IN T ERGOV ERNMENTA L PA NEL ON climate change CLIMATE CHANGE 2013 The Physical Science Basis WG I WORKING GROUP I CONTRIBUTION TO THE FIFTH ASSESSMENT REPORT OF THE INTERGOVERNMENTAL PANEL ON CLIMATE CHANGE Climate Change 2013 Foreword The Physical Science Basis Working Group I Contribution to the Fifth Assessment Report of the Intergovernmental Panel on Climate Change Edited by Thomas F. Stocker Dahe Qin Working Group I Co-Chair Working Group I Co-Chair University of Bern China Meteorological Administration Gian-Kasper Plattner Melinda M.B. Tignor Simon K. Allen Judith Boschung Director of Science Director of Operations Senior Science Officer Administrative Assistant Alexander Nauels Yu Xia Vincent Bex Pauline M. Midgley Science Assistant Science Officer IT Officer Head Working Group I Technical Support Unit Foreword CAMBRIDGE UNIVERSITY PRESS Cambridge, New York, Melbourne, Madrid, Cape Town, Singapore, Sao Paolo, Delhi, Mexico City Cambridge University Press 32 Avenue of the Americas, New York, NY 10013-2473, USA www.cambridge.org Information on this title: www.cambridge.org/9781107661820 (c) Intergovernmental Panel on Climate Change 2013 This publication is in copyright. Subject to statutory exception and to the provisions of relevant collective licensing agreements, no reproduction of any part may take place without the written permission of Cambridge University Press. First published 2013 Printed in the United States of America A catalog record for this publication is available from the British Library. ISBN 978-1-107-05799-1 hardback ISBN 978-1-107-66182-0 paperback Cambridge University Press has no responsibility for the persistence or accuracy of URLs for external or third-party Internet Web sites referred to in this publication and does not guarantee that any content on such Web sites is, or will remain, accurate or appropriate. Please use the following reference to the whole report: IPCC, 2013: Climate Change 2013: The Physical Science Basis. Contribution of Working Group I to the Fifth Assessment Report of the Intergovern- mental Panel on Climate Change [Stocker, T.F., D. Qin, G.-K. Plattner, M. Tignor, S.K. Allen, J. Boschung, A. Nauels, Y. Xia, V. Bex and P.M. Midgley (eds.)]. Cambridge University Press, Cambridge, United Kingdom and New York, NY, USA, 1535 pp. Cover photo: Folgefonna glacier on the high plateaus of Srfjorden, Norway (60°03 N - 6°20 E) (c) Yann Arthus-Bertrand / Altitude. ii Introduction Chapter 2 Foreword Foreword, Preface and Dedication iii Foreword Climate Change 2013: The Physical Science Basis presents clear and We are also grateful to the governments that supported their scien- robust conclusions in a global assessment of climate change science tists participation in developing this report and that contributed to Foreword not the least of which is that the science now shows with 95 percent the IPCC Trust Fund to provide for the essential participation of experts certainty that human activity is the dominant cause of observed warm- from developing countries and countries with economies in transition. ing since the mid-20th century. The report confirms that warming in We would like to express our appreciation to the government of Italy the climate system is unequivocal, with many of the observed changes for hosting the scoping meeting for the IPCC s Fifth Assessment Report, unprecedented over decades to millennia: warming of the atmosphere to the governments of China, France, Morocco and Australia for host- and the ocean, diminishing snow and ice, rising sea levels and increas- ing drafting sessions of the Working Group I contribution and to the ing concentrations of greenhouse gases. Each of the last three decades government of Sweden for hosting the Twelfth Session of Working has been successively warmer at the Earth s surface than any preced- Group I in Stockholm for approval of the Working Group I Report. The ing decade since 1850. generous financial support by the government of Switzerland, and the logistical support by the University of Bern (Switzerland), enabled the These and other findings confirm and enhance our scientific under- smooth operation of the Working Group I Technical Support Unit. This standing of the climate system and the role of greenhouse gas emis- is gratefully acknowledged. sions; as such, the report demands the urgent attention of both policy- makers and the general public. We would particularly like to thank Dr. Rajendra Pachauri, Chairman of the IPCC, for his direction and guidance of the IPCC and we express our As an intergovernmental body jointly established in 1988 by the World deep gratitude to Professor Qin Dahe and Professor Thomas Stocker, Meteorological Organization (WMO) and the United Nations Environ- the Co-Chairs of Working Group I for their tireless leadership through- ment Programme (UNEP), the Intergovernmental Panel on Climate out the development and production of this report. Change (IPCC) has provided policymakers with the most authorita- tive and objective scientific and technical assessments. Beginning in 1990, this series of IPCC Assessment Reports, Special Reports, Tech- nical Papers, Methodology Reports and other products have become standard works of reference. This Working Group I contribution to the IPCC s Fifth Assessment Report contains important new scientific knowledge that can be used to produce climate information and services for assisting society to act to address the challenges of climate change. The timing is particularly M. Jarraud significant, as this information provides a new impetus, through clear Secretary-General and indisputable physical science, to those negotiators responsible for World Meteorological Organization concluding a new agreement under the United Nations Framework Convention on Climate Change in 2015. Climate change is a long-term challenge, but one that requires urgent action given the pace and the scale by which greenhouse gases are accumulating in the atmosphere and the risks of a more than 2 degree Celsius temperature rise. Today we need to focus on the fundamentals A. Steiner and on the actions otherwise the risks we run will get higher with Executive Director every year. United Nations Environment Programme This Working Group I assessment was made possible thanks to the commitment and dedication of many hundreds of experts worldwide, representing a wide range of disciplines. WMO and UNEP are proud that so many of the experts belong to their communities and networks. We express our deep gratitude to all authors, review editors and expert reviewers for devoting their knowledge, expertise and time. We would like to thank the staff of the Working Group I Technical Support Unit and the IPCC Secretariat for their dedication. v Preface The Working Group I contribution to the Fifth Assessment Report of The Summary for Policymakers and Technical Summary of this report the Intergovernmental Panel on Climate Change (IPCC) provides a follow a parallel structure and each includes cross-references to the comprehensive assessment of the physical science basis of climate chapter and section where the material being summarised can be change. It builds upon the Working Group I contribution to the IPCC s found in the underlying report. In this way, these summary compo- Fourth Assessment Report in 2007 and incorporates subsequent new nents of the report provide a road-map to the contents of the entire findings from the Special Report on Managing the Risks of Extreme report and a traceable account of every major finding. Events and Disasters to Advance Climate Change Adaptation, as well as from research published in the extensive scientific and technical In order to facilitate the accessibility of the findings of the Working literature. The assessment considers new evidence of past, present and Group I assessment for a wide readership and to enhance their usabil- projected future climate change based on many independent scien- ity for stakeholders, each section of the Summary for Policymakers has tific analyses from observations of the climate system, paleoclimate a highlighted headline statement. Taken together, these 19 headline Preface archives, theoretical studies of climate processes and simulations using statements provide an overarching summary in simple and quotable climate models. language that is supported by the scientists and approved by the member governments of the IPCC. Another innovative feature of this report is the presentation of Thematic Focus Elements in the Techni- Scope of the Report cal Summary that provide end to end assessments of important cross- cutting issues in the physical science basis of climate change. During the process of scoping and approving the outline of its Fifth Assessment Report, the IPCC focussed on those aspects of the current Introduction (Chapter 1): This chapter provides information on the understanding of the science of climate change that were judged to be progress in climate change science since the First Assessment Report most relevant to policymakers. of the IPCC in 1990 and gives an overview of key concepts, indica- tors of climate change, the treatment of uncertainties and advances in In this report, Working Group I has extended coverage of future climate measurement and modelling capabilities. This includes a description of change compared to earlier reports by assessing near-term projections the future scenarios and in particular the Representative Concentration and predictability as well as long-term projections and irreversibility Pathway scenarios used across all Working Groups for the IPCC s Fifth in two separate chapters. Following the decisions made by the Panel Assessment Report. during the scoping and outline approval, a set of new scenarios, the Representative Concentration Pathways, are used across all three Observations and Paleoclimate Information (Chapters 2, 3, 4, 5): These Working Groups for projections of climate change over the 21st cen- chapters assess information from all climate system components on tury. The coverage of regional information in the Working Group I climate variability and change as obtained from instrumental records report is expanded by specifically assessing climate phenomena such and climate archives. They cover all relevant aspects of the atmosphere as monsoon systems and their relevance to future climate change in including the stratosphere, the land surface, the oceans and the cryo- the regions. sphere. Timescales from days to decades (Chapters 2, 3 and 4) and from centuries to many millennia (Chapter 5) are considered. The Working Group I Report is an assessment, not a review or a text book of climate science, and is based on the published scientific and Process Understanding (Chapters 6 and 7): These chapters cover all technical literature available up to 15 March 2013. Underlying all relevant aspects from observations and process understanding to pro- aspects of the report is a strong commitment to assessing the science jections from global to regional scales for two key topics. Chapter 6 comprehensively, without bias and in a way that is relevant to policy covers the carbon cycle and its interactions with other biogeochemical but not policy prescriptive. cycles, in particular the nitrogen cycle, as well as feedbacks on the climate system. For the first time, there is a chapter dedicated to the assessment of the physical science basis of clouds and aerosols, their Structure of the Report interactions and chemistry, and the role of water vapour, as well as their role in feedbacks on the climate system (Chapter 7). This report consists of a short Summary for Policymakers, a longer Technical Summary and fourteen thematic chapters plus annexes. An From Forcing to Attribution of Climate Change (Chapters 8, 9, 10): All innovation in this Working Group I assessment is the Atlas of Global the information on the different drivers (natural and anthropogenic) and Regional Climate Projections (Annex I) containing time series and of climate change is collected, expressed in terms of Radiative Forc- maps of temperature and precipitation projections for 35 regions of ing and assessed in Chapter 8. In Chapter 9, the hierarchy of climate the world, which enhances accessibility for stakeholders and users. models used in simulating past and present climate change is assessed and evaluated against observations and paleoclimate reconstructions. vii Preface Information regarding detection of changes on global to regional in the online versions of the report to provide an additional level of scales and their attribution to the increase in anthropogenic green- detail, such as description of datasets, models, or methodologies used house gases is assessed in Chapter 10. in chapter analyses, as well as material supporting the figures in the Summary for Policymakers. Future Climate Change, Predictability and Irreversibility (Chapters 11 and 12): These chapters assess projections of future climate change derived from climate models on time scales from decades to centuries The Process at both global and regional scales, including mean changes, variabil- ity and extremes. Fundamental questions related to the predictability This Working Group I Assessment Report represents the combined of climate as well as long term climate change, climate change com- efforts of hundreds of leading experts in the field of climate science mitments and inertia in the climate system are addressed. Knowledge and has been prepared in accordance with rules and procedures estab- on irreversible changes and surprises in the climate system is also lished by the IPCC. A scoping meeting for the Fifth Assessment Report assessed. was held in July 2009 and the outlines for the contributions of the three Working Groups were approved at the 31st Session of the Panel Preface Integration (Chapters 13 and 14): These chapters synthesise all relevant in November 2009. Governments and IPCC observer organisations information for two key topics of this assessment: sea level change nominated experts for the author team. The team of 209 Coordinat- (Chapter 13) and climate phenomena across the regions (Chapter 14). ing Lead Authors and Lead Authors plus 50 Review Editors selected Chapter 13 presents an end to end assessment of information on sea by the Working Group I Bureau was accepted at the 41st Session of level change based on paleoclimate reconstructions, observations and the IPCC Bureau in May 2010. In addition, more than 600 Contribut- process understanding, and provides projections from global to region- ing Authors provided draft text and information to the author teams al scales. Chapter 14 assesses the most important modes of variability at their request. Drafts prepared by the authors were subject to two in the climate system, such as El Nino-Southern Oscillation, monsoon rounds of formal review and revision followed by a final round of gov- and many others, as well as extreme events. Furthermore, this chapter ernment comments on the Summary for Policymakers. A total of 54,677 deals with interconnections between the climate phenomena, their written review comments were submitted by 1089 individual expert regional expressions and their relevance for future regional climate reviewers and 38 governments. The Review Editors for each chapter change. monitored the review process to ensure that all substantive review comments received appropriate consideration. The Summary for Poli- Maps assessed in Chapter 14, together with Chapters 11 and 12, form cymakers was approved line-by-line and the underlying chapters were the basis of the Atlas of Global and Regional Climate Projections in then accepted at the 12th Session of IPCC Working Group I from 23 27 Annex I, which is also available in digital format. Radiative forcings September 2007. and estimates of future atmospheric concentrations from Chapters 7, 8, 11 and 12 form the basis of the Climate System Scenario Tables presented in Annex II. All material including high-resolution versions of Acknowledgements the figures, underlying data and Supplementary Material to the chap- ters is also available online: www.climatechange2013.org. We are very grateful for the expertise, hard work, commitment to excellence and integrity shown throughout by the Coordinating Lead The scientific community and the climate modelling centres around the Authors and Lead Authors with important help by the many Contribut- world brought together their activities in the Coordinated Modelling ing Authors. The Review Editors have played a critical role in assist- Intercomparison Project Phase 5 (CMIP5), providing the basis for most ing the author teams and ensuring the integrity of the review process. of the assessment of future climate change in this report. Their efforts We express our sincere appreciation to all the expert and government enable Working Group I to deliver comprehensive scientific informa- reviewers. We would also like to thank the members of the Bureau of tion for the policymakers and the users of this report, as well as for Working Group I: Jean Jouzel, Abdalah Mokssit, Fatemeh Rahimizadeh, the specific assessments of impacts carried out by IPCC Working Group Fredolin Tangang, David Wratt and Francis Zwiers, for their thoughtful II, and of costs and mitigation strategies, carried out by IPCC Working advice and support throughout the preparation of the report. Group III. We gratefully acknowledge the long-term efforts of the scientific com- Following the successful introduction in the previous Working Group I munity, organized and facilitated through the World Climate Research assessment in 2007, all chapters contain Frequently Asked Questions. Programme, in particular CMIP5. In this effort by climate modelling In these the authors provide scientific answers to a range of general centres around the world, more than 2 million gigabytes of numerical questions in a form that will be accessible to a broad readership and data have been produced, which were archived and distributed under serves as a resource for teaching purposes. Finally, the report is accom- the stewardship of the Program for Climate Model Diagnosis and Inter- panied by extensive Supplementary Material which is made available comparison. This represents an unprecedented concerted effort by the scientific community and their funding institutions. viii Preface Our sincere thanks go to the hosts and organizers of the four Working Finally our particular appreciation goes to the Working Group I Techni- Group I Lead Author Meetings and the 12th Session of Working Group cal Support Unit: Gian-Kasper Plattner, Melinda Tignor, Simon Allen, I. We gratefully acknowledge the support from the host countries: Judith Boschung, Alexander Nauels, Yu Xia, Vincent Bex and Pauline China, France, Morocco, Australia and Sweden. The support for their Midgley for their professionalism, creativity and dedication. Their tire- scientists provided by many governments as well as through the IPCC less efforts to coordinate the Working Group I Report ensured a final Trust Fund is much appreciated. The efficient operation of the Working product of high quality. They were assisted in this by Adrien Michel Group I Technical Support Unit was made possible by the generous and Flavio Lehner with further support from Zhou Botao and Sun Ying. financial support provided by the government of Switzerland and logis- In addition, the following contributions are gratefully acknowledged: tical support from the University of Bern (Switzerland). David Hansford (editorial assistance with the Frequently Asked Ques- tions), UNEP/GRID-Geneva and University of Geneva (graphics assis- We would also like to thank Renate Christ, Secretary of the IPCC, and tance with the Frequently Asked Questions), Theresa Kornak (copyedit), the staff of the IPCC Secretariat: Gaetano Leone, Jonathan Lynn, Mary Marilyn Anderson (index) and Michael Shibao (design and layout). Jean Burer, Sophie Schlingemann, Judith Ewa, Jesbin Baidya, Werani Zabula, Joelle Fernandez, Annie Courtin, Laura Biagioni and Amy Preface Smith. Thanks are due to Francis Hayes who served as the conference officer for the Working Group I Approval Session. Rajendra K. Pachauri Qin Dahe Thomas F. Stocker IPCC Chair IPCC WGI Co-Chair IPCC WGI Co-Chair ix Dedication Dedication Bert Bolin (15 May 1925 30 December 2007) The Working Group I contribution to the Fifth Assessment Report of the Intergovernmental Panel on Climate Change (IPCC) Climate Change 2013: The Physical Science Basis is dedicated to the memory of Bert Bolin, the first Chair of the IPCC. As an accomplished scientist who published on both atmospheric dynamics and the carbon cycle, including processes in the atmosphere, oceans and biosphere, Bert Bolin realised the complexity of the climate system and its sensitivity to anthropogenic perturbation. He made a fundamental contribution to the organisation of international cooperation in climate research, being involved in the establishment of a number of global programmes. Bert Bolin played a key role in the creation of the IPCC and its assessments, which are carried out in a unique and formalized process in order to provide a robust scientific basis for informed decisions regarding one of the greatest challenges of our time. His vision and leadership of the Panel as the founding Chair from 1988 to 1997 laid the basis for subsequent assessments includ- ing this one and are remembered with deep appreciation. xi Contents Front Matter Foreword . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . v Foreword Preface . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . vii Dedication . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . xi SPM Summary for Policymakers . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3 TS Technical Summary . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 33 Chapters Chapter 1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 119 Chapter 2 Observations : Atmosphere and Surface . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 159 Chapter 3 Observations: Ocean . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 255 Chapter 4 Observations: Cryosphere . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 317 Chapter 5 Information from Paleoclimate Archives . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 383 Chapter 6 Carbon and Other Biogeochemical Cycles . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 465 Chapter 7 Clouds and Aerosols . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 571 Chapter 8 Anthropogenic and Natural Radiative Forcing . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 659 Chapter 9 Evaluation of Climate Models . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 741 Chapter 10 Detection and Attribution of Climate Change: from Global to Regional . . . . . . . . . . . . . . . . 867 Chapter 11 Near-term Climate Change: Projections and Predictability . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 953 Chapter 12 Long-term Climate Change: Projections, Commitments and Irreversibility . . . . . . . . . . . . 1029 Chapter 13 Sea Level Change . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1137 Chapter 14 Climate Phenomena and their Relevance for Future Regional Climate Change . . . . . . 1217 Annexes Annex I Atlas of Global and Regional Climate Projections . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1311 Annex II Climate System Scenario Tables . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1395 Annex III Glossary . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1447 Annex IV Acronyms . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1467 Annex V Contributors to the IPCC WGI Fifth Assessment Report . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1477 Annex VI Expert Reviewers of the IPCC WGI Fifth Assessment Report . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1497 Index . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1523 Introduction Chapter 2 Summary Chapter 1 for Policymakers 1 SPM Summary for Policymakers Drafting Authors: Lisa V. Alexander (Australia), Simon K. Allen (Switzerland/New Zealand), Nathaniel L. Bindoff (Australia), François-Marie Bréon (France), John A. Church (Australia), Ulrich Cubasch (Germany), Seita Emori (Japan), Piers Forster (UK), Pierre Friedlingstein (UK/Belgium), Nathan Gillett (Canada), Jonathan M. Gregory (UK), Dennis L. Hartmann (USA), Eystein Jansen (Norway), Ben Kirtman (USA), Reto Knutti (Switzerland), Krishna Kumar Kanikicharla (India), Peter Lemke (Germany), Jochem Marotzke (Germany), Valérie Masson-Delmotte (France), Gerald A. Meehl (USA), Igor I. Mokhov (Russian Federation), Shilong Piao (China), Gian-Kasper Plattner (Switzerland), Qin Dahe (China), Venkatachalam Ramaswamy (USA), David Randall (USA), Monika Rhein (Germany), Maisa Rojas (Chile), Christopher Sabine (USA), Drew Shindell (USA), Thomas F. Stocker (Switzerland), Lynne D. Talley (USA), David G. Vaughan (UK), Shang- Ping Xie (USA) Draft Contributing Authors: Myles R. Allen (UK), Olivier Boucher (France), Don Chambers (USA), Jens Hesselbjerg Christensen (Denmark), Philippe Ciais (France), Peter U. Clark (USA), Matthew Collins (UK), Josefino C. Comiso (USA), Viviane Vasconcellos de Menezes (Australia/Brazil), Richard A. Feely (USA), Thierry Fichefet (Belgium), Arlene M. Fiore (USA), Gregory Flato (Canada), Jan Fuglestvedt (Norway), Gabriele Hegerl (UK/Germany), Paul J. Hezel (Belgium/USA), Gregory C. Johnson (USA), Georg Kaser (Austria/Italy), Vladimir Kattsov (Russian Federation), John Kennedy (UK), Albert M. G. Klein Tank (Netherlands), Corinne Le Quéré (UK), Gunnar Myhre (Norway), Timothy Osborn (UK), Antony J. Payne (UK), Judith Perlwitz (USA), Scott Power (Australia), Michael Prather (USA), Stephen R. Rintoul (Australia), Joeri Rogelj (Switzerland/Belgium), Matilde Rusticucci (Argentina), Michael Schulz (Germany), Jan Sedláèek (Switzerland), Peter A. Stott (UK), Rowan Sutton (UK), Peter W. Thorne (USA/Norway/UK), Donald Wuebbles (USA) This Summary for Policymakers should be cited as: IPCC, 2013: Summary for Policymakers. In: Climate Change 2013: The Physical Science Basis. Contribution of Working Group I to the Fifth Assessment Report of the Intergovernmental Panel on Climate Change [Stocker, T.F., D. Qin, G.-K. Plattner, M. Tignor, S.K. Allen, J. Boschung, A. Nauels, Y. Xia, V. Bex and P.M. Midgley (eds.)]. Cambridge University Press, Cambridge, United Kingdom and New York, NY, USA. 3 Summary for Policymakers A. Introduction The Working Group I contribution to the IPCC s Fifth Assessment Report (AR5) considers new evidence of climate change based on many independent scientific analyses from observations of the climate system, paleoclimate archives, theoretical SPM studies of climate processes and simulations using climate models. It builds upon the Working Group I contribution to the IPCC s Fourth Assessment Report (AR4), and incorporates subsequent new findings of research. As a component of the fifth assessment cycle, the IPCC Special Report on Managing the Risks of Extreme Events and Disasters to Advance Climate Change Adaptation (SREX) is an important basis for information on changing weather and climate extremes. This Summary for Policymakers (SPM) follows the structure of the Working Group I report. The narrative is supported by a series of overarching highlighted conclusions which, taken together, provide a concise summary. Main sections are introduced with a brief paragraph in italics which outlines the methodological basis of the assessment. The degree of certainty in key findings in this assessment is based on the author teams evaluations of underlying scientific understanding and is expressed as a qualitative level of confidence (from very low to very high) and, when possible, probabilistically with a quantified likelihood (from exceptionally unlikely to virtually certain). Confidence in the validity of a finding is based on the type, amount, quality, and consistency of evidence (e.g., data, mechanistic understanding, theory, models, expert judgment) and the degree of agreement1. Probabilistic estimates of quantified measures of uncertainty in a finding are based on statistical analysis of observations or model results, or both, and expert judgment2. Where appropriate, findings are also formulated as statements of fact without using uncertainty qualifiers. (See Chapter 1 and Box TS.1 for more details about the specific language the IPCC uses to communicate uncertainty). The basis for substantive paragraphs in this Summary for Policymakers can be found in the chapter sections of the underlying report and in the Technical Summary. These references are given in curly brackets. B. Observed Changes in the Climate System Observations of the climate system are based on direct measurements and remote sensing from satellites and other platforms. Global-scale observations from the instrumental era began in the mid-19th century for temperature and other variables, with more comprehensive and diverse sets of observations available for the period 1950 onwards. Paleoclimate reconstructions extend some records back hundreds to millions of years. Together, they provide a comprehensive view of the variability and long-term changes in the atmosphere, the ocean, the cryosphere, and the land surface. Warming of the climate system is unequivocal, and since the 1950s, many of the observed changes are unprecedented over decades to millennia. The atmosphere and ocean have warmed, the amounts of snow and ice have diminished, sea level has risen, and the concentrations of greenhouse gases have increased (see Figures SPM.1, SPM.2, SPM.3 and SPM.4). {2.2, 2.4, 3.2, 3.7, 4.2 4.7, 5.2, 5.3, 5.5 5.6, 6.2, 13.2} 1 In this Summary for Policymakers, the following summary terms are used to describe the available evidence: limited, medium, or robust; and for the degree of agreement: low, medium, or high. A level of confidence is expressed using five qualifiers: very low, low, medium, high, and very high, and typeset in italics, e.g., medium confidence. For a given evidence and agreement statement, different confidence levels can be assigned, but increasing levels of evidence and degrees of agreement are correlated with increasing confidence (see Chapter 1 and Box TS.1 for more details). 2 In this Summary for Policymakers, the following terms have been used to indicate the assessed likelihood of an outcome or a result: virtually certain 99 100% probability, very likely 90 100%, likely 66 100%, about as likely as not 33 66%, unlikely 0 33%, very unlikely 0 10%, exceptionally unlikely 0 1%. Additional terms (extremely likely: 95 100%, more likely than not >50 100%, and extremely unlikely 0 5%) may also be used when appropriate. Assessed likelihood is typeset in italics, e.g., very likely (see Chapter 1 and Box TS.1 for more details). 4 Summary for Policymakers B.1 Atmosphere Each of the last three decades has been successively warmer at the Earth s surface than any preceding decade since 1850 (see Figure SPM.1). In the Northern Hemisphere, 1983 2012 was likely the warmest 30-year period of the last 1400 years (medium confidence). {2.4, 5.3} SPM The globally averaged combined land and ocean surface temperature data as calculated by a linear trend, show a warming of 0.85 [0.65 to 1.06] °C3, over the period 1880 to 2012, when multiple independently produced datasets exist. The total increase between the average of the 1850 1900 period and the 2003 2012 period is 0.78 [0.72 to 0.85] °C, based on the single longest dataset available 4 (see Figure SPM.1). {2.4} For the longest period when calculation of regional trends is sufficiently complete (1901 to 2012), almost the entire globe has experienced surface warming (see Figure SPM.1). {2.4} In addition to robust multi-decadal warming, global mean surface temperature exhibits substantial decadal and interannual variability (see Figure SPM.1). Due to natural variability, trends based on short records are very sensitive to the beginning and end dates and do not in general reflect long-term climate trends. As one example, the rate of warming over the past 15 years (1998 2012; 0.05 [ 0.05 to 0.15] °C per decade), which begins with a strong El Nino, is smaller than the rate calculated since 1951 (1951 2012; 0.12 [0.08 to 0.14] °C per decade)5. {2.4} Continental-scale surface temperature reconstructions show, with high confidence, multi-decadal periods during the Medieval Climate Anomaly (year 950 to 1250) that were in some regions as warm as in the late 20th century. These regional warm periods did not occur as coherently across regions as the warming in the late 20th century (high confidence). {5.5} It is virtually certain that globally the troposphere has warmed since the mid-20th century. More complete observations allow greater confidence in estimates of tropospheric temperature changes in the extratropical Northern Hemisphere than elsewhere. There is medium confidence in the rate of warming and its vertical structure in the Northern Hemisphere extra-tropical troposphere and low confidence elsewhere. {2.4} Confidence in precipitation change averaged over global land areas since 1901 is low prior to 1951 and medium afterwards. Averaged over the mid-latitude land areas of the Northern Hemisphere, precipitation has increased since 1901 (medium confidence before and high confidence after 1951). For other latitudes area-averaged long-term positive or negative trends have low confidence (see Figure SPM.2). {TS TFE.1, Figure 2; 2.5} Changes in many extreme weather and climate events have been observed since about 1950 (see Table SPM.1 for details). It is very likely that the number of cold days and nights has decreased and the number of warm days and nights has increased on the global scale6. It is likely that the frequency of heat waves has increased in large parts of Europe, Asia and Australia. There are likely more land regions where the number of heavy precipitation events has increased than where it has decreased. The frequency or intensity of heavy precipitation events has likely increased in North America and Europe. In other continents, confidence in changes in heavy precipitation events is at most medium. {2.6} 3 In the WGI contribution to the AR5, uncertainty is quantified using 90% uncertainty intervals unless otherwise stated. The 90% uncertainty interval, reported in square brackets, is expected to have a 90% likelihood of covering the value that is being estimated. Uncertainty intervals are not necessarily symmetric about the corresponding best estimate. A best estimate of that value is also given where available. 4 Both methods presented in this bullet were also used in AR4. The first calculates the difference using a best fit linear trend of all points between 1880 and 2012. The second calculates the difference between averages for the two periods 1850 1900 and 2003 2012. Therefore, the resulting values and their 90% uncertainty intervals are not directly comparable. {2.4} 5 Trends for 15-year periods starting in 1995, 1996, and 1997 are 0.13 [0.02 to 0.24] °C per decade, 0.14 [0.03 to 0.24] °C per decade, and, 0.07 [ 0.02 to 0.18] °C per decade, respectively. 6 See the Glossary for the definition of these terms: cold days/cold nights, warm days/warm nights, heat waves. 5 Summary for Policymakers Observed globally averaged combined land and ocean (a) surface temperature anomaly 1850 2012 0.6 Annual average 0.4 SPM 0.2 Temperature anomaly (°C) relative to 1961 1990 0.0 0.2 0.4 0.6 0.6 Decadal average 0.4 0.2 0.0 0.2 0.4 0.6 1850 1900 1950 2000 Year (b) Observed change in surface temperature 1901 2012 0.6 0.4 0.2 0 0.2 0.4 0.6 0.8 1.0 1.25 1.5 1.75 2.5 (°C) Figure SPM.1 | (a) Observed global mean combined land and ocean surface temperature anomalies, from 1850 to 2012 from three data sets. Top panel: annual mean values. Bottom panel: decadal mean values including the estimate of uncertainty for one dataset (black). Anomalies are relative to the mean of 1961 1990. (b) Map of the observed surface temperature change from 1901 to 2012 derived from temperature trends determined by linear regression from one dataset (orange line in panel a). Trends have been calculated where data availability permits a robust estimate (i.e., only for grid boxes with greater than 70% complete records and more than 20% data availability in the first and last 10% of the time period). Other areas are white. Grid boxes where the trend is significant at the 10% level are indicated by a + sign. For a listing of the datasets and further technical details see the Technical Summary Supplementary Material. {Figures 2.19 2.21; Figure TS.2} 6 Table SPM.1 | Extreme weather and climate events: Global-scale assessment of recent observed changes, human contribution to the changes, and projected further changes for the early (2016 2035) and late (2081 2100) 21st century. Bold indicates where the AR5 (black) provides a revised* global-scale assessment from the SREX (blue) or AR4 (red). Projections for early 21st century were not provided in previous assessment reports. Projections in the AR5 are relative to the reference period of 1986 2005, and use the new Representative Concentration Pathway (RCP) scenarios (see Box SPM.1) unless otherwise specified. See the Glossary for definitions of extreme weather and climate events. Phenomenon and Assessment that changes occurred (typically Assessment of a human Likelihood of further changes direction of trend since 1950 unless otherwise indicated) contribution to observed changes Early 21st century Late 21st century Warmer and/or fewer Very likely {2.6} Very likely {10.6} Likely {11.3} Virtually certain {12.4} cold days and nights Very likely Likely Virtually certain over most land areas Very likely Likely Virtually certain  Warmer and/or more Very likely {2.6} Very likely {10.6} Likely {11.3} Virtually certain {12.4} frequent hot days and Very likely Likely Virtually certain nights over most land areas Very likely Likely (nights only) Virtually certain Warm spells/heat waves. Medium confidence on a global scale Likelya Not formally assessedb Very likely Frequency and/or duration Likely in large parts of Europe, Asia and Australia {2.6} {10.6} {11.3} {12.4} increases over most Medium confidence in many (but not all) regions Not formally assessed Very likely land areas Likely More likely than not Very likely Heavy precipitation events. Likely more land areas with increases than decreasesc Medium confidence Likely over many land areas Very likely over most of the mid-latitude land Increase in the frequency, {2.6} {7.6, 10.6} {11.3} masses and over wet tropical regions {12.4} intensity, and/or amount Likely more land areas with increases than decreases Medium confidence Likely over many areas of heavy precipitation Likely over most land areas More likely than not Very likely over most land areas Low confidence on a global scale Low confidence {10.6} Low confidenceg {11.3} Likely (medium confidence) on a regional to Increases in intensity Likely changes in some regionsd {2.6} global scaleh {12.4} and/or duration of drought Medium confidence in some regions Medium confidence f Medium confidence in some regions Likely in many regions, since 1970e More likely than not Likelye Low confidence in long term (centennial) changes Low confidencei Low confidence More likely than not in the Western North Pacific Virtually certain in North Atlantic since 1970 {2.6} {10.6} {11.3} and North Atlantic j {14.6} Increases in intense tropical cyclone activity Low confidence Low confidence More likely than not in some basins Likely in some regions, since 1970 More likely than not Likely Increased incidence and/or Likely (since 1970) {3.7} Likely k {3.7} Likely l {13.7} Very likely l {13.7} magnitude of extreme Likely (late 20th century) Likely k Very likely m high sea level Likely More likely than not k Likely * The direct comparison of assessment findings between reports is difficult. For some climate variables, different aspects have been assessed, and the revised guidance note on uncertainties has been used for the SREX and AR5. The availability of new information, improved scientific understanding, continued analyses of data and models, and specific differences in methodologies applied in the assessed studies, all contribute to revised assessment findings. Notes: a Attribution is based on available case studies. It is likely that human influence has more than doubled the probability of occurrence of some observed heat waves in some locations. b Models project near-term increases in the duration, intensity and spatial extent of heat waves and warm spells. c In most continents, confidence in trends is not higher than medium except in North America and Europe where there have been likely increases in either the frequency or intensity of heavy precipitation with some seasonal and/or regional variation. It is very likely that there have been increases in central North America. d The frequency and intensity of drought has likely increased in the Mediterranean and West Africa, and likely decreased in central North America and north-west Australia. e AR4 assessed the area affected by drought. f SREX assessed medium confidence that anthropogenic influence had contributed to some changes in the drought patterns observed in the second half of the 20th century, based on its attributed impact on precipitation and temperature changes. SREX assessed low confidence in the attribution of changes in droughts at the level of single regions. g There is low confidence in projected changes in soil moisture. h Regional to global-scale projected decreases in soil moisture and increased agricultural drought are likely (medium confidence) in presently dry regions by the end of this century under the RCP8.5 scenario. Soil moisture drying in the Mediterranean, Southwest US and southern African regions is consistent with projected changes in Hadley circulation and increased surface temperatures, so there is high confidence in likely surface drying in these regions by the end of this century under the RCP8.5 scenario. i There is medium confidence that a reduction in aerosol forcing over the North Atlantic has contributed at least in part to the observed increase in tropical cyclone activity since the 1970s in this region. j Based on expert judgment and assessment of projections which use an SRES A1B (or similar) scenario. k Attribution is based on the close relationship between observed changes in extreme and mean sea level. 7 Summary for Policymakers l There is high confidence that this increase in extreme high sea level will primarily be the result of an increase in mean sea level. There is low confidence in region-specific projections of storminess and associated storm surges. m SREX assessed it to be very likely that mean sea level rise will contribute to future upward trends in extreme coastal high water levels. SPM Summary for Policymakers Observed change in annual precipitation over land 1901 2010 1951 2010 SPM 100 50 25 10 5 2.5 0 2.5 5 10 25 50 100 (mm yr per decade) -1 Figure SPM.2 | Maps of observed precipitation change from 1901 to 2010 and from 1951 to 2010 (trends in annual accumulation calculated using the same criteria as in Figure SPM.1) from one data set. For further technical details see the Technical Summary Supplementary Material. {TS TFE.1, Figure 2; Figure 2.29} B.2 Ocean Ocean warming dominates the increase in energy stored in the climate system, accounting for more than 90% of the energy accumulated between 1971 and 2010 (high confidence). It is virtually certain that the upper ocean (0 700 m) warmed from 1971 to 2010 (see Figure SPM.3), and it likely warmed between the 1870s and 1971. {3.2, Box 3.1} On a global scale, the ocean warming is largest near the surface, and the upper 75 m warmed by 0.11 [0.09 to 0.13] °C per decade over the period 1971 to 2010. Since AR4, instrumental biases in upper-ocean temperature records have been identified and reduced, enhancing confidence in the assessment of change. {3.2} ­ It is likely that the ocean warmed between 700 and 2000 m from 1957 to 2009. Sufficient observations are available for the period 1992 to 2005 for a global assessment of temperature change below 2000 m. There were likely no significant observed temperature trends between 2000 and 3000 m for this period. It is likely that the ocean warmed from 3000 m to the bottom for this period, with the largest warming observed in the Southern Ocean. {3.2} More than 60% of the net energy increase in the climate system is stored in the upper ocean (0 700 m) during the relatively well-sampled 40-year period from 1971 to 2010, and about 30% is stored in the ocean below 700 m. The increase in upper ocean heat content during this time period estimated from a linear trend is likely 17 [15 to 19] × 1022 J 7 (see Figure SPM.3). {3.2, Box 3.1} It is about as likely as not that ocean heat content from 0 700 m increased more slowly during 2003 to 2010 than during 1993 to 2002 (see Figure SPM.3). Ocean heat uptake from 700 2000 m, where interannual variability is smaller, likely continued unabated from 1993 to 2009. {3.2, Box 9.2} It is very likely that regions of high salinity where evaporation dominates have become more saline, while regions of low salinity where precipitation dominates have become fresher since the 1950s. These regional trends in ocean salinity provide indirect evidence that evaporation and precipitation over the oceans have changed (medium confidence). {2.5, 3.3, 3.5} There is no observational evidence of a trend in the Atlantic Meridional Overturning Circulation (AMOC), based on the decade-long record of the complete AMOC and longer records of individual AMOC components. {3.6} 7 A constant supply of heat through the ocean surface at the rate of 1 W m 2 for 1 year would increase the ocean heat content by 1.1 × 1022 J. 8 Summary for Policymakers B.3 Cryosphere Over the last two decades, the Greenland and Antarctic ice sheets have been losing mass, glaciers have continued to shrink almost worldwide, and Arctic sea ice and Northern Hemisphere spring snow cover have continued to decrease in extent (high confidence) (see SPM Figure SPM.3). {4.2 4.7} The average rate of ice loss8 from glaciers around the world, excluding glaciers on the periphery of the ice sheets9, was very likely 226 [91 to 361] Gt yr 1 over the period 1971 to 2009, and very likely 275 [140 to 410] Gt yr 1 over the period 1993 to 200910. {4.3} The average rate of ice loss from the Greenland ice sheet has very likely substantially increased from 34 [ 6 to 74] Gt yr 1 over the period 1992 to 2001 to 215 [157 to 274] Gt yr 1 over the period 2002 to 2011. {4.4} The average rate of ice loss from the Antarctic ice sheet has likely increased from 30 [ 37 to 97] Gt yr 1 over the period 1992 2001 to 147 [72 to 221] Gt yr 1 over the period 2002 to 2011. There is very high confidence that these losses are mainly from the northern Antarctic Peninsula and the Amundsen Sea sector of West Antarctica. {4.4} The annual mean Arctic sea ice extent decreased over the period 1979 to 2012 with a rate that was very likely in the range 3.5 to 4.1% per decade (range of 0.45 to 0.51 million km2 per decade), and very likely in the range 9.4 to 13.6% per decade (range of 0.73 to 1.07 million km2 per decade) for the summer sea ice minimum (perennial sea ice). The average decrease in decadal mean extent of Arctic sea ice has been most rapid in summer (high confidence); the spatial extent has decreased in every season, and in every ­ uccessive decade since 1979 (high confidence) (see Figure SPM.3). s There is medium confidence from reconstructions that over the past three decades, Arctic summer sea ice retreat was unprecedented and sea surface temperatures were anomalously high in at least the last 1,450 years. {4.2, 5.5} It is very likely that the annual mean Antarctic sea ice extent increased at a rate in the range of 1.2 to 1.8% per decade (range of 0.13 to 0.20 million km2 per decade) between 1979 and 2012. There is high confidence that there are strong regional differences in this annual rate, with extent increasing in some regions and decreasing in others. {4.2} There is very high confidence that the extent of Northern Hemisphere snow cover has decreased since the mid-20th century (see Figure SPM.3). Northern Hemisphere snow cover extent decreased 1.6 [0.8 to 2.4] % per decade for March and April, and 11.7 [8.8 to 14.6] % per decade for June, over the 1967 to 2012 period. During this period, snow cover extent in the Northern Hemisphere did not show a statistically significant increase in any month. {4.5} There is high confidence that permafrost temperatures have increased in most regions since the early 1980s. Observed warming was up to 3°C in parts of Northern Alaska (early 1980s to mid-2000s) and up to 2°C in parts of the Russian European North (1971 to 2010). In the latter region, a considerable reduction in permafrost thickness and areal extent has been observed over the period 1975 to 2005 (medium confidence). {4.7} Multiple lines of evidence support very substantial Arctic warming since the mid-20th century. {Box 5.1, 10.3} 8 All references to ice loss or mass loss refer to net ice loss, i.e., accumulation minus melt and iceberg calving. 9 For methodological reasons, this assessment of ice loss from the Antarctic and Greenland ice sheets includes change in the glaciers on the periphery. These peripheral glaciers are thus excluded from the values given for glaciers. 10 100 Gt yr 1 of ice loss is equivalent to about 0.28 mm yr 1 of global mean sea level rise. 9 Summary for Policymakers (a) Northern Hemisphere spring snow cover 45 40 (million km2) SPM 35 30 1900 1920 1940 1960 1980 2000 Year (b) Arctic summer sea ice extent 14 12 (million km2) 10 8 6 4 1900 1920 1940 1960 1980 2000 Year (c) Change in global average upper ocean heat content 20 10 (1022 J) 0 10 20 1900 1920 1940 1960 1980 2000 Year (d) Global average sea level change 200 150 100 (mm) 50 0 50 1900 1920 1940 1960 1980 2000 Year Figure SPM.3 | Multiple observed indicators of a changing global climate: (a) Extent of Northern Hemisphere March-April (spring) average snow cover; (b) extent of Arctic July-August-September (summer) average sea ice; (c) change in global mean upper ocean (0 700 m) heat content aligned to 2006 2010, and relative to the mean of all datasets for 1970; (d) global mean sea level relative to the 1900 1905 mean of the longest running dataset, and with all datasets aligned to have the same value in 1993, the first year of satellite altimetry data. All time-series (coloured lines indicating different data sets) show annual values, and where assessed, uncertainties are indicated by coloured shading. See Technical Summary Supplementary Material for a listing of the datasets. {Figures 3.2, 3.13, 4.19, and 4.3; FAQ 2.1, Figure 2; Figure TS.1} 10 Summary for Policymakers B.4 Sea Level The rate of sea level rise since the mid-19th century has been larger than the mean rate during the previous two millennia (high confidence). Over the period 1901 to 2010, global mean sea level rose by 0.19 [0.17 to 0.21] m (see Figure SPM.3). {3.7, 5.6, 13.2} SPM Proxy and instrumental sea level data indicate a transition in the late 19th to the early 20th century from relatively low mean rates of rise over the previous two millennia to higher rates of rise (high confidence). It is likely that the rate of global mean sea level rise has continued to increase since the early 20th century. {3.7, 5.6, 13.2} It is very likely that the mean rate of global averaged sea level rise was 1.7 [1.5 to 1.9] mm yr 1 between 1901 and 2010, 2.0 [1.7 to 2.3] mm yr 1 between 1971 and 2010, and 3.2 [2.8 to 3.6] mm yr 1 between 1993 and 2010. Tide-gauge and satellite altimeter data are consistent regarding the higher rate of the latter period. It is likely that similarly high rates occurred between 1920 and 1950. {3.7} Since the early 1970s, glacier mass loss and ocean thermal expansion from warming together explain about 75% of the observed global mean sea level rise (high confidence). Over the period 1993 to 2010, global mean sea level rise is, with high confidence, consistent with the sum of the observed contributions from ocean thermal expansion due to warming (1.1 [0.8 to 1.4] mm yr 1), from changes in glaciers (0.76 [0.39 to 1.13] mm yr 1), Greenland ice sheet (0.33 [0.25 to 0.41] mm yr 1), Antarctic ice sheet (0.27 [0.16 to 0.38] mm yr 1), and land water storage (0.38 [0.26 to 0.49] mm yr 1). The sum of these contributions is 2.8 [2.3 to 3.4] mm yr 1. {13.3} There is very high confidence that maximum global mean sea level during the last interglacial period (129,000 to 116,000 years ago) was, for several thousand years, at least 5 m higher than present, and high confidence that it did not exceed 10 m above present. During the last interglacial period, the Greenland ice sheet very likely contributed between 1.4 and 4.3 m to the higher global mean sea level, implying with medium confidence an additional contribution from the Antarctic ice sheet. This change in sea level occurred in the context of different orbital forcing and with high-latitude surface temperature, averaged over several thousand years, at least 2°C warmer than present (high confidence). {5.3, 5.6} B.5 Carbon and Other Biogeochemical Cycles The atmospheric concentrations of carbon dioxide, methane, and nitrous oxide have increased to levels unprecedented in at least the last 800,000 years. Carbon dioxide concentrations have increased by 40% since pre-industrial times, primarily from fossil fuel emissions and secondarily from net land use change emissions. The ocean has absorbed about 30% of the emitted anthropogenic carbon dioxide, causing ocean acidification (see Figure SPM.4). {2.2, 3.8, 5.2, 6.2, 6.3} The atmospheric concentrations of the greenhouse gases carbon dioxide (CO2), methane (CH4), and nitrous oxide (N2O) have all increased since 1750 due to human activity. In 2011 the concentrations of these greenhouse gases were 391 ppm11, 1803 ppb, and 324 ppb, and exceeded the pre-industrial levels by about 40%, 150%, and 20%, respectively. {2.2, 5.2, 6.1, 6.2} Concentrations of CO2, CH4, and N2O now substantially exceed the highest concentrations recorded in ice cores during the past 800,000 years. The mean rates of increase in atmospheric concentrations over the past century are, with very high confidence, unprecedented in the last 22,000 years. {5.2, 6.1, 6.2} ppm (parts per million) or ppb (parts per billion, 1 billion = 1,000 million) is the ratio of the number of gas molecules to the total number of molecules of dry air. For example, 11 300 ppm means 300 molecules of a gas per million molecules of dry air. 11 Summary for Policymakers Annual CO2 emissions from fossil fuel combustion and cement ­ roduction were 8.3 [7.6 to 9.0] GtC12 yr 1 averaged over p 2002 2011 (high confidence) and were 9.5 [8.7 to 10.3] GtC yr 1 in 2011, 54% above the 1990 level. Annual net CO2 emissions from ­ nthropogenic land use change were 0.9 [0.1 to 1.7] GtC yr 1 on average during 2002 to 2011 (medium a confidence). {6.3} SPM From 1750 to 2011, CO2 emissions from fossil fuel combustion and cement production have released 375 [345 to 405] GtC to the atmosphere, while deforestation and other land use change are estimated to have released 180 [100 to 260] GtC. This results in cumulative anthropogenic emissions of 555 [470 to 640] GtC. {6.3} Of these cumulative anthropogenic CO2 emissions, 240 [230 to 250] GtC have accumulated in the atmosphere, 155 [125 to 185] GtC have been taken up by the ocean and 160 [70 to 250] GtC have accumulated in natural terrestrial ecosystems (i.e., the cumulative residual land sink). {Figure TS.4, 3.8, 6.3} Ocean acidification is quantified by decreases in pH13. The pH of ocean surface water has decreased by 0.1 since the beginning of the industrial era (high confidence), corresponding to a 26% increase in hydrogen ion concentration (see Figure SPM.4). {3.8, Box 3.2} Atmospheric CO2 (a) 400 380 CO2 (ppm) 360 340 320 300 1950 1960 1970 1980 1990 2000 2010 Year Surface ocean CO2 and pH (b) 400 380 pCO2 ( atm) 360 340 320 8.12 in situ pH unit 8.09 8.06 1950 1960 1970 1980 1990 2000 2010 Year Figure SPM.4 | Multiple observed indicators of a changing global carbon cycle: (a) atmospheric concentrations of carbon dioxide (CO2) from Mauna Loa (19°32 N, 155°34 W red) and South Pole (89°59 S, 24°48 W black) since 1958; (b) partial pressure of dissolved CO2 at the ocean surface (blue curves) and in situ pH (green curves), a measure of the acidity of ocean water. Measurements are from three stations from the Atlantic (29°10 N, 15°30 W dark blue/dark green; 31°40 N, 64°10 W blue/green) and the Pacific Oceans (22°45 N, 158°00 W light blue/light green). Full details of the datasets shown here are provided in the underlying report and the Technical Summary Supplementary Material. {Figures 2.1 and 3.18; Figure TS.5} 1 Gigatonne of carbon = 1 GtC = 1015 grams of carbon. This corresponds to 3.667 GtCO2. 12 pH is a measure of acidity using a logarithmic scale: a pH decrease of 1 unit corresponds to a 10-fold increase in hydrogen ion concentration, or acidity. 13 12 Summary for Policymakers C. Drivers of Climate Change Natural and anthropogenic substances and processes that alter the Earth s energy budget are drivers of climate change. Radiative forcing14 (RF) quantifies the change in energy fluxes caused by changes in these drivers for 2011 relative to 1750, unless otherwise indicated. Positive RF leads to surface warming, negative RF leads to surface cooling. RF is estimated based on in-situ and remote observations, properties of greenhouse gases and aerosols, and calculations using numerical models SPM representing observed processes. Some emitted compounds affect the atmospheric concentration of other substances. The RF can be reported based on the concentration changes of each substance15. Alternatively, the emission-based RF of a compound can be reported, which provides a more direct link to human activities. It includes contributions from all substances affected by that emission. The total anthropogenic RF of the two approaches are identical when considering all drivers. Though both approaches are used in this Summary for Policymakers, emission-based RFs are emphasized. Total radiative forcing is positive, and has led to an uptake of energy by the climate system. The largest contribution to total radiative forcing is caused by the increase in the atmospheric concentration of CO2 since 1750 (see Figure SPM.5). {3.2, Box 3.1, 8.3, 8.5} The total anthropogenic RF for 2011 relative to 1750 is 2.29 [1.13 to 3.33] W m 2 (see Figure SPM.5), and it has increased more rapidly since 1970 than during prior decades. The total anthropogenic RF best estimate for 2011 is 43% higher than that reported in AR4 for the year 2005. This is caused by a combination of continued growth in most greenhouse gas concentrations and improved estimates of RF by aerosols indicating a weaker net cooling effect (negative RF). {8.5} The RF from emissions of well-mixed greenhouse gases (CO2, CH4, N2O, and Halocarbons) for 2011 relative to 1750 is 3.00 [2.22 to 3.78] W m 2 (see Figure SPM.5). The RF from changes in concentrations in these gases is 2.83 [2.26 to 3.40] W m 2. {8.5} Emissions of CO2 alone have caused an RF of 1.68 [1.33 to 2.03] W m 2 (see Figure SPM.5). Including emissions of other carbon-containing gases, which also contributed to the increase in CO2 concentrations, the RF of CO2 is 1.82 [1.46 to 2.18] W m 2. {8.3, 8.5} Emissions of CH4 alone have caused an RF of 0.97 [0.74 to 1.20] W m 2 (see Figure SPM.5). This is much larger than the concentration-based estimate of 0.48 [0.38 to 0.58] W m 2 (unchanged from AR4). This difference in estimates is caused by concentration changes in ozone and stratospheric water vapour due to CH4 emissions and other emissions indirectly affecting CH4. {8.3, 8.5} Emissions of stratospheric ozone-depleting halocarbons have caused a net positive RF of 0.18 [0.01 to 0.35] W m 2 (see Figure SPM.5). Their own positive RF has outweighed the negative RF from the ozone depletion that they have induced. The positive RF from all halocarbons is similar to the value in AR4, with a reduced RF from CFCs but increases from many of their substitutes. {8.3, 8.5} Emissions of short-lived gases contribute to the total anthropogenic RF. Emissions of carbon monoxide (CO) are virtually certain to have induced a positive RF, while emissions of nitrogen oxides (NOx) are likely to have induced a net negative RF (see Figure SPM.5). {8.3, 8.5} The RF of the total aerosol effect in the atmosphere, which includes cloud adjustments due to aerosols, is 0.9 [ 1.9 to 0.1] W m 2 (medium confidence), and results from a negative forcing from most aerosols and a positive contribution The strength of drivers is quantified as Radiative Forcing (RF) in units watts per square metre (W m 2) as in previous IPCC assessments. RF is the change in energy flux 14 caused by a driver, and is calculated at the tropopause or at the top of the atmosphere. In the traditional RF concept employed in previous IPCC reports all surface and tropospheric conditions are kept fixed. In calculations of RF for well-mixed greenhouse gases and aerosols in this report, physical variables, except for the ocean and sea ice, are allowed to respond to perturbations with rapid adjustments. The resulting forcing is called Effective Radiative Forcing (ERF) in the underlying report. This change reflects the scientific progress from previous assessments and results in a better indication of the eventual temperature response for these drivers. For all drivers other than well-mixed greenhouse gases and aerosols, rapid adjustments are less well characterized and assumed to be small, and thus the traditional RF is used. {8.1} This approach was used to report RF in the AR4 Summary for Policymakers. 15 13 Summary for Policymakers from black carbon absorption of solar radiation. There is high confidence that ­ erosols and their interactions with clouds a have offset a substantial portion of global mean forcing from well-mixed greenhouse gases. They continue to contribute the largest uncertainty to the total RF estimate. {7.5, 8.3, 8.5} The forcing from stratospheric volcanic aerosols can have a large impact on the climate for some years after volcanic SPM eruptions. Several small eruptions have caused an RF of 0.11 [ 0.15 to 0.08] W m 2 for the years 2008 to 2011, which is approximately twice as strong as during the years 1999 to 2002. {8.4} The RF due to changes in solar irradiance is estimated as 0.05 [0.00 to 0.10] W m 2 (see Figure SPM.5). Satellite obser- vations of total solar irradiance changes from 1978 to 2011 indicate that the last solar minimum was lower than the previous two. This results in an RF of 0.04 [ 0.08 to 0.00] W m 2 between the most recent minimum in 2008 and the 1986 minimum. {8.4} The total natural RF from solar irradiance changes and stratospheric volcanic aerosols made only a small contribution to the net radiative forcing throughout the last century, except for brief periods after large volcanic eruptions. {8.5} Emitted Resulting atmospheric Level of compound drivers Radiative forcing by emissions and drivers confidence CO2 CO2 1.68 [1.33 to 2.03] VH Well-mixed greenhouse gases CH4 CO2 H2Ostr O3 CH4 0.97 [0.74 to 1.20] H Halo- O3 CFCs HCFCs 0.18 [0.01 to 0.35] H carbons N 2O N 2O 0.17 [0.13 to 0.21] VH CO Anthropogenic CO2 CH4 O3 0.23 [0.16 to 0.30] M Short lived gases and aerosols NMVOC CO2 CH4 O3 0.10 [0.05 to 0.15] M NOx Nitrate CH4 O3 -0.15 [-0.34 to 0.03] M Aerosols and Mineral dust Sulphate Nitrate precursors Organic carbon Black carbon -0.27 [-0.77 to 0.23] H (Mineral dust, SO2, NH3, Organic carbon Cloud adjustments -0.55 [-1.33 to -0.06] L and Black carbon) due to aerosols Albedo change -0.15 [-0.25 to -0.05] M due to land use Changes in Natural 0.05 [0.00 to 0.10] M solar irradiance 2.29 [1.13 to 3.33] 2011 H Total anthropogenic 1980 1.25 [0.64 to 1.86] H RF relative to 1750 1950 0.57 [0.29 to 0.85] M 1 0 1 2 3 Radiative forcing relative to 1750 (W m 2) Figure SPM.5 | Radiative forcing estimates in 2011 relative to 1750 and aggregated uncertainties for the main drivers of climate change. Values are global average radiative forcing (RF14), partitioned according to the emitted compounds or processes that result in a combination of drivers. The best esti- mates of the net radiative forcing are shown as black diamonds with corresponding uncertainty intervals; the numerical values are provided on the right of the figure, together with the confidence level in the net forcing (VH very high, H high, M medium, L low, VL very low). Albedo forcing due to black carbon on snow and ice is included in the black carbon aerosol bar. Small forcings due to contrails (0.05 W m 2, including contrail induced cirrus), and HFCs, PFCs and SF6 (total 0.03 W m 2) are not shown. Concentration-based RFs for gases can be obtained by summing the like-coloured bars. Volcanic forcing is not included as its episodic nature makes is difficult to compare to other forcing mechanisms. Total anthropogenic radiative forcing is provided for three different years relative to 1750. For further technical details, including uncertainty ranges associated with individual components and processes, see the Technical Summary Supplementary Material. {8.5; Figures 8.14 8.18; Figures TS.6 and TS.7} 14 Summary for Policymakers D. Understanding the Climate System and its Recent Changes Understanding recent changes in the climate system results from combining observations, studies of feedback processes, and model simulations. Evaluation of the ability of climate models to simulate recent changes requires consideration of the state of all modelled climate system components at the start of the simulation and the natural and anthropogenic forcing used to drive the models. Compared to AR4, more detailed and longer observations and improved climate models now enable the SPM attribution of a human contribution to detected changes in more climate system components. Human influence on the climate system is clear. This is evident from the increasing greenhouse gas concentrations in the atmosphere, positive radiative forcing, observed warming, and understanding of the climate system. {2 14} D.1 Evaluation of Climate Models Climate models have improved since the AR4. Models reproduce observed continental- scale surface temperature patterns and trends over many decades, including the more rapid warming since the mid-20th century and the cooling immediately following large volcanic eruptions (very high confidence). {9.4, 9.6, 9.8} The long-term climate model simulations show a trend in global-mean surface temperature from 1951 to 2012 that agrees with the observed trend (very high confidence). There are, however, differences between simulated and observed trends over periods as short as 10 to 15 years (e.g., 1998 to 2012). {9.4, Box 9.2} The observed reduction in surface warming trend over the period 1998 to 2012 as compared to the period 1951 to 2012, is due in roughly equal measure to a reduced trend in radiative forcing and a cooling contribution from natural internal variability, which includes a possible redistribution of heat within the ocean (medium confidence). The reduced trend in radiative forcing is primarily due to volcanic eruptions and the timing of the downward phase of the 11-year solar cycle. However, there is low confidence in quantifying the role of changes in radiative forcing in causing the reduced warming trend. There is medium confidence that natural internal decadal variability causes to a substantial degree the difference between observations and the simulations; the latter are not expected to reproduce the timing of natural internal variability. There may also be a contribution from forcing inadequacies and, in some models, an overestimate of the response to increasing greenhouse gas and other anthropogenic forcing (dominated by the effects of aerosols). {9.4, Box 9.2, 10.3, Box 10.2, 11.3} On regional scales, the confidence in model capability to simulate surface temperature is less than for the larger scales. However, there is high confidence that regional-scale surface temperature is better simulated than at the time of the AR4. {9.4, 9.6} There has been substantial progress in the assessment of extreme weather and climate events since AR4. Simulated global-mean trends in the frequency of extreme warm and cold days and nights over the second half of the 20th century are generally consistent with observations. {9.5} There has been some improvement in the simulation of continental-­ cale patterns of precipitation since the AR4. At s regional scales, precipitation is not simulated as well, and the assessment is hampered by observational uncertainties. {9.4, 9.6} Some important climate phenomena are now better reproduced by models. There is high confidence that the statistics of monsoon and El Nino-Southern Oscillation (ENSO) based on multi-model simulations have improved since AR4. {9.5} 15 Summary for Policymakers Climate models now include more cloud and aerosol processes, and their interactions, than at the time of the AR4, but there remains low confidence in the representation and quantification of these processes in models. {7.3, 7.6, 9.4, 9.7} There is robust evidence that the downward trend in Arctic summer sea ice extent since 1979 is now reproduced by more models than at the time of the AR4, with about one-quarter of the models showing a trend as large as, or larger than, the trend in the observations. Most models simulate a small downward trend in Antarctic sea ice extent, albeit with large SPM inter-model spread, in contrast to the small upward trend in observations. {9.4} Many models reproduce the observed changes in upper-ocean heat content (0 700 m) from 1961 to 2005 (high confidence), with the multi-model mean time series falling within the range of the available observational estimates for most of the period. {9.4} Climate models that include the carbon cycle (Earth System Models) simulate the global pattern of ocean-atmosphere CO2 fluxes, with outgassing in the tropics and uptake in the mid and high latitudes. In the majority of these models the sizes of the simulated global land and ocean carbon sinks over the latter part of the 20th century are within the range of observational estimates. {9.4} D.2 Quantification of Climate System Responses Observational and model studies of temperature change, climate feedbacks and changes in the Earth s energy budget together provide confidence in the magnitude of global warming in response to past and future forcing. {Box 12.2, Box 13.1} The net feedback from the combined effect of changes in water vapour, and differences between atmospheric and surface warming is extremely likely positive and therefore amplifies changes in climate. The net radiative feedback due to all cloud types combined is likely positive. Uncertainty in the sign and magnitude of the cloud feedback is due primarily to continuing uncertainty in the impact of warming on low clouds. {7.2} The equilibrium climate sensitivity quantifies the response of the climate system to constant radiative forcing on multi- century time scales. It is defined as the change in global mean surface temperature at equilibrium that is caused by a doubling of the atmospheric CO2 concentration. Equilibrium climate sensitivity is likely in the range 1.5°C to 4.5°C (high confidence), extremely unlikely less than 1°C (high confidence), and very unlikely greater than 6°C (medium confidence)16. The lower temperature limit of the assessed likely range is thus less than the 2°C in the AR4, but the upper limit is the same. This assessment reflects improved understanding, the extended temperature record in the atmosphere and ocean, and new estimates of radiative forcing. {TS TFE.6, Figure 1; Box 12.2} The rate and magnitude of global climate change is determined by radiative forcing, climate feedbacks and the storage of energy by the climate system. Estimates of these quantities for recent decades are consistent with the assessed likely range of the equilibrium climate sensitivity to within assessed uncertainties, providing strong evidence for our understanding of anthropogenic climate change. {Box 12.2, Box 13.1} The transient climate response quantifies the response of the climate system to an increasing radiative forcing on a decadal to century timescale. It is defined as the change in global mean surface temperature at the time when the atmospheric CO2 concentration has doubled in a scenario of concentration increasing at 1% per year. The transient climate response is likely in the range of 1.0°C to 2.5°C (high confidence) and extremely unlikely greater than 3°C. {Box 12.2} A related quantity is the transient climate response to cumulative carbon emissions (TCRE). It quantifies the transient response of the climate system to cumulative carbon emissions (see Section E.8). TCRE is defined as the global mean ­ No best estimate for equilibrium climate sensitivity can now be given because of a lack of agreement on values across assessed lines of evidence and studies. 16 16 Summary for Policymakers s ­ urface temperature change per 1000 GtC emitted to the atmosphere. TCRE is likely in the range of 0.8°C to 2.5°C per 1000 GtC and applies for cumulative emissions up to about 2000 GtC until the time temperatures peak (see Figure SPM.10). {12.5, Box 12.2} Various metrics can be used to compare the contributions to climate change of emissions of different substances. The most appropriate metric and time horizon will depend on which aspects of climate change are considered most important SPM to a particular application. No single metric can accurately compare all consequences of different emissions, and all have limitations and uncertainties. The Global Warming Potential is based on the cumulative radiative forcing over a particular time horizon, and the Global Temperature Change Potential is based on the change in global mean surface temperature at a chosen point in time. Updated values are provided in the underlying Report. {8.7} D.3 Detection and Attribution of Climate Change Human influence has been detected in warming of the atmosphere and the ocean, in changes in the global water cycle, in reductions in snow and ice, in global mean sea level rise, and in changes in some climate extremes (see Figure SPM.6 and Table SPM.1). This evidence for human influence has grown since AR4. It is extremely likely that human influence has been the dominant cause of the observed warming since the mid-20th century. {10.3 10.6, 10.9} It is extremely likely that more than half of the observed increase in global average surface temperature from 1951 to 2010 was caused by the anthropogenic increase in greenhouse gas concentrations and other anthropogenic forcings together. The best estimate of the human-induced contribution to warming is similar to the observed warming over this period. {10.3} Greenhouse gases contributed a global mean surface warming likely to be in the range of 0.5°C to 1.3°C over the period 1951 to 2010, with the contributions from other anthropogenic forcings, including the cooling effect of aerosols, likely to be in the range of 0.6°C to 0.1°C. The contribution from natural forcings is likely to be in the range of 0.1°C to 0.1°C, and from natural internal variability is likely to be in the range of 0.1°C to 0.1°C. Together these assessed contributions are consistent with the observed warming of approximately 0.6°C to 0.7°C over this period. {10.3} Over every continental region except Antarctica, anthropogenic forcings have likely made a substantial contribution to surface temperature increases since the mid-20th century (see Figure SPM.6). For Antarctica, large observational uncer- tainties result in low confidence that anthropogenic forcings have contributed to the observed warming averaged over available stations. It is likely that there has been an anthropogenic contribution to the very substantial Arctic warming since the mid-20th century. {2.4, 10.3} It is very likely that anthropogenic influence, particularly greenhouse gases and stratospheric ozone depletion, has led to a detectable observed pattern of tropospheric warming and a corresponding cooling in the lower stratosphere since 1961. {2.4, 9.4, 10.3} It is very likely that anthropogenic forcings have made a substantial contribution to increases in global upper ocean heat content (0 700 m) observed since the 1970s (see Figure SPM.6). There is evidence for human influence in some individual ocean basins. {3.2, 10.4} It is likely that anthropogenic influences have affected the global water cycle since 1960. Anthropogenic influences have contributed to observed increases in atmospheric moisture content in the atmosphere (medium confidence), to global- scale changes in precipitation patterns over land (medium confidence), to intensification of heavy precipitation over land regions where data are sufficient (medium confidence), and to changes in surface and sub-surface ocean salinity (very likely). {2.5, 2.6, 3.3, 7.6, 10.3, 10.4} 17 Summary for Policymakers SPM Global averages Land surface Land and ocean surface Ocean heat content Observations Models using only natural forcings Models using both natural and anthropogenic forcings Figure SPM.6 | Comparison of observed and simulated climate change based on three large-scale indicators in the atmosphere, the cryosphere and the ocean: change in continental land surface air temperatures (yellow panels), Arctic and Antarctic September sea ice extent (white panels), and upper ocean heat content in the major ocean basins (blue panels). Global average changes are also given. Anomalies are given relative to 1880 1919 for surface temperatures, 1960 1980 for ocean heat content and 1979 1999 for sea ice. All time-series are decadal averages, plotted at the centre of the decade. For temperature panels, observations are dashed lines if the spatial coverage of areas being examined is below 50%. For ocean heat content and sea ice panels the solid line is where the coverage of data is good and higher in quality, and the dashed line is where the data coverage is only adequate, and thus, uncertainty is larger. Model results shown are Coupled Model Intercomparison Project Phase 5 (CMIP5) multi-model ensemble ranges, with shaded bands indicating the 5 to 95% confidence intervals. For further technical details, including region definitions see the Technical Summary Supplementary Material. {Figure 10.21; Figure TS.12} 18 Summary for Policymakers There has been further strengthening of the evidence for human influence on temperature extremes since the SREX. It is now very likely that human influence has contributed to observed global scale changes in the frequency and intensity of daily temperature extremes since the mid-20th century, and likely that human influence has more than doubled the probability of occurrence of heat waves in some locations (see Table SPM.1). {10.6} Anthropogenic influences have very likely contributed to Arctic sea ice loss since 1979. There is low confidence in the SPM scientific understanding of the small observed increase in Antarctic sea ice extent due to the incomplete and competing scientific explanations for the causes of change and low confidence in estimates of natural internal variability in that region (see Figure SPM.6). {10.5} Anthropogenic influences likely contributed to the retreat of glaciers since the 1960s and to the increased surface mass loss of the Greenland ice sheet since 1993. Due to a low level of scientific understanding there is low confidence in attributing the causes of the observed loss of mass from the Antarctic ice sheet over the past two decades. {4.3, 10.5} It is likely that there has been an anthropogenic contribution to observed reductions in Northern Hemisphere spring snow cover since 1970. {10.5} It is very likely that there is a substantial anthropogenic contribution to the global mean sea level rise since the 1970s. This is based on the high confidence in an anthropogenic influence on the two largest contributions to sea level rise, that is thermal expansion and glacier mass loss. {10.4, 10.5, 13.3} There is high confidence that changes in total solar irradiance have not contributed to the increase in global mean surface temperature over the period 1986 to 2008, based on direct satellite measurements of total solar irradiance. There is medium confidence that the 11-year cycle of solar variability influences decadal climate fluctuations in some regions. No robust association between changes in cosmic rays and cloudiness has been identified. {7.4, 10.3, Box 10.2} E. Future Global and Regional Climate Change Projections of changes in the climate system are made using a hierarchy of climate models ranging from simple climate models, to models of intermediate complexity, to comprehensive climate models, and Earth System Models. These models simulate changes based on a set of scenarios of anthropogenic forcings. A new set of scenarios, the Representative Concentration Pathways (RCPs), was used for the new climate model simulations carried out under the framework of the Coupled Model Intercomparison Project Phase 5 (CMIP5) of the World Climate Research Programme. In all RCPs, atmospheric CO2 concentrations are higher in 2100 relative to present day as a result of a further increase of cumulative emissions of CO2 to the atmosphere during the 21st century (see Box SPM.1). Projections in this Summary for Policymakers are for the end of the 21st century (2081 2100) given relative to 1986 2005, unless otherwise stated. To place such projections in historical context, it is necessary to consider observed changes between different periods. Based on the longest global surface temperature dataset available, the observed change between the average of the period 1850 1900 and of the AR5 reference period is 0.61 [0.55 to 0.67] °C. However, warming has occurred beyond the average of the AR5 reference period. Hence this is not an estimate of historical warming to present (see Chapter 2) . Continued emissions of greenhouse gases will cause further warming and changes in all c ­omponents of the climate system. Limiting climate change will require substantial and sustained reductions of greenhouse gas emissions. {6, 11 14} Projections for the next few decades show spatial patterns of climate change similar to those projected for the later 21st century but with smaller magnitude. Natural internal variability will continue to be a major influence on climate, particularly in the near-term and at the regional scale. By the mid-21st century the magnitudes of the projected changes are substantially affected by the choice of emissions scenario (Box SPM.1). {11.3, Box 11.1, Annex I} 19 Summary for Policymakers Projected climate change based on RCPs is similar to AR4 in both patterns and magnitude, after accounting for scenario differences. The overall spread of projections for the high RCPs is narrower than for comparable scenarios used in AR4 because in contrast to the SRES emission scenarios used in AR4, the RCPs used in AR5 are defined as concentration pathways and thus carbon cycle uncertainties affecting atmospheric CO2 concentrations are not considered in the concentration-driven CMIP5 simulations. Projections of sea level rise are larger than in the AR4, primarily because of SPM improved modelling of land-ice contributions.{11.3, 12.3, 12.4, 13.4, 13.5} E.1 Atmosphere: Temperature Global surface temperature change for the end of the 21st century is likely to exceed 1.5°C relative to 1850 to 1900 for all RCP scenarios except RCP2.6. It is likely to exceed 2°C for RCP6.0 and RCP8.5, and more likely than not to exceed 2°C for RCP4.5. Warming will continue beyond 2100 under all RCP scenarios except RCP2.6. Warming will continue to exhibit interannual-to-decadal variability and will not be regionally uniform (see Figures SPM.7 and SPM.8). {11.3, 12.3, 12.4, 14.8} The global mean surface temperature change for the period 2016 2035 relative to 1986 2005 will likely be in the range of 0.3°C to 0.7°C (medium confidence). This assessment is based on multiple lines of evidence and assumes there will be no major volcanic eruptions or secular changes in total solar irradiance. Relative to natural internal variability, near-term increases in seasonal mean and annual mean temperatures are expected to be larger in the tropics and subtropics than in mid-latitudes (high confidence). {11.3} Increase of global mean surface temperatures for 2081 2100 relative to 1986 2005 is projected to likely be in the ranges derived from the concentration-driven CMIP5 model simulations, that is, 0.3°C to 1.7°C (RCP2.6), 1.1°C to 2.6°C (RCP4.5), 1.4°C to 3.1°C (RCP6.0), 2.6°C to 4.8°C (RCP8.5). The Arctic region will warm more rapidly than the global mean, and mean warming over land will be larger than over the ocean (very high confidence) (see Figures SPM.7 and SPM.8, and Table SPM.2). {12.4, 14.8} Relative to the average from year 1850 to 1900, global surface temperature change by the end of the 21st century is projected to likely exceed 1.5°C for RCP4.5, RCP6.0 and RCP8.5 (high confidence). Warming is likely to exceed 2°C for RCP6.0 and RCP8.5 (high confidence), more likely than not to exceed 2°C for RCP4.5 (high confidence), but unlikely to exceed 2°C for RCP2.6 (medium confidence). Warming is unlikely to exceed 4°C for RCP2.6, RCP4.5 and RCP6.0 (high confidence) and is about as likely as not to exceed 4°C for RCP8.5 (medium confidence). {12.4} It is virtually certain that there will be more frequent hot and fewer cold temperature extremes over most land areas on daily and seasonal timescales as global mean temperatures increase. It is very likely that heat waves will occur with a higher frequency and duration. Occasional cold winter extremes will continue to occur (see Table SPM.1). {12.4} E.2 Atmosphere: Water Cycle Changes in the global water cycle in response to the warming over the 21st century will not be uniform. The contrast in precipitation between wet and dry regions and between wet and dry seasons will increase, although there may be regional exceptions (see Figure SPM.8). {12.4, 14.3} Projected changes in the water cycle over the next few decades show similar large-scale patterns to those towards the end of the century, but with smaller magnitude. Changes in the near-term, and at the regional scale will be strongly influenced by natural internal variability and may be affected by anthropogenic aerosol emissions. {11.3} 20 Summary for Policymakers (a) Global average surface temperature change Mean over 6.0 2081 2100 historical RCP2.6 4.0 RCP8.5 39 SPM (oC) 2.0 RCP8.5 RCP6.0 42 RCP4.5 0.0 32 RCP2.6 2.0 1950 2000 2050 2100 (b) Northern Hemisphere September sea ice extent 10.0 39 (5) 8.0 (106 km2) 6.0 29 (3) 4.0 2.0 37 (5) 0.0 RCP2.6 RCP4.5 RCP6.0 RCP8.5 1950 2000 2050 2100 (c) Global ocean surface pH 8.2 12 9 (pH unit) 8.0 RCP2.6 RCP4.5 10 RCP6.0 7.8 RCP8.5 7.6 1950 2000 2050 2100 Year Figure SPM.7 | CMIP5 multi-model simulated time series from 1950 to 2100 for (a) change in global annual mean surface temperature relative to 1986 2005, (b) Northern Hemisphere September sea ice extent (5-year running mean), and (c) global mean ocean surface pH. Time series of projections and a measure of uncertainty (shading) are shown for scenarios RCP2.6 (blue) and RCP8.5 (red). Black (grey shading) is the modelled historical evolution using historical reconstructed forcings. The mean and associated uncertainties averaged over 2081 2100 are given for all RCP scenarios as colored verti- cal bars. The numbers of CMIP5 models used to calculate the multi-model mean is indicated. For sea ice extent (b), the projected mean and uncertainty (minimum-maximum range) of the subset of models that most closely reproduce the climatological mean state and 1979 to 2012 trend of the Arctic sea ice is given (number of models given in brackets). For completeness, the CMIP5 multi-model mean is also indicated with dotted lines. The dashed line represents nearly ice-free conditions (i.e., when sea ice extent is less than 106 km2 for at least five consecutive years). For further technical details see the Technical Summary Supplementary Material {Figures 6.28, 12.5, and 12.28 12.31; Figures TS.15, TS.17, and TS.20} 21 Summary for Policymakers RCP 2.6 RCP 8.5 (a) Change in average surface temperature (1986 2005 to 2081 2100) 32 39 SPM (°C) 2 1.5 1 0.5 0 0.5 1 1.5 2 3 4 5 7 9 11 (b) Change in average precipitation (1986 2005 to 2081 2100) 32 39 (%) 50 40 30 20 10 0 10 20 30 40 50 (c) Northern Hemisphere September sea ice extent (average 2081 2100) 29 (3) 37 (5) CMIP5 multi-model average 1986 2005 CMIP5 multi-model average 2081 2100 CMIP5 subset average 1986 2005 CMIP5 subset average 2081 2100 (d) Change in ocean surface pH (1986 2005 to 2081 2100) 9 10 (pH unit) 0.6 0.55 0.5 0.45 0.4 0.35 0.3 0.25 0.2 0.15 0.1 0.05 Figure SPM.8 | Maps of CMIP5 multi-model mean results for the scenarios RCP2.6 and RCP8.5 in 2081 2100 of (a) annual mean surface temperature change, (b) average percent change in annual mean precipitation, (c) Northern Hemisphere September sea ice extent, and (d) change in ocean surface pH. Changes in panels (a), (b) and (d) are shown relative to 1986 2005. The number of CMIP5 models used to calculate the multi-model mean is indicated in the upper right corner of each panel. For panels (a) and (b), hatching indicates regions where the multi-model mean is small compared to natural internal variability (i.e., less than one standard deviation of natural internal variability in 20-year means). Stippling indicates regions where the multi-model mean is large compared to natural internal variability (i.e., greater than two standard deviations of natural internal variability in 20-year means) and where at least 90% of models agree on the sign of change (see Box 12.1). In panel (c), the lines are the modelled means for 1986 2005; the filled areas are for the end of the century. The CMIP5 multi-model mean is given in white colour, the projected mean sea ice extent of a subset of models (number of models given in brackets) that most closely reproduce the climatological mean state and 1979 to 2012 trend of the Arctic sea ice extent is given in light blue colour. For further technical details see the Technical Summary Supplementary Material. {Figures 6.28, 12.11, 12.22, and 12.29; Figures TS.15, TS.16, TS.17, and TS.20} 22 Summary for Policymakers The high latitudes and the equatorial Pacific Ocean are likely to experience an increase in annual mean precipitation by the end of this century under the RCP8.5 scenario. In many mid-latitude and subtropical dry regions, mean precipitation will likely decrease, while in many mid-latitude wet regions, mean precipitation will likely increase by the end of this century under the RCP8.5 scenario (see Figure SPM.8). {7.6, 12.4, 14.3} Extreme precipitation events over most of the mid-latitude land masses and over wet tropical regions will very likely become more intense and more frequent by the end of this century, as global mean surface temperature increases (see Table SPM.1). {7.6, 12.4} Globally, it is likely that the area encompassed by monsoon systems will increase over the 21st century. While monsoon winds are likely to weaken, monsoon precipitation is likely to intensify due to the increase in atmospheric moisture. Monsoon onset dates are likely to become earlier or not to change much. Monsoon retreat dates will likely be delayed, resulting in lengthening of the monsoon season in many regions. {14.2} There is high confidence that the El Nino-Southern Oscillation (ENSO) will remain the dominant mode of interannual variability in the tropical Pacific, with global effects in the 21st century. Due to the increase in moisture availability, ENSO- related precipitation variability on regional scales will likely intensify. Natural variations of the amplitude and spatial pattern of ENSO are large and thus confidence in any specific projected change in ENSO and related regional phenomena for the 21st century remains low. {5.4, 14.4} Table SPM.2 | Projected change in global mean surface air temperature and global mean sea level rise for the mid- and late 21st century relative to the reference period of 1986 2005. {12.4; Table 12.2, Table 13.5} 2046 2065 2081 2100 Scenario Mean Likely range c Mean Likely rangec RCP2.6 1.0 0.4 to 1.6 1.0 0.3 to 1.7 Global Mean Surface RCP4.5 1.4 0.9 to 2.0 1.8 1.1 to 2.6 Temperature Change (°C) a RCP6.0 1.3 0.8 to 1.8 2.2 1.4 to 3.1 RCP8.5 2.0 1.4 to 2.6 3.7 2.6 to 4.8 Scenario Mean Likely range d Mean Likely ranged RCP2.6 0.24 0.17 to 0.32 0.40 0.26 to 0.55 Global Mean Sea Level RCP4.5 0.26 0.19 to 0.33 0.47 0.32 to 0.63 Rise (m)b RCP6.0 0.25 0.18 to 0.32 0.48 0.33 to 0.63 RCP8.5 0.30 0.22 to 0.38 0.63 0.45 to 0.82 Notes: a Based on the CMIP5 ensemble; anomalies calculated with respect to 1986 2005. Using HadCRUT4 and its uncertainty estimate (5 95% confidence interval), the observed warming to the reference period 1986 2005 is 0.61 [0.55 to 0.67] °C from 1850 1900, and 0.11 [0.09 to 0.13] °C from 1980 1999, the reference period for projections used in AR4. Likely ranges have not been assessed here with respect to earlier reference periods because methods are not generally available in the literature for combining the uncertainties in models and observations. Adding projected and observed changes does not account for potential effects of model biases compared to observations, and for natural internal variability during the observational reference period {2.4; 11.2; Tables 12.2 and 12.3} b Based on 21 CMIP5 models; anomalies calculated with respect to 1986 2005. Where CMIP5 results were not available for a particular AOGCM and scenario, they were estimated as explained in Chapter 13, Table 13.5. The contributions from ice sheet rapid dynamical change and anthropogenic land water storage are treated as having uniform probability distributions, and as largely independent of scenario. This treatment does not imply that the contributions concerned will not depend on the scenario followed, only that the current state of knowledge does not permit a quantitative assessment of the dependence. Based on current understanding, only the collapse of marine-based sectors of the Antarctic ice sheet, if initiated, could cause global mean sea level to rise substantially above the likely range during the 21st century. There is medium confidence that this additional contribution would not exceed several tenths of a meter of sea level rise during the 21st century. Calculated from projections as 5 95% model ranges. These ranges are then assessed to be likely ranges after accounting for additional uncertainties or different levels c of confidence in models. For projections of global mean surface temperature change in 2046 2065 confidence is medium, because the relative importance of natural internal variability, and uncertainty in non-greenhouse gas forcing and response, are larger than for 2081 2100. The likely ranges for 2046 2065 do not take into account the possible influence of factors that lead to the assessed range for near-term (2016 2035) global mean surface temperature change that is lower than the 5 95% model range, because the influence of these factors on longer term projections has not been quantified due to insufficient scientific understanding. {11.3} d Calculated from projections as 5 95% model ranges. These ranges are then assessed to be likely ranges after accounting for additional uncertainties or different levels of confidence in models. For projections of global mean sea level rise confidence is medium for both time horizons. 23 Summary for Policymakers E.3 Atmosphere: Air Quality The range in projections of air quality (ozone and PM2.517 in near-surface air) is driven primarily by emissions (including CH4), rather than by physical climate change (medium confidence). There is high confidence that globally, warming decreases background surface ozone. High CH4 levels (as in RCP8.5) can offset this decrease, raising background surface SPM ozone by year 2100 on average by about 8 ppb (25% of current levels) relative to scenarios with small CH4 changes (as in RCP4.5 and RCP6.0) (high confidence). {11.3} Observational and modelling evidence indicates that, all else being equal, locally higher surface temperatures in polluted regions will trigger regional feedbacks in chemistry and local emissions that will increase peak levels of ozone and PM2.5 (medium confidence). For PM2.5, climate change may alter natural aerosol sources as well as removal by precipitation, but no confidence level is attached to the overall impact of climate change on PM2.5 distributions. {11.3} E.4 Ocean The global ocean will continue to warm during the 21st century. Heat will penetrate from the surface to the deep ocean and affect ocean circulation. {11.3, 12.4} The strongest ocean warming is projected for the surface in tropical and Northern Hemisphere subtropical regions. At greater depth the warming will be most pronounced in the Southern Ocean (high confidence). Best estimates of ocean warming in the top one hundred meters are about 0.6°C (RCP2.6) to 2.0°C (RCP8.5), and about 0.3°C (RCP2.6) to 0.6°C (RCP8.5) at a depth of about 1000 m by the end of the 21st century. {12.4, 14.3} It is very likely that the Atlantic Meridional Overturning Circulation (AMOC) will weaken over the 21st century. Best estimates and ranges18 for the reduction are 11% (1 to 24%) in RCP2.6 and 34% (12 to 54%) in RCP8.5. It is likely that there will be some decline in the AMOC by about 2050, but there may be some decades when the AMOC increases due to large natural internal variability. {11.3, 12.4} It is very unlikely that the AMOC will undergo an abrupt transition or collapse in the 21st century for the scenarios considered. There is low confidence in assessing the evolution of the AMOC beyond the 21st century because of the limited number of analyses and equivocal results. However, a collapse beyond the 21st century for large sustained warming cannot be excluded. {12.5} E.5 Cryosphere It is very likely that the Arctic sea ice cover will continue to shrink and thin and that Northern Hemisphere spring snow cover will decrease during the 21st century as global mean surface temperature rises. Global glacier volume will further decrease. {12.4, 13.4} Year-round reductions in Arctic sea ice extent are projected by the end of the 21st century from multi-model averages. These reductions range from 43% for RCP2.6 to 94% for RCP8.5 in September and from 8% for RCP2.6 to 34% for RCP8.5 in February (medium confidence) (see Figures SPM.7 and SPM.8). {12.4} PM2.5 refers to particulate matter with a diameter of less than 2.5 micrometres, a measure of atmospheric aerosol concentration. 17 The ranges in this paragraph indicate a CMIP5 model spread. 18 24 Summary for Policymakers Based on an assessment of the subset of models that most closely reproduce the climatological mean state and 1979 to 2012 trend of the Arctic sea ice extent, a nearly ice-free Arctic Ocean19 in September before mid-century is likely for RCP8.5 (medium confidence) (see Figures SPM.7 and SPM.8). A projection of when the Arctic might become nearly ice- free in September in the 21st century cannot be made with confidence for the other scenarios. {11.3, 12.4, 12.5} In the Antarctic, a decrease in sea ice extent and volume is projected with low confidence for the end of the 21st century SPM as global mean surface temperature rises. {12.4} By the end of the 21st century, the global glacier volume, excluding glaciers on the periphery of Antarctica, is projected to decrease by 15 to 55% for RCP2.6, and by 35 to 85% for RCP8.5 (medium confidence). {13.4, 13.5} The area of Northern Hemisphere spring snow cover is projected to decrease by 7% for RCP2.6 and by 25% in RCP8.5 by the end of the 21st century for the model average (medium confidence). {12.4} It is virtually certain that near-surface permafrost extent at high northern latitudes will be reduced as global mean surface temperature increases. By the end of the 21st century, the area of permafrost near the surface (upper 3.5 m) is projected to decrease by between 37% (RCP2.6) to 81% (RCP8.5) for the model average (medium confidence). {12.4} E.6 Sea Level Global mean sea level will continue to rise during the 21st century (see Figure SPM.9). Under all RCP scenarios, the rate of sea level rise will very likely exceed that observed during 1971 to 2010 due to increased ocean warming and increased loss of mass from glaciers and ice sheets. {13.3 13.5} Confidence in projections of global mean sea level rise has increased since the AR4 because of the improved physical understanding of the components of sea level, the improved agreement of process-based models with observations, and the inclusion of ice-sheet dynamical changes. {13.3 13.5} Global mean sea level rise for 2081 2100 relative to 1986 2005 will likely be in the ranges of 0.26 to 0.55 m for RCP2.6, 0.32 to 0.63 m for RCP4.5, 0.33 to 0.63 m for RCP6.0, and 0.45 to 0.82 m for RCP8.5 (medium confidence). For RCP8.5, the rise by the year 2100 is 0.52 to 0.98 m, with a rate during 2081 to 2100 of 8 to 16 mm yr 1 (medium confidence). These ranges are derived from CMIP5 climate projections in combination with process-based models and literature assessment of glacier and ice sheet contributions (see Figure SPM.9, Table SPM.2). {13.5} In the RCP projections, thermal expansion accounts for 30 to 55% of 21st century global mean sea level rise, and glaciers for 15 to 35%. The increase in surface melting of the Greenland ice sheet will exceed the increase in snowfall, leading to a positive contribution from changes in surface mass balance to future sea level (high confidence). While surface melt- ing will remain small, an increase in snowfall on the Antarctic ice sheet is expected (medium confidence), resulting in a negative contribution to future sea level from changes in surface mass balance. Changes in outflow from both ice sheets combined will likely make a contribution in the range of 0.03 to 0.20 m by 2081 2100 (medium confidence). {13.3 13.5} Based on current understanding, only the collapse of marine-based sectors of the Antarctic ice sheet, if initiated, could cause global mean sea level to rise substantially above the likely range during the 21st century. However, there is medium confidence that this additional contribution would not exceed several tenths of a meter of sea level rise during the 21st century. {13.4, 13.5} Conditions in the Arctic Ocean are referred to as nearly ice-free when the sea ice extent is less than 106 km2 for at least five consecutive years. 19 25 Summary for Policymakers Global mean sea level rise 1.0 Mean over 2081 2100 0.8 SPM 0.6 (m) 0.4 RCP8.5 RCP6.0 RCP4.5 RCP2.6 0.2 0.0 2000 2020 2040 2060 2080 2100 Year Figure SPM.9 | Projections of global mean sea level rise over the 21st century relative to 1986 2005 from the combination of the CMIP5 ensemble with process-based models, for RCP2.6 and RCP8.5. The assessed likely range is shown as a shaded band. The assessed likely ranges for the mean over the period 2081 2100 for all RCP scenarios are given as coloured vertical bars, with the corresponding median value given as a horizontal line. For further technical details see the Technical Summary Supplementary Material {Table 13.5, Figures 13.10 and 13.11; Figures TS.21 and TS.22} The basis for higher projections of global mean sea level rise in the 21st century has been considered and it has been concluded that there is currently insufficient evidence to evaluate the probability of specific levels above the assessed likely range. Many semi-empirical model projections of global mean sea level rise are higher than process-based model projections (up to about twice as large), but there is no consensus in the scientific community about their reliability and there is thus low confidence in their projections. {13.5} Sea level rise will not be uniform. By the end of the 21st century, it is very likely that sea level will rise in more than about 95% of the ocean area. About 70% of the coastlines worldwide are projected to experience sea level change within 20% of the global mean sea level change. {13.1, 13.6} E.7 Carbon and Other Biogeochemical Cycles Climate change will affect carbon cycle processes in a way that will exacerbate the increase of CO2 in the atmosphere (high confidence). Further uptake of carbon by the ocean will increase ocean acidification. {6.4} Ocean uptake of anthropogenic CO2 will continue under all four RCPs through to 2100, with higher uptake for higher concentration pathways (very high confidence). The future evolution of the land carbon uptake is less certain. A majority of models projects a continued land carbon uptake under all RCPs, but some models simulate a land carbon loss due to the combined effect of climate change and land use change. {6.4} Based on Earth System Models, there is high confidence that the feedback between climate and the carbon cycle is positive in the 21st century; that is, climate change will partially offset increases in land and ocean carbon sinks caused by rising atmospheric CO2. As a result more of the emitted anthropogenic CO2 will remain in the atmosphere. A positive feedback between climate and the carbon cycle on century to millennial time scales is supported by paleoclimate observations and modelling. {6.2, 6.4} 26 Summary for Policymakers Table SPM.3 | Cumulative CO2 emissions for the 2012 to 2100 period compatible with the RCP atmospheric concentrations simulated by the CMIP5 Earth System Models. {6.4, Table 6.12, Figure TS.19} Cumulative CO2 Emissions 2012 to 2100a Scenario GtC GtCO2 Mean Range Mean Range SPM RCP2.6 270 140 to 410 990 510 to 1505 RCP4.5 780 595 to 1005 2860 2180 to 3690 RCP6.0 1060 840 to 1250 3885 3080 to 4585 RCP8.5 1685 1415 to 1910 6180 5185 to 7005 Notes: a 1 Gigatonne of carbon = 1 GtC = 1015 grams of carbon. This corresponds to 3.667 GtCO2. Earth System Models project a global increase in ocean acidification for all RCP scenarios. The corresponding decrease in surface ocean pH by the end of 21st century is in the range18 of 0.06 to 0.07 for RCP2.6, 0.14 to 0.15 for RCP4.5, 0.20 to 0.21 for RCP6.0, and 0.30 to 0.32 for RCP8.5 (see Figures SPM.7 and SPM.8). {6.4} Cumulative CO2 emissions20 for the 2012 to 2100 period compatible with the RCP atmospheric CO2 concentrations, as derived from 15 Earth System Models, range18 from 140 to 410 GtC for RCP2.6, 595 to 1005 GtC for RCP4.5, 840 to 1250 GtC for RCP6.0, and 1415 to 1910 GtC for RCP8.5 (see Table SPM.3). {6.4} By 2050, annual CO2 emissions derived from Earth System Models following RCP2.6 are smaller than 1990 emissions (by 14 to 96%). By the end of the 21st century, about half of the models infer emissions slightly above zero, while the other half infer a net removal of CO2 from the atmosphere. {6.4, Figure TS.19} The release of CO2 or CH4 to the atmosphere from thawing permafrost carbon stocks over the 21st century is assessed to be in the range of 50 to 250 GtC for RCP8.5 (low confidence). {6.4} E.8 Climate Stabilization, Climate Change Commitment and Irreversibility Cumulative emissions of CO2 largely determine global mean surface warming by the late 21st century and beyond (see Figure SPM.10). Most aspects of climate change will persist for many centuries even if emissions of CO2 are stopped. This represents a substantial multi-century climate change commitment created by past, present and future emissions of CO2. {12.5} Cumulative total emissions of CO2 and global mean surface temperature response are approximately linearly related (see Figure SPM.10). Any given level of warming is associated with a range of cumulative CO2 emissions21, and therefore, e.g., higher emissions in earlier decades imply lower emissions later. {12.5} Limiting the warming caused by anthropogenic CO2 emissions alone with a probability of >33%, >50%, and >66% to less than 2°C since the period 1861 188022, will require cumulative CO2 emissions from all anthropogenic sources to stay between 0 and about 1570 GtC (5760 GtCO2), 0 and about 1210 GtC (4440 GtCO2), and 0 and about 1000 GtC (3670 GtCO2) since that period, respectively23. These upper amounts are reduced to about 900 GtC (3300 GtCO2), 820 GtC (3010 GtCO2), and 790 GtC (2900 GtCO2), respectively, when accounting for non-CO2 forcings as in RCP2.6. An amount of 515 [445 to 585] GtC (1890 [1630 to 2150] GtCO2), was already emitted by 2011. {12.5} From fossil fuel, cement, industry, and waste sectors. 20 Quantification of this range of CO2 emissions requires taking into account non-CO2 drivers. 21 22 The first 20-year period available from the models. 23 This is based on the assessment of the transient climate response to cumulative carbon emissions (TCRE, see Section D.2). 27 Summary for Policymakers A lower warming target, or a higher likelihood of remaining below a specific warming target, will require lower cumulative CO2 ­ missions. Accounting for warming effects of increases in non-CO2 greenhouse gases, reductions in aerosols, or the e release of greenhouse gases from permafrost will also lower the cumulative CO2 emissions for a specific warming target (see Figure SPM.10). {12.5} A large fraction of anthropogenic climate change resulting from CO2 emissions is irreversible on a multi-century to SPM millennial time scale, except in the case of a large net removal of CO2 from the atmosphere over a sustained period. Surface temperatures will remain approximately constant at elevated levels for many centuries after a complete cessation of net anthropogenic CO2 emissions. Due to the long time scales of heat transfer from the ocean surface to depth, ocean warming will continue for centuries. Depending on the scenario, about 15 to 40% of emitted CO2 will remain in the atmosphere longer than 1,000 years. {Box 6.1, 12.4, 12.5} It is virtually certain that global mean sea level rise will continue beyond 2100, with sea level rise due to thermal expansion to continue for many centuries. The few available model results that go beyond 2100 indicate global mean sea level rise above the pre-industrial level by 2300 to be less than 1 m for a radiative forcing that corresponds to CO2 concentrations that peak and decline and remain below 500 ppm, as in the scenario RCP2.6. For a radiative forcing that corresponds to a CO2 concentration that is above 700 ppm but below 1500 ppm, as in the scenario RCP8.5, the projected rise is 1 m to more than 3 m (medium confidence). {13.5} Cumulative total anthropogenic CO2 emissions from 1870 (GtCO2) 1000 2000 3000 4000 5000 6000 7000 8000 5 2100 Temperature anomaly relative to 1861 1880 (°C) 4 2100 3 2100 2050 2 2050 2050 2050 2100 2030 2030 1 2010 RCP2.6 Historical 2000 RCP4.5 RCP range 1950 RCP6.0 1% yr -1 CO2 1980 RCP8.5 1% yr -1 CO2 range 0 1890 0 500 1000 1500 2000 2500 Cumulative total anthropogenic CO2 emissions from 1870 (GtC) Figure SPM.10 | Global mean surface temperature increase as a function of cumulative total global CO2 emissions from various lines of evidence. Multi- model results from a hierarchy of climate-carbon cycle models for each RCP until 2100 are shown with coloured lines and decadal means (dots). Some decadal means are labeled for clarity (e.g., 2050 indicating the decade 2040 2049). Model results over the historical period (1860 to 2010) are indicated in black. The coloured plume illustrates the multi-model spread over the four RCP scenarios and fades with the decreasing number of available models in RCP8.5. The multi-model mean and range simulated by CMIP5 models, forced by a CO2 increase of 1% per year (1% yr 1 CO2 simulations), is given by the thin black line and grey area. For a specific amount of cumulative CO2 emissions, the 1% per year CO2 simulations exhibit lower warming than those driven by RCPs, which include additional non-CO2 forcings. Temperature values are given relative to the 1861 1880 base period, emissions relative to 1870. Decadal averages are connected by straight lines. For further technical details see the Technical Summary Supplementary Material. {Figure 12.45; TS TFE.8, Figure 1} 28 Summary for Policymakers Sustained mass loss by ice sheets would cause larger sea level rise, and some part of the mass loss might be irreversible. There is high confidence that sustained warming greater than some threshold would lead to the near-complete loss of the Greenland ice sheet over a millennium or more, causing a global mean sea level rise of up to 7 m. Current estimates indicate that the threshold is greater than about 1°C (low confidence) but less than about 4°C (medium confidence) global mean warming with respect to pre-industrial. Abrupt and irreversible ice loss from a potential instability of marine- based sectors of the Antarctic ice sheet in response to climate forcing is possible, but current evidence and understanding SPM is insufficient to make a quantitative assessment. {5.8, 13.4, 13.5} Methods that aim to deliberately alter the climate system to counter climate change, termed geoengineering, have been proposed. Limited evidence precludes a comprehensive quantitative assessment of both Solar Radiation Management (SRM) and Carbon D ioxide Removal (CDR) and their impact on the climate system. CDR methods have biogeochemical and technological limitations to their potential on a global scale. There is insufficient knowledge to quantify how much CO2 emissions could be partially offset by CDR on a century timescale. Modelling indicates that SRM methods, if realizable, have the potential to substantially offset a global temperature rise, but they would also modify the global water cycle, and would not reduce ocean acidification. If SRM were terminated for any reason, there is high confidence that global surface temperatures would rise very rapidly to values consistent with the greenhouse gas forcing. CDR and SRM methods carry side effects and long-term consequences on a global scale. {6.5, 7.7} Box SPM.1: Representative Concentration Pathways (RCPs) Climate change projections in IPCC Working Group I require information about future emissions or concentrations of greenhouse gases, aerosols and other climate drivers. This information is often expressed as a scenario of human activities, which are not assessed in this report. Scenarios used in Working Group I have focused on anthropogenic emissions and do not include changes in natural drivers such as solar or volcanic forcing or natural emissions, for example, of CH4 and N2O. For the Fifth Assessment Report of IPCC, the scientific community has defined a set of four new scenarios, denoted Representative Concentration Pathways (RCPs, see Glossary). They are identified by their approximate total radiative forcing in year 2100 relative to 1750: 2.6 W m-2 for RCP2.6, 4.5 W m-2 for RCP4.5, 6.0 W m-2 for RCP6.0, and 8.5 W m-2 for RCP8.5. For the Coupled Model Intercomparison Project Phase 5 (CMIP5) results, these values should be understood as indicative only, as the climate forcing resulting from all drivers varies between models due to specific model characteristics and treatment of short-lived climate forcers. These four RCPs include one mitigation scenario leading to a very low forcing level (RCP2.6), two stabilization scenarios (RCP4.5 and RCP6), and one scenario with very high greenhouse gas emissions (RCP8.5). The RCPs can thus represent a range of 21st century climate policies, as compared with the no-climate policy of the Special Report on Emissions Scenarios (SRES) used in the Third Assessment Report and the Fourth Assessment Report. For RCP6.0 and RCP8.5, radiative forcing does not peak by year 2100; for RCP2.6 it peaks and declines; and for RCP4.5 it stabilizes by 2100. Each RCP provides spatially resolved data sets of land use change and sector-based emissions of air pollutants, and it specifies annual greenhouse gas concentrations and anthropogenic emissions up to 2100. RCPs are based on a combination of integrated assessment models, simple climate models, atmospheric chemistry and global carbon cycle models. While the RCPs span a wide range of total forcing values, they do not cover the full range of emissions in the literature, particularly for aerosols. Most of the CMIP5 and Earth System Model simulations were performed with prescribed CO2 concentrations reaching 421 ppm (RCP2.6), 538 ppm (RCP4.5), 670 ppm (RCP6.0), and 936 ppm (RCP 8.5) by the year 2100. Including also the prescribed concentrations of CH4 and N2O, the combined CO2-equivalent concentrations are 475 ppm (RCP2.6), 630 ppm (RCP4.5), 800 ppm (RCP6.0), and 1313 ppm (RCP8.5). For RCP8.5, additional CMIP5 Earth System Model simulations are performed with prescribed CO2 emissions as provided by the integrated assessment models. For all RCPs, additional calculations were made with updated atmospheric chemistry data and models (including the Atmospheric Chemistry and Climate component of CMIP5) using the RCP prescribed emissions of the chemically reactive gases (CH4, N2O, HFCs, NOx, CO, NMVOC). These simulations enable investigation of uncertainties related to carbon cycle feedbacks and atmospheric chemistry. 29 Introduction Chapter 2 TS Technical Summary 31 TS Technical Summary Coordinating Lead Authors: Thomas F. Stocker (Switzerland), Qin Dahe (China), Gian-Kasper Plattner (Switzerland) Lead Authors: Lisa V. Alexander (Australia), Simon K. Allen (Switzerland/New Zealand), Nathaniel L. Bindoff (Australia), François-Marie Bréon (France), John A. Church (Australia), Ulrich Cubasch (Germany), Seita Emori (Japan), Piers Forster (UK), Pierre Friedlingstein (UK/Belgium), Nathan Gillett (Canada), Jonathan M. Gregory (UK), Dennis L. Hartmann (USA), Eystein Jansen (Norway), Ben Kirtman (USA), Reto Knutti (Switzerland), Krishna Kumar Kanikicharla (India), Peter Lemke (Germany), Jochem Marotzke (Germany), Valérie Masson-Delmotte (France), Gerald A. Meehl (USA), Igor I. Mokhov (Russian Federation), Shilong Piao (China), Venkatachalam Ramaswamy (USA), David Randall (USA), Monika Rhein (Germany), Maisa Rojas (Chile), Christopher Sabine (USA), Drew Shindell (USA), Lynne D. Talley (USA), David G. Vaughan (UK), Shang-Ping Xie (USA) Contributing Authors: Myles R. Allen (UK), Olivier Boucher (France), Don Chambers (USA), Jens Hesselbjerg Christensen (Denmark), Philippe Ciais (France), Peter U. Clark (USA), Matthew Collins (UK), Josefino C. Comiso (USA), Viviane Vasconcellos de Menezes (Australia/Brazil), Richard A. Feely (USA), Thierry Fichefet (Belgium), Gregory Flato (Canada), Jesús Fidel González Rouco (Spain), Ed Hawkins (UK), Paul J. Hezel (Belgium/USA), Gregory C. Johnson (USA), Simon A. Josey (UK), Georg Kaser (Austria/Italy), Albert M.G. Klein Tank (Netherlands), Janina Körper (Germany), Gunnar Myhre (Norway), Timothy Osborn (UK), Scott B. Power (Australia), Stephen R. Rintoul (Australia), Joeri Rogelj (Switzerland/Belgium), Matilde Rusticucci (Argentina), Michael Schulz (Germany), Jan Sedláèek (Switzerland), Peter A. Stott (UK), Rowan Sutton (UK), Peter W. Thorne (USA/Norway/UK), Donald Wuebbles (USA) Review Editors: Sylvie Joussaume (France), Joyce Penner (USA), Fredolin Tangang (Malaysia) This Technical Summary should be cited as: Stocker, T.F., D. Qin, G.-K. Plattner, L.V. Alexander, S.K. Allen, N.L. Bindoff, F.-M. Bréon, J.A. Church, U. Cubasch, S. Emori, P. Forster, P. Friedlingstein, N. Gillett, J.M. Gregory, D.L. Hartmann, E. Jansen, B. Kirtman, R. Knutti, K. Krishna Kumar, P. Lemke, J. Marotzke, V. Masson-Delmotte, G.A. Meehl, I.I. Mokhov, S. Piao, V. Ramaswamy, D. Randall, M. Rhein, M. Rojas, C. Sabine, D. Shindell, L.D. Talley, D.G. Vaughan and S.-P. Xie, 2013: Technical Sum- mary. In: Climate Change 2013: The Physical Science Basis. Contribution of Working Group I to the Fifth Assess- ment Report of the Intergovernmental Panel on Climate Change [Stocker, T.F., D. Qin, G.-K. Plattner, M. Tignor, S.K. Allen, J. Boschung, A. Nauels, Y. Xia, V. Bex and P.M. Midgley (eds.)]. Cambridge University Press, Cambridge, United Kingdom and New York, NY, USA. 33 Table of Contents TS.1 Introduction......................................................................... 35 TS.5 Projections of Global and Regional Box TS.1: Treatment of Uncertainty............................................ 36 Climate Change.................................................................. 79 TS.5.1 Introduction................................................................. 79 TS.2 Observation of Changes in the Climate System....... 37 TS.5.2 Future Forcing and Scenarios....................................... 79 TS.2.1 Introduction................................................................. 37 Box TS.6: The New Representative Concentration Pathway Scenarios and Coupled Model Intercomparison Project TS.2.2 Changes in Temperature.............................................. 37 Phase 5 Models............................................................................. 79 TS.2.3 Changes in Energy Budget and Heat Content.............. 39 TS.5.3 Quantification of Climate System Response................. 81 TS.2.4 Changes in Circulation and Modes of Variability.......... 39 TS.5.4 Near-term Climate Change.......................................... 85 TS TS.2.5 Changes in the Water Cycle and Cryosphere................ 40 TS.5.5 Long-term Climate Change.......................................... 89 TS.2.6 Changes in Sea Level................................................... 46 TS.5.6 Long-term Projections of Carbon and Other TS.2.7 Changes in Extremes.................................................... 46 Biogeochemical Cycles................................................. 93 TS.2.8 Changes in Carbon and Other Box TS.7: Climate Geoengineering Methods............................. 98 Biogeochemical Cycles................................................. 50 TS.5.7 Long-term Projections of Sea Level Change................. 98 TS.5.8 Climate Phenomena and Regional TS.3 Drivers of Climate Change.............................................. 53 Climate Change......................................................... 105 TS.3.1 Introduction................................................................. 53 TS.3.2 Radiative Forcing from Greenhouse Gases................... 53 TS.6 Key Uncertainties............................................................. 114 Box TS.2: Radiative Forcing and Effective TS.6.1 Key Uncertainties in Observation of Changes in Radiative Forcing.......................................................................... 53 the Climate System.................................................... 114 TS.3.3 Radiative Forcing from Anthropogenic Aerosols........... 55 TS.6.2 Key Uncertainties in Drivers of Climate Change......... 114 TS.3.4 Radiative Forcing from Land Surface Changes TS.6.3 Key Uncertainties in Understanding the Climate and Contrails................................................................ 55 System and Its Recent Changes................................. 114 TS.3.5 Radiative Forcing from Natural Drivers of TS.6.4 Key Uncertainties in Projections of Global and Climate Change........................................................... 55 Regional Climate Change........................................... 115 TS.3.6 Synthesis of Forcings; Spatial and Temporal Evolution...................................................... 56 Thematic Focus Elements TS.3.7 Climate Feedbacks....................................................... 57 TFE.1 Water Cycle Change.................................................. 42 TS.3.8 Emission Metrics.......................................................... 58 TFE.2 Sea Level Change: Scientific Understanding and Uncertainties...................................................... 47 TS.4 Understanding the Climate System and TFE.3 Comparing Projections from Previous IPCC Its Recent Changes............................................................ 60 Assessments with Observations.............................. 64 TS.4.1 Introduction................................................................. 60 TFE.4 The Changing Energy Budget of the Global Climate System.......................................................... 67 TS.4.2 Surface Temperature.................................................... 60 TFE.5 Irreversibility and Abrupt Change........................... 70 Box TS.3: Climate Models and the Hiatus in Global Mean Surface Warming of the Past 15 Years............................. 61 TFE.6 Climate Sensitivity and Feedbacks......................... 82 TS.4.3 Atmospheric Temperature............................................ 66 TFE.7 Carbon Cycle Perturbation and Uncertainties....... 96 TS.4.4 Oceans......................................................................... 68 TFE.8 Climate Targets and Stabilization......................... 102 TS.4.5 Cryosphere................................................................... 69 TFE.9 Climate Extremes.................................................... 109 TS.4.6 Water Cycle.................................................................. 72 Supplementary Material TS.4.7 Climate Extremes......................................................... 72 Supplementary Material is available in online versions of the report. TS.4.8 From Global to Regional.............................................. 73 Box TS.4: Model Evaluation......................................................... 75 Box TS.5: Paleoclimate................................................................. 77 34 Technical Summary TS.1 Introduction the scientific studies considered4. Confidence is expressed qualita- tively. Quantified measures of uncertainty in a finding are expressed Climate Change 2013: The Physical Science Basis is the contribution probabilistically and are based on a combination of statistical analy- of Working Group I (WGI) to the Fifth Assessment Report (AR5) of the ses of observations or model results, or both, and expert judgement. Intergovernmental Panel on Climate Change (IPCC). This comprehen- Where appropriate, findings are also formulated as statements of fact sive assessment of the physical aspects of climate change puts a focus without using uncertainty qualifiers (see Chapter 1 and Box TS.1 for on those elements that are relevant to understand past, document cur- more details). rent and project future climate change. The assessment builds on the IPCC Fourth Assessment Report (AR4)1 and the recent Special Report The Technical Summary is structured into four main sections presenting on Managing the Risk of Extreme Events and Disasters to Advance Cli- the assessment results following the storyline of the WGI contribution mate Change Adaptation (SREX)2 and is presented in 14 chapters and 3 to AR5: Section TS.2 covers the assessment of observations of changes annexes. The chapters cover direct and proxy observations of changes in the climate system; Section TS.3 summarizes the information on in all components of the climate system; assess the current knowledge the different drivers, natural and anthropogenic, expressed in terms of various processes within, and interactions among, climate system of RF; Section TS.4 presents the assessment of the quantitative under- components, which determine the sensitivity and response of the standing of observed climate change; and Section TS.5 summarizes the TS system to changes in forcing; and quantify the link between the chang- assessment results for projections of future climate change over the es in atmospheric constituents, and hence radiative forcing (RF)3, and 21st century and beyond from regional to global scale. Section TS.6 the consequent detection and attribution of climate change. Projec- combines and lists key uncertainties from the WGI assessment from tions of changes in all climate system components are based on model Sections TS.2 to TS.5. The overall nine TFEs, cutting across the various simulations forced by a new set of scenarios. The Report also provides components of the WGI AR5, are dispersed throughout the four main a comprehensive assessment of past and future sea level change in a TS sections, are visually distinct from the main text and should allow dedicated chapter. Regional climate change information is presented in stand-alone reading. the form of an Atlas of Global and Regional Climate Projections (Annex I). This is complemented by Annex II: Climate System Scenario Tables The basis for substantive paragraphs in this Technical Summary can be and Annex III: Glossary. found in the chapter sections of the underlying report. These references are given in curly brackets. The primary purpose of this Technical Summary (TS) is to provide the link between the complete assessment of the multiple lines of inde- pendent evidence presented in the 14 chapters of the main report and the highly condensed summary prepared as the WGI Summary for Policymakers (SPM). The Technical Summary thus serves as a starting point for those readers who seek the full information on more specific topics covered by this assessment. This purpose is facilitated by includ- ing pointers to the chapters and sections where the full assessment can be found. Policy-relevant topics, which cut across many chapters and involve many interlinked processes in the climate system, are pre- sented here as Thematic Focus Elements (TFEs), allowing rapid access to this information. An integral element of this report is the use of uncertainty language that permits a traceable account of the assessment (Box TS.1). The degree of certainty in key findings in this assessment is based on the author teams evaluations of underlying scientific understanding and is expressed as a level of confidence that results from the type, amount, quality and consistency of evidence and the degree of agreement in 1 IPCC, 2007: Climate Change 2007: The Physical Science Basis. Contribution of Working Group I to the Fourth Assessment Report of the Intergovernmental Panel on Climate Change [Solomon, S., D. Qin, M. Manning, Z. Chen, M. Marquis, K.B. Averyt, M. Tignor and H.L. Miller (eds.)]. Cambridge University Press, Cambridge, United Kingdom and New York, NY, USA, 996 pp. 2 IPCC, 2012: Managing the Risks of Extreme Events and Disasters to Advance Climate Change Adaptation. A Special Report of Working Groups I and II of the Intergovernmental Panel on Climate Change [Field, C.B., V. Barros, T.F. Stocker, D. Qin, D.J. Dokken, K.L. Ebi, M.D. Mastrandrea, K.J. Mach, G.-K. Plattner, S.K. Allen, M. Tignor and P. M. Midgley (eds.)]. Cambridge University Press, Cambridge, UK, and New York, NY, USA, 582 pp. 3 Radiative forcing (RF) is a measure of the net change in the energy balance of the Earth system in response to some external perturbation. It is expressed in watts per square metre (W m 2); see Box TS.2. 4 Mastrandrea, M.D., C.B. Field, T.F. Stocker, O. Edenhofer, K.L. Ebi, D.J. Frame, H. Held, E. Kriegler, K.J. Mach, P.R. Matschoss, G.-K. Plattner, G.W. Yohe, and F.W. Zwiers, 2010: Guidance Note for Lead Authors of the IPCC Fifth Assessment Report on Consistent Treatment of Uncertainties. Intergovernmental Panel on Climate Change (IPCC). 35 Technical Summary Box TS.1 | Treatment of Uncertainty Based on the Guidance Note for Lead Authors of the IPCC Fifth Assessment Report on Consistent Treatment of Uncertainties, this WGI Technical Summary and the WGI Summary for Policymakers rely on two metrics for communicating the degree of certainty in key find- ings, which is based on author teams evaluations of underlying scientific understanding: Confidence in the validity of a finding, based on the type, amount, quality and consistency of evidence (e.g., mechanistic under- standing, theory, data, models, expert judgement) and the degree of agreement. Confidence is expressed qualitatively. Quantified measures of uncertainty in a finding expressed probabilistically (based on statistical analysis of observations or model results, or expert judgement). The AR5 Guidance Note refines the guidance provided to support the IPCC Third and Fourth Assessment Reports. Direct comparisons between assessment of uncertainties in findings in this Report and those in the AR4 and the SREX are difficult, because of the applica- tion of the revised guidance note on uncertainties, as well as the availability of new information, improved scientific understanding, TS continued analyses of data and models and specific differences in methodologies applied in the assessed studies. For some climate variables, different aspects have been assessed and therefore a direct comparison would be inappropriate. Each key finding is based on an author team s evaluation of associated evidence and agreement. The confidence metric provides a qualitative synthesis of an author team s judgement about the validity of a finding, as determined through evaluation of evidence and agreement. If uncertainties can be quantified probabilistically, an author team can characterize a finding using the calibrated likelihood language or a more precise presentation of probability. Unless otherwise indicated, high or very high confidence is associated with findings for which an author team has assigned a likelihood term. The following summary terms are used to describe the available evidence: limited, medium, or robust; and for the degree of agreement: low, medium, or high. A level of confidence is expressed using five qualifiers very low, low, medium, high, and very high, and typeset in italics, e.g., medium confidence. Box TS.1, Figure 1 depicts summary statements for evidence and agreement and their relationship to confidence. There is flexibility in this relationship; for a given evidence and agreement statement, different confidence levels can be assigned, but increasing levels of evidence and degrees of agreement correlate with increasing confidence. High agreement High agreement High agreement Limited evidence Medium evidence Robust evidence Medium agreement Medium agreement Medium agreement Agreement Limited evidence Medium evidence Robust evidence Low agreement Low agreement Low agreement Limited evidence Medium evidence Robust evidence Confidence Scale Evidence (type, amount, quality, consistency) Box TS.1, Figure 1 | A depiction of evidence and agreement statements and their relationship to confidence. Confidence increases toward the top right corner as suggested by the increasing strength of shading. Generally, evidence is most robust when there are multiple, consistent independent lines of high quality. {Figure 1.11} The following terms have been used to indicate the assessed likelihood, and typeset in italics: Term* Likelihood of the outcome Virtually certain 99 100% probability Very likely 90 100% probability Likely 66 100% probability About as likely as not 33 66% probability Unlikely 0 33% probability Very unlikely 0 10% probability Exceptionally unlikely 0 1% probability * Additional terms (extremely likely: 95 100% probability, more likely than not: >50 100% probability, and extremely unlikely: 0 5% probability) may also be used when appropriate. 36 Technical Summary TS.2 Observation of Changes in the land have increased on a global scale since 1950.7 {2.4.1, 2.4.3; Chapter ­ Climate System 2 Supplementary Material Section 2.SM.3} TS.2.1 Introduction Despite the robust multi-decadal warming, there exists substantial interannual to decadal variability in the rate of warming, with several Observations of the climate system are based on direct physical and periods exhibiting weaker trends (including the warming hiatus since biogeochemical measurements, and remote sensing from ground sta- 1998) (Figure TS.1). The rate of warming over the past 15 years (1998 tions and satellites; information derived from paleoclimate archives 2012; 0.05 [ 0.05 to +0.15] °C per decade) is smaller than the trend provides a long-term context. Global-scale observations from the since 1951 (1951 2012; 0.12[0.08 to 0.14] °C per decade). Trends for instrumental era began in the mid-19th century, and paleoclimate short periods are uncertain and very sensitive to the start and end reconstructions extend the record of some quantities back hundreds to years. For example, trends for 15-year periods starting in 1995, 1996, millions of years. Together, they provide a comprehensive view of the and 1997 are 0.13 [0.02 to 0.24] °C per decade, 0.14 [0.03 to 0.24] variability and long-term changes in the atmosphere, the ocean, the °C per decade and 0.07 [ 0.02 to 0.18] °C per decade, respectively. cryosphere and at the land surface. Several independently analysed data records of global and regional land surface air temperature obtained from station observations are TS The assessment of observational evidence for climate change is sum- in broad agreement that land surface air temperatures have increased. marized in this section. Substantial advancements in the availability, Sea surface temperatures (SSTs) have also increased. Intercomparisons acquisition, quality and analysis of observational data sets for the of new SST data records obtained by different measurement methods, atmosphere, land surface, ocean and cryosphere have occurred since including satellite data, have resulted in better understanding of errors the AR4. Many aspects of the climate system are showing evidence of and biases in the records. {2.4.1 2.4.3; Box 9.2} a changing climate. {2, 3, 4, 5, 6, 13} It is unlikely that any uncorrected urban heat island effects and land TS.2.2 Changes in Temperature use change effects have raised the estimated centennial globally aver- aged land surface air temperature trends by more than 10% of the TS.2.2.1 Surface reported trend. This is an average value; in some regions that have rapidly developed urban heat island and land use change impacts on It is certain that global mean surface temperature (GMST) has increased regional trends may be substantially larger. {2.4.1} since the late 19th century (Figures TS.1 and TS.2). Each of the past three decades has been successively warmer at the Earth s surface than any There is high confidence that annual mean surface warming since the the previous decades in the instrumental record, and the decade of the 20th century has reversed long-term cooling trends of the past 5000 2000 s has been the warmest. The globally averaged combined land and years in mid-to-high latitudes of the Northern Hemisphere (NH). For ocean temperature data as calculated by a linear trend5, show a warm- average annual NH temperatures, the period 1983 2012 was very likely ing of 0.85 [0.65 to 1.06] °C6, over the period 1880 2012, when mul- the warmest 30-year period of the last 800 years (high confidence) tiple independently produced datasets exist, about 0.89 [0.69 to 1.08] and likely the warmest 30-year period of the last 1400 years (medium °C over the period 1901 2012, and about 0.72 [0.49 to 0.89] °C over confidence). This is supported by comparison of instrumental tempera- the period 1951 2012 when based on three independently-produced tures with multiple reconstructions from a variety of proxy data and data sets. The total increase between the average of the 1850 1900 statistical methods, and is consistent with AR4. Continental-scale sur- period and the 2003 2012 period is 0.78 [0.72 to 0.85] °C, based on face temperature reconstructions show, with high confidence, multi- the Hadley Centre/Climatic Research Unit gridded surface temperature decadal periods during the Medieval Climate Anomaly (950 1250) data set 4 (HadCRUT4), the global mean surface temperature dataset that were in some regions as warm as in the mid-20th century and with the longest record of the three independently-produced data sets. in others as warm as in the late 20th century. With high confidence, The warming from 1850 1900 to 1986 2005 (reference period for the these regional warm periods were not as synchronous across regions modelling chapters and the Atlas in Annex I) is 0.61 [0.55 to 0.67] °C, as the warming since the mid-20th century. Based on the comparison when calculated using HadCRUT4 and its uncertainty estimates. It is between reconstructions and simulations, there is high confidence that also virtually certain that maximum and minimum temperatures over not only external orbital, solar and volcanic forcing, but also internal 5 The warming is reported as an unweighted average based on linear trend estimates calculated from Hadley Centre/Climatic Research Unit gridded surface temperature data set 4 (HadCRUT4), Merged Land Ocean Surface Temperature Analysis (MLOST) and Goddard Institute for Space Studies Surface Temperature Analysis (GISTEMP) data sets (see Figure TS.2; Section 2.4.3). 6 In the WGI contribution to the AR5, uncertainty is quantified using 90% uncertainty intervals unless otherwise stated. The 90% uncertainty interval, reported in square brackets, is expected to have a 90% likelihood of covering the value that is being estimated. The upper endpoint of the uncertainty interval has a 95% likelihood of exceed- ing the value that is being estimated and the lower endpoint has a 95% likelihood of being less than that value. A best estimate of that value is also given where available. Uncertainty intervals are not necessarily symmetric about the corresponding best estimate. 7 Both methods presented in this paragraph to calculate temperature change were also used in AR4. The first calculates the difference using a best fit linear trend of all points between two years, e.g., 1880 and 2012. The second calculates the difference between averages for the two periods, e.g., 1850 to 1900 and 2003 to 2012. Therefore, the resulting values and their 90% uncertainty intervals are not directly comparable. 37 Technical Summary variability, contributed substantially to the spatial pattern and timing radiosondes. Hence there is only medium confidence in the rate of of surface temperature changes between the Medieval Climate Anom- change and its vertical structure in the NH extratropical troposphere aly and the Little Ice Age (1450 1850). {5.3.5, 5.5.1} and low confidence elsewhere. {2.4.4} TS.2.2.2 Troposphere and Stratosphere TS.2.2.3 Ocean Based on multiple independent analyses of measurements from radio- It is virtually certain that the upper ocean (above 700 m) has warmed sondes and satellite sensors, it is virtually certain that globally the from 1971 to 2010, and likely that it has warmed from the 1870s to 1971 troposphere has warmed and the stratosphere has cooled since the (Figure TS.1). There is less certainty in changes prior to 1971 because mid-20th century (Figure TS.1). Despite unanimous agreement on the of relatively sparse sampling in earlier time periods. ­ Instrumental sign of the trends, substantial disagreement exists between available biases in historical upper ocean temperature ­ easurements have been m estimates as to the rate of temperature changes, particularly outside i ­dentified and reduced since AR4, diminishing artificial decadal varia- the NH extratropical troposphere, which has been well sampled by tion in temperature and upper ocean heat content, most prominent during the 1970s and 1980s. {3.2.1 3.2.3, 3.5.3} TS 1.0 Land surface air temperature: 4 datasets 0.6 Tropospheric temperature: 0.4 7 datasets anomaly (C) Temperature 0.5 0.2 anomaly (C) Temperature 0.0 0.0 -0.2 -0.5 -0.4 -0.6 -1.0 -0.8 20 0.4 Sea-surface temperature: 5 datasets Ocean heat content(0-700m): Ocean heat content 5 datasets anomaly (1022 J) 0.2 10 anomaly (C) Temperature 0.0 0 -0.2 -0.4 -10 -0.6 0.4 0.4 Marine air temperature: 2 datasets Specific humidity: 4 datasets Specific humidity anomaly (g/kg) 0.2 0.2 anomaly (C) Temperature 0.0 0.0 -0.2 -0.4 -0.2 -0.6 100 Sea level: 6 datasets 6 Northern hemisphere (March- 50 4 April) snow cover: 2 datasets Mass balance (1015GT) Extent anomaly (106km2) anomaly (mm) 0 2 Sea level -50 0 -100 -2 -150 -4 -200 -6 12 10 Glacier mass balance: 5 3 datasets Extent (106km2) 10 Summer arctic sea-ice extent: 6 datasets 0 8 -5 6 -10 4 -15 1850 1900 1950 2000 1940 1960 1980 2000 Year Year Figure TS.1 | Multiple complementary indicators of a changing global climate. Each line represents an independently derived estimate of change in the climate element. The times series presented are assessed in Chapters 2, 3 and 4. In each panel all data sets have been normalized to a common period of record. A full detailing of which source data sets go into which panel is given in Chapter 2 Supplementary Material Section 2.SM.5 and in the respective chapters. Further detail regarding the related Figure SPM.3 is given in the TS Supplementary Material. {FAQ 2.1, Figure 1; 2.4, 2.5, 3.2, 3.7, 4.5.2, 4.5.3} 38 Technical Summary It is likely that the ocean warmed between 700-2000 m from 1957 to TS.2.3 Changes in Energy Budget and Heat Content 2009, based on 5-year averages. It is likely that the ocean warmed from 3000 m to the bottom from 1992 to 2005, while no significant trends The Earth has been in radiative imbalance, with more energy from the in global average temperature were observed between 2000 and 3000 Sun entering than exiting the top of the atmosphere, since at least m depth from circa 1992 to 2005. Below 3000 m depth, the largest about 1970. It is virtually certain that the Earth has gained substantial warming is observed in the Southern Ocean. {3.2.4, 3.5.1; Figures 3.2b, energy from 1971 to 2010. The estimated increase in energy inventory 3.3; FAQ 3.1} between 1971 and 2010 is 274 [196 to 351] × 1021 J (high confidence), with a heating rate of 213 × 1012 W from a linear fit to the annual values over that time period (see also TFE.4). {Boxes 3.1, 13.1} HadCRUT4 1901-2012 Ocean warming dominates that total heating rate, with full ocean depth warming accounting for about 93% (high confidence), and warming of the upper (0 to 700 m) ocean accounting for about 64%. Melting ice (including Arctic sea ice, ice sheets and glaciers) and warm- ing of the continents each account for 3% of the total. Warming of the TS atmosphere makes up the remaining 1%. The 1971 2010 estimated rate of ocean energy gain is 199 × 1012 W from a linear fit to data over that time period, equivalent to 0.42 W m 2 heating applied continu- ously over the Earth s entire surface, and 0.55 W m 2 for the portion owing to ocean warming applied over the ocean s entire surface area. The Earth s estimated energy increase from 1993 to 2010 is 163 [127 MLOST 1901-2012 to 201] × 1021 J with a trend estimate of 275 × 1015 W. The ocean por- tion of the trend for 1993 2010 is 257 × 1012 W, equivalent to a mean heat flux into the ocean of 0.71 W m 2. {3.2.3, 3.2.4; Box 3.1} It is about as likely as not that ocean heat content from 0 700 m increased more slowly during 2003 to 2010 than during 1993 to 2002 (Figure TS.1). Ocean heat uptake from 700 2000 m, where interannual variability is smaller, likely continued unabated from 1993 to 2009. {3.2.3, 3.2.4; Box 9.2} TS.2.4 Changes in Circulation and Modes of Variability GISS 1901-2012 Large variability on interannual to decadal time scales hampers robust conclusions on long-term changes in atmospheric circulation in many instances. Confidence is high that the increase of the northern mid- latitude westerly winds and the North Atlantic Oscillation (NAO) index from the 1950s to the 1990s, and the weakening of the Pacific Walker Circulation from the late 19th century to the 1990s, have been largely offset by recent changes. With high confidence, decadal and multi- decadal changes in the winter NAO index observed since the 20th cen- tury are not unprecedented in the context of the past 500 years. {2.7.2, 2.7.5, 2.7.8, 5.4.2; Box 2.5; Table 2.14} It is likely that circulation features have moved poleward since the -0.6 -0.4 -0.2 0 0.2 0.4 0.6 0.8 1 1.25 1.5 1.75 2.5 1970s, involving a widening of the tropical belt, a poleward shift of Trend (C over period) storm tracks and jet streams and a contraction of the northern polar vortex. Evidence is more robust for the NH. It is likely that the Southern Figure TS.2 | Change in surface temperature over 1901 2012 as determined by linear Annular Mode (SAM) has become more positive since the 1950s. The trend for three data sets. White areas indicate incomplete or missing data. Trends have increase in the strength of the observed summer SAM since 1950 has been calculated only for those grid boxes with greater than 70% complete records and more than 20% data availability in the first and last 10% of the time period. Black plus been anomalous, with medium confidence, in the context of the past signs (+) indicate grid boxes where trends are significant (i.e., a trend of zero lies out- 400 years. {2.7.5, 2.7.6, 2.7.8, 5.4.2; Box 2.5; Table 2.14} side the 90% confidence interval). Differences in coverage primarily reflect the degree of interpolation to account for data void regions undertaken by the data set providers New results from high-resolution coral records document with high ranging from none beyond grid box averaging (Hadley Centre/Climatic Research Unit confidence that the El Nino-Southern Oscillation (ENSO) system has gridded surface temperature data set 4 (HadCRUT4)) to substantial (Goddard Institute for Space Studies Surface Temperature Analysis (GISTEMP)). Further detail regarding the remained highly variable throughout the past 7000 years, showing no related Figure SPM.1 is given in the TS Supplementary Material. {Figure 2.21} discernible evidence for an orbital modulation of ENSO. {5.4.1} 39 Technical Summary Recent observations have strengthened evidence for variability in The spatial patterns of the salinity trends, mean salinity and the mean major ocean circulation systems on time scales from years to decades. distribution of evaporation minus precipitation are all similar (TFE.1, It is very likely that the subtropical gyres in the North Pacific and Figure 1). These similarities provide indirect evidence that the pattern South Pacific have expanded and strengthened since 1993. Based on of evaporation minus precipitation over the oceans has been enhanced measurements of the full Atlantic Meridional Overturning Circulation since the 1950s (medium confidence). Uncertainties in currently avail- (AMOC) and its individual components at various latitudes and differ- able surface fluxes prevent the flux products from being reliably used ent time periods, there is no evidence of a long-term trend. There is also to identify trends in the regional or global distribution of evaporation no evidence for trends in the transports of the Indonesian Throughflow, or precipitation over the oceans on the time scale of the observed salin- the Antarctic Circumpolar Current (ACC) or in the transports between ity changes since the 1950s. {3.3.2 3.3.4, 3.4.2, 3.4.3, 3.9; FAQ 3.2} the Atlantic Ocean and Nordic Seas. However, a southward shift of the ACC by about 1° of latitude is observed in data spanning the time TS.2.5.3 Sea Ice period 1950 2010 with medium confidence. {3.6} Continuing the trends reported in AR4, there is very high confidence TS.2.5 Changes in the Water Cycle and Cryosphere that the Arctic sea ice extent (annual, multi-year and perennial) decreased over the period 1979 2012 (Figure TS.1). The rate of the TS TS.2.5.1 Atmosphere annual decrease was very likely between 3.5 and 4.1% per decade (range of 0.45 to 0.51 million km2 per decade). The average decrease in Confidence in precipitation change averaged over global land areas decadal extent of annual Arctic sea ice has been most rapid in summer is low prior to 1951 and medium afterwards because of insufficient and autumn (high confidence), but the extent has decreased in every data, particularly in the earlier part of the record (for an overview of season, and in every successive decade since 1979 (high confidence). observed and projected changes in the global water cycle see TFE.1). The extent of Arctic perennial and multi-year ice decreased between Further, when virtually all the land area is filled in using a reconstruc- 1979 and 2012 (very high confidence). The rates are very likely 11.5 tion method, the resulting time series shows little change in land- [9.4 to 13.6]% per decade (0.73 to 1.07 million km2 per decade) for the based precipitation since 1901. NH mid-latitude land areas do show sea ice extent at summer minimum (perennial ice) and very likely 13.5 a likely overall increase in precipitation (medium confidence prior to [11 to 16] % per decade for multi-year ice. There is medium confidence 1951, but high confidence afterwards). For other latitudes area-aver- from reconstructions that the current (1980 2012) Arctic summer sea aged long-term positive or negative trends have low confidence (TFE.1, ice retreat was unprecedented and SSTs were anomalously high in the Figure 1). {2.5.1} perspective of at least the last 1,450 years. {4.2.2, 5.5.2} It is very likely that global near surface and tropospheric air specif- It is likely that the annual period of surface melt on Arctic perennial ic humidity have increased since the 1970s. However, during recent sea ice lengthened by 5.7 [4.8 to 6.6] days per decade over the period years the near-surface moistening trend over land has abated (medium 1979 2012. Over this period, in the region between the East Siberian confidence) (Figure TS.1). As a result, fairly widespread decreases in Sea and the western Beaufort Sea, the duration of ice-free conditions relative humidity near the surface are observed over the land in recent increased by nearly 3 months. {4.2.2} years. {2.4.4, 2.5.5, 2.5.6} There is high confidence that the average winter sea ice thickness Although trends of cloud cover are consistent between independent within the Arctic Basin decreased between 1980 and 2008. The aver- data sets in certain regions, substantial ambiguity and therefore low age decrease was likely between 1.3 m and 2.3 m. High confidence in confidence remains in the observations of global-scale cloud variability this assessment is based on observations from multiple sources: sub- and trends. {2.5.7} marine, electromagnetic probes and satellite altimetry; and is consistent with the decline in multi-year and perennial ice extent. Satellite mea- ­ TS.2.5.2 Ocean and Surface Fluxes surements made in the period 2010 2012 show a decrease in sea ice volume compared to those made over the period 2003 2008 (medium It is very likely that regional trends have enhanced the mean geograph- confidence). There is high confidence that in the Arctic, where the sea ical contrasts in sea surface salinity since the 1950s: saline surface ice thickness has decreased, the sea ice drift speed has increased. {4.2.2} waters in the evaporation-dominated mid-latitudes have become more saline, while relatively fresh surface waters in rainfall-dominated tropi- It is very likely that the annual Antarctic sea ice extent increased at a cal and polar regions have become fresher. The mean contrast between rate of between 1.2 and 1.8% per decade (0.13 to 0.20 million km2 high- and low-salinity regions increased by 0.13 [0.08 to 0.17] from per decade) between 1979 and 2012 (very high confidence). There was 1950 to 2008. It is very likely that the inter-basin contrast in freshwater a greater increase in sea ice area, due to a decrease in the percent- content has increased: the Atlantic has become saltier and the Pacific age of open water within the ice pack. There is high confidence that and Southern Oceans have freshened. Although similar conclusions there are strong regional differences in this annual rate, with some were reached in AR4, recent studies based on expanded data sets and regions increasing in extent/area and some decreasing. There are also new analysis approaches provide high confidence in this assessment. contrasting regions around the Antarctic where the ice-free season has {3.3.2, 3.3.3, 3.9; FAQ 3.2} lengthened, and others where it has decreased over the satellite period (high confidence). {4.2.3} 40 Technical Summary TS.2.5.4 Glaciers and Ice Sheets There is high confidence that current glacier extents are out of balance with current climatic conditions, indicating that glaciers will continue to There is very high confidence that glaciers world-wide are persistently shrink in the future even without further temperature increase. {4.3.3} shrinking as revealed by the time series of measured changes in glacier length, area, volume and mass (Figures TS.1 and TS.3). The few excep- There is very high confidence that the Greenland ice sheet has lost ice tions are regionally and temporally limited. Measurements of glacier during the last two decades. Combinations of satellite and airborne change have increased substantially in number since AR4. Most of the remote sensing together with field data indicate with high confidence new data sets, along with a globally complete glacier inventory, have that the ice loss has occurred in several sectors and that large rates of been derived from satellite remote sensing {4.3.1, 4.3.3} mass loss have spread to wider regions than reported in AR4 (Figure TS.3). There is high confidence that the mass loss of the Greenland There is very high confidence that, during the last decade, the largest ice sheet has accelerated since 1992: the average rate has very likely contributions to global glacier ice loss were from glaciers in Alaska, the increased from 34 [ 6 to 74] Gt yr 1 over the period 1992 2001 (sea Canadian Arctic, the periphery of the Greenland ice sheet, the South- level equivalent, 0.09 [ 0.02 to 0.20] mm yr 1), to 215 [157 to 274] Gt ern Andes and the Asian mountains. Together these areas account for yr 1 over the period 2002 2011 (0.59 [0.43 to 0.76] mm yr 1). There is more than 80% of the total ice loss. Total mass loss from all glaciers high confidence that ice loss from Greenland resulted from increased TS in the world, excluding those on the periphery of the ice sheets, was surface melt and runoff and increased outlet glacier discharge, and very likely 226 [91 to 361] Gt yr 1 (sea level equivalent, 0.62 [0.25 to these occurred in similar amounts. There is high confidence that the 0.99] mm yr 1) in the period 1971 2009, 275 [140 to 410] Gt yr 1 (0.76 area subject to summer melt has increased over the last two decades. [0.39 to 1.13] mm yr 1) in the period 1993 2009 and 301 [166 to 436] {4.4.2, 4.4.3} Gt yr 1 (0.83 [0.46 to 1.20] mm yr 1) between 2005 and 20098. {4.3.3; Tables 4.4, 4.5} (a) (b) 16 5000 Glaciers 14 Cumulative ice mass loss (Gt) Greenland 4000 12 Antarctica 10 SLE (mm) 3000 8 2000 6 4 1000 2 0 0 -2 1992 1994 1996 1998 2000 2002 2004 2006 2008 2010 2012 Year Figure TS.3 | (Upper) Distribution of ice loss determined from Gravity Recovery and Climate Experiment (GRACE) time-variable gravity for (a) Antarctica and (b) Greenland, shown in centimetres of water per year (cm of water yr 1) for the period 2003 2012. (Lower) The assessment of the total loss of ice from glaciers and ice sheets in terms of mass (Gt) and sea level equivalent (mm). The contribution from glaciers excludes those on the periphery of the ice sheets. {4.3.4; Figures 4.12 4.14, 4.16, 4.17, 4.25} 8 100 Gt yr 1 of ice loss corresponds to about 0.28 mm yr 1 of sea level equivalent. 41 Technical Summary Thematic Focus Elements TFE.1 | Water Cycle Change The water cycle describes the continuous movement of water through the climate system in its liquid, solid and vapour forms, and storage in the reservoirs of ocean, cryosphere, land surface and atmosphere. In the atmosphere, water occurs primarily as a gas, water vapour, but it also occurs as ice and liquid water in clouds. The ocean is pri- marily liquid water, but the ocean is partly covered by ice in polar regions. Terrestrial water in liquid form appears as surface water (lakes, rivers), soil moisture and groundwater. Solid terrestrial water occurs in ice sheets, glaciers, snow and ice on the surface and permafrost. The movement of water in the climate system is essential to life on land, as much of the water that falls on land as precipitation and supplies the soil moisture and river flow has been evaporated from the ocean and transported to land by the atmosphere. Water that falls as snow in winter can provide soil moisture in springtime and river flow in summer and is essential to both natural and human systems. The movement of fresh water between the atmosphere and the ocean can also influence oceanic salinity, which is TS an important driver of the density and circulation of the ocean. The latent heat contained in water vapour in the atmosphere is critical to driving the circulation of the atmosphere on scales ranging from individual thunderstorms to the global circulation of the atmosphere. {12.4.5; FAQ 3.2, FAQ 12.2} Observations of Water Cycle Change Because the saturation vapour pressure of air increases with temperature, it is expected that the amount of water vapour in air will increase with a warming climate. Observations from surface stations, radiosondes, global posi- tioning systems and satellite measurements indicate increases in tropospheric water vapour at large spatial scales (TFE.1, Figure 1). It is very likely that tropospheric specific humidity has increased since the 1970s. The magnitude of the observed global change in tropospheric water vapour of about 3.5% in the past 40 years is consistent with the observed temperature change of about 0.5°C during the same period, and the relative humidity has stayed approximately constant. The water vapour change can be attributed to human influence with medium confidence. {2.5.4, 10.3.2} Changes in precipitation are harder to measure with the existing records, both because of the greater difficulty in sampling precipitation and also because it is expected that precipitation will have a smaller fractional change than the water vapour content of air as the climate warms. Some regional precipitation trends appear to be robust (TFE.1, Figure 2), but when virtually all the land area is filled in using a reconstruction method, the resulting time series of global mean land precipitation shows little change since 1900. At present there is medium confidence that there has been a significant human influence on global scale changes in precipitation patterns, including increases in Northern Hemisphere (NH) mid-to-high latitudes. Changes in the extremes of precipitation, and other climate extremes related to the water cycle are comprehensively discussed in TFE.9. {2.5.1, 10.3.2} Although direct trends in precipitation and evaporation are difficult to measure with the available records, the observed oceanic surface salinity, which is strongly dependent on the difference between evaporation and pre- cipitation, shows significant trends (TFE.1, Figure 1). The spatial patterns of the salinity trends since 1950 are very similar to the mean salinity and the mean distribution of evaporation minus precipitation: regions of high salinity where evaporation dominates have become more saline, while regions of low salinity where rainfall dominates have become fresher (TFE.1, Figure 1). This provides indirect evidence that the pattern of evaporation minus pre- cipitation over the oceans has been enhanced since the 1950s (medium confidence). The inferred changes in evapo- ration minus precipitation are consistent with the observed increased water vapour content of the warmer air. It is very likely that observed changes in surface and subsurface salinity are due in part to anthropogenic climate forc- ings. {2.5, 3.3.2 3.3.4, 3.4, 3.9, 10.4.2; FAQ 3.2} In most regions analysed, it is likely that decreasing numbers of snowfall events are occurring where increased winter temperatures have been observed. Both satellite and in situ observations show significant reductions in the NH snow cover extent over the past 90 years, with most of the reduction occurring in the 1980s. Snow cover decreased most in June when the average extent decreased very likely by 53% (40 to 66%) over the period 1967 to 2012. From 1922 to 2012 only data from March and April are available and show very likely a 7% (4.5 to 9.5%) decline. Because of earlier spring snowmelt, the duration of the NH snow season has declined by 5.3 days per decade since the 1972/1973 winter. It is likely that there has been an anthropogenic component to these observed reductions in snow cover since the 1970s. {4.5.2, 10.5.1, 10.5.3} (continued on next page) 42 Technical Summary TFE.1 (continued) 1.6 (a) Trend in 0.8 total precipitable water vapour 0.0 (1988-2010) 0.8 1.6 (kg m-2 per decade) TS 100 (b) Mean evaporation 0 minus precipitation 100 (cm yr-1) 0.8 (c) Trend in 0.4 surface salinity 0.0 (1950-2000) 0.4 0.8 (PSS78 per decade) 37 (d) Mean surface salinity 35 33 31 (PSS78) 0.09 0.06 (e) High salinity salinity (PSS78) minus low Salinity 0.03 0 -0.03 -0.06 -0.09 1950 1960 1970 1980 1990 2000 2010 Year TFE.1, Figure 1 | Changes in sea surface salinity are related to the atmospheric patterns of evaporation minus precipitation (E P) and trends in total precipitable water: (a) Linear trend (1988 to 2010) in total precipitable water (water vapour integrated from the Earth s surface up through the entire atmosphere) (kg m 2 per decade) from satellite observations. (b) The 1979 2005 climatological mean net evaporation minus precipitation (cm yr 1) from meteorological reanalysis data. (c) Trend (1950 2000) in surface salinity (Practical Salinity Scale 78 (PSS78) per 50 years). (d) The climatological mean surface salinity (PSS78) (blues <35; yellows-reds >35). (e) Global difference between salinity averaged over regions where the sea surface salinity is greater than the global mean sea surface salinity ( High Salinity ) and salinity averaged over regions with values below the global mean ( Low Salinity ). For details of data sources see Figure 3.21 and FAQ 3.2, Figure 1. {3.9} 43 Technical Summary TFE.1 (continued) CRU 1901-2010 CRU 1951-2010 GHCN 1901-2010 GHCN 1951-2010 TS GPCC 1901-2010 GPCC 1951-2010 -100 -50 -25 -10 -5 -2.5 0 2.5 5 10 25 50 100 -1 Trend (mm yr per decade) TFE.1, Figure 2 | Maps of observed precipitation change over land from 1901 to 2010 (left-hand panels) and 1951 to 2010 (right-hand panels) from the Climatic Research Unit (CRU), Global Historical Climatology Network (GHCN) and Global Precipitation Climatology Centre (GPCC) data sets. Trends in annual accumulation have been calculated only for those grid boxes with greater than 70% complete records and more than 20% data availability in first and last decile of the period. White areas indicate incomplete or missing data. Black plus signs (+) indicate grid boxes where trends are significant (i.e., a trend of zero lies outside the 90% confidence interval). Further detail regarding the related Figure SPM.2 is given in the TS Supplementary Material. {Figure 2.29; 2.5.1} The most recent and most comprehensive analyses of river runoff do not support the IPCC Fourth Assessment Report (AR4) conclusion that global runoff has increased during the 20th century. New results also indicate that the AR4 conclusions regarding global increasing trends in droughts since the 1970s are no longer supported. {2.5.2, 2.6.2} Projections of Future Changes Changes in the water cycle are projected to occur in a warming climate (TFE.1, Figure 3, see also TS 4.6, TS 5.6, Annex I). Global-scale precipitation is projected to gradually increase in the 21st century. The precipitation increase is projected to be much smaller (about 2% K 1) than the rate of lower tropospheric water vapour increase (about 7% K 1­ , due to global energetic constraints. Changes of average precipitation in a much warmer world will not be ) uniform, with some regions experiencing increases, and others with decreases or not much change at all. The high latitude land masses are likely to experience greater amounts of precipitation due to the additional water carrying capacity of the warmer troposphere. Many mid-latitude and subtropical arid and semi-arid regions will likely experi- ence less precipitation. The largest precipitation changes over northern Eurasia and North America are projected to occur during the winter. {12.4.5, Annex I} (continued on next page) 44 Technical Summary TFE.1 (continued) Regional to global-scale projections of soil moisture and drought remain relatively uncertain compared to other aspects of the water cycle. Nonetheless, drying in the Mediterranean, southwestern USA and southern African regions are consistent with projected changes in the Hadley Circulation, so drying in these regions as global temper- atures increase is likely for several degrees of warming under the Representative Concentration Pathway RCP8.5. Decreases in runoff are likely in southern Europe and the Middle East. Increased runoff is likely in high northern latitudes, and consistent with the projected precipitation increases there. {12.4.5} Precipitation Evaporation TS Relative humidity E-P Runoff Soil moisture TFE.1, Figure 3 | Annual mean changes in precipitation (P), evaporation (E), relative humidity, E P, runoff and soil moisture for 2081 2100 relative to 1986 2005 under the Representative Concentration Pathway RCP8.5 (see Box TS.6). The number of Coupled Model Intercomparison Project Phase 5 (CMIP5) models to calculate the multi-model mean is indicated in the upper right corner of each panel. Hatching indicates regions where the multi-model mean change is less than one standard deviation of internal variability. Stippling indicates regions where the multi-model mean change is greater than two standard deviations of internal variability and where 90% of models agree on the sign of change (see Box 12.1). {Figures 12.25 12.27} 45 Technical Summary There is high confidence that the Antarctic ice sheet has been losing ice above present, implying reduced volume of polar ice sheets. The best ­ during the last two decades (Figure TS.3). There is very high confidence estimates from various methods imply with high confidence that sea that these losses are mainly from the northern Antarctic Peninsula and level has not exceeded +20 m during the warmest periods of the the Amundsen Sea sector of West Antarctica and high confidence that Pliocene, due to deglaciation of the Greenland and West Antarctic ice they result from the acceleration of outlet glaciers. The average rate sheets and areas of the East Antarctic ice sheet. {5.6.1, 13.2} of ice loss from Antarctica likely increased from 30 [ 37 to 97] Gt yr 1 (sea level equivalent, 0.08 [ 0.10 to 0.27] mm yr 1) over the period There is very high confidence that maximum GMSL during the last inter- 1992 2001, to 147 [72 to 221] Gt yr 1 over the period 2002 2011 glacial period (129 to 116 ka) was, for several thousand years, at least (0.40 [0.20 to 0.61] mm yr 1). {4.4.2, 4.4.3} 5 m higher than present and high confidence that it did not exceed 10 m above present, implying substantial contributions from the Green- There is high confidence that in parts of Antarctica floating ice shelves land and Antarctic ice sheets. This change in sea level occurred in the are undergoing substantial changes. There is medium confidence that context of different orbital forcing and with high-latitude surface tem- ice shelves are thinning in the Amundsen Sea region of West Antarctica, perature, averaged over several thousand years, at least 2°C warmer and low confidence that this is due to high ocean heat flux. There than present (high confidence). Based on ice sheet model simulations is high confidence that ice shelves around the Antarctic Peninsula consistent with elevation changes derived from a new Greenland ice TS continue a long-term trend of retreat and partial collapse that began core, the Greenland ice sheet very likely contributed between 1.4 m decades ago. {4.4.2, 4.4.5} and 4.3 m sea level equivalent, implying with medium confidence a contribution from the Antarctic ice sheet to the GMSL during the Last TS.2.5.5 Snow Cover, Freshwater Ice and Frozen Ground Interglacial Period. {5.3.4, 5.6.2, 13.2.1} There is very high confidence that snow cover extent has decreased in Proxy and instrumental sea level data indicate a transition in the late the NH, especially in spring (Figure TS.1). Satellite records indicate that 19th to the early 20th century from relatively low mean rates of rise over the period 1967 2012, snow cover extent very likely decreased; over the previous two millennia to higher rates of rise (high confi- the largest change, 53% [ 40 to 66%], occurred in June. No month dence) {3.7, 3.7.4, 5.6.3, 13.2} had statistically significant increases. Over the longer period, 1922 2012, data are available only for March and April, but these show very GMSL has risen by 0.19 [0.17 to 0.21] m, estimated from a linear trend likely a 7% [4.5 to 9.5%] decline and a negative correlation ( 0.76) over the period 1901 2010, based on tide gauge records and addition- with March to April 40°N to 60°N land temperature. In the Southern ally on satellite data since 1993. It is very likely that the mean rate of Hemisphere (SH), evidence is too limited to conclude whether changes sea level rise was 1.7 [1.5 to 1.9] mm yr 1 between 1901 and 2010. have occurred. {4.5.2, 4.5.3} Between 1993 and 2010, the rate was very likely higher at 3.2 [2.8 to 3.6] mm yr 1; similarly high rates likely occurred between 1920 and Permafrost temperatures have increased in most regions around the 1950. The rate of GMSL rise has likely increased since the early 1900s, world since the early 1980s (high confidence). These increases were with estimates ranging from 0.000 [ 0.002 to 0.002] to 0.013 [ 0.007 in response to increased air temperature and to changes in the timing to 0.019] mm yr 2. {3.7, 5.6.3, 13.2} and thickness of snow cover (high confidence). The temperature increase for colder permafrost was generally greater than for warmer TS.2.7 Changes in Extremes permafrost (high confidence). {4.7.2; Table 4.8} TS.2.7.1 Atmosphere TS.2.6 Changes in Sea Level Recent analyses of extreme events generally support the AR4 and SREX The primary contributions to changes in the volume of water in the conclusions (see TFE.9 and in particular TFE.9, Table 1, for a synthesis). ocean are the expansion of the ocean water as it warms and the trans- It is very likely that the number of cold days and nights has decreased fer to the ocean of water currently stored on land, particularly from and the number of warm days and nights has increased on the global glaciers and ice sheets. Water impoundment in reservoirs and ground scale between 1951 and 2010. Globally, there is medium confidence water depletion (and its subsequent runoff to the ocean) also affect that the length and frequency of warm spells, including heat waves, sea level. Change in sea level relative to the land (relative sea level) has increased since the middle of the 20th century, mostly owing to can be significantly different from the global mean sea level (GMSL) lack of data or studies in Africa and South America. However, it is likely change because of changes in the distribution of water in the ocean, that heat wave frequency has increased over this period in large parts vertical movement of the land and changes in the Earth s gravitational of Europe, Asia and Australia. {2.6.1; Tables 2.12, 2.13} field. For an overview on the scientific understanding and uncertain- ties associated with recent (and projected) sea level change see TFE.2. It is likely that since about 1950 the number of heavy precipitation {3.7.3, 13.1} events over land has increased in more regions than it has decreased. Confidence is highest for North America and Europe where there have During warm intervals of the mid Pliocene (3.3 to 3.0 Ma), when been likely increases in either the frequency or intensity of heavy pre- there is medium confidence that GMSTs were 1.9°C to 3.6°C warmer cipitation with some seasonal and regional variations. It is very likely than for pre-industrial climate and carbon dioxide (CO2) levels were that there have been trends towards heavier precipitation events in between 350 and 450 ppm, there is high confidence that GMSL was central North America. {2.6.2; Table 2.13} 46 Technical Summary Thematic Focus Elements TFE.2 | Sea Level Change: Scientific Understanding and Uncertainties After the Last Glacial Maximum, global mean sea levels (GMSLs) reached close to present-day values several thou- sand years ago. Since then, it is virtually certain that the rate of sea level rise has increased from low rates of sea level change during the late Holocene (order tenths of mm yr 1) to 20th century rates (order mm yr 1, Figure TS1). {3.7, 5.6, 13.2} Ocean thermal expansion and glacier mass loss are the dominant contributors to GMSL rise during the 20th century (high confidence). It is very likely that warming of the ocean has contributed 0.8 [0.5 to 1.1] mm yr 1 of sea level change during 1971 2010, with the majority of the contribution coming from the upper 700 m. The model mean rate of ocean thermal expansion for 1971 2010 is close to observations. {3.7, 13.3} Observations, combined with improved methods of analysis, indicate that the global glacier contribution (excluding the peripheral glaciers around Greenland and Antarctica) to sea level was 0.25 to 0.99 mm yr 1 sea level equivalent TS during 1971 2010. Medium confidence in global glacier mass balance models used for projections of glacier chang- es arises from the process-based understanding of glacier surface mass balance, the consistency of observations and models of glacier changes, and the evidence that Atmosphere Ocean General Circulation Model (AOGCM) climate simulations can provide realitistic climate input. A simulation using observed climate data shows a larger rate of glacier mass loss during the 1930s than the simulations using AOGCM input, possibly a result of an episode of warm- ing in Greenland associated with unforced regional climate variability. {4.3, 13.3} Observations indicate that the Greenland ice sheet has very likely experienced a net loss of mass due to both increased surface melting and runoff, and increased ice discharge over the last two decades (Figure TS.3). Regional climate models indicate that Greenland ice sheet surface mass balance showed no significant trend from the 1960s to the 1980s, but melting and consequent runoff has increased since the early 1990s. This tendency is related to pronounced regional warming, which may be attributed to a combination of anomalous regional variability in recent years and anthropogenic climate change. High confidence in projections of future warming in Greenland and increased surface melting is based on the qualitative agreements of models in projecting amplified warming at high northern latitudes for well-understood physical reasons. {4.4, 13.3} There is high confidence that the Antarctic ice sheet is in a state of net mass loss and its contribution to sea level is also likely to have increased over the last two decades. Acceleration in ice outflow has been observed since the 1990s, especially in the Amundsen Sea sector of West Antarctica. Interannual variability in accumulation is large and as a result no significant trend is present in accumulation since 1979 in either models or observations. Surface melting is currently negligible in Antarctica. {4.4, 13.3} Model-based estimates of climate-related changes in water storage on land (as snow cover, surface water, soil mois- ture and ground water) do not show significant long-term contributions to sea level change for recent decades. However, human-induced changes (reservoir impoundment and groundwater depletion) have each contributed at least several tenths of mm yr 1 to sea level change. Reservoir impoundment exceeded groundwater depletion for the majority of the 20th century but the rate of groundwater depletion has increased and now exceeds the rate of impoundment. Their combined net contribution for the 20th century is estimated to be small. {13.3} The observed GMSL rise for 1993 2010 is consistent with the sum of the observationally estimated contributions (TFE.2, Figure 1e). The closure of the observational budget for recent periods within uncertainties represents a significant advance since the IPCC Fourth Assessment Report in physical understanding of the causes of past GMSL change, and provides an improved basis for critical evaluation of models of these contributions in order to assess their reliability for making projections. {13.3} The sum of modelled ocean thermal expansion and glacier contributions and the estimated change in land water storage (which is relatively small) accounts for about 65% of the observed GMSL rise for 1901 1990, and 90% for 1971 2010 and 1993 2010 (TFE.2, Figure 1). After inclusion of small long-term contributions from ice sheets and the possible greater mass loss from glaciers during the 1930s due to unforced climate variability, the sum of the modelled contribution is close to the observed rise. The addition of the observed ice sheet contribution since 1993 improves the agreement further between the observed and modelled sea level rise (TFE.2, Figure 1). The evidence now available gives a clearer account than in previous IPCC assessments of 20th century sea level change. {13.3} (continued on next page) 47 Technical Summary TFE.2 (continued) (a) Tide gauge (b) TS (c) (d) (e) Year TFE.2, Figure 1 | (a) The observed and modelled sea level for 1900 to 2010. (b) The rates of sea level change for the same period, with the satellite altimeter data shown as a red dot for the rate. (c) The observed and modelled sea level for 1961 to 2010. (d) The observed and modelled sea level for 1990 to 2010. Panel (e) com- pares the sum of the observed contributions (orange) and the observed sea level from the satellite altimeter data (red). Estimates of GMSL from different sources are given, with the shading indicating the uncertainty estimates (two standard deviations). The satellite altimeter data since 1993 are shown in red. The grey lines in panels (a)-(d) are the sums of the contributions from modelled ocean thermal expansion and glaciers (excluding glaciers peripheral to the Antarctic ice sheet), plus changes in land-water storage (see Figure 13.4). The black line is the mean of the grey lines plus a correction of thermal expansion for the omission of volcanic forcing in the Atmosphere Ocean General Circulation Model (AOGCM) control experiments (see Section 13.3.1). The dashed black line (adjusted model mean) is the sum of the cor- rected model mean thermal expansion, the change in land water storage, the glacier estimate using observed (rather than modelled) climate (see Figure 13.4), and an illustrative long-term ice-sheet contribution (of 0.1 mm yr 1). The dotted black line is the adjusted model mean but now including the observed ice-sheet contributions, which begin in 1993. Because the observational ice-sheet estimates include the glaciers peripheral to the Greenland and Antarctic ice sheets (from Section 4.4), the contribution from glaciers to the adjusted model mean excludes the peripheral glaciers (PGs) to avoid double counting. {13.3; Figure 13.7} 48 Technical Summary TFE.2 (continued) When calibrated appropriately, recently improved dynamical ice sheet models can reproduce the observed rapid changes in ice sheet outflow for individual glacier systems (e.g., Pine Island Glacier in Antarctica; medium confi- dence). However, models of ice sheet response to global warming and particularly ice sheet ocean interactions are incomplete and the omission of ice sheet models, especially of dynamics, from the model budget of the past means that they have not been as critically evaluated as other contributions. {13.3, 13.4} TS TFE.2, Figure 2 | Compilation of paleo sealevel data (purple), tide gauge data (blue, red and green), altimeter data (light blue) and central estimates and likely ranges for projections of global mean sea level rise from the combination of CMIP5 and process-based models for RCP2.6 (blue) and RCP8.5 (red) scenarios, all relative to pre-industrial values. {Figures 13.3, 13.11, 13.27} GMSL rise for 2081 2100 (relative to 1986 2005) for the Representative Concentration Pathways (RCPs) will likely be in the 5 to 95% ranges derived from Coupled Model Intercomparison Project Phase 5 (CMIP5) climate projec- tions in combination with process-based models of other contributions (medium confidence), that is, 0.26 to 0.55 m (RCP2.6), 0.32 to 0.63 m (RCP4.5), 0.33 to 0.63 m (RCP6.0), 0.45 to 0.82 (RCP8.5) m (see Table TS.1 and Figure TS.15 for RCP forcing). For RCP8.5 the range at 2100 is 0.52 to 0.98 m. Confidence in the projected likely ranges comes from the consistency of process-based models with observations and physical understanding. It is assessed that there is currently insufficient evidence to evaluate the probability of specific levels above the likely range. Based on current understanding, only the collapse of marine-based sectors of the Antarctic ice sheet, if initiated, could cause GMSL to rise substantially above the likely range during the 21st century. There is a lack of consensus on the probability for such a collapse, and the potential additional contribution to GMSL rise cannot be precisely quantified, but there is medium confidence that it would not exceed several tenths of a metre of sea level rise during the 21st century. It is virtually certain that GMSL rise will continue beyond 2100. {13.5.1, 13.5.3} Many semi-empirical models projections of GMSL rise are higher than process-based model projections, but there is no consensus in the scientific community about their reliability and there is thus low confidence in their projections. {13.5.2, 13.5.3} TFE.2, Figure 2 combines the paleo, tide gauge and altimeter observations of sea level rise from 1700 with the pro- jected GMSL change to 2100. {13.5, 13.7, 13.8} 49 Technical Summary There is low confidence in a global-scale observed trend in drought or with very high confidence from polar ice cores. Since AR4 these records dryness (lack of rainfall), owing to lack of direct observations, depen- have been extended from 650 ka to 800 ka. {5.2.2} dencies of inferred trends on the index choice and geographical incon- sistencies in the trends. However, this masks important regional chang- With very high confidence, the current rates of CO2, CH4 and N2O rise es and, for example, the frequency and intensity of drought have likely in atmospheric concentrations and the associated increases in RF are increased in the Mediterranean and West Africa and likely decreased unprecedented with respect to the highest resolution ice core records in central North America and northwest Australia since 1950. {2.6.2; of the last 22 kyr. There is medium confidence that the rate of change Table 2.13} of the observed GHG rise is also unprecedented compared with the lower resolution records of the past 800 kyr. {2.2.1, 5.2.2} There is high confidence for droughts during the last millennium of greater magnitude and longer duration than those observed since the In several periods characterized by high atmospheric CO2 concentra- beginning of the 20th century in many regions. There is medium confi- tions, there is medium confidence that global mean temperature was dence that more megadroughts occurred in monsoon Asia and wetter significantly above pre-industrial level. During the mid-Pliocene (3.3 conditions prevailed in arid Central Asia and the South American mon- to 3.0 Ma), atmospheric CO2 concentration between 350 ppm and soon region during the Little Ice Age (1450 1850) compared to the 450 ppm (medium confidence) occurred when GMST was 1.9°C to TS Medieval Climate Anomaly (950 1250). {5.5.4, 5.5.5} 3.6°C warmer (medium confidence) than for pre-industrial climate. During the Early Eocene (52 to 48 Ma), atmospheric CO2 concentra- Confidence remains low for long-term (centennial) changes in tropi- tion exceeded about 1000 ppm when GMST was 9°C to 14°C higher cal cyclone activity, after accounting for past changes in observing (medium confidence) than for pre-industrial conditions. {5.3.1} capabilities. However, for the years since the 1970s, it is virtually cer- tain that the frequency and intensity of storms in the North Atlantic TS.2.8.1 Carbon Dioxide have increased although the reasons for this increase are debated (see TFE.9). There is low confidence of large-scale trends in storminess over Between 1750 and 2011, CO2 emissions from fossil fuel combustion the last century and there is still insufficient evidence to determine and cement production are estimated from energy and fuel use sta- whether robust trends exist in small-scale severe weather events such tistics to have released 375 [345 to 405] PgC9. In 2002 2011, average as hail or thunderstorms. {2.6.2 2.6.4} fossil fuel and cement manufacturing emissions were 8.3 [7.6 to 9.0] PgC yr 1 (high confidence), with an average growth rate of 3.2% yr 1 With high confidence, floods larger than recorded since the 20th cen- (Figure TS.4). This rate of increase of fossil fuel emissions is higher than tury occurred during the past five centuries in northern and central during the 1990s (1.0% yr 1). In 2011, fossil fuel emissions were 9.5 Europe, the western Mediterranean region and eastern Asia. There [8.7 to 10.3] PgC. {2.2.1, 6.3.1; Table 6.1} is medium confidence that in the Near East, India and central North America, modern large floods are comparable or surpass historical Between 1750 and 2011, land use change (mainly deforestation), floods in magnitude and/or frequency. {5.5.5} derived from land cover data and modelling, is estimated to have released 180 [100 to 260] PgC. Land use change emissions between TS.2.7.2 Oceans 2002 and 2011 are dominated by tropical deforestation, and are esti- mated at 0.9 [0.1 to 1.7] PgC yr 1 (medium confidence), with possibly a It is likely that the magnitude of extreme high sea level events has small decrease from the 1990s due to lower reported forest loss during increased since 1970 (see TFE.9, Table 1). Most of the increase in this decade. This estimate includes gross deforestation emissions of extreme sea level can be explained by the mean sea level rise: changes around 3 PgC yr 1 compensated by around 2 PgC yr 1 of forest regrowth in extreme high sea levels are reduced to less than 5 mm yr 1 at 94% in some regions, mainly abandoned agricultural land. {6.3.2; Table 6.2} of tide gauges once the rise in mean sea level is accounted for. There is medium confidence based on reanalysis forced model hindcasts and Of the 555 [470 to 640] PgC released to the atmosphere from fossil ship observations that mean significant wave height has increased fuel and land use emissions from 1750 to 2011, 240 [230 to 250] PgC since the 1950s over much of the North Atlantic north of 45°N, with accumulated in the atmosphere, as estimated with very high accuracy typical winter season trends of up to 20 cm per decade. {3.4.5, 3.7.5} from the observed increase of atmospheric CO2 concentration from 278 [273 to 283] ppm10 in 1750 to 390.5 [390.4 to 390.6] ppm in TS.2.8 Changes in Carbon and Other Biogeochemical 2011. The amount of CO2 in the atmosphere grew by 4.0 [3.8 to 4.2] Cycles PgC yr 1 in the first decade of the 21st century. The distribution of observed atmospheric CO2 increases with latitude clearly shows that Concentrations of the atmospheric greenhouse gases (GHGs) carbon the increases are driven by anthropogenic emissions that occur primar- dioxide (CO2), methane (CH4) and nitrous oxide (N2O) in 2011 exceed ily in the industrialized countries north of the equator. Based on annual the range of concentrations recorded in ice cores during the past 800 average concentrations, stations in the NH show slightly higher con- kyr. Past changes in atmospheric GHG concentrations are determined centrations than stations in the SH. An independent line of evidence 9 1 Petagram of carbon = 1 PgC = 1015 grams of carbon = 1 Gigatonne of carbon = 1 GtC. This corresponds to 3.667 GtCO2. 10 ppm (parts per million) or ppb (parts per billion, 1 billion = 1000 million) is the ratio of the number of greenhouse gas molecules to the total number of molecules of dry air. For example, 300 ppm means 300 molecules of a greenhouse gas per million molecules of dry air. 50 Technical Summary for the ­ nthropogenic origin of the observed atmospheric CO2 increase a inventory of anthropogenic carbon increased from 1994 to 2010. In comes from the observed consistent decrease in atmospheric oxygen 2011, it is estimated to be 155 [125 to 185] PgC. The annual global (O2) content and a decrease in the stable isotopic ratio of CO2 (13C/12C) oceanic uptake rates calculated from independent data sets (from in the atmosphere (Figure TS.5). {2.2.1, 6.1.3} changes in the oceanic inventory of anthropogenic carbon, from mea- surements of the atmospheric oxygen to nitrogen ratio (O2/N2) or from The remaining amount of carbon released by fossil fuel and land CO2 partial pressure (pCO2) data) and for different time periods agree use emissions has been re-absorbed by the ocean and terrestrial with each other within their uncertainties, and very likely are in the e ­cosystems. Based on high agreement between independent esti- range of 1.0 to 3.2 PgC yr 1. Regional observations of the storage mates using different methods and data sets (e.g., oceanic carbon, rate of anthropogenic carbon in the ocean are in broad agreement oxygen and transient tracer data), it is very likely that the global ocean with the expected rate resulting from the increase in atmospheric CO2 1750 1800 1850 1900 1950 2000 10 cement CO2 emissions (PgC yr 1) gas TS Fossil fuel and cement oil coal 5 0 10 fossil fuel and cement from energy statistics land use change from data and models residual land sink measured atmospheric growth rate Annual anthropogenic CO2 emissions ocean sink from data and models 5 and partitioning (PgC yr 1) emissions 0 partitioning 5 10 1750 1800 1850 1900 1950 2000 Year Figure TS.4 | Annual anthropogenic CO2 emissions and their partitioning among the atmosphere, land and ocean (PgC yr 1) from 1750 to 2011. (Top) Fossil fuel and cement CO2 emissions by category, estimated by the Carbon Dioxide Information Analysis Center (CDIAC). (Bottom) Fossil fuel and cement CO2 emissions as above. CO2 emissions from net land use change, mainly deforestation, are based on land cover change data (see Table 6.2). The atmospheric CO2 growth rate prior to 1959 is based on a spline fit to ice core observations and a synthesis of atmospheric measurements from 1959. The fit to ice core observations does not capture the large interannual variability in atmospheric CO2 and is represented with a dashed line. The ocean CO2 sink is from a combination of models and observations. The residual land sink (term in green in the figure) is computed from the residual of the other terms. The emissions and their partitioning include only the fluxes that have changed since 1750, and not the natural CO2 fluxes (e.g., atmospheric CO2 uptake from weathering, outgassing of CO2 from lakes and rivers and outgassing of CO2 by the ocean from carbon delivered by rivers; see Figure 6.1) between the atmosphere, land and ocean reservoirs that existed before that time and still exist today. The uncertainties in the various terms are discussed in Chapter 6 and reported in Table 6.1 for decadal mean values. {Figure 6.8} 51 Technical Summary the beginning of the industrial era (high confidence), corresponding to a 26% increase in hydrogen ion concentration. The observed pH trends range between 0.0014 and 0.0024 per year in surface waters. In the ocean interior, natural physical and biological processes, as well as uptake of anthropogenic CO2, can cause changes in pH over decadal and longer time scales. {3.8.2; Box 3.2; Table 3.2; FAQ 3.3} TS.2.8.3 Methane The concentration of CH4 has increased by a factor of 2.5 since pre- industrial times, from 722 [697 to 747] ppb in 1750 to 1803 [1799 to 1807] ppb in 2011 (Figure TS.5). There is very high confidence that the atmospheric CH4 increase during the Industrial Era is caused by anthro- pogenic activities. The massive increase in the number of ruminants, the emissions from fossil fuel extraction and use, the expansion of TS rice paddy agriculture and the emissions from landfills and waste are the dominant anthropogenic CH4 sources. Anthropogenic emissions account for 50 to 65% of total emissions. By including natural geologi- cal CH4 emissions that were not accounted for in previous budgets, the fossil component of the total CH4 emissions (i.e., anthropogenic emis- sions related to leaks in the fossil fuel industry and natural geological leaks) is now estimated to amount to about 30% of the total CH4 emis- sions (medium confidence). {2.2.1, 6.1, 6.3.3} In recent decades, CH4 growth in the atmosphere has been variable. CH4 concentrations were relatively stable for about a decade in the 1990s, but then started growing again starting in 2007. The exact drivers of Figure TS.5 | Atmospheric concentration of CO2, oxygen, 13C/12C stable isotope ratio this renewed growth are still debated. Climate-driven fluctuations of in CO2, as well as CH4 and N2O atmospheric concentrations and oceanic surface obser- CH4 emissions from natural wetlands (177 to 284 ×1012 g (CH4) yr 1 for vations of CO2 partial pressure (pCO2) and pH, recorded at representative time series 2000 2009 based on bottom-up estimates) are the main drivers of the stations in the Northern and the Southern Hemispheres. MLO: Mauna Loa Observatory, Hawaii; SPO: South Pole; HOT: Hawaii Ocean Time-Series station; MHD: Mace Head, global interannual variability of CH4 emissions (high confidence), with Ireland; CGO: Cape Grim, Tasmania; ALT: Alert, Northwest Territories, Canada. Further a smaller contribution from biomass burning emissions during high fire detail regarding the related Figure SPM.4 is given in the TS Supplementary Material. years {2.2.1, 6.3.3; Table 6.8}. {Figures 3.18, 6.3; FAQ 3.3, Figure 1} TS.2.8.4 Nitrous Oxide concentrations, but with significant spatial and temporal variations. {3.8.1, 6.3} Since pre-industrial times, the concentration of N2O in the atmosphere has increased by a factor of 1.2 (Figure TS.5). Changes in the nitro- Natural terrestrial ecosystems (those not affected by land use change) gen cycle, in addition to interactions with CO2 sources and sinks, affect are estimated by difference from changes in other reservoirs to have emissions of N2O both on land and from the ocean. {2.2.1, 6.4.6} accumulated 160 [70 to 250] PgC between 1750 and 2011. The gain of carbon by natural terrestrial ecosystems is estimated to take place TS.2.8.5 Oceanic Oxygen mainly through the uptake of CO2 by enhanced photosynthesis at higher CO2 levels and nitrogen deposition and longer growing seasons High agreement among analyses provides medium confidence that in mid and high latitudes. Natural carbon sinks vary regionally owing oxygen concentrations have decreased in the open ocean thermocline to physical, biological and chemical processes acting on different time in many ocean regions since the 1960s. The general decline is con- scales. An excess of atmospheric CO2 absorbed by land ecosystems sistent with the expectation that warming-induced stratification leads gets stored as organic matter in diverse carbon pools, from short-lived to a decrease in the supply of oxygen to the thermocline from near (leaves, fine roots) to long-lived (stems, soil carbon). {6.3; Table 6.1} surface waters, that warmer waters can hold less oxygen and that changes in wind-driven circulation affect oxygen concentrations. It is TS.2.8.2 Carbon and Ocean Acidification likely that the tropical oxygen minimum zones have expanded in recent decades. {3.8.3} Oceanic uptake of anthropogenic CO2 results in gradual acidification of the ocean. The pH11 of ocean surface water has decreased by 0.1 since pH is a measure of acidity: a decrease in pH value means an increase in acidity, that is, acidification. 11 52 Technical Summary TS.3 Drivers of Climate Change in well-mixed greenhouse gas (WMGHG) concentrations during the Industrial Era (see Section TS.2.8 and TFE.7). As historical WMGHG TS.3.1 Introduction concentrations since the pre-industrial are well known based on direct measurements and ice core records, and WMGHG radiative proper- Human activities have changed and continue to change the Earth s ties are also well known, the computation of RF due to concentra- surface and atmospheric composition. Some of these changes have tion changes provides tightly constrained values (Figure TS.6). There a direct or indirect impact on the energy balance of the Earth and are has not been significant change in our understanding of WMGHG thus drivers of climate change. Radiative forcing (RF) is a measure of radiative impact, so that the changes in RF estimates relative to AR4 the net change in the energy balance of the Earth system in response to are due essentially to concentration increases. The best estimate for some external perturbation (see Box TS.2), with positive RF leading to WMGHG ERF is the same as RF, but the uncertainty range is twice as a warming and negative RF to a cooling. The RF concept is valuable for large due to the poorly constrained cloud responses. Owing to high- comparing the influence on GMST of most individual agents affecting quality observations, it is certain that increasing atmospheric burdens the Earth s radiation balance. The quantitative values provided in AR5 of most WMGHGs, especially CO2, resulted in a further increase in their are consistent with those in previous IPCC reports, though there have RF from 2005 to 2011. Based on concentration changes, the RF of all been some important revisions (Figure TS.6). Effective radiative forc- WMGHGs in 2011 is 2.83 [2.54 to 3.12] W m 2 (very high confidence). TS ing (ERF) is now used to quantify the impact of some forcing agents This is an increase since AR4 of 0.20 [0.18 to 0.22] W m 2, with nearly that involve rapid adjustments of components of the atmosphere and all of the increase due to the increase in the abundance of CO2 since surface that are assumed constant in the RF concept (see Box TS.2). 2005. The Industrial Era RF for CO2 alone is 1.82 [1.63 to 2.01] W m 2. RF and ERF are estimated from the change between 1750 and 2011, Over the last 15 years, CO2 has been the dominant contributor to the referred to as Industrial Era , if other time periods are not explicitly increase in RF from the WMGHGs, with RF of CO2 having an average stated. Uncertainties are given associated with the best estimates of growth rate slightly less than 0.3 W m 2 per decade. The uncertainty in RF and ERF, with values representing the 5 to 95% (90%) confidence the WMGHG RF is due in part to its radiative properties but mostly to range. {8.1, 7.1} the full accounting of atmospheric radiative transfer including clouds. {2.2.1, 5.2, 6.3, 8.3, 8.3.2; Table 6.1} In addition to the global mean RF or ERF, the spatial distribution and temporal evolution of forcing, as well as climate feedbacks, play a After a decade of near stability, the recent increase of CH4 concentra- role in determining the eventual impact of various drivers on climate. tion led to an enhanced RF compared to AR4 by 2% to 0.48 [0.43 to Land surface changes may also impact the local and regional climate 0.53] W m 2. It is very likely that the RF from CH4 is now larger than that through processes that are not radiative in nature. {8.1, 8.3.5, 8.6} of all halocarbons combined. {2.2.1, 8.3.2} TS.3.2 Radiative Forcing from Greenhouse Gases Atmospheric N2O has increased by 6% since AR4, causing an RF of 0.17 [0.14 to 0.20] W m 2. N2O concentrations continue to rise while those Human activity leads to change in the atmospheric composition either of dichlorodifluoromethane (CF2Cl2, CFC-12), the third largest WMGHG directly (via emissions of gases or particles) or indirectly (via atmo- contributor to RF for several decades, are decreasing due to phase- spheric chemistry). Anthropogenic emissions have driven the changes out of emissions of this chemical under the Montreal Protocol. Since Box TS.2 | Radiative Forcing and Effective Radiative Forcing RF and ERF are used to quantify the change in the Earth s energy balance that occurs as a result of an externally imposed change. They are expressed in watts per square metre (W m 2). RF is defined in AR5, as in previous IPCC assessments, as the change in net downward flux (shortwave + longwave) at the tropopause after allowing for stratospheric temperatures to readjust to radiative equilibrium, while holding other state variables such as tropospheric temperatures, water vapour and cloud cover fixed at the unperturbed values (see Glossary). {8.1.1} Although the RF concept has proved very valuable, improved understanding has shown that including rapid adjustments of the Earth s surface and troposphere can provide a better metric for quantifying the climate response. These rapid adjustments occur over a variety of time scales, but are relatively distinct from responses to GMST change. Aerosols in particular impact the atmosphere temperature profile and cloud properties on a time scale much shorter than adjustments of the ocean (even the upper layer) to forcings. The ERF concept defined in AR5 allows rapid adjustments to perturbations, for all variables except for GMST or ocean temperature and sea ice cover. The ERF and RF values are significantly different for the anthropogenic aerosols, owing to their influence on clouds and on snow or ice cover. For other components that drive the Earth s energy balance, such as GHGs, ERF and RF are fairly similar, and RF may have comparable utility given that it requires fewer computational resources to calculate and is not affected by meteorological variability and hence can better isolate small forcings. In cases where RF and ERF differ substantially, ERF has been shown to be a better indicator of the GMST response and is therefore emphasized in AR5. {7.1, 8.1; Box 8.1} 53 Technical Summary AR4, N2O has overtaken CFC-12 to become the third largest WMGHG RF from HCFC-22. There is high confidence that the growth rate in RF contributor to RF. The RF from halocarbons is very similar to the value from all WMGHG is weaker over the last decade than in the 1970s and in AR4, with a reduced RF from CFCs but increases in many of their 1980s owing to a slower increase in the non-CO2 RF. {2.2.1, 8.3.2} replacements. Four of the halocarbons (trichlorofluoromethane (CFCl3, CFC-11), CFC-12, trichlorotrifluoroethane (CF2ClCFCl2, CFC-113) and The short-lived GHGs ozone (O3) and stratospheric water vapour also chlorodifluoromethane (CHF2Cl, HCFC-22) account for 85% of the total contribute to anthropogenic forcing. Observations indicate that O3 halocarbon RF. The former three compounds have declining RF over likely increased at many undisturbed (background) locations through the last 5 years but are more than compensated for by the increased the 1990s. These increases have continued mainly over Asia (though Radiative forcing of climate between 1750 and 2011 Confidence Forcing agent Level CO2 Very High Well Mixed Halocarbons Greenhouse Gases Other WMGHG CH4 N2O Very High TS Ozone Stratospheric Tropospheric High Anthropogenic Stratospheric water AR4 estimates Medium vapour from CH4 Surface Albedo Land Use Black carbon High/Low on snow Medium Contrails Contrail induced cirrus Low High Aerosol-Radiation Interac. Medium Aerosol-Cloud Interac. Low Total anthropogenic Natural Solar irradiance Medium -1 0 1 2 3 Radiative Forcing (W m-2) AR4 RF 1.2 Greenhouse 1.0 gases Probability density function 0.8 Aerosols Total anthropogenic 0.6 0.4 0.2 0.0 -2 0 2 4 Effective radiative forcing (W m-2) Figure TS.6 | Radiative forcing (RF) and Effective radiative forcing (ERF) of climate change during the Industrial Era. (Top) Forcing by concentration change between 1750 and 2011 with associated uncertainty range (solid bars are ERF, hatched bars are RF, green diamonds and associated uncertainties are for RF assessed in AR4). (Bottom) Probability density functions (PDFs) for the ERF, for the aerosol, greenhouse gas (GHG) and total. The green lines show the AR4 RF 90% confidence intervals and can be compared with the red, blue and black lines which show the AR5 ERF 90% confidence intervals (although RF and ERF differ, especially for aerosols). The ERF from surface albedo changes and combined contrails and contrail-induced cirrus is included in the total anthropogenic forcing, but not shown as a separate PDF. For some forcing mechanisms (ozone, land use, solar) the RF is assumed to be representative of the ERF but an additional uncertainty of 17% is added in quadrature to the RF uncertainty. {Figures 8.15, 8.16} 54 Technical Summary observations cover a limited area) and flattened over Europe during microphysical effects on mixed-phase, ice and convective clouds. This the last decade. The total RF due to changes in O3 is 0.35 [0.15 to 0.55] range was obtained by giving equal weight to satellite-based studies W m 2 (high confidence), with RF due to tropospheric O3 of 0.40 [0.20 and estimates from climate models. It is consistent with multiple lines to 0.60] W m 2 (high confidence) and due to stratospheric O3 of 0.05 of evidence suggesting less negative estimates for aerosol cloud inter- [ 0.15 to +0.05] W m 2 (high confidence). O3 is not emitted directly actions than those discussed in AR4. {7.4, 7.5, 8.5} into the atmosphere; instead it is formed by photochemical reactions. In the troposphere these reactions involve precursor compounds that The RF from black carbon (BC) on snow and ice is assessed to be 0.04 are emitted into the atmosphere from a variety of natural and anthro- [0.02 to 0.09] W m 2 (low confidence). Unlike in the previous IPCC pogenic sources. Tropospheric O3 RF is largely attributed to increases assessment, this estimate includes the effects on sea ice, accounts for in emissions of CH4, carbon monoxide, volatile organics and nitrogen more physical processes and incorporates evidence from both models oxides, while stratospheric RF results primarily from O3 depletion by and observations. This RF causes a two to four times larger GMST anthropogenic halocarbons. However, there is now strong evidence change per unit forcing than CO2 primarily because all of the forc- for substantial links between the changes in tropospheric and strato- ing energy is deposited directly into the cryosphere, whose evolution spheric O3 and a total O3 RF of 0.50 [0.30 to 0.70] W m 2 is attributed drives a positive albedo feedback on climate. This effect thus can rep- to tropospheric O3 precursor emissions and 0.15 [ 0.30 to 0.00] W resent a significant forcing mechanism in the Arctic and other snow- or TS m 2 to O3 depletion by halocarbons. There is strong evidence that tro- ice-covered regions. {7.3, 7.5.2, 8.3.4, 8.5} pospheric O3 also has a detrimental impact on vegetation physiology, and therefore on its CO2 uptake. This reduced uptake leads to an indi- Despite the large uncertainty ranges on aerosol forcing, there is a high rect increase in the atmospheric CO2 concentration. Thus a fraction of confidence that aerosols have offset a substantial portion of GHG the CO2 RF should be attributed to ozone or its precursors rather than forcing. Aerosol cloud interactions can influence the character of indi- direct emission of CO2, but there is a low confidence on the quantita- vidual storms, but evidence for a systematic aerosol effect on storm or tive estimates. RF for stratospheric water vapour produced from CH4 precipitation intensity is more limited and ambiguous. {7.4, 7.6, 8.5} oxidation is 0.07 [0.02 to 0.12] W m 2. Other changes in stratospheric water vapour, and all changes in water vapour in the troposphere, are TS.3.4 Radiative Forcing from Land Surface Changes regarded as a feedback rather than a forcing. {2.2.2, 8.1 8.3; FAQ 8.1} and Contrails TS.3.3 Radiative Forcing from Anthropogenic Aerosols There is robust evidence that anthropogenic land use changes such as deforestation have increased the land surface albedo, which leads to Anthropogenic aerosols are responsible for an RF of climate through an RF of 0.15 [ 0.25 to 0.05] W m 2. There is still a large spread of multiple processes which can be grouped into two types: aerosol radi- quantitative estimates owing to different assumptions for the albedo of ation interactions (ari) and aerosol cloud interactions (aci). There has natural and managed surfaces (e.g., croplands, pastures). In addition, been progress since AR4 on observing and modelling climate-relevant the time evolution of the land use change, and in particular how much aerosol properties (including their size distribution, hygroscopicity, was already completed in the reference year 1750, are still debated. chemical composition, mixing state, optical and cloud nucleation prop- Furthermore, land use change causes other modifications that are not erties) and their atmospheric distribution. Nevertheless, substantial radiative but impact the surface temperature, including modifications uncertainties remain in assessments of long-term trends of global in the surface roughness, latent heat flux, river runoff and irrigation. aerosol optical depth and other global properties of aerosols due to These are more uncertain and they are difficult to quantify, but they difficulties in measurement and lack of observations of some relevant tend to offset the impact of albedo changes at the global scale. As a parameters, high spatial and temporal variability and the relatively consequence, there is low agreement on the sign of the net change short observational records that exist. The anthropogenic RFari is given in global mean temperature as a result of land use change. Land use a best estimate of 0.35 [ 0.85 to +0.15] W m 2 (high confidence) change, and in particular deforestation, also has significant impacts on using evidence from aerosol models and some constraints from obser- WMGHG concentrations. It contributes to the corresponding RF associ- vations. The RFari is caused by multiple aerosol types (see Section ated with CO2 emissions or concentration changes. {8.3.5} TS3.6). The rapid adjustment to RFari leads to further negative forcing, in particular through cloud adjustments, and is attributable primarily Persistent contrails from aviation contribute a positive RF of 0.01 to black carbon. As a consequence, the ERFari is more negative than [0.005 to 0.03] W m 2 (medium confidence) for year 2011, and the the RFari (low confidence) and given a best estimate of 0.45 [ 0.95 to combined contrail and contrail-cirrus ERF from aviation is assessed to +0.05] W m 2. The assessment for RFari is less negative than reported be 0.05 [0.02 to 0.15] W m 2 (low confidence). This forcing can be much in AR4 because of a re-evaluation of aerosol absorption. The uncer- larger regionally but there is now medium confidence that it does not tainty estimate is wider but more robust. {2.2.3, 7.3, 7.5.2} produce observable regional effects on either the mean or diurnal range of surface temperature. {7.2.7} Improved understanding of aerosol cloud interactions has led to a reduction in the magnitude of many global aerosol cloud forcings esti- TS.3.5 Radiative Forcing from Natural Drivers of mates. The total ERF due to aerosols (ERFari+aci, excluding the effect Climate Change of absorbing aerosol on snow and ice) is assessed to be 0.9 [ 1.9 to 0.1] W m 2 (medium confidence). This estimate encompasses all Solar and volcanic forcings are the two dominant natural contributors rapid adjustments, including changes to the cloud lifetime and aerosol to global climate change during the Industrial Era. Satellite observations 55 Technical Summary of total solar irradiance (TSI) changes since 1978 show quasi-periodic to AR4, the confidence level has been elevated for seven forcing agents cyclical variation with a period of roughly 11 years. Longer term forc- owing to improved evidence and understanding. {8.5; Figure 8.14} ing is typically estimated by comparison of solar minima (during which variability is least). This gives an RF change of 0.04 [ 0.08 to 0.00] W The time evolution of the total anthropogenic RF shows a nearly con- m 2 between the most recent (2008) minimum and the 1986 minimum. tinuous increase from 1750, primarily since about 1860. The total There is some diversity in the estimated trends of the composites of anthropogenic RF increase rate since 1960 has been much greater than various satellite data, however. Secular trends of TSI before the start during earlier Industrial Era periods, driven primarily by the continuous of satellite observations rely on a number of indirect proxies. The best increase in most WMGHG concentrations. There is still low agreement estimate of RF from TSI changes over the industrial era is 0.05 [0.00 on the time evolution of the total aerosol ERF, which is the primary to 0.10] W m 2 (medium confidence), which includes greater RF up to factor for the uncertainty in the total anthropogenic forcing. The frac- around 1980 and then a small downward trend. This RF estimate is tional uncertainty in the total anthropogenic forcing decreases gradual- substantially smaller than the AR4 estimate due to the addition of the ly after 1950 owing to the smaller offset of positive WMGHG forcing by latest solar cycle and inconsistencies in how solar RF was estimated in negative aerosol forcing. There is robust evidence and high agreement earlier IPCC assessments. The recent solar minimum appears to have that natural forcing is a small fraction of the WMGHG forcing. Natural been unusually low and long-lasting and several projections indicate forcing changes over the last 15 years have likely offset a substantial TS lower TSI for the forthcoming decades. However, current abilities to fraction (at least 30%) of the anthropogenic forcing increase during project solar irradiance are extremely limited so that there is very low this period (Box TS.3). Forcing by CO2 is the largest single contribu- confidence concerning future solar forcing. Nonetheless, there is a high tor to the total forcing during the Industrial Era and from 1980 2011. confidence that 21st century solar forcing will be much smaller than Compared to the entire Industrial Era, the dominance of CO2 forcing the projected increased forcing due to WMGHGs. {5.2.1, 8.4.1; FAQ 5.1} is larger for the 1980 2011 change with respect to other WMGHGs, and there is high confidence that the offset from aerosol forcing to Changes in solar activity affect the cosmic ray flux impinging upon WMGHG forcing during this period was much smaller than over the the Earth s atmosphere, which has been hypothesized to affect climate 1950 1980 period. {8.5.2} through changes in cloudiness. Cosmic rays enhance aerosol nucleation and thus may affect cloud condensation nuclei production in the free Forcing can also be attributed to emissions rather than to the result- troposphere, but the effect is too weak to have any climatic influence ing concentration changes (Figure TS.7). Carbon dioxide is the largest during a solar cycle or over the last century (medium evidence, high single contributor to historical RF from either the perspective of chang- agreement). No robust association between changes in cosmic rays es in the atmospheric concentration of CO2 or the impact of changes in and cloudiness has been identified. In the event that such an associa- net emissions of CO2. The relative importance of other forcing agents tion existed, a mechanism other than cosmic ray induced nucleation can vary markedly with the perspective chosen, however. In particu- of new aerosol particles would be needed to explain it. {7.3, 7.4.6} lar, CH4 emissions have a much larger forcing (about 1.0 W m 2 over the Industrial Era) than CH4 concentration increases (about 0.5 W m 2) The RF of stratospheric volcanic aerosols is now well understood and due to several indirect effects through atmospheric chemistry. In addi- there is a large RF for a few years after major volcanic eruptions (Box tion, carbon monoxide emissions are virtually certain to cause a posi- TS.5, Figure 1). Although volcanic eruptions inject both mineral par- tive forcing, while emissions of reactive nitrogen oxides likely cause a ticles and sulphate aerosol precursors into the atmosphere, it is the net negative forcing but uncertainties are large. Emissions of ozone- latter, because of their small size and long lifetimes, that are respon- depleting halocarbons very likely cause a net positive forcing as their sible for RF important for climate. The emissions of CO2 from volcanic direct radiative effect is larger than the impact of the stratospheric eruptions are at least 100 times smaller than anthropogenic emissions, ozone depletion that they induce. Emissions of SO2, organic carbon and and inconsequential for climate on century time scales. Large tropical ammonia cause a negative forcing, while emissions of black carbon volcanic eruptions have played an important role in driving annual to lead to positive forcing via aerosol radiation interactions. Note that decadal scale climate change during the Industrial Era owing to their mineral dust forcing may include a natural component or a climate sometimes very large negative RF. There has not been any major vol- feedback effect. {7.3, 7.5.2, 8.5.1} canic eruption since Mt Pinatubo in 1991, which caused a 1-year RF of about 3.0 W m 2, but several smaller eruptions have caused an Although the WMGHGs show a spatially fairly homogeneous forcing, RF averaged over the years 2008 2011 of 0.11 [ 0.15 to 0.08] W other agents such as aerosols, ozone and land use changes are highly m 2 (high confidence), twice as strong in magnitude compared to the heterogeneous spatially. RFari showed maximum negative values over 1999 2002 average. The smaller eruptions have led to better under- eastern North America and Europe during the early 20th century, with standing of the dependence of RF on the amount of material from large negative values extending to East and Southeast Asia, South high-latitude injections as well as the time of the year when they take America and central Africa by 1980. Since then, however, the magnitude place. {5.2.1, 5.3.5, 8.4.2; Annex II} has decreased over eastern North America and Europe due to pollution control, and the peak negative forcing has shifted to South and East TS.3.6 Synthesis of Forcings; Spatial and Temporal Asia primarily as a result of economic growth and the resulting increase Evolution in emissions in those areas. Total aerosol ERF shows similar behaviour for locations with maximum negative forcing, but also shows substan- A synthesis of the Industrial Era forcing finds that among the forcing tial positive forcing over some deserts and the Arctic. In contrast, the agents, there is a very high confidence only for the WMGHG RF. Relative global mean whole atmosphere ozone forcing increased throughout 56 Technical Summary the 20th century, and has peak positive amplitudes around 15°N to the RCP scenarios suggest only small changes in aerosol ERF between 30°N but negative values over Antarctica. Negative land use forcing 2000 and 2030, followed by a strong reduction in the aerosols and a by albedo changes has been strongest in industrialized and biomass substantial weakening of the negative total aerosol ERF. Nitrate aero- burning regions. The inhomogeneous nature of these forcings can cause sols are an exception to this reduction, with a substantially increased them to have a substantially larger influence on the hydrologic cycle negative forcing which is a robust feature among the few available than an equivalent global mean homogeneous forcing. {8.3.5, 8.6} models. The divergence across the RCPs indicates that, although a cer- tain amount of future climate change is already in the system due to Over the 21st century, anthropogenic RF is projected to increase under the current radiative imbalance caused by historical emissions and the the Representative Concentration Pathways (RCPs; see Box TS.6). long lifetime of some atmospheric forcing agents, societal choices can Simple model estimates of the RF resulting from the RCPs, which still have a very large effect on future RF, and hence on climate change. include WMGHG emissions spanning a broad range of possible futures, {8.2, 8.5.3, 12.3; Figures 8.22, 12.4} show anthropogenic RF relative to 1750 increasing to 3.0 to 4.8 W m 2 in 2050, and 2.7 to 8.4 W m 2 at 2100. In the near term, the RCPs TS.3.7 Climate Feedbacks are quite similar to one another (and emissions of near-term climate forcers do not span the literature range of possible futures), with RF Feedbacks will also play an important role in determining future cli- TS at 2030 ranging only from 2.9 to 3.3 W m 2 (additional 2010 to 2030 mate change. Indeed, climate change may induce modification in the RF of 0.7 to 1.1 W m 2), but they show highly diverging values for the water, carbon and other biogeochemical cycles which may reinforce second half of the 21st century driven largely by CO2. Results based on (positive feedback) or dampen (negative feedback) the expected Figure TS.7 | Radiative forcing (RF) of climate change during the Industrial Era shown by emitted components from 1750 to 2011. The horizontal bars indicate the overall uncer- tainty, while the vertical bars are for the individual components (vertical bar lengths proportional to the relative uncertainty, with a total length equal to the bar width for a +/-50% uncertainty). Best estimates for the totals and individual components (from left to right) of the response are given in the right column. Values are RF except for the effective radiative forcing (ERF) due to aerosol cloud interactions (ERFaci) and rapid adjustment associated with the RF due to aerosol-radiation interaction (RFari Rapid Adjust.). Note that the total RF due to aerosol-radiation interaction ( 0.35 Wm 2) is slightly different from the sum of the RF of the individual components ( 0.33 Wm 2). The total RF due to aerosol-radiation interaction is the basis for Figure SPM.5. Secondary organic aerosol has not been included since the formation depends on a variety of factors not currently sufficiently quantified. The ERF of contrails includes contrail induced cirrus. Combining ERFaci 0.45 [ 1.2 to 0.0] Wm 2 and rapid adjustment of ari 0.1 [ 0.3 to +0.1] Wm 2 results in an integrated component of adjustment due to aerosols of 0.55 [ 1.33 to 0.06] Wm 2. CFCs = chlorofluorocarbons, HCFCs = hydrochlorofluorocarbons, HFCs = hydrofluorocarbons, PFCs = perfluorocarbons, NMVOC = Non-Methane Volatile Organic Compounds, BC = black carbon. Further detail regarding the related Figure SPM.5 is given in the TS Supplementary Material. {Figure 8.17} 57 Technical Summary temperature increase. Snow and ice albedo feedbacks are known to climate forcers are higher than GTPs due to the equal time weighting be positive. The combined water vapour and lapse rate feedback is in the integrated forcing used in the GWP. Hence the choice of metric extremely likely to be positive and now fairly well quantified, while can greatly affect the relative importance of near-term climate forcers cloud feedbacks continue to have larger uncertainties (see TFE.6). In and WMGHGs, as can the choice of time horizon. Analysis of the impact addition, the new Coupled Model Intercomparison Project Phase 5 of current emissions (1-year pulse of emissions) shows that near-term (CMIP5) models consistently estimate a positive carbon-cycle feed- climate forcers, such as black carbon, sulphur dioxide or CH4, can have back, that is, reduced natural CO2 sinks in response to future climate contributions comparable to that of CO2 for short time horizons (of change. In particular, carbon-cycle feedbacks in the oceans are positive either the same or opposite sign), but their impacts become progres- in the models. Carbon sinks in tropical land ecosystems are less con- sively less for longer time horizons over which emissions of CO2 domi- sistent, and may be susceptible to climate change via processes such nate (Figure TS.8 top). {8.7} as drought and fire that are sometimes not yet fully represented. A key update since AR4 is the introduction of nutrient dynamics in some of A large number of other metrics may be defined down the driver the CMIP5 land carbon models, in particular the limitations on plant response impact chain. No single metric can accurately compare all growth imposed by nitrogen availability. The net effect of accounting consequences (i.e., responses in climate parameters over time) of dif- for the nitrogen cycle is a smaller projected land sink for a given trajec- ferent emissions, and a metric that establishes equivalence with regard TS tory of anthropogenic CO2 emissions (see TFE.7). {6.4, Box 6.1, 7.2} to one effect will not give equivalence with regard to other effects. The choice of metric therefore depends strongly on the particular conse- Models and ecosystem warming experiments show high agreement quence one wants to evaluate. It is important to note that the metrics that wetland CH4 emissions will increase per unit area in a warmer do not define policies or goals, but facilitate analysis and implementa- climate, but wetland areal extent may increase or decrease depending tion of multi-component policies to meet particular goals. All choices on regional changes in temperature and precipitation affecting wet- of metric contain implicit value-related judgements such as type of land hydrology, so that there is low confidence in quantitative projec- effect considered and weighting of effects over time. Whereas GWP tions of wetland CH4 emissions. Reservoirs of carbon in hydrates and integrates the effects up to a chosen time horizon (i.e., giving equal permafrost are very large, and thus could potentially act as very pow- weight to all times up to the horizon and zero weight thereafter), the erful feedbacks. Although poorly constrained, the 21st century global GTP gives the temperature just for one chosen year with no weight on release of CH4 from hydrates to the atmosphere is likely to be low due years before or after. {8.7} to the under-saturated state of the ocean, long ventilation time of the ocean and slow propagation of warming through the seafloor. There is The GWP and GTP have limitations and suffer from inconsistencies high confidence that release of carbon from thawing permafrost pro- related to the treatment of indirect effects and feedbacks, for instance, vides a positive feedback, but there is low confidence in quantitative if climate carbon feedbacks are included for the reference gas CO2 but projections of its strength. {6.4.7} not for the non-CO2 gases. The uncertainty in the GWP increases with time horizon, and for the 100-year GWP of WMGHGs the uncertainty Aerosol-climate feedbacks occur mainly through changes in the source can be as large as +/-40%. Several studies also point out that this metric strength of natural aerosols or changes in the sink efficiency of natu- is not well suited for policies with a maximum temperature target. ral and anthropogenic aerosols; a limited number of modelling studies Uncertainties in GTP also increase with time as they arise from the have assessed the magnitude of this feedback to be small with a low same factors contributing to GWP uncertainties along with additional confidence. There is medium confidence for a weak feedback (of uncer- contributions from it being further down the driver response impact tain sign) involving dimethylsulphide, cloud condensation nuclei and chain and including climate response. The GTP metric is better suited cloud albedo due to a weak sensitivity of cloud condensation nuclei to target-based policies, but is again not appropriate for every goal. population to changes in dimethylsulphide emissions. {7.3.5} Updated metric values accounting for changes in knowledge of life- times and radiative efficiencies and for climate carbon feedbacks are TS.3.8 Emission Metrics now available. {8.7, Table 8.7, Table 8.A.1, Chapter 8 Supplementary Material Table 8.SM.16} Different metrics can be used to quantify and communicate the relative and absolute contributions to climate change of emissions of different With these emission metrics, the climate impact of past or current substances, and of emissions from regions/countries or sources/sectors. emissions attributable to various activities can be assessed. Such activ- Up to AR4, the most common metric has been the Global Warming ity-based accounting can provide additional policy-relevant informa- Potential (GWP) that integrates RF out to a particular time horizon. This tion, as these activities are more directly affected by particular societal metric thus accounts for the radiative efficiencies of the various sub- choices than overall emissions. A single year s worth of emissions (a stances, and their lifetimes in the atmosphere, and gives values relative pulse) is often used to quantify the impact on future climate. From this to those for the reference gas CO2. There is now increasing focus on perspective and with the absolute GTP metric used to illustrate the the Global Temperature change Potential (GTP), which is based on the results, energy and industry have the largest contributions to warm- change in GMST at a chosen point in time, again relative to that caused ing over the next 50 to 100 years (Figure TS.8, bottom). Household by the reference gas CO2, and thus accounts for climate response along fossil and biofuel, biomass burning and on-road transportation are also with radiative efficiencies and atmospheric lifetimes. Both the GWP relatively large contributors to warming over these time scales, while and the GTP use a time horizon (Figure TS.8 top), the choice of which current emissions from sectors that emit large amounts of CH4 (animal is subjective and context dependent. In general, GWPs for near-term husbandry, waste/landfills and agriculture) are also important over 58 Technical Summary shorter time horizons (up to about 20 years). Another useful perspec- emissions from those sectors can lead to opposite global mean tem- tive is to examine the effect of sustained current emissions. Because perature responses at short and long time scales. The relative impor- emitted substances are removed according to their residence time, tance of the other sectors depends on the time and perspective chosen. short-lived species remain at nearly constant values while long-lived As with RF or ERF, uncertainties in aerosol impacts are large, and in gases accumulate in this analysis. In both cases, the sectors that have particular attribution of aerosol cloud interactions to individual com- the greatest long-term warming impacts (energy and industry) lead ponents is poorly constrained. {8.7; Chapter 8 Supplementary Material to cooling in the near term (primarily due to SO2 emissions), and thus Figures 8.SM.9, 8.SM.10} GWP GTP 10 CO2 equivalent emissions (Pg CO2-eq) CO2 equivalent emissions (PgC-eq) 20 5 TS 0 0 CO2 CH4 N 2O NOX CO -5 -20 SO2 BC OC 10 yrs 20 yrs 100 yrs 10 yrs 20 yrs 100 yrs 10 Temperature impact (10-3 K) 0 Waste/landfill Biomass burning Agricultural waste burning Agriculture -10 Animal husbandry Household fossil & biofuel Shipping Non-road Road -20 Aviation Industry Energy 10 20 30 40 50 60 Time Horizon (yr) Figure TS.8 | (Upper) Global anthropogenic present-day emissions weighted by the Global Warming Potential (GWP) and the Global Temperature change Potential (GTP) for the chosen time horizons. Year 2008 (single-year pulse) emissions weighted by GWP, which is the global mean radiative forcing (RF) per unit mass emitted integrated over the indicated number of years relative to the forcing from CO2 emissions, and GTP which estimates the impact on global mean temperature based on the temporal evolution of both RF and cli- mate response per unit mass emitted relative to the impact of CO2 emissions. The units are CO2 equivalents , which reflects equivalence only in the impact parameter of the chosen metric (integrated RF over the chosen time horizon for GWP; temperature change at the chosen point in time for GTP), given as Pg(CO2)eq (left axis) and PgCeq (right axis). (Bottom) The Absolute GTP (AGTP) as a function of time multiplied by the present-day emissions of all compounds from the indicated sectors is used to estimate global mean temperature response (AGTP is the same as GTP, except is not normalized by the impact of CO2 emissions). There is little change in the relative values for the sectors over the 60 to 100-year time horizon. The effects of aerosol cloud interactions and contrail-induced cirrus are not included in the upper panel. {Figures 8.32, 8.33} 59 Technical Summary TS.4 Understanding the Climate System and Its Recent Changes TS.4.1 Introduction Understanding of the climate system results from combining obser- vations, theoretical studies of feedback processes and model simula- tions. Compared to AR4, more detailed observations and improved climate models (see Box TS.4) now enable the attribution of detected changes to human influences in more climate system components. The consistency of observed and modelled changes across the climate system, including in regional temperatures, the water cycle, global energy budget, cryosphere and oceans (including ocean acidification), points to global climate change resulting primarily from anthropogenic increases in WMGHG concentrations. {10} TS TS.4.2 Surface Temperature Several advances since the AR4 have allowed a more robust quantifica- tion of human influence on surface temperature changes. Observational uncertainty has been explored much more thoroughly than previously and the assessment now considers observations from the first decade of the 21st century and simulations from a new generation of climate models whose ability to simulate historical climate has improved in many respects relative to the previous generation of models consid- ered in AR4. Observed GMST anomalies relative to 1880 1919 in recent years lie well outside the range of GMST anomalies in CMIP5 simula- tions with natural forcing only, but are consistent with the ensemble of CMIP5 simulations including both anthropogenic and natural forc- ing (Figure TS.9) even though some individual models overestimate the warming trend, while others underestimate it. Simulations with WMGHG changes only, and no aerosol changes, generally exhibit stron- ger warming than has been observed (Figure TS.9). Observed temper- ature trends over the period 1951 2010, which are characterized by warming over most of the globe with the most intense warming over the NH continents, are, at most observed locations, consistent with the temperature trends in CMIP5 simulations including anthropogenic and natural forcings and inconsistent with the temperature trends in CMIP5 simulations including natural forcings only. A number of studies have investigated the effects of the Atlantic Multi-decadal Oscillation (AMO) on GMST. Although some studies find a significant role for the AMO in driving multi-decadal variability in GMST, the AMO exhibited little trend over the period 1951 2010 on which the current assessments are based, and the AMO is assessed with high confidence to have made little contribution to the GMST trend between 1951 and 2010 (consider- ably less than 0.1°C). {2.4, 9.8.1, 10.3; FAQ 9.1} Figure TS.9 | Three observational estimates of global mean surface temperature (black It is extremely likely that human activities caused more than half of the lines) from the Hadley Centre/Climatic Research Unit gridded surface temperature data observed increase in global average surface temperature from 1951 to set 4 (HadCRUT4), Goddard Institute for Space Studies Surface Temperature Analysis (GISTEMP), and Merged Land Ocean Surface Temperature Analysis (MLOST), com- 2010. This assessment is supported by robust evidence from multiple pared to model simulations (CMIP3 models thin blue lines and CMIP5 models thin studies using different methods. In particular, the temperature trend yellow lines) with anthropogenic and natural forcings (a), natural forcings only (b) and attributable to all anthropogenic forcings combined can be more close- greenhouse gas forcing only (c). Thick red and blue lines are averages across all avail- ly constrained in multi-signal detection and attribution analyses. Uncer- able CMIP5 and CMIP3 simulations respectively. All simulated and observed data were tainties in forcings and in climate models responses to those forcings, masked using the HadCRUT4 coverage (as this data set has the most restricted spatial coverage), and global average anomalies are shown with respect to 1880 1919, where together with difficulty in distinguishing the patterns of temperature all data are first calculated as anomalies relative to 1961 1990 in each grid box. Inset response due to WMGHGs and other anthropogenic forcings, prevent to (b) shows the three observational data sets distinguished by different colours. {Figure as precise a quantification of the temperature changes attributable to ­ 10.1} 60 Technical Summary Box TS.3 | Climate Models and the Hiatus in Global Mean Surface Warming of the Past 15 Years The observed GMST has shown a much smaller increasing linear trend over the past 15 years than over the past 30 to 60 years (Box TS.3, Figure 1a, c). Depending on the observational data set, the GMST trend over 1998 2012 is estimated to be around one third to one half of the trend over 1951 2012. For example, in HadCRUT4 the trend is 0.04°C per decade over 1998 2012, compared to 0.11°C per decade over 1951 2012. The reduction in observed GMST trend is most marked in NH winter. Even with this hiatus in GMST trend, the decade of the 2000s has been the warmest in the instrumental record of GMST. Nevertheless, the occurrence of the hiatus in GMST trend during the past 15 years raises the two related questions of what has caused it and whether climate models are able to reproduce it. {2.4.3, 9.4.1; Box 9.2; Table 2.7} Fifteen-year-long hiatus periods are common in both the observed and CMIP5 historical GMST time series. However, an analysis of the full suite of CMIP5 historical simulations (augmented for the period 2006 2012 by RCP4.5 simulations) reveals that 111 out of 114 realizations show a GMST trend over 1998 2012 that is higher than the entire HadCRUT4 trend ensemble (Box TS.3, Figure 1a; CMIP5 ensemble mean trend is 0.21°C per decade). This difference between simulated and observed trends could be caused by some combina- TS tion of (a) internal climate variability, (b) missing or incorrect RF, and (c) model response error. These potential sources of the difference, which are not mutually exclusive, are assessed below, as is the cause of the observed GMST trend hiatus. {2.4.3, 9.3.2, 9.4.1; Box 9.2} Internal Climate Variability Hiatus periods of 10 to 15 years can arise as a manifestation of internal decadal climate variability, which sometimes enhances and sometimes counteracts the long-term externally forced trend. Internal variability thus diminishes the relevance of trends over periods as short as 10 to 15 years for long-term climate change. Furthermore, the timing of internal decadal climate variability is not expected to be matched by the CMIP5 historical simulations, owing to the predictability horizon of at most 10 to 20 years (CMIP5 historical simulations are typically started around nominally 1850 from a control run). However, climate models exhibit individual decades of GMST trend hiatus even during a prolonged phase of energy uptake of the climate system, in which case the energy budget would be balanced by increasing subsurface ocean heat uptake. {2.4.3, 9.3.2, 11.2.2; Boxes 2.2, 9.2} Owing to sampling limitations, it is uncertain whether an increase in the rate of subsurface ocean heat uptake occurred during the past 15 years. However, it is very likely that the climate system, including the ocean below 700 m depth, has continued to accumulate energy over the period 1998 2010. Consistent with this energy accumulation, GMSL has continued to rise during 1998 2012, at a rate only slightly and insignificantly lower than during 1993 2012. The consistency between observed heat content and sea level changes yields high confidence in the assessment of continued ocean energy accumulation, which is in turn consistent with the positive radiative imbalance of the climate system. By contrast, there is limited evidence that the hiatus in GMST trend has been accompanied by a slower rate of increase in ocean heat content over the depth range 0 to 700 m, when comparing the period 2003 2010 against 1971 2010. There is low agreement on this slowdown, as three of five analyses show a slowdown in the rate of increase while the other two show the increase continuing unabated. {3.2.3, 3.2.4, 3.7, 8.5.1, 13.3; Boxes 3.1, 13.1} During the 15-year period beginning in 1998, the ensemble of HadCRUT4 GMST trends lies below almost all model-simulated trends (Box TS.3, Figure 1a), whereas during the 15-year period ending in 1998, it lies above 93 out of 114 modelled trends (Box TS.3, Figure 1b; HadCRUT4 ensemble mean trend 0.26°C per decade, CMIP5 ensemble mean trend 0.16°C per decade). Over the 62-year period 1951 2012, observed and CMIP5 ensemble mean trend agree to within 0.02°C per decade (Box TS.3, Figure 1c; CMIP5 ensemble mean trend 0.13°C per decade). There is hence very high confidence that the CMIP5 models show long-term GMST trends consistent with observations, despite the disagreement over the most recent 15-year period. Due to internal climate variability, in any given 15-year period the observed GMST trend sometimes lies near one end of a model ensemble, an effect that is pronounced in Box TS.3, Figure 1a, b as GMST was influenced by a very strong El Nino event in 1998. {Box 9.2} Unlike the CMIP5 historical simulations referred to above, some CMIP5 predictions were initialized from the observed climate state during the late 1990s and the early 21st century. There is medium evidence that these initialized predictions show a GMST lower by about 0.05°C to 0.1°C compared to the historical (uninitialized) simulations and maintain this lower GMST during the first few years of the sim- ulation. In some initialized models this lower GMST occurs in part because they correctly simulate a shift, around 2000, from a positive to a negative phase of the Inter-decadal Pacific Oscillation (IPO). However, the improvement of this phasing of the IPO through initialization is not universal across the CMIP5 predictions. Moreover, although part of the GMST reduction through initialization indeed results from initializing at the correct phase of internal variability, another part may result from correcting a model bias that was caused by incorrect past forcing or incorrect model response to past forcing, especially in the ocean. The relative magnitudes of these effects are at present unknown; moreover, the quality of a forecasting system cannot be evaluated from a single prediction (here, a 10-year prediction within (continued on next page) 61 Technical Summary Box TS.3 (continued) the period 1998 2012). Overall, there is medium confidence that initialization leads to simulations of GMST during 1998 2012 that are more consistent with the observed trend hiatus than are the uninitialized CMIP5 historical simulations, and that the hiatus is in part a consequence of internal variability that is predictable on the multi-year time scale. {11.1, 11.2.3; Boxes 2.5, 9.2, 11.1, 11.2} Radiative Forcing On decadal to interdecadal time scales and under continually increasing ERF, the forced component of the GMST trend responds to the ERF trend relatively rapidly and almost linearly (medium confidence). The expected forced-response GMST trend is related to the ERF trend by a factor that has been estimated for the 1% per year CO2 increases in the CMIP5 ensemble as 2.0 [1.3 to 2.7] W m 2 C 1 (90% uncertainty range). Hence, an ERF trend can be approximately converted to a forced-response GMST trend, permitting an assessment of how much of the change in the GMST trends shown in Box TS.3, Figure 1 is due to a change in ERF trend. {Box 9.2} The AR5 best-estimate ERF trend over 1998 2011 is 0.22 [0.10 to 0.34] W m 2 per decade (90% uncertainty range), which is substan- TS tially lower than the trend over 1984 1998 (0.32 [0.22 to 0.42] W m 2 per decade; note that there was a strong volcanic eruption in 1982) and the trend over 1951 2011 (0.31 [0.19 to 0.40] W m 2 per decade; Box TS.3, Figure 1d f; the end year 2011 is chosen because data availability is more limited than for GMST). The resulting forced-response GMST trend would approximately be 0.12 [0.05 to 0.29] C per decade, 0.19 [0.09 to 0.39] C per decade, and 0.18 [0.08 to 0.37] C per decade for the periods 1998 2011, 1984 1998, and 1951 2011, respectively (the uncertainty ranges assume that the range of the conversion factor to GMST trend and the range of ERF trend itself are independent). The AR5 best-estimate ERF forcing trend difference between 1998 2011 and 1951 2011 thus might explain about one-half (0.05 C per decade) of the observed GMST trend difference between these periods (0.06 to 0.08 C per decade, depending on observational data set). {8.5.2} The reduction in AR5 best-estimate ERF trend over 1998 2011 compared to both 1984 1998 and 1951 2011 is mostly due to decreas- ing trends in the natural forcings, 0.16 [ 0.27 to 0.06] W m 2 per decade over 1998 2011 compared to 0.01 [ 0.00 to +0.01] W m 2 per decade over 1951 2011. Solar forcing went from a relative maximum in 2000 to a relative minimum in 2009, with a peak-to-peak difference of around 0.15 W m 2 and a linear trend over 1998 2011 of around 0.10 W m 2 per decade. Furthermore, a series of small volcanic eruptions has increased the observed stratospheric aerosol loading after 2000, leading to an additional negative ERF linear- trend contribution of around 0.06 W m 2 per decade over 1998 2011 (Box TS.3, Figure 1d, f). By contrast, satellite-derived estimates of tropospheric aerosol optical depth suggests little overall trend in global mean aerosol optical depth over the last 10 years, implying little change in ERF due to aerosol radiative interaction (low confidence because of low confidence in aerosol optical depth trend itself). Moreover, because there is only low confidence in estimates of ERF due to aerosol cloud interaction, there is likewise low con- fidence in its trend over the last 15 years. {2.2.3, 8.4.2, 8.5.1, 8.5.2, 10.3.1; Box 10.2; Table 8.5} For the periods 1984 1998 and 1951 2011, the CMIP5 ensemble mean ERF trend deviates from the AR5 best-estimate ERF trend by only 0.01 W m 2 per decade (Box TS.3, Figure 1e, f). After 1998, however, some contributions to a decreasing ERF trend are missing in the CMIP5 models, such as the increasing stratospheric aerosol loading after 2000 and the unusually low solar minimum in 2009. None- theless, over 1998 2011 the CMIP5 ensemble mean ERF trend is lower than the AR5 best-estimate ERF trend by 0.03 W m 2 per decade (Box TS.3, Figure 1d). Furthermore, global mean aerosol optical depth in the CMIP5 models shows little trend over 1998 2012, similar to the observations. Although the forcing uncertainties are substantial, there are no apparent incorrect or missing global mean forcings in the CMIP5 models over the last 15 years that could explain the model observations difference during the warming hiatus. {9.4.6} Model Response Error The discrepancy between simulated and observed GMST trends during 1998 2012 could be explained in part by a tendency for some CMIP5 models to simulate stronger warming in response to increases in greenhouse-gas concentration than is consistent with obser- vations. Averaged over the ensembles of models assessed in Section 10.3.1, the best-estimate GHG and other anthropogenic scaling factors are less than one (though not significantly so, Figure 10.4), indicating that the model-mean GHG and other anthropogenic respons- es should be scaled down to best match observations. This finding provides evidence that some CMIP5 models show a larger response to GHGs and other anthropogenic factors (dominated by the effects of aerosols) than the real world (medium confidence). As a consequence, it is argued in Chapter 11 that near-term model projections of GMST increase should be scaled down by about 10%. This downward scal- ing is, however, not sufficient to explain the model mean overestimate of GMST trend over the hiatus period. {10.3.1, 11.3.6} Another possible source of model error is the poor representation of water vapour in the upper atmosphere. It has been suggested that a reduction in stratospheric water vapour after 2000 caused a reduction in downward longwave radiation and hence a surface-cooling contribution, possibly missed by the models. However, this effect is assessed here to be small, because there was a recovery in strato- spheric water vapour after 2005. {2.2.2, 9.4.1; Box 9.2} (continued on next page) 62 Technical Summary Box TS.3 (continued) In summary, the observed recent warming hiatus, defined as the reduction in GMST trend during 1998 2012 as compared to the trend during 1951 2012, is attributable in roughly equal measure to a cooling contribution from internal variability and a reduced trend in external forcing (expert judgement, medium confidence). The forcing trend reduction is due primarily to a negative forcing trend from both volcanic eruptions and the downward phase of the solar cycle. However, there is low confidence in quantifying the role of forcing trend in causing the hiatus, because of uncertainty in the magnitude of the volcanic forcing trend and low confidence in the aerosol forcing trend. {Box 9.2} Almost all CMIP5 historical simulations do not reproduce the observed recent warming hiatus. There is medium confidence that the GMST trend difference between models and observations during 1998 2012 is to a substantial degree caused by internal variability, with pos- sible contributions from forcing error and some CMIP5 models overestimating the response to increasing GHG forcing. The CMIP5 model trend in ERF shows no apparent bias against the AR5 best estimate over 1998 2012. However, confidence in this assessment of CMIP5 ERF trend is low, primarily because of the uncertainties in model aerosol forcing and processes, which through spatial heterogeneity TS might well cause an undetected global mean ERF trend error even in the absence of a trend in the global mean aerosol loading. {Box 9.2} The causes of both the observed GMST trend hiatus and of the model observation GMST trend difference during 1998 2012 imply that, barring a major volcanic eruption, most 15-year GMST trends in the near-term future will be larger than during 1998 2012 (high confidence; see Section 11.3.6 for a full assessment of near-term projections of GMST). The reasons for this implication are fourfold: first, anthropogenic GHG concentrations are expected to rise further in all RCP scenarios; second, anthropogenic aerosol concentration is expected to decline in all RCP scenarios, and so is the resulting cooling effect; third, the trend in solar forcing is expected to be larger over most near-term 15-year periods than over 1998 2012 (medium confidence), because 1998 2012 contained the full downward phase of the solar cycle; and fourth, it is more likely than not that internal climate variability in the near term will enhance and not counteract the surface warming expected to arise from the increasing anthropogenic forcing. {Box 9.2} (a) 1998-2012 (b) 1984-1998 (c) 1951-2012 8 HadCRUT4 Normalized density 6 4 CMIP5 2 0 0.0 0.2 0.4 0.6 0.0 0.2 0.4 0.6 0.0 0.2 0.4 0.6 (°C per decade) (°C per decade) (°C per decade) (d) 1998-2011 (e) 1984-1998 (f) 1951-2011 5 4 Normalized density 3 2 1 0 -0.3 0.0 0.3 0.6 0.9 -0.3 0.0 0.3 0.6 0.9 -0.3 0.0 0.3 0.6 0.9 (W m-2 per decade) (W m-2 per decade) (W m-2 per decade) Box TS.3, Figure 1 | (Top) Observed and simulated GMST trends in °C per decade, over the periods 1998 2012 (a), 1984 1998 (b), and 1951 2012 (c). For the observations, 100 realizations of the Hadley Centre/Climatic Research Unit gridded surface temperature data set 4 (HadCRUT4) ensemble are shown (red, hatched). The uncertainty displayed by the ensemble width is that of the statistical construction of the global average only, in contrast to the trend uncertainties quoted in Section 2.4.3, which include an estimate of internal climate variability. Here, by contrast, internal variability is characterized through the width of the model ensemble. For the models, all 114 available CMIP5 historical realizations are shown, extended after 2005 with the RCP4.5 scenario and through 2012 (grey, shaded). (Bottom) Trends in effective radiative forcing (ERF, in W m 2 per decade) over the periods 1998 2011 (d), 1984 1998 (e), and 1951 2011 (f). The figure shows AR5 best-estimate ERF trends (red, hatched) and CMIP5 ERF (grey, shaded). Black lines are smoothed versions of the histograms. Each histogram is normalized so that its area sums up to one. {2.4.3, 8.5.2; Box 9.2; Figure 8.18; Box 9.2, Figure 1} 63 Technical Summary Thematic Focus Elements TFE.3 | Comparing Projections from Previous IPCC Assessments with Observations Verification of projections is arguably the most convincing way of establishing the credibility of climate change science. Results of projected changes in carbon dioxide (CO2), global mean surface temperature (GMST) and global mean sea level (GMSL) from previous IPCC assessment reports are quantitatively compared with the best available observational estimates. The comparison between the four previous reports highlights the evolution in our under- standing of how the climate system responds to changes in both natural and anthropogenic forcing and provides an assessment of how the projections compare with observational estimates. TFE.3, Figure 1, for example, shows the projected and observed estimates of: (1) CO2 changes (top row), (2) GMST anomaly relative to 1961 1990 (middle row) and (3) GMSL relative to 1961 1990 (bottom row). Results from previous assessment reports are in the left- hand column, and for completeness results from current assessment are given in the right-hand column. {2.4, 3.7, 6.3, 11.3, 13.3} (continued on next page) TS SAR TAR FAR AR4 AR4 RCP Observations TAR Observations 8.5 500 A1B A2 500 FAR SAR 475 B1 475 RCP RCP 4.5 RCP CO2 concentration 450 450 2.6 6.0 425 425 (ppm) 400 400 375 375 350 350 325 325 1960 1975 1990 2005 2020 2035 1960 1975 1990 2005 2020 2035 AR4 } } FAR A1B 2 A2 2 AR4 CMIP3 Indicative Temperature anomaly (°C) TAR B1 AR5 CMIP5 likely } } 1.5 1.5 range w.r.t. 1961 1990 Observations SAR Observations 1 1 0.5 0.5 0 0 0.5 0.5 1960 1975 1990 2005 2020 2035 1960 1975 1990 2005 2020 2035 } } FAR 35 Estimates derived 35 Estimates derived 30 from tide-gauge data SAR 30 from tide-gauge data Post-AR4 RCP RCP RCP RCP 25 Estimates derived from 25 2.6 4.5 6.0 8.5 sea level rise (cm) TAR Estimates derived from 20 sea-surface altimetry 20 sea-surface altimetry Global mean A1B B1 A2 15 15 10 10 5 5 0 0 5 5 1960 1975 1990 2005 2020 2035 1960 1975 1990 2005 2020 2035 Year Year TFE.3, Figure 1 | (Top left) Observed globally and annually averaged CO2 concentrations in parts per million (ppm) since 1950 compared with projections from the previous IPCC assessments. Observed global annual CO2 concentrations are shown in dark blue. The shading shows the largest model projected range of global annual CO2 concentrations from 1950 to 2035 from FAR (First Assessment Report; Figure A.3 in the Summary for Policymakers (SPM) of IPCC 1990), SAR (Second Assessment Report; Figure 5b in the TS of IPCC 1996), TAR (Third Assessment Report; Appendix II of IPCC 2001), and for the IPCC Special Report on Emission Scenarios (SRES) A2, A1B and B1 scenarios presented in the AR4 (Fourth Assessment Report; Figure 10.26). The publication years of the assessment reports are shown. (Top right) Same observed globally averaged CO2 concentrations and the projections from this report. Only RCP8.5 has a range of values because the emission-driven senarios were carried out only for this RCP. For the other RCPs the best estimate is given. (Middle left) Estimated changes in the observed globally and annually averaged surface temperature anomaly relative to 1961 1990 (in °C) since 1950 compared with the range of projections from the previous IPCC assessments. Values are harmonized 64 Technical Summary TFE.3 (continued) to start from the same value at 1990. Observed global annual temperature anomaly, relative to 1961 1990, from three data sets is shown as squares and smoothed time series as solid lines from the Hadley Centre/Climatic Research Unit gridded surface temperature data set 4 (HadCRUT4; bright green), Merged Land Ocean Surface Temperature Analysis (MLOST; warm mustard) and Goddard Institute for Space Studies Surface Temperature Analysis (GISTEMP; dark blue) data sets. The coloured shading shows the projected range of global annual mean near surface temperature change from 1990 to 2035 for models used in FAR (Figure 6.11), SAR (Figure 19 in the TS of IPCC 1996), TAR (full range of TAR, Figure 9.13(b)). TAR results are based on the simple climate model analyses presented in this assessment and not on the individual full three-dimensional climate model simulations. For the AR4 results are presented as single model runs of the CMIP3 ensemble for the historical period from 1950 to 2000 (light grey lines) and for three SRES scenarios (A2, A1B and B1) from 2001 to 2035. For the three SRES scenarios the bars show the CMIP3 ensemble mean and the likely range given by 40 % to +60% of the mean as assessed in Chapter 10 of AR4. (Middle right) Projections of annual mean global mean surface air temperature (GMST) for 1950 2035 (anomalies relative to 1961 1990) under different RCPs from CMIP5 models (light grey and coloured lines, one ensemble member per model), and observational estimates the same as the middle left panel. The grey shaded region shows the indicative likely range for annual mean GMST during the period 2016 2035 for all RCPs (see Figure TS.14 for more details). The grey bar shows this same indicative likely range for the year 2035. (Bottom left) Estimated changes in the observed global annual mean sea level (GMSL) since 1950. Different estimates of changes in global annual sea level anomalies from tide gauge data (dark blue, warm mustard, dark green) and based on annual averages of altimeter data (light blue ) starting in 1993 (the values have been aligned to fit the 1993 value of the tide gauge data). Squares indicate annual mean values, solid lines smoothed values. The shading shows the largest model projected range of global annual sea level rise from 1950 to 2035 for FAR (Figures 9.6 and 9.7), SAR (Figure 21 in TS of IPCC, 1996), TAR (Appendix II of IPCC, 2001) and based on the CMIP3 model results TS available at the time of AR4 using the SRES A1B scenario. Note that in the AR4 no full range was given for the sea level projections for this period. Therefore, the figure shows results that have been published subsequent to the AR4. The bars at the right hand side of each graph show the full range given for 2035 for each assessment report. (Bottom right) Same observational estimate as bottom left. The bars are the likely ranges (medium confidence) for global mean sea level rise at 2035 with respect to 1961 1990 following the four RCPs. Appendix 1.A provides details on the data and calculations used to create these figures. See Chapters 1, 11 and 13 for more details. {Figures 1.4, 1.5, 1.10, 11.9, 11.19, 11.25, 13.11} Carbon Dioxide Changes From 1950 to 2011 the observed concentrations of atmospheric CO2 have steadily increased. Considering the period 1990 2011, the observed CO2 concentration changes lie within the envelope of the scenarios used in the four assessment reports. As the most recent assessment prior to the current, the IPCC Fourth Assessment Report (AR4) (TFE.3.Figure 1; top left) has the narrowest scenario range and the observed concentration follows this range. The results from the IPCC Fifth Assessment Report (AR5) (TFE.3, Figure 1; top right) are consistent with AR4, and during 2002 2011, atmospheric CO2 concentrations increased at a rate of 1.9 to 2.1 ppm yr 1. {2.2.1, 6.3; Table 6.1} Global Mean Temperature Anomaly Relative to the 1961 1990 mean, the GMST anomaly has been positive and larger than 0.25°C since 2001. Observa- tions are generally well within the range of the extent of the earlier IPCC projections (TFE.3, Figure1, middle left) This is also true for the Coupled Model Intercomparison Project Phase 5 (CMIP5) results (TFE.3, Figure 1; middle right) in the sense that the observed record lies within the range of the model projections, but on the lower end of the plume. Mt Pinatubo erupted in 1991 (see FAQ 11.2 for discussion of how volcanoes impact the climate system), leading to a brief period of relative global mean cooling during the early 1990s. The IPCC First, Second and Third Assessment Reports (FAR, SAR and TAR) did not include the effects of volcanic eruptions and thus failed to include the cooling associated with the Pinatubo eruption. AR4 and AR5, however, did include the effects from volcanoes and did simulate successfully the associated cooling. During 1995 2000 the global mean temperature anomaly was quite variable a significant fraction of this variability was due to the large El Nino in 1997 1998 and the strong back-to-back La Ninas in 1999 2001. The projections associated with these assessment reports do not attempt to capture the actual evolution of these El Nino and La Nina events, but include them as a source of uncertainty due to natural variability as encompassed by, for example, the range given by the individual CMIP3 and CMIP5 simula- tions and projection (TFE.3, Figure 1). The grey wedge in TFE.3, Figure 1 (middle right) corresponds to the indicative likely range for annual temperatures, which is determined from the Representative Concentration Pathways (RCPs) assessed value for the 20-year mean 2016 2035 (see discussion of Figure TS.14 and Section 11.3.6 for details). From 1998 to 2012 the observational estimates have largely been on the low end of the range given by the scenarios alone in previous assessment reports and CMIP3 and CMIP5 projections. {2.4; Box 9.2} Global Mean Sea Level Based on both tide gauge and satellite altimetry data, relative to 1961 1990, the GMSL has continued to rise. While the increase is fairly steady, both observational records show short periods of either no change or a slight decrease. The observed estimates lie within the envelope of all the projections except perhaps in the very early 1990s. The sea level rise uncertainty due to scenario-related uncertainty is smallest for the most recent assessments (AR4 and AR5) and observed estimates lie well within this scenario-related uncertainty. It is virtually certain that over the 20th century sea level rose. The mean rate of sea level increase was 1.7 mm yr 1 with a very likely range between 1.5 to 1.9 between 1901 and 2010 and this rate increased to 3.2 with a likely range of 2.8 to 3.6 mm yr 1 between 1993 and 2010 (see TFE.2). {3.7.2, 3.7.4} 65 Technical Summary observed warming differs from those associated with internal variabil- ity. Based on this evidence, the contribution of internal variability to the 1951 2010 GMST trend was assessed to be likely between 0.1°C and 0.1°C, and it is virtually certain that warming since 1951 cannot be explained by internal variability alone. {9.5, 10.3, 10.7} The instrumental record shows a pronounced warming during the first half of the 20th century. Consistent with AR4, it is assessed that the early 20th century warming is very unlikely to be due to internal vari- ability alone. It remains difficult to quantify the contributions to this early century warming from internal variability, natural forcing and anthropogenic forcing, due to forcing and response uncertainties and incomplete observational coverage. {10.3} TS.4.3 Atmospheric Temperature TS Figure TS.10 | Assessed likely ranges (whiskers) and their midpoints (bars) for warming trends over the 1951 2010 period due to well-mixed greenhouse gases (GHG), anthro- A number of studies since the AR4 have investigated the consistency of pogenic forcings (ANT) anthropogenic forcings other than well-mixed greenhouse gases simulated and observed trends in free tropospheric temperatures (see (OA), natural forcings (NAT) and internal variability. The trend in the Hadley Centre/ section TS.2). Most, though not all, CMIP3 and CMIP5 models overes- Climatic Research Unit gridded surface temperature data set 4 (HadCRUT4) observa- timate the observed warming trend in the tropical troposphere during tions is shown in black with its 5 to 95% uncertainty range due only to observational uncertainty in this record. {Figure 10.5} the satellite period 1979 2012. Roughly one half to two thirds of this difference from the observed trend is due to an overestimate of the SST trend, which is propagated upward because models attempt to WMGHGs and other anthropogenic forcings individually. Consistent maintain static stability. There is low confidence in these assessments, with AR4, it is assessed that more than half of the observed increase however, owing to the low confidence in observed tropical tropospher- in global average surface temperature from 1951 to 2010 is very likely ic trend rates and vertical structure. Outside the tropics, and over the due to the observed anthropogenic increase in WMGHG concentra- period of the radiosonde record beginning in 1961, the discrepancy tions. WMGHGs contributed a global mean surface warming likely between simulated and observed trends is smaller. {2.4.4, 9.4, 10.3} to be between 0.5°C and 1.3°C over the period between 1951 and 2010, with the contributions from other anthropogenic forcings likely Analysis of both radiosonde and satellite data sets, combined with to be between 0.6°C and 0.1°C and from natural forcings likely to be CMIP5 and CMIP3 simulations, continues to find that observed tro- between 0.1°C and 0.1°C. Together these assessed contributions are pospheric warming is inconsistent with internal variability and simu- consistent with the observed warming of approximately 0.6°C over lations of the response to natural forcings alone. Over the period this period (Figure TS.10). {10.3} 1961 2010 CMIP5 models simulate tropospheric warming driven by WMGHG changes, with only a small offsetting cooling due to the Solar forcing is the only known natural forcing acting to warm the combined effects of changes in reflecting and absorbing aerosols and climate over the 1951 2010 period but it has increased much less tropospheric ozone. Taking this evidence together with the results of than WMGHG forcing, and the observed pattern of long-term tropo- multi-signal detection and attribution analyses, it is likely that anthro- spheric warming and stratospheric cooling is not consistent with the pogenic forcings, dominated by WMGHGs, have contributed to the expected response to solar irradiance variations. Considering this warming of the troposphere since 1961. Uncertainties in radiosonde evidence together with the assessed contribution of natural forcings and satellite records makes assessment of causes of observed trends in to observed trends over this period, it is assessed that the contribu- the upper troposphere less confident than an assessment of the overall tion from solar forcing to the observed global warming since 1951 atmospheric temperature changes. {2.4.4, 9.4, 10.3} is extremely ­ nlikely to be larger than that from WMGHGs. Because u solar forcing has very likely decreased over a period with direct satel- CMIP5 simulations including WMGHGs, ozone and natural forcing lite measurements of solar output from 1986 to 2008, there is high changes broadly reproduce the observed evolution of lower strato- confidence that changes in total solar irradiance have not contributed spheric temperature, with some tendency to underestimate the to global warming during that period. However, there is medium con- observed cooling trend over the satellite era (see Section TS.2). New fidence that the 11-year cycle of solar variability influences decadal studies of stratospheric temperature, considering the responses to nat- climate fluctuations in some regions through amplifying mechanisms. ural forcings, WMGHGs and ozone-depleting substances, demonstrate {8.4, 10.3; Box 10.2} that it is very likely that anthropogenic forcings, dominated by the depletion of the ozone layer due to ozone depleting substances have Observed warming over the past 60 years is far outside the range of contributed to the cooling of the lower stratosphere since 1979. CMIP5 internal climate variability estimated from pre-instrumental data, and it models simulate only a very weak cooling of the lower stratosphere in is also far outside the range of internal variability simulated in climate response to historical WMGHG changes, and the influence of WMGHGs models. Model-based simulations of internal variability are assessed to on lower stratospheric temperature has not been formally detected. be adequate to make this assessment. Further, the spatial pattern of Considering both regions together, it is very likely that anthropogenic 66 Technical Summary Thematic Focus Elements TFE.4 | The Changing Energy Budget of the Global Climate System The global energy budget is a fundamental aspect of the Earth s climate system and depends on many phenomena within it. The ocean has stored about 93% of the increase in energy in the climate system over recent decades, resulting in ocean thermal expansion and hence sea level rise. The rate of storage of energy in the Earth system must be equal to the net downward radiative flux at the top of the atmosphere, which is the difference between effective radiative forcing (ERF) due to changes imposed on the system and the radiative response of the system. There are also significant transfers of energy between components of the climate system and from one location to another. The focus here is on the Earth s global energy budget since 1970, when better global observational data coverage is available. {3.7, 9.4, 13.4; Box 3.1} The ERF of the climate system has been positive as a result of increases in well-mixed (long-lived) greenhouse gas (GHG) concentrations, changes in short-lived GHGs (tropospheric and stratospheric ozone and stratospheric water vapour), and an increase in solar irradiance (TFE.4, Figure 1a). This has been partly compensated by a negative TS contribution to the ERF of the climate system as a result of changes in tropospheric aerosol, which predominantly reflect sunlight and furthermore enhance the brightness of clouds, although black carbon produces positive forc- ing. Explosive volcanic eruptions (such as El Chichón in Mexico in 1982 and Mt Pinatubo in the Philippines in 1991) (continued on next page) Year Year TFE.4, Figure 1 | The Earth s energy budget from 1970 through 2011. (a) The cumulative energy inflow into the Earth system from changes in well-mixed and short- lived greenhouse gases, solar forcing, tropospheric aerosol forcing, volcanic forcing and changes in surface albedo due to land use change (all relative to 1860 1879) are shown by the coloured lines; these contributions are added to give the total energy inflow (black; contributions from black carbon on snow and contrails as well as contrail-induced cirrus are included but not shown separately). (b) The cumulative total energy inflow from (a, black) is balanced by the sum of the energy uptake of the Earth system (blue; energy absorbed in warming the ocean, the atmosphere and the land, as well as in the melting of ice) and an increase in outgoing radiation inferred from changes in the global mean surface temperature. The sum of these two terms is given for a climate feedback parameter of 2.47, 1.23 and 0.82 W m 2 °C 1, corresponding to an equilibrium climate sensitivity of 1.5°C, 3.0°C and 4.5°C, respectively; 1.5°C to 4.5°C is assessed to be the likely range of equilibrium climate sensitivity. The energy budget would be closed for a particular value of a if the corresponding line coincided with the total energy inflow. For clarity, all uncertainties (shading) shown are likely ranges. {Box 12.2; Box 13.1, Figure 1} 67 Technical Summary TFE.4 (continued) can inject sulphur dioxide into the stratosphere, giving rise to stratospheric aerosol, which persists for several years. Stratospheric aerosol reflects some of the incoming solar radiation and thus gives a negative forcing. Changes in surface albedo from land use change have also led to a greater reflection of shortwave radiation back to space and hence a negative forcing. Since 1970, the net ERF of the climate system has increased, and the integrated impact of these forcings is an energy inflow over this period (TFE.4, Figure 1a). {2.3, 8.5; Box 13.1} As the climate system warms, energy is lost to space through increased outgoing radiation. This radiative response by the system is due predominantly to increased thermal radiation, but it is modified by climate feedbacks such as changes in water vapour, clouds and surface albedo, which affect both outgoing longwave and reflected shortwave radiation. The top of the atmosphere fluxes have been measured by the Earth Radiation Budget Experiment (ERBE) satellites from 1985 to 1999 and the Cloud and the Earth s Radiant Energy System (CERES) satellites from March 2000 to the present. The top of the atmosphere radiative flux measurements are highly precise, allowing identifi- TS cation of changes in the Earth s net energy budget from year to year within the ERBE and CERES missions, but the absolute calibration of the instruments is not sufficiently accurate to allow determination of the absolute top of the atmosphere energy flux or to provide continuity across missions. TFE.4, Figure 1b relates the cumulative total energy change of the Earth system to the change in energy storage and the cumulative outgoing radiation. Calcu- lation of the latter is based on the observed global mean surface temperature multiplied by the climate feedback parameter , which in turn is related to the equilibrium climate sensitivity. The mid-range value for , 1.23 W m 2 °C 1, corresponds to an ERF for a doubled carbon dioxide (CO2) concentration of 3.7 [2.96 to 4.44] W m 2 combined with an equilibrium climate sensitivity of 3.0°C. The climate feedback parameter is likely to be in the range from 0.82 to 2.47 W m 2 °C 1 (corresponding to the likely range in equilibrium climate sensitivity of 1.5°C to 4.5°C). {9.7.1; Box 12.2} If ERF were fixed, the climate system would eventually warm sufficiently that the radiative response would balance the ERF, and there would be no further change in energy storage in the climate system. However, the forcing is increasing, and the ocean s large heat capacity means that the climate system is not in radiative equilibrium and its energy content is increasing (TFE.4, Figure 1b). This storage provides strong evidence of a changing climate. The majority of this additional heat is in the upper 700 m of the ocean, but there is also warming in the deep and abys- sal ocean. The associated thermal expansion of the ocean has contributed about 40% of the observed sea level rise since 1970. A small amount of additional heat has been used to warm the continents, warm and melt glacial and sea ice and warm the atmosphere. {13.4.2; Boxes 3.1, 13.1} In addition to these forced variations in the Earth s energy budget, there is also internal variability on decadal time scales. Observations and models indicate that, because of the comparatively small heat capacity of the atmosphere, a decade of steady or even decreasing surface temperature can occur in a warming world. Climate model simula- tions suggest that these periods are associated with a transfer of heat from the upper to the deeper ocean, of the order 0.1 W m 2, with a near-steady or an increased radiation to space, again of the order 0.1 W m 2. Although these natural fluctuations represent a large amount of heat, they are significantly smaller than the anthropogenic forcing of the Earth s energy budget, particularly on time scales of several decades or longer. {9.4; Boxes 9.2, 13.1} The available independent estimates of ERF, of observed heat storage, and of surface warming combine to give an energy budget for the Earth that is consistent with the assessed likely range of equilibrium climate sensitiv- ity to within estimated uncertainties (high confidence). Quantification of the terms in the Earth s energy budget and verification that these terms balance over recent decades provides strong evidence for our understanding of anthropogenic climate change. {Box 13.1} forcing, particularly WMGHGs and stratospheric ozone depletion, has AR4 using updated observations and more simulations (see Section led to a detectable observed pattern of tropospheric warming and TS.2.2). The long term trends and variability in the observations are lower stratospheric cooling since 1961. {2.4, 9.4, 10.3} most consistent with simulations of the response to both anthropo- genic forcing and volcanic forcing. The anthropogenic fingerprint in TS.4.4 Oceans observed upper-ocean warming, consisting of global mean and basin- scale pattern changes, has also been detected. This result is robust to The observed upper-ocean warming during the late 20th and early 21st a number of observational, model and methodological or structural centuries and its causes have been assessed more completely since uncertainties. It is very likely that anthropogenic forcings have made 68 Technical Summary a substantial ­ontribution to upper ocean warming (above 700 m) c The anthropogenic signal is also detectable for individual months from observed since the 1970s. This anthropogenic ocean warming has May to December, suggesting that human influence, strongest in late contributed to global sea level rise over this period through thermal summer, now also extends into colder seasons. From these simulations expansion. {3.2.2, 3.2.3, 3.7.2, 10.4.1, 10.4.3; Box 3.1} of sea ice and observed sea ice extent from the instrumental record with high agreement between studies, it is concluded that anthropo- Observed surface salinity changes also suggest a change in the global genic forcings are very likely to have contributed to Arctic sea ice loss water cycle has occurred (see TFE.1). The long-term trends show that since 1979 (Figure TS.12). {10.5.1} there is a strong positive correlation between the mean climate of the surface salinity and the temporal changes of surface salinity from 1950 For Antarctic sea ice extent, the shortness of the observed record and to 2000. This correlation shows an enhancement of the climatological differences in simulated and observed variability preclude an assess- salinity pattern so fresh areas have become fresher and salty areas ment of whether or not the observed increase since 1979 is inconsis- saltier. The strongest anthropogenic signals are in the tropics (30°S to tent with internal variability. Untangling the processes involved with 30°N) and the Western Pacific. The salinity contrast between the Pacific trends and variability in Antarctica and surrounding waters remains and Atlantic Oceans has also increased with significant contributions complex and several studies are contradictory. In conclusion, there is from anthropogenic forcing. {3.3, 10.3.2, 10.4.2; FAQ 3.2} low confidence in the scientific understanding of the observed increase TS in Antarctic sea ice extent since 1979, due to the large differences On a global scale, surface and subsurface salinity changes (1955 2004) between sea ice simulations from CMIP5 models and to the incom- over the upper 250 m of the water column do not match changes plete and competing scientific explanations for the causes of change expected from natural variability but do match the modelled distribu- and low confidence in estimates of internal variability (Figure TS.12). tion of forced changes (WMGHGs and tropospheric aerosols). Natural {9.4.3, 10.5.1; Table 10.1} external variability taken from the simulations with just the variations in solar and volcanic forcing does not match the observations at all, The Greenland ice sheet shows recent major melting episodes in thus excluding the hypothesis that observed trends can be explained response to record temperatures relative to the 20th century associ- by just solar or volcanic variations. These lines of evidence and our ated with persistent shifts in early summer atmospheric circulation, understanding of the physical processes leads to the conclusion that and these shifts have become more pronounced since 2007. Although it is very likely that anthropogenic forcings have made a discernible many Greenland instrumental records are relatively short (two contribution to surface and subsurface oceanic salinity changes since decades), regional modelling and observations tell a consistent story of the 1960s. {10.4.2; Table 10.1} the response of Greenland temperatures and ice sheet runoff to shifts in regional atmospheric circulation associated with larger scale flow Oxygen is an important physical and biological tracer in the ocean. patterns and global temperature increases. Mass loss and melt is also Global analyses of oxygen data from the 1960s to 1990s extend the occurring in Greenland through the intrusion of warm water into the spatial coverage from local to global scales and have been used in major fjords containing glaciers such as Jacobshaven Glacier. It is likely attribution studies with output from a limited range of Earth System that anthropogenic forcing has contributed to surface melting of the Models (ESMs). It is concluded that there is medium confidence that Greenland ice sheet since 1993. {10.5.2; Table 10.1} the observed global pattern of decrease in dissolved oxygen in the oceans can be attributed in part to human influences. {3.8.3, 10.4.4; Estimates of ice mass in Antarctica since 2000 show that the great- Table 10.1} est losses are at the edges. An analysis of observations underneath a floating ice shelf off West Antarctica leads to the conclusion that ocean The observations show distinct trends for ocean acidification (which is warming in this region and increased transport of heat by ocean circu- observed to be between 0.0014 and 0.0024 pH units per year). There lation are largely responsible for accelerating melt rates. The observa- is high confidence that the pH of ocean surface seawater decreased by tional record of Antarctic mass loss is short and the internal variability about 0.1 since the beginning of the industrial era as a consequence of the ice sheet is poorly understood. Due to a low level of scientific of the oceanic uptake of anthropogenic CO2. {3.8.2, 10.4.4; Box 3.2; understanding there is low confidence in attributing the causes of the Table 10.1} observed loss of mass from the Antarctic ice sheet since 1993. {3.2, 4.2, 4.4.3, 10.5.2} TS.4.5 Cryosphere The evidence for the retreat of glaciers due to warming and moisture The reductions in Arctic sea ice extent and NH snow cover extent and change is now more complete than at the time of AR4. There is high widespread glacier retreat and increased surface melt of Greenland confidence in the estimates of observed mass loss and the estimates of are all evidence of systematic changes in the cryosphere. All of these natural variations and internal variability from long-term glacier records. changes in the cryosphere have been linked to anthropogenic forcings. Based on these factors and our understanding of glacier response to cli- {4.2.2, 4.4 4.6, 10.5.1, 10.5.3; Table 10.1} matic drivers there is high confidence that a substantial part of the mass loss of glaciers is likely due to human influence. It is likely that there has Attribution studies, comparing the seasonal evolution of Arctic sea been an anthropogenic component to observed reductions in NH snow ice extent from observations from the 1950s with that simulated by cover since 1970. {4.3.3, 10.5.2, 10.5.3; Table 10.1} coupled model simulations, demonstrate that human influence on the sea ice extent changes can be robustly detected since the early 1990s. 69 Technical Summary Thematic Focus Elements TFE.5 | Irreversibility and Abrupt Change A number of components or phenomena within the climate system have been proposed as potentially exhibiting threshold behaviour. Crossing such thresholds can lead to an abrupt or irreversible transition into a different state of the climate system or some of its components. Abrupt climate change is defined in this IPCC Fifth Assessment Report (AR5) as a large-scale change in the climate system that takes place over a few decades or less, persists (or is anticipated to persist) for at least a few decades and causes substantial disruptions in human and natural systems. There is information on potential consequences of some abrupt changes, but in general there is low confidence and little consensus on the likelihood of such events over the 21st century. Examples of components susceptible to such abrupt change are the strength of the Atlantic Meridional Overturning Circulation (AMOC), clathrate methane release, tropical and boreal forest dieback, disap- pearance of summer sea ice in the Arctic Ocean, long-term drought and monsoonal circulation. {5.7, 6.4.7, 12.5.5; TS Table 12.4} A change is said to be irreversible if the recovery time scale from this state due to natural processes is significantly longer than the time it takes for the system to reach this perturbed state. Such behaviour may arise because the time scales for perturbations and recovery processes are different, or because climate change may persist due to the long residence time of a carbon dioxide (CO2) perturbation in the atmosphere (see TFE.8). Whereas changes in Arctic Ocean summer sea ice extent, long-term droughts and monsoonal circulation are assessed to be reversible within years to decades, tropical or boreal forest dieback may be reversible only within centuries. Changes in clath- rate methane and permafrost carbon release, Greenland and Antarctic ice sheet collapse may be irreversible during millennia after the causal perturbation. {5.8, 6.4.7, 12.5.5, 13.4.3, 13.4.4; Table 12.4} Abrupt Climate Change Linked with AMOC New transient climate model simulations have confirmed with high confidence that strong changes in the strength of the AMOC produce abrupt climate changes at global scale with magnitude and pattern resembling past glacial Dansgaard Oeschger events and Heinrich stadials. Confidence in the link between changes in North Atlantic cli- mate and low-latitude precipitation has increased since the IPCC Fourth Assessment Report (AR4). From new paleo- climate reconstructions and modelling studies, there is very high confidence that a reduced strength of the AMOC and the associated surface cooling in the North Atlantic region caused southward shifts of the Atlantic Intertropical Convergence Zone and affected the American (north and south), African and Asian monsoons. {5.7} The interglacial mode of the AMOC can recover (high confidence) from a short-lived freshwater input into the sub- polar North Atlantic. Approximately 8.2 ka, a sudden freshwater release occurred during the final stages of North America ice sheet melting. Paleoclimate observations and model results indicate, with high confidence, a marked reduction in the strength of the AMOC followed by a rapid recovery, within approximately 200 years after the perturbation. {5.8.2} Although many more model simulations have been conducted since AR4 under a wide range of future forcing scenarios, projections of the AMOC behaviour have not changed. It remains very likely that the AMOC will weaken over the 21st century relative to 1850-1900 values. Best estimates and ranges for the reduction from the Coupled Model Intercomparison Project Phase 5 (CMIP5) are 11% (1 to 24%) for the Representative Concentration Path- way RCP2.6 and 34% (12 to 54%) for RCP8.5, but there is low confidence on the magnitude of weakening. It also remains very unlikely that the AMOC will undergo an abrupt transition or collapse in the 21st century for the sce- narios considered (high confidence) (TFE.5, Figure 1). For an abrupt transition of the AMOC to occur, the sensitivity of the AMOC to forcing would have to be far greater than seen in current models, or would require meltwater flux from the Greenland ice sheet greatly exceeding even the highest of current projections. Although neither pos- sibility can be excluded entirely, it is unlikely that the AMOC will collapse beyond the end of the 21st century for the scenarios considered, but a collapse beyond the 21st century for large sustained warming cannot be excluded. There is low confidence in assessing the evolution of AMOC beyond the 21st century because of limited number of analyses and equivocal results. {12.4.7, 12.5.5} Potential Irreversibility of Changes in Permafrost, Methane Clathrates and Forests In a warming climate, permafrost thawing may induce decomposition of carbon accumulated in frozen soils which could persist for hundreds to thousands of years, leading to an increase of atmospheric CO2 and/or methane (CH4) (continued on next page) 70 Technical Summary TFE.5 (continued) TS TFE.5, Figure 1 | Atlantic Meridional Overturning Circulation (AMOC) strength at 30°N (Sv) as a function of year, from 1850 to 2300 as simulated by different Atmo- sphere Ocean General Circulation Models in response to scenario RCP2.6 (left) and RCP8.5 (right). The vertical black bar shows the range of AMOC strength measured at 26°N, from 2004 to 2011 {Figures 3.11, 12.35} concentrations. The existing modelling studies of permafrost carbon balance under future warming that take into account at least some of the essential permafrost-related processes do not yield consistent results, beyond the fact that present-day permafrost will become a net emitter of carbon during the 21st century under plausible future warming scenarios (low confidence). This also reflects an insufficient understanding of the relevant soil processes during and after permafrost thaw, including processes leading to stabilization of unfrozen soil carbon, and pre- cludes any quantitative assessment of the amplitude of irreversible changes in the climate system potentially relat- ed to permafrost degassing and associated feedbacks. {6.4.7, 12.5.5} Anthropogenic warming will very likely lead to enhanced CH4 emissions from both terrestrial and oceanic clathrates. Deposits of CH4 clathrates below the sea floor are susceptible to destabilization via ocean warming. However, sea level rise due to changes in ocean mass enhances clathrate stability in the ocean. While difficult to formally assess, initial estimates of the 21st century feedback from CH4 clathrate destabilization are small but not insignificant. It is very unlikely that CH4 from clathrates will undergo catastrophic release during the 21st century (high confidence). On multi-millennial time scales, such CH4 emissions may provide a positive feedback to anthropogenic warming and may be irreversible, due to the diffference between release and accumulation time scales. {6.4.7, 12.5.5} The existence of critical climate change driven dieback thresholds in the Amazonian and other tropical rainforests purely driven by climate change remains highly uncertain. The possibility of a critical threshold being crossed in precipitation volume and duration of dry seasons cannot be ruled out. The response of boreal forest to projected climate change is also highly uncertain, and the existence of critical thresholds cannot at present be ruled out. There is low confidence in projections of the collapse of large areas of tropical and/or boreal forests. {12.5.5} Potential Irreversibility of Changes in the Cryosphere The reversibility of sea ice loss has been directly assessed in sensitivity studies to CO2 increase and decrease with Atmosphere Ocean General Circulation Models (AOGCMs) or Earth System Models (ESMs). None of them show evi- dence of an irreversible change in Arctic sea ice at any point. By contrast, as a result of the strong coupling between surface and deep waters in the Southern Ocean, the Antarctic sea ice in some models integrated with ramp-up and ramp-down atmospheric CO2 concentration exhibits some hysteresis behaviour. {12.5.5} At present, both the Greenland and Antarctic ice sheets have a positive surface mass balance (snowfall exceeds melting), although both are losing mass because ice outflow into the sea exceeds the net surface mass balance. A positive feedback operates to reduce ice sheet volume and extent when a decrease of the surface elevation of the ice sheet induces a decreased surface mass balance. This arises generally through increased surface melting, and therefore applies in the 21st century to Greenland, but not to Antarctica, where surface melting is currently very small. Surface melting in Antarctica is projected to become important after several centuries under high well-mixed greenhouse gas radiative forcing scenarios. {4.4, 13.4.4; Boxes 5.2, 13.2} Abrupt change in ice sheet outflow to the sea may be caused by unstable retreat of the grounding line in regions where the bedrock is below sea level and slopes downwards towards the interior of the ice sheet. This mainly (continued on next page) 71 Technical Summary TFE.5 (continued) applies to West Antarctica, but also to parts of East Antarctica and Greenland. Grounding line retreat can be trig- gered by ice shelf decay, due to warmer ocean water under ice shelves enhancing submarine ice shelf melt, or melt water ponds on the surface of the ice shelf promoting ice shelf fracture. Because ice sheet growth is a slow process, such changes would be irreversible in the definition adopted here. {4.4.5; Box 13.2} There is high confidence that the volumes of the Greenland and West Antarctic ice sheets were reduced during periods of the past few million years that were globally warmer than present. Ice sheet model simulations and geo- logical data suggest that the West Antarctic ice sheet is very sensitive to subsurface ocean warming and imply with medium confidence a West Antarctic ice sheet retreat if atmospheric CO2 concentration stays within, or above, the range of 350 450 ppm for several millennia. {5.8.1, 13.4.4; Box 13.2} The available evidence indicates that global warming beyond a threshold would lead to the near-complete loss of the Greenland ice sheet over a millennium or longer, causing a global mean sea level rise of approximately 7 m. TS Studies with fixed present-day ice sheet topography indicate that the threshold is greater than 2°C but less than 4°C (medium confidence) of global mean surface temperature rise above pre-industrial. The one study with a dynamical ice sheet suggests the threshold is greater than about 1°C (low confidence) global mean warming with respect to pre-industrial. Considering the present state of scientific uncertainty, a likely range cannot be quantified. The com- plete loss of the Greenland ice sheet is not inevitable because this would take a millennium or more; if temperatures decline before the ice sheet has completely vanished, the ice sheet might regrow. However, some part of the mass loss might be irreversible, depending on the duration and degree of exceedance of the threshold, because the ice sheet may have multiple steady states, due to its interaction with regional climate. {13.4.3, 13.4.4} TS.4.6 Water Cycle TS.4.7 Climate Extremes Since the AR4, new evidence has emerged of a detectable human influ- Several new attribution studies have found a detectable anthropo- ence on several aspects of the water cycle. There is medium confidence genic influence in the observed increased frequency of warm days and that observed changes in near-surface specific humidity since 1973 nights and decreased frequency of cold days and nights. Since the AR4 contain a detectable anthropogenic component. The anthropogenic and SREX, there is new evidence for detection of human influence on water vapour fingerprint simulated by an ensemble of climate models extremely warm daytime temperature and there is new evidence that has been detected in lower tropospheric moisture content estimates the influence of anthropogenic forcing may be detected separately derived from Special Sensor Microwave/Imager (SSM/I) data covering from the influence of natural forcing at global scales and in some con- the period 1988 2006. An anthropogenic contribution to increases in tinental and sub-continental regions. This strengthens the conclusions tropospheric specific humidity is found with medium confidence. {2.5, from both AR4 and SREX, and it is now very likely that anthropogenic 10.3} forcing has contributed to the observed changes in the frequency and intensity of daily temperature extremes on the global scale since the Attribution studies of global zonal mean terrestrial precipitation and mid-20th century. It is likely that human influence has significantly Arctic precipitation both find a detectable anthropogenic influence. increased the probability of occurrence of heat waves in some loca- Overall there is medium confidence in a significant human influence tions. See TFE.9 and TFE.9, Table 1 for a summary of the assessment of on global scale changes in precipitation patterns, including increases extreme weather and climate events. {10.6} in NH mid-to-high latitudes. Remaining observational and modelling uncertainties and the large effect of internal variability on observed Since the AR4, there is some new limited direct evidence for an anthro- precipitation preclude a more confident assessment. {2.5, 7.6, 10.3} pogenic influence on extreme precipitation, including a formal detec- tion and attribution study and indirect evidence that extreme precip- Based on the collected evidence for attributable changes (with varying itation would be expected to have increased given the evidence of levels of confidence and likelihood) in specific humidity, terrestrial pre- anthropogenic influence on various aspects of the global hydrological cipitation and ocean surface salinity through its connection to precipi- cycle and high confidence that the intensity of extreme precipitation tation and evaporation, and from physical understanding of the water events will increase with warming, at a rate well exceeding that of the cycle, it is likely that human influence has affected the global water mean precipitation. In land regions where observational coverage is cycle since 1960. This is a major advance since AR4. {2.4, 2.5, 3.3, 9.4.1, sufficient for assessment, there is medium confidence that anthropo- 10.3, 10.4.2; Table 10.1; FAQ 3.2} genic forcing has contributed to a global-scale intensification of heavy precipitation over the second half of the 20th century. {7.6, 10.6} 72 Technical Summary Globally, there is low confidence in attribution of changes in tropical just global mean changes, but also distinctive regional patterns con- cyclone activity to human influence. This is due to insufficient observa- sistent with the expected fingerprints of change from anthropogenic tional evidence, lack of physical understanding of the links between forcings and the expected responses from volcanic eruptions (Figure anthropogenic drivers of climate and tropical cyclone activity, and the TS.12). {10.3 10.6, 10.9} low level of agreement between studies as to the relative importance of internal variability, and anthropogenic and natural forcings. In the Human influence has been detected in nearly all of the major assessed North Atlantic region there is medium confidence that a reduction in components of the climate system (Figure TS.12). Taken together, the aerosol forcing over the North Atlantic has contributed at least in part combined evidence increases the overall level of confidence in the to the observed increase in tropical cyclone activity there since the attribution of observed climate change, and reduces the uncertainties 1970s. There remains substantial disagreement on the relative impor- associated with assessment based on a single climate variable. From tance of internal variability, WMGHG forcing and aerosols for this this combined evidence it is virtually certain that human influence has observed trend. {2.6, 10.6, 14.6} warmed the global climate system. Anthropogenic influence has been identified in changes in temperature near the surface of the Earth, in Although the AR4 concluded that it is more likely than not that anthro- the atmosphere and in the oceans, as well as in changes in the cryo- pogenic influence has contributed to an increased risk of drought in the sphere, the water cycle and some extremes. There is strong evidence TS second half of the 20th century, an updated assessment of the obser- that excludes solar forcing, volcanoes and internal variability as the vational evidence indicates that the AR4 conclusions regarding global strongest drivers of warming since 1950. {10.9; Table 10.1; FAQ 5.1} increasing trends in hydrological droughts since the 1970s are no longer supported. Owing to the low confidence in observed large-scale trends Over every continent except Antarctica, anthropogenic influence has in dryness combined with difficulties in distinguishing decadal-scale likely made a substantial contribution to surface temperature increas- variability in drought from long-term climate change, there is now low es since the mid-20th century (Figure TS.12). It is likely that there has confidence in the attribution of changes in drought over global land been a significant anthropogenic contribution to the very substantial since the mid-20th century to human influence. {2.6, 10.6} warming in Arctic land surface temperatures over the past 50 years. For Antarctica large observational uncertainties result in low confidence TS.4.8 From Global to Regional that anthropogenic influence has contributed to observed warming averaged over available stations. Detection and attribution at regional Taking a longer term perspective shows the substantial role played by external forcings in driving climate variability on hemispheric scales in pre-industrial times (Box TS.5). It is very unlikely that NH temperature SAM trend (hPa per decade) variations from 1400 to 1850 can be explained by internal variability 1.5 alone. There is medium confidence that external forcing contributed to NH temperature variability from 850 to 1400 and that external forcing contributed to European temperature variations over the last 5 centu- 1.0 ries. {5.3.3, 5.5.1, 10.7.2, 10.7.5; Table 10.1} 0.5 Changes in atmospheric circulation are important for local climate change because they could lead to greater or smaller changes in cli- 0.0 mate in a particular region than elsewhere. It is likely that human influ- ence has altered sea level pressure patterns globally. There is medium confidence that stratospheric ozone depletion has contributed to the -0.5 observed poleward shift of the southern Hadley Cell border during aus- MAM JJA SON DJF tral summer. It is likely that stratospheric ozone depletion has contrib- Season uted to the positive trend in the SAM seen in austral summer since the historical control mid-20th century which corresponds to sea level pressure reductions historicalGHG HadSLP2 historicalAer 20CR over the high latitudes and increase in the subtropics (Figure TS.11). historicalOz historicalNat {10.3} The evidence is stronger that observed changes in the climate system Figure TS.11 | Simulated and observed 1951 2011 trends in the Southern Annular Mode (SAM) index by season. The SAM index is a difference between zonal mean sea can now be attributed to human activities on global and regional scales level pressure (SLP) at 40°S and 65°S. The SAM index is defined without normaliza- in many components (Figure TS.12). Observational uncertainty has been tion, so that the magnitudes of simulated and observed trends can be compared. Black explored much more thoroughly than previously, and fingerprints of lines show observed trends from the Hadley Centre Sea Level Pressure 2r (HadSLP2r) human influence have been deduced from a new generation of climate data set (solid), and the 20th Century Reanalysis (dotted). Grey bars show 5th to 95th models. There is improved understanding of ocean changes, including percentile ranges of control trends, and red boxes show the 5th to 95th percentile range of trends in historical simulations including anthropogenic and natural forcings. salinity changes, that are consistent with large scale intensification of ­ Coloured bars show ensemble mean trends and their associated 5 to 95% confidence the water cycle predicted by climate models. The changes in near sur- ranges simulated in response to well-mixed greenhouse gas (light green), aerosol (dark face temperatures, free atmosphere temperatures, ocean temperatures green), ozone (magenta) and natural forcing changes (blue) in CMIP5 individual-forcing and NH snow cover and sea ice extent, when taken together, show not simulations. {Figure 10.13b} 73 Technical Summary scales is complicated by the greater role played by dynamical factors ocean heat content show emerging anthropogenic and natural signals (circulation changes), a greater range of forcings that may be regionally in both records, and a clear separation from the alternative hypothesis important, and the greater difficulty of modelling relevant processes at of just natural variations. These signals do not appear just in the global regional scales. Nevertheless, human influence has likely contributed to means, but also appear at regional scales on continents and in ocean temperature increases in many sub-continental regions. {10.3; Box 5.1} basins in each of these variables. Sea ice extent emerges clearly from the range of internal variability for the Arctic. At sub-continental scales The coherence of observed changes with simulations of anthropogenic human influence is likely to have substantially increased the probabil- and natural forcing in the physical system is remarkable (Figure TS.12), ity of occurrence of heat waves in some locations. {Table 10.1} particularly for temperature-related variables. Surface temperature and TS Global averages Ocean surface Land surface Land and ocean surface Ocean heat content Observations Models using only natural forcings Models using both natural and anthropogenic forcings Figure TS.12 | Comparison of observed and simulated change in the climate system, at regional scales (top panels) and global scales (bottom four panels). Brown panels are land surface temperature time series, blue panels are ocean heat content time series and white panels are sea ice time series (decadal averages). Each panel shows observations (black or black and shades of grey), and the 5 to 95% range of the simulated response to natural forcings (blue shading) and natural and anthropogenic forcings (pink shading), together with the corresponding ensemble means (dark blue and dark red respectively). The observed surface temperature is from the Hadley Centre/Climatic Research Unit gridded surface temperature data set 4 (HadCRUT4). Three observed records of ocean heat content (OHC) are shown. Sea ice anomalies (rather than absolute values) are plotted and based on models in Figure 10.16. The observations lines are either solid or dashed and indicate the quality of the observations and estimates. For land and ocean surface temperatures panels and precipitation panels, solid observation lines indicate where spatial coverage of areas being examined is above 50% coverage and dashed observation lines where coverage is below 50%. For example, data coverage of Antarctica never goes above 50% of the land area of the continent. For ocean heat content and sea ice panels the solid observations line is where the coverage of data is good and higher in quality, and the dashed line is where the data coverage is only adequate. This figure is based on Figure 10.21 except presented as decadal averages rather than yearly averages. Further detail regarding the related Figure SPM.6 is given in the TS Supplementary Material. {Figure 10.21} 74 Technical Summary Box TS.4 | Model Evaluation Climate models have continued to be improved since the AR4, and many models have been extended into Earth System Models (ESMs) by including the representation of biogeochemical cycles important to climate change. Box TS.4, Figure 1 provides a partial overview of model capabilities as assessed in this report, including improvements or lack thereof relative to models that were assessed in the AR4 or that were available at the time of the AR4. {9.1, 9.8.1; Box 9.1} The ability of climate models to simulate surface temperature has improved in many, though not all, important aspects relative to the generation of models assessed in the AR4. There continues to be very high confidence that models reproduce the observed large-scale time-mean surface temperature patterns (pattern correlation of about 0.99), although systematic errors of several degrees Celsius are found in some regions. There is high confidence that on the regional scale (sub-continental and smaller), time-mean surface tempera- ture is better simulated than at the time of the AR4; however, confidence in model capability is lower than for the large scale. Models are able to reproduce the magnitude of the observed global mean or northern-hemisphere-mean temperature variability on interannual to centennial time scales. Models are also able to reproduce the large-scale patterns of temperature during the Last Glacial Maximum TS indicating an ability to simulate a climate state much different from the present (see also Box TS.5). {9.4.1, 9.6.1} There is very high confidence that models reproduce the general features of the global and annual mean surface temperature changes over the historical period, including the warming in the second half of the 20th century and the cooling immediately following large volcanic eruptions. Most simulations of the historical period do not reproduce the observed reduction in global mean surface warming trend over the last 10 to 15 years (see Box TS.3). There is medium confidence that the trend difference between models and observa- tions during 1998 2012 is to a substantial degree caused by internal variability, with possible contributions from forcing inadequacies in models and some models overestimating the response to increasing greenhouse gas forcing. Most, though not all, models overesti- mate the observed warming trend in the tropical troposphere over the last 30 years, and tend to underestimate the long-term lower- stratospheric cooling trend. {9.4.1; Box 9.2} The simulation of large-scale patterns of precipitation has improved somewhat since the AR4, although models continue to perform less well for precipitation than for surface temperature. The spatial pattern correlation between modelled and observed annual mean precipitation has increased from 0.77 for models available at the time of the AR4 to 0.82 for current models. At regional scales, precipi- tation is not simulated as well, and the assessment remains difficult owing to observational uncertainties. {9.4.1, 9.6.1} Many models are able to reproduce the observed changes in upper-ocean heat content from 1961 to 2005. The time series of the multi- model mean falls within the range of the available observational estimates for most of the period. {9.4.2} There is robust evidence that the downward trend in Arctic summer sea ice extent is better simulated than at the time of the AR4. About one quarter of the models show a trend as strong as, or stronger, than the trend in observations over the satellite era 1979 2012. Most models simulate a small decreasing trend in Antarctic sea ice extent, albeit with large inter-model spread, in contrast to the small increasing trend in observations. {9.4.3} There has been substantial progress since the AR4 in the assessment of model simulations of extreme events. Changes in the frequency of extreme warm and cold days and nights over the second half of the 20th century are consistent between models and observations, with the ensemble mean global mean time series generally falling within the range of observational estimates. The majority of models underestimate the sensitivity of extreme precipitation to temperature variability or trends, especially in the tropics. {9.5.4} In the majority of the models that include an interactive carbon cycle, the simulated global land and ocean carbon sinks over the latter part of the 20th century fall within the range of observational estimates. However, models systematically underestimate the NH land sink implied by atmospheric inversion techniques. {9.4.5} Regional downscaling methods provide climate information at the smaller scales needed for many climate impact studies. There is high confidence that downscaling adds value both in regions with highly variable topography and for various small-scale phenomena. {9.6.4} The model spread in equilibrium climate sensitivity ranges from 2.1°C to 4.7°C and is very similar to the assessment in the AR4. There is very high confidence that the primary factor contributing to the spread in equilibrium climate sensitivity continues to be the cloud feedback. This applies to both the modern climate and the last glacial maximum. There is likewise very high confidence that, consis- tent with observations, models show a strong positive correlation between tropospheric temperature and water vapour on regional to global scales, implying a positive water vapour feedback in both models and observations. {5.3.3, 9.4.1, 9.7} (continued on next page) 75 Technical Summary Box TS.4 (continued) Climate models are based on physical principles, and they reproduce many important elements of observed climate. Both aspects contribute to our confidence in the models suitability for their application in detection and attribution studies (see Chapter 10) and for quantitative future predictions and projections (see Chapters 11 to 14). There is increasing evidence that some elements of observed variability or trends are well correlated with inter-model differences in model projections for quantities such as Arctic summer sea ice trends, the snow albedo feedback, and the carbon loss from tropical land. However, there is still no universal strategy for transferring a model s past performance to a relative weight of this model in a multi-model-ensemble mean of climate projections. {9.8.3} (a) Trends (b) Extremes fgCO2 ArctSIE TAS Hurric -hr TAS_ext High High TS TC-hr NBP OHC PR_ext PR_ext -hr Medium Medium Model performance Model performance TotalO3 TAS_ext -t LST PR_ext -t Doughts TTT AntSIE TC Low Low Very low Low Medium High Very high Very low Low Medium High Very high Confidence in assessment Confidence in assessment Degradation since the AR4 No change since the AR4 Improvement since the AR4 Not assessed in the AR4 Box TS.4, Figure1 | Summary of how well the current-generation climate models simulate important features of the climate of the 20th century. Confidence in the assessment increases towards the right as suggested by the increasing strength of shading. Model quality increases from bottom to top. The colour coding indicates improvements from the models available at the time of the AR4 to the current assessment. There have been a number of improvements since the AR4, and some some modelled quantities are not better simulated. The major climate quantities are listed in this summary and none shows degradation. The assessment is based mostly on the multi-model mean, not excluding that deviations for individual models could exist. Assessed model quality is simplified for representation in this figure; details of each assessment are found in Chapter 9. {9.8.1; Figure 9.44} The figure highlights the following key features, with the sections that back up the assessment added in brackets: (a) Trends in: AntSIE Antarctic sea ice extent {9.4.3} ArctSIE Arctic sea ice extent {9.4.3} fgCO2 Global ocean carbon sink {9.4.5} LST Lower-stratospheric temperature {9.4.1.} NBP Global land carbon sink {9.4.5} OHC Global ocean heat content {9.4.2} TotalO3 Total-column ozone {9.4.1} TAS Surface air temperature {9.4.1} TTT Tropical tropospheric temperature {9.4.1} (b) Extremes: Droughts Droughts {9.5.4} Hurric-hr Year-to-year count of Atlantic hurricanes in high-resolution AGCMs {9.5.4} PR_ext Global distribution of precipitation extremes {9.5.4} PR_ext-hr Global distribution of precipitation extremes in high-resolution AGCMs {9.5.4} PR_ext-t Global trends in precipitation extremes {9.5.4} TAS_ext Global distributions of surface air temperature extremes {9.5.4} TAS_ext-t Global trends in surface air temperature extremes {9.5.4} TC Tropical cyclone tracks and intensity {9.5.4} TC-hr Tropical cyclone tracks and intensity in high-resolution AGCMs {9.5.4} 76 Technical Summary Box TS.5 | Paleoclimate Reconstructions from paleoclimate archives allow current changes in atmospheric composition, sea level and climate (including extreme events such as droughts and floods), as well as future projections, to be placed in a broader perspective of past climate variability (see Section TS.2). {5.2 5.6, 6.2, 10.7} Past climate information also documents the behaviour of slow components of the climate system including the carbon cycle, ice sheets and the deep ocean for which instrumental records are short compared to their characteristic time scales of responses to perturba- tions, thus informing on mechanisms of abrupt and irreversible changes. Together with the knowledge of past external climate forcings, syntheses of paleoclimate data have documented polar amplification, characterized by enhanced temperature changes in the Arctic compared to the global mean, in response to high or low CO2 concentrations. {5.2.1, 5.2.2, 5.6, 5.7, 5.8, 6.2, 8.4.2, 13.2.1, 13.4; Boxes 5.1, 5.2} Since AR4, the inclusion of paleoclimate simulations in the PMIP3 (Paleoclimate Modelling Intercomparison Project)/CMIP5 framework TS has enabled paleoclimate information to be more closely linked with future climate projections. Paleoclimate information for the mid- Holocene (6 ka), the Last Glacial Maximum (approximately 21 ka), and last millennium has been used to test the ability of models to simulate realistically the magnitude and large-scale patterns of past changes. Combining information from paleoclimate simulations and reconstructions enables to quantify the response of the climate system to radiative perturbations, constraints to be placed on the range of equilibrium climate sensitivity, and past patterns of internal climate variability to be documented on inter-annual to multi- centennial scales. {5.3.1 5.3.5, 5.4, 5.5.1, 9.4.1, 9.4.2, 9.5.3, 9.7.2, 10.7.2, 14.1.2} Box TS.5, Figure 1 illustrates the comparison between the last millennium Paleoclimate Modelling Intercomparison Project Phase 3 (PMIP3)/CMIP5 simulations and reconstructions, together with the associated solar, volcanic and WMGHG RFs. For average annual NH temperatures, the period 1983 2012 was very likely the warmest 30-year period of the last 800 years (high confidence) and likely the warmest 30-year period of the last 1400 years (medium confidence). This is supported by comparison of instrumental temperatures with multiple reconstructions from a variety of proxy data and statistical methods, and is consistent with AR4. In response to solar, volcanic and anthropogenic radiative changes, climate models simulate multi-decadal temperature changes in the last 1200 years in the NH that are generally consistent in magnitude and timing with reconstructions, within their uncertainty ranges. Continental-scale temperature reconstructions show, with high confidence, multi-decadal periods during the Medieval Climate Anomaly (about 950 to 1250) that were in some regions as warm as the mid-20th century and in others as warm as in the late 20th century. With high confi- dence, these regional warm periods were not as synchronous across regions as the warming since the mid-20th century. Based on the comparison between reconstructions and simulations, there is high confidence that not only external orbital, solar and volcanic forcing but also internal variability contributed substantially to the spatial pattern and timing of surface temperature changes between the Medieval Climate Anomaly and the Little Ice Age (about 1450 to 1850). However, there is only very low confidence in quantitative esti- mates of their relative contributions. It is very unlikely that NH temperature variations from 1400 to 1850 can be explained by internal variability alone. There is medium confidence that external forcing contributed to Northern Hemispheric temperature variability from 850 to 1400 and that external forcing contributed to European temperature variations over the last 5 centuries. {5.3.5, 5.5.1, 10.7.2, 10.7.5; Table 10.1} (continued on next page) 77 Technical Summary Box TS.5 (continued) (a) Radiative forcing (W m-2) 0 -5 Volcanic -10 -15 0.1 -20 0.0 TSI 2.5 -0.1 2.0 -0.2 Well mixed GHGs 1.5 -0.3 1.0 0.5 0.0 -0.5 1000 1200 1400 1600 1800 2000 Time (Year CE) TS (b) Reconstructed (grey) and simulated (red) NH temperature Temp. anomaly wrt 1500 - 1850 (°C) 1.0 0.5 0.0 -0.5 1000 1200 1400 1600 1800 2000 Time (Year CE) (c) Arctic ANN (d) North America ANN Temp. anomaly wrt 1500 1850 (°C) Temp. anomaly wrt 1881 1980 (°C) Temp. anomaly wrt 1500 1850 (°C) Temp. anomaly wrt 1881 1980 (°C) 2 2 1 1.5 1.5 1.5 0.5 1 1 1 0 0.5 0.5 0.5 0.5 0 0 0 1 0.5 0.5 0.5 1.5 1 1 1 2 1.5 1.5 Arctic 1.5 2 2 Asia 2 1000 1200 1400 1600 1800 2000 North 1000 1200 1400 1600 1800 2000 America Europe Time (Year CE) Time (Year CE) (e) Europe JJA (f) Asia JJA Temp. anomaly wrt 1500 1850 (°C) Temp. anomaly wrt 1500 1850 (°C) Temp. anomaly wrt 1881 1980 (°C) Temp. anomaly wrt 1881 1980 (°C) 2 2 1.5 1.5 1.5 1.5 1 1 1 1 0.5 0.5 0.5 0.5 0 0 0 0 0.5 0.5 0.5 0.5 1 1 1 1 1.5 1.5 1.5 1.5 2 2 2 2 1000 1200 1400 1600 1800 2000 1000 1200 1400 1600 1800 2000 Time (Year CE) Time (Year CE) Box TS.5, Figure 1 | Last-millennium simulations and reconstructions. (a) 850 2000 PMIP3/CMIP5 radiative forcing due to volcanic, solar and well-mixed green- house gases. Different colours illustrate the two existing data sets for volcanic forcing and four estimates of solar forcing. For solar forcing, solid (dashed) lines stand for reconstruction variants in which background changes in irradiance are (not) considered; (b) 850 2000 PMIP3/CMIP5 simulated (red) and reconstructed (shading) Northern Hemisphere (NH) temperature changes. The thick red line depicts the multi-model mean while the thin red lines show the multi-model 90% range. The overlap of reconstructed temperatures is shown by grey shading; all data are expressed as anomalies from their 1500 1850 mean and smoothed with a 30-year filter. Note that some reconstructions represent a smaller spatial domain than the full NH or a specific season, while annual temperatures for the full NH mean are shown for the simulations. (c), (d), (e) and (f) Arctic and North America annual mean temperature, and Europe and Asia June, July and August (JJA) temperature, from 950 to 2000 from reconstructions (black line), and PMIP3/CMIP5 simulations (thick red, multi-model mean; thin red, 90% multi-model range). All red curves are expressed as anomalies from their 1500 1850 mean and smoothed with a 30-year filter. The shaded envelope depicts the uncertainties from each reconstruction (Arctic: 90% confidence bands, North American: +/-2 standard deviation. Asia: +/-2 root mean square error. Europe: 95% confidence bands). For comparison with instrumental record, the Climatic Research Unit land station Temperature (CRUTEM4) data set is shown (yellow line). These instrumental data are not necessarily those used in calibration of the reconstrctions, and thus may show greater or lesser correspondence with the reconstructions than the instrumental data actually used for calibration; cutoff timing may also lead to end effects for smoothed data shown. All lines are smoothed by applying a 30-year moving average. Map shows the individual regions for each reconstruction. {5.3.5; Table 5.A.1; Figures 5.1, 5.8, 5.12} 78 Technical Summary TS.5 Projections of Global and Regional cycles; projections in sea level change; and finally changes to climate Climate Change phenomena and other aspects of regional climate over the 21st cen- tury. Projected changes are given relative to the 1986 2005 average TS.5.1 Introduction unless indicated otherwise. Projections of climate change on longer term and information on climate stabilization and targets are provided Projections of changes in the climate system are made using a hierar- in TFE.8. Methods to counter climate change, termed geoengineering, chy of climate models ranging from simple climate models, to models have been proposed and an overview is provided in Box TS.7. {11.3, of intermediate complexity, to comprehensive climate models, and 12.3 12.5, 13.5 13.7, 14.1 14.6, Annex I} Earth System Models (ESMs). These models simulate changes based on a set of scenarios of anthropogenic forcings. A new set of scenarios, TS.5.2 Future Forcing and Scenarios the Representative Concentration Pathways (RCPs), was used for the new climate model simulations carried out under the framework of In this assessment report a series of new RCPs are used that largely the Coupled Model Intercomparison Project Phase 5 (CMIP5) of the replace the IPCC Special Report on Emission Scenarios (SRES) scenarios World Climate Research Programme. A large number of comprehensive (see Box TS.6 and Annex II for Climate System Scenario Tables). They climate models and ESMs have participated in CMIP5, whose results produce a range of responses from ongoing warming, to ­approximately TS form the core of the climate system projections. stabilized forcing, to a stringent mitigation scenario (RCP2.6) that sta- bilizes and then slowly reduces the RF after mid-21st century. In con- This section summarizes the assessment of these climate change pro- trast to the AR4, the climate change from the RCP scenarios in the AR5 jections. First, future forcing and scenarios are presented. The following is framed as a combination of adaptation and mitigation. Mitigation subsections then address various aspects of projections of global and actions starting now in the various RCP scenarios do not produce dis- regional climate change, including near-term (up to about mid-century) cernibly different climate change outcomes for the next 30 years or and long-term (end of the 21st century) projections in the atmosphere, so, whereas long-term climate change after mid-century is appreciably ocean and cryosphere; projections of carbon and other biogeochemical different across the RCPs. {Box 1.1} Box TS.6 | The New Representative Concentration Pathway Scenarios and Coupled Model Intercomparison Project Phase 5 Models Future anthropogenic emissions of GHGs, aerosol particles and other forcing agents such as land use change are dependent on socio- economic factors, and may be affected by global geopolitical agreements to control those emissions to achieve mitigation. AR4 made extensive use of the SRES scenarios that do not include additional climate initiatives, which means that no scenarios were available that explicitly assume implementation of the United Nations Framework Convention on Climate Change (UNFCCC) or the emissions targets of the Kyoto Protocol. However, GHG emissions are directly affected by non-climate change policies designed for a wide range of other purposes. The SRES scenarios were developed using a sequential approach, that is, socioeconomic factors fed into emissions scenarios, which were then used in simple climate models to determine concentrations of GHGs, and other agents required to drive the more complex AOGCMs. In this report, outcomes of climate simulations that use new scenarios (some of which include implied policy actions to achieve mitigation) referred to as RCPs are assessed. These RCPs represent a larger set of mitigation scenarios and were selected to have different targets in terms of radiative forcing at 2100 (about 2.6, 4.5, 6.0 and 8.5 W m 2; Figure TS.15). The scenarios should be considered plausible and illustrative, and do not have probabilities attached to them. {12.3.1; Box 1.1} The RCPs were developed using Integrated Assessment Models (IAMs) that typically include economic, demographic, energy, and simple climate components. The emission scenarios they produce are then run through a simple model to produce time series of GHG concentrations that can be run in AOGCMs. The emission time series from the RCPs can then be used directly in ESMs that include interactive biogeochemistry (at least a land and ocean carbon cycle). {12.3.1; Box 1.1} The CMIP5 multi-model experiment (coordinated through the World Climate Research Programme) presents an unprecedented level of information on which to base assessments of climate variability and change. CMIP5 includes new ESMs in addition to AOGCMs, new model experiments and more diagnostic output. CMIP5 is much more comprehensive than the preceding CMIP3 multi-model experi- ment that was available at the time of the IPCC AR4. CMIP5 has more than twice as many models, many more experiments (that also include experiments to address understanding of the responses in the future climate change scenario runs), and nearly 2 × 1015 bytes of data (as compared to over 30 × 1012 bytes of data in CMIP3). A larger number of forcing agents are treated more completely in the CMIP5 models, with respect to aerosols and land use particularly. Black carbon aerosol is now a commonly included forcing agent. Considering CO2, both concentrations-driven projections and emissions-driven projections are assessed from CMIP5. These allow quantification of the physical response uncertainties as well as climate carbon cycle interactions. {1.5.2} (continued on next page) 79 Technical Summary Box TS.6 (continued) The assessment of the mean values and ranges of global mean temperature changes in AR4 would not have been substantially dif- ferent if the CMIP5 models had been used in that report. The differences in global temperature projections can largely be attributed to the different scenarios. The global mean temperature response simulated by CMIP3 and CMIP5 models is very similar, both in the mean and the model range, transiently and in equilibrium. The range of temperature change across all scenarios is wider because the RCPs include a strong mitigation scenario (RCP2.6) that had no equivalent among the SRES scenarios used in CMIP3. For each scenario, the 5 to 95% range of the CMIP5 projections is obtained by approximating the CMIP5 distributions by a normal distribution with same mean and standard deviation and assessed as being likely for projections of global temperature change for the end of the 21st century. Probabilistic projections with simpler models calibrated to span the range of equilibrium climate sensitivity assessed by the AR4 provide uncertainty ranges that are consistent with those from CMIP5. In AR4 the uncertainties in global temperature projections were found to be approximately constant when expressed as a fraction of the model mean warming (constant fractional uncertainty). For the higher RCPs, the uncertainty is now estimated to be smaller than with the AR4 method for long-term climate change, because the carbon cycle climate feedbacks are not relevant for the concentration-driven RCP projections (in contrast, the assessed projection TS uncertainties of global temperature in AR4 did account of carbon cycle climate feedbacks, even though these were not part of the CMIP3 models). When forced with RCP8.5, CO2 emissions, as opposed to the RCP8.5 CO2 concentrations, CMIP5 ESMs with interactive carbon cycle simulate, on average, a 50 ( 140 to +210) ppm (CMIP5 model spread) larger atmospheric CO2 concentration and 0.2°C larger global surface temperature increase by 2100. For the low RCPs the fractional uncertainty is larger because internal variability and non-CO2 forcings make a larger relative contribution to the total uncertainty. {12.4.1, 12.4.8, 12.4.9} (continued on next page) Temperature scaled by global T (°C per °C) Precipitation scaled by global T (% per °C) CMIP3 : 2080-2099 CMIP3 : 2080-2099 CMIP5 : 2081-2100 CMIP5 : 2081-2100 (°C per °C global mean change) (% per °C global mean change) Box TS.6, Figure 1 | Patterns of temperature (left column) and percent precipitation change (right column) for the CMIP3 models average (first row) and CMIP5 models average (second row), scaled by the corresponding global average temperature changes. The patterns are computed in both cases by taking the difference between the averages over the last 20 years of the 21st century experiments (2080 2099 for CMIP3 and 2081 2100 for CMIP5) and the last 20 years of the historic experiments (1980 1999 for CMIP3, 1986 2005 for CMIP5) and rescaling each difference by the corresponding change in global average temperature. This is done first for each individual model, then the results are averaged across models. Stippling indicates a measure of significance of the difference between the two correspond- ing patterns obtained by a bootstrap exercise. Two subsets of the pooled set of CMIP3 and CMIP5 ensemble members of the same size as the original ensembles, but without distinguishing CMIP3 from CMIP5 members, were randomly sampled 500 times. For each random sample the corresponding patterns and their difference are computed, then the true difference is compared, grid-point by grid-point, to the distribution of the bootstrapped differences, and only grid-points at which the value of the difference falls in the tails of the bootstrapped distribution (less than the 2.5th percentiles or the 97.5th percentiles) are stippled. {Figure 12.41} 80 Technical Summary Box TS.6 (continued) There is overall consistency between the projections of temperature and precipitation based on CMIP3 and CMIP5, both for large-scale patterns and magnitudes of change (Box TS.6, Figure 1). Model agreement and confidence in projections depends on the variable and on spatial and temporal averaging, with better agreement for larger scales. Confidence is higher for temperature than for those quan- tities related to the water cycle or atmospheric circulation. Improved methods to quantify and display model robustness have been developed to indicate where lack of agreement across models on local trends is a result of internal variability, rather than models actu- ally disagreeing on their forced response. Understanding of the sources and means of characterizing uncertainties in long-term large scale projections of climate change has not changed significantly since AR4, but new experiments and studies have continued to work towards a more complete and rigorous characterization. {9.7.3, 12.2, 12.4.1, 12.4.4, 12.4.5, 12.4.9; Box 12.1} The well-established stability of geographical patterns of temperature and precipitation change during a transient experiment remains valid in the CMIP5 models (Box TS.6, Figure 1). Patterns are similar over time and across scenarios and to first order can be scaled by the global mean temperature change. There remain limitations to the validity of this technique when it is applied to strong mitigation TS scenarios, to scenarios where localized forcings (e.g., aerosols) are significant and vary in time and for variables other than average seasonal mean temperature and precipitation. {12.4.2} The range in anthropogenic aerosol emissions across all scenarios has and timing of future eruptions is unknown. Except for the 11-year solar a larger impact on near-term climate projections than the correspond- cycle, changes in the total solar irradiance are uncertain. Except where ing range in long-lived GHGs, particularly on regional scales and for explicitly indicated, future volcanic eruptions and changes in total solar hydrological cycle variables. The RCP scenarios do not span the range irradiance additional to a repeating 11-year solar cycle are not included of future aerosol emissions found in the SRES and alternative scenarios in the projections of near- and long-term climate assessed. {8, 11.3.6} (Box TS.6). {11.3.1, 11.3.6} TS.5.3 Quantification of Climate System Response If rapid reductions in sulphate aerosol are undertaken for improving air quality or as part of decreasing fossil-fuel CO2 emissions, then there is Estimates of the equilibrium climate sensitivity (ECS) based on medium confidence that this could lead to rapid near-term warming. observed climate change, climate models and feedback analysis, as There is evidence that accompanying controls on CH4 emissions would well as paleoclimate evidence indicate that ECS is positive, likely in offset some of this sulphate-induced warming, although the cool- the range 1.5°C to 4.5°C with high confidence, extremely unlikely less ing from CH4 mitigation will emerge more slowly than the warming than 1°C (high confidence) and very unlikely greater than 6°C (medium from sulphate mitigation due to the different time scales over which confidence). Earth system sensitivity over millennia time scales includ- atmospheric concentrations of these substances decrease in response ing long-term feedbacks not typically included in models could be sig- to decreases in emissions. Although removal of black carbon aerosol nificantly higher than ECS (see TFE.6 for further details). {5.3.1, 10.8; could also counter warming associated with sulphate removal, uncer- Box 12.2} tainties are too large to constrain the net sign of the global tempera- ture response to black carbon emission reductions, which depends on With high confidence the transient climate response (TCR) is positive, reduction of co-emitted (reflective) aerosols and on aerosol indirect likely in the range 1°C to 2.5°C and extremely unlikely greater than effects. {11.3.6} 3°C, based on observed climate change and climate models (see TFE.6 for further details). {10.8; Box 12.2} Including uncertainties in projecting the chemically reactive GHGs CH4 and N2O from RCP emissions gives a range in abundance pathways The ratio of GMST change to total cumulative anthropogenic carbon that is likely 30% larger than the range in RCP concentrations used to emissions is relatively constant and independent of the scenario, but is force the CMIP5 climate models. Including uncertainties in emission model dependent, as it is a function of the model cumulative airborne estimates from agricultural, forest and land use sources, in atmospheric fraction of carbon and the transient climate response. For any given lifetimes, and in chemical feedbacks, results in a much wider range of temperature target, higher emissions in earlier decades therefore imply abundances for N2O, CH4 and HFCs and their RF. In the case of CH4, lower emissions by about the same amount later on. The transient cli- by year 2100 the likely range of RCP8.5 CH4 abundance extends 520 mate response to cumulative carbon emission (TCRE) is likely between ppb above the single-valued RCP8.5 CH4 abundance, and RCP2.6 CH4 0.8°C to 2.5°C per 1000 PgC (high confidence), for cumulative carbon extends 230 ppb below RCP2.6 CH4. {11.3.5} emissions less than about 2000 PgC until the time at which tempera- tures peak (see TFE.8 for further details). {10.8, 12.5.4; Box 12.2} There is very low confidence in projections of natural forcing. Major volcanic eruptions cause a negative RF up to several watts per square metre, with a typical lifetime of one year, but the possible occurrence 81 Technical Summary Thematic Focus Elements TFE.6 | Climate Sensitivity and Feedbacks The description of climate change as a response to a forcing that is amplified by feedbacks goes back many decades. The concepts of radiative forcing (RF) and climate feedbacks continue to be refined, and limitations are now better understood; for instance, feedbacks may be much faster than the surface warming, feedbacks depend on the type of forcing agent (e.g., greenhouse gas (GHG) vs. solar forcing), or may have intrinsic time scales (associated mainly with vegetation change and ice sheets) of several centuries to millennia. The analysis of physical feedbacks in models and from observations remains a powerful framework that provides constraints on transient future warming for differ- ent scenarios, on climate sensitivity and, combined with estimates of carbon cycle feedbacks (see TFE.5), determines the GHG emissions that are compatible with climate stabilization or targets (see TFE.8). {7.1, 9.7.2, 12.5.3; Box 12.2} The water vapour/lapse rate, albedo and cloud feedbacks are the principal determinants of equilibrium climate sensitivity. All of these feedbacks are assessed to be positive, but with different levels of likelihood assigned rang- TS ing from likely to extremely likely. Therefore, there is high confidence that the net feedback is positive and the black body response of the climate to a forcing will therefore be amplified. Cloud feedbacks continue to be the largest uncertainty. The net feedback from water vapour and lapse rate changes together is extremely likely posi- tive and approximately doubles the black body response. The mean value and spread of these two processes in climate models are essentially unchanged from the IPCC Fourth Assessment Report (AR4), but are now supported by stronger observational evidence and better process understanding of what determines relative humidity dis- tributions. Clouds respond to climate forcing mechanisms in multiple ways and individual cloud feedbacks can be positive or negative. Key issues include the representation of both deep and shallow cumulus convection, micro- physical processes in ice clouds and partial cloudiness that results from small-scale variations of cloud-producing and cloud-dissipating processes. New approaches to diagnosing cloud feedback in General Circulation Models (GCMs) have clarified robust cloud responses, while continuing to implicate low cloud cover as the most important source of intermodel spread in simulated cloud feedbacks. The net radiative feedback due to all cloud types is likely posi- tive. This conclusion is reached by considering a plausible range for unknown contributions by processes yet to be accounted for, in addition to those occurring in current climate models. Observations alone do not currently pro- vide a robust, direct constraint, but multiple lines of evidence now indicate positive feedback contributions from changes in both the height of high clouds and the horizontal distribution of clouds. The additional feedback from low cloud amount is also positive in most climate models, but that result is not well understood, nor effectively constrained by observations, so confidence in it is low. {7.2.4 7.2.6, 9.7.2} The representation of aerosol cloud processes in climate models continues to be a challenge. Aerosol and cloud variability at scales significantly smaller than those resolved in climate models, and the subtle responses of clouds to aerosol at those scales, mean that, for the foreseeable future, climate models will continue to rely on parameteriza- tions of aerosol cloud interactions or other methods that represent subgrid variability. This implies large uncertain- ties for estimates of the forcings associated with aerosol cloud interactions. {7.4, 7.5.3, 7.5.4} Equilibrium climate sensitivity (ECS) and transient climate response (TCR) are useful metrics summarising the global climate system s temperature response to an externally imposed RF. ECS is defined as the equilibrium change in annual mean global mean surface temperature (GMST) following a doubling of the atmospheric carbon dioxide (CO2) concentration, while TCR is defined as the annual mean GMST change at the time of CO2 doubling following a linear increase in CO2 forcing over a period of 70 years (see Glossary). Both metrics have a broader application than these definitions imply: ECS determines the eventual warming in response to stabilisation of atmospheric composi- tion on multi-century time scales, while TCR determines the warming expected at a given time following any steady increase in forcing over a 50- to 100-year time scale. {Box 12.2; 12.5.3} ECS and TCR can be estimated from various lines of evidence (TFE.6, Figures 1 and 2). The estimates can be based on the values of ECS and TCR diagnosed from climate models, or they can be constrained by analysis of feedbacks in climate models, patterns of mean climate and variability in models compared to observations, temperature fluctua- tions as reconstructed from paleoclimate archives, observed and modelled short term perturbations of the energy balance like those caused by volcanic eruptions, and the observed surface and ocean temperature trends since pre- industrial. For many applications, the limitations of the forcing-feedback analysis framework and the dependence of feedbacks on time scales and the climate state must be kept in mind. {5.3.1, 5.3.3, 9.7.1 9.7.3, 10.8.1, 10.8.2, 12.5.3; Box 5.2; Table 9.5} (continued on next page) 82 Technical Summary TFE.6 (continued) Newer studies of constraints on ECS are based on the observed warming since pre-industrial, analysed using simple and intermediate complexity models, improved statistical methods and several different and newer data sets. Together with paleoclimate constraints but without considering the CMIP based evidence these studies show ECS is likely between 1.5°C to 4.5°C (medium confidence) and extremely unlikely less than 1.0°C. {5.3.1, 5.3.3, 10.8.2; Boxes 5.2, 12.2} Estimates based on Atmosphere Ocean General Circulation Models (AOGCMs) and feedback analysis indicate a range of 2°C to 4.5°C, with the Coupled Model Intercomparison Project Phase 5 (CMIP5) model mean at 3.2°C, similar to CMIP3. High climate sensitivities are found in some perturbed parameter ensembles models, but recent comparisons of perturbed-physics ensembles against the observed climate find that models with ECS values in the range 3°C to 4°C show the smallest errors for many fields. Relationships between climatological quantities and cli- mate sensitivity are often found within a specific perturbed parameter ensemble model but in many cases the rela- tionship is not robust across perturbed parameter ensembles models from different models or in CMIP3 and CMIP5. TS The assessed literature suggests that the range of climate sensitivities and transient responses covered by CMIP3 and CMIP5 cannot be narrowed significantly by constraining the models with observations of the mean climate and variability. Studies based on perturbed parameter ensembles models and CMIP3 support the conclusion that a credible representation of the mean climate and variability is very difficult to achieve with ECSs below Instrumental 2°C. {9.2.2, 9.7.3; Box 12.2} New estimates of ECS based on reconstructions and simulations of the Last Glacial Maximum (21 ka to 19 ka) show that values below 1°C as well as above 6°C are very unlikely. In some models climate sensitivity differs between warm and cold climates because of differenc- es in the representation of cloud feedbacks. Estimates of an Earth system sensitivity including slow feedbacks (e.g., ice sheets or vegetation) are even more difficult to Climatological constraints relate to climate sensitivity of the current climate state. The main limitations of ECS estimates from paleoclimate states are uncertainties in proxy data, spatial coverage Raw model range of the data, uncertainties in some forcings, and struc- tural limitations in models used in model data compari- sons. {5.3, 10.8.2, 12.5.3} CMIP3 AOGCMs CMIP5 AOGCMs Bayesian methods to constrain ECS or TCR are sensitive Palaeoclimate to the assumed prior distributions. They can in principle yield narrower estimates by combining constraints from the observed warming trend, volcanic eruptions, model climatology and paleoclimate, and that has been done in some studies, but there is no consensus on how this should be done robustly. This approach is sensitive to Combination the assumptions regarding the independence of the var- ious lines of evidence, the possibility of shared biases in models or feedback estimates and the assumption that 0 1 2 3 4 5 6 7 8 9 10 each individual line of evidence is unbiased. The combi- Equilibrium Climate Sensitivity (°C) nation of different estimates in this assessment is based on expert judgement. {10.8.2; Box 12.2} TFE.6, Figure 1 | Probability density functions, distributions and ranges for equi- librium climate sensitivity, based on Figure 10.20b plus climatological constraints Based on the combined evidence from observed climate shown in IPCC AR4 (Box AR4 10.2 Figure 1), and results from CMIP5 (Table 9.5). change including the observed 20th century warming, The grey shaded range marks the likely 1.5°C to 4.5°C range, grey solid line the climate models, feedback analysis and paleoclimate, as extremely unlikely less than 1°C, the grey dashed line the very unlikely greater discussed above, ECS is likely in the range 1.5°C to 4.5°C than 6°C. See Figure 10.20b and Chapter 10 Supplementary Material for full with high confidence. ECS is positive, extremely unlikely caption and details. {Box 12.2, Figure 1} (continued on next page) 83 Technical Summary TFE.6 (continued) less than 1°C (high confidence), and very unlikely great- er than 6°C (medium confidence). The tails of the ECS distribution are now better understood. Multiple lines of evidence provide high confidence that an ECS value less than 1°C is extremely unlikely. The upper limit of the likely range is unchanged compared to AR4. The lower limit of the likely range of 1.5°C is less than the lower limit of 2°C in AR4. This change reflects the evidence Probability / Relative Frequency (°C ) 1 from new studies of observed temperature change, using the extended records in atmosphere and ocean. These studies suggest a best fit to the observed surface and ocean warming for ECS values in the lower part of TS the likely range. Note that these studies are not purely observational, because they require an estimate of the response to RF from models. In addition, the uncertainty 1.5 in ocean heat uptake remains substantial. Accounting for short-term variability in simple models remains chal- lenging, and it is important not to give undue weight to any short time period which might be strongly affect- 1 ed by internal variability. On the other hand, AOGCMs with observed climatology with ECS values in the upper Black histogram part of the 1.5 to 4.5°C range show very good agree- CMIP5 models 0.5 Dashed lines ment with observed climatology, but the simulation of AR4 studies key feedbacks like clouds remains challenging in those models. The estimates from the observed warming, paleoclimate, and from climate models are consistent 0 within their uncertainties, each is supported by many 0 1 2 3 4 studies and multiple data sets, and in combination they Transient Climate Response (°C) provide high confidence for the assessed likely range. TFE.6, Figure 2 | Probability density functions, distributions and ranges (5 to Even though this assessed range is similar to previous 95%) for the transient climate response from different studies, based on Figure reports, confidence today is much higher as a result 10.20a, and results from CMIP5 (black histogram, Table 9.5). The grey shaded of high quality and longer observational records with range marks the likely 1°C to 2.5°C range, the grey solid line marks the extremely a clearer anthropogenic signal, better process under- unlikely greater than 3°C. See Figure 10.20a and Chapter 10 Supplementary Material for full caption and details. {Box 12.2, Figure 2} standing, more and better understood evidence from paleoclimate reconstructions, and better climate models with higher resolution that capture many more processes more realistically. All these lines of evidence individually support the assessed likely range of 1.5°C to 4.5°C. {3.2, 9.7.3, 10.8; Boxes 9.2, 13.1} On time scales of many centuries and longer, additional feedbacks with their own intrinsic time scales (e.g., vegeta- tion, ice sheets) may become important but are not usually modelled in AOGCMs. The resulting equilibrium tem- perature response to a doubling of CO2 on millennial time scales or Earth system sensitivity is less well constrained but likely to be larger than ECS, implying that lower atmospheric CO2 concentrations are compatible with limiting warming to below a given temperature level. These slow feedbacks are less likely to be proportional to global mean temperature change, implying that Earth system sensitivity changes over time. Estimates of Earth system sensitivity are also difficult to relate to climate sensitivity of the current climate state. {5.3.3, 10.8.2, 12.5.3} For scenarios of increasing RF, TCR is a more informative indicator of future climate change than ECS. This assess- ment concludes with high confidence that the TCR is likely in the range 1°C to 2.5°C, close to the estimated 5 to 95% range of CMIP5 (1.2°C to 2.4°C), is positive and extremely unlikely greater than 3°C. As with the ECS, this is an expert-assessed range, supported by several different and partly independent lines of evidence, each based on multiple studies, models and data sets. TCR is estimated from the observed global changes in surface temperature, ocean heat uptake and RF including detection/attribution studies identifying the response patterns to increasing GHG concentrations, and the results of CMIP3 and CMIP5. Estimating TCR suffers from fewer difficulties in terms of state- or time-dependent feedbacks, and is less affected by uncertainty as to how much energy is taken up by the (continued on next page) 84 Technical Summary TFE.6 (continued) ocean. Unlike ECS, the ranges of TCR estimated from the observed warming and from AOGCMs agree well, increas- ing our confidence in the assessment of uncertainties in projections over the 21st century. The assessed ranges of ECS and TCR are largely consistent with the observed warming, the estimated forcing and the projected future warming. In contrast to AR4, no best estimate for ECS is given because of a lack of agreement on the best estimate across lines of evidence and studies and an improved understanding of the uncertainties in estimates based on the observed warming. Climate models with ECS values in the upper part of the likely range show very good agreement with observed climatology, whereas estimates derived from observed climate change tend to best fit the observed surface and ocean warming for ECS values in the lower part of the likely range. In esti- mates based on the observed warming the most likely value is sensitive to observational and model uncertainties, internal climate variability and to assumptions about the prior distribution of ECS. In addition, best estimate and most likely value are defined in various ways in different studies. {9.7.1, 10.8.1, 12.5.3; Table 9.5} TS TS.5.4 Near-term Climate Change when verified against observations over large regions of the planet and of the global mean. Observation-based initialization of the fore- Near-term decadal climate prediction provides information not avail- casts contributes to the skill of predictions of annual mean tempera- able from existing seasonal to interannual (months to a year or two) ture for the first couple of years and to the skill of predictions of the predictions or from long-term (mid 21st century and beyond) climate GMST and the temperature over the North Atlantic, regions of the change projections. Prediction efforts on seasonal to interannual time South Pacific and the tropical Indian Ocean up to 10 years (high confi- scales require accurate estimates of the initial climate state with less dence) partly due to a correction of the forced response. Probabilistic focus extended to changes in external forcing12, whereas long-term temperature predictions are statistically reliable (see Section 11.2.3 for climate projections rely more heavily on estimations of external forcing definition of reliability) owing to the correct representation of global with little reliance on the initial state of internal variability. Estimates trends, but still unreliable at the regional scale when probabilities are of near-term climate depend on the committed warming (caused by computed from the multi-model ensemble. Predictions initialized over the inertia of the oceans as they respond to historical external forcing) 2000 2005 improve estimates of the recent global mean temperature the time evolution of internally generated climate variability, and the hiatus. Predictions of precipitation over continental areas with large future path of external forcing. Near-term predictions out to about a forced trends also exhibit positive skill. {11.2.2, 11.2.3; Box 9.2} decade (Figure TS.13) depend more heavily on an accurate depiction of the internally generated climate variability. {11.1, 12, 14} TS.5.4.1 Projected Near-term Changes in Climate Further near-term warming from past emissions is unavoidable owing Projections of near-term climate show small sensitivity to GHG sce- to thermal inertia of the oceans. This warming will be increased by narios compared to model spread, but substantial sensitivity to uncer- ongoing emissions of GHGs over the near term, and the climate tainties in aerosol emissions, especially on regional scales and for observed in the near term will also be strongly influenced by the inter- hydrological cycle variables. In some regions, the local and regional nally generated variability of the climate system. Previous IPCC Assess- responses in precipitation and in mean and extreme temperature to ments only described climate-change projections wherein the exter- land use change will be larger than those due to large-scale GHGs and nally forced component of future climate was included but no attempt aerosol forcing. These scenarios presume that there are no major vol- was made to initialize the internally generated climate ­ariability. v canic eruptions and that anthropogenic aerosol emissions are rapidly Decadal climate predictions, on the other hand, are intended to pre- reduced during the near term. {11.3.1, 11.3.2, 11.3.6} dict both the externally forced component of future climate change, and the internally generated component. Near-term predictions do not TS.5.4.2 Projected Near-term Changes in Temperature provide detailed information of the evolution of weather. Instead they can provide estimated changes in the time evolution of the statistics of In the absence of major volcanic eruptions which would cause sig- near-term climate. {11.1, 11.2.2; Box 11.1; FAQ 11.1} nificant but temporary cooling and, assuming no significant future long-term changes in solar irradiance, it is likely that the GMST Retrospective prediction experiments have been used to assess fore- anomaly for the period 2016 2035, relative to the reference period of cast quality. There is high confidence that the retrospective prediction 1986 2005 will be in the range 0.3°C to 0.7°C (medium confidence). experiments for forecast periods of up to 10 years exhibit positive skill This is based on multiple lines of evidence. This range is consistent 12 Seasonal-to-interannual predictions typically include the impact of external forcing. 85 Technical Summary with the range obtained by using CMIP5 5 to 95% model trends for Higher concentrations of GHGs and lower amounts of sulphate aero- 2012 2035. It is also consistent with the CMIP5 5 to 95% range for sol lead to greater warming. In the near-term, differences in global all four RCP ­ cenarios of 0.36°C to 0.79°C, using the 2006 2012 refer- s mean surface air temperature across RCP scenarios for a single climate ence period, after the upper and lower bounds are reduced by 10% to model are typically smaller than across climate models for a single take into account the evidence that some models may be too sensitive RCP scenario. In 2030, the CMIP5 ensemble median values for global to anthropogenic forcing (see Table TS.1 and Figure TS.14). {11.3.6} mean temperature differ by at most 0.2°C between the RCP scenarios, whereas the model spread (defined as the 17 to 83% range ) for each RCP is around 0.4°C. The inter-scenario spread increases in time and Global mean surface temperature change Atlantic multidecadal variability by 2050 is comparable to the model spread. Regionally, the largest dif- ferences in surface air temperature between RCP scenarios are found 0.2 0.4 in the Arctic. {11.3.2. 11.3.6} (C) 0.0 0.0 The projected warming of global mean temperatures implies high confidence that new levels of warming relative to 1850-1900 mean -0.2 -0.4 1960 1970 1980 1990 2000 2010 1960 1970 1980 1990 2000 2010 climate will be crossed, particularly under higher GHG emissions sce- TS Year Year narios. Relative to a reference period of 1850 1900, under RCP4.5 or 0.9 RCP6.0, it is more likely than not that the mean GMST for the period 0.9 2016 2035 will be more than 1°C above the mean for 1850 1900, Correlation 0.6 and very unlikely that it will be more than 1.5°C above the 1850 1900 0.3 mean (medium confidence). {11.3.6} 0.6 0.0 1-4 2-5 3-8 4-7 5-8 6-9 1-4 2-5 3-8 4-7 5-8 6-9 Forecast time (yr) Forecast time (yr) A future volcanic eruption similar in size to the 1991 eruption of Mt Pinatubo would cause a rapid drop in global mean surface air tem- 0.15 0.15 perature of about 0.5°C in the following year, with recovery over the 0.05 0.10 0.10 next few years. Larger eruptions, or several eruptions occurring close rmse (C) together in time, would lead to larger and more persistent effects. 0.05 {11.3.6} 0.00 0.00 1-4 2-5 3-8 4-7 5-8 6-9 1-4 2-5 3-8 4-7 5-8 6-9 Forecast time (yr) Forecast time (yr) Possible future changes in solar irradiance could influence the rate at CMIP5 Init CMIP5 NoInit which GMST increases, but there is high confidence that this influence will be small in comparison to the influence of increasing concentra- Figure TS.13 | Decadal prediction forecast quality of several climate indices. (Top row) tions of GHGs in the atmosphere. {11.3.6} Time series of the 2- to 5-year average ensemble mean initialized hindcast anoma- lies and the corresponding non-initialized experiments for three climate indices: global mean surface temperature (GMST, left) and the Atlantic Multi-decadal Variability (AMV, The spatial patterns of near-term warming projected by the CMIP5 right). The observational time series, Goddard Institute of Space Studies Goddard Insti- models following the RCP scenarios (Figure TS.15) are broadly con- tute for Space Studies Surface Temperature Analysis (GISTEMP) global mean tempera- sistent with the AR4. It is very likely that anthropogenic warming of ture and Extended Reconstructed Sea Surface Tempearture (ERSST) for the AMV, are surface air temperature over the next few decades will proceed more represented with dark grey (positive anomalies) and light grey (negative anomalies) vertical bars, where a 4-year running mean has been applied for consistency with the rapidly over land areas than over oceans, and it is very likely that time averaging of the predictions. Predicted time series are shown for the CMIP5 Init the anthropogenic warming over the Arctic in winter will be greater (solid) and NoInit (dotted) simulations with hindcasts started every 5 years over the than the global mean warming, consistent with the AR4. Relative to period 1960 2005. The lower and upper quartile of the multi-model ensemble are plot- background levels of internally generated variability there is high ted using thin lines. The AMV index was computed as the sea surface temperature (SST) confidence that the anthropogenic warming relative to the reference anomalies averaged over the region Equator to 60N and 80W to 0W minus the SST anomalies averaged over 60S to 60N. Note that the vertical axes are different for each period is expected to be larger in the tropics and subtropics than in time series. (Middle row) Correlation of the ensemble mean prediction with the observa- mid-latitudes. {11.3.2} tional reference along the forecast time for 4-year averages of the three sets of CMIP5 hindcasts for Init (solid) and NoInit (dashed). The one-sided 95% confidence level with It is likely that in the next decades the frequency of warm days and a t distribution is represented in grey. The effective sample size has been computed warm nights will increase in most land regions, while the frequency of taking into account the autocorrelation of the observational time series. A two-sided t test (where the effective sample size has been computed taking into account the cold days and cold nights will decrease. Models also project increases autocorrelation of the observational time series) has been used to test the differences in the duration, intensity and spatial extent of heat waves and warm between the correlation of the initialized and non-initialized experiments, but no differ- spells for the near term. These changes may proceed at a different ences were found statistically significant with a confidence equal or higher than 90%. rate than the mean warming. For example, several studies project that (Bottom row) Root mean square error (RMSE) of the ensemble mean prediction along European high-percentile summer temperatures are projected to warm the forecast time for 4-year averages of the CMIP5 hindcasts for Init (solid) and NoInit (dashed). A two-sided F test (where the effective sample size has been computed taking faster than mean temperatures (see also TFE.9). {11.3.2} into account the autocorrelation of the observational time series) has been used to test the ratio between the RMSE of the Init and NoInit, and those forecast times with differ- ences statistically significant with a confidence equal or higher than 90% are indicated with an open square. {Figure 11.3} 86 Technical Summary Global mean temperature near term projections relative to 1986 2005 2.5 Observations (4 datasets) (a) Temperature anomaly (°C) Historical (42 models) 2 RCP 2.6 (32 models) RCP 4.5 (42 models) 1.5 RCP 6.0 (25 models) RCP 8.5 (39 models) 1 0.5 0 0.5 Historical RCPs 1990 2000 2010 2020 2030 2040 2050 TS 2.5 Indicative likely range for annual means (b) 3 Temperature anomaly (°C) ALL RCPs (5 95% range, two reference periods) 2 Relative to 1850 1900 ALL RCPs min max (299 ensemble members) Observational uncertainty (HadCRUT4) 1.5 Observations (4 datasets) 2 ALL RCPs Assessed likely range 1 for 2016 2035 mean 0.5 1 0 Assuming no future large volcanic eruptions 0.5 Historical RCPs 0 1990 2000 2010 2020 2030 2040 2050 1.5 Projections of 2016 2035 mean (c) Temperature anomaly (°C) 1 Using trends 0.5 B1 A1B A2 A1B 4.5 4.5 8.5 2.6 4.5 6.0 8.5 ALL ALL SRES CMIP3 Obs. Constrained RCPs CMIP5 Assessed 0 Key: 5% 17 83% 95% Figure TS.14 | Synthesis of near-term projections of global mean surface air temperature (GMST). (a) Projections of annual mean GMST 1986 2050 (anomalies relative to 1986 2005) under all RCPs from CMIP5 models (grey and coloured lines, one ensemble member per model), with four observational estimates (Hadley Centre/Climatic Research Unit gridded surface temperature data set 4 (HadCRUT4), European Centre for Medium Range Weather Forecasts (ECMWF) interim re-analysis of the global atmosphere and surface conditions (ERA-Interim), Goddard Institute for Space Studies Surface Temperature Analysis (GISTEMP), National Oceanic and Atmospheric Administration (NOAA)) for the period 1986 2012 (black lines). (b) As (a) but showing the 5 to 95% range of annual mean CMIP5 projections (using one ensemble member per model) for all RCPs using a reference period of 1986 2005 (light grey shade) and all RCPs using a reference period of 2006 2012, together with the observed anomaly for (2006 2012) minus (1986 2005) of 0.16°C (dark grey shade). The percentiles for 2006 onwards have been smoothed with a 5-year running mean for clarity. The maximum and minimum values from CMIP5 using all ensemble members and the 1986 2005 reference period are shown by the grey lines (also smoothed). Black lines show annual mean observational estimates. The red shaded region shows the indicative likely range for annual mean GMST during the period 2016 2035 based on the ALL RCPs Assessed likely range for the 20-year mean GMST anomaly for 2016 2035, which is shown as a black bar in both (b) and (c) (see text for details). The temperature scale relative to 1850-1900 mean climate on the right-hand side assumes a warming of GMST prior to 1986 2005 of 0.61°C estimated from HadCRUT4. (c) A synthesis of projections for the mean GMST anomaly for 2016 2035 relative to 1986 2005. The box and whiskers represent the 66% and 90% ranges. Shown are: unconstrained SRES CMIP3 and RCP CMIP5 projections; observationally constrained projections for the SRES A1B and, the RCP4.5 and 8.5 scenarios; unconstrained projections for all four RCP scenarios using two reference periods as in (b) (light grey and dark grey shades), consistent with (b); 90% range estimated using CMIP5 trends for the period 2012 2035 and the observed GMST anomaly for 2012; an overall likely (>66%) assessed range for all RCP scenarios. The dots for the CMIP5 estimates show the maximum and minimum values using all ensemble members. The medians (or maximum likelihood estimate; green filled bar) are indicated by a grey band. (Adapted from Figure 11.25.) See Section 11.3.6 for details. {Figure 11.25} 87 Technical Summary TS.5.4.3 Projected Near-term Changes in the Water Cycle It is likely that salinity will increase in the tropical and (especially) sub- tropical Atlantic, and decrease in the western tropical Pacific over the Zonal mean precipitation will very likely increase in high and some of next few decades. Overall, it is likely that there will be some decline the mid latitudes, and will more likely than not decrease in the subtrop- in the Atlantic Meridional Overturning Circulation by 2050 (medium ics. At more regional scales precipitation changes may be dominated confidence). However, the rate and magnitude of weakening is very by a combination of natural internal variability, volcanic forcing and uncertain and decades when this circulation increases are also to be anthropogenic aerosol effects. {11.3.2} expected. {11.3.3} Over the next few decades increases in near-surface specific humidity TS.5.4.6 Projected Near-term Changes in the Cryosphere are very likely. It is likely that there will be increases in evaporation in many regions. There is low confidence in projected changes in soil A nearly ice-free Arctic Ocean (sea ice extent less than 106 km2 for at moisture and surface runoff. {11.3.2} least five consecutive years) in September is likely before mid-century under RCP8.5 (medium confidence). This assessment is based on a In the near term, it is likely that the frequency and intensity of heavy subset of models that most closely reproduce the climatological mean precipitation events will increase over land. These changes are primar- state and 1979 to 2012 trend of Arctic sea ice cover. It is very likely that TS ily driven by increases in atmospheric water vapour content, but also there will be further shrinking and thinning of Arctic sea ice cover, and affected by changes in atmospheric circulation. The impact of anthro- decreases of northern high-latitude spring time snow cover and near pogenic forcing at regional scales is less obvious, as regional-scale surface permafrost as GMST rises (Figures TS.17 and TS.18). There is changes are strongly affected by natural variability and also depend on low confidence in projected near-term decreases in the Antarctic sea the course of future aerosol emissions, volcanic forcing and land use ice extent and volume. {11.3.4} changes (see also TFE.9). {11.3.2} TS.5.4.7 Possibility of Near-term Abrupt Changes in Climate TS.5.4.4 Projected Near-term Changes in Atmospheric Circulation There are various mechanisms that could lead to changes in global or Internally generated climate variability and multiple RF agents (e.g., regional climate that are abrupt by comparison with rates experienced volcanoes, GHGs, ozone and anthropogenic aerosols) will all contrib- in recent decades. The likelihood of such changes is generally lower ute to near-term changes in the atmospheric circulation. For example, for the near term than for the long term. For this reason the relevant it is likely that the annual mean Hadley Circulation and the SH mid-lat- mechanisms are primarily assessed in the TS.5 sections on long-term itude westerlies will shift poleward, while it is likely that the projected changes and in TFE.5. {11.3.4} recovery of stratospheric ozone and increases in GHG concentrations will have counteracting impacts on the width of the Hadley Circula- TS.5.4.8 Projected Near-term Changes in Air Quality tion and the meridional position of the SH storm track. Therefore it is unlikely that they will continue to expand poleward as rapidly as in The range in projections of air quality (O3 and PM2.5 in surface air) is recent decades. {11.3.2} driven primarily by emissions (including CH4), rather than by physi- cal climate change (medium confidence). The response of air qual- There is low confidence in near-term projections of the position and ity to climate-driven changes is more uncertain than the response strength of NH storm tracks. Natural variations are larger than the pro- to emission-driven changes (high confidence). Globally, warming jected impact of GHGs in the near term. {11.3.2} decreases background surface O3 (high confidence). High CH4 levels (such as RCP8.5 and SRES A2) can offset this decrease, raising 2100 There is low confidence in basin-scale projections of changes in inten- background surface O3 on average by about 8 ppb (25% of current sity and frequency of tropical cyclones in all basins to the mid-21st levels) relative to scenarios with small CH4 changes (such as RCP4.5 century. This low confidence reflects the small number of studies and RCP6.0) (high confidence). On a continental scale, projected air exploring near-term tropical cyclone activity, the differences across pollution levels are lower under the new RCP scenarios than under the published projections of tropical cyclone activity, and the large role for SRES scenarios because the SRES did not incorporate air quality legis- natural variability. There is low confidence in near-term projections for lation (high confidence). {11.3.5, 11.3.5.2; Figures 11.22 and 11.23ab, increased tropical cyclone intensity in the Atlantic; this projection is in AII.4.2, AII.7.1 AII.7.4} part due to projected reductions in aerosol loading. {11.3.2} Observational and modelling evidence indicates that, all else being TS.5.4.5 Projected Near-term Changes in the Ocean equal, locally higher surface temperatures in polluted regions will trigger regional feedbacks in chemistry and local emissions that will It is very likely that globally averaged surface and vertically averaged increase peak levels of O3 and PM2.5 (medium confidence). Local emis- ocean temperatures will increase in the near-term. In the absence of sions combined with background levels and with meteorological con- multiple major volcanic eruptions, it is very likely that globally aver- ditions conducive to the formation and accumulation of pollution are aged surface and depth-averaged temperatures averaged for 2016 known to produce extreme pollution episodes on local and regional 2035 will be warmer than those averaged over 1986 2005. {11.3.3} scales. There is low confidence in projecting changes in meteorologi- cal blocking associated with these extreme episodes. For PM2.5, cli- mate change may alter natural aerosol sources (wildfires, wind-lofted 88 Technical Summary dust, biogenic precursors) as well as precipitation scavenging, but no The 5 to 95% range of CMIP5 for global mean temperature change c ­ onfidence level is attached to the overall impact of climate change on is also assessed as likely for mid-21st century, but only with medium PM2.5 distributions. {11.3.5, 11.3.5.2; Box 14.2} confidence. With respect to 1850 1900 mean conditions, global t ­ emperatures averaged in the period 2081 2100 are projected to likely TS.5.5 Long-term Climate Change exceed 1.5°C above 1850 1900 values for RCP4.5, RCP6.0 and RCP8.5 (high confidence) and are likely to exceed 2°C above 1850 1900 TS.5.5.1 Projected Long-term Changes in Global Temperature values for RCP6.0 and RCP8.5 (high confidence). Temperature change above 2°C relative to 1850 1900 under RCP2.6 is unlikely (medium Global mean temperatures will continue to rise over the 21st century confidence). Warming above 4°C by 2081 2100 is unlikely in all RCPs under all of the RCPs. From around the mid-21st century, the rate of (high confidence) except for RCP8.5, where it is about as likely as not global warming begins to be more strongly dependent on the scenario (medium confidence). {12.4.1; Tables 12.2, 12.3} (Figure TS.15). {12.4.1} TS.5.5.2 Projected Long-term Changes in Regional Temperature Under the assumptions of the concentration-driven RCPs, GMSTs for 2081 2100, relative to 1986 2005 will likely be in the 5 to 95% range There is very high confidence that globally averaged changes over land TS of the CMIP5 models; 0.3°C to 1.7°C (RCP2.6), 1.1 to 2.6°C (RCP4.5), will exceed changes over the ocean at the end of the 21st century by 1.4°C to 3.1°C (RCP6.0), 2.6°C to 4.8°C (RCP8.5) (see Table TS.1). With a factor that is likely in the range 1.4 to 1.7. In the absence of a strong high confidence, the 5 to 95% range of CMIP5 is assessed as likely reduction in the Atlantic Meridional Overturning, the Arctic region rather than very likely based on the assessment of TCR (see TFE.6). is projected to warm most (very high confidence) (Figure TS.15). As 12 17 39 12 25 42 32 42 models Figure TS.15 | (Top left) Total global mean radiative forcing for the four RCP scenarios based on the Model for the Assessment of Greenhouse-gas Induced Climate Change (MAGICC) energy balance model. Note that the actual forcing simulated by the CMIP5 models differs slightly between models. (Bottom left) Time series of global annual mean surface air temperature anomalies (relative to 1986 2005) from CMIP5 concentration-driven experiments. Projections are shown for each RCP for the multi-model mean (solid lines) and +/-1.64 standard deviation (5 to 95%) across the distribution of individual models (shading), based on annual means. The 1.64 standard deviation range based on the 20 yr averages 2081 2100, relative to 1986 2005, are interpreted as likely changes for the end of the 21st century. Discontinuities at 2100 are due to different numbers of models performing the extension runs beyond the 21st century and have no physical meaning. Numbers in the same colours as the lines indicate the number of different models contribut- ing to the different time periods. Maps: Multi-model ensemble average of annual mean surface air temperature change (compared to 1986 2005 base period) for 2016 2035 and 2081 2100, for RCP2.6, 4.5, 6.0 and 8.5. Hatching indicates regions where the multi-model mean signal is less than one standard deviation of internal variability. Stippling indicates regions where the multi-model mean signal is greater than two standard deviations of internal variability and where 90% of the models agree on the sign of change. The number of CMIP5 models used is indicated in the upper right corner of each panel. Further detail regarding the related Figures SPM.7a and SPM.8.a is given in the TS Supplementary Material. {Box 12.1; Figures 12.4, 12.5, 12.11; Annex I} 89 Technical Summary Table TS.1 | Projected change in global mean surface air temperature and global mean sea level rise for the mid- and late 21st century relative to the reference period of 1986 2005. {12.4.1; Tables 12.2,13.5} 2046 2065 2081 2100 Scenario Mean Likely rangec Mean Likely rangec RCP2.6 1.0 0.4 to 1.6 1.0 0.3 to 1.7 Global Mean Surface RCP4.5 1.4 0.9 to 2.0 1.8 1.1 to 2.6 Temperature Change (°C) a RCP6.0 1.3 0.8 to 1.8 2.2 1.4 to 3.1 RCP8.5 2.0 1.4 to 2.6 3.7 2.6 to 4.8 Scenario Mean Likely range d Mean Likely ranged RCP2.6 0.24 0.17 to 0.32 0.40 0.26 to 0.55 Global Mean Sea Level RCP4.5 0.26 0.19 to 0.33 0.47 0.32 to 0.63 Rise (m)b RCP6.0 0.25 0.18 to 0.32 0.48 0.33 to 0.63 TS RCP8.5 0.30 0.22 to 0.38 0.63 0.45 to 0.82 Notes: a Based on the CMIP5 ensemble; anomalies calculated with respect to 1986 2005. Using HadCRUT4 and its uncertainty estimate (5 95% confidence interval), the observed warming to the reference period 1986 2005 is 0.61 [0.55 to 0.67] °C from 1850 1900, and 0.11 [0.09 to 0.13] °C from 1980 1999, the reference period for projections used in AR4. Likely ranges have not been assessed here with respect to earlier reference periods because methods are not generally available in the literature for combining the uncertainties in models and observations. Adding projected and observed changes does not account for potential effects of model biases compared to observations, and for natural internal variability during the observational reference period. {2.4; 11.2; Tables 12.2 and 12.3} b Based on 21 CMIP5 models; anomalies calculated with respect to 1986 2005. Where CMIP5 results were not available for a particular AOGCM and scenario, they were estimated as explained in Chapter 13, Table 13.5. The contributions from ice sheet rapid dynamical change and anthropogenic land water storage are treated as having uniform probability distributions, and as largely independent of scenario. This treatment does not imply that the contributions concerned will not depend on the scenario followed, only that the current state of knowledge does not permit a quantitative assessment of the dependence. Based on current understanding, only the collapse of marine-based sectors of the Antarctic ice sheet, if initiated, could cause global mean sea level to rise substantially above the likely range during the 21st century. There is medium confidence that this additional contribution would not exceed several tenths of a metre of sea level rise during the 21st century. c Calculated from projections as 5 95% model ranges. These ranges are then assessed to be likely ranges after accounting for additional uncertainties or different levels of confidence in models. For projections of global mean surface temperature change in 2046 2065 confidence is medium, because the relative importance of natural internal variability, and uncertainty in non-greenhouse gas forcing and response, are larger than for 2081 2100. The likely ranges for 2046 2065 do not take into account the possible influence of factors that lead to the assessed range for near-term (2016 2035) global mean surface temperature change that is lower than the 5 95% model range, because the influence of these factors on longer term projections has not been quantified due to insufficient scientific understanding. {11.3} d Calculated from projections as 5 95% model ranges. These ranges are then assessed to be likely ranges after accounting for additional uncertainties or different levels of confidence in models. For projections of global mean sea level rise confidence is medium for both time horizons. GMST rises, the pattern of atmospheric zonal mean temperatures show Models simulate a decrease in cloud amount in the future over most of warming throughout the troposphere and cooling in the stratosphere, the tropics and mid-latitudes, due mostly to reductions in low clouds. consistent with previous assessments. The consistency is especially Changes in marine boundary layer clouds are most uncertain. Increases clear in the tropical upper troposphere and the northern high latitudes. in cloud fraction and cloud optical depth and therefore cloud reflection {12.4.3; Box 5.1} are simulated in high latitudes, poleward of 50°. {12.4.3} It is virtually certain that, in most places, there will be more hot TS.5.5.3 Projected Long-term Changes in Atmospheric Circulation and fewer cold temperature extremes as global mean temperatures increase. These changes are expected for events defined as extremes Mean sea level pressure is projected to decrease in high latitudes and on both daily and seasonal time scales. Increases in the frequency, increase in the mid-latitudes as global temperatures rise. In the trop- duration and magnitude of hot extremes along with heat stress are ics, the Hadley and Walker Circulations are likely to slow down. Pole- expected; however, occasional cold winter extremes will continue to ward shifts in the mid-latitude jets of about 1 to 2 degrees latitude occur. Twenty-year return values of low-temperature events are pro- are likely at the end of the 21st century under RCP8.5 in both hemi- jected to increase at a rate greater than winter mean temperatures spheres (medium confidence), with weaker shifts in the NH. In austral in most regions, with the largest changes in the return values of low summer, the additional influence of stratospheric ozone recovery in temperatures at high latitudes. Twenty-year return values for high- the SH opposes changes due to GHGs there, though the net response temperature events are projected to increase at a rate similar to or varies strongly across models and scenarios. Substantial uncertainty greater than the rate of increase of summer mean temperatures in and thus low confidence remains in projecting changes in NH storm most regions. Under RCP8.5 it is likely that, in most land regions, a cur- tracks, especially for the North Atlantic basin. The Hadley Cell is likely rent 20-year high-temperature event will occur more frequently by the to widen, which translates to broader tropical regions and a pole- end of the 21st century (at least doubling its frequency, but in many ward encroachment of subtropical dry zones. In the stratosphere, the regions becoming an annual or 2-year event) and a current 20-year Brewer Dobson circulation is likely to strengthen. {12.4.4} low-temperature event will become exceedingly rare (See also TFE.9). {12.4.3} 90 Technical Summary TS.5.5.4 Projected Long-term Changes in the Water Cycle century under the RCP8.5 scenario. Many mid-latitude and subtropical arid and semi-arid regions will likely experience less precipitation and On the planetary scale, relative humidity is projected to remain roughly many moist mid-latitude regions will likely experience more precipita- constant, but specific humidity to increase in a warming climate. The tion by the end of this century under the RCP8.5 scenario. Maps of projected differential warming of land and ocean promotes changes in precipitation change for the four RCP scenarios are shown in Figure atmospheric moistening that lead to small decreases in near-surface TS.16. {12.4.2, 12.4.5} relative humidity over most land areas with the notable exception of parts of tropical Africa (medium confidence) (see TFE.1, Figure 1). Globally, for short-duration precipitation events, a shift to more intense {12.4.5} individual storms and fewer weak storms is likely as temperatures increase. Over most of the mid-latitude land masses and over wet tropi- It is virtually certain that, in the long term, global precipitation will cal regions, extreme precipitation events will very likely be more intense increase with increased GMST. Global mean precipitation will increase and more frequent in a warmer world. The global average sensitivity at a rate per °C smaller than that of atmospheric water vapour. It will of the 20-year return value of the annual maximum daily precipitation likely increase by 1 to 3% °C 1 for scenarios other than RCP2.6. For ranges from 4% °C 1 of local temperature increase (average of CMIP3 RCP2.6 the range of sensitivities in the CMIP5 models is 0.5 to 4% °C 1 models) to 5.3% °C 1 of local temperature increase (average of CMIP5 TS at the end of the 21st century. {7.6.2, 7.6.3, 12.4.1} models), but regionally there are wide variations. {12.4.2, 12.4.5} Changes in average precipitation in a warmer world will exhibit sub- Annual surface evaporation is projected to increase as global tempera- stantial spatial variation under RCP8.5. Some regions will experience tures rise over most of the ocean and is projected to change over land increases, other regions will experience decreases and yet others will following a similar pattern as precipitation. Decreases in annual runoff not experience significant changes at all (see Figure TS.16). There are likely in parts of southern Europe, the Middle East and southern is high confidence that the contrast of annual mean precipitation Africa by the end of this century under the RCP8.5 scenario. Increases in between dry and wet regions and that the contrast between wet annual runoff are likely in the high northern latitudes corresponding to and dry seasons will increase over most of the globe as temperatures large increases in winter and spring precipitation by the end of the 21st increase. The general pattern of change indicates that high latitudes ­ century under the RCP8.5 scenario. Regional to global-scale projected are very likely to experience greater amounts of precipitation due to decreases in soil moisture and increased risk of agricultural drought the increased specific humidity of the warmer troposphere as well as are likely in presently dry regions and are projected with medium confi- increased transport of water vapour from the tropics by the end of this dence by the end of this century under the RCP8.5 scenario. Prominent Figure TS.16 | Maps of multi-model results for the scenarios RCP2.6, RCP4.5, RCP6.0 and RCP8.5 in 2081 2100 of average percent change in mean precipitation. Changes are shown relative to 1986 2005. The number of CMIP5 models to calculate the multi-model mean is indicated in the upper right corner of each panel. Hatching indicates regions where the multi- model mean signal is less than 1 standard deviation of internal variability. Stippling indicates regions where the multi- model mean signal is greater than 2 standard deviations of internal variability and where 90% of models agree on the sign of change (see Box 12.1). Further detail regarding the related Figure SPM.8b is given in the TS Supplementary Material. {Figure 12.22; Annex I} 91 Technical Summary areas of projected decreases in evaporation include southern Africa than observed over the last decade and it is likely that such instances and northwestern Africa along the Mediterranean. Soil moisture drying of rapid ice loss will occur in the future. There is little evidence in global in the Mediterranean and southern African regions is consistent with climate models of a tipping point (or critical threshold) in the transition projected changes in Hadley Circulation and increased surface tem- from a perennially ice-covered to a seasonally ice-free Arctic Ocean peratures, so surface drying in these regions as global temperatures beyond which further sea ice loss is unstoppable and irreversible. In increase is likely with high confidence by the end of this century under the Antarctic, the CMIP5 multi-model mean projects a decrease in sea the RCP8.5 scenario. In regions where surface moistening is projected, ice extent that ranges from 16% for RCP2.6 to 67% for RCP8.5 in changes are generally smaller than natural variability on the 20-year February and from 8% for RCP2.6 to 30% for RCP8.5 in September time scale. A summary of the projected changes in the water cycle from for 2081 2100 compared to 1986 2005. There is, however, low con- the CMIP5 models is shown in TFE.1, Figure 1. {12.4.5; Box 12.1} fidence in those projections because of the wide inter-model spread and the inability of almost all of the available models to reproduce the TS.5.5.5 Projected Long-term Changes in the Cryosphere overall increase of the Antarctic sea ice areal coverage observed during the satellite era. {12.4.6, 12.5.5} It is very likely that the Arctic sea ice cover will continue shrinking and thinning year-round in the course of the 21st century as GMST rises. It is very likely that NH snow cover will reduce as global temperatures TS At the same time, in the Antarctic, a decrease in sea ice extent and rise over the coming century. A retreat of permafrost extent with rising volume is expected, but with low confidence. The CMIP5 multi-model global temperatures is virtually certain. Snow cover changes result projections give average reductions in Arctic sea ice extent for 2081 from precipitation and ablation changes, which are sometimes oppo- 2100 compared to 1986 2005 ranging from 8% for RCP2.6 to 34% site. Projections of the NH spring snow covered area by the end of the for RCP8.5 in February and from 43% for RCP2.6 to 94% for RCP8.5 in 21st century vary between a decrease of 7 [3 to 10] % (RCP2.6) and 25 September (medium confidence) (Figure TS.17). A nearly ice-free Arctic [18 to 32] % (RCP8.5) (Figure TS.18), but confidence is those numbers Ocean (sea ice extent less than 106 km2 for at least five consecutive is only medium because snow processes in global climate models are years) in September before mid-century is likely under RCP8.5 (medium strongly simplified. The projected changes in permafrost are a response confidence), based on an assessment of a subset of models that most not only to warming, but also to changes in snow cover, which exerts a closely reproduce the climatological mean state and 1979 2012 trend control on the underlying soil. By the end of the 21st century, diagnosed of the Arctic sea ice cover. Some climate projections exhibit 5- to near-surface permafrost area is projected to decrease by between 37% 10-year periods of sharp summer Arctic sea ice decline even steeper (RCP2.6) to 81% (RCP8.5) (medium confidence). {12.4.6} NH September sea-ice extent 2081 2100 RCP2.6 RCP6.0 ) 29(3) 39(5) ( 21(2) RCP4.5 RCP8.5 observations historical RCP2.6 RCP4.5 ) RCP6.0 RCP8.5 39(5) 39(5) ( 37(5) Figure TS.17 | Northern Hemisphere (NH) sea ice extent in September over the late 20th century and the whole 21st century for the scenarios RCP2.6, RCP4.5, RCP6.0 and RCP8.5 in the CMIP5 models, and corresponding maps of multi-model results in 2081 2100 of NH September sea ice extent. In the time series, the number of CMIP5 models to calculate the multi-model mean is indicated (subset in brackets). Time series are given as 5-year running means. The projected mean sea ice extent of a subset of models that most closely reproduce the climatological mean state and 1979 2012 trend of the Arctic sea ice is given (solid lines), with the minimum to maximum range of the subset indicated with shading. Black (grey shading) is the modelled historical evolution using historical reconstructed forcings. The CMIP5 multi-model mean is indicated with dashed lines. In the maps, the CMIP5 multi-model mean is given in white and the results for the subset in grey. Filled areas mark the averages over the 2081 2100 period, lines mark the sea ice extent averaged over the 1986 2005 period. The observed sea ice extent is given in pink as a time series and averaged over 1986 2005 as a pink line in the map. Further detail regarding the related Figures SPM.7b and SPM.8c is given in the TS Supplementary Material. {Figures 12.18, 12.29, 12.31} 92 Technical Summary Snow cover extent change in higher concentration pathways. The future evolution of the land carbon uptake is much more uncertain. A majority of CMIP5 ESMs proj- ect a continued net carbon uptake by land ecosystems through 2100. Yet, a minority of models simulate a net CO2 source to the atmosphere by 2100 due to the combined effect of climate change and land use change. In view of the large spread of model results and incomplete (%) process representation, there is low confidence on the magnitude of modelled future land carbon changes. {6.4.3} There is high confidence that climate change will partially offset increases in global land and ocean carbon sinks caused by rising atmos- Near surface permafrost area pheric CO2. Yet, there are regional differences among CMIP5 ESMs in the response of ocean and land CO2 fluxes to climate. There is high agreement between models that tropical ecosystems will store less carbon in a warmer climate. There is medium agreement between the TS (106km2) CMIP5 ESMs that at high latitudes warming will increase land carbon storage, although none of these models accounts for decomposition of carbon in permafrost which may offset increased land carbon storage. There is high confidence that reductions in permafrost extent due to warming will cause thawing of some currently frozen carbon. However, there is low confidence on the magnitude of carbon losses through CO2 and CH4 emissions to the atmosphere with a range from 50 to 250 PgC between 2000 and 2100 for RCP8.5. {6.4.2, 6.4.3} Figure TS.18 | (Top) Northern Hemisphere (NH) spring (March to April average) rela- tive snow-covered area (RSCA) in CMIP5, obtained by dividing the simulated 5-year The loss of carbon from frozen soils constitutes a positive radiative box smoothed spring snow-covered area (SCA) by the simulated average spring SCA feedback that is missing in current coupled ESM projections. There is of 1986 2005 reference period. (Bottom) NH diagnosed near-surface permafrost area high agreement between CMIP5 ESMs that ocean warming and cir- in CMIP5, using 20-year average monthly surface air temperatures and snow depths. culation changes will reduce the rate of ocean carbon uptake in the Lines indicate the multi model average, shading indicates the inter-model spread (one Southern Ocean and North Atlantic, but that carbon uptake will never- standard deviation). {Figures 12.32, 12.33} theless persist in those regions. {6.4.2} TS.5.5.6 Projected Long-term Changes in the Ocean It is very likely, based on new experimental results and modelling, that nutrient shortage will limit the effect of rising atmospheric CO2 Over the course of the 21st century, the global ocean will warm in on future land carbon sinks for the four RCP scenarios. There is high all RCP scenarios. The strongest ocean warming is projected for the confidence that low nitrogen availability will limit carbon storage on surface in subtropical and tropical regions. At greater depth the land even when considering anthropogenic nitrogen deposition. The warming is projected to be most pronounced in the Southern Ocean. role of phosphorus limitation is more uncertain. {6.4.6} Best estimates of ocean warming in the top one hundred metres are about 0.6°C (RCP2.6) to 2.0°C (RCP8.5), and 0.3°C (RCP2.6) to 0.6°C For the ESMs simulations driven by CO2 concentrations, representation (RCP8.5) at a depth of about 1 km by the end of the 21st century. For of the land and ocean carbon cycle allows quantification of the fossil RCP4.5 by the end of the 21st century, half of the energy taken up by fuel emissions compatible with the RCP scenarios. Between 2012 and the ocean is in the uppermost 700 m, and 85% is in the uppermost 2100, ESM results imply cumulative compatible fossil fuel emissions of 2000 m. Due to the long time scales of this heat transfer from the 270 [140 to 410] PgC for RCP2.6, 780 [595 to 1005] PgC for RCP4.5, surface to depth, ocean warming will continue for centuries, even if 1060 [840 to 1250] PgC for RCP6.0 and 1685 [1415 to 1910] PgC for GHG emissions are decreased or concentrations kept constant, and will RCP8.5 (values quoted to nearest 5 PgC, range +/-1 standard devia- result in a continued contribution to sea level rise (see Section TS5.7). tion derived from CMIP5 model results) (Figure TS.19). For RCP2.6, the {12.4.3, 12.4.7} models project an average 50% (range 14 to 96%) emission reduction by 2050 relative to 1990 levels. By the end of the 21st century, about TS.5.6 Long-term Projections of Carbon and Other half of the models infer emissions slightly above zero, while the other Biogeochemical Cycles half infer a net removal of CO2 from the atmosphere (see also Box TS.7). {6.4.3; Table 6.12} Projections of the global carbon cycle to 2100 using the CMIP5 ESMs represent a wider range of complex interactions between the carbon When forced with RCP8.5 CO2 emissions, as opposed to the RCP8.5 cycle and the physical climate system. {6} CO2 concentrations, CMIP5 ESMs with interactive carbon cycles simu- late, on average, a 50 ( 140 to +210) ppm larger atmospheric CO2 With very high confidence, ocean carbon uptake of anthropogenic CO2 concentration and a 0.2 ( 0.4 to +0.9) °C larger global surface tem- will continue under all four RCPs through to 2100, with higher uptake perature increase by 2100 (CMIP5 model spread ). {12.4.8} 93 Technical Summary It is virtually certain that the increased storage of carbon by the ocean RCPs. The corresponding decrease in surface ocean pH by the end of will increase acidification in the future, continuing the observed trends 21st century is 0.065 (0.06 to 0.07) for RCP2.6, 0.145 (0.14 to 0.15) of the past decades. Ocean acidification in the surface ocean will for RCP4.5, 0.203 (0.20 to 0.21) for RCP6.0 and 0.31 (0.30 to 0.32) follow atmospheric CO2 and it will also increase in the deep ocean as for RCP8.5 (CMIP5 model spread) (Figure TS.20). Surface waters are CO2 ­continues to penetrate the abyss. The CMIP5 models ­ onsistently c projected to become seasonally corrosive to aragonite in parts of the project worldwide increased ocean acidification to 2100 under all Arctic and in some coastal upwelling systems within a decade, and 30 Fossil-fuel emissions 25 1000 20 CMIP5 mean 800 RCP8.5 IAM scenario RCP6.0 TS (PgC yr-1) RCP4.5 15 600 RCP2.6 400 10 200 1850 1900 1950 2000 2050 2100 5 0 -5 1850 1900 1950 2000 2050 2100 Years 2000 Cumulative fossil-fuel emissions Historical emission inventories (1860-2005) RCP8.5 (2006-2100) 1500 RCP6.0 (2006-2100) RCP4.5 (2006-2100) RCP2.6 (2006-2100) (PgC) 1000 Historical IMAGE ESMs ESMs MESSAGE 500 GCAM ESMs ESMs ESMs AIM 0 Figure TS.19 | Compatible fossil fuel emissions simulated by the CMIP5 models for the four RCP scenarios. (Top) Time series of annual emission (PgC yr 1). Dashed lines represent the historical estimates and RCP emissions calculated by the Integrated Assessment Models (IAMs) used to define the RCP scenarios, solid lines and plumes show results from CMIP5 Earth System Models (ESMs, model mean, with one standard deviation shaded). (Bottom) Cumulative emissions for the historical period (1860 2005) and 21st century (defined in CMIP5 as 2006 2100) for historical estimates and RCP scenarios. Left bars are cumulative emissions from the IAMs, right bars are the CMIP5 ESMs multi-model mean estimate and dots denote individual ESM results. From the CMIP5 ESMs results, total carbon in the land-atmosphere ocean system can be tracked and changes in this total must equal fossil fuel emissions to the system. Hence the compatible emissions are given by cumulative emissions = CA + CL + CO , while emission rate = d/dt [CA +CL + CO], where CA, CL, CO are carbon stored in atmosphere, land and ocean respectively. Other sources and sinks of CO2 such as from volcanism, sedimentation or rock weathering, which are very small on centennial time scales are not considered here. {Box 6.4; Figure 6.25} 94 Technical Summary in parts of the Southern Ocean within one to three decades in most oceans, caused by enhanced stratification, reduced ventilation and scenarios. Aragonite, a less stable form of calcium carbonate, under- warming. However, there is no consensus on the future development of saturation becomes widespread in these regions at atmospheric CO2 the volume of hypoxic and suboxic waters in the open ocean because levels of 500 to 600 ppm. {6.4.4} of large uncertainties in potential biogeochemical effects and in the evolution of tropical ocean dynamics. {6.4.5} It is very likely that the dissolved oxygen content of the ocean will decrease by a few percent during the 21st century in response to With very high confidence, the carbon cycle in the ocean and on land surface warming. CMIP5 models suggest that this decrease in dis- will continue to respond to climate change and atmospheric CO2 solved oxygen will predominantly occur in the subsurface mid-latitude increases that arise during the 21st century (see TFE.7 and TFE 8). {6.4} (a) Global ocean surface pH 12 TS 9 11 pH 10 4 (b) Change in ocean surface pH (2081-2100) RCP2.6 RCP4.5 RCP6.0 RCP8.5 4 -0.6 -0.55 -0.5 -0.45 -0.4 -0.35 -0.3 -0.25 -0.2 -0.15 -0.1 -0.05 0 Figure TS.20 | (a) Time series (model averages and minimum to maximum ranges) and (b) maps of multi-model surface ocean pH for the scenarios RCP2.6, RCP4.5, RCP6.0 and RCP8.5 in 2081 2100. The maps in (b) show change in global ocean surface pH in 2081 2100 relative to 1986 2005. The number of CMIP5 models to calculate the multi-model mean is indicated in the upper right corner of each panel. Further detail regarding the related Figures SPM.7c and SPM.8.d is given in the TS Supplementary Material. {Figure 6.28} 95 Technical Summary Thematic Focus Elements TFE.7 | Carbon Cycle Perturbation and Uncertainties The natural carbon cycle has been perturbed since the beginning of the Industrial Revolution (about 1750) by the anthropogenic release of carbon dioxide (CO2) to the atmosphere, virtually all from fossil fuel combustion and land use change, with a small contribution from cement production. Fossil fuel burning is a process related to energy production. Fossil fuel carbon comes from geological deposits of coal, oil and gas that were buried in the Earth crust for millions of years. Land use change CO2 emissions are related to the conversion of natural ecosystems into man- aged ecosystems for food, feed and timber production with CO2 being emitted from the burning of plant material or from the decomposition of dead plants and soil organic carbon. For instance when a forest is cleared, the plant material may be released to the atmosphere quickly through burning or over many years as the dead biomass and soil carbon decay on their own. {6.1, 6.3; Table 6.1} The human caused excess of CO2 in the atmosphere is partly removed from the atmosphere by carbon sinks in land TS ecosystems and in the ocean, currently leaving less than half of the CO2 emissions in the atmosphere. Natural carbon sinks are due to physical, biological and chemical processes acting on different time scales. An excess of atmospheric CO2 supports photosynthetic CO2 fixation by plants that is stored as plant biomass or in the soil. The residence times of stored carbon on land depends on the compartments (plant/soil) and composition of the organic carbon, with time horizons varying from days to centuries. The increased storage in terrestrial ecosystems not affected by land use change is likely to be caused by enhanced photosynthesis at higher CO2 levels and nitrogen deposition, and changes in climate favoring carbon sinks such as longer growing seasons in mid-to-high latitudes. {6.3, 6.3.1} The uptake of anthropogenic CO2 by the ocean is primarily a response to increasing CO2 in the atmosphere. Excess atmospheric CO2 absorbed by the surface ocean or transported to the ocean through aquatic systems (e.g., rivers, groundwaters) gets buried in coastal sediments or transported to deep waters where it is stored for decades to centuries. The deep ocean carbon can dissolve ocean carbonate sediments to store excess CO2 on time scales of cen- turies to millennia. Within a 1 kyr, the remaining atmospheric fraction of the CO2 emissions will be between 15 and 40%, depending on the amount of carbon released (TFE.7, Figure 1). On geological time scales of 10 kyr or longer, additional CO2 is removed very slowly from the atmosphere by rock weathering, pulling the remaining atmospheric CO2 fraction down to 10 to 25% after 10 kyr. {Box 6.1} The carbon cycle response to future climate and CO2 changes can be viewed as two strong and opposing feedbacks. The concentration carbon feedback deter- mines changes in storage due to elevated CO2, and the climate carbon feedback determines changes in carbon storage due to changes in climate. There is high confidence that increased atmospheric CO2 will lead to increased land and ocean carbon uptake but by an uncertain amount. Models agree on the positive sign of land and ocean response to rising CO2 but show only medium and low agreement for the magnitude of ocean and land carbon uptake respectively (TFE.7, Figure 2). Future climate change will decrease land and ocean carbon uptake compared to the case with constant climate (medium confidence). This is further supported by paleoclimate observations and modelling TFE.7, Figure 1 | Percentage of initial atmospheric CO2 perturbation remaining in the atmosphere in response to an idealized instantaneous CO2 emission pulse indicating that there is a positive feedback between cli- in year 0 as calculated by a range of coupled climate carbon cycle models. Multi- mate and the carbon cycle on century to millennial time model mean (line) and the uncertainty interval (maximum model range, shading) scales. Models agree on the sign, globally negative, of simulated during 100 years (left) and 1 kyr (right) following the instantaneous land and ocean response to climate change but show emission pulse of 100 PgC (blue) and 5,000 PgC (red). {Box 6.1, Figure 1} low agreement on the magnitude of this response, espe- cially for the land (TFE.7, Figure 2). A key update since the IPCC Fourth Assessment Report (AR4) is the introduction of nutrient dynamics in some land carbon models, in particular the limitations on plant growth imposed by nitrogen availability. There is high confidence that, at the global scale, relative to the Coupled Model Intercomparison Project Phase 5 (CMIP5) carbon-only Earth System (continued on next page) 96 Technical Summary TFE.7 (continued) Models (ESMs), CMIP5 ESMs including a land nitrogen cycle will reduce the strength of both the concentration carbon feedback and the climate carbon feedback of land ecosystems (TFE.7, Figure 2). Inclusion of nitrogen-cycle processes increases the spread across the CMIP5 ensemble. The CMIP5 spread in ocean sensitivity to CO2 and climate appears reduced compared to AR4 (TFE.7, Figure 2). {6.2.3, 6.4.2} Climate response C4MIP to CO2 CMIP5 0.002 0.004 0.006 0.008 K ppm-1 Land C C4MIP TS response to CO2 CMIP5 Ocean C C4MIP response to CO2 CMIP5 0.5 1.0 1.5 2.0 2.5 3.0 PgC ppm-1 Land C C4MIP response to climate CMIP5 Ocean C C4MIP response to climate CMIP5 -200 -160 -120 -80 -40 0 PgC K-1 TFE.7, Figure 2 | Comparison of carbon cycle feedback metrics between the ensemble of seven General Circulation Models (GCMs) and four Earth System Models of Intermediate Complexity (EMICs) at the time of AR4 (Coupled Carbon Cycle Climate Model Intercomparison Project (C4MIP)) under the SRES A2 scenario and the eight CMIP5 models under the 140-year 1% CO2 increase per year scenario. Black dots represent a single model simulation and coloured bars the mean of the multi-model results, grey dots are used for models with a coupled terrestrial nitrogen cycle. The comparison with C4MIP models is for context, but these metrics are known to be variable across different scenarios and rates of change (see Section 6.4.2). The SRES A2 scenario is closer in rate of change to a 0.5% CO2 increase per year scenario and as such it should be expected that the CMIP5 climate carbon sensitivity terms are comparable, but the concentration carbon sensitivity terms are likely to be around 20% smaller for CMIP5 than for C4MIP due to lags in the ability of the land and ocean to respond to higher rates of CO2 increase. This dependence on scenario reduces confidence in any quantitative statements of how CMIP5 carbon cycle feedbacks differ from C4MIP. {Figure 6.21} With very high confidence, ocean carbon uptake of anthropogenic CO2 emissions will continue under all four Repre- sentative Concentration Pathways (RCPs) through to 2100, with higher uptake corresponding to higher concentra- tion pathways. The future evolution of the land carbon uptake is much more uncertain, with a majority of models projecting a continued net carbon uptake under all RCPs, but with some models simulating a net loss of carbon by the land due to the combined effect of climate change and land use change. In view of the large spread of model results and incomplete process representation, there is low confidence on the magnitude of modelled future land carbon changes. {6.4.3; Figure 6.24} Biogeochemical cycles and feedbacks other than the carbon cycle play an important role in the future of the climate system, although the carbon cycle represents the strongest of these. Changes in the nitrogen cycle, in addition to interactions with CO2 sources and sinks, affect emissions of nitrous oxide (N2O) both on land and from the ocean. The human-caused creation of reactive nitrogen has increased steadily over the last two decades and is dominated by the production of ammonia for fertilizer and industry, with important contributions from legume cultivation and combustion of fossil fuels. {6.3} Many processes, however, are not yet represented in coupled climate-biogeochemistry models (e.g., other processes involving other biogenic elements such as phosphorus, silicon and iron) so their magnitudes have to be estimated in offline or simpler models, which make their quantitative assessment difficult. It is likely that there will be nonlinear interactions between many of these processes, but these are not yet well quantified. Therefore any assessment of the future feedbacks between climate and biogeochemical cycles still contains large uncertainty. {6.4} 97 Technical Summary Box TS.7 | Climate Geoengineering Methods Geoengineering is defined as the deliberate large-scale intervention in the Earth system to counter undesirable impacts of climate change on the planet. Carbon Dioxide Reduction (CDR) aims to slow or perhaps reverse projected increases in the future atmospheric CO2 concentrations, accelerating the natural removal of atmospheric CO2 and increasing the storage of carbon in land, ocean and geo- logical reservoirs. Solar Radiation Management (SRM) aims to counter the warming associated with increasing GHG concentrations by reducing the amount of sunlight absorbed by the climate system. A related technique seeks to deliberately decrease the greenhouse effect in the climate system by altering high-level cloudiness. {6.5, 7.7; FAQ 7.3} CDR methods could provide mitigation of climate change if CO2 can be reduced, but there are uncertainties, side effects and risks, and implementation would depend on technological maturity along with economic, political and ethical considerations. CDR would likely need to be deployed at large-scale and over at least one century to be able to significantly reduce CO2 concentrations. There are biogeochemical, and currently technical limitations that make it difficult to provide quantitative estimates of the potential for CDR. It TS is virtually certain that CO2 removals from the atmosphere by CDR would be partially offset by outgassing of CO2 previously stored in ocean and terrestrial carbon reservoirs. Some of the climatic and environmental side effects of CDR methods are associated with altered surface albedo from afforestation, ocean de-oxygenation from ocean fertilization, and enhanced N2O emissions. Land-based CDR methods would probably face competing demands for land. The level of confidence on the effectiveness of CDR methods and their side effects on carbon and other biogeochemical cycles is low. {6.5; Box 6.2; FAQ 7.3} SRM remains unimplemented and untested but, if realizable, could offset a global temperature rise and some of its effects. There is medium confidence that SRM through stratospheric aerosol injection is scalable to counter the RF and some of the climate effects expected from a twofold increase in CO2 concentration. There is no consensus on whether a similarly large RF could be achieved from cloud brightening SRM due to insufficient understanding of aerosol cloud interactions. It does not appear that land albedo change SRM could produce a large RF. Limited literature on other SRM methods precludes their assessment. {7.7.2, 7.7.3} Numerous side effects, risks and shortcomings from SRM have been identified. SRM would produce an inexact compensation for the RF by GHGs. Several lines of evidence indicate that SRM would produce a small but significant decrease in global precipitation (with larger differences on regional scales) if the global surface temperature were maintained. Another side effect that is relatively well characterized is the likelihood of modest polar stratospheric ozone depletion associated with stratospheric aerosol SRM. There could also be other as yet unanticipated consequences. {7.6.3, 7.7.3, 7.7.4} As long as GHG concentrations continued to increase, the SRM would require commensurate increase, exacerbating side effects. In addition, scaling SRM to substantial levels would carry the risk that if the SRM were terminated for any reason, there is high confidence that surface temperatures would increase rapidly (within a decade or two) to values consistent with the GHG forcing, which would stress systems sensitive to the rate of climate change. Finally, SRM would not compensate for ocean acidification from increasing CO2. {7.7.3, 7.7.4} TS.5.7 Long-term Projections of Sea Level Change similar at the end of the century, RCP4.5 has a greater rate of rise earlier in the century than RCP6.0. GMSL rise depends on the pathway of CO2 TS.5.7.1 Projections of Global Mean Sea Level Change for emissions, not only on the cumulative total; reducing emissions earlier the 21st Century rather than later, for the same cumulative total, leads to a larger mitiga- tion of sea level rise. {12.4.1, 13.4.1, 13.5.1; Table 13.5} GMSL rise for 2081 2100 (relative to 1986 2005) for the RCPs will likely be in the 5 to 95% ranges derived from CMIP5 climate projections Confidence in the projected likely ranges comes from the consistency in combination with process-based models of glacier and ice sheet sur- of process-based models with observations and physical understand- face mass balance, with possible ice sheet dynamical changes assessed ing. The basis for higher projections has been considered and it has from the published literature. These likely ranges are 0.26 to 0.55 m been concluded that there is currently insufficient evidence to evalu- (RCP2.6), 0.32 to 0.63 m (RCP4.5), 0.33 to 0.63 m (RCP6.0) and 0.45 ate the probability of specific levels above the likely range. Based on to 0.82 m (RCP8.5) (medium confidence) (Table TS.1, Figure TS.21). For current understanding, only the collapse of marine-based sectors of RCP8.5 the range at 2100 is 0.52 to 0.98 m. The central projections for the Antarctic ice sheet, if initiated, could cause GMSL to rise substan- GMSL rise in all scenarios lie within a range of 0.05 m until the middle tially above the likely range during the 21st century. There is a lack of the century, when they begin to diverge; by the late 21st century, of consensus on the probability for such a collapse, and the potential they have a spread of 0.25 m. Although RCP4.5 and RCP6.0 are very additional contribution to GMSL rise cannot be precisely quantified, 98 Technical Summary but there is medium confidence that it would not exceed several tenths There is medium confidence in the ability to model future rapid chang- of a metre of sea level rise during the 21st century. {13.5.1, 13.5.3} es in ice sheet dynamics on decadal time scales. At the time of the AR4, scientific understanding was not sufficient to allow an assessment of Under all the RCP scenarios, the time-mean rate of GMSL rise during the possibility of such changes. Since the publication of the AR4, there the 21st century is very likely to exceed the rate observed during 1971 has been substantial progress in understanding the relevant processes 2010. In the projections, the rate of rise initially increases. In RCP2.6 as well as in developing new ice sheet models that are capable of it becomes roughly constant (central projection about 4.5 mm yr 1) simulating them. However, the published literature as yet provides only before the middle of the century, and subsequently declines slightly. a partially sufficient basis for making projections related to particular The rate of rise becomes roughly constant in RCP4.5 and RCP6.0 by the scenarios. In our projections of GMSL rise by 2081 2100, the likely end of the 21st century, whereas acceleration continues throughout range from rapid changes in ice outflow is 0.03 to 0.20 m from the two the century in RCP8.5 (reaching 11 [8 to 16] mm yr 1 during 2081 ice sheets combined, and its inclusion is the most important reason 2100). {13.5.1; Table 13.5} why the projections are greater than those given in the AR4. {13.1.5, 13.5.1, 13.5.3} In all RCP scenarios, thermal expansion is the largest contribution, accounting for about 30 to 55% of the total. Glaciers are the next Semi-empirical models are designed to reproduce the observed sea TS largest, accounting for 15-35%. By 2100, 15 to 55% of the present level record over their period of calibration, but do not attribute sea glacier volume is projected to be eliminated under RCP2.6, and 35 to level rise to its individual physical components. For RCPs, some semi- 85% under RCP8.5 (medium confidence). The increase in surface melt- empirical models project a range that overlaps the process-based likely ing in Greenland is projected to exceed the increase in accumulation, range while others project a median and 95-percentile that are about and there is high confidence that the surface mass balance changes on twice as large as the process-based models. In nearly every case, the the Greenland ice sheet will make a positive contribution to sea level semi-empirical model 95th percentile is higher than the process-based rise over the 21st century. On the Antarctic ice sheet, surface melting likely range. For 2081 2100 (relative to 1986 2005) under RCP4.5, is projected to remain small, while there is medium confidence that semi-empirical models give median projections in the range 0.56 to snowfall will increase (Figure TS.21). {13.3.3, 13.4.3, 13.4.4, 13.5.1; 0.97 m, and their 95th percentiles extend to about 1.2 m. This differ- Table 13.5} ence implies either that there is some contribution which is presently 1.2 Sum 2081-2100 relative to 1986-2005 Thermal expansion 1.0 Glaciers Greenland ice sheet (including dynamical change) Antarctic ice sheet (including dynamical change) Global mean sea level rise (m) Land water storage 0.8 Greenland ice-sheet rapid dynamical change Antarctic ice-sheet rapid dynamical change 0.6 0.4 0.2 0.0 A1B RCP2.6 RCP4.5 RCP6.0 RCP8.5 Figure TS.21 | Projections from process-based models with likely ranges and median values for global mean sea level (GMSL) rise and its contributions in 2081 2100 relative to 1986 2005 for the four RCP scenarios and scenario SRES A1B used in the AR4. The contributions from ice sheets include the contributions from ice sheet rapid dynamical change, which are also shown separately. The contributions from ice sheet rapid dynamics and anthropogenic land water storage are treated as having uniform probability distributions, and as independent of scenario (except that a higher rate of change is used for Greenland ice sheet outflow under RCP8.5). This treatment does not imply that the contributions concerned will not depend on the scenario followed, only that the current state of knowledge does not permit a quantitative assessment of the dependence. See discussion in Sections 13.5.1 and 13.5.3 and Supplementary Material for methods. Based on current understanding, only the collapse of the marine-based sectors of the Antarctic ice sheet, if initiated, could cause GMSL to rise substantially above the likely range during the 21st century. This potential additional contribution cannot be precisely quantified but there is medium confidence that it would not exceed several tenths of a metre during the 21st century. {Figure 13.10} 99 Technical Summary Global mean sea level rise about 0.43 m sea level equivalent) decreases. In Antarctica, beyond 1.0 2100 and with higher GHG scenarios, the increase in surface melting Mean over 2081 2100 could exceed the increase in accumulation. {13.5.2, 13.5.4} 0.8 The available evidence indicates that global warming greater than a certain threshold would lead to the near-complete loss of the Green- 0.6 land ice sheet over a millennium or more, causing a GMSL rise of about 7 m. Studies with fixed present-day ice sheet topography indicate the (m) threshold is greater than 2°C but less than 4°C of GMST rise with 0.4 respect to pre-industrial (medium confidence). The one study with a RCP8.5 dynamical ice sheet suggests the threshold is greater than about 1°C (low confidence) global mean warming with respect to pre-industrial. RCP6.0 RCP4.5 0.2 Considering the present state of scientific uncertainty, a likely range RCP2.6 cannot be quantified. The complete loss of the ice sheet is not inevi- table because this would take a millennium or more; if temperatures TS 0.0 2000 2020 2040 2060 2080 2100 decline before the ice sheet is eliminated, the ice sheet might regrow. Year However, some part of the mass loss might be irreversible, depending on the duration and degree of exceedance of the threshold, because Figure TS.22 | Projections from process-based models of global mean sea level the ice sheet may have multiple steady states, due to its interaction (GMSL) rise relative to 1986 2005 for the four RCP scenarios. The solid lines show the with its regional climate. {13.4.3, 13.5.4} median projections, the dashed lines show the likely ranges for RCP4.5 and RCP6.0, and the shading the likely ranges for RCP2.6 and RCP8.5. The time means for 2081 2100 are shown as coloured vertical bars. See Sections 13.5.1 and 13.5.3 and Supplementary Currently available information indicates that the dynamical contribu- Material for methods. Based on current understanding, only the collapse of the marine- tion of the ice sheets will continue beyond 2100, but confidence in based sectors of the Antarctic ice sheet, if initiated, could cause GMSL to rise substan- projections is low. In Greenland, ice outflow induced from interaction tially above the likely range during the 21st century. This potential additional contribu- with the ocean is self-limiting as the ice sheet margin retreats inland tion cannot be precisely quantified but there is medium confidence that it would not exceed several tenths of a metre during the 21st century. Further detail regarding the from the coast. By contrast, the bedrock topography of Antarctica is related Figure SPM.9 is given in the TS Supplementary Material. {Table 13.5; Figures such that there may be enhanced rates of mass loss as the ice retreats. 13.10, 13.11} About 3.3 m of equivalent global sea level of the West Antarctic ice sheet is grounded on areas with downward sloping bedrock, which may be subject to potential ice loss via the marine ice sheet instability. unidentified or underestimated by process-based models, or that the Abrupt and irreversible ice loss from a potential instability of marine- projections of semi-empirical models are overestimates. Making pro- based sectors of the Antarctic Ice Sheet in response to climate forcing jections with a semi-empirical model assumes that sea level change in is possible, but current evidence and understanding is insufficient to the future will have the same relationship as it has had in the past to make a quantitative assessment. Due to relatively weak snowfall on RF or global mean temperature change. This may not hold if potentially Antarctica and the slow ice motion in its interior, it can be expected nonlinear physical processes do not scale in the future in ways which that the West Antarctic ice sheet would take at least several thousand can be calibrated from the past. There is no consensus in the scientific years to regrow if it was eliminated by dynamic ice discharge. Conse- community about the reliability of semi-empirical model projections, quently any significant ice loss from West Antarctic that occurs within and confidence in them is assessed to be low. {13.5.2, 13.5.3} the next century will be irreversible on a multi-centennial to millennial time scale. {5.8, 13.4.3, 13.4.4, 13.5.4} TS.5.7.2 Projections of Global Mean Sea Level Change Beyond 2100 TS.5.7.3 Projections of Regional Sea Level Change It is virtually certain that GMSL rise will continue beyond 2100. The few Regional sea level will change due to dynamical ocean circulation available model results that go beyond 2100 indicate global mean sea changes, changes in the heat content of the ocean, mass redistribution level rise above the pre-industrial level (defined here as an equilibrium in the entire Earth system and changes in atmospheric pressure. Ocean 280 ppm atmospheric CO2 concentration) by 2300 to be less than 1 m dynamical change results from changes in wind and buoyancy forc- for a RF that corresponds to CO2 concentrations that peak and decline ing (heat and freshwater), associated changes in the circulation, and and remain below 500 ppm, as in the scenario RCP2.6. For a RF that redistribution of heat and freshwater. Over time scales longer than a corresponds to a CO2 concentration that is above 700 ppm but below few days, regional sea level also adjusts nearly isostatically to regional 1500 ppm, as in the scenario RCP8.5, the projected rise is 1 m to more changes in sea level atmospheric pressure relative to its mean over than 3 m (medium confidence). {13.5.4} the ocean. Ice sheet mass loss (both contemporary and past), glacier mass loss and changes in terrestrial hydrology cause water mass redis- Sea level rise due to ocean thermal expansion will continue for cen- tribution among the cryosphere, the land and the oceans, giving rise turies to millennia. The amount of ocean thermal expansion increases to distinctive regional changes in the solid Earth, Earth rotation and with global warming (models give a range of 0.2 to 0.6 m °C 1). The the gravity field. In some coastal locations, changes in the hydrologi- glacier contribution decreases over time as their volume (currently cal cycle, ground subsidence associated with anthropogenic activity, 100 Technical Summary tectonic processes and coastal processes can dominate the relative sea TS.5.7.4 Projections of Change in Sea Level Extremes and Waves level change, that is, the change in sea surface height relative to the During the 21st Century land. {13.1.3, 13.6.2, 13.6.3, 13.6.4} It is very likely that there will be a significant increase in the occurrence By the end of the 21st century, sea level change will have a strong of future sea level extremes by the end of the 21st century, with a likely regional pattern, which will dominate over variability, with many increase in the early 21st century (see TFE.9, Table 1). This increase will regions likely experiencing substantial deviations from the global primarily be the result of an increase in mean sea level (high confi- mean change (Figure TS.23). It is very likely that over about 95% of dence), with extreme return periods decreasing by at least an order of the ocean will experience regional relative sea level rise, while most magnitude in some regions by the end of the 21st century. There is low regions experiencing a sea level fall are located near current and confidence in region-specific projections of storminess and associated former glaciers and ice sheets. Local sea level changes deviate more storm surges. {13.7.2} than 10% and 25% from the global mean projection for as much as 30% and 9% of the ocean area, respectively, indicating that spatial It is likely (medium confidence) that annual mean significant wave variations can be large. Regional changes in sea level reach values of heights will increase in the Southern Ocean as a result of enhanced up to 30% above the global mean value in the Southern Ocean and wind speeds. Southern Ocean generated swells are likely to affect TS around North America, between 10% and 20% in equatorial regions, heights, periods and directions of waves in adjacent basins. It is very and up to 50% below the global mean in the Arctic region and some likely that wave heights and the duration of the wave season will regions near Antarctica. About 70% of the coastlines worldwide are increase in the Arctic Ocean as a result of reduced sea ice extent. In projected to experience a relative sea level change within 20% of the general, there is low confidence in region-specific projections due to GMSL change. Over decadal periods, the rates of regional relative sea the low confidence in tropical and extratropical storm projections, and level change as a result of climate variability can differ from the global to the challenge of down-scaling future wind states from coarse reso- average rate by more than 100%. {13.6.5} lution climate models. {13.7.3} Relative Sea Level Change 2081-2100 relative to 1986-2005 a) RCP2.6 b) RCP4.5 c) RCP6.0 d) RCP8.5 (m) 0.4 0.2 0.0 0.2 0.4 0.6 0.8 Figure TS.23 | Ensemble mean net regional relative sea level change (metres) evaluated from 21 CMIP5 models for the RCP scenarios (a) 2.6, (b) 4.5, (c) 6.0 and (d) 8.5 between 1986 2005 and 2081 2100. Each map includes effects of atmospheric loading, plus land-ice, glacial isostatic adjustment (GIA) and terrestrial water sources. {Figure 13.20} 101 Technical Summary Thematic Focus Elements TFE.8 | Climate Targets and Stabilization The concept of stabilization is strongly linked to the ultimate objective of the United Nations Framework Conven- tion on Climate Change (UNFCCC), which is to achieve [ ] stabilization of greenhouse gas concentrations in the atmosphere at a level that would prevent dangerous anthropogenic interference with the climate system . Recent policy discussions focused on limits to a global temperature increase, rather than to greenhouse gas (GHG) con- centrations, as climate targets in the context of the UNFCCC objectives. The most widely discussed is that of 2°C, that is, to limit global temperature increase relative to pre-industrial times to below 2°C, but targets other than 2°C have been proposed (e.g., returning warming to well below 1.5°C global warming relative to pre-industrial, or returning below an atmospheric carbon dioxide (CO2) concentration of 350 ppm). Climate targets generally mean avoiding a warming beyond a predefined threshold. Climate impacts, however, are geographically diverse and sector specific, and no objective threshold defines when dangerous interference is reached. Some changes may TS be delayed or irreversible, and some impacts could be beneficial. It is thus not possible to define a single critical objective threshold without value judgements and without assumptions on how to aggregate current and future costs and benefits. This TFE does not advocate or defend any threshold or objective, nor does it judge the economic or political feasibility of such goals, but assesses, based on the current understanding of climate and carbon cycle feedbacks, the climate projections following the Representative Concentration Pathways (RCPs) in the context of climate targets, and the implications of different long-term temperature stabilization objectives on allowed carbon emissions. Further below it is highlighted that temperature stabilization does not necessarily imply stabilization of the entire Earth system. {12.5.4} Temperature targets imply an upper limit on the total radiative forcing (RF). Differences in RF between the four RCP scenarios are relatively small up to 2030, but become very large by the end of the 21st century and dominated by CO2 forcing. Consequently, in the near term, global mean surface temperatures (GMSTs) are projected to continue to rise at a similar rate for the four RCP scenarios. Around the mid-21st century, the rate of global warming begins to be more strongly dependent on the scenario. By the end of the 21st century, global mean temperatures will be warmer than present day under all the RCPs, global temperature change being largest (>0.3°C per decade) in the highest RCP8.5 and significantly lower in RCP2.6, particularly after about 2050 when global surface temperature response stabilizes (and declines thereafter) (see Figure TS.15). {11.3.1, 12.3.3, 12.4.1} In the near term (2016 2035), global mean surface warming is more likely than not to exceed 1°C and very unlikely to be more than 1.5°C relative to the average from year 1850 to 1900 (assuming 0.61°C warming from 1850-1900 to 1986 2005) (medium confidence). By the end of the 21st century (2081 2100), global mean surface warming, relative to 1850-1900, is likely to exceed 1.5°C for RCP4.5, RCP6.0 and RCP8.5 (high confidence) and is likely to exceed 2°C for RCP6.0 and RCP8.5 (high confidence). It is more likely than not to exceed 2°C for RCP4.5 (medium confidence). Global mean surface warming above 2°C under RCP2.6 is unlikely (medium confidence). Global mean surface warming above 4°C by 2081 2100 is unlikely in all RCPs (high confidence) except for RCP8.5 where it is about as likely as not (medium confidence). {11.3.6, 12.4.1; Table 12.3} Continuing GHG emissions beyond 2100 as in the RCP8.5 extension induces a total RF above 12 W m 2 by 2300, with global warming reaching 7.8 [3.0 to 12.6] °C for 2281 2300 relative to 1986 2005. Under the RCP4.5 extension, where radiative forcing is kept constant (around 4.5 W m-2) beyond 2100, global warming reaches 2.5 [1.5 to 3.5] °C. Global warming reaches 0.6 [0.0 to 1.2] °C under the RCP2.6 extension where sustained negative emissions lead to a further decrease in RF, reaching values below present-day RF by 2300. See also Box TS.7. {12.3.1, 12.4.1, 12.5.1} The total amount of anthropogenic CO2 released in the atmosphere since pre-industrial (often termed cumulative carbon emission, although it applies only to CO2 emissions) is a good indicator of the atmospheric CO2 concentration and hence of the global warming response. The ratio of GMST change to total cumulative anthropogenic CO2 emis- sions is relatively constant over time and independent of the scenario. This near-linear relationship between total CO2 emissions and global temperature change makes it possible to define a new quantity, the transient climate response to cumulative carbon emission (TCRE), as the transient GMST change for a given amount of cumulated anthropo- genic CO2 emissions, usually 1000 PgC (TFE.8, Figure 1). TCRE is model dependent, as it is a function of the cumulative CO2 airborne fraction and the transient climate response, both quantities varying significantly across models. Taking into account the available information from multiple lines of evidence (observations, models and process under- standing), the near linear relationship between cumulative CO2 emissions and peak global mean temperature is (continued on next page) 102 Technical Summary TFE.8 (continued) well established in the literature and robust for cumulative total CO2 emissions up to about 2000 PgC. It is consistent with the relationship inferred from past cumulative CO2 emissions and observed warming, is supported by process understanding of the carbon cycle and global energy balance, and emerges as a robust result from the entire hier- archy of models. Expert judgment based on the available evidence suggests that TCRE is likely between 0.8°C and 2.5°C per 1000 PgC, for cumulative emissions less than about 2000 PgC until the time at which temperature peaks (TFE.8, Figure 1a). {6.4.3, 12.5.4; Box 12.2} CO2-induced warming is projected to remain approximately constant for many centuries following a complete cessation of emissions. A large fraction of climate change is thus irreversible on a human time scale, except if net anthropogenic CO2 emissions were strongly negative over a sustained period. Based on the assessment of TCRE (assuming a normal distribution with a +/-1 standard deviation range of 0.8 to 2.5°C per 1000 PgC), limiting the warming caused by anthropogenic CO2 emissions alone (i.e., ignoring other radiative forcings) to less than 2°C since the period 1861 1880 with a probability of >33%, >50% and >66%, total CO2 emissions from all anthropo- TS genic sources would need to be below a cumulative budget of about 1570 PgC, 1210 PgC and 1000 PgC since 1870, respectively. An amount of 515 [445 to 585] PgC was emitted between 1870 and 2011 (TFE.8, Figure 1a,b). Higher emissions in earlier decades therefore imply lower or even negative emissions later on. Accounting for non-CO2 forcings contributing to peak warming implies lower cumulated CO2 emissions. Non-CO2 forcing constituents are important, requiring either assumptions on how CO2 emission reductions are linked to changes in other forcings, or separate emission budgets and climate modelling for short-lived and long-lived gases. So far, not many studies have considered non-CO2 forcings. Those that do consider them found significant effects, in particular warming of several tenths of a degree for abrupt reductions in emissions of short-lived species, like aerosols. Accounting for an unanticipated release of GHGs from permafrost or methane hydrates, not included in studies assessed here, would also reduce the anthropogenic CO2 emissions compatible with a given temperature target. Requiring a higher likeli- hood of temperatures remaining below a given temperature target would further reduce the compatible emissions (TFE.8, Figure 1c). When accounting for the non-CO2 forcings as in the RCP scenarios, compatible carbon emissions since 1870 are reduced to about 900 PgC, 820 PgC and 790 PgC to limit warming to less than 2°C since the period 1861 1880 with a probability of >33%, >50%, and >66%, respectively. These estimates were derived by computing the fraction of the Coupled Model Intercomparison Project Phase 5 (CMIP5) Earth System Models (ESMs) and Earth System Models of Intermediate Complexity (EMICs) that stay below 2°C for given cumulative emissions following RCP8.5, as shown in TFE.8 Fig. 1c. The non-CO2 forcing in RCP8.5 is higher than in RCP2.6. Because all likelihood statements in calibrated IPCC language are open intervals, the estimates provided are thus both conservative and consistent choices valid for non-CO2 forcings across all RCP scenarios. There is no RCP scenario which limits warming to 2°C with probabilities of >33% or >50%, and which could be used to directly infer compatible cumulative emis- sions. For a probability of >66% RCP2.6 can be used as a comparison. Combining the average back-calculated fossil fuel carbon emissions for RCP2.6 between 2012 and 2100 (270 PgC) with the average historical estimate of 515 PgC gives a total of 785 PgC, i.e., 790 PgC when rounded to 10 PgC. As the 785 PgC estimate excludes an explicit assess- ment of future land-use change emissions, the 790 PgC value also remains a conservative estimate consistent with the overall likelihood assessment. The ranges of emissions for these three likelihoods based on the RCP scenarios are rather narrow, as they are based on a single scenario and on the limited sample of models available (TFE.8 Fig. 1c). In contrast to TCRE they do not include observational constraints or account for sources of uncertainty not sampled by the models. The concept of a fixed cumulative CO2 budget holds not just for 2°C, but for any temperature level explored with models so far (up to about 5°C, see Figures 12.44 to 12.46). Higher temperature targets would allow larger cumulative budgets, while lower temperature target would require lower cumulative budgets (TFE.8, Figure 1). {6.3.1, 12.5.2, 12.5.4} The climate system has multiple time scales, ranging from annual to multi-millennial, associated with different thermal and carbon reservoirs. These long time scales induce a commitment warming already in the pipe-line . Stabilization of the forcing would not lead to an instantaneous stabilization of the warming. For the RCP scenarios and their extensions to 2300, the fraction of realized warming, at that time when RF stabilizes, would be about 75 to 85% of the equilibrium warming. For a 1% yr 1 CO2 increase to 2 × CO2 or 4 × CO2 and constant forcing there- after, the fraction of realized warming would be much smaller, about 40 to 70% at the time when the forcing is kept constant. Owing to the long time scales in the deep ocean, full equilibrium is reached only after hundreds to thousands of years. {12.5.4} (continued on next page) 103 Technical Summary TFE.8 (continued) 5 a b Temperature anomaly relative to 1861-1880 (°C) 1 4 0.5 0 3 0 200 400 600 TS 2 1 Observations TCRE assessment CMIP5 ESM 1% CO2 runs Masked ESM RCP2.6 range 1% CO2 runs RCP4.5 range Historical RCP6 range RCP2.6 RCP8.5 range 0 RCP4.5 2000-2009 average RCP6.0 2040-2049 average Cumulative emissions RCP8.5 2090-2099 average estimate 1870-2011 0 500 1000 1500 2000 2500 Cumulative total anthropogenic CO2 emissions from 1870 (PgC) Peak warming limit (°C) 3 c 2.5 2 1.5 0 500 1000 1500 2000 2500 Consistent cum. total anthropogenic CO2 emissions given warming by all forcers in RCP8.5 (PgC) 90% of models 66% of models 50% of models 33% of models 10% of models TFE.8, Figure 1 | Global mean temperature increase since 1861 1880 as a function of cumulative total global CO2 emissions from various lines of evidence. (a) Decadal average results are shown over all CMIP5 Earth System Model of Intermediate Complexity (EMICs) and Earth System Models (ESMs) for each RCP respectively, with coloured lines (multi-model average), decadal markers (dots) and with three decades (2000 2009, 2040 2049 and 2090 2099) highlighted with a star, square and diamond, respectively. The historical time period up to decade 2000 2009 is taken from the CMIP5 historical runs prolonged by RCP8.5 for 2006 2010 and is indicated with a black thick line and black symbols. Coloured ranges illustrate the model spread (90% range) over all CMIP5 ESMs and EMICs and do not represent a formal uncertainty assessment. Ranges are filled as long as data of all models is available and until peak temperature. They are faded out for illustrative purposes afterward. CMIP5 simulations with 1% yr 1 CO2 increase only are illustrated by the dark grey area (range definition similar to RCPs above) and the black thin line (multi- model average). The light grey cone represents this Report s assessment of the transient climate response to emissions (TCRE) from CO2 only. Estimated cumulative historical CO2 emissions from 1870 to 2011 with associated uncertainties are illustrated by the grey bar at the bottom of (a). (b) Comparison of historical model results with observations. The magenta line and uncertainty ranges are based on observed emissions from Carbon Dioxide Information Analysis Center (CDIAC) extended by values of the Global Carbon project until 2010 and observed temperature estimates of the Hadley Centre/Climatic Research Unit gridded surface temperature data set 4 (HadCRUT4). The uncertainties in the last decade of observations are based on the assessment in this report. The black thick line is identical to the one in (a). The thin green line with crosses is as the black line but for ESMs only. The yellow-brown line and range show these ESM results until 2010, when corrected for HadCRUT4 s incomplete geographical coverage over time. All values are given relative to the 1861 1880 base period. All time-series are derived from decadal averages to illustrate the long-term trends. Note that observations are in addition subject to internal climate variability, adding an uncertainty of about 0.1°C. (c) Cumulative CO2 emis- sions over the entire industrial era, consistent with four illustrative peak global temperature limits (1.5°C, 2°C, 2.5°C and 3°C, respectively) when taking into account warming by all forcers. Horizontal bars indicate consistent cumulative emission budgets as a function of the fraction of models (CMIP5 ESMs and EMICs) that at least hold warming below a given temperature limit. Note that the fraction of models cannot be interpreted as a probability. The budgets are derived from the RCP8.5 runs, with relative high non-CO2 forcing over the 21st century. If non-CO2 are significantly reduced, the CO2 emissions compatible with a specific temperature limit might be slightly higher, but only to a very limited degree, as illustrated by the other coloured lines in (a), which assume significantly lower non-CO2 forcing. Further detail regarding the related Figure SPM.10 is given in the TS Supplementary Material. {Figure 12.45} 104 Technical Summary TFE.8 (continued) The commitment to past emissions is a persistent warming for hundreds of years, continuing at about the level of warming that has been realized when emissions were ceased. The persistence of this CO2-induced warming after emission have ceased results from a compensation between the delayed commitment warming described above and the slow reduction in atmospheric CO2 resulting from ocean and land carbon uptake. This persistence of warm- ing also results from the nonlinear dependence of RF on atmospheric CO2, that is, the relative decrease in forcing being smaller than the relative decrease in CO2 concentration. For high climate sensitivities, and in particular if sulphate aerosol emissions are eliminated at the same time as GHG emissions, the commitment from past emission can be strongly positive, and is a superposition of a fast response to reduced aerosols emissions and a slow response to reduced CO2. {12.5.4} Stabilization of global temperature does not imply stabilization for all aspects of the climate system. Processes related to vegetation change, changes in the ice sheets, deep ocean warming and associated sea level rise and potential feedbacks linking, for example, ocean and the ice sheets have their own intrinsic long time scales. Ocean TS acidification will very likely continue in the future as long as the oceans will continue to take up atmospheric CO2. Committed land ecosystem carbon cycle changes will manifest themselves further beyond the end of the 21st century. It is virtually certain that global mean sea level rise will continue beyond 2100, with sea level rise due to thermal expansion to continue for centuries to millennia. Global mean sea level rise depends on the pathway of CO2 emissions, not only on the cumulative total; reducing emissions earlier rather than later, for the same cumulative total, leads to a larger mitigation of sea level rise. {6.4.4, 12.5.4, 13.5.4} TS.5.8 Climate Phenomena and Regional Climate Change small. There is medium confidence in that the Indian summer ­ onsoon m c ­ irculation weakens, but this is compensated by increased atmospheric This section assesses projected changes over the 21st century in large- moisture content, leading to more rainfall. For the East Asian summer scale climate phenomena that affect regional climate (Table TS.2). monsoon, both monsoon circulation and rainfall are projected to Some of these phenomena are defined by climatology (e.g., mon- increase. {14.2.2, 14.8.9, 14.8.11, 14.8.13} soons), and some by interannual variability (e.g., El Nino), the latter affecting climate extremes such as floods, droughts and heat waves. There is low confidence in projections of the North American and South Changes in statistics of weather phenomena such as tropical cyclones American monsoon precipitation changes, but medium confidence that and extratropical storms are also summarized here. {14.8} the North American monsoon will arrive and persist later in the annual cycle, and high confidence in expansion of South American Monsoon TS.5.8.1 Monsoon Systems area. {14.2.3, 14.8.3 14.8.5} Global measures of monsoon by the area and summer precipitation are There is low confidence in projections of a small delay in the West likely to increase in the 21st century, while the monsoon circulation African rainy season, with an intensification of late-season rains. The weakens. Monsoon onset dates are likely to become earlier or not to limited skills of model simulations for the region suggest low confi- change much while monsoon withdrawal dates are likely to delay, result- dence in the projections. {14.2.4, 14.8.7} ing in a lengthening of the monsoon season in many regions (Figure TS.24). The increase in seasonal mean precipitation is pronounced in TS.5.8.2 Tropical Phenomena the East and South Asian summer monsoons while the change in other monsoon regions is subject to larger uncertainties. {14.2.1} Precipitation change varies in space, increasing in some regions and decreasing in some others. The spatial distribution of tropical rainfall There is medium confidence that monsoon-related interannual rainfall changes is likely shaped by the current climatology and ocean warm- variability will increase in the future. Future increase in precipitation ing pattern. The first effect is to increase rainfall near the currently extremes related to the monsoon is very likely in South America, Africa, rainy regions, and the second effect increases rainfall where the ocean East Asia, South Asia, Southeast Asia and Australia. {14.2.1, 14.8.5, warming exceeds the tropical mean. There is medium confidence that 14.8.7, 14.8.9, 14.8.11 14.8.13} tropical rainfall projections are more reliable for the seasonal than annual mean changes. {7.6.2, 12.4.5, 14.3.1} There is medium confidence that overall precipitation associated with the Asian-Australian monsoon will increase but with a north south There is medium confidence in future increase in seasonal mean pre- asymmetry: Indian monsoon rainfall is projected to increase, while cipitation on the equatorial flank of the Intertropical Convergence projected changes in the Australian summer monsoon rainfall are Zone and a decrease in precipitation in the subtropics including parts 105 Technical Summary Table TS.2 | Overview of projected regional changes and their relation to major climate phenomena. A phenomenon is considered relevant when there is both sufficient confidence that it has an influence on the given region, and when there is sufficient confidence that the phenomenon will change, particularly under the RCP4.5 or higher end scenarios. See Section 14.8 and Tables 14.2 and 14.3 for full assessment of the confidence in these changes, and their relevance for regional climate. {14.8; Tables 14.2, 14.3} Regions Projected Major Changes in Relation to Phenomena Arctic Wintertime changes in temperature and precipitation resulting from the small projected increase in North Atlantic Oscillation (NAO); enhanced warming {14.8.2} and sea ice melting; significant increase in precipitation by mid-century due mostly to enhanced precipitation in extratropical cyclones. North America Monsoon precipitation will shift later in the annual cycle; increased precipitation in extratropical cyclones will lead to large increases in wintertime {14.8.3} precipitation over the northern third of the continent; extreme precipitation increases in tropical cyclones making landfall along the western coast of USA and Mexico, the Gulf Mexico, and the eastern coast of USA and Canada. Central America and Caribbean Projected reduction in mean precipitation and increase in extreme precipitation; more extreme precipitation in tropical cyclones making landfall along {14.8.4} the eastern and western coasts. South America A southward displaced South Atlantic Convergence Zone increases precipitation in the southeast; positive trend in the Southern Annular Mode displaces {14.8.5} the extratropical storm track southward, decreasing precipitation in central Chile and increasing it at the southern tip of South America. Europe and Mediterranean Enhanced extremes of storm-related precipitation and decreased frequency of storm-related precipitation over the eastern Mediterranean. {14.8.6} Africa Enhanced summer monsoon precipitation in West Africa; increased short rain in East Africa due to the pattern of Indian Ocean warming; increased TS {14.8.7} rainfall extremes of landfall cyclones on the east coast (including Madagascar). Central and North Asia Enhanced summer precipitation; enhanced winter warming over North Asia. {14.8.8} East Asia Enhanced summer monsoon precipitation; increased rainfall extremes of landfall typhoons on the coast; reduction in the midwinter suppression of {14.8.9} extratropical cyclones. West Asia Increased rainfall extremes of landfall cyclones on the Arabian Peninsula; decreased precipitation in northwest Asia due to a northward shift of extra- {14.8.10} tropical storm tracks. South Asia Enhanced summer monsoon precipitation; increased rainfall extremes of landfall cyclones on the coasts of the Bay of Bengal and Arabian Sea. {14.8.11} Southeast Asia Reduced precipitation in Indonesia during July to October due to the pattern of Indian Ocean warming; increased rainfall extremes of landfall cyclones {14.8.12} on the coasts of the South China Sea, Gulf of Thailand and Andaman Sea. Australia and New Zealand Summer monsoon precipitation may increase over northern Australia; more frequent episodes of the zonal South Pacific Convergence Zone may reduce {14.8.13} precipitation in northeastern Australia; increased warming and reduced precipitation in New Zealand and southern Australia due to projected positive trend in the Southern Annular Mode; increased extreme precipitation associated with tropical and extratropical storms Pacific Islands Tropical convergence zone changes affect rainfall and its extremes; more extreme precipitation associated with tropical cyclones {14.8.14} Antarctica Increased warming over Antarctic Peninsula and West Antarctic related to the positive trend in the Southern Annular Mode; increased precipitation in {14.8.15} coastal areas due to a poleward shift of storm track. of North and Central Americas, the Caribbean, South America, Africa It is currently not possible to assess how the Madden Julian Oscilla- and West Asia. There is medium confidence that the interannual occur- ­ tion will change owing to the poor skill in model simulations of this rence of zonally oriented South Pacific Convergence Zone events will intraseasonal phenomenon and the sensitivity to ocean warming pat- increase, leading possibly to more frequent droughts in the southwest terns. Future projections of regional climate extremes in West Asia, Pacific. There is medium confidence that the South Atlantic Conver- Southeast Asia and Australia are therefore of low confidence. {9.5.2, gence Zone will shift southwards, leading to a precipitation increase 14.3.4, 14.8.10, 14.8.12, 14.8.13} over southeastern South America and a reduction immediately north ­ of the convergence zone. {14.3.1, 14.8.3 14.8.5, 14.8.7, 14.8.11, TS.5.8.3 El Nino-Southern Oscillation 14.8.14} There is high confidence that the El Nino-Southern Oscillation (ENSO) The tropical Indian Ocean is likely to feature a zonal pattern with will remain the dominant mode of natural climate variability in the reduced warming and decreased rainfall in the east (including Indone- 21st century with global influences in the 21st century, and that sia), and enhanced warming and increased rainfall in the west (includ- regional rainfall variability it induces likely intensifies. Natural varia- ing East Africa). The Indian Ocean dipole mode of interannual variabil- tions of the amplitude and spatial pattern of ENSO are so large that ity is very likely to remain active, affecting climate extremes in East confidence in any projected change for the 21st century remains low. Africa, Indonesia and Australia. {14.3.3, 14.8.7, 14.8.12} The projected change in El Nino amplitude is small for both RCP4.5 and RCP8.5 compared to the spread of the change among models (Figure There is low confidence in the projections for the tropical Atlantic TS.25). Over the North Pacific and North America, patterns of tempera- both for the mean and interannual modes, because of large errors in ture and precipitation anomalies related to El Nino and La Nina (tele- model simulations in the region. Future projections in Atlantic hurri- connections) are likely to move eastwards in the future (medium confi- canes and tropical South American and West African precipitation are dence), while confidence is low in changes in climate impacts on other therefore of low confidence. {14.3.4, 14.6.1, 14.8.5,14.8.7} regions including Central and South Americas, the Caribbean, Africa, most of Asia, Australia and most Pacific Islands. In a warmer climate, the increase in atmospheric moisture intensifies temporal variability 106 Technical Summary 60 60 60 (b) NAMS (c) NAF (d) SAS 40 40 40 Change (% or days) 90 % tile 20 20 20 75 % tile 0 50 % tile 0 0 25 % tile -20 -20 -20 10 % tile -40 -40 -40 -60 -60 -60 Pav Psd R5d DUR Pav Psd R5d DUR Pav Psd R5d DUR 40 60 (a) GLOBAL Regional land monsoon domain (e) EAS 40 N 40 20 20 N EAS 20 NAMS NAF SAS EQ 0 0 20 S SAF AUSMC -20 40 S SAMS TS 120 W 60 W 0 60 E 120 E 180 -40 -20 GMA GMI Psd R5d DUR -60 Pav Psd R5d DUR 60 60 60 (h) SAMS (g) SAF (f) AUSMC 40 40 40 20 20 20 0 0 0 -20 -20 -20 -40 -40 -40 -60 -60 -60 Pav Psd R5d DUR Pav Psd R5d DUR Pav Psd R5d DUR Figure TS.24 | Future change in monsoon statistics between the present-day (1986 2005) and the future (2080 2099) based on CMIP5 ensemble from RCP2.6 (dark blue; 18 models), RCP4.5 (blue; 24), RCP6.0 (yellow; 14), and RCP8.5 (red; 26) simulations. (a) GLOBAL: Global monsoon area (GMA), global monsoon intensity (GMI), standard deviation of inter-annual variability in seasonal precipitation (Psd), seasonal maximum 5-day precipitation total (R5d) and monsoon season duration (DUR). Regional land monsoon domains determined by 24 multi-model mean precipitation in the present-day. (b) (h) Future change in regional land monsoon statistics: seasonal average precipitation (Pav), Psd, R5d, and DUR in (b) North America (NAMS), (c) North Africa (NAF), (d) South Asia (SAS), (e) East Asia (EAS), (f) Australia-Maritime continent (AUSMC), (g) South Africa (SAF) and (h) South America (SAMS). Units are % except for DUR (days). Box-and-whisker plots show the 10th, 25th, 50th, 75th and 90th percentiles. All the indices are calculated for the summer season (May to September for the Northern, and November to March for the Southern Hemisphere) over each model s monsoon domains. {Figures 14.3, 14.4, 14.6, 14.7} 1.2 of precipitation even if atmospheric circulation variability remains the Standard deviation of Nino3 index (°C) same. This applies to ENSO-induced precipitation variability but the possibility of changes in ENSO teleconnections complicates this gener- 1 al conclusion, making it somewhat regional-dependent. {12.4.5, 14.4, 14.8.3 14.8.5, 14.8.7, 14.8.9, 14.8.11 14.8.14} 0.8 TS.5.8.4 Cyclones 0.6 Projections for the 21st century indicate that it is likely that the global frequency of tropical cyclones will either decrease or remain essentially unchanged, concurrent with a likely increase in both global mean trop- 0.4 ical cyclone maximum wind speed and rain rates (Figure TS.26). The PI 20C RCP4.5 RCP8.5 influence of future climate change on tropical cyclones is likely to vary by region, but there is low confidence in region-specific projections. Figure TS.25 | Standard deviation in CMIP5 multi-model ensembles of sea surface The frequency of the most intense storms will more likely than not temperature variability over the eastern equatorial Pacific Ocean (Nino3 region: 5°S to 5°N, 150°W to 90°W), a measure of El Nino amplitude, for the pre-industrial (PI) control increase in some basins. More extreme precipitation near the centers and 20th century (20C) simulations, and 21st century projections using RCP4.5 and of tropical cyclones making landfall is projected in North and Central RCP8.5. Open circles indicate multi-model ensemble means, and the red cross symbol is America, East Africa, West, East, South and Southeast Asia as well as the observed standard deviation for the 20th century. Box-and-whisker plots show the in Australia and many Pacific islands (medium confidence). {14.6.1, 16th, 25th, 50th, 75th and 84th percentiles. {Figure 14.14} 14.8.3, 14.8.4, 14.8.7, 14.8.9 14.8.14} 107 Technical Summary The global number of extratropical cyclones is unlikely to decrease by North America and Eurasia. The austral summer/autumn positive trend more than a few percent and future changes in storms are likely to in Southern Annular Mode (SAM) is likely to weaken considerably as be small compared to natural interannual variability and substantial stratospheric ozone recovers through the mid-21st century with some, variations between models. A small poleward shift is likely in the SH but not very well documented, implications for South America, Africa, storm track but the magnitude of this change is model dependent. It is Australia, New Zealand and Antarctica. {11.3.2, 14.5.2,14.8.5, 14.8.7, unlikely that the response of the North Atlantic storm track in climate 14.8.13, 14.8.15} projections is a simple poleward shift. There is medium confidence in a projected poleward shift in the North Pacific storm track. There is low TS.5.8.6 Additional Phenomena confidence in the impact of storm track changes on regional climate at the surface. More precipitation in extratropical cyclones leads to a It is unlikely that the Atlantic Multi-decadal Oscillation (AMO will winter precipitation increase in Arctic, Northern Europe, North America change its behaviour as the mean climate changes. However, natural and the mid-to-high-latitude SH. {11.3.2, 12.4.4, 14.6.2, 14.8.2, 14.8.3, fluctuations in the AMO over the coming few decades are likely to 14.8.5, 14.8.6, 14.8.13, 14.8.15} influence regional climates at least as strongly as will human-induced changes with implications for Atlantic major hurricane frequency, the TS.5.8.5 Annular and Dipolar Modes of Variability West African monsoon and North American and European summer con- TS ditions. {14.2.4, 14.5.1, 14.6.1, 14.7.6, 14.8.2, 14.8.3, 14.8.6, 14.8.8} Future boreal wintertime North Atlantic Oscillation (NAO is very likely to exhibit large natural variations as observed in the past. The NAO is There is medium confidence that the frequency of NH and SH blocking likely to become slightly more positive (on average), with some, but not will not increase, while the trends in blocking intensity and persistence very well documented implications for winter conditions in the Arctic, remain uncertain. {Box 14.2} Western North Pacific North Atlantic 50 200 % Eastern North Pacific 50 North Indian % Change % Change 0 50 50 0 % Change % Change 50 insf. d. insf. d. 0 0 50 -100 % I II IIII IV 50 I II IIII IV 50 I II IIII IV I II IIII IV South Pacific South Indian 50 50 % Change % Change insf. d. insf. d. 0 0 Tropical Cyclone (TC) Metrics: 50 I All TC frequency 50 II Category 4-5 TC frequency I II IIII IV III Lifetime Maximum Intensity I II IIII IV IV Precipitation rate SOUTHERN HEMISPHERE GLOBAL NORTHERN HEMISPHERE 50 50 50 % Change % Change % Change insf. d. insf. d. 0 0 0 insf. d. insf. d. 50 50 50 I II IIII IV I II IIII IV I II IIII IV Figure TS.26 | Projected changes in tropical cyclone statistics. All values represent expected percent change in the average over period 2081 2100 relative to 2000 2019, under an A1B-like scenario, based on expert judgement after subjective normalization of the model projections. Four metrics were considered: the percent change in I) the total annual frequency of tropical storms, II) the annual frequency of Category 4 and 5 storms, III) the mean Lifetime Maximum Intensity (LMI; the maximum intensity achieved during a storm s lifetime) and IV) the precipitation rate within 200 km of storm center at the time of LMI. For each metric plotted, the solid blue line is the best guess of the expected percent change, and the coloured bar provides the 67% (likely) confidence interval for this value (note that this interval ranges across 100% to +200% for the annual frequency of Category 4 and 5 storms in the North Atlantic). Where a metric is not plotted, there are insufficient data (denoted X) available to complete an assessment. A randomly drawn (and coloured) selection of historical storm tracks are underlaid to identify regions of tropical cyclone activity. See Section 14.6.1 for details. {14.6.1} 108 Technical Summary Thematic Focus Elements TFE.9 | Climate Extremes Assessing changes in climate extremes poses unique challenges, not just because of the intrinsically rare nature of these events, but because they invariably happen in conjunction with disruptive conditions. They are strongly influenced by both small- and large-scale weather patterns, modes of variability, thermodynamic processes, land atmosphere feedbacks and antecedent conditions. Much progress has been made since the IPCC Fourth Assessment Report (AR4) including the comprehensive assessment of extremes undertaken by the IPCC Special Report on Man- aging the Risk of Extreme Events and Disasters to Advance Climate Change Adaptation (SREX) but also because of the amount of observational evidence available, improvements in our understanding and the ability of models to simulate extremes. {1.3.3, 2.6, 7.6, 9.5.4} For some climate extremes such as droughts, floods and heat waves, several factors need to be combined to produce an extreme event. Analyses of rarer extremes such as 1-in-20- to 1-in-100-year events using Extreme Value Theory are making their way into a growing body of literature. Other recent advances concern the notion of fraction of TS attributable risk that aims to link a particular extreme event to specific causal relationships. {1.3.3, 2.6.1, 2.6.2, 10.6.2, 12.4.3; Box 2.4} TFE.9, Table 1 indicates the changes that have been observed in a range of weather and climate extremes over the last 50 years, the assessment of the human contribution to those changes, and how those extremes are expected to change in the future. The table also compares the current assessment with that of the AR4 and the SREX where applicable. {2.6, 3.7, 10.6, 11.3, 12.4, 14.6} Temperature Extremes, Heat Waves and Warm Spells It is very likely that both maximum and minimum temperature extremes have warmed over most land areas since the mid-20th century. These changes are well simulated by current climate models, and it is very likely that anthro- pogenic forcing has affected the frequency of these extremes and virtually certain that further changes will occur. This supports AR4 and SREX conclusions although with greater confidence in the anthropogenic forcing compo- nent. {2.6.1, 9.5.4, 10.6.1, 12.4.3} For land areas with sufficient data there has been an overall increase in the number of warm days and nights. Simi- lar decreases are seen in the number of cold days and nights. It is very likely that increases in unusually warm days and nights and/or reductions in unusually cold days and nights including frosts have occurred over this period across most continents. Warm spells or heat waves containing consecutive extremely hot days or nights are often associ- ated with quasi-stationary anticyclonic circulation anomalies and are also affected by pre-existing soil conditions and the persistence of soil moisture anomalies that can amplify or dampen heat waves particularly in moisture- limited regions. Most global land areas, with a few exceptions, have experienced more heat waves since the middle of the 20th century. Several studies suggest that increases in mean temperature account for most of the changes in heat wave frequency, however, heat wave intensity/amplitude is highly sensitive to changes in temperature vari- ability and the shape of the temperature distribution and heat wave definition also plays a role. Although in some regions instrumental periods prior to the 1950s had more heat waves (e.g., USA), for other regions such as Europe, an increase in heat wave frequency in the period since the 1950s stands out in long historical temperature series. {2.6, 2.6.1, 5.5.1; Box 2.4; Tables 2.12, 2.13; FAQ 2.2} The observed features of temperature extremes and heat waves are well simulated by climate models and are simi- lar to the spread among observationally based estimates in most regions. Regional downscaling now offers cred- ible information on the spatial scales required for assessing extremes and improvements in the simulation of the El Nino-Southern Oscillation from Coupled Model Intercomparison Project Phase 3 (CMIP3) to Phase 5 (CMIP5) and other large-scale phenomena is crucial. However simulated changes in frequency and intensity of extreme events is limited by observed data availability and quality issues and by the ability of models to reliably simulate certain feedbacks and mean changes in key features of circulation such as blocking. {2.6, 2.7, 9.4, 9.5.3, 9.5.4, 9.6, 9.6.1, 10.3, 10.6, 14.4; Box 14.2} Since AR4, the understanding of mechanisms and feedbacks leading to changes in extremes has improved. There continues to be strengthening evidence for a human influence on the observed frequency of extreme temperatures and heat waves in some regions. Near-term (decadal) projections suggest likely increases in temperature extremes but with little distinguishable separation between emissions scenarios (TFE.9, Figure 1). Changes may proceed at (continued on next page) 109 TS 110 TFE.9, Table 1 | Extreme weather and climate events: Global-scale assessment of recent observed changes, human contribution to the changes and projected further changes for the early (2016 2035) and late (2081 2100) 21st century. Bold indicates where the AR5 (black) provides a revised* global-scale assessment from the Special Report on Managing the Risk of Extreme Events and Disasters to Advance Climate Change Adaptation (SREX, blue) or AR4 (red). Projections for early 21st century were not provided in previous assessment reports. Projections in the AR5 are relative to the reference period of 1986 2005, and use the new RCP scenarios unless otherwise specified. See the Glossary for definitions of extreme weather and climate events. Phenomenon and Assessment that changes occurred (typically Assessment of a human Likelihood of further changes Technical Summary direction of trend since 1950 unless otherwise indicated) contribution to observed changes Early 21st century Late 21st century Warmer and/or fewer Very likely {2.6} Very likely {10.6} Likely {11.3} Virtually certain {12.4} cold days and nights Very likely Likely Virtually certain over most land areas Very likely Likely Virtually certain  Warmer and/or more Very likely {2.6} Very likely {10.6} Likely {11.3} Virtually certain {12.4} frequent hot days and Very likely Likely Virtually certain nights over most land areas Very likely Likely (nights only) Virtually certain Warm spells/heat waves. Medium confidence on a global scale Likelya Not formally assessedb Very likely {12.4} Frequency and/or duration Likely in large parts of Europe, Asia and Australia {2.6} {10.6} {11.3} increases over most Medium confidence in many (but not all) regions Not formally assessed Very likely land areas Likely More likely than not Very likely Heavy precipitation events. Likely more land areas with increases than decreasesc Medium confidence Likely over many land areas Very likely over most of the mid-latitude land Increase in the frequency, {2.6} {7.6, 10.6} {11.3} masses and over wet tropical regions {12.4} intensity, and/or amount Likely more land areas with increases than decreases Medium confidence Likely over many areas of heavy precipitation Likely over most land areas More likely than not Very likely over most land areas Low confidence on a global scale Low confidence {10.6} Low confidenceg {11.3} Likely (medium confidence) on a regional to Increases in intensity Likely changes in some regionsd {2.6} global scaleh {12.4} and/or duration of drought Medium confidence in some regions Medium confidence f Medium confidence in some regions Likely in many regions, since 1970e More likely than not Likelye Low confidence in long term (centennial) changes Low confidencei {10.6} Low confidence More likely than not in the Western North Pacific Virtually certain in North Atlantic since 1970 {2.6} {11.3} and North Atlantic j {14.6} Increases in intense tropical cyclone activity Low confidence Low confidence More likely than not in some basins Likely in some regions, since 1970 More likely than not Likely Increased incidence and/or Likely (since 1970) {3.7} Likely k {3.7} Likely l {13.7} Very likely l {13.7} magnitude of extreme Likely (late 20th century) Likely k Very likely m high sea level Likely More likely than not k Likely * The direct comparison of assessment findings between reports is difficult. For some climate variables, different aspects have been assessed, and the revised guidance note on uncertainties has been used for the SREX and AR5. The availability of new information, improved scientific understanding, continued analyses of data and models, and specific differences in methodologies applied in the assessed studies, all contribute to revised assessment findings. Notes: a Attribution is based on available case studies. It is likely that human influence has more than doubled the probability of occurrence of some observed heat waves in some locations. b Models project near-term increases in the duration, intensity and spatial extent of heat waves and warm spells. c In most continents, confidence in trends is not higher than medium except in North America and Europe where there have been likely increases in either the frequency or intensity of heavy precipitation with some seasonal and/or regional variation. It is very likely that there have been increases in central North America. d The frequency and intensity of drought has likely increased in the Mediterranean and West Africa and likely decreased in central North America and north-west Australia. e AR4 assessed the area affected by drought. f SREX assessed medium confidence that anthropogenic influence had contributed to some changes in the drought patterns observed in the second half of the 20th century, based on its attributed impact on precipitation and temperature changes. SREX assessed low confidence in the attribution of changes in droughts at the level of single regions. g There is low confidence in projected changes in soil moisture. h Regional to global-scale projected decreases in soil moisture and increased agricultural drought are likely (medium confidence) in presently dry regions by the end of this century under the RCP8.5 scenario. Soil moisture drying in the Mediterranean, Southwest USA and southern African regions is consistent with projected changes in Hadley circulation and increased surface temperatures, so there is high confidence in likely surface drying in these regions by the end of this century under the RCP8.5 scenario. i There is medium confidence that a reduction in aerosol forcing over the North Atlantic has contributed at least in part to the observed increase in tropical cyclone activity since the 1970s in this region. j Based on expert judgment and assessment of projections which use an SRES A1B (or similar) scenario. k Attribution is based on the close relationship between observed changes in extreme and mean sea level. l There is high confidence that this increase in extreme high sea level will primarily be the result of an increase in mean sea level. There is low confidence in region-specific projections of storminess and associated storm surges. m SREX assessed it to be very likely that mean sea level rise will contribute to future upward trends in extreme coastal high water levels. Technical Summary TFE.9 (continued) a different rate than the mean warming however, with several studies showing that projected European high- percentile summer temperatures will warm faster than mean temperatures. Future changes associated with the warming of temperature extremes in the long-term are virtually certain and scale with the strength of emissions scenario, that is, greater anthropogenic emissions correspond to greater warming of extremes (TFE.9, Figure 1). For high-emissions scenarios, it is likely that, in most land regions, a current 1-in-20-year maximum temperature event (continued on next page) (a) Cold days (TX10p) (b) Wettest consecutive five days (RX5day) 12 18 12 historical 18 RCP2.6 20 20 RCP4.5 10 10 RCP8.5 15 15 Exceedance rate (%) Relative change (%) 8 8 TS 10 10 6 6 5 5 4 4 historical 2 RCP2.6 2 0 0 RCP4.5 RCP8.5 0 0 5 5 1960 1980 2000 2020 2040 2060 2080 2100 1960 1980 2000 2020 2040 2060 2080 2100 Year Year (c) Warm days (TX90p) (d) Precipitation from very wet days (R95p) historical 18 historical 18 70 RCP2.6 70 RCP2.6 60 60 RCP4.5 RCP4.5 60 RCP8.5 60 RCP8.5 Exceedance rate (%) Relative change (%) 50 50 40 40 40 40 30 30 20 20 20 20 0 0 10 10 1960 1980 2000 2020 2040 2060 2080 2100 1960 1980 2000 2020 2040 2060 2080 2100 Year Year (e) Future change in 20yr RV of warmest daily Tmax (TXx) (f) Future RP for present day 20yr RV of wettest day (RX1day) 29 29 (°C) Years -2 -1.5 -1 -0.5 0 0.5 1 1.5 2 3 4 5 7 9 11 2 4 6 8 10 12 14 16 18 20 TFE.9, Figure 1 | Global projections of the occurrence of (a) cold days (TX10p)- percentage of days annually with daily maximum surface air temperature (Tmax) below the 10th percentile of Tmax for 1961 to 1990, (b) wettest consecutive 5 days (RX5day) percentage change relative to 1986 2005 in annual maximum consecutive 5-day precipitation totals, (c) warm days (TX90p) percentage of days annually with daily maximum surface air temperature (Tmax) exceeding the 90th percentile of Tmax for 1961 to 1990 and (d) very wet day precipitation (R95p) percentage change relative to 1986 2005 of annual precipitation from days >95th percentile. Results are shown from CMIP5 for the RCP2.6, RCP4.5 and RCP8.5 scenarios. Solid lines indicate the ensemble median and shading indicates the interquartile spread between individual projections (25th and 75th percentiles). Maps show (e) the change from 1986 2005 to 2081 2100 in 20-year return values (RV) of daily maximum temperatures, TXx, and (f) the 2081 2100 return period (RP) for rare daily precipitation values, RX1day, that have a 20-year return period during 1986 2005. Both maps are based on the CMIP5 RCP8.5 scenario. The number of models used to calculate the multi-model mean is indicated in each panel. See Box 2.4, Table 1 for index definitions. {Figures 11.17, 12.14, 12.26, 12.27} 111 Technical Summary TFE.9 (continued) will at least double in frequency but in many regions will become an annual or a 1-in-2-year event by the end of the 21st century. The magnitude of both high and low temperature extremes is expected to increase at least at the same rate as the mean, but with 20-year return values for low temperature events projected to increase at a rate greater than winter mean temperatures in most regions. {10.6.1, 11.3.2, 12.4.3} Precipitation Extremes It is likely that the number of heavy precipitation events over land has increased in more regions than it has decreased in since the mid-20th century, and there is medium confidence that anthropogenic forcing has contrib- uted to this increase. {2.6.2, 10.6.1} There has been substantial progress between CMIP3 and CMIP5 in the ability of models to simulate more realistic precipitation extremes. However, evidence suggests that the majority of models underestimate the sensitivity of extreme precipitation to temperature variability or trends especially in the tropics, which implies that models may TS underestimate the projected increase in extreme precipitation in the future. While progress has been made in understanding the processes that drive extreme precipitation, challenges remain in quantifying cloud and convec- tive effects in models for example. The complexity of land surface and atmospheric processes limits confidence in regional projections of precipitation change, especially over land, although there is a component of a wet-get- wetter and dry-get-drier response over oceans at the large scale. Even so, there is high confidence that, as the climate warms, extreme precipitation rates (e.g., on daily time scales) will increase faster than the time average. Changes in local extremes on daily and sub-daily time scales are expected to increase by roughly 5 to 10% per °C of warming (medium confidence). {7.6, 9.5.4} For the near and long term, CMIP5 projections confirm a clear tendency for increases in heavy precipitation events in the global mean seen in the AR4, but there are substantial variations across regions (TFE.9, Figure 1). Over most of the mid-latitude land masses and over wet tropical regions, extreme precipitation will very likely be more intense and more frequent in a warmer world. {11.3.2, 12.4.5} Floods and Droughts There continues to be a lack of evidence and thus low confidence regarding the sign of trend in the magnitude and/or frequency of floods on a global scale over the instrumental record. There is high confidence that past floods larger than those recorded since 1900 have occurred during the past five centuries in northern and central Europe, western Mediterranean region, and eastern Asia. There is medium confidence that modern large floods are com- parable to or surpass historical floods in magnitude and/or frequency in the Near East, India and central North America. {2.6.2, 5.5.5} Compelling arguments both for and against significant increases in the land area affected by drought and/or dry- ness since the mid-20th century have resulted in a low confidence assessment of observed and attributable large- scale trends. This is due primarily to a lack and quality of direct observations, dependencies of inferred trends on the index choice, geographical inconsistencies in the trends and difficulties in distinguishing decadal scale variability from long term trends. On millennial time scales, there is high confidence that proxy information provides evidence of droughts of greater magnitude and longer duration than observed during the 20th century in many regions. There is medium confidence that more megadroughts occurred in monsoon Asia and wetter conditions prevailed in arid Central Asia and the South American monsoon region during the Little Ice Age (1450 to 1850) compared to the Medieval Climate Anomaly (950 to 1250). {2.6.2, 5.5.4, 5.5.5, 10.6.1} Under the Representative Concentration Pathway RCP8.5, projections by the end of the century indicate an increased risk of drought is likely (medium confidence) in presently dry regions linked to regional to global-scale projected decreases in soil moisture. Soil moisture drying is most prominent in the Mediterranean, Southwest USA, and southern Africa, consistent with projected changes in the Hadley Circulation and increased surface tempera- tures, and surface drying in these regions is likely (high confidence) by the end of the century under RCP8.5. {12.4.5} Extreme Sea Level It is likely that the magnitude of extreme high sea level events has increased since 1970 and that most of this rise can be explained by increases in mean sea level. When mean sea level changes is taken into account, changes in extreme high sea levels are reduced to less than 5 mm yr 1 at 94% of tide gauges. In the future it is very likely that there will be a significant increase in the occurrence of sea level extremes and similarly to past observations, this increase will primarily be the result of an increase in mean sea level. {3.7.5, 13.7.2} (continued on next page) 112 Technical Summary TFE.9 (continued) Tropical and Extratropical Cyclones There is low confidence in long-term (centennial) changes in tropical cyclone activity, after accounting for past changes in observing capabilities. However over the satellite era, increases in the frequency and intensity of the strongest storms in the North Atlantic are robust (very high confidence). However, the cause of this increase is debated and there is low confidence in attribution of changes in tropical cyclone activity to human influence owing to insufficient observational evidence, lack of physical understanding of the links between anthropogenic drivers of climate and tropical cyclone activity and the low level of agreement between studies as to the relative importance of internal variability, and anthropogenic and natural forcings. {2.6.3, 10.6.1, 14.6.1} Some high-resolution atmospheric models have realistically simulated tracks and counts of tropical cyclones and models generally are able to capture the general characteristics of storm tracks and extratropical cyclones with evi- dence of improvement since the AR4. Storm track biases in the North Atlantic have improved slightly, but models still produce a storm track that is too zonal and underestimate cyclone intensity. {9.4.1, 9.5.4} TS While projections indicate that it is likely that the global frequency of tropical cyclones will either decrease or remain essentially unchanged, concurrent with a likely increase in both global mean tropical cyclone maximum wind speed and rainfall rates, there is lower confidence in region-specific projections of frequency and intensity. However, due to improvements in model resolution and downscaling techniques, it is more likely than not that the frequency of the most intense storms will increase substantially in some basins under projected 21st century warm- ing (see Figure TS.26). {11.3.2, 14.6.1} Research subsequent to the AR4 and SREX continues to support a likely poleward shift of storm tracks since the 1950s. However over the last century there is low confidence of a clear trend in storminess due to inconsistencies between studies or lack of long-term data in some parts of the world (particularly in the Southern Hemisphere (SH)). {2.6.4, 2.7.6} Despite systematic biases in simulating storm tracks, most models and studies are in agreement that the global number of extratropical cyclones is unlikely to decrease by more than a few per cent. A small poleward shift is likely in the SH storm track. It is more likely than not (medium confidence) for a projected poleward shift in the North Pacific storm track but it is unlikely that the response of the North Atlantic storm track is a simple poleward shift. There is low confidence in the magnitude of regional storm track changes, and the impact of such changes on regional surface climate. {14.6.2} 113 Technical Summary TS.6 Key Uncertainties The number of continuous observational time series measuring the strength of climate relevant ocean circulation features (e.g., the This final section of the Technical Summary provides readers with a meridional overturning circulation) is limited and the existing time short overview of key uncertainties in the understanding of the climate series are still too short to assess decadal and longer trends. {3.6}. system and the ability to project changes in response to anthropogenic influences. The overview is not comprehensive and does not describe in In Antarctica, available data are inadequate to assess the status detail the basis for these findings. These are found in the main body of of change of many characteristics of sea ice (e.g., thickness and this Technical Summary and in the underlying chapters to which each volume). {4.2.3} bullet points in the curly brackets. On a global scale the mass loss from melting at calving fronts and TS.6.1 Key Uncertainties in Observation of Changes in iceberg calving are not yet comprehensively assessed. The largest the Climate System uncertainty in estimated mass loss from glaciers comes from the Antarctic, and the observational record of ice ocean interactions There is only medium to low confidence in the rate of change of around both ice sheets remains poor. {4.3.3, 4.4} tropospheric warming and its vertical structure. Estimates of tro- TS pospheric warming rates encompass surface temperature warm- TS.6.2 Key Uncertainties in Drivers of Climate Change ing rate estimates. There is low confidence in the rate and vertical structure of the stratospheric cooling. {2.4.4} Uncertainties in aerosol cloud interactions and the associated radiative forcing remain large. As a result, uncertainties in aerosol Confidence in global precipitation change over land is low prior forcing remain the dominant contributor to the overall uncertainty to 1951 and medium afterwards because of data incompleteness. in net anthropogenic forcing, despite a better understanding of {2.5.1} some of the relevant atmospheric processes and the availability of global satellite monitoring. {2.2, 7.3 7.5, 8.5} Substantial ambiguity and therefore low confidence remains in the observations of global-scale cloud variability and trends. {2.5.6} The cloud feedback is likely positive but its quantification remains difficult. {7.2} There is low confidence in an observed global-scale trend in drought or dryness (lack of rainfall), due to lack of direct observa- Paleoclimate reconstructions and Earth System Models indicate tions, methodological uncertainties and choice and geographical that there is a positive feedback between climate and the carbon inconsistencies in the trends. {2.6.2} cycle, but confidence remains low in the strength of this feedback, particularly for the land. {6.4} There is low confidence that any reported long-term (centen- nial) changes in tropical cyclone characteristics are robust, after TS.6.3 Key Uncertainties in Understanding the Climate accounting for past changes in observing capabilities. {2.6.3} System and Its Recent Changes Robust conclusions on long-term changes in large-scale atmos- The simulation of clouds in AOGCMs has shown modest improve- pheric circulation are presently not possible because of large vari- ment since AR4; however, it remains challenging. {7.2, 9.2.1, 9.4.1, ability on interannual to decadal time scales and remaining differ- 9.7.2} ences between data sets. {2.7} Observational uncertainties for climate variables other than tem- Different global estimates of sub-surface ocean temperatures have perature, uncertainties in forcings such as aerosols, and limits in variations at different times and for different periods, suggesting process understanding continue to hamper attribution of changes that sub-decadal variability in the temperature and upper heat in many aspects of the climate system. {10.1, 10.3, 10.7} content (0 to to 700 m) is still poorly characterized in the historical record. {3.2} Changes in the water cycle remain less reliably modelled in both their changes and their internal variability, limiting confidence in Below ocean depths of 700 m the sampling in space and time is attribution assessments. Observational uncertainties and the large too sparse to produce annual global ocean temperature and heat effect of internal variability on observed precipitation also pre- content estimates prior to 2005. {3.2.4} cludes a more confident assessment of the causes of precipitation changes. {2.5.1, 2.5.4, 10.3.2} Observational coverage of the ocean deeper than 2000 m is still limited and hampers more robust estimates of changes in global Modelling uncertainties related to model resolution and incorpo- ocean heat content and carbon content. This also limits the quan- ration of relevant processes become more important at regional tification of the contribution of deep ocean warming to sea level scales, and the effects of internal variability become more signifi- rise. {3.2, 3.7, 3.8; Box 3.1} cant. Therefore, challenges persist in attributing observed change to external forcing at regional scales. {2.4.1, 10.3.1} 114 Technical Summary The ability to simulate changes in frequency and intensity of extreme events is limited by the ability of models to reliably simu- late mean changes in key features. {10.6.1} In some aspects of the climate system, including changes in drought, changes in tropical cyclone activity, Antarctic warming, Antarctic sea ice extent, and Antarctic mass balance, confidence in attribution to human influence remains low due to model- ling uncertainties and low agreement between scientific studies. {10.3.1, 10.5.2, 10.6.1} TS.6.4 Key Uncertainties in Projections of Global and Regional Climate Change Based on model results there is limited confidence in the predict- TS ability of yearly to decadal averages of temperature both for the global average and for some geographical regions. Multi-model results for precipitation indicate a generally low predictability. Short-term climate projection is also limited by the uncertainty in projections of natural forcing. {11.1, 11.2, 11.3.1, 11.3.6; Box 11.1} There is medium confidence in near-term projections of a north- ward shift of NH storm track and westerlies. {11.3.2} There is generally low confidence in basin-scale projections of sig- nificant trends in tropical cyclone frequency and intensity in the 21st century. {11.3.2, 14.6.1} Projected changes in soil moisture and surface run off are not robust in many regions. {11.3.2, 12.4.5} Several components or phenomena in the climate system could potentially exhibit abrupt or nonlinear changes, but for many phe- nomena there is low confidence and little consensus on the likeli- hood of such events over the 21st century. {12.5.5} There is low confidence on magnitude of carbon losses through CO2 or CH4 emissions to the atmosphere from thawing perma- frost. There is low confidence in projected future CH4 emissions from natural sources due to changes in wetlands and gas hydrate release from the sea floor. {6.4.3, 6.4.7} There is medium confidence in the projected contributions to sea level rise by models of ice sheet dynamics for the 21st century, and low confidence in their projections beyond 2100. {13.3.3} There is low confidence in semi-empirical model projections of global mean sea level rise, and no consensus in the scientific com- munity about their reliability. {13.5.2, 13.5.3} There is low confidence in projections of many aspects of climate phenomena that influence regional climate change, including changes in amplitude and spatial pattern of modes of climate vari- ability. {9.5.3, 14.2 14.7} 115 Introduction Chapter 2 Chapters 117 1 Introduction Coordinating Lead Authors: Ulrich Cubasch (Germany), Donald Wuebbles (USA) Lead Authors: Deliang Chen (Sweden), Maria Cristina Facchini (Italy), David Frame (UK/New Zealand), Natalie Mahowald (USA), Jan-Gunnar Winther (Norway) Contributing Authors: Achim Brauer (Germany), Lydia Gates (Germany), Emily Janssen (USA), Frank Kaspar (Germany), Janina Körper (Germany), Valérie Masson-Delmotte (France), Malte Meinshausen (Australia/Germany), Matthew Menne (USA), Carolin Richter (Switzerland), Michael Schulz (Germany), Uwe Schulzweida (Germany), Bjorn Stevens (Germany/USA), Rowan Sutton (UK), Kevin Trenberth (USA), Murat Türkeº (Turkey), Daniel S. Ward (USA) Review Editors: Yihui Ding (China), Linda Mearns (USA), Peter Wadhams (UK) This chapter should be cited as: Cubasch, U., D. Wuebbles, D. Chen, M.C. Facchini, D. Frame, N. Mahowald, and J.-G. Winther, 2013: Introduction. In: Climate Change 2013: The Physical Science Basis. Contribution of Working Group I to the Fifth Assessment Report of the Intergovernmental Panel on Climate Change [Stocker, T.F., D. Qin, G.-K. Plattner, M. Tignor, S.K. Allen, J. Boschung, A. Nauels, Y. Xia, V. Bex and P.M. Midgley (eds.)]. Cambridge University Press, Cambridge, United Kingdom and New York, NY, USA. 119 Table of Contents Executive Summary...................................................................... 121 1 1.1 Chapter Preview............................................................... 123 1.2 Rationale and Key Concepts of the WGI Contribution............................................................. 123 1.2.1 Setting the Stage for the Assessment......................... 123 1.2.2 Key Concepts in Climate Science................................ 123 1.2.3 Multiple Lines of Evidence for Climate Change.......... 129 1.3 Indicators of Climate Change....................................... 130 1.3.1 Global and Regional Surface Temperatures................ 131 1.3.2 Greenhouse Gas Concentrations................................ 132 1.3.3 Extreme Events.......................................................... 134 1.3.4 Climate Change Indicators......................................... 136 1.4 Treatment of Uncertainties........................................... 138 1.4.1 Uncertainty in Environmental Science........................ 138 1.4.2 Characterizing Uncertainty......................................... 138 1.4.3 Treatment of Uncertainty in IPCC............................... 139 1.4.4 Uncertainty Treatment in This Assessment................. 139 1.5 Advances in Measurement and Modelling Capabilities........................................................................ 142 1.5.1 Capabilities of Observations...................................... 142 1.5.2 Capabilities in Global Climate Modelling................... 144 Box 1.1: Description of Future Scenarios................................ 147 1.6 Overview and Road Map to the Rest of the Report.......................................................................... 151 1.6.1 Topical Issues............................................................. 151 References .................................................................................. 152 Appendix 1.A: Notes and Technical Details on Figures Displayed in Chapter 1................................................................ 155 Frequently Asked Questions FAQ 1.1 If Understanding of the Climate System Has Increased, Why Hasn t the Range of Temperature Projections Been Reduced?............ 140 120 Introduction Chapter 1 Executive Summary Observations of CO2 concentrations, globally averaged temper- ature and sea level rise are generally well within the range of Human Effects on Climate the extent of the earlier IPCC projections. The recently observed increases in CH4 and N2O concentrations are smaller than those Human activities are continuing to affect the Earth s energy assumed in the scenarios in the previous assessments. Each budget by changing the emissions and resulting atmospheric IPCC assessment has used new projections of future climate change concentrations of radiatively important gases and aerosols and that have become more detailed as the models have become more 1 by changing land surface properties. Previous assessments have advanced. Similarly, the scenarios used in the IPCC assessments have already shown through multiple lines of evidence that the climate is themselves changed over time to reflect the state of knowledge. The changing across our planet, largely as a result of human activities. The range of climate projections from model results provided and assessed most compelling evidence of climate change derives from observations in the first IPCC assessment in 1990 to those in the 2007 AR4 provides of the atmosphere, land, oceans and cryosphere. Unequivocal evidence an opportunity to compare the projections with the actually observed from in situ observations and ice core records shows that the atmos- changes, thereby examining the deviations of the projections from the pheric concentrations of important greenhouse gases such as carbon observations over time. {1.3.1, 1.3.2, 1.3.4; Figures 1.4, 1.5, 1.6, 1.7, dioxide (CO2), methane (CH4), and nitrous oxide (N2O) have increased 1.10} over the last few centuries. {1.2.2, 1.2.3} Climate change, whether driven by natural or human forcing, The processes affecting climate can exhibit considerable natural can lead to changes in the likelihood of the occurrence or variability. Even in the absence of external forcing, periodic and strength of extreme weather and climate events or both. Since chaotic variations on a vast range of spatial and temporal scales the AR4, the observational basis has increased substantially, so that are observed. Much of this variability can be represented by simple some extremes are now examined over most land areas. Furthermore, (e.g., unimodal or power law) distributions, but many components of more models with higher resolution and a greater number of regional the climate system also exhibit multiple states for instance, the gla- models have been used in the simulations and projections of extremes. cial interglacial cycles and certain modes of internal variability such {1.3.3; Figure 1.9} as El Nino-Southern Oscillation (ENSO). Movement between states can occur as a result of natural variability, or in response to external forc- Treatment of Uncertainties ing. The relationship among variability, forcing and response reveals the complexity of the dynamics of the climate system: the relationship For AR5, the three IPCC Working Groups use two metrics to com- between forcing and response for some parts of the system seems rea- municate the degree of certainty in key findings: (1) Confidence sonably linear; in other cases this relationship is much more complex. is a qualitative measure of the validity of a finding, based on the type, {1.2.2} amount, quality and consistency of evidence (e.g., data, mechanis- tic understanding, theory, models, expert judgment) and the degree Multiple Lines of Evidence for Climate Change of agreement1; and (2) Likelihood provides a quantified measure of uncertainty in a finding expressed probabilistically (e.g., based on sta- Global mean surface air temperatures over land and oceans tistical analysis of observations or model results, or both, and expert have increased over the last 100 years. Temperature measure- judgement)2. {1.4; Figure 1.11} ments in the oceans show a continuing increase in the heat content of the oceans. Analyses based on measurements of the Earth s radi- Advances in Measurement and Modelling Capabilities ative budget suggest a small positive energy imbalance that serves to increase the global heat content of the Earth system. Observations Over the last few decades, new observational systems, especial- from satellites and in situ measurements show a trend of significant ly satellite-based systems, have increased the number of obser- reductions in the mass balance of most land ice masses and in Arctic vations of the Earth s climate by orders of magnitude. Tools to sea ice. The oceans uptake of CO2 is having a significant effect on analyse and process these data have been developed or enhanced to the chemistry of sea water. Paleoclimatic reconstructions have helped cope with this large increase in information, and more climate proxy place ongoing climate change in the perspective of natural climate var- data have been acquired to improve our knowledge of past chang- iability. {1.2.3; Figure 1.3} es in climate. Because the Earth s climate system is characterized on multiple spatial and temporal scales, new observations may reduce the uncertainties surrounding the understanding of short timescale In this Report, the following summary terms are used to describe the available evidence: limited, medium, or robust; and for the degree of agreement: low, medium, or high. 1 A level of confidence is expressed using five qualifiers: very low, low, medium, high, and very high, and typeset in italics, e.g., medium confidence. For a given evidence and agreement statement, different confidence levels can be assigned, but increasing levels of evidence and degrees of agreement are correlated with increasing confidence (see Section 1.4 and Box TS.1 for more details). In this Report, the following terms have been used to indicate the assessed likelihood of an outcome or a result: Virtually certain 99 100% probability, Very likely 90 100%, 2 Likely 66 100%, About as likely as not 33 66%, Unlikely 0 33%, Very unlikely 0 10%, Exceptionally unlikely 0 1%. Additional terms (Extremely likely: 95 100%, More likely than not >50 100%, and Extremely unlikely 0 5%) may also be used when appropriate. Assessed likelihood is typeset in italics, e.g., very likely (see Section 1.4 and Box TS.1 for more details). 121 Chapter 1 Introduction processes quite rapidly. However, processes that occur over longer timescales may require very long observational baselines before much progress can be made. {1.5.1; Figure 1.12} Increases in computing speed and memory have led to the development of more sophisticated models that describe phys- ical, chemical and biological processes in greater detail. Model- 1 ling strategies have been extended to provide better estimates of the uncertainty in climate change projections. The model comparisons with observations have pushed the analysis and development of the models. The inclusion of long-term simulations has allowed incorporation of information from paleoclimate data to inform projections. Within uncertainties associated with reconstructions of past climate variables from proxy record and forcings, paleoclimate information from the Mid Holocene, Last Glacial Maximum, and Last Millennium have been used to test the ability of models to simulate realistically the magnitude and large-scale patterns of past changes. {1.5.2; Figures 1.13, 1.14} As part of the process of getting model analyses for a range of alter- native images of how the future may unfold, four new scenarios for future emissions of important gases and aerosols have been developed for the AR5, referred to as Representative Concentration Pathways (RCPs). {Box 1.1} 122 Introduction Chapter 1 1.1 Chapter Preview c ­onducts this critical revision through processes such as the peer review. At conferences and in the procedures that surround publica- This introductory chapter serves as a lead-in to the science presented in tion in peer-reviewed journals, scientific claims about environmental the Working Group I (WGI) contribution to the Intergovernmental Panel processes are analysed and held up to scrutiny. Even after publication, on Climate Change (IPCC) Fifth Assessment Report (AR5). Chapter 1 in findings are further analysed and evaluated. That is the self-correcting the IPCC Fourth Assessment Report (AR4) (Le Treut et al., 2007) provid- nature of the scientific process (more details are given in AR4 Chapter ed a historical perspective on the understanding of climate science and 1 and Le Treut et al., 2007). 1 the evidence regarding human influence on the Earth s climate system. Since the last assessment, the scientific knowledge gained through Science strives for objectivity but inevitably also involves choices and observations, theoretical analyses, and modelling studies has contin- judgements. Scientists make choices regarding data and models, which ued to increase and to strengthen further the evidence linking human processes to include and which to leave out. Usually these choices activities to the ongoing climate change. In AR5, Chapter 1 focuses on are uncontroversial and play only a minor role in the production of the concepts and definitions applied in the discussions of new findings research. Sometimes, however, the choices scientists make are sources in the other chapters. It also examines several of the key indicators for of disagreement and uncertainty. These are usually resolved by fur- a changing climate and shows how the current knowledge of those ther scientific enquiry into the sources of disagreement. In some cases, indicators compares with the projections made in previous assess- experts cannot reach a consensus view. Examples in climate science ments. The new scenarios for projected human-related emissions used include how best to evaluate climate models relative to observations, in this assessment are also introduced. Finally, the chapter discusses how best to evaluate potential sea level rise and how to evaluate prob- the directions and capabilities of current climate science, while the abilistic projections of climate change. In many cases there may be no detailed discussion of new findings is covered in the remainder of the definitive solution to these questions. The IPCC process is aimed at WGI contribution to the AR5. assessing the literature as it stands and attempts to reflect the level of reasonable scientific consensus as well as disagreement. 1.2 Rationale and Key Concepts of the To assess areas of scientific controversy, the peer-reviewed literature is WGI Contribution considered and evaluated. Not all papers on a controversial point can be discussed individually in an assessment, but every effort has been 1.2.1 Setting the Stage for the Assessment made here to ensure that all views represented in the peer-reviewed literature are considered in the assessment process. A list of topical The IPCC was set up in 1988 by the World Meteorological Organiza- issues is given in Table 1.3. tion and the United Nations Environment Programme to provide gov- ernments with a clear view of the current state of knowledge about The Earth sciences study the multitude of processes that shape our the science of climate change, potential impacts, and options for environment. Some of these processes can be understood through a ­ daptation and mitigation through regular assessments of the most ­idealized laboratory experiments, by altering a single element and then recent information published in the scientific, technical and socio-eco- tracing through the effects of that controlled change. However, as in nomic literature worldwide. The WGI contribution to the IPCC AR5 other natural and the social sciences, the openness of environmental assesses the current state of the physical sciences with respect to cli- systems, in terms of our lack of control of the boundaries of the system, mate change. This report presents an assessment of the current state their spatially and temporally multi-scale character and the complexity of research results and is not a discussion of all relevant papers as of interactions, often hamper scientists ability to definitively isolate would be included in a review. It thus seeks to make sure that the causal links. This in turn places important limits on the understand- range of scientific views, as represented in the peer-reviewed literature, ing of many of the inferences in the Earth sciences (e.g., Oreskes et is considered and evaluated in the assessment, and that the state of al., 1994). There are many cases where scientists are able to make the science is concisely and accurately presented. A transparent review inferences using statistical tools with considerable evidential support process ensures that disparate views are included (IPCC, 2012a). and with high degrees of confidence, and conceptual and numerical modelling can assist in forming understanding and intuition about the As an overview, Table 1.1 shows a selection of key findings from earlier interaction of dynamic processes. IPCC assessments. This table provides a non-comprehensive selection of key assessment statements from previous assessment reports 1.2.2 Key Concepts in Climate Science IPCC First Assessment Report (FAR, IPCC, 1990), IPCC Second Assess- ment Report (SAR, IPCC, 1996), IPCC Third Assessment Report (TAR, Here, some of the key concepts in climate science are briefly described; IPCC, 2001) and IPCC Fourth Assessment Report (AR4, IPCC, 2007) many of these were summarized more comprehensively in earlier IPCC with a focus on policy-relevant quantities that have been evaluated in assessments (Baede et al., 2001). We focus only on a certain number of each of the IPCC assessments. them to facilitate discussions in this assessment. Scientific hypotheses are contingent and always open to revision in First, it is important to distinguish the meaning of weather from cli- light of new evidence and theory. In this sense the distinguishing fea- mate. Weather describes the conditions of the atmosphere at a cer- tures of scientific enquiry are the search for truth and the willingness tain place and time with reference to temperature, pressure, humid- to subject itself to critical re-examination. Modern research science ity, wind, and other key parameters (meteorological elements); the 123 1 Table 1.1 | Historical overview of major conclusions of previous IPCC assessment reports. The table provides a non-comprehensive selection of key statements from previous assessment reports IPCC First Assessment Report (FAR; IPCC, 124 1990), IPCC Second Assessment Report (SAR; IPCC, 1996), IPCC Third Assessment Report (TAR; IPCC, 2001) and IPCC Fourth Assessment Report (AR4; IPCC, 2007) with a focus on global mean surface air temperature and sea level change as two policy relevant quantities that have been covered in IPCC since the first assessment report. Chapter 1 Topic FAR SPM Statement SAR SPM Statement TAR SPM Statement AR4 SPM Statement There is a natural greenhouse effect which already keeps Greenhouse gas concentrations have Emissions of greenhouse gases and aerosols Global atmospheric concentrations of carbon dioxide, methane the Earth warmer than it would otherwise be. Emissions continued to increase. These trends due to human activities continue to alter the and nitrous oxide have increased markedly as a result of human resulting from human activities are substantially increasing can be attributed largely to human atmosphere in ways that are expected to affect activities since 1750 and now far exceed pre-industrial values the atmospheric concentrations of the greenhouse gases activities, mostly fossil fuel use, land the climate. The atmospheric concentration of determined from ice cores spanning many thousands of years. carbon dioxide, methane, chlorofluorocarbons and nitrous use change and agriculture. CO2 has increased by 31% since 1750 and that The global increases in carbon dioxide concentration are due oxide. These increases will enhance the greenhouse effect, of methane by 151%. primarily to fossil fuel use and land use change, while those of resulting on average in an additional warming of the Earth s Anthropogenic aerosols are short- methane and nitrous oxide are primarily due to agriculture. Human and Natural lived and tend to produce negative Anthropogenic aerosols are short-lived and Drivers of Climate Change surface. radiative forcing. mostly produce negative radiative forcing by Very high confidence that the global average net effect of human Continued emissions of these gases at present rates would their direct effect. There is more evidence for activities since 1750 has been one of warming, with a radiative commit us to increased concentrations for centuries ahead. their indirect effect, which is negative, although forcing of +1.6 [+0.6 to +2.4] W m 2. of very uncertain magnitude. Natural factors have made small contributions to radiative forcing over the past century. Global mean surface air temperature has increased by 0.3°C Climate has changed over the past An increasing body of observations gives a col- Warming of the climate system is unequivocal, as is now evident to 0.6°C over the last 100 years, with the five global-aver- century. Global mean surface tem- lective picture of a warming world and other from observations of increases in global average air and ocean age warmest years being in the 1980s. perature has increased by between changes in the climate system. temperatures, widespread melting of snow and ice, and rising about 0.3 and 0.6°C since the late global average sea level. 19th century. Recent years have been The global average temperature has increased among the warmest since 1860, de- since 1861. Over the 20th century the increase Eleven of the last twelve years (1995 2006) rank among the 12 Temperature spite the cooling effect of the 1991 has been 0.6°C. warmest years in the instrumental record of global surface tem- Direct perature (since 1850). The updated 100-year linear trend (1906 Mt. Pinatubo volcanic eruption. Some important aspects of climate appear not Observations to 2005) of 0.74°C [0.56°C to 0.92°C] is therefore larger than the of Recent to have changed. corresponding trend for 1901 to 2000 given in the TAR of 0.6°C Climate [0.4°C to 0.8°C]. Change Some aspects of climate have not been observed to change. Over the same period global sea level has increased by 10 Global sea level has risen by between Tide gauge data show that global average sea Global average sea level rose at an average rate of 1.8 [1.3 to to 20 cm These increases have not been smooth with time, 10 and 25 cm over the past 100 years level rose between 0.1 and 0.2 m during the 2.3] mm per year over 1961 to 2003. The rate was faster over Sea Level nor uniform over the globe. and much of the rise may be related 20th century. 1993 to 2003: about 3.1 [2.4 to 3.8] mm per year. The total 20th to the increase in global mean tem- century rise is estimated to be 0.17 [0.12 to 0.22] m. perature. Climate varies naturally on all timescales from hundreds The limited available evidence from New analyses of proxy data for the Northern Palaeoclimatic information supports the interpretation that the of millions of years down to the year-to-year. Prominent in proxy climate indicators suggests Hemisphere indicate that the increase in tem- warmth of the last half century is unusual in at least the previous the Earth s history have been the 100,000 year glacial in- that the 20th century global mean perature in the 20th century is likely to have 1,300 years. terglacial cycles when climate was mostly cooler than at temperature is at least as warm as been the largest of any century during the past present. Global surface temperatures have typically varied any other century since at least 1400 1,000 years. It is also likely that, in the Northern The last time the polar regions were significantly warmer than by 5°C to 7°C through these cycles, with large changes in AD. Data prior to 1400 are too sparse Hemisphere, the 1990s was the warmest decade present for an extended period (about 125,000 years ago), re- ice volume and sea level, and temperature changes as great to allow the reliable estimation of and 1998 the warmest year. Because less data ductions in polar ice volume led to 4 to 6 m of sea level rise. A Palaeoclimatic Perspective as 10°C to 15°C in some middle and high latitude regions global mean temperature. are available, less is known about annual aver- of the Northern Hemisphere. Since the end of the last ice ages prior to 1,000 years before present and for age, about 10,000 years ago, global surface temperatures conditions prevailing in most of the Southern have probably fluctuated by little more than 1°C. Some fluc- Hemisphere prior to 1861. tuations have lasted several centuries, including the Little Ice Age which ended in the nineteenth century and which appears to have been global in extent. Introduction (continued on next page) (Table 1.1 continued) Topic FAR SPM Statement SAR SPM Statement TAR SPM Statement AR4 SPM Statement The size of this warming is broadly consistent with predic- The balance of evidence suggests a There is new and stronger evidence that most Most of the observed increase in global average temperatures tions of climate models, but it is also of the same magnitude discernible human influence on glob- of the warming observed over the last 50 years since the mid-20th century is very likely due to the observed as natural climate variability. Thus the observed increase al climate. Simulations with coupled is attributable to human activities. There is a increase in anthropogenic greenhouse gas concentrations. Understanding and Introduction could be largely due to this natural variability; alternatively atmosphere ocean models have pro- longer and more scrutinized temperature record Discernible human influences now extend to other aspects of Attributing Climate this variability and other human factors could have offset vided important information about and new model estimates of variability. Recon- climate, including ocean warming, continental-average tempera- Change a still larger human-induced greenhouse warming. The un- decade to century timescale natural structions of climate data for the past 1,000 tures, temperature extremes and wind patterns. equivocal detection of the enhanced greenhouse effect from internal climate variability. years indicate this warming was unusual and is observations is not likely for a decade or more. unlikely to be entirely natural in origin. Under the IPCC Business-as-Usual emissions of greenhouse Climate is expected to continue to Global average temperature and sea level are For the next two decades, a warming of about 0.2°C per decade gases, a rate of increase of global mean temperature during change in the future. For the mid- projected to rise under all IPCC SRES scenarios. is projected for a range of SRES emission scenarios. Even if the the next century of about 0.3°C per decade (with an uncer- range IPCC emission scenario, IS92a, The globally averaged surface temperature is concentrations of all greenhouse gases and aerosols had been tainty range of 0.2°C to 0.5°C per decade); this is greater assuming the best estimate value of projected to increase by 1.4°C to 5.8°C over the kept constant at year 2000 levels, a further warming of about than that seen over the past 10,000 years. climate sensitivity and including the period 1990 to 2100. 0.1°C per decade would be expected. effects of future increases in aerosols, Temperature models project an increase in global Confidence in the ability of models to project There is now higher confidence in projected patterns of warm- Projections mean surface air temperature rela- future climate has increased. ing and other regional-scale features, including changes in wind of Future patterns, precipitation and some aspects of extremes and of ice. Changes in tive to 1990 of about 2°C by 2100. Anthropogenic climate change will persist for Climate many centuries. Anthropogenic warming and sea level rise would continue for centuries, even if greenhouse gas concentrations were to be stabilised. An average rate of global mean sea level rise of about 6 cm Models project a sea level rise of 50 Global mean sea level is projected to rise by Global sea level rise for the range of scenarios is projected as Sea Level per decade over the next century (with an uncertainty range cm from the present to 2100. 0.09 to 0.88 m between 1990 and 2100. 0.18 to 0.59 m by the end of the 21st century. of 3 to 10 cm per decade) is projected. Chapter 1 125 1 Chapter 1 Introduction p ­ resence of clouds, precipitation; and the occurrence of special phe- ture has been relatively constant over many centuries, the incoming nomena, such as thunderstorms, dust storms, tornados and others. solar energy must be nearly in balance with outgoing radiation. Of Climate in a narrow sense is usually defined as the average weather, the incoming solar shortwave radiation (SWR), about half is absorbed or more rigorously, as the statistical description in terms of the mean by the Earth s surface. The fraction of SWR reflected back to space and variability of relevant quantities over a period of time ranging from by gases and aerosols, clouds and by the Earth s surface (albedo) is months to thousands or millions of years. The relevant quantities are approximately 30%, and about 20% is absorbed in the atmosphere. most often surface variables such as temperature, precipitation and Based on the temperature of the Earth s surface the majority of the 1 wind. Classically the period for averaging these variables is 30 years, outgoing energy flux from the Earth is in the infrared part of the spec- as defined by the World Meteorological Organization. Climate in a trum. The longwave radiation (LWR, also referred to as infrared radi- wider sense also includes not just the mean conditions, but also the ation) emitted from the Earth s surface is largely absorbed by certain associated statistics (frequency, magnitude, persistence, trends, etc.), atmospheric constituents water vapour, carbon dioxide (CO2), meth- often combining parameters to describe phenomena such as droughts. ane (CH4), nitrous oxide (N2O) and other greenhouse gases (GHGs); Climate change refers to a change in the state of the climate that can see Annex III for Glossary and clouds, which themselves emit LWR be identified (e.g., by using statistical tests) by changes in the mean into all directions. The downward directed component of this LWR adds and/or the variability of its properties, and that persists for an extended heat to the lower layers of the atmosphere and to the Earth s surface period, typically decades or longer. (greenhouse effect). The dominant energy loss of the infrared radiation from the Earth is from higher layers of the troposphere. The Sun pro- The Earth s climate system is powered by solar radiation (Figure 1.1). vides its energy to the Earth primarily in the tropics and the subtropics; Approximately half of the energy from the Sun is supplied in the vis- this energy is then partially redistributed to middle and high latitudes ible part of the electromagnetic spectrum. As the Earth s tempera- by atmospheric and oceanic transport processes. Natural Fluctuations in SWR Reflected by Outgoing Longwave Radiation (OLR) Incoming Solar Output the Atmosphere Shortwave SWR Radiation (SWR) Chemical SWR Absorbed by Aerosol/cloud Reactions the Atmosphere Interactions Clouds Ozone Greenhouse Aerosols SWR, LWR SWR, LWR Gases and Large Aerosols SWR Chemical LWR Reactions Emission of Back Gases Latent Longwave LWR and Aeros Heat Flux Emitted ols Sensible Radiation Heat Flux (LWR) from Surface SWR Absorbed by SWR Reflected by the Surface the Surface Ice/Snow Cover Vegetation Color Changes Ocean ight He Surface Wave Albedo Changes SWR Figure 1.1 | Main drivers of climate change. The radiative balance between incoming solar shortwave radiation (SWR) and outgoing longwave radiation (OLR) is influenced by global climate drivers . Natural fluctuations in solar output (solar cycles) can cause changes in the energy balance (through fluctuations in the amount of incoming SWR) (Section 2.3). Human activity changes the emissions of gases and aerosols, which are involved in atmospheric chemical reactions, resulting in modified O3 and aerosol amounts (Section 2.2). O3 and aerosol particles absorb, scatter and reflect SWR, changing the energy balance. Some aerosols act as cloud condensation nuclei modifying the properties of cloud droplets and possibly affecting precipitation (Section 7.4). Because cloud interactions with SWR and LWR are large, small changes in the properties of clouds have important implications for the radiative budget (Section 7.4). Anthropogenic changes in GHGs (e.g., CO2, CH4, N2O, O3, CFCs) and large aerosols (>2.5 m in size) modify the amount of outgoing LWR by absorbing outgoing LWR and re-emitting less energy at a lower temperature (Section 2.2). Surface albedo is changed by changes in vegetation or land surface properties, snow or ice cover and ocean colour (Section 2.3). These changes are driven by natural seasonal and diurnal changes (e.g., snow cover), as well as human influence (e.g., changes in vegetation types) (Forster et al., 2007). 126 Introduction Chapter 1 Changes in the global energy budget derive from either changes in land reduces carbon storage in the vegetation, adds CO2 to the atmos- the net incoming solar radiation or changes in the outgoing longwave phere, and changes the reflectivity of the land (surface albedo), rates of radiation (OLR). Changes in the net incoming solar radiation derive evapotranspiration and longwave emissions (Figure 1.1). from changes in the Sun s output of energy or changes in the Earth s albedo. Reliable measurements of total solar irradiance (TSI) can be Changes in the atmosphere, land, ocean, biosphere and cryosphere made only from space, and the precise record extends back only to both natural and anthropogenic can perturb the Earth s radiation 1978. The generally accepted mean value of the TSI is about 1361 W budget, producing a radiative forcing (RF) that affects climate. RF is 1 m 2 (Kopp and Lean, 2011; see Chapter 8 for a detailed discussion on a measure of the net change in the energy balance in response to an the TSI); this is lower than the previous value of 1365 W m 2 used in the external perturbation. The drivers of changes in climate can include, for earlier assessments. Short-term variations of a few tenths of a percent example, changes in the solar irradiance and changes in ­atmospheric are common during the approximately 11-year sunspot solar cycle (see trace gas and aerosol concentrations (Figure 1.1). The concept of RF Sections 5.2 and 8.4 for further details). Changes in the outgoing LWR cannot capture the interactions of anthropogenic aerosols and clouds, can result from changes in the temperature of the Earth s surface or for example, and thus in addition to the RF as used in previous assess- atmosphere or changes in the emissivity (measure of emission effi- ments, Sections 7.4 and 8.1 introduce a new concept, effective radi- ciency) of LWR from either the atmosphere or the Earth s surface. For ative forcing (ERF), that accounts for rapid response in the climate the atmosphere, these changes in emissivity are due predominantly to system. ERF is defined as the change in net downward flux at the top changes in cloud cover and cloud properties, in GHGs and in aerosol of the atmosphere after allowing for atmospheric temperatures, water concentrations. The radiative energy budget of the Earth is almost in vapour, clouds and land albedo to adjust, but with either sea surface balance (Figure 1.1), but ocean heat content and satellite measure- temperatures (SSTs) and sea ice cover unchanged or with global mean ments indicate a small positive imbalance (Murphy et al., 2009; Tren- surface temperature unchanged. berth et al., 2009; Hansen et al., 2011) that is consistent with the rapid changes in the atmospheric composition. Once a forcing is applied, complex internal feedbacks determine the eventual response of the climate system, and will in general cause this In addition, some aerosols increase atmospheric reflectivity, whereas response to differ from a simple linear one (IPCC, 2001, 2007). There others (e.g., particulate black carbon) are strong absorbers and also are many feedback mechanisms in the climate system that can either modify SWR (see Section 7.2 for a detailed assessment). Indirectly, aer- amplify ( positive feedback ) or diminish ( negative feedback ) the osols also affect cloud albedo, because many aerosols serve as cloud effects of a change in climate forcing (Le Treut et al., 2007) (see Figure condensation nuclei or ice nuclei. This means that changes in aerosol 1.2 for a representation of some of the key feedbacks). An example of ­ types and distribution can result in small but important changes in a positive feedback is the water vapour feedback whereby an increase cloud albedo and lifetime (Section 7.4). Clouds play a critical role in in surface temperature enhances the amount of water vapour pres- climate because they not only can increase albedo, thereby cooling ent in the atmosphere. Water vapour is a powerful GHG: increasing the planet, but also because of their warming effects through infra- its atmospheric concentration enhances the greenhouse effect and red radiative transfer. Whether the net radiative effect of a cloud is leads to further surface warming. Another example is the ice albedo one of cooling or of warming depends on its physical properties (level feedback, in which the albedo decreases as highly reflective ice and of occurrence, vertical extent, water path and effective cloud particle snow surfaces melt, exposing the darker and more absorbing surfaces size) as well as on the nature of the cloud condensation nuclei pop- below. The dominant negative feedback is the increased emission of ulation (Section 7.3). Humans enhance the greenhouse effect direct- energy through LWR as surface temperature increases (sometimes also ly by emitting GHGs such as CO2, CH4, N2O and chlorofluorocarbons referred to as blackbody radiation feedback). Some feedbacks oper- (CFCs) (Figure 1.1). In addition, pollutants such as carbon monoxide ate quickly (hours), while others develop over decades to centuries; (CO), volatile organic compounds (VOC), nitrogen oxides (NOx) and in order to understand the full impact of a feedback mechanism, its sulphur dioxide (SO2), which by themselves are negligible GHGs, have timescale needs to be considered. Melting of land ice sheets can take an indirect effect on the greenhouse effect by altering, through atmos- days to millennia. pheric chemical reactions, the abundance of important gases to the amount of outgoing LWR such as CH4 and ozone (O3), and/or by acting A spectrum of models is used to project quantitatively the climate as precursors of secondary aerosols. Because anthropogenic emission response to forcings. The simplest energy balance models use one sources simultaneously can emit some chemicals that affect climate box to represent the Earth system and solve the global energy bal- and others that affect air pollution, including some that affect both, ance to deduce globally averaged surface air temperature. At the other atmospheric chemistry and climate science are intrinsically linked. extreme, full complexity three-dimensional climate models include the explicit solution of energy, momentum and mass conservation In addition to changing the atmospheric concentrations of gases and e ­ quations at millions of points on the Earth in the atmosphere, land, aerosols, humans are affecting both the energy and water budget of ocean and cryosphere. More recently, capabilities for the explicit sim- the planet by changing the land surface, including redistributing the ulation of the biosphere, the carbon cycle and atmospheric chemistry balance between latent and sensible heat fluxes (Sections 2.5, 7.2, 7.6 have been added to the full complexity models, and these models are and 8.2). Land use changes, such as the conversion of forests to culti- called Earth System Models (ESMs). Earth System Models of Interme- vated land, change the characteristics of vegetation, including its colour, diate Complexity include the same processes as ESMs, but at reduced seasonal growth and carbon content (Houghton, 2003; Foley et al., resolution, and thus can be simulated for longer periods (see Annex III 2005). For example, clearing and burning a forest to prepare ­agricultural for Glossary and Section 9.1). 127 Chapter 1 Introduction An equilibrium climate experiment is an experiment in which a cli- is a measure of the strength and rapidity of the surface temperature mate model is allowed to adjust fully to a specified change in RF. Such response to GHG forcing. It can be more meaningful for some problems experiments provide information on the difference between the initial as well as easier to derive from observations (see Figure 10.20; Sec- and final states of the model simulated climate, but not on the time-de- tion 10.8; Chapter 12; Knutti et al., 2005; Frame et al., 2006; Forest et pendent response. The equilibrium response in global mean surface air al., 2008), but such experiments are not intended to replace the more temperature to a doubling of atmospheric concentration of CO2 above realistic scenario evaluations. pre-industrial levels (e.g., Arrhenius, 1896; see Le Treut et al., 2007 for 1 a comprehensive list) has often been used as the basis for the concept Climate change commitment is defined as the future change to which of equilibrium climate sensitivity (e.g., Hansen et al., 1981; see Meehl the climate system is committed by virtue of past or current forcings. et al., 2007 for a comprehensive list). For more realistic simulations of The components of the climate system respond on a large range of climate, changes in RF are applied gradually over time, for example, timescales, from the essentially rapid responses that characterise some using historical reconstructions of the CO2, and these simulations are radiative feedbacks to millennial scale responses such as those associ- called transient simulations. The temperature response in these tran- ated with the behaviour of the carbon cycle (Section 6.1) and ice sheets sient simulations is different than in an equilibrium simulation. The (see Figure 1.2 and Box 5.1). Even if anthropogenic emissions were transient climate response is defined as the change in global surface immediately ceased (Matthews and Weaver, 2010) or if climate forcings temperature at the time of atmospheric CO2 doubling in a global cou- were fixed at current values (Wigley, 2005), the climate system would pled ocean atmosphere climate model simulation where concentra- continue to change until it came into equilibrium with those forcings tions of CO2 were increased by 1% yr 1. The transient climate response (Section 12.5). Because of the slow response time of some components Longwave radiation Snow/ice Clouds Lapse rate albedo Water vapor Ocean Emission of non-CO2 circulation greenhouse gases and aerosols Peat and permafrost decomposition HOURS DAYS YEARS CENTURIES Longwave rad. Air-land CO2 exchange Lapse rate and biogeochemical Air-sea CO2 Water vapor Clouds processes exchange Snow/sea ice albedo Air-land CO2 exchange Biogeophysics Non-CO2 GHG and aerosols Air-sea CO2 exchange Biogeophysical Peat/Permafrost Land ice processes Ocean circ. Figure 1.2 | Climate feedbacks and timescales. The climate feedbacks related to increasing CO2 and rising temperature include negative feedbacks ( ) such as LWR, lapse rate (see Glossary in Annex III), and air sea carbon exchange and positive feedbacks (+) such as water vapour and snow/ice albedo feedbacks. Some feedbacks may be positive or negative (+/-): clouds, ocean circulation changes, air land CO2 exchange, and emissions of non-GHGs and aerosols from natural systems. In the smaller box, the large difference in timescales for the various feedbacks is highlighted. 128 Introduction Chapter 1 of the climate system, equilibrium conditions will not be reached for ble evidence from in situ observations and ice core records that the many centuries. Slow processes can sometimes be ­ onstrained only by c atmospheric concentrations of GHGs such as CO2, CH4, and N2O have data collected over long periods, giving a particular salience to paleo- increased substantially over the last 200 years (Sections 6.3 and 8.3). climate data for understanding equilibrium processes. Climate change In addition, instrumental observations show that land and sea sur- commitment is indicative of aspects of inertia in the climate system face temperatures have increased over the last 100 years (Chapter 2). because it captures the ongoing nature of some aspects of change. Satellites allow a much broader spatial distribution of measurements, especially over the last 30 years. For the upper ocean temperature the 1 A summary of perturbations to the forcing of the climate system from observations indicate that the temperature has increased since at least changes in solar radiation, GHGs, surface albedo and aerosols is pre- 1950 (Willis et al., 2010; Section 3.2). Observations from satellites and sented in Box 13.1. The energy fluxes from these perturbations are bal- in situ measurements suggest reductions in glaciers, Arctic sea ice and anced by increased radiation to space from a warming Earth, reflection ice sheets (Sections 4.2, 4.3 and 4.4). In addition, analyses based on of solar radiation and storage of energy in the Earth system, principally measurements of the radiative budget and ocean heat content sug- the oceans (Box 3.1, Box 13.1). gest a small imbalance (Section 2.3). These observations, all published in peer-reviewed journals, made by diverse measurement groups in The processes affecting climate can exhibit considerable natural var- multiple countries using different technologies, investigating various iability. Even in the absence of external forcing, periodic and chaotic climate-relevant types of data, uncertainties and processes, offer a variations on a vast range of spatial and temporal scales are observed. wide range of evidence on the broad extent of the changing climate Much of this variability can be represented by simple (e.g., unimodal or throughout our planet. power law) distributions, but many components of the climate system also exhibit multiple states for instance, the glacial-interglacial Conceptual and numerical models of the Earth s climate system offer cycles and certain modes of internal variability such as El Nino-South- another line of evidence on climate change (discussions in Chapters ern Oscillation (ENSO) (see Box 2.5 for details on patterns and indices 5 and 9 provide relevant analyses of this evidence from paleoclimat- of climate variability). Movement between states can occur as a result ic to recent periods). These use our basic understanding of the cli- of natural variability, or in response to external forcing. The relation- mate system to provide self-consistent methodologies for calculating ship between variability, forcing and response reveals the complexity impacts of processes and changes. Numerical models include the cur- of the dynamics of the climate system: the relationship between forc- rent knowledge about the laws of physics, chemistry and biology, as ing and response for some parts of the system seems reasonably linear; well as hypotheses about how complicated processes such as cloud in other cases this relationship is much more complex, characterised by formation can occur. Because these models can represent only the hysteresis (the dependence on past states) and a non-additive combi- existing state of knowledge and technology, they are not perfect; they nation of feedbacks. are, however, important tools for analysing uncertainties or unknowns, for testing different hypotheses for causation relative to observations, Related to multiple climate states, and hysteresis, is the concept of and for making projections of possible future changes. irreversibility in the climate system. In some cases where multiple states and irreversibility combine, bifurcations or tipping points can One of the most powerful methods for assessing changes occurring in been reached (see Section 12.5). In these situations, it is difficult if not climate involves the use of statistical tools to test the analyses from impossible for the climate system to revert to its previous state, and the models relative to observations. This methodology is generally called change is termed irreversible over some timescale and forcing range. detection and attribution in the climate change community (Section A small number of studies using simplified models find evidence for 10.2). For example, climate models indicate that the temperature global-scale tipping points (e.g., Lenton et al., 2008); however, there response to GHG increases is expected to be different than the effects is no evidence for global-scale tipping points in any of the most com- from aerosols or from solar variability. Radiosonde measurements prehensive models evaluated to date in studies of climate evolution in and satellite retrievals of atmospheric temperature show increases the 21st century. There is evidence for threshold behaviour in certain in tropospheric temperature and decreases in stratospheric tempera- aspects of the climate system, such as ocean circulation (see Section tures, consistent with the increases in GHG effects found in climate 12.5) and ice sheets (see Box 5.1), on multi-centennial-to-millennial model simulations (e.g., increases in CO2, changes in O3), but if the timescales. There are also arguments for the existence of regional tip- Sun was the main driver of current climate change, stratospheric and ping points, most notably in the Arctic (e.g., Lenton et al., 2008; Duarte tropospheric temperatures would respond with the same sign (Hegerl et al., 2012; Wadhams, 2012), although aspects of this are contested et al., 2007). (Armour et al., 2011; Tietsche et al., 2011). Resources available prior to the instrumental period historical 1.2.3 Multiple Lines of Evidence for Climate Change sources, natural archives, and proxies for key climate variables (e.g., tree rings, marine sediment cores, ice cores) can provide quantita- While the first IPCC assessment depended primarily on observed tive information on past regional to global climate and atmospheric changes in surface temperature and climate model analyses, more composition variability and these data contribute another line of evi- recent assessments include multiple lines of evidence for climate dence. Reconstructions of key climate variables based on these data change. The first line of evidence in assessing climate change is based sets have provided important information on the responses of the on careful analysis of observational records of the atmosphere, land, Earth system to a variety of external forcings and its internal variabil- ocean and cryosphere systems (Figure 1.3). There is incontroverti- ity over a wide range of timescales (Hansen et al., 2006; Mann et al., 129 Chapter 1 Introduction 2008). Paleoclimatic reconstructions thus offer a means for placing important climate parameters are discussed in this section and all are the current changes in climate in the perspective of natural climate assessed in much more detail in other chapters. variability (Section 5.1). AR5 includes new information on external RFs caused by variations in volcanic and solar activity (e.g., Steinhilber As was done to a more limited extent in AR4 (Le Treut et al., 2007), this et al., 2009; see Section 8.4). Extended data sets on past changes section provides a test of the planetary-scale hypotheses of climate in atmospheric concentrations and distributions of atmospheric GHG change against observations. In other words, how well do the projec- concentrations (e.g., Lüthi et al., 2008; Beerling and Royer, 2011) and tions used in the past assessments compare with observations to date? 1 mineral aerosols (Lambert et al., 2008) have also been used to attrib- Seven additional years of observations are now available to evaluate ute reconstructed paleoclimate temperatures to past variations in earlier model projections. The projected range that was given in each external forcings (Section 5.2). assessment is compared to observations. The largest possible range of scenarios available for a specific variable for each of the previous assessment reports is shown in the figures. 1.3 Indicators of Climate Change Based on the assessment of AR4, a number of the key climate and There are many indicators of climate change. These include physical associated environmental parameters are presented in Figure 1.3, responses such as changes in the following: surface temperature, which updates the similar figure in the Technical Summary (TS) of IPCC atmospheric water vapour, precipitation, severe events, glaciers, ocean (2001). This section discusses the recent changes in several indicators, and land ice, and sea level. Some key examples of such changes in while more thorough assessments for each of these indicators are Atmosphere Stratosphere Cooling Stratospheric temperature (Chapter 2.4). Changes in winter polar vortex strength (Chapter 2.7). Troposphere Warming from the surface through much of the Increasing concentration of CO2 and other greenhouse troposphere (Chapter 2.4). gases from human activities (Chapter 2.2). Long-term changes in the large-scale atmospheric Changes in cloud cover (Chapter 2.5). circulation, including a poleward shift of jet Increasing tropospheric water vapour (Chapter 2.5). streams (Chapter 2.7). Changes in aerosole burden and ozone concentrations (Chapter 2.2) Observations of Climate Changes from AR4 (points to AR5) Near Surface Rising global average near surface temperature (Chapter 2.4). Warming of sea surface temperatures (Chapter 2.4). Increasing surface humidity (Chapter 2.5). Warming throughout much of the More frequent warm days and nights. Fewer worlds ocean (Chapter 3.2). cold days and nights (Chapter 2.6). Shrinking annual average Increasing rates of global mean Reductions in the number of frost days Arctic sea ice extent sea level rise (Chapter 3.7). (Chapter 2.6). (Chapter 4.2). Changes in ocean Decreasing snow cover in most regions Widespread glacier salinity (Chapter 3.3). (Chapter 4.5). retreat (Chapter 4.3). Acidification of the oceans Degrading permafrost in areal Changes in ice sheets in (Chapter 3.8). extent and thickness (Chapter 4.6). Greenland and Antarctica (Chapter 4.4). Large scale precipitation changes (Chapter 2.5). Increase in the number of heavy precipitation events (Chapter 2.6). Ocean Land Ice Figure 1.3 | Overview of observed climate change indicators as listed in AR4. Chapter numbers indicate where detailed discussions for these indicators are found in AR5 (temperature: red; hydrological: blue; others: black). 130 Introduction Chapter 1 p ­ rovided in other chapters. Also shown in parentheses in Figure 1.3 are models generally simulate global temperatures that compare well with the chapter and section where those indicators of change are assessed o­ servations over climate timescales (Section 9.4). Even though the b in AR5. projections from the models were never intended to be predictions over such a short timescale, the observations through 2012 generally Note that projections presented in the IPCC assessments are not pre- fall within the projections made in all past assessments. The 1990 dictions (see the Glossary in Annex III); the analyses in the discussion 2012 data have been shown to be consistent with the FAR projections below only examine the short-term plausibility of the projections up to (IPCC, 1990), and not consistent with zero trend from 1990, even in 1 AR4, including the scenarios for future emissions and the models used the presence of substantial natural variability (Frame and Stone, 2013). to simulate these scenarios in the earlier assessments. Model results from the Coupled Model Intercomparison Project Phase 5 (CMIP5) The scenarios were designed to span a broad range of plausible (Taylor et al., 2012) used in AR5 are therefore not included in this sec- futures, but are not aimed at predicting the most likely outcome. The tion; Chapters 11 and 12 describe the projections from the new mod- scenarios considered for the projections from the earlier reports (FAR, elling studies. Note that none of the scenarios examined in the IPCC SAR) had a much simpler basis than those of the Special Report on assessments were ever intended to be short-term predictors of change. Emission Scenarios (SRES) (IPCC, 2000) used in the later assessments. For example, the FAR scenarios did not specify future aerosol distribu- 1.3.1 Global and Regional Surface Temperatures tions. AR4 presented a multiple set of projections that were simulated using comprehensive ocean atmosphere models provided by CMIP3 Observed changes in global mean surface air temperature since 1950 and these projections are continuations of transient simulations of the (from three major databases, as anomalies relative to 1961 1990) 20th century climate. These projections of temperature provide in addi- are shown in Figure 1.4. As in the prior assessments, global climate tion a measure of the natural variability that could not be obtained Figure 1.4 | Estimated changes in the observed globally and annually averaged surface temperature anomaly relative to 1961 1990 (in °C) since 1950 compared with the range of projections from the previous IPCC assessments. Values are harmonized to start from the same value in 1990. Observed global annual mean surface air temperature anomaly, relative to 1961 1990, is shown as squares and smoothed time series as solid lines (NASA (dark blue), NOAA (warm mustard), and the UK Hadley Centre (bright green) reanalyses). The coloured shading shows the projected range of global annual mean surface air temperature change from 1990 to 2035 for models used in FAR (Figure 6.11 in Bretherton et al., 1990), SAR (Figure 19 in the TS of IPCC, 1996), TAR (full range of TAR Figure 9.13(b) in Cubasch et al., 2001). TAR results are based on the simple climate model analyses presented and not on the individual full three-dimensional climate model simulations. For the AR4 results are presented as single model runs of the CMIP3 ensemble for the historical period from 1950 to 2000 (light grey lines) and for three scenarios (A2, A1B and B1) from 2001 to 2035. The bars at the right-hand side of the graph show the full range given for 2035 for each assessment report. For the three SRES scenarios the bars show the CMIP3 ensemble mean and the likely range given by 40% to +60% of the mean as assessed in Meehl et al. (2007). The publication years of the assessment reports are shown. See Appendix 1.A for details on the data and calculations used to create this figure. 131 Chapter 1 Introduction from the earlier projections based on models of intermediate complex- their experimental design these episodes cannot be duplicated with ity (Cubasch et al., 2001). the same timing as the observed episodes in most of the model simu- lations; this affects the interpretation of recent trends in the scenario Note that before TAR the climate models did not include natural forc- evaluations (Section 11.2). Notwithstanding these points, there is evi- ing (such as volcanic activity and solar variability). Even in AR4 not all dence that early forecasts that carried formal estimates of uncertainty models included natural forcing and some also did not include aero- have proved highly consistent with subsequent observations (Allen et sols. Those models that allowed for aerosol effects presented in the al., 2013). If the contributions of solar variability, volcanic activity and 1 AR4 simulated, for example, the cooling effects of the 1991 Mt Pinatu- ENSO are removed from the observations the remaining trend of sur- bo eruption and agree better with the observed temperatures than the face air temperature agree better with the modelling studies (Rahm- previous assessments that did not include those effects. storf et al., 2012). The bars on the side for FAR, SAR and TAR represent the range of 1.3.2 Greenhouse Gas Concentrations results for the scenarios at the end of the time period and are not error bars. In contrast to the previous reports, the AR4 gave an assessment Key indicators of global climate change also include the changing con- of the individual scenarios with a mean estimate (cross bar; ensemble centrations of the radiatively important GHGs that are significant driv- mean of the CMIP3 simulations) and a likely range (full bar; 40% to ers for this change (e.g., Denman et al., 2007; Forster et al., 2007). Fig- +60% of the mean estimate) (Meehl et al., 2007). ures 1.5 through 1.7 show the recent globally and annually averaged observed concentrations for the gases of most concern, CO2, CH4, and In summary, the trend in globally averaged surface temperatures falls N2O (see Sections 2.2, 6.3 and 8.3 for more detailed discussion of these within the range of the previous IPCC projections. During the last and other key gases). As discussed in the later chapters, accurate meas- decade the trend in the observations is smaller than the mean of the urements of these long-lived gases come from a number of monitoring projections of AR4 (see Section 9.4.1, Box 9.2 for a detailed assessment stations throughout the world. The observations in these figures are of the hiatus in global mean surface warming in the last 15 years). compared with the projections from the previous IPCC assessments. As shown by Hawkins and Sutton (2009), trends in the observations during short-timescale periods (decades) can be dominated by natural The model simulations begin with historical emissions up to 1990. The variability in the Earth s climate system. Similar episodes are also seen further evolution of these gases was described by scenario projections. in climate model experiments (Easterling and Wehner, 2009). Due to TAR and AR4 model concentrations after 1990 are based on the SRES Figure 1.5 | Observed globally and annually averaged CO2 concentrations in parts per million (ppm) since 1950 compared with projections from the previous IPCC assessments. Observed global annual CO2 concentrations are shown in dark blue. The shading shows the largest model projected range of global annual CO2 concentrations from 1950 to 2035 from FAR (Figure A.3 in the Summary for Policymakers of IPCC, 1990); SAR (Figure 5b in the Technical Summary of IPCC, 1996); TAR (Appendix II of IPCC, 2001); and from the A2, A1B and B1 scenarios presented in the AR4 (Figure 10.26 in Meehl et al., 2007). The bars at the right-hand side of the graph show the full range given for 2035 for each assessment report. The publication years of the assessment reports are shown. See Appendix 1.A for details on the data and calculations used to create this figure. 132 Introduction Chapter 1 1 Figure 1.6 | Observed globally and annually averaged CH4 concentrations in parts per billion (ppb) since 1950 compared with projections from the previous IPCC assessments. Estimated observed global annual CH4 concentrations are shown in dark blue. The shading shows the largest model projected range of global annual CH4 concentrations from 1950 to 2035 from FAR (Figure A.3 of the Annex of IPCC, 1990); SAR (Table 2.5a in Schimel et al., 1996); TAR (Appendix II of IPCC, 2001); and from the A2, A1B and B1 scenarios pre- sented in the AR4 (Figure 10.26 in Meehl et al., 2007). The bars at the right-hand side of the graph show the full range given for 2035 for each assessment report. The publication years of the assessment reports are shown. See Appendix 1.A for details on the data and calculations used to create this figure. Figure 1.7 | Observed globally and annually averaged N2O concentrations in parts per billion (ppb) since 1950 compared with projections from the previous IPCC assessments. Observed global annual N2O concentrations are shown in dark blue. The shading shows the largest model projected range of global annual N2O concentrations from 1950 to 2035 from FAR (Figure A3 in the Annex of IPCC, 1990), SAR (Table 2.5b in Schimel et al., 1996), TAR (Appendix II of IPCC, 2001), and from the A2, A1B and B1 scenarios presented in the AR4 (Figure 10.26 in Meehl et al., 2007). The bars at the right hand side of the graph show the full range given for 2035 for each assessment report. The publication years of the assessment reports are shown. See Appendix 1.A for details on the data and calculations used to create this figure. 133 Chapter 1 Introduction scenarios but those model results may also account for historical emis- Temperature sions analyses. The recent observed trends in CO2 concentrations tend (a) Increase in mean to be in the middle of the scenarios used for the projections (Figure 1.5). Fewer cold extremes More hot extremes As discussed in Dlugokencky et al. (2009), trends in CH4 showed a stabilization from 1999 to 2006, but CH4 concentrations have been 1 increasing again starting in 2007 (see Sections 2.2 and 6.3 for more discussion on the budget and changing concentration trends for CH4). Because at the time the scenarios were developed (e.g., the SRES Cold Average Hot scenarios were developed in 2000), it was thought that past trends Temperature would continue, the scenarios used and the resulting model projec- tions assumed in FAR through AR4 all show larger increases than those (b) Increase in variance observed (Figure 1.6). More cold extremes More hot extremes Concentrations of N2O have continued to increase at a nearly constant rate (Elkins and Dutton, 2010) since about 1970 as shown in Figure 1.7. The observed trends tend to be in the lower part of the projections for the previous assessments. Cold Average Hot 1.3.3 Extreme Events Temperature Climate change, whether driven by natural or human forcings, can lead (c) Increase in mean and variance to changes in the likelihood of the occurrence or strength of extreme weather and climate events such as extreme precipitation events or More/Fewer cold extremes More hot extremes warm spells (see Chapter 3 of the IPCC Special Report on Managing the Risks of Extreme Events and Disasters to Advance Climate Change Adaptation (SREX); Seneviratne et al., 2012). An extreme weather event is one that is rare at a particular place and/or time of year. Defi- nitions of rare vary, but an extreme weather event would normally be as rare as or rarer than the 10th or 90th percentile of a probabili- Cold Average Hot ty density function estimated from observations (see also Glossary in Annex III and FAQ 2.2). By definition, the characteristics of what is Precipitation called extreme weather may vary from place to place in an absolute (d) Change in skewness sense. At present, single extreme events cannot generally be directly attributed to anthropogenic influence, although the change in likeli- hood for the event to occur has been determined for some events by accounting for observed changes in climate (see Section 10.6). When a pattern of extreme weather persists for some time, such as a season, it may be classified as an extreme climate event, especially if it yields More heavy precipitation an average or total that is itself extreme (e.g., drought or heavy rainfall over a season). For some climate extremes such as drought, floods and heat waves, several factors such as duration and intensity need to be combined to produce an extreme event (Seneviratne et al., 2012). Light Average Heavy The probability of occurrence of values of a climate or weather variable Figure 1.8 | Schematic representations of the probability density function of daily tem- can be described by a probability density function (PDF) that for some perature, which tends to be approximately Gaussian, and daily precipitation, which has variables (e.g., temperature) is shaped similar to a Gaussian curve. A a skewed distribution. Dashed lines represent a previous distribution and solid lines a changed distribution. The probability of occurrence, or frequency, of extremes is denoted PDF is a function that indicates the relative chances of occurrence of by the shaded areas. In the case of temperature, changes in the frequencies of extremes different outcomes of a variable. Simple statistical reasoning indicates are affected by changes (a) in the mean, (b) in the variance or shape, and (c) in both that substantial changes in the frequency of extreme events (e.g., the the mean and the variance. (d) In a skewed distribution such as that of precipitation, a maximum possible 24-hour rainfall at a specific location) can result change in the mean of the distribution generally affects its variability or spread, and thus from a relatively small shift in the distribution of a weather or climate an increase in mean precipitation would also imply an increase in heavy precipitation extremes, and vice-versa. In addition, the shape of the right-hand tail could also change, variable. Figure 1.8a shows a schematic of such a PDF and illustrates affecting extremes. Furthermore, climate change may alter the frequency of precipita- the effect of a small shift in the mean of a variable on the frequency of tion and the duration of dry spells between precipitation events. (Parts a c modified extremes at either end of the distribution. An increase in the frequency from Folland et al., 2001, and d modified from Peterson et al., 2008, as in Zhang and of one extreme (e.g., the number of hot days) can be accompanied by Zwiers, 2012.) 134 Introduction Chapter 1 a decline in the opposite extreme (in this case the number of cold days of regional models have been used in the simulation and projection of such as frost days). Changes in the variability, skewness or the shape extremes, and ensemble integrations now provide information about of the distribution can complicate this simple picture (Figure 1.8b, c PDFs and extremes. and d). Since the TAR, climate change studies have especially focused on While the SAR found that data and analyses of extremes related to cli- changes in the global statistics of extremes, and observed and pro- mate change were sparse, improved monitoring and data for changes jected changes in extremes have been compiled in the so-called 1 in extremes were available for the TAR, and climate models were being Extremes -Table (Figure 1.9). This table has been modified further to analysed to provide projections of extremes. In AR4, the observation- account for the SREX assessment. For some extremes ( higher maximum al basis of analyses of extremes had increased substantially, so that temperature , higher minimum temperature , precipitation extremes , some extremes were now examined over most land areas (e.g., rainfall droughts or dryness ), all of these assessments found an increasing extremes). More models with higher resolution, and a larger number trend in the observations and in the projections. In the observations for 1 More intense precipitation events Heavy precipitation events. Frequency (or proportion of total rainfall from heavy falls) increases Statistically significant trends in the number of heavy precipitation events in some regions. It is likely that more of these regions have experienced increases than decreases. 4 See SREX Table 3-3 for details on precipitation extremes for the different regions. 5 Increased summer continental drying and associated risk of drought 6 Area affected by droughts increases 7 Some areas include southern Europe and the Mediterranean region, central Europe, central North America and Mexico, northeast Brazil and southern Africa 8 Increase in tropical cyclone peak wind intensities 9 Increase in intense tropical cyclone activity 10 In any observed long-term (i.e., 40 years or more) after accounting for past changes in observing capabilities (see SREX, section 3.4.4) 11 Increase in average tropical cyclone maximum wind speed is, although not in all ocean basins; either decrease or no change in the global frequency of tropical cyclones 12 Increase in extreme coastal high water worldwide related to increases in mean sea level in the late 20th century 13 Mean sea level rise will contribute to upward trends in extreme coastal high water levels Figure 1.9 | Change in the confidence levels for extreme events based on prior IPCC assessments: TAR, AR4 and SREX. Types of extreme events discussed in all three reports are highlighted in green. Confidence levels are defined in Section 1.4. Similar analyses for AR5 are discussed in later chapters. Please note that the nomenclature for confidence level changed from AR4 to SREX and AR5. 135 Chapter 1 Introduction the higher maximum temperature the likelihood level was raised from non-uniform density change, circulation changes, and deformation of likely in the TAR to very likely in SREX. While the diurnal temperature ocean basins, the evidence indicates that the global mean sea level is range was assessed in the Extremes-Table of the TAR, it was no longer rising, and that this is likely (according to AR4 and SREX) resulting from included in the Extremes-Table of AR4, since it is not considered a cli- global climate change (ocean warming plus land ice melt; see Chapter mate extreme in a narrow sense. Diurnal temperature range was, how- 13 for AR5 findings). The historical tide gauge record shows that the ever, reported to decrease for 21st century projections in AR4 (Meehl average rate of global mean sea level rise over the 20th century was et al., 2007). In projections for precipitation extremes, the spatial rel- 1.7 +/- 0.2 mm yr 1 (e.g., Church and White, 2011). This rate increased 1 evance has been improved from very likely over many Northern Hemi- to 3.2 +/- 0.4 mm yr 1 since 1990, mostly because of increased thermal ­ sphere mid-latitudes to high latitudes land areas from the TAR to very expansion and land ice contributions (Church and White, 2011; IPCC, likely for all regions in AR4 (these uncertainty labels are discussed in 2012b). Although the long-term sea level record shows decadal and Section 1.4). However, likelihood in trends in projected precipitation multi-decadal oscillations, there is evidence that the rate of global extremes was downscaled to likely in the SREX as a result of a percep- mean sea level rise during the 20th century was greater than during tion of biases and a fairly large spread in the precipitation projections the 19th century. in some regions. SREX also had less confidence than TAR and AR4 in the trends for droughts and dryness, due to lack of direct observations, All of the previous IPCC assessments have projected that global sea some geographical inconsistencies in the trends, and some dependen- level will continue to rise throughout this century for the scenarios cies of inferred trends on the index choice (IPCC, 2012b). examined. Figure 1.10 compares the observed sea level rise since 1950 with the projections from the prior IPCC assessments. Earlier models For some extremes (e.g., changes in tropical cyclone activity ) the defi- had greater uncertainties in modelling the contributions, because of nition changed between the TAR and the AR4. Whereas the TAR only limited observational evidence and deficiencies in theoretical under- made a statement about the peak wind speed of tropical cyclones, standing of relevant processes. Also, projections for sea level change in the AR4 also stressed the overall increase in intense tropical cyclone the prior assessments are scenarios for the response to anthropogenic activity. The low confidence for any long term trend (>40 years) in the forcing only; they do not include unforced or natural interannual vari- observed changes of the tropical cyclone activities is due to uncertain- ability. Nonetheless, the results show that the actual change is in the ties in past observational capabilities (IPCC, 2012b). The increase in middle of projected changes from the prior assessments, and towards extreme sea level has been added in the AR4. Such an increase is likely the higher end of the studies from TAR and AR4. according to the AR4 and the SREX for observed trends, and very likely for the climate projections reported in the SREX. 1.3.4.2 Ocean Acidification The assessed likelihood of anthropogenic contributions to trends is The observed decrease in ocean pH resulting from increasing concen- lower for variables where the assessment is based on indirect evidence. trations of CO2 is another indicator of global change. As discussed Especially for extremes that are the result of a combination of factors in AR4, the ocean s uptake of CO2 is having a significant impact on such as droughts, linking a particular extreme event to specific causal the chemistry of sea water. The average pH of ocean surface waters relationships is difficult to determine (e.g., difficult to establish the has fallen by about 0.1 units, from about 8.2 to 8.1 (total scale) since clear role of climate change in the event) (see Section 10.6 and Peter- 1765 (Section 3.8). Long time series from several ocean sites show son et al., 2012). In some cases (e.g., precipitation extremes), however, ongoing declines in pH, consistent with results from repeated pH it may be possible to estimate the human-related contribution to such measurements on ship transects spanning much of the globe (Sec- changes in the probability of occurrence of extremes (Pall et al., 2011; tions 3.8 and 6.4; Byrne et al., 2010; Midorikawa et al., 2010). Ocean Seneviratne et al., 2012). time-series in the North Atlantic and North Pacific record a decrease in pH ranging between 0.0015 and 0.0024 per year (Section 3.8). Due 1.3.4 Climate Change Indicators to the increased storage of carbon by the ocean, ocean acidification will increase in the future (Chapter 6). In addition to other impacts Climate change can lead to other effects on the Earth s physical system of global climate change, ocean acidification poses potentially serious that are also indicators of climate change. Such integrative indicators threats to the health of the world s oceans ecosystems (see AR5 WGII include changes in sea level (ocean warming + land ice melt), in ocean assessment). acidification (ocean uptake of CO2) and in the amount of ice on ocean and land (temperature and hydrological changes). See Chapters 3, 4 1.3.4.3 Ice and 13 for detailed assessment. Rapid sea ice loss is one of the most prominent indicators of Arctic 1.3.4.1 Sea Level climate change (Section 4.2). There has been a trend of decreasing Northern Hemisphere sea ice extent since 1978, with the summer of Global mean sea level is an important indicator of climate change (Sec- 2012 being the lowest in recorded history (see Section 4.2 for details). tion 3.7 and Chapter 13). The previous assessments have all shown The 2012 minimum sea ice extent was 49% below the 1979 to 2000 that observations indicate that the globally averaged sea level is rising. average and 18% below the previous record from 2007. The amount of Direct observations of sea level change have been made for more multi-year sea ice has been reduced, i.e., the sea ice has been thinning than 150 years with tide gauges, and for more than 20 years with and thus the ice volume is reduced (Haas et al., 2008; Kwok et al., satellite radar altimeters. Although there is regional variability from 2009). These changes make the sea ice less resistant to wind forcing. 136 Introduction Chapter 1 FAR } 35 Estimates derived from tide-gauge data 30 Estimates derived from sea-surface altimetry SAR 25 1 Global mean sea level rise (cm) Church et al. TAR (2011) 20 A1B B1 A2 15 10 5 0 FAR SAR TAR AR4 5 1950 1960 1970 1980 1990 2000 2010 2020 2030 Year Figure 1.10 | Estimated changes in the observed global annual mean sea level (GMSL) since 1950 relative to 1961 1990. Estimated changes in global annual sea level anomalies are presented based on tide gauge data (warm mustard: Jevrejeva et al., 2008; dark blue: Church and White, 2011; dark green: Ray and Douglas, 2011) and based on sea surface altimetry (light blue). The altimetry data start in 1993 and are harmonized to start from the mean 1993 value of the tide gauge data. Squares indicate annual mean values and solid lines smoothed values. The shading shows the largest model projected range of global annual sea level rise from 1950 to 2035 for FAR (Figures 9.6 and 9.7 in Warrick and Oerlemans, 1990), SAR (Figure 21 in TS of IPCC, 1996), TAR (Appendix II of IPCC, 2001) and for Church et al. (2011) based on the Coupled Model Intercomparison Project Phase 3 (CMIP3) model results not assessed at the time of AR4 using the SRES B1, A1B and A2 scenarios. Note that in the AR4 no full range was given for the sea level projections for this period. Therefore, the figure shows results that have been published subsequent to the AR4. The bars at the right-hand side of the graph show the full range given for 2035 for each assessment report. For Church et al. (2011) the mean sea level rise is indicated in addition to the full range. See Appendix 1.A for details on the data and calculations used to create this figure. Sea ice extent has been diminishing significantly faster than projected of West Antarctica and the northern Antarctic Peninsula. The ice sheet by most of the AR4 climate models (SWIPA, 2011). While AR4 found no on the rest of the continent is relatively stable or thickening slightly consistent trends in Antarctica sea ice, more recent studies indicate a (Lemke et al., 2007; Scott et al., 2009; Turner et al., 2009). Since AR4, small increase (Section 4.2). Various studies since AR4 suggest that this there have been improvements in techniques of measurement, such as has resulted in a deepening of the low-pressure systems in West Ant- gravity, altimetry and mass balance, and understanding of the change arctica that in turn caused stronger winds and enhanced ice production (Section 4.4). in the Ross Sea (Goosse et al., 2009; Turner and Overland, 2009). As discussed in the earlier assessments, most glaciers around the globe AR4 concluded that taken together, the ice sheets in Greenland and have been shrinking since the end of the Little Ice Age, with increasing Antarctica have very likely been contributing to sea level rise. The rates of ice loss since the early 1980s (Section 4.3). The vertical profiles Greenland Ice Sheet has lost mass since the early 1990s and the rate of temperature measured through the entire thickness of mountain of loss has increased (see Section 4.4). The interior, high-altitude areas glaciers, or through ice sheets, provide clear evidence of a warming are thickening due to increased snow accumulation, but this is more climate over recent decades (e.g., Lüthi and Funk, 2001; Hoelzle et al., than counterbalanced by the ice loss due to melt and ice discharge 2011). As noted in AR4, the greatest mass losses per unit area in the (AMAP, 2009; Ettema et al., 2009). Since 1979, the area experiencing last four decades have been observed in Patagonia, Alaska, northwest surface melting has increased significantly (Tedesco, 2007; Mernild et USA, southwest Canada, the European Alps, and the Arctic. Alaska and al., 2009), with 2010 breaking the record for surface melt area, runoff, the Arctic are especially important regions as contributors to sea level and mass loss, and the unprecedented areal extent of surface melt of rise (Zemp et al., 2008, 2009). the Greenland Ice Sheet in 2012 (Nghiem et al., 2012). Overall, the Antarctic continent now experiences a net loss of ice (Section 4.4). Significant mass loss has been occurring in the Amundsen Sea sector 137 Chapter 1 Introduction 1.4 Treatment of Uncertainties Model uncertainty is an important contributor to uncertainty in cli- mate predictions and projections. It includes, but is not restricted to, 1.4.1 Uncertainty in Environmental Science the uncertainties introduced by errors in the model s representation of dynamical and physical and bio-geochemical aspects of the climate Science always involves uncertainties. These arise at each step of the system as well as in the model s response to external forcing. The scientific method: in the development of models or hypotheses, in phrase model uncertainty is a common term in the climate change measurements and in analyses and interpretation of scientific assump- literature, but different studies use the phrase in different senses: some 1 tions. Climate science is not different in this regard from other areas of use it to represent the range of behaviours observed in ensembles of science. The complexity of the climate system and the large range of climate model (model spread), while others use it in more comprehen- processes involved bring particular challenges because, for example, sive senses (see Sections 9.2, 11.2 and 12.2). Model spread is often gaps in direct measurements of the past can be filled only by recon- used as a measure of climate response uncertainty, but such a measure structions using proxy data. is crude as it takes no account of factors such as model quality (Chap- ter 9) or model independence (e.g., Masson and Knutti, 2011; Pennell Because the Earth s climate system is characterized by multiple spatial and Reichler, 2011), and not all variables of interest are adequately and temporal scales, uncertainties do not usually reduce at a single, simulated by global climate models. predictable rate: for example, new observations may reduce the uncer- tainties surrounding short-timescale processes quite rapidly, while To maintain a degree of terminological clarity this report distinguishes longer timescale processes may require very long observational base- between model spread for this narrower representation of climate lines before much progress can be made. Characterization of the inter- model responses and model uncertainty which describes uncertainty action between processes, as quantified by models, can be improved about the extent to which any particular climate model provides an by model development, or can shed light on new areas in which uncer- accurate representation of the real climate system. This uncertainty tainty is greater than previously thought. The fact that there is only arises from approximations required in the development of models. a single realization of the climate, rather than a range of different Such approximations affect the representation of all aspects of the cli- c ­ limates from which to draw, can matter significantly for certain lines mate including the response to external forcings. of enquiry, most notably for the detection and attribution of causes of climate change and for the evaluation of projections of future states. Model uncertainty is sometimes decomposed further into parametric and structural uncertainty, comprising, respectively, uncertainty in the 1.4.2 Characterizing Uncertainty values of model parameters and uncertainty in the underlying model structure (see Section 12.2). Some scientific research areas, such as Uncertainty is a complex and multifaceted property, sometimes orig- detection and attribution and observationally-constrained model pro- inating in a lack of information, and at other times from quite funda- jections of future climate, incorporate significant elements of both mental disagreements about what is known or even knowable (Moss observational and model-based science, and in these instances both and Schneider, 2000). Furthermore, scientists often disagree about the sets of relevant uncertainties need to be incorporated. best or most appropriate way to characterize these uncertainties: some can be quantified easily while others cannot. Moreover, appropriate Scenario uncertainty refers to the uncertainties that arise due to limita- characterization is dependent on the intended use of the information tions in our understanding of future emissions, concentration or forcing and the particular needs of that user community. trajectories. Scenarios help in the assessment of future developments in complex systems that are either inherently unpredictable, or that Scientific uncertainty can be partitioned in various ways, in which the have high scientific uncertainties (IPCC, 2000). The societal choices details of the partitioning usually depend on the context. For instance, defining future climate drivers are surrounded by considerable uncer- the process and classifications used for evaluating observational tainty, and these are explored by examining the climate response to uncertainty in climate science is not the same as that employed to a wide range of possible futures. In past reports, emissions scenarios evaluate projections of future change. Uncertainty in measured quan- from the SRES (IPCC, 2000) were used as the main way of exploring tities can arise from a range of sources, such as statistical variation, uncertainty in future anthropogenic climate drivers. Recent research variability, inherent randomness, inhomogeneity, approximation, sub- has made use of Representative Concentration Pathways (RCP) (van jective judgement, and linguistic imprecision (Morgan et al., 1990), Vuuren et al., 2011a, 2011b). or from calibration methodologies, instrumental bias or instrumental limitations (JCGM, 2008). Internal or natural variability, the natural fluctuations in climate, occur in the absence of any RF of the Earth s climate (Hawkins and Sutton, In the modelling studies that underpin projections of future climate 2009). Climate varies naturally on nearly all time and space scales, and change, it is common to partition uncertainty into four main catego- quantifying precisely the nature of this variability is challenging, and ries: scenario uncertainty, due to uncertainty of future emissions of is characterized by considerable uncertainty. The analysis of internal GHGs and other forcing agents; model uncertainty associated with and forced contributions to recent climate is discussed in Chapter 10. climate models; internal variability and initial condition uncertainty; The fractional contribution of internal variability compared with other and forcing and boundary condition uncertainty for the assessment of forms of uncertainty varies in time and in space, but usually diminish- historical and paleoclimate simulations (e.g., Collins and Allen, 2002; es with time as other sources of uncertainty become more significant Yip et al., 2011). (Hawkins and Sutton, 2009; see also Chapter 11 and FAQ 1.1). 138 Introduction Chapter 1 In the WGI contribution to the AR5, uncertainty is quantified using and procedures to improve presentation of uncertainty. Many of the 90% uncertainty intervals unless otherwise stated. The 90% uncer- recommendations of these groups are addressed in the revised Guid- tainty interval, reported in square brackets, is expected to have a 90% ance Notes. One key revision relates to clarification of the relation- likelihood of covering the value that is being estimated. The value that ship between the confidence and likelihood language, and pertains is being estimated has a 5% likelihood of exceeding the upper end- to demarcation between qualitative descriptions of confidence and point of the uncertainty interval, and the value has a 5% likelihood of the numerical representations of uncertainty that are expressed by being less than that the lower endpoint of the uncertainty interval. A the likelihood scale. In addition, a finding that includes a probabilistic 1 best estimate of that value is also given where available. Uncertainty measure of uncertainty does not require explicit mention of the level intervals are not necessarily symmetric about the corresponding best of confidence associated with that finding if the level of confidence is estimate. high or very high. This is a concession to stylistic clarity and readabil- ity: if something is described as having a high likelihood, then in the In a subject as complex and diverse as climate change, the information absence of additional qualifiers it should be inferred that it also has available as well as the way it is expressed, and often the interpreta- high or very high confidence. tion of that material, varies considerably with the scientific context. In some cases, two studies examining similar material may take different 1.4.4 Uncertainty Treatment in This Assessment approaches even to the quantification of uncertainty. The interpretation of similar numerical ranges for similar variables can differ from study All three IPCC Working Groups in the AR5 have agreed to use two met- to study. Readers are advised to pay close attention to the caveats rics for communicating the degree of certainty in key findings (Mas- and conditions that surround the results presented in peer-­eviewed r trandrea et al., 2010): studies, as well as those presented in this assessment. To help readers in this complex and subtle task, the IPCC draws on specific, calibrat- Confidence in the validity of a finding, based on the type, amount, ed language scales to express uncertainty (Mastrandrea et al., 2010), quality, and consistency of evidence (e.g., data, mechanistic under- as well as specific procedures for the expression of uncertainty (see standing, theory, models, expert judgment) and the degree of Table 1.2). The aim of these structures is to provide tools through which agreement. Confidence is expressed qualitatively. chapter teams might consistently express uncertainty in key results. Quantified measures of uncertainty in a finding expressed proba- 1.4.3 Treatment of Uncertainty in IPCC bilistically (based on statistical analysis of observations or model results, or expert judgement). In the course of the IPCC assessment procedure, chapter teams review the published research literature, document the findings (including A level of confidence synthesizes the Chapter teams judgements about uncertainties), assess the scientific merit of this information, identify the validity of findings as determined through evaluation of the availa- the key findings, and attempt to express an appropriate measure of ble evidence and the degree of scientific agreement. The evidence and the uncertainty that accompanies these findings using a shared guid- agreement scale underpins the assessment, as it is on the basis of evi- ance procedure. This process has changed over time. The early Assess- dence and agreement that statements can be made with scientific con- ment Reports (FAR and SAR) were largely qualitative. As the field has fidence (in this sense, the evidence and agreement scale replaces the grown and matured, uncertainty is being treated more explicitly, with level of scientific understanding scale used in previous WGI assess- a greater emphasis on the expression, where possible and appropriate, ments). There is flexibility in this relationship; for a given evidence and of quantified measures of uncertainty. agreement statement, different confidence levels could be assigned, but increasing levels of evidence and degrees of agreement are cor- Although IPCC s treatment of uncertainty has become more sophis- related with increasing confidence. Confidence cannot necessarily be ­ ticated since the early reports, the rapid growth and considerable assigned for all combinations of evidence and agreement, but where diversity of climate research literature presents ongoing challenges. In key variables are highly uncertain, the available evidence and scientific the wake of the TAR the IPCC formed a Cross-Working Group team agreement regarding that variable are presented and discussed. Confi- charged with identifying the issues and compiling a set of Uncertainty dence should not be interpreted probabilistically, and it is distinct from Guidance Notes that could provide a structure for consistent treatment statistical confidence . of uncertainty across the IPCC s remit (Manning et al., 2004). These expanded on the procedural elements of Moss and Schneider (2000) The confidence level is based on the evidence (robust, medium and and introduced calibrated language scales designed to enable chap- limited) and the agreement (high, medium and low). A combination of ter teams to use the appropriate level of precision to describe find- different methods, e.g., observations and modelling, is important for ings. These notes were revised between the TAR and AR4 and again evaluating the confidence level. Figure 1.11 shows how the combined between AR4 and AR5 (Mastrandrea et al., 2010). evidence and agreement results in five levels for the confidence level used in this assessment. Recently, increased engagement of social scientists (e.g., Patt and Schrag, 2003; Kandlikar et al., 2005; Risbey and Kandlikar, 2007; The qualifier likelihood provides calibrated language for describ- Broomell and Budescu, 2009; Budescu et al., 2009; CCSP, 2009) and ing quantified uncertainty. It can be used to express a probabilistic expert advisory panels (CCSP, 2009; InterAcademy Council, 2010) in es ­ ­timate of the occurrence of a single event or of an outcome, for the area of uncertainty and climate change has helped clarify issues example, a climate parameter, observed trend, or projected change 139 Chapter 1 Introduction Frequently Asked Questions FAQ 1.1 | If Understanding of the Climate System Has Increased, Why Hasn t the Range of T ­ emperature Projections Been Reduced? 1 The models used to calculate the IPCC s temperature projections agree on the direction of future global change, but the projected size of those changes cannot be precisely predicted. Future greenhouse gas (GHG) emission rates could take any one of many possible trajectories, and some underlying physical processes are not yet completely understood, making them difficult to model. Those uncertainties, combined with natural year-to-year climate variability, produce an uncertainty range in temperature projections. The uncertainty range around projected GHG and aerosol precursor emissions (which depend on projections of future social and economic conditions) cannot be materially reduced. Nevertheless, improved understanding and climate models along with observational constraints may reduce the uncertainty range around some factors that influence the climate s response to those emission changes. The complexity of the climate system, however, makes this a slow process. (FAQ1.1, Figure 1) Climate science has made many important advances since the last IPCC assessment report, thanks to improvements in measurements and data analysis in the cryosphere, atmosphere, land, biosphere and ocean systems. Scientists also have better understanding and tools to model the role of clouds, sea ice, aerosols, small-scale ocean mixing, the carbon cycle and other processes. More observations mean that models can now be evaluated more thoroughly, and projections can be better constrained. For example, as models and observational analysis have improved, projections of sea level rise have become more accurate, balancing the current sea level rise budget. Despite these advances, there is still a range in plausible projections for future global and regional climate what scientists call an uncertainty range . These uncertainty ranges are specific to the variable being considered (precipitation vs. temperature, for instance) and the spatial and temporal extent (such as regional vs. global averages). Uncertainties in climate projections arise from natural variability and uncertainty around the rate of future emissions and the climate s response to them. They can also occur because representations of some known processes are as yet unrefined, and because some processes are not included in the models. There are fundamental limits to just how precisely annual temperatures can be projected, because of the chaotic nature of the climate system. Furthermore, decadal-scale projections are sensitive to prevailing conditions such as the temperature of the deep ocean that are less well known. Some natural variability over decades arises from interactions between the ocean, atmosphere, land, biosphere and cryosphere, and is also linked to phenomena such as the El Nino-Southern Oscillation (ENSO) and the North Atlantic Oscillation (see Box 2.5 for details on patterns and indices of climate variability). Volcanic eruptions and variations in the sun s output also contribute to natural variability, although they are externally forced and explainable. This natural variability can be viewed as part of the noise in the climate record, which provides the backdrop against which the signal of anthropogenic climate change is detected. Natural variability has a greater influence on uncertainty at regional and local scales than it does over continental or global scales. It is inherent in the Earth system, and more knowledge will not eliminate the uncertainties it brings. However, some progress is possible particularly for projections up to a few years ahead which exploit advances in knowledge of, for instance, the cryosphere or ocean state and processes. This is an area of active research. When climate variables are averaged over decadal timescales or longer, the relative importance of internal variability diminishes, making the long-term signals more evident (FAQ1.1, Figure 1). This long-term perspective is consistent with a common definition of climate as an average over 30 years. A second source of uncertainty stems from the many possible trajectories that future emission rates of GHGs and aerosol precursors might take, and from future trends in land use. Nevertheless, climate projections rely on input from these variables. So to obtain these estimates, scientists consider a number of alternative scenarios for future human society, in terms of population, economic and technological change, and political choices. They then estimate the likely emissions under each scenario. The IPCC informs policymaking, therefore climate projections for different emissions scenarios can be useful as they show the possible climatic consequences of different policy choices. These scenarios are intended to be compatible with the full range of emissions scenarios described in the current scientific literature, with or without climate policy. As such, they are designed to sample uncertainty in future ­scenarios. (continued on next page) 140 Introduction Chapter 1 FAQ 1.1 (continued) Projections for the next few years and decades are sensitive to emissions of short-lived compounds such as aerosols and methane. More distant projections, however, are more sensitive to alternative scenarios around long-lived GHG emissions. These scenario-dependent uncertainties will not be reduced by improvements in climate science, and will become the dominant uncertainty in projections over longer timescales (e.g., 2100) (FAQ 1.1, Figure 1). 1 The final contribution to the uncertainty range comes from our imperfect knowledge of how the climate will respond to future anthropogenic emissions and land use change. Scientists principally use computer-based global climate models to estimate this response. A few dozen global climate models have been developed by different groups of scientists around the world. All models are built on the same physical principles, but some approximations are needed because the climate system is so complex. Different groups choose slightly different approximations to represent specific processes in the atmosphere, such as clouds. These choices produce differences in climate projections from different models. This contribution to the uncertainty range is described as response uncertainty or model uncertainty . The complexity of the Earth system means that future climate could follow many different scenarios, yet still be consistent with current understanding and models. As observational records lengthen and models improve, researchers should be able, within the limitations of the range of natural variability, to narrow that range in probable temperature in the next few decades (FAQ 1.1, Figure 1). It is also possible to use information about the current state of the oceans and cryosphere to produce better projections up to a few years ahead. As science improves, new geophysical processes can be added to climate models, and representations of those already included can be improved. These developments can appear to increase model-derived estimates of climate response uncertainty, but such increases merely reflect the quantification of previously unmeasured sources of uncertainty (FAQ1.1, Figure 1). As more and more important processes are added, the influence of unquantified processes lessens, and there can be more confidence in the projections. Global average temperature change (°C) Decadal mean temperature anomalies (a) 4 (b) 4 Observations 3.5 Global average temperature change (°C) 3 Natural variability 2.5 3.5 Climate response uncertainty 2 Emission uncertainty 1.5 3 Historical GCM uncertainty 1 All 90% uncertainty ranges 0.5 2.5 0 1960 1980 2000 2020 2040 2060 2080 2100 2 Year Global average temperature change (°C) 1.5 4 (c) 3.5 3 1 2.5 2 0.5 1.5 1 0 0.5 0 1960 1980 2000 2020 2040 2060 2080 2100 1960 1980 2000 2020 2040 2060 2080 2100 Year Year FAQ 1.1, Figure 1 | Schematic diagram showing the relative importance of different uncertainties, and their evolution in time. (a) Decadal mean surface temperature change (°C) from the historical record (black line), with climate model estimates of uncertainty for historical period (grey), along with future climate projections and uncertainty. Values are normalised by means from 1961 to 1980. Natural variability (orange) derives from model interannual variability, and is assumed constant with time. Emission uncertainty (green) is estimated as the model mean difference in projections from different scenarios. Climate response uncertainty (blue-solid) is based on climate model spread, along with added uncertainties from the carbon cycle, as well as rough estimates of additional uncertainty from poorly modelled processes. Based on Hawkins and Sutton (2011) and Huntingford et al. (2009). (b) Climate response uncertainty can appear to increase when a new process is discovered to be relevant, but such increases reflect a quantification of previously unmeasured uncertainty, or (c) can decrease with additional model improvements and observational constraints. The given uncertainty range of 90% means that the temperature is estimated to be in that range, with a probability of 90%. 141 Chapter 1 Introduction such as climate change characterized by complexity of process and High agreement High agreement High agreement h ­ eterogeneity of data constraints some degree of expert judgement Limited evidence Medium evidence Robust evidence is inevitable (Mastrandrea et al., 2010). Medium agreement Medium agreement Medium agreement These issues were brought to the attention of chapter teams so that contributors to the AR5 might be sensitized to the ways presentation, Agreement Limited evidence Medium evidence Robust evidence framing, context and potential biases might affect their own assess- 1 ments and might contribute to readers understanding of the infor- Low agreement Low agreement Low agreement Limited evidence Medium evidence Robust evidence Con dence mation presented in this assessment. There will always be room for Scale debate about how to summarize such a large and growing literature. Evidence (type, amount, quality, consistency) The uncertainty guidance is aimed at providing a consistent, cali- brated set of words through which to communicate the uncertainty, Figure 1.11 | The basis for the confidence level is given as a combination of evidence c ­ onfidence and degree of consensus prevailing in the scientific litera- (limited, medium, robust) and agreement (low, medium and high) (Mastrandrea et al., ture. In this sense the guidance notes and practices adopted by IPCC 2010). for the presentation of uncertainties should be regarded as an inter- ­ disciplinary work in progress, rather than as a finalized, comprehensive lying in a given range. Statements made using the likelihood scale approach. Moreover, one precaution that should be considered is that may be based on statistical or modelling analyses, elicitation of expert translation of this assessment from English to other languages may views, or other quantitative analyses. Where sufficient information is lead to a loss of precision. available it is preferable to eschew the likelihood qualifier in favour of the full probability distribution or the appropriate probability range. See Table 1.2 for the list of likelihood qualifiers to be used in AR5. 1.5 Advances in Measurement and Modelling Capabilities Many social sciences studies have found that the interpretation of uncertainty is contingent on the presentation of information, the con- Since AR4, measurement capabilities have continued to advance. The text within which statements are placed and the interpreter s own models have been improved following the progress in the understand- lexical preferences. Readers often adjust their interpretation of prob- ing of physical processes within the climate system. This section illus- abilistic language according to the magnitude of perceived potential trates some of those developments. consequences (Patt and Schrag, 2003; Patt and Dessai, 2005). Further- more, the framing of a probabilistic statement impinges on how it is 1.5.1 Capabilities of Observations interpreted (Kahneman and Tversky, 1979): for example, a 10% chance of dying is interpreted more negatively than a 90% chance of surviving. Improved understanding and systematic monitoring of Earth s climate requires observations of various atmospheric, oceanic and terrestrial In addition, work examining expert judgement and decision making parameters and therefore has to rely on various technologies (ranging shows that people including scientific experts are prone to a range from ground-based instruments to ships, buoys, ocean profilers, bal- of heuristics and biases that affect their judgement (e.g., Kahneman loons, aircraft, satellite-borne sensors, etc.). The Global Climate Observ- et al., 1982). For example, in the case of expert judgements there ing System (GCOS, 2009) defined a list of so-called Essential Climate is a tendency towards overconfidence both at the individual level Variables, that are technically and economically feasible to observe, (Morgan et al., 1990) and at the group level as people converge on a but some of the associated observing systems are not yet operated in view and draw confidence in its reliability from each other. However, a systematic manner. However, during recent years, new observational in an assessment of the state of scientific knowledge across a field systems have increased the number of observations by orders of mag- nitude and observations have been made at places where there have been no data before (see Chapters 2, 3 and 4 for an assessment of Table 1.2 | Likelihood terms associated with outcomes used in the AR5. changes in observations). Parallel to this, tools to analyse and process the data have been developed and enhanced to cope with the increase Term Likelihood of the Outcome of information and to provide a more comprehensive picture of the Virtually certain 99 100% probability Earth s climate. At the same time, it should be kept in mind that there Very likely 90 100% probability has been some limited progress in developing countries in filling gaps Likely 66 100% probability in their in situ observing networks, but developed countries have made About as likely as not 33 66% probability little progress in ensuring long-term continuity for several important Unlikely 0 33% probability observing systems (GCOS, 2009). In addition, more proxy (non-instru- Very unlikely 0 10% probability mental) data have been acquired to provide a more comprehensive Exceptionally unlikely 0 1% probability picture of climate changes in the past (see Chapter 5). Efforts are also occurring to digitize historic observations, mainly of ground-station Notes: Additional terms that were used in limited circumstances in the AR4 (extremely likely = data from periods prior to the second half of the 20th century (Brunet 95 100% probability, more likely than not = >50 100% probability, and extremely unlikely = and Jones, 2011). 0 5% probability) may also be used in the AR5 when appropriate. 142 Introduction Chapter 1 Geophysical Year satellites International rockets radiosonde 1 ozone sonde pilot balloons aircraft (chemistry) aircraft kites cloud obs. total ozone / remote sensing in-situ air chemistry surface stations (temperature, pressure, wind, radiation, turbidity, carbon flux) marine observations (e.g., sea surface temperature, precipitation) 1880 1900 1920 1940 1960 96 60 19 1980 9 2000 0 70 Number of satellite data sources used 60 First Year of Temperature Record 2010 in GHCN Daily Database 2000 50 1990 1980 40 1970 1960 1950 30 1940 1930 20 1920 1910 10 1900 0 1996 2000 2005 2010 Figure 1.12 | Development of capabilities of observations. Top: Changes in the mix and increasing diversity of observations over time create challenges for a consistent climate record (adapted from Brönnimann et al., 2008). Bottom left: First year of temperature data in Global Historical Climatology Network (GHCN) daily database (available at http:// www.ncdc.noaa.gov/oa/climate/ghcn-daily/; Menne et al., 2012). Bottom right: Number of satellite instruments from which data have been assimilated in the European Centre for Medium-Range Weather Forecasts production streams for each year from 1996 to 2010. This figure is used as an example to demonstrate the fivefold increase in the usage of satellite data over this time period. Reanalysis is a systematic approach to produce gridded dynamically example, information from Global Positioning System radio occultation consistent data sets for climate monitoring and research by assimilat- measurements has increased significantly since 2007. The increases in ing all available observations with help of a climate model (Box 2.3). data from fixed stations are often associated with an increased fre- Model-based reanalysis products play an important role in obtaining quency of reporting, rather than an increase in the number of stations. a consistent picture of the climate system. However, their usefulness Increases in data quality come from improved instrument design or in detecting long-term climate trends is currently limited by changes from more accurate correction in the ground-station processing that is over time in observational coverage and biases, linked to the presence applied before the data are transmitted to users and data centres. As of biases in the assimilating model (see also Box 2.3 in Chapter 2). an example for in situ data, temperature biases of radiosonde measure- Because AR4 both the quantity and quality of the observations that ments from radiation effects have been reduced over recent years. The are assimilated through reanalysis have increased (GCOS, 2009). As new generation of satellite sensors such as the high spectral resolution ­ an example, there has been some overall increase in mostly atmos- infrared sounders (such as the Atmospheric Infrared Sounder and the pheric observations assimilated in European Centre for Medium-Range Infrared Atmospheric Sounding Interferometer) are instrumental to Weather Forecasts Interim Reanalysis since 2007 (Dee et al., 2011). achieving a better temporal stability for recalibrating sensors such The overwhelming majority of the data, and most of the increase over as the High-Resolution Infrared Radiation Sounder. Few instruments recent years, come from satellites (Figure 1.12) (GCOS, 2011). For (e.g., the Advanced Very High Resolution Radiometer) have now been 143 Chapter 1 Introduction in orbit for about three decades, but these were not originally designed Progress has also been made with regard to observation of terrestri- for climate applications and therefore require careful re-calibration. al Essential Climate Variables. Major advances have been achieved in remote sensing of soil moisture due to the launch of the Soil Moisture A major achievement in ocean observation is due to the implementa- and Oceanic Salinity mission in 2009 but also due to new retrieval tion of the Argo global array of profiling floats system (GCOS, 2009). techniques that have been applied to data from earlier and ongoing Deployment of Argo floats began in 2000, but it took until 2007 for missions (see Seneviratne et al., 2010 for a detailed review). ­However, numbers to reach the design target of 3000 floats. Since 2000 the ice- these measurements have limitations. For example, the methods fail 1 free upper 2000 m of the ocean have been observed systematically under dense vegetation and they are restricted to the surface soil. for temperature and salinity for the first time in history, because both Updated Advanced Very High Resolution Radiometer-based ­Normalized the Argo profiling float and surface drifting buoy arrays have reached Differenced Vegetation Index data provide new information on the global coverage at their target numbers (in January 2009, there were change in vegetation. During the International Polar Year 2007 2009 3291 floats operating). Biases in historical ocean data have been iden- the number of borehole sites was significantly increased and therefore tified and reduced, and new analytical approaches have been applied allows a better monitoring of the large-scale permafrost features (see (e.g., Willis et al., 2009). One major consequence has been the reduc- Section 4.7). tion of an artificial decadal variation in upper ocean temperature and heat content that was apparent in the observational assessment for 1.5.2 Capabilities in Global Climate Modelling AR4 (see Section 3.2). The spatial and temporal coverage of bioge- ochemical measurements in the ocean has also expanded. Satellite Several developments have especially pushed the capabilities in mod- observations for sea level (Sections 3.7 and 13.2), sea surface salinity elling forward over recent years (see Figure 1.13 and a more detailed (Section 3.3), sea ice (Section 4.2) and ocean colour have also been discussion in Chapters 6, 7 and 9). further developed over the past few years. Mid-1970s Mid-1980s FAR SAR TAR AR4 AR5 Atmosphere C O U Land P Surface L E D Ocean & Sea Ice C L Aerosols I M A T Carbon Cycle E M Dynamic O Vegetation D E Atmospheric L Chemistry Land Ice Mid-1970s Mid-1980s FAR SAR TAR AR4 AR5 Figure 1.13 | The development of climate models over the last 35 years showing how the different components were coupled into comprehensive climate models over time. In each aspect (e.g., the atmosphere, which comprises a wide range of atmospheric processes) the complexity and range of processes has increased over time (illustrated by growing cylinders). Note that during the same time the horizontal and vertical resolution has increased considerably e.g., for spectral models from T21L9 (roughly 500 km horizontal resolu- tion and 9 vertical levels) in the 1970s to T95L95 (roughly 100 km horizontal resolution and 95 vertical levels) at present, and that now ensembles with at least three independent experiments can be considered as standard. 144 Introduction Chapter 1 a) 1 x 87.5 km 87.5 km b) m x 30.0 k 30.0 km Figure 1.14 | Horizontal resolutions considered in today s higher resolution models and in the very high resolution models now being tested: (a) Illustration of the European topography at a resolution of 87.5 × 87.5 km; (b) same as (a) but for a resolution of 30.0 × 30.0 km. There has been a continuing increase in horizontal and vertical resolu- Representations of Earth system processes are much more extensive tion. This is especially seen in how the ocean grids have been refined, and improved, particularly for the radiation and the aerosol cloud inter- and sophisticated grids are now used in the ocean and atmosphere actions and for the treatment of the cryosphere. The representation of models making optimal use of parallel computer architectures. More the carbon cycle was added to a larger number of models and has been models with higher resolution are available for more regions. Figure improved since AR4. A high-resolution stratosphere is now included in 1.14a and 1.14b show the large effect on surface representation from many models. Other ongoing process development in climate models a horizontal grid spacing of 87.5 km (higher resolution than most cur- includes the enhanced representation of nitrogen effects on the carbon rent global models and similar to that used in today s highly resolved cycle. As new processes or treatments are added to the models, they models) to a grid spacing of 30.0 km (similar to the current regional are also evaluated and tested relative to available observations (see climate models). Chapter 9 for more detailed discussion). 145 Chapter 1 Introduction Ensemble techniques (multiple calculations to increase the statistical The capabilities of ESMs continue to be enhanced. For example, there sample, to account for natural variability, and to account for ­uncertainty are currently extensive efforts towards developing advanced treat- in model formulations) are being used more frequently, with larger ments for the processes affecting ice sheet dynamics. Other enhance- samples and with different methods to generate the samples (different ­ ments are being aimed at land surface hydrology, and the effects of models, different physics, different initial conditions). Coordinated agriculture and urban environments. projects have been set up to generate and distribute large samples (ENSEMBLES, climateprediction.net, Program for Climate Model Diag- As part of the process of getting model analyses for a range of alter- 1 nosis and Intercomparison). native assumptions about how the future may unfold, scenarios for future emissions of important gases and aerosols have been ­ enerated g The model comparisons with observations have pushed the analysis for the IPCC assessments (e.g., see the SRES scenarios used in TAR and development of the models. CMIP5, an important input to the AR5, and AR4). The emissions scenarios represent various development has produced a multi-model data set that is designed to advance our pathways based on well-defined assumptions. The scenarios are used understanding of climate variability and climate change. Building on to calculate future changes in climate, and are then archived in the previous CMIP efforts, such as the CMIP3 model analysis reported in Climate Model Intercomparison Project (e.g., CMIP3 for AR4; CMIP5 AR4, CMIP5 includes long-term simulations of 20th century climate for AR5). For CMIP5, four new scenarios, referred to as Representative and projections for the 21st century and beyond. See Chapters 9, 10, 11 Concentration Pathways (RCPs) were developed (Section 12.3; Moss et and 12 for more details on the results derived from the CMIP5 archive. al., 2010). See Box 1.1 for a more thorough discussion of the RCP sce- narios. Because results from both CMIP3 and CMIP5 will be presented Since AR4, the incorporation of long-term paleoclimate simulations in the later chapters (e.g., Chapters 8, 9, 11 and 12), it is worthwhile in the CMIP5 framework has allowed incorporation of information considering the differences and similarities between the SRES and the from paleoclimate data to inform projections. Within uncertainties RCP scenarios. Figure 1.15, acting as a prelude to the discussion in Box a ­ ssociated with reconstructions of past climate variables from proxy 1.1, shows that the RF for several of the SRES and RCP scenarios are records and forcings, paleoclimate information from the Mid Holocene, similar over time and thus should provide results that can be used to Last Glacial Maximum and Last Millennium have been used to test compare climate modelling studies. the ability of models to simulate realistically the magnitude and large- scale patterns of past changes (Section 5.3, Box 5.1 and 9.4). 9 RCP2.6 RCP4.5 8 RCP6.0 RCP8.5 SRES A1B 7 SRES A2 SRES B1 6 IS92a RF total (Wm-2) 5 4 3 2 1 0 1950 1975 2000 2025 2050 2075 2100 Year Figure 1.15 | Historical and projected total anthropogenic RF (W m 2) relative to preindustrial (about 1765) between 1950 and 2100. Previous IPCC assessments (SAR IS92a, TAR/ AR4 SRES A1B, A2 and B1) are compared with representative concentration pathway (RCP) scenarios (see Chapter 12 and Box 1.1 for their extensions until 2300 and Annex II for the values shown here). The total RF of the three families of scenarios, IS92, SRES and RCP, differ for example, for the year 2000, resulting from the knowledge about the emissions assumed having changed since the TAR and AR4. 146 Introduction Chapter 1 Box 1.1 | Description of Future Scenarios Long-term climate change projections require assumptions on human activities or natural effects that could alter the climate over decades and centuries. Defined scenarios are useful for a variety of reasons, e.g., assuming specific time series of emissions, land use, atmospheric concentrations or RF across multiple models allows for coherent climate model intercomparisons and synthesis. Scenarios can be formed in a range of ways, from simple, idealized structures to inform process understanding, through to comprehensive 1 scenarios produced by Integrated Assessment Models (IAMs) as internally consistent sets of assumptions on emissions and socio- economic drivers (e.g., regarding population and socio-economic development). Idealized Concentration Scenarios As one example of an idealized concentration scenario, a 1% yr 1 compound increase of atmospheric CO2 concentration until a doubling or a quadrupling of its initial value has been widely used in the past (Covey et al., 2003). An exponential increase of CO2 concentrations induces an essentially linear increase in RF (Myhre et al., 1998) due to a saturation effect of the strong absorbing bands. Such a linear ramp function is highly useful for comparative diagnostics of models climate feedbacks and inertia. The CMIP5 intercomparison project again includes such a stylized pathway up to a quadrupling of CO2 concentrations, in addition to an instantaneous quadrupling case. The Socio-Economic Driven SRES Scenarios The SRES suite of scenarios were developed using IAMs and resulted from specific socio-economic scenarios from storylines about future demographic and economic development, regionalization, energy production and use, technology, agriculture, forestry and land use (IPCC, 2000). The climate change projections undertaken as part of CMIP3 and discussed in AR4 were based primarily on the SRES A2, A1B and B1 scenarios. However, given the diversity in models carbon cycle and chemistry schemes, this approach implied differences in models long lived GHG and aerosol concentrations for the same emissions scenario. As a result of this and other shortcomings, revised scenarios were developed for AR5 to allow atmosphere-ocean general circulation model (AOGCM) (using concentrations) simulations to be compared with those ESM simulations that use emissions to calculate concentrations. Representative Concentration Pathway Scenarios and Their Extensions Representative Concentration Pathway (RCP) scenarios (see Section 12.3 for a detailed description of the scenarios; Moss et al., 2008; Moss et al., 2010; van Vuuren et al., 2011b) are new scenarios that specify concentrations and corresponding emissions, but are not directly based on socio-economic storylines like the SRES scenarios. The RCP scenarios are based on a different approach and include more consistent short-lived gases and land use changes. They are not necessarily more capable of representing future developments than the SRES scenarios. Four RCP scenarios were selected from the published literature (Fujino et al., 2006; Smith and Wigley, 2006; Riahi et al., 2007; van Vuuren et al., 2007; Hijioka et al., 2008; Wise et al., 2009) and updated for use within CMIP5 (Masui et al., 2011; Riahi et al., 2011; Thomson et al., 2011; van Vuuren et al., 2011a). The four scenarios are identified by the 21st century peak or stabilization value of the RF derived by the reference model (in W m 2) (Box 1.1, Figure 1): the lowest RCP, RCP2.6 (also referred to as (continued on next page) History RCPs ECPs 14 RCP8.5 12 Radiative forcing (Wm-2) 10 ~8.5 (Wm-2) 8 RCP6 6 ~6 (Wm-2) SCP6to4.5 RCP4.5 ~4.5 (Wm-2) 4 ~3.0 (Wm-2) 2 RCP2.6 0 2 1800 1900 2000 2100 2200 2300 2400 2500 Year Box 1.1, Figure 1 | Total RF (anthropogenic plus natural) for RCPs and extended concentration pathways (ECP) for RCP2.6, RCP4.5, and RCP6, RCP8.5, as well as a supplementary extension RCP6 to 4.5 with an adjustment of emissions after 2100 to reach RCP4.5 concentration levels in 2250 and thereafter. Note that the stated RF levels refer to the illustrative default median estimates only. There is substantial uncertainty in current and future RF levels for any given scenario. Short-term variations in RF are due to both volcanic forcings in the past (1800 2000) and cyclical solar forcing assuming a constant 11-year solar cycle (following the CMIP5 recommenda- tion), except at times of stabilization. (Reproduced from Figure 4 in Meinshausen et al., 2011.) 147 Chapter 1 Introduction Box 1.1 (continued) RCP3-PD) which peaks at 3 W m 2 and then declines to approximately 2.6 W m 2 by 2100; the medium-low RCP4.5 and the medium- high RCP6 aiming for stabilization at 4.5 and 6 W m 2, respectively around 2100; and the highest one, RCP8.5, which implies a RF of 8.5 W m 2 by 2100, but implies rising RF beyond that date (Moss et al., 2010). In addition there is a supplementary extension SCP6to4.5 with an adjustment of emissions after 2100 to reach RCP 4.5 concentration levels in 2250 and thereafter. The RCPs span the full range 1 of RF associated with emission scenarios published in the peer-reviewed literature at the time of the development of the RCPs, and the two middle scenarios where chosen to be roughly equally spaced between the two extremes (2.6 and 8.5 W m 2). These forcing values should be understood as comparative labels representative of the forcing associated with each scenario, which will vary somewhat from model to model. This is because concentrations or emissions (rather than the RF) are prescribed in the CMIP5 climate model runs. Various steps were necessary to turn the selected raw RCPs into emission scenarios from IAMs and to turn these into data sets usable by the climate modelling community, including the extension with historical emissions (Granier et al., 2011; Meinshausen et al., 2011), the harmonization (smoothly connected historical reconstruction) and gridding of land use data sets (Hurtt et al., 2011), the provision of atmospheric chemistry modelling studies, particularly for tropospheric ozone (Lamarque et al., 2011), analyses of 2000 2005 GHG emission levels, and extension of GHG concentrations with historical GHG concentrations and harmonization with analyses of 2000 2005 GHG concentrations levels (Meinshausen et al., 2011). The final RCP data sets comprise land use data, harmonized GHG emissions and concentrations, gridded reactive gas and aerosol emissions, as well as ozone and aerosol abundance fields ( Figures 2, 3, and 4 in Box 1.1). (continued on next page) History RCPs ECPs History RCPs ECPs 2000 a) Carbon ppb ppm RCP8.5 b) Methane Dioxide RCP8.5 1500 3500 3000 1000 2500 900 2000 800 RCP6 RCP6 RCP4.5 700 1500 SCP6to4.5 RCP2.6 600 1000 RCP4.5 500 500 c) Nitrous ppb RCP2.6 400 Oxide RCP8.5 500 300 ppt 450 1000 d) CFC12 eq RCP6 900 SCP6to4.5 800 400 RCP4.5 700 600 350 RCP2.6 500 400 300 300 200 100 0 250 1800 1900 2000 2100 2200 2300 1800 1900 2000 2100 2200 2300 Box 1.1, Figure 2 | Concentrations of GHG following the 4 RCPs and their extensions (ECP) to 2300. (Reproduced from Figure 5 in Meinshausen et al., 2011.) Also see Annex II Table AII.4.1 for CO2, Table AII.4.2 for CH4, Table AII.4.3 for N2O. 148 Introduction Chapter 1 Box 1.1 (continued) a) History RCPs ECPs 1000 SRES A1FI RCP8.5 SRES A2 CO2 equivalent (CO2-eq ppm) 900 RCP6 1 800 SRES A1B SCP6to4.5 SRES B2 700 SRES A1T 600 RCP4.5 SRES B1 500 400 RCP2.6 300 1800 1850 1900 1950 2000 2050 2100 2150 2200 2250 2300 Global CO2 (fossil & ind.) emissions (GtC yr-1) b) History RCPs ECPs 30 SRES A1FI SRES 25 A2 RCP8.5 20 SRES A1B 15 SRES B2 SRES 10 B1 SRES 5 A1T RCP6 RCP4.5 0 RCP2.6 SCP6to4.5 5 1800 1850 1900 1950 2000 2050 2100 2150 2200 2250 2300 Box 1.1, Figure 3 | (a) Equivalent CO2 concentration and (b) CO2 emissions (except land use emissions) for the four RCPs and their ECPs as well as some SRES scenarios. To aid model understanding of longer-term climate change implications, these RCPs were extended until 2300 (Meinshausen et al., 2011) under reasonably simple and somewhat arbitrary assumptions regarding post-2100 GHG emissions and concentrations. In order to continue to investigate a broad range of possible climate futures, the two outer RCPs, RCP2.6 and RCP8.5 assume constant emissions after 2100, while the two middle RCPs aim for a smooth stabilization of concentrations by 2150. RCP8.5 stabilizes concentrations only by 2250, with CO2 concentrations of approximately 2000 ppm, nearly seven times the pre-industrial levels. As the RCP2.6 implies netnegative CO2 emissions after around 2070 and throughout the extension, CO2 concentrations are slowly reduced towards 360 ppm by 2300. Comparison of SRES and RCP Scenarios The four RCP scenarios used in CMIP5 lead to RF values that span a range larger than that of the three SRES scenarios used in CMIP3 (Figure 12.3). RCP4.5 is close to SRES B1, RCP6 is close to SRES A1B (more after 2100 than during the 21st century) and RCP8.5 is somewhat higher than A2 in 2100 and close to the SRES A1FI scenario (Figure 3 in Box 1.1). RCP2.6 is lower than any of the SRES scenarios (see also Figure 1.15). (continued on next page) 149 Chapter 1 Introduction Box 1.1 (continued) a) 30 Anthropogenic BC emissions 25 1 20 (Tg yr ) -1 15 10 5 0 2000 2010 2020 2030 2040 2050 2060 2070 2080 2090 2100 b) 120 Anthropogenic NOX emissions RCP2.6 105 RCP4.5 90 RCP6.0 (TgN yr ) -1 RCP8.5 75 SRES A2 60 SRES B1 45 30 15 2000 2010 2020 2030 2040 2050 2060 2070 2080 2090 2100 c) 120 Anthropogenic SOX emissions 105 90 75 (TgS yr ) -1 60 45 30 15 2000 2010 2020 2030 2040 2050 2060 2070 2080 2090 2100 Note: Primary anthropogenic sulphur emissions as SO2 measured here as Tg of S (see Annex II Table AII.2.20) Box 1.1, Figure 4 | (a) Anthropogenic BC emissions (Annex II Table AII.2.22), (b) anthropogenic NOx emissions (Annex II Table AII.2.18), and (c) anthropogenic SOx emissions (Annex II Table II.2.20). 150 Introduction Chapter 1 1.6 Overview and Road Map to the Rest climate change. Maps produced and assessed in Chapter 14, together of the Report with Chapters 11 and 12, form the basis of the Atlas of Global and Regional Climate Projections in Annex I. RFs and estimates of future As this chapter has shown, understanding of the climate system and atmospheric concentrations from Chapters 7, 8, 11 and 12 form the the changes occurring in it continue to advance. The notable scientific basis of the Climate System Scenario Tables in Annex II. advances and associated peer-reviewed publications since AR4 provide the basis for the assessment of the science as found in Chapters 2 to 1.6.1 Topical Issues 1 14. Below a quick summary of these chapters and their objectives is provided. A number of topical issues are discussed throughout the assessment. These issues include those of areas where there is contention in the Observations and Paleoclimate Information (Chapters 2, 3, 4 and peer-reviewed literature and where questions have been raised that 5): These chapters assess information from all climate system compo- are being addressed through ongoing research. Table 1.3 provides a nents on climate variability and change as obtained from instrumental non-comprehensive list of many of these and the chapters where they records and climate archives. This group of chapters covers all relevant are discussed. aspects of the atmosphere including the stratosphere, the land surface, the oceans and the cryosphere. Information on the water cycle, includ- Table 1.3 | Key topical issues discussed in the assessment. ing evaporation, precipitation, runoff, soil moisture, floods, drought, etc. is assessed. Timescales from daily to decades (Chapters 2, 3 and Topic Section 4) and from centuries to many millennia (Chapter 5) are considered. Abrupt change and irreversibility 5.7, 12.5, 13.4 Aerosols 6.4, 7.3, 7.4, 7.5, 7.6, 8.3, 11.3, 14.1 Process Understanding (Chapters 6 and 7): These chapters cover Antarctic climate change 5.8, 9.4, 10.3, 13.3 all relevant aspects from observations and process understanding, to Arctic sea ice change 4.2, 5.5, 9.4, 10.3, 11.3, 12.4 projections from global to regional scale. Chapter 6 covers the carbon cycle and its interactions with other biogeochemical cycles, in ­particular Hydrological cycle changes 2.5, 2.6, 3.3, 3.4, 3.5, 7.6, 10.3, 12.4 the nitrogen cycle, as well as feedbacks on the climate system. Chapter Carbon-climate feedbacks 6.4, 12.4 7 treats in detail clouds and aerosols, their interactions and chemistry, Climate sensitivity 5.3, 9.7, 10.8, 12.5 the role of water vapour, as well as their role in feedbacks on the cli- Climate stabilization 6.3, 6.4, 12.5 mate system. Cloud feedbacks 5.3, 7.2, 9.7, 11.3, 12.4 Cosmic ray effects on clouds 7.4 From Forcing to Attribution of Climate Change (Chapters 8, 9 Decadal climate variability 5.3, 9.5, 10.3 and 10): In these chapters, all the information on the different drivers Earth s Energy (trends, distribution and (natural and anthropogenic) of climate change is collected, expressed budget) 2.3, 3.2, 13.3 in terms of RF, and assessed (Chapter 8). As part of this, the science of El Nino-Southern Oscillation 2.7, 5.4, 9.4, 9.5, 14.4 metrics commonly used in the literature to compare radiative effects Geo-engineering 6.4, 7.7 from a range of agents (Global Warming Potential, Global Temperature Glacier change 4.3, 5.5, 10.5, 13.3 Change Potential and others) is covered. In Chapter 9, the hierarchy of climate models used in simulating past and present climate change is Ice sheet dynamics and mass balance 4.4, 5.3, 5.6, 10.5, 13.3 assessment assessed. Information regarding detection and attribution of changes Monsoons 2.7, 5.5, 9.5, 14.2 on global to regional scales is assessed in Chapter 10. Ocean acidification 3.8, 6.4 Future Climate Change and Predictability (Chapters 11 and 12): Permafrost change 4.7, 6.3, 10.5 These chapters assess projections of future climate change derived from Solar effects on climate change 5.2, 8.4 climate models on timescales from decades to centuries at both global Sea level change, including regional effects 3.7, 5.6, 13.1 and regional scales, including mean changes, variability and extremes. Temperature trends since 1998 2.4, 3.2, 9.4 Fundamental questions related to the predictability of ­ limate as well c Tropical cyclones 2.6, 10.6, 14.6 as long-term climate change, climate change commitments and inertia Upper troposphere temperature trends 2.4, 9.4 in the climate system are addressed. Integration (Chapters 13 and 14): These chapters integrate all rel- evant information for two key topics in WGI AR5: sea level change (Chapter 13) and climate phenomena across the regions (Chapter 14). Chapter 13 assesses information on sea level change ranging from observations and process understanding to projections from global to regional scales. Chapter 14 assesses the most important modes of variability in the climate system and extreme events. Furthermore, this chapter deals with interconnections between the climate phenome- na, their regional expressions, and their relevance for future regional 151 Chapter 1 Introduction References Allen, M. R., J. F. B. Mitchell, and P. A. Stott, 2013: Test of a decadal climate forecast. Dlugokencky, E. J., et al., 2009: Observational constraints on recent increases in the Nature Geosci., 6, 243 244. atmospheric CH4 burden. Geophys. Res. Lett., 36, L18803. AMAP, 2009: Summary The Greenland Ice Sheet in a Changing Climate: Snow, Duarte, C. M., T. M. Lenton, P. Wadhams, and P. Wassmann, 2012: Commentary: Water, Ice and Permafrost in the Arctic (SWIPA). Arctic Monitoring and Assess- Abrupt climate change in the Arctic. Nature Clim. Change, 2, 60 62. ment Programme (AMAP), 22 pp. Easterling, D. R., and M. F. Wehner, 2009: Is the climate warming or cooling? Geo- 1 Armour, K. C., I. Eisenman, E. Blanchard-Wrigglesworth, K. E. McCusker, and C. M. phys. Res. Lett., 36, L08706. Bitz, 2011: The reversibility of sea ice loss in a state-of-the-art climate model. Elkins, J., and G. Dutton, 2010: Nitrous oxide and sulfur hexaflouride. Section in State Geophys. Res. Lett., 38. of the Climate in 2009. Bull. Am. Meteorol. Soc., 91, 44 45. Arrhenius, S., 1896: On the influence of carbonic acid in the air upon the temperature Ettema, J., M. R. van den Broeke, E. van Meijgaard, W. J. van de Berg, J. L. Bamber, of the ground. Philos. Mag., 41, 237 276. J. E. Box, and R. C. Bales, 2009: Higher surface mass balance of the Greenland Baede, A. P. M., E. Ahlonsou, Y. Ding, and D. Schimel, 2001: The climate system: An ice sheet revealed by high-resolution climate modeling. Geophys. Res. Lett., 36, overview. In: Climate Change 2001: The Scientific Basis. Contribution of Work- L12501. ing Group I to the Third Assessment Report of the Intergovernmental Panel on Foley, J., et al., 2005: Global consequences of land use. Science, 309, 570 574. Climate Change [J. T. Houghton, Y. Ding, D. J. Griggs, M. Noquer, P. J. van der Folland, C. K., et al., 2001: Observed climate variability and change. In: Climate Linden, X. Dai, K. Maskell and C. A. Johnson (eds.)]. Cambridge University Press, Change 2001: The Scientific Basis. Contribution of Working Group I to the Third Cambridge, United Kingdom and New York, NY, USA. Assessment Report of the Intergovernmental Panel on Climate Change [J. T. Beerling, D. J., and D. L. Royer, 2011: Convergent Cenozoic CO2 history. Nature Houghton, Y. Ding, D. J. Griggs, M. Noquer, P. J. van der Linden, X. Dai, K. Maskell Geosci., 4, 418 420. and C. A. Johnson (eds.)]. Cambridge University Press, Cambridge, United King- Bretherton, F. P., K. Bryan, and J. D. Woodes, 1990: Time-dependent greenhouse-gas- dom and New York, NY, USA, 101 181. induced climate change. In: Climate Change: The IPCC Scientific Assessment [J. Forest, C. E., P. H. Stone, and A. P. Sokolov, 2008: Constraining climate model param- T. Houghton, G. J. Jenkins and J. J. Ephraums (eds.)]. Cambridge University Press, eters from observed 20th century changes. Tellus A, 60, 911 920. Cambridge, United Kingdom and New York, NY, USA, 177 193. Forster, P., et al., 2007: Changes in atmospheric constituents and in radiative forcing. Brönnimann, S., T. Ewen, J. Luterbacher, H. F. Diaz, R. S. Stolarski, and U. Neu, 2008: A In: Climate Change 2007: The Physical Science Basis. Contribution of Working focus on climate during the past 100 years. In: Climate Variability and Extremes Group I to the Fourth Assessment Report of the Intergovernmental Panel on during the Past 100 Years [S. Brönnimann, J. Luterbacher, T. Ewen, H. F. Diaz, R. Climate Change [Solomon, S., D. Qin, M. Manning, Z. Chen, M. Marquis, K. B. S. Stolarski and U. Neu (eds.)]. Springer Science+Business Media, Heidelberg, Averyt, M. Tignor and H. L. Miller (eds.)]. Cambridge University Press, Cambridge, Germany and New York, NY, USA, pp. 1 25. United Kingdom and New York, NY, USA, 131 234. Broomell, S., and D. Budescu, 2009: Why are experts correlated? Decomposing cor- Frame, D. J., and D. A. Stone, 2013: Assessment of the first consensus prediction on relations between judges. Psychometrika, 74, 531 553. climate change. Nature Clim. Change, 3, 357 359. Brunet, M., and P. Jones, 2011: Data rescue initiatives: Bringing historical climate Frame, D. J., D. A. Stone, P. A. Stott, and M. R. Allen, 2006: Alternatives to stabilization data into the 21st century. Clim. Res., 47, 29 40. scenarios. Geophys. Res. Lett., 33. Budescu, D., S. Broomell, and H.-H. Por, 2009: Improving communication of uncer- Fujino, J., R. Nair, M. Kainuma, T. Masui, and Y. Matsuoka, 2006: Multi-gas mitiga- tainty in the reports of the Intergovernmental Panel on Climate Change. Psychol. tion analysis on stabilization scenarios using aim global model. Energy J., 0, Sci., 20, 299 308. 343 353. Byrne, R., S. Mecking, R. Feely, and X. Liu, 2010: Direct observations of basin-wide GCOS, 2009: Progress Report on the Implementation of the Global Observing System acidification of the North Pacific Ocean. Geophys. Res. Lett., 37. for Climate in Support of the UNFCCC 2004 2008, GCOS-129 (WMO/TD-No. CCSP, 2009: Best Practice Approaches for Characterizing, Communicating, and Incor- 1489; GOOS-173; GTOS-70) , Geneva, Switzerland. porating Scientific Uncertainty in Climate Decision Making. U.S. Climate Change GCOS , 2011: Systematic Observation Requirements for Satellite-based Products for Science Program, Washington, DC, USA, 96 pp. Climate Supplemental details to the satellite-based component of the Imple- Church, J. A., and N. J. White, 2011: Sea-level rise from the late 19th to the early 21st mentation Plan for the Global Observing System for Climate in Support of the century. Surv. Geophys., 32, 585 602. UNFCCC 2011 Update, (GCOS-154) December 2011, Geneva, Switzerland. Church, J. A., J. M. Gregory, N. J. White, S. M. Platten, and J. X. Mitrovica, 2011: Goosse, H., W. Lefebvre, A. de Montety, E. Crespin, and A. H. Orsi, 2009: Consistent Understanding and projecting sea level change. Oceanography, 24, 130 143. past half-century trends in the atmosphere, the sea ice and the ocean at high Cleveland, W. S., 1979: Robust locally weighted regression and smoothing scatter- southern latitudes. Clim. Dyn., 33, 999 1016. plots. J. Am. Stat. Assoc., 74, 829 836. Granier, C., et al., 2011: Evolution of anthropogenic and biomass burning emissions Collins, M., and M. R. Allen, 2002: Assessing the relative roles of initial and bound- of air pollutants at global and regional scales during the 1980 2010 period. ary conditions in interannual to decadal climate predictability. J. Clim., 15, Clim. Change, 109, 163 190. 3104 3109. Haas, C., A. Pfaffling, S. Hendricks, L. Rabenstein, J. L. Etienne, and I. Rigor, 2008: Covey, C., et al., 2003: An overview of results from the Coupled Model Intercompari- Reduced ice thickness in Arctic Transpolar Drift favors rapid ice retreat. Geophys. son Project. Global Planet. Change, 37, 103 133. Res. Lett., 35, L17501. Cubasch, U., et al., 2001: Projections of future climate change. In: Climate Change Hansen, J., D. Johnson, A. Lacis, S. Lebedeff, P. Lee, D. Rind, and G. Russell, 1981: Cli- 2001: The Scientific Basis. Contribution of Working Group I to the Third Assess- mate impact of increasing atmospheric carbon dioxide. Science, 213, 957 966. ment Report of the Intergovernmental Panel on Climate Change [J. T. Houghton, Hansen, J., M. Sato, R. Ruedy, K. Lo, D. W. Lea, and M. Medina-Elizade, 2006: Global Y. Ding, D. J. Griggs, M. Noquer, P. J. van der Linden, X. Dai, K. Maskell and C. A. temperature change. Proc. Natl. Acad. Sci. U.S.A., 103, 14288 14293. Johnson (eds.)]. Cambridge University Press, Cambridge, United Kingdom and Hansen, J., R. Ruedy, M. Sato, and K. Lo, 2010: Global surface temperature change. New York, NY, USA, 527 582. Rev. Geophys., 48, RG4004. Dee, D. P., et al., 2011: The ERA-Interim reanalysis: Configuration and performance of Hansen, J., M. Sato, P. Kharecha, and K. von Schuckmann, 2011: Earth s energy the data assimilation system. Q. J. R. Meteorol. Soc., 137, 553 597. imbalance and implications. Atmos. Chem. Phys., 11, 13421 13449. Denman, K. L., et al., 2007: Couplings between changes in the climate system and Hawkins, E., and R. Sutton, 2009: The potential to narrow uncertainty in regional biogeochemistry. In: Climate Change 2007: The Physical Science Basis. Contribu- climate predictions. Bull. Am. Meteorol. Soc., 90, 1095 1107. tion of Working Group I to the Fourth Assessment Report of the Intergovern- Hawkins, E., and R. Sutton, 2011: The potential to narrow uncertainty in projections mental Panel on Climate Change [Solomon, S., D. Qin, M. Manning, Z. Chen, M. of regional precipitation change. Clim. Dyn., 37, 407 418. Marquis, K. B. Averyt, M. Tignor and H. L. Miller (eds.)]. Cambridge University Press, Cambridge, United Kingdom and New York, NY, USA, 501 587. 152 Introduction Chapter 1 Hegerl, G. C., et al., 2007: Understanding and attributing climate change. In: Climate Kwok, R., G. F. Cunningham, M. Wensnahan, I. Rigor, H. J. Zwally, and D. Yi, 2009: Change 2007: The Physical Science Basis. Contribution of Working Group I to the Thinning and volume loss of the Arctic Ocean sea ice cover: 2003 2008. J. Geo- Fourth Assessment Report of the Intergovernmental Panel on Climate Change phys. Res. Oceans, 114, C07005. [Solomon, S., D. Qin, M. Manning, Z. Chen, M. Marquis, K. B. Averyt, M. Tignor Lamarque, J. F., et al., 2011: Global and regional evolution of short-lived radiatively- and H. L. Miller (eds.)]. Cambridge University Press, Cambridge, United Kingdom active gases and aerosols in the Representative Concentration Pathways. Clim. and New York, NY, USA, 665 745. Change, 109, 191 212. Hijioka, Y., Y. Matsuoka, H. Nishomoto, M. Masui, and M. Kainuma, 2008: Global Lambert, F., et al., 2008: Dust-climate couplings over the past 800,000 years from the GHG emission scenarios under GHG concentration stabilization targets. JGEE, EPICA Dome C ice core. Nature, 452, 616 619. 13, 97 108. Lemke, P., et al., 2007: Observations: Changes in snow, ice and frozen ground. In: Cli- 1 Hoelzle, M., G. Darms, M. P. Lüthi, and S. Suter, 2011: Evidence of accelerated engla- mate Change 2007: The Physical Science Basis. Contribution of Working Group cial warming in the Monte Rosa area, Switzerland/Italy. Cryosphere, 5, 231 243. I to the Fourth Assessment Report of the Intergovernmental Panel on Climate Houghton, R., 2003: Revised estimates of the annual net flux of carbon to the atmo- Change [Solomon, S., D. Qin, M. Manning, Z. Chen, M. Marquis, K. B. Averyt, M. sphere from changes in land use and land management 1850 2000. Tellus B, Tignor and H. L. Miller (eds.)]. Cambridge University Press, Cambridge, United 55, 378 390. Kingdom and New York, NY, USA, 339 383. Huntingford, C., J. Lowe, B. Booth, C. Jones, G. Harris, L. Gohar, and P. Meir, 2009: Lenton, T., H. Held, E. Kriegler, J. Hall, W. Lucht, S. Rahmstorf, and H. Schellnhuber, Contributions of carbon cycle uncertainty to future climate projection spread. 2008: Tipping elements in the Earth s climate system. Proc. Natl. Acad. Sci.U.S.A., Tellus B, doi:10.1111/j.1600 0889.2009.00414.x, 355 360. 105, 1786 1793. Hurtt, G. C., et al., 2011: Harmonization of land-use scenarios for the period 1500 Le Treut, H., et al., 2007: Historical Overview of Climate Change. In: Climate Change 2100: 600 years of global gridded annual land-use transitions, wood harvest, 2007: The Physical Science Basis. Contribution of Working Group I to the Fourth and resulting secondary lands. Clim. Change, 109, 117 161. Assessment Report of the Intergovernmental Panel on Climate Change [Solo- InterAcademy Council, 2010: Climate change assessments. In: Review of the Pro- mon, S., D. Qin, M. Manning, Z. Chen, M. Marquis, K. B. Averyt, M. Tignor and H. cesses and Procedures of the IPCC, Amsterdam, The Netherlands. L. Miller (eds.)]. Cambridge University Press, Cambridge, United Kingdom and IPCC, 1990: Climate Change: The IPCC Scientific Assessment [J. T. Houghton, G. J. Jen- New York, NY, USA, pp. 94 127. kins and J. J. Ephraums (eds.)]. Cambridge University Press, Cambridge, United Lüthi, M., and M. Funk, 2001: Modelling heat flow in a cold, high-altitude glacier: Kingdom and New York, NY, USA, 212 pp. Interpretation of measurements from Colle Gnifetti, Swiss Alps. J. Glaciol., 47, IPCC , 1996: Climate Change 1995: The Science of Climate Change. Contribution of 314 324. Working Group I to the Second Assessment Report of the Intergovernmental Lüthi, D., et al., 2008: High-resolution carbon dioxide concentration record 650,000 Panel on Climate Change. Cambridge University Press, Cambridge, United King- 800,000 years before present. Nature, 453, 379 382. dom and New York, NY, USA, 584 pp. Mann, M., Z. Zhang, M. Hughes, R. Bradley, S. Miller, S. Rutherford, and F. Ni, 2008: IPCC , 2000: IPCC Special Report on Emissions Scenarios. Prepared by Working Proxy-based reconstructions of hemispheric and global surface temperature Group III of the Intergovernmental Panel on Climate Change, Cambridge Univer- variations over the past two millennia. Proc. Natl. Acad. Sci. U.S.A., 105, 13252 sity Press, Cambrudge, United Kingdom, pp 570. 13257. IPCC , 2001: Climate Change 2001: The Scientific Basis. Contribution of Working Manning, M., et al., 2004: IPCC workshop Report: Describing scientific uncertainties Group I to the Third Assessment Report of the Intergovernmental Panel on Cli- in climate change to support analysis of risk and of options [IPCC IPCC Working mate Change [J. T. Houghton, Y. Ding, D. J. Griggs, M. Noquer, P. J. van der Linden, Group I Technical Support Unit (ed.)]. Available at http://www.ipcc.ch/ (accessed X. Dai, K. Maskell and C. A. Johnson (eds.)]. Cambridge University Press, Cam- 07-10-2013), 138. bridge, United Kingdom and New York, NY, USA, 881 pp. Masson, D., and R. Knutti, 2011: Climate model genealogy. Geophys. Res. Lett., 38, IPCC , 2007: Climate Change 2007: The Physical Science Basis. Contribution of Work- L08703. ing Group I to the Fourth Assessment Report of the Intergovernmental Panel on Mastrandrea, M. D., et al., 2010: Guidance notes for lead authors of the IPCC Fifth Climate Change (IPCC) [Solomon, S., D. Qin, M. Manning, Z. Chen, M. Marquis, K. Assessment Report on Consistent Treatment of Uncertainties. Available at http:// B. Averyt, M. Tignor and H. L. Miller (eds.)]. Cambridge University Press, 996 pp. www.ipcc.ch (accessed 07-10-2013). IPCC , 2012a: Procedures for the preparation, review, acceptance, adoption, approv- Masui, T., et al., 2011: An emission pathway for stabilization at 6 Wm 2 radiative al and publication of IPCC reports. Appendix A to the Principles Governing IPCC forcing. Clim. Change, 109, 59 76. Work, Geneva, Switzerland, 6-9 June 2012, 29 pp. Matthews, H. D., and A. J. Weaver, 2010: Committed climate warming. Nature IPCC , 2012b: Managing the Risks of Extreme Events and Disasters to Advance Cli- Geosci., 3, 142 143. mate Change Adaptation. Special Report of the Intergovernmental Panel on Cli- Meehl, G. A., et al., 2007: Global climate projections. In: Climate Change 2007: The mate Change. [ Field, C. B., V. Barros, T. F. Stocker, D. Qin, D. J. Dokken, K. L. Ebi, M. Physical Science Basis. Contribution of Working Group I to the Fourth Assess- D. Mastrandrea, K. J. Mach, G.-K. Plattner, S. K. Allen, M. Tignor, and P. M. Midgley ment Report of the Intergovernmental Panel on Climate Change [Solomon, S., (Eds.)]. Cambridge University Press, Cambridge, United Kingdom, 582 pp. D. Qin, M. Manning, Z. Chen, M. Marquis, K. B. Averyt, M. Tignor and H. L. Miller JCGM, 2008: JCGM 100: 2008. GUM 1995 with minor corrections. Evaluation of (eds.)]. Cambridge University Press, Cambridge, United Kingdom and New York, measurement data Guide to the expression of uncertainty in measurement. NY, USA, 749 845. Joint Committee for Guides in Metrology. Meinshausen, M., et al., 2011: The RCP greenhouse gas concentrations and their Jevrejeva, S., J. C. Moore, A. Grinsted, and P. L. Woodworth, 2008: Recent global sea extensions from 1765 to 2300. Clim. Change, 109, 213 241. level acceleration started over 200 years ago? Geophys. Res. Lett., 35, L08715. Menne, M. J., I. Durre, R. S. Vose, B. E. Gleason, and T. G. Houston, 2012: An overview Kahneman, D., and A. Tversky, 1979: Prospect theory: An analysis of decision under of the Global Historical Climatology Network-Daily Database. J. Atmos. Ocean. risk. Econometrica, 47, 263 291. Technol., 29, 897 910. Kahneman, D., P. Slovic, and A. Tversky, Eds., 1982: Judgment under Uncertainty: Mernild, S. H., G. E. Liston, C. A. Hiemstra, K. Steffen, E. Hanna, and J. H. Christensen, Heuristics and Biases. Cambridge University Press, Cambridge, United Kingdom 2009: Greenland ice sheet surface mass-balance modelling and freshwater flux and New York, NY, USA, 544 pp. for 2007, and in a 1995 2007 perspective. Hydrol. Proc,, 23, 2470 2484. Kandlikar, M., J. Risbey, and S. Dessai, 2005: Representing and communicating deep Midorikawa, T., et al., 2010: Decreasing pH trend estimated from 25-yr time series uncertainty in climate-change assessments. C. R. Geosci., 337, 443 455. of carbonate parameters in the western North Pacific. Tellus B, 62, 649 659. Knutti, R., F. Joos, S. A. Müller, G. K. Plattner, and T. F. Stocker, 2005: Probabilistic Morgan, M. G., M. Henrion, and M. Small, 1990: Uncertainty: A Guide to Dealing climate change projections for CO2 stabilization profiles. Geophys. Res. Lett., with Uncertainty in Quantitative Risk and Policy Analysis. Cambridge University 32, L20707. Press, Cambridge, United Kingdom and New York, NY, USA, 332 pp. Knutti, R., et al., 2008: A review of uncertainties in global temperature projections Morice, C. P., J. J. Kennedy, N. A. Rayner, and P. D. Jones, 2012: Quantifying uncertain- over the twenty-first century. J. Clim., 21, 2651 2663. ties in global and regional temperature change using an ensemble of observa- Kopp, G., and J. L. Lean, 2011: A new, lower value of total solar irradiance: Evidence tional estimates: The HadCRUT4 data set. J. Geophys. Res. Atmos., 117, D08101. and climate significance. Geophys. Res. Lett., 38, L01706. 153 Chapter 1 Introduction Moss, R. H., and S. H. Schneider, 2000: Uncertainties in the IPCC TAR: Recommenda- SWIPA, 2011: Snow, water, ice and permafrost in the Arctic. SWIPA 2011 Executive tions to lead authors for more consistent assessment and reporting. In: Guid- Summary. AMAP, Oslo, Norway, 16 pp. ance Papers on the Cross Cutting Issues of the Third Assessment Report of the Taylor, K. E., R. J. Stouffer, and G. A. Meehl, 2012: An overview of CMIP5 and the IPCC. World Meteorological Organization, Geneva, pp. 33 51. experiment design. Bull. Am. Meteorol. Soc., 93, 485 498. Moss, R., et al., 2008: Towards New Scenarios for Analysis of Emissions, Climate Tedesco, M., 2007: A new record in 2007 for melting in Greenland. EOS, Trans. Am. Change, Impacts, and Response Strategies. Geneva, Intergovernmental Panel on Geophys. Union, 88, 383. Climate Change, 132 pp. Thomson, A. M., et al., 2011: RCP4.5: A pathway for stabilization of radiative forcing Moss, R., et al., 2010: The next generation of scenarios for climate change research by 2100. Clim. Change, 109, 77 94. 1 and assessment. Nature, 463, 747 756. Tietsche, S., D. Notz, J. H. Jungclaus, and J. Marotzke, 2011: Recovery mechanisms of Murphy, D., S. Solomon, R. Portmann, K. Rosenlof, P. Forster, and T. Wong, 2009: An Arctic summer sea ice. Geophys. Res. Lett., 38, L02707. observationally based energy balance for the Earth since 1950. J. Geophys. Res. Trenberth, K. E., J. T. Fasullo, and J. Kiehl, 2009: Earth s global energy budget. Bull. Atmos., 114, D17107. Am. Meteorol. Soc., 90, 311 323. Myhre, G., E. Highwood, K. Shine, and F. Stordal, 1998: New estimates of radiative Turner, J., and J. E. Overland, 2009: Contrasting climate change in the two polar forcing due to well mixed greenhouse gases. Geophys. Res. Lett., 25, 2715 2718. regions. Polar Res., 28, 146 164. Nghiem, S. V., et al., 2012: The extreme melt across the Greenland ice sheet in 2012. Turner, J., et al., 2009: Antarctic Climate Change and the environment. Scientific Geophys. Res. Lett., 39, L20502. Committee on Antarctic Research, Cambridge, United Kingdom, 526 pp. Oreskes, N., K. Shrader-Frechette, and K. Belitz, 1994: Verification, validation, and van Vuuren, D., et al., 2007: Stabilizing greenhouse gas concentrations at low levels: confirmation of numerical models in the earth sciences. Science, 263, 641 646. An assessment of reduction strategies and costs. Clim. Change, 81, 119 159. Pall, P., et al., 2011: Anthropogenic greenhouse gas contribution to flood risk in Eng- van Vuuren, D. P., et al., 2011a: RCP2.6: Exploring the possibility to keep global mean land and Wales in autumn 2000. Nature, 470, 382 385. temperature increase below 2°C. Clim. Change, 109, 95 116. Patt, A. G., and D. P. Schrag, 2003: Using specific language to describe risk and prob- van Vuuren, D. P., et al., 2011b: The representative concentration pathways: An over- ability. Clim. Change, 61, 17 30. view. Clim. Change, 109, 5 31. Patt, A. G., and S. Dessai, 2005: Communicating uncertainty: Lessons learned and Wadhams, P., 2012: Arctic ice cover, ice thickness and tipping points. Ambio, 41, suggestions for climate change assessment. C. R. Geosci., 337, 425 441. 23 33. Pennell, C., and T. Reichler, 2011: On the effective number of climate models. J. Clim., Warrick, R., and J. Oerlemans, 1990: Sea level rise. In: Climate Change: The IPCC 24, 2358 2367. Scientific Assessment [J. T. Houghton, G. J. Jenkins and J. J. Ephraums (eds.)]. Peterson, T. C., P. A. Stott, and S. Herring, 2012: Explaining extreme events of 2011 Cambridge University Press, Cambridge, United Kingdom and New York, NY, from a climate perspective. Bull. Am. Meteorol. Soc., 93, 1041 1067. USA, 261 281. Peterson, T. C., et al., 2008: Why weather and climate extremes matter. In: Weather Wigley, T. M. L., 2005: The climate change commitment. Science, 307, 1766 1769. and Climate Extremes in a Changing Climate. Regions of Focus: North America, Willis, J. K., J. M. Lyman, G. C. Johnson, and J. Gilson, 2009: In situ data biases and Hawaii, Caribbean, and U.S. Pacific Islands, [Karl, T. R., G. A. Meehl, C. D. Miller, recent ocean heat content variability. J. Atmos. Ocean. Technol., 26, 846 852. S. J. Hassol, A. M. Waple, and W. L. Murray (eds.)]. A Report by the U.S.Climate Willis, J., D. Chambers, C. Kuo, and C. Shum, 2010: Global sea level rise recent prog- Change Science Program and the Subcommittee on Global Change Research, ress and challenges for the decade to come. Oceanography, 23, 26 35. Washington, DC., USA, 11 33. Wise, M., et al., 2009: Implications of limiting CO2 concentrations for land use and Rahmstorf, S., G. Foster, and A. Cazenave, 2012: Comparing climate projections to energy. Science, 324, 1183 1186. observations up to 2011. Environ. Res. Lett., 7, 044035. Yip, S., C. A. T. Ferro, D. B. Stephenson, and E. Hawkins, 2011: A simple, coherent Ray, R. D., and B. C. Douglas, 2011: Experiments in reconstructing twentieth-century framework for partitioning uncertainty in climate predictions. J. Climate, 24, sea levels. Prog. Oceanogr., 91, 496 515. 4634 4643. Riahi, K., A. Grübler, and N. Nakicenovic, 2007: Scenarios of long-term socio-eco- Zemp, M., I. Roer, A. Kääb, M. Hoelzle, F. Paul, and W. Haeberli, 2008: Global glacier nomic and environmental development under climate stabilization. Technol. changes: Facts and figures. United Nations Environment Programme and World ForecastSoc Change, 74, 887 935. Glacier Monitoring Service, 88 pp. Riahi, K., et al., 2011: RCP 8.5 A scenario of comparatively high greenhouse gas Zemp, M., M. Hoelzle, and W. Haeberli, 2009: Six decades of glacier mass-balance emissions. Clim. Change, 109, 33 57. observations: A review of the worldwide monitoring network. Ann. Glaciol., 50, Risbey, J. S., and M. Kandlikar, 2007: Expressions of likelihood and confidence in the 101 111. IPCC uncertainty assessment process. Clim. Change, 85, 19 31. Zhang, X., and F. Zwiers, 2012: Statistical indices for the diagnosing and detect- Schimel, D., et al., 1996: Radiative forcing of climate change. In: Climate Change ing changes in extremes. In: Extremes in a Changing Climate: Detection, Analy- 1995: The Science of Climate Change, Contribution of Working Group I to the sis and Uncertainty [A. AghaKouchak, D. Easterling, K. Hsu, S. Schubert, and S. Second Assessment Report of the Intergovernmental Panel on Climate Change Sorooshian (eds.)]. Springer Science+Business Media, Heidelberg, Germany and [J. T. Houghton, L. G. Meiro Filho, B. A. Callander, N. Harris, A. Kattenburg and K. New York, NY, USA, 1 14. Maskell (eds.)]. Cambridge University Press, Cambridge, United Kingdom and New York, NY, USA, 69 131. Scott, J. T. B., G. H. Gudmundsson, A. M. Smith, R. G. Bingham, H. D. Pritchard, and D. G. Vaughan, 2009: Increased rate of acceleration on Pine Island Glacier strongly coupled to changes in gravitational driving stress. The Cryosphere, 3, 125 131. Seneviratne, S., et al., 2010: Investigating soil moisture-climate interactions in a changing climate: A review. Earth-Sci. Rev., 99, 125 161. Seneviratne, S. I., et al., 2012: Chapter 3: Changes in climate extremes and their Impacts on the Natural Physical Environment. In: SREX: Special Report on Man- aging the Risks of Extreme Events and Disasters to Advance Climate Change Adaptation [C. B. Field, et al. (eds.]. Cambridge University Press, Cambridge, United Kingdom and New York, NY, USA, pp.109 230. Smith, S., and T. Wigley, 2006: Multi-gas forcing stabilization with Minicam. Energy J., 373 391. Smith, T. M., R. W. Reynolds, T. C. Peterson, and J. Lawrimore, 2008: Improvements to NOAA s historical merged land-ocean surface temperature analysis (1880 2005). J. Clim., 21, 2283 2296. Steinhilber, F., J. Beer, and C. Fröhlich, 2009: Total solar irradiance during the Holo- cene. Geophys. Res. Lett., 36, L19704. 154 Introduction Chapter 1 Appendix 1.A: Table 1.A.3 | TAR: The data have been digitized using a graphics tool from Figure Notes and Technical Details on Figures Displayed 9.13(b) (Cubasch et al., 2001) in 5-year increments based on the GFDL_R15_a and DOE PCM parameter settings (°C). in Chapter 1 Year Lower Bound Upper Bound Figure 1.4: Documentation of Data Sources 1990 0.00 0.00 1995 0.05 0.09 Observed Temperature 2000 0.11 0.20 1 NASA GISS evaluation of the observations: Hansen et al. (2010) updat- 2005 0.14 0.34 ed: The data were downloaded from http://data.giss.nasa.gov/gistemp/ 2010 0.17 0.52 tabledata_v3/GLB.Ts+dSST.txt. Annual means are used (January to December) and anomalies are calculated relative to 1961 1990. 2015 0.22 0.70 2020 0.28 0.87 NOAA NCDC evaluation of the observations: Smith et al. (2008) 2025 0.37 1.08 updated: The data were downloaded from ftp://ftp.ncdc.noaa.gov/pub/ 2030 0.43 1.28 data/anomalies/annual.land_ocean.90S.90N.df_1901 2000mean.dat. 2035 0.52 1.50 Annual mean anomalies are calculated relative to 1961 1990. Hadley Centre evaluation of the observations: Morice et al. (2012): The AR4: The temperature projections of the AR4 are presented for three data were downloaded from http://www.metoffice.gov.uk/hadobs/ SRES scenarios: B1, A1B and A2. Annual mean anomalies relative to hadcrut4/data/current/download.html#regional_series. Annual mean 1961 1990 of the individual CMIP3 ensemble simulations (as used in anomalies are calculated relative to 1961 1990 based on the ensem- AR4 SPM Figure SPM5) are shown. One outlier has been eliminated ble median. based on the advice of the model developers because of the model drift that leads to an unrealistic temperature evolution. As assessed IPCC Range of Projections by Meehl et al. (2007), the likely range for the temperature change is given by the ensemble mean temperature change +60% and 40% Table 1.A.1 | FAR: The data have been digitized using a graphics tool from FAR Chap- of the ensemble mean temperature change. Note that in the AR4 the ter 6, Figure 6.11 (Bretherton et al., 1990) in 5-year increments as anomalies relative to 1990 (°C). uncertainty range was explicitly estimated for the end of the 21st cen- tury results. Here, it is shown for 2035. The time dependence of this Lower Bound Upper Bound range has been assessed in Knutti et al. (2008). The relative uncertainty Year (Scenario D) (Business as Usual) is approximately constant over time in all estimates from different 1990 0.00 0.00 sources, except for the very early decades when natural variability is 1995 0.09 0.14 being considered (see Figure 3 in Knutti et al., 2008). 2000 0.15 0.30 2005 0.23 0.53 Data Processing 2010 0.28 0.72 2015 0.33 0.91 Observations The observations are shown from 1950 to 2012 as annual mean anom- 2020 0.39 1.11 aly relative to 1961 1990 (squares). For smoothing, first, the trend of 2025 0.45 1.34 each of the observational data sets was calculated by locally weighted 2030 0.52 1.58 scatter plot smoothing (Cleveland, 1979; f = 1/3). Then, the 11-year 2035 0.58 1.86 running means of the residuals were determined with reflected ends for the last 5 years. Finally, the trend was added back to the 11-year Table 1.A.2 | SAR: The data have been digitized using a graphics tool from Figure 19 running means of the residuals. of the TS (IPCC, 1996) in 5-year increments as anomalies relative to 1990. The scenarios include changes in aerosols beyond 1990 (°C). Projections Year Lower Bound Upper Bound For FAR, SAR and TAR, the projections have been harmonized to match (IS92c/1.5) (IS92e/4.5) the average of the three smoothed observational data sets at 1990. 1990 0.00 0.00 1995 0.05 0.09 2000 0.11 0.17 2005 0.16 0.28 2010 0.19 0.38 2015 0.23 0.47 2020 0.27 0.57 2025 0.31 0.67 2030 0.36 0.79 2035 0.41 0.92 155 Chapter 1 Introduction Figure 1.5: Documentation of Data Sources Figure 1.6: Documentation of Data Sources Observed CO2 Concentrations Observed CH4 Concentrations Global annual mean CO2 concentrations are presented as annual mean Global annual mean CH4 concentrations are presented as annual mean values from Annex II Table AII.1.1a. values from Annex II Table AII.1.1a. IPCC Range of Projections IPCC Range of Projections 1 Table 1.A.4 | FAR: The data have been digitized using a graphics tool from Figure A.3 Table 1.A.6 | FAR: The data have been digitized using a graphics tool from FAR SPM (Annex, IPCC, 1990) as anomalies compared to 1990 in 5-year increments (ppm) and Figure 5 (IPCC, 1990) in 5-year increments (ppb) as anomalies compared to 1990 the the observed 1990 value (353.6) has been added. observed 1990 value (1714.4) has been added. Lower Bound Upper Bound Lower Bound Upper Bound Year Year (Scenario D) (Business as Usual) (Scenario D) (Business as Usual) 1990 353.6 353.6 1990 1714.4 1714.4 1995 362.8 363.7 1995 1775.7 1816.7 2000 370.6 373.3 2000 1809.7 1938.7 2005 376.5 386.5 2005 1819.0 2063.8 2010 383.2 401.5 2010 1823.1 2191.1 2015 390.2 414.3 2015 1832.3 2314.1 2020 396.6 428.8 2020 1847.7 2441.3 2025 401.5 442.0 2025 1857.9 2562.3 2030 406.0 460.7 2030 1835.3 2691.6 2035 410.0 480.3 2035 1819.0 2818.8 Table 1.A.5 | SAR: The data have been digitized using a graphics tool from Figure 5b in the TS (IPCC, 1996) in 5-year increments (ppm) as anomalies compared to 1990 and SAR: The data were taken in 5-year increments from Table 2.5a (Schimel the observed 1990 value (353.6) has been added. et al., 1996). The scenarios that give the upper bound or lower bound respectively vary over time. Lower Bound Upper Bound Year (IS92c) (IS92e) TAR: The data were taken in 10-year increments from Appendix II SRES 1990 353.6 353.6 Data Tables Table II.2.2 (IPCC, 2001). The upper bound is given by the 1995 358.4 359.0 A1p scenario, the lower bound by the B1p scenario. 2000 366.8 369.2 2005 373.7 380.4 AR4: The data used was obtained from Figure 10.26 in Chapter 10 2010 382.3 392.9 of AR4 (Meehl et al., 2007, provided by Malte Meinshausen). Annual means are used. 2015 391.4 408.0 2020 400.7 423.0 Data Processing 2025 408.0 439.6 2030 416.9 457.7 The observations are shown as annual means. The projections have 2035 424.5 477.7 been harmonized to start from the same value in 1990. TAR: The data were taken in 10-year increments from table Appendix II (IPCC, 2001) SRES Data Tables Table II.2.1 (ISAM model high and low setting). The scenarios that give the upper bound or lower bound respectively vary over time. AR4: The data used was obtained from Figure 10.26 in Chapter 10 of AR4 (Meehl et al., 2007, provided by Malte Meinshausen). Annual means are used. Data Processing The projections have been harmonized to start from the observed value in 1990. 156 Introduction Chapter 1 Figure 1.7: Documentation of Data Sources Figure 1.10: Documentation of Data Sources Observed N2O Concentrations Observed Global Mean Sea Level Rise Global annual mean N2O concentrations are presented as annual mean Three data sets based on tide gauge measurements are presented: values from Annex II Table AII.1.1a. Church and White (2011), Jevrejeva et al. (2008), and Ray and Douglas (2011). Annual mean anomalies are calculated relative to 1961 1990. IPCC Range of Projections 1 Estimates based on sea surface altimetry are presented as the ensem- Table 1.A.7: FAR | The data have been digitized using a graphics tool from FAR A.3 ble mean of five different data sets (Section 3.7, Figure 3.13, Section (Annex, IPCC, 1990) in 5-year increments (ppb) as anomalies compared to 1990 and the 13.2, Figure 13.3) from 1993 to 2012. Annual means have been calcu- observed 1990 value (308.7) has been added. lated. The data are harmonized to start from the mean of the three tide Lower Bound Upper Bound gauge based estimates (see above) at 1993. Year (Scenario D) (Business as Usual) 1990 308.7 308.7 IPCC Range of Projections 1995 311.7 313.2 Table 1.A.8 | FAR: The data have been digitized using a graphics tool from Chapter 2000 315.4 317.7 9, Figure 9.6 for the upper bound and Figure 9.7 for the lower bound (Warrick and 2005 318.8 322.9 Oerlemans, 1990) in 5-year increments as anomalies relative to 1990 (cm) and the 2010 322.1 328.0 observed anomaly relative to 1961 1990 (2.0 cm) has been added. 2015 325.2 333.0 Lower Bound Upper Bound Year 2020 328.2 337.9 (Scenario D) (Business as Usual) 2025 331.7 343.0 1990 2.0 2.0 2030 334.0 348.9 1995 2.7 5.0 2035 336.1 354.1 2000 3.7 7.9 2005 4.6 11.3 2010 5.5 15.0 SAR: The data were taken in 5-year increments from Table 2.5b (Schimel 2015 6.3 18.7 et al., 1996). The upper bound is given by the IS92e and IS92f scenario, 2020 6.9 22.8 the lower bound by the IS92d scenario. 2025 7.7 26.7 TAR: The data were taken in 10-year increments from Appendix II SRES 2030 8.4 30.9 Data Tables Table II.2.3 (IPCC, 2001). The upper bound is given by the 2035 9.2 35.4 A1FI scenario, the lower bound by the B2 and A1T scenario. Table 1.A.9 | SAR: The data have been digitized using a graphics tool from Figure AR4: The data used was obtained from Figure 10.26 in Chapter 10 21 (TS, IPCC, 1996) in 5-year increments as anomalies relative to 1990 (cm) and the observed anomaly relative to 1961 1990 (2.0 cm) has been added. of AR4 (Meehl et al., 2007, provided by Malte Meinshausen). Annual means are used. Lower Bound Upper Bound Year (IS92c/1.5) (IS92e/4.5) Data Processing 1990 2.0 2.0 1995 2.4 4.3 The observations are shown as annual means. No smoothing is applied. 2000 2.7 6.5 The projections have been harmonized to start from the same value in 2005 3.1 9.0 1990. 2010 3.4 11.7 2015 3.8 14.9 2020 4.4 18.3 2025 5.1 21.8 2030 5.7 25.4 2035 6.4 29.2 TAR: The data are given in Table II.5.1 in 10-year increments. They are harmonized to start from mean of the observed anomaly relative to 1961 1990 at 1990 (2.0 cm). 157 Chapter 1 Introduction AR4: The AR4 did not give a time-dependent estimate of sea level rise. These analyses have been conducted post AR4 by Church et al. (2011) based on the CMIP3 model results that were available at the time of AR4. Here, the SRES B1, A1B and A2 scenarios are shown from Church et al. (2011). The data start in 2001 and are given as anomalies with respect to 1990. They are displayed from 2001 to 2035, but the anoma- lies are harmonized to start from mean of the observed anomaly rela- 1 tive to 1961 1990 at 1990 (2.0 cm). Data Processing The observations are shown from 1950 to 2012 as the annual mean anomaly relative to 1961 1990 (squares) and smoothed (solid lines). For smoothing, first, the trend of each of the observational data sets was calculated by locally weighted scatterplot smoothing (Cleveland, 1979; f = 1/3). Then, the 11-year running means of the residuals were determined with reflected ends for the last 5 years. Finally, the trend was added back to the 11-year running means of the residuals. 158 2 Observations: Atmosphere and Surface Coordinating Lead Authors: Dennis L. Hartmann (USA), Albert M.G. Klein Tank (Netherlands), Matilde Rusticucci (Argentina) Lead Authors: Lisa V. Alexander (Australia), Stefan Brönnimann (Switzerland), Yassine Abdul-Rahman Charabi (Oman), Frank J. Dentener (EU/Netherlands), Edward J. Dlugokencky (USA), David R. Easterling (USA), Alexey Kaplan (USA), Brian J. Soden (USA), Peter W. Thorne (USA/Norway/UK), Martin Wild (Switzerland), Panmao Zhai (China) Contributing Authors: Robert Adler (USA), Richard Allan (UK), Robert Allan (UK), Donald Blake (USA), Owen Cooper (USA), Aiguo Dai (USA), Robert Davis (USA), Sean Davis (USA), Markus Donat (Australia), Vitali Fioletov (Canada), Erich Fischer (Switzerland), Leopold Haimberger (Austria), Ben Ho (USA), John Kennedy (UK), Elizabeth Kent (UK), Stefan Kinne (Germany), James Kossin (USA), Norman Loeb (USA), Carl Mears (USA), Christopher Merchant (UK), Steve Montzka (USA), Colin Morice (UK), Cathrine Lund Myhre (Norway), Joel Norris (USA), David Parker (UK), Bill Randel (USA), Andreas Richter (Germany), Matthew Rigby (UK), Ben Santer (USA), Dian Seidel (USA), Tom Smith (USA), David Stephenson (UK), Ryan Teuling (Netherlands), Junhong Wang (USA), Xiaolan Wang (Canada), Ray Weiss (USA), Kate Willett (UK), Simon Wood (UK) Review Editors: Jim Hurrell (USA), Jose Marengo (Brazil), Fredolin Tangang (Malaysia), Pedro Viterbo (Portugal) This chapter should be cited as: Hartmann, D.L., A.M.G. Klein Tank, M. Rusticucci, L.V. Alexander, S. Brönnimann, Y. Charabi, F.J. Dentener, E.J. Dlugokencky, D.R. Easterling, A. Kaplan, B.J. Soden, P.W. Thorne, M. Wild and P.M. Zhai, 2013: Observations: Atmosphere and Surface. In: Climate Change 2013: The Physical Science Basis. Contribution of Working Group I to the Fifth Assessment Report of the Intergovernmental Panel on Climate Change [Stocker, T.F., D. Qin, G.-K. Plattner, M. Tignor, S.K. Allen, J. Boschung, A. Nauels, Y. Xia, V. Bex and P.M. Midgley (eds.)]. Cambridge University Press, Cambridge, United Kingdom and New York, NY, USA. 159 Table of Contents Executive Summary...................................................................... 161 2.7 Changes in Atmospheric Circulation and Patterns of Variability..................................................... 223 2.1 Introduction....................................................................... 164 2.7.1 Sea Level Pressure...................................................... 223 2.7.2 Surface Wind Speed................................................... 224 2.2 Changes in Atmospheric Composition....................... 165 2.7.3 Upper-Air Winds......................................................... 226 2.2.1 Well-Mixed Greenhouse Gases.................................. 165 2.7.4 Tropospheric Geopotential Height and Box 2.1: Uncertainty in Observational Records...................... 165 Tropopause................................................................ 226 2.2.2 Near-Term Climate Forcers......................................... 170 2.7.5 Tropical Circulation.................................................... 226 2.2.3 Aerosols..................................................................... 174 2.7.6 Jets, Storm Tracks and Weather Types........................ 229 2 Box 2.2: Quantifying Changes in the Mean: 2.7.7 Stratospheric Circulation............................................ 230 Trend Models and Estimation................................................... 179 2.7.8 Changes in Indices of Climate Variability................... 230 2.3 Changes in Radiation Budgets..................................... 180 Box 2.5: Patterns and Indices of Climate Variability.............. 232 2.3.1 Global Mean Radiation Budget.................................. 181 References .................................................................................. 237 2.3.2 Changes in Top of the Atmosphere Radiation Budget....................................................... 182 Frequently Asked Questions 2.3.3 Changes in Surface Radiation Budget........................ 183 FAQ 2.1 How Do We Know the World Has Warmed?......... 198 Box 2.3: Global Atmospheric Reanalyses................................ 185 FAQ 2.2 Have There Been Any Changes in Climate Extremes?.................................................. 218 2.4 Changes in Temperature................................................ 187 2.4.1 Land Surface Air Temperature.................................... 187 Supplementary Material 2.4.2 Sea Surface Temperature and Marine Supplementary Material is available in online versions of the report. Air Temperature......................................................... 190 2.4.3 Global Combined Land and Sea Surface Temperature.................................................. 192 2.4.4 Upper Air Temperature............................................... 194 2.5 Changes in Hydrological Cycle..................................... 201 2.5.1 Large-Scale Changes in Precipitation......................... 201 2.5.2 Streamflow and Runoff.............................................. 204 2.5.3 Evapotranspiration Including Pan Evaporation........... 205 2.5.4 Surface Humidity........................................................ 205 2.5.5 Tropospheric Humidity............................................... 206 2.5.6 Clouds........................................................................ 208 2.6 Changes in Extreme Events........................................... 208 2.6.1 Temperature Extremes............................................... 209 2.6.2 Extremes of the Hydrological Cycle............................ 213 2.6.3 Tropical Storms.......................................................... 216 2.6.4 Extratropical Storms................................................... 217 Box 2.4: Extremes Indices.......................................................... 221 160 Observations: Atmosphere and Surface Chapter 2 Executive Summary Confidence is low in ozone changes across the Southern Hemi- sphere (SH) owing to limited measurements. It is likely3 that sur- The evidence of climate change from observations of the atmosphere face ozone trends in eastern North America and Western Europe since and surface has grown significantly during recent years. At the same 2000 have levelled off or decreased and that surface ozone strongly time new improved ways of characterizing and quantifying uncertainty increased in East Asia since the 1990s. Satellite and surface obser- have highlighted the challenges that remain for developing long-term vations of ozone precursor gases NOx, CO, and non-methane volatile global and regional climate quality data records. Currently, the obser- organic carbons indicate strong regional differences in trends. Most vations of the atmosphere and surface indicate the following changes: notably NO2 has likely decreased by 30 to 50% in Europe and North America and increased by more than a factor of 2 in Asia since the Atmospheric Composition mid-1990s. {2.2.2.3, 2.2.2.4} It is certain that atmospheric burdens of the well-mixed green- It is very likely that aerosol column amounts have declined over house gases (GHGs) targeted by the Kyoto Protocol increased Europe and the eastern USA since the mid 1990s and increased from 2005 to 2011. The atmospheric abundance of carbon dioxide over eastern and southern Asia since 2000. These shifting aerosol (CO2) was 390.5 ppm (390.3 to 390.7)1 in 2011; this is 40% greater regional patterns have been observed by remote sensing  of aerosol than in 1750. Atmospheric nitrous oxide (N2O) was 324.2 ppb (324.0 to optical depth (AOD), a measure of total atmospheric  aerosol load. 2 324.4) in 2011 and has increased by 20% since 1750. Average annual Declining aerosol loads over Europe and North America are consistent increases in CO2 and N2O from 2005 to 2011 are comparable to those with ground-based in situ monitoring of particulate mass. Confidence observed from 1996 to 2005. Atmospheric methane (CH4) was 1803.2 in satellite based global average AOD trends is low. {2.2.3} ppb (1801.2 to 1805.2) in 2011; this is 150% greater than before 1750. CH4 began increasing in 2007 after remaining nearly constant from Radiation Budgets 1999 to 2006. Hydrofluorocarbons (HFCs), perfluorocarbons (PFCs), and sulphur hexafluoride (SF6) all continue to increase relatively rapid- Satellite records of top of the atmosphere radiation fluxes have ly, but their contributions to radiative forcing are less than 1% of the been substantially extended since AR4, and it is unlikely that total by well-mixed GHGs. {2.2.1.1} significant trends exist in global and tropical radiation budgets since 2000. Interannual variability in the Earth s energy imbalance For ozone-depleting substances (Montreal Protocol gases), it is related to El Nino-Southern Oscillation is consistent with ocean heat certain that the global mean abundances of major chlorofluoro- content records within observational uncertainty. {2.3.2} carbons (CFCs) are decreasing and HCFCs are increasing. Atmos- pheric burdens of major CFCs and some halons have decreased since Surface solar radiation likely underwent widespread decadal 2005. HCFCs, which are transitional substitutes for CFCs, continue to changes after 1950, with decreases ( dimming ) until the 1980s increase, but the spatial distribution of their emissions is changing. and subsequent increases ( brightening ) observed at many {2.2.1.2} land-based sites. There is medium confidence for increasing down- ward thermal and net radiation at land-based observation sites since Because of large variability and relatively short data records, the early 1990s. {2.3.3} confidence2 in stratospheric H2O vapour trends is low. ­Near-global satellite measurements of stratospheric water vapour show substantial Temperature variability but small net changes for 1992 2011. {2.2.2.1} It is certain that Global Mean Surface Temperature has increased It is certain that global stratospheric ozone has declined from since the late 19th century. Each of the past three decades has pre-1980 values. Most of the decline occurred prior to the mid 1990s; been successively warmer at the Earth s surface than all the pre- since then ozone has remained nearly constant at about 3.5% below vious decades in the instrumental record, and the first decade the 1964 1980 level. {2.2.2.2} of the 21st century has been the warmest. The globally averaged combined land and ocean surface temperature data as calculated by a Confidence is medium in large-scale increases of tropospheric linear trend, show a warming of 0.85 [0.65 to 1.06] °C, over the period ozone across the Northern Hemisphere (NH) since the 1970s. 1880 2012, when multiple independently produced datasets exist, and Values in parentheses are 90% confidence intervals. Elsewhere in this chapter usually the half-widths of the 90% confidence intervals are provided for the estimated change 1 from the trend method. In this Report, the following summary terms are used to describe the available evidence: limited, medium, or robust; and for the degree of agreement: low, medium, or high. 2 A level of confidence is expressed using five qualifiers: very low, low, medium, high, and very high, and typeset in italics, e.g., medium confidence. For a given evidence and agreement statement, different confidence levels can be assigned, but increasing levels of evidence and degrees of agreement are correlated with increasing confidence (see Section 1.4 and Box TS.1 for more details). In this Report, the following terms have been used to indicate the assessed likelihood of an outcome or a result: Virtually certain 99 100% probability, Very likely 90 100%, 3 Likely 66 100%, About as likely as not 33 66%, Unlikely 0 33%, Very unlikely 0 10%, Exceptionally unlikely 0 1%. Additional terms (Extremely likely: 95 100%, More likely than not >50 100%, and Extremely unlikely 0 5%) may also be used when appropriate. Assessed likelihood is typeset in italics, e.g., very likely (see Section 1.4 and Box TS.1 for more details). 161 Chapter 2 Observations: Atmosphere and Surface about 0.72°C [0.49°C to 0.89°C] over the period 1951 2012. The total Northern Hemisphere, precipitation has likely increased since increase between the average of the 1850 1900 period and the 2003 1901 (medium confidence before and high confidence after 2012 period is 0.78 [0.72 to 0.85] °C and the total increase between 1951). For other latitudinal zones area-averaged long-term positive the average of the 1850 1900 period and the reference period for pro- or negative trends have low confidence due to data quality, data jections, 1986 2005, is 0.61 [0.55 to 0.67] °C, based on the single completeness or disagreement amongst available estimates. {2.5.1.1, longest dataset available. For the longest period when calculation of 2.5.1.2} regional trends is sufficiently complete (1901 2012), almost the entire globe has experienced surface warming. In addition to robust multi- It is very likely that global near surface and tropospheric air decadal warming, global mean surface temperature exhibits substan- specific humidity have increased since the 1970s. However, tial decadal and interannual variability. Owing to natural variability, during recent years the near surface moistening over land has abated trends based on short records are very sensitive to the beginning and (medium confidence). As a result, fairly widespread decreases in rel- end dates and do not in general reflect long-term climate trends. As one ative humidity near the surface are observed over the land in recent example, the rate of warming over the past 15 years (1998 2012; 0.05 years. {2.4.4, 2.5.4, 2.5.5} [ 0.05 to +0.15] °C per decade), which begins with a strong El Nino, is smaller than the rate calculated since 1951 (1951 2012; 0.12 [0.08 to While trends of cloud cover are consistent between independent 2 0.14] °C per decade). Trends for 15-year periods starting in 1995, 1996, data sets in certain regions, substantial ambiguity and there- and 1997 are 0.13 [0.02 to 0.24], 0.14 [0.03 to 0.24] and 0.07 [ 0.02 fore low confidence remains in the observations of global-scale ­ to 0.18], respectively. Several independently analyzed data records of cloud variability and trends. {2.5.6} global and regional land-surface air temperature (LSAT) obtained from station observations are in broad agreement that LSAT has increased. Extreme Events Sea surface temperatures (SSTs) have also increased. Intercomparisons of new SST data records obtained by different measurement methods, It is very likely that the numbers of cold days and nights have including satellite data, have resulted in better understanding of uncer- decreased and the numbers of warm days and nights have tainties and biases in the records. {2.4.1, 2.4.2, 2.4.3; Box 9.2} increased globally since about 1950. There is only medium con- fidence that the length and frequency of warm spells, including heat It is unlikely that any uncorrected urban heat-island effects and waves, has increased since the middle of the 20th century mostly owing land use change effects have raised the estimated centennial to lack of data or of studies in Africa and South America. However, it is globally averaged LSAT trends by more than 10% of the report- likely that heatwave frequency has increased during this period in large ed trend. This is an average value; in some regions with rapid devel- parts of Europe, Asia and Australia. {2.6.1} opment, urban heat island and land use change impacts on regional trends may be substantially larger. {2.4.1.3} It is likely that since about 1950 the number of heavy precipita- tion events over land has increased in more regions than it has Confidence is medium in reported decreases in observed global decreased. Confidence is highest for North America and Europe where diurnal temperature range (DTR), noted as a key uncertainty in there have been likely increases in either the frequency or intensity of the AR4. Several recent analyses of the raw data on which many pre- heavy precipitation with some seasonal and/or regional variation. It is vious analyses were based point to the potential for biases that differ- very likely that there have been trends towards heavier precipitation ently affect maximum and minimum average temperatures. However, events in central North America. {2.6.2.1} apparent changes in DTR are much smaller than reported changes in average temperatures and therefore it is virtually certain that maxi- Confidence is low for a global-scale observed trend in drought mum and minimum temperatures have increased since 1950. {2.4.1.2} or dryness (lack of rainfall) since the middle of the 20th centu- ry, owing to lack of direct observations, methodological uncer- Based on multiple independent analyses of measurements from tainties and geographical inconsistencies in the trends. Based on radiosondes and satellite sensors it is virtually certain that updated studies, AR4 conclusions regarding global increasing trends globally the troposphere has warmed and the stratosphere has in drought since the 1970s were probably overstated. However, this cooled since the mid-20th century. Despite unanimous agreement masks important regional changes: the frequency and intensity of on the sign of the trends, substantial disagreement exists among avail- drought have likely increased in the Mediterranean and West Africa able estimates as to the rate of temperature changes, particularly out- and likely decreased in central North America and north-west Australia side the NH extratropical troposphere, which has been well sampled since 1950. {2.6.2.2} by radiosondes. Hence there is only medium confidence in the rate of change and its vertical structure in the NH extratropical troposphere Confidence remains low for long-term (centennial) changes in and low confidence elsewhere. {2.4.4} tropical cyclone activity, after accounting for past changes in observing capabilities. However, it is virtually certain that the fre- Hydrological Cycle quency and intensity of the strongest tropical cyclones in the North Atlantic has increased since the 1970s. {2.6.3} Confidence in precipitation change averaged over global land areas since 1901 is low for years prior to 1951 and medium Confidence in large-scale trends in storminess or storminess afterwards. Averaged over the mid-latitude land areas of the proxies over the last century is low owing to inconsistencies 162 Observations: Atmosphere and Surface Chapter 2 between studies or lack of long-term data in some parts of the world (particularly in the SH). {2.6.4} Because of insufficient studies and data quality issues con- fidence is also low for trends in small-scale severe weather events such as hail or thunderstorms. {2.6.2.4} Atmospheric Circulation and Indices of Variability It is likely that circulation features have moved poleward since the 1970s, involving a widening of the tropical belt, a poleward shift of storm tracks and jet streams, and a contraction of the northern polar vortex. Evidence is more robust for the NH. It is likely that the Southern Annular Mode has become more positive since the 1950s. {2.7.5, 2.7.6, 2.7.8; Box 2.5} 2 Large variability on interannual to decadal time scales hampers robust conclusions on long-term changes in atmospheric circu- lation in many instances. Confidence is high that the increase in the northern mid-latitude westerly winds and the North Atlantic Oscilla- tion (NAO) index from the 1950s to the 1990s and the weakening of the Pacific Walker circulation from the late 19th century to the 1990s have been largely offset by recent changes. {2.7.5, 2.7.8, Box 2.5} Confidence in the existence of long-term changes in remaining aspects of the global circulation is low owing to observational limitations or limited understanding. These include surface winds over land, the East Asian summer monsoon circulation, the tropical cold-point tropopause temperature and the strength of the Brewer Dobson circulation. {2.7.2, 2.7.4, 2.7.5, 2.7.7} 163 Chapter 2 Observations: Atmosphere and Surface 2.1 Introduction This chapter starts with an assessment of the observations of the abun- dances of greenhouse gases (GHGs) and of aerosols, the main drivers This chapter assesses the scientific literature on atmospheric and of climate change (Section 2.2). Global trends in GHGs are indicative surface observations since AR4 (IPCC, 2007). The most likely changes of the imbalance between sources and sinks in GHG budgets, and play in physical climate variables or climate forcing agents are identified an important role in emissions verification on a global scale. The radia- based on current knowledge, following the IPCC AR5 uncertainty guid- tive forcing (RF) effects of GHGs and aerosols are assessed in Chapter ance (Mastrandrea et al., 2011). 8. The observed changes in radiation budgets are discussed in Sec- tion 2.3. Aerosol cloud interactions are assessed in Chapter 7. Sec- As described in AR4 (Trenberth et al., 2007), the climate comprises a tion 2.4 provides an assessment of observed changes in surface and variety of space- and timescales: from the diurnal cycle, to interannual atmospheric temperature. Observed change in the hydrological cycle, variability such as the El Nino-Southern Oscillation (ENSO), to mul- including precipitation and clouds, is assessed in Section 2.5. Changes ti-decadal variations. Climate change refers to a change in the state in variability and extremes (such as cold spells, heat waves, droughts of the climate that can be identified by changes in the mean and/or and tropical cyclones) are assessed in Section 2.6. Section 2.7 assesses the variability of its properties and that persists for an extended period observed changes in the circulation of the atmosphere and its modes of time (Annex III: Glossary). In this chapter, climate change is exam- of variability, which help determine seasonal and longer-term anoma- 2 ined for the period with instrumental observations, since about 1850. lies at regional scales (Chapter 14). Change prior to this date is assessed in Chapter 5. The word trend is used to designate a long-term movement in a time series that may Trends have been assessed where possible for multi-decadal periods be regarded, together with the oscillation and random component, as starting in 1880, 1901 (referred to as long-term trends) and in 1951, composing the observed values (Annex III: Glossary). Where numerical 1979 (referred to as short-term trends). The time elapsed since AR4 values are given, they are equivalent linear changes (Box 2.2), though extends the period for trend calculation from 2005 to 2012 for many more complex nonlinear changes in the variable will often be clear variables. The GMST trend since 1998 has also been considered (see from the description and plots of the time series. also Box 9.2) as well as the trends for sequential 30-year segments of the time series. For many variables derived from satellite data, infor- In recent decades, advances in the global climate observing system mation is available for 1979 2012 only. In general, trend estimates have contributed to improved monitoring capabilities. In particular, sat- are more reliable for longer time intervals, and trends computed on ellites provide additional observations of climate change, which have short intervals have a large uncertainty. Trends for short intervals are been assessed in this and subsequent chapters together with more very sensitive to the start and end years. An exception to this is trends traditional ground-based and radiosonde observations. Since AR4, in GHGs, whose accurate measurement and long lifetimes make them substantial developments have occurred including the production of well-mixed and less susceptible to year-to-year variability, so that revised data sets, more digital data records, and new data set efforts. trends computed on relatively short intervals are very meaningful for New dynamical reanalysis data sets of the global atmosphere have these variables. Where possible, the time interval 1961 1990 has been been published (Box 2.3). These various innovations have improved chosen as the climatological reference period (or normal period) for understanding of data issues and uncertainties (Box 2.1). averaging. This choice enables direct comparisons with AR4, but is different from the present-day climate period (1986 2005) used as a Developing homogeneous long-term records from these different reference in the modelling chapters of AR5 and Annex I: Atlas of Global sources remains a challenge. The longest observational series are and Regional Climate Projections. land surface air temperatures (LSATs) and sea surface temperatures (SSTs). Like all physical climate system measurements, they suffer from It is important to note that the question of whether the observed n ­ on-climatic artefacts that must be taken into account (Box 2.1). The changes are outside the possible range of natural internal climate global combined LSAT and SST remains an important climate change variability and consistent with the climate effects from changes in measure for several reasons. Climate sensitivity is typically assessed atmospheric composition is not addressed in this chapter, but rather in in the context of global mean surface temperature (GMST) responses Chapter 10. No attempt has been undertaken to further describe and to a doubling of CO2 (Chapter 8) and GMST is thus a key metric in interpret the observed changes in terms of multi-decadal oscillatory the climate change policy framework. Also, because it extends back in (or low-frequency) variations, (long-term) persistence and/or secular time farther than any other global instrumental series, GMST is key to trends (e.g., as in Cohn and Lins, 2005; Koutsoyiannis and Montanari, understanding both the causes of change and the patterns, role and 2007; Zorita et al., 2008; Lennartz and Bunde, 2009; Mills, 2010; Mann, magnitude of natural variability (Chapter 10). Starting at various points 2011; Wu et al., 2011; Zhou and Tung, 2012; Tung and Zhou, 2013). in the 20th century, additional observations, including balloon-borne In this chapter, the robustness of the observed changes is assessed measurements and satellite measurements, and reanalysis products in relation to various sources of observational uncertainty (Box 2.1). allow analyses of indicators such as atmospheric composition, radia- In addition, the reported trend significance and statistical confidence tion budgets, hydrological cycle changes, extreme event characteriza- intervals provide an indication of how large the observed trend is tions and circulation indices. A full understanding of the climate system compared to the range of observed variability in a given aspect of the characteristics and changes requires analyses of all such variables climate system (see Box 2.2 for a description of the statistical trend as well as ocean (Chapter 3) and cryosphere (Chapter 4) indicators. model applied). Unless otherwise stated, 90% confidence intervals Through such a holistic analysis, a clearer and more robust assessment are given. The chapter also examines the physical consistency across of the changing climate system emerges (FAQ 2.1). 164 Observations: Atmosphere and Surface Chapter 2 different observations, which helps to provide additional confidence is an increase in the average growth rate of atmospheric methane in the reported changes. ­ dditional information about data sources A (CH4) from ~0.5 ppb yr 1 during 1999 2006 to ~6 ppb yr 1 from 2007 and methods is described in the Supplementary Material to Chapter 2. through 2011. Current observation networks are sufficient to quanti- fy global annual mean burdens used to calculate RF and to constrain global emission rates (with knowledge of loss rates), but they are not 2.2 Changes in Atmospheric Composition sufficient for accurately estimating regional scale emissions and how they are changing with time. 2.2.1 Well-Mixed Greenhouse Gases The globally, annually averaged well-mixed GHG mole fractions report- AR4 (Forster et al., 2007; IPCC, 2007) concluded that increasing atmos- ed here are used in Chapter 8 to calculate RF. A direct, inseparable con- pheric burdens of well-mixed GHGs resulted in a 9% increase in their nection exists between observed changes in atmospheric composition RF from 1998 to 2005. Since 2005, the atmospheric abundances of and well-mixed GHG emissions and losses (discussed in Chapter 6 for many well-mixed GHG increased further, but the burdens of some CO2, CH4, and N2O). A global GHG budget consists of the total atmos- ozone-depleting substances (ODS) whose production and use were pheric burden, total global rate of production or emission (i.e., sources), controlled by the Montreal Protocol on Substances that Deplete the and the total global rate of destruction or removal (i.e., sinks). Precise, Ozone Layer (1987; hereinafter, Montreal Protocol ) decreased. accurate systematic observations from independent globally distribut- 2 ed measurement networks are used to estimate global annual mean Based on updated in situ observations, this assessment concludes well-mixed GHG mole fractions at the Earth s surface, and these allow that these trends resulted in a 7.5% increase in RF from GHGs from estimates of global burdens. Emissions are predominantly from surface 2005 to 2011, with carbon dioxide (CO2) contributing 80%. Of note sources, which are described in Chapter 6 for CO2, CH4, and N2O. Direct ­ Box 2.1 | Uncertainty in Observational Records The vast majority of historical (and modern) weather observations were not made explicitly for climate monitoring purposes. Measure- ments have changed in nature as demands on the data, observing practices and technologies have evolved. These changes almost always alter the characteristics of observational records, changing their mean, their variability or both, such that it is necessary to process the raw measurements before they can be considered useful for assessing the true climate evolution. This is true of all observ- ing techniques that measure physical atmospheric quantities. The uncertainty in observational records encompasses instrumental/ recording errors, effects of representation (e.g., exposure, observing frequency or timing), as well as effects due to physical changes in the instrumentation (such as station relocations or new satellites). All further processing steps (transmission, storage, gridding, interpolating, averaging) also have their own particular uncertainties. Because there is no unique, unambiguous, way to identify and account for non-climatic artefacts in the vast majority of records, there must be a degree of uncertainty as to how the climate system has changed. The only exceptions are certain atmospheric composition and flux measurements whose measurements and uncertainties are rigorously tied through an unbroken chain to internationally recognized absolute measurement standards (e.g., the CO2 record at Mauna Loa; Keeling et al., 1976a). Uncertainty in data set production can result either from the choice of parameters within a particular analytical framework paramet- ric uncertainty, or from the choice of overall analytical framework structural uncertainty. Structural uncertainty is best estimated by having multiple independent groups assess the same data using distinct approaches. More analyses assessed now than in AR4 include published estimates of parametric or structural uncertainty. It is important to note that the literature includes a very broad range of approaches. Great care has been taken in comparing the published uncertainty ranges as they almost always do not constitute a like- for-like comparison. In general, studies that account for multiple potential error sources in a rigorous manner yield larger uncertainty ranges. This yields an apparent paradox in interpretation as one might think that smaller uncertainty ranges should indicate a better product. However, in many cases this would be an incorrect inference as the smaller uncertainty range may instead reflect that the pub- lished estimate considered only a subset of the plausible sources of uncertainty. Within the timeseries figures, where this issue would be most acute, such parametric uncertainty estimates are therefore not generally included. Consistent with AR4 HadCRUT4 uncertainties in GMST are included in Figure 2.19, which in addition includes structural uncertainties in GMST. To conclude, the vast majority of the raw observations used to monitor the state of the climate contain residual non-climatic influences. Removal of these influences cannot be done definitively and neither can the uncertainties be unambiguously assessed. Therefore, care is required in interpreting both data products and their stated uncertainty estimates. Confidence can be built from: redundancy in efforts to create products; data set heritage; and cross-comparisons of variables that would be expected to co-vary for physical reasons, such as LSATs and SSTs around coastlines. Finally, trends are often quoted as a way to synthesize the data into a single number. Uncer- tainties that arise from such a process and the choice of technique used within this chapter are described in more detail in Box 2.2. 165 Chapter 2 Observations: Atmosphere and Surface use of observations of well-mixed GHG to model their regional budg- Table 2.1 summarizes globally, annually averaged well-mixed GHG ets can also play an important role in verifying inventory estimates of mole fractions from four independent measurement programs. Sam- emissions (Nisbet and Weiss, 2010). pling strategies and techniques for estimating global means and their uncertainties vary among programs. Differences among measurement Systematic measurements of well-mixed GHG in ambient air began programs are relatively small and will not add significantly to uncer- at various times during the last six decades, with earlier atmospheric tainty in RF. Time series of the well-mixed GHG are plotted in Figures 2.1 histories being reconstructed from measurements of air stored in air (CO2), 2.2 (CH4), 2.3 (N2O), and 2.4 (halogen-containing compounds). archives and trapped in polar ice cores or in firn. In contrast to the physical meteorological parameters discussed elsewhere in this chap- 2.2.1.1 Kyoto Protocol Gases (Carbon Dioxide, Methane, ter, measurements of well-mixed GHG are reported relative to stand- Nitrous Oxide, Hydrofluorocarbons, Perfluorocarbons ards developed from fundamental SI base units (SI = International and Sulphur Hexafluoride) System of Units) as dry-air mole fractions, a unit that is conserved with changes in temperature and pressure (Box 2.1). This eliminates dilution 2.2.1.1.1 Carbon Dioxide by H2O vapour, which can reach 4% of total atmospheric composition. Here, the following abbreviations are used: ppm = umol mol 1; ppb = Precise, accurate systematic measurements of atmospheric CO2 at 2 nmol mol 1; and ppt = pmol mol 1. Unless noted otherwise, averag- Mauna Loa, Hawaii and South Pole were started by C. D. Keeling from es of National Oceanic and Atmospheric Administration (NOAA) and Scripps Institution of Oceanography in the late 1950s (Keeling et al., Advanced Global Atmospheric Gases Experiment (AGAGE) annually 1976a; Keeling et al., 1976b). The 1750 globally averaged abundance averaged surface global mean mole fractions is described in Section of atmospheric CO2 based on measurements of air extracted from ice 2.2.1 (see Supplementary Material 2.SM.2 for further species not listed cores and from firn is 278 +/- 2 ppm (Etheridge et al., 1996). Globally here). averaged CO2 mole fractions since the start of the instrumental record Table 2.1 | Global annual mean surface dry-air mole fractions and their change since 2005 for well-mixed greenhouse gases (GHGs) from four measurement networks. Units are ppt except where noted. Uncertainties are 90% confidence intervalsa. REs (radiative efficiency) and lifetimes (except CH4 and N2O, which are from Prather et al., 2012) are from Chapter 8. 2011 Global Annual Mean Global Increase from 2005 to 2011 Species Lifetime (yr) RE (W m 2 ppb 1) UCI SIOb/AGAGE NOAA UCI SIOb/AGAGE NOAA CO2 (ppm) 1.37 × 10 5 390.48 +/- 0.28 390.44 +/- 0.16 11.67 +/- 0.37 11.66 +/- 0.13 CH4 (ppb) 9.1 3.63 × 10 4 1798.1 +/- 0.6 1803.1 +/- 4.8 1803.2 +/- 1.2 26.6 +/- 0.9 28.9 +/- 6.8 28.6 +/- 0.9 N2O (ppb) 131 3.03 × 10 3 324.0 +/- 0.1 324.3 +/- 0.1 4.7 +/- 0.2 5.24 +/- 0.14 SF6 3200 0.575 7.26 +/- 0.02 7.31 +/- 0.02 1.65 +/- 0.03 1.64 +/-0.01 CF4 50,000 0.1 79.0 +/- 0.1 4.0 +/- 0.2 C2F6 10,000 0.26 4.16 +/- 0.02 0.50 +/- 0.03 HFC-125 28.2 0.219 9.58 +/- 0.04 5.89 +/- 0.07 HFC-134a 13.4 0.159 63.4 +/- 0.9 62.4 +/- 0.3 63.0 +/- 0.6 27.7 +/- 1.4 28.2 +/- 0.4 28.2 +/- 0.1 HFC-143a 47.1 0.159 12.04 +/- 0.07 6.39 +/- 0.10 HFC-152a 1.5 0.094 6.4 +/- 0.1 3.0 +/- 0.2 HFC-23 222 0.176 24.0 +/- 0.3 5.2 +/- 0.6 CFC-11 45 0.263 237.9 +/- 0.8 236.9 +/- 0.1 238.5 +/- 0.2 13.2 +/- 0.8 12.7 +/- 0.2 13.0 +/- 0.1 CFC-12 100 0.32 525.3 +/- 0.8 529.5 +/- 0.2 527.4 +/- 0.4 12.8 +/- 0.8 13.4 +/- 0.3 14.1 +/- 0.1 CFC-113 85 0.3 74.9 +/- 0.6 74.29 +/- 0.06 74.40 +/- 0.04 4.6 +/- 0.8 4.25 +/- 0.08 4.35 +/-0.02 HCFC-22 11.9 0.2 209.0 +/- 1.2 213.4 +/- 0.8 213.2 +/- 1.2 41.5 +/- 1.4 44.6 +/- 1.1 44.3 +/- 0.2 HCFC-141b 9.2 0.152 20.8 +/- 0.5 21.38 +/- 0.09 21.4 +/- 0.2 3.7 +/- 0.5 3.70 +/- 0.1 3.76 +/- 0.03 HCFC-142b 17.2 0.186 21.0 +/- 0.5 21.35 +/- 0.06 21.0 +/- 0.1 4.9 +/- 0.5 5.72 +/- 0.09 5.73 +/- 0.04 CCl4 26 0.175 87.8 +/- 0.6 85.0 +/- 0.1 86.5 +/- 0.3 6.4 +/- 0.5 6.9 +/- 0.2 7.8 +/- 0.1 CH3CCl3 5 0.069 6.8 +/- 0.6 6.3 +/- 0.1 6.35 +/- 0.07 14.8 +/- 0.5 11.9 +/- 0.2 12.1 +/- 0.1 Notes: AGAGE = Advanced Global Atmospheric Gases Experiment; NOAA = National Oceanic and Atmospheric Administration, Earth System Research Laboratory, Global Monitoring Division; SIO = Scripps Institution of Oceanography, University of California, San Diego; UCI = University of California, Irvine, Department of Chemistry. HFC-125 = CHF2CF3; HFC-134a = CH2FCF3; HFC-143a = CH3CF3; HFC-152a = CH3CHF2; HFC-23 = CHF3; CFC-11 = CCl3F; CFC-12 = CCl2F2; CFC-113 = CClF2CCl2F; HCFC-22 = CHClF2; HCFC-141b = CH3CCl2F; HCFC-142b = CH3CClF2. a Each program uses different methods to estimate uncertainties. b SIO reports only CO2; all other values reported in these columns are from AGAGE. SIO CO2 program and AGAGE are not affiliated with each other. Budget lifetimes are shown; for CH4 and N2O, perturbation lifetimes (12.4 years for CH4 and 121 years for N2O) are used to estimate global warming potentials (Chapter 8). Year 1750 values determined from air extracted from ice cores are below detection limits for all species except CO2 (278 +/- 2 ppm), CH4 (722 +/- 25 ppb), N2O (270 +/- 7 ppb) and CF4 (34.7 +/- 0.2 ppt). Centennial variations up to 10 ppm CO2, 40 ppb CH4, and 10 ppb occur throughout the late-Holocene (Chapter 6). 166 Observations: Atmosphere and Surface Chapter 2 are plotted in Figure 2.1. The main features in the contemporary CO2 (a) record are the long-term increase and the seasonal cycle resulting from 1800 1750 CH4 (ppb) photosynthesis and respiration by the terrestrial biosphere, mostly in the Northern Hemisphere (NH). The main contributors to increasing 1700 1650 atmospheric CO2 abundance are fossil fuel combustion and land use 1600 change (Section 6.3). Multiple lines of observational evidence indicate 1550 that during the past few decades, most of the increasing atmospheric burden of CO2 is from fossil fuel combustion (Tans, 2009). Since the 25 (b) d(CH4)/dt (ppb yr -1) last year for which the AR4 reported (2005), CO2 has increased by 11.7 20 ppm to 390.5 ppm in 2011 (Table 2.1). From 1980 to 2011, the average 15 annual increase in globally averaged CO2 (from 1 January in one year 10 to 1 January in the next year) was 1.7 ppm yr 1 (1 standard deviation 5 = 0.5 ppm yr 1; 1 ppm globally corresponds to 2.1 PgC increase in the 0 atmospheric burden). Since 2001, CO2 has increased at 2.0 ppm yr 1 (1 -5 standard deviation = 0.3 ppm yr 1). The CO2 growth rate varies from year to year; since 1980 the range in annual increase is 0.7 +/- 0.1 ppm 1980 1990 2000 2010 2 in 1992 to 2.9 +/- 0.1 ppm in 1998. Most of this interannual variability Figure 2.2 | (a) Globally averaged CH4 dry-air mole fractions from UCI (green; four in growth rate is driven by small changes in the balance between pho- values per year, except prior to 1984, when they are of lower and varying frequency), tosynthesis and respiration on land, each having global fluxes of ~120 AGAGE (red; monthly), and NOAA/ESRL/GMD (blue; quasi-weekly). (b) Instantaneous growth rate for globally averaged atmospheric CH4 using the same colour code as in (a). PgC yr 1 (Chapter 6). Growth rates were calculated as in Figure 2.1. 2.2.1.1.2 Methane 2009). Assuming no long-term trend in hydroxyl radical (OH) concen- Globally averaged CH4 in 1750 was 722 +/- 25 ppb (after correction tration, the observed decrease in CH4 growth rate from the early 1980s to the NOAA-2004 CH4 standard scale) (Etheridge et al., 1998; Dlu- through 2006 indicates an approach to steady state where total global gokencky et al., 2005), although human influences on the global CH4 emissions have been approximately constant at ~550 Tg (CH4) yr 1. budget may have begun thousands of years earlier than this time that Superimposed on the long-term pattern is significant interannual vari- is normally considered pre-industrial (Ruddiman, 2003; Ferretti et al., ability; studies of this variability are used to improve understanding of 2005; Ruddiman, 2007). In 2011, the global annual mean was 1803 the global CH4 budget (Chapter 6). The most likely drivers of increased +/- 2 ppb. Direct atmospheric measurements of CH4 of sufficient spa- atmospheric CH4 were anomalously high temperatures in the Arctic in tial coverage to calculate global annual means began in 1978 and are 2007 and greater than average precipitation in the tropics during 2007 plotted through 2011 in Figure 2.2a. This time period is characterized and 2008 (Dlugokencky et al., 2009; Bousquet, 2011). Observations of by a decreasing growth rate (Figure 2.2b) from the early 1980s until the difference in CH4 between zonal averages for northern and south- 1998, stabilization from 1999 to 2006, and an increasing ­ tmospheric a ern polar regions (53° to 90°) (Dlugokencky et al., 2009, 2011) suggest burden from 2007 to 2011 (Rigby et al., 2008; Dlugokencky et al., that, so far, it is unlikely that there has been a permanent measureable increase in Arctic CH4 emissions from wetlands and shallow sub-sea CH4 clathrates. (a) 380 Reaction with the hydroxyl radical (OH) is the main loss process for CO2 (ppm) 360 CH4 (and for hydrofluorocarbons (HFCs) and hydrochlorofluorocarbons (HCFCs)), and it is the largest term in the global CH4 budget. Therefore, 340 trends and interannual variability in OH concentration significantly 320 impact our understanding of changes in CH4 emissions. Methyl chloro- form (CH3CCl3; Section 2.2.1.2) has been used extensively to estimate d(CO2)/dt (ppm yr -1) (b) globally averaged OH concentrations (e.g., Prinn et al., 2005). AR4 3 reported no trend in OH from 1979 to 2004, and there is no evidence from this assessment to change that conclusion for 2005 to 2011. 2 Montzka et al. (2011a) exploited the exponential decrease and small emissions in CH3CCl3 to show that interannual variations in OH con- 1 centration from 1998 to 2007 are 2.3 +/- 1.5%, which is consistent with estimates based on CH4, tetrachloroethene (C2Cl4), dichloromethane 1960 1970 1980 1990 2000 2010 (CH2Cl2), chloromethane (CH3Cl) and bromomethane (CH3Br). Figure 2.1 | (a) Globally averaged CO2 dry-air mole fractions from Scripps Institution of Oceanography (SIO) at monthly time resolution based on measurements from Mauna 2.2.1.1.3 Nitrous Oxide Loa, Hawaii and South Pole (red) and NOAA/ESRL/GMD at quasi-weekly time resolution (blue). SIO values are deseasonalized. (b) Instantaneous growth rates for globally aver- Globally averaged N2O in 2011 was 324.2 ppb, an increase of 5.0 ppb aged atmospheric CO2 using the same colour code as in (a). Growth rates are calculated over the value reported for 2005 in AR4 (Table 2.1). This is an increase as the time derivative of the deseasonalized global averages (Dlugokencky et al., 1994). 167 Chapter 2 Observations: Atmosphere and Surface (a) pheric abundances of these species are summarized in Table 2.1 and 320 plotted in Figure 2.4. 315 N2O (ppb) 310 Atmospheric HFC abundances are low and their contribution to RF is 305 small relative to that of the CFCs and HCFCs they replace (less than 1% of the total by well-mixed GHGs; Chapter 8). As they replace CFCs and 300 HCFCs phased out by the Montreal Protocol, however, their contribu- (b) tion to future climate forcing is projected to grow considerably in the d(N2O)/dt (ppb yr -1) 1.25 absence of controls on global production (Velders et al., 2009). 1.00 0.75 HFC-134a is a replacement for CFC-12 in automobile air conditioners 0.50 and is also used in foam blowing applications. In 2011, it reached 62.7 0.25 ppt, an increase of 28.2 ppt since 2005. Based on analysis of high-fre- quency measurements, the largest emissions occur in North America, 1980 1985 1990 1995 2000 2005 2010 Europe and East Asia (Stohl et al., 2009). 2 Figure 2.3 | (a) Globally averaged N2O dry-air mole fractions from AGAGE (red) and NOAA/ESRL/GMD (blue) at monthly resolution. (b) Instantaneous growth rates for glob- HFC-23 is a by-product of HCFC-22 production. Direct measurements ally averaged atmospheric N2O. Growth rates were calculated as in Figure 2.1. of HFC-23 in ambient air at five sites began in 2007. The 2005 global annual mean used to calculate the increase since AR4 in Table 2.1, 5.2 ppt, is based on an archive of air collected at Cape Grim, Tasmania of 20% over the estimate for 1750 from ice cores, 270 +/- 7 ppb (Prather (Miller et al., 2010). In 2011, atmospheric HFC-23 was at 24.0 ppt. Its et al., 2012). Measurements of N2O and its isotopic composition in firn growth rate peaked in 2006 as emissions from developing countries air suggest the increase, at least since the early 1950s, is dominated by emissions from soils treated with synthetic and organic (manure) nitrogen fertilizer (Rockmann and Levin, 2005; Ishijima et al., 2007; 600 Davidson, 2009; Syakila and Kroeze, 2011). Since systematic measure- ments began in the late 1970s, N2O has increased at an average rate of 500 ~0.75 ppb yr 1 (Figure 2.3). Because the atmospheric burden of CFC-12 CFC-12 is decreasing, N2O has replaced CFC-12 as the third most important well-mixed GHG contributing to RF (Elkins and Dutton, 2011). 400 Gas (ppt) Persistent latitudinal gradients in annually averaged N2O are observed at background surface sites, with maxima in the northern subtropics, 300 values about 1.7 ppb lower in the Antarctic, and values about 0.4 ppb CFC-11 lower in the Arctic (Huang et al., 2008). These persistent gradients HCFC-22 200 contain information about anthropogenic emissions from fertilizer use CH 3 CCl3 at northern tropical to mid-latitudes and natural emissions from soils CCl 4 and ocean upwelling regions of the tropics. N2O time series also con- 100 tain seasonal variations with peak-to-peak amplitudes of about 1 ppb HFC-134a CF 4 in high latitudes of the NH and about 0.4 ppb at high southern and HFC-23 tropical latitudes. In the NH, exchange of air between the stratosphere 0 HFC-125 C 3 F8 (where N2O is destroyed by photochemical processes) and troposphere 12 1980 1985 1990 1995 2000 2005 2010 HFC-143a HFC-32 is the dominant contributor to observed seasonal cycles, not seasonali- HFC-152a HFC-245fa ty in emissions (Jiang et al., 2007). Nevison et al. (2011) found correla- 9 Gas (ppt) C2F C2F66 HFC-365mfc tions between the magnitude of detrended N2O seasonal minima and SF SF66 lower stratospheric temperature, providing evidence for a stratospheric C3F8 6 influence on the timing and amplitude of the seasonal cycle at surface monitoring sites. In the Southern Hemisphere (SH), observed seasonal cycles are also affected by stratospheric influx, and by ventilation and 3 thermal out-gassing of N2O from the oceans. 0 2.2.1.1.4 Hydrofluorocarbons, Perfluorocarbons, Sulphur 1980 1985 1990 1995 2000 2005 2010 Hexafluoride and Nitrogen Trifluoride Figure 2.4 | Globally averaged dry-air mole fractions at the Earth s surface of the major halogen-containing well-mixed GHG. These are derived mainly using monthly The budgets of HFCs, PFCs and SF6 were recently reviewed in Chapter mean measurements from the AGAGE and NOAA/ESRL/GMD networks. For clarity, only 1 of the Scientific Assessment of Ozone Depletion: 2010 (Montzka et the most abundant chemicals are shown in different compound classes and results from al., 2011b), so only a brief description is given here. The current atmos- different networks have been combined when both are available. 168 Observations: Atmosphere and Surface Chapter 2 increased, then declined as emissions were reduced through abate- for its global annual mean mole fraction in 2008, growing from almost ment efforts under the Clean Development Mechanism (CDM) of the zero in 1978. In 2011, NF3 was 0.86 ppt, increasing by 0.49 ppt since UNFCCC. Estimates of total global emissions based on atmospheric 2005. These abundances were updated from the first work to quantify observations and bottom-up inventories agree within uncertainties NF3 by Weiss et al. (2008). Initial bottom-up inventories underestimat- (Miller et al., 2010; Montzka et al., 2010). Currently, the largest emis- ed its emissions; based on the atmospheric observations, NF3 emissions sions of HFC-23 are from East Asia (Yokouchi et al., 2006; Kim et al., were 1.18 +/- 0.21Gg in 2011 (Arnold et al., 2013). 2010; Stohl et al., 2010); developed countries emit less than 20% of the global total. Keller et al. (2011) found that emissions from developed In summary, it is certain that atmospheric burdens of well-mixed GHGs countries may be larger than those reported to the UNFCCC, but their targeted by the Kyoto Protocol increased from 2005 to 2011. The atmos- contribution is small. The lifetime of HFC-23 was revised from 270 to pheric abundance of CO2 was 390.5 +/- 0.2 ppm in 2011; this is 40% 222 years since AR4. greater than before 1750. Atmospheric N2O was 324.2 +/- 0.2 ppb in 2011 and has increased by 20% since 1750. Average annual increases After HFC-134a and HFC-23, the next most abundant HFCs are HFC- in CO2 and N2O from 2005 to 2011 are comparable to those observed 143a at 12.04 ppt in 2011, 6.39 ppt greater than in 2005; HFC-125 from 1996 to 2005. Atmospheric CH4 was 1803.2 +/- 2.0 ppb in 2011; this (O Doherty et al., 2009) at 9.58 ppt, increasing by 5.89 ppt since 2005; is 150% greater than before 1750. CH4 began increasing in 2007 after HFC-152a (Greally et al., 2007) at 6.4 ppt with a 3.0 ppt increase since remaining nearly constant from 1999 to 2006. HFCs, PFCs, and SF6 all 2 2005; and HFC-32 at 4.92 ppt in 2011, 3.77 ppt greater than in 2005. continue to increase relatively rapidly, but their contributions to RF are Since 2005, all of these were increasing exponentially except for HFC- less than 1% of the total by well-mixed GHGs (Chapter 8). 152a, whose growth rate slowed considerably in about 2007 (Figure 2.4). HFC-152a has a relatively short atmospheric lifetime of 1.5 years, 2.2.1.2 Ozone-Depleting Substances (Chlorofluorocarbons, so its growth rate will respond quickly to changes in emissions. Its Chlorinated Solvents, and Hydrochlorofluorocarbons) major uses are as a foam blowing agent and aerosol spray propellant while HFC-143a, HFC-125, and HFC-32 are mainly used in refriger- CFC atmospheric abundances are decreasing (Figure 2.4) because of the ant blends. The reasons for slower growth in HFC-152a since about successful reduction in emissions resulting from the Montreal Protocol. 2007 are unclear. Total global emissions of HFC-125 estimated from By 2010, emissions from ODSs had been reduced by ~11 Pg CO2-eq the observations are within about 20% of emissions reported to the yr 1, which is five to six times the reduction target of the first com- UNFCCC, after accounting for estimates of unreported emissions from mitment period (2008 2012) of the Kyoto Protocol (2 PgCO2-eq yr 1) East Asia (O Doherty et al., 2009). (Velders et al., 2007). These avoided equivalent-CO2 emissions account for the offsets to RF by stratospheric O3 depletion caused by ODSs and CF4 and C2F6 (PFCs) have lifetimes of 50 kyr and 10 kyr, respective- the use of HFCs as substitutes for them. Recent observations in Arctic ly, and they are emitted as by-products of aluminium production and and Antarctic firn air further confirm that emissions of CFCs are entirely used in plasma etching of electronics. CF4 has a natural lithospheric anthropogenic (Martinerie et al., 2009; Montzka et al., 2011b). CFC-12 source (Deeds et al., 2008) with a 1750 level determined from Green- has the largest atmospheric abundance and GWP-weighted emissions land and Antarctic firn air of 34.7 +/- 0.2 ppt (Worton et al., 2007; Muhle (which are based on a 100-year time horizon) of the CFCs. Its tropo- et al., 2010). In 2011, atmospheric abundances were 79.0 ppt for CF4, spheric abundance peaked during 2000 2004. Since AR4, its global increasing by 4.0 ppt since 2005, and 4.16 ppt for C2F6, increasing by annual mean mole fraction declined by 13.8 ppt to 528.5 ppt in 2011. 0.50 ppt. The sum of emissions of CF4 reported by aluminium produc- CFC-11 continued the decrease that started in the mid-1990s, by 12.9 ers and for non-aluminium production in EDGAR (Emission Database ppt since 2005. In 2011, CFC-11 was 237.7 ppt. CFC-113 decreased by for Global Atmospheric Research) v4.0 accounts for only about half 4.3 ppt since 2005 to 74.3 ppt in 2011. A discrepancy exists between of global emissions inferred from atmospheric observations (Muhle et top-down and bottom-up methods for calculating CFC-11 emissions al., 2010). For C2F6, emissions reported to the UNFCCC are also sub- (Montzka et al., 2011b). Emissions calculated using top-down methods stantially lower than those estimated from atmospheric observations come into agreement with bottom-up estimates when a lifetime of 64 (Muhle et al., 2010). years is used for CFC-11 in place of the accepted value of 45 years; this longer lifetime (64 years) is at the upper end of the range estimated The main sources of atmospheric SF6 emissions are electricity distri- by Douglass et al. (2008) with models that more accurately simulate bution systems, magnesium production, and semi-conductor manufac- stratospheric circulation. Future emissions of CFCs will largely come turing. Global annual mean SF6 in 2011 was 7.29 ppt, increasing by from banks (i.e., material residing in existing equipment or stores) 1.65 ppt since 2005. SF6 has a lifetime of 3200 years, so its emissions rather than current production. accumulate in the atmosphere and can be estimated directly from its observed rate of increase. Levin et al. (2010) and Rigby et al. (2010) The mean decrease in globally, annually averaged carbon tetrachlo- showed that SF6 emissions decreased after 1995, most likely because ride (CCl4) based on NOAA and AGAGE measurements since 2005 was of emissions reductions in developed countries, but then increased 7.4 ppt, with an atmospheric abundance of 85.8 ppt in 2011 (Table after 1998. During the past decade, they found that actual SF6 emis- 2.1). The observed rate of decrease and inter-hemispheric difference sions from developed countries are at least twice the reported values. of CCl4 suggest that emissions determined from the observations are on average greater and less variable than bottom-up emission esti- NF3 was added to the list of GHG in the Kyoto Protocol with the Doha mates, although large uncertainties in the CCl4 lifetime result in large Amendment, December, 2012. Arnold et al. (2013) determined 0.59 ppt uncertainties in the top-down estimates of emissions (Xiao et al., 2010; 169 Chapter 2 Observations: Atmosphere and Surface Montzka et al., 2011b). CH3CCl3 has declined exponentially for about a The longest continuous time series of stratospheric water vapour abun- decade, decreasing by 12.0 ppt since 2005 to 6.3 ppt in 2011. dance is from in situ measurements made with frost point hygrome- ters starting in 1980 over Boulder, USA (40°N, 105°W) (Scherer et al., HCFCs are classified as transitional substitutes by the Montreal Proto- 2008), with values ranging from 3.5 to 5.5 ppm, depending on altitude. col. Their global production and use will ultimately be phased out, but These observations have been complemented by long-term global sat- their global production is not currently capped and, based on changes ellite observations from SAGE II (1984 2005; Stratospheric Aerosol in observed spatial gradients, there has likely been a shift in emissions and Gas Experiment II (Chu et al., 1989)), HALOE (1991 2005; HAL- within the NH from regions north of about 30°N to regions south of ogen Occultation Experiment (Russell et al., 1993)), Aura MLS (2004 30°N (Montzka et al., 2009). Global levels of the three most abundant present; Microwave Limb Sounder (Read et al., 2007)) and Envisat HCFCs in the atmosphere continue to increase. HCFC-22 increased by MIPAS (2002-2012; Michelson Interferometer for Passive Atmospheric 44.5 ppt since 2005 to 213.3 ppt in 2011. Developed country emissions Sounding (Milz et al., 2005; von Clarmann et al., 2009)). Discrepancies of HCFC-22 are decreasing, and the trend in total global emissions is in water vapour mixing ratios from these different instruments can be driven by large increases from south and Southeast Asia (Saikawa et attributed to differences in the vertical resolution of measurements, al., 2012). HCFC-141b increased by 3.7 ppt since 2005 to 21.4 ppt in along with other factors. For example, offsets of up to 0.5 ppm in lower 2011, and for HCFC-142b, the increase was 5.73 ppt to 21.1 ppt in stratospheric water vapour mixing ratios exist between the most cur- 2 2011. The rates of increase in these three HCFCs increased since 2004, rent versions of HALOE (v19) and Aura MLS (v3.3) retrievals during but the change in HCFC-141b growth rate was smaller and less persis- their 16-month period of overlap (2004 to 2005), although such biases tent than for the other two, which approximately doubled from 2004 can be removed to generate long-term records. Since AR4, new studies to 2007 (Montzka et al., 2009). characterize the uncertainties in measurements from individual types of in situ H2O sensors (Vömel et al., 2007b; Vömel et al., 2007a; Wein- In summary, for ODS, whose production and consumption are con- stock et al., 2009), but discrepancies between different instruments trolled by the Montreal Protocol, it is certain that the global mean (50 to 100% at H2O mixing ratios less than 10 ppm), particularly for abundances of major CFCs are decreasing and HCFCs are increasing. high-altitude measurements from aircraft, remain largely unexplained. Atmospheric burdens of CFC-11, CFC-12, CFC-113, CCl4, CH3CCl3 and some halons have decreased since 2005. HCFCs, which are transitional Observed anomalies in stratospheric H2O from the near-global com- substitutes for CFCs, continue to increase, but the spatial distribution bined HALOE+MLS record (1992 2011) (Figure 2.5) include effects of their emissions is changing. linked to the stratospheric quasi-biennial oscillation (QBO) influence on tropopause temperatures, plus a step-like drop after 2001 (noted in 2.2.2 Near-Term Climate Forcers AR4), and an increasing trend since 2005. Variability during 2001 2011 was large yet there was only a small net change from 1992 through This section covers observed trends in stratospheric water vapour; 2011. These interannual water vapour variations for the satellite record stratospheric and tropospheric ozone (O3); the O3 precursor gases, are closely linked to observed changes in tropical tropopause temper- nitrogen dioxide (NO2) and carbon monoxide (CO); and column and atures (Fueglistaler and Haynes, 2005; Randel et al., 2006; Rosenlof surface aerosol. Since trend estimates from the cited literature are used and Reid, 2008; Randel, 2010), providing reasonable understanding of here, issues such as data records of different length, potential lack of observed changes. The longer record of Boulder balloon measurements comparability among measurement methods and different trend calcu- (since 1980) has been updated and reanalyzed (Scherer et al., 2008; lation methods, add to the uncertainty in assessing trends. Hurst et al., 2011), showing deca dal-scale variability and a long-term stratospheric (16 to 26 km) increase of 1.0 +/- 0.2 ppm for 1980 2010. 2.2.2.1 Stratospheric Water Vapour Agreement between interannual changes inferred from the Boulder and HALOE+MLS data is good for the period since 1998 but was poor Stratospheric H2O vapour has an important role in the Earth s radi- during 1992 1996. About 30% of the positive trend during 1980 2010 ative balance and in stratospheric chemistry. Increased stratospher- determined from frost point hygrometer data (Fujiwara et al., 2010; ic H2O vapour causes the troposphere to warm and the stratosphere Hurst et al., 2011) can be explained by increased production of H2O to cool (Manabe and Strickler, 1964; Solomon et al., 2010), and also from CH4 oxidation (Rohs et al., 2006), but the remainder cannot be causes increased rates of stratospheric O3 loss (Stenke and Grewe, explained by changes in tropical tropopause temperatures (Fueglistal- 2005). Water vapour enters the stratosphere through the cold tropical er and Haynes, 2005) or other known factors. tropopause. As moisture-rich air masses are transported through this region, most water vapour condenses resulting in extremely dry lower In summary, near-global satellite measurements of stratospheric H2O stratospheric air. Because tropopause temperature varies seasonally, show substantial variability for 1992 2011, with a step-like decrease so does H2O abundance there. Other contributions include oxidation after 2000 and increases since 2005. Because of this large variability of methane within the stratosphere, and possibly direct injection of and relatively short time series, confidence in long-term stratospher- H2O vapour in overshooting deep convection (Schiller et al., 2009). AR4 ic H2O trends is low. There is good understanding of the relationship reported that stratospheric H2O vapour showed significant long-term between the satellite-derived H2O variations and tropical tropopause variability and an upward trend over the last half of the 20th century, temperature changes. Stratospheric H2O changes from temporally but no net increase since 1996. This updated assessment finds large sparse balloon-borne observations at one location (Boulder, Colorado) interannual variations that have been observed by independent meas- are in good agreement with satellite observations from 1998 to the urement techniques, but no significant net changes since 1996. present, but discrepancies exist for changes during 1992 1996. Long- 170 Observations: Atmosphere and Surface Chapter 2 Water Vapour Anomaly (ppm) HALOE+MLS: 60°S-60°N 20 Water Vapour Anomaly (%) HALOE+MLS: 30°N-50°N 0.5 NOAA FPH: Boulder (40°N) 10 0.0 0 -10 -0.5 (a) -20 1.0 30 Water Vapour Anomaly (%) Water Vapour Anomaly (ppm) 20 0.5 10 2 0.0 0 -10 -0.5 -20 -1.0 -30 -1.5 (b) -40 1980 1990 2000 2010 Figure 2.5 | Water vapour anomalies in the lower stratosphere (~16 to 19 km) from satellite sensors and in situ measurements normalized to 2000 2011. (a) Monthly mean water vapour anomalies at 83 hPa for 60°S to 60°N (blue) determined from HALOE and MLS satellite sensors. (b) Approximately monthly balloon-borne measurements of stratospheric water vapour from Boulder, Colorado at 40°N (green dots; green curve is 15-point running mean) averaged over 16 to 18 km and monthly means as in (a), but averaged over 30°N to 50°N (black) term balloon measurements from Boulder indicate a net increase of Two altitude regions are mainly responsible for long-term changes in 1.0 +/- 0.2 ppm over 16 to 26 km for 1980 2010, but these long-term total column ozone (Douglass et al., 2011). In the upper stratosphere increases cannot be fully explained by changes in tropical tropopause (35 to 45 km), there was a strong and statistically significant decline temperatures, methane oxidation or other known factors. (about 10%) up to the mid-1990s and little change or a slight increase since. The lower stratosphere, between 20 and 25 km over mid-lat- 2.2.2.2 Stratospheric Ozone itudes, also experienced a statistically significant decline (7 to 8%) between 1979 and the mid-1990s, followed by stabilization or a slight AR4 did not explicitly discuss measured stratospheric ozone trends. For (2 to 3%) ozone increase. the current assessment report such trends are relevant because they are the basis for revising the RF from 0.05 +/- 0.10 W m 2 in 1750 to Springtime averages of total ozone poleward of 60° latitude in the 0.10 +/- 0.15 W m 2 in 2005 (Section 8.3.3.2). These values strongly Arctic and Antarctic are shown in Figure 2.6e. By far the strongest depend on the vertical distribution of the stratospheric ozone changes. ozone loss in the stratosphere occurs in austral spring over Antarctica (ozone hole) and its impact on SH climate is discussed in Chapters Total ozone is a good proxy for stratospheric ozone because tropo- 11, 12 and 14. Interannual variability in polar stratospheric ozone spheric ozone accounts for only about 10% of the total ozone column. abundance and chemistry is driven by variability in temperature and Long-term total ozone changes over various latitudinal belts, derived transport due to year-to-year differences in dynamics. This variability is from Weber et al. (2012), are illustrated in Figure 2.6 (a d). Annual- particularly large in the Arctic, where the most recent large depletion ly averaged total column ozone declined during the 1980s and early occurred in 2011, when chemical ozone destruction was, for the first 1990s and has remained constant for the past decade, about 3.5 and time in the observational record, comparable to that in the Antarctic 2.5% below the 1964 1980 average for the entire globe (not shown) (Manney et al., 2011). and 60°S to 60°N, respectively, with changes occurring mostly outside the tropics, particularly the SH, where the current extratropical (30S In summary, it is certain that global stratospheric ozone has declined to 60S) mean values are 6% below the 1964 1980 average, com- from pre-1980 values. Most of the decline occurred prior to the mid- pared to 3.5% for the NH extratropics (Douglass et al., 2011). In the 1990s; since then there has been little net change and ozone has NH, the 1993 minimum of about 6% was caused primarily by ozone remained nearly constant at about 3.5% below the 1964 1980 level. loss through heterogeneous reactions on volcanic aerosols from Mt. Pinatubo. 171 Chapter 2 Observations: Atmosphere and Surface (a) 2.2.2.3 Tropospheric Ozone Tropospheric ozone is a short-lived trace gas that either originates in O3 (DU) the stratosphere or is produced in situ by precursor gases and sunlight (e.g., Monks et al., 2009). An important GHG with an estimated RF of 0.40 +/- 0.20 W m 2 (Chapter 8), tropospheric ozone also impacts human health and vegetation at the surface. Its average atmospheric (b) lifetime of a few weeks produces a global distribution highly variable by season, altitude and location. These characteristics and the paucity of long-term measurements make the assessment of long-term global ozone trends challenging. However, new studies since AR4 provide O3 (DU) greater understanding of surface and free tropospheric ozone trends from the 1950s through 2010. An extensive compilation of meas- ured ozone trends is presented in the Supplementary Material, Figure 2.SM.1 and Table 2.SM.2. 2 WOUDC (Brewer, Dobson, Filter) GOME/SCIAMACHY/GOME2 (GSG) BUV/TOMS/SBUV/OMI (MOD V8) The earliest (1876 1910) quantitative ozone observations are limited (c) to Montsouris near Paris where ozone averaged 11 ppb (Volz and Kley, 1988). Semiquantitative ozone measurements from more than 40 loca- O3 (DU) tions around the world in the late 1800s and early 1900s range from 5 to 32 ppb with large uncertainty (Pavelin et al., 1999). The low 19th century ozone values cannot be reproduced by most models (Section 8.2.3.1), and this discrepancy is an important factor contributing to (d) uncertainty in RF calculations (Section 8.3.3.1). Limited quantitative measurements from the 1870s to 1950s indicate that surface ozone in Europe increased by more than a factor of 2 compared to observations made at the end of the 20th century (Marenco et al., 1994; Parrish et O3 (DU) al., 2012). Satellite-based tropospheric column ozone retrievals across the tropics and mid-latitudes reveal a greater burden in the NH than in the SH (Ziemke et al., 2011). Tropospheric column ozone trend analyses are few. An analysis by Ziemke et al. (2005) found no trend over the trop- ical Pacific Ocean but significant positive trends (5 to 9% per decade) (e) in the mid-latitude Pacific of both hemispheres during 1979 2003. Sig- nificant positive trends (2 to 9% per decade) were found across broad regions of the tropical South Atlantic, India, southern China, southeast Asia, Indonesia and the tropical regions downwind of China (Beig and Singh, 2007). Long-term ozone trends at the surface and in the free troposphere (of O3 (DU) importance for calculating RF, Chapter 8) can be assessed only from in situ measurements at a limited number of sites, leaving large areas such as the tropics and SH sparsely sampled (Table 2.SM.2, Figure 2.7). Nineteen predominantly rural surface sites or regions around the globe have long-term records that stretch back to the 1970s, and in two cases the 1950s (Lelieveld et al., 2004; Parrish et al., 2012; Oltmans et WOUDC (Brewer, Dobson, Filter) al., 2013). Thirteen of these sites are in the NH, and 11 sites have statis- GOME/SCIAMACHY/GOME2 (GSG) tically significant positive trends of 1 to 5 ppb per decade, correspond- BUV/TOMS/SBUV/OMI (MOD V8) ing to >100% ozone increases since the 1950s and 9 to 55% ozone 1970 1980 1990 2000 2010 increases since the 1970s. In the SH, three of six sites have signifi- cant trends of approximately 2 ppb per decade and three have insig- Figure 2.6 | Zonally averaged, annual mean total column ozone in Dobson Units nificant trends. Free tropospheric monitoring since the 1970s is more (DU; 1 DU = 2.69 × 1016 O3/cm2) from ground-based measurements combining Brewer, limited. Significant positive trends since 1971 have been observed Dobson, and filter spectrometer data WOUDC (red), GOME/SCIAMACHY/GOME-2 GSG (green) and merged satellite BUV/TOMS/SBUV/OMI MOD V8 (blue) for (a) Non-Polar using ozone sondes above Western Europe, Japan and coastal Antarc- Global (60°S to 60°N), (b) NH (30°N to 60°N), (c) Tropics (25°S to 25°N), (d) SH (30°S tica (rates of increase range from 1 to 3 ppb per decade), but not at to 60°S) and (e) March NH Polar (60°N to 90°N) and October SH Polar. (Adapted from all levels (Oltmans et al., 2013). In addition, aircraft have measured Weber et al., 2012; see also for abbreviations.) 172 Observations: Atmosphere and Surface Chapter 2 significant upper tropospheric trends in one or more seasons above (a) Jungfraujoch - 46N, 3.6 km the north-eastern USA, the North Atlantic Ocean, Europe, the Middle Zugspitze - 47N, 3.0 km East, northern India, southern China and Japan (Schnadt Poberaj et 60 Arosa - 47N, 1.0 km al., 2009). Insignificant free tropospheric trends were found above the Hohenpeissenberg - 48N, 1.0 km Mid-Atlantic USA (1971 2010) (Oltmans et al., 2013) and in the upper troposphere above the western USA (1975 2001) (Schnadt Poberaj et Europe O3 (ppb) al., 2009). No site or region showed a significant negative trend. 40 In recent decades ozone precursor emissions have decreased in Europe and North America and increased in Asia (Granier et al., 2011), impact- ing ozone production on regional and hemispheric scales (Skeie et al., 20 2011). Accordingly, 1990 2010 surface ozone trends vary regionally. In Europe ozone generally increased through much of the 1990s but since 2000 ozone has either levelled off or decreased at rural and mountain- Mace Head - 55N, 0.2 km Arkona-Zingst - 54N, 0.0 km top sites, as well as for baseline ozone coming ashore at Mace Head, 0 Ireland (Tarasova et al., 2009; Logan et al., 2012; Parrish et al., 2012; 2 Oltmans et al., 2013). In North America surface ozone has increased in (b) Mt. Happo - 36N, 1.9 km eastern and Arctic Canada, but is unchanged in central and western 60 Japanese MBL - 38-45N, 0.1 km Canada (Oltmans et al., 2013). Surface ozone has increased in baseline air masses coming ashore along the west coast of the USA (Parrish et al., 2012) and at half of the rural sites in the western USA during spring O3 (ppb) (Cooper et al., 2012). In the eastern USA surface ozone has decreased 40 Asia strongly in summer, is largely unchanged in spring and has increased North America in winter (Lefohn et al., 2010; Cooper et al., 2012). East Asian surface ozone is generally increasing (Table 2.SM.2) and at downwind sites ozone is increasing at Mauna Loa, Hawaii but decreasing at Minami 20 Tori Shima in the subtropical western North Pacific (Oltmans et al., Lassen NP - 41N, 1.8 km 2013). In the SH ozone has increased at the eight available sites, U.S. Pacific MBL - 38-48N, 0.2 km although trends are insignificant at four sites (Helmig et al., 2007; Olt- mans et al., 2013). 0 Summit, Greenland - 72.6N, 3.2 km Owing to methodological changes, free tropospheric ozone obser- Barrow, Alaska - 71.3N, 0.0 km (c) Storhofdi, Iceland - 63.3N, 1.0 km vations are most reliable since the mid-1990s. Ozone has decreased 60 Mauna Loa, Hawaii - 19.5N, 3.4 km above Europe since 1998 (Logan et al., 2012) and is largely unchanged Samoa - 14.2S, 0.1 km above Japan (Oltmans et al., 2013). Otherwise the remaining regions Cape Point, South Africa - 34.4S, 0.2 km Cape Grim, Tasmania - 40.7S, 0.1 km with measurements (North America, North Pacific Ocean, SH) show a range of positive trends (both significant and insignificant) depending O3 (ppb) 40 on altitude, with no site having a negative trend at any altitude (Table 2.SM.2). Other latitudes In summary, there is medium confidence from limited measurements 20 in the late 19th through mid-20th century that European surface ozone more than doubled by the end of the 20th century. There is medium confidence from more widespread measurements beginning in the Arrival Heights, Antarctica - 77.8S, 0.1 km 1970s that surface ozone has increased at most (non-urban) sites in South Pole- 90.0S, 2.8 km 0 the NH (1 to 5 ppb per decade), while there is low confidence for ozone 1950 1960 1970 1980 1990 2000 2010 increases (2 ppb per decade) in the SH. Since 1990 surface ozone has likely increased in East Asia, while surface ozone in the eastern USA Figure 2.7 | Annual average surface ozone concentrations from regionally representa- tive ozone monitoring sites around the world. (a) Europe. (b) Asia and North America. and Western Europe has levelled off or is decreasing. Ozone monitor- (c) Remote sites in the Northern and Southern Hemispheres. The station name in the ing in the free troposphere since the 1970s is very limited and indicates legend is followed by its latitude and elevation. Time series include data from all times of a weaker rate of increase than at the surface. Satellite instruments day and trend lines are linear regressions following the method of Parrish et al. (2012). can now quantify the present-day tropospheric ozone burden on a Trend lines are fit through the full time series at each location, except for Jungfraujoch, near-global basis; significant tropospheric ozone column increases Zugspitze, Arosa and Hohenpeissenberg where the linear trends end in 2000 (indicated by the dashed vertical line in (a)). Twelve of these 19 sites have significant positive were observed over extended tropical regions of southern Asia, as well ozone trends (i.e., a trend of zero lies outside the 95% confidence interval); the seven as mid-latitude regions of the South and North Pacific Ocean since sites with non-significant trends are: Japanese MBL (marine boundary layer), Summit 1979. (Greenland), Barrow (Alaska), Storhofdi (Iceland), Samoa (tropical South Pacific Ocean), Cape Point (South Africa) and South Pole (Antarctica). 173 Chapter 2 Observations: Atmosphere and Surface 2.2.2.4 Carbon Monoxide, Non-Methane Volatile Organic CE-U.S. Compounds and Nitrogen Dioxide EC-China NC-India Japan Emissions of carbon monoxide (CO), non-methane volatile organic Middle East cont. U.S. compounds (NMVOCs) and NOx (NO + NO2) do not have a direct effect W-Europe on RF, but affect climate indirectly as precursors to tropospheric O3 and aerosol formation, and their impacts on OH concentrations and CH4 lifetime. NMVOCs include aliphatic, aromatic and oxygenated hydrocarbons (e.g., aldehydes, alcohols and organic acids), and have atmospheric lifetimes ranging from hours to months. Global cover- age of NMVOC measurements is poor, except for a few compounds. GOME SCIAMACHY Reports on trends generally indicate declines in a range of NMVOCs 1996 1998 2000 2002 2004 2006 2008 2010 in urban and rural regions of North America and Europe on the order of a few percent to more than 10% yr 1. Global ethane levels reported Figure 2.8 | Relative changes in tropospheric NO2 column amounts (logarithmic scale) by Simpson et al. (2012) declined by about 21% from 1986 to 2010. in seven selected world regions dominated by high NOx emissions. Values are normal- 2 Measurements of air extracted from firn suggest that NMVOC concen- ized for 1996 and derived from the GOME (Global Ozone Monitoring Experiment) instrument from 1996 to 2002 and SCIAMACHY (Scanning Imaging Spectrometer for trations were growing until 1980 and declined afterwards (Aydin et al., Atmospheric Cartography) from 2003 to 2011 (Hilboll et al., 2013). The regions are 2011; Worton et al., 2012). Satellite retrievals of formaldehyde column indicated in the map inset. abundances from 1997 to 2007 show significant positive trends over northeastern China (4% yr 1) and India (1.6% yr 1), possibly related to strong increases in anthropogenic NMVOC emissions, whereas nega- NOx emissions, with Castellanos and Boersma (2012) reporting overall tive trends of about 3% yr 1 are observed over Tokyo, Japan and the increases in global emissions, driven by Asian emission increases of northeast USA urban corridor as a result of pollution regulation (De up to 29% yr 1 (1996 2006), while moderate decreases up to 7% yr 1 Smedt et al., 2010). (1996 2006) are reported for North America and Europe. The major sources of atmospheric CO are in situ production by oxida- In summary, satellite and surface observations of ozone precursor tion of hydrocarbons (mostly CH4 and isoprene) and direct emission gases NOx, CO, and non-methane volatile organic carbons indicate resulting from incomplete combustion of biomass and fossil fuels. An strong regional differences in trends. Most notably, NO2 has likely analysis of MOPITT (Measurements of Pollutants in the Troposphere) decreased by 30 to 50% in Europe and North America and increased and AIRS (Atmospheric Infrared Sounder) satellite data suggest a clear by more than a factor of 2 in Asia since the mid-1990s. and consistent decline of CO columns for 2002 2010 over a number of polluted regions in Europe, North America and Asia with a global 2.2.3 Aerosols trend of about 1% yr 1 (Yurganov et al., 2010; Fortems-Cheiney et al., 2011; Worden et al., 2013). Analysis of satellite data using two more This section assesses trends in aerosol resulting from both anthro- instruments for recent overlapping years shows qualitatively similar pogenic and natural sources. The significance of aerosol changes for decreasing trends (Worden et al., 2013), but the magnitude of trends global dimming and brightening is discussed in Section 2.3. Chapter 7 remains uncertain owing to the presence of instrument biases. Small provides additional discussion of aerosol properties, while Chapter 8 CO decreases observed in the NOAA and AGAGE networks are consist- discusses future RF and the ice-core records that contain information ent with slight declines in global anthropogenic CO emissions over the on aerosol changes prior to the 1980s. Chapter 11 assesses air quali- same time (Supplementary Material 2.SM.2). ty climate change interactions. Because of the short lifetime (days to weeks) of tropospheric aerosol, trends have a strong regional signa- Due to its short atmospheric lifetime (approximately hours), NOx con- ture. Aerosol from anthropogenic sources (i.e., fossil and biofuel burn- centrations are highly variable in time and space. AR4 described the ing) are confined mainly to populated regions in the NH, whereas aer- potential of satellite observations of NO2 to verify and improve NOx osol from natural sources, such as desert dust, sea salt, volcanoes and emission inventories and their trends and reported strong NO2 increas- the biosphere, are important in both hemispheres and likely dependent es by 50% over the industrial areas of China from 1996 to 2004. An on climate and land use change (Carslaw et al., 2010). Owing to inter- extension of this analysis reveals increases between a factor of 1.7 annual variability, long-term trends in aerosols from natural sources and 3.2 over parts of China, while over Europe and the USA NO2 has are more difficult to identify (Mahowald et al., 2010). decreased by 30 to 50% between 1996 and 2010 (Hilboll et al., 2013). 2.2.3.1 Aerosol Optical Depth from Remote Sensing Figure 2.8 shows the changes relative to 1996 in satellite-derived trop- ospheric NO2 columns, with a strong upward trend over central eastern AOD is a measure of the integrated columnar aerosols load and is an China and an overall downward trend in Japan, Europe and the USA. important parameter for evaluating aerosol radiation interactions. NO2 reductions in the USA are very pronounced after 2004, related to AR4 described early attempts to retrieve AOD from satellites but did differences in effectiveness of NOx emission abatements in the USA and not provide estimates of temporal changes in tropospheric aerosol. also to changes in atmospheric chemistry of NOx (Russell et al., 2010). Little high-accuracy information on AOD changes exists prior to 1995. Increasingly, satellite data are used to derive trends in anthropogenic Better satellite sensors and ground-based sun-photometer networks, 174 Observations: Atmosphere and Surface Chapter 2 along with improved retrieval methods and methodological intercom- Aerosol products from dedicated satellite sensors complement sur- parisons, allow assessment of regional AOD trends since about 1995. face-based AOD with better spatial coverage. The quality of the satel- lite-derived AOD strongly depends on the retrieval s ability to remove AOD sun photometer measurements at two stations in northern Ger- scenes contaminated by clouds and to accurately account for reflectivi- many, with limited regional representativity, suggest a long-term ty at the Earth s surface. Due to relatively weak reflectance of incoming decline of AOD in Europe since 1986 (Ruckstuhl et al., 2008). Ground- sunlight by the sea surface, the typical accuracy of retrieved AOD over based, cloud-screened solar broadband radiometer measurements oceans (uncertainty of 0.03 +0.05*AOD; Kahn et al. (2007)) is usually provide longer time-records than spectrally selective sun-photometer better than over continents (uncertainty of 0.05 +0.15*AOD, Levy et data, but are less specific for aerosol retrieval. Multi-decadal records al. (2010)). over Japan (Kudo et al., 2011) indicate an AOD increase until the mid- 1980s, followed by an AOD decrease until the late 1990s and almost Satellite-based AOD trends at 550 nm over oceans from conservatively constant AOD in the 2000s. Similar broad-band solar radiative flux cloud-screened MODIS data (Zhang and Reid, 2010) for 2000 2009 are multi-decadal trends have been observed for urban industrial regions presented in Figure 2.9. Strongly positive AOD trends were observed of Europe and North America (Wild et al., 2005), and were linked to over the oceans adjacent to southern and eastern Asia. Positive AOD successful measures to reduce sulphate (precursor) emissions since the trends are also observed over most tropical oceans. The negative mid-1980s (Section 2.3). An indirect method to estimate AOD is offered MODIS AOD trends observed over coastal regions of Europe and near 2 by ground-based visibility observations. These data are more ambigu- the east coast of the USA are in agreement with sun photometer obser- ous to interpret, but records go further back in time than broadband, vations and in situ measurements (Section 2.2.3.2) of aerosol mass sun photometer and satellite data. A multi-regional analysis for 1973 in these regions. These regional changes over oceans are consistent 2007 (Wang et al., 2009a) shows that prior to the 1990s visibility-de- with analyses of AVHRR (Advanced Very High Resolution Radiometer) rived AOD was relatively constant in most regions analysed (except for trends for 1981 2005 (Mishchenko et al., 2007; Cermak et al., 2010; positive trends in southern Asia), but after 1990 positive AOD trends Zhao et al., 2011), except over the Southern Ocean (45°S to 60°S), were observed over Asia, and parts of South America, Australia and where negative AOD trends of AVHRR retrievals are neither confirmed Africa, and mostly negative AOD trends were found over Europe. In by MODIS after 2001 (Zhang and Reid, 2010) nor by ATSR-2 (Along North America, a small stepwise decrease of visibility after 1993 was Track Scanning Radiometer) for 1995 2001 (Thomas et al., 2010). likely related to methodological changes (Wang et al., 2012f). Satellite-based AOD changes for both land and oceans (Figure 2.9b) AOD can be determined most accurately with sun photometers that were examined with re-processed SeaWiFS (Sea-viewing Wide Field- measure direct solar intensity in the absence of cloud interferences of-view Sensor) AOD data for 1998 2010 (Hsu et al., 2012). A small with an absolute uncertainty of single measurements of +/- 0.01% positive global average AOD trend is reported, which is likely influ- (Holben et al., 1998). AERONET (AErosol RObotic NETwork) is a global enced by interannual natural aerosol emissions variability (e.g., related sun photometer network (Holben et al., 1998), with densest coverage to ENSO or North Atlantic Oscillation (NAO); Box 2.5), and compen- over Europe and North America. AERONET AOD temporal trends were sating larger positive and negative regional AOD trends. In addition, examined in independent studies (de Meij et al., 2012; Hsu et al., 2012; temporal changes in aerosol composition are ignored in the retrieval Yoon et al., 2012), using different data selection and statistical meth- algorithms, giving more uncertain trends than suggested by statisti- ods. Hsu et al. (2012) investigated AOD trends at 12 AERONET sites cal analysis alone (Mishchenko et al., 2012). Thus, confidence is low with data coverage of at least 10 years between 1997 and 2010. Yoon for global satellite derived AOD trends over these relatively short time et al. (2012) investigated AOD and size trends at 14 AERONET sites periods. with data coverage varying between 4 and 12 years between 1997 and 2009. DeMeij et al. (2012) investigated AOD trends between 2000 The sign and magnitude of SeaWiFS regional AOD trends over conti- and 2009 (550 nm; monthly data) at 62 AERONET sites mostly located nents are in agreement with most AOD trends by ground-based sun in USA and Europe. Each of these studies noted an increase in AOD photometer data (see above) and with MODIS trends (Figure 2.9). The over East Asia and reductions in North America and Europe. The only strong positive AOD trend over the Arabian Peninsula occurs mainly dense sun photometer network over southern Asia, ARFINET (Aerosol during spring (MAM) and summer (JJA), during times of dust transport, Radiative Forcing over India NETwork), shows an increase in AOD of and is also visible in MODIS data (Figure 2.9). The positive AOD trend about 2% yr 1 during the last one to two decades (Krishna Moorthy et over southern and eastern Asia is strongest during the dry seasons (DJF, al., 2013), with an absolute uncertainty of +/- 0.02 at 500 nm (Krishna MAM), when reduced wet deposition allows anthropogenic aerosol to Moorthy et al., 2007). In contrast, negative AOD trends are identified at accumulate in the troposphere. AOD over the Saharan outflow region more than 80% of examined European and North American AERONET off western Africa displays the strongest seasonal AOD trend differ- sites (de Meij et al., 2012). Decreasing AOD is also observed near the ences, with AOD increases only in spring, but strong AOD decreases west coast of northern Africa, where aerosol loads are dominated by during the other seasons. SeaWifs AOD decreases over Europe and the Saharan dust outflow. Positive AOD trends are found over the Arabi- USA and increases over southern and eastern Asia (especially during an Peninsula, where aerosol is dominated by dust. Inconsistent AOD the dry season) are in agreement with reported temporal trends in trends reported for stations in central Africa result from the use of rela- anthropogenic emissions, and surface observations (Section 2.2.3.2). tively short time series with respect to the large interannual variability caused by wildfires and dust emissions. In summary, based on satellite- and surface-based remote sensing it is very likely that AOD has decreased over Europe and the eastern 175 Chapter 2 Observations: Atmosphere and Surface (a) -1.2 -0.4 0 0.4 0.8 1.2 1.6 Aerosol Optical Depth trend x 100 (yr -1) 2 (b) -0.04 -0.03 -0.02 -0.01 0 0.01 0.02 0.03 0.04 Aerosol Optical Depth trend (yr -1) Figure 2.9 | (a) Annual average aerosol optical depth (AOD) trends at 0.55 m for 2000 2009, based on de-seasonalized, conservatively cloud-screened MODIS aerosol data over oceans (Zhang and Reid, 2010). Negative AOD trends off Mexico are due to enhanced volcanic activity at the beginning of the record. Most non-zero trends are significant (i.e., a trend of zero lies outside the 95% confidence interval). (b) Seasonal average AOD trends at 0.55 m for 1998 2010 using SeaWiFS data (Hsu et al., 2012). White areas indicate incomplete or missing data. Black dots indicate significant trends (i.e., a trend of zero lies outside the 95% confidence interval). USA since the mid 1990s and increased over eastern and southern with aerodynamic diameters <10 and <2.5 m, respectively), sulphate Asia since 2000. In the 2000s dust-related AOD has been increasing and equivalent black carbon/elemental carbon, from regionally repre- over the Arabian Peninsula and decreasing over the North Atlantic sentative measurement networks. An overview of current networks Ocean. Aerosol trends over other regions are less strong or not signifi- and definitions pertinent to aerosol measurements is given in Sup- cant during this period owing to relative strong interannual variability. plementary Material 2.SM.2.3. Studies reporting trends representa- Overall, confidence in satellite-based global average AOD trends is low. tive of regional changes are presented in Table 2.2. Long-term data are almost entirely from North America and Europe, whereas a few 2.2.3.2 In Situ Surface Aerosol Measurements individual studies on aerosol trends in India and China are reported in Supplementary Material 2.SM.2.3. Figure 2.10 gives an overview AR4 did not report trends in long-term surface-based in situ meas- of observed PM10, PM2.5, and sulphate trends in North America and urements of particulate matter, its components or its properties. This Europe for 1990 2009 and 2000 2009. s ­ ection summarizes reported trends of PM10, PM2.5 (particulate matter 176 Observations: Atmosphere and Surface Chapter 2 (a) PM10, 2000-2009 Trend (% yr-1) (b) PM2.5, 2000-2009 (d) PM2.5, 1990-2009 2 (c) Sulphate, 2000-2009 (e) Sulphate, 1990-2009 Figure 2.10 | Trends in particulate matter (PM10 and PM2.5 with aerodynamic diameters <10 and <2.5 m, respectively) and sulphate in Europe and USA for two overlapping periods 2000 2009 (a, b, c) and 1990 2009 (d, e). The trends are based on measurements from the EMEP (Torseth et al., 2012) and IMPROVE (Hand et al., 2011) networks in Europe and USA, respectively. Sites with significant trends (i.e., a trend of zero lies outside the 95% confidence interval) are shown in colour; black dots indicate sites with non- significant trends. In Europe, strong downward trends are observed for PM10, PM2.5 and and 2.1% yr 1 at all sites, and PM10 decreases of 3.1% yr 1 for 2000 sulphate from the rural stations in the EMEP (European Monitoring 2009. Declines of PM2.5 and SO42 in Canada are very similar (Hidy and and Evaluation Programme) network. For 2000 2009, PM2.5 shows Pennell, 2010), with annual mean PM2.5 at urban measurement sites an average reduction of 3.9% yr 1 for the six stations with significant decreasing by 3.6% yr 1 during 1985 2006 (Canada, 2012). trends, while trends are not significant at seven other stations. Over 2000 2009, PM10 at 12 (out of 24) sites shows significant downward In the eastern and southwestern USA, IMPROVE data show strong sul- trend of on average 2.6% yr 1. Similarly sulphate strongly decreased phate declines, which range from 2 to 6% yr 1, with an average of at 3.1% yr 1 from 1990 to 2009 with 26 of 30 sites having signifi- 2.3% yr 1 for the sites with significant negative trends for 1990 2009. cant reductions. The largest decrease occurred before 2000, while for However, four IMPROVE sites show strong SO42 increases from 2000 2000 2009, the trends were weaker and less robust. This is consistent to 2009, amounting to 11.9% yr 1, at Hawaii (1225 m above sea level), with reported emission reductions of 65% from 1990 to 2000 and 28% and 4 to 7% yr 1 at three sites in southwest Alaska. from 2001 to 2009 (Yttri et al., 2011; Torseth et al., 2012). Model anal- ysis (Pozzoli et al., 2011) attributed the trends in large part to emission A recent study on long-term trends in aerosol optical properties from changes. 24 globally distributed background sites (Collaud Coen et al., 2013) reported statistically significant trends at 16 locations, but the sign and In the USA, the largest reductions in PM and sulphate are observed in magnitude of the trends varied largely with the aerosol property con- the 2000s, rather than the 1990s as in Europe. IMPROVE (U.S. Inter- sidered and geographical region (Table 2.3). Among the sites, this study agency Monitoring of Protected Visual Environments Network) PM2.5 reported strong increases in absorption and scattering coefficients in measurements (Hand et al., 2011) show significant downward trends the free troposphere at Mauna Loa, Hawaii (3400 m above sea level), averaging 4.0% yr 1 for 2000 2009 at sites with significant trends, which is a regional feature also evident in the satellite-based AOD ­ 177 Chapter 2 Observations: Atmosphere and Surface Table 2.2 | Trend estimates for various aerosol variables reported in the literature, using data sets with at least 10 years of measurements. Unless otherwise noted, trends of indi- vidual stations were reported in % yr 1, and 95% confidence intervals. The standard deviation (in parentheses) is determined from the individual trends of a set of regional stations. Aerosol Trend, % yr 1 (1, Period Reference Comments variable standard deviation) Europe 2.9 (1.31) (Adapted from Torseth et al., 2012) 13 sites available, 6 sites show statistically significant results. Average change was PM2.5 3.9 (0.87)b 2000 2009 Regional background sites 0.37 and 0.52b mg m 3 yr 1. 1.9 (1.43) 24 sites available, 12 sites show statistically significant results. Average change was PM10 2000 2009 2.6 (1.19)b 0.29 and 0.40b mg m 3 yr 1 . 3.0 (0.82) 30 sites available, 26 sites show statistically significant results. Average change was SO42 1990 2009 3.1 (0.72)b 0.04 and 0.04b mg m 3 yr 1. 1.5 (1.41) 30 sites available, 10 sites show statistically significant results. Average change was SO42 2000 2009 2.0 (1.8)b 0.01 and 0.01b mg m 3 yr 1. (Barmpadimos et al., 2012) 10 sites in Switzerland. The trend is adjusted for change in meteorology unadjusted PM10 1.9 1991 2008 Rural and urban sites data did not differ strongly. The average change was 0.51 mg m 3 yr 1. 2 USA 2.1 (2.08) Adapted from (Hand et al., 2011) 153 sites available, 52 sites show statistically significant negative results. Only 1 site PM2.5 2000 2009 4.0 (1.01)b Regional background sites shows statisticallly positive trend. 1.5 (1.25) PM2.5 1990 2009 153 sites available, 39 sites show statistically significant results. 2.1 (0.97)b 1.7 (2.00) PM10 2000 2009 154 sites available, 37 sites show statistically significant results. 3.1 (1.65)b 3.0 (2.86) 154 sites available, 83 sites show statistically significant negative results. 4 sites SO42 2000 2009 showed statistical positive trend. 3.0 (0.62)b 2.0 (1.07) SO42 1990 2009 103 sites available, 41 sites show statistically significant results. 2.3 (0.85)b (Hand et al., 2011) The trend interval includes about 50 sites mainly located along the East and West Total Carbon 2.5 to 7.5 1989 2008 Regional background sites Coasts of the USA; fewer sites were situated in the central part of the continent. Arctic EBCa 3.8 (0.7)c 1989 2008 (Hirdman et al., 2010) Alert, Canada 62.3°W 82.5°N SO42 3.0 (0.6) c 1985 2006 EBCa Not sig. c 1998 2008 Barrow, Alaska, 156.6°W 71.3° N SO4 2 Not sig. c 1997 2008 EBCa 9.0 (5.0) c 2002 2009 Zeppelin, Svalbard, 11.9°E 78.9° N SO4 2 1.9 (1.7) c 1990 2008 Notes: a Equivalent black carbon. b Trend numbers indicated refer to the subset of stations with significant changes over time generally in regions strongly influenced by anthropogenic emissions (Figure 2.10). Trend values significant at 1% level. c trends (illustrated in Figure 2.9). Possible explanations for these chang- large interannual variability. Collaud Coen et al. (2013) reported con- es include the influence of increasing Asian emissions and changes sistent negative trends in the aerosol absorption coefficient at stations in clouds and removal processes. More and longer Asian time series, in the continental USA, Arctic and Antarctica, but mostly insignificant coupled with transport analyses, are needed to corroborate these find- trends in Europe over the last decade. ings. Aerosol number concentrations (Asmi et al., 2013) are declining significantly at most sites in Europe, North America, the Pacific and the In the Arctic, changes in aerosol impact the atmosphere s radiative bal- Caribbean, but increasing at South Pole based on a study of 17 globally ance as well as snow and ice albedo. Similar to Europe and the USA, distributed remote sites. Hirdman et al. (2010) reported downward trends in equivalent black carbon and SO42 for two out of total three Arctic stations and attribut- Total carbon (= light absorbing carbon + organic carbon) measure- ed them to emission changes. ments indicate highly significant downward trends between 2.5 and 7.5% yr 1 along the east and west coasts of the USA, and smaller and In summary, declining AOD in Europe and North America is corrobo- less significant trends in other regions of the USA from 1989 to 2008 rated by very likely downward trends in ground-based in situ particu- (Hand et al., 2011; Murphy et al., 2011). late matter measurements since the mid-1980s. Robust evidence from around 200 regional background sites with in situ ground based aer- In Europe, Torseth et al. (2012) suggest a slight reduction in elemental osol measurements indicate downward trends in the last two decades carbon concentrations at two stations from 2001 to 2009, subject to of PM2.5 in parts of Europe (2 to 6% yr 1) and the USA (1 to 2.5% yr 1), 178 Observations: Atmosphere and Surface Chapter 2 Table 2.3 | Summary table of aerosol optical property trends reported in the literature, using data sets with at least 10 years of measurements. Otherwise as in Table 2.2. Trend, % yr 1(1, Region Period Reference Comments standard deviation) Scattering coefficient +0.6 (1.9) Europe (4/1) +2.7a 2.0 (2.5) Trend study including 24 regional background sites with more than 10 USA (14/10) 2.9 (2.4)a Adapted from (Collaud Coen et al., years of observations. Regional averages for last 10 years are included 2001 2010 2013) Regional background sites here. Values in parenthesis show total number of sites/number of sites with Mauna Loa (1/1) +2.7 significant trend. Arctic (1/0) +2.4 Antarctica (1/0) +2.5 Absorption coefficient Europe (3/0) +0.3 (0.4) USA (1/1) 2.0 Trend study of aerosol optical properties including 24 regional background Mauna Loa (1/1) +9.0 2001 2010 Adapted from (Collaud Coen et al., sites with more than 10 years of observations. Regional averages for last 2 2013) Regional background sites 10 years are included here. Values in parenthesis show total number of Arctic (1/1) 6.5 sites and number of sites with significant trend. Antarctica (1/1) 0.1 Particle number concentration 0.9 (1.8) Europe (4/2) 2.3 (1.0)a North America and 5.3 (2.8) Trend study of particle number concentration (N) and size distribution Caribbean (3/3) 6.6 (1.1)a including 17 regional background sites. Regional averages of particle Adapted from (Asmi et al., 2013) 2001 2010 number concentration for last 10 years are included here. Values in Regional background sites Mauna Loa (1/1) 3.5 parentheses show total number of sites and number of sites with significant trend. Arctic (1/0) 1.3 Antarctica (2/2) +2.7 (1.4) Notes: a Trend numbers indicated refer to the subset of stations with significant changes over time generally in regions strongly influenced by anthropogenic emissions (Figure 2.10). Box 2.2 | Quantifying Changes in the Mean: Trend Models and Estimation Many statistical methods exist for estimating trends in environmental time series (see Chandler and Scott, 2011 for a review). The assessment of long-term changes in historical climate data requires trend models that are transparent and robust, and that can provide credible uncertainty estimates. Linear Trends Historical climate trends are frequently described and quantified by estimating the linear component of the change over time (e.g., AR4). Such linear trend modelling has broad acceptance and understanding based on its frequent and widespread use in the published research assessed in this report, and its strengths and weaknesses are well known (von Storch and Zwiers, 1999; Wilks, 2006). Chal- lenges exist in assessing the uncertainty in the trend and its dependence on the assumptions about the sampling distribution (Gaussian or otherwise), uncertainty in the data, dependency models for the residuals about the trend line, and treating their serial correlation (Von Storch, 1999; Santer et al., 2008). The quantification and visualization of temporal changes are assessed in this chapter using a linear trend model that allows for first- order autocorrelation in the residuals (Santer et al., 2008; Supplementary Material 2.SM.3). Trend slopes in such a model are the same as ordinary least squares trends; uncertainties are computed using an approximate method. The 90% confidence interval quoted is solely that arising from sampling uncertainty in estimating the trend. Structural uncertainties, to the extent sampled, are apparent from the range of estimates from different data sets. Parametric and other remaining uncertainties (Box 2.1), for which estimates are provided with some data sets, are not included in the trend estimates shown here, so that the same method can be applied to all data sets considered. Nonlinear Trends There is no a priori physical reason why the long-term trend in climate variables should be linear in time. Climatic time series often have trends for which a straight line is not a good approximation (e.g., Seidel and Lanzante, 2004). The residuals from a linear fit in time often do not follow a simple autoregressive or moving average process, and linear trend estimates can easily change when recalculated (continued on next page) 179 Chapter 2 Observations: Atmosphere and Surface Box 2.2 (continued) for shorter or longer time periods or when new data are added. When linear trends for two parts of a longer time 0.6 (a) series are calculated separately, the trends calculated for two 0.4 shorter periods may be very different (even in sign) from the trend in the full period, if the time series exhibits significant 0.2 nonlinear behavior in time (Box 2.2, Table 1). 0.0 Temperature anomaly (C) Many methods have been developed for estimating the long- -0.2 term change in a time series without assuming that the change is linear in time (e.g., Wu et al., 2007; Craigmile and Guttorp, -0.4 2011). Box 2.2, Figure 1 shows the linear least squares and -0.6 a nonlinear trend fit to the GMST values from the HadCRUT4 2 data set (Section 2.4.3). The nonlinear trend is obtained by 0.6 (b) fitting a smoothing spline trend (Wood, 2006; Scinocca et 0.4 al., 2010) while allowing for first-order autocorrelation in 0.2 the residuals (Supplementary Material 2.SM.3). The results indicate that there are significant departures from linearity 0.0 in the trend estimated this way. -0.2 Box 2.2, Table 1 shows estimates of the change in the GMST -0.4 from the two methods. The methods give similar estimates with 90% confidence intervals that overlap one another. -0.6 Smoothing methods that do not assume the trend is linear 1850 1900 1950 2000 can provide useful information on the structure of change that is not as well treated with linear fits. The linear trend fit Box 2.2, Figure 1 | (a) Global mean surface temperature (GMST) anomalies relative to a 1961 1990 climatology based on HadCRUT4 annual data. The straight black is used in this chapter because it can be applied consistently lines are least squares trends for 1901 2012, 1901 1950 and 1951 2012. (b) Same to all the data sets, is relatively simple, transparent and data as in (a), with smoothing spline (solid curve) and the 90% confidence interval on easily comprehended, and is frequently used in the published the smooth curve (dashed lines). Note that the (strongly overlapping) 90% confidence research assessed here. intervals for the least square lines in (a) are omitted for clarity. See Figure 2.20 for the other two GMST data products. Box 2.2, Table 1 | Estimates of the mean change in global mean surface temperature (GMST) between 1901 and 2012, 1901 and 1950, and 1951 and 2012, obtained from the linear (least squares) and nonlinear (smoothing spline) trend models. Half-widths of the 90% confidence intervals are also provided for the estimated changes from the two trend methods. Trends in °C per decade Method 1901 2012 1901 1950 1951 2012 Least squares 0.075 +/- 0.013 0.107 +/- 0.026 0.106 +/- 0.027 Smoothing spline 0.081 +/- 0.010 0.070 +/- 0.016 0.090 +/- 0.018 and also for SO42 (2 to 5% yr 1). The strongest decreases were in the heating. Spatial and temporal energy imbalances due to radiation and 1990s in Europe and in the 2000s in the USA. There is robust evidence latent heating produce the general circulation of the atmosphere and for downward trends of light absorbing aerosol in the USA and the oceans. Anthropogenic influence on climate occurs primarily through Arctic, while elsewhere in the world in situ time series are lacking or perturbations of the components of the Earth radiation budget. not long enough to reach statistical significance. The radiation budget at the top of the atmosphere (TOA) includes the absorption of solar radiation by the Earth, determined as the difference 2.3 Changes in Radiation Budgets between the incident and reflected solar radiation at the TOA, as well as the thermal outgoing radiation emitted to space. The surface radia- The radiation budget of the Earth is a central element of the climate tion budget takes into account the solar fluxes absorbed at the Earth s system. On average, radiative processes warm the surface and cool the surface, as well as the upward and downward thermal radiative fluxes atmosphere, which is balanced by the hydrological cycle and sensible emitted by the surface and atmosphere, respectively. In view of new 180 Observations: Atmosphere and Surface Chapter 2 observational evidence since AR4, the mean state as well as multi-dec- The estimate for the reflected solar radiation at the TOA in Figure 2.11, adal changes of the surface and TOA radiation budgets are assessed 100 W m 2, is a rounded value based on the CERES Energy Balanced in the following. and Filled (EBAF) satellite data product (Loeb et al., 2009, 2012b) for the period 2001 2010. This data set adjusts the solar and thermal TOA 2.3.1 Global Mean Radiation Budget fluxes within their range of uncertainty to be consistent with inde- pendent estimates of the global heating rate based on in situ ocean Since AR4, knowledge on the magnitude of the radiative energy fluxes observations (Loeb et al., 2012b). This leaves 240 W m 2 of solar radia- in the climate system has improved, requiring an update of the global tion absorbed by the Earth, which is nearly balanced by thermal emis- annual mean energy balance diagram (Figure 2.11). Energy exchanges sion to space of about 239 W m 2 (based on CERES EBAF), considering between Sun, Earth and Space are observed from space-borne plat- a global heat storage of 0.6 W m 2 (imbalance term in Figure 2.11) forms such as the Clouds and the Earth s Radiant Energy System (CERES, based on Argo data from 2005 to 2010 (Hansen et al., 2011; Loeb et Wielicki et al., 1996) and the Solar Radiation and Climate Experiment al., 2012b; Box 3.1). The stated uncertainty in the solar reflected TOA (SORCE, Kopp and Lawrence, 2005) which began data collection in fluxes from CERES due to uncertainty in absolute calibration alone is 2000 and 2003, respectively. The total solar irradiance (TSI) incident at about 2% (2-sigma), or equivalently 2 W m 2 (Loeb et al., 2009). The the TOA is now much better known, with the SORCE Total Irradiance uncertainty of the outgoing thermal flux at the TOA as measured by Monitor (TIM) instrument reporting uncertainties as low as 0.035%, CERES due to calibration is ~3.7 W m 2 (2). In addition to this, there is 2 compared to 0.1% for other TSI instruments (Kopp et al., 2005). During uncertainty in removing the influence of instrument spectral response the 2008 solar minimum, SORCE/TIM observed a solar irradiance of on measured radiance, in radiance-to-flux conversion, and in time 1360.8 +/- 0.5 W m 2 compared to 1365.5 +/- 1.3 W m 2 for instruments space averaging, which adds up to another 1 W m 2 (Loeb et al., 2009). launched prior to SORCE and still operating in 2008 (Section 8.4.1.1). Kopp and Lean (2011) conclude that the SORCE/TIM value of TSI is the The components of the radiation budget at the surface are generally most credible value because it is validated by a National Institute of more uncertain than their counterparts at the TOA because they cannot Standards and Technology calibrated cryogenic radiometer. This revised be directly measured by passive satellite sensors and surface measure- TSI estimate corresponds to a solar irradiance close to 340 W m 2 glob- ments are not always regionally or globally representative. Since AR4, ally averaged over the Earth s sphere (Figure 2.11). new estimates for the downward thermal infrared (IR) radiation at incoming solar reflected thermal outgoing Units (Wm-2) solar TOA TOA TOA 340 100 239 (340, 341) (96, 100) (236, 242) atmospheric 79 window (74, 91) greenhouse solar absorbed latent heat gases atmosphere solar solar down 185 24 reflected (179, 189) (22,26) surface surface 161 84 20 398 342 (154, 166) (70, 85) (15, 25) (394, 400) (338, 348) imbalance 0.6 solar absorbed evapo- sensible thermal thermal (0.2, 1.0) surface ration heat up surface down surface Figure 2.11: | Global mean energy budget under present-day climate conditions. Numbers state magnitudes of the individual energy fluxes in W m 2, adjusted within their uncertainty ranges to close the energy budgets. Numbers in parentheses attached to the energy fluxes cover the range of values in line with observational constraints. (Adapted from Wild et al., 2013.) 181 Chapter 2 Observations: Atmosphere and Surface the surface have been established that incorporate critical information 2.3.2 Changes in Top of the Atmosphere Radiation on cloud base heights from space-borne radar and lidar instruments Budget (L Ecuyer et al., 2008; Stephens et al., 2012a; Kato et al., 2013). In line with studies based on direct surface radiation measurements (Wild et While the previous section emphasized the temporally-averaged state al., 1998, 2013) these studies propose higher values of global mean of the radiation budget, the focus in the following is on the temporal downward thermal radiation than presented in previous IPCC assess- (multi-decadal) changes of its components. Variations in TSI are dis- ments and typically found in climate models, exceeding 340 W m 2 cussed in Section 8.4.1. AR4 reported large changes in tropical TOA (Figure 2.11). This aligns with the downward thermal radiation in the radiation between the 1980s and 1990s based on observations from ERA-Interim and ERA-40 reanalyses (Box 2.3), of 341 and 344 W m 2, the Earth Radiation Budget Satellite (ERBS) (Wielicki et al., 2002; respectively (Berrisford et al., 2011). Estimates of global mean down- Wong et al., 2006). Although the robust nature of the large decadal ward thermal radiation computed as a residual of the other terms of changes in tropical radiation remains to be established, several studies the surface energy budget (Kiehl and Trenberth, 1997; Trenberth et al., have suggested links to changes in atmospheric circulation (Allan and 2009) are lower (324 to 333 W m 2), highlighting remaining uncertain- Slingo, 2002; Chen et al., 2002; Clement and Soden, 2005; Merrifield, ties in estimates of both radiative and non-radiative components of the 2011) (Section 2.7). surface energy budget. 2 Since AR4, CERES enabled the extension of satellite records of TOA Estimates of absorbed solar radiation at the Earth s surface include fluxes into the 2000s (Loeb et al., 2012b). The extended records from considerable uncertainty. Published global mean values inferred from CERES suggest no noticeable trends in either the tropical or global satellite retrievals, reanalyses and climate models range from below radiation budget during the first decade of the 21st century (e.g., 160 W m 2 to above 170 W m 2. Recent studies taking into account Andronova et al., 2009; Harries and Belotti, 2010; Loeb et al., 2012a, surface observations as well as updated spectroscopic parameters 2012b). Comparisons between ERBS/CERES thermal radiation and that and continuum absorption for water vapor favour values towards derived from the NOAA High Resolution Infrared Radiation Sounder the lower bound of this range, near 160 W m 2, and an atmospheric (HIRS) (Lee et al., 2007) show good agreement until approximate- solar absorption around 80 W m 2 (Figure 2.11) (Kim and Ramanathan, ly 1998, corroborating the rise of 0.7 W m 2 between the 1980s and 2008; Trenberth et al., 2009; Kim and Ramanathan, 2012; Trenberth 1990s reported in AR4. Thereafter the HIRS thermal fluxes show much and Fasullo, 2012b; Wild et al., 2013). The ERA-Interim and ERA-40 higher values, likely due to changes in the channels used for HIRS/3 reanalyses further support an atmospheric solar absorption of this instruments launched after October 1998 compared to earlier HIRS magnitude (Berrisford et al., 2011). Latest satellite-derived estimates instruments (Lee et al., 2007). constrained by CERES now also come close to these values (Kato et al., in press). Recent independently derived surface radiation estimates On a global scale, interannual variations in net TOA radiation and favour therefore a global mean surface absorbed solar flux near 160 ocean heating rate (OHR) should correspond, as oceans have a much W m 2 and a downward thermal flux slightly above 340 W m 2, respec- larger effective heat capacity than land and atmosphere, and therefore tively (Figure 2.11). serve as the main reservoir for heat added to the Earth atmosphere system (Box 3.1). Wong et al. (2006) showed that interannual varia- The global mean latent heat flux is required to exceed 80 W m 2 to tions in these two data sources are in good agreement for 1992 2003. close the surface energy balance in Figure 2.11, and comes close to the In the ensuing 5 years, however, Trenberth and Fasullo (2010) note 85 W m 2 considered as upper limit by Trenberth and Fasullo (2012b) that the two diverge with ocean in situ measurements (Levitus et al., in view of current uncertainties in precipitation retrieval in the Global 2009), indicating a decline in OHR, in contrast to expectations from the Precipitation Climatology Project (GPCP, Adler et al., 2012) (the latent observed net TOA radiation. The divergence after 2004 is referred to as heat flux corresponds to the energy equivalent of evaporation, which missing energy by Trenberth and Fasullo (2012b), who further argue globally equals precipitation; thus its magnitude may be constrained that the main sink of the missing energy likely occurs at ocean depths by global precipitation estimates). This upper limit has recently been below 275 m. Loeb et al. (2012b) compared interannual variations in challenged by Stephens et al. (2012b). The emerging debate reflects CERES net radiation with OHRs derived from three independent ocean potential remaining deficiencies in the quantification of the radiative heat content anomaly analyses and included an error analysis of both and non-radiative energy balance components and associated uncer- CERES and the OHRs. They conclude that the apparent decline in OHR tainty ranges, as well as in the consistent representation of the global is not statistically robust and that differences between interannual var- mean energy and water budgets (Stephens et al., 2012b; Trenberth and iations in OHR and satellite net TOA flux are within the uncertainty Fasullo, 2012b; Wild et al., 2013). Relative uncertainty in the globally of the measurements (Figure 2.12). They further note that between averaged sensible heat flux estimate remains high owing to the very January 2001 and December 2012, the Earth has been steadily accu- limited direct observational constraints (Trenberth et al., 2009; Ste- mulating energy at a rate of 0.50 +/- 0.43 W m 2 (90% CI). Hansen et al. phens et al., 2012b). (2011) obtained a similar value for 2005 2010 using an independent analysis of the ocean heat content anomaly data (von Schuckmann In summary, newly available observations from both space-borne and and Le Traon, 2011). The variability in the Earth s energy imbalance is surface-based platforms allow a better quantification of the Global strongly influenced by ocean circulation changes relating to the ENSO Energy Budget, even though notable uncertainties remain, particu- (Box 2.5); during cooler La Nina years (e.g., 2009) less thermal radia- larly in the estimation of the non-radiative surface energy balance tion is emitted and the climate system gains heat while the reverse is ­components. true for warmer El Nino years (e.g., 2010) (Figure 2.12). 182 Observations: Atmosphere and Surface Chapter 2 In summary, satellite records of TOA radiation fluxes have been sub- Since AR4, numerous studies have substantiated the findings of sig- stantially extended since AR4. It is unlikely that significant trends exist nificant decadal SSR changes observed both at worldwide distributed in global and tropical radiation budgets since 2000. Interannual vari- terrestrial sites (Dutton et al., 2006; Wild et al., 2008; Gilgen et al., ability in the Earth s energy imbalance related to ENSO is consistent 2009; Ohmura, 2009; Wild, 2009 and references therein) as well as in with ocean heat content records within observational uncertainty. specific regions. In Europe, Norris and Wild (2007) noted a dimming between 1971 and 1986 of 2.0 to 3.1 W m 2 per decade and subse- 2.3.3 Changes in Surface Radiation Budget quent brightening of 1.1 to 1.4 W m 2 per decade from 1987 to 2002 in a pan-European time series comprising 75 sites. Similar tendencies 2.3.3.1 Surface Solar Radiation were found at sites in northern Europe (Stjern et al., 2009), Estonia (Russak, 2009) and Moscow (Abakumova et al., 2008). Chiacchio and Changes in radiative fluxes at the surface can be traced further back Wild (2010) pointed out that dimming and subsequent brightening in in time than the satellite-based TOA fluxes, although only at selected Europe is seen mainly in spring and summer. Brightening in Europe terrestrial locations where long-term records exist. Monitoring of radi- from the 1980s onward was further documented at sites in Switzer- ative fluxes from land-based stations began on a widespread basis in land, Germany, France, the Benelux, Greece, Eastern Europe and the the mid-20th century, predominantly measuring the downward solar Iberian Peninsula (Ruckstuhl et al., 2008; Wild et al., 2009; Zerefos et component, also known as global radiation or surface solar radiation al., 2009; Sanchez-Lorenzo et al., 2013). Concurrent brightening of 2 2 (SSR). to 8 W m 2 per decade was also noted at continental sites in the USA (Long et al., 2009; Riihimaki et al., 2009; Augustine and Dutton, 2013). AR4 reported on the first indications for substantial decadal changes The general pattern of dimming and consecutive brightening was fur- in observational records of SSR. Specifically, a decline of SSR from the ther found at numerous sites in Japan (Norris and Wild, 2009; Ohmura, beginning of widespread measurements in the 1950s until the mid- 2009; Kudo et al., 2011) and in the SH in New Zealand (Liley, 2009). 1980s has been observed at many land-based sites (popularly known Analyses of observations from sites in China confirmed strong declines as global dimming ; Stanhill and Cohen, 2001; Liepert, 2002), as well in SSR from the 1960s to 1980s on the order of 2 to 8 W m 2 per decade, as a partial recovery from the 1980s onward ( brightening ; Wild et al., which also did not persist in the 1990s (Che et al., 2005; Liang and 2005) (see the longest available SSR series of Stockholm, Sweden, in Xia, 2005; Qian et al., 2006; Shi et al., 2008; Norris and Wild, 2009; Figure 2.13 as an illustrative example). Xia, 2010a). On the other hand, persistent dimming since the mid-20th Figure 2.12 | Comparison of net top of the atmosphere (TOA) flux and upper ocean heating rates (OHRs). Global annual average (July to June) net TOA flux from CERES observa- tions (based on the EBAF-TOA_Ed2.6r product) (black line) and 0 700 (blue) and 0 1800 m (red) OHR from the Pacific Marine Environmental Laboratory/Jet Propulsion Laboratory/ Joint Institute for Marine and Atmospheric Research (PMEL/JPL/JIMAR), 0 700 m OHR from the National Oceanic Data Center (NODC) (green; Levitus et al., 2009), and 0 700 m OHR from the Hadley Center (brown; Palmer et al., 2007). The length of the coloured bars corresponds to the magnitude of OHR. Thin vertical lines are error bars, corresponding to the magnitude of uncertainties. Uncertainties for all annual OHR are given at one standard error derived from ocean heat content anomaly uncertainties (Lyman et al., 2010). CERES net TOA flux uncertainties are given at the 90% confidence level determined following Loeb et al. (2012b). (Adapted from Loeb et al., 2012b.) 183 Chapter 2 Observations: Atmosphere and Surface century with no ­evidence for a trend reversal was noted at sites in India (Wild et al., 2005; Kumari et al., 2007; Kumari and Goswami, 2010; 130 Soni et al., 2012) and in the Canadian Prairie (Cutforth and Judiesch, Radiation (Wm-2) 2007). Updates on latest SSR changes observed since 2000 provide 120 a less coherent picture (Wild, 2012). They suggest a continuation of brightening at sites in Europe, USA, and parts of Asia, a levelling off at 110 sites in Japan and Antarctica, and indications for a renewed dimming 100 in parts of China (Wild et al., 2009; Xia, 2010a). 90 The longest observational SSR records, extending back to the 1920s 1920 1940 1960 1980 2000 and 1930s at a few sites in Europe, further indicate some brightening during the first half of the 20th century, known as early brightening Figure 2.13 | Annual mean Surface Solar Radiation (SSR) as observed at Stockholm, Sweden, from 1923 to 2010. Stockholm has the longest SSR record available world- (cf. Figure 2.13) (Ohmura, 2009; Wild, 2009). This suggests that the wide. (Updated from Wild (2009) and Ohmura (2009).) decline in SSR, at least in Europe, was confined to a period between the 1950s and 1980s. 2 Overall, these proxies provide independent evidence for the existence A number of issues remain, such as the quality and representativeness of large-scale multi-decadal variations in SSR. Specifically, widespread of some of the SSR data as well as the large-scale significance of the observations of declines in pan evaporation from the 1950s to the phenomenon (Wild, 2012). The historic radiation records are of varia- 1980s were related to SSR dimming amongst other factors (Roderick ble quality and rigorous quality control is necessary to avoid spurious and Farquhar, 2002). The observed decline in DTR over global land trends (Dutton et al., 2006; Shi et al., 2008; Gilgen et al., 2009; Tang surfaces from the 1950s to the 1980s (Section 2.4.1.2), and its stabi- et al., 2011; Wang et al., 2012e; Sanchez-Lorenzo et al., 2013). Since lisation thereafter fits to a large-scale dimming and subsequent bright- the mid-1990s, high-quality data are becoming increasingly available ening, respectively (Wild et al., 2007). Widespread brightening after from new sites of the Baseline Surface Radiation Network (BSRN) and 1980 is further supported by reconstructions from sunshine duration Atmospheric Radiation Measurement (ARM) Program, which allow the records (Wang et al., 2012e). Over Europe, SSR dimming and subse- determination of SSR variations with unprecedented accuracy (Ohmura quent brightening is consistent with concurrent declines and increas- et al., 1998). Alpert et al. (2005) and Alpert and Kishcha (2008) argued es in sunshine duration (Sanchez-Lorenzo et al., 2008), evaporation that the observed SSR decline between 1960 and 1990 was larger in in energy limited environments (Teuling et al., 2009), visibility records densely populated than in rural areas. The magnitude of this urbani- (Vautard et al., 2009; Wang et al., 2009b) and DTR (Makowski et al., zation effect in the radiation data is not yet well quantified. Dimming 2009). The early brightening in the 1930s and 1940s seen in a few and brightening is, however, also notable at remote and rural sites European SSR records is in line with corresponding changes in sun- (Dutton et al., 2006; Karnieli et al., 2009; Liley, 2009; Russak, 2009; shine duration and DTR (Sanchez-Lorenzo et al., 2008; Wild, 2009; Wild, 2009; Wang et al., 2012d). Sanchez-Lorenzo and Wild, 2012). In China, the levelling off in SSR in the 1990s after decades of decline coincides with similar tendencies Globally complete satellite estimates have been available since the in the pan evaporation records, sunshine duration and DTR (Liu et al., early 1980s (Hatzianastassiou et al., 2005; Pinker et al., 2005; Hin- 2004a; Liu et al., 2004b; Qian et al., 2006; Ding et al., 2007; Wang et al., kelman et al., 2009). Because satellites do not directly measure the 2012d). Dimming up to the 1980s and subsequent brightening is also surface fluxes, they have to be inferred from measurable TOA signals indicated in a set of 237 sunshine duration records in South America using empirical or physical models to remove atmospheric pertur- (Raichijk, 2011). bations. Available satellite-derived products qualitatively agree on a brightening from the mid-1980s to 2000 averaged globally as well as 2.3.3.2 Surface Thermal and Net Radiation over oceans, on the order of 2 to 3 W m 2 per decade (Hatzianastas- siou et al., 2005; Pinker et al., 2005; Hinkelman et al., 2009). Averaged Thermal radiation, also known as longwave, terrestrial or far-IR radi- over land, however, trends are positive or negative depending on the ation is sensitive to changes in atmospheric GHGs, temperature and respective satellite product (Wild, 2009). Knowledge of the decadal humidity. Long-term measurements of the thermal surface com- variation of aerosol burdens and optical properties, required in satel- ponents as well as surface net radiation are available at far fewer lite retrievals of SSR and considered relevant for dimming/brightening sites than SSR. Downward thermal radiation observations started to particularly over land, is very limited (Section 2.2.3). Extensions of sat- become available during the early 1990s at a limited number of glob- ellite-derived SSR beyond 2000 indicate tendencies towards a renewed ally distributed terrestrial sites. From these records, Wild et al. (2008) dimming at the beginning of the new millennium (Hinkelman et al., determined an overall increase of 2.6 W m 2 per decade over the 1990s, 2009; Hatzianastassiou et al., 2012). in line with model projections and the expectations of an increasing greenhouse effect. Wang and Liang (2009) inferred an increase in Reconstructions of SSR changes from more widely measured mete- downward thermal radiation of 2.2 W m 2 per decade over the period orological variables can help to increase their spatial and temporal 1973 2008 from globally available terrestrial observations of temper- coverage. Multi-decadal SSR changes have been related to observed ature, humidity and cloud fraction. Prata (2008) estimated a slightly changes in sunshine duration, atmospheric visibility, diurnal tempera- lower increase of 1.7 W m 2 per decade for clear sky conditions over ture range (DTR; Section 2.4.1.2) and pan evaporation (Section 2.5.3). the earlier period 1964 1990, based on observed temperature and 184 Observations: Atmosphere and Surface Chapter 2 humidity profiles from globally distributed land-based radiosonde sta- 2.3.3.3 Implications from Observed Changes in Related tions and radiative transfer calculations. Philipona et al. (2004; 2005) Climate Elements and Wacker et al. (2011) noted increasing downward thermal fluxes recorded in the Swiss Alpine Surface Radiation Budget (ASRB) network The observed multi-decadal SSR variations cannot be explained by since the mid-1990s, corroborating an increasing greenhouse effect. changes in TSI, which are an order of magnitude smaller (Willson and For mainland Europe, Philipona et al. (2009) estimated an increase of Mordvinov, 2003). They therefore have to originate from alterations downward thermal radiation of 2.4 to 2.7 W m 2 per decade for the in the transparency of the atmosphere, which depends on the pres- period 1981 2005. ence of clouds, aerosols and radiatively active gases (Kvalevag and Myhre, 2007; Kim and Ramanathan, 2008). Cloud cover changes (Sec- There is limited observational information on changes in surface net tion 2.5.7) effectively modulate SSR on an interannual basis, but their radiation, in large part because measurements of upward fluxes at the contribution to the longer-term SSR trends is ambiguous. Although surface are made at only a few sites and are not spatially representa- cloud cover changes were found to explain the trends in some areas tive. Wild et al. (2004, 2008) inferred a decline in land surface net radi- (e.g., Liley, 2009), this is not always the case, particularly in relatively ation on the order of 2 W m 2 per decade from the 1960s to the 1980s, polluted regions (Qian et al., 2006; Norris and Wild, 2007, 2009; Wild, and an increase at a similar rate from the 1980s to 2000, based on esti- 2009; Kudo et al., 2012). SSR dimming and brightening has also been mated changes of the individual radiative components that constitute observed under cloudless atmospheres at various locations, pointing to 2 the surface net radiation. Philipona et al. (2009) estimated an increase a prominent role of atmospheric aerosols (Wild et al., 2005; Qian et al., in surface net radiation of 1.3 to 2 W m 2 per decade for central Europe 2007; Ruckstuhl et al., 2008; Sanchez-Lorenzo et al., 2009; Wang et al., and the Alps between 1981 and 2005. 2009b; Zerefos et al., 2009). Box 2.3 | Global Atmospheric Reanalyses Dynamical reanalyses are increasingly used for assessing weather and climate phenomena. Given their more frequent use in this assessment compared to AR4, their characteristics are described in more detail here. Reanalyses are distinct from, but complement, more traditional statistical approaches to assessing the raw observations. They aim to produce continuous reconstructions of past atmospheric states that are consistent with all observations as well as with atmospheric physics as represented in a numerical weather prediction model, a process termed data assimilation. Unlike real-world observations, reanalyses are uniform in space and time and provide non-observable variables (e.g., potential vorticity). Several groups are actively pursuing reanalysis development at the global scale, and many of these have produced several generations of reanalyses products (Box 2.3, Table 1). Since the first generation of reanalyses produced in the 1990s, substantial development has taken place. The NASA Modern-Era Retrospective Analysis for Research and Applications (MERRA) and ERA-Interim reanalyses show improved tropical precipitation and hence better represent the global hydrological cycle (Dee et al., 2011b). The NCEP/CFSR reanalysis (continued on next page) Box 2.3, Table 1 | Overview of global dynamical reanalysis data sets (ranked by start year; the period extends to present if no end year is provided). A further description of reanalyses and their technical derivation is given in pp. S33 35 of Blunden et al. (2011). Approximate resolution is calculated as 1000 km * 20/N (with N denoting the spectral truncation, Laprise, 1992). Approximate Resolution Institution Reanalysis Period Reference at Equator Cooperative Institute for Research in Environmental Sciences (CIRES), 20th Century Reanalysis, 1871 2010 320 km Compo et al. (2011) National Oceanic and Atmospheric Administration (NOAA), USA Vers. 2 (20CR) National Centers for Environmental Prediction (NCEP) and National NCEP/NCAR R1 (NNR) 1948 320 km Kistler et al. (2001) Center for Atmospheric Research (NCAR), USA European Centre for Medium-Range Weather Forecasts (ECMWF) ERA-40 1957 2002 125 km Uppala et al. (2005) Japan Meteorological Agency (JMA) JRA-55 1958 60 km Ebita et al. (2011) National Centers for Environmental Prediction (NCEP), US Department NCEP/DOE R2 1979 320 km Kanamitsu et al. (2002) of Energy, USA Japan Meteorological Agency (JMA) JRA-25 1979 190 km Onogi et al. (2007) National Aeronautics and Space Administration (NASA), USA MERRA 1979 75 km Rienecker et al. (2011) European Centre for Medium-Range Weather Forecasts (ECMWF) ERA-Interim 1979 80 km Dee et al. (2011b) National Centers for Environmental Prediction (NCEP), USA CFSR 1979 50 km Saha et al. (2010) 185 Chapter 2 Observations: Atmosphere and Surface Box 2.3 (continued) uses a coupled ocean atmosphere land-sea ice model (Saha et al., 2010). The 20th Century Reanalyses (20CR, Compo et al., 2011) is a 56-member ensemble and covers 140 years by assimilating only surface and sea level pressure (SLP) information. This variety of groups and approaches provides some indication of the robustness of reanalyses when compared. In addition to the global reanalyses, several regional reanalyses exist or are currently being produced. Reanalyses products provide invaluable information on time scales ranging from daily to interannual variability. However, they may often be unable to characterize long-term trends (Trenberth et al., 2011). Although reanalyses projects by definition use a frozen assimilation system, there are many other sources of potential errors. In addition to model biases, changes in the observational systems (e.g., coverage, introduction of satellite data) and time-dependent errors in the underlying observations or in the boundary conditions lead to step changes in time, even in latest generation reanalyses (Bosilovich et al., 2011). Errors of this sort were ubiquitous in early generation reanalyses and rendered them of limited value for trend characterization (Thorne 2 and Vose, 2010). Subsequent products have improved and uncertainties are better understood (Dee et al., 2011a), but artefacts are still present. As a consequence, trend adequacy depends on the variable under consideration, the time period and the region of inter- est. For example, surface air temperature and humidity trends over land in the ERA-Interim reanalysis compare well with observations (Simmons et al., 2010), but polar tropospheric temperature trends in ERA-40 disagree with trends derived from radiosonde and satel- lite observations (Bitz and Fu, 2008; Grant et al., 2008; Graversen et al., 2008; Thorne, 2008; Screen and Simmonds, 2011) owing to problems that were resolved in ERA-Interim (Dee et al., 2011a). Studies based on reanalyses are used cautiously in AR5 and known inadequacies are pointed out and referenced. Later generation reanalyses are preferred where possible; however, literature based on these new products is still sparse. Aerosols can directly attenuate SSR by scattering and absorbing solar Reanalyses and observationally based methods have been used to radiation, or indirectly, through their ability to act as cloud condensa- show that increased atmospheric moisture with warming (Willett et tion nuclei, thereby changing cloud reflectivity and lifetime (Chapter al., 2008; Section 2.5) enhances thermal radiative emission of the 7). SSR dimming and brightening is often reconcilable with trends in atmosphere to the surface, leading to reduced net thermal cooling of anthropogenic emission histories and atmospheric aerosol loadings the surface (Prata, 2008; Allan, 2009; Philipona et al., 2009; Wang and (Stern, 2006; Streets et al., 2006; Mishchenko et al., 2007; Ruckstuhl et Liang, 2009). al., 2008; Ohvril et al., 2009; Russak, 2009; Streets et al., 2009; Cermak et al., 2010; Wild, 2012). Recent trends in aerosol optical depth derived In summary, the evidence for widespread multi-decadal variations in from satellites indicate a decline in Europe since 2000 (Section 2.2.3), solar radiation incident on land surfaces has been substantiated since in line with evidence from SSR observations. However, direct aerosol AR4, with many of the observational records showing a decline from effects alone may not be able to account for the full extent of the the 1950s to the 1980s ( dimming ), and a partial recovery thereafter observed SSR changes in remote regions with low pollution levels ( brightening ). Confidence in these changes is high in regions with (Dutton and Bodhaine, 2001; Schwartz, 2005). Aerosol indirect effects high station densities such as over Europe and parts of Asia. These have not yet been well quantified, but have the potential to amplify likely changes are generally supported by observed changes in related, aerosol-induced SSR trends, particularly in relatively pristine environ- but more widely measured variables, such as sunshine duration, DTR ments, such as over oceans (Wild, 2012). and hydrological quantities, and are often in line with aerosol emission patterns. Over some remote land areas and over the oceans, confi- SSR trends are also qualitatively in line with observed multi-decadal dence is low owing to the lack of direct observations, which hamper a surface warming trends (Chapter 10), with generally smaller warm- truly global assessment. Satellite-derived SSR fluxes support the exist- ing rates during phases of declining SSR, and larger warming rates ence of brightening also over oceans, but are less consistent over land in phases of increasing SSR (Wild et al., 2007). This is seen more pro- surface where direct aerosol effects become more important. There are nounced for the relatively polluted NH than the more pristine SH (Wild, also indications for increasing downward thermal and net radiation 2012). For Europe, Vautard et al. (2009) found that a decline in the fre- at terrestrial stations since the early 1990s with medium confidence. quency of low-visibility conditions such as fog, mist and haze over the past 30 years and associated SSR increase may be responsible for 10 to 20% of Europe s recent daytime warming, and 50% of Eastern Euro- pean warming. Philipona (2012) noted that both warming and bright- ening are weaker in the European Alps compared to the surrounding lowlands with stronger aerosol declines since 1981. 186 Observations: Atmosphere and Surface Chapter 2 2.4 Changes in Temperature 1.0 GHCN CRUTEM Berkeley GISS Temperature anomaly (C) 2.4.1 Land Surface Air Temperature 0.5 2.4.1.1 Large-Scale Records and Their Uncertainties 0.0 AR4 concluded global land-surface air temperature (LSAT) had increased over the instrumental period of record, with the warming -0.5 rate approximately double that reported over the oceans since 1979. Since AR4, substantial developments have occurred including the pro- duction of revised data sets, more digital data records, and new data -1.0 set efforts. These innovations have improved understanding of data 1850 1900 1950 2000 issues and uncertainties, allowing better quantification of regional Figure 2.14 | Global annual average land-surface air temperature (LSAT) anomalies changes. This reinforces confidence in the reported globally averaged relative to a 1961 1990 climatology from the latest versions of four different data sets LSAT time series behaviour. (Berkeley, CRUTEM, GHCN and GISS). 2 Global Historical Climatology Network Version 3 (GHCNv3) incorpo- rates many improvements (Lawrimore et al., 2011) but was found to Particular controversy since AR4 has surrounded the LSAT record over be virtually indistinguishable at the global mean from version 2 (used the United States, focussed on siting quality of stations in the US His- in AR4). Goddard Institute of Space Studies (GISS) continues to provide torical Climatology Network (USHCN) and implications for long-term an estimate based upon primarily GHCN, accounting for urban impacts trends. Most sites exhibit poor current siting as assessed against offi- through nightlights adjustments (Hansen et al., 2010). CRUTEM4 cial WMO siting guidance, and may be expected to suffer potentially (Jones et al., 2012) incorporates additional station series and also large siting-induced absolute biases (Fall et al., 2011). However, overall newly homogenized versions of many individual station records. A new biases for the network since the 1980s are likely dominated by instru- data product from a group based predominantly at Berkeley (Rohde ment type (owing to replacement of Stevenson screens with maximum et al., 2013a) uses a method that is substantially distinct from ear- minimum temperature systems (MMTS) in the 1980s at the majori- lier efforts (further details on all the data sets and data availability ty of sites), rather than siting biases (Menne et al., 2010; Williams et are given in Supplementary Material 2.SM.4). Despite the range of al., 2012). A new automated homogeneity assessment approach (also approaches, the long-term variations and trends broadly agree among used in GHCNv3, Menne and Williams, 2009) was developed that has these various LSAT estimates, particularly after 1900. Global LSAT has been shown to perform as well or better than other contemporary increased (Figure 2.14, Table 2.4). approaches (Venema et al., 2012). This homogenization procedure likely removes much of the bias related to the network-wide changes Since AR4, various theoretical challenges have been raised over the in the 1980s (Menne et al., 2010; Fall et al., 2011; Williams et al., 2012). verity of global LSAT records (Pielke et al., 2007). Globally, sam- Williams et al. (2012) produced an ensemble of data set realizations pling and methodological independence has been assessed through using perturbed settings of this procedure and concluded through sub-sampling (Parker et al., 2009; Jones et al., 2012), creation of an assessment against plausible test cases that there existed a propensity entirely new and structurally distinct product (Rohde et al., 2013b) and to under-estimate adjustments. This propensity is critically dependent a complete reprocessing of GHCN (Lawrimore et al., 2011). None of upon the (unknown) nature of the inhomogeneities in the raw data these yielded more than minor perturbations to the global LSAT records records. Their homogenization increases both minimum temperature since 1900. Willett et al. (2008) and Peterson et al. (2011) explicitly and maximum temperature centennial-time-scale USA average LSAT showed that changes in specific and relative humidity (Section 2.5.5) trends. Since 1979 these adjusted data agree with a range of reanalysis were physically consistent with reported temperature trends, a result products whereas the raw records do not (Fall et al., 2010; Vose et al., replicated in the ERA reanalyses (Simmons et al., 2010). Various inves- 2012a). tigators (Onogi et al., 2007; Simmons et al., 2010; Parker, 2011; Vose et al., 2012a) showed that LSAT estimates from modern reanalyses were Regional analyses of LSAT have not been limited to the United States. in quantitative agreement with observed products. Various national and regional studies have undertaken assessments for Europe (Winkler, 2009; Bohm et al., 2010; Tietavainen et al., 2010; van Table 2.4: | Trend estimates and 90% confidence intervals (Box 2.2) for LSAT global average values over five common periods. Trends in °C per decade Data Set 1880 2012 1901 2012 1901 1950 1951 2012 1979 2012 CRUTEM4.1.1.0 (Jones et al., 2012) 0.086 +/- 0.015 0.095 +/- 0.020 0.097 +/- 0.029 0.175 +/- 0.037 0.254 +/- 0.050 GHCNv3.2.0 (Lawrimore et al., 2011) 0.094 +/- 0.016 0.107 +/- 0.020 0.100 +/- 0.033 0.197 +/- 0.031 0.273 +/- 0.047 GISS (Hansen et al., 2010) 0.095 +/- 0.015 0.099 +/- 0.020 0.098 +/- 0.032 0.188 +/- 0.032 0.267 +/- 0.054 Berkeley (Rohde et al., 2013) 0.094 +/- 0.013 0.101 +/- 0.017 0.111 +/- 0.034 0.175 +/- 0.029 0.254 +/- 0.049 187 Chapter 2 Observations: Atmosphere and Surface der Schrier et al., 2011), China (Li et al., 2009; Zhen and Zhong-Wei, using HadEX2 (Section 2.6) find significant decreasing DTR trends 2009; Li et al., 2010a; Tang et al., 2010), India (Jain and Kumar, 2012), in more than half of the land areas assessed but less than 10% of Australia (Trewin, 2012), Canada (Vincent et al., 2012), South America, land with significant increases since 1951. Available trend estimates (Falvey and Garreaud, 2009) and East Africa (Christy et al., 2009). These ( 0.04 +/- 0.01°C per decade over 1950 2011 (Rohde et al., 2013b) analyses have used a range of methodologies and, in many cases, more and 0.066°C per decade over 1950 2004 (Vose et al., 2005a)) are data and metadata than available to the global analyses. Despite the much smaller than global mean LSAT average temperature trends range of analysis techniques they are generally in broad agreement over 1951 2012 (Table 2.4). It therefore logically follows that globally with the global products in characterizing the long-term changes in averaged maximum and minimum temperatures over land have both mean temperatures. This includes some regions, such as the Pacific increased by in excess of 0.1°C per decade since 1950. coast of South America, that have exhibited recent cooling (Falvey and Garreaud, 2009). Of specific importance for the early global records, Regionally, Makowski et al. (2008) found that DTR behaviour in Europe large (>1°C) summer time warm bias adjustments for many European over 1950 to 2005 changed from a decrease to an increase in the 19th century and early 20th century records were revisited and broadly 1970s in Western Europe and in the 1980s in Eastern Europe. Sen Roy confirmed by a range of approaches (Bohm et al., 2010; Brunet et al., and Balling (2005) found significant increases in both maximum and 2011). minimum temperatures for India, but little change in DTR over 1931 2 2002. Christy et al. (2009) reported that for East Africa there has been Since AR4 efforts have also been made to interpolate Antarctic records no pause in the narrowing of DTR in recent decades. Zhou and Ren from the sparse, predominantly coastal ground-based network (Chap- (2011) reported a significant decrease in DTR over mainland China of man and Walsh, 2007; Monaghan et al., 2008; Steig et al., 2009; 0.15°C per decade during 1961 2008. O Donnell et al., 2011). Although these agree that Antarctica as a whole has warmed since the late 1950s, substantial multi-annual to Various investigators (e.g., Christy et al. (2009), Pielke and Matsui multi-decadal variability and uncertainties in reconstructed magnitude (2005), Zhou and Ren (2011)) have raised doubts about the physical and spatial trend structure yield only low confidence in the details of interpretation of minimum temperature trends, hypothesizing that pan-Antarctic regional LSAT changes. microclimate and local atmospheric composition impacts are more apparent because the dynamical mixing at night is much reduced. In summary, it is certain that globally averaged LSAT has risen since the Parker (2006) investigated this issue arguing that if data were affected late 19th century and that this warming has been particularly marked in this way, then a trend difference would be expected between calm since the 1970s. Several independently analyzed global and regional and windy nights. However, he found no such minimum temperature LSAT data products support this conclusion. There is low confidence differences on a global average basis. Using more complex boundary in changes prior to 1880 owing to the reduced number of estimates, layer modelling techniques, Steeneveld et al. (2011) and McNider et al. non-standardized measurement techniques, the greater spread among (2012) showed much lower sensitivity to windspeed variations than the estimates and particularly the greatly reduced observational sam- posited by Pielke and Matsui but both concluded that boundary layer pling. Confidence is also low in the spatial detail and magnitude of understanding was key to understanding the minimum temperature LSAT trends in sparsely sampled regions such as Antarctica. Since AR4 changes. Data analysis and long-term side-by-side instrumentation significant efforts have been undertaken to identify and adjust for data field studies show that real non-climatic data artefacts certainly affect issues and new estimates have been produced. These innovations have maximum and minimum differently in the raw records for both recent further strengthened overall understanding of the global LSAT records. (Fall et al., 2011; Williams et al., 2012) and older (Bohm et al., 2010; Brunet et al., 2011) records. Hence there could be issues over interpre- 2.4.1.2 Diurnal Temperature Range tation of apparent DTR trends and variability in many regions (Christy et al., 2006, 2009; Fall et al., 2011; Zhou and Ren, 2011; Williams et In AR4 diurnal temperature range (DTR) was found, globally, to have al., 2012), particularly when accompanied by regional-scale land-use/ narrowed since 1950, with minimum daily temperatures increasing land-cover (LULC) changes (Christy et al., 2006). faster than maximum daily temperatures. However, significant mul- ti-decadal variability was highlighted including a recent period from In summary, confidence is medium in reported decreases in observed 1997 to 2004 of no change, as both maximum and minimum temper- global DTR, noted as a key uncertainty in AR4. Several recent analyses atures rose at similar rates. The Technical Summary of AR4 highlight- of the raw data on which many previous analyses were based point to ed changes in DTR and their causes as a key uncertainty. Since AR4, the potential for biases that differently affect maximum and minimum uncertainties in DTR and its physical interpretation have become even average temperatures. However, apparent changes in DTR are much more apparent. smaller than reported changes in average temperatures and therefore it is virtually certain that maximum and minimum temperatures have No dedicated global analysis of DTR has been undertaken subsequent increased since 1950. to Vose et al. (2005a), although global behaviour has been discussed in two broader ranging analyses. Rohde et al. (2012) and Wild et al. 2.4.1.3 Land Use Change and Urban Heat Island Effects (2007) note an apparent reversal since the mid-1980s; with DTR sub- sequently increasing. This decline and subsequent increase in DTR over In AR4 Urban Heat Island (UHI) effects were concluded to be real local global land surfaces is qualitatively consistent with the dimming and phenomena with negligible impact on large-scale trends. UHI and subsequent brightening noted in Section 2.3.3.1. Donat et al. (2013c) land-use land-cover change (LULC) effects arise mainly because the 188 Observations: Atmosphere and Surface Chapter 2 modified surface affects the storage and transfer of heat, water and titioning of moist and dry energy terms. Reanalyses have also been airflow. For single discrete locations these impacts may dominate all used to estimate the LULC signature in LSAT trends. Fall et al. (2010) other factors. found that the North American Regional Reanalysis generated over- all surface air temperature trends for 1979 2003 similar to observed Regionally, most attention has focused on China. A variety of investi- records. Observations-minus-reanalysis trends were most positive for gations have used methods as diverse as SST comparisons (e.g., Jones barren and urban areas, in accord with the results of Lim et al. (2008) et al., 2008), urban minus rural (e.g., Ren et al., 2008; Yang et al., 2011), using the NCEP/NCAR and ERA-40 reanalyses, and negative in agricul- satellite observations (Ren and Ren, 2011) and observations minus rea- tural areas. nalysis (e.g., Hu et al., 2010; Yang et al., 2011). Interpretation is com- plicated because often studies have used distinct versions of station McKitrick and Michaels (2004) and de Laat and Maurellis (2006) series. For example, the effect in Beijing is estimated at 80% (Ren et assessed regression of trends with national socioeconomic and geo- al., 2007) or 40% (Yan et al., 2010) of the observed trend depending graphical indicators, concluding that UHI and related LULC have on data corrections applied. A representative sample of these stud- caused much of the observed LSAT warming. AR4 concluded that ies suggest the effect of UHI and LULC is approximately 20% of the this correlation ceases to be statistically significant if one takes into trend in Eastern China as a whole and of the order 0.1°C per decade account the fact that the locations of greatest socioeconomic devel- nationally (Table 1 in Yang et al., 2011) over the last 30 years, but with opment are also those that have been most warmed by atmospheric 2 very substantial uncertainties. These effects have likely been partially circulation changes but provided no explicit evidence for this overall or completely accounted for in many homogenized series (e.g., Li et assessment result. Subsequently McKitrick and Michaels (2007) con- al., 2010b; Yan et al., 2010). Fujibe (2009) ascribes about 25% of Jap- cluded that about half the reported warming trend in global-average anese warming trends in 1979 2006 to UHI effects. Das et al. (2011) land surface air temperature in 1980 2002 resulted from local land confirmed that many Japanese sites have experienced UHI warming surface changes and faults in the observations. Schmidt (2009) under- but that rural stations show unaffected behaviour when compared to took a quantitative analysis that supported AR4 conclusions that much nearby SSTs. of the reported correlation largely arose due to naturally occurring climate variability and model over-fitting and was not robust. Taking There is an important distinction to be made between UHI trend effects these factors into account, modified analyses by McKitrick (2010) and in regions underseeing rapid development and those that have been McKitrick and Nierenberg (2010) still yielded significant evidence for developed for a long time. Jones and Lister (2009) and Wilby et al. such contamination of the record. (2011) using data from London (UK) concluded that some sites that have always been urban and where the UHI has not grown in mag- In marked contrast to regression based studies, several studies have nitude will exhibit regionally indicative trends that agree with nearby shown the methodologically diverse set of modern reanalysis products rural locations and that in such cases the time series may exhibit mul- and the various LSAT records at global and regional levels to be similar ti-decadal trends driven primarily by synoptic variations. A lack of obvi- since at least the mid-20th century (Simmons et al., 2010; Parker, 2011; ous time-varying UHI influences was also noted for Sydney, Melbourne Ferguson and Villarini, 2012; Jones et al., 2012; Vose et al., 2012a). and Hobart in Australia by Trewin (2012). The impacts of urbanization These reanalyses do not directly assimilate the LSAT measurements but also will be dependent on the natural LULC characteristics that they rather infer LSAT estimates from an observational constraint provided replace. Zhang et al. (2010) found no evidence for urban influences in by much of the rest of the global observing system, thus representing the desert North West region of China despite rapid urbanization. an independent estimate. A hypothesized residual significant warming artefact argued for by regression-based analyses is therefore physical- Global adjusted data sets likely account for much of the UHI effect pres- ly inconsistent with many other components of the global observing ent in the raw data. For the US network, Hausfather et al. (2013) showed system according to a broad range of state-of-the-art data assimilation that the adjustments method used in GHCNv3 removed much of an models (Box 2.3). Further, Efthymiadis and Jones (2010) estimated an apparent systematic difference between urban and rural locations, con- absolute upper limit on urban influence globally of 0.02°C per decade, cluding that this arose from adjustment of biased urban location data. or about 15% of the total LSAT trends, in 1951 2009 from trends of Globally, Hansen et al. (2010) used satellite-based nightlight radiances coastal land and SST. to estimate the worldwide influence on LSAT of local urban develop- ment. Adjustments reduced the global 1900 2009 temperature change In summary, it is indisputable that UHI and LULC are real influenc- (averaged over land and ocean) only from 0.71°C to 0.70°C. Wickham es on raw temperature measurements. At question is the extent to et al. (2013) also used satellite data and found that urban locations in which they remain in the global products (as residual biases in broader the Berkeley data set exhibited even less warming than rural stations, regionally representative change estimates). Based primarily on the although not statistically significantly so, over 1950 to 2010. range of urban minus rural adjusted data set comparisons and the degree of agreement of these products with a broad range of rea- Studies of the broader effects of LULC since AR4 have tended to focus nalysis products, it is unlikely that any uncorrected urban heat-island on the effects of irrigation on temperatures, with a large number of effects and LULC change effects have raised the estimated centennial studies in the Californian central belt (Christy et al., 2006; Kueppers et globally averaged LSAT trends by more than 10% of the reported trend al., 2007; Bonfils et al., 2008; Lo and Famiglietti, 2013). They find cooler (high confidence, based on robust evidence and high agreement). This average temperatures and a marked reduction in DTR in areas of active is an average value; in some regions with rapid development, UHI and irrigation and ascribe this to increased humidity; effectively a repar- LULC change impacts on regional trends may be substantially larger. 189 Chapter 2 Observations: Atmosphere and Surface 2.4.2 Sea Surface Temperature and Marine Air HadSST2 HadSST3 Temperature 0.4 ICOADS HadNMAT2 Temperature anomaly (C) 0.2 AR4 concluded that recent warming (since the 1950s) is strongly evi- dent at all latitudes in SST over each ocean. Prominent spatio-temporal 0.0 structures including the ENSO and decadal variability patterns in the -0.2 Pacific Ocean (Box 2.5) and a hemispheric asymmetry in the Atlantic -0.4 Ocean were highlighted as contributors to the regional differences in surface warming rates, which in turn affect atmospheric circulation. -0.6 Since AR4 the availability of metadata has increased, data complete- -0.8 ness has improved and a number of new SST products have been pro- duced. Intercomparisons of data obtained by different measurement 1850 1900 1950 2000 methods, including satellite data, have resulted in better understand- ing of errors and biases in the record. Figure 2.16 | Global annual average sea surface temperature (SST) and Night Marine Air Temperature (NMAT) relative to a 1961 1990 climatology from gridded data sets 2 2.4.2.1 Advances in Assembling Data Sets and in of SST observations (HadSST2 and its successor HadSST3), the raw SST measurement archive (ICOADS, v2.5) and night marine air temperatures data set HadNMAT2 (Kent et Understanding Data Errors al., 2013). HadSST2 and HadSST3 both are based on SST observations from versions of the ICOADS data set, but differ in degree of measurement bias correction. 2.4.2.1.1 In situ data records Historically, most SST observations were obtained from moving ships. observer instructions and other related documents. Early data were Buoy measurements comprise a significant and increasing fraction systematically cold biased because they were made using canvas or of in situ SST measurements from the 1980s onward (Figure 2.15). wooden buckets that, on average, lost heat to the air before the meas- Improvements in the understanding of uncertainty have been expe- urements were taken. This effect has long been recognized (Brooks, dited by the use of metadata (Kent et al., 2007) and the recovery of 1926), and prior to AR4 represented the only artefact adjusted in grid- ded SST products, such as HadSST2 (Rayner et al., 2006) and ERSST 1.0 (a) (Smith et al., 2005, 2008), which were based on bucket correction Fractional contribution to methods by Folland and Parker (1995) and Smith and Reynolds (2002), global average SST 0.8 respectively. The adjustments, made using ship observations of Night 0.6 Marine Air Temperature (NMAT) and other sources, had a striking effect 0.4 on the SST global mean estimates: note the difference in 1850 1941 0.2 between HadSST2 and International Comprehensive Ocean-Atmos- 0.0 1920 1940 1960 1980 2000 phere Data Set (ICOADS) curves in Figure 2.16 (a brief description of SST and NMAT data sets and their methods is given in Supplementary SST anomaly (°C) relative 0.8 0.6 (b) All Bucket Material 2.SM.4.3). ERI/Hull contact sensors Buoy to 1961-1990 0.4 0.2 Buckets of improved design and measurement methods with smaller, 0.0 on average, biases came into use after 1941 (Figure 2.15, top); aver- -0.2 -0.4 age biases were reduced further in recent decades, but not eliminated -0.6 (Figure 2.15, bottom). Increasing density of SST observations made 1950 1960 1970 1980 1990 2000 possible the identification (Reynolds et al., 2002, 2010; Kennedy et al., Figure 2.15 | Temporal changes in the prevalence of different measurement methods 2012) and partial correction of more recent period biases (Kennedy et in the International Comprehensive Ocean-Atmosphere Data Set (ICOADS). (a) Fraction- al., 2011a). In particular, it is hypothesized that the proximity of the al contributions of observations made by different measurement methods: bucket obser- hot engine often biases engine room intake (ERI) measurements warm vations (blue), engine room intake (ERI) and hull contact sensor observations (green), (Kent et al., 2010). Because of the prevalence of the ERI measurements moored and drifting buoys (red), and unknown (yellow). (b) Global annual average sea surface temperature (SST) anomalies based on different kinds of data: ERI and hull among SST data from ships, the ship SSTs are biased warm by 0.12°C contact sensor (green), bucket (blue), buoy (red), and all (black). Averages are computed to 0.18°C on average compared to the buoy data (Reynolds et al., 2010; over all 5° × 5° grid boxes where both ERI/hull and bucket measurements, but not Kennedy et al., 2011a, 2012). An assessment of the potential impact necessarily buoy data, were available. (Adapted from Kennedy et al., 2011a.) of modern biases can be ascertained by considering the difference Table 2.5 | Trend estimates and 90% confidence intervals (Box 2.2) for two subsequent versions of the HadSST data set over five common periods. HadSST2 has been used in AR4; HadSST3 is used in this chapter. Trends in °C per decade Data Set 1880 2012 1901 2012 1901 1950 1951 2012 1979 2012 HadSST3 (Kennedy et al., 2011a) 0.054 +/- 0.012 0.067 +/- 0.013 0.117 +/- 0.028 0.074 +/- 0.027 0.124 +/- 0.030 HadSST2 (Rayner et al., 2006) 0.051 +/- 0.015 0.069 +/- 0.012 0.084 +/- 0.055 0.098 +/- 0.017 0.121 +/- 0.033 190 Observations: Atmosphere and Surface Chapter 2 between HadSST3 (bias corrections applied throughout) and HadSST2 0.6 (bucket corrections only) global means (Figure 2.16): it is particularly prominent in 1945 1970 period, when rapid changes in prevalence of ERI and bucket measurements during and after the World War II affect 0.4 SST anomaly (°C) HadSST2 owing to the uncorrected measurement biases (Thompson et al., 2008), while these are corrected in HadSST3. Nevertheless, for peri- 0.2 ods longer than a century the effect of HadSST3-HadSST2 differences on linear trend slopes is small relative to the trend uncertainty (Table 2.5). Some degree of independent check on the validity of HadSST3 ARC D3 0.0 adjustments comes from a comparison to sub-surface temperature ARC D2 data (Gouretski et al., 2012) (see Section 3.2). HadSST3 -0.2 ATSR-1 ATSR-2 AATSR The traditional approach to modeling random error of in situ SST 1992 1994 1996 1998 2000 2002 2004 2006 2008 2010 data assumed the independence of individual measurements. Kent and Berry (2008) identified the need to account for error correlation Figure 2.17 | Global monthly mean sea surface temperature (SST) anomalies relative for measurements from the same platform (i.e., an individual ship to a 1961 1990 climatology from satellites (ATSRs) and in situ records (HadSST3). Black 2 or buoy), while measurement errors from different platforms remain lines: the 100-member HadSST3 ensemble. Red lines: ATSR-based nighttime subsurface independent.. Kennedy et al. (2011b) achieved that by introducing temperature at 0.2 m depth (SST0.2m) estimates from the ATSR Reprocessing for Climate (ARC) project. Retrievals based on three spectral channels (D3, solid line) are more platform-dependent biases, which are constant within the same plat- accurate than retrievals based on only two (D2, dotted line). Contributions of the three form, but change randomly from one platform to another. Accounting different ATSR missions to the curve shown are indicated at the bottom. The in situ and for such correlated errors in HadSST3 resulted in estimated error for satellite records were co-located within 5° × 5° monthly grid boxes: only those where global and hemispheric monthly means that are more than twice the both data sets had data for the same month were used in the comparison. (Adapted estimates given by HadSST2. The uncertainty in many, but not all, com- from Merchant et al. 2012.) ponents of the HadSST3 product is represented by the ensemble of its realizations (Figure 2.17). bined record starts in August 1991 and exceeds two decades (it stopped with the demise of the ENVISAT platform in 2012). The (A) ATSRs are ­ Data sets of marine air temperatures (MATs) have traditionally been dual-view IR radiometers intended to allow atmospheric effects restricted to nighttime series only (NMAT data sets) due to the direct removal without the use of in situ observations. Since AR4, (A)ATSR solar heating effect on the daytime measurements, although corrected observations have been reprocessed with new estimation techniques daytime MAT records for 1973 present are already available (Berry (Embury and Merchant, 2011). The resulting SST products seem to be and Kent, 2009). Other major biases, affecting both nighttime and day- more accurate than many in situ observations (Embury et al., 2011). In time MAT are due to increasing deck height with the general increase terms of monthly global means, the agreement is illustrated in Figure in the size of ships over time and non-standard measurement prac- 2.17. By analyzing (A)ATSR and in situ data together, Kennedy at al. tices. Recently these biases were re-examined and explicit uncertainty (2012) verified and extended existing models for biases and random calculation undertaken for NMAT by Kent et al. (2013), resulting in the errors of in situ data. HadNMAT2 data set. 2.4.2.2 Interpolated SST Products and Trends 2.4.2.1.2 Satellite SST data records SST data sets form a major part of global surface temperature anal- Satellite SST data sets are based on measuring electromagnetic radia- yses considered in this assessment report. To use an SST data set as tion that left the ocean surface and got transmitted through the atmos- a boundary condition for atmospheric reanalyses products (Box 2.3) phere. Because of the complexity of processes involved, the majority or in atmosphere-only climate simulations (considered in Chapter 9 of such data has to be calibrated on the basis of in situ observations. onwards), gridded data sets with complete coverage over the global The resulting data sets, however, provide a description of global SST ocean are typically needed. These are usually based on a special form fields with a level of spatial detail unachievable by in situ data only. of kriging (optimal interpolation) procedure that retains large-scale The principal IR sensor is the Advanced Very High Resolution Radiom- correlation structures and can accommodate very sparse data cover- eter (AVHRR). Since AR4, the AVHRR time series has been reprocessed age. For the pre-satellite era (generally, before October 1981) only in consistently back to March 1981 (Casey et al., 2010) to create the situ data are used; for the latter period some products also use AVHRR AVHRR Pathfinder v5.2 data set. Passive microwave data sets of SST data. Figure 2.18 compares interpolated SST data sets that extend are available since 1997 equatorward of 40° and near-globally since back to the 19th century with the uninterpolated HadSST3 and Had- 2002 (Wentz et al., 2000; Gentemann et al., 2004). They are generally NMAT2 products. Linear trend estimates for global mean SSTs from less accurate than IR-based SST data sets, but their superior coverage those products updated through 2012 are presented in Table 2.6. Dif- in areas of persistent cloudiness provides SST estimates where the IR ferences between the trends from different data sets are larger when record has none (Reynolds et al., 2010). the calculation period is shorter (1979 2012) or has lower quality data (1901 1950); these are due mainly to different data coverage of The (Advanced) Along Track Scanning Radiometer (A)ATSR) series of underlying observational data sets and bias correction methods used three sensors was designed for climate monitoring of SST; their com- in these products. 191 Chapter 2 Observations: Atmosphere and Surface Table 2.6 | Trend estimates and 90% confidence intervals (Box 2.2) for interpolated SST data sets (uninterpolated state-of-the-art HadSST3 data set is included for comparison). Dash indicates not enough data available for trend calculation. Trends in °C per decade Data Set 1880 2012 1901 2012 1901 1950 1951 2012 1979 2012 HadISST (Rayner et al., 2003) 0.042 +/- 0.007 0.052 +/- 0.007 0.067 +/- 0.024 0.064 +/- 0.015 0.072 +/- 0.024 COBE-SST (Ishii et al., 2005) 0.058 +/- 0.007 0.066 +/- 0.032 0.071 +/- 0.014 0.073 +/- 0.020 ERSSTv3b (Smith et al., 2008) 0.054 +/- 0.015 0.071 +/- 0.011 0.097 +/- 0.050 0.088 +/- 0.017 0.105 +/- 0.031 HadSST3 (Kennedy et al., 2011a) 0.054 +/- 0.012 0.067 +/- 0.013 0.117 +/- 0.028 0.074 +/- 0.027 0.124 +/- 0.030 COBE ERSST HadISST Starting in the 1980s each decade has been significantly warmer at 0.4 HadSST3 HadNMAT2 the Earth s surface than any preceding decade since the 1850s in Had- Temperature anomaly (C) 0.2 CRUT4, a data set that explicitly quantifies a large number of sources of uncertainty (Figure 2.19). Each of the last three decades is also the 2 0.0 warmest in the other two GMST data sets, but these have substan- -0.2 tially less mature and complete uncertainty estimates, precluding such an assessment of significance of their decadal differences. The GISS -0.4 and MLOST data sets fall outside the 90% CI of HadCRUT4 for several decades in the 20th century (Figure 2.19). These decadal differences -0.6 could reflect residual biases in one or more data set, an incomplete treatment of uncertainties in HadCRUT4.1 or a combination of these 1850 1900 1950 2000 effects (Box 2.1). The data sets utilize different LSAT (Section 2.4.1) Figure 2.18 | Global annual average sea surface temperature (SST) and Night Marine and SST (Section 2.4.2) component records (Supplementary Material Air Temperature (NMAT) relative to a 1961 1990 climatology from state of the art data 2.SM.4.3.4) that in the case of SST differ somewhat in their multi-dec- sets. Spatially interpolated products are shown by solid lines; non-interpolated products by dashed lines. adal trend behaviour (Table 2.6 compare HadSST3 and ERSSTv3b). In summary, it is certain that global average sea surface temperatures 2000s (SSTs) have increased since the beginning of the 20th century. Since 1990s AR4, major improvements in availability of metadata and data com- pleteness have been made, and a number of new global SST records 1980s have been produced. Intercomparisons of new SST data records 1970s obtained by different measurement methods, including satellite data, 1960s have resulted in better understanding of uncertainties and biases in the records. Although these innovations have helped highlight and 1950s quantify uncertainties and affect our understanding of the character of 1940s changes since the mid-20th century, they do not alter the conclusion 1930s that global SSTs have increased both since the 1950s and since the late 19th century. 1920s 1910s 2.4.3 Global Combined Land and Sea Surface 1900s Temperature 1890s AR4 concluded that the GMST had increased, with the last 50 years 1880s increasing at almost double the rate of the last 100 years. Subsequent 1870s developments in LSAT and SST have led to better understanding of the GISS data and their uncertainties as discussed in preceding sections. This 1860s MLOST HadCRUT4 improved understanding has led to revised global products. 1850s Changes have been made to all three GMST data sets that were used -0.6 -0.4 -0.2 0.0 0.2 0.4 0.6 Temperature difference from 1961-1990 ( C) o in AR4 (Hansen et al., 2010; Morice et al., 2012; Vose et al., 2012b). These are now in somewhat better agreement with each other over Figure 2.19 | Decadal global mean surface temperature (GMST) anomalies (white recent years, in large part because HadCRUT4 now better samples the vertical lines in grey blocks) and their uncertainties (90% confidence intervals as grey NH high latitude land regions (Jones et al., 2012; Morice et al., 2012) blocks) based upon the land-surface air temperature (LSAT) and sea surface tempera- which comparisons to reanalyses had shown led to a propensity for ture (SST) combined HadCRUT4 (v4.1.1.0) ensemble (Morice et al., 2012). Anomalies are relative to a 1961 1990 climatology. 1850s indicates the period 1850-1859, and HadCRUT3 to underestimate recent warming (Simmons et al., 2010). so on. NCDC MLOST and GISS data set best-estimates are also shown. 192 Observations: Atmosphere and Surface Chapter 2 All ten of the warmest years have occurred since 1997, with 2010 and Much interest has focussed on the period since 1998 and an observed 2005 effectively tied for the warmest year on record in all three prod- reduction in warming trend, most marked in NH winter (Cohen et al., ucts. However, uncertainties on individual annual values are sufficient- 2012). Various investigators have pointed out the limitations of such ly large that the ten warmest years are statistically indistinguishable short-term trend analysis in the presence of auto-correlated series var- from one another. The global-mean trends are significant for all data iability and that several other similar length phases of no warming sets and multi-decadal periods considered in Table 2.7. Using Had- exist in all the observational records and in climate model simulations CRUT4 and its uncertainty estimates, the warming from 1850 1900 to 1986 2005 (reference period for the modelling chapters and Annex I) is 0.61 [0.55 to 0.67] °C (90% confidence interval), and the warming HadCRUT4 1901-2012 from 1850 1900 to 2003 2012 (the most recent decade) is 0.78 [0.72 to 0.85] °C (Supplementary Material 2.SM.4.3.3). Differences between data sets are much smaller than both interannual variability and the long-term trend (Figure 2.20). Since 1901 almost the whole globe has experienced surface warming (Figure 2.21). Warming has not been linear; most warming occurred in two periods: around 2 1900 to around 1940 and around 1970 onwards (Figure 2.22. Shorter periods are noisier and so proportionately less of the sampled globe exhibits statistically significant trends at the grid box level (Figure MLOST 1901-2012 2.22). The two periods of global mean warming exhibit very distinct spatial signatures. The early 20th century warming was largely a NH mid- to high-latitude phenomenon, whereas the more recent warm- ing is more global in nature. These distinctions may yield important information as to causes (Chapter 10). Differences between data sets are larger in earlier periods (Figures 2.19, 2.20), particularly prior to the 1950s when observational sampling is much more geographically incomplete (and many of the well sampled areas may have been glob- ally unrepresentative (Brönnimann, 2009)), data errors and subsequent methodological impacts are larger (Thompson et al., 2008), and differ- ent ways of accounting for data void regions are more important (Vose GISS 1901-2012 et al., 2005b). 0.6 MLOST HadCRUT4 GISS Temperature anomaly (C) 0.4 0.2 0.0 -0.2 -0.6 -0.4 -0.2 0 0.2 0.4 0.6 0.8 1. 1.25 1.5 1.75 2.5 -0.4 Trend (C over period) -0.6 Figure 2.21 | Trends in surface temperature from the three data sets of Figure 2.20 1950 for 1901 2012. White areas indicate incomplete or missing data. Trends have been 1850 1900 2000 calculated only for those grid boxes with greater than 70% complete records and more Figure 2.20 | Annual global mean surface temperature (GMST) anomalies relative to a than 20% data availability in first and last decile of the period. Black plus signs (+) 1961 1990 climatology from the latest version of the three combined land-surface air indicate grid boxes where trends are significant (i.e., a trend of zero lies outside the 90% temperature (LSAT) and sea surface temperature (SST) data sets (HadCRUT4, GISS and confidence interval). Differences in coverage primarily reflect the degree of interpolation NCDC MLOST). Published data set uncertainties are not included for reasons discussed to account for data void regions undertaken by the data set providers ranging from none in Box 2.1. beyond grid box averaging (HadCRUT4) to substantial (GISS). Table 2.7 | Same as Table 2.4, but for global mean surface temperature (GMST) over five common periods. Trends in °C per decade Data Set 1880 2012 1901 2012 1901 1950 1951 2012 1979 2012 HadCRUT4 (Morice et al., 2012) 0.062 +/- 0.012 0.075 +/- 0.013 0.107 +/- 0.026 0.106 +/- 0.027 0.155 +/- 0.033 NCDC MLOST (Vose et al., 2012b) 0.064 +/- 0.015 0.081 +/- 0.013 0.097 +/- 0.040 0.118 +/- 0.021 0.151 +/- 0.037 GISS (Hansen et al., 2010) 0.065 +/- 0.015 0.083 +/- 0.013 0.090 +/- 0.034 0.124 +/- 0.020 0.161 +/- 0.033 193 Chapter 2 Observations: Atmosphere and Surface MLOST 1911-1940 three decades has been warmer than all the previous decades in the instrumental record, and the decade of the 2000s has been the warmest. The globally averaged combined land and ocean surface temperature data as calculated by a linear trend, show a warming of 0.85 [0.65 to 1.06] °C, over the period 1880 2012, when multiple independently produced datasets exist, about 0.89°C [0.69 to 1.08] °C over the period 1901 2012, and about 0.72 [0.49° to 0.89] °C over the period 1951 2012. The total increase between the average of the 1850 1900 period and the 2003 2012 period is 0.78 [0.72 to 0.85] °C and the total increase between the average of the 1850 1900 period and the reference period for projections 1986 2005 is 0.61 [0.55 to MLOST 1951-1980 0.67] °C, based on the single longest dataset available. For the lon- gest period when calculation of regional trends is sufficiently complete (1901 2012), almost the entire globe has experienced surface warm- ing. In addition to robust multi-decadal warming, global mean surface 2 temperature exhibits substantial decadal and interannual variability. Owing to natural variability, trends based on short records are very sensitive to the beginning and end dates and do not in general reflect long-term climate trends. As one example, the rate of warming over the past 15 years (1998 2012; 0.05 [ 0.05 to +0.15] °C per decade), which begins with a strong El Nino, is smaller than the rate calculated since 1951 (1951 2012; 0.12 [0.08 to 0.14] °C per decade)Trends for MLOST 1981-2012 15-year periods starting in 1995, 1996, and 1997 are 0.13 [0.02 to 0.24], 0.14 [0.03 to 0.24] and 0.07 [ 0.02 to 0.18], respectively.. 2.4.4 Upper Air Temperature AR4 summarized that globally the troposphere had warmed at a rate greater than the GMST over the radiosonde record, while over the shorter satellite era the GMST and tropospheric warming rates were indistinguishable. Trends in the tropics were more uncertain than global trends although even this region was concluded to be warming. Globally, the stratosphere was reported to be cooling over the satellite -1.25 -1 -0.8 -0.6 -0.5 -0.4 -0.3 -0.2 -0.1 0 0.1 0.2 0.3 0.4 0.5 0.6 0.8 1 1.25 era starting in 1979. New advances since AR4 have highlighted the Trend (°C per decade) substantial degree of uncertainty in both satellite and balloon-borne radiosonde records and led to some revisions and improvements in Figure 2.22 | Trends in surface temperature from NCDC MLOST for three non- consectutive shorter periods (1911 1940; 1951 1980; 1981 2012). White areas existing products and the creation of a number of new data products. indicate incomplete or missing data. Trends and significance have been calculated as in Figure 2.21. 2.4.4.1 Advances in Multi-Decadal Observational Records (Easterling and Wehner, 2009; Peterson et al., 2009; Liebmann et al., The major global radiosonde records extend back to 1958, with tem- 2010; Foster and Rahmstorf, 2011; Santer et al., 2011). This issue is peratures, measured as the balloon ascends, reported at mandatory discussed in the context of model behaviour, forcings and natural var- pressure levels. Satellites have monitored tropospheric and lower strat- iability in Box 9.2 and Section 10.3.1. Regardless, all global combined ospheric temperature trends since late 1978 through the Microwave LSAT and SST data sets exhibit a statistically non-significant warming Sounding Unit (MSU) and its follow-on Advanced Microwave Sound- trend over 1998 2012 (0.042°C +/- 0.093°C per decade (HadCRUT4); ing Unit (AMSU) since 1998. These measures of upwelling radiation 0.037°C +/- 0.085°C per decade (NCDC MLOST); 0.069°C +/- 0.082°C per represent bulk (volume averaged) atmospheric temperature (Figure decade (GISS)). An average of the trends from these three data sets 2.23). The Mid-Tropospheric (MT) MSU channel that most directly cor- yields an estimated change for the 1998 2012 period of 0.05 [ 0.05 to responds to the troposphere has 10 to 15% of its signal from both the +0.15] °C per decade. Trends of this short length are very sensitive to skin temperature of the Earth s surface and the stratosphere. Two alter- the precise period selection with trends calculated in the same manner native approaches have been suggested for removing the stratospheric for the 15-year periods starting in 1995, 1996, and 1997 being 0.13 component based on differencing of view angles (LT) and statistical [0.02 to 0.24], 0.14 [0.03 to 0.24] and 0.07 [ 0.02 to 0.18] (all °C per recombination (*G) with the Lower Stratosphere (LS) channel (Spen- decade), respectively. cer and Christy, 1992; Fu et al., 2004). The MSU satellite series also included a Stratospheric Sounding Unit (SSU) that measured at higher In summary, it is certain that globally averaged near surface temper- altitudes (Seidel et al., 2011). atures have increased since the late 19th century. Each of the past 194 Observations: Atmosphere and Surface Chapter 2 ics effects were inter-satellite offset determinations and, for tropospheric 0.1 LT *G MT LS SSU 25 SSU 26 SSU 27 Tro p les 60 Po channels, diurnal drift. Uncertainties were concluded to be of the order 0.1°C per decade at the global mean for both tropospheric channels 1.0 50 Mesosphere (where it is of comparable magnitude to the long-term trends) and the Pressure (hPa) stratospheric channel. 10 40 Stratosphere 30 SSU provides the only long-term near-global temperature data above 25 the lower stratosphere, with the series terminating in 2006. Some 100 20 Tropopause AMSU-A channels have replaced this capability and efforts to under- 15 Level 10 stand the effect of changed measurement properties have been under- 1000 5 Troposphere Surface taken (Kobayashi et al., 2009). Until recently only one SSU data set Height (km) existed (Nash and Edge, 1989), updated by Randel et al. (2009). Liu and Weight Weng (2009) have produced an intermediate analysis for Channels 25 Figure 2.23 | Vertical weighting functions for those satellite temperature retrievals and 26 (but not Channel 27). Wang et al. (2012g), building on insights discussed in this chapter (modified from Seidel et al. (2011)). The dashed line indicates from several of these recent studies, have produced a more complete the typical maximum altitude achieved in the historical radiosonde record. The three SSU analysis. Differences between the independent estimates are much 2 channels are denoted by the designated names 25, 26 and 27. LS (Lower Stratosphere) larger than differences between MSU records or radiosonde records and MT (Mid Troposphere) are two direct MSU measures and LT (Lower Troposphere) at lower levels, with substantial inter-decadal time series behaviour and *G (Global Troposphere) are derived quantities from one or more of these that attempt to remove the stratospheric component from MT. departures, zonal trend structure, and global trend differences of the order 0.5°C per decade (Seidel et al., 2011; Thompson et al., 2012; Wang et al., 2012g). Although all SSU data sets agree that the strato- At the time of AR4 there were only two global radiosonde data sets sphere is cooling, there is therefore low confidence in the details above that included treatment of homogeneity issues: RATPAC (Free et al., the lower stratosphere. 2005) and HadAT (Thorne et al., 2005). Three additional estimates have appeared since AR4 based on novel and distinct approaches. In summary, many new data sets have been produced since AR4 from A group at the University of Vienna have produced RAOBCORE and radiosondes and satellites with renewed interest in satellite measure- RICH (Haimberger, 2007; Haimberger et al., 2008, 2012) using ERA ments above the lower stratosphere. Several studies have attempted reanalysis products (Box 2.3). Sherwood and colleagues developed an to quantify the parametric uncertainty (Box 2.1) more rigorously. These iterative universal kriging approach for radiosonde data to create IUK various data sets and analyses have served to highlight the degree of (Sherwood et al., 2008) and concluded that non-climatic data issues uncertainty in the data and derived products. leading to spurious cooling remained in the deep tropics even after homogenization. The HadAT group created an automated version, 2.4.4.2 Intercomparisons of Various Long-Term Radiosonde undertook systematic experimentation and concluded that the para- and MSU Products metric uncertainty (Box 2.1) was of the same order of magnitude as the apparent climate signal (McCarthy et al., 2008; Titchner et al., Since AR4 there have been a large number of intercomparisons between 2009; Thorne et al., 2011). A similar ensemble approach has also been radiosonde and MSU data sets. Interpretation is complicated, as most applied to the RICH product (Haimberger et al., 2012). These various studies considered data set versions that have since been superseded. ensembles and new products exhibit more tropospheric warming / less Several studies compared UAH and RSS products to local, regional or stratospheric cooling than pre-existing products at all levels. Globally global raw/homogenized radiosonde data (Christy and Norris, 2006, the radiosonde records all imply the troposphere has warmed and the 2009; Christy et al., 2007, 2010, 2011; Randall and Herman, 2008; stratosphere cooled since 1958 but with uncertainty that grows with Mears et al., 2012; Po-Chedley and Fu, 2012). Early studies focussed on height and is much greater outside the better-sampled NH extra-trop- the time of transition from NOAA-11 to NOAA-12 (early 1990s) which ics (Thorne et al., 2011; Haimberger et al., 2012), where it is of the indicated an apparent issue in RSS. Christy et al. (2007) noted that this order 0.1°C per decade. coincided with the Mt Pinatubo eruption and that RSS was the only product, either surface or tropospheric, that exhibited tropical warm- For MSU, AR4 considered estimates produced from three groups: UAH ing immediately after the eruption when cooling would be expected. (University of Alabama in Huntsville); RSS (Remote Sensing Systems) Using reanalysis data Bengtsson and Hodges (2011) also found evi- and VG2 (now no longer updated). A new product has been creat- dence of a potential jump in RSS in 1993 over the tropical oceans. ed by NOAA labelled STAR, using a fundamentally distinct approach Mears et al. (2012) cautioned that an El Nino event quasi-simultane- for the critical inter-satellite warm target calibration step (Zou et al., ous with Pinatubo complicates interpretation. They also highlighted 2006a). STAR exhibits more warming/less cooling at all levels than several other periods of disagreement between radiosonde records UAH and RSS. For MT and LS, Zou and Wang (2010) concluded that and MSU records. All MSU records were most uncertain when satellite this does not relate primarily to use of their inter-satellite calibration orbits are drifting rapidly (Christy and Norris, 2006, 2009). Mears et technique but rather differences in other processing steps. RSS also al. (2011) found that trend differences between RSS and other data produced a parametric uncertainty ensemble (Box 2.1) employing a sets could not be explained in many cases by parametric uncertainties Monte Carlo approach allowing methodological inter-dependencies to in RSS alone. It was repeatedly cautioned that there were potential be fully expressed (Mears et al., 2011). For large-scale trends dominant common biases (of varying magnitude) between the different MSU 195 Chapter 2 Observations: Atmosphere and Surface records or between the different radiosonde records which complicate 2.4.4.4 Synthesis of Free Atmosphere Temperature Estimates intercomparisons (Christy and Norris, 2006, 2009; Mears et al., 2012). Global-mean lower tropospheric temperatures have increased since the In summary, assessment of the large body of studies comparing var- mid-20th century (Figure 2.24, bottom). Structural uncertainties (Box ious long-term radiosonde and MSU products since AR4 is hampered 2.1) are larger than at the surface but it can still be concluded that glob- by data set version changes, and inherent data uncertainties. These ally the troposphere has warmed (Table 2.8). On top of this long-term factors substantially limit the ability to draw robust and consistent trend are superimposed short-term variations that are highly correlated inferences from such studies about the true long-term trends or the with those at the surface but of somewhat greater amplitude. Global value of different data products. mean lower stratospheric temperatures have decreased since the mid- 20th century punctuated by short-lived warming events associated with 2.4.4.3 Additional Evidence from Other Technologies explosive volcanic activity (Figure 2.24a). However, since the mid-1990s and Approaches little net change has occurred. Cooling rates are on average greater from radiosonde data sets than MSU data sets. This very likely relates Global Positioning System (GPS) radio occultation (RO) currently repre- to widely recognized cooling biases in radiosondes (Mears et al., 2006) sents the only self-calibrated SI traceable raw satellite measurements which all data set producers explicitly caution are likely to remain to 2 (Anthes et al., 2008; Anthes, 2011). The fundamental observation is some extent in their final products (Free and Seidel, 2007; Haimberger time delay of the occulted signal s phase traversing the atmosphere. et al., 2008; Sherwood et al., 2008; Thorne et al., 2011). The time delay is a function of several atmospheric physical state vari- ables. Subsequent analysis converts the time delay to temperature and In comparison to the surface (Figure 2.22), tropospheric layers exhibit other parameters, which inevitably adds some degree of uncertainty to smoother geographic trends (Figure 2.25) with warming dominating the derived temperature data. Intercomparisons of GPS-RO products cooling north of approximately 45°S and greatest warming in high show that differences are largest for derived geophysical parameters northern latitudes. The lower stratosphere cooled almost everywhere (including temperature), but are still small relative to other observing but this cooling exhibits substantial large-scale structure. Cooling is technologies (Ho et al., 2012). Comparisons to MSU and radiosondes greatest in the highest southern latitudes and smallest in high northern (Kuo et al., 2005; Ho et al., 2007, 2009a, 2009b; He et al., 2009; Bar- latitudes. There are also secondary stratospheric cooling maxima in the inger et al., 2010; Sun et al., 2010; Ladstadter et al., 2011) show sub- mid-latitude regions of each hemisphere. stantive agreement in interannual behaviour, but also some multi-year drifts that require further examination before this additional data Available global and regional trends from radiosondes since 1958 source can usefully arbitrate between different MSU and radiosonde (Figure 2.26) show agreement that the troposphere has warmed and trend estimates. the stratosphere cooled. While there is little ambiguity in the sign of the changes, the rate and vertical structure of change are distinctly data Atmospheric winds are driven by thermal gradients. Radiosonde winds set dependent, particularly in the stratosphere. Differences are greatest are far less affected by time-varying biases than their temperatures in the tropics and SH extra-tropics where the historical radiosonde data (Gruber and Haimberger, 2008; Sherwood et al., 2008; Section 2.7.3). coverage is poorest. Not shown in the figure for clarity are estimates Allen and Sherwood (2007) initially used radiosonde wind to infer of parametric data set uncertainties or trend-fit ­uncertainties both of temperatures within the Tropical West Pacific warm pool region, then which are of the order of at least 0.1°C per decade (Section 2.4.4.1). extended this to a global analysis (Allen and Sherwood, 2008) yielding a distinct tropical upper tropospheric warming trend maximum within Differences in trends between available radiosonde data sets are the vertical profile, but with large uncertainty. Winds can only quan- greater during the satellite era than for the full radiosonde period of tify relative changes and require an initialization (location and trend record in all regions and at most levels (Figure 2.27; cf. Figure 2.26). The at that location) (Allen and Sherwood, 2008). The large uncertainty RAOBCORE product exhibits greater vertical trend gradients than other range was predominantly driven by this initialization choice, a finding data sets and it has been posited that this relates to its dependency later confirmed by Christy et al. (2010), who in addition questioned on reanalysis fields (Sakamoto and Christy, 2009; Christy et al., 2010). the stability given the sparse geographical sampling, particularly in the MSU trend estimates in the troposphere are generally bracketed by the tropics, and possible systematic sampling effects amongst other poten- radiosonde range. In the stratosphere MSU deep layer estimates tend tial issues. Initializing closer to the tropics tended to reduce or remove to show slightly less cooling. Over both 1958 2011 and 1979 2011 the appearance of a tropical upper tropospheric warming trend maxi- there is some evidence in the radiosonde products taken as a whole mum (Allen and Sherwood, 2008; Christy et al., 2010). There is only low that the tropical tropospheric trends increase with height. But the mag- confidence in trends inferred from thermal winds given the relative nitude and the structure is highly data set dependent. immaturity of the analyses and their large uncertainties. In summary, based on multiple independent analyses of measurements In summary, new technologies and approaches have emerged since from radiosondes and satellite sensors it is virtually certain that global- AR4. However, these new technologies and approaches either consti- ly the troposphere has warmed and the stratosphere has cooled since tute too short a record or are too immature to inform assessments of the mid-20th century. Despite unanimous agreement on the sign of the long-term trends at the present time. trends, substantial disagreement exists among available estimates as to the rate of temperature changes, particularly outside the NH extra- tropical troposphere, which has been well sampled by radiosondes. 196 Observations: Atmosphere and Surface Chapter 2 Table 2.8 | Trend estimates and 90% confidence intervals (Box 2.2) for radiosonde and MSU data set global average values over the radiosonde (1958 2012) and satellite periods (1979 2012). LT indicates Lower Troposphere, MT indicates Mid Troposphere and LS indicates Lower Stratosphere (Figure 2.23. Satellite records start only in 1979 and STAR do not produce an LT product. Trends in °C per decade Data Set 1958 2012 1979 2012 LT MT LS LT MT LS HadAT2 (Thorne et al., 2005) 0.159 +/- 0.038 0.095 +/- 0.034 0.339 +/- 0.086 0.162 +/- 0.047 0.079 +/- 0.057 0.436 +/- 0.204 RAOBCORE 1.5 (Haimberger et al., 2012) 0.156 +/- 0.031 0.109 +/- 0.029 0.186 +/- 0.087 0.139 +/- 0.049 0.079 +/- 0.054 0.266 +/- 0.227 RICH-obs (Haimberger et al., 2012) 0.162 +/- 0.031 0.102 +/- 0.029 0.285 +/- 0.087 0.158 +/- 0.046 0.081 +/- 0.052 0.331 +/- 0.241 RICH-tau (Haimberger et al., 2012) 0.168 +/- 0.032 0.111 +/- 0.030 0.280 +/- 0.085 0.160 +/- 0.046 0.083 +/- 0.052 0.345 +/- 0.238 RATPAC (Free et al., 2005) 0.136 +/- 0.028 0.076 +/- 0.028 0.338 +/- 0.092 0.128 +/- 0.044 0.039 +/- 0.051 0.468 +/- 0.225 UAH (Christy et al., 2003) 0.138 +/- 0.043 0.043 +/- 0.042 0.372 +/- 0.201 RSS (Mears and Wentz, 2009a, 2009b) 0.131 +/- 0.045 0.079 +/- 0.043 0.268 +/- 0.177 STAR (Zou and Wang, 2011) 0.123 +/- 0.047 0.320 +/- 0.175 2 1.5 (a) Lower stratosphere Temperature anomaly (C) 1.0 0.5 0.0 RAOBCORE 1.5 HadAT2 RICH - obs -0.5 RATPACB RICH - tau (b) Lower troposphere 0.4 Temperature anomaly (C) 0.2 0.0 -0.2 -0.4 -0.6 UAH STAR -0.8 RSS 1960 1970 1980 1990 2000 2010 Figure 2.24 | Global annual average lower stratospheric (top) and lower tropospheric (bottom) temperature anomalies relative to a 1981 2010 climatology from different data sets. STAR does not produce a lower tropospheric temperature product. Note that the y-axis resolution differs between the two panels. 197 Chapter 2 Observations: Atmosphere and Surface Frequently Asked Questions FAQ 2.1 | How Do We Know the World Has Warmed? Evidence for a warming world comes from multiple independent climate indicators, from high up in the atmosphere to the depths of the oceans. They include changes in surface, atmospheric and oceanic temperatures; glaciers; snow cover; sea ice; sea level and atmospheric water vapour. Scientists from all over the world have independently veri- fied this evidence many times. That the world has warmed since the 19th century is unequivocal. Discussion about climate warming often centres on potential residual biases in temperature records from land- based weather stations. These records are very important, but they only represent one indicator of changes in the climate system. Broader evidence for a warming world comes from a wide range of independent physically consis- tent measurements of many other, strongly interlinked, elements of the climate system (FAQ 2.1, Figure 1). A rise in global average surface temperatures is the best-known indicator of climate change. Although each year and 2 even decade is not always warmer than the last, global surface temperatures have warmed substantially since 1900. Warming land temperatures correspond closely with the observed warming trend over the oceans. Warming oce- anic air temperatures, measured from aboard ships, and temperatures of the sea surface itself also coincide, as borne out by many independent analyses. The atmosphere and ocean are both fluid bodies, so warming at the surface should also be seen in the lower atmo- sphere, and deeper down into the upper oceans, and observations confirm that this is indeed the case. Analyses of measurements made by weather balloon radiosondes and satellites consistently show warming of the troposphere, the active weather layer of the atmosphere. More than 90% of the excess energy absorbed by the climate system since at least the 1970s has been stored in the oceans as can be seen from global records of ocean heat content going back to the 1950s. (continued on next page) Glacier Volume Air Temperature in the lowest few Km (troposphere) Water Vapor Temperature Over Land Sea Ice Area Snow Cover Marine Air Temperature Sea Surface Temperature Sea Level Ocean Heat Content FAQ 2.1, Figure 1 | Independent analyses of many components of the climate system that would be expected to change in a warming world exhibit trends consistent with warming (arrow direction denotes the sign of the change), as shown in FAQ 2.1, Figure 2. 198 Observations: Atmosphere and Surface Chapter 2 FAQ 2.1 (continued) As the oceans warm, the water itself expands. This expansion is one of the main drivers of the independently observed rise in sea levels over the past century. Melting of glaciers and ice sheets also contribute, as do changes in storage and usage of water on land. A warmer world is also a moister one, because warmer air can hold more water vapour. Global analyses show that specific humidity, which measures the amount of water vapour in the atmosphere, has increased over both the land and the oceans. The frozen parts of the planet known collectively as the cryosphere affect, and are affected by, local changes in temperature. The amount of ice contained in glaciers globally has been declining every year for more than 20 years, and the lost mass contributes, in part, to the observed rise in sea level. Snow cover is sensitive to changes in temperature, particularly during the spring, when snow starts to melt. Spring snow cover has shrunk across the NH since the 1950s. Substantial losses in Arctic sea ice have been observed since satellite records began, particularly at the time of the mimimum extent, which occurs in September at the end of the annual melt season. By contrast, the 2 increase in Antarctic sea ice has been smaller. Individually, any single analysis might be unconvincing, but analysis of these different indicators and independent data sets has led many independent research groups to all reach the same conclusion. From the deep oceans to the top of the troposphere, the evidence of warmer air and oceans, of melting ice and rising seas all points unequivo- cally to one thing: the world has warmed since the late 19th century (FAQ 2.1, Figure 2). 1.0 Land surface air temperature: 4 datasets 0.6 Tropospheric temperature: 0.4 7 datasets anomaly (C) Temperature 0.5 0.2 anomaly (C) Temperature 0.0 0.0 -0.2 -0.5 -0.4 -0.6 -1.0 -0.8 20 0.4 Sea-surface temperature: 5 datasets Ocean heat content(0-700m): Ocean heat content 5 datasets anomaly (1022 J) 0.2 10 anomaly (C) Temperature 0.0 0 -0.2 -0.4 -10 -0.6 0.4 0.4 Marine air temperature: 2 datasets Specific humidity: 4 datasets Specific humidity anomaly (g/kg) 0.2 0.2 anomaly (C) Temperature 0.0 0.0 -0.2 -0.4 -0.2 -0.6 100 Sea level: 6 datasets 6 Northern hemisphere (March- Mass balance (1015GT) Extent anomaly (106km2) 50 4 April) snow cover: 2 datasets anomaly (mm) 0 2 Sea level -50 0 -100 -2 -150 -4 -200 -6 12 10 Glacier mass balance: 5 3 datasets Extent (106km2) 10 Summer arctic sea-ice extent: 6 datasets 0 8 -5 6 -10 4 -15 1850 1900 1950 2000 1940 1960 1980 2000 Year Year FAQ 2.1, Figure 2 | Multiple independent indicators of a changing global climate. Each line represents an independently derived estimate of change in the climate element. In each panel all data sets have been normalized to a common period of record. A full detailing of which source data sets go into which panel is given in the Supplementary Material 2.SM.5. 199 Chapter 2 Observations: Atmosphere and Surface UAH LS RSS LS UAH LT RSS LT 2 -1.25 -1.0 -0.8 -0.6 -0.5 -0.4 -0.3 -0.2 -0.1 0 0.1 0.2 0.3 0.4 0.5 0.6 0.8 1.0 1.25 Trend (°C per decade) Figure 2.25 | Trends in MSU upper air temperature over 1979 2012 from UAH (left-hand panels) and RSS (right-hand panels) and for LS (top row) and LT (bottom row). Data are temporally complete within the sampled domains for each data set. White areas indicate incomplete or missing data. Black plus signs (+) indicate grid boxes where trends are significant (i.e., a trend of zero lies outside the 90% confidence interval). Global Radiosonde Datasets LS MT HadAT2 LT 30 50 RICH-obs 70 Pressure (hPa) 100 150 RAOBCORE1.5 200 250 300 400 RICH-tau 500 700 850 RATPACB -1.0 -0.8 -0.6 -0.4 -0.2 0.0 0.2 0.4 Trend (C per decade) SH Extra-Tropics Tropics NH Extra-Tropics LS LS LS MT MT MT LT LT LT 30 30 30 50 50 50 70 70 70 Pressure (hPa) Pressure (hPa) 100 Pressure (hPa) 100 100 150 150 150 200 200 200 250 250 250 300 300 300 400 400 400 500 500 500 700 700 700 850 850 850 -1.0 -0.8 -0.6 -0.4 -0.2 0.0 0.2 0.4 -1.0 -0.8 -0.6 -0.4 -0.2 0.0 0.2 0.4 -1.0 -0.8 -0.6 -0.4 -0.2 0.0 0.2 0.4 Trend (C per decade) Trend (C per decade) Trend (C per decade) Figure 2.26 | Trends in upper air temperature for all available radiosonde data products that contain records for 1958 2012 for the globe (top) and tropics (20°N to 20°S) and extra-tropics (bottom). The bottom panel trace in each case is for trends on distinct pressure levels. Note that the pressure axis is not linear. The top panel points show MSU layer equivalent measure trends. MSU layer equivalents have been processed using the method of Thorne et al. (2005). No attempts have been made to sub-sample to a common data mask. 200 Observations: Atmosphere and Surface Chapter 2 Radiosonde Datasets Global Satellite Datasets LS HadAT2 MT RSS LT 30 RICH-obs 50 UAH 70 Pressure (hPa) 100 150 RAOBCORE1.5 200 STAR 250 300 RICH-tau 400 500 700 850 RATPACB -1.0 -0.8 -0.6 -0.4 -0.2 0.0 0.2 0.4 Trend (C per decade) SH Extra-Tropics Tropics NH Extra-Tropics LS LS LS 2 MT MT MT LT LT LT 30 30 30 50 50 50 70 70 70 Pressure (hPa) Pressure (hPa) Pressure (hPa) 100 100 100 150 150 150 200 200 200 250 250 250 300 300 300 400 400 400 500 500 500 700 700 700 850 850 850 -1.0 -0.8 -0.6 -0.4 -0.2 0.0 0.2 0.4 -1.0 -0.8 -0.6 -0.4 -0.2 0.0 0.2 0.4 -1.0 -0.8 -0.6 -0.4 -0.2 0.0 0.2 0.4 Trend (C per decade) Trend (C per decade) Trend (C per decade) Figure 2.27 | As Figure 2.26 except for the satellite era 1979 2012 period and including MSU products (RSS, STAR and UAH). Hence there is only medium confidence in the rate of change and its Since AR4, existing data sets have been updated and a new data set vertical structure in the NH extratropical troposphere and low confi- developed. Figure 2.28 shows the century-scale variations and trends dence elsewhere. on globally and zonally averaged annual precipitation using five data sets: GHCN V2 (updated through 2011; Vose et al., 1992), Global Pre- 2.5 Changes in Hydrological Cycle cipitation Climatology Project V2.2 (GPCP) combined raingauge satel- lite product (Adler et al., 2003), CRU TS 3.10.01 (updated from Mitchell This section covers the main aspects of the hydrological cycle, including and Jones, 2005), Global Precipitation Climatology Centre V6 (GPCC) large-scale average precipitation, stream flow and runoff, soil mois- data set (Becker et al., 2013) and a reconstructed data set by Smith et ture, atmospheric water vapour, and clouds. Meteorological drought is al. (2012). Each data product incorporates a different number of station assessed in Section 2.6. Ocean precipitation changes are assessed in series for each region. The Smith et al. product is a statistical recon- Section 3.4.3 and changes in the area covered by snow in Section 4.5. struction using Empirical Orthogonal Functions, similar to the NCDC MLOST global temperature product (Section 2.4.3) that does provide 2.5.1 Large-Scale Changes in Precipitation coverage for most of the global surface area although only land is included here. The data sets based on in situ observations only start in 2.5.1.1 Global Land Areas 1901, but the Smith et al. data set ends in 2008, while the other three data sets contain data until at least 2010. AR4 concluded that precipitation has generally increased over land north of 30°N over the period 1900 2005 but downward trends dom- For the longest common period of record (1901 2008) all datasets inate the tropics since the 1970s. AR4 included analysis of both the exhibit increases in globally averaged precipitation, with three of the GHCN (Vose et al., 1992) and CRU (Mitchell and Jones, 2005) gauge- four showing statistically significant changes (Table 2.9). However, based precipitation data sets for the globally averaged annual pre- there is a factor of almost three spread in the magnitude of the change cipitation over land. For both data sets the overall linear trend from which serves to create low confidence. Global trends for the shorter 1900 to 2005 (1901 2002 for CRU) was positive but not statistically period (1951 2008) show a mix of statistically non-significant positive significant (Table 3.4 from AR4). Other periods covered in AR4 (1951 and negative trends amongst the four data sets with the infilled Smith 2005 and 1979 2005) showed a mix of negative and positive trends et al. (2012) analysis showing increases and the remainder decreases. depending on the data set. These differences among data sets indicate that long-term increases 201 Chapter 2 Observations: Atmosphere and Surface 60 60N-90N 2.5.1.2 Spatial Variability of Observed Trends 30 0 CRU The latitude band plots in Figure 2.28 suggest that precipitation over GHCN -30 GPCC tropical land areas (30°S to 30°N) has increased over the last decade -60 Smith et al. GPCP reversing the drying trend that occurred from the mid-1970s to mid- -90 1990s. As a result the period 1951 2008 shows no significant overall 30N-60N trend in tropical land precipitation in any of the datasets (Table 2.10). 30 Longer term trends (1901 2008) in the tropics, shown in Table 2.10, 0 are also non-significant for each of the four data sets. The mid-latitudes -30 of the NH (30°N to 60°N) show an overall increase in precipitation -60 from 1901 to 2008 with statistically significant trends for each data Precipitation anomaly (mm yr -1) set. For the shorter period (1951 2008) the trends are also positive 120 30S-30N 90 but non-significant for three of the four data sets. For the high lat- 60 itudes of the NH (60°N to 90°N) where data completeness permits 30 trend calculations solely for the 1951 2008 period, all datasets show 2 0 -30 increases but there is a wide range of magnitudes and the infilled -60 -90 Smith et al. series shows small and insignificant trends (Table 2.10). 120 60S-30S Fewer data from high latitude stations make these trends less certain 90 60 and yield low confidence in resulting zonal band average estimates. 30 In the mid-latitudes of the SH (60°S to 30°S) there is limited evidence 0 -30 of long-term increases with three data sets showing significant trends -60 for the 1901 2008 period but GHCN having negative trends that are -90 not significant. For the 1951 2008 period changes in SH mid-latitude 60 Global precipitation are less certain, with one data set showing a significant 30 trend towards drying, two showing non-significant drying trends and the final dataset suggesting increases in precipitation. All data sets 0 show an abrupt decline in SH mid-latitude precipitation in the early -30 2000s (Figure 2.28) consistent with enhanced drying that has very -60 recently recovered. These results for latitudinal changes are broadly 1900 1910 1920 1930 1940 1950 1960 1970 1980 1990 2000 2010 consistent with the global satellite observations for the 1979 2008 Figure 2.28 | Annual precipitation anomalies averaged over land areas for four period (Allan et al., 2010) and land-based gauge measurements for the latitudinal bands and the globe from five global precipitation data sets relative to a 1950 1999 period (Zhang et al., 2007a). 1981 2000 climatology. In AR4, maps of observed trends of annual precipitation for 1901 2005 were calculated using GHCN interpolated to a 5° × 5° latitude/longi- in global precipitation discussed in AR4 are uncertain, owing in part tude grid. Trends (in percent per decade) were calculated for each grid to issues in data coverage in the early part of the 20th century (Wan box and showed statistically significant changes, particularly increas- et al., 2013). es in eastern and northwestern North America, parts of Europe and Russia, southern South America and Australia, declines in the Sahel In summary, confidence in precipitation change averaged over global region of Africa, and a few scattered declines elsewhere. land areas is low for the years prior to 1950 and medium afterwards because of insufficient data, particularly in the earlier part of the record. Figure 2.29 shows the spatial variability of long-term trends (1901 Available globally incomplete records show mixed and non-significant 2010) and more recent trends (1951 2010) over land in annual precip- long-term trends in reported global mean changes. Further, when vir- itation using the CRU, GHCN and GPCC data sets. The trends are com- tually all the land area is filled in using a reconstruction method, the puted from land-only grid box time series using each native data set resulting time series shows less change in land-based precipitation grid resolution. The patterns of these absolute trends (in mm yr 1 per since 1900. decade) are broadly similar to the trends (in percent per decade) relative ­ Table 2.9 | Trend estimates and 90% confidence intervals (Box 2.2) for annual precipitation for each time series in Figure 2.28 over two common periods of record. Trends in mm yr 1 per decade Data Set Area 1901 2008 1951 2008 CRU TS 3.10.01 (updated from Mitchell and Jones, 2005) Global 2.77 +/- 1.46 2.12 +/- 3.52 GHCN V2 (updated through 2011; Vose et al., 1992) Global 2.08 +/- 1.66 2.77 +/- 3.92 GPCC V6 (Becker et al., 2013) Global 1.48 +/- 1.65 1.54 +/- 4.50 Smith et al. (2012) Global 1.01 +/- 0.64 0.68 +/- 2.07 202 Observations: Atmosphere and Surface Chapter 2 Table 2.10 | Trend estimates and 90% confidence intervals (Box 2.2) for annual precipitation for each time series in Figure 2.28 over two periods. Dashes indicate not enough data available for trend calculation. For the latitudinal band 90°S to 60°S not enough data exist for each product in either period. Trends in mm yr 1 per decade Data Set Area 1901 2008 1951 2008 60°N 90°N 5.82 +/- 2.72 CRU TS 3.10.01 (updated from Mitchell and 30°N 60°N 3.82 +/- 1.14 1.13 +/- 2.01 Jones, 2005) 30°S 30°N 0.89 +/- 2.89 4.22 +/- 8.27 60°S 30°S 3.88 +/- 2.28 3.73 +/- 5.94 60°N 90°N 4.52 +/- 2.64 30°N 60°N 3.23 +/- 1.10 1.39 +/- 1.98 GHCN V2 (updated through 2011; Vose et al., 1992) 30°S 30°N 1.01 +/- 3.00 5.15 +/- 7.28 60°S 30°S 0.57 +/- 2.27 8.01 +/- 5.63 60°N 90°N 2.69 +/- 2.54 30°N 60°N 3.14 +/- 1.05 1.50 +/- 1.93 2 GPCC V6 (Becker et al., 2013) 30°S 30°N 0.48 +/- 3.35 4.16 +/- 9.65 60°S 30°S 2.40 +/- 2.01 0.51 +/- 5.45 60°N 90°N 0.63 +/- 1.27 30°N 60°N 1.44 +/- 0.50 0.97 +/- 0.88 Smith et al. (2012) 30°S 30°N 0.43 +/- 1.48 0.67 +/- 4.75 60°S 30°S 2.94 +/- 1.40 0.78 +/- 3.31 CRU 1901-2010 CRU 1951-2010 GHCN 1901-2010 GHCN 1951-2010 GPCC 1901-2010 GPCC 1951-2010 Trend (mm yr-1 per decade) Figure 2.29 | Trends in annual precipitation over land from the CRU, GHCN and GPCC data sets for 1901 2010 (left-hand panels) and 1951 2010 (right-hand panels). Trends have been calculated only for those grid boxes with greater than 70% complete records and more than 20% data availability in first and last decile of the period. White areas indicate incomplete or missing data. Black plus signs (+) indicate grid boxes where trends are significant (i.e., a trend of zero lies outside the 90% confidence interval). 203 Chapter 2 Observations: Atmosphere and Surface to local climatology (Supplementary Material 2.SM.6.1). Increases for with increases in maximum and minimum temperatures in the west- the period 1901 2010 are seen in the mid- and higher-latitudes of ern Himalaya. Serquet et al. (2011) analyzed snowfall and rainfall days both the NH and SH consistent with the reported changes for latitu- since 1961 and found the proportion of snowfall days to rainfall days dinal bands. At the grid box scale, statistically significant trends occur in Switzerland was declining in association with increasing temper- in most of the same areas, in each data set but are far more limited atures. Scherrer and Appenzeller (2006) found a trend in a pattern than for temperature over a similar length period (cf. Figure 2.21). The of variability of snowfall in the Swiss Alps that indicated decreasing GPCC map shows the most areas with significant trends. Comparing snow at low altitudes relative to high altitudes, but with large decadal the maps in Figure 2.29, most areas for which trends can be calculated variability in key snow indicators (Scherrer et al., 2013). Van Ommen for both periods show similar trends between the 1901 2010 period and Morgan (2010) draw a link between increased snowfall in coastal and the 1951 2010 period with few exceptions (e.g., South Eastern East Antarctica and increased southwest Western Australia drought. Australia, ). Trends over shorter periods can differ from those implied However, Monaghan and Bromwich (2008) found an increase in snow for the longest periods. For example, since the late 1980s trends in the accumulation over all Antarctica from the late 1950s to 1990, then a Sahel region have been significantly positive (not shown). decline to 2004. Thus snowfall changes in Antarctica remain uncertain. In summary, when averaged over the land areas of the mid-latitudes In summary, in most regions analyzed, it is likely that decreasing num- 2 of the NH, all datasets show a likely overall increase in precipitation bers of snowfall events are occurring where increased winter temper- (medium confidence since 1901, but high confidence after 1951). atures have been observed (North America, Europe, Southern and East For all other zones one or more of data sparsity, quality, or a lack Asia). Confidence is low for the changes in snowfall over Antarctica. of quantitative agreement amongst available estimates yields low ­confidence in characterisation of such long-term trends in zonally 2.5.2 Streamflow and Runoff averaged ­ recipitation. Nevertheless, changes in some more regional p or shorter-term recent changes can be quantified. It is likely there was AR4 concluded that runoff and river discharge generally increased at an abrupt decline in SH mid-latitude precipitation in the early 2000s high latitudes, with some exceptions. No consistent long-term trend in consistent with enhanced drying that has very recently recovered. Pre- discharge was reported for the world s major rivers on a global scale. cipitation in the tropical land areas has increased (medium confidence) over the last decade, reversing the drying trend that occurred from the River discharge is unique among water cycle components in that it mid-1970s to mid-1990s reported in AR4. both spatially and temporally integrates surplus waters upstream within a catchment (Shiklomanov et al., 2010) which makes it well 2.5.1.3 Changes in Snowfall suited for in situ monitoring (Arndt et al., 2010). The most recent com- prehensive analyses (Milliman et al., 2008; Dai et al., 2009) do not sup- AR4 draws no conclusion on global changes in snowfall. Changes port earlier work (Labat et al., 2004) that reported an increasing trend in snowfall are discussed on a region-by-region basis, but focussed in global river discharge associated with global warming during the mainly on North America and Eurasia. Statistically significant increases 20th century. It must be noted that many if not most large rivers, espe- were found in most of Canada, parts of northern Europe and Russia. A cially those for which a long-term streamflow record exists, have been number of areas showed a decline in the number of snowfall events, impacted by human influences such as dam construction or land use, especially those where climatological averaged temperatures were so results must be interpreted with caution. Dai et al. (2009) assem- close to 0°C and where warming led to earlier onset of spring. Also, bled a data set of 925 most downstream stations on the largest rivers an increase in lake-effect snowfall was found for areas near the North monitoring 80% of the global ocean draining land areas and capturing American Great Lakes. 73% of the continental runoff. They found that discharges in about one-third of the 200 largest rivers (including the Congo, Mississippi, Since AR4, most published literature has considered again changes in Yenisey, Paraná, Ganges, Colombia, Uruguay and Niger) show sta- snowfall in North America. These studies have confirmed that more tistically significant trends during 1948 2004, with the rivers having winter-time precipitation is falling as rain rather than snow in the downward trends (45) outnumbering those with upward trends (19). western USA (Knowles et al., 2006), the Pacific Northwest and Central Decreases in streamflow were found over many low and mid-latitude USA (Feng and Hu, 2007). Kunkel et al. (2009) analyzed trends using river basins such as the Yellow River in northern China since 1960s a specially quality-controlled data set of snowfall observations over (Piao et al., 2010) where precipitation has decreased. Increases in the contiguous USA and found that snowfall has been declining in the streamflow during the latter half of the 20th century also have been western USA, northeastern USA and southern margins of the season- reported over regions with increased precipitation, such as parts of al snow region, but increasing in the western Great Plains and Great the USA (Groisman et al., 2004), and in the Yangtze River in southern Lakes regions. Snowfall in Canada has increased mainly in the north China (Piao et al., 2010). In the Amazon basin an increase of discharge while a significant decrease was observed in the southwestern part of extremes is observed over recent decades (Espinoza Villar et al., 2009). the country for 1950 2009 (Mekis and Vincent, 2011). For France, Giuntoli et al. (2013) found that the sign of the temporal trends in natural streamflows varies with period studied. In that case Other regions that have been analyzed include Japan (Takeuchi et al., study, significant correlations between median to low flows and the 2008), where warmer winters in the heavy snowfall areas on Honshu Atlantic Multidecadal Oscillation (AMO; Section 2.7.8) result in long are associated with decreases in snowfall and precipitation in general. quasi-periodic oscillations. Shekar et al. (2010) found declines in total seasonal snowfall along 204 Observations: Atmosphere and Surface Chapter 2 At high latitudes, increasing winter base flow and mean annual Zhang et al. (2007b) found decreasing pan evaporation at stations stream flow resulting from possible permafrost thawing were report- across the Tibetan Plateau, even with increasing air temperature. Sim- ed in northwest Canada (St. Jacques and Sauchyn, 2009). Rising min- ilarly, decreases in pan evaporation were also found for northeast- imum daily flows also have been observed in northern Eurasian rivers ern India (Jhajharia et al., 2009) and the Canadian Prairies (Burn and (Smith et al., 2007). For ocean basins other than the Arctic, and for Hesch, 2007). A continuous decrease in reference and pan evapora- the global ocean as a whole, the data for continental discharge show tion for the period 1960 2000 was reported by Xu et al. (2006a) for a small or downward trends, which are statistically significant for the humid region in China, consistent with reported continuous increase Pacific ( 9.4 km3 yr 1). Precipitation is a major driver for the discharge in aerosol levels over China (Qian et al., 2006; Section 2.2.4). Rod- trends and for the large interannual-to-decadal variations (Dai et al., erick et al. (2007) examined the relationship between pan evapora- 2009). However, for the Arctic drainage areas, Adam and Lettenmaier tion changes and many of the possible causes listed above using a (2008) found that upward trends in streamflow are not accompanied physical model and conclude that many of the decreases (USA, China, by increasing precipitation, especially over Siberia, based on available Tibetan Plateau, Australia) cited previously are related to declining observations. Zhang et al. (2012a) argued that precipitation measure- wind speeds and to a lesser extent decreasing solar radiation. Fu et al. ments are sparse and exhibit large cold-season biases in the Arctic (2009) provided an overview of pan evaporation trends and conclud- drainage areas and hence there would be large uncertianties using ed the major possible causes, changes in wind speed, humidity and these data to investigate their influence on streamflow. solar radiation, have been occurring, but that the importance of each 2 is regionally dependent. Recently, Stahl et al. (2010) and Stahl and Tallaksen (2012) investigat- ed streamflow trends based on a data set of near-natural streamflow The recent increase in incoming shortwave radiation in regions with records from more than 400 small catchments in 15 countries across decreasing aerosol concentrations (Section 2.2.3) can explain positive Europe for 1962 2004. A regional coherent pattern of annual stream- evapotranspiration trends only in the humid part of Europe. In semiarid flow trends was revealed with negative trends in southern and eastern and arid regions, trends in evapotranspiration largely follow trends in regions, and generally positive trends elsewhere. Subtle regional dif- precipitation (Jung et al., 2010). Trends in surface winds (Section 2.7.2) ferences in the subannual changes in various streamflow metrics also and CO2 (Section 2.2.1.1.1) also alter the partitioning of available can be captured in regional studies such as by Monk et al. (2011) for energy into evapotranspiration and sensible heat. While surface wind Canadian rivers. trends may explain pan evaporation trends over Australia (Rayner, 2007; Roderick et al., 2007), their impact on actual evapotranspiration In summary, the most recent comprehensive analyses lead to the con- is limited due to the compensating effect of boundary-layer feedbacks clusion that confidence is low for an increasing trend in global river (van Heerwaarden et al., 2010). In vegetated regions, where a large discharge during the 20th century. part of evapotranspiration comes from transpiration through plants stomata, rising CO2 concentrations can lead to reduced stomatal open- 2.5.3 Evapotranspiration Including Pan Evaporation ing and evapotranspiration (Idso and Brazel, 1984; Leakey et al., 2006). Additional regional effects that impact evapotranspiration trends are AR4 concluded that decreasing trends were found in records of pan lengthening of the growing season and land use change. evaporation over recent decades over the USA, India, Australia, New Zealand, China and Thailand and speculated on the causes including In summary, there is medium confidence that pan evaporation contin- decreased surface solar radiation, sunshine duration, increased spe- ued to decline in most regions studied since AR4 related to changes cific humidity and increased clouds. However, AR4 also reported that in wind speed, solar radiation and humidity. On a global scale, evapo- direct measurements of evapotranspiration over global land areas transpiration over land increased (medium confidence) from the early are scarce, and concluded that reanalysis evaporation fields are not 1980s up to the late 1990s. After 1998, a lack of moisture availability reliable because they are not well constrained by precipitation and in SH land areas, particularly decreasing soil moisture, has acted as a radiation. constraint to further increase of global evapotranspiration. Since AR4 gridded data sets have been developed that estimate actual 2.5.4 Surface Humidity evapotranspiration from either atmospheric forcing and thermal remote sensing, sometimes in combination with direct measurements AR4 reported widespread increases in surface air moisture content (e.g., from FLUXNET, a global network of flux towers), or interpolation since 1976, along with near-constant relative humidity over large of FLUXNET data using regression techniques, providing an unprec- scales though with some significant changes specific to region, time edented look at global evapotranspiration (Mueller et al., 2011). On of day or season. a global scale, evapotranspiration over land increased from the early 1980s up to the late 1990s (Wild et al., 2008; Jung et al., 2010; Wang et In good agreement with previous analysis from Dai (2006), Willett et al. al., 2010) and Wang et al. (2010) found that global evapotranspiration (2008) show widespread increasing specific humidity across the globe increased at a rate of 0.6 W m 2 per decade for the period 1982 2002. from the homogenized gridded monthly mean anomaly product Had- After 1998, a lack of moisture availability in SH land areas, particularly CRUH (1973 2003). Both Dai and HadCRUH products that are blended decreasing soil moisture, has acted as a constraint to further increase land and ocean data products end in 2003 but HadISDH (1973 2012) of global evapotranspiration (Jung et al., 2010). (Willett et al., 2013) and the NOCS product (Berry and Kent, 2009) are available over the land and ocean respectively through 2012. There 205 Chapter 2 Observations: Atmosphere and Surface (a) 1973 2012 increase expected from the Clausius Clapeyron relation (about 7% °C 1; Annex III: Glossary) with high confidence (Willett et al., 2010). Land surface humidity trends are similar in ERA-Interim to observed estimates of homogeneity-adjusted data sets (Simmons et al., 2010; Figure 2.30b). Since 2000 surface specific humidity over land has remained largely unchanged (Figure 2.30) whereas land areas have on average warmed slightly (Figure 2.14), implying a reduction in land region relative humidity. This may be linked to the greater warming of the land surface relative to the ocean surface (Joshi et al., 2008). The marine specific humidity (Berry and Kent, 2009), like that over land, shows widespread increases that correlate strongly with SST. However, there is a marked -0.5 -0.25 -0.2 -0.15 -0.1 -0.05 0. 0.05 0.1 0.15 0.2 0.25 0.5 Trend (g kg-1 per decade) decline in marine relative humidity around 1982. This is reported in Wil- lett et al. (2008) where its origin is concluded to be a non-climatic data 2 (b) issue owing to a change in reporting practice for dewpoint temperature. Specific humidity anomaly (g kg-1) 0.4 In summary, it is very likely that global near surface air specific humidi- ty has increased since the 1970s. However, during recent years the near 0.2 surface moistening over land has abated (medium confidence). As a result, fairly widespread decreases in relative humidity near the surface 0.0 are observed over the land in recent years. -0.2 2.5.5 Tropospheric Humidity HadISDH Dai HadCRUH ERA-Interim As reported in AR4, observations from radiosonde and GPS meas- -0.4 1980 1990 2000 2010 urements over land, and satellite measurements over ocean indicate increases in tropospheric water vapour at near-global spatial scales Figure 2.30 | (a) Trends in surface specific humidity from HadISDH and NOCS over 1973 2012. Trends have been calculated only for those grid boxes with greater than which are consistent with the observed increase in atmospheric tem- 70% complete records and more than 20% data availability in first and last decile perature over the last several decades. Tropospheric water vapour of the period. White areas indicate incomplete or missing data. Black plus signs (+) plays an important role in regulating the energy balance of the surface indicate grid boxes where trends are significant (i.e., a trend of zero lies outside the and TOA, provides a key feedback mechanism and is essential to the 90% confidence interval). (b) Global annual average anomalies in land surface spe- formation of clouds and precipitation. cific humidity from Dai (2006; red), HadCRUH (Willett et al., 2013; orange), HadISDH (Willett et al., 2013; black), and ERA-Interim (Simmons et al., 2010; blue). Anomalies are relative to the 1979 2003 climatology. 2.5.5.1 Radiosonde Radiosonde humidity data for the troposphere were used sparingly are some small isolated but coherent areas of drying over some of the in AR4, noting a renewed appreciation for biases with the operation- more arid land regions (Figure 2.30a). Moistening is largest in the trop- al radiosonde data that had been highlighted by several major field ics and in the extratropics during summer over both land and ocean. campaigns and intercomparisons. Since AR4 there have been three Large uncertainty remains over the SH where data are sparse. Global distinct efforts to homogenize the tropospheric humidity records from specific humidity is sensitive to large-scale phenomena such as ENSO operational radiosonde measurements (Durre et al., 2009; McCarthy (Figure 2.30b; Box 2.5). It is strongly correlated with land surface tem- et al., 2009; Dai et al., 2011) (Supplementary Material 2.SM.6.1, Table perature averages over the 23 Giorgi and Francisco (2000) regions for 2.SM.9). Over the common period of record from 1973 onwards, the the period 1973 1999 and exhibits increases mostly at or above the resulting estimates are in substantive agreement regarding specific Table 2.11 | Trend estimates and 90% confidence intervals (Box 2.2) for surface humidity over two periods. Trends in % per decade Data Set 1976 2003 1973 2012 HadISDH (Willett et al., 2008) 0.127 +/- 0.037 0.091 +/- 0.023 Land HadCRUH_land (Willett et al., 2008) 0.128 +/- 0.043 Dai_land (Dai, 2006) 0.099 +/- 0.046 NOCS (Berry and Kent, 2009) 0.114 +/- 0.064 0.090 +/- 0.033 Ocean HadCRUH_marine (Willett et al., 2008) 0.065 +/- 0.049 Dai_marine (Dai, 2006) 0.058 +/- 0.044 206 Observations: Atmosphere and Surface Chapter 2 humidity trends at the largest geographical scales. On average, the (a) 1998 - 2012 impact of the correction procedures is to remove an artificial temporal trend towards drying in the raw data and indicate a positive trend in free tropospheric specific humidity over the period of record. In each analysis, the rate of increase in the free troposphere is concluded to be largely consistent with that expected from the Clausius ­ lapeyron C relation (about 7% per degree Celsius). There is no evidence for a significant change in free tropospheric relative humidity, although a decrease in relative humidity at lower levels is observed (Section 2.5.5). Indeed, McCarthy et al. (2009) show close agreement between their radiosonde product at the lowest levels and HadCRUH (Willett et al., 2008). -10 -5 -2.5 -1 -0.5 -0.25 0. 0.25 0.5 1 2.5 5 10 2.5.5.2 Global Positioning System Trend (g kg-1 per decade) (b) Since the early 1990s, estimates of column integrated water vapour 2 Water vapour (kg m-2 per decade) 0.6 have been obtained from ground-based Global Positioning System (GPS) receivers. An international network started with about 100 0.4 stations in 1997 and has currently been expanded to more than 500 0.2 (primarily land-based) stations. Several studies have compiled GPS water vapour data sets for climate studies (Jin et al., 2007; Wang et 0.0 al., 2007; Wang and Zhang, 2008, 2009). Using such data, Mears et al. -0.2 (2010) demonstrated general agreement of the interannual anomalies -0.4 between ocean-based satellite and land-based GPS column integrat- ed water vapour data. The interannual water vapour anomalies are -0.6 closely tied to the atmospheric temperature changes in a manner con- sistent with that expected from the Clausius Clapeyron relation. Jin 1990 1995 2000 2005 2010 et al. (2007) found an average column integrated water vapour trend Figure 2.31 | (a) Trends in column integrated water vapour over ocean surfaces from of about 2 kg m 2 per decade during 1994 2006 for 150 (primarily Special Sensor Microwave Imager (Wentz et al., 2007) for the period 1988 2010. land-based) stations over the globe, with positive trends at most NH Trends have been calculated only for those grid boxes with greater than 70% complete stations and negative trends in the SH. However, given the short length records and more than 20% data availability in first and last decile of the period. Black (about 10 years) of the GPS records, the estimated trends are very sen- plus signs (+) indicate grid boxes where trends are significant (i.e., a trend of zero lies outside the 90% confidence interval). (b) Global annual average anomalies in column sitive to the start and end years and the analyzed time period (Box 2.2). integrated water vapour averaged over ocean surfaces. Anomalies are relative to the 1988 2007 average. 2.5.5.3 Satellite AR4 reported positive decadal trends in lower and upper tropospheric b ­ ehavior at large spatial scales (Dessler et al., 2008; Gettelman and water vapour based on satellite observations for the period 1988 2004. Fu, 2008; Chung et al., 2010). On decadal time-scales, increased GHG Since AR4, there has been continued evidence for increases in lower concentrations reduce clear-sky outgoing long-wave radiation (Allan, tropospheric water vapour from microwave satellite measurements of 2009; Chung and Soden, 2010), thereby influencing inferred relation- column integrated water vapour over oceans (Santer et al., 2007; Wentz ships between moisture and temperature. Using Meteosat IR radianc- et al., 2007) and globally from satellite measurements of spectrally es, Brogniez et al. (2009) demonstrated that interannual variations in resolved reflected solar radiation (Mieruch et al., 2008). The interannual free tropospheric humidity over subtropical dry regions are heavily variability and longer-term trends in column-integrated water vapour influenced by meridional mixing between the deep tropics and the over oceans are closely tied to changes in SST at the global scale and extra tropics. Regionally, upper tropospheric humidity changes in the interannual anomalies show remarkable agreement with low-level spe- tropics were shown to relate strongly to the movement of the ITCZ cific humidity anomalies from HadCRUH (O Gorman et al., 2012). The based upon microwave satellite data (Xavier et al., 2010). Shi and rate of moistening at large spatial scales over oceans is close to that Bates (2011) found an increase in upper tropospheric humidity over expected from the Clausius Clapeyron relation (about 7% per degree the equatorial tropics from 1979 to 2008. However there was no signif- Celsius) with invariant relative humidity (Figure 2.31). Satellite meas- icant trend found in tropical-mean or global-mean averages, indicating urements also indicate that the globally averaged upper tropospheric that on these time and space scales the upper troposphere has seen relative humidity has changed little over the period 1979 2010 while little change in relative humidity over the past 30 years. While micro- the troposphere has warmed, implying an increase in the mean water wave satellite measurements have become increasingly relied upon for vapour mass in the upper troposphere (Shi and Bates, 2011). studies of upper tropospheric humidity, the absence of a homogenized data set across multiple satellite platforms presents some difficulty in Interannual variations in temperature and upper tropospheric water documenting coherent trends from these records (John et al., 2011). vapour from IR satellite data are consistent with a constant RH 207 Chapter 2 Observations: Atmosphere and Surface 2.5.5.4 Reanalyses eastward shift in tropical convection and total cloud cover from the western to central equatorial Pacific occurred over the 20th century Using NCEP reanalyses for the period 1973 2007, Paltridge et al. and attributed it to a long-term weakening of the Walker circulation (2009) found negative trends in specific humidity above 850 hPa over (Section 2.7.5). Eastman et al. (2011) report that, after the remov- both the tropics and southern mid-latitudes, and above 600 hPa in the al of apparently spurious globally coherent variability, cloud cover NH mid-latitudes. However, as noted in AR4, reanalysis products suffer decreased in all subtropical stratocumulus regions from 1954 to 2008. from time dependent biases and have been shown to simulate unreal- istic trends and variability over the ocean (Mears et al., 2007; John et 2.5.6.2 Satellite Observations al., 2009) (Box 2.3). Some reanalysis products do reproduce observed variability in low level humidity over land (Simmons et al., 2010), more Satellite cloud observations offer the advantage of much better spa- complete assesments of multiple reanalysis products yield substan- tial and temporal coverage compared to surface observations. How- tially different and even opposing trends in free tropospheric specific ever they require careful efforts to identify and correct for temporal humidity (Chen et al., 2008; Dessler and Davis, 2010). Consequently, discontinuities in the data sets associated with orbital drift, sensor reanalysis products are still considered to be unsuitable for the analysis degradation, and inter-satellite calibration differences. AR4 noted that of tropospheric water vapour trends (Sherwood et al., 2010). there were substantial uncertainties in decadal trends of cloud cover 2 in all satellite data sets available at the time and concluded that there In summary, radiosonde, GPS and satellite observations of tropospher- was no clear consensus regarding the decadal changes in total cloud ic water vapour indicate very likely increases at near global scales cover. Since AR4 there has been continued effort to assess the quality since the 1970s occurring at a rate that is generally consistent with of and develop improvements to multi-decadal cloud products from the Clausius-Clapeyron relation (about 7% per degree Celsius) and operational satellite platforms (Evan et al., 2007; O Dell et al., 2008; the observed increase in atmospheric temperature. Significant trends Heidinger and Pavolonis, 2009). in tropospheric relative humidity at large spatial scales have not been observed, with the exception of near-surface air over land where rela- Several satellite data sets offer multi-decadal records of cloud cover tive humidity has decreased in recent years (Section 2.5.5). (Stubenrauch et al., 2013). AR4 noted that there were discrepancies in global cloud cover trends between ISCCP and other satellite data prod- 2.5.6 Clouds ucts, notably a large downward trend of global cloudiness in ISCCP since the late 1980s which is inconsistent with PATMOS-x and surface 2.5.6.1 Surface Observations observations (Baringer et al., 2010). Recent work has confirmed the conclusion of AR4, that much of the downward trend in ISCCP is spuri- AR4 reported that surface-observed total cloud cover may have ous and an artefact of changes in satellite viewing geometry (Evan et increased over many land areas since the middle of the 20th centu- al., 2007). An assesment of long-term variations in global-mean cloud ry, including the USA, the former USSR, Western Europe, mid-latitude amount from nine different satellite data sets by Stubenrauch et al. Canada and Australia. A few regions exhibited decreases, including (2013) found differences between data sets were comparable in mag- China and central Europe. Trends were less globally consistent since nitude to the interannual variability (2.5 to 3.5%). Such inconsistencies the early 1970s, with regional reductions in cloud cover reported for result from differnces in sampling as well as changes in instrument western Asia and Europe but increases over the USA. calibration and inhibit an accurate assessment of global-scale cloud cover trends. Analyses since AR4 have indicated decreases in cloud occurrence/cover in recent decades over Poland (Wibig, 2008), China and the Tibetan Satellite observations of low-level marine clouds suggest no long-term Plateau (Duan and Wu, 2006; Endo and Yasunari, 2006; Xia, 2010b), trends in cloud liquid water path or optical properties (O Dell et al., in particular for upper level clouds (Warren et al., 2007) and also over 2008; Rausch et al., 2010). On regional scales, trends in cloud proper- Africa, Eurasia and in particular South America (Warren et al., 2007). ties over China have been linked to changes in aerosol concentrations Increased frequency of overcast conditions has been reported for some (Qian et al., 2009; Bennartz et al., 2011) (Section 2.2.3). regions, such as Canada, from 1953 to 2002 (Milewska, 2004), with no statistically significant trends evident over Australia (Jovanovic et In summary, surface-based observations show region- and height-spe- al., 2011) and North America (Warren et al., 2007). A global analysis cific variations and trends in cloudiness but there remains substantial of surface observations spanning the period 1971 2009 (Eastman and ambiguity regarding global-scale cloud variations and trends, especial- Warren, 2012) indicates a small decline in total cloud cover of about ly from satellite observations. Although trends of cloud cover are con- 0.4% per decade which is largely attributed to declining mid- and sistent between independent data sets in certain regions, substantial high-level cloud cover and is most prominent in the middle latitudes. ambiguity and therefore low confidence remains in the observations of global-scale cloud variability and trends. Regional variability in surface-observed cloudiness over the ocean appeared more credible than zonal and global mean variations in AR4. Multidecadal changes in upper-level cloud cover and total cloud cover 2.6 Changes in Extreme Events over particular areas of the tropical Indo-Pacific Ocean were consist- ent with island precipitation records and SST variability. This has been AR4 highlighted the importance of understanding changes in extreme extended more recently by Deser et al. (2010a), who found that an climate events (Annex III: Glossary) because of their disproportionate 208 Observations: Atmosphere and Surface Chapter 2 impact on society and ecosystems compared to changes in mean cli- A large amount of evidence continues to support the conclusion that mate (see also IPCC Working Group II). More recently a comprehensive most global land areas analysed have experienced significant warming assessment of observed changes in extreme events was undertaken by of both maximum and minimum temperature extremes since about the IPCC Special Report on Managing the Risks of Extreme Events and 1950 (Donat et al., 2013c). Changes in the occurrence of cold and Disasters to Advance Climate Change Adaptation (SREX) (Seneviratne warm days (based on daily maximum temperatures) are generally et al., 2012; Section 1.3.3). less marked (Figure 2.32). ENSO (Box 2.5) influences both maximum and minimum temperature variability especially around the Pacific Data availability, quality and consistency especially affect the statistics Rim (e.g., Kenyon and Hegerl, 2008; Alexander et al., 2009) but often of extremes and some variables are particularly sensitive to chang- affecting cold and warm extremes differently. Different data sets using ing measurement practices over time. For example, historical tropical different gridding methods and/or input data (Supplementary Mate- cyclone records are known to be heterogeneous owing to changing rial 2.SM.7) indicate large coherent trends in temperature extremes observing technology and reporting protocols (Section 14.6.1) and globally, associated with warming (Figure 2.32). The level of quality when records from multiple ocean basins are combined to explore control varies between these data sets. For example, HadEX2 (Donat global trends, because data quality and reporting protocols vary sub- et al., 2013c) uses more rigorous quality control which leads to a stantially between regions (Knapp and Kruk, 2010). Similar problems reduced station sample compared to GHCNDEX (Donat et al., 2013a) have been discovered when analysing wind extremes, because of the or HadGHCND (Caesar et al., 2006). However, despite these issues 2 sensitivity of measurements to changing instrumentation and observ- data sets compare remarkably consistently even though the station ing practice (e.g., Smits et al., 2005; Wan et al., 2010). networks vary through time (Figure 2.32; Table 2.12). Other data sets that have assessed these indices, but cover a shorter period, also agree Numerous regional studies indicate that changes observed in the very well over the period of overlapping data, e.g., HadEX (Alexander frequency of extremes can be explained or inferred by shifts in the et al., 2006) and Duke (Morak et al., 2011, 2013). overall probability distribution of the climate variable (Griffiths et al., 2005; Ballester et al., 2010; Simolo et al., 2011). However, it should be The shift in the distribution of nighttime temperatures appears great- noted that these studies refer to counts of threshold exceedance er than daytime temperatures although whether distribution changes frequency, duration which closely follow mean changes. Departures are simply linked to increases in the mean or other moments is an from high percentiles/return periods (intensity, severity, magnitude) active area of research (Ballester et al., 2010; Simolo et al., 2011; Donat are highly sensitive to changes in the shape and scale parameters of and Alexander, 2012; Hansen et al., 2012). Indeed, all data sets exam- the distribution (Schär et al., 2004; Clark et al., 2006; Della-Marta et ined (Duke, GHCNDEX, HadEX, HadEX2 and HadGHCND), indicate a al., 2007a, 2007b; Fischer and Schär, 2010) and geographical location. faster increase in minimum temperature extremes than maximum Debate continues over whether variance as well as mean changes are temperature extremes. While DTR declines have only been assessed affecting global temperature extremes (Hansen et al., 2012; Rhines and with medium confidence (Section 2.4.1.2), confidence of accelerated Huybers, 2013) as illustrated in Figure 1.8 and FAQ 2.2, Figure 1. In increases in minimum temperature extremes compared to maximum the following sections the conclusions from both AR4 and SREX are temperature extremes is high due to the more consistent patterns of reviewed along with studies subsequent to those assessments. warming in minimum temperature extremes globally. 2.6.1 Temperature Extremes Regional changes in a range of climate indices are assessed in Table 2.13. These indicate likely increases across most continents in unusu- AR4 concluded that it was very likely that a large majority of global ally warm days and nights and/or reductions in unusually cold days land areas had experienced decreases in indices of cold extremes and and nights including frosts. Some regions have experienced close to a increases in indices of warm extremes, since the middle of the 20th doubling of the occurrence of warm and a halving of the occurrence century, consistent with warming of the climate. In addition, global- of cold nights, for example, parts of the Asia-Pacific region (Choi et ly averaged multi-day heat events had likely exhibited increases over al., 2009) and parts of Eurasia (Klein Tank et al., 2006; Donat et al., a similar period. SREX updated AR4 but came to similar conclusions 2013a, 2013c) since the mid-20th century. Changes in both local and while using the revised AR5 uncertainty guidance (Seneviratne et al., global SST patterns (Section 2.4.2) and large scale circulation patterns 2012). Further evidence since then indicates that the level of confi- (Section 2.7) have been shown to be associated with regional changes dence that the majority of warm and cool extremes show warming in temperature extremes (Barrucand et al., 2008; Scaife et al., 2008; remains high. Table 2.12 | Trend estimates and 90% confidence intervals (Box 2.2) for global values of cold nights (TN10p), cold days (TX10p), warm nights (TN90p) and warm days (TX90p) over the periods 1951 2010 and 1979 2010 (see Box 2.4, Table 1 for more information on indices). Trends in % per decade Data Set TN10p TX10p TN90p TX90p 1951 2010 1979 2010 1951 2010 1979 2010 1951 2010 1979 2010 1951 2010 1979 2010 HadEX2 (Donat et al., 2013c) 3.9 +/- 0.6 4.2 +/- 1.2 2.5 +/- 0.7 4.1 +/- 1.4 4.5 +/- 0.9 6.8 +/- 1.8 2.9 +/- 1.2 6.3 +/- 2.2 HadGHCND (Caesar et al., 2006) 4.5 +/- 0.7 4.0 +/- 1.5 3.3 +/- 0.8 5.0 +/- 1.6 5.8 +/- 1.3 8.6 +/- 2.3 4.2 +/- 1.8 9.4 +/- 2.7 GHCNDEX (Donat et al., 2013a) 3.9 +/- 0.6 3.9 +/- 1.3 2.6 +/- 0.7 3.9 +/- 1.4 4.3 +/- 0.9 6.3 +/- 1.8 2.9 +/- 1.2 6.1 +/- 2.2 209 Chapter 2 Observations: Atmosphere and Surface (a) Cold Nights 20 Anomaly (days) 10 0 -10 -20 1950 1960 1970 1980 1990 2000 2010 Trend (days per decade) (b) Cold Days 2 20 Anomaly (days) 10 0 -10 -20 1950 1960 1970 1980 1990 2000 2010 Trend (days per decade) (c) Warm Nights 30 Anomaly (days) 20 10 0 -10 1950 1960 1970 1980 1990 2000 2010 Trend (days per decade) (d) Warm Days 30 Anomaly (days) 20 10 0 -10 1950 1960 1970 1980 1990 2000 2010 HadEX2 Trend (days per decade) HadGHCND GHCNDEX Figure 2.32 | Trends in annual frequency of extreme temperatures over the period 1951 2010, for (a) cold nights (TN10p), (b) cold days (TX10p), (c) warm nights (TN90p) and (d) warm days (TX90p) (Box 2.4, Table 1). Trends were calculated only for grid boxes that had at least 40 years of data during this period and where data ended no earlier than 2003. Grey areas indicate incomplete or missing data. Black plus signs (+) indicate grid boxes where trends are significant (i.e., a trend of zero lies outside the 90% confidence interval). The data source for trend maps is HadEX2 (Donat et al., 2013c) updated to include the latest version of the European Climate Assessment data set (Klok and Tank, 2009). Beside each map are the near-global time series of annual anomalies of these indices with respect to 1961 1990 for three global indices data sets: HadEX2 (red); HadGHCND (Caesar et al., 2006; blue) and updated to 2010 and GHCNDEX (Donat et al., 2013a; green). Global averages are only calculated using grid boxes where all three data sets have at least 90% of data over the time period. Trends are significant (i.e., a trend of zero lies outside the 90% confidence interval) for all the global indices shown. 210 Observations: Atmosphere and Surface Chapter 2 Table 2.13 | Regional observed changes in a range of climate indices since the middle of the 20th century. Assessments are based on a range of global studies and assessments (Groisman et al., 2005; Alexander et al., 2006; Caesar et al., 2006; Sheffield and Wood, 2008; Dai, 2011a, 2011b, 2013; Seneviratne et al., 2012; Sheffield et al., 2012; Donat et al., 2013a, 2013c; van der Schrier et al., 2013) and selected regional studies as indicated. Bold text indicates where the assessment is somewhat different to SREX Table 3-2. In each such case a footnote explains why the assessment is different. See also Figures 2.32 and 2.33. Extreme Warm Days Cold Days Warm Nights Cold Nights/Frosts Heat Waves / Precipitation Dryness (e.g,. Region (e.g., TX90pa) (e.g., TX10pa) (e.g., TN90pa, TRa) (e.g., TN10pa, FDa) Warm Spellsg (e.g., RX1daya, CDDa) / Droughth R95pa, R99pa) High confidence: High confidence: High confidence: High confidence: Medium confidence: High confidence: Medium confidence: Likely overall Likely overall Likely overall Likely overall increases in more Likely overall decrease1 but increase but spatially decrease but increase1,2 decrease1,2 regions than increase1,2,5 but some spatially varying North America varying trends1,2 with spatially decreases1,3 but 1930s spatial variation trends and Central varying trends1,2 dominates longer term America trends in the USA4 High confidence: High confidenceb: Very likely increase Likely decrease central North central North America6,7 America4 Medium Medium Medium Medium Low confidence: Medium Low confidence: confidenceb: confidenceb: confidenceb: confidenceb: insufficient evidence confidenceb: limited literature 2 Overall increase8 Overall decrease8 Overall increase8 Overall decrease8 (lack of literature) Increases in and spatially and spatially varying more regions varying trends8 South America trends but some than decreases8,9 evidence of increases but spatially in more areas varying trends than decreases8 High confidence: High confidence: High confidence: High confidence: High confidenceb: High confidenceb,c: Medium confidence: Likely overall Likely overall Likely overall Likely overall Likely increases in Likely increases spatially varying increase10,11,12 decrease11,12 increase11,12 decrease10,11,12 most regions3,13 in more regions trends Europe and than decreases5,15,16 Mediterranean but regional and High confidenceb: seasonal Likely increase in variation Mediterranean17,18 Low to medium Low to medium Medium Medium Low confidenced: Low confidenced: Medium confidenced: confidenceb,d: confidenceb,d: confidenceb,d: confidenceb,d: insufficient evidence insufficient evidence increase19,22,24 limited data in limited data in limited data in limited data in (lack of literature) and spatially many regions but many regions but many regions but many regions but varying trends High confidenceb: increases in most decreases in most increases in most decreases in most Medium confidence: Likely increase in regions assessed regions assessed regions assessed regions assessed increase in North Medium West Africa25,26 Africa and confidenceb: although 1970s Medium Medium Medium Medium Middle East and increases in more Sahel drought Africa and confidenceb: confidenceb: confidenceb: confidenceb: southern regions than dominates Middle East increase North decrease North increase North decrease North Africa3,19,21,22 decreases in the trend Africa and Africa and Africa and Africa and southern Africa but Middle East19,20 Middle East19,20 Middle East19,20 Middle East19,20 spatially varying trends depending High confidenceb: High confidenceb: High confidenceb: High confidenceb: on index5,21,22 Likely increase Likely decrease Likely increase Likely decrease southern southern Africa21,22,23 southern southern Africa21,22,23 Africa21,22,23 Africa21,22,23 High confidenceb,e: High confidenceb,e: High confidenceb,e: High confidenceb,e: Medium Low to medium Low to medium Likely overall Likely overall Likely overall Likely overall confidenceb,e: confidenceb,e: confidenceb,e increase27,28,29,30,31,32 decrease27,28,29,30,31,32 increase27,28,29,30,31,32 increase27,28,29,30,31,32 Spatially varying trends and Low confidence Medium insufficient data due to insufficient confidence: Asia (excluding in some regions evidence or spatially Increase in South-east varying trends. eastern Asia36,37 Asia) High confidenceb,c: Likely more areas Medium confidence: of increases than increases in more decreases3,28,33 regions than decreases5,34,35,36 High confidenceb,f: High confidenceb,f: High confidenceb,f: High confidenceb,f: Low confidence (due Low confidence Low to medium Likely overall Likely overall Likely overall Likely overall lack of literature) (lack of literature) confidenceb,f: increase27,38,39,40 decrease27,38,39 increase27,38,39,40 decrease27,38,39 to high confidenceb,f to high confidenceb,f inconsistent trends depending on region between studies High confidence: in SE Asia. Overall High confidence2: Likely decrease in increase in dryness South-east Asia Likely overall southern Australia42,43 in southern and and Oceania increase in but index and eastern Australia Australia3,14,41 season dependent High confidenceb: Likely decrease northwest Australia25,26,44 (continued on next page) 211 Chapter 2 Observations: Atmosphere and Surface (Table 2.13 continued) Notes: a See Table 1 in Box 2.4, for definitions. b More recent literature updates the assessment from SREX Table 3-2 (including global studies). c This represents a measure of the area affected which is different from what was assessed in SREX Table 3-2. d This represents a slightly different region than that assessed in SREX Table 3-2 as it includes the Middle East. e This represents a slightly different region than that assessed in SREX Table 3-2 as it excludes Southeast Asia. f This represents a slightly different region than that assessed in SREX Table 3-2 as it combines SE Asia and Oceania. g Definitions for warm spells and heat waves vary (Perkins and Alexander, 2012) but here we are commonly assessing the Warm Spell Duration Index (WSDI; Zhang et al., 2011) or other heat wave indices (e.g., HWF, HWM; (Fischer and Schär, 2010; Perkins et al., 2012) that have defined multi-day heat extremes relative to either daily maximum or minimum temperatures (or both) above a high (commonly 90th) percentile relative to a late-20th century reference period. h See Box 2.4 and Section 2.6.1 for definitions. 1 Kunkel et al. (2008), 2 Peterson et al. (2008), 3 Perkins et al. (2012), 4 Peterson et al. (2013), 5 Westra et al. (2013), 6 Groisman et al. (2012), 7 Villarini et al. (2013), 8 Skansi et al. (2013), 9 Haylock et al. (2006), 10 Andrade et al. (2012), 11 Efthymiadis et al. (2011), 12 Moberg et al. (2006), 13 Della-Marta et al. (2007a), 14 Perkins and Alexander (2012), 15 Van den Besselaar et al. (2012), 16 Zolina et al. (2009), 17 Sousa et al. (2011), 18 Hoerling et al. (2012), 19 Donat et al. (2013b), 20 Zhang et al. (2005), 21 Kruger and Sekele (2013), 22 New et al. (2006), 23 Vincent et al. (2011), 24 Aguilar et al. (2009), 25 Dai (2013), 26 Sheffield et al. (2012), 27 Choi et al. (2009), 28 Rahimzadeh et al. (2009), 29 Revadekar et al. (2012), 30 Tank et al. (2006), 31 You et al. (2010), 32 Zhou and Ren (2011), 33 Ding et al. (2010), 34 Krishna Moorthy et al. (2009), 35 Pattanaik and Rajeevan (2010), 36 Wang et al. (2012b), 37 Fischer et al. (2011), 38 Caesar et al. (2011), 39 Chambers and Griffiths (2008), 40 Wang et 2 al. (2013), 41 Tryhorn and Risbey (2006), 42 Gallant et al. (2007), 43 King et al. (2013), 44 Jones et al. (2009). Alexander et al., 2009; Li et al., 2012), particularly in regions around ta et al., 2007b; Vautard et al., 2007), or evapotranspiration excesses the Pacific Rim (Kenyon and Hegerl, 2008). Globally, there is evidence (Black and Sutton, 2007; Fischer et al., 2007), or a combination of both of large-scale warming trends in the extremes of temperature, espe- (Seneviratne et al., 2010). This amplification of soil moisture temper- cially minimum temperature, since the beginning of the 20th century ature feedbacks is suggested to have partly enhanced the duration of (Donat et al., 2013c). extreme summer heat waves in southeastern Europe during the latter part of the 20th century (Hirschi et al., 2011), with evidence emerging There are some exceptions to this large-scale warming of temperature of a signature in other moisture-limited regions (Mueller and Senevi- extremes including central North America, eastern USA (Alexander et ratne, 2012). al., 2006; Kunkel et al., 2008; Peterson et al., 2008) and some parts of South America (Alexander et al., 2006; Rusticucci and Renom, 2008; Table 2.13 shows that there has been a likely increasing trend in the Skansi et al., 2013) which indicate changes consistent with cooling in frequency of heatwaves since the middle of the 20th century in Europe these locations. However, these exceptions appear to be mostly associ- and Australia and across much of Asia where there are sufficient data. ated with changes in maximum temperatures (Donat et al., 2013c). The However, confidence on a global scale is medium owing to lack of so-called warming hole in central North America and eastern USA, studies over Africa and South America but also in part owing to dif- where temperatures have cooled relative to the significant warming ferences in trends depending on how heatwaves are defined (Perkins elsewhere in the region, is associated with observed changes in the et al., 2012). Using monthly means as a proxy for heatwaves Coumou hydrological cycle and land atmosphere interaction (Pan et al., 2004; et al. (2013) and Hansen et al. (2012) indicate that record-breaking Portmann et al., 2009a; Portmann et al., 2009b; Misra et al., 2012) temperatures in recent decades substantially exceed what would be and decadal and multi-decadal variability linked with the Atlantic and expected by chance but caution is required when making inferences Pacific Oceans (Meehl et al., 2012; Weaver, 2012). between these studies and those that deal with multi-day events and/ or use more complex definitions for heatwave events. There is also Since AR4 many studies have analysed local to regional changes in evidence in some regions that periods prior to the 1950s had more multi-day temperature extremes in more detail, specifically address- heatwaves (e.g., over the USA, the decade of the 1930s stands out ing different heat wave aspects such as frequency, intensity, duration and is also associated with extreme drought conditions (Peterson et and spatial extent (Box 2.4, FAQ 2.2). Several high-profile heat waves al., 2013) whereas conversely in other regions heatwave trends may have occurred in recent years (e.g., in Europe in 2003 (Beniston, 2004), have been underestimated owing to poor quality and/or consistency of Australia in 2009 (Pezza et al., 2012), Russia in 2010 (Barriopedro et data (e.g., Della-Marta et al. (2007a) over Western Europe; Kuglitsch et al., 2011; Dole et al., 2011; Trenberth and Fasullo, 2012a) and USA in al. (2009, 2010) over the Mediterranean). Recent available studies also 2010/2011 (Hoerling et al., 2012) (Section 10.6.2) which have had suggest that the number of cold spells has reduced significantly since severe impacts (see WGII). Heat waves are often associated with qua- the 1950s (Donat et al., 2013a, 2013c). si-stationary anticyclonic circulation anomalies that produce prolonged hot conditions at the surface (Black and Sutton, 2007; Garcia-Herrera In summary, new analyses continue to support the AR4 and SREX et al., 2010), but long-term changes in the persistence of these anom- conclusions that since about 1950 it is very likely that the numbers alies are still relatively poorly understood (Section 2.7). Heat waves of cold days and nights have decreased and the numbers of warm can also be amplified by pre-existing dry soil conditions in transitional days and nights have increased overall on the global scale, that is, for climate zones (Ferranti and Viterbo, 2006; Fischer et al., 2007; Senevi- land areas with sufficient data. It is likely that such changes have also ratne et al., 2010; Mueller and Seneviratne, 2012) and the persistence occurred across most of North America, Europe, Asia and Australia. of those soil-mositure anomalies (Lorenz et al., 2010). Dry soil-mois- There is low to medium confidence in historical trends in daily ture conditions are either induced by precipitation deficits (Della-Mar- temperature extremes in Africa and South America as there is either 212 Observations: Atmosphere and Surface Chapter 2 insufficient data or trends vary across these regions. This, combined have occurred during the whole of the 20th century (Pryor et al., 2009; with issues with defining events, leads to the assessment that there Donat et al., 2013c; Villarini et al., 2013). For South America the most is medium confidence that globally the length and frequency of warm recent integrative studies indicate heavy rain events are increasing in spells, including heat waves, has increased since the middle of the 20th frequency and intensity over the contient as a whole (Donat et al., century although it is likely that heatwave frequency has increased 2013c; Skansi et al., 2013). For Europe and the Mediterranean, the during this period in large parts of Europe, Asia and Australia. assessment masks some regional and seasonal variation. For example, much of the increase reported in Table 2.13 is found in winter although 2.6.2 Extremes of the Hydrological Cycle with decreasing trends in some other regions such as northern Italy, Poland and some Mediterranean coastal sites (Pavan et al., 2008; Lupi- In Section 2.5 mean state changes in different aspects of the hydrolog- kasza, 2010; Toreti et al., 2010). There are mixed regional trends across ical cycle are discussed. In this section we focus on the more extreme Asia and Oceania but with some indication that increases are being aspects of the cycle including extreme rainfall, severe local weather observed in more regions than decreases while recent studies focused events like hail, flooding and droughts. Extreme events associated with on Africa, in general, have not found significant trends in extreme pre- tropical and extratropical storms are discussed in Sections 2.6.3 and cipitation (see Chapter 14 for more on regional variations and trends). 2.6.4 respectively. The above studies generally use indices which reflect moderate 2 2.6.2.1 Precipitation Extremes extremes, for example, events occurring as often as 5% or 10% of the time (Box 2.4). Only a few regions have sufficient data to assess trends AR4 concluded that substantial increases are found in heavy precipi- in rarer precipitation events reliably, for example, events occurring on tation events. It was likely that annual heavy precipitation events had average once in several decades. Using Extreme Value Theory, DeGae- disproportionately increased compared to mean changes between tano (2009) showed a 20% reduction in the return period for extreme 1951 and 2003 over many mid-latitude regions, even where there had precipitation events over large parts of the contiguous USA from 1950 been a reduction in annual total precipitation. Rare precipitation (such to 2007. For Europe from 1951 to 2010, Van den Besselaar et al. (2012) as the highest annual daily precipitation total) events were likely to reported a median reduction in 5- to 20-year return periods of 21%, have increased over regions with sufficient data since the late 19th with a range between 2% and 58% depending on the subregion and century. SREX supported this view, as have subsequent analyses, but season. This decrease in return times for rare extremes is qualitatively noted large spatial variability within and between regions (Table 3.2 of similar to the increase in moderate extremes for these regions reported Seneviratne et al., 2012). above, and also consistent with earlier local results for the extreme tail of the distribution reported in AR4. Given the diverse climates across the globe, it has been difficult to pro- vide a universally valid definition of extreme precipitation . However, The aforementioned studies refer to daily precipitation extremes, Box 2.4 Table 1 indicates some of the common definitions that are used although rainfall will often be limited to part of the day only. The litera- in the scientific literature. In general, statistical tests indicate changes ture on sub-daily scales is too limited for a global assessment although in precipitation extremes are consistent with a wetter climate (Sec- it is clear that analysis and framing of questions regarding sub-daily tion 7.6.5), although with a less spatially coherent pattern of change precipitation extremes is becoming more critical (Trenberth, 2011). than temperature, in that there are large areas that show increasing Available regional studies have shown results that are even more com- trends and large areas that show decreasing trends and a lower level plex than for daily precipitation and with variations in the spatial pat- of statistical significance than for temperature change (Alexander et terns of trends depending on event formulation and duration. However, al., 2006; Donat et al., 2013a, 2013c). Using R95p and SDII indices (Box regional studies show indications of more increasing than decreasing 2.4), Figures 2.33a and 2.33b show these areas for heavy precipitation trends (Sen Roy, 2009; for India) (Sen Roy and Rouault, 2013; for South amounts and precipitation intensity where sufficient data are available Africa) (Westra and Sisson, 2011; for Australia). Some studies present in the HadEX2 data set (Donat et al., 2013c) although there are more evidence of scaling of sub-daily precipitation with temperature that areas showing significant increases than decreases. Although chang- is outside that expected from the Clausius Clapeyron relation (about es in large-scale circulation patterns have a substantial influence on 7% per degree Celsius) (Lenderink and Van Meijgaard, 2008; Haerter precipitation extremes globally (Alexander et al., 2009; Kenyon and et al., 2010; Jones et al., 2010; Lenderink et al., 2011; Utsumi et al., Hegerl, 2010), Westra et al. (2013) showed, using in situ data over land, 2011), but scaling beyond that expected from thermodynamic theories that trends in the wettest day of the year indicate more increases than is controversial (Section 7.6.5). would be expected by chance. Over the tropical oceans satellite meas- urements show an increase in the frequency of the heaviest rainfall In summary, further analyses continue to support the AR4 and SREX during warmer (El Nino) years (Allan and Soden, 2008). conclusions that it is likely that since 1951 there have been statistically significant increases in the number of heavy precipitation events (e.g., Regional trends in precipitation extremes since the middle of the 20th above the 95th percentile) in more regions than there have been sta- century are varied (Table 2.13). In most continents confidence in trends tistically significant decreases, but there are strong regional and sub- is not higher than medium except in North America and Europe where regional variations in the trends. In particular, many regions present there have been likely increases in either the frequency or intensity of statistically non-significant or negative trends, and, where seasonal heavy precipitation. This assessment increases to very likely for cen- changes have been assessed, there are also variations between seasons tral North America. For North America it is also likely that increases (e.g., more consistent trends in winter than in summer in Europe). The 213 Chapter 2 Observations: Atmosphere and Surface overall most consistent trends towards heavier precipitation events are be comparable across climate zones. A self-calibrating (sc-) PDSI can found in central North America (very likely increase) but assessment for replace the fixed empirical constants in PDSI with values representa- Europe shows likely increases in more regions than decreases. tive of the local climate (Wells et al., 2004). Furthermore, for studies using simulated soil moisture, the type of potential evapotranspiration 2.6.2.2 Floods model used can lead to significant differences in the estimation of the regions affected and the areal extent of drought (Sheffield et al., 2012), AR4 WGI Chapter 3 (Trenberth et al., 2007) did not assess changes in but the overall effect of a more physically realistic parameterisation is floods but AR4 WGII concluded that there was not a general global debated (van der Schrier et al., 2013). trend in the incidence of floods (Kundzewicz et al., 2007). SREX went further to suggest that there was low agreement and thus low confi- Because drought is a complex variable and can at best be incompletely dence at the global scale regarding changes in the magnitude or fre- represented by commonly used drought indices, discrepancies in the quency of floods or even the sign of changes. interpretation of changes can result. For example, Sheffield and Wood (2008) found decreasing trends in the duration, intensity and severity AR5 WGII assesses floods in regional detail accounting for the fact of drought globally. Conversely, Dai (2011a,b) found a general global that trends in floods are strongly influenced by changes in river man- increase in drought, although with substantial regional variation and 2 agement (see also Section 2.5.2). Although the most evident flood individual events dominating trend signatures in some regions (e.g., trends appear to be in northern high latitudes, where observed warm- the 1970s prolonged Sahel drought and the 1930s drought in the USA ing trends have been largest, in some regions no evidence of a trend and Canadian Prairies). Studies subsequent to these continue to pro- in extreme flooding has been found, for example, over Russia based vide somewhat different conclusions on trends in global droughts and/ on daily river discharge (Shiklomanov et al., 2007). Other studies for or dryness since the middle of the 20th century (Sheffield et al., 2012; Europe (Hannaford and Marsh, 2008; Renard et al., 2008; Petrow and Dai, 2013; Donat et al., 2013c; van der Schrier et al., 2013). Merz, 2009; Stahl et al., 2010) and Asia (Jiang et al., 2008; Delgado et al., 2010) show evidence for upward, downward or no trend in the Van der Schrier et al. (2013), using monthly sc-PDSI, found no strong magnitude and frequency of floods, so that there is currently no clear case either for notable drying or moisture increase on a global scale and widespread evidence for observed changes in flooding except for over the periods 1901 2009 or 1950 2009, and this largely agrees the earlier spring flow in snow-dominated regions (Seneviratne et al., with the results of Sheffield et al. (2012) over the latter period. A 2012). comparison between the sc-PDSI calculated by van der Schrier et al. (2013) and that of Dai (2011a) shows that the dominant mode of In summary, there continues to be a lack of evidence and thus low con- variability is very similar, with a temporal evolution suggesting a trend fidence regarding the sign of trend in the magnitude and/or frequency toward drying. However, the same analysis for the 1950 2009 period of floods on a global scale. shows an initial increase in drying in the Van der Schrier et al. data set, followed by a decrease from the mid-1980s onwards, while the Dai 2.6.2.3 Droughts data show a continuing increase until 2000. The difference in trends between the sc-PDSI data set of Van der Schrier et al. and Dai appears AR4 concluded that droughts had become more common, especial- to be due to the different calibration periods used, the shorter 1950 ly in the tropics and sub-tropics since about 1970. SREX provided a 1979 period in the latter study resulting in higher index values from comprehensive assessment of changes in observed droughts (Section 1980 onwards, although the associated spatial patterns are similar. In 3.5.1 and Box 3.3 of SREX), updated the conclusions provided by AR4 addition, the observed precipitation forcing data set differs between and stated that the type of drought considered and the complexities studies, with van der Schrier et al. (2013) and Sheffield et al. (2012) in defining drought (Annex III: Glossary) can substantially affect the using CRU TS 3.10.01 (updated from Mitchell and Jones, 2005). This conclusions regarding trends on a global scale (Chapter 10). Based on data set uses fewer stations and has been wetter than some other evidence since AR4, SREX concluded that there were not enough direct precipitation products in the last couple of decades (Figure 2.29, observations of dryness to suggest high confidence in observed trends Table 2.9), although the best data set to use is still an open question. globally, although there was medium confidence that since the 1950s Despite this, a measure of sc-PDSI with potential evapotranspiration some regions of the world have experienced more intense and longer estimated using the Penman Montieth equation shows an increase droughts. The differences between AR4 and SREX are due primarily to in the percentage of land area in drought since 1950 (Sheffield et analyses post-AR4, differences in how both assessments considered al., 2012; Dai, 2013), while van der Schrier et al. (2013) also finds a drought and updated IPCC uncertainty guidance. slight increase in the percentage of land area in severe drought using the same measure. This is qualitatively consistent with the trends There are very few direct measurements of drought related variables, in surface soil moisture found for the shorter period 1988 2010 by such as soil moisture (Robock et al., 2000), so drought proxies (e.g., Dorigo et al. (2012) using a new multi-satellite data set and changes PDSI, SPI, SPEI; Box 2.4) and hydrological drought proxies (e.g., Vidal in observed streamflow (Dai, 2011b). However all these studies draw et al., 2010; Dai, 2011b) are often used to assess drought. The chosen somewhat different conclusions and the compelling arguments both proxy (e.g., precipitation, evapotranspiration, soil moisture or stream- for (Dai, 2011b, 2013) and against (Sheffield et al., 2012; van der flow) and time scale can strongly affect the ranking of drought events Schrier et al., 2013) a significant increase in the land area experienc- (Sheffield et al., 2009; Vidal et al., 2010). Analyses of these indirect ing drought has hampered global assessment. indices come with substantial uncertainties. For example, PDSI may not 214 Observations: Atmosphere and Surface Chapter 2 Studies that support an increasing trend towards the land area affect- Despite differences between the conclusions drawn by global studies, ed by drought seem to be at odds with studies that look at trends there are some areas in which they agree. Table 2.13 indicates that in dryness (i.e., lack of rainfall). For example, Donat et al. (2013c) there is medium confidence of an increase in dryness or drought in East found that the annual maximum number of consecutive dry days Asia with high confidence that this is the case in the Mediterannean has declined since the 1950s in more regions than it has increased and West Africa. There is also high confidence of decreases in dryness (Figure 2.33c). However, only regions in Russia and the USA indicate or drought in central North America and north-west Australia. significant changes and there is a lack of information for this index over large regions, especially Africa. Most other studies focussing on In summary, the current assessment concludes that there is not enough global dryness find similar results, with decadal variability dominating evidence at present to suggest more than low confidence in a glob- longer-term trends (Frich et al., 2002; Alexander et al., 2006; Donat et al-scale observed trend in drought or dryness (lack of rainfall) since the al., 2013a). However, Giorgi et al. (2011) indicate that hydroclimatic middle of the 20th century, owing to lack of direct observations, geo- intensity (Box 2.4, Chapter 7), a measure which combines both dry graphical inconsistencies in the trends, and dependencies of inferred spell length and precipitation intensity, has increased over the latter trends on the index choice. Based on updated studies, AR4 conclusions part of the 20th century in response to a warming climate. They show regarding global increasing trends in drought since the 1970s were that positive trends (reflecting an increase in the length of drought probably overstated. However, it is likely that the frequency and inten- and/or extreme precipitation events) are most marked in Europe, sity of drought has increased in the Mediterranean and West Africa 2 India, parts of South America and East Asia although trends appear to and decreased in central North America and north-west Australia since have decreased (reflecting a decrease in the length of drought and/or 1950. extreme precipitation events) in Australia and northern South America (Figure 2.33c). Data availability, quality and length of record remain issues in drawing conclusions on a global scale, however. (a) R95p 1951-2010 (b) SDII 1951-2010 -20 -15 -10 -5 0 5 10 15 20 -20 -15 -10 -5 0 5 10 15 20 Trend (% per decade) Trend (% per decade) (c) CDD 1951-2010 (d) HY-INT 1976-2000 -20 -15 -10 -5 0 5 10 15 20 -0.8 - 0.4 -0.2 0 0.2 0.4 0.8 Trend (% per decade) Trend (Normalised units) Figure 2.33 | Trends in (a) annual amount of precipitation from days >95th percentile (R95p), (b) daily precipitation intensity (SDII) and (c) frequency of the annual maximum number of consecutive dry days (CDD) (Box 2.4, Table 1). Trends are shown as relative values for better comparison across different climatic regions. Trends were calculated only for grid boxes that had at least 40 years of data during this period and where data ended no earlier than 2003. Grey areas indicate incomplete or missing data. Black plus signs (+) indicate grid boxes where trends are significant (i.e., a trend of zero lies outside the 90% confidence interval). The data source for trend maps is HadEX2 (Donat et al., 2013a) updated to include the latest version of the European Climate Assessment data set (Klok and Tank, 2009). (d) Trends (normalized units) in hydroclimatic intensity (HY-INT: a multipli- cative measure of length of dry spell and precipitation intensity) over the period 1976 2000 (adapted from Giorgi et al., 2011). An increase (decrease) in HY-INT reflects an increase (decrease) in the length of drought and /or extreme precipitation events. 215 Chapter 2 Observations: Atmosphere and Surface 2.6.2.4 Severe Local Weather Events (a) Normalized units Another extreme aspect of the hydrological cycle is severe local weather phenomena such as hail or thunder storms. These are not well observed in many parts of the world because the density of surface meteorological observing stations is too coarse to measure all such events. Moreover, homogeneity of existing reporting is questionable Landfalling Tropical Cyclones, Eastern Australia (Verbout et al., 2006; Doswell et al., 2009). Alternatively, measures of severe thunderstorms or hailstorms can be derived by assessing the (b) Normalized units environmental conditions that are favourable for their formation but this method is very uncertain (Seneviratne et al., 2012). SREX high- lighted studies such as those of Brooks and Dotzek (2008), who found significant variability but no clear trend in the past 50 years in severe thunderstorms in a region east of the Rocky Mountains in the USA, Cao (2008), who found an increasing frequency of severe hail events in Landfalling Hurricanes, United States 2 Ontario, Canada during the period 1979 2002 and Kunz et al. (2009), who found that hail days significantly increased during the period (c) Normalized units 1974 2003 in southwest Germany. Hailpad studies from Italy (Eccel et al., 2012) and France (Berthet et al., 2011) suggest slight increases in larger hail sizes and a correlation between the fraction of precipitation falling as hail with average summer temperature while in Argentina between 1960 and 2008 the annual number of hail events was found to be increasing in some regions and decreasing in others (Mezher et Landfalling Typhoons, China al., 2012). In China between 1961 and 2005, the number of hail days 1900 1950 2000 has been found to generally decrease, with the highest occurrence between 1960 and 1980 but with a sharp drop since the mid-1980s Figure 2.34 | Normalized 5-year running means of the number of (a) adjusted land (CMA, 2007; Xie et al., 2008). However, there is little consistency in hail falling eastern Australian tropical cyclones (adapted from Callaghan and Power (2011) size changes in different regions of China since 1980 (Xie et al., 2010). and updated to include 2010//2011 season) and (b) unadjusted land falling U.S. hur- ricanes (adapted from Vecchi and Knutson (2011) and (c) land-falling typhoons in China Remote sensing offers a potential alterative to surface-based meteor- (adapted from CMA, 2011). Vertical axis ticks represent one standard deviation, with all ological networks for detecting changes in small scale severe weather series normalized to unit standard deviation after a 5-year running mean was applied. phenomenon such as proxy measurements of lightning from satel- lites (Zipser et al., 2006) but there remains little convincing evidence that changes in severe thunderstorms or hail have occurred since the North Atlantic and these appear robust since the 1970s (Kossin et al. middle of the 20th century (Brooks, 2012). 2007) (very high confidence). However, argument reigns over the cause of the increase and on longer time scales the fidelity of these trends In summary, there is low confidence in observed trends in small-scale is debated (Landsea et al., 2006; Holland and Webster, 2007; Land- severe weather phenomena such as hail and thunderstorms because sea, 2007; Mann et al., 2007b) with different methods for estimating ­ of historical data inhomogeneities and inadequacies in monitoring undercounts in the earlier part of the record providing mixed conclu- ­systems. sions (Chang and Guo, 2007; Mann et al., 2007a; Kunkel et al., 2008; Vecchi and Knutson, 2008, 2011). No robust trends in annual numbers 2.6.3 Tropical Storms of tropical storms, hurricanes and major hurricanes counts have been identified over the past 100 years in the North Atlantic basin. Measures AR4 concluded that it was likely that an increasing trend had occurred of land-falling tropical cyclone frequency (Figure 2.34) are generally in intense tropical cyclone activity since 1970 in some regions but that considered to be more reliable than counts of all storms which tend there was no clear trend in the annual numbers of tropical cyclones. to be strongly influenced by those that are weak and/or short lived. Subsequent assessments, including SREX and more recent literature Callaghan and Power (2011) find a statistically significant decrease indicate that it is difficult to draw firm conclusions with respect to the in Eastern Australia land-falling tropical cyclones since the late 19th confidence levels associated with observed trends prior to the satellite century although including 2010/2011 season data this trend becomes era and in ocean basins outside of the North Atlantic. non-significant (i.e., a trend of zero lies just inside the 90% confidence interval). Significant trends are not found in other oceans on shorter Section 14.6.1 discusses changes in tropical storms in detail. Current data time scales (Chan and Xu, 2009; Kubota and Chan, 2009; Mohapatra sets indicate no significant observed trends in global tropical cyclone et al., 2011; Weinkle et al., 2012), although Grinsted et al. (2012) find frequency over the past century and it remains uncertain whether any a significant positive trend in eastern USA using tide-guage data from reported long-term increases in tropical cyclone frequency are robust, 1923 2008 as a proxy for storm surges associated with land-falling after accounting for past changes in observing capabilities (Knutson hurricanes. Differences between tropical cyclone studies highlight the et al., 2010). Regional trends in tropical cyclone frequency and the fre- challenges that still lie ahead in assessing long-term trends. quency of very intense tropical cyclones have been identified in the ­ 216 Observations: Atmosphere and Surface Chapter 2 Arguably, storm frequency is of limited usefulness if not considered 2009), with substantial decadal and longer fluctuations but with some in tandem with intensity and duration measures. Intensity measures regional and seasonal trends (Wang et al., 2009c, 2011). Figure 2.35 in historical records are especially sensitive to changing technology shows some of these changes for boreal winter using geostrophic wind and improving methodology. However, over the satellite era, increases speeds indicating that decreasing trends outnumber increasing trends in the intensity of the strongest storms in the Atlantic appear robust (Wang et al., 2011), although with few that are statistically significant. (Kossin et al., 2007; Elsner et al., 2008) but there is limited evidence Although Donat et al. (2011) and Wang et al. (2012h) find significant for other regions and the globe. Time series of cyclone indices such increases in both the strength and frequency of wintertime storms for as power dissipation, an aggregate compound of tropical cyclone large parts of Europe using the 20CR (Compo et al., 2011), there is frequency, duration and intensity that measures total wind energy debate over whether this is an artefact of the changing number of by tropical cyclones, show upward trends in the North Atlantic and assimilated observations over time (Cornes and Jones, 2011; Krueger weaker upward trends in the western North Pacific since the late 1970s et al., 2013) even though Wang et al. (2012h) find good agreement (Emanuel, 2007), but interpretation of longer-term trends is again con- between the 20CR trends and those derived from geostropic wind strained by data quality concerns (Landsea et al., 2011). extremes in the North Sea region. In summary, this assessment does not revise the SREX conclusion of SREX noted that available studies using reanalyses indicate a decrease low confidence that any reported long-term (centennial) increases in in extratropical cyclone activity (Zhang et al., 2004) and intensity 2 tropical cyclone activity are robust, after accounting for past changes (Zhang et al., 2004; Wang et al., 2009d) over the last 50 years has been in observing capabilities. More recent assessments indicate that it is reported for northern Eurasia (60°N to 40°N) linked to a ­possible north- unlikely that annual numbers of tropical storms, hurricanes and major ward shift with increased cyclone frequency in the higher ­atitudes and l hurricanes counts have increased over the past 100 years in the North decrease in the lower latitudes. The decrease at lower latitudes was Atlantic basin. Evidence, however, is for a virtually certain increase in also found in East Asia (Wang et al., 2012h) and is also supported by a the frequency and intensity of the strongest tropical cyclones since the study of severe storms by Zou et al. (2006b) who used sub-daily in situ 1970s in that region. pressure data from a number of stations across China. 2.6.4 Extratropical Storms SREX also notes that, based on reanalyses, North American cyclone numbers have increased over the last 50 years, with no statistically AR4 noted a likely net increase in frequency/intensity of NH extreme significant change in cyclone intensity (Zhang et al., 2004). Hourly extratropical cyclones and a poleward shift in storm tracks since the SLP data from Canadian stations showed that winter cyclones have 1950s. SREX further consolidated the AR4 assessment of poleward become significantly more frequent, longer lasting, and stronger in shifting storm tracks, but revised the assessment of the confidence the lower Canadian Arctic over the last 50 years (1953 2002), but levels associated with regional trends in the intensity of extreme extra- less frequent and weaker in the south, especially along the southeast tropical cyclones. and southwest Canadian coasts (Wang et al., 2006a). Further south, a tendency toward weaker low-pressure systems over the past few dec- ­ Studies using reanalyses continue to support a northward and eastward ades was found for U.S. east coast winter cyclones using reanalyses, shift in the Atlantic cyclone activity during the last 60 years with both but no statistically significant trends in the frequency of occurrence of more frequent and more intense wintertime cyclones in the high-lati- systems (Hirsch et al., 2001). tude Atlantic (Schneidereit et al., 2007; Raible et al., 2008; Vilibic and Sepic, 2010) and fewer in the mid-latitude Atlantic (Wang et al., 2006b; Using the 20CR (Compo et al., 2011), Wang et al. (2012h) found sub- Raible et al., 2008). Some studies show an increase in intensity and stantial increases in extratropical cyclone activity in the SH (20°S to number of extreme Atlantic cyclones (Paciorek et al., 2002; Lehmann 90°S). However, for southeast Australia, a decrease in activity is found et al., 2011) while others show opposite trends in eastern Pacific and and this agrees well with geostrophic wind extremes derived from North America (Gulev et al., 2001). Comparisons between studies are in situ surface pressure observations (Alexander et al., 2011). This hampered because of the sensitivities in identification schemes and/ strengthens the evidence of a southward shift in storm tracks previous- or different definitions for extreme cyclones (Ulbrich et al., 2009; Neu ly noted using older reanalyses products (Fyfe, 2003; Hope et al., 2006). et al., 2012). The fidelity of research findings also rests largely with Frederiksen and Frederiksen (2007) linked the reduction in cyclogenesis the underlying reanalyses products that are used (Box 2.3). See also at 30°S and southward shift to a decrease in the vertical mean meridi- Section 14.6.2. onal temperature gradient. There is some inconsistency among reanal- ysis products for the SH regarding trends in the frequency of intense Over longer periods studies of severe storms or storminess have been extratropical cyclones (Lim and Simmonds, 2007; Pezza et al., 2007; performed for Europe where long running in situ pressure and wind Lim and Simmonds, 2009) although studies tend to agree on a trend observations exist. Direct wind speed measurements, however, either towards more intense systems, even when inhomogeneities associated have short records or are hampered by inconsistencies due to changing with changing numbers of observations have been taken into account instrumentation and observing practice over time (Smits et al., 2005; (Wang et al., 2012h). However, further undetected contamination of Wan et al., 2010). In most cases, therefore wind speed or storminess these trends owing to issues with the reanalyses products cannot be proxies are derived from in situ pressure measurements or reanalyses ruled out (Box 2.3) and this lowers our confidence in long-term trends. data, the quality and consistency of which vary. In situ observations Links between extratropical cyclone activity and large-scale variability indicate no clear trends over the past century or longer (Hanna et al., are discussed in Sections 2.7 and 14.6.2. 2008; Matulla et al., 2008; Allan et al., 2009; Barring and Fortuniak, 217 Chapter 2 Observations: Atmosphere and Surface Frequently Asked Questions FAQ 2.2 | Have There Been Any Changes in Climate Extremes? There is strong evidence that warming has lead to changes in temperature extremes including heat waves since the mid-20th century. Increases in heavy precipitation have probably also occurred over this time, but vary by region. However, for other extremes, such as tropical cyclone frequency, we are less certain, except in some limited regions, that there have been discernable changes over the observed record. From heat waves to cold snaps or droughts to flooding rains, recording and analysing climate extremes poses unique challenges, not just because these events are rare, but also because they invariably happen in conjunction with disruptive conditions. Furthermore, there is no consistent definition in the scientific literature of what consti- tutes an extreme climatic event, and this complicates comparative global assessments. Although, in an absolute sense, an extreme climate event will vary from place to place a hot day in the tropics, 2 for instance, may be a different temperature to a hot day in the mid-latitudes international efforts to monitor extremes have highlighted some significant global changes. For example, using consistent definitions for cold (<10th percentile) and warm (>90th percentile) days 0.08 (a) Daily Minimum and nights it is found that warm days and nights have Temperatures increased and cold days and nights have decreased for most regions of the globe; a few exceptions being cen- 0.06 tral and eastern North America, and southern South Probability America but mostly only related to daytime tempera- 0.04 tures. Those changes are generally most apparent in minimum temperature extremes, for example, warm nights. Data limitations make it difficult to establish 0.02 a causal link to increases in average temperatures, but FAQ 2.2, Figure 1 indicates that daily global tem- perature extremes have indeed changed. Whether 0.08 these changes are simply associated with the average (b) Daily Maximum of daily temperatures increasing (the dashed lines in Temperatures FAQ 2.2, Figure 1) or whether other changes in the 0.06 distribution of daytime and nighttime temperatures Probability have occurred is still under debate. 0.04 Warm spells or heat waves, that is, periods contain- ing consecutive extremely hot days or nights, have also been assessed, but there are fewer studies of 0.02 heat wave characteristics than those that compare changes in merely warm days or nights. Most global land areas with available data have experienced more heat waves since the middle of the 20th century. One -15 -10 -5 0 10 5 15 Temperature Anomaly (C) exception is the south-eastern USA, where heat wave frequency and duration measures generally show FAQ 2.2, Figure 1 | Distribution of (a) daily minimum and (b) daily maxi- decreases. This has been associated with a so-called mum temperature anomalies relative to a 1961 1990 climatology for two peri- warming hole in this region, where precipitation ods: 1951 1980 (blue) and 1981 2010 (red) using the HadGHCND data set. has also increased and may be related to interactions The shaded blue and red areas represent the coldest 10% and warmest 10% respectively of (a) nights and (b) days during the 1951 1980 period. The darker between the land and the atmosphere and long-term shading indicates by how much the number of the coldest days and nights has variations in the Atlantic and Pacific Oceans. Howev- reduced (dark blue) and by how much the number of the warmest days and er, for large regions, particularly in Africa and South nights has increased (dark red) during the 1981 2010 period compared to the America, information on changes in heatwaves is 1951 1980 period. ­limited. For regions such as Europe, where historical temperature reconstructions exist going back several hundreds of years, indications are that some areas have experienced a disproportionate number of extreme heat waves in recent decades. (continued on next page) 218 Observations: Atmosphere and Surface Chapter 2 FAQ 2.2 (continued) Changes in extremes for other climate variables are generally less coherent than those observed for temperature, owing to data limitations and inconsistencies between studies, regions and/or seasons. However, increases in pre- cipitation extremes, for example, are consistent with a warmer climate. Analyses of land areas with sufficient data indicate increases in the frequency and intensity of extreme precipitation events in recent decades, but results vary strongly between regions and seasons. For instance, evidence is most compelling for increases in heavy precipitation in North America, Central America and Europe, but in some other regions such as southern Australia and western Asia there is evidence of decreases. Likewise, drought studies do not agree on the sign of the global trend, with regional inconsistencies in trends also dependent on how droughts are defined. However, indications exist that droughts have increased in some regions (e.g., the Mediterranean) and decreased in others (e.g., central North America) since the middle of the 20th century. Considering other extremes, such as tropical cyclones, the latest assessments show that due to problems with past observing capabilities, it is difficult to make conclusive statements about long-term trends. There is very strong evi- 2 dence, however, that storm activity has increased in the North Atlantic since the 1970s. Over periods of a century or more, evidence suggests slight decreases in the frequency of tropical cyclones making landfall in the North Atlantic and the South Pacific, once uncertainties in observing methods have been considered. Little evidence exists of any longer-term trend in other ocean basins. For extratropical cyclones, a poleward shift is evident in both hemispheres over the past 50 years, with further but limited evidence of a decrease in wind storm frequency at mid-latitudes. Several studies suggest an increase in intensity, but data sampling issues hamper these assessments. FAQ 2.2, Figure 2 summarizes some of the observed changes in climate extremes. Overall, the most robust global changes in climate extremes are seen in measures of daily temperature, including to some extent, heat waves. Precipitation extremes also appear to be increasing, but there is large spatial variability, and observed trends in droughts are still uncertain except in a few regions. While robust increases have been seen in tropical cyclone fre- quency and activity in the North Atlantic since the 1970s, the reasons for this are still being debated. There is limited evidence of changes in extremes associated with other climate variables since the mid-20th century. Heavy Precipitation Events Droughts Mediterranean, West Africa Droughts Central North America Northwest Australia Hot Days and Nights; Warm Spells and Heat Waves Cold Days and Nights Strongest Tropical Cyclones North Atlantic FAQ 2.2, Figure 2 | Trends in the frequency (or intensity) of various climate extremes (arrow direction denotes the sign of the change) since the middle of the 20th century (except for North Atlantic storms where the period covered is from the 1970s). 219 Chapter 2 Observations: Atmosphere and Surface Studies that have examined trends in wind extremes from observa- reanalysis products must be treated with caution however, although tions or regional reanalysis products tend to point to declining trends usually with later generation products providing improvements over in extremes in mid-latitudes (Pirazzoli and Tomasin, 2003; Smits et al., older products (Box 2.3). 2005; Pryor et al., 2007; Zhang et al., 2007b) and increasing trends in high latitudes (Lynch et al., 2004; Turner et al., 2005; Hundecha et In summary, confidence in large scale changes in the intensity of al., 2008; Stegall and Zhang, 2012). Other studies have compared the extreme extratropical cyclones since 1900 is low. There is also low con- trends from observations with reanalysis data and reported differing fidence for a clear trend in storminess proxies over the last century or even opposite trends in the reanalysis products (Smits et al., 2005; due to inconsistencies between studies or lack of long-term data in McVicar et al., 2008). On the other hand, declining trends reported some parts of the world (particularly in the SH). Likewise, confidence in by Xu et al. (2006b) over China between 1969 and 2000 were gener- trends in extreme winds is low, owing to quality and consistency issues ally consistent with trends in NCEP reanalysis. Trends extracted from with analysed data. Jan Mayen 15W 2 1 Bodo 1 0 0 -1 1900 1950 2000 -1 1 1900 1950 2000 1 0 0 -1 Torshavn -1 1900 1950 2000 1 1 1900 1950 2000 0 0 Bergen 1 -1 -1 1 0 Stockholm 1900 1950 2000 1900 1950 2000 0 1 -1 -1 1900 1950 2000 0 Aberdeen 1900 1950 2000 -1 Vestervig 1 1 1900 1950 2000 0 0 1 -1 -1 0 1900 1950 2000 1900 1950 2000 -1 1 1900 1950 2000 de Bilt0 Valentia 1 -1 1900 1950 2000 0 1 -1 0 1900 1950 2000 Paris-Orly -1 1 1900 1950 2000 0 1 Kremsmuenster -1 0 1900 1950 2000 -1 1 1 1900 1950 2000 1 0 0 Milan 45N 0 -1 -1 -1 1900 1950 2000 1900 1950 2000 1900 1950 2000 La_corunya 1 1 0 0 -1 Barcelona 1900 1950 2000 -1 1900 1950 2000 Madrid 1 Lisboa 0 1 -1 0 1900 1950 2000 -1 1900 1950 2000 Gibraltar Figure 2.35 | 99th percentiles of geostrophic wind speeds for winter (DJF). Triangles show regions where geostrophic wind speeds have been calculated from in situ surface pres- sure observations. Within each pressure triangle, Gaussian low-pass filtered curves and estimated linear trends of the 99th percentile of these geostrophic wind speeds for winter are shown. The ticks of the time (horizontal) axis range from 1875 to 2005, with an interval of 10 years. Disconnections in lines show periods of missing data. Red (blue) trend lines indicate upward (downward) significant trends (i.e., a trend of zero lies outside the 95% confidence interval). (From Wang et al., 2011.) 220 Observations: Atmosphere and Surface Chapter 2 Box 2.4 | Extremes Indices As SREX highlighted, there is no unique definition of what constitutes a climate extreme in the scientific literature given variations in regions and sectors affected (Stephenson et al., 2008). Much of the available research is based on the use of so-called extremes indices (Zhang et al., 2011). These indices can either be based on the probability of occurrence of given quantities or on absolute or percentage threshold exceedances (relative to a fixed climatological period) but also include more complex definitions related to duration, intensity and persistence of extreme events. For example, the term heat wave can mean very different things depending on the index formula- tion for the application for which it is required (Perkins and Alexander, 2012). Box 2.4, Table 1 lists a number of specific indices that appear widely in the literature and have been chosen to provide some consistency across multiple chapters in AR5 (along with the location of associated figures and text). These indices have been generally chosen for their robust statistical properties and their applicability across a wide range of climates. Another important criterion is that data for these indices are broadly available over both space and time. The existing near-global land-based data sets cover at least the post-1950 period but for regions such as Europe, North America, parts of Asia and Australia much longer analyses are available. The same indices 2 used in observational studies (this chapter) are also used to diagnose climate model output (Chapters 9, 10, 11 and 12). The types of indices discussed here do not include indices such as NINO3 representing positive and negative phases of ENSO (Box 2.5), nor do they include extremes such as 1 in 100 year events. Typically extreme indices assessed here reflect more moderate extremes, for example, events occurring as often as 5% or 10% of the time (Box 2.4, Table 1). Predefined extreme indices are usually easier to obtain than the underlying daily climate data, which are not always freely exchanged by meteorological services. However, some of these indices do represent rarer events, for example, annual maxima or minima. Analyses of these and rarer extremes (e.g., with longer (continued on next page) Box 2.4, Table 1 | Definitions of extreme temperature and precipitation indices used in IPCC (after Zhang et al., 2011). The most common units are shown but these may be shown as normalized or relative depending on application in different chapters. Index Descriptive name Definition Units Figures/Tables Section TXx Warmest daily Tmax Seasonal/annual maximum value of daily maximum C Box 2.4, Figure 1, Figures 9.37, Box 2.4, 9.5.4.1, 10.6.1.1, temperature 10.17, 12.13 12.4.3.3 TNx Warmest daily Tmin Seasonal/annual maximum value of daily minimum C Figures 9.37, 10.17 9.5.4.1, 10.6.1.1 temperature TXn Coldest daily Tmax Seasonal/annual minimum value of daily maximum C Figures 9.37, 10.17, 12.13 9.5.4.1, 10.6.1.1, 12.4.3.3 temperature TNn Coldest daily Tmin Seasonal/annual minimum value of daily minimum C Figures 9.37, 10.17, 12.13 9.5.4.1, 10.6.1.1 temperature TN10p Cold nights Days (or fraction of time) when daily minimum Days (%) Figures 2.32, 9.37, 10.17 2.6.1, 9.5.4.1, 10.6.1.1, temperature <10th percentile Tables 2.11, 2.12 11.3.2.5.1 TX10p Cold days Days (or fraction of time) when daily maximum Days (%) Figures 2.32, 9.37, 10.17, 11.17 2.6.1, 9.5.4.1, 10.6.1.1, temperature <10th percentile 11.3.2.5.1, TN90p Warm nights Days (or fraction of time) when daily minimum Days (%) Figures 2.32, 9.37, 10.17 2.6.1, 9.5.4.1, 10.6.1.1, temperature >90th percentile Tables 2.11, 2.12 11.3.2.5.1 TX90p Warm days Days (or fraction of time) when daily maximum Days (%) Figures 2.32, 9.37, 10.17, 11.17 2.6.1, 9.5.4.1, 10.6.1.1, temperature >90th percentile Tables 2.11, 2.12 11.3.2.5.1, FD Frost days Frequency of daily minimum temperature <0°C Days Figures 9.37, 12.13 2.6.1, 9.5.4.1, 10.6.1.1, Table 2.12 12.4.3.3 TR Tropical nights Frequency of daily minimum temperature >20°C Days Figures 9.37, 12.13 9.5.4.1, 12.4.3.3 RX1day Wettest day Maximum 1-day precipitation mm Figures 9.37, 10.10 2.6.2.1, 9.5.4.1, 10.6.1.2, Table 2.12, 12.27 12.4.5.5 RX5day Wettest consecutive five days Maximum of consecutive 5-day precipitation mm Figures 9.37, 12.26, 14.1 9.5.4.1, 10.6.1.2, 12.4.5.5, 14.2.1 SDII Simple daily intensity index Ratio of annual total precipitation to the number of mm day 1 Figures 2.33, 9.37, 14.1 2.6.2.1, 9.5.4.1, 14.2.1 wet days ( 1 mm) R95p Precipitation from very wet Amount of precipitation from days >95th percentile mm Figures 2.33, 9.37, 11.17 2.6.2.1, 9.5.4.1, 11.3.2.5.1 days Table 2.12 CDD Consecutive dry days Maximum number of consecutive days when Days Figures 2.33, 9.37, 12.26, 14.1 2.6.2.3, 9.5.4.1, 12.4.5.5, precipitation <1 mm 14.2.1 221 Chapter 2 Observations: Atmosphere and Surface Box 2.4 (continued) return period thresholds) are making their way into a (a) HadEX2 1951-2010 growing body of literature which, for example, are using Extreme Value Theory (Coles, 2001) to study climate extremes (Zwiers and Kharin, 1998; Brown et al., 2008; Sillmann et al., 2011; Zhang et al., 2011; Kharin et al., 2013). Extreme indices are more generally defined for daily temperature and precipitation characteristics (Zhang et al., 2011) although research is developing on the analysis of sub-daily events but mostly only on regional scales (Sen Roy, 2009; Shiu et al., 2009; Jones et al., 2 2010; Jakob et al., 2011; Lenderink et al., 2011; Shaw et al., 2011). Temperature and precipitation indices (b) HadGHCND 1951-2010 are sometimes combined to investigate extremeness (e.g., hydroclimatic intensity, HY-INT; Giorgi et al., 2011) and/or the areal extent of extremes (e.g., the Climate Extremes Index (CEI) and its variants (Gleason et al., 2008; Gallant and Karoly, 2010; Ren et al., 2011). Indi- ces rarely include other weather and climate variables, such as wind speed, humidity or physical impacts (e.g., streamflow) and phenomena. Some examples are avail- able in the literature for wind-based (Della-Marta et al., 2009) and pressure-based (Beniston, 2009) indices, for health-relevant indices combining temperature and rel- ative humidity characteristics (Diffenbaugh et al., 2007; Fischer and Schär, 2010) and for a range of dryness or -1 -0.75 -0.5 -0.25 0 0.25 0.5 0.75 1 Trend (°C per decade) drought indices (e.g., Palmer Drought Severity Index (PDSI) Palmer, 1965; Standardised Precipitation Index (c) Global land average (SPI), Standardised Precipitation Evapotranspiration 2.0 Temperature anomaly (C) Index (SPEI) Vicente-Serrano et al., 2010) and wetness HadEX2 indices (e.g., Standardized Soil Wetness Index (SSWI); 1.5 HadGHCND Vidal et al., 2010). (continued on next page) 1.0 0.5 In addition to the complication of defining an index, 0.0 the results depend also on the way in which indices are calculated (to create global averages, for example). -0.5 This is due to the fact that different algorithms may be 1950 1960 1970 1980 1990 2000 2010 employed to create grid box averages from station data, or that extremes indices may be calculated from grid- Box 2.4, Figure 1 | Trends in the warmest day of the year using different data sets for ded daily data or at station locations and then gridded. the period 1951 2010. The data sets are (a) HadEX2 (Donat et al., 2013c) updated to All of these factors add uncertainty to the calculation of include the latest version of the European Climate Assessment data set (Klok and Tank, an extreme. For example, the spatial patterns of trends 2009), (b) HadGHCND (Caesar et al., 2006) using data updated to 2010 (Donat et al., 2013a) and (c) Globally averaged annual warmest day anomalies for each data set. in the hottest day of the year differ slightly between Trends were calculated only for grid boxes that had at least 40 years of data during this data sets, although when globally averaged, trends are period and where data ended no earlier than 2003. Grey areas indicate incomplete or similar over the second half of the 20th century (Box missing data. Black plus signs (+) indicate grid boxes where trends are significant (i.e., a 2.4, Figure 1). Further discussion of the parametric and trend of zero lies outside the 90% confidence interval). Anomalies are calculated using structural uncertainties in data sets is given in Box 2.1. grid boxes only where both data sets have data and where 90% of data are available. 222 Observations: Atmosphere and Surface Chapter 2 2.7 Changes in Atmospheric Circulation and 2.7.1 Sea Level Pressure Patterns of Variability AR4 concluded that SLP in December to February decreased between Changes in atmospheric circulation and indices of climate variability, as 1948 and 2005 in the Arctic, Antarctic and North Pacific. More recent expressed in sea level pressure (SLP), wind, geopotential height (GPH), studies using updated data for the period 1949 2009 (Gillett and Stott, and other variables were assessed in AR4. Substantial multi-decadal 2009) also find decreases in SLP in the high latitudes of both hemi- variability was reported in the large-scale atmospheric circulation over spheres in all seasons and increasing SLP in the tropics and subtropics the Atlantic and the Pacific. With respect to trends, a decrease was most of the year. However, due to decadal variability SLP trends are found in tropospheric GPH over high latitudes of both hemispheres sensitive to the choice of the time period (Box 2.2), and they depend and an increase over the mid-latitudes in boreal winter for the period on the data set. 1979 2001. These changes were found to be associated with an inten- sification and poleward displacement of Atlantic and southern mid-lat- The spatial distribution of SLP represents the distribution of atmos- itude jet streams and enhanced storm track activity in the NH from the pheric mass, which is the surface imprint of the atmospheric circula- 1960s to at least the 1990s. Changes in the North Atlantic Oscillation tion. Barometric measurements are made in weather stations or on (NAO) and the Southern Annular Mode (SAM) towards their positive board ships. Fields are produced from the observations by interpolation phases were observed, but it was noted that the NAO returned to its or using data assimilation into weather models. One of the most widely 2 long-term mean state from the mid-1990s to the early 2000s. used observational data sets is HadSLP2 (Allan and Ansell, 2006), which integrates 2228 historical global terrestrial stations with marine Since AR4, more and improved observational data sets and reanalysis observations from the ICOADS on a 5° × 5°grid. Other observation data sets (Box 2.3) have been published. Uncertainties and inaccuracies products (e.g., Trenberth and Paolino, 1980; for the extratropical NH) in all data sets are better understood (Box 2.1). The studies since AR4 or reanalyses are also widely used to address changes in SLP. Although assessed in this section support the poleward movement of circulation the quality of SLP data is considered good, there are discrepancies features since the 1970s and the change in the SAM. At the same time, between gridded SLP data sets in regions with sparse observations, large decadal-to-multidecadal variability in atmospheric circulation is e.g., over Antarctica (Jones and Lister, 2007). found that partially offsets previous trends in other circulation features such as the NAO or the Pacific Walker circulation. Van Haren et al. (2012) found a strong SLP decrease over the Mediter- ranean in January to March from 1961 to 2000. For the more recent This section assesses observational evidence for changes in atmos- period (1979 2012) trends in SLP, consistent across different data sets pheric circulation in fields of SLP, GPH, and wind, in circulation features (shown in Figure 2.36 for ERA-Interim), are negative in the tropical (such as the Hadley and Walker circulation, monsoons, or jet streams; and northern subtropical Atlantic during most of the year as well as, Annex III: Glossary), as well as in circulation variability modes. Regional in May to October, in northern Siberia. Positive trends are found year- climate effects of the circulation changes are discussed in Chapter 14. round over the North and South Pacific and South Atlantic. Trends in Sea-level pressure 500hPa geopotential height 100hPa geopotential height Nov-Apr May-Oct -1.2 -0.6 0 0.6 1.2 -30 -15 0 15 30 -40 -20 0 20 40 Trend (hPa per decade) Trend (gpm per decade) Trend (gpm per decade) Figure 2.36 | Trends in (left) sea level pressure (SLP), (middle) 500 hPa geopotential height (GPH) and (right) 100 hPa GPH in (top) November to April 1979/1980 to 2011/2012 and (bottom) May to October 1979 to 2011 from ERA-Interim data. Trends are shown only if significant (i.e., a trend of zero lies outside the 90% confidence interval). 223 Chapter 2 Observations: Atmosphere and Surface November-April May-October 2 1004 hPa 1020.5 hPa 1961-1970 1971-1980 1981-1990 1991-2000 2001-2010 Figure 2.37 | Decadal averages of sea level pressure (SLP) from the 20th Century Reanalysis (20CR) for (left) November of previous year to April and (right) May to October shown by two selected contours: 1004 hPa (dashed lines) and 1020.5 hPa (solid lines). Topography above 2 km above mean sea level in 20CR is shaded in dark grey. the equatorial Pacific zonal SLP gradient during the 20th century (e.g., Surface wind measurements over land and ocean are based on largely Vecchi et al., 2006; Power and Kociuba, 2011a, 2011b) are discussed separate observing systems. Early marine observations were based on in Section 2.7.5. ship speed through the water or sails carried or on visual estimates of sea state converted to the wind speed using the Beaufort scale. The position and strength of semi-permanent pressure centres show Anemometer measurements were introduced starting in the 1950s. no clear evidence for trends since 1951. However, prominent variability The transition from Beaufort to measured winds introduced a spurious is found on decadal time scales (Figure 2.37). Consistent across differ- trend, compounded by an increase in mean anemometer height over ent data sets, the Azores high and the Icelandic low in boreal winter, time (Kent et al., 2007; Thomas et al., 2008). ICOADS release 2.5 (Wood- as captured by the high and low SLP contours, were both small in the ruff et al., 2011) contains information on measurement methods and 1960s and 1970s, large in the 1980s and 1990s, and again smaller in wind measurement heights, permitting adjustment for these effects. the 2000s. Favre and Gershunov (2006) find an eastward shift of the The ICOADS-based data set WASWind (1950 2010; Tokinaga and Xie, Aleutian low from the mid-1970s to 2001, which persisted during the 2011a) and the interpolated product NOCS v.2.0 (1973 present; Berry 2000s (Figure 2.37). The Siberian High exhibits pronounced decad- and Kent, 2011) include such corrections, among other improvements. al-to-multidecadal variability (Panagiotopoulos et al., 2005; Huang et al., 2010), with a recent (1998 to 2012) strengthening and northwest- Marine surface winds are also measured from space using various ward expansion (Zhang et al., 2012b). In boreal summer, the Atlantic and microwave range instruments: scatterometers and synthetic aper- Pacific high-pressure systems extended more westward in the 1960s ture radars retrieve wind vectors, while altimeters and passive radi- and 1970s than later. On interannual time scales, variations in pressure ometers measure wind speed only (Bourassa et al., 2010). The latter centres are related to modes of climate variability. Trends in the indices type provides the longest continuous record, starting in July 1987. that capture the strength of these modes are reported in Section 2.7.8, Satellite-based interpolated marine surface wind data sets use objec- their characteristics and impacts are discussed in Chapter 14. tive analysis methods to blend together data from different satellites and atmospheric reanalyses. The latter provide wind directions as in In summary, sea level pressure has likely decreased from 1979 to 2012 Blended Sea Winds (BSW; Zhang et al., 2006), or background fields over the tropical Atlantic and increased over large regions of the Pacific as in Cross-Calibrated Multi-Platform winds (CCMP; Atlas et al., 2011) and South Atlantic, but trends are sensitive to the time period analysed and OAFlux (Yu and Weller, 2007). CCMP uses additional dynamical owing to large decadal variability. ­constraints, in situ data and a recently homogenized data set of SSM/I observations (Wentz et al., 2007), among other satellite sources. 2.7.2 Surface Wind Speed Figure 2.38 compares 1988 2010 linear trends in surface wind speeds AR4 concluded that mid-latitude westerly winds have general- from interpolated data sets based on satellite data, from interpolat- ly increased in both hemispheres. Because of shortcomings in the ed and non-interpolated data sets based on in situ data, and from o ­ bservations, SREX stated that confidence in surface wind trends is a ­ tmospheric reanalyses. Note that these trends over a 23-year-long low. Further studies assessed here confirm this assessment. period primarily reflect decadal variability in winds, rather than long- 224 Observations: Atmosphere and Surface Chapter 2 term climate change (Box 2.2). Kent et al. (2012) recently intercom- used for trend analysis. Global data sets lack important meta informa- pared several of these data sets and found large differences. The differ- tion on instrumentation and siting (McVicar et al., 2012). Long, homog- ences in trend patterns in Figure 2.38 are large as well. Nevertheless, enized instrumental records are rare (e.g., Usbeck et al., 2010; Wan et some statistically significant features are present in most data sets, al., 2010). Moreover, wind speed trends are sensitive to the anemome- including a pattern of positive and negative trend bands across the ter height (Troccoli et al., 2012). Winds near the surface can be derived North Atlantic Ocean (Section 2.7.6.2.) and positive trends along the from reanalysis products (Box 2.3), but discrepancies are found when west coast of North America. Strengthening of the Southern Ocean comparing trends therein with trends for land stations (Smits et al., winds, consistent with the increasing trend in the SAM (Section 2.7.8) 2005; McVicar et al., 2008). and with the observed changes in wind stress fields described in Sec- tion 3.4.4, can be seen in satellite-based analyses and atmospheric Over land, a weakening of seasonal and annual mean as well as max- reanalyses in Figure 2.38. Alternating Southern Ocean trend signs in imum winds is reported for many regions from around the 1960s or the NOCS v.2.0 panel are due to interpolation of very sparse in situ 1970s to the early 2000s (a detailed review is given in McVicar et al. data (cf. the panel for the uninterpolated WASWind product). (2012)), including China and the Tibetan Plateau (Xu et al., 2006b; Guo et al., 2010) (but levelling off since 2000; Lin et al., 2012), Western and Surface winds over land have been measured with anemometers on a southern Europe (e.g., Earl et al., 2013), much of the USA (Pryor et global scale for decades, but until recently the data have been rarely al., 2007), Australia (McVicar et al., 2008) and southern and western 2 (a) CCMP (b) OAFlux (c) BSW (d) ERA Interim (e) NNR (f) 20CR (g) NOCS v2.0 (h) WASWind (i) Surface winds on the land 0.5 0.4 0.3 0.2 0.1 0 0.1 0.2 0.3 0.4 0.5 Trend (m s-1 per decade) Figure 2.38 | Trends in surface wind speed for 1988 2010. Shown in the top row are data sets based on the satellite wind observations: (a) Cross-Calibrated Multi-Platform wind product (CCMP; Atlas et al., 2011); (b) wind speed from the Objectively Analyzed Air-Sea Heat Fluxes data set, release 3 (OAFlux); (c) Blended Sea Winds (BSW; Zhang et al., 2006); in the middle row are data sets based on surface observations: (d) ERA-Interim; (e) NCEP-NCAR, v.1 (NNR); (f) 20th Century Reanalysis (20CR, Compo et al., 2011), and in the bottom row are surface wind speeds from atmospheric reanalyses: (g) wind speed from the Surface Flux Data set, v.2, from NOC, Southampton, UK (Berry and Kent, 2009); (h) Wave- and Anemometer-based Sea Surface Wind (WASWind; Tokinaga and Xie, 2011a)); and (i) Surface Winds on the Land (Vautard et al., 2010). Wind speeds correspond to 10 m heights in all products. Land station winds (panel f) are also for 10 m (but anemometer height is not always reported) except for the Australian data where they correspond to 2 m height. To improve readability of plots, all data sets (including land station data) were averaged to the 4° × 4° uniform longitude-latitude grid. Trends were computed for the annually averaged timeseries of 4° × 4° cells. For all data sets except land station data, an annual mean was considered available only if monthly means for no less than eight months were available in that calendar year. Trend values were computed only if no less than 17 years had values and at least 1 year was available among the first and last 3 years of the period. White areas indicate incomplete or missing data. Black plus signs (+) indicate grid boxes where trends are significant (i.e., a trend of zero lies outside the 90% confidence interval). 225 Chapter 2 Observations: Atmosphere and Surface Canada (Wan et al., 2010). Increasing wind speeds were found at high 2.7.4 Tropospheric Geopotential Height and Tropopause latitudes in both hemispheres, namely in Alaska from 1921 to 2001 (Lynch et al., 2004), in the central Canadian Arctic and Yukon from the AR4 concluded that over the NH between 1960 and 2000, boreal 1950 to the 2000s (Wan et al., 2010) and in coastal Antarctica over the winter and annual means of tropospheric GPH decreased over high second half of the 20th century (Turner et al., 2005). A global review latitudes and increased over the mid-latitudes. AR4 also reported an of 148 studies showed that near-surface terrestrial wind speeds are increase in tropical tropopause height and a slight cooling of the trop- declining in the Tropics and the mid-latitudes of both hemispheres at ical cold-point tropopause. a rate of 0.14 m s 1 per decade (McVicar et al., 2012). Vautard et al. (2010), analysing a global land surface wind data set from 1979 to Changes in GPH, which can be addressed using radiosonde data or 2008, found negative trends on the order of 0.1 m s 1 per decade reanalysis data (Box 2.3), reflect SLP and temperature changes in the over large portions of NH land areas. The wind speed trend pattern atmospheric levels below. The spatial gradients of the trend indicate over land inferred from their data (1988 2010, Figure 2.38) has many changes in the upper-level circulation. As for SLP, tropopsheric GPH points with magnitudes much larger than those in the reanalysis prod- trends strongly depend on the period analysed due to pronounced dec- ucts, which appear to underestimate systematically the wind speed adal variability. For the 1979 2012 period, trends for 500 hPa GPH over land, as well as in coastal regions (Kent et al., 2012). from the ERA-Interim reanalysis (Figure 2.36) as well as for other rea- 2 nalyses show a significant decrease only at southern high latitudes in In summary, confidence is low in changes in surface wind speed over November to April, but significant positive GPH trends in the subtrop- the land and over the oceans owing to remaining uncertainties in data ics and northern high latitudes. Hence the change in the time period sets and measures used. leads to a different trend pattern as compared to AR4. The seasonality and spatial dependence of 500 hPa GPH trends over Antarctica was 2.7.3 Upper-Air Winds highlighted by Neff et al. (2008), based upon radiosonde data over the period 1957 2007. In contrast to surface winds, winds above the planetary bounda- ry layer have received little attention in AR4. Radiosondes and pilot Minimum temperatures near the tropical tropopause (and therefore balloon observations are available from around the 1930s (Stickler et tropical tropopause height) are important as they affect the water al., 2010). Temporal inhomogeneities in radiosonde wind records are vapour input into the stratosphere (Section 2.2.2.1). Studies since AR4 less common, but also less studied, than those in radiosonde temper- confirm the increase in tropopause height (Wang et al., 2012c). For ature records (Gruber and Haimberger, 2008; Section 2.4.4.3). Upper tropical tropopause temperatures, studies based on radiosonde data air winds can also be derived from tracking clouds or water vapour and reanalyses partly support a cooling between the 1990s and the in satellite imagery (Menzel, 2001) or from measurements using wind early 2000s (Randel et al., 2006; Randel and Jensen, 2013), but uncer- profilers, aircraft or thermal observations, all of which serve as an input tainties in long-term trends of the tropical cold-point tropopause tem- to reanalyses (Box 2.3). perature from radiosondes (Wang et al., 2012c; Randel and Jensen, 2013) and reanalyses (Gettelman et al., 2010) are large and confidence In the past few years, interest in an accurate depiction of upper air is therefore low. winds has grown, as they are essential for estimating the state and changes of the general atmospheric circulation and for explaining In summary, tropospheric geopotential height likely decreased from changes in the surface winds (Vautard et al., 2010). Allen and Sher- 1979 to 2012 at SH high latitudes in austral summer and increased wood (2008), analysing wind shear from radiosonde data, found sig- in the subtropics and NH high latitudes. Confidence in trends of the nificant positive zonal mean zonal wind trends in the northern extrat- tropical cold-point tropopause is low owing to remaining uncertainties ropics in the upper troposphere and stratosphere and negative trends in the data. in the tropical upper troposphere for the period 1979 2005. Vautard et al. (2010) find increasing wind speed in radiosonde observations 2.7.5 Tropical Circulation in the lower and middle troposphere from 1979 to 2008 over Europe and North America and decreasing wind speeds over Central and East In AR4, large interannual variability of the Hadley and Walker circula- Asia. However, systematic global trend analyses of radiosonde winds tion was highlighted, as well as the difficulty in addressing changes in are rare, prohibiting an assessment of upper-air wind trends (specific these features in the light of discrepancies between data sets. AR4 also features such as monsoons, jet streams and storms are discussed in found that rainfall in many monsoon systems exhibits decadal chang- Sections 2.7.5, 2.7.6 and 2.6, respectively). es, but that data uncertainties restrict confidence in trends. SREX also attributed low confidence to observed trends in monsoons. In summary, upper-air winds are less studied than other aspects of the circulation, and less is known about the quality of data products, hence Observational evidence for trends and variability in the strength of the confidence in upper-air wind trends is low. Hadley and Walker circulations (Annex III: Glossary), the monsoons, and the width of the tropical belt is based on radiosonde and ­reanalyses data (Box 2.3). In addition, changes in the tropical circulation imprint on other fields that are observed from space (e.g., total ozone, outgo- ing longwave radiation). Changes in the average state of the tropical circulation are constrained to some extent by changes in the water ­ 226 Observations: Atmosphere and Surface Chapter 2 cycle (Held and Soden, 2006; Schneider et al., 2010). Changes in the 1920s is also found in SLP gradients (Zhou et al., 2009a). However, monsoon systems are expressed through altered circulation, moisture trends derived from wind observations and circulation trends from transport and convergence, and precipitation. Only a few monsoon reanalysis data carry large uncertainties (Figure 2.38), and monsoon studies address circulation changes, while most work focuses on pre- rainfall trends depend, for example, on the definition of the monsoon cipitation. area (Hsu et al., 2011). For instance, using a new definition of monsoon area, an increase in northern hemispheric and global summer monsoon Several studies report a weakening of the global monsoon circulations (land and ocean) precipitation is reported from 1979 to 2008 (Hsu et as well as a decrease of global land monsoon rainfall or of the number al., 2011; Wang et al., 2012a). of precipitation days over the past 40 to 50 years (Zhou et al., 2008, see also SREX; Liu et al., 2011). Concerning the East Asian Monsoon, The additional data sets that became available since AR4 confirm the a year-round decrease is reported for wind speeds over China at the large interannual variability of the Hadley and Walker circulation. The surface and in the lower troposphere based on surface observations strength of the northern Hadley circulation (Figure 2.39) in boreal and radiosonde data (Guo et al., 2010; Jiang et al., 2010; Vautard et winter and of the Pacific Walker circulation in boreal fall and winter is al., 2010; Xu et al., 2010). The changes in wind speed are concomi- largely related to the ENSO (Box 2.5). This association dominates inter- tant with changes in pressure centres such as a westward extension annual variability and affects trends. Data sets do not agree well with of the Western Pacific Subtropical High (Gong and Ho, 2002; Zhou et respect to trends in the Hadley circulation (Figure 2.39). Two widely 2 al., 2009b). A weakening of the East Asian summer monsoon since the used reanalysis data sets, NNR and ERA-40, both have demonstrated Reanalyses (individual and spread) Reconstructions SSMI ICOADS/WASWIND HADSPL2 4 (a) Hadley Circulation indices 2 max (1010 kg s-1) 0 -2 -4 Pacific Walker circulation indices 0.04 (b) (Pa s-1) 0.02 0 -0.02 c (octas) -0.04 -0.5 0.0 -0.06 0.5 -2 u (m s-1) -1 0 1 2 -0.6 VE (m s-1) 3 -0.4 2 -0.2 SLP (hPa) 1 0.0 0 -1 -2 1880 1900 1920 1940 1960 1980 2000 Figure 2.39 | (a) Indices of the strength of the northern Hadley circulation in December to March ( max is the maximum of the meridional mass stream function at 500 hPa between the equator and 40°N). (b) Indices of the strength of the Pacific Walker circulation in September to January ( is the difference in the vertical velocity between [10°S to 10°N, 180°W to 100°W] and [10°S to 10°N, 100°E to 150°E] as in Oort and Yienger (1996), c is the difference in cloud cover between [6°N to 12°S, 165°E to 149°W] and [18°N to 6°N, 165°E to 149°W] as in Deser et al. (2010a), vE is the effective wind index from SSM/I satellite data, updated from Sohn and Park (2010), u is the zonal wind at 10 m averaged in the region [10°S to 10°N, 160°E to 160°W], SLP is the SLP difference between [5°S to 5°N, 160°W to 80°W] and [5°S to 5°N, 80°E to 160°E] as in Vecchi et al. (2006)). Reanalysis data sets include 20CR, NCEP/NCAR, ERA-Interim, JRA-25, MERRA, and CFSR, except for the zonal wind at 10 m (20CR, NCEP/NCAR, ERA-Interim), where available until January 2013. ERA-40 and NCEP2 are not shown as they are outliers with respect to the strength trend of the northern Hadley circulation (Mitas and Clement, 2005; Song and Zhang, 2007; Hu et al., 2011; Stachnik and Schumacher, 2011). Observation data sets include HadSLP2 (Section 2.7.1), ICOADS (Section 2.7.2; only 1957 2009 data are shown) and WASWIND (Section 2.7.2), reconstructions are from Brönnimann et al. (2009). Where more than one time series was available, anomalies from the 1980/1981 to 2009/2010 mean values of each series are shown. 227 Chapter 2 Observations: Atmosphere and Surface shortcomings with respect to tropical circulation; hence their increases circulation have reversed (Figure 2.39; Luo et al., 2012). This is evident in the Hadley circulation strength since the 1970s might be artificial from changes in SLP (see equatorial Southern Oscillation Index (SOI) (Mitas and Clement, 2005; Song and Zhang, 2007; Hu et al., 2011; trends in Table 2.14 and Box 2.5, Figure 1), vertical velocity (Compo et Stachnik and Schumacher, 2011). Later generation reanalysis data sets al., 2011), water vapour flux from satellite and reanalysis data (Sohn including ERA-Interim (Brönnimann et al., 2009; Nguyen et al., 2013) and Park, 2010), or sea level height (Merrifield, 2011). It is also con- as well as satellite humidity data (Sohn and Park, 2010) also suggest sistent with the SST trend pattern since 1979 (Meng et al., 2012; see a strengthening from the mid 1970s to present, but the magnitude is also Figure 2.22). strongly data set dependent. Observed changes in several atmospheric parameters suggest that the Consistent changes in different observed variables suggest a weaken- width of the tropical belt has increased at least since 1979 (Seidel et ing of the Pacific Walker circulation during much of the 20th century al., 2008; Forster et al., 2011; Hu et al., 2011). Since AR4, wind, tem- that has been largely offset by a recent strengthening. A weakening perature, radiation, and ozone information from radiosondes, satellites, is indicated by trends in the zonal SLP gradient across the equato- and reanalyses had been used to diagnose the tropical belt width and rial Pacific (Section 2.7.1, Table 2.14) from 1861 to 1992 (Vecchi et estimate their trends. Annual mean time series of the tropical belt al., 2006), or from 1901 to 2004 (Power and Kociuba, 2011b). Boreal width from various sources are shown in Figure 2.40. 2 spring and summer contribute most strongly to the centennial trend (Nicholls, 2008; Karnauskas et al., 2009), as well as to the trend in the Since 1979 the region of low column ozone values typical of the tropics second half of the 20th century (Tokinaga et al., 2012). For boreal fall has expanded in the NH (Hudson et al., 2006; Hudson, 2012). Based on and winter, when the circulation is strongest, no trend is found in the radiosonde observations and reanalyses, the region of the high tropical Pacific Walker circulation based on the vertical velocity at 500 hPa from tropopause has expanded since 1979, and possibly since 1960 (Seidel reanalyses (Compo et al., 2011), equatorial Pacific 10 m zonal winds, or and Randel, 2007; Birner, 2010; Lucas et al., 2012), although widening SLP in Darwin (Nicholls, 2008; Figure 2.39). However, there are incon- estimates from different reanalyses and using different methodologies sistencies between ERA-40 and NNR (Chen et al., 2008). Deser et al. show a range of magnitudes (Seidel and Randel, 2007; Birner, 2010). (2010a) find changes in marine air temperature and cloud cover over the Pacific that are consistent with a weakening of the Walker circu- Several lines of evidence indicate that climate features at the edges lation during most of the 20th century (Section 2.5.7.1 and Yu and of the Hadley cell have also moved poleward since 1979. Subtropi- Zwiers, 2010). Tokinaga et al. (2012) find robust evidence for a weak- cal jet metrics from reanalysis zonal winds (Strong and Davis, 2007, ening of the Walker circulation (most notably over the Indian Ocean) 2008; Archer and Caldeira, 2008b, 2008a) and layer-average satellite from 1950 to 2008 based on observations of cloud cover, surface wind, ­temperatures (Fu et al., 2006; Fu and Lin, 2011) also indicate widening, and SLP. Since the 1980s or 1990s, however, trends in the Pacific Walker although 1979 2009 wind-based trends (Davis and Rosenlof, 2011) Tropical belt width Northern Hemisphere 80 Tropical edge latitude (°N) 38 75 68 70 34 65 67 Total tropical width (°lat) 30 68 66 1980 1990 2000 2010 76 Southern Hemisphere 64 75 Tropical edge latitude (°S) -30 73 70 -34 71 65 Ozone -38 Jet Stream Tropopause Hadley Cell Outgoing Longwave Radiation 60 1980 1990 2000 2010 1980 1990 2000 2010 Figure 2.40 | Annual average tropical belt width (left) and tropical edge latitudes in each hemisphere (right). The tropopause (red), Hadley cell (blue), and jet stream (green) metrics are based on reanalyses (NCEP/NCAR, ERA-40, JRA25, ERA-Interim, CFSR, and MERRA, see Box 2.3); outgoing longwave radiation (orange) and ozone (black) metrics are based on satellite measurements. The ozone metric refers to equivalent latitude (Hudson et al., 2006; Hudson, 2012). Adapted and updated from Seidel et al. (2008) using data presented in Davis and Rosenlof (2011) and Hudson (2012). Where multiple data sets are available for a particular metric, all are shown as light solid lines, with shading showing their range and a heavy solid line showing their median. 228 Observations: Atmosphere and Surface Chapter 2 are not statistically significant. Changes in subtropical outgoing long- Caldeira, 2008a; Fu and Lin, 2011) but no clear trend is found in the SH wave radiation, a surrogate for high cloud, also suggest widening (Hu (Swart and Fyfe, 2012). There is inconsistency with respect to jet speed and Fu, 2007), but the methodology and results are disputed (Davis trends based upon whether one uses an SMW-based or isobaric-based and Rosenlof, 2011). Widening of the tropical belt is also found in pre- approach (Strong and Davis, 2007, 2008; Archer and Caldeira, 2008b, cipitation patterns (Hu and Fu, 2007; Davis and Rosenlof, 2011; Hu et 2008a) and the choice of analysis periods due to inhomogeneities in al., 2011; Kang et al., 2011; Zhou et al., 2011), including in SH regions reanalyses (Archer and Caldeira, 2008a). In general, jets have become (Cai et al., 2012). more common (and jet speeds have increased) over the western and central Pacific, eastern Canada, the North Atlantic and Europe (Strong The qualitative consistency of these observed changes in independent and Davis, 2007; Barton and Ellis, 2009), trends that are concomitant data sets suggests a widening of the tropical belt between at least with regional increases in GPH gradients and circumpolar vortex con- 1979 and 2005 (Seidel et al., 2008), and possibly longer. Widening esti- traction (Frauenfeld and Davis, 2003; Angell, 2006). From a climate mates range between around 0° and 3° latitude per decade, but their dynamics perspective, these trends are driven by regional patterns of uncertainties have been only partially explored (Birner, 2010; Davis and tropospheric and lower stratospheric warming or cooling and thus are Rosenlof, 2011). coupled to large-scale circulation variability. In summary, large interannual-to-decadal variability is found in the The North Atlantic storm track is closely associated with the NAO 2 strength of the Hadley and Walker circulation. The confidence in trends (Schneidereit et al., 2007). Studies based on ERA-40 reanalysis (Schnei- in the strength of the Hadley circulation is low due to uncertainties in dereit et al., 2007), SLP measurements from ships (Chang, 2007), sea reanalysis data sets. Recent strengthening of the Pacific Walker circu- level time series (Vilibic and Sepic, 2010), and cloud analyses (Bender lation has largely offset the weakening trend from the 19th century et al., 2012) support a poleward shift and intensification of the North to the 1990s (high confidence). Several lines of independent evidence Atlantic cyclone tracks from the 1950s to the early 2000s (Sorteberg indicate a widening of the tropical belt since the 1970s. The suggested and Walsh, 2008; Cornes and Jones, 2011). weakening of the East Asian monsoon has low confidence, given the nature and quality of the evidence. 2.7.6.2 Weather Types and Blocking 2.7.6 Jets, Storm Tracks and Weather Types In AR4, weather types were not assessed as such, but an increase in blocking frequency in the Western Pacific and a decrease in North 2.7.6.1 Mid-latitude and Subtropical Jets and Storm Track Atlantic were noted. Position Changes in the frequency of weather types are of interest since weath- AR4 reported a poleward displacement of Atlantic and southern polar er extremes are often associated with specific weather types. For front jet streams from the 1960s to at least the mid-1990s and a pole- instance, persistent blocking of the westerly flow was essential in the ward shift of the northern hemispheric storm tracks. However, it was development of the 2010 heat wave in Russia (Dole et al., 2011) (Sec- also noted that uncertainties are large and that NNR and ERA-40 dis- tion 9.5.2.2 and Box 14.2). Synoptic classifications or statistical clus- agree in important aspects. SREX also reported a poleward shift of NH tering (Philipp et al., 2007) are commonly used to classify the weather and SH storm tracks. Studies since AR4 confirm that in the NH, the jet on a given day. Feature-based methods are also used (Croci-Maspoli core has been migrating towards the pole since the 1970s, but trends et al., 2007a). All these methods require daily SLP or upper-level fields. in the jet speed are uncertain. Additional studies assessed here further support the poleward shift of the North Atlantic storm track from the Trends in synoptic weather types have been best analysed for central 1950s to the early 2000s. Europe since the mid-20th century, where several studies describe an increase in westerly or cyclonic weather types in winter but an increase Subtropical and mid-latitude jet streams are three-dimensional enti- of anticyclonic, dry weather types in summer (Philipp et al., 2007; ties that vary meridionally, zonally, and vertically. The position of the Werner et al., 2008; Trnka et al., 2009). An eastward shift of blocking mid-latitude jet streams is related to the position of the mid-latitude events over the North Atlantic (fewer cases of blocking over Green- storm tracks; regions of enhanced synoptic activity due to the pas- land and more frequent blocking over the eartern North Atlantic) and sage of cyclones (Section 2.6). Jet stream winds can be determined the North Pacific was found by Davini et al. (2012) using NCEP/NCAR from radiosonde measurements of GPH using quasi-geostrophic flow reanalysis since 1951 and by Croci-Maspoli et al. (2007a) in ERA-40 assumptions. Using reanalysis data sets (Box 2.3), it is possible to track reanalysis during the period 1957 2001. Mokhov et al. (2013) find an three-dimensional jet variations by identifying a surface of maximum increase in blocking duration over the NH year-round since about 1990 wind (SMW), although a high vertical resolution is required for identi- in a study based on NCEP/NCAR reanalysis data from 1969 2011. For fication of jets. the SH, Dong et al. (2008) found a decrease in number of blocking days but increase in intensity of blocking over the period 1948 1999. Dif- Various new analyses based on NCEP/NCAR and ERA-40 reanalyses as ferences in blocking index definitions, the sensitivity of some indices to well as MSU/AMSU lower stratospheric temperatures (Section 2.4.4) changes in the mean field, and strong interannual variability in all sea- confirm that the jet streams (mid-latitude and subtropical) have been sons (Kreienkamp et al., 2010), partly related to circulation variability moving poleward in most regions in the NH over the last three decades modes (Croci-Maspoli et al., 2007b), complicate a global assessment (Fu et al., 2006; Hu and Fu, 2007; Strong and Davis, 2007; Archer and of blocking trends. 229 Chapter 2 Observations: Atmosphere and Surface In summary, there is evidence for a poleward shift of storm tracks and In summary, it is likely that lower-stratopheric geopotential height over jet streams since the 1970s. Based on the consistency of these trends Antarctica has decreased in spring and summer at least since 1979. with the widening of the tropical belt (Section 2.7.5), trends that are Owing to uncertainties in the data and approaches used, confidence in based on many different data sets, variables, and approaches, it is likely trends in the Brewer Dobson circulation is low. that circulation features have moved poleward since the 1970s. Meth- odological differences between studies mean there is low confidence 2.7.8 Changes in Indices of Climate Variability in characterizing the global nature of any change in blocking. AR4 assessed changes in indices of climate variability. The NAO and 2.7.7 Stratospheric Circulation SAM were found to exhibit positive trends (strengthened mid-latitude westerlies) from the 1960s to 1990s, but the NAO has returned to its Changes in the polar vortices were assessed in AR4. A significant long-term mean state since then. decrease in lower-stratospheric GPH in summer over Antarctica since 1969 was found, whereas trends in the Northern Polar Vortex were Indices of climate variability describe the state of the climate system considered uncertain owing to its large variability. with regards to individual modes of climate variability. Together with corresponding spatial patterns, they summarize large fractions of spa- 2 The most important characteristics of the stratospheric circulation for tio-temporal climate variability. Inferences about significant trends in climate and for trace gas distribution are the winter and spring polar indices are generally hampered by relative shortness of climate records, vortices and Sudden Stratospheric Warmings (rapid warmings of the their uncertainties and the presence of large variability on decadal and middle stratosphere that may lead to a collapse of the Polar Vortex), multidecadal time scales. the Quasi-Biennial Oscillation (an oscillation of equatorial zonal winds with a downward phase propagation) and the Brewer-Dobson circu- Table 2.14 summarizes observed changes in well-known indices of lation (BDC, the meridional overturning circulation transporting air climate variability (see Box 2.5, Table 1 for precise definitions). Even upward in the tropics, poleward to the winter hemisphere, and down- the indices that explicitly include detrending of the entire record (e.g., ward at polar and subpolar latitudes; Annex III: Glossary). Radiosonde Deser et al., 2010b), can exhibit statistically significant trends over observations, reanalysis data sets and space-borne temperature or shorter sub-periods. Confidence intervals in Table 2.14 that do not con- trace gas observations are used to address changes in the stratospher- tain zero indicate trend significance at 10% level; however, the trends ic circulation, but all of these sources of information carry large trend significant at 5% and 1% levels are emphasized in the discussion that uncertainties. follows. Chapter 14 discusses the main features and physical meaning of individual climate modes. The AR4 assessment was corroborated further in Forster et al. (2011) and in updated 100 hPa GPH trends from ERA-Interim reanalysis (Box The NAO index reached very low values in the winter of 2010 (Osborn, 2.3, Figure 2.36). There is high confidence that lower stratospheric GPH 2011). As a result, with the exception of the principal component over Antarctica has decreased in spring and summer at least since (PC) -based NAO index, which still shows a 5% significant positive 1979. Cohen et al. (2009) reported an increase in the number of Arctic trend from 1951 to present, other NAO or North Annular Mode (NAM) sudden stratospheric warmings during the last two decades. However, indices do not show significant trends of either sign for the periods interannual variability in the Arctic Polar Vortex is large, uncertainties in presented in Table 2.14. In contrast, the SAM maintained the upward reanalysis products are high (Tegtmeier et al., 2008), and trends depend trend (Table 2.14). Fogt et al. (2009) found a positive trend in the SAM strongly on the time period analysed (Langematz and Kunze, 2008). index from 1957 to 2005. Visbeck (2009), in a station-based index, found an increase in recent decades (1970s to 2000s). The BDC is only indirectly observable via wave activity diagnostics (which represent the main driving mechanism of the BDC), via tem- The observed detrended multidecadal SST anomaly averaged over the peratures or via the distribution of trace gases which may allow the North Atlantic Ocean area is often called Atlantic Multi-decadal Oscil- determination of the age of air (i.e., the time an air parcel has resided lation Index (AMO; see Box 2.5, Table 1, Figure 1). The warming trend in the stratosphere after its entry from the troposphere). Randel et al. in the revised AMO index since 1979 is significant at 1% level (Table (2006), found a sudden decrease in global lower stratospheric water 2.14) but cannot be readily interpreted because of the difficulty with vapour and ozone around 2001 that is consistent with an increase in reliable removal of the SST warming trend from it (Deser et al., 2010b). the mean tropical upwelling, that is, the tropical branch of the BDC (Rosenlof and Reid, 2008; Section 2.2.2.1; Lanzante, 2009; Randel and On decadal and inter-decadal time scales the Pacific climate shows Jensen, 2013). On the other hand, Engel et al. (2009) found no statisti- an irregular oscillation with long periods of persistence in individu- cally significant change in the age of air in the 24-35 km layer over the al stages and prominent shifts between them. Pacific Decadal Oscil- NH mid-latitudes from measurements of chemically inert trace gases lation (PDO), Inter-decadal Pacific Oscillation (IPO) and North Pacific from 1975 to 2005. However, this does not rule out trends in the lower Index (NPI) indices characterize this variability for both hemispheres stratospheric branch of the BDC or trends in mid to low latitude mixing and agree well with each other (Box 2.5, Figure 1). While AR4 noted (Bonisch et al., 2009; Ray et al., 2010). All of these methods are subject climate impacts of the 1976 1977 PDO phase transition, the shift in to considerable uncertainties, and they might shed light only on some the opposite direction, both in PDO and IPO, may have occurred at the aspects of the BDC. Confidence in trends in the BDC is therefore low. end of 1990s (Cai and van Rensch, 2012; Dai, 2012). Significance of 1979 2012 trends in PDO and NPI then would be an artefact of this 230 Observations: Atmosphere and Surface Chapter 2 Table 2.14 | Trends for selected indices listed in Box 2.5, Table 1. Each index was standardized for its longest available period contained within the 1870 2012 interval. Standard- ization was done on the December-to-March (DJFM) means for the NAO, NAM and Pacific-North American pattern (PNA), on seasonal anomalies for Pacific-South American patterns (PSA1,PSA2) and on monthly anomalies for all other indices. Standardized monthly and seasonal anomalies were further averaged to annual means. Trend values computed for annual or DJFM means are given in standard deviation per decade with their 90% confidence intervals. Index records where the source is not explicitly indicated were computed from either HadISST1 (for SST-based indices), or HadSLP2r (for SLP-based indices) or NNR fields of 500 hPa or 850 hPa geopotential height. CoA stands for Centers of Action index definitions. Linear trends for 1870 2012 were removed from ATL3, BMI and DMI. Trends in standard deviation units per decade Index Name 1901 2012 1951 2012 1979 2012 ( 1)*SOI from CPC 0.004 +/- 0.103 0.243 +/- 0.233 ( 1)*SOI Troup from BOM records 0.012 +/- 0.039 0.018 +/- 0.104 0.247 +/- 0.236 SOI Darwin from BOM records 0.028 +/- 0.036 0.082 +/- 0.085 0.116 +/- 0.195 ( 1)*EQSOI 0.001 +/- 0.051 0.076 +/- 0.143 0.558b +/- 0.297 NINO3.4 0.003 +/- 0.042 0.012 +/- 0.105 0.156 +/- 0.274 NINO3.4 (ERSST v.3b) 0.067 +/- 0.045 a 0.054 +/- 0.103 0.085 +/- 0.259 2 NINO3.4 (COBE SST) 0.024 +/- 0.041 0.008 +/- 0.107 0.154 +/- 0.289 NINO3 0.007 +/- 0.039 0.043 +/- 0.095 0.143 +/- 0.256 NINO3 (ERSST v.3b) 0.069 +/- 0.039 0.098 +/- 0.092 0.073 +/- 0.236 NINO3 (COBE SST) 0.034 +/- 0.036 0.054 +/- 0.096 0.113 +/- 0.258 NINO4 0.026 +/- 0.054 0.068 +/- 0.145 0.102 +/- 0.380 EMI 0.059 +/- 0.061 0.119 +/- 0.189 0.131 +/- 0.580 ( 1)*TNI 0.019 +/- 0.052 0.066 +/- 0.167 0.030 +/- 0.550 PDO from Mantua et al. (1997) 0.017 +/- 0.071 0.112 +/- 0.189 0.460a +/- 0.284 ( 1)*NPI 0.026a +/- 0.022 0.010 +/- 0.046 0.169a +/- 0.105 AMO revised 0.001 +/- 0.111 0.012 +/- 0.341 0.779b +/- 0.291 NAO stations from Jones et al. (1997) 0.044 +/- 0.056 0.095 +/- 0.149 0.136 +/- 0.394 NAO stations from Hurrell (1995) 0.001 +/- 0.066 0.171 +/- 0.179 0.214 +/- 0.400 NAO PC from Hurrell (1995) 0.012 +/- 0.059 0.198a +/- 0.148 0.037 +/- 0.401 NAM PC 0.003 +/- 0.048 0.141 +/- 0.123 0.029 +/- 0.360 SAM Z850 PC 0.268b +/- 0.063 0.100 +/- 0.109 SAM SLP grid 40°S to 70°S 0.139 +/- 0.026 b 0.198 +/- 0.052 b 0.294b +/- 0.131 SAM SLP stations from Marshall (2003) 0.128a +/- 0.097 PNA CoA 0.113 +/- 0.114 0.103 +/- 0.298 PNA RPC from CPC 0.202b +/- 0.111 0.019 +/- 0.271 PSA Karoly (1989) CoA definition 0.267 +/- 0.079 b 0.233a +/- 0.174 ( 1)*PSA Yuan and Li (2008) CoA definition 0.211b +/- 0.069 0.208 +/- 0.189 PSA1 PC 0.163 +/- 0.103 a 0.368a +/- 0.245 PSA2 PC 0.200b +/- 0.066 0.036 +/- 0.156 ATL3 0.035 +/- 0.043 0.125 +/- 0.088 a 0.186 +/- 0.193 AONM PC 0.064a +/- 0.051 0.138a +/- 0.109 0.327a +/- 0.230 AMM PC 0.019 +/- 0.058 0.015 +/- 0.155 0.309 +/- 0.324 IOBM PC 0.075a +/- 0.051 0.314b +/- 0.082 0.201 +/- 0.206 BMI 0.072 +/- 0.050 a 0.294 +/- 0.083 b 0.189 +/- 0.206 IODM PC 0.016 +/- 0.034 0.031 +/- 0.093 0.052 +/- 0.203 DMI 0.030 +/- 0.033 0.080 +/- 0.090 0.211 +/- 0.210 Notes: Trend values significant at the 5% level. a b Trend values significant at the 1% level. 231 Chapter 2 Observations: Atmosphere and Surface change; incidentally, no significant trends in these indices were seen of ENSO events. Takahashi et al. (2011) and Ren and Jin (2011) have for longer periods (Table 2.14). Nevertheless, Pacific changes since the presented many of the popular ENSO indices as elements in a two-di- 1980s (positive for NPI and negative for PDO and IPO) are consist- mensional linear space spanned by a pair of such indices. ENSO indices ent with the observed SLP changes (Section 2.7.1) and with reversing that involve central and western Pacific SST (NINO4, EMI, TNI) show no trends in the Walker Circulation (Section 2.7.5), which was reported to significant trends. be slowing down during much of the 20th century but sped up again since the 1990s. Equatorial SOI shows an increasing trend since 1979 Significant positive PNA trends and negative and positive trends in at 1% significance; more traditionally defined SOI indices do not show the first and second PSA modes respectively are observed over the significant trends (Table 2.14). last 60 years (Table 2.14). However, the level of significance of these trends depends on the index definition and on the data set used. The NINO3.4 and NINO3 show a century-scale warming trend significant at positive trend in the Atlantic Ocean Nino mode (AONM) index and in 5% level, if computed from the ERSSTv3b data set (Section 2.4.2) but ATL3 are due to the intensified warming in the eastern Tropical Atlantic not if calculated from other data sets (Table 2.14). Furthermore, the that causes the the weakening of the Atlantic equatorial cold tongue: sign (and significance) of the trend in east west SST gradient across these changes were noticed by Tokinaga and Xie (2011b) with regards the Pacific remains ambiguous (Vecchi and Soden, 2007; Bunge and to the last 60-year period. The Indian Ocean Basin Mode (IOBM) has a 2 Clarke, 2009; Karnauskas et al., 2009; Deser et al., 2010a) (Section strong warming trend (significant at 1% since the middle of the 20th 14.4.1). century). This phenomenon is well-known (Du and Xie, 2008) and its consequences for the regional climate is a subject of active research In addition to changes in the mean values of climate indices, changes (Du et al., 2009; Xie et al., 2009). in the associated spatial patterns are also possible. In particular, the diversity of detail of different ENSO events and possible distinction In summary, large variability on interannual to decadal time scales and between their flavors have received significant attention (Section remaining differences between data sets precludes robust conclusions 14.4.2). These efforts also intensified the discussion of useful ENSO on long-term changes in indices of climate variability. Confidence is indices in the literature. Starting from the work of Trenberth and Stepa- high that the increase in the NAO index from the 1950s to the 1990s niak (2001), who proposed to characterize the evolution of ENSO has been largely offset by recent changes. It is likely that the SAM events with the Trans-Nino Index (TNI), which is virtually uncorrelated index has become more positive since the 1950s. with the standard ENSO index NINO3.4, other alternative ENSO indices have been introduced and proposals were made for classifying ENSO events according to the indices they primarily maximize. While a tradi- tional, canonical El Nino event type (Rasmusson and Carpenter, 1982) is viewed as the eastern Pacific type, some of the alternative indices purport to identify events that have central Pacific maxima and are called dateline El Nino (Larkin and Harrison, 2005), Modoki (Ashok et al., 2007), or Central Pacific El Nino (Kao and Yu, 2009). However, no consensus has been reached regarding the appropriate classification Box 2.5 | Patterns and Indices of Climate Variability Much of the spatial structure of climate variability can be described as a combination of preferred patterns. The most prominent of these are known as modes of climate variability and they impact weather and climate on many spatial and temporal scales (Chapter 14). Individual climate modes historically have been identified through spatial teleconnections: correlations between regional climate variations at widely separated, geographically fixed spatial locations. An index describing temporal variations of the climate mode in question can be formed, for example, by adding climate anomalies calculated from meteorological records at stations exhibiting the strongest correlation with the mode and subtracting anomalies at stations exhibiting anticorrelation. By regressing climate records from other places on this index, one derives a spatial climate pattern characterizing this mode. Patterns of climate variability have also been derived using a variety of mathematical techniques such as principal component analysis (PCA). These patterns and their indices are useful both because they efficiently describe climate variability in terms of a few preferred modes and also because they can provide clues about how the variablility is sustained (Box 14.1 provides formal definitions of these terms). Box 2.5, Table 1 lists some prominent modes of large-scale climate variability and indices used for defining them. Changes in these indices are associated with large-scale climate variations on interannual and longer time scales. With some exceptions, indices shown have been used by a variety of authors. They are defined relatively simply from raw or statistically analyzed observations of a single climate variable, which has a history of surface observations. For most of these indices at least a century-long record is available for climate research. (continued on next page) 232 Observations: Atmosphere and Surface Chapter 2 Box 2.5, Table 1 | Established indices of climate variability with global or regional influence. Z500, Z700 and Z850 denote geopotential height at the 500, 700 and 850 hPa levels, respectively. The subscripts s and a denote standardized and anomalies , respectively. Further information is given in Supplementary Material 2.SM.8. Climate impacts of these modes are listed in Box 14.1. (continued on next page) Climate Phenomenon Index Name Index Definition Primary Reference(s) NINO1+2 SSTa averaged over [10°S 0°, 90°W 80°W] Rasmusson and Wallace NINO3 Same as above but for [5°S 5°N, 150°W 90°W] (1983), Cane (1986) NINO4 Same as above but for [5°S 5°N, 160°E 150°W] Traditional NINO3.4 Same as above but for [5°S 5°N, 170°W 120°W] Trenberth (1997) indices of Troup Southern Oscilla- Standardized for each calendar month SLPa difference: Tahiti minus Darwin, Troup (1965) ENSO-related El Nino tion Index (SOI) ×10 Tropical Southern Pacific SOI Standardized difference of SLPsa: Tahiti minus Darwin Trenberth (1984); Ropelewski Oscillation ­variability and Jones (1987) (ENSO) Darwin SOI Darwin SLPsa Trenberth and Hoar (1996) Equatorial SOI (EQSOI) Standardized difference of standardized averages of SLPa over equatorial Bell and Halpert (1998) [5°S 5°N] Pacific Ocean areas: [130°W 80°W] minus [90°E 140°E] 2 Indices of Trans-Nino Index (TNI) NINO1+2s minus NINO4s Trenberth and Stepaniak (2001) ENSO events El Nino Modoki Index SSTa [165°E 140°W, 10°S 10°N] minus 1/2*[110°W 70°W, 15°S 5°N] minus Ashok et al. (2007) evolution and type (EMI) 1/2*[125°E 145°E, 10°S 20°N] Pacific Decadal Oscillation 1st PC of monthly N. Pacific SSTa field [20°N 70°N] with subtracted global Mantua et al. (1997); Zhang et (PDO) mean al. (1997) Pacific Decadal and Inter-decadal Pacific Projection of a global SSTa onto the IPO pattern, which is found as one of the Folland et al. (1999); Power et ­Interdecadal Variability Oscillation (IPO) leading Empirical Orthogonal Functions of a low-pass filtered global SSTa field al. (1999); Parker et al. (2007) North Pacific Index (NPI) SLPa averaged over [30°N 65°N; 160°E 140°W] Trenberth and Hurrell (1994) Azores-Iceland NAO Index SLPsa difference: Lisbon/Ponta Delgada minus Stykkisholmur/ Reykjavik Hurrell (1995) North Atlantic Oscillation PC-based NAO Index Leading PC of SLPa over the Atlantic sector Hurrell (1995) (NAO) Gibraltar South-west Standardized for each calendar month SLPa difference: Gibraltar minus SW Jones et al. (1997) Iceland NAO Index Iceland / Reykjavik Northern PC-based NAM or Arctic 1st PC of the monthly mean SLPa poleward of 20°N Thompson and Wallace (1998, ­Annular Oscillation (AO) index 2000) Mode (NAM) PC-based SAM or Ant- 1st PC of Z850a or Z700a south of 20°S Thompson and Wallace (2000) Annular arctic Oscillation (AAO) modes Southern index ­Annular Grid-based SAM index: Difference between standardized zonally averaged SLPa at 40°S and 70°S, Nan and Li (2003) Mode (SAM) 40°S 70°S difference using gridded SLP fields Station-based SAM index: Difference in standardized zonal mean SLPa at 40°S and 65°S, using station Marshall (2003) 40°S 65°S data PNA index based on 1/4[(20°N, 160°W) (45°N, 165°W) + (55°N, 115°W) (30°N, 85°W)] in the Wallace and Gutzler (1981) Pacific/North America (PNA) centers of action Z500sa field atmospheric teleconnection PNA from rotated PCA Rotated PC (RPC) from the analysis of the NH Z500a field Barnston and Livezey (1987) PSA1 and PSA2 mode 2nd and 3rd PCs respectively of SH seasonal Z500a Mo and Paegle (2001) Pacific/South America (PSA) indices (PC-based) atmospheric teleconnection PSA index (centers of [ (35°S, 150°W) + (60°S, 120°W) (45°S, 60°W)] in the Z500a field Karoly (1989) action) [(45°S, 170°W) (67.5°S, 120°W) + (50°S, 45°W)]/3 in the Z500a field Yuan and Li (2008) Atlantic Multi-decadal 10-year running mean of linearly detrended Atlantic mean SSTa [0° 70°N] Enfield et al. (2001) Atlantic Ocean Multidecadal Oscillation (AMO) index Variability Revised AMO index As above, but detrended by subtracting SSTa [60°S 60°N] mean Trenberth and Shea (2006) Atlantic ATL3 SSTa averaged over [3°S 3°N, 20°W 0°] Zebiak (1993) Ocean PC-based AONM 1st PC of the detrended tropical Atlantic monthly SSTa (20°S 20°N) Deser et al. (2010b) Tropical Nino Mode ­Atlantic (AONM) Ocean Tropical PC-based AMM Index 2nd PC of the detrended tropical Atlantic monthly SSTa (20°S 20°N) ­Variability Atlantic Meridional Mode (AMM) Indian Ocean Basin mean index (BMI) SSTa averaged over [40° 110°E, 20°S 20°N] Yang et al. (2007) Basin Mode IOBM, PC-based Index The first PC of the IO detrended SSTa (40°E 110° E, 20°S 20°N) Deser et al. (2010b) Tropical (IOBM) ­Indian Ocean Indian Ocean PC-based IODM index The second PC of the IO detrended SSTa (40°E 110° E, 20°S 20°N) Variability Dipole Mode Dipole Mode Index (DMI) SSTa difference: [50°E 70°E, 10°S 10°N] minus [90°E 110°E, 10°S 0°] Saji et al. (1999) (IODM) 233 Chapter 2 Observations: Atmosphere and Surface Box 2.5 (continued) Most climate modes are illustrated by several indices (Box 2.5, Figure 1), which often behave similarly to each other. Spatial pat- terns of SST or SLP associated with these climate modes are illustrated in Box 2.5, Figure 2. They can be interpreted as a change in the SST or SLP field associated with one standard deviation change in the index. (continued on next page) (a) Traditional indices of ENSO (b) NINO3.4 ( C), SOIs (s.d.) Additional indices of ENSO (HadISST1) NINO3.4 Troup ( 1)*SOI ( 1)*EQSOI (HadISST1) (HadSLP2r) NINO4 ( 1)*TNI EMI 2 2 Indices (s.d.) 0 0 o 2 2 1880 1900 1920 1940 1960 1980 2000 1880 1900 1920 1940 1960 1980 2000 2 (c) Indices of Pacific Decadal/Interdecadal Variability (d) AMO indices (HadISST1): annual & 10 yr r.m. PDO ( 1)*NPI, 24 mon IPO, 11 yr LPF 4 r.m. (HadSLP2r) 0.6 AMO 10 yr r.m. Revised 10 yr r.m. Indices (s.d.) 0.4 Indices ( C) 2 o 0.2 0 0 2 0.2 0.4 1880 1900 1920 1940 1960 1980 2000 1880 1900 1920 1940 1960 1980 2000 (e) Indices of NAO and NAM/AO, DJFM means (f) Indices of SAM/AAO 4 NAO stations NAO PC NAM (HadSLP2r) 2 Z850 PC grid 40S 70S stations (NNR) (HadSLP2r) 40S 65S (Hurrell,1995) Indices (s.d.) Indices (s.d.) 2 0 0 2 2 4 1880 1900 1920 1940 1960 1980 2000 1880 1900 1920 1940 1960 1980 2000 (g) PNA indices, DJFM means (h) PSA1 mode indices (NNR), 24 mon r.m. Centers of action (NNR) RPC PSA1 PC Karoly PSA Yuan&Li ( 1)*PSA 2 2 Indices (s.d.) Indices (s.d.) 1 1 0 0 1 1 2 2 1880 1900 1920 1940 1960 1980 2000 1880 1900 1920 1940 1960 1980 2000 (i) Atlantic Ocean Nino Mode indices (HadISST1) (j) Tropical Atlantic Meridional Mode index (HadISST1) 3 AONM PC ATL3 (detrended) 3 AMM PC AMM index (s.d.) 2 2 Indices (s.d.) 1 1 0 0 1 1 2 1880 1900 1920 1940 1960 1980 2000 1880 1900 1920 1940 1960 1980 2000 (k) Indian Ocean Basin Mode indices (HadISST1) (l) Indian Ocean Dipole Mode indices (HadISST1) IOBM PC BMI (detrended) 2 IODM PC DMI (detrended) Indices (s.d.) Indices (s.d.) 2 1 0 0 1 2 2 1880 1900 1920 1940 1960 1980 2000 1880 1900 1920 1940 1960 1980 2000 Box 2.5, Figure 1 | Some indices of climate variability, as defined in Box 2.5, Table 1, plotted in the 1870 2012 interval. Where HadISST1 , HadSLP2r , or NNR are indicated, the indices were computed from the sea surface temperature (SST) or sea level pressure (SLP) values of the former two data sets or from 500 or 850 hPa geopotential height fields from the NNR. Data set references given in the panel titles apply to all indices shown in that panel. Where no data set is specified, a publicly available version of an index from the authors of a primary reference given in Box 2.5, Table 1 was used. All indices were standardized with regard to 1971 2000 period except for NINO3.4 (centralized for 1971 2000) and AMO indices (centralized for 1901 1970). Indices marked as detrended had their linear trend for 1870 2012 removed. All indices are shown as 12-month running means except when the temporal resolution is explicitly indicated (e.g., DJFM for December-to-March averages) or smoothing level (e.g., 11-year LPF for a low-pass filter with half-power at 11 years). 234 Observations: Atmosphere and Surface Chapter 2 Box 2.5 (continued) The difficulty of identifying a universally best index for any particular climate mode is due to the fact that no simply defined indicator can achieve a perfect separation of the target phenomenon from all other effects occurring in the climate system. As a result, each index is affected by many climate phenomena whose relative contributions may change with the time period and the data set used. Limited length and quality of the observational record further compound this problem. Thus the choice of index is always application specific. El Nino Southern Oscillation NINO3.4 (-1)*TNI (a) (b) 2 Decadal to Multi-decadal Variability of Paci c and Atlantic Oceans PDO AMO (revised) (c) (d) 0.6 0.4 0.2 0 0.2 0.4 0.6 SST change per index s.d. (C per s.d.) Hemispheric-Scale Modes of Atmospheric Variability NAO SAM PNA PSA1 & PSA2 Stations (Hurrell) Z850 PC Centers of Action PC (e) (f) (g) (h) 2 1 0 1 2 MSLP change per index s.d. (hPa per s.d.) Tropical Variability of Atlantic and Indian Oceans (i) AONM ATL3 (j) AMM PC (k) IOBM BMI (l) IODM DMI 0.4 0.3 0.2 0.1 0 0.1 0.2 0.3 0.4 SST change per index s.d. (C per s.d.) Box 2.5, Figure 2 | Spatial patterns of climate modes listed in Box 2.5, Table 1. All patterns shown here are obtained by regression of either sea surface temperature (SST) or sea level pressure (SLP) fields on the standardized index of the climate mode. For each climate mode one of the specific indices shown in Box 2.5, Figure 1 was used, as identified in the panel subtitles. SST and SLP fields are from HadISST1 and HadSLP2r data sets (interpolated gridded products based on data sets of historical observations). Regressions were done on monthly means for all patterns except for NAO and PNA, which were done with the DJFM means, and for PSA1 and PSA2, where seasonal means were used. Each regression was done for the longest period within the 1870-2012 interval when the index was available. For each pattern the time series was linearly de-trended over the entire regression interval. All patterns are shown by color plots, except for PSA2, which is shown by white contours over the PSA1 color plot (contour steps are 0.5 hPa, zero contour is skipped, negative values are indicated by dash). 235 Chapter 2 Observations: Atmosphere and Surface Acknowledgements The authors of Chapter 2 wish to thank Wenche Aas (NILU, Kjeller), Erika Coppola (ICTP, Trieste), Ritesh Gautam (NASA GSFC, Greenbelt), Jenny Hand (CIRA, Fort Collins), Andreas Hilboll (U. Bremen, Bremen), Glenn Hyatt (NOAA NCDC, Asheville), David Parrish (NOAA ESRL-CSD, Boulder), Deborah Misch (LMI, Inc, Asheville), Jared Rennie (CICS- NC, Asheville), Deborah Riddle (NOAA NCDC, Asheville), Sara Veasey (NOAA NCDC, Asheville), Mark Weber (U. Bremen, Bremen), Yin Xun- gang (STG Inc., Asheville), Teresa Young (STG, Asheville) and Jianglong Zhang (U. North Dakota, Grand Forks) for their critical contributions to the production of figures in this work. 2 236 Observations: Atmosphere and Surface Chapter 2 References Abakumova, G. M., E. V. Gorbarenko, E. I. Nezval, and O. A. Shilovtseva, 2008: Fifty Arnold, T., et al., 2013: Nitrogen trifluoride global emissions estimated from updated years of actinometrical measurements in Moscow. Int. J. Remote Sens., 29, atmospheric measurements. Proc. Natl. Acad. Sci. U.S.A., 110, 2029 2034. 2629 2665. Ashok, K., S. K. Behera, S. A. Rao, H. Y. Weng, and T. Yamagata, 2007: El Nino Modoki Adam, J. C., and D. P. Lettenmaier, 2008: Application of new precipitation and recon- and its possible teleconnection. J. Geophys. Res. Oceans, 112, C11007. structed streamflow products to streamflow trend attribution in northern Eur- Asmi, A., et al., 2013: Aerosol decadal trends Part 2: In-situ aerosol particle number asia. J. Clim., 21, 1807 1828. concentrations at GAW and ACTRIS stations. Atmos. Chem. Phys., 13, 895 916. Adler, R. F., G. J. Gu, and G. J. Huffman, 2012: Estimating climatological bias errors for Atlas, R., R. Hoffman, J. Ardizzone, S. Leidner, J. Jusem, D. Smith, and D. Gombos, the global Precipitation Climatology Project (GPCP). J. Appl. Meteor. Climatol., 2011: A cross-calibrated mutiplatform ocean wind velocity product for meteor- 51, 84 99. logical and oceanographic applications. Bull. Am. Meteor. Soc., 92, 157 . Adler, R. F., et al., 2003: The version-2 global precipitation climatology project (GPCP) Augustine, J. A., and E. G. Dutton, 2013: Variability of the surface radiation budget monthly precipitation analysis (1979 present). J. Hydrometeor., 4, 1147 1167. over United States from 1996 through 2011 from high-quality measurements. J. Aguilar, E., et al., 2009: Changes in temperature and precipitation extremes in west- Geophys. Res., 118, 43-53, ern central Africa, Guinea Conakry, and Zimbabwe, 1955 2006. J. Geophys. Res. Aydin, M., et al., 2011: Recent decreases in fossil-fuel emissions of ethane and meth- Atmos., 114, D02115. ane derived from firn air. Nature, 476, 198 201. Alexander, L. V., P. Uotila, and N. Nicholls, 2009: Influence of sea surface temperature Ballester, J., F. Giorgi, and X. Rodo, 2010: Changes in European temperature extremes 2 variability on global temperature and precipitation extremes. J. Geophys. Res. can be predicted from changes in PDF central statistics. Clim. Change, 98, 277 Atmos., 114, D18116. 284. Alexander, L. V., X. L. L. Wang, H. Wan, and B. Trewin, 2011: Significant decline in Baringer, M. O., D. S. Arndt, and M. R. Johnson, 2010: State of the Climate in 2009. storminess over southeast Australia since the late 19th century. Aust. Meteor. Bull. Am. Meteor. Soc., 91, S1 . Ocean. J., 61, 23 30. Barmpadimos, I., J. Keller, D. Oderbolz, C. Hueglin, and A. S. H. Prévôt, 2012: One Alexander, L. V., et al., 2006: Global observed changes in daily climate extremes of decade of parallel fine (PM2.5) and coarse (PM10 PM2.5) particulate matter temperature and precipitation. J. Geophys. Res. Atmos., 111, D05109. measurements in Europe: trends and variability. Atmos. Chem. Phys., 12, 3189 Allan, R., and T. Ansell, 2006: A new globally complete monthly historical gridded 3203. mean sea level pressure dataset (HadSLP2): 1850 2004. J. Clim., 19, 5816 5842. Barnston, A. G., and R. E. Livezey, 1987: Classification, seasonality and persistence Allan, R., S. Tett, and L. Alexander, 2009: Fluctuations in autumn-winter severe of low-frequency atmospheric circulation patterns. Mon. Weather Rev., 115, storms over the British Isles: 1920 to present. Int. J. Climatol., 29, 357 371. 1083 1126. Allan, R. P., 2009: Examination of relationships between clear-sky longwave radia- Barring, L., and K. Fortuniak, 2009: Multi-indices analysis of southern Scandinavian tion and aspects of the atmospheric hydrological cycle in climate models, reanal- storminess 1780 2005 and links to interdecadal variations in the NW Europe- yses, and observations. J. Clim., 22, 3127 3145. North Sea region. Int. J. Climatol., 29, 373 384. Allan, R. P., and A. Slingo, 2002: Can current climate model forcings explain the Barriopedro, D., E. M. Fischer, J. Luterbacher, R. Trigo, and R. Garcia-Herrera, 2011: spatial and temporal signatures of decadal OLR variations? Geophys. Res. Lett., The hot summer of 2010: Redrawing the temperature record map of Europe. 29, 1141. Science, 332, 220 224. Allan, R. P., and B. J. Soden, 2008: Atmospheric warming and the amplification of Barrucand, M., M. Rusticucci, and W. Vargas, 2008: Temperature extremes in the precipitation extremes. Science, 321, 1481 1484. south of South America in relation to Atlantic Ocean surface temperature and Allan, R. P., B. J. Soden, V. O. John, W. Ingram, and P. Good, 2010: Current changes in Southern Hemisphere circulation. J. Geophys. Res. Atmos., 113, D20111. tropical precipitation. Environ. Res. Lett., 5, 025205. Barton, N. P., and A. W. Ellis, 2009: Variability in wintertime position and strength of Allen, R. J., and S. C. Sherwood, 2007: Utility of radiosonde wind data in representing the North Pacific jet stream as represented by re-analysis data. Int. J. Climatol., climatological variations of tropospheric temperature and baroclinicity in the 29, 851 862. western tropical Pacific. J. Clim., 20, 5229 5243. Becker, A., et al., 2013: A description of the global land-surface precipitation data Allen, R. J., and S. C. Sherwood, 2008: Warming maximum in the tropical upper tro- products of the Global Precipitation Climatology Centre with sample applica- posphere deduced from thermal winds. Nature Geosci., 1, 399 403. tions including centennial (trend) analysis from 1901 present. Earth Syst. Sci. Alpert, P., and P. Kishcha, 2008: Quantification of the effect of urbanization on solar Data, 5, 71 99. dimming. Geophys. Res. Lett., 35, L08801. Beig, G., and V. Singh, 2007: Trends in tropical tropospheric column ozone from satel- Alpert, P., P. Kishcha, Y. J. Kaufman, and R. Schwarzbard, 2005: Global dimming or lite data and MOZART model. Geophys. Res. Lett., 34, L17801. local dimming? Effect of urbanization on sunlight availability. Geophys. Res. Bell, G. D., and M. S. Halpert, 1998: Climate assessment for 1997. Bull. Am. Meteor. Lett., 32, L17802. Soc., 79, S1 S50. Andrade, C., S. Leite, and J. Santos, 2012: Temperature extremes in Europe: Over- Bender, F. A. M., V. Ramanathan, and G. Tselioudis, 2012: Changes in extratropical view of their driving atmospheric patterns. Nat. Hazards Earth Syst. Sci., 12, storm track cloudiness 1983 2008: Observational support for a poleward shift. 1671 1691. Clim. Dyn., 38, 2037 2053. Andronova, N., J. E. Penner, and T. Wong, 2009: Observed and modeled evolution of Bengtsson, L., and K. I. Hodges, 2011: On the evaluation of temperature trends in the the tropical mean radiation budget at the top of the atmosphere since 1985. J. tropical troposphere. Clim. Dyn., 36, 419 430. Geophys. Res. Atmos., 114, D14106. Beniston, M., 2004: The 2003 heat wave in Europe: A shape of things to come? An Angell, J. K., 2006: Changes in the 300-mb North Circumpolar Vortex, 1963 2001. J. analysis based on Swiss climatological data and model simulations. Geophys. Clim., 19, 2984 2994. Res. Lett., 31, L02202. Anthes, R. A., 2011: Exploring Earth s atmosphere with radio occultation: contribu- Beniston, M., 2009: Decadal-scale changes in the tails of probability distribution tions to weather, climate and space weather. Atmos. Meas. Tech., 4, 1077 1103. functions of climate variables in Switzerland. Int. J. Climatol., 29, 1362 1368. Anthes, R. A., et al., 2008: The COSMOC/FORMOSAT-3 Mission Early results. Bull. Bennartz, R., J. Fan, J. Rausch, L. Leung, and A. Heidinger, 2011: Pollution from China Am. Meteor. Soc., 89, 313. increases cloud droplet number, suppresses rain over the East China Sea. Geo- Archer, C. L., and K. Caldeira, 2008a: Reply to comment by Courtenay Strong and phys. Res. Lett., 38, L09704. Robert E. Davis on Historical trends in the jet streams . Geophys. Res. Lett., Berrisford, P., et al., 2011: Atmospheric conservation properties in ERA-Interim. Q. J. 35. L24807. R. Meteor. Soc., 137, 1381 1399. Archer, C. L., and K. Caldeira, 2008b: Historical trends in the jet streams. Geophys. Berry, D., and E. Kent, 2011: Air-Sea fluxes from ICOADS: The construction of a new Res. Lett., 35, L08803. gridded dataset with uncertainty estimates. Int. J. Climatol., 31, 987 1001. Arndt, D. S., M. O. Baringer, and M. R. Johnson, 2010: State of the Climate in 2009. Berry, D. I., and E. C. Kent, 2009: A new air-sea interaction gridded dataset from Bull. Am. Meteor. Soc., 91, S1 . ICOADS with uncertainty estimates. Bull. Am. Meteor. Soc., 90, 645-656. 237 Chapter 2 Observations: Atmosphere and Surface Berthet, C., J. Dessens, and J. Sanchez, 2011: Regional and yearly variations of hail Carslaw, K. S., O. Boucher, D. Spracklen, G. Mann, J. G. Rae, S. Woodward, and M. frequency and intensity in France. Atmos. Res., 100, 391 400. Kumala, 2010: A review of natural aerosol interactions and feedbacks within the Birner, T., 2010: Recent widening of the tropical belt from global tropopause statis- Earth system. Atmos. Chem. Phys., 10, 1701 1737. tics: Sensitivities. J. Geophys. Res. Atmos., 115, D23109. Casey, K. S., T. B. Brandon, P. Cornillon, and R. Evans, 2010: The past, present Bitz, C. M., and Q. Fu, 2008: Arctic warming aloft is data set dependent. Nature, and future of the AVHRR Pathfinder SST Program. In: Oceanography from 455, E3 E4. Space: Revisited [V. Barale, J. F. R. Gower, and L. Alberotanza (eds.)]. Springer Black, E., and R. Sutton, 2007: The influence of oceanic conditions on the hot Euro- Science+Business Media, New York, 323-341. pean summer of 2003. Clim. Dyn., 28, 53 66. Castellanos, P., and K. F. Boersma, 2012: Reductions in nitrogen oxides over Europe Blunden, J., D. S. Arndt, and M. O. Baringer, 2011: State of the Climate in 2010. Bull. driven by environmental policy and economic recession. Sci. Rep., 2. 265. Am. Meteor. Soc., 92, S17 . Cermak, J., M. Wild, R. Knutti, M. I. Mishchenko, and A. K. Heidinger, 2010: Consisten- Bohm, R., P. D. Jones, J. Hiebl, D. Frank, M. Brunetti, and M. Maugeri, 2010: The early cy of global satellite-derived aerosol and cloud data sets with recent brightening instrumental warm-bias: A solution for long central European temperature series observations. Geophys. Res. Lett., 37, L21704. 1760 2007. Clim. Change, 101, 41 67. Chambers, L., and G. Griffiths, 2008: The changing nature of temperature extremes in Bonfils, C., P. B. Duffy, B. D. Santer, T. M. L. Wigley, D. B. Lobell, T. J. Phillips, and C. Dou- Australia and New Zealand. Aust. Meteorol. Mag., 57, 13 35. triaux, 2008: Identification of external influences on temperatures in California. Chan, J. C. L., and M. Xu, 2009: Inter-annual and inter-decadal variations of landfall- Clim. Change, 87, S43 S55. ing tropical cyclones in East Asia. Part I: time series analysis. Int. J. Climatol., 29, Bonisch, H., A. Engel, J. Curtius, T. Birner, and P. Hoor, 2009: Quantifying transport 1285 1293. into the lowermost stratosphere using simultaneous in-situ measurements of Chandler, R. E., and E. M. Scott, 2011: Statistical Methods for Trend Detection and 2 SF6 and CO2. Atmos. Chem. Phys., 9, 5905 5919. Analysis in the Environmental Sciences. John Wiley & Sons, Hoboken, NJ. Bosilovich, M. G., F. R. Robertson, and J. Chen, 2011: Global energy and water bud- Chang, E. K. M., 2007: Assessing the increasing trend in Northern Hemisphere winter gets in MERRA. J. Clim., 24, 5721-5739.. storm track activity using surface ship observations and a statistical storm track Bourassa, M. A., S. T. Gille, D. L. Jackson, J. B. Roberts, and G. A. Wick, 2010: Ocean model. J. Clim., 20, 5607 5628. winds and turbulent air-sea fluxes inferred from remote sensing. Oceanography, Chang, E. K. M., and Y. J. Guo, 2007: Is the number of North Atlantic tropical cyclones 23, 36 51. significantly underestimated prior to the availability of satellite observations? Bousquet, P., 2011: Source attribution of the changes in atmospheric methane for Geophys. Res. Lett., 34. L14801. 2006 2008. Atmos. Chem. Phys. Discuss., 10, 27603 27630. Chapman, W. L., and J. E. Walsh, 2007: A synthesis of Antarctic temperatures. J. Clim., Brogniez, H., R. Roca, and L. Picon, 2009: Study of the free tropospheric humid- 20, 4096 4117. ity interannual variability using meteosat data and an advection-condensation Che, H. Z., et al., 2005: Analysis of 40 years of solar radiation data from China, transport model. J. Clim., 22, 6773 6787. 1961 2000. Geophys. Res. Lett., 32, L06803. Brönnimann, S., 2009: Early twentieth-century warming. Nature Geosci., 2, 735 736. Chen, J. Y., B. E. Carlson, and A. D. Del Genio, 2002: Evidence for strengthening of the Brönnimann, S., et al., 2009: Variability of large-scale atmospheric circulation indi- tropical general circulation in the 1990s. Science, 295, 838 841. ces for the Northern Hemisphere during the past 100 years. Meteorol. Z., 18, Chen, J. Y., A. D. Del Genio, B. E. Carlson, and M. G. Bosilovich, 2008: The spatio- 379 396. temporal structure of twentieth-century climate variations in observations and Brooks, C. F., 1926: Observing water-surface temperatures at sea. Mon. Wea. Rev., reanalyses. Part I: Long-term trend. J. Clim., 21, 2611 2633. 54, 241 253. Chiacchio, M., and M. Wild, 2010: Influence of NAO and clouds on long-term sea- Brooks, H., 2012: Severe thunderstorms and climate change. Atmos. Res., 123, SI, sonal variations of surface solar radiation in Europe. J. Geophys. Res. Atmos., 129-138. 115, D00d22. Brooks, H. E., and N. Dotzek, 2008: The spatial distribution of severe convective Choi, G., et al., 2009: Changes in means and extreme events of temperature and storms and an analysis of their secular changes. In: Climate Extremes and Soci- precipitation in the Asia Pacific Network region, 1955 2007. Int. J. Climatol., ety [H. F. Diaz and R. J. Murnane (eds.] Cambridge University Press, pp. 35 53. 29, 1906 1925. Brown, S. J., J. Caesar, and C. A. T. Ferro, 2008: Global changes in extreme daily tem- Christy, J. R., and W. B. Norris, 2006: Satellite and VIZ-radiosonde intercomparisons perature since 1950. J. Geophys. Res. Atmos., 113, D05115. for diagnosis of nonclimatic influences. J. Atmos. Ocean. Technol., 23, 1181 Brunet, M., et al., 2011: The minimization of the screen bias from ancient Western 1194. Mediterranean air temperature records: an exploratory statistical analysis. Int. J. Christy, J. R., and W. B. Norris, 2009: Discontinuity issues with radiosonde and satel- Climatol., 31, 1879 1895. lite temperatures in the Australian Region 1979 2006. J. Atmos. Ocean Technol., Bunge, L., and A. J. Clarke, 2009: A verified estimation of the El Nino Index Nino-3.4 26, 508 522. since 1877. J. Clim., 22, 3979 3992. Christy, J. R., W. B. Norris, and R. T. McNider, 2009: Surface temperature variations in Burn, D. H., and N. M. Hesch, 2007: Trends in evaporation for the Canadian prairies. East Africa and possible causes. J. Clim., 22, 3342 3356. J. Hydrol., 336, 61 73. Christy, J. R., R. W. Spencer, and W. B. Norris, 2011: The role of remote sensing in Caesar, J., L. Alexander, and R. Vose, 2006: Large-scale changes in observed daily monitoring global bulk tropospheric temperatures. Int. J. Remote Sens., 32, maximum and minimum temperatures: Creation and analysis of a new gridded 671 685. data set. J. Geophys. Res. Atmos., 111, D05101. Christy, J. R., W. B. Norris, K. Redmond, and K. P. Gallo, 2006: Methodology and Caesar, J., et al., 2011: Changes in temperature and precipitation extremes over the results of calculating central california surface temperature trends: Evidence of Indo-Pacific region from 1971 to 2005. Int. J. Climatol., 31, 791 801. human-induced climate change? J. Clim., 19, 548 563. Cai, W., and P. van Rensch, 2012: The 2011 southeast Queensland extreme summer Christy, J. R., W. B. Norris, R. W. Spencer, and J. J. Hnilo, 2007: Tropospheric tempera- rainfall: A confirmation of a negative Pacific Decadal Oscillation phase? Geo- ture change since 1979 from ,tropical radiosonde and satellite measurements. J. phys. Res. Lett., 39, L08702. Geophys. Res. Atmos., 112, D06102. Cai, W., T. Cowan, and M. Thatcher, 2012: Rainfall reductions over Southern Hemi- Christy, J. R., R. W. Spencer, W. B. Norris, W. D. Braswell, and D. E. Parker, 2003: sphere semi-arid regions: The role of subtropical dry zone expansion. Sci. Rep., Error estimates of version 5.0 of MSU-AMSU bulk atmospheric temperatures. J. 2, 702. Atmos. Ocean Technol., 20, 613 629. Callaghan, J., and S. B. Power, 2011: Variability and decline in the number of severe Christy, J. R., et al., 2010: What do observational datasets say about modeled tropo- tropical cyclones making land-fall over eastern Australia since the late nine- spheric temperature trends since 1979? , 2148 2169. teenth century. Clim. Dyn., 37, 647 662. Chu, W. P., M. P. McCormick, J. Lenoble, C. Brogniez, and P. Pruvost, 1989: SAGE II Canada, 2012: Canadian Smog Science Assessment Highlights and Key Messages. inversion algorithm. J. Geophys. Res. Atmos., 94, 8339 8351. Environment Canada and Health Canada, 64 pp. Chung, E. S., and B. J. Soden, 2010: Investigating the influence of carbon dioxide and Cane, M. A., 1986: El-Nino. Annu. Rev. Earth Planet. Sci., 14, 43 70. the stratosphere on the long-term tropospheric temperature monitoring from Cao, Z. H., 2008: Severe hail frequency over Ontario, Canada: Recent trend and vari- HIRS. J. Appl. Meteor. Climatol., 49, 1927 1937. ability. Geophys. Res. Lett., 35. L14803. 238 Observations: Atmosphere and Surface Chapter 2 Chung, E. S., D. Yeomans, and B. J. Soden, 2010: An assessment of climate feedback De Smedt, I., T. Stavrakou, J. F. Müller, R. J. van der A, and M. Van Roozendael, 2010: processes using satellite observations of clear-sky OLR. Geophys. Res. Lett., 37, Trend detection in satellite observations of formaldehyde tropospheric columns. L02702. Geophys. Res. Lett., 37, L18808. Clark, R. T., S. J. Brown, and J. M. Murphy, 2006: Modeling Northern Hemisphere Dee, D. P., E. Kallen, A. J. Simmons, and L. Haimberger, 2011a: Comments on Reanal- summer heat extreme changes and their uncertainties using a physics ensemble yses suitable for characterizing long-term trends . Bull. Am. Meteor. Soc., 92, of climate sensitivity experiments. J. Clim., 19, 4418 4435. 65 70. Clement, A. C., and B. Soden, 2005: The sensitivity of the tropical-mean radiation Dee, D. P., et al., 2011b: The ERA-Interim reanalysis: Configuration and performance budget. J. Clim., 18, 3189 3203. of the data assimilation system. Q. J. R. Meteor. Soc., 137, 553 597. CMA, 2007: Atlas of China Disastrous Weather and Climate. Chinese Meteorological Deeds, D., et al., 2008: Evidence for crustal degassing of CF4 and SF6 in Mojave Administration. Beijing, China. Desert groundwaters. Geochim. Cosmochim. Acta, 72, 999 1013. CMA, 2011: China Climate Change Bulletin. Chinese Meteorological Administration. DeGaetano, A. T., 2009: Time-dependent changes in extreme-precipitation return- Beijing, China. period amounts in the continental United States. J. Appl. Meteor. Climatol., 48, Cohen, J., M. Barlow, and K. Saito, 2009: Decadal fluctuations in planetary wave 2086 2099. forcing modulate global warming in late boreal winter. J. Clim., 22, 4418 4426. Delgado, J. M., H. Apel, and B. Merz, 2010: Flood trends and variability in the Mekong Cohen, J. L., J. C. Furtado, M. Barlow, V. A. Alexeev, and J. E. Cherry, 2012: Asymmetric River. Hydrol. Earth Syst. Sci., 14, 407 418. seasonal temperature trends. Geophys. Res. Lett., 39, L04705. Della-Marta, P. M., M. R. Haylock, J. Luterbacher, and H. Wanner, 2007a: Doubled Cohn, T. A., and H. F. Lins, 2005: Nature s style: Naturally trendy. Geophys. Res. Lett., length of western European summer heat waves since 1880. J. Geophys. Res. 32, L23402. Atmos., 112, D15103. Coles, S., 2001: An Introduction to Statistical Modeling of Extreme Values. Springer Della-Marta, P. M., J. Luterbacher, H. von Weissenfluh, E. Xoplaki, M. Brunet, and 2 Science+Business Media, New York, 208 pp. H. Wanner, 2007b: Summer heat waves over western Europe 1880 2003, their Collaud Coen, M., et al., 2013: Aerosol decadal trends Part 1: In-situ optical mea- relationship to large-scale forcings and predictability. Clim. Dyn., 29, 251 275. surements at GAW and IMPROVE stations. Atmos. Chem. Phys., 13, 869 894. Della-Marta, P. M., H. Mathis, C. Frei, M. A. Liniger, J. Kleinn, and C. Appenzeller, 2009: Compo, G. P., et al., 2011: The twentieth century reanalysis project. Q. J. Roy. Meteo- The return period of wind storms over Europe. Int. J. Climatol., 29, 437 459. rol. Soc., 137, 1 28. Deser, C., A. S. Phillips, and M. A. Alexander, 2010a: Twentieth century tropical sea Cooper, O. R., R. S. Gao, D. Tarasick, T. Leblanc, and C. Sweeney, 2012: Long-term surface temperature trends revisited. Geophys. Res. Lett., 37, L10701. ozone trends at rural ozone monitoring sites across the United States, 1990 Deser, C., M. A. Alexander, S. P. Xie, and A. S. Phillips, 2010b: Sea surface temperature 2010. J. Geophys. Res., 117, D22307. variability: Patterns and mechanisms. Annu. Rev. Mar. Sci., 2, 115 143. Cornes, R. C., and P. D. Jones, 2011: An examination of storm activity in the northeast Dessler, A. E., and S. M. Davis, 2010: Trends in tropospheric humidity from reanalysis Atlantic region over the 1851 2003 period using the EMULATE gridded MSLP systems. J. Geophys. Res. Atmos., 115, D19127. data series. J. Geophys. Res. Atmos., 116, D16110. Dessler, A. E., Z. Zhang, and P. Yang, 2008: Water-vapor climate feedback inferred Coumou, D., A. Robinson, and S. Rahmstorf, 2013: Global increase in record-breaking from climate fluctuations, 2003 2008. Geophys. Res. Lett., 35, L20704. monthly-mean temperatures. Clim. Change, 118, 771-782. Diffenbaugh, N. S., J. S. Pal, F. Giorgi, and X. J. Gao, 2007: Heat stress intensification Craigmile, P. F., and P. Guttorp, 2011: Space-time modelling of trends in temperature in the Mediterranean climate change hotspot. Geophys. Res. Lett., 34, L11706. series. J. Time Ser. Anal., 32, 378 395. Ding, T., W. H. Qian, and Z. W. Yan, 2010: Changes in hot days and heat waves in Croci-Maspoli, M., C. Schwierz, and H. C. Davies, 2007a: A multifaceted climatology China during 1961 2007. Int. J. Climatol., 30, 1452 1462. of atmospheric blocking and its recent linear trend. J. Clim., 20, 633 649. Ding, Y. H., G. Y. Ren, Z. C. Zhao, Y. Xu, Y. Luo, Q. P. Li, and J. Zhang, 2007: Detection, Croci-Maspoli, M., C. Schwierz, and H. C. Davies, 2007b: Atmospheric blocking: causes and projection of climate change over China: An overview of recent prog- Space-time links to the NAO and PNA. Clim. Dyn., 29, 713 725. ress. Adv. Atmos. Sci., 24, 954 971. Cutforth, H. W., and D. Judiesch, 2007: Long-term changes to incoming solar energy Dlugokencky, E., E. Nisbet, R. Fisher, and D. Lowry, 2011: Global atmospheric meth- on the Canadian Prairie. Agr. Forest Meteor., 145, 167 175. ane: Budget, changes and dangers. Philos. Trans. R. Soc. London Ser. A, 369, Dai, A., 2006: Recent climatology, variability, and trends in global surface humidity. 2058 2072. J. Clim., 19, 3589 3606. Dlugokencky, E., et al., 2005: Conversion of NOAA atmospheric dry air CH4 mole Dai, A., 2011a: Characteristics and trends in various forms of the Palmer Drought fractions to a gravimetrically prepared standard scale. J. Geophys. Res. Atmos., Severity Index during 1900 2008. J. Geophys. Res. Atmos., 116, D12115. 110, D18306. Dai, A., 2012: The influence of the inter-decadal Pacific oscillation on US precipitation Dlugokencky, E., et al., 2009: Observational constraints on recent increases in the during 1923 2010. Clim. Dyn., 41, 633-646. atmospheric CH4 burden. Geophys. Res. Lett., L18803. Dai, A., 2013: Increasing drought under global warming in observations and models. Dlugokencky, E. J., K. A. Masaire, P. M. Lang, P. P. Steele, and E. G. Nisbet, 1994: A Nature Clim. Change, 3, 52 58. dramatic decrease in the growth rate of atmospheric methane in the Northern Dai, A., T. T. Qian, K. E. Trenberth, and J. D. Milliman, 2009: Changes in continental Hemisphere during 1992. Geophys. Res. Lett., 21, 45 48. freshwater diSchärge from 1948 to 2004. J. Clim., 22, 2773 2792. Dole, R., et al., 2011: Was there a basis for anticipating the 2010 Russian heat wave? Dai, A. G., 2011b: Drought under global warming: A review. Clim. Change, 2, 45 65. Geophys. Res. Lett., 38, L06702. Dai, A. G., J. H. Wang, P. W. Thorne, D. E. Parker, L. Haimberger, and X. L. L. Wang, Donat, M. G., and L. V. Alexander, 2012: The shifting probability distribution of global 2011: A new approach to homogenize daily radiosonde humidity data. J. Clim., daytime and night-time temperatures. Geophys. Res. Lett., 39, L14707. 24, 965 991. Donat, M. G., D. Renggli, S. Wild, L. V. Alexander, G. C. Leckebusch, and U. Ulbrich, Das, L., J. D. Annan, J. C. Hargreaves, and S. Emori, 2011: Centennial scale warming 2011: Reanalysis suggests long-term upward trends in European storminess over Japan: Are the rural stations really rural? Atmos. Sci. Lett., 12, 362-367. since 1871. Geophys. Res. Lett., 38, L14703. Davidson, E., 2009: The contribution of manure and fertilizer nitrogen to atmospher- Donat, M. G., L. V. Alexander, H. Yang, I. Durre, R. Vose, and J. Caesar, 2013a: Global ic nitrous oxide since 1860. Nature Geosci., 2, 659 662. land-based datasets for monitoring climatic extremes. Bull. Am. Meteor. Soc., Davini, P., C. Cagnazzo, S. Gualdi, and A. Navarra, 2012: Bidimensional diagnostics, 94, 997-1006. variability, and trends of Northern Hemisphere blocking. J. Clim., 25, 6496 6509. Donat, M. G., et al., 2013b: Changes in extreme temperature and precipitation in the Davis, S. M., and K. H. Rosenlof, 2011: A multidiagnostic intercomparison of tropi- Arab region: Long-term trends and variability related to ENSO and NAO. Int. J. cal width time series using reanalyses and satellite observations. J. Clim., 25, Climatol., doi:10.1002/joc.3707. 1061-1078. Donat, M. G., et al., 2013c: Updated analyses of temperature and precipitation De Laat, A. T. J., and A. N. Maurellis, 2006: Evidence for influence of anthropogenic extreme indices since the beginning of the twentieth century: The HadEX2 data- surface processes on lower tropospheric and surface temperature trends. Int. J. set. J. Geophys. Res. Atmos., 118, 2098-2118. Climatol., 26, 897 913. Dong, L., T. J. Vogelsang, and S. J. Colucci, 2008: Interdecadal trend and ENSO-related de Meij, A., A. Pozzer, and J. Lelieveld, 2012: Trend analysis in aerosol optical depths interannual variability in Southern Hemisphere blocking. J. Clim., 21, 3068 3077. and pollutant emission estimates between 2000 and 2009. Atmos. Environ., 51, 75 85. 239 Chapter 2 Observations: Atmosphere and Surface Dorigo, W., R. de Jeu, D. Chung, R. Parinussa, Y. Liu, W. Wagner, and D. Fernández-Pri- Etheridge, D., L. Steele, R. Francey, and R. Langenfelds, 1998: Atmospheric methane eto, 2012: Evaluating global trends (1988 2010) in harmonized multi-satellite between 1000 AD and present: Evidence of anthropogenic emissions and cli- surface soil moisture. Geophys. Res. Lett., 39, L18405. matic variability. J. Geophys. Res. Atmos., 15979 15993. Doswell, C., H. Brooks, and N. Dotzek, 2009: On the implementation of the enhanced Etheridge, D. M., L. P. Steele, R. L. Langenfelds, R. J. Francey, J. M. Barnola, and V. I. Fujita scale in the USA. Atmos. Res., 93, 554 563. Morgan, 1996: Natural and anthropogenic changes in atmospheric CO2 over Douglass, A., et al., 2008: Relationship of loss, mean age of air and the distribution the last 1000 years from air in Antarctic ice and firn. J. Geophys. Res. Atmos., of CFCs to stratospheric circulation and implications for atmospheric lifetimes. J. 4115 4128. Geophys. Res. Atmos., 113, D14309. Evan, A. T., A. K. Heidinger, and D. J. Vimont, 2007: Arguments against a physical Douglass, A., et al., 2011: WMO/UNEP scientific assessment of ozone depletion: long-term trend in global ISCCP cloud amounts. Geophys. Res. Lett., 34, L04701. 2010. In: Stratospheric Ozone and Surface Ultraviolet Radiation. World Meteoro- Fall, S., D. Niyogi, A. Gluhovsky, R. A. Pielke, E. Kalnay, and G. Rochon, 2010: Impacts logical Organisation, Geneva, Switzerland. of land use land cover on temperature trends over the continental United States: Du, Y., and S. Xie, 2008: Role of atmospheric adjustments in the tropical Indian Assessment using the North American regional reanalysis. Int. J. Climatol., 30, Ocean warming during the 20th century in climate models. Geophys. Res. Lett., 1980 1993. 35, L08712. Fall, S., A. Watts, J. Nielsen-Gammon, E. Jones, D. Niyogi, J. R. Christy, and R. A. Pielke, Du, Y., S. Xie, G. Huang, and K. Hu, 2009: Role of air-sea interaction in the long persis- 2011: Analysis of the impacts of station exposure on the US Historical Clima- tence of El Nino-induced North Indian Ocean warming. J. Clim., 22, 2023 2038. tology Network temperatures and temperature trends. J. Geophys. Res. Atmos., Duan, A. M., and G. X. Wu, 2006: Change of cloud amount and the climate warming 116, D14120. on the Tibetan Plateau. Geophys. Res. Lett., 33, L22704. Falvey, M., and R. D. Garreaud, 2009: Regional cooling in a warming world: Recent 2 Durre, I., C. N. Williams, X. G. Yin, and R. S. Vose, 2009: Radiosonde-based trends in temperature trends in the southeast Pacific and along the west coast of sub- precipitable water over the Northern Hemisphere: An update. J. Geophys. Res. tropical South America (1979 2006). J. Geophys. Res. Atmos., 114, D04102. Atmos., 114, D05112. Favre, A., and A. Gershunov, 2006: Extra-tropical cyclonic/anticyclonic activity in Dutton, E. G., and B. A. Bodhaine, 2001: Solar irradiance anomalies caused by clear- north-eastern Pacific and air temperature extremes in western North America. sky transmission variations above Mauna Loa: 1958 99. J. Clim., 14, 3255 3262. Clim. Dyn., 26, 617 629. Dutton, E. G., D. W. Nelson, R. S. Stone, D. Longenecker, G. Carbaugh, J. M. Harris, and Feng, S., and Q. Hu, 2007: Changes in winter snowfall/precipitation ratio in the con- J. Wendell, 2006: Decadal variations in surface solar irradiance as observed in a tiguous United States. J. Geophys. Res. Atmos., 112, D15109. globally remote network. J. Geophys. Res. Atmos., 111, D19101. Ferguson, C. R., and G. Villarini, 2012: Detecting inhomogeneities in the twentieth Earl, N., S. Dorling, R. Hewston, and R. von Glasow, 2013: 1980 2010 Variability in century reanalysis over the central United States. J. Geophys. Res. Atmos., 117, U.K. surface wind climate. J. Climate, 26, 1172 1191. D05123. Easterling, D., and M. Wehner, 2009: Is the climate warming or cooling? Geophys. Ferranti, L., and P. Viterbo, 2006: The European summer of 2003: Sensitivity to soil Res. Lett., 36, L08706. water initial conditions. J. Clim., 19, 3659 3680. Eastman, R., and S. G. Warren, 2012: A 39-yr survey of cloud changes from land sta- Ferretti, D., et al., 2005: Unexpected changes to the global methane budget over the tions worldwide 1971 2009: Long-term trends, relation to aerosols, and expan- past 2000 years. Science, 309, 1714 1717. sion of the Tropical Belt. J. Clim., 26, 1286 1303. Fischer, E. M., and C. Schär, 2010: Consistent geographical patterns of changes in Eastman, R., S. G. Warren, and C. J. Hahn, 2011: Variations in cloud cover and cloud high-impact European heatwaves. Nature Geosci., 3, 398 403. types over the ocean from surface observations, 1954 2008. J. Clim., 24, 5914 Fischer, E. M., S. I. Seneviratne, P. L. Vidale, D. Luthi, and C. Schär, 2007: Soil mois- 5934. ture atmosphere interactions during the 2003 European summer heat wave. J. Ebita, A., et al., 2011: The Japanese 55-year reanalysis JRA-55 : An interim report. Clim., 20, 5081 5099. Sola, 7, 149 152. Fischer, T., M. Gemmer, L. Liu, and B. Su, 2011: Temperature and precipitation trends Eccel, E., P. Cau, K. Riemann-Campe, and F. Biasioli, 2012: Quantitative hail monitor- and dryness/wetness pattern in the Zhujiang River Basin, South China, 1961 ing in an alpine area: 35-year climatology and links with atmospheric variables. 2007. Quatern. Int., 244, 138 148. Int. J. Climatol., 32, 503 517. Fogt, R. L., J. Perlwitz, A. J. Monaghan, D. H. Bromwich, J. M. Jones, and G. J. Mar- Efthymiadis, D., C. M. Goodess, and P. D. Jones, 2011: Trends in Mediterranean grid- shall, 2009: Historical SAM variability. Part II: Twentieth-century variability and ded temperature extremes and large-scale circulation influences. Nat. Hazards trends from reconstructions, observations, and the IPCC AR4 models. J. Clim., Earth Syst. Sci., 11, 2199 2214. 22, 5346 5365. Efthymiadis, D. A., and P. D. Jones, 2010: Assessment of maximum possible urbaniza- Folland, C. K., and D. E. Parker, 1995: Correction of instrumental biases in historical tion influences on land temperature data by comparison of land and marine sea-surface temperature data. Q. J. R. Meteor. Soc., 121, 319 367. data around coasts. Atmosphere, 1, 51 61. Folland, C. K., D. E. Parker, A. Colman, and W. R., 1999: Large scale modes of ocean Elkins, J. W., and G. S. Dutton, 2011: Nitrous oxide and sulfur hexaflouride. Bull. Am. surface temperature since the late nineteenth century. In: Beyond El Nino: Meteor. Soc., 92, 2. Decadal and Interdecadal Climate Variability [A. Navarra (ed.)] Springer-Verlag, Elsner, J. B., J. P. Kossin, and T. H. Jagger, 2008: The increasing intensity of the stron- New York, pp. 73 102. gest tropical cyclones. Nature, 455, 92 95. Forster, P., et al., 2007: Changes in atmospheric constituents and in radiative forcing. Emanuel, K., 2007: Environmental factors affecting tropical cyclone power dissipa- In: Climate Change 2007: The Physical Science Basis. Contribution of Working tion. J. Clim., 20, 5497 5509. Group I to the Fourth Assessment Report of the Intergovernmental Panel on Embury, O., and C. J. Merchant, 2011: Reprocessing for climate of sea surface tem- Climate Change [Solomon, S., D. Qin, M. Manning, Z. Chen, M. Marquis, K. B. perature from the along-track scanning radiometers: A new retrieval scheme. Averyt, M. Tignor and H. L. Miller (eds.)] Cambridge University Press, Cambridge, Remote Sens. Environ., 116, 47-61. United Kingdom and New York, NY, USA, 129-234. Embury, O., C. J. Merchant, and G. K. Corlett, 2011: A reprocessing for climate of Forster, P. M., et al., 2011: Stratospheric changes and climate. Scientific Assessment sea surface temperature from the along-track scanning radiometers: Preliminary of Ozone Depletion: 2010. Global Ozone Research and Monitoring Project validation, accounting for skin and diurnal variability. Remote Sens. Environ., Report No. 52. World Meteorological Organization, Geneva, Switzerland, 1-60. 116, 62-78. Fortems-Cheiney, A., F. Chevallier, I. Pison, P. Bousquet, S. Szopa, M. N. Deeter, and C. Endo, N., and T. Yasunari, 2006: Changes in low cloudiness over China between 1971 Clerbaux, 2011: Ten years of CO emissions as seen from Measurements of Pollu- and 1996. J. Clim., 19, 1204 1213. tion in the Troposphere (MOPITT). J. Geophys. Res., 116, D05304. Enfield, D. B., A. M. Mestas-Nunez, and P. J. Trimble, 2001: The Atlantic multidecadal Foster, G., and S. Rahmstorf, 2011: Global temperature evolution 1979 2010. Envi- oscillation and its relation to rainfall and river flows in the continental US. Geo- ron. Res. Lett., 6, 044022. phys. Res. Lett., 28, 2077 2080. Frauenfeld, O. W., and R. E. Davis, 2003: Northern Hemisphere circumpolar vortex Engel, A., et al., 2009: Age of stratospheric air unchanged within uncertainties over trends and climate change implications. J. Geophys. Res. Atmos., 108, 4423. the past 30 years. Nature Geosci., 2, 28 31. Frederiksen, J. S., and C. S. Frederiksen, 2007: Interdecadal changes in Southern Espinoza Villar, J. C., et al., 2009: Contrasting regional diSchärge evolutions in the Hemisphere winter storm track modes. Tellus A, 59, 599 617. Amazon basin (1974 2004). J. Hydrol., 375, 297 311. 240 Observations: Atmosphere and Surface Chapter 2 Free, M., and D. J. Seidel, 2007: Comments on biases in stratospheric and tropo- Greally, B., et al., 2007: Observations of 1,1-difluoroethane (HFC-152a) at AGAGE spheric temperature trends derived from historical radiosonde data . J. Clim., and SOGE monitoring stations in 1994 2004 and derived global and regional 20, 3704 3709. emission estimates. J. Geophys. Res. Atmos., 112, D06308. Free, M., D. J. Seidel, J. K. Angell, J. Lanzante, I. Durre, and T. C. Peterson, 2005: Radio- Griffiths, G. M., et al., 2005: Change in mean temperature as a predictor of extreme sonde Atmospheric Temperature Products for Assessing Climate (RATPAC): A temperature change in the Asia-Pacific region. Int. J. Climatol., 25, 1301 1330. new data set of large-area anomaly time series. J. Geophys. Res. Atmos., 110, Grinsted, A., J. C. Moore, and S. Jevrejeva, 2012: Homogeneous record of Atlantic hur- D22101. ricane surge threat since 1923. Proc. Natl. Acad. Sci. U.S.A. 109, 19601-19605. Frich, P., L. V. Alexander, P. Della-Marta, B. Gleason, M. Haylock, A. Tank, and T. Peter- Groisman, P., R. Knight, and T. Karl, 2012: Changes in intense precipitation over the son, 2002: Observed coherent changes in climatic extremes during the second central United States. J. Hydrometeor., 13, 47 66. half of the twentieth century. Clim. Res., 19, 193 212. Groisman, P., R. Knight, T. R. Karl, D. Easterling, B. M. Sun, and J. Lawrimore, 2004: Fu, G. B., S. P. Charles, and J. J. Yu, 2009: A critical overview of pan evaporation trends Contemporary changes of the hydrological cycle over the contiguous United over the last 50 years. Clim. Change, 97, 193 214. States: Trends derived from in situ observations. J. Hydrometeor., 5, 64 85. Fu, Q., and P. Lin, 2011: Poleward shift of subtropical jets inferred from satellite- Groisman, P. Y., R. W. Knight, D. R. Easterling, T. R. Karl, G. C. Hegerl, and V. A. N. observed lower stratospheric temperatures. J. Clim., 24, 5597 5603. Razuvaev, 2005: Trends in intense precipitation in the climate record. J. Clim., Fu, Q., C. M. Johanson, S. G. Warren, and D. J. Seidel, 2004: Contribution of strato- 18, 1326 1350. spheric cooling to satellite-inferred tropospheric temperature trends. Nature, Gruber, C., and L. Haimberger, 2008: On the homogeneity of radiosonde wind time 429, 55 58. series. Meteorol. Z., 17, 631 643. Fu, Q., C. M. Johanson, J. M. Wallace, and T. Reichler, 2006: Enhanced mid-latitude Gulev, S. K., O. Zolina, and S. Grigoriev, 2001: Extratropical cyclone variability in the tropospheric warming in satellite measurements. Science, 312, 1179 1179. Northern Hemisphere winter from the NCEP/NCAR reanalysis data. Clim. Dyn., 2 Fueglistaler, S., and P. H. Haynes, 2005: Control of interannual and longer-term vari- 17, 795 809. ability of stratospheric water vapor. J. Geophys. Res. Atmos., 110, D24108. Guo, H., M. Xu, and Q. Hub, 2010: Changes in near-surface wind speed in China: Fujibe, F., 2009: Detection of urban warming in recent temperature trends in Japan. 1969 2005. Int. J. Climatol., 31, 349-358. Int. J. Climatol., 29, 1811 1822. Haerter, J., P. Berg, and S. Hagemann, 2010: Heavy rain intensity distributions on Fujiwara, M., et al., 2010: Seasonal to decadal variations of water vapor in the varying time scales and at different temperatures. J. Geophys. Res. Atmos., 115, tropical lower stratosphere observed with balloon-borne cryogenic frost point D17102. hygrometers. J. Geophys. Res. Atmos., 115, D18304. Haimberger, L., 2007: Homogenization of radiosonde temperature time series using Fyfe, J. C., 2003: Extratropical southern hemisphere cyclones: Harbingers of climate innovation statistics. J. Clim., 20, 1377 1403. change? J. Clim., 16, 2802 2805. Haimberger, L., C. Tavolato, and S. Sperka, 2008: Toward elimination of the warm Gallant, A., K. Hennessy, and J. Risbey, 2007: Trends in rainfall indices for six Austra- bias in historic radiosonde temperature records Some new results from a com- lian regions: 1910 2005. Aust. Meteor. Mag., 56, 223 239. prehensive intercomparison of upper-air data. J. Clim., 21, 4587 4606. Gallant, A. J. E., and D. J. Karoly, 2010: A Combined Climate Extremes Index for the Haimberger, L., C. Tavolato, and S. Sperka, 2012: Homogenization of the global radio- Australian Region. J. Clim., 23, 6153 6165. sonde temperature dataset through combined comparison with reanalysis back- Garcia-Herrera, R., J. Diaz, R. M. Trigo, J. Luterbacher, and E. M. Fischer, 2010: A ground series and neighboring stations. J. Clim., 25, 8108 8131. review of the European summer heat wave of 2003. Crit. Rev. Environ. Sci. Tech- Hand, J. L., et al., 2011: IMPROVE, spatial and seasonal patterns and temporal vari- nol., 40, 267 306. ability of haze and its constituents in the United States. Cooperative Institute for Gentemann, C., F. Wentz, C. Mears, and D. Smith, 2004: In situ validation of Tropical Research in the Atmosphere and Colorado University. Rainfall Measuring Mission microwave sea surface temperatures. J. Geophys. Hanna, E., J. Cappelen, R. Allan, T. Jonsson, F. Le Blancq, T. Lillington, and K. Hickey, Res. Oceans, 109, C04021. 2008: New insights into North European and North Atlantic surface pressure Gettelman, A., and Q. Fu, 2008: Observed and simulated upper-tropospheric water variability, storminess, and related climatic change since 1830. J. Clim., 21, vapor feedback. J. Clim., 21, 3282 3289. 6739 6766. Gettelman, A., et al., 2010: Multimodel assessment of the upper troposphere and Hannaford, J., and T. Marsh, 2008: High-flow and flood trends in a network of undis- lower stratosphere: Tropics and global trends. J. Geophys. Res. Atmos., 115, turbed catchments in the UK. Int. J. Climatol., 28, 1325 1338. D00M08. Hansen, J., M. Sato, and R. Ruedy, 2012: Perception of climate change. Proc. Natl. Gilgen, H., A. Roesch, M. Wild, and A. Ohmura, 2009: Decadal changes in shortwave Acad. Sci. U.S.A., 109, E2415 E2423. irradiance at the surface in the period from 1960 to 2000 estimated from Global Hansen, J., R. Ruedy, M. Sato, and K. Lo, 2010: Global surface temperature change. Energy Balance Archive Data. J. Geophys. Res. Atmos., 114, D00d08. Rev. Geophys., 48, RG4004. Gillett, N. P., and P. A. Stott, 2009: Attribution of anthropogenic influence on seasonal Hansen, J., M. Sato, P. Kharecha, and K. von Schuckmann, 2011: Earth s energy sea level pressure. Geophys. Res. Lett., 36, L23709. imbalance and implications. Atmos. Chem. Phys., 11, 13421 13449. Giorgi, F., and R. Francisco, 2000: Evaluating uncertainties in the prediction of region- Harries, J. E., and C. Belotti, 2010: On the variability of the global net radiative energy al climate change. Geophys. Res. Lett., 27, 1295 1298. balance of the nonequilibrium Earth. J. Clim., 23, 1277 1290. Giorgi, F., E. S. Im, E. Coppola, N. S. Diffenbaugh, X. J. Gao, L. Mariotti, and Y. Shi, 2011: Hatzianastassiou, N., C. Matsoukas, A. Fotiadi, K. G. Pavlakis, E. Drakakis, D. Hatzi- Higher hydroclimatic intensity with global warming. J. Clim., 24, 5309 5324. dimitriou, and I. Vardavas, 2005: Global distribution of Earth s surface shortwave Giuntoli, I., B. Renard, J. P. Vidal, and A. Bard, 2013: Low flows in France and their radiation budget. Atmos. Chem. Phys., 5, 2847 2867. relationship to large-scale climate indices. J. Hydrol., 482, 105-118. Hatzianastassiou, N., C. D. Papadimas, C. Matsoukas, K. Pavlakis, A. Fotiadi, M. Gleason, K. L., J. H. Lawrimore, D. H. Levinson, T. R. Karl, and D. J. Karoly, 2008: A Wild, and I. Vardavas, 2012: Recent regional surface solar radiation dimming revised US Climate Extremes Index. J. Clim., 21, 2124 2137. and brightening patterns: inter-hemispherical asymmetry and a dimming in the Gong, D. Y., and C. H. Ho, 2002: The Siberian High and climate change over middle to Southern Hemisphere. Atmos. Sci. Lett., 13, 43 48. high latitude Asia. Theor. Appl. Climatol., 72, 1 9. Hausfather, Z., M. J. Menne, C. N. Williams, T. Masters, R. Broberg, and D. Jones, 2013: Gouretski, V., J. Kennedy, T. Boyer, and A. Kohl, 2012: Consistent near-surface ocean Quantifying the effect of urbanization on U.S. Historical Climatology Network warming since 1900 in two largely independent observing networks. Geophys. temperature records. J. Geophys. Res. Atmos., 118, 481-494. Res. Lett., 39, L19606. Haylock, M. R., et al., 2006: Trends in total and extreme South American rainfall in Granier, C., et al., 2011: Evolution of anthropogenic and biomass burning emissions 1960 2000 and links with sea surface temperature. J. Clim., 19, 1490 1512. of air pollutants at global and regional scales during the 1980 2010 period. He, W. Y., S. P. Ho, H. B. Chen, X. J. Zhou, D. Hunt, and Y. H. Kuo, 2009: Assessment Clim. Change, 109, 163 190. of radiosonde temperature measurements in the upper troposphere and lower Grant, A. N., S. Brönnimann, and L. Haimberger, 2008: Recent Arctic warming vertical stratosphere using COSMIC radio occultation data. Geophys. Res. Lett., 36, structure contested. Nature, 455, E2 E3. L17807. Graversen, R. G., T. Mauritsen, M. Tjernstrom, E. Kallen, and G. Svensson, 2008: Verti- Heidinger, A. K., and M. J. Pavolonis, 2009: Gazing at cirrus clouds for 25 years cal structure of recent Arctic warming. Nature, 451, 53 U54. through a split window. Part I: Methodology. J. Appl. Meteor. Climatol., 48, 1100 1116. 241 Chapter 2 Observations: Atmosphere and Surface Held, I. M., and B. J. Soden, 2006: Robust responses of the hydrological cycle to Hurrell, J. W., 1995: Decadal trends in the North Atlantic Oscillation: Regional tem- global warming. J. Clim., 19, 5686 5699. peratures and precipitation. Science, 269, 676 679. Helmig, D., et al., 2007: A review of surface ozone in the polar regions. Atmos. Envi- Hurst, D., 2011: Stratospheric water vapor trends over Boulder, Colorado: Analysis of ron., 41, 5138 5161. the 30 year Boulder record. J. Geophys. Res., 116, D02306. Hidy, G. M., and G. T. Pennell, 2010: Multipollutant air quality management: 2010 Idso, S. B., and A. J. Brazel, 1984: Rising atmospheric carbon-dioxide concentrations critical review. J. Air Waste Manage. Assoc., 60, 645 674. may increase streamflow. Nature, 312, 51 53. Hilboll, A., A. Richter, and J. P. Burrows, 2013: Long-term changes of tropospheric IPCC, 2007: Clim. Change 2007: The Physical Science Basis. Contribution of Working NO2 over megacities derived from multiple satellite instruments. Atmos. Chem. Group I to the Fourth Assessment Report of the Intergovernmental Panel on Cli- Phys., 13, 4145-4169. mate Change (IPCC) [Solomon, S., D. Qin, M. Manning, Z. Chen, M. Marquis, K. B. Hinkelman, L. M., P. W. Stackhouse, B. A. Wielicki, T. P. Zhang, and S. R. Wilson, 2009: Averyt, M. Tignor and H. L. Miller (eds.)]. Cambridge University Press, Cambridge, Surface insolation trends from satellite and ground measurements: Comparisons United Kingdom and New York, NY, USA, 996 pp. and challenges. J. Geophys. Res. Atmos., 114, D00d20. Ishii, M., A. Shouji, S. Sugimoto, and T. Matsumoto, 2005: Objective analyses of sea- Hirdman, D., et al., 2010: Long-term trends of black carbon and sulphate aerosol surface temperature and marine meteorological variables for the 20th century in the Arctic: Changes in atmospheric transport and source region emissions. using icoads and the Kobe collection. Int. J. Climatol., 25, 865 879. Atmos. Chem. Phys., 10, 9351 9368. Ishijima, K., et al., 2007: Temporal variations of the atmospheric nitrous oxide con- Hirsch, M. E., A. T. DeGaetano, and S. J. Colucci, 2001: An East Coast winter storm centration and its delta N-15 and delta O-18 for the latter half of the 20th cen- climatology. J. Clim., 14, 882 899. tury reconstructed from firn air analyses. J. Geophys. Res. Atmos., 112, D03305 . Hirschi, M., et al., 2011: Observational evidence for soil-moisture impact on hot Jain, S. K., and V. Kumar, 2012: Trend analysis of rainfall and temperature data for 2 extremes in southeastern Europe. Nature Geosci., 4, 17 21. India. Curr. Sci., 102, 37 49. Ho, S. P., W. He, and Y. H. Kuo, 2009a: Construction of consistent temperature records Jakob, D., D. Karoly, and A. Seed, 2011: Non-stationarity in daily and sub-daily in the lower stratosphere using Global Positioning System Radio Occultation intense rainfall Part 2: Regional assessment for sites in south-east Australia. Data and Microwave Sounding measurements. New Horizons in Occultation Nat. Hazards Earth Syst. Sci., 11, 2273 2284. Research, Springer-Verlag Berlin, 207 217. Jhajharia, D., S. Shrivastava, D. Sarkar, and S. Sarkar, 2009: Temporal characteristics Ho, S. P., Y. H. Kuo, Z. Zeng, and T. C. Peterson, 2007: A comparison of lower strato- of pan evaporation trends under humid conditions of northeast India. Agr. Forest sphere temperature from microwave measurements with CHAMP GPS RO data. Meteorol., 336, 61 73. Geophys. Res. Lett., 34, L15701. Jiang, T., Z. W. Kundzewicz, and B. Su, 2008: Changes in monthly precipitation and Ho, S. P., M. Goldberg, Y. H. Kuo, C. Z. Zou, and W. Schreiner, 2009b: Calibration flood hazard in the Yangtze River Basin, China. Int. J. Climatol., 28, 1471 1481. of temperature in the lower stratosphere from microwave measurements using Jiang, X., W. Ku, R. Shia, Q. Li, J. Elkins, R. Prinn, and Y. Yung, 2007: Seasonal cycle of COSMIC radio occultation data: Preliminary results. Terr. Atmos. Ocean. Sci., 20, N2O: Analysis of data. Global Biogeochem. Cycles, 21, GB1006. 87 100. Jiang, Y., Y. Luo, Z. C. Zhao, and S. W. Tao, 2010: Changes in wind speed over China Ho, S. P., et al., 2012: Reproducibility of GPS radio occultation data for climate moni- during 1956 2004. Theor. Appl. Climatol., 99, 421 430. toring: Profile-to-profile inter-comparison of CHAMP climate records 2002 to Jin, S. G., J. U. Park, J. H. Cho, and P. H. Park, 2007: Seasonal variability of GPS-derived 2008 from six data centers. J. Geophys. Res. Atmos., 117, D18111. zenith tropospheric delay (1994 2006) and climate implications. J. Geophys. Hoerling, M., et al., 2012: Anatomy of an extreme event. J. Clim., 26, 2811 2832. Res. Atmos., 112, D09110. Holben, B. N., et al., 1998: AERONET A federated instrument network and data John, V. O., R. P. Allan, and B. J. Soden, 2009: How robust are observed and simu- archive for aerosol characterization. Remote Sens. Environ., 66, 1 16. lated precipitation responses to tropical ocean warming? Geophys. Res. Lett., Holland, G. J., and P. J. Webster, 2007: Heightened tropical cyclone activity in the 36, L14702. North Atlantic: Natural variability or climate trend? Philos. Trans. R. Soc. London John, V. O., G. Holl, R. P. Allan, S. A. Buehler, D. E. Parker, and B. J. Soden, 2011: Clear- Ser. A, 365, 2695 2716. sky biases in satellite infrared estimates of upper tropospheric humidity and its Hope, P. K., W. Drosdowsky, and N. Nicholls, 2006: Shifts in the synoptic systems trends. J. Geophys. Res. Atmos., 116, D14108. influencing southwest Western Australia. Clim. Dyn., 26, 751 764. Jones, D. A., W. Wang, and R. Fawcett, 2009: High-quality spatial climate data-sets Hsu, N. C., et al., 2012: Global and regional trends of aerosol optical depth over for Australia. Australian, Meteor. Ocean. J., 58, 233 248. land and ocean using SeaWiFS measurements from 1997 to 2010. Atmos. Chem. Jones, P. D., and D. H. Lister, 2007: Intercomparison of four different Southern Hemi- Phys. Discuss., 12, 8465 8501. sphere sea level pressure datasets. Geophys. Res. Lett., 34, L10704. Hsu, P. C., T. Li, and B. Wang, 2011: Trends in global monsoon area and precipitation Jones, P. D., and D. H. Lister, 2009: The urban heat island in Central London and over the past 30 years. Geophys. Res. Lett., 38, L08701. urban-related warming trends in Central London since 1900. Weather, 64, Hu, Y., and Q. Fu, 2007: Observed poleward expansion of the Hadley circulation since 323 327. 1979. Atmos. Chem. Phys., 7, 5229 5236. Jones, P. D., T. Jonsson, and D. Wheeler, 1997: Extension to the North Atlantic Oscil- Hu, Y. C., W. J. Dong, and Y. He, 2010: Impact of land surface forcings on mean and lation using early instrumental pressure observations from Gibraltar and south- extreme temperature in eastern China. J. Geophys. Res. Atmos., 115, 11. west Iceland. Int. J. Climatol., 17, 1433 1450. Hu, Y. Y., C. Zhou, and J. P. Liu, 2011: Observational evidence for the poleward expan- Jones, P. D., D. H. Lister, and Q. Li, 2008: Urbanization effects in large-scale tempera- sion of the Hadley circulation. Adv. Atmos. Sci., 28, 33 44. ture records, with an emphasis on China. J. Geophys. Res. Atmos., 113, D16122. Huang, J., et al., 2008: Estimation of regional emissions of nitrous oxide from 1997 Jones, P. D., D. H. Lister, T. J. Osborn, C. Harpham, M. Salmon, and C. P. Morice, 2012: to 2005 using multinetwork measurements, a chemical transport model, and an Hemispheric and large-scale land-surface air temperature variations: An exten- inverse method. J. Geophys. Res. Atmos., 113, D17313. sive revision and an update to 2010. J. Geophys. Res. Atmos., 117, D05127. Huang, W.-R., S.-Y. Wang, and J. C. L. Chan, 2010: Discrepancies between global Jones, R., S. Westra, and A. Sharma, 2010: Observed relationships between extreme reanalyses and observations in the interdecadal variations of Southeast Asian sub-daily precipitation, surface temperature, and relative humidity. Geophys. cold surge. Int. J. Climatol., 31, 2272-2280.. Res. Lett., 37, L22805. Hudson, R. D., 2012: Measurements of the movement of the jet streams at mid-lati- Joshi, M. M., J. M. Gregory, M. J. Webb, D. M. H. Sexton, and T. C. Johns, 2008: Mecha- tudes, in the Northern and Southern Hemispheres, 1979 to 2010. Atmos. Chem. nisms for the land/sea warming contrast exhibited by simulations of climate Phys., 12, 7797 7808. change. Clim. Dyn., 30, 455 465. Hudson, R. D., M. F. Andrade, M. B. Follette, and A. D. Frolov, 2006: The total ozone Jovanovic, B., D. Collins, K. Braganza, D. Jakob, and D. A. Jones, 2011: A high-quality field separated into meteorological regimes Part II: Northern Hemisphere mid- monthly total cloud amount dataset for Australia. Clim. Change, 108, 485-517. latitude total ozone trends. Atmos. Chem. Phys., 6, 5183 5191. Jung, M., et al., 2010: Recent decline in the global land evapotranspiration trend due Hundecha, Y., A. St-Hilaire, T. Ouarda, S. El Adlouni, and P. Gachon, 2008: A nonsta- to limited moisture supply. Nature, 467, 951 954. tionary extreme value analysis for the assessment of changes in extreme annual Kahn, R. A., et al., 2007: Satellite-derived aerosol optical depth over dark water from wind speed over the Gulf of St. Lawrence, Canada. J. Appl. Meteor. Climatol., 47, MISR and MODIS: Comparisons with AERONET and implications for climatologi- 2745 2759. cal studies. J. Geophys. Res. Atmos., 112, D18205. 242 Observations: Atmosphere and Surface Chapter 2 Kanamitsu, M., W. Ebisuzaki, J. Woollen, S. K. Yang, J. J. Hnilo, M. Fiorino, and G. Kistler, R., et al., 2001: The NCEP-NCAR 50-year reanalysis: Monthly means CD-ROM L. Potter, 2002: NCEP-DOE AMIP-II reanalysis (R-2). Bull. Am. Meteor. Soc., 83, and documentation. Bull. Am. Meteor. Soc., 82, 247 267. 1631 1643. Klein Tank, A. M. G., et al., 2006: Changes in daily temperature and precipitation Kang, S. M., L. M. Polvani, J. C. Fyfe, and M. Sigmond, 2011: Impact of polar ozone extremes in central and south Asia. J. Geophys. Res. Atmos., 111, D16105. depletion on subtropical Precipitation. Science, 332, 951 954. Klok, E. J., and A. Tank, 2009: Updated and extended European dataset of daily cli- Kao, H. Y., and J. Y. Yu, 2009: Contrasting Eastern-Pacific and Central-Pacific types of mate observations. Int. J. Climatol., 29, 1182 1191. ENSO. J. Clim., 22, 615 632. Knapp, K. R., and M. C. Kruk, 2010: Quantifying interagency differences in tropical Karnauskas, K. B., R. Seager, A. Kaplan, Y. Kushnir, and M. A. Cane, 2009: Observed cyclone best-track wind speed estimates. Mon. Weather Rev., 138, 1459 1473. strengthening of the zonal sea surface temperature gradient across the equato- Knowles, N., M. D. Dettinger, and D. R. Cayan, 2006: Trends in snowfall versus rainfall rial Pacific Ocean. J. Clim., 22, 4316 4321. in the western United States. J. Clim., 19, 4545 4559. Karnieli, A., et al., 2009: Temporal trend in anthropogenic sulfur aerosol transport Knutson, T. R., et al., 2010: Tropical cyclones and climate change. Nature Geosci., 3, from central and eastern Europe to Israel. J. Geophys. Res. Atmos., 114, D00d19. 157 163. Karoly, D., 1989: Southern-Hemisphere circulation features associated with El Nino- Kobayashi, S., M. Matricardi, D. Dee, and S. Uppala, 2009: Toward a consistent reanal- Southern Oscillation. J. Clim., 2, 1239 1252. ysis of the upper stratosphere based on radiance measurements from SSU and Kato, S., et al.: Surface irradiances consistent with CERES-derived top-of-atmosphere AMSU-A. Q. J. R. Meteorol. Soc., 135, 2086 2099. shortwave and longwave irradiances. J. Clim. 26, 2719-2740 Kopp, G., and G. Lawrence, 2005: The Total Irradiance Monitor (TIM): Instrument Keeling, C., R. Bacastow, A. Bainbridge, C. Ekdahl, P. Guenther, L. Waterman, and design. Solar Phys., 230, 91 109. J. Chin, 1976a: Atmospheric Carbon-Dioxide Variations at Mauna-Loa Observa- Kopp, G., and J. L. Lean, 2011: A new, lower value of total solar irradiance: Evidence tory, Hawaii. Tellus, 28, 538 551. and climate significance. Geophys. Res. Lett., 38, L01706. 2 Keeling, C. D., J. A. Adams, and C. A. Ekdahl, 1976b: Atmospheric carbo-dioxide varia- Kopp, G., G. Lawrence, and G. Rottman, 2005: The Total Irradiance Monitor (TIM): tions at South Pole. Tellus, 28, 553 564. Science results. Solar Phys., 230, 129 139. Keller, C., D. Brunner, S. Henne, M. Vollmer, S. O Doherty, and S. Reimann, 2011: Evi- Kossin, J. P., K. R. Knapp, D. J. Vimont, R. J. Murnane, and B. A. Harper, 2007: A glob- dence for under-reported western European emissions of the potent greenhouse ally consistent reanalysis of hurricane variability and trends. Geophys. Res. Lett., gas HFC-23. Geophys. Res. Lett., 38, L15808. 34, L04815. Kennedy, J. J., N. A. Rayner, and R. O. Smith, 2012: Using AATSR data to assess the Koutsoyiannis, D., and A. Montanari, 2007: Statistical analysis of hydroclimatic time quality of in situ sea surface temperature observations for climate studies. series: Uncertainty and insights. Water Resour. Res., 43, W05429. Remote Sens. Environ., 116, 79 92. Kreienkamp, F., A. Spekat, and W. Enke, 2010: Stationarity of atmospheric waves and Kennedy, J. J., N. A. Rayner, R. O. Smith, D. E. Parker, and M. Saunby, 2011a: Reas- blocking over Europe-based on a reanalysis dataset and two climate scenarios. sessing biases and other uncertainties in sea surface temperature observations Theor. Appl. Climatol., 102, 205 212. measured in situ since 1850: 2. Biases and homogenization. J. Geophys. Res. Krishna Moorthy, K., S. Suresh Babu, and S. K. Satheesh, 2007: Temporal heteroge- Atmos., 116, D14104. neity in aerosol characteristics and the resulting radiative impact at a tropical Kennedy, J. J., N. A. Rayner, R. O. Smith, M. Saunby, and D. E. Parker, 2011b: Reas- coastal station Part 1: Microphysical and optical properties. Ann. Geophys., sessing biases and other uncertainties in sea surface temperature observations 25, 2293 2308. since 1850, part 1: Measurement and sampling uncertainties. J. Geophys. Res., Krishna Moorthy, K., S. Suresh Babu, M. R. Manoj, and S. K. Satheesh, 2013: Buildup 116, D14103. of Aerosols over the Indian Region. Geophys. Res. Lett., 40, 1011-1014 Kent, E. C., and D. I. Berry, 2008: Assessment of the Marine Observing System Krishna Moorthy, K., S. S. Babu, S. K. Satheesh, S. Lal, M. M. Sarin, and S. Ramach- (ASMOS): Final report. National Oceanography Centre Southampton Research andran, 2009: Climate implications of atmospheric aerosols and trace gases: and Consultancy Report, 55 pp. Indian Scenario, Climate Sense. World Meteorological Organisation, Geneva, Kent, E. C., S. D. Woodruff, and D. I. Berry, 2007: Metadata from WMO publication Switzerland, pp. 157 160. no. 47 and an assessment of voluntary observing ship observation heights in Krueger, O., F. Schenk, F. Feser, and R. Weisse, 2013: Inconsistencies between long- ICOADS. J. Atmos. Ocean Technol., 24, 214 234. term trends in storminess derived from the 20CR reanalysis and observations. J. Kent, E. C., S. Fangohr, and D. I. Berry, 2012: A comparative assessment of monthly Clim., 26, 868 874. mean wind speed products over the global ocean. Int. J. Climatol., 33, 2530- Kruger, A., and S. Sekele, 2013: Trends in extreme temperature indices in South 2541. Africa: 1962 2009. Int. J. Climatol., 33, 661-676. Kent, E. C., J. J. Kennedy, D. I. Berry, and R. O. Smith, 2010: Effects of instrumenta- Kubota, H., and J. C. L. Chan, 2009: Interdecadal variability of tropical cyclone land- tion changes on sea surface temperature measured in situ. Clim. Change, 1, fall in the Philippines from 1902 to 2005. Geophys. Res. Lett., 36, L12802. 718 728. Kudo, R., A. Uchiyama, A. Yamazaki, T. Sakami, and O. Ijima, 2011: Decadal changes Kent, E. C., N. A. Rayner, D. I. Berry, M. Saunby, B. I. Moat, J. J. Kennedy, and D. E. in aerosol optical thickness and single scattering albedo estimated from ground- Parker, 2013: Global analysis of night marine air temperature and its uncertainty based broadband radiometers: A case study in Japan. J. Geophys. Res., 116, since 1880, the HadNMAT2 Dataset, J. Geophys. Res., 118, 1281-1298. D03207. Kenyon, J., and G. C. Hegerl, 2008: Influence of modes of climate variability on global Kudo, R., A. Uchiyama, O. Ijima, N. Ohkawara, and S. Ohta, 2012: Aerosol impact on temperature extremes. J. Clim., 21, 3872 3889. the brightening in Japan. J. Geophys. Res. Atmos., 117, 11. Kenyon, J., and G. C. Hegerl, 2010: Influence of modes of climate variability on global Kueppers, L. M., M. A. Snyder, and L. C. Sloan, 2007: Irrigation cooling effect: Region- precipitation extremes. J. Clim., 23, 6248 6262. al climate forcing by land-use change. Geophys. Res. Lett., 34, L03703. Kharin, V., F. Zwiers, X. Zhang, and M. Wehner, 2013: Changes in temperature and Kuglitsch, F. G., A. Toreti, E. Xoplaki, P. M. Della-Marta, J. Luterbacher, and H. Wanner, precipitation extremes in the CMIP5 ensemble. Climatic Change, 119, 345-357. 2009: Homogenization of daily maximum temperature series in the Mediter- Kiehl, J. T., and K. E. Trenberth, 1997: Earth s annual global mean energy budget. Bull. ranean. J. Geophys. Res. Atmos., 114, D15108. Am. Meteor. Soc., 78, 197 208. Kuglitsch, F. G., A. Toreti, E. Xoplaki, P. M. Della-Marta, C. S. Zerefos, M. Turkes, and J. Kim, D., and V. Ramanathan, 2012: Improved estimates and understanding of global Luterbacher, 2010: Heat wave changes in the eastern Mediterranean since 1960. albedo and atmospheric solar absorption. Geophys. Res. Lett., 39, L24704. Geophys. Res. Lett., 37, L04802. Kim, D. Y., and V. Ramanathan, 2008: Solar radiation budget and radiative forcing Kumari, B. P., and B. N. Goswami, 2010: Seminal role of clouds on solar dimming over due to aerosols and clouds. J. Geophys. Res. Atmos., 113, D02203. the Indian monsoon region. Geophys. Res. Lett., 37, L06703. Kim, J., et al., 2010: Regional atmospheric emissions determined from measure- Kumari, B. P., A. L. Londhe, S. Daniel, and D. B. Jadhav, 2007: Observational evidence ments at Jeju Island, Korea: Halogenated compounds from China. Geophys. Res. of solar dimming: Offsetting surface warming over India. Geophys. Res. Lett., Lett., 37, L12801. 34, L21810. King, A., L. Alexander, and M. Donat, 2013: The efficacy of using gridded data to examine extreme rainfall characteristics: A case study for Australia. Inter. J. Cli- matol., 33, 2376-2387. 243 Chapter 2 Observations: Atmosphere and Surface Kundzewicz, Z. W., et al., 2007: Freshwater resources and their management. Cli- Lenderink, G., and E. Van Meijgaard, 2008: Increase in hourly precipitation extremes mate Change 2007: Impacts, Adaptation and Vulnerability. Contribution of beyond expectations from temperature changes. Nature Geosci., 1, 511 514. Working Group II to the Fourth Assessment Report of the Intergovernmental Lenderink, G., H. Y. Mok, T. C. Lee, and G. J. van Oldenborgh, 2011: Scaling and trends Panel on Climate Change [Solomon, S., D. Qin, M. Manning, Z. Chen, M. Marquis, of hourly precipitation extremes in two different climate zones – Hong K. B. Averyt, M. Tignor and H. L. Miller (eds.)].Cambridge University Press, Cam- Kong and the Netherlands. Hydrol. Earth Syst. Sci. Discuss., 8, 4701 4719. bridge, United Kingdom and New York, NY, USA,172-210. Lennartz, S., and A. Bunde, 2009: Trend evaluation in records with long-term memory: Kunkel, K. E., M. A. Palecki, L. Ensor, D. Easterling, K. G. Hubbard, D. Robinson, and Application to global warming. Geophys. Res. Lett., 36, L16706. K. Redmond, 2009: Trends in twentieth-century US extreme snowfall seasons. J. Levin, I., et al., 2010: The global SF6 source inferred from long-term high preci- Clim., 22, 6204 6216. sion atmospheric measurements and its comparison with emission inventories. Kunkel, K. E., et al., 2008: Observed changes in weather and climate extremes. In: Atmos. Chem. Phys., 10, 2655 2662. Weather and Climate Extremes in a Changing Climate. Regions of Focus: North Levitus, S., J. I. Antonov, T. P. Boyer, R. A. Locarnini, H. E. Garcia, and A. V. Mishonov, America, Hawaii, Caribbean, and U.S. Pacific Islands [T. R. Karl, G. A. Meehl, D. M. 2009: Global ocean heat content 1955 2008 in light of recently revealed instru- Christopher, S. J. Hassol, A. M. Waple, and W. L. Murray (eds.)]. A Report by the mentation problems. Geophys. Res. Lett., 36, 5. U.S. Climate Change Science Program and the Subcommittee on Global Change Levy, R. C., L. A. Remer, R. G. Kleidman, S. Mattoo, C. Ichoku, R. Kahn, and T. F. Eck, Research. 2010: Global evaluation of the Collection 5 MODIS dark-target aerosol products Kunz, M., J. Sander, and C. Kottmeier, 2009: Recent trends of thunderstorm and hail- over land. Atmos. Chem. Phys., 10, 10399 10420. storm frequency and their relation to atmospheric characteristics in southwest Li, Q., H. Zhang, X. Liu, J. Chen, W. Li, and P. Jones, 2009: A mainland China homog- Germany. Int. J. Climatol., 29, 2283 2297. enized historical temperature dataset of 1951 2004. Bull. Am. Meteor. Soc., 90, 2 Kuo, Y. H., W. S. Schreiner, J. Wang, D. L. Rossiter, and Y. Zhang, 2005: Comparison 1062 1065. of GPS radio occultation soundings with radiosondes. Geophys. Res. Lett., 32, Li, Q., W. Dong, W. Li, X. Gao, P. Jones, J. Kennedy, and D. Parker, 2010a: Assessment of L05817. the uncertainties in temperature change in China during the last century. Chin. Kvalevag, M. M., and G. Myhre, 2007: Human impact on direct and diffuse solar Sci. Bull., 55, 1974 1982. radiation during the industrial era. J. Clim., 20, 4874 4883. Li, Q. X., et al., 2010b: Assessment of surface air warming in northeast China, with L Ecuyer, T. S., N. B. Wood, T. Haladay, G. L. Stephens, and P. W. Stackhouse, 2008: emphasis on the impacts of urbanization. Theor. Appl. Climatol., 99, 469 478. Impact of clouds on atmospheric heating based on the R04 CloudSat fluxes and Li, Z., et al., 2012: Changes of daily climate extremes in southwestern China during heating rates data set. J. Geophys. Res. Atmos., 113, 15. 1961 2008. Global Planet. Change, 80 81, 255 272. Labat, D., Y. Godderis, J. L. Probst, and J. L. Guyot, 2004: Evidence for global runoff Liang, F., and X. A. Xia, 2005: Long-term trends in solar radiation and the associated increase related to climate warming. Adv. Water Resour., 27, 631 642. climatic factors over China for 1961 2000. Ann. Geophys., 23, 2425 2432. Ladstadter, F., A. K. Steiner, U. Foelsche, L. Haimberger, C. Tavolato, and G. Kirch- Liebmann, B., R. M. Dole, C. Jones, I. Blade, and D. Allured, 2010: Influence of choice nebngast, 2011: An assessment of differences in lower stratospheric tempera- of time period on global surface temperature trend estimates. Bull. Am. Meteor. ture records from (A)MSU, radiosondes and GPS radio occultation. Atmos. Meas. Soc., 91, 1485 U1471. Tech., 4, 1965 1977. Liepert, B. G., 2002: Observed reductions of surface solar radiation at sites in the Landsea, C. W., 2007: Counting Atlantic tropical cyclones back to 1900. EOS Trans. United States and worldwide from 1961 to 1990. Geophys. Res. Lett., 29, 1421. (AGU), 88, 197 202. Liley, J. B., 2009: New Zealand dimming and brightening. J. Geophys. Res. Atmos., Landsea, C. W., B. A. Harper, K. Hoarau, and J. A. Knaff, 2006: Can we detect trends 114, D00d10. in extreme tropical cyclones? Science, 313, 452 454. Lim, E. P., and I. Simmonds, 2007: Southern Hemisphere winter extratropical cyclone Landsea, C. W., et al., 2011: A reanalysis of the 1921 30 Atlantic Hurricane Data- characteristics and vertical organization observed with the ERA-40 data in base. J. Clim., 25, 865 885. 1979 2001. J. Clim., 20, 2675 2690. Langematz, U., and M. Kunze, 2008: Dynamical changes in the Arctic and Antarctic Lim, E. P., and I. Simmonds, 2009: Effect of tropospheric temperature change on the stratosphere during spring. In: Climate Variability and Extremes during the Past zonal mean circulation and SH winter extratropical cyclones. Clim. Dyn., 33, 100 Years. Advances in Global Change Research [S. Brönnimann, J. Luterbacher, T. 19 32. Ewen, H. F. Diaz, R. S. Stolarski, and U. Neu (eds.)], Springer, pp. 293 301. Lim, Y. K., M. Cai, E. Kalnay, and L. Zhou, 2008: Impact of vegetation types on surface Lanzante, J. R., 2009: Comment on Trends in the temperature and water vapor temperature change. J. Appl. Meteor. Climatol., 47, 411 424. content of the tropical lower stratosphere: Sea surface connection by Karen H. Lin, C., K. Yang, J. Qin, and R. Fu, 2012: Observed coherent trends of surface and Rosenlof and George C. Reid. J. Geophys. Res. Atmos., 114, D12104. upper-air wind speed over China since 1960. J. Clim.,26, 2891-2903.. Laprise, R., 1992: The resolution of global spectroal models. Bull. Am. Meteor. Soc., Liu, B., M. Xu, and M. Henderson, 2011: Where have all the showers gone? Regional 73, 1453 1454. declines in light precipitation events in China, 1960 2000. Int. J. Climatol., 31, Larkin, N. K., and D. E. Harrison, 2005: On the definition of El Nino and associated 1177 1191. seasonal average US weather anomalies. Geophys. Res. Lett., 32, L13705. Liu, B. H., M. Xu, M. Henderson, and W. G. Gong, 2004a: A spatial analysis of pan Lawrimore, J. H., M. J. Menne, B. E. Gleason, C. N. Williams, D. B. Wuertz, R. S. Vose, evaporation trends in China, 1955 2000. J. Geophys. Res. Atmos., 109, D15102. and J. Rennie, 2011: An overview of the Global Historical Climatology Network Liu, B. H., M. Xu, M. Henderson, Y. Qi, and Y. Q. Li, 2004b: Taking China s temperature: monthly mean temperature data set, version 3. J. Geophys. Res. Atmos., 116, Daily range, warming trends, and regional variations, 1955 2000. J. Clim., 17, D19121. 4453 4462. Leakey, A. D. B., M. Uribelarrea, E. A. Ainsworth, S. L. Naidu, A. Rogers, D. R. Ort, and Liu, Q. H., and F. Z. Weng, 2009: Recent stratospheric temperature observed from S. P. Long, 2006: Photosynthesis, productivity, and yield of maize are not affected satellite measurements. Sola, 5, 53 56. by open-air elevation of CO2 concentration in the absence of drought. Plant Lo, M.-H., and J. S. Famiglietti, 2013: Irrigation in California s Central Valley strength- Physiol., 140, 779 790. ens the southwestern U.S. water cycle. Geophys. Res. Lett., 40, 301-306. Lee, H. T., A. Gruber, R. G. Ellingson, and I. Laszlo, 2007: Development of the HIRS Loeb, N. G., et al., 2009: Toward optimal closure of the Earth s top-of-atmosphere outgoing longwave radiation climate dataset. J. Atmos. Ocean Technol., 24, radiation budget. J. Clim., 22, 748 766. 2029 2047. Loeb, N. G., et al., 2012a: Advances in understanding top-of-atmosphere radiation Lefohn, A. S., D. Shadwick, and S. J. Oltmans, 2010: Characterizing changes in surface variability from satellite observations. Surv. Geophys., 33, 359 385. ozone levels in metropolitan and rural areas in the United States for 1980 2008 Loeb, N. G., et al., 2012b: Observed changes in top-of-the-atmosphere radiation and and 1994 2008. Atmos. Environ., 44, 5199 5210. upper-ocean heating consistent within uncertainty. Nature Geosci., 5, 110 113. Lehmann, A., K. Getzlaff, and J. Harlass, 2011: Detailed assessment of climate vari- Logan, J. A., et al., 2012: Changes in ozone over Europe since 1990: Analysis of ozone ability in the Baltic Sea area for the period 1958 to 2009. Clim. Res., 46, 185 measurements from sondes, regular aircraft (MOZAIC), and alpine surface sites. 196. J. Geophys. Res., 117, D09301. Lelieveld, J., J. van Aardenne, H. Fischer, M. de Reus, J. Williams, and P. Winkler, 2004: Long, C. N., E. G. Dutton, J. A. Augustine, W. Wiscombe, M. Wild, S. A. McFarlane, and Increasing ozone over the Atlantic Ocean. Science, 304, 1483 1487. C. J. Flynn, 2009: Significant decadal brightening of downwelling shortwave in the continental United States. J. Geophys. Res. Atmos., 114, D00d06. 244 Observations: Atmosphere and Surface Chapter 2 Lorenz, R., E. B. Jaeger, and S. I. Seneviratne, 2010: Persistence of heat waves and its McVicar, T. R., et al., 2012: Global review and synthesis of trends in observed ter- link to soil moisture memory. Geophys. Res. Lett., 37, L09703. restrial near-surface wind speeds: Implications for evaporation. J. Hydrol., 416, Lucas, C., H. Nguyen, and B. Timbal, 2012: An observational analysis of Southern 182 205. Hemisphere tropical expansion. J. Geohys. Res., 117, D17112. Mears, C., J. Wang, S. Ho, L. Zhang, and X. Zhou, 2010: Total column water vapor, in Luo, J. J., W. Sasaki, and Y. Masumoto, 2012: Indian Ocean warming modulates Pacific State of the Climate in 2009. Bull. Am. Meteor. Soc. [D. S. Arndt, M. O. Baringer, climate change. Proc. Natl. Acad. Sci. U.S.A., 109, 18701 18706. and M. R. Johnson (eds.)]. Lupikasza, E., 2010: Spatial and temporal variability of extreme precipitation in Mears, C. A., and F. J. Wentz, 2009a: Construction of the remote sensing systems V3.2 Poland in the period 1951 2006. Int. J. Climatol., 30, 991 1007. atmospheric temperature records from the MSU and AMSU microwave sound- Lyman, J. M., et al., 2010: Robust warming of the global upper ocean. Nature, 465, ers. J. Atmos. Ocean Technol., 26, 1040 1056. 334 337. Mears, C. A., and F. J. Wentz, 2009b: Construction of the RSS V3.2 lower-tropospheric Lynch, A. H., J. A. Curry, R. D. Brunner, and J. A. Maslanik, 2004: Toward an integrated temperature dataset from the MSU and AMSU microwave sounders. J. Atmos. assessment of the impacts of extreme wind events on Barrow, Alaska. Bull. Am. Ocean Technol., 26, 1493 1509. Meteor. Soc., 85, 209 . Mears, C. A., F. J. Wentz, and P. W. Thorne, 2012: Assessing the value of Microwave Mahowald, N., et al., 2010: Observed 20th century desert dust variability: Impact on Sounding Unit-radiosonde comparisons in ascertaining errors in climate data climate and biogeochemistry. Atmos. Chem. Phys., 10, 10875 10893. records of tropospheric temperatures. J. Geophys. Res. Atmos., 117, D19103. Makowski, K., M. Wild, and A. Ohmura, 2008: Diurnal temperature range over Mears, C. A., F. J. Wentz, P. Thorne, and D. Bernie, 2011: Assessing uncertainty in Europe between 1950 and 2005. Atmos. Chem. Phys., 8, 6483 6498. estimates of atmospheric temperature changes from MSU and AMSU using a Makowski, K., E. B. Jaeger, M. Chiacchio, M. Wild, T. Ewen, and A. Ohmura, 2009: On Monte-Carlo estimation technique. J. Geophys. Res. Atmos., 116. the relationship between diurnal temperature range and surface solar radiation Mears, C. A., C. E. Forest, R. W. Spencer, R. S. Vose, and R. W. Reynolds, 2006: What is 2 in Europe. J. Geophys. Res. Atmos., 114, D00d07. our understanding of the contribution made by observational or methodologi- Manabe, S., and R. F. Strickler, 1964: Thermal equilibrium of the atmosphere with cal uncertainties to the previously reported vertical differences in temperature a convective adjustment. Journal of the Atmospheric Sciences, 21, 361 385. trends? In: Temperature Trends in the Lower Tmosphere: Steps for Understand- Mann, M. E., 2011: On long range dependence in global surface temperature series. ing and Reconciling Differences [T. R. Karl, S. J. Hassol, C. D. Miller, and W. L. Clim. Change, 107, 267 276. Murray (eds.)], 71-88. Mann, M. E., T. A. Sabbatelli, and U. Neu, 2007a: Evidence for a modest undercount Mears, C. A., B. D. Santer, F. J. Wentz, K. E. Taylor, and M. F. Wehner, 2007: Relation- bias in early historical Atlantic tropical cyclone counts. Geophys. Res. Lett., 34, ship between temperature and precipitable water changes over tropical oceans. L22707. Geophys. Res. Lett., 34, L24709. Mann, M. E., K. A. Emanual, G. J. Holland, and P. J. Webster, 2007b: Atlantic tropical Meehl, G. A., J. M. Arblaster, and G. Branstator, 2012: Mechanisms contributing to the cyclones revisited. EOS Transactions (AGU), 88, 349 350. warming hole and the consequent U.S. East West differential of heat extremes. Manney, G. L., et al., 2011: Unprecedented Arctic ozone loss in 2011. Nature, 478, J. Clim., 25, 6394 6408. 469 475. Mekis, É., and L. A. Vincent, 2011: An overview of the second generation adjusted Mantua, N. J., S. R. Hare, Y. Zhang, J. M. Wallace, and R. C. Francis, 1997: A Pacific daily precipitation dataset for trend analysis in Canada. Atmosphere-Ocean, 49, interdecadal climate oscillation with impacts on salmon production. Bull. Am. 163 177. Meteor. Soc., 78, 1069 1079. Meng, Q. J., M. Latif, W. Park, N. S. Keenlyside, V. A. Semenov, and T. Martin, 2012: Marenco, A., H. Gouget, P. Nédélec, and J. P. Pagés, 1994: Evidence of a long-term Twentieth century Walker Circulation change: Data analysis and model experi- increase in tropospheric ozone from Pic du Midi series: Consequences: positive ments. Clim. Dyn., 38, 1757 1773. radiative forcing. J. Geophys. Res., 99, 16,617 616, 632. Menne, M. J., and C. N. Williams, 2009: Homogenization of temperature series via Marshall, G. J., 2003: Trends in the southern annular mode from observations and pairwise comparisons. J. Clim., 22, 1700 1717. reanalyses. J. Clim., 16, 4134 4143. Menne, M. J., C. N. Williams, and M. A. Palecki, 2010: On the reliability of the US Martinerie, P., et al., 2009: Long-lived halocarbon trends and budgets from atmo- surface temperature record. J. Geophys. Res. Atmos., 115, D11108. spheric chemistry modelling constrained with measurements in polar firn. Menzel, W. P., 2001: Cloud tracking with satellite imagery: From the pioneering work Atmos. Chem. Phys., 3911 3934. of Ted Fujita to the present. Bull. Am. Meteor. Soc., 82, 33 47. Mastrandrea, M., et al., 2011: The IPCC AR5 guidance note on consistent treatment Merchant, C. J., et al., 2012: A 20 year independent record of sea surface tempera- of uncertainties: A common approach across the working groups. Clim. Change, ture for climate from Along Track Scanning Radiometer. J. Geophys. Res., 117. 108, 675 691. C12013. Matulla, C., W. Schoner, H. Alexandersson, H. von Storch, and X. L. Wang, 2008: Euro- Merrifield, M. A., 2011: A shift in western tropical Pacific sea level trends during the pean storminess: Late nineteenth century to present. Clim. Dyn., 31, 125 130. 1990s. J. Clim., 24, 4126 4138. McCarthy, M. P., P. W. Thorne, and H. A. Titchner, 2009: An analysis of tropospheric Mezher, R. N., M. Doyle, and V. Barros, 2012: Climatology of hail in Argentina. Atmos. humidity trends from radiosondes. J. Clim., 22, 5820 5838. Res., 114 115, 70 82. McCarthy, M. P., H. A. Titchner, P. W. Thorne, S. F. B. Tett, L. Haimberger, and D. E. Mieruch, S., S. Noel, H. Bovensmann, and J. P. Burrows, 2008: Analysis of global water Parker, 2008: Assessing bias and uncertainty in the HadAT-adjusted radiosonde vapour trends from satellite measurements in the visible spectral range. Atmos. climate record. J. Clim., 21, 817 832. Chem. Phys., 8, 491 504. McKitrick, R., 2010: Atmospheric circulations do not explain the temperature-indus- Milewska, E. J., 2004: Baseline cloudiness trends in Canada 1953 2002. Atmos. trialization correlation. Stat. Politics Policy, 1, issue 1 . Ocean, 42, 267 280. McKitrick, R., and P. J. Michaels, 2004: A test of corrections for extraneous signals in Miller, B., et al., 2010: HFC-23 (CHF3) emission trend response to HCFC-22 (CHClF2) gridded surface temperature data. Clim. Res., 26, 159 173. production and recent HFC-23 emission abatement measures. Atmos. Chem. McKitrick, R., and N. Nierenberg, 2010: Socioeconomic patterns in climate data. J. Phys., 10, 7875 7890. Econ. Soc. Meas, 35, 149 175. Milliman, J. D., K. L. Farnsworth, P. D. Jones, K. H. Xu, and L. C. Smith, 2008: Cli- McKitrick, R. R., and P. J. Michaels, 2007: Quantifying the influence of anthropogenic matic and anthropogenic factors affecting river diSchärge to the global ocean, surface processes and inhomogeneities on gridded global climate data. J. Geo- 1951 2000. Global Planet. Change, 62, 187 194. phys. Res. Atmos., 112, D24S09. Mills, T. C., 2010: Skinning a cat : Alternative models of representing temperature McNider, R. T., et al., 2012: Response and sensitivity of the nocturnal boundary layer trends. Clim. Change, 101, 415 426. over land to added longwave radiative forcing. J. Geophys. Res., 117, D14106. Milz, M., et al., 2005: Water vapor distributions measured with the Michelson Inter- McVicar, T. R., T. G. Van Niel, L. T. Li, M. L. Roderick, D. P. Rayner, L. Ricciardulli, and R. ferometer for passive atmospheric sounding on board Envisat (MIPAS/Envisat). J. Donohue, 2008: Wind speed climatology and trends for Australia, 1975 2006: J. Geophys. Res., 110, D24307. Capturing the stilling phenomenon and comparison with near-surface reanalysis Mishchenko, M. I., et al., 2007: Long-term satellite record reveals likely recent aero- output. Geophys. Res. Lett., 35, L20403. sol trend. Science, 315, 1543 1543. 245 Chapter 2 Observations: Atmosphere and Surface Mishchenko, M. I., et al., 2012: Aerosol retrievals from channel-1 and -2 AVHRR radi- Neu, U., et al., 2012: IMILAST: A community effort to intercompare extratropical ances: Long-term trends updated and revisited. J. Quant. Spectr. Radiat. Trans., cyclone detection and tracking algorithms. Bull. Am. Meteor. Soc., 94, 529 547. 113, 1974 1980. Nevison, C., et al., 2011: Exploring causes of interannual variability in the seasonal Misra, V., J. P. Michael, R. Boyles, E. P. Chassignet, M. Griffin, and J. J. O Brien, 2012: cycles of tropospheric nitrous oxide. Atmos. Chem. Phys., 11, 3713 3730. Reconciling the spatial distribution of the surface temperature trends in the New, M., et al., 2006: Evidence of trends in daily climate extremes over southern and southeastern United States. J. Clim., 25, 3610 3618. west Africa. J. Geophys. Res. Atmos., 111, D14102. Mitas, C. M., and A. Clement, 2005: Has the Hadley cell been strengthening in recent Nguyen, H., B. Timbal, I. Smith, A. Evans, and C. Lucas, 2013: The Hadley circulation in decades? Geophys. Res. Lett., 32, L030809. reanalyses: Climatology, variability and change. J. Clim., 26, 3357 3376. Mitchell, T. D., and P. D. Jones, 2005: An improved method of constructing a database Nicholls, N., 2008: Recent trends in the seasonal and temporal behaviour of the El of monthly climate observations and associated high-resolution grids. Int. J. Cli- Nino-Southern Oscillation. Geophys. Res. Lett., 35, L19703. matol., 25, 693 712. Nisbet, E., and R. Weiss, 2010: Top-down versus bottom-up. Science, 328, 1241- Mo, K., and J. Paegle, 2001: The Pacific-South American modes and their downstream 1243. effects. Int. J. Climatol., 21, 1211 1229. Norris, J. R., and M. Wild, 2007: Trends in aerosol radiative effects over Europe Moberg, A., et al., 2006: Indices for daily temperature and precipitation extremes inferred from observed cloud cover, solar dimming and solar brightening . J. in Europe analyzed for the period 1901 2000. J. Geophys. Res. Atmos., 111, Geophys. Res. Atmos., 112, D08214. D22106. Norris, J. R., and M. Wild, 2009: Trends in aerosol radiative effects over China and Mohapatra, M., B. K. Mandyopadhyay, and A. Tyagi, 2011: Best track parameters of Japan inferred from observed cloud cover, solar dimming, and solar brightening. tropical cyclones over the North Indian Ocean: A review. Natural Hazards, 63, J. Geophys. Res. Atmos., 114, D00d15. 2 1285-1317. O Dell, C. W., F. J. Wentz, and R. Bennartz, 2008: Cloud liquid water path from satel- Mokhov, I. I., M. G. Akperov, M. A. Prokofyeva, A. V. Timazhev, A. R. Lupo, and H. Le lite-based passive microwave observations: A new climatology over the global Treut, 2013: Blockings in the Northern Hemisphere and Euro-Atlantic region: oceans. J. Clim., 21, 1721 1739. Estimates of changes from reanalyses data and model simulations. Doklady, O Doherty, S., et al., 2009: Global and regional emissions of HFC-125 (CHF2CF3) Earth Sci., 449, 430-433. from in situ and air archive atmospheric observations at AGAGE and SOGE Monaghan, A. J., and D. H. Bromwich, 2008: Advances describing recent Antarctic observatories. J. Geophys. Res. Atmos., 109, D06310. climate variability. Bull. Am. Meteorol. Soc., 89, 1295 1306. O Donnell, R., N. Lewis, S. McIntyre, and J. Condon, 2011: Improved methods for Monaghan, A. J., D. H. Bromwich, W. Chapman, and J. C. Comiso, 2008: Recent vari- PCA-based reconstructions: Case study using the Steig et al. (2009) Antarctic ability and trends of Antarctic near-surface temperature. J. Geophys. Res. Atmos., Temperature Reconstruction. J. Clim., 24, 2099 2115. 113, D04105. O Gorman, P., R. P. Allan, M. P. Byrne, and M. Previdi, 2012: Energentic constraints on Monk, W., D. L. Peters, D. J. Baird, and R. A. Curry, 2011: Trends in indicator hydrologi- precipitation under climate change. Surv. Geophys., 33, 585 608. cal variables for Canadian rivers. Hydrol. Proc., 25, 3086 3100. Ohmura, A., 2009: Observed decadal variations in surface solar radiation and their Monks, P. S., et al., 2009: Atmospheric composition change global and regional air causes. J. Geophys. Res. Atmos., 114, D00d05. quality. Atmos. Environ., 43, 5268 5350. Ohmura, A., et al., 1998: Baseline Surface Radiation Network (BSRN/WCRP): New Montzka, S., B. Hall, and J. Elkins, 2009: Accelerated increases observed for hydro- precision radiometry for climate research. Bull. Am. Meteor. Soc., 79, 2115 2136. chlorofluorocarbons since 2004 in the global atmosphere. Geophys. Res. Lett., Ohvril, H., et al., 2009: Global dimming and brightening versus atmospheric column 36, L03804 . transparency, Europe, 1906 2007. J. Geophys. Res. Atmos., 114, D00d12. Montzka, S., M. Krol, E. Dlugokencky, B. Hall, P. Jockel, and J. Lelieveld, 2011: Small Oltmans, S. J., et al., 2013: Recent tropospheric ozone changes A pattern domi- interannual variability of global atmospheric hydroxyl. Science, 331, 67-69. nated by slow or no growth. Atmos. Environ., 67, 331 351. Montzka, S., L. Kuijpers, M. Battle, M. Aydin, K. Verhulst, E. Saltzman, and D. Fahey, Onogi, K., et al., 2007: The JRA-25 reanalysis. J. Meteorol. Soc. Jpn., 85, 369 432. 2010: Recent increases in global HFC-23 emissions. Geophys. Res. Lett., 37, Oort, A. H., and J. J. Yienger, 1996: Observed interannual variability in the Hadley L02808. circulation and its connection to ENSO. J. Clim., 9, 2751 2767. Montzka, S. A., et al., 2011b: Ozone-depleting substances (ODSs) and related chemi- Osborn, T. J., 2011: Winter 2009/2010 temperatures and a record breaking North cals. In Scientific Assessment of Ozone Depletion: 2010, Global Ozone Research Atlantic Oscillation index. Weather, 66, 19 21. and Monitoring Project Report No. 52. World Meteorological Organization, Paciorek, C. J., J. S. Risbey, V. Ventura, and R. D. Rosen, 2002: Multiple indices of Geneva, Switzerland, 516 pp. Northern Hemisphere cyclone activity, winters 1949 99. J. Clim., 15, 1573 1590. Morak, S., G. C. Hegerl, and J. Kenyon, 2011: Detectable regional changes in the Palmer, M. D., K. Haines, S. F. B. Tett, and T. J. Ansell, 2007: Isolating the signal of number of warm nights. Geophys. Res. Lett., 38, 5. ocean global warming. Geophys. Res. Lett., 34, 6. Morak, S., G. C. Hegerl, and N. Christidis, 2013: Detectable changes in the frequency Palmer, W. C., 1965: Meteorological drought. US Weather Bureau Research Paper, of temperature extremes. J. Clim., 26, 1561 1574. 45, 58 pages. Morice, C. P., J. J. Kennedy, N. A. Rayner, and P. D. Jones, 2012: Quantifying uncertain- Paltridge, G., A. Arking, and M. Pook, 2009: Trends in middle- and upper-level tropo- ties in global and regional temperature change using an ensemble of obser- spheric humidity from NCEP reanalysis data. Theor. Appl. Climatol., 98, 351 359. vational estimates: The HadCRUT4 data set. J. Geophys. Res. Atmos., 117, 22. Pan, Z. T., R. W. Arritt, E. S. Takle, W. J. Gutowski, C. J. Anderson, and M. Segal, 2004: Mueller, B., and S. Seneviratne, 2012: Hot days induced by precipitation deficits at Altered hydrologic feedback in a warming climate introduces a warming hole . the global scale. Proc. Natl. Acad. Sci. U.S.A., 109, 12398-12403. Geophys. Res. Lett., 31, L17109. Mueller, B., et al., 2011: Evaluation of global observations-based evapotranspiration Panagiotopoulos, F., M. Shahgedanova, A. Hannachi, and D. B. Stephenson, 2005: datasets and IPCC AR4 simulations. Geophys. Res. Lett., 38, L06402. Observed trends and teleconnections of the Siberian high: A recently declining Muhle, J., et al., 2010: Perfluorocarbons in the global atmosphere: tetrafluorometh- center of action. J. Clim., 18, 1411 1422. ane, hexafluoroethane, and octafluoropropane. Atmos. Chem. Phys., 10, 5145- Parker, D., C. Folland, A. Scaife, J. Knight, A. Colman, P. Baines, and B. Dong, 2007: 5164. Decadal to multidecadal variability and the climate change background. J. Geo- Murphy, D. M., et al., 2011: Decreases in elemental carbon and fine particle mass in phys. Res. Atmos., 112. D18115. the United States. Atmos. Chem. Phys., 11, 4679 4686. Parker, D. E., 2006: A demonstration that large-scale warming is not urban. J. Clim., Nan, S., and J. P. Li, 2003: The relationship between the summer precipitation in the 19, 2882 2895. Yangtze River Valley and the boreal spring Southern Hemisphere annular mode. Parker, D. E., 2011: Recent land surface air temperature trends assessed using the Geophys. Res. Lett., 30, 2266. 20th century reanalysis. J. Geophys. Res. Atmos., 116, D20125. Nash, J., and P. R. Edge, 1989: Temperature changes in the stratosphere and lower Parker, D. E., P. Jones, T. C. Peterson, and J. Kennedy, 2009: Comment on Unresolved mesosphere 197 1988 inferred from TOVS radiance observations. Adv. Space issues with the assessment of multidecadal global land surface temperature Res., 9, 333 341. trends by Roger A. Pielke Sr. et al. J. Geophys. Res. Atmos., 114. D05104. Neff, W., J. Perlwitz, and M. Hoerling, 2008: Observational evidence for asymmetric Parrish, D. D., et al., 2012: Long-term changes in lower tropospheric baseline ozone changes in tropospheric heights over Antarctica on decadal time scales. Geo- concentrations at northern mid-latitudes. Atmos. Chem. Phys., 12, 11485 11504. phys. Res. Lett., 35, L18703. 246 Observations: Atmosphere and Surface Chapter 2 Pattanaik, D. R., and M. Rajeevan, 2010: Variability of extreme rainfall events over Power, S. B., and G. Kociuba, 2011b: What caused the observed twentieth-century India during southwest monsoon season. Meteorol. Appl., 17, 88 104. weakening of the Walker Circulation? J. Clim., 24, 6501 6514. Pavan, V., R. Tomozeiu, C. Cacciamani, and M. Di Lorenzo, 2008: Daily precipitation Pozzoli, L., et al., 2011: Reanalysis of tropospheric sulfate aerosol and ozone for observations over Emilia-Romagna: Mean values and extremes. Int. J. Climatol., the period 1980 2005 using the aerosol-chemistry-climate model ECHAM5 28, 2065 2079. HAMMOZ. Atmos. Chem. Phys., 11, 9563 9594. Pavelin, E. G., C. E. Johnson, S. Rughooputh, and R. Toumi, 1999: Evaluation of Prata, F., 2008: The climatological record of clear-sky longwave radiation at the pre-industrial surface ozone measurements made using Sch\onbein s method. Earth s surface: Evidence for water vapour feedback? Int. J. Remote Sens., 29, Atmos. Environ., 33, 919 929. 5247 5263. Perkins, S. E., and L. V. Alexander, 2012: On the measurement of heat waves. J. Clim., Prather, M., C. Holmes, and J. Hsu, 2012: Reactive greenhouse gas scenarios: Sys- 26, 4500-4517 . tematic exploration of uncertainties and the role of atmospheric chemistry. Geo- Perkins, S. E., L. V. Alexander, and J. R. Nairn, 2012: Increasing frequency, intensity phys. Res. Lett., 39, L09803. and duration of observed global heatwaves and warm spells. Geophys. Res. Prinn, R., et al., 2005: Evidence for variability of atmospheric hydroxyl radicals over Lett., 39. L20714. the past quarter century. Geophys. Res. Lett., L07809. Peterson, T. C., K. M. Willett, and P. W. Thorne, 2011: Observed changes in surface Pryor, S. C., R. J. Barthelmie, and E. S. Riley, 2007: Historical evolution of wind cli- atmospheric energy over land. Geophys. Res. Lett., 38, L16707. mates in the USA - art. no. 012065. In: Science of Making Torque from Wind [M. Peterson, T. C., X. B. Zhang, M. Brunet-India, and J. L. Vazquez-Aguirre, 2008: Chang- O. L. Hansen and K. S. Hansen (eds.)], 75, 12065 12065. es in North American extremes derived from daily weather data. J. Geophys. Res. Pryor, S. C., J. A. Howe, and K. E. Kunkel, 2009: How spatially coherent and statisti- Atmos., 113, D07113. cally robust are temporal changes in extreme precipitation in the contiguous Peterson, T. C., et al., 2009: State of the Climate in 2008. Bull. Am. Meteor. Soc., 90, USA? Int. J. Climatol., 29, 31 45. 2 S13-. Qian, Y., D. P. Kaiser, L. R. Leung, and M. Xu, 2006: More frequent cloud-free sky and Peterson, T. C., et al., 2013: Monitoring and understanding changes in heat waves, less surface solar radiation in China from 1955 to 2000. Geophys. Res. Lett., 33, cold waves, floods and droughts in the United States: State of knowledge. Bull. L01812. Am. Meteor. Soc., 94, 821-834. Qian, Y., W. G. Wang, L. R. Leung, and D. P. Kaiser, 2007: Variability of solar radiation Petrow, T., and B. Merz, 2009: Trends in flood magnitude, frequency and seasonality under cloud-free skies in China: The role of aerosols. Geophys. Res. Lett., 34, in Germany in the period 1951 2002. J. Hydrol., 371, 129 141. L12804. Pezza, A., P. van Rensch, and W. Cai, 2012: Severe heat waves in Southern Australia: Qian, Y., D. Gong, J. Fan, L. Leung, R. Bennartz, D. Chen, and W. Wang, 2009: Heavy Synoptic climatology and large scale connections. Clim. Dyn., 38, 209 224. pollution suppresses light rain in China: Observations and modeling. J. Geophys. Pezza, A. B., I. Simmonds, and J. A. Renwick, 2007: Southern Hemisphere cyclones Res. Atmos., 114, D00K02. and anticyclones: Recent trends and links with decadal variability in the Pacific Rahimzadeh, F., A. Asgari, and E. Fattahi, 2009: Variability of extreme temperature Ocean. Int. J. Climatol., 27, 1403 1419. and precipitation in Iran during recent decades. Int. J. Climatol., 29, 329 343. Philipona, R., 2012: Greenhouse warming and solar brightening in and around the Raible, C. C., P. M. Della-Marta, C. Schwierz, H. Wernli, and R. Blender, 2008: North- Alps. Int. J. Climatol., 33, 1530-1537. ern hemisphere extratropical cyclones: A comparison of detection and tracking Philipona, R., K. Behrens, and C. Ruckstuhl, 2009: How declining aerosols and rising methods and different reanalyses. Mon. Weather Rev., 136, 880 897. greenhouse gases forced rapid warming in Europe since the 1980s. Geophys. Raichijk, C., 2011: Observed trends in sunshine duration over South America. Int. J. Res. Lett., 36, L02806. Climatol., 32, 669-680. Philipona, R., B. Dürr, A. Ohmura, and C. Ruckstuhl, 2005: Anthropogenic greenhouse Randall, R. M., and B. M. Herman, 2008: Using limited time period trends as a means forcing and strong water vapor feedback increase temperature in Europe. Geo- to determine attribution of discrepancies in microwave sounding unit-derived phys. Res. Lett., 32, L19809. tropospheric temperature time series. J. Geophys. Res. Atmos., 113, D05105. Philipona, R., B. Dürr, C. Marty, A. Ohmura, and M. Wild, 2004: Radiative forcing- Randel, W. J., 2010: Variability and trends in stratospheric temperature and water measured at Earth s surface corroborate the increasing greenhouse effect. vapor. The Stratosphere: Dynamics, Transport and Chemistry, S. Polvani, and Geophys. Res. Lett., 31, L03202. Waugh, Ed., American Geophysical Union, 123 135. Philipp, A., P. M. Della-Marta, J. Jacobeit, D. R. Fereday, P. D. Jones, A. Moberg, and H. Randel, W. J., and E. J. Jensen, 2013: Physical processes in the tropical tropopause Wanner, 2007: Long-term variability of daily North Atlantic-European pressure layer and their roles in a changing climate. Nature Geosci., 169 176. patterns since 1850 classified by simulated annealing clustering. J. Clim., 20, Randel, W. J., F. Wu, H. Vömel, G. E. Nedoluha, and P. Forster, 2006: Decreases in 4065 4095. stratospheric water vapor after 2001: Links to changes in the tropical tropo- Piao, S., et al., 2010: The impacts of climate change on water resources and agricul- pause and the Brewer-Dobson circulation. J. Geophys. Res. Atmos., 111, D12312. ture in China. Nature, 467, 43 51. Randel, W. J., et al., 2009: An update of observed stratospheric temperature trends. J. Pielke, R. A., and T. Matsui, 2005: Should light wind and windy nights have the same Geophys. Res. Atmos., 114, D02107. temperature trends at individual levels even if the boundary layer averaged heat Rasmusson, E. M., and T. H. Carpenter, 1982: Variations in tropical sea surface tem- content change is the same? Geophys. Res. Lett., 32, L21813. perature and surface wind fields associated with the Southern Oscillation/El Pielke, R. A., Sr., et al., 2007: Unresolved issues with the assessment of multidecadal Nino. Mon. Weather Rev., 110, 354 384. global land surface temperature trends. J. Geophys. Res. Atmos., 112, D24S08. Rasmusson, E. M., and J. M. Wallace, 1983: Meteorological aspects of the El Nino- Pinker, R. T., B. Zhang, and E. G. Dutton, 2005: Do satellites detect trends in surface Southern Oscillation. Science, 222, 1195 1202. solar radiation? Science, 308, 850 854. Rausch, J., A. Heidinger, and R. Bennartz, 2010: Regional assessment of microphysi- Pirazzoli, P. A., and A. Tomasin, 2003: Recent near-surface wind changes in the cen- cal properties of marine boundary layer cloud using the PATMOS-x dataset. J. tral Mediterranean and Adriatic areas. Int. J. Climatol., 23, 963 973. Geophys. Res. Atmos., 115, D23212. Po-Chedley, S., and Q. Fu, 2012: A bias in the Midtropospheric Channel Warm Target Ray, E. A., et al., 2010: Evidence for changes in stratospheric transport and mixing Factor on the NOAA-9 Microwave Sounding Unit. J. Atmos. Ocean Technol., 29, over the past three decades based on multiple data sets and tropical leaky pipe 646 652. analysis. J. Geophys. Res. Atmos., 115, D21304. Portmann, R., S. Solomon, and G. Hegerl, 2009a: Spatial and seasonal patterns in Rayner, D. P., 2007: Wind run changes: The dominant factor affecting pan evapora- climate change, temperatures, and precipitation across the United States. Proc. tion trends in Australia. J. Clim., 20, 3379 3394. Natl. Acad. Sci. U.S.A., 106, 7324 7329. Rayner, N. A., et al., 2003: Global analyses of sea surface temperature, sea ice, and Portmann, R. W., S. Solomon, and G. C. Hegerl, 2009b: Linkages between climate night marine air temperature since the late nineteenth century. J. Geophys. Res. change, extreme temperature and precipitation across the United States. Proc. Atmos., 108, 37. Natl. Acad. Sci. U.S.A., 106, 7324 7329. Rayner, N. A., et al., 2006: Improved analyses of changes and uncertainties in sea Power, S., T. Casey, C. Folland, A. Colman, and V. Mehta, 1999: Inter-decadal modula- surface temperature measured in situ sice the mid-nineteenth century: The tion of the impact of ENSO on Australia. Clim. Dyn., 15, 319 324. HadSST2 dataset. J. Clim., 19, 446 469. Power, S. B., and G. Kociuba, 2011a: The impact of global warming on the Southern Oscillation Index. Clim. Dynamics, 37, 1745 1754. 247 Chapter 2 Observations: Atmosphere and Surface Read, W. G., et al., 2007: Aura Microwave Limb Sounder upper tropospheric and Russak, V., 2009: Changes in solar radiation and their influence on temperature lower stratospheric H2O and relative humidity with respect to ice validation. J. trend in Estonia (1955 2007). J. Geophys. Res. Atmos., 114, D00d01. Geophys. Res. Atmos., 112, D24S35. Russell, A. R., L. C. Valin, E. J. Bucsela, M. O. Wenig, and R. C. Cohen, 2010: Space- Ren, G., et al., 2011: Change in climatic extremes over mainland China based on an based constraints on spatial and temporal patterns of NOx emissions in Califor- integrated extreme climate index. Clim. Res., 50, 113 124. nia, 2005 2008. Environ. Sci. Technol., 44, 3608 3615. Ren, G. Y., Z. Y. Chu, Z. H. Chen, and Y. Y. Ren, 2007: Implications of temporal change Russell, J., et al., 1993: The halogen occultation experiment. J. Geophys. Res. Atmos., in urban heat island intensity observed at Beijing and Wuhan stations. Geophys. 98, 10777 10797. Res. Lett., 34, L05711. Rusticucci, M., and M. Renom, 2008: Variability and trends in indices of quality-con- Ren, G. Y., Y. Q. Zhou, Z. Y. Chu, J. X. Zhou, A. Y. Zhang, J. Guo, and X. F. Liu, 2008: trolled daily temperature extremes in Uruguay. Int. J. Climatol., 28, 1083 1095. Urbanization effects on observed surface air temperature trends in north China. Saha, S., et al., 2010: The NCEP climate forecaset system reanalysis. Bull. Am. Meteor. J. Clim., 21, 1333 1348. Soc., 91, 1015 1057. Ren, H.-L., and F.-F. Jin, 2011: Nino indices for two types of ENSO. Geophys. Res. Saikawa, E., et al., 2012: Global and regional emissions estimates for HCFC-22. Lett., 38, L04704. Atmos. Chem. Phys. Discuss., 12, 18423 18285. Ren, Y. Y., and G. Y. Ren, 2011: A remote-sensing method of selecting reference sta- Saji, N. H., B. N. Goswami, P. N. Vinayachandran, and T. Yamagata, 1999: A dipole tions for evaluating urbanization effect on surface air temperature trends. J. mode in the tropical Indian Ocean. Nature, 401, 360 363. Clim., 24, 3179 3189. Sakamoto, M., and J. R. Christy, 2009: The influences of TOVS radiance assimilation Renard, B., et al., 2008: Regional methods for trend detection: Assessing field signifi- on temperature and moisture tendencies in JRA-25 and ERA-40. J. Atmos. Ocean cance and regional consistency. Water Resourc. Res., 44, W08419. Technol., 26, 1435 1455. 2 Revadekar, J., D. Kothawale, S. Patwardhan, G. Pant, and K. Kumar, 2012: About the Sanchez-Lorenzo, A., and M. Wild, 2012: Decadal variations in estimated surface observed and future changes in temperature extremes over India. Nat. Hazards, solar radiation over Switzerland since the late 19th century. Atmos. Chem. Phys. 60, 1133 1155. Discussion, 12, 10815 10843. Reynolds, R., N. Rayner, T. Smith, D. Stokes, and W. Wang, 2002: An improved in situ Sanchez-Lorenzo, A., J. Calbo, and J. Martin-Vide, 2008: Spatial and temporal trends and satellite SST analysis for climate. J. Clim., 15, 1609 1625. in sunshine duration over Western Europe (1938 2004). J. Clim., 21, 6089 6098. Reynolds, R. W., C. L. Gentemann, and G. K. Corlett, 2010: Evaluation of AATSR and Sanchez-Lorenzo, A., J. Calbo, and M. Wild, 2013: Global and diffuse solar radiation TMI Satellite SST Data. J. Clim., 23, 152 165. in Spain: Building a homogeneous dataset and assessing their trends. Global Rhines, A., and P. Huybers, 2013: Frequent summer temperature extremes reflect Planet. Change, 100, 343 352. changes in the mean, not the variance. Proc. Natl. Acad. Sci. U.S.A., 110, E546 Sanchez-Lorenzo, A., J. Calbo, M. Brunetti, and C. Deser, 2009: Dimming/brightening E546. over the Iberian Peninsula: Trends in sunshine duration and cloud cover and their Rienecker, M. M., Suarez, M.J., Gelaro, R., Todling, R., Bacmeister, J., Liu, E., Bosi- relations with atmospheric circulation. J. Geophys. Res. Atmos., 114, D00d09. lovich, M. G., Schubert, S. D., Takacs, L., Kim, G.-K., Bloom, S., Chen, J., Collins, Santer, B., et al., 2008: Consistency of modelled and observed temperature trends in D., Conaty, A., da Silva, A., Gu, W., Joiner, J., Koster, R. D., Lucchesi, R., Molod, the tropical troposphere. Int. J. Climatol., 28, 1703 1722. A., Owens, T., Pawson, S., Pegion, P., Redder, C. R., Reichle, R., Robertson, F. R., Santer, B. D., et al., 2007: Identification of human-induced changes in atmospheric Ruddick, A. G., Sienkiewicz, M., and Woollen, J., 2011: MERRA: NASA s modern- moisture content. Proc. Natl. Acad. Sci. U.S.A., 104, 15248 15253. era retrospective analysis for research and applications. J. Clim., 24, 3624-3648. Santer, B. D., et al., 2011: Separating signal and noise in atmospheric temperature Rigby, M., et al., 2008: Renewed growth of atmospheric methane. Geophys. Res. changes: The importance of timescale. J. Geophys. Res. Atmos., 116, D22105. Lett., 35, L22805. Scaife, A., C. Folland, L. Alexander, A. Moberg, and J. Knight, 2008: European climate Rigby, M., et al., 2010: History of atmospheric SF6 from 1973 to 2008. Atmos. Chem. extremes and the North Atlantic Oscillation. J. Clim., 21, 72 83. Phys., 10, 10305-10320. Schär, C., P. L. Vidale, D. Luthi, C. Frei, C. Haberli, M. A. Liniger, and C. Appenzeller, Riihimaki, L. D., F. E. Vignola, and C. N. Long, 2009: Analyzing the contribution of 2004: The role of increasing temperature variability in European summer heat- aerosols to an observed increase in direct normal irradiance in Oregon. J. Geo- waves. Nature, 427, 332 336. phys. Res. Atmos., 114, D00d02. Scherer, M., H. Vömel, S. Fueglistaler, S. J. Oltmans, and J. Staehelin, 2008: Trends and Robock, A., et al., 2000: The Global Soil Moisture Data Bank. Bull. Am. Meteor. Soc., variability of midlatitude stratospheric water vapour deduced from the re-eval- 81, 1281 1299. uated Boulder balloon series and HALOE. Atmos. Chem. Phys., 8, 1391 1402. Rockmann, T., and I. Levin, 2005: High-precision determination of the changing Scherrer, S. C., and C. Appenzeller, 2006: Swiss Alpine snow pack variability: Major isotopic composition of atmospheric N2O from 1990 to 2002. J. Geophys. Res. patterns and links to local climate and large-scale flow. Clim. Res., 32, 187 199. Atmos., 110, D21304. Scherrer, S. C., C. Wüthrich, M. Croci-Maspoli, R. Weingartner, and C. Appenzeller, Roderick, M. L., and G. D. Farquhar, 2002: The cause of decreased pan evaporation 2013: Snow variability in the Swiss Alps 1864 2009. Int. J. Climatol., doi: over the past 50 years. Science, 298, 1410 1411. 10.1002/joc.3653. Roderick, M. L., L. D. Rotstayn, G. D. Farquhar, and M. T. Hobbins, 2007: On the attri- Schiller, C., J. U. Grooss, P. Konopka, F. Plager, F. H. Silva dos Santos, and N. Spelten, bution of changing pan evaporation. Geophys. Res. Lett., 34, L17403. 2009: Hydration and dehydration at the tropical tropopause. Atmos. Chem. Rohde, R., et al., 2013a: A new estimate of the average Earth surface land tempera- Phys., 9, 9647 9660. ture spanning 1753 to 2011. Geoinfor. Geostat.: An Overview, 1, doi:10.4172/ Schmidt, G. A., 2009: Spurious correlations between recent warming and indices of gigs.1000101. local economic activity. Int. J. Climatol., 29, 2041 2048. Rohde, R., et al., 2013b: Berkeley Earth temperature averaging process. Geoinfor Schnadt Poberaj, C., J. Staehelin, D. Brunner, V. Thouret, H. De Backer, and R. Stübi, Geostat: An Overview, 1, doi:10.4172/gigs.1000103. 2009: Long-term changes in UT/LS ozone between the late 1970s and the 1990s Rohs, S., et al., 2006: Long-term changes of methane and hydrogen in the strato- deduced from the GASP and MOZAIC aircraft programs and from ozonesondes. sphere in the period 1978 2003 and their impact on the abundance of strato- Atmos. Chem. Phys., 9, 5343 5369. spheric water vapor. J. Geophys. Res. Atmos., 111, D14315. Schneider, T., P. A. O Gorman, and X. J. Levine, 2010: Water vapour and the dynamics Ropelewski, C. F., and P. D. Jones, 1987: An extension of the Tahiti Darwin Southern of climate changes. Rev. Geophys., RG3001. Oscillation Index. Mon. Weather Rev., 115, 2161 2165. Schneidereit, A., R. Blender, K. Fraedrich, and F. Lunkeit, 2007: Icelandic climate and Rosenlof, K. H., and G. C. Reid, 2008: Trends in the temperature and water vapor north Atlantic cyclones in ERA-40 reanalyses. Meteorol. Z., 16, 17 23. content of the tropical lower stratosphere: Sea surface connection. J. Geophys. Schwartz, R. D., 2005: Global dimming: Clear-sky atmospheric transmission from Res. Atmos., 113, D06107. astronomical extinction measurements. J. Geophys. Res. Atmos., 110. Ruckstuhl, C., et al., 2008: Aerosol and cloud effects on solar brightening and the Scinocca, J. F., D. B. Stephenson, T. C. Bailey, and J. Austin, 2010: Estimates of past recent rapid warming. Geophys. Res. Lett., 35, L12708. and future ozone trends from multimodel simulations using a flexible smoothing Ruddiman, W., 2003: The anthropogenic greenhouse era began thousands of years spline methodology. J. Geophys. Res. Atmos., 115. ago. Clim. Change, 261 293. Screen, J. A., and I. Simmonds, 2011: Erroneous Arctic temperature trends in the Ruddiman, W., 2007: The early anthropogenic hypothesis: Challenges and responses. ERA-40 reanalysis: A closer look. J. Clim., 24, 2620 2627. Rev. Geophys., 45, RG4001. 248 Observations: Atmosphere and Surface Chapter 2 Seidel, D. J., and J. R. Lanzante, 2004: An assessment of three alternatives to linear Skansi, M., et al., 2013: Warming and wetting signals emerging from analysis of trends for characterizing global atmospheric temperature changes. J. Geophys. changes in climate extreme indices over South America. Global Planet. Change, Res. Atmos., 109, D14108. 100, 295 307. Seidel, D. J., and W. J. Randel, 2007: Recent widening of the tropical belt: Evidence Skeie, R. B., T. K. Berntsen, G. Myhre, K. Tanaka, M. M. Kvalevag, and C. R. Hoyle, from tropopause observations. J. Geophys. Res. Atmos., 112, D20113. 2011: Anthropogenic radiative forcing time series from pre-industrial times until Seidel, D. J., Q. Fu, W. J. Randel, and T. J. Reichler, 2008: Widening of the tropical belt 2010. Atmos. Chem. Phys., 11, 11827 11857. in a changing climate. Nature Geosci., 1, 21 24. Smith, L. C., T. Pavelsky, G. MacDonald, I. A. Shiklomanov, and R. Lammers, 2007: Seidel, D. J., N. P. Gillett, J. R. Lanzante, K. P. Shine, and P. W. Thorne, 2011: Strato- Rising minimum daily flows in northern Eurasian rivers suggest a growing influ- spheric temperature trends: Our evolving understanding. Clim. Change, 2, ence of groundwater in the high-latitude water cycle. J. Geophys. Res., 112, 592 616. G04S47. Sen Roy, S., 2009: A spatial analysis of extreme hourly precipitation patterns in India. Smith, T. M., and R. W. Reynolds, 2002: Bias corrections for historical sea surface Int. J. Climatol., 29, 345 355. temperatures based on marine air temperatures. J. Clim., 15, 73 87. Sen Roy, S., and R. C. Balling, 2005: Analysis of trends in maximum and minimum Smith, T. M., T. C. Peterson, J. H. Lawrimore, and R. W. Reynolds, 2005: New surface temperature, diurnal temperature range, and cloud cover over India. Geophys. temperature analyses for climate monitoring. Geophys. Res. Lett., 32, L14712. Res. Lett., 32, L12702. Smith, T. M., R. W. Reynolds, T. C. Peterson, and J. Lawrimore, 2008: Improvements Sen Roy, S., and M. Rouault, 2013: Spatial patterns of seasonal scale trends in to NOAA s historical merged land-ocean surface temperature analysis (1880 extreme hourly precipitation in South Africa. Appl. Geogr., 39, 151 157. 2006). J. Clim., 21, 2283 2296. Seneviratne, S. I., et al., 2010: Investigating soil moisture-climate interactions in a Smith, T. M., P. A. Arkin, L. Ren, and S. S. P. Shen, 2012: Improved reconstruction of changing climate: A review. Earth Sci. Rev., 99, 125 161. global precipitation since 1900. J. Atmos. Ocean. Technol., 29, 1505 1517. 2 Seneviratne, S. I., et al., 2012: Changes in climate extremes and their impacts on the Smits, A., A. Tank, and G. P. Konnen, 2005: Trends in storminess over the Netherlands, natural physical environment. In: IPCC Special Report on Extremes, 109-230. 1962 2002. Int. J. Climatol., 25, 1331 1344. Serquet, G., C. Marty, J. P. Dulex, and M. Rebetez, 2011: Seasonal trends and tem- Sohn, B. J., and S. C. Park, 2010: Strengthened tropical circulations in past three perature dependence of the snowfall/precipitation-day ratio in Switzerland. decades inferred from water vapor transport. J. Geophys. Res. Atmos., 115, Geophys. Res. Lett., 38, L07703. D15112. Shaw, S. B., A. A. Royem, and S. J. Riha, 2011: The relationship between extreme Solomon, S., K. Rosenlof, R. Portmann, J. Daniel, S. Davis, T. Sanford, and G. Plattner, hourly precipitation and surface temperature in different hydroclimatic regions 2010: Contributions of stratospheric water vapor to decadal changes in the rate of the United States. J. Hydrometeor., 12, 319 325. of global warming. Science, 327, 1219-1223. Sheffield, J., and E. F. Wood, 2008: Global trends and variability in soil moisture and Song, H., and M. H. Zhang, 2007: Changes of the boreal winter Hadley circulation drought characteristics, 1950 2000, from observation-driven simulations of the in the NCEP-NCAR and ECMWF reanalyses: A comparative study. J. Clim., 20, terrestrial hydrologic cycle. J. Clim., 21, 432 458. 5191 5200. Sheffield, J., E. Wood, and M. Roderick, 2012: Little change in global drought over Soni, V. K., G. Pandithurai, and D. S. Pai, 2012: Evaluation of long-term changes of the past 60 years. Nature, 491, 435 . solar radiation in India. Int. J. Climatol., 32, 540 551. Sheffield, J., K. Andreadis, E. Wood, and D. Lettenmaier, 2009: Global and continen- Sorteberg, A., and J. E. Walsh, 2008: Seasonal cyclone variability at 70 degrees N and tal drought in the second half of the twentieth century: Severity-area-duration its impact on moisture transport into the Arctic. Tellus A, 60, 570 586. analysis and temporal variability of large-scale events. J. Clim., 22, 1962 1981. Sousa, P., R. Trigo, P. Aizpurua, R. Nieto, L. Gimeno, and R. Garcia-Herrera, 2011: Shekar, M., H. Chand, S. Kumar, K. Srinivasan, and A. Ganju, 2010: Climate change Trends and extremes of drought indices throughout the 20th century in the studies in the western Himalaya. Ann. Glaciol., 51, 105-112. Mediterranean. Nat. Hazards Earth Syst. Sci., 11, 33 51. Sherwood, S. C., R. Roca, and T. M. Weckwerth, 2010: Tropospheric water vapor, con- Spencer, R. W., and J. R. Christy, 1992: Precision and radiosonde validation of sat- vection, and climate. Rev. Geophys., 48, RG2001. ellite gridpoint temperature anomalies. 2. A troposhperic retrieval and trends Sherwood, S. C., C. L. Meyer, R. J. Allen, and H. A. Titchner, 2008: Robust tropospheric during 1979 90. J. Clim., 5, 858 866. warming revealed by iteratively homogenized radiosonde data. J. Clim., 21, St. Jacques, J.-M., and D. Sauchyn, 2009: Increasing winter baseflow and mean 5336 5350. annual streamflow from possible permafrost thawing in the Northwest Territo- Shi, G. Y., et al., 2008: Data quality assessment and the long-term trend of ground ries, Canada. Geophys. Res. Lett., 36, L01401. solar radiation in China. J. Appl. Meteor. Climatol., 47, 1006 1016. Stachnik, J. P., and C. Schumacher, 2011: A comparison of the Hadley circulation in Shi, L., and J. J. Bates, 2011: Three decades of intersatellite-calibrated High-Resolu- modern reanalyses. J. Geophys. Res. Atmos., 116, D22102. tion Infrared Radiation Sounder upper tropospheric water vapor. J. Geophys. Res. Stahl, K., and L. M. Tallaksen, 2012: Filling the white space on maps of European Atmos., 116, D04108. runoff trends: Estimates from a multi-model ensemble. Hydrol. Earth Syst. Sci. Shiklomanov, A. I., R. B. Lammers, M. A. Rawlins, L. C. Smith, and T. M. Pavelsky, 2007: Discuss., 9, 2005 2032. Temporal and spatial variations in maximum river diSchärge from a new Russian Stahl, K., et al., 2010: Streamflow trends in Europe: Evidence from a dataset of near- data set. J. Geophys. Res. Biogeosci., 112. natural catchments. Hydrol. Earth Syst. Sci., 14, 2367 2382. Shiklomanov, I. A., V. Y. Georgievskii, V. I. Babkin, and Z. A. Balonishnikova, 2010: Stanhill, G., and S. Cohen, 2001: Global dimming: A review of the evidence for a Research problems of formation and estimation of water resources and water widespread and significant reduction in global radiation with discussion of its availability changes of the Russian Federation. Russ. Meteorol, Hydrol., 35, probable causes and possible agricultural consequences. Agr. Forest Meteorol., 13 19. 107, 255 278. Shiu, C. J., S. C. Liu, and J. P. Chen, 2009: Diurnally asymmetric trends of temperature, Steeneveld, G. J., A. A. M. Holtslag, R. T. McNider, and R. A. Pielke, 2011: Screen level humidity, and precipitation in Taiwan. J. Clim., 22, 5635 5649. temperature increase due to higher atmospheric carbon dioxide in calm and Sillmann, J., M. Croci-Maspoli, M. Kallache, and R. W. Katz, 2011: Extreme cold winter windy nights revisited. J. Geophys. Res. Atmos., 116. temperatures in Europe under the influence of North Atlantic atmospheric block- Stegall, S., and J. Zhang, 2012: Wind field climatology, changes, and extremes in the ing. J. Clim., 24, 5899 5913. Chukchi-Beaufort Seas and Alaska North Slope during 1979 2009. J. Clim., 25, Simmons, A. J., K. M. Willett, P. D. Jones, P. W. Thorne, and D. P. Dee, 2010: Low- 8075 8089. frequency variations in surface atmospheric humidity, temperature, and precipi- Steig, E. J., D. P. Schneider, S. D. Rutherford, M. E. Mann, J. C. Comiso, and D. T. Shin- tation: Inferences from reanalyses and monthly gridded observational data sets. dell, 2009: Warming of the Antarctic ice-sheet surface since the 1957 Interna- J. Geophys. Res. Atmos., 115, D01110. tional Geophysical Year. Nature, 460, 766 766. Simolo, C., M. Brunetti, M. Maugeri, and T. Nanni, 2011: Evolution of extreme tem- Stenke, A., and V. Grewe, 2005: Simulation of stratospheric water vapor trends: peratures in a warming climate. Geophys. Res. Lett., 38, 6. Impact on stratospheric ozone chemistry. Atmos. Chem. Phys., 5, 1257 1272. Simpson, I. J., et al., 2012: Long-term decline of global atmospheric ethane concen- Stephens, G. L., M. Wild, P. W. Stackhouse, T. L Ecuyer, S. Kato, and D. S. Hender- trations and implications for methane. Nature, 488, 490 494. son, 2012a: The global character of the flux of downward longwave radiation. J. Clim., 25, 2329 2340. 249 Chapter 2 Observations: Atmosphere and Surface Stephens, G. L., et al., 2012b: An update on Earth s energy balance in light of the Thompson, D. W. J., and J. M. Wallace, 1998: The Arctic Oscillation signature in the latest global observations. Nature Geosci., 5, 691 696. wintertime geopotential height and temperature fields. Geophys. Res. Lett., 25, Stephenson, D. B., H. F. Diaz, and R. J. Murnane, 2008: Definition, diagnosis and origin 1297 1300. of extreme weather and climate events. In: Climate Extremes and Society [R. J. Thompson, D. W. J., and J. M. Wallace, 2000: Annular modes in the extratropical circu- Murnane, and H. F. Diaz (eds.)] Cambridge University Press, Cambridge, United lation. Part I: Month-to-month variability. J. Clim., 13, 1000 1016. Kingdom and New York, NY, USA, pp. 11 23. Thompson, D. W. J., J. J. Kennedy, J. M. Wallace, and P. D. Jones, 2008: A large discon- Stern, D. I., 2006: Reversal of the trend in global anthropogenic sulfur emissions. tinuity in the mid-twentieth century in observed global-mean surface tempera- Global Environ. Change Hum. Policy Dimens., 16, 207 220. ture. Nature, 453, 646 649. Stickler, A., et al., 2010: The Comprehensive Historical Upper-Air Network. Bull. Am. Thompson, D. W. J., et al., 2012: The mystery of recent stratospheric temperature Meteor. Soc., 91, 741 751. trends. Nature, 491, 692 697. Stjern, C. W., J. E. Kristjansson, and A. W. Hansen, 2009: Global dimming and global Thorne, P. W., 2008: Arctic tropospheric warming amplification? Nature, 455, E1 E2. brightening - an analysis of surface radiation and cloud cover data in northern Thorne, P. W., and R. S. Vose, 2010: Reanalyses suitable for characterizing long-term Europe. Int. J. Climatol., 29, 643 653. trends: Are They Really Achievable? Bull. Am. Meteor. Soc., 91, 353 . Stohl, A., et al., 2009: An analytical inversion method for determining regional and Thorne, P. W., D. E. Parker, S. F. B. Tett, P. D. Jones, M. McCarthy, H. Coleman, and P. global emissions of greenhouse gases: Sensitivity studies and application to Brohan, 2005: Revisiting radiosonde upper air temperatures from 1958 to 2002. halocarbons. Atmos. Chem. Phys., 1597 1620. J. Geophys. Res. Atmos., 110. Stohl, A., et al., 2010: Hydrochlorofluorocarbon and hydrofluorocarbon emissions Thorne, P. W., et al., 2011: A quantification of uncertainties in historical tropical tro- in East Asia determined by inverse modeling. Atmos. Chem. Phys., 3545 3560. pospheric temperature trends from radiosondes. J. Geophys. Res. Atmos., 116, 2 Streets, D. G., Y. Wu, and M. Chin, 2006: Two-decadal aerosol trends as a likely expla- D12116. nation of the global dimming/brightening transition. Geophys. Res. Lett., 33, Tietavainen, H., H. Tuomenvirta, and A. Venalainen, 2010: Annual and seasonal mean L15806. temperatures in Finland during the last 160 years based on gridded temperature Streets, D. G., et al., 2009: Anthropogenic and natural contributions to regional trends data. Int. J. Climatol., 30, 2247 2256. in aerosol optical depth, 1980 2006. J. Geophys. Res. Atmos., 114, D00D18. Titchner, H. A., P. W. Thorne, M. P. McCarthy, S. F. B. Tett, L. Haimberger, and D. E. Strong, C., and R. E. Davis, 2007: Winter jet stream trends over the Northern Hemi- Parker, 2009: Critically reassessing tropospheric temperature trends from radio- sphere. Q. J. R. Meteorol. Soc., 133, 2109 2115. sondes using realistic validation experiments. J. Clim., 22, 465 485. Strong, C., and R. E. Davis, 2008: Comment on Historical trends in the jet streams Tokinaga, H., and S.-P. Xie, 2011a: Wave and anemometer-based sea surface wind by Cristina L. Archer and Ken Caldeira. Geophys. Res. Lett., L24806. (WASWind) for climate change analysis. J. Clim., 267-285. Stubenrauch, C. J., et al., 2013: Assessment of global cloud datasets from satellite: Tokinaga, H., and S. P. Xie, 2011b: Weakening of the equatorial Atlantic cold tongue Project and database initiated by the GEWEX radiation panel. Bull. Am. Meteo- over the past six decades. Nature Geosci., 4, 222-226. rol. Soc., 94, 1031-1049. Tokinaga, H., S. P. Xie, A. Timmermann, S. McGregor, T. Ogata, H. Kubota, and Y. M. Sun, B. M., A. Reale, D. J. Seidel, and D. C. Hunt, 2010: Comparing radiosonde and Okumura, 2012: Regional patterns of tropical Indo-Pacific climate change: Evi- COSMIC atmospheric profile data to quantify differences among radiosonde dence of the Walker circulation weakening. J. Clim., 25, 1689 1710. types and the effects of imperfect collocation on comparison statistics. J. Geo- Toreti, A., E. Xoplaki, D. Maraun, F. G. Kuglitsch, H. Wanner, and J. Luterbacher, 2010: phys. Res. Atmos., 115, D23104. Characterisation of extreme winter precipitation in Mediterranean coastal sites Swart, N. C., and J. C. Fyfe, 2012: Observed and simulated changes in the Southern and associated anomalous atmospheric circulation patterns. Nat. Hazards Earth Hemisphere surface westerly wind-stress. Geophys. Res. Lett., 39, L16711. Syst. Sci., 10, 1037 1050. Syakila, A., and C. Kroeze, 2011: The global nitrous oxide budget revisited. Green- Torseth, K., et al., 2012: Introduction to the European Monitoring and Evaluation house Gas Meas. Management, 1, 17 26. Programme (EMEP) and observed atmospheric composition change during Takahashi, K., A. Montecinos, K. Goubanova, and B. Dewitte, 2011: ENSO regimes: 1972 2009. Atmos. Chem. Phys. Discuss., 12, 1733 1820. Reinterpreting the canonical and Modoki El Nino. Geophys. Res. Lett., 38, Trenberth, K., 2011: Changes in precipitation with climate change. Clim. Res., 47, L10704. 123 138. Takeuchi, Y., Y. Endo, and S. Murakami, 2008: High correlation between winter pre- Trenberth, K., and J. Fasullo, 2012a: Climate extremes and climate change: The Rus- cipitation and air temperature in heavy-snowfall areas in Japan. Ann. Glaciol., sian heat wave and other climate extremes of 2010. J. Geophys. Res. Atmos., 49, 7 10. 117, D17103. Tang, G., Y. Ding, S. Wang, G. Ren, H. Liu, and L. Zhang, 2010: Comparative analysis Trenberth, K. E., 1984: Signal versus noise in the Southern Oscillation. Mon. Weather of China surface air temperature series for the past 100 years. Adv. Climate Rev., 112, 326 332. Change Res., 1, 11 19. Trenberth, K. E., 1997: The definition of El Nino. Bull. Am. Meteor. Soc., 78, 2771 Tang, W. J., K. Yang, J. Qin, C. C. K. Cheng, and J. He, 2011: Solar radiation trend 2777. across China in recent decades: a revisit with quality-controlled data. Atmos. Trenberth, K. E., and D. A. Paolino, 1980: The Northern Hemisphere Sea-Level Pres- Chem. Phys., 11, 393 406. sure Data Set Trends, errors, and discontinuities. Mon. Weather Rev., 108, Tank, A., et al., 2006: Changes in daily temperature and precipitation extremes in 855 872. central and south Asia. J. Geophys. Res. Atmos., 111, D16105. Trenberth, K. E., and J. W. Hurrell, 1994: Decadal atmosphere-ocean variations in the Tans, P., 2009: An accounting of the observed increase in oceanic and atmospheric Pacific. Clim. Dyn., 9, 303 319. CO2 and an outlook for the future. Oceanography, 26 35. Trenberth, K. E., and T. J. Hoar, 1996: The 1990 1995 El Nino Southern Oscillation Tarasova, O. A., I. A. Senik, M. G. Sosonkin, J. Cui, J. Staehelin, and A. S. H. PrA(c)vA´t, event: Longest on record. Geophys. Res. Lett., 23, 57 60. 2009: Surface ozone at the Caucasian site Kislovodsk High Mountain Station Trenberth, K. E., and D. P. Stepaniak, 2001: Indices of El Nino evolution. J. Clim., 14, and the Swiss Alpine site Jungfraujoch: Data analysis and trends (1990 2006). 1697 1701. Atmos. Chem. Phys., 9, 4157 4175. Trenberth, K. E., and D. J. Shea, 2006: Atlantic hurricanes and natural variability in Tegtmeier, S., K. Kruger, I. Wohltmann, K. Schoellhammer, and M. Rex, 2008: Varia- 2005. Geophys. Res. Lett., 33, L12704. tions of the residual circulation in the Northern Hemispheric winter. J. Geophys. Trenberth, K. E., and J. T. Fasullo, 2010: Climate change tracking Earth s energy. Sci- Res. Atmos., 113, D16109. ence, 328, 316 317. Teuling, A. J., et al., 2009: A regional perspective on trends in continental evapora- Trenberth, K. E., and J. T. Fasullo, 2012b: Tracking Earth s energy: From El Nino to tion. Geophys. Res. Lett., 36, L02404. global warming. Surv. Geophys., 33, 413 426. Thomas, B., E. Kent, V. Swail, and D. Berry, 2008: Trends in ship wind speeds adjusted Trenberth, K. E., J. T. Fasullo, and J. Kiehl, 2009: Earth s Global energy budget. Bull. for observation method and height. Int. J. Climatol., 28, 747 763. Am. Meteor. Soc., 90, 311. Thomas, G. E., et al., 2010: Validation of the GRAPE single view aerosol retrieval for Trenberth, K. E., J. T. Fasullo, and J. Mackaro, 2011: Atmospheric moisture transports ATSR-2 and insights into the long term global AOD trend over the ocean. Atmos. from ocean to land and global energy flows in reanalyses. J. Clim., 24, 4907 Chem. Phys., 10, 4849 4866. 4924. 250 Observations: Atmosphere and Surface Chapter 2 Trenberth, K. E., et al., 2007: Observations: Surface and atmospheric climate change. Venema, V. K. C., et al., 2012: Benchmarking homogenization algorithms for monthly In: Climate Change 2007: The Physical Science Basis. Contribution of Working data. Clim. Past, 8, 89 115. Group I to the Fourth Assessment Report of the Intergovernmental Panel on Verbout, S., H. Brooks, L. Leslie, and D. Schultz, 2006: Evolution of the US tornado Climate Change [Solomon, S., D. Qin, M. Manning, Z. Chen, M. Marquis, K. B. database: 1954 2003. Weather Forecast., 21, 86 93. Averyt, M. Tignor and H. L. Miller (eds.)]. Cambridge University Press, Cambridge, Vicente-Serrano, S. M., S. Begueria, and J. I. Lopez-Moreno, 2010: A multiscalar United Kingdom and New York, NY, USA. drought index sensitive to global warming: The Standardized Precipitation Trewin, B., 2012: A daily homogenized temperature data set for Australia. Int. J. Cli- Evapotranspiration Index. J. Clim., 23, 1696 1718. matol., 33, 1510-1529. Vidal, J., E. Martin, L. Franchisteguy, F. Habets, J. Soubeyroux, M. Blanchard, and Trnka, M., J. Kysely, M. Mozny, and M. Dubrovsky, 2009: Changes in Central-Euro- M. Baillon, 2010: Multilevel and multiscale drought reanalysis over France with pean soil-moisture availability and circulation patterns in 1881 2005. Int. J. Cli- the Safran-Isba-Modcou hydrometeorological suite. Hydrol. Earth Syst. Sci., 14, matol., 29, 655 672. 459 478. Troccoli, A., K. Muller, P. Coppin, R. Davy, C. Russell, and A. L. Hirsch, 2012: Long-term Vilibic, I., and J. Sepic, 2010: Long-term variability and trends of sea level storminess wind speed trends over Australia. J. Climate, 25, 170 183. and extremes in European Seas. Global Planet. Change, 71, 1 12. Troup, A. J., 1965: Southern Oscillation. Q. J. R. Meteorol. Soc., 91, 490 . Villarini, G., J. Smith, and G. Vecchi, 2013: Changing frequency of heavy rainfall over Tryhorn, L., and J. Risbey, 2006: On the distribution of heat waves over the Australian the central United States. J. Clim., 26, 351 357. region. Aust. Meteorol. Mag., 55, 169 182. Vincent, L., et al., 2011: Observed trends in indices of daily and extreme temperature Tung, K.-K., and J. Zhou, 2013: Using data to attribute episodes of warming and and precipitation for the countries of the western Indian Ocean, 1961 2008. J. cooling in instrumental records. Proc. Natl. Acad. Sci. U.S.A., 110, 2058-2063. Geophys. Res. Atmos., 116, D10108. Turner, J., et al., 2005: Antarctic climate change during the last 50 years. Int. J. Cli- Vincent, L. A., X. L. L. Wang, E. J. Milewska, H. Wan, F. Yang, and V. Swail, 2012: A 2 matol., 25, 279 294. second generation of homogenized Canadian monthly surface air temperature Ulbrich, U., G. C. Leckebusch, and J. G. Pinto, 2009: Extra-tropical cyclones in the for climate trend analysis. J. Geophys. Res. Atmos., 117, D18110. present and future climate: A review. Theor. Appl. Climatol., 96, 117 131. Visbeck, M., 2009: A station-based Southern Annular Mode Index from 1884 to Uppala, S. M., et al., 2005: The ERA-40 re-analysis. Q. J. R. Meteorol. Soc., 131, 2005. J. Clim., 22, 940 950. 2961 3012. Volz, A., and D. Kley, 1988: Evaluation of the Montsouris series of ozone measure- Usbeck, T., T. Wohlgemuth, C. Pfister, R. Volz, M. Beniston, and M. Dobbertin, 2010: ments made in the 19th century. Nature, 332, 240 242. Wind speed measurements and forest damage in Canton Zurich (Central Europe) Vömel, H., D. E. David, and K. Smith, 2007a: Accuracy of tropospheric and strato- from 1891 to winter 2007. Int. J. Climatol., 30, 347 358. spheric water vapor measurements by the cryogenic frost point hygrometer: Utsumi, N., S. Seto, S. Kanae, E. Maeda, and T. Oki, 2011: Does higher surface tem- Instrumental details and observations. J. Geophys. Res. Atmos., 112, D08305. perature intensify extreme precipitation? Geophys. Res. Lett., 38, L16708. Vömel, H., et al., 2007b: Validation of Aura Microwave Limb Sounder water vapor by van den Besselaar, E. J. M., A. M. G. Klein Tank, and T. A. Buishand, 2012: Trends in balloon-borne cryogenic frost point hygrometer measurements. J. Geophys. Res. European precipitation extremes over 1951 2010. Int. J. Climatol., 33, 2682 Atmos., 112, D24S37. 2689. von Clarmann, T., et al., 2009: Retrieval of temperature, H2O, O3, HNO3, CH4, N2O, van der Schrier, G., A. van Ulden, and G. J. van Oldenborgh, 2011: The construction of ClONO2 and ClO from MIPAS reduced resolution nominal mode limb emission a Central Netherlands temperature. Clim. Past, 7, 527 542. measurements. Atmos. Meas. Tech., 2, 159 175. van der Schrier, G., J. Barichivich, K. R. Briffa, and P. D. Jones, 2013: A scPDSI-based von Schuckmann, K., and P.-Y. Le Traon, 2011: How well can we derive global ocean global dataset of dry and wet spells for 1901 2009. J. Geophys. Res. Atmos., indicators from Argo data? Ocean Sci., 7, 783 791. 118, 4025-4048. Von Storch, H., 1999: Misuses of statistical analysis in climate research. Analysis of van Haren, R., G. J. van Oldenborgh, G. Lenderink, M. Collins, and W. Hazeleger, Climate Variability: Applications of Statistical Techniques, 2nd edition [H. Von 2012: SST and circulation trend biases cause an underestimation of European Storch and A. Navarra (eds.)]. Springer-Verlag, New York, and Heidelberg, Ger- precipitation trends. Clim. Dyn., 40, 1-20. many, pp. 11 26. van Heerwaarden, C. C., J. V. G. de Arellano, and A. J. Teuling, 2010: Land-atmosphere von Storch, H., and F. W. Zwiers, 1999: Statistical Analysis in Climate Research. Cam- coupling explains the link between pan evaporation and actual evapotranspira- bridge University Press, Cambridge, United Kingdom and New York, NY, USA, tion trends in a changing climate. Geophys. Res. Lett., 37, L21401. 484 pp. van Ommen, T. D., and V. Morgan, 2010: Snowfall increase in coastal East Antarctica Vose, R. S., D. R. Easterling, and B. Gleason, 2005a: Maximum and minimum tem- linked with southwest Western Australian drought. Nature Geosci., 3, 267 272. perature trends for the globe: An update through 2004. Geophys. Res. Lett., 32, Vautard, R., P. Yiou, and G. J. van Oldenborgh, 2009: Decline of fog, mist and haze in L23822. Europe over the past 30 years. Nature Geosci., 2, 115 119. Vose, R. S., D. Wuertz, T. C. Peterson, and P. D. Jones, 2005b: An intercomparison of Vautard, R., J. Cattiaux, P. Yiou, J. N. The paut, and P. Ciais, 2010: Northern Hemi- trends in surface air temperature analyses at the global, hemispheric, and grid- sphere atmospheric stilling partly attributed to an increase in surface roughness. box scale. Geophys. Res. Lett., 32, L18718. Nature Geosci., 3, 756-761. Vose, R. S., S. Applequist, M. J. Menne, C. N. Williams, Jr., and P. Thorne, 2012a: An Vautard, R., et al., 2007: Summertime European heat and drought waves induced intercomparison of temperature trends in the US Historical Climatology Network by wintertime Mediterranean rainfall deficit. Geophys. Res. Lett., 34., L07711 and recent atmospheric reanalyses. Geophys. Res. Lett., 39, L10703. Vecchi, G. A., and B. J. Soden, 2007: Global warming and the weakening of the tropi- Vose, R. S., Oak Ridge National Laboratory. Environmental Sciences Division., cal circulation. J. Clim., 20, 4316 4340. U.S. Global Change Research Program, United States. Dept. of Energy. Office Vecchi, G. A., and T. R. Knutson, 2008: On estimates of historical north Atlantic tropi- of Health and Environmental Research., Carbon Dioxide Information Analysis cal cyclone activity. J. Clim., 21, 3580 3600. Center (U.S.), and Martin Marietta Energy Systems Inc., 1992: The Global Histori- Vecchi, G. A., and T. R. Knutson, 2011: Estimating annual numbers of Atlantic hurri- cal Climatology Network: Long-Term Monthly Temperature, Precipitation, Sea canes missing from the HURDAT database (1878 1965) using ship track density. Level Pressure, and Station Pressure Data. Carbon Dioxide Information Analysis J. Clim., 24, 1736 1746. Center. Available to the public from N.T.I.S., 1 v. (various pagings) Vecchi, G. A., B. J. Soden, A. T. Wittenberg, I. M. Held, A. Leetmaa, and M. J. Harrison, Vose, R. S., et al., 2012b: NOAA s Merged Land-Ocean Surface Temperature Analysis. 2006: Weakening of tropical Pacific atmospheric circulation due to anthropo- Bull. Am. Meteor. Soc., 93, 1677 1685. genic forcing. Nature, 441, 73 76. Wacker, S., J. Grobner, K. Hocke, N. Kampfer, and L. Vuilleumier, 2011: Trend analysis Velders, G., S. Andersen, J. Daniel, D. Fahey, and M. McFarland, 2007: The importance of surface cloud-free downwelling long-wave radiation from four Swiss sites. J. of the Montreal Protocol in protecting climate. Proc. Natl. Acad. Sci. U.S.A., 104, Geophys. Res. Atmos., 116, 13. 4814-4819. Wallace, J. M., and D. S. Gutzler, 1981: Teleconnections in the geopotential height Velders, G., D. Fahey, J. Daniel, M. McFarland, and S. Andersen, 2009: The large con- field during the Northern Hemisphere winter. Mon. Weather Rev., 109, 784 812. tribution of projected HFC emissions to future climate forcing. Proc. Natl. Acad. Wan, H., X. L. Wang, and V. R. Swail, 2010: Homogenization and trend analysis of Sci. U.S.A., 106, 10949-10954. Canadian near-surface wind speeds. J. Clim., 23, 1209 1225. 251 Chapter 2 Observations: Atmosphere and Surface Wan, H., X. Zhang, F. Zwiers, S. Emori, and H. Shiogama, 2013: Effect of data cover- Weinstock, E. M., et al., 2009: Validation of the Harvard Lyman-alpha in situ water age on the estimation of mean and variability of precipitation at global and vapor instrument: Implications for the mechanisms that control stratospheric regional scales. J. Geophys. Res., 118, 534 546. water vapor. J. Geophys. Res. Atmos., 114. Wang, B., J. Liu, H. J. Kim, P. J. Webster, and S. Y. Yim, 2012a: Recent change of the Weiss, R., J. Muhle, P. Salameh, and C. Harth, 2008: Nitrogen trifluoride in the global global monsoon precipitation (1979 2008). Clim. Dyn., 39, 1123 1135. atmosphere. Geophys. Res. Lett., 35, L20821. Wang, H., et al., 2012b: Extreme climate in China: Facts, simulation and projection. Wells, N., S. Goddard, and M. J. Hayes, 2004: A self-calibrating Palmer Drought Sever- Meteorol. Z., 21, 279 304. ity Index. J. Clim., 17, 2335 2351. Wang, J. H., and L. Y. Zhang, 2008: Systematic errors in global radiosonde precipi- Wentz, F., C. Gentemann, D. Smith, and D. Chelton, 2000: Satellite measurements of table water data from comparisons with ground-based GPS measurements. J. sea surface temperature through clouds. Science, 288, 847 850. Clim., 21, 2218 2238. Wentz, F. J., L. Ricciardulli, K. Hilburn, and C. Mears, 2007: How much more rain will Wang, J. H., and L. Y. Zhang, 2009: Climate applications of a global, 2-hourly atmo- global warming bring? Science, 317, 233 235. spheric precipitable water dataset derived from IGS tropospheric products. J. Werner, P. C., F. W. Gerstengarbe, and F. Wechsung, 2008: Grosswetterlagen and pre- Geodes., 83, 209 217. cipitation trends in the Elbe River catchment. Meteorol. Z., 17, 61 66. Wang, J. H., L. Y. Zhang, A. Dai, T. Van Hove, and J. Van Baelen, 2007: A near-global, Westra, S., and S. Sisson, 2011: Detection of non-stationarity in precipitation 2-hourly data set of atmospheric precipitable water from ground-based GPS extremes using a max-stable process model. J. Hydrol., 406, 119 128. measurements. J. Geophys. Res. Atmos., 112, D11107. Westra, S., L. Alexander, and F. Zwiers, 2013: Global increasing trends in annual Wang, J. S., D. J. Seidel, and M. Free, 2012c: How well do we know recent climate maximum daily precipitation. J. Clim., 26, 3904-3918. trends at the tropical tropopause? J. Geophys. Res. Atmos., 117, D09118. Wibig, J., 2008: Cloudiness variations in Lodz in the second half of the 20th century. 2 Wang, K., R. E. Dickinson, and S. Liang, 2009a: Clear sky visibility has decreased over Int. J. Climatol., 28, 479 491. land globally from 1973 to 2007. Science, 323, 1468 1470. Wickham, C., et al., 2013: Influence of urban heating on the global temperature Wang, K., H. Ye, F. Chen, Y. Z. Xiong, and C. P. Wang, 2012d: Urbanization effect on land average using rural sites identified from MODIS classifications. Geoinfor the diurnal temperature range: Different roles under solar dimming and bright- Geostat: An Overview, 1, 1:2. doi:10.4172/gigs.1000104. ening. J. Clim., 25, 1022 1027. Wielicki, B. A., B. R. Barkstrom, E. F. Harrison, R. B. Lee, G. L. Smith, and J. E. Cooper, Wang, K. C., and S. L. Liang, 2009: Global atmospheric downward longwave radia- 1996: Clouds and the Earth s radiant energy system (CERES): An Earth observing tion over land surface under all-sky conditions from 1973 to 2008. J. Geophys. system experiment. Bull. Am. Meteor. Soc., 77, 853 868. Res. Atmos., 114, D19101. Wielicki, B. A., et al., 2002: Evidence for large decadal variability in the tropical mean Wang, K. C., R. E. Dickinson, and S. L. Liang, 2009b: Clear sky visibility has decreased radiative energy budget. Science, 295, 841 844. over land globally from 1973 to 2007. Science, 323, 1468 1470. Wilby, R. L., P. D. Jones, and D. H. Lister, 2011: Decadal variations in the nocturnal Wang, K. C., R. E. Dickinson, M. Wild, and S. L. Liang, 2010: Evidence for decadal heat island of London. Weather, 66, 59 64. variation in global terrestrial evapotranspiration between 1982 and 2002: 2. Wild, M., 2009: Global dimming and brightening: A review. J. Geophys. Res. Atmos., Results. J. Geophys. Res. Atmos., 115, D20113. 114, D00D16. Wang, K. C., R. E. Dickinson, M. Wild, and S. Liang, 2012e: Atmospheric impacts on Wild, M., 2012: Enlightening global dimming and brightening. Bull. Am. Meteor. climatic variability of surface incident solar radiation. Atmos. Chem. Phys., 12, Soc., 93, 27 37. 9581 9592. Wild, M., A. Ohmura, and K. Makowski, 2007: Impact of global dimming and bright- Wang, K. C., R. E. Dickinson, L. Su, and K. E. Trenberth, 2012f: Contrasting trends ening on global warming. Geophys. Res. Lett., 34, L04702. of mass and optical properties of aerosols over the Northern Hemisphere from Wild, M., J. Grieser, and C. Schaer, 2008: Combined surface solar brightening and 1992 to 2011. Atmos. Chem. Phys., 12, 9387 9398. increasing greenhouse effect support recent intensification of the global land- Wang, L. K., C. Z. Zou, and H. F. Qian, 2012g: Construction of stratospheric tempera- based hydrological cycle. Geophys. Res. Lett., 35, L17706. ture data records from Stratospheric Sounding Units. J. Clim., 25, 2931 2946. Wild, M., A. Ohmura, H. Gilgen, and D. Rosenfeld, 2004: On the consistency of trends Wang, X., H. Wan, and V. Swail, 2006a: Observed changes in cyclone activity in in radiation and temperature records and implications for the global hydrologi- Canada and their relationships to major circulation regimes. J. Clim., 19, 896 cal cycle. Geophys. Res. Lett., 31, L11201. 915. Wild, M., A. Ohmura, H. Gilgen, E. Roeckner, M. Giorgetta, and J. J. Morcrette, 1998: Wang, X., B. Trewin, Y. Feng, and D. Jones, 2013: Historical changes in Australian tem- The disposition of radiative energy in the global climate system: GCM-calculated perature extremes as inferred from extreme value distribution analysis. Geophys. versus observational estimates. Clim. Dyn., 14, 853 869. Res. Lett., 40, 573-578. Wild, M., D. Folini, C. Schär, N. Loeb, E. G. Dutton, and G. König-Langlo, 2013: The Wang, X., Y. Feng, G. P. Compo, V. R. Swail, F. W. Zwiers, R. J. Allan, and P. D. global energy balance from a surface perspective. Clim. Dyn., 40, 3107-3134. Sardeshmukh, 2012: Trends and low frequency variability of extra-tropical Wild, M., B. Truessel, A. Ohmura, C. N. Long, G. Konig-Langlo, E. G. Dutton, and A. cyclone activity in the ensemble of twentieth century reanalysis. Clim. Dyn., 40, Tsvetkov, 2009: Global dimming and brightening: An update beyond 2000. J. 2775-2800. Geophys. Res. Atmos., 114, D00d13. Wang, X., et al., 2011: Trends and low-frequency variability of storminess over west- Wild, M., et al., 2005: From dimming to brightening: Decadal changes in solar radia- ern Europe, 1878 2007. Clim. Dyn., 37, 2355-2371. tion at Earth s surface. Science, 308, 847 850. Wang, X. L. L., V. R. Swail, and F. W. Zwiers, 2006b: Climatology and changes of extra- Wilks, D. S., 2006: Statistical Methods in the Atmospheric Sciences, 2nd edition. Else- tropical cyclone activity: Comparison of ERA-40 with NCEP-NCAR reanalysis for vier, Philadelphia, 627 pp. 1958 2001. J. Clim., 19, 3145 3166. Willett, K. M., P. D. Jones, N. P. Gillett, and P. W. Thorne, 2008: Recent Changes in Sur- Wang, X. L. L., F. W. Zwiers, V. R. Swail, and Y. Feng, 2009c: Trends and variability face Humidity: Development of the HadCRUH dataset. J. Clim., 21, 5364 5383. of storminess in the Northeast Atlantic region, 1874 2007. Clim. Dyn., 33, Willett, K. M., P. D. Jones, P. W. Thorne, and N. P. Gillett, 2010: A comparison of large 1179 1195. scale changes in surface humidity over land in observations and CMIP3 general Wang, X. M., P. M. Zhai, and C. C. Wang, 2009d: Variations in extratropical cyclone circulation models. Environ. Res. Lett., 5. activity in northern East Asia. Adv. Atmos. Sci., 26, 471 479. Willett, K. M., et al., 2013: HadISDH: an updateable land surface specific humidity Warren, S. G., R. M. Eastman, and C. J. Hahn, 2007: A survey of changes in cloud product for climate monitoring. Clim. Past, 9, 657 677. cover and cloud types over land from surface observations, 1971 96. J. Clim., Williams, C. N., M. J. Menne, and P. W. Thorne, 2012: Benchmarking the performance 20, 717 738. of pairwise homogenization of surface temperatures in the United States. J. Geo- Weaver, S. J., 2012: Factors associated with decadal variability in Great Plains sum- phys. Res. Atmos., 117. mertime surface temperatures. J. Clim., 26, 343 350. Willson, R. C., and A. V. Mordvinov, 2003: Secular total solar irradiance trend during Weber, M., W. Steinbrecht, C. Long, V. E. Fioletov, S. H. Frith, R. Stolarski, and P. A. solar cycles 21 23. Geophys. Res. Lett., 30, 1199. Newman, 2012: Stratospheric ozone [in State of the Climate in 2011 ]. Bull. Winkler, P., 2009: Revision and necessary correction of the long-term temperature Am. Met. Soc., 93, S46 S44. series of Hohenpeissenberg, 1781 2006. Theor. Appl. Climatol., 98, 259 268. Weinkle, J., R. Maue, and R. Pielke, 2012: Historical global tropical cyclone landfalls. J. Clim., 25, 4729 4735. 252 Observations: Atmosphere and Surface Chapter 2 Wong, T., B. A. Wielicki, R. B. Lee, ., G. L. Smith, K. A. Bush, and J. K. Willis, 2006: Reex- Yu, L., and R. Weller, 2007: Objectively analyzed air-sea heat fluxes for the global amination of the observed decadal variability of the earth radiation budget using ice-free oceans (1981 2005). Bull. Am. Meteor. Soc., 88, 527-539. altitude-corrected ERBE/ERBS nonscanner WFOV data. J. Clim., 19, 4028 4040. Yuan, X., and C. Li, 2008: Climate modes in southern high latitudes and their impacts Wood, S. N., 2006: Generalized Additive Models: An Introduction with R. CRC/Chap- on Antarctic sea ice. J. Geophys. Res. Oceans, 113, C06S91. man & Hall, Boca Raton, FL, USA. Yurganov, L., W. McMillan, E. Grechko, and A. Dzhola, 2010: Analysis of global and Woodruff, S. D., et al., 2011: ICOADS Release 2.5: Extensions and enhancements to regional CO burdens measured from space between 2000 and 2009 and vali- the Surface Marine Meteorological Archive. Int. J. Climatol., 31, 951 967. dated by ground-based solar tracking spectrometers. Atmos. Chem. Phys., 10, Worden, H. M., et al., 2013: Decadal record of satellite carbon monoxide observa- 3479 3494. tions. Atmos. Chem. Phys., 13, 837 850. Zebiak, S. E., 1993: Air-sea interaction in the equatorial Atlantic region. J. Clim., 6, Worton, D., et al., 2007: Atmospheric trends and radiative forcings of CF4 and C2F6 1567 1568. inferred from firn air. Environ. Sci. Technol., 41, 2184-2189. Zerefos, C. S., et al., 2009: Solar dimming and brightening over Thessaloniki, Greece, Worton, D. R., et al., 2012: Evidence from firn air for recent decreases in non-meth- and Beijing, China. Tellus B, 61, 657 665. ane hydrocarbons and a 20th century increase in nitrogen oxides in the northern Zhang, A. Y., G. Y. Ren, J. X. Zhou, Z. Y. Chu, Y. Y. Ren, and G. L. Tang, 2010: On the hemisphere. Atmos. Environ., 54, 592 602. urbanization effect on surface air temperature trends over China. Acta Meteorol. Wu, Z., N. E. Huang, S. R. Long, and C.-K. Peng, 2007: On the trend, detrending, Sin., 68, 957 966. and variability of nonlinear and nonstationary time series. Proc. Natl. Acad. Sci. Zhang, H., J. Bates, and R. Reynolds, 2006: Assessment of composite global sam- U.S.A., 104, 14889 14894. pling: Sea srface wind speed. Geophys. Res. Lett., 33, L17714. Wu, Z., N. E. Huang, J. M. Wallace, B. V. Smoliak, and X. Chen, 2011: On the time- Zhang, J., and J. S. Reid, 2010: A decadal regional and global trend analysis of the varying trend in global-mean surface temperature. Clim. Dyn., 37, 759 773. aerosol optical depth using a data-assimilation grade over-water MODIS and 2 Xavier, P. K., V. O. John, S. A. Buehler, R. S. Ajayamohan, and S. Sijikumar, 2010: Vari- Level 2 MISR aerosol products. Atmos. Chem. Phys., 10, 10949 10963. ability of Indian summer monsoon in a new upper tropospheric humidity data Zhang, X., J. He, J. Zhang, I. Polaykov, R. Gerdes, J. Inoue, and P. Wu, 2012a: Enhanced set. Geophys. Res. Lett., 37, L05705. poleward moisture transport and amplified northern high-latitude wetting Xia, X., 2010a: A closer looking at dimming and brightening in China during 1961 trend. Nature Clim. Change, 3, 47-51. 2005. Ann. Geophys., 28, 1121 1132. Zhang, X., et al., 2007a: Detection of human influence on twentieth-century precipi- Xia, X. G., 2010b: Spatiotemporal changes in sunshine duration and cloud amount tation trends. Nature, 448, 461 U464. as well as their relationship in China during 1954 2005. J. Geophys. Res. Atmos., Zhang, X., et al., 2011: Indices for monitoring changes in extremes based on daily 115, D00K06. temperature and precipitation data. Wiley Interdis. Rev. Clim. Change, 2, 851- Xiao, X., et al., 2010: Atmospheric three-dimensional inverse modeling of regional 870. industrial emissions and global oceanic uptake of carbon tetrachloride. Atmos. Zhang, X. B., et al., 2005: Trends in Middle East climate extreme indices from 1950 to Chem. Phys., 10, 10421 10434. 2003. J. Geophys. Res. Atmos., 110, D22104. Xie, B., Q. Zhang, and Y. Wang, 2010: Observed characteristics of hail size in four Zhang, X. D., C. H. Lu, and Z. Y. Guan, 2012b: Weakened cyclones, intensified anticy- regions in China during 1980 2005. J. Clim., 23, 4973 4982. clones and recent extreme cold winter weather events in Eurasia. Environ. Res. Xie, B. G., Q. H. Zhang, and Y. Q. Wang, 2008: Trends in hail in China during 1960 Lett., 7, 044044. 2005. Geophys. Res. Lett., 35, L13801. Zhang, X. D., J. E. Walsh, J. Zhang, U. S. Bhatt, and M. Ikeda, 2004: Climatology Xie, S., K. Hu, J. Hafner, H. Tokinaga, Y. Du, G. Huang, and T. Sampe, 2009: Indian and interannual variability of Arctic cyclone activity: 1948 2002. J. Clim., 17, Ocean capacitor effect on Indo-Western Pacific climate during the summer fol- 2300 2317. lowing El Nino. J. Clim., 22, 730 747. Zhang, Y., J. M. Wallace, and D. S. Battisti, 1997: ENSO-like interdecadal variability: Xu, C. Y., L. B. Gong, J. Tong, and D. L. Chen, 2006a: Decreasing reference evapotrans- 1900 93. J. Clim., 10, 1004 1020. piration in a warming climate A case of Changjiang (Yangtze) River catchment Zhang, Y. Q., C. M. Liu, Y. H. Tang, and Y. H. Yang, 2007b: Trends in pan evaporation during 1970 2000. Adv. Atmos. Sci., 23, 513 520. and reference and actual evapotranspiration across the Tibetan Plateau. J. Geo- Xu, K. H., J. D. Milliman, and H. Xu, 2010: Temporal trend of precipitation and runoff phys. Res. Atmos., 112, D12110. in major Chinese Rivers since 1951. Global Planet. Change, 73, 219 232. Zhao, X. P. T., A. K. Heidinger, and K. R. Knapp, 2011: Long-term trends of zonally Xu, M., C. P. Chang, C. B. Fu, Y. Qi, A. Robock, D. Robinson, and H. M. Zhang, 2006b: averaged aerosol optical thickness observed from operational satellite AVHRR Steady decline of east Asian monsoon winds, 1969 2000: Evidence from direct instrument. Meteorol. Appl., 18, 440 445. ground measurements of wind speed. J. Geophys. Res. Atmos., 111. Zhen, L., and Y. Zhong-Wei, 2009: Homogenized daily mean.maximum/minimum Yan, Z. W., Z. Li, Q. X. Li, and P. Jones, 2010: Effects of site change and urbanisation temperature series for China from 1960 2008. 237 243. in the Beijing temperature series 1977 2006. Int. J. Climatol., 30, 1226 1234. Zhou, J., and K.-K. Tung, 2012: Deducing multidecadal anthropogenic global warm- Yang, J., Q. Liu, S.-P. Xie, Z. Liu, and L. Wu, 2007: Impact of the Indian Ocean SST basin ing trends using multiple regression Analysis. J. Atmos. Sci., 70, 3 8. mode on the Asian summer monsoon. Geophys. Res. Lett., 34, L02708. Zhou, T., L. Zhang, and H. Li, 2008: Changes in global land monsoon area and total Yang, X. C., Y. L. Hou, and B. D. Chen, 2011: Observed surface warming induced by rainfall accumulation over the last half century. Geophys. Res. Lett., 35, L16707. urbanization in east China. J. Geophys. Res. Atmos., 116, 12. Zhou, T. J., D. Y. Gong, J. Li, and B. Li, 2009a: Detecting and understanding the multi- Yokouchi, Y., S. Taguchi, T. Saito, Y. Tohjima, H. Tanimoto, and H. Mukai, 2006: High decadal variability of the East Asian summer monsoon Recent progress and frequency measurements of HFCs at a remote site in east Asia and their implica- state of affairs. Meteorol. Z., 18, 455 467. tions for Chinese emissions. Geophys. Res. Lett., 33, L21814. Zhou, T. J., et al., 2009b: Why the Western Pacific subtropical high has extended Yoon, J., W. von Hoyningen-Huene, A. A. Kokhanovsky, M. Vountas, and J. P. Bur- westward since the late 1970s. J. Clim., 22, 2199 2215. rows, 2012: Trend analysis of aerosol optical thickness and Angstrom exponent Zhou, Y. P., K. M. Xu, Y. C. Sud, and A. K. Betts, 2011: Recent trends of the tropical derived from the global AERONET spectral observations. Atmos. Meas. Tech., 5, hydrological cycle inferred from Global Precipitation Climatology Project and 1271 1299. International Satellite Cloud Climatology Project data. J. Geophys. Res. Atmos., You, Q., et al., 2010: Changes in daily climate extremes in China and their connec- 116, D09101 tion to the large scale atmospheric circulation during 1961 2003. Clim. Dyn., Zhou, Y. Q., and G. Y. Ren, 2011: Change in extreme temperature event frequency 36, 2399-2417. over mainland China, 1961 2008. Clim. Res., 50, 125 139. Yttri, K. E., et al., 2011: Transboundary particulate matter in Europe, Status Report Ziemke, J. R., S. Chandra, and P. K. Bhartia, 2005: A 25-year data record of atmo- 2011. In: Co-operative Programme for Monitoring and Evaluation of the Long spheric ozone in the Pacific from Total Ozone Mapping Spectrometer (TOMS) Range Transmission of Air Pollutants (Joint CCC, MSC-W, CEIP and CIAM report cloud slicing: Implications for ozone trends in the stratosphere and troposphere. 2011). NILU - Chemical Coordinating Centre - CCC. http://emep.int/publ/ J. Geophys. Res., 110, D15105. common_publications.html Ziemke, J. R., S. Chandra, G. J. Labow, P. K. Bhartia, L. Froidevaux, and J. C. Witte, Yu, B., and F. W. Zwiers, 2010: Changes in equatorial atmospheric zonal circulations 2011: A global climatology of tropospheric and stratospheric ozone derived from in recent decades. Geophys. Res. Lett., 37, L05701. Aura OMI and MLS measurements. Atmos. Chem. Phys., 11, 9237 9251. 253 Chapter 2 Observations: Atmosphere and Surface Zipser, E. J., C. Liu, D. J. Cecil, S. W. Nesbitt, and D. P. Yorty, 2006: Where are the most intense thunderstorms on Earth? Bull. Am. Meteor. Soc., 87, 1057 1071. Zolina, O., C. Simmer, K. Belyaev, A. Kapala, and S. Gulev, 2009: Improving estimates of heavy and extreme precipitation using daily records from European rain gauges. J. Hydrometeor., 10, 701 716. Zorita, E., T. F. Stocker, and H. von Storch, 2008: How unusual is the recent series of warm years? Geophys. Res. Lett., 35, L24706. Zou, C. Z., and W. H. Wang, 2010: Stability of the MSU-derived atmospheric tempera- ture trend. J. Atmos. Ocean Technol., 27, 1960 1971. Zou, C. Z., and W. H. Wang, 2011: Intersatellite calibration of AMSU-A observations for weather and climate applications. J. Geophys. Res. Atmos., 116, D23113. Zou, C. Z., M. D. Goldberg, Z. H. Cheng, N. C. Grody, J. T. Sullivan, C. Y. Cao, and D. Tar- pley, 2006a: Recalibration of microwave sounding unit for climate studies using simultaneous nadir overpasses. J. Geophys. Res. Atmos., 111, L17701. Zou, X., L. V. Alexander, D. Parker, and J. Caesar, 2006b: Variations in severe storms over China. Geophys. Res. Lett., 33. Zwiers, F. W., and V. V. Kharin, 1998: Changes in the extremes of the climate simu- lated by CCC GCM2 under CO2 doubling. J. Clim., 11, 2200 2222. 2 254 3 Observations: Ocean Coordinating Lead Authors: Monika Rhein (Germany), Stephen R. Rintoul (Australia) Lead Authors: Shigeru Aoki (Japan), Edmo Campos (Brazil), Don Chambers (USA), Richard A. Feely (USA), Sergey Gulev (Russian Federation), Gregory C. Johnson (USA), Simon A. Josey (UK), Andrey Kostianoy (Russian Federation), Cecilie Mauritzen (Norway), Dean Roemmich (USA), Lynne D. Talley (USA), Fan Wang (China) Contributing Authors: Ian Allison (Australia), Michio Aoyama (Japan), Molly Baringer (USA), Nicholas R. Bates (Bermuda), Timothy Boyer (USA), Robert H. Byrne (USA), Sarah Cooley (USA), Stuart Cunningham (UK), Thierry Delcroix (France), Catia M. Domingues (Australia), Scott Doney (USA), John Dore (USA), Paul. J. Durack (USA/Australia), Rana Fine (USA), Melchor González-Dávila (Spain), Simon Good (UK), Nicolas Gruber (Switzerland), Mark Hemer (Australia), David Hydes (UK), Masayoshi Ishii (Japan), Stanley Jacobs (USA), Torsten Kanzow (Germany), David Karl (USA), Georg Kaser (Austria/Italy), Alexander Kazmin (Russian Federation), Robert Key (USA), Samar Khatiwala (USA), Joan Kleypas (USA), Ronald Kwok (USA), Kitack Lee (Republic of Korea), Eric Leuliette (USA), Melisa Menéndez (Spain), Calvin Mordy (USA), Jon Olafsson (Iceland), James Orr (France), Alejandro Orsi (USA), Geun-Ha Park (Republic of Korea), Igor Polyakov (USA), Sarah G. Purkey (USA), Bo Qiu (USA), Gilles Reverdin (France), Anastasia Romanou (USA), Sunke Schmidtko (UK), Raymond Schmitt (USA), Koji Shimada (Japan), Doug Smith (UK), Thomas M. Smith (USA), Uwe Stöber (Germany), Lothar Stramma (Germany), Toshio Suga (Japan), Neil Swart (Canada/ South Africa), Taro Takahashi (USA), Toste Tanhua (Germany), Karina von Schuckmann (France), Hans von Storch (Germany), Xiaolan Wang (Canada), Rik Wanninkhof (USA), Susan Wijffels (Australia), Philip Woodworth (UK), Igor Yashayaev (Canada), Lisan Yu (USA) Review Editors: Howard Freeland (Canada), Silvia Garzoli (USA), Yukihiro Nojiri (Japan) This chapter should be cited as: Rhein, M., S.R. Rintoul, S. Aoki, E. Campos, D. Chambers, R.A. Feely, S. Gulev, G.C. Johnson, S.A. Josey, A. Kostianoy, C. Mauritzen, D. Roemmich, L.D. Talley and F. Wang, 2013: Observations: Ocean. In: Climate Change 2013: The Physical Science Basis. Contribution of Working Group I to the Fifth Assessment Report of the Intergovernmental Panel on Climate Change [Stocker, T.F., D. Qin, G.-K. Plattner, M. Tignor, S.K. Allen, J. Boschung, A. Nauels, Y. Xia, V. Bex and P.M. Midgley (eds.)]. Cambridge University Press, Cambridge, United Kingdom and New York, NY, USA. 255 Table of Contents Executive Summary...................................................................... 257 3.6.4 The Antarctic Meridional Overturning Circulation...... 284 3.6.5 Water Exchange Between Ocean Basins.................... 284 3.1 Introduction....................................................................... 260 3.6.6 Conclusions................................................................ 285 3.2 Changes in Ocean Temperature and 3.7 Sea Level Change, Including Extremes...................... 285 Heat Content..................................................................... 260 3.7.1 Introduction and Overview of Sea Level 3.2.1 Effects of Sampling on Ocean Heat Measurements........................................................... 285 Content Estimates...................................................... 260 3.7.2 Trends in Global Mean Sea Leve 3.2.2 Upper Ocean Temperature......................................... 261 and Components........................................................ 286 3.2.3 Upper Ocean Heat Content........................................ 262 3.7.3 Regional Distribution of Sea Level Change................ 288 3.2.4 Deep Ocean Temperature and Heat Content.............. 263 3.7.4 Assessment of Evidence for Accelerations in 3.2.5 Conclusions................................................................ 263 Sea Level Rise............................................................ 289 Box 3.1: Change in Global Energy Inventory.......................... 264 3.7.5 Changes in Extreme Sea Level................................... 290 3.7.6 Conclusions................................................................ 291 3.3 Changes in Salinity and Freshwater Content.......... 265 3.3.1 Introduction............................................................... 265 3.8 Ocean Biogeochemical Changes, Including 3 Anthropogenic Ocean Acidification............................ 291 3.3.2 Global to Basin-Scale Trends...................................... 267 3.8.1 Carbon....................................................................... 292 3.3.3 Regional Changes in Upper Ocean Salinity................ 271 3.8.2 Anthropogenic Ocean Acidification............................ 293 3.3.4 Evidence for Change of the Hydrological Cycle from Salinity Changes................................................ 273 3.8.3 Oxygen....................................................................... 294 3.3.5 Conclusions................................................................ 273 Box 3.2: Ocean Acidification..................................................... 295 3.8.4 Nutrients.................................................................... 298 3.4 Changes in Ocean Surface Fluxes................................ 273 3.8.5 Conclusions................................................................ 300 3.4.1 Introduction............................................................... 273 3.4.2 Air Sea Heat Fluxes................................................... 274 3.9 Synthesis................................................................ 301 3.4.3 Ocean Precipitation and Freshwater Flux................... 275 References .................................................................................. 303 3.4.4 Wind Stress................................................................ 276 3.4.5 Changes in Surface Waves......................................... 277 Appendix 3.A: Availability of Observations for 3.4.6 Conclusions................................................................ 278 Assessment of Change in the Oceans.................................... 311 3.A.1 Subsurface Ocean Temperature and Heat Content..... 311 3.5 Changes in Water-Mass Properties............................. 278 3.A.2 Salinity....................................................................... 312 3.5.1 Introduction............................................................... 278 3.A.3 Sea Level.................................................................... 312 3.5.2 Intermediate Waters................................................... 278 3.A.4 Biogeochemistry........................................................ 312 3.5.3 Deep and Bottom Waters........................................... 279 3.5.4 Conclusions................................................................ 280 Frequently Asked Questions FAQ 3.1 Is the Ocean Warming?........................................... 266 3.6 Changes in Ocean Circulation...................................... 281 FAQ 3.2 Is There Evidence for Changes in the 3.6.1 Global Observations of Ocean Earth s Water Cycle?............................................... 269 Circulation Variability................................................. 281 FAQ 3.3 How Does Anthropogenic Ocean Acidification 3.6.2 Wind-Driven Circulation Variability in the Relate to Climate Change?.................................... 297 Pacific Ocean.............................................................. 281 3.6.3 The Atlantic Meridional Overturning Circulation........ 282 256 Observations: Ocean Chapter 3 Executive Summary i ­ntegrated ocean heat content in some of the 0 to 700 m estimates increased more slowly from 2003 to 2010 than over the previous Temperature and Heat Content Changes decade, ocean heat uptake from 700 to 2000 m likely continued una- bated during this period. {3.2.4, Figure 3.2, Box 9.2} It is virtually certain1 that the upper ocean (above 700 m) has warmed from 1971 to 2010, and likely that it has warmed from Ocean warming dominates the global energy change inventory. the 1870s to 1971. Confidence in the assessment for the time period Warming of the ocean accounts for about 93% of the increase since 1971 is high2 based on increased data coverage after this date in the Earth s energy inventory between 1971 and 2010 (high and on a high level of agreement among independent observations of confidence), with warming of the upper (0 to 700 m) ocean subsurface temperature [3.2], sea surface temperature [2.4.2], and sea accounting for about 64% of the total. Melting ice (including Arctic level rise, which is known to include a substantial component due to sea ice, ice sheets and glaciers) and warming of the continents and thermal expansion [3.7, Chapter 13]. There is less certainty in changes atmosphere account for the remainder of the change in energy. The prior to 1971 because of relatively sparse sampling in earlier time peri- estimated net increase in the Earth s energy storage between 1971 and ods. The strongest warming is found near the sea surface (0.11 [0.09 2010 is 274 [196 to 351] ZJ (1 ZJ = 1021 Joules), with a heating rate of to 0.13] °C per decade in the upper 75 m between 1971 and 2010), 213 TW from a linear fit to annual inventories over that time period, decreasing to about 0.015°C per decade at 700 m. It is very likely that equivalent to 0.42 W m 2 heating applied continuously over the Earth s the surface intensification of this warming signal increased the ther- entire surface, and 0.55 W m 2 for the portion due to ocean warming mal stratification of the upper ocean by about 4% between 0 and 200 applied over the ocean surface area. {Section 3.2.3, Figure 3.2, Box 3.1} m depth. Instrumental biases in historical upper ocean temperature measurements have been identified and reduced since AR4, diminish- Salinity and Freshwater Content Changes ing artificial decadal variation in temperature and upper ocean heat content, most prominent during the 1970s and 1980s. {3.2.1 3.2.3, It is very likely that regional trends have enhanced the mean Figures 3.1, 3.2 and 3.9} geographical contrasts in sea surface salinity since the 1950s: saline surface waters in the evaporation-dominated mid-­ 3 It is likely that the ocean warmed between 700 and 2000 m latitudes have become more saline, while relatively fresh sur- from 1957 to 2009, based on 5-year averages. It is likely that face waters in rainfall-dominated tropical and polar regions the ocean warmed from 3000 m to the bottom from 1992 to have become fresher. The mean contrast between high- and low- 2005, while no significant trends in global average tempera- salinity regions increased by 0.13 [0.08 to 0.17] from 1950 to 2008. ture were observed between 2000 and 3000 m depth during It is very likely that the interbasin contrast in freshwater content has this period. Warming below 3000 m is largest in the Southern Ocean increased: the Atlantic has become saltier and the Pacific and Southern {3.2.4, 3.5.1, Figures 3.2b and 3.3, FAQ 3.1} oceans have freshened. Although similar conclusions were reached in AR4,  recent studies based on expanded data sets and new analysis It is virtually certain that upper ocean (0 to 700 m) heat content approaches provide  high confidence  in the assessment of trends in increased during the relatively well-sampled 40-year period ocean salinity. {3.3.2, 3.3.3, 3.3.5, Figures 3.4, 3.5 and 3.21d, FAQ 3.2} from 1971 to 2010. Published rates for that time period range from 74 TW to 137 TW, with generally smaller trends for estimates that It is very likely that large-scale trends in salinity have also assume zero anomalies in regions with sparse data. Using a statistical occurred in the ocean interior. It is likely that both the subduction analysis of ocean variability to estimate change in sparsely sampled of surface water anomalies formed by changes in evaporation pre- areas and to estimate uncertainties results in a rate of increase of cipitation (E P) and the movement of density surfaces due to warm- global upper ocean heat content of 137 [120 154] TW (medium confi- ing have contributed to the observed changes in subsurface salinity. dence). Although not all trends agree within their statistical uncertain- {3.3.2 3.3.4, Figures 3.5 and 3.9} ties, all are positive, and all are statistically different from zero. {3.2.3, Figure 3.2} The spatial patterns of the salinity trends, mean salinity and the mean distribution of E P are all similar. This provides, with Warming of the ocean between 700 and 2000 m likely contrib- medium confidence, indirect evidence that the pattern of E P over the uted about 30% of the total increase in global ocean heat con- oceans has been enhanced since the 1950s. {3.3.2 3.3.4, Figures 3.4, tent (0 to 2000 m) between 1957 and 2009. Although globally 3.5 and 3.20d, FAQ 3.2}. In this Report, the following terms have been used to indicate the assessed likelihood of an outcome or a result: Virtually certain 99 100% probability, Very likely 90 100%, 1 Likely 66 100%, About as likely as not 33 66%, Unlikely 0 33%, Very unlikely 0-10%, Exceptionally unlikely 0 1%. Additional terms (Extremely likely: 95 100%, More likely than not >50 100%, and Extremely unlikely 0 5%) may also be used when appropriate. Assessed likelihood is typeset in italics, e.g., very likely (see Section 1.4 and Box TS.1 for more details). In this Report, the following summary terms are used to describe the available evidence: limited, medium, or robust; and for the degree of agreement: low, medium, or high. 2 A level of confidence is expressed using five qualifiers: very low, low, medium, high, and very high, and typeset in italics, e.g., medium confidence. For a given evidence and agreement statement, different confidence levels can be assigned, but increasing levels of evidence and degrees of agreement are correlated with increasing confidence (see Section 1.4 and Box TS.1 for more details). 257 Chapter 3 Observations: Ocean Air Sea Flux and Wave Height Changes Circumpolar Current (ACC), or between the Atlantic Ocean and Nordic Seas. However, there is medium confidence that the ACC shifted south Uncertainties in air sea heat flux data sets are too large to allow between 1950 and 2010, at a rate equivalent to about 1° of latitude in detection of the change in global mean net air-sea heat flux, of 40 years. {3.6, Figures 3.10, 3.11} the order of 0.5 W m 2 since 1971, required for consistency with the observed ocean heat content increase. The products cannot Sea Level Change yet be reliably used to directly identify trends in the regional or global distribution of evaporation or precipitation over the oceans on the time Global mean sea level (GMSL) has risen by 0.19 [0.17 to 0.21] m scale of the observed salinity changes since 1950. {3.4.2, 3.4.3, Figures over the period 1901 2010, calculated using the mean rate over 3.6 and 3.7} these 110 years, based on tide gauge records and since 1993 additionally on satellite data. It is very likely that the mean Basin-scale wind stress trends at decadal to centennial time rate was 1.7 [1.5 to 1.9] mm yr 1 between 1901 and 2010 and scales have been observed in the North Atlantic, Tropical Pacif- increased to 3.2 [2.8 to 3.6] mm yr 1 between 1993 and 2010. ic and Southern Ocean with low to medium confidence. These This assessment is based on high agreement among multiple studies results are based largely on atmospheric reanalyses, in some cases a using different methods, long tide gauge records corrected for verti- single product, and the confidence level is dependent on region and cal land motion and independent observing systems (tide gauges and time scale considered. The evidence is strongest for the Southern altimetry) since 1993 (see also TFE.2, Figure 1). It is likely that GMSL Ocean, for which there is medium confidence that zonal mean wind rose between 1920 and 1950 at a rate comparable to that observed stress has increased in strength since the early 1980s. {3.4.4, Figure between 1993 and 2010, as individual tide gauges around the world 3.8} and reconstructions of GMSL show increased rates of sea level rise during this period. Rates of sea level rise over broad regions can be There is medium confidence based on ship observations and several times larger or smaller than that of GMSL for periods of sev- reanalysis forced wave model hindcasts that mean significant eral decades due to fluctuations in ocean circulation. High agreement 3 wave height has increased since the 1950s over much of the between studies with and without corrections for vertical land motion North Atlantic north of 45°N, with typical winter season trends suggests that it is very unlikely that estimates of the global average of up to 20 cm per decade. {3.4.5} rate of sea level change are significantly biased owing to vertical land motion that has been unaccounted for. {3.7.2, 3.7.3, Table 3.1, Figures Changes in Water Masses and Circulation 3.12, 3.13, 3.14} Observed changes in water mass properties likely reflect the It is very likely that warming of the upper 700 m has been con- combined effect of long-term trends in surface forcing (e.g., tributing an average of 0.6 [0.4 to 0.8] mm yr 1 of sea level rise warming of the surface ocean and changes in E P) and inter- since 1971. It is likely that warming between 700 m and 2000 m has annual-to-multi-decadal variability related to climate modes. been contributing an additional 0.1 mm yr 1 [0 to 0.2] of sea level rise Most of the observed temperature and salinity changes in the ocean since 1971, and that warming below 2000 m has been contributing interior can be explained by subduction and spreading of water masses another 0.1 [0.0 to 0.2] mm yr 1 of sea level rise since the early 1990s. with properties that have been modified at the sea surface. From 1950 {3.7.2, Figure 3.13} to 2000, it is likely that subtropical salinity maximum waters became more saline, while fresh intermediate waters formed at higher latitude It is likely that the rate of sea level rise increased from the early have generally become fresher. For Upper North Atlantic Deep Water 19th century to the early 20th century, and increased further changes in properties and formation rates are very likely dominated over the 20th century. The inference of 19th century change is by decadal variability. The Lower North Atlantic Deep Water has likely based on a small number of very long tide gauge records from north- cooled from 1955 to 2005, and the freshening trend highlighted in AR4 ern Europe and North America. Multiple long tide gauge records and reversed in the mid-1990s. It is likely that the Antarctic Bottom Water reconstructions of global mean sea level confirm a higher rate of rise warmed and contracted globally since the 1980s and freshened in the from the late 19th century. It is likely that the average acceleration Indian/Pacific sectors from 1970 to 2008. {3.5, FAQ 3.1} over the 20th century is [ 0.002 to 0.019] mm yr 2, as two of three reconstructions extending back to at least 1900 show an acceleration Recent observations have strengthened evidence for variability during the 20th century. {3.7.4} in major ocean circulation systems on time scales from years to decades. It is very likely that the subtropical gyres in the North Pacific It is likely that the magnitude of extreme high sea level events and South Pacific have expanded and strengthened since 1993. It is has increased since 1970. A rise in mean sea level can explain most about as likely as not that this is linked to decadal variability in wind of the increase in extreme sea levels: changes in extreme high sea forcing rather than being part of a longer-term trend. Based on meas- levels are reduced to less than 5 mm yr 1 at 94% of tide gauges once urements of the full Atlantic Meridional Overturning Circulation and its the rise in mean sea level is accounted for. {3.7.5, Figure 3.15} individual components at various latitudes and different time periods, there is no evidence of a long-term trend. There is also no evidence for trends in the transports of the Indonesian Throughflow, the Antarctic 258 Observations: Ocean Chapter 3 Changes in Ocean Biogeochemistry Based on high agreement between independent estimates using different methods and data sets (e.g., oceanic carbon, oxygen, and transient tracer data), it is very likely that the global ocean inventory of anthropogenic carbon (Cant) increased from 1994 to 2010. The oceanic Cant inventory in 2010 is estimated to be 155 PgC with an uncertainty of +/-20%. The annual global oceanic uptake rates calculated from independent data sets (from oceanic Cant inven- tory changes, from atmospheric O2/N2 measurements or from partial pressure of carbon dioxide (pCO2) data) and for different time periods agree with each other within their uncertainties and very likely are in the range of 1.0 to 3.2 PgC yr 1 {3.8.1, Figure 3.16} Uptake of anthropogenic CO2 results in gradual acidification of the ocean. The pH of surface seawater has decreased by 0.1 since the beginning of the industrial era, corresponding to a 26% increase in hydrogen ion concentration (high confidence). The observed pH trends range between 0.0014 and 0.0024 yr 1 in surface waters. In the ocean interior, natural physical and biological processes, as well as uptake of anthropogenic CO2, can cause changes in pH over decadal and longer time scales. {3.8.2, Table 3.2, Box 3.2, Figures 3.18, 3.19, FAQ 3.3} 3 High agreement among analyses provides medium confidence that oxygen concentrations have decreased in the open ocean thermocline in many ocean regions since the 1960s. The general decline is consistent with the expectation that warming-induced strati- fication leads to a decrease in the supply of oxygen to the thermocline from near surface waters, that warmer waters can hold less oxygen, and that changes in wind-driven circulation affect oxygen concentra- tions. It is likely that the tropical oxygen minimum zones have expand- ed in recent decades. {3.8.3, Figure 3.20} Synthesis The observations summarized in this chapter provide strong evidence that ocean properties of relevance to climate have changed during the past 40 years, including temperature, salin- ity, sea level, carbon, pH, and oxygen. The observed patterns of change in the subsurface ocean are consistent with changes in the surface ocean in response to climate change and natural variability and with known physical and biogeochemical pro- cesses in the ocean, providing high confidence in this assess- ment. {3.9, Figures 3.21, 3.22} 259 Chapter 3 Observations: Ocean 3.1 Introduction in Section 3.2; changes in sea surface temperature (SST) are covered in Chapter 2. Changes in ocean heat content dominate changes in the The ocean influences climate by storing and transporting large global energy inventory (Box 3.1). Recent studies have strengthened amounts of heat, freshwater, and carbon, and by exchanging these the evidence for regional changes in ocean salinity and their link to properties with the atmosphere. About 93% of the excess heat energy changes in evaporation and precipitation over the oceans (Section 3.3), stored by the Earth over the last 50 years is found in the ocean (Church a connection already identified in AR4. Evidence for changes in the et al., 2011; Levitus et al., 2012). The ability of the ocean to store vast fluxes of heat, water and momentum (wind stress) across the air sea amounts of heat reflects the large mass and heat capacity of seawater interface is assessed in Section 3.4. Considering ocean changes from relative to air and the fact that ocean circulation connects the surface a water-mass perspective adds additional insight into the nature and and interior ocean. More than three quarters of the total exchange causes of ocean change (Section 3.5). Although direct observations of water between the atmosphere and the Earth s surface through of ocean circulation are more limited than those of temperature and evaporation and precipitation takes place over the oceans (Schmitt, salinity, there is growing evidence of variability and change of ocean 2008). The ocean contains 50 times more carbon than the atmosphere current patterns relevant to climate (Section 3.6). Observations of sea (Sabine et al., 2004) and is at present acting to slow the rate of climate level change are summarized in Section 3.7; Chapter 13 builds on the change by absorbing about 30% of human emissions of carbon diox- evidence presented in this and other chapters to provide an overall ide (CO2) from fossil fuel burning, cement production, deforestation synthesis of past and future sea level change. Biogeochemical chang- and other land use change (Mikaloff-Fletcher et al., 2006; Le Quéré et es in the ocean, including ocean acidification, are covered in Section al., 2010). Changes in the ocean may result in climate feedbacks that 3.8. Chapter 6 combines observations with models to discuss past and either increase or reduce the rate of climate change. Climate variabil- present changes in the carbon cycle. Section 3.9 provides an overall ity and change on time scales from seasons to millennia is therefore synthesis of changes observed in the ocean during the instrumental closely linked to the ocean and its interactions with the atmosphere period and highlights key uncertainties. Unless otherwise noted, uncer- and cryosphere. The large inertia of the oceans means that they nat- tainties (in square brackets) represent 5 to 95% confidence intervals. urally integrate over short-term variability and often provide a clearer 3 signal of longer-term change than other components of the climate system. Observations of ocean change therefore provide a means to 3.2 Changes in Ocean Temperature and track the evolution of climate change, and a relevant benchmark for Heat Content climate models. 3.2.1 Effects of Sampling on Ocean Heat Content The lack of long-term measurements of the global ocean and chang- Estimates es in the observing system over time makes documenting and under- standing change in the oceans a difficult challenge (Appendix 3.A). Temperature is the most often measured subsurface ocean variable. Many of the issues raised in Box 2.1 regarding uncertainty in atmos- Historically, a variety of instruments have been used to measure tem- pheric climate records are common to oceanographic data. Despite the perature, with differing accuracies, precisions, and sampling depths. limitations of historical records, AR4 identified significant trends in a Both the mix of instruments and the overall sampling patterns have number of ocean variables relevant to climate change, including ocean changed in time and space (Boyer et al., 2009), complicating efforts heat content, sea level, regional patterns of salinity, and biogeochem- to determine and interpret long-term change. The evolution of the ical parameters (Bindoff et al., 2007). Since AR4, substantial progress observing system for ocean temperature is summarized in Appendix has been made in improving the quality and coverage of ocean obser- 3.A. Upper ocean temperature (hence heat content) varies over multi- vations. Biases in historical measurements have been identified and ple time scales including seasonal (e.g., Roemmich and Gilson, 2009), reduced, providing a clearer record of past change. The Argo array of interannual (e.g. associated with El Nino, which has a strong influence profiling floats has provided near-global, year-round measurements of on ocean heat uptake, Roemmich and Gilson, 2011), decadal (e.g., temperature and salinity in the upper 2000 m since 2005. The satellite Carson and Harrison, 2010), and centennial (Gouretski et al., 2012; altimetry record is now more than 20 years in length. Longer continu- Roemmich et al., 2012). Ocean data assimilation products using these ous time series of important components of the meridional overturning data exhibit similar significant variations (e.g., Xue et al., 2012). Sparse circulation and tropical oceans have been obtained. The spatial and historical sampling coupled with large amplitude variations on shorter temporal coverage of biogeochemical measurements in the ocean has time and spatial scales raise challenges for estimating globally aver- expanded. As a result of these advances, there is now stronger evi- aged upper ocean temperature changes. Uncertainty analyses indicate dence of change in the ocean, and our understanding of the causes of that the historical data set begins to be reasonably well suited for this ocean change is improved. purpose starting around 1970 (e.g., Domingues et al., 2008; Lyman and Johnson, 2008; Palmer and Brohan, 2011). UOHC uncertainty estimates This chapter summarizes the observational evidence of change in the shrink after 1970 with improved sampling, so this assessment focus- ocean, with an emphasis on basin- and global-scale changes relevant to es on changes since 1971. Estimates of UOHC have been extended climate, with a focus on studies published since the AR4. As in Chapter back to 1950 by averaging over longer time intervals, such as 5-year 2, the robustness of observed changes is assessed relative to sources running means, to compensate for sparse data distributions in earlier of observational uncertainty. The attribution of ocean change, includ- time periods (e.g., Levitus et al., 2012). These estimates may be most ing the degree to which observed changes are consistent with anthro- appropriate in the deeper ocean, where strong interannual variability pogenic climate change, is addressed in Chapter 10. The evidence for in upper ocean temperature distributions such as that associated with changes in subsurface ocean temperature and heat content is assessed El Nino (Roemmich and Gilson, 2011) is less likely to be aliased. 260 Observations: Ocean Chapter 3 Since AR4 the significant impact of measurement biases in some of used for Figure 3.1 and because warming in the upper 1000 m of the the widely used instruments (the expendable (XBT) and mechanical Southern Ocean was stronger between the 1930s and the 1970s than bathythermograph (MBT) as well as a subset of Argo floats) on esti- between the 1970s and 1990s (Gille, 2008). Another warming maxi- mates of ocean temperature and upper (0 to 700 m) ocean heat con- mum is present at 25°N to 65°N. Both warming signals extend to 700 tent (hereafter UOHC) changes has been recognized (Gouretski and m (Levitus et al., 2009, Figure 3.1b), and are consistent with poleward Koltermann, 2007; Barker et al., 2011). Careful comparison of meas- displacement of the mean temperature field. Other zonally averaged urements from the less accurate instruments with those from the more temperature changes are also consistent with poleward displacement accurate ones has allowed some of the biases to be identified and of the mean temperatures. For example, cooling at depth between reduced (Wijffels et al., 2008; Ishii and Kimoto, 2009; Levitus et al., 30°S and the equator (Figure 3.1b) is consistent with a southward shift 2009; Gouretski and Reseghetti, 2010; Hamon et al., 2012). One major of cooler water near the equator. Poleward displacements of some sub- consequence of this bias reduction has been the reduction of an artifi- tropical and subpolar zonal currents and associated wind changes are cial decadal variation in upper ocean heat content that was apparent discussed in Section 3.6. in the observational assessment for AR4, in notable contrast to climate model output (Domingues et al., 2008). Substantial time-dependent Globally averaged ocean temperature anomalies as a function of depth XBT and MBT biases introduced spurious warming in the 1970s and and time (Figure 3.1c) relative to a 1971 2010 mean reveal warm- cooling in the early 1980s in the analyses assessed in AR4. Most ocean ing at all depths in the upper 700 m over the relatively well-sampled state estimates that assimilate biased data (Carton and Santorelli, 40-year period considered. Strongest warming is found closest to the 2008) also showed this artificial decadal variability while one (Stam- mer et al., 2010) apparently rejected these data on dynamical grounds. More recent estimates assimilating better-corrected data sets (Giese (a) et al., 2011) also result in reduced artificial decadal variability during this time period. Recent estimates of upper ocean temperature change also differ in 3 their treatment of unsampled regions. Some studies (e.g., Ishii and Kimoto, 2009; Levitus et al., 2012) effectively assume a temperature anomaly of zero in these regions, while other studies (Palmer et al., 2007; Lyman and Johnson, 2008) assume that the averages of sampled regions are representative of the global mean in any given year, and yet (b) others (Smith and Murphy, 2007; Domingues et al., 2008) use ocean 0 22 26 24 8 100 20 16 statistics (from numerical model output and satellite altimeter data, 18 200 0 0 14 Depth (m) respectively) to extrapolate temperature anomalies in sparsely sam- 2 300 10 12 6 4 pled areas and estimate uncertainties. These differences in approach, 400 2 coupled with choice of background climatology, can lead to significant 500 8 4 6 divergence in basin-scale averages (Gleckler et al., 2012), especially in 600 4 700 sparsely sampled regions (e.g., the extratropical Southern Hemisphere 80°S 60°S 40°S 20°S 0°S 20°N 40°N 60°N 80°N (SH) prior to Argo), and as a result can produce different global averag- (c) Latitude 0 es (Lyman et al., 2010). However, for well-sampled regions and times, 0.3 100 (a,b) Temp. trend (°C per decade) the various analyses of temperature changes yield results in closer 0.25 200 0.2 Depth (m) agreement, as do reanalyses (Xue et al., 2012). 300 0.15 (c) Temp. anom. (°C) 400 0.1 3.2.2 Upper Ocean Temperature 500 0.05 600 0 700 0.05 Depth-averaged 0 to 700 m ocean temperature trends from 1971 to (d) 1960 1970 1980 1990 2000 2010 0.1 2010 are positive over most of the globe (Levitus et al., 2009; Figure 0.15 6.7 T0 T200 (°C) 0.2 3.1a). The warming is more prominent in the Northern Hemisphere 6.5 0.25 (NH), especially the North Atlantic. This result holds in different anal- 6.3 0.3 yses, using different time periods, bias corrections and data sources 6.1 Year (e.g., with or without XBT or MBT data) (e.g., Palmer et al., 2007; Durack and Wijffels, 2010; Gleckler et al., 2012; Figures 3.1 and 3.9). Figure 3.1 | (a) Depth-averaged 0 to 700 m temperature trend for 1971 2010 However, the greater volume of the SH oceans increases the contribu- (longitude vs. latitude, colours and grey contours in degrees Celsius per decade). (b) tion of their warming to global heat content. Zonally averaged upper Zonally averaged temperature trends (latitude vs. depth, colours and grey contours in ocean temperature trends show warming at nearly all latitudes and degrees Celsius per decade) for 1971 2010 with zonally averaged mean temperature depths (Levitus et al., 2009, Figure 3.1b). A maximum in warming south over-plotted (black contours in degrees Celsius). (c) Globally averaged temperature anomaly (time vs. depth, colours and grey contours in degrees Celsius) relative to the of 30°S appears in Figure 3.1b, but is not as strong as in other analyses 1971 2010 mean. (d) Globally averaged temperature difference between the ocean (e.g., Gille, 2008), likely because the data are relatively sparse in this surface and 200 m depth (black: annual values, red: 5-year running mean). All panels location so anomalies are attenuated by the objectively analyzed fields are constructed from an update of the annual analysis of Levitus et al. (2009). 261 Chapter 3 Observations: Ocean sea surface, and the near-surface trends are consistent with indepen- (a) dently measured SST (Chapter 2). The global average warming over 150 this period is 0.11 [0.09 to 0.13] °C per decade in the upper 75 m, decreasing to 0.015°C per decade by 700 m (Figure 3.1c). Comparison of Argo data to Challenger expedition data from the 1870s suggests 100 that warming started earlier than 1971, and was also larger in the 0 700 m OHC (ZJ) Atlantic than in the Pacific over that longer time interval (Roemmich et 50 al., 2012). An observational analysis of temperature in the upper 400 m of the global ocean starting in the year 1900 (Gouretski et al., 2012) finds warming between about 1900 and 1945, as well as after 1970, 0 with some evidence of slight cooling between 1945 and 1970. Levitus The globally averaged temperature difference between the ocean sur- 50 Ishii Domingues face and 200 m (Figure 3.1d) increased by about 0.25C from 1971 to Palmer Smith 2010 (Levitus et al., 2009). This change, which corresponds to a 4% 100 increase in density stratification, is widespread in all the oceans north 1950 1960 1970 1980 1990 2000 2010 of about 40°S. (b) Year 1950 1960 1970 1980 1990 2000 2010 50 A potentially important impact of ocean warming is the effect on sea Deep OHC (ZJ) ice, floating glacial ice and ice sheet dynamics (see Chapter 4 for a discussion of these topics). Although some of the global integrals of 0 UOHC neglect changes poleward of +/-60° (Ishii and Kimoto, 2009) or 700 2000 m +/-65° (Domingues et al., 2008) latitude, at least some parts of the Arctic 2000 6000 m 50 3 have warmed: In the Arctic Ocean, subsurface pulses of relatively warm water of Atlantic origin can be traced around the Eurasian Basin, and Figure 3.2: | (a) Observation-based estimates of annual global mean upper (0 to 700 analyses of data from 1950 2010 show a decadal warming of this m) ocean heat content in ZJ (1 ZJ = 1021 Joules) updated from (see legend): Levitus et water mass since the late 1970s (Polyakov et al., 2012), as well as a al. (2012), Ishii and Kimoto (2009), Domingues et al. (2008), Palmer et al. (2007) and shoaling, by 75 to 90 m (Polyakov et al., 2010). Arctic surface waters Smith and Murphy (2007). Uncertainties are shaded and plotted as published (at the have also warmed, at least in the Canada Basin, from 1993 to 2007 one standard error level, except one standard deviation for Levitus, with no uncertain- (Jackson et al., 2010). ties provided for Smith). Estimates are shifted to align for 2006 2010, 5 years that are well measured by Argo, and then plotted relative to the resulting mean of all curves for 1971, the starting year for trend calculations. (b) Observation-based estimates of 3.2.3 Upper Ocean Heat Content annual 5-year running mean global mean mid-depth (700 to 2000 m) ocean heat con- tent in ZJ (Levitus et al., 2012) and the deep (2000 to 6000 m) global ocean heat Global integrals of 0 to 700 m UOHC (Figure 3.2a) estimated from ocean content trend from 1992 to 2005 (Purkey and Johnson, 2010), both with one standard temperature measurements all show a gain from 1971 to 2010 (Palmer error uncertainties shaded (see legend). et al., 2007; Smith and Murphy, 2007; e.g., Domingues et al., 2008; Ishii and Kimoto, 2009; Levitus et al., 2012) . These estimates usually start around 1950, although as noted in Section 3.2.1 and discussed in the the heating rate required to account for this warming: 118 [82 to 154] Appendix, historical data coverage is sparse, so global integrals are TW (1 TW = 1012 watts) for Levitus et al. (2012), 98 [67 to 130] TW increasingly uncertain for earlier years, especially prior to 1970. There for Ishii and Kimoto (2009), 137 [120 to 154] TW for Domingues et is some convergence towards agreement in instrument bias correction al. (2008), 108 [80 to 136] TW for Palmer et al. (2007), and 74 [43 to algorithms since AR4 (Section 3.2.1), but other sources of uncertainty 105] TW for Smith and Murphy (2007). Uncertainties are calculated include the different assumptions regarding mapping and integrating as 90% confidence intervals for an ordinary least squares fit, taking UOHCs in sparsely sampled regions, differences in quality control of into account the reduction in the degrees of freedom implied by the temperature data, and differences among baseline climatologies used temporal correlation of the residuals. Although these rates of energy for estimating changes in heat content (Lyman et al., 2010). Although gain do not all agree within their statistical uncertainties, all are pos- there are still apparent interannual variations about the upward trend itive, and all are statistically different from zero. Generally the smaller of global UOHC since 1970, different global estimates have variations trends are for estimates that assume zero anomalies in areas of sparse at different times and for different periods, suggesting that sub-decadal data, as expected for that choice, which will tend to reduce trends and variability in the time rate of change is still quite uncertain in the his- variability. Hence the assessment of the Earth s energy uptake (Box torical record. Most of the estimates in Figure 3.2a do exhibit decreas- 3.1) employs a global UOHC estimate (Domingues et al., 2008) chosen es for a few years immediately following major volcanic eruptions in because it fills in sparsely sampled areas and estimates uncertainties 1963, 1982 and 1991 (Domingues et al., 2008). using a statistical analysis of ocean variability patterns. Again, all of the global integrals of UOHC in Figure 3.2a have increased Globally integrated ocean heat content in three of the five 0 to 700 m between 1971 and 2010. Linear trends fit to the UOHC estimates for estimates appear to be increasing more slowly from 2003 to 2010 than the relatively well-sampled 40-year period from 1971 to 2010 estimate over the previous decade (Figure 3.2a). Although this apparent change 262 Observations: Ocean Chapter 3 (a) is concurrent with a slowing of the increase global mean surface tem- perature, as discussed in Box 9.2, this is also a time period when the 1000 ocean observing system transitioned from predominantly XBT to pre- 2000 dominantly Argo temperature measurements (Johnson and Wijffels, Depth (m) 2011). Shifts in observing systems can sometimes introduce spurious 3000 Global signals, so this apparent recent change should be viewed with caution. Southern 4000 3.2.4 Deep Ocean Temperature and Heat Content 5000 6000 Below 700 m data coverage is too sparse to produce annual global 0.02 0.01 0 0.01 0.02 0.03 0.04 0.05 0.06 0.07 ocean heat content estimates prior to about 2005, but from 2005 to Warming rate (°C per decade) 2010 and 0 to 1500 m the global ocean warmed (von Schuckmann and (b) Le Traon, 2011). Five-year running mean estimates yield a 700 to 2000 m global ocean heat content trend from 1957 to 2009 (Figure 3.2b) that is about 30% of that for 0 to 2000 m over the length of the record (Levitus et al., 2012). Ocean heat uptake from 700 to 2000 m likely continues unabated since 2003 (Figure 3.2b); as a result, ocean heat content from 0 to 2000 m shows less slowing after 2003 than does 0 to 700 m heat content (Levitus et al., 2012). Global sampling of the ocean below 2000 m is limited to a number of repeat oceanographic transects, many occupied only in the last few decades (Figure 3.3b), and several time-series stations, some of 0.05 0 0.05 which extend over decades. This sparse sampling in space and time (°C per decade) 3 makes assessment of global deep ocean heat content variability less Figure 3.3 | (a) Areal mean warming rates (C per decade) versus depth (thick lines) certain than that for the upper ocean (Ponte, 2012), especially at mid- with 5 to 95% confidence limits (shading), both global (orange) and south of the depths, where vertical gradients are still sufficiently large for transient Sub-Antarctic Front (purple), centred on 1992 2005. (b) Mean warming rates (C per v ­ ariations (ocean eddies, internal waves, and internal tides) to alias decade) below 4000 m (colour bar) estimated for deep ocean basins (thin black out- estimates made from sparse data sets. However, the deep North Atlan- lines), centred on 1992 2005. Stippled basin warming rates are not significantly differ- tic Ocean is better sampled than the rest of the globe, making esti- ent from zero at 95% confidence. The positions of the Sub-Antarctic Front (purple line) and the repeat oceanographic transects from which these warming rates are estimated mates of full-depth deep ocean heat content changes there feasible (thick black lines) also shown. (Data from Purkey and Johnson, 2010.) north of 20N since the mid-1950s (Mauritzen et al., 2012). Based on the limited information available, it is likely that the global In the North Atlantic, strong decadal variability in North Atlantic Deep ocean did not show a significant temperature trend between 2000 and Water (NADW) temperature and salinity (Wang et al., 2010), largely 3000 m depth from about 1992 2005 (Figures 3.2b and 3.3a; Kouketsu associated with the North Atlantic Oscillation (NAO, Box 2.5) (e.g., Yas- et al., 2011). At these depths it has been around a millennium on aver- hayaev, 2007; Sarafanov et al., 2008), complicates efforts to determine age since waters in the Indian and Pacific Oceans were last exposed to long-term trends from the historical record. Heat content in the North air sea interaction (Gebbie and Huybers, 2012). Atlantic north of 20°N from 2000 m to the ocean floor increased slight- ly from 1955 to 1975, and then decreased more strongly from 1975 Warming from 1992 to 2005 is likely greater than zero from 3000 m to 2005 (Mauritzen et al., 2012), with a net cooling trend of 4 TW to the ocean floor (Figures 3.2b and 3.3a; Kouketsu et al., 2011), espe- from 1955 2005 estimated from a linear fit. The global trend estimate cially in recently formed Antarctic Bottom Water (AABW). South of the below 2000 m is +35 TW from 1992 to 2005 (Purkey and Johnson, Sub-Antarctic Front (Figure 3.3b), much of the water column warmed 2010), with strong warming in the Southern Ocean. between 1992 and 2005 (Purkey and Johnson, 2010). Globally, deep warming rates are highest near 4500 m (Figure 3.3a), usually near 3.2.5 Conclusions the sea floor where the AABW influence is strongest, and attenuate towards the north (Figure 3.3b), where the AABW influence weakens. It is virtually certain that the upper ocean (0 to 700 m) warmed from Global scale abyssal warming on relatively short multi-decadal time 1971 to 2010. This result is supported by three independent and con- scales is possible because of communication of signals by planetary sistent methods of observation including (1) multiple analyses of waves originating within the Southern Ocean, reaching even such subsurface temperature measurements described here; (2) SST data remote regions as the North Pacific (Kawano et al., 2010; Masuda et (Section 2.4.2) from satellites and in situ measurements from surface al., 2010). This AABW warming may partly reflect a recovery from cool drifters and ships; and (3) the record of sea level rise, which is known conditions induced by the 1970s Weddell Sea Polynya (Robertson et to include a substantial component owing to thermosteric expansion al., 2002), but further north, in the Vema Channel of the South Atlantic, (Section 3.7 and Chapter 13). The warming rate is 0.11 [0.09 to 0.13]°C observations since 1970 suggest strong bottom water warming did not per decade in the upper 75 m, decreasing to about 0.015°C per decade commence there until about 1991 (Zenk and Morozov, 2007). by 700 m. It is very likely that surface intensification of the warming 263 Chapter 3 Observations: Ocean Box 3.1 | Change in Global Energy Inventory The Earth has been in radiative imbalance, with less energy exiting the top of the atmosphere than entering, since at least about 1970 (Murphy et al., 2009; Church et al., 2011; Levitus et al., 2012). Quantifying this energy gain is essential for understanding the response of the climate system to radiative forcing. Small amounts of this excess energy warm the atmosphere and continents, evaporate water and melt ice, but the bulk of it warms the ocean (Box 3.1, Figure 1). The ocean dominates the change in energy because of its large mass and high heat capacity compared to the atmosphere. In addition, the ocean has a very low albedo and absorbs solar radiation much more readily than ice. The global atmospheric energy change inventory accounting for specific heating and water evaporation is estimated by combining satellite estimates for temperature anomalies in the lower troposphere (Mears and Wentz, 2009a; updated to version 3.3) from 70°S to 82.5°N and the lower stratosphere (Mears and Wentz, 2009b; updated to version 3.3) from 82.5°S to 82.5°N weighted by the ratio of the portions of atmospheric mass they sample (0.87 and 0.13, respectively). These temperature anomalies are converted to energy changes using a total atmospheric mass of 5.14 × 1018 kg, a mean total water vapor mass of 12.7 × 1015 kg (Trenberth and Smith, 2005), a heat capacity of 1 J g 1 °C 1, a latent heat of vaporization of 2.464 J kg 1 and a fractional increase of integrated water vapor con- tent of 0.075 °C 1 (Held and Soden, 2006). Smaller changes in potential and kinetic energy are considered negligible. Standard 300 deviations for each year of data are used for uncertainties, and Upper ocean the time series starts in 1979. The warming trend from a linear fit Deep ocean from 1979 to 2010 amounts to 2 TW (1 TW = 1012 watts). 250 Ice Land Atmosphere 3 The global average rate of continental warming and its uncer- Uncertainty tainty has been estimated from borehole temperature profiles 200 from 1500 to 2000 at 50-year intervals (Beltrami et al., 2002). The 1950 2000 estimate of land warming, 6 TW, is extended into the first decade of the 21st century, although that extrapolation 150 is almost certainly an underestimate of the energy absorbed, as Energy (ZJ) land surface air temperatures for years since 2000 are some of the warmest on record (Section 2.4.1). 100 All annual ice melt rates (for glaciers and ice-caps, ice sheets and sea ice from Chapter 4) are converted into energy change 50 using a heat of fusion (334 × 103 J kg 1) and density (920 kg m 3) for freshwater ice. The heat of fusion and density of ice may vary, but only slightly among the different ice types, and warm- 0 ing the ice from sub-freezing temperatures requires much less energy than that to melt it, so these second-order contributions are neglected here. The linear trend of energy storage from 1971 50 to 2010 is 7 TW. For the oceans, an estimate of global upper (0 to 700 m depth) 100 1980 1990 2000 2010 ocean heat content change using ocean statistics to extrapo- Year late to sparsely sampled regions and estimate uncertainties (Domingues et al., 2008) is used (see Section 3.2), with a linear Box 3.1, Figure 1 | Plot of energy accumulation in ZJ (1 ZJ = 1021 J) within trend from 1971 to 2010 of 137 TW. For the ocean from 700 to distinct components of the Earth s climate system relative to 1971 and from 1971 2000 m, annual 5-year running mean estimates are used from to 2010 unless otherwise indicated. See text for data sources. Ocean warming 1970 to 2009 and annual estimates for 2010 2011 (Levitus et (heat content change) dominates, with the upper ocean (light blue, above 700 m) al., 2012). For the ocean from 2000 m to bottom, a uniform rate contributing more than the mid-depth and deep ocean (dark blue, below 700 m; including below 2000 m estimates starting from 1992). Ice melt (light grey; for of energy gain of 35 [6 to 61] TW from warming rates centred on glaciers and ice caps, Greenland and Antarctic ice sheet estimates starting from 1992 2005 (Purkey and Johnson, 2010) is applied from 1992 to 1992, and Arctic sea ice estimate from 1979 to 2008); continental (land) warming 2011, with no warming below 2000 m assumed prior to 1992. (orange); and atmospheric warming (purple; estimate starting from 1979) make Their 5 to 95% uncertainty estimate may be too small, as it smaller contributions. Uncertainty in the ocean estimate also dominates the total (continued on next page) uncertainty (dot-dashed lines about the error from all five components at 90% confidence intervals). 264 Observations: Ocean Chapter 3 Box 3.1 (continued) assumes the usually sparse sampling in each deep ocean basin analysed is representative of the mean trend in that basin. The linear trend for heating the ocean below 700 m is 62 TW for 1971 2010. It is virtually certain that the Earth has gained substantial energy from 1971 to 2010 the estimated increase in energy inventory between 1971 and 2010 is 274 [196 to 351] ZJ (1 ZJ = 1021 J), with a rate of 213 TW from a linear fit to the annual values over that time period (Box 3.1, Figure 1). An energy gain of 274 ZJ is equivalent to a heating rate of 0.42 W m-2 applied continuously over the surface area of the earth (5.10 × 1014 m2). Ocean warming dominates the total energy change inventory, accounting for roughly 93% on average from 1971 to 2010 (high confidence). The upper ocean (0-700 m) accounts for about 64% of the total energy change inventory. Melting ice (including Arctic sea ice, ice sheets and glaciers) accounts for 3% of the total, and warming of the continents 3%. Warming of the atmosphere makes up the remaining 1%. The 1971 2010 estimated rate of oceanic energy gain is 199 TW from a linear fit to data over that time period, implying a mean heat flux of 0.55 W m 2 across the global ocean surface area (3.60 × 1014 m2). The Earth s net estimated energy increase from 1993 to 2010 is 163 [127 to 201] ZJ with a trend estimate of 275 TW. The ocean portion of the trend for 1993 2010 is 257 TW, equivalent to a mean heat flux into the ocean of 0.71 W m 2 over the global ocean surface area. signal increased the thermal stratification of the upper ocean by about has increased since the 1970s, at a rate consistent with the observed 4% (between 0 and 200 m depth) from 1971 to 2010. It is also likely warming (Sections 2.4.4, 2.5.5 and 2.5.6). that the upper ocean warmed over the first half of the 20th century, based again on these same three independent and consistent, although It has not been possible to detect robust trends in regional precipita- much sparser, observations. Deeper in the ocean, it is likely that the tion and evaporation over the ocean because observations over the 3 waters from 700 to 2000 m have warmed on average between 1957 ocean are sparse and uncertain (Section 3.4.2). Ocean salinity, on the and 2009 and likely that no significant trend was observed between other hand, naturally integrates the small difference between these 2000 and 3000 m from 1992 to 2005. It is very likely that the deep two terms and has the potential to act as a rain gauge for precip- (2000 m to bottom) North Atlantic Ocean north of 20°N warmed from itation minus evaporation over the ocean (e.g., Lewis and Fofonoff, 1955 to 1975, and then cooled from 1975 to 2005, with an overall 1979; Schmitt, 2008; Yu, 2011; Pierce et al., 2012; Terray et al., 2012; cooling trend. It is likely that most of the water column south of the Section 10.4). Diagnosis and understanding of ocean salinity trends Sub-Antarctic Front warmed at a rate of about 0.03°C per decade from is also important because salinity changes, like temperature changes, 1992 to 2005, and waters of Antarctic origin warmed below 3000 m at affect circulation and stratification, and therefore the ocean s capacity a global average rate approaching 0.01°C per decade at 4500 m over to store heat and carbon as well as to change biological productivity. the same time period. For the deep ocean. Sparse sampling is the larg- Salinity changes also contribute to regional sea level change (Steele est source of uncertainty below 2000 m depth. and Ermold, 2007). In AR4, surface and subsurface salinity changes consistent with a 3.3 Changes in Salinity and Freshwater Content warmer climate were highlighted, based on linear trends for the period between 1955 and 1998 in the historical global salinity data set (Boyer 3.3.1 Introduction et al., 2005) as well as on more regional studies. In the early few dec- ades the salinity data distribution was good in the NH, especially the The ocean plays a pivotal role in the global water cycle: about 85% of North Atlantic, but the coverage was poor in some regions such as the the evaporation and 77% of the precipitation occurs over the ocean central South Pacific, central Indian and polar oceans (Appendix 3.A). (Schmitt, 2008). The horizontal salinity distribution of the upper ocean However, Argo provides much more even spatial and temporal cover- largely reflects this exchange of freshwater, with high surface salinity age in the 2000s. These additional observations, improvements in the generally found in regions where evaporation exceeds precipitation, availability and quality of historical data and new analysis approaches and low salinity found in regions of excess precipitation and runoff now allow a more complete assessment of changes in salinity. (Figure 3.4a,b). Ocean circulation also affects the regional distribution of surface salinity. The subduction (Section 3.5) of surface waters trans- Salinity refers to the weight of dissolved salts in a kilogram of sea- fers the surface salinity signal into the ocean interior, so that subsurface water. Because the total amount of salt in the ocean does not change, salinity distributions are also linked to patterns of evaporation, precip- the salinity of seawater can be changed only by addition or removal of itation and continental run-off at the sea surface. Melting and freezing fresh water. All salinity values quoted in the chapter are expressed on of ice (both sea ice and glacial ice) also influence ocean salinity. the Practical Salinity Scale 1978 (PSS78) (Lewis and Fofonoff, 1979). Regional patterns and amplitudes of atmospheric moisture transport could change in a warmer climate, because warm air can contain more moisture (FAQ 3.2). The water vapour content of the troposphere likely 265 Chapter 3 Observations: Ocean Frequently Asked Questions FAQ 3.1 | Is the Ocean Warming? Yes, the ocean is warming over many regions, depth ranges and time periods, although neither everywhere nor constantly. The signature of warming emerges most clearly when considering global, or even ocean basin, averages over time spans of a decade or more. Ocean temperature at any given location can vary greatly with the seasons. It can also fluctuate substantially from year to year or even decade to decade because of variations in ocean currents and the exchange of heat between ocean and atmosphere. Ocean temperatures have been recorded for centuries, but it was not until around 1971 that measurements were sufficiently comprehensive to estimate the average global temperature of the upper several hundred meters of the ocean confidently for any given year. In fact, before the international Argo temperature/salinity profiling float array first achieved worldwide coverage in 2005, the global average upper ocean temperature for any given year was sensitive to the methodology used to estimate it. Global mean upper ocean temperatures have increased over decadal time scales from 1971 to 2010. Despite large uncertainty in most yearly means, this warming is a robust result. In the upper 75 m of the ocean, the global average warming trend has been 0.11 [0.09 to 0.13]°C per decade over this time. That trend generally lessens from the surface to mid-depth, reducing to about 0.04°C per decade by 200 m, and to less than 0.02°C per decade by 500 m. Temperature anomalies enter the subsurface ocean by paths in addition to mixing from above (FAQ3.1, Figure 3 1). Colder hence denser waters from high latitudes can sink from the surface, then spread toward the equator beneath warmer, lighter, waters at lower latitudes. At a few locations the northern North Atlantic Ocean and the Southern Ocean around Antarctica ocean water is cooled so much that it sinks to great depths, even to the sea floor. This water then spreads out to fill much of the rest of the deep ocean. As ocean surface waters warm, these sinking waters also warm with time, increasing temperatures in the ocean interior much more quickly than would downward mixing of surface heating alone. In the North Atlantic, the temperature of these deep waters varies from decade to decade sometimes warming, sometimes cooling depending on prevailing winter atmospheric patterns. Around Antarctica, bottom waters have warmed detectably from about 1992 2005, perhaps due to the strengthening and southward shift of westerly winds around the Southern Ocean over the last several decades. This warming signal in the deepest coldest bottom waters of the world ocean is detectable, although it weakens northward in the Indian, Atlantic and Pacific Oceans. Deep warming rates are generally less pronounced than ocean surface rates (around 0.03C per decade since the 1990s in the deep and bottom waters around Antarctica, and smaller in many other locations). However, they occur over a large volume, so deep ocean warming contributes significantly to the total increase in ocean heat. Estimates of historical changes in global average ocean temperature have become more accurate over the past several years, largely thanks to the recognition, and reduction, of systematic measurement errors. By carefully comparing less accurate measurements with sparser, more accurate ones at adjacent locations and similar times, scientists have reduced some spurious instrumental biases in the historical record. These improvements revealed that the global average ocean temperature has increased much more steadily from year to year than was reported prior to 2008. Nevertheless, the global average warming rate may not be uniform in time. In some years, the ocean appears to warm faster than average; in others, the warming rate seems to slow. The ocean s large mass and high heat capacity allow it to store huge amounts of energy more than 1000 times that in the atmosphere for an equivalent increase in temperature. The Earth is absorbing more heat than it is emitting back into space, and nearly all this excess heat is entering the oceans and being stored there. The ocean has absorbed about 93% of the combined heat stored by warmed air, sea, and land, and melted ice between 1971 and 2010. The ocean s huge heat capacity and slow circulation lend it significant thermal inertia. It takes about a decade for near-surface ocean temperatures to adjust in response to climate forcing (Section 12.5), such as changes in greenhouse gas concentrations. Thus, if greenhouse gas concentrations could be held at present levels into the future, increases in the Earth s surface temperature would begin to slow within about a decade. However, deep ocean temperature would continue to warm for centuries to millennia (Section 12.5), and thus sea levels would continue to rise for centuries to millennia as well (Section 13.5). (continued on next page) 266 Observations: Ocean Chapter 3 FAQ 3.1 (continued) A D B C A Pacific Ocean B C West Atlantic Ocean D Surface Surface ter A n t a r ti c B Wa ee p 2500m 2500m N o rt h A t l a n t i c D ott o m W te te r r a Wa Antarctic B ott o m 5000m 5000m N S S N Arctic Equator Antarctica Antarctica Equator Arctic Surface Surface S ub tropical Waters Subtropical W ater s 3 r 500m ate 500m iate W Intermed ediate Water erm Int 1000m 1000m Equator Equator FAQ 3.1, Figure 1 | Ocean heat uptake pathways. The ocean is stratified, with the coldest, densest water in the deep ocean (upper panels: use map at top for orienta- tion). Cold Antarctic Bottom Water (dark blue) sinks around Antarctica then spreads northward along the ocean floor into the central Pacific (upper left panel: red arrows fading to white indicate stronger warming of the bottom water most recently in contact with the ocean surface) and western Atlantic oceans (upper right panel), as well as the Indian Ocean (not shown). Less cold, hence lighter, North Atlantic Deep Water (lighter blue) sinks in the northern North Atlantic Ocean (upper right panel: red and blue arrow in the deep water indicates decadal warming and cooling), then spreads south above the Antarctic Bottom Water. Similarly, in the upper ocean (lower left panel shows Pacific Ocean detail, lower right panel the Atlantic), cool Intermediate Waters (cyan) sink in sub-polar regions (red arrows fading to white indicating warming with time), before spreading toward the equator under warmer Subtropical Waters (green), which in turn sink (red arrows fading to white indicate stronger warming of the intermediate and subtropical waters most recently in contact with the surface) and spread toward the equator under tropical waters, the warmest and lightest (orange) in all three oceans. Excess heat or cold entering at the ocean surface (top curvy red arrows) also mixes slowly downward (sub-surface wavy red arrows). 3.3.2 Global to Basin-Scale Trends d ­ ominates (Figure 3.4). For example, salinity generally increased in the surface salinity maxima formed in the evaporation-dominated subtrop- The salinity of near-surface waters is changing on global and basin ical gyres. The surface salinity minima at subpolar latitudes and the scales, with an increase in the more evaporative regions and a decrease intertropical convergence zones have generally freshened. Interbasin in the precipitation-dominant regions in almost all ocean basins. salinity differences are also enhanced: the relatively salty Atlantic has become more saline on average, while the relatively fresh Pacific has 3.3.2.1 Sea Surface Salinity become fresher (Figures 3.5 and 3.9). No well-defined trend is found in the subpolar North Atlantic , which is dominated by decadal varia- Multi-decadal trends in sea surface salinity have been documented in bility from atmospheric modes like the North Atlantic Oscillation (NAO, studies published since AR4 (Boyer et al., 2007; Hosoda et al., 2009; Box 2.5). The 50-year salinity trends in Figure 3.4c, both positive and Roemmich and Gilson, 2009; Durack and Wijffels, 2010), confirm- negative, are statistically significant at the 99% level over 43.8% of ing the trends reported in AR4 based mainly on Boyer et al. (2005). the global ocean surface (Durack and Wijffels, 2010); trends were less The spatial pattern of surface salinity change is similar to the distri- significant over the remainder of the surface. The patterns of salinity bution of surface salinity itself: salinity tends to increase in regions change in the complementary Hosoda et al. (2009) study of differences of high mean salinity, where evaporation exceeds precipitation, and between the periods 1960 1989 and 2003 2007 (Figure 3.4d), using a tends to decrease in regions of low mean salinity, where precipitation different methodology, have a point-to-point correlation of 0.64 with 267 Chapter 3 Observations: Ocean (a) (b) 60°N mean SSS 60°N mean E -P 33 0 34 0 30°N 35 30°N 1 1 37 36 34 36 0° 0° 36 37 30°S 36 30°S 1 0 35 35 1 35 0 34 34 60°S 60°S 60°E 120°E 180° 120°W 60°W 0° (PSS78) 60°E 120°E 180° 120°W 60°W 0° (m yr-1) 32 33 34 35 36 37 38 -3 -2 -1 0 1 2 3 (c) (d) 60°N SSS change 60°N SSS diff 30°N 30°N 0 0 0° 0° 0 0 30°S 0 30°S 0 0 0 60°S 60°S 60°E 120°E 180° 120°W 60°W 0° (PSS78) 60°E 120°E 180° 120°W 60°W 0° (PSS78) -0.5 -0.4 -0.3 -0.2 -0.1 0 0.1 0.2 0.3 0.4 0.5 -0.25 -0.2 -0.15 -0.1 -0.05 0 0.05 0.1 0.15 0.2 0.25 Figure 3.4 | (a) The 1955 2005 climatological-mean sea surface salinity (World Ocean Atlas 2009 of Antonov et al., 2010) colour contoured at 0.5 PSS78 intervals (black lines). 3 (b) Annual mean evaporation precipitation averaged over the period 1950 2000 (NCEP) colour contoured at 0.5 m yr 1 intervals (black lines). (c) The 58-year (2008 minus 1950) sea surface salinity change derived from the linear trend (PSS78), with seasonal and El Nino-Southern Oscillation (ENSO) signals removed (Durack and Wijffels, 2010) colour con- toured at 0.116 PSS78 intervals (black lines). (d) The 30-year (2003 2007 average centred at 2005, minus the 1960 1989 average, centred at 1975) sea surface salinity difference (PSS78) (Hosoda et al., 2009) colour contoured at 0.06 PSS78 intervals (black lines). Contour intervals in (c) and (d) are chosen so that the trends can be easily compared, given the different time intervals in the two analyses. White areas in (c) to (d) are marginal seas where the calculations are not carried out. Regions where the change is not significant at the 99% confidence level are stippled in grey. the Durack and Wijffels (2010) results, with significant differences only Freshwater content in the upper 500 m very likely changed, based in limited locations such as adjacent to the West Indies, Labrador Sea, on the World Ocean Database 2009 (Boyer et al., 2009), analyzed by and some coastlines (Figure 3.4c and d). Durack and Wijffels (2010) and independently as an update to Boyer et al. (2005) for 1955 2010 (Figure 3.5a, b, e, f). Both show freshening in It is very likely that the globally averaged contrast between regions of the North Pacific, salinification in the North Atlantic south of 50°N and high and low salinity relative to the global mean salinity has increased. salinification in the northern Indian Ocean (trends significant at 90% The contrast between high and low salinity regions, averaged over the confidence). A significant freshening is observed in the circumpolar ocean area south of 70°N, increased by 0.13 [0.08 to 0.17] PSS78 from Southern Ocean south of 50S. 1950 to 2008 using the data set of Durack and Wijffels (2010) , and by 0.12 [0.10 to 0.15] PSS78 using the data set of Boyer et al. (2009) with Density layers that are ventilated (connected to the sea surface) in the range reported in brackets signifying a 99% confidence interval precipitation-dominated regions have freshened, while those venti- (Figure 3.21d). lated in evaporation-dominated regions have increased in salinity, compatible with an enhancement of the mean surface freshwater flux 3.3.2.2 Upper Ocean Subsurface Salinity pattern (Helm et al., 2010). In addition, where warming has caused surface outcrops of density layers to move (poleward) into higher Compatible with observed changes in surface salinity, robust mul- salinity surface waters, the subducted salinity in the density layers has ti-decadal trends in subsurface salinity have been detected (Boyer et increased; where outcrops have moved into fresher surface waters, the al., 2005; Boyer et al., 2007; Steele and Ermold, 2007; Böning et al., subducted salinity decreased (Durack and Wijffels, 2010). Vertical and 2008; Durack and Wijffels, 2010; Helm et al., 2010; Wang et al., 2010). lateral shifts of density surfaces, due to both changes in water mass Global, zonally averaged multi-decadal salinity trends (1950 2008) in renewal rates and wind-driven circulation, have also contributed to the upper 500 m (Figures 3.4, 3.5, 3.9 and Section 3.5) show salinity the observed subsurface salinity changes (Levitus, 1989; Bindoff and increases at the salinity maxima of the subtropical gyres, freshening McDougall, 1994). of the low-salinity intermediate waters sinking in the Southern Ocean (Subantarctic Mode Water and Antarctic Intermediate Water) and A change in total, globally integrated freshwater content and salini- North Pacific (North Pacific Intermediate Water). On average, the Pacific ­ ty requires an addition or removal of freshwater; the only significant freshened, and the Atlantic became more saline. These trends, shown source is land ice (ice sheets and glaciers). The estimate of change in in Figures 3.5 and 3.9, are significant at a 95% confidence interval. globally averaged salinity and freshwater content remains smaller than 268 Observations: Ocean Chapter 3 Frequently Asked Questions FAQ 3.2 | Is There Evidence for Changes in the Earth s Water Cycle? The Earth s water cycle involves evaporation and precipitation of moisture at the Earth s surface. Changes in the atmosphere s water vapour content provide strong evidence that the water cycle is already responding to a warming climate. Further evidence comes from changes in the distribution of ocean salinity, which, due to a lack of long-term observations of rain and evaporation over the global oceans, has become an important proxy rain gauge. The water cycle is expected to intensify in a warmer climate, because warmer air can be moister: the atmosphere can hold about 7% more water vapour for each degree Celsius of warming. Observations since the 1970s show increases in surface and lower atmospheric water vapour (FAQ 3.2, Figure 1a), at a rate consistent with observed warming. Moreover, evaporation and precipitation are projected to intensify in a warmer climate. Recorded changes in ocean salinity in the last 50 years support that projection. Seawater contains both salt and fresh water, and its salinity is a function of the weight of dissolved salts it contains. Because the total amount of salt which comes from the weathering of rocks does not change over human time scales, seawater s salinity can only be altered over days or centuries by the addition or removal of fresh water. The atmosphere connects the ocean s regions of net fresh water loss to those of fresh water gain by moving evaporated water vapour from one place to another. The distribution of salinity at the ocean surface largely reflects the spatial pattern of evaporation minus precipitation, runoff from land, and sea ice processes. There is some shifting of the patterns relative to each other, because of the ocean s currents. Subtropical waters are highly saline, because evaporation exceeds rainfall, whereas seawater at high latitudes 3 and in the tropics where more rain falls than evaporates is less so (FAQ 3.2, Figure 1b, d). The Atlantic, the saltiest ocean basin, loses more freshwater through evaporation than it gains from precipitation, while the Pacific is nearly neutral (i.e., precipitation gain nearly balances evaporation loss), and the Southern Ocean (region around Antarctica) is dominated by precipitation. Changes in surface salinity and in the upper ocean have reinforced the mean salinity pattern. The evaporation- dominated subtropical regions have become saltier, while the precipitation-dominated subpolar and tropical regions have become fresher. When changes over the top 500 m are considered, the evaporation-dominated Atlantic has become saltier, while the nearly neutral Pacific and precipitation-dominated Southern Ocean have become fresher (FAQ 3.2, Figure 1c). Observing changes in precipitation and evaporation directly and globally is difficult, because most of the exchange of fresh water between the atmosphere and the surface happens over the 70% of the Earth s surface covered by ocean. Long-term precipitation records are available only from over the land, and there are no long-term measurements of evaporation. Land-based observations show precipitation increases in some regions, and decreases in others, making it difficult to construct a globally integrated picture. Land-based observations have shown more extreme rainfall events, and more flooding associated with earlier snow melt at high northern latitudes, but there is strong regionality in the trends. Land-based observations are so far insufficient to provide evidence of changes in drought. Ocean salinity, on the other hand, acts as a sensitive and effective rain gauge over the ocean. It naturally reflects and smoothes out the difference between water gained by the ocean from precipitation, and water lost by the ocean through evaporation, both of which are very patchy and episodic. Ocean salinity is also affected by water runoff from the continents, and by the melting and freezing of sea ice or floating glacial ice. Fresh water added by melting ice on land will change global-averaged salinity, but changes to date are too small to observe. Data from the past 50 years show widespread salinity changes in the upper ocean, which are indicative of systematic changes in precipitation and runoff minus evaporation, as illustrated in FAQ 3.2, Figure 1. FAQ 3.2 is based on observations reported in Chapters 2 and 3, and on model analyses in Chapters 9 and 12. (continued on next page) 269 Chapter 3 Observations: Ocean FAQ 3.2 (continued) 1.6 (a) Trend in 0.8 total precipitable water vapour 0.0 (1988-2010) 0.8 1.6 (kg m-2 per decade) 100 (b) Mean evaporation 0 minus precipitation 3 100 (cm yr-1) 0.8 (c) Trend in 0.4 surface salinity 0.0 (1950-2000) 0.4 0.8 (PSS78 per decade) 37 (d) Mean surface salinity 35 33 31 (PSS78) FAQ 3.2, Figure 1 | Changes in sea surface salinity are related to the atmospheric patterns of evaporation minus precipitation (E P) and trends in total precipitable water: (a) Linear trend (1988 2010) in total precipitable water (water vapor integrated from the Earth s surface up through the entire atmosphere) (kg m 2 per decade) from satellite observations (Special Sensor Microwave Imager) (after Wentz et al., 2007) (blues: wetter; yellows: drier). (b) The 1979 2005 climatological mean net E P (cm yr 1) from meteorological reanalysis (National Centers for Environmental Prediction/National Center for Atmospheric Research; Kalnay et al., 1996) (reds: net evaporation; blues: net precipitation). (c) Trend (1950 2000) in surface salinity (PSS78 per 50 years) (after Durack and Wijffels, 2010) (blues freshening; yellows-reds saltier). (d) The climatological-mean surface salinity (PSS78) (blues: <35; yellows reds: >35). 270 Observations: Ocean Chapter 3 its uncertainty, as was true in the AR4 assessment. For instance, the 60% of the density decrease of 0.004 kg m 3 yr 1 from 1968 to 1998 globally averaged sea surface salinity change from 1950 to 2008 is (Ono et al., 2001). small (+0.003 [ 0.056 to 0.062]) compared to its error estimate (Durack and Wijffels, 2010). Thus a global freshening due to land ice loss has 3.3.3.2 Atlantic Ocean not yet been discerned in global surface salinity change even if it were assumed that all added freshwater were in the ocean s surface layer. The net evaporative North Atlantic has become saltier as a whole over the past 50 years (Figure 3.9; Boyer et al., 2007). The largest increase in 3.3.3 Regional Changes in Upper Ocean Salinity the upper 700 m occurred in the Gulf Stream region (0.006 per decade between 1955 1959 and 2002 2006) (Wang et al., 2010). Salinity Regional changes in ocean salinity are broadly consistent with the increase is also evident following the circulation pathway of Mediter- conclusion that regions of net precipitation (precipitation greater than ranean Outflow Water (Figure 3.9; Fusco et al., 2008). This increase evaporation) have very likely become fresher, while regions of net can be traced back to the western basin of the Mediterranean, where evaporation have become more saline. This pattern is seen in salinity salinity of the deep water increased during the period from 1943 to the trend maps (Figure 3.4); zonally averaged salinity trends and freshwa- mid-2000s (Smith et al., 2008; Vargas-Yánez et al., 2010). ter inventories for each ocean (Figure 3.5); and the globally averaged contrast between regions of high and low salinity (Figure 3.21d). In During the time period between 1955 1959 and 2002 2006 (using the high-latitude regions, higher runoff, increased melting of ice and salinities averaged over the indicated 5-year ranges), the upper 700 changes in freshwater transport by ocean currents have likely also con- m of the subpolar North Atlantic freshened by up to 0.002 per decade tributed to observed salinity changes (Bersch et al., 2007; Polyakov et (Wang et al., 2010), while an increase in surface salinity was found al., 2008; Jacobs and Giulivi, 2010). between the average taken over 1960 1989 and the 5-year average over 2003 2007 (Hosoda et al., 2009). Decadal and multi-decadal 3.3.3.1 Pacific and Indian Oceans variability in the subpolar gyre and Nordic Seas is vigorous and has been related to various climate modes such as the NAO, the Atlantic In the tropical Pacific, surface salinity has declined by 0.1 to 0.3 over multi-decadal oscillation (AMO, Box 2.5), and even El Nino-Southern 3 50 years in the precipitation-dominated western equatorial regions Oscillation (ENSO; Polyakov et al., 2005; Yashayaev and Loder, 2009), and by up to 0.6 to 0.75 in the Intertropical Convergence Zone and obscuring long-term trends. The 1970s to 1990s freshening of the the South Pacific Convergence Zone (Cravatte et al., 2009), while sur- northern North Atlantic and Nordic Seas (Dickson et al., 2002; Curry face salinity has increased by up to 0.1 over the same period in the et al., 2003; Curry and Mauritzen, 2005) reversed to salinification (0 to evaporation-dominated zones in the southeastern and north-central 2000 m depth) starting in the late 1990s (Boyer et al., 2007; Holliday et tropical Pacific (Figure 3.9). The fresh, low-density waters in the warm al., 2008), and the propagation of this signal could be followed along pool of the western equatorial Pacific expanded in area as the surface the eastern boundary from south of 60°N in the Northeast Atlantic to salinity front migrated eastward by 1500 to 2500 km over the period Fram Strait at 79°N (Holliday et al., 2008). Advection has also played 1955 2003 (Delcroix et al., 2007; Cravatte et al., 2009). Similarly, in the a role in moving higher salinity subtropical waters to the subpolar Indian Ocean, the net precipitation regions in the Bay of Bengal and gyre (Hatun et al., 2005; Bersch et al., 2007; Lozier and Stewart, 2008; the warm pool contiguous with the tropical Pacific warm pool have Valdimarsson et al., 2012). The variability of the cross equatorial trans- been freshening by up to 0.1 to 0.2, while the saline Arabian Sea and port contribution to this budget is highly uncertain. Reversals of North south Indian Ocean have been getting saltier by up to 0.2 (Durack and Atlantic surface salinity of similar amplitude and duration to those Wijffels, 2010). observed in the last 50 years are apparent in the early 20th century (Reverdin et al., 2002; Reverdin, 2010). The evaporation-dominated In the North Pacific, the subtropical thermocline has freshened by 0.1 subtropical South Atlantic has become saltier by 0.1 to 0.3 during the since the early 1990s, following surface freshening that began around period from 1950 to 2008 (Hosoda et al., 2009; Durack and Wijffels, 1984 (Ren and Riser, 2010); the freshening extends down through the 2010; Figure 3.4). intermediate water that is formed in the northwest Pacific (Nakano et al., 2007), continuing the freshening documented by Wong et al. 3.3.3.3 Arctic Ocean (1999). Warming of the surface water that subducts to supply the inter- mediate water is one reason for this signal, as the freshwater from the Sea ice in the Arctic has declined significantly in recent decades (Sec- subpolar North Pacific is now entering the subtropical thermocline at tion 4.2), which might be expected to reduce the surface salinity and lower density. increase freshwater content as freshwater locked in multi-year sea ice is released. Generally, strong multi-decadal variability, regional vari- Salinity changes, together with temperature changes (Section 3.2.2), ability, and the lack of historical observations have made it difficult affect stratification; salinity has more impact than temperature in some to assess long-term trends in ocean salinity and freshwater content regions. In the western tropical Pacific, for example, the density chang- for the Arctic as a whole (Rawlins et al., 2010). The signal that is now es from 1970 to 2003 at a trend of 0.013 kg m 3 yr 1, about 60% of emerging, including salinity observations from 2005 to 2010, indicates that due to salinity (Delcroix et al., 2007). The decreasing density trend increased freshwater content, with medium confidence. mainly occurs near the surface only, which should affect stratification across the base of the mixed layer. In the Oyashio region of the west- Over the 20th century (1920 2003) the central Arctic Ocean in the ern North Pacific, salinity decrease near the surface accounts for about upper 150 m became fresher in the 1950s and then more saline by 271 Chapter 3 Observations: Ocean Latitude Latitude Freshwater content (km3 deg-1) 80 60 40 20 0 20 40 60 80 80 60 40 20 0 20 40 60 80 2000 2000 1000 1000 0 0 1000 1000 (a) Atlantic (b) Pacific 0 0.02 0.04 0.01 0 0 0 0.02 0.01 -0.03 0.02 0.03 - -0.01 0.01 -0.02 0.01 0.03 -0.01 -0.05 100 100 0.03 0.03 0.01 0.01 0.01 -0.01 0.01 200 0.02 200 Depth (m) 300 300 0.01 400 0.01 400 -0.01 (c) Atlantic (d) Pacific -0.02 500 500 3 80 60 40 20 0 20 40 60 80 80 60 40 20 0 20 40 60 80 Latitude Latitude Freshwater content (km3 deg-1) 80 60 40 20 0 20 40 60 80 80 60 40 20 0 20 40 60 80 2000 2000 1000 1000 0 0 1000 1000 (e) Indian (f) World 0 0 . 0.01 -0.01 0.02 0.03 100 -0.005 100 -0.03 0.01 0.01 -0.01 0.04 0.01 Depth (m) 200 0.01 200 0.01 -0.02 0.02 -0.01 300 300 400 400 0.01 (g) Indian -0.01 (h) World 500 500 80 60 40 20 0 20 40 60 80 80 60 40 20 0 20 40 60 80 Latitude Latitude Figure 3.5 | Zonally integrated freshwater content changes (FWCC; km3 per degree of latitude) in the upper 500 m over one-degree zonal bands and linear trends (1955 2010) of zonally averaged salinity (PSS78; lower panels) in the upper 500 m of the (a) and (c) Atlantic, (b) and (d) Pacific, (e) and (g) Indian and (f) and (h) World Oceans. The FWCC time period is from 1955 to 2010 (Boyer et al., 2005; blue lines) and 1950 to 2008 (Durack and Wijffels, 2010; red lines). Data are updated from Boyer et al. (2005) and calculations of FWCC are done according to the method of Boyer et al. (2007), using 5-year averages of salinity observations and fitting a linear trend to these averages. Error estimates are 95% confidence intervals. The contour interval of salinity trend in the lower panels is 0.01 PSS78 per decade and dashed contours are 0.005 PSS78 per decade. Red shading indicates values equal to or greater than 0.05 PSS78 per decade and blue shading indicates values equal to or less than 0.005 PSS78 per decade. 272 Observations: Ocean Chapter 3 the early 2000s, with a net small salinification over the whole record transport from regions of net evaporation to regions of net precipita- (Polyakov et al., 2008), while at the Siberian Shelf the river discharge tion. A similar conclusion was reached in AR4 (Bindoff et al., 2007). The increased (Shiklomanov and Lammers, 2009) and the shelf waters water vapour in the troposphere has likely increased since the 1970s, became fresher (Polyakov et al., 2008). due to warming (2.4.4, 2.5.5, 2.5.6; FAQ 3.2). The inferred enhanced pattern of net E P can be related to water vapor increase, although Upper ocean freshening has also been observed regionally in the the linkage is complex (Emori and Brown, 2005; Held and Soden, southern Canada basin from the period 1950 1980 to the period 2006). From 1950 to 2000, the large-scale pattern of surface salinity 1990 2000s (Proshutinsky et al., 2009; Yamamoto-Kawai et al., 2009). has amplified at a rate that is larger than model simulations for the his- These are the signals reflected in the freshwater content trend from torical 20th century and 21st century projections. The observed rate of 1955 to 2010 shown in Figure 3.5a, f: salinification at the highest lati- surface salinity amplification is comparable to the rate expected from tudes and a band of freshening at about 70°N to 80°N. Ice production a water cycle response following the Clausius Clapeyron relationship and sustained export of freshwater from the Arctic Ocean in response (Durack et al., 2012). to winds are suggested as key contributors to the high- latitude salin- ification (Polyakov et al., 2008; McPhee et al., 2009). The contrasting Studies published since AR4, based on expanded data sets and new changes in different regions of the Arctic have been attributed to the analysis approaches, have substantially decreased the level of uncer- effects of Ekman transport, sea ice formation (and melt) and a shift in tainty in the salinity and freshwater content trends (e.g., Stott et al., the pathway of Eurasian river runoff (McPhee et al., 2009; Yamamoto- 2008; Hosoda et al., 2009; Roemmich and Gilson, 2009; Durack and Kawai et al., 2009; Morison et al., 2012). Wijffels, 2010; Helm et al., 2010), and thus increased confidence in the inferred changes of evaporation and precipitation over the ocean. Between the periods 1992 1999 and 2006 2008, not only the cen- tral Arctic Ocean freshened (Rabe et al., 2011; Giles et al., 2012), but 3.3.5 Conclusions also freshening is now observed in all regions including those that were becoming more saline through the early 2000s (updated from Both positive and negative trends in ocean salinity and freshwater con- Polyakov et al., 2008). Moreover, freshwater transport out of the Arctic tent have been observed throughout much of the ocean, both at the 3 has increased in that time period (McPhee et al., 2009). sea surface and in the ocean interior. While similar conclusions were reached in AR4, the recent studies summarized here, based on expand- 3.3.3.4 Southern Ocean ed data sets and new analysis approaches, provide high confidence in the assessment of trends in ocean salinity. It is virtually certain that Widespread freshening (trend of 0.01 per decade, significant at 95% the salinity contrast between regions of high and low surface salinity confidence, from the 1980s to 2000s) of the upper 1000 m of the has increased since the 1950s. It is very likely that since the 1950s, Southern Ocean was inferred by taking differences between modern the mean regional pattern of upper ocean salinity has been enhanced: data (mostly Argo) and a long-term climatology along mean stream- saline surface waters in the evaporation-dominated mid-latitudes have lines (Böning et al., 2008). Decadal variability, although notable, does become more saline, while the relatively fresh surface waters in rain- not overwhelm this trend (Böning et al., 2008). Both a southward shift fall-dominated tropical and polar regions have become fresher. Simi- of the Antarctic Circumpolar Current and water-mass changes contrib- larly, it is very likely that the interbasin contrast between saline Atlan- ute to the observed trends during the period 1992 2009 (Meijers et tic and fresh Pacific surface waters has increased, and it is very likely al., 2011). The zonally averaged freshwater content for each ocean and that freshwater content in the Southern Ocean has increased. There is the world (Figure 3.5) shows this significant Southern Ocean freshen- medium confidence that these patterns in salinity trends are caused by ing, which exceeds other regional trends and is present in each basin increased horizontal moisture transport in the atmosphere, suggesting (Indian, Atlantic and Pacific, Figure 3.9). changes in evaporation and precipitation over the ocean as the lower atmosphere has warmed. 3.3.4 Evidence for Change of the Hydrological Cycle from Salinity Changes Trends in salinity have been observed in the ocean interior as well. It is likely that the subduction of surface water mass anomalies and The similarity between the geographic distribution of significant salin- the movement of density surfaces have contributed to the observed ity and freshwater content trends (Figures 3.4, 3.5 and 3.21) and both salinity changes on depth levels. Changes in freshwater flux and the the mean salinity pattern and the distribution of mean evaporation migration of surface density outcrops caused by surface warming (e.g., precipitation (E P; Figure 3.4) indicates, with medium confidence, that to regions of lower or higher surface salinity) have likely both contrib- the large-scale pattern of net evaporation minus precipitation over the uted to the formation of salinity anomalies on density surfaces. oceans has been enhanced. Whereas the surface salinity pattern could be enhanced by increased stratification due to surface warming, the large-scale changes in column-integrated freshwater content are very 3.4 Changes in Ocean Surface Fluxes unlikely to result from changes in stratification in the thin surface layer. Furthermore, the large spatial scale of the observed changes in fresh- 3.4.1 Introduction water content cannot be explained by changes in ocean ­irculation c such as shifts of gyre boundaries. The observed changes in surface and Exchanges of heat, water and momentum (wind stress) at the sea sur- subsurface salinity require additional horizontal atmospheric water face are important factors for driving the ocean circulation. Changes 273 Chapter 3 Observations: Ocean in the air sea fluxes may result from variations in the driving surface overall uncertainty of the annually averaged global ocean mean for meteorological state variables (air temperature and humidity, SST, each term is expected to be in the range 10 to 20%. In the case of the wind speed, cloud cover, precipitation) and can impact both water- latent heat flux term, this corresponds to an uncertainty of up to 20 W mass formation rates and ocean circulation. Air sea fluxes also influ- m 2. In comparison, changes in global mean values of individual heat ence temperature and humidity in the atmosphere and, therefore, the flux components expected as a result of anthropogenic climate change hydrological cycle and atmospheric circulation. AR4 concluded that, since 1900 are at the level of <2 W m 2 (Pierce et al., 2006). at the global scale, the accuracy of the observations is insufficient to permit a direct assessment of changes in heat flux (AR4 Section 5.2.4). Many new turbulent heat flux data sets have become available since As described in Section 3.4.2, although substantial progress has been AR4 including products based on atmospheric reanalyses, satellite and made since AR4, that conclusion still holds for this assessment. in situ observations, and hybrid or synthesized data sets that combine information from these three different sources. It is not possible to The net air sea heat flux is the sum of two turbulent (latent and sensi- identify a single best product as each has its own strengths and weak- ble) and two radiative (shortwave and longwave) components. Ocean nesses (Gulev et al., 2010); several data sets are summarised here to heat gain from the atmosphere is defined to be positive according to illustrate the key issues. The Hamburg Ocean-Atmosphere Parameters the sign convention employed here. The latent and sensible heat fluxes and Fluxes from Satellite (HOAPS) data product provides global tur- are computed from the state variables using bulk parameterizations; bulent heat fluxes (and precipitation) developed from observations they depend primarily on the products of wind speed and the verti- at microwave and infrared wavelengths (Andersson et al., 2011). In cal near-sea-surface gradients of humidity and temperature respec- common with other satellite data sets it provides globally complete tively. The air sea freshwater flux is the difference of precipitation (P) fields, however, it spans a relatively short period (1987 onwards) and and evaporation (E). It is linked to heat flux through the relationship is thus of limited utility for identifying long-term changes. A significant between evaporation and latent heat flux. Thus, when considering advance in flux data set development methodology is the 1 × 1 degree potential trends in the global hydrological cycle, consistency between grid Objectively Analysed Air Sea heat flux (OAFlux) data set that observed heat budget and evaporation changes is required in areas covers 1958 onwards and for the first time synthesizes state variables 3 where evaporation is the dominant term in hydrological cycle changes. (SST, air temperature and humidity, wind speed) from reanalyses and Ocean surface shortwave and longwave radiative fluxes can be inferred satellite observations, prior to flux calculation (Yu and Weller, 2007). from satellite measurements using radiative transfer models, or com- OAFlux has the potential to minimize severe spatial sampling errors puted using empirical formulae, involving astronomical parameters, that limit the usefulness of data sets based on ship observations alone atmospheric humidity, cloud cover and SST. The wind stress is given by and provides a new resource for temporal variability studies. However, the product of the wind speed squared, and the drag coefficient. For the data sources for OAFlux changed in the 1980s, with the advent of detailed discussion of all terms see, for example, Gulev et al. (2010). satellite data, and the consequences of this change need to be assessed. In an alternative approach, Large and Yeager (2009) modified NCEP1 Atmospheric reanalyses, discussed in Box 2.3, are referred to fre- reanalysis state variables prior to flux calculation using various adjust- quently in the following sections and for clarity the products cited are ment techniques, to produce the hybrid Coordinated Ocean-ice Refer- summarised here: ECMWF 40-year Reanalysis (referred to as ERA40 ence Experiments (CORE) turbulent fluxes for 1948 2007 (Griffies et hereafter, Uppala et al., 2005), ECMWF Interim Reanalysis (ERAI, Dee al., 2009). However, as the adjustments employed to produce the CORE et al., 2011), NCEP/NCAR Reanalysis 1 (NCEP1, Kalnay et al., 1996), fluxes were based on limited periods (e.g., 2000 2004 for wind speed) NCEP/DOE Reanalysis 2 (NCEP2, Kanamitsu et al., 2002), NCEP Climate it is not clear to what extent CORE can be reliably used for studies of Forecast System Reanalysis (CFSR, Saha et al., 2010), NASA Modern interdecadal variability over the 60-year period that it spans. Era Reanalysis for Research and Applications (MERRA, Rienecker et al., 2011) and NOAA-CIRES 20th Century Reanalysis, version 2 (20CRv2, Analysis of OAFlux suggests that global mean evaporation may vary Compo et al., 2011). at inter-decadal time scales, with the variability being relatively small compared to the mean (Yu, 2007; Li et al., 2011; Figure 3.6a). Changing 3.4.2 Air Sea Heat Fluxes data sources, particularly as satellite observations became available in the 1980s, may contribute to this variability (Schanze et al., 2010) and 3.4.2.1 Turbulent Heat Fluxes and Evaporation it is not yet possible to identify how much of the variability is due to changes in the observing system. The latent heat flux variations (Figure The latent and sensible heat fluxes have a strong regional dependence, 3.6b) closely follow those in evaporation (with allowance for the sign with typical values varying in the annual mean from close to zero to definition which results in negative values of latent heat flux corre- 220 W m 2 and 70 W m 2 respectively over strong heat loss sites (Yu sponding to positive values of evaporation) but do not scale exactly and Weller, 2007). Estimates of these terms have many potential sourc- as there is an additional minor dependence on SST through the latent es of error (e.g., sampling issues, instrument biases, changing data heat of evaporation. The large uncertainty ranges that are evident in sources, uncertainty in the flux computation algorithms). These sources each of the time series highlight the difficulty in establishing whether may be spatially and temporally dependent, and are difficult to quan- there is a trend in global ocean mean evaporation or latent heat flux. tify (Gulev et al., 2007); consequently flux error estimates have a high The uncertainty range for latent heat flux is much larger than the 0.5 W degree of uncertainty. Spurious temporal trends may arise as a result m 2 level of net heat flux change expected from the ocean heat content of variations in measurement method for the driving meteorological increase (Box 3.1). Thus, it is not yet possible to use such data sets to state variables, in particular wind speed (Tokinaga and Xie, 2011). The establish global ocean multi-decadal trends in evaporation or latent 274 Observations: Ocean Chapter 3 130 representation in reanalyses, sampling issues and changing satellite 125 (a) sensors (Gulev et al., 2010). As for the turbulent fluxes, the uncertainty Evaporation (cm yr-1) 120 of the annually averaged global ocean mean shortwave or longwave 115 flux is difficult to determine and in the range 10 20%. 110 High accuracy in situ radiometer measurements are available at land 105 sites since the 1960s (see Wild, 2009 Figure 1), allowing analysis of 100 decadal variations in the surface shortwave flux. However, this is not 95 the case over the oceans, where there are very few in situ measure- 90 ments (the exception being moored buoy observations in the tropical 1960 1970 1980 1990 2000 2010 band 15°S to 15°N since the 1990s, Pinker et al., 2009). Consequently, Year for global ocean shortwave analyses it is necessary to rely on satellite observations, which are less accurate (compared to in situ determi- 0 (b) nation of radiative fluxes), restrict the period that can be considered -10 to the mid-1980s onwards, but do provide homogeneous sampling. Detailed discussion of variations in global (land and ocean) averaged Sensible -20 surface solar radiation is given in Section 2.3.3; confidence in variabili- ty of radiation averaged over the global ocean is low owing to the lack -30 of direct observations. Latent and sensible heat (W m-2) Satellite -40 3.4.2.3 Net Heat Flux and Ocean Heat Storage Constraints -50 The most reliable source of information for changes in the global mean Reanalysis net air sea heat flux comes from the constraints provided by analyses 3 -60 of changes in ocean heat storage. The estimate of increase in global ocean heat content for 1971 2010 quantified in Box 3.1 corresponds -70 to an increase in mean net heat flux from the atmosphere to the ocean of 0.55 W m 2. In contrast, closure of the global ocean mean net sur- -80 face heat flux budget to within 20 W m 2 from observation based sur- face flux data sets has still not been reliably achieved (e.g., Trenberth et -90 al., 2009). The increase in mean net air sea heat flux is thus small com- pared to the uncertainties of the global mean. Large and Yeager (2012) -100 Latent examined global ocean average net heat flux variability using the -110 CORE data set over 1984 2006 and concluded that natural variability, 1960 1970 1980 1990 2000 2010 rather than long-term climate change, dominates heat flux changes Year over this relatively short, recent period. Since AR4, some studies have shown consistency in regional net heat flux variability at sub-basin Figure 3.6 | Time series of annual mean global ocean average evaporation (red line, scale since the 1980s, notably in the Tropical Indian Ocean (Yu et al., a), sensible heat flux (green line, b) and latent heat flux (blue line, b) from 1958 to 2012 2007) and North Pacific (Kawai et al., 2008). However, detection of a determined by Yu from a revised and updated version of the original OAFlux data set Yu change in air sea fluxes responsible for the long-term ocean warming and Weller (2007). Shaded bands show uncertainty estimates and the black horizontal bars in (b) show the time periods for which reanalysis output and satellite observations remains beyond the ability of currently available surface flux data sets. were employed in the OAFlux analysis; they apply to both panels. 3.4.3 Ocean Precipitation and Freshwater Flux heat flux at this level. The globally averaged sensible heat flux is small- Assessment of changes in ocean precipitation at multi-decadal time er in magnitude than the latent heat flux and has a smaller absolute scales is very difficult owing to the lack of reliable observation based range of uncertainty (Figure 3.6b). data sets prior to the satellite era. The few studies available rely on reconstruction techniques. Remote sensing based precipitation 3.4.2.2 Surface Fluxes of Shortwave and Longwave Radiation o ­ bservations from the Global Precipitation Climatology Project (GPCP) for 1979 2003 have been used by Smith et al. (2009, 2012) to recon- The surface shortwave flux has a strong latitudinal dependence with struct precipitation for 1900 2008 (over 75°S to 75°N) by employ- typical annual mean values of 250 W m 2 in the tropics. The annual mean ing statistical techniques that make use of the correlation between surface net longwave flux ranges from 30 to 70 W m 2. Estimates of precipitation and both SST and sea level pressure (SLP). Each of the these terms are available from in situ climatologies, from atmospher- reconstructions shows both centennial and decadal variability in global ic reanalyses, and, since the 1980s, from satellite observations. These ocean mean precipitation (Figure 3.7). The trend from 1900 to 2008 is data sets have many potential sources of error that include: uncer- 1.5 mm per month per century according to Smith et al. (2012). For the tainty in the satellite retrieval algorithms and in situ formulae, cloud period of overlap, the reconstructed global ocean mean precipitation 275 Chapter 3 Observations: Ocean 3 3.4.4 Wind Stress Smith et al. (2009) Wind stress fields are available from reanalyses, satellite-based data Smith et al. (2012) Precipitation anomaly (mm per month) 2 sets, and in situ observations. Basin scale wind stress trends at decadal GPCP to centennial time scales have been reported for the Southern Ocean, the North Atlantic and the Tropical Pacific as detailed below. Howev- 1 er, these results are based largely on atmospheric reanalyses, in some cases a single product, and consequently the confidence level is low to 0 medium depending on region and time scale considered. In the Southern Ocean, the majority of reanalyses in the most compre- -1 hensive study available show an increase in the annual mean zonal wind stress (Swart and Fyfe, 2012; Figure 3.8). They find an increase -2 in annual mean wind stress strength in four (NCEP1, NCEP2, ERAI and 20CRv2) of the six reanalyses considered (Figure 3.8). The mean of all reanalyses available at a given time (Figure 3.8, black line) also shows -3 an upward trend from about 0.15 N m 2 in the early 1950s to 0.20 N m 2 in the early 2010s. An earlier study, covering 1979 2009, found a wind stress increase in two of four reanalyses considered (Xue et al., -4 2010). A positive trend of zonal wind stress from 1980 to 2000 was 1900 1910 1920 1930 1940 1950 1960 1970 1980 1990 2000 2010 also reported by Yang et al. (2007) using a single reanalysis (ERA40) Year and found to be consistent with increases in wind speed observations Figure 3.7 | Long-term reconstruction of ocean precipitation anomaly averaged over made on Macquarie Island (54.5°S, 158.9°E) and by the SSM/I satel- 3 75°S to 75°N from Smith et al. (2012): Annual values, thin blue line; low-pass filtered lite (data from 1987 onwards). The wind stress strengthening is found (15-year running mean) values, bold blue line with uncertainty estimates (shading). by Yang et al. (2007) to have a seasonal dependence, with strongest Smith et al. (2009) low-pass filtered values, dotted grey line. Also shown is the cor- trends in January, and has been linked by them to changes in the responding GPCPv2.2 derived ocean precipitation anomaly time series averaged over Southern Annular Mode (SAM, Box 2.5), which has continued to show the same latitudinal range (annual values, thin magenta line; low-pass filtered values, bold magenta line); note Smith et al. (2012) employed an earlier version of the GPCP an upward trend since AR4 (Section 2.7.8). Taken as a whole, these data set leading to minor differences relative to the published time series in their paper. studies provide medium confidence that Southern Ocean wind stress Precipitation anomalies were taken relative to the 1979 2008 period. has strengthened since the early 1980s. A strengthening of the related wind speed field in the Southern Ocean, consistent with the increasing trend in the SAM, has also been noted in Section 2.7.2 from satel- time series show consistent variability with GPCP as is to be expected lite-based analyses and atmospheric reanalyses. (Figure 3.7). Focusing on the Tropical Ocean (25°S to 25°N) for the recent period 1979 2005, Gu et al. (2007) have identified a precipi- In the Tropical Pacific, a reanalysis based study found a strengthening tation trend of 0.06 mm day 1 per decade using GPCP. Concerns have of the trade wind associated wind stress for 1990 2009, but for the been expressed in the cited studies over the need for further work both earlier period 1959 1989 there is no clear trend (Merrifield, 2011). to determine the most reliable approach to precipitation reconstruc- Strengthening of the related Tropical Pacific Ocean wind speed field tion and to evaluate the remotely sensed precipitation data sets. Given in recent decades is evident in reanalysis and satellite based data sets. these concerns, confidence in ocean precipitation trend results is low. Taken together with evidence for rates of sea level rise in the western Pacific larger than the global mean (Section 3.7.3) these studies pro- Evaporation and precipitation fields from atmospheric reanalyses vide medium confidence that Tropical Pacific wind stress has increased can be tested for internal consistency of different components of the since 1990. This increase may be related to the Pacific Decadal Oscilla- hydrological cycle. Specifically, the climatological mean value for E P tion (Merrifield et al., 2012). At centennial time scales, attempts have averaged over the global ocean should equal both the correspond- been made to reconstruct the wind stress field in the Tropical Pacific by ing mean for P E averaged over land and the moisture transport making use of the relationship between wind stress and SLP in combi- from ocean to land. Trenberth et al. (2011) find in an assessment of nation with historic SLP data. Vecchi et al. (2006), using this approach, eight atmospheric reanalyses that this is not the case for each product found a reduction of 7% in zonal mean wind stress across the Equa- c ­ onsidered, and they also report spurious trends due to variations in torial Pacific from the 1860s to the 1990s and related it to a possible the observing system with time. Schanze et al. (2010) examine interan- weakening of the tropical Walker circulation. Observations discussed in nual variability within the OAFlux evaporation and GPCP precipitation Section 2.7.5 indicate that this weakening has largely been offset by a data sets, and find that use of satellite data prior to 1987 is limited by stronger Walker circulation since the 1990s. discontinuities attributable to variations in data type. Thus, it is not yet possible to use such data sets to establish whether there are significant Changes in winter season wind stress curl over the North Atlantic multi-decadal trends in mean E P. However, regional trends in surface from 1950 to early 2000s from NCEP1 and ERA40 have leading modes salinity since the 1950s do suggest trends in E P over the same time that are highly correlated with the NAO and East Atlantic circulation (see Section 3.3.4). patterns; each of these patterns demonstrates a trend towards more 276 Observations: Ocean Chapter 3 positive index values superimposed on pronounced decadal variability 0.25 over the period from the early 1960s to the late 1990s (Sugimoto and Hanawa, 2010). Wu et al. (2012) find a poleward shift over the past 0.225 Wind stress (N m -2 ) century of the zero wind stress curl line by 2.5° [1.5° to 3.5°] in the North Atlantic and 3.0° [1.6° to 4.4°] in the North Pacific from 20CRv2. 0.2 Confidence in these results is low as they are based on a single prod- 0.175 uct, 20CRv2 (the only century time scale reanalysis), which may be affected by temporal inhomogeneity in the number of observations 0.15 assimilated (Krueger et al., 2013). 0.125 3.4.5 Changes in Surface Waves 1950 1960 1970 1980 1990 2000 2010 Year Surface wind waves are generated by wind forcing and are partitioned Figure 3.8 | Time series of annual average maximum zonal-mean zonal wind stress into two components, namely wind sea (wind-forced waves propagat- (N m 2) over the Southern Ocean for various atmospheric reanalyses: CFSR (orange), ing slower than surface wind) and swell (resulting from the wind sea NCEP1 (cyan), NCEP2 (red), ERAI (dark blue), MERRA (green), 20CR (grey), and mean development and propagating typically faster than surface wind). Sig- of all reanalyses at a given time (thick black), see Box 2.3 for details of reanalyses. nificant wave height (SWH) represents the measure of the wind wave Updated version of Figure 1a in Swart and Fyfe (2012), with CFSR, MERRA and the mean of all reanalyses added. field consisting of wind sea and swell and is approximately equal to the highest one-third of wave heights. Local wind changes influence wind sea properties, while changes in remote storms affect swell. Thus, patterns of wind wave and surface wind variability may differ the USA (Komar and Allan, 2008; Ruggiero et al., 2010) and the north- because wind waves integrate wind properties over a larger domain. east Pacific coast (Menéndez et al., 2008). However, Gemmrich et al. As wind waves integrate characteristics of atmospheric dynamics over (2011) found for the Pacific buoys that some trends may be artefacts a range of scales they potentially serve as an indicator of climate var- due to step-type historical changes in the instrument types, observa- 3 iability and change. Global and regional time series of wind waves tional practices and post-processing procedures. Analysis of data from characteristics are available from buoy data, Voluntary Observing Ship a single buoy deployed west of Tasmania showed no significant trend (VOS) reports, satellite measurements and model wave hindcasts. No in the frequency of extreme waves contrary to a significant positive source is superior, as all have their strengths and weaknesses (Sterl trend seen in the ERA40 reanalysis (Hemer, 2010). and Caires, 2005; Gulev and Grigorieva, 2006; Wentz and Ricciardulli, 2011). 3.4.5.3 Changes in Surface Waves from Satellite Data 3.4.5.1 Changes in Surface Waves from Voluntary Observing Satellite altimeter observations provide a further data source for wave Ship and Wave Model Hindcasts Forced by Reanalyses height variability since the mid-1980s. Altimetry is of particular value in the southern hemisphere, and in some poorly sampled regions of AR4 reported statistically significant positive SWH trends during 1900 the northern hemisphere, where analysis of SWH trends remains a 2002 in the North Pacific (up to 8 to 10 cm per decade) and strong- challenge due to limited in situ data and temporal inhomogeneity in er trends (up to 14 cm per decade) from 1950 to 2002 for most of the data used for reanalysis products. In the Southern Ocean, altime- the mid-latitudinal North Atlantic and North Pacific, with insignificant ter-derived SWH and model output both show regions with increas- trends, or small negative trends, in most other regions (Trenberth et ing wave height although these regions cover narrower areas in the al., 2007). Studies since AR4 have provided further evidence for SWH altimeter analysis than in the models and have smaller trends (Hemer trends with more detailed quantification and regionalization. et al., 2010). Young et al. (2011a) compiled global maps of mean and extreme (90th and 99th percentile) surface wind speed and SWH Model hindcasts based on 20CRv2 (spanning 1871 2010) and ERA40 trends for 1985 2008 using altimeter measurements. As the length (spanning 1958 2001) show increases in annual and winter mean of the data set is short, it is not possible to determine whether their SWH in the north-east Atlantic, although the trend magnitudes depend results reflect long-term SWH and wind speed trends, or are part of on the reanalysis products used (Sterl and Caires, 2005; Wang et al., a multi-decadal oscillation. For mean SWH, their analysis shows pos- 2009, 2012; Semedo et al., 2011). Analysis of VOS observations for itive linear trends of up to 10 to 15 cm per decade in some parts of 1958 2002 reveals increases in winter mean SWH over much of the the Southern Ocean (with the strongest changes between 80°E and North Atlantic, north of 45°N, and the central to eastern mid-latitude 160°W) that may reflect the increase in strength of the wind stress North Pacific with typical trends of up to 20 cm per decade (Gulev and since the early 1980s (see Section 3.4.4). Young et al. (2011a) note, Grigorieva, 2006). however, that globally the level of statistical significance is generally low in the mean and 90th percentile SWH trends but increases for the 3.4.5.2 Changes in Surface Waves from Buoy Data 99th percentile. Small negative mean SWH trends are found in many NH ocean regions and these are of opposite sign to, and thus incon- Positive regional trends in extreme wave heights have been reported sistent with, trends in wind speed the latter being primarily positive. at several buoy locations since the late 1970s, with some evidence for Nevertheless, for the 99th SWH percentile, strong positive trends up to seasonal dependence, including at sites on the east and west coasts of 50 to 60 cm per decade were identified in the Southern Ocean, North 277 Chapter 3 Observations: Ocean Atlantic and North Pacific and these are consistent in sign with the supply the subtropical salinity maximum waters found in the upper extreme wind speed trends. Subsequent analysis has shown that the few hundred meters in each basin (Figure 3.9). Relatively fresh water Young et al. (2011a) wind speed trends tend to be biased high when masses produced at higher latitude, where precipitation exceeds evap- compared with microwave radiometer data (Wentz and Ricciardulli, oration, sink and spread equatorward to form salinity minimum layers 2011; Young et al., 2011b). at intermediate depths. Outflow of saline water from the Mediterrane- an Sea and Red Sea, where evaporation is very strong, accounts for the 3.4.6 Conclusions relatively high salinity observed in the upper 1000 m in the subtropical North Atlantic and North Indian basins, respectively. Uncertainties in air sea heat flux data sets are too large to allow detec- tion of the change in global mean net air sea heat flux, on the order Many of the observed changes in zonally averaged salinity, density and of 0.5 W m 2 since 1971, required for consistency with the observed temperature are aligned with the spreading paths of the major water ocean heat content increase. The accuracy of reanalysis and satellite masses (Figure 3.9, trends from 1950 to 2000 shown in colours and observation based freshwater flux products is limited by changing data white contours), illustrating how the formation and spreading of water sources. Consequently, the products cannot yet be reliably used to masses transfer anomalies in surface climate to the ocean interior. The directly identify trends in the regional or global distribution of evapora- strongest anomalies in a water mass are found near its source region. tion or precipitation over the oceans on the time scale of the observed For instance, bottom and deep water anomalies are strongest in the salinity changes since 1950. Southern Ocean and the northern North Atlantic, with lessening ampli- tudes along the spreading paths of these water masses. In each basin, Basin scale wind stress trends at decadal to centennial time scales the subtropical salinity maximum waters have become more saline, have been observed in the North Atlantic, Tropical Pacific, and South- while the low-salinity intermediate waters have become fresher (Figure ern Oceans with low to medium confidence. These results are based 3.9 a, d, g, j; see also Section 3.3). Strongest warming is observed in largely on atmospheric reanalyses, in some cases a single product, and the upper 100 m, which has warmed almost everywhere, with reduced the confidence level is dependent on region and time scale consid- warming (Atlantic) or regions of cooling (Indian and Pacific) observed 3 ered. The evidence is strongest for the Southern Ocean for which there between 100 and 500 m depth. is medium confidence that zonal mean wind stress has increased in strength since the early 1980s. Warming is observed throughout the upper 2000 m south of 40°S in each basin. Shifts in the location of ocean circulation features can also There is medium confidence based on ship observations and reanalysis contribute to the observed trends in temperature and salinity, as dis- forced wave model hindcasts that mean significant wave height has cussed in Section 3.2. Density decreased throughout most of the upper increased since the 1950s over much of the North Atlantic north of 2000 m of the global ocean (middle column of Figure 3.9). The decrease 45°N, with typical winter season trends of up to 20 cm per decade. in near-surface density (hence increase in stratification) is largest in the Pacific, where warming and freshening both act to reduce density, and smallest in the Atlantic where the salinity and temperature trends have 3.5 Changes in Water-Mass Properties opposite effects on density. 3.5.1 Introduction The remainder of this section focuses on evidence of change in globally relevant intermediate, deep and bottom water masses. To a large degree, water properties are set at the sea surface through interaction between the ocean and the overlying atmosphere (and ice, 3.5.2 Intermediate Waters in polar regions). The water characteristics resulting from these interac- tions (e.g., temperature, salinity and concentrations of dissolved gases 3.5.2.1 North Pacific Intermediate Water and nutrients) are transferred to various depths in the world ocean, depending on the density of the water. Warm, light water masses The North Pacific Intermediate Water (NPIW) has freshened over supply (or ventilate ) the upper ocean at low to mid-latitudes, while the last two decades (Wong et al., 1999; Nakano et al., 2007; Figure the colder, denser water masses formed at higher latitudes supply the 3.9g) and has warmed since the 1950s, as reported in AR4, Chapter 5. intermediate and deep layers of the ocean (see schematic in FAQ 3.1, NPIW in the northwestern North Pacific warmed by 0.5°C from 1955 Figure 1). The formation and subduction of water masses are important ­ to 2004 and is now entering the subtropics at lower density; oxygen for the ocean s capacity to store heat, freshwater, carbon, oxygen and c ­ oncentrations in the NPIW have declined, indicating weaker ventila- other properties relevant to climate. In this section, the evidence for tion (Nakanowatari et al., 2007; Kouketsu et al., 2010). The strongest change in some of the major water masses of the world ocean is trends are in the Sea of Okhotsk, where NPIW is formed, and have assessed. been tentatively linked to increased air temperature and decreased sea-ice extent in winter (Nakanowatari et al., 2007; Figure 3.9i). The zonal-mean distributions of salinity, density, and temperature in each ocean basin (black contours in Figure 3.9) reflect the formation 3.5.2.2 Antarctic Intermediate Water of water masses at the sea surface and their subsequent spreading into the ocean interior. For example, warm, salty waters formed in the In AR4, Chapter 5, Antarctic Intermediate Water (AAIW) was reported regions of net evaporation between 10° and 30° latitude (Figure 3.4b) to have warmed and freshened since the 1960s (Figure 3.9). In most 278 Observations: Ocean Chapter 3 recent studies, usually but not always a dipole pattern was found: roughly 0.5 Sv in 2003 2005 (Rhein et al., 2011), and since 1997, only on isopycnals denser than the AAIW salinity minimum, a warming and less dense LSW was formed compared to the high NAO years before. salinification was observed and on isopycnals lighter than the AAIW There is, however, evidence that formation of denser LSW occurred in salinity minimum, a cooling and freshening trend (Böning et al., 2008; 2008 (Vage et al., 2009; Yashayaev and Loder, 2009), but not in the Durack and Wijffels, 2010; Helm et al., 2010; McCarthy et al., 2011). following years (Yashayaev and Loder, 2009; Rhein et al., 2011). The salinity minimum core of the AAIW also underwent changes con- sistent with these patterns on isopycnals: In 1970 2009, south of 30°S, The strong variability in the formation of UNADW affected significantly the AAIW salinity minimum core showed a strong, large-scale shoal- the heat transfer into the deep North Atlantic (Mauritzen et al., 2012). ing (30 to 50 dbar per decade) and warming (0.05°C to 0.15°C per Substantial heat entered the deep North Atlantic during the low NAO decade), leading to lighter densities (up to 0.03 kg m 3 per decade), years of the 1960s, when salinity was large enough to compensate for while the salinity trends varied regionally. A long-term freshening of the high temperatures, and dense LSW was still formed and exported the AAIW core is found in the southwest Atlantic, southeast Pacific, to the subtropics. and south-central Indian oceans, with salinification south of Africa and Australia. All trends were strongest close to the AAIW formation 3.5.3.2 Lower North Atlantic Deep Water l ­atitude just north of the Antarctic Circumpolar Current (Schmidtko and Johnson, 2012). Dense waters overflowing the sills between Greenland and Scotland supply the Lower North Atlantic Deep Water (LNADW). Both overflows Both an increase in precipitation evaporation and poleward migra- freshened from the mid-1960s to the mid-1990s (Dickson et al., 2008). tion of density surfaces caused by warming have likely contributed The salinity of the Faroe Bank overflow increased by 0.015 to 0.02 to the observed trends (Section 3.3; Böning et al., 2008; Durack and from 1997 to 2004, implying a density increase on the order of 0.01 Wijffels, 2010; Helm et al., 2010; McCarthy et al., 2011). Changes in kg m 3 (Hansen and Osterhus, 2007). The other main overflow, through AAIW properties in particular locations have also been linked to other Denmark Strait, shows large interannual variability in temperature processes, including exchange between the Indian and Atlantic basins and salinity, but no trends for the time period 1996 2011 (Jochum- (McCarthy et al., 2011) and changes in surface forcing related to modes sen et al., 2012). Observations of the transport of the dense overflows 3 of climate variability like ENSO and the SAM (Garabato et al., 2009). are dominated by short-term variability and there is no evidence of Whether these changes in properties also affected the formation rates a trend in the short time series available (see Section 3.6). As both of AAIW cannot be assessed from the available observations. overflow components descend into the North Atlantic, they entrain substantial amounts of ambient subpolar waters to create LNADW. As 3.5.3 Deep and Bottom Waters a whole, the LNADW in the North Atlantic cooled from the 1950s to 2005 (Mauritzen et al., 2012), a signal thus stemming primarily from Deep and bottom layers of the ocean are supplied by roughly equal the entrained waters, possibly an adjustment from an unusually warm volumes of dense water sinking in the northern North Atlantic (Lower period observed in the 1920s and 1930s (Drinkwater, 2006). North Atlantic Deep Water, LNADW) and around Antarctica (Antarctic Bottom Water, AABW) (FAQ 3.1, Figure 1). 3.5.3.3 Antarctic Bottom Water 3.5.3.1 Upper North Atlantic Deep Water The Antarctic Bottom Water (AABW) has warmed since the 1980s or 1990s, most noticeably near Antarctica (Aoki et al., 2005; Rintoul, Upper North Atlantic Deep Water (UNADW) is formed by deep convec- 2007; Johnson et al., 2008a; Purkey and Johnson, 2010; Kouketsu et tion in the Labrador Sea between Canada and Greenland, so is also al., 2011), but with warming detectable into the North Pacific and known as Labrador Sea Water (LSW). It is the shallowest component of North Atlantic Oceans (Johnson et al., 2008b; Kawano et al., 2010). The the NADW, located above the overflow water masses that supply the warming of AABW between the 1990s and 2000s contributed to global Lower North Atlantic Deep Water (LNADW). AR4 Chapter 5 assessed ocean heat uptake (Section 3.2). The global volume of the AABW layer the variability in water mass properties of LSW from the 1950s. Recent decreased by 8.2 [5.6 to 10.8] Sv during the last two decades (John- studies have confirmed the large interannual-to-multi-decadeal varia- son et al., 2008b; Mauritzen et al., 2012; Purkey and Johnson, 2012), bility of LSW properties and provided new information on variability making it more likely than not that at least the export rate of AABW in formation rates and the impact on heat and carbon (Section 3.8.1) from the Southern Ocean declined during this period. uptake by the deep ocean. The sources of AABW in the Indian and Pacific sectors of the Southern During the 1970s and 1980s and especially the 1990s the UNADW has Ocean have freshened in recent decades. The strongest signal (0.03 been cold and fresh. In Figure 3.9A it is the strong freshening signal per decade, between 1970 and 2008) is observed in the Ross Sea and from the 1960s to the 1990s that dominates the trend. This freshen- has been linked to inflow of glacial melt water from the Amundsen ing trend reversed in the late 1990s (Boyer et al., 2007; Holliday et and Bellingshausen Seas (Shepherd et al., 2004; Rignot et al., 2008; al., 2008; see Section 3.3.3.2). Estimates of the LSW formation rate3 Jacobs and Giulivi, 2010). Freshening has been observed in AABW decreased from about 7.6 to 8.9 Sv in 1997 1999 (Kieke et al., 2006) to since the 1970s in the Indian sector (Rintoul, 2007) and between the The formation rate of a water mass is the volume of water per year that is transformed into the density range of this water mass by surface processes (for instance cooling), 3 eventually modified through ocean interior processes (for instance mixing). Formation rates are reported in Sverdrups (Sv). 1 Sv equals 106 m3 s 1. 279 Chapter 3 Observations: Ocean 1990s and 2000s in the Pacific sector (Swift and Orsi, 2012; Purkey 3.5.4 Conclusions and Johnson, 2013). AR4 Chapter 5 concluded that observed changes in upper ocean water In the Weddell Sea (the primary source of AABW in the Atlantic), a masses reflect the combination of long-term trends and interannual to contraction of the bottom water mass was observed between 1984 decadal variability related to climate modes like ENSO, NAO and SAM. and 2008 at the Prime Meridian, accompanied by warming of about The time series are still generally too short and incomplete to distinguish 0.015°C, and by salinity variability on a multi-annual time scale. Tran- decadal variability from long-term trends, but understanding of the sient tracer observations between 1984 and 2011 confirmed that the nature and causes of variability has improved in this assessment. The AABW there has become less well ventilated over that time period. The observed patterns of change in subsurface temperature and salinity changes in the AABW, however, seem to be caused by the much strong- (Sections 3.2 and 3.3) are consistent with understanding of how and er trends observed in the Warm Deep Water, as WDW is entrained into where water masses form, enhancing the level of confidence in the the AABW while sinking to the bottom, and not by changes in the assessment of the observed changes. AABW formation rate (Huhn et al., 2008; Huhn et al., 2013). 0 24 25 100 25 36 20 35.5 26 5 200 27 10 35 35 0 28 300 35 Pressure (dbar) 10 5 400 15 36 27 500 34 .5 27 27 10 1000 0 35 5 35 1500 0 2000 ATL A B C 3 0 35 23 25 100 24 25 20 200 26 36 15 300 Pressure (dbar) 35 5 400 0 500 27 1000 35 34.5 5 1500 2000 IND D E F 0 23 25 5 100 24 20 25 200 15 35 300 Pressure (dbar) 26 34 5 400 10 500 27 5 .5 1000 34 34.5 1500 2000 PAC G H I 0 23 25 100 24 25 20 27 0 5 200 10 26 15 35 300 Pressure (dbar) 35 10 5 5 400 500 27 34.5 28 1000 5 0 1500 28 2000 Salinity GLO J Density K Temperature L 70S 50S 30S 10S 10N 30N 50N 70N 50S 30S 10S 10N 30N 50N 70N 50S 30S 10S 10N 30N 50N 70N Latitude Latitude Latitude 0.2 0.15 0.1 0.05 0 0.05 0.1 0.15 0.2 0.3 0.2 0.1 0 0.1 0.2 0.3 1 0.75 0.5 0.25 0 0.25 0.5 0.75 1 (PSS78 per 50 yr) (kg m-3 per 50 yr) (°C per 50 yr) Figure 3.9 | Upper 2000 dbar zonally-averaged linear trend (1950 to 2000) (colours with white contours) of salinity changes (column 1, PSS-78 per 50 yr), neutral density changes (column 2, kg m-3 per 50 yr), and potential temperature changes (column 3, °C per 50 yr), for the Atlantic Ocean (ATL) in row 1, Indian Ocean (IND), row 2, Pacific Ocean (PAC), row 3, and global ocean (GLO) in row 4. Mean fields are shown as black lines (salinity: thick black contours 0.5 PSS-78, thin contours 0.25 PSS-78; neutral density: thick black contours 1.0 kg m-3, thin contours 0.25 kg m-3; potential temperature: thick black contours 5.0°C, thin contours 2.5°C). Trends are calculated on pressure surfaces (1 dbar pressure is approximately equal to 1 m in depth). Regions where the resolved linear trend is not significant at the 90% confidence level are stippled in grey. Salinity results are republished from Durack and Wijffels (2010) with the unpublished temperature and density results from that study also presented. 280 Observations: Ocean Chapter 3 Recent studies showed that the warming of the upper ocean (Section influenced by buoyancy loss and water-mass formation as well as wind 3.2.2) very likely affects properties of water masses in the interior, in forcing. The MOCs are responsible for much of the ocean s capacity direct and indirect ways. Transport of SST and SSS anomalies caused to carry excess heat from the tropics to middle latitudes, and also are by changes in surface heat and freshwater fluxes are brought into the important in the ocean s sequestration of carbon. The connections ocean s interior by contact with the surface ocean (Sections 3.2 and between ocean basins (Section 3.6.5) have also been subject to study 3.3). Vertical and horizontal displacements of isopycnals due to surface because of the significance of inter-basin exchanges in wind-driven warming could change salinity and temperature (Section 3.3). Circu- and thermohaline variability, and also because these can be logistically lation changes (Section 3.6) could also change salinity by shifting the advantageous regions for measurement ( chokepoints ). An assess- outcrop area of this isopycnal in regions with higher (or lower) E P. ment is now possible of the recent mean and the changes in global Properties of several deep and bottom water masses are the product of geostrophic circulation over the previous decade (Figure 3.10, and dis- near surface processes and significant mixing or entrainment of other cussion in Section 3.6.2). In general, changes in the slope of SSH across ambient water masses (Section 3.5). Changes in the properties of the ocean basins indicate changes in the major gyres and the interior entrained or admixed water mass could dominate the observed deep component of MOCs. Changes occurring in high gradient regions such and bottom water mass changes, for instance, in the LNADW and the as the Antarctic Circumpolar Current (ACC) may indicate shifts in the AABW in the Weddell Sea. location of those currents. In the following, the best-studied and most significant aspects of circulation variability and change are assessed From 1950 to 2000, it is likely that subtropical salinity maximum waters including wind-driven circulation in the Pacific, the Atlantic and Ant- have become more saline, while fresh intermediate waters formed at arctic MOCs, and selected interbasin exchanges. higher latitudes have generally become fresher. In the extratropical North Atlantic, it is very likely that the temperature, salinity, and 3.6.2 Wind-Driven Circulation Variability in the formation rate of the UNADW is dominated by strong decadal Pacific Ocean variability related to NAO. It is likely that LNADW has cooled from 1955 to 2005. It is likely that the abyssal layer ventilated by AABW warmed The Pacific covers over half of the global ocean area and its wind-­ over much of the globe since the 1980s or 1990s respectively, and the driven variability is of interest both for its consistency with wind stress 3 volume of cold AABW has been reduced over this time period. observations and for potential air sea feedbacks that could influence climate. Changes in Pacific Ocean circulation since the early 1990s to the present, from the subarctic gyre to the southern ocean, observed 3.6 Changes in Ocean Circulation with satellite ocean data and in situ ocean measurements, are in good agreement and consistent with the expected dynamical response to 3.6.1 Global Observations of Ocean Circulation observed changes in wind stress forcing. Variability The subarctic gyre in the North Pacific poleward of 40°N consists of the The present-day ocean observing system includes global observa- Alaska Gyre to the east and the Western Subarctic Gyre (WSG). Since tions of velocity made at the sea surface by the Global Drifter Pro- 1993, the cyclonic Alaska Gyre has intensified while decreasing in size. gram (Dohan et al., 2010), and at 1000 m depth by the Argo Program The shrinking is seen in the northward shift of the North Pacific Current (Freeland et al., 2010). In addition, Argo observes the geostrophic shear between 2000 m and the sea surface. These two recently imple- mented observing systems, if sustained, will continue to document the large-spatial scale, long-time-scale variability of circulation in the upper ocean. The drifter program achieved its target of 1250 drifters in 2005, and Argo its target of 3000 floats in 2007. Historically, global measurements of ocean circulation are much spars- (cm per decade) er, so estimates of decadal and longer-term changes in circulation are very limited. Since 1992, high-precision satellite altimetry has meas- ured the time variations in sea surface height (SSH), whose horizontal gradients are proportional to the surface geostrophic velocity. In addi- tion, a single global top-to-bottom hydrographic survey was carried out by the World Ocean Circulation Experiment (WOCE), mostly during 1991 1997, measuring geostrophic shear as well as velocity from mid- Figure 3.10 | Mean steric height of the sea surface relative to 2000 decibars (black depth floats and from lowered acoustic Doppler current profilers. A contours at 10-cm intervals) shows the pattern of geostrophic flow for the Argo era subset of WOCE and pre-WOCE transects is being repeated at 5- to (2004 2012) based on Argo profile data, updated from Roemmich and Gilson (2009). 10-year intervals (Hood et al., 2010). The sea surface height (SSH) trend (cm per decade, colour shading) for the period 1993 2011 is based on the AVISO altimetry reference product (Ducet et al., 2000). Spatial gradients in the SSH trend, divided by the (latitude-dependant) Coriolis param- Ocean circulation studies in relation to climate have focused on var- eter, are proportional to changes in surface geostrophic velocity. For display, the mean iability in the wind-driven gyres (Section 3.6.2) and changes in the steric height contours and SSH trends are spatially smoothed over 5° longitude and 3° meridional overturning circulations (MOCs, Sections 3.6.3 and 3.6.4) latitude. 281 Chapter 3 Observations: Ocean (NPC, the high gradient region centred about 40°N in Figure 3.10) and the ACC and the southern limb of the subtropical gyres, by about 1° has been described using the satellite altimeter, XBT/hydrography, and, of latitude per 40 years (Gille, 2008). The warming and corresponding more recently, Argo profiling float data (Douglass et al., 2006; Cum- sea level rise signals are not confined to the South Pacific, but are seen mins and Freeland, 2007). A similar 20-year trend is detected in the globally in zonal mean fields (e.g., at 40°S to 50°S in Figures 3.9 I and WSG, with the northern WSG in the Bering Sea having intensified while 3.10). Alory et al. (2007) describe the broad warming consistent with a the southern WSG south of the Aleutian Islands has weakened. These southward shift of the ACC in the South Indian Ocean. In the Atlantic, decadal changes are attributable to strengthening and northward a southward trend in the location of the Brazil-Malvinas confluence (at expansion of the Pacific High and Aleutian Low atmospheric pressure around 39°S) is described from surface drifters and altimetry by Lump- systems over the subarctic North Pacific Ocean (Carton et al., 2005). kin and Garzoli (2011), and in the location of the Brazil Current sep- aration point from SST and altimetry by Goni et al. (2011). Enhanced The subtropical gyre in the North Pacific also expanded along its surface warming and poleward displacement, globally, of the western southern boundary over the past two decades. The North Equatorial boundary currents is described by Wu et al. (2012). Current (NEC) shifted southward along the 137°E meridian (Qiu and Chen, 2012; also note the SSH increase east of the Philippines in Figure Changes in Pacific Ocean circulation over the past two decades since 3.10 indicating the southward shift). The NEC s bifurcation latitude 1993, observed with medium to high confidence, include intensifica- along the Philippine coast migrated southward from a mean latitude tion of the North Pacific subpolar gyre, the South Pacific subtropical of 13°N in the early 1990s to 11°N in the late 2000s (Qiu and Chen, gyre, and the subtropical cells, plus expansion of the North Pacific sub- 2010). These changes are due to a recent strengthening of the Walker tropical gyre and a southward shift of the ACC. It is likely that these circulation generating a positive wind stress curl anomaly (Tanaka et wind-driven changes are predominantly due to interannual-to-decadal al., 2004; Mitas and Clement, 2005). The enhanced regional sea level variability, and in the case of the subtropical cells represent reversal rise, >10 mm yr 1 in the western tropical North Pacific Ocean (Timmer- of earlier multi-decadal change. Sustained time series of wind stress mann et al., 2010, Figure 3.10), is indicative of the changes in ocean forcing and ocean circulation will permit increased skill in separating circulation. The 20-year time-scale expansion of the North Pacific sub- interannual and decadal variability from long-term trends (e.g., Zhang 3 tropical gyre has high confidence owing to the good agreement seen and Church, 2012). in satellite altimetry, subsurface ocean data and wind stress changes. This sea level increase in the western tropical Pacific also indicates a 3.6.3 The Atlantic Meridional Overturning Circulation strengthening of the equatorward geostrophic limb of the subtropical cells. However, the 20-year increase reversed a longer term weakening The Atlantic Meridional Overturning Circulation (AMOC) consists of the subtropical cells (Feng et al., 2010), illustrating the high difficulty of an upper limb with net northward transport between the surface of separating secular trends from multi-decadal variability. and approximately 1200 m depth, and a lower limb of denser, colder, fresher waters returning southward between 1200 m and 5000 m. The Variability in the mid-latitude South Pacific over the past two decades AMOC is responsible for most of the meridional transport of heat and is characterized by a broad increase in SSH in the 35°S to 50°S band carbon by the mid-latitude NH ocean and associated with the produc- and a lesser increase south of 50°S along the path of the ACC (Figure tion of about half of the global ocean s deep waters in the northern 3.10). These SSH fluctuations are induced by the intensification in the North Atlantic. Coupled climate models find that a slowdown of the SH westerlies (i.e., the SAM; see also Section 3.4.4), generating positive AMOC in the next decades is very likely, though with uncertain magni- and negative wind stress curl anomalies north and south of 50°S. In tude (Section 11.3.3.3). Observations of the AMOC are directed toward response, the southern limb of the South Pacific subtropical gyre has detecting possible long-term changes in its amplitude, its northward intensified in the past two decades (Cai, 2006; Qiu and Chen, 2006; energy transport, and in the ocean s capacity to absorb excess heat Roemmich et al., 2007) along with a southward expansion of the East and greenhouse gases, as well as characterizing short-term variability Australian Current (EAC) into the Tasman Sea (Hill et al., 2008). The and its relationship to changes in forcing. intensification in the South Pacific gyre extends to a greater depth (>1800 m) than that in the North Pacific gyre (Roemmich and Gilson, Presently, variability in the full AMOC and meridional heat flux are 2009). As in the north, the 20-year changes in the South Pacific are being estimated on the basis of direct observations at 26.5°N by the seen with high confidence as they occur consistently in multiple lines RAPID/MOCHA array (Cunningham et al., 2007; Kanzow et al., 2007; of medium and high-quality data. Multiple linear regression analysis of Johns et al., 2011). The array showed a mean AMOC magnitude of 18 the 20-year Pacific SSH field (Zhang and Church, 2012) indicated that +/- 1.0 Sv (+/-1 standard deviation of annual means) between April 2004 interannual and decadal modes explain part of the circulation varia- and April 2009, with 10-day values ranging from 3 to 32 Sv (McCarthy bility seen in SSH gradients, and once the aliasing by these modes is et al., 2012). Earlier estimates of AMOC strength from five shipboard removed, the SSH trends are weaker and more spatially uniform than expeditions over 47 years at 24°N (Bryden et al., 2005) were in the in a single variable trend analysis. range of variability seen by RAPID/MOCHA. For the 1-year period 1 April 2009 to 31 March 2010, the AMOC mean strength decreased The strengthening of SH westerlies is a multi-decadal signal, as seen in to 12.8 Sv. This decrease was manifest in a shift of southward interi- SLP difference between middle and high southern latitudes from 1949 or transport from the deep layers to the upper 1000 m. Although the to 2009 (Gillett and Stott, 2009; also Section 3.4.4). The multi-decadal AMOC weakening in 2009/2010 was large, it subsequently rebounded warming in the Southern Ocean (e.g., Figure 3.1, and Gille, 2008, for and with the large year-to-year changes no trend is detected in the the past 50 to 70 years) is consistent with a poleward displacement of updated time-series (Figure 3.11b). 282 Observations: Ocean Chapter 3 (a) NOAA Cable Sanford cable 50 NOAA dropsonde NR dropsonde (offset by 2 Sv) BN dropsonde (offset by 2 Sv) 45 Pegasus (RSMAS & NOAA) Pegasus as dropsonde Florida current transport (Sv) 40 35 30 25 20 1965 1970 1975 1980 1985 1990 1995 2000 2005 2010 Year (b) 3 20 RAPID/MOCHA: 26 oN: 17.5 +/- 3.8 Sv Transport positive northward (Sv) 10 41oN: 13.8 +/- 3.3 Sv Upper limb 0 Lower limb o MOVE: 16 N: 20.3 +/- 4.8 Sv 10 20 30 2000 2002 2005 2007 2010 2012 Year Figure 3.11 | (a) Volume transport in Sverdrups (Sv; where 1 Sv = 106 m3 s 1) of the Florida Current between Florida and the Bahamas, from dropsonde measurements (symbols) and cable voltages (continuous line), extending the time-series shown in Meinen et al. (2010) (b) Atlantic Meridional Overturning Circulation (AMOC) transport estimates (Sv): 1. RAPID/MOCHA (Rapid Climate Change programme / Meridional Ocean Circulation and Heatflux Array) at 26.5°N (red). The array monitors the top-to-bottom Atlantic wide circula- tion, ensuring a closed mass balance across the section, and hence a direct measure of the upper and lower limbs of the AMOC. 2. 41°N (black): An index of maximum AMOC strength from Argo float measurements in the upper 2000 m only, combined with satellite altimeter data. The lower limb is not measured. 3. Meridional Overturning Variability Experiment (MOVE) at 16°N (blue) measuring transport of North Atlantic Deep Water in the lower limb of the AMOC between 1100 m and 4800 m depth between the Caribbean and the mid-Atlantic Ridge. This transport is thought to be representative of maximum MOC variability based on model validation experiments. The temporal resolution of the three time series is 10 days for 16°N and 26°N and 1 month for 41°N. The data have been 3-month low-pass filtered. Means and standard deviations for the common period of 2 April 2004 to 1 April 2010 are 17.5 +/- 3.8 Sv, 13.8 +/- 3.3 Sv and 20.3 +/- 4.8 Sv (negative indicating the southward lower limb) for 26.5°N, 41°N and 16°N respectively. The means over this period are indicated by the horizontal line on each time series. 283 Chapter 3 Observations: Ocean Observations targeting one limb of the AMOC include Willis (2010) at periods, agree that the range of interannual variability is 5 Sv (Figure 41°N combining velocities from Argo drift trajectories, Argo tempera- 3.11b). These estimates do not have trends, in either the subtropical or ture/salinity profiles, and satellite altimeter data (Figure 3.11b). Here the subpolar gyre. However, the observational record of AMOC varia- the upper limb AMOC magnitude is 15.5 Sv +/- 2.4 from 2002 to 2009 bility is short, and there is insufficient evidence to support a finding of (Figure 3.11b). This study suggests an increase in the AMOC strength by change in the transport of the AMOC. about 2.6 Sv from 1993 to 2010, though with low confidence because it is based on SSH alone in the pre-Argo interval of 1993 2001. At 3.6.4 The Antarctic Meridional Overturning Circulation 16°N, geostrophic array-based estimates of the southward transport of the AMOC s lower limb, in the depth range 1100 to 4700 m, have Sinking of AABW near Antarctica supplies about half of the deep and been made continuously since 2000 (Kanzow et al., 2008). These are abyssal waters in the global ocean (Orsi et al., 1999). AABW spreads the longest continuous measurements of the southward flow of NADW northward as part of the global overturning circulation and ventilates in the western basin. Whereas the period 2000 to mid-2009 suggested the bottom-most portions of much of the ocean. Observed widespread a downward trend (Send et al., 2011), the updated time series (Figure warming of AABW in recent decades (Section 3.5.4) implies a con- 3.11b) has no apparent trend. In the South Atlantic at 35°S, estimates comitant reduction in its northward spread. Reductions of 1 to 4 Sv of the AMOC upper limb were made using 27 high-resolution XBT tran- in northward transports of AABW across 24°N have been estimated sects (2002 2011) and Argo float data (Garzoli et al., 2013). The upper- by geostrophic calculations using repeat oceanographic section data limb AMOC magnitude was 18.1 Sv +/- 2.3 (1 standard deviation based between 1981 and 2010 in the North Atlantic Ocean (Johnson et al., on cruise values), consistent with the NH estimates. 2008b; Frajka-Williams et al., 2011) and between 1985 and 2005 in the North Pacific (Kouketsu et al., 2009). A global full-depth ocean data The continuous AMOC estimates at 16°N, 26.5°N and 41°N have assimilation study shows a reduction of northward AABW flow across time series of length 11, 7, and 9 years respectively (Figure 3.11b). All 35°S of >2 Sv in the South Pacific starting around 1985 and >1 Sv in show a substantial variability of ~3 to 5 Sv for 3-month low-pass time the western South Atlantic since around 1975 (Kouketsu et al., 2011). series, with a peak-to-peak interannual variability of 5 Sv. The short- This reduction is consistent with the contraction in volume of AABW 3 ness of these time series and the relatively large interannual variability (Purkey and Johnson, 2012) discussed in Section 3.5.4. e ­ merging in them suggests that trend estimates be treated cautiously, and no trends are seen at 95% confidence in any of the time series. Several model studies have suggested that changes in wind stress over the Southern Ocean (Section 3.4) may drive a change in the Southern Continuous time series of AMOC components, longer than those of Ocean overturning circulation (e.g., Le Quéré et al., 2007). A recent the complete system at 26.5°N, have been obtained using moored analysis of changes in chlorofluorocarbon (CFC) concentrations in the instrumentation. These include the inflow into the Arctic through Fram Southern Ocean supports the idea that the overturning cell formed Strait (since 1997, Schauer and Beszczynska-Möller, 2009) and through by upwelling of deep water and sinking of intermediate waters has the Barents Sea (since 1997, Ingvaldsen et al., 2004; Mauritzen et al., slowed, but does not quantify the change in transport (Waugh et al., 2011), dense inflows across sills between Greenland and Scotland 2013). (since 1999 and 1995 respectively, Olsen et al., 2008; Jochumsen et al., 2012) and North Atlantic Deep Water carried southward within the 3.6.5 Water Exchange Between Ocean Basins Deep Western Boundary Current at 53°N (since 1997, Fischer et al., 2010) and at 39°N (Line W, since 2004, Toole et al., 2011). The longest 3.6.5.1 The Indonesian Throughflow time series of observations of ocean transport in the world (dropsonde and cable voltage measurements in the Florida Straits), extend from The transport of water from the Pacific to the Indian Ocean via the the mid-1960s to the present (Meinen et al., 2010), with small decad- Indonesian archipelago is the only low-latitude exchange between al variability of about 1 Sv and no evidence of a multi-decadal trend oceans, and is significant because it is a fluctuating sink/source for (Figure 3.11a). Similarly, none of the other direct, continuous transport very warm tropical water in the two oceans. The Indonesian Through- estimates of single components of the AMOC exhibit long-term trends flow (ITF) transport has been estimated from hydrographic and XBT at 95% significance. transects between Australia and Indonesia, and as a synthesis of these together with satellite altimetry, wind stress, and other data (Wunsch, Indirect estimates of the annual average AMOC strength and variability 2010), and from moorings in the principal Indonesian passages. The can be made (Grist et al., 2009; Josey et al., 2009) from diapycnal trans- most comprehensive observations were obtained in 2004 2006 in ports driven by air sea fluxes (NCEP-NCAR reanalysis fields from 1960 three passages by the INSTANT mooring array (Sprintall et al., 2009), to 2007) or by inverse techniques (Lumpkin and Speer, 2007). Decadal and show a westward transport of 15.0 (+/-4) Sv. For the main pas- fluctuations of up to 2 Sv are seen, but no trend. Consistent with Grist sage, Makassar Strait, Susanto et al. (2012) find 13.3 (+/-3.6) Sv in the et al. (2009), the sea level index of the strength of the AMOC, based on period 2004 2009, with small year-to-year differences. On a longer several coherent western boundary tide gauge records between 39°N time scale, the Wunsch (2010) estimate for 1992 2007 was 11.5 Sv and 43°N at the American coast (Bingham and Hughes, 2009) shows (+/-2.4) westward, and thus consistent with INSTANT. Wainwright et al. no long-term trend from 1960 to 2007. (2008) analyzed data between Australia and Indonesia  beginning in the early 1950s, and found a change in the slope of the thermocline In summary, measurements of the AMOC and of circulation elements for data before and after 1976, indicating a decrease in geostrophic contributing to it, at various latitudes and covering different time transport by 23%, consistent with a weakening of the tradewinds (e.g., 284 Observations: Ocean Chapter 3 Vecchi et al. (2006), who described a downward trend in the Walker cir- 3.6.6 Conclusions culation since the late 19th century). Other transport estimates based on the IX1 transect show correlation with ENSO variability (Potemra Recent observations have greatly increased the knowledge of the and Schneider, 2007) and no significant trend for the period since 1984 amplitude of variability in major ocean circulation systems on time having continuous sampling along IX1 (Sprintall et al., 2002). Overall, scales from years to decades. It is very likely that the subtropical gyres the limited evidence provides low confidence that a trend in ITF trans- in the North Pacific and South Pacific have expanded and strength- port has been observed. ened since 1993, but it is about as likely as not that this reflects a decadal oscillation linked to changes in wind forcing, including chang- 3.6.5.2 The Antarctic Circumpolar Current es in winds associated with the modes of climate variability. There is no evidence for a long-term trend in the AMOC amplitude, based on There is medium confidence that the westerly winds in the Southern a decade of continuous observations plus several decades of sparse Ocean have increased since the early 1980s (Section 3.4.4), associated hydrographic transects, or in the longer records of components of the with a positive trend in the SAM (Marshall, 2003); also see Sections AMOC such as the Florida Current (since 1965), although there are 3.4.4 and 3.6.3). Although a few observational studies have found large interannual fluctuations. Nor is there evidence of a trend in the evidence for correlation between SAM and ACC transport on subsea- transports of the ITF (over about 20 years), the ACC (about 30 years sonal to interannual scales (e.g., Hughes et al., 2003; Meredith et al., sparsely sampled), or between the Atlantic and Nordic Seas (about 20 2004), there is no significant observational evidence of an increase in years). Given the short duration of direct measurements of ocean cir- ACC transport associated with the multi-decadal trend in wind forcing culation, we have very low confidence that multi-decadal trends can over the Southern Ocean. Repeat hydrographic sections spread une- be separated from decadal variability. venly over 35 years in Drake Passage (e.g., Cunningham et al., 2003; Koshlyakov et al., 2007, 2011; Gladyshev et al., 2008), south of Africa (Swart et al., 2008) and south of Australia (Rintoul et al., 2002) reveal 3.7 Sea Level Change, Including Extremes moderate variability but no significant trends in these sparse and dis- continuous records. A comparison of recent Argo data and a long-term 3.7.1 Introduction and Overview of Sea Level 3 climatology showed that the slope of density surfaces (hence ­baroclinic Measurements transport) associated with the ACC had not changed in recent decades (Böning et al., 2008). Eddy-resolving models suggest the ACC trans- Sea level varies as the ocean warms or cools, as water is transferred port is relatively insensitive to trends in wind forcing, consistent with between the ocean and continents, between the ocean and ice sheets, the ACC being in an eddy-saturated state where increases in wind and as water is redistributed within the ocean due to the tides and forcing are compensated by changes in the eddy field (Hallberg and changes in the oceanic and atmospheric circulation. Sea level can rise Gnanadesikan, 2006; Farneti et al., 2010; Spence et al., 2010). While or fall on time scales ranging from hours to centuries, spatial scales there is limited evidence for (or against) multi-decadal changes in from <1 km to global, and with height changes from a few millimeters transport of the ACC, observations of changes in temperature, salinity to a meter or more (due to tides). Sea level integrates and reflects and SSH indicate the current system has shifted poleward (medium multiple climatic and dynamical signals. Measurements of sea level are confidence) (Böning et al., 2008; Gille, 2008; Morrow et al., 2008; Soko- the longest-running ocean observation system. This section assesses lov and Rintoul, 2009; Kazmin, 2012). interannual and longer variations in non-tidal sea level from the instru- mented period (late 18th century to the present). Sections 4.3.3 and 3.6.5.3 North Atlantic/Nordic Seas Exchange 4.4.2 assess contributions of glaciers and ice sheets to sea level, Sec- tion 5.6 assess reconstructions of sea level from the geological record, There is no observational evidence of changes during the past two Section 10.4.3 assesses detection and attribution of human influences decades in the flow across the Greenland Scotland Ridge, which con- on sea level change, and Chapter 13 synthesizes results and assesses nects the North Atlantic with the Norwegian and Greenland Seas. projections of sea level change. Direct current measurements since the mid-1990s have not shown any significant trends in volume transport for any of the three inflow The sea level observing system has evolved over time. There are inter- branches (Osterhus et al., 2005; Hansen et al., 2010; Mauritzen et al., mittent records of sea level at four sites in Northern Europe starting 2011; Jónsson and Valdimarsson, 2012). in the 1700s. By the late 1800s, there were more tide gauges being operated in Northern Europe, on both North American coasts, and in The two primary pathways for the deep southward overflows across Australia and New Zealand in the SH (Appendix 3.A). Tide gauges the Greenland Scotland Ridge are the Denmark Strait and Faroe began to be placed on islands far from continental coasts starting in Bank Channel. Moored measurements of the Denmark Strait overflow the early 20th century, but a majority of deep-ocean islands did not demonstrate significant interannual transport variations (Macrander have an operating tide gauge suitable for climate studies until the et al., 2005; Jochumsen et al., 2012), but the time series is not long early 1970s. enough to detect a multi-decadal trend. Similarly, a 10-year time series of moored measurements in the Faroe Bank channel (Olsen et al., Tide gauge records measure the combined effect of ocean volume 2008) does not show a trend in transport. change and vertical land motion (VLM). For detecting climate related variability of the ocean volume, the VLM signal must be removed. One component that can be accounted for to a certain extent is the VLM 285 Chapter 3 Observations: Ocean associated with glacial isostatic adjustment (GIA) (Peltier, 2001). In significant interannual and decadal-scale fluctuations about the aver- some areas, however, VLM from tectonic activity, groundwater mining, age rate of sea level rise in all records. Different approaches have been or hydrocarbon extraction is greater than GIA (e.g., Wöppelmann et used to compute the mean rate of 20th century global mean sea level al., 2009; King et al., 2012); these effects can be reduced by selecting (GMSL) rise from the available tide gauge data: computing average gauges with no known tectonic or subsidence issues (e.g., Douglas, rates from only very long, nearly continuous records (Douglas, 2001; 2001) or by selecting gauges where GIA models have small differences Holgate, 2007); using more numerous but shorter records and filters to (Spada and Galassi, 2012). More recently, Global Positioning System separate nonlinear trends from decadal-scale quasi-periodic variability (GPS) receivers have been installed at tide gauge sites to measure VLM (Jevrejeva et al., 2006, 2008); neural network methods (Wenzel and as directly as possible (e.g., Wöppelmann et al., 2009; King et al., 2012). Schroeter, 2010); computing regional sea level for specific basins then However, these measurements of VLM are only available since the late 1990s at the earliest, and either have to be extrapolated into the past to (a) apply to older records, or used to identify sites without extensive VLM. 200 Satellite radar altimeters in the 1970s and 1980s made the first nearly 150 New York City, USA Newlyn, UK MSL anomaly (mm) global observations of sea level, but these early measurements were 100 highly uncertain and of short duration. The first precise record began with the launch of TOPEX/Poseidon (T/P) in 1992. This satellite and its 50 successors (Jason-1, Jason-2) have provided continuous measurements of sea level variability at 10-day intervals between approximately +/-66° 0 latitude. Additional altimeters in different orbits (ERS-1, ERS-2, Envi- sat, Geosat Follow-on) have allowed for measurements up to +/-82° -50 latitude and at different temporal sampling (3 to 35 days), although -100 these measurements are not as accurate as those from the T/P and 1880 1900 1920 1940 1960 1980 2000 3 Jason satellites. Unlike tide gauges, altimetry measures sea level rela- (b) Year tive to a geodetic reference frame (classically a reference ellipsoid that 200 coincides with the mean shape of the Earth, defined within a globally realized terrestrial reference frame) and thus will not be affected by 150 San Francisco, USA VLM, although a small correction that depends on the area covered by Sydney, Australia MSL anomaly (mm) the satellite (~0.3 mm yr 1) must be added to account for the change in 100 location of the ocean bottom due to GIA relative to the reference frame 50 of the satellite (Peltier, 2001; see also Section 13.1.2). 0 Tide gauges and satellite altimetry measure the combined effect of ocean warming and mass changes on ocean volume. Although var- -50 iations in the density related to upper-ocean salinity changes cause -100 regional changes in sea level, when globally averaged their effect on 1880 1900 1920 1940 1960 1980 2000 sea level rise is an order of magnitude or more smaller than thermal (c) Year effects (Lowe and Gregory, 2006). The thermal contribution to sea level 200 can be calculated from in situ temperature measurements (Section 3.2). It has only been possible to directly measure the mass compo- 150 Mumbai, India nent of sea level since the launch of the Gravity Recovery and Climate Fremantle, Australia MSL anomaly (mm) Experiment (GRACE) in 2002 (Chambers et al., 2004). Before that, esti- 100 mates were based either on estimates of glacier and ice sheet mass 50 losses or using residuals between sea level measured by altimetry or tide gauges and estimates of the thermosteric component (e.g., Willis 0 et al., 2004; Domingues et al., 2008), which allowed for the estima- tion of seasonal and interannual variations as well. GIA also causes a -50 gravitational signal in GRACE data that must be removed in order to -100 determine present-day mass changes; this correction is of the same 1880 1900 1920 1940 1960 1980 2000 order of magnitude as the expected trend and is still uncertain at the Year 30% level (Chambers et al., 2010). Figure 3.12 | 3-year running mean sea level anomalies (in millimeters) relative to 3.7.2 Trends in Global Mean Sea Level and Components 1900 1905 from long tide gauge records representing each ocean basin from the Permanent Service for Mean Sea Level (PSMSL) (http://www.psmsl.org), obtained May 2011. Data have been corrected for Glacial Isostatic Adjustment (GIA) (Peltier, 2004), Tide gauges with the longest nearly continuous records of sea using values available from http://www.psmsl.org/train_and_info/geo_signals/gia/pel- level show increasing sea level over the 20th century (Figure 3.12; tier/. Error bars reflect the 5 to 95% confidence interval, based on the residual monthly W ­ oodworth et al., 2009; Mitchum et al., 2010). There are, however, variability about the 3-year running mean. 286 Observations: Ocean Chapter 3 (a) (b) 200 70 Church & White, 2011 60 150 Tide gauge Jevrejeva et al., 2008 Altimeter GMSL anomaly (mm) GMSL anomaly (mm) Ray & Douglas, 2011 50 100 40 50 30 20 0 10 -50 0 -100 -10 1880 1900 1920 1940 1960 1980 2000 1992 1994 1996 1998 2000 2002 2004 2006 2008 2010 2012 Year Year (c) (d) 100 15 Sea level (Altimeter) 80 Sea level Mass (GRACE) + Steric (ARGO) 10 Thermosteric component GMSL anomaly (mm) GMSL anomaly (mm) 60 5 40 20 0 3 0 -5 -20 -10 1970 1975 1980 1985 1990 1995 2000 2005 2010 2005 2006 2007 2008 2009 2010 2011 2012 Year Year Figure 3.13 | Global mean sea level anomalies (in mm) from the different measuring systems as they have evolved in time, plotted relative to 5-year mean values that start at (a) 1900, (b) 1993, (c) 1970 and (d) 2005. (a) Yearly average GMSL reconstructed from tide gauges (1900 2010) by three different approaches (Jevrejeva et al., 2008; Church and White, 2011; Ray and Douglas, 2011). (b) GMSL (1993 2010) from tide gauges and altimetry (Nerem et al., 2010) with seasonal variations removed and smoothed with a 60-day running mean. (c) GMSL (1970 2010) from tide gauges along with the thermosteric component to 700 m (3-year running mean) estimated from in situ temperature profiles (updated from Domingues et al., 2008). (d) The GMSL (nonseasonal) from altimetry and that computed from the mass component (GRACE) and steric component (Argo) from 2005 to 2010 (Leuliette and Willis, 2011), all with a 3-month running mean filter. All uncertainty bars are one standard error as reported by the authors. The thermosteric component is just a portion of total sea level, and is not expected to agree with total sea level. averaging (Jevrejeva et al., 2006, 2008; Merrifield et al., 2009; Wöp- models to correct tide gauge measurements results in differences less pelmann et al., 2009); or projecting tide gauge records onto empirical than 0.2 mm yr 1 (one standard error), and rates of GMSL rise com- orthogonal functions (EOFs) computed from modern altimetry (Church puted from uncorrected tide gauges differ from rates computed from et al., 2004; Church and White, 2011; Ray and Douglas, 2011) or EOFs GIA-corrected gauges by only 0.4 mm yr 1 (Spada and Galassi, 2012), from ocean models (Llovel et al., 2009; Meyssignac et al., 2012). Dif- again within uncertainty estimates. This agreement gives increased ferent approaches show very similar long-term trends, but noticeably confidence that the 20th century rate of GMSL rise is not biased high different interannual and decadal-scale variability (Figure 3.13a). Only due to unmodeled VLM at the gauges. the time series from Church and White (2011) extends to 2010, so it is used in the assessment of rates of sea level rise. The rate from 1901 to Satellite altimetry can resolve interannual fluctuations in GMSL better 2010 is 1.7 [1.5 to 1.9] mm yr 1 (Table 3.1), which is unchanged from than tide gauge records because less temporal smoothing is required the value in AR4. Rates computed using alternative approaches over (Figure 3.13b). It is clear that deviations from the long-term trend can the longest common interval (1900 2003) agree with this estimate exist for periods of several years, especially during El Nino (e.g., 1997 within the uncertainty. 1998) and La Nina (e.g., 2011) events (Nerem et al., 1999; Boening et al., 2012; Cazenave et al., 2012). The rate of GMSL rise from 1993 Since AR4, significant progress has been made in quantifying the uncer- 2010 is 3.2 [2.8 to 3.6] mm yr 1 based on the average of altimeter time tainty in GMSL associated with unknown VLM and uncertainty in GIA series published by multiple groups (Ablain et al., 2009; Beckley et al., models. Differences between rates of GMSL rise computed with and 2010; Leuliette and Scharroo, 2010; Nerem et al., 2010; Church and without VLM from GPS are smaller than the estimated uncertainties ­ White, 2011; Masters et al., 2012, Figure 3.13). As noted in AR4, this (Merrifield et al., 2009; Wöppelmann et al., 2009). Use of different GIA rate continues to be statistically higher than that for the 20th ­ entury c 287 Chapter 3 Observations: Ocean (Table 3.1). There is high confidence that this change is real and not required. Considerable progress has been made since AR4, and the an artefact of the different sampling or change in instrumentation, mass component of sea level measured by GRACE has been increas- as the trends estimated over the same period from tide gauges and ing at a rate between 1 and 2 mm yr 1 since 2002 (Willis et al., 2008, altimetry are consistent. Although the rate of GMSL rise has a slightly 2010; Cazenave et al., 2009; Leuliette and Miller, 2009; Chambers et lower trend between 2005 and 2010 (Nerem et al., 2010), this variation al., 2010; Llovel et al., 2010; Leuliette and Willis, 2011). Differences is consistent with earlier interannual fluctuations in the record (e.g., between studies are due partially to the time periods used to com- 1993 1997), mostly attributable to El Nino/La Nina cycles (Box 9.2). pute trends, as there are significant interannual variations in the mass At least 15 years of data are required to reduce the impact of interan- component of GMSL (Willis et al., 2008; Chambers et al., 2010; Llovel nual variations associated with El Nino or La Nina on estimated trends et al., 2010; Boening et al., 2012), but also to substantial differences in (Nerem et al., 1999). GIA corrections applied, of order 1 mm yr 1. Recent evaluations of the GIA correction have found explanations for the difference (Chambers Since AR4, estimates of both the thermosteric component and mass et al., 2010; Peltier et al., 2012), but uncertainty of 0.3 mm yr 1 is still component of GMSL rise have improved, although estimates of the probable. Measurements of sea level from altimetry and the sum of mass component are possible only since the start of the GRACE meas- observed steric and mass components are also consistent at monthly urements in 2002. After correcting for biases in older XBT data [3.2], scales during the time period when Argo data have global distribu- the rate of thermosteric sea level rise in the upper 700 m since 1971 tion (Figure 3.13d), which gives high confidence that the current ocean is 50% higher than estimates used for AR4 (Domingues et al., 2008; observing system is capable of resolving the rate of sea level rise and Wijffels et al., 2008). Because of much sparser upper ocean measure- its components. ments before 1971, we estimate the trend only since then (Section 3.2). The warming of the upper 700 m from 1971 to 2010 caused an 3.7.3 Regional Distribution of Sea Level Change estimated mean thermosteric rate of rise of 0.6 [0.4 to 0.8] mm yr 1 (90% confidence), which is 30% of the observed rate of GMSL rise Large-scale spatial patterns of sea level change are known to high for the same period (Table 3.1; Figure 3.13c). Although still a short precision only since 1993, when satellite altimetry became available 3 record, more numerous, better distributed, and higher quality profile (Figure 3.10). These data have shown a persistent pattern of change measurements from the Argo program are now being used to estimate since the early 1990s in the Pacific, with rates of rise in the Warm Pool the steric component for the upper 700 m as well as for the upper of the western Pacific up to three times larger than those for GMSL, 2000 m (Domingues et al., 2008; Willis et al., 2008, 2010; Cazenave et while rates over much of the eastern Pacific are near zero or nega- al., 2009; Leuliette and Miller, 2009; Leuliette and Willis, 2011; Llovel tive (Beckley et al., 2010). The increasing sea level in the Warm Pool et al., 2011; von Schuckmann and Le Traon, 2011; Levitus et al., 2012). started shortly before the launch of TOPEX/Poseidon (Merrifield, 2011), However, these data have been shown to be best suited for global and is caused by an intensification of the trade winds (Merrifield and analyses after 2005 owing to a combination of interannual variabil- Maltrud, 2011) since the late 1980s that may be related to the Pacific ity and large biases when using data before 2005 owing to sparser Decadal Oscillation (PDO) (Merrifield et al., 2012; Zhang and Church, sampling (Leuliette and Miller, 2009; von Schuckmann and Le Traon, 2012). The lower rate of sea level rise since 1993 along the western 2011). Comparison of sparse but accurate temperature measurements coast of the United States has also been attributed to changes in the from the World Ocean Circulation Experiment in the 1990s with Argo wind stress curl over the North Pacific associated with the PDO (Bro- data from 2006 to 2008 also indicates a significant rise in global ther- mirski et al., 2011). While global maps can be created using EOF anal- mosteric sea level, although the estimate is uncertain owing to rela- ysis (e.g., Church et al., 2004; Llovel et al., 2009), pre-1993 results are tively sparse 1990s sampling (Freeland and Gilbert, 2009). still uncertain, as the method assumes that the EOFs since 1993 are capable of representing the patterns in previous decades, and results Observations of the contribution to sea level rise from warming below may be biased in the middle of the ocean where there are no tide 700 m are still uncertain due to limited historical data, especially in the gauges to constrain the estimate (Ray and Douglas, 2011). Several Southern Ocean (Section 3.2). Before Argo, they are based on 5-year studies have examined individual long tide gauge records in the North averages to 2000 m depth (Levitus et al., 2012). From 1971 to 2010, Atlantic and found coherent decadal-scale fluctuations along both the the estimated trend for the contribution between 700 m and 2000 m USA east coast (Sturges and Hong, 1995; Hong et al., 2000; Miller and is 0.1 [0 to 0.2] mm yr 1 (Table 3.1; Levitus et al., 2012). To measure Douglas, 2007), the European coast (Woodworth et al., 2010; Sturges the contribution of warming below 2000 m, much sparser but very and Douglas, 2011; Calafat et al., 2012), and the marginal seas in the accurate temperature profiles along repeat hydrographic sections are western North Pacific (Marcos et al., 2012), all related to natural cli- utilized (Purkey and Johnson, 2010; Kouketsu et al., 2011). The studies mate variability. have found a significant warming trend between 1000 and 4000 m within and south of the Sub-Antarctic Front (Figure 3.3). The estimated There is still considerable uncertainty on how long large-scale pat- total contribution of warming below 2000 m to global mean sea level terns of regional sea level change can persist, especially in the Pacif- rise between about 1992 and 2005 is 0.1 [0.0 to 0.2] mm yr 1 (95% ic where the majority of tide gauge records are less than 40 years confidence as reported by authors; Purkey and Johnson, 2010). long. Based on analyses of the longest records in the Atlantic, Indian and Pacific Oceans (including the available gauges in the Southern Detection of the mass component of sea level from the GRACE mis- Ocean) there are significant multi-decadal variations in regional sea sion was not assessed in AR4, as the record was too short and there level (Holgate, 2007; Woodworth et al., 2009, 2011; Mitchum et al., was still considerable uncertainty in the measurements and corrections 2010; Chambers et al., 2012). Hence local rates of sea level rise can 288 Observations: Ocean Chapter 3 be considerably higher or lower than the global mean rate for periods level around the United States and Australia since 1920 (Houston and of a decade or more. Dean, 2011; Watson, 2011), or large positive quadratic values since 1950 along the U.S. east coast (Sallenger et al., 2012). However, fitting The preceding discussion of regional sea level trends has focused on a quadratic term to tide gauge data after 1920 results in highly varia- effects that appear to be related to regional ocean volume change, and ble, insignificant quadratic terms (Rahmstorf and Vermeer, 2011), and not those due to vertical land motion. As discussed in Section 3.7.1, so only studies that use data before 1920 and that extend until 2000 or vertical land motion can dramatically affect local sea level change. beyond are suitable for evaluating long-term acceleration of sea level. Some extreme examples of vertical land motion are in Neah Bay, Washington, where the signal is +3.8 mm yr 1 (uplift from tectonic A long time scale is needed because significant multi-decadal varia- activity); Galveston, Texas, where the value is 5.9 mm yr 1 (subsid- bility appears in numerous tide gauge records during the 20th century ence from groundwater mining); and Nedre Gavle, Sweden where the (Holgate, 2007; Woodworth et al., 2009, 2011; Mitchum et al., 2010; value is +7.1 mm yr 1 (uplift from GIA), all computed from nearby GPS Chambers et al., 2012). The multi-decadal variability is marked by an receivers (Wöppelmann et al., 2009). These areas will all have long- increasing trend starting in 1910 1920, a downward trend (i.e., level- term rates of sea level rise that are significantly higher or lower than ing of sea level if a long-term trend is not removed) starting around those due to ocean volume change alone, but as these rates are not 1950, and an increasing trend starting around 1980. The pattern can related to climate change, they are not discussed here. be seen in New York, Mumbai and Fremantle records, for instance (Figure 3.12), as well as 14 other gauges representing all ocean basins 3.7.4 Assessment of Evidence for Accelerations ( ­ Chambers et al., 2012), and in all reconstructions (Figure 3.14). It is in Sea Level Rise also seen in an analysis of upper 400 m temperature (Gouretski et al., 2012; Section 3.3.2). Although the calculations of 18-year rates of AR4 concluded that there was high confidence that the rate of global GMSL rise based on the different reconstruction methods disagree by sea level rise increased from the 19th to the 20th century but could as much as 2 mm yr 1 before 1950 and on details of the variability not be certain as to whether the higher rate since 1993 was reflective of (Figure 3.14), all do indicate 18-year trends that were significantly decadal variability or a further increase in the longer-term trend. Since higher than the 20th century average at certain times (1920 1950, 3 AR4, there has been considerable effort to quantify the level of decadal 1990 present) and lower at other periods (1910 1920, 1955 1980), and multi-decadal variability and to detect acceleration in GMSL and likely related to multi-decadal variability. Several studies have suggest- mean sea level at individual tide gauges. It has been clear for some ed these variations may be linked to climate fluctuations like the Atlan- time that there was a significant increase in the rate of sea level rise tic Multi-decadal Oscillation (AMO) and/or Pacific Decadal Oscillation in the four oldest records from Northern Europe starting in the early (PDO, Box 2.5) (Holgate, 2007; Jevrejeva et al., 2008; Chambers et al., to mid-19th century (Ekman, 1988; Woodworth, 1990, 1999; Mitchum 2012), but these results are not conclusive. et al., 2010). Estimates of the change in the rate have been computed, either by comparing trends over 100-year intervals for the Stockholm While technically correct that these multi-decadal changes represent site (Ekman, 1988; Woodworth, 1990), or by fitting a quadratic term to acceleration/deceleration of sea level, they should not be interpreted all the long records starting before 1850 (Woodworth, 1990, 1999). The as change in the longer-term rate of sea level rise, as a time series results are consistent and indicate a significant acceleration that start- longer than the variability is required to detect those trends. Using data ed in the early to mid-19th century (Woodworth, 1990, 1999), although some have argued it may have started in the late 1700s (Jevrejeva et al., 2008). The increase in the rate of sea level rise at Stockholm (the 5 longest record that extends past 1900) has been based on differenc- Church & White Jevrejeva et al. ing 100-year trends from 1774 1884 and 1885 1985. The estimated 4 18-year GMSL trends (mm yr-1) Ray & Douglas Altimeter change is 1.0 [0.7 to 1.3] mm yr 1 per century (1 standard error, as cal- culated by Woodworth, 1990). Although sites in other ocean basins do 3 show an increased trend after 1860 (e.g., Figure 3.12), it is impossible to detect a change in the early to mid-1800s in other parts of the ocean 2 using tide gauge data alone, as there are no observations. 1 Numerous studies have attempted to quantify if a detectable accelera- tion has continued into the 20th century, typically by fitting a quadratic 0 to data at individual tide gauges (Woodworth, 1990; Woodworth et al., 2009, 2011; Houston and Dean, 2011; Watson, 2011) as well as to -1 1900 1910 1920 1930 1940 1950 1960 1970 1980 1990 2000 reconstructed time series of GMSL (Church and White, 2006; Jevrejeva et al., 2008; Church and White, 2011; Rahmstorf and Vermeer, 2011), or Year by examining differences in long-term rates computed at different tide Figure 3.14 | 18-year trends of GMSL rise estimated at 1-year intervals. The time is gauges (Sallenger et al., 2012). Woodworth et al. (2011) find significant the start date of the 18-year period, and the shading represents the 90% confidence. quadratic terms at the sites that begin before 1860 (all in the NH). The estimate from satellite altimetry is also given, with the 90% confidence given as Other authors using more numerous but significantly shorter records an error bar. Uncertainty is estimated by the variance of the residuals about the fit, and accounts for serial correlation in the residuals as quantified by the lag-1 autocorrelation. have found either insignificant or small negative quadratic terms in sea 289 Chapter 3 Observations: Ocean extending from 1900 to after 2000, the quadratic term computed from high water level, changes in number of high storm surge events, or both individual tide gauge records and GMSL reconstructions is signif- changes in 99th percentile events (e.g., Church et al., 2006; D Onofrio icantly positive (Jevrejeva et al., 2008; Church and White, 2011; Rahm- et al., 2008; Marcos et al., 2009; Haigh et al., 2010; Letetrel et al., 2010; storf and Vermeer, 2011; Woodworth et al., 2011). Church and White Tsimplis and Shaw, 2010; Vilibic and Sepic, 2010; Grinsted et al., 2012). (2006) report that the estimated acceleration term in GMSL (twice the A global analysis of tide gauge records has been performed for data quadratic parameter) is 0.009 [0.006 to 0.012] mm yr 2 (1 standard from the 1970s onwards when the global data sampling has been deviation) from 1880 to 2009, which is consistent with the other pub- robust and finds that the magnitude of extreme sea level events has lished estimates (e.g., Jevrejeva et al., 2008; Woodworth et al., 2009) increased in all regions studied since that time (Woodworth and Black- that use records longer than 100 years. Chambers et al. (2012) find that man, 2004; Menéndez and Woodworth, 2010; Woodworth et al., 2011). modelling a period near 60 years removes much of the multi-decadal variability of the 20th century in the tide gauge reconstruction time The height of a 50-year flood event has increased anywhere from 2 series. When a 60-year oscillation is modeled along with an accelera- to more than 10 cm per decade since 1970 (Figure 3.15a), although tion term, the estimated acceleration in GMSL since 1900 ranges from: some areas have seen a negative rate because vertical land motion is 0.000 [ 0.002 to 0.002] mm yr 2 in the Ray and Douglas (2011) record, much larger than the rate of mean sea level rise. However, when the 0.013 [0.007 to 0.019] mm yr 2 in the Jevrejeva et al. (2008) record, annual median height at each gauge is removed to reduce the effect and 0.012 [0.009 to 0.015] mm yr 2 in the Church and White (2011) of local mean sea level rise, interannual and decadal fluctuations, and record. Thus, while there is more disagreement on the value of a 20th vertical land motion, the rate of extreme sea level change drops in century acceleration in GMSL when accounting for multi-decadal fluc- 49% of the gauges to below significance (Figure 3.15b), while at 45% tuations, two out of three records still indicate a significant positive it fell to less than 5 mm yr 1. Only 6% of tide gauge records evaluated value. The trend in GMSL observed since 1993, however, is not signif- had a change in the amplitude of more than 5 mm yr 1 after removing icantly larger than the estimate of 18-year trends in previous decades mean sea level variations, mainly in the southeast United States, the (e.g., 1920 1950). western Pacific, Southeast Asia and a few locations in Northern Europe. The higher rates in the southeastern United States have been linked to 3 3.7.5 Changes in Extreme Sea Level larger storm surge events unconnected to global sea level rise (Grin- sted et al., 2012). Aside from non-climatic events such as tsunamis, extremes in sea level (i.e., coastal flooding, storm surge, high water events, etc.) tend to be (a) caused by large storms, especially when they occur at times of high tide. However, any low-pressure system offshore with associated high winds can cause a coastal flooding event depending on the duration and direction of the winds. Evaluation of changes in frequency and intensity of storms have been treated in Sections 2.6.3 and 2.6.4, as well as SREX Chapter 3 (Section 3.5.2). The main conclusions from both are that there is low confidence of any trend or long term change in tropical or extratropic storm frequency or intensity in any ocean basin, although there is robust evidence for an increase in the most intense tropical cyclones in the North Atlantic basin since the 1970s. The mag- nitude and frequency of extreme events can still increase without a change in storm intensity, however, if the mean water level is also (b) increasing. AR4 concluded that the highest water levels have been increasing since the 1950s in most regions of the world, caused mainly by increasing mean sea level. Studies published since AR4 continue to ­ support this conclusion, although higher regional extremes are also caused by large interannual and multi-decadal variations in sea level associated with climate fluctuations such as ENSO, the North Atlantic Oscillation and the Atlantic Multi-decadal Oscillation, among others (e.g., Abeysirigunawardena and Walker, 2008; Haigh et al., 2010; Menéndez and Woodworth, 2010; Park et al., 2011). Global analyses of the changes in extreme sea level are limited, and most reports are based on analysis of regional data (see Lowe et al., 2010 for a review). Estimates of changes in extremes rely either on the 12 10 8 6 4 -2 0 2 4 6 8 10 12 analysis of local tide gauge data, or on multi-decadal hindcasts of a (cm per decade) dynamical model (WASA-Group, 1998). Most analyses have focused Figure 3.15 | Estimated trends (cm per decade) in the height of a 50-year event in on specific regions and find that extreme values have been increas- extreme sea level from (a) total elevation and (b) total elevation after removal of annual ing since the 1950s, using various statistical measures such as annual medians. Only trends significant at the 95% confidence level are shown. (Data are from maximum surge, annual maximum surge-at-high-water, monthly mean Menéndez and Woodworth, 2010.) 290 Observations: Ocean Chapter 3 3.7.6 Conclusions m likely has been contributing another 0.1 [0.0 to 0.2] mm yr 1 of sea level rise since the early 1990s. It is virtually certain that globally averaged sea level has risen over the 20th century, with a very likely mean rate between 1900 and 2010 of It is very likely that the rate of mean sea level rise along Northern 1.7 [1.5 to 1.9] mm yr 1 and 3.2 [2.8 and 3.6] mm yr 1 between 1993 European coastlines has accelerated since the early 1800s and that this and 2010. This assessment is based on high agreement among multi- has continued through the 20th century, as the increased rate since ple studies using different methods, and from independent observing 1875 has been observed in multiple long tide gauge records and by systems (tide gauges and altimetry) since 1993. It is likely that a rate different groups using different analysis techniques. It is likely that sea comparable to that since 1993 occurred between 1920 and 1950, pos- level rise throughout the NH has also accelerated since 1850, as this is sibly due to a multi-decadal climate variation, as individual tide gauges also observed in a smaller number of gauges along the coast of North around the world and all reconstructions of GMSL show increased America. Two of the three time series based on reconstructing GMSL rates of sea level rise during this period. Although local vertical land from tide gauge data back to 1900 or earlier indicate a significant motion can cause even larger rates of sea level rise (or fall) relative to positive acceleration, while one does not. The range is 0.002 to 0.019 the coastline, it is very likely that this does not affect the estimates of mm yr 2, so it is likely that GMSL has accelerated since 1900. Finally, it the global average rate, based on multiple estimations of the average is likely that extreme sea levels have increased since 1970, largely as a with and without VLM corrections. result of the rise in mean sea level. It is virtually certain that interannual and decadal changes in the large-scale winds and ocean circulation can cause significantly higher 3.8 Ocean Biogeochemical Changes, Including or lower rates over shorter periods at individual locations, as this has Anthropogenic Ocean Acidification been observed in tide gauge records around the world. Warming of the upper 700 m of the ocean has very likely contributed an average of 0.6 The oceans can store large amounts of CO2. The reservoir of inorganic [0.4 to 0.8] mm yr 1 of sea level change since 1971. Warming between carbon in the ocean is roughly 50 times that of the atmosphere (Sabine 700 m and 2000 m has likely been contributing an additional 0.1 mm et al., 2004). Therefore even small changes in the ocean reservoir can 3 yr 1 [0 to 0.2] of sea level rise since 1971, and warming below 2000 have an impact on the atmospheric concentration of CO2. The ocean Table 3.1 | Estimated trends in GMSL and components over different periods from representative time-series. Trends and uncertainty have been estimated from a time series provided by the authors using ordinary least squares with the uncertainty representing the 90% confidence interval. The model fit for yearly averaged time series was a bias + trend; the model fit for monthly and 10-day averaged data was a bias + trend + seasonal sinusoids. Uncertainty accounts for correlations in the residuals. Trend Quantity Period Source Resolution (mm yr 1) 1901 2010 1.7 [1.5 to 1.9] Tide Gauge Reconstruction (Church and White, 2011) Yearly 1901 1990 1.5 [1.3 to 1.7] Tide Gauge Reconstruction (Church and White, 2011) Yearly GMSL 1971 2010 2.0 [1.7 to 2.3] Tide Gauge Reconstruction (Church and White, 2011) Yearly 1993 2010 2.8 [2.3 to 3.3] Tide Gauge Reconstruction (Church and White, 2011) Yearly 1993 2010 3.2 [2.8 to 3.6] a Altimetry (Nerem et al., 2010) time-series 10-Day Thermosteric Component 1971 2010 0.6 [0.4 to 0.8] XBT Reconstruction (updated from Domingues et al., 2008) 3-Year running means (upper 700 m) 1993 2010 0.8 [0.5 to 1.1] XBT Reconstruction (updated from Domingues et al., 2008) 3-Year running means Thermosteric Component 1971 2010 0.1 [0 to 0.2] Objective mapping of historical temperature data (Levitus et al., 2012) 5-Year averages (700 to 2000 m) 1993 2010 0.2 [0.1 to 0.3] Objective mapping of historical temperature data (Levitus et al., 2012) 5-Year averages Thermosteric Component 1992 2005 0.11 [0.01 to 0.21]b Deep hydrographic sections (Purkey and Johnson, 2010) Trend only (below 2000 m) Thermosteric Component 1971 2010 0.8 [0.5 to 1.1]c Combination of estimates from 0 to 700 m, 700 to 2000 m, and below 2000 mc Trend only (whole depth) 1993 2010 1.1 [0.8 to 1.4]c Combination of estimates from 0 700 m, 700 to 2000 m, and below 2000 mc Trend only Notes: a Uncertainty estimated from fit to Nerem et al. (2010) time series and includes potential systematic error owing to drift of altimeter, estimated to be +/-0.4 mm yr 1 (Beckley et al., 2010; Nerem et al., 2010), applied as the root-sum-square (RSS) with the least squares error estimate. The uncertainty in drift contains uncertainty in the reference frame, orbit and instrument. b Trend value taken from Purkey and Johnson (2010), Table 1. Uncertainty represents the 2.5 97.5% confidence interval. Assumes no trend below 2000 m before 1 January 1992, then value from Purkey and Johnson (2010) afterwards. Uncertainty for 0 to 700 m, 700 to 2000 m and below 2000 m is assumed to c be uncorrelated, and uncertainty is calculated as RSS of the uncertainty for each layer. 291 Chapter 3 Observations: Ocean also provides an important sink for carbon dioxide released by human uptake (Schuster et al., 2013). Uptake of CO2 in the Subtropical Mode activities, the anthropogenic CO2 (Cant). Currently, an amount of CO2 Water (STMW) of the North Atlantic was enhanced during the 1990s, equivalent to approximately 30% of the total human emissions of CO2 a predominantly positive phase of the NAO, and much reduced in the to the atmosphere is accumulating in the ocean (Mikaloff-Fletcher et 2000s when the NAO phase was neutral or negative (Bates, 2012). al., 2006; Le Quéré et al., 2010). In this section, observations of change Observations in the Indian and Pacific sectors of the Southern Ocean in the ocean uptake of carbon, the inventory of Cant, and ocean acidifi- were interpreted as evidence for reduced winter-time CO2 uptake as a cation are assessed, as well as changes in oxygen and nutrients. Chap- result of increased winds, increased upwelling and outgassing of natu- ter 6 provides a synthesis of the overall carbon cycle, including the ral CO2 (Metzl, 2009; Lenton et al., 2012). ocean, atmosphere and biosphere and considering both past trends and future projections. 3.8.1.2 Changes in the Oceanic Inventory of Anthropogenic Carbon Dioxide 3.8.1 Carbon Ocean carbon uptake and storage is inferred from changes in the 3.8.1.1 Ocean Uptake of Carbon inventory of anthropogenic carbon. Cant cannot be measured direct- ly but is calculated from observations of ocean properties (Appendix The air sea flux of CO2 is computed from the observed difference in the 3.A discusses the sampling on which the ocean carbon inventory is partial pressure of CO2 (pCO2) across the air water interface ( pCO2 based). Two independent data-based methods to calculate anthropo- = pCO2,sw- pCO2,air), the solubility of CO2 in seawater, and the gas genic carbon inventories exist: the C* method (Sabine et al., 2004), transfer velocity (Wanninkhof et al., 2009). However, the limited geo- and the transit time distribution (TTD) method (Waugh et al., 2006). graphic and temporal coverage of the pCO2 measurement as well as The Green s function approach that applies the maximum entropy uncertainties in wind forcing and transfer velocity parameterizations de-convolution methodology (Khatiwala et al., 2009) is related to the mean that uncertainties in global and regional fluxes calculated from latter. These approaches use different tracer data, for instance, the TTD measurements of pCO2 can be as larges as +/-50% (Wanninkhof et al., method is based mostly on chlorofluorcarbon measurements. Changes 3 2013). Using pCO2 data in combination with the riverine input Gruber due to variability in ocean productivity (Chavez et al., 2011) are not et al. (2009) estimated a global uptake rate of 1.9 [1.2 to 2.5] PgC considered. yr 1 for the time period 1995 2000 and Takahashi et al. (2009) found 2.0 [1.0 to 3.0] PgC yr 1 normalized to the year 2000. Uncertainties in Estimates of the global inventory of Cant (including marginal seas) cal- fluxes calculated from pCO2 are too large to detect trends in global culated using these methods have a mean value of 118 PgC and a ocean carbon uptake. range of 93 to 137 PgC in 1994 and a mean of 160 PgC and range of 134 to 186 PgC in 2010 (Sabine et al., 2004; Waugh et al., 2006; Khati- Trends in surface ocean pCO2 are calculated from ocean time series sta- wala et al., 2009, 2013). When combined with model results (Mikaloff- tions and repeat hydrographic sections in the North Atlantic and North Fletcher et al., 2006; Doney et al., 2009; Gerber et al., 2009; Graven Pacific (Table 3.2). At all locations and for all time periods shown, pCO2 et al., 2012), Khatiwala et al. (2013) arrive at a best estimate of in both the atmosphere and ocean has increased, while pH and [CO32 ] the global ocean inventory (including marginal seas) of anthropogenic have decreased. At some sites, oceanic surface pCO2 increased faster carbon from 1750 to 2010 of 155 PgC with an uncertainty of +/-20% than the atmospheric trend, implying a decreasing uptake of atmos- (Figure 3.16). While the estimates of total inventory agree within their pheric CO2 at those locations. The oceanic pCO2 trend can differ from uncertainty, the different methods result in significant differences in that in the atmosphere owing to changes in the intensity of biological the inferred spatial distribution of Cant, particularly at high latitudes. production and changes in physical conditions, for instance between El Nino and La Nina (Keeling et al., 2004; Midorikawa et al., 2005; The Cant inventory best estimate of 155 PgC (Khatiwala et al., 2013; Yoshikawa-Inoue and Ishii, 2005; Takahashi et al., 2006, 2009; Schuster Figure 3.16) corresponds to an uptake rate of 2.3 (range of 1.7 to 2.9) and Watson, 2007; Ishii et al., 2009; McKinley et al., 2011; Bates, 2012; PgC yr 1 from 2000 to 2010, in close agreement with an independent Lenton et al., 2012). estimate of 2.5 (range of 1.8 to 3.2) PgC yr 1 based on atmospheric O2/ N2 measurements obtained for the same period (Ishidoya et al., 2012). Although local variations of pCO2 with time have little effect on the The O2/N2 method resulted in 2.2 +/- 0.6 PgC yr 1 for the time period atmospheric CO2 growth rate in the short term, they provide impor- 1990 to 2000 and 2.5 +/- 0.6 for the period from 2000 to 2010 (Keeling tant information on the dynamics of the ocean carbon cycle and the and Manning, 2014). These estimates are also consistent with an inde- potential for longer-term climate feedbacks. For example, El Nino and pendent estimate of 1.9 +/- 0.4 PgC yr 1 for the period between 1970 La Nina can drive large changes in the efflux of CO2 in the Pacific. and 1990 based on depth-integrated d13C changes (Quay et al., 2003) Differences in pCO2 can exceed 100 uatm in the eastern and central and with estimates inferred from pCO2. equatorial Pacific between El Nino and La Nina; an increase in pCO2 observed between 1998 and 2004 was attributed to wind and circula- The storage rate of anthropogenic CO2 is assessed by calculating the tion changes associated with the Pacific Decadal Oscillation (Feely et change in Cant concentrations between two time periods. Regional al., 2006). CO2 uptake in the North Atlantic decreased by 0.24 [0.19 observations of the storage rate are in general agreement with that 0.29] PgC yr 1 between 1994 and 2003 (Schuster and Watson, 2007) expected from the increase in atmospheric CO2 concentrations and and has partially recovered since then (Watson et al., 2009). Linear with the tracer-based estimates. However, there are significant spatial trends for the North Atlantic from 1995 to 2009 reveal an increased and temporal variations in the degree to which the inventory of Cant 292 Observations: Ocean Chapter 3 180oW 15 (mol m-2) o 80 N o W 0o 0 E 15 160 o o 70 N 140 W 12 o 0o 40 N 0 o 12 o 120 E 80 N o 100 90 E 0 90oW o 80 o 40 S 60 E 60 60 o 40 Wo o 20 80 S 30 o o E o 60 E 120 E o 180 W o o 120 W o 60 W 0 o W 30 0 0o Figure 3.16 | Compilation of the 2010 column inventories (mol m 2) of anthropogenic CO2: the global Ocean excluding the marginal seas (updated from Khatiwala et al., 2009) 150 +/- 26 PgC; Arctic Ocean (Tanhua et al., 2009) 2.7 to 3.5 PgC; the Nordic Seas (Olsen et al., 2010) 1.0 to 1.6 PgC; the Mediterranean Sea (Schneider et al., 2010) 1.6 to 2.5 PgC; the Sea of Japan(Park et al., 2006) 0.40 +/- 0.06 PgC. From Khatiwala et al. (2013). tracks changes in the atmosphere (Figure 3.17). The North Atlantic, in that it is unlikely that on a global scale both land and ocean sinks particular, is an area with high variability in circulation and deep water decreased (Ballantyne et al., 2012). formation, influencing the Cant inventory. As a result of the decline in Labrador Sea Water (LSW) formation since 1997 (Rhein et al., 2011), In summary, the high agreement between multiple lines of independ- the Cant increase between 1997 and 2003 was smaller in the subpolar ent evidence for increases in the ocean inventory of Cant underpins the North Atlantic than expected from the atmospheric increase, in con- conclusion that it is virtually certain that the ocean is sequestering 3 trast to the subtropical and equatorial Atlantic (Steinfeldt et al., 2009). anthropogenic carbon dioxide and very likely that the oceanic Cant Perez et al. (2010) also noted the dependence of the Cant storage rate inventory increased from 1994 to 2010. Oceanic carbon uptake rates in the North Atlantic on the NAO, with high Cant storage rate during calculated using different data sets and methods agree within their phases of high NAO (i.e., high LSW formation rates) and low storage uncertainties and very likely range between 1.0 and 3.2 PgC yr 1. during phases of low NAO (low formation). Wanninkhof et al. (2010) found a smaller inventory increase in the North Atlantic compared to 3.8.2 Anthropogenic Ocean Acidification the South Atlantic between 1989 and 2005. The uptake of CO2 by the ocean changes the chemical balance of Ocean observations are insufficient to assess whether there has been seawater through the thermodynamic equilibrium of CO2 with sea- a change in the rate of total (anthropogenic plus natural) carbon water. Dissolved CO2 forms a weak acid (H2CO3) and, as CO2 in sea- uptake by the global ocean. Evidence from regional ocean studies water increases, the pH, carbonate ion (CO32 ), and calcium carbonate (often covering relatively short time periods), atmospheric observa- (CaCO3) saturation state of seawater decrease while bicarbonate ion tions and models is equivocal, with some studies suggesting the ocean (HCO3 ) increases (FAQ 3.3). Variations in oceanic total dissolved inor- uptake rate of total CO2 may have declined (Le Quéré et al., 2007; ganic carbon (CT = CO2 + CO32 + HCO3 ) and pCO2 reflect changes in Schuster and Watson, 2007; McKinley et al., 2011) while others con- both the natural carbon cycle and the uptake of anthropogenic CO2 clude that there is little evidence for a decline (Knorr, 2009; Gloor et from the atmosphere. The mean pH (total scale) of surface waters al., 2010; Sarmiento et al., 2010). A study based on atmospheric CO2 ranges between 7.8 and 8.4 in the open ocean, so the ocean remains o ­ bservations and emission inventories concluded that global carbon mildly basic (pH > 7) at present (Orr et al., 2005a; Feely et al., 2009). uptake by land and oceans doubled from 1960 to 2010, implying Ocean uptake of CO2 results in gradual acidification of seawater; this Atlantic Ocean (mol m-2 y-1) Pacific Ocean (mol m-2 y-1) Indian Ocean (mol m-2 y-1) 2.4 0.8 0.9 60°N 60°N 60°N 0.7 0.8 2 0.6 0.7 30°N 30°N 30°N 1.6 0.6 0.5 0.5 0° 1.2 0° 0.4 0° 0.4 0.3 0.8 0.3 30°S 30°S 0.2 30°S 0.2 0.4 0.1 0.1 60°S 60°S 60°S 0 1 0 0 25°W 0° 25° E 15 2 1 1 °E °E 00° 25 °W °W 0° °W 0°W0°W 90 15 E 50 75 °E E 75 50 15 180 E °E 0° °W 0°E 25 12 Figure 3.17 | Maps of storage rate distribution of anthropogenic carbon (mol m 2 yr 1) for the three ocean basins (left to right: Atlantic, Pacific and Indian Ocean) averaged over 1980 2005 estimated by the Green s function approach (Khatiwala et al., 2009). Note that a different colour scale is used in each basin. 293 Chapter 3 Observations: Ocean process is termed ocean acidification (Box 3.2) (Broecker and Clark, 420 ESTOC BATS 2001; Caldeira and Wickett, 2003). The observed decrease in ocean 390 pH of 0.1 since the beginning of the industrial era corresponds to a 26% increase in the hydrogen ion concentration [H+] concentration of pCO2 (uatm) 360 seawater (Orr et al., 2005b; Feely et al., 2009). The consequences of changes in pH, CO32 , and the saturation state of CaCO3 minerals for 330 marine organisms and ecosystems are just beginning to be understood (see WGII Chapters 5, 6, 28 and 30). 300 ALOHA A global mean decrease in surface water pH of 0.08 from 1765 to 1994 270 1985 1990 1995 2000 2005 2010 was calculated based on the inventory of anthropogenic CO2 (Sabine 8.20 et al., 2004), with the largest reduction ( 0.10) in the northern North BATS Atlantic and the smallest reduction ( 0.05) in the subtropical South in situ pH (total scale) ALOHA 8.15 Pacific. These regional variations in the size of the pH decrease are con- sistent with the generally lower buffer capacities of the high latitude oceans compared to lower latitudes (Egleston et al., 2010). 8.10 Direct measurements on ocean time-series stations in the North Atlan- tic and North Pacific record decreasing pH with rates ranging between 8.05 ESTOC 0.0014 and 0.0024 yr 1 (Table 3.2, Figure 3.18; Bates, 2007, 2012; 1985 1990 1995 2000 2005 2010 Santana-Casiano et al., 2007; Dore et al., 2009; Olafsson et al., 2009; 270 González-Dávila et al., 2010). Directly measured pH differences in the BATS surface mixed layer along repeat transects in the central North Pacific 250 3 [CO2 ] ( mol kg 1) Ocean between Hawaii and Alaska showed a 0.0017 yr 1 decline in pH between 1991 and 2006, in agreement with observations at the time-series sites (Byrne et al., 2010). This rate of pH change is also 230 ALOHA consistent with repeat transects of CO2 and pH measurements in the 3 western North Pacific (winter: 0.0018 +/- 0.0002 yr 1; summer: 0.0013 210 ESTOC +/- 0.0005 yr 1) (Midorikawa et al., 2010). The pH changes in southern ocean surface waters are less certain because of the paucity of long- 1985 1990 1995 2000 2005 2010 term time-series observations there, but pCO2 measurements collect- Year ed by ships-of-opportunity indicate similar rates of pH decrease there Figure 3.18 | Long-term trends of surface seawater pCO2 (top), pH (middle) and car- (Takahashi et al., 2009). bonate ion (bottom) concentration at three subtropical ocean time series in the North Atlantic and North Pacific Oceans, including (a) Bermuda Atlantic Time-series Study Uptake of anthropogenic CO2 is the dominant cause of observed (BATS, 31°40 N, 64°10 W; green) and Hydrostation S (32°10 , 64°30 W) from 1983 changes in the carbonate chemistry of surface waters (Doney et al., to present (updated from Bates, 2007); (b) Hawaii Ocean Time-series (HOT) at Station ALOHA (A Long-term Oligotrophic Habitat Assessment; 22°45 N, 158°00 W; orange) 2009). Changes in carbonate chemistry in subsurface waters can also from 1988 to present (updated from Dore et al., 2009) and (c) European Station for reflect local physical and biological variability. As an example, while Time series in the Ocean (ESTOC, 29°10 N, 15°30 W; blue) from 1994 to present pH changes in the mixed layer of the North Pacific Ocean can be (updated from González-Dávila et al., 2010). Atmospheric pCO2 (black) from the Mauna explained solely by equilibration with atmospheric CO2, declines in pH Loa Observatory Hawaii is shown in the top panel. Lines show linear fits to the data, between 800 m and the mixed layer in the time period 1991 2006 whereas Table 3.2 give results for harmonic fits to the data (updated from Orr, 2011). were attributed in approximately equal measure to anthropogenic and natural variations (Byrne et al., 2010). Figure 3.19 shows the portion 3.8.3 Oxygen of pH changes between the surface and 1000 m that were attributed solely to the effects of anthropogenic CO2. Seawater pH and [CO32 ] As a consequence of the early introduction of standardized methods decreased by 0.0014 to 0.0024 yr 1 and ~0.4 to 0.9 umol kg 1 yr 1, and the relatively wide interest in the distribution of dissolved oxygen, respectively, between 1988 and 2009 (Table 3.2). Over longer time the historical record of marine oxygen observations is generally richer periods, anthropogenic changes in ocean chemistry are expected to than that of other biogeochemical parameters, although still sparse become increasingly prominent relative to changes imparted by physi- compared to measurements of temperature and salinity (Appendix cal and biological variability. 3.A). Dissolved oxygen changes in the ocean thermocline has generally decreased since 1960, but with strong regional variations (Keeling et The consistency of these observations demonstrates that the pH of al., 2010; Keeling and Manning, 2014). Oxygen concentrations at 300 surface waters has decreased as a result of ocean uptake of anthropo- dbar decreased between 50°S and 50°N at a mean rate of 0.63 umol genic CO2 from the atmosphere. There is high confidence that the pH kg 1 per decade between 1960 and 2010 (Stramma et al., 2012). For decreased by 0.1 since the preindustrial era. the period 1970 to 1990, the mean annual global oxygen loss between 100 m and 1000 m was calculated to be 0.55 +/- 0.13 × 1014 mol yr 1 (Helm et al., 2011). 294 Observations: Ocean Chapter 3 Box 3.2 | Ocean Acidification Ocean acidification refers to a reduction in pH of the ocean over an extended period, typically decades or longer, caused primarily by the uptake of carbon dioxide (CO2) from the atmosphere. Ocean acidification can also be caused by other chemical additions or subtractions from the oceans that are natural (e.g., increased volcanic activity, methane hydrate releases, long-term changes in net respiration) or human-induced (e.g., release of nitrogen and sulphur compounds into the atmosphere). Anthropogenic ocean acidification refers to the component of pH reduction that is caused by human activity (IPCC, 2011). Since the beginning of the industrial era, the release of CO2 from industrial and agricultural activities has resulted in atmospheric CO2 concentrations that have increased from approximately 280 8.40 ppm to about 392 ppm in 2012 (Chapter 6). The oceans have absorbed approximately 155 PgC from the atmosphere over the last two and a half centuries (Sabine et al., 2004; Khatiwala 8.35 et al., 2013). This natural process of absorption has benefited humankind by significantly reducing the greenhouse gas levels 8.30 in the atmosphere and abating some of the impacts of global warming. However, the ocean s uptake of carbon dioxide is having a significant impact on the chemistry of seawater. The 1875 Model 8.25 average pH of ocean surface waters has already fallen by about 0.1 units, from about 8.2 to 8.1 (total scale), since the beginning of the industrial revolution (Orr et al., 2005a; Figure 1; Feely et 8.20 3 al., 2009). Estimates of future atmospheric and oceanic carbon dioxide concentrations indicate that, by the end of this century, 8.15 pH the average surface ocean pH could be lower than it has been for more than 50 million years (Caldeira and Wickett, 2003). 8.10 The major controls on seawater pH are atmospheric CO2 exchange, the production and respiration of dissolved and 1995 Model particulate organic matter in the water column, and the formation 8.05 and dissolution of calcium carbonate minerals. Oxidation of organic matter lowers dissolved oxygen concentrations, adds 8.00 CO2 to solution, reduces pH, carbonate ion (CO32 ) and calcium carbonate (CaCO3) saturation states (Box 3.2, Figure 2), and lowers the pH of seawater in subsurface waters (Byrne et al., 7.95 2010). As a result of these processes, minimum pH values in the oceanic water column are generally found near the depths of the oxygen minimum layer. When CO2 reacts with seawater it forms 7.90 carbonic acid (H2CO3), which is highly reactive and reduces the GLODAP Observations concentration of carbonate ion (Box 3.2, Figure 2) and can affect shell formation for marine animals such as corals, plankton, Box 3.2, Figure 1 | National Center for Atmospheric Research Community and shellfish. This process could affect fundamental biological Climate System Model 3.1 (CCSM3)-modeled decadal mean pH at the sea surface centred on the years 1875 (top) and 1995 (middle). Global Ocean Data Analysis and chemical processes of the sea in coming decades (Fabry et Project (GLODAP)-based pH at the sea surface, nominally for 1995 (bottom). al., 2008; Doney et al., 2009; WGII Chapters 5, 6, 28 and 30). Deep and shallow-water coral reefs are indicated with magenta dots. White areas (continued on next page) indicate regions with no data. (After Feely et al., 2009.) The long-term deoxygenation of the open ocean thermocline is consist- reduced ventilation due to increased stratification (Helm et al., 2011; ent with the expectation that warmer waters can hold less dissolved see Table 6.14). oxygen (solubility effect), and that warming-induced stratification leads to a decrease in the transport of dissolved oxygen from surface Oxygen concentrations in the tropical ocean thermocline decreased to subsurface waters (stratification effect) (Matear and Hirst, 2003; in each of the ocean basins over the last 50 years (Ono et al., 2001; Deutsch et al., 2005; Frölicher et al., 2009). Observations of oxygen Stramma et al., 2008; Keeling et al., 2010; Helm et al., 2011), resulting change suggested that about 15% of the oxygen decline between in an expansion of the dissolved oxygen minimum zones. A comparison 1970 and 1990 could be explained by warming and the remainder by of data between 1960 and 1974 with those from 1990 to 2008 showed 295 Chapter 3 Observations: Ocean Box 3.2 (continued) (a) Pacific Atlantic Indian 0 200 400 600 Depth (m) 800 pH (total scale) 1000 2000 4000 6000 7.6 7.8 8.0 8.2 7.6 7.8 8.0 8.2 7.6 7.8 8.0 8.2 (b) pH (total scale) 0 200 400 CO32- Depth (m) (umol kg-1) 600 Aragonite saturation 800 Calcite 1000 saturation 3 2000 4000 6000 50 150 250 50 150 250 50 150 250 Carbonate ion concentration (umol kg-1) Box 3.2, Figure 2 | Distribution of (a) pH and (b) carbonate (CO32 ) ion concentration in the Pacific, Atlantic and Indian Oceans. The data are from the World Ocean Circulation Experiment/Joint Global Ocean Flux Study/Ocean Atmosphere Carbon Exchange Study global carbon dioxide (CO2) survey (Sabine et al., 2005). The lines show the mean pH (red solid line, top panel), mean CO32 (red solid line, bottom panel), and aragonite and calcite (black solid and dashed lines, bottom panel) satura- tion values for each of these basins (modified from Feely et al., 2009). The shaded areas show the range of values within the ocean basins. Dissolution of aragonite and calcite shells and skeletons occurs when CO32 concentrations drop below the saturation level, reducing the ability of calcifying organisms to produce their shells and skeletons. that oxygen concentrations decreased in most tropical regions at an show regions of alternating sign (e.g., Stramma et al., 2010), reflecting average rate of 2 to 3 umol kg 1 per decade (Figure 3.20; Stramma et differences in data and period considered. al., 2010). Data from one of the longest time-series sites in the subpo- lar North Pacific (Station Papa, 50°N, 145°W) reveal a persistent declin- CLIVAR P16N (2006) - WOCE P16N (1991): pH (anthropogenic) ing oxygen trend in the thermocline over the last 50 years (Whitney et 0 0.08 -0.03 al., 2007), superimposed on oscillations with time scales of a few years 24.8 25.8 26 0.06 25.2 25.6 26.2 to two decades. Stendardo and Gruber (2012) found dissolved oxygen 250 25.4 0.04 -0.0 decreases in upper water masses of the North Atlantic and increases 26.4 1 0 Depth (m) 0.02 in intermediate water masses. The changes were caused by changes in -0.02 26.6 -0. 01 26.8 pH solubility as well as changes in ventilation and circulation over time. 500 0 0 Alaska In contrast to the widely distributed oxygen declines, oxygen increased -0.02 in the thermoclines of the Indian and South Pacific Oceans from the 750 27 0 -0.04 1990s to the 2000s (McDonagh et al., 2005; Álvarez et al., 2011), 27.2 -0.06 apparently due to strengthened circulation driven by stronger winds 0 0.01 Hawaii.4 27 (Cai, 2006; Roemmich et al., 2007). In the southern Indian Ocean below 1000 -0.08 25° 30° 35° 40° 45° 50° 55°N the thermocline, east of 75°E, oxygen decreased between 1960 and 2010 most prominently on the isopycnals q = 26.9 to 27.0 (Kobayas- Figure 3.19 | pHant: pH change attributed to the uptake of anthropogenic carbon hi et al., 2012). While some studies suggest a widespread decline of between 1991 and 2006, at about 150°W, Pacific Ocean (from Byrne et al., 2010). The oxygen in the Southern Ocean (e.g., Helm et al., 2011), other studies red lines show the layers of constant density. 296 Observations: Ocean Chapter 3 Frequently Asked Questions FAQ 3.3 | How Does Anthropogenic Ocean Acidification Relate to Climate Change? Both anthropogenic climate change and anthropogenic ocean acidification are caused by increasing carbon dioxide concentrations in the atmosphere. Rising levels of carbon dioxide (CO2), along with other greenhouse gases, indi- rectly alter the climate system by trapping heat as it is reflected back from the Earth s surface. Anthropogenic ocean acidification is a direct consequence of rising CO2 concentrations as seawater currently absorbs about 30% of the anthropogenic CO2 from the atmosphere. Ocean acidification refers to a reduction in pH over an extended period, typically decades or longer, caused primari- ly by the uptake of CO2 from the atmosphere. pH is a dimensionless measure of acidity. Ocean acidification describes the direction of pH change rather than the end point; that is, ocean pH is decreasing but is not expected to become acidic (pH < 7). Ocean acidification can also be caused by other chemical additions or subtractions from the oceans that are natural (e.g., increased volcanic activity, methane hydrate releases, long-term changes in net respiration) or human-induced (e.g., release of nitrogen and sulphur compounds into the atmosphere). Anthropogenic ocean acidification refers to the component of pH reduction that is caused by human activity. Since about 1750, the release of CO2 from industrial and agricultural activities has resulted in global average atmo- spheric CO2 concentrations that have increased from 278 to 390.5 ppm in 2011. The atmospheric concentration of CO2 is now higher than experienced on the Earth for at least the last 800,000 years and is expected to continue to rise because of our dependence on fossil fuels for energy. To date, the oceans have absorbed approximately 155 +/- 30 PgC from the atmosphere, which corresponds to roughly one-fourth of the total amount of CO2 emitted (555 +/- 85 PgC) by human activities since preindustrial times. This natural process of absorption has significantly reduced 3 the greenhouse gas levels in the atmosphere and minimized some of the impacts of global warming. However, the ocean s uptake of CO2 is having a significant impact on the chemistry of seawater. The average pH of ocean surface waters has already fallen by about 0.1 units, from about 8.2 to 8.1 since the beginning of the Industrial Revolution. Estimates of projected future atmospheric and oceanic CO2 concentrations indicate that, by the end of this century, the average surface ocean pH could be 0.2 to 0.4 lower than it is today. The pH scale is logarithmic, so a change of 1 unit corresponds to a 10-fold change in hydrogen ion concentration. When atmospheric CO2 exchanges across the air sea interface it reacts with seawater through a series of four chem- ical reactions that increase the concentrations of the carbon species: dissolved carbon dioxide (CO2(aq)), carbonic acid (H2CO3) and bicarbonate (HCO3 ): CO2(atmos) CO2(aq) (1) CO2(aq) + H2O H2CO3 (2) H2CO3 H+ + HCO3 (3) HCO3 H+ + CO32 (4) Hydrogen ions (H+) are produced by these reactions. This increase in the ocean s hydrogen ion concentration cor- responds to a reduction in pH, or an increase in acidity. Under normal seawater conditions, more than 99.99% of the hydrogen ions that are produced will combine with carbonate ion (CO32 ) to produce additional HCO3 . Thus, the addition of anthropogenic CO2 into the oceans lowers the pH and consumes carbonate ion. These reactions are fully reversible and the basic thermodynamics of these reactions in seawater are well known, such that at a pH of approximately 8.1 approximately 90% the carbon is in the form of bicarbonate ion, 9% in the form of carbonate ion, and only about 1% of the carbon is in the form of dissolved CO2. Results from laboratory, field, and modeling studies, as well as evidence from the geological record, clearly indicate that marine ecosystems are highly suscep- tible to the increases in oceanic CO2 and the corresponding decreases in pH and carbonate ion. Climate change and anthropogenic ocean acidification do not act independently. Although the CO2 that is taken up by the ocean does not contribute to greenhouse warming, ocean warming reduces the solubility of carbon dioxide in seawater; and thus reduces the amount of CO2 the oceans can absorb from the atmosphere. For example, under doubled preindustrial CO2 concentrations and a 2°C temperature increase, seawater absorbs about 10% less CO2 (10% less total carbon, CT) than it would with no temperature increase (compare columns 4 and 6 in Table 1), but the pH remains almost unchanged. Thus, a warmer ocean has less capacity to remove CO2 from the atmosphere, yet still experiences ocean acidification. The reason for this is that bicarbonate is converted to carbonate in a warmer ocean, releasing a hydrogen ion thus stabilizing the pH. (continued on next page) 297 Chapter 3 Observations: Ocean FAQ 3.3 (continued) CO2 Time Series in the North Pacific 400 8.30 160°W 158°W 156°W 23°N Station Aloha 22°N 21°N 8.25 375 20°N Station Mauna Loa 19°N pCO2 (uatm) CO2(ppm) 8.20 350 pH 8.15 325 8.10 300 8.05 275 8.00 1990 1992 1994 1998 1996 2000 2002 2004 2006 2008 2010 2012 Year FAQ 3.3, Figure 1 | A smoothed time series of atmospheric CO2 mole fraction (in ppm) at the atmospheric Mauna Loa Observatory (top red line), surface ocean partial pressure of CO2 (pCO2; middle blue line) and surface ocean pH (bottom green line) at Station ALOHA in the subtropical North Pacific north of Hawaii for the 3 period from1990 2011 (after Doney et al., 2009; data from Dore et al., 2009). The results indicate that the surface ocean pCO2 trend is generally consistent with the atmospheric increase but is more variable due to large-scale interannual variability of oceanic processes. FAQ 3.3, Table 1 | Oceanic pH and carbon system parameter changes in surface water for a CO2 doubling from the preindustrial atmosphere without and with a 2°C warminga. Pre-industrial 2 × Pre-industrial (% change relative 2 × Pre-industrial (% change relative Parameter (280 ppmv) (560 ppmv) to pre-industrial) (560 ppmv) to pre-industrial) 20°C 20°C 22°C pH 8.1714 7.9202 7.9207 H (mol kg ) + 1 6.739e 9 1.202e 8 (78.4) 1.200e 8 (78.1) CO2(aq) (umol kg 1) 9.10 18.10 (98.9) 17.2 (89.0) HCO3 (umol kg ) 1 1723.4 1932.8 (12.15) 1910.4 (10.9) CO32 (umol kg 1) 228.3 143.6 (-37.1) 152.9 ( 33.0) CT (umol kg 1) 1960.8 2094.5 (6.82) 2080.5 (6.10) Notes: a CO 2(aq) = dissolved CO2, H2CO3 = carbonic acid, HCO3 = bicarbonate, CO3 = carbonate, CT = total carbon = CO2(aq) + HCO3 + CO3 ). 2 2 Coastal regions have also experienced long-term dissolved oxygen than in the open ocean, and an increase in the number of hypoxic changes. Bograd et al. (2008) reported a substantial reduction of the zones was observed since the 1960s (Diaz and Rosenberg, 2008). thermocline oxygen content in the southern part of the California Cur- rent from 1984 to 2002, resulting in a shoaling of the hypoxic bound- 3.8.4 Nutrients ary (marked by oxygen concentrations of about 60 umol kg 1). Off the British Columbia coast, oxygen concentrations in the near bottom Nutrient concentrations in the surface ocean surface are influenced by waters decreased an average of 1.1 umol kg 1 yr 1 over a 30-year human impacts on coastal runoff and on atmospheric deposition, and period (Chan et al., 2008). These changes along the west coast of North by changing nutrient supply from the ocean s interior into the mixed A ­ merica appear to have been largely caused by the open ocean dis- layer (for instance due to increased stratification). Changing nutrient solved oxygen decrease and local processes associated with decreased distributions might influence the magnitude and variability of the vertical dissolved oxygen transport following near-surface warming ocean s biological carbon pump. and increased stratification. Gilbert et al. (2010) found evidence that for the time period 1976 2000 oxygen concentrations between 0 and Globally, the manufacture of nitrogen fertilizers has continued to 300 m depth were declining about 10 times faster in the coastal ocean increase (Galloway et al., 2008) accompanied by increasing eutrophi- 298 Observations: Ocean Chapter 3 Table 3.2 | Published and updated long-term trends of atmospheric (pCO2atm) and seawater carbonate chemistry (i.e., surface-water pCO2, and corresponding calculated pH, CO32 , and aragonite saturation state (a) at four ocean time series in the North Atlantic and North Pacific oceans: (1) Bermuda Atlantic Time-series Study (BATS, 31°40 N, 64°10 W) and Hydrostation S (32°10 N, 64°30 W) from 1983 to present (Bates, 2007); (2) Hawaii Ocean Time series (HOT) at Station ALOHA (A Long-term Oligotrophic Habitat Assess- ment; 22°45 N, 158°00 W) from 1988 to the present (Dore et al., 2009); (3) European Station for Time series in the Ocean (ESTOC, 29°10 N, 15°30 W) from 1994 to the present (González-Dávila et al., 2010); and (4) Iceland Sea (IS, 68.0°N, 12.67°W) from 1985 to 2006 (Olafsson et al., 2009). Trends at the first three time-series sites are from observations with the seasonal cycle removed. Also reported are the wintertime trends in the Iceland Sea as well as the pH difference trend for the North Pacific Ocean between transects in 1991 and 2006 (Byrne et al., 2010) and repeat sections in the western North Pacific between 1983 and 2008 (Midorikawa et al., 2010). pCO2atm pCO2sea pH* [CO32 ] a Site Period ( atm yr 1) ( atm yr 1) (yr 1) ( mol kg 1 yr 1) (yr 1) a. Published trends BATS 1983 2005a 1.78 +/- 0.02 1.67 +/- 0.28 0.0017 +/- 0.0003 0.47 +/- 0.09 0.007 +/- 0.002 1983 2005 b 1.80 +/- 0.02 1.80 +/- 0.13 0.0017 +/- 0.0001 0.52 +/- 0.02 0.006 +/- 0.001 ALOHA 1988 2007c 1.68 +/- 0.03 1.88 +/- 0.16 0.0019 +/- 0.0002 0.0076 +/- 0.0015 1998 2007d 0.0014 +/- 0.0002 ESTOC 1995 2004e 1.55 +/- 0.43 0.0017 +/- 0.0004 1995 2004 f 1.6 +/- 0.7 1.55 0.0015 +/- 0.0007 0.90 +/- 0.08 0.0140 +/- 0.0018 IS 1985 2006g 1.69 +/- 0.04 2.15 +/- 0.16 0.0024 +/- 0.0002 0.0072 +/- 0.0007g N. Pacific 1991 2006h 0.0017 N. Pacific 1983 2008 i Summer 1.54 +/- 0.08 Summer 1.37 +/- 0.33 Summer 0.0013 +/- 0.0005 Winter 1.65 +/- 0.05 Winter 1.58 +/- 0.12 Winter 0.0018 +/- 0.0002 Coast of western 1994 2008k 1.99 +/- 0.02 1.54 +/- 0.33 0.0020 +/- 0.0007 0.012 +/- 0.005 N. Pacific 3 b. Updated trends j,l BATS 1983 2009 1.66 +/- 0.01 1.92 +/- 0.08 0.0019 +/- 0.0001 0.59 +/- 0.04 0.0091 +/- 0.0006 1985 2009 1.67 +/- 0.01 2.02 +/- 0.08 0.0020 +/- 0.0001 0.68 +/- 0.04 0.0105 +/- 0.0006 1988 2009 1.73 +/- 0.01 2.22 +/- 0.11 0.0022 +/- 0.0001 0.87 +/- 0.05 0.0135 +/- 0.0008 1995 2009 1.90 +/- 0.01 2.16 +/- 0.18 0.0021 +/- 0.0002 0.80 +/- 0.08 0.0125 +/- 0.0013 ALOHA 1988 2009 1.73 +/- 0.01 1.82 +/- 0.07 0.0018 +/- 0.0001 0.52 +/- 0.04 0.0083 +/- 0.0007 1995 2009 1.92 +/- 0.01 1.58 +/- 0.13 0.0015 +/- 0.0001 0.40 +/- 0.07 0.0061 +/- 0.0028 ESTOC 1995 2009 1.88 +/- 0.02 1.83 +/- 0.15 0.0017 +/- 0.0001 0.72 +/- 0.05 0.0123 +/- 0.0015 IS 1985 2009m 1.75 +/- 0.01 2.07 +/- 0.15 0.0024 +/- 0.0002 0.47 +/- 0.04 0.0071 +/- 0.0006 1988 2009 m 1.70 +/- 0.01 1.96 +/- 0.22 0.0023 +/- 0.0003 0.48 +/- 0.05 0.0073 +/- 0.0008 1995 2009m 1.90 +/- 0.01 2.01 +/- 0.37 0.0022 +/- 0.0004 0.40 +/- 0.08 0.0062 +/- 0.0012 Notes: * pH on the total scale. a Bates (2007, Table 1): Simple linear fit. b Bates (2007, Table 2): Seasonally detrended (including linear term for time). c Dore et al. (2009): Linear fit with calculated pH and pCO2 from measured DIC and TA (full time series); corresponding a from Feely et al. (2009). d Dore et al. (2009): Linear fit with measured pH (partial time series). e Santana-Casiano et al. (2007): Seasonal detrending (including linear terms for time and temperature). f González-Dávila et al. (2010): Seasonal detrending (including linear terms for time, temperature and mixed-layer depth). g Olafsson et al. (2009): Multivariable linear regression (linear terms for time and temperature) for winter data only. h Byrne et al. (2010): Meridional section originally occupied in 1991 and repeated in 2006. Midorikawa et al. (2010): Winter and summer observations along 137°E. i Trends are for linear time term in seasonal detrending with harmonic periods of 12, 6 and 4 months. Harmonic analysis made after interpolating data to regular monthly grids (except for IS, which was j sampled much less frequently): 1983 2009 = September 1983 to December 2009 (BATS/Hydrostation S sampling period), 1985 2009 = February 1985 to December 2009 (IS sampling period), 1988 2009 = November 1988 to December 2009 (ALOHA/HOT sampling period), and 1995 2009 = September 1995 to December 2009 (ESTOC sampling period). k Ishii et al. (2011) - time-series observations in the coast of western North Pacific, with the seasonal cycle removed Atmospheric pCO2 trends computed from same harmonic analysis (12-, 6- and 4-month periods) on the GLOBALVIEW-CO2 (2010) data product for the marine boundary layer referenced to the latitude l of the nearest atmospheric measurement station (BME = Bermuda; MLO = ALOHA; IZO = ESTOC; ICE = Iceland). m Winter ocean data, collected during dark period (between 19 January and 7 March), as per Olafsson et al. (2009) to reduce scatter from large interannual variations in intense short-term bloom events, undersampled in time, fit linearly (y = at + bT + c). 299 Chapter 3 Observations: Ocean cation of coastal waters (Diaz and Rosenberg, 2008; Seitzinger et al., tracer data), it is very likely that the global ocean inventory of anthropo- 2010; Kim et al., 2011), which amplifies the drawdown of CO2 (Borges genic carbon (Cant) increased from 1994 to 2010. The oceanic Cant inven- and Gypens, 2010; Provoost et al., 2010). In addition, atmospheric tory in 2010 is estimated to be 155 PgC with an uncertainty of +/-20%. deposition of anthropogenic fixed nitrogen may now account for up The annual global oceanic uptake rates calculated from independent to about 3% of oceanic new production, and this nutrient source is data sets (from oceanic Cant inventory changes, from atmospheric O2/ projected to increase (Duce et al., 2008). N2 measurements or from pCO2 data) and for different time periods agree with each other within their uncertainties and very likely are in Satellite observations of chlorophyll reveal that oligotrophic provinc- the range of 1.0 to 3.2 PgC yr 1. (Section 3.8.1, Figures 3.16 and 3.17) es in four of the world s major oceans expanded at average rates of 0.8 to 4.3% yr 1 from 1998 to 2006 (Polovina et al., 2008; Irwin and Oceanic uptake of anthropogenic CO2 results in gradual acidification Oliver, 2009), consistent with a reduction in nutrient availability owing of the ocean. The pH of surface seawater has decreased by 0.1 since to increases in stratification. Model and observational studies suggest the beginning of the industrial era, corresponding to a 26% increase interannual and multi-decadal fluctuations in nutrients are coupled in hydrogen ion concentration. The observed pH trends range between with variability of mode water and the NAO in the Atlantic Ocean 0.0014 and 0.0024 yr 1 in surface waters. In the ocean interior, nat- (Cianca et al., 2007; Pérez et al., 2010), climate modes of variability ural physical and biological processes, as well as uptake of anthro- in the Pacific Ocean (Wong et al., 2007; Di Lorenzo et al., 2009), and pogenic CO2, can cause changes in pH over decadal and longer time variability of subtropical gyre circulation in the Indian Ocean (Álvarez scales (Section 3.8.2, Table 3.2, Box 3.2, Figures 3.18 and 3.19, FAQ et al., 2011). However, there are no published studies quantifying long- 3.3). term trends in ocean nutrient concentrations. High agreement among analyses provides medium confidence that 3.8.5 Conclusions oxygen concentrations have decreased in the open ocean thermocline in many ocean regions since the 1960s. The general decline is consist- Based on high agreement between independent estimates using differ- ent with the expectation that warming-induced stratification leads to a 3 ent methods and data sets (e.g., oceanic carbon, oxygen, and transient decrease in the supply of oxygen to the thermocline from near surface 60°W 0° 60°E 120°E 180°W 120°W 60°W 300 30°N 200 (dbar) 15°N 200 (umol kg-1) 0° 15°S 100 30°S (a) 0 30°N 200 (dbar) 15°N 0° 15°S 30°S (b) 30°N 200-700 (dbar) 16 15°N 8 (umol kg-1) 0° 0 15°S -8 30°S (c) -16 60°W 0° 60°E 120°E 180°W 120°W 60°W Figure 3.20 | Dissolved oxygen (DO) distributions (in umol kg 1) between 40°S and 40°N for: (a) the climatological mean (Boyer et al., 2006) at 200 dbar, as well as changes between 1960 and 1974 and 1990 and 2008 of (b) dissolved oxygen ( DO) at 200 dbar and (c) DO vertically averaged over 200 to 700 dbar. In (b) and (c) increases are red and decreases blue, and areas with differences below the 95% confidence interval are shaded by black horizontal lines. (After Stramma et al., 2010.) 300 Observations: Ocean Chapter 3 waters, that warmer waters can hold less oxygen, and that changes in This consistency is illustrated here with two simple figures (Figures 3.21 wind-driven circulation affect oxygen concentrations. It is likely that and 3.22). Four global measures of ocean change have increased since the tropical oxygen minimum zones have expanded in recent decades the 1950s: the inventory of anthropogenic CO2, global mean sea level, (Section 3.8.3, Figure 3.20). upper ocean heat content, and the salinity contrast between regions of high and low sea surface salinity (Figure 3.21). High agreement among multiple lines of evidence based on independent data and different 3.9 Synthesis methods provides high confidence in the observed increase in these global metrics of ocean change. Substantial progress has been made since AR4 in documenting and understanding change in the ocean. The major findings of this chapter The distributions of trends in subsurface water properties, summarized are largely consistent with those of AR4, but in many cases statements in a schematic zonally averaged view in Figure 3.22, are consistent can now be made with greater confidence because more data are both with each other and with well-understood dynamics of ocean available, biases in historical data have been identified and reduced, circulation and water mass formation. The largest changes in and new analytical approaches have been applied. temperature, salinity, anthropogenic CO2, and other properties are observed along known ventilation pathways (indicated by arrows in Changes have been observed in a number of ocean properties of Figure 3.22), where surface waters are transferred to the ocean interior, relevance to climate. It is virtually certain that the upper ocean (0 to or in regions where changes in ocean circulation (e.g., contraction or 700 m) has warmed from 1971 to 2010 (Section 3.2.2, Figures 3.1 and expansion of gyres, or a southward shift of the Antarctic Circumpolar 3.2). Warming between 700 and 2000 m likely contributed about 30% of the total increase in global ocean heat content between 1957 and 2009 (Section 3.2.4, Figure 3.2). Global mean sea level has risen by (a) 125 0.19 [0.17 to 0.21] m over the period 1901 2010. It is very likely that 100 the mean rate was 1.7 [1.5 to 1.9] mm yr 1 between 1901 and 2010 and increased to 3.2 [2.8 to 3.6] mm yr 1 between 1993 and 2010 75 Carbon 3 (PgC) (Section 3.7, Figure 3.13). The rise in mean sea level can explain most 50 of the observed increase in extreme sea levels (Figure 3.15). Regional 25 trends in sea surface salinity have very likely enhanced the mean 0 geographical contrasts in sea surface salinity since the 1950s: saline 1950 1960 1970 1980 1990 2000 2010 surface waters in evaporation-dominated regions have become more (b) 150 saline, while fresh surface waters in rainfall-dominated regions have 100 become fresher. It is very likely that trends in salinity have also occurred Global mean sea level (mm) in the ocean interior. These salinity changes provide indirect evidence 50 that the pattern of evaporation minus precipitation over the oceans has been enhanced since the 1950s (Section 3.4, Figures 3.4 and 3.5]. 0 Observed changes in water mass properties likely reflect the combined 1950 1960 1970 1980 1990 2000 2010 effect of long-term trends in surface forcing (e.g., warming and (c) 250 Upper ocean heat content changes in evaporation minus precipitation) and variability associated 200 with climate modes (Section 3.5, Figure 3.9). It is virtually certain that 150 (ZJ) the ocean is storing anthropogenic CO2 and very likely that the ocean 100 inventory of anthropogenic CO2 increased from 1994 to 2010 (Section 50 3.8, Figures 3.16 and 3.17). The uptake of anthropogenic CO2 has very likely caused acidification of the ocean (Section 3.8.2, Box 3.2). 0 1950 1960 1970 1980 1990 2000 2010 (d) 0.09 High salinity minus low salinity For some ocean properties, the short and incomplete observational 0.06 (PSS-78) record is not sufficient to detect trends. For example, there is no obser- 0.03 vational evidence for or against a change in the strength of the AMOC 0 (Section 3.6, Figure 3.11). However, recent observations have strength- -0.03 ened evidence for variability in major ocean circulation systems and -0.06 water mass properties on time scales from years to decades. Much of -0.09 1950 1960 1970 1980 1990 2000 2010 the variability observed in ocean currents and in water masses can be Year linked to changes in surface forcing, including wind changes associat- ed with the major modes of climate variability such as the NAO, SAM, Figure 3.21 | Time series of changes in large-scale ocean climate properties. From ENSO, PDO and the AMO (Section 3.6, Box 2.5). top to bottom: global ocean inventory of anthropogenic carbon dioxide, updated from Khatiwala et al. (2009); global mean sea level (GMSL), from Church and White (2011); global upper ocean heat content anomaly, updated from Domingues et al. (2008); the The consistency between the patterns of change in a number of difference between salinity averaged over regions where the sea surface salinity is independent ocean parameters enhances confidence in the assessment greater than the global mean sea surface salinity ( High Salinity ) and salinity averaged that the physical and biogeochemical state of the oceans has changed. over regions values below the global mean ( Low Salinity ), from Boyer et al. (2009). 301 Chapter 3 Observations: Ocean Current) result in large anomalies. Zonally averaged warming trends Improvements in the quality and quantity of ocean observations has are widespread throughout the upper 2000 m, with largest warming allowed for a more definitive assessment of ocean change than was near the sea surface. Water masses formed in the precipitation- possible in AR4. However, substantial uncertainties remain. In many dominated mid to high latitudes have freshened, while water masses cases, the observational record is still too short or incomplete to detect formed in the evaporation-dominated subtropics have become saltier. trends in the presence of energetic variability on time scales of years Anthropogenic CO2 has accumulated in surface waters and been to decades. Recent improvements in the ocean observing system, most transferred into the interior, primarily by water masses formed in the notably the Argo profiling float array, mean that temperature and North Atlantic and Southern Oceans. salinity are now being sampled routinely in most of the ocean above 2000 m depth for the first time. However, sparse sampling of the deep In summary, changes have been observed in ocean properties of rele- ocean and of many biogeochemical variables continues to limit the vance to climate during the past 40 years, including temperature, salin- ability to detect and understand changes in the global ocean. ity, sea level, carbon, pH, and oxygen. The observed patterns of change are consistent with changes in the surface ocean (warming, changes in salinity and an increase in Cant) in response to climate change and variability and with known physical and biogeochemical processes in the ocean, providing high confidence in this assessment. Chapter 10 discusses the extent to which these observed changes can be attribut- ed to human or natural forcing. 3 Depth (m) Temperature change (°C per decade) Figure 3.22 | Summary of observed changes in zonal averages of global ocean properties. Temperature trends (degrees Celsius per decade) are indicated in colour (red = warm- ing, blue = cooling); salinity trends are indicated by contour lines (dashed = fresher; solid = saltier) for the upper 2000 m of the water column (50-year trends from data set of Durack and Wijffels (2010); trends significant at >90% confidence are shown). Arrows indicate primary ventilation pathways. Changes in other physical and chemical properties are summarised to the right of the figure, for each depth range (broken axes symbols delimit changes in vertical scale). Increases are shown in red, followed by a plus sign; decreases are shown in blue, followed by a minus sign; the number of + and signs indicates the level of confidence associated with the observation of change (+++, high confidence; ++, medium confidence; +, low confidence). T = temperature, S = salinity, Strat = stratification, Cant = anthropogenic carbon, CO32 = carbonate ion, NA = North Atlantic, SO = Southern Ocean, AABW = Antarctic Bottom Water. S > S refers to the salinity averaged over regions where the sea surface salinity is greater than the global mean sea surface salinity; S < S refers to the average over regions with values below the global mean. 302 Observations: Ocean Chapter 3 References Abeysirigunawardena, D. S., and I. J. Walker, 2008: Sea level responses to climatic Boyer, T. P., S. Levitus, J. I. Antonov, R. A. Locarnini, and H. E. Garcia, 2005: Linear variability and change in Northern British Columbia. Atmos. Ocean, 46, 277 trends in salinity for the World Ocean, 1955 1998. Geophys. Res. Lett., 32, 296. L01604. Ablain, M., A. Cazenave, S. Guinehut, and G. Valladeau, 2009: A new assessment Boyer, T. P., et al., 2006: Introduction. World Ocean Database 2005 (DVD), NOAA Atlas of global mean sea level from altimeters highlights a reduction of global slope NESDIS, Vol. 60 [S. Levitus, (ed.)]. US Government Printing Office, Washington, from 2005 to 2008 in agreement with in-situ measurements. Ocean Sci., 5, 193 DC, pp. 15 37. - 201. Boyer, T. P., et al., 2009: Chapter 1: Introduction. World Ocean Database 2009, NOAA Alory, G., S. Wijffels, and G. Meyers, 2007: Observed temperature trends in the Indian Atlas NESDIS 66, DVD ed., S. Levitus, Ed., U.S. Gov. Printing Office, Wash., D.C., Ocean over 1960 1999 and associated mechanisms. Geophys. Res. Lett., 34, USA, pp. 216. L02606. Broecker, W., and E. Clark, 2001: A dramatic Atlantic dissolution event at the onset of Álvarez, M., T. Tanhua, H. Brix, C. Lo Monaco, N. Metzl, E. L. McDonagh, and H. L. the last glaciation. Geochem. Geophys. Geosyst., 2, 2001GC000185. Bryden, 2011: Decadal biogeochemical changes in the subtropical Indian Ocean Bromirski, P. D., A. J. Miller, R. E. Flick, and G. Auad, 2011: Dynamical suppression of associated with Subantarctic Mode Water. J. Geophys. Res. Oceans, 116, C09016. sea level rise along the Pacific coast of North America: Indications for imminent Andersson, A., C. Klepp, K. Fennig, S. Bakan, H. Grassl, and J. Schulz, 2011: Evaluation acceleration. J. Geophys. Res. Oceans, 116, C07005. of HOAPS-3 ocean surface freshwater flux components. J. Appl. Meteorol. Bryden, H. L., H. R. Longworth, and S. A. Cunningham, 2005: Slowing of the Atlantic Climatol., 50, 379 398. meridional overturning circulation at 25°N. Nature, 438, 655 657. Antonov, J. I., et al., 2010: World Ocean Atlas 2009, Vol. 2: Salinity. NOAA Atlas Byrne, R. H., S. Mecking, R. A. Feely, and X. W. Liu, 2010: Direct observations of basin- NESDIS 68. S. Levitus, Ed. U.S. Government Printing Office, Washington, DC, wide acidification of the North Pacific Ocean. Geophys. Res. Lett., 37, L02601. USA, 184 pp. Cai, W., 2006: Antarctic ozone depletion causes an intensification of the Southern Aoki, S., S. R. Rintoul, S. Ushio, S. Watanabe, and N. L. Bindoff, 2005: Freshening of the Ocean super-gyre circulation. Geophys. Res. Lett., 33, L03712. Adelie Land Bottom Water near 140°E. Geophys. Res. Lett., 32, L23601. Calafat, F. M., D. P. Chambers, and M. N. Tsimplis, 2012: Mechanisms of decadal Ballantyne, A. P., C. B. Alden, J. B. Miller, P. P. Tans, and J. W. C. White, 2012: Increase sea level variability in the eastern North Atlantic and the Mediterranean Sea. J. in observed net carbon dioxide uptake by land and oceans during the past 50 Geophys. Res. Oceans, 117, C09022. years. Nature, 488, 70 72. Caldeira, K., and M. E. Wickett, 2003: Anthropogenic carbon and ocean pH. Nature, Barker, P. M., J. R. Dunn, C. M. Domingues, and S. E. Wijffels, 2011: Pressure sensor 425, 365 365. 3 drifts in Argo and their impacts. J. Atmos. Ocean. Technol., 28, 1036 1049. Carson, M., and D. E. Harrison, 2010: Regional interdecadal variability in bias- Bates, N. R., 2007: Interannual variability of the oceanic CO2 sink in the subtropical corrected ocean temperature data. J. Clim., 23, 2847 2855. gyre of the North Atlantic Ocean over the last 2 decades. J. Geophys. Res. Carton, J. A., and A. Santorelli, 2008: Global decadal upper-ocean heat content as Oceans, 112, C09013. viewed in nine analyses. J. Clim., 21, 6015 6035. Bates, N. R., 2012: Multi-decadal uptake of carbon dioxide into subtropical mode Carton, J. A., B. S. Giese, and S. A. Grodsky, 2005: Sea level rise and the warming of water of the North Atlantic Ocean. Biogeosciences, 9, 2649 2659. the oceans in the Simple Ocean Data Assimilation (SODA) ocean reanalysis. J. Beckley, B. D., et al., 2010: Assessment of the Jason-2 extension to the TOPEX/ Geophys. Res. Oceans, 110, C09006. Poseidon, Jason-1 sea-surface height time series for global mean sea level Cazenave, A., et al., 2009: Sea level budget over 2003 2008: A re-evaluation from monitoring. Mar. Geodesy, 33, 447 471. GRACE space gravimetry, satellite altimetry and Argo. Mar. Geodesy, 65, 447 Beltrami, H., J. E. Smerdon, H. N. Pollack, and S. P. Huang, 2002: Continental heat gain - 471. in the global climate system. Geophys. Res. Lett., 29, 3. Cazenave, A., et al., 2012: Estimating ENSO influence on the global mean sea level, Bersch, M., I. Yashayaev, and K. P. Koltermann, 2007: Recent changes of the 1993 2010. Mar. Geodesy, 35, 82 97. thermohaline circulation in the subpolar North Atlantic. Ocean Dyn., 57, 223 Chambers, D. P., J. Wahr, and R. S. Nerem, 2004: Preliminary observations of global 235. ocean mass variations with GRACE. Geophys. Res. Lett., 31, L13310. Bindoff, N. L., and T. J. McDougall, 1994: Diagnosing climate-change and ocean Chambers, D. P., M. A. Merrifield, and R. S. Nerem, 2012: Is there a 60-year oscillation ventilation using hydrographic data. J. Phys. Oceanogr., 24, 1137 1152. in global mean sea level? Geophys. Res. Lett., 39, L18607. Bindoff, N. L., et al., 2007: Observations: Oceanic climate change and sea level. Chambers, D. P., J. Wahr, M. E. Tamisiea, and R. S. Nerem, 2010: Ocean mass from In: Climate Change 2007: The Physical Science Basis. Contribution of Working GRACE and glacial isostatic adjustment. J. Geophys. Res. Sol. Ea., 115, B11415. Group I to the Fourth Assessment Report of the Intergovernmental Panel on Chan, F., J. A. Barth, J. Lubchenco, A. Kirincich, H. Weeks, W. T. Peterson, and B. A. Climate Change [Solomon, S., D. Qin, M. Manning, Z. Chen, M. Marquis, K. B. Menge, 2008: Emergence of anoxia in the California current large marine Averyt, M. Tignor and H. L. Miller (eds.)] Cambridge University Press, Cambridge, ecosystem. Science, 319, 920 920. United Kingdom and New York, NY, USA. Chavez, F. P., M. Messié, and J. T. Pennington, 2011: Marine primary production in Bingham, R. J., and C. W. Hughes, 2009: Signature of the Atlantic meridional relation to climate variability and change. Annu. Rev. Mar. Sci., 3, 227 260. overturning circulation in sea level along the east coast of North America. Church, J. A., and N. J. White, 2006: A 20th century acceleration in global sea-level Geophys. Res. Lett., 36, L02603. rise. Geophys. Res. Lett., 33, L01602. Boening, C., J. K. Willis, F. W. Landerer, R. S. Nerem, and J. Fasullo, 2012: The 2011 La Church, J. A., and N. J. White, 2011: Sea-level rise from the late 19th to the early 21st Nina: So strong, the oceans fell. Geophys. Res. Lett., 39, L19602. century. Surv. Geophys., 32, 585 602. Bograd, S. J., C. G. Castro, E. Di Lorenzo, D. M. Palacios, H. Bailey, W. Gilly, and F. P. Church, J. A., J. R. Hunter, K. L. McInnes, and N. J. White, 2006: Sea-level rise around Chavez, 2008: Oxygen declines and the shoaling of the hypoxic boundary in the the Australian coastline and the changing frequency of extreme sea-level events. California Current. Geophys. Res. Lett., 35, L12607. Aust. Meteorol. Mag., 55, 253 260. Böning, C. W., A. Dispert, M. Visbeck, S. R. Rintoul, and F. U. Schwarzkopf, 2008: The Church, J. A., N. J. White, R. Coleman, K. Lambeck, and J. X. Mitrovica, 2004: Estimates response of the Antarctic Circumpolar Current to recent climate change. Nature of the regional distribution of sea level rise over the 1950 2000 period. J. Clim., Geosci., 1, 864 869. 17, 2609 2625. Borges, A. V., and N. Gypens, 2010: Carbonate chemistry in the coastal zone responds Church, J. A., et al., 2011: Revisiting the Earth s sea-level and energy budgets from more strongly to eutrophication than to ocean acidification. Limnol. Oceanogr., 1961 to 2008. Geophys. Res. Lett., 38, L18601. 55, 346 353. Cianca, A., P. Helmke, B. Mourino, M. J. Rueda, O. Llinas, and S. Neuer, 2007: Decadal Boyer, T., S. Levitus, J. Antonov, R. Locarnini, A. Mishonov, H. Garcia, and S. A. Josey, analysis of hydrography and in situ nutrient budgets in the western and eastern 2007: Changes in freshwater content in the North Atlantic Ocean 1955 2006. North Atlantic subtropical gyre. J. Geophys. Res. Oceans, 112, C07025. Geophys. Res. Lett., 34, L16603. Compo, G. P., et al., 2011: The Twentieth Century Reanalysis Project. Q. J. R. Meteor. Soc., 137, 1 28. 303 Chapter 3 Observations: Ocean Cravatte, S., T. Delcroix, D. X. Zhang, M. McPhaden, and J. Leloup, 2009: Observed Emori, S., and S. J. Brown, 2005: Dynamic and thermodynamic changes in mean and freshening and warming of the western Pacific Warm Pool. Clim. Dyn., 33, 565 extreme precipitation under changed climate. Geophys. Res. Lett., 32, L17706. 589. Fabry, V. J., B. A. Seibel, R. A. Feely, and J. C. Orr, 2008: Impacts of ocean acidification Cummins, P. F., and H. J. Freeland, 2007: Variability of the North Pacific current and on marine fauna and ecosystem processes. Ices J. Mar. Sci., 65, 414 432. its bifurcation. Prog. Oceanogr., 75, 253 265. Farneti, R., T. L. Delworth, A. J. Rosati, S. M. Griffies, and F. Zeng, 2010: The role of Cunningham, S. A., S. G. Alderson, B. A. King, and M. A. Brandon, 2003: Transport and mesoscale eddies in the rectification of the Southern Ocean response to climate variability of the Antarctic Circumpolar Current in Drake Passage. J. Geophys. change. J. Phys. Oceanogr., 40, 1539 1557. Res. Oceans, 108, 8084. Feely, R. A., S. C. Doney, and S. R. Cooley, 2009: Ocean acidification: Present Cunningham, S. A., et al., 2007: Temporal variability of the Atlantic meridional conditions and future changes in a high-CO2 world. Oceanography, 22, 36 47. overturning circulation at 26.5°N. Science, 317, 935 938. Feely, R. A., T. Takahashi, R. Wanninkhof, M. J. McPhaden, C. E. Cosca, S. C. Curry, R., and C. Mauritzen, 2005: Dilution of the northern North Atlantic Ocean in Sutherland, and M. E. Carr, 2006: Decadal variability of the air-sea CO2 fluxes in recent decades. Science, 308, 1772 1774. the equatorial Pacific Ocean. J. Geophys. Res. Oceans, 111, C08s90. Curry, R., B. Dickson, and I. Yashayaev, 2003: A change in the freshwater balance of Feng, M., M. J. McPhaden, and T. Lee, 2010: Decadal variability of the Pacific the Atlantic Ocean over the past four decades. Nature, 426, 826 829. subtropical cells and their influence on the southeast Indian Ocean. Geophys. D Onofrio, E. E., M. M. E. Fiore, and J. L. Pousa, 2008: Changes in the regime of storm Res. Lett., 37, L09606. surges at Buenos Aires, Argentina. J. Coast. Res., 24, 260 265. Fischer, J., M. Visbeck, R. Zantopp, and N. Nunes, 2010: Interannual to decadal Dee, D. P., et al., 2011: The ERA-Interim reanalysis: Configuration and performance of variability of outflow from the Labrador Sea. Geophys. Res. Lett., 37, L24610. the data assimilation system. Q. J. R. Meteor. Soc., 137, 553 597. Frajka-Williams, E., S. A. Cunningham, H. Bryden, and B. A. King, 2011: Variability of Delcroix, T., S. Cravatte, and M. J. McPhaden, 2007: Decadal variations and trends Antarctic Bottom Water at 24.5°N in the Atlantic. J. Geophys. Res. Oceans, 116, in tropical Pacific sea surface salinity since 1970. J. Geophys. Res. Oceans, 112, C11026. C03012. Freeland, H., et al., 2010: Argo A decade of progress. In: Proceedings of Deutsch, C., S. Emerson, and L. Thompson, 2005: Fingerprints of climate change in OceanObs 09: Sustained Ocean Observations and Information for Society (Vol. North Pacific oxygen. Geophys. Res. Lett., 32, L16604. 2). Venice, Italy,21-25 September 2009, Hall, J., Harrison, D.E. & Stammer, D., Eds., Di Lorenzo, E., et al., 2009: Nutrient and salinity decadal variations in the central and European Space Agency, ESA Publication WPP-306, doi:10.5270/OceanObs09. eastern North Pacific. Geophys. Res. Lett., 36, L14601. cwp.32 Diaz, R. J., and R. Rosenberg, 2008: Spreading dead zones and consequences for Freeland, H. J., and D. Gilbert, 2009: Estimate of the steric contribution to global marine ecosystems. Science, 321, 926 929. sea level rise from a comparison of the WOCE one-time survey with 2006 2008 3 Dickson, B., I. Yashayaev, J. Meincke, B. Turrell, S. Dye, and J. Holfort, 2002: Rapid Argo observations. Atmos. Ocean, 47, 292 298. freshening of the deep North Atlantic Ocean over the past four decades. Nature, Frölicher, T. L., F. Joos, G. K. Plattner, M. Steinacher, and S. C. Doney, 2009: Natural 416, 832 837. variability and anthropogenic trends in oceanic oxygen in a coupled carbon Dickson, R. R., et al., 2008: The overflow flux west of Iceland: variability, origins cycle-climate model ensemble. Global Biogeochem. Cycles, 23, Gb1003. and forcing. In: Arctic-Subarctic Ocean Fluxes [R. R. Dickson, J. Meincke, and Fusco, G., V. Artale, Y. Cotroneo, and G. Sannino, 2008: Thermohaline variability of P. B. Rhines (eds.)] Springer Science+Business Media, New York, NY, USA, and Mediterranean Water in the Gulf of Cadiz, 1948 1999. Deep-Sea Res. Pt. I, 55, Heidelberg, Germany, 443-474. 1624 1638. Dohan, K., et al., 2010: Measuring the global ocean surface circulation with satellite Galloway, J. N., et al., 2008: Transformation of the nitrogen cycle: Recent trends, and in situ observations. In: Proceedings of OceanObs 09: Sustained Ocean questions, and potential solutions. Science, 320, 889 892. Observations and Information for Society (Vol. 2). Venice, Italy,21-25 September Garabato, A. C. N., L. Jullion, D. P. Stevens, K. J. Heywood, and B. A. King, 2009: 2009, Hall, J., Harrison, D.E. & Stammer, D., Eds., European Space Agency, ESA Variability of Subantarctic Mode Water and Antarctic Intermediate Water in Publication WPP-306, doi:10.5270/OceanObs09.cwp.23 the Drake Passage during the late-twentieth and early-twenty-first centuries. J. Domingues, C. M., J. A. Church, N. J. White, P. J. Gleckler, S. E. Wijffels, P. M. Barker, Clim., 22, 3661 3688. and J. R. Dunn, 2008: Improved estimates of upper-ocean warming and multi- Garzoli, S. L., M. O. Baringer, S. F. Dong, R. C. Perez, and Q. Yao, 2013: South Atlantic decadal sea-level rise. Nature, 453, 1090 1093. meridional fluxes. Deep-Sea Res. Pt. I, 71, 21 32. Doney, S. C., V. J. Fabry, R. A. Feely, and J. A. Kleypas, 2009: Ocean Acidification: The Gebbie, G., and P. Huybers, 2012: The mean age of ocean waters inferred from other CO2 problem. Annu. Rev. Mar. Sci., 1, 169 192. radiocarbon observations: sensitivity to surface sources and accounting for Dore, J. E., R. Lukas, D. W. Sadler, M. J. Church, and D. M. Karl, 2009: Physical and mixing histories. J. Phys. Oceanogr., 42, 291 305. biogeochemical modulation of ocean acidification in the central North Pacific. Gemmrich, J., B. Thomas, and R. Bouchard, 2011: Observational changes and trends Proc. Natl. Acad. Sci. U.S.A., 106, 12235 12240. in northeast Pacific wave records. Geophys. Res. Lett., 38, L22601. Douglas, B. C., 2001: Sea level change in the era of the recording tide gauge. In: Sea Gerber, M., F. Joos, M. Vázquez-Rodríguez, F. Touratier, and C. Goyet, 2009: Regional Level Rise: History and Consequences [B. C. Douglas, M. S. Kearney, and S. P. air-sea fluxes of anthropogenic carbon inferred with an Ensemble Kalman Filter. Leatherman (eds.)]. Academic Press, San Diego,CA, USA, pp. 37 64. Global Biogeochem. Cycles, 23, Gb1013. Douglass, E., D. Roemmich, and D. Stammer, 2006: Interannual variability in northeast Giese, B. S., G. A. Chepurin, J. A. Carton, T. P. Boyer, and H. F. Seidel, 2011: Impact of Pacific circulation. J. Geophys. Res. Oceans, 111, C04001. bathythermograph temperature bias models on an ocean reanalysis. J. Clim., Drinkwater, K. F., 2006: The regime shift of the 1920s and 1930s in the North Atlantic. 24, 84 93. Prog. Oceanogr., 68, 134 151. Gilbert, D., N. N. Rabalais, R. J. Diaz, and J. Zhang, 2010: Evidence for greater oxygen Duce, R. A., et al., 2008: Impacts of atmospheric anthropogenic nitrogen on the open decline rates in the coastal ocean than in the open ocean. Biogeosciences, 7, ocean. Science, 320, 893 897. 2283 2296. Ducet, N., P. Y. Le Traon, and G. Reverdin, 2000: Global high-resolution mapping Giles, K. A., S. W. Laxon, A. L. Ridout, D. J. Wingham, and S. Bacon, 2012: Western of ocean circulation from TOPEX/Poseidon and ERS-1 and-2. J. Geophys. Res. Arctic Ocean freshwater storage increased by wind-driven spin-up of the Oceans, 105, 19477 19498. Beaufort Gyre. Nature Geosci., 5, 194 197. Durack, P. J., and S. E. Wijffels, 2010: Fifty-year trends in global ocean salinities and Gille, S. T., 2008: Decadal-scale temperature trends in the Southern Hemisphere their relationship to broad-scale warming. J. Clim., 23, 4342 4362. ocean. J. Clim., 21, 4749 4765. Durack, P. J., S. E. Wijffels, and R. J. Matear, 2012: Ocean salinities reveal strong Gillett, N. P., and P. A. Stott, 2009: Attribution of anthropogenic influence on seasonal global water cycle intensification during 1950 to 2000. Science, 336, 455 458. sea level pressure. Geophys. Res. Lett., 36, L23709. Egleston, E. S., C. L. Sabine, and F. M. M. Morel, 2010: Revelle revisited: Buffer factors Gladyshev, S. V., M. N. Koshlyakov, and R. Y. Tarakanov, 2008: Currents in the Drake that quantify the response of ocean chemistry to changes in DIC and alkalinity. Passage based on observations in 2007. Oceanology, 48, 759 770. Global Biogeochem. Cycles, 24, GB1002. Gleckler, P. J., et al., 2012: Human-induced global ocean warming on multidecadal Ekman, M., 1988: The world s longest continued series of sea-level observations. timescales. Nature Clim. Change, 2, 524 529. Pure Appl. Geophys., 127, 73 77. 304 Observations: Ocean Chapter 3 Gloor, M., J. L. Sarmiento, and N. Gruber, 2010: What can be learned about carbon Hemer, M. A., J. A. Church, and J. R. Hunter, 2010: Variability and trends in the cycle climate feedbacks from the CO2 airborne fraction? Atmos. Chem. Phys., directional wave climate of the Southern Hemisphere. Int. J. Climatol., 30, 475 10, 7739 7751. 491. Goni, G. J., F. Bringas, and P. N. DiNezio, 2011: Observed low frequency variability of Hill, K. L., S. R. Rintoul, R. Coleman, and K. R. Ridgway, 2008: Wind forced low the Brazil Current front. J. Geophys. Res. Oceans, 116, C10037. frequency variability of the East Australia Current. Geophys. Res. Lett., 35, González-Dávila, M., J. M. Santana-Casiano, M. J. Rueda, and O. Llinas, 2010: The L08602. water column distribution of carbonate system variables at the ESTOC site from Holgate, S. J., 2007: On the decadal rates of sea level change during the twentieth 1995 to 2004. Biogeosciences, 7, 3067 3081. century. Geophys. Res. Lett., 34, L01602. Gouretski, V., and K. P. Koltermann, 2007: How much is the ocean really warming? Holliday, N., et al., 2008: Reversal of the 1960s to 1990s freshening trend in the Geophys. Res. Lett., 34, L01610. northeast North Atlantic and Nordic Seas. Geophys. Res. Lett., 35, L03614. Gouretski, V., and F. Reseghetti, 2010: On depth and temperature biases in Hong, B. G., W. Sturges, and A. J. Clarke, 2000: Sea level on the US East Coast: bathythermograph data: Development of a new correction scheme based on Decadal variability caused by open ocean wind-curl forcing. J. Phys. Oceanogr., analysis of a global ocean database. Deep-Sea Res. Pt. I, 57, 812 833. 30, 2088 2098. Gouretski, V., J. Kennedy, T. Boyer, and A. Kohl, 2012: Consistent near-surface ocean Hood, M., et al., 2010: Ship-based repeat hydrography: A strategy for a sustained warming since 1900 in two largely independent observing networks. Geophys. global program. In: Proceedings of OceanObs 09: Sustained Ocean Observations Res. Lett., 39, L19606. and Information for Society (Vol. 2). Venice, Italy. 21-25 September 2009, Hall, Graven, H. D., N. Gruber, R. Key, S. Khatiwala, and X. Giraud, 2012: Changing controls J., Harrison, D.E. & Stammer, D., Eds., European Space Agency, ESA Publication on oceanic radiocarbon: New insights on shallow-to-deep ocean exchange and WPP-306, doi:10.5270/OceanObs09.cwp.44 anthropogenic CO2 uptake. J. Geophys. Res. Oceans, 117, C10005. Hosoda, S., T. Suga, N. Shikama, and K. Mizuno, 2009: Global surface layer salinity Griffies, S. M., et al., 2009: Coordinated Ocean-ice Reference Experiments (COREs). change detected by Argo and its implication for hydrological cycle intensification. Ocean Model., 26, 1 46. J. Oceanogr., 65, 579 586. Grinsted, A., J. C. Moore, and S. Jevrejeva, 2012: Homogeneous record of Atlantic Houston, J. R., and R. G. Dean, 2011: Sea-level acceleration based on US tide gauges hurricane surge threat since 1923. Proc. Natl. Acad. Sci. U.S.A., 109, 19601 and extensions of previous global-gauge analyses. J. Coast. Res., 27, 409 417. 19605. Hughes, C. W., P. L. Woodworth, M. P. Meredith, V. Stepanov, T. Whitworth, and A. R. Grist, J. P., R. Marsh, and S. A. Josey, 2009: On the relationship between the North Pyne, 2003: Coherence of Antarctic sea levels, Southern Hemisphere Annular Atlantic Meridional Overturning Circulation and the surface-forced overturning Mode, and flow through Drake Passage. Geophys. Res. Lett., 30, 1464. streamfunction. J. Clim., 22, 4989 5002. Huhn, O., M. Rhein, M. Hoppema, and S. van Heuven, 2013: Decline of deep and Gruber, N., et al., 2009: Oceanic sources, sinks, and transport of atmospheric CO2. bottom water ventilation and slowing down of anthropogenic carbon storage in Global Biogeochem. Cycles, 23, Gb1005. the Weddell Sea, 1984 2011. Deep-Sea Res. Pt. I, 76, 66 84. 3 Gu, G. J., R. F. Adler, G. J. Huffman, and S. Curtis, 2007: Tropical rainfall variability Huhn, O., H. H. Hellmer, M. Rhein, C. Rodehacke, W. G. Roether, M. P. Schodlok, on interannual-to-interdecadal and longer time scales derived from the GPCP and M. Schröder, 2008: Evidence of deep- and bottom-water formation in the monthly product. J. Clim., 20, 4033 4046. western Weddell Sea. Deep-Sea Res. Pt. II, 55, 1098 1116. Gulev, S., T. Jung, and E. Ruprecht, 2007: Estimation of the impact of sampling Ingvaldsen, R. B., L. Asplin, and H. Loeng, 2004: Velocity field of the western entrance errors in the VOS observations on air-sea fluxes. Part II: Impact on trends and to the Barents Sea. J. Geophys. Res. Oceans, 109, C03021. interannual variability. J. Clim., 20, 302 315. IPCC, 2011: Workshop Report of the Intergovernmental Panel on Climate Change Gulev, S., et al., 2010: Surface Energy and CO2 Fluxes in the Global Ocean-Atmosphere- Workshop on Impacts of Ocean Acidification on Marine Biology and Ecosystems Ice System. In: Proceedings of OceanObs 09: Sustained Ocean Observations and [C. B. Field, V. Barros, T. F. Stocker, D. Qin, K. J. Mach, G.-K. Plattner, M. D. Information for Society. Venice, Italy. 21-25 September 2009, Hall, J., Harrison, Mastrandrea, M. Tignor and K. L. Ebi (eds.)]. IPCC Working Group II Technical D.E. & Stammer, D., Eds., European Space Agency, ESA Publication WPP-306, Support Unit, Carnegie Institution, Stanford, CA, USA 164 pp. doi:10.5270/OceanObs09.pp.19 Irwin, A. J., and M. J. Oliver, 2009: Are ocean deserts getting larger? Geophys. Res. Gulev, S. K., and V. Grigorieva, 2006: Variability of the winter wind waves and swell Lett., 36, L18609. in the North Atlantic and North Pacific as revealed by the voluntary observing Ishidoya, S., S. Aoki, D. Goto, T. Nakazawa, S. Taguchi, and P. K. Patra, 2012: Time and ship data. J. Clim., 19, 5667 5685. space variations of the O2/N2 ratio in the troposphere over Japan and estimation Haigh, I., R. Nicholls, and N. Wells, 2010: Assessing changes in extreme sea levels: of the global CO2 budget for the period 2000 2010. Tellus B, 64, 18964. Application to the English Channel, 1900 2006. Cont. Shelf Res., 30, 1042 1055. Ishii, M., and M. Kimoto, 2009: Reevaluation of historical ocean heat content Hallberg, R., and A. Gnanadesikan, 2006: The role of eddies in determining the variations with time-varying XBT and MBT depth bias corrections. J. Oceanogr., structure and response of the wind-driven Southern Hemisphere overturning: 65, 287 299. Results from the Modeling Eddies in the Southern Ocean (MESO) project. J. Phys. Ishii, M., N. Kosugi, D. Sasano, S. Saito, T. Midorikawa, and H. Y. Inoue, 2011: Ocean Oceanogr., 36, 2232 2252. acidification off the south coast of Japan: A result from time series observations Hamon, M., G. Reverdin, and P. Y. Le Traon, 2012: Empirical correction of XBT data. J. of CO2 parameters from 1994 to 2008. J. Geophys. Res. Oceans, 116, C06022. Atmos. Ocean. Technol., 29, 960 973. Ishii, M., et al., 2009: Spatial variability and decadal trend of the oceanic CO2 in the Hansen, B., and S. Osterhus, 2007: Faroe Bank Channel overflow 1995 2005. Prog. western equatorial Pacific warm/fresh water. Deep-Sea Res. Pt. II., 56, 591 606. Oceanogr., 75, 817 856. Jackson, J. M., E. C. Carmack, F. A. McLaughlin, S. E. Allen, and R. G. Ingram, 2010: Hansen, B., H. Hatun, R. Kristiansen, S. M. Olsen, and S. Osterhus, 2010: Stability and Identification, characterization, and change of the near-surface temperature forcing of the Iceland-Faroe inflow of water, heat, and salt to the Arctic. Ocean maximum in the Canada Basin, 1993 2008. J. Geophys. Res. Oceans, 115, Sci., 6, 1013 1026. C05021. Hatun, H., A. B. Sando, H. Drange, B. Hansen, and H. Valdimarsson, 2005: Influence Jacobs, S. S., and C. F. Giulivi, 2010: Large multidecadal salinity trends near the of the Atlantic subpolar gyre on the thermohaline circulation. Science, 309, Pacific-Antarctic continental margin. J. Clim., 23, 4508 4524. 1841 1844. Jevrejeva, S., A. Grinsted, J. C. Moore, and S. Holgate, 2006: Nonlinear trends and Held, I. M., and B. J. Soden, 2006: Robust responses of the hydrological cycle to multiyear cycles in sea level records. J. Geophys. Res. Oceans, 111, C09012. global warming. J. Clim., 19, 5686 5699. Jevrejeva, S., J. C. Moore, A. Grinsted, and P. L. Woodworth, 2008: Recent global sea Helm, K. P., N. L. Bindoff, and J. A. Church, 2010: Changes in the global hydrological- level acceleration started over 200 years ago? Geophys. Res. Lett., 35, L08715. cycle inferred from ocean salinity. Geophys. Res. Lett., 37, L18701. Jochumsen, K., D. Quadfasel, H. Valdimarsson, and S. Jonsson, 2012: Variability of the Helm, K. P., N. L. Bindoff, and J. A. Church, 2011: Observed decreases in oxygen Denmark Strait overflow: Moored time series from 1996 2011. J. Geophys. Res. content of the global ocean. Geophys. Res. Lett., 38, L23602. Oceans, 117, C12003. Hemer, M. A., 2010: Historical trends in Southern Ocean storminess: Long-term Johns, W. E., et al., 2011: Continuous, array-based estimates of Atlantic Ocean heat variability of extreme wave heights at Cape Sorell, Tasmania. Geophys. Res. Lett., transport at 26.5°N. J. Clim., 24, 2429 2449. 37, L18601. Johnson, G. C., and S. E. Wijffels, 2011: Ocean density change contributions to sea level rise. Oceanography, 24, 112 121. 305 Chapter 3 Observations: Ocean Johnson, G. C., S. G. Purkey, and J. L. Bullister, 2008a: Warming and freshening in the Kouketsu, S., et al., 2009: Changes in water properties and transports along 24°N in abyssal southeastern Indian Ocean. J. Clim., 21, 5351 5363. the North Pacific between 1985 and 2005. J. Geophys. Res. Oceans, 114, C01008. Johnson, G. C., S. G. Purkey, and J. M. Toole, 2008b: Reduced Antarctic meridional Kouketsu, S., et al., 2011: Deep ocean heat content changes estimated from overturning circulation reaches the North Atlantic Ocean. Geophys. Res. Lett., observation and reanalysis product and their influence on sea level change. J. 35, L22601. Geophys. Res. Oceans, 116, C03012. Jónsson, S., and H. Valdimarsson, 2012: Water mass transport variability to the North Krueger, O., F. Schenk, F. Feser, and R. Weisse, 2013: Inconsistencies between long- Icelandic shelf, 1994 2010. Ices J. Mar. Sci., 69, 809 815. term trends in storminess derived from the 20CR reanalysis and observations. J. Josey, S. A., J. P. Grist, and R. Marsh, 2009: Estimates of meridional overturning Clim., 26, 868 874. circulation variability in the North Atlantic from surface density flux fields. J. Large, W. G., and S. G. Yeager, 2009: The global climatology of an interannually Geophys. Res. Oceans, 114, C09022. varying air-sea flux data set. Clim. Dyn., 33, 341 364. Kalnay, E., et al., 1996: The NCEP/NCAR 40-year reanalysis project. Bull. Am. Large, W. G., and S. G. Yeager, 2012: On the observed trends and changes in global Meteorol. Soc., 77, 437 471. sea surface temperature and air sea heat fluxes (1984 2006). J. Clim., 25, Kanamitsu, M., W. Ebisuzaki, J. Woollen, S. K. Yang, J. J. Hnilo, M. Fiorino, and G. L. 6123 6135. Potter, 2002: NCEP-DOE AMIP-II reanalysis (R-2). Bull. Am. Meteorol. Soc., 83, Le Quéré, C., T. Takahashi, E. T. Buitenhuis, C. Roedenbeck, and S. C. Sutherland, 1631 1643. 2010: Impact of climate change and variability on the global oceanic sink of Kanzow, T., U. Send, and M. McCartney, 2008: On the variability of the deep CO2. Global Biogeochem. Cycles, 24, Gb4007. meridional transports in the tropical North Atlantic. Deep-Sea Res. Pt. I, 55, Le Quéré, C., et al., 2007: Saturation of the Southern Ocean CO2 sink due to recent 1601 1623. climate change. Science, 316, 1735 8. Kanzow, T., et al., 2007: Observed flow compensation associated with the MOC at Lenton, A., et al., 2012: The observed evolution of oceanic pCO2 and its drivers over 26.5°N in the Atlantic. Science, 317, 938 941. the last two decades. Global Biogeochem. Cycles, 26, Gb2021. Kawai, Y., T. Doi, H. Tomita, and H. Sasaki, 2008: Decadal-scale changes in meridional Letetrel, C., M. Marcos, B. M. Miguez, and G. Wöppelmann, 2010: Sea level extremes heat transport across 24°N in the Pacific Ocean. J. Geophys. Res. Oceans, 113, in Marseille (NW Mediterranean) during 1885 2008. Cont. Shelf Res., 30, 1267 C08021. 1274. Kawano, T., T. Doi, H. Uchida, S. Kouketsu, M. Fukasawa, Y. Kawai, and K. Katsumata, Leuliette, E. W., and L. Miller, 2009: Closing the sea level rise budget with altimetry, 2010: Heat content change in the Pacific Ocean between the 1990s and 2000s. Argo, and GRACE. Geophys. Res. Lett., 36, L04608. Deep-Sea Res. Pt. II, 57, 1141 1151. Leuliette, E. W., and R. Scharroo, 2010: Integrating Jason-2 into a multiple-altimeter Kazmin, A. S., 2012: Variability of the large-scale frontal zones: analysis of the climate data record. Mar. Geodesy, 33, 504 517. global satellite information. Mod. Prob. Remote Sens. Ea. Space, 9, 213 218 Leuliette, E. W., and J. K. Willis, 2011: Balancing the sea level budget. Oceanography, 3 (in Russian). 24, 122 129. Keeling, C. D., H. Brix, and N. Gruber, 2004: Seasonal and long-term dynamics of the Levitus, S., 1989: Interpentadal variability of temperature and salinity at intermediate upper ocean carbon cycle at Station ALOHA near Hawaii. Global Biogeochem. depths of the North-Atlantic Ocean, 1970 1974 variabilityersus 1955 1959. J. Cycles, 18, GB4006. Geophys. Res. Oceans, 94, 6091 6131. Keeling, R.F. and A. C. Manning, 2014: Studies of Recent Changes in Atmospheric O2 Levitus, S., J. I. Antonov, T. P. Boyer, R. A. Locarnini, H. E. Garcia, and A. V. Mishonov, Content. In: Holland, H.D. and Turekian, K.K., eds. Treatise on Geochemistry, 2nd 2009: Global ocean heat content 1955 2008 in light of recently revealed Edition, Volume 5, pp.385-404. Oxford: Elsevier instrumentation problems. Geophys. Res. Lett., 36, L07608. Keeling, R. F., A. Kortzinger, and N. Gruber, 2010: Ocean deoxygenation in a warming Levitus, S., et al., 2012: World ocean heat content and thermosteric sea level change world. Annu. Rev. Mar. Sci., 2, 199 229. (0 2000m) 1955 2010. Geophys. Res. Lett., 39, L10603. Key, R. M., et al., 2004: A global ocean carbon climatology: Results from Global Data Lewis, E. L., and N. P. Fofonoff, 1979: A practical salinity scale. J. Phys. Oceanogr., Analysis Project (GLODAP). Global Biogeochem. Cycles, 18, Gb4031. 9, 446. Khatiwala, S., F. Primeau, and T. Hall, 2009: Reconstruction of the history of Li, G., B. Ren, J. Zheng, and C. Yang, 2011: Trend singular value decomposition anthropogenic CO2 concentrations in the ocean. Nature, 462, 346 349. analysis and its application to the global ocean surface latent heat flux and SST Khatiwala, S., et al., 2013: Global ocean storage of anthropogenic carbon. anomalies. J. Clim., 24, 2931 2948. Biogeosciences, 10, 2169 2191. Llovel, W., S. Guinehut, and A. Cazenave, 2010: Regional and interannual variability Kieke, D., M. Rhein, L. Stramma, W. M. Smethie, D. A. LeBel, and W. Zenk, 2006: in sea level over 2002 2009 based on satellite altimetry, Argo float data and Changes in the CFC inventories and formation rates of Upper Labrador Sea GRACE ocean mass. Ocean Dyn., 60, 1193 1204. Water, 1997 2001. J. Phys. Oceanogr., 36, 64 86. Llovel, W., B. Meyssignac, and A. Cazenave, 2011: Steric sea level variations over Kim, T. W., K. Lee, R. G. Najjar, H. D. Jeong, and H. J. Jeong, 2011: Increasing N 2004 2010 as a function of region and depth: Inference on the mass component abundance in the northwestern Pacific Ocean due to atmospheric nitrogen variability in the North Atlantic Ocean. Geophys. Res. Lett., 38, L15608. deposition. Science, 334, 505 509. Llovel, W., A. Cazenave, P. Rogel, A. Lombard, and M. B. Nguyen, 2009: Two- King, M. A., M. Keshin, P. L. Whitehouse, I. D. Thomas, G. Milne, and R. E. M. Riva, dimensional reconstruction of past sea level (1950 2003) from tide gauge data 2012: Regional biases in absolute sea-level estimates from tide gauge data due and an Ocean General Circulation Model. Clim. Past, 5, 217 227. to residual unmodeled vertical land movement. Geophys. Res. Lett., 39, L14604. Lowe, J. A., and J. M. Gregory, 2006: Understanding projections of sea level rise in Knorr, W., 2009: Is the airborne fraction of anthropogenic CO2 emissions increasing? a Hadley Centre coupled climate model. J. Geophys. Res. Oceans, 111, C11014. Geophys. Res. Lett., 36, L21710. Lowe, J. A., et al., 2010: Past and future changes in extreme sea levels and waves. Kobayashi, T., K. Mizuno, and T. Suga, 2012: Long-term variations of surface and In: Understanding Sea-Level Rise and Variability [J. A. Church, P. L. Woodworth, intermediate waters in the southern Indian Ocean along 32°S. J. Oceanogr., 68, T. Aarup, and W. S. Wilson (eds.)]. Wiley-Blackwell, New York, NY, USA, 326-375. 243 265. Lozier, M. S., and N. M. Stewart, 2008: On the temporally varying northward Komar, P. D., and J. C. Allan, 2008: Increasing hurricane-generated wave heights penetration of Mediterranean Overflow Water and eastward penetration of along the US East Coast and their climate controls. J. Coast. Res., 24, 479 488. Labrador Sea water. J. Phys. Oceanogr., 38, 2097 2103. Koshlyakov, M. N., Lisina, II, E. G. Morozov, and R. Y. Tarakanov, 2007: Absolute Lumpkin, R., and K. Speer, 2007: Global ocean meridional overturning. J. Phys. geostrophic currents in the Drake Passage based on observations in 2003 and Oceanogr., 37, 2550 2562. 2005. Oceanology, 47, 451 463. Lumpkin, R., and S. Garzoli, 2011: Interannual to decadal changes in the western Koshlyakov, M. N., S. V. Gladyshev, R. Y. Tarakanov, and D. A. Fedorov, 2011: Currents South Atlantic s surface circulation. J. Geophys. Res. Oceans, 116, C01014. in the western Drake Passage by the observations in January 2010. Oceanology, Lyman, J. M., and G. C. Johnson, 2008: Estimating annual global upper-ocean heat 51, 187 198. content anomalies despite irregular in situ ocean sampling. J. Clim., 21, 5629 Kouketsu, S., M. Fukasawa, D. Sasano, Y. Kumamoto, T. Kawano, H. Uchida, and T. 5641. Doi, 2010: Changes in water properties around North Pacific intermediate water Lyman, J. M., et al., 2010: Robust warming of the global upper ocean. Nature, 465, between the 1980s, 1990s and 2000s. Deep-Sea Res. Pt. II, 57, 1177 1187. 334 337. 306 Observations: Ocean Chapter 3 Macrander, A., U. Send, H. Valdimarsson, S. Jonsson, and R. H. Kase, 2005: Interannual Metzl, N., 2009: Decadal increase of oceanic carbon dioxide in Southern Indian changes in the overflow from the Nordic Seas into the Atlantic Ocean through Ocean surface waters (1991 2007). Deep-Sea Res. Pt. II, 56, 607 619. Denmark Strait. Geophys. Res. Lett., 32, L06606. Meyssignac, B., M. Becker, W. Llovel, and A. Cazenave, 2012: An assessment of Marcos, M., M. N. Tsimplis, and A. G. P. Shaw, 2009: Sea level extremes in southern two-dimensional past sea level reconstructions over 1950 2009 based on tide- Europe. J. Geophys. Res. Oceans, 114, C01007. gauge data and different input sea level grids. Surv. Geophys., 33, 945 972. Marcos, M., M. N. Tsimplis, and F. M. Calafat, 2012: Inter-annual and decadal sea Midorikawa, T., K. Nemoto, H. Kamiya, M. Ishii, and H. Y. Inoue, 2005: Persistently level variations in the north-western Pacific marginal seas. Prog. Oceanogr., 105, strong oceanic CO2 sink in the western subtropical North Pacific. Geophys. Res. 4 21. Lett., 32, L05612. Marshall, G. J., 2003: Trends in the southern annular mode from observations and Midorikawa, T., et al., 2010: Decreasing pH trend estimated from 25 yr time series reanalyses. J. Clim., 16, 4134 4143. of carbonate parameters in the western North Pacific. Tellus B, 62, 649 659. Masters, D., R. S. Nerem, C. Choe, E. Leuliette, B. Beckley, N. White, and M. Ablain, Mikaloff-Fletcher, S. E., et al., 2006: Inverse estimates of anthropogenic CO2 uptake, 2012: Comparison of global mean sea level time series from TOPEX/Poseidon, transport, and storage by the ocean. Global Biogeochem. Cycles, 20, Gb2002. Jason-1, and Jason-2. Mar. Geodesy, 35, 20 41. Miller, L., and B. C. Douglas, 2007: Gyre-scale atmospheric pressure variations and Masuda, S., et al., 2010: Simulated rapid warming of abyssal North Pacific waters. their relation to 19th and 20th century sea level rise. Geophys. Res. Lett., 34, Science, 329, 319 322. L16602. Matear, R. J., and A. C. Hirst, 2003: Long-term changes in dissolved oxygen Mitas, C. M., and A. Clement, 2005: Has the Hadley cell been strengthening in recent concentrations in the ocean caused by protracted global warming. Global decades? Geophys. Res. Lett., 32, L03809. Biogeochem. Cycles, 17, 1125. Mitchum, G. T., R. S. Nerem, M. A. Merrifield, and W. R. Gehrels, 2010: Modern sea- Mauritzen, C., A. Melsom, and R. T. Sutton, 2012: Importance of density-compensated level-change estimates. In: Understanding Sea-Level Rise and Variability [J. A. temperature change for deep North Atlantic Ocean heat uptake. Nature Geosci., Church, P. L. Woodworth, T. Aarup, and W. S. Wilson (eds.)]. Wiley-Blackwell, New 5, 905 910. York, NY, USA, 122-142. Mauritzen, C., et al., 2011: Closing the loop Approaches to monitoring the state of Morison, J., R. Kwok, C. Peralta-Ferriz, M. Alkire, I. Rigor, R. Andersen, and M. Steele, the Arctic Mediterranean during the International Polar Year 2007 2008. Prog. 2012: Changing Arctic Ocean freshwater pathways. Nature, 481, 66 70. Oceanogr., 90, 62 89. Morrow, R., G. Valladeau, and J. B. Sallee, 2008: Observed subsurface signature of McCarthy, G., E. McDonagh, and B. King, 2011: Decadal variability of thermocline Southern Ocean sea level rise. Prog. Oceanogr., 77, 351 366. and intermediate waters at 24°S in the South Atlantic. J. Phys. Oceanogr., 41, Murphy, D. M., S. Solomon, R. W. Portmann, K. H. Rosenlof, P. M. Forster, and T. Wong, 157 165. 2009: An observationally based energy balance for the Earth since 1950. J. McCarthy, G., et al., 2012: Observed interannual variability of the Atlantic meridional Geophys. Res. Atmos., 114, D17107. overturning circulation at 26.5°N. Geophys. Res. Lett., 39, L19609. Nakano, T., I. Kaneko, T. Soga, H. Tsujino, T. Yasuda, H. Ishizaki, and M. Kamachi, 2007: 3 McDonagh, E. L., H. L. Bryden, B. A. King, R. J. Sanders, S. A. Cunningham, and R. Mid-depth freshening in the North Pacific subtropical gyre observed along the Marsh, 2005: Decadal changes in the south Indian Ocean thermocline. J. Clim., JMA repeat and WOCE hydrographic sections. Geophys. Res. Lett., 34, L23608. 18, 1575 1590. Nakanowatari, T., K. Ohshima, and M. Wakatsuchi, 2007: Warming and oxygen McKinley, G. A., A. R. Fay, T. Takahashi, and N. Metzl, 2011: Convergence of decrease of intermediate water in the northwestern North Pacific, originating atmospheric and North Atlantic carbon dioxide trends on multidecadal from the Sea of Okhotsk, 1955 2004. Geophys. Res. Lett., 34, L04602. timescales. Nature Geosci., 4, 606 610. Nerem, R. S., D. P. Chambers, C. Choe, and G. T. Mitchum, 2010: Estimating mean McPhee, M. G., A. Proshutinsky, J. H. Morison, M. Steele, and M. B. Alkire, 2009: sea level change from the TOPEX and Jason altimeter missions. Mar. Geodesy, Rapid change in freshwater content of the Arctic Ocean. Geophys. Res. Lett., 33, 435 446. 36, L10602. Nerem, R. S., D. P. Chambers, E. W. Leuliette, G. T. Mitchum, and B. S. Giese, 1999: Mears, C. A., and F. J. Wentz, 2009a: Construction of the RSS V3.2 lower-tropospheric Variations in global mean sea level associated with the 1997 1998 ENSO event: temperature dataset from the MSU and AMSU microwave sounders. J. Atmos. Implications for measuring long term sea level change. Geophys. Res. Lett., 26, Ocean. Technol., 26, 1493 1509. 3005 3008. Mears, C. A., and F. J. Wentz, 2009b: Construction of the Remote Sensing Systems Olafsson, J., S. R. Olafsdottir, A. Benoit-Cattin, M. Danielsen, T. S. Arnarson, and T. V3.2 atmospheric temperature records from the MSU and AMSU microwave Takahashi, 2009: Rate of Iceland Sea acidification from time series measurements. sounders. J. Atmos. Ocean. Technol., 26, 1040 1056. Biogeosciences, 6, 2661 2668. Meijers, A. J. S., N. L. Bindoff, and S. R. Rintoul, 2011: Frontal movements and property Olsen, A., A. M. Omar, E. Jeansson, L. G. Anderson, and R. G. J. Bellerby, 2010: Nordic fluxes: Contributions to heat and freshwater trends in the Southern Ocean. J. seas transit time distributions and anthropogenic CO2. J. Geophys. Res. Oceans, Geophys. Res. Oceans, 116, C08024. 115, C05005. Meinen, C. S., M. O. Baringer, and R. F. Garcia, 2010: Florida Current transport Olsen, S. M., B. Hansen, D. Quadfasel, and S. Osterhus, 2008: Observed and modelled variability: An analysis of annual and longer-period signals. Deep-Sea Res. Pt. stability of overflow across the Greenland-Scotland ridge. Nature, 455, 519 22. I, 57, 835 846. Ono, T., T. Midorikawa, Y. W. Watanabe, K. Tadokoro, and T. Saino, 2001: Temporal Menéndez, M., and P. L. Woodworth, 2010: Changes in extreme high water levels increases of phosphate and apparent oxygen utilization in the subsurface based on a quasi-global tide-gauge data set. J. Geophys. Res. Oceans, 115, waters of western subarctic Pacific from 1968 to 1998. Geophys. Res. Lett., 28, C10011. 3285 3288. Menéndez, M., F. J. Méndez, I. J. Losada, and N. E. Graham, 2008: Variability of extreme Orr, J. C., 2011: Recent and future changes in ocean carbonate chemistry. In: Ocean wave heights in the northeast Pacific Ocean based on buoy measurements. Acidification [J.-P. Gattuso and L. Hansson (eds.)]. Oxford University Press, Geophys. Res. Lett., 35, L22607. Oxford, UK, and New York, NY, USA, pp. 41 66. Meredith, M. P., P. L. Woodworth, C. W. Hughes, and V. Stepanov, 2004: Changes in Orr, J. C., S. Pantoja, and H. O. Pörtner, 2005a: Introduction to special section: The the ocean transport through Drake Passage during the 1980s and 1990s, forced ocean in a high-CO2 world. J. Geophys. Res. Oceans, 110, C09S01. by changes in the Southern Annular Mode. Geophys. Res. Lett., 31, L21305. Orr, J. C., et al., 2005b: Anthropogenic ocean acidification over the twenty-first Merrifield, M. A., 2011: A shift in western tropical Pacific sea level trends during the century and its impact on calcifying organisms. Nature, 437, 681 686. 1990s. J. Clim., 24, 4126 4138. Orsi, A. H., G. C. Johnson, and J. L. Bullister, 1999: Circulation, mixing, and production Merrifield, M. A., and M. E. Maltrud, 2011: Regional sea level trends due to a Pacific of Antarctic Bottom Water. Prog. Oceanogr., 43, 55 109. trade wind intensification. Geophys. Res. Lett., 38, L21605. Osterhus, S., W. R. Turrell, S. Jonsson, and B. Hansen, 2005: Measured volume, heat, Merrifield, M. A., S. T. Merrifield, and G. T. Mitchum, 2009: An anomalous recent and salt fluxes from the Atlantic to the Arctic Mediterranean. Geophys. Res. Lett., acceleration of global sea level rise. J. Clim., 22, 5772 5781. 32, L07603. Merrifield, M. A., P. R. Thompson, and M. Lander, 2012: Multidecadal sea level Palmer, M., and P. Brohan, 2011: Estimating sampling uncertainty in fixed-depth anomalies and trends in the western tropical Pacific. Geophys. Res. Lett., 39, and fixed-isotherm estimates of ocean warming. Int. J. Climatol., 31, 980 986. L13602. Palmer, M., K. Haines, S. Tett, and T. Ansell, 2007: Isolating the signal of ocean global warming. Geophys. Res. Lett., 34, L23610. 307 Chapter 3 Observations: Ocean Park, G. H., et al., 2006: Large accumulation of anthropogenic CO2 in the East (Japan) Rahmstorf, S., and M. Vermeer, 2011: Discussion of: Houston, J.R. and Dean, R.G., Sea and its significant impact on carbonate chemistry. Global Biogeochem. 2011. Sea-level acceleration based on U.S. tide gauges and extensions of Cycles, 20, Gb4013. previous global-gauge analyses. J. Coast. Res., 27(3), 409 417. J. Coast. Res., Park, J., J. Obeysekera, M. Irizarry, J. Barnes, P. Trimble, and W. Park-Said, 2011: Storm 27, 784 787. surge projections and implications for water management in South Florida. Clim. Rawlins, M. A., et al., 2010: Analysis of the Arctic System for freshwater cycle Change, 107, 109 128. intensification: Observations and expectations. J. Clim., 23, 5715 5737. Peltier, W. R., 2001: Global glacial isostatic adjustment and modern instrumental Ray, R. D., and B. C. Douglas, 2011: Experiments in reconstructing twentieth-century records of relative sea level history. In: Sea Level Rise [B. C. Douglas, M. S. sea levels. Prog. Oceanogr., 91, 496 515. Kearney, and S. P. Leatherman (eds.)]. Elsevier, Amsterdam, the Netherlands, and Ren, L., and S. C. Riser, 2010: Observations of decadal time scale salinity changes Philadelphia, PA, USA, pp. 65 95. in the subtropical thermocline of the North Pacific Ocean. Deep-Sea Res. Pt. II, Peltier, W. R., 2004: Global glacial isostasy and the surface of the ice-age earth: The 57, 1161 1170. ice-5G (VM2) model and grace. Annu. Rev. Earth Planet. Sci., 32, 111 149. Reverdin, G., 2010: North Atlantic subpolar gyre surface variability (1895 2009). J. Peltier, W. R., R. Drummond, and K. Roy, 2012: Comment on Ocean mass from Clim., 23, 4571 4584. GRACE and glacial isostatic adjustment by D. P. Chambers et al. J. Geophys. Reverdin, G., F. Durand, J. Mortensen, F. Schott, H. Valdimarsson, and W. Zenk, 2002: Res. Sol. Ea., 117, B11403. Recent changes in the surface salinity of the North Atlantic subpolar gyre. J. Pérez, F. F., M. Vázquez-Rodríguez, H. Mercier, A. Velo, P. Lherminier, and A. F. Ríos, Geophys. Res. Oceans, 107, 8010. 2010: Trends of anthropogenic CO2 storage in North Atlantic water masses. Rhein, M., et al., 2011: Deep water formation, the subpolar gyre, and the meridional Biogeosciences, 7, 1789 1807. overturning circulation in the subpolar North Atlantic. Deep-Sea Res. Pt. II, 58, Pierce, D. W., P. J. Gleckler, T. P. Barnett, B. D. Santer, and P. J. Durack, 2012: The 1819 1832. fingerprint of human-induced changes in the ocean s salinity and temperature Rienecker, M. M., et al., 2011: MERRA: NASA s Modern-Era Retrospective Analysis for fields. Geophys. Res. Lett., 39, L21704. Research and Applications. J. Clim., 24, 3624 3648. Pierce, D. W., T. P. Barnett, K. M. AchutaRao, P. J. Gleckler, J. M. Gregory, and W. M. Rignot, E., J. L. Bamber, M. R. Van Den Broeke, C. Davis, Y. H. Li, W. J. Van De Berg, and Washington, 2006: Anthropogenic warming of the oceans: Observations and E. Van Meijgaard, 2008: Recent Antarctic ice mass loss from radar interferometry model results. J. Clim., 19, 1873 1900. and regional climate modelling. Nature Geosci., 1, 106 110. Pinker, R. T., H. M. Wang, and S. A. Grodsky, 2009: How good are ocean buoy Rintoul, S. R., 2007: Rapid freshening of Antarctic Bottom Water formed in the Indian observations of radiative fluxes? Geophys. Res. Lett., 36, L10811. and Pacific oceans. Geophys. Res. Lett., 34, L06606. Polovina, J. J., E. A. Howell, and M. Abecassis, 2008: Ocean s least productive waters Rintoul, S. R., S. Sokolov, and J. Church, 2002: A 6 year record of baroclinic transport are expanding. Geophys. Res. Lett., 35, L03618. variability of the Antarctic Circumpolar Current at 140°E derived from 3 Polyakov, I. V., A. V. Pnyushkov, and L. A. Timokhov, 2012: Warming of the Intermediate expendable bathythermograph and altimeter measurements. J. Geophys. Res. Atlantic Water of the Arctic Ocean in the 2000s. J. Clim., 25, 8362 8370. Oceans, 107, 3155. Polyakov, I. V., V. A. Alexeev, U. S. Bhatt, E. I. Polyakova, and X. D. Zhang, 2010: North Robertson, R., M. Visbeck, A. L. Gordon, and E. Fahrbach, 2002: Long-term Atlantic warming: patterns of long-term trend and multidecadal variability. Clim. temperature trends in the deep waters of the Weddell Sea. Deep-Sea Res. Pt. Dyn., 34, 439 457. II, 49, 4791 4806. Polyakov, I. V., U. S. Bhatt, H. L. Simmons, D. Walsh, J. E. Walsh, and X. Zhang, 2005: Roemmich, D., and J. Gilson, 2009: The 2004 2008 mean and annual cycle of Multidecadal variability of North Atlantic temperature and salinity during the temperature, salinity, and steric height in the global ocean from the Argo twentieth century. J. Clim., 18, 4562 4581. Program. Prog. Oceanogr., 82, 81 100. Polyakov, I. V., et al., 2008: Arctic ocean freshwater changes over the past 100 years Roemmich, D., and J. Gilson, 2011: The global ocean imprint of ENSO. Geophys. Res. and their causes. J. Clim., 21, 364 384. Lett., 38, L13606. Ponte, R. M., 2012: An assessment of deep steric height variability over the global Roemmich, D., W. J. Gould, and J. Gilson, 2012: 135 years of global ocean warming ocean. Geophys. Res. Lett., 39, L04601. between the Challenger expedition and the Argo Programme. Nature Clim. Potemra, J. T., and N. Schneider, 2007: Interannual variations of the Indonesian Change, 2, 425 428. throughflow. J. Geophys. Res. Oceans, 112, C05035. Roemmich, D., J. Gilson, R. Davis, P. Sutton, S. Wijffels, and S. Riser, 2007: Decadal Proshutinsky, A., et al., 2009: Beaufort Gyre freshwater reservoir: State and variability spinup of the South Pacific subtropical gyre. J. Phys. Oceanogr., 37, 162 173. from observations. J. Geophys. Res. Oceans, 114, C00A10. Ruggiero, P., P. D. Komar, and J. C. Allan, 2010: Increasing wave heights and extreme Provoost, P., S. van Heuven, K. Soetaert, R. W. P. M. Laane, and J. J. Middelburg, value projections: The wave climate of the U.S. Pacific Northwest. Coast. Engng., 2010: Seasonal and long-term changes in pH in the Dutch coastal zone. 57, 539 552. Biogeosciences, 7, 3869 3878. Sabine, C. L., et al., 2005: Global Ocean Data Analysis Project (GLODAP): Results and Purkey, S. G., and G. C. Johnson, 2010: Warming of global abyssal and deep Southern data. ORNL/CDIAC-145, NDP-083. Carbon Dioxide Information Analysis Center, Ocean waters between the 1990s and 2000s: Contributions to global heat and Oak Ridge National Laboratory, U.S. Department of Energy, 110 pp. sea level rise budgets. J. Clim., 23, 6336 6351. Sabine, C. L., et al., 2004: The oceanic sink for anthropogenic CO2. Science, 305, , 2012: Global contraction of Antarctic Bottom Water between the 1980s and 367 371. 2000s. J. Clim., 25, 5830 5844. Saha, S., et al., 2010: The NCEP Climate Forecast System Reanalysis. Bull. Am. Purkey, S. G., and G. C. Johnson, 2013: Antarctic Bottom Water warming and Meteorol. Soc., 91, 1015 1057. freshening: Contributions to sea level rise, ocean freshwater budgets, and global Sallenger, A. H., K. S. Doran, and P. A. Howd, 2012: Hotspot of accelerated sea-level heat gain. J. Clim., doi:10.1175/JCLI-D-12 00834.1. rise on the Atlantic coast of North America. Nature Clim. Change, 2, 884 888. Qiu, B., and S. Chen, 2006: Decadal variability in the large-scale sea surface height Santana-Casiano, J. M., M. González-Dávila, M. J. Rueda, O. Llinas, and E. F. field of the South Pacific Ocean: Observations and causes. J. Phys. Oceanogr., González-Dávila, 2007: The interannual variability of oceanic CO2 parameters in 36, 1751 1762. the northeast Atlantic subtropical gyre at the ESTOC site. Global Biogeochem. Qiu, B., and S. Chen, 2010: Interannual-to-decadal variability in the bifurcation of the Cycles, 21, GB1015. North Equatorial Current off the Philippines. J. Phys. Oceanogr., 40, 2525 2538. Sarafanov, A., A. Falina, A. Sokov, and A. Demidov, 2008: Intense warming and Qiu, B., and S. Chen, 2012: Multi-decadal sea level and gyre circulation variability in salinification of intermediate waters of southern origin in the eastern subpolar the northwestern tropical Pacific Ocean. J. Phys. Oceanogr., 42, 193 206. North Atlantic in the 1990s to mid-2000s. J. Geophys. Res. Oceans, 113, C12022. Quay, P., R. Sonnerup, T. Westby, J. Stutsman, and A. McNichol, 2003: Changes Sarmiento, J. L., et al., 2010: Trends and regional distributions of land and ocean in the C-13/C-12 of dissolved inorganic carbon in the ocean as a tracer of carbon sinks. Biogeosciences, 7, 2351 2367. anthropogenic CO2 uptake. Global Biogeochem. Cycles, 17, 1004. Schanze, J. J., R. W. Schmitt, and L. L. Yu, 2010: The global oceanic freshwater cycle: Rabe, B., et al., 2011: An assessment of Arctic Ocean freshwater content changes A state-of-the-art quantification. J. Mar. Res., 68, 569 595. from the 1990s to the 2006 2008 period. Deep-Sea Res. Pt. I, 58, 173 185. Schauer, U., and A. Beszczynska-Möller, 2009: Problems with estimation and interpretation of oceanic heat transport conceptual remarks for the case of Fram Strait in the Arctic Ocean. Ocean Sci., 5, 487 494. 308 Observations: Ocean Chapter 3 Schmidtko, S., and G. C. Johnson, 2012: Multi-decadal warming and shoaling of Sturges, W., and B. G. Hong, 1995: Wind forcing of the Atlantic thermocline along Antarctic Intermediate Water. J. Clim., 25, 201 221. 32°N at low-frequencies. J. Phys. Oceanogr., 25, 1706 1715. Schmitt, R. W., 2008: Salinity and the global water cycle. Oceanography, 21, 12 19. Sturges, W., and B. C. Douglas, 2011: Wind effects on estimates of sea level rise. J. Schneider, T., P. A. O Gorman, and X. J. Levine, 2010: Water vapor and the dynamics Geophys. Res. Oceans, 116, C06008. of climate changes. Rev. Geophys., 48, Rg3001. Sugimoto, S., and K. Hanawa, 2010: The wintertime wind stress curl field in the North Schuster, U., and A. J. Watson, 2007: A variable and decreasing sink for atmospheric Atlantic and its relation to atmospheric teleconnection patterns. J. Atmos. Sci., CO2 in the North Atlantic. J. Geophys. Res. Oceans, 112, C11006. 67, 1687 1694. Schuster, U., et al., 2013: An assessment of the Atlantic and Arctic sea-air CO2 fluxes, Susanto, R. D., A. Ffield, A. L. Gordon, and T. R. Adi, 2012: Variability of Indonesian 1990 2009. Biogeosciences, 10, 607 627. throughflow within Makassar Strait, 2004 2009. J. Geophys. Res. Oceans, 117, Seitzinger, S. P., et al., 2010: Global river nutrient export: A scenario analysis of past C09013. and future trends. Global Biogeochem. Cycles, 24, Gb0a08. Swart, N. C., and J. C. Fyfe, 2012: Observed and simulated changes in the Southern Semedo, A., K. Suselj, A. Rutgersson, and A. Sterl, 2011: A global view on the wind Hemisphere surface westerly wind-stress. Geophys. Res. Lett., 39, L16711. sea and swell climate and variability from ERA-40. J. Clim., 24, 1461 1479. Swart, S., S. Speich, I. J. Ansorge, G. J. Goni, S. Gladyshev, and J. R. E. Lutjeharms, 2008: Send, U., M. Lankhorst, and T. Kanzow, 2011: Observation of decadal change in Transport and variability of the Antarctic Circumpolar Current South of Africa. J. the Atlantic Meridional Overturning Circulation using 10 years of continuous Geophys. Res. Oceans, 113, C09014. transport data. Geophys. Res. Lett., 38, L24606. Swift, J. H., and A. H. Orsi, 2012: Sixty-four days of hydrography and storms: RVIB Shepherd, A., D. Wingham, and E. Rignot, 2004: Warm ocean is eroding West Nathaniel B. Palmer s 2011 S04P Cruise. Oceanography, 25, 54 55. Antarctic Ice Sheet. Geophys. Res. Lett., 31, L23402. Takahashi, T., S. C. Sutherland, R. A. Feely, and R. Wanninkhof, 2006: Decadal change Shiklomanov, A. I., and R. B. Lammers, 2009: Record Russian river discharge in 2007 of the surface water pCO2 in the North Pacific: A synthesis of 35 years of and the limits of analysis. Environ. Res. Lett., 4, 045015. observations. J. Geophys. Res. Oceans, 111, C07s05. Smith, D. M., and J. M. Murphy, 2007: An objective ocean temperature and salinity Takahashi, T., et al., 2009: Climatological mean and decadal change in surface ocean analysis using covariances from a global climate model. J. Geophys. Res. Oceans, pCO2, and net sea-air CO2 flux over the global oceans (vol 56, pg 554, 2009). 112, C02022. Deep-Sea Res. Pt. I, 56, 2075 2076. Smith, R. O., H. L. Bryden, and K. Stansfield, 2008: Observations of new western Tanaka, H. L., N. Ishizaki, and A. Kitoh, 2004: Trend and interannual variability of Mediterranean deep water formation using Argo floats 2004 2006. Ocean Sci., Walker, monsoon and Hadley circulations defined by velocity potential in the 4, 133 149. upper troposphere. Tellus A, 56, 250 269. Smith, T. M., P. A. Arkin, and M. R. P. Sapiano, 2009: Reconstruction of near-global Tanhua, T., E. P. Jones, E. Jeansson, S. Jutterstrom, W. M. Smethie, D. W. R. Wallace, and annual precipitation using correlations with sea surface temperature and sea L. G. Anderson, 2009: Ventilation of the Arctic Ocean: Mean ages and inventories level pressure. J. Geophys. Res. Atmos., 114, D12107. of anthropogenic CO2 and CFC-11. J. Geophys. Res. Oceans, 114, C01002. 3 Smith, T. M., P. A. Arkin, L. Ren, and S. S. P. Shen, 2012: Improved reconstruction of Terray, L., L. Corre, S. Cravatte, T. Delcroix, G. Reverdin, and A. Ribes, 2012: Near- global precipitation since 1900. J. Atmos. Ocean. Technol., 29, 1505 1517. surface salinity as nature s rain gauge to detect human influence on the tropical Sokolov, S., and S. R. Rintoul, 2009: Circumpolar structure and distribution of the water cycle. J. Clim., 25, 958 977. Antarctic Circumpolar Current fronts: 2. Variability and relationship to sea Timmermann, A., S. McGregor, and F. F. Jin, 2010: Wind effects on past and future surface height. J. Geophys. Res. Oceans, 114, C11019. regional sea level trends in the southern Indo-Pacific. J. Clim., 23, 4429 4437. Spada, G., and G. Galassi, 2012: New estimates of secular sea level rise from tide Tokinaga, H., and S.-P. Xie, 2011: Wave- and Anemometer-based Sea surface Wind gauge data and GIA modelling. Geophys. J. Int., 191, 1067 1094. (WASWind) for climate change analysis. J. Clim., 24, 267 285. Spence, P., J. C. Fyfe, A. Montenegro, and A. J. Weaver, 2010: Southern Ocean Toole, J. M., R. G. Curry, T. M. Joyce, M. McCartney, and B. Pena-Molino, 2011: response to strengthening winds in an eddy-permitting global climate model. Transport of the North Atlantic Deep Western Boundary Current about 39°N, J. Clim., 23, 5332 5343. 70°W: 2004 2008. Deep-Sea Res. Pt. II, 58, 1768 1780. Sprintall, J., S. Wijffels, T. Chereskin, and N. Bray, 2002: The JADE and WOCE I10/ Trenberth, K. E., and L. Smith, 2005: The mass of the atmosphere: A constraint on IR6 Throughflow sections in the southeast Indian Ocean. Part 2: velocity and global analyses. J. Clim., 18, 864 875. transports. Deep-Sea Res. Pt. II, 49, 1363 1389. Trenberth, K. E., J. T. Fasullo, and J. Kiehl, 2009: Earth s global energy budget. Bull. Sprintall, J., S. E. Wijffels, R. Molcard, and I. Jaya, 2009: Direct estimates of the Am. Meteorol. Soc., 90, 311 323. Indonesian Throughflow entering the Indian Ocean: 2004 2006. J. Geophys. Trenberth, K. E., J. T. Fasullo, and J. Mackaro, 2011: Atmospheric moisture transports Res. Oceans, 114, C07001. from ocean to land and global energy flows in reanalyses. J. Clim., 24, 4907 Stammer, D. et al, 2010: Ocean Information Provided Through Ensemble Ocean 4924. Syntheses in Proceedings of OceanObs 09: Sustained Ocean Observations and Trenberth, K. E., et al., 2007: Observations: Surface and atmospheric climate change. Information for Society (Vol. 2), Venice, Italy, 21-25 September 2009, Hall, J., In: Climate Change 2007: The Physical Science Basis. Contribution of Working Harrison, D.E. & Stammer, D., Eds., European Space Agency, ESA Publication Group I to the Fourth Assessment Report of the Intergovernmental Panel on WPP-306, doi:10.5270/OceanObs09.cwp.85 Climate Change [Solomon, S., D. Qin, M. Manning, Z. Chen, M. Marquis, K. B. Steele, M., and W. Ermold, 2007: Steric sea level change in the Northern Seas. J. Averyt, M. Tignor and H. L. Miller (eds.)] Cambridge University Press, Cambridge, Clim., 20, 403 417. United Kingdom and New York, NY, USA. Steinfeldt, R., M. Rhein, J. L. Bullister, and T. Tanhua, 2009: Inventory changes in Tsimplis, M. N., and A. G. P. Shaw, 2010: Seasonal sea level extremes in the anthropogenic carbon from 1997 2003 in the Atlantic Ocean between 20°S and Mediterranean Sea and at the Atlantic European coasts. Nat. Hazards Earth Syst. 65°N. Global Biogeochem. Cycles, 23, GB3010. Sci., 10, 1457 1475. Stendardo, I., and N. Gruber, 2012: Oxygen trends over five decades in the North Uppala, S. M., et al., 2005: The ERA-40 re-analysis. Q. J. R. Meteor. Soc., 131, 2961 Atlantic. J. Geophys. Res. Oceans, 117, C11004. 3012. Sterl, A., and S. Caires, 2005: Climatology, variability and extrema of ocean waves: Vage, K., et al., 2009: Surprising return of deep convection to the subpolar North The web-based KNMI/ERA-40 wave atlas. Int. J. Climatol., 25, 963 977. Atlantic Ocean in winter 2007 2008. Nature Geosci., 2, 67 72. Stott, P. A., R. T. Sutton, and D. M. Smith, 2008: Detection and attribution of Atlantic Valdimarsson, H., O. S. Astthorsson, and J. Palsson, 2012: Hydrographic variability in salinity changes. Geophys. Res. Lett., 35, L21702. Icelandic waters during recent decades and related changes in distribution of Stramma, L., A. Oschlies, and S. Schmidtko, 2012: Mismatch between observed some fish species. Ices J. Mar. Sci., 69, 816 825. and modeled trends in dissolved upper-ocean oxygen over the last 50 yr. Vargas-Yánez, M., et al., 2010: How much is the western Mediterranean really Biogeosciences, 9, 4045 4057. warming and salting? J. Geophys. Res. Oceans, 115, C04001. Stramma, L., G. C. Johnson, J. Sprintall, and V. Mohrholz, 2008: Expanding oxygen- Vecchi, G. A., B. J. Soden, A. T. Wittenberg, I. M. Held, A. Leetmaa, and M. J. Harrison, minimum zones in the tropical oceans. Science, 320, 655 658. 2006: Weakening of tropical Pacific atmospheric circulation due to anthropogenic Stramma, L., S. Schmidtko, L. A. Levin, and G. C. Johnson, 2010: Ocean oxygen minima forcing. Nature, 441, 73 76. expansions and their biological impacts. Deep-Sea Res. Pt. I, 57, 587 595. Vilibic, I., and J. Sepic, 2010: Long-term variability and trends of sea level storminess and extremes in European Seas. Global Planet. Change, 71, 1 12. 309 Chapter 3 Observations: Ocean von Schuckmann, K., and P. Y. Le Traon, 2011: How well can we derive Global Ocean Woodworth, P. L., and D. L. Blackman, 2004: Evidence for systematic changes in Indicators from Argo data? Ocean Sci., 7, 783 791. extreme high waters since the mid-1970s. J. Clim., 17, 1190 1197. Wainwright, L., G. Meyers, S. Wijffels, and L. Pigot, 2008: Change in the Indonesian Woodworth, P. L., N. Pouvreau, and G. Woeppelmann, 2010: The gyre-scale circulation Throughflow with the climatic shift of 1976/77. Geophys. Res. Lett., 35, L03604. of the North Atlantic and sea level at Brest. Ocean Sci., 6, 185 190. Wang, C. Z., S. F. Dong, and E. Munoz, 2010: Seawater density variations in the Woodworth, P. L., M. Menéndez, and W. R. Gehrels, 2011: Evidence for century- North Atlantic and the Atlantic meridional overturning circulation. Clim. Dyn., timescale acceleration in mean sea levels and for recent changes in extreme sea 34, 953 968. levels. Surv. Geophys., 32, 603 618. Wang, X., Y. Feng, and V. R. Swail, 2012: North Atlantic wave height trends as Woodworth, P. L., N. J. White, S. Jevrejeva, S. J. Holgate, J. A. Church, and W. R. reconstructed from the Twentieth Century Reanalysis. Geophys. Res. Lett., 39, Gehrels, 2009: Evidence for the accelerations of sea level on multi-decade and L18705. century timescales. Int. J. Climatol., 29, 777 789. Wang, X. L. L., and V. R. Swail, 2006: Climate change signal and uncertainty in Wöppelmann, G., et al., 2009: Rates of sea-level change over the past century in a projections of ocean wave heights. Clim. Dyn., 26, 109 126. geocentric reference frame. Geophys. Res. Lett., 36, L12607. Wang, X. L. L., V. R. Swail, F. W. Zwiers, X. B. Zhang, and Y. Feng, 2009: Detection of Wu, L., et al., 2012: Enhanced warming over the global subtropical western boundary external influence on trends of atmospheric storminess and northern oceans currents. Nature Clim. Change, 2, 161 166. wave heights. Clim. Dyn., 32, 189 203. Wunsch, C., 2010: Variability of the Indo-Pacific Ocean exchanges. Dyn. Atmos. Wanninkhof, R., W. E. Asher, D. T. Ho, C. Sweeney, and W. R. McGillis, 2009: Advances Oceans, 50, 157 173. in quantifying air-sea gas exchange and environmental forcing. Annu. Rev. Mar. Xue, Y., B. Huang, Z.-Z. Hu, A. Kumar, C. Wen, D. Behringer, and S. Nadiga, 2010: An Sci., 1, 213 244. assessment of oceanic variability in the NCEP climate forecast system reanalysis. Wanninkhof, R., S. C. Doney, J. L. Bullister, N. M. Levine, M. Warner, and N. Gruber, Clim. Dyn., 37, 2541 2550. 2010: Detecting anthropogenic CO2 changes in the interior Atlantic Ocean Xue, Y., et al., 2012: A comparative analysis of upper ocean heat content variability between 1989 and 2005. J. Geophys. Res., 115, C11028. from an ensemble of operational ocean reanalyses. J. Clim., 25, 6905 6929. Wanninkhof, R., G. H. Park, T. Takahashi, R. A. Feely, J. L. Bullister, and S. C. Doney, Yamamoto-Kawai, M., F. A. McLaughlin, E. C. Carmack, S. Nishino, K. Shimada, and N. 2013: Changes in deep-water CO2 concentrations over the last several decades Kurita, 2009: Surface freshening of the Canada Basin, 2003 2007: River runoff determined from discrete pCO2 measurements. Deep-Sea Res. Pt. I, 74, 48 63. versus sea ice meltwater. J. Geophys. Res. Oceans, 114, C00A05. WASA-Group, 1998: Changing waves and storm in the Northern Atlantic? Bull. Am. Yang, X. Y., R. X. Huang, and D. X. Wang, 2007: Decadal changes of wind stress Meteorol. Soc., 79, 741 760. over the Southern Ocean associated with Antarctic ozone depletion. J. Clim., Watson, A. J., et al., 2009: Tracking the variable North Atlantic sink for atmospheric 20, 3395 3410. CO2. Science, 326, 1391 1393. Yashayaev, I., 2007: Hydrographic changes in the Labrador Sea, 1960 2005. Prog. 3 Watson, P. J., 2011: Is there evidence yet of an acceleration in mean sea level rise Oceanogr., 73, 242 276. around mainland Australia? J. Coast. Res., 27, 368 377. Yashayaev, I., and J. W. Loder, 2009: Enhanced production of Labrador Sea Water in Waugh, D. M., F. Primeau, T. DeVries, and M. Holzer, 2013: Recent changes in the 2008. Geophys. Res. Lett., 36, L01606. ventilation of the Southern Oceans. Science, 339, 568 570. Yoshikawa-Inoue, H., and M. Ishii, 2005: Variations and trends of CO2 in the surface Waugh, D. W., T. M. Hall, B. I. McNeil, R. Key, and R. J. Matear, 2006: Anthropogenic seawater in the Southern Ocean south of Australia between 1969 and 2002. CO2 in the oceans estimated using transit-time distributions. Tellus B, 58, 376 Tellus B, 57, 58 69. 389. Young, I. R., S. Zieger, and A. V. Babanin, 2011a: Global trends in wind speed and Wentz, F. J., and L. Ricciardulli, 2011: Comment on Global trends in wind speed and wave height. Science, 332, 451 455. wave height. Science, 334, 905 905. Young, I. R., A. V. Babanin, and S. Zieger, 2011b: Response to comment on Global Wentz, F. J., L. Ricciardulli, K. Hilburn, and C. Mears, 2007: How much more rain will trends in wind speed and wave height . Science, 334, 905 905. global warming bring? Science, 317, 233 235. Yu, L., 2011: A global relationship between the ocean water cycle and near-surface Wenzel, M., and J. Schroeter, 2010: Reconstruction of regional mean sea level salinity. J. Geophys. Res. Oceans, 116, C10025. anomalies from tide gauges using neural networks. J. Geophys. Res. Oceans, Yu, L., and R. A. Weller, 2007: Objectively analyzed air-sea flux fields for the global 115, C08013. ice-free oceans (1981 2005). Bull. Am. Meteorol. Soc., 88, 527 539. Whitney, F. A., H. J. Freeland, and M. Robert, 2007: Persistently declining oxygen Yu, L. S., 2007: Global variations in oceanic evaporation (1958 2005): The role of the levels in the interior waters of the eastern subarctic Pacific. Prog. Oceanogr., changing wind speed. J. Clim., 20, 5376 5390. 75, 179 199. Yu, L. S., X. Z. Jin, and R. A. Weller, 2007: Annual, seasonal, and interannual variability Wijffels, S. E., et al., 2008: Changing expendable bathythermograph fall rates and of air-sea heat fluxes in the Indian Ocean. J. Clim., 20, 3190 3209. their impact on estimates of thermosteric sea level rise. J. Clim., 21, 5657 5672. Zenk, W., and E. Morozov, 2007: Decadal warming of the coldest Antarctic Bottom Wild, M., 2009: Global dimming and brightening: A review. J. Geophys. Res. Atmos., Water flow through the Vema Channel. Geophys. Res. Lett., 34, L14607. 114, D00D16. Zhang, X. B., and J. A. Church, 2012: Sea level trends, interannual and decadal Willis, J. K., 2010: Can in situ floats and satellite altimeters detect long-term changes variability in the Pacific Ocean. Geophys. Res. Lett., 39, L21701. in Atlantic Ocean overturning? Geophys. Res. Lett., 37, L06602. Willis, J. K., D. Roemmich, and B. Cornuelle, 2004: Interannual variability in upper ocean heat content, temperature, and thermosteric expansion on global scales. J. Geophys. Res. Oceans, 109, C12036. Willis, J. K., D. P. Chambers, and R. S. Nerem, 2008: Assessing the globally averaged sea level budget on seasonal to interannual timescales. J. Geophys. Res. Oceans, 113, C06015. Willis, J. K., D. P. Chambers, C.-Y. Kuo, and C. K. Shum, 2010: Global sea level rise: Recent progress and challenges for the decade to come. Oceanography, 23, 26 35. Wong, A. P. S., N. L. Bindoff, and J. A. Church, 1999: Large-scale freshening of intermediate waters in the Pacific and Indian oceans. Nature, 400, 440 443. Wong, C. S., L. S. Xie, and W. W. Hsieh, 2007: Variations in nutrients, carbon and other hydrographic parameters related to the 1976/77 and 1988/89 regime shifts in the sub-arctic Northeast Pacific. Prog. Oceanogr., 75, 326 342. Woodworth, P. L., 1990: A search for accelerations in records of European mean sea- level. Int. J. Climatol., 10, 129 143. Woodworth, P. L., 1999: High waters at Liverpool since 1768: the UK s longest sea level record. Geophys. Res. Lett., 26, 1589 1592. 310 Observations: Ocean Chapter 3 Appendix 3.A: Early measurements of temperature were made using reversing ther- Availability of Observations for Assessment of mometers and Nansen bottles that were lowered from ships on station Change in the Oceans (not moving). Starting in the 1960s conductivity-temperature-depth (CTD) instruments with Niskin bottles gradually gained dominance for Sampling of the ocean has been highly heterogeneous since 1950. high-quality data and deep data collected on station during oceano- The coverage in space, time, depth and number of ocean variables graphic cruises. From at least 1950 through about 1970, most subsur- has evolved over time, reflecting changes in technology and the con- face measurements of ocean temperature were made with mechanical tribution of major oceanographic research programs. Changes in the bathythermographs, an advance because these instruments could be distribution and quality of ocean measurements over time complicate deployed from a moving ship, albeit a slowly moving one, but these efforts to detect and interpret change in the ocean. This Appendix pro- casts were generally limited to depths shallower than 250 m. Expend- vides some illustrative examples of the evolution of the ocean observ- able bathythermographs (XBTs) that could be deployed from a rapidly ing system on which the assessment of ocean change in this chapter moving ship and sampled to 400 m came into widespread use in the is based. A more comprehensive discussion of ocean sampling is pro- late 1960s, and those that sampled to 700 m became predominant vided in the literature cited in this chapter. Sampling of sea surface in the 1990s, greatly expanding oceanographic sampling. Starting in temperature is discussed in Chapter 2. 2000, Argo floats began sampling the ocean to a target depth of 2000 m, building to near-global coverage by 2005. Prior to the Argo era, 3.A.1 Subsurface Ocean Temperature and Heat Content sampling of the ocean below 700 m was almost solely achieved from ships on station deploying Nansen bottles with reversing thermom- Temperature is the best-sampled oceanographic variable, but even eters or later using CTDs with Niskin bottles. Today ship-based station for temperature sampling is far from ideal or complete. Early oceano- data still dominates sampling for waters deeper than 2000 m depth graphic expeditions included the Challenger voyage around the world (the maximum depth currently sampled by Argo floats). An illustration in the 1870s, the Meteor survey of the Atlantic in the 1920s, and the of the limited data available for assessment of change in the deep Discovery investigations of the Southern Ocean starting in the 1920s. ocean is provided in Figure 3.3, which shows locations of full-depth More frequent basin-scale sampling commenced in the late 1950s with oceanographic CTD sections that have been occupied more than once 3 the International Geophysical Year. The number of profiles available for since about 1980. The depth coverage of the ocean observing system assessment of changes in temperature and ocean heat content in the has changed over time (Figure 3A.2, top panel) with a hemispheric bias upper 700 m generally increases with time since the 1950s (Figure (Figure 3.A.2, middle and lower panels). The Northern Hemisphere (NH) 3.A.1). Near-global coverage of the upper half of the ocean was not has been consistently better sampled than the Southern Hemisphere achieved until the widespread deployment of Argo profiling floats in (SH) prior to the Argo era. the 2000s (Figure 3.A.2). 60°N 1950s 1960s 1970s 30°N 0° 30°S 60°S 60°N 1980s 1990s 2000s 30°N 0° 30°S 60°S 60°E 160°W 20°W 60°E 160°W 20°W 60°E 160°W 20°W No. Temperature Profiles 0 700 m ( 1° x 1° ) 5 15 25 Figure 3.A.1 | Number of temperature profiles extending to 700 m depth in each 1° × 1° square, by decade, between 65°N and 65°S. 311 Chapter 3 Observations: Ocean Ocean temperature profiles Yearly coverage the relatively well sampled North Atlantic, information on changes in 80 ocean salinity is largely restricted to the repeat hydrographic transects 0 100 m GLOBAL (see Figure 3.3). 0 200 m 60 Global coverage (%) 0 300 m 3.A.3 Sea Level 0 400 m 40 0 700 m Direct observations of sea level are made using tide gauges since the 0 900 m 20 1700s and high-precision satellite altimeters since 1992. Tide gauge 0 1500 m measurements are limited to coastlines and islands. There are intermit- 0 1800 m tent records of sea level at Amsterdam from 1700 and at three more 0 sites in Northern Europe starting after 1770. By the late 1800s, more 1950 1960 1970 1980 1990 2000 2010 tide gauges were being operated in Northern Europe and in North 0 America, as well as in Australia and New Zealand (Figure 3.A.4). It was not until the late 1970s to early 1980s that a majority of deep- 500 ocean islands had operating tide gauges suitable for climate studies. Although tide gauges have continued to be deployed since 1990, they Depth (m) have been complemented by continuous, near-global measurements 1000 of sea level from space since 1992. Measurements are made along the satellite s ground track on the Earth surface, typically averaged over 1500 NORTHERN HEMISPHERE approximately 7 km to reduce noise and improve precision. The maxi- mum latitude extent of the measurement is limited by the inclination 2000 of the orbital plane, which has been between +/-66° for the TOPEX/ 1950 1960 1970 1980 1990 2000 2010 Poseidon and Jason series of altimeters. The spacing between ground 3 0 tracks is much greater than the spacing along the ground track. As an example, the groundtrack separation of the TOPEX/Poseidon-type 500 of orbits is about 300 km at the equator, but is less than 100 km at latitudes poleward of 50° latitude. On average, the spacing is between Depth (m) 1000 100 and 200 km. Satellites are limited in the temporal sampling as well due to the orbit configuration. For a specific location along a groundtrack, the return time for a TOPEX/Poseidon-type of orbit is 9.9 1500 SOUTHERN HEMISPHERE days. If one relaxes the requirement to a measurement within a 300 km radius, the return time can be as short as a few hours at high latitudes 2000 to about 3 days at the equator. As noted in Section 3.6, satellite altim- 1950 1960 1970 1980 1990 2000 2010 eter observations of sea level are also an important tool for observing Global coverage (%) large-scale ocean circulation. 0 10 20 30 40 50 60 70 3.A.4 Biogeochemistry Figure 3.A.2 | (Top) Percentage of global coverage of ocean temperature profiles as a function of depth in 1° latitude by 1° longitude by 1-year bins (top panel) shown versus The data available for assessing changes in biogeochemical param- time. Different colours indicate profiles to different depths (middle panel). Percentage of eters is less complete than for temperature and salinity. The global global coverage as a function of depth and time, for the Northern Hemisphere. (Bottom data base on which the Global Ocean Data Analysis Project (GLODAP, panel) As above, but for the Southern Hemisphere. Key et al., 2004) ocean carbon inventory is based is illustrated in Figure 3.A.5. Changes in the ocean inventory of anthropogenic CO2 have been 3.A.2 Salinity estimated using measurements of carbon parameters and other tracers collected at these roughly 12,000 stations, mostly occupied since 1990. Measurements of subsurface salinity have relied almost solely on data The majority of these stations extend through the full water depth. A collected from bottle and CTD casts from ships on station (and, more subset of these stations have been repeated one or more times. The recently, using profiling floats that sample both temperature and salin- distribution of oxygen measurements at 300 m depth in 10-year peri- ity with CTDs). Hence fewer measurements of salinity are available ods since 1960 is shown in Figure 3.A.6, as used in the global study of than of temperature (by roughly a factor of 3). However, the evolution Stramma et al. (2012). As for temperature and salinity, the sampling with time of subsurface salinity sampling shows a progression simi- is heterogeneous, coverage generally improves with time but shifts lar to that of temperature (Figure 3.A.3). Coverage generally improves between basins as major field programs come and go, and tends to be with time, but there is a strong NH bias, particularly in the North concentrated in the NH. Atlantic. A shift in focus from basin-to-basin as major field programs were carried out is evident. Near-global coverage of ocean salinity above 2000 m was not achieved until after 2005, when the Argo array approached full deployment. For depths greater than 2000 m, outside 312 Observations: Ocean Chapter 3 75N 1950 to 1955 1955 to 1960 1960 to 1965 55N Total: 45556 Total: 58519 Total: 82505 Atlantic: 25182 Atlantic: 29123 Atlantic: 38697 Pacific: 10605 Pacific: 14385 Pacific: 17573 35N Indian: 226 Indian: 1184 Indian: 5584 15N Latitude 15S 35S 55S 75S 75N 1965 to 1970 1970 to 1975 1975 to 1980 55N Total: 117464 Total: 137089 Total: 140434 Atlantic: 50564 Atlantic: 68844 Atlantic: 67843 Pacific: 29262 Pacific: 24345 Pacific: 27029 35N Indian: 6845 Indian: 8614 Indian: 10213 15N Latitude 15S 35S 55S 75S 75N 1980 to 1985 1985 to 1990 1990 to 1995 55N Total: 152305 Total: 164376 Total: 126420 Atlantic: 84119 Atlantic: 94367 Atlantic: 61562 Pacific: 28252 Pacific: 28323 Pacific: 31443 35N Indian: 8359 Indian: 8625 Indian: 4195 15N 3 Latitude 15S 35S 55S 75S 75N 1995 to 2000 2000 to 2005 2005 to 2010 55N Total: 63356 Total: 98169 Total: 369960 Atlantic: 36210 Atlantic: 30290 Atlantic: 74633 Pacific: 9123 Pacific: 46613 Pacific: 207567 35N Indian: 5614 Indian: 18133 Indian: 75888 15N Latitude 15S 35S 55S 75S 0 60E 120E 180 120W 60W 0 0 60E 120E 180 120W 60W 0 0 60E 120E 180 120W 60W 0 Longitude Longitude Longitude Figure 3.A.3 | Hydrographic profile data used in the Durack and Wijffels (2010) study. Station locations for 5-year temporal bins from 1950 1955 (top left) to 2005 2010 (bottom right). 313 Chapter 3 Observations: Ocean 1880-1890 1900-1910 1920-1930 1940-1950 3 1960-1970 1980-1990 Figure 3.A.4 | Location of tide gauges (red dots) that had at least 1 year of observations within the decade indicated. GLODAP Stations (12011) 60oN o 30 N o 0 o 30 S o 60 S Figure 3.A.5 | Location of profiles used to construct the Global Ocean Data Analysis Project (GLODAP) ocean carbon climatology. 314 Observations: Ocean Chapter 3 1950 1960 1960 1970 60oN 60oN 30oN 30oN 0o 0o 30oS 30oS 60oS 60oS 180oW 120oW 60oW 0o 60oE 120oE 180oW 180oW 120oW 60oW 0o 60oE 120oE 180oW 1970 1980 1980 1990 60oN 60oN 30oN 30oN 0o 0o 30oS 30oS 60oS 60oS 180oW 120oW 60oW 0o 60oE 120oE 180oW 180oW 120oW 60oW 0o 60oE 120oE 180oW 3 1990 2000 2000 2010 60oN 60oN 30oN 30oN 0o 0o 30oS 30oS 60oS 60oS 180oW 120oW 60oW 0o 60oE 120oE 180oW 180oW 120oW 60oW 0o 60oE 120oE 180oW Figure 3.A.6 | Distribution of oxygen measurements at 300 dbar for the decades 1950 to 1960 (upper left) to 2000 to 2010 (lower right frame). (From Stramma et al., 2012.) [Note that additional oxygen data have become available for the 2000 2010 period since that study was completed.] 315 4 Observations: Cryosphere Coordinating Lead Authors: David G. Vaughan (UK), Josefino C. Comiso (USA) Lead Authors: Ian Allison (Australia), Jorge Carrasco (Chile), Georg Kaser (Austria/Italy), Ronald Kwok (USA), Philip Mote (USA), Tavi Murray (UK), Frank Paul (Switzerland/Germany), Jiawen Ren (China), Eric Rignot (USA), Olga Solomina (Russian Federation), Konrad Steffen (USA/Switzerland), Tingjun Zhang (USA/China) Contributing Authors: Anthony A. Arendt (USA), David B. Bahr (USA), Michiel van den Broeke (Netherlands), Ross Brown (Canada), J. Graham Cogley (Canada), Alex S. Gardner (USA), Sebastian Gerland (Norway), Stephan Gruber (Switzerland), Christian Haas (Canada), Jon Ove Hagen (Norway), Regine Hock (USA), David Holland (USA), Matthias Huss (Switzerland), Thorsten Markus (USA), Ben Marzeion (Austria), Rob Massom (Australia), Geir Moholdt (USA), Pier Paul Overduin (Germany), Antony Payne (UK), W. Tad Pfeffer (USA), Terry Prowse (Canada), Valentina Radiæ (Canada), David Robinson (USA), Martin Sharp (Canada), Nikolay Shiklomanov (USA), Sharon Smith (Canada), Sharon Stammerjohn (USA), Isabella Velicogna (USA), Peter Wadhams (UK), Anthony Worby (Australia), Lin Zhao (China) Review Editors: Jonathan Bamber (UK), Philippe Huybrechts (Belgium), Peter Lemke (Germany) This chapter should be cited as: Vaughan, D.G., J.C. Comiso, I. Allison, J. Carrasco, G. Kaser, R. Kwok, P. Mote, T. Murray, F. Paul, J. Ren, E. Rignot, O. Solomina, K. Steffen and T. Zhang, 2013: Observations: Cryosphere. In: Climate Change 2013: The Physical Sci- ence Basis. Contribution of Working Group I to the Fifth Assessment Report of the Intergovernmental Panel on Climate Change [Stocker, T.F., D. Qin, G.-K. Plattner, M. Tignor, S.K. Allen, J. Boschung, A. Nauels, Y. Xia, V. Bex and P.M. Midgley (eds.)]. Cambridge University Press, Cambridge, United Kingdom and New York, NY, USA. 317 Table of Contents Executive Summary...................................................................... 319 4.8 Synthesis............................................................................. 367 4.1 Introduction....................................................................... 321 References .................................................................................. 369 4.2 Sea Ice................................................................................. 323 Appendix 4.A: Details of Available and Selected Ice Sheet Mass Balance Estimates from 1992 to 2012............ 380 4.2.1 Background................................................................ 323 4.2.2 Arctic Sea Ice............................................................. 323 Frequently Asked Questions 4.2.3 Antarctic Sea Ice........................................................ 330 FAQ 4.1 How Is Sea Ice Changing in the Arctic and Antarctic?......................................................... 333 4.3 Glaciers................................................................................ 335 FAQ 4.2 Are Glaciers in Mountain Regions 4.3.1 Current Area and Volume of Glaciers......................... 335 Disappearing?..............................................................x 4.3.2 Methods to Measure Changes in Glacier Length, Area and Volume/Mass.............................................. 335 Supplementary Material 4.3.3 Observed Changes in Glacier Length, Area Supplementary Material is available in online versions of the report. and Mass................................................................... 338 4.4 Ice Sheets........................................................................... 344 4.4.1 Background................................................................ 344 4.4.2 Changes in Mass of Ice Sheets................................... 344 4.4.3 Total Ice Loss from Both Ice Sheets............................ 353 4.4.4 Causes of Changes in Ice Sheets................................ 353 4.4.5 Rapid Ice Sheet Changes............................................ 355 4 4.5 Seasonal Snow.................................................................. 358 4.5.1 Background................................................................ 358 4.5.2 Hemispheric View....................................................... 358 4.5.3 Trends from In Situ Measurements............................. 359 4.5.4 Changes in Snow Albedo........................................... 359 Box 4.1: Interactions of Snow within the Cryosphere..................................................................... 360 4.6 Lake and River Ice............................................................ 361 4.7 Frozen Ground................................................................... 362 4.7.1 Background................................................................ 362 4.7.2 Changes in Permafrost............................................... 362 4.7.3 Subsea Permafrost..................................................... 364 4.7.4 Changes in Seasonally Frozen Ground....................... 364 318 Observations: Cryosphere Chapter 4 Executive Summary 4.5, 4.6} Satellite measurements made in the period 2010 2012 show a decrease in sea ice volume compared to those made over the period The cryosphere, comprising snow, river and lake ice, sea ice, glaciers, 2003 2008 (medium confidence). There is high confidence that in the ice shelves and ice sheets, and frozen ground, plays a major role in Arctic, where the sea ice thickness has decreased, the sea ice drift the Earth s climate system through its impact on the surface energy speed has increased. {4.2.2, Figure 4.6} budget, the water cycle, primary productivity, surface gas exchange and sea level. The cryosphere is thus a fundamental control on the It is likely that the annual period of surface melt on Arctic per- physical, biological and social environment over a large part of the ennial sea ice lengthened by 5.7 +/- 0.9 days per decade over the Earth s surface. Given that all of its components are inherently sen- period 1979 2012. Over this period, in the region between the East sitive to temperature change over a wide range of time scales, the Siberian Sea and the western Beaufort Sea, the duration of ice-free cryosphere is a natural integrator of climate variability and provides conditions increased by nearly 3 months. {4.2.2, Figure 4.6} some of the most visible signatures of climate change. It is very likely that the annual Antarctic sea ice extent increased Since AR4, observational technology has improved and key time series at a rate of between 1.2 and 1.8% per decade (0.13 to 0.20 of measurements have been lengthened, such that our identification million km2 per decade) between 1979 and 2012. There was a and measurement of changes and trends in all components of the greater increase in sea ice area, due to a decrease in the percentage cryosphere has been substantially improved, and our understanding of open water within the ice pack. There is high confidence that there of the specific processes governing their responses has been refined. are strong regional differences in this annual rate, with some regions Since the AR4, observations show that there has been a continued net increasing in extent/area and some decreasing {4.2.3, Figure 4.7} loss of ice from the cryosphere, although there are significant differ- ences in the rate of loss between cryospheric components and regions. Glaciers The major changes occurring to the cryosphere are as follows. Since AR4, almost all glaciers worldwide have continued to Sea Ice shrink as revealed by the time series of measured changes in glacier length, area, volume and mass (very high confidence). Continuing the trends reported in AR4, the annual Arctic sea Measurements of glacier change have increased substantially in ice extent decreased over the period 1979 2012. The rate of number since AR4. Most of the new data sets, along with a globally this decrease was very likely1 between 3.5 and 4.1% per decade complete glacier inventory, have been derived from satellite remote (0.45 to 0.51 million km2 per decade). The average decrease in sensing. {4.3.1, 4.3.3, Figures 4.9, 4.10, 4.11} decadal extent of Arctic sea ice has been most rapid in summer and autumn (high confidence2), but the extent has decreased in every Between 2003 and 2009, most of the ice lost was from glaciers season, and in every successive decade since 1979 (high confidence). in Alaska, the Canadian Arctic, the periphery of the Greenland 4 {4.2.2, Figure 4.2} ice sheet, the Southern Andes and the Asian Mountains (very high confidence). Together these regions account for more than 80% The extent of Arctic perennial and multi-year sea ice decreased of the total ice loss. {4.3.3, Figure 4.11, Table 4.4} between 1979 and 2012 (very high confidence). The perennial sea ice extent (summer minimum) decreased between 1979 and 2012 at Total mass loss from all glaciers in the world, excluding those 11.5 +/- 2.1% per decade (0.73 to 1.07 million km2 per decade) (very on the periphery of the ice sheets, was very likely 226 +/- 135 likely) and the multi-year ice (that has survived two or more summers) Gt yr 1 (sea level equivalent, 0.62 +/- 0.37 mm yr 1) in the period decreased at a rate of 13.5 +/- 2.5% per decade (0.66 to 0.98 million 1971 2009, 275 +/- 135 Gt yr 1 (0.76 +/- 0.37 mm yr 1) in the period km2 per decade) (very likely). {4.2.2, Figures 4.4, 4.6} 1993 2009, and 301 +/- 135 Gt yr 1 (0.83 +/- 0.37 mm yr 1) between 2005 and 2009. {4.3.3, Figure 4.12, Table 4.5} The average winter sea ice thickness within the Arctic Basin decreased between 1980 and 2008 (high confidence). The aver- Current glacier extents are out of balance with current climatic age decrease was likely between 1.3 and 2.3 m. High confidence in this conditions, indicating that glaciers will continue to shrink in the assessment is based on observations from multiple sources: submarine, future even without further temperature increase (high confi- electro-magnetic (EM) probes, and satellite altimetry, and is consistent dence). {4.3.3} with the decline in multi-year and perennial ice extent {4.2.2, Figures In this Report, the following terms have been used to indicate the assessed likelihood of an outcome or a result: Virtually certain 99 100% probability, Very likely 90 100%, 1 Likely 66 100%, About as likely as not 33 66%, Unlikely 0 33%, Very unlikely 0 10%, Exceptionally unlikely 0 1%. Additional terms (Extremely likely: 95 100%, More likely than not >50 100%, and Extremely unlikely 0 5%) may also be used when appropriate. Assessed likelihood is typeset in italics, e.g., very likely (see Section 1.4 and Box TS.1 for more details). In this Report, the following summary terms are used to describe the available evidence: limited, medium, or robust; and for the degree of agreement: low, medium, or high. 2 A level of confidence is expressed using five qualifiers: very low, low, medium, high, and very high, and typeset in italics, e.g., medium confidence. For a given evidence and agreement statement, different confidence levels can be assigned, but increasing levels of evidence and degrees of agreement are correlated with increasing confidence (see Section 1.4 and Box TS.1 for more details). 319 Chapter 4 Observations: Cryosphere Ice Sheets Station observations of snow, nearly all of which are in the Northern Hemisphere, generally indicate decreases in spring, The Greenland ice sheet has lost ice during the last two decades especially at warmer locations (medium confidence). Results (very high confidence). Combinations of satellite and airborne depend on station elevation, period of record, and variable measured remote sensing together with field data indicate with high (e.g., snow depth or duration of snow season), but in almost every confidence that the ice loss has occurred in several sectors and study surveyed, a majority of stations showed decreasing trends, and that large rates of mass loss have spread to wider regions than stations at lower elevation or higher average temperature were the reported in AR4. {4.4.2, 4.4.3, Figures 4.13, 4.15, 4.17} most liable to show decreases. In the Southern Hemisphere, evidence is too limited to conclude whether changes have occurred. {4.5.2, 4.5.3, The rate of ice loss from the Greenland ice sheet has accelerated Figures 4.19, 4.20, 4.21} since 1992. The average rate has very likely increased from 34 [ 6 to 74] Gt yr 1 over the period 1992 2001 (sea level Freshwater Ice equivalent, 0.09 [ 0.02 to 0.20] mm yr 1), to 215 [157 to 274] Gt yr 1 over the period 2002 2011 (0.59 [0.43 to 0.76] mm yr 1). The limited evidence available for freshwater (lake and river) ice {4.4.3, Figures 4.15, 4.17} indicates that ice duration is decreasing and average seasonal ice cover shrinking (low confidence). For 75 Northern Hemisphere Ice loss from Greenland is partitioned in approximately similar lakes, for which trends were available for 150-, 100- and 30-year peri- amounts between surface melt and outlet glacier discharge ods ending in 2005, the most rapid changes were in the most recent (medium confidence), and both components have increased period (medium confidence), with freeze-up occurring later (1.6 days (high confidence). The area subject to summer melt has per decade) and breakup earlier (1.9 days per decade). In the North increased over the last two decades (high confidence). {4.4.2} American Great Lakes, the average duration of ice cover declined 71% over the period 1973 2010. {4.6} The Antarctic ice sheet has been losing ice during the last two decades (high confidence). There is very high confidence that Frozen Ground these losses are mainly from the northern Antarctic Peninsula and the Amundsen Sea sector of West Antarctica, and high Permafrost temperatures have increased in most regions since confidence that they result from the acceleration of outlet the early 1980s (high confidence) although the rate of increase glaciers. {4.4.2, 4.4.3, Figures 4.14, 4.16, 4.17} has varied regionally. The temperature increase for colder perma- frost was generally greater than for warmer permafrost (high confi- The average rate of ice loss from Antarctica likely increased dence). {4.7.2, Table 4.8, Figure 4.24} from 30 [ 37 to 97] Gt yr 1 (sea level equivalent, 0.08 [ 0.10 to 4 0.27] mm yr 1) over the period 1992 2001, to 147 [72 to 221] Significant permafrost degradation has occurred in the Russian Gt yr 1 over the period 2002 2011 (0.40 [0.20 to 0.61] mm yr 1). European North (medium confidence). There is medium confidence {4.4.3, Figures 4.16, 4.17} that, in this area, over the period 1975 2005, warm permafrost up to 15 m thick completely thawed, the southern limit of discontinuous per- In parts of Antarctica, floating ice shelves are undergoing mafrost moved north by up to 80 km and the boundary of continuous substantial changes (high confidence). There is medium confidence permafrost moved north by up to 50 km. {4.7.2} that ice shelves are thinning in the Amundsen Sea region of West Antarctica, and medium confidence that this is due to high ocean In situ measurements and satellite data show that surface sub- heat flux. There is high confidence that ice shelves round the Antarctic sidence associated with degradation of ice-rich permafrost Peninsula continue a long-term trend of retreat and partial collapse occurred at many locations over the past two to three decades that began decades ago. {4.4.2, 4.4.5} (medium confidence). {4.7.4} Snow Cover In many regions, the depth of seasonally frozen ground has changed in recent decades (high confidence). In many areas since Snow cover extent has decreased in the Northern Hemisphere, the 1990s, active layer thicknesses increased by a few centimetres to especially in spring (very high confidence). Satellite records indi- tens of centimetres (medium confidence). In other areas, especially in cate that over the period 1967 2012, annual mean snow cover extent northern North America, there were large interannual variations but decreased with statistical significance; the largest change, 53% [very few significant trends (high confidence). The thickness of the season- likely, 40% to 66%], occurred in June. No months had statistically ally frozen ground in some non-permafrost parts of the Eurasian conti- significant increases. Over the longer period, 1922 2012, data are nent likely decreased, in places by more than 30 cm from 1930 to 2000 available only for March and April, but these show a 7% [very likely, (high confidence) {4.7.4} 4.5% to 9.5%] decline and a strong negative [ 0.76] correlation with March April 40°N to 60°N land temperature. {4.5.2, 4.5.3} 320 Observations: Cryosphere Chapter 4 4.1 Introduction climate-meter , responsive not only to temperature but also to other climate variables (e.g., precipitation). However, it remains the case that The cryosphere is the collective term for the components of the Earth the conspicuous and widespread nature of changes in the cryosphere system that contain a substantial fraction of water in the frozen state (in particular, sea ice, glaciers and ice sheets) means these changes are (Table 4.1). The cryosphere comprises several components: snow, river frequently used emblems of the impact of changing climate. It is thus and lake ice; sea ice; ice sheets, ice shelves, glaciers and ice caps; and imperative that we understand the context of current change within the frozen ground which exist, both on land and beneath the oceans (see framework of past changes and natural variability. Glossary and Figure 4.1). The lifespan of each component is very differ- ent. River and lake ice, for example, are transient features that general- The cryosphere is, however, not simply a passive indicator of climate ly do not survive from winter to summer; sea ice advances and retreats change; changes in each component of the cryosphere have a signifi- with the seasons but especially in the Arctic can survive to become cant and lasting impact on physical, biological and social systems. Ice multi-year ice lasting several years. The East Antarctic ice sheet, on the sheets and glaciers exert a major control on global sea level (see Chap- other hand, is believed to have become relatively stable around 14 ters 5 and 13), ice loss from these systems may affect global ocean million years ago (Barrett, 2013). Nevertheless, all components of the circulation and marine ecosystems, and the loss of glaciers near popu- cryosphere are inherently sensitive to changes in air temperature and lated areas as well as changing seasonal snow cover may have direct precipitation, and hence to a changing climate (see Chapter 2). impacts on water resources and tourism (see WGII Chapters 3 and 24). Similarly, reduced sea ice extent has altered, and in the future may Changes in the longer-lived components of the cryosphere (e.g., glaciers) continue to alter, ocean circulation, ocean productivity and regional are the result of an integrated response to climate, and the cryosphere is climate and will have direct impacts on shipping and mineral and oil often referred to as a natural thermometer . But as our understanding exploration (see WGII, Chapter 28). Furthermore, decline in snow cover of the complexity of this response has grown, it is increasingly clear that and sea ice will tend to amplify regional warming through snow and elements of the cryosphere should rather be considered as a natural ice-albedo feedback effects (see Glossary and Chapter 9). In ­ ddition, a Table 4.1 | Representative statistics for cryospheric components indicating their general significance. Ice on Land Percent of Global Land Surfacea Sea Level Equivalentb (metres) Antarctic ice sheetc 8.3 58.3 Greenland ice sheetd 1.2 7.36 Glaciers e 0.5 0.41 Terrestrial permafrostf 9 12 0.02 0.10g Seasonally frozen groundh 33 Not applicable Seasonal snow cover 4 1.3 30.6 0.001 0.01 (seasonally variable)i Northern Hemisphere freshwater (lake and river) icej 1.1 Not applicable Totalk 52.0 55.0% ~66.1 Ice in the Ocean Percent of Global Ocean Areaa Volumel (103 km3) Antarctic ice shelves 0.45m ~380 Antarctic sea ice, austral summer (spring)n 0.8 (5.2) 3.4 (11.1) Arctic sea ice, boreal autumn (winter/spring)n 1.7 (3.9) 13.0 (16.5) Sub-sea permafrosto ~0.8 Not available Totalp 5.3 7.3 Notes: a Assuming a global land area of 147.6 Mkm2 and ocean area of 362.5 Mkm2. b See Glossary. Assuming an ice density of 917 kg m 3, a seawater density of 1028 kg m 3, with seawater replacing ice currently below sea level. c Area of grounded ice sheet not including ice shelves is 12.295 Mkm2 (Fretwell et al., 2013). d Area of ice sheet and peripheral glaciers is 1.801 Mkm2 (Kargel et al., 2012). SLE (Bamber et al., 2013). e Calculated from glacier outlines (Arendt et al., 2012), includes glaciers around Greenland and Antarctica. For sources of SLE see Table 4.2. f Area of permafrost excluding permafrost beneath the ice sheets is 13.2 to 18.0 Mkm2 (Gruber, 2012). g Value indicates the full range of estimated excess water content of Northern Hemisphere permafrost (Zhang et al., 1999). h Long-term average maximum of seasonally frozen ground is 48.1 Mkm2 (Zhang et al., 2003); excludes Southern Hemisphere. i Northern Hemisphere only (Lemke et al., 2007). j Areas and volume of freshwater (lake and river ice) were derived from modelled estimates of maximum seasonal extent (Brooks et al., 2012). k To allow for areas of permafrost and seasonally frozen ground that are also covered by seasonal snow, total area excludes seasonal snow cover. l Antarctic austral autumn (spring) (Kurtz and Markus, 2012); and Arctic boreal autumn (winter) (Kwok et al., 2009). For the Arctic, volume includes only sea ice in the Arctic Basin. m Area is 1.617 Mkm2 (Griggs and Bamber, 2011). n Maximum and minimum areas taken from this assessment, Sections 4.2.2 and 4.2.3. o Few estimates of the area of sub-sea permafrost exist in the literature. The estimate shown, 2.8 Mkm2, has significant uncertainty attached and was assembled from other publications by Gruber (2012). p Summer and winter totals assessed separately. 321 Chapter 4 Observations: Cryosphere 4 Figure 4.1 | The cryosphere in the Northern and Southern Hemispheres in polar projection. The map of the Northern Hemisphere shows the sea ice cover during minimum summer extent (13 September 2012). The yellow line is the average location of the ice edge (15% ice concentration) for the yearly minima from 1979 to 2012. Areas of continuous perma- frost (see Glossary) are shown in dark pink, discontinuous permafrost in light pink. The green line along the southern border of the map shows the maximum snow extent while the black line across North America, Europe and Asia shows the 50% contour for frequency of snow occurrence. The Greenland ice sheet (blue/grey) and locations of glaciers (small gold circles) are also shown. The map of the Southern Hemisphere shows approximately the maximum sea ice cover during an austral winter (13 September 2012). The yellow line shows the average ice edge (15% ice concentration) during maximum extent of the sea ice cover from 1979 to 2012. Some of the elements (e.g., some glaciers and snow) located at low latitudes are not visible in this projection (see Figure 4.8). The source of the data for sea ice, permafrost, snow and ice sheet are data sets held at the National Snow and Ice Data Center (NSIDC), University of Colorado, on behalf of the North American Atlas, Instituto Nacional de Estadística, Geografía e Informática (Mexico), Natural Resources Canada, U.S. Geological Survey, Government of Canada, Canada Centre for Remote Sensing and The Atlas of Canada. Glacier locations were derived from the multiple data sets compiled in the Randolph Glacier Inventory (Arendt et al., 2012). 322 Observations: Cryosphere Chapter 4 changes in frozen ground (in particular, ice-rich permafrost) will sea ice motion create areas of open water where, during colder months, damage some vulnerable Arctic infrastructure (see WGII, Chapter 28), new ice can quickly form and grow. On the other hand, convergent ice and could substantially alter the carbon budget through the release of motion causes the ice cover to thicken by deformation. Two relatively methane (see Chapter 6). thin floes colliding with each other can raft , stacking one on top of the other and thickening the ice. When thicker floes collide, thick ridges Since AR4, substantial progress has been made in most types of cry- may be built from broken pieces, with a height above the surface (ridge ospheric observations. Satellite technologies now permit estimates sail) of 2 m or more, and a much greater thickness (~10 m) and width of regional and temporal changes in the volume and mass of the below the ocean surface (ridge keel). ice sheets. The longer time series now available enable more accu- rate assessments of trends and anomalies in sea ice cover and rapid Sea ice thickness also increases by basal freezing during winter months. identification of unusual events such as the dramatic decline of Arctic But the thicker the ice becomes the more it insulates heat loss from the summer sea ice extent in 2007 and 2012. Similarly, Arctic sea ice thick- ocean to the atmosphere and the slower the basal growth is. There is ness can now be estimated using satellite altimetry, allowing pan-Arc- an equilibrium thickness for basal ice growth that is dependent on the tic measurements of changes in volume and mass. A new global glacier surface energy balance and heat from the deep ocean below. Snow inventory includes nearly all glaciers (Arendt et al., 2012) (42% in AR4) cover lying on the surface of sea ice provides additional insulation, and and allows for much better estimates of the total ice volume and its also alters the surface albedo and aerodynamic roughness. But also, past and future changes. Remote sensing measurements of regional and particularly in the Antarctic, a heavy snow load on thin sea ice glacier volume change are also now available widely and modelling of can depress the ice surface and allow seawater to flood the snow. This glacier mass change has improved considerably. Finally, fluctuations in saturated snow layer freezes quickly to form snow ice (see FAQ 4.1). the cryosphere in the distant and recent past have been mapped with increasing certainty, demonstrating the potential for rapid ice loss, Because sea ice is formed from seawater it contains sea salt, mostly compared to slow recovery, particularly when related to sea level rise. in small pockets of concentrated brine. The total salt content in newly formed sea ice is only 25 to 50% of that in the parent seawater, and the This chapter describes the current state of the cryosphere and its indi- residual salt rejected as the sea ice forms alters ocean water density vidual components, with a focus on recent improvements in under- and stability. The salinity of the ice decreases as it ages, and particu- standing of the observed variability, changes and trends. Projections larly during the Arctic summer when melt water (including from melt of future cryospheric changes (e.g., Chapter 13) and potential drivers ponds that form on the surface) drains through and flushes the ice. (Chapter 10) are discussed elsewhere. Earlier IPCC reports used cry- The salinity and porosity of sea ice affect its mechanical strength, its ospheric terms that have specific scientific meanings (see Cogley et thermal properties and its electrical properties the latter being very al., 2011), but have rather different meanings in everyday language. important for remote sensing. To avoid confusion, this chapter uses the term glaciers for what was previously termed glaciers and ice caps (e.g., Lemke et al., 2007). For Geographical constraints play a dominant but not an exclusive role in 4 the two largest ice masses of continental size, those covering Green- determining the quite different characteristics of sea ice in the Arctic land and Antarctica, we use the term ice sheets . For simplicity, we use and the Antarctic (see FAQ 4.1). This is one of the reasons why changes units such as gigatonnes (Gt, 109 tonnes, or 1012 kg). One gigatonne is in sea ice extent and thickness are very different in the north and the approximately equal to one cubic kilometre of freshwater (1.1 km3 of south. We also have much more information on Arctic sea ice thickness ice), and 362.5 Gt of ice removed from the land and immersed in the than we do on Antarctic sea ice thickness, and so discuss Arctic and oceans will cause roughly 1 mm of global sea level rise (Cogley, 2012). Antarctic separately in this assessment. 4.2.2 Arctic Sea Ice 4.2 Sea Ice Regional sea ice observations, which span more than a century, have 4.2.1 Background revealed significant interannual changes in sea ice coverage (Walsh and Chapman, 2001). Since the advent of satellite multichannel pas- Sea ice (see Glossary) is an important component of the climate sive microwave imaging systems in 1979, which now provide more system. A sea ice cover on the ocean changes the surface albedo, insu- than 34 years of continuous coverage, it has been possible to monitor lates the ocean from heat loss, and provides a barrier to the exchange the entire extent of sea ice with a temporal resolution of less than a of momentum and gases such as water vapour and CO2 between the day. A number of procedures have been used to convert the observed ocean and atmosphere. Salt ejected by growing sea ice alters the den- microwave brightness temperature into sea ice concentration the sity structure and modifies the circulation of the ocean. Regional cli- fractional area of the ocean covered by ice and thence to derive sea mate changes affect the sea ice characteristics and these changes can ice extent and area (Markus and Cavalieri, 2000; Comiso and Nishio, feed back on the climate system, both regionally and globally. Sea ice 2008). Sea ice extent is defined as the sum of ice covered areas with is also a major component of polar ecosystems; plants and animals at concentrations of at least 15%, while ice area is the product of the ice all trophic levels find a habitat in, or are associated with, sea ice. concentration and area of each data element within the ice extent. A brief description of the different techniques for deriving sea ice con- Most sea ice exists as pack ice, and wind and ocean currents drive the centration is provided in the Supplementary Material. The trends in the drift of individual pieces of ice (called floes). Divergence and shear in sea ice concentration, ice extent and ice area, as inferred from data 323 Chapter 4 Observations: Cryosphere derived from the different techniques, are generally compatible. A com- bars are also comparable in summer and winter during the first decade parison of derived ice extents from different sources is presented in the but become progressively larger for summer compared to winter in next section and in the Supplementary Material. Results presented in subsequent decades. These results indicate that the largest interannual this assessment are based primarily on a single technique (Comiso and variability has occurred in the summer and in the recent decade. Nishio, 2008) but the use of data from other techniques would provide generally the same conclusions. Although relatively short as a climate record, the 34-year satellite record is long enough to allow determination of significant and con- Arctic sea ice cover varies seasonally, with average ice extent varying sistent trends of the time series of monthly anomalies (i.e., difference between about 6 × 106 km2 in the summer and about 15 × 106 km2 in between the monthly and the averages over the 34-year record) of ice the winter (Comiso and Nishio, 2008; Cavalieri and Parkinson, 2012; extent, area and concentration. The trends in ice concentration for the Meier et al., 2012). The summer ice cover is confined to mainly the winter, spring, summer and autumn for the period November 1978 to Arctic Ocean basin and the Canadian Arctic Archipelago, while winter December 2012 are shown in Figure 4.2 (b, c, d and e). The seasonal sea ice reaches as far south as 44°N, into the peripheral seas. At the trends for different regions, except the Bering Sea, are negative. Ice end of summer, the Arctic sea ice cover consists primarily of the pre- cover changes are relatively large in the eastern Arctic Basin and most viously thick, old and ridged ice types that survived the melt period. peripheral seas in winter and spring, while changes are pronounced Interannual variability is largely determined by the extent of the ice almost everywhere in the Arctic Basin, except at greater than 82°N, in cover in the peripheral seas in winter and by the ice cover that survives summer and autumn. In connection with a comprehensive observation- the summer melt in the Arctic Basin. al research program during the International Polar Year 2007 2008, regional studies primarily on the Canadian side of the Arctic revealed 4.2.2.1 Total Arctic Sea Ice Extent and Concentration very similar patterns of spatial and interannual variability of the sea ice cover (Derksen et al., 2012). Figure 4.2 (derived from passive microwave data) shows both the sea- sonality of the Arctic sea ice cover and the large decadal changes that From the monthly anomaly data, the trend in sea ice extent in the have occurred over the last 34 years. Typically, Arctic sea ice reaches Northern Hemisphere (NH) for the period from November 1978 to its maximum seasonal extent in February or March whereas the min- December 2012 is 3.8 +/- 0.3% per decade (very likely) (see FAQ 4.1). imum occurs in September at the end of summer melt. Changes in The error quoted is calculated from the standard deviation of the slope decadal averages in Arctic ice extent are more pronounced in summer of the regression line. The baseline for the monthly anomalies is the than in winter. The change in winter extent between 1979 1988 and average of all data for each month from November 1978 to December 1989 1998 was negligible. Between 1989 1998 and 1999 2008, 2012. The trends for different regions vary greatly, ranging from +7.3% there was a decrease in winter extent of around 0.6 × 106 km2. This per decade in the Bering Sea to 13.8% per decade in the Gulf of St. can be contrasted to a decrease in ice extent at the end of the summer Lawrence. This large spatial variability is associated with the complex- 4 (September) of 0.5 × 106 km2 between 1979 1988 and 1989 1998, ity of the atmospheric and oceanic circulation system as manifested in followed by a further decrease of 1.2 × 106 km2 between 1989 1998 the Arctic Oscillation (Thompson and Wallace, 1998). The trends also and 1999 2008. Figure 4.2 also shows that the change in extent from differ with season (Comiso and Nishio, 2008; Comiso et al., 2011). For 1979 1988 to 1989 1998 was statistically significant mainly in spring the entire NH, the trends in ice extent are 2.3 +/- 0.5%, 1.8 +/- 0.5%, and summer while the change from 1989 1998 to 1999 2008 was 6.1 +/- 0.8% and 7.0 +/- 1.5% per decade (very likely) in winter, spring, statistically significant during winter and summer. The largest inter- summer and autumn, respectively. The corresponding trends in ice area annual changes occur during the end of summer when only the thick are 2.8 +/- 0.5%, 2.2 +/- 0.5%, 7.2 +/- 1.0%, and 7.8 +/- 1.3% per components of the winter ice cover survive the summer melt (Comiso decade (very likely). Similar results were obtained by (Cavalieri and et al., 2008; Comiso, 2012). Parkinson, 2012) but cannot be compared directly since their data are for the period from 1979 to 2010 (see Supplementary Material). The For comparison, the average extents during the 2009 2012 period are trends for ice extent and ice area are comparable except in the summer also presented: the extent during this period was considerably less and autumn, when the trend in ice area is significantly more than that than in earlier periods in all seasons, except spring. The summer min- in ice extent. This is due in part to increasing open water areas within imum extent was at a record low in 2012 following an earlier record the pack that may be caused by more frequent storms and more diver- set in 2007 (Stroeve et al., 2007; Comiso et al., 2008). The minimum gence in the summer (Simmonds et al., 2008). The trends are larger in ice extent in 2012 was 3.44 × 106 km2 while the low in 2007 was 4.22 the summer and autumn mainly because of the rapid decline in the × 106 km2. For comparison, the record high value was 7.86 × 106 km2 multi-year ice cover (Comiso, 2012), as discussed in Section 4.2.2.3. in 1980. The low extent in 2012 (which is 18.5% lower than in 2007) The trends in km2 yr 1 were estimated as in Comiso and Nishio (2008) was probably caused in part by an unusually strong storm in the Cen- and Comiso (2012) but the percentage trends presented in this chapter tral Arctic Basin on 4 to 8 August 2012 (Parkinson and Comiso, 2013). were calculated differently. Here the percentage is calculated as a dif- The extents for 2007 and 2012 were almost the same from June until ference from the first data point on the trend line whereas the earlier the storm period in 2012, after which the extent in 2012 started to estimations used the difference from the mean value. The new percent- trend considerably lower than in 2007. The error bars, which represent age trends are only slightly different from the previous ones and the 1 standard deviation (1) of samples used to estimate each data point, conclusions about changes are the same. are smallest in the first decade and get larger with subsequent decades indicating much higher interannual variability in recent years. The error 324 Observations: Cryosphere Chapter 4 4.2.2.2 Longer Records of Arctic Ice Extent ­terrestrial proxies (e.g., Macias Fauria et al., 2010; Kinnard et al., 2011). The records constructed by Kinnard et al. (2011) and Macias Fauria et For climate analysis, the variability of the sea ice cover prior to the al. (2010) suggest that the decline of sea ice over the last few decades commencement of the satellite record in 1979 is also of interest. There has been unprecedented over the past 1450 years (see Section 5.5.2). are a number of pre-satellite records, some based on regional obser- In a study of the marginal seas near the Russian coastline using ice vations taken from ships or aerial reconnaissance (e.g., Walsh and extent data from 1900 to 2000, Polyakov et al. (2003) found a low Chapman, 2001; Polyakov et al., 2003) while others were based on frequency multi-decadal oscillation near the Kara Sea that shifted to a dominant decadal oscillation in the Chukchi Sea. 18 A more comprehensive basin-wide record, compiled by Walsh and a) Daily ice extent Chapman (2001), showed very little interannual variability until the 16 last three to four decades. For the period 1901 to 1998, their results show a summer mode that includes an anomaly of the same sign over 14 Ice extent (10 6 km2) nearly the entire Arctic and that captures the sea-ice trend from recent satellite data. Figure 4.3 shows an updated record of the Walsh and 12 Chapman data set with longer time coverage (1870 to 1978) that is more robust because it includes additional historical sea ice observa- 10 tions (e.g., from Danish meteorological stations). A comparison of this updated data set with that originally reported by Walsh and Chapman (2001) shows similar interannual variability that is dominated by a 8 1979-1988 nearly constant extent of the winter (January February March) and 1989-1998 autumn (October November December) ice cover from 1870 to the 6 1999-2008 1950s. The absence of interannual variability during that period is due 2009-2012 to the use of climatology to fill gaps, potentially masking the natural 4 signal. Sea ice data from 1900 2011 as compiled by Met Office Hadley Centre are also plotted for comparison. In this data set, the 1979 2011 J F M A M J J A S O N D values were derived from various sources, including satellite data, as described by Rayner et al. (2003). Since the 1950s, more in situ data are b) Winter (DJF) c) Spring (MAM) available and have been homogenized with the satellite record (Meier 50oN et al., 2012). These data show a consistent decline in the sea ice cover 60oN 90oE that is relatively moderate during the winter but more dramatic during the summer months. Satellite data from other sources are also plotted 4 in Figure 4.3, including Scanning Multichannel Microwave Radiome- ter (SMMR) and Special Sensor Microwave/Imager (SSM/I) data using the Bootstrap Algorithm (SBA) as described by Comiso and Nishio (2008) and National Aeronautics and Space Administration (NASA) 90oW Team Algorithm (NT1) as described by Cavalieri et al. (1984) (see Sup- plementary Material). Data from the Advanced Microwave Scanning Radiometer - Earth Observing System (AMSR-E) using the Bootstrap d) Summer (JJA) e) Autumn (SON) Algorithm (ABA) and the NASA Team Algorithm Version 2 (NT2) are also presented. The error bars represent one standard deviation of the interannual variability during the satellite period. Because of the use of climatology to fill data gaps from 1870 to 1953, the error bars in the Walsh and Chapman data were set to twice that of the satellite period and 1.5 times higher for 1954 to 1978. The apparent reduction of the sea ice extent from 1978 to 1979 is in part due to the change from surface observations to satellite data. Generally, the temporal distri- butions from the various sources are consistent with some exceptions that may be attributed to possible errors in the data (e.g., Screen, 2011 and Supplementary Material). Taking this into account, the various sources provide similar basic information and conclusions about the changing extent and variability of the Arctic sea ice cover. -2.4 -1.6 -0.8 0.0 0.8 1.6 2.4 Trend (% IC yr-1) 4.2.2.3 Multi-year/Seasonal Ice Coverage Figure 4.2 | (a) Plots of decadal averages of daily sea ice extent in the Arctic (1979 to 1988 in red, 1989 to 1998 in blue, 1999 to 2008 in gold) and a 4-year average daily The winter extent and area of the perennial and multi-year ice cover ice extent from 2009 to 2012 in black. Maps indicate ice concentration trends (1979 in the Central Arctic (i.e., excluding Greenland Sea multi-year ice) for 2012) in (b) winter, (c) spring, (d) summer and (e) autumn (updated from Comiso, 2010). 325 Chapter 4 Observations: Cryosphere ice area were strongly negative at 11.5 +/- 2.1 and 12.5 +/- 2.1% per decade (very likely) respectively. These values indicate an increased rate of decline from the 6.4% and 8.5% per decade, respectively, reported for the 1979 to 2000 period by Comiso (2002). The trends in multi-year ice extent and area are even more negative, at 13.5 +/- 2.5 and 14.7 +/- 3.0% per decade (very likely), respectively, as updated for the period 1979 to 2012 (Comiso, 2012). The more negative trend in ice area than in ice extent indicates that the average ice concentration of multi-year ice in the Central Arctic has also been declining. The rate of decline in the extent and area of multi-year ice cover is consistent with the observed decline of old ice types from the analysis of ice drift and ice age by Maslanik et al. (2007), confirming that older and thicker ice types in the Arctic have been declining significantly. The more negative trend for the thicker multi-year ice area than that for the perennial ice area implies that the average thickness of the ice, and hence the ice volume, has also been declining. Drastic changes in the multi-year ice coverage from QuikScat (satellite radar scatterometer) data, validated using high-resolution Synthetic Aperture Radar data (Kwok, 2004; Nghiem et al., 2007), have also been reported. Some of these changes have been attributed to the near zero replenishment of the Arctic multi-year ice cover by ice that survives the summer (Kwok, 2007). Figure 4.3 | Ice extent in the Arctic from 1870 to 2011. (a) Annual ice extent and (b) seasonal ice extent using averages of mid-month values derived from in situ and other sources including observations from the Danish meteorological stations from 1870 to 1978 (updated from, Walsh and Chapman, 2001). Ice extent from a joint Hadley and National Oceanic and Atmospheric Administration (NOAA) project (called HADISST1_ Ice) from 1900 to 2011 is also shown. The yearly and seasonal averages for the period 4 from 1979 to 2011 are shown as derived from Scanning Multichannel Microwave Radi- ometer (SMMR) and Special Sensor Microwave/Imager (SSM/I) passive microwave data using the Bootstrap Algorithm (SBA) and National Aeronautics and Space Administra- tion (NASA) Team Algorithm, Version 1 (NT1), using procedures described in Comiso and Nishio (2008), and Cavalieri et al. (1984), respectively; and from Advanced Microwave Scanning Radiometer, Version 2 (AMSR2) using algorithms called AMSR Bootstrap Algo- rithm (ABA) and NASA Team Algorithm, Version 2 (NT2), described in Comiso and Nishio (2008) and Markus and Cavalieri (2000). In (b), data from the different seasons are shown in different colours to illustrate variation between seasons, with SBA data from the procedure in Comiso and Nishio (2008) shown in black. 1979 2012 are shown in Figure 4.4. Perennial ice is that which survives the summer, and the ice extent at summer minimum has been used as a measure of its coverage (Comiso, 2002). Multi-year ice (as defined by World Meteorological Organization) is ice that has survived at least two summers. Generally, multi-year ice is less saline and has a distinct microwave signature that differs from the seasonal ice, and thus can be discriminated and monitored with satellite microwave radiometers (Johannessen et al., 1999; Zwally and Gloersen, 2008; Comiso, 2012). Figure 4.4 shows similar interannual variability and large trends for Figure 4.4 | Annual perennial (blue) and multi-year (green) sea ice extent (a) and sea ice area (b) in the Central Arctic from 1979 to 2012 as derived from satellite passive both perennial and multi-year ice for the period 1979 to 2012. The microwave data (updated from Comiso, 2012). Perennial ice values are derived from extent of the perennial ice cover, which was about 7.9 × 106 km2 in summer minimum ice extent, while the multi-year ice values are averages of those from 1980, decreased to as low as 3.5 × 106 km2 in 2012. Similarly, the December, January and February. The gold lines (after 2002) are from AMSR-E data. multi-year ice extent decreased from about 6.2 × 106 km2 in 1981 to Uncertainties in the observations (very likely range) are indicated by representative error about 2.5 × 106 km2 in 2012. The trends in perennial ice extent and bars, and uncertainties in the trends are given (very likely range). 326 Observations: Cryosphere Chapter 4 4.2.2.4 Ice Thickness and Volume be considered as significant. Envisat observations showed a large decrease in thickness (0.25 m) following September 2007 when ice For the Arctic, there are several techniques available for estimating extent was the second lowest on record (Giles et al., 2008b). This was the thickness distribution of sea ice. Combined data sets of draft and associated with the large retreat of the summer ice cover, with thinning thickness from submarine sonars, satellite altimetry and airborne elec- regionally confined to the Beaufort and Chukchi seas, but with no sig- tromagnetic sensing provide broadly consistent and strong evidence of nificant changes in the eastern Arctic. These results are consistent with decrease in Arctic sea ice thickness in recent years (Figure 4.6c). those from the NASA Ice, Cloud and land Elevation Satellite (ICESat) laser altimeter (see comment on ICESat data in Section 4.4.2.1), which Data collected by upward-looking sonar on submarines operating show thinning in the same regions between 2007 and 2008 (Kwok, beneath the Arctic pack ice provided the first evidence of basin-wide 2009) (Figure 4.5). Large decreases in thickness due to the 2007 mini- decreases in ice thickness (Wadhams, 1990). Sonar measurements are mum in summer ice are clearly seen in both the radar and laser altim- of average draft (the submerged portion of sea ice), which is converted eter thickness estimates. to thickness by assuming an average density for the measured floe including its snow cover. With the then available submarine records, The coverage of the laser altimeter on ICESat (which ceased opera- Rothrock et al. (1999) found that ice draft in the mid-1990s was less tion in 2009) extended to 86°N and provided a more complete spatial than that measured between 1958 and 1977 in each of six regions pattern of the thickness distribution in the Arctic Basin (Figure 4.6c). within the Arctic Basin. The change was least ( 0.9 m) in the Beau- Thickness estimates are consistently within 0.5 m of sonar measure- fort and Chukchi seas and greatest ( 1.7 m) in the Eurasian Basin. The ments from near-coincident submarine tracks and profiles from sonar decrease averaged about 42% of the average 1958 to 1977 thickness. moorings in the Chukchi and Beaufort seas (Kwok, 2009). Ten ICESat This decrease matched the decline measured in the Eurasian Basin campaigns between autumn 2003 and spring 2008 showed seasonal between 1976 and 1996 using UK submarine data (Wadhams and differences in thickness and thinning and volume losses of the Arctic Davis, 2000), which was 43%. Ocean ice cover (Kwok, 2009). Over these campaigns, the multi-year sea ice thickness in spring declined by ~0.6 m (Figure 4.5), while the A subsequent analysis of US Navy submarine ice draft (Rothrock et average thickness of the first-year ice (~2 m) had a negligible trend. al., 2008) used much richer and more geographically extensive data The average sea ice volume inside the Arctic Basin in spring (February/ from 34 cruises within a data release area that covered almost 38% March) was ~14,000 km3. Between 2004 and 2008, the total multi-year of the area of the Arctic Ocean. These cruises were equally distributed ice volume in spring (February/March) experienced a net loss of 6300 in spring and autumn over a 25-year period between 1975 and 2000. km3 (>40%). Residual differences between sonar mooring and satellite Observational uncertainty associated with the ice draft from these is thicknesses suggest basin-scale volume uncertainties of approximate- 0.5 m (Rothrock and Wensnahan, 2007). Multiple regression analysis ly 700 km3. The rate of volume loss ( 1237 km3 yr 1) during autumn was used to separate the interannual changes (Figure 4.6c), the annual (October/November), while highlighting the large changes during the cycle and the spatial distribution of draft in the observations. Results of short ICESat record compares with a more moderate loss rate ( 280 4 that analysis show that the annual mean ice thickness declined from a +/- 100 km3 yr 1) over a 31-year period (1979 2010) estimated from a peak of 3.6 m in 1980 to 2.4 m in 2000, a decrease of 1.2 m. Over the sea ice reanalysis study using the Pan-Arctic Ice-Ocean Modelling and period, the most rapid change was 0.08 m yr 1 in 1990. Assimilation system (Schweiger et al., 2011). The most recent submarine record, Wadhams et al. (2011), found that The CryoSat-2 radar altimeter (launched in 2010), which provides cov- tracks north of Greenland repeated between the winters of 2004 and erage up to 89°N, has provided new thickness and volume estimates 2007 showed a continuing shift towards less multi-year ice. of Arctic Ocean sea ice (Laxon et al., 2013). These show that the ice volume inside the Arctic Basin decreased by a total of 4291 km3 in Satellite altimetry techniques are now capable of mapping sea ice free- autumn (October/November) and 1479 km3 in winter (February/March) board to provide relatively comprehensive pictures of the distribution between the ICESat (2003 2008) and CryoSat-2 (2010 2012) periods. of Arctic sea ice thickness. Similar to the estimation of sea ice thick- Based on ice thickness estimates from sonar moorings, an inter-satel- ness from ice draft, satellite measured freeboard (the height of sea ice lite bias between ICESat and CryoSat-2 of 700 km3 can be expected. above the water surface) is converted to thickness, assuming an aver- This is much less than the change in volume between the two periods. age density of ice and snow. The principal challenges to accurate thick- ness estimation using satellite altimetry are in the discrimination of Airborne electro-magnetic (EM) sounding measures the distance ice and open water, and in estimating the thickness of the snow cover. between an EM instrument near the surface or on an aircraft and the ice/water interface, and provides another method to measure ice thick- Since 1993, radar altimeters on the European Space Agency (ESA), ness. Uncertainties in these thickness estimates are 0.1 m over level European Remote Sensing (ERS) and Envisat satellites have provided ice. Comparison with drill-hole measurements over a mix of level and Arctic observations south of 81.5°N. With the limited latitudinal reach ridged ice found differences of 0.17 m (Haas et al., 2011). of these altimeters, however, it has been difficult to infer basin-wide changes in thickness. The ERS-1 estimates of ice thickness show a Repeat EM surveys in the Arctic, though restricted in time and space, downward trend but, because of the high variability and short time have provided a regional view of the changing ice cover. From repeat series (1993 2001), Laxon et al. (2003) concluded that the trend in a ground-based and helicopter-borne EM surveys, Haas et al. (2008) region of mixed seasonal and multi-year ice (i.e., below 81.5°N) cannot found significant thinning in the region of the Transpolar Drift (an 327 Chapter 4 Observations: Cryosphere 2005 2006 2007 Greenland Greenland Greenland 4.0 Overall 2004 2008 MY ice 3.5 FM06 FY ice Thickness (m) 3.0 0.83 m Trend = -0.17+/- 0.05 m yr-1 2.5 Greenland Greenland 2.0 MA07 Thickness (m) 0.0 5.0 1.5 2004 2005 2006 2007 2008 Figure 4.5 | The distribution of winter sea ice thickness in the Arctic and the trends in average, first-year (FY and multi-year (MY) ice thickness derived from ICESat data between 2004 and 2008 (Kwok, 2009). a ­ verage wind-driven drift pattern that transports sea ice from the Sibe- Drifting buoys have been used to measure Arctic sea ice motion since 4 rian coast of Russia across the Arctic Basin to Fram Strait). Between 1979. From the record of buoy drift archived by the International Arctic 1991 and 2004, the modal ice thickness decreased from 2.5 m to 2.2 Buoy Programme, Rampal et al. (2009) found an increase in average m, with a larger decline to 0.9 m in 2007. Mean ice thicknesses also drift speed between 1978 and 2007 of 17 +/- 4.5% per decade in winter decreased strongly. This thinning was associated with reduction of the and 8.5 +/- 2.0% per decade in summer. Using daily satellite ice motion age of the ice, and replacement of second-year ice by first-year ice in Greenland fields, which provide a basin-wide picture of the ice drift, Spreen et 2007 (following the large decline in summer ice extent in 2007) as al. (2011) found that, between 1992 and 2008, the spatially averaged seen in satellite observations. Ice thickness estimates from EM surveys FM08 winter ice drift speed increased by 10.6 +/- 0.9% per decade, but varied near the North Pole can be compared to submarine estimates (Figure regionally between 4 and +16% per decade (Figure 4.6d). Increases 4.6c). Airborne EM measurements from the Lincoln Sea between 83°N in drift speed are seen over much of the Arctic except in areas with and 84°N since 2004 (Haas et al., 2010) showed some of the thickest thicker ice (Figure 4.6b, e.g., north of Greenland and the Canadian ice in the Arctic, with mean and modal thicknesses of more than 4.5 m Archipelago). The largest increases occurred during the second half of and 4 m, respectively. Since 2008, the modal thickness in this region the period (2001 2009), coinciding with the years of rapid ice thinning has declined to 3.5 m, which is most likely related to the narrowing discussed in Section 4.2.2.4. Both Rampal et al. (2009) and Spreen et al. of the remaining band of old ice along the northern coast of Canada. (2011) suggest that, since atmospheric reanalyses do not show strong- er winds, the positive trend in drift speed is probably due to a weaker 4.2.2.5 Arctic Sea Ice Drift and thinner ice cover, especially during the period after 2003. Ice motion influences the distribution of sea ice thickness in the Arctic In addition to freezing and melting, sea ice export through Fram Strait Basin: locally, through deformation and creation of open water areas; is a major component of the Arctic Ocean ice mass balance. Approxi- regionally, through advection of ice from one area to another; and mately10% of the area of Arctic Ocean ice is exported annually. Over a basin-wide, through export of ice from polar seas to lower latitudes 32-year satellite record (1979 2010), the mean annual outflow of ice where it melts. The drift and deformation of sea ice is forced primarily area through Fram Strait was 699 +/- 112 × 103 km2 with a peak during by winds and surface currents, but depends also on ice strength, top the 1994 1995 winter (updated from , Kwok, 2009), but with no sig- and bottom surface roughness, and ice concentration. On time scales nificant decadal trend. Decadal trends in ice volume export a more of days to weeks, winds are responsible for most of the variance in sea definitive measure of change is far less certain owing to the lack of ice motion. an extended record of the thickness of sea ice exported through Fram 328 Observations: Cryosphere Chapter 4 Strait. Comparison of volume outflow using ICESat thickness estimates increased at a rate of 5.7 +/- 0.9 days per decade. The largest and most (Spreen et al., 2009) with earlier estimates by Kwok and Rothrock significant trends (at the 99% level) of more than 10 days per decade (1999) and Vinje (2001) using thicknesses from moored upward look- are seen in the coastal margins and peripheral seas: Hudson Bay, the ing sonars shows no discernible change. East Greenland Sea, the Laptev/East Siberian seas, and the Chukchi/ Beaufort seas. Between 2005 and 2008, more than a third of the thicker and older sea ice loss occurred by transport of thick, multi-year ice, typically found 4.2.2.7 Arctic Polynyas west of the Canadian Archipelago, into the southern Beaufort Sea, where it melted in summer (Kwok and Cunningham, 2010). Uncertain- High sea ice production in coastal polynyas (anomalous regions of ties remain in the relative contributions of in-basin melt and export to open water or low ice concentration) over the continental shelves of observed changes in Arctic ice volume loss, and it has also been shown the Arctic Ocean is responsible for the formation of cold saline water, that export of thicker ice through Nares Strait could account for a small which contributes to the maintenance of the Arctic Ocean halocline fraction of the loss (Kwok, 2005). (see Glossary). A new passive microwave algorithm has been used to estimate thin sea ice thicknesses (<0.15 m) in the Arctic Ocean (Tamura 4.2.2.6 Timing of Sea Ice Advance, Retreat and Ice Season and Ohshima, 2011), providing the first circumpolar mapping of sea ice Duration; Length of Melt Season production in coastal polynyas. High sea ice production is confined to the most persistent Arctic coastal polynyas, with the highest ice pro- Importantly from both physical and biological perspectives, strong duction rate being in the North Water Polynya. The mean annual sea ice regional changes have occurred in the seasonality of sea ice in both production in the 10 major Arctic polynyas is estimated to be 2942 +/- polar regions (Massom and Stammerjohn, 2010; Stammerjohn et al., 373 km3 and decreased by 462 km3 between 1992 and 2007 (Tamura 2012). However, there are distinct regional differences in when sea- and Ohshima, 2011). sonally the change is strongest (Stammerjohn et al., 2012). 4.2.2.8 Arctic Land-Fast Ice Seasonality collectively describes the annual time of sea ice advance and retreat, and its duration (the time between day of advance and Shore- or land-fast ice is sea ice attached to the coast. Land-fast ice retreat). Daily satellite ice-concentration records (1979 2012) are used along the Arctic coast is usually grounded in shallow water, with the to determine the day to which sea ice advanced, and the day from seaward edge typically around the 20 to 30 m isobath (Mahoney et al., which it retreated, for each satellite pixel location. Maps of the timing of 2007). In fjords and confined bays, land-fast ice extends into deeper sea ice advance, retreat and duration are derived from these data (see water. Parkinson (2002) and Stammerjohn et al. (2008) for detailed methods). There are no reliable estimates of the total area or interannual variabil- Most regions in the Arctic show trends towards shorter ice season ity of land-fast ice in the Arctic. However, both significant and non-sig- 4 duration. One of the most rapidly changing areas (showing great- nificant trends have been observed regionally. Long-term monitoring er than 2 days yr 1 change) extends from the East Siberian Sea to near Hopen, Svalbard, revealed thinning of land-fast ice in the Barents the western Beaufort Sea. Here, between 1979 and 2011, sea ice Sea region by 11 cm per decade between 1966 and 2007 (Gerland et advance occurred 41 +/- 6 days later (or 1.3 +/- 0.2 days yr 1), sea al., 2008). Between 1936 and 2000, the trends in land-fast ice thick- ice retreat 49 +/- 7 days earlier ( 1.5 +/- 0.2 days yr 1), and duration ness (in May) at four Siberian sites (Kara Sea, Laptev Sea, East Siberi- became 90 +/- 16 days shorter ( 2.8 +/- 0.5 days yr 1) (Stammerjohn an Sea, Chukchi Sea) are insignificant (Polyakov et al., 2003). A more et al., 2012). This 3-month lengthening of the summer ice-free season recent composite time series of land-fast ice thickness between the places Arctic summer sea ice extent loss into a seasonal perspective mid 1960s and early 2000s from 15 stations along the Siberian coast and underscores impacts to the marine ecosystem (e.g., Grebmeier et revealed an average rate of thinning of 0.33 cm yr 1(Polyakov et al., al., 2010). 2010). End-of-winter ice thickness for three stations in the Canadian Arctic reveal a small downward trend at Eureka, a small positive trend The timing of surface melt onset in spring, and freeze-up in autumn, at Resolute Bay, and a negligible trend at Cambridge Bay (updated can be derived from satellite microwave data as the emissivity of the from Brown and Coté, 1992; Melling, 2012), but these trends are small surface changes significantly with snow melt (Smith, 1998; Drobot and and not statistically significant. Even though the trend in the land-fast Anderson, 2001; Belchansky et al., 2004). The amount of solar energy ice extent near Barrow, Alaska has not been significant (Mahoney et absorbed by the ice cover increases with the length of the melt season. al., 2007), relatively recent observations by Mahoney et al. (2007) and Longer melt seasons with lower albedo surfaces (wet snow, melt ponds Druckenmiller et al. (2009) found longer ice-free seasons and thinner and open water) increase absorption of incoming shortwave radiation land-fast ice compared to earlier records (Weeks and Gow, 1978; Barry and ice melt (Perovich et al., 2007). Hudson (2011) estimates that the et al., 1979). As freeze-up happens later, the growth season shortens observed reduction in Arctic sea ice has contributed approximately and the thinner ice breaks up and melts earlier. 0.1 W m 2 of additional global radiative forcing, and that an ice-free summer Arctic Ocean will result in a forcing of about 0.3 W m 2. The 4.2.2.9 Decadal Trends in Arctic Sea Ice satellite record (Markus et al., 2009) shows a trend toward earlier melt and later freeze-up nearly everywhere in the Arctic (Figure 4.6e). Over The average decadal extent of Arctic sea ice has decreased in every the last 34 years, the mean melt season over the Arctic ice cover has season and in every successive decade since satellite observations 329 Chapter 4 Observations: Cryosphere commenced. The data set is robust with continuous and consistent 4.2.3.1 Total Antarctic Sea Ice Extent and Concentration global coverage on a daily basis thereby providing very reliable trend results (very high confidence). The annual Arctic sea ice cover very Figure 4.7a shows the seasonal variability of Antarctic sea ice extent likely declined within the range 3.5 to 4.1% per decade (0.45 to 0.51 using 34 years of satellite passive microwave data updated from million km2 per decade) during the period 1979 2012 with larger Comiso and Nishio (2008). In contrast to the Arctic, decadal monthly changes occurring in summer and autumn (very high confidence). averages almost overlap with each other, and the seasonal variability Much larger changes apply to the perennial ice (the summer minimum of the total Antarctic sea ice cover has not changed much over the extent) which very likely decreased in the range from 9.4 % to 13.6 % period. In winter, the values for the 1999 2008 decade were slightly per decade (0.73 to 1.07 million km2 per decade) and multiyear sea ice higher than those of the other decades; whereas in autumn the values (more than 2 years old) which very likely declined in the range from for 1989 1998 and 1999 2008 decades were higher than those of 11.0 % to 16.0% per decade (0.66 to 0.98 million km2 per decade) 1979 1988. There was more seasonal variability in the period 2009 (very high confidence; Figure 4.4b). The rate of decrease in ice area 2012 than for earlier decadal periods, with relatively high values in late has been greater than that in extent (Figure 4.4b) because the ice con- autumn, winter and spring. centration has also decreased. The decline in multiyear ice cover as observed by QuikScat from 1992 to 1910 is presented in Figure 4.6b Trend maps for winter, spring, summer and autumn extent are present- and shown to be consistent with passive microwave data (Figure 4.4b). ed in Figure 4.7 (b, c, d and e respectively). The seasonal trends are sig- nificant mainly near the ice edge, with the values alternating between The decrease in perennial and multi-year ice coverage has resulted in a positive and negative around Antarctica. Such an alternating pattern is strong decrease in ice thickness, and hence in ice volume. Declassified similar to that described previously as the Antarctic Circumpolar Wave submarine sonar measurements, covering ~38% of the Arctic Ocean, (ACW) (White and Peterson, 1996) but the ACW may not be associated indicate an overall mean winter thickness of 3.64 m in 1980, which with the trends because the trends have been strongly positive in the likely decreased by 1.8 [1.3 to 2.3] m by 2008 (high confidence, Figure Ross Sea and negative in the Bellingshausen/Amundsen seas but with 4.6c). Between 1975 and 2000, the steepest rate of decrease was 0.08 almost no trend in the other regions (Comiso et al., 2011). In the winter, m yr 1 in 1990 compared to a slightly higher winter/summer rate of negative trends are evident at the tip of the Antarctic Peninsula and 0.10/0.20 m yr 1 in the 5-year ICESat record (2003 2008). This com- the western part of the Weddell Sea, while positive trends are prev- bined analysis (Figure 4.6c) shows a long-term trend of sea ice thinning alent in the Ross Sea. The patterns in spring are very similar to those that spans five decades. Satellite measurements made in the period of winter, whereas in summer and autumn negative trends are mainly 2010 2012 show a decrease in basin-scale sea ice volume compared confined to the Bellingshausen/Amundsen seas, while positive trends to those made over the period 2003 2008 (medium confidence). The are dominant in the Ross Sea and the Weddell Sea. Arctic sea ice is becoming increasingly seasonal with thinner ice, and it will take several years for any recovery. The regression trend in the monthly anomalies of Antarctic sea ice 4 extent from November 1978 to December 2012 (updated from Comiso The decreases in both concentration and thickness reduces sea ice and Nishio, 2008) is slightly positive, at 1.5 +/- 0.3% per decade, or 0.13 strength reducing its resistance to wind forcing, and drift speed has to 0.20 million km2 per decade (very likely) (see FAQ 4.1). The seasonal increased (Figure 4.6d) (Rampal et al., 2009; Spreen et al., 2011). Other trends in ice extent are 1.2 +/- 0.5%, 1.0 +/- 0.5%, 2.5 +/- 2.0% and 3.0 significant changes to the Arctic Ocean sea ice include lengthening in +/- 2.0% per decade (very likely) in winter, spring, summer and autumn, the duration of the surface melt on perennial ice of 6 days per decade respectively, as updated from Comiso et al. (2011). The corresponding (Figure 4.6e) and a nearly 3-month lengthening of the ice-free season trends in ice area (also updated) are 1.9 +/- 0.7%, 1.6 +/- 0.5%, 3.0 +/- in the region from the East Siberian Sea to the western Beaufort Sea. 2.1%, and 4.4 +/- 2.3% per decade (very likely). The values are all pos- itive, with the largest trends occurring in the autumn. The trends are 4.2.3 Antarctic Sea Ice consistently higher for ice area than ice extent, indicating less open water (possibly due to less storms and divergence) within the pack in The Antarctic sea ice cover is largely seasonal, with average extent var- later years. Trends reported by Parkinson and Cavalieri (2012) using ying from a minimum of about 3 × 106 km2 in February to a maximum data from 1978 to 2010 are slightly different, in part because they of about 18 × 106 km2 in September (Zwally et al., 2002a; Comiso et al., cover a different time period (see Supplementary Material). The overall 2011). The relatively small fraction of Antarctic sea ice that survives the interannual trends for various sectors around Antarctica are given in summer is found mostly in the Weddell Sea, but with some perennial FAQ 4.1, and show large regional variability. Changes in ice drift and ice also surviving on the western side of the Antarctic Peninsula and wind patterns as reported by Holland and Kwok (2012) may be related in small patches around the coast. As well as being mostly first-year to this phenomenon. ice, Antarctic sea ice is also on average thinner, warmer, more saline and more mobile than Arctic ice (Wadhams and Comiso, 1992). These 4.2.3.2 Antarctic Sea Ice Thickness and Volume characteristics, which reduce the capabilities of some remote sensing techniques, together with its more distant location from inhabited con- Since AR4, some advances have been made in determining the thick- tinents, result in far less being known about the properties of Antarctic ness of Antarctic sea ice, particularly in the use of ship-based obser- sea ice than of that in the Arctic. vations and satellite altimetry. However, there is still no information on large-scale Antarctic ice thickness change. Worby et al. (2008) compiled 25 years of ship-based data from 83 Antarctic voyages on 330 Observations: Cryosphere Chapter 4 Ice concentration a) Annual ice extent -10.0 (% per decade) 10.0 1.0 -3.8+/-0.3 (% per decade) anomaly (106km2) 0.5 Ice extent 0.0 -0.5 -1.0 -1.5 1980 1985 1990 1995 2000 2005 2010 Multiyear ice concentration b) Multiyear ice coverage (Jan-1) 5.0 -50.0 (% per decade) 50.0 MYice area (106km2) 4.5 4.0 3.5 3.0 -0.80+/-0.2 x106 (km2 per decade) 2.5 2.0 1980 1985 1990 1995 2000 2005 2010 Thickness c) Ice thickness 4.0 -1.0 (m per decade) 0.0 3.5 Winter Ice thickness (m) -0.62 (m per decade) 3.0 2.5 2.0 ICESat 1.5 Regression of submarine observations EM surveys (North Pole) 1.0 1980 1985 1990 1995 2000 2005 2010 d) Sea ice drift speed Drift speed 4 2 Buoy drift: 0.55+/-0.04 (km day per decade) -1 -2.5 (km day-1 per decade) 2.5 anomaly (km day-1) Sea ice drift speed 1 0 -1 Satellite ice drift (Oct-May): 0.94+/-0.3 (km day-1 per decade) -2 1980 1985 1990 1995 2000 2005 2010 Length of melt season e) Average length of melt season 130 -30.0 (day per decade) 30.0 Length of melt (day) 120 110 100 5.7+/-0.9 (day per decade) 90 1980 1985 1990 1995 2000 2005 2010 Figure 4.6 | Summary of linear decadal trends (red lines) and pattern of changes in the following: (a) Anomalies in Arctic sea ice extent from satellite passive microwave observa- tions (Comiso and Nishio, 2008, updated to include 2012). Uncertainties are discussed in the text. (b) Multi-year sea ice coverage on January 1st from analysis of the QuikSCAT time series (Kwok, 2009); grey band shows uncertainty in the retrieval. (c) Sea ice thickness from submarine (blue), satellites (black) (Kwok and Rothrock, 2009), and in situ/electro- magnetic (EM) surveys (circles) (Haas et al., 2008); trend in submarine ice thickness is from multiple regression of available observations within the data release area (Rothrock et al., 2008). Error bars show uncertainties in observations. (d) Anomalies in buoy (Rampal et al., 2009) and satellite-derived sea ice drift speed (Spreen et al., 2011). (e) Length of melt season (updated from Markus et al., 2009); grey band shows the basin-wide variability. 331 Chapter 4 Observations: Cryosphere development but progress is limited by knowledge of snow thickness 20 a) Daily ice extent and the paucity of suitable validation data sets. A recent analysis of the ICESat record by Kurtz and Markus (2012), assuming zero ice freeboard, found negligible trends in ice thickness over the 5-year record. Ice extent (10 6 km2) 15 4.2.3.3 Antarctic Sea Ice Drift Using a 19-year data set (1992 2010) of satellite-tracked sea ice 10 motion, Holland and Kwok (2012) found large and statistically sig- 1979-1988 nificant decadal trends in Antarctic ice drift that in most sectors are 1989-1998 caused by changes in local winds. These trends suggest acceleration of 5 1999-2008 the wind-driven Ross Gyre and deceleration of the Weddell Gyre. The 2009-2012 changes in meridional ice transport affect the freshwater budget near the Antarctic coast. This is consistent with the increase of 30,000 km2 yr 1 in the net area export of sea ice from the Ross Sea shelf coastal 0 J F M A M J J A S O N D polynya region between 1992 and 2008 (Comiso et al., 2011). Assum- ing an annual average thickness of 0.6 m, Comiso et al. (2011) estimat- b) Winter (JJA) c) Spring (SON) ed an increase in volume export of 20 km3 yr 1 which is similar to the 45oE rate of production in the Ross Sea coastal polynya region for the same period discussed in Section 4.2.3.5. 4.2.3.4 Timing of Sea Ice Advance, Retreat and Ice Season Duration 60oS In the Antarctic there are regionally different patterns of strong change 135oW 50oS in ice duration (>2 days yr 1). In the northeast and west Antarctic Peninsula and southern Bellingshausen Sea region, later ice advance d) Summer (DJF) e) Autumn (MAM) (+61 +/- 15 days), earlier retreat ( 39 +/- 13 days) and shorter duration (+100 +/- 31 days, a trend of 3.1 +/- 1.0 days yr 1) occurred over the period 1979/1980 2010/2011 (Stammerjohn et al., 2012). These changes have strong impacts on the marine ecosystem (Montes- 4 Hugo et al., 2009; Ducklow et al., 2011). The opposite is true in the adjacent western Ross Sea, where substantial lengthening of the ice season of 79 +/- 12 days has occurred (+2.5 +/- 0.4 days yr 1) due to earlier advance (+42 +/- 8 days) and later retreat ( 37 +/- 8 days). Patterns of change in the relatively narrow East Antarctic sector are generally of a lower magnitude and zonally complex, but in certain -2.4 -0.8 -1.60.0 0.8 1.6 2.4 regions involve changes in the timing of sea ice advance and retreat Trend (% IC yr-1) of the order of +/-1 to 2 days yr 1 (for the period 1979 2009) (Massom Figure 4.7 | (a) Plots of decadal averages of daily sea ice extent in the Antarctic et al., 2013). (1979 1988 in red, 1989 1998 in blue, 1999 2008 in gold) and a 4-year average daily ice extent from 2009 to 2012 in black. Maps indicate ice concentration trends 4.2.3.5 Antarctic Polynyas (1979 2012) in (b) winter, (c) spring, (d) summer and (e) autumn (updated from Comiso, 2010). Polynyas are commonly found along the coast of Antarctica. There are two different processes that cause a polynya. Warm water upwelling which routine observations of sea ice and snow properties were made. keeps the surface water near the freezing point and reduces ice pro- Their compilation included a gridded data set that reflects the regional duction (sensible heat polynya), and wind or ocean currents move ice differences in sea ice thickness. A subset of these ship observations, away and increase further ice production (latent heat polynya). and ice charts, was used by DeLiberty et al. (2011) to estimate the annual cycle of sea ice thickness and volume in the Ross Sea, and to An increase in the extent of coastal polynyas in the Ross Sea caused investigate the relationship between ice thickness and extent. They increased ice production (latent heat effect) that is primarily respon- found that maximum sea ice volume was reached later than maxi- sible for the positive trend in ice extent in the Antarctic (Comiso et mum extent. While ice is advected to the northern edge and melts, the al., 2011). Drucker et al. (2011) show that in the Ross Sea, the net interior of the sea ice zone is supplied with ice from higher latitudes ice export equals the annual ice production in the Ross Sea polynya and continues to thicken by thermodynamic growth and deformation. (approximately 400 km3 in 1992), and that ice production increased by Satellite retrievals of sea ice freeboard and thickness in the Antarctic 20 km3 yr 1 from 1992 to 2008. However, the ice production in the Wed- (Mahoney et al., 2007; Zwally et al., 2008; Xie et al., 2011) are under dell Sea, which is three times less, has had no statistically significant 332 Observations: Cryosphere Chapter 4 Frequently Asked Questions FAQ 4.1 | How Is Sea Ice Changing in the Arctic and Antarctic? The sea ice covers on the Arctic Ocean and on the Southern Ocean around Antarctica have quite different charac- teristics, and are showing different changes with time. Over the past 34 years (1979 2012), there has been a down- ward trend of 3.8% per decade in the annual average extent of sea ice in the Arctic. The average winter thickness of Arctic Ocean sea ice has thinned by approximately 1.8 m between 1978 and 2008, and the total volume (mass) of Arctic sea ice has decreased at all times of year. The more rapid decrease in the extent of sea ice at the summer minimum is a consequence of these trends. In contrast, over the same 34-year period, the total extent of Antarctic sea ice shows a small increase of 1.5% per decade, but there are strong regional differences in the changes around the Antarctic. Measurements of Antarctic sea ice thickness are too few to be able to judge whether its total volume (mass) is decreasing, steady, or increasing. A large part of the total Arctic sea ice cover lies above 60°N (FAQ 4.1, Figure 1) and is surrounded by land to the south with openings to the Canadian Arctic Archipelago, and the Bering, Barents and Greenland seas. Some of the ice within the Arctic Basin survives for several seasons, growing in thickness by freezing of seawater at the base and by deformation (ridging and rafting). Seasonal sea ice grows to only ~2 m in thickness but sea ice that is more than 1 year old (perennial ice) can be several metres thicker. Arctic sea ice drifts within the basin, driven by wind and ocean currents: the mean drift pattern is dominated by a clockwise circulation pattern in the western Arctic and a Transpolar Drift Stream that transports Siberian sea ice across the Arctic and exports it from the basin through the Fram Strait. Satellites with the capability to distinguish ice and open water have provided a picture of the sea ice cover changes. Since 1979, the annual average extent of ice in the Arctic has decreased by 3.8% per decade. The decline in extent at the end of summer (in late September) has been even greater at 11% per decade, reaching a record minimum in 2012. The decadal average extent of the September minimum Arctic ice cover has decreased for each decade since satellite records began. Submarine and satellite records suggest that the thickness of Arctic ice, and hence the total volume, is also decreasing. Changes in the relative amounts of perennial and seasonal ice are contributing to the reduction in ice volume. Over the 34-year record, approximately 17% of this type of sea ice per decade has been lost to melt and export out of the basin since 1979 and 40% since 1999. Although the area of Arctic sea ice coverage can fluctuate from year to year because of variable seasonal production, the proportion of thick perennial ice, and the total sea ice volume, can recover only slowly. 4 Unlike the Arctic, the sea ice cover around Antarctica is constrained to latitudes north of 78°S because of the pres- ence of the continental land mass. The Antarctic sea ice cover is largely seasonal, with an average thickness of only ~1 m at the time of maximum extent in September. Only a small fraction of the ice cover survives the summer minimum in February, and very little Antarctic sea ice is more than 2 years old. The ice edge is exposed to the open ocean and the snowfall rate over Antarctic sea ice is higher than in the Arctic. When the snow load from snowfall is sufficient to depress the ice surface below sea level, seawater infiltrates the base of the snow pack and snow-ice is formed when the resultant slush freezes. Consequently, snow-to-ice conversion (as well as basal freezing as in the Arctic) contributes to the seasonal growth in ice thickness and total ice volume in the Antarctic. Snow-ice forma- tion is sensitive to changes in precipitation and thus changes in regional climate. The consequence of changes in precipitation on Antarctic sea ice thickness and volume remains a focus for research. Unconstrained by land boundaries, the latitudinal extent of the Antarctic sea ice cover is highly variable. Near the Antarctic coast, sea ice drift is predominantly from east to west, but further north, it is from west to east and highly divergent. Distinct clockwise circulation patterns that transport ice northward can be found in the Weddell and Ross seas, while the circulation is more variable around East Antarctica. The northward extent of the sea ice cover is controlled in part by the divergent drift that is conducive in winter months to new ice formation in persistent open water areas (polynyas) along the coastlines. These zones of ice formation result in saltier and thus denser ocean water and become one of the primary sources of the deepest water found in the global oceans. Over the same 34-year satellite record, the annual extent of sea ice in the Antarctic increased at about 1.5% per decade. However, there are regional differences in trends, with decreases seen in the Bellingshausen and Amundsen seas, but a larger increase in sea ice extent in the Ross Sea that dominates the overall trend. Whether the smaller overall increase in Antarctic sea ice extent is meaningful as an indicator of climate is uncertain because the extent (continued on next page) 333 Chapter 4 Observations: Cryosphere FAQ 4.1 (continued) varies so much from year to year and from place to place around the continent. Results from a recent study suggest that these contrasting trends in ice coverage may be due to trends in regional wind speed and patterns. Without better ice thickness and ice volume estimates, it is difficult to characterize how Antarctic sea ice cover is responding to changing climate, or which climate parameters are most influential. There are large differences in the physical environment and processes that affect the state of Arctic and Antarctic sea ice cover and contribute to their dissimilar responses to climate change. The long, and unbroken, record of satellite observations have provided a clear picture of the decline of the Arctic sea ice cover, but available evidence precludes us from making robust statements about overall changes in Antarctic sea ice and their causes. +1.3% Arctic Antarctic ea +7.3% Siberia dell S Wed +3.2% ing Ber a Se Alaska and 2.2% ingsh en Seas Barents Sea ausen Antarctica Bell nds 9.3% 4.3% Amu 60°N 2.5% 60°S Greenland 6.1% Ross S ea +1.3% 4.6% 7.0% +4.3% 5 km per day 10 km per day 4 Extent anomalies Extent anomalies 1.0 1.0 (106 km2) (106 km2) 0.0 0.0 1.0 1.0 -3.8% per decade +1.5% per decade 1990 2000 2010 1990 2000 2010 FAQ 4.1, Figure 1 | The mean circulation pattern of sea ice and the decadal trends (%) in annual anomalies in ice extent (i.e., after removal of the seasonal cycle), in different sectors of the Arctic and Antarctic. Arrows show the average direction and magnitude of ice drift. The average sea ice cover for the period 1979 through 2012, from satellite observations, at maximum (minimum) extent is shown as orange (grey) shading. trend over the same period. Variability in the ice cover in this region 4.2.3.6 Antarctic Land-Fast Ice is linked to changes in the Southern Annular Mode (SAM). Between 1974 and 1976, the large Weddell Sea Polynya, which is a sensible heat Land-fast ice forms around the coast of Antarctica, typically in narrow polynya, was created by the injection of relatively warm deep water coastal bands of varying width up to 150 km from the coast and in into the surface layer due to sustained deep-ocean convection (sensi- water depths of up to 400 to 500 m. Around East Antarctica, it com- ble heat effect) during negative SAM, but since the late 1970s the SAM prises generally between 5% (winter) and 35% (summer) of the overall has been mainly positive, resulting in warmer and wetter condition sea ice area (Fraser et al., 2012), and a greater fraction of ice volume forestalling any reoccurrence of the Weddell Sea Polynya (Gordon et (Giles et al., 2008a). al., 2007). Variability in the distribution and extent of land-fast ice is sensitive to processes of ice formation and to processes such as ocean swell and 334 Observations: Cryosphere Chapter 4 waves, and strong wind events that cause the ice to break-up. Histor- Section 4.3.3.4 and Chapter 13). In the following, we report global ical records of Antarctic land-fast ice extent, such as that of Kozlovsky glacier coverage (Section 4.3.1), how changes in length, area, volume et al. (1977) covering 0° to 160°E, were limited by sparse and sporad- and mass are determined (Section 4.3.2) and the observed changes in ic sampling. Recently, using cloud-free Moderate Resolution Imaging these parameters through time (Section 4.3.3). Spectrometer (MODIS) composite images, Fraser et al. (2012) derived a high-resolution time series of land-fast sea ice extent along the 4.3.1 Current Area and Volume of Glaciers East Antarctic coast, showing a statistically significant increase (1.43 +/- 0.30% yr 1) between March 2000 and December 2008. There is a The total area covered by glaciers was only roughly known in AR4, strong increase in the Indian Ocean sector (20°E to 90°E, 4.07 +/- 0.42% resulting in large uncertainties for all related calculations (e.g., overall yr 1), and a non-significant decrease in the sector from 90°E to 160°E glacier volume or mass changes). Since AR4, the world glacier invento- ( 0.40 +/- 0.37% yr 1). An apparent shift from a negative to a positive ry (WGMS, 1989) was gradually extended by Cogley (2009a) and Radiæ trend was noted in the Indian Ocean sector from 2004, which coincid- and Hock (2010); and for AR5, a new globally complete data set of gla- ed with greater interannual variability. Although significant changes cier outlines (Randolph Glacier Inventory (RGI)) was compiled from a are observed, this record is only 9 years in length. wide range of data sources from the 1950s to 2010 with varying levels of detail and quality (Arendt et al., 2012). Regional glacier-covered 4.2.3.7 Decadal Trends in Antarctic Sea Ice areas for 19 regions were extracted from the RGI and supplemented with the percentage of the area covered by glaciers terminating in tide- For the Antarctic, any changes in many sea ice characteristics are water (Figure 4.8 and Table 4.2). The areas covered by glaciers that are unknown. There has been a small but significant increase in total annual in contact with freshwater lakes are only locally available. The separa- mean sea ice extent that is very likely in the range of 1.2 to 1.8 % per tion of so-called peripheral glaciers from the ice sheets in Greenland decade between 1979 and 2012 (0.13 to 0.20 million km2 per decade) and Antarctica is not easy. A new detailed inventory of the glaciers in (very high confidence). There was also a greater increase in ice area Greenland (Rastner et al., 2012) allows for estimation of their area, associated with an increase in ice concentration. But there are strong volume, and mass balance separately from those of the ice sheet. This regional differences within this total, with some regions increasing in separation is still incomplete for Antarctica, and values discussed here extent/area and some decreasing (high confidence). Similarly, there are (Figures 4.1, 4.8 to 4.11, Tables 4.2 and 4.4) refer to the glaciers on contrasting regions around the Antarctic where the ice-free season has the islands in the Antarctic and Sub-Antarctic (Bliss et al., 2013) but lengthened, and others where it has decreased over the satellite period exclude glaciers on the mainland of Antarctica that are separate from (high confidence). There are still inadequate data to make any assess- the ice sheet. Regionally variable accuracy of the glacier outlines leads ment of changes to Antarctic sea ice thickness and volume. to poorly quantified uncertainties. These uncertainties, along with the regional variation in the minimum size of glaciers included in the inventory, and the subdivision of contiguous ice masses, also makes 4.3 Glaciers the total number of glaciers uncertain; the current best estimate is 4 around 170,000 covering a total area of about 730,000 km2. When This section considers all perennial surface land ice masses (defined in summed up, nearly 80% of the glacier area found in regions Antarctic 4.1 and Glossary) outside of the Antarctic and Greenland ice sheets. and Subantarctic (region 19), Canadian Arctic (regions 3 and 4), High Glaciers occur where climate conditions and topographic ­characteristics Mountain Asia (regions 13, 14 and 15), Alaska (region 5), and Green- allow snow to accumulate over several years and to transform grad- land (region 17) (Table 4.2). ually into firn (snow that persists for at least one year) and finally to ice. Under the force of gravity, this ice flows downwards to elevations From the glacier areas in the new inventory, total glacier volumes with higher temperatures where various processes of ablation (loss of and masses have been determined by applying both simple scaling snow and ice) dominate over accumulation (gain of snow and ice). The relations and ice-dynamical considerations (Table 4.2, and references sum of all accumulation and ablation processes determines the mass therein), however, both methods are calibrated with only a few hun- balance of a glacier. Accumulation is in most regions due mainly to dred glacier thickness measurements. This small sample means that solid precipitation (in general snow), but also results from refreezing uncertainties are large and difficult to quantify. The range of values as of liquid water, especially in polar regions or at high altitudes where derived from four global-scale studies for each of the 19 RGI regions firn remains below melting temperature. Ablation is, in most regions, is given in Table 4.2, suggesting a global glacier mass that is likely mainly due to surface melting with subsequent runoff, but loss of ice between 114,000 and 192,000 Gt (314 to 529 mm SLE). The numbers by calving (on land or in water; see Glossary) or sublimation (important and areas of glaciers reported in Table 4.2 are directly taken from RGI in dry regions) can also dominate. Re-distribution of snow by wind 2.0 (Arendt et al., 2012), with updates for the Low Latitudes (region 16) and avalanches can contribute to both accumulation and ablation. The and the Southern Andes (region 17). energy and mass fluxes governing the surface mass balance are direct- ly linked to atmospheric conditions and are modified by topography 4.3.2 Methods to Measure Changes in Glacier Length, (e.g., due to shading). Glaciers are sensitive climate indicators because Area and Volume/Mass they adjust their size in response to changes in climate (e.g., tempera- ture and precipitation) (FAQ 4.2). Glaciers are also important season- To measure changes in glacier length, area, mass and volume, a wide al to long-term hydrologic reservoirs (WGII, Chapter 3) on a regional range of observational techniques has been developed. Each technique scale and a major contributor to sea level rise on a global scale (see has individual benefits over specific spatial and temporal scales; their 335 Chapter 4 Observations: Cryosphere main characteristics are summarized in Table 4.3. Monitoring ­programs difficult to access, aerial photography and satellite imaging have been include complex climate-related observations at a few glaciers, index used to determine glacier length changes over the past decades. For measurements of mass balance at about a hundred glaciers, annual selected glaciers globally, historic terminus positions have been recon- length changes for a few hundred glaciers, and repeat geodetic esti- structed from maps, photographs, satellite imagery, also paintings, mates of area and volume changes at regional scales using remote dated moraines and other sources (e.g., Masiokas et al., 2009; Lopez et sensing methods (e.g., Haeberli et al., 2007). Although in situ meas- al., 2010; Nussbaumer et al., 2011; Davies and Glasser, 2012; Leclercq urements of glacier changes are biased towards glaciers that are easily and Oerlemans, 2012; Rabatel et al., 2013). Early reconstructions are accessible, comparatively small and simple to interpret, a large propor- sparsely distributed in both space and time, generally at intervals of tion of all glaciers in the world is debris covered or tidewater calving decades. The terminus fluctuations of some individual glaciers have (see Table 4.2) and changes of such glaciers are more difficult to inter- been reconstructed for periods of more than 3000 years (Holzhauser pret in climatic terms (Yde and Pasche, 2010). In addition, many of the et al., 2005), with a much larger number of records available as far remote-sensing based assessments do not discriminate these types. back as the 16th or 17th centuries (Zemp et al., 2011, and references therein). The reconstructed glacier length records are globally well dis- 4.3.2.1 Length Change Measurements tributed and were used, for example, to determine the contribution of glaciers to global sea level rise (Leclercq et al., 2011) (Section 4.3.3.4), For the approximately 500 glaciers worldwide that are regularly and for an independent temperature reconstruction at a hemispheric observed, front variations (commonly called length changes) are usu- scale (Leclercq and Oerlemans, 2012). ally obtained through annual measurements of the glacier terminus position. Globally coordinated observations were started in 1894, providing one of the longest available time series of environmental change (WGMS, 2008). More recently, particularly in regions that are Table 4.2 | The 19 regions used throughout this chapter and their respective glacier numbers and area (absolute and in percent) are derived from the RGI 2.0 (Arendt et al., 2012); the tidewater fraction is from Gardner et al. (2013). The minimum and maximum values of glacier mass are the minimum and maximum of the estimates given in four studies: Grinsted (2013), Huss and Farinotti (2012), Marzeion et al. (2012) and Radiæ et al. (2013). The mean sea level equivalent (SLE) of the mean glacier mass is the mean of estimates from the same four studies, using an ocean area of 362.5 × 106 km2 for conversion. All values were derived with globally consistent methods; deviations from more precise national data sets are thus possible. Ongoing improvements may lead to revisions of these (RGI 2.0) numbers in future releases of the RGI. Mass Mass Number of Area Percent of Tidewater Mean SLE Region Region Name (minimum) (maximum) Glaciers ( km2) total area fraction (%) (mm) (Gt) (Gt) 1 Alaska 23,112 89,267 12.3 13.7 16,168 28,021 54.7 4 Western Canada 2 15,073 14,503.5 2.0 0 906 1148 2.8 and USA 3 Arctic Canada North 3318 103,990.2 14.3 46.5 22,366 37,555 84.2 4 Arctic Canada South 7342 40,600.7 5.6 7.3 5510 8845 19.4 5 Greenland 13,880 87,125.9 12.0 34.9 10,005 17,146 38.9 6 Iceland 290 10,988.6 1.5 0 2390 4640 9.8 7 Svalbard 1615 33,672.9 4.6 43.8 4821 8700 19.1 8 Scandinavia 1799 2833.7 0.4 0 182 290 0.6 9 Russian Arctic 331 51,160.5 7.0 64.7 11,016 21,315 41.2 10 North Asiaa 4403 3425.6 0.4 0 109 247 0.5 11 Central Europe 3920 2058.1 0.3 0 109 125 0.3 12 Caucasus 1339 1125.6 0.2 0 61 72 0.2 13 Central Asia 30,200 64,497 8.9 0 4531 8591 16.7 14 South Asia (West) 22,822 33,862 4.7 0 2900 3444 9.1 15 South Asia (East) 14,006 21,803.2 3.0 0 1196 1623 3.9 16 Low Latitudes a 2601 2554.7 0.6 0 109 218 0.5 17 Southern Andesa 15,994 29,361.2 4.5 23.8 4241 6018 13.5 18 New Zealand 3012 1160.5 0.2 0 71 109 0.2 Antarctic and 19 3274 13,2267.4 18.2 97.8 27,224 43,772 96.3 Sub-Antarctic Total 168,331 726,258.3 38.5 113,915 191,879 412.0 Notes: a For regions 10, 16 and 17 the number and area of glaciers are corrected to allow for over-inclusion of seasonal snow in the glacierized extent of RGI 2.0 and for improved outlines (region 10) compared to RGI 2.0 (updated from, Arendt et al., 2012). 336 Observations: Cryosphere Chapter 4 3 7 9 5 1 8 10 4 6 2 11 13 12 14 15 ( ( ) 16 17 18 19 Figure 4.8 | Global distribution of glaciers (yellow, area increased for visibility) and area covered (diameter of the circle), sub-divided into the 19 RGI regions (white number) referenced in Table 4.2. The area percentage covered by tidewater (TW) glaciers in each region is shown in blue. Data from Arendt et al. (2012) and Gardner et al. (2013). 4.3.2.2 Area Change Measurements over glacier regions and types. Annual measurements began in the 4 1940s on a few glaciers, with about 100 glaciers being measured since Glacier area changes are reported in increasing number and cover- the 1980s and only 37 glaciers have been measured without inter- age based on repeat satellite imagery (WGMS, 2008). Although satel- ruption for more than 40 years (WGMS, 2009). Potential mass loss from lite-based observations are available only for the past four decades, calving or from basal ablation is not included in the surface measure- studies using aerial photography, old maps, as well as mapped and ments. At present, it is not possible to quantify all sources of uncer- dated moraines and trim lines show glacier areas back to the end of tainty in mass budgets extrapolated from measurements of individual the so-called Little Ice Age (LIA, see Glossary) about 150 years ago (cf. glaciers (Cogley, 2009b). Figure 6 in Rabatel et al., 2008) and beyond (e.g., Citterio et al., 2009; Davies and Glasser, 2012). The observed area changes depend (in A second method determines the volume change of all glaciers in a most regions) on glacier size (with smaller glaciers shrinking at faster region by measurement of surface-elevation changes (Section 4.3.3.3 percentage rates) and tend to vary greatly within any one mountain and Figure 4.11). The information is derived by subtracting digital ter- range. Moreover, the time spans of the measurements of change vary rain models from two points in time, including those from repeat air- from study to study and regional or global-scale estimates are there- borne or satellite altimetry (particularly suitable for larger and flatter fore difficult to generate. The focus here is thus on the comparison of ice surfaces). The conversion from volume to mass change can cause mean annual relative area changes averaged over entire mountain a major uncertainty, in particular over short periods, as density infor- regions. mation is required, but is generally available only from field measure- ments (Gardner et al., 2013, and references therein). 4.3.2.3 Volume and Mass Change Measurements Since 2003, a third method used to estimate overall mass change is Several methods are in use for measuring mass changes of glaciers. through measurement of the changing gravity field from satellites Traditionally, the annual surface mass balance is derived from repeated (GRACE mission). The coarse spatial resolution (about 300 km) and the snow density and snow/ice stake readings on individual glaciers. Esti- difficulties of separating different mass change signals such as hydro- mates over larger regions are obtained by extrapolating from the mea- logical storage and glacial isostatic adjustment limit this method to sured glaciers. This labour-intensive method is generally restricted to a regions with large continuous ice extent (Gardner et al., 2013). limited number of accessible glaciers, which are unevenly distributed ­ 337 Chapter 4 Observations: Cryosphere A fourth method calculates the mass balance of individual glaciers, typically show a pattern with the largest (flatter) glaciers tending to or a glacier region, with models that either convert particular glacier retreat continuously and by large cumulative distances, medium-sized variables such as length changes or the altitude of the equilibrium line (steeper) glaciers showing decadal fluctuations, and smaller glaciers (see Glossary) (e.g., Rabatel et al., 2005; Luethi et al., 2010; Leclercq showing high variability superimposed on smaller cumulative retreats et al., 2011) into mass changes, or use time series of atmospheric tem- (Figure 4.9). perature and other meteorological variables to simulate glacier mass balances at different levels of complexity (e.g., Hock et al., 2009; Mach- The exceptional terminus advances of a few individual glaciers in Scan- guth et al., 2009; Marzeion et al., 2012; Hirabayashi et al., 2013). The dinavia and New Zealand in the 1990s may be related to locally spe- models improve fidelity and physical completeness, and add value to cific climatic conditions such as increased winter precipitation (Nesje the scarce direct measurements. et al., 2000; Chinn et al., 2005; Lemke et al., 2007). In other regions, such as Iceland, the Karakoram and Svalbard, observed advances A fifth method determines glacier mass changes as residuals of the were often related to dynamical instabilities (surging) of glaciers (e.g., water balance for hydrological basins rather than for glacier regions. Murray et al., 2003; Quincey et al., 2011; Bolch et al., 2012; Björnsson Results from all but this last method are used in the following regional et al., 2013). Glaciers with calving instabilities can retreat exceptionally and global assessment of glacier mass changes (Sections 4.3.3.3 and rapidly (Pfeffer, 2007), while those with heavily debris-covered tongues 4.3.3.4). are often close to stationary (Scherler et al., 2011). More regionally- focused studies of length change over different time periods (e.g., Cit- 4.3.3 Observed Changes in Glacier Length, Area and Mass terio et al., 2009; Masiokas et al., 2009; Lopez et al., 2010; Bolch et al., 2012) justify high confidence about the trend of glacier length varia- 4.3.3.1 Length Changes tions shown in Figure 4.9. Despite their variability due to different response times and local con- 4.3.3.2 Area Changes ditions (see FAQ 4.2), the annually measured glacier terminus fluctu- ations from about 500 glaciers worldwide reveal a largely homoge- From the large number of published studies on glacier area changes neous trend of retreat (WGMS, 2008). In Figure 4.9, a selection of the in all parts of the world since AR4 (see Table 4.SM.1) a selection with available long-term records of field measurements is shown for 14 out examples from 16 out of the 19 RGI regions is shown in Figure 4.10. The of the 19 RGI regions. Cumulative values of retreat for large, land-ter- studies reveal that (1) total glacier area has decreased in all regions, minating valley glaciers typically reach a few kilometres over the 120- (2) the rates of change cover a similar range of values in all regions, year period of observation. For mid-latitude mountain and valley gla- (3) there is considerable variability of the rates of change within each ciers, typical retreat rates are of the order of 5 to 20 m yr 1. Rates of up region, (4) highest loss rates are found in regions 2, 11 and 16, and (5) to 100 m yr 1 (or even more) are seen to occur under special conditions, the rates of loss have a tendency to be higher over more recent time 4 such as the complete loss of a tongue on a steep slope (see FAQ 4.2, periods. The last point (5) requires studies comparing the same sample Figure 1c), or the disintegration of a very flat tongue. A non-calving of glaciers over multiple similar time periods. For 14 out of 19 regions valley glacier in Chile had reported mean annual retreat rates of 125 m listed in Table 4.SM.1 (see Supplementary Material) with such an anal- from 1961 to 2011 (Rivera et al., 2012). The general tendency of retreat ysis, higher loss rates were found for the more recent period. in the 20th century was interrupted in several regions (e.g., regions 2, 8, 11 and 17) by phases of stability lasting one or two decades, or While points (1) and (2) give high confidence in the global-scale shrink- even advance, for example in the 1920s, 1970s and 1990s (regionally age in glacier area, (3) points to a considerable regional to local-scale variable). In regions for which long-term field measurements of sev- scatter of observed change rates. The shorter the period of investiga- eral glaciers of different sizes are available, the terminus fluctuations tion and the smaller the sample of glaciers analysed, the more variable Table 4.3 | Overview of methods used to determine changes in glacier length, area and volume mass along with some typical characteristics. The techniques are not exclusive. The last three columns provide only indicative values. Number of Repeat Parameter Method Technique Typical Accuracy Earliest Data Glaciers Interval Decadal Various Reconstruction 10 m Dozens Holocene centuries Length change Field In situ measurement 1m Hundreds Annual 19th century Remote sensing Photogrammetric survey Two image pixels (depending on resolution) Hundreds Annual 20th century Maps Cartographic 5% of the area Hundreds Decadal 19th century Area change Remote sensing Image processing 5% of the area Thousands Sub-decadal 20th century Remote sensing Laser and radar profiling 0.1 m Hundreds Annual 21st century Volume change Remote sensing DEM differencing 0.5 m Thousands Decadal 20th century Field Direct mass balance measurement 0.2 m Hundreds Seasonal 20th century Mass change Remote sensing Gravimetry (GRACE) Dependent on the region Global Seasonal 21st century 338 Observations: Cryosphere Chapter 4 1 Alaska 2 Western Canada and US 5 Greenland 500 500 500 0 0 0 Cumulative length change (m) -500 -500 -500 -1000 -1000 -1000 -1500 -1500 -1500 Blue Sermikavsak Grif n Sorqaup -2000 -2000 Nisqually -2000 Tunorssuaq Peyto Sigssarigsut Sasketchewan Serminguaq -2500 -2500 Sentinel -2500 Motzfeld Nuka Wedgemount Okpilak Sermitsiaq -3000 -3000 -3000 1860 1880 1900 1920 1940 1960 1980 2000 1860 1880 1900 1920 1940 1960 1980 2000 1860 1880 1900 1920 1940 1960 1980 2000 6 Iceland 7 Svalbard 8 Scandinavia 500 500 500 0 0 0 Cumulative length change (m) -500 -500 -500 -1000 -1000 -1000 -1500 -1500 -1500 Brikdalsbreen Breidamerkurjokull Engabreen -2000 Fjallsjokull G-Sel -2000 -2000 Faabergstoelsbreen Gljufurarjokull Nigardsbreen Mulajokull Hansbreen Storbreen -2500 Skeidararjokull E3 -2500 Nordenskioldbreen -2500 Storglaciaeren Solheimajokull Waldemarbreen Styggedalbreen -3000 -3000 -3000 1860 1880 1900 1920 1940 1960 1980 2000 1860 1880 1900 1920 1940 1960 1980 2000 1860 1880 1900 1920 1940 1960 1980 2000 10 North Asia 11 Central Europe 12 Caucasus and Middle East 500 500 500 0 0 0 Cumulative length change (m) -500 -500 -500 -1000 -1000 -1000 -1500 -1500 -1500 Abano Gr. Aletsch Bezengi -2000 -2000 Hornkees -2000 Bolshoy Azau Kara-batkak Lys Devdoraki Kljuev Pasterze Djankuat -2500 Korumdu -2500 Pizol -2500 Gergeti Koryto Trient Khakel -3000 -3000 -3000 1860 1880 1900 1920 1940 1960 1980 2000 1860 1880 1900 1920 1940 1960 1980 2000 1860 1880 1900 1920 1940 1960 1980 2000 4 13 Central Asia 16 Low Latitudes 17 Southern Andes 500 500 500 0 0 0 -500 Cumulative length change (m) -500 -500 -1000 -1500 -1000 -1000 -2000 -2500 -1500 -1500 Broggi -3000 Chacaltaya Azufre -2000 -2000 Charquini Norte -3500 Esperanza Norte Geblera Lewis Frias Levi Aktru Meren -4000 Guessfeldt -2500 Maliy Aktru -2500 Tyndall Upsala Ts. Tuyuksuyskiy Zongo -4500 Vacas -3000 -3000 -5000 1860 1880 1900 1920 1940 1960 1980 2000 1860 1880 1900 1920 1940 1960 1980 2000 1860 1880 1900 1920 1940 1960 1980 2000 18 New Zealand 19 Antarctic and Subantarctic Region overview 500 500 0 0 -500 Cumulative length change (m) -500 -1000 -1500 -1000 -2000 -1500 -2500 -2000 -3000 Fox Franz Josef -2500 Heaney -3500 Stocking Hodges -4000 -3000 1860 1880 1900 1920 1940 1960 1980 2000 1860 1880 1900 1920 1940 1960 1980 2000 Figure 4.9 | Selection of long-term cumulative glacier length changes as compiled from in situ measurements (WGMS, 2008), reconstructed data points added to measured time series (region 5) from Leclercq et al. (2012), and additional time series from reconstructions (regions 1, 2, 7, 10, 12, 16, 17 and 18) from Leclercq and Oerlemans (2012). Indepen- dent of their (highly variable) temporal density, all measurement points are connected by straight lines. The glacier Mulajokull (region 6) is of surge type and some of the glaciers showing strong retreat either terminate (Guessfeldt and Upsala in region 17) or terminated (Engabreen and Nigardsbreen in region 8) in lakes. For region 11, many more time series are available (see WGMS, 2008), but are not shown for graphical reasons. 339 Chapter 4 Observations: Cryosphere are the rates of change reported for a specific region (Table 4.SM.1). 4.3.3.3 Regional Scale Glacier Volume and Mass Changes In many regions of the world, rates of area loss have increased (Table 4.SM.1), confirming that glaciers are still too large for the current cli- In AR4, global and regional scale glacier mass changes were extrapo- mate and will continue to shrink (see FAQ 4.2 and Section 4.3.3.3). lated from in situ measurements of mass balance on individual glaciers (Kaser et al., 2006; Lemke et al., 2007). In some regions, such as Alaska, Several studies have reported the disappearance of glaciers, among Patagonia and the Russian Arctic, very few if any such records were others in Arctic Canada (Thomson et al., 2011), the Rocky Mountains available. Since AR4, geodetically derived ice volume changes have (Bolch et al., 2010; Tennant et al., 2012) and North Cascades (Pelto, been assimilated (Cogley, 2009b), providing more consistent regional 2006), Patagonia (Bown et al., 2008; Davies and Glasser, 2012), sever- coverage and better representation of the proportion of calving gla- al tropical mountain ranges (Coudrain et al., 2005; Klein and Kincaid, ciers. In addition, the new near complete inventory (RGI) of glacier-cov- 2006; Cullen et al., 2013), the European Alps (Citterio et al., 2007; ered areas (Arendt et al., 2012) has improved knowledge about region- Knoll and Kerschner, 2009; Diolaiuti et al., 2012), the Tien Shan (Hagg al and global glacier volume and mass changes. et al., 2012; Kutuzov and Shahgedanova, 2009) in Asia and on James Ross Island in Antarctica (Carrivick et al., 2012). In total, the disap- Figure 4.11 shows a compilation of available mean mass-balance rates pearance of more than 600 glaciers has been reported, but the real for 1960 2010 for each of the 19 RGI regions. Where error estimates number is certainly higher. Also some of the glaciers, whose annual are reported, the 90% confidence bounds are shown. Most results mass balance has been measured over several years or even decades, shown are calculated using a single method, some merge multiple have disappeared or started to disintegrate (Ramirez et al., 2001; Car- methods; those from Gardner et al. (2013) are reconciled estimates turan and Seppi, 2007; Thibert et al., 2008). Though the number of for 2003 2009 obtained by selecting the most reliable results of dif- glaciers that have disappeared is difficult to compare directly (e.g., the ferent observation methods, after region-by-region reanalysis and time periods analysed or the disappearance-criteria applied differ), ­comparison. glaciers that have disappeared provide robust evidence that the ELA (see Glossary) has risen above the highest peaks in many mountain Despite the great progress made since AR4, uncertainties inherent to ranges (see FAQ 4.2). specific methods, and arising from the differences between methods (cf. 0 -0.2 4 Relative area change rate (% yr-1) -0.4 -0.6 -0.8 1 Alaska -1 2 Western Canada / US 3 Arctic Canada N 4 Arctic Canada S 6 Iceland -1.2 7 Svalbard 8 Scandinavia 10 North Asia -1.4 11 Central Europe 13 Central Asia 14 South Asia W 15 South Asia E -1.6 16 Low Latitudes 17 Southern Andes 18 New Zealand -1.8 19 Antarcic / Subantarctic 1940 1950 1960 1970 1980 1990 2000 2010 Year Figure 4.10 | Mean annual relative area loss rates for 16 out of the 19 RGI regions of Figure 4.8. Each line shows a measurement of the rate of percentage change in area over a mountain range from a specific publication (for sources see Table 4.SM.1), the length of the line shows the period used for averaging. 340 Observations: Cryosphere Chapter 4 Box 2.1), remain large, and confidence about the absolute value of mass Gardner et al. (2013) discussed inconsistencies among, and differences loss is medium at both regional and global scales (Figure 4.11). The between, methods and their respective results for 2003 2009. They highest density of measurement and best time resolution are available found that results from the GRACE gravimetric mission agree well with for Scandinavia (region 8) and Central Europe (region 11). The least cov- results from ICESat laser altimetry in regions of extensive ice cover (see erage and some of the highest uncertainties are in the Arctic (regions 3, comment on ICESat data in Section 4.4.2.1), but show much more vari- 4, 5, 9) and the Antarctic and Sub-Antarctic (region 19). able and uncertain mass changes in regions with small or scattered ice 1 Alaska 2 Western Canada/US 3 Arctic Canada North 4 Arctic Canada South 1000 1000 1000 1000 Mass budget (kg m-2 yr-1) Mass budget (kg m-2 yr-1) Mass budget (kg m-2 yr-1) Mass budget (kg m-2 yr-1) 0 0 0 0 1000 1000 1000 1000 2000 2000 2000 2000 -1000 kg m-2 = 0.243 mm SLE -1000 kg m-2 = 0.04 mm SLE -1000 kg m-2 = 0.289 mm SLE -1000 kg m-2 = 0.113 mm SLE 1960 1970 1980 1990 2000 2010 1960 1970 1980 1990 2000 2010 1960 1970 1980 1990 2000 2010 1960 1970 1980 1990 2000 2010 5 Greenland 6 Iceland 7 Svalbard 8 Scandinavia 1000 1000 1000 1000 Mass budget (kg m-2 yr-1) Mass budget (kg m-2 yr-1) Mass budget (kg m-2 yr-1) Mass budget (kg m-2 yr-1) 0 0 0 0 1000 1000 1000 1000 2000 2000 2000 2000 -1000 kg m-2 = 0.248 mm SLE -1000 kg m-2 = 0.031 mm SLE -1000 kg m-2 = 0.094 mm SLE -1000 kg m-2 = 0.008 mm SLE 1960 1970 1980 1990 2000 2010 1960 1970 1980 1990 2000 2010 1960 1970 1980 1990 2000 2010 1960 1970 1980 1990 2000 2010 9 Russian Arctic 10 North Asia 11 Central Europe Mass budget (kg m-2 yr-1) 1000 Mass budget (kg m-2 yr-1) Mass budget (kg m-2 yr-1) 1000 1000 0 0 0 1000 1000 1000 2000 2000 2000 -1000 kg m-2 = 0.142 mm SLE -1000 kg m-2 = 0.009 mm SLE -1000 kg m-2 = 0.006 mm SLE 1960 1970 1980 1990 2000 2010 1960 1970 1980 1990 2000 2010 1960 1970 1980 1990 2000 2010 12 Caucasus & Middle East 13 Central Asia 14 South Asia (West) 15 South Asia (East) 4 1000 Mass budget (kg m-2 yr-1) 1000 1000 Mass budget (kg m-2 yr-1) 1000 Mass budget (kg m-2 yr-1) Mass budget (kg m-2 yr-1) 0 0 0 0 1000 1000 1000 1000 2000 2000 2000 2000 -1000 kg m-2 = 0.003 mm SLE -1000 kg m-2 = 0.178 mm SLE -1000 kg m-2 = 0.093 mm SLE -1000 kg m-2 = 0.06 mm SLE 1960 1970 1980 1990 2000 2010 1960 1970 1980 1990 2000 2010 1960 1970 1980 1990 2000 2010 1960 1970 1980 1990 2000 2010 16 Low Latitudes 17 Southern Andes 18 New Zealand 19 Antarctic & Subantarctic 1000 1000 1000 1000 Mass budget (kg m-2 yr-1) Mass budget (kg m-2 yr-1) Mass budget (kg m-2 yr-1) Mass budget (kg m-2 yr-1) 0 0 0 0 1000 1000 1000 1000 2000 2000 2000 2000 -1000 kg m-2 = 0.011 mm SLE -1000 kg m-2 = 0.087 mm SLE -1000 kg m-2 = 0.003 mm SLE -1000 kg m-2 = 0.368 mm SLE 1960 1970 1980 1990 2000 2010 1960 1970 1980 1990 2000 2010 1960 1970 1980 1990 2000 2010 1960 1970 1980 1990 2000 2010 modeled with climate data repeat volume area scaling repeat topography repeat gravimetry interpolated local records Gardner et al. 2013 [mixed] Figure 4.11 | Regional glacier mass budgets in units of kg m 2 yr 1 for the world s 19 glacierized regions (Figure 4.8 and Table 4.2). Estimates are from modelling with climate data (blue: Hock et al., 2009; Marzeion et al., 2012), repeat gravimetry (green: Chen et al., 2007; Luthcke et al., 2008; Peltier, 2009; Matsuo and Heki, 2010; Wu et al., 2010; Gardner et al., 2011; Ivins et al., 2011; Schrama and Wouters, 2011; Jacob et al., 2012, updated for RGI regions), repeat volume area scaling (magenta: Glazovsky and Macheret, 2006), interpolation of local glacier records (black: Cogley, 2009a; Huss, 2012), or airborne and/or satellite repeat topographic mapping (orange: Arendt et al., 2002; Rignot et al., 2003; Abdalati et al., 2004; Schiefer et al., 2007; Paul and Haeberli, 2008; Berthier et al., 2010; Moholdt et al., 2010, 2012; Nuth et al., 2010; Gardner et al., 2011, 2012; Willis et al., 2012; Björnsson et al., 2013; Bolch et al., 2013). Mass-budget estimates are included only for study domains that cover about 50% or more of the total regional glacier area. Mass-budget estimates include 90% confidence envelopes (not available from all studies). Conversions from specific mass budget in kg m 2 to mm SLE are given for each region. Gravimetric estimates are often not accompanied by estimates of glacierized area (required for conversion from Gt yr 1 to kg m 2 yr 1); in such cases the RGI regional glacier areas were used. 341 Chapter 4 Observations: Cryosphere cover such as Western Canada/USA (region 2) and the Qinghai-Xizang ple, with losses on Antarctic Peninsula islands and gains on Ellsworth (Tibet) Plateau (e.g., Yao et al., 2012, and references therein). Based Land islands. The picture is also heterogeneous in High Mountain Asia ­ on ICESat measurements, Gardner et al. (2013) also found that glaciers (region 13 to 15) (e.g., Bolch et al., 2012; Yao et al., 2012), where with in situ measurements tend to be located in sub-regions that are glaciers in the Himalaya and the Hindu Kush have been losing mass thinning more rapidly than the region as a whole. Thus, extrapolation (Kääb et al., 2012) while those in the Karakoram are close to balance from in situ measurements has a negative bias in regions with sparse (Gardelle et al., 2012). measurements. Based on this analysis, Gardner et al. (2013) excluded GRACE results for regions with small or scattered glacier coverage and Several studies of recent glacier velocity change (Heid and Kääb, 2012; excluded results based on extrapolation of local records for remote, Azam et al., 2012) and of the worldwide present-day sizes of accu- sparsely sampled regions. mulation areas (Bahr et al., 2009) indicate that the world s glaciers are out of balance with the present climate and thus committed to The 2003 2009 regionally differentiated results are given in Table 4.4. losing considerable mass in the future, even without further changes in There is very high confidence that, between 2003 and 2009, most mass climate. Increasing ice temperatures recorded at high elevation sites in loss was from glaciers in the Canadian Arctic (regions 3 and 4), Alaska the tropical Andes (Gilbert et al., 2010) and in the European Alps (Col (region 1), Greenland (region 5), the Southern Andes (region 17) and du Dome on Mont Blanc and Monte Rosa) (Vincent et al., 2007; Hoelzle the Asian Mountains (region 13 to 15), which together account for et al., 2011), as well as the ongoing thinning of the cold surface layer more than 80% of the global ice loss. on Storglaciären in northern Sweden (Gusmeroli et al., 2012), support this conclusion and give it high confidence. Despite the considerable scatter, Figure 4.11 shows mass losses in all 19 regions over the past five decades that, together with their consist- 4.3.3.4 Global Scale Glacier Mass Changes The ency with length (Section 4.3.3.1) and area changes (Section 4.3.3.2), Contribution to Sea Level provide robust evidence and very high confidence in global glacier shrinkage. In many regions, ice loss has likely increased during the last Global time series are required to assess the continuing contribution two decades, with slightly smaller losses in some regions during the of glacier mass changes to sea level (see Section 13.4.2 for discus- most recent years, since around 2005. In Central Europe (region 11), sion of the small proportion of ice loss from glaciers that does not the increase of loss rates was earliest and strongest. In the Russian contribute to sea level rise). A series of recent studies, some updated Arctic (region 9) and in the Antarctic and Sub-Antarctic (region 19), to RGI areas for this report by their respective authors, provides very the signal is highly uncertain and trends are least clear. Gardner et al. high confidence in a considerable and continuous mass loss, despite (2013) present values close to balance for the Antarctic and Subant- only medium agreement on the specific rates (Figure 4.12 and Table arctic (region 19) that result from complex regional patterns, for exam- 4.5). Cogley (updated from, 2009b) compiled 4,817 directly meas- ured annual mass budgets, and 983 volume change measurements by 4 extending the data set of WGMS (2009, and earlier issues ). Global Table 4.4 | Regional mass change rates in units of kg m 2 yr 1 and Gt yr 1 for the 5-year averages for 1961 2010, with uncertainties, were estimated period 2003 2009 from Gardner et al. (2013). Central Asia (region 13), South Asia from these using an inverse-distance-weighted interpolation. Newly West (region14), and South Asia East (region 15) are merged into a single region. For available volume change measurements increased the proportion of the division of regions see Figure 4.8. observations from calving glaciers from 3% to 16% compared to ear- No. Region Name (kg m 2 yr 1) (Gt yr 1) lier estimates reported by Lemke et al. (2007). This proportion is more 1 Alaska 570 +/- 200 50 +/- 17 realistic, but may still underestimate the relative importance of calving glaciers (Figure 4.8 and Section 4.3.3.3). 2 Western Canada and USA 930 +/- 230 14 +/- 3 3 Arctic Canada North 310 +/- 40 33 +/- 4 Leclercq et al. (updated from 2011) used length variations from 382 4 Arctic Canada South 660 +/- 110 27 +/- 4 glaciers worldwide as a proxy for glacier mass loss since 1800. The 5 Greenland periphery 420 +/- 70 38 +/- 7 length/mass change conversion was calibrated against mass bal- 6 Iceland 910 +/- 150 10 +/- 2 ance observations for 1950 2005 from Cogley (2009b) and provide 7 Svalbard 130 +/- 60 5 +/- 2 one estimate based on the arithmetic mean and another based on 8 Scandinavia 610 +/- 140 2 +/- 0 area-weighted extrapolation of regional averages. Uncertainty was 9 Russian Arctic 210 +/- 80 11 +/- 4 estimated from upper and lower bounds of the calibration parameter 10 North Asia 630 +/- 310 2 +/- 1 assumptions, and cumulatively propagated backward in time. For the 11 Central Europe 1060 +/- 170 2 +/- 0 19th century, the information was constrained by a limited number of observations, particularly in extensively glacierized regions that con- 12 Caucasus and Middle East 900 +/- 160 1 +/- 0 tribute most to the global mass budget. 13 15 High Mountain Asia 220 +/- 100 26 +/- 12 16 Low Latitudes 1080 +/- 360 4 +/- 1 Two global-scale time series are obtained from mass-balance mod- 17 Southern Andes 990 +/- 360 29 +/- 10 elling based on temperature and precipitation data (Marzeion et al., 18 New Zealand 320 +/- 780 0+/-1 2012; Hirabayashi et al., 2013). Glacier size adjustments are simulated 19 Antarctic and Sub-Antarctic 50 +/- 70 6 +/- 10 by using area volume power-law relations as proposed by Bahr et al. Total 350 +/- 40 259 +/- 28 (1997) for the approximately 170,000 individual glaciers delineated in 342 Observations: Cryosphere Chapter 4 Figure 4.12 | Global cumulative (top graphs) and annual (lower graphs) glacier mass change for (a) 1801 2010 and (b) 1961 2010. The cumulative estimates are all set to zero mean over 1986 2005. Estimates are based on glacier length variations (updated from Leclercq et al., 2011), from area-weighted extrapolations of individual directly and geodetically measured glacier mass budgets (updated from Cogley, 2009b), and from modelling with atmospheric variables as input (Marzeion et al., 2012; Hirabayashi et al., 2013). Uncertainties are based on comprehensive error analyses in Cogley (2009b) and Marzeion et al. (2012) and on assumptions about the representativeness of the sampled glaciers in Leclercq et al. (2011). Hirabayashi et al. (2013) give a bulk error estimate only. For clarity in the bottom panels, uncertainties are shown only for the Cogley and Marzeion 4 curves excluding Greenland (GL). The blue bars (a, top) show the number of measured single-glacier mass balances per pentad in the updated Cogley (2009b) time series. The mean 2003 2009 estimate of Gardner et al. (2013) is added to b, bottom. the RGI. Marzeion et al. (2012) derive mass balances for 1902 2009 have been made simply by extrapolating the global mean to region from monthly mean temperature and precipitation obtained from 19 (Cogley, 2009a; Marzeion et al., 2012). One (1961 2004) mean Mitchell and Jones (2005). The model is calibrated against meas- glacier mass loss estimate based on an ECMWF 40-year reanalysis ured time series and validated against independent measurements. (ERA-40) driven simulation (Hock et al., 2009), is for a glacier area U ­ ncertainty estimates are obtained from comprehensive error propa- differing from the RGI region 19. For these reasons, the Antarctic and gation, first accumulated temporally for each glacier, and then region- S ­ ub-Antarctic (region 19) is excluded from this global glacier mass ally and globally. The model does not account for the subsurface mass change assessment. The contribution of region 19 to sea level is ­ balance or calving, but reproduces geodetically measured volume assumed to be within the uncertainty bounds of the Antarctic ice sheet changes for land-based glaciers within the uncertainties; however, it assessment (Section 4.4.2). Whereas Hirabayashi et al. (2013) exclude underestimates volume loss slightly for calving glaciers. Hirabayashi et the Antarctic and Greenland from their simulations and Leclercq et al. al. (2013) force an extended positive degree-day model with data from (2011) implicitly include Greenland, both Cogley (2009a) and Mar- an observation-based global set of daily precipitation and near-surface zeion et al. (2012) explicitly estimate mass changes in Greenland. In temperature as updated from earlier work (Hirabayashi et al., 2008). Figure 4.12, cumulative mass changes and corresponding rates are Annual mass balance is provided for 1948 2005 with a constant root shown for global glaciers excluding regions 5 and 19 (bold lines), mean square error of 500 km3 yr 1, estimated from comparison of mod- and also for global glaciers excluding only region 19 (thin lines). The elled with measured mass balances. cumulative curves are normalized such that their 1986 2005 aver- ages are all zero. For the Antarctic and Sub-Antarctic (region 19) observational infor- mation is limited and difficult to incorporate into this assessment. The arithmetic-mean estimate of Leclercq et al. (2011) indicates con- For some studies the time spans do not match (e.g., Gardner et al., tinuous mass loss from glaciers after about 1850 (Figure 4.12a, top). 2013 give only mean mass change for 2003 2009). Some estimates During the 1920s their area-weighted extrapolation reaches consid- 343 Chapter 4 Observations: Cryosphere erably higher rates (Figure 4.12a, bottom) than the other estimates, those in regions 5 and 19, which are included in the assessment of but the reasons remain unclear. After 1950, mass loss rates including ice sheets (Section 4.4.2). For the more recent periods, the time series Greenland are all within the uncertainty bounds of those that exclude of Cogley (2009a) and Marzeion et al. (2012) are combined, while Greenland, except for the 2001 2005 period when the Greenland for 1901 1990 the Leclercq et al. (2011) series were separated by ­ contribution was slightly outside the uncertainty bounds for both the area-weighting and combined with the Marzeion et al. (2012) values. Cogley and the Marzeion et al. estimates. Most notable is the rapid Each rate in Table 4.5 is thus the arithmetic mean of two series, with loss from Greenland glaciers in the Marzeion et al. simulations during a confidence bound calculated from their difference, and assessed to the 1930s. Other studies support rapid Greenland mass loss around represent the 90% likelihood range. Because differences between the this time (Zeeberg and Forman, 2001; Yde and Knudsen, 2007, and two time series vary considerably, the average confidence bound of references therein; Bjrk et al., 2012; Zdanowicz et al., 2012); howev- 1971 2009 is also applied uniformly to the two sub-periods 1993 er, the neighbouring regions in the Canadian Arctic (south and north) 2009 and 2005 2009. The 2003 2009 estimate of Gardner et al. and Iceland have mass loss anomalies an order of magnitude lower (2013) is lower than the Cogley and Marzeion averages but those for than predicted for Greenland in the same simulation. This discrepancy all glaciers excluding regions 5 and 19 are within the 2005 2009 90% may be an artefact of the uncertainties in the forcing and methods confidence bound. The 1991 2009 assessment is shown as a cumula- of Marzeion et al. that are considerably larger in the first than in the tive time series in Section 4.8 (see Figure 4.25). Earlier studies of the second half of the 20th century, so that the rates may well be overes- long-term contribution of glaciers to sea level change (Meier, 1984; timated. The Marzeion et al. rates are also considerably greater in the Zuo and Oerlemans, 1997; Gregory and Oerlemans, 1998; Kaser et al., 1950s and 1960s than in the other studies; during this period, the most 2006; Lemke et al., 2007; Oerlemans et al., 2007; Hock et al., 2009, rapid losses are in Arctic Canada and the Russian Arctic (Marzeion et with removal of Antarctic glacier contribution) all give smaller esti- al., 2012). mates than those assessed here. Overall, there is very high confidence that globally, the mass loss from glaciers has increased since the 1960s, and this is evident in region- 4.4 Ice Sheets al-scale estimates (Figure 4.11). For 2003 2009, Gardner et al. (2013) indicate that some regional (Section 4.3.3.3 and Figure 4.11) and 4.4.1 Background also the global time series may overestimate mass loss (Figure 4.12b, bottom). That glaciers with measured mass balances are concentrated Since AR4, satellite, airborne and in situ observations have greatly in sub-regions with higher mass losses definitely biases the estimates improved our ability to identify and quantify change in the vast polar of Cogley (2009a) (Section 4.3.3.3), but this explanation cannot hold ice sheets of Antarctica and Greenland. As a direct consequence, our for the Marzeion et al. (2012) time series, for which mass changes are understanding of the underlying drivers of ice-sheet change is also simulated separately for every single glacier in the inventory. It also much improved. These observations and the insights they yield are dis- 4 remains unclear whether the 2003 2009 inconsistency identified by cussed throughout Section 4.4, while the attribution of recent ice sheet Gardner et al. (2013) applies to earlier times, and if so how it should be change, projection of future changes in ice sheets and their future con- reconciled. Neither the evidence nor our level of understanding war- tribution to sea level rise are discussed in Chapter 10 and Chapter 13 rants any simple correction of the longer time series at present. respectively. Table 4.5 summarizes global-scale glacier mass losses for different 4.4.2 Changes in Mass of Ice Sheets periods relevant to discussions on sea level change (Chapter 13) and the global energy budget (Chapter 3). Values are given separately for The current state of mass balance of the Greenland and Antarctic ice the Greenland glaciers alone (region 5) and for all glaciers excluding ­ sheets is assessed in sections 4.4.2.2 and 4.4.2.3, but is introduced by Table 4.5 | Average annual rates of global mass change in Gt yr 1 and in sea level equivalents (mm SLE yr 1) for different time periods (Chapter 13) for (a) glaciers around the Greenland ice sheet (region 5 as defined by Rastner et al., 2012) and (b) all glaciers globally, excluding peripheral glaciers around the Antarctic and Greenland ice sheets (see dis- cussion in Section 4.4.2). The values are derived by averaging the results from the references listed and uncertainty ranges give 90% confidence level. The uncertainty calculated for 1971 2009 is also applied for the sub-periods 1993 2009 and 2005 2009. The global values for 2003 2009 from Gardner et al. (2013) are within this likelihood range (italics). (a) Greenland glaciers (region 5) (b) All glaciers excluding ice sheet peripheries Reference Gt yr 1 mm SLE yr 1 Gt yr 1 mm SLE yr 1 Marzeion et al. (2012); Leclercq 1901 1990 54 +/- 16 0.15 +/- 0.05 197 +/- 24 0.54 +/- 0.07 et al. (2011), updateda 1971 2009 Cogley (2009a); Marzeion et al. (2012) 21 +/- 10 0.06 +/- 0.03 226 +/- 135 0.62 +/- 0.37 1993 2009 Cogley (2009a); Marzeion et al. (2012) 37 +/- 10 0.10 +/- 0.03 275 +/- 135 0.76 +/- 0.37 2005 2009 Cogley (2009a); Marzeion et al. (2012) 56 +/- 10 0.15 +/- 0.03 301 +/- 135 0.83 +/- 0.37 2003 2009 Gardner et al. (2013) 38 +/- 7 0.10 +/- 0.02 215 +/- 26 0.59 +/- 0.07 Notes: a Isolation of (a) from (b) made by applying the respective annual ratios in Marzeion et al. (2012). 344 Observations: Cryosphere Chapter 4 Frequently Asked Questions FAQ 4.2 | Are Glaciers in Mountain Regions Disappearing? In many mountain ranges around the world, glaciers are disappearing in response to the atmospheric temperature increases of past decades. Disappearing glaciers have been reported in the Canadian Arctic and Rocky Mountains; the Andes; Patagonia; the European Alps; the Tien Shan; tropical mountains in South America, Africa and Asia and elsewhere. In these regions, more than 600 glaciers have disappeared over the past decades. Even if there is no further warming, many more glaciers will disappear. It is also likely that some mountain ranges will lose most, if not all, of their glaciers. In all mountain regions where glaciers exist today, glacier volume has decreased considerably over the past 150 years. Over that time, many small glaciers have disappeared. With some local exceptions, glacier shrinkage (area and volume reduction) was globally widespread already and particularly strong during the 1940s and since the 1980s. However, there were also phases of relative stability during the 1890s, 1920s and 1970s, as indicated by long- term measurements of length changes and by modelling of mass balance. Conventional in situ measurements and increasingly, airborne and satellite measurements offer robust evidence in most glacierized regions that the rate of reduction in glacier area was higher over the past two decades than previously, and that glaciers continue to shrink. In a few regions, however, individual glaciers are behaving differently and have advanced while most others were in retreat (e.g., on the coasts of New Zealand, Norway and Southern Patagonia (Chile), or in the Karakoram range in Asia). In general, these advances are the result of special topographic and/or climate conditions (e.g., increased precipitation). It can take several decades for a glacier to adjust its extent to an instantaneous change in climate, so most glaciers are currently larger than they would be if they were in balance with current climate. Because the time required for the adjustment increases with glacier size, larger glaciers will continue to shrink over the next few decades, even if temperatures stabilise. Smaller glaciers will also continue to shrink, but they will adjust their extent faster and many will ultimately disappear entirely. Many factors influence the future development of each glacier, and whether it will disappear: for instance, its size, slope, elevation range, distribution of area with elevation, and its surface characteristics (e.g., the amount of debris cover). These factors vary substantially from region to region, and also between neighbouring glaciers. External fac- tors, such as the surrounding topography and the climatic regime, are also important for future glacier evolution. 4 Over shorter time scales (one or two decades), each glacier responds to climate change individually and differently in detail. Over periods longer than about 50 years, the response is more coherent and less dependent on local environmental details, which means that long-term trends in glacier development can be well modelled. Such models are built on an understanding of basic physical principles. For example, an increase in local mean air temperature, with no change in precipitation, will cause an upward shift of the equilibrium line altitude (ELA; see Glossary) by about 150 m for each degree Celsius of atmospheric warming. Such an upward shift and its consequences for glaciers of dif- ferent size and elevation range are illustrated in FAQ 4.2, Figure 1. Initially, all glaciers have an accumulation area (white) above and an ablation area (light blue) below the ELA (FAQ 4.2, Figure 1a). As the ELA shifts upwards, the accumulation area shrinks and the ablation area expands, thus increasing the area over which ice is lost through melt (FAQ 4.2, Figure 1b). This imbalance results in an overall loss of ice. After several years, the glacier front retreats, and the ablation area shrinks until the glacier has adjusted its extent to the new climate (FAQ 4.2, Figure 1c). Where climate change is sufficiently strong to raise the ELA per- manently above the glacier s highest point (FAQ 4.2, Figure 1b, right) the glacier will eventually disappear entirely (FAQ 4.2, Figure 1c, right). Higher glaciers, which retain their accumulation areas, will shrink but not disappear (FAQ 4.2, Figure 1c, left and middle). A large valley glacier might lose much of its tongue, probably leaving a lake in its place (FAQ 4.2, Figure 1c, left). Besides air temperature, changes in the quantity and seasonality of precipitation influence the shift of the ELA as well. Glacier dynamics (e.g., flow speed) also plays a role, but is not considered in this simplified scheme. Many observations have confirmed that different glacier types do respond differently to recent climate change. For example, the flat, low-lying tongues of large valley glaciers (such as in Alaska, Canada or the Alps) currently show the strongest mass losses, largely independent of aspect, shading or debris cover. This type of glacier is slow in (continued on next page) 345 Chapter 4 Observations: Cryosphere FAQ 4.2 (continued) adjusting its extent to new climatic conditions a) Before climate change and reacts mainly by thinning without substan- Valley Glacier tial terminus retreat. In contrast, smaller moun- Mountain Glacier tain glaciers, with fairly constant slopes, adjust Small Glacier more quickly to the new climate by changing ELA1 the size of their ablation area more rapidly (FAQ 4.2, Figure 1c, middle). The long-term response of most glacier types can be determined very well with the approach illustrated in FAQ 4.2, Figure 1. However, mod- elling short-term glacier response, or the long- b) After climate change but before glacier readjustment term response of more complex glacier types (e.g., those that are heavily debris-covered, fed by avalanche snow, have a disconnected accu- ELA2 mulation area, are of surging type, or calve into ELA1 water), is difficult. These cases require detailed knowledge of other glacier characteristics, such as mass balance, ice thickness distribution, and internal hydraulics. For the majority of glaciers worldwide, such data are unavailable, and their response to climate change can thus only c) After readjustment to climate change be approximated with the simplified scheme shown in FAQ 4.2, Figure 1. The Karakoram Himalaya mountain range, for ELA2 instance, has a large variety of glacier types and climatic conditions, and glacier character- istics are still only poorly known. This makes 4 determining their future evolution particularly uncertain. However, gaps in knowledge are expected to decrease substantially in coming years, thanks to increased use of satellite data (e.g., to compile glacier inventories or derive FAQ 4.2, Figure 1 | Schematic of three types of glaciers located at different elevations, and their response to an upward shift of the equilibrium line altitude (ELA). (a) For a given flow velocities) and extension of the ground- climate, the ELA has a specific altitude (ELA1), and all glaciers have a specific size. (b) Due based measurement network. to a temperature increase, the ELA shifts upwards to a new altitude (ELA2), initially resulting in reduced accumulation and larger ablation areas for all glaciers. (c) After glacier size has In summary, the fate of glaciers will be variable, adjusted to the new ELA, the valley glacier (left) has lost its tongue and the small glacier depending on both their specific characteristics (right) has disappeared entirely. and future climate conditions. More glaciers will disappear; others will lose most of their low-lying portions and others might not change substantially. Where the ELA is already above the highest elevation on a particular glacier, that glacier is destined to disappear entirely unless climate cools. Similarly, all glaciers will disappear in those regions where the ELA rises above their highest elevation in the future. a discussion of the improvements in techniques of measurement and temporal variations in the Earth s gravity field. Each method has been understanding of the change made since AR4 (e.g., Lemke et al., 2007; applied to both ice sheets by multiple groups, and over time scales Cazenave et al., 2009; Chen et al., 2011). ranging from multiple years to decades (Figures 4.13 and 4.14). The peripheral glaciers, surrounding but not strictly a part of the ice sheets, 4.4.2.1 Techniques are not treated in the same manner by each technique. Peripheral gla- ciers are generally excluded from estimates using the mass budget The three broad techniques for measuring ice-sheet mass balance are method, they are sometimes, but not always, included in altimetric the mass budget method, repeated altimetry and measurement of estimates, and they are almost always included in gravity estimates. 346 Observations: Cryosphere Chapter 4 4.4.2.1.1 Mass budget method al., 2013; Fretwell et al., 2013) and velocity data from satellite radar i ­nterferometry and other techniques (Joughin et al., 2010b; Rignot et The mass budget method (see Glossary) relies on estimating the dif- al., 2011a). However, incomplete ice thickness mapping still causes ference between net surface balance over the ice sheet (input) and uncertainties in ice discharge of 2 to 15% in Antarctica (Rignot et al., perimeter ice discharge flux (output). This method requires compari- 2008b) and 10% in Greenland (Howat et al., 2011; Rignot et al., 2011c). son of two very large numbers, and even small percentage errors in either may result in large errors in total mass balance. For ice discharge, Regional atmospheric climate models (see Glossary) verified using perimeter fluxes are calculated from measurements of ice velocity and independent in situ data are increasingly preferred to produce esti- ice thickness at the grounding line. Knowledge of perimeter fluxes mates of surface mass balance over models that are recalibrated or has improved significantly since AR4 for both ice sheets (Rignot et al., corrected with in situ data (Box et al., 2009), downscaling of global 2011b) as a result of more complete ice-thickness data (Bamber et re-analysis data (see Glossary) (Hanna et al., 2011), or interpolation of a b c 0 500 (km) 103 (kg m-2 yr-1) (m yr-1) (m yr-1) 4 -3 -1 -0.3 0 0.3 1 3 1 10 100 1000 -1.5 -0.5 -0.2 0 0.2 0.5 2003-2012 03 2012 2012 2003-2006 2006-2012 d e f (cm y (cm yr-1) -10 -8 -6 -4 -2 0 2 4 Figure 4.13 | Key variable related to the determination of the Greenland ice sheet mass changes. (a) Mean surface mass balance for 1989 2004 from regional atmospheric climate modelling (Ettema et al., 2009). (b) Ice sheet velocity for 2007 2009 determined from satellite data, showing fastest flow in red, fast flow in blue and slower flow in green and yellow (Rignot and Mouginot, 2012). (c) Changes in ice sheet surface elevation for 2003 2008 determined from ICESat altimetry, with elevation decrease in red to increase in blue (Pritchard et al., 2009). (d, e) Temporal evolution of ice loss determined from GRACE time-variable gravity, shown in centimetres of water per year for the periods (a) 2003 2012, (b) 2003 2006 and (c) 2006 2012, colour coded red (loss) to blue (gain) (Velicogna, 2009). Fields shown in (a) and (b) are used together with ice thickness (see Figure 4.18) in the mass budget method. 347 Chapter 4 Observations: Cryosphere a b c 103 (kg m-2yr-1) (m yr-1) (m yr-1) -3 -1 -0.3 0 0.3 1 3 1 10 100 1000 -1.5 -0.5 -0.2 0 0.2 0.5 d 2003-2012 e 2003-2006 f 2006-2012 0 500 (km) (cm y -1) ( yr -10 -8 -6 -4 -2 10 0 2 4 Figure 4.14 | Key fields relating to the determination of Antarctica ice sheet mass changes. (a) Mean surface mass balance for 1989 2004 from regional atmospheric climate modelling (van den Broeke et al., 2006). (b) Ice sheet velocity for 2007 2009 determined from satellite data, showing fastest flow in red, fast flow in blue, and slower flow in green and yellow (Rignot et al., 2011a). (c) Changes in ice sheet surface elevation for 2003 2008 determined from ICESat altimetry, with elevation decrease in red to increase in blue (Pritchard et al., 2009). (d, e) Temporal evolution of ice loss determined from GRACE time-variable gravity, shown in centimetres of water per year for the periods (a) 2003 2012, 4 (b) 2003 2006 and (c) 2006 2012, colour coded red (loss) to blue (gain) (Velicogna, 2009). Fields shown in (a) and (b) are used together with ice thickness (see Figure 4.18) in the mass budget method. in situ measurements (Arthern et al., 2006; Bales et al., 2009). In Ant- snow density and bed elevation; or if the ice is floating, for tides and arctica, surface mass balance (excluding ice shelves) for 1979 2010 is sea level) reveals changes in ice sheet mass. Satellite radar ­ ltimetry a estimated at 1983 +/- 122 Gt yr 1 (van de Berg et al., 2006; Lenaerts et (SRALT) has been widely used (Thomas et al., 2008b; Wingham et al., 2012) with interannual variability of 114 Gt yr 1 driven by snow- al., 2009), as has laser altimetry from airplanes (Krabill et al., 2002; fall variability (Figure 4.14). Comparison with 750 in situ observations Thomas et al., 2009) and satellites (Pritchard et al., 2009; Abdalati et indicates an overall uncertainty of 6% for total ice sheet mass balance, al., 2010; Sorensen et al., 2011; Zwally et al., 2011). Both radar and ranging from 5 to 20% for individual drainage basins (van de Berg et laser methods have significant challenges. The field-of-view of early al., 2006; Rignot et al., 2008b; Lenaerts et al., 2012; Shepherd et al., SRALT sensors was ~20 km in diameter, and as a consequence, inter- 2012). In Greenland, total snowfall (697 Gt yr 1) and rainfall (46 Gt pretation of the data they acquired over ice sheets with undulating yr 1) minus runoff (248 Gt yr 1) and evaporation/sublimation (26 Gt surfaces or significant slopes was complex. Also, for radar altimeters, yr 1) yield a surface mass balance of 469 +/- 82 Gt yr 1 for 1958 2007 estimates are affected by penetration of the radar signal below the (Ettema et al., 2009). The 17% uncertainty is based on a comparison of surface, which depends on characteristics such as snow density and model outputs with 350 in situ accumulation observations and, in the wetness, and by wide orbit separation (Thomas et al., 2008b). Errors absence of runoff data, an imposed 20% uncertainty in runoff (Howat in surface-elevation change are typically determined from the internal et al., 2011). Interannual variability in surface mass balance is large consistency of the measurements, often after iterative removal of sur- ­ (107 Gt yr 1) due to the out-of-phase relationship between the vari- face elevation-change values that exceed some multiple of the local ability in precipitation (78 Gt yr 1) and runoff (67 Gt yr 1). value of their standard deviation; this results in very small error esti- mates (Zwally et al., 2005). 4.4.2.1.2 Repeated altimetry Laser altimeters have been used from aircraft for many years, but satel- Repeated altimetric survey allows measurement of rates of sur- lite laser altimetry, available for the first time from NASA s ICESat satel- face-elevation change, and after various corrections (for changes in lite launched in 2003, has provided many new results since AR4. Laser 348 Observations: Cryosphere Chapter 4 altimetry is easier to validate and interpret than radar data; the field of 10 years, estimates of ice sheet mass change from GRACE have lower view is small (1 m diameter for airborne lasers, 60 m for ICESat), and uncertainties than in AR4 (e.g., Harig and Simons, 2012; King et al., there is negligible penetration below the surface. However, clouds limit 2012). The ice-loss signal from the last decade is also more distinct data acquisition, and accuracy is affected by atmospheric conditions, because the numbers have grown significantly higher (e.g., Wouters laser-pointing errors, and data scarcity. et al., 2008; Cazenave et al., 2009; Chen et al., 2009; Velicogna, 2009). The estimates of ice loss based on data from GRACE vary between pub- Knowledge of the density of the snow and firn in the upper layers of lished studies due to the time-variable nature of the signal, along with an ice sheet is required to convert altimetric measurements to mass other factors that include (1) data-centre specific processing, (2) specif- change. However, snow densification rates are sensitive to snow tem- ic methods used to calculate the mass change, and (3) contamination perature and wetness. Warm conditions favour more rapid densifica- by other signals within the ice sheet (e.g., glacial isostatic adjustment tion (Arthern et al., 2010; Li and Zwally, 2011). Consequently, recent or GIA, see Glossary) or outside the ice sheet (continental hydrology, Greenland warming has probably caused surface lowering simply from ocean circulation). Many of these differences have been reduced in this effect. Corrections are inferred from models that are difficult to studies published since AR4, resulting in greater agreement between validate and are typically less than 2 cm yr 1. ICESat derived surface GRACE estimates (Shepherd et al., 2012). elevation changes supplemented with differenced ASTER (Advanced Spaceborne Thermal Emission and Reflection Radiometer) satellite In Antarctica, the GIA signal is similar in magnitude to the ice-loss digital elevation models were used for outlet glaciers in southeast signal, with an uncertainty of +/-80 Gt yr 1 (Velicogna and Wahr, 2006b; Greenland (Howat et al., 2008) and for the northern Antarctic Penin- Riva et al., 2009; Velicogna, 2009). Correction for the GIA signal is sula (Shuman et al., 2011). Laser surveys from airborne platforms over addressed using numerical models (e.g., Ivins and James, 2005; Paul- Greenland yield elevation estimates accurate to 10 cm along reference son et al., 2007; Peltier, 2009). A comparison of recent GIA models targets (Krabill et al., 1999; Thomas et al., 2009) and 15 cm for ICESat (Tarasov and Peltier, 2002; Fleming and Lambeck, 2004; Peltier, 2004; using ground-based high-resolution GPS measurements (Siegfried et Ivins and James, 2005; Simpson et al., 2009; Whitehouse et al., 2012) al., 2011). For a 5-year separation between surveys, this is an uncer- with improved constraints on ice-loading history, indicate better agree- tainty of 2.0 cm yr 1 for airborne platforms and 3 cm yr 1 for ICESat. ment with direct observations of vertical land movements (Thomas et al., 2011a), despite a potential discrepancy between far-field sea level Early in 2013, NASA released an elevation correction for ICESat records and common NH deglaciation models. In Greenland, the GIA (National Snow and Ice Data Center, 2013) that is relevant to several correction is less than 10% of the GRACE signal with an error of +/-19 studies cited in this chapter, but was provided too late to be includ- Gt yr 1. However, because the GIA rate is constant over the satellite s ed in those studies. This correction improves shot-to-shot variability lifetime, GIA uncertainty does not affect the estimate of any change in in ICESat elevations, although spatial averaging and application of the rate of ice mass loss (acceleration/deceleration). In Antarctica, the inter-campaign bias corrections derived from calibration data and used adoption of new GIA models has resulted in a lowering of estimated in many studies already mitigates the impact of the higher variability. ice-sheet mass loss (King et al., 2012; Shepherd et al., 2012). 4 The correction also changes elevation trend estimates over the 2003 2009 ICESat mission period by up to 1.4 cm yr 1. In addition to GRACE, the elastic response of the crustal deformation shown in GPS measurements of uplift rates confirms increasing rates To date, a thorough treatment of the impact of this finding has not of ice loss in Greenland (Khan et al., 2010b; Khan et al., 2010a) and been published in the peer-reviewed literature, but the overall magni- Antarctica (Thomas et al., 2011a). Analysis of a 34-year time series tude of the effect is reported to be at the level of 1.4 cm yr 1. For many of the Earth s oblateness (J2) by satellite laser ranging also suggests studies of glaciers, ice sheets and sea ice this is substantially lower (in that ice loss from Greenland and Antarctica has progressively dom- some cases, an order of magnitude lower) than the signal of change, inated the change in oblateness trend since the 1990s (Nerem and but elsewhere (e.g, for elevation changes in East Antarctica) it may Wahr, 2011). have an impact. However, multiple lines of evidence, of which ICESat is only one, are used to arrive at the conclusions presented in this chapter. 4.4.2.2 Greenland To the degree to which it can be assessed, there is high confidence that the substantive conclusions this chapter will not be affected by There is very high confidence that the Greenland ice sheet has lost ice revisions of the ICESat data products. and contributed to sea level rise over the last two decades (Ewert et al., 2012; Sasgen et al., 2012; Shepherd et al., 2012). Recent GRACE results 4.4.2.1.3 Temporal variations in Earth gravity field are in better agreement than in AR4 as discussed in Section 4.4.2.1 (Baur et al., 2009; Velicogna, 2009; Pritchard et al., 2010; Wu et al., Since 2002, the GRACE (Gravity Recovery and Climate Experiment) 2010; Chen et al., 2011; Schrama and Wouters, 2011). Altimetry mis- satellite mission has surveyed the Earth s time-variable gravity field. sions report losses comparable to those from the mass budget method Time-variable gravity provides a direct estimate of the ice-mass and from the time-variable gravity method (Thomas et al., 2006; Zwally change at a spatial resolution of about 300 km (Wahr, 2007). GRACE et al., 2011) (Figure 4.13f). data yielded early estimates of trends in ice-mass changes over the Greenland and Antarctic ice sheets and confirmed regions of ice loss in Figure 4.15 shows the cumulative ice mass loss from the Greenland coastal Greenland and West Antarctica (Luthcke et al., 2006; Velicogna ice sheet over the period 1992 2012 derived from 18 recent studies and Wahr, 2006a, 2006b). With extended time series, now more than made by 14 different research groups (Baur et al., 2009; Cazenave et 349 Chapter 4 Observations: Cryosphere al., 2009; Slobbe et al., 2009; Velicogna, 2009; Pritchard et al., 2010; ­individual studies (Velicogna, 2009; Chen et al., 2011; Rignot et al., Wu et al., 2010; Chen et al., 2011; Rignot et al., 2011c; Schrama and 2011c; Zwally et al., 2011) (Figure 4.13a c). The average ice mass Wouters, 2011; Sorensen et al., 2011; Zwally et al., 2011; Ewert et al., change to Greenland from the present assessment has been 121 2012; Harig and Simons, 2012; Sasgen et al., 2012). These studies do [ 149 to 94] Gt yr 1 (a sea level equivalent of 0.33 [0.41 to 0.26] not include earlier estimates from the same researchers when those mm yr 1) over the period 1993 to 2010, and 229 [ 290 to 169] Gt have been updated by more recent analyses using extended data. They yr 1 (0.63 [0.80 to 0.47] mm yr 1 sea level equivalent) over the period include estimates made from satellite gravimetry, satellite altimetry 2005 2010. and the mass budget method. Details of the studies used for Greenland are listed in Appendix Table 4.A.1 (additional studies not selected are Greenland changes that include and exclude peripheral glaciers cannot listed in Table 4.A.2). be cleanly separated from the mixture of studies and techniques in this assessment, but for the post 2003 period there is a prevalence of grav- The mass balance for each year is estimated as a simple average of all ity studies, which do include the peripheral glaciers. Hence, although the selected estimates available for that particular year. Figure 4.15 the estimated mass change in Greenland peripheral glaciers of 38 +/- 7 shows an accumulation of these estimates since an arbitrary zero on 1 Gt yr 1over the period 2003 2009 (Gardner et al., 2013) is discussed in January 1992. The number of estimates available varies with time, with Section 4.3.3 (Table 4.5), these changes are included within the values as few as two estimates per year in the 1990s and up to 18 per year for ice-sheet change quoted in this section, and not as part of the total from 2004. The cumulative uncertainty in Figure 4.15 is based on the mass change for glaciers. uncertainty cited in the original studies which, when the confidence level is not specifically given, is assumed to be at the 1 standard devi- A reconciliation of apparent disparities between the different satel- ation (1) level. However, the annual estimates from different studies lite methods was made by the Ice-sheet Mass Balance Intercompari- often do not overlap within the original uncertainties, and hence the son Experiment (IMBIE) (Shepherd et al., 2012). This intercomparison error limits used in this assessment are derived from the absolute max- combined an ensemble of satellite altimetry, interferometry, airborne imum and minimum mass balance estimate for each year. These have radio-echo sounding and airborne gravimetry data and regional ­ been converted to the 90% confidence interval (5 to 95%, or 1.65). atmospheric climate model output products, for common geographical The cumulative error is weighted by 1/n , where n is the number of regions and for common time intervals. Good agreement was obtained years accumulated. between the estimates from the different methods and, whereas the uncertainties of any method are sometimes large, the combination of Despite year-to-year differences between the various original analyses, methods considerably improves the overall certainty. (Note that Shep- this multi-study assessment yields very high confidence that Green- herd et al. (2012) also cannot cleanly separate estimates including or land has lost mass over the last two decades and high confidence that excluding peripheral glaciers). For Greenland, Shepherd et al. (2012) the rate of loss has increased. The increase is also shown in several estimate a change in mass over the period 1992 2011, averaged­ 4 Figure 4.15 | Cumulative ice mass loss (and sea level equivalent, SLE) from Greenland derived as annual averages from 18 recent studies (see main text and Appendix 4.A for details). 350 Observations: Cryosphere Chapter 4 across the ensemble for each method, of 142 +/- 49 Gt yr 1 (0.39 +/- major outlet glaciers that confirm the dominance of dynamic losses in 0.14 mm yr 1 of sea level rise). For the same period, this present assess- these regions (van den Broeke et al., 2009). In particular, major outlet ment, which averages across individual studies, yields a slightly slower glacier speed-up reported in AR4 occurred in west Greenland between loss, with a rate of mass change of 125 +/- 25 Gt yr 1 at the 90% con- 1996 and 2000 (Rignot and Kanagaratnam, 2006) and in southeast fidence level (0.34 +/- 0.07 mm yr 1 SLE). Averaging across technique Greenland from 2001 to 2006 (Rignot and Kanagaratnam, 2006; ensembles in the present assessment yields a loss at a rate of 129 Gt Joughin et al., 2010b). In the southeast, many outlet glaciers slowed yr 1 (0.36 mm). Shepherd et al. (2012) confirm an increasing mass loss after 2005 (Howat et al., 2007; Howat et al., 2011), with many flow from Greenland, although they also identify mass balance variations speeds decreasing back towards those of the early 2000s (Murray et over intermediate (2- to 4-year) periods. al., 2010; Moon et al., 2012), although most are still flowing faster and discharging more ice into the ocean than they did in 1996 (Rignot and The mass budget method shows that ice loss from the Greenland ice Kanagaratnam, 2006; Howat et al., 2011). sheet is partitioned in approximately similar amounts between sur- face mass balance (i.e., runoff) and discharge from ice flow across the In the northwest, the increase in the rate of ice loss from 1996 2006 to grounding line (van den Broeke et al., 2009) (medium confidence). 2006 2010 was probably caused partially by a higher accumulation in However, there are significant differences in the relative importance the late 1990s compared to earlier and later years (Sasgen et al., 2012), of ice discharge and surface mass balance in various regions of Green- but ice dynamic changes also played a role as outlet glaciers in the land (Howat et al., 2007; Pritchard et al., 2009; van den Broeke et al., northwest showed an increase in speed from 2000 to 2010, with the 2009; Sasgen et al., 2012). Dynamic losses dominate in southeast and greatest increase from 2007 to 2010 (Moon et al., 2012). Longer-term central west regions, and also influence losses in northwest Greenland, observations of surface topography in the northwest sector confirm whereas in the central north, southwest and northeast sectors, chang- the dynamic component of this mass loss and suggest two periods of es in surface mass balance appear to dominate. loss in 1985 1993 and 2005 2010 separated by limited mass changes (Kjaer et al., 2012). In the southeast, an 80-year long record reveals There is high confidence that over the last two decades, surface mass that many land-terminating glaciers retreated more rapidly in the balance has become progressively more negative as a result of an 1930s compared to the 2000s, but marine-terminating glaciers retreat- increase in surface melt and runoff, and that ice discharge across the ed more rapidly during the recent warming (Bjrk et al., 2012). grounding line has also been enhanced due to the increased speed of some outlet glaciers. Altimetric measurements of surface height 4.4.2.3 Antarctica suggest slight inland thickening in 1994 2006 (Thomas et al., 2006, 2009), but this is not confirmed by regional atmospheric climate model Antarctic results from the gravity method are also now more numerous outputs for the period 1957 2009 (Ettema et al., 2009), nor recent ice and consistent than in AR4 (Figure 4.14a-c). Methods combining GPS core (see Glossary) data (Buchardt et al., 2012), hence there is low and GRACE at the regional level indicate with high confidence that confidence in an increase in precipitation in Greenland in recent dec- the Antarctic Peninsula is losing ice (Ivins et al., 2011; Thomas et al., 4 ades. Probable changes in accumulation are, however, exceeded by the 2011a). In other areas, large uncertainties remain in the global GRACE- increased runoff especially since 2006 (van den Broeke et al., 2009). GPS solutions (Wu et al., 2010). The four highest runoff years over the last 140 years occurred since 1995 (Hanna et al., 2011). The SMB reconstructions used in the mass budget method have improved considerably since AR4 (e.g., Rignot et al. 2008b; van den The total surface melt area has continued to increase since AR4 and Broeke et al., 2006; Lenaerts et al., 2012; Shepherd et al., 2012). Recon- has accelerated in the past few years (Fettweis et al., 2011; Tedesco structed snowfall from regional atmospheric climate models indicates et al., 2011), with an extreme melt event covering more than 90% of higher accumulation along the coastal sectors than in previous esti- the ice sheet for a few days in July 2012 (Nghiem et al., 2012; Tedesco mates, but little difference in total snowfall. There is medium confidence et al., 2013). Annual surface mass balance in 2011 2012 was 2 stand- that there has been no long-term trend in the total accumulation over ard deviations (2) below the 2003 2012 mean. Such extreme melt the continent over the past few decades (Monaghan et al., 2006; van events are rare and have been observed in ice core records only twice, den Broeke et al., 2006; Bromwich et al., 2011; Frezzotti et al., 2012; once in 1889, and once more, seven centuries earlier in the Medieval Lenaerts et al., 2012). Although anomalies in accumulation have been Warm Period (Meese et al., 1994; Alley and Anandakrishnan, 1995). noted in recent decades in Eastern Wilkes Land (Boening et al., 2012; Over the past decade, the surface albedo of the Greenland ice sheet Shepherd et al., 2012) and Law Dome (Van Ommen and Morgan, 2010) has decreased by up to 18% in coastal regions, with a statistically in East Antarctica, their overall impact on total mass balance is not sig- s ­ ignificant increase over 87% of the ice sheet due to melting and snow nificant. Satellite laser altimetry indicates that ice volume changes are metamorphism, allowing more solar energy to be absorbed for surface concentrated on outlet glaciers and ice streams (see Glossary), as illus- melting (Box et al., 2012). trated by the strong correspondence between areas of thinning (Figure 4.14f) and areas of fast flow (Figure 4.14e) (Pritchard et al., 2009). GRACE results show ice loss was largest in southeast Greenland during 2005 and increased in the northwest after 2007 (Khan et al., 2010a; Figure 4.16 shows the cumulative ice mass loss from the Antarctic ice Chen et al., 2011; Schrama and Wouters, 2011; Harig and Simons, sheet over the period 1992 2012 derived from recent studies made 2012). Subsequent to 2005, ice loss decreased in the southeast. These by 10 different research groups (Cazenave et al., 2009; Chen et al., GRACE results agree with measurements of ice discharge from the 2009; E et al., 2009; Horwath and Dietrich, 2009; Velicogna, 2009; Wu 351 Chapter 4 Observations: Cryosphere Figure 4.16 | Cumulative ice mass loss (and sea level equivalent, SLE) from Antarctica derived as annual averages from 10 recent studies (see main text and Appendix 4.A for details). et al., 2010; Rignot et al., 2011c; Shi et al., 2011; King et al., 2012; Tang the present assessment, rather than individual estimates, yields no sig- et al., 2012). These studies do not include earlier estimates from the nificant difference. same researchers when those have been updated by more recent anal- yses using extended data. They include estimates made from satellite There is low confidence that the rate of Antarctic ice loss has increased gravimetry, satellite altimetry and the mass balance method. Details over the last two decades (Chen et al., 2009; Velicogna, 2009; Rignot et 4 of the studies used for Antarctica are listed in Table 4.A.3 (additional al., 2011c; Shepherd et al., 2012); however, GRACE data gives medium studies not selected are listed in Table 4.A.4). The number of estimates confidence of increasing loss over the last decade (Chen et al., 2009; available varies with time, with only one estimate per year in the 1990s Velicogna, 2009) (Figure 4.16). For GRACE, this conclusion is independ- and up to 10 per year from 2003. The cumulative curves and associated ent of the GIA signal, which is constant over the measurement period. errors are derived in the same way as those for Figure 4.15 (see Section The mass budget method suggests that the increase in loss from the 4.4.2.2). mass budget method is caused by an increase in glacier flow-speed in the eastern part of the Pacific sector of West Antarctica (Rignot, Overall, there is high confidence that the Antarctic ice sheet is current- 2008; Joughin et al., 2010a) and the Antarctic Peninsula (Scambos et ly losing mass. The average ice mass change to Antarctica from the al., 2004; Pritchard and Vaughan, 2007; Rott et al., 2011). Comparison present assessment has been 97 [ 135 to 58] Gt yr 1 (a sea level of GRACE and the mass budget method for 1992 2010 indicates an equivalent of 0.27 mm yr 1 [0.37 to 0.16] mm yr 1) over the period increase in the rate of ice loss of, on average, 14 +/- 2 Gt yr 1 per year 1993 2010, and 147 [ 221 to 74] Gt yr 1 (0.41 [0.61 to 0.20] mm compared with 21 +/- 2 Gt yr 1 per year on average for Greenland during yr 1) over the period 2005 2010. These assessments include the Ant- the same time period (Rignot et al., 2011c). The recent IMBIE analysis arctic peripheral glaciers. (Shepherd et al., 2012) shows that the West Antarctic ice sheet and the Antarctic Peninsula are losing mass at an increasing rate, but that East The recent IMBIE intercomparison (Shepherd et al., 2012) for Antarc- Antarctica gained an average of 21 +/- 43 Gt yr 1 between 1992 and tica, where the GIA signal is less well known than in Greenland, used 2011. Zwally and Giovinetto (2011) also estimate a mass gain for East two new GIA models (an updated version of Ivins and James (2005), Antarctica (+16 Gt yr 1 between 1992 and 2001). Their reassessment for details see Shepherd et al. (2012); and Whitehouse et al. (2012)). of total Antarctic change made a correction for the ice discharge esti- These new models had the effect of reducing the estimates of East Ant- mates from regions of the ice sheet not observed in the mass budget arctic ice mass loss from GRACE data, compared with some previous method (see Section 4.4.2.1.1). The analysis of Shepherd et al. (2012) estimates. For Antarctica, Shepherd et al. (2012) estimate an average indicated that the missing regions contribute little to the total mass change in mass for 1992 2011 of 71 +/- 53 Gt yr 1 (0.20 +/- 0.15 mm change. yr 1 of sea level equivalent). For the same period this present assess- ment estimates a loss of 88 +/- 35 Gt yr 1 at the 90% confidence level In the near-absence of surface runoff and, as discussed in this section, (0.24 +/- 0.10 mm yr 1 SLE). Averaging across technique ensembles in with no evidence of multi-decadal change in total snowfall, there is 352 Observations: Cryosphere Chapter 4 high confidence that Antarctic multi-decadal changes in grounded ice 4.4.3 Total Ice Loss from Both Ice Sheets mass must be due to increased ice discharge, although the observation- al record of ice dynamics extends only from the 1970s and is spatially The total ice loss from both ice sheets for the 20 years 1992 2011 incomplete for much of this period. Over shorter time scales, howev- (inclusive) has been 4260 [3060 to 5460] Gt, equivalent to 11.7 [8.4 er, the interannual to decadal variability in snowfall has an important to 15.1] mm of sea level. However, the rate of change has increased impact on ice sheet mass balance (Rignot et al., 2011c). with time and most of this ice has been lost in the second decade of the 20-year period. From the data presented in Figure 4.17, the average The three techniques are in excellent agreement as to the spatial pat- loss in Greenland has very likely increased from 34 [ 6 to 74] Gt yr 1 tern of ice loss (thinning) and gain (thickening) over Antarctica (Figure over the decade 1992 2001 (sea level equivalent, 0.09 [ 0.02 to 0.20] 4.14). There is very high confidence that the largest ice losses are locat- mm yr 1), to 215 [157 to 274] Gt yr 1 over the decade 2002 2011 (0.59 ed along the northern tip of the Antarctic Peninsula where the collapse [0.43 to 0.76] mm yr 1). In Antarctica, the loss has likely increased 30 of several ice shelves in the last two decades triggered the acceleration [ 37 to 97] Gt yr 1 (sea level equivalent, 0.08 [ 0.10 to 0.27] mm yr 1) of outlet glaciers, and in the Amundsen Sea, in West Antarctica (Figure for 1992 2001, to 147 [72 to 221] Gt yr 1 for 2002 2011 (0.40 [0.20 4.14).. On the Antarctic Peninsula, there is evidence that precipita- to 0.61] mm yr 1). Over the last five years (2007 2011), the loss from tion has increased (Thomas et al., 2008a) but the resulting ice-gain both ice sheets combined has been equivalent to 1.2 +/- 0.4 mm yr 1 of is insufficient to counteract the losses (Wendt et al., 2010; Ivins et al., sea level (Figure 4.17 and Table 4.6). 2011). There is medium confidence that changes in the Amundsen Sea region are due to the thinning of ice shelves (Pritchard et al., 2012), 4.4.4 Causes of Changes in Ice Sheets and medium confidence that this is due to high ocean heat flux (Jacobs et al., 2011), which caused grounding line retreat (1 km yr 1) (Joughin 4.4.4.1 Climatic Forcing et al., 2010a) and glacier thinning (Wingham et al., 2009). Indications of dynamic change are also evident in East Antarctica, primarily around Changes in ice sheet mass balance are the result of an integrated Totten Glacier, from GRACE (Chen et al., 2009), altimetry (Wingham response to climate, and it is imperative that we understand the con- et al., 2006; Shepherd and Wingham, 2007; Pritchard et al., 2009; Fla- text of current change within the framework of past changes and nat- ment and Remy, 2012), and satellite radar interferometry (Rignot et ural variability. al., 2008b). The contribution to the total ice loss from these areas is, however, small and not well understood. 4.4.4.1.1 Snowfall and surface temperature 4.4.2.4 Ice Shelves and Floating Ice Tongues Ice sheets experience large interannual variability in snowfall, and local trends may deviate significantly from the long-term trend in integrated As much as 74% of the ice discharged from the grounded ice sheet in snowfall. However, as in AR4, the available data do not suggest any Antarctica passes through ice shelves and floating ice tongues (Bind- significant long-term change in accumulation in Antarctica, except for 4 schadler et al., 2011). Ice shelves help to buttress and restrain flow of the Antarctic Peninsula (Monaghan et al., 2006; Ettema et al., 2009; the grounded ice (Rignot et al., 2004; Scambos et al., 2004; Hulbe et al., van den Broeke et al., 2009; Bromwich et al., 2011). 2008), and so changes in thickness (Shepherd et al., 2003, 2010; Fricker and Padman, 2012), and extent (Doake and Vaughan, 1991; Scambos Increasing air temperature will (when above the freezing point) et al., 2004) of ice shelves influence current ice sheet change. Indeed, increase the amount of surface melt, and can also increase the mois- nearly all of the outlet glaciers and ice streams that are experiencing ture bearing capacity of the air, and hence can increase snowfall. Over high rates of ice loss flow into thinning or disintegrated ice shelves Greenland, temperature has risen significantly since the early 1990s, (Pritchard et al., 2012). Many of the larger ice shelves however, exhibit reaching values similar to those in the 1930s (Box et al., 2009). The stable conditions (King et al., 2009; Shepherd et al., 2010; Pritchard et al., 2012). Table 4.6 | Average rates of ice sheet loss given as mm of sea level equivalent, derived as described for Figure 4.15 and Figure 4.16 using estimates listed in Appendix Tables Around the Antarctic Peninsula, the reduction in ice-shelf extent has 4.A.1 and 4.A.3. been ongoing for several decades (Cook and Vaughan, 2010; Fricker and Padman, 2012), and has continued since AR4 with substantial col- Ice sheet loss Period lapse of a section of Wilkins Ice Shelf (Humbert et al., 2010), which had (mm yr 1 SLE) been retreating since the late1990s (Scambos et al., 2000). Overall, 7 of Greenland 12 ice shelves around the Peninsula have retreated in recent decades 2005 2010 (6-year) 0.63 +/-0.17 with a total loss of 28,000 km2, and a continuing rate of loss of around 1993 2010 (18-year) 0.33 +/-0.08 6000 km2 per decade (Cook and Vaughan, 2010). There is high confi- Antarctica dence that this retreat of ice shelves along the Antarctic Peninsula has 2005 2010 (6-year) 0.41 +/-0.20 been related to changing atmospheric temperatures (e.g., Scambos et 1993 2010 (18-year) 0.27 +/-0.11 al., 2000; Morris and Vaughan, 2003; Marshall et al., 2006; Holland et Combined al., 2011). There is low confidence that changes in the ocean have also 2005 2010 (6-year) 1.04 +/-0.37 contributed (e.g., Shepherd et al., 2003; Holland et al., 2011; Nicholls 1993 2010 (18-year) 0.60 +/-0.18 et al., 2012; Padman et al., 2012). 353 Chapter 4 Observations: Cryosphere Figure 4.17 | Rate of ice sheet loss in sea level equivalent averaged over 5-year periods between 1992 and 2011. These estimates are derived from the data in Figures 4.15 and 4.16. year 2010 was an exceptionally warm year in west Greenland with Ocean circulation delivers warm waters to ice sheets. Variations in Nuuk having the warmest year since the start of the temperature wind patterns associated with the North Atlantic Oscillation (Jacobs record in 1873 (Tedesco et al., 2011). In West Antarctica, the warming et al., 1992; Hurrell, 1995), and tropical circulations influencing West 4 since the 1950s (Steig et al., 2009; Ding et al., 2011; Schneider et al., Antarctica (Ding et al., 2011; Steig et al., 2012), are probable drivers of 2012; Bromwich et al., 2013), the magnitude and seasonality of which increasing melt at some ice-sheet margins. In some parts of Antarctica, are still debated, has not manifested itself in enhanced surface melt- changes in the Southern Annular Mode (Thompson and Wallace, 2000, ing (Tedesco and Monaghan, 2009; Kuipers Munneke et al., 2012) nor see Glossary) may also be important. Observations have established in increased snowfall (Monaghan et al., 2006; Bromwich et al., 2011; that warm waters of subtropical origin are present within several fjords Lenaerts et al., 2012). Statistically significant summer warming has in Greenland (Holland et al., 2008; Myers et al., 2009; Straneo et al., been observed on the east coast of the northern Antarctic Peninsula 2010; Christoffersen et al., 2011; Daniault et al., 2011). (Marshall et al., 2006; Chapman and Walsh, 2007), with extension of summer melt duration (Barrand et al., 2013), while East Antarctica has Satellite records and in situ observations indicate warming of the showed summer cooling (Turner et al., 2005). In contrast, the signifi- Southern Ocean (see Chapter 3) since the 1950s (Gille, 2002, 2008). cant winter warming at Faraday/Vernadsky station on the western Ant- This warming is confirmed by data from robotic ocean buoys (Argo arctic Peninsula is attributable to a reduction of sea ice extent (Turner floats) (Boening et al., 2008) but the observational record remains et al., 2005). short and, close to Antarctica, there are only limited observations from ships (Jacobs et al., 2011), short-duration moorings and data from 4.4.4.1.2 Ocean thermal forcing instrumented seals (Charrassin et al., 2008; Costa et al., 2008). Since AR4, observational evidence has contributed to medium confi- 4.4.4.2 Ice Sheet Processes dence that the interaction between ocean waters and the periphery of large ice sheets plays a major role in present ice sheet changes 4.4.4.2.1 Basal lubrication ( ­ Holland et al., 2008; Pritchard et al., 2012). Ocean waters provide the heat that can drive high melt rates beneath ice shelves (Jacobs et al., Ice flows in part by sliding over the underlying rock and sediment, 1992; Holland and Jenkins, 1999; Rignot and Jacobs, 2002; Pritchard which is lubricated by water at the ice base: a process known as basal et al., 2012) and at marine-terminating glacier fronts (Holland et al., lubrication (see Glossary). In many regions close to the Greenland ice 2008; Rignot et al., 2010; Jacobs et al., 2011). sheet margin, abundant summer meltwater on the surface of the ice sheet forms large lakes. This surface water can drain to the ice sheet 354 Observations: Cryosphere Chapter 4 bed, thus increasing basal water pressure, reducing basal friction and of sub-glacial melt water at the glacier base (Motyka et al., 2003; Jen- increasing ice flow speed (Zwally et al., 2002b). kins, 2011; Straneo et al., 2012; Xu et al., 2012). In South Greenland, there is medium confidence that the acceleration of glaciers from the Such drainage events are common in southwest and northeast Green- mid-1990s to mid-2000s was due to the intrusion of ocean waters of land, but rare in the most rapidly changing southeast and northwest subtropical origin into glacial fjords (Holland et al., 2008; Howat et al., regions (Selmes et al., 2011). The effect can be seen in diurnal flow var- 2008; Murray et al., 2010; Straneo et al., 2010; Christoffersen et al., iations of some land-terminating regions (Das et al., 2008; Shepherd et 2011; Motyka et al., 2011; Straneo et al., 2011; Rignot and Mouginot, al., 2009), and after lake-drainage events, when 50 to 110% short-term 2012). Models suggest that the increase in ice melting by the ocean speed-up of flow has been observed. However, the effect is temporally contributed to the reduction of backstress experienced by glaciers and and spatially restricted (Das et al., 2008). The summer increase in speed subsequent acceleration (Payne et al., 2004; Schoof, 2007; Nick et al., over the annual mean is only ~10 20%, the increase is less at higher 2009; Nick et al., 2013; O Leary and Christoffersen, 2013): changes in elevations (Bartholomew et al., 2011), and observations suggest most the floating mixture of sea ice, iceberg debris and blown snow in front lake drainages do not affect ice sheet velocity (Hoffman et al., 2011). of the glacier may also play a part (Amundson et al., 2010). Theory and field studies suggest an initial increase in flow rate with increased surface meltwater supply (Bartholomew et al., 2011; Palmer 4.4.4.2.5 Iceberg calving et al., 2011), but if the supply of surface water continues to increase and subglacial drainage becomes more efficient, basal water pressure, Calving of icebergs from marine-terminating glaciers and ice shelves and thus basal motion, is reduced (van de Wal et al., 2008; Schoof, is important in their overall mass balance, but the processes that ini- 2010; Sundal et al., 2011; Shannon et al., 2012). Overall, there is high tiate calving range from seasonal melt-driven processes (Benn et al., confidence that basal lubrication is important in modulating flow in 2007) to ocean swells and tsunamis (MacAyeal et al., 2006; Brunt et some regions, especially southwest Greenland, but there is also high al., 2011), or the culmination of a response to gradual change (Doake confidence that it does not explain recent dramatic regional speed-ups et al., 1998; Scambos et al., 2000). Some of these processes show that have resulted in rapid increases in ice loss from calving glaciers. strong climate influence, while others do not. Despite arguments of rather limited progress in this area (Pfeffer, 2011), there have been 4.4.4.2.2 Cryo-hydrologic warming some recent advances (Joughin et al., 2008a; Blaszczyk et al., 2009; Amundson et al., 2010; Nick et al., 2010, 2013), and continental-scale Percolation and refreezing of surface meltwater that drains through ice sheet models currently rely on improved parameterisations (Alley et the ice column may alter the thermal regime of the ice sheet on decad- al., 2008; Pollard and DeConto, 2009; Levermann et al., 2012). Recently al time scales (Phillips et al., 2010). This process is known as cryo-hy- more realistic models have been developed allowing the dependence drologic warming, and it could affect ice rheology and hence ice flow. of calving and climate to be explicitly investigated (e.g., Nick et al., 2013). 4.4.4.2.3 Ice shelf buttressing 4 4.4.5 Rapid Ice Sheet Changes Recent changes in marginal regions of the Greenland and Antarctic ice sheets include some thickening and slowdown of outlet glaciers, but The projections of sea level rise presented in AR4 explicitly excluded mostly thinning and acceleration (e.g., Pritchard et al., 2009; Sorensen future rapid dynamical changes (see Glossary) in ice flow, and stated et al., 2011), with some glacier speeds increasing two- to eight-fold that understanding of these processes is limited and there is no con- (Joughin et al., 2004; Rignot et al., 2004; Scambos et al., 2004; Luck- sensus on their magnitude . Considerable efforts have been made man and Murray, 2005; Rignot and Kanagaratnam, 2006; Howat et al., since AR4 to fill this knowledge gap. Chapter 13 discusses observed 2007). Many of the largest and fastest glacier changes appear to be and likely future sea level, including model projections of changes in at least partly a response to thinning, shrinkage or loss of ice shelves the volume stored in the ice sheets: in this section we summarise the or floating ice tongues (MacGregor et al., 2012; Pritchard et al., 2012). processes thought to be potential causes of rapid changes in ice flow This type of glacier response is consistent with classical models of ice and emphasise new observational evidence that these processes are shelf buttressing proposed 40 years ago (Hughes, 1973; Weertman, already underway. 1974; Mercer, 1978; Thomas and Bentley, 1978). Rapid ice sheet changes are defined as changes that are of sufficient 4.4.4.2.4 Ice ocean interaction speed and magnitude to impact on the mass budget and hence rate of sea level rise on time scales of several decades or shorter. A further con- Since AR4 it has become far more evident that the rates of subma- sideration is whether and under what circumstances any such changes rine melting can be very large (e.g., Motyka et al., 2003). The rate of are irreversible , that is, would take several decades to centuries to melting is proportional to the product of ocean thermal forcing (dif- reverse under a different climate forcing. For example, an effectively ference between ocean temperature and the in situ freezing point of irreversible change might be the loss of a significant fraction of the seawater) and water flow speed at the ice ocean interface (Holland Greenland ice sheet, because at its new lower (and therefore warmer) and Jenkins, 1999). Melt rates along marine-terminating glacier mar- surface elevation, the ice sheet would be able to grow thicker only gins are one-to-two orders of magnitude greater than for ice shelves slowly even in a cooler climate (Ridley et al., 2010) (Section 13.4.3.3). because of the additional buoyancy forces provided by the discharge 355 Chapter 4 Observations: Cryosphere Observations suggest that some observed changes in ice shelves and absorb more solar radiation); both processes further increase melt. glaciers on the Antarctic Peninsula are irreversible. These ice bodies The warm summers of the last two decades (van den Broeke et al., continue to experience rapid and irreversible retreat, coincident with 2009; Hanna et al., 2011), and especially in 2012 (Hall et al., 2013), are air temperatures rising at four to six times the global average rate unusual in the multi-centennial record. Exceptionally high melt events at some stations (Vaughan et al., 2003), and with warm Circumpolar have affected even the far north of Greenland, for example, with the Deep Water becoming widespread on the western continental shelf partial collapse of the floating ice tongues of Ostenfeld Gletscher and (Martinson et al., 2008). Collapse of floating ice shelves on the Ant- Zachariae Isstrom in 2000 2006 (Moon and Joughin, 2008). arctic Peninsula, such as the 2002 collapse of the Larsen B Ice Shelf which is unprecedented in the last 10,000 years, has resulted in speed The importance that subsurface warm waters play in melting the up of tributary glaciers by 300 to 800% (De Angelis and Skvarca, 2003; periphery of ice sheets in Greenland and Antarctica, and the evolution Rignot et al., 2004; Scambos et al., 2004; Rott et al., 2011). Even if ice- of these ice sheets, has become much clearer since AR4 (see Sections berg calving was to cease entirely, regrowth of the Larsen B ice shelf to 4.4.3.1 and 4.4.3.2). New observations in Greenland and Antarctica, its pre-collapse state would take centuries based on the ice-shelf speed as well as advances in theoretical understanding, show that regions and size prior to its collapse (Rignot et al., 2004). of ice sheets that are grounded well below sea level are most likely to experience rapid ice mass loss, especially if the supply of heat to the Surface melt that becomes runoff is a major contributor to mass loss ice margin increases (Schoof, 2007; Holland et al., 2008; Joughin and from the Greenland ice sheet, which results in a lower (hence warmer) Alley, 2011; Motyka et al., 2011; Young et al., 2011a; Joughin et al., ice sheet surface and a lower surface albedo (allowing the surface to 2012; Ross et al., 2012) (See also Figure 4.18.) Where this ice meets the A Pe nta ni rct ns ic F ul a W East Antarctica 4 C West P A Antarctica D J T S K B Wilkes H Amundsen Land To Sea A C B 0 500 1000 Co Scale (km) 0 500 1000 1500 2000 Scale (km) Bed elevation (m) -2000 -1000 0 1000 1500 A A B B C C 4000 4000 4000 2000 2000 2000 (m) (m) (m) 0 0 0 -2000 -2000 -2000 0 1000 (km) 0 1000 2000 (km) 0 1000 2000 (km) Figure 4.18 | Subglacial and seabed topography for Greenland and Antarctica derived from digital compilations (Bamber et al., 2013; Fretwell et al., 2013). Blue areas highlight the marine-based parts of the ice sheets, which are extensive in Antarctica, but in Greenland, relate to specific glacier troughs. Selected sections through the ice sheet show reverse bed gradients that exist beneath some glaciers in both ice sheets. 356 Observations: Cryosphere Chapter 4 ocean and does not form an ice shelf, warm waters can increase melt- Thwaites Glacier sector are not inconsistent with the development of ing at the ice front, causing undercutting, higher calving rates, ice-front a marine ice sheet instability triggered by a change in climate forcing, retreat (Motyka et al., 2003; Benn et al., 2007; Thomas et al., 2011a) but neither are they inconsistent solely with a response to external and consequent speed-up and thinning. Surface runoff also increases environmental (probably oceanic) forcing. subglacial water discharge at the grounding line, which enhances ice melting at the ice front (Jenkins, 2011; Xu et al., 2012). Where an ice In Greenland, there is medium confidence that the recent rapid retreat shelf is present, ice melt by the ocean may cause thinning of the shelf of Jakobshavn Isbrae was caused by the intrusion of warm ocean water as well as migration of the grounding line further inland into deep beneath the floating ice tongue (Holland et al., 2008; Motyka et al., basins, with a major impact on buttressing, flow speed and thinning 2011) combined with other factors, such as weakening of the float- rate (Thomas et al., 2011a). ing mixture of sea ice, iceberg debris and blown snow within ice rifts (Joughin et al., 2008b; Amundson et al., 2010). There is medium con- The influence of the ocean on the ice sheets is controlled by the delivery fidence that recent variations in southeast Greenland s glaciers have of heat to the ice sheet margins, particularly to ocean cavities beneath been caused by intrusion of warm waters of subtropical origin into ice shelves and to calving fronts (Jenkins and Doake, 1991; Jacobs et glacial fjords. Since AR4 it has become clear that the mid-2000s speed al., 2011). The amount of heat delivered is a function of the tempera- up of southeast Greenland glaciers, which caused a doubling of ice ture and salinity of ocean waters; ocean circulation; and the bathyme- loss from the Greenland ice sheet (Luthcke et al., 2006; Rignot and try of continental shelves, in fjords near glacier fronts and beneath ice Kanagaratnam, 2006; Howat et al., 2008; Wouters et al., 2008), was a shelves, most of which are not known in sufficient detail (Jenkins and pulse that was followed by a partial slow down (Howat et al., 2008; Jacobs, 2008; Holland et al., 2010; Dinniman et al., 2012; Galton-Fenzi Murray et al., 2010). Although changes in elevation in the north are not et al., 2012; Padman et al., 2012). Changes in any of these parameters as large as in the south, marine sectors were thinning in 2003 2008 would have a direct and rapid impact on melt rates and potentially on (Pritchard et al., 2009; Sorensen et al., 2011). calving fluxes (see Chapter 13). In contrast to the rapidly changing marine margins of the ice sheets, Ice grounded on a reverse bed-slope, deepening towards the ice sheet land-terminating regions of the Greenland ice sheet are changing more interior, is potentially subject to the marine ice sheet instability (Weert- slowly, and these changes are explained largely by changes in the input man, 1974; Schoof, 2007) (see Box 13.2). Much of the bed of the of snow and loss of meltwater (Sole et al., 2011). Surface meltwater, West Antarctic Ice Sheet (WAIS) lies below sea level and on a reverse although abundant on the Greenland ice sheet, does not seem to be bed-slope, with basins extending to depths greater than 2 km (Figure driving significant changes in basal lubrication that impact on ice sheet 4.18). The marine parts of the WAIS contain ~3.4 m of equivalent sea flow (Joughin et al., 2008b; Selmes et al., 2011; Sundal et al., 2011). level rise (Bamber et al., 2013; Fretwell et al., 2013), and a variety of evidence strongly suggests that the ice sheet volume has been much In Greenland, the observed changes are not all irreversible. The Helheim smaller than present in the last 1 million years, during periods with Glacier in southeast Greenland accelerated, retreated and increased its 4 temperatures similar to those predicted in the next century (see also calving flux during the period 2002 2005 (Howat et al., 2011; Andre- Chapter 5) (Kopp et al., 2009). Potentially unstable marine ice sheets sen et al., 2012), but its calving flux similarly increased during the late also exist in East Antarctica, for example, in Wilkes Land (Young et al., 1930s early 1940s (Andresen et al., 2012): an episode from which 2011a), and these contain more ice than WAIS (9 m sea level equiv- the glacier subsequently recovered and re-advanced (Joughin et al., alent for Wilkes Land). In northern Greenland, ice is also grounded 2008b). The collapse of the floating tongue of Jakobshavn Isbrae in below sea level, with reverse slopes (Figure 4.18; Joughin et al., 1999). 2002 and consequent loss of buttressing has considerably increased ice flow speeds and discharge from the ice sheet. At present, the gla- Observations since AR4 confirm that rapid changes are indeed occur- cier grounding line is retreating 0.5 0.6 km yr 1 (Thomas et al., 2011b; ring at the marine margins of ice sheets, and that these changes have Rosenau et al., 2013), with speeds in excess of 11 km yr 1 (Moon et been observed to penetrate hundreds of kilometres inland (Pritchard et al., 2012), and the glacier is retreating on a bed that deepens further al., 2009; Joughin et al., 2010b). inland, which could be conducive to a marine instability. However, there is evidence that Jakobshavn Isbrae has undergone significant The Amundsen Sea sector of West Antarctica is grounded significantly margin changes over the last approximately 8000 years which may below sea level and is the region of Antarctica changing most rapidly at have been both more and less extensive than the recent ones (Young present. Pine Island Glacier has sped up 73% since 1974 (Rignot, 2008) et al., 2011b). and has thinned throughout 1995 2008 at increasing rates (Wingham et al., 2009) due to grounding line retreat. There is medium confidence Since AR4, many new observations indicate that changes in ice sheets that retreat was caused by the intrusion of warm ocean water into the can happen more rapidly than was previously recognised. Similarly, sub-ice shelf cavity (Jenkins et al., 2010; Jacobs et al., 2011; Steig et al., evidence presented since AR4 indicates that interactions with both the 2012). The neighbouring Thwaites, Smith and Kohler glaciers are also atmosphere and ocean are key drivers of decadal ice-sheet change. So, speeding-up, thinning and contributing to increasing mass loss (Figure although our understanding of the detailed processes that control the 4.14). The present rates of thinning are more than one order of magni- evolution of ice sheets in a warming climate remains incomplete, there tude larger than millennial-scale thinning rates in this area (Johnson et is no indication in observations of a slowdown in the mass loss from al., 2008). Changes in velocity, elevation, thickness and grounding line ice sheets; instead, recent observations suggest an ongoing increase position observed in the past two to three decades in the Pine Island/ in mass loss. 357 Chapter 4 Observations: Cryosphere 4.5 Seasonal Snow per decade or 53% [40 to 66%] total over the 1967 2012 period and 14.8% [10.3 to 19.3%] per decade over the 1979 2012 period (all 4.5.1 Background ranges very likely). Note that these percentages differ from those given by Brown and Robinson (2011) which were calculated relative to the Snowfall is a component of total precipitation and, in that context, is mean over the 1979 2000 period, rather than relative to the start- discussed in Chapter 2 (See Section 2.5.1.3); here we discuss accumu- ing point. The loss rate of June SCE exceeds the loss rate for Coupled lated snow as a climatological indicator. Snow is measured using a Model Intercomparison Project Phase 5 (CMIP5) model projections of variety of instruments and techniques, and reported using several met- June SCE and also exceeds the well-known loss of September sea ice rics, including snow cover extent (SCE; see Glossary); the seasonal sum extent (Derksen and Brown, 2012). Viewed another way, the NOAA of daily snowfall; snow depth (SD); snow cover duration (SCD), that is, SCE data indicate that, owing to earlier spring snowmelt, the duration number of days with snow exceeding a threshold depth; or snow water of the snow season averaged over NH grid points declined by 5.3 days equivalent (SWE; see Glossary). per decade since winter 1972 1973 (Choi et al., 2010). Long-duration, consistent records of snow are rare owing to many Over Eurasia, in situ data show significant increases in winter snow challenges in making accurate and representative measurements. accumulation but a shorter snowmelt season (Bulygina et al., 2009). Although weather stations in snowy inhabited areas often report snow From analysis of passive microwave satellite data since 1979, signif- depth, records of snowfall are often patchy or use techniques that icant trends toward a shortening of the snowmelt season have been change over time (e.g., Kunkel et al., 2007). The density of stations and identified over much of Eurasia (Takala et al., 2009) and the pan-Arctic the choice of metric also varies considerably from country to coun- region (Tedesco et al., 2009), with a trend toward earlier melt of about try. The longest satellite-based record of SCE is the visible-wavelength 5 days per decade for the beginning of the melt season, and a trend of weekly product of the National Oceanic and Atmospheric Administra- about 10 days per decade later for the end of the melt season. tion (NOAA) dating to 1966 (Robinson et al., 1993), but this covers only the NH. Satellite mapping of snow depth and SWE has lower accuracy The correlation between spring temperature and SCE (Figure 4.20) than SCE, especially in mountainous and heavily forested areas. Meas- demonstrates that trends in spring SCE are linked to rising tempera- urement challenges are particularly acute in the Southern Hemisphere ture, and for a well-understood reason: The spring snow cover-albedo (SH), where only about 11 long-duration in situ records continue to feedback. This feedback contributes substantially to the hemispheric recent times: seven in the central Andes and four in southeast Aus- response to rising greenhouse gases and provides a useful test of global tralia. Owing to concerns about quality and duration, global satellite microwave retrievals of SWE are of less use in the data-rich NH than in the data-poor SH. 6 March-April 4 SCE anomaly (106 km2) 4 4.5.2 Hemispheric View 2 By blending in situ and satellite records, Brown and Robinson (2011) have updated a key indicator of climate change, namely the time series 0 of NH SCE (Figure 4.19). This time series shows significant reductions over the past 90 years with most of the reductions occurring in the -2 1980s, and is an improvement over that presented in AR4 in several ways, not least because the uncertainty estimates are explicitly derived -4 June through the statistical analysis of multiple data sets, which leads to very -6 high confidence. Snow cover decreases are largest in spring (Table 4.7), 1920 1940 1960 1980 2000 2020 and the rate of decrease increases with latitude in response to larger Year albedo feedbacks (Déry and Brown, 2007). Averaged March and April NH SCE decreased 0.8% [0.5 to 1.1%] per decade over the 1922 2012 Figure 4.19 | March April NH snow cover extent (SCE, circles) over the period of period, 1.6% [0.8 to 2.4%] per decade over the 1967 2012 period, available data, filtered with a 13-term smoother and with shading indicating the 95% confidence interval; and June SCE (red crosses, from satellite data alone), also filtered and 2.2% [1.1 to 3.4%] per decade over the 1979 2012 period. In a with a 13-term smoother. The width of the smoothed 95% confidence interval is influ- new development since AR4, both absolute and relative losses in June enced by the interannual variability in SCE. Updated from Brown and Robinson (2011). SCE now exceed the losses in March April SCE: 11.7% [8.8 to 14.6%] For both time series the anomalies are calculated relative to the 1971 2000 mean. Table 4.7 | Least-squares linear trend in Northern Hemisphere snow cover extent (SCE) in 106 km2 per decade for 1967 2012. The equivalent trends for 1922 2012 (available only for March and April) are 0.19* March and 0.40* April. Annual Jan Feb March April May June July Aug Sep Oct Nov Dec 0.40* 0.03 0.13 0.50* 0.63* 0.90* 1.31* n/a n/a n/a n/a 0.17 0.34 Notes: *Denotes statistical significance at p = 0.05. 358 Observations: Cryosphere Chapter 4 snowmelt, or both. However, unravelling the competing effects of 38 rising temperatures and changing precipitation remains an important challenge in understanding and interpreting observed changes. Figure 4.21 shows a compilation of many published trends observed at indi- vidual locations; data were obtained either from tables in the pub- 36 lished papers, or (when the numerical results in the figures were not tabulated) directly from the author, in some cases including updates to the published data sets. The figure shows that in most studies, a SCE (106 km2) majority of sites experienced declines during the varying periods of record, and where data on site mean temperature or elevation were 34 available, warmer/lower sites (red circles) were more likely to experi- ence declines. Some in situ studies in addition to those in Figure 4.21 deserve discus- 32 sion. Ma and Qin (2012) described trends by season at 754 stations aggregated by region in China over 1951 2009; they found statisti- cally significant trends: positive in winter SD in northwest China, and Slope -4.5 (% °C-1) negative in SD and SWE in spring for China as a whole and spring 30 SWE for the Qinghai-Xizang (Tibet) Plateau. Marty and Meister (2012) noted changes at six high-elevation (>2200 m) sites in the European -2 -1 0 1 2 Alps of Switzerland, Austria, and Germany, consistent with Figure 4.21: 40-60°N Temperature anomaly (°C) no change in SD in midwinter, shortening of SCD in spring and reduc- tion in spring SWE and SD coincident with warming. For the Pyrenees, Figure 4.20 | Relationship between NH April SCE and corresponding land air tem- Lopez-Moreno and Vicente-Serrano (2007) derived proxy SD for 106 perature anomalies over 40°N to 60°N from the CRUtem4 data set (Jones et al., 2012). Red circles indicate the years 2000 2012. The correlation is 0.76. Updated from Brown sites since 1950 from actual SD measurements since 1985 and weath- and Robinson (2011). er measurements; they noted declines in spring SD that were related to changes in atmospheric circulation. In the SH, of seven records in the Andes, none have significant trends in maximum SWE (Masiokas climate models (Fernandes et al., 2009) (see also Chapter 9). Indeed, et al., 2010) over their periods of record. Of four records in Australia the observed declines in land snow cover and sea ice have contributed discussed in AR4, all show decreases in spring SWE over their respec- roughly the same amount to changes in the surface energy fluxes, and tive periods of record (Nicholls, 2005), and the only one that has been the albedo feedback of the NH cryosphere is likely in the range 0.3 to updated since the Nicholls (2005) paper shows a statistically signifi- 4 1.1 W m 2 K 1 (Flanner et al., 2011). Brown et al. (2010) used satellite, cant decrease of 37% (Sanchez-Bayo and Green, 2013). reanalyses and in situ observations to document variability and trend in Arctic spring (May June) SCE over the 1967 2008 period. In June, 4.5.4 Changes in Snow Albedo with Arctic albedo feedback at a maximum, SCE decreased 46% (as of 2012, now 53%) and air temperature explains 56% of the variability. In addition to reductions in snow cover extent, which will reduce the mean reflectivity of particular regions, the reflectivity (albedo) of the For the SH, as noted above (see Section 4.5.1), there are no corre- snow itself may also be changing in response to human activities. spondingly long visible-wavelength satellite records, but microwave Unfortunately, there are extremely limited data on the changes of data date from 1979. Foster et al. (2009) presented the first satel- albedo over time, and we must rely instead on analyses from ice cores, lite study of variability and trends in any measure of snow for South direct recent observations, and modelling. Flanner et al. (2007), using a America, in this case SWE from microwave data. They focused on the detailed snow radiative model coupled to a global climate model and May-September period and noted large year-to-year variability and estimates of biomass burning, estimated that the human-induced radi- some lower frequency variability the July with most extensive snow ative forcing by deposition of black carbon on snow cover is +0.054 cover had almost six times as much as the July with the least extensive (0.007 0.13) W m 2 globally, of which 80% is from fossil fuels. How- snow cover but identified no trends. ever, spatially comprehensive surveys of impurities in Arctic snow in the late 2000s and mid-1980s suggested that impurities decreased 4.5.3 Trends from In Situ Measurements between those two periods (Doherty et al., 2010) and hence albedo changes have probably not made a significant contribution to recent AR4 stimulated a review paper (Brown and Mote, 2009) that synthe- reductions in Arctic ice and snow. sized modelling results as well as observations from many countries. They showed that decreases in various metrics of snow are most likely to be observed in spring and at locations where air temperatures are close to the freezing point, because changes in air temperature there are most effective at reducing snow accumulation, increasing 359 Chapter 4 Observations: Cryosphere a. 31% 9 -2 -1 0 1 2 # days per year high elev low elev o o -10 C -5 C 0 oC 5 oC b: 50% 1 c: 60% 59 71 d: 65% e: 66% 3 2 f: 72% 8 g: 73% h: 80% i: 100% j: 100% -2 -1 0 1 2 % per year Figure 4.21 | Compilation of studies (rows) showing trends at individual stations (symbols in each row, with percentage of trends that are negative) showing that most sites studied show decreases in snow, especially at lower and/or warmer locations. For each study, if more than one quantity was presented, only the one representing spring conditions is shown. (a) Number of days per year with SD >20 cm at 675 sites in northern Eurasia, 1966 2010 (Bulygina et al., 2011). (b) March April May snowfall for 500 stations in California, aggregated into four regions (Christy, 2012). (c) maximum SWE at 393 sites in Norway, 1961 2009 (Skaugen et al., 2012); statistically significant trends are denoted by solid circles. (d) SD at 560 sites in China, 1957 2012 (Ma and Qin, 2012); statistically significant trends are denoted by solid red circles. (e) Snow cover duration at 15 sites in the Romanian Carpathians, 1961 2003 (Micu, 2009). (f) 1 April SWE at 799 sites, 1950 2000, in western North America (Mote, 2006). (g) Difference between 1990s and 1960s 4 March SD at 89 sites in Japan (Ishizaka, 2004). (h) SCD at 15 sites for starting years near 1931, ending 2000 (Petkova et al., 2004). (i) SCD at 18 sites in Italy, 1950 2009 (Valt and Cianfarra, 2010). (j) SCD at 34 sites in Switzerland, 1948 2007, from Marty (2008). See text for definitions of abbreviations. For (b) through ( j), the quantity plotted is the percentage change of a linear fit divided by the number of years of the fit. For studies with more than 50 sites, the median, upper and lower quartiles are shown with vertical lines. In a few cases, some trends lie beyond the edges of the graph; these are indicated by a numeral at the corresponding edge of the graph, for example, two sites >2% yr 1 in row (f). Colours indicate temperature or, for studies e) and i), elevation using the lowest and highest site in the respective data set to set the colour scale. Note the prevalence of negative trends at lower/warmer sites. Box 4.1 | Interactions of Snow within the Cryosphere Snow is just one component of the cryosphere, but snow also sustains ice sheets and glaciers, and has strong interactions with all the other cryospheric components, except sub-sea permafrost. For example, snow can affect the rate of sea-ice production, and can alter frozen ground through its insulating effect. Snowfall and the persistence of snow cover are strongly dependent on atmospheric tem- perature and precipitation, and are thus likely to change in complex ways in a changing climate (e.g, Brown and Mote, 2009). For the Earth s climate in general, and more specifically, the cryospheric components on which snow falls, the two most important physical properties of snow are its high albedo (reflectivity of solar radiation) and its low thermal conductivity, which results because its high air content makes it an excellent thermal insulator. Both factors substantially alter the flux of energy between the atmosphere and the material beneath the snow cover. Snow also has a major impact on the total energy balance of the Earth s surface because large regions in the NH are seasonally covered by snow (e.g., Barry and Gran, 2011). When seasonal snow melts it is also an important fresh water resource. The high albedo of snow has a strong impact on the radiative energy balance of all surfaces on which it lies, most of which (including glaciers and sea ice) are much less reflective. For example, the albedo of bare glacier or sea ice is typically only 20 to 30%, and hence (continued on next page) 360 Observations: Cryosphere Chapter 4 Box 4.1 (continued) 70 to 80% of solar radiation is absorbed at the surface. For ice at the melting point, this energy melts the ice. With a fresh snow cover over ice, the albedo changes to 80% or even higher and melting is greatly reduced (e.g., Oerlemans, 2001). The effect is similar for other land surfaces bare soil, frozen ground, low-lying vegetation but here the thermal properties of the snow cover also play an important role by insulating the ground from changes in ambient air temperature. While an insulating snow cover can reduce the growth of sea ice, a heavy snow load, particularly in the Antarctic, often depresses the sea ice surface below sea level and this leads to faster transformation of snow to ice (see FAQ 4.1). Even without flooding, the basal snow layer on Antarctic sea ice tends to be moist and saline because brine is wicked up through the snow cover. In regions of heavily ridged and deformed sea ice, snow redistributed by wind smoothes the ice surface, reducing the drag of the air on the ice and thus slowing ice drift and reducing heat exchange (Massom et al., 2001) . For frozen ground, the insulation characteristics of snow cover are particularly important. If the air above is colder than the material on which it lies, the presence of snow will reduce heat transfer upwards, especially for fresh snow with a low density. This could, for example, reduce the seasonal freezing of soil, slow down the freezing of the active layer (seasonally thawed layer) or protect permafrost from cooling. Alternatively, if the air is warmer than the material beneath the snow, heat transfer downwards from the air is reduced and the presence of snow cover can increase the thickness of seasonal soil freeze and protect permafrost from warming. Which process applies depends on the timing of the snowfall, its thickness, and its duration (e.g., Zhang, 2005; Smith et al., 2012). For the preceding reasons, the timing of snowfall and the persistence of snow cover are of major importance. Whereas snow falling on glaciers and ice sheets in summer has a strongly positive (sustaining) effect on the mass budget, early snow cover can reduce radiative and conductive cooling and freezing of the active layer. During winter, snowfall is the most important source of nourishment for most glaciers, but radiative cooling of frozen ground is strongly reduced by thick snow cover (Zhang, 2005). 4.6 Lake and River Ice In the only reported study since the 1990s of ice on SH lakes, Green (2011) suggested on the basis of available evidence that break-up of The assessment of changes in lake and river ice is made more difficult ice cover on Blue Lake in the Snowy Mountains of Australia had shifted by several factors. Until the satellite era, some nations collected data from November to October between observation periods 1970 1972 4 from numerous lakes and rivers and others none; many published stud- and 1998 2010. ies focus on a single lake or river. Many records have been discontinued (Prowse et al., 2011), and consistency of observational methods is a Several studies made quantitative connections between ice cover and challenge, especially for date of ice break-up of ice on rivers when the temperature. For instance, Benson et al. (2012) found significant corre- process of break-up can take as long as 3 months (Beltaos and Prowse, lations between mean ice duration and mean NH land air temperature 2009). in fall-winter-spring (r2 = 0.48) and between spring air temperature and breakup (r2 = 0.36); see also the review by Prowse et al. (2011). The most comprehensive description is the analysis of 75 lakes, mostly in Scandinavia and the northern USA, but with one each in Switzerland Studies of changes in river ice have used both disparate data and time and Russia (Benson et al., 2012). Examining 150-, 100-, and 30-year intervals, ranging in duration from multi-decade to more than two cen- periods ending in spring 2005, they found the most rapid changes in turies, and most focus on a single river. Beltaos and Prowse (2009), the most recent 30-year period (medium confidence) with trends in summarizing most available information for northern rivers, noted an freeze-up 1.6 days per decade later and breakup 1.9 days per decade almost universal trend towards earlier break-up dates but considerable earlier. Wang et al. (2012) found a total ice cover reduction on the north spatial variability in those for freeze-up, and noted too that changes American Great Lakes of 71% over the 1973 2010 period of record, were often more pronounced during the last few decades of the 20th using weekly ice charts derived from satellite observations (medium century. They noted that the 20th century increase in mean air tem- confidence). Jensen et al. (2007) examined data from 65 water bodies perature in spring and autumn has produced in many areas a change in the Great Lakes region between Minnesota and New York (not of about 10 to 15 days toward earlier break-up and later freeze-up, including the Great Lakes themselves) and found trends in freeze-up although the relationship with air temperatures is complicated by the 3.3 days per decade later, trends in breakup 2.1 days per decade ear- roles of snow accumulation and spring runoff. lier, and rates of change over 1975 2004 that were bigger than those over 1846 1995. Spatial patterns in trends are ambiguous: Latifovic In summary, the limited evidence available for freshwater (lake and and Pouliot (2007) found larger trends in higher latitudes over Canada, river) ice indicates that ice duration is decreasing and average sea- but Hodgkins et al. (2002) found larger trends in lower latitudes in the sonal ice cover shrinking (low confidence), and the following general northeastern USA. patterns (each of which has exceptions): rates of change in timing are 361 Chapter 4 Observations: Cryosphere generally, but not universally, (1) higher for spring breakup than fall high Arctic regions and gradually increase southwards, but substantial freeze-up; (2) higher for more recent periods; (3) higher at higher eleva- differences do occur at the same latitude. For example, as a result of tions (Jensen et al., 2007) and (4) quantitatively related to temperature the proximity to warm ocean currents, the southern limit of permafrost changes. is farther north, and permafrost temperature is higher in Scandinavia and north-western Russia than it is in Arctic regions of Siberia and North America (Romanovsky et al., 2010a). 4.7 Frozen Ground In Russia, permafrost temperature measurements reach back to the 4.7.1 Background early 1930s (Romanovsky et al., 2010b), in North America to the late 1940s (Brewer, 1958) and in China to the early 1960s (Zhou et al., Frozen ground occurs across the world at high latitudes, in mountain 2000; Zhao et al., 2010). Systematic measurements, however, began regions, beneath glacial ice and beneath lakes and seas. It is a product mostly in the late 1970s and early 1980s (Zhou et al., 2000; Oster- of cold weather and climate, and can be diurnal, seasonal or peren- kamp, 2007; Smith et al., 2010). In addition, since the AR4, consider- nial. Wherever the ground remains at or below 0°C for at least two able effort (especially during the International Polar Year) has gone consecutive years, it is called permafrost (Van Everdingen, 1998), and into enhancing the observation network and establishing a base- this too can occur beneath the land surface (terrestrial permafrost) and line against which future changes in permafrost can be measured beneath the seafloor (subsea permafrost). In this chapter, the term (Romanovsky et al., 2010a). However, it should be noted that there permafrost refers to terrestrial permafrost unless specified. still exist comparatively few measurements of permafrost tempera- ture in the SH (Vieira et al., 2010). Both the temperature and extent of permafrost are highly sensitive to climate change, but the responses may be complex and highly het- erogeneous (e.g., Osterkamp, 2007). Similarly, the annual freezing and thawing of seasonally frozen ground is coupled to the land sur- face energy and moisture fluxes, and thus to climate. Since, perma- frost and seasonally frozen ground, can contain significant fractions of ice, changes in landscapes, ecosystems and hydrological processes can occur when it forms or degrades (Jorgenson et al., 2006; Gruber and Haeberli, 2007; White et al., 2007). Furthermore, frozen organic soils contain considerable quantities of carbon, more than twice the amount currently in the atmosphere (Tarnocai et al., 2009), and perma- frost thawing exposes previously frozen carbon to microbial degrada- 4 tion and releases radiatively active gases, such as carbon dioxide (CO2) and methane (CH4), into the atmosphere (Zimov et al., 2006; Schuur et al., 2009; Schaefer et al., 2011) (for a detailed assessment of this issue, see Chapter 6). Similarly, recent evidence suggests that degradation of permafrost may also permit the release of nitrous oxide (N2O), which is also radiatively active (Repo et al., 2009; Marushchak et al., 2011). Finally, permafrost degradation may directly affect the lives of people, both in northern and high-mountain areas, through impacts on the landscape, vegetation and infrastructure (WGII, Chapter 28). 4.7.2 Changes in Permafrost 4.7.2.1 Permafrost Temperature The ice content and temperature of permafrost are the key parame- ters that determine its physical state. Permafrost temperature is a key Figure 4.22 | Time series of mean annual ground temperatures at depths between parameter used to document changes to permafrost. Permafrost tem- 10 and 20 m for boreholes throughout the circumpolar northern permafrost regions perature measured at a depth where seasonal variations cease to occur (Romanovsky et al., 2010a). Data sources are from Romanovsky et al. (2010b) and Christiansen et al. (2010). Measurement depth is 10 m for Russian boreholes, 15 m is generally used as an indicator of long-term change and to represent for Gulkana and Oldman, and 20 m for all other boreholes. Borehole locations are: the mean annual ground temperature (Romanovsky et al., 2010a). For ZS-124, 67.48°N 063.48°E; 85-8A, 61.68°N 121.18°W; Gulkana, 62.28°N 145.58°W; most sites this depth occurs in the upper 20 m. YA-1, 67.58°N 648°E; Oldman, 66.48°N 150.68°W; Happy Valley, 69.18°N 148.88°W; Svalbard, 78.28°N 016.58°E; Deadhorse, 70.28°N 148.58°W and West Dock, 70.48°N In the SH, permafrost temperatures as low as 23.6°C have been 148.58°W. The rate of change (degrees Celsius per decade) in permafrost temperature over the period of each site record is: ZS-124: 0.53 +/- 0.07; YA-1: 0.21 +/- 0.02; West observed in the Antarctic (Vieira et al., 2010), but in the NH, permafrost Dock: 0.64 +/- 0.08; Deadhorse: 0.82 +/- 0.07; Happy Valley: 0.34 +/- 0.05; Gaibrath Lake: temperatures generally range from 15°C to close to the freezing point 0.35 +/- 0.07; Gulkana: 0.15 +/- 0.03; Old Man: 0.40 +/- 0.04 and Svalvard: 0.63 +/- 0.09. (Figure 4.22) (Romanovsky et al., 2010a). They are usually coldest in (The trends are very likely range, 90%.) 362 Observations: Cryosphere Chapter 4 In most regions, and at most sites, permafrost temperatures have sometimes nearly isothermal with depth; as is observed in mountain increased during the past three decades (high confidence): at rather regions such as the European Alps (Noetzli and Vonder Muehll, 2010), fewer sites, permafrost temperatures show little change, or a slight Scandinavia (Christiansen et al., 2010), the Western Cordillera of North decrease (Figure 4.22; Table 4.8). However, it is important to discrim- America (Smith et al., 2010; Lewkowicz et al., 2011), the Qinghai-Xi- inate between cold permafrost, with mean annual ground tempera- zang (Tibet) Plateau (Zhao et al., 2010; Wu et al., 2012) and in the tures below 2°C, and warm permafrost at temperatures above 2°C northern high latitudes in the southern margins of discontinuous per- (Cheng and Wu, 2007; Smith et al., 2010; Wu and Zhang, 2010). Warm mafrost regions (Romanovsky et al., 2010b; Smith et al., 2010). In such permafrost is found mostly in the discontinuous permafrost zone, areas, permafrost temperatures have shown little or no change, indi- while cold permafrost exists in the continuous permafrost zone and cating that permafrost is thawing internally but remaining very close only occasionally in the discontinuous permafrost zone (Romanovsky to the melting point (Smith et al., 2010). Cooling of permafrost due to et al., 2010a). atmospheric temperature fluctuations has been observed; for exam- ple, in the eastern Canadian Arctic until the mid-1990s (Smith et al., Overall, permafrost temperature increases are greater in cold 2010); but some examples have been short-lived and others controlled permafrost than they are in warm permafrost (high confidence). This by site-specific conditions (Marchenko et al., 2007; Wu and Zhang, is especially true for warm ice-rich permafrost, due to heat absorbed 2008; Noetzli and Vonder Muehll, 2010; Zhao et al., 2010). In at least by partial melting of interstitial ice, slowing and attenuating temper- one case in the Antarctic, permafrost warming has been observed in a ature change (Romanovsky et al., 2010a). The temperatures of cold region with almost stable air temperatures (Guglielmin and Cannone, permafrost across a range of regions have increased by up to 2°C since 2012). the 1970s (Table 4.8 and Callaghan et al., 2011); however, the timing of warming events has shown considerable spatial variability (Romano- Permafrost warming is mainly in response to increased air temperature vsky et al., 2010a). and changing snow cover (see Box 4.1). In cold permafrost regions, especially in tundra regions with low ice content (such as bedrock) Temperatures of warm permafrost have also increased over the last where permafrost warming rates have been greatest, changes in snow three decades, but generally by less than 1°C. Warm permafrost is cover may play an important role (Zhang, 2005; Smith et al., 2010). Table 4.8 | Permafrost temperatures during the International Polar Year (2007 2009) and their recent changes. Each line may refer to one or more measurements sites. Permafrost Permafrost Depth Region Temperature Temperature Period of Record Source (m) During IPY (°C) Change (°C) North America Northern Alaska 5.0 to 10.0 0.6 3 10 20 Early 1980s 2009 Osterkamp (2005, 2007); Smith et al. (2010); Romanovsky et al. (2010a) 4 Mackenzie Delta and Beau- 0.5 to 8.0 1.0 2.0 12 20 Late 1960s 2009 Burn and Kokelj (2009); Burn and Zhang fort coastal region (2009); Smith et al. (2010) Canadian High Arctic 11.8 to 14.3 1.2 1.7 12 15 1978 2008 Smith et al. (2010, 2012) Interior of Alaska, 0.0 to 5.0 0.0 0.8 15 20 1985 2009 Osterkamp (2008); Smith et al. (2010); Romanovsky et al. (2010a) Central and Southern Mackenzie Valley > 2.2 0.0 0.5 10 12 1984 2008 Smith et al. (2010) Northern Quebec > 5.6 0.0 1.8 12 20 1993 2008 Allard et al. (1995); Smith et al. (2010) Europe European Alps > 3 0.0 0.4 15 20 1990s 2010 Haeberli et al. (2010); Noetzli and Vonder Muehll (2010); Christiansen et al. (2012) Russian European North 0.1 to 4.1 0.3 2.0 8 22 1971 2010 Malkova (2008); Oberman (2008); Romanovsky et al. (2010b); Oberman (2012) Nordic Countries 0.1 to 5.6 0.0 1.0 2 15 1999 2009 Christiansen et al. (2010); Isaksen et al. (2011) Northern and Central Asia Northern Yakutia 4.3 to 10.8 0.5 1.5 14 25 early 1950s 2009 Romanovsky et al. (2010b) Trans-Baykal region 4.7 to 5.1 0.5 0.8 19 20 late 1980s 2009 Romanovsky et al. (2010b) Qinghai-Xizang Plateau 0.2 to 3.4 0.2 0.7 6 1996 2010 Cheng and Wu (2007); Li et al. (2008); Wu and Zhang (2008); Zhao et al. (2010) Tian Shan 0.4 to 1.1 0.3 0.9 10 25 1974 2009 (Marchenko et al. (2007); Zhao et al. (2010) Mongolia 0.0 to < 2.0 0.2 0.6 10 15 1970 2009 Sharkhuu et al. (2007); Zhao et al. (2010); Ishikawa et al. (2012) Others Maritime Antarctica 0.5 to 3.1 NA 20 25 2007 2009 Vieira et al. (2010) Continental Antarctica 13.9 to 19.1 NA 20 30 2005 2008 Vieira et al. (2010); Guglielmin et al. (2011) East Greenland 8.1 NA 3.25 2008 2009 Christiansen et al. (2010) 363 Chapter 4 Observations: Cryosphere In forested areas, especially in warm ice-rich permafrost, changes Noetzli and Vonder Muehll, 2010; Schoeneich et al., 2010; Delaloye et in permafrost temperature are reduced by the effects of the surface al., 2011). Similarly, photo-comparison and photogrammetry have indi- insulation (Smith et al., 2012; Throop et al., 2012) and latent heat cated collapse-like features on some rock glaciers (Roer et al., 2008). (Romanovsky et al., 2010a). The clear relationship between mean annual air temperature at the rock glacier front and rock glacier velocity points to a likely temper- 4.7.2.2 Permafrost Degradation ature influence and a plausible causal connection to climate (Kaab et al., 2007). Strong surface lowering of rock glaciers has been reported in Permafrost degradation refers to a decrease in thickness and/or areal the Andes (Bodin et al., 2010), indicating melting of ground ice in rock extent. In particular, the degradation can be manifested by a deepen- glaciers and permafrost degradation. ing of summer thaw, or top-down or bottom-up permafrost thawing, and a development of taliks (see Glossary). Other manifestations of 4.7.3 Subsea Permafrost degradation include geomorphologic changes such as the formation of thermokarst terrain (see Glossary and Jorgenson et al., 2006), expan- Subsea permafrost is similar to its terrestrial counterpart, but lies sion of thaw lakes (Sannel and Kuhry, 2011) active-layer detachment beneath the coastal seas. And as with terrestrial permafrost, subsea slides along slopes, rock falls (Ravanel et al., 2010), and destabilized permafrost is a substantial reservoir and/or a confining layer for gas rock glaciers (Kääb et al., 1997; Haeberli et al., 2006; Haeberli et al., hydrates (Koch et al., 2009). It is roughly estimated that subsea perma- 2010). Although most permafrost has been degrading since the Little frost contains 2 to 65 Pg of CH4 hydrate (McGuire et al., 2009). Obser- Ice Age (Halsey et al., 1995), the trend was relatively modest until the vations of gas release on the East Siberian Shelf and high methane past two decades, during which the rate of degradation has increased concentrations in water-column and air above (Shakhova et al., 2010a, in some regions (Romanovsky et al., 2010b). 2010b) have led to the suggestion that permafrost thawing creates pathways for gas release. Significant permafrost degradation has been reported in the Russian European North (medium confidence). Warm permafrost with a thick- Subsea permafrost in the Arctic is generally relict terrestrial perma- ness of 10 to 15 m thawed completely in the period 1975 2005 in frost (Vigdorchik, 1980), inundated after the last glaciation and now the Vorkuta area (Oberman, 2008). And although boundaries between degrading under the overlying shelf sea. Permafrost may, however, also permafrost types are not easy to map, the southern permafrost bound- form when the sea is shallow, permitting sediment freezing through ary in this region is reported to have moved north by about 80 km and bottom-fast winter sea ice (Solomon et al., 2008; Stevens et al., 2010). the boundary of continuous permafrost has moved north by 15 to 50 A 76-year record of bottom water temperature in the Laptev Sea (Dmi- km (Oberman, 2008) (medium confidence). Taliks have also developed trenko et al., 2011) showed warming of 2.1°C since 1985 in the near- in relatively thick permafrost during the past several decades. In the shore zone (<10 m water depth), as lengthening summers reduced Vorkuta region, the thickness of existing closed taliks increased by 0.6 sea ice extent and increased solar heating. Degradation rates of the 4 to 6.7 m over the past 30 years (Romanovsky et al., 2010b). Permafrost ice-bearing permafrost following inundation have been estimated to thawing and talik formation has occurred in the Nadym and Urengoy be 1 to 20 cm a 1 on the East Siberian Shelf (Overduin et al., 2007) and regions in north- western Russian (Drozdov et al., 2010). Long-term 1 to 4 cm a 1 in the Alaskan Beaufort Sea (Overduin et al., 2012). permafrost thawing has been reported around the city of Yakutsk, but this in this case, the thawing may have been caused mainly by forest 4.7.4 Changes in Seasonally Frozen Ground fires or human disturbance (Fedorov and Konstantinov, 2008). Perma- frost degradation has also been reported on the Qinghai-Xizang (Tibet) Seasonally frozen ground is a soil layer that freezes and thaws annu- Plateau (Cheng and Wu, 2007; Li et al., 2008). ally, which may or may not overlie terrestrial permafrost, and also includes some portions of the Arctic seabed that freeze in winter. A key Coastal erosion and permafrost degradation appear to be evident parameter regarding seasonally frozen ground overlying permafrost along many Arctic coasts in recent years, with complex interactions is the active-layer thickness (ALT; see Glossary), which indicates the between them (Jones et al., 2009). In part, these interactions arise depth of the seasonal freeze thaw cycle, and which is dependent on from the thermal and chemical impact of sea water on cold terrestrial climate and other factors; for example, vegetation cover (Smith et al., permafrost (Rachold et al., 2007). Similar impacts arise for permafrost 2009). Many observations across many regions have revealed trends in beneath new thaw lakes, which have been formed in recent years (e.g., the thickness of the active laver (high confidence). Sannel and Kuhry, 2011). In northern Alaska, estimates of permafrost thawing under thaw lakes are in the range 0.9 to 1.7 cm a 1 (Ling and 4.7.4.1 Changes in Active-Layer Thickness Zhang, 2003). Many observations have revealed a general positive trend in the thick- Since AR4, destabilized rock glaciers have received increased attention ness of the active layer (see Glossary) for discontinuous permafrost from researchers. A rock glacier is a mass of perennially frozen rock regions at high latitudes (medium confidence). Based on measurements fragments on a slope, that contains ice in one or more forms and shows from the International Permafrost Association (IPA) Circumpolar Active evidence of past or present movement (Van Everdingen, 1998; Haeberli Layer Monitoring (CALM) programme, active-layer thickening has been et al., 2006). Time series acquired over recent decades by terrestrial observed since the 1970s and has accelerated since 1995 in north- surveys indicate acceleration of some rock glaciers as well as seasonal ern Europe (Akerman and Johansson, 2008; Callaghan et al., 2010), velocity changes related to ground temperatures (Bodin et al., 2009; and on Svalbard and Greenland since the late-1990s ­Christiansen et ( 364 Observations: Cryosphere Chapter 4 al., 2010). The ALT has increased significantly in the Russian European a) Northern America North (Mazhitova, 2008), East Siberia (Fyodorov-Davydov et al., 2008), and Chukotka (Zamolodchikov, 2008) since the mid-1990s. Burn and Kokelj (2009) found, for a site in the Mackenzie Delta area, that ALT increased by 8 cm between 1983 and 2008, although the record does exhibit high interannual variability as has been observed at other sites in the region (Smith et al., 2009). ALT has increased since the mid- 1990s in the eastern portion of the Canadian Arctic, with the largest increase occurring at bedrock sites in the discontinuous permafrost zone (Smith et al., 2010). The interannual variations and trends of the active-layer thickness in b) European North Northern America, Northern Europe and Northern Asia from 1990 to 2012 are presented in Figure 4.23. Large regional variations in the yearly variability patterns and trends are apparent. While increases in ALT are occurring in the Eastern Canadian Region (Smith et al., 2009), a slightly declining trend is observed in the Western Canadian Region (Figure 4.23a). In Northern Europe, the trends in the study areas are similar and consistently positive (Figure 4.23b). On the other hand, in Northern Asia, trends are generally strongly positive with the excep- tion of West Siberia, where the trend is slightly negative (Figure 4.23c). On the interior of Alaska, slightly increasing ALT from 1990 to 2010 c) Northern Asia was followed by anomalous increases in 2011 and in 2012. Overall, a general increase in ALT since the 1990s has been observed at many stations in many regions (medium confidence). The general increase is shown in Figure 4.23d, which shows the results of analysis of data from about 44 stations in Russia indicating a change of almost 0.2 m from 1950 to 2008. At some measurement sites on the Qinghai-Xizang (Tibet) Plateau, ALT was reported to be increasing at 7.8 cm yr 1 over a period from 1995 through 2010 (Wu and Zhang, 2010). The high rates may have been the 4 result of local disturbances since more recent studies indicate rates of 1.33 cm yr 1 for the period 1981 2010 and 3.6 cm yr 1 for the period d) Composite ALT from RHM station 1998 2010 (e.g., Zhao et al., 2010; Li et al., 2012a). During the past decade, increases in ALT up to 4.0 cm yr 1 were observed in Mongolian sites characterized as a warm permafrost region (Sharkhuu et al., 2007). Changes in ALT were also detected in Tian Shan (Marchenko et al., 2007; Zhao et al., 2010), and in the European Alps, where increases in ALT were largest during years of hot summers but a strong dependence on surface and subsurface characteristics was noted (Noetzli and Vonder Muehll, 2010). Figure 4.23 | Active layer thickness from different locations for slightly different peri- ods between 1990 and 2012 in (a) Northern America, (b) Northern Europe, and (c) In several areas, across North America and in West Siberia, large-inter Northern Asia. The dashed lines represents linear fit to each set of data. ALT data for annual variations obscure any trends in ALT (high confidence, Figure Northern America, Northern Asia and Northern Europe were obtained from the Inter- 4.23). No trend in ALT was observed on the Alaskan North Slope from national Permafrost Association (IPA) CALM website (http://www.udel.edu/Geography/ 1993 to 2010 (Streletskiy et al., 2008; Shiklomanov et al., 2010) and calm/about/permafrost.html). The number of Russian Hydrometeorological Stations also in the Mackenzie Valley (Smith et al., 2009) and in West Siberia (RHM) stations has expanded from 31 stations as reported from Frauenfeld et al. (2004) and Zhang et al. (2005) to 44 stations and the time series has extended from 1990 to (Vasiliev et al., 2008) since the mid-1990s (Figure 4.23). At some sites, 2008. (d) Departures from the mean of active layer thickness in Siberia from 1950 to such as at Western Canada (C5) and Western Siberia (R1) (Figure 4.23), 2008. The red asterisk represents the mean composite value, the shaded area indicates the active layer thickness was actually decreasing. the standard deviation and the black line is the trend. Data for Siberia stations were obtained from the Russian Hydrometeorological Stations (RHM). The penetration of thaw into ice-rich permafrost at the base of the active layer is often accompanied by loss of volume due to consolida- tion. At several sites, this has been shown to cause surface subsidence (medium confidence). Results from ground-based measurements at 365 Chapter 4 Observations: Cryosphere selected sites on the North Slope of Alaska indicate 11 to 13 cm in sur- of seasonally frozen ground decreased by about 0.32 m during the face subsidence over the period 2001 2006 (Streletskiy et al., 2008), 4 period 1930 2000 (high confidence, Figure 4.24) (Frauenfeld and to 10 cm from 2003 to 2005 in the Brooks Range (Overduin and Kane, Zhang, 2011). Inter-decadal variability was such that no trend could 2006) and up to 20 cm in the Russian European North (Mazhitova and be identified until the late 1960s, after which seasonal freeze depths Kaverin, 2007). Subsidence has also been identified using space-borne decreased significantly until the early 1990s. From then, until about interferometric synthetic aperture radar (InSAR) data. Surface defor- 2008, no further change was evident. Such changes are closely linked mation was detected using InSAR over permafrost on the North Slope with the freezing index, but also with mean annual air temperatures of Alaska during the 1992 2000 thaw seasons and a long-term surface and snow depth (Frauenfeld and Zhang, 2011). subsidence of 1 to 4 cm per decade (Liu et al., 2010). Such subsidence could explain why in situ measurements at some locations reveal neg- Thickness of seasonally frozen ground in western China decreased by ligible trends in ALT changes during the past two decades, despite the 20 to 40 cm since the early 1960s (Li et al., 2008), whereas on the fact that atmospheric and permafrost temperatures increased during Qinghai-Xizang (Tibet) Plateau, the seasonally frozen depth decreased that time. by up to 33 cm since the middle of 1980s (Li et al., 2009). Evidence from the satellite record indicates that the onset dates of spring thaw 4.7.4.2 Changes in Seasonally Frozen Ground in Areas Not advanced by 14 days, whereas the autumn freeze date was delayed by Underlain by Permafrost 10 days on the Qinghai-Xizang (Tibet) Plateau from 1988 through 2007 (Li et al., 2012b) An estimate based on monthly mean soil temperatures from 387 sta- tions across part of the Eurasian continent suggested that the ­thickness o o o o o o 60 N 70 N 80 N 80 N 70 N 60 N o o 10 E 170 W o o 20 E 180 E o o 30 E 170 E o o 40 E 160 E o o 50 E 150 E o o o o o o o o o 100 E 110 E 120 E 130 E 140 E 4 60 E 70 E 80 E 90 E Figure 4.24 | Annual anomalies of the average thickness of seasonally frozen depth in Russia from 1930 to 2000. Each data point represents a composite from 320 stations as compiled at the Russian Hydrometeorological Stations (RHM) (upper right inset). The composite was produced by taking the sum of the thickness measurements from each station and dividing the result by the number of stations operating in that year. Although the total number of stations is 320, the number providing data may be different for each year but the minimum was 240. The yearly anomaly was calculated by subtracting the 1971 2000 mean from the composite for each year. The thin lines indicate the 1 standard deviation (1) (likely) uncertainty range. The line shows a negative trend of 4.5 cm per decade or a total decrease in the thickness of seasonally frozen ground of 31.9 cm from 1930 to 2000 (Frauenfeld and Zhang, 2011). 366 Observations: Cryosphere Chapter 4 4.8 Synthesis in trend analyses, and also in process studies, has enabled increased confidence in the quantification of most of the changes. A graphical Observations show that the cryosphere has been in transition during depiction and a text summary of observed changes in the various com- the last few decades and that the strong and significant changes ponents of the cryosphere are provided in Figure 4.25. They reveal a reported in AR4 have continued, and in many cases accelerated. The general decline in all components of the cryosphere, but the magnitude number of in situ and satellite observations of cryospheric parame- of the decline varies regionally and there are isolated cases where an ters has increased considerably since AR4 and the use of the new data increase is observed. Changes in the Cryosphere Ice Sheet Glaciers Ice Shelf Sea Ice Lake & Snow Cover River Ice Frozen Ground Frozen Ground: increasing permafrost tempera- tures by up to 2C and active layer thickness by up to 90 cm since early 1980s. In the NH, southern limit Sea Ice: between 1979 and 2012, Arctic sea of permafrost moving north since mid 1970s, and ice extent declined at a rate of 3.8% per decreasing thickness of seasonal frozen ground by 32 decade with larger losses in summer and autumn. cm since 1930s. Over the same period, the extent of thick multiyear ice in the Arctic declined at a higher rate of 13.5% per Snow cover: between 1967 and 2012, satellite data show decade. Mean sea ice thickness decreased by 1.3 - decreases through the year, with largest decreases (53%) 2.3 m between 1980 and 2008. in June. Most stations report decreases in now especially in spring. Ice Shelves and ice tongues: continuing retreat and Lake and river ice: contracting winter ice duration with collapse of ice shelves along the Antarctic Peninsula. delays in autumn freeze-up proceeding more slowly than Progressive thinning of some other ice shelves/ice advances in spring break-up, with evidence of recent tongues in Antarctica and Greenland. 4 acceleration in both across the NH. Glaciers: are major contributors to sea level rise. Ice mass Ice Sheets: both Greenland and Antarctic ice sheets loss from glaciers has increased since the 1960s. Loss lost mass and contributed to sea level change over the rates from glaciers outside Greenland and Antarctica last 20 years. Rate of total loss and discharge from were 0.76 mm yr SLE during the 1993 to 2009 period -1 a number of major outlet glaciers in Antarctica and and 0.83 mm yr SLE over the 2005 to 2009 period. -1 Greenland increased over this period. Contribution of Glaciers and Ice Sheets to Sea Level Change 16 5000 Glaciers 14 Cumulative ice mass loss (Gt) Greenland 4000 12 Antarctica 10 SLE (mm) 3000 8 2000 6 4 1000 2 0 0 -2 1992 1994 1996 1998 2000 2002 2004 2006 2008 2010 2012 Year Cumulative ice mass loss from glacier and ice sheets (in sea level equivalent) is 1.0 to 1.4 mm yr-1 for 1993-2009 and 1.2 to 2.2 mm yr-1 for 2005-2009. Figure 4.25 | Schematic summary of the dominant observed variations in the cryosphere. The inset figure summarises the assessment of the sea level equivalent of ice loss from the ice sheets of Greenland and Antarctica, together with the contribution from all glaciers except those in the periphery of the ice sheets (Section 4.3.3 and 4.4.2). 367 Chapter 4 Observations: Cryosphere Some of the observed changes since AR4 have been considerable and The sea level equivalent of mass loss from the Greenland and Antarctic unexpected. One of the most visible was the dramatic decline in the ice sheets over the period 1993 2010, has been about 5.9 mm (includ- September minimum sea ice cover in the Arctic in 2007, which was ing 1.7 mm from glaciers around Greenland) and 4.8 mm, respectively. followed by a record low value in 2012, supporting observations that The reliability of observations of ice loss from the ice sheets has been the thicker components of the Arctic sea ice cover are decreasing. The enhanced with the introduction of advanced satellite observation tech- trend in extent for Arctic sea ice is 3.8 +/- 0.3% per decade (very likely) niques. The ice loss from glaciers between 1993 and 2009 measured while that for multi-year ice is 13.5 +/- 2.5% per decade (very likely). in terms of sea level equivalent (excluding those peripheral to the ice Observations also show marked decreases in Arctic ice thickness and sheets) is estimated to be 13 mm. The inset to Figure 4.25 shows the volume. The pattern of melt on the surface of the Greenland ice sheet cumulative sea level equivalent from glaciers and the ice sheets in has also changed radically, with melt occurring in 2012 over almost the Greenland and Antarctica. These have been contributing dominantly to entire surface of the ice sheet for the first time during the satellite era. sea level rise in recent decades. The contribution of the cryosphere to The ice mass loss in Greenland has been observed to have increased sea level change is discussed more fully in Chapter 13. from 34 [ 6 to 74] Gt yr 1 for the period 1992 2001 to 215 [157 to 274] Gt yr 1 for the period 2002 2011 while the estimates of mass The overall consistency in the negative changes observed in the vari- loss in Antarctica have increased from 30 [ 37 to 97] Gt yr 1 during the ous components of the cryosphere (Figure 4.25), and the acceleration 1992 2001 period to 147 [72 to 221] Gt yr 1 during the 2002 2011 of these changes in recent decades, provides a strong signal of climate period. Observed mass loss from glaciers has also increased, with the change. Regional differences in the magnitude and direction of the sig- global mass loss (excluding the glaciers peripheral to the ice sheets) nals are apparent, but these are not unexpected considering the large estimated to be 226 [91 to 361] Gt yr 1 during the 1971 2009 period, variability and complexity of atmospheric and oceanic circulations. It 275 [140 to 410] Gt yr 1 over the 1993 2009 period, and 301 [166 to is very likely, however, that the Arctic has changed substantially since 436] over the 2005 2009 period. A large majority of observing stations 1979. report decreasing trends in snow depth, snow duration, or snow water equivalent, and the largest decreases are typically observed at loca- tions with temperatures close to freezing. Most lakes and rivers with Acknowledgements long-term records have exhibited declines in ice duration and average seasonal ice cover. Permafrost has also been degrading and retreating We acknowledge the kind contributions of C. Starr (NASA Visualization to the north while permafrost temperatures have increased in most Group), U. Blumthaler, S. Galos (University of Innsbruck) and P. Fretwell regions since the 1980s. (British Antarctic Survey), who assisted in drafting figures. M. Mahrer, R. Graber (University of Zurich) and G. Hiess (BAS) undertook valuable The observed positive trend of sea ice extent in the Antarctic that was literature reviews, and N. E. Barrand (BAS) assisted with collation of regarded as small and insignificant in AR4, has persisted, and increased references. 4 slightly to about 1.5 +/- 0.2% per decade. The higher-than-average Ant- arctic sea ice extent in recent years has been mainly due to increases in the Ross Sea region, which more than offset the declines in the Bell- ingshausen Sea and Amundsen Sea. Ice production in coastal polynyas (regarded as sea ice factories ) along the Ross Sea ice shelves have been observed to be increasing. Recent work suggests strengthening of the zonal (east-west) winds and accompanying ice drift accounts for some of the increasing sea ice extent. Satellite data have provided the ability to observe large-scale changes in the cryosphere at relatively good temporal and spatial resolution throughout the globe. Largely because of the availability of high reso- lution satellite data, the first near-complete global glacial inventory has been generated, leading to a more precise determination of the past, current and future contribution of glaciers to sea level rise. As more data accumulate, and as more capable sensors are launched, the data become more valuable for studies related to change assessment. The advent of new satellites and airborne missions has provided powerful tools that have enabled breakthroughs in the capability to measure some parameters and enhance our ability to interpret results. However, a longer record of measurements of the cryosphere will help increase confidence in the results, reduce uncertainties in the long-term trends, and bring more critical insights into the physical processes controlling the changes. There is thus a need for the continuation of the satellite records, and a requirement for longer and more reliable historical data from in situ measurements and proxies. 368 Observations: Cryosphere Chapter 4 References Abdalati, W., et al., 2004: Elevation changes of ice caps in the Canadian Arctic Benson, B. J., et al., 2012: Extreme events, trends, and variability in Northern Archipelago. J. Geophys. Res. Earth Surf., 109, 11 (F04007). Hemisphere lake-ice phenology (1855 2005). Clim. Change, 112, 299 323. Abdalati, W., et al., 2010: The ICESat-2 Laser Altimetry Mission. Proc. IEEE, 98, Berthier, E., E. Schiefer, G. K. C. Clarke, B. Menounos, and F. Remy, 2010: Contribution 735 751. of Alaskan glaciers to sea-level rise derived from satellite imagery. Nature Akerman, H. J., and M. Johansson, 2008: Thawing permafrost and thicker active Geosci., 3, 92 95. layers in sub-Arctic Sweden. Permafr. Process., 19, 279 292. Bindschadler, R., et al., 2011: Getting around Antarctica: new high-resolution Allard, M., B. L. Wang, and J. A. Pilon, 1995: Recent cooling along the southern mappings of the grounded and freely-floating boundaries of the Antarctic ice shore of the Hudson Strait, Quebec, Canada, documented from permafrost sheet created for the International Polar Year. Cryosphere, 5, 569 588. temperatuure-measurements. Arct. Alp. Res., 27, 157 166. Bjrk, A. A., et al., 2012: An aerial view of 80 years of climate-related glacier Alley, R. B., and S. Anandakrishnan, 1995: Variations in melt-layer frequency in fluctuations in southeast Greenland. Nature Geosci., 5, 427 432. the GISP2 ice core: Implications for Holocene summer temperatures central Björnsson, H., et al., 2013: Contribution of Icelandic ice caps to sea level rise: trends Greenland. Ann. Glaciol., 21, 64 70. and variability since the Little Ice Age. Geophys. Res. Lett., 40, 1546-1550 Alley, R. B., et al., 2008: A simple law for ice-shelf calving. Science, 322, 1344 1344. Blaszczyk, M., J. A. Jania, and J. O. Hagen, 2009: Tidewater glaciers of Svalbard: Amundson, J. M., M. Fahnestock, M. Truffer, J. Brown, M. P. Luthi, and R. J. Motyka, Recent changes and estimates of calving fluxes. Pol. Polar Res., 30, 85 142. 2010: Ice melange dynamics and implications for terminus stability, Jakobshavn Bliss, A., R. Hock, and J. G. Cogley, 2013: A new inventory of mountain glaciers and Isbrae Greenland. J. Geophys. Res. Earth Surf., 115, 12 ice caps for the Antarctic periphery. Ann. Glaciol., 54, 191 199. Andresen, C. S., et al., 2012: Rapid response of Helheim Glacier in Greenland to Bodin, X., F. Rojas, and A. Brenning, 2010: Status and evolution of the cryosphere in climate variability over the past century. Nature Geosci., 5, 37 41 the Andes of Santiago (Chile, 33.5 degrees S.). Geomorphology, 118, 453 464. Arendt, A. A., K. A. Echelmeyer, W. D. Harrison, C. S. Lingle, and V. B. Valentine, 2002: Bodin, X., et al., 2009: Two Decades of Responses (1986 2006) to Climate by the Rapid wastage of Alaska glaciers and their contribution to rising sea level. Laurichard Rock Glacier, French Alps. Permafr. Periglac. Process., 20, 331 344. Science, 297, 382 386. Boening, C., M. Lebsock, F. Landerer, and G. Stephens, 2012: Snowfall-driven mass Arendt, A., et al., 2012: Randolph Glacier Inventory [v2.0]: A Dataset of Global change on the East Antarctic ice sheet. Geophys. Res. Lett., 39, L21501. Glacier Outlines. Global Land Ice Measurements from Space, Boulder Colorado, Boening, C. W., A. Dispert, M. Visbeck, S. R. Rintoul, and F. U. Schwarzkopf, 2008: The USA. Digital Media 32 pp. [Available online at: http://www.glims.org/RGI/RGI_ response of the Antarctic Circumpolar Current to recent climate change. Nature Tech_Report_V2.0.pdf] Geosci., 1, 864 869. Arthern, R. J., D. P. Winebrenner, and D. G. Vaughan, 2006: Antarctic snow Bolch, T., B. Menounos, and R. Wheate, 2010: Landsat-based inventory of glaciers in accumulation mapped using polarization of 4.3-cm wavelength microwave western Canada, 1985 2005. Remote Sens. Environ., 114, 127 137. emission. J. Geophys. Res. Atmos., 111, D06107. Bolch, T., L. Sandberg Srensen, S. B. Simonsen, N. Moelg, H. Machguth, P. Rastner, Arthern, R. J., D. G. Vaughan, A. M. Rankin, R. Mulvaney, and E. R. Thomas, 2010: and F. Paul, 2013: Mass loss of Greenland s glaciers and ice caps 2003 2008 In-situ measurements of Antarctic snow compaction, compared with predictions revealed from ICESat data. Geophys. Res. Lett., 40, 875 881. of models. J. Geophys. Res., 115, F03011. Bolch, T., et al., 2012: The state and fate of Himalayan glaciers. Science, 336, 310 Azam, M. F., et al., 2012: From balance to imbalance: a shift in the dynamic behaviour 314. of Chhota Shigri glacier, western Himalaya, India. J. Glaciol., 58, 315 324. Bown, F., A. Rivera, and C. Acuna, 2008: Recent glacier variations at the Aconcagua Bahr, D. B., M. F. Meier, and S. D. Peckham, 1997: The physical basis of glacier volume- basin, central Chilean Andes. Ann. Glaciol., 48, 43 48. area scaling. J. Geophys. Res. Sol. Ea., 102, 20355 20362. Box, J. E., L. Yang, D. H. Bromwich, and L. S. Bai, 2009: Greenland ice sheet surface air 4 Bahr, D. B., M. Dyurgerov, and M. F. Meier, 2009: Sea-level rise from glaciers and ice temperature variability: 1840 2007. J. Clim., 22, 4029 4049. caps: A lower bound. Geophys. Res. Lett., 36, 4 (L03501). Box, J. E., X. Fettweis, J. C. Stroeve, M. Tedesco, D. K. Hall, and K. Steffen, 2012: Bales, R. C., et al., 2009: Annual accumulation for Greenland updated using ice core Greenland ice sheet albedo feedback: Thermodynamics and atmospheric drivers. data developed during 2000 2006 and analysis of daily coastal meteorological Cryosphere, 6, 821 839. data. J. Geophys. Res. Atmos., 114, D06116. Brewer, M. C., 1958: Some results of geothermal investigations of permafrost. Am. Bamber, J. L., et al., 2013: A new bed elevation dataset for Greenland. Cryosphere, Geophys. Union Trans. 39, 19 26. 7, 499 510. Bromwich, D. H., J. P. Nicolas, and A. J. Monaghan, 2011: An assessment of Barrand, N., D. G. Vaughan, N. Steiner, M. Tedesco, P. Kuipers Munneke, M. R. van den precipitation changes over Antarctica and the Southern Ocean since 1989 in Broeke, and J. S. Hosking, 2013: Trends in Antarctic Peninsula surface melting contemporary global reanalyses. J. Clim., 24, 4189 4209. conditions from observations and regional climate modelling. J. Geophys. Res., Bromwich, D. H., J. P. Nicolas, A. J. Monaghan, M. A. Lazzara, L. M. Keller, G. A. 118, 1 16. Weidner, and A. B. Wilson, 2013: Central West Antarctica among the most rapidly Barrett, P. J., 2013: Resolving views on Antarctic Neogene glacial history the Sirius warming regions on Earth. Nature Geosci., 6, 139 145. debate. Earth Environ. Sci. Trans. R. Soc. Edinburgh, 104, 29 51. Brooks, R. N., T. D. Prowse, and I. J. O Connell, 2012: Quantifying Northern Hemisphere Barry, R., and T. Y. Gran, 2011: The Global Cryosphere: Past, Present and Future. freshwater ice. Geophys. Res. Lett., 40, 1128 1131. Cambridge University Press, Cambridge, UK, and New York, NY, USA, 498 pp. Brown, R., C. Derksen, and L. B. Wang, 2010: A multi-data set analysis of variability Barry, R. G., R. E. Moritz, and J. C. Rogers, 1979: Fast ice regimes of the Beaufort and and change in Arctic spring snow cover extent, 1967 2008. J. Geophys. Res. Chukchi sea coasts, Alaska. Cold Reg. Sci. Technol., 1, 129 152. Atmos., 115, D16111. Bartholomew, I. D., P. Nienow, A. Sole, D. Mair, T. Cowton, M. A. King, and S. Palmer, Brown, R. D., and P. Coté, 1992: Interannual variability of landfast ice thickness in the 2011: Seasonal variations in Greenland Ice Sheet motion: Inland extent and Canadian High Arctic, 1950 89. Arctic, 45, 273 284. behaviour at higher elevations. Earth Planet. Sci. Lett., 307, 271 278. Brown, R. D., and P. W. Mote, 2009: The response of Northern Hemisphere snow Baur, O., M. Kuhn, and W. E. Featherstone, 2009: GRACE-derived ice-mass variations cover to a changing climate. J. Clim., 22, 2124 2145. over Greenland by accounting for leakage effects. J. Geophys. Res. Sol. Ea., 114, Brown, R. D., and D. A. Robinson, 2011: Northern Hemisphere spring snow cover 13 (B06407). variability and change over 1922 2010 including an assessment of uncertainty. Belchansky, G. I., D. C. Douglas, and N. G. Platonov, 2004: Duration of the Arctic Sea Cryosphere, 5, 219 229. ice melt season: Regional and interannual variability, 1979 2001. J. Clim., 17, Brunt, K. M., E. A. Okal, and D. R. MacAyeal, 2011: Antarctic ice-shelf calving triggered 67 80. by the Honshu (Japan) earthquake and tsunami, March 2011. J. Glaciol., 57, Beltaos, S., and T. Prowse, 2009: River-ice hydrology in a shrinking cryosphere. 785 788. Hydrol. Process., 23, 122 144. Buchardt, S. L., H. B. Clausen, B. M. Vinther, and D. Dahl-Jensen, 2012: Investigating Benn, D. I., C. R. Warren, and R. H. Mottram, 2007: Calving processes and the the past and recent delta18O-accumulation relationship seen in Greenland ice dynamics of calving glaciers. Earth Sci. Rev., 82, 143 179. cores. Clim. Past, 8, 2053 2059. 369 Chapter 4 Observations: Cryosphere Bulygina, O. N., V. N. Razuvaev, and N. N. Korshunova, 2009: Changes in snow cover Cogley, J. G., 2009b: Geodetic and direct mass-balance measurements: comparison over Northern Eurasia in the last few decades. Environ. Res. Lett., 4, 045026. and joint analysis. Ann. Glaciol., 50, 96 100. Bulygina, O. N., P. Y. Groisman, V. N. Razuvaev, and N. N. Korshunova, 2011: Changes Cogley, J. G., 2012: Area of the ocean. Mar. Geodesy, 35, 379 388. in snow cover characteristics over Northern Eurasia since 1966. Environ. Res. Cogley, J. G., et al., 2011: Glossary of Glacier Mass Balance and Related Terms. Lett., 6, 045204. IHP-VII Technical Documents in Hydrology No. 86, International Association of Burn, C. R., and S. V. Kokelj, 2009: The environment and permafrost of the Mackenzie Cryospheric Sciences, Contribution No. 2, UNESCO-IHP. 114 pp. Delta Area. Permafr. Periglac. Process., 20, 83 105. Comiso, J. C., 2002: A rapidly declining perennial sea ice cover in the Arctic. Geophys. Burn, C. R., and Y. Zhang, 2009: Permafrost and climate change at Herschel Island Res. Lett., 29, 1956. (Qikiqtaruq), Yukon Territory, Canada. J. Geophys. Res., 114, F02001. Comiso, J. C., 2010: Polar Oceans from Space. Springer Science+Business Media, Callaghan, T. V., F. Bergholm, T. R. Christensen, C. Jonasson, U. Kokfelt, and M. New York, NY, USA and Heidelberg, Germany. Johansson, 2010: A new climate era in the sub-Arctic: Accelerating climate Comiso, J. C., 2012: Large decadal decline in the Arctic multiyear ice cover. J. Clim., changes and multiple impacts. Geophys. Res. Lett., 37, L14705. 25, 1176 1193. Callaghan, T. V., M. Johansson, O. Anisimov, H. H. Christiansen, A. Instanes, V. Comiso, J. C., and F. Nishio, 2008: Trends in the sea ice cover using enhanced and Romanovsky, and S. Smith, 2011: Changing permafrost and its impacts. In: compatible AMSR-E, SSM/I, and SMMR data. J. Geophys. Res. Oceans, 113, Snow, Water, Ice and Permafrost in the Arctic (SWIPA). Arctic Monitoring and C02S07. Assessment Program (AMAP). Comiso, J. C., C. L. Parkinson, R. Gersten, and L. Stock, 2008: Accelerated decline in Carrivick, J. L., B. J. Davies, N. F. Glasser, D. Nyvlt, and M. J. Hambrey, 2012: Late- the Arctic Sea ice cover. Geophys. Res. Lett., 35, L01703. Holocene changes in character and behaviour of land-terminating glaciers on Comiso, J. C., R. Kwok, S. Martin, and A. L. Gordon, 2011: Variability and trends in James Ross Island, Antarctica. J. Glaciol., 58, 1176 1190. sea ice extent and ice production in the Ross Sea. J. Geophys. Res. Oceans, 116, Carturan, L., and R. Seppi, 2007: Recent mass balance results and morphological C04021. evolution of Careser glacier (Central Alps). Geograf. Fis. Dinam. Quat., 30, 33 42. Cook, A. J., and D. G. Vaughan, 2010: Overview of areal changes of the ice shelves on Cavalieri, D. J., and C. L. Parkinson, 2012: Arctic sea ice variability and trends, 1979 the Antarctic Peninsula over the past 50 years. Cryosphere, 4, 77 98. 2010. Cryosphere, 6, 957 979. Costa, D. P., J. M. Klinck, E. E. Hofmann, M. S. Dinniman, and J. M. Burns, 2008: Cavalieri, D. J., P. Gloersen, and W. J. Campbell, 1984: Determination of sea ice Upper ocean variability in west Antarctic Peninsula continental shelf waters as parameters with the Nimbus-7 SMMR. J. Geophys. Res. Atmos., 89, 5355 5369. measured using instrumented seals. Deep-Sea Res. Pt. Ii, 55, 323 337. Cazenave, A., et al., 2009: Sea level budget over 2003 2008: A reevaluation from Coudrain, A., B. Francou, and Z. W. Kundzewicz, 2005: Glacier shrinkage in the Andes GRACE space gravimetry, satellite altimetry and Argo. Global Planet. Change, and consequences for water resources. Hydrol. Sci. J. J. Sci. Hydrol., 50, 925 932. 65, 83 88. Cullen, N. J., P. Sirguey, T. Moelg, G. Kaser, M. Winkler, and S. J. Fitzsimons, 2013: Chapman, W. L., and J. E. Walsh, 2007: A synthesis of Antarctic temperatures. J. Clim., A century of ice retreat on Kilimanjaro: The mapping reloaded. Cryosphere, 7, 20, 4096 4117. 419 431. Charrassin, J. B., et al., 2008: Southern Ocean frontal structure and sea-ice formation Daniault, N., H. Mercier, and P. Lherminier, 2011: The 1992 2009 transport variability rates revealed by elephant seals. Proc. Natl. Acad. Sci. U.S.A., 105, 11634 11639. of the East Greenland-Irminger Current at 60 degrees N. Geophys. Res. Lett., Chen, J. L., C. R. Wilson, and B. D. Tapley, 2006: Satellite gravity measurements 38, 4 (L07601). confirm accelerated melting of Greenland ice sheet. Science, 313, 1958 1960. Das, S. B., I. Joughin, M. D. Behn, I. M. Howat, M. A. King, D. Lizarralde, and M. Chen, J. L., C. R. Wilson, and B. D. Tapley, 2011: Interannual variability of Greenland P. Bhatia, 2008: Fracture propagation to the base of the Greenland Ice Sheet ice losses from satellite gravimetry. J. Geophys. Res. Sol. Ea., 116, 11( B07406). during supraglacial lake drainage. Science, 320, 778 781. Chen, J. L., C. R. Wilson, D. Blankenship, and B. D. Tapley, 2009: Accelerated Antarctic Davies, B. J., and N. F. Glasser, 2012: Accelerating shrinkage of Patagonian glaciers ice loss from satellite gravity measurements. Nature Geosci., 2, 859 862. from the Little Ice Age (c. AD 1870) to 2011. J. Glaciol., 58, 1063 1084. 4 Chen, J. L., C. R. Wilson, B. D. Tapley, D. D. Blankenship, and E. R. Ivins, 2007: Patagonia De Angelis, H., and P. Skvarca, 2003: Glacier surge after ice shelf collapse. Science, icefield melting observed by gravity recovery and climate experiment (GRACE). 299, 1560 1562. Geophys. Res. Lett., 34, 6 (L22501). Delaloye, R., et al., cited 2011: Recent interannual variations of rock glacier creep in Cheng, G. D., and T. H. Wu, 2007: Responses of permafrost to climate change and the European Alps. [Available online at http://www.zora.uzh.ch/7031/.] their environmental significance, Qinghai-Tibet Plateau. J. Geophys. Res., 112, DeLiberty, T. L., C. A. Geiger, S. F. Ackley, A. P. Worby, and M. L. Van Woert, 2011: F02S03. Estimating the annual cycle of sea-ice thickness and volume in the Ross Sea. Chinn, T., S. Winkler, M. J. Salinger, and N. Haakensen, 2005: Srecent glacier Deep-Sea Res. Pt. Ii, 58, 1250 1260. advances in Norway and New Zealand: A comparison of their glaciological and Derksen, C., and R. Brown, 2012: Spring snow cover extent reductions in the meteorological causes. Geograf. Annal. A, 87A, 141 157. 2008 2012 period exceeding climate model projections. Geophys. Res. Lett., 39, Choi, G., D. A. Robinson, and S. Kang, 2010: Changing Northern Hemisphere snow L19504. seasons. J. Clim., 23, 5305 5310. Derksen, C., et al., 2012: Variability and change in the Canadian cryosphere. Clim. Christiansen, H. H., M. Guglielmin, J. Noetzli, V. Romanovsky, N. Shiklomanov, S. Change, 115, 59 88. Smith, and L. Zhao, 2012: Cryopsphere, Permafrost thermal state. Special Suppl. Déry, S. J., and R. D. Brown, 2007: Recent Northern Hemisphere snow cover extent to Bull. Am. Meteorol. Soc., 93( July) [J. Blunden and D. S. Arndt (eds.)], S19 S21. trends and implications for the snow-albedo feedback. Geophys. Res. Lett., 34, Christiansen, H. H., et al., 2010: The thermal state of permafrost in the Nordic area 6 (L22504). during the International Polar Year 2007 2009. Permafr. Periglac. Process., 21, Ding, Q. H., E. J. Steig, D. S. Battisti, and M. Kuttel, 2011: Winter warming in West 156 181. Antarctica caused by central tropical Pacific warming. Nature Geosci., 4, 398 Christoffersen, P., et al., 2011: Warming of waters in an East Greenland fjord prior 403. to glacier retreat: Mechanisms and connection to large-scale atmospheric Dinniman, M. S., J. M. Klinck, and E. E. Hofmann, 2012: Sensitivity of circumpolar conditions. Cryosphere, 5, 701 714. deep water transport and ice shelf basal melt along the West Antarctic Peninsula Christy, J. R., 2012: Searching for information in 133 years of California snowfall to changes in the winds. J. Clim., 25, 4799 4816. observations. J. Hydrometeorol., 13, 895 912. Diolaiuti, G., D. Bocchiola, C. D Agata, and C. Smiraglia, 2012: Evidence of climate Citterio, M., F. Paul, A. P. Ahlstrom, H. F. Jepsen, and A. Weidick, 2009: Remote sensing change impact upon glaciers recession within the Italian Alps The case of of glacier change in West Greenland: accounting for the occurrence of surge- Lombardy glaciers. Theor. Appl. Climatol., 109, 429 445. type glaciers. Ann. Glaciol., 50, 70 80. Dmitrenko, I. A., et al., 2011: Recent changes in shelf hydrography in the Siberian Citterio, M., G. Diolaiuti, C. Smiraglia, C. D Agata, T. Carnielli, G. Stella, and G. B. Arctic: Potential for subsea permafrost instability. J. Geophys. Res. Oceans, 116, Siletto, 2007: The fluctuations of Italian glaciers during the last century: A 10 (C10027). contribution to knowledge about Alpine glacier changes. Geograf. Annal. A, Doake, C. S. M., and D. G. Vaughan, 1991: Rapid disintegration of the wordie ice shelf 89A, 167 184. in response to atmospheric warming. Nature, 350, 328 330. Cogley, J. G., 2009a: A more complete version of the World Glacier Inventory. Ann. Glaciol., 50, 32 38. 370 Observations: Cryosphere Chapter 4 Doake, C. S. M., H. F. J. Corr, H. Rott, P. Skvarca, and N. W. Young, 1998: Breakup and Fricker, H. A., and L. Padman, 2012: Thirty years of elevation change on Antarctic conditions for stability of the northern Larsen Ice Shelf, Antarctica. Nature, 391, Peninsula ice shelves from multimission satellite radar altimetry. J. Geophys. Res. 778 780. Oceans, 117, C02026. Doherty, S. J., S. G. Warren, T. C. Grenfell, A. D. Clarke, and R. E. Brandt, 2010: Light- Fyodorov-Davydov, D. G., A. L. Kholodov, V. E. Ostroumov, G. N. Kraev, V. A. Sorokovikov, absorbing impurities in Arctic snow. Atmos. Chem. Phys., 10, 11647 11680. S. P. Davudov, and A. A. Merekalova, 2008: Seasonal thaw of soils in the North Drobot, S. D., and M. R. Anderson, 2001: An improved method for determining Yakutian ecosystems. In: Proceedings of the 9th International Conference on snowmelt onset dates over Arctic sea ice using scanning multichannel Permafrost, 29 June 3 July 2008, Institute of Northern Engineering, University microwave radiometer and Special Sensor Microwave/Imager data. J. Geophys. of Alaska, Fairbanks [D. L. Kane, and K. M. Hinkel (eds.)], pp. 481 486. Res. Atmos., 106, 24033 24049. Galton-Fenzi, B. K., J. R. Hunter, R. Coleman, and N. Young, 2012: A decade of change Drozdov, D. S., N. G. Ukraintseva, A. M. Tsarev, and S. N. Chekrygina, 2010: Changes in the hydraulic connection between an Antarctic epishelf lake and the ocean. J. in the temperature field and in the state of the geosystems within the territory Glaciol., 58, 223 228. of the Urengoy field during the last 35 years (1974 2008). Earth Cryosphere, Gardelle, J., E. Berthier, and Y. Arnaud, 2012: Slight mass gain of Karakoram glaciers 14, 22 31. in the early twenty-first century. Nature Geosci., 5, 322 325. Druckenmiller, M. L., H. Eicken, M. A. Johnson, D. J. Pringle, and C. C. Williams, 2009: Gardner, A., G. Moholdt, A. Arendt, and B. Wouters, 2012: Accelerated contributions Toward an integrated coastal sea-ice observatory: System components and a of Canada s Baffin and Bylot Island glaciers to sea level rise over the past half case study at Barrow, Alaska. Cold Reg. Sci. Technol., 56, 61 72. century. Cryosphere, 6, 1103 1125. Drucker, R., S. Martin, and R. Kwok, 2011: Sea ice production and export from coastal Gardner, A. S., et al., 2011: Sharply increased mass loss from glaciers and ice caps in polynyas in the Weddell and Ross Seas. Geophys. Res. Lett., 38, 4 (L17502). the Canadian Arctic Archipelago. Nature, 473, 357 360. Ducklow, H., et al., 2011: The marine system of the Western Antarctic Peninsula In: Gardner, A. S., et al., 2013: A reconciled estimate of glacier contributions to sea level Antarctica: An Extreme Environment in a Changing World [A. D. Rogers (ed.)]. rise: 2003 to 2009. Science, 340, 852 857. New York, NY, USA, John Wiley & Sons. 121-159. Gerland, S., A. H. H. Renner, F. Godtliebsen, D. Divine, and T. B. Loyning, 2008: Decrease E, D.-C., Y.-D. Yang, and D.-B. Chao, 2009: The sea level change from the Antarctic ice of sea ice thickness at Hopen, Barents Sea, during 1966 2007. Geophys. Res. sheet based on GRACE. Chin. J. Geophys. Chin. Ed., 52, 2222 2228. Lett., 35, L06501. Ettema, J., M. R. van den Broeke, E. van Meijgaard, W. J. van de Berg, J. L. Bamber, Gilbert, A., P. Wagnon, C. Vincent, P. Ginot, and M. Funk, 2010: Atmospheric warming J. E. Box, and R. C. Bales, 2009: Higher surface mass balance of the Greenland at a high-elevation tropical site revealed by englacial temperatures at Illimani, ice sheet revealed by high-resolution climate modeling. Geophys. Res. Lett., 36, Bolivia (6340 m above sea level, 16 degrees S, 67 degrees W). J. Geophys. Res., L12501. 115, D10109. Ewert, H., A. Groh, and R. Dietrich, 2012: Volume and mass changes of the Greenland Giles, A. B., R. A. Massom, and V. I. Lytle, 2008a: Fast-ice distribution in East ice sheet inferred from ICESat and GRACE. J. Geodyn., 59, 111 123. Antarctica during 1997 and 1999 determined using RADARSAT data. J. Geophys. Fedorov, A. N., and P. Y. Konstantinov, 2008: Recent changes in ground temperature Res. Oceans, 113, 15 (C02S14). and the effect on permafrost landscapes in Central Yakutia. In: Proceeding of Giles, K. A., S. W. Laxon, and A. L. Ridout, 2008b: Circumpolar thinning of Arctic the 9th International Conference on Permafrost, 29 June 3 July 2008, Institute sea ice following the 2007 record ice extent minimum. Geophys. Res. Lett., 35, of Northern Engineering, University of Alaska, Fairbanks [D. L. Kane, and K. M. L22502. Hinkel (eds.)], pp. 433 438. Gille, S. T., 2002: Warming of the Southern Ocean since the 1950s. Science, 295, Fernandes, R., H. X. Zhao, X. J. Wang, J. Key, X. Qu, and A. Hall, 2009: Controls on 1275 1277. Northern Hemisphere snow albedo feedback quantified using satellite Earth Gille, S. T., 2008: Decadal-scale temperature trends in the Southern Hemisphere observations. Geophys. Res. Lett., 36, L21702. Ocean. J. Clim., 21, 4749 4765. Fettweis, X., M. Tedesco, M. R. van den Broeke, and J. Ettema, 2011: Melting trends Glazovsky, A., and Y. Macheret, 2006: Eurasian Arctic. In: Glaciation in North and over the Greenland ice sheet (1958 2009) from spaceborne microwave data Central Eurasia in Present Time. [V. M. Kotlyakov (ed.)]. Nauka, Saint Petersburg, 4 and regional climate models. Cryosphere, 5, 359 375. Russian Federation, pp. 438 445. Flament, T., and F. Remy, 2012: Dynamic thinning of Antarctic glaciers from along- Gordon, A. L., M. Visbeck, and J. C. Comiso, 2007: A possible link between the track repeat radar altimetry. J. Glaciol., 58, 830 840. Weddell Polynya and the Southern Annular Mode. J. Clim., 20, 2558 2571. Flanner, M. G., C. S. Zender, J. T. Randerson, and P. J. Rasch, 2007: Present-day climate Grebmeier, J. M., S. E. Moore, J. E. Overland, K. E. Frey, and R. Gradinger, 2010: forcing and response from black carbon in snow. J. Geophys. Res., 112, D11202. Biological response to recent Pacific Arctic sea ice retreats. EOS Trans. Am. Flanner, M. G., K. M. Shell, M. Barlage, D. K. Perovich, and M. A. Tschudi, 2011: Geophys. Union, 91, 161 163. Radiative forcing and albedo feedback from the Northern Hemisphere Green, K., 2011: Interannual and seasonal changes in the ice cover of glacial lakes in cryosphere between 1979 and 2008. Nature Geosci., 4, 151 155. the Snowy Mountains of Australia. J. Mount. Sci., 8, 655 663. Fleming, K., and K. Lambeck, 2004: Constraints on the Greenland Ice Sheet since the Gregory, J. M., and J. Oerlemans, 1998: Simulated future sea-level rise due to glacier Last Glacial Maximum from sea-level observations and glacial-rebound models. melt based on regionally and seasonally resolved temperature changes. Nature, Quat. Sci. Rev., 23, 1053 1077. 391, 474 476. Foster, J. L., D. K. Hall, R. E. J. Kelly, and L. Chiu, 2009: Seasonal snow extent and Griggs, J., and J. L. Bamber, 2011: Antarctic ice-shelf thickness from satellite radar snow mass in South America using SMMR and SSM/I passive microwave data altimetry. J. Glaciol., 57, 485 498. (1979 2006). Remote Sens. Environ., 113, 291 305. Grinsted, A., 2013: An estimate of global glacier volume. Cryosphere, 7, 141 151. Fraser, A. D., R. A. Massom, K. J. Michael, B. K. Galton-Fenzi, and J. L. Lieser, 2012: Gruber, S., 2012: Derivation and analysis of a high-resolution estimate of East Antarctic landfast sea ice distribution and variability, 2000 2008. J. Clim., globalpermafrost zonation. Cryosphere, 6, 221 233. 25, 1137 1156. Gruber, S., and W. Haeberli, 2007: Permafrost in steep bedrock slopes and its Frauenfeld, O. W., and T. J. Zhang, 2011: An observational 71-year history of temperature-related destabilization following climate change. J. Geophys. Res., seasonally frozen ground changes in the Eurasian high latitudes. Environ. Res. 112, 10 (F02S18). Lett., 6, 044024. Guglielmin, M., and N. Cannone, 2012: A permafrost warming in a cooling Frauenfeld, O. W., T. J. Zhang, R. G. Barry, and D. Gilichinsky, 2004: Interdecadal Antarctica? Clim. Change, 111, 177 195. changes in seasonal freeze and thaw depths in Russia. J. Geophys. Res., 109, Guglielmin, M., M. R. Balks, L. S. Adlam, and F. Baio, 2011: Permafrost thermal regime D05101. from two 30-m deep boreholes in Southern Victoria Land, Antarctica. Permafr. Fretwell, P. T., et al., 2013: Bedmap2: improved ice bed, surface and thickness Periglac. Process., 22, 129 139. datasets for Antarctica. Cryosphere, 7, 375 393. Gunter, B., et al., 2009: A comparison of coincident GRACE and ICESat data over Frezzotti, M., C. Scarchilli, S. Becagli, M. Proposito, and S. Urbini, 2012: A synthesis of Antarctica. J. Geodesy, 83, 1051 1060. the Antarctic Surface Mass Balance during the last eight centuries. Cryosphere, Gusmeroli, A., P. Jansson, R. Pettersson, and T. Murray, 2012: Twenty years of cold 7, 303 319. surface layer thinning at Storglaciären, sub-Arctic Sweden, 1989 2009. J. Glaciol., 58, 3 10. 371 Chapter 4 Observations: Cryosphere Haas, C., S. Hendricks, H. Eicken, and A. Herber, 2010: Synoptic airborne thickness Howat, I. M., I. Joughin, M. Fahnestock, B. E. Smith, and T. A. Scambos, 2008: surveys reveal state of Arctic sea ice cover. Geophys. Res. Lett., 37, L09501. Synchronous retreat and acceleration of southeast Greenland outlet glaciers Haas, C., H. Le Goff, S. Audrain, D. Perovich, and J. Haapala, 2011: Comparison of 2000 06: Ice dynamics and coupling to climate. J. Glaciol., 54, 646 660. seasonal sea-ice thickness change in the Transpolar Drift observed by local ice Howat, I. M., Y. Ahn, I. Joughin, M. R. van den Broeke, J. T. M. Lenaerts, and B. Smith, mass-balance observations and floe-scale EM surveys. Ann. Glaciol., 52, 97 102. 2011: Mass balance of Greenland s three largest outlet glaciers, 2000 2010. Haas, C., A. Pfaffling, S. Hendricks, L. Rabenstein, J. L. Etienne, and I. Rigor, 2008: Geophys. Res. Lett., 38, 5 (L12501). Reduced ice thickness in Arctic Transpolar Drift favors rapid ice retreat. Geophys. Hudson, S. R., 2011: Estimating the global radiative impact of the sea ice-albedo Res. Lett., 35, L17501. feedback in the Arctic. J. Geophys. Res. Atmos., 116, D16102. Haeberli, W., M. Hoelzle, F. Paul, and M. Zemp, 2007: Integrated monitoring of Hughes, T. J., 1973: Is the West Antarctic ice sheet disintegrating? J. Geophys. Res., mountain glaciers as key indicators of global climate change: the European Alps. 78, 7884 7910. Ann. Glaciol., 46, 150 160. Hulbe, C. L., T. A. Scambos, T. Youngberg, and A. K. Lamb, 2008: Patterns of glacier Haeberli, W., et al., 2006: Permafrost creep and rock glacier dynamics. Permafr. response to disintegration of the Larsen B ice shelf, Antarctic Peninsula. Global Periglac. Process., 17, 189 214. Planet. Change, 63, 1 8. Haeberli, W., et al., 2010: Mountain permafrost: development and challenges of a Humbert, A., et al., 2010: Deformation and failure of the ice bridge on the Wilkins Ice young research field. J. Glaciol., 56, 1043 1058. Shelf, Antarctica. Ann. Glaciol., 51, 49 55. Hagg, W., C. Mayer, A. Lambrecht, D. Kriegel, and E. Azizov, 2012: Glacier changes Hurrell, J. W., 1995: Decadal trends in the North-Atlantic oscillation regional in the Big Naryn basin, Central Tian Shan. Global Planet. Change, doi:10.1016/j. temperatures and precipitation. Science, 269, 676 679. gloplacha.2012.07.010. Huss, M., 2012: Extrapolating glacier mass balance to the mountain-range scale: The Hall, D. K., J. C. Comiso, N. E. DiGirolamo, C. A. Shuman, J. E. Box, and L. S. Koenig, European Alps 1900 2100. Cryosphere, 6, 713 727. 2013: Variability in the surface temperature and melt extent of the Greenland Ice Huss, M., and D. Farinotti, 2012: Distributed ice thickness and volume of 180,000 Sheet from MODIS. Geophys. Res. Lett., 10, 2114-2120. glaciers around the globe. J. Geophys. Res., 117, F04010. Halsey, L. A., D. H. Vitt, and S. C. Zoltai, 1995: Disequilibrium response of permafrost Isaksen, K., et al., 2011: Degrading mountain permafrost in southern Norway: Spatial in boreal continental western Canada to climate-change. Clim. Change, 30, and temporal variability of mean ground temperatures, 1999 2009. Permafr. 57 73. Periglac. Process., 22, 361 377. Hanna, E., et al., 2011: Greenland Ice Sheet surface mass balance 1870 to 2010 Ishikawa, M., N. Sharkhuu, Y. Jambaljav, G. Davaa, K. Yoshikawa, and T. Ohata, based on Twentieth Century Reanalysis, and links with global climate forcing. J. 2012: Thermal state of Mongolian permafrost. In: Proceedings of the 10th Geophys. Res., 116, D24121. International Conference on Permafrost, June, 2012, Salekhard, Yamel-nenets Harig, C., and F. J. Simons, 2012: Mapping Greenland s mass loss in space and time. Autonomous District, Russian Federation, v1.[K. M. Hinkel (ed)]. The Northern Proc. Natl. Acad. Sci. U.S.A., 109, 19934 19937. Publisher, Salekhard, Russia, pp. 173 178. Heid, T., and A. Kääb, 2012: Repeat optical satellite images reveal widespread and Ishizaka, M., 2004: Climatic response of snow depth to recent warmer winter long term decrease in land-terminating glacier speeds. Cryosphere , 6, 467 478 seasons in heavy-snowfall areas in Japan. Ann. Glaciol., 38, 299 304. Hirabayashi, Y., S. Kanae, K. Masuda, K. Motoya, and P. Doell, 2008: A 59-year (1948 Ivins, E. R., and T. S. James, 2005: Antarctic glacial isostatic adjustment: A new 2006) global near-surface meteorological data set for land surface models. Part assessment. Antarct. Sci., 17, 541 553. I: Development of daily forcing and assessment of precipitation intensity. Hydrol. Ivins, E. R., M. M. Watkins, D. N. Yuan, R. Dietrich, G. Casassa, and A. Rulke, 2011: Res. Lett., 2, 36 40. On-land ice loss and glacial isostatic adjustment at the Drake Passage: 2003 Hirabayashi, Y., Y. Zhang, S. Watanabe, S. Koirala, and S. Kanae, 2013: Projection of 2009. J. Geophys. Res. Sol. Ea., 116, 24 (B02403). glacier mass changes under a high-emission climate scenario using the global Jacob, T., J. Wahr, W. T. Pfeffer, and S. Swenson, 2012: Recent contributions of glaciers glacier model HYOGA2. Hydrol. Res. Lett., 7, 6 11. and ice caps to sea level rise. Nature, 482, 514 518. 4 Hock, R., M. de Woul, V. Radic, and M. Dyurgerov, 2009: Mountain glaciers and ice Jacobs, S. S., A. Jenkins, C. F. Giulivi, and P. Dutrieux, 2011: Stronger ocean circulation caps around Antarctica make a large sea-level rise contribution. Geophys. Res. and increased melting under Pine Island Glacier ice shelf. Nature Geosci., 4, Lett., 36, L07501. 519 523. Hodgkins, A. G., I. C. James, and T. G. Huntington, 2002: Historical changes in lake Jacobs, S. S., H. H. Helmer, C. S. M. Doake, A. Jenkins, and R. M. Frolich, 1992: Melting ice-out dates as indicators of climate change in New England, 1850 2000. Int. of the ice shelves and the mass balance of Antarctica. J. Glaciol., 38, 375 387. J. Clim., 22, 1819 1827. Jenkins, A., 2011: Convection-driven melting near the grounding lines of ice shelves Hoelzle, M., G. Darms, M. P. Lüthi, and S. Suter, 2011: Evidence of accelerated and tidewater glaciers. J. Clim., 41, 2279 2294. englacial warming in the Monte Rosa area, Switzerland/Italy. Cryosphere, 5, Jenkins, A., and C. S. M. Doake, 1991: Ice-Ocean interaction on Ronne ice shelf, 231 243. Antarctica J. Geophys. Res. Oceans, 96, 791 813. Hoffman, M. J., G. A. Catania, T. A. Neumann, L. C. Andrews, and J. A. Rumrill, 2011: Jenkins, A., and S. Jacobs, 2008: Circulation and melting beneath George VI Ice Shelf, Links between acceleration, melting, and supraglacial lake drainage of the Antarctica. J. Geophys. Res. Oceans, 113, C04013. western Greenland Ice Sheet. J. Geophys. Res. Earth Surf., 116, F04035. Jenkins, A., P. Dutrieux, S. S. Jacobs, S. D. McPhail, J. R. Perrett, A. T. Webb, and D. Holland, D. M., and A. Jenkins, 1999: Modeling thermodynamic ice-ocean interactions White, 2010: Observations beneath Pine Island Glacier in West Antarctica and at the base of an ice shelf. J. Phys. Oceanogr., 29, 1787 1800. implications for its retreat. Nature Geosci., 3, 468 472. Holland, D. M., R. H. Thomas, B. De Young, M. H. Ribergaard, and B. Lyberth, 2008: Jensen, O. P., B. J. Benson, J. J. Magnuson, V. M. Card, M. N. Futter, P. A. Soranno, Acceleration of Jakobshavn Isbrae triggered by warm subsurface ocean waters. and K. M. Stewart, 2007: Spatial analysis of ice phenology trends across the Nature Geosci., 1, 659 664. Laurentian Great Lakes Region during a recent warming period. Limnol. Holland, P. R., and R. Kwok, 2012: Wind-driven trends in Antarctic sea ice motion. Oceanogr., 52, 2013 2026. Nature Geosci., 5, 872 875. Jia, L. L., H. S. Wang, and L. W. Xiang, 2011: Effect of glacio-static adjustment on the Holland, P. R., A. Jenkins, and D. M. Holland, 2010: Ice and ocean processes in the estimate of ice mass balance over Antarctic and uncertainties. Chin. J. Geophys., Bellingshausen Sea, Antarctica. J. Geophys. Res. Oceans, 115, C05020. 54, 1466 1477. Holland, P. R., H. F. J. Corr, H. D. Pritchard, D. G. Vaughan, R. J. Arthern, A. Jenkins, Johannessen, O. M., E. V. Shalina, and M. W. Miles, 1999: Satellite evidence for an and M. Tedesco, 2011: The air content of Larsen Ice Shelf. Geophys. Res. Lett., Arctic sea ice cover in transformation. Science, 286, 1937 1939. 38, L10503. Johnson, J. S., M. J. Bentley, and K. Gohl, 2008: First exposure ages from the Holzhauser, H., M. Magny, and H. J. Zumbuhl, 2005: Glacier and lake-level variations Amundsen Sea embayment, West Antarctica: The late quaternary context for in west-central Europe over the last 3500 years. Holocene, 15, 789 801. recent thinning of Pine Island, Smith, and Pope Glaciers. Geology, 36, 223 226. Horwath, M., and R. Dietrich, 2009: Signal and error in mass change inferences from Jones, B. M., C. D. Arp, M. T. Jorgenson, K. M. Hinkel, J. A. Schmutz, and P. L. Flint, GRACE: The case of Antarctica. Geophys. J. Int., 177, 849 864. 2009: Increase in the rate and uniformity of coastline erosion in Arctic Alaska. Howat, I. M., I. Joughin, and T. A. Scambos, 2007: Rapid changes in ice discharge Geophys. Res. Lett., 36, 5 (L03503). from Greenland outlet glaciers. Science, 315, 1559 1561. 372 Observations: Cryosphere Chapter 4 Jones, P. D., D. H. Lister, T. J. Osborn, C. Harpham, M. Salmon, and C. P. Morice, Krabill, W. B., et al., 2002: Aircraft laser altimetry measurement of elevation changes 2012: Hemispheric and large-scale land-surface air temperature variations: An of the Greenland ice sheet: Technique and accuracy assessment. J. Geodyn., 34, extensive revision and an update to 2010. J. Geophys. Res. Atmos., 117, D05127. 357 376. Jorgenson, M. T., Y. L. Shur, and E. R. Pullman, 2006: Abrupt increase in permafrost Kuipers Munneke, P., G. Picard, M. R. van den Broeke, J. T. M. Lenaerts, and E. Van degradation in Arctic Alaska. Geophys. Res. Lett., 33, 4 (L02503). Meijgaard, 2012: Insignificant change in Antarctic snowmelt volume since 1979. Joughin, I., and R. B. Alley, 2011: Stability of the West Antarctic ice sheet in a Geophys. Res. Lett., 39, (L01501). warming world. Nature Geosci., 4, 506 513. Kunkel, K. E., M. A. Palecki, K. G. Hubbard, D. A. Robinson, K. T. Redmond, and D. Joughin, I., W. Abdalati, and M. Fahnestock, 2004: Large fluctuations in speed on R. Easterling, 2007: Trend identification in twentieth-century US snowfall: The Greenland s Jakobshavn Isbrae glacier. Nature, 432, 608 610. challenges. J. Atmos. Ocean. Technol., 24, 64 73. Joughin, I., B. E. Smith, and D. M. Holland, 2010a: Sensitivity of 21st century sea Kurtz, N. T., and T. Markus, 2012: Satellite observations of Antarctic sea ice thickness level to ocean-induced thinning of Pine Island Glacier, Antarctica. Geophys. Res. and volume. J. Geophys. Res. Oceans, 117, C08025 Lett., 37, L20502. Kutuzov, S., and M. Shahgedanova, 2009: Glacier retreat and climatic variability Joughin, I., R. B. Alley, and D. M. Holland, 2012: Ice-sheet response to oceanic in the eastern Terskey-Alatoo, inner Tien Shan between the middle of the 19th forcing. Science, 338, 1172 1176. century and beginning of the 21st century. Global Planet. Change, 69, 59 70. Joughin, I., M. Fahnestock, R. Kwok, P. Gogineni, and C. Allen, 1999: Ice flow of Kwok, R., 2004: Annual cycles of multiyear sea ice coverage of the Arctic Ocean: Humboldt, Petermann and Ryder Gletscher, northern Greenland. J. Glaciol., 45, 1999 2003. J. Geophys. Res.-Oceans, 109, C11004. 231 241. Kwok, R., 2005: Variability of Nares Strait ice flux. Geophys. Res. Lett., 32, L24502. Joughin, I., B. E. Smith, I. M. Howat, T. Scambos, and T. Moon, 2010b: Greenland flow Kwok, R., 2007: Near zero replenishment of the Arctic multiyear sea ice cover at the variability from ice-sheet-wide velocity mapping. J. Glaciol., 56, 415 430. end of 2005 summer. Geophys. Res. Lett., 34, L05501. Joughin, I., S. B. Das, M. A. King, B. E. Smith, I. M. Howat, and T. Moon, 2008a: Kwok, R., 2009: Outflow of Arctic Ocean Sea Ice into the Greenland and Barents Seasonal speedup along the western flank of the Greenland ice sheet. Science, Seas: 1979 2007. J. Clim., 22, 2438 2457. 320, 781 783. Kwok, R., and D. A. Rothrock, 1999: Variability of Fram Strait ice flux and North Joughin, I., et al., 2008b: Ice-front variation and tidewater behavior on Helheim and Atlantic Oscillation. J. Geophys. Res. Oceans, 104, 5177 5189. Kangerdlugssuaq Glaciers, Greenland. J. Geophys. Res. Earth Surf., 113, F01004. Kwok, R., and D. A. Rothrock, 2009: Decline in Arctic sea ice thickness from submarine Kääb, A., R. Frauenfelder, and I. Roer, 2007: On the response of rockglacier creep to and ICESat records: 1958 2008. Geophys. Res. Lett., 36, L15501. surface temperature increase. Global Planet. Change, 56, 172 187. Kwok, R., and G. F. Cunningham, 2010: Contribution of melt in the Beaufort Sea to Kääb, A., W. Haeberli, and G. H. Gudmundsson, 1997: Analysing the creep of mountain the decline in Arctic multiyear sea ice coverage: 1993 2009. Geophys. Res. Lett., permafrost using high precision aerial photogrammetry: 25 years of monitoring 37, L20501. Gruben Rock Glacier, Swiss Alps. Permafr. Periglac. Process., 8, 409 426. Kwok, R., G. F. Cunningham, M. Wensnahan, I. Rigor, H. J. Zwally, and D. Yi, 2009: Kääb, A., E. Berthier, C. Nuth, J. Gardelle, and Y. Arnaud, 2012: Contrasting patterns Thinning and volume loss of the Arctic Ocean sea ice cover: 2003 2008. J. of early twenty-first-century glacier mass change in the Himalayas. Nature, 488, Geophys. Res. Oceans, 114, C07005. 495 498. Latifovic, R., and D. Pouliot, 2007: Analysis of climate change impacts on lake ice Kargel, J. S., et al., 2012: Greenland s shrinking ice cover: Fast times but not that phenology in Canada using the historical satellite data record. Remote Sens. fast. Cryosphere, 6, 533 537. Environ., 106, 492 507. Kaser, G., J. G. Cogley, M. B. Dyurgerov, M. F. Meier, and A. Ohmura, 2006: Mass Laxon, S., N. Peacock, and D. Smith, 2003: High interannual variability of sea ice balance of glaciers and ice caps: Consensus estimates for 1961 2004. Geophys. thickness in the Arctic region. Nature, 425, 947 950. Res. Lett., 33, L19501. Laxon, S. W., et al., 2013: CryoSat-2 estimates of Arctic sea ice thickness and volume. Khan, S. A., J. Wahr, M. Bevis, I. Velicogna, and E. Kendrick, 2010a: Spread of ice mass Geophys. Res. Lett., 40, 732 737. loss into northwest Greenland observed by GRACE and GPS. Geophys. Res. Lett., Leclercq, P. W., and J. Oerlemans, 2012: Global and hemispheric temperature 4 37, L06501. reconstruction from glacier length fluctuations. Clim. Dyn., 38, 1065 1079. Khan, S. A., L. Liu, J. Wahr, I. Howat, I. Joughin, T. van Dam, and K. Fleming, 2010b: Leclercq, P. W., J. Oerlemans, and J. G. Cogley, 2011: Estimating the glacier GPS measurements of crustal uplift near Jakobshavn Isbrae due to glacial ice contribution to sea-level rise for the period 1800 2005. Surv. Geophys., 32, mass loss. J. Geophys. Res. Sol. Ea., 115, 13 (B09405). 519 535. King, M. A., R. J. Bingham, P. Moore, P. L. Whitehouse, M. J. Bentley, and G. A. Milne, Leclercq, P. W., A. Weidick, F. Paul, T. Bolch, M. Citterio, and Oerlemans.J, 2012: 2012: Lower satellite-gravimetry estimates of Antarctic sea-level contribution. Brief communication Historical glacier length changes in West Greenland. Nature, 491, 586-589. Cryosphere, 6, 1339 1343. King, M. A., et al., 2009: A 4 decade record of elevation change of the Amery Ice Lemke, P., et al., 2007: Observations: Changes in snow, ice and frozen ground. In: Shelf, East Antarctica. J. Geophys. Res. Earth Surf., 114, F01010. Climate Change 2007: The Physical Science Basis. Contribution of Working Kinnard, C., C. M. Zdanowicz, D. A. Fisher, E. Isaksson, A. De Vernal, and L. G. Group I to the Fourth Assessment Report of the Intergovernmental Panel on Thompson, 2011: Reconstructed changes in Arctic sea ice over the past 1,450 Climate Change [Solomon, S., D. Qin, M. Manning, Z. Chen, M. Marquis, K. B. years. Nature, 479, 509 U231. Averyt, M. Tignor and H. L. Miller (eds.)] Cambridge University Press, Cambridge, Kjaer, K. H., et al., 2012: Aerial photographs reveal late-20th-century dynamic ice United Kingdom and New York, NY, USA, pp. 337 383. loss in northwestern Greenland. Science, 337, 569 573. Lenaerts, J. T. M., M. R. van den Broeke, W. J. van de Berg, E. van Meijgaard, and Klein, A. G., and J. L. Kincaid, 2006: Retreat of glaciers on Puncak Jaya, Irian Jaya, P. Kuipers Munneke, 2012: A new, high resolution surface mass balance map determined from 2000 and 2002 IKONOS satellite images. J. Glaciol., 52, 65 79. of Antarctica (1979 2010) based on regional climate modeling. Geophys. Res. Knoll, C., and H. Kerschner, 2009: A glacier inventory for South Tyrol, Italy, based on Lett., 39, 1 5 (L04501). airborne laser-scanner data. Ann. Glaciol., 50, 46 52. Levermann, A., T. Albrecht, R. Winkelmann, M. A. Martin, M. Haseloff, and I. Joughin, Koch, K., C. Knoblauch, and D. Wagner, 2009: Methanogenic community composition 2012: Kinematic first-order calving law implies potential for abrupt ice-shelf and anaerobic carbon turnover in submarine permafrost sediments of the retreat. Cryosphere, 6, 273 286. Siberian Laptev Sea. Environ. Microbiol., 11, 657 668. Lewkowicz, A. G., B. Etzelmuller, and S. L. Smith, 2011: Characteristics of Kopp, R. E., F. J. Simons, J. X. Mitrovica, A. C. Maloof, and M. Oppenheimer, 2009: discontinuous permafrost based on ground temperature measurements and Probabilistic assessment of sea level during the last interglacial stage. Nature, electrical resistivity tomography, Southern Yukon, Canada. Permafr. Periglac. 462, 863 867. Process., 22, 320 342. Kozlovsky, A. M., Y. L. Nazintsev, V. I. Fedotov, and N. V. Cherepanov, 1977: Fast ice Li, J., and H. J. Zwally, 2011: Modeling of firn compaction for estimating ice-sheet of the Eastern Antarctic (in Russian). Proc. Soviet Antarct. Expedit., 63, 1 129. mass change from observed ice-sheet elevation change. Ann. Glaciol., 52, 1 7. Krabill, W., et al., 1999: Rapid thinning of parts of the southern Greenland ice sheet. Li, R., L. Zhao, and Y. Ding, 2009: The climatic characteristics of the maximum seasonal Science, 283, 1522 1524. frozen depth in the Tibetan plateau. J. Glaciol. Geocryol., 31, 1050 1056. Krabill, W., et al., 2000: Greenland ice sheet: High-elevation balance and peripheral Li, R., et al., 2012a: Temporal and spatial variations of the active layer along the thinning. Science, 289, 428 430. Qinghai-Tibet Highway in a permafrost region. Chin. Sci. Bull., 57, 4609 4616. 373 Chapter 4 Observations: Cryosphere Li, X., R. Jin, X. D. Pan, T. J. Zhang, and J. W. Guo, 2012b: Changes in the near-surface Masiokas, M. H., R. Villalba, B. H. Luckman, and S. Mauget, 2010: Intra- to soil freeze-thaw cycle on the Qinghai-Tibetan Plateau. Int. J. Appl. Earth Obs. multidecadal variations of snowpack and streamflow records in the Andes of Geoinf., 17, 33 42. Chile and Argentina between 30 degrees and 37 degrees S. J. Hydrometeorol., Li, X., et al., 2008: Cryospheric change in China. Global Planet. Change, 62, 210 218. 11, 822 831. Ling, F., and T. Zhang, 2003: Numerical simulation of permafrost thermal regime and Masiokas, M. H., A. Rivera, L. E. Espizua, R. Villalba, S. Delgado, and J. C. Aravena, talik development under shallow thaw lakes on the Alaskan Arctic Coastal Plain. 2009: Glacier fluctuations in extratropical South America during the past 1000 J. Geophys. Res. Atmos., 108, 11. years. Palaeogeogr. Palaeoclimatol. Palaeoecol., 281, 242 268. Liu, L., T. Zhang, and J. Wahr, 2010: InSAR measurements of surface deformation Maslanik, J. A., C. Fowler, J. Stroeve, S. Drobot, J. Zwally, D. Yi, and W. Emery, 2007: A over permafrost on the North Slope of Alaska. J. Geophys. Res. Earth Surf., 115, younger, thinner Arctic ice cover: Increased potential for rapid, extensive sea-ice F03023 loss. Geophys. Res. Lett., 34, L24501. Lopez, P., P. Chevallier, V. Favier, B. Pouyaud, F. Ordenes, and J. Oerlemans, 2010: A Massom, R. A., and S. Stammerjohn, 2010: Antarctic sea ice change and variability regional view of fluctuations in glacier length in southern South America. Global Physical and ecological implications. Polar Sci., 149 186. Planet. Change, 71, 85 108. Massom, R. A., P. Reid, B. Raymond, S. Stammerjohn, A. D. Fraser, and S. Ushio, 2013: Lopez-Moreno, J. I., and S. M. Vicente-Serrano, 2007: Atmospheric circulation Change and variability in East Antarctic Sea Ice Seasonality, 1979/80 2009/10. influence on the interannual variability of snow pack in the Spanish Pyrenees PLoS ONE, 8, e64756. during the second half of the 20th century. Nordic Hydrol., 38, 33 44. Massom, R. A., et al., 2001: Snow on Antarctic Sea ice: A review of physical Luckman, A., and T. Murray, 2005: Seasonal variation in velocity before retreat of characteristics. Rev. Geophys., 39, 413 445. Jakobshavn Isbrae, Greenland. Geophys. Res. Lett., 32, 4. Matsuo, K., and K. Heki, 2010: Time-variable ice loss in Asian high mountains from Luethi, M. P., A. Bauder, and M. Funk, 2010: Volume change reconstruction of Swiss satellite gravimetry. Earth Planet. Sci. Lett., 290, 30 36. glaciers from length change data. J. Geophys. Res. Earth Surf., 115, F04022. Mazhitova, G. G., 2008: Soil temperature regimes in the discontinuous permafrost Luthcke, S. B., A. A. Arendt, D. D. Rowlands, J. J. McCarthy, and C. F. Larsen, 2008: zone in the east European Russian Arctic. Euras. Soil Sci., 41, 48 62. Recent glacier mass changes in the Gulf of Alaska region from GRACE mascon Mazhitova, G. G., and D. A. Kaverin, 2007: Thaw depth dynamics and soil surface solutions. J. Glaciol., 54, 767 777. subsidence at a Circumpolar Active Layer Monitoring (CALM) site in the East Luthcke, S. B., et al., 2006: Recent Greenland ice mass loss by drainage system from European Russian Arctic. Kriosfera Zemli, XI, N, 20 30. satellite gravity observations. Science, 314, 1286 1289. McGuire, A. D., et al., 2009: Sensitivity of the carbon cycle in the Arctic to climate Ma, L., and D. Qin, 2012: Temporal-spatial characteristics of observed key parameters change. Ecol. Monogr., 79, 523 555. of snow cover in China during 1957 2009. Sci. Cold Arid Reg., 4(5), 384 393. Meese, D. A., et al., 1994: The accumulation record from the Gisp2 Core as and MacAyeal, D. R., et al., 2006: Transoceanic wave propagation links iceberg calving indicator of climate change throughout the holocene. Science, 266, 1680 1682. margins of Antarctica with storms in tropics and Northern Hemisphere. Geophys. Meier, M. F., 1984: Contribution of small glaciers to global sea level. Science, 226, Res. Lett., 33, 4 (L17502). 1418 1421. MacGregor, J. A., G. A. Catania, M. S. Markowski, and A. G. Andrews, 2012: Meier, W. N., J. Stroeve, A. Barrett, and F. Fetterer, 2012: A simple approach to Widespread rifting and retreat of ice-shelf margins in the eastern Amundsen Sea providing a more consistent Arctic sea ice extent time series from the 1950s to Embayment between 1972 and 2011. J. Glaciol., 58, 458 466. present. Cryosphere, 6, 1359 1368. Machguth, H., F. Paul, S. Kotlarski, and M. Hoelzle, 2009: Calculating distributed Melling, H., 2012: Sea-Ice Observation: Advances and challenges. In: Arctic Climate glacier mass balance for the Swiss Alps from regional climate model output: A Change: The ACSYS Decade and Beyond [P. Lemke and H.-W. Jacobi (eds.)]. methodical description and interpretation of the results. J. Geophys. Res. Atmos., Atmospheric and Oceanographic Sciences Library. Springer Science, New York, 114, D19106. NY, USA, and Heidelberg, Germany. 27-115. Mahoney, A., H. Eicken, and L. Shapiro, 2007: How fast is landfast sea ice? A study of Mercer, J. H., 1978: West Antarctic ice sheet and CO2 greenhouse effect threat of 4 the attachment and detachment of nearshore ice at Barrow, Alaska. Cold Reg. disaster. Nature, 271, 321 325. Sci. Technol., 47, 233 255. Micu, D., 2009: Snow pack in the Romanian Carpathians under changing climatic Macias Fauria, M., et al., 2010: Unprecedented low twentieth century winter sea conditions. Meteorol. Atmos. Phys., 105, 1 16. ice extent in the Western Nordic Seas since AD 1200. Clim. Dyn., 34, 781 795. Mitchell, T. D., and P. D. Jones, 2005: An improved method of constructing a database Malkova, G. V., 2008: The last twenty-five years of changes in permafrost of monthly climate observations and associated high-resolution grids. Int. J. temperature of the European Russian Arctic. In: Proceedings of the 9th Climatol., 25, 693 712. International Conference on Permafrost, 29 June 3 July 2008, Institute of Moholdt, G., B. Wouters, and A. S. Gardner, 2012: Recent contribution to sea-level Northern Engineering, University of Alaska, Fairbanks [D. L. Kane, and K. M. rise from glaciers and ice caps in the Russian High Arctic. Geophys. Res. Lett., Hinkel (eds.)], pp. 1119 1124. 39, L10502. Marchenko, S. S., A. P. Gorbunov, and V. E. Romanovsky, 2007: Permafrost warming Moholdt, G., C. Nuth, J. O. Hagen, and J. Kohler, 2010: Recent elevation changes of in the Tien Shan Mountains, Central Asia. Global Planet. Change, 56, 311 327. Svalbard glaciers derived from ICESat laser altimetry. Remote Sens. Environ., Markus, T., and D. J. Cavalieri, 2000: An enhancement of the NASA Team sea ice 114, 2756 2767. algorithm. IEEE Trans. Geosci. Remote Sens., 38, 1387 1398. Monaghan, A. J., D. H. Bromwich, and S. H. Wang, 2006: Recent trends in Antarctic Markus, T., J. C. Stroeve, and J. Miller, 2009: Recent changes in Arctic sea ice melt snow accumulation from Polar MM5 simulations. Philos. Trans. R. Soc. A, 364, onset, freezeup, and melt season length. J. Geophys. Res. Oceans, 114, C12024. 1683 1708. Marshall, G. J., A. Orr, N. P. M. van Lipzig, and J. C. King, 2006: The impact of a Montes-Hugo, M., S. C. Doney, H. W. Ducklow, W. Fraser, D. Martinson, S. E. changing Southern Hemisphere Annular Mode on Antarctic Peninsula summer Stammerjohn, and O. Schofield, 2009: Recent changes in phytoplankton temperatures. J. Clim., 19, 5388 5404. communities associated with rapid regional climate change along the western Martinson, D. G., S. E. Stammerjohn, R. A. Iannuzzi, R. C. Smith, and M. Vernet, Antarctic Peninsula. Science, 323, 1470 1473. 2008: Western Antarctic Peninsula physical oceanography and spatio-temporal Moon, T., and I. Joughin, 2008: Changes in ice front position on Greenland s outlet variability. Deep-Sea Res. Pt. Ii, 55, 1964 1987. glaciers from 1992 to 2007. J. Geophys. Res. Earth Surf., 113, F02022. Marty, C., 2008: Regime shift of snow days in Switzerland. Geophys. Res. Lett., 35, Moon, T., I. Joughin, B. Smith, and I. Howat, 2012: 21st-Century evolution of L12501. Greenland outlet glacier velocities. Science, 336, 576 578. Marty, C., and R. Meister, 2012: Long-term snow and weather observations at Moore, P., and M. A. King, 2008: Antarctic ice mass balance estimates from GRACE: Weissfluhjoch and its relation to other high-altitude observatories in the Alps. Tidal aliasing effects. J. Geophys. Res. Earth Surf., 113, F02005. Theor. Appl. Climatol., 110, 573 583. Morris, E. M., and D. G. Vaughan, 2003: Spatial and temporal variation of surface Marushchak, M. E., A. Pitkamaki, H. Koponen, C. Biasi, M. Seppala, and P. J. temperature on the Antarctic Peninsula and the limit of viability of ice shelves. Martikainen, 2011: Hot spots for nitrous oxide emissions found in different types In: Antarctic Peninsula Climate Variability: Historical and Paleoenvironmental of permafrost peatlands. Global Change Biol., 17, 2601 2614. Perspectives [E. Domack, A. Leventer, A. Burnett, R. Bindschadler, P. Convey, and Marzeion, B., A. H. Jarosch, and M. Hofer, 2012: Past and future sea-level change M. Kirby (eds.)]. Antarctic Research Series, 79, American Geophysical Union, from the surface mass balance of glaciers. Cryosphere, 6, 1295 1322. Washington, DC, pp. 61 68. 374 Observations: Cryosphere Chapter 4 Mote, P. W., 2006: Climate-driven variability and trends in mountain snowpack in Osterkamp, T. E., 2007: Characteristics of the recent warming of permafrost in western North America. J. Clim., 19, 6209 6220. Alaska. J. Geophys. Res. Earth Surf., 112, 10 (F02S02). Motyka, R. J., L. Hunter, K. A. Echelmeyer, and C. Connor, 2003: Submarine melting Osterkamp, T. E., 2008: Thermal state of permafrost in Alaska during the fourth quarter at the terminus of a temperate tidewater glacier, LeConte Glacier, Alaska, USA. of the twentieth century. In: Proceedings of the 9th International Conference on Ann. Glaciol., 36, 57 65. Permafrost, 29 June 3 July 2008, Institute of Northern Engineering,University Motyka, R. J., M. Truffer, M. Fahnestock, J. Mortensen, S. Rysgaard, and I. Howat, of Alaska, Fairbanks, Alaska [D. L. Kane, and K. M. Hinkel (eds.)], pp. 1333 1338. 2011: Submarine melting of the 1985 Jakobshavn Isbrae floating tongue and Overduin, P. P., and D. L. Kane, 2006: Frost boils and soil ice content: Field observations. the triggering of the current retreat. J. Geophys. Res. Earth Surf., 116, F01007. Permafr. Periglac. Process., 17, 291 307. Murray, T., T. Strozzi, A. Luckman, H. Jiskoot, and P. Christakos, 2003: Is there a single Overduin, P. P., H.-W. Hubberten, V. Rachold, N. Romanovskii, M. N. Grigoriev, and surge mechanism? Contrasts in dynamics between glacier surges in Svalbard M. Kasymskaya, 2007: Evolution and degradation of coastal and offshore and other regions. J. Geophys. Res. Sol. Ea., 108, 2237. permafrost in the Laptev and East Siberian Seas during the last climatic cycle. Murray, T., et al., 2010: Ocean regulation hypothesis for glacier dynamics in GSA Special Papers, 426, 97 111. southeast Greenland and implications for ice sheet mass changes. J. Geophys. Overduin, P. P., S. Westermann, K. Yoshikawa, T. Haberlau, V. Romanovsky, and S. Res. Earth Surf., 115, F03026. Wetterich, 2012: Geoelectric observations of the degradation of nearshore Myers, P. G., C. Donnelly, and M. H. Ribergaard, 2009: Structure and variability of submarine permafrost at Barrow (Alaskan Beaufort Sea). J. Geophys. Res. Earth the West Greenland Current in Summer derived from 6 repeat standard sections. Surf., 117, F02004. Prog. Oceanogr., 80, 93 112. Padman, L., et al., 2012: Oceanic controls on the mass balance of Wilkins Ice Shelf, National Snow and Ice Data Center, 2013: http://nsidc.org/data/icesat/correction-to- Antarctica. J. Geophys. Res. Oceans, 117, C01010 product-surface-elevations.html. Palmer, S., A. Shepherd, P. Nienow, and I. Joughin, 2011: Seasonal speedup of the Nerem, R. S., and J. Wahr, 2011: Recent changes in the Earth s oblateness driven Greenland Ice Sheet linked to routing of surface water. Earth Planet. Sci. Lett., by Greenland and Antarctic ice mass loss. Geophys. Res. Lett., 38, 6 (L13501). 302, 423 428. Nesje, A., O. Lie, and S. O. Dahl, 2000: Is the North Atlantic Oscillation reflected in Parkinson, C. L., 2002: Trends in the length of the Southern Ocean sea-ice season, Scandinavian glacier mass balance records? J. Quat. Sci., 15, 587 601. 1979 99. Ann. Glaciol., 34, 435 440. Nghiem, S. V., I. G. Rigor, D. K. Perovich, P. Clemente-Colon, J. W. Weatherly, and G. Parkinson, C. L., and D. J. Cavalieri, 2012: Antarctic sea ice variability and trends, Neumann, 2007: Rapid reduction of Arctic perennial sea ice. Geophys. Res. Lett., 1979 2010. Cryosphere, 6, 871 880. 34, 6 (L19504). Parkinson, C. L., and J. C. Comiso, 2013: On the 2012 record low Arctic sea ice cover: Nghiem, S. V., et al., 2012: The extreme melt across the Greenland ice sheet in 2012. Combined impact of preconditioning and an August storm. Geophys. Res. Lett., Geohys. Res. Lett., 39, L20502. 40, 1356 1361. Nicholls, K. W., K. Makinson, and E. J. Venables, 2012: Ocean circulation beneath Paul, F., and W. Haeberli, 2008: Spatial variability of glacier elevation changes in the Larsen C Ice Shelf, Antarctica from in situ observations. Geophys. Res. Lett., 39, Swiss Alps obtained from two digital elevation models. Geophys. Res. Lett., 35, L19608. 5 (L21502). Nicholls, N., 2005: Climate variability, climate change and the Australian snow Paulson, A., S. J. Zhong, and J. Wahr, 2007: Inference of mantle viscosity from GRACE season. Aust. Meteorol. Mag., 54, 177 185. and relative sea level data. Geophys. J. Int., 171, 497 508. Nick, F. M., A. Vieli, I. M. Howat, and I. Joughin, 2009: Large-scale changes in Payne, A. J., A. Vieli, A. P. Shepherd, D. J. Wingham, and E. Rignot, 2004: Recent Greenland outlet glacier dynamics triggered at the terminus. Nature Geosci., dramatic thinning of largest West Antarctic ice stream triggered by oceans. 2, 110 114. Geophys. Res. Lett., 31, L23401. Nick, F. M., C. J. van der Veen, A. Vieli, and D. Benn, 2010: A physically based calving Peltier, W. R., 2004: Global glacial isostasy and the surface of the ice-age earth: The model applied to marine outlet glaciers and implications for their dynamics. J. ice-5G (VM2) model and grace. Annu. Rev. Earth Planet. Sci., 32, 111 149. Glaciol., 56, 781 794. Peltier, W. R., 2009: Closure of the budget of global sea level rise over the GRACE era: 4 Nick, F. M., et al., 2013: Future sea level rise from Greenland s major outlet glaciers The importance and magnitudes of the required corrections for global glacial in a warming climate. Nature, 497, 235 238. isostatic adjustment. Quat. Sci. Rev., 28, 1658 1674. Noetzli, J., and D. Vonder Muehll, 2010: Permafrost in Switzerland 2006/2007 Pelto, M. S., 2006: The current disequilibrium of North Cascade glaciers. Hydrol. and 2007/2008. Glaciological Report (Permafrost) No. 8/9 of the Cryospheric Process., 20, 769 779. Commission of the Swiss Academy of Sciences. Cryospheric Commission of the Perovich, D. K., B. Light, H. Eicken, K. F. Jones, K. Runciman, and S. V. Nghiem, 2007: Swiss Academy of Sciences, 68 pp.   Increasing solar heating of the Arctic Ocean and adjacent seas, 1979 2005: Nussbaumer, S. U., A. Nesje, and H. J. Zumbuhl, 2011: Historical glacier fluctuations of Attribution and role in the ice-albedo feedback. Geophys. Res. Lett., 34, L19505. Jostedalsbreen and Folgefonna (southern Norway) reassessed by new pictorial Petkova, N., E. Koleva, and V. Alexandrov, 2004: Snow cover variability and change in and written evidence. Holocene, 21, 455 471. mountainous regions of Bulgaria, 1931 2000. Meteorol. Z., 13, 19 23. Nuth, C., G. Moholdt, J. Kohler, J. O. Hagen, and A. Kaab, 2010: Svalbard glacier Pfeffer, W. T., 2007: A simple mechanism for irreversible tidewater glacier retreat. J. elevation changes and contribution to sea level rise. J. Geophys. Res. Earth Surf., Geophys. Res. Earth Surf., 112, F03S25. 115, 16 (F01008). Pfeffer, W. T., 2011: Land ice and sea level rise: A thirty-year perspective. Oberman, N. G., 2008: Contemporary permafrost degradation of Northern European Oceanography, 24, 94 111. Russia. In: Proceedings of the 9th International Conference on Permafrost, Phillips, T., H. Rajaram, and K. Steffen, 2010: Cryo-hydrologic warming: A potential 29 June 3 July 2008, Institute of Northern Engineering, University of Alaska, mechanism for rapid thermal response of ice sheets. Geophys. Res. Lett., 37, Fairbanks [D. L. Kane, and K. M. Hinkel (eds.)], pp. 1305 1310. L20503. Oberman, N. G., 2012: Long-term temperature regime of the Northeast European Pollard, D., and R. M. DeConto, 2009: Modelling West Antarctic ice sheet growth and permafrost region during contemporary climate warming. In: Proceedings collapse through the past five million years. Nature, 458, 329 333. of the 10th International Conference on Permafrost, June, 2012, Salekhard, Polyakov, I. V., et al., 2003: Long-term ice variability in Arctic marginal seas. J. Clim., Yamel-Nenets Autonomous District, Russian Federation, v2. [V. P. Melnikov, D. S. 16, 2078 2085. Drozdov and V. E. Romanovsky (eds)]. The Northern Publisher, Salekhard, Russia. Polyakov, I. V., et al., 2010: Arctic Ocean warming contributes to reduced polar ice pp. 287 291. cap. J. Phys. Oceanogr., 40, 2743 2756 Oerlemans, J., 2001: Glaciers and Climate Change. A. A. Balkema, Lisse, the Pritchard, H. D., and D. G. Vaughan, 2007: Widespread acceleration of tidewater Netherlands, 160 pp. glaciers on the Antarctic Peninsula. J. Geophys. Res. Earth Surf., 112, F03S29. Oerlemans, J., M. Dyurgerov, and R. De Wal, 2007: Reconstructing the glacier Pritchard, H. D., S. B. Luthcke, and A. H. Fleming, 2010: Understanding ice-sheet mass contribution to sea-level rise back to 1850. Cryosphere, 1, 59 65. balance: Progress in satellite altimetry and gravimetry. J. Glaciol., 56, 1151 O Leary, M., and P. Christoffersen, 2013: Calving on tidewater glaciers amplified by 1161. submarine frontal melting. Cryosphere, 7, 119 128. Pritchard, H. D., R. J. Arthern, D. G. Vaughan, and L. A. Edwards, 2009: Extensive Osterkamp, T.E., 2005: The recent warming of permafrost in Alaska. Global Planet. dynamic thinning on the margins of the Greenland and Antarctic ice sheets. Change, 49, 187 202. Nature, 461, 971 975. 375 Chapter 4 Observations: Cryosphere Pritchard, H. D., S. R. M. Ligtenberg, H. A. Fricker, D. G. Vaughan, M. R. van den Broeke, Rignot, E., J. L. Bamber, M. R. van den Broeke, C. Davis, Y. H. Li, W. J. van de Berg, and E. and L. Padman, 2012: Antarctic ice loss driven by ice-shelf melt. Nature, 484, Van Meijgaard, 2008b: Recent Antarctic ice mass loss from radar interferometry 502 505. and regional climate modelling. Nature Geosci., 1, 106 110. Prowse, T., et al., 2011: Arctic freshwater ice and its climatic role. Ambio, 40, 46 52. Riva, R. E. M., et al., 2009: Glacial Isostatic Adjustment over Antarctica from combined Quincey, D. J., M. Braun, N. F. Glasser, M. P. Bishop, K. Hewitt, and A. Luckman, 2011: ICESat and GRACE satellite data. Earth Planet. Sci. Lett., 288, 516 523. Karakoram glacier surge dynamics. Geophys. Res. Lett., 38, L18504. Rivera, A., F. Bown, D. Carrion, and P. Zenteno, 2012: Glacier responses to recent Rabatel, A., J. P. Dedieu, and C. Vincent, 2005: Using remote-sensing data to volcanic activity in Southern Chile. Environ. Res. Lett., 7, 014036. determine equilibrium-line altitude and mass-balance time series: validation on Robinson, D. A., K. F. Dewey, and R. R. Heim, 1993: Global snow cover monitoring three French glaciers, 1994 2002. J. Glaciol., 51, 539 546. An update. Bull. Am. Meteorol. Soc., 74, 1689 1696. Rabatel, A., B. Francou, V. Jomelli, P. Naveau, and D. Grancher, 2008: A chronology Roer, I., W. Haeberli, M. Avian, V. Kaufmann, R. Delaloye, C. Lambiel, and A. Kääb, of the Little Ice Age in the tropical Andes of Bolivia (16 degrees S) and its 2008: Observations and considerations on destabilizing active rock glaciers implications for climate reconstruction. Q. Res., 70, 198 212. in the European Alps. In: Proceedings of the 9th International Conference on Rabatel, A., et al., 2013: Current state of glaciers in the tropical Andes: A multi- Permafrost, 29 June 3 July 2008, Institute of Northern Engineering, University century perspective on glacier evolution and climate change. Cryosphere, 7, of Alaska, Fairbanks [D. L. Kane, and K. M. Hinkel (eds.)], pp. 1505 1510. 81 102. Romanovsky, V. E., S. L. Smith, and H. H. Christiansen, 2010a: Permafrost thermal Rachold, V., et al., 2007: Near-shore Arctic subsea permafrost in transition. EOS Trans. state in the polar Northern Hemisphere during the International Polar Year Am. Geophys. Union, 88, 149 156. 2007 2009: A Synthesis. Permafr. Periglac. Process., 21, 106 116. Radiæ, V., and R. Hock, 2010: Regional and global volumes of glaciers derived from Romanovsky, V. E., et al., 2010b: Thermal state of permafrost in Russia. Permafr. statistical upscaling of glacier inventory data. J. Geophys. Res. Earth Surf., 115, Periglac. Process., 21, 136 155. F01010. Rosenau, R., E. Schwalbe, H.-G. Maas, M. Baessler, and R. Dietrich, 2013: Grounding Radiæ, V., A. Bliss, A. C. Beedlow, R. Hock, E. Miles, and J. G. Cogley, 2013: Regional line migration and high resolution calving dynamics of Jakobshavn Isbrae, West and global projections of 21st century glacier mass changes in response to Greenland. J. Geophys. Res., 118, 382-395. climate scenarios from global climate models. Clim. Dyn., doi:10.1007/s00382- Ross, N., et al., 2012: Steep reverse bed slope at the grounding line of the Weddell 013-1719-7. Sea sector in West Antarctica. Nature Geosci, 5, 393 396. Ramillien, G., A. Lombard, A. Cazenave, E. R. Ivins, M. Llubes, F. Remy, and R. Rothrock, D. A., and M. Wensnahan, 2007: The accuracy of sea ice drafts measured Biancale, 2006: Interannual variations of the mass balance of the Antarctica and from US Navy submarines. J. Atmos. Ocean. Technol., 24, 1936 1949. Greenland ice sheets from GRACE. Global Planet. Change, 53, 198 208. Rothrock, D. A., Y. Yu, and G. A. Maykut, 1999: Thinning of the Arctic sea-ice cover. Ramirez, E., et al., 2001: Small glaciers disappearing in the tropical Andes: a case- Geophys. Res. Lett., 26, 3469 3472. study in Bolivia: Glaciar Chacaltaya (16 degrees S). J. Glaciol., 47, 187 194. Rothrock, D. A., D. B. Percival, and M. Wensnahan, 2008: The decline in arctic sea-ice Rampal, P., J. Weiss, and D. Marsan, 2009: Positive trend in the mean speed and thickness: Separating the spatial, annual, and interannual variability in a quarter deformation rate of Arctic sea ice, 1979 2007. J. Geophys. Res. Oceans, 114, century of submarine data. J. Geophys. Res. Oceans, 113, C05003. C05013. Rott, H., F. Muller, T. Nagler, and D. Floricioiu, 2011: The imbalance of glaciers after Rastner, P., T. Bolch, N. Mölg, H. Machguth, and F. Paul, 2012: The first complete disintegration of Larsen-B ice shelf, Antarctic Peninsula. Cryosphere, 5, 125 134. glacier inventory for entire Greenland. Cryosphere, 6, 1483 1495. Sanchez-Bayo, F., and K. Green, 2013: Australian snowpack disappearing under the Ravanel, L., F. Allignol, P. Deline, S. Gruber, and M. Ravello, 2010: Rock falls in the influence of global warming and solare activity. Arct., Antarct. Alp. Res., 45, Mont Blanc Massif in 2007 and 2008. Landslides, 7, 493 501. 107 118. Rayner, N. A., et al., 2003: Global analyses of SST, sea ice and night marine air Sannel, A. B. K., and P. Kuhry, 2011: Warming-induced destabilization of peat plateau/ temperature since the late nineteenth century. J. Geophys. Res., 108, 4407. thermokarst lake complexes. J. Geophys. Res. Biogeosci., 116, 16 (G03035). 4 Repo, M. E., et al., 2009: Large N2O emissions from cryoturbated peat soil in tundra. Sasgen, I., et al., 2012: Timing and origin of recent regional ice-mass loss in Nature Geosci., 2, 189 192. Greenland. Earth Planet. Sci. Lett., 333, 293 303. Ridley, J., J. M. Gregory, P. Huybrechts, and J. Lowe, 2010: Thresholds for irreversible Scambos, T. A., C. Hulbe, M. Fahnestock, and J. Bohlander, 2000: The link between decline of the Greenland ice sheet. Clim. Dyn., 35, 1065 1073. climate warming and break-up of ice shelves in the Antarctic Peninsula. J. Rignot, E., 2008: Changes in West Antarctic ice stream dynamics observed with ALOS Glaciol., 46, 516 530. PALSAR data. Geophys. Res. Lett., 35, L12505. Scambos, T. A., J. A. Bohlander, C. A. Shuman, and P. Skvarca, 2004: Glacier Rignot, E., and S. S. Jacobs, 2002: Rapid bottom melting widespread near Antarctic acceleration and thinning after ice shelf collapse in the Larsen B embayment, ice sheet grounding lines. Science, 296, 2020 2023. Antarctica. Geophys. Res. Lett., 31, 4. Rignot, E., and R. H. Thomas, 2002: Mass balance of polar ice sheets. Science, 297, Schaefer, K., T. J. Zhang, L. Bruhwiler, and A. P. Barrett, 2011: Amount and timing 1502 1506. of permafrost carbon release in response to climate warming. Tellus B, 63, Rignot, E., and P. Kanagaratnam, 2006: Changes in the velocity structure of the 165 180. Greenland ice sheet. Science, 311, 986 990. Scherler, D., B. Bookhagen, and M. R. Strecker, 2011: Spatially variable response of Rignot, E., and J. Mouginot, 2012: Ice flow in Greenland for the International Polar Himalayan glaciers to climate change affected by debris cover. Nature Geosci., Year 2008 2009. Geophys. Res. Lett., 39, L11501. 4, 156 159 Rignot, E., A. Rivera, and G. Casassa, 2003: Contribution of the Patagonia Icefields of Schiefer, E., B. Menounos, and R. Wheate, 2007: Recent volume loss of British South America to sea level rise. Science, 302, 434 437. Columbian glaciers, Canada. Geophys. Res. Lett., 34, 6 (L16503). Rignot, E., M. Koppes, and I. Velicogna, 2010: Rapid submarine melting of the calving Schneider, D. P., C. Deser, and Y. Okumura, 2012: An assessment and interpretation faces of West Greenland glaciers. Nature Geosci., 3, 187 191. of the observed warming of West Antarctica in the austral spring. Clim. Dyn., Rignot, E., J. Mouginot, and B. Scheuchl, 2011a: Ice flow of the Antarctic ice sheet. 38, 323 347. Science, 333, 1427 1430. Schoeneich, P., X. Bodin, J. Krysiecki, P. Deline, and L. Ravanel, 2010: Permafrost in Rignot, E., J. Mouginot, and B. Scheuchl, 2011b: Antarctic grounding line mapping France, 1st Report. Institut de Géographie Alpine, Université Joseph Fourier, from differential satellite radar interferometry. Geophys. Res. Lett., 38, L10504. Grenoble, France. 68 pp. Rignot, E., J. E. Box, E. Burgess, and E. Hanna, 2008a: Mass balance of the Greenland Schoof, C., 2007: Ice sheet grounding line dynamics: Steady states, stability, and ice sheet from 1958 to 2007. Geophys. Res. Lett., 35, L20502. hysteresis. J. Geophys. Res. Earth Surf., 112, F03S28. Rignot, E., I. Velicogna, M. R. van den Broeke, A. Monaghan, and J. Lenaerts, 2011c: Schoof, C., 2010: Ice-sheet acceleration driven by melt supply variability. Nature, Acceleration of the contribution of the Greenland and Antarctic ice sheets to sea 468, 803 806. level rise. Geophys. Res. Lett., 38, 5 (L05503). Schrama, E. J. O., and B. Wouters, 2011: Revisiting Greenland ice sheet mass loss Rignot, E., G. Casassa, P. Gogineni, W. Krabill, A. Rivera, and R. Thomas, 2004: observed by GRACE. J. Geophys. Res. Sol. Ea., 116, B02407. Accelerated ice discharge from the Antarctic Peninsula following the collapse of Schuur, E. A. G., J. G. Vogel, K. G. Crummer, H. Lee, J. O. Sickman, and T. E. Osterkamp, Larsen B ice shelf. Geophys. Res. Lett., 31, 4 (L18401). 2009: The effect of permafrost thaw on old carbon release and net carbon exchange from tundra. Nature, 459, 556 559. 376 Observations: Cryosphere Chapter 4 Schweiger, A., R. Lindsay, J. L. Zhang, M. Steele, H. Stern, and R. Kwok, 2011: Solomon, S. M., A. E. Taylor, and C. W. Stevens, 2008: Nearshore ground temperatures, Uncertainty in modeled Arctic sea ice volume. J. Geophys. Res. Oceans, 116, seasonal ice bonding and permafrost formation within the bottom-fast ice zone, C00D06. Mackenzie Delta, NWT. In: Proceedings of the 9th International Conference of Screen, J. A., 2011: Sudden increase in Antarctic sea ice: Fact or artifact? Geophys. Permafrost, 29 June 3 July 2008, Institute of Northern Engineering, University of Res. Lett., 38, L13702. Alaska, Fairbanks [D. L. Kane, and K. M. Hinkel (eds.)], pp. 1675 1680. Selmes, N., T. Murray, and T. D. James, 2011: Fast draining lakes on the Greenland Ice Sorensen, L. S., et al., 2011: Mass balance of the Greenland ice sheet (2003 2008) Sheet. Geophys. Res. Lett., 38, 5 (L15501). from ICESat data: The impact of interpolation, sampling and firn density. Shakhova, N., I. Semiletov, A. Salyuk, V. Yusupov, D. Kosmach, and O. Gustafsson, Cryosphere, 5, 173 186. 2010a: Extensive methane venting to the atmosphere from sediments of the Spreen, G., R. Kwok, and D. Menemenlis, 2011: Trends in Arctic sea ice drift and role East Siberian Arctic Shelf. Science, 327, 1246 1250. of wind forcing: 1992 2009. Geophys. Res. Lett., 38, 6 (L19501). Shakhova, N., I. Semiletov, I. Leifer, A. Salyuk, P. Rekant, and D. Kosmach, 2010b: Spreen, G., S. Kern, D. Stammer, and E. Hansen, 2009: Fram Strait sea ice volume Geochemical and geophysical evidence of methane release over the East export estimated between 2003 and 2008 from satellite data. Geophys. Res. Siberian Arctic Shelf. J. Geophys. Res. Oceans, 115, 14 (C08007). Lett., 36, L19502. Shannon, S., et al., 2012: Enhanced basal lubrication and the contribution of the Stammerjohn, S., R. Massom, D. Rind, and D. Martinson, 2012: Regions of rapid sea Greenland ice sheet to future sea level rise. Proc. Natl. Acad. Sci. U.S.A. 110 (35), ice change: An inter-hemispheric seasonal comparison. Geophys. Res. Lett., 39, 14156-14161. L06501. Sharkhuu, A., et al., 2007: Permafrost monitoring in the Hovsgol mountain region, Stammerjohn, S. E., D. G. Martinson, R. C. Smith, X. Yuan, and D. Rind, 2008: Trends Mongolia. J. Geophys. Res. Earth Surf., 112, 11 (F02S06). in Antarctic annual sea ice retreat and advance and their relation to El Nino- Shepherd, A., and D. Wingham, 2007: Recent sea-level contributions of the Antarctic Southern Oscillation and Southern Annular Mode variability. J. Geophys. Res. and Greenland ice sheets. Science, 315, 1529 1532. Oceans, 113, C03S90. Shepherd, A., D. Wingham, T. Payne, and P. Skvarca, 2003: Larsen ice shelf has Steig, E. J., Q. Ding, D. S. Battisti, and A. Jenkins, 2012: Tropical forcing of Circumpolar progressively thinned. Science, 302, 856 859. Deep Water Inflow and outlet glacier thinning in the Amundsen Sea Embayment, Shepherd, A., A. Hubbard, P. Nienow, M. King, M. McMillan, and I. Joughin, 2009: West Antarctica. Ann. Glaciol., 53, 19 28. Greenland ice sheet motion coupled with daily melting in late summer. Geophys. Steig, E. J., D. P. Schneider, S. D. Rutherford, M. E. Mann, J. C. Comiso, and D. T. Shindell, Res. Lett., 36, L01501. 2009: Warming of the Antarctic ice-sheet surface since the 1957 International Shepherd, A., D. Wingham, D. Wallis, K. Giles, S. Laxon, and A. V. Sundal, 2010: Recent Geophysical Year. Nature, 457, 459 462. loss of floating ice and the consequent sea level contribution. Geophys. Res. Stevens, C. W., B. J. Moorman, and S. M. Solomon, 2010: Modeling ground thermal Lett., 37, 5 (L13503). conditions and the limit of permafrost within the nearshore zone of the Shepherd, A., et al., 2012: A reconciled estimate of ice-sheet mass balance. Science, Mackenzie Delta, Canada. J. Geophys. Res. Earth Surf., 115, F04027. 338, 1183 1189. Straneo, F., R. G. Curry, D. A. Sutherland, G. S. Hamilton, C. Cenedese, K. Vage, and L. Shi, H. L., Y. Lu, Z. L. Du, L. L. Jia, Z. Z. Zhang, and C. X. Zhou, 2011: Mass change A. Stearns, 2011: Impact of fjord dynamics and glacial runoff on the circulation detection in Antarctic ice sheet using ICESat block analysis techniques from near Helheim Glacier. Nature Geosci., 4, 322 327. 2003 similar to 2008. Chin. J. Geophys. Chin. Ed., 54, 958 965. Straneo, F., et al., 2010: Rapid circulation of warm subtropical waters in a major Shiklomanov, N. I., et al., 2010: Decadal variations of active-layer thickness in glacial fjord in East Greenland. Nature Geosci., 3, 182 186. moisture-controlled landscapes, Barrow, Alaska. J. Geophys. Res. Biogeosci., Straneo, F., et al., 2012: Characteristics of ocean waters reaching Greenland s 115, G00I04. glaciers. Ann. Glaciol., 53, 202 210. Shuman, C. A., E. Berthier, and T. A. Scambos, 2011: 2001 2009 elevation and mass Streletskiy, D. A., N. I. Shiklomanov, F. E. Nelson, and A. E. Klene, 2008: 13 Years losses in the Larsen A and B embayments, Antarctic Peninsula. J. Glaciol., 57, of Observations at Alaskan CALM Sites: Long-term Active Layer and Ground 737 754. Surface Temperature Trends. In: Proceedings of the 9th International Conference 4 Siegfried, M. R., R. L. Hawley, and J. F. Burkhart, 2011: High-resolution ground-based on Permafrost, 29 June 3 July 2008, Institute of Northern Engineering, University GPS measurements show intercampaign bias in ICESat Elevation data near of Alaska, Fairbanks [D. L. Kane, and K. M. Hinkel (eds.)], pp. 1727 1732. Summit, Greenland. IEEE Trans. Geosci. Remote Sens., 49, 3393 3400. Stroeve, J., M. M. Holland, W. Meier, T. Scambos, and M. Serreze, 2007: Arctic sea ice Simmonds, I., C. Burke, and K. Keay, 2008: Arctic climate change as manifest in decline: Faster than forecast. Geophys. Res. Lett., 34, L09501. cyclone behavior. J. Clim., 21, 5777 5796. Sundal, A. V., A. Shepherd, P. Nienow, E. Hanna, S. Palmer, and P. Huybrechts, 2011: Simpson, M. J. R., G. A. Milne, P. Huybrechts, and A. J. Long, 2009: Calibrating a Melt-induced speed-up of Greenland ice sheet offset by efficient subglacial glaciological model of the Greenland ice sheet from the Last Glacial Maximum drainage. Nature, 469, 522 U583. to present-day using field observations of relative sea level and ice extent. Quat. Takala, M., J. Pulliainen, S. J. Metsamaki, and J. T. Koskinen, 2009: Detection of Sci. Rev., 28, 1631 1657. snowmelt using spaceborne microwave radiometer data in Eurasia from 1979 Skaugen, T., H. B. Stranden, and T. Saloranta, 2012: Trends in snow water equivalent to 2007. IEEE Trans. Geosci. Remote Sens., 47, 2996 3007. in Norway (1931 2009). Hydrol. Res., 43, 489 499. Tamura, T., and K. I. Ohshima, 2011: Mapping of sea ice production in the Arctic Slobbe, D. C., P. Ditmar, and R. C. Lindenbergh, 2009: Estimating the rates of mass coastal polynyas. J. Geophys. Res. Oceans, 116, 20 (C07030). change, ice volume change and snow volume change in Greenland from ICESat Tang, J. S., H. W. Cheng, and L. Liu, 2012: Using nonlinear programming to correct and GRACE data. Geophys. J. Int., 176, 95 106. leakage and estimate mass change from GRACE observation and its application Smith, D. M., 1998: Recent increase in the length of the melt season of perennial to Antarctica. J. Geophys. Res. Sol. Ea., 117, B11410. Arctic sea ice. Geophys. Res. Lett., 25, 655 658. Tarasov, L., and W. R. Peltier, 2002: Greenland glacial history and local geodynamic Smith, S. L., J. Throop, and A. G. Lewkowicz, 2012: Recent changes in climate and consequences. Geophys. J. Int., 150, 198 229. permafrost temperatures at forested and polar desert sites in northern Canada. Tarnocai, C., J. G. Canadell, E. A. G. Schuur, P. Kuhry, G. Mazhitova, and S. Zimov, 2009: Can. J. Earth Sci., 49, 914 924. Soil organic carbon pools in the northern circumpolar permafrost region. Global Smith, S. L., S. A. Wolfe, D. W. Riseborough, and F. M. Nixon, 2009: Active-layer Biogeochem. Cycles, 23, 11 (GB2023). characteristics and summer climatic indices, Mackenzie Valley, Northwest Tedesco, M., and A. J. Monaghan, 2009: An updated Antarctic melt record through Territories, Canada. Permafr. Periglac. Process., 20, 201 220. 2009 and its linkages to high-latitude and tropical climate variability. Geophys. Smith, S. L., et al., 2010: Thermal state of permafrost in North America: A contribution Res. Lett., 36, L18502. to the International Polar Year. Permafr. Periglac. Process., 21, 117 135. Tedesco, M., M. Brodzik, R. Armstrong, M. Savoie, and J. Ramage, 2009: Pan arctic Sole, A. J., D. W. F. Mair, P. W. Nienow, I. D. Bartholomew, M. A. King, M. J. Burke, terrestrial snowmelt trends (1979 2008) from spaceborne passive microwave and I. Joughin, 2011: Seasonal speedup of a Greenland marine-terminating data and correlation with the Arctic Oscillation. Geophys. Res. Lett., 36, L21402. outlet glacier forced by surface melt-induced changes in subglacial hydrology. J. Tedesco, M., X. Fettweis, T. Mote, J. Wahr, P. Alexander, J. E. Box, and B. Wouters, 2013: Geophys. Res. Earth Surf., 116, 11 (F03014) Evidence and analysis of 2012 Greenland records from spaceborne observations, a regional climate model and reanalysis data. Cryosphere, 7, 615 630. 377 Chapter 4 Observations: Cryosphere Tedesco, M., et al., 2011: The role of albedo and accumulation in the 2010 melting Vieira, G., et al., 2010: Thermal state of permafrost and active-layer monitoring in record in Greenland. Environ. Res. Lett., 6, 6 (014005). the Antarctic: Advances during the International Polar Year 2007 2009. Permafr. Tennant, C., B. Menounos, R. Wheate, and J. J. Clague, 2012: Area change of glaciers Periglac. Process., 21, 182 197. in the Canadian Rocky Mountains, 1919 to 2006. Cryosphere, 6, 1541 1552. Vigdorchik, M. E., 1980: Arctic Pleistocene History and the Development of Thibert, E., R. Blanc, C. Vincent, and N. Eckert, 2008: Glaciological and volumetric Submarine Permafrost. Westview Press, Boulder, CO, USA, 286 pp. mass-balance measurements: error analysis over 51 years for Glacier de Vincent, C., E. Le Meur, D. Six, P. Possenti, E. Lefebvre, and M. Funk, 2007: Climate Sarennes, French Alps. J. Glaciol., 54, 522 532. warming revealed by englacial temperatures at Col du Dome (4250 m, Mont Thomas, E. R., G. J. Marshall, and J. R. McConnell, 2008a: A doubling in snow Blanc area). Geophys. Res. Lett., 34. accumulation in the western Antarctic Peninsula since 1850. Geophys. Res. Lett., Vinje, T., 2001: Fram strait ice fluxes and atmospheric circulation: 1950 2000. J. 35, 5 (L01706) Clim., 14, 3508 3517. Thomas, I. D., et al., 2011a: Widespread low rates of Antarctic glacial isostatic Wadhams, P., 1990: Evidence for thinning of the Arctic ice cover north of Greenland. adjustment revealed by GPS observations. Geophys. Res. Lett., 38, L22302. Nature, 345, 795 797. Thomas, R., E. Frederick, W. Krabill, S. Manizade, and C. Martin, 2006: Progressive Wadhams, P., and J. C. Comiso, 1992: The ice thickness distribution inferred using increase in ice loss from Greenland. Geophys. Res. Lett., 33, 4 (L10503). remote sensing techniques. In: Microware Remote Sensing of Sea Ice [F. Carsey Thomas, R., E. Frederick, W. Krabill, S. Manizade, and C. Martin, 2009: Recent changes (ed.)]. American Geophysical Union, Washington, DC, pp. 375 383. on Greenland outlet glaciers. J. Glaciol., 55, 147 162. Wadhams, P., and N. R. Davis, 2000: Further evidence of ice thinning in the Arctic Thomas, R., C. Davis, E. Frederick, W. Krabill, Y. H. Li, S. Manizade, and C. Martin, Ocean. Geophys. Res. Lett., 27, 3973 3975. 2008b: A comparison of Greenland ice-sheet volume changes derived from Wadhams, P., N. Hughes, and J. Rodrigues, 2011: Arctic sea ice thickness altimetry measurements. J. Glaciol., 54, 203 212. characteristics in winter 2004 and 2007 from submarine sonar transects. J. Thomas, R., E. Frederick, J. Li, W. Krabill1, S. Manizade, J. Paden, J. Sonntag, R. Swift, Geophys. Res. Oceans, 116, C00E02. J. Yungel., 2011b: Accelerating ice loss from the fastest Greenland and Antarctic Wahr, J. M., 2007: Time-variable gravity from satellites. In: Treatise on Geophysics glaciers. Geophys. Res. Lett., 38, L10502. [T. A. Herring (ed.)]. Elsevier, Amsterdam, the Netherlands, and Philadelphia, PA, Thomas, R. H., and C. R. Bentley, 1978: A model for Holocene retreat of the West USA, pp. 213 237. Antarctic Ice Sheet. Quat. Res., 10, 150 170. Walsh, J. E., and W. L. Chapman, 2001: 20th-century sea-ice variations from Thompson, D. W. J., and J. M. Wallace, 1998: The Arctic Oscillation signature in the observational data. Ann. Glaciol., 33, 444 448. wintertime geopotential height and temperature fields. Geophys. Res. Lett., 25, Wang, J., X. Bai, H. Hu, A. Clites, M. Colton, and B. Lofgren, 2012: Temporal and 1297 1300. spatial variability of Great Lakes ice cover, 1973 2010. J. Clim., 25, 13181329. Thompson, D. W. J., and J. M. Wallace, 2000: Annular modes in the extratropical Weeks, W. F., and A. J. Gow, 1978: Preferred crystal orientations in fast ice along circulation. Part I: Month-to-month variability. J. Clim., 13, 1000 1016. margins of Arctic Ocean J. Geophys. Res. Oceans Atmos., 83, 5105 5121. Thomson, L. I., G. R. Osinski, and C. S. L. Ommanney, 2011: Glacier change on Axel Weertman, J., 1974: Stability of the junction of an ice sheet and an ice shelf. J. Heiberg Island, Nunavut, Canada. J. Glaciol., 57, 1079 1086 Glaciol., 13, 3 11. Throop, J., A. G. Lewkowicz, and S. L. Smith, 2012: Climate and ground temperature Wendt, J., A. Rivera, A. Wendt, F. Bown, R. Zamora, G. Casassa, and C. Bravo, 2010: relations at sites across the continuous and discontinuous permafrost zones, Recent ice-surface-elevation changes of Fleming Glacier in response to the northern Canada. Can. J. Earth Sci., 49, 865 876. removal of the Wordie Ice Shelf, Antarctic Peninsula. Ann. Glaciol., 51, 97 102. Turner, J., et al., 2005: Antarctic climate change during the last 50 years. Int. J. WGMS, 1989: World glacier inventory Status 1988. IAHS (ICSI)/UNEP/ Climatol., 25, 279 294. UNESCO,[Haeberli, W., H. Bösch, K. Scherler, G. Ostrem and C. C. Wallén (eds.)] Valt, M., and P. Cianfarra, 2010: Recent snow cover variability in the Italian Alps. World Glacier Monitoring Service, Zurich, Switzerland, 458 pp. Cold Reg. Sci. Technol., 64, 146 157. WGMS, 2008: Global Glacier Changes: Facts and Figures. [Zemp, M, I. Roer, A. Kääb, 4 van de Berg, W. J., M. R. van den Broeke, C. H. Reijmer, and E. Van Meijgaard, 2006: M. Hoelzle, F. Paul, W. G. Haeberli (eds.)] UNEP and World Glacier Monitoring Reassessment of the Antarctic surface mass balance using calibrated output of Service, Zurich, Switzerland, 88 pp. a regional atmospheric climate model. J. Geophys. Res. Atmos., 111, D11104. WGMS, 2009: Glacier Mass Balance Bulletin No. 10 (2006 2007). ICSU (WDS)/IUGG van de Wal, R. S. W., W. Boot, M. R. van den Broeke, C. Smeets, C. H. Reijmer, J. J. A. (IACS) /UNEP/UNESCO/WMO. [Haeberli, W., I. Gärtner-Roer, M. Hoelzle, F. Paul, Donker, and J. Oerlemans, 2008: Large and rapid melt-induced velocity changes M.l Zemp (eds.)] World Glacier Monitoring Service, Zurich, Switzerland. 96 pp.  in the ablation zone of the Greenland Ice Sheet. Science, 321, 111 113. White, D., et al., 2007: The arctic freshwater system: Changes and impacts. J. van den Broeke, M., W. J. van de Berg, and E. Van Meijgaard, 2006: Snowfall in Geophys. Res. Biogeosci., 112, G04S54. coastal West Antarctica much greater than previously assumed. Geophys. Res. White, W. B., and R. G. Peterson, 1996: An Antarctic circumpolar wave in surface Lett., 33, L02505. pressure, wind, temperature and sea-ice extent. Nature, 380, 699 702. van den Broeke, M., et al., 2009: Partitioning recent Greenland mass loss. Science, Whitehouse, P. L., M. J. Bentley, G. A. Milne, M. A. King, and I. D. Thomas, 2012: A new 326, 984 986. glacial isostatic adjustment model for Antarctica: Calibrated and tested using Van Everdingen, R. (ed.), 1998: Multi-language Glossary of Permafrost and Related observations of relative sea-level change and present-day uplift rates. Geophys. Ground-Ice Terms. National Snow and Ice Data Center /World Data Center for J. Int., 190, 1464 1482. Glaciology. Willis, M. J., A. K. Melkonian, M. E. Pritchard, and A. Rivera, 2012: Ice loss from the Van Ommen, T. D., and V. Morgan, 2010: Snowfall increase in coastal East Antarctica Southern Patagonian Icefield. Geophys. Res. Lett., 39, L17501. linked with southwest Western Australian drought. Nature Geosci., 3, 267 272. Wingham, D. J., D. W. Wallis, and A. Shepherd, 2009: Spatial and temporal evolution Vasiliev, A. A., M. O. Leibman, and N. G. Moskalenko, 2008: Active layer monitoring of Pine Island Glacier thinning, 1995 2006. Geophys. Res. Lett., 36, L17501. in West Siberia under the CALM II Program. In: Proceedings of the 9th Wingham, D. J., A. Shepherd, A. Muir, and G. J. Marshall, 2006: Mass balance of the International Conference on Permafrost, 29 June 3 July 2008, Institute of Antarctic ice sheet. Philos. Trans. R. Soc. A, 364, 1627 1635. Northern Engineering, University of Alaska, Fairbanks, [D. L. Kane, and K. M. Wingham, D. J., A. J. Ridout, R. Scharroo, R. J. Arthern, and C. K. Shum, 1998: Antarctic Hinkel (eds.)], pp. 1815 1821. elevation change from 1992 to 1996. Science, 282, 456 458. Vaughan, D. G., et al., 2003: Recent rapid regional climate warming on the Antarctic Worby, A. P., C. A. Geiger, M. J. Paget, M. L. Van Woert, S. F. Ackley, and T. L. DeLiberty, Peninsula. Clim. Change, 60, 243 274. 2008: Thickness distribution of Antarctic sea ice. J. Geophys. Res. Oceans, 113, Velicogna, I., 2009: Increasing rates of ice mass loss from the Greenland and C05S92. Antarctic ice sheets revealed by GRACE. Geophys. Res. Lett., 36, L19503. Wouters, B., D. Chambers, and E. J. O. Schrama, 2008: GRACE observes small-scale Velicogna, I., and J. Wahr, 2006a: Acceleration of Greenland ice mass loss in spring mass loss in Greenland. Geophys. Res. Lett., 35, L20501. 2004. Nature, 443, 329 331. Wu, Q., T. Zhang, and Y. Liu, 2012: Thermal state of the active layer and permafrost Velicogna, I., and J. Wahr, 2006b: Measurements of time-variable gravity show mass along the Qinghai-Xizang (Tibet) Railway from 2006 to 2010. Cryosphere, 6, loss in Antarctica. Science, 311, 1754 1756. 607 612. 378 Observations: Cryosphere Chapter 4 Wu, Q. B., and T. J. Zhang, 2008: Recent permafrost warming on the Qinghai-Tibetan Zwally, H. J., et al., 2005: Mass changes of the Greenland and Antarctic ice sheets and plateau. J. Geophys. Res. Atmos., 113, D13108. shelves and contributions to sea-level rise: 1992 2002. J. Glaciol., 51, 509 527. Wu, Q. B., and T. J. Zhang, 2010: Changes in active layer thickness over the Qinghai- Zwally, H. J., et al., 2011: Greenland ice sheet mass balance: Distribution of increased Tibetan Plateau from 1995 to 2007. J. Geophys. Res. Atmos., 115, D09107. mass loss with climate warming; 2003 07 versus 1992 2002. J. Glaciol., 57, Wu, X. P., et al., 2010: Simultaneous estimation of global present-day water transport 88 102. and glacial isostatic adjustment. Nature Geosci., 3, 642 646. Xie, H., et al., 2011: Sea-ice thickness distribution of the Bellingshausen Sea from surface measurements and ICESat altimetry. Deep-Sea Res. Pt. Ii, 58, 1039 1051. Xu, Y., E. Rignot, D. Menemenlis, and M. Koppes, 2012: Numerical experiments on subaqueous melting of Greenland tidewater glaciers in response to ocean warming and enhanced subglacial discharge. Ann. Glaciol., 53, 229 234. Yao, T., et al., 2012: Different glacier status with atmospheric circulations in Tibetan Plateau and surroundings. Nature Clim. Change, 2, 663 667 Yde, J. C., and N. T. Knudsen, 2007: 20th-century glacier fluctuations on Disko Island (Qeqertarsuaq), Greenland. Ann. Glaciol., 46, 209 214. Yde, Y. C., and O. Pasche, 2010: Reconstructing climate change: Not all glaciers suitable. EOS, 91, 189 190. Young, D. A., et al., 2011a: A dynamic early East Antarctic Ice Sheet suggested by ice-covered fjord landscapes. Nature, 474, 72 75. Young, N. E., J. P. Briner, Y. Axford, B. Csatho, G. S. Babonis, D. H. Rood, and R. C. Finkel, 2011b: Response of a marine-terminating Greenland outlet glacier to abrupt cooling 8200 and 9300 years ago. Geophys. Res. Lett., 38, L24701. Zamolodchikov, D., 2008: Recent climate and active layer changes in northeast Russia: Regional output of Circumpolar Active Layer Monitoring (CALM). In: Proceedings of the 9th International Conference on Permafrost, 29 June 3 July 2008, Institute of Northern Engineering, University of Alaska, Fairbanks [D. L. Kane, and K. M. Hinkel (eds.)], pp. 2021 2027. Zdanowicz, C., A. Smetny-Sowa, D. Fisher, N. Schaffer, L. Copland, J. Eley, and F. Dupont, 2012: Summer melt rates on Penny Ice Cap, Baffin Island: Past and recent trends and implications for regional climate. J. Geophys. Res., 117, F02006. Zeeberg, J., and S. L. Forman, 2001: Changes in glacier extent on north Novaya Zemlya in the twentieth century. Holocene, 11, 161 175. Zemp, M., H. J. Zumbhul, S. U. Nussbaumer, M. H. Masiokas, L. E. Espizua, and P. Pitte, 2011: Extending glacier monitoring into the Little Ice Age and beyond. PAGES News, 19, 67 69. Zhang, T., R. G. Barry, K. Knowles, J. A. Heginbottom, and J. Brown, 1999: Statistics and characteristics of permafrost and ground ice distribution in the Northern Hemisphere. Polar Geogr., 23, 147 169. 4 Zhang, T., R. G. Barry, K. Knowles, F. Ling, and R. L. Armstrong, 2003: Distribution of seasonally and perennially frozen ground in the Northern Hemisphere. In: Proceedings of the 8th International Conference on Permafrost, 21 25 July 2003, Zurich, Switerland [Phillips, M., S.M. Springman, and L.U. Arenson (eds)]. A. A. Balkema, Lisse, the Netherlands, pp. 1289 1294. Zhang, T. J., 2005: Influence of the seasonal snow cover on the ground thermal regime: An overview. Rev. Geophys., 43, RG4002 Zhang, T. J., et al., 2005: Spatial and temporal variability in active layer thickness over the Russian Arctic drainage basin. J. Geophys. Res. Atmos., 110, D16101. Zhao, L., Q. B. Wu, S. S. Marchenko, and N. Sharkhuu, 2010: Thermal state of permafrost and active layer in central Asia during the International Polar Year. Permafr. Periglac. Process., 21, 198 207. Zhou, Y., D. Guo, G. Qiu, G. Cheng, and S. Li, 2000: Geocryology in China. Science Press, Beijing, China, 450 pp. Zimov, S. A., E. A. G. Schuur, and F. S. Chapin, 2006: Permafrost and the global carbon budget. Science, 312, 1612 1613. Zuo, Z., and J. Oerlemans, 1997: Contribution of glacier melt to sea-level rise since AD 1865: A regionally differentiated calculation. Clim. Dyn., 13, 835 845. Zwally, H. J., and P. Gloersen, 2008: Arctic sea ice surviving the summer melt: interannual variability and decreasing trend. J. Glaciol., 54, 279 296. Zwally, H. J., and M. B. Giovinetto, 2011: Overview and assessment of Antarctic Ice- Sheet mass balance estimates: 1992 2009. Surv. Geophys., 32, 351 376. Zwally, H. J., D. H. Yi, R. Kwok, and Y. H. Zhao, 2008: ICESat measurements of sea ice freeboard and estimates of sea ice thickness in the Weddell Sea. J. Geophys. Res. Oceans, 113, C02S15. Zwally, H. J., J. C. Comiso, C. L. Parkinson, D. J. Cavalieri, and P. Gloersen, 2002a: Variability of Antarctic sea ice 1979 1998. J. Geophys. Res., 107, 1029 1047. Zwally, H. J., W. Abdalati, T. Herring, K. Larson, J. Saba, and K. Steffen, 2002b: Surface melt-induced acceleration of Greenland ice-sheet flow. Science, 297, 218 222. 379 Chapter 4 Observations: Cryosphere Appendix 4.A: Details of Available and Selected Ice Sheet Mass Balance Estimates from 1992 to 2012 All comprehensive mass balance estimates available for Greenland, and the subset of those selected for this assessment (Section 4.4.2) are listed in Tables 4.A.1 and 4.A.2. Those available for Antarctica are shown in Tables 4.A.3 and 4.A.4. These studies include estimates made from satellite gravimetry (GRACE), satellite altimetry (radar and laser) and the mass balance (flux) method. The studies selected for this assessment are the latest made by different research groups, for each of Greenland and Antarctica. The tables indicate whether smaller glaciers peripheral to the ice sheet are included, or excluded, in the estimate, and explain why some studies were not selected (e.g., ear- lier estimates from the same researchers have been updated by more recent analyses using extended data). Table 4.A.1 | Sources used for calculation of ice loss from Greenland. Peripheral Source Method Start End Gt yr 1 Uncertainty Comment Glaciers Ewert et al. (2012) Laser alt. 2003.8 2008.2 185 28 Excluded GRACE 2002.7 2009.5 191 21 Included Harig and Simons (2012) GRACE 2003.0 2011.0 200 6 Included Yearly estimates used in compilation. GIA uncertainty not provided. Sasgen et al. (2012) GRACE 2002.7 2011.7 240 18 Included Yearly estimates used in compilation. Flux 2002.7 2011.7 244 53 Excluded Yearly estimates used in compilation. Laser alt. 2003.8 2009.8 245 28 Included Chen et al. (2011) GRACE 2002.3 2005.3 144 25 Included 4 GRACE 2005.3 2009.9 248 43 Included Rignot et al. (2011c) Flux 1992.0 2010.0 154 51 Excluded Yearly estimates used in compilation. Schrama and Wouters (2011) GRACE 2003.2 2010.1 201 19 Included 2 standard deviation (2) uncertainty Sorensen et al. (2011) Laser alt. 2003.8 2008.2 221 28 Included Zwally et al. (2011) Radar alt. 1992.3 2002.8 7 3 Excluded Laser alt. 2003.8 2007.8 171 4 Excluded Pritchard et al. (2010) GRACE 2003.6 2009.6 195 30 Included Wu et al. (2010) GRACE 2002.4 2009.0 104 23 Included Global inversion technique. +GPS Baur et al. (2009) GRACE 2002.6 2008.6 159 11 Included No GIA correction. Cazenave et al. (2009) GRACE 2003.0 2008.0 136 18 Included Slobbe et al. (2009) GRACE 2002.5 2007.5 178 78 Included Laser alt. 2003.1 2007.3 139 68 Included Velicogna (2009) GRACE 2002.3 2009.1 269 33 Included Time series extended to 2012 using new data and published method. Yearly estimates derived from cited trend. 380 Observations: Cryosphere Chapter 4 Table 4.A.2 | Sources NOT used for calculation of ice loss from Greenland. Peripheral Source Method Start End Gt yr 1 Uncertainty Comment Glaciers Shepherd et al. (2012) Flux 1992.0 2009.9 154 51 Excluded This comprehensive inter-comparison rec- onciles estimates from different techniques. The reconciled value is the best estimate from all techniques. This source is discussed separately and not included within the average assessment presented here. GRACE 2002.2 2012.0 212 27 Included Laser alt. 2004.5 2007.4 198 23 Excluded Reconciled 1992.0 2011.0 142 49 - van den Broeke (2009) Flux 2003.0 2009.0 237 20 Excluded Superseded by Rignot et al. (2011c). Rignot et al. (2008a) Flux 1996.0 1997.0 97 47 Excluded Superseded by Rignot et al. (2011c). Flux 2000.0 2001.0 156 44 Excluded Flux 2004.0 2008.0 264 39 Excluded Wouters et al. (2008) GRACE 2003.2 2008.1 179 25 Included Superseded by Schrama and Wouters (2011). Chen et al. (2006) GRACE 2002.3 2005.9 219 21 Included Superseded by Chen et al. (2011). Luthcke et al. (2006) GRACE 2003.5 2005.5 101 16 Included Superseded by Pritchard et al. (2010). Ramillien et al. (2006) GRACE 2002.5 2005.2 129 15 Included Superseded by Cazenave et al. (2009). Rignot and Kanagaratnam (2006) Flux 1996.0 1997.0 83 28 Excluded Superseded by Rignot et al. (2011c). Flux 2000.0 2001.0 127 28 Excluded Flux 2005.0 2006.0 205 38 Excluded Thomas et al. (2006) Radar alt. 1994.0 1999.0 27 23 Excluded Includes only half the ice sheet and fills in the rest with a melt model. Radar alt. 1999.0 2005.0 81 24 Excluded Velicogna and Wahr (2006a) GRACE 2002.3 2004.3 95 49 Included Superseded by Velicogna (2009). GRACE 2004.3 2006.3 313 60 Included Zwally et al. (2005) Radar alt. 1992.3 2002.8 11 3 Not known Superseded by Zwally et al. (2011). Krabill et al. (2000) Laser alt. (aircraft) 1993.5 1999.5 47 Excluded Includes only half the ice sheet and fills in the rest with a melt model. 4 Table 4.A.3 | Sources used for calculation of ice loss from Antarctica. Peripheral Source Method Start End Gt yr 1 Uncertainty Comment Glaciers King et al. (2012) GRACE 2002.6 2011.0 69 18 Included 2 standard deviation (2) uncertainty. This study treats systematic uncertainty as bounds not random error as in other GRACE studies. Tang et al. (2012) GRACE 2006.0 2011.4 211 75 Included Rignot et al. (2011c) Flux 1992.0 2010.0 83 91 Excluded Yearly estimates used in compilation. Shi et al. (2011) Laser alt. 2003.1 2008.2 78 5 Not known Methodology and error budget incompletely described. Wu et al. (2010) GRACE 2002.4 2009.0 87 43 Included Global inversion technique. +GPS Cazenave et al. (2009) GRACE 2003.0 2008.0 198 22 Included Chen et al. (2009) GRACE 2002.3 2006.0 144 58 Included GRACE 2006.0 2009.1 220 89 Included E et al. (2009) GRACE 2002.5 2007.7 78 37 Included Error budget incompletely explained. Horwath and Dietrich (2009) GRACE 2002.6 2008.1 109 48 Included Velicogna (2009) GRACE 2002.3 2013.0 184 73 Included Time series extended to 2012 using new data and published method. Yearly estimates derived from cited trend. 381 Chapter 4 Observations: Cryosphere Table 4.A.4 | Sources NOT used for calculation of ice loss from Antarctica. Peripheral Source Method Start End Gt yr 1 Uncertainty Comment Glaciers Shepherd et al. (2012) Flux 1992.0 2010.0 110 89 Excluded This comprehensive inter-comparison reconciles estimates from different techniques. Estimates are made separately for East Antarctica, West Antarctica and the Antarctic Peninsula. The reconciled value is the best estimate from all techniques. The results from this study are discussed separately and not included within the average assessment presented here. GRACE 2003.0 2011.0 90 44 Included Laser alt. 2003.8 2008.7 +21 76 Excluded Reconciled 1992.0 2011.0 -71 53 - Jia et al. (2011) GRACE 2002.6 2010.0 82 29 Included No consideration of gravity signal leakage. Zwally and Giovinetto (2011) Radar alt. 1992.3 2001.3 31 12 Excluded Same data analysis as Zwally et al. (2005). Excludes Antarctic Peninsula. Gunter et al. (2009) Laser alt. 2003.1 2007.1 100 ? Not known No error bar and no final estimate. Moore and King (2008) GRACE 2002.3 2006.0 150 73 Included Superseded by King et al. (2012) Rignot et al. (2008b) Flux 1996.0 1997.0 112 91 Excluded Superseded by Rignot et al. (2011c). Flux 2006.0 2007.0 196 92 Excluded Ramillien et al. (2006) GRACE 2002.5 2005.2 40 36 Included Superseded by Cazenave et al. (2009). Velicogna and Wahr (2006b) GRACE 2002.3 2005.8 139 73 Included Superseded by Velicogna (2009). Zwally et al. (2005) Radar alt. 1992.3 2001.3 31 52 Excluded Antarctic Peninsula excluded. Wingham et al. (2006) Radar alt. 1992.8 2003.1 27 29 Not known No data in Antarctic Peninsula; series truncated within 100 km of coast. Rignot and Thomas (2002) Flux Not specific Not 26 37 Excluded Not an ice-sheet wide estimate. specific Wingham et al. (1998) Radar alt. 1992.3 1997.0 60 76 Not known Superseded by Wingham et al. (2006). 4 382 Information from Paleoclimate Archives 5 Coordinating Lead Authors: Valérie Masson-Delmotte (France), Michael Schulz (Germany) Lead Authors: Ayako Abe-Ouchi (Japan), Jürg Beer (Switzerland), Andrey Ganopolski (Germany), Jesus Fidel González Rouco (Spain), Eystein Jansen (Norway), Kurt Lambeck (Australia), Jürg Luterbacher (Germany), Tim Naish (New Zealand), Timothy Osborn (UK), Bette Otto-Bliesner (USA), Terrence Quinn (USA), Rengaswamy Ramesh (India), Maisa Rojas (Chile), XueMei Shao (China), Axel Timmermann (USA) Contributing Authors: Kevin Anchukaitis (USA), Julie Arblaster (Australia), Patrick J. Bartlein (USA), Gerardo Benito (Spain), Peter Clark (USA), Josefino C. Comiso (USA), Thomas Crowley (UK), Patrick De Deckker (Australia), Anne de Vernal (Canada), Barbara Delmonte (Italy), Pedro DiNezio (USA), Trond Dokken (Norway), Harry J. Dowsett (USA), R. Lawrence Edwards (USA), Hubertus Fischer (Switzerland), Dominik Fleitmann (UK), Gavin Foster (UK), Claus Fröhlich (Switzerland), Aline Govin (Germany), Alex Hall (USA), Julia Hargreaves (Japan), Alan Haywood (UK), Chris Hollis (New Zealand), Ben Horton (USA), Masa Kageyama (France), Reto Knutti (Switzerland), Robert Kopp (USA), Gerhard Krinner (France), Amaelle Landais (France), Camille Li (Norway/Canada), Dan Lunt (UK), Natalie Mahowald (USA), Shayne McGregor (Australia), Gerald Meehl (USA), Jerry X. Mitrovica (USA/Canada), Anders Moberg (Sweden), Manfred Mudelsee (Germany), Daniel R. Muhs (USA), Stefan Mulitza (Germany), Stefanie Müller (Germany), James Overland (USA), Frédéric Parrenin (France), Paul Pearson (UK), Alan Robock (USA), Eelco Rohling (Australia), Ulrich Salzmann (UK), Joel Savarino (France), Jan Sedláèek (Switzerland), Jeremy Shakun (USA), Drew Shindell (USA), Jason Smerdon (USA), Olga Solomina (Russian Federation), Pavel Tarasov (Germany), Bo Vinther (Denmark), Claire Waelbroeck (France), Dieter Wolf- Gladrow (Germany), Yusuke Yokoyama (Japan), Masakazu Yoshimori (Japan), James Zachos (USA), Dan Zwartz (New Zealand) Review Editors: Anil K. Gupta (India), Fatemeh Rahimzadeh (Iran), Dominique Raynaud (France), Heinz Wanner (Switzerland) This chapter should be cited as: Masson-Delmotte, V., M. Schulz, A. Abe-Ouchi, J. Beer, A. Ganopolski, J.F. González Rouco, E. Jansen, K. Lambeck, J. Luterbacher, T. Naish, T. Osborn, B. Otto-Bliesner, T. Quinn, R. Ramesh, M. Rojas, X. Shao and A. Timmermann, 2013: Information from Paleoclimate Archives. In: Climate Change 2013: The Physical Science Basis. Contribution of Working Group I to the Fifth Assessment Report of the Intergovernmental Panel on Climate Change [Stocker, T.F., D. Qin, G.-K. Plattner, M. Tignor, S.K. Allen, J. Boschung, A. Nauels, Y. Xia, V. Bex and P.M. Midgley (eds.)]. Cambridge University Press, Cambridge, United Kingdom and New York, NY, USA. 383 Table of Contents Executive Summary...................................................................... 385 5.8 Paleoclimate Perspective on Irreversibility in the Climate System.......................................................... 433 5.1 Introduction....................................................................... 388 5.8.1 Ice Sheets................................................................... 433 5.8.2 Ocean Circulation...................................................... 433 5.2 Pre-Industrial Perspective on Radiative Forcing Factors.................................................................. 388 5.8.3 Next Glacial Inception................................................ 435 5.2.1 External Forcings........................................................ 388 5.9 Concluding Remarks........................................................ 435 5.2.2 Radiative Perturbations from Greenhouse Gases and Dust.................................................................... 391 References .................................................................................. 436 Box 5.1: Polar Amplification...................................................... 396 Appendix 5.A: Additional Information on Paleoclimate 5.3 Earth System Responses and Feedbacks at Archives and Models................................................................... 456 Global and Hemispheric Scales.................................... 398 5.3.1 High-Carbon Dioxide Worlds and Temperature.......... 398 Frequently Asked Questions 5.3.2 Glacial Interglacial Dynamics.................................... 399 FAQ 5.1 Is the Sun a Major Driver of Recent Changes in Climate?............................................................... 392 Box 5.2: Climate-Ice Sheet Interactions.................................. 402 FAQ 5.2 How Unusual is the Current Sea Level Rate 5.3.3 Last Glacial Maximum and Equilibrium of Change?............................................................... 430 Climate Sensitivity..................................................... 403 5.3.4 Past Interglacials........................................................ 407 5.3.5 Temperature Variations During the Last 2000 Years.......................................................... 409 5.4 Modes of Climate Variability........................................ 415 5.4.1 Tropical Modes........................................................... 415 5.4.2 Extratropical Modes................................................... 415 5.5 Regional Changes During the Holocene................... 417 5.5.1 Temperature............................................................... 417 5.5.2 Sea Ice....................................................................... 420 5.5.3 Glaciers...................................................................... 421 5 5.5.4 Monsoon Systems and Convergence Zones............... 421 5.5.5 Megadroughts and Floods......................................... 422 5.6 Past Changes in Sea Level............................................. 425 5.6.1 Mid-Pliocene Warm Period......................................... 425 5.6.2 The Last Interglacial................................................... 425 5.6.3 Last Glacial Termination and Holocene...................... 428 5.7 Evidence and Processes of Abrupt Climate Change................................................................ 432 384 Information from Paleoclimate Archives Chapter 5 Executive Summary CO2 concentrations between 350 ppm and 450 ppm (medium confi- dence) occurred when global mean surface temperatures were 1.9°C Greenhouse-Gas Variations and Past Climate Responses to 3.6°C (medium confidence) higher than for pre-industrial climate {5.3.1}. During the Early Eocene (52 to 48 million years ago), atmos- It is a fact that present-day (2011) concentrations of the atmos- pheric CO2 concentrations exceeded ~1000 ppm (medium confidence) pheric greenhouse gases (GHGs) carbon dioxide (CO2), methane when global mean surface temperatures were 9°C to 14°C (medium (CH4) and nitrous oxide (N2O) exceed the range of concentra- confidence) higher than for pre-industrial conditions. {5.3.1} tions recorded in ice cores during the past 800,000 years. Past changes in atmospheric GHG concentrations can be determined with New temperature reconstructions and simulations of past very high confidence1 from polar ice cores. Since AR4 these records climates show with high confidence polar amplification in have been extended from 650,000 years to 800,000 years ago. {5.2.2} response to changes in atmospheric CO2 concentration. For high CO2 climates such as the Early Eocene (52 to 48 million years ago) or With very high confidence, the current rates of CO2, CH4 and N2O mid-Pliocene (3.3 to 3.0 million years ago), and low CO2 climates such rise in atmospheric concentrations and the associated radiative as the Last Glacial Maximum (21,000 to 19,000 years ago), sea sur- forcing are unprecedented with respect to the highest resolu- face and land surface air temperature reconstructions and simulations tion ice core records of the last 22,000 years. There is medium show a stronger response to changes in atmospheric GHG concentra- confidence that the rate of change of the observed GHG rise is also tions at high latitudes as compared to the global average. {Box 5.1, unprecedented compared with the lower resolution records of the past 5.3.1, 5.3.3} 800,000 years. {5.2.2} Global Sea Level Changes During Past Warm Periods There is high confidence that changes in atmospheric CO2 con- centration play an important role in glacial interglacial cycles. The current rate of global mean sea level change, starting in the Although the primary driver of glacial interglacial cycles lies in the late 19th-early 20th century, is, with medium confidence, unu- seasonal and latitudinal distribution of incoming solar energy driven by sually high in the context of centennial-scale variations of the changes in the geometry of the Earth s orbit around the Sun ( orbital last two millennia. The magnitude of centennial-scale global mean forcing ), reconstructions and simulations together show that the full sea level variations did not exceed 25 cm over the past few millennia magnitude of glacial interglacial temperature and ice volume changes (medium confidence). {5.6.3} cannot be explained without accounting for changes in atmospher- ic CO2 content and the associated climate feedbacks. During the last There is very high confidence that the maximum global mean deglaciation, it is very likely2 that global mean temperature increased sea level during the last interglacial period (129,000 to 116,000 by 3°C to 8°C. While the mean rate of global warming was very likely years ago) was, for several thousand years, at least 5 m higher 0.3°C to 0.8°C per thousand years, two periods were marked by faster than present and high confidence that it did not exceed 10 m warming rates, likely between 1°C and 1.5°C per thousand years, above present. The best estimate is 6 m higher than present. Based although regionally and on shorter time scales higher rates may have on ice sheet model simulations consistent with elevation changes occurred. {5.3.2} derived from a new Greenland ice core, the Greenland ice sheet very likely contributed between 1.4 and 4.3 m sea level equivalent, implying New estimates of the equilibrium climate sensitivity based on with medium confidence a contribution from the Antarctic ice sheet reconstructions and simulations of the Last Glacial Maximum to the global mean sea level during the last interglacial period. {5.6.2} (21,000 years to 19,000 years ago) show that values below 1°C as well as above 6°C for a doubling of atmospheric CO2 concen- There is high confidence that global mean sea level was above 5 tration are very unlikely. In some models climate sensitivity differs present during some warm intervals of the mid-Pliocene (3.3 between warm and cold climates because of differences in the rep- to 3.0 million years ago), implying reduced volume of polar ice resentation of cloud feedbacks. {5.3.3} sheets. The best estimates from various methods imply with high con- fidence that sea level has not exceeded +20 m during the warmest With medium confidence, global mean surface temperature periods of the Pliocene, due to deglaciation of the Greenland and West was significantly above pre-industrial levels during several past Antarctic ice sheets and areas of the East Antarctic ice sheet. {5.6.1} periods characterised by high atmospheric CO2 concentrations. During the mid-Pliocene (3.3 to 3.0 million years ago), atmospheric In this Report, the following summary terms are used to describe the available evidence: limited, medium, or robust; and for the degree of agreement: low, medium, or high. 1 A level of confidence is expressed using five qualifiers: very low, low, medium, high, and very high, and typeset in italics, e.g., medium confidence. For a given evidence and agreement statement, different confidence levels can be assigned, but increasing levels of evidence and degrees of agreement are correlated with increasing confidence (see Section 1.4 and Box TS.1 for more details). In this Report, the following terms have been used to indicate the assessed likelihood of an outcome or a result: Virtually certain 99 100% probability, Very likely 90 100%, 2 Likely 66 100%, About as likely as not 33 66%, Unlikely 0 33%, Very unlikely 0 10%, Exceptionally unlikely 0 1%. Additional terms (Extremely likely: 95 100%, More likely than not >50 100%, and Extremely unlikely 0 5%) may also be used when appropriate. Assessed likelihood is typeset in italics, e.g., very likely (see Section 1.4 and Box TS.1 for more details). 385 Chapter 5 Information from Paleoclimate Archives Observed Recent Climate Change in the Context of late 20th century. With high confidence, these regional warm peri- Interglacial Climate Variability ods were not as synchronous across regions as the warming since the mid-20th century. Based on the comparison between reconstructions New temperature reconstructions and simulations of the warm- and simulations, there is high confidence that not only external orbit- est millennia of the last interglacial period (129,000 to 116,000 al, solar and volcanic forcing, but also internal variability, contributed years ago) show with medium confidence that global mean substantially to the spatial pattern and timing of surface temperature annual surface temperatures were never more than 2°C higher changes between the Medieval Climate Anomaly and the Little Ice Age than pre-industrial. High latitude surface temperature, averaged over (1450 to 1850). {5.3.5.3, 5.5.1} several thousand years, was at least 2°C warmer than present (high confidence). Greater warming at high latitudes, seasonally and annu- There is high confidence for droughts during the last millennium ally, confirm the importance of cryosphere feedbacks to the seasonal of greater magnitude and longer duration than those observed orbital forcing. During these periods, atmospheric GHG concentrations since the beginning of the 20th century in many regions. There were close to the pre-industrial level. {5.3.4, Box 5.1} is medium confidence that more megadroughts occurred in monsoon Asia and wetter conditions prevailed in arid Central Asia and the South There is high confidence that annual mean surface warming American monsoon region during the Little Ice Age (1450 to 1850) since the 20th century has reversed long-term cooling trends compared to the Medieval Climate Anomaly (950 to 1250). {5.5.4 and of the past 5000 years in mid-to-high latitudes of the Northern 5.5.5} Hemisphere (NH). New continental- and hemispheric-scale annual surface temperature reconstructions reveal multi-millennial cooling With high confidence, floods larger than those recorded since trends throughout the past 5000 years. The last mid-to-high latitude 1900 occurred during the past five centuries in northern and cooling trend persisted until the 19th century, and can be attributed central Europe, western Mediterranean region and eastern Asia. with high confidence to orbital forcing, according to climate model There is medium confidence that modern large floods are comparable simulations. {5.5.1} to or surpass historical floods in magnitude and/or frequency in the Near East, India and central North America. {5.5.5} There is medium confidence from reconstructions that the cur- rent (1980 2012) summer sea ice retreat was unprecedented Past Changes in Climate Modes and sea surface temperatures in the Arctic were anomalously high in the perspective of at least the last 1450 years. Lower than New results from high-resolution coral records document with late 20th century summer Arctic sea ice cover is reconstructed and sim- high confidence that the El Nino-Southern Oscillation (ENSO) ulated for the period between 8000 and 6500 years ago in response to system has remained highly variable throughout the past 7000 orbital forcing. {5.5.2} years, showing no discernible evidence for an orbital modula- tion of ENSO. This is consistent with the weak reduction in mid-Hol- There is high confidence that minima in NH extratropical glacier ocene ENSO amplitude of only 10% simulated by the majority of cli- extent between 8000 and 6000 years ago were primarily due mate models, but contrasts with reconstructions reported in AR4 that to high summer insolation (orbital forcing). The current glacier showed a reduction in ENSO variance during the first half of the Hol- retreat occurs within a context of orbital forcing that would be favour- ocene. {5.4.1} able for NH glacier growth. If glaciers continue to reduce at current rates, most extratropical NH glaciers will shrink to their minimum With high confidence, decadal and multi-decadal changes in the extent, which existed between 8000 and 6000 years ago, within this winter North Atlantic Oscillation index (NAO) observed since 5 century (medium confidence). {5.5.3} the 20th century are not unprecedented in the context of the past 500 years. Periods of persistent negative or positive winter NAO For average annual NH temperatures, the period 1983 2012 phases, similar to those observed in the 1960s and 1990 to 2000s, was very likely the warmest 30-year period of the last 800 years respectively, are not unusual in the context of NAO reconstructions (high confidence) and likely the warmest 30-year period of the during at least the past 500 years. {5.4.2} last 1400 years (medium confidence). This is supported by com- parison of instrumental temperatures with multiple reconstructions The increase in the strength of the observed summer Southern from a variety of proxy data and statistical methods, and is consistent Annular Mode since 1950 has been anomalous, with medium with AR4. In response to solar, volcanic and anthropogenic radiative confidence, in the context of the past 400 years. No similar spa- changes, climate models simulate multi-decadal temperature changes tially coherent multi-decadal trend can be detected in tree-ring indices over the last 1200 years in the NH, that are generally consistent in from New Zealand, Tasmania and South America. {5.4.2} magnitude and timing with reconstructions, within their uncertainty ranges. {5.3.5} Abrupt Climate Change and Irreversibility Continental-scale surface temperature reconstructions show, With high confidence, the interglacial mode of the Atlantic with high confidence, multi-decadal periods during the Medie- Ocean meridional overturning circulation (AMOC) can recover val Climate Anomaly (950 to 1250) that were in some regions as from a short-term freshwater input into the subpolar North warm as in the mid-20th century and in others as warm as in the Atlantic. Approximately 8200 years ago, a sudden freshwater release 386 Information from Paleoclimate Archives Chapter 5 occurred during the final stages of North America ice sheet melting. Paleoclimate observations and model results indicate, with high con- fidence, a marked reduction in the strength of the AMOC followed by a rapid recovery, within approximately 200 years after the perturba- tion. {5.8.2} Confidence in the link between changes in North Atlantic climate and low-latitude precipitation patterns has increased since AR4. From new paleoclimate reconstructions and modelling studies, there is very high confidence that reduced AMOC and the associated surface cooling in the North Atlantic region caused southward shifts of the Atlantic Intertropical Convergence Zone, and also affected the Ameri- can (North and South), African and Asian monsoon systems. {5.7} It is virtually certain that orbital forcing will be unable to trig- ger widespread glaciation during the next 1000 years. Paleo- climate records indicate that, for orbital configurations close to the present one, glacial inceptions only occurred for atmospheric CO2 concentrations significantly lower than pre-industrial levels. Climate models simulate no glacial inception during the next 50,000 years if CO2 concentrations remain above 300 ppm. {5.8.3, Box 6.2} There is high confidence that the volumes of the Greenland and West Antarctic ice sheets were reduced during periods of the past few million years that were globally warmer than pres- ent. Ice sheet model simulations and geological data suggest that the West Antarctic ice sheet is very sensitive to subsurface Southern Ocean warming and imply with medium confidence a West Antarctic ice sheet retreat if atmospheric CO2 concentration stays within or above the range of 350 ppm to 450 ppm for several millennia. {5.3.1, 5.6.1, 5.8.1} 5 387 Chapter 5 Information from Paleoclimate Archives 5.1 Introduction Additional information to this chapter is available in the Appendix. Pro- cessed data underlying the figures are stored in the PANGAEA data- This chapter assesses the information on past climate obtained prior to base (www.pangaea.de), while model output from PMIP3 is available the instrumental period. The information is based on data from various from pmip3.lsce.ipsl.fr. In all sections, information is structured by time, paleoclimatic archives and on modelling of past climate, and updates going from past to present. Table 5.1 summarizes the past periods Chapter 6 of AR4 of IPCC Working Group I (Jansen et al., 2007). assessed in the subsections. The Earth system has responded and will continue to respond to various external forcings (solar, volcanic and orbital) and to changes 5.2 Pre-Industrial Perspective on Radiative in atmospheric composition. Paleoclimate data and modelling pro- Forcing Factors vide quantitative information on the Earth system response to these forcings. Paleoclimate information facilitates understanding of Earth 5.2.1 External Forcings system feedbacks on time scales longer than a few centuries, which cannot be evaluated from short instrumental records. Past climate 5.2.1.1 Orbital Forcing changes also document transitions between different climate states, including abrupt events, which occurred on time scales of decades to The term orbital forcing is used to denote the incoming solar radiation a few centuries. They inform about multi-centennial to millennial base- changes originating from variations in the Earth s orbital parameters line variability, against which the recent changes can be compared to as well as changes in its axial tilt. Orbital forcing is well known from assess whether or not they are unusual. precise astronomical calculations for the past and future (Laskar et al., 2004). Changes in eccentricity, longitude of perihelion (related to Major progress since AR4 includes the acquisition of new and more precession) and axial tilt (obliquity) (Berger and Loutre, 1991) predom- precise information from paleoclimate archives, the synthesis of inantly affect the seasonal and latitudinal distribution and magnitude regional information, and Paleoclimate Modelling Intercomparison of solar energy received at the top of the atmosphere (AR4, Box 6.1; Project Phase III (PMIP3) and Coupled Model Intercomparison Project Jansen et al., 2007), and the durations and intensities of local seasons. Phase 5 (CMIP5) simulations using the same models as for projections Obliquity also modulates the annual mean insolation at any given (see Chapter 1). This chapter assesses the understanding of past cli- latitude, with opposite effects at high and low latitudes. Orbital forc- mate variations, using paleoclimate reconstructions as well as climate ing is considered the pacemaker of transitions between glacials and models of varying complexity, while the model evaluation based on interglacials (high confidence), although there is still no consensus on paleoclimate information is covered in Chapter 9. Additional paleo- exactly how the different physical processes influenced by insolation climate perspectives are included in Chapters 6, 10 and 13 (see Table changes interact to influence ice sheet volume (Box 5.2; Section 5.3.2). 5.1). The different orbital configurations make each glacial and interglacial period unique (Yin and Berger, 2010; Tzedakis et al., 2012a). Multi-mil- The content of this chapter is largely restricted to topics for which sub- lennial trends of temperature, Arctic sea ice and glaciers during the stantial new information has emerged since AR4. Examples include current interglacial period, and specifically the last 2000 years, have proxy-based estimates of the atmospheric carbon dioxide (CO2) con- been related to orbital forcing (Section 5.5). tent during the past ~65 million years (Section 5.2.2) and magnitude of sea level variations during interglacial periods (Section 5.6.2). Infor- 5.2.1.2 Solar Forcing mation from glacial climates has been included only if the underlying processes are of direct relevance for an assessment of projected cli- Solar irradiance models (e.g., Wenzler et al., 2005) have been improved 5 mate change. The impacts of past climate changes on biological sys- to explain better the instrumental measurements of total solar irradi- tems and past civilizations are not covered, as these topics are beyond ance (TSI) and spectral (wavelength dependent) solar irradiance (SSI). the scope of Working Group I. Typical changes measured over an 11-year solar cycle are 0.1% for TSI and up to several percent for the ultraviolet (UV) part of SSI (see Sec- The chapter proceeds from evidence for pre-industrial changes in tion 8.4). Changes in TSI directly impact the Earth s surface (see solar atmospheric composition and external solar and volcanic forcings Box 10.2), whereas changes in UV primarily affect the stratosphere, but (Section 5.2, FAQ 5.1), to global and hemispheric responses (Section can influence the tropospheric circulation through dynamical coupling 5.3). After evaluating the evidence for past changes in climate modes (Haigh, 1996). Most models attribute all TSI and SSI changes exclusively of variability (Section 5.4), a specific focus is given to regional changes to magnetic phenomena at the solar surface (sunspots, faculae, mag- in temperature, cryosphere and hydroclimate during the current inter- netic network), neglecting any potential internal phenomena such as glacial period (Section 5.5). Sections on sea level change (Section 5.6, changes in energy transport (see also Section 8.4). The basic concept in FAQ 5.2), abrupt climate changes (Section 5.7) and illustrations of irre- solar models is to divide the solar surface into different magnetic fea- versibility and recovery time scales (Section 5.8) conclude the chapter. tures each with a specific radiative flux. The balance of contrasting dark While polar amplification of temperature changes is addressed in Box sunspots and bright faculae and magnetic network leads to a higher TSI 5.1, the relationships between ice sheets, sea level, atmospheric CO2 value during solar cycle maxima and at most wavelengths, but some concentration and climate are addressed in several sections (Box 5.2, wavelengths may be out of phase with the solar cycle (Harder et al., Sections 5.3.1, 5.5, and 5.8.1). 2009; Cahalan et al., 2010; Haigh et al., 2010). TSI and SSI are calculated by adding the radiative fluxes of all features plus the contribution from 388 Information from Paleoclimate Archives Chapter 5 Table 5.1 | Summary of past periods for which climate information is assessed in the various sections of this chapter and other chapters of AR5. Calendar ages are expressed in Common Era (CE), geological ages are expressed in thousand years (ka) or million years (Ma) before present (BP), with present defined as 1950. Radiocarbon-based ages are quoted as the published calibrated ages. Chapter 5 Sections Other Time Period Age 5.2 5.3 5.4 5.5 5.6 5.7 5.8 Chapters Holocenea 11.65 kag to present 6, 9, 10 Pre-industrial period refers to times before 1850 or 1850 valuesh Little Ice Age (LIA) 1450 1850i 10 Medieval Climate Anomaly (MCA) b 950 1250i 10 Last Millennium 1000 1999j 9, 10 Mid-Holocene (MH) ~6 ka 9, 13 8.2-ka event ~8.2 kag Last Glacial Terminationc 6 Younger Dryasd 12.85 11.65 kag 6 Blling-Allerde 14.64 12.85 kag 6 Meltwater Pulse 1A (MWP-1A) 14.65 14.31 ka k Heinrich stadial 1 (HS1) ~19 14.64 kal Last Glacial Maximum (LGM) ~21 19 ka m 6, 9 Last Interglacial (LIG)f ~129 116 kan 13 Mid-Pliocene Warm Period (MPWP) ~3.3 3.0 Ma o 13 Early Eocene Climatic Optimum (EECO) ~52 50 Map Paleocene-Eocene Thermal Maximum (PETM) ~55.5 55.3 Maq Notes: a Also known as Marine Isotopic Stage (MIS) 1 or current interglacial. b Also known as Medieval Climate Optimum or the Medieval Warm Period. c Also known as Termination I or the Last Deglaciation. Based on sea level, Last Glacial Termination occurred between ~19 and ~6 ka. d Also known as Greenland Stadial GS-1. e Also known as Greenland Interstadial GI-a-c-e. f Also known as MIS5e, which overlaps with the Eemian (Shackleton et al., 2003). g As estimated from the Greenland ice core GICC05 chronology (Rasmussen et al., 2006; Thomas et al., 2007). h In this chapter, when referring to comparison of radiative forcing or climate variables, pre-industrial refers to 1850 values in accordance with Taylor et al. (2012). Otherwise it refers to an extended period of time before 1850 as stated in the text. Note that Chapter 7 uses 1750 as the reference pre-industrial period. i Different durations are reported in the literature. In Section 5.3.5, time intervals 950 1250 and 1450 1850 are used to calculate Northern Hemisphere temperature anomalies representative of the MCA and LIA, respectively. j Note that CMIP5 Last Millennium simulations have been performed for the period 850 1850 (Taylor et al., 2012). k As dated on Tahiti corals (Deschamps et al., 2012). l The duration of Heinrich stadial 1 (e.g., Stanford et al., 2011) is longer than the associated Heinrich event, which is indicated by ice-rafted debris in deep sea sediment cores from the North Atlantic Ocean (Hemming, 2004). m Period based on MARGO Project Members (2009). LGM simulations are performed for 21 ka. Note that maximum continental ice extent had already occurred at 26.5 ka (Clark et al., 2009). n Ages are maximum date for the onset and minimum age for the end from tectonically stable sites (cf. Section 5.6.2). 5 o Dowsett et al. (2012). p Zachos et al. (2008). q Westerhold et al. (2007). the magnetically inactive surface. These models can successfully repro- radionuclides (10Be and 14C) for the past millennium (Muscheler et al., duce the measured TSI changes between 1978 and 2003 (Balmaceda et 2007; Delaygue and Bard, 2011) and the Holocene (Table 5.1) (Stein- al., 2007; Crouch et al., 2008), but not necessarily the last minimum of hilber et al., 2009; Vieira et al., 2011). 10Be and 14C records reflect not 2008 (Krivova et al., 2011). This approach requires detailed information only solar activity, but also the geomagnetic field intensity and effects of all the magnetic features and their temporal changes (Wenzler et al., of their respective geochemical cycles and transport pathways (Pedro 2006; Krivova and Solanki, 2008) (see Section 8.4). et al., 2011; Steinhilber et al., 2012). The corrections for these non-solar components, which are difficult to quantify, contribute to the overall The extension of TSI and SSI into the pre-satellite period poses two error of the reconstructions (grey band in Figure 5.1c). main challenges. First, the satellite period (since 1978) used to cali- brate the solar irradiance models does not show any significant long- TSI reconstructions are characterized by distinct grand solar minima term trend. Second, information about the various magnetic features lasting 50 to 100 years (e.g., the Maunder Minimum, 1645 1715) at the solar surface decreases back in time and must be deduced from that are superimposed upon long-term changes. Spectral analysis of proxies such as sunspot counts for the last 400 years and cosmogenic TSI records reveals periodicities of 87, 104, 150, 208, 350, 510, ~980 389 Chapter 5 Information from Paleoclimate Archives and ~2200 years (Figure 5.1d) (Stuiver and Braziunas, 1993), but with Since AR4, most recent reconstructions show a considerably smaller time-varying amplitudes (Steinhilber et al., 2009; Vieira et al., 2011). All difference (<0.1%) in TSI between the late 20th century and the Late reconstructions rely ultimately on the same data (sunspots and cosmo- Maunder Minimum (1675 1715) when the sun was very quiet, com- genic radionuclides), but differ in the details of the methodologies. As pared to the often used reconstruction of Lean et al. (1995b) (0.24%) a result the reconstructions agree rather well in their shape, but differ and Shapiro et al. (2011) (~0.4%). The Lean et al. (1995a) reconstruc- in their amplitude (Figure 5.1b) (Wang et al., 2005; Krivova et al., 2011; tion has been used to scale solar forcing in simulations of the last Lean et al., 2011; Schrijver et al., 2011) (see Section 8.4.1). millennium prior to PMIP3/CMIP5 (Table 5.A.1). PMIP3/CMIP5 last 0 Volcanic Forcing -5 (W m-2) -10 CEA -15 GRA (a) -20 (b) TSI (W m-2) 0 -1 DB MEA -2 SBF VSK WLS LBB Solar Forcing -3 1000 1100 1200 1300 1400 1500 1600 1700 1800 1900 2000 (c) TSI (W m-2) 0 -1 64 (d) Amplitude (dimensionless) 32 16 32 8 Period (yr) 64 4 5 128 2 1 256 1/2 512 1/4 1/8 1024 1/16 2048 1/32 1/64 -6000 -4000 -2000 0 2000 Time (Year BCE/CE) Figure 5.1 | (a) Two reconstructions of volcanic forcing for the past 1000 years derived from ice core sulphate and used for Paleoclimate Modelling Intercomparison Project Phase III (PMIP3) and Coupled Model Intercomparison Project Phase 5 (CMIP5) simulations (Schmidt et al., 2011). GRA: Gao et al. (2012); CEA: Crowley and Unterman (2013). Volcanic sulphate peaks identified from their isotopic composition as originating from the stratosphere are indicated by squares (green: Greenland; brown: Antarctica) (Baroni et al., 2008; Cole-Dai et al., 2009). (b) Reconstructed total solar irradiance (TSI) anomalies back to the year 1000. Proxies of solar activity (e.g., sunspots, 10Be) are used to estimate the parameters of the models or directly TSI. All records except LBB (Lean et al., 1995b) have been used for PMIP3/CMIP5 simulations (Schmidt et al., 2011). DB: Delaygue and Bard (2011); MEA: Muscheler et al. (2007); SBF: Steinhilber et al. (2009); WLS: Wang et al. (2005); VSK: Vieira et al. (2011). For the years prior to 1600, the 11-year cycle has been added artificially to the original data with an amplitude proportional to the mean level of TSI. (c) Reconstructed TSI anomalies (100-year low-pass filtered; grey shading: 1 standard deviation uncertainty range) for the past 9300 years (Steinhilber et al., 2009). The reconstruction is based on 10Be and calibrated using the relationship between instrumental data of the open magnetic field, which modulates the production of 10Be, and TSI for the past four solar minima. The yellow band indicates the past 1000 years shown in more details in (a) and (b). Anomalies are relative to the 1976 2006 mean value (1366.14 W m 2) of Wang et al. (2005). (d) Wavelet analysis (Torrence and Compo, 1998) of TSI anomalies from (c) with dashed white lines highlighting significant periodicities (Stuiver and Braziunas, 1993). 390 Information from Paleoclimate Archives Chapter 5 millennium simulations have used the weak solar forcing of recent A ­ ntarctic ice core sulphur isotope data (Baroni et al., 2008; Cole-Dai et reconstructions of TSI (Schmidt et al., 2011, 2012b) calibrated (Mus- al., 2009; Schmidt et al., 2012b). cheler et al., 2007; Delaygue and Bard, 2011) or spliced (Steinhilber et al., 2009; Vieira and Solanki, 2010) to Wang et al. (2005). The larger The use of different volcanic forcing reconstructions in pre-PMIP3/ range of past TSI variability in Shapiro et al. (2011) is not supported by CMIP5 (see AR4 Chapter 6) and PMIP3/CMIP5 last millennium simu- studies of magnetic field indicators that suggest smaller changes over lations (Schmidt et al., 2011) (Table 5.A.1), together with the methods the 19th and 20th centuries (Svalgaard and Cliver, 2010; Lockwood used to implement these volcanic indices with different representa- and Owens, 2011). tions of aerosols in climate models, is a source of uncertainty in model intercomparisons. The impact of volcanic forcing on climate variations Note that: (1) the recent new measurement of the absolute value of of the last millennium climate is assessed in Sections 5.3.5, 5.4, 5.5.1 TSI and TSI changes during the past decades are assessed in Section and 10.7.1. 8.4.1.1; (2) the current state of understanding the effects of galactic cosmic rays on clouds is assessed in Sections 7.4.6 and 8.4.1.5 and 5.2.2 Radiative Perturbations from Greenhouse (3) the use of solar forcing in simulations of the last millennium is Gases and Dust discussed in Section 5.3.5. 5.2.2.1 Atmospheric Concentrations of Carbon Dioxide, 5.2.1.3 Volcanic Forcing Methane and Nitrous Oxide from Ice Cores Volcanic activity affects global climate through the radiative impacts Complementing instrumental data, air enclosed in polar ice pro- of atmospheric sulphate aerosols injected by volcanic eruptions (see vides a direct record of past atmospheric well-mixed greenhouse gas Sections 8.4.2 and 10.3.1). Quantifying volcanic forcing in the pre-sat- (WMGHG) concentrations albeit smoothed by firn diffusion (Joos and ellite period is important for historical and last millennium climate Spahni, 2008; Köhler et al., 2011). Since AR4, the temporal resolution simulations, climate sensitivity estimates and detection and attribution of ice core records has been enhanced (MacFarling Meure et al., 2006; studies. Reconstructions of past volcanic forcing are based on sulphate Ahn and Brook, 2008; Loulergue et al., 2008; Lüthi et al., 2008; Mischler deposition from multiple ice cores from Greenland and Antarctica, et al., 2009; Schilt et al., 2010; Ahn et al., 2012; Bereiter et al., 2012). combined with atmospheric modelling of aerosol distribution and opti- During the pre-industrial part of the last 7000 years, millennial (20 ppm cal depth. CO2, 125 ppb CH4) and centennial variations (up to 10 ppm CO2, 40 ppb CH4 and 10 ppb N2O) are recorded (see Section 6.2.2 and Figure 6.6). Since AR4, two new reconstructions of the spatial distribution of vol- Significant centennial variations in CH4 during the last glacial occur in canic aerosol optical depth have been generated using polar ice cores, phase with Northern Hemisphere (NH) rapid climate changes, while spanning the last 1500 years (Gao et al., 2008, 2012) and 1200 years millennial CO2 changes coincide with their Southern Hemisphere (SH) (Crowley and Unterman, 2013) (Figure 5.1a). Although the relative size bipolar seesaw counterpart (Ahn and Brook, 2008; Loulergue et al., of eruptions for the past 700 years is generally consistent among these 2008; Lüthi et al., 2008; Grachev et al., 2009; Capron et al., 2010b; and earlier studies (Jansen et al., 2007), they differ in the absolute Schilt et al., 2010; Bereiter et al., 2012). amplitude of peaks. There are also differences in the reconstructions of Icelandic eruptions, with an ongoing debate on the magnitude of strat- Long-term records have been extended from 650 ka in AR4 to 800 ka ospheric inputs for the 1783 Laki eruption (Thordarson and Self, 2003; (Figures 5.2 and 5.3) (Loulergue et al., 2008; Lüthi et al., 2008; Schilt et Wei et al., 2008; Lanciki et al., 2012; Schmidt et al., 2012a). The recur- al., 2010). During the last 800 ka, the pre-industrial ice core WMGHG rence time of past large volcanic aerosol injections (eruptions changing concentrations stay within well-defined natural limits with maximum the radiative forcing (RF) by more than 1 W m 2) varies from 3 to 121 interglacial concentrations of approximately 300 ppm, 800 ppb and 5 years, with long-term mean value of 35 years (Gao et al., 2012) and 39 300 ppb for CO2, CH4 and N2O, respectively, and minimum glacial years (Crowley and Unterman, 2013), and only two or three periods of concentrations of approximately 180 ppm, 350 ppb, and 200 ppb. The 100 years without such eruptions since 850. new data show lower than pre-industrial (280 ppm) CO2 concentra- tions during interglacial periods from 800 to 430 ka (MIS19 to MIS13) Hegerl et al. (2006) estimated the uncertainty of the RF for a given (Figure 5.3). It is a fact that present-day (2011) concentrations of CO2 volcanic event to be approximately 50%. Differences between recon- (390.5 ppm), CH4 (1803 ppb) and N2O (324 ppm) (Annex II) exceed the structions (Figure 5.1a) arise from different proxy data, identification range of concentrations recorded in the ice core records during the past of the type of injection, methodologies to estimate particle distribution 800 ka. With very high confidence, the rate of change of the observed and optical depth (Kravitz and Robock, 2011), and parameterization of anthropogenic WMGHG rise and its RF is unprecedented with respect scavenging for large events (Timmreck et al., 2009). Key limitations are to the highest resolution ice core record back to 22 ka for CO2, CH4 and associated with ice core chronologies (Plummer et al., 2012; Sigl et al., N2O, accounting for the smoothing due to ice core enclosure processes 2013), and deposition patterns (Moore et al., 2012). (Joos and Spahni, 2008; Schilt et al., 2010). There is medium confidence that the rate of change of the observed anthropogenic WMGHG rise is A new independent methodology has recently been developed to also unprecedented with respect to the lower resolution records of the distinguish between tropospheric and stratospheric volcanic aerosol past 800 ka. deposits (Baroni et al., 2007). The stratospheric character of several large eruptions has started to be assessed from Greenland and/or Progress in understanding the causes of past WMGHG variations is reported in Section 6.2. 391 Chapter 5 Information from Paleoclimate Archives Frequently Asked Questions FAQ 5.1 | Is the Sun a Major Driver of Recent Changes in Climate? Total solar irradiance (TSI, Chapter 8) is a measure of the total energy received from the sun at the top of the atmo- sphere. It varies over a wide range of time scales, from billions of years to just a few days, though variations have been relatively small over the past 140 years. Changes in solar irradiance are an important driver of climate vari- ability (Chapter 1; Figure 1.1) along with volcanic emissions and anthropogenic factors. As such, they help explain the observed change in global surface temperatures during the instrumental period (FAQ 5.1, Figure 1; Chapter 10) and over the last millennium. While solar variability may have had a discernible contribution to changes in global surface temperature in the early 20th century, it cannot explain the observed increase since TSI started to be mea- sured directly by satellites in the late 1970s (Chapters 8, 10). The Sun s core is a massive nuclear fusion reactor that converts hydrogen into helium. This process produces energy that radiates throughout the solar system as electromagnetic radiation. The amount of energy striking the top of Earth s atmosphere varies depending on the generation and emission of electromagnetic energy by the Sun and on the Earth s orbital path around the Sun. Satellite-based instruments have directly measured TSI since 1978, and indicate that on average, ~1361 W m 2 reach- es the top of the Earth s atmosphere. Parts of the Earth s surface and air pollution and clouds in the atmosphere act as a mirror and reflect about 30% of this power back into space. Higher levels of TSI are recorded when the Sun is more active. Irradiance variations follow the roughly 11-year sunspot cycle: during the last cycles, TSI values fluctu- ated by an average of around 0.1%. For pre-satellite times, TSI variations have to be estimated from sunspot numbers (back to 1610), or from radioiso- topes that are formed in the atmosphere, and archived in polar ice and tree rings. Distinct 50- to 100-year periods of very low solar activity such as the Maunder Minimum between 1645 and 1715 are commonly referred to as grand solar minima. Most estimates of TSI changes between the Maunder Minimum and the present day are in the order of 0.1%, similar to the amplitude of the 11-year variability. How can solar variability help explain the observed global surface temperature record back to 1870? To answer this question, it is important to understand that other climate drivers are involved, each producing characteristic patterns of regional climate responses. However, it is the combination of them all that causes the observed climate change. Solar variability and volcanic eruptions are natural factors. Anthropogenic (human-produced) factors, on the other hand, include changes in the concentrations of greenhouse gases, and emissions of visible air pollution (aerosols) and other substances from human activities. Internal variability refers to fluctuations within the climate system, for example, due to weather variability or phenomena like the El Nino-Southern Oscillation. The relative contributions of these natural and anthropogenic factors change with time. FAQ 5.1, Figure 1 illustrates those contributions based on a very simple calculation, in which the mean global surface temperature variation rep- resents the sum of four components linearly related to solar, volcanic, and anthropogenic forcing, and to internal 5 variability. Global surface temperature has increased by approximately 0.8°C from 1870 to 2010 (FAQ 5.1, Figure 1a). However, this increase has not been uniform: at times, factors that cool the Earth s surface volcanic eruptions, reduced solar activity, most anthropogenic aerosol emissions have outweighed those factors that warm it, such as greenhouse gases, and the variability generated within the climate system has caused further fluctuations unre- lated to external influences. The solar contribution to the record of global surface temperature change is dominated by the 11-year solar cycle, which can explain global temperature fluctuations up to approximately 0.1°C between minima and maxima (FAQ 5.1, Figure 1b). A long-term increasing trend in solar activity in the early 20th century may have augmented the warming recorded during this interval, together with internal variability, greenhouse gas increases and a hiatus in volcanism. However, it cannot explain the observed increase since the late 1970s, and there was even a slight decreasing trend of TSI from 1986 to 2008 (Chapters 8 and 10). Volcanic eruptions contribute to global surface temperature change by episodically injecting aerosols into the atmosphere, which cool the Earth s surface (FAQ 5.1, Figure 1c). Large volcanic eruptions, such as the eruption of Mt. Pinatubo in 1991, can cool the surface by around 0.1°C to 0.3°C for up to three years. (continued on next page) 392 Information from Paleoclimate Archives Chapter 5 FAQ 5.1 (continued) (a) Global Surface Temperature 0.8 The most important component of internal cli- Anomaly (°C) 0.4 mate variability is the El Nino Southern Oscillation, which has a major effect on year-to-year variations 0.0 of tropical and global mean temperature (FAQ 5.1, -0.4 Figure 1d). Relatively high annual temperatures -0.8 have been encountered during El Nino events, such as in 1997 1998. (b) Solar Component 0.2 The variability of observed global surface tempera- Anomaly (°C) tures from 1870 to 2010 (Figure 1a) reflects the com- bined influences of natural (solar, volcanic, internal; 0.1 FAQ 5.1, Figure 1b d) factors, superimposed on the multi-decadal warming trend from anthropogenic factors (FAQ 5.1, Figure 1e). 0.0 Prior to 1870, when anthropogenic emissions (c) Volcanic Component 0.0 of greenhouse gases and aerosols were smaller, Anomaly (°C) changes in solar and volcanic activity and internal v ­ ariability played a more important role, although -0.1 the specific contributions of these individual fac- tors to global surface temperatures are less certain. Solar minima lasting several decades have often -0.2 been associated with cold conditions. However, these periods are often also affected by volcanic (d) Internal Variability 0.2 eruptions, making it difficult to quantify the solar contribution. Anomaly (°C) At the regional scale, changes in solar activity have 0.0 been related to changes in surface climate and atmospheric circulation in the Indo-Pacific, North- ern Asia and North Atlantic areas. The mechanisms -0.2 that amplify the regional effects of the relatively (e) Anthropogenic Component small fluctuations of TSI in the roughly 11-year solar cycle involve dynamical interactions between the 0.8 Anomaly (°C) upper and the lower atmosphere, or between the 0.6 ocean sea surface temperature and atmosphere, 0.4 and have little effect on global mean temperatures (see Box 10.2). 0.2 5 Finally, a decrease in solar activity during the past 0.0 1880 1900 1920 1940 1960 1980 2000 solar minimum a few years ago (FAQ 5.1, Figure Year 1b) raises the question of its future influence on climate. Despite uncertainties in future solar activ- FAQ 5.1, Figure 1 | Global surface temperature anomalies from 1870 to 2010, and the natural (solar, volcanic, and internal) and anthropogenic factors that ity, there is high confidence that the effects of solar influence them. (a) Global surface temperature record (1870 2010) relative to activity within the range of grand solar maxima and the average global surface temperature for 1961 1990 (black line). A model minima will be much smaller than the changes due of global surface temperature change (a: red line) produced using the sum of to anthropogenic effects. the impacts on temperature of natural (b, c, d) and anthropogenic factors (e). (b) Estimated temperature response to solar forcing. (c) Estimated temperature response to volcanic eruptions. (d) Estimated temperature variability due to internal variability, here related to the El Nino-Southern Oscillation. (e) Esti- mated temperature response to anthropogenic forcing, consisting of a warm- ing component from greenhouse gases, and a cooling component from most aerosols. 393 Chapter 5 Information from Paleoclimate Archives 5.2.2.2 Atmospheric Carbon Dioxide Concentrations from and did not exceed ~450 ppm during the Pliocene, with interglacial Geological Proxy Data values in the upper part of that range between 350 and 450 ppm. Geological proxies provide indirect information on atmospheric CO2 5.2.2.3 Past Changes in Mineral Dust Aerosol Concentrations concentrations for time intervals older than those covered by ice core records (see Section 5.2.2.1). Since AR4, the four primary proxy Past changes in mineral dust aerosol (MDA) are important for estimates CO2 methods have undergone further development (Table 5.A.2). A of climate sensitivity (see Section 5.3.3) and for its supply of nutrients, reassessment of biological respiration and carbonate formation has especially iron to the Southern Ocean (see Section 6.2). MDA concen- reduced CO2 reconstructions based on fossil soils by approximately tration is controlled by variations in dust sources, and by changes in 50% (Breecker et al., 2010). Bayesian statistical techniques for calibrat- atmospheric circulation patterns acting on its transport and lifetime. ing leaf stomatal density reconstructions produce consistently higher CO2 estimates than previously assessed (Beerling et al., 2009), result- Since AR4, new records of past MDA flux have been obtained from ing in more convergence between estimates from these two terrestrial deep-sea sediment and ice cores. A 4 million-year MDA-flux recon- proxies. Recent CO2 reconstructions using the boron isotope proxy pro- struction from the Southern Ocean (Figure 5.2) implies reduced dust vide an improved understanding of foraminifer species effects and evo- generation and transport during the Pliocene compared to Holo- lution of seawater alkalinity (Hönisch and Hemming, 2005) and sea- cene levels, followed by a significant rise around 2.7 Ma when NH water boron isotopic composition (Foster et al., 2012). Quantification ice volume increased (Martinez-Garcia et al., 2011). Central Antarc- of the phytoplankton cell-size effects on carbon isotope fractionation tic ice core records show that local MDA deposition fluxes are ~20 has also improved the consistency of the alkenone method (Henderiks times higher during glacial compared to interglacial periods (Fischer and Pagani, 2007). These proxies have also been applied more widely et al., 2007; Lambert et al., 2008; Petit and Delmonte, 2009). This is and at higher temporal resolution to a range of geological archives, due to enhanced dust production in southern South America and per- resulting in an increased number of atmospheric CO2 estimates since haps Australia (Gaiero, 2007; De Deckker et al., 2010; Gabrielli et al., 65 Ma (Beerling and Royer, 2011). Although there is improved consen- 2010; Martinez-Garcia et al., 2011; Wegner et al., 2012). The impact of sus between the proxy CO2 estimates, especially the marine proxy esti- changes in MDA lifetime (Petit and Delmonte, 2009) on dust fluxes in mates, a significant degree of variation among the different techniques Antarctica remains uncertain (Fischer et al., 2007; Wolff et al., 2010). remains. All four techniques have been included in the assessment, as Equatorial Pacific glacial interglacial MDA fluxes co-vary with Antarc- there is insufficient knowledge to discriminate between different proxy tic records, but with a glacial interglacial ratio in the range of approx- estimates on the basis of confidence (assessed in Table 5.A.2). imately three to four (Winckler et al., 2008), attributed to enhanced dust production from Asian and northern South American sources in In the time interval between 65 and 23 Ma, all proxy estimates of CO2 glacial times (Maher et al., 2010). The dominant dust source regions concentration span a range of 300 ppm to 1500 ppm (Figure 5.2). An (e.g., North Africa, Arabia and Central Asia) show complex patterns independent constraint on Early Eocene atmospheric CO2 concentra- of variability (Roberts et al., 2011). A glacial increase of MDA source tion is provided by the occurrence of the sodium carbonate mineral strength by a factor of 3 to 4 requires low vegetation cover, seasonal nahcolite, in about 50 Ma lake sediments, which precipitates in asso- aridity, and high wind speeds (Fischer et al., 2007; McGee et al., 2010). ciation with halite at the sediment water interface only at CO2 levels In Greenland ice cores, MDA ice concentrations are higher by a factor >1125 ppm (Lowenstein and Demicco, 2006), and thus provides a of 100 and deposition fluxes by a factor 20 during glacial periods potential lower bound for atmospheric concentration (medium confi- (Ruth et al., 2007). This is due mainly to changes in the dust sources dence) during the warmest period of the last 65 Ma, the Early Eocene for Greenland (Asian desert areas), increased gustiness (McGee et al., Climatic Optimum (EECO; 52 to 50 Ma; Table 5.1), which is inconsist- 2010) and atmospheric lifetime and transport of MDA (Fischer et al., 5 ent with lower estimates from stomata and paleosoils. Although the 2007). A strong coherence is observed between dust in Greenland ice reconstructions indicate a general decrease in CO2 concentrations cores and aeolian deposition in European loess formations (Antoine et since about 50 Ma (Figure 5.2), the large scatter of proxy data pre- al., 2009). cludes a robust assessment of the second-order variation around this overall trend. Global data synthesis shows two to four times more dust deposition at the Last Glacial Maximum (LGM; Table 5.1) than today (Derbyshire, Since 23 Ma, CO2 proxy estimates are at pre-industrial levels with 2003; Maher et al., 2010). Based on data model comparisons, esti- exception of the Middle Miocene climatic optimum (17 to 15 Ma) mates of global mean LGM dust RF vary from 3 W m 2 to +0.1 W m 2, and the Pliocene (5.3 to 2.6 Ma), which have higher concentrations. due to uncertainties in radiative properties. The best estimate value Although new CO2 reconstructions for the Pliocene based on marine remains at 1 W m 2 as in AR4 (Claquin et al., 2003; Mahowald et proxies have produced consistent estimates mostly in the range 350 al., 2006, 2011; Patadia et al., 2009; Takemura et al., 2009; Yue et al., ppm to 450 ppm (Pagani et al., 2010; Seki et al., 2010; Bartoli et al., 2010). Models may underestimate the MDA RF at high latitudes (Lam- 2011), the uncertainties associated with these marine estimates remain bert et al., 2013). difficult to quantify. Several boron-derived data sets agree within error (+/-25 ppm) with the ice core records (Foster, 2008; Hönisch et al., 2009), but alkenone data for the ice core period are outside the error limits (Figure 5.2). We conclude that there is medium confidence that CO2 levels were above pre-industrial interglacial concentration (~280 ppm) 394 Information from Paleoclimate Archives Chapter 5 MPWP Dust accumulation Southern Ocean (g m 2 yr 1) 1 2 5 10 0 20 sea level (m) Global Tropical sea surface temperature (°C) 100 28 26 500 24 Atmospheric CO2 400 (ppm) 300 200 100 3 2 1 0 Age (Ma) 2000 1000 Atmospheric CO2 (ppm) 500 200 CO2 proxies Phytoplankton Boron Stomata Liverworts Nahcolite Paleosols 100 5 60 50 40 30 20 10 0 Age (Ma) Figure 5.2 | (Top) Orbital-scale Earth system responses to radiative forcings and perturbations from 3.5 Ma to present. Reconstructed dust mass accumulation rate is from the Atlantic sector of the Southern Ocean (red) (Martinez-Garcia et al., 2011). Sea level curve (blue) is the stacked d18O proxy for ice volume and ocean temperature (Lisiecki and Raymo, 2005) calibrated to global average eustatic sea level (Naish and Wilson, 2009; Miller et al., 2012a). Also shown are global eustatic sea level reconstructions for the last 500 kyr based on sea level calibration of the d18O curve using dated coral shorelines (green line; Waelbroeck et al., 2002) and the Red Sea isotopic reconstruction (red line; Rohling et al., 2009). Weighted mean estimates (2 standard deviation uncertainty) for far-field reconstructions of eustatic peaks are shown for mid-Pliocene interglacials (red dots; Miller et al., 2012a). The dashed horizontal line represents present-day sea level. Tropical sea surface temperature (black line) based on a stack of four alkenone-based sea surface temperature reconstructions (Herbert et al., 2010). Atmospheric carbon dioxide (CO2) measured from Antarctic ice cores (green line, Petit et al., 1999; Siegenthaler et al., 2005; Lüthi et al., 2008), and estimates of CO2 from boron isotopes (d11B) in foraminifera in marine sediments (blue triangles; Hönisch et al., 2009; Seki et al., 2010; Bartoli et al., 2011), and phytoplankton alkenone-derived carbon isotope proxies (red diamonds; Pagani et al., 2010; Seki et al., 2010), plotted with 2 standard deviation uncertainty. Present (2012) and pre-industrial CO2 concentrations are indicated with long-dashed and short-dashed grey lines, respectively. (Bottom) Concentration of atmospheric CO2 for the last 65 Ma is reconstructed from marine and terrestrial proxies (Cerling, 1992; Freeman and Hayes, 1992; Koch et al., 1992; Stott, 1992; van der Burgh et al., 1993; Sinha and Stott, 1994; Kürschner, 1996; McElwain, 1998; Ekart et al., 1999; Pagani et al., 1999a, 1999b, 2005a, 2005b, 2010, 2011; Kürschner et al., 2001, 2008; Royer et al., 2001a, 2001b; Beerling et al., 2002, 2009; Beerling and Royer, 2002; Nordt et al., 2002; Greenwood et al., 2003; Royer, 2003; Lowenstein and Demicco, 2006; Fletcher et al., 2008; Pearson et al., 2009; Retallack, 2009b, 2009a; Tripati et al., 2009;Seki et al., 2010; Smith et al., 2010; Bartoli et al., 2011; Doria et al., 2011; Foster et al., 2012). Individual proxy methods are colour-coded (see also Table A5.1). The light blue shading is a 1-standard deviation uncertainty band constructed using block bootstrap resampling (Mudelsee et al., 2012) for a kernel regression through all the data points with a bandwidth of 8 Myr prior to 30 Ma, and 1 Myr from 30 Ma to present. Most of the data points for CO2 proxies are based on duplicate and multiple analyses. The red box labelled MPWP represents the mid-Pliocene Warm Period (3.3 to 3.0 Ma; Table 5.1). 395 Chapter 5 Information from Paleoclimate Archives Box 5.1 | Polar Amplification Polar amplification occurs if the magnitude of zonally averaged surface temperature change at high latitudes exceeds the globally averaged temperature change, in response to climate forcings and on time scales greater than the annual cycle. Polar amplification is of global concern due to the potential effects of future warming on ice sheet stability and, therefore, global sea level (see Sections 5.6.1, 5.8.1 and Chapter 13) and carbon cycle feedbacks such as those linked with permafrost melting (see Chapter 6). Some external climate forcings have an enhanced radiative impact at high latitudes, such as orbital forcing (Section 5.2.1.1), or black carbon (Section 8.3.4). Here, we focus on the latitudinal response of surface temperature to CO2 perturbations. The magnitude of polar amplification depends on the relative strength and duration of different climate feedbacks, which determine the transient and equilib- rium response to external forcings. This box first describes the different feedbacks operating in both polar regions, and then contrasts polar amplification depicted for past high CO2 and low CO2 climates with projected temperature patterns for the RCP8.5 future green- house gas (WMGHG) emission scenario. In the Arctic, the sea ice/ocean surface albedo feedback plays an important role (Curry et al., 1995; Serreze and Barry, 2011). With retreating sea ice, surface albedo decreases, air temperatures increase and the ocean can absorb more heat. The resulting ocean warming contributes to further sea ice melting. The sea ice/ocean surface albedo feedback can exhibit threshold behaviour when temperatures exceed the freezing point of sea ice. This may also translate into a strong seasonality of the response characteristics. Other feedbacks, including water vapour and cloud feedbacks have been suggested as important amplifiers of Arctic climate change (Vavrus, 2004; Abbot and Tziperman, 2008, 2009; Graversen and Wang, 2009; Lu and Cai, 2009; Screen and Simmonds, 2010; Bintanja et al., 2011). In continental Arctic regions with seasonal snow cover, changes in radiative forcing (RF) can heavily influence snow cover (Ghatak et al., 2010), and thus surface albedo. Other positive feedbacks operating on time scales of decades-to-centuries in continen- tal high-latitude regions are associated with surface vegetation changes (Bhatt et al., 2010) and thawing permafrost (e.g., Walter et al., 2006). On glacial-to-interglacial time scales, the very slow ice sheet albedo response to external forcings (see Box 5.2) is a major contributor to polar amplification in the Northern Hemisphere. An amplified response of Southern Ocean sea surface temperature (SST) to radiative perturbations also emerges from the sea ice albedo feedback. However, in contrast to the Arctic Ocean, which in parts is highly stratified, mixed-layer depths in the Southern Ocean typically exceed several hundreds of meters, which allows the ocean to take up vast amounts of heat (Böning et al., 2008; Gille, 2008; Sokolov and Rintoul, 2009) and damp the SST response to external forcing. This process, and the presence of the ozone hole over the Antarctic ice sheet (Thompson and Solomon, 2002, 2009), can affect the transient response of surface warming of the Southern Ocean and Antarctica, and lead to different patterns of future polar amplification on multi-decadal to multi-centennial time scales. In response to rapid atmospheric CO2 changes, climate models indeed project an asymmetric warming between the Arctic and Southern Oceans, with an earlier response in the Arctic and a delayed response in the Southern Ocean (Section 12.4.3). Above the Antarctic ice sheet, however, surface air temperature can respond quickly to radiative perturbations owing to the limited role of latent heat flux in the surface energy budget of Antarctica. 5 These differences in transient and equilibrium responses of surface temperatures on Antarctica, the Southern Ocean and over conti- nents and oceans in the Arctic domain can explain differences in the latitudinal temperature patterns depicted in Box 5.1, Figure 1 for past periods (equilibrium response) and future projections (transient response). Box 5.1, Figure 1 illustrates the polar amplification phenomenon for three different periods of the Earth s climate history using tem- perature reconstructions from natural archives and climate model simulations for: (i) the Early Eocene Climatic Optimum (EECO, 54 to 48 Ma) characterised by CO2 concentrations of 1000 to 2000 ppm (Section 5.2.2.2) and the absence of continental ice sheets; (ii) the mid-Pliocene Warm Period (MPWP, 3.3 to 3.0 Ma), characterized by CO2 concentrations in the range of 350 to 450 ppm (Section 5.2.2.2) and reduced Greenland and Antarctic ice sheets compared to today (see Section 5.6.1), (iii) the Last Glacial Maximum (LGM, 21 to 19 ka), characterized by CO2 concentrations around 200 ppm and large continental ice sheets covering northern Europe and North America. Throughout all three time periods, reconstructions and simulations reveal Arctic and Antarctic surface air temperature amplification of up to two times the global mean (Box 5.1, Figure 1c, d), and this bipolar amplification appears to be a robust feature of the equilibrium Earth system response to changes of CO2 concentration, irrespective of climate state. The absence (EECO), or expansion (LGM) of conti- nental ice sheets has the potential to affect the zonally averaged surface temperatures due to the lapse-rate effect (see Box 5.2), hence contributing to polar amplification. However, polar amplification is also suppressed in zonally averaged gradients of SST compared with terrestrial surface air temperature (Box 5.1, Figure 1), owing to the presence of high-latitude sea ice in the pre-industrial control, (continued on next page) 396 Information from Paleoclimate Archives Chapter 5 Box 5.1 (continued) which places a lower limit on SST. Global mean temperature estimates for these three past climates also imply an Earth system climate sensitivity to radiative perturbations up to two times higher than the equilibrium climate sensitivity (Lunt et al., 2010; Haywood et al., 2013) (see Section 5.3.1 and Box 12.2). Polar amplification explains in part why Greenland Ice Sheet (GIS) and the West Antarctic Ice Sheet (WAIS) appear to be highly sensitive to relatively small increases in CO2 concentration and global mean temperature. For example, global sea level during MPWP may have been up to +20m higher than present day when atmospheric CO2 concentrations were ~350 to 450 ppm and global mean surface temperature was 2°C to 3°C above pre-industrial levels (see Sections 5.6.1 and 5.8.1). (continued on next page) (a) (b) (c) (d) SST anomaly (°C) SST anomaly (°C) SAT anomaly (°C) SAT anomaly (°C) RCP 8.5 2081 2100 90°N 90°N land +4.9°C 0° +2.5°C 0° +3.7°C global 90°S 90°S 0 10 0 20 40 LGM 21 ka 90°N 90°N land 7.2°C 0° 2.2°C 0° 4.4°C global 90°S 90°S 0 10 0 20 40 MPWP 3.3 3 Ma 90°N 90°N land +3.7°C 0° +1.7°C 0° +2.7°C global 90°S 90°S 0 10 0 20 40 EECO 54 48 Ma 90°N 90°N land +14.1°C 5 0° +9.6°C 0° +12.7°C global 90°S 90°S 12 10 8 6 4 2 0 2 4 6 8 10 12 0 10 0 20 40 24 20 16 12 8 4 0 4 8 12 16 20 24 Confidence SST anomaly (°C) Low Medium High Very High Not assessed SAT anomaly (°C) Box 5.1, Figure 1 | Comparison of data and multi-model mean (MMM) simulations, for four periods of time, showing (a) sea surface temperature (SST) anomalies, (b) zonally averaged SST anomalies, (c) zonally averaged global (green) and land (grey) surface air temperature (SAT) anomalies and (d) land SAT anomalies. The time periods are 2081 2100 for the Representative Concentration Pathway (RCP) 8.5 (top row), Last Glacial Maximum (LGM, second row), mid-Pliocene Warm Period (MPWP, third row) and Early Eocene Climatic Optimum (EECO, bottom row). Model temperature anomalies are calculated relative to the pre-industrial value of each model in the ensemble prior to calculating the MMM anomaly (a, d; colour shading). Zonal MMM gradients (b, c) are plotted with a shaded band indicating 2 standard deviations. Site specific temperature anomalies estimated from proxy data are calculated relative to present site temperatures and are plotted (a, d) using the same colour scale as the model data, and a circle-size scaled to estimates of confidence. Proxy data compilations for the LGM are from Multiproxy Approach for the Reconstruction of the Glacial Ocean surface (MARGO) Project Members (2009) and Bartlein et al. (2011), for the MPWP are from Dowsett et al. (2012), Salzmann et al. (2008) and Haywood et al. (2013) and for the EECO are from Hollis et al. (2012) and Lunt et al. (2012). Model ensemble simulations for 2081 2100 are from the CMIP5 ensemble using RCP 8.5, for the LGM are seven Paleoclimate Modelling Intercomparison Project Phase III (PMIP3) and Coupled Model Intercomparison Project Phase 5 (CMIP5) models, for the Pliocene are from Haywood et al., (2013), and for the EECO are after Lunt et al. (2012). 397 Chapter 5 Information from Paleoclimate Archives Box 5.1 (continued) Based on earlier climate data model comparisons, it has been claimed (summarised in Huber and Caballero, 2011), that models under- estimated the strength of polar amplification for high CO2 climates by 30 to 50%. While recent simulations of the EECO and the MPWP exhibit a wide inter-model variability, there is generally good agreement between new simulations and data, particularly if seasonal biases in some of the marine SST proxies from high-latitude sites are considered (Hollis et al., 2012; Lunt et al., 2012; Haywood et al., 2013). Transient polar amplification as recorded in historical instrumental data and as projected by coupled climate models for the 21st century involves a different balance of feedbacks than for the equilibrium past states featured in Box 5.1, Figure 1. Since 1875, the Arctic north of 60°N latitude has warmed at a rate of 1.36°C per century, approximately twice as fast as the global average (Bekryaev et al., 2010), and since 1979, Arctic land surface has warmed at an even higher rate of 0.5°C per decade (e.g., Climatic Research Unit (CRU) Gridded Dataset of Global Historical Near-Surface Air TEMperature Anomalies Over Land version 4 (CRUTEM4), Jones et al., 2012; Hadley Centre/CRU gridded surface temperature data set version 4 (HadCRUT4), Morice et al., 2012) (see Section 2.4). This recent warming appears unusual in the context of reconstructions spanning the past 2000 years (Section 5.5) and has been attributed primari- ly to anthropogenic factors (Gillett et al., 2008) (see Section 10.3.1.1.4). The fact that the strongest warming occurs in autumn and early winter (Chylek et al., 2009; Serreze et al., 2009; Polyakov et al., 2010; Screen and Simmonds, 2010; Semenov et al., 2010; Spielhagen et al., 2011) strongly links Arctic amplification to feedbacks associated with the seasonal reduction in sea ice extent and duration, as well as the insulating effect of sea ice in winter (e.g., Soden et al., 2008; Serreze et al., 2009; Serreze and Barry, 2011). For future model projections (Box 5.1, Figure 1), following the RCP8.5 scenarios, annual mean Arctic (68°N to 90°N) warming is expected to exceed the global average by 2.2 to 2.4 times for the period 2081 2100 compared to 1986 2005 (see Section 12.4.3.1), which corresponds to the higher end of polar amplification implied by paleo-reconstructions. The transient response of Antarctic and Southern Ocean temperatures to the anthropogenic perturbation appears more complex, than for the Arctic region. Zonal mean Antarctic surface warming has been modest at 0.1°C per decade over the past 50 years (Steig et al., 2009; O Donnell et al., 2010). The Antarctic Peninsula is experiencing one of the strongest regional warming trends (0.5°C per decade over the past 50 years), more than twice that of the global mean temperature. Central West Antarctica may have also experienced a similar strong warming trend, as depicted by the only continuous meteorological station during the last 50 years (Bromwich et al., 2013), and borehole measurements spanning the same period (Orsi et al., 2012). Ice core records show enhanced summer melting in the Antarctic Peninsula since the 1950, which is unprecedented over the past 1000 years (Abram et al., 2013), and warming in West Antarctica that cannot be distinguished from natural variability over the last 2000 years (Steig et al., 2013) (see also Section 10.3.1.1.4, and Section 5.5). Polar amplification in the Southern Ocean and Antarctica is virtually absent in the transient CMIP5 RCP4.5 future simulations (2081 2100 versus 1986 2005) (see Section 12.4.3.1), although CMIP5 RCP8.5 exhibits an amplified warming in the Southern Ocean (Box 5.1, Figure 1), much smaller in magnitude than the equilibrium response implied from paleo-reconstructions for a high-CO2 world. In summary, high confidence exists for polar amplification in either one or both hemispheres, based on robust and consistent evidence from temperature reconstructions of past climates, recent instrumental temperature records and climate model simulations of past, 5 present and future climate changes. 5.3 Earth System Responses and Feedbacks factors such as tectonics and the evolution of biological systems, which at Global and Hemispheric Scales play an important role in the carbon cycle (e.g., Zachos et al., 2008). Although new reconstructions of deep-ocean temperatures have been This section updates the information available since AR4 on changes in compiled since AR4 (e.g., Cramer et al., 2011), low confidence remains surface temperature on million-year to orbital time scales and for the in the precise relationship between CO2 and deep-ocean temperature last 2000 years. New information on changes of the monsoon systems (Beerling and Royer, 2011). on glacial interglacial time scales is also assessed. Since AR4 new proxy and model data have become available from three 5.3.1 High-Carbon Dioxide Worlds and Temperature Cenozoic warm periods to enable an assessment of forcing, feedbacks and the surface temperature response (e.g., Dowsett et al., 2012; Lunt et Cenozoic (last 65 Ma) geological archives provide examples of natural al., 2012; Haywood et al., 2013). These are the Paleocene Eocene Ther- climate states globally warmer than the present, which are associated mal Maximum (PETM; Table 5.1), the Early Eocene Climatic Optimum with atmospheric CO2 concentrations above pre-industrial levels. This (EECO; Table 5.1) and the mid-Pliocene Warm Period (MPWP; Table 5.1). relationship between global warmth and high CO2 is complicated by Reconstructions of surface temperatures based on proxy data remain 398 Information from Paleoclimate Archives Chapter 5 challenged by (i) the limited number and uneven geographical dis- tive to the 1901 1920 mean (Haywood et al., 2013). Weakened merid- tribution of sites, (ii) seasonal biases and (iii) the validity of assump- ional temperature gradients are shown by all GCM simulations, and tions required by each proxy method (assessed in Table 5.A.3). There have significant implications for the stability of polar ice sheets and is also a lack of consistency in the way uncertainties are reported for sea level (see Box 5.1 and Section 5.6). SST gradients and the Pacific proxy climate estimates. In most cases error bars represent the analyti- Ocean thermocline gradient along the equator were greatly reduced cal and calibration error. In some compilations qualitative confidence compared to present (Fedorov et al., 2013) (Section 5.4). Vegetation assessments are reported to account for the quality of the age control, reconstructions (Salzmann et al., 2008) imply that the global extent of number of samples, fossil preservation and abundance, performance of arid deserts decreased and boreal forests replaced tundra, and GCMs the proxy method utilized and agreement of multiple proxy estimates predict an enhanced hydrological cycle, but with large inter-model (e.g., Multiproxy Approach for the Reconstruction of the Glacial Ocean spread (Haywood et al., 2013). The East Asian Summer Monsoon, as surface (MARGO) Project Members, 2009; Dowsett et al., 2012). well as other monsoon systems, may have been enhanced at this time (e.g., Wan et al., 2010). The PETM was marked by a massive carbon release and corresponding global ocean acidification (Zachos et al., 2005; Ridgwell and Schmidt, Climate reconstructions for the warm periods of the Cenozoic also pro- 2010) and, with low confidence, global warming of 4°C to 7°C rel- vide an opportunity to assess Earth-system and equilibrium climate ative to pre-PETM mean climate (Sluijs et al., 2007; McInerney and sensitivities. Uncertainties on both global temperature and CO2 recon- Wing, 2011). The carbon release of 4500 to 6800 PgC over 5 to 20 kyr structions preclude deriving robust quantitative estimates from the translates into a rate of emissions of ~0.5 to 1.0 PgC yr 1 (Panchuk et available PETM data. The limited number of models for MPWP, which al., 2008; Zeebe et al., 2009). GHG emissions from marine methane take into account slow feedbacks such as ice sheets and the carbon hydrate and terrestrial permafrost may have acted as positive feed- cycle, imply with medium confidence that Earth-system sensitivity may backs (DeConto et al., 2012). be up to two times the model equilibrium climate sensitivity (ECS) (Lunt et al., 2010; Pagani et al., 2010; Haywood et al., 2013). However, The EECO represents the last time atmospheric CO2 concentrations if the slow amplifying feedbacks associated with ice sheets and CO2 may have reached a level of ~1000 ppm (Section 5.2.2.2). There were are considered as forcings rather than feedbacks, climate records of no substantial polar ice sheets, and oceanic and continental configu- the past 65 Myr yield an estimate of 1.1°C to 7°C (95% confidence rations, vegetation type and distribution were significantly different interval) for ECS (PALAEOSENS Project Members, 2012) (see also Sec- from today. Whereas simulated SAT are in reasonable agreement with tion 5.3.3.2). reconstructions (Huber and Caballero, 2011; Lunt et al., 2012) (Box 5.1, Figure 1d), there are still significant discrepancies between simulat- 5.3.2 Glacial Interglacial Dynamics ed and reconstructed mean annual SST, which are reduced if seasonal biases in some of the marine proxies are considered for the high-lati- 5.3.2.1 Role of Carbon Dioxide in Glacial Cycles tude sites (Hollis et al., 2012; Lunt et al., 2012). Medium confidence is placed on the reconstructed global mean surface temperature anomaly Recent modelling work provides strong support for the important role estimate of 9°C to 14°C. of variations in the Earth s orbital parameters in generating long-term climate variability. In particular, new simulations with GCMs (Carlson The Pliocene is characterized by a long-term increase in global ice et al., 2012; Herrington and Poulsen, 2012) support the fundamental volume and decrease in temperature from ~3.3 2.6 Ma (Lisiecki and premise of the Milankovitch theory that a reduction in NH summer Raymo, 2005; Mudelsee and Raymo, 2005; Fedorov et al., 2013), which insolation generates sufficient cooling to initiate ice sheet growth. Cli- marks the onset of continental-scale glaciations in the NH. Superim- mate ice sheet models with varying degrees of complexity and forced posed on this trend, benthic d18O (Lisiecki and Raymo, 2005) and an by variations in orbital parameters and reconstructed atmospheric CO2 5 ice proximal geological archive (Lisiecki and Raymo, 2005; Naish et al., concentrations simulate ice volume variations and other climate char- 2009a) imply moderate fluctuations in global ice volume paced by the acteristics during the last and several previous glacial cycles consistent 41 kyr obliquity cycle. This orbital variability is also evident in far-field with paleoclimate records (Abe-Ouchi et al., 2007; Bonelli et al., 2009; sea level reconstructions (Miller et al., 2012a), tropical Pacific SST (Her- Ganopolski et al., 2010) (see Figure 5.3). bert et al., 2010) and Southern Ocean MDA records (Martinez-Garcia et al., 2011), and indicate a close coupling between temperature, atmos- There is high confidence that orbital forcing is the primary external pheric circulation and ice volume/sea level (Figure 5.2). The MPWP and driver of glacial cycles (Kawamura et al,. 2007; Cheng et al., 2009; the following 300 kyr represent the last time atmospheric CO2 concen- Lisiecki, 2010; Huybers, 2011). However, atmospheric CO2 content trations were in the range 350 to 450 ppm (Section 5.2.2.2, Figure 5.2). plays an important internal feedback role. Orbital-scale variability Model data comparisons (Box 5.1, Figure 1) provide high confidence in CO2 concentrations over the last several hundred thousand years that mean surface temperature was warmer than pre-industrial for the covaries (Figure 5.3) with variability in proxy records including recon- average interglacial climate state during the MPWP (Dowsett et al., structions of global ice volume (Lisiecki and Raymo, 2005), climatic 2012; Haywood et al., 2013). Global mean SST is estimated at +1.7°C conditions in central Asia (Prokopenko et al., 2006), tropical (Herbert (without uncertainty) above the 1901 1920 mean based on large data et al., 2010) and Southern Ocean SST (Pahnke et al., 2003; Lang and syntheses (Lunt et al., 2010; Dowsett et al., 2012). General circulation Wolff, 2011), Antarctic temperature (Parrenin et al., 2013), deep-ocean model (GCM) results agree with this SST anomaly (to within +/-0.5°C), temperature (Elderfield et al., 2010), biogeochemical conditions in the and produce a range of global mean SAT of +1.9°C and +3.6°C rela- North Pacific (Jaccard et al., 2010) and deep-ocean ventilation (Lisiecki 399 Chapter 5 Information from Paleoclimate Archives et al., 2008). Such close linkages between CO2 concentration and cli- For the last glacial termination, a large-scale temperature reconstruc- mate variability are consistent with modelling results suggesting with tion (Shakun et al., 2012) documents that temperature change in the high confidence that glacial interglacial variations of CO2 and other SH lead NH temperature change. This lead can be explained by the GHGs explain a considerable fraction of glacial interglacial climate bipolar thermal seesaw concept (Stocker and Johnsen, 2003) (see also variability in regions not directly affected by the NH continental ice Section 5.7) and the related changes in the inter-hemispheric ocean sheets (Timmermann et al., 2009; Shakun et al., 2012). heat transport, caused by weakening of the Atlantic Ocean meridional overturning circulation (AMOC) during the last glacial termination 5.3.2.2 Last Glacial Termination (Ganopolski and Roche, 2009). SH warming prior to NH warming can also be explained by the fast sea ice response to changes in austral It is very likely that global mean surface temperature increased by 3°C spring insolation (Stott et al., 2007; Timmermann et al., 2009). Accord- to 8°C over the last deglaciation (see Table 5.2), which gives a very ing to these mechanisms, SH temperature lead over the NH is fully likely average rate of change of 0.3 to 0.8°C kyr 1. Deglacial global consistent with the NH orbital forcing of deglacial ice volume chang- warming occurred in two main steps from 17.5 to 14.5 ka and 13.0 es (high confidence) and the importance of the climate carbon cycle to 10.0 ka that likely reached maximum rates of change between feedbacks in glacial interglacial transitions. The tight coupling is fur- 1°C kyr 1 and 1.5°C kyr 1 at the millennial time scale (cf. Shakun et ther highlighted by the near-zero lag between the deglacial rise in CO2 al., 2012; Figure 5.3i), although regionally and on shorter time scales and averaged deglacial Antarctic temperature recently reported from higher rates may have occurred, in particular during a sequence of improved estimates of gas-ice age differences (Pedro et al., 2012; Par- abrupt climate change events (see Section 5.7). renin et al., 2013). Previous studies (Monnin et al., 2001; Table 5.A.4) 5 Figure 5.3 | Orbital parameters and proxy records over the past 800 kyr. (a) Eccentricity. (b) Obliquity. (c) Precessional parameter (Berger and Loutre, 1991). (d) Atmospheric concentration of CO2 from Antarctic ice cores (Petit et al., 1999; Siegenthaler et al., 2005; Ahn and Brook, 2008; Lüthi et al., 2008). (e) Tropical sea surface temperature stack (Herbert et al., 2010). (f) Antarctic temperature stack based on up to seven different ice cores (Petit et al., 1999; Blunier and Brook, 2001; Watanabe et al., 2003; European Project for Ice Coring in Antarctica (EPICA) Community Members, 2006; Jouzel et al., 2007; Stenni et al., 2011). (g) Stack of benthic 18O, a proxy for global ice volume and deep-ocean temperature (Lisiecki and Raymo, 2005). (h) Reconstructed sea level (dashed line: Rohling et al., 2010; solid line: Elderfield et al., 2012). Lines represent orbital forcing and proxy records, shaded areas represent the range of simulations with climate models (Grid Enabled Integrated Earth System Model-1, GENIE-1, Holden et al., 2010a; Bern3D, Ritz et al., 2011), climate ice sheet models of intermediate complexity (CLIMate and BiosphERe model, CLIMBER-2, Ganopolski and Calov, 2011) and an ice sheet model (ICe sheet model for Integrated Earth system studies, IcIES, Abe-Ouchi et al., 2007) forced by variations of the orbital parameters and the atmospheric concentrations of the major greenhouse gases. (i) Rate of changes of global mean temperature during Termination I based on Shakun et al. (2012). 400 Information from Paleoclimate Archives Chapter 5 suggesting a temperature lead of 800 +/- 600 years over the deglacial data (Clemens et al., 2010) document that speleothem d18O variations CO2 rise probably overestimated gas-ice age differences. in some monsoon regions can be explained as a combination of chang- es in local precipitation and large-scale moisture transport. 5.3.2.3 Monsoon Systems This subsection focuses on the response of monsoon systems to orbital Since AR4, new high-resolution hydroclimate reconstructions using forcing on glacial interglacial time scales. Proxy data including spe- speleothems (Sinha et al., 2007; Hu et al., 2008; Wang et al., 2008; Cruz leothem d18O from southeastern China (Wang et al., 2008), northern et al., 2009; Asmerom et al., 2010; Berkelhammer et al., 2010; Stríkis et Borneo (Meckler et al., 2013), eastern Brazil (Cruz et al., 2005) and al., 2011; Kanner et al., 2012), lake sediments (Shanahan et al., 2009; the Arabian Peninsula (Bar-Matthews et al., 2003), along with marine- Stager et al., 2009; Wolff et al., 2011), marine sediments (Weldeab et based records off northwestern Africa (Weldeab et al., 2007a) and from al., 2007b; Mulitza et al., 2008; Tjallingii et al., 2008; Ponton et al., the Arabian Sea (Schulz et al., 1998) document hydrological chang- 2012) and tree-ring chronologies (Buckley et al., 2010; Cook et al., es that are dominated by eccentricity-modulated precessional cycles. 2010a) have provided a more comprehensive view on the dynamics Increasing boreal summer insolation can generate a strong inter-hemi- of monsoon systems on a variety of time scales. Water isotope-ena- spheric surface temperature gradient that leads to large-scale decreas- bled modelling experiments (LeGrande and Schmidt, 2009; Lewis et es in precipitation in the SH summer monsoon systems and increased al., 2010; Pausata et al., 2011) and evaluation of marine and terrestrial hydrological cycle in the NH tropics (Figure 5.4a, d, g). Qualitatively (a) Insolation at 20°N/20°S (b) Greenland: NGRIP (c) Proxy data locations HS5 HS4 35 b 480 f Temperature (°C) Insolation (W m-2) d1 40 e d2 460 i1 h 45 i2 g 440 50 80 60 40 20 0 50 45 40 Age (ka) Age (ka) (d) China: Hulu (d1), Sanbao (d2) caves (e) China: XiaoBailong cave (f) China: Huangye (f1), Wanxian (f2) caves Standardized hydrological change Standardized hydrological change 13 HS5 HS4 2 12 2 11 18O ( ) 0 0 10 2 2 9 LIA 80 60 40 20 50 45 40 1000 1500 2000 Age (ka) Age (ka) Time (Year CE) (g) Brazil: Botuvéra cave (h) Peru: Pacupahuain cave (i) Peru: Cascayunga (i1) cave, Pumacocha (i2) lake Standardized hydrological change Standardized hydrological change 4 17 2 2 16 0 18O ( ) 0 15 5 2 2 HS5 HS4 LIA 14 4 80 60 40 20 50 45 40 1000 1500 2000 Age (ka) Age (ka) Time (Year CE) Figure 5.4 | Inter-hemispheric response of monsoon systems at orbital, millennial and centennial scales. (a) Boreal summer insolation changes at 20°N (red) (W m 2) and austral summer insolation changes at 20°S (blue). (b) Temperature changes in Greenland (degrees Celsius) reconstructed from North Greenland Ice Core Project (NGRIP) ice core on SS09 time scale (Huber et al., 2006), location indicated by orange star in c. (c) Location of proxy records displayed in panels a, b, d i in relation to the global monsoon regions (cyan shad- ing) (Wang and Ding, 2008). (d) Reconstructed (red) standardized negative d18O anomaly in East Asian Summer Monsoon region derived from Hulu (Wang et al., 2001) and Sanbao (Wang et al., 2008) cave speleothem records, China and simulated standardized multi-model average (black) of annual mean rainfall anomalies averaged over region 108°E to 123°E and 25°N to 40°N using the transient runs conducted with LOch Vecode-Ecbilt-CLio-agIsm Model (LOVECLIM, Timm et al., 2008), FAst Met Office/UK Universities Simulator (FAMOUS, Smith and Gregory, 2012), and the Hadley Centre Coupled Model (HadCM3) snapshot simulations (Singarayer and Valdes, 2010). (e) d18O from Xiaobailong cave, China (Cai et al., 2010). (f) Standardized negative d18O anomalies (red) in Huangye (Tan et al., 2011) and Wanxian (Zhang et al., 2008) caves, China and simulated standardized annual mean and 30-year low-pass filtered rainfall anomalies (black) in region 100°E to 110°E, 20°N to 35°N, ensemble averaged over externally forced Atmosphere-Ocean General Circulation Model (AOGCM) experiments conducted with Community Climate System Model-4 (CCSM4), ECHAM4+HOPE-G (ECHO-G), Max Planck Institute Earth System Model (MPI-ESM), Commonwealth Scientific and Industrial Research Organisation model (CSIRO-Mk3L-1-2), Model for Interdisciplinary Research on Climate (MIROC), HadCM3 (Table 5.A.1). (g) Standardized negative d18O anomaly (blue) from Botuvéra speleothem, Brazil (Cruz et al., 2005) and simulated standardized multi-model average (black) of annual mean rainfall anomalies averaged over region 45°W to 60°W and 35°S to 15°S using same experiments as in panel d. (h) Standardized d18O anomaly (blue) from Pacupahuain cave, Peru (Kanner et al., 2012). (i) Standardized negative d18O anomalies (blue) from Cascayunga Cave, Peru (Reuter et al., 2009) and Pumacocha Lake, Peru (Bird et al., 2011) and simulated standardized annual mean and 30-year low-pass filtered rainfall anomalies (black) in region 76°W to 70°W, 16°S to 8°S, ensemble averaged over the same model simulations as in f. HS4/5 denote Heinrich stadials 4 and 5, and LIA denotes Little Ice Age (Table 5.1). 401 Chapter 5 Information from Paleoclimate Archives similar, out-of-phase inter-hemispheric responses to insolation forcing al., 2006; Marzin and Braconnot, 2009). For example, in the mid-Holo- have also been documented in coupled time-slice and transient GCM cene drier conditions occurred in central North America (Diffenbaugh simulations (Braconnot et al., 2008; Kutzbach et al., 2008) (see also et al., 2006) and wetter conditions in northern Africa (Liu et al., 2007b; Figure 5.4d, g). The similarity in response in both proxy records and Hély et al., 2009; Tierney et al., 2011). There is further evidence for models provide high confidence that orbital forcing induces inter-hem- east west shifts of precipitation in response to orbital forcing in South ispheric rainfall variability. Across longitudes, the response of precipi- America (Cruz et al., 2009). tation may, however, be different for the same orbital forcing (Shin et Box 5.2 | Climate-Ice Sheet Interactions Ice sheets have played an essential role in the Earth s climate history (see Sections 5.3, 5.6 and 5.7). They interact with the atmosphere, the ocean sea ice system, the lithosphere and the surrounding vegetation (see Box 5.2, Figure 1). They serve as nonlinear filters and integrators of climate effects caused by orbital and GHG forcings (Ganopolski and Calov, 2011), while at the same time affecting the global climate system on a variety of time scales (see Section 5.7). Ice sheets form when annual snow accumulation exceeds melting. Growing ice sheets expand on previously vegetated areas, thus leading to an increase of surface albedo, further cooling and an increase in net surface mass balance. As ice sheets grow in height and area, surface temperatures drop further as a result of the lapse-rate effect, but also snow accumulation decreases because colder air holds less moisture (inlay in Box 5.2, Figure 1). This so-called elevation-desert effect (Oerlemans, 1980) is an important negative feedback for ice sheets which limits their growth. Higher elevation ice sheets can be associated with enhanced calving at their margins, because the ice flow will be accelerated directly by increased surface slopes and indirectly by lubrication at the base of the ice sheet. Calving, grounding line processes, basal lubrication and other forms of thermo-mechanical coupling may have played important roles in accelerating glacial terminations following phases of relatively slow ice sheet growth, hence contributing to the temporal saw-tooth structure of the recent glacial interglacial cycles (Figure 5.3). Large glacial ice sheets also deflect the path of the extratropical NH westerly winds (Cook and Held, 1988), generating anticyclonic circulation anomalies (Box 5.2, Figure 1), which tend to warm the western side of the ice sheet and cool the remainder (e.g., Roe and Lindzen, 2001). Furthermore, the orographic effects of ice sheets lead to reorganizations of the global atmosphere circulation by chang- ing the major stationary wave patterns (e.g., Abe-Ouchi et al., 2007; Yin et al., 2008) and trade wind systems (Timmermann et al., 2004). This allows for a fast transmission of ice sheet signals to remote regions. The enormous weight of ice sheets depresses the underlying bedrocks causing a drop in ice sheet height and a surface warming as a result of the lapse-rate effect. The lithospheric adjustment has been shown to play an important role in modulating the ice sheet response to orbital forcing (Birchfield et al., 1981; van den Berg et al., 2008). The presence of terrestrial sedimentary materials (regolith) on top of the unweathered bedrock affects the friction at the base of an ice sheet, and may further alter the response of continental ice sheets to external forcings, with impacts on the dominant periodicities of glacial cycles (Clark and Pollard, 1998). 5 An area of very active research is the interaction between ice sheets, ice shelves and the ocean (see Sections 4.4, 13.4.3 and 13.4.4). The mass balance of marine ice sheets is strongly determined by ocean temperatures (Joughin and Alley, 2011). Advection of warmer waters below ice shelves can cause ice shelf instabilities, reduced buttressing, accelerated ice stream flow (De Angelis and Skvarca, 2003) and grounding line retreat in regions with retrograde bedrock slopes (Schoof, 2012), such as West Antarctica. On orbital and millennial time scales such processes may have played an essential role in driving ice volume changes of the West Antarctic ice sheet (Pollard and DeConto, 2009) and the Laurentide ice sheet (Alvarez-Solas et al., 2010). Massive freshwater release from retreating ice sheets, can feed back to the climate system by altering sea level, oceanic deep convection, ocean circulation, heat transport, sea ice and the global atmospheric circulation (Sections 5.6.3 and 5.7). Whereas the initial response of ice sheets to external forcings can be quite fast, involving for instance ice shelf processes and outlet glaciers (10 to 103 years), their long-term adjustment can take much longer (104 to 105 years) (see Section 12.5.5.3). As a result, the climate cryosphere system is not even in full equilibrium with the orbital forcing. This also implies that future anthropogenic radiative perturbations over the next century can determine the evolution of the Greenland (Charbit et al., 2008) and Antarctic ice sheets for centuries and millennia to come with a potential commitment to significant global sea level rise (Section 5.8). (continued on next page) 402 Information from Paleoclimate Archives Chapter 5 Box 5.2 (continued) Tem p era tur stationary wave feedback e Mo * istu Height * * * re *** * ** atmosphere-ice sheet interaction ** ** * *** k albedo snow at accumulation ab ocean-atmosphere at interaction ic in w land-ice sheet d sea-ice dust interaction ice shelf calving ablation zone geothermal 103 105 heatflux years moulins icebergs ocean-ice sheet iceberg interaction soot 1 103 years ocean subglacial lakes fire bedrock adjustment Box 5.2, Figure 1 | Schematic illustration of multiple interactions between ice sheets, solid earth and the climate system which can drive internal variability and affect the coupled ice sheet climate response to external forcings on time scales of months to millions of years. The inlay figure represents a typical height profile of atmospheric temperature and moisture in the troposphere. 5 5.3.3 Last Glacial Maximum and Equilibrium s ­ynthesis expanded earlier work (CLIMAP Project Members, 1976, Climate Sensitivity 1981; Sarnthein et al., 2003a; Sarnthein et al., 2003b) by using multiple proxies (Table 5.2). The land SAT synthesis is based on pollen data, The LGM is characterized by a large temperature response (Section following the Cooperative Holocene Mapping Project (COHMAP Mem- 5.3.3.1) to relatively well-defined radiative perturbations (Section bers, 1988). 5.2), linked to atmospheric CO2 concentration around 200 ppm (Sec- tion 5.2.2) and large ice sheets covering northern Europe and North Climate models and proxy data consistently show that mean annual America. This can be used to evaluate climate models (Braconnot et SST change (relative to pre-industrial) is largest in the mid-latitude al. (2012b); see Sections 9.7 and 10.8) and to estimate ECS from the North Atlantic (up to 10C), and the Mediterranean (about 6C) combined use of proxy information and simulations (Section 5.3.3.2). (MARGO Project Members, 2009, Box 5.1, Table 5.2). Warming and seasonally ice-free conditions are reconstructed, however, in the north- 5.3.3.1 Last Glacial Maximum Climate eastern North Atlantic, in the eastern Nordic Seas and north Pacific, albeit with large uncertainty because of the different interpretation of Since AR4, synthesis of proxy LGM temperature estimates was com- proxy data (de Vernal et al., 2006). SAT reconstructions generally shows pleted for SST (MARGO Project Members, 2009), and for land SAT year-round cooling, with regional exceptions such as Alaska (Bartlein (Bartlein et al., 2011) (Box 5.1, Figure 1). The Multiproxy Approach et al., 2011). Modelling studies show how atmospheric dynamics influ- for the Reconstruction of the Glacial Ocean Surface (MARGO) SST enced by ice sheets affect regional temperature patterns in the North 403 Chapter 5 Information from Paleoclimate Archives Table 5.2 | Summary of Last Glacial Maximum (LGM) sea surface temperature (SST) and surface air temperature (SAT) reconstructions (anomalies with respect to pre-industrial climate) using proxy data and model ensemble constrained by proxy data. Cooling ranges indicate 90% confidence intervals (C.I., where available). Region Cooling (°C) 90% C.I. Methods Reference and remarks Sea Surface Temperature (SST) Global 0.7 2.7 Multi-proxy MARGO Project Members (2009) Mid-latitude North Atlantic up to 10 Multi-proxy MARGO Project Members (2009) Southern Ocean 2 6 Multi-proxy MARGO Project Members (2009) MARGO Project Members (2009) 1.7°C +/- 1°C: 15°S to 15°N Low-latitude 0.3 2.7 Multi-proxy 2.9°C +/- 1.3°C: Atlantic 15°S to 15°N (30°S to 30°N) 1.2°C +/- 1.1°C : Pacific 15°S to 15°N (1.2°C +/- 1°C based on microfossil assemblages; 2.5°C +/- 1°C based on Mg/Ca ratios and alkenones) Low-latitude 2.2 3.2 Multi-proxy Ballantyne et al. (2005) (30°S to 30°N) Lea et al. (2000); de Garidel-Thoron et al. (2007); Leduc et al. Low-latitude (western and 2 3 Multi-proxy (2007); Pahnke et al. (2007); Stott et al. (2007); Koutavas and eastern tropical Pacific) Sachs (2008); Steinke et al. (2008); Linsley et al. (2010) Surface Air Temperature (SAT) Eastern Antarctica 7 10 Water stable isotopes from ice core Stenni et al. (2010); Uemura et al. (2012) Central Greenland 21 25 Borehole paleothermometry Cuffey et al. (1995); Johnsen et al. (1995); Dahl-Jensen et al. (1998) Single-EMIC ensemble with microfossil- Global 4.4 7.2 Schneider von Deimling et al. (2006) assemblage derived tropical Atlantic SST Single-EMIC ensemble with multi- Global 4.6 8.3 Holden et al. (2010a) proxy derived tropical SST Single-EMIC ensemble with Global 1.7 3.7 Schmittner et al. (2011) global multi-proxy data Global 3.9 4.6 Multi-proxy Shakun et al. (2012); for the interval 17.5 9.5 ka Multi-AOGCM ensemble with Global 3.4 4.6 Annan and Hargreaves (2013) global multi-proxy data Global 3.1 5.9 Multi-AOGCM ensemble PMIP2 and PMIP3/CMIP5 Notes: AOGCM = Atmosphere-Ocean General Circulation Model; CMIP5 = Coupled Model Intercomparison Project Phase 5; EMIC = Earth System Model of Intermediate Complexity; MARGO = Multiproxy Approach for the Reconstruction of the Glacial Ocean surface; PMIP2 and PMIP3 = Paleoclimate Modelling Intercomparison Project Phase II and III, respectively. Atlantic region (Lainé et al., 2009; Pausata et al., 2011; Unterman et dustrial period (Lea et al., 2000, 2006; de Garidel-Thoron et al., 2007; al., 2011; Hofer et al., 2013), and in the north Pacific region (Yanase Leduc et al., 2007; Pahnke et al., 2007; Stott et al., 2007; Koutavas and 5 and Abe-Ouchi, 2010). Larger cooling over land compared to ocean Sachs, 2008; Steinke et al., 2008; Linsley et al., 2010). AOGCMs tend to is a robust feature of observations and multiple atmosphere ocean underestimate longitudinal patterns of tropical SST (Otto-Bliesner et general circulation models (AOGCM) (Izumi et al., 2013). As in AR4, al., 2009) and atmospheric circulation (DiNezio et al., 2011). central Greenland temperature change during the LGM is underesti- mated by PMIP3/CMIP5 simulations, which show 2°C to 18°C cooling, Larger sea ice seasonality is reconstructed for the LGM compared to compared to 21°C to 25°C cooling reconstructed from ice core data the pre-industrial period around Antarctica (Gersonde et al., 2005). Cli- (Table 5.2). A mismatch between reconstructions and model results mate models underestimate this feature, as well as the magnitude of may arise because of missing Earth system feedbacks (dust, vegeta- Southern Ocean cooling (Roche et al., 2012) (see Box 5.1, Figure 1). In tion) (see Section 5.2.2.3) or insufficient integration time to reach an ­ Antarctica 7°C to 10°C cooling relative to the pre-industrial period is equilibrium for LGM boundary conditions. reconstructed from ice cores (Stenni et al., 2010; Uemura et al., 2012) and captured in most PMIP3/CMIP5 simulations (Figure 5.5d). Uncertainties remain on the magnitude of tropical SST cooling during the LGM. Previous estimates of tropical cooling (2.7°C +/- 0.5°C, Bal- The combined use of proxy reconstructions, with incomplete spatial lantyne et al., 2005) are greater than more recent estimates (1.5°C +/- coverage, and model simulations is used to estimate LGM global mean 1.2°C, MARGO Project Members, 2009). Such discrepancies may arise temperature change (Table 5.2). One recent such study, combining from seasonal productivity (Leduc et al., 2010) and habitat depth (Tel- multi-proxy data with multiple AOGCMs, estimates LGM global cooling ford et al., 2013) biases. In the western and eastern tropical Pacific, at 4.0°C +/- 0.8°C (95% confidence interval) (Annan and Hargreaves, many proxy records show 2°C to 3°C cooling relative to the pre-in- 2013). This result contrasts with the wider range of global cooling 404 Information from Paleoclimate Archives Chapter 5 (1.7°C to 8.3°C) obtained using Earth-system models of intermediate 5.3.3.2 Last Glacial Maximum Constraints on Equilibrium complexity (EMIC) (Schneider von Deimling et al., 2006; Holden et al., Climate Sensitivity 2010b; Schmittner et al., 2011). The source of these differences in the estimate of LGM cooling may be the result of (i) the proxy data used Temperature change recorded in proxies results from various feed- to constrain the simulations, such as data sets associated with mild back processes, and external forcings vary before equilibrium of the cooling in the tropics and the North Atlantic; (ii) model resolution and whole Earth system is reached. Nevertheless, the equilibrium climate structure, which affects their ability to resolve the land sea contrast s ­ ensitivity (ECS) can be estimated from past temperatures by explic- (Annan and Hargreaves, 2013) and polar amplification (Fyke and Eby, itly counting the slow components of the processes (e.g., ice sheets) 2012); (iii) the experimental design of the simulations, where the lack as forcings, rather than as feedbacks (PALAEOSENS Project Members, of dust and vegetation feedbacks (Section 5.2.2.3) and insufficient 2012). This is achieved in three fundamentally different ways (Edwards integration time. Based on the results and the caveats in the studies et al., 2007); see also Sections 9.7.3.2 and 10.8.2.4. assessed here (Table 5.2), it is very likely that global mean surface tem- perature during the LGM was cooler than pre-industrial by 3°C to 8°C. In the first approach, ECS is estimated by scaling the reconstructed global mean temperature change in the past with the RF difference of Some recent AOGCM simulations produce a stronger AMOC under the past and 2 × CO2 (Hansen et al., 2008; Köhler et al., 2010) (Table LGM conditions, leading to mild cooling over the North Atlantic and 5.3). The results are subject to uncertainties in the estimate of global GIS (Otto-Bliesner et al., 2007; Weber et al., 2007). This finding con- mean surface temperature based on proxy records of incomplete spa- trasts with proxy-based information (Lynch-Stieglitz et al., 2007; Hesse tial coverage (see Section 5.3.3.1). Additional uncertainty is introduced et al., 2011). Changes in deep-ocean temperature and salinity during when the sensitivity to LGM forcing is scaled to the sensitivity to 2 × the LGM have been constrained by pore-water chemistry in deep-sea CO2 forcing, as some but not all (Brady et al., 2013) models show that sediments. For example, pore-water data indicate that deep water in these sensitivities differ due to the difference in cloud feedbacks (Cruci- the Atlantic Ocean cooled by between 1.7°C +/- 0.9°C and 4.5°C +/- fix, 2006; Hargreaves et al., 2007; Yoshimori et al., 2011) (Figure 5.5a, b). 0.2°C, and became saltier by up to 2.4 +/- 0.2 psu in the South Atlan- tic and at least 0.95 +/- 0.07 psu in the North Atlantic (cf. Adkins et In the second approach, an ensemble of LGM simulations is carried al., 2002). The magnitude of deep-water cooling is supported by other out using a single climate model in which each ensemble member marine proxy data (e.g., Dwyer et al., 2000; Martin et al., 2002; Elder- differs in model parameters and the ensemble covers a range of ECS field et al., 2010), while the increase in salinity is consistent with inde- (Annan et al., 2005; Schneider von Deimling et al., 2006; Holden et al., pendent estimates based on d18O (Duplessy et al., 2002; Waelbroeck 2010a; Schmittner et al., 2011) (Table 5.3). Model parameters are then et al., 2002). Average salinity increased due to storage of freshwater constrained by comparison with LGM temperature proxy information, in ice sheets. The much larger than average salinity increase in deep generating a probability distribution of ECS. Although EMICSs are often Southern Ocean compared to the North Atlantic is probably due to used in order to attain sufficiently large ensemble size, the uncertainty increased salt rejection through sea ice freezing processes around Ant- arising from asymmetric cloud feedbacks cannot be addressed because arctica (Miller et al., 2012b) (see also Section 9.4.2.3.2 ). they are parameterized in the EMICs. As a result of prevailing LGM modelling uncertainties (Chavaillaz et al., In the third approach, multiple GCM simulations are compared to proxy 2013; Rojas, 2013) and ambiguities in proxy interpretations (Kohfeld et data, and performance of the models and indirectly their ECSs are al., 2013), it cannot be determined robustly whether LGM SH wester- assessed (Otto-Bliesner et al., 2009; Braconnot et al., 2012b). Because lies changed in amplitude and position relative to today. the cross-model correlation between simulated LGM global cooling Table 5.3 | Summary of equilibrium climate sensitivity (ECS) estimates based on Last Glacial Maximum (LGM) climate. Uncertainty ranges are 5 to 95% confidence intervals, 5 with the exception of Multiproxy Approach for the Reconstruction of the Glacial Ocean surface (MARGO) Project Members (2009), where the published interval is reported here. Method (number follows the ECS Estimate (°C) Reference and Model Name approach in the text) 1.0 3.6 MARGO Project Members (2009) 1. Proxy data 1.4 5.2 Köhler et al. (2010) <6 Annan et al. (2005), MIROC3.2 AOGCM 1.2 4.3 Schneider von Deimling et al. (2006), CLIMBER-2 2. Single-model ensemble constrained by proxy data 2.0 5.0 Holden et al. (2010a), GENIE-1 1.4 2.8 Schmittner et al. (2011), UVic ~3.6 Fyke and Eby (2012), UVic 1.2 4.2 Hargreaves et al. (2012), updated with addition of PMIP3/CMIP5 AOGCMs 3. Multi-GCM ensemble constrained by proxy data 1.6 4.5 Hargreaves et al. (2012), updated with addition of PMIP3/CMIP5 AOGCMsa Notes: a Temperature constraints in the tropics were lowered by 0.4°C according to Annan and Hargreaves (2013). AOGCM = Atmosphere-Ocean General Circulation Model; CMIP5 = Coupled Model Intercomparison Project Phase 5; CLIMBER-2 = CLIMate and BiosphERe model-2; GENIE-1 = Grid ENabled Integrated Earth system model, version 1; MIROC3.2 = Model for Interdisciplinary Research on Climate 3.2; UVic = University of Victoria Earth system model; PMIP3 = Paleoclimate Modelling Intercomparison Project Phase III. 405 Chapter 5 Information from Paleoclimate Archives (a) Equilibrium climate sensitivity (°C) (b) (c) (d) Figure 5.5 | (a) Relation between equilibrium climate sensitivity (ECS) estimated from Last Glacial Maximum (LGM) simulations and that estimated from equilibrium 2 × CO2 or abrupt 4 × CO2 experiments. All experiments are referenced to pre-industrial simulations. Flexible Global Ocean Atmosphere Land System model version 1 (FGOALS1), Institut Pierre Simon Laplace version 4 (IPSL4), Max Planck Institute version 5 (MPI5), and ECBILT of Paleoclimate Modelling Intercomparison Project Phase II (PMIP2) models stand 5 for FGOALS-1.0g, IPSL-CM4-V1-MR, European Centre Hamburg Model 5 and Max Planck Institute Ocean Model Lund-Potsdam-Jena Dynamic Global Model (LPJ), and Coupled Atmosphere Ocean Model from de Bilt (ECBilt) with Coupled Large-scale Ice-Ocean model (CLIO), respectively. Flexible Global Ocean Atmosphere Land System model-2 (FGOALS2), Institut Pierre Simon Laplace 5 (IPSL5), Model for Interdisciplinary Research on Climate (MIROC3), Max Planck Institute für Meteorologie Earth system model (MPIE), Meteorological Research Institute of Japan Meteorological Agency version 3 (MRI3) and Centre National de Recherches Météorologiques version 5 (CNRM5) of Paleoclimate Modelling Intercomparison Project Phase III (PMIP3) models stand for FGOALS-g2, IPSL-CM5A-LR, MIROC-ESM, MPI-ESM-P, MRI-CGCM3, and CNRM-CM5, respectively. ECS based on LGM simulations (abscissa) was derived by multiplying the LGM global mean temperature anomaly (DTLGM) and the ratio of radiative forcing between 2 × CO2 and LGM (DFratio). DFratio for three PMIP2 models (Community Climate System Model-3 (CCSM3), Met Office Hadley Centre climate prediction models-3 (HadCM3) and IPSL4) were taken from Crucifix (2006), and it was taken from Yoshimori et al. (2009) for MIROC3. Its range, 0.80 to 0.56, with a mean of 0.69 was used for other PMIP2 and all PMIP3 models. ECS of PMIP2 models (ordinate) was taken from Hargreaves et al. (2012). ECS of PMIP3 models was taken from Andrews et al. (2012) and Brady et al. (2013), or computed using the method of Andrews et al. (2012) for FGOALS2. Also plotted is a one-to-one line. (b) Strength of individual feedbacks for the PMIP3/CMIP5 abrupt 4 × CO2 (131 to 150 years) and LGM (stable states) experiments following the method in Yoshimori et al. (2011). WV+LR, A, CSW and CLW denote water vapour plus lapse rate, surface albedo, shortwave cloud, and longwave cloud feedbacks, respectively. All denotes the sum of all feedbacks except for the Planck response. Feedback parameter here is defined as the change in net radiation at the top of the atmosphere due to the change in individual fields, such as water vapour, with respect to the pre-industrial simulations. It was normalized by the global mean surface air temperature change. Positive (negative) value indicates that the feedback amplifies (damp) the initial temperature response. Only models with all necessary data available for the analysis are displayed. (c) Relation between LGM tropical (20°S to 30°N) surface air temperature anomaly from pre-industrial simulations and ECS across models. A dark blue bar represents a 90% confidence interval for the estimate of reconstructed temperature anomaly of Annan and Hargreaves (2013). A light blue bar represents the same with additional 0.4°C anomaly increase according to the result of the sensitivity experiment conducted in Annan and Hargreaves (2013), in which additional 1°C lowering of tropical SST proxy data was assumed. (d) Same as in (c) but for the average of East Antarctica ice core sites of Dome F, Vostok, European Project for Ice Coring in Antarctica Dome C and Droning Maud Land ice cores. A dark blue bar represents a range of reconstructed temperature anomaly based on stable isotopes at these core sites (Stenni et al., 2010; Uemura et al., 2012). A value below zero is not displayed (the result of CNRM5). Note that the Antarctic ice sheet used for PMIP3 simulations, based on several different methods (Tarasov and Peltier, 2007; Argus and Peltier, 2010; Lambeck et al., 2010) differs in elevation from that used in PMIP2 (d). 406 Information from Paleoclimate Archives Chapter 5 and ECS is poor (Figure 5.5a), ECS cannot be constrained by the LGM this transition (Jouzel et al., 2007) and marine records of deep-wa- global mean cooling. Models that show weaker sensitivity to LGM forc- ter temperatures are characterized by generally higher values during ing than 4 × CO2 forcing in Figure 5.5a tend to show weaker shortwave later interglacials than earlier interglacials (Lang and Wolff, 2011). In cloud feedback and hence weaker total feedback under LGM forcing c ­ ontrast, similar interglacial magnitudes are observed across the ~430 than 4 × CO2 forcing (Figure 5.5b), consistent with previous studies ka boundary in some terrestrial archives from Eurasia (Prokopenko et (Crucifix, 2006). The relation found between simulated LGM tropical al., 2002; Tzedakis et al., 2006; Candy et al., 2010). Simulations with cooling and ECS across multi-GCM (Hargreaves et al., 2012; Figure an EMIC relate global and southern high latitude mean annual surface 5.5c) has been used to constrain the ECS with the reconstructed LGM temperature variations to changes in CO2 variations, while orbital forc- tropical cooling (Table 5.3). Again, the main caveats of this approach ing and associated feedbacks of vegetation and sea ice have a major are the uncertainties in proxy reconstructions and missing dust and impact on the simulated northern high-latitude mean annual surface vegetation effects which may lead to underestimated LGM cooling in temperature (Yin and Berger, 2012). The highest and lowest interglacial the simulations. temperatures occur in models when WMGHG concentrations and local insolation reinforce each other (Yin and Berger, 2010, 2012; Herold et Based on these different approaches, estimates of ECS yield low prob- al., 2012). ability for values outside the range 1°C to 5°C (Table 5.3). Even though there is some uncertainty in these studies owing to problems in both At the time of the AR4, a compilation of Arctic records and two AOGCM the paleoclimate data and paleoclimate modelling as discussed in this simulations allowed an assessment of LIG summer temperature chang- section, it is very likely that ECS is greater than 1°C, and very unlikely es. New quantitative data syntheses (Figure 5.6a) now allow estima- that ECS exceeds 6°C. tion of maximum annual surface temperatures around the globe for the LIG (Turney and Jones, 2010; McKay et al., 2011). A caveat is that 5.3.4 Past Interglacials these data syntheses assume that the warmest phases were globally synchronous (see Figure 5.6 legend for details). However, there is high Past interglacials are characterized by different combinations of orbit- confidence that warming in the Southern Ocean (Cortese et al., 2007; al forcing (Section 5.2.1.1), atmospheric composition (Section 5.2.2.1) Schneider Mor et al., 2012) and over Antarctica (Masson-Delmotte et and climate responses (Tzedakis et al., 2009; Lang and Wolff, 2011). al., 2010b) occurred prior to peak warmth in the North Atlantic, Nordic Documenting natural interglacial climate variability in the past pro- Seas, and Greenland (Bauch et al., 2011; Govin et al., 2012; North vides a deeper understanding of the physical climate responses to Greenland Eemian Ice Drilling (NEEM) community members, 2013). orbital forcing. This section reports on interglacials of the past 800 kyr, Overall, higher annual temperatures than pre-industrial are recon- with emphasis on the Last Interglacial (LIG, Table 5.1) which has more structed for high latitudes of both hemispheres. At ~128 ka, East Ant- data and modelling studies for assessing regional and global tempera- arctic ice cores record early peak temperatures ~5°C above the present ture changes than earlier interglacials. The LIG sea level responses are (Jouzel et al., 2007; Sime et al., 2009; Stenni et al., 2010). Higher tem- assessed in Section 5.6.2. Section 5.5 is devoted to the current inter- peratures are derived for northern Eurasia and Alaska, with sites near glacial, the Holocene. the Arctic coast in Northeast Siberia indicating warming of more than 10°C as compared to late Holocene (Velichko et al., 2008). Greenland The phasing and strengths of the precessional parameter and obliquity warming of 8°C +/- 4°C at 126 ka is estimated from the new Greenland varied over past interglacials (Figure 5.3b, c), influencing their timing, NEEM ice core, after accounting for ice sheet elevation changes (NEEM duration, and intensity (Tzedakis et al., 2012b; Yin and Berger, 2012) community members, 2013). Seasonally open waters off northern (Figure 5.3e, f, h). Since 800 ka, atmospheric CO2 concentrations during Greenland and in the central Arctic are recorded during the LIG (Nr- interglacials were systematically higher than during glacial periods. gaard-Pedersen et al., 2007; Adler et al., 2009). Changes in Arctic sea Prior to ~430 ka, ice cores from Antarctica record lower interglacial ice cover (Sime et al., 2013) may have affected the Greenland water 5 CO2 concentrations than for the subsequent interglacial periods (Sec- stable isotope temperature relationship, adding some uncertainty to tion 5.2.2.1; Figure 5.3d). While LIG WMGHG concentrations were LIG Greenland temperature reconstructions. Marine proxies from the similar to the pre-industrial Holocene values, orbital conditions were Atlantic indicate warmer than late Holocene year-round SSTs north of very different with larger latitudinal and seasonal insolation variations. 30°N, whereas SST changes were more variable south of this latitude Large eccentricity and the phasing of precession and obliquity (Figure (McKay et al., 2011). 5.3a c) during the LIG resulted in July 65°N insolation peaking at ~126 ka and staying above the Holocene maximum values from ~129 to 123 Transient LIG simulations with EMICs and low-resolution AOGCMs dis- ka. The high obliquity (Figure 5.3b) contributed to small, but positive play peak NH summer warmth between 128 ka and 125 ka in response annual insolation anomalies at high latitudes in both hemispheres and to orbital and WMGHG forcings. This warming is delayed when NH ice negative anomalies at low latitudes. sheets are allowed to evolve (Bakker et al., 2013). Time-slice climate simulations run by 13 modelling groups with a hierarchy of climate New data and syntheses from marine and terrestrial archives, with models forced with orbital and WMGHG changes for 128 to 125 ka updated age models, have provided an expanded view of tempera- (Figure 5.6b) simulate the reconstructed pattern of NH annual warm- ture patterns during interglacials since 800 ka (Masson-Delmotte et al., ing (Figure 5.6a). Positive feedbacks with the cryosphere (sea ice and 2010a; Lang and Wolff, 2011; Rohling et al., 2012). There is currently no snow cover) provide the memory that allows simulated NH high-lati- consensus on whether interglacials changed intensity after ~430 ka. tude warming, annually as well as seasonally, in response to the sea- EPICA Dome C Antarctic ice cores record warmer temperatures after sonal orbital forcing (Schurgers et al., 2007; Yin and Berger, 2012). The 407 Chapter 5 Information from Paleoclimate Archives (a) Data (c) SST anom (°C) (d) SAT anom (°C) Latitude (°) (b) Models [16] Latitude (°) ( ) Figure 5.6 | Changes in surface temperature for the Last Interglacial (LIG) as reconstructed from data and simulated by an ensemble of climate model experiments in response to orbital and well-mixed greenhouse gas (WMGHG) forcings. (a) Proxy data syntheses of annual surface temperature anomalies as published by Turney and Jones (2010) and McKay et al. (2011). McKay et al., (2011) calculated an annual anomaly for each record as the average sea surface temperature (SST) of the 5-kyr period centred on the warmest temperature between 135 ka and 118 ka and then subtracting the average SST of the late Holocene (last 5 kyr). Turney and Jones (2010) calculated the annual temperature anomalies relative to 1961 1990 by averaging the LIG temperature estimates across the isotopic plateau in the marine and ice records and the period of maximum warmth in the terrestrial records 5 (assuming globally synchronous terrestrial warmth). (b) Multi-model average of annual surface air temperature anomalies simulated for the LIG computed with respect to pre- industrial. The results for the LIG are obtained from 16 simulations for 128 to 125 ka conducted by 13 modelling groups (Lunt et al., 2013). (c) Seasonal SST anomalies. Multi-model zonal averages are shown as solid line with shaded bands indicating 2 standard deviations. Plotted values are the respective seasonal multi-mean global average. Symbols are individual proxy records of seasonal SST anomalies from McKay et al. (2011). (d) Seasonal terrestrial surface temperature anomalies (SAT). As in (c) but with symbols representing terrestrial proxy records as compiled from published literature (Table 5.A.5). Observed seasonal terrestrial anomalies larger than 10°C or less than 6°C are not shown. In (c) and (d) JJA denotes June July August and DJF December January February, respectively. magnitude of observed NH annual warming though is only reached seesaw response to persistent iceberg melting at high northern lati- in summer in the simulations (Lunt et al., 2013) (Figure 5.6c, d). The tudes (Govin et al., 2012) and disintegration of the WAIS (Overpeck et reasons for this discrepancy are not yet fully determined. Error bars on al., 2006) (see Section 5.6.2.3) are better able to reproduce the early temperature reconstructions vary significantly between methods and LIG Antarctic warming (Holden et al., 2010b). regions, due to the effects of seasonality and resolution (Kienast et al., 2011; McKay et al., 2011; Tarasov et al., 2011). Differences may also be From data synthesis, the LIG global mean annual surface temperature related to model representations of cloud and sea ice processes (Born is estimated to be ~1°C to 2°C warmer than pre-industrial (medium et al., 2010; Fischer and Jungclaus, 2010; Kim et al., 2010; Otto-Bliesner confidence) (Turney and Jones, 2010; Otto-Bliesner et al., 2013), et al., 2013), and that most LIG simulations set the vegetation and ice albeit proxy reconstructions may overestimate the global temperature sheets to their pre-industrial states (Schurgers et al., 2007; Holden et change. High latitude surface temperature, averaged over several thou- al., 2010b; Bradley et al., 2013). Simulations accounting for the bipolar sand years, was at least 2°C warmer than present (high confidence). In 408 Information from Paleoclimate Archives Chapter 5 response to orbital forcing and WMGHG concentration changes, time the last 2000 years (Section 5.3.5.1) and their associated uncertain- slice simulations for 128 to 125 ka exhibit global mean annual surface ties (Section 5.3.5.2), and supported more extensive comparisons with temperature changes of 0.0°C +/- 0.5°C as compared to pre-industrial. GCM simulations (Section 5.3.5.3). Data and models suggest a land ocean contrast in the responses to the LIG forcing (Figure 5.6c, d). Peak global annual SST warming is 5.3.5.1 Recent Warming in the Context of New Reconstructions estimated from data to be 0.7°C +/- 0.6°C (medium confidence) (McKay et al., 2011). Models give more confidence to the lower bound. The New paleoclimate reconstruction efforts since AR4 (Figure 5.7; Table ensemble of climate model simulations gives a large range of global 5.4; Appendix 5.A.1) have provided further insights into the charac- annual land temperature change relative to pre-industrial, 0.4°C to teristics of the Medieval Climate Anomaly (MCA; Table 5.1) and the 1.7°C, when sampled at the data locations and cooler than when Little Ice Age (LIA; Table 5.1). The timing and spatial structure of the averaged for all model land areas, pointing to difficulties in estimating MCA and LIA are complex (see Box 6.4 in AR4 and Diaz et al., 2011; global mean annual surface temperature with current spatial data cov- and Section 5.5), with different reconstructions exhibiting warm and erage (Otto-Bliesner et al., 2013). cold conditions at different times for different regions and seasons. The median of the NH temperature reconstructions (Figure 5.7) indicates 5.3.5 Temperature Variations During the Last 2000 Years mostly warm conditions from about 950 to about 1250 and colder con- ditions from about 1450 to about 1850; these time intervals are chosen The last two millennia allow comparison of instrumental records with here to represent the MCA and the LIA, respectively. multi-decadal-to-centennial variability arising from external forcings and internal climate variability. The assessment benefits from high-res- Based on multiple lines of evidence (using different statistical meth- olution proxy records and reconstructions of natural and anthropogen- ods or different compilations of proxy records; see Appendix 5.A.1 ic forcings back to at least 850 (Section 5.2), used as boundary condi- for a description of reconstructions and selection criteria), published tions for transient GCM simulations. Since AR4, expanded proxy data reconstructions and their uncertainty estimates indicate, with high con- networks and better understanding of reconstruction methods have fidence, that the mean NH temperature of the last 30 or 50 years very supported new reconstructions of surface temperature changes during likely exceeded any previous 30- or 50-year mean during the past 800 1.0 PS04bore Ma08cpsl Ma08eivl LO12glac Sh13pcar LM08ave NH temperature anomaly (oC from 1881-1980) Fr07treecps He07tls Da06treecps Mo05wave Ju07cvm Ma09regm Ma08min7eivf CL12loc Lj10cps HadCRUT4 NH CRUTEM4 NH CRUTEM4 30-90N 0.5 0.0 -0.5 5 -1.0 (a) Northern Hemisphere 1 400 800 1200 1600 2000 Global (oC from 1881-1980) 1.0 1.0 SH (oC from 1881-1980) PS04bore Ma08cpsl Ma08eivl LO12glac PS04bore Ma08eivl LO12glac Ma08eivf HadCRUT4 SH CRUTEM4 SH Ma08eivf HadCRUT4 GL CRUTEM4 GL 0.5 0.5 0.0 0.0 -0.5 -0.5 -1.0 (b) Southern Hemisphere -1.0 (c) Global 1 400 800 1200 1600 2000 1 400 800 1200 1600 2000 Year Year Figure 5.7 | Reconstructed (a) Northern Hemisphere and (b) Southern Hemisphere, and (c) global annual temperatures during the last 2000 years. Individual reconstructions (see Appendix 5.A.1 for further information about each one) are shown as indicated in the legends, grouped by colour according to their spatial representation (red: land-only all latitudes; orange: land-only extratropical latitudes; light blue: land and sea extra-tropical latitudes; dark blue: land and sea all latitudes) and instrumental temperatures shown in black (Hadley Centre/ Climatic Research Unit (CRU) gridded surface temperature-4 data set (HadCRUT4) land and sea, and CRU Gridded Dataset of Global Historical Near-Surface Air TEMperature Anomalies Over Land version 4 (CRUTEM4) land-only; Morice et al., 2012). All series represent anomalies (°C) from the 1881 1980 mean (horizontal dashed line) and have been smoothed with a filter that reduces variations on time scales less than about 50 years. 409 Chapter 5 Information from Paleoclimate Archives years (Table 5.4). The timing of warm and cold periods is mostly consist- NH reconstructions covering part or all of the first millennium suggest ent across reconstructions (in some cases this is because they use simi- that some earlier 50-year periods might have been as warm as the lar proxy compilations) but the magnitude of the changes is clearly sen- 1963 2012 mean instrumental temperature, but the higher temper- sitive to the statistical method and to the target domain (land or land ature of the last 30 years appear to be at least likely the warmest and sea; the full hemisphere or only the extra-tropics; Figure 5.7a). Even 30-year period in all reconstructions (Table 5.4). However, the confi- accounting for these uncertainties, almost all reconstructions agree that dence in this finding is lower prior to 1200, because the evidence is each 30-year (50-year) period from 1200 to 1899 was very likely colder less reliable and there are fewer independent lines of evidence. There in the NH than the 1983 2012 (1963 2012) instrumental temperature. are fewer proxy records, thus yielding less independence among the Table 5.4 | Comparison of recent hemispheric and global temperature estimates with earlier reconstructed values, using published uncertainty ranges to assess likelihood of unusual warmth. Each reconstructed N-year mean temperature within the indicated period is compared with both the warmest N-year mean reconstructed after 1900 and with the most recent N-year mean instrumental temperature, for N = 30 and N = 50 years. Blue symbols indicate the periods and reconstructions where the reconstructed temperatures are very likely cooler than the post-1900 reconstruction (c), or otherwise very likely ( ) or likely (T) cooler than the most recent instrumental temperatures; indicates that some reconstructed temperatures were as likely warmer or colder than recent temperatures. Region NH SH Global Domain Land & Sea Land Extratropics Land Land Study 1 2 3 4 5 6 7 8 9 10 11 12 13 14 7 6 9 7 9 15 50-year means 1600 1899 c c c c c c c c c c c c c c 1400 1899 c c c c c c c c c c 1200 1899 c c c c c c c 1000 1899 T c c c 800 1899 T T c c T 600 1899 T T c T 400 1899 T 200 1899 1 1899 30-year means 1600 1899 c c c c c c c c c c c c 1400 1899 c c c c c c c c c 1200 1899 c c c c c c c c 1000 1899 c c c 5 800 1899 T c c c 600 1899 T c c 400 1899 T 200 1899 T 1 1899 Notes: Symbols indicate the likelihood (based on the published multi-decadal uncertainty ranges) that each N-year mean of the reconstructed temperature during the indicated period was colder than the warmest N-year mean after 1900. A reconstructed mean temperature X is considered to be likely (very likely) colder than a modern temperature Y if X+aE < Y, where E is the reconstruction standard error and a = 0.42 (1.29) corresponding to a 66% (90%) one-tailed confidence interval assuming the reconstruction error is normally distributed. Symbols indicate that the reconstructed temperatures were either: T likely colder than the 1983 2012 or 1963 2012 mean instrumental temperature; very likely colder than the 1983 2012 or 1963 2012 mean instrumental temperature; c very likely colder than the 1983 2012 or 1963 2012 mean instrumental temperature and additionally very likely colder than the warmest 30- or 50-year mean of the post-1900 reconstruction (which is typically not as warm as the end of the instrumental record); indicates that at least one N-year reconstructed mean is about as likely colder or warmer than the 1983 2012 or 1963 2012 mean instrumental temperature. No symbol is given where the reconstruction does not fully cover the indicated period. Identification and further information for each study is given in Table 5.A.6 of Appendix 5.A.1: 1 = Mo05wave; 2 = Ma08eivf; 3 = Ma09regm; 4 = Ju07cvm; 5 = LM08ave; 6 = Ma08cpsl; 7 = Ma08eivl; 8 = Sh13pcar; 9 = LO12gla; 10 = Lj10cps; 11 = CL12loc; 12 = He07tls; 13 = Da06treecps; 14 = Fr07treecps; 15 = PS04bore. 410 Information from Paleoclimate Archives Chapter 5 reconstructions while making them more susceptible to errors in indi- problem will be larger: (i) for cases with weaker correlation between vidual proxy records. The published uncertainty ranges do not include instrumental temperatures and proxies (Lee et al., 2008; Christiansen all sources of error (Section 5.3.5.2), and some proxy records and et al., 2009; Smerdon et al., 2011); (ii) if errors in the proxy data are uncertainty estimates do not fully represent variations on time scales not incorporated correctly (Hegerl et al., 2007; Ammann et al., 2010); as short as the 30 years considered in Table 5.4. Considering these or (iii) if the data are detrended in the calibration phase (Lee et al., caveats, there is medium confidence that the last 30 years were likely 2008; Christiansen et al., 2009). The 20th-century trends in proxies may the warmest 30-year period of the last 1400 years. contain relevant temperature information (Ammann and Wahl, 2007) but calibration with detrended or undetrended data has been an issue Increasing numbers of proxy records and regional reconstructions are of debate (von Storch et al., 2006; Wahl et al., 2006; Mann et al., 2007) being developed for the SH (see Section 5.5), but few reconstructions because trends in proxy records can be induced by other (non-temper- of SH or global mean temperatures have been published (Figure 5.7b, ature) climate and non-climatic influences (Jones et al., 2009; Gagen c). The SH and global reconstructions with published uncertainty esti- et al., 2011). Recent developments mitigate the loss of low-frequen- mates indicate that each 30- or 50-year interval during the last four cy variance in global and hemispheric reconstructions by increasing centuries was very likely colder than the warmest 30- or 50-year inter- the correlation between proxies and temperature through temporal val after 1900 (Table 5.4). However, there is only limited proxy evidence smoothing (Lee et al., 2008) or by correctly attributing part or all of the and therefore low confidence that the recent warming has exceeded temperature-proxy differences to imperfect proxy data (Hegerl et al., the range of reconstructed temperatures for the SH and global scales. 2007; Juckes et al., 2007; Mann et al., 2008). Pseudoproxy experiments have shown that the latter approach used with a site-by-site calibra- 5.3.5.2 Reconstruction Methods, Limitations and Uncertainties tion (Christiansen, 2011; Christiansen and Ljungqvist, 2012) can also avoid attenuation of low-frequency variability, though it is debated Reconstructing NH, SH or global-mean temperature variations over whether it might instead inflate the variability and thus constitute an the last 2000 years remains a challenge due to limitations of spatial upper bound for low-frequency variability (Moberg, 2013). Even those sampling, uncertainties in individual proxy records and challenges field reconstruction methods that do not attenuate the low-frequency associated with the statistical methods used to calibrate and integrate variability of global or hemispheric means may still suffer from attenu- multi-proxy information (Hughes and Ammann, 2009; Jones et al., ation and other errors at regional scales (Smerdon et al., 2011; Annan 2009; Frank et al., 2010a). Since AR4, new assessments of the statis- and Hargreaves, 2012; Smerdon, 2012; Werner et al., 2013). tical methods used to reconstruct either global/hemispheric temper- ature averages or spatial fields of past temperature anomalies have The fundamental limitations for deriving past temperature variabili- been published. The former include approaches for simple compositing ty at global/hemispheric scales are the relatively short instrumental and scaling of local or regional proxy records into global and hemi- period and the number, temporal and geographical distribution, reli- spheric averages using uniform or proxy-dependent weighting (Hegerl ability and climate signal of proxy records (Jones et al., 2009). The et al., 2007; Juckes et al., 2007; Mann et al., 2008; Christiansen and database of high-resolution proxies has been expanded since AR4 Ljungqvist, 2012). The latter correspond to improvements in climate (Mann et al., 2008; Wahl et al., 2010; Neukom and Gergis, 2011; PAGES field reconstruction methods (Mann et al., 2009; Smerdon et al., 2011) 2k Consortium, 2013), but data are still sparse in the tropics, SH and that apply temporal and spatial relationships between instrumen- over the oceans (see new developments in Section 5.5). Integration of tal and proxy records to the pre-instrumental period. New develop- low-resolution records (e.g., marine or some lake sediment cores and ments for both reconstruction approaches include implementations of some speleothem records) with high-resolution tree-ring, ice core and Bayesian inference (Li et al., 2010a; Tingley and Huybers, 2010, 2012; coral records in global/hemispheric reconstructions is still challenging. McShane and Wyner, 2011; Werner et al., 2013). In particular, Bayesian Dating uncertainty, limited replication and the possibility of tempo- hierarchical models enable a more explicit representation of the under- ral lags in low-resolution records (Jones et al., 2009) make regres- 5 lying processes that relate proxy (and instrumental) records to climate, sion-based calibration particularly difficult (Christiansen et al., 2009) allowing a more systematic treatment of the multiple uncertainties and can be potentially best addressed in the future with Bayesian hier- that affect the climate reconstruction process. This is done by speci- archical models (Tingley et al., 2012). The short instrumental period fying simple parametric forms for the proxy-temperature relationships and the paucity of proxy data in specific regions may preclude obtain- that are then used to estimate a probability distribution of the recon- ing accurate estimates of the covariance of temperature and proxy structed temperature evolution that is compatible with the available records (Juckes et al., 2007), impacting the selection and weighting data (Tingley et al., 2012). of proxy records in global/hemispheric reconstructions (Bürger, 2007; Osborn and Briffa, 2007; Emile-Geay et al., 2013b) and resulting in An improved understanding of potential uncertainties and biases asso- regional errors in climate field reconstructions (Smerdon et al., 2011). ciated with reconstruction methods has been achieved, particularly by using millennial GCM simulations as a surrogate reality in which pseu- Two further sources of uncertainty have been only partially considered do-proxy records are created and reconstruction methods are replicated in the published literature. First, some studies have used multiple statis- and tested (Smerdon, 2012). A key finding is that the methods used for tical models (Mann et al., 2008) or generated ensembles of reconstruc- many published reconstructions can underestimate the amplitude of tions by sampling parameter space (Frank et al., 2010b), but this type of the low-frequency variability (Lee et al., 2008; Christiansen et al., 2009; structural and parameter uncertainty needs further examination (Chris- Smerdon et al., 2010). The magnitude of this amplitude attenuation in tiansen et al., 2009; Smerdon et al., 2011). Second, proxy-temperature real-world reconstructions is uncertain, but for affected methods the relationships may change over time due to the effect of other climate 411 Chapter 5 Information from Paleoclimate Archives and non-climate influences on a proxy, a prominent example being the selecting simulations from decade-by-decade ensembles to obtain the divergence between some tree-ring width and density chronologies closest match to reconstructed climate patterns (Annan and Harg- and instrumental temperature trends during the last decades of the reaves, 2012; Goosse et al., 2012a). The resulting simulations provide 20th century (Briffa et al., 1998). In cases that do show divergence, a insight into the relative roles of internal variability and external forcing number of factors may be responsible, such as direct temperature or (Goosse et al., 2012b), and processes that may account for the spatial drought stress on trees, delayed snowmelt, changes in seasonality and distribution of past climate anomalies (Crespin et al., 2009; Palastanga reductions in solar radiation (Lloyd and Bunn, 2007; D Arrigo et al., et al., 2011). 2008; Porter and Pisaric, 2011). However, this phenomenon does not affect all tree-ring records (Wilson et al., 2007; Esper and Frank, 2009) Figure 5.8b d provides additional tests of model-data agreement by and in some cases where divergence is apparent it may arise from the compositing the temperature response to a number of distinct forcing use of inappropriate statistical standardization of the data (Melvin and events. The models simulate a significant NH cooling in response to Briffa, 2008; Briffa and Melvin, 2011) and not from a genuine change volcanic events (Figure 5.8b; peaks between 0.1°C and 0.5°C depend- in the proxy temperature relationship. For the European Alps and Sibe- ing on model) that lasts 3 to 5 years, overlapping with the signal ria, Büntgen et al., (2008) and Esper et al. (2010) demonstrate that inferred from reconstructions with annual resolution (0.05°C to 0.3°C). divergence can be avoided by careful selection of sites and standardi- CMIP5 simulations tend to overestimate cooling following the major zation methods together with large sample replication. 1809 and 1815 eruptions relative to early instrumental data (Brohan et al., 2012). Such differences could arise from uncertainties in volcan- Limitations in proxy data and reconstruction methods suggest that ic forcing (Section 5.2.1.3) and its implementation in climate models published uncertainties will underestimate the full range of uncertain- (Joshi and Jones, 2009) or from errors in the reconstructions (Section ties of large-scale temperature reconstructions (see Section 5.3.5.1). 5.3.5.2). Since many reconstructions do not have annual resolution, While this has fostered debate about the extent to which proxy-based similar composites (Figure 5.8c) are formed to show the response to reconstructions provide useful climate information (e.g., McShane and changes in multi-decadal volcanic forcings (representing clusters of Wyner, 2011 and associated comments and rejoinder), it is well estab- eruptions). Both the simulated and reconstructed responses are sig- lished that temperature and external forcing signals are detectable in nificant and comparable in magnitude, although simulations show a proxy reconstructions (Sections 5.3.5.3 and 10.7.2). Recently, model faster recovery (<5 years) than reconstructions. Solar forcing estimat- experiments assuming a nonlinear sensitivity of tree-rings to climate ed over the last millennium shows weaker variations than volcanic (Mann et al., 2012) have been used to suggest that the tree-ring forcing (Figure 5.8d), even at multi-decadal time scales. Compositing response to volcanic cooling may be attenuated and lagged. Tree-ring the response to multi-decadal fluctuations in solar irradiance shows data and additional tree-growth model assessments (Anchukaitis et cooling in simulations and reconstructions of NH temperature between al., 2012; Esper et al., 2013) have challenged this interpretation and 0.0°C and 0.15°C. In both cases, the cooling may be partly a response analyses of instrumental data suggest hemispheric temperature recon- to concurrent variations in volcanic forcing (green line in Figure 5.8d). structions agree well with the degree of volcanic cooling during early 19th-century volcanic events (Brohan et al., 2012; see Section 5.3.5.3). Temperature differences between the warmest and coolest centennial These lines of evidence leave the representation of volcanic events in or multi-centennial periods provide an additional comparison of the tree-ring records and associated hemispheric scale temperature recon- amplitude of NH temperature variations in the reconstructions and structions as an emerging area of investigation. simulations: between 950 and 1250 (nominally the MCA) and 1450 1850 (nominally the LIA; Figures 5.8e; 5.9a c) and between the LIA 5.3.5.3 Comparing Reconstructions and Simulations and the 20th century (Figures 5.8f; 5.9g i). Despite similar multi-mod- el and multi-reconstruction means for the warming from the LIA to 5 The number of GCM simulations of the last millennium has increased the present, the range of individual results is very wide (see Sections since AR4 (Fernández-Donado et al., 2013). The simulations have 9.5.3.1 and 10.7.1 for a comparison of reconstructed and simulated used different estimates of natural and anthropogenic forcings (Table variability across various frequency ranges) and there is no clear dif- 5.A.1). In particular, the PMIP3/CMIP5 simulations are driven by small- ference between runs with weaker or stronger solar forcing (Figure er long-term changes in TSI (Section 5.2.1; Figure 5.1b): TSI increases 5.8f; Section 10.7.2). The difference between the MCA and LIA temper- by 0.10% from the Late Maunder Minimum (LMM; 1675 1715) to atures, however, has a smaller range for the model simulations than the late 20th century (Schmidt et al., 2011), while most previous simu- the reconstructions, and the simulations (especially those with weaker lations use increases between 0.23% and 0.29% (Fernández-Donado solar forcing) lie within the lower half of the reconstructed range of et al., 2013). Simulated NH temperatures during the last millennium lie temperature changes (Figure 5.8e). Recent studies have assessed the mostly within the uncertainties of the available reconstructions (Figure consistency of model simulations and temperature reconstructions at 5.8a). This agreement between GCM simulations and reconstructions the hemispheric scale. Hind and Moberg (2012) found closer data-mod- provides neither strong constraints on forcings nor on model sensitiv- el agreement for simulations with 0.1% TSI increase than 0.24% TSI ities because internal variability and uncertainties in the forcings and increase, but the result is sensitive to the reconstruction uncertainty reconstructions are considerable factors. and the climate sensitivity of the model. Simulations with an EMIC using a much stronger solar forcing (0.44% TSI increase from LMM Data have also been assimilated into climate models (see Sections 5.5 to present, Shapiro et al., 2011) appear to be incompatible with most and 10.7, Figure 10.19) by either nudging simulations to follow local temperature reconstructions (Feulner, 2011). or regional proxy-based reconstructions (Widmann et al., 2010) or by 412 Information from Paleoclimate Archives Chapter 5 (a) reconstructed (grey) and simulated (red/blue) NH temperature Strong solar 1.0 variability simulations Temperature anomaly (°C) 0.5 0.0 -0.5 Weak MCA LIA 20C solar variability simulations 1000 1200 1400 1600 1800 2000 Time (Year CE) (b) (c) (d) 0 0.2 0.2 0.0 0.0 Volcanic forcing (W m-2) Volcanic forcing (W m-2) Solar forcing (W m-2) -2 -0.2 -0.2 -0.4 -0.4 Volcanic forcing -4 coincident with -0.6 -0.6 solar variability -0.8 -0.8 -6 Forcing -1.0 Forcing -1.0 Forcing Temperature Temperature Temperature NH temperature anomaly (°C) NH temperature anomaly (°C) NH temperature anomaly (°C) -0.0 -0.0 -0.0 -0.2 -0.2 -0.2 -0.4 -0.4 -0.4 Individual volcanic events Multi-decadal volcanic activity Multi-decadal solar variability -0.6 -0.6 -0.6 -5 0 5 10 -40 -20 0 20 40 -40 -20 0 20 40 Year from peak forcing Year from peak forcing Year from peak forcing Fr07treecps Ma08cpsl Da06treecps He07tls Sh13pcar Ju07cvm Ma09regm Lj10cps Ma08min7eivf Ma08eivl Mo05wave LM08ave BCCcsm1-1-P ECHAM5-SW IPSLCM5A-P CSIRO-Mk3L MPI-ESM-P CSM1.4 MIROC-P CCSM3 CSIRO-Mk3L-P HadCM3-P FGOALS-g1 GISS-E2R-P CCSM4-P CNRM-CM3.3 ECHAM5-SS ECHO-G LO12glac Ma08eivl Ma09regm Ma08cpsl Lj10cps Sh13pcar Ju07cvm Ma08min7eivf Mo05wave ECHAM5-SW ECHAM5-SS MIROC-P HadCM3-P CNRM-CM3.3 CSIRO-Mk3L FGOALS-g1 GISS-E2R-P 1.0 1.0 MCA - LIA NH temp (°C) 0.8 0.8 20C - LIA NH temp (°C) 0.6 0.6 0.4 0.4 5 CSIRO-Mk3L-P BCCcsm1-1-P IPSLCM5A-P Da06treecps Fr07treecps MPI-ESM-P CCSM4-P PS04bore 0.2 0.2 ECHO-G CL12loc CL12loc CSM1.4 CCSM3 0.0 0.0 -0.2 (e) NH temp (950-1250) minus (1450-1850) -0.2 (f) NH temp (1900-2000) minus (1450-1850) Figure 5.8 | Comparisons of simulated and reconstructed NH temperature changes. (a) Changes over the last millennium (Medieval Climate Anomaly, MCA; Little Ice Age, LIA; 20th century, 20C) (b) Response to individual volcanic events. (c) Response to multi-decadal periods of volcanic activity. (d) Response to multi-decadal variations in solar activity. (e) Mean change from the MCA to the LIA. (f) Mean change from 20th century to LIA. Note that some reconstructions represent a smaller spatial domain than the full Northern Hemisphere (NH) or a specific season, while annual temperatures for the full NH mean are shown for the simulations. (a) Simulations shown by coloured lines (thick lines: multi- model-mean; thin lines: multi-model 90% range; red/blue lines: models forced by stronger/weaker solar variability, though other forcings and model sensitivities also differ between the red and blue groups); overlap of reconstructed temperatures shown by grey shading; all data are expressed as anomalies from their 1500 1850 mean and smoothed with a 30-year filter. Superposed composites (time segments from selected periods positioned so that the years with peak negative forcing are aligned) of the forcing and temperature response to: (b) 12 of the strongest individual volcanic forcing events after 1400 (the data shown are not smoothed); (c) multi-decadal changes in volcanic activity; (d) multi-decadal changes in solar irradiance. Upper panels show volcanic or solar forcing for the individual selected periods together with the composite mean (thick line); in (d), the composite mean of volcanic forcing (green) during the solar composite is also shown. Lower panels show the NH temperature composite means and 90% range of spread between simulations (red line, pink shading) or reconstructions (grey line and shading), with overlap indicated by darker shading. Mean NH temperature difference between (e) MCA (950 1250) and LIA (1450 1850) and (f) 20th century (1900 2000) and LIA, from reconstructions (grey), multi-reconstruction mean and range (dark grey), multi-model mean and range and individual simulations (red/blue for models forced by stronger/weaker solar variability). Where an ensemble of simulations is available from one model, the ensemble mean is shown in solid and the individual ensemble members by open circles. Results are sorted into ascending order and labelled. Reconstructions, models and further details are given in Appendix 5.A.1 and Tables 5.A.1 and 5.A.6. 413 Chapter 5 Information from Paleoclimate Archives Simulations Reconstructions Stronger TSI change Weaker TSI change (a) (b) (c) MCA-LIA (d) (e) (f) Present-MCA (g) (h) (i) Present-LIA (j) Coral 1.0 0.8 0.6 0.4 0.2 0.0 0.2 0.4 0.6 0.8 1.0 Speleothem Sediment Shaded and contours: Temperature difference (°C) Documentary Ice core Dots in (c) are dimensionless proxy anomalies / 2 MXD TRW TRW-RC CFR 0 200 400 600 800 1000 1200 1400 1600 1800 2000 Starting year Figure 5.9 | Simulated and reconstructed temperature changes for key periods in the last millennium. Annual temperature differences for: (a) to (c) Medieval Climate Anomaly (MCA, 950 1250) minus Little Ice Age (LIA, 1450 1850); (d) to (f) present (1950 2000) minus MCA; (g) to (i) present minus LIA. Model temperature differences (left and middle columns) are average temperature changes in the ensemble of available model simulations of the last millennium, grouped into those using stronger (total solar irradiance (TSI) change from the Late Maunder Minimum (LMM) to present >0.23%; left column; SS in Table 5.A.1) or weaker solar forcing changes (TSI change from the LMM to present <0.1%; middle column; SW in Table 5.A.1). Right column panels (c, f, i) show differences (shading) for the Mann et al. (2009) field reconstruction. In (c), dots represent additionally proxy differences from Ljungqvist et al. (2012), scaled by 0.5 for display purposes. The distribution, type and temporal span of the input data used in the field reconstruction of Mann et al. (2009) are shown in (j); proxy types are included in the legend, acronyms stand for: tree-ring maximum latewood density (MXD); tree-ring width (TRW); regional TRW composite (TRW-RC); and multi-proxy 5 climate field reconstruction (CFR). Dotted grid-cells indicate non-significant differences (<0.05 level) in reconstructed fields (right) or that <80% of the simulations showed significant changes of the same sign (left and middle). For simulations starting after 950, the period 1000 1250 was used to estimate MCA values. Grid cells outside the domain of the Mann et al. (2009) reconstruction are shaded grey in the model panels to enable easier comparison, though contours (interval 0.2 K) illustrate model output over the complete global domain. Only simulations spanning the whole millennium and including at least solar, volcanic and greenhouse gas forcing have been used (Table 5.A.1): BCC-csm1-1 (1), CCSM3 (1), CCSM4 (1), CNRM-CM3.3, CSIRO-mk3L-1-2 (4), CSM1.4 (1), ECHAM5-MPIOM (8), ECHO-G (1), MPI-ESM-P (1), FGOALS-gl (1), GISS-E2-R (3), HadCM3 (1), IPSL-CM5A-LR (1). Averages for each model are calculated first, to avoid models with multiple simulations having greater influence on the ensemble means shown here. The spatial distributions of simulated and reconstructed (Mann et al., of the Pacific Ocean). This is not surprising because greater regional 2009; Ljungqvist et al., 2012) temperature changes between the MCA, variability is expected in the reconstructions compared with the mean LIA and 20th century are shown in Figure 5.9. Simulated changes tend of multiple model simulations, though reconstructed changes for such to be larger, particularly with stronger TSI forcing, over the continents areas with few or no proxy data (Figure 5.9i) should also be interpreted and ice/snow-covered regions, showing polar amplification (see Box with caution (Smerdon et al., 2011). The reconstructed temperature dif- 5.1). The largest simulated and reconstructed changes are between ferences between MCA and LIA (Figure 5.9c) indicate higher medieval the LIA and present, with reconstructions (Figure 5.9i) indicating wide- temperatures over the NH continents in agreement with simulations spread warming except for the cooling south of Greenland. Models also (Figure 5.9a, b). The reconstructed MCA warming is higher than in the simulate overall warming between the MCA and present (Figure 5.9d, simulations, even for stronger TSI changes and individual simulations e), whereas the reconstructions indicate significant regional cooling (in (Fernández-Donado et al., 2013). Simulations with proxy assimilation the North Atlantic, southeastern North America, and the mid-latitudes show that this pattern of change is compatible with a direct response 414 Information from Paleoclimate Archives Chapter 5 to a relatively weak solar forcing and internal variability patterns sim- using sub-annually resolved d18O from central equatorial Pacific coral ilar to a positive Northern Annular Mode (NAM) phase and northward segments (Cobb et al., 2013) reveals no evidence for orbitally-induced shifts of the Kuroshio and Gulf Stream currents (Goosse et al., 2012b). changes in interannual ENSO amplitude throughout the last 7 ka (high For the tropical regions, an enhanced zonal SST gradient produced by confidence), which is consistent with the weak reduction in mid-Hol- either a warmer Indian Ocean (Graham et al., 2011) or a cooler eastern ocene ENSO amplitude of only ~10% simulated by the majority of cli- Pacific (La Nina-like state) (Seager et al., 2007; Mann et al., 2009) could mate models (Fig. 5.10), but contrasts with reconstructions reported explain the reconstructed MCA patterns (Figure 5.9c). However, the in AR4 that showed a reduction in ENSO variance during the first half enhanced gradients are not reproduced by model simulations (Figure of the Holocene. The same study revealed an ENSO system that expe- 5.9a, b) and are not robust when considering the reconstruction uncer- rienced very large internal variance changes on decadal and centen- tainties and the limited proxy records in these tropical ocean regions nial time scales. This latter finding is also confirmed by the analysis (Emile-Geay et al., 2013b) (Sections 5.4.1 and 5.5.1). This precludes an of about 2000 years of annually varved lake sediments (Wolff et al., assessment of the role of external forcing and/or internal variability in 2011) in the ENSO-teleconnected region of equatorial East Africa. Fur- these reconstructed patterns. thermore, Cobb et al. (2013) identify the late 20th century as a period of anomalously high, although not unprecedented, ENSO variability relative to the average reconstructed variance over the last 7000 years. 5.4 Modes of Climate Variability Reconstructions of ENSO for the last millennium also document mul- Since AR4, new proxy reconstructions and model simulations have ti-decadal-to-centennial variations in the amplitude of reconstructed provided additional insights into the forced and unforced behaviour interannual eastern equatorial Pacific SST anomalies (McGregor et al., of modes of climate variability. This section focuses only on the inter- 2010; Wilson et al., 2010; Li et al., 2011; Emile-Geay et al., 2013a). Sta- annual ENSO, the NAM and NAO, the Southern Annular Mode (SAM) tistical efforts to determine ENSO variance changes in different annu- and longer term variability associated with the Atlantic Multidecadal ally resolved ENSO proxies (D Arrigo et al., 2005; Braganza et al., 2009; Oscillation (AMO) (see Glossary and Chapter 14 for definitions and McGregor et al., 2010; Fowler et al., 2012; Hereid et al., 2013) and from illustrations and Box 2.5). It is organized from low to high latitudes and documentary sources (Garcia-Herrera et al., 2008; Gergis and Fowler, from interannual to decadal-scale modes of variability. 2009) reveal (medium confidence) extended periods of low ENSO activity during parts of the LIA compared to the 20th century. Direct TSI 5.4.1 Tropical Modes effects on reconstructed multi-decadal ENSO variance changes cannot be identified (McGregor et al., 2010). According to reconstructions of During the MPWP, climate conditions in the equatorial Pacific were volcanic events (Section 5.2.1.3) and some ENSO proxies, a slightly characterized by weaker zonal (Wara et al., 2005) and cross-equatorial increased probability exists (medium confidence) for the occurrence (Steph et al., 2010) SST gradients, consistent with the absence of an of El Nino events 1 to 2 years after major volcanic eruptions (Adams eastern equatorial cold tongue. This state still supported interannual et al., 2003; McGregor et al., 2010; Wilson et al., 2010). This response variability, according to proxy records (Scroxton et al., 2011; Watanabe is not captured robustly by GCMs (McGregor and Timmermann, 2010; et al., 2011). These results together with recent GCM experiments Ohba et al., 2013). (Haywood et al., 2007) indicate (medium confidence) that interannual ENSO variability existed, at least sporadically, during the warm back- 5.4.2 Extratropical Modes ground state of the Pliocene (Section 5.3.1). Robust evidence from LGM simulations indicates a weakening of the LGM GCM simulations display wide ranges in the behaviour of ENSO NAM variability, connected with stronger planetary wave activity (Lü and the eastern equatorial Pacific annual cycle of SST with little con- et al., 2010). A significant but model-dependent distortion of the sim- 5 sistency (Liu et al., 2007a; Zheng et al., 2008) (Figure 5.10). Currently ulated LGM NAO pattern may result from the strong topographic ice ENSO variance reconstructions for the LGM are too uncertain to help sheet forcing (Justino and Peltier, 2005; Handorf et al., 2009; Pausata et constrain the simulated responses of the annual cycle and ENSO to al., 2009; Riviere et al., 2010). A multimodel analysis of NAO behaviour LGM boundary conditions. GCMs show that a reduced AMOC very in mid-Holocene GCM simulations (Gladstone et al., 2005) reveals an likely induces intensification of ENSO amplitude and for the majority NAO structure, similar to its pre-industrial state, but a tendency for of climate models also a reduction of the amplitude of the SST annual more positive NAO values during the early Holocene (Rimbu et al., cycle in the eastern equatorial Pacific (Timmermann et al., 2007; Merkel 2003), with no consistent change in its interannual variability. Robust et al., 2010; Braconnot et al., 2012a) (Figure 5.10). About 75% of the proxy evidence to test these model-based results has not yet been PMIP2 and PMIP3/CMIP5 mid-Holocene simulations exhibit a weak- established. A new 5200-year-long lake sediment record from south- ening of interannual SST amplitude in the eastern equatorial Pacific western Greenland (Olsen et al., 2012) suggests that around 4500 relative to pre-industrial conditions. More than 87% of these simu- and 650 years ago variability associated with the NAO changed from lations also show a concomitant substantial weakening in the ampli- generally positive to variable, intermittently negative conditions. Since tude of the annual cycle of eastern equatorial Pacific SST. Model results AR4, a few cold-season NAO reconstructions for the last centuries have are consistent with a reduction of total variance of d18O variations of been published. They are based on long instrumental pressure series individual foraminifera in the eastern equatorial Pacific, indicative of (Cornes et al., 2012), a combination of instrumental and ship log-book an orbital effect on eastern equatorial Pacific SST variance (Koutavas data (Küttel et al., 2010) and two proxy records (Trouet et al., 2009). and Joanides, 2012). In contrast to these findings, a recent proxy study Whereas these and earlier NAO reconstructions (Cook et al., 2002; 415 Chapter 5 Information from Paleoclimate Archives Annual cycle ENSO Relative change in annual cycle amplitude (%) Relative change in ENSO amplitude (%) 80 40 60 30 40 20 20 10 0 0 20 10 40 20 30 60 40 80 Mid Holocene LGM Hosing Mid Holocene LGM Hosing Figure 5.10 | Relative changes in amplitude of the annual cycle of sea surface temperature (SST) in Nino 3 region (average over 5°S to 5°N and 150°W to 90°W) (left) and in amplitude of interannual SST anomalies in the Nino 3.4 region (average over 5°S to 5°N and 170°W to 120°W) (right) simulated by an ensemble of climate model experiments in response to external forcing. Left: Multi-model average of relative changes (%) in amplitude of the mean seasonal cycle of Nino 3 SST for mid Holocene (MH) and Last Glacial Maximum (LGM) time-slice experiments and for freshwater perturbation experiments (Hosing) that lead to a weakening of the Atlantic Ocean meridional overturning circulation (AMOC) by more than 50%. Bars encompass the 25 and 75 percentiles, with the red horizontal lines indicating the median in the respective multi-model ensemble, red crosses are values in the upper and lower quartile of the distribution; Right: same as left, but for the SST anomalies in the Nino 3.4 region, representing El Nino-Southern Oscillation (ENSO) variability. The MH ensemble includes 4 experiments performed by models participating in Paleoclimate Modelling Intercomparison Project Phase II (PMIP2) (FGOALS1.0g, IPSL- CM4, MIROC3.2 medres, CCSM3.0) and 7 experiments (mid-Holocene) performed by models participating in PMIP3/CMIP5 (CCSM4.0, CSIRO-Mk3-6-0, HadGEM2-CC, HadGEM2- ES, MIROC-ESM, MPI-ESM-P, MRI-CGCM3). The LGM ensemble includes 5 experiments performed by models participating in PMIP2 (FGOALS1.0g, IPSL-CM4, MIROC3.2 medres, CCSM3.0, HadCM3) and 5 experiments (LGM) performed by models participating in PMIP3/CMIP5 (CCSM4, GISS-E2-R, IPSL-CM5A-LR, MIROC-ESM, and MPI-ESM-P). The changes in response to MH and LGM forcing are computed with respect to the pre-industrial control simulations coordinated by PMIP2 and PMIP3/CMIP5. The results for Hosing are obtained from freshwater perturbation experiments conducted with CCSM2.0, CCSM3.0, HadCM3, ECHAM5-MPIOM, GFDL-CM2.1 (Timmermann et al., 2007), CSM1.4 (Bozbiyik et al., 2011) for pre-industrial or present-day conditions and with CCSM3 for glacial conditions (Merkel et al., 2010). The changes in response to fresh water forcing are computed with respect the portion of simulations when the AMOC is high. Luterbacher et al., 2002; Timm et al., 2004; Pinto and Raible, 2012) al., 2011), wildfires (Holz and Veblen, 2011) as well as on LGM climate differ in several aspects, and taking into consideration associated (Justino and Peltier, 2008). A first hemispheric-wide, tree-ring-based reconstruction uncertainties, they demonstrate with high confidence reconstruction of the austral summer SAM (Villalba et al., 2012) indi- that the strong positive NAO phases of the early 20th century and the cates that the late 20th century positive trend may have been anoma- mid-1990s are not unusual in the context of the past half millennium. lous in the context of the last 600 years, thus supporting earlier South 5 Trouet et al. (2009) presented a winter NAO reconstruction that yielded American proxy evidence for the last 400 years (e.g., Lara et al., 2008) a persistent positive phase during the MCA in contrast to higher fre- and GCM (Wilmes et al., 2012). Hence, there is medium confidence that quency variability during the LIA. This is not consistent with the strong the positive trend in SAM since 1950 may be anomalous compared to NAO imprint in Greenland ice core data (Vinther et al., 2010) and recent the last 400 years. results from transient model simulations that neither support such a persistent positive NAO during the MCA, nor a strong NAO phase shift The AMO (Delworth and Mann, 2000; Knight et al., 2005) (see also during the LIA (Lehner et al., 2012; Yiou et al., 2012). A recent pseu- Sections 9.5.3.3.2 and 14.7.6) has been reconstructed using marine do-proxy-based assessment of low-frequency NAO behaviour (Lehner (Black et al., 2007; Kilbourne et al., 2008; Sicre et al., 2008; Chiessi et et al., 2012) infers weaknesses in the reconstruction method used by al., 2009; Saenger et al., 2009) and terrestrial proxy records (Gray et Trouet et al. (2009). Last millennium GCM simulations reveal no signif- al., 2004; Shanahan et al., 2009) from different locations. Correlations icant response of the NAO to solar forcing (Yiou et al., 2012), except among different AMO reconstructions decrease rapidly prior to 1900 for the GISS-ER coupled model which includes ozone photochemistry, (Winter et al., 2011). An 8000-year long AMO reconstruction (Knudsen extends into the middle atmosphere and exhibits changes in NAO that et al., 2011) shows no correlation with TSI changes, and is interpreted are weak during the MCA compared to the LIA (Mann et al., 2009). as internally generated ocean-atmosphere variability. However, GCM experiments (Waple et al., 2002; Ottera et al., 2010) using solar and/ Changes in the SAM modulate the strength and position of the mean or volcanic forcing reconstructions indicate that external forcings may SH westerlies, and leave an important signature on SH present-day sur- have played a role in driving or at least acting as pacemaker for AMO face climate (Gillett et al., 2006) past tree-ring growth (e.g., Urrutia et variations. 416 Information from Paleoclimate Archives Chapter 5 5.5 Regional Changes During the Holocene (Spielhagen et al., 2011). However, different results are obtained using dinocysts from the same sediment core (Bonnet et al. (2010) showing a Reconstructions and simulations of regional changes that have cooling trend over the last 2000 years without a 20th century rise, and emerged since AR4 are assessed. Most emphasis is on the last 2000 warmest intervals centered at years 100 and 600. years, which has the best data coverage. Tree-ring data and lake sediment information from the North American 5.5.1 Temperature treeline (McKay et al., 2008; Bird et al., 2009; D Arrigo et al., 2009; Anchukaitis et al., 2012) suggest common variability between different 5.5.1.1 Northern Hemisphere Mid to High Latitudes records during the last centuries. An annually resolved, tree-ring based 800-year temperature reconstruction over temperate North America New studies confirm the spatial patterns of SAT and SST distribution as (PAGES 2k Consortium, 2013) and a 1500-year long pollen-based tem- summarised in AR4 (Jansen et al., 2007). According to a recent compi- perature estimate (Viau et al., 2012) show cool periods 500 700 and lation of proxy data, the global mean annual temperatures around 8 to 1200 1900 as well as a warm period between 750 and 1100. The gen- 6 ka were about 0.7°C higher, and extratropical NH temperatures were erally colder conditions until 1900 are in broad agreement with other about 1°C higher than for pre-industrial conditions (Marcott et al., pollen, tree-ring and lake-sediment evidence from northwest Canada, 2013). Spatial variability in the temperature anomalies and the timing the Canadian Rockies and Colorado (Luckman and Wilson, 2005; Salzer of the thermal maximum implicate atmospheric or oceanic dynamical and Kipfmueller, 2005; Loso, 2009; MacDonald et al., 2009; Thomas feedbacks including effects from remaining ice sheets (e.g., Wanner and Briner, 2009). It is very likely that the most recent decades have et al., 2008; Leduc et al., 2010; Bartlein et al., 2011; Renssen et al., been, on average, the warmest across mid-latitude western and tem- 2012). The peak early-to-mid-Holocene North Atlantic and sub-Arctic perate North America over at least 500 years (Wahl and Smerdon, SST anomalies are reconstructed and simulated to primarily occur in 2012; PAGES 2k Consortium, 2013; Figure 5.12). summer and in the stratified uppermost surface-ocean layer (Hald et al., 2007; Andersson et al., 2010). Terrestrial MH (~6 ka, Table 5.1) sum- New warm-season temperature reconstructions (PAGES 2k Consor- mer-season temperatures were higher than modern in the mid-to-high tium, 2013; Figure 5.12) covering the past 2 millennia show that warm latitudes of the NH, consistent with minimum glacier extents (Section European summer conditions were prevalent during 1st century, fol- 5.5.3) and PMIP2 and PMIP3/CMIP5 simulated responses to orbital lowed by cooler conditions from the 4th to the 7th century. Persistent forcing (Figure 5.11) (Braconnot et al., 2007; Bartlein et al., 2011; Izumi warm conditions also occurred during the 8th 11th centuries, peaking et al., 2013). There is also robust evidence for warmer MH winters com- throughout Europe during the 10th century. Prominent periods with pared to the late 20th century (e.g., Wanner et al., 2008; Sundqvist et cold summers occurred in the mid-15th and early 19th centuries. There al., 2010; Bartlein et al., 2011) (Figure 5.11), but the simulated high is high confidence that northern Fennoscandia from 900 to 1100 was latitude winter warming is model dependent and is sensitive to ocean as warm as the mid-to-late 20th century (Helama et al., 2010; Linder- and sea-ice changes (Otto et al., 2009; Zhang et al., 2010). Overall, holm et al., 2010; Büntgen et al., 2011a; Esper et al., 2012a; 2012b; models underestimate the reduction in the latitudinal gradient of Euro- McCarroll et al., 2013; Melvin et al., 2013). The evidence also suggests pean winter temperatures during the MH (Brewer et al., 2007). There warm conditions during the 1st century, but comparison with recent is a general, gradual NH cooling after ~5 ka, linked to orbital forcing, temperatures is restricted because long-term temperature trends from and increased amplitude of millennial-scale variability (Wanner et al., tree-ring data are uncertain (Esper et al., 2012a). In the European Alps 2008; Vinther et al., 2009; Kobashi et al., 2011; Marcott et al., 2013). region, tree-ring based summer temperature reconstructions (Büntgen et al., 2005; Nicolussi et al., 2009; Corona et al., 2010, 2011; Büntgen et Since AR4, regional temperature reconstructions have been produced al., 2011b) show higher temperatures in the last decades than during for the last 2 kyr (Figure 5.12; PAGES 2k Consortium, 2013). A recent any time in the MCA, while reconstructions based on lake sediments 5 multi-proxy 2000-year Arctic temperature reconstruction shows that (Larocque-Tobler et al., 2010; Trachsel et al., 2012) show as high, or temperatures during the first centuries were comparable or even slightly higher temperatures during parts of the MCA compared to higher than during the 20th century (Hanhijärvi et al., 2013; PAGES most recent decades. The longest summer temperature reconstructions 2k Consortium, 2013). During the MCA, portions of the Arctic and from parts of the Alps show several intervals during Roman and earlier sub-Arctic experienced periods warmer than any subsequent period, times as warm (or warmer) than most of the 20th century (Büntgen et except for the most recent 50 years (Figure 5.12) (Kaufman et al., 2009; al., 2011b; Stewart et al., 2011). Kobashi et al., 2010, 2011; Vinther et al., 2010; Spielhagen et al., 2011). Tingley and Huybers (2013) provided a statistical analysis of northern Since AR4, new temperature reconstructions have also been generat- high-latitude temperature reconstructions back to 1400 and found that ed for Asia. A tree-ring based summer temperature reconstruction for recent extreme hot summers are unprecedented over this time span. temperate East Asia back to 800 indicates warm conditions during the Marine proxy records indicate anomalously high SSTs north of Iceland period 850 1050, followed by cooler conditions during 1350 1880 and the Norwegian Sea from 900 to 1300, followed by a generally and a subsequent 20th century warming (Cook et al., 2012; PAGES colder period that ended in the early 20th century. Modern SSTs in 2k Consortium, 2013; Figure 5.12). Tree-ring reconstructions from the this region may still be lower than the warmest intervals of the 900 western Himalayas, Tibetan Plateau, Tianshan Mountains and western 1300 period (Cunningham et al., 2013). Further north, in Fram Strait, High Asia depict warm conditions from the 10th to the 15th centuries, modern SSTs from Atlantic Water appear warmer than those recon- lower temperature afterwards and a 20th century warming (Esper et structed from foraminifera for any prior period of the last 2000 years al., 2007a; Zhu et al., 2008; Zhang et al., 2009; Yadav et al., 2011). 417 Chapter 5 Information from Paleoclimate Archives MTCO MH Anomalies MTWA MH Anomalies p ( ) Data Simulations -2 -1.5 -1 -0.5 0 0.5 1 1.5 2 3 4 5 7 9 11 (°C) -2 -1.5 -1 -0.5 0 0.5 1 1.5 2 3 4 5 7 9 11 (°C) 6 6 MTCO (Northern Hemisphere Extratropics, Land) MTWA (Northern Hemisphere Extratropics, Land) MH - PI control Anomaly (oC) MH - PI control Anomaly (oC) 3 3 0 0 -3 -3 PMIP2 & CMIP5/PMIP3 ensemble averages CMIP5/PMIP3 OA coupled models CMIP5/PMIP3 OAC coupled models -6 -6 C C A 6 6 MTCO (Tropics, Land) MTWA (Tropics, Land) MH - PI control Anomaly (oC) MH - PI control Anomaly (oC) 4 3 2 0 0 -2 -3 5 -4 -6 -6 CMIP5 IPSL-CM5A-LR OAC CMIP5 IPSL-CM5A-LR OAC CMIP5 MIROC-ESM OAC CMIP5 MIROC-ESM OAC CMIP5 BCC-CSM1.1 OAC CMIP5 BCC-CSM1.1 OAC CMIP5 MPI-ESM-P OA CMIP5 MPI-ESM-P OA CMIP5 HadGEM2-CC OAC CMIP5 HadGEM2-CC OAC CMIP5 MRI-CGCM3 OA CMIP5 MRI-CGCM3 OA CMIP5 FGOALS-g2 OA CMIP5 FGOALS-g2 OA CMIP5 CCSM4 OA CMIP5 CCSM4 OA MH MTWA Data PMIP23 ensemble CMIP5 HadGEM2-ES OAC MH MTCO Data PMIP23 ensemble CMIP5 HadGEM2-ES OAC CMIP5 FGOALS-s2 OA CMIP5 FGOALS-s2 OA CMIP5 EC-EARTH-2-2 OA CMIP5 GISS-E2-R OA CMIP5 EC-EARTH-2-2 OA CMIP5 GISS-E2-R OA CMIP5 CNRM-CM5 OA CMIP5 CNRM-CM5 OA PMIP2 ensemble MH-OA PMIP2 ensemble MH-OA PMIP2 ensemble PMIP2 ensemble CMIP5 CSIRO-Mk3-6-0 OA CMIP5 CSIRO-Mk3-6-0 OA CMIP5 ensemble CMIP5 ensemble CMIP5 CSIRO-Mk3L-1-2 OA CMIP5 CSIRO-Mk3L-1-2 OA PMIP2 ensemble MH-OAV PMIP2 ensemble MH-OAV Figure 5.11 | Model-data comparison of surface temperature anomalies for the mid-Holocene (6 ka). MTCO is the mean temperature of the coldest month; MTWA is the mean temperature of the warmest month. Top panels are pollen-based reconstructions of Bartlein et al., (2011) with anomalies defined as compared to modern, which varies among the records. The bulk of the records fall within the range of 5.5 to 6.5 ka, with only 3.5% falling outside this range. Middle panels are corresponding surface temperature anomalies simulated by the Paleoclimate Modelling Intercomparison Project Phase II (PMIP2) and Paleoclimate Modelling Intercomparison Project Phase III (PMIP3)/ Coupled Model Inter- comparison Project Phase 5 (CMIP5) models for 6 ka as compared to pre-industrial. Bottom panels contain boxplots for reconstructions (grey), for model ensembles and for the individual CMIP5 models interpolated to the locations of the reconstructions. Included are OA (ocean atmosphere), OAV (ocean atmosphere vegetation), and OAC (ocean atmo- sphere carbon cycle) models. The boxes are drawn using the 25th, 50th and 75th percentiles (bottom, middle and top of the box, respectively), and whiskers extend to the 5th and 95th percentiles of data or model results within each area. The northern extratropics are defined as 30°N to 90N and the tropics as 30S to 30N. For additional model data comparisons for the mid-Holocene, see Section 9.4.1.4 and Figures 9.11 and 9.12. 418 Information from Paleoclimate Archives Chapter 5 Arctic ANN North America ANN 2 1.5 2 1.5 1.5 1 Temp anomaly wrt 1500 1850 (°C) Temp anomaly wrt 1881 1980 (°C) Temp anomaly wrt 1500 1850 (°C) Temp anomaly wrt 1881 1980 (°C) 1.5 1 0.5 1 1 0 0.5 0.5 0.5 0.5 0 0 0 1 0.5 0.5 0.5 1.5 1 1 2 1 1.5 2.5 1.5 2 1.5 2 2 1000 1100 1200 1300 1400 1500 1600 1700 1800 1900 2000 1000 1100 1200 1300 1400 1500 1600 1700 1800 1900 2000 Time (Year CE) Time (Year CE) Europe JJA Asia JJA 2 2 1.5 1.5 1.5 1.5 1 1 Temp anomaly wrt 1500 1850 (°C) Temp anomaly wrt 1881 1980 (°C) Temp anomaly wrt 1500 1850 (°C) Temp anomaly wrt 1881 1980 (°C) 1 1 0.5 0.5 0.5 0.5 0 0 0 0 0.5 0.5 0.5 0.5 1 1 1 1 1.5 1.5 1.5 1.5 2 2 2 2 1000 1100 1200 1300 1400 1500 1600 1700 1800 1900 2000 1000 1100 1200 1300 1400 1500 1600 1700 1800 1900 2000 Time (Year CE) Time (Year CE) Australasia SONDJF South America DJF 2 2 1.5 1.5 1.5 1.5 Temp anomaly wrt 1500 1850 (°C) Temp anomaly wrt 1881 1980 (°C) Temp anomaly wrt 1500 1850 (°C) Temp anomaly wrt 1881 1980 (°C) 1 1 1 1 0.5 0.5 0.5 0.5 0 0 0 0 0.5 0.5 0.5 0.5 1 1 1 1 1.5 1.5 1.5 1.5 2 2 2 2 1000 1100 1200 1300 1400 1500 1600 1700 1800 1900 2000 1000 1100 1200 1300 1400 1500 1600 1700 1800 1900 2000 Time (Year CE) Time (Year CE) Antarctica ANN MCA - LIA 2 2 ARC 0.4 1.5 0.4 1.5 0.4 (°C) 0.4 EUR (°C) ASIA Temp anomaly wrt 1500 1850 (°C) Temp anomaly wrt 1881 1980 (°C) (°C) NAM 0 o (°C) 1 0.2 0.2 1 0.2 0.5 0 0 0.5 0 0.4 0.4 0 (°C) 0 AUS (°C) SSA 0.4 ANT 0.2 (°C) 0.5 0.2 0.5 1 1 0 0.2 0 5 0 1.5 1.5 Reconstruction Strong solar forcing 2 1000 1100 1200 1300 1400 1500 1600 1700 1800 1900 2000 Time (Year CE) Weak solar forcing CRUTEM4 Figure 5.12 | Regional temperature reconstructions, comparison with model simulations over the past millennium (950 2010). Temperature anomalies (thick black line), and uncertainty estimated provided by each individual reconstruction (grey envelope). Uncertainties: Arctic: 90% confidence bands. Antarctica, Australasia, North American pollen and South America: +/-2 standard deviation. Asia: +/-2 root mean square error. Europe: 95% confidence bands. North American trees: upper/lower 5% bootstrap bounds. Simulations are separated into 2 groups: High solar forcing (red thick line), and weak solar forcing (blue thick line). For each model sub-group, uncertainty is shown as 1.645 times sigma level (light red and blue lines). For comparison with instrumental record, the Climatic Research Unit (CRU) Gridded Dataset of Global Historical Near-Surface Air TEMperature Anomalies Over Land version 4 (CRUTEM4) data set is shown (yellow line). These instrumental data are not necessarily those used in calibration of the reconstructions, and thus may show greater or lesser correspondence with the reconstructions than the instrumental data actually used for calibration; cut-off timing may also lead to end effects for the smoothed data shown. Cf. PAGES 2k Consortium (2013, SOM) in this regard for the North America reconstruction. Green bars in rectangles on top of each panel indicate the 30 warmest years in the 950 1250 period (left rectangle) and 1800 2010 period (right rectangle). All lines are smoothed by applying a 30 year moving average. Map at bottom right shows the individual regions for each reconstruction, and in bars the Medieval Climate Anomaly (MCA, 950-1250) Little Ice Age (LIA, 1450-1850) differences over those regions. Reconstructions: from PAGES 2k Consortium (2013). Models used: simulations with strong solar forcing (mostly pre-Paleoclimate Modelling Intercomparison Project Phase III (pre-PMIP3) simulations): CCSM3 (1), CNRM-CM3.3 (1), CSM1.4 (1), CSIRO-MK3L-1-2 (3), ECHAM5/MPIOM (3), ECHO-G (1) IPSLCM4 (1), FGOALS-gl (1). Simulations with weak solar forcing (mostly PMIP3/ CMIP5 simulations): BCC-csm1-1 (1), CCSM4 (1), CSIRO-MK3L-1-2 (1), GISS-E2-R (3, ensemble members 121, 124, 127), HadCM3 (1), MPI-ESM, ECHAM5/MPIOM (5), IPSL-CM5A- LR (1). In parenthesis are the number of simulations used for each model. All simulations are treated individually, in the time series as well as in the MCA LIA bars. More information about forcings used in simulations and corresponding references are given in Table 5.A.1. Time periods for averaging are JJA for June July August, SONDJF for the months from September to February, and DJF for December January February, respectively, while ANN denotes annual mean. 419 Chapter 5 Information from Paleoclimate Archives Taking associated uncertainties into consideration, 20th century tem- strength of the westerlies (Moros et al., 2009; Euler and Ninnemann, peratures in those regions were likely not higher than during the first 2010; Shevenell et al., 2011). The Holocene land-surface temperature part of the last millennium. In different regions of China, temperatures history in the SH is difficult to assess. Individual reconstructions gener- appear higher during recent decades than during earlier centuries, ally track the trends registered by Antarctic ice core records with peak although with large uncertainties (e.g., Ge et al., 2006, 2010; Wang values at around 12 to 10 ka (Masson-Delmotte et al., 2011b; Marcott et al., 2007; Holmes et al., 2009; Yang et al., 2009; Zhang et al., 2009; et al., 2013; Mathiot et al., 2013). Pollen-based records indicate posi- Cook et al., 2012). In northeast China, an alkenone-based reconstruc- tive MH temperature anomalies in southern South Africa that are not tion indicates that the growing season temperature during the peri- reproduced in the PMIP3/CMIP5 simulations (Figure 5.11). ods 480 860, 1260 1300, 1510 1570 and 1800 1900 was about 1°C lower compared with the 20th century (Chu et al., 2011). These Indices for the position of Southern Ocean fronts and the strength and reconstructed NH regional temperature evolutions appear consistent position of the westerlies diverge (Moros et al., 2009; e.g., Shevenell with last millennium GCM simulations using a range of solar forcing et al., 2011). For the mid-to-late-Holocene, climate models of different estimates (Figure 5.12). complexity consistently show a poleward shift and intensification of the SH westerlies in response to orbital forcing (Varma et al., 2012). There is high confidence that in the extratropical NH, both regionally However, the magnitude, spatial pattern and seasonal response vary and on a hemispheric basis, the surface warming of the 20th century significantly among the models. reversed the long term cooling trend due to orbital forcing. New high-resolution, climate reconstructions for the last millennium 5.5.1.2 Tropics are based on tree-ring records from the subtropical and central Andes, northern and southern Patagonia, Tierra del Fuego, New Zealand and Marcott et al. (2013) provide a compilation of tropical SST reconstruc- Tasmania (Cook et al., 2006; Boninsegna et al., 2009; Villalba et al., tions, showing a gradual warming of about 0.5°C until 5 ka and little 2009), ice cores, lake and marine sediments and documentary evidence change thereafter. Holocene tropical SST trends are regionally hetero- from southern South America (Prieto and García Herrera, 2009; Vimeux geneous and variable in magnitude. Alkenone records from the eastern et al., 2009; von Gunten et al., 2009; Tierney et al., 2010; Neukom et al., tropical Pacific, western tropical Atlantic, and the Indonesian archipel- 2011), terrestrial and shallow marine geological records from eastern ago document a warming trend of ~0.5°C to 2°C from the early Hol- Antarctica (Verleyen et al., 2011), ice cores from Antarctica (Goosse ocene to present (Leduc et al., 2010), consistent with local insolation. et al., 2012c; Abram et al., 2013; Steig et al., 2013), boreholes from In contrast, regional trends of planktonic foraminiferal Mg/Ca records western Antarctica (Orsi et al., 2012) and coral records from the Indian are heterogeneous, and imply smaller magnitude SST changes (Leduc and Pacific Oceans (Linsley et al., 2008; Zinke et al., 2009; Lough, 2011; et al., 2010; Schneider et al., 2010). Foraminiferal Mg/Ca records in the DeLong et al., 2012). There is medium confidence that southern South Indo-Pacific warm-pool region show cooling trends with varying mag- America (Neukom et al., 2011) austral summer temperatures during nitudes (Stott et al., 2004; Linsley et al., 2010). When comparing SST 950 1350 were warmer than the 20th century. A 1000-year temper- records from different paleoclimate proxies it is important to note that ature reconstruction for land and ocean representing Australasia indi- they can have different, and also regionally varying, seasonal biases cates a warm period during 1160 1370 though this reconstruction is (Schneider et al., 2010). based on only three records before 1430 (PAGES 2k Consortium, 2013). In Australasia, 1971 2000 temperatures were very likely higher than Terrestrial temperature reconstruction efforts have mostly focussed on any other 30-year period over the last 580 years (PAGES 2k Consor- Africa and to some extent on southeast Asia (Figure 5.11), with a lack tium, 2013). of syntheses from South America and Australia (Bartlein et al., 2011). 5 The PMIP2 and PMIP3/CMIP5 MH simulations show summer cooling Antarctica was likely warmer than 1971 2000 during the late 17th compared to pre-industrial conditions and a shorter growing season century, and during the period from approximately the mid-2nd centu- in the tropical monsoon regions of Africa and southeast Asia (Figure ry to 1250 (PAGES 2k Consortium, 2013). 5.11), attributed to increased cloudiness and local evaporation (Brac- onnot et al., 2007). In contrast, MH simulations and reconstructions for In conclusion, continental scale surface temperature reconstructions the entire tropics (30°S to 30°N) show generally higher mean tempera- from 950 to 1250 show multi-decadal periods that were in some ture of the warmest month and lower mean temperature of the coldest regions as warm as in the mid-20th century and in others as warm as month than for the mid-20th century. in the late 20th century (high confidence). These regional warm periods were not as synchronous across regions as the warming since the mid- 5.5.1.3 Southern Hemisphere Mid to High Latitudes 20th century (high confidence). In the high latitude Southern Ocean, Holocene SST trends follow the 5.5.2 Sea Ice decrease in austral summer duration, with a cooling trend from the early Holocene into the late Holocene (Kaiser et al., 2008; Shevenell Since AR4 several new Holocene sea ice reconstructions for the Arctic et al., 2011). Similar cooling trends are found in the Australian-New and sub-Arctic have been made available that resolve multi-decadal Zealand region (Bostock et al., 2013). Increased amplitude of millenni- to century-scale variability. Proxies of sea-ice extent have been fur- al-to-centennial scale SST variability between 5 ka and 4 ka is record- ther developed from biomarkers in deep sea sediments (e.g., IP25, ed in several locations, possibly due to variations in the position and Belt et al., 2007; Müller et al., 2011) and from sea-ice biota preserved 420 Information from Paleoclimate Archives Chapter 5 in sediments (e.g., Justwan and Koç, 2008). Indirect information on regarding potential inter-hemispheric synchronicity of sub-millennial sea-ice conditions based on drift wood and beach erosion has also scale glacier fluctuations (Wanner et al., 2008; Jomelli et al., 2009; Lic- been compiled (Funder et al., 2011). In general, these sea-ice recon- ciardi et al., 2009; Schaefer et al., 2009; Winkler and Matthews, 2010; structions parallel regional SST, yet they display spatial heterogeneity, Wanner et al., 2011). and differences between the methods, making it difficult to provide quantitative estimates of past sea-ice extent. Summer sea-ice cover Glacial chronologies for the last 2 kyr are better constrained (Yang et was reduced compared to late 20th century levels both in the Arctic al., 2008; Clague et al., 2010; Wiles et al., 2011; Johnson and Smith, Ocean and along East Greenland between 8 ka and 6.5 ka (e.g., Moros 2012). Multi-centennial glacier variability has been linked with var- et al., 2006; Polyak et al., 2010; Funder et al., 2011), a feature which is iations in solar activity (Holzhauser et al., 2005; Wiles et al., 2008), captured by some MH simulations (Berger et al., 2013). The response volcanic forcing (Anderson et al., 2008) and changes in North Atlantic of this sea ice cover to summer insolation warming was shown to be circulation (Linderholm and Jansson, 2007; Nesje, 2009; Marzeion and central for explaining the reconstructed warmer winter temperatures Nesje, 2012). Glacier response is more heterogeneous and complex over the adjacent land (Otto et al., 2009; Zhang et al., 2010). During the during the MCA than the uniform global glacier recession observed at last 6 kyr available records show a long-term trend of a more extensive present (see Section 4.3). Glaciers were smaller during the MCA than Arctic sea ice cover driven by the orbital forcing (e.g., Polyak et al., in the early 21st Century in the western Antarctic Peninsula (Hall et al., 2010), but punctuated by strong century-to-millennial scale variability. 2010) and Southern Greenland (Larsen et al., 2011). However, promi- Consistent with Arctic temperature changes (see Section 5.5.1), sea nent advances occurred within the MCA in the Alps (Holzhauser et al., ice proxies indicate relatively reduced sea-ice cover from 800 to 1200 2005), Patagonia (Luckman and Villalba, 2001), New Zealand (Schaefer followed by a subsequent increase during the LIA (Polyak et al., 2010). et al., 2009), East Greenland (Lowell et al., 2013) and SE Tibet (Yang et Proxy reconstructions document the 20th-century ice loss trend, which al., 2008). Glaciers in northwestern North America were similar in size is also observed in historical sea ice data sets with a decline since during the MCA compared to the peak during the LIA, probably driven the late 19th century (Divine and Dick, 2006). There is medium con- by increased winter precipitation (Koch and Clague, 2011). fidence that the current ice loss was unprecedented and that current SSTs in the Arctic were anomalously high at least in the context of the There is high confidence that glaciers at times have been smaller than last 1450 years (England et al., 2008; Kinnard et al., 2008; Kaufman et at the end of the 20th century in the Alps (Joerin et al., 2008; Ivy-Ochs al., 2009; Macias Fauria et al., 2010; Polyakov et al., 2010; Kinnard et et al., 2009; Goehring et al., 2011), Scandinavia (Nesje et al., 2011), al., 2011; Spielhagen et al., 2011). Fewer high-resolution records exist Altai in Central Asia (Agatova et al., 2012), Baffin Island (Miller et al., from the Southern Ocean. Data from the Indian Ocean sector docu- 2005), Greenland (Larsen et al., 2011; Young et al., 2011), Spitsbergen ment an increasing sea-ice trend during the Holocene, with a rather (Humlum et al., 2005), but the precise glacier extent in the previous abrupt increase between 5 ka and 4 ka, consistent with regional tem- warm periods of the Holocene is often difficult to assess. While ear- peratures (see Section 5.5.1.3) (Denis et al., 2010). ly-to-mid-Holocene glacier minima can be attributed with high con- fidence to high summer insolation (see Section 5.5.1.1), the current 5.5.3 Glaciers glacier retreat, however, occurs within a context of orbital forcing that would be favourable for NH glacier growth. If retreats continue at cur- Due to the response time of glacier fronts, glacier length variations rent rates, most extratropical NH glaciers will shrink to their minimum resolve only decadal- to centennial-scale climate variability. Since AR4 extent, that existed between 8 ka and 6 ka (medium confidence) (e.g., new and improved chronologies of glacier size variations were pub- Anderson et al., 2008); and ice shelves on the Antarctic peninsula will lished (Anderson et al., 2008; Joerin et al., 2008; Yang et al., 2008; retreat to an extent unprecedented through Holocene (Hodgson, 2011; Jomelli et al., 2009; Licciardi et al., 2009; Menounos et al., 2009; Mulvaney et al., 2012). Schaefer et al., 2009; Wiles et al., 2011; Hughes et al., 2012). Studies 5 of sediments from glacier-fed lakes and marine deposits have allowed 5.5.4 Monsoon Systems and Convergence Zones new continuous reconstructions of glacier fluctuations (Matthews and Dresser, 2008; Russell et al., 2009; Briner et al., 2010; Bowerman and This subsection focuses on internally and externally driven variability of Clark, 2011; Larsen et al., 2011; Bertrand et al., 2012; Vasskog et al., monsoon systems during the last millennium. Abrupt monsoon chang- 2012). Reconstructions of the history of ice shelves and ice sheets/caps es associated with Dansgaard Oeschger and Heinrich events (Figure have also emerged (Antoniades et al., 2011; Hodgson, 2011; Simms et 5.4b, e, h) are further assessed in Section 5.7.1. Orbital-scale monsoon al., 2011; Smith et al., 2011; Kirshner et al., 2012). New data confirm (Figure 5.4a, d, g) changes are evaluated in Section 5.3.2.3. a general increase of glacier extent in the NH and decrease in the SH during the Holocene (Davis et al., 2009; Menounos et al., 2009), con- Hydrological proxy data characterizing the intensity of the East Asian sistent with the local trends in summer insolation and temperatures. monsoon (South American monsoon) show decreased (increased) Some exceptions exist (e.g., in the eastern Himalayas), where glaciers hydrological activity during the LIA as compared to the MCA (medium were most extensive in the early Holocene (Gayer et al., 2006; Seong confidence) (Figure 5.4f, i) (Zhang et al., 2008; Bird et al., 2011; Vuille et et al., 2009), potentially due to monsoon changes (Rupper et al., 2009). al., 2012). These shifts were accompanied by changes in the occurrence Due to dating uncertainties, incompleteness and heterogeneity of most of megadroughts (high confidence) in parts of the Asian monsoon existing glacial chronologies, it is difficult to compare glacier variations region (Buckley et al., 2010; Cook et al., 2010a) (Figure 5.13). Lake sed- between regions at centennial and shorter time scales (Heyman et al., iment data from coastal eastern Africa document dry conditions in the 2011; Kirkbride and Winkler, 2012). There are no definitive conclusions late MCA, a wet LIA, and return toward dry conditions in the 18th or 421 Chapter 5 Information from Paleoclimate Archives early 19th century (Verschuren et al., 2000; Stager et al., 2005; Versch- tions show a multi-centennial summer drought phase during Medieval uren et al., 2009; Tierney et al., 2011; Wolff et al., 2011), qualitatively times (900 1350) (Helama et al., 2009), while lake sediment proxies similar to the South American monsoon proxies in Figure 5.4i, whereas from the same region suggest wetter winters (Luoto et al., 2013). some inland and southern African lakes suggest dry spells during the New tree-ring reconstructions from the southern-central (Wilson et al., LIA (Garcin et al., 2007; Anchukaitis and Tierney, 2013). Rainfall pat- 2013) and southeastern British Isles (Cooper et al., 2013) do not reveal terns associated with the Pacific ITCZ also shifted southward during multi-­ entennial drought during medieval times, but rather alternating c the MCA/LIA transition in the central equatorial Pacific (Sachs et al., multidecades of dry and wet periods. Wilson et al. (2013) reconstructed 2009). Extended intervals of monsoon failures and dry spells have been drier conditions between ~1300 and the early 16th century. Büntgen reconstructed for the last few millennia for west Africa (Shanahan et et al. (2011b) identified exceptionally dry conditions in central Europe al., 2009), east Africa (Wolff et al., 2011), northern Africa (Esper et al., from 200 to 350 and between 400 and 600. Numerous tree-ring 2007b; Touchan et al., 2008; Touchan et al., 2011), India and southeast- records from the eastern Mediterranean testify to the regular occur- ern Asia (Zhang et al., 2008; Berkelhammer et al., 2010; Buckley et al., rence of droughts in the past few millennia (e.g., Akkemik et al., 2008; 2010; Cook et al., 2010a) and Australia (Mohtadi et al., 2011). Nicault et al., 2008; Luterbacher et al., 2012). In northern Africa, Esper et al. (2007b) and Touchan et al. (2008; 2011) show severe drought On multi-decadal-to-centennial time scales, influences of North Atlan- events through the last millennium, particularly prior to 1300, in the tic SST variations have been demonstrated for the North and South 1400s, between 1700 and 1900, and in the most recent instrumental African monsoon, and the Indian and East Asian summer monsoons data. Using multiple proxies from Chile, Boucher et al. (2011) inferred (see Figure 5.4c for monsoon regions), both using proxy reconstruc- wetter conditions during 1000 1250, followed by much drier period tions (Feng and Hu, 2008; Shanahan et al., 2009) and GCM simula- until 1400 and wetter conditions similar to present afterwards, while tions (Lu et al., 2006; Zhang and Delworth, 2006; Wang et al., 2009; Ledru et al. (2013) reconstructed a dry MCA-LIA transition until 1550. Luo et al., 2011). These simulations suggest that solar and volcanic For the South American Altiplano Morales et al. (2012) found periods forcing (Fan et al., 2009; Liu et al., 2009a; Man et al., 2012) may exert of drier conditions in the 14th, 16th, and 18th centuries, as well as a only weak regional influences on monsoon systems (Figure 5.4f, i). A modern drying trend. five-member multi-model ensemble mean of PMIP3/CMIP5 simula- tions (Table 5.A.1) exhibits decreased standardized monsoon rainfall Reconstruction of past flooding from sedimentary, botanical and his- accompanying periods of reduced solar forcing during the LIA in the torical records (Brázdil et al., 2006; Baker, 2008; Brázdil et al., 2012) East Asian monsoon regions (Figure 5.4f). There is, however, a consid- provides a means to compare recent large, rare floods, and to ana- erable inter-model spread in the simulated annual mean precipitation lyse links between flooding and climate variability. During the last few response to solar forcing with the multi-model mean, explaining on millennia, flood records reveal strong decadal to secular variability average only ~25 +/- 15% (1 standard deviation) of the variance of the and non-stationarity in flood frequency and clustering of paleofloods, individual model simulations. An assessment of the pre-instrumental which varied among regions. In Europe, modern flood magnitudes are response of monsoon systems to volcanic forcing using paleo-proxy not unusual within the context of the last 1000 years (e.g., Brázdil et data has revealed wetter conditions over southeast Asia in the year of al., 2012). In Central Europe, the Elbe and the Oder/Odra Rivers show a major volcanic eruption and drier conditions in central Asia (Anchu- a decrease in the frequency of winter floods during the last 80 to 150 kaitis et al., 2010), in contrast to GCM simulations (Oman et al., 2005; years compared to earlier centuries, while summer floods indicate no Brovkin et al., 2008; Fan et al., 2009; Schneider et al., 2009). significant trend (Mudelsee et al., 2003) (Figure 5.14f i). In the Alps, paleoflood records derived from lake sediments have shown a higher 5.5.5 Megadroughts and Floods flood frequency during cool and/or wet phases (Stewart et al., 2011; Giguet-Covex et al., 2012; Wilhelm et al., 2012), a feature also found 5 Multiple lines of proxy evidence from tree rings, lake sediments, and in Central Europe (Starkel et al., 2006) and the British Isles (Macklin speleothems indicate with high confidence that decadal or mul- et al., 2012). In the western Mediterranean, winter floods were more ti-decadal episodes of drought have been a prominent feature of frequent during relatively cool and wet climate conditions of the LIA North American Holocene hydroclimate (e.g., Axelson et al., 2009; St. (Benito et al., 2003b; Piccarreta et al., 2011; Luterbacher et al., 2012; George et al., 2009; Cook et al., 2010a, 2010b; Shuman et al., 2010; Figure 5.14a), whereas autumn floods reflect multi-decadal variations Woodhouse et al., 2010; Newby et al., 2011; Oswald and Foster, 2011; (Benito et al., 2010; Machado et al., 2011; Figure 5.14b, c). In China, Routson et al., 2011; Stahle et al., 2011; Stambaugh et al., 2011; Laird extraordinary paleoflood events in the Yellow, Weihe and Qishuihe et al., 2012; Ault et al., 2013). During the last millennium, western rivers, occurred synchronously with severe droughts and dust accu- North America drought reconstructions based on tree ring informa- mulations coinciding with a monsoonal shift, the most severe floods tion (Figure 5.13) show longer and more severe droughts than today, dated at 3.1 ka (Zha et al., 2009; Huang et al., 2012). In India, flood particularly during the MCA in the southwestern and central United frequencies since 1950 are the largest for the last several hundred States (Meko et al., 2007; Cook et al., 2010b). The mid-14th century years for eight rivers, interpreted as a strengthening of the monsoon cooling coincides in southwestern North America with a shift towards conditions after the LIA (Kale, 2008). In southwestern United States, overall wetter conditions (Cook et al., 2010a). In the Pacific Northwest, increased frequency of high-magnitude paleofloods coincide with peri- contrasting results emerge from lake sediment records, indicating ods of cool, wet climate, whereas warm intervals including the MCA, wetter conditions during the MCA (Steinman et al., 2013), and tree- corresponded with significant decreases in the number of large floods ring data showing no substantial change (Zhang and Hebda, 2005; (Ely et al., 1993). In the Great Plains of North America, the frequency Cook et al., 2010a). In Scandinavia, new tree-ring based reconstruc- of large floods increased significantly around 850 with magnitudes 422 Information from Paleoclimate Archives Chapter 5 0 300 (a) Severity Interval 48ON 250 Severity (Cumulative PDSI) 2 200 Interval (yr) 36 ON 4 150 6 24ON 100 8 50 12 N O (b) 10 0 40 20 0 0 2 4 6 8 10 Count Duration (yr) 0 0O 20 40 Count 60 80 75 OE 90 OE 105 OE 120 OE 135 OE 2 (c) 1 0 PDSI 1 2 1350 1450 1550 1650 1750 1850 1950 Time (Year CE) (d) 0 600 Severity 60 ON Severity (Cumulative PDSI) Interval 500 5 400 Interval (yr) 48 ON 10 300 15 200 36 ON 20 100 (e) 25 0 24 ON 60 40 20 0 0 5 10 15 Count Duration (yr) 0 20 Count 40 12 N O 60 135 W O 120 W O 105 W O 90 WO 75 W O 80 4 (f) 5 2 0 PDSI 2 4 800 900 1000 1100 1200 1300 1400 1500 1600 1700 1800 1900 Time (Year CE) Figure 5.13 | Severity, duration, and frequency of droughts in the Monsoon Asia (Cook et al., 2010b) and North American (Cook et al., 2004) Drought Atlases. The box in (a) and (d) indicates the region over which the tree-ring reconstructed Palmer Drought Severity Index (PDSI) values have been averaged to form the regional mean time series shown in (c) and (f), respectively. Solid black lines in (c) and (f) are a 9-year Gaussian smooth on the annual data shown by the red and blue bars. The covariance of drought (PDSI <0) duration and cumulative severity for each region is shown in panels (b) and (e) by the red circles (corresponding to the left y-axes), along with the respective marginal frequency histograms for each quantity. Not shown in b) is an outlier with an apparent duration of 24 years, corresponding to the Strange Parallels drought identified in Cook et al. (2010b). Intervals between droughts of given durations are shown in the same panels and are estimated as the mean interval between their occurrence, with the minimum and maximum reconstructed intervals indicated (corresponding to the right y-axes, shown as connected lines and their corresponding range). No error bars are present if there are fewer than three observations of a drought of that duration. The period of analysis is restricted by the availability of tree-ring data to the period 1300 1989 for Monsoon Asia, following Cook et al. (2010a), and from 800 to 1978 for southwestern North America, following Cook et al. (2004). 423 Chapter 5 Information from Paleoclimate Archives (a) Tagus River at Aranjuez, Central Spain (f) Vltava River at Prague, Czech Republic 10 10 Large - catastrophic floods (437 yr; n= 9; T=48 yr) Flow Large - catastrophic floods (505 yr; n=19; T=26 yr) Gauge Bidecadal frequency Bidecadal frequency 8 regulation 8 record Extraordinary floods (437 yr; n=31; T=14 yr) Extraordinary floods (505 yr; n=63; T=8 yr) 6 6 Discontinuous documentary archives 4 4 2 2 0 0 1100 1200 1300 1400 1500 1600 1700 1800 1900 2000 1100 1200 1300 1400 1500 1600 1700 1800 1900 2000 (b) Segura River, SE Spain (g) Elbe River, Germany 14 Large - catastrophic floods (710 yr; n=49; T=15 yr) Flow 14 Large - catastrophic floods (685 yr; n=21; T=33 yr) Gauge Bidecadal frequency Bidecadal frequency 12 regulation 12 Extraordinary floods (625 yr; n=105; T=6 yr) Extraordinary floods (941 yr; n=91; T=10 yr) record 10 10 8 8 6 6 4 4 2 2 0 0 1100 1200 1300 1400 1500 1600 1700 1800 1900 2000 1100 1200 1300 1400 1500 1600 1700 1800 1900 2000 10 (c) Gardon River, Sourthern France (h) Oder River, Western Poland/Germany 14 Large - catastrophic floods (504 yr; n=26; T=19 yr) Large - catastrophic floods (600 yr; n=13; T=46 yr) Bidecadal frequency Gauge Gauge Bidecadal frequency 8 12 record Extraordinary floods (270 yr; n=20; T=13 yr) record Extraordinary floods (650 yr; n=67; T=10 yr) 10 6 8 4 6 4 2 2 0 0 1100 1200 1300 1400 1500 1600 1700 1800 1900 2000 1100 1200 1300 1400 1500 1600 1700 1800 1900 2000 Time (Year CE) (d) Tiber River, Rome, Italy 10 Large - catastrophic floods (819 yr; n=20; T=41 yr) Gauge Bidecadal frequency 8 record -20° -10° 0° 10° 20° 30° 40° 50° Extraordinary floods (517 yr; n=40; T=13 yr) 60° 6 4 2 50° e 0 g h 1100 1200 1300 1400 1500 1600 1700 1800 1900 f (e) Ouse River at York, UK 20 Gauge c Large -castastrophic floods (410 yr; n=25; T=16 yr) 16 record 40° a Bidecadal frequency Extraordinary floods (410 yr; n=65; T=6 yr) d 12 b 8 0° 10° 0 500 1000 1500 4 (km) 20° 30° 5 0 1100 1200 1300 1400 1500 1600 1700 1800 1900 2000 Time (Year CE) Figure 5.14 | Flood frequency from paleofloods, historical and instrumental records in selected European rivers. Depicted is the number of floods exceeding a particular discharge threshold or flood height over periods of 20 years (bidecadal). Flood categories include rare or catastrophic floods (CAT) associated with high flood discharge or severe damages, and extraordinary floods (EXT) causing inundation of the floodplain with moderate-to-minor damages. Legend at each panel indicates for each category the period of record in years, number of floods (n) over the period, and the average occurrence interval (T in years). (a) Tagus River combined paleoflood, historical and instrumental flood records from Aranjuez and Toledo with thresholds of 100 400 m3 s 1 (EXT) and >400 m3 s 1 (CAT) (data from Benito et al., 2003a; 2003b). (b) Segura River Basin (SE Spain) documentary and instrumental records at Murcia (Barriendos and Rodrigo, 2006; Machado et al., 2011). (c) Gardon River combined discharges from paleofloods at La Baume (Sheffer et al., 2008), documented floods (since the 15th century) and historical and daily water stage readings at Anduze (1741 2005; Neppel et al., 2010). Discharge thresholds referred to Anduze are 1000 to 3000 m3 s 1 (EXT), >3000 m3 s 1 (CAT). At least five floods larger than the 2002 flood (the largest in the gauged record) occurred in the period 1400 1800 (Sheffer et al., 2008). (d) Tiber River floods in Rome from observed historical stages (since 1100; Camuffo and Enzi, 1996; Calenda et al., 2005) and continuous stage readings (1870 to present) at the Ripetta landing (Calenda et al., 2005). Discharge thresholds set at 2300 to 2900 m3 s 1 (EXT) and >2900 m3 s 1 (CAT; >17 m stage at Ripetta). Recent flooding is difficult to evaluate in context due to river regulation structures. (e) River Ouse at York combined documentary and instrumental flood record (Macdonald and Black, 2010). Discharge thresholds for large floods were set at 500 m3 s 1 (CAT) and 350 to 500 m3 s 1 (EXT). (f) Vltava River combined documentary and instrumental flood record at Prague (Brázdil et al., 2005) discharge thresholds: CAT, flood index 2 and 3 or discharge >2900 m3 s 1 ; EXT flood index 1 or discharge 2000 to 2900 m3 s 1 . (g) Elbe River combined documentary and instrumental flood record (Mudelsee et al., 2003). Classes refer to Mudelsee et al. (2003) strong (EXT) and exceptionally strong (CAT) flooding. (h) Oder River combined documentary and instrumental flood record (Mudelsee et al., 2003). The map shows the location of rivers used in the flood frequency plots. Note that flood frequencies obtained from historical sources may be down biased in the early part of the reported periods owing to document loss. 424 Information from Paleoclimate Archives Chapter 5 roughly two to three times larger than those of the 1972 flood (Harden The first appearance of ice-rafted debris across the entire North Atlan- et al., 2011). South America large flooding in the Atacama and Peruvi- tic indicates that continental-scale ice sheets in North America and an desert streams originated in the highland Altiplano and were par- Eurasia did not develop until about 2.7 Ma (Kleiven et al., 2002). This ticularly intense during El Nino events (Magilligan et al., 2008). In the suggests that MPWP high sea levels were due to mass loss from the winter rainfall zone of southern Africa, the frequency of large floods GIS, the WAIS and possibly the East Antarctic Ice sheet (EAIS). Sed- decreased during warmer conditions (e.g., from 1425 to 1600 and imentary record from the Ross Sea indicates that the WAIS and the after 1925) and increased during wetter, colder conditions (Benito et marine margin of EAIS retreated periodically during obliquity-paced al., 2011). interglacial periods of MPWP (Naish et al., 2009a). Reconstructed SSTs for the ice free seasons in the Ross Sea range from 2°C to 8°C (McKay In summary, there is high confidence that past floods larger than et al., 2012b), with mean values >5°C being, according to one ice sheet recorded since the 20th century have occurred during the past 500 model, above the stability threshold for ice shelves and marine portions years in northern and central Europe, western Mediterranean region, of the WAIS and EAIS (Pollard and DeConto, 2009; see also Section and eastern Asia. There is, however, medium confidence that in the 5.8.1). A synthesis of the geological evidence from the coastal regions Near East, India, central North America, modern large floods are com- of the Transantarctic Mountains (Barrett, 2013) and an iceberg-raft- parable to or surpass historical floods in magnitude and/or frequency. ed debris record offshore of Prydz Bay (Passchier, 2011) also supports coastal thinning and retreat of the EAIS between about 5 to 2.7 Ma. In response to Pliocene climate, ice sheet models consistently produce 5.6 Past Changes in Sea Level near-complete deglaciation of GIS (+7 m) and WAIS (+4 m) and retreat of the marine margins of EAIS (+3 m) (Lunt et al., 2008; Pollard and This section discusses evidence for global mean sea level (GMSL) DeConto, 2009; Hill et al., 2010), altogether corresponding to a GMSL change from key periods. The MPWP (Table 5.1) has been selected as a rise of up to 14 m. period of higher than present sea level (Section 5.6.1), warmer temper- ature (Section 5.3.1) and 350-450 ppm atmospheric CO2 concentration In summary, there is high confidence that GMSL was above present, (Section 5.2.2.2). Of the recent interglacial periods with evidence for due to deglaciation of GIS, WAIS and areas of EAIS, and that sea level higher than present sea level, the LIG has the best-preserved record was not higher than 20 m above present during the interglacials of (Section 5.6.2). For testing glacio-isostatic adjustment (GIA) models, the the MPWP. principal characteristics of Termination I, including Meltwater Pulse-1A (Section 5.6.3) is assessed. For the Holocene, the emphasis is on the 5.6.2 The Last Interglacial last 6000 years when ice volumes stabilized near present-day values, providing the baseline for discussion of anthropogenic contributions. Proxy indicators of sea level, including emergent shoreline deposits (Blanchon et al., 2009; Thompson et al., 2011; Dutton and Lambeck, 5.6.1 Mid-Pliocene Warm Period 2012) and foraminiferal d18O records (Siddall et al., 2003; Rohling et al., 2008a; Grant et al., 2012) are used to reconstruct LIG sea levels. Implic- Estimates of peak sea levels during the MPWP (Table 5.1, Section it in these reconstructions is that geophysical processes affecting the 5.3.1) based on a variety of geological records are consistent in sug- elevation of the sea level indicators (uplift, subsidence, GIA) have been gesting higher-than-present sea levels, but they range widely (10 to 30 properly modelled and/or that the sea level component of the stable m; Miller et al., 2012a), and are each subject to large uncertainties. For isotope signal has been properly isolated. Particularly important issues example, coastal records (shorelines, continental margin sequences) with regard to the LIG are (1) the ongoing debate on its initiation and are influenced by GIA, with magnitudes of the order of 5 m to 30 m duration (cf. 130 to 116 ka, Stirling et al., 1998; 124 to 119 ka, Thomp- for sites in the far and near fields of ice sheets, respectively (Raymo et son and Goldstein, 2005), due to coral geochronology issues; (2) the 5 al., 2011), and global mantle dynamic processes (Moucha et al., 2008; magnitude of its maximum rise; and (3) sea level variability within the Müller et al., 2008) may contribute up to an additional +/-10 m. Conse- interval. Foraminiferal d18O records can constrain (2) and possibly (3). quently, both signals can be as large as the sea level estimate itself and Emergent shorelines on tectonically active coasts can constrain (1) and current estimates of their amplitudes are uncertain. (3) and possibly (2) if vertical tectonic rates are independently known. Shorelines on tectonically stable coasts can constrain all three issues. Benthic 18O records are better dated than many coastal records Here evidence from emergent shorelines that can be dated directly is and provide a continuous time series, but the 18O signal reflects ice emphasized. volume, temperature and regional hydrographic variability. During the mid-Pliocene warm interval, the 0.1 to 0.25 anomalies recorded in 5.6.2.1 Magnitude of the Last Interglacial Sea Level Rise the LR04 benthic 18O stack (Lisiecki and Raymo, 2005) would trans- late into ~12 to 31 m higher than present GMSL, if they reflected only AR4 assessed that global sea level was likely between 4 and 6 m higher ice volume. Conversely, these anomalies could be explained entirely by during the LIG than in the 20th century. Since AR4, two studies (Kopp warmer deep-water temperatures (Dowsett et al., 2009). Attempts to et al., 2009; Dutton and Lambeck, 2012) have addressed GIA effects constrain the temperature component in benthic 18O records conclude from observations of coastal sites. higher than present GMSL during the MPWP with large uncertainties (+/-10 m) (Dwyer and Chandler, 2009; Naish and Wilson, 2009; Sosdian Kopp et al. (2009) obtained a probabilistic estimate of GMSL based and Rosenthal, 2009; Miller et al., 2012a). on a large and geographically broadly distributed database of LIG sea 425 Chapter 5 Information from Paleoclimate Archives level indicators (Figure 5.15a). Their analysis accounted for GIA effects 0.9 m per century, and a revised chronology of the Red Sea sea level as well as uncertainties in geochronology, the interpretation of sea record adjusted to ages from Soreq Cave yields estimates of sea level level indicators, and regional tectonic uplift and subsidence. They con- rise rates of up to 0.7 m per century when sea level was above present cluded that GMSL was very likely +6.6 m and likely +8.0 m relative level during the LIG (Grant et al., 2012). to present, and that it is unlikely to have exceeded +9.4 m, although some of the most rapid and sustained rates of change occur in the In their probabilistic assessment of LIG sea level, Kopp et al. (2013) early period when GMSL was still below present (Figure 5.15a). concluded that it was extremely likely that there were at least two peaks in sea level during the LIG. They further concluded that during Dutton and Lambeck (2012) used data from two tectonically stable far- the interval following the initial peak at ~126 ka (Figure 5.15a) it is field areas (areas far from the former centres of glaciation), Australia likely that there was a period in which GMSL rose at an average rate and the Seychelles islands. At these sites, in contrast to sites near the exceeding 3 m kyr 1, but unlikely that this rate exceeded 7 m kyr 1. former ice margins, the isostatic signals are less sensitive to the choice of parameters defining the Earth rheology and the glacial ice sheets In summary, there is evidence for two intra-LIG sea level peaks (high (Lambeck et al., 2012). On the west coast of Australia, the highest LIG confidence) during which sea level varied by up to 4 m (medium confi- reef elevations are at +3.5 m and the inferred paleo-sea level, allowing dence). The millennial-scale rate of sea level rise during these periods for possible reef erosion, is about +5.5 m relative to present. In the exceeded 2 m kyr 1 (high confidence). Seychelles, LIG coral reefs occur from 0 m to 6 m, but also possibly as high as ~9 m (Israelson and Wohlfarth, 1999, and references therein). 5.6.2.3 Implications for Ice Sheet Loss During the Ten of the eleven LIG coral samples from the Seychelles used in Dutton Last Interglacial and Lambeck (2012) have reef elevation estimates ranging from +2.1 to +4 m relative to present; whereas a single LIG coral sample has a The principal sources for the additional LIG meltwater are the GIS, reef elevation estimate of +6 m. Additional results are needed to sup- WAIS and the low elevation, marine-based margins of the EAIS. An port an estimate of a maximum LIG sea level at the Seychelles of + 9 upper limit for the contributions from mountain glaciers is ~0.42 +/- m relative to present. 0.11 m if all present-day mountain glaciers melted (cf. Section 4.3). The estimated LIG ocean thermal expansion contribution is 0.4 +/- 0.3 In conclusion, there is very high confidence that the maximum GMSL m (McKay et al., 2011). during the LIG was, for several thousand years, at least 5 m higher than present but that GMSL at this time did not exceed 10 m (high Sedimentological evidence indicates that southern Greenland was not confidence). The best estimate from the two available studies is 6 m ice-free during the LIG (Colville et al., 2011). Since AR4, the evidence higher than present. for LIG ice layers in Greenland ice cores, which was ambiguous from Dye 3 and unequivocal from Summit and NGRIP ice cores (summarised 5.6.2.2 Evidence for Last Interglacial Sea Level Variability in Masson-Delmotte et al., 2011a), has been strengthened. Data from the new NEEM ice core (NEEM community members, 2013; see Figure Since AR4, there is evidence for meter-scale variability in local LIG sea 5.16 for locations) point to an unequivocal existence of ice throughout level between 126 ka and 120 ka (Thompson and Goldstein, 2005; the LIG with elevations differing a few hundred meter from present, Hearty et al., 2007; Rohling et al., 2008a; Kopp et al., 2009; Thompson possibly decreasing in elevation by ~ 400 m +/- 350 m between 128 and et al., 2011). However, there are considerable differences in the timing 122 ka BP. GIS simulations give an average contribution of ~2.3 m to and amplitude of the reported fluctuations due to regional sea level LIG GMSL (1.5 m, 1.9 m, 1.4 m and 4.3 m respectively for four models variability and uncertainties in sea level proxies and their ages. illustrated in Figure 5.16). Each model result has been selected from 5 a series of runs within a range of parameter uncertainties that yield Two episodes of reef building during the LIG have been reported on the predictions consistent with the occurrence of ice at NEEM and the ele- Yucatan coast (Blanchon et al., 2009) and in the Bahamas (Chen et al., vation of that ice reconstructed from the ice core record. In summary, 1991; Thompson et al., 2011). Blanchon et al. (2009) provide evidence the GIS simulations that are consistent with elevation changes from of Yucatan reef growth early in the LIG at a relative sea level of +3 m, the ice core analysis show limited ice retreat during this period such followed by a later episode at +6 m. Thompson et al. (2011) inferred a that this ice sheet very likely contributed between 1.4 and 4.3 m sea +4 m relative sea level at ~123 ka, followed by a fall to near present, level equivalent, implying with medium confidence a contribution from and finally a rise to +6 m at ~119 ka. This yields a rate of sea level the Antarctic ice sheet. change in the Bahamas of ~2.6 m kyr 1, although the higher estimate at the end of the interval may reflect GIA effects that result in a rise in One model of WAIS glacial interglacial variability shows very little relative sea level at these locations (Dutton and Lambeck, 2012). difference in ice volumes between the LIG and present (Pollard and DeConto, 2009) (Figure 5.15g), when the surface climate and ocean LIG sea level rise rates of between 1.1 and 2.6 m per century have melt term were parameterised using the global benthic 18O record for been estimated based on a foraminiferal d18O record from the Red Sea the last 5 Ma. Direct geological evidence of fluctuations in the extent (Rohling et al., 2008a). However, the original Red-Sea chronology was of WAIS margin during the LIG is equivocal due to inadequate age based on a short LIG duration of 124 to 119 ka, after Thompson and control on two sediment cores which imply that open-water conditions Goldstein (2005). The longer LIG duration of 130 to 116 ka indicated existed in the southeastern sector of the Ross Ice Shelf at some time in by the coral data (Stirling et al., 1998) reduces these rates to 0.4 to the last 1 Ma (Scherer et al., 1998; McKay et al., 2011; Vaughan et al., 426 Information from Paleoclimate Archives Chapter 5 15 Sea-level change (m) 10 (a) 4 (b) Sea-level change (m) 5 2 0 0 -5 -10 -2 mean esl -15 67% probability limits Western Australia 95% probability limits -4 all sites -20 135 130 125 120 115 135 130 125 120 115 3 (c) Caribbean Bermuda Yucatan 2 (d) Western Australia 12 Sea-level change (m) Sea-level change (m) San Salvador Margaret River 1 8 Gt Inagua 0 Haiti 4 -1 Rottnest Island NW Cape Barbados Geraldton 0 -2 -3 135 130 125 120 115 F 135 130 125 120 115 2.5 4 (e) 2 (f) Sea-level change (m) Sea-level change (m) 1.5 2 1 0 0.5 0 -2 Observed rsl GMSL Q -0.5 predicted rsl for reference esl GMSL R -4 inferred GMSL -1 GMSL S -1.5 135 130 125 120 115 135 130 125 120 115 4 6 2 (g) (h) JJA Temp anomaly (degC) 4 Sea-level change (m) 0 2 -2 0 -4 esl Q+PD -2 temp Q -6 esl R+PD temp R esl S+PD -4 -8 temp S esl PD -10 135 130 125 120 115 -6 135 130 125 120 115 5 Age (ka) Age (ka) Figure 5.15 | Sea level during the Last Interglacial (LIG) period. (a) Proxy-derived estimate of global mean sea level (GMSL) anomaly from Kopp et al. (2013). Mean GMSL (red line), 67% confidence limits (blue dashed lines) and 95% confidence limits (green dashed lines). The chronology is based on open-system U/Th dates. (b) Local LIG relative sea level reconstructions from Western Australia based on in situ coral elevations (red) that pass diagenetic screening (Dutton and Lambeck, 2012). Age error bars correspond to 2 standard deviation uncertainties. All elevations have been normalised to the upper growth limit of corals corresponding to mean low water spring or mean low sea level. The blue line indicates the simplest interpretation of local sea level consistent with reef stratigraphy and should be considered as lower limits by an amount indicated by the blue upper limit error bars. The chronology is based on closed system U/Th dates. (c) Predicted sea levels for selected sites in the Caribbean and North Atlantic in the absence of tectonics with the assump- tion that ice volumes during the interval from 129 to 116 ka are equal to those of today (Lambeck et al., 2012) illustrating the spatial variability expected across the region due to glacio-isostatic effects of primarily the MIS-6 and MIS-2 deglaciations (see also Raymo and Mitrovica, 2012). The reference ice-volume model for the LIG interval (blue shaded), earth rheology and ice sheet parameters are based on rebound analyses from different regions spanning the interval from Marine Isotope Stage 6 to the present (c.f. Lambeck et al., 2006). LIG sea level observations from these sites contain information on these ice histories and on GMSL. (d) Same as (c) but for different sites along the Western Australia coast contributing to the data set in (b). The dependence on details of the ice sheet and on Earth-model parameters is less important at these sites than for those in (c). Thus data from these locations, assuming tectonic stability, is more appropriate for estimating GMSL. (e) The Western Australian reconstructed evidence (blue) from (b), compared with the model- predicted result (red) for a reference site midway between the northern and southern most localities. The difference between the reconstructed and predicted functions provides an estimate of GMSL (green). Uncertainties in this estimate (67% confidence limits) include the observational uncertainties from (b) and model uncertainties (see e.g., Lambeck et al., 2004a, for a treatment of model errors). (f) Simulated contribution of GIS to GMSL change (black, Q, Quiquet et al. (2013); red, R, Robinson et al. (2011); blue, S, Stone et al. (2013), The Q, R, S correspond to the labels in Figure 5.16). (g) Simulated total GMSL contribution from the GIS (Q, R, S as in panel (f)) and the Antarctic ice sheet contribution (PD) according to Pollard and DeConto (2009). (h) Central Greenland surface-air temperature anomalies for summer (June August, JJA) used for ice sheet simulations displayed in panel (f) and in Figure 5.16. Anomalies in all panels are calculated relative to present. 427 Chapter 5 Information from Paleoclimate Archives Figure 5.16 | Simulated GIS elevation at the Last Interglacial (LIG) in transient (Q, R, S) and constant-forcing experiments (B). (Q) GRISLI ice sheet model with transient climate forc- ing derived from IPSL simulations and paleoclimate reconstructions (Quiquet et al., 2013). (R) Simulation, most consistent with independent evidence from ice cores, from ensemble runs SICOPOLIS ice sheet model driven by transient LIG climate simulations downscaled from CLIMBER2 with the regional model REMBO (Robinson et al., 2011). (S) As R but from ensemble simulations with the Glimmer ice sheet model forced with transient climate forcing from 135 to 120 ka with HadCM3 (Stone et al., 2013). (B) SICOPOLIS ice model forced with a constant Eemian climate simulation of IPSL (at 126 ka), running for 6000 years starting from fully glaciated present-day GIS (Born and Nisancioglu, 2012). White squares in each panel show the locations of ice core sites: Greenland Ice Core Project/Greenland Ice Sheet Project (GRIP/GISP) from the summit (G), North Greenland Ice Core Project (NGRIP) (NG), North Greenland Eemian Ice Drilling (NEEM) (NE), Camp Century (C), and Dye3 (D). For ice sheet simulations using transient climate forcing, the minimum in ice volume is illustrated. All panels use original model resolution and grids. Below each panel, maximum contribution to global mean sea level rise and time of minimum ice volume are denoted together with information on experimental design (either transient run of the Interglacial period starting from the former glacial or equilibrium run of a time slice at the peak interglacial). The differences in model outputs regarding timing and ice elevations result from different methodologies (e.g., transient climate change or equilibrium climate, with the latter assumption leading to the highest estimate of the four models), melt schemes (van de Berg et al., 2011), and the reference climate input (Quiquet et al., 2013). 2011) and in the vicinity of the northwestern Ross Ice Shelf within the et al. (2012) from a new Tahiti coral record. At this location sea level last 250,000 years during MIS 7 or LIG (McKay et al., 2012a). Ackert et rose between 14 and 18 m at a rate approaching 5 m per century. The al. (2011) dated glacial erratics and moraines across the Ohio Moun- source of MWP-1A continues to be widely debated with most attention tain Range of the Transantarctic Mountains and concluded that the being on scenarios in which the Antarctic ice sheet contributed either ice elevations were similar during the LIG and today, but such results significantly (Clark et al., 2002, 2009; Bassett et al., 2005) or very little cannot be extrapolated beyond this region. East Antarctic ice core LIG (Bentley et al., 2010; Mackintosh et al., 2011). Evidence of rapid WAIS data may reflect the impact of a reduced WAIS due to climatic effects retreat at around the time of MWP-1A is also indicated by analysis of (Holden et al., 2010b) but not through isostatic effects (Bradley et al., marine sediment cores (e.g., Kilfeather et al., 2011; Smith et al., 2011). 2012). Modelling and ice core data suggest EAIS may have retreated in the Wilkes Basin (Bradley et al., 2012). If the Antarctic ice sheet was the major contributor to MWP-1A then it must have contained at least 7.106 km3 more ice than at present 5 In summary, no reliable quantification of the contribution of the Ant- (equivalent to ~17 m GMSL), which is about twice the difference in arctic ice volume to LIG sea level is currently possible. The only availa- Antarctic ice volume between the LGM and present found by White- ble transient ice sheet model simulation (Pollard and DeConto, 2009) house et al. (2012). Because of the Earth-ocean (including gravita- does not have realistic boundary conditions, not enough is known tional, deformational and rotational) response to rapid changes in ice about the subsurface temperatures and there are few direct observa- volume, the amplitude of the associated sea level change is spatially tional constraints on the location of ice margins during this period. If variable (Clark et al., 2002) and can provide insight into the source the above inference of the contribution to GMSL from the GIS (5.6.2.1) region. Based on the comparison of the new Tahiti record with records is correct, the full GMSL change (section 5.6.2.2) implies significantly from Barbados (Fairbanks, 1989) and the Sunda Shelf (Hanebuth et al., less LIG ice in Antarctica than today, but as yet this cannot be support- 2011), Deschamps et al. (2012) conclude that a significant meltwater ed by the observational and model evidence. contribution to GMSL, of at least 7 m, originated from Antarctica. From ice sheet modelling, Gregoire et al. (2012) argued that the separation 5.6.3 Last Glacial Termination and Holocene of the North American Laurentide and Cordilleran ice sheets may in part be the cause of MWP-1A, contributing ~9 m in 500 years. Another The onset of melting of the LGM ice sheets occurred at approximately ice sheet modelling study Carlson et al. (2012) suggests a contribution 20 ka and was followed by a GMSL rise of ~130 m in ~13 kyr (Lambeck of 6 to 8 m in 500 years from the Laurentide at the onset of the Blling et al., 2002b). Coeval with the onset of the Blling warming in the NH, warming over North America. These studies indicate that there are no a particularly rapid rise of ~ 20 m occurred within ~ 340 years (Melt- glaciological impediments to a major North American contribution to water Pulse 1A, MWP-1A), as most recently documented by Deschamps MWP-1A. In contrast, there are as yet no modelling results that show a rapid retreat or partial collapse of the Antarctic ice sheet at that time. 428 Information from Paleoclimate Archives Chapter 5 Since AR4, high-resolution sea level records from different localities ration events have been proposed: a multi-stage draining of glacial suggest further periods of rapid ice-mass loss. For example, records Lake Agassiz (Hijma and Cohen, 2010), although estimates of the from Singapore indicate a rise of ~14 m from ~9.5 to 8.0 ka followed amount of water stored in this lake are less than the required amount; by a short interval of a smaller rise centred on about 7.2 ka (Bird et a rapid melting of the Labrador and Baffin ice domes (Carlson et al., al., 2010, for Singapore) and records from the US Atlantic (Cronin et 2007; Gregoire et al., 2012); or to Antarctic ice sheet decay (Bird et al., al., 2007) and North Sea coasts (Hijma and Cohen, 2010) suggest a 2007; Cronin et al., 2007). rise at around ~9.0 7.5 ka that is possibly punctuated by one or two short intervals of higher rates. These and similar rapid events have to Ocean volume between about 7 ka and 3 ka is likely to have increased be interpreted against a background of rapid rise that is spatially vari- by an equivalent sea level rise of 2 to 3 m (Lambeck et al., 2004b, 2010) able because of the residual isostatic response to the last deglaciation (Figure 5.17). About 10% of this increase can be attributed to a mid- (Milne and Mitrovica, 2008). Different explanations of these short-du- to-late-Holocene ice reduction over Marie Byrd Land, West Antarctica Regional observations of relative sea level Global mean sea level Regional observations of relative sea level from salt-marsh records for past 2000 years corrected for isostatic and tectonic contributions from different proxy records for past 7000 years 0.5 0.2 0.6 0 (a) North Carolina USA (e) Global mean sea level Global mean sea level (m) Kemp et al., 2011 0.1 Jevrejeva et al., 2008 Relative sea level (m) 0.4 Relative sea level (m) -0.5 Tump Point 0.2 -1 0 0 -1.5 -0.1 -0.2 -2 -0.4 -2.5 Sand Point -0.2 (g) Kiritimati Atoll -0.6 Woodroffe et al., 2012 -3 -0.3 2000 1500 1000 500 0 1700 1750 1800 1850 1900 1950 2000 7 6 5 4 3 2 1 0 Age (year BP) Time (Year CE) Age (ka BP) 0.5 0.5 2 Globally averaged sea level (m) (b) Louisianna USA 0 F (h) North Queensland Gonzales and Tornqvist, 2009 1.5 Chapell et al., 1983 Relative sea level (m) Relative sea level (m) 0 Lambeck, 2002 -0.5 1 -1 rsl -0.5 0.5 -1.5 0 -2 -1 -2.5 (f) Global mean sea level -0.5 Lambeck et al., 2010 -1.5 -3 -1 2000 1500 1000 500 0 7 6 5 4 3 2 1 0 7 6 5 4 3 2 1 0 Age (year BP) Age (ka BP) Age (ka BP) 0.5 0.5 (i) Mediterranean France Relative sea level (m) 0 (Giens, Port Gros, La Ciotat) relative sea level (m) 0 Laborel et al., 1994 -0.5 j -0.5 b a d i -1 -1 (c) South Island, New Zealand g -1.5 Gehrels et al., 2008 -1.5 h -2 2000 1500 1000 500 0 7 6 5 4 3 2 1 0 Age (year BP) c Age (ka BP) 0.5 2 (j) Bilboa, Spain Leorri et al., 2012 Relative sea level (m) 0 0 relative sea level (m) -2 5 -0.5 -4 -1 (d) Bay of Biscay, Spain Leorri et al., 2008 and -6 Garcia-Artola et al., 2009 -1.5 -8 2000 1500 1000 500 0 7 6 5 4 3 2 1 0 Age (year BP) Age (ka BP) Figure 5.17 | Observational evidence for sea level change in the recent and late Holocene. Left panels (a d): High-resolution, relative sea level results from salt-marsh data at representative sites, without corrections for glacial isostatic movement of land and sea surfaces. Locations are given on the map. The North Carolina (a) result is based on two nearby locations, Tump Point (dark blue) and Sand Point (light blue). They are representative of other North American Atlantic coast locations (e.g., b; Kemp et al., 2011). The rate of change occurring late in the 19th century are seen in all high resolution salt-marsh records e.g., in New Zealand (c) (Gehrels et al., 2008; Gehrels and Woodworth, 2013) and in Spain (d) (Leorri et al., 2008; García-Artola et al., 2009) that extend into modern time and is consistent with Roman archaeological evidence (Lambeck et al., 2004b). The oscillation in sea level seen in the North Carolina record at about 1000 years ago occurs in some (González and Törnqvist, 2009) but not all records (cf. Gehrels et al., 2011; Kemp et al., 2011). Right hand side panels (g j): Observational evidence for sea level change from lower resolution but longer period records. All records are uncorrected for isostatic effects resulting in spatially variable near-linear trend in sea level over the 7000-year period. The Kiritimati record (Christmas Island) (g) consists of coral microatoll elevations whose fossil elevations are with respect to the growth position of living microatolls (Woodroffe et al., 2012). The North Queensland record (h) is also based on microatoll evidence from several sites on Orpheus Island (Chappell, 1983; Lambeck et al., 2002a). The data from Mediterranean France (i) is based on biological indicators (Laborel et al., 1994) restricted to three nearby locations between which differential isostatic effects are less than the observational errors (Lambeck and Bard, 2000). The Spanish record (j) from estuarine sedimentary deposits is for two nearby localities; Bilboa (dark blue) and Urdaibai (light blue) (Leorri et al., 2012). The two global records (central panels) are estimates of change in global mean sea level from (i) the instrumental record (Jevrejeva et al., 2008) that overlaps the salt-marsh records, and (j) from a range of geological and archaeological indicators from different localities around the world (Lambeck et al., 2010), with the contributing records corrected individually for the isostatic effects at each location. 429 Chapter 5 Information from Paleoclimate Archives Frequently Asked Questions FAQ 5.2 | How Unusual is the Current Sea Level Rate of Change? The rate of mean global sea level change averaging 1.7 +/- 0.2 mm yr 1 for the entire 20th century and between 2.8 and 3.6 mm yr 1 since 1993 (Chapter 13) is unusual in the context of centennial-scale variations of the last two millennia. However, much more rapid rates of sea level change occurred during past periods of rapid ice sheet dis- integration, such as transitions between glacial and interglacial periods. Exceptional tectonic effects can also drive very rapid local sea level changes, with local rates exceeding the current global rates of change. Sea level is commonly thought of as the point where the ocean meets the land. Earth scientists define sea level as a measure of the position of the sea surface relative to the land, both of which may be moving relative to the center of the Earth. A measure of sea level therefore reflects a combination of geophysical and climate factors. Geophysi- cal factors affecting sea level include land subsidence or uplift and glacial isostatic adjustments the earth ocean system s response to changes in mass distribution on the Earth, specifically ocean water and land ice. Climate influences include variations in ocean temperatures, which cause sea water to expand or contract, changes in the volume of glaciers and ice sheets, and shifts in ocean currents. Local and regional changes in these climate and geophysical factors produce significant deviations from the global estimate of the mean rate of sea level change. For example, local sea level is falling at a rate approaching 10 mm yr 1 along the northern Swedish coast (Gulf of Bothnia), due to ongoing uplift caused by continental ice that melted after the last glacial period. In con- trast, local sea level rose at a rate of ~20 mm yr 1 from 1960 to 2005 south of Bangkok, mainly in response to subsid- ence due to ground water extraction. For the past ~150 years, sea level change has been recorded at tide gauge stations, and for the past ~20 years, with satellite altimeters. Results of these two data sets are consistent for the overlapping period. The globally averaged rate of sea level rise of ~1.7 +/- 0.2 mm yr 1 over the 20th century and about twice that over the past two decades may seem small compared with observations of wave and tidal oscillations around the globe that can be orders of magnitude larger. However, if these rates persist over long time intervals, the magnitude carries important con- sequences for heavily populated, low-lying coastal regions, where even a small increase in sea level can inundate large land areas. Prior to the instrumental period, local rates of sea level change are estimated from indirect measures recorded in sedimentary, fossil and archaeological archives. These proxy records are spatially limited and reflect both local and global conditions. Reconstruction of a global signal is strengthened, though, when individual proxy records from widely different environmental settings converge on a common signal. It is important to note that geologic archives particularly those before about 20,000 years ago most commonly only capture millennial-scale changes in sea level. Estimates of century-scale rates of sea level change are therefore based on millennial-scale information, but it must be recognised that such data do not necessarily preclude more rapid rates of century-scale changes in sea level. 5 Sea level reconstructions for the last two millennia offer an opportunity to use proxy records to overlap with, and extend beyond, the instrumental period. A recent example comes from salt-marsh deposits on the Atlantic Coast of the United States, combined with sea level reconstructions based on tide-gauge data and model predictions, to document an average rate of sea level change since the late 19th century of 2.1 +/- 0.2 mm yr 1. This century-long rise exceeds any other century-scale change rate in the entire 2000-year record for this same section of coast. On longer time scales, much larger rates and amplitudes of sea level changes have sometimes been encountered. Glacial interglacial climate cycles over the past 500,000 years resulted in global sea level changes of up to about 120 to 140 m. Much of this sea level change occurred in 10,000 to 15,000 years, during the transition from a full glacial period to an interglacial period, at average rates of 10 to 15 mm yr 1. These high rates are only sustainable when the Earth is emerging from periods of extreme glaciation, when large ice sheets contact the oceans. For example, during the transition from the last glacial maximum (about 21,000 years ago) to the present interglacial (Holocene, last 11,650 years), fossil coral reef deposits indicate that global sea level rose abruptly by 14 to 18 m in less than 500 years. This event is known as Meltwater Pulse 1A, in which the rate of sea level rise reached more than 40 mm yr 1. These examples from longer time scales indicate rates of sea level change greater than observed today, but it should be remembered that they all occurred in special circumstances: at times of transition from full glacial to intergla- cial condition; at locations where the long-term after-effects of these transitions are still occurring; at ­locations of (continued on next page) 430 Information from Paleoclimate Archives Chapter 5 FAQ 5.2 (continued) major tectonic upheavals or in major deltas, where subsidence due to sediment compaction sometimes amplified by ground-fluid extraction dominates. The instrumental and geologic record support the conclusion that the current rate of mean global sea level change is unusual relative to that observed and/or estimated over the last two millennia. Higher rates have been observed in the geological record, especially during times of transition between glacial and interglacial periods. (a) 22,000 to 7,000 14,600 2,000 years ago years ago years ago to 1899 1900-1999 1993-2012 60 50 (b) 4 Rate of sea-level change (mm yr-1) Rate of sea-level change (mm yr-1) 40 3 2 30 1 20 0 Last 2 20th Century Satellite Millennia Altimetry Era -1 10 0 Average Meltwater Last 2 20th Century Satellite Glacial-to-Interglacial Pulse 1A Millennia Altimetry Era -10 FAQ 5.2, Figure 1 | (a) Estimates of the average rate of global mean sea level change (in mm yr 1) for five selected time intervals: last glacial-to-interglacial transition; Meltwater Pulse 1A; last 2 millennia; 20th century; satellite altimetry era (1993 2012). Blue columns denote time intervals of transition from a glacial to an interglacial period, whereas orange columns denote the current interglacial period. Black bars indicate the range of likely values of the average rate of global mean sea level change. Note the overall higher rates of global mean sea level change characteristic of times of transition between glacial and interglacial periods. (b) Expanded view of the rate of global mean sea level change during three time intervals of the present interglacial. 5 (Stone et al., 2003). Elevation histories derived from central Greenland (Woodroffe and McLean, 1990; Smithers and Woodroffe, 2001; Good- ice core data (Vinther et al., 2009; Lecavalier et al., 2013) have present- win and Harvey, 2008); and coastal archaeological features construct- ed evidence for thinning from 8 ka to 6 ka but no integrated observa- ed with direct (e.g., fish ponds and certain harbour structures) or indi- tion-based estimate for the total ice sheet is available. Contributions rect (e.g., changes in water-table level in ancient wells) relationships from mountain glaciers for this interval are unknown. to sea level (Lambeck et al., 2004b; Sivan et al., 2004; Auriemma and Solinas, 2009; Anzidei et al., 2011). Of these, the salt-marsh records Resolving decimeter-scale sea level fluctuations is critical for under- are particularly important because they have been validated against standing the causes of sea level change during the last few millen- regional tide-gauge records and because they can provide near-contin- nia. Three types of proxies have this capability: salt-marsh plants and uous records. The most robust signal captured in the salt-marsh proxy microfauna (foraminifera and diatoms) that form distinctive elevation sea level records from both the NH and SH is an increase in rate, late zones reflecting variations in tolerances to the frequency and duration in the 19th or in the early 20th century (Figure 5.17), that marks a of tidal inundation (Donnelly et al., 2004; Horton and Edwards, 2006; transition from relatively low rates of change during the late Holocene Gehrels et al., 2008; Kemp et al., 2009; Long et al., 2012); coral micro- (order tenths of mm yr 1) to modern rates (order mm yr 1) (see also FAQ atolls found in intertidal environments close to lowest spring tides 5.2). Variability in both the magnitude and the timing (1840 1920) of 431 Chapter 5 Information from Paleoclimate Archives this acceleration has been reported (Gehrels et al., 2006, 2008, 2011; accompanied by abrupt shifts in dust and deuterium excess, indicative Kemp et al., 2009, 2011), but Gehrels and Woodworth (2013) have of reorganizations in atmospheric circulation (Steffensen et al., 2008; concluded that these mismatches can be reconciled within the obser- Thomas et al., 2009). Reconstructions from the subtropical Atlantic and vational uncertainties. Combined with the instrumental evidence (see Mediterranean reveal concomitant SST changes attaining values up to Section 3.7) and with inferences drawn from archaeological evidence 5°C (e.g., Martrat et al., 2004; Martrat et al., 2007). from 2000 years ago (Lambeck et al., 2004b), rates of sea level rise exceeded the late Holocene background rate after about 1900 (high In spite of the visible presence of DO events in many paleoclimate confidence) (Figure 5.17). records from both hemispheres, the underlying mechanisms still remain unresolved and range from internally generated atmosphere ocean Regionally, as along the US Atlantic coast and Gulf of Mexico coast, ice sheet events (Timmermann et al., 2003; Ditlevsen and Ditlevsen, the salt-marsh records reveal some consistency in multi-decadal and 2009), to solar-forced variability (Braun et al., 2008; Braun and Kurths, centennial time scales deviations from the linear trends expected from 2010). However, given the lack of observational evidence for a direct the GIA signal (see e.g., panels (a) and (b) in Figure 5.17) (van de Plass- linear modulation of solar irradiance on DO time scales, (Muscheler che et al., 1998; González and Törnqvist, 2009; Kemp et al., 2011) but and Beer, 2006), solar forcing is an improbable candidate to generate they have not yet been identified as truly global phenomena. For the DO events. There is robust evidence from multiple lines of paleoceano- past 5 millennia the most complete sea level record from a single loca- graphic information and modelling that DO variability is often associ- tion consists of microatoll evidence from Kiritimati (Christmas Island; ated with AMOC changes, as suggested by climate models of varying Pacific Ocean) (Woodroffe et al., 2012) that reveals with medium confi- complexity (Ganopolski and Rahmstorf, 2001; Arzel et al., 2009) and dence that amplitudes of any fluctuations in GMSL during this interval marine proxy records (Piotrowski et al., 2005; Kissel et al., 2008; Barker did not exceed approximately +/-25 cm on time scales of a few hundred et al., 2010; Roberts et al., 2010); but also potential influences of sea- years. Proxy data from other localities with quasi-continuous records ice cover (Li et al., 2010b), atmosphere circulation and ice sheet topog- for parts of this pre-industrial period, likewise, do not identify signifi- raphy (Wunsch, 2006) have been proposed. cant global oscillations on centennial time scales (Figure 5.17). The widespread presence of massive layers of ice-rafted detritus in North Atlantic marine sediments provide robust evidence that some 5.7 Evidence and Processes of Abrupt DO GS, known as Heinrich stadials, were associated with iceberg dis- Climate Change charges originating from the Northern Hemispheric ice sheets. During these periods global sea level rose by up to several tens of meters Many paleoclimate archives document climate changes that happened (Chappell, 2002; Rohling et al., 2008b; Siddall et al., 2008; González at rates considerably exceeding the average rate of change for longer- and Dupont, 2009; Yokoyama and Esat, 2011), with remaining uncer- term averaging periods prior and after this change (see Glossary for tainties in timing and amplitude of sea level rise, stadial cooling and other definition of Abrupt Climate Change). A variety of mechanisms ocean circulation changes relative to the iceberg discharge (Hall et al., have been suggested to explain the emergence of such abrupt climate 2006; Arz et al., 2007; Siddall et al., 2008; González and Dupont, 2009; changes (see Section 12.5.5). Most of them invoke the existence of Sierro et al., 2009; Hodell et al., 2010). Internal instabilities of the Lau- nonlinearities or, more specifically, thresholds in the underlying dynam- rentide ice sheet can cause massive calving and meltwater events sim- ics of one or more Earth-system components. Both internal dynamics ilar to those reconstructed from proxy records (Calov et al., 2002, 2010; and external forcings can generate abrupt changes in the climate state. Marshall and Koutnik, 2006). Alternatively, an initial weakening of the Documentation of abrupt climate changes in the past using multiple AMOC can lead to subsurface warming in parts of the North Atlantic sources of proxy evidence can provide important benchmarks to test (Shaffer et al., 2004) and subsequent basal melting of the Labrador 5 instability mechanisms in climate models. This assessment of abrupt ice shelves, and a resulting acceleration of ice streams and iceberg climate change on time scales of 10 to 100 years focuses on Dans- discharge (Alvarez-Solas et al., 2010; Marcott et al., 2011). At present, gaard-Oeschger (DO) events and iceberg/meltwater discharges during unresolved dynamics in ice sheet models and limited proxy information Heinrich events, especially the advances since AR4 in reconstructing do not allow us to distinguish the two mechanisms with confidence. and understanding their global impacts and in extending the record of millennial-scale variability to about 800 ka. Since AR4, climate model simulations (Liu et al., 2009b; Otto-Bliesner and Brady, 2010; Menviel et al., 2011; Kageyama et al., 2013) have Twenty-five abrupt DO events (North Greenland Ice Core Project mem- further confirmed the finding (high confidence) that changes in AMOC bers, 2004) and several centennial-scale events (Capron et al., 2010b) strength induce abrupt climate changes with magnitude and patterns occurred during the last glacial cycle (see Section 5.3.2). DO events resembling reconstructed paleoclimate-proxy data of DO and Heinrich in Greenland were marked by an abrupt transition (within a few dec- events. ades) from a cold phase, referred to as Greenland Stadial (GS) into a warm phase, known as Greenland Interstadial (GI). Subsequently Recent studies have presented a better understanding of the global but within a GI, a gradual cooling preceded a rapid jump to GS that imprints of DO events and Heinrich events, for various regions. Wide- lasted for centuries up to millennia. Thermal gas-fractionation methods spread North Atlantic cooling and sea-ice anomalies during GS induced (Landais et al., 2004; Huber et al., 2006) suggest that for certain DO atmospheric circulation changes (high confidence) (Krebs and Tim- events Greenland temperatures increased by up to 16°C +/- 2.5°C (1 mermann, 2007; Clement and Peterson, 2008; Kageyama et al., 2010; standard deviation) within several decades. Such transitions were also Merkel et al., 2010; Otto-Bliesner and Brady, 2010; Timmermann et 432 Information from Paleoclimate Archives Chapter 5 al., 2010) which in turn affected inter-hemispheric tropical rainfall The existence of threshold behaviour in the EAIS is consistent with patterns, leading to drying in Northern South America (Peterson and an abrupt increase in Antarctic ice volume at the Eocene/Oligocene Haug, 2006), the Mediterranean (Fletcher and Sánchez Goni, 2008; boundary, 33 Ma, attributed to gradual atmospheric CO2 concentra- Fleitmann et al., 2009), equatorial western Africa and Arabia (Higgin- tion decline on geological time scale (Pagani et al., 2005b; Pearson son et al., 2004; Ivanochko et al., 2005; Weldeab et al., 2007a; Mulitza et al., 2009) (Figure 5.2, Section 5.2.2). Ice sheet models produce a et al., 2008; Tjallingii et al., 2008; Itambi et al., 2009; Weldeab, 2012), hysteresis behaviour of the EAIS with respect to CO2 concentrations, wide parts of Asia (Wang et al., 2008; Cai et al., 2010) (see Figure leading to EAIS glaciation when CO2 concentration declined to 600 5.4e) as well as in the Australian-Indonesian monsoon region (Mohtadi 900 ppm (DeConto and Pollard, 2003; Langebroek et al., 2009) and et al., 2011). Concomitant wetter conditions have been reconstructed deglaciation for CO2 above 1200 ppm (Pollard and DeConto, 2009). for southwestern North America (Asmerom et al., 2010; Wagner et al., Proxy records suggest that the WAIS might have collapsed during last 2010) and southern South America (Kanner et al., 2012) (Figure 5.4h). interglacials (Naish et al., 2009b; Vaughan et al., 2011) and was absent Moreover, atmospheric circulation changes have been invoked (Zhang during warm periods of the Pliocene when CO2 concentration was 350 and Delworth, 2005; Xie et al., 2008; Okumura et al., 2009) to explain to 450 ppm (see Section 5.2.2.2) and global sea level was higher than temperature variations in the North Pacific that varied in unison with present (see Section 5.6.1). These reconstructions and one ice sheet abrupt climate change in the North Atlantic region (Harada et al., 2008, model simulation (Pollard and DeConto, 2009) suggest that WAIS is 2012; Pak et al., 2012). Other factors that may have contributed to very sensitive to the subsurface ocean temperature. This implies, with North Pacific climate anomalies include large-scale Pacific Ocean cir- medium confidence, that a large part of the WAIS will be eventually culation changes (Saenko et al., 2004; Schmittner et al., 2007; Harada lost if the atmospheric CO2 concentration stays within, or above, the et al., 2009; Okazaki et al., 2010) during phases of a weak AMOC. range of 350 to 450 ppm for several millennia. Recent high-resolution ice core studies (EPICA Community Members, 2006; Capron et al., 2010a, 2010b, 2012; Stenni et al., 2011) show Observational evidence suggest that the GIS was also much smaller that Antarctica warmed gradually for most GS, reaching maximum than today during the MPWP (see Sections 5.6.1 and 5.2.2), consist- values at the time of GS/GI transitions, which is in agreement with ent with the results of simulations with ice sheet models (Dolan et the bipolar seesaw concept (Stocker and Johnsen, 2003; Stenni et al., al., 2011; Koenig et al., 2011). Ice sheet model simulations and proxy 2011). A recent global temperature compilation (Shakun et al., 2012), records show that the volume of the GIS was also reduced during the Southern Ocean temperature records (Lamy et al., 2007; Barker et al., past interglacial period (Section 5.6.2). This supports modelling results 2009; De Deckker et al., 2012), evidence from SH terrestrial records that indicate temperature or CO2 thresholds for melting and re-growth (Kaplan et al., 2010; Putnam et al., 2010) and transient climate model of the GIS may lie in close proximity to the present and future levels experiments (Menviel et al., 2011) provide multiple lines of evidence (Gregory and Huybrechts, 2006; Lunt et al., 2008) (Section 5.6.1) and for the inter-hemispheric character of millennial-scale variability during that the GIS may have multiple equilibrium states under present-day the last glacial termination and for DO events (high confidence). climate state (Ridley et al., 2010). Newly available marine records (Martrat et al., 2007; Grützner and Hig- Therefore, proxy records and results of model simulations indicate gins, 2010; Margari et al., 2010; Kleiven et al., 2011), Antarctic WMGHG with medium confidence that the GIS and WAIS could be destabi- records (Loulergue et al., 2008; Schilt et al., 2010) and statistical analy- lized by projected climate changes, although the time scales of the ice ses of Antarctic ice core data (Siddall et al., 2010; Lambert et al., 2012) sheets response to climate change are very long (several centuries to combined with bipolar seesaw modelling (Siddall et al., 2006; Barker et ­millennia). al., 2011) document with high confidence that abrupt climate change events, similar to the DO events and Heinrich stadials of the last glacial 5.8.2 Ocean Circulation cycle, occurred during previous glacial periods extending back about 5 800 ka and, with medium confidence, to 1100 ka. Numerous modelling studies demonstrate that increased freshwater flux into the North Atlantic leads to weakening of the AMOC. Results of EMICs (Rahmstorf et al., 2005) and coupled GCMs also suggest that 5.8 Paleoclimate Perspective on AMOC may have multiple equilibrium states under present or glacial Irreversibility in the Climate System climate conditions (Hawkins et al., 2011; Hu et al., 2012). Experiments with climate models provide evidence that the sensitivity of the AMOC For an introduction of the concept of irreversibility see Glossary. to freshwater perturbation is larger for glacial boundary conditions than for interglacial conditions (Swingedouw et al., 2009) and that the 5.8.1 Ice Sheets recovery time scale of the AMOC is longer for LGM conditions than for the Holocene (Bitz et al., 2007). Modelling studies suggest the existence of multiple equilibrium states for ice sheets with respect to temperature, CO2 concentration The abrupt climate-change event at 8.2 ka permits the study of and orbital forcing phase spaces (DeConto and Pollard, 2003; Calov the recovery time of the AMOC to freshwater perturbation under and ­Ganopolski, 2005; Ridley et al., 2010). This implies a possibility of near-modern boundary conditions (Rohling and Pälike, 2005). Since irreversible changes in the climate-cryosphere system in the past and AR4, new proxy records and simulations confirm that the pattern of future. surface-ocean and atmospheric climate anomalies is consistent with a reduction in the strength of the AMOC (Figure 5.18a, b, d). ­ vailable A 433 Chapter 5 Information from Paleoclimate Archives proxy records from the North Atlantic support the hypothesis that and atmospheric temperatures in the North Atlantic and in Greenland freshwater input into the North Atlantic reduced the amount of deep has been observed (Figure 5.18a, b) with the climate anomaly asso- and central water-mass formation, Nordic Seas overflows, intermediate ciated with the event lasting 100 160 years (Daley et al., 2011). The water temperatures and the ventilation state of North Atlantic Deep additional freshwater that entered the North Atlantic during the 8.2 ka Water (Figure 5.18c, d) (McManus et al., 2004; Ellison et al., 2006; Kleiv- event is estimated between 1.6.1014 m3 and 8.1014 m3 (von Grafenstein en et al., 2008; Bamberg et al., 2010). A concomitant decrease of SST et al., 1998; Barber et al., 1999; Clarke et al., 2004). The duration of the 34 (a) SST (°C) Foram transfer function 18O Greenland ice cores ( ) -28 Temperature (oC) 34.5 (e) T2m (°C) 35 -29 Lake Agassiz Drainage of 35.5 -30 (b) SST (°C) Foram transfer function 11.2 12.5 10.8 12 10.4 11.5 10 11 9.6 10.5 9.2 10 8.8 (c) (f) prec (%) Sortable silt (SS um) 20 Mg/Ca - IMWT (°C) 9.0 19 8.5 18 8.0 17 16 1.2 (d) 100 13C C.wuellerstorfi ( ) AMOC strength (%) 0.8 0.4 0 50 5 -0.4 9 8.5 8 7.5 7 Age (ka) Figure 5.18 | Compilation of selected paleoenvironmental and climate model data for the abrupt Holocene cold event at 8.2 ka, documenting temperature and ocean-circulation changes around the event and the spatial extent of climate anomalies following the event. Published age constraints for the period of release of freshwater from glacier lakes Agassiz and Ojibway are bracketed inside the vertical blue bar. Vertical grey bar denotes the time of the main cold event as found in Greenland ice core records (Thomas et al., 2007). Thick lines in (a d) denote 5-point running mean of underlying data in thin lines. (a) Black curve: North Greenland Ice Core Project (NGRIP) d18O (temperature proxy) from Greenland Summit (North Greenland Ice Core Project members, 2004). Red curve: Simulated Greenland temperature in an 8.2 ka event simulation with the ECBilt-CLIO-VECODE model (Wiersma et al., 2011). Blue curve: Simulated Greenland temperature in an 8.2 ka event simulation with the CCSM3 model (Morrill et al., 2011). (b) North Atlantic/Nordic Seas sea surface temperature (SST) reconstructions, age models are aligned on the peak of the cold-event (less than 100-year adjustment). Blue curve: Nordic Seas (Risebrobakken et al., 2011). Black curve: Gardar Drift south of Iceland (Ellison et al., 2006). (c) Deep- and intermediate-water records. Black curve: Sortable silt (SS) record (overflow strength proxy) from Gardar Drift south of Iceland (Ellison et al., 2006), Atlantic intermediate water temperature reconstruction (Bamberg et al., 2010). (d) Black curve: d13C (deep water ventilation proxy) at 3.4 km water depth south of Greenland (Kleiven et al., 2008). Age model is aligned on the minimum overflow strength in (c) (less than 100-year adjustment). Modelled change in the strength of the Atlantic Ocean meridional overturning circulation (AMOC) Green curve: an 8.2 ka event simulation with the GISS model (LeGrande et al., 2006). Red curve: an 8.2 ka event simulation with the ECBilt-CLIO-VECODE (v. 3) model (Wiersma et al., 2011). Blue curve: an 8.2 ka event simulation with the CCSM3 model (Morrill et al., 2011). (e) Spatial distribution of the 4-member ensemble mean annual mean surface temperature anomaly (°C) compared with the control experiment from model simulations of the effects of a freshwater release at 8.2 ka (based on Morrill et al., 2013a). White dots indicate regions where less than 3 models agree on the sign of change. Coloured circles show paleoclimate data from records resolving the 8.2 ka event: purple = cold anomaly, yellow = warm anomaly, grey = no significant anomaly. Data source and significance thresholds are as summarized by Morrill et al. (2013b). (f) Same as (e) but for annual mean precipitation anomalies in %. Coloured circles show paleoclimate data from records resolving the 8.2 ka event: purple = dry anomaly, yellow = wet anomaly, grey = no significant anomaly. 434 Information from Paleoclimate Archives Chapter 5 meltwater release may have been as short as 0.5 years (Clarke et al., 5.9 Concluding Remarks 2004), but new drainage estimates indicate an up to 200 year-duration in two separate stages (Gregoire et al., 2012). A four-model ensem- The assessments in this chapter are based on a rapidly growing body of ble with a one-year freshwater perturbation of 2.5 Sv only gives tem- new evidence from the peer-review literature. Since AR4, there exists a perature anomalies half of what has been reconstructed and with a wide range of new information on past changes in atmospheric com- shorter duration than observed, resulting from unresolved processes position, sea level, regional climates including droughts and floods, in models, imprecise representation of the initial climate state or a too as well as new results from internationally coordinated model exper- short duration of the freshwater forcing (Morrill et al., 2013a). These iments on past climates (PMIP3/CMIP5). At the regional scale proxy- marine-based reconstructions consistently show that the recovery time based temperature estimates are still scarce for key regions such as scale of the shallow and deep overturning circulation is on the order Africa, India and parts of the Americas. Syntheses of past precipitation of 200 years (Ellison et al., 2006; Bamberg et al., 2010) (Figure 5.18c, changes were too limited to support regional assessments. d), with one record pointing to a partial recovery on a decadal time scale (Kleiven et al., 2008). Both recovery time scale and sensitivity Precise knowledge of past changes in atmospheric concentrations of of the AMOC to the freshwater perturbation are generally consistent well-mixed GHGs prior to the period for which ice core records are with model experiments for the 8.2 ka event using coarse-resolution available remains a strong limitation on assessing longer-term climate models, GCMs and eddy permitting models (LeGrande and Schmidt, change. Key limitations to our knowledge of past climate continues 2008; Spence et al., 2008; Li et al., 2009). The recovery of temperatures to be associated with uncertainties of the quantitative information out of the cold anomaly appears overprinted with natural variability derived from climate proxies, in particular due to seasonality effects, in the proxy data, and is more gradual in data than in the AOGCM the lack of proxy records sensitive to winter temperature, or the precise experiments (Figure 5.18c, d). In summary, multiple lines of evidence water depth at which ocean proxies signals form. Moreover, method- indicate, with high confidence, that the interglacial mode of the AMOC ological uncertainties associated with regional, hemispheric or global can recover from a short-term freshwater input into the subpolar North syntheses need to be further investigated and quantified. Atlantic. Despite progress on developing proxy records of past changes in sea The characteristic teleconnection patterns associated with a colder ice it is not yet possible to provide quantitative and spatially coherent North Atlantic Ocean as described in Section 5.7 are evident for the assessments of past sea ice cover in both polar oceans. 8.2 ka event in both models and proxy data (Figure 5.18e, f). While this assessment could build on improved reconstructions of 5.8.3 Next Glacial Inception abrupt climate changes during glacial periods, key questions remain open regarding the underlying cause of these changes. Large uncer- Since orbital forcing can be accurately calculated for the future (see tainties remain on the variations experienced by the West and East Section 5.2.1), efforts can be made to predict the onset of the next Antarctic ice sheets over various time scales of the past. Regarding glacial period. However, the glaciation threshold depends not only on past sea level change, major difficulties are associated with deconvolv- insolation but also on the atmospheric CO2 concentration (Archer and ing changes in ocean geodynamic effects, as well as for inferring global Ganopolski, 2005). Models of different complexity have been used to signals from regional reconstructions. investigate the response to orbital forcing in the future for a range of atmospheric CO2 levels. These results consistently show that a glacial The PMIP3/CMIP5 model framework offers the opportunity to directly inception is not expected to happen within the next approximate 50 incorporate information from paleoclimate data and simulations into kyr if either atmospheric CO2 concentration remains above 300 ppm assessments of future projections. This is an emerging field for which or cumulative carbon emissions exceed 1000 PgC (Loutre and Berger, only preliminary information was available for AR5. 5 2000; Archer and Ganopolski, 2005; Cochelin et al., 2006). Only if atmospheric CO2 content was below the pre-industrial level would a glaciation be possible under present orbital configuration (Loutre and Acknowledgements Berger, 2000; Cochelin et al., 2006; Kutzbach et al., 2011; Vettoretti and Peltier, 2011; Tzedakis et al., 2012a). Simulations with climate carbon The compilation of this chapter has benefited greatly from the techni- cycle models show multi-millennial lifetime of the anthropogenic CO2 cal support by the chapter s scientific assistants Vera Bender (Germa- in the atmosphere (see Box 6.1). Even for the lowest RCP 2.6 scenario, ny), Hiroshi Kawamura (Germany/Japan), and Anna Peregon (France/ atmospheric CO2 concentrations will exceed 300 ppm until the year Russian Federation). We are indebted to Hiroshi and Vera for compiling 3000. It is therefore virtually certain that orbital forcing will not trigger the various drafts, managing the ever-growing reference list and their a glacial inception before the end of the next millennium. skilful stylistic overhaul of figures. Anna is thanked for help with output from PMIP3 simulations, for tracking acronyms, and for identifying entries for the glossary. 435 Chapter 5 Information from Paleoclimate Archives References Abbot, D. S., and E. Tziperman, 2008: A high-latitude convective cloud feedback and Annan, J. D., and J. C. Hargreaves, 2012: Identification of climatic state with limited equable climates. Q. J. R. Meteorol. Soc., 134, 165 185. proxy data. Clim. Past, 8, 1141 1151. Abbot, D. S., and E. Tziperman, 2009: Controls on the activation and strength of a , 2013: A new global reconstruction of temperature changes at the Last Glacial high-latitude convective cloud feedback. J. Atmos. Sci., 66, 519 529. Maximum. Clim. Past, 9, 367 376. Abe-Ouchi, A., T. Segawa, and F. Saito, 2007: Climatic Conditions for modelling the Annan, J. D., J. C. Hargreaves, R. Ohgaito, A. Abe-Ouchi, and S. Emori, 2005: Efficiently Northern Hemisphere ice sheets throughout the ice age cycle. Clim. Past, 3, constraining climate sensitivity with ensembles of paleoclimate simulations. Sci. 423 438. Online Lett. Atmos., 1, 181 184. Abram, N. J., et al., 2013: Acceleration of snow melt in an Antarctic Peninsula ice core Antoine, P., et al., 2009: Rapid and cyclic aeolian deposition during the Last Glacial during the twentieth century. Nature Geosci., 6, 404 411. in European loess: a high-resolution record from Nussloch, Germany. Quat. Sci. Ackert Jr, R. P., S. Mukhopadhyay, D. Pollard, R. M. DeConto, A. E. Putnam, and H. W. Rev., 28, 2955 2973. Borns Jr, 2011: West Antarctic Ice Sheet elevations in the Ohio Range: Geologic Antoniades, D., P. Francus, R. Pienitz, G. St-Onge, and W. F. Vincent, 2011: Holo- constraints and ice sheet modeling prior to the last highstand. Earth Planet. Sci. cene dynamics of the Arctic s largest ice shelf. Proc. Natl. Acad. Sci. U.S.A., 108, Lett., 307, 83 93. 18899 18904. Adams, J. B., M. E. Mann, and C. M. Ammann, 2003: Proxy evidence for an El Nino- Anzidei, M., F. Antonioli, A. Benini, K. Lambeck, D. Sivan, E. Serpelloni, and P. Stocchi, like response to volcanic forcing. Nature, 426, 274 278. 2011: Sea level change and vertical land movements since the last two millen- Adkins, J. F., K. McIntyre, and D. P. Schrag, 2002: The salinity, temperature, and 18O nia along the coasts of southwestern Turkey and Israel. Quat. Int., 232, 13 20. of the glacial deep ocean. Science, 298, 1769 1773. Archer, D., and A. Ganopolski, 2005: A movable trigger: Fossil fuel CO2 and the onset Adler, R. E., et al., 2009: Sediment record from the western Arctic Ocean with an of the next glaciation. Geochem. Geophys., Geosyst., 6, Q05003. improved Late Quaternary age resolution: HOTRAX core HLY0503 8JPC, Men- Argus, D. F., and W. R. Peltier, 2010: Constraining models of postglacial rebound deleev Ridge. Global Planet. Change, 68, 18 29. using space geodesy: A detailed assessment of model ICE-5G (VM2) and its Agatova, A. R., A. N. Nazarov, R. K. Nepop, and H. Rodnight, 2012: Holocene glacier relatives. Geophys. J. Int., 181, 697 723. fluctuations and climate changes in the southeastern part of the Russian Altai Arz, H. W., F. Lamy, A. Ganopolski, N. Nowaczyk, and J. Pätzold, 2007: Dominant (South Siberia) based on a radiocarbon chronology. Quat. Sci. Rev., 43, 74 93. Northern Hemisphere climate control over millennial-scale glacial sea level vari- Ahn, J., and E. J. Brook, 2008: Atmospheric CO2 and climate on millennial time scales ability. Quat. Sci. Rev., 26, 312 321. during the last glacial period. Science, 322, 83 85. Arzel, O., A. Colin de Verdiere, and M. H. England, 2009: The role of oceanic heat Ahn, J., E. J. Brook, A. Schmittner, and K. Kreutz, 2012: Abrupt change in atmospheric transport and wind stress forcing in abrupt millennial-scale climate transitions. CO2 during the last ice age. Geophys. Res. Lett., 39, L18711. J. Clim., 23, 2233 2256. Akkemik, Ü., R. D Arrigo, P. Cherubini, N. Köse, and G. C. Jacoby, 2008: Tree-ring Asmerom, Y., V. J. Polyak, and S. J. Burns, 2010: Variable winter moisture in the south- reconstructions of precipitation and streamflow for north-western Turkey. Int. J. western United States linked to rapid glacial climate shifts. Nature Geosci., 3, Climatol., 28, 173 183. 114 117. Alvarez-Solas, J., S. Charbit, C. Ritz, D. Paillard, G. Ramstein, and C. Dumas, 2010: Ault, T. R., et al., 2013: The continuum of hydroclimate variability in western North Links between ocean temperature and iceberg discharge during Heinrich events. America during the last millennium. J. Clim., 26, 5863-5878. Nature Geosci., 3, 122 126. Auriemma, R., and E. Solinas, 2009: Archaeological remains as sea level change Ammann, C. M., and E. R. Wahl, 2007: The importance of the geophysical context in markers: A review. Quat. Int., 206, 134 146. statistical evaluations of climate reconstruction procedures. Clim. Change, 85, Axelson, J. N., D. J. Sauchyn, and J. Barichivich, 2009: New reconstructions of stream- 71 88. flow variability in the South Saskatchewan River Basin from a network of tree Ammann, C. M., M. G. Genton, and B. Li, 2010: Technical Note: Correcting for signal ring chronologies, Alberta, Canada. Water Resourc. Res., 45, W09422. attenuation from noisy proxy data in climate reconstructions. Clim. Past, 6, Baker, V. R., 2008: Paleoflood hydrology: Origin, progress, prospects. Geomorphol- 273 279. ogy, 101, 1 13. Ammann, C. M., G. A. Meehl, W. M. Washington, and C. S. Zender, 2003: A monthly Bakker, P., et al., 2013: Last interglacial temperature evolution a model inter-com- and latitudinally varying volcanic forcing dataset in simulations of 20th century parison. Clim. Past, 9, 605 619. climate. Geophys. Res. Lett., 30, 1657. Ballantyne, A. P., M. Lavine, T. J. Crowley, J. Liu, and P. B. Baker, 2005: Meta-analysis Ammann, C. M., F. Joos, D. S. Schimel, B. L. Otto-Bliesner, and R. A. Tomas, 2007: of tropical surface temperatures during the Last Glacial Maximum. Geophys. Solar influence on climate during the past millennium: Results from transient Res. Lett., 32, L05712. simulations with the NCAR Climate System Model. Proc. Natl. Acad. Sci. U.S.A., Balmaceda, L., N. A. Krivova, and S. K. Solanki, 2007: Reconstruction of solar irradi- 5 104, 3713 3718. ance using the Group sunspot number. Adv. Space Res., 40, 986 989. Anchukaitis, K. J., and J. E. Tierney, 2013: Identifying coherent spatiotemporal modes Bamberg, A., Y. Rosenthal, A. Paul, D. Heslop, S. Mulitza, C. Rühlemann, and M. in time-uncertain proxy paleoclimate records. Clim. Dyn., 41, 1291 - 1306. Schulz, 2010: Reduced north Atlantic central water formation in response to Anchukaitis, K. J., B. M. Buckley, E. R. Cook, B. I. Cook, R. D. D Arrigo, and C. M. early Holocene ice-sheet melting. Geophys. Res. Lett., 37, L17705. Ammann, 2010: Influence of volcanic eruptions on the climate of the Asian mon- Bar-Matthews, M., A. Ayalon, M. Gilmour, A. Matthews, and C. J. Hawkesworth, 2003: soon region. Geophys. Res. Lett., 37, L22703. Sea land oxygen isotopic relationships from planktonic foraminifera and spe- Anchukaitis, K. J., et al., 2012: Tree rings and volcanic cooling. Nature Geosci., 5, leothems in the Eastern Mediterranean region and their implication for paleo- 836 837. rainfall during interglacial intervals. Geochim Cosmochim. Acta, 67, 3181 3199. Anderson, R. K., G. H. Miller, J. P. Briner, N. A. Lifton, and S. B. DeVogel, 2008: A millen- Barber, D. C., et al., 1999: Forcing of the cold event of 8,200 years ago by cata- nial perspective on Arctic warming from 14C in quartz and plants emerging from strophic drainage of Laurentide lakes. Nature, 400, 344 348. beneath ice caps. Geophys. Res. Lett., 35, L01502. Bard, E., G. Raisbeck, F. Yiou, and J. Jouzel, 2000: Solar irradiance during the last Andersson, C., F. S. R. Pausata, E. Jansen, B. Risebrobakken, and R. J. Telford, 2010: 1200 years based on cosmogenic nuclides. Tellus B, 52, 985 992. Holocene trends in the foraminifer record from the Norwegian Sea and the Barker, S., G. Knorr, M. J. Vautravers, P. Diz, and L. C. Skinner, 2010: Extreme deepen- North Atlantic Ocean. Clim. Past, 6, 179 193. ing of the Atlantic overturning circulation during deglaciation. Nature Geosci., Andreev, A. A., et al., 2004: Late Saalian and Eemian palaeoenvironmental history 3, 567 571. of the Bol shoy Lyakhovsky Island (Laptev Sea region, Arctic Siberia). Boreas, Barker, S., P. Diz, M. J. Vautravers, J. Pike, G. Knorr, I. R. Hall, and W. S. Broecker, 2009: 33, 319 348. Interhemispheric Atlantic seesaw response during the last deglaciation. Nature, Andrews, T., J. M. Gregory, M. J. Webb, and K. E. Taylor, 2012: Forcing, feedbacks and 457, 1097 1102. climate sensitivity in CMIP5 coupled atmosphere-ocean climate models. Geo- Barker, S., et al., 2011: 800,000 years of abrupt climate variability. Science, 334, phys. Res. Lett., 39, L09712. 347 351. 436 Information from Paleoclimate Archives Chapter 5 Baroni, M., M. H. Thiemens, R. J. Delmas, and J. Savarino, 2007: Mass-independent Berger, M., J. Brandefelt, and J. Nilsson, 2013: The sensitivity of the Arctic sea ice to sulfur isotopic compositions in stratospheric volcanic eruptions. Science, 315, orbitally induced insolation changes: a study of the mid-Holocene Paleoclimate 84 87. Modelling Intercomparison Project 2 and 3 simulations. Clim. Past, 9, 969 982. Baroni, M., J. Savarino, J. H. Cole-Dai, V. K. Rai, and M. H. Thiemens, 2008: Anomalous Berkelhammer, M., A. Sinha, M. Mudelsee, H. Cheng, R. L. Edwards, and K. Cannari- sulfur isotope compositions of volcanic sulfate over the last millennium in Ant- ato, 2010: Persistent multidecadal power of the Indian Summer Monsoon. Earth arctic ice cores. J. Geophys. Res., 113, D20112. Planet. Sci. Lett., 290, 166 172. Barrett, P. J., 2013: Resolving views on Antarctic Neogene glacial history the Bertrand, S., K. A. Hughen, F. Lamy, J.-B. W. Stuut, F. Torrejón, and C. B. Lange, 2012: Sirius debate. Trans. R. Soc. Edinburgh, published online 7 May 2013, CJ02013, Precipitation as the main driver of Neoglacial fluctuations of Gualas glacier, doi:10.1017/S175569101300008X. Northern Patagonian Icefield. Clim. Past, 8, 519 534. Barriendos, M., and F. S. Rodrigo, 2006: Study of historical flood events on Spanish Bhatt, U. S., et al., 2010: Circumpolar arctic tundra vegetation change is linked to sea rivers using documentary data. Hydrol. Sci. J., 51, 765 783. ice decline. Earth Interact., 14, 1 20. Bartlein, P. J., et al., 2011: Pollen-based continental climate reconstructions at 6 and Bintanja, R., R. G. Graversen, and W. Hazeleger, 2011: Arctic winter warming ampli- 21 ka: A global synthesis. Clim. Dyn., 37, 775 802. fied by the thermal inversion and consequent low infrared cooling to space. Bartoli, G., B. Hönisch, and R. E. Zeebe, 2011: Atmospheric CO2 decline during the Nature Geosci., 4, 758 761. Pliocene intensification of Northern Hemisphere glaciations. Paleoceanography, Birchfield, G. E., J. Weertman, and A. T. Lunde, 1981: A paleoclimate model of north- 26, PA4213. ern hemisphere ice sheets. Quat. Res., 15, 126 142. Bassett, S. E., G. A. Milne, J. X. Mitrovica, and P. U. Clark, 2005: Ice sheet and solid Bird, B. W., M. B. Abbott, B. P. Finney, and B. Kutchko, 2009: A 2000 year varve-based Earth influences on far-field sea level histories. Science, 309, 925 928. climate record from the central Brooks Range, Alaska. J. Paleolimnol., 41, 25 41. Battle, M., et al., 1996: Atmospheric gas concentrations over the past century mea- Bird, B. W., M. B. Abbott, D. T. Rodbell, and M. Vuille, 2011: Holocene tropical South sured in air from firn at the South Pole. Nature, 383, 231 235. American hydroclimate revealed from a decadally resolved lake sediment 18O Bauch, H. A., E. S. Kandiano, J. Helmke, N. Andersen, A. Rosell-Melé, and H. Erlen- record. Earth Planet. Sci. Lett., 310, 192 202. keuser, 2011: Climatic bisection of the last interglacial warm period in the Polar Bird, M. I., L. K. Fifield, T. S. Teh, C. H. Chang, N. Shirlaw, and K. Lambeck, 2007: An North Atlantic. Quat. Sci. Rev., 30, 1813 1818. inflection in the rate of early mid-Holocene eustatic sea level rise: A new sea Beerling, D. J., and D. L. Royer, 2002: Fossil plants as indicator of the Phanerozoic level curve from Singapore. Estuar. Coast. Shelf Sci., 71, 523 536. global carbon cycle. Annu. Rev. Earth Planet. Sci., 30, 527 556. Bird, M. I., W. E. N. Austin, C. M. Wurster, L. K. Fifield, M. Mojtahid, and C. Sargeant, Beerling, D. J., and D. L. Royer, 2011: Convergent Cenozoic CO2 history. Nature 2010: Punctuated eustatic sea level rise in the early mid-Holocene. Geology, Geosci., 4, 418 420. 38, 803 806. Beerling, D. J., A. Fox, and C. W. Anderson, 2009: Quantitative uncertainty analyses of Bitz, C. M., J. C. H. Chiang, W. Cheng, and J. J. Barsugli, 2007: Rates of thermohaline ancient atmospheric CO2 estimates from fossil leaves. Am. J. Sci., 309, 775 787. recovery from freshwater pluses in modern, Last Glacial Maximum, and green- Beerling, D. J., B. H. Lomax, D. L. Royer, G. R. Upchurch, and L. R. Kump, 2002: An house warming climates. Geophys. Res. Lett., 34, L07708. atmospheric pCO2 reconstruction across the Cretaceous-Tertiary boundary from Black, D. E., M. A. Abahazi, R. C. Thunell, A. Kaplan, E. J. Tappa, and L. C. Peterson, leaf megafossils. Proc. Natl. Acad. Sci. U.S.A., 99, 7836 7840. 2007: An 8 century tropical Atlantic SST record from the Cariaco Basin: Baseline Beets, D. J., C. J. Beets, and P. Cleveringa, 2006: Age and climate of the late Saalian variability, twentieth-century warming, and Atlantic hurricane frequency. Pale- and early Eemian in the type-area, Amsterdam basin, The Netherlands. Quat. Sci. oceanography, 22, PA4204. Rev., 25, 876 885. Blanchon, P., A. Eisenhauer, J. Fietzke, and V. Liebetrau, 2009: Rapid sea level rise Bekryaev, R. V., I. V. Polyakov, and V. A. Alexeev, 2010: Role of polar amplification and reef back-stepping at the close of the last interglacial highstand. Nature, in long-term surface air temperature variations and modern Arctic warming. J. 458, 881 884. Clim., 23, 3888 3906. Blunier, T., and E. Brook, 2001: Timing of millennial-scale climate change in Antarc- Belt, S. T., G. Massé, S. J. Rowland, M. Poulin, C. Michel, and B. LeBlanc, 2007: A novel tica and Greenland during the last glacial period. Science, 291, 109 112. chemical fossil of palaeo sea ice: IP25. Org. Geochem., 38, 16 27. Blunier, T., J. Chappellaz, J. Schwander, B. Stauffer, and D. Raynaud, 1995: Variations Benito, G., A. Díez-Herrero, and M. Fernández De Villalta, 2003a: Magnitude and fre- in atmospheric methane concentration during the Holocene epoch. Nature, 374, quency of flooding in the Tagus basin (Central Spain) over the last millennium. 46 49. Clim. Change, 58, 171 192. Blunier, T., R. Spahni, J. M. Barnola, J. Chappellaz, L. Loulergue, and J. Schwander, Benito, G., A. Sopena, Y. Sánchez-Moya, M. J. Machado, and A. Pérez-González, 2007: Synchronization of ice core records via atmospheric gases. Clim. Past, 3, 2003b: Palaeoflood record of the Tagus River (Central Spain) during the Late 325 330. Pleistocene and Holocene. Quat. Sci. Rev., 22, 1737 1756. Blunier, T., et al., 1997: Timing of the Antarctic cold reversal and the atmospheric Benito, G., M. Rico, Y. Sánchez-Moya, A. Sopena, V. R. Thorndycraft, and M. Barrien- CO2 increase with respect to the Younger Dryas event. Geophys. Res. Lett., 24, dos, 2010: The impact of late Holocene climatic variability and land use change 2683 2686. 5 on the flood hydrology of the Guadalentín River, southeast Spain. Global Planet. Bonelli, S., S. Charbit, M. Kageyama, M. N. Woillez, G. Ramstein, C. Dumas, and A. Change, 70, 53 63. Quiquet, 2009: Investigating the evolution of major Northern Hemisphere ice Benito, G., et al., 2011: Hydrological response of a dryland ephemeral river to sheets during the last glacial-interglacial cycle. Clim. Past, 5, 329 345. southern African climatic variability during the last millennium. Quat. Res., 75, Böning, C. W., A. Dispert, M. Visbeck, S. R. Rintoul, and F. U. Schwarzkopf, 2008: The 471 482. response of the Antarctic Circumpolar Current to recent climate change. Nature Bentley, M. J., C. J. Fogwill, A. M. Le Brocq, A. L. Hubbard, D. E. Sugden, T. J. Dunai, Geosci., 1, 864 869. and S. P. H. T. Freeman, 2010: Deglacial history of the West Antarctic Ice Sheet in Boninsegna, J. A., et al., 2009: Dendroclimatological reconstructions in South Ameri- the Weddell Sea embayment: Constraints on past ice volume change. Geology, ca: A review. Palaeogeography, Palaeoclimatol. Palaeoecol., 281, 210 228. 38, 411 414. Bonnet, S., A. de Vernal, C. Hillaire-Marcel, T. Radi, and K. Husum, 2010: Variability of Bereiter, B., D. Lüthi, M. Siegrist, S. Schüpbach, T. F. Stocker, and H. Fischer, 2012: sea-surface temperature and sea-ice cover in the Fram Strait over the last two Mode change of millennial CO2 variability during the last glacial cycle associ- millennia. Mar. Micropaleontol., 74, 59 74. ated with a bipolar marine carbon seesaw. Proc. Natl. Acad. Sci. U.S.A., 109, Born, A., and K. H. Nisancioglu, 2012: Melting of Northern Greenland during the last 9755 9760. interglaciation. Cryosphere, 6, 1239 1250. Berger, A., and M. F. Loutre, 1991: Insolation values for the climate of the last 10 Born, A., K. Nisancioglu, and P. Braconnot, 2010: Sea ice induced changes in ocean million years. Quat. Sci. Rev., 10, 297 317. circulation during the Eemian. Clim. Dyn., 35, 1361 1371. Berger, A. L., 1978: Long-term variations of daily insolation and Quaternary climatic Bostock, H. C., et al., 2013: A review of the Australian New Zealand sector of the changes. J. Atmos. Sci., 35, 2362 2367. Southern Ocean over the last 30 ka (Aus-INTIMATE project). Quat. Sci. Rev., 74, Berger, G. W., and P. M. Anderson, 2000: Extending the geochronometry of Arctic 35-57. lake cores beyond the radiocarbon limit by using thermoluminescence. J. Geo- Boucher, É., J. Guiot, and E. Chapron, 2011: A millennial multi-proxy reconstruction phys. Res., 105, 15439 15455. of summer PDSI for Southern South America. Clim. Past, 7, 957 974. 437 Chapter 5 Information from Paleoclimate Archives Boucher, O., and M. Pham, 2002: History of sulfate aerosol radiative forcings. Geo- Bromwich, D. H., J. P. Nicolas, A. J. Monaghan, M. A. Lazzara, L. M. Keller, G. A. Wei- phys. Res. Lett., 29, 22 1 22 4. dner, and A. B. Wilson, 2013: Central West Antarctica among the most rapidly Bowerman, N. D., and D. H. Clark, 2011: Holocene glaciation of the central Sierra warming regions on Earth. Nature Geosci., 6, 139 145. Nevada, California. Quat. Sci. Rev., 30, 1067 1085. Brovkin, V., J.-H. Kim, M. Hofmann, and R. Schneider, 2008: A lowering effect of Bozbiyik, A., M. Steinacher, F. Joos, T. F. Stocker, and L. Menviel, 2011: Fingerprints of reconstructed Holocene changes in sea surface temperatures on the atmospher- changes in the terrestrial carbon cycle in response to large reorganizations in ic CO2 concentration. Global Biogeochem. Cycles, 22, GB1016. ocean circulation. Clim. Past, 7, 319 338. Buckley, B. M., et al., 2010: Climate as a contributing factor in the demise of Angkor, Braconnot, P., Y. Luan, S. Brewer, and W. Zheng, 2012a: Impact of Earth s orbit and Cambodia. Proc. Natl. Acad. Sci. U.S.A., 107, 6748 6752. freshwater fluxes on Holocene climate mean seasonal cycle and ENSO charac- Büntgen, U., D. C. Frank, D. Nievergelt, and J. Esper, 2006: Summer temperature teristics. Clim. Dyn., 38, 1081 1092. variations in the European Alps, AD 755 2004. J. Clim., 19, 5606 5623. Braconnot, P., C. Marzin, L. Grégoire, E. Mosquet, and O. Marti, 2008: Monsoon Büntgen, U., J. Esper, D. Frank, K. Nicolussi, and M. Schmidhalter, 2005: A 1052 year response to changes in Earth s orbital parameters: comparisons between simu- tree-ring proxy for Alpine summer temperatures. Clim. Dyn., 25, 141 153. lations of the Eemian and of the Holocene. Clim. Past, 4, 281 294. Büntgen, U., D. Frank, R. Wilson, M. Carrer, C. Urbinati, and J. Esper, 2008: Testing for Braconnot, P., et al., 2012b: Evaluation of climate models using palaeoclimatic data. tree-ring divergence in the European Alps. Global Change Biol., 14, 2443 2453. Nature Clim. Change, 2, 417 424. Büntgen, U., et al., 2011a: Causes and consequences of past and projected Scandina- Braconnot, P., et al., 2007: Results of PMIP2 coupled simulations of the mid-Holo- vian summer temperatures, 500 2100 AD. PLoS ONE, 6, e25133. cene and Last Glacial Maximum - Part 1: experiments and large-scale features. Büntgen, U., et al., 2011b: 2500 years of european climate variability and human Clim. Past, 3, 261 277. susceptibility. Science, 331, 578 582. Bradley, S. L., M. Siddall, G. A. Milne, V. Masson-Delmotte, and E. Wolff, 2012: Where Bürger, G., 2007: Comment on The spatial extent of 20th-century warmth in the might we find evidence of a Last Interglacial West Antarctic Ice Sheet collapse in context of the past 1200 years . Science, 316, 1844a. Antarctic ice core records? Global Planet. Change, 88 89, 64 75. Cahalan, R. F., G. Wen, J. W. Harder, and P. Pilewskie, 2010: Temperature responses to Bradley, S. L., M. Siddall, G. A. Milne, V. Masson-Delmotte, and E. Wolff, 2013: Com- spectral solar variability on decadal time scales. Geophys. Res. Lett., 37, L07705. bining ice core records and ice sheet models to explore the evolution of the East Cai, Y. J., et al., 2010: The variation of summer monsoon precipitation in central China Antarctic Ice sheet during the Last Interglacial period. Global Planet. Change, since the last deglaciation. Earth Planet. Sci. Lett., 291, 21 31. 100, 278 290. Caillon, N., J. P. Severinghaus, J. Jouzel, J.-M. Barnola, J. Kang, and V. Y. Lipenkov, Brady, E. C., B. L. Otto-Bliesner, J. E. Kay, and N. Rosenbloom, 2013: Sensitivity to 2003: Timing of atmospheric CO2 and Antarctic temperature changes across Ter- Glacial Forcing in the CCSM4. J. Clim., 26, 1901 1925. mination III. Science, 299, 1728 1731. Braganza, K., J. L. Gergis, S. B. Power, J. S. Risbey, and A. M. Fowler, 2009: A multi- Calenda, G., C. P. Mancini, and E. Volpi, 2005: Distribution of the extreme peak floods proxy index of the El Nino Southern Oscillation, A.D. 1525 1982. J. Geophys. of the Tiber River from the XV century. Adv. Water Resourc., 28, 615 625. Res., 114, D05106. Calov, R., and A. Ganopolski, 2005: Multistability and hysteresis in the climate-cryo- Braun, H., and J. Kurths, 2010: Were Dansgaard-Oeschger events forced by the Sun? sphere system under orbital forcing. Geophys. Res. Lett., 32, L21717. Eur. Phys. J. Special Top., 191, 117 129. Calov, R., A. Ganopolski, V. Petoukhov, M. Claussen, and R. Greve, 2002: Large-scale Braun, H., P. Ditlevsen, and D. R. Chialvo, 2008: Solar forced Dansgaard-Oeschger instabilities of the Laurentide ice sheet simulated in a fully coupled climate- events and their phase relation with solar proxies. Geophys. Res. Lett., 35, system model. Geophys. Res. Lett., 29, 2216. L06703. Calov, R., et al., 2010: Results from the Ice-Sheet Model Intercomparison Project- Brázdil, R., Z. W. Kundzewicz, and G. Benito, 2006: Historical hydrology for studying Heinrich Event INtercOmparison (ISMIP HEINO). J. Glaciol., 56, 371 383. flood risk in Europe. Hydrol. Sci. J., 51, 739 764. Camuffo, D., and S. Enzi, 1996: The analysis of two bi-millenary series: Tiber and Po Brázdil, R., C. Pfister, H. Wanner, H. von Storch, and J. Luterbacher, 2005: Historical river floods. In: Climatic Variations and Forcing Mechanisms of the Last 2000 climatology in Europe The state of the art. Clim. Change, 70, 363 430. Years [P. D. Jones, R. S. Bradley, and J. Jouzel (eds.)]. Springer-Verlag, Heidelberg, Brázdil, R., Z. W. Kundzewicz, G. Benito, G. Demaree, N. MacDonald, and L. A. Roald, Germany, and New York, NY, USA, pp. 433 450. 2012: Historical floods in Europe in the past millennium. In: Changes of Flood Candy, I., G. R. Coope, J. R. Lee, S. A. Parfitt, R. C. Preece, J. Rose, and D. C. Schreve, Risk in Europe [Z. W. Kundzewicz (ed.)]. CRC Press, Boca Raton, Fl, USA, pp. 2010: Pronounced warmth during early Middle Pleistocene interglacials: Investi- 121 166. gating the Mid-Brunhes Event in the British terrestrial sequence. Earth Sci. Rev., Breecker, D. O., Z. D. Sharp, and L. D. McFadden, 2010: Atmospheric CO2 concentra- 103, 183 196. tions during ancient greenhouse climates were similar to those predicted for Capron, E., et al., 2012: A global picture of the first abrupt climatic event occurring A.D. 2100. Proc. Natl. Acad. Sci. U.S.A., 107, 576 580. during the last glacial inception. Geophys. Res. Lett., 39, L15703. Bretagnon, P., and G. Francou, 1988: Planetary theories in rectangular and spherical Capron, E., et al., 2010a: Synchronising EDML and NorthGRIP ice cores using 18O of 5 variables - VSOP 87 solutions. Astronomy & Astrophysics, 202, 309 315. atmospheric oxygen (18Oatm) and CH4 measurements over MIS5 (80 123 kyr). Brewer, S., J. Guiot, and F. Torre, 2007: Mid-Holocene climate change in Europe: A Quat. Sci. Rev., 29, 222 234. data-model comparison. Clim. Past, 3, 499 512. Capron, E., et al., 2010b: Millennial and sub-millennial scale climatic variations Briffa, K. R., and T. M. Melvin, 2011: A closer look at Regional Curve Standardiza- recorded in polar ice cores over the last glacial period. Clim. Past, 6, 345 365. tion of tree-ring records: Justification of the need, a warning of some pitfalls, Carlson, A. E., P. U. Clark, G. M. Raisbeck, and E. J. Brook, 2007: Rapid Holocene and suggested improvements in its application. In: Dendroclimatology: Prog- deglaciation of the Labrador sector of the Laurentide Ice Sheet. J. Clim., 20, ress and Prospects [M. K. Hughes, H. F. Diaz, and T. W. Swetnam (eds]. Springer 5126 5133. Science+Business Media, Dordrecht, the Netherlands, pp. 113 145. Carlson, A. E., D. J. Ullman, F. S. Anslow, F. He, P. U. Clark, Z. Liu, and B. L. Otto-Bliesner, Briffa, K. R., F. H. Schweingruber, P. D. Jones, T. J. Osborn, S. G. Shiyatov, and E. A. 2012: Modeling the surface mass-balance response of the Laurentide Ice Sheet Vaganov, 1998: Reduced sensitivity of recent tree-growth to temperature at high to Blling warming and its contribution to Meltwater Pulse 1A. Earth Planet. Sci. northern latitudes. Nature, 391, 678 682. Lett., 315 316, 24 29. Briffa, K. R., T. J. Osborn, F. H. Schweingruber, I. C. Harris, P. D. Jones, S. G. Shiyatov, Cerling, T. E., 1992: Use of carbon isotopes in paleosols as an indicator of the pCO2 and E. A. Vaganov, 2001: Low-frequency temperature variations from a northern of the paleoatmosphere. Global Biogeochem. Cycles, 6, 307 314. tree ring density network. J. Geophys. Res., 106, 2929 2941. Chappell, J., 1983: Evidence for smoothly falling sea level relative to north Briner, J. P., H. A. M. Stewart, N. E. Young, W. Philipps, and S. Losee, 2010: Using Queensland, Australia, during the past 6,000 yr. Nature, 302, 406 408. proglacial-threshold lakes to constrain fluctuations of the Jakobshavn Isbrae ice Chappell, J., 2002: Sea level changes forced ice breakouts in the Last Glacial cycle: margin, western Greenland, during the Holocene. Quat. Sci. Rev., 29, 3861 3874. new results from coral terraces. Quat. Sci. Rev., 21, 1229 1240. Brohan, P., R. Allan, E. Freeman, D. Wheeler, C. Wilkinson, and F. Williamson, 2012: Charbit, S., D. Paillard, and G. Ramstein, 2008: Amount of CO2 emissions irreversibly Constraining the temperature history of the past millennium using early instru- leading to the total melting of Greenland. Geophys. Res. Lett., 35, L12503. mental observations. Clim. Past, 8, 1551 1563. Chavaillaz, Y., F. Codron, and M. Kageyama, 2013: Southern Westerlies in LGM and future (RCP4.5) climates. Clim. Past, 9, 517 524. 438 Information from Paleoclimate Archives Chapter 5 Chen, J. H., H. A. Curran, B. White, and G. J. Wasserburg, 1991: Precise chronology of Cook, E. R., P. J. Krusic, K. J. Anchukaitis, B. M. Buckley, T. Nakatsuka, and M. Sano, the last interglacial period: 234U-230Th data from fossil coral reefs in the Bahamas. 2012: Tree-ring reconstructed summer temperature anomalies for temperate Geol. Soc. Am. Bull., 103, 82 97. East Asia since 800 C.E. Clim. Dyn., doi:10.1007/s00382-012-1611-x, 1-16, pub- Cheng, H., et al., 2009: Ice Age Terminations. Science, 326, 248 252. lished online 5 December 2012. Chiessi, C. M., S. Mulitza, J. Pätzold, G. Wefer, and J. A. Marengo, 2009: Possible Cook, E. R., B. M. Buckley, J. G. Palmer, P. Fenwick, M. J. Peterson, G. Boswijk, and A. impact of the Atlantic Multidecadal Oscillation on the South American summer Fowler, 2006: Millennia-long tree-ring records from Tasmania and New Zealand: monsoon. Geophys. Res. Lett., 36, L21707. A basis for modelling climate variability and forcing, past, present and future. J. Christiansen, B., 2011: Reconstructing the NH mean temperature: Can underestima- Quat. Sci., 21, 689 699. tion of trends and variability be avoided? J. Clim., 24, 674 692. Cook, K. H., and I. M. Held, 1988: Stationary Waves of the Ice Age Climate. J. Clim., Christiansen, B., and F. C. Ljungqvist, 2012: The extra-tropical Northern Hemisphere 1, 807 819. temperature in the last two millennia: Reconstructions of low-frequency vari- Cooper, R. J., T. M. Melvin, I. Tyers, R. J. S. Wilson, and K. R. Briffa, 2013: A tree-ring ability. Clim. Past, 8, 765 786. reconstruction of East Anglian (UK) hydroclimate variability over the last millen- Christiansen, B., T. Schmith, and P. Thejll, 2009: A surrogate ensemble study of climate nium. Clim. Dyn., 40, 1019 1039. reconstruction methods: stochasticity and robustness. J. Clim., 22, 951 976. Cornes, R. C., P. D. Jones, K. R. Briffa, and T. J. Osborn, 2012: Estimates of the North Chu, G., et al., 2011: Seasonal temperature variability during the past 1600 years Atlantic Oscillation back to 1692 using a Paris-London westerly index. Int. J. recorded in historical documents and varved lake sediment profiles from north- Climatol., 32, 1135 1150. eastern China. Holocene, 22, 785 792. Corona, C., J. Guiot, J.-L. Edouard, F. Chalie, U. Büntgen, P. Nola, and C. Urbinati, Chylek, P., C. K. Folland, G. Lesins, M. K. Dubey, and M. Wang, 2009: Arctic air tem- 2010: Millennium-long summer temperature variations in the European Alps as perature change amplification and the Atlantic Multidecadal Oscillation. Geo- reconstructed from tree rings. Clim. Past, 6, 379 400. phys. Res. Lett., 36, L14801. Corona, C., J.-L. Edouard, F. Guibal, J. Guiot, S. Bernard, A. Thomas, and N. Denelle, Clague, J. J., J. Koch, and M. Geertsema, 2010: Expansion of outlet glaciers of the 2011: Long-term summer (AD 751 2008) temperature fluctuation in the French Juneau Icefield in northwest British Columbia during the past two millennia. Alps based on tree-ring data. Boreas, 40, 351 366. Holocene, 20, 447 461. Cortese, G., A. Abelmann, and R. Gersonde, 2007: The last five glacial-interglacial Claquin, T., et al., 2003: Radiative forcing of climate by ice-age atmospheric dust. transitions: A high-resolution 450,000 year record from the subantarctic Atlan- Clim. Dyn., 20, 193 202. tic. Paleoceanography, 22, PA4203. Clark, P. U., and D. Pollard, 1998: Origin of the middle Pleistocene transition by ice Cramer, B. S., K. G. Miller, P. J. Barrett, and J. D. Wright, 2011: Late Cretaceous-Neo- sheet erosion of regolith. Paleoceanography, 13, 1 9. gene trends in deep ocean temperature and continental ice volume: Reconciling Clark, P. U., J. X. Mitrovica, G. A. Milne, and M. E. Tamisiea, 2002: Sea level finger- records of benthic foraminiferal geochemistry (18O and Mg/Ca) with sea level printing as a direct test for the source of global meltwater pulse IA. Science, history. J. Geophys. Res., 116, C12023. 295, 2438 2441. Crespin, E., H. Goosse, T. Fichefet, and M. E. Mann, 2009: The 15th century Arctic Clark, P. U., et al., 2009: The Last Glacial Maximum. Science, 325, 710 714. warming in coupled model simulations with data assimilation. Clim. Past, 5, Clarke, G. K. C., D. W. Leverington, J. T. Teller, and A. S. Dyke, 2004: Paleohydraulics 389 401. of the last outburst flood from glacial Lake Agassiz and the 8200 BP cold event. Cronin, T. M., P. R. Vogt, D. A. Willard, R. Thunell, J. Halka, M. Berke, and J. Pohlman, Quat. Sci. Rev., 23, 389 407. 2007: Rapid sea level rise and ice sheet response to 8,200 year climate event. Clemens, S. C., W. L. Prell, and Y. Sun, 2010: Orbital-scale timing and mechanisms Geophys. Res. Lett., 34, L20603. driving late Pleistocene Indo-Asian summer monsoons: reinterpreting cave spe- Crouch, A. D., P. Charbonneau, G. Beaubien, and D. Paquin-Ricard, 2008: A model leothem 18O. Paleoceanography, 25, PA4207. for the total solar irradiance based on active region decay. Astrophys. J., 677, Clement, A. C., and L. C. Peterson, 2008: Mechanisms of abrupt climate change of 723 741. the last glacial period. Rev. Geophys., 46, RG4002. Crowley, T. J., 2000: Causes of Climate Change Over the Past 1000 Years. Science, CLIMAP Project Members, 1976: The surface of the Ice-Age Earth. Science, 191, 289, 270 277. 1131 1137. Crowley, T. J., and M. B. Unterman, 2013: Technical details concerning develop- CLIMAP Project Members, 1981: Seasonal reconstructions of the earth s surface at ment of a 1200 year proxy index for global volcanism. Earth Syst. Sci. Data, the last glacial maximum. Geol. Soc. Am., MC-36. 5, 187 197. Cobb, K. M., et al., 2013: Highly variable El Nino-Southern Oscillation throughout the Crowley, T. J., S. K. Baum, K.-Y. Kim, G. C. Hegerl, and W. T. Hyde, 2003: Modeling Holocene. Science, 339, 67 70. ocean heat content changes during the last millennium. Geophys. Res. Lett., Cochelin, A.-S. B., L. A. Mysak, and Z. Wang, 2006: Simulation of long-term future 30, 1932. climate changes with the green McGill paleoclimate model: The next glacial Crucifix, M., 2006: Does the Last Glacial Maximum constrain climate sensitivity? inception. Clim. Change, 79, 381 401. Geophys. Res. Lett., 33, L18701. 5 COHMAP Members, 1988: Climatic changes of the last 18,000 years: observations Cruz, F. W., et al., 2005: Insolation-driven changes in atmospheric circulation over the and model simulations. Science, 241, 1043 1052. past 116,000 years in subtropical Brazil. Nature, 434, 63 66. Cole-Dai, J., D. Ferris, A. Lanciki, J. Savarino, M. Baroni, and M. Thiemens, 2009: Cold Cruz, F. W., et al., 2009: Orbitally driven east-west antiphasing of South American decade (AD 1810 1819) caused by Tambora (1815) and another (1809) strato- precipitation. Nature Geosci., 2, 210 214. spheric volcanic eruption. Geophys. Res. Lett., 36, L22703. Cuffey, K. M., G. D. Clow, R. B. Alley, M. Stuiver, E. D. Waddington, and R. W. Saltus, Colville, E. J., A. E. Carlson, B. L. Beard, R. G. Hatfield, J. S. Stoner, A. V. Reyes, and D. 1995: Large Arctic temperature change at the Wisconsin-Holocene glacial transi- J. Ullman, 2011: Sr-Nd-Pb isotope evidence for ice-sheet presence on southern tion. Science, 270, 455 458. Greenland during the Last Interglacial. Science, 333, 620 623. Cunningham, L. K., et al., 2013: Reconstructions of surface ocean conditions from Cook, E. R., R. D. D Arrigo, and M. E. Mann, 2002: A well-verified, multiproxy recon- the northeast Atlantic and Nordic seas during the last millennium. Holocene, struction of the winter North Atlantic Oscillation index since AD 1400. J. Clim., 23, 921-935. 15, 1754 1764. Curry, J. A., J. L. Schramm, and E. E. Ebert, 1995: Sea ice-albedo climate feedback Cook, E. R., C. A. Woodhouse, C. M. Eakin, D. M. Meko, and D. W. Stahle, 2004: Long- mechanism. J. Clim., 8, 240 247. term aridity changes in the western United States. Science, 306, 1015 1018. D Arrigo, R., R. Wilson, and G. Jacoby, 2006: On the long-term context for late twen- Cook, E. R., K. J. Anchukaitis, B. M. Buckley, R. D. D Arrigo, G. C. Jacoby, and W. E. tieth century warming. J. Geophys. Res., 111, D03103. Wright, 2010a: Asian Monsoon Failure and Megadrought During the Last Mil- D Arrigo, R., R. Wilson, B. Liepert, and P. Cherubini, 2008: On the Divergence Prob- lennium. Science, 328, 486 489. lem in Northern Forests: A review of the tree-ring evidence and possible causes. Cook, E. R., R. Seager, R. R. Heim Jr, R. S. Vose, C. Herweijer, and C. Woodhouse, Global Planet. Change, 60, 289 305. 2010b: Megadroughts in North America: placing IPCC projections of hydrocli- D Arrigo, R., E. R. Cook, R. J. Wilson, R. Allan, and M. E. Mann, 2005: On the variability matic change in a long-term palaeoclimate context. J. Quat. Sci., 25, 48 61. of ENSO over the past six centuries. Geophys. Res. Lett., 32, L03711. D Arrigo, R., et al., 2009: Tree growth and inferred temperature variability at the North American Arctic treeline. Global Planet. Change, 65, 71 82. 439 Chapter 5 Information from Paleoclimate Archives Dahl-Jensen, D., K. Mosegaard, N. Gundestrup, G. D. Clow, S. J. Johnsen, A. W. Dreimanis, A., 1992: Transition from the Sangamon interglaciation to the Wisconsin Hansen, and N. Balling, 1998: Past temperatures directly from the Greenland Ice glaciation along the southeastern margin of the Laurentide Ice Sheet, North Sheet. Science, 282, 268 271. America. In: Start of a Glacial, NATO ASI Series, 13 [G. T. Kukla, and E. Went Daley, T. J., et al., 2011: The 8200 yr BP cold event in stable isotope records from the (eds.)]. Springer-Verlag, Heidelberg, Germany, and New York, NY, USA, pp. North Atlantic region. Global Planet. Change, 79, 288 302. 225 251. Davis, P. T., B. Menounos, and G. Osborn, 2009: Holocene and latest Pleistocene Duplessy, J.-C., L. Labeyrie, and C. Waelbroeck, 2002: Constraints on the ocean alpine glacier fluctuations: A global perspective. Quat. Sci. Rev., 28, 2021 2238. oxygen isotopic enrichment between the Last Glacial Maximum and the Holo- De Angelis, H., and P. Skvarca, 2003: Glacier surge after ice shelf collapse. Science, cene: Paleoceanographic implications. Quat. Sci. Rev., 21, 315 330. 299, 1560 1562. Dutton, A., and K. Lambeck, 2012: Ice volume and sea level during the Last Intergla- De Deckker, P., M. Moros, K. Perner, and E. Jansen, 2012: Influence of the tropics and cial. Science, 337, 216 219. southern westerlies on glacial interhemispheric asymmetry. Nature Geosci., 5, Dwyer, G. S., and M. A. Chandler, 2009: Mid-Pliocene sea level and continental ice 266 269. volume based on coupled benthic Mg/Ca palaeotemperatures and oxygen iso- De Deckker, P., M. Norman, I. D. Goodwin, A. Wain, and F. X. Gingele, 2010: Lead topes. Philos. Trans. R. Soc. London A, 367, 157 168. isotopic evidence for an Australian source of aeolian dust to Antarctica at times Dwyer, G. S., T. M. Cronin, P. A. Baker, and J. Rodriguez-Lazaro, 2000: Changes in over the last 170,000 years. Palaeogeogr. Palaeoclimatol. Palaeoecol., 285, North Atlantic deep-sea temperature during climatic fluctuations of the last 205 223. 25,000 years based on ostracode Mg/Ca ratios. Geochem. Geophys. Geosyst. de Garidel-Thoron, T., Y. Rosenthal, L. Beaufort, E. Bard, C. Sonzogni, and A. C. Mix, 1, 1028. 2007: A multiproxy assessment of the western equatorial Pacific hydrography Edwards, T. L., M. Crucifix, and S. P. Harrison, 2007: Using the past to constrain the during the last 30 kyr. Paleoceanography, 22, PA3204. future: How the palaeorecord can improve estimates of global warming. Prog. de Vernal, A., et al., 2006: Comparing proxies for the reconstruction of LGM sea-sur- Phys. Geogr., 31, 481 500. face conditions in the northern North Atlantic. Quat. Sci. Rev., 25, 2820 2834. Ekart, D. D., T. E. Cerling, I. P. Montanez, and N. J. Tabor, 1999: A 400 million year DeConto, R. M., and D. Pollard, 2003: Rapid Cenozoic glaciation of Antarctica carbon isotope record of pedogenic carbonate; implications for paleoatmo- induced by declining atmospheric CO2. Nature, 421, 245 249. spheric carbon dioxide. Am. J. Sci., 299, 805 827. DeConto, R. M., et al., 2012: Past extreme warming events linked to massive carbon Elderfield, H., P. Ferretti, M. Greaves, S. Crowhurst, I. N. McCave, D. Hodell, and A. M. release from thawing permafrost. Nature, 484, 87 91. Piotrowski, 2012: Evolution of ocean temperature and ice volume through the Delaygue, G., and E. Bard, 2011: An Antarctic view of Beryllium-10 and solar activity mid-Pleistocene climate transition. Science, 337, 704 709. for the past millennium. Clim. Dyn., 36, 2201 2218. Elderfield, H., et al., 2010: A record of bottom water temperature and seawater 18O DeLong, K. L., T. M. Quinn, F. W. Taylor, K. Lin, and C.-C. Shen, 2012: Sea surface for the Southern Ocean over the past 440 kyr based on Mg/Ca of benthic fora- temperature variability in the southwest tropical Pacific since AD 1649. Nature miniferal Uvigerina spp. Quat. Sci. Rev., 29, 160 169. Clim. Change, 2, 799 804. Ellison, C. R. W., M. R. Chapman, and I. R. Hall, 2006: Surface and deep ocean interac- Delworth, T. L., and M. E. Mann, 2000: Observed and simulated multidecadal vari- tions during the cold climate event 8200 years ago. Science, 312, 1929 1932. ability in the Northern Hemisphere. Clim. Dyn., 16, 661 676. Ely, L. L., Y. Enzel, V. R. Baker, and D. R. Cayan, 1993: A 5000-year record of extreme Denis, D., X. Crosta, L. Barbara, G. Massé, H. Renssen, O. Ther, and J. Giraudeau, 2010: floods and climate change in the southwestern United States. Science, 262, Sea ice and wind variability during the Holocene in East Antarctica: insight on 410 412. middle high latitude coupling. Quat. Sci. Rev., 29, 3709 3719. Emile-Geay, J., K. M. Cobb, M. E. Mann, and A. T. Wittenberg, 2013a: Estimating Derbyshire, E., 2003: Loess, and the dust indicators and records of terrestrial and central equatorial Pacific SST variability over the past millennium. Part I: Meth- marine palaeoenvironments (DIRTMAP) database. Quat. Sci. Rev., 22, 1813 odology and validation. J. Clim., 26, 2302 2328. 1819. Emile-Geay, J., K. M. Cobb, M. E. Mann, and A. T. Wittenberg, 2013b: Estimating Deschamps, P., et al., 2012: Ice-sheet collapse and sea level rise at the Blling warm- central equatorial Pacific SST variability over the past millennium. Part II: Recon- ing 14,600 years ago. Nature, 483, 559 564. structions and implications. J. Clim., 26, 2329 2352. Diaz, H. F., R. M. Trigo, M. K. Hughes, M. E. Mann, E. Xoplaki, and D. Barriopedro, England, J. H., T. R. Lakeman, D. S. Lemmen, J. M. Bednarski, T. G. Stewart, and D. J. 2011: Spatial and temporal characteristics of Climate in medieval times revis- A. Evans, 2008: A millennial-scale record of Arctic Ocean sea ice variability and ited. Bull. Am. Meteorol. Soc., 92, 1487 1500. the demise of the Ellesmere Island ice shelves. Geophys. Res. Lett., 35, L19502. Diffenbaugh, N. S., M. Ashfaq, B. Shuman, J. W. Williams, and P. J. Bartlein, 2006: EPICA Community Members, 2006: One-to-one coupling of glacial climate variability Summer aridity in the United States: Response to mid-Holocene changes in inso- in Greenland and Antarctica. Nature, 444, 195 198. lation and sea surface temperature. Geophys. Res. Lett., 33, L22712. Esper, J., and D. Frank, 2009: Divergence pitfalls in tree-ring research. Clim. Change, DiNezio, P. N., A. Clement, G. A. Vecchi, B. Soden, A. J. Broccoli, B. L. Otto-Bliesner, 94, 261 266. 5 and P. Braconnot, 2011: The response of the Walker circulation to Last Glacial Esper, J., U. Büntgen, M. Timonen, and D. C. Frank, 2012a: Variability and extremes Maximum forcing: Implications for detection in proxies. Paleoceanography, 26, of northern Scandinavian summer temperatures over the past two millennia. PA3217. Global Planet. Change, 88 89, 1 9. Ditlevsen, P. D., and O. D. Ditlevsen, 2009: On the stochastic nature of the rapid Esper, J., U. Büntgen, J. Luterbacher, and P. J. Krusic, 2013: Testing the hypothesis of climate shifts during the Last Ice Age. J. Clim., 22, 446 457. globally missing rings in temperature sensitive dendrochronological data. Den- Divine, D. V., and C. Dick, 2006: Historical variability of sea ice edge position in the drochronologia, 31, 216-222. Nordic Seas. J. Geophys. Res., 111, C01001. Esper, J., D. Frank, R. Wilson, U. Büntgen, and K. Treydte, 2007a: Uniform growth Dolan, A. M., A. M. Haywood, D. J. Hill, H. J. Dowsett, S. J. Hunter, D. J. Lunt, and S. J. trends among central Asian low- and high-elevation juniper tree sites. Trees, Pickering, 2011: Sensitivity of Pliocene ice sheets to orbital forcing. Palaeogeogr. 21, 141 150. Palaeoclimatol. Palaeoecol. 309, 98 110. Esper, J., D. Frank, U. Büntgen, A. Verstege, J. Luterbacher, and E. Xoplaki, 2007b: Donnelly, J. P., P. Cleary, P. Newby, and R. Ettinger, 2004: Coupling instrumental and Long-term drought severity variations in Morocco. Geophys. Res. Lett., 34, geological records of sea level change: Evidence from southern New England of L17702. an increase in the rate of sea level rise in the late 19th century. Geophys. Res. Esper, J., D. Frank, U. Büntgen, A. Verstege, R. M. Hantemirov, and A. V. Kirdyanov, Lett., 31, L05203. 2010: Trends and uncertainties in Siberian indicators of 20th century warming. Doria, G., D. L. Royer, A. P. Wolfe, A. Fox, J. A. Westgate, and D. J. Beerling, 2011: Global Change Biol., 16, 386 398. Declining atmospheric CO2 during the late Middle Eocene climate transition. Esper, J., et al., 2012b: Orbital forcing of tree-ring data. Nature Clim. Change, 2, Am. J. Sci., 311, 63 75. 862 866. Dowsett, H. J., M. M. Robinson, and K. M. Foley, 2009: Pliocene three-dimensional Etheridge, D. M., L. P. Steele, R. J. Francey, and R. L. Langenfelds, 1998: Atmospheric global ocean temperature reconstruction. Clim. Past, 5, 769 783. methane between 1000 A.D. and present: Evidence of anthropogenic emissions Dowsett, H. J., et al., 2012: Assessing confidence in Pliocene sea surface tempera- and climatic variability. J. Geophys. Res., 103, 15979 15993. tures to evaluate predictive models. Nature Clim. Change, 2, 365 371. 440 Information from Paleoclimate Archives Chapter 5 Etheridge, D. M., L. P. Steele, R. L. Langenfelds, R. J. Francey, J. M. Barnola, and V. Fyke, J., and M. Eby, 2012: Comment on Climate sensitivity estimated from tem- I. Morgan, 1996: Natural and anthropogenic changes in atmospheric CO2 over perature reconstructions of the Last Glacial Maximum . Science, 337, 1294. the last 1000 years from air in Antarctic ice and firn. J. Geophys. Res., 101, Gabrielli, P., et al., 2010: A major glacial-interglacial change in aeolian dust com- 4115 4128. position inferred from Rare Earth Elements in Antarctic ice. Quat. Sci. Rev., 29, Euler, C., and U. S. Ninnemann, 2010: Climate and Antarctic Intermediate Water cou- 265 273. pling during the late Holocene. Geology, 38, 647 650. Gagen, M., et al., 2011: Cloud response to summer temperatures in Fennoscandia Fairbanks, R. G., 1989: A 17,000 year glacio-eustatic sea level record: Influence of over the last thousand years. Geophys. Res. Lett., 38, L05701. glacial melting rates on the Younger Dryas event and deep ocean circulation. Gaiero, D. M., 2007: Dust provenance in Antarctic ice during glacial periods: From Nature, 342, 637 642. where in southern South America? Geophys. Res. Lett., 34, L17707. Fan, F. X., M. E. Mann, and C. M. Ammann, 2009: Understanding changes in the Ganopolski, A., and S. Rahmstorf, 2001: Rapid changes of glacial climate simulated Asian summer monsoon over the past millennium: Insights from a long-term in a coupled climate model. Nature, 409, 153 158. coupled model simulation. J. Clim., 22, 1736 1748. Ganopolski, A., and D. M. Roche, 2009: On the nature of lead-lag relationships Fedorov, A. V., C. M. Brierley, K. T. Lawrence, Z. Liu, P. S. Dekens, and A. C. Ravelo, during glacial-interglacial climate transitions. Quat. Sci. Rev., 28, 3361 3378. 2013: Patterns and mechanisms of early Pliocene warmth. Nature, 496, 43 49. Ganopolski, A., and R. Calov, 2011: The role of orbital forcing, carbon dioxide and Feng, S., and Q. Hu, 2008: How the North Atlantic Multidecadal Oscillation may have regolith in 100 kyr glacial cycles. Clim. Past, 7, 1415 1425. influenced the Indian summer monsoon during the past two millennia. Geophys. Ganopolski, A., R. Calov, and M. Claussen, 2010: Simulation of the last glacial cycle Res. Lett., 35, L01707. with a coupled climate ice-sheet model of intermediate complexity. Clim. Past, Fernández-Donado, L., et al., 2013: Large-scale temperature response to external 6, 229 244. forcing in simulations and reconstructions of the last millennium. Clim. Past, 9, Gao, C., A. Robock, and C. Ammann, 2008: Volcanic forcing of climate over the past 393 421. 1500 years: An improved ice core-based index for climate models. J. Geophys. Feulner, G., 2011: Are the most recent estimates for Maunder Minimum solar irradi- Res., 113, D23111. ance in agreement with temperature reconstructions? Geophys. Res. Lett., 38, , 2012: Correction to Volcanic forcing of climate over the past 1500 years: L16706. An improved ice core-based index for climate models . J. Geophys. Res., 117, Fischer, H., M. Wahlen, J. Smith, D. Mastroiani, and B. Deck, 1999: Ice core records D16112. of atmospheric CO2 around the last three glacial terminations. Science, 283, García-Artola, A., A. Cearreta, E. Leorri, M. Irabien, and W. Blake, 2009: Las maris- 1712 1714. mas costeras como archivos geológicos de las variaciones recientes en el nivel Fischer, H., M. L. Siggaard-Andersen, U. Ruth, R. Röthlisberger, and E. Wolff, 2007: marino/Coastal salt-marshes as geological archives of recent sea level changes. Glacial/interglacial changes in mineral dust and sea-salt records in polar ice Geogaceta, 47, 109 112. cores: Sources, transport, and deposition. Rev. Geophys., 45, RG1002. Garcia-Herrera, R., D. Barriopedro, E. Hernández, H. F. Diaz, R. R. Garcia, M. R. Prieto, Fischer, N., and J. H. Jungclaus, 2010: Effects of orbital forcing on atmosphere and and R. Moyano, 2008: A chronology of El Nino events from primary documentary ocean heat transports in Holocene and Eemian climate simulations with a com- sources in northern Peru. J. Clim., 21, 1948 1962. prehensive Earth system model. Clim. Past, 6, 155 168. Garcin, Y., et al., 2007: Solar and anthropogenic imprints on Lake Masoko (southern Fleitmann, D., et al., 2009: Timing and climatic impact of Greenland interstadials Tanzania) during the last 500 years. J. Paleolimnol., 37, 475 490. recorded in stalagmites from northern Turkey. Geophys. Res. Lett., 36, L19707. Gayer, E., J. Lavé, R. Pik, and C. France-Lanord, 2006: Monsoonal forcing of Holocene Fletcher, B. J., S. J. Brentnall, C. W. Anderson, R. A. Berner, and D. J. Beerling, 2008: glacier fluctuations in Ganesh Himal (Central Nepal) constrained by cosmogenic Atmospheric carbon dioxide linked with Mesozoic and early Cenozoic climate 3He exposure ages of garnets. Earth Planet. Sci. Lett., 252, 275 288. change. Nature Geosci., 1, 43 48. Ge, Q.-S., J.-Y. Zheng, Z.-X. Hao, X.-M. Shao, W.-C. Wang, and J. Luterbacher, 2010: Fletcher, W. J., and M. F. Sánchez Goni, 2008: Orbital- and sub-orbital-scale cli- Temperature variation through 2000 years in China: an uncertainty analysis of mate impacts on vegetation of the western Mediterranean basin over the last reconstruction and regional difference. Geophys. Res. Lett., 37, L03703. 48,000 yr. Quat. Res., 70, 451 464. Ge, Q. S., S. B. Wang, and J. Y. Zheng, 2006: Reconstruction of temperature series in Flückiger, J., A. Dällenbach, T. Blunier, B. Stauffer, T. F. Stocker, D. Raynaud, and J.-M. China for the last 5000 years. Prog. Nat. Sci., 16, 838 845. Barnola, 1999: Variations in atmospheric N2O concentration during abrupt cli- Gehrels, W. R., and P. L. Woodworth, 2013: When did modern rates of sea level rise matic changes. Science, 285, 227 230. start? Global Planet. Change, 100, 263 277. Flückiger, J., et al., 2002: High-resolution Holocene N2O ice core record and its rela- Gehrels, W. R., B. W. Hayward, R. M. Newnham, and K. E. Southall, 2008: A 20th tionship with CH4 and CO2. Global Biogeochem. Cycles, 16, 1010. century acceleration of sea level rise in New Zealand. Geophys. Res. Lett., 35, 2 Foster, G. L., 2008: Seawater pH, pCO2 and [CO3 ] variations in the Caribbean Sea L02717. over the last 130 kyr: a boron isotope and B/Ca study of planktic forminifera. Gehrels, W. R., B. P. Horton, A. C. Kemp, and D. Sivan, 2011: Two millennia of sea level Earth Planet. Sci. Lett., 271, 254 266. data: The key to predicting change. Eos Trans. AGU, 92, 289 290. 5 Foster, G. L., C. H. Lear, and J. W. B. Rae, 2012: The evolution of pCO2, ice volume and Gehrels, W. R., et al., 2006: Rapid sea level rise in the North Atlantic Ocean since the climate during the middle Miocene. Earth Planet. Sci. Lett., 341 344, 243 254. first half of the nineteenth century. The Holocene, 16, 949 965. Fowler, A. M., et al., 2012: Multi-centennial tree-ring record of ENSO-related activity Gergis, J. L., and A. M. Fowler, 2009: A history of ENSO events since A.D. 1525: Impli- in New Zealand. Nature Clim. Change, 2, 172 176. cations for future climate change. Clim. Change, 92, 343 387. Frank, D., J. Esper, and E. R. Cook, 2007: Adjustment for proxy number and coherence Gersonde, R., X. Crosta, A. Abelmann, and L. Armand, 2005: Sea-surface temperature in a large-scale temperature reconstruction. Geophys. Res. Lett., 34, L16709. and sea ice distribution of the Southern Ocean at the EPILOG Last Glacial Maxi- Frank, D., J. Esper, E. Zorita, and R. Wilson, 2010a: A noodle, hockey stick, and spa- mum a circum-Antarctic view based on siliceous microfossil records. Quat. Sci. ghetti plate: a perspective on high-resolution paleoclimatology. Clim. Change, Rev., 24, 869 896. 1, 507 516. Ghatak, D., A. Frei, G. Gong, J. Stroeve, and D. Robinson, 2010: On the emergence of Frank, D. C., J. Esper, C. C. Raible, U. Büntgen, V. Trouet, B. Stocker, and F. Joos, 2010b: an Arctic amplification signal in terrestrial Arctic snow extent. J. Geophys. Res., Ensemble reconstruction constraints on the global carbon cycle sensitivity to 115, D24105. climate. Nature, 463, 527 530. Giguet-Covex, C., et al., 2012: Frequency and intensity of high-altitude floods over Fréchette, B., A. P. Wolfe, G. H. Miller, P. J. H. Richard, and A. de Vernal, 2006: Vegeta- the last 3.5 ka in northwestern French Alps (Lake Anterne). Quat. Res., 77, 12 22. tion and climate of the last interglacial on Baffin Island, Arctic Canada. Palaeo- Gille, S. T., 2008: Decadal-scale temperature trends in the southern hemisphere geogr. Palaeoclimatol. Palaeoecol. 236, 91 106. ocean. J. Clim., 21, 4749 4765. Freeman, K. H., and J. M. Hayes, 1992: Fractionation of carbon isotopes by phyto- Gillett, N., T. Kell, and P. Jones, 2006: Regional climate impacts of the Southern Annu- plankton and estimates of ancient CO2 levels. Global Biogeochem. Cycles, 6, lar Mode. Geophys. Res. Lett., 33, L23704. 185 198. Gillett, N., et al., 2008: Attribution of polar warming to human influence. Nature Funder, S., et al., 2011: A 10,000-year record of Arctic Ocean sea-ice variability Geosci., 1, 750 754. view from the beach. Science, 333, 747 750. Gladstone, R. M., et al., 2005: Mid-Holocene NAO: A PMIP2 model intercomparison. Geophys. Res. Lett., 32, L16707. 441 Chapter 5 Information from Paleoclimate Archives Goehring, B. M., et al., 2011: The Rhone Glacier was smaller than today for most of Hanebuth, T. J. J., H. K. Voris, Y. Yokoyama, Y. Saito, and J. i. Okuno, 2011: Formation the Holocene. Geology, 39, 679 682. and fate of sedimentary depocentres on Southeast Asia s Sunda Shelf over the Goldewijk, K. K., 2001: Estimating global land use change over the past 300 years: past sea level cycle and biogeographic implications. Earth Sci. Rev., 104, 92 110. The HYDE Database. Global Biogeochem. Cycles, 15, 417 433. Hanhijärvi, S., M. P. Tingley, and A. Korhola, 2013: Pairwise comparisons to recon- González-Rouco, F., H. von Storch, and E. Zorita, 2003: Deep soil temperature as struct mean temperature in the Arctic Atlantic Region over the last 2,000 years. proxy for surface air-temperature in a coupled model simulation of the last thou- Clim. Dyn., 41, 2039-2060. sand years. Geophys. Res. Lett., 30, 2116. Hansen, J., and M. Sato, 2004: Greenhouse gas growth rates. Proc. Natl. Acad. Sci. González-Rouco, J. F., H. Beltrami, E. Zorita, and H. von Storch, 2006: Simulation and U.S.A., 101, 16109 16114. inversion of borehole temperature profiles in surrogate climates: Spatial distri- Hansen, J., et al., 2008: Target atmospheric CO2: Where should humanity aim? Open bution and surface coupling. Geophys. Res. Lett., 33, L01703. Atmos. Sci. J., 2, 217 231. González, C., and L. Dupont, 2009: Tropical salt marsh succession as sea level indica- Harada, N., M. Sato, and T. Sakamoto, 2008: Freshwater impacts recorded in tetraun- tor during Heinrich events. Quat. Sci. Rev., 28, 939 946. saturated alkenones and alkenone sea surface temperatures from the Okhotsk González, J. L., and T. E. Törnqvist, 2009: A new Late Holocene sea level record from Sea across millennial-scale cycles. Paleoceanography, 23, PA3201. the Mississippi Delta: evidence for a climate/sea level connection? Quat. Sci. Harada, N., K. Kimoto, Y. Okazaki, K. Nagashima, A. Timmermann, and A. Abe-Ouchi, Rev., 28, 1737 1749. 2009: Millennial time scale changes in surface to intermediate-deep layer cir- Goodwin, I. D., and N. Harvey, 2008: Subtropical sea level history from coral microat- culation recorded in sediment cores from the northwestern North Pacific. Quat. olls in the Southern Cook Islands, since 300 AD. Mar. Geol., 253, 14 25. Res. (Daiyonki-Kenkyu), 48, 179 194. Goosse, H., J. Guiot, M. E. Mann, S. Dubinkina, and Y. Sallaz-Damaz, 2012a: The medi- Harada, N., et al., 2012: Sea surface temperature changes in the Okhotsk Sea and eval climate anomaly in Europe: Comparison of the summer and annual mean adjacent North Pacific during the last glacial maximum and deglaciation. Deep- signals in two reconstructions and in simulations with data assimilation. Global Sea Res. Pt. II, 61 64, 93 105. Planet. Change, 84 85, 35 47. Harden, T. M., J. E. O Connor, D. G. Driscoll, and J. F. Stamm, 2011: Flood-frequency Goosse, H., et al., 2012b: The role of forcing and internal dynamics in explaining the analyses from paleoflood investigations for Spring, Rapid, Boxelder, and Elk Medieval Climate Anomaly . Clim. Dyn., 39, 2847 2866. Creeks, Black Hills, western South Dakota. U.S. Geological Survey Scientific Goosse, H., et al., 2012c: Antarctic temperature changes during the last millennium: Investigations Report 2011 5131, 136 pp. evaluation of simulations and reconstructions. Quat. Sci. Rev., 55, 75 90. Harder, J. W., J. M. Fontenla, P. Pilewskie, E. C. Richard, and T. N. Woods, 2009: Trends Govin, A., et al., 2012: Persistent influence of ice sheet melting on high northern in solar spectral irradiance variability in the visible and infrared. Geophys. Res. latitude climate during the early Last Interglacial. Clim. Past, 8, 483 507. Lett., 36, L07801. Grachev, A. M., E. J. Brook, J. P. Severinghaus, and N. G. Pisias, 2009: Relative timing Hargreaves, J., A. Abe-Ouchi, and J. Annan, 2007: Linking glacial and future climates and variability of atmospheric methane and GISP2 oxygen isotopes between 68 through an ensemble of GCM simulations. Clim. Past, 3, 77 87. and 86 ka. Global Biogeochem. Cycles, 23, GB2009. Hargreaves, J. C., J. D. Annan, M. Yoshimori, and A. Abe-Ouchi, 2012: Can the Last Graham, N., C. Ammann, D. Fleitmann, K. Cobb, and J. Luterbacher, 2011: Support Glacial Maximum constrain climate sensitivity? Geophys. Res. Lett., 39, L24702. for global climate reorganization during the Medieval Climate Anomaly . Clim. Hawkins, E., R. S. Smith, L. C. Allison, J. M. Gregory, T. J. Woollings, H. Pohlmann, and Dyn., 37, 1217 1245. B. de Cuevas, 2011: Bistability of the Atlantic overturning circulation in a global Grant, K. M., et al., 2012: Rapid coupling between ice volume and polar temperature climate model and links to ocean freshwater transport. Geophys. Res. Lett., 38, over the past 150,000 years. Nature, 491, 744 747. L10605. Graversen, R. G., and M. H. Wang, 2009: Polar amplification in a coupled climate Haywood, A. M., P. J. Valdes, and V. L. Peck, 2007: A permanent El Nino-like state model with locked albedo. Clim. Dyn., 33, 629 643. during the Pliocene? Paleoceanography, 22, PA1213. Gray, S. T., L. J. Graumlich, J. L. Betancourt, and G. T. Pederson, 2004: A tree-ring based Haywood, A. M., et al., 2013: Large-scale features of Pliocene climate: results from reconstruction of the Atlantic Multidecadal Oscillation since 1567 A.D. Geophys. the Pliocene Model Intercomparison Project. Clim. Past, 9, 191 209. Res. Lett., 31, L12205. Hearty, P. J., J. T. Hollin, A. C. Neumann, M. J. O Leary, and M. McCulloch, 2007: Global Greenwood, D. R., M. J. Scarr, and D. C. Christophel, 2003: Leaf stomatal frequency sea level fluctuations during the Last Interglaciation (MIS 5e). Quat. Sci. Rev., in the Australian tropical rainforest tree Neolitsea dealbata (Lauraceae) as a 26, 2090 2112. proxy measure of atmospheric pCO2. Palaeogeogr. Palaeoclimatol. Palaeoecol. Hegerl, G., T. Crowley, W. Hyde, and D. Frame, 2006: Climate sensitivity constrained 196, 375 393. by temperature reconstructions over the past seven centuries. Nature, 440, Gregoire, L. J., A. J. Payne, and P. J. Valdes, 2012: Deglacial rapid sea level rises caused 1029 1032. by ice-sheet saddle collapses. Nature, 487, 219 222. Hegerl, G. C., T. J. Crowley, M. Allen, W. T. Hyde, H. N. Pollack, J. Smerdon, and E. Gregory, J. M., and P. Huybrechts, 2006: Ice-sheet contributions to future sea level Zorita, 2007: Detection of human influence on a new, validated 1500 year tem- 5 change. Philos. Trans. R. Soc. A, 364, 1709 1732. perature reconstruction. J. Clim., 20, 650 666. Grichuk, V. P., 1985: Reconstructed climatic indexes by means of floristic data and Helama, S., J. Meriläinen, and H. Tuomenvirta, 2009: Multicentennial megadrought in an estimation of their accuracy. In: Metody reconstruktsii paleoklimatov [A. A. northern Europe coincided with a global El Nino Southern Oscillation drought Velichko and Y. Y. Gurtovaya (eds.)]. Nauka-press, St. Petersburg, Russian Federa- pattern during the Medieval Climate Anomaly. Geology, 37, 175 178. tion, pp. 20 28 (in Russian). Helama, S., M. M. Fauria, K. Mielikäinen, M. Timonen, and M. Eronen, 2010: Sub- Grützner, J., and S. M. Higgins, 2010: Threshold behavior of millennial scale variabil- Milankovitch solar forcing of past climates: mid and late Holocene perspectives. ity in deep water hydrography inferred from a 1.1 Ma long record of sediment Geol. Soc. Am. Bull., 122, 1981 1988. provenance at the southern Gardar Drift. Paleoceanography, 25, PA4204. Hély, C., P. Braconnot, J. Watrin, and W. Zheng, 2009: Climate and vegetation: Simu- Haigh, J. D., 1996: The impact of solar variability on climate. Science, 272, 981 984. lating the African humid period. C. R. Geosci., 341, 671 688. Haigh, J. D., A. R. Winning, R. Toumi, and J. W. Harder, 2010: An influence of solar Hemming, S. R., 2004: Heinrich events: Massive late Pleistocene detritus layers of spectral variations on radiative forcing of climate. Nature, 467, 696 699. the North Atlantic and their global climate imprint. Rev. Geophys., 42, RG1005. Hald, M., et al., 2007: Variations in temperature and extent of Atlantic Water in the Henderiks, J., and M. Pagani, 2007: Refining ancient carbon dioxide estimates: Sig- northern North Atlantic during the Holocene. Quat. Sci. Rev., 26, 3423 3440. nificance of coccolithophore cell size for alkenone-based pCO2 records. Pale- Hall, B. L., T. Koffman, and G. H. Denton, 2010: Reduced ice extent on the western oceanography, 22, PA3202. Antarctic Peninsula at 700 970 cal. yr B.P. Geology, 38, 635 638. Herbert, T. D., L. C. Peterson, K. T. Lawrence, and Z. Liu, 2010: Tropical ocean tempera- Hall, I. R., S. B. Moran, R. Zahn, P. C. Knutz, C. C. Shen, and R. L. Edwards, 2006: tures over the past 3.5 million years. Science, 328, 1530 1534. Accelerated drawdown of meridional overturning in the late-glacial Atlantic Hereid, K. A., T. M. Quinn, F. W. Taylor, C.-C. Shen, R. L. Edwards, and H. Cheng, 2013: triggered by transient pre-H event freshwater perturbation. Geophys. Res. Lett., Coral record of reduced El Nino activity in the early 15th to middle 17th century. 33, L16616. Geology, 41, 51 54. Handorf, D., K. Dethloff, A. G. Marshall, and A. Lynch, 2009: Climate regime variability Herold, N., Q. Z. Yin, M. P. Karami, and A. Berger, 2012: Modeling the diversity of the for past and present time slices simulated by the Fast Ocean Atmosphere Model. warm interglacials. Clim. Dyn., 56, 126 141. J. Clim., 22, 58 70. 442 Information from Paleoclimate Archives Chapter 5 Herrington, A., and C. Poulsen, 2012: Terminating the Last Interglacial: the role of Huber, M., and R. Caballero, 2011: The early Eocene equable climate problem revis- ice sheet-climate feedbacks in a GCM asynchronously coupled to an Ice Sheet ited. Clim. Past, 7, 603 633. Model. J. Clim., 25, 1871 1882. Hughes, A. L. C., E. Rainsley, T. Murray, C. J. Fogwill, C. Schnabel, and S. Xu, 2012: Hesse, T., M. Butzin, T. Bickert, and G. Lohmann, 2011: A model-data comparison of Rapid response of Helheim Glacier, southeast Greenland, to early Holocene cli- 13C in the glacial Atlantic Ocean. Paleoceanography, 26, PA3220. mate warming. Geology, 40, 427 430. Heusser, C. J., and L. E. Heusser, 1990: Long continental pollen sequence from Wash- Hughes, M. K., and C. M. Ammann, 2009: The future of the past an Earth system ington State (U.S.A.): Correlation of upper levels with marine pollen-oxygen iso- framework for high resolution paleoclimatology: editorial essay. Clim. Change, tope stratigraphy through substage 5e. Palaeogeogr. Palaeoclimatol. Palaeoecol., 94, 247 259. 79, 63 71. Humlum, O., B. Elberling, A. Hormes, K. Fjordheim, O. H. Hansen, and J. Heinemeier, Heyman, J., A. P. Stroeven, J. M. Harbor, and M. W. Caffee, 2011: Too young or too 2005: Late-Holocene glacier growth in Svalbard, documented by subglacial relict old: evaluating cosmogenic exposure dating based on an analysis of compiled vegetation and living soil microbes. Holocene, 15, 396 407. boulder exposure ages. Earth Planet. Sci. Lett., 302, 71 80. Hurtt, G. C., et al., 2006: The underpinnings of land-use history: three centuries of Higginson, M. J., M. A. Altabet, D. W. Murray, R. W. Murray, and T. D. Herbert, 2004: global gridded land-use transitions, wood-harvest activity, and resulting second- Geochemical evidence for abrupt changes in relative strength of the Arabian ary lands. Global Change Biol., 12, 1208 1229. monsoons during a stadial/interstadial climate transition. Geochim Cosmochim. Huybers, P., 2011: Combined obliquity and precession pacing of Late Pleistocene Acta, 68, 3807 3826. deglaciations. Nature, 480, 229 232. Hijma, M. P., and K. M. Cohen, 2010: Timing and magnitude of the sea level jump Israelson, C., and B. Wohlfarth, 1999: Timing of the last-interglacial high sea level on preluding the 8200 yr event. Geology, 38, 275 278. the Seychelles Islands, Indian Ocean. Quat. Res., 51, 306 316. Hill, D. J., A. M. Dolan, A. M. Haywood, S. J. Hunter, and D. K. Stoll, 2010: Sensitivity Itambi, A. C., T. von Dobeneck, S. Mulitza, T. Bickert, and D. Heslop, 2009: Millennial- of the Greenland Ice Sheet to Pliocene sea surface temperatures. Stratigraphy, scale northwest African droughts related to Heinrich events and Dansgaard- 7, 111 122. Oeschger cycles: Evidence in marine sediments from offshore Senegal. Pale- Hind, A., and A. Moberg, 2012: Past millennial solar forcing magnitude: A statisti- oceanography, 24, PA1205. cal hemispheric-scale climate model versus proxy data comparison. Clim. Dyn., Ivanochko, T. S., R. S. Ganeshram, G.-J. A. Brummer, G. Ganssen, S. J. A. Jung, S. G. doi:10.1007/s00382 012 1526 6, published online 22 September 2012. Moreton, and D. Kroon, 2005: Variations in tropical convection as an amplifier Hodell, D. A., H. F. Evans, J. E. T. Channell, and J. H. Curtis, 2010: Phase relationships of of global climate change at the millennial scale. Earth Planet. Sci. Lett., 235, North Atlantic ice-rafted debris and surface-deep climate proxies during the last 302 314. glacial period. Quat. Sci. Rev., 29, 3875 3886. Ivy-Ochs, S., H. Kerschner, M. Maisch, M. Christl, P. W. Kubik, and C. Schlüchter, 2009: Hodgson, D. A., 2011: First synchronous retreat of ice shelves marks a new phase of Latest Pleistocene and Holocene glacier variations in the European Alps. Quat. polar deglaciation. Proc. Natl. Acad. Sci. U.S.A., 108, 18859 18860. Sci. Rev., 28, 2137 2149. Hofer, D., C. Raible, and T. Stocker, 2011: Variations of the Atlantic Meridional circula- Izumi, K., P. J. Bartlein, and S. P. Harrison, 2013: Consistent large-scale temperature tion in control and transient simulations of the last millennium. Clim. Past, 7, responses in warm and cold climates. Geophys. Res. Lett., 40, 1817-1823. 133 150. Jaccard, S. L., E. D. Galbraith, D. M. Sigman, and G. H. Haug, 2010: A pervasive link Hofer, D., C. C. Raible, N. Merz, A. Dehnert, and J. Kuhlemann, 2013: Simulated winter between Antarctic ice core and subarctic Pacific sediment records over the past circulation types in the North Atlantic and European region for preindustrial and 800 kyrs. Quat. Sci. Rev., 29, 206 212. glacial conditions. Geophys. Res. Lett., 39, L15805. Jansen, E., et al., 2007: Palaeoclimate. In: Climate Change 2007: The Physical Science Holden, P., N. Edwards, K. Oliver, T. Lenton, and R. Wilkinson, 2010a: A probabilistic Basis. Contribution of Working Group I to the Fourth Assessment Report of the calibration of climate sensitivity and terrestrial carbon change in GENIE-1. Clim. Intergovernmental Panel on Climate Change [Solomon, S., D. Qin, M. Manning, Z. Dyn., 35, 785 806. Chen, M. Marquis, K. B. Averyt, M. Tignor and H. L. Miller (eds.)] Cambridge Uni- Holden, P. B., N. R. Edwards, E. W. Wolff, N. J. Lang, J. S. Singarayer, P. J. Valdes, and versity Press, Cambridge, United Kingdom and New York, NY, USA, pp. 433 497. T. F. Stocker, 2010b: Interhemispheric coupling, the West Antarctic Ice Sheet and Jevrejeva, S., J. C. Moore, A. Grinsted, and P. L. Woodworth, 2008: Recent global sea warm Antarctic interglacials. Clim. Past, 6, 431 443. level acceleration started over 200 years ago? Geophys. Res. Lett., 35, L08715. Hollis, C. J., et al., 2012: Early Paleogene temperature history of the Southwestern Joerin, U. E., K. Nicolussi, A. Fischer, T. F. Stocker, and C. Schlüchter, 2008: Holocene Pacific Ocean: reconciling proxies and models. Earth Planet. Sci. Lett., 349 350, optimum events inferred from subglacial sediments at Tschierva Glacier, Eastern 53 66. Swiss Alps. Quat. Sci. Rev., 27, 337 350. Holmes, J. A., E. R. Cook, and B. Yang, 2009: Climate change over the past 2000 years Johns, T. C., et al., 2003: Anthropogenic climate change for 1860 to 2100 simulated in Western China. Quaternary International, 194, 91 107. with the HadCM3 model under updated emissions scenarios. Clim. Dyn., 20, Holz, A., and T. T. Veblen, 2011: Variability in the Southern Annular Mode determines 583 612. wildfire activity in Patagonia. Geophys. Res. Lett., 38, L14710. Johnsen, S. J., D. Dahl-Jensen, W. Dansgaard, and N. Gundestrup, 1995: Greenland 5 Holzhauser, H., M. Magny, and H. J. Zumbühl, 2005: Glacier and lake-level variations palaeotemperatures derived from GRIP bore hole temperature and ice core iso- in west-central Europe over the last 3500 years. Holocene, 15, 789 801. tope profiles. Tellus B, 47, 624 629. Hönisch, B., and N. G. Hemming, 2005: Surface ocean pH response to variations in Johnson, K., and D. J. Smith, 2012: Dendroglaciological reconstruction of late-Holo- pCO2 through two full glacial cycles. Earth Planet. Sci. Lett., 236, 305 314. cene glacier activity at White and South Flat glaciers, Boundary Range, northern Hönisch, B., N. G. Hemming, D. Archer, M. Siddall, and J. F. McManus, 2009: Atmo- British Columbia Coast Mountains, Canada. Holocene, 22, 987 995. spheric carbon dioxide concentration across the Mid-Pleistocene transition. Sci- Jomelli, V., V. Favier, A. Rabatel, D. Brunstein, G. Hoffmann, and B. Francou, 2009: ence, 324, 1551 1554. Fluctuations of glaciers in the tropical Andes over the last millennium and pal- Horton, B., and R. Edwards, 2006: Quantifying Holocene Sea Level Change Using aeoclimatic implications: A review. Palaeogeogr. Palaeoclimatol. Palaeoecol. Intertidal Foraminifera: Lessons from the British Isles. Journal of Foraminiferal 281, 269 282. Research, Special publication 40, 1 97. Jones, P. D., D. H. Lister, T. J. Osborn, C. Harpham, M. Salmon, and C. P. Morice, 2012: Hu, A. X., et al., 2012: Role of the Bering Strait on the hysteresis of the ocean con- Hemispheric and large-scale land-surface air temperature variations: an exten- veyor belt circulation and glacial climate stability. Proc. Natl. Acad. Sci. U.S.A., sive revision and an update to 2010. J. Geophys. Res., 117, D05127. 109, 6417 6422. Jones, P. D., et al., 2009: High-resolution palaeoclimatology of the last millennium: A Hu, C., G. M. Henderson, J. Huang, S. Xie, Y. Sun, and K. R. Johnson, 2008: Quantifica- review of current status and future prospects. Holocene, 19, 3 49. tion of Holocene Asian monsoon rainfall from spatially separated cave records. Joos, F., and R. Spahni, 2008: Rates of change in natural and anthropogenic radiative Earth Planet. Sci. Lett., 266, 221 232. forcing over the past 20,000 years. Proc. Natl. Acad. Sci. U.S.A., 105, 1425 1430. Huang, C. C., J. Pang, X. Zha, Y. Zhou, H. Su, H. Wan, and B. Ge, 2012: Sedimentary Joos, F., et al., 2001: Global warming feedbacks on terrestrial carbon uptake under records of extraordinary floods at the ending of the mid-Holocene climatic opti- the Intergovernmental Panel on Climate Change (IPCC) Emission Scenarios. mum along the Upper Weihe River, China. Holocene, 22, 675 686. Global Biogeochem. Cycles, 15, 891 907. Huber, C., et al., 2006: Isotope calibrated Greenland temperature record over Marine Isotope Stage 3 and its relation to CH4. Earth Planet. Sci. Lett., 243, 504 519. 443 Chapter 5 Information from Paleoclimate Archives Joshi, M. M., and G. S. Jones, 2009: The climatic effects of the direct injection of Kirshner, A. E., J. B. Anderson, M. Jakobsson, M. O Regan, W. Majewski, and F. O. water vapour into the stratosphere by large volcanic eruptions. Atmos. Chem. Nitsche, 2012: Post-LGM deglaciation in Pine Island Bay, West Antarctica. Quat. Phys., 9, 6109 6118. Sci. Rev., 38, 11 26. Joughin, I., and R. B. Alley, 2011: Stability of the West Antarctic ice sheet in a warm- Kissel, C., C. Laj, A. M. Piotrowski, S. L. Goldstein, and S. R. Hemming, 2008: Millen- ing world. Nature Geosci., 4, 506 513. nial-scale propagation of Atlantic deep waters to the glacial Southern Ocean. Jouzel, J., et al., 2007: Orbital and millennial Antarctic climate variability over the Paleoceanography, 23, PA2102. past 800,000 years. Science, 317, 793 796. Kleiven, H. F., E. Jansen, T. Fronval, and T. M. Smith, 2002: Intensification of Northern Juckes, M. N., et al., 2007: Millennial temperature reconstruction intercomparison Hemisphere glaciations in the circum Atlantic region (3.5 2.4 Ma) ice-rafted and evaluation. Clim. Past, 3, 591 609. detritus evidence. Palaeogeogr. Palaeoclimatol. Palaeoecol., 184, 213 223. Jungclaus, J. H., et al., 2010: Climate and carbon-cycle variability over the last millen- Kleiven, H. F., I. R. Hall, I. N. McCave, G. Knorr, and E. Jansen, 2011: Coupled deep- nium. Clim. Past, 6, 723 737. water flow and climate variability in the middle Pleistocene North Atlantic. Geol- Justino, F., and W. R. Peltier, 2005: The glacial North Atlantic Oscillation. Geophys. ogy, 39, 343 346. Res. Lett., 32, L21803. Kleiven, H. F., C. Kissel, C. Laj, U. S. Ninnemann, T. O. Richter, and E. Cortijo, 2008: Justino, F., and W. R. Peltier, 2008: Climate anomalies induced by the arctic and Reduced North Atlantic Deep Water coeval with the glacial Lake Agassiz fresh- antarctic oscillations: glacial maximum and present-day perspectives. J. Clim., water outburst. Science, 319, 60 64. 21, 459 475. Klotz, S., J. Guiot, and V. Mosbrugger, 2003: Continental European Eemian and Justwan, A., and N. Koç, 2008: A diatom based transfer function for reconstructing early Würmian climate evolution: comparing signals using different quantita- sea ice concentrations in the North Atlantic. Mar. Micropaleontol., 66, 264 278. tive reconstruction approaches based on pollen. Global Planet. Change, 36, Kageyama, M., A. Paul, D. M. Roche, and C. J. Van Meerbeeck, 2010: Modelling gla- 277 294. cial climatic millennial-scale variability related to changes in the Atlantic meridi- Knight, J. R., R. J. Allan, C. K. Folland, M. Vellinga, and M. E. Mann, 2005: A signature onal overturning circulation: a review. Quat. Sci. Rev., 29, 2931 2956. of persistent natural thermohaline circulation cycles in observed climate. Geo- Kageyama, M., et al., 2013: Climatic impacts of fresh water hosing under Last Glacial phys. Res. Lett., 32, L20708. Maximum conditions: a multi-model study. Clim. Past, 9, 935 953. Knudsen, M. F., M.-S. Seidenkrantz, B. H. Jacobsen, and A. Kuijpers, 2011: Track- Kaiser, J., E. Schefuß, F. Lamy, M. Mohtadi, and D. Hebbeln, 2008: Glacial to Holo- ing the Atlantic Multidecadal Oscillation through the last 8,000 years. Nature cene changes in sea surface temperature and coastal vegetation in north central Commun., 2, 178. Chile: high versus low latitude forcing. Quat. Sci. Rev., 27, 2064 2075. Kobashi, T., J. P. Severinghaus, J. M. Barnola, K. Kawamura, T. Carter, and T. Nakaega- Kale, V. S., 2008: Palaeoflood hydrology in the Indian context. J. Geol. Soc. India, 71, wa, 2010: Persistent multi-decadal Greenland temperature fluctuation through 56 66. the last millennium. Clim. Change, 100, 733 756. Kanner, L. C., S. J. Burns, H. Cheng, and R. L. Edwards, 2012: High-latitude forcing Kobashi, T., et al., 2011: High variability of Greenland surface temperature over the of the South American Summer Monsoon during the Last Glacial. Science, 335, past 4000 years estimated from trapped air in an ice core. Geophys. Res. Lett., 570 573. 38, L21501. Kaplan, J. O., K. M. Krumhardt, E. C. Ellis, W. F. Ruddiman, C. Lemmen, and K. K. Koch, J., and J. Clague, 2011: Extensive glaciers in northwest North America during Goldewijk, 2011: Holocene carbon emissions as a result of anthropogenic land medieval time. Clim. Change, 107, 593 613. cover change. Holocene, 21, 775 791. Koch, P. L., J. C. Zachos, and P. D. Gingerich, 1992: Correlation between isotope Kaplan, M. R., et al., 2010: Glacier retreat in New Zealand during the Younger Dryas records in marine and continental carbon reservoirs near the Palaeocene/Eocene stadial. Nature, 467, 194 197. boundary. Nature, 358, 319 322. Kaufman, D. S., et al., 2009: Recent warming reverses long-term Arctic cooling. Sci- Koenig, S. J., R. M. DeConto, and D. Pollard, 2011: Late Pliocene to Pleistocene sen- ence, 325, 1236 1239. sitivity of the Greenland Ice Sheet in response to external forcing and internal Kawamura, K., et al., 2007: Northern Hemisphere forcing of climatic cycles in Antarc- feedbacks. Clim. Dyn., 37, 1247 1268. tica over the past 360,000 years. Nature, 448, 912 916. Kohfeld, K. E., R. M. Graham, A. M. de Boer, L. C. Sime, E. W. Wolff, C. Le Quéré, and L. Kemp, A. C., B. P. Horton, J. P. Donnelly, M. E. Mann, M. Vermeer, and S. Rahmstorf, Bopp, 2013: Southern hemisphere westerly wind changes during the Last Glacial 2011: Climate related sea level variations over the past two millennia. Proc. Natl. Maximum: paleo-data synthesis. Quat. Sci. Rev., 68, 76 95. Acad. Sci. U.S.A., 108, 11017 11022. Köhler, P., G. Knorr, D. Buiron, A. Lourantou, and J. Chappellaz, 2011: Abrupt rise in Kemp, A. C., et al., 2009: Timing and magnitude of recent accelerated sea level rise atmospheric CO2 at the onset of the Blling/Allerd: in-situ ice core data versus (North Carolina, United States). Geology, 37, 1035 1038. true atmospheric signals. Clim. Past, 7, 473 486. Kienast, F., et al., 2011: Paleontological records indicate the occurrence of open Köhler, P., R. Bintanja, H. Fischer, F. Joos, R. Knutti, G. Lohmann, and V. Masson- woodlands in a dry inland climate at the present-day Arctic coast in western Delmotte, 2010: What caused Earth s temperature variations during the last 5 Beringia during the Last Interglacial. Quat. Sci. Rev., 30, 2134 2159. 800,000 years? Data-based evidence on radiative forcing and constraints on Kilbourne, K. H., T. M. Quinn, R. Webb, T. Guilderson, J. Nyberg, and A. Winter, 2008: climate sensitivity. Quat. Sci. Rev., 29, 129 145. Paleoclimate proxy perspective on Caribbean climate since the year 1751: Evi- Kopp, R. E., F. J. Simons, J. X. Mitrovica, A. C. Maloof, and M. Oppenheimer, 2009: dence of cooler temperatures and multidecadal variability. Paleoceanography, Probabilistic assessment of sea level during the last interglacial stage. Nature, 23, PA3220. 462, 863 867. Kilfeather, A. A., C. Ó Cofaigh, J. M. Lloyd, J. A. Dowdeswell, S. Xu, and S. G. Moreton, Kopp, R. E., F. J. Simons, J. X. Mitrovica, A. C. Maloof, and M. Oppenheimer, 2013: A 2011: Ice-stream retreat and ice-shelf history in Marguerite Trough, Antarctic probabilistic assessment of sea level variations within the last interglacial stage. Peninsula: Sedimentological and foraminiferal signatures. Geol. Soc. Am. Bull., Geophys. J. Int., 193, 711 716. 123, 997 1015. Koutavas, A., and J. P. Sachs, 2008: Northern timing of deglaciation in the eastern Kim, S. J., et al., 2010: Climate response over Asia/Arctic to change in orbital param- equatorial Pacific from alkenone paleothermometry. Paleoceanography, 23, eters for the last interglacial maximum. Geosci. J., 14, 173 190. PA4205. Kinnard, C., C. M. Zdanowicz, R. M. Koerner, and D. A. Fisher, 2008: A changing Arctic Koutavas, A., and S. Joanides, 2012: El Nino-Southern Oscillation extrema in the seasonal ice zone: Observations from 1870 2003 and possible oceanographic Holocene and Last Glacial Maximum. Paleoceanography, 27, PA4208. consequences. Geophys. Res. Lett., 35, L02507. Kravitz, B., and A. Robock, 2011: Climate effects of high-latitude volcanic eruptions: Kinnard, C., C. M. Zdanowicz, D. A. Fisher, E. Isaksson, A. de Vernal, and L. G. Thomp- Role of the time of year. J. Geophys. Res., 116, D01105. son, 2011: Reconstructed changes in Arctic sea ice over the past 1,450 years. Krebs, U., and A. Timmermann, 2007: Tropical air-sea interactions accelerate the Nature, 479, 509 512. recovery of the Atlantic Meridional Overturning Circulation after a major shut- Kirkbride, M. P., and S. Winkler, 2012: Correlation of Late Quaternary moraines: down. J. Clim., 20, 4940 4956. Impact of climate variability, glacier response, and chronological resolution. Krivova, N., and S. Solanki, 2008: Models of solar irradiance variations: Current Quat. Sci. Rev., 46, 1 29. status. J. Astrophys. Astron., 29, 151 158. 444 Information from Paleoclimate Archives Chapter 5 Krivova, N., L. Balmaceda, and S. Solanki, 2007: Reconstruction of solar total irra- Lambert, F., et al., 2008: Dust-climate couplings over the past 800,000 years from the diance since 1700 from the surface magnetic flux. Astron. Astrophys., 467, EPICA Dome C ice core. Nature, 452, 616 619. 335 346. Lamy, F., et al., 2007: Modulation of the bipolar seesaw in the southeast Pacific Krivova, N., S. Solanki, and Y. Unruh, 2011: Towards a long-term record of solar total during Termination 1. Earth Planet. Sci. Lett., 259, 400 413. and spectral irradiance. J. Atmos. Solar-Terres. Phys., 73, 223 234. Lanciki, A., J. Cole-Dai, M. H. Thiemens, and J. Savarino, 2012: Sulfur isotope evidence Kühl, N., 2003: Die Bestimmung botanisch-klimatologischer Transferfunktionen und of little or no stratospheric impact by the 1783 Laki volcanic eruption. Geophys. die Rekonstruktion des bodennahen Klimazustandes in Europa während der Res. Lett., 39, L01806. Eem-Warmzeit. Vol. 375, Dissertationes Botanicae, Cramer, Berlin, 149 pp. Landais, A., et al., 2004: A continuous record of temperature evolution over a Kürschner, W. M., 1996: Leaf stomata as biosensors of paleoatmospheric CO2 levels. sequence of Dansgaard-Oeschger events during marine isotopic stage 4 (76 to LPP Contributions Series, 5, 1 153. 62 kyr BP). Geophys. Res. Lett., 31, L22211. Kürschner, W. M., Z. Kvaèek, and D. L. Dilcher, 2008: The impact of Miocene atmo- Landrum, L., B. L. Otto-Bliesner, E. R. Wahl, A. Conley, P. J. Lawrence, and H. Teng, spheric carbon dioxide fluctuations on climate and the evolution of terrestrial 2013: Last millennium climate and its variability in CCSM4. J. Clim., 26, 1085 ecosystems. Proc. Natl. Acad. Sci. U.S.A., 105, 449 453. 1111. Kürschner, W. M., F. Wagner, D. L. Dilcher, and H. Visscher, 2001: Using fossil leaves Lang, N., and E. W. Wolff, 2011: Interglacial and glacial variability from the last 800 for the reconstruction of Cenozoic paleoatmospheric CO2 concentrations. In: ka in marine, ice and terrestrial archives. Clim. Past, 7, 361 380. Geological Perspectives of Global Climate Change: APPG Studies in Geology 47, Langebroek, P. M., A. Paul, and M. Schulz, 2009: Antarctic ice-sheet response to Tulsa, [L. C. Gerhard, W. E. Harrison, and B. M. Hanson (eds.)]. The American atmospheric CO2 and insolation in the Middle Miocene. Clim. Past, 5, 633 646. Association of Petroleum Geologists, pp. 169 189. Lara, A., R. Villalba, and R. Urrutia, 2008: A 400 year tree-ring record of the Puelo Küttel, M., et al., 2010: The importance of ship log data: Reconstructing North Atlan- river summer fall streamflow in the valdivian rainforest eco-region, Chile. Clim. tic, European and Mediterranean sea level pressure fields back to 1750. Clim. Change, 86, 331 356. Dyn., 34, 1115 1128. Larocque-Tobler, I., M. Grosjean, O. Heiri, M. Trachsel, and C. Kamenik, 2010: Thou- Kutzbach, J. E., X. D. Liu, Z. Y. Liu, and G. S. Chen, 2008: Simulation of the evolutionary sand years of climate change reconstructed from chironomid subfossils pre- response of global summer monsoons to orbital forcing over the past 280,000 served in varved lake Silvaplana, Engadine, Switzerland. Quat. Sci. Rev., 29, years. Clim. Dyn., 30, 567 579. 1940 1949. Kutzbach, J. E., S. J. Vavrus, W. F. Ruddiman, and G. Philippon-Berthier, 2011: Com- Larocque-Tobler, I., M. M. Stewart, R. Quinlan, M. Trachsel, C. Kamenik, and M. Gros- parisons of atmosphere ocean simulations of greenhouse gas-induced climate jean, 2012: A last millennium temperature reconstruction using chironomids change for pre-industrial and hypothetical no-anthropogenic radiative forcing, preserved in sediments of anoxic Seebergsee (Switzerland): Consensus at local, relative to present day. Holocene, 21, 793 801. regional and central European scales. Quat. Sci. Rev., 41, 49 56. Laborel, J., C. Morhange, R. Lafont, J. Le Campion, F. Laborel-Deguen, and S. Sar- Larsen, N. K., K. H. Kjaer, J. Olsen, S. Funder, K. K. Kjeldsen, and N. Nrgaard-Pedersen, toretto, 1994: Biological evidence of sea level rise during the last 4500 years 2011: Restricted impact of Holocene climate variations on the southern Green- on the rocky coasts of continental southwestern France and Corsica. Mar. Geol., land Ice Sheet. Quat. Sci. Rev., 30, 3171 3180. 120, 203 223. Laskar, J., P. Robutel, F. Joutel, M. Gastineau, A. C. M. Correia, and B. Levrard, 2004: A Lainé, A., et al., 2009: Northern hemisphere storm tracks during the last glacial long-term numerical solution for the insolation quantities of the earth. Astron. maximum in the PMIP2 ocean-atmosphere coupled models: Energetic study, Astrophys., 428, 261 285. seasonal cycle, precipitation. Clim. Dyn., 32, 593 614. Lea, D. W., D. K. Pak, and H. J. Spero, 2000: Climate impact of late Quaternary equato- Laird, K. R., et al., 2012: Expanded spatial extent of the Medieval Climate Anomaly rial Pacific sea surface temperature variations. Science, 289, 1719 1724. revealed in lake-sediment records across the boreal region in northwest Ontario. Lea, D. W., D. K. Pak, C. L. Belanger, H. J. Spero, M. A. Hall, and N. J. Shackleton, 2006: Global Change Biol., 18, 2869 2881. Paleoclimate history of Galápagos surface waters over the last 135,000 yr. Quat. Lamarque, J. F., et al., 2010: Historical (1850 2000) gridded anthropogenic and Sci. Rev., 25, 1152 1167. biomass burning emissions of reactive gases and aerosols: methodology and Lean, J., J. Beer, and R. Bradley, 1995a: Reconstruction of solar irradiance since 1610: application. Atmos. Chem. Phys., 10, 7017 7039. implications for climate change. Geophys. Res. Lett., 22, 3195 3198. Lambeck, K., and E. Bard, 2000: Sea level change along the French Mediterranean Lean, J. L., O. R. White, and A. Skumanich, 1995b: On the solar ultraviolet spec- coast for the past 30 000 years. Earth Planet. Sci. Lett., 175, 203 222. tral irradiance during the Maunder Minimum. Global Biogeochem. Cycles, 9, Lambeck, K., Y. Yokoyama, and T. Purcell, 2002a: Into and out of the Last Glacial 171 182. Maximum: sea level change during oxygen isotope stages 3 and 2. Quat. Sci. Lean, J. L., T. N. Woods, F. G. Eparvier, R. R. Meier, D. J. Strickland, J. T. Correira, and J. Rev., 21, 343 360. S. Evans, 2011: Solar extreme ultraviolet irradiance: Present, past, and future. J. Lambeck, K., T. Esat, and E. Potter, 2002b: Links between climate and sea levels for Geophys. Res., 116, A01102. the past three million years. Nature, 419, 199 206. Lecavalier, B. S., G. A. Milne , B. M. Vinther, D. A. Fisher, A. S. Dyke, and M. J. R. Simp- 5 Lambeck, K., A. Purcell, and A. Dutton, 2012: The anatomy of interglacial sea levels: son, 2013: Revised estimates of Greenland ice sheet thinning histories based on The relationship between sea levels and ice volumes during the Last Interglacial. ice-core records. Quat. Sci. Rev., 63, 73 82. Earth Planet. Sci. Lett., 315 316, 4 11. Leclercq, P. W., and J. Oerlemans, 2012: Global and Hemispheric temperature recon- Lambeck, K., F. Antonioli, A. Purcell, and S. Silenzi, 2004a: Sea level change along the struction from glacier length fluctuations. Clim. Dyn., 38, 1065 1079. Italian coast for the past 10,000 yr. Quat. Sci. Rev., 23, 1567 1598. Ledru, M. P., V. Jomelli, P. Samaniego, M. Vuille, S. Hidalgo, M. Herrera, and C. Ceron, Lambeck, K., M. Anzidei, F. Antonioli, A. Benini, and A. Esposito, 2004b: Sea level in 2013: The Medieval Climate Anomaly and the Little Ice Age in the eastern Ecua- Roman time in the Central Mediterranean and implications for recent change. dorian Andes. Clim. Past, 9, 307 321. Earth Planet. Sci. Lett., 224, 563 575. Leduc, G., R. Schneider, J. H. Kim, and G. Lohmann, 2010: Holocene and Eemian sea Lambeck, K., A. Purcell, S. Funder, K. H. Kjaer, E. Larsen, and P. E. R. Moller, 2006: surface temperature trends as revealed by alkenone and Mg/Ca paleothermom- Constraints on the late Saalian to early middle Weichselian ice sheet of Eurasia etry. Quat. Sci. Rev., 29, 989 1004. from field data and rebound modelling. Boreas, 35, 539 575. Leduc, G., L. Vidal, K. Tachikawa, F. Rostek, C. Sonzogni, L. Beaufort, and E. Bard, Lambeck, K., C. D. Woodroffe, F. Antonioli, M. Anzidei, W. R. Gehrels, J. Laborel, and A. 2007: Moisture transport across Central America as a positive feedback on J. Wright, 2010: Paleoenvironmental records, geophysical modelling, and recon- abrupt climatic changes. Nature, 445, 908 911. struction of sea level trends and variability on centennial and longer timescales. Lee, T. C. K., F. W. Zwiers, and M. Tsao, 2008: Evaluation of proxy-based millennial In: Understanding Sea Level Rise and Variability [J. A. Church, P. L. Woodworth, T. reconstruction methods. Clim. Dyn., 31, 263 281. Aarup, and W. S. Wilson (eds.)]. Wiley-Blackwell, Hoboken, NJ, USA, pp. 61 121. Lefohn, A. S., J. D. Husar, and R. B. Husar, 1999: Estimating historical anthropogenic Lambert, F., M. Bigler, J. P. Steffensen, M. A. Hutterli, and H. Fischer, 2012: Centennial global sulfur emission patterns for the period 1850 1990. Atmos. Environ., 33, mineral dust variability in high-resolution ice core data from Done C, Antarctica. 3435 3444. Clim. Past, 8, 609 623. LeGrande, A. N., and G. A. Schmidt, 2008: Ensemble, water isotope-enabled, coupled Lambert, F., et al., 2013: The role of mineral dust aerosols in polar amplification. general circulation modeling insights into the 8.2 ka event. Paleoceanography, Nature Clim. Change, 3, 487 491. 23, PA3207. 445 Chapter 5 Information from Paleoclimate Archives LeGrande, A. N., and G. A. Schmidt, 2009: Sources of Holocene variability of oxygen Loehle, C., and J. H. McCulloch, 2008: Correction to: A 2000-year global temperature isotopes in paleoclimate archives. Clim. Past, 5, 441 455. reconstruction based on non-tree ring proxies. Energy Environ., 19, 93 100. LeGrande, A. N., et al., 2006: Consistent simulation of multiple proxy responses to Long, A. J., S. A. Woodroffe, G. A. Milne, C. L. Bryant, M. J. R. Simpson, and L. M. Wake, an abrupt climate change event. Proc. Natl. Acad. Sci. U.S.A., 103, 10527 10527. 2012: Relative sea level change in Greenland during the last 700 yrs and ice Lehner, F., C. C. Raible, and T. F. Stocker, 2012: Testing the robustness of a precipita- sheet response to the Little Ice Age. Earth Planet. Sci. Lett., 315 316, 76 85. tion proxy-based North Atlantic Oscillation reconstruction. Quat. Sci. Rev., 45, Loso, M. G., 2009: Summer temperatures during the Medieval Warm Period and 85 94. Little Ice Age inferred from varved proglacial lake sediments in southern Alaska. Lemieux-Dudon, B., et al., 2010: Consistent dating for Antarctic and Greenland ice J. Paleolimnol., 41, 117 128. cores. Quat. Sci. Rev., 29, 8 20. Lough, J. M., 2011: Great Barrier Reef coral luminescence reveals rainfall variabil- Leorri, E., B. P. Horton, and A. Cearreta, 2008: Development of a foraminifera-based ity over northeastern Australia since the 17th century. Paleoceanography, 26, transfer function in the Basque marshes, N. Spain: implications for sea level PA2201. studies in the Bay of Biscay. Mar. Geol., 251, 60 74. Loulergue, L., et al., 2008: Orbital and millennial-scale features of atmospheric CH4 Leorri, E., A. Cearreta, and G. Milne, 2012: Field observations and modelling of Holo- over the past 800,000 years. Nature, 453, 383 386. cene sea level changes in the southern Bay of Biscay: implication for under- Loutre, M. F., and A. Berger, 2000: Future climatic changes: are we entering an excep- standing current rates of relative sea level change and vertical land motion tionally long interglacial? Clim. Change, 46, 61 90. along the Atlantic coast of SW Europe. Quat. Sci. Rev., 42, 59 73. Lowell, T. V., et al., 2013: Late Holocene expansion of Istorvet ice cap, Liverpool Land, Lewis, S. C., A. N. LeGrande, M. Kelley, and G. A. Schmidt, 2010: Water vapour source east Greenland. Quat. Sci. Rev., 63, 128 140. impacts on oxygen isotope variability in tropical precipitation during Heinrich Lowenstein, T. K., and R. V. Demicco, 2006: Elevated Eocene atmospheric CO2 and its events. Clim. Past, 6, 325 343. subsequent decline. Science, 313, 1928 1928. Li, B., D. W. Nychka, and C. M. Ammann, 2010a: The value of multiproxy reconstruc- Lozhkin, A. V., and P. A. Anderson, 2006: A reconstruction of the climate and vegeta- tion of past climate. J. Am. Stat. Assoc., 105, 883 895. tion of northeastern Siberia based on lake sediments. Paleontol. J., 40, 622 628. Li, C., D. S. Battisti, and C. M. Bitz, 2010b: Can North Atlantic sea ice anomalies Lu, J., and M. Cai, 2009: Seasonality of polar surface warming amplification in cli- account for Dansgaard-Oeschger climate signals? J. Clim., 23, 5457 5475. mate simulations. Geophys. Res. Lett., 36, L16704. Li, J., et al., 2011: Interdecadal modulation of El Nino amplitude during the past mil- Lü, J. M., S. J. Kim, A. Abe-Ouchi, Y. Q. Yu, and R. Ohgaito, 2010: Arctic oscillation lennium. Nature Clim. Change, 1, 114 118. during the mid-Holocene and Last Glacial Maximum from PMIP2 coupled model Li, Y. X., H. Renssen, A. P. Wiersma, and T. E. Törnqvist, 2009: Investigating the impact simulations. J. Clim., 23, 3792 3813. of Lake Agassiz drainage routes on the 8.2 ka cold event with a climate model. Lu, R., B. Dong, and H. Ding, 2006: Impact of the Atlantic multidecadal oscillation on Clim. Past, 5, 471 480. the Asian summer monsoon. Geophys. Res. Lett., 33, L24701. Licciardi, J. M., J. M. Schaefer, J. R. Taggart, and D. C. Lund, 2009: Holocene glacier Luckman, B. H., and R. Villalba, 2001: Assessing the synchroneity of glacier fluc- fluctuations in the Peruvian Andes indicate northern climate linkages. Science, tuations in the western Cordillera of the Americas during the last millenium. 325, 1677 1679. In: Interhemispheric Climate Linkages [V. Markgraf (ed.)]. Academic Press, San Linderholm, H. W., and P. Jansson, 2007: Proxy data reconstructions of the Storgla- Diego, CA, USA, pp. 119 140. ciaren (Sweden) mass-balance record back to AD 1500 on annual to decadal Luckman, B. H., and R. J. S. Wilson, 2005: Summer temperatures in the Canadian timescales. Ann. Glaciol., 46, 261 267. Rockies during the last millennium: a revised record. Clim. Dyn., 24, 131 144. Linderholm, H. W., et al., 2010: Dendroclimatology in Fennoscandia from past Lunt, D. J., G. L. Foster, A. M. Haywood, and E. J. Stone, 2008: Late Pliocene Green- accomplishments to future potential. Clim. Past, 5, 1415 1462. land glaciation controlled by a decline in atmospheric CO2 levels. Nature, 454, Linsley, B. K., Y. Rosenthal, and D. W. Oppo, 2010: Holocene evolution of the Indone- 1102 1105. sian throughflow and the western Pacific warm pool. Nature Geosci., 3, 578 583. Lunt, D. J., A. M. Haywood, G. A. Schmidt, U. Salzmann, P. J. Valdes, and H. J. Dow- Linsley, B. K., P. P. Zhang, A. Kaplan, S. S. Howe, and G. M. Wellington, 2008: Inter- sett, 2010: Earth system sensitivity inferred from Pliocene modelling and data. decadal-decadal climate variability from multicoral oxygen isotope records in Nature Geosci., 3, 60 64. the South Pacific convergence zone region since 1650 AD. Paleoceanography, Lunt, D. J., T. Dunkleay Jones, M. Heinemann, M. Huber, A. Legrande, A. Winguth, C. 23, PA2219. Lopston, J. Marotzke, C.D. Roberts, J. Tindall, P. Valdes, C. Winguth, 2012: A mod- Lisiecki, L. E., and M. E. Raymo, 2005: A Pliocene-Pleistocene stack of 57 globally el-data comparison for a multi-model ensemble of early Eocene atmosphere- distributed benthic 18O records. Paleoceanography, 20, PA1003. ocean simulations: EoMIP. Climate of the Past, 8, 1717 1736. Lisiecki, L. E., M. E. Raymo, and W. B. Curry, 2008: Atlantic overturning responses to Lunt, D. J., et al., 2013: A multi-model assessment of last interglacial temperatures. late Pleistocene climate forcings. Nature, 456, 85 88. Clim. Past, 9, 699 717. Lisiecki, L.E., 2010: Links between eccentricity forcing and the 100,000-year glacial Luo, F. F., S. L. Li, and T. Furevik, 2011: The connection between the Atlantic multi- 5 cycle. Nature Geoscience, 3, 349 352. decadal oscillation and the Indian summer monsoon in Bergen climate model Liu, J., B. Wang, Q. Ding, X. Kuang, W. Soon, and E. Zorita, 2009a: Centennial varia- version 2.0. J. Geophys. Res., 116, D19117. tions of the global monsoon precipitation in the last millennium: results from Luoto, T. P., S. Helama, and L. Nevalainen, 2013: Stream flow intensity of the Saavan- ECHO-G model. J. Clim., 22, 2356 2371. joki River, eastern Finland, during the past 1500 years reflected by mayfly and Liu, X. D., Z. Y. Liu, S. Clemens, W. Prell, and J. Kutzbach, 2007a: A coupled model caddisfly mandibles in adjacent lake sediments. J. Hydrol., 476, 147 153. study of glacial Asian monsoon variability and Indian ocean dipole. J. Meteorol. Luterbacher, J., et al., 2002: Reconstruction of sea level pressure fields over the East- Soc. Jpn., 85, 1 10. ern North Atlantic and Europe back to 1500. Clim. Dyn., 18, 545 561. Liu, Z., et al., 2007b: Simulating the transient evolution and abrupt change of North- Luterbacher, J., et al., 2012: A review of 2000 years of paleoclimatic evidence in the ern Africa atmosphere-ocean-terrestrial ecosystem in the Holocene. Quat. Sci. Mediterranean. In: The Climate of the Mediterranean Region: From the Past to Rev., 26, 1818 1837. the Future [P. Lionello (ed.)]. Elsevier, Philadelphia, PA, USA, pp. 87 185. Liu, Z., et al., 2009b: Transient simulation of last deglaciation with a new mechanism Lüthi, D., et al., 2008: High-resolution carbon dioxide concentration record 650,000 for Blling-Allerd warming. Science, 325, 310 314. 800,000 years before present. Nature, 453, 379 382. Ljungqvist, F. C., 2010: A new reconstruction of temperature variability in the extra- Lynch-Stieglitz, J., et al., 2007: Atlantic meridional overturning circulation during the tropical northern hemisphere during the last two millennia. Geograf. Annal. A, Last Glacial Maximum. Science, 316, 66 69. 92, 339 351. MacDonald, G. M., D. F. Porinchu, N. Rolland, K. V. Kremenetsky, and D. S. Kaufman, Ljungqvist, F. C., P. J. Krusic, G. Brattström, and H. S. Sundqvist, 2012: Northern hemi- 2009: Paleolimnological evidence of the response of the central Canadian sphere temperature patterns in the last 12 centuries. Clim. Past, 8, 227 249. treeline zone to radiative forcing and hemispheric patterns of temperature Lloyd, A. H., and A. G. Bunn, 2007: Responses of the circumpolar boreal forest to change over the past 2000 years. J. Paleolimnol., 41, 129 141. 20th century climate variability. Environ. Res. Lett., 2, 045013. Macdonald, N., and A. R. Black, 2010: Reassessment of flood frequency using histori- Lockwood, M., and M. J. Owens, 2011: Centennial changes in the heliospheric mag- cal information for the River Ouse at York, UK (1200 2000). Hydrol. Sci. J., 55, netic field and open solar flux: the consensus view from geomagnetic data and 1152 1162. cosmogenic isotopes and its implications. J. Geophys. Res., 116, A04109. 446 Information from Paleoclimate Archives Chapter 5 MacFarling Meure, C. M., et al., 2006: Law Dome CO2, CH4 and N2O ice core records Marzin, C., and P. Braconnot, 2009: Variations of Indian and African monsoons extended to 2000 years BP. Geophys. Res. Lett., 10, L14810. induced by insolation changes at 6 and 9.5 kyr BP. Clim. Dyn., 33, 215 231. Machado, M. J., G. Benito, M. Barriendos, and F. S. Rodrigo, 2011: 500 years of rainfall Masson-Delmotte, V., et al., 2011a: Sensitivity of interglacial Greenland temperature variability and extreme hydrological events in southeastern Spain drylands. J. and 18O: ice core data, orbital and increased CO2 climate simulations. Clim. Arid Environ., 75, 1244 1253. Past, 7, 1041 1059. Machida, T., T. Nakazawa, Y. Fujii, S. Aoki, and O. Watanabe, 1995: Increase in the Masson-Delmotte, V., et al., 2010a: EPICA Dome C record of glacial and interglacial atmospheric nitrous oxide concentration during the last 250 years. Geophys. intensities. Quat. Sci. Rev., 29, 113 128. Res. Lett., 22, 2921 2924. Masson-Delmotte, V., et al., 2011b: A comparison of the present and last interglacial Macias Fauria, M., et al., 2010: Unprecedented low twentieth century winter sea periods in six Antarctic ice cores. Clim. Past, 7, 397 423. ice extent in the western Nordic Seas since AD 1200. Clim. Dyn., 34, 781 795. Masson-Delmotte, V., et al., 2010b: Abrupt change of Antarctic moisture origin at the Mackintosh, A., et al., 2011: Retreat of the East Antarctic ice sheet during the last end of Termination II. Proc. Natl. Acad. Sci. U.S.A., 107, 12091 12094. glacial termination. Nature Geosci., 4, 195 202. Mathiot, P., et al., 2013: Using data assimilation to investigate the causes of South- Macklin, M. G., J. Lewin, and J. C. Woodward, 2012: The fluvial record of climate ern Hemisphere high latitude cooling from 10 to 8 ka BP. Clim. Past, 9, 887 901. change. Philos. Trans. R. Soc. London A, 370, 2143 2172. Matthews, J. A., and P. Q. Dresser, 2008: Holocene glacier variation chronology of Magilligan, F. J., P. S. Goldstein, G. B. Fisher, B. C. Bostick, and R. B. Manners, 2008: the Smrstabbtindan massif, Jotunheimen, southern Norway, and the recogni- Late Quaternary hydroclimatology of a hyper-arid Andean watershed: climate tion of century- to millennial-scale European Neoglacial Events. Holocene, 18, change, floods, and hydrologic responses to the El Nino-Southern Oscillation in 181 201. the Atacama Desert. Geomorphology, 101, 14 32. McCarroll, D., et al., 2013: A 1200 year multiproxy record of tree growth and summer Maher, B. A., J. M. Prospero, D. Mackie, D. Gaiero, P. P. Hesse, and Y. Balkanski, 2010: temperature at the northern pine forest limit of Europe. Holocene, 23, 471 484. Global connections between aeolian dust, climate and ocean biogeochemistry McElwain, J. C., 1998: Do fossil plants signal palaeoatmospheric CO2 concentration at the present day and at the last glacial maximum. Earth Sci. Rev., 99, 61 97. in the geological past? Philos. Trans. R. Soc. London B, 353, 83 96. Mahowald, N., S. Albani, S. Engelstaedter, G. Winckler, and M. Goman, 2011: Model McGee, D., W. S. Broecker, and G. Winckler, 2010: Gustiness: the driver of glacial insight into glacial interglacial paleodust records. Quat. Sci. Rev., 30, 832 854. dustiness? Quat. Sci. Rev., 29, 2340 2350. Mahowald, N. M., M. Yoshioka, W. D. Collins, A. J. Conley, D. W. Fillmore, and D. B. McGregor, S., and A. Timmermann, 2010: The effect of explosive tropical volcanism Coleman, 2006: Climate response and radiative forcing from mineral aerosols on ENSO. J. Clim., 24, 2178 2191. during the last glacial maximum, pre-industrial, current and doubled-carbon McGregor, S., A. Timmermann, and O. Timm, 2010: A unified proxy for ENSO and PDO dioxide climates. Geophys. Res. Lett., 33, L20705. variability since 1650. Clim. Past, 6, 1 17. Man, W. M., T. J. Zhou, and J. H. Jungclaus, 2012: Simulation of the East Asian McInerney, F. A., and S. L. Wing, 2011: The Paleocene-Eocene Thermal Maximum: A Summer Monsoon during the last millennium with the MPI Earth System Model. perturbation of carbon cycle, climate, and biosphere with implications for the J. Clim., 25, 7852 7866. future. Annu. Rev. Earth Planet. Sci., 39, 489 516. Mann, M. E., J. D. Fuentes, and S. Rutherford, 2012: Underestimation of volcanic McKay, N. P., D. S. Kaufman, and N. Michelutti, 2008: Biogenic silica concentration as cooling in tree-ring-based reconstructions of hemispheric temperatures. Nature a high-resolution, quantitative temperature proxy at Hallet Lake, south-central Geosci., 5, 202 205. Alaska. Geophys. Res. Lett., 35, L05709. Mann, M. E., S. Rutherford, E. R. Wahl, and C. Ammann, 2007: Robustness of proxy- McKay, N. P., J. T. Overpeck, and B. L. Otto-Bliesner, 2011: The role of ocean thermal based climate field reconstruction methods. J. Geophys. Res., 112, D12109. expansion in Last Interglacial sea level rise. Geophys. Res. Lett., 38, L14605. Mann, M. E., Z. H. Zhang, M. K. Hughes, R. S. Bradley, S. K. Miller, S. Rutherford, and McKay, R., et al., 2012a: Pleistocene variability of Antarctic ice sheet extent in the F. B. Ni, 2008: Proxy-based reconstructions of hemispheric and global surface Ross embayment. Quat. Sci. Rev., 34, 93 112. temperature variations over the past two millennia. Proc. Natl. Acad. Sci. U.S.A., McKay, R., et al., 2012b: Antarctic and Southern Ocean influences on Late Pliocene 105, 13252 13257. global cooling. Proc. Natl. Acad. Sci. U.S.A., 109, 6423-6428. Mann, M. E., et al., 2009: Global signatures and dynamical origins of the Little Ice McManus, J., R. Francois, J. Gherardi, L. Keigwin, and S. Brown-Leger, 2004: Collapse Age and Medieval Climate Anomaly. Science, 326, 1256 1260. and rapid resumption of Atlantic meridional circulation linked to deglacial cli- Marcott, S. A., J. D. Shakun, P. U. Clark, and A. C. Mix, 2013: A reconstruction of mate changes. Nature, 428, 834 837. regional and global temperature for the past 11,300 years. Science, 339, 1198 McShane, B. B., and A. J. Wyner, 2011: A statistical analysis of multiple temperature 1201. proxies: Are reconstructions of surface temperatures over the last 1000 years Marcott, S. A., et al., 2011: Ice-shelf collapse from subsurface warming as a trigger reliable? Ann. Appl. Stat., 5, 5 44. for Heinrich events. Proc. Natl. Acad. Sci. U.S.A., 108, 13415 13419. Meckler, A. N., M. O. Clarkson, K. M. Cobb, H. Sodemann, and J. F. Adkins, 2013: Inter- Margari, V., L. C. Skinner, P. C. Tzedakis, A. Ganopolski, M. Vautravers, and N. J. Shack- glacial hydroclimate in the tropical West Pacific through the Late Pleistocene. leton, 2010: The nature of millennial-scale climate variability during the past two Science, 336, 1301 1304. 5 glacial periods. Nature Geosci., 3, 127 131. Meko, D. M., C. A. Woodhouse, C. A. Baisan, T. Knight, J. J. Lukas, M. K. Hughes, and MARGO Project Members, 2009: Constraints on the magnitude and patterns of M. W. Salzer, 2007: Medieval drought in the upper Colorado River Basin. Geo- ocean cooling at the Last Glacial Maximum. Nature Geosci., 2, 127 132. phys. Res. Lett., 34, L10705. Marra, M. J., 2003: Last interglacial beetle fauna from New Zealand. Quat. Res., 59, Melvin, T. M., and K. R. Briffa, 2008: A signal-free approach to dendroclimatic 122 131. standardisation. Dendrochronologia, 26, 71 86. Marshall, S. J., and M. R. Koutnik, 2006: Ice sheet action versus reaction: distin- Melvin, T. M., H. Grudd, and K. R. Briffa, 2013: Potential bias in updating tree-ring guishing between Heinrich events and Dansgaard-Oeschger cycles in the North chronologies using regional curve standardisation: Re-processing 1500 years of Atlantic. Paleoceanography, 21, PA2021. Torneträsk density and ring-width data. Holocene, 23, 364 373. Martin, P. A., D. W. Lea, Y. Rosenthal, N. J. Shackleton, M. Sarnthein, and T. Papenfuss, Menounos, B., G. Osborn, J. Clague, and B. Luckman, 2009: Latest Pleistocene and 2002: Quaternary deep sea temperature histories derived from benthic forami- Holocene glacier fluctuations in western Canada. Quat. Sci. Rev., 28, 2049 2074. niferal Mg/Ca. Earth Planet. Sci. Lett., 198, 193 209. Menviel, L., A. Timmermann, O. E. Timm, and A. Mouchet, 2011: Deconstructing the Martínez-Garcia, A., A. Rosell-Melé, S. L. Jaccard, W. Geibert, D. M. Sigman, and G. last glacial termination: the role of millennial and orbital-scale forcings. Quat. H. Haug, 2011: Southern Ocean dust-climate coupling over the past four million Sci. Rev., 30, 1155 1172. years. Nature, 476, 312 315. Merkel, U., M. Prange, and M. Schulz, 2010: ENSO variability and teleconnections Martrat, B., J. O. Grimalt, N. J. Shackleton, L. de Abreu, M. A. Hutterli, and T. F. Stocker, during glacial climates. Quat. Sci. Rev., 29, 86 100. 2007: Four climate cycles of recurring deep and surface water destabilizations Miller, G. H., A. P. Wolfe, J. P. Briner, P. E. Sauer, and A. Nesje, 2005: Holocene glacia- on the Iberian margin. Science, 317, 502 507. tion and climate evolution of Baffin Island, Arctic Canada. Quat. Sci. Rev., 24, Martrat, B., et al., 2004: Abrupt temperature changes in the western Mediterranean 1703 1721. over the past 250,000 years. Science, 306, 1762 1765. Miller, G. H., et al., 1999: Stratified interglacial lacustrine sediments from Baffin Marzeion, B., and A. Nesje, 2012: Spatial patterns of North Atlantic Oscillation influ- Island, Arctic Canada: Chronology and paleoenvironmental implications. Quat. ence on mass balance variability of European glaciers. Cryosphere, 6, 661 673. Sci. Rev., 18, 789 810. 447 Chapter 5 Information from Paleoclimate Archives Miller, K. G., et al., 2012a: High tide of the warm Pliocene: implications of global sea Muscheler, R., and J. Beer, 2006: Solar forced Dansgaard/Oeschger events? Geophys. level for Antarctic deglaciation. Geology, 40, 407 410. Res. Lett., 33, L20706. Miller, M. D., J. F. Adkins, D. Menemenlis, and M. P. Schodlok, 2012b: The role of ocean Muscheler, R., F. Joos, J. Beer, S. A. Müller, M. Vonmoos, and I. Snowball, 2007: Solar cooling in setting glacial southern source bottom water salinity. Paleoceanog- activity during the last 1000 yr inferred from radionuclide records. Quat. Sci. raphy, 27, PA3207. Rev., 26, 82 97. Milne, G., and J. Mitrovica, 2008: Searching for eustasy in deglacial sea level histo- Naish, T., et al., 2009a: Obliquity-paced Pliocene West Antarctic ice sheet oscillations. ries. Quat. Sci. Rev., 27, 2292 2302. Nature, 458, 322 328. Mischler, J. A., et al., 2009: Carbon and hydrogen isotopic composition of methane Naish, T. R., and G. S. Wilson, 2009: Constraints on the amplitude of mid-Pliocene over the last 1000 years. Global Biogeochem. Cycles, 23, GB4024. (3.6 2.4 a) eustatic sea level fluctuations from the New Zealand shallow- M Moberg, A., 2013: Comments on Reconstruction of the extra-tropical NH mean marine sediment record. Philos. Trans. R. Soc. London A, 367, 169 187. temperature over the last millennium with a method that preserves low-fre- Naish, T. R., L. Carter, E. Wolff, D. Pollard, and R. D. Powell, 2009b: Late Pliocene quency variability . J. Clim., 25, 7991 7997. Pleistocene Antarctic climate variability at orbital and suborbital scale: Ice sheet, Moberg, A., D. M. Sonechkin, K. Holmgren, N. M. Datsenko, and W. Karlén, 2005: ocean and atmospheric interactions. In: Developments in Earth & Environmen- Highly variable Northern Hemisphere temperatures reconstructed from low- and tal Sciences [F. Florindo and S. M. (eds.)]. Elsevier, Philadelphia, PA, USA, pp. high-resolution proxy data. Nature, 433, 613 617. 465 529. Mohtadi, M., D. W. Oppo, S. Steinke, J.-B. W. Stuut, R. De Pol-Holz, D. Hebbeln, and Nakagawa, T., et al., 2008: Regulation of the monsoon climate by two different A. Lückge, 2011: Glacial to Holocene swings of the Australian-Indonesian mon- orbital rhythms and forcing mechanisms. Geology, 36, 491 494. soon. Nature Geosci., 4, 540 544. NEEM community members, 2013: Eemian interglacial reconstructed from Green- Monnin, E., et al., 2001: Atmospheric CO2 concentrations over the last glacial termi- land folded ice core. Nature, 493, 489 494. nation. Science, 291, 112 114. Neppel, L., et al., 2010: Flood frequency analysis using historical data: Accounting for Moore, J. C., E. Beaudon, S. Kang, D. Divine, E. Isaksson, V. A. Pohjola, and R. S. W. van random and systematic errors. Hydrol. Sci. J. J. Sci. Hydrol., 55, 192 208. de Wal, 2012: Statistical extraction of volcanic sulphate from nonpolar ice cores. Nesje, A., 2009: Latest Pleistocene and Holocene alpine glacier fluctuations in Scan- J. Geophys. Res., 117, D03306. dinavia. Quat. Sci. Rev., 28, 2119 2136. Morales, M. S., et al., 2012: Precipitation changes in the South American Altiplano Nesje, A., et al., 2011: The climatic significance of artefacts related to prehistoric since 1300 AD reconstructed by tree-rings. Clim. Past, 8, 653 666. reindeer hunting exposed at melting ice patches in southern Norway. Holocene, Morice, C. P., J. J. Kennedy, N. A. Rayner, and P. D. Jones, 2012: Quantifying uncertain- 22, 485 496. ties in global and regional temperature change using an ensemble of observa- Neukom, R., and J. Gergis, 2011: Southern Hemisphere high-resolution palaeocli- tional estimates: The HadCRUT4 data set. J. Geophys. Res., 117, D08101. mate records of the last 2000 years. Holocene, 22, 501 524. Moros, M., J. T. Andrews, D. D. Eberl, and E. Jansen, 2006: Holocene history of drift Neukom, R., et al., 2011: Multiproxy summer and winter surface air temperature ice in the northern North Atlantic: Evidence for different spatial and temporal field reconstructions for southern South America covering the past centuries. modes. Paleoceanography, 21, PA2017. Clim. Dyn., 37, 35 51. Moros, M., P. De Deckker, E. Jansen, K. Perner, and R. J. Telford, 2009: Holocene cli- Newby, P. E., B. N. Shuman, J. P. Donnelly, and D. MacDonald, 2011: Repeated cen- mate variability in the Southern Ocean recorded in a deep-sea sediment core off tury-scale droughts over the past 13,000 yr near the Hudson River watershed, South Australia. Quat. Sci. Rev., 28, 1932 1940. USA. Quat. Res., 75, 523 530. Morrill, C., A. J. Wagner, B. L. Otto-Bliesner, and N. Rosenbloom, 2011: Evidence for Nicault, A., S. Alleaume, S. Brewer, M. Carrer, P. Nola, and J. Guiot, 2008: Mediter- significant climate impacts in monsoonal Asia at 8.2 ka from multiple proxies ranean drought fluctuation during the last 500 years based on tree-ring data. and model simulations. J. Earth Environ., 2, 426 441. Clim. Dyn., 31, 227 245. Morrill, C., A. N. LeGrande, H. Renssen, P. Bakker, and B. L. Otto-Bliesner, 2013a: Nicolussi, K., M. Kaufmann, T. M. Melvin, J. van der Plicht, P. Schießling, and A. Thurn- Model sensitivity to North Atlantic freshwater forcing at 8.2 ka. Clim. Past, 9, er, 2009: A 9111 year long conifer tree-ring chronology for the European Alps: 955 968. a base for environmental and climatic investigations. Holocene, 19, 909 920. Morrill, C., et al., 2013b: Proxy benchmarks for intercomparison of 8.2 ka simula- Nordt, L., S. Atchley, and S. I. Dworkin, 2002: Paleosol barometer indicates extreme tions. Clim. Past, 9, 423 432. fluctuations in atmospheric CO2 across the Cretaceous-Tertiary boundary. Geol- Moucha, R., A. M. Forte, J. X. Mitrovica, D. B. Rowley, S. Quéré, N. A. Simmons, and S. ogy, 30, 703 706. P. Grand, 2008: Dynamic topography and long-term sea level variations: There Nrgaard-Pedersen, N., N. Mikkelsen, S. J. Lassen, Y. Kristoffersen, and E. Sheldon, is no such thing as a stable continental platform. Earth Planet. Sci. Lett., 271, 2007: Reduced sea ice concentrations in the Arctic Ocean during the last inter- 101 108. glacial period revealed by sediment cores off northern Greenland. Paleoceanog- Mudelsee, M., 2001: The phase relations among atmospheric CO2 content, tempera- raphy, 22, PA1218. 5 ture and global ice volume over the past 420 ka. Quat. Sci. Rev., 20, 583 589. North Greenland Ice Core Project members, 2004: High-resolution record of North- Mudelsee, M., and M. E. Raymo, 2005: Slow dynamics of the Northern Hemisphere ern Hemisphere climate extending into the last interglacial period. Nature, 431, glaciation. Paleoceanography, 20, PA4022. 147 151. Mudelsee, M., J. Fohlmeister, and D. Scholz, 2012: Effects of dating errors on non- Novenko, E. Y., M. Seifert-Eulen, T. Boettger, and F. W. Junge, 2008: Eemian and early parametric trend analyses of speleothem time series. Clim. Past, 8, 1637 1648. Weichselian vegetation and climate history in Central Europe: a case study from Mudelsee, M., M. Börngen, G. Tetzlaff, and U. Grünewald, 2003: No upward trends the Klinge section (Lusatia, eastern Germany). Rev. Palaeobot. Palynol., 151, in the occurrence of extreme floods in central Europe. Nature, 425, 166 169. 72 78. Mulitza, S., et al., 2008: Sahel megadroughts triggered by glacial slowdowns of O Donnell, R., N. Lewis, S. McIntyre, and J. Condon, 2010: Improved methods for Atlantic meridional overturning. Paleoceanography, 23, PA4206. PCA-based reconstructions: case study using the Steig et al. (2009) Antarctic Müller, J., A. Wagner, K. Fahl, R. Stein, M. Prange, and G. Lohmann, 2011: Towards temperature reconstruction. J. Clim., 24, 2099 2115. quantitative sea ice reconstructions in the northern North Atlantic: A combined Oerlemans, J., 1980: Model experiments on the 100,000 yr glacial cycle. Nature, biomarker and numerical modelling approach. Earth Planet. Sci. Lett., 306, 287, 430 432. 137 148. Ohba, M., H. Shiogama, T. Yokohata, and M. Watanabe, 2013: Impact of strong tropi- Müller, R. D., M. Sdrolias, C. Gaina, B. Steinberger, and C. Heine, 2008: Long-term sea cal volcanic eruptions on ENSO simulated in a coupled GCM. J. Clim., 26, 5169- level fluctuations driven by ocean basin dynamics. Science, 319, 1357 1362. 5182. Müller, U., 2001: Die Vegetations-und Klimaentwicklung im jüngeren Quartär Okazaki, Y., et al., 2010: Deepwater formation in the north Pacific during the Last anhand ausgewählter Profile aus dem südwestdeutschen Alpenvorland. Tübin- Glacial Termination. Science, 329, 200 204. ger Geowissenschaftliche Arbeiten D7, Geographisches Institut der Universität Okumura, Y. M., C. Deser, A. Hu, A. Timmermann, and S. P. Xie, 2009: North Pacific Tübingen, 118 pp. climate response to freshwater forcing in the subarctic North Atlantic: Oceanic Mulvaney, R., et al., 2012: Recent Antarctic Peninsula warming relative to Holocene and atmospheric pathways. J. Clim., 22, 1424 1445. climate and ice-shelf history. Nature, 489, 141 144. Olsen, J., N. J. Anderson, and M. F. Knudsen, 2012: Variability of the North Atlantic Oscillation over the past 5,200 years. Nature Geosci., 5, 808 812. 448 Information from Paleoclimate Archives Chapter 5 Oman, L., A. Robock, G. Stenchikov, G. A. Schmidt, and R. Ruedy, 2005: Climatic Passchier, S., 2011: Linkages between East Antarctic ice sheet extent and Southern response to high-latitude volcanic eruptions. J. Geophys. Res., 110, D13103. Ocean temperatures based on a Pliocene high-resolution record of ice-rafted Orsi, A. J., B. D. Cornuelle, and J. P. Severinghaus, 2012: Little Ice Age cold interval in debris off Prydz Bay, East Antarctica. Paleoceanography, 26, PA4204. West Antarctica: Evidence from borehole temperature at the West Antarctic Ice Patadia, F., E.-S. Yang, and S. A. Christopher, 2009: Does dust change the clear sky top Sheet (WAIS) Divide. Geophys. Res. Lett., 39, L09710. of atmosphere shortwave flux over high surface reflectance regions? Geophys. Osborn, T., and K. Briffa, 2007: Response to comment on The spatial extent of 20th- Res. Lett., 36, L15825. century warmth in the context of the past 1200 years . Science, 316, 1844. Pausata, F. S. R., C. Li, J. J. Wettstein, K. H. Nisancioglu, and D. S. Battisti, 2009: Chang- Osborn, T., S. Raper, and K. Briffa, 2006: Simulated climate change during the last es in atmospheric variability in a glacial climate and the impacts on proxy data: 1,000 years: Comparing the ECHO-G general circulation model with the MAGICC A model intercomparison. Clim. Past, 5, 489 502. simple climate model. Clim. Dyn., 27, 185 197. Pausata, F. S. R., C. Li, J. J. Wettstein, M. Kageyama, and K. H. Nisancioglu, 2011: The Oswald, W. W., and D. R. Foster, 2011: A record of late-Holocene environmental key role of topography in altering North Atlantic atmospheric circulation during change from southern New England, USA. Quat. Res., 76, 314 318. the last glacial period. Clim. Past, 7, 1089 1101. Ottera, O. H., M. Bentsen, H. Drange, and L. L. Suo, 2010: External forcing as a metro- Pearson, P. N., G. L. Foster, and B. S. Wade, 2009: Atmospheric carbon dioxide through nome for Atlantic multidecadal variability. Nature Geosci., 3, 688 694. the Eocene-Oligocene climate transition. Nature, 461, 1110 1113. Otto-Bliesner, B., et al., 2009: A comparison of PMIP2 model simulations and the Pedro, J., et al., 2011: The last deglaciation: timing the bipolar seesaw. Clim. Past, MARGO proxy reconstruction for tropical sea surface temperatures at Last Gla- 7, 671 683. cial Maximum. Clim. Dyn., 32, 799 815. Pedro, J. B., S. O. Rasmussen, and T. D. van Ommen, 2012: Tightened constraints on Otto-Bliesner, B. L., and E. C. Brady, 2010: The sensitivity of the climate response to the time-lag between Antarctic temperature and CO2 during the last deglacia- the magnitude and location of freshwater forcing: Last glacial maximum experi- tion. Clim. Past, 8, 1213 1221. ments. Quat. Sci. Rev., 29, 56 73. Pépin, L., D. Raynaud, J.-M. Barnola, and M. F. Loutre, 2001: Hemispheric roles of cli- Otto-Bliesner, B. L., N. Rosenbloom, E. J. Stone, N. McKay, D. Lunt, E. C. Brady, and J. mate forcings during glacial-interglacial transitions as deduced from the Vostok T. Overpeck, 2013: How warm was the last interglacial? New model-data com- record and LLN-2D model experiments. J. Geophys. Res., 106, 31885 31892. parisons. Philos. Trans. R. Soc. London A, 371, 20130097, published online 16 Peschke, P., C. Hannss, and S. Klotz, 2000: Neuere Ergebnisse aus der Banquette September 2013. von Barraux (Grésivaudan, französische Nordalpen) zur spätpleistozänen Vege- Otto-Bliesner, B. L., et al., 2007: Last Glacial Maximum ocean thermohaline circula- tationsentwicklung mit Beiträgen zur Reliefgenese und Klimarekonstruktion. tion: PMIP2 model intercomparisons and data constraints. Geophys. Res. Lett., Eiszeitalter Gegenwart, 50, 1 24. 34, L12706. Peterson, L. C., and G. H. Haug, 2006: Variability in the mean latitude of the Atlantic Otto, J., T. Raddatz, M. Claussen, V. Brovkin, and V. Gayler, 2009: Separation of atmo- Intertropical Convergence Zone as recorded by riverine input of sediments to sphere-ocean-vegetation feedbacks and synergies for mid-Holocene climate. the Cariaco Basin (Venezuela). Palaeogeogr. Palaeoclimatol. Palaeoecol., 234, Geophys. Res. Lett., 36, L09701. 97 113. Overpeck, J., B. Otto-Bliesner, G. Miller, D. Muhs, R. Alley, and J. Kiehl, 2006: Paleocli- Petit, J. R., and B. Delmonte, 2009: A model for large glacial interglacial climate- matic evidence for future ice-sheet instability and rapid sea level rise. Science, induced changes in dust and sea salt concentrations in deep ice cores (central 311, 1747 1750. Antarctica): palaeoclimatic implications and prospects for refining ice core chro- Pagani, M., K. H. Freeman, and M. A. Arthur, 1999a: Late Miocene Atmospheric CO2 nologies. Tellus B, 61B, 768 790. concentrations and the expansion of C4 grasses. Science, 285, 876 879. Petit, J. R., et al., 1999: Climate and atmospheric history of the past 420,000 years Pagani, M., M. A. Arthur, and K. H. Freeman, 1999b: Miocene evolution of atmo- from the Vostok ice core, Antarctica. Nature, 399, 429 436. spheric carbon dioxide. Paleoceanography, 14, 273 292. Phipps, S., et al., 2013: Palaeoclimate data-model comparison and the role of climate Pagani, M., D. Lemarchand, A. Spivack, and J. Gaillardet, 2005a: A critical evaluation forcings over the past 1500 years. J. Clim., 26, 6915-6936. of the boron isotope-pH proxy: The accuracy of ancient ocean pH estimates. Piccarreta, M., M. Caldara, D. Capolongo, and F. Boenzi, 2011: Holocene geomorphic Geochim. Cosmochim. Acta, 69, 953 961. activity related to climatic change and human impact in Basilicata, Southern Pagani, M., Z. H. Liu, J. LaRiviere, and A. C. Ravelo, 2010: High Earth-system climate Italy. Geomorphology, 128, 137 147. sensitivity determined from Pliocene carbon dioxide concentrations. Nature Pinto, J. G., and C. C. Raible, 2012: Past and recent changes in the North Atlantic Geosci., 3, 27 30. Oscillation. WIREs Clim. Change, 3, 79 90. Pagani, M., J. C. Zachos, K. H. Freeman, B. Tipple, and S. Bohaty, 2005b: Marked Piotrowski, A. M., S. L. Goldstein, S. R. Hemming, and R. G. Fairbanks, 2005: Temporal decline in atmospheric carbon dioxide concentrations during the Paleogene. Sci- relationships of carbon cycling and ocean circulation at glacial boundaries. Sci- ence, 309, 600 603. ence, 307, 1933 1938. Pagani, M., et al., 2011: The role of carbon dioxide during the onset of Antarctic Plummer, C. T., et al., 2012: An independently dated 2000 yr volcanic record from glaciation. Science, 334, 1261 1264. Law Dome, East Antarctica, including a new perspective on the dating of the 5 PAGES 2k Consortium, 2013: Continental-scale temperature variability during the 1450s CE eruption of Kuwae, Vanuatu. Clim. Past, 8, 1929 1940. last two millennia. Nature Geosci., 6, 339 346. Pollack, H. N., and J. E. Smerdon, 2004: Borehole climate reconstructions: Spatial Pahnke, K., R. Zahn, H. Elderfield, and M. Schulz, 2003: 340,000-year centennial- structure and hemispheric averages. J. Geophys. Res., 109, D11106. scale marine record of Southern Hemisphere climatic oscillation. Science, 301, Pollard, D., and R. M. DeConto, 2009: Modelling West Antarctic ice sheet growth and 948 952. collapse through the past five million years. Nature, 458, 329 332. Pahnke, K., J. P. Sachs, L. Keigwin, A. Timmermann, and S. P. Xie, 2007: Eastern tropi- Polyak, L., et al., 2010: History of sea ice in the Arctic. Quat. Sci. Rev., 29, 1757 1778. cal Pacific hydrologic changes during the past 27,000 years from D/H ratios in Polyakov, I. V., et al., 2010: Arctic Ocean warming contributes to reduced polar ice alkenones. Paleoceanography, 22, PA4214. cap. J. Phys. Oceanogr., 40, 2743 2756. Pak, D. K., D. W. Lea, and J. P. Kennett, 2012: Millennial scale changes in sea surface Pongratz, J., C. Reick, T. Raddatz, and M. Claussen, 2008: A reconstruction of global temperature and ocean circulation in the northeast Pacific, 10 60 kyr BP. Pale- agricultural areas and land cover for the last millennium. Global Biogeochem. oceanography, 27, PA1212. Cycles, 22, GB3018. PALAEOSENS Project Members, 2012: Making sense of palaeoclimate sensitivity. Pongratz, J., T. Raddatz, C. H. Reick, M. Esch, and M. Claussen, 2009: Radiative forc- Nature, 491, 683 691. ing from anthropogenic land cover change since A.D. 800. Geophys. Res. Lett., Palastanga, V., G. van der Schrier, S. Weber, T. Kleinen, K. Briffa, and T. Osborn, 2011: 36, L02709. Atmosphere and ocean dynamics: contributors to the European Little Ice Age? Ponton, C., L. Giosan, T. I. Eglinton, D. Q. Fuller, J. E. Johnson, P. Kumar, and T. S. Collett, Clim. Dyn., 36, 973 987. 2012: Holocene aridification of India. Geophys. Res. Lett., 39, L03704. Panchuk, K., A. Ridgwell, and L. R. Kump, 2008: Sedimentary response to Paleocene- Porter, T. J., and M. F. J. Pisaric, 2011: Temperature-growth divergence in white spruce Eocene Thermal Maximum carbon release: a model-data comparison. Geology, forests of Old Crow Flats, Yukon Territory, and adjacent regions of northwestern 36, 315 318. North America. Global Change Biol., 17, 3418 3430. Parrenin, F., et al., 2013: Synchronous change of atmospheric CO2 and Antarctic tem- perature during the last deglacial warming. Science, 339, 1060 1063. 449 Chapter 5 Information from Paleoclimate Archives Prieto, M. d. R., and R. García Herrera, 2009: Documentary sources from South Roeckner, E., L. Bengtsson, J. Feichter, J. Lelieveld, and H. Rodhe, 1999: Transient America: Potential for climate reconstruction. Palaeogeogr. Palaeoclimatol. Pal- climate change simulations with a coupled atmosphere ocean GCM including aeoecol., 281, 196 209. the tropospheric sulfur cycle. J. Clim., 12, 3004 3032. Prokopenko, A., L. Hinnov, D. Williams, and M. Kuzmin, 2006: Orbital forcing of conti- Rohling, E. J., and H. Pälike, 2005: Centennial-scale climate cooling with a sudden nental climate during the Pleistocene: A complete astronomically tuned climatic cold event around 8,200 years ago. Nature, 434, 975 979. record from Lake Baikal, SE Siberia. Quat. Sci. Rev., 25, 3431 3457. Rohling, E. J., M. Medina-Elizalde, J. G. Shepherd, M. Siddall, and J. D. Stanford, 2012: Prokopenko, A. A., D. F. Williams, M. I. Kuzmin, E. B. Karabanov, G. K. Khursevich, and Sea surface and high-latitude temperature sensitivity to radiative forcing of cli- J. A. Peck, 2002: Muted climate variations in continental Siberia during the mid- mate over several glacial cycles. J. Clim., 25, 1635 1656. Pleistocene epoch. Nature, 418, 65 68. Rohling, E. J., K. Grant, C. Hemleben, M. Siddall, B. A. A. Hoogakker, M. Bolshaw, and Putnam, A. E., et al., 2010: Glacier advance in southern middle-latitudes during the M. Kucera, 2008a: High rates of sea level rise during the last interglacial period. Antarctic Cold Reversal. Nature Geosci., 3, 700 704. Nature Geosci., 1, 38 42. Quiquet, A., C. Ritz, H. J. Punge, and D. Salas y Mélia, 2013: Greenland ice sheet con- Rohling, E. J., K. Grant, M. Bolshaw, A. P. Roberts, M. Siddall, C. Hemleben, and M. tribution to sea level rise during the last interglacial period: A modelling study Kucera, 2009: Antarctic temperature and global sea level closely coupled over driven and constrained by ice core data. Clim. Past, 8, 353 366. the past five glacial cycles. Nature Geosci., 2, 500 504. Rahmstorf, S., et al., 2005: Thermohaline circulation hysteresis: A model intercom- Rohling, E. J., K. Braun, K. Grant, M. Kucera, A. P. Roberts, M. Siddall, and G. Trommer, parison. Geophys. Res. Lett., 32, L23605. 2010: Comparison between Holocene and Marine Isotope Stage-11 sea level Ramankutty, N., and J. A. Foley, 1999: Estimating historical changes in global land histories. Earth Planet. Sci. Lett., 291, 97 105. cover: Croplands from 1700 to 1992. Global Biogeochem. Cycles, 13, 997 1027. Rohling, E. J., et al., 2008b: New constraints on the timing of sea level fluctuations Rasmussen, S. O., et al., 2006: A new Greenland ice core chronology for the last during early to middle marine isotope stage 3. Paleoceanography, 23, PA3219. glacial termination. J. Geophys. Res., 111, D06102. Rojas, M., 2013: Sensitivity of Southern Hemisphere circulation to LGM and 4 × CO2 Raymo, M. E., and J. X. Mitrovica, 2012: Collapse of polar ice sheets during the stage climates. Geophys. Res. Lett., 40, 965 970. 11 interglacial. Nature, 483, 453 456. Rousseau, D.-D., C. Hatté, D. Duzer, P. Schevin, G. Kukla, and J. Guiot, 2007: Estimates Raymo, M. E., J. X. Mitrovica, M. J. O Leary, R. M. DeConto, and P. J. Hearty, 2011: of temperature and precipitation variations during the Eemian interglacial: New Departures from eustasy in Pliocene sea level records. Nature Geosci., 4, 328 data from the grande pile record (GP XXI). In: Developments in Quaternary Sci- 332. ences [F. Sirocko, M. Claussen, M. F. Sánchez Goni, and T. Litt (eds.)]. Elsevier, Renssen, H., H. Seppä, X. Crosta, H. Goosse, and D. M. Roche, 2012: Global character- Philadelphia, PA, USA, pp. 231 238. ization of the Holocene Thermal Maximum. Quat. Sci. Rev., 48, 7 19. Routson, C. C., C. A. Woodhouse, and J. T. Overpeck, 2011: Second century mega- Retallack, G. J., 2009a: Refining a pedogenic-carbonate CO2 paleobarometer to drought in the Rio Grande headwaters, Colorado: How unusual was medieval quantify a middle Miocene greenhouse spike. Palaeogeogr. Palaeoclimatol. Pal- drought? Geophys. Res. Lett., 38, L22703. aeoecol. 281, 57 65. Royer, D. L., 2003: Estimating latest Cretaceous and Tertiary atmospheric CO2 from Retallack, G. J, 2009b: Greenhouse crises of the past 300 million years. Geol. Soc. stomatal indices. In: Causes and Consequences of Globally Warm Climates in Am. Bull., 121, 1441 1455. the Early Paleogen [S. L. Wing, P. D. Gingerich, B. Schmitz and E. Thomas (eds.)]. Reuter, J., L. Stott, D. Khider, A. Sinha, H. Cheng, and R. L. Edwards, 2009: A new Geological Society of America Special Paper 369, pp.79 93. perspective on the hydroclimate variability in northern South America during the Royer, D. L., R. A. Berner, and D. J. Beerling, 2001a: Phanerozoic atmospheric CO2 Little Ice Age. Geophys. Res. Lett., 36, L21706. change: evaluating geochemical and paleobiological approaches. Earth Sci. Rev., Ridgwell, A., and D. N. Schmidt, 2010: Past constraints on the vulnerability of marine 54, 349 392. calcifiers to massive carbon dioxide release. Nature Geosci., 3, 196 200. Royer, D. L., S. L. Wing, D. J. Beerling, D. W. Jolley, P. L. Koch, L. J. Hickey, and R. Ridley, J., J. Gregory, P. Huybrechts, and J. Lowe, 2010: Thresholds for irreversible A. Berner, 2001b: Paleobotanical evidence for near present-day levels of atmo- decline of the Greenland ice sheet. Clim. Dyn., 35, 1049 1057. spheric CO2 during part of the Tertiary. Science, 292, 2310 2313. Rimbu, N., G. Lohmann, J. H. Kim, H. W. Arz, and R. Schneider, 2003: Arctic/North Rupper, S., G. Roe, and A. Gillespie, 2009: Spatial patterns of Holocene glacier Atlantic Oscillation signature in Holocene sea surface temperature trends as advance and retreat in Central Asia. Quat. Res., 72, 337 346. obtained from alkenone data. Geophys. Res. Lett., 30, 4. Russell, J., H. Eggermont, R. Taylor, and D. Verschuren, 2009: Paleolimnological Risebrobakken, B., T. Dokken, L. H. Smedsrud, C. Andersson, E. Jansen, M. Moros, and records of recent glacier recession in the Rwenzori Mountains, Uganda-D. R. E. V. Ivanova, 2011: Early Holocene temperature variability in the Nordic Seas: Congo. J. Paleolimnol., 41, 253 271. The role of oceanic heat advection versus changes in orbital forcing. Paleocean- Ruth, U., et al., 2007: Ice core evidence for a very tight link between North Atlantic ography, 26, PA4206. and east Asian glacial climate. Geophys. Res. Lett., 34, L03706. Ritz, S., T. Stocker, and F. Joos, 2011: A coupled dynamical ocean-energy balance Sachs, J. P., D. Sachse, R. H. Smittenberg, Z. H. Zhang, D. S. Battisti, and S. Golubic, 5 atmosphere model for paleoclimate studies. J. Clim., 24, 349 375. 2009: Southward movement of the Pacific intertropical convergence zone AD Riviere, G., A. Laîné, G. Lapeyre, D. Salas-Melia, and M. Kageyama, 2010: Links 1400 1850. Nature Geosci., 2, 519 525. between Rossby wave breaking and the North Atlantic Oscillation-Arctic Oscil- Saenger, C., A. Cohen, D. Oppo, R. Halley, and J. Carilli, 2009: Surface-temperature lation in present-day and Last Glacial Maximum climate simulations. J. Clim., trends and variability in the low-latitude North Atlantic since 1552. Nature 23, 2987 3008. Geosci., 2, 492 495. Roberts, A. P., E. J. Rohling, K. M. Grant, J. C. Larrasoana, and Q. Liu, 2011: Atmo- Saenko, O. A., A. Schmittner, and A. J. Weaver, 2004: The Atlantic-Pacific Seesaw. J. spheric dust variability from Arabia and China over the last 500,000 years. Quat. Clim., 17, 2033 2038. Sci. Rev., 30, 3537 3541. Salisbury, E. J., 1928: On the causes and ecological significance of stomatal fre- Roberts, N. L., A. M. Piotrowski, J. F. McManus, and L. D. Keigwin, 2010: Synchronous quency, with special reference to the woodland flora. Philos. Trans. R. Soc. B, deglacial overturning and water mass source changes. Science, 327, 75 78. 216, 1 65. Robertson, A., et al., 2001: Hypothesized climate forcing time series for the last 500 Salzer, M., and K. Kipfmueller, 2005: Reconstructed temperature and precipitation on years. J. Geophys. Res., 106, 14783 14803. a millennial timescale from tree-rings in the Southern Colorado Plateau, USA. Robinson, A., R. Calov, and A. Ganopolski, 2011: Greenland ice sheet model param- Clim. Change, 70, 465 487. eters constrained using simulations of the Eemian Interglacial. Clim. Past, 7, Salzmann, U., A. M. Haywood, D. J. Lunt, P. J. Valdes, and D. J. Hill, 2008: A new global 381 396. biome reconstruction and data-model comparison for the Middle Pliocene. Roche, D. M., X. Crosta, and H. Renssen, 2012: Evaluating Southern Ocean sea-ice for Global Ecol. Biogeogr., 17, 432 447. the Last Glacial Maximum and pre-industrial climates: PMIP-2 models and data Sarnthein, M., U. Pflaumann, and M. Weinelt, 2003a: Past extent of sea ice in the evidence. Quat. Sci. Rev., 56, 99 106. northern North Atlantic inferred from foraminiferal paleotemperature estimates. Roe, G. H., and R. S. Lindzen, 2001: The mutual interaction between continental-scale Paleoceanography, 18, 1047. ice sheets and atmospheric stationary waves. J. Clim., 14, 1450 1465. 450 Information from Paleoclimate Archives Chapter 5 Sarnthein, M., S. Van Kreveld, H. Erlenkeuser, P. Grootes, M. Kucera, U. Pflaumann, Serreze, M. C., and R. G. Barry, 2011: Processes and impacts of Arctic amplification: and M. Schulz, 2003b: Centennial-to-millennial-scale periodicities of Holocene A research synthesis. Global Planet. Change, 77, 85 96. climate and sediment injections off the western Barents shelf, 75°N. Boreas, Serreze, M. C., A. P. Barrett, J. C. Stroeve, D. N. Kindig, and M. M. Holland, 2009: The 32, 447 461. emergence of surface-based Arctic amplification. Cryosphere, 3, 11 19. Schaefer, J. M., et al., 2009: High-frequency Holocene glacier fluctuations in New Servonnat, J., P. Yiou, M. Khodri, D. Swingedouw, and S. Denvil, 2010: Influence of Zealand differ from the northern signature. Science, 324, 622 625. solar variability, CO2 and orbital forcing between 1000 and 1850 AD in the Scherer, D., M. Gude, M. Gempeler, and E. Parlow, 1998: Atmospheric and hydrologi- IPSLCM4 model. Clim. Past, 6, 445 460. cal boundary conditions for slushflow initiation due to snowmelt. Ann. Glaciol., Shackleton, N. J., 2000: The 100,000 year ice-age cycle identified and found to lag 26, 377 380. temperature, carbon dioxide, and orbital eccentricity. Science, 289, 1897 1902. Schilt, A., M. Baumgartner, T. Blunier, J. Schwander, R. Spahni, H. Fischer, and T. F. Shackleton, N. J., M. F. Sánchez-Goni, D. Pailler, and Y. Lancelot, 2003: Marine Isotope Stocker, 2010: Glacial interglacial and millennial-scale variations in the atmo- Substage 5e and the Eemian interglacial. Global Planet. Change, 36, 151 155. spheric nitrous oxide concentration during the last 800,000 years. Quat. Sci. Shaffer, G., S. M. Olsen, and C. J. Bjerrum, 2004: Ocean subsurface warming as a Rev., 29, 182 192. mechanism for coupling Dansgaard-Oeschger climate cycles and ice-rafting Schmidt, A., T. Thordarson, L. D. Oman, A. Robock, and S. Sell, 2012a: Climatic impact events. Geophys. Res. Lett., 31, L24202. of the long-lasting 1783 Laki eruption: Inapplicability of mass-independent Shakun, J. D., et al., 2012: Global warming preceded by increasing carbon dioxide sulfur isotopic composition measurements. J. Geophys. Res., 117, D23116. concentrations during the last deglaciation. Nature, 484, 49 54. Schmidt, G. A., et al., 2011: Climate forcing reconstructions for use in PMIP simula- Shanahan, T. M., et al., 2009: Atlantic forcing of persistent drought in west Africa. tions of the last millennium (v1.0). Geoscientif. Model Dev., 4, 33 45. Science, 324, 377 380. Schmidt, G. A., et al., 2012b: Climate forcing reconstructions for use in PMIP simula- Shapiro, A. I., W. Schmutz, E. Rozanov, M. Schoell, M. Haberreiter, A. V. Shapiro, and tions of the Last Millennium (v1.1). Geoscientif. Model Dev., 5, 185 191. S. Nyeki, 2011: A new approach to the long-term reconstruction of the solar Schmittner, A., E. D. Galbraith, S. W. Hostetler, T. F. Pedersen, and R. Zhang, 2007: irradiance leads to large historical solar forcing. Astron. Astrophys., 529, 1 8. Large fluctuations of dissolved oxygen in the Indian and Pacific oceans during Sheffer, N. A., M. Rico, Y. Enzel, G. Benito, and T. Grodek, 2008: The Palaeoflood record Dansgaard-Oeschger oscillations caused by variations of North Atlantic Deep of the Gardon River, France: A comparison with the extreme 2002 flood event. Water subduction. Paleoceanography, 22, PA3207. Geomorphology, 98, 71 83. Schmittner, A., et al., 2011: Climate sensitivity estimated from temperature recon- Shevenell, A. E., A. E. Ingalls, E. W. Domack, and C. Kelly, 2011: Holocene Southern structions of the Last Glacial Maximum. Science, 334, 1385 1388. Ocean surface temperature variability west of the Antarctic Peninsula. Nature, Schneider, B., G. Leduc, and W. Park, 2010: Disentangling seasonal signals in Holo- 470, 250 254. cene climate trends by satellite-model-proxy integration. Paleoceanography, 25, Shi, F., et al., 2013: Northern hemisphere temperature reconstruction during the last PA4217. millennium using multiple annual proxies. Clim. Res., 56, 231 244. Schneider, D. P., C. M. Ammann, B. L. Otto-Bliesner, and D. S. Kaufman, 2009: Climate Shin, S. I., P. D. Sardeshmukh, R. S. Webb, R. J. Oglesby, and J. J. Barsugli, 2006: Under- response to large, high-latitude and low-latitude volcanic eruptions in the Com- standing the mid-Holocene climate. J. Clim., 19, 2801 2817. munity Climate System Model. J. Geophys. Res., 114, D15101. Shuman, B., P. Pribyl, T. A. Minckley, and J. J. Shinker, 2010: Rapid hydrologic shifts Schneider Mor, A., R. Yam, C. Bianchi, M. Kunz-Pirrung, R. Gersonde, and A. Shemesh, and prolonged droughts in Rocky Mountain headwaters during the Holocene. 2012: Variable sequence of events during the past seven terminations in two Geophys. Res. Lett., 37, L06701. deep-sea cores from the Southern Ocean. Quat. Res., 77, 317 325. Sicre, M. A., et al., 2008: A 4500-year reconstruction of sea surface temperature vari- Schneider von Deimling, T., H. Held, A. Ganopolski, and S. Rahmstorf, 2006: Climate ability at decadal time-scales off North Iceland. Quat. Sci. Rev., 27, 2041 2047. sensitivity estimated from ensemble simulations of glacial climate. Clim. Dyn., Siddall, M., E. J. Rohling, W. G. Thompson, and C. Waelbroeck, 2008: Marine isotope 27, 149 163. stage 3 sea level fluctuations: Data synthesis and new outlook. Rev. Geophys., Schoof, C., 2012: Marine ice sheet stability. J. Fluid Mech., 698, 62 72. 46, RG4003. Schrijver, C. J., W. C. Livingston, T. N. Woods, and R. A. Mewaldt, 2011: The minimal Siddall, M., E. J. Rohling, T. Blunier, and R. Spahni, 2010: Patterns of millennial vari- solar activity in 2008 2009 and its implications for long-term climate modeling. ability over the last 500 ka. Clim. Past, 6, 295 303. Geophys. Res. Lett., 38, L06701. Siddall, M., T. F. Stocker, T. Blunier, R. Spahni, J. F. McManus, and E. Bard, 2006: Using a Schulz, H., U. von Rad, and H. Erlenkeuser, 1998: Correlation between Arabian Sea maximum simplicity paleoclimate model to simulate millennial variability during and Greenland climate oscillations of the past 110,000 years. Nature, 393, the last four glacial periods. Quat. Sci. Rev., 25, 3185 3197. 54 57. Siddall, M., E. J. Rohling, A. Almogi-Labin, C. Hemleben, D. Meischner, I. Schmel- Schurer, A., G. C. Hegerl, M. E. Mann, S. F. B. Tett, and S. J. Phipps, 2013: Separating zer, and D. A. Smeed, 2003: Sea level fluctuations during the last glacial cycle. forced from chaotic climate variability over the past millennium. J. Clim., 26, Nature, 423, 853 858. 6954-6973. Siegenthaler, U., et al., 2005: Stable carbon cycle-climate relationship during the late 5 Schurgers, G., U. Mikolajewicz, M. Gröger, E. Maier-Reimer, M. Vizcaino, and A. Wing- Pleistocene. Science, 310, 1313 1317. uth, 2007: The effect of land surface changes on Eemian climate. Clim. Dyn., 29, Sierro, F. J., et al., 2009: Phase relationship between sea level and abrupt climate 357 373. change. Quat. Sci. Rev., 28, 2867 2881. Screen, J. A., and I. Simmonds, 2010: The central role of diminishing sea ice in recent Sigl, M., et al., 2013: A new bipolar ice core record of volcanism from WAIS Divide Arctic temperature amplification. Nature, 464, 1334 1337. and NEEM and implications for climate forcing of the last 2000 years. J. Geophys. Scroxton, N., S. G. Bonham, R. E. M. Rickaby, S. H. F. Lawrence, M. Hermoso, and A. Res., 118, 1151-1169. M. Haywood, 2011: Persistent El Nino-Southern Oscillation variation during the Sime, L. C., E. W. Wolff, K. I. C. Oliver, and J. C. Tindall, 2009: Evidence for warmer Pliocene Epoch. Paleoceanography, 26, PA2215. interglacials in East Antarctic ice cores. Nature, 462, 342 345. Seager, R., N. Graham, C. Herweijer, A. Gordon, Y. Kushnir, and E. Cook, 2007: Blue- Sime, L. C., C. Risib, J. C. Tindall, J. Sjolted, E. W. Wolff, V. Masson-Delmotte, and prints for Medieval hydroclimate. Quat. Sci. Rev., 26, 2322 2336. E. Caprona, 2013: Warm climate isotopic simulations: What do we learn about Seki, O., G. L. Foster, D. N. Schmidt, A. Mackensen, K. Kawamura, and R. D. Pancost, interglacial signals in Greenland ice cores? Quat. Sci. Rev., 67, 59 80. 2010: Alkenone and boron-based Pliocene pCO2 records. Earth Planet. Sci. Lett., Simms, A. R., K. T. Milliken, J. B. Anderson, and J. S. Wellner, 2011: The marine record 292, 201 211. of deglaciation of the South Shetland Islands, Antarctica since the Last Glacial Semenov, V. A., M. Latif, D. Dommenget, N. S. Keenlyside, A. Strehz, T. Martin, and Maximum. Quat. Sci. Rev., 30, 1583 1601. W. Park, 2010: The Impact of North Atlantic-Arctic multidecadal variability on Singarayer, J. S., and P. J. Valdes, 2010: High-latitude climate sensitivity to ice-sheet northern hemisphere surface air temperature. J. Clim., 23, 5668 5677. forcing over the last 120 kyr. Quat. Sci. Rev., 29, 43 55. Seong, Y., L. Owen, C. Yi, and R. Finkel, 2009: Quaternary glaciation of Muztag Sinha, A., and L. D. Stott, 1994: New atmospheric pCO2 estimates from palesols Ata and Kongur Shan: Evidence for glacier response to rapid climate changes during the late Paleocene/early Eocene global warming interval. Global Planet. throughout the Late Glacial and Holocene in westernmost Tibet. Geol. Soc. Am. Change, 9, 297 307. Bull., 129, 348 365. 451 Chapter 5 Information from Paleoclimate Archives Sinha, A., et al., 2007: A 900-year (600 to 1500 A.D.) record of the Indian summer Steinhilber, F., J. Beer, and C. Fröhlich, 2009: Total solar irradiance during the Holo- monsoon precipitation from the core monsoon zone of India. Geophys. Res. cene. Geophys. Res. Lett., 36, L19704. Lett., 34, L16707. Steinhilber, F., et al., 2012: 9,400 years of cosmic radiation and solar activity from ice Sivan, D., K. Lambeck, R. Toueg, A. Raban, Y. Porath, and B. Shirman, 2004: Ancient cores and tree rings. Proc. Natl. Acad. Sci. U.S.A., 109, 5967-5971. coastal wells of Caesarea Maritima, Israel, an indicator for relative sea level Steinke, S., M. Kienast, J. Groeneveld, L.-C. Lin, M.-T. Chen, and R. Rendle-Bühring, changes during the last 2000 years. Earth Planet. Sci. Lett., 222, 315 330. 2008: Proxy dependence of the temporal pattern of deglacial warming in the Sluijs, A., et al., 2007: Environmental precursors to rapid light carbon injection at the tropical South China Sea: toward resolving seasonality. Quat. Sci. Rev., 27, Palaeocene/Eocene boundary. Nature, 450, 1218 1221. 688 700. Smerdon, J. E., 2012: Climate models as a test bed for climate reconstruction meth- Steinman, B. A., M. B. Abbott, M. E. Mann, N. D. Stansell, and B. P. Finney, 2013: ods: pseudoproxy experiments. Rev. Clim. Change, 3, 63 77. 1,500  year quantitative reconstruction of winter precipitation in the Pacific Smerdon, J. E., A. Kaplan, D. Chang, and M. N. Evans, 2010: A pseudoproxy evalua- Northwest. Proc. Natl. Acad. Sci. U.S.A., 109, 11619-11623. tion of the CCA and RegEM methods for reconstructing climate fields of the last Stendel, M., I. Mogensen, and J. Christensen, 2006: Influence of various forcings on millennium. J. Clim., 23, 4856 4880. global climate in historical times using a coupled atmosphere ocean general Smerdon, J. E., A. Kaplan, E. Zorita, J. F. González-Rouco, and M. N. Evans, 2011: circulation model. Clim. Dyn., 26, 1 15. Spatial performance of four climate field reconstruction methods targeting the Stenni, B., et al., 2010: The deuterium excess records of EPICA Dome C and Dronning Common Era. Geophys. Res. Lett., 38, L11705. Maud Land ice cores (East Antarctica). Quat. Sci. Rev., 29, 146 159. Smith, J. A., et al., 2011: Deglacial history of the West Antarctic Ice Sheet in the west- Stenni, B., et al., 2011: Expression of the bipolar see-saw in Antarctic climate records ern Amundsen Sea Embayment. Quat. Sci. Rev., 30, 488 505. during the last deglaciation. Nature Geosci., 4, 46 49. Smith, R., and J. Gregory, 2012: The last glacial cycle: Transient simulations with an Steph, S., et al., 2010: Early Pliocene increase in thermohaline overturning: A pre- AOGCM. Clim. Dyn., 38, 1545 1559. condition for the development of the modern equatorial Pacific cold tongue. Smith, R. Y., D. R. Greenwood, and J. F. Basinger, 2010: Estimating paleoatmospheric Paleoceanography, 25, PA2202. pCO2 during the Early Eocene Climatic Optimum from stomatal frequency of Stewart, M. M., I. Larocque-Tobler, and M. Grosjean, 2011: Quantitative inter-annual Ginkgo, Okanagan Highlands, British Columbia, Canada. Palaeogeogr. Palaeocli- and decadal June July August temperature variability ca. 570 BC to AD 120 matol. Palaeoecol. 293, 120 131. (Iron Age Roman Period) reconstructed from the varved sediments of Lake Sil- Smithers, S. G., and C. D. Woodroffe, 2001: Coral microatolls and 20th century sea vaplana, Switzerland. J. Quat. Sci., 26, 491 501. level in the eastern Indian Ocean. Earth Planet. Sci. Lett., 191, 173 184. Stirling, C., T. Esat, K. Lambeck, and M. McCulloch, 1998: Timing and duration of Soden, B. J., I. M. Held, R. Colman, K. M. Shell, J. T. Kiehl, and C. A. Shields, 2008: the Last Interglacial: Evidence for a restricted interval of widespread coral reef Quantifying climate feedbacks using radiative kernels. J. Clim., 21, 3504 3520. growth. Earth Planet. Sci. Lett., 160, 745 762. Sokolov, S., and S. R. Rintoul, 2009: Circumpolar structure and distribution of the Stocker, T., and S. Johnsen, 2003: A minimum thermodynamic model for the bipolar Antarctic Circumpolar Current fronts: 1. Mean circumpolar paths. J. Geophys. seesaw. Paleoceanography, 18, 1087. Res., 114, C11018. Stone, E. J., D. J. Lunt, J. D. Annan, and J. C. Hargreaves, 2013: Quantification of the Solanki, S. K., I. G. Usoskin, B. Kromer, M. Schussler, and J. Beer, 2004: Unusual activ- Greenland ice sheet contribution to Last Interglacial sea level rise. Clim. Past, ity of the Sun during recent decades compared to the previous 11,000 years. 9, 621 639. Nature, 431, 1084 1087. Stone, J. O., G. A. Balco, D. E. Sugden, M. W. Caffee, L. C. Sass, S. G. Cowdery, and C. Sosdian, S., and Y. Rosenthal, 2009: Deep-sea temperature and ice volume changes Siddoway, 2003: Holocene Deglaciation of Marie Byrd Land, West Antarctica. across the Pliocene-Pleistocene climate transitions. Science, 325, 306 310. Science, 299, 99 102. Sowers, T., and M. Bender, 1995: Climate records covering the Last Deglaciation. Stott, L., A. Timmermann, and R. Thunell, 2007: Southern hemisphere and deep-sea Science, 269, 210 214. warming led deglacial atmospheric CO2 rise and tropical warming. Science, 318, Spence, J. P., M. Eby, and A. J. Weaver, 2008: The sensitivity of the Atlantic Meridional 435 438. Overturning Circulation to freshwater forcing at eddy-permitting resolutions. J. Stott, L., K. Cannariato, R. Thunell, G. H. Haug, A. Koutavas, and S. Lund, 2004: Decline Clim., 21, 2697 2710. of surface temperature and salinity in the western tropical Pacific Ocean in the Spielhagen, R. F., et al., 2011: Enhanced modern heat transfer to the Arctic by warm Holocene epoch. Nature, 431, 56 59. Atlantic water. Science, 331, 450 453. Stott, L. D., 1992: Higher temperatures and lower oceanic pCO2: A climate enigma at St. George, S., et al., 2009: The tree-ring record of drought on the Canadian prairies. the end of the Paleocene epoch. Paleoceanography, 7, 395 404. J. Clim., 22, 689 710. Stríkis, N. M., et al., 2011: Abrupt variations in South American monsoon rainfall Stager, J. C., D. Ryves, B. F. Cumming, L. D. Meeker, and J. Beer, 2005: Solar variability during the Holocene based on a speleothem record from central-eastern Brazil. and the levels of Lake Victoria, East Africa, during the last millenium. J. Paleo- Geology, 39, 1075 1078. 5 limnol., 33, 243 251. Stuiver, M., and T. F. Braziunas, 1993: Sun, ocean, climate and atmospheric 14CO2: An Stager, J. C., C. Cocquyt, R. Bonnefille, C. Weyhenmeyer, and N. Bowerman, 2009: A evaluation of causal and spectral relationships. Holocene, 3, 289 305. late Holocene paleoclimatic history of Lake Tanganyika, East Africa. Quat. Res., Sundqvist, H. S., Q. Zhang, A. Moberg, K. Holmgren, H. Körnich, J. Nilsson, and G. 72, 47 56. Brattström, 2010: Climate change between the mid and late Holocene in north- Stahle, D. W., et al., 2011: Major Mesoamerican droughts of the past millennium. ern high latitudes - Part 1: survey of temperature and precipitation proxy data. Geophys. Res. Lett., 38, L05703. Clim. Past, 6, 591 608. Stambaugh, M. C., R. P. Guyette, E. R. McMurry, E. R. Cook, D. M. Meko, and A. R. Svalgaard, L., and E. W. Cliver, 2010: Heliospheric magnetic field 1835 2009. J. Geo- Lupo, 2011: Drought duration and frequency in the U.S. Corn Belt during the last phys. Res., 115, A09111. millennium (AD 992 2004). Agr. For. Meteorol., 151, 154 162. Svensson, A., et al., 2008: A 60 000 year Greenland stratigraphic ice core chronology. Stanford, J. D., E. J. Rohling, S. Bacon, A. P. Roberts, F. E. Grousset, and M. Bolshaw, Clim. Past, 4, 47 57. 2011: A new concept for the paleoceanographic evolution of Heinrich event 1 in Swingedouw, D., J. Mignot, P. Braconnot, E. Mosquet, M. Kageyama, and R. Alkama, the North Atlantic. Quat. Sci. Rev., 30, 1047 1066. 2009: Impact of freshwater release in the North Atlantic under different climate Starkel, L., R. Soja, and D. J. Michczyñska, 2006: Past hydrological events reflected in conditions in an OAGCM. J. Clim., 22, 6377 6403. Holocene history of Polish rivers. CATENA, 66, 24 33. Swingedouw, D., L. Terray, C. Cassou, A. Voldoire, D. Salas-Mélia, and J. Servonnat, Steffensen, J. P., et al., 2008: High-resolution Greenland ice core data show abrupt 2011: Natural forcing of climate during the last millennium: Fingerprint of solar climate change happens in few years. Science, 321, 680 684. variability. Clim. Dyn., 36, 1349 1364. Steig, E. J., D. P. Schneider, S. D. Rutherford, M. E. Mann, J. C. Comiso, and D. T. Shin- Takemura, T., M. Egashira, K. Matsuzawa, H. Ichijo, R. O ishi, and A. Abe-Ouchi, 2009: dell, 2009: Warming of the Antarctic ice-sheet surface since the 1957 Interna- A simulation of the global distribution and radiative forcing of soil dust aerosols tional Geophysical Year. Nature, 457, 459 462. at the Last Glacial Maximum. Atmos. Chem. Phys., 9, 3061 3073. Steig, E. J., et al., 2013: Recent climate and ice-sheet changes in West Antarctica compared with the past 2,000 years. Nature Geosci., 6, 372-375. 452 Information from Paleoclimate Archives Chapter 5 Tan, L., Y. Cai, R. Edwards, H. Cheng, C. Shen, and H. Zhang, 2011: Centennial- to Timmermann, A., et al., 2007: The influence of a weakening of the Atlantic meridi- decadal-scale monsoon precipitation variability in the semi-humid region, onal overturning circulation on ENSO. J. Clim., 20, 4899 4919. northern China during the last 1860 years: Records from stalagmites in Huangye Timmreck, C., S. J. Lorenz, T. J. Crowley, S. Kinne, T. J. Raddatz, M. A. Thomas, and J. Cave. Holocene, 21, 287 296. H. Jungclaus, 2009: Limited temperature response to the very large AD 1258 Tarasov, L., and W. R. Peltier, 2007: Coevolution of continental ice cover and perma- volcanic eruption. Geophys. Res. Lett., 36, L21708. frost extent over the last glacial-interglacial cycle in North America. J. Geophys. Tingley, M. P., and P. Huybers, 2010: A Bayesian algorithm for reconstructing climate Res., 112, F02S08. anomalies in space and time. Part I: development and applications to paleocli- Tarasov, P., W. Granoszewski, E. Bezrukova, S. Brewer, M. Nita, A. Abzaeva, and H. mate reconstruction problems. J. Clim., 23, 2759 2781. Oberhänsli, 2005: Quantitative reconstruction of the last interglacial vegetation Tingley, M. P., and P. Huybers, 2013: Recent temperature extremes at high northern and climate based on the pollen record from Lake Baikal, Russia. Clim. Dyn., 25, latitudes unprecedented in the past 600 years. Nature, 496, 201 205. 625 637. Tingley, M. P., P. F. Craigmile, M. Haran, B. Li, E. Mannshardt, and B. Rajaratnam, Tarasov, P. E., et al., 2011: Progress in the reconstruction of Quaternary climate 2012: Piecing together the past: statistical insights into paleoclimatic recon- dynamics in the Northwest Pacific: A new modern analogue reference dataset structions. Quat. Sci. Rev., 35, 1 22. and its application to the 430-kyr pollen record from Lake Biwa. Earth Sci. Rev., Tjallingii, R., et al., 2008: Coherent high- and low-latitude control of the northwest 108, 64 79. African hydrological balance. Nature Geosci., 1, 670 675. Taylor, K. E., R. J. Stouffer, and G. A. Meehl, 2012: An overview of CMIP5 and the Torrence, C., and G. P. Compo, 1998: A practical guide to wavelet analysis. Bull. Am. experiment design. Bull. Am. Meteorol. Soc., 93, 485 498. Meteorol. Soc., 79, 61 78. Telford, R. J., C. Li, and M. Kucera, 2013: Mismatch between the depth habitat of Touchan, R., K. J. Anchukaitis, D. M. Meko, S. Attalah, C. Baisan, and A. Aloui, 2008: planktonic foraminifera and the calibration depth of SST transfer functions may Long term context for recent drought in northwestern Africa. Geophys. Res. Lett., bias reconstructions. Clim. Past, 9, 859 870. 35, L13705. Tett, S., et al., 2007: The impact of natural and anthropogenic forcings on climate and Touchan, R., K. Anchukaitis, D. Meko, M. Sabir, S. Attalah, and A. Aloui, 2011: Spatio- hydrology since 1550. Clim. Dyn., 28, 3 34. temporal drought variability in northwestern Africa over the last nine centuries. Thomas, E., and J. Briner, 2009: Climate of the past millennium inferred from varved Clim. Dyn., 37, 237 252. proglacial lake sediments on northeast Baffin Island, Arctic Canada. J. Paleolim- Trachsel, M., et al., 2012: Multi-archive summer temperature reconstruction for the nol., 41, 209 224. European Alps, AD 1053 1996. Quat. Sci. Rev., 46, 66 79. Thomas, E. R., E. W. Wolff, R. Mulvaney, S. J. Johnsen, J. P. Steffensen, and C. Arrow- Tripati, A. K., C. D. Roberts, and R. A. Eagle, 2009: Coupling of CO2 and ice sheet smith, 2009: Anatomy of a Dansgaard-Oeschger warming transition: high-res- stability over major climate transitions of the last 20 million years. Science, 326, olution analysis of the North Greenland Ice Core Project ice core. J. Geophys. 1394 1397. Res., 114, D08102. Trouet, V., J. Esper, N. E. Graham, A. Baker, J. D. Scourse, and D. C. Frank, 2009: Per- Thomas, E. R., et al., 2007: The 8.2 ka event from Greenland ice cores. Quat. Sci. sistent positive north Atlantic oscillation mode dominated the Medieval Climate Rev., 26, 70 81. Anomaly. Science, 324, 78 80. Thompson, D. W. J., and S. Solomon, 2002: Interpretation of recent southern hemi- Turney, C. S. M., and R. T. Jones, 2010: Does the Agulhas Current amplify global tem- sphere climate change. Science, 296, 895 899. peratures during super-interglacials? J. Quat. Sci., 25, 839 843. Thompson, D. W. J., and S. Solomon, 2009: Understanding recent stratospheric cli- Tzedakis, P. C., H. Hooghiemstra, and H. Pälike, 2006: The last 1.35 million years at mate change. J. Clim., 22, 1934 1943. Tenaghi Philippon: revised chronostratigraphy and long-term vegetation trends. Thompson, W. G., and S. L. Goldstein, 2005: Open-system coral ages reveal persistent Quat. Sci. Rev., 25, 3416 3430. suborbital sea level cycles. Science, 308, 401 404. Tzedakis, P. C., J. E. T. Channell, D. A. Hodell, H. F. Kleiven, and L. C. Skinner, 2012a: Thompson, W. G., H. Allen Curran, M. A. Wilson, and B. White, 2011: Sea level oscilla- Determining the natural length of the current interglacial. Nature Geosci., 5, tions during the last interglacial highstand recorded by Bahamas corals. Nature 138 141. Geosci., 4, 684 687. Tzedakis, P. C., D. Raynaud, J. F. McManus, A. Berger, V. Brovkin, and T. Kiefer, 2009: Thordarson, T., and S. Self, 2003: Atmospheric and environmental effects of the Interglacial diversity. Nature Geosci., 2, 751 755. 1783 1784 Laki eruption: A review and reassessment. J. Geophys. Res., 108, Tzedakis, P. C., E. W. Wolff, L. C. Skinner, V. Brovkin, D. A. Hodell, J. F. McManus, and D14011. D. Raynaud, 2012b: Can we predict the duration of an interglacial? Clim. Dyn., Tierney, J., M. Mayes, N. Meyer, C. Johnson, P. Swarzenski, A. Cohen, and J. Russell, 8, 1473 1485. 2010: Late-twentieth-century warming in Lake Tanganyika unprecedented since Uemura, R., V. Masson-Delmotte, J. Jouzel, A. Landais, H. Motoyama, and B. Stenni, AD 500. Nature Geosci., 3, 422 425. 2012: Ranges of moisture-source temperature estimated from Antarctic ice cores Tierney, J. E., S. C. Lewis, B. I. Cook, A. N. LeGrande, and G. A. Schmidt, 2011: Model, stable isotope records over glacial interglacial cycles. Clim. Past, 8, 1109 1125. proxy and isotopic perspectives on the east African humid period. Earth Planet. Unterman, M. B., T. J. Crowley, K. I. Hodges, S. J. Kim, and D. J. Erickson, 2011: Paleo- 5 Sci. Lett., 307, 103 112. meteorology: High resolution Northern Hemisphere wintertime mid-latitude Tiljander, M. I. A., M. Saarnisto, A. E. K. Ojala, and T. Saarinen, 2003: A 3000 year pal- dynamics during the Last Glacial Maximum. Geophys. Res. Lett., 38, L23702. aeoenvironmental record from annually laminated sediment of Lake Korttajarvi, Urrutia, R., A. Lara, R. Villalba, D. Christie, C. Le Quesne, and A. Cuq, 2011: Multicen- central Finland. Boreas, 32, 566 577. tury tree ring reconstruction of annual streamflow for the Maule River water- Timm, O., E. Ruprecht, and S. Kleppek, 2004: Scale-dependent reconstruction of the shed in south central Chile. Water Resourc. Res., 47, W06527. NAO index. J. Clim., 17, 2157 2169. van de Berg, W. J., M. van den Broeke, J. Ettema, E. van Meijgaard, and F. Kaspar, Timm, O., A. Timmermann, A. Abe-Ouchi, F. Saito, and T. Segawa, 2008: On the defini- 2011: Significant contribution of insolation to Eemian melting of the Greenland tion of seasons in paleoclimate simulations with orbital forcing. Paleoceanog- ice sheet. Nature Geosci., 4, 679 683. raphy, 23, PA2221. van de Plassche, O., K. van der Borg, and A. F. M. de Jong, 1998: Sea level climate Timmermann, A., H. Gildor, M. Schulz, and E. Tziperman, 2003: Coherent resonant correlation during the past 1400 yr. Geology, 26, 319 322. millennial-scale climate oscillations triggered by massive meltwater pulses. J. van den Berg, J., R. S. W. van de Wal, G. A. Milne, and J. Oerlemans, 2008: Effect of Clim., 16, 2569 2585. isostasy on dynamical ice sheet modeling: A case study for Eurasia. J. Geophys. Timmermann, A., O. Timm, L. Stott, and L. Menviel, 2009: The roles of CO2 and orbital Res., 113, B05412. forcing in driving southern hemispheric temperature variations during the last van der Burgh, J., H. Visscher, D. L. Dilcher, and W. M. Kürschner, 1993: Paleoatmo- 21 000 yr. J. Clim., 22, 1626 1640. spheric signatures in Neogene fossil leaves. Science, 260, 1788 1790. Timmermann, A., F. Justino, F. F. Jin, U. Krebs, and H. Goosse, 2004: Surface tempera- van Leeuwen, R. J., et al., 2000: Stratigraphy and integrated facies analysis of the ture control in the North and tropical Pacific during the last glacial maximum. Saalian and Eemian sediments in the Amsterdam-Terminal borehole, the Nether- Clim. Dyn., 23, 353 370. lands. Geolog.Mijnbouw / Netherlands J. Geosci., 79, 161 196. Timmermann, A., et al., 2010: Towards a quantitative understanding of millennial- Varma, V., et al., 2012: Holocene evolution of the Southern Hemisphere westerly scale Antarctic warming events. Quat. Sci. Rev., 29, 74 85. winds in transient simulations with global climate models. Clim. Past, 8, 391 402. 453 Chapter 5 Information from Paleoclimate Archives Vasskog, K., O. Paasche, A. Nesje, J. F. Boyle, and H. J. B. Birks, 2012: A new approach Wahl, E. R., and J. E. Smerdon, 2012: Comparative performance of paleoclimate field for reconstructing glacier variability based on lake sediments recording input and index reconstructions derived from climate proxies and noise-only predic- from more than one glacier. Quat. Res., 77, 192 204. tors. Geophys. Res. Lett., 39, L06703. Vaughan, D. G., D. K. A. Barnes, P. T. Fretwell, and R. G. Bingham, 2011: Potential Wahl, E. R., D. M. Ritson, and C. M. Ammann, 2006: Comment on Reconstructing seaways across West Antarctica. Geochem., Geophys., Geosyst., 12, Q10004. past climate from noisy data . Science, 312, 529. Vavrus, S., 2004: The impact of cloud feedbacks on Arctic climate under greenhouse Walter, K. M., S. A. Zimov, J. P. Chanton, D. Verbyla, and F. S. Chapin, 2006: Methane forcing. J. Clim., 17, 603 615. bubbling from Siberian thaw lakes as a positive feedback to climate warming. Velichko, A. A., O. K. Borisova, and E. M. Zelikson, 2008: Paradoxes of the Last Inter- Nature, 443, 71 75. glacial climate: Reconstruction of the northern Eurasia climate based on palaeo- Wan, S., J. Tian, S. Steinke, A. Li, and T. Li, 2010: Evolution and variability of the East floristic data. Boreas, 37, 1 19. Asian summer monsoon during the Pliocene: Evidence from clay mineral records Verleyen, E., et al., 2011: Post-glacial regional climate variability along the East of the South China Sea. Palaeogeogr. Palaeoclimatol. Palaeoecol. 293, 237 247. Antarctic coastal margin Evidence from shallow marine and coastal terrestrial Wang, B., and Q. Ding, 2008: Global monsoon: Dominant mode of annual variation records. Earth Sci. Rev., 104, 199 212. in the tropics. Dyn. Atmos. Oceans, 44, 165 183. Verschuren, D., K. Laird, and B. Cumming, 2000: Rainfall and drought in equatorial Wang, S., X. Wen, Y. Luo, W. Dong, Z. Zhao, and B. Yang, 2007: Reconstruction of tem- East Africa during the past 1000 years. Nature, 403, 410 414. perature series of China for the last 1000 years. Chin. Sci. Bull., 52, 3272 3280. Verschuren, D., J. S. Sinninghe Damste, J. Moernaut, I. Kristen, M. Blaauw, M. Fagot, Wang, Y. J., H. Cheng, R. L. Edwards, Z. S. An, J. Y. Wu, C. C. Shen, and J. A. Dorale, and G. H. Haug, 2009: Half-precessional dynamics of monsoon rainfall near the 2001: A high-resolution absolute-dated Late Pleistocene monsoon record from East African Equator. Nature, 462, 637 641. Hulu Cave, China. Science, 294, 2345 2348. Vettoretti, G., and W. R. Peltier, 2011: The impact of insolation, greenhouse gas forc- Wang, Y. J., et al., 2008: Millennial- and orbital-scale changes in the East Asian mon- ing and ocean circulation changes on glacial inception. Holocene, 21, 803 817. soon over the past 224,000 years. Nature, 451, 1090 1093. Viau, A. E., M. Ladd, and K. Gajewski, 2012: The climate of North America during Wang, Y. M., J. Lean, and N. Sheeley, 2005: Modeling the Sun s magnetic field and the past 2000 years reconstructed from pollen data. Global Planet. Change, irradiance since 1713. Astrophys. J., 625, 522 538. 84 85, 75 83. Wang, Y. M., S. L. Li, and D. H. Luo, 2009: Seasonal response of Asian monsoonal Vieira, L. E., S. K. Solanki , A. V. Krivov, and I. G. Usoskin 2011: Evolution of the solar climate to the Atlantic Multidecadal Oscillation. J. Geophys. Res., 114, D02112. irradiance during the Holocene. Astron. Astrophys., 531, A6. Wanner, H., O. Solomina, M. Grosjean, S. P. Ritz, and M. Jetel, 2011: Structure and Vieira, L. E. A., and S. K. Solanki, 2010: Evolution of the solar magnetic flux on time origin of Holocene cold events. Quat. Sci. Rev., 30, 3109 3123. scales of years to millenia. Astron. Astrophys., 509, A100. Wanner, H., et al., 2008: Mid- to Late Holocene climate change: an overview. Quat. Villalba, R., M. Grosjean, and T. Kiefer, 2009: Long-term multi-proxy climate recon- Sci. Rev., 27, 1791 1828. structions and dynamics in South America (LOTRED-SA): State of the art and Waple, A. M., M. E. Mann, and R. S. Bradley, 2002: Long-term patterns of solar irradi- perspectives. Palaeogeogr. Palaeoclimatol. Palaeoecol., 281, 175 179. ance forcing in model experiments and proxy based surface temperature recon- Villalba, R., et al., 2012: Unusual Southern Hemisphere tree growth patterns induced structions. Clim. Dyn., 18, 563 578. by changes in the Southern Annular Mode. Nature Geosci., 5, 793 798. Wara, M. W., A. C. Ravelo, and M. L. Delaney, 2005: Permanent El Nino-like condi- Vimeux, F., P. Ginot, M. Schwikowski, M. Vuille, G. Hoffmann, L. G. Thompson, and tions during the Pliocene Warm Period. Science, 309, 758 761. U. Schotterer, 2009: Climate variability during the last 1000 years inferred from Watanabe, O., J. Jouzel, S. Johnsen, F. Parrenin, H. Shoji, and N. Yoshida, 2003: Homo- Andean ice cores: A review of methodology and recent results. Palaeogeogr. Pal- geneous climate variability across East Antarctica over the past three glacial aeoclimatol. Palaeoecol., 281, 229 241. cycles. Nature, 422, 509 512. Vinther, B., P. Jones, K. Briffa, H. Clausen, K. Andersen, D. Dahl-Jensen, and S. Johnsen, Watanabe, T., et al., 2011: Permanent El Nino during the Pliocene warm period not 2010: Climatic signals in multiple highly resolved stable isotope records from supported by coral evidence. Nature, 471, 209 211. Greenland. Quat. Sci. Rev., 29, 522 538. Weber, S. L., et al., 2007: The modern and glacial overturning circulation in the Atlan- Vinther, B. M., et al., 2009: Holocene thinning of the Greenland ice sheet. Nature, tic ocean in PMIP coupled model simulations. Clim. Past, 3, 51 64. 461, 385 388. Wegmüller, S., 1992: Vegetationsgeschichtliche und stratigraphische Untersu- von Grafenstein, U., E. Erlenkeuser, J. Müller, J. Jouzel, and S. Johnsen, 1998: The cold chungen an Schieferkohlen des nördlichen Alpenvorlandes. Denkschriften der event 8,200 years ago documented in oxygen isotope records of precipitation in Schweizerischen Akademie der Naturwissenschaften, 102, Birkhauser, Basel, Europe and Greenland. Clim. Dyn., 14, 73 81. 445 454 pp. von Gunten, L., M. Grosjean, B. Rein, R. Urrutia, and P. Appleby, 2009: A quantita- Wegner, A., et al., 2012: Change in dust variability in the Atlantic sector of Antarctica tive high-resolution summer temperature reconstruction based on sedimentary at the end of the last deglaciation. Clim. Past, 8, 135 147. pigments from Laguna Aculeo, central Chile, back to AD 850. Holocene, 19, Wei, L. J., E. Mosley-Thompson, P. Gabrielli, L. G. Thompson, and C. Barbante, 2008: 5 873 881. Synchronous deposition of volcanic ash and sulfate aerosols over Greenland in von Königswald, W., 2007: Mammalian faunas from the interglacial periods in Cen- 1783 from the Laki eruption (Iceland). Geophys. Res. Lett., 35, L16501. tral Europe and their stratigraphic correlation. In: Developments in Quaternary Weldeab, S., 2012: Bipolar modulation of millennial-scale West African monsoon Science [F. Sirocko, M. Claussen, M. F. Sánchez Goni and T. Litt (eds.)]. Elsevier, variability during the last glacial (75,000 25,000 years ago). Quat. Sci. Rev., Philadelphia, PA, USA, pp. 445 454. 40, 21 29. von Storch, H., E. Zorita, J. Jones, F. González-Rouco, and S. Tett, 2006: Response Weldeab, S., D. W. Lea, R. R. Schneider, and N. Andersen, 2007a: 155,000 years of to comment on Reconstructing past climate from noisy data . Science, 312, West African monsoon and ocean thermal evolution. Science, 316, 1303 1307. 1872 1873. Weldeab, S., D. W. Lea, R. R. Schneider, and N. Andersen, 2007b: Centennial scale Vuille, M., et al., 2012: A review of the South American Monsoon history as recorded climate instabilities in a wet early Holocene West African monsoon. Geophys. in stable isotopic proxies over the past two millennia. Clim. Past, 8, 1309 1321. Res. Lett., 34, L24702. Waelbroeck, C., et al., 2002: Sea level and deep water temperature changes derived Welten, M., 1988: Neue pollenanalytische Ergebnisse über das jüngere Quartär des from benthic foraminifera isotopic records. Quat. Sci. Rev., 21, 295 305. nördlichen Alpenvorlandes der Schweiz (Mittel-und Jungpleistozän). Beiträge Wagner, J. D. M., J. E. Cole, J. W. Beck, P. J. Patchett, G. M. Henderson, and H. R. zur Geologischen Karte der Schweiz, 162, Stämpfli, 40 pp. Barnett, 2010: Moisture variability in the southwestern United States linked to Wenzler, T., S. Solanki, and N. Krivova, 2005: Can surface magnetic fields reproduce abrupt glacial climate change. Nature Geosci., 3, 110 113. solar irradiance variations in cycles 22 and 23? Astron. Astrophys., 432, 1057 Wagner, S., et al., 2007: Transient simulations, empirical reconstructions and forcing 1061. mechanisms for the Mid-holocene hydrological climate in southern Patagonia. Wenzler, T., S. K. Solanki, N. A. Krivova, and C. Fröhlich, 2006: Reconstruction of solar Clim. Dyn., 29, 333 355. irradiance variations in cycles 21 23 based on surface magnetic fields. Astron. Wahl, E., et al., 2010: An archive of high-resolution temperature reconstructions over Astrophys., 460, 583 595. the past 2+ millennia. Geochem. Geophys. Geosyst., 11, Q01001. Werner, J. P., J. Luterbacher, and J. E. Smerdon, 2013: A pseudoproxy evaluation of Bayesian hierarchical modelling and canonical correlation analysis for climate field reconstructions over Europe. J. Clim., 26, 851 867. 454 Information from Paleoclimate Archives Chapter 5 Westerhold, T., U. Röhl, J. Laskar, I. Raffi, J. Bowles, L. J. Lourens, and J. C. Zachos, Yang, B., J. Wang, A. Bräuning, Z. Dong, and J. Esper, 2009: Late Holocene climatic 2007: On the duration of magnetochrons C24r and C25n and the timing of early and environmental changes in and central Asia. Quat. Int., 194, 68 78. Eocene global warming events: Implications from the Ocean Drilling Program Yin, Q., and A. Berger, 2012: Individual contribution of insolation and CO2 to the Leg 208 Walvis Ridge depth transect. Paleoceanography, 22, PA2201. interglacial climates of the past 800,000 years. Clim. Dyn., 38, 709 724. Whitehouse, P. L., M. J. Bentley, G. A. Milne, M. A. King, and I. D. Thomas, 2012: A new Yin, Q. Z., and A. Berger, 2010: Insolation and CO2 contribution to the interglacial glacial isostatic adjustment model for Antarctica: Calibrated and tested using climate before and after the Mid-Brunhes Event. Nature Geosci., 3, 243 246. observations of relative sea level change and present-day uplift rates. Geophys. Yin, Q. Z., A. Berger, E. Driesschaert, H. Goosse, M. F. Loutre, and M. Crucifix, 2008: J. Int., 190, 1464 1482. The Eurasian ice sheet reinforces the East Asian summer monsoon during the Widmann, M., H. Goosse, G. van der Schrier, R. Schnur, and J. Barkmeijer, 2010: Using interglacial 500 000 years ago. Clim. Past, 4, 79 90. data assimilation to study extratropical Northern Hemisphere climate over the Yiou, P., J. Servonnat, M. Yoshimori, D. Swingedouw, M. Khodri, and A. Abe-Ouchi, last millennium. Clim. Past, 6, 627 644. 2012: Stability of weather regimes during the last millennium from climate Wiersma, A., D. Roche, and H. Renssen, 2011: Fingerprinting the 8.2 ka event climate simulations. Geophys. Res. Lett., 39, L08703. response in a coupled climate model. J. Quat. Sci., 26, 118 127. Yokoyama, Y., and T. M. Esat, 2011: Global climate and sea level: Enduring variability Wiles, G. C., D. J. Barclay, P. E. Calkin, and T. V. Lowell, 2008: Century to millennial- and rapid fluctuations over the past 150,000 years. Oceanography, 24, 54 69. scale temperature variations for the last two thousand years indicated from gla- Yoshimori, M., T. Yokohata, and A. Abe-Ouchi, 2009: A comparison of climate feed- cial geologic records of Southern Alaska. Global Planet. Change, 60, 115 125. back strength between CO2 doubling and LGM experiments. J. Clim., 22, 3374 Wiles, G. C., D. E. Lawson, E. Lyon, N. Wiesenberg, and R. D. D Arrigo, 2011: Tree-ring 3395. dates on two pre-Little Ice Age advances in Glacier Bay National Park and Pre- Yoshimori, M., J. C. Hargreaves, J. D. Annan, T. Yokohata, and A. Abe-Ouchi, 2011: serve, Alaska, USA. Quat. Res., 76, 190 195. Dependency of feedbacks on forcing and climate state in physics parameter Wilhelm, B., et al., 2012: 1400 years of extreme precipitation patterns over the Medi- ensembles. J. Clim., 24, 6440 6455. terranean French Alps and possible forcing mechanisms. Quat. Res., 78, 1 12. Young, N. E., J. P. Briner, H. A. M. Stewart, Y. Axford, B. Csatho, D. H. Rood, and R. C. Wilmes, S. B., C. C. Raible, and T. F. Stocker, 2012: Climate variability of the mid- and Finkel, 2011: Response of Jakobshavn Isbrae, Greenland, to Holocene climate high-latitudes of the Southern Hemisphere in ensemble simulations from 1500 change. Geology, 39, 131 134. to 2000 AD. Clim. Past, 8, 373 390. Yue, X., H. Wang, H. Liao, and D. Jiang, 2010: Simulation of the direct radiative effect Wilson, M. F., and A. Henderson-Sellers, 1985: A global archive of land cover and of mineral dust aerosol on the climate at the Last Glacial Maximum. J. Clim., soils data for use in general circulation climate models. J. Climatol., 5, 119 143. 24, 843 858. Wilson, R., E. Cook, R. D Arrigo, N. Riedwyl, M. N. Evans, A. Tudhope, and R. Allan, Zachos, J. C., G. R. Dickens, and R. E. Zeebe, 2008: An early Cenozoic perspective on 2010: Reconstructing ENSO: the influence of method, proxy data, climate forcing greenhouse warming and carbon-cycle dynamics. Nature, 451, 279 283. and teleconnections. J. Quat. Sci., 25, 62 78. Zachos, J. C., et al., 2005: Rapid acidification of the ocean during the Paleocene- Wilson, R., D. Miles, N. Loader, T. Melvin, L. Cunningham, R. Cooper, and K. Briffa, Eocene Thermal Maximum. Science, 308, 1611 1615. 2013: A millennial long march july precipitation reconstruction for southern- Zagwijn, W. H., 1996: An analysis of Eemian climate in western and central Europe. central England. Clim. Dyn., 40, 997 1017. Quat. Sci. Rev., 15, 451 469. Wilson, R., et al., 2007: A matter of divergence: Tracking recent warming at hemi- Zeebe, R. E., J. C. Zachos, and G. R. Dickens, 2009: Carbon dioxide forcing alone spheric scales using tree ring data. J. Geophys. Res., 112, D17103. insufficient to explain Palaeocene-Eocene Thermal Maximum warming. Nature Winckler, G., R. F. Anderson, M. Q. Fleisher, D. McGee, and N. Mahowald, 2008: Geosci., 2, 576 580. Covariant Glacial-Interglacial Dust Fluxes in the Equatorial Pacific and Antarc- Zha, X., C. Huang, and J. Pang, 2009: Palaeofloods recorded by slackwater deposits tica. Science, 320, 93 96. on the Qishuihe river in the middle Yellow river. J. Geograph. Sci., 19, 681 690. Winkler, S., and J. Matthews, 2010: Holocene glacier chronologies: Are high-res- Zhang, P. Z., et al., 2008: A test of climate, sun, and culture relationships from an olution global and inter-hemispheric comparisons possible? Holocene, 20, 1810 year Chinese cave record. Science, 322, 940 942. 1137 1147. Zhang, Q.-B., and R. J. Hebda, 2005: Abrupt climate change and variability in the Winter, A., et al., 2011: Evidence for 800 years of North Atlantic multi-decadal vari- past four millennia of the southern Vancouver Island, Canada. Geophys. Res. ability from a Puerto Rican speleothem. Earth Planet. Sci. Lett., 308, 23 28. Lett., 32, L16708. Wolff, C., et al., 2011: Reduced interannual rainfall variability in east Africa during Zhang, Q., H. S. Sundqvist, A. Moberg, H. Kornich, J. Nilsson, and K. Holmgren, 2010: the Last Ice Age. Science, 333, 743 747. Climate change between the mid and late Holocene in northern high latitudes Wolff, E. W., et al., 2010: Changes in environment over the last 800,000 years from Part 2: Model-data comparisons. Clim. Past, 6, 609 626. chemical analysis of the EPICA Dome C ice core. Quat. Sci. Rev., 29, 285 295. Zhang, R., and T. L. Delworth, 2005: Simulated tropical response to a substantial Woodhouse, C. A., D. M. Meko, G. M. MacDonald, D. W. Stahle, and E. R. Cook, 2010: weakening of the Atlantic thermohaline circulation. J. Clim., 18, 1853 1860. A 1,200 year perspective of 21st century drought in southwestern North Amer- Zhang, R., and T. L. Delworth, 2006: Impact of Atlantic multidecadal oscillations on 5 ica. Proc. Natl. Acad. Sci. U.S.A., 107, 21283 21288. India/Sahel rainfall and Atlantic hurricanes. Geophys. Res. Lett., 33, L17712. Woodroffe, C., and R. McLean, 1990: Microatolls and recent sea level change on Zhang, Y., Z. Kong, S. Yan, Z. Yang, and J. Ni, 2009: Medieval Warm Period on the coral atolls. Nature, 344, 531 534. northern slope of central Tianshan Mountains, Xinjiang, NW China. Geophys. Woodroffe, C. D., H. V. McGregor, K. Lambeck, S. G. Smithers, and D. Fink, 2012: Mid- Res. Lett., 36, L11702. Pacific microatolls record sea level stability over the past 5000 yr. Geology, 40, Zheng, W., P. Braconnot, E. Guilyardi, U. Merkel, and Y. Yu, 2008: ENSO at 6ka and 951 954. 21ka from ocean-atmosphere coupled model simulations. Clim. Dyn., 30, 745 Wunsch, C., 2006: Abrupt climate change: An alternative view. Quat. Res., 65, 191 762. 203. Zhou, T., B. Li, W. Man, L. Zhang, and J. Zhang, 2011: A comparison of the Medieval Xie, S. P., Y. Okumura, T. Miyama, and A. Timmermann, 2008: Influences of Atlantic Warm Period, Little Ice Age and 20th century warming simulated by the FGOALS climate change on the tropical Pacific via the Central American Isthmus. J. Clim., climate system model. Chin. Sci. Bull., 56, 3028 3041. 21, 3914 3928. Zhu, H., F. Zheng, X. Shao, X. Liu, X. Yan, and E. Liang, 2008: Millennial temperature Yadav, R., A. Braeuning, and J. Singh, 2011: Tree ring inferred summer temperature reconstruction based on tree-ring widths of Qilian juniper from Wulan, Qinghai variations over the last millennium in western Himalaya, India. Clim. Dyn., 36, province, China. Chin. Sci. Bull., 53, 3914 3920. 1545 1554. Zinke, J., M. Pfeiffer, O. Timm, W. C. Dullo, and G. Brummer, 2009: Western Indian Yanase, W., and A. Abe-Ouchi, 2010: A numerical study on the atmospheric circula- Ocean marine and terrestrial records of climate variability: A review and new tion over the midlatitude North Pacific during the Last Glacial Maximum. J. Clim., concepts on land ocean interactions since AD 1660. Int. J. Earth Sci., 98, 115 23, 135 151. 133. Yang, B., A. Bräuning, Z. Dong, Z. Zhang, and J. Keqing, 2008: Late Holocene mon- soonal temperate glacier fluctuations on the Tibetan Plateau. Global Planet. Change, 60, 126 140. 455 Chapter 5 Information from Paleoclimate Archives Appendix 5.A: Additional Information on Paleoclimate Archives and Models Table 5.A.1 | Summary of the Atmosphere-Ocean General Circulation Model (AOGCM) simulations available and assessed for Sections 5.3.5 and 5.5.1. Acronyms describing forcings are: SS (solar forcing, stronger variability), SW (solar forcing, weaker variability), V (volcanic activity), G (greenhouse gases concentration), A (aerosols), L (land use land cover), and O (orbital). The table is divided into Paleoclimate Modelling Intercomparison Project Phase III (PMIP3) and Coupled Model Intercomparison Project Phase 5 (CMIP5) and non-PMIP3/CMIP5 experiments (Braconnot et al., 2012b; Taylor et al., 2012). Superscript indices in forcing acronyms identify the forcing reconstructions used and are listed in the table footnotes. PMIP3 experiments follow forcing guidelines provided in Schmidt et al. (2011, 2012b). See Fernández-Donado et al. (2013) for more information on pre-PMIP3/ CMIP5 forcing configurations. See Chapter 8 and Table 9.1 for the forcing and model specifications of the CMIP5 historical runs. The simulations highlighted in red were excluded from Figures 5.8, 5.9 and 5.12 because they did not include at least solar, volcanic and greenhouse gas forcings, they did not span the whole of the last millennium, or for a reason given in the table notes. Model (No. runs) Period Forcingsa Reference Pre PMIP3/CMIP5 Experiments (1×) 1000 2000 CCSM3 SS11.V22.G30,31,35 Hofer et al. (2011) (4×) 1500 2000 CNRM-CM3.3 (1×) 1001 1999 SS11.V21.G30,34,35.A44.L54 Swingedouw et al. (2011) CSM1.4 (1×) 850 1999 SS .V .G 10 21 .A 30,31,35 41 Ammann et al. (2007) (3×) 1 2001 SW14 CSIRO-MK3L-1-2 (3×) 1 2001 SW14.G34.O60 Phipps et al. (2013) (3×) 501 2001 SW14.V24.G34.O60 ECHAM4/OPYC (1×) 1500 2000 SS11.V21,26.G38.A42.L55 Stendel et al. (2006) (5×) 800 2005 SW13.V25.G34,39.A40.L53.O61 ECHAM5/MPIOM Jungclaus et al. (2010) (3×) 800 2005 SS10.V25.G34,39.A40.L53.O61 (1×) 1000 1990 SS11.V20.G31,36,37 González-Rouco et al. (2003)b ECHO-G (1×) 1000 1990 SS11.V20.G31,36,37 González-Rouco et al. (2006) (2×) 7000 1998 SS12.G30.O62 Wagner et al. (2007) HadCM3 (1×) 1492 1999 SS11.V23.G32.A43.L50,54,55.O60 Tett et al. (2007) IPSLCM4 (1×) 1001 2000 SS11.G30,34,35.A44.O63 Servonnat et al. (2010) FGOALS-gl (1×) 1000 1999 SS11.V20.G30,31,35 Zhou et al. (2011)c PMIP3/CMIP5 Experiments BCC-csm1-1 (1×) 850 2005 SW15.V24.G30,33,34. A45.O60 CCSM4 (1×) 850 2004 SW15.V24.G30,33,34. A45 .L51.O60 Landrum et al. (2013) CSIRO-MK3L-1-2 (1×) 851 2000 SW14.V25.G30,33,34.O60 SW14.V25.G30,33,34.A45.L51.O60 SW14.V24.G30,33,34.A45.L51.O60 SW14.G30,33,34.A4.L51.O60 SW15.V25.G30,33,34.A45.L51.O60 GISS-E2-R (8×) 850 2004 d SW15.V24.G30,33,34.A45.L52.O60 SW15.G30,33,34.A4.L51.O60 SW15.V25.G30,33,34.A45.L52.O60 SW15.V24.G30,33,34.A45.L51.O60 HadCM3 (1×) 800 2000 SW14.V25.G30,32,34.A43.L51.O60 Schurer et al. (2013) 5 IPSL-CM5A-LR (1×) 850 2005 SW .V .G 15 27 30,33,34 .O60 MIROC-ESM (1×) 850 2005 SW16.V25.G30,34,39.O60 e MPI-ESM-P (1×) 850 2005 SW15.V25.G30,33,34.A45.L51.O60 Notes: a Key for superscript indices in forcing acronyms: [23] Crowley et al. (2003) [38] Robertson et al. (2001) [1] Solar: [24] Gao et al. (2008). In the GISS-E2-R simulations this [39] CO2 diagnosed by the model. [10] Bard et al. (2000) forcing was implemented twice as large as in Gao [4] Aerosols: [11] Bard et al. (2000) spliced to Lean et al. (1995a) et al. (2008). [40] Lefohn et al. (1999) [12] Solanki et al. (2004) [25] Crowley and Unterman (2013) [41] Joos et al. (2001) [13] Krivova et al. (2007) [26] Robertson et al. (2001) [42] Roeckner et al. (1999) [14] Steinhilber et al. (2009) spliced to Wang et al. (2005) [27] Ammann et al. (2007) [43] Johns et al. (2003) [15] Vieira and Solanki (2010) spliced to Wang et al. (2005) [3] WMGHGs: [44] Boucher and Pham (2002) [16] Delaygue and Bard (2011) spliced to Wang [30] Flückiger et al., (1999; 2002); Machida et al. (1995) [45] Lamarque et al. (2010). See Sections 8.2 and 8.3 et al. (2005) [31] Etheridge et al. (1996) [5] Land use, land cover: [2] Volcanic: [32] Johns et al. (2003) [50] Wilson and Henderson-Sellers (1985) [20] Crowley (2000) [33] Hansen and Sato (2004) [51] Pongratz et al. (2009) spliced to Hurtt et al. (2006) [21] Ammann et al. (2003) [34] MacFarling Meure et al. (2006) [52] Kaplan et al. (2011) [22] Total solar irradiances from Crowley (2000) converted [35] Blunier et al. (1995) [53] Pongratz et al. (2008) to aerosol masses using Ammann et al. (2003) [36] Etheridge et al. (1998) regression coefficients. [37] Battle et al. (1996) (continued on next page) 456 Information from Paleoclimate Archives Chapter 5 Table 5.A.1 Notes (continued) [54] Ramankutty and Foley (1999) b This simulation was only used in Figure 5.8, using NH temperature adjusted by Osborn et al. (2006). [55] Goldewijk (2001) c The FGOALS-gl experiment is available in the PMIP3 repository, but the forcing configuration is different from [6] Orbital: Schmidt et al (2011; 2012b) recommendations so it is included here within the pre-PMIP3 ensemble. [60] Berger (1978) d The GISS-E2-R experiments with Gao et al. (2008) volcanic forcing were not used in Figures 5.8, 5.9 or 5.12. See [24]. [61] Bretagnon and Francou (1988) e This simulation was only used in Figure 5.8, using drift-corrected NH temperature. [62] Berger and Loutre (1991) [63] Laskar et al. (2004) Table 5.A.2 | Summary of atmospheric carbon dioxide (CO2) proxy methods and confidence assessment of their main assumptions. Scientific Estimated Method Limitations Main Assumptions (relative confidence) Rationale Applicability Alkenone Measurements of 100 to Alkenones are often rare in Measured alkenone carbon isotope ratio is accurate and precise (high). (phytoplankton carbon isotope ratios ~4000 ppm; oligotrophic areas and some- Ambient aqueous partial pressure of carbon dioxide (pCO2) has a quantifiable relation- biomarker) of marine sedi- 0 to 100 Ma times absent. Method relies on ship with p that can be distinguished from the nutrient-related physiological factors carbon isotopes mentary alkenones empirical calibration and 13C is such as algal growth rate, cell size, cell geometry and light-limited growth (medium). (or other organic sensitive to other environmental Aqueous pCO2 is in equilibrium with atmospheric pCO2 (medium). compounds) allows factors, especially nutrient-relat- determination of the ed variables. Method has been Carbon isotope fractionation in modern alkenone-producing species is the same in isotopic fraction- used successfully to reconstruct ancient species and constant through time (medium). ation factor during glacial interglacial changes. Levels of biological productivity (e.g., dissolved phosphate concentrations) can carbon fixation ( p) be calculated (high). from which pCO2 Carbon isotope ratio of aqueous CO2 in the mixed layer can be determined (medium). can be calculated. Sea surface temperature can be determined (high). Atmospheric partial pressure of oxygen (pO2) is known or assumed (medium). Diagenetic effects are minimal, or can be quantified (medium). Boron isotopes Boron isotope ratios 100 to Calculated pCO2 is very sensi- Measured boron isotope ratio is accurate and precise (high). in foraminifera (11B) in foraminifera ~4000 ppm; tive to the boron isotope ratio The equilibrium constant for dissociation of boric acid and boron isotopic fractionation (or other calcifying 0 to 100 Ma of seawater which is relatively between B(OH)3 and B(OH)4 are well known (high). organisms) give poorly known, especially for Boron incorporation into carbonate is predominantly from borate ion (high). paleo-pH from the earlier Cenozoic. Effects which pCO2 can of foraminiferal preserva- Boron isotope ratio of foraminifer calcification reflects ambient surface seawater be calculated if a tion are not well understood. pH (high). value for a second Method has been used Aqueous pCO2 is in equilibrium with atmospheric pCO2 (medium). carbonate system successfully to reconstruct Habitats of extinct species can be determined (high). parameter (e.g., alka- glacial interglacial changes. There is no vital effect fractionation in extinct species, or it can be determined (medium). linity) is assumed. The boron isotope ratio of seawater (11Bsw) can be determined (medium). Ocean alkalinity or concentration of Total Dissolved Inorganic Carbon can be determined (high). Sea surface temperature (SST) and salinity (SSS) can be determined (high). Diagenetic effects are minimal or can be quantified (high). Carbon isotopes Atmospheric pCO2 1000 to Method works better for Isotopic composition of soil CO2 is reflected in soil carbonates below a depth in soil carbonate affects the relation- ~4000 ppm; some soil types than others. of 50 cm. (medium). and organic ship between the 0 to 400 Ma CO2 loss is difficult to The concentration of respired CO2 in the soil is known or assumed (medium). matter 13C of soil CO2 quantify and method and Isotopic composition of atmospheric CO2 is known or can be inferred (low). and the 13C of soil effects of late diagenesis may organic matter at be difficult to determine. Soil carbonates were precipitated in the vadose zone in exchange with atmospheric CO2 (high). 5 depth in certain soil types, hence mea- The original depth profile of a paleosoil can be determined (low). surement of these Burial (late) diagenetic effects are minimal or can be quantified (high). parameters in paleo- sols can be used to calculate past pCO2. Stomata in The relative frequen- 100 to Closely related species have Measured stomatal index is accurate and precise (high). plant leaves cy of stomata on ~1000 ppm; very different responses to Measured stomatal index is representative of the plant (high). fossil leaves (Stoma- 0 to 400 Ma pCO2. The assumption that The target plants adjust their stomatal index of leaves to optimize CO2 uptake (medium). tal Index; (Salisbury, short-term response is the same 1928) can be used to as the evolutionary response Atmospheric pCO2 close to the plant is representative of the atmosphere as calculate past atmo- is difficult to test. This and a whole (medium). spheric CO2 levels. the shape of the calibra- The quantitative relationship between stomatal index and CO2 observed on short time tion curves mean that much scales (ecophenotypic or plastic response ) applies over evolutionary time (low). greater certainty applies to low Environmental factors such as irradiance, atmospheric moisture, water availability, pCO2 and short time scales. temperature, and nutrient availability do not affect the relationship between stomatal index and CO2 (medium). Stomatal index response to CO2 of extinct species can be determined or assumed (low). Taphonomic processes do not affect stomatal index counts (high). Diagenetic processes do not affect stomatal index counts (high). 457 Chapter 5 Information from Paleoclimate Archives Table 5.A.3 | Summary of sea surface temperature (SST) proxy methods and confidence assessment of their main assumptions. Estimated Method Scientific Rationale Applica- Limitations Main Assumptions (relative confidence) bility 18O of mixed- Partitioning of 18O/16O from 0°C to 50°C; The 18O/16O ratios of recrystallized Analytical errors are negligible (high). layer planktonic seawater into calcite shells of 0 to 150 Ma planktonic foraminifer shells in carbonate- Sensitivity to T is high and similar to modern descendants (high). foraminifera all foraminifera is temperature rich sediments are biased toward colder Seawater 18O is known. The uncertainty varies with time dependent. Verified by theoretical, seafloor temperatures, and at most, can depending on presence of continental ice-sheets, though field and laboratory studies. Utilizes only constrain the lower limit of SST. The error is negligible in the Pleistocene and during minimal ice extant and extinct species that transition in preservation is progressive with periods such as the Eocene (<+/-0.25°C). Error doubles during resided in the photic zone. age. Well-preserved forams from clay-rich periods of Oligocene and early Neogene glaciation because sequences on continental margins are of weak constraints on ice-volume (medium to high). preferred. Diagenetic calcite is detectable by visual and microscopic techniques. Species lives in the mixed-layer and thus records SST (high). Local salinity/seawater 18O is known (low to medium). Carbonate ion/pH is similar to modern (medium, high). Foraminifera from clay-rich sequences are well preserved and 18O/16O ratios unaffected by diagenesis (high). Foraminifera from carbonate-rich pelagic sequences are well preserved and ratios unaffected by diagenesis (medium to low; decreasing confidence with age). Biased towards summer SST in polar oceans (medium). Mg/Ca in mixed- Partitioning of Mg/Ca from seawater 5°C to 35°C; Diagenetic recrystallization of foram shells Analytical errors are negligible (high). layer planktonic into calcite shells is temperature 0 to 65 Ma can bias ratios, though the direction of Mg containing oxide and organic contaminants have been foraminifera dependent. Calibration to T is based bias is unknown and comparisons with removed by oxidative/reductive cleaning (high). on empirical field and laboratory other proxies suggest it is minor. The Mg/ Sensitivity to T in extinct species is similar to modern culturing studies, as Mg concentra- Ca is also slightly sensitive to seawater species (medium). tions of inorganically precipitated pH. Long-term changes in seawater Mg/ calcite are an order of magnitude Ca, on the order of a 2 5%/10 Myr, Species lives in the mixed-layer and thus records SST (high). higher than in biogenic calcite. must be constrained via models. Seawater Mg/Ca is known (high to low: decreasing There is no ice-volume influence confidence with time). on seawater Mg/Ca, though Surface water carbonate ion/pH is similar to modern sensitivity does change with (medium). seawater Mg concentration. Foraminfera from clay-rich sequences are well preserved and ratios unaffected by diagenesis (high). Foraminifera from carbonate-rich pelagic sequences are well preserved and ratios unaffected by diagenesis (high to low; decreasing confidence with age). Biased towards summer SST in polar oceans (medium). TEX86 index The ratio of cyclopentane rings in 1°C to 40°C; The depth from which the bulk of sedi- Analytical errors are small (high). in Archea archaeal tetraether lipids (TEX), i.e., 0 to 150 Ma mentary GDGT s are produced is assumed Sensitivity to T similar to modern (medium). isoprenoid glycerol dibiphytanyl to be the mixed-layer though this cannot Species that produced tropical sedimentary GDGT s resided glycerol tetraethers (GDGTs), is sen- be verified, for the modern or past. At mainly in the mixed-layer and thus records SST sitive to the temperature of growth least two species with differing ecologies (high to medium). environment. The relationship and appear to be producing the tetraethers. The calibration with temperature is GDGT signal is ultimately an integrated Species that produced the sedimentary GDGT s in the sub-polar empirical (based on core tops), as community signal allowing the potential for to polar regions mainly resided in the mixed-layer and thus the underlying mechanism(s) for this evolutionary changes to influence regional records SST (low). relationship has yet to be identified. signals over time. Tetraethers are found in No alteration of GDGT ratios during degradation of compounds Verification of field calibrations measurable abundances on continental (medium to low: decreasing confidence with age). with laboratory cultures is still shelves and/or organic rich sediments. No contamination by GDGT s derived from terrestrial sources 5 in progress. The compounds are (high to medium if BIT index <0.3). extracted from bulk sediments. Biased towards summer SST in polar oceans (medium). UK37 Index Based on the relative concentra- 5°C to 28°C; The distribution of haptophyte algae Analytical errors are negligible (high). in Algae tion of C37 methyl ketones derived 0 to 50 Ma ranges from sub-polar to tropical. Sensitivity to SST similar to modern (high to medium; decreasing from the cells of haptophyte confidence with time). phytoplankton. Calibrations Species that produced the sedimentary alkenones lived in the are empirically derived through mixed-layer and thus record SST (high). field and culture studies. No alteration of alkenone saturation index during degradation of compounds (medium; decreasing confidence with age). Biased towards summer SST in polar oceans (medium). Microfossil Utilises a statistical correla- 0°C to 40°C; Dependent on quality, coverage, size The composition of modern assemblages can be correlated census modern tion between extant planktonic 0 to 5 Ma and representativeness of the core to SST (high). analogue microfossil assemblage data (most top modern analogue data base. Sensitivity of paleo-assemblages to SST is similar to modern techniques commonly foraminifera, but also Extant species reduce with increasing age. (high, but decreases with increasing age). diatoms and radiolarians) and This and paleogeographic and ocean Eurythermal assemblages responding to non-temperature (e.g., climate parameters. Most commonly circulation differences with age-limit nutrient availability) influences can be identified (medium). used statistical methods are modern applicability to less than 1 Ma. analogue technique (MAT) and That the extant species used to reconstruct SST mainly reside artificial neural network (ANN). in the mixed layer (medium to high). Depositional and post-depositional processes have not biased the assemblage (medium to high). 458 Table 5.A.4 | Assessment of leads and lags between Antarctic, hemispheric temperatures and atmospheric CO2 concentration during terminations. Chronological synthesis of publications, main findings, incorporation in IPCC assessments and key uncertainties. Lag Between Tempera- Investigated Source Tempera- Reference Source CO2 Data Lag Quantification Method ture and CO2 (positive, Key Limitations Period ture Data temperature lead) TAR: From a detailed study of the last three glacial terminations in the Vostok ice core, Fischer et al. (1999) conclude that CO2 increases started 600 +/- 400 years after the Antarctic warming. However, considering the large uncertainty in the ages of the CO2 and ice (1000 years or more if we consider the ice accumulation rate uncertainty), Petit et al. (1999) felt it premature to ascertain the sign of the phase relationship between CO2 and Antarctic temperature at the initiation of the terminations. In any event, CO2 changes parallel Antarctic temperature changes during deglaciations (Sowers and Bender, 1995; Blunier et al., 1997; Petit et al., 1999). Fischer et Termination I Taylor Dome, Byrda (CH4 Byrd d18O, Vostok D Maximum at onset of Antarctica: Ice core synchronization for Termination I (~300 years). al. (1999) synchonized age scales) (CH4 synchonized age) interglacial periods 600 +/- 400 years Gas age-ice age difference simulated by firn models for interglacial a a conditions could be overestimated by ~400 years. Terminations Vostok Vostok D (GT4 I, II, III (gas age scales based ice age scale) Signal-to-noise ratio. on firn modelling) Resolution of CO2 measurements and firnification smoothing Information from Paleoclimate Archives (~300 years). Petit et al. Terminations Vostoka Vostok D Onset of transitions Antarctica: (1999) I, II, III, IV (GT4 gas age scale based (GT4 ice age scale) in phase within uncertainties Gas age-ice age difference simulated by firn models for glacial Pépin et al. on firn modelling) conditions could be overestimated by up to 1500 years. (2001) Positive Resolution of CO2 measurements and firnification smoothing Mudelsee 0 420 ka Lagged generalised least square Antarctica : (~300 years). Vostok a Vostok D (2001) regression with parametric bootstrap 1300 +/- 1000 years Signal to noise ratio (1 ice core). (GT4 gas age scale) (GT4 ice age scale) resampling, entire record AR4: High-resolution ice core records of temperature proxies and CO2 during deglaciation indicates that Antarctic temperature starts to rise several hundred years before CO2 (Monnin et al., 2001; Caillon et al., 2003). During the last deglaciation, and possibly also the three previous ones, the onset of warming at both high southern and northern latitudes preceded by several thousand years the first signals of significant sea level increase resulting from the melting of the northern ice sheets linked with the rapid warming at high northern latitudes (Petit et al., 1999; Shackleton, 2000; Pépin et al., 2001). Current data are not accurate enough to identify whether warming started earlier in the SH or NH, but a major deglacial feature is the difference between North and South in terms of the magnitude and timing of strong reversals in the warming trend, which are not in phase between the hemispheres and are more pronounced in the NH (Blunier and Brook, 2001). Monnin et Termination I High resolution data from EDC (EPICA (European Crossing points of linear fit Antarctica: Gas age ice age difference (+/-1000 years). al. (2001) EDC on EDC1 gas age scale Project for Ice Coring in 800 +/- 600 years Signal to noise ratio (1 ice core). (based on firn modelling) Antarctica Dome C) on EDC1 ice age scale Caillon et Termination III Vostok on GT4 gas age scale Vostok 40Ar on GT4 Maximum lagged correlation Antarctica: Relationship between 40Ar and temperature assumed to be al. (2003) gas age scale 800 +/- 200 years instantaneous. The 800 years is a minimum CO2-temperature lag which does not account for a possible delayed response of firn gravitational fractionation to surface temperature change. AR5: For the last glacial termination, a large-scale temperature reconstruction (Shakun et al., 2012) documents that temperature change in the SH lead NH temperature change. This lead can be explained by the bipolar thermal seesaw concept (Stocker and Johnsen, 2003) (see also Section 5.7) and the related changes in the inter-hemispheric ocean heat transport, caused by weakening of the Atlantic Ocean meridional overturning circulation (AMOC) during the last glacial termination (Ganopolski and Roche, 2009). SH warming prior to NH warming can also be explained by the fast sea ice response to changes in austral spring insolation (Stott et al., 2007; Timmermann et al., 2009). According to these mechanisms, SH temperature lead over the NH is fully consistent with the NH orbital forcing of deglacial ice volume changes (high confidence) and the importance of the climate carbon cycle feedbacks in glacial interglacial transitions. The tight coupling is further highlighted by the near-zero lag between the deglacial rise in CO2 and averaged deglacial Antarctic temperature recently reported from improved estimates of gas-ice age differences (Pedro et al., 2012; Parrenin et al., 2013). Previous studies (Monnin et al., 2001) suggesting a temperature lead of 800 +/- 600 years over the deglacial CO2 rise probably overestimated gas-ice age differences. Shakun et Termination I EDC age scale synchro- NH: stack of 50 records Lag correlation (20 10 kyr) using SH: Uncertainties in the original age scales of each record: e.g., reservoir al. (2012) nized to GICC05b(Lemieux- including 2 Greenland Monte-Carlo statistics 620 +/- 660 years ages of marine sediments, radiocarbon calibration (intCal04), Dudon et al., 2010) ice cores Antarctic gas /ice chronology. NH: Assumption that time scale errors (e.g., from reservoir ages or ice core SH: stack of 30 records 720 +/- 660 years chronologies) are independent from each other. This could lead incl. 4 ice cores (Vostok, to higher-than-reported lag estimation uncertainties. EDML, EDC, Dome F)a on Global: their original age scale 460 +/- 340 years Similar limitations as in earlier studies for Antarctic temperature lead on CO2. Non stability of the phase lags: global temperature leads CO2 at the onset of deglacial warming. Chapter 5 459 (continued on next page) 5 5 Table 5.A.4 (continued) 460 Investigated Lag Between Tempera- Reference Source CO2 Data Source Tempera- Key Limitations Period Lag Quantification Method ture and CO2 (positive, Chapter 5 ture Data temperature lead) Pedro et al. Siple Dome and Byrd, synchro- 18O composite (Law Lag correlation (9 21 kyr) and Antarctica: Uncertainty on gas ice age difference in high accumulation (2012) nized to GICC05b age scale Dome, Siple Dome, Byrd, derivative lag correlation 60 to 380 years sites (<300 years) and on synchronization methods to GICC05. EDML and TALDICEa ice cores) synchronized to Data resolution (145 year for Byrd CO2, 266 year for Siple CO2). GICC05b using firn model- The CO2 data were resampled at 20 year resolution prior to the ling (Pedro et al., 2011) lag analysis, which may lead to an underestimation of the statistical error in the lag determination. Temperature versus other (e.g., elevation, moisture origin) signals in coastal ice core 18O. Correlation method sensitive to minima, maxima and inflexion points. (Parrenin et EDC, new gas age scale produced Stack temperature profile Monte-Carlo algorithm at Antarctica: Accuracy, resolution and interpolation of d15N of N2; assumption al., 2013) from the modified EDC3 ice derived from water isotopes linear break points Warming onset: of no firn convective zone at EDC under glacial conditions. age scale using lock-in depth from EDCa, Vostoka, Dome 10 +/- 160 years derived from d15N of N2 and Fujia, TALDICEa and EDMLa Blling onset: Data resolution and noise (e.g., precipitation intermittency adjusted to be consistent with synchronized to a modified 260 +/- 130 years biases in stable isotope records). GICC05b gas age scale. Processes EDC3 ice age scale Younger Dryas onset: affecting the gas lock-in depth 60 +/- 120 years such as impurities are implicitly Holocene onset: taken into account when using 500 +/- 90 years d15N (no use of firn models). Notes: a Names of different Antarctic ice cores (Byrd, Taylor Dome, Vostok, Siple Dome, Law Dome, TALDICE, Dome Fuji, EDML, EDC), with different locations, surface climate and firnification conditions. For the most inland sites (Vostok, EDC, Dome Fuji), at a given ice core depth, gas ages are lower than ice ages by 1500 to 2000 years (interglacial conditions) and 5000 5500 years (glacial conditions) while this gas age ice age difference is lower (400 to 800. years) for coastal, higher accumulation sites (Byrd, Law Dome, Siple Dome). b GICC05: Greenland Ice Core Chronology 2005, based on annual layer counting in Greenland (NGRIP, GRIP and DYE3 ice cores) (Rasmussen et al., 2006), back to 60 ka (Svensson et al., 2008). The synchronism between rapid shifts in Greenland climate and in atmospheric CH4 variations allows to transfer GICC05 to Greenland and then to Antarctic CH4 variations (Blunier et al., 2007). Additional point: CO2-Antarctic temperature phase during AIM events. Studies on CO2 phasing relative to CH4 during Dansgaard Oeschger (DO) event onsets (Ahn and Brook, 2008; Ahn et al., 2012; Bereiter et al., 2012) suggest a lag of maximum CO2 concentration relative to the Antarctic Isotope Maxima (AIM) 19, 20, 21, 23 and 24 by 260 +/- 220 years during MIS5 and 670 to 870 years +/- 360 years relative to AIM 12, 14, 17 during MIS3 (Bereiter et al., 2012). Accordingly, the lag is dependent on the climate state. A lag is not discernible for shorter AIM. This study avoids the ice age gas age difference problem, but relies on the bipolar seesaw concept, i.e., it assumes that maximum Antarctic temperatures are coincident to the onset of DO events and the concurrent CH4 increase. Information from Paleoclimate Archives Table 5.A.5 | Summary of seasonal estimates of terrestrial surface temperature anomalies (°C) for the Last Interglacial (LIG) plotted in Figure 5.6. pdf-method stands for probability-density function method. Dating methods: AMS=Accelerator mass spectrometry; IRSL=Infrared stimulated luminescence; OSL=Optically stimulated luminescence; TL=thermoluminescence. Temperature Latitude Longitude Elevation Anomaly (°C) Site Dating Proxy References (°N) (°E) (m asl) July January Netherlands, Amsterdam Terminal 52.38 4.91 1 Eemian, U/Th Pollen, diatoms, molluscs, foraminifera, dinoflagellates, ostracods, 2 3 (Zagwijn, 1996; van Leeuwen heavy minerals, paleomagnetism, grain-size, trace elements et al., 2000; Beets et al., 2006) E Canada, Addington 45.65 62.1 50 Uranium-series Pollen 4 (Dreimanis, 1992) Forks, Nova Scotia NW America, Humptulips 47.28 123.55 100 interpolation with 14C dates Pollen 1 (Heusser and Heusser, 1990) (peat) of the same core NE Siberia, Lake El gygytgyn 67.5 172 492 TL Pollen 6 14 (Lozhkin and Anderson, 2006) NW Alaska, Squirrel Lake 67.1 160.38 91 TL Pollen, plant macrofossils 1.5 2 (Berger and Anderson, 2000) Information from Paleoclimate Archives SE Baffin Island, Robinson Lake 63.38 64.25 170 TL, IRSL Pollen, diatoms, macrofossils 5 (Miller et al., 1999; Fréchette et al., 2006) Sweden, Leveäniemi 67.63 21.02 380 125 ka Pollen, pdf method 2.1 6.6 (Kühl, 2003, and ref. therein) Finland, Evijärvi 63.43 23.33 67 125 ka Pollen, pdf method 2.3 10.3 (Kühl, 2003, and ref. therein) Finland, Norinkylä 62.58 22.02 110 125 ka Pollen, pdf method 1.3 7.7 (Kühl, 2003, and ref. therein) Estland, Prangli 59.65 25.08 5 125 ka Pollen, pdf method 1.7 3.2 (Kühl, 2003, and ref. therein) Estland, Waewa-Ringen 58.33 26.73 50 125 ka Pollen, pdf method 1.3 6.8 (Kühl, 2003, and ref. therein) Norway, Fjsanger 60.35 5.33 5 125 ka Pollen, pdf method 2.9 1.6 (Kühl, 2003, and ref. therein) Denmark, Hollerup 56.7 9.83 40 125 ka Pollen, pdf method 1.1 3.7 (Kühl, 2003, and ref. therein) Germany, Husum 54.52 9.17 2 125 ka Pollen, pdf method 2.3 0.3 (Kühl, 2003, and ref. therein) Germany, Rederstall 54.28 9.25 0 125 ka Pollen, pdf method 0.3 1 (Kühl, 2003, and ref. therein) Germany, Odderade 54.23 9.28 7 125 ka Pollen, pdf method 1.8 1.4 (Kühl, 2003, and ref. therein) Germany, Helgoland 53.95 8.85 1 125 ka Pollen & macrofossils, pdf method 2 0.6 (Kühl, 2003, and ref. therein) Germany, Oerel 53.48 9.07 12.5 125 ka Pollen, pdf method 1.1 0.6 (Kühl, 2003, and ref. therein) Germany, Quakenbrück 52.4 7.57 26 125 ka Pollen, pdf method 1.4 0.3 (Kühl, 2003, and ref. therein) Netherlands, Amersfoort 52.15 5.38 3 125 ka Pollen, pdf method 0.3 0.5 (Kühl, 2003, and ref. therein) Germany, Wallensen 52 9.4 160 125 ka Pollen & macrofossils, pdf method 1.9 0.7 (Kühl, 2003, and ref. therein) Germany, Neumark-Nord 51.33 11.88 90 125 ka Pollen & macrofossils, pdf method 1.4 0.5 (Kühl, 2003, and ref. therein) Germany, Grabschütz 51.48 12.28 100 125 ka Pollen & macrofossils, pdf method 1.3 0.2 (Kühl, 2003, and ref. therein) Germany, Schönfeld 51.8 13.89 65 125 ka Pollen, pdf method 0.5 2.6 (Kühl, 2003, and ref. therein) Germany, Kittlitz 51.43 14.78 150 125 ka Pollen, pdf method 1.4 2.4 (Kühl, 2003, and ref. therein) Poland, Imbramovice 50.88 16.57 175 125 ka Pollen & macrofossils, pdf method 2.5 3.4 (Kühl, 2003, and ref. therein) Poland, Zgierz-Rudunki 51.87 19.42 200 125 ka Pollen & macrofossils, pdf method 0.2 2.6 (Kühl, 2003, and ref. therein) Poland, Wladyslawow 52.13 18.47 100 125 ka Pollen & macrofossils, pdf method 2.4 0.9 (Kühl, 2003, and ref. therein) Poland, Glowczyn 52.48 20.21 124 125 ka Pollen, pdf method 1.4 3.6 (Kühl, 2003, and ref. therein) Poland, Gora Kalwaria 51.98 21.18 100 125 ka Pollen & macrofossils, pdf method 0.3 1.9 (Kühl, 2003, and ref. therein) Poland, Naklo 53.15 17.6 62 125 ka Pollen & macrofossils, pdf method 0.6 3 (Kühl, 2003, and ref. therein) Poland, Grudziadz 53.48 18.75 10 125 ka Pollen, pdf method 0.5 1.6 (Kühl, 2003, and ref. therein) England, Wing 52.62 0.78 119 125 ka Pollen, pdf method 2.4 0.5 (Kühl, 2003, and ref. therein) Chapter 5 461 (continued on next page) 5 5 Table 5.A.5 (continued) 462 Temperature Latitude Longitude Elevation Anomaly (°C) Site Dating Proxy References (°N) (°E) (m asl) Chapter 5 July January England, Bobbitshole 52.05 1.15 3 125 ka Pollen & macrofossils, pdf method 2.5 2.3 (Kühl, 2003, and ref. therein) England, Selsey 50.42 0.48 0 125 ka Pollen & macrofossils, pdf method 0.7 2.2 (Kühl, 2003, and ref. therein) England, Stone 50.42 1.02 0 125 ka Pollen & macrofossils, pdf method 2.9 2.3 (Kühl, 2003, and ref. therein) France, La Grande Pile 47.73 6.5 330 125 ka Pollen, pdf method 0.5 0.7 (Kühl, 2003, and ref. therein) Germany, Krumbach 48.04 9.5 606 125 ka Pollen, pdf method 0.5 2.3 (Kühl, 2003, and ref. therein) Germany, Jammertal 48.1 9.72 578 125 ka Pollen, pdf method 0 0.5 (Kühl, 2003, and ref. therein) Germany, Samerberg 47.75 12.2 600 125 ka Pollen & macrofossils, pdf method 2.7 4.1 (Kühl, 2003, and ref. therein) Germany, Zeifen 47.93 12.83 427 125 ka Pollen & macrofossils, pdf method 3.4 2.5 (Kühl, 2003, and ref. therein) Austria, Mondsee 47.51 13.21 534 125 ka Pollen & macrofossils, vmethod 4.3 1.3 (Kühl, 2003, and ref. therein) Germany, Eurach 47.29 11.13 610 125 ka Pollen, pdf method 6.4 4.7 (Kühl, 2003, and ref. therein) Germany, Füramoos 47.91 9.95 662 125 ka Pollen, modern analogue vegetation (MAV) and probability mutual climatic 2.8 1.2 (Müller, 2001) spheres (PCS) Swiss, Gondiswil-Seilern 47.12 7.88 639 125 ka Pollen, pdf method 0.1 0.4 (Kühl, 2003, and ref. therein) Swiss, Meikirch 47 7.37 620 125 ka Pollen, pdf method 0.3 0.3 (Kühl, 2003, and ref. therein) Swiss, Meikirch II 47.01 7.33 620 125 ka Pollen, modern analogue vegetation (MAV) and probability mutual climatic 1.2 4.5 (Welten, 1988) spheres (PCS) Swiss, Beerenmösli 47.06 7.51 649 125 ka Pollen, modern analogue vegetation (MAV) and probability mutual climatic 1.1 5.5 (Wegmüller, 1992) spheres (PCS) France, Lac Du Bouchet 44.55 3.47 1200 125 ka Pollen, pdf method 1.7 0.2 (Kühl, 2003, and ref. therein) Italia, Valle di Castiglione 41.85 12.73 110 125 ka Pollen, pdf method 3.4 5.9 (Kühl, 2003, and ref. therein) Romania, Turbuta 47.25 23.3 275 U/Th, 125 ka Pollen, pdf method 1.2 2.4 (Kühl, 2003, and ref. therein) Greece, Tenaghi Phillipon 41.17 24.33 40 125 ka Pollen, pdf method 0.9 2 (Kühl, 2003, and ref. therein) Greece, Ioannina 39.67 20.85 472 125 ka Pollen, pdf method 1.9 1.9 (Kühl, 2003, and ref. therein) Germany, Bispingen 53.08 9.98 100 TL,125ka Pollen, pdf method 1.2 0.9 (Kühl, 2003, and ref. therein) Germany, Gröbern 52.02 12.08 95 TL, 125 ka Pollen & macrofossils, pdf method 0.4 1.8 (Kühl, 2003, and ref. therein) Germany, Klinge 51.75 14.52 10 pollen correlation Pollen (Grichuk, 1985) 0 2 (Novenko et al., 2008) Germany, Ober-Rheinebene 49.82 8.4 90 Eem Vegetation, mammals (von Königswald, 2007) near Darmstadt France, La Flachere 45.23 5.58 333 125 ka Pollen, modern analogue vegetation (MAV) and probability mutual climatic 0.9 14.4 (Peschke et al., 2000) spheres (PCS) France, Lathuile 45.75 6.14 452 125 ka Pollen, modern analogue vegetation (MAV) and probability mutual climatic 0.5 2.2 (Klotz et al., 2003) spheres (PCS) France, La Grande Pile 47.73 6.5 330 TL, 125 ka Pollen, carbon isotopes 10 15 (Rousseau et al., 2007) Japan, Lake Biwa 35.33 136.17 86 tephrochronological and mag- Pollen 3 2.5 (Nakagawa et al., 2008) netostratigraphic information Pollen 5.5 1.5 (Tarasov et al., 2011) Siberia, Lake Baikal, Conti- 53.95 108.9 386 AMS, 125 ka Pollen 2 1 (Tarasov et al., 2005) nent Ridge CON01-603-2 Bol shoi Lyakhovsky Island 73.33 141.5 40 MIS 5, ca. 130 110 ka (IRSL) Pollen, beetles, chironomids, rhizopods, palaeomagnetic, BMA 4.5 (Andreev et al., 2004) Wairarapa Valley; New Zealand 41.37 175.07 10 OSL, MIS 5e Beetles 2.8 winter 2.1 summer (Marra, 2003) Information from Paleoclimate Archives Information from Paleoclimate Archives Chapter 5 5.A.1 Additional Information to Section 5.3.5 centred on the five years (1259, 1456, 1599, 1695 and 1814) during 850 1999 when the Crowley and Unterman (2013) volcanic forcing, Section 5.3.5 assesses knowledge of changes in hemispheric and smoothed with a 40-year Gaussian-weighted filter, exceeds 0.2 W m 2 global temperature over the last 2 ka from a range of studies, recon- below the 1500 1899 mean volcanic forcing, except that a year is not structions and simulations. Tables 5.A.1 and 5.A.6 provide further selected if it is within 39 years of another year that has a larger nega- information about the datasets used in Figures 5.7 5.9 and 5.12, and tive 40-year smoothed volcanic forcing. The composite of the strong- the construction of Figure 5.8 is described in more detail. All recon- est multidecadal changes in the solar forcing shown in (d) is formed structions assessed in, or published since, AR4 were considered, but from 80-year periods centred on the seven years (1044, 1177, 1451, those that have been superseded by a related study using an expanded 1539, 1673, 1801 and 1905) during 850 1999 when the Ammann et proxy dataset and/or updated statistical methods were excluded. al. (2007) solar forcing, band-pass filtered to retain variations on time scales between 20 and 160 years, is reduced by at least 0.1 W m 2 over Figure 5.8 compares simulated and reconstructed NH temperature a 40-year period. Reconstructed and simulated temperature timeseries changes (see caption). Some reconstructions represent a smaller spa- were smoothed with a 40-year Gaussian-weighted filter in (c) or 20- tial domain than the full NH or a specific season, while annual tem- to-160-year band-pass filtered in (d), and each composite was shifted peratures for the full NH mean are shown for the simulations. Mul- to have zero mean during the (b) 5 or (c, d) 40 years preceding the peak ti-model means and estimated 90% multi-model ranges are shown by negative forcing. the thick and thin lines, respectively, for two groups of simulations (Table 5.A.1): those forced by stronger (weaker) solar variability in red (blue). Note that the strength of the solar variability is not the only difference between these groups: the GCMs and the other forcings are also different between the groups. In Figure 5.7, the reconstructions are shown as deviations from their 1881 1980 means, which allows them to be compared with the instrumental record. In Figure 5.8a, all timeseries are expressed as anomalies from their 1500 1850 mean (prior to smoothing with a 30-year Gaussian-weighted filter, truncated 7 years from the end of each series to reduce end-effects of the filter) because the comparison of simulations and reconstructions is less sen- sitive to errors in anthropogenic aerosol forcing applied to the models when a pre-industrial reference period is used, and less sensitive to different realisations of internal variability with a multi-century refer- ence period. The grey shading represents a measure of the overlapping reconstruction confidence intervals, with scores of 1 and 2 assigned to temperatures within +/-1.645 standard deviation (90% confidence range) or +/-1 standard deviation, respectively, then summed over all reconstructions and scaled so that the maximum score is dark grey, and minimum score is pale grey. This allows the multi-model ensem- bles to be compared with the ensemble of reconstructed NH tempera- tures, taking into account the published confidence intervals. The superposed composites (time segments from selected periods 5 positioned so that the years with peak negative forcing are aligned; top panels of Figure 5.8b d) compare the simulated and reconstructed temperatures (bottom panels) associated with (b) individual volcanic forcing events; (c) multi-decadal changes in volcanic activity; (d) mul- ti-decadal changes in solar irradiance. Only reconstructions capable of resolving (b) interannual or (c, d) interdecadal variations are used. The thick green line in Figure 5.8d shows the composite mean of the vol- canic forcing, also band-pass filtered, but constructed using the solar composite periods to demonstrate the changes in volcanic forcing that are coincident with solar variability. The composite of individual vol- canic events shown in (b) is formed by aligned time segments centred on the 12 years (1442, 1456, 1600, 1641, 1674, 1696, 1816, 1835, 1884, 1903, 1983 and 1992) during 1400 1999 that the Crowley and Unterman (2013) volcanic forcing history exceeds 1.0 W m 2 below the 1500 1899 mean volcanic forcing, excluding events within 7 years (before or after) of a stronger event. The composite of multi-decadal changes in volcanic forcing shown in (c) is formed from 80-year periods 463 Chapter 5 Information from Paleoclimate Archives Table 5.A.6 | Hemispheric and global temperature reconstructions assessed in Table 5.4 and used in Figures 5.7 to 5.9. Reference Proxy Coverageb Period (CE) Resolution Regiona Method & Data [Identifier] H M L O Principal component forward regression of regional Briffa et al. (2001) [only composite averages. used in Figure 5.8b d due 1402 1960 Annual (summer) L 20°N to 90°N T Tree-ring density network, age effect removed via to divergence issue] age-band decomposition. Composite average of local records Christiansen and Ljungqvist (2012) [CL12loc] 1 1973 Annual L+S 30°N to 90°N calibrated by local inverse regression. Multi-proxy network. Forward linear regression of composite average. D Arrigo et al. (2006) [Da06treecps] 713 1995 Annual L 20°N to 90°N T Network of long tree-ring width chronologies, age effect removed by Regional Curve Standardisation. Variance matching of composite average, adjusted for artificial changes in variance. Frank et al. (2007) [Fr07treecps] 831 1992 Annual L 20°N to 90°N T T Network of long tree-ring width chronologies, age effect removed by Regional Curve Standardisation. Total Least Squares regression. Hegerl et al. (2007) [He07tls] 558 1960 Decadal L 30°N to 90°N T T Multi-proxy network. Variance matching of composite average. Juckes et al. (2007) [Ju07cvm] 1000 1980 Annual L+S 0° to 90°N T T Multi-proxy network. L 0° to 90°N Leclercq and Oerlemans Inversion of glacier length response model. (2012) [LO12glac] 1600 2000 Multidecadal L 90S to 0° T T 308 glacier records. L 90°S to 90°N Variance matching of composite average. Ljungqvist (2010) [Lj10cps] 1 1999 Decadal L+S 30°N to 90°N T T Multi-proxy network. Loehle and McCulloch Average of calibrated local records. (2008) [LM08ave] 16 1935 Multidecadal L+S mostly 0° to 90°N T T T Multi-proxy network (almost no tree-rings). Mann et al. (2008) [Ma08cpsl] L [cpsl/eivl] and L+S [eivf] (i) Variance matching of composite average. [Ma08eivl] [Ma08eivf] 200 1980 Decadal versions, 0° to 90°N, 0° to T T (ii) Total Least Squares regression. 90°S, and 90°S to 90°N Multi-proxy network.c [Ma08min7eivf] Regularized Expectation Maximization with Mann et al. (2009) [Ma09regm] 500 1849 Decadal L+S 0 to 90°N T T Truncated Total Least Squares. Multi-proxy network.c Variance matching of composites of wavelet Moberg et al. (2005) decomposed records. [Mo05wave] 1 1979 Annual L+S 0° to 90°N T T Tree-ring width network for short time scales; non-tree-ring network for long time scales. L 0° to 90°N Pollack and Smerdon (2004) [PS04bore] 1500 2000 Centennial L 0° to 90°S T T Borehole temperature profiles inversion L 90°S to 90°N Principal component regression with autoregressive timeseries model. Shi et al. (2013) [Sh13pcar] 1000 1998 Annual L 0 to 90°N T Multi-proxy network (tree-ring and non-tree-ring versions). 5 Notes: a Region: L = land only, L+S = land and sea, latitude range indicated. b Proxy location and coverage: H = high latitude, M = mid latitude, L = low latitude, O = oceans, = none or very few, T = limited, = moderate c These studies also present versions without tree-rings or without seven inhomogeneous proxies (including the Lake Korttajärvi sediment records; Tiljander et al., 2003). The latter version is used in Figure 5.7a (Ma08min7eivf) in preference to the reconstruction from the full network. The impact of these seven proxies on the other NH reconstructions is negligible (MA08cpsl) or results in a slightly warmer pre-900 reconstruction compared to the version without them (Ma09regm). 464 Carbon and Other Biogeochemical Cycles Coordinating Lead Authors: 6 Philippe Ciais (France), Christopher Sabine (USA) Lead Authors: Govindasamy Bala (India), Laurent Bopp (France), Victor Brovkin (Germany/Russian Federation), Josep Canadell (Australia), Abha Chhabra (India), Ruth DeFries (USA), James Galloway (USA), Martin Heimann (Germany), Christopher Jones (UK), Corinne Le Quéré (UK), Ranga B. Myneni (USA), Shilong Piao (China), Peter Thornton (USA) Contributing Authors: Anders Ahlström (Sweden), Alessandro Anav (UK/Italy), Oliver Andrews (UK), David Archer (USA), Vivek Arora (Canada), Gordon Bonan (USA), Alberto Vieira Borges (Belgium/Portugal), Philippe Bousquet (France), Lex Bouwman (Netherlands), Lori M. Bruhwiler (USA), Kenneth Caldeira (USA), Long Cao (China), Jérôme Chappellaz (France), Frédéric Chevallier (France), Cory Cleveland (USA), Peter Cox (UK), Frank J. Dentener (EU/Netherlands), Scott C. Doney (USA), Jan Willem Erisman (Netherlands), Eugenie S. Euskirchen (USA), Pierre Friedlingstein (UK/Belgium), Nicolas Gruber (Switzerland), Kevin Gurney (USA), Elisabeth A. Holland (Fiji/ USA), Brett Hopwood (USA), Richard A. Houghton (USA), Joanna I. House (UK), Sander Houweling (Netherlands), Stephen Hunter (UK), George Hurtt (USA), Andrew D. Jacobson (USA), Atul Jain (USA), Fortunat Joos (Switzerland), Johann Jungclaus (Germany), Jed O. Kaplan (Switzerland/Belgium/USA), Etsushi Kato (Japan), Ralph Keeling (USA), Samar Khatiwala (USA), Stefanie Kirschke (France/Germany), Kees Klein Goldewijk (Netherlands), Silvia Kloster (Germany), Charles Koven (USA), Carolien Kroeze (Netherlands), Jean-François Lamarque (USA/Belgium), Keith Lassey (New Zealand), Rachel M. Law (Australia), Andrew Lenton (Australia), Mark R. Lomas (UK), Yiqi Luo (USA), Takashi Maki (Japan), Gregg Marland (USA), H. Damon Matthews (Canada), Emilio Mayorga (USA), Joe R. Melton (Canada), Nicolas Metzl (France), Guy Munhoven (Belgium/Luxembourg), Yosuke Niwa (Japan), Richard J. Norby (USA), Fiona O Connor (UK/Ireland), James Orr (France), Geun-Ha Park (USA), Prabir Patra (Japan/ India), Anna Peregon (France/Russian Federation), Wouter Peters (Netherlands), Philippe Peylin (France), Stephen Piper (USA), Julia Pongratz (Germany), Ben Poulter (France/USA), Peter A. Raymond (USA), Peter Rayner (Australia), Andy Ridgwell (UK), Bruno Ringeval (Netherlands/ France), Christian Rödenbeck (Germany), Marielle Saunois (France), Andreas Schmittner (USA/Germany), Edward Schuur (USA), Stephen Sitch (UK), Renato Spahni (Switzerland), Benjamin Stocker (Switzerland), Taro Takahashi (USA), Rona L. Thompson (Norway/New Zealand), Jerry Tjiputra (Norway/Indonesia), Guido van der Werf (Netherlands), Detlef van Vuuren (Netherlands), Apostolos Voulgarakis (UK/Greece), Rita Wania (Austria), Sönke Zaehle (Germany), Ning Zeng (USA) Review Editors: Christoph Heinze (Norway), Pieter Tans (USA), Timo Vesala (Finland) This chapter should be cited as: Ciais, P., C. Sabine, G. Bala, L. Bopp, V. Brovkin, J. Canadell, A. Chhabra, R. DeFries, J. Galloway, M. Heimann, C. Jones, C. Le Quéré, R.B. Myneni, S. Piao and P. Thornton, 2013: Carbon and Other Biogeochemical Cycles. In: Cli- mate Change 2013: The Physical Science Basis. Contribution of Working Group I to the Fifth Assessment Report of the Intergovernmental Panel on Climate Change [Stocker, T.F., D. Qin, G.-K. Plattner, M. Tignor, S.K. Allen, J. Boschung, A. Nauels, Y. Xia, V. Bex and P.M. Midgley (eds.)]. Cambridge University Press, Cambridge, United Kingdom and New York, NY, USA. 465 Table of Contents Executive Summary...................................................................... 467 6.5 Potential Effects of Carbon Dioxide Removal Methods and Solar Radiation Management on the Carbon Cycle........................................................ 546 6.1 Introduction....................................................................... 470 6.5.1 Introduction to Carbon Dioxide 6.1.1 Global Carbon Cycle Overview................................... 470 Removal Methods...................................................... 546 Box 6.1: Multiple Residence Times for an Excess of Carbon 6.5.2 Carbon Cycle Processes Involved in Carbon Dioxide Emitted in the Atmosphere......................................... 472 Dioxide Removal Methods......................................... 547 6.1.2 Industrial Era.............................................................. 474 6.5.3 Impacts of Carbon Dioxide Removal Methods on 6.1.3 Connections Between Carbon and the Nitrogen Carbon Cycle and Climate.......................................... 550 and Oxygen Cycles..................................................... 475 6.5.4 Impacts of Solar Radiation Management on the Box 6.2: Nitrogen Cycle and Climate-Carbon Carbon Cycle.............................................................. 551 Cycle Feedbacks.......................................................................... 477 6.5.5 Synthesis.................................................................... 552 6.2 Variations in Carbon and Other Biogeochemical References .................................................................................. 553 Cycles Before the Fossil Fuel Era................................. 480 6.2.1 Glacial Interglacial Greenhouse Gas Changes........... 480 Frequently Asked Questions 6.2.2 Greenhouse Gas Changes over the Holocene............ 483 FAQ 6.1 Could Rapid Release of Methane and Carbon 6.2.3 Greenhouse Gas Changes over the Dioxide from Thawing Permafrost or Ocean Last Millennium......................................................... 485 Warming Substantially Increase Warming?......... 530 FAQ 6.2 What Happens to Carbon Dioxide After It Is 6.3 Evolution of Biogeochemical Cycles Since the Emitted into the Atmosphere?.............................. 544 Industrial Revolution...................................................... 486 6.3.1 Carbon Dioxide Emissions and Their Fate Supplementary Material Since 1750................................................................. 486 Supplementary Material is available in online versions of the report. 6.3.2 Global Carbon Dioxide Budget................................... 488 Box 6.3: The Carbon Dioxide Fertilisation Effect.................... 502 6.3.3 Global Methane Budget............................................. 508 6.3.4 Global Nitrogen Budgets and Global Nitrous Oxide Budget in the 1990s......................................... 510 6.4. Projections of Future Carbon and Other Biogeochemical Cycles................................................... 514 6.4.1 Introduction............................................................... 514 6.4.2 Carbon Cycle Feedbacks in Climate Modelling Intercomparison Project Phase 5 Models................... 514 Box 6.4: Climate Carbon Cycle Models and Experimental Design.................................................................. 516 6.4.3 Implications of the Future Projections for the Carbon Cycle and Compatible Emissions................... 523 6.4.4 Future Ocean Acidification......................................... 528 6 6.4.5 Future Ocean Oxygen Depletion................................ 532 6.4.6 Future Trends in the Nitrogen Cycle and Impact on Carbon Fluxes....................................................... 535 6.4.7 Future Changes in Methane Emissions...................... 539 6.4.8 Other Drivers of Future Carbon Cycle Changes.......... 542 6.4.9 The Long-term Carbon Cycle and Commitments........ 543 466 Carbon and Other Biogeochemical Cycles Chapter 6 Executive Summary carbon cycle reservoirs. The ocean reservoir stored 155 +/- 30 PgC. Veg- etation biomass and soils not affected by land use change stored 160 This chapter addresses the biogeochemical cycles of carbon dioxide +/- 90 PgC. {6.1, 6.3, 6.3.2.3, Table 6.1, Figure 6.8} (CO2), methane (CH4) and nitrous oxide (N2O). The three greenhouse gases (GHGs) have increased in the atmosphere since pre-industrial Carbon emissions from fossil fuel combustion and cement pro- times, and this increase is the main driving cause of climate change duction increased faster during the 2000 2011 period than (Chapter 10). CO2, CH4 and N2O altogether amount to 80% of the total during the 1990 1999 period. These emissions were 9.5 +/- 0.8 PgC radiative forcing from well-mixed GHGs (Chapter 8). The increase of yr 1 in 2011, 54% above their 1990 level. Anthropogenic net CO2 emis- CO2, CH4 and N2O is caused by anthropogenic emissions from the use sions from land use change were 0.9 +/- 0.8 PgC yr 1 throughout the of fossil fuel as a source of energy and from land use and land use past decade, and represent about 10% of the total anthropogenic CO2 changes, in particular agriculture. The observed change in the atmos- emissions. It is more likely than not2 that net CO2 emissions from land pheric concentration of CO2, CH4 and N2O results from the dynamic use change decreased during 2000 2011 compared to 1990 1999. balance between anthropogenic emissions, and the perturbation of {6.3, Table 6.1, Table 6.2, Figure 6.8} natural processes that leads to a partial removal of these gases from the atmosphere. Natural processes are linked to physical conditions, Atmospheric CO2 concentration increased at an average rate chemical reactions and biological transformations and they respond of 2.0 +/- 0.1 ppm yr 1 during 2002 2011. This decadal rate of themselves to perturbed atmospheric composition and climate change. increase is higher than during any previous decade since direct Therefore, the physical climate system and the biogeochemical cycles atmospheric concentration measurements began in 1958. Glob- of CO2, CH4 and N2O are coupled. This chapter addresses the present ally, the size of the combined natural land and ocean sinks of CO2 human-caused perturbation of the biogeochemical cycles of CO2, CH4 approximately followed the atmospheric rate of increase, removing and N2O, their variations in the past coupled to climate variations and 55% of the total anthropogenic emissions every year on average their projected evolution during this century under future scenarios. during 1958 2011. {6.3, Table 6.1} The Human-Caused Perturbation in the Industrial Era After almost one decade of stable CH4 concentrations since the late 1990s, atmospheric measurements have shown renewed CO2 increased by 40% from 278 ppm about 1750 to 390.5 ppm CH4 concentrations growth since 2007. The drivers of this renewed in 2011. During the same time interval, CH4 increased by 150% growth are still debated. The methane budget for the decade of 2000 from 722 ppb to 1803 ppb, and N2O by 20% from 271 ppb to 2009 (bottom-up estimates) is 177 to 284 Tg(CH4) yr 1 for natural 324.2 ppb in 2011. It is unequivocal that the current concentrations wetlands emissions, 187 to 224 Tg(CH4) yr 1 for agriculture and waste of atmospheric CO2, CH4 and N2O exceed any level measured for at (rice, animals and waste), 85 to 105 Tg(CH4) yr 1 for fossil fuel related least the past 800,000 years, the period covered by ice cores. Further- emissions, 61 to 200 Tg(CH4) yr 1 for other natural emissions including, more, the average rate of increase of these three gases observed over among other fluxes, geological, termites and fresh water emissions, the past century exceeds any observed rate of change over the previ- and 32 to 39 Tg(CH4) yr 1 for biomass and biofuel burning (the range ous 20,000 years. {2.2, 5.2, 6.1, 6.2} indicates the expanse of literature values). Anthropogenic emissions account for 50 to 65% of total emissions. By including natural geo- Anthropogenic CO2 emissions to the atmosphere were 555 +/- 85 logical CH4 emissions that were not accounted for in previous budg- PgC (1 PgC = 1015 gC) between 1750 and 2011. Of this amount, ets, the fossil component of the total CH4 emissions (i.e., anthropo- fossil fuel combustion and cement production contributed 375 +/- 30 genic emissions related to leaks in the fossil fuel industry and natural PgC and land use change (including deforestation, afforestation and geological leaks) is now estimated to amount to about 30% of the reforestation) contributed 180 +/- 80 PgC. {6.3.1, Table 6.1} total CH4 emissions (medium confidence). Climate driven fluctuations of CH4 emissions from natural wetlands are the main drivers of the With a very high level of confidence1, the increase in CO2 emis- global interannual variability of CH4 emissions (high confidence), with sions from fossil fuel burning and those arising from land a smaller contribution from the variability in emissions from biomass use change are the dominant cause of the observed increase burning during high fire years. {6.3.3, Figure 6.2, Table 6.8} in atmospheric CO2  concentration. About half of the emissions remained in the atmosphere (240 +/- 10 PgC) since 1750. The rest The concentration of N2O increased at a rate of 0.73 +/- 0.03 ppb was removed from the atmosphere by sinks and stored in the natural yr 1  over the last three decades. Emissions of N2O to the atmos- phere are mostly caused by nitrification and de-nitrification reactions 6 In this Report, the following summary terms are used to describe the available evidence: limited, medium, or robust; and for the degree of agreement: low, medium, or 1 high. A level of confidence is expressed using five qualifiers: very low, low, medium, high, and very high, and typeset in italics, e.g., medium confidence. For a given evi- dence and agreement statement, different confidence levels can be assigned, but increasing levels of evidence and degrees of agreement are correlated with increasing confidence (see Section 1.4 and Box TS.1 for more details). In this Report, the following terms have been used to indicate the assessed likelihood of an outcome or a result: Virtually certain 99 100% probability, Very likely 2 90 100%, Likely 66 100%, About as likely as not 33 66%, Unlikely 0 33%, Very unlikely 0 10%, Exceptionally unlikely 0 1%. Additional terms (Extremely likely: 95 100%, More likely than not >50 100%, and Extremely unlikely 0 5%) may also be used when appropriate. Assessed likelihood is typeset in italics, e.g., very likely (see Section 1.4 and Box TS.1 for more details). 467 Chapter 6 Carbon and Other Biogeochemical Cycles of reactive nitrogen in soils and in the ocean. Anthropogenic N2O emis- under all RCPs, but with some models simulating a net loss of carbon sions increased steadily over the last two decades and were 6.9 (2.7 by the land due to the combined effect of climate change and land use to 11.1) TgN (N2O) yr 1 in 2006. Anthropogenic N2O emissions are 1.7 change. In view of the large spread of model results and incomplete to 4.8 TgN (N2O) yr 1 from the application of nitrogenous fertilisers in process representation, there is low confidence on the magnitude of agriculture, 0.2 to 1.8 TgN (N2O) yr 1 from fossil fuel use and industrial modelled future land carbon changes. {6.4.3, Figure 6.24} processes, 0.2 to 1.0 TgN (N2O) yr 1 from biomass burning (including biofuels) and 0.4 to 1.3 TgN (N2O) yr 1 from  land emissions due to There is high confidence that climate change will partially offset atmospheric nitrogen deposition (the range indicates expand of liter- increases in global land and ocean carbon sinks caused by rising ature values). Natural N2O emissions derived from soils, oceans and a atmospheric CO2. Yet, there are regional differences among Climate small atmospheric source are together 5.4 to 19.6 TgN (N2O) yr 1. {6.3, Modelling Intercomparison Project Phase 5 (CMIP5) Earth System 6.3.4, Figure 6.4c, Figure 6.19, Table 6.9} Models, in the response of ocean and land CO2 fluxes to climate. There is a high agreement between models that tropical ecosystems will store The human-caused creation of reactive nitrogen in 2010 was at less carbon in a warmer climate. There is medium agreement between least two times larger than the rate of natural terrestrial cre- models that at high latitudes warming will increase land carbon stor- ation. The human-caused creation of reactive nitrogen is dominated age, although none of the models account for decomposition of carbon by the production of ammonia for fertiliser and industry, with impor- in permafrost, which may offset increased land carbon storage. There tant contributions from legume cultivation and combustion of fossil is high agreement between CMIP5 Earth System models that ocean fuels. Once formed, reactive nitrogen can be transferred to waters warming and circulation changes will reduce the rate of carbon uptake and the atmosphere. In addition to N2O, two important nitrogen com- in the Southern Ocean and North Atlantic, but that carbon uptake will pounds emitted to the atmosphere are NH3 and NOx both of which nevertheless persist in those regions. {6.4.2, Figures 6.21 and 6.22} influence tropospheric O3 and aerosols through atmospheric chemis- try. All of these effects contribute to radiative forcing. It is also likely It is very likely, based on new experimental results {6.4.6.3} and that reactive nitrogen deposition over land currently increases natural modelling, that nutrient shortage will limit the effect of rising CO2 sinks, in particular forests, but the magnitude of this effect varies atmospheric CO2 on future land carbon sinks, for the four RCP between regions. {6.1.3, 6.3, 6.3.2.6.5, 6.3.4, 6.4.6, Figures 6.4a and scenarios. There is high confidence that low nitrogen availability will 6.4b, Table 6.9, Chapter 7} limit carbon storage on land, even when considering anthropogenic nitrogen deposition. The role of phosphorus limitation is more uncer- Before the Human-Caused Perturbation tain. Models that combine nitrogen limitations with rising CO2 and changes in temperature and precipitation thus produce a systematical- During the last 7000 years prior to 1750, atmospheric CO2 from ly larger increase in projected future atmospheric CO2, for a given fossil ice cores shows only very slow changes (increase) from 260 fuel emissions trajectory. {6.4.6, 6.4.6.3, 6.4.8.2, Figure 6.35} ppm to 280 ppm, in contrast to the human-caused increase of CO2 since pre-industrial times. The contribution of CO2 emis- Taking climate and carbon cycle feedbacks into account, we sions from early anthropogenic land use is unlikely sufficient can quantify the fossil fuel emissions compatible with the to explain the CO2 increase prior to 1750. Atmospheric CH4 from RCPs. Between 2012 and 2100, the RCP2.6, RCP4.5, RCP6.0, and ice cores increased by about 100 ppb between 5000 years ago and RCP8.5 scenarios imply cumulative compatible fossil fuel emis- around 1750. About as likely as not, this increase can be attributed to sions of 270 (140 to 410) PgC, 780 (595 to 1005) PgC, 1060 (840 early human activities involving livestock, human-caused fires and rice to 1250) PgC and 1685 (1415 to 1910) PgC respectively (values cultivation. {6.2, Figures 6.6 and 6.7} quoted to nearest 5 PgC, range derived from CMIP5 model results). For RCP2.6, an average 50% (range 14 to 96%) emission reduction is Further back in time, during the past 800,000 years prior to required by 2050 relative to 1990 levels. By the end of the 21st century, 1750, atmospheric CO2 varied from 180 ppm during glacial about half of the models infer emissions slightly above zero, while the (cold) up to 300 ppm during interglacial (warm) periods. This other half infer a net removal of CO2 from the atmosphere. {6.4.3, Table is well established from multiple ice core measurements. Variations in 6.12, Figure 6.25} atmospheric CO2 from glacial to interglacial periods were caused by decreased ocean carbon storage (500 to 1200 PgC), partly compensat- There is high confidence that reductions in permafrost extent ed by increased land carbon storage (300 to 1000 PgC). {6.2.1, Figure due to warming will cause thawing of some currently frozen 6.5} carbon. However, there is low confidence on the magnitude of 6 carbon losses through CO2 and CH4 emissions to the atmosphere, Future Projections with a range from 50 to 250 PgC between 2000 and 2100 under the RCP8.5 scenario. The CMIP5 Earth System Models did not include With very high confidence, ocean carbon uptake of anthropo- frozen carbon feedbacks. {6.4.3.4, Chapter 12} genic CO2 emissions will continue under all four Representative Concentration Pathways (RCPs) through to 2100, with higher There is medium confidence that emissions of CH4 from wet- uptake corresponding to higher concentration pathways. The lands are likely to increase under elevated CO2 and a warmer future evolution of the land carbon uptake is much more uncertain, climate. But there is low confidence in quantitative projections of with a majority of models projecting a continued net carbon uptake these changes. The likelihood of the future release of CH4 from marine 468 Carbon and Other Biogeochemical Cycles Chapter 6 gas hydrates in response to seafloor warming is poorly understood. In region considered because of different responses of the underlying the event of a significant release of CH4 from hydrates in the sea floor physical and biological mechanisms at different time scales. {6.4, Table by the end of the 21st century, it is likely that subsequent emissions to 6.10, Figures 6.14 and 6.17} the atmosphere would be in the form of CO2, due to CH4 oxidation in the water column. {6.4.7, Figure 6.37} The removal of human-emitted CO2 from the atmosphere by natural processes will take a few hundred thousand years (high It is likely that N2O emissions from soils will increase due to the confidence). Depending on the RCP scenario considered, about 15 to increased demand for feed/food and the reliance of agriculture 40% of emitted CO2 will remain in the atmosphere longer than 1,000 on nitrogen fertilisers. Climate warming will likely amplify agricul- years. This very long time required by sinks to remove anthropogenic tural and natural terrestrial N2O sources, but there is low confidence in CO2 makes climate change caused by elevated CO2 irreversible on quantitative projections of these changes. {6.4.6, Figure 6.32} human time scale. {Box 6.1} It is virtually certain that the increased storage of carbon by the Geoengineering Methods and the Carbon Cycle ocean will increase acidification in the future, continuing the observed trends of the past decades. Ocean acidification in the Unconventional ways to remove CO2 from the atmosphere on surface ocean will follow atmospheric CO2 while it will also increase a large scale are termed Carbon Dioxide Removal (CDR) meth- in the deep ocean as CO2 continues to penetrate the abyss. The CMIP5 ods. CDR could in theory be used to reduce CO2 atmospheric models consistently project worldwide increased ocean acidification to concentrations but these methods have biogeochemical and 2100 under all RCPs. The corresponding decrease in surface ocean pH technological limitations to their potential. Uncertainties make it by the end of the 21st century is 0.065 (0.06 to 0.07) for RCP2.6, 0.145 difficult to quantify how much CO2 emissions could be offset by CDR (0.14 to 0.15) for RCP4.5, 0.203 (0.20 to 0.21) for RCP6.0, and 0.31 on a human time scale, although it is likely that CDR would have to be (0.30 to 0.32) for RCP8.5 (range from CMIP5 models spread). Surface deployed at large-scale for at least one century to be able to signifi- waters become seasonally corrosive to aragonite in parts of the Arctic cantly reduce atmospheric CO2. In addition, it is virtually certain that and in some coastal upwelling systems within a decade, and in parts of the removal of CO2 by CDR will be partially offset by outgassing of CO2 the Southern Ocean within 1 to 3 decades in most scenarios. Aragonite from the ocean and land ecosystems. {6.5, Figures 6.39 and 6.40, Table undersaturation becomes widespread in these regions at atmospheric 6.15, Box 6.1, FAQ 7.3} CO2 levels of 500 to 600 ppm. {6.4.4, Figures 6.28 and 6.29} The level of confidence on the side effects of CDR methods It is very likely that the dissolved oxygen content of the ocean on carbon and other biogeochemical cycles is low. Some of the will decrease by a few percent during the 21st century. CMIP5 climatic and environmental effects of CDR methods are associated models suggest that this decrease in dissolved oxygen will predomi- with altered surface albedo (for afforestation), de-oxygenation and nantly occur in the subsurface mid-latitude oceans, caused by enhanced enhanced N2O emissions (for artificial ocean fertilisation). Solar Radia- stratification, reduced ventilation and warming. However, there is no tion Management (SRM) methods (Chapter 7) will not directly interfere consensus on the future development of the volume of hypoxic and with the effects of elevated CO2 on the carbon cycle, such as ocean suboxic waters in the open-ocean because of large uncertainties in acidification, but will impact carbon and other biogeochemical cycles potential biogeochemical effects and in the evolution of tropical ocean through their climate effects. {6.5.3, 6.5.4, 7.7, Tables 6.14 and 6.15} dynamics. {6.4.5, Figure 6.30} Irreversible Long-Term Impacts of Human-Caused Emissions With very high confidence, the physical, biogeochemical carbon cycle in the ocean and on land will continue to respond to cli- mate change and rising atmospheric CO2 concentrations created during the 21st century. Ocean acidification will very likely continue in the future as long as the oceans take up atmospheric CO2. Com- mitted land ecosystem carbon cycle changes will manifest themselves further beyond the end of the 21st century. In addition, it is virtually certain that large areas of permafrost will experience thawing over multiple centuries. There is, however, low confidence in the magnitude 6 of frozen carbon losses to the atmosphere, and the relative contribu- tions of CO2 and CH4 emissions. {6.4.4, 6.4.9, Chapter 12} The magnitude and sign of the response of the natural carbon reservoirs to changes in climate and rising CO2 vary substan- tially over different time scales. The response to rising CO2 is to increase cumulative land and ocean uptake, regardless of the time scale. The response to climate change is variable, depending of the 469 Chapter 6 Carbon and Other Biogeochemical Cycles 6.1 Introduction carbon in the atmosphere, the ocean, surface ocean sediments and on land in vegetation, soils and freshwaters. Reservoir turnover times, The radiative properties of the atmosphere are strongly influenced defined as reservoir mass of carbon divided by the exchange flux, by the abundance of well-mixed GHGs (see Glossary), mainly carbon range from a few years for the atmosphere to decades to millennia dioxide (CO2), methane (CH4) and nitrous oxide (N2O), which have sub- for the major carbon reservoirs of the land vegetation and soil and the stantially increased since the beginning of the Industrial Era (defined various domains in the ocean. A second, slow domain consists of the as beginning in the year 1750), due primarily to anthropogenic emis- huge carbon stores in rocks and sediments which exchange carbon sions (see Chapter 2). Well-mixed GHGs represent the gaseous phase with the fast domain through volcanic emissions of CO2, chemical of global biogeochemical cycles, which control the complex flows and weathering (see Glossary), erosion and sediment formation on the sea transformations of the elements between the different components floor (Sundquist, 1986). Turnover times of the (mainly geological) reser- of the Earth System (atmosphere, ocean, land, lithosphere) by biotic voirs of the slow domain are 10,000 years or longer. Natural exchange and abiotic processes. Since most of these processes are themselves fluxes between the slow and the fast domain of the carbon cycle are also dependent on the prevailing environment, changes in climate and relatively small (<0.3 PgC yr 1, 1 PgC = 1015 gC) and can be assumed human impacts on ecosystems (e.g., land use and land use change) as approximately constant in time (volcanism, sedimentation) over the also modify the atmospheric concentrations of CO2, CH4 and N2O. last few centuries, although erosion and river fluxes may have been During the glacial-interglacial cycles (see Glossary), in absence of sig- modified by human-induced changes in land use (Raymond and Cole, nificant direct human impacts, long variations in climate also affected 2003). CO2, CH4 and N2O and vice versa (see Chapter 5, Section 5.2.2). In the coming century, the situation would be quite different, because of the During the Holocene (beginning 11,700 years ago) prior to the Indus- dominance of anthropogenic emissions that affect global biogeochem- trial Era the fast domain was close to a steady state, as evidenced by ical cycles, and in turn, climate change (see Chapter 12). Biogeochemi- the relatively small variations of atmospheric CO2 recorded in ice cores cal cycles thus constitute feedbacks in the Earth System. (see Section 6.2), despite small emissions from human-caused changes in land use over the last millennia (Pongratz et al., 2009). By contrast, This chapter summarizes the scientific understanding of atmospher- since the beginning of the Industrial Era, fossil fuel extraction from ic budgets, variability and trends of the three major biogeochemical geological reservoirs, and their combustion, has resulted in the transfer greenhouse gases, CO2, CH4 and N2O, their underlying source and sink of significant amount of fossil carbon from the slow domain into the processes and their perturbations caused by direct human impacts, fast domain, thus causing an unprecedented, major human-induced past and present climate changes as well as future projections of cli- perturbation in the carbon cycle. A schematic of the global carbon cycle mate change. After the introduction (Section 6.1), Section 6.2 assess- with focus on the fast domain is shown in Figure 6.1. The numbers es the present understanding of the mechanisms responsible for the represent the estimated current pool sizes in PgC and the magnitude of variations of CO2, CH4 and N2O in the past emphasizing glacial-inter- the different exchange fluxes in PgC yr 1 averaged over the time period glacial changes, and the smaller variations during the Holocene (see 2000 2009 (see Section 6.3). Glossary) since the last glaciation and over the last millennium. Sec- tion 6.3 focuses on the Industrial Era addressing the major source and In the atmosphere, CO2 is the dominant carbon bearing trace gas with sink processes, and their variability in space and time. This information a current (2011) concentration of approximately 390.5 ppm (Dlugo- is then used to evaluate critically the models of the biogeochemical kencky and Tans, 2013a), which corresponds to a mass of 828 PgC cycles, including their sensitivity to changes in atmospheric compo- (Prather et al., 2012; Joos et al., 2013). Additional trace gases include sition and climate. Section 6.4 assesses future projections of carbon methane (CH4, current content mass ~3.7 PgC) and carbon monox- and other biogeochemical cycles computed, in particular, with CMIP5 ide (CO, current content mass ~0.2 PgC), and still smaller amounts of Earth System Models. This includes a quantitative assessment of the hydrocarbons, black carbon aerosols and organic compounds. direction and magnitude of the various feedback mechanisms as rep- resented in current models, as well as additional processes that might The terrestrial biosphere reservoir contains carbon in organic com- become important in the future but which are not yet fully understood. pounds in vegetation living biomass (450 to 650 PgC; Prentice et al., Finally, Section 6.5 addresses the potential effects and uncertainties of 2001) and in dead organic matter in litter and soils (1500 to 2400 PgC; deliberate carbon dioxide removal methods (see Glossary) and solar Batjes, 1996). There is an additional amount of old soil carbon in wet- radiation management (see Glossary) on the carbon cycle. land soils (300 to 700 PgC; Bridgham et al., 2006) and in permafrost soils (see Glossary) (~1700 PgC; Tarnocai et al., 2009); albeit some over- 6.1.1 Global Carbon Cycle Overview lap with these two quantities. CO2 is removed from the atmosphere by 6 plant photosynthesis (Gross Primary Production (GPP), 123+/-8 PgC yr 1, 6.1.1.1 Carbon Dioxide and the Global Carbon Cycle (Beer et al., 2010)) and carbon fixed into plants is then cycled through plant tissues, litter and soil carbon and can be released back into the Atmospheric CO2 represents the main atmospheric phase of the global atmosphere by autotrophic (plant) and heterotrophic (soil microbial carbon cycle. The global carbon cycle can be viewed as a series of reser- and animal) respiration and additional disturbance processes (e.g., voirs of carbon in the Earth System, which are connected by exchange sporadic fires) on a very wide range of time scales (seconds to mil- fluxes of carbon. Conceptually, one can distinguish two domains in lennia). Because CO2 uptake by photosynthesis occurs only during the the global carbon cycle. The first is a fast domain with large exchange growing season, whereas CO2 release by respiration occurs nearly year- fluxes and relatively rapid reservoir turnovers, which consists of round, the greater land mass in the Northern Hemisphere (NH) imparts 470 Carbon and Other Biogeochemical Cycles Chapter 6 10 + 240 +/- gC yr -1)) ere 589 Rock weathering 0.3 A tmosph c increase: 4 (P d flux Volcanism 0.1 spheri Net lan (avera ge atmo Net land use change 1.1 +/-0.8 1.7 7.8 +/-0.6 2.6 +/-1.2 Freshwater outgassing 1.0 an flux Net oce Fossil fuels (coal, oil, gas) 118.7 = 107.2 + 11.6 Total respiration and fire 123 = 108.9 + 14.1 Gross photosynthesis cement production 0.7 2.3 +/-0.7 Ocean-atmosphere 78.4 = 60.7 + 17.7 gas exchange 80 = 60 + 20 Rock ring weathe 0.1 from Export vers so ils to ri 1.7 50 Marine biota ocean Rivers Surface 0 3 90 37 Burial on 0.9 0.2 Vegetati0 2 450-65 101 90 -30 +/-45 ost 11 Soils Permafr ed Dissolvic 00 ~1700 diate Interme sea 1500-24 organ & deep 0 carb on es 37,1 0 2 el reserv 700 Fossil fu 83-1135 +155 +/-3 0 Gas: 3 -264 Oil: 173 41 0.2 6-5 Coal: 44 30 Units yr -1) (PgC -365 +/- Fluxes: (PgC) floor Stocks: Ocean iments sed surface ,750 1 Figure 6.1 | Simplified schematic of the global carbon cycle. Numbers represent reservoir mass, also called carbon stocks in PgC (1 PgC = 1015 gC) and annual carbon exchange fluxes (in PgC yr 1). Black numbers and arrows indicate reservoir mass and exchange fluxes estimated for the time prior to the Industrial Era, about 1750 (see Section 6.1.1.1 for references). Fossil fuel reserves are from GEA (2006) and are consistent with numbers used by IPCC WGIII for future scenarios. The sediment storage is a sum of 150 PgC of the organic carbon in the mixed layer (Emerson and Hedges, 1988) and 1600 PgC of the deep-sea CaCO3 sediments available to neutralize fossil fuel CO2 (Archer et al., 1998). Red arrows and numbers indicate annual anthropogenic fluxes averaged over the 2000 2009 time period. These fluxes are a perturbation of the carbon cycle during Industrial Era post 1750. These fluxes (red arrows) are: Fossil fuel and cement emissions of CO2 (Section 6.3.1), Net land use change (Section 6.3.2), and the Average atmospheric increase of CO2 in the atmosphere, also called CO2 growth rate (Section 6.3). The uptake of anthropogenic CO2 by the ocean and by terrestrial ecosystems, often called carbon sinks are the red arrows part of Net land flux and Net ocean flux. Red numbers in the reservoirs denote cumulative changes of anthropogenic carbon over the Industrial Period 1750 2011 (column 2 in Table 6.1). By convention, a positive cumulative change means that a reservoir has gained carbon since 1750. The cumulative change of anthropogenic carbon in the terrestrial reservoir is the sum of carbon cumulatively lost through land use change and carbon accumulated since 1750 in other ecosystems (Table 6.1). Note that the mass balance of the two ocean carbon stocks Surface ocean and Intermediate and deep ocean includes a yearly accumulation of anthropogenic carbon (not shown). Uncertainties are reported as 90% confidence intervals. Emission estimates and land and ocean sinks (in red) are from Table 6.1 in Section 6.3. The change of gross terrestrial fluxes (red arrows of Gross 6 photosynthesis and Total respiration and fires) has been estimated from CMIP5 model results (Section 6.4). The change in air sea exchange fluxes (red arrows of ocean atmosphere gas exchange) have been estimated from the difference in atmospheric partial pressure of CO2 since 1750 (Sarmiento and Gruber, 2006). Individual gross fluxes and their changes since the beginning of the Industrial Era have typical uncertainties of more than 20%, while their differences (Net land flux and Net ocean flux in the figure) are determined from independent measurements with a much higher accuracy (see Section 6.3). Therefore, to achieve an overall balance, the values of the more uncertain gross fluxes have been adjusted so that their difference matches the Net land flux and Net ocean flux estimates. Fluxes from volcanic eruptions, rock weathering (silicates and carbonates weathering reactions resulting into a small uptake of atmospheric CO2), export of carbon from soils to rivers, burial of carbon in freshwater lakes and reservoirs and transport of carbon by rivers to the ocean are all assumed to be pre-industrial fluxes, that is, unchanged during 1750 2011. Some recent studies (Section 6.3) indicate that this assumption is likely not verified, but global estimates of the Industrial Era perturbation of all these fluxes was not available from peer-reviewed literature. The atmospheric inventories have been calculated using a conversion factor of 2.12 PgC per ppm (Prather et al., 2012). 471 Chapter 6 Carbon and Other Biogeochemical Cycles a characteristic sawtooth seasonal cycle in atmospheric CO2 (Keeling, phytoplankton and other microorganisms, represent a small organic 1960) (see Figure 6.3). A significant amount of terrestrial carbon (1.7 carbon pool (~3 PgC), which is turned over very rapidly in days to a PgC yr 1; Figure 6.1) is transported from soils to rivers headstreams. A few weeks. fraction of this carbon is outgassed as CO2 by rivers and lakes to the atmosphere, a fraction is buried in freshwater organic sediments and Carbon is transported within the ocean by three mechanisms (Figure the remaining amount (~0.9 PgC yr 1; Figure 6.1) is delivered by rivers 6.1): (1) the solubility pump (see Glossary), (2) the biological pump to the coastal ocean as dissolved inorganic carbon, dissolved organic (see Glossary), and (3) the marine carbonate pump that is generated carbon and particulate organic carbon (Tranvik et al., 2009). by the formation of calcareous shells of certain oceanic microorganisms in the surface ocean, which, after sinking to depth, are re-mineralized Atmospheric CO2 is exchanged with the surface ocean through gas back into DIC and calcium ions. The marine carbonate pump operates exchange. This exchange flux is driven by the partial CO2 pressure dif- counter to the marine biological soft-tissue pump with respect to its ference between the air and the sea. In the ocean, carbon is availa- effect on CO2: in the formation of calcareous shells, two bicarbonate ble predominantly as Dissolved Inorganic Carbon (DIC, ~38,000 PgC; ions are split into one carbonate and one dissolved CO2 molecules, Figure 6.1), that is carbonic acid (dissolved CO2 in water), bicarbonate which increases the partial CO2 pressure in surface waters (driving a and carbonate ions, which are tightly coupled via ocean chemistry. In release of CO2 to the atmosphere). Only a small fraction (~0.2 PgC yr 1) addition, the ocean contains a pool of Dissolved Organic Carbon (DOC, of the carbon exported by biological processes (both soft-tissue and ~700 PgC), of which a substantial fraction has a turnover time of 1000 carbonate pumps) from the surface reaches the sea floor where it can years or longer (Hansell et al., 2009). The marine biota, predominantly be stored in sediments for millennia and longer (Denman et al., 2007). Box 6.1 | Multiple Residence Times for an Excess of Carbon Dioxide Emitted in the Atmosphere On an average, CO2 molecules are exchanged between the atmosphere and the Earth surface every few years. This fast CO2 cycling through the atmosphere is coupled to a slower cycling of carbon through land vegetation, litter and soils and the upper ocean (decades to centuries); deeper soils and the deep sea (centuries to millennia); and geological reservoirs, such as deep-sea carbonate sediments and the upper mantle (up to millions of years) as explained in Section 6.1.1.1. Atmospheric CO2 represents only a tiny fraction of the carbon in the Earth System, the rest of which is tied up in these other reservoirs. Emission of carbon from fossil fuel reserves, and addi- tionally from land use change (see Section 6.3) is now rapidly increasing atmospheric CO2 content. The removal of all the human-emitted CO2 from the atmosphere by natural processes will take a few hundred thousand years (high confidence) as shown by the timescales of the removal process shown in the table below (Archer and Brovkin, 2008). For instance, an extremely long atmospheric CO2 recovery time scale from a large emission pulse of CO2 has been inferred from geological evidence when during the Paleocene Eocene thermal maximum event about 55 million years ago a large amount of CO2 was released to the atmosphere (McInerney and Wing, 2011). Based on the amount of CO2 remaining in the atmosphere after a pulse of emissions (data from Joos et al. 2013) and on the magnitude of the historical and future emissions for each RCP scenario, we assessed that about 15 to 40% of CO2  emitted until 2100 will remain in the atmosphere longer than 1000 years. Box 6.1, Table 1 | The main natural processes that remove CO2 consecutive to a large emission pulse to the atmosphere, their atmospheric CO2 adjustment time scales, and main (bio)chemical reactions involved. Processes Time scale (years) Reactions Land uptake: Photosynthesis respiration 1 102 6CO2 + 6H2O + photons C6H12O6 + 6O2 C6H12O6 + 6O2 6CO2 + 6H2O + heat Ocean invasion: Seawater buffer 10 103 CO2 + CO32 + H2O 2HCO3 Reaction with calcium carbonate 10 10 3 4 CO2 + CaCO3 + H2O Ca2+ + 2HCO3 Silicate weathering 10 10 4 6 CO2 + CaSiO3 CaCO3 + SiO2 These processes are active on all time scales, but the relative importance of their role in the CO2 removal is changing with time and 6 depends on the level of emissions. Accordingly, the times of atmospheric CO2 adjustment to anthropogenic carbon emissions can be divided into three phases associated with increasingly longer time scales. Phase 1. Within several decades of CO2 emissions, about a third to half of an initial pulse of anthropogenic CO2 goes into the land and ocean, while the rest stays in the atmosphere (Box 6.1, Figure 1a). Within a few centuries, most of the anthropogenic CO2 will be in the form of additional dissolved inorganic carbon in the ocean, thereby decreasing ocean pH (Box 6.1, Figure 1b). Within a thousand years, the remaining atmospheric fraction of the CO2 emissions (see Section 6.3.2.4) is between 15 and 40%, depending on the amount of carbon released (Archer et al., 2009b). The carbonate buffer capacity of the ocean decreases with higher CO2, so the larger the cumula- tive emissions, the higher the remaining atmospheric fraction (Eby et al., 2009; Joos et al., 2013). (continued on next page) 472 Carbon and Other Biogeochemical Cycles Chapter 6 Box 6.1 (continued) Phase 2. In the second stage, within a few thousands of years, the pH of the ocean that has decreased in Phase 1 will be restored by reaction of ocean dissolved CO2 and calcium carbonate (CaCO3) of sea floor sediments, partly replenishing the buffer capacity of the ocean and further drawing down atmospheric CO2 as a new balance is re-established between CaCO3 sedimentation in the ocean and terrestrial weathering (Box 6.1, Figure 1c right). This second phase will pull the remaining atmospheric CO2 fraction down to 10 to 25% of the original CO2 pulse after about 10 kyr (Lenton and Britton, 2006; Montenegro et al., 2007; Ridgwell and Hargreaves, 2007; Tyrrell et al., 2007; Archer and Brovkin, 2008). Phase 3. In the third stage, within several hundred thousand years, the rest of the CO2 emitted during the initial pulse will be removed from the atmosphere by silicate weathering, a very slow process of CO2 reaction with calcium silicate (CaSiO3) and other minerals of igneous rocks (e.g., Sundquist, 1990; Walker and Kasting, 1992). Involvement of extremely long time scale processes into the removal of a pulse of CO2 emissions into the atmosphere complicates comparison with the cycling of the other GHGs. This is why the concept of a single, characteristic atmospheric lifetime is not applicable to CO2 (Chapter 8). Box 6.1, Figure 1 | A percentage of emitted CO2 remaining in the atmosphere in response to an idealised instantaneous CO2 pulse emitted to the atmosphere in year 0 as calculated by a range of coupled climate carbon cycle models. (Left and middle panels, a and b) Multi-model mean (blue line) and the uncertainty interval (+/-2 standard deviations, shading) simulated during 1000 years following the instantaneous pulse of 100 PgC (Joos et al., 2013). (Right panel, c) A mean of models with oceanic and terrestrial carbon components and a maximum range of these models (shading) for instantaneous CO2 pulse in year 0 of 100 PgC (blue), 1000 PgC (orange) and 5000 PgC (red line) on a time interval up to 10 kyr (Archer et al., 2009b). Text at the top of the panels indicates the dominant processes that remove the excess of CO2 emitted in the atmosphere on the successive time scales. Note that higher pulse of CO2 emissions leads to higher remaining CO2 fraction (Section 6.3.2.4) due to reduced carbonate buffer capacity of the ocean and positive climate carbon cycle feedback (Section 6.3.2.6.6). 6.1.1.2 Methane Cycle paddy agriculture, ruminant livestock, landfills, man-made lakes and wetlands and waste treatment. In general, biogenic CH4 is produced CH4 absorbs infrared radiation relatively stronger per molecule com- from organic matter under low oxygen conditions by fermentation pro- pared to CO2 (Chapter 8), and it interacts with photochemistry. On cesses of methanogenic microbes (Conrad, 1996). Atmospheric CH4 is the other hand, the methane turnover time (see Glossary) is less than removed primarily by photochemistry, through atmospheric chemistry 10 years in the troposphere (Prather et al., 2012; see Chapter 7). The reactions with the OH radicals. Other smaller removal processes of sources of CH4 at the surface of the Earth (see Section 6.3.3.2) can be atmospheric CH4 take place in the stratosphere through reaction with thermogenic including (1) natural emissions of fossil CH4 from geolog- chlorine and oxygen radicals, by oxidation in well aerated soils, and 6 ical sources (marine and terrestrial seepages, geothermal vents and possibly by reaction with chlorine in the marine boundary layer (Allan mud volcanoes) and (2) emissions caused by leakages from fossil fuel et al., 2007; see Section 6.3.3.3). extraction and use (natural gas, coal and oil industry; Figure 6.2). There are also pyrogenic sources resulting from incomplete burning of fossil A very large geological stock (globally 1500 to 7000 PgC, that is 2 x fuels and plant biomass (both natural and anthropogenic fires). The 106 to 9.3 x 106 Tg(CH4) in Figure 6.2; Archer (2007); with low confi- biogenic sources include natural biogenic emissions predominantly dence in estimates) of CH4 exists in the form of frozen hydrate deposits from wetlands, from termites and very small emissions from the ocean ( clathrates ) in shallow ocean sediments and on the slopes of con- (see Section 6.3.3). Anthropogenic biogenic emissions occur from rice tinental shelves, and permafrost soils. These CH4 hydrates are stable 473 Chapter 6 Carbon and Other Biogeochemical Cycles under ­ onditions of low temperature and high pressure. Warming or c is releasing large amounts of CO2 into the atmosphere (Rotty, 1983; changes in pressure could render some of these hydrates unstable with Boden et al., 2011; see Section 6.3.2.1). The amount of fossil fuel CO2 a potential release of CH4 to the overlying soil/ocean and/or atmos- emitted to the atmosphere can be estimated with an accuracy of about phere. Possible future CH4 emissions from CH4 released by gas hydrates 5 to 10% for recent decades from statistics of fossil fuel use (Andres et are discussed in Section 6.4.7.3. al., 2012). Total cumulative emissions between 1750 and 2011 amount to 375 +/- 30 PgC (see Section 6.3.2.1 and Table 6.1), including a contri- 6.1.2 Industrial Era bution of 8 PgC from the production of cement. 6.1.2.1 Carbon Dioxide and the Global Carbon Cycle The second major source of anthropogenic CO2 emissions to the atmosphere is caused by changes in land use (mainly deforestation), Since the beginning of the Industrial Era, humans have been produc- which causes globally a net reduction in land carbon storage, although ing energy by burning of fossil fuels (coal, oil and gas), a process that r ­ecovery from past land use change can cause a net gain in in land 45 2970 +/- r -1)) 1984 + g CH4 y Oxidations in soils 9-47 osphere crease: 17 +/-9 (T 33-75 Geological sources Atm heric in Termites 2-22 Biomass burning 32-39 e atmosp (averag Fossil fuels 85-105 Landfills and waste 67-90 Wetlands 177-284 Freshwaters 8-73 Rice cultivation 33-40 Livestock 87-94 Hydrates 2-9 Tropospheric OH 454-617 Tropospheric CL 13-37 Stratospheric OH 16-84 ost Permafr s hydrate 0 <5 30,00 rves Gas rese 13,000 ,5 s 51 1,000-1 hydrate Units H yr-1) Ocean -8,000,000 (Tg C 4 2,000,0 00 Fluxes: (Tg CH4) Stocks: 6 Figure 6.2 | Schematic of the global cycle of CH4. Numbers represent annual fluxes in Tg(CH4) yr 1 estimated for the time period 2000 2009 and CH4 reservoirs in Tg (CH4): the atmosphere and three geological reservoirs (hydrates on land and in the ocean floor and gas reserves) (see Section 6.3.3). Black arrows denote natural fluxes, that is, fluxes that are not directly caused by human activities since 1750, red arrows anthropogenic fluxes, and the light brown arrow denotes a combined natural + anthropogenic flux. Note that human activities (e.g., land use) may have modified indirectly the global magnitude of the natural fluxes (Section 6.3.3). Ranges represent minimum and maximum values from cited references as given in Table 6.8 in Section 6.3.3. Gas reserves are from GEA (2006) and are consistent with numbers used by IPCC WG III for future scenarios. Hydrate reservoir sizes are from Archer et al. (2007). The atmospheric inventories have been calculated using a conversion factor of 2.7476 TgCH4 per ppb (Prather et al., 2012). The assumed preindustrial annual mean globally averaged CH4 concentration was 722 +/- 25 ppb taking the average of the Antarctic Law Dome ice core observations (MacFarling-Meure et al., 2006) and the measurements from the GRIP ice core in Greenland (Blunier et al., 1995; see also Table 2.1). The atmospheric inventory in the year 2011 is based on an annual globally averaged CH4 concentration of 1803 +/- 4 ppb in the year 2011 (see Table 2.1). It is the sum of the atmospheric increase between 1750 and 2011 (in red) and of the pre-industrial inventory (in black). The average atmospheric increase each year, also called growth rate, is based on a measured concentration increase of 2.2 ppb yr 1 during the time period 2000 2009 (Dlugokencky et al., 2011). 474 Carbon and Other Biogeochemical Cycles Chapter 6 carbon storage in some regions. Estimation of this CO2 source to the 6.1.3 Connections Between Carbon and the Nitrogen atmosphere requires knowledge of changes in land area as well as and Oxygen Cycles estimates of the carbon stored per area before and after the land use change. In addition, longer term effects, such as the decomposition of 6.1.3.1 Global Nitrogen Cycle Including Nitrous Oxide soil organic matter after land use change, have to be taken into account (see Section 6.3.2.2). Since 1750, anthropogenic land use changes The biogeochemical cycles of nitrogen and carbon are tightly coupled have resulted into about 50 million km2 being used for cropland and with each other owing to the metabolic needs of organisms for these pasture, corresponding to about 38% of the total ice-free land area two elements. Changes in the availability of one element will influence (Foley et al., 2007, 2011), in contrast to an estimated cropland and pas- not only biological productivity but also availability and requirements ture area of 7.5 to 9 million km2 about 1750 (Ramankutty and Foley, for the other element (Gruber and Galloway, 2008) and in the longer 1999; Goldewijk, 2001). The cumulative net CO2 emissions from land term, the structure and functioning of ecosystems as well. use changes between 1750 and 2011 are estimated at approximately 180 +/- 80 PgC (see Section 6.3 and Table 6.1). Before the Industrial Era, the creation of reactive nitrogen Nr (all nitro- gen species other than N2) from non-reactive atmospheric N2 occurred Multiple lines of evidence indicate that the observed atmospher- primarily through two natural processes: lightning and biological ic increase in the global CO2 concentration since 1750 (Figure 6.3) nitrogen fixation (BNF). BNF is a set of reactions that convert N2 to is caused by the anthropogenic CO2 emissions (see Section 6.3.2.3). ammonia in a microbially mediated process. This input of Nr to the The rising atmospheric CO2 content induces a disequilibrium in the land and ocean biosphere was in balance with the loss of Nr though exchange fluxes between the atmosphere and the land and oceans denitrification, a process that returns N2 back to the atmosphere (Ayres respectively. The rising CO2 concentration implies a rising atmospheric et al., 1994). This equilibrium has been broken since the beginning of CO2 partial pressure (pCO2) that induces a globally averaged net-air- the Industrial Era. Nr is produced by human activities and delivered to to-sea flux and thus an ocean sink for CO2 (see Section 6.3.2.5). On ecosystems. During the last decades, the production of Nr by humans land, the rising atmospheric CO2 concentration fosters photosynthesis has been much greater than the natural production (Figure 6.4a; Sec- via the CO2 fertilisation effect (see Section 6.3.2.6). However, the effi- tion 6.3.4.3). There are three main anthropogenic sources of Nr: (1) the cacy of these oceanic and terrestrial sinks does also depend on how Haber-Bosch industrial process, used to make NH3 from N2, for nitrogen the excess carbon is transformed and redistributed within these sink fertilisers and as a feedstock for some industries; (2) the cultivation of reservoirs. The magnitude of the current sinks is shown in Figure 6.1 legumes and other crops, which increases BNF; and (3) the combustion (averaged over the years 2000 2009, red arrows), together with the of fossil fuels, which converts atmospheric N2 and fossil fuel nitrogen cumulative reservoir content changes over the industrial era (1750 into nitrogen oxides (NOx) emitted to the atmosphere and re-deposited 2011, red numbers) (see Table 6.1, Section 6.3). at the surface (Figure 6.4a). In addition, there is a small flux from the mobilization of sequestered Nr from nitrogen-rich sedimentary rocks 6.1.2.2 Methane Cycle (Morford et al., 2011) (not shown in Figure 6.4a). After 1750, atmospheric CH4 levels rose almost exponentially with The amount of anthropogenic Nr converted back to non-reactive N2 by time, reaching 1650 ppb by the mid-1980s and 1803 ppb by 2011. denitrification is much smaller than the amount of Nr produced each Between the mid-1980s and the mid-2000s the atmospheric growth year, that is, about 30 to 60% of the total Nr production, with a large of CH4 declined to nearly zero (see Section 6.3.3.1, see also Chapter uncertainty (Galloway et al., 2004; Canfield et al., 2010; Bouwman et 2). More recently since 2006, atmospheric CH4 is observed to increase al., 2013). What is more certain is the amount of N2O emitted to the again (Rigby et al., 2008); however, it is unclear if this is a short-term atmosphere. Anthropogenic sources of N2O are about the same size fluctuation or a new regime for the CH4 cycle (Dlugokencky et al., 2009). as natural terrestrial sources (see Section 6.3.4 and Table 6.9 for the global N2O budget). In addition, emissions of Nr to the atmosphere, There is very high level of confidence that the atmospheric CH4 as NH3 and NOx, are caused by agriculture and fossil fuel combustion. increase during the Industrial Era is caused by anthropogenic activities. A portion of the emitted NH3 and NOx is deposited over the conti- The massive increase in the number of ruminants (Barnosky, 2008), nents, while the rest gets transported by winds and deposited over the emissions from fossil fuel extraction and use, the expansion of rice the oceans. This atmospheric deposition flux of Nr over the oceans is paddy agriculture and the emissions from landfills and waste are the comparable to the flux going from soils to rivers and delivered to the dominant anthropogenic CH4 sources. Total anthropogenic sources coastal ocean (Galloway et al., 2004; Suntharalingam et al., 2012). contribute at present between 50 and 65% of the total CH4 sources The increase of Nr creation during the Industrial Era, the connections (see Section 6.3.3). The dominance of CH4 emissions located mostly in among its impacts, including on climate and the connections with the 6 the NH (wetlands and anthropogenic emissions) is evidenced by the carbon cycle are presented in Box 6.2. observed positive north south gradient in CH4 concentrations (Figure 6.3). Satellite-based CH4 concentration measurements averaged over For the global ocean, the best BNF estimate is 160 TgN yr 1, which the entire atmospheric column also indicate higher concentrations of is roughly the midpoint of the minimum estimate of 140 TgN yr 1 of CH4 above and downwind of densely populated and intensive agricul- Deutsch et al. (2007) and the maximum estimate of 177 TgN yr 1 (Gro- ture areas where anthropogenic emissions occur (Frankenberg et al., szkopf et al., 2012). The probability that this estimate will need an 2011). upward revision in the near future is high because several additional processes are not yet considered (Voss et al., 2013). 475 Chapter 6 Carbon and Other Biogeochemical Cycles 6 Figure 6.3 | Atmospheric concentration of CO2, oxygen, 13C/12C stable isotope ratio in CO2, CH4 and N2O recorded over the last decades at representative stations (a) CO2 from Mauna Loa (MLO) Northern Hemisphere and South Pole Southern Hemisphere (SPO) atmospheric stations (Keeling et al., 2005). (b) O2 from Alert Northern Hemisphere (ALT) and Cape Grim Southern Hemisphere (CGO) stations (http://scrippso2.ucsd.edu/ right axes, expressed relative to a reference standard value). (c) 13C/12C: Mauna Loa, South Pole (Keeling et al., 2005). (d) CH4 from Mauna Loa and South Pole stations (Dlugokencky et al., 2012). (e) N2O from Mace-Head Northern Hemisphere (MHD) and Cape Grim stations (Prinn et al., 2000). 476 Carbon and Other Biogeochemical Cycles Chapter 6 Box 6.2 | Nitrogen Cycle and Climate-Carbon Cycle Feedbacks Human creation of reactive nitrogen by the Haber Bosch process (see Sections 6.1.3 and 6.3.4), fossil fuel combustion and agricultural biological nitrogen fixation (BNF) dominate Nr creation relative to biological nitrogen fixation in natural terrestrial ecosystems. This dominance impacts on the radiation balance of the Earth (covered by the IPCC; see, e.g., Chapters 7 and 8), and affects human health and ecosystem health as well (EPA, 2011b; Sutton et al., 2011). The Nr creation from 1850 to 2005 is shown in Box 6.2 (Figure 1). After mid-1970s, human production of Nr exceeded natural production. During the 2000s food production (mineral fertilisers, legumes) accounts for three-quarters of Nr created by humans, with fossil fuel combustion and industrial uses accounting equally for the remainder (Galloway et al., 2008; Canfield et al., 2010; Sutton et al., 2011). The three most relevant questions regarding the anthro- pogenic perturbation of the nitrogen cycle with respect to Haber Bosch Process global change are: (1) What are the interactions with the Biological Nitrogen Fixation carbon cycle, and the effects on carbon sinks (see Sections 150 Fossil Fuel Burning Nr Creation (TgN yr-1) 6.3.2.6.5 and 6.4.2.1), (2) What are the effects of increased Total Nr Creation Nr on the radiative forcing of nitrate aerosols (Chapter 7, 7.3.2) and tropospheric ozone (Chapters 8), (3) What are 100 the impacts of the excess of Nr on humans and ecosystems (health, biodiversity, eutrophication, not treated in this 50 report, but see, for example, EPA, 2011b; Sutton et al., 2011). Essentially all of the Nr formed by human activity is spread 0 into the environment, either at the point of creation (i.e., 1850 1900 1950 2000 fossil fuel combustion) or after it is used in food production Year and in industry. Once in the environment, Nr has a number of negative impacts if not converted back into N2. In addi- Box 6.2, Figure 1 | Anthropogenic reactive nitrogen (Nr) creation rates (in TgN yr 1) from fossil fuel burning (orange line), cultivation-induced biological nitrogen fixation tion to its contributions to climate change and stratospheric (blue line), Haber Bosch process (green line) and total creation (red line). Source: ozone depletion, Nr contributes to the formation of smog; Galloway et al. (2003), Galloway et al. (2008). Note that updates are given in Table increases the haziness of the troposphere; contributes to the 6.9. The only one with significant changes in the more recent literature is cultivation- acidification of soils and freshwaters; and increases the pro- induced BNF) which Herridge et al. (2008) estimated to be 60 TgN yr 1. The data are ductivity in forests, grasslands, open and coastal waters and only reported since 1850, as no published estimate is available since 1750. open ocean, which can lead to eutrophication and reduction in biodiversity in terrestrial and aquatic ecosystems. In addition, Nr-induced increases in nitrogen oxides, aerosols, tropospheric ozone, and nitrates in drinking water have negative impacts on human health (Galloway et al., 2008; Davidson et al., 2012). Once the nitrogen atoms become reactive (e.g., NH3, NOx), any given Nr atom can contribute to all of the impacts noted above in sequence. This is called the nitrogen cascade (Galloway et al., 2003; Box 6.2, Figure 2). The nitrogen cascade is the sequential transfer of the same Nr atom through the atmosphere, terrestrial ecosystems, freshwater ecosystems and marine ecosystems that results in multiple effects in each reservoir. Because of the nitrogen cascade, the creation of any molecule of Nr from N2, at any location, has the potential to affect climate, either directly or indirectly, as explained in this box This potential exists until the Nr gets converted back to N2. The most important processes causing direct links between anthropogenic Nr and climate change include (Erisman et al., 2011): (1) N2O formation during industrial processes (e.g., fertiliser production), combustion, or microbial conversion of substrate containing nitrogen notably after fertiliser and manure application to soils. N2O is a strong greenhouse gas (GHG), (2) emission of anthropogenic NOx leading to (a) formation of tropospheric O3, (which is the third most important GHG), (b) a decrease of CH4 and (c) the formation of nitrate aerosols. Aerosol formation affects radiative forcing, as nitrogen-containing aerosols have a direct cooling effect in addition to an indirect cooling effect through cloud formation and (3) NH3 emission to the atmosphere which contributes to aerosol formation. The 6 first process has a warming effect. The second has both a warming (as a GHG) and a cooling (through the formation of the OH radical in the troposphere which reacts with CH4, and through aerosol formation) effect. The net effect of all three NOx-related contributions is cooling. The third process has a cooling effect. The most important processes causing an indirect link between anthropogenic Nr and climate change include: (1) n ­ itrogen-dependent changes in soil organic matter decomposition and hence CO2 emissions, affecting heterotrophic respiration; (2) alteration of the biospheric CO2 sink due to increased supply of Nr. About half of the carbon that is emitted to the atmosphere is ­ (continued on next page) 477 Chapter 6 Carbon and Other Biogeochemical Cycles Box 6.2 (continued) taken up by the biosphere; Nr affects net CO2 uptake from the atmosphere in terrestrial systems, rivers, estuaries and the open ocean in a positive direction (by increasing productivity or reducing the rate of organic matter breakdown) and negative direction (in situations where it accelerates organic matter breakdown). CO2 uptake in the ocean causes ocean acidification, which reduces CO2 uptake; (3) changes in marine primary productivity, generally an increase, in response to Nr deposition; and (4) O3 formed in the troposphere as a result of NOx and volatile organic compound emissions reduces plant productivity, and therefore reduces CO2 uptake from the atmos- phere. On the global scale the net influence of the direct and indirect contributions of Nr on the radiative balance was estimated to be 0.24 W m 2 (with an uncertainty range of +0.2 to 0.5 W m 2) (Erisman et al., 2011). Nr is required for both plants and soil microorganisms to grow, and plant and microbial processes play important roles in the global carbon cycle. The increasing concentration of atmospheric CO2 is observed to increase plant photosynthesis (see Box 6.3) and plant growth, which drives an increase of carbon storage in terrestrial ecosystems. Plant growth is, however, constrained by the availability of Nr in soils (see Section 6.3.2.6.5). This means that in some nitrogen-poor ecosystems, insufficient Nr availability will limit carbon sinks, while the deposition of Nr may instead alleviate this limitation and enable larger carbon sinks (see Section 6.3.2.6.5). Therefore, human production of Nr has the potential to mitigate CO2 emissions by providing additional nutrients for plant growth in some regions. Microbial growth can also be limited by the availability of Nr, particularly in cold, wet environments, so that human production of Nr also has the potential to accelerate the decomposition of organic matter, increasing release of CO2. The availability of Nr also changes in response to climate change, generally increasing with warmer temperatures and increased precipitation (see Section 6.4.2.1), but with complex interactions in the case of seasonally inundated environments. This complex network of feedbacks is amenable to study through observation and experimentation (Section 6.3) and Earth System modelling (Section 6.4). Even though we do not yet have a thorough understanding of how nitrogen and carbon cycling will interact with climate change, elevated CO2 and human Nr production in the future, given scenarios of human activity, current observations and model results all indicate that low nitrogen availability will limit carbon storage on land in the 21st century (see Section 6.4.2.1). Atmosphere Stratospheric Particulate Effects Matter Ozone N2O Effects Effects NOx Greenhouse Effects NHx Energy production NOx NH3 NOy N2O Terrestrial Ecosystems Forest & NHx Grassland Effects NOy Food production Agro-ecosystem Plant NHx Effects Crop Animal N 2O Soil (land) Soil - NO3 Norganic Coastal People (food; fiber) Effects Surface Water N2O Effects (water) Aquatic Ecosystem Ocean Effects 6 Groundwater Effects Box 6.2, Figure 2 | Illustration of the nitrogen cascade showing the sequential effects that a single atom of nitrogen in its various molecular forms can have in various reservoirs after it has been converted from nonreactive N2 to a reactive form by energy and food production (orange arrows). Once created the reactive nitrogen has the potential to continue to contribute to impacts until it is converted back to N2. The small black circle indicates that there is the potential for denitrification to occur within that reservoir. NH3 = ammonia; NHx = ammonia plus ammonium; NO3 = nitrate; NOx = nitrogen oxides; NOy = NOx and other combinations of nitrogen and oxygen (except N2O); N2O = nitrous oxide. (Adapted with permission from the GEO Yearbook 2003, United Nations Environmental Programme (UNEP), 2004 which was based on Galloway et al., 2003.) 478 Carbon and Other Biogeochemical Cycles Chapter 6 (N2) a) lecular Nitrogen Natural biological 58 (50-100) h eric mo Atmosp Fossil fuel 30 (27-33) Industrial 124 (117-126) Denitrification 109 (101-118) Cultivation 60 (50-70) Lightining 4 (3-5) Biological 160 (140-177) combustion Denitrification 300 (200-400) fixation fixation fixation fixation (Haber Bosch) Units -1) (Tg N yr Fluxes: O) xcept N2 ecies (e b) 2 (NH3) rogen sp Soils under natural 7 (NOx) e nit biofuel burning 9 (NH3) Reactiv Biomass and 6 (NOx) combustion and 0.5 (NH3) osphere: NOx emission, fossil fuel 28 (NOx) Atm 36 (NHx) Deposition 27 (NOy) agriculture and 30 (NH3) NH3 emission, 4 (NOx) 17 (NHx) vegetation Deposition 20 (NOy) industrial processes NH3 emission 8.2 sewage Units -1) (Tg N yr Fluxes: 50) + 213 +/- c) ] yr )) -1 O (1340 .15 (Tg N [N2O Soils under natural 6.6 (3.3-9.0) here: N2 +/-0 Atmosp increase: 3.6 From atmospheric 0.4 (0.3-0.9) heric Biomass and 0.7 (0.2-1.0) atmosp Fossil fuels and 0.7 (0.2-1.8) e (averag Human excreta 0.2 (0.1-0.3) Rivers, estuaries, 0.6 (0.1-2.9) Agriculture 4.1 (1.7-4.8) Atmospheric 0.6 (0.3-1.2) deposition on land Stratospheric sink 14.3 (4.3-27.2) vegetation biofuel burning From atmospheric 0.2 (0.1-0.4) Oceans 3.8 (1.8-9.4) industry coastal zone chemistry deposition on ocean Units ] yr-1) O -1 (Tg N [N2 Fluxes: (Tg N [N2O] yr Stocks : ) 6 Figure 6.4 | Schematic of the global nitrogen cycle. (a) The natural and anthropogenic processes that create reactive nitrogen and the corresponding rates of denitrification that convert reactive nitrogen back to N2. (b) The flows of the reactive nitrogen species NOy and NHx. (c) The stratospheric sink of N2O is the sum of losses via photolysis and reaction with O(1D) (oxygen radical in the 1D excited state; Table 6.9). The global magnitude of this sink is adjusted here in order to be equal to the difference between the total sources and the observed growth rate. This value falls within literature estimates (Volk et al., 1997). The atmospheric inventories have been calculated using a conversion factor of 4.79 TgN (N2O) per ppb (Prather et al., 2012). 479 Chapter 6 Carbon and Other Biogeochemical Cycles A global denitrification rate is much more difficult to constrain than rates of increase of CO2, CH4 and N2O are larger during the Industrial the BNF considering the changing paradigms of nitrogen cycling in the Era (see Section 6.3) than during any comparable period of at least the oxygen minimum zones or the unconstrained losses in permeable sed- past 22,000 years (Joos and Spahni, 2008). iments on the continental shelves (Gao et al., 2012). The coastal ocean may have losses in the range of 100 to 250 (Voss et al., 2011). For the 6.2.1 Glacial Interglacial Greenhouse Gas Changes open and distal ocean Codispoti (2007) estimated an upper limit of denitrification of 400 TgN yr 1. Voss et al. (2013) used a conservative 6.2.1.1 Processes Controlling Glacial Carbon Dioxide estimate of 100 TgN yr 1 for the coastal ocean, and 200 to 300 TgN yr 1 for the open ocean. Because the upper limit in the global ocean is 400 Ice cores recovered from the Antarctic ice sheet reveal that the con- TgN yr 1, 300 +/- 100 TgN yr 1 is the best estimate for global ocean losses centration of atmospheric CO2 at the Last Glacial Maximum (LGM; see of reactive nitrogen (Table 6.9). Glossary) at 21 ka was about one third lower than during the sub- sequent interglacial (Holocene) period started at 11.7 ka (Delmas et This chapter does not describe the phosphorus and sulphur biogeo- al., 1980; Neftel et al., 1982; Monnin et al., 2001). Longer (to 800 ka) chemical cycles, but phosphorus limitations on carbon sinks are briefly records exhibit similar features, with CO2 values of ~180 to 200 ppm addressed in Section 6.4.8.2 and future sulphur deposition in Section during glacial intervals (Petit et al., 1999). Prior to 420 ka, interglacial 6.4.6.2. CO2 values were 240 to 260 ppm rather than 270 to 290 ppm after that date (Lüthi et al., 2008). 6.1.3.2 Oxygen A variety of proxy reconstructions as well as models of different com- Atmospheric oxygen is tightly coupled with the global carbon cycle plexity from conceptual to complex Earth System Models (ESM; see (sometimes called a mirror of the carbon cycle). The burning of fossil Glossary) have been used to test hypotheses for the cause of lower fuels removes oxygen from the atmosphere in a tightly defined stoichi- LGM atmospheric CO2 concentrations (e.g., Köhler et al., 2005; Sigman ometric ratio depending on fuel carbon content. As a consequence of et al., 2010). The mechanisms of the carbon cycle during the LGM the burning of fossil fuels, atmospheric O2 levels have been observed which lead to low atmospheric CO2 can be broken down by individual to decrease steadily over the last 20 years (Keeling and Shertz, 1992; drivers (Figure 6.5). It should be recognized, however, that this sep- Manning and Keeling, 2006) (Figure 6.3b). Compared to the atmos- aration is potentially misleading, as many of the component drivers pheric oxygen content of about 21% this decrease is very small; how- shown in Figure 6.5 may combine nonlinearly (Bouttes et al., 2011). ever, it provides independent evidence that the rise in CO2 must be due Only well-established individual drivers are quantified (Figure 6.5), and to an oxidation process, that is, fossil fuel combustion and/or organic discussed here. carbon oxidation, and is not caused by, for example, volcanic emissions or by outgassing of dissolved CO2 from a warming ocean. The atmos- 6.2.1.1.1 Reduced land carbon pheric oxygen measurements furthermore also show the north south concentration O2 difference (higher in the south and mirroring the CO2 Despite local evidence of larger carbon storage in permafrost regions north south concentration difference) as expected from the stronger during glacial periods (Zimov et al., 2009; Zech et al., 2011), the 13C fossil fuel consumption in the NH (Keeling et al., 1996). record of ocean waters as preserved in benthic foraminiferal shells has been used to infer that global terrestrial carbon storage was reduced On land, during photosynthesis and respiration, O2 and CO2 are in glacial times, thus opposite to recorded changes in atmospheric CO2. exchanged in nearly a 1:1 ratio. However, with respect to exchanges Data-based estimates of the deficit between LGM and pre-industrial with the ocean, O2 behaves quite differently from CO2, because com- land carbon storage range from a few hundreds to 1000 PgC (e.g., pared to the atmosphere only a small amount of O2 is dissolved in the Bird et al., 1996; Ciais et al., 2012). Dynamic vegetation models tend ocean whereas by contrast the oceanic CO2 content is much larger due to simulate values at the higher end (~800 PgC) (Kaplan et al., 2002; to the carbonate chemistry. This different behaviour of the two gases Otto et al., 2002) and indicate a role for the physiological effects of low with respect to ocean exchange provides a powerful method to assess CO2 on photosynthesis at the LGM at least as large as that of colder independently the partitioning of the uptake of anthropogenic CO2 by and dryer climate conditions in determining the past extent of forests land and ocean (Manning and Keeling, 2006), Section 6.3.2.3. (Prentice and Harrison, 2009). 6.2.1.1.2 Lower sea surface temperatures 6.2 Variations in Carbon and Other 6 Biogeochemical Cycles Before the Fossil Reconstructions of sea surface temperatures (SSTs) during the LGM Fuel Era suggest that the global surface ocean was on average 3°C to 5°C cooler compared to the Holocene. Because the solubility of CO2 The Earth System mechanisms that were responsible for past variations increases at colder temperature (Zeebe and Wolf-Gladrow, 2001), a in atmospheric CO2, CH4, and N2O will probably operate in the future colder glacial ocean will hold more carbon. However, uncertainty in as well. Past archives of GHGs and climate therefore provide useful reconstructing the LGM pattern of ocean temperature, particularly knowledge, including constraints for biogeochemical models applied in the tropics (Archer et al., 2000; Waelbroeck et al., 2009), together to the future projections described in Section 6.4. In addition, past with problems in transforming this pattern to the resolution of models archives of GHGs also show with very high confidence that the average in light of the nonlinear nature of the CO2 temperature relationship 480 Carbon and Other Biogeochemical Cycles Chapter 6 (Ridgwell, 2001), creates a large spread in modelled estimates, Most 6.2.1.1.5 Iron fertilisation ocean general circulation models (OGCM) projections, however, cluster more tightly and suggest that lower ocean temperatures contribute to Both marine and terrestrial sediment records indicate higher rates of lower CO2 values by 25 ppm during the LGM (Figure 6.5). deposition of dust and hence iron (Fe) supply at the LGM (Mahow- ald et al., 2006), implying a potential link between Fe fertilisation of 6.2.1.1.3 Lower sea level and increased salinity marine productivity and lower glacial CO2 (Martin, 1990). However, despite the fact that ocean carbon cycle models generally employ sim- During the LGM, sea level was about ~120 m lower than today, and ilar reconstructions of glacial dust fluxes (i.e., Mahowald et al., 1999; this change in ocean volume had several well-understood effects on Mahowald et al., 2006), there is considerable disagreement among atmospheric CO2 concentrations. Lower sea level impacts the LGM them in the associated CO2 change. OGCM that include a descrip- ocean carbon cycle in two main ways. First, the resulting higher LGM tion of the Fe cycle tend to cluster at the lower end of simulated CO2 ocean surface salinity causes atmospheric CO2 to be higher than during changes between glacial and interglacial (e.g., Archer at al., 2000; the Holocene. Second, the total dissolved inorganic carbon and alka- Bopp et al., 2003), whereas box models (e.g., Watson et al., 2000) or linity (a measure of the capacity of an aqueous solution to neutralize Earth System Models of Intermediate Complexity (EMICs, e.g., Brovkin acid) become more concentrated in equal proportions, and this process et al., 2007) tend to produce CO2 changes which are at the higher also causes atmospheric CO2 to be higher during the LGM. In total, end (Parekh et al., 2008). An alternative view comes from inferences lower sea level is estimated to contribute to higher CO2 values by 15 drawn from the timing and magnitude of changes in dust and CO2 in ppm during the LGM (Figure 6.5), implying that other processes must ice cores (Röthlisberger et al., 2004), assigning a 20 ppm limit for the explain the lower CO2 values measured in ice cores. lowering of CO2 during the LGM in response to an Southern Ocean Fe fertilisation effect, and a 8 ppm limit for the same effect in the North 6.2.1.1.4 Ocean circulation and sea ice Pacific. Reorganization in ocean circulation during glacial periods that pro- 6.2.1.1.6 Other glacial carbon dioxide drivers moted the retention of dissolved inorganic carbon in the deep ocean during the LGM has become the focus of most research on the gla- A number of further aspects of altered climate and biogeochemistry cial interglacial CO2 problem. That ocean circulation plays a key role in at the LGM are also likely to have affected atmospheric CO2. Reduced low glacial period atmospheric CO2 concentration is exemplified by the bacterial metabolic rates and remineralization (see Glossary) of organ- tight coupling observed between reconstructed deep ocean tempera- ic matter (Matsumoto, 2007; Menviel et al., 2012), increased glacial tures and atmospheric CO2 (Shackleton, 2000). Evidence from marine supply of dissolved silica (required by diatoms to form frustules) bore hole sites (Adkins et al., 2002) and from marine sediment cores (Harrison, 2000), silica leakage (Brzezinski et al., 2002; Matsumoto (Jaccard et al., 2005; Skinner et al., 2010) show that the glacial ocean et al., 2002), changes in net global weathering rates (Berner, 1992; was highly stratified compared to interglacial conditions and may thus Munhoven, 2002), reduction in coral reef growth and other forms of have held a larger store of carbon during glacial times. 13CO2 ice core shallow water CaCO3 accumulation (Berger, 1982), carbonate com- records (Lourantou et al., 2010a, 2010b; Schmitt et al., 2012), as well pensation (Ridgwell and Zeebe, 2005) and changes in the CaCO3 to as radiocarbon records from deep-sea corals demonstrate the role of organic matter rain ratio to the sediments (Archer and Maier-Reimer, a deep and stratified Southern Ocean in the higher LGM ocean carbon 1994) will act to amplify or diminish the effect of many of the afore- storage. However, conflicting hypotheses exist on the drivers of this mentioned drivers on glacial CO2. increase in the Southern Ocean stratification, for example, northward shift and weakening of Southern Hemisphere (SH) westerly winds (Tog- 6.2.1.1.7 Summary gweiler et al., 2006), reduced air sea buoyancy fluxes (Watson and Garabato, 2006) or massive brine rejections during sea ice formation All of the major drivers of the glacial-to-interglacial atmospheric (Bouttes et al., 2011, 2012). Ocean carbon cycle models have simulated CO2 changes (Figure 6.5) are likely to have already been identified. a circulation-induced effect on LGM CO2 that can explain lower values However, Earth System Models have been unable to reproduce the than during interglacial by 3 ppm (Bopp et al., 2003) to 57 ppm (Tog- full magnitude of the glacial-to-interglacial CO2 changes. Significant gweiler, 1999). uncertainties exist in glacial boundary conditions and on some of the primary controls on carbon storage in the ocean and in the land. These A long-standing hypothesis is that increased LGM sea ice cover acted uncertainties prevent an unambiguous attribution of individual mech- as a barrier to air sea gas exchange and hence reduced the leakage anisms as controllers of the low glacial CO2 concentrations. Further of CO2 during winter months from the ocean to the atmosphere during assessments of the interplay of different mechanisms prior to degla- 6 glacial periods (Broecker and Peng, 1986). However, concurrent chang- cial transitions or in glacial inceptions will provide additional insights es in ocean circulation and biological productivity complicate the esti- into the drivers and processes that caused the glacial decrease of CO2. mation of the impact of increased sea ice extent on LGM atmospher- Because several of these identified drivers (e.g., organic matter rem- ic CO2 (Kurahashi-Nakamura et al., 2007). With the exception of the ineralization, ocean stratification) are sensitive to climate change in results of an idealised box model (Stephens and Keeling, 2000), ocean general, improved understanding drawn from the glacial interglacial carbon models are relatively consistent in projecting a small effect of cycles will help constrain the magnitude of future ocean feedbacks higher sea ice extent on maintaining atmospheric CO2 lower during on atmospheric CO2. Other drivers (e.g., iron fertilisation) are involved LGM (Archer et al., 2003). in geoengineering methods (see Glossary), such that improved under- 481 Chapter 6 Carbon and Other Biogeochemical Cycles Glacial to Interglacial sea surface H Ocean temperature sea-level and salinity H ocean circulation M Fe fertilisation M Drivers coral reef & carbonate M compensation Geol. Land CO2 fertilisation H & biome shifts terrestrial weathering L ice-core 20 ka to 0 ka Data record -30 -20 -10 0 10 20 30 40 50 60 70 80 Holocene sea surface L Ocean temperature coral reef & carbonate M compensation CO2 fertilisation M Land biome shifts M Drivers peat accumulation M Data Human Geol. volcanic outgassing L land use M ice-core 7 ka to 0 ka * record -30 -20 -10 0 10 20 30 40 50 CO2 change (ppm) Figure 6.5 | Mechanisms contributing to carbon dioxide concentrations changes from Last Glacial Maximum (LGM) to late Holocene (top) and from early/mid Holocene (7 ka) to late Holocene (bottom). Filled black circles represent individual model-based estimates for individual ocean, land, geological or human mechanisms. Solid colour bars represent expert judgment (to the nearest 5 ppm) rather than a formal statistical average. References for the different model results used for explaining CO2 changes from LGM to late Holocene are as per (Kohfeld and Ridgwell, 2009) with excluded model projections in grey. References for the different model results used for explaining CO2 changes during the Holocene are: Joos et al. (2004), Brovkin et al. (2002, 2008), Kleinen et al. (2010, 2012), Broecker et al. (1999), Ridgwell et al. (2003), Schurgers et al. (2006), Yu (2011), Ruddiman (2003, 2007), Strassmann et al. (2008), Olofsson and Hickler (2008), Pongratz et al. (2009), Kaplan et al. (2011), Lemmen (2009), Stocker et al. (2011), Roth and Joos (2012). Confidence levels for each mechanism are indicated in the left column H for high confidence, M for medium confidence and L for low confidence. standing could also help constrain the potential and applicability of changes. N2O isotopes suggest a similar increase in marine and terres- these methods (see Section 6.5.2). trial N2O emissions during the last deglaciation (Sowers et al., 2003). Marine sediment proxies of ocean oxygenation suggest that most of 6.2.1.2 Processes Controlling Glacial Methane and Nitrous Oxide the observed N2O deglacial rise was of marine origin (Jaccard and Gal- braith, 2012). D and 14C isotopic composition measurements of CH4 Ice core measurements show that atmospheric CH4 and N2O were have shown that catastrophic methane hydrate degassing events are much lower under glacial conditions compared to interglacial ones. unlikely to have caused the last deglaciation CH4 increase (Sowers, Their reconstructed history encompasses the last 800 ka (Loulergue et 2006; Petrenko et al., 2009; Bock et al., 2010). 13C and D meas- 6 al., 2008; Schilt et al., 2010a). Glacial CH4 mixing ratios are in the 350 urements of CH4 combined with interpolar atmospheric CH4 gradient to 400 ppb range during the eight glacial maxima covered by the ice changes (Greenland minus Antarctica ice cores) suggest that most of core record. This is about half the levels observed during interglacial the deglacial CH4 increase was caused by increased emissions from conditions. The N2O concentration amounts to 202 +/- 8 ppb at the LGM, boreal and tropical wetlands and an increase in CH4 atmospheric res- compared to the early Holocene levels of about 270 ppb (Flückiger et idence time due to a reduced oxidative capacity of the atmosphere al., 1999). (Fischer et al., 2008). The biomass burning source apparently changed little on the same time scale, whereas this CH4 source experienced CH4 and N2O isotopic ratio measurements in ice cores provide impor- large fluctuations over the last millennium (Mischler et al., 2009; Wang tant constraints on the mechanisms responsible for their temporal et al., 2010b). Recent modelling studies, however, suggest that ­changes 482 Carbon and Other Biogeochemical Cycles Chapter 6 in the atmospheric oxidising capacity of the atmosphere at the LGM ­ Conflicting hypotheses exist on the drivers of these millennial-scale are probably negligible compared to changes in sources (Levine et al., changes. Some model simulations suggest that both CO2 and N2O 2011) and that tropical temperature influencing tropical wetlands and fluctuations can be explained by changes in the Atlantic meridional global vegetation were the dominant controls for CH4 atmospheric overturning ocean circulation (Schmittner and Galbraith, 2008), CO2 changes on glacial interglacial time scales (Konijnendijk et al., 2011). variations being explained mainly by changes in the efficiency of the biological pump which affects deep ocean carbon storage (Bouttes et 6.2.1.3 Processes Controlling Changes in Carbon Dioxide, al., 2011), whereas N2O variations could be due to changes in produc- Methane, and Nitrous Oxide During Abrupt Glacial tivity and oxygen concentrations in the subsurface ocean (Schmittner Events and Galbraith, 2008). Other studies, however, suggest that the millen- nial-scale CO2 fluctuations can be explained by changes in the land Ice core measurements of CO2, CH4 and N2O show sharp (millen- carbon storage (Menviel et al., 2008; Bozbiyik et al., 2011). For CH4, nial-scale) changes in the course of glaciations, associated with the models have difficulties in reproducing changes in wetland emissions so-called Dansgaard/Oeschger (DO) climatic events (see Section 5.7), compatible with DO atmospheric variations (Hopcroft et al., 2011), and but their amplitude, shape and timing differ. During these millennial the changes in the atmospheric oxidizing capacity of the atmosphere scale climate events, atmospheric CO2 concentrations varied by about during DO events seem to be too weak to explain the CH4 changes 20 ppm, in phase with Antarctic, but not with Greenland tempera- (Levine et al., 2012). tures. CO2 increased during cold (stadial) periods in Greenland, several t ­ housands years before the time of the rapid warming event in Green- 6.2.2 Greenhouse Gas Changes over the Holocene land (Ahn and Brook, 2008). CH4 and N2O showed rapid transitions in phase with Greenland temperatures with little or no lag. CH4 changes 6.2.2.1 Understanding Processes Underlying Holocene Carbon are in the 50 to 200 ppb range (Flückiger et al., 2004), in phase with Dioxide Changes Greenland temperature warming at a decadal time scale (Huber et al., 2006). N2O changes are large, of same magnitude than glacial inter- The evolution of the atmospheric CO2, CH4, and N2O concentrations glacial changes, and for the warmest and longest DO events N2O starts during the Holocene, the interglacial period which began 11.7 ka, is to increase several centuries before Greenland temperature and CH4 known with high certainty from ice core measurements (Figure 6.6). A (Schilt et al., 2010b). decrease in atmospheric CO2 of about 7 ppm is measured in ice cores ) ( ) ( ) 6 ( ( ) Figure 6.6 | Variations of CO2, CH4, and N2O concentrations during the Holocene. The data are for Antarctic ice cores: European Programme for Ice Coring in Antarctica EPICA Dome C (Flückiger et al., 2002; Monnin et al., 2004), triangles; EPICA Dronning Maud Land (Schilt et al., 2010b), crosses; Law Dome (MacFarling-Meure et al., 2006), circles; and for Greenland Ice Core Project (GRIP) (Blunier et al., 1995), squares. Lines correspond to spline fits. 483 Chapter 6 Carbon and Other Biogeochemical Cycles between 11 and 7 ka, followed by a 20 ppm CO2 increase until the ing the intensification and decline of the Afro-Asian monsoon and the onset of the Industrial Era in 1750 (Indermühle et al., 1999; Monnin et mid-Holocene warming of the high latitudes of the NH are estimated al., 2004; Elsig et al., 2009). These variations in atmospheric CO2 over in models to have caused changes in vegetation distribution and hence the past 11 kyr preceding industrialisation are more than five times of terrestrial carbon storage. These climate-induced carbon storage smaller than the CO2 increase observed during the Industrial Era (see changes are estimated using models to have been smaller than the Section 6.3.2.3). Despite the small magnitude of CO2 variations prior increase due to CO2 fertilisation (Brovkin et al., 2002; Schurgers et al., to the Industrial Era, these changes are nevertheless useful for under- 2006). The Holocene accumulation of carbon in peatlands has been standing the role of natural forcing in carbon and other biogeochemi- reconstructed globally, suggesting a land carbon additional storage of cal cycles during interglacial climate conditions. several hundred petagrams of carbon between the early Holocene and the Industrial Era, although uncertainties remain on this estimate (Tar- Since the IPCC AR4, the mechanisms underlying the observed 20 ppm nocai et al., 2009; Yu, 2011; Kleinen et al., 2012). Volcanic CO2 emis- CO2 increase between 7 ka and the Industrial Era have been a matter sions to the atmosphere between 12 and 7 ka were estimated to be of intensive debate. During three interglacial periods prior to the Holo- two to six times higher than during the last millennium, of about 0.1 cene, CO2 did not increase, and this led to a hypothesis that pre-indus- PgC yr 1 (Huybers and Langmuir, 2009; Roth and Joos, 2012). However, trial anthropogenic CO2 emissions could be associated with early land a peak in the inferred volcanic emissions coincides with the period of use change and forest clearing (Ruddiman, 2003, 2007). However, ice decreasing atmospheric CO2 and the confidence in changes of volcanic core CO2 data (Siegenthaler et al., 2005b) indicate that during Marine CO2 emissions is low. Isotope Stage 11 (see Section 5.2.2), an interglacial period that lasted from 400 to 420 ka, CO2 increased similarly to the Holocene period. Global syntheses of the observational, paleoecological and archaeolog- Drivers of atmospheric CO2 changes during the Holocene can be divid- ical records for Holocene land use change are not currently available ed into oceanic and terrestrial processes (Figure 6.5) and their roles are (Gaillard et al., 2010). Available reconstructions of anthropogenic land examined below. use and land cover change (LULCC) prior to the last millennium cur- rently extrapolate using models and assumptions from single regions 6.2.2.1.1 Oceanic processes to changes in all regions of the world (Goldewijk et al., 2011; Kaplan et al., 2011). Because of regional differences in land use systems and The change in oceanic carbonate chemistry could explain the slow uncertainty in historical population estimates, the confidence in spa- atmospheric CO2 increase during the Holocene since 7 ka. Proposed tially explicit LULCC reconstructions is low. mechanisms include: (1) a shift of oceanic carbonate sedimentation from deep sea to the shallow waters due to sea level rise onto con- Some recent studies focused on reconstructing LULCC and making very tinental shelves causing accumulation of CaCO3 on shelves including simple assumptions regarding the effect of land use on carbon (Olofs- coral reef growth, a process that releases CO2 to the atmosphere (Ridg- son and Hickler, 2008; Lemmen, 2009). Other studies relied on more well et al., 2003; Kleinen et al., 2010), (2) a carbonate compensation sophisticated terrestrial biosphere models to simulate carbon storage in response to the release of carbon from the deep ocean during degla- and loss in response to pre-industrial LULCC during the late Holocene ciation and to the buildup of terrestrial biosphere in the early Holocene (Strassmann et al., 2008; Pongratz et al., 2009; Stocker et al., 2011). (Broecker et al., 1999; Joos et al., 2004; Elsig et al., 2009; Menviel and The conclusion of the aforementioned studies was that cumulative Joos, 2012). Proxies for carbonate ion concentration in the deep sea (Yu Holocene carbon emissions as a result of pre-industrial LULCC were et al., 2010) and a decrease in modern CaCO3 preservation in equatori- not large enough (~50 to 150 PgC during the Holocene before 1850) al Pacific sediments (Anderson et al., 2008) support the hypothesis that to have had an influence larger than an increase of ~10 ppm on late the ocean was a source of CO2 to the atmosphere during the Holocene. Holocene observed CO2 concentration increase (Figure 6.5). However, a Changes in SSTs over the last 7 kyr (Kim et al., 2004) could have con- modelling study by Kaplan et al. (2011) suggested that more than 350 tributed to slightly lower (Brovkin et al., 2008) or higher (Menviel and PgC could have been released as a result of LULCC between 8 ka and Joos, 2012) atmospheric CO2 concentration but, very likely, SST-driven 1850 as a result of a much stronger loss of soil carbon in response to CO2 change represents only a minor contribution to the observed CO2 land use change, than in other studies. increase during the Holocene after 7 ka (Figure 6.5). In addition to clearing of forests, large-scale biomass burning activ- 6.2.2.1.2 Terrestrial processes ity, inferred from synthesized charcoal records and bog sediments has been hypothesized to correlate with the observed Late Holocene The 13C of atmospheric CO2 trapped in ice cores can be used to infer atmospheric CO2 (Carcaillet et al., 2002). A global extensive synthesis 6 changes in terrestrial biospheric carbon pools. Calculations based of charcoal records for the last 21 kyr (Power et al., 2008) and updates on inferred 13C of atmospheric CO2 during the Holocene suggest of those shows that fire activity followed climate variability on global an increase in terrestrial carbon storage of about 300 PgC between (Marlon et al., 2008; Daniau et al., 2012) and regional scale (Archibald 11 and 5 ka and small overall terrestrial changes thereafter (Elsig et et al., 2009; Mooney et al., 2011; Marlon et al., 2012; Power et al., al., 2009). Modelling studies suggest that CO2 fertilisation (Box 6.3) 2013). There is no evidence, however, for a distinct change in fire activ- in response to increasing atmospheric CO2 concentration after 7 ka ity linked to human activity alone as hypothesized from a regional contributed to a substantially increased terrestrial carbon storage charcoal record synthesis for the tropical Americas (Nevle and Bird, (>100 PgC) on Holocene time scales (Kaplan et al., 2002; Joos et al., 2008; Nevle et al., 2011). Fire being a newly studied component, no 2004; Kleinen et al., 2010). Orbitally forced climate variability, includ- estimate for its role is given in Figure 6.5. 484 Carbon and Other Biogeochemical Cycles Chapter 6 6.2.2.2 Holocene Methane and Nitrous Oxide Drivers 6.2.3 Greenhouse Gas Changes over the Last Millennium The atmospheric CH4 levels decreased from the early Holocene to about 6.2.3.1 A Decrease of Carbon Dioxide around Year 1600 and 6 ka, were lowest at around 5 ka, and increased between 5 ka and year Possible Explanations for this Event 1750 by about 100 ppb (Figure 6.6). Major Holocene agricultural devel- opments, in particular rice paddy cultivation and widespread domes- High resolution ice cores records reveal that atmospheric CO2 during tication of ruminants, have been proposed as an explanation for the the last millennium varied with a drop in atmospheric CO2 concen- Late Holocene CH4 rise (Ruddiman, 2007). The most recent syntheses tration by 7 to 10 ppm around year 1600, followed by a CO2 increase of archaeological data point to an increasing anthropogenic CH4 source during the 17th century (Trudinger et al., 2002; Siegenthaler et al., from domesticated ruminants after 5 ka and from rice cultivation after 4 2005a; MacFarling-Meure et al., 2006; Ahn et al., 2012). This is shown ka (Ruddiman, 2007; Fuller et al., 2011). The modelling support for either in Figure 6.7. The CO2 decrease during the 17th century was used to natural or anthropogenic explanations of the Late Holocene increase in evaluate the response of atmospheric CO2 concentration to a centu- the atmospheric CH4 concentration is equivocal. A study by Kaplan et ry-scale shift in global temperature (Scheffer et al., 2006; Cox and al. (2006) suggested that a part of the Late Holocene CH4 rise could Jones, 2008; Frank et al., 2010) which was found to be dependent on be explained by anthropogenic sources. Natural wetland CH4 models the choice of global temperature reconstructions used in the model. driven by simulated climate changes are able (Singarayer et al., 2011) or unable (Konijnendijk et al., 2011) to simulate Late Holocene increase ­ One of the possible explanations for the drop in atmospheric CO2 around in the CH4 concentration, reflecting a large spread in present-day CH4 year 1600 is enhanced land and/or ocean carbon uptake in response emissions simulated by this type of models (Melton et al., 2013; see Sec- to the cooling caused by reduced solar irradiance during the Maunder tion 6.3.3.2). Consequently, about as likely as not, the atmospheric CH4 Minimum (Section 5.3.5.3). However, simulations using Earth System increase after 5000 years ago can be attributed to early human activi- Models of Intermediate Complexity (EMICs)(Gerber et al., 2003; Brovkin ties. The mechanisms causing the N2O concentration changes during the et al., 2004) and by complex Earth System Models (ESMs) (Jungclaus Holocene are not firmly identified (Flückiger et al., 2002). et al., 2010) suggest that solar irradiance forcing alone is not sufficient ) ( ) ( ) ( 6 Figure 6.7 | Variations of CO2, CH4, and N2O during 900 1900 from ice cores. The data are for Antarctic ice cores: Law Dome (Etheridge et al., 1996; MacFarling-Meure et al., 2006), circles; West Antarctic Ice Sheet (Mitchell et al., 2011; Ahn et al., 2012), triangles; Dronning Maud Land (Siegenthaler et al., 2005a), squares. Lines are spline fits to individual measurements. 485 Chapter 6 Carbon and Other Biogeochemical Cycles to explain the magnitude of the CO2 decrease. The drop in atmospheric to changes in the wetland CH4 source, changes in biomass burning have CO2 around year 1600 could also be caused by a cooling from increased been invoked to explain the last millennium CH4 record (Ferretti et al., volcanic eruptions (Jones and Cox, 2001; Brovkin et al., 2010; Frölicher 2005; Mischler et al., 2009), ice core CO and CO isotopes (Wang et al., et al., 2011). A third hypothesis calls for a link between CO2 and epidem- 2010b) and global charcoal depositions (Marlon et al., 2008). Chang- ics and wars associated with forest regrowth over abandoned lands and es in anthropogenic CH4 emissions during times of war and plague increased carbon storage, especially in Central America. Here, results are hypothetically contributed to variability in atmospheric CH4 concentra- model and scenario dependent. Simulations by Pongratz et al. (2011a) tion (Mitchell et al., 2011). Ice core 13CH4 measurements suggested do not reproduce a decrease in CO2, while simulations by Kaplan et al. pronounced variability in both natural and anthropogenic CH4 sources (2011) suggest a considerable increase in land carbon storage around over the 1000 1800 period (Sapart et al., 2012). No studies are known year 1600. The temporal resolution of Central American charcoal and about mechanisms of N2O changes for the last ­ illennium. m pollen records is insufficient to support or falsify these model results (e.g., Nevle and Bird, 2008; Marlon et al., 2008). 6.3 Evolution of Biogeochemical Cycles Ensemble simulations over the last 1200 years have been conducted Since the Industrial Revolution using an ESM (Jungclaus et al., 2010) and EMICs (Eby et al., 2013) including a fully interactive carbon cycle. The sensitivity of atmospheric 6.3.1 Carbon Dioxide Emissions and Their Fate CO2 concentration to NH temperature changes in ESM was modeled to Since 1750 be of 2.7 to 4.4 ppm °C 1, while EMICs show on average a higher sen- sitivity of atmospheric CO2 to global temperature changes of 8.6 ppm Prior to the Industrial Era, that began in 1750, the concentration of °C 1.These sensitivities fall within the range of 1.7 to 21.4 ppm °C 1 of atmospheric CO2 fluctuated roughly between 180 ppm and 290 ppm a recent reconstruction based on tree-ring NH temperature reconstruc- for at least 2.1 Myr (see Section 5.2.2 and Hönisch et al., 2009; Lüthi tions (Frank et al., 2010). et al., 2008; Petit et al., 1999). Between 1750 and 2011, the combus- tion of fossil fuels (coal, gas, oil and gas flaring) and the production of 6.2.3.2 Mechanisms Controlling Methane and Nitrous Oxide cement have released 375 +/- 30 PgC (1 PgC = 1015 gC) to the atmos- during the Last Millennium phere (Table 6. 1; Boden et al., 2011). Land use change activities, mainly deforestation, has released an additional 180 +/- 80 PgC (Table 6.1). This Recent high-resolution ice core records confirm a CH4 decrease in the carbon released by human activities is called anthropogenic carbon. late 16th century by about 40 ppb (MacFarling-Meure et al., 2006; Mitchell et al., 2011), as shown in Figure 6.7. Correlations between this Of the 555 +/- 85 PgC of anthropogenic carbon emitted to the atmos- drop in atmospheric CH4 and the lower temperatures reconstructed phere from fossil fuel and cement and land use change, less than half during the 15th and 16th centuries suggest that climate change may have accumulated in the atmosphere (240 +/- 10 PgC) (Table 6.1). The have reduced CH4 emissions by wetlands during this period. In addition remaining anthropogenic carbon has been absorbed by the ocean and Table 6.1 | Global anthropogenic CO2 budget, accumulated since the Industrial Revolution (onset in 1750) and averaged over the 1980s, 1990s, 2000s, as well as the last 10 years until 2011. By convention, a negative ocean or land to atmosphere CO2 flux is equivalent to a gain of carbon by these reservoirs. The table does not include natural exchanges (e.g., rivers, weathering) between reservoirs. The uncertainty range of 90% confidence interval presented here differs from how uncertainties were reported in AR4 (68%). 1750 2011 1980 1989 1990 1999 2000 2009 2002 2011 Cumulative PgC yr 1 PgC yr 1 PgC yr 1 PgC yr 1 PgC Atmospheric increasea 240 +/- 10f 3.4 +/- 0.2 3.1 +/- 0.2 4.0 +/- 0.2 4.3 +/- 0.2 Fossil fuel combustion and cement production b 375 +/- 30 f 5.5 +/- 0.4 6.4 +/- 0.5 7.8 +/- 0.6 8.3 +/- 0.7 Ocean-to-atmosphere fluxc 155 +/- 30f 2.0 +/- 0.7 2.2 +/- 0.7 2.3 +/- 0.7 2.4 +/- 0.7 Land-to-atmosphere flux 30 +/- 45f 0.1 +/- 0.8 1.1 +/- 0.9 1.5 +/- 0.9 1.6 +/- 1.0 Partitioned as follows Net land use changed 180 +/- 80f,g 1.4 +/- 0.8 1.5 +/- 0.8 1.1 +/- 0.8 0.9 +/- 0.8 Residual land sinke 160 +/- 90f 1.5 +/- 1.1 2.6 +/- 1.2 2.6 +/- 1.2 2.5 +/- 1.3 Notes: 6 a Data from Charles D. Keeling, (http://scrippsco2.ucsd.edu/data/data.html), Thomas Conway and Pieter Tans, National Oceanic and Atmospheric Administration Earth System Research Laboratory (NOAA ESRL, www.esrl.noaa.gov/gmd/ccgg/trends/) using a conversion factor of 2.120 PgC per ppm (Prather et al., 2012). Prior to the atmospheric record in 1960, ice core data is used (Neftel et al., 1982; Friedli et al., 1986; Etheridge et al., 1996). b Estimated by the Carbon Dioxide Information Analysis Center (CDIAC) based on UN energy statistics for fossil fuel combustion (up to 2009) and US Geological Survey for cement production (Boden et al., 2011), and updated to 2011 using BP energy statistics. c Based on observations for 1990 1999, with the trends based on existing global estimates (see Section 6.3.2.5 and Table 6.4). d Based on the bookkeeping land use change flux accounting model of Houghton et al. (2012) until 2010, and assuming constant LUC emissions for 2011, consistent with satellite-based fire emissions (Le Quéré et al., 2013; see Section 6.3.2.2 and Table 6.2). e Calculated as the sum of the Land-to-atmosphere flux minus Net land use change flux, assuming the errors on each term are independent and added quadratically. f The 1750 2011 estimate and its uncertainty is rounded to the nearest 5 PgC. g Estimated from the cumulative net land use change emissions of Houghton et al. (2012) during 1850 2011 and the average of four publications (Pongratz at al., 2009; van Minnen et al., 2009; Shevliakova et al., 2009; Zaehle et al., 2011) during 1750 1850. 486 Carbon and Other Biogeochemical Cycles Chapter 6 in terrestrial ecosystems: the carbon sinks (Figure 6.8). The ocean tems, those affected by land use change and the others, is thus close stored 155 +/- 30 PgC of anthropogenic carbon since 1750 (see Sec- to neutral since 1750, with an average loss of 30 +/- 45 (see Figure 6.1). tion 6.3.2.5.3 and Box 6.1). Terrestrial ecosystems that have not been This increased storage in terrestrial ecosystems not affected by land affected by land use change since 1750, have accumulated 160 +/- 90 use change is likely to be caused by enhanced photosynthesis at higher PgC of anthropogenic carbon since 1750 (Table 6.1), thus not fully CO2 levels and nitrogen deposition, and changes in climate favouring compensating the net CO2 losses from terrestrial ecosystems to the carbon sinks such as longer growing seasons in mid-to-high latitudes. atmosphere from land use change during the same period estimated Forest area expansion and increased biomass density of forests that of 180 +/- 80 PgC (Table 6.1). The net balance of all terrestrial ecosys- result from changes in land use change are also carbon sinks, and they 1750 1800 1850 1900 1950 2000 10 cement CO2 emissions (PgC yr 1) gas Fossil fuel and cement oil coal 5 0 10 fossil fuel and cement from energy statistics land use change from data and models residual land sink measured atmospheric growth rate Annual anthropogenic CO2 emissions ocean sink from data and models 5 and partitioning (PgC yr 1) emissions 0 partitioning 5 10 1750 1800 1850 1900 1950 2000 Year Figure 6.8 | Annual anthropogenic CO2 emissions and their partitioning among the atmosphere, land and ocean (PgC yr 1) from 1750 to 2011. (Top) Fossil fuel and cement CO2 emissions by category, estimated by the Carbon Dioxide Information Analysis Center (CDIAC) based on UN energy statistics for fossil fuel combustion and US Geological Survey 6 for cement production (Boden et al., 2011). (Bottom) Fossil fuel and cement CO2 emissions as above. CO2 emissions from net land use change, mainly deforestation, are based on land cover change data and estimated for 1750 1850 from the average of four models (Pongratz et al., 2009; Shevliakova et al., 2009; van Minnen et al., 2009; Zaehle et al., 2011) before 1850 and from Houghton et al. (2012) after 1850 (see Table 6.2). The atmospheric CO2 growth rate (term in light blue atmosphere from measurements in the figure) prior to 1959 is based on a spline fit to ice core observations (Neftel et al., 1982; Friedli et al., 1986; Etheridge et al., 1996) and a synthesis of atmospheric measurements from 1959 (Ballantyne et al., 2012). The fit to ice core observations does not capture the large interannual variability in atmospheric CO2 and is represented with a dashed line. The ocean CO2 sink prior to 1959 (term in dark blue ocean from indirect observations and models in the figure) is from Khatiwala et al. (2009) and from a combination of models and observations from 1959 from (Le Quéré et al., 2013). The residual land sink (term in green in the figure) is computed from the residual of the other terms, and represents the sink of anthropogenic CO2 in natural land ecosystems. The emissions and their partitioning only include the fluxes that have changed since 1750, and not the natural CO2 fluxes (e.g., atmospheric CO2 uptake from weathering, outgassing of CO2 from lakes and rivers, and outgassing of CO2 by the ocean from carbon delivered by rivers; see Figure 6.1) between the atmosphere, land and ocean reservoirs that existed before that time and still exist today. The uncertainties in the various terms are discussed in the text and reported in Table 6.1 for decadal mean values. 487 Chapter 6 Carbon and Other Biogeochemical Cycles are accounted in Table 6.1 as part of the net flux from land use change. revised data on the rates of land use change conversion from country The increased terrestrial carbon storage in ecosystems not affected by statistics (FAO, 2010) now providing an arguably more robust estimate land use change is called the Residual land sink in Table 6.1 because of the land use change flux (Houghton et al., 2012; Section 6.3.2.2); it is inferred from mass balance as the difference between fossil and (2) a new global compilation of forest inventory data that provides an net land use change emissions and measured atmospheric and oceanic independent estimate of the amount of carbon that has been gained storage increase. by forests over the past two decades, albeit with very scarce measure- ments for tropical forest (Pan et al., 2011); (3) over 2 million new obser- 6.3.2 Global Carbon Dioxide Budget vations of the partial pressure of CO2 (pCO2) at the ocean surface have been taken and added to the global databases (Takahashi et al., 2009; Since the IPCC AR4 (Denman et al., 2007), a number of new advance- Pfeil et al., 2013) and used to quantify ocean CO2 sink variability and ments in data availability and data-model synthesis have allowed the trends (Section 6.3.2.5) and to evaluate and constrain models (Schuster establishment of a more constrained anthropogenic CO2 budget and et al., 2013; Wanninkhof et al., 2013); and (4) the use of multiple con- better attribution of its flux components. The advancements are: (1) straints with atmospheric inversions and combined atmosphere ocean Number of inversion models 6 Figure 6.9 | Interannual surface CO2 flux anomalies from inversions of the TRANSCOM project for the period 1981 2010 (Peylin et al., 2013). The ensemble of inversion results contains up to 17 atmospheric inversion models. The orange bars in the bottom panel indicate the number of available inversion models for each time period. The ensemble mean is bounded by the 1- inter-model spread in ocean atmosphere (blue) and land atmosphere (green) CO2 fluxes (PgC yr 1) grouped into large latitude bands, and the global. For each flux and each region, the CO2 flux anomalies were obtained by subtracting the long-term mean flux from each inversion and removing the seasonal signal. Grey shaded regions indicate El Nino episodes, and the black bars indicate the cooling period following the Mt. Pinatubo eruption, during which the growth rate of CO2 remained low. A positive flux means a larger than normal source of CO2 to the atmosphere (or a smaller CO2 sink). 488 Carbon and Other Biogeochemical Cycles Chapter 6 inversions (so called top down approaches; Jacobson et al., 2007) and of organic carbon; and they also include the long-term CO2 uptake by the up-scaling of reservoir-based observations using models (so called forest regrowth and soil carbon storage on abandoned agricultural bottom up approaches) provides new coarse scale consistency checks lands, afforestation and storage changes of wood products (Houghton on CO2 flux estimates for land and ocean regions (McGuire et al., 2009; et al., 2012; Mason Earles et al., 2012). The net flux of land use change Piao et al., 2009b; Schulze et al., 2009; Ciais et al., 2010; Schuster et al., is the balance among all source and sink processes involved in a given 2013). The causes of the year-to-year variability observed in the annual timeframe. The net flux of land use change is globally a net source to atmospheric CO2 accumulation shown in Figure 6.8 are estimated with the atmosphere (Table 6.1; Figure 6.8). a medium to high confidence to be mainly driven by terrestrial pro- cesses occurring in tropical latitudes as inferred from atmospheric CO2 Approaches to estimate global net CO2 fluxes from land use fall into inversions and supported by ocean data and models (Bousquet et al., three categories: (1) the bookkeeping method that tracks carbon in 2000; Raupach et al., 2008; Sarmiento et al., 2010) (Figures 6.9 and living vegetation, dead plant material, wood products and soils with 6.13; Section 6.3.2.5) and land models (Figure 6.16; Section 6.3.2.6). cultivation, harvesting and reforestation using country-level reports on changes in forest area and biome-averaged biomass values (Houghton, 6.3.2.1 Carbon Dioxide Emissions from Fossil Fuel Combustion 2003); (2) process-based terrestrial ecosystem models that simulate and Cement Production on a grid-basis the carbon stocks (biomass, soils) and exchange fluxes between vegetation, soil and atmosphere (see references in Table 6.2) Global CO2 emissions from the combustion of fossil fuels used for this and (3) detailed regional (primarily tropical forests) analyses based on chapter are determined from national energy consumption statistics satellite data that estimate changes in forest area or biomass (DeFries and converted to emissions by fuel type (Marland and Rotty, 1984). et al., 2002; Achard et al., 2004; Baccini et al., 2012; Harris et al., 2012). Estimated uncertainty for the annual global emissions are on the order Satellite-derived estimates of CO2 emissions to the atmosphere from of +/-8% (converted from +/-10% uncertainty for 95% confidence inter- so-called deforestation fires (van der Werf et al., 2010) provide addi- vals in Andres et al. (2012) to the 90% confidence intervals used here). tional constraints on the spatial attribution and variability of land use The uncertainty has been increasing in recent decades because a larger change gross emissions. Most global estimates do not include emis- fraction of the global emissions originate from emerging economies sions from peat burning or decomposition after a land use change, where energy statistics and emission factors per fuel type are more which are estimated to be 0.12 PgC yr 1 over 1997 2006 for peat fires uncertain (Gregg et al., 2008). CO2 emissions from cement production (van der Werf et al., 2008) and between 0.10 and 0.23 PgC yr 1 from were 4% of the total emissions during 2000 2009, compared to 3% in the decomposition of drained peat (Hooijer et al., 2010). The processes the 1990s (Boden et al., 2011). Additional emissions from gas flaring and time scales captured by these methods to estimate net land use represent <1% of the global emissions. change CO2 emissions are diverse, creating difficulties with comparison of different estimates (Houghton et al., 2012; Table 6.2). The bookkeep- Global CO2 emissions from fossil fuel combustion and cement produc- ing method of Houghton et al. (2012) was used for Table 6.1 because it tion were 7.8 +/- 0.6 PgC yr 1 on average during 2000 2009, 6.4 +/- 0.5 is closest to observations and includes the most extensive set of man- PgC yr 1 during 1990 1999 and 5.5 +/- 0.4 PgC yr 1 during 1980 1989 agement practices (Table 6.2). Methods that do not include long-term (Table 6.1; Figure 6.8). Global fossil fuel CO2 emissions increased by legacy fluxes from soils caused by deforestation (Table 6.2) underes- 3.2% yr 1 on average during the decade 2000 2009 compared to timate net land use change CO2 emissions by 13 to 62% depending on 1.0% yr 1 in the 1990s and 1.9% yr 1 in the 1980s. Francey et al. (2013) the starting year and decade (Ramankutty et al., 2006), and methods recently suggested a cumulative underestimation of 8.8 PgC emissions that do not include the fate of carbon wood harvest and shifting cul- during the period 1993 2004, which would reduce the contrast in tivation underestimate CO2 emissions by 25 to 35% (Houghton et al., emissions growth rates between the two decades. The global financial 2012). crisis in 2008 2009 induced only a short-lived drop in global emis- sions in 2009 ( 0.3%), with the return to high annual growth rates Global net CO2 emissions from land use change are estimated at 1.4, of 5.1% and 3.0% in 2010 and 2011, respectively, and fossil fuel and 1.5 and 1.1 PgC yr 1 for the 1980s, 1990s and 2000s, respectively, by cement CO2 emissions of 9.2 +/- 0.8 PgC in 2010 and 9.5 +/- 0.8 PgC in the bookkeeping method of Houghton et al. (2012) (Table 6.2; Figure 2011(Peters et al., 2013). 6.10). This estimate is consistent with global emissions simulated by process-based terrestrial ecosystem models using mainly three land 6.3.2.2 Net Land Use Change Carbon Dioxide Flux cover change data products as input for time-varying maps of land use change (Table 6.2). The bookkeeping method estimate is also generally CO2 is emitted to the atmosphere by land use and land use change consistent although higher than the satellite-based methods (tropics activities, in particular deforestation, and taken up from the atmos- only). Part of the discrepancy can be accounted for by emissions from 6 phere by other land uses such as afforestation (the deliberate creation extratropical regions (~0.1 PgC yr 1; Table 6.3) and by legacy fluxes for of new forests) and vegetation regrowth on abandoned lands. A critical land cover change prior to 1980s (~0.2 PgC yr 1) that are not covered distinction in estimating land use change is the existence of gross and by satellite-based methods used in Table 6.2, and by the fact that the net fluxes. Gross fluxes are the individual fluxes from multiple pro- bookkeeping method accounts for degradation and shifting agriculture cesses involved in land use change that can be either emissions to CO2 losses not detected in the satellite-based method reported in Table or removals from the atmosphere occurring at different time scales. 6.2. We adopt an uncertainty of +/-0.8 PgC yr 1 as representative of For example, gross emissions include instantaneous emissions from 90% uncertainty intervals. This is identical to the uncertainty of +/-0.5 deforestation fires and long-term emissions from the decomposition PgC yr 1 representing +/-1- interval (68% if Gaussian distributed error) 489 Chapter 6 Carbon and Other Biogeochemical Cycles from Houghton et al. (2012). This uncertainty of +/-0.8 PgC yr 1 on net Different estimates of net land use change CO2 emissions are shown in land use change CO2 fluxes is smaller than the one that was reported Figure 6.10. The lower net land use change CO2 emissions reported in in AR4 of 0.5 to 2.7 PgC yr 1 for the 1990s (68% confidence interval). In the 2000s compared to the 1990s, by 0.5 PgC yr 1 in the bookkeeping this chapter, uncertainty is estimated based on expert judgment of the method based on FAO (2010), and by 0.3 to 0.5 PgC yr 1 from five available evidence, including improved accuracy of land cover change process-based ecosystem models based on the HistorY Database of the incorporating satellite data, the larger number of independent meth- global Environment (HYDE) land cover change data updated to 2009 ods to quantify emissions and the consistency of the reported results (Goldewijk et al., 2011), are within the error bar of the data and meth- (Table 6.2; Figure 6.10). ods. The bookkeeping method suggests that most of the LUC emissions Table 6.2 | Estimates of net land to atmosphere CO2 flux from land use change covering recent decades (PgC yr 1). Positive values indicate CO2 losses to the atmosphere. Vari- ous forms of land management are also included in the different estimates, including wood harvest (W), shifting cultivation (C) and harvesting (H) of crops and peat burning and peat drainage (P). All methods include the vegetation degradation after land clearance. Additional processes included are initial biomass loss during the year of deforestation (I), decomposition of slash and soil carbon during the year of initial loss (D), regrowth (R), change in storage in wood products pools (S), the effect of increasing CO2, (C), the effect of observed climate variability between decades (M) and legacy long-term decomposition flux carried over from land use change transitions prior to start of time period used for reporting in the table (L). In the absence of data on L in the assessed estimates, the studies have either assumed instantaneous loss of all biomass and soil carbon (I, a committed future flux) or did not consider the legacy flux L. Satellite-based methods have examined Land Use Change (LUC) emissions in the tropical regions only. Numbers in parentheses are ranges in uncertainty provided in some studies. Data for Land Biomass Processes 1980 1989 1990 1999 2000 2009 Land Use Management Data Included PgC yr 1 PgC yr 1 PgC yr 1 Change Areaa Included Bookkeeping Method (global) Houghton et al. (2012) FAO-2010 Observedb W, C, H I, D, R, S, L 1.4 1.5 1.1 Baccini et al. (2012) FAO-2010 Satellite data W, C, H I, D, R, S, L 1.0 Satellite-based Methods (tropics only) Achard et al. (2004) Landsat Observedb I, D, R, S, C, M 0.9 (0.5 1.4)c DeFries et al. (2002) AVHRR Observedb I, D, R, Sd, C, M 0.6 (0.3 0.8) 0.9 (0.5 1.4) Van der Werf et al. (2010) GFED CASA e P I, D, C, M 1.2 (0.6 1.8)f Process Models (global) Shevliakova et al. (2009) HYDE LM3V W, C I, D, R, S, L, C 1.1 1.1 Shevliakova et al. (2009) SAGE LM3V W, C I, D, R, S, L, C 1.4 1.3 van Minnen et al. (2009)g HYDE IMAGE 2e W I, D, R, S, L, C 1.8 1.4 1.2 Strassmann et al. (2008) HYDE BernCCe I, D, R, S, L, C 1.3 1.3 Stocker et al. (2011)g HYDE BernCCe H I, D, R, S, L, C 1.4 0.9 0.6 Yang et al. (2010) SAGE ISAM e W I, D, R, S, L, C 1.7 1.7 Yang et al. (2010) FAO-2005 ISAMe W I, D, R, S, L, C 1.7 1.8 Yang et al. (2010) g HYDE ISAM e W I, D, R, S, L, C 2.2 1.5 1.2 Arora and Boer (2010) SAGE CTEMe H I, D, R, S, L, C 1.1h 1.1h Arora and Boer (2010) g HYDE CTEMe H I, D, R, S, L, C 0.4h 0.4h Poulter et al. (2010)g HYDE LPJmLe I, D, R, S, L, C 1.0 0.9 0.5 Kato et al. (2013)g HYDE VISITe C I, D, R, S, L, C 1.2 1.0 0.5 Zaehle et al. (2011) HYDE O-CN I, D, R, S, L, C 1.2 1.0 Average of process modelsi 1.3 +/- 0.7 1.2 +/- 0.6 0.8 +/- 0.6 Range of process models [0.4 2.2] [0.4 1.8] [0.5 1.2] Notes: a References for the databases used: FAO (2010) as applied in Houghton et al. (2012); FAO (2005) as applied in Houghton (2003), updated; GFED (van der Werf et al., 2009); HYDE (Goldewijk et al., 2011), SAGE (Ramankutty and Foley, 1999). Landsat and AVHRR are satellite-based data and GFED is derived from satellite products as described in the references. 6 b Based on average estimates by biomes compiled from literature data (see details in corresponding references). c 1990 1997 only. d Legacy fluxes for land cover change prior to 1980 are not included and are estimated to add about 0.2 PgC yr 1 to the 1980s and 0.1 PgC yr 1 to the 1990s estimates, based on Ramankutty et al. (2006). e The vegetation and soil biomass is computed using a vegetation model described in the reference. f 1997 2006 average based on estimates of carbon emissions from deforestation and degradation fires, including peat fires and oxidation. Estimates were doubled to account for emissions other than fire including respiration of leftover plant materials and soil carbon following deforestation following (Olivier et al., 2005). g Method as described in the reference but updated to 2010 using the land cover change data listed in column 2. h The large variability produced by the calculation method is removed for comparison with other studies by averaging the flux over the two decades. i Average of estimates from all process models and 90% confidence uncertainty interval; note that the spread of the different estimates does not follow a Gaussian distribution. AVHRR = Advanced Very High Resolution Radiometer; FAO = Food and Agriculture Organization (UN); GFED = Global Fire Emissions Database; HYDE = HistorY Database of the global Environ- ment; SAGE = Center for Sustainability and the Global Environment. 490 Carbon and Other Biogeochemical Cycles Chapter 6 3 Houghton et al. (2012) Process models (Table 6.2) Satellite-based methods (see legend) Net Land Use Change CO2 emissions (PgC yr-1) Average of four models (see Secton 6.3.2.2) 2 1 0 1750 1800 1850 1900 1950 2000 Figure 6.10 | Net land use change CO2 emissions (PgC yr 1). All methods are based on land cover change data (see Table 6.2) and are smoothed with a 10-year filter to remove interannual variability. The bookkeeping estimate of Houghton et al. (2012) (thick black over 1850 2011) and the average of four process models (dash black) over 1750 1850 (see 6.3.2.2) are used in Table 6.1. The process model results for net land use change CO2 emissions from Table 6.2 are shown in blue. Satellite-based methods are available for the tropics only, from (red) van der Werf et al. (2010), (blue) DeFries et al. (2002), and (green) Achard et al. (2004). Note that the definitions of land use change fluxes vary between models (Table 6.2). The grey shading shows a constant uncertainty of +/-0.8 PgC yr-1 around the mean estimate used in Table 6.3. originate from Central and South America, Africa and Tropical Asia permanent deforestation are in agreement between the bookkeeping since the 1980s (Table 6.3). The process models based on the HYDE method of Houghton et al. (2012) and the satellite data analysis of database allocate about 30% of the global land use change emissions Harris et al. (2012). to East Asia, but this is difficult to reconcile with the large afforestation programmes reported in this region. Inconsistencies in the available Over the 1750 2011 period, cumulative net CO2 emissions from land land cover change reconstructions and in the modelling results pre- use change of 180 +/- 80 PgC are estimated (Table 6.1). The uncertainty vent a firm assessment of recent trends and their partitioning among is based on the spread of the available estimates (Figure 6.10). The regions (see regional data in Table 6.3). cumulative net CO2 emissions from land use change have been domi- nated by deforestation and other land use change in the mid-northern In this chapter, we do not assess individual gross fluxes that sum up latitudes prior to 1980s, and in the tropics since the 1980s, largely from to make the net land use change CO2 emission, because there are too deforestation in tropical America and Asia with smaller contributions few independent studies. Gross emissions from tropical deforestation from tropical Africa. Deforestation from 800 to 1750 has been estimat- and degradation were 3.0 +/- 0.5 PgC yr 1 for the 1990s and 2.8 +/- 0.5 ed at 27 PgC using a process-based ecosystem model (Pongratz et al., 6 PgC yr 1 for the 2000s using forest inventory data, FAO (2010) and the 2009). bookeeping method (Pan et al., 2011). These gross emissions are about double the net emissions because of the presence of a large regrowth 6.3.2.3 Atmospheric Carbon Dioxide Concentration Growth Rate that compensates for about half of the gross emissions. A recent anal- ysis estimated a lower gross deforestation of 0.6 to 1.2 PgC yr 1 (Harris Since the beginning of the Industrial Era (1750), the concentration of et al., 2012). That study primarily estimated permanent deforestation CO2 in the atmosphere has increased by 40%, from 278 +/- 5 ppm to and excluded additional gross emissions from degraded forests, shift- 390.5 +/- 0.1 ppm in 2011 (Figure 6.11; updated from Ballantyne et al. ing agriculture and some carbon pools. In fact, gross emissions from (2012), corresponding to an increase in CO2 of 240 +/- 10 PgC in the 491 Chapter 6 Carbon and Other Biogeochemical Cycles atmosphere. Atmospheric CO2 grew at a rate of 3.4 +/- 0.2 PgC yr 1 in tinuous atmospheric CO2 concentration measurements at background the 1980s, 3.1 +/- 0.2 PgC yr 1 in the 1990s and 4.0 +/- 0.2 PgC yr 1 in stations (e.g., Keeling et al., 1976). the 2000s (Conway and Tans, 2011) (Table 6.1). The increase of atmos- pheric CO2 between 1750 and 1957, prior to direct measurements in The ice core record of atmospheric CO2 during the past century exhibits the atmosphere, is established from measurements of CO2 trapped in interesting variations, which can be related to climate induced-chang- air bubbles in ice cores (e.g., Etheridge et al., 1996). After 1957, the es in the carbon cycle. Most conspicuous is the interval from about increase of atmospheric CO2 is established from highly precise con- 1940 to 1955, during which atmospheric CO2 concentration stabilised Table 6.3 | Estimates of net land to atmosphere flux from land use change (PgC yr 1; except where noted) for decadal periods from 1980s to 2000s by region. Positive values indicate net CO2 losses from land ecosystems affected by land use change to the atmosphere. Uncertainties are reported as 90% confidence interval (unlike 68% in AR4). Numbers in parentheses are ranges in uncertainty provided in some studies. Tropical Asia includes the Middle East, India and surrounding countries, Indonesia and Papua New Guinea. East Asia includes China, Japan, Mongolia and Korea. Land Cover Central and Tropical North Africa Eurasia East Asia Oceania Data South Americas Asia America 2000s van der Werf et al. (2010)a,b GFED 0.33 0.15 0.35 DeFries and Rosenzweig (2010)c MODIS 0.46 0.08 0.36 Houghton et al. (2012) FAO-2010 0.48 0.31e 0.25 0.01 0.07d 0.01e van Minnen et al. (2009) a HYDE 0.45 0.21 0.20 0.09 0.08 0.10 0.03 Stocker et al. (2011)a HYDE 0.19 0.18 0.21 0.019 0.067 0.12 0.011 Yang et al. (2010)a HYDE 0.14 0.03 0.25 0.25 0.39 0.12 0.02 Poulter et al. (2010) a HYDE 0.09 0.13 0.14 0.01 0.03 0.05 0.00 Kato et al. (2013)a HYDE 0.36 0.09 0.23 0.05 0.04 0.10 0.00 Average 0.31 +/- 0.25 0.13 +/- 0.20 0.25 +/- 0.12 0.05 +/- 0.17 0.12 +/- 0.31 0.08 +/- 0.07 0.01 +/- 0.02 1990s DeFries et al. (2002) AVHRR 0.5 0.1 0.4 (0.2 0.7) (0.1 0.2) (0.2 0.6) Achard et al. (2004) Landsat 0.3 0.2 0.4 (0.3 0.4) (0.1 0.2) (0.3 0.5) Houghton et al. (2012) FAO-2010 0.67 0.32e 0.45 0.05 0.04d 0.05e van Minnen et al. (2009)a HYDE 0.48 0.22 0.34 0.07 0.08 0.20 0.07 Stocker et al. (2011)a HYDE 0.30 0.14 0.19 0.072 0.11 0.27 0.002 Yang et al. (2010)a HYDE 0.20 0.04 0.31 0.27 0.47 0.19 0.00 Poulter et al. (2010)a HYDE 0.26 0.13 0.12 0.07 0.16 0.11 0.01 Kato et al. (2013)a HYDE 0.53 0.07 0.25 0.04 0.01 0.16 0.02 Average 0.41 +/- 0.27 0.15 +/- 0.15 0.31 +/- 0.19 0.08 +/- 0.19 0.16 +/- 0.30 0.16 +/- 0.13 0.02 +/- 0.05 1980s DeFries et al. (2002) AVHRR 0.4 0.1 0.2 (0.2 0.5) (0.08 0.14) (01 0.3) Houghton et al. (2012) FAO-2010 0.79 0.22e 0.32 0.04 0.00d 0.07e van Minnen et al. (2009) a HYDE 0.70 0.18 0.43 0.07 0.06 0.37 0.04 Stocker et al. (2011)a HYDE 0.44 0.16 0.25 0.085 0.11 0.40 0.009 Yang et al. (2010)a HYDE 0.26 0.01 0.34 0.30 0.71 0.59 0.00 Poulter et al. (2010)a HYDE 0.37 0.11 0.19 0.02 0.03 0.29 0.01 Kato et al. (2013)a HYDE 0.61 0.07 0.25 0.04 0.02 0.35 0.01 Average 0.51 +/- 0.32 0.12 +/- 0.12 0.28 +/- 0.14 0.08 +/- 0.19 0.15 +/- 0.46 0.35 +/- 0.28 0.01 +/- 0.03 6 Notes: a Method as described in the reference but updated to 2010 using the HYDE land cover change data. b 1997 2006 average based on estimates of CO emissions from deforestation and degradation fires, including peat carbon emissions. Estimates were doubled to account for emissions other than 2 fire including respiration of leftover plant materials and soil carbon following deforestation following (Olivier et al., 2005). Estimates include peat fires and peat soil oxidation. If peat fires are excluded, estimate in tropical Asia is 0.23 and Pan-tropical total is 0.71. c CO estimates were summed for dry and humid tropical forests, converted to C and normalized to annual values. Estimates are based on satellite-derived deforestation area (Hansen et al., 2010), 2 and assume 0.6 fraction of biomass emitted with deforestation. Estimates do not include carbon uptake by regrowth or legacy fluxes from historical deforestation. Estimates cover emissions from 2000 to 2005. d Includes China only. e East Asia and Oceania are averaged in one region. The flux is split in two equally for computing the average; North Africa and the Middle East are combined with Eurasia. AVHRR = Advanced Very High Resolution Radiometer; FAO = Food and Agriculture Organization (UN); GFED = Global Fire Emissions Database; HYDE = HistorY Database of the global Environ- ment; MODIS = Moderate Resolution Imaging Spectrometer. 492 Carbon and Other Biogeochemical Cycles Chapter 6 (Trudinger et al., 2002), and the CH4 and N2O growth slowed down cause of the observed increase in atmospheric CO2 concentration. Sev- (MacFarling-Meure et al., 2006), possibly caused by slightly decreasing eral lines of evidence support this conclusion: temperatures over land in the NH (Rafelski et al., 2009). The observed decrease in atmospheric O2 content over past two There is substantial evidence, for example, from 13C carbon isotopes in decades and the lower O2 content in the northern compared to atmospheric CO2 (Keeling et al., 2005) that source/sink processes on the SH are consistent with the burning of fossil fuels (see Figure land generate most of the interannual variability in the atmospheric 6.3 and Section 6.1.3.2; Keeling et al., 1996; Manning and Keeling, CO2 growth rate (Figure 6.12). The strong positive anomalies of the CO2 2006). growth rate in El Nino years (e.g., 1986 1987 and 1997 1998) orig- inated in tropical latitudes (see Sections 6.3.6.3 and 6.3.2.5.4), while CO2 from fossil fuels and from the land biosphere has a lower the anomalies in 2003 and 2005 originated in northern mid-latitudes, 13C/12C stable isotope ratio than the CO in the atmosphere. This 2 perhaps reflecting the European heat wave in 2003 (Ciais et al., 2005). induces a decreasing temporal trend in the atmospheric 13C/12C Volcanic forcing also contributes to multi-annual variability in carbon ratio of atmospheric CO2 concentration as well as, on annual aver- storage on land and in the ocean (Jones and Cox, 2001; Gerber et al., age, slightly lower 13C/12C values in the NH (Figure 6.3). These sig- 2003; Brovkin et al., 2010; Frölicher et al., 2011). nals are measured in the atmosphere. With a very high confidence, the increase in CO2 emissions from fossil Because fossil fuel CO2 is devoid of radiocarbon (14C), reconstruc- fuel burning and those arising from land use change are the dominant tions of the 14C/C isotopic ratio of atmospheric CO2 from tree rings 400 380 360 CO2 ppm 340 320 300 280 260 1800 1600 1400 CH4 ppb 1200 1000 800 600 330 320 310 300 N2 O ppb 290 280 6 270 260 250 0 500 1000 1500 1750 1800 1900 2000 2020 Year Year Figure 6.11 | Atmospheric CO2, CH4, and N2O concentrations history over the industrial era (right) and from year 0 to the year 1750 (left), determined from air enclosed in ice cores and firn air (colour symbols) and from direct atmospheric measurements (blue lines, measurements from the Cape Grim observatory) (MacFarling-Meure et al., 2006). 493 Chapter 6 Carbon and Other Biogeochemical Cycles show a declining trend, as expected from the addition of fossil CO2 the South Pole (see Figure 6.3). The annually averaged concentra- (Stuiver and Quay, 1981; Levin et al., 2010). Yet nuclear weapon tion difference between the two stations has increased in propor- tests in the 1950s and 1960s have been offsetting that declining tion of the estimated increasing difference in fossil fuel combus- trend signal by adding 14C to the atmosphere. Since this nuclear tion emissions between the hemispheres (Figure 6.13; Keeling et weapon induced 14C pulse in the atmosphere has been fading, the al., 1989; Tans et al., 1989; Fan et al., 1999). 14C/C isotopic ratio of atmospheric CO is observed to resume its 2 declining trend (Naegler and Levin, 2009; Graven et al., 2012). The rate of CO2  emissions from fossil fuel burning and land use change was almost exponential, and the rate of CO2 increase in Most of the fossil fuel CO2 emissions take place in the industri- the atmosphere was also almost exponential and about half that alised countries north of the equator. Consistent with this, on of the emissions, consistent with a large body of evidence about annual average, atmospheric CO2 measurement stations in the NH changes of carbon inventory in each reservoir of the carbon cycle record increasingly higher CO2 concentrations than stations in the presented in this chapter. SH, as witnessed by the observations from Mauna Loa, Hawaii, and 1960 1970 1980 1990 2000 2010 4 3 (ppm yr-1) 2 1 0 90N 60N 45N 30N 15N Latitude EQ 15S 30S 45S 60S 90S 1960 1970 1980 1990 2000 2010 Year 6 -1 0 1 2 3 4 -1 (ppm yr ) Figure 6.12 | (Top) Global average atmospheric CO2 growth rate, computed from the observations of the Scripps Institution of Oceanography (SIO) network (light green line: Keeling et al. 2005, updated) and from the marine boundary layer air reference measurements of the National Oceanic and Atmospheric Administration Global Monitoring Division (NOAA GMD) network (dark green line: Conway et al., 1994; Dlugokencky and Tans, 2013b). (Bottom) Atmospheric growth rate of CO2 as a function of latitude determined from the National Oceanic and Atmospheric Administration Earth System Research Laboratory (NOAA ESRL) network, representative of stations located in the marine boundary layer at each given latitude (Masarie and Tans, 1995; Dlugokencky and Tans, 2013b). Sufficient observations are available only since 1979. 494 Carbon and Other Biogeochemical Cycles Chapter 6 6.3.2.5 Ocean Carbon Dioxide Sink 4 6.3.2.5.1 Global ocean sink and decadal change The estimated mean anthropogenic ocean CO2 sink assessed in AR4 3 was 2.2 +/- 0.7 PgC yr 1 for the 1990s based on observations (McNeil et CMLO CSPO ppm al., 2003; Manning and Keeling, 2006; Mikaloff-Fletcher et al., 2006), and is supported by several contemporary estimates (see Chapter 3). Note that the uncertainty of +/-0.7 PgC yr 1 reported here (90% confi- 2 dence interval) is the same as the +/-0.4 PgC yr 1 uncertainty reported in AR4 (68% confidence intervals). The uptake of anthropogenic CO2 by the ocean is primarily a response to increasing CO2 in the atmos- 1 Assmann et al. (2010) Graven et al. (2012) 0 updated from Le Quere et al. (2010) updated from Doney et al. (2009) 2 3 4 5 6 7 8 9 1 a. Climate effect only Park et al. (2010) Qfoss,N Qfoss,S PgC yr -1 Figure 6.13 | Blue points: Annually averaged CO2 concentration difference between the station Mauna Loa in the Northern Hemisphere and the station South Pole in the 0 Southern Hemisphere (vertical axis; Keeling et al., 2005, updated) versus the difference in fossil fuel combustion CO2 emissions between the hemispheres (Boden et al., 2011). Dark red dashed line: regression line fitted to the data points. -1 6.3.2.4 Carbon Dioxide Airborne Fraction 1960 1970 1980 1990 2000 2010 Ocean CO2 sink anomalies (PgC yr ) -1 Until recently, the uncertainty in CO2 emissions from land use change 1 b. CO2 effect only emissions was large and poorly quantified which led to the use of an airborne fraction (see Glossary) based on CO2 emissions from fossil fuel only (e.g., Figure 7.4 in AR4 and Figure 6.26 of this chapter). However, ­ reduced uncertainty of emissions from land use change and larger 0 agreement in its trends over time (Section 6.3.2.2) allow making use of an airborne fraction that includes all anthropogenic emissions. The updated from Khatiwala et al. airborne fraction will increase if emissions are too fast for the uptake (2009) of CO2 by the carbon sinks (Bacastow and Keeling, 1979; Gloor et al., -1 2010; Raupach, 2013). It is thus controlled by changes in emissions 1960 1970 1980 1990 2000 2010 rates, and by changes in carbon sinks driven by rising CO2, changes in climate and all other biogeochemical changes. 1 c. CO2 and climate effects combined A positive trend in airborne fraction of ~0.3% yr 1 relative to the mean of 0.44 +/-0.06 (or about 0.05 increase over 50 years) was found by all recent studies (Raupach et al., 2008, and related papers; Knorr, 2009; 0 Gloor et al., 2010) using the airborne fraction of total anthropogenic CO2 emissions over the approximately 1960 2010 period (for which the most accurate atmospheric CO2 data are available). However, there is no consensus on the significance of the trend because of differences -1 6 in the treatment of uncertainty and noise (Raupach et al., 2008; Knorr, 1960 1970 1980 1990 2000 2010 2009). There is also no consensus on the cause of the trend (Canadell Year et al., 2007b; Raupach et al., 2008; Gloor et al., 2010). Land and ocean Figure 6.14 | Anomalies in the ocean CO2 ocean-to-atmosphere flux in response to carbon cycle model results attributing the trends of fluxes to underly- (a) changes in climate, (b) increasing atmospheric CO2 and (c) the combined effects of ing processes suggest that the effect of climate change and variability increasing CO2 and changes in climate (PgC yr 1). All estimates are shown as anomalies on ocean and land sinks have had a significant influence (Le Quéré et with respect to the 1990 2000 averages. Estimates are updates from ocean models al., 2009), including the decadal influence of volcanic eruptions (Fröli- (in colours) and from indirect methods based on observations (Khatiwala et al., 2009; Park et al., 2010). A negative ocean-to-atmosphere flux represents a sink of CO2, as in cher et al., 2013). Table 6.1. 495 Chapter 6 Carbon and Other Biogeochemical Cycles Table 6.4 | Decadal changes in the ocean CO2 sink from models and from data-based methods (a positive change between two decades means an increasing sink with time). It is reminded that the total CO2 sink for the 1990s is estimated at 2.2 +/- 0.7 PgC yr 1 based on observations. 1990s Minus 1980s 2000s Minus 1990s   Method PgC yr 1 PgC yr 1 CO2 effects only Khatiwala et al. (2009) Data-basedc 0.24 0.20 Mikaloff-Fletcher et al. (2006) a Data-based d 0.40 0.44 Assmann et al. (2010) (to 2007 only) Model 0.28 0.35 Graven et al. (2012) Model 0.15 0.25 Doney et al. (2009) Model 0.15 0.39 Le Quéré et al. (2010) NCEP Model 0.16 0.32 Le Quéré et al. (2010) ECMWF Model 0.39 Le Quéré et al. (2010) JPL Model 0.32 Average b 0.23 +/- 0.15 0.33 +/- 0.13 Climate effects only Park et al. (2010) Data-basede 0.15 Assmann et al. (2010) (to 2007 only) Model 0.07 0.00 Graven et al. (2012) Model 0.02 0.27 Doney et al. (2009) Model 0.02 0.21 Le Quéré et al. (2010) NCEP Model 0.02 0.27 Le Quéré et al. (2010) ECMWF Model 0.14 Le Quéré et al. (2010) JPL Model 0.36 Average b 0.02 +/- 0.05 0.19 +/- 0.18 CO2 and climate effects combined 0.25 +/- 0.16 0.14 +/- 0.22 Notes: a As published by Sarmiento et al. (2010). b Average of all estimates +/-90% confidence interval. The average includes results by Le Quéré et al. (2010) NCEP only because the other Le Quéré et al. model versions do not differ sufficiently to be considered separately. c Based on observed patterns of atmospheric minus oceanic pCO , assuming the difference increases with time following the increasing atmospheric CO . 2 2 d Ocean inversion, assuming constant oceanic transport through time. e Based on observed fit between the variability in temperature and pCO , and observed variability in temperature. 2 ECMWF = European Centre for Medium-Range Weather Forecasts; JPL = Jet Propulsion Laboratory; NCEP = National Centers for Environmental Prediction. phere and is limited mainly by the rate at which anthropogenic CO2 is 6.14). The decadal estimates in the ocean CO2 sink reported in Table 6.4 transported from the surface waters into the deep ocean (Sarmiento as CO2 effects only are entirely explained by the faster rate of increase et al., 1992; Graven et al., 2012). This anthropogenic ocean CO2 sink of atmospheric CO2 in the later decade. On the other hand, climate occurs on top of a very active natural oceanic carbon cycle. Recent effects only in Table 6.4 are assessed to have no noticeable effect on climate trends, such as ocean warming, changes in ocean circulation the sink difference between the 1980s and the 1990s (0.02 +/- 0.05 PgC and changes in marine ecosystems and biogeochemical cycles, can yr 1), but are estimated to have reduced the ocean anthropogenic CO2 have affected both the anthropogenic ocean CO2 sink as well as the sink by 0.19 +/- 0.18 PgC yr 1 between the 1990s and the 2000s (Table natural air sea CO2 fluxes. We report a decadal mean uptake of 2.0 6.4). +/- 0.7 PgC yr 1 for the 1980s and of 2.3 +/- 0.7 PgC yr 1 for the 2000s (Table 6.4). The methods used are: (1) an empirical Green s function 6.3.2.5.2 Regional changes in ocean dissolved inorganic carbon approach fitted to observations of transient ocean tracers (Khatiwala et al., 2009), (2) a model-based Green s function approach fitted to Observational-based estimates for the global ocean inventory of anthropogenic carbon reconstructions (Mikaloff-Fletcher et al., 2006), anthropogenic carbon are obtained from shipboard repeated hydro- 6 (3) estimates based on empirical relationships between observed graphic cross sections (Sabine et al., 2004; Waugh et al., 2006; Khati- ocean surface pCO2 and temperature and salinity (Park et al., 2010) wala et al., 2009). These estimates agree well among each other, with and (4) process-based global ocean biogeochemical models forced an average value of 155 +/- 30 PgC of increased dissolved inorgan- by observed meteorological fields (Doney et al., 2009; Assmann et al., ic carbon for the period 1750 2011 (see Chapter 3). The uptake of 2010; Le Quéré et al., 2010; Graven et al., 2012). All these different anthropogenic carbon into the ocean is observed to be larger in the methods suggest that in the absence of recent climate change and high latitudes than in the tropics and subtropics over the entire Indus- climate variability, the ocean anthropogenic CO2 sink should have trial Era, because of the more vigorous ocean convection in the high increased by 0.23 +/- 0.15 PgC yr 1 between the 1980s and the 1990s, latitudes (Khatiwala et al., 2009). A number of ocean cross sections and by 0.33 +/- 0.13 PgC yr 1 between the 1990s and the 2000s (Figure have been repeated over the last decade, and the observed changes 496 Carbon and Other Biogeochemical Cycles Chapter 6 Table 6.5 | Regional rates of change in inorganic carbon storage from shipboard repeated hydrographic cross sections. Storage Rate Section Time Data Source (mol C m 2 yr 1) Global average (used in Table 6.1) 2007 2008 0.5 +/- 0.2 Khatiwala et al. (2009) Pacific Ocean Section along 30°S 1992 2003 1.0 +/- 0.4 Murata et al. (2007) N of 50°S, 120°W to 180°W 1974 1996 0.9 +/- 0.3 Peng et al. (2003) 154°W, 20°N to 50°S 1991 2006 0.6 +/- 0.1 Sabine et al. (2008) 140°E to 170°W, 45°S to 65°S 1968 1991/1996 0.4 +/- 0.2 Matear and McNeil (2003) 149° W, 4°S to 10°N 1993 2005 0.3 +/- 0.1 Murata et al. (2009) 149° W, 24°N to 30°N 1993 2005 0.6 +/- 0.2 Murata et al. (2009) Northeast Pacific 1973 1991 1.3 +/- 0.5 Peng et al. (2003) ~160°E, ~45°N 1997 2008 0.4 +/- 0.1 Wakita et al. (2010) North of 20°N 1994 2004/2005 0.4 +/- 0.2 Sabine et al. (2008) 150°W, 20°S to 20°N 1991/1992 2006 0.3 +/- 0.1 Sabine et al. (2008) Indian Ocean 20°S to 10°S 1978 1995 0.1 Peng et al. (1998) 10°S to 5°N 1978 1995 0.7 Peng et al. (1998) Section along 20°S 1995 2003/2004 1.0 +/- 0.1 Murata et al. (2010) Atlantic Ocean Section along 30°S 1992/1993 2003 0.6 +/- 0.1 Murata et al. (2010) ~30°W, 56°S to 15°S 1989 2005 0.8 Wanninkhof et al. (2010) 20°W, 64°N to 15°N 1993 2003 0.6 Wanninkhof et al. (2010) ~25°W, 15°N to 15°S 1993 2003 0.2 Wanninkhof et al. (2010) 40°N to 65°N 1981 1997/1999 2.2 +/- 0.7 Friis et al. (2005) 20°N to 40°N 1981 2004 1.2 +/- 0.3 Tanhua et al. (2007) Nordic Seas 1981 2002/2003 0.9 +/- 0.2 Olsen et al. (2006) Sub-decadal variations Irminger Sea 1981 1991 0.6 +/- 0.4 Pérez et al. (2008) Irminger Sea 1991 1996 2.3 +/- 0.6 Pérez et al. (2008) Irminger Sea 1997 2006 0.8 +/- 0.2 Pérez et al. (2008) in carbon storage (Table 6.5) suggest that some locations have rates in this region (Schuster et al., 2013). Interannual variability of 0.1 to of carbon accumulation that are higher and others that are lower than 0.2 PgC yr 1 was also estimated by models and one atmospheric inver- the global average estimated by Khatiwala et al. (2009). Model results sion in the Southern Ocean (Le Quéré et al., 2007), possibly driven by suggest that there may be an effect of climate change and variability the Southern Annular Mode of climate variability (Lenton and Matear, in the storage of total inorganic carbon in the ocean (Table 6.4), but 2007; Lovenduski et al., 2007; Lourantou and Metzl, 2011). that this effect is small (~2 PgC over the past 50 years; Figure 6.14) compared to the cumulative uptake of anthropogenic carbon during 6.3.2.5.4 Regional ocean carbon dioxide partial pressure trends the same period. Observations of the partial pressure of CO2 at the ocean surface (pCO2) 6.3.2.5.3 Interannual variability in air-sea CO2 fluxes show that ocean pCO2 has been increasing generally at about the same rate as CO2 in the atmosphere when averaged over large ocean regions The interannual variability in the global ocean CO2 sink is estimated during the past two to three decades (Yoshikawa-Inoue and Ishii, 2005; to be of about +/-0.2 PgC yr 1 (Wanninkhof et al., 2013) which is small Takahashi et al., 2009; McKinley et al., 2011). However, analyses of 6 compared to the interannual variability of the terrestrial CO2 sink (see regional observations highlight substantial regional and temporal var- Sections 6.3.2.3 and 6.3.2.6.3; Figure 6.12). In general, the ocean takes iations around the mean trend. up more CO2 during El Nino episodes (Park et al., 2010) because of the temporary suppression of the source of CO2 to the atmosphere over In the North Atlantic, repeated observations show ocean pCO2 increas- the eastern Pacific upwelling. Interannual variability of ~0.3 PgC yr 1 ing regionally either at the same rate or faster than atmospheric CO2 has been reported for the North Atlantic ocean region alone (Watson between about 1990 and 2006 (Schuster et al., 2009), thus indicating et al., 2009) but there is no agreement among estimates regarding a constant or decreasing sink for CO2 in that region, in contrast to the the exact magnitude of driving factors of air sea CO2 flux variability increasing sink expected from the response of the ocean to increasing 497 Chapter 6 Carbon and Other Biogeochemical Cycles atmospheric CO2 alone. The anomalous North Atlantic trends appear contribution has been estimated to be between 0.1 and 0.4 PgC yr 1 to be related to sea surface warming and its effect on solubility (Cor- around the year 2000 using models (Duce et al., 2008; Reay et al., biere et al., 2007) and/or changes in ocean circulation (Schuster and 2008; Krishnamurthy et al., 2009; Suntharalingam et al., 2012). Simi- Watson, 2007; Schuster et al., 2009) and deep convection (Metzl et al., larly, increases in iron deposition over the ocean from dust generated 2010). Recent changes have been associated with decadal variability by human activity is estimated to have enhanced the ocean cumulative in the North Atlantic Oscillation (NAO) and the Atlantic Multidecadal CO2 uptake by 8 PgC during the 20th century (or about 0.05 PgC yr 1 in Variability (AMV) (Thomas et al., 2007; Ullman et al., 2009; McKinley et the past decades) (Mahowald et al., 2010). Although changes in ocean al., 2011; Tjiputra et al., 2012). A systematic analysis of trends estimat- circulation and in global biogeochemical drivers have the potential to ed in this region show no agreement regarding the drivers of change alter the ocean carbon fluxes through changes in marine ecosystems, (Schuster et al., 2013). modelling studies show only small variability in ocean biological pump, which has not significantly impacted the response of the ocean carbon In the Southern Ocean, an approximately constant sink was inferred cycle over the recent period (Bennington et al., 2009). from atmospheric (Le Quéré et al., 2007) and oceanic (Metzl, 2009; Takahashi et al., 2009) CO2 observations but the uncertainties are large Model studies suggest that the response of the air sea CO2 fluxes to (Law et al., 2008). Most ocean biogeochemistry models reproduce the climate change and variability in recent decades has decreased the constant sink and attribute it as a response to an increase in Southern rate at which anthropogenic CO2 is absorbed by the ocean (Sarmiento Ocean winds driving increased upwards transport of carbon-rich deep et al. (2010); Figure 6.14 and Table 6.4). This result is robust to the waters (Lenton and Matear, 2007; Verdy et al., 2007; Lovenduski et al., model or climate forcing used (Figure 6.13), but no formal attribution 2008; Le Quéré et al., 2010). The increase in winds has been attribut- to anthropogenic climate change has been made. There is insuffi- ed to the depletion of stratospheric ozone (Thompson and Solomon, cient data coverage to separate the impact of climate change on the 2002) with a contribution from GHGs (Fyfe and Saenko, 2006). global ocean CO2 sink directly from observations, though the regional trends described in Section 6.3.2.5.4 suggest that surface ocean pCO2 Large decadal variability has been observed in the Equatorial Pacific responds to changes in ocean properties in a significant and measur- (Ishii et al., 2009) associated with changes in the phasing of the Pacif- able way. ic Decadal Oscillation (see Glossary) and its impact on gas transfer velocity (Feely et al., 2006; Valsala et al., 2012). By contrast, ocean 6.3.2.5.6 Model evaluation of global and regional ocean pCO2 appears to have increased at a slower rate than atmospheric CO2 carbon balance (thus a growing ocean CO2 sink in that region) in the northern North Pacific Ocean (Takahashi et al., 2006). There is less evidence available Ocean process-based carbon cycle models are capable of reproduc- to attribute the observed changes in other regions to changes in under- ing the mean air sea fluxes of CO2 derived from pCO2 observations lying processes or climate change and variability. (Takahashi et al., 2009), including their general patterns and amplitude (Sarmiento et al., 2000), the anthropogenic uptake of CO2 (Orr et al., 6.3.2.5.5 Processes driving variability and trends in air sea 2001; Wanninkhof et al., 2013) and the regional distribution of air sea carbon dioxide fluxes fluxes (Gruber et al., 2009). The spread between different model results for air sea CO2 fluxes is the largest in the Southern Ocean (Matsu- Three type of processes are estimated to have an important effect on moto et al., 2004), where intense convection occurs. Tracer observa- the air sea CO2 fluxes on century time scales: (1) the dissolution of tions (Schmittner et al., 2009) and water mass analysis (Iudicone et al., CO2 at the ocean surface and its chemical equilibrium with other forms 2011) have been used to reduce the model uncertainty associated with of carbon in the ocean (mainly carbonate and bicarbonate), (2) the this process and improve the simulation of carbon fluxes. The models transport of carbon between the surface and the intermediate and reproduce the observed seasonal cycle of pCO2 in the sub-tropics but deep ocean and (3) changes in the cycling of carbon through marine generally do poorly in sub-polar regions where the balance of process- ecosystem processes (the ocean biological pump; see Section 6.1.1.1). es is more difficult to simulate well (McKinley et al., 2006; Schuster The surface dissolution and equilibration of CO2 with the atmosphere et al., 2013). Less information is available to evaluate specifically the is well understood and quantified. It varies with the surface ocean con- representation of biological fluxes in the models, outside of their real- ditions, in particular with temperature (solubility effect) and alkalinity. istic representation of surface ocean chlorophyll distributions. Ocean The capacity of the ocean to take up additional CO2 for a given alka- process-based carbon cycle models used in AR5 reproduce the relative- linity decreases at higher temperature (4.23% per degree warming; ly small interannual variability inferred from observations (Figure 6.12; Takahashi et al., 1993) and at elevated CO2 concentrations (about 15% Wanninkhof et al., 2013). See also Section 9.4.5. 6 per 100 ppm, computed from the so called Revelle factor; Revelle and Suess, 1957). Sensitivity of modelled air sea fluxes to CO2. Data-based studies esti- mated a cumulative carbon uptake of ~155 +/- 30 PgC across stud- Recent changes in nutrient supply in the ocean are also thought to ies for the 1750 2011 time period (Sabine et al., 2004; Waugh et al., have changed the export of organic carbon from biological process- 2006; Khatiwala et al., 2009), a mean anthropogenic CO2 sink of 2.2 es below the surface layer, and thus the ocean CO2 sink (Duce et al., +/- 0.7 PgC yr 1 for the 1990s, and decadal trends of 0.13 PgC yr 1 per 2008). Anthropogenic reactive nitrogen Nr (see Box 6.2) entering the decade during the two decades 1990 2009 (Wanninkhof et al., 2013; ocean via atmospheric deposition or rivers acts as a fertiliser and from atmospheric inversions), respectively. Models that have estimat- may enhance carbon export to depth and hence the CO2 sink. This Nr ed these quantities give a total ocean uptake of 170 +/- 25 PgC for 498 Carbon and Other Biogeochemical Cycles Chapter 6 1750 2011 (from the model ensemble of Orr et al., (2005) until 1994, 6.3.2.6 Land Carbon Dioxide Sink plus an additional 40 PgC from estimates in Table 6.4 for 1995 2011), a mean anthropogenic CO2 sink of 2.1 +/- 0.6 PgC yr 1 for 1990 1999 (Le 6.3.2.6.1 Global residual land sink and atmosphere-to-land Quéré et al., 2013) and a decadal trend of 0.14 PgC yr 1 per decade for carbon dioxide flux 1990 2009 (Wanninkhof et al., 2013). Therefore, although the ocean models do not reproduce all the details of the regional structure and The residual land CO2 sink, that is, the uptake of CO2 in ecosystems changes in air sea CO2 fluxes, their globally integrated ocean CO2 sink excluding the effects of land use change, is 1.5 +/- 1.1, 2.6 +/- 1.2 and 2.6 and decadal rate of change of this sink is in good agreement with the +/- 1.2 PgC yr 1 for the 1980s, 1990s and 2000s, respectively (Table 6.1). available observations. After including the net land use change emissions, the atmosphere- to-land flux of CO2 (Table 6.1) corresponds to a net sink of CO2 by all Sensitivity of modelled air sea fluxes to climate. The relationship terrestrial ecosystems. This sink has intensified globally from a neutral between air sea CO2 flux and climate is strongly dependent on the CO2 flux of 0.1 +/- 0.8 PgC yr 1 in the 1980s to a net CO2 sink of 1.1 +/- oceanic region and on the time scale. Ocean carbon cycle models of 0.9 PgC yr 1 and 1.5 +/- 0.9 PgC yr 1 during the 1990s and 2000s, respec- the type used in AR5 estimate a reduction in cumulative ocean CO2 tively (Table 6.1; Sarmiento et al., 2010). This growing land sink is also uptake of 1.6 to 5.4 PgC over the period 1959 2008 (1.5 to 5.4%) in supported by an atmospheric inversion (Gurney and Eckels, 2011) and response to climate change and variability compared to simulations by process-based models (Le Quéré et al., 2009). with no changes in climate (Figure 6.14), partly due to changes in the equatorial Pacific and to changes in the Southern Ocean. The only 6.3.2.6.2 Regional atmosphere-to-land carbon dioxide fluxes observation-based estimate available to evaluate the climate response of the global air sea CO2 flux is from Park et al. (2010), which is at The results from atmospheric CO2 inversions, terrestrial ecosystem the low end of the model estimate for the past two decades (Table models and forest inventories consistently show that there is a large 6.4). However, this estimate does not include the nonlinear effects of net CO2 sink in the northern extratropics, albeit the very limited availa- changes in ocean circulation and warming on the global air sea CO2 bility of observations in the tropics (Jacobson et al., 2007; Gurney and flux, which could amplify the response of the ocean CO2 sink to climate Eckels, 2011; Pan et al., 2011). Inversion estimates of atmosphere land by 20 to 30% (Le Quéré et al., 2010; Zickfeld et al., 2011). CO2 fluxes show net atmosphere-to-land CO2 flux estimates ranging from neutral to a net source of 0.5 to 1.0 PgC yr 1 (Jacobson et al., Processes missing in ocean models. The most important processes 2007; Gurney and Eckels, 2011) (Figure 6.15). However, Stephens et al. missing in ocean carbon cycle models used in the AR5 are those rep- (2007) selected from an ensemble of inversion models those that were resenting explicitly small-scale physical circulation (e.g., eddies, brine consistent with independent aircraft cross-validation data, and con- formation), which are parameterised in models. These processes have strained an atmosphere-to-land CO2 flux of 0.1 +/- 0.8 PgC yr 1 during an important influence on the vertical transport of water, heat, salt the period 1992 1996, and a NH net CO2 sink of 1.5 +/- 0.6 PgC yr 1. and carbon (Loose and Schlosser, 2011; Sallée et al., 2012). In particu- These results shows that after subtracting emissions from land use lar, changes in vertical transport in the Southern Ocean are thought change, tropical land ecosystems might also be large CO2 sinks. to explain part of the changes in atmospheric CO2 between glacial and interglacial conditions, a signal that is not entirely reproduced by Based on repeated forest biomass inventory data, estimated soil models (Section 6.2) suggesting that the sensitivity of ocean models carbon changes, and CO2 emissions from land use change from the could be underestimated. bookkeeping method of Houghton et al. (2012), Pan et al. (2011) esti- mated a global forest carbon accumulation of 0.5 +/- 0.1 PgCyr 1 in Processes related to marine ecosystems in global ocean models are boreal forests, and of 0.8 +/- 0.1 PgC yr 1 in temperate forests for the also limited to the simulation of lower trophic levels, with crude period 2000 2007. Tropical forests were found to be near neutral with parameterizations for sinking processes, bacterial and other loss pro- net emissions from land use change being compensated by sinks in cesses at the surface and in the ocean interior and their temperature established tropical forests (forests not affected by land use change), dependence (Kwon et al., 2009). Projected changes in carbon fluxes therefore consistent with the Stephens et al. (2007) inversion estimate from the response of marine ecosystems to changes in temperature of tropical atmosphere land CO2 fluxes. (Beaugrand et al., 2010), ocean acidification (Riebesell et al., 2009) (see Glossary) and pressure from fisheries (Pershing et al., 2010) are Since AR4, a number of studies have compared and attempted to rec- all considered potentially important, though not yet quantified. Several oncile regional atmosphere-to-land CO2 flux estimates from multiple processes have been specifically identified that could lead to changes approaches and so providing further spatial resolution of the regional in the ocean CO2 sink, in particular the temperature effects on marine contributions of carbon sources and sinks (Table 6.6). A synthesis of 6 ecosystem processes (Riebesell et al., 2009; Taucher and Oschlies, regional contributions estimated a 1.7 PgC yr 1 sink in the NH regions 2011) and the variable nutrient ratios induced by ocean acidification above 20°N with consistent estimates from terrestrial models and or ecosystem changes (Tagliabue et al., 2011). Coastal ocean process- inventories (uncertainty: +/-0.3 PgC yr 1) and atmospheric CO2 inver- es are also poorly represented in global and may influence the ocean sions (uncertainty: +/-0.7 PgC yr 1) (Ciais et al., 2010). CO2 sink. Nevertheless, the fit of ocean model results to the integrated CO2 sink and decadal trends discussed above suggest that, up to now, the missing processes have not had a dominant effect on ocean CO2 beyond the limits of the uncertainty of the data. 499 Chapter 6 Carbon and Other Biogeochemical Cycles 6 Figure 6.15 | (Top) Bar plots showing decadal average CO2 fluxes for 11 land regions (1) as estimated by 10 different atmospheric CO2 inversions for the 1990s (yellow) and 2000s (red) (Peylin et al., 2013; data source: http://transcom.lsce.ipsl.fr/), and (2) as simulated by 10 dynamic vegetation models (DGVMs) for the 1990s (green) and 2000s (light green) (Piao et al., 2013; data source: http://www-lscedods.cea.fr/invsat/RECCAP/). The divisions of land regions are shown in the map. (Bottom) Bar plots showing decadal average CO2 fluxes for 11 ocean regions (1) as estimated by 10 different atmospheric CO2 inversions for the 1990s (yellow) and 2000s (red) (data source: http://transcom.lsce.ipsl.fr/), (2) inversion of contemporary interior ocean carbon measurements using 10 ocean transport models (dark blue) (Gruber et al., 2009) and (3) surface ocean pCO2 measurements based air-sea exchange climatology (Takahashi et al., 2009). The divisions of 11 ocean regions are shown in the map. 500 Carbon and Other Biogeochemical Cycles Chapter 6 Table 6.6 | Regional CO2 budgets using top-down estimates (atmospheric inversions) and bottom-up estimates (inventory data, biogeochemical modelling, eddy-covariance), excluding fossil fuel emissions. A positive sign indicates a flux from the atmosphere to the land (i.e., a land sink). Region CO2 Sink (PgC yr 1) Uncertaintya Period Reference Artic Tundra 0.1 +/-0.3b 2000 2006 McGuire et al. (2012) Australia 0.04 +/-0.03 1990 2009 Haverd et al. (2013) East Asia 0.25 +/-0.1 1990 2009 Piao et al. (2012) Europe 0.9 +/-0.2 2001 2005 Luyssaert et al. (2012) North America 0.6 +/-0.02 2000 2005 King et al. (2012) Russian Federation 0.6 0.3 to 1.3 1990 2007 Dolman et al. (2012) South Asia 0.15 +/-0.24 2000 2009 Patra et al. (2013) South America 0.3 +/-0.3 2000 2005 Gloor et al. (2012) Notes: a One standard deviation from mean unless indicated otherwise. b Based on range provided. 6.3.2.6.3 Interannual variability in atmosphere-to-land carbon Regional DIC concentrations in rivers has increased during the Indus- dioxide fluxes trial Era (Oh and Raymond, 2006; Hamilton et al., 2007; Perrin et al., 2008). Agricultural practices coupled with climate change can lead to The interannual variability of the residual land sink shown in Figures large increases in regional scale DIC export in watersheds with a large 6.12 and 6.16 accounts for most of the interannual variability of the agricultural footprint (Raymond et al., 2008). Furthermore, region- atmospheric CO2 growth rate (see Section 6.3.2.3). Atmospheric CO2 al urbanization also elevates DIC fluxes in rivers (Baker et al., 2008; inversion results suggest that tropical land ecosystems dominate the Barnes and Raymond, 2009), which suggests that anthropogenic activ- global CO2 variability, with positive anomalies during El Nino episodes ities have contributed a significant portion of the annual global river (Bousquet et al., 2000; Rödenbeck et al., 2003; Baker et al., 2006), DIC flux to the ocean. which is consistent with the results of one inversion of atmospher- ic 13C and CO2 measurements (Rayner et al., 2008). A combined El Land clearing and management are thought to produce an acceleration Nino-Southern Oscillation (ENSO) Volcanic index time series explains of POC transport, much of which is trapped in alluvial and colluvial dep- 75% of the observed variability (Raupach et al., 2008). A positive phase osition zones, lakes, reservoirs and wetlands (Stallard, 1998; Smith et of ENSO (El Nino, see Glossary) is generally associated with enhanced al., 2001b; Syvitski et al., 2005). Numerous studies have demonstrated land CO2 source, and a negative phase (La Nina) with enhanced land an increase in the concentration of DOC in rivers in the northeastern CO2 sink (Jones and Cox, 2001; Peylin et al., 2005). Observations from United States and northern/central Europe over the past two to four eddy covariance networks suggest that interannual carbon flux varia- decades (Worrall et al., 2003; Evans et al., 2005; Findlay, 2005; Mon- bility in the tropics and temperate regions is dominated by precipita- teith et al., 2007; Lepistö et al., 2008). Owing to the important role of tion, while boreal ecosystem fluxes are more sensitive to temperature wetlands in DOC production, the mobilization of DOC due to human-in- and shortwave radiation variation (Jung et al., 2011), in agreement duced changes in wetlands probably represents an important cause with the results from process-based terrestrial ecosystem models (Piao of changes in global river DOC fluxes to date (Seitzinger et al., 2005), et al., 2009a). Terrestrial biogeochemical models suggest that inter- although a global estimate of this alteration is not available. A robust annual net biome productivity (NBP) variability is dominated by GPP partitioning between natural and anthropogenic carbon fluxes in fresh- (see Glossary) rather than terrestrial ecosystem respiration (Piao et al., water systems is not yet possible, nor a quantification of the ultimate 2009b; Jung et al., 2011). fate of carbon delivered by rivers to the coastal and open oceans. 6.3.2.6.4 Carbon fluxes from inland water 6.3.2.6.5 Processes driving terrestrial atmosphere-to-land carbon dioxide fluxes Global analyses estimate that inland waters receive about 1.7 to 2.7 PgC yr 1 emitted by soils to rivers headstreams, of which, 0.2 to 0.6 Assessment of experimental data, observations and model results sug- PgC yr 1 is buried in aquatic sediments, 0.8 to 1.2 PgC yr 1 returns to gests that the main processes responsible for the residual land sink the atmosphere as CO2, and 0.9 PgC yr 1 is delivered to the ocean (Cole include the CO2 fertilisation effect on photosynthesis (see Box 6.3), et al., 2007; Battin et al., 2009; Aufdenkampe et al., 2011). Estimates nitrogen fertilisation by increased deposition (Norby, 1998; Thornton 6 of the transport of carbon from land ecosystems to the coastal ocean et al., 2007; Bonan and Levis, 2010; Zaehle and Dalmonech, 2011) by rivers are ~0.2 PgC yr 1 for Dissolved Organic Carbon (DOC), 0.3 and climate effects (Nemani et al., 2003; Gloor et al., 2009). It is likely PgC yr 1 for Dissolved Inorganic Carbon (DIC), and 0.1 to 0.4 PgC yr 1 that reactive nitrogen deposition over land currently increases natu- for Particulate Organic Carbon (POC) (Seitzinger et al., 2005; Syvitski ral CO2 in particular in forests, but the magnitude of this effect varies et al., 2005; Mayorga et al., 2010). For the DIC fluxes, only about two- between regions (Norby, 1998; Thornton et al., 2007; Bonan and Levis, thirds of it originates from atmospheric CO2 and the rest of the carbon 2010; Zaehle and Dalmonech, 2011). Processes responsible for the is supplied by weathered carbonate rocks (Suchet and Probst, 1995; net atmosphere-to-land CO2 sink on terrestrial ecosystems include, Gaillardet et al., 1999; Oh and Raymond, 2006; Hartmann et al., 2009). in addition, forest regrowth and afforestation (Myneni et al., 2001; 501 Chapter 6 Carbon and Other Biogeochemical Cycles Box 6.3 | The Carbon Dioxide Fertilisation Effect Elevated atmospheric CO2 concentrations lead to higher leaf photosynthesis and reduced canopy transpiration, which in turn lead to increased plant water use efficiency and reduced fluxes of surface latent heat. The increase in leaf photosynthesis with rising CO2, the so-called CO2 fertilisation effect, plays a dominant role in terrestrial biogeochemical models to explain the global land carbon sink (Sitch et al., 2008), yet it is one of most unconstrained process in those models. Field experiments provide a direct evidence of increased photosynthesis rates and water use efficiency (plant carbon gains per unit of water loss from transpiration) in plants growing under elevated CO2. These physiological changes translate into a broad range of higher plant carbon accumulation in more than two-thirds of the experiments and with increased net primary productivity (NPP) of about 20 to 25% at double CO2 from pre-industrial concentrations (Ainsworth and Long, 2004; Luo et al., 2004, 2006; Nowak et al., 2004; Norby et al., 2005;Canadell et al., 2007a; Denman et al., 2007; Ainsworth et al., 2012; Wang et al., 2012a). Since the AR4, new evidence is available from long-term Free-air CO2 Enrichment (FACE) experiments in temperate ecosystems showing the capacity of ecosystems exposed to elevated CO2 to sustain higher rates of carbon accumulation over multiple years (Liberloo et al., 2009; McCarthy et al., 2010; Aranjuelo et al., 2011; Dawes et al., 2011; Lee et al., 2011; Zak et al., 2011). However, FACE experiments also show the diminishing or lack of CO2 fertilisation effect in some ecosystems and for some plant species (Dukes et al., 2005; Adair et al., 2009; Bader et al., 2009; Norby et al., 2010; Newingham et al., 2013). This lack of response occurs despite increased water use efficiency, also confirmed with tree ring evidence (Gedalof and Berg, 2010; Penuelas et al., 2011). Nutrient limitation is hypothesized as primary cause for reduced or lack of CO2 fertilisation effect observed on NPP in some experi- ments (Luo et al., 2004; Dukes et al., 2005; Finzi et al., 2007; Norby et al., 2010). Nitrogen and phosphorus are very likely to play the most important role in this limitation of the CO2 fertilisation effect on NPP, with nitrogen limitation prevalent in temperate and boreal ecosystems, and phosphorus limitation in the tropics (Luo et al., 2004; Vitousek et al., 2010; Wang et al., 2010a; Goll et al., 2012). Micronutrients interact in diverse ways with other nutrients in constraining NPP such as molybdenum and phosphorus in the tropics (Wurzburger et al., 2012). Thus, with high confidence, the CO2 fertilisation effect will lead to enhanced NPP, but significant uncertainties remain on the magnitude of this effect, given the lack of experiments outside of temperate climates. Pacala et al., 2001; Houghton, 2010; Bellassen et al., 2011; Williams et al., 2009). Changes in the climate are also associated with disturbanc- al., 2012a), changes in forest management and reduced harvest rates es such as fires, insect damage, storms, droughts and heat waves which (Nabuurs et al., 2008). are already significant processes of interannual variability and possibly trends of regional land carbon fluxes (Page et al., 2002; Ciais et al., Process attribution of the global land CO2 sink is difficult due to lim- 2005; Chambers et al., 2007; Kurz et al., 2008b; Clark et al., 2010; van ited availability of global data sets and biogeochemical models that der Werf et al., 2010; Lewis et al., 2011) (see Section 6.3.2.2). include all major processes. However, regional studies shed light on key drivers and their interactions. The European and North American Warming (and possibly the CO2 fertilisation effect) has also been cor- carbon sinks are explained by the combination of forest regrowth in related with global trends in satellite greenness observations, which abandoned lands and decreased forest harvest along with the fertilis- resulted in an estimated 6% increase of global NPP, or the accumu- ation effects of rising CO2 and nitrogen deposition (Pacala et al., 2001; lation of 3.4 PgC on land over the period 1982 1999 (Nemani et al., Ciais et al., 2008; Sutton et al., 2008; Schulze et al., 2010; Bellassen et 2003). This enhanced NPP was attributed to the relaxation of climatic al., 2011; Williams et al., 2012a). In the tropics, there is evidence from constraints to plant growth, particularly in high latitudes. Concomi- forest inventories that increasing forest growth rates are not explained tant to the increased of NPP with warming, global soil respiration also by the natural recovery from disturbances, suggesting that increasing increased between 1989 and 2008 (Bond-Lamberty and Thomson, atmospheric CO2 and climate change play a role in the observed sink 2010), reducing the magnitude of the net land sink. A recent study in established forests (Lewis et al., 2009; Pan et al., 2011). There is also suggests a declining NPP trend over 2000 2009 (Zhao and Running, 6 recent evidence of tropical nitrogen deposition becoming more notable 2010) although the model used to reconstruct NPP trends from satel- although its effects on the net carbon balance have not been assessed lite observation has not been widely accepted (Medlyn, 2011; Samanta (Hietz et al., 2011). et al., 2011). The land carbon cycle is very sensitive to climate changes (e.g., precip- 6.3.2.6.6 Model evaluation of global and regional terrestrial itation, temperature, diffuse vs. direct radiation), and thus the changes carbon balance in the physical climate from increasing GHGs as well as in the diffuse fraction of sunlight are likely to be causing significant changes in the Evaluation of global process-based land carbon models was performed carbon cycle (Jones et al., 2001; Friedlingstein et al., 2006; Mercado et against ground and satellite observations including (1) measured CO2 502 Carbon and Other Biogeochemical Cycles Chapter 6 fluxes and carbon storage change at particular sites around the world, is compatible with model estimates with afforestation ( 63 gC m 2 in particular sites from the Fluxnet global network (Baldocchi et al., yr 1; Luyssaert et al., 2010), while modelled NPP was 43% larger than 2001; Jung et al., 2007; Stöckli et al., 2008; Schwalm et al., 2010; Tan the inventory estimate. In North America, the ability of 22 terrestrial et al., 2010), (2) observed spatio-temporal change in leaf area index carbon cycle models to simulate the seasonal cycle of land atmos- (LAI) (Lucht et al., 2002; Piao et al., 2006) and (3) interannual and phere CO2 exchange from 44 eddy covariance flux towers was poor seasonal change in atmospheric CO2 (Randerson et al., 2009; Cadule with a difference between observations and simulations of 10 times et al., 2010). the observational uncertainty (Schwalm et al., 2010). Model short- comings included spring phenology, soil thaw, snow pack melting Figure 6.16 compares the global land CO2 sink driven by climate and lag responses to extreme climate events (Keenan et al., 2012). In change and rising CO2 as simulated by different process based carbon China, the magnitude of the carbon sink estimated by five terrestrial cycle models (without land use change), with the residual land sink ecosystem models ( 0.22 to 0.13 PgC yr 1) was comparable to the computed as the sum of fossil fuel and cement emissions and land use observation-based estimate ( 0.18 +/- 0.73 PgC yr 1; Piao et al., 2009a), change emissions minus the sum of CO2 growth rate and ocean sink but modelled interannual variation was weakly correlated to observed (Le Quéré et al., 2009; Friedlingstein et al., 2010). Although these two regional land atmosphere CO2 fluxes (Piao et al., 2011). quantities are not the same, the multi-model mean reproduces well the trend and interannual variability of the residual land sink which is Sensitivity of the terrestrial carbon cycle to rising atmospheric carbon dominated by climate variability and climate trends and CO2, respec- dioxide. An inter-comparison of 10 process-based models showed tively, both represented in models (Table 6.7). Limited availability of increased NPP by 3% to 10% over the last three decades, during which in situ measurements, particularly in the tropics, limits the progress CO2 increased by ~50 ppm (Piao et al., 2013). These results are con- towards reducing uncertainty on model parameterizations. sistent within the broad range of responses from experimental studies (see Box 6.3). However, Hickler et al. (2008) suggested that currently Regional and local measurements can be used to evaluate and improve available FACE results (largely from temperate regions) are not appli- global models. Regionally, forest inventory data show that the forest cable to vegetation globally because there may be large spatial heter- carbon sink density over Europe is of 89 +/- 19 gC m 2 yr 1, which ogeneity in vegetation responses to CO2 fertilisation. Table 6.7 | Estimates of the land CO2 sink from process-based terrestrial ecosystem models driven by rising CO2 and by changes in climate. The land sink simulated by these models is close to but not identical to the terrestrial CO2 sink from Table 6.1 because the models calculate the effect of CO2 and climate over managed land, and many do not include nitrogen limitation and disturbances. Natural Fire CO2 Model Name Nitrogen Limitation 1980 1989 1990 1999 2000 2009 Emissions   (Yes/No) (Yes/No) PgC yr 1 PgC yr 1 PgC yr 1 CLM4Cb,c No Yes 1.98 2.11 2.64 CLM4CNb,c Yes Yes 1.27 1.25 1.67 Hylandd No No 2.21 2.92 3.99 LPJ e No Yes 1.14 1.90 2.60 LPJ_GUESSf No Yes 1.15 1.54 2.07 OCNg Yes No 1.75 2.18 2.36 ORC h No No 2.08 3.05 3.74 SDGVMi Yes Yes 1.25 1.95 2.30 TRIFFIDj No No 1.85 2.52 3.00 VEGAS k No No 1.40 1.68 1.89 Averagea 1.61 +/- 0.65 2.11 +/- 0.93 2.63 +/- 1.22 Notes: a Average of all models +/-90% confidence interval. Woodward and Lomas (2004). i b Oleson et al. (2010). Cox (2001). j k Zeng (2003). c Lawrence et al. (2011). d Levy et al. (2004). 6 e Sitch et al. (2003). f Smith et al. (2001a). g Zaehle and Friend (2010). h Krinner et al. (2005). All of these models run are forced by rising CO2 concentration and time-varying historical reconstructed weather and climate fields using the same protocol from the TRENDY project (Piao et al., 2013). (http://www.globalcarbonproject.org/global/pdf/DynamicVegetationModels.pdf). CLM4C = Community Land Model for Carbon; CLM4CN = Community Land Model for Carbon Nitrogen; GUESS = General Ecosystem Simulator; LPJ = Lund-Potsdam-Jena Dynamic Global Vegeta- tion Model; OCN = Cycling of Carbon and Nitrogen on land, derived from ORCHIDEE model; ORC = ORCHIDEE, ORganizing Carbon and Hydrology in Dynamic EcosystEms model; SDGVM = Sheffield Dynamic Global Vegetation Model; TRIFFID = Top-down Representation of Interactive Foliage and Flora Including Dynamics; VEGAS = VEgetation-Global-Atmosphere-Soil terrestrial carbon cycle model. 503 Chapter 6 Carbon and Other Biogeochemical Cycles 6 Residual terrestrial sink (Table 6.1) Process models (Table 6.6) Process model average 5 4 Terrestrial CO2 sink (PgC yr-1) 3 2 1 0 -1 -2 1960 1970 1980 1990 2000 2010 Year Figure 6.16 | The black line and gray shading represent the estimated value of the residual land sink (PgC yr 1) and its uncertainty from Table 6.1, which is calculated from the dif- ference between emissions from fossil fuel and land use change plus emissions from net land use change, minus the atmospheric growth rate and the ocean sink. The atmosphere- to-land flux simulated by process land ecosystem models from Table 6.7 are shown in thin green, and their average in thick green. A positive atmosphere-to-land flux represents a sink of CO2. The definition of the atmosphere-to-land flux simulated by these models is close to but not identical to the residual land sink from Table 6.1 (see Table 6.7). Sensitivity of terrestrial carbon cycle to climate trends and variability. ­ outbreaks and the resulting variation in forest age structure which is Warming exerts a direct control on the net land atmosphere CO2 known to affect the net carbon exchange (Kurz et al., 2008c; Bellassen exchange because both photosynthesis and respiration are sensitive et al., 2010; Higgins and Harte, 2012). Second, many key processes rel- to changes in temperature. From estimates of interannual variations evant to decomposition of carbon are missing in models (Todd-Brown in the residual land sink, 1°C of positive global temperature anomaly et al., 2012), and particularly for permafrost carbon and for carbon in leads to a decrease of 4 PgC yr 1 of the global land CO2 sink (Figure boreal and tropical wetlands and peatlands, despite the large amount 6.17). This observed interannual response is close to the response of of carbon stored in these ecosystems and their vulnerability to warming the models listed in Table 6.7 ( 3.5 +/- 1.5 PgC yr 1°C 1 in Piao et al., and land use change (Tarnocai et al., 2009; Hooijer et al., 2010; Page et 2013), albeit individual models show a range going from 0.5 to 6.2 al., 2011). However, progress has been made (Wania et al., 2009; Koven PgC yr 1 °C 1. The sensitivity of atmospheric CO2 concentration to cen- et al., 2011; Schaefer et al., 2011). Third, nutrient dynamics are taken tury scale temperature change was estimated at about 3.6 to 45.6 PgC into account only by few models despite the fact it is well established °C 1 (or 1.7 to 21.4 ppm CO2 °C 1) using the ice core observed CO2 drop that nutrient constrains NPP and nitrogen deposition enhances NPP during the Little Ice Age (see Section 6.2; Frank et al., 2010). (Elser et al., 2007; Magnani et al., 2007; LeBauer and Treseder, 2008); see Section 6.3.2.6.5. Very few models have phosphorus dynamics Terrestrial carbon cycle models used in AR5 generally underestimate (Zhang et al., 2011; Goll et al., 2012). Fourth, the negative effects of 6 GPP in the water limited regions, implying that these models do not elevated tropospheric ozone on NPP have not been taken into account correctly simulate soil moisture conditions, or that they are too sensi- by most current carbon cycle models (Sitch et al., 2007). Fifth, transfer tive to changes in soil moisture (Jung et al., 2007). Most models (Table of radiation, water and heat in the vegetation soil atmosphere con- 6.7) estimated that the interannual precipitation sensitivity of the tinuum are treated very simply in the global ecosystem models. Finally, global land CO2 sink to be higher than that of the observed residual processes that transport carbon at the surface (e.g., water and tillage land sink ( 0.01 PgC yr 1 mm 1; Figure 6.17). erosion; Quinton et al., 2010) and human managements including fer- tilisation and irrigation (Gervois et al., 2008) are poorly or not repre- Processes missing in terrestrial carbon cycle models. First, many models sented at all. Broadly, models are still at their early stages in dealing do not explicitly take into account the various forms of disturbanc- with land use, land use change and forestry. es or ecosystem dynamics: migration, fire, logging, harvesting, insect 504 Carbon and Other Biogeochemical Cycles Chapter 6 residual land sink Precipitation Interannual Variations (PgC yr 1 100 mm 1) (see Fig 6.15 and Table 6.1) Sensitivity of Land CO2 Sink Interannual Variations to CLM4C 6 CLM4CN HYLAND LPJ 4 LPJ GUESS OCN ORCHIDEE SDGVM 2 TRIFFID VEGAS 0 2 7 5 3 1 1 Sensitivity of Land CO2 Sink Interannual Variations to Temperature Interannual Variations (PgC yr 1 oC 1) Figure 6.17 | The response of interannual land CO2 flux anomaly to per 1°C interannual temperature anomaly and per 100 mm interannual precipitation anomaly during 1980 2009. Black circles show climate sensitivity of land CO2 sink estimated from the residual land sink (see Figure 6.15 and Table 6.1), which is the sum of fossil fuel and cement emissions and land use change emissions minus the sum of observed atmospheric CO2 growth rate and modeled ocean sink sink (Le Quéré et al., 2009; Friedlingstein and Prentice, 2010). Coloured circles show land CO2 sink estimated by 10 process-based terrestrial carbon cycle models (CLM4C (Community Land Model for Carbon), CLM4CN (Community Land Model for Carbon Nitrogen), HYLAND (HYbrid LAND terrestrial ecosystem model), LPJ (Lund-Potsdam-Jena Dynamic Global Vegetation Model), LPJ GUESS (LPJ General Ecosystem Simulator, OCN (Cycling of Carbon and Nitrogen on land, derived from ORCHIDEE model), ORCHIDEE (ORganizing Carbon and Hydrology in Dynamic EcosystEms model), SDGVM (Sheffield Dynamic Global Vegetation Model), TRIFFID (Top-down Representation of Interactive Foliage and Flora Including Dynamics) and VEGAS (terrestrial vegetation and carbon model)). Error bars show standard error of the sensitivity estimates. Dashed error bars indicate the estimated sensitivity by the regression approach is statistically insignificant (P > 0.05). Grey area denoted the area bounded by the estimated climate sensitivity of the residual land sink +/- the standard error of the estimated climate sensitivity of the residual land sink. The sensitivity of land CO2 sink interannual variations to interannual variations of temperature (or precipitation) is estimated as the regression coefficient of temperature (or precipitation) in a multiple regression of detrended anomaly of land CO2 sink against detrended anomaly of annual mean temperature and annual precipitation. 6.3.3 Global Methane Budget CH4 is currently measured by a network of more than 100 surface sites (Blake et al., 1982; Cunnold et al., 2002; Langenfelds et al., 2002; Dlu- AR5 is the first IPCC assessment report providing a consistent synthesis gokencky et al., 2011), aircraft profiles (Brenninkmeijer et al., 2007), of the CH4 budget per decade using multiple atmospheric CH4 inver- satellite (Wecht et al., 2012; Worden et al., 2012) and before 1979 from sion models (top-down) and process-based models and inventories analyses of firn air and ice cores (see Sections 5.2.2 and Section 6.2, (bottom-up). Table 6.8 shows the budgets for the decades of 1980s, and Figure 6.11). The growth of CH4 in the atmosphere is largely in 1990s and 2000s. Uncertainties on emissions and sinks are listed using response to increasing anthropogenic emissions. The vertically aver- minimum and maximum of each published estimate for each decade. aged atmospheric CH4 concentration field can be mapped by remote Bottom-up approaches are used to attribute decadal budgets to indi- sensing from the surface using Fourier Transform Infrared Spectroscopy vidual processes emitting CH4 (see Section 6.1.1.2 for a general over- (FTIR) instruments (Total Carbon Column Observing Network, TCCON, view). Top-down inversions provide an atmospheric-based constraint http://www.tccon.caltech.edu/) and from space by several satellite mostly for the total CH4 source per region, and the use of additional instruments: Atmospheric Infrared Sounder (AIRS, since 2002; http:// observations (e.g., isotopes) allows inferring emissions per source type. airs.jpl.nasa.gov), Tropospheric Emission Spectrometer (TES, since Estimates of CH4 sinks in the troposphere by reaction with tropospheric 2004; http://tes.jpl.nasa.gov), Infrared Atmospheric Sounder Interfer- 6 OH, in soils and in the stratosphere are also presented. Despite signif- ometer (IASI, since 2006; Crévoisier et al., 2009), Scanning Imaging icant progress since the AR4, large uncertainties remain in the present Spectrometer for Atmospheric Cartography (SCIAMACHY, 2003 2012; knowledge of the budget and its evolution over time. Frankenberg et al., 2008), and Greenhouse Gases Observing Satel- lite-Thermal And Near infrared Sensor for carbon Observation Fouri- 6.3.3.1 Atmospheric Changes er-Transform Spectrometer (GOSAT-TANSO-FTS, since 2009; Morino et al., 2011). As an example, SCIAMACHY shows the column CH4 gradient Since the beginning of the Industrial Era, the atmospheric CH4 concen- between the two hemispheres as well as increased concentrations over tration increased by a factor of 2.5 (from 722 ppb to 1803 ppb in 2011). Southeast Asia, due to emissions from agriculture, wetlands, waste and 505 Chapter 6 Carbon and Other Biogeochemical Cycles energy production (Frankenberg et al., 2008). In situ observations pro- emissions (Bousquet et al., 2006; Chen and Prinn, 2006); (3) significant vide very precise measurements (~0.2%) but unevenly located at the (Rigby et al., 2008) to small (Montzka et al., 2011) changes in OH con- surface of the globe. Satellite data offer a global coverage at the cost centrations and/or based on two different 13CH4 data sets; (4) reduced of a lower precision on individual measurements (~2%) and possible emissions from rice paddies attributed to changes in agricultural prac- biases (Bergamaschi et al., 2009). tices (Kai et al., 2011); or (5) stable microbial and fossil fuel emissions from 1990 to 2005 (Levin et al., 2012). The growth rate of CH4 has declined since the mid-1980s, and a near zero growth rate (quasi-stable concentrations) was observed during Since 2007, atmospheric CH4 has been observed to increase again 1999 2006, suggesting an approach to steady state where the sum (Rigby et al., 2008; Dlugokencky et al., 2009) with positive anoma- of emissions are in balance with the sum of sinks (Dlugokencky et al., lies of emissions of 21 Tg(CH4) yr 1 and 18 Tg(CH4) yr 1 estimated by 2003; Khalil et al., 2007; Patra et al., 2011; Figure 6.18). The reasons inversions during 2007 and 2008, respectively (Bousquet et al., 2011) for this growth rate decline after the mid-1980s are still debated, and as compared to the 1999 2006 period. The increase of emissions in results from various studies provide possible scenarios: (1) a reduc- 2007 2008 was dominated by tropical regions (Bousquet et al., 2011), tion of anthropogenic emitting activities such as coal mining, gas with a major contribution from tropical wetlands and some contribu- industry and/or animal husbandry, especially in the countries of the tion from high-latitude wetlands during the 2007 anomaly (Dlugo- former Soviet Union (Dlugokencky et al., 2003; Chen and Prinn, 2006; kencky et al., 2009; Bousquet et al., 2011). This increase is suggested by Savolainen et al., 2009; Simpson et al., 2012); (2) a compensation the growth rate over latitude in Figure 6.18 (Dlugokencky et al., 2009). between increasing anthropogenic emissions and decreasing wetland The recent increase of CH4 concentration since 2007 is also consistent 1985 1990 1995 2000 2005 2010 15 10 (ppb yr-1) 5 0 -5 90N 60N 45N 30N 15N Latitude EQ 15S 30S 45S 60S 90S 1985 1990 1995 2000 2005 2010 6 Year -15 -10 -5 0 5 10 15 20 25 -1 (ppb yr ) Figure 6.18 | (Top) Globally averaged growth rate of atmospheric CH4 in ppb yr 1 determined from the National Oceanic and Atmospheric Administration Earth System Research Laboratory (NOAA ESRL) network, representative for the marine boundary layer. (Bottom) Atmospheric growth rate of CH4 as a function of latitude (Masarie and Tans, 1995; Dlugokencky and Tans, 2013b). 506 Carbon and Other Biogeochemical Cycles Chapter 6 Table 6.8 | Global CH4 budget for the past three decades (in Tg(CH4) yr 1) and present day (2011)38. The bottom-up estimates for the decade of 2000 2009 are used in the Executive Summary and in Figure 6.2. T-D stands for Top-Down inversions and B-U for Bottom-Up approaches. Only studies covering at least 5 years of each decade have been used. Reported values correspond to the mean of the cited references and therefore not always equal (max-min)/2; likewise, ranges [in brackets] represent minimum and maximum values of the cited references. The sum of sources and sinks from B-U approaches does not automatically balance the atmospheric changes. For B-U studies, individual source types are also presented. For T-D inversions, the 1980s decade starts in 1984. As some atmospheric inversions did not reference their global sink, balance with the atmosphere and the sum of the sources has been assumed. One biomass burning estimate (Schultz et al., 2007) excludes biofuels (a). Stratospheric loss for B-U is the sum of the loss by OH radicals, a 10 Tg yr 1 loss due to O1D radicals (Neef et al., 2010) and a 20 to 35% contribution due to Cl radicals24 (Allan et al., 2007). Present day budgets39 adopt a global mean lifetime of 9.14 yr (+/-10%). 1980 1989 1990 1999 2000 2009 Tg(CH4) yr 1 Top-Down Bottom-Up Top-Down Bottom-Up Top-Down Bottom-Up Natural Sources 193 [150 267] 355 [244 466] 182 [167 197] 336 [230 465] 218 [179 273] 347 [238 484] 175 [142 Natural wetlands 157 [115 231] 1,2,3 225 [183 266] 4,5 150 [144 160] 1,28,29 206 [169 265] 4,5,27 217 [177 284]4,5,27 208]1,29,33,34,35,36 Other sources 36 [35 36]1,2 130 [61 200] 32 [23 37]1,28,29 130 [61 200] 43 [37 65]1,29,33,34,35,36 130 [61 200] Freshwater (lakes and rivers) 40 [8 73] 6,7,8 40 [8 73] 6,7,8 40 [8 73]6,7,8 Wild animals 15 [15 15] 9 15 [15 15] 9 15 [15 15]9 Wildfires 3 [1 5]9,10,11,12,13 3 [1 5]9,10,11,12,13 3 [1 5]9,10,11,12,13 Termites 11 [2 22] 9,10,14,15,x 11 [2 22] 9,10,14,15,x 11 [2 22]9,10,14,15,x Geological (incl. oceans) 54 [33 75] 10,16,17 54 [33 75] 10,16,17 54 [33 75]10,16,17 Hydrates 6 [2 9]9,18,19 6 [2 9]9,18,19 6 [2 9]9,18,19 Permafrost (excl. lakes 1 [0 1]10 1 [0 1]10 1 [0 1]10 and wetlands) Anthropogenic Sources 348 [305 383] 308 [292 323] 372 [290 453] 313 [281 347] 335 [273 409] 331 [304 368] 209 [180 Agriculture and waste 208 [187 220]1,2,3 185 [172 197]20 239 [180 301]1,28,29 187 [177 196]20,30,31 200 [187 224]20,30,31 241]1,29,33,34,35,36 Rice 45 [41 47]20 35 [32 37]20,27,30,31 36 [33 40]20,27,30,31 Ruminants 85 [81 90] 20 87 [82 91] 20,30,31 89 [87 94]20,30,31 Landfills and waste 55 [50 60]20 65 [63 68]20,30,31 75 [67 90]20,30,31 Biomass burning (incl. biofuels) 46 [43 55]1,2,3 34 [31 37]20,21,22a,38 38 [26 45]1,28,29 42 [38 45]13,20,21,22a,32,38 30 [24 45]1,29,33,34,35,36 35 [32 39]13,20,21,32,37,38 96 [77 Fossil fuels 94 [75 108]1,2,3 89 [89 89]20 95 [84 107]1,28,29 84 [66 96]20,30,31 96 [85 105]20,30,31 123]1,29,33,34,35,36 Sinks 518 [510 Total chemical loss 490 [450 533]1,2,3 539 [411 671]23,24,25,26 515 [491 554]1,28,29 571 [521 621]23,24,25,26 604 [483 738]23,24,25,26 538]1,29,33,34,36 Tropospheric OH 468 [382 567]26 479 [457 501]26 528 [454 617]25,26 Stratospheric OH 46 [16 67]23,25,26 67 [51 83]23,25,26 51 [16 84]23,25,26 Tropospheric Cl 25 [13 37] 24 25 [13 37] 24 25 [13 37]24 Soils 21 [10 27] 1,2,3 28 [9 47] 27,34,36 27 [27 27] 1 28 [9 47] 27,34,36 32 [26 42] 1,33,34,35,36 28 [9 47]27,34,36 Global Sum of sources 541 [500 592] 663 [536 789] 554 [529 596] 649 [511 812] 553 [526 569] 678 [542 852] Sum of sinks 511 [460 559] 567 [420 718] 542 [518 579] 599 [530 668] 550 [514 560] 632 [592 785] Imbalance (sources 30 [16 40] 12 [7 17] 3 [ 4 19] minus sinks) Atmospheric growth rate 34 17 6 Global top-down (year 2011) 2011 (AR5)38 Burden (Tg CH4) 4954+/-10 Atmospheric loss (Tg CH4 yr ) -1 542+/-56 6 Atmos. increase (Tg CH4 yr-1) 14+/-3 Total source (Tg CH4 yr-1) 556+/-56 Anthropogenic source 354+/-45 (Tg CH4 yr-1) Natural source (Tg CH4 yr ) -1 202+/-35 References: 1 Bousquet et al. (2011) 3 Hein et al. (1997) 5 Ringeval et al. (2011) 7 Bastviken et al. (2011) 9 Denman et al. (2007) 2 Fung et al. (1991) 4 Hodson et al. (2011) 6 Bastviken et al. (2004) 8 Walter et al. (2007) 10 EPA (2010) 507 Chapter 6 Carbon and Other Biogeochemical Cycles Table 6.8 References (continued) 11 Hoelzemann et al. (2004) 18 Dickens (2003) 24 Allan et al. (2007) 31 EPA (2011a) 38 Andreae and Merlet (2001) 12 Ito and Penner (2004) 19 Shakhova et al. (2010) 25 Williams et al. (2012b) 32 van der Werf (2004) 39 Prather et al. (2012), updated to 13 van der Werf et al. (2010) 20 EDGAR4-database (2009) 26 Voulgarakis et al. (2013) 33 Bergamaschi et al. (2009) 2011 (Table 2.1) and used in Curry (2007) Chapter 11 projections; 14 Sanderson (1996) 21 Mieville et al. (2010) 27 Spahni et al. (2011) 34 uncertainties evaluated as 15 Sugimoto et al. (1998) 22 Schultz et al. (2007) 28 Chen and Prinn (2006) 35 Spahni et al. (2011) 68% confidence intervals, see 16 Etiope et al. (2008) (excluding biofuels) 29 Pison et al. (2009) 36 Ito and Inatomi (2012) also Annex II.2.2 and II.4.2. 17 Rhee et al. (2009) 23 Neef et al. (2010) 30 Dentener et al. (2005) 37 Wiedinmyer et al. (2011) with anthropogenic emission inventories, which show more (EDGAR unconstrained in spite the existence of some remote sensing products v4.2) or less (EPA, 2011a) rapidly increasing anthropogenic CH4 emis- (Papa et al., 2010). It has been observed that wetland CH4 emissions sions in the period 2000 2008. This is related to increased energy increase in response to elevated atmospheric CO2 concentrations (van production in growing Asian economies (EDGAR, edgar.jrc.ec.europa. Groenigen et al., 2011). van Groenigen et al. attribute such an increase eu; EPA, http://www.epa.gov/nonco2/econ-inv/international.html). The in CH4 emissions from natural wetlands to increasing soil moisture due atmospheric increase has continued after 2009, at a rate of 4 to 5 ppb to the reduced plant demand for water under higher CO2. However, the yr 1 (Sussmann et al., 2012). sign and magnitude of the CH4 emission response to changes in tem- perature and precipitation vary among models but show, on average, 6.3.3.2 Methane Emissions a decrease of wetland area and CH4 flux with increasing temperature, especially in the tropics, and a modest (~4%) increase of wetland area \The CH4 growth rate results from the balance between emissions and and CH4 flux with increasing precipitation (Melton et al., 2013). sinks. Methane emissions around the globe are biogenic, thermogenic or pyrogenic in origin (Neef et al., 2010), and they can be the direct In AR4, natural geological sources were estimated between 4 and 19 result of either human activities and/or natural processes (see Section Tg(CH4) yr 1. Since then, Etiope et al. (2008) provided improved emis- 6.1.1.2 and Table 6.8). Biogenic sources are due to degradation of sion estimates from terrestrial (13 to 29 Tg(CH4) yr 1) and marine (~20 organic matter in anaerobic conditions (natural wetlands, ruminants, Tg(CH4) yr 1) seepages, mud volcanoes (6 to 9 Tg(CH4) yr 1), hydrates (5 waste, landfills, rice paddies, fresh waters, termites). Thermogenic to 10 Tg(CH4) yr 1) and geothermal and volcanic areas (3 to 6 Tg(CH4) sources come from the slow transformation of organic matter into yr 1), which represent altogether between 42 and 64 Tg(CH4) yr 1 (see fossil fuels on geological time scales (natural gas, coal, oil). Pyrogenic Table 6.8 for full range of estimates). This contribution from natural, sources are due to incomplete combustion of organic matter (biomass geological and partly fossil CH4 is larger than in AR4 and consistent and biofuel burning). Some sources can eventually combine a biogenic with a 14CH4 reanalysis showing natural and anthropogenic fossil con- and a thermogenic origin (e.g., natural geological sources such as oce- tributions to the global CH4 budget to be around 30% (medium confi- anic seeps, mud volcanoes or hydrates). Each of these three types of dence) (Lassey et al., 2007) and not around 20% as previously estimat- emissions is characterized by ranges in its isotopic composition in13C- ed (e.g., AR4). However, such a large percentage was not confirmed by CH4: typically 55 to 70 for biogenic, 25 to 45 for thermogenic, an analysis of the global atmospheric record of ethane (Simpson et al., and 13 to 25 for pyrogenic. These isotopic distinctions provide a 2012) which is co-emitted with geological CH4. basis for attempting to separate the relative contribution of different methane sources using the top-down approach (Bousquet et al., 2006; Of the natural sources of CH4, emissions from thawing permafrost and Neef et al., 2010; Monteil et al., 2011). CH4 hydrates in the northern circumpolar region will become poten- tially important in the 21st century because they could increase dra- During the decade of the 2000s, natural sources of CH4 account for 35 matically due to the rapid climate warming of the Arctic and the large to 50% of the decadal mean global emissions (Table 6.8). The single carbon pools stored there (Tarnocai et al., 2009; Walter Anthony et al., most dominant CH4 source of the global flux and interannual variability 2012) (see Section 6.4.3.4). Hydrates are, however, estimated to rep- is CH4 emissions from wetlands (177 to 284 Tg(CH4) yr 1). With high resent only a very small emission, between 2 and 9 Tg(CH4) yr 1 under confidence, climate driven changes of emissions from wetlands are the the current time period (Table 6.8). Supersaturation of dissolved CH4 at main drivers of the global inter-annual variability of CH4 emissions. the bottom and surface waters in the East Siberian Arctic Shelf indicate The term wetlands denotes here a variety of ecosystems emitting some CH4 activity across the region, with a net sea air flux of 10.5 CH4 in the tropics and the high latitudes: wet soils, swamps, bogs and Tg(CH4) yr 1 which is similar in magnitude to the flux for the entire 6 peatlands. These emissions are highly sensitive to climate change and ocean (Shakhova et al., 2010) but it is not possible to say whether this variability, as shown, for instance, from the high CH4 growth rate in source has always been present or is a consequence of recent Arctic 2007 2008 that coincides with positive precipitation and temperature changes. The ebullition of CH4 from decomposing, thawing lake sed- anomalies (Dlugokencky et al., 2009). Several process-based models of iments in north Siberia with an estimated flux of ~4 Tg(CH4) yr 1 is methane emissions from wetlands have been developed and improved another demonstration of the activity of this region and of its potential since AR4 (Hodson et al., 2011; Ringeval et al., 2011; Spahni et al., importance in the future (Walter et al., 2006; van Huissteden et al., 2011; Melton et al., 2013), yet the confidence in modeled wetland 2011). The sum of all natural emission estimates other than wetlands CH4 emissions remains low, particularly because of limited observa- is still very uncertain based on bottom-up studies [see Table 6.8, range tional data sets available for model calibration and evaluation. Spatial of 238 to 484 Tg(CH4) yr 1 for 2000 2009]. distribution and temporal variability of wetlands also remains highly 508 Carbon and Other Biogeochemical Cycles Chapter 6 Pyrogenic sources of CH4 (biomass burning in Table 6.8) are assessed 6.3.3.3 Sinks of Atmospheric Methane to have a small contribution in the global flux for the 2000s (32 to 39 Tg(CH4) yr 1). Biomass burning of tropical and boreal forests (17 to The main sink of atmospheric CH4 is its oxidation by OH radicals, a 21 Tg(CH4) yr 1) play a much smaller role than wetlands in interannual chemical reaction that takes place mostly in the troposphere and strat- variability of emissions, except during intensive fire periods (Langen- osphere (Table 6.8). OH removes each year an amount of CH4 equiva- felds et al., 2002; Simpson et al., 2006). Only during the 1997 1998 lent to 90% of all surface emissions (Table 6.8), that is, 9% of the total record strong El Nino, burning of forests and peatland that took place burden of CH4 in the atmosphere, which defines a partial atmospheric in Indonesia and Malaysia, released ~12 Tg(CH4) and contributed to the lifetime with respect to OH of 7 to 11 years for an atmospheric burden observed growth rate anomaly (Langenfelds et al., 2002; van der Werf of 4800 Tg(CH4) (4700 to 4900 TgCH4 as computed by Atmospheric et al., 2004). Other smaller fire CH4 emissions positive anomalies were Chemistry and Climate Model Intercomparison Project (ACCMIP) suggested over the northern mid-latitudes in 2002 2003, in particular atmospheric chemistry models in Voulgarakis et al. (2013), thus slightly over Eastern Siberia in 2003 (van der Werf et al., 2010) and Russia in different from Figure 6.2; see Section 8.2.3.3 for ACCMIP models). A 2010. Traditional biofuel burning is estimated to be a source of 14 to 17 recent estimate of the CH4 lifetime is 9.1 +/- 0.9 years (Prather et al., Tg(CH4) yr 1(Andreae and Merlet, 2001; Yevich and Logan, 2003). 2012). A small sink of atmospheric CH4 is suspected, but still debated, in the marine boundary layer due to a chemical reaction with chlorine Keppler at al. (2006) reported that plants under aerobic conditions (Allan et al., 2007). Another small sink is the reaction of CH4 with Cl were able to emit CH4, and thus potentially could constitute a large radicals and O(1D) in the stratosphere (Shallcross et al., 2007; Neef additional emission, which had not been previously considered in the et al., 2010). Finally, oxidation in upland soils (with oxygen) by meth- global CH4 budget. Later studies do not support plant emissions as a anotrophic bacterias removes about 9 to 47 Tg(CH4) yr 1 (Curry, 2007; widespread mechanism (Dueck et al., 2007; Wang et al., 2008; Nisbet Dutaur and Verchot, 2007; Spahni et al., 2011; Ito and Inatomi, 2012). et al., 2009) or show small to negligible emissions in the context of the global CH4 budget (Vigano et al., 2008; Nisbet et al., 2009; Bloom There have been a number of published estimates of global OH con- et al., 2010). Alternative mechanisms have been suggested to explain centrations and variations over the past decade (Prinn et al., 2001; an apparent aerobic CH4 production, which involve (1) adsorption Dentener et al., 2003; Bousquet et al., 2005; Prinn et al., 2005; Rigby and desorption (Kirschbaum and Walcroft, 2008; Nisbet et al., 2009), et al., 2008; Montzka et al., 2011). The very short lifetime of OH makes (2) degradation of organic matter under strong ultraviolet (UV) light it almost impossible to measure directly global OH concentrations in (Dueck et al., 2007; Nisbet et al., 2009) and (3) methane in the ground- the atmosphere. Chemistry transport models (CTMs), chemistry climate water emitted through internal air spaces in tree bodies (Terazawa models (CCMs) or proxy methods have to be used to obtain a global et al., 2007). Overall, a significant emission of CH4 by plants under mean value and time variations. For the 2000s, CTMs and CCMs (Young aerobic conditions is very unlikely, and this source is not reported in et al., 2013) estimate a global chemical loss of methane due to OH Table 6.8. of 604 Tg(CH4) yr 1 (509 to 764 Tg(CH4) yr 1). This loss is larger, albeit compatible considering the large uncertainties, with a recent exten- Anthropogenic CH4 sources are estimated to range between 50% and sive analysis by Prather et al. (2012) inferring a global chemical loss of 65% of the global emissions for the 2000s (Table 6.8). They include 554 +/- 56 Tg(CH4) yr 1. Top-down inversions using methyl-chloroform rice paddies agriculture, ruminant animals, sewage and waste, land- (MCF) measurements to infer OH provide a smaller chemical loss of fills, and fossil fuel extraction, storage, transformation, transportation 518 Tg(CH4) yr 1 with a more narrow range of 510 to 538 Tg(CH4) yr 1 and use (coal mining, gas and oil industries). Anthropogenic sources in the 2000s. However, inversion estimates probably do not account for are dominant over natural sources in top-down inversions (~65%) but all sources of uncertainties (Prather et al., 2012). they are of the same magnitude in bottom-up models and inventories (Table 6.8). Rice paddies emit between 33 to 40 Tg(CH4) yr 1 and 90% CCMs and CTMs simulate small interannual variations of OH radicals, of these emissions come from tropical Asia, with more than 50% from typically of 1 to 3% (standard deviation over a decade) due to a high China and India (Yan et al., 2009). Ruminant livestock, such as cattle, buffering of this radical by atmospheric photochemical reactions (Voul- sheep, goats, etc. produce CH4 by food fermentation in their anoxic garakis et al., 2013; Young et al., 2013). Atmospheric inversions show rumens with a total estimate of between 87 and 94 Tg(CH4) yr 1. Major much larger variations for the 1980s and the 1990s (5 to 10%), because regional contributions of this flux come from India, China, Brazil and of their oversensitivity to uncertainties on MCF emissions, when meas- the USA (EPA, 2006; Olivier and Janssens-Maenhout, 2012), EDGAR urements of this tracer are used to reconstruct OH (Montzka et al., v4.2. India, with the world s largest livestock population emitted 11.8 2011), although reduced variations are inferred after 1998 by Prinn et Tg(CH4) yr 1 in 2003, including emission from enteric fermentation al. (2005). For the 2000s, the reduction of MCF in the atmosphere, due (10.7 Tg(CH4) yr 1) and manure management (1.1 Tg(CH4) yr 1; Chhabra to the Montreal protocol (1987) and its further amendments, allows 6 et al., 2013). Methanogenesis in landfills, livestock manure and waste a consistent estimate of small OH variations between atmospheric waters produces between 67 and 90 Tg(CH4) yr 1 due to anoxic con- inversions (<+/-5%) and CCMs/CTMs (<+/-3%). However, the very low ditions and a high availability of acetate, CO2 and H2. Loss of natural atmospheric values reached by MCF (few ppt in 2010) impose the gas (~90% CH4) is the largest contributor to fossil fuel related fugitive need to find another tracer to reconstruct global OH in the upcoming emissions, estimated between 85 and 105 Tg(CH4) yr 1 in the USA (EPA, years. Finally, evidence for the role of changes in OH concentrations in 2006; Olivier and Janssens-Maenhout, 2012), EDGAR v4.2. explaining the increase in atmospheric methane since 2007 is variable, ranging from a significant contribution (Rigby et al., 2008) to only a small role (Bousquet et al., 2011). 509 Chapter 6 Carbon and Other Biogeochemical Cycles 6.3.3.4 Global Methane Budget for the 2000s Since AR4 (Table 6.9 for the 1990s), a number of studies allow us to update some of the N2O emission estimates. First and most important- Based on the inversion of atmospheric measurements of CH4 from sur- ly, the IPCC Guidelines were revised in 2006 (De Klein et al., 2007) and face stations, global CH4 emissions for the 2000s are of 553 Tg(CH4) in particular emission factors for estimating agricultural N2O emissions. yr 1, with a range of 526 to 569 Tg(CH4) yr 1 (Table 6.8). The total loss of Applying these 2006 emission factors to global agricultural statistics atmospheric methane is of 550 Tg(CH4) yr 1 with a range of 514 to 560 results in higher direct emissions from agriculture (from fertilised soils Tg(CH4) yr 1, determining a small imbalance of about 3 Tg(CH4) yr 1, in and animal production) than in AR4, but into indirect emissions (asso- line with the small growth rate of 6 Tg(CH4) yr 1 observed for the 2000s. ciated with leaching and runoff of Nr resulting in N2O emissions from groundwater, riparian zones and surface waters) that are considerably Based on bottom-up models and inventories, a larger global CH4 emis- lower than reported in AR4 (Table 6.9). It should be noted that emis- sions of 678 Tg(CH4) yr 1 are found, mostly because of the still debated sions of N2O show large uncertainties when default emission factors upward re-evaluation of geological (Etiope et al., 2008) and freshwa- are applied at the global scale (Crutzen et al., 2008; Davidson, 2009; ter (Walter et al., 2007; Bastviken et al., 2011) emission sources. An Smith et al., 2012). Second, estimates of the anthropogenic source of averaged total loss of 632 Tg(CH4) yr 1 is found, by an ensemble of N2O from the open ocean have been made for the first time. These Atmospheric Chemistry models (Lamarque et al., 2013) leading to an emissions result from atmospheric deposition of anthropogenic Nr imbalance of about 45 Tg(CH4) yr 1 during the 2000s, as compared (nitrogen oxides and ammonia/ammonium) (Duce et al., 2008; Sun- to the observed mean growth rate of 6 Tg(CH4) yr 1(Table 6.8; Dlugo- tharalingam et al., 2012). This anthropogenic ocean N2O source was kencky et al., 2011). There is no constraint that applies to the sum of implicitly included as part of the natural ocean N2O source in AR4, but emissions in the bottom-up approach, unlike for top-down inversions is now given as a separate anthropogenic source of 0.2 (0.1 to 0.4) when these have constrained OH fields (e.g., from MCF). Therefore, TgN yr 1 in Table 6.9. Finally, a first estimate of global N2O uptake at top-down inversions can help constrain global CH4 emissions in the the surface is now available (Syakila et al., 2010; Syakila and Kroeze, global budget, although they do not resolve the same level of detail in 2011), based on reviews of measurements of N2O uptake in soils and the mix of sources than the bottom-up approaches, and thus provide sediments (Chapuis-Lardy et al., 2007; Kroeze et al., 2007). The uncer- more limited information about processes (Table 6.8). tainty in this sink of N2O is large. On the global scale, this surface sink is negligible, but at the local scale it may not be irrelevant. 6.3.4 Global Nitrogen Budgets and Global Nitrous Oxide Budget in the 1990s 6.3.4.1 Atmosphere Nitrous Oxide Burden and Growth Rate The atmospheric abundance of N2O has been increasing mainly as a The concentration of N2O is currently 20% higher than pre-industrial result of agricultural intensification to meet the food demand for a levels (Figure 6.11; MacFarling-Meure et al., 2006). Figure 6.19 shows growing human population. Use of synthetic fertiliser (primarily from the annual growth rate of atmospheric N2O estimated from direct the Haber Bosch process) and manure applications increase the pro- measurements (National Oceanic and Atmospheric Administration duction of N2O in soils and sediments, via nitrification and denitrifica- Global Monitoring Division (NOAA GMD) network of surface stations). tion pathways, leading to increased N2O emissions to the atmosphere. On decadal time scales, the concentration of N2O has been increasing Increased emissions occur not only in agricultural fields, but also in at a rate of 0.73 +/- 0.03 ppb yr 1. The interannual variability in mid- to aquatic systems after nitrogen leaching and runoff, and in natural high-latitude N2O abundance in both the NH and SH was found to cor- soils and ocean surface waters as a result of atmospheric deposition relate with the strength of the stratospheric Brewer Dobson circula- of nitrogen originating from agriculture, fossil fuel combustion and tion (Nevison et al., 2011). Variability in stratosphere to troposphere air industrial activities. Food production is likely responsible for 80% of mass exchange, coupled with the stratospheric N2O sink is likely to be the increase in atmospheric N2O (Kroeze et al., 1999; Davidson, 2009; responsible for a fraction of the interannual variability in tropospheric Williams and Crutzen, 2010; Syakila and Kroeze, 2011; Zaehle et al., N2O, but the understanding of this process is poor (Huang et al., 2008). 2011; Park et al., 2012), via the addition of nitrogen fertilisers. Global This removal process signal is obscured in the SH by the timing of oce- emissions of N2O are difficult to estimate owing to heterogeneity in anic thermal and biological ventilation signals (Nevison et al., 2011) space and time. Table 6.9 presents global emissions based on upscaling and terrestrial sources (Ishijima et al., 2009). These two factors may of local flux measurements at the surface. Modelling of the atmos- thus also be important determinants of seasonal and interannual vari- pheric lifetime of N2O and atmospheric inversions constrain global and ability of N2O in the atmosphere. Quantitative understanding of terres- regional N2O budgets (Hirsch et al., 2006; Huang et al., 2008; Rhee et trial N2O emissions variability is poor, although emissions are known al., 2009; Prather et al., 2012), although there is uncertainty in these to be sensitive to soil water content (Ishijima et al., 2009). A first pro- 6 estimates because of uncertainty in the dominant loss term of N2O, cess model-based estimate suggests that the mainly climate-driven that is, the destruction of N2O by photolysis and reaction with O(1D) variability in the terrestrial source may account for only 0.07 ppb yr 1 in the stratosphere. The long atmospheric lifetime of N2O (118 to 131 variability in atmospheric N2O growth rate, which would be difficult to years, Volk et al., 1997; Hsu and Prather, 2010; Fleming et al., 2011; see detect in the observed growth rate (Zaehle et al., 2011). Chapter 8) implies that it will take more than a century before atmos- pheric abundances stabilise after the stabilization of global emissions. Most N2O is produced by biological (microbial) processes such as nitri- This is of concern not only because of its contribution to the radiative fication and denitrification in terrestrial and aquatic systems, including forcing (see Glossary), but also because of the relative importance of rivers, estuaries, coastal seas and the open ocean (Table 6.9; Freing et N2O and other GHGs in affecting the ozone layer (Ravishankara et al., al., 2012). In general, more N2O is formed when more reactive nitrogen 2009; Fleming et al., 2011). 510 Carbon and Other Biogeochemical Cycles Chapter 6 is available. The production of N2O shows large spatial and temporal Table 6.9 does not include the formation of atmospheric N2O from variability. Emission estimates for tropical regions and for aquatic sys- abiotic decomposition of ammonium nitrate in the presence of light, tems are relatively uncertain. Inverse modelling studies show that the appropriate relative humidity and a surface. This process recently has errors in emissions are large, especially in (sub)-tropical regions (e.g., been proposed as a potentially important source of N2O (­Rubasinghege Hirsch et al., 2006; Huang et al., 2008). Emissions from rivers, estuar- et al., 2011); however, a global estimate does not yet exist. Table 6.9 ies and continental shelves have been the subject of debate for many indicates that the global N2O emissions in the mid-1990s amount to years (Seitzinger and Kroeze, 1998; De Klein et al., 2007). Recent stud- 17.5 (8.1 to 30.7) TgN (N2O) yr 1. The uncertainty range is consistent ies confirm that rivers can be important sources of N2O, which could with that of atmospheric inversions studies (14.1 to 17.8) by Huang et be a reason to reconsider recent estimates of aquatic N2O emissions al. (2008). The estimates of anthropogenic N2O emissions of Table 6.9 (Beaulieu et al., 2011; Rosamond et al., 2012). are in line with the top-down estimates by Prather et al. (2012) of 6.5 +/- 1.3 TgN (N2O) yr 1, and somewhat higher than their estimates for Table 6.9 | Section 1 gives the global nitrogen budget (TgN yr 1): (a) creation of reactive nitrogen, (b) emissions of NOx, NH3 in 2000s to atmosphere, (c) deposition of nitrogen to continents and oceans, (d) discharge of total nitrogen to coastal ocean and (e) conversion of Nr to N2 by denitrification. Section 2 gives the N2O budget for the year 2006, and for the 1990s compared to AR4. Unit: Tg(N2O-N) yr 1. SECTION 1 (NOy and NHx) a. Conversion of N2 to Nr 2005 2005 References Anthropogenic sources Fossil fuel combustion 30 (27 33) Fowler et al. (2013) Haber Bosch process Fertiliser 100 (95 100) Galloway et al. (2008), Fowler et al. (2013) Industrial feedstock 24 (22 26) Galloway et al. (2008), Fowler et al. (2013) Biological nitrogen fixation (BNF) 60 (50 70) Herridge et al. (2008) Anthropogenic total 210 Natural sources BNF, terrestrial 58 (50 100) Vitousek et al. (2013) BNF, marine 160 (140 177) Voss et al. (2013), Codispoti (2007) Lightning 4 (3 5) AR4 Natural total 220 Total conversion of N2 to reactive N 440 b. Emissions to Atmosphere NOx NH3 Fossil fuel combustion industrial processes 28.3 0.5 Dentener et al. (2006) Agriculture 3.7 30.4 Dentener et al. (2006) Biomass and biofuel burning 5.5 9.2 Dentener et al. (2006) Anthropogenic total 37.5 40.1 Natural sources Soils under natural vegetation 7.3 (5 8) 2.4 (1 10) AR4 8.2 Oceans AR4 (3.6) Lightning 4 (3 5) AR4 Natural total 11.3 10.6 AR4 Total sources 48.8 50.7 c. Deposition from the Atmosphere NOy NHx 6 Continents 27.1 36.1 Lamarque et al. (2010) Oceans 19.8 17.0 Lamarque et al. (2010) Total 46.9 53.1 d. Discharge to Coastal Ocean Surface water N flux 45 Mayorga et al. (2010), Seitzinger et al. (2010) e. Conversion of Nr to N2 by Denitrification Continents 109 (101 118) Bouwman et al. (2013) (continued on next page) 511 Chapter 6 Carbon and Other Biogeochemical Cycles Table 6.9 (continued) SECTION 2 (N2O) AR5 (2006/2011) AR5 (mid-1990s) AR4 (1990s) Anthropogenic sources  Fossil fuel combustion and industrial processes  0.7 (0.2 1.8)a 0.7 (0.2 1.8)a 0.7 (0.2 1.8) Agriculture  4.1 (1.7 4.8)b 3.7 (1.7 4.8) b 2.8(1.7 4.8) Biomass and biofuel burning  0.7(0.2 1.0)a 0.7(0.2 1.0)a 0.7(0.2 1.0) Human excreta  0.2 (0.1 0.3)a 0.2 (0.1 0.3)a 0.2 (0.1 0.3) Rivers, estuaries, coastal zones  0.6 (0.1 2.9)c 0.6 (0.1 2.9)c 1.7(0.5 2.9) Atmospheric deposition on land  0.4 (0.3 0.9)d 0.4 (0.3 0.9)d 0.6 (0.3 0.9) Atmospheric deposition on ocean 0.2 (0.1 0.4)e 0.2 (0.1 0.4)e Surface sink 0.01 (0 -1) f 0.01 (0 -1) f Total anthropogenic sources 6.9 (2.7 11.1) 6.5 (2.7 11.1) 6.7 (2.7 11.1) Natural sourcesa Soils under natural vegetation  6.6 (3.3 9.0) 6.6 (3.3 9.0) 6.6 (3.3 9.0) Oceans  3.8(1.8 9.4) 3.8(1.8 9.4) 3.8(1.8 5.8) Lightning  Atmospheric chemistry  0.6 (0.3 1.2) 0.6 (0.3 1.2) 0.6 (0.3 1.2) Total natural sources 11.0 (5.4 19.6) 11.0 (5.4 19.6) 11.0 (5.4 19.6) Total natural + anthropogenic sources  17.9 (8.1 30.7) 17.5 (8.1 30.7) 17.7 (8.5 27.7) Stratospheric sink 14.3 (4.3 27.2)g Observed growth rate 3.61 (3.5 3.8)h Global top-down (year 2011) i Burden (Tg N) 1553 Atmospheric Loss 11.9+/-0.9 Atmospheric Increase 4.0+/-0.5 Total Source 15.8+/-1.0 Natural Source 9.1+/-1.0 Anthropogenic Source 6.7+/-1.3 Notes: a All units for N O fluxes are in TgN (N O) yr 1 as in AR4 (not based on 2006 IPCC Guidelines). Lower end of range in the natural ocean from Rhee et al. (2009); higher end of the range from Bianchi 2 2 et al. (2012) and Olivier and Janssens-Maenhout (2012); natural soils in line with Stocker et al. (2013). b Direct soil emissions and emissions from animal production; calculated following 2006 IPCC Guidelines (Syakila and Kroeze, 2011); range from AR4 (Olivier and Janssens-Maenhout, 2012). c Following 2006 IPCC Guidelines (Kroeze et al., 2010; Syakila and Kroeze, 2011). Higher end of range from AR4; lower end of range from 1996 IPCC Guidelines (Mosier et al., 1998). Note that a recent study indicates that emissions from rivers may be underestimated in the IPCC assessments (Beaulieu et al., 2011). d Following 2006 IPCC Guidelines (Syakila and Kroeze, 2011). e Suntharalingam et al. (2012). f Syakila et al. (2010). g The stratospheric sink regroups losses via photolysis and reaction with O(1D) that account for 90% and 10% of the sink, respectively (Minschwaner et al., 1993). The global magnitude of the stratospheric sink was adjusted in order to be equal to the difference between the total sources and the observed growth rate. This value falls within literature estimates (Volk et al., 1997). h Data from Sections 6.1 and 6.3 (see Figure 6.4c). The range on the observed growth rate in this table is given by the 90% confidence interval of Figure 6.4c. i Based on Prather et al. (2012), updated to 2011 (Table 2.1) and used in Chapter 11 projections; uncertainties evaluated as 68% confidence intervals, N O budget reduced based on recently 2 published longer lifetimes of 131+/-10 yrs, see Annex II.2.3 and II.4.3. natural (9.1 +/- 1.3 TgN (N2O) yr 1) and total (15.7 +/- 1.1 TgN (N2O) yr 1) ily responsible for the historic increase in N2O (Röckmann and Levin, emissions. Anthropogenic emissions have steadily increased over the 2005; Sutka et al., 2006; Park et al., 2012). 6 last two decades and were 6.9 (2.7 to 11.1) TgN (N2O) yr 1 in 2006, or 6% higher than the value in mid-1990s (Davidson, 2009; Zaehle et 6.3.4.2 Sensitivity of Nitrous Oxide Fluxes to Climate and al., 2011) (see also Figure 6.4c). Overall, anthropogenic N2O emissions Elevated Carbon Dioxide are now a factor of 8 greater than their estimated level in 1900. These trends are consistent with observed increases in atmospheric N2O Previous studies suggested a considerable positive feedback between (Syakila et al., 2010). Human activities strongly influence the source of N2O and climate (Khalil and Rasmussen, 1989) supported by observed N2O, as nitrogen fertiliser used in agriculture is now the main source glacial interglacial increases of ~70 ppb in atmospheric N2O (Flück- of nitrogen for nitrification and denitrification (Opdyke et al., 2009). iger et al., 1999). Climate change influences marine and terrestrial N2O Nitrogen stable isotope ratios confirm that fertilised soils are primar- sources, but their individual contribution and even the sign of their 512 Carbon and Other Biogeochemical Cycles Chapter 6 response to long-term climate variations are difficult to estimate (see frequency distribution of precipitation, and also because agricultural Section 6.2). Simulations  by terrestrial biosphere models suggest a emissions themselves may also be sensitive to climate. moderate increase of global N2O emissions with recent climate chang- es, related mainly to changes in land temperature (Zaehle and Dal- N2O production will be affected by climate change through the effects monech, 2011; Xu-Ri et al., 2012), thus suggesting a possible posi- on the microbial nitrification and denitrification processes (Barnard et tive feedback to the climate system. Nonetheless, the recent change al., 2005; Singh et al., 2010; Butterbach-Bahl and Dannenmann, 2011). in atmospheric N2O is largely dominated to anthropogenic reactive Warming experiments tend to show enhanced N2O emission (Lohila et nitrogen (Nr) and industrial emissions (Holland et al., 2005; David- al., 2010; Brown et al., 2011; Chantarel et al., 2011; Larsen et al., 2011). son, 2009; Zaehle and Dalmonech, 2011). Stocker et al. (2013) have Elevated CO2 predominantly increases N2O emissions(van Groenigen et found, using a global coupled model of climate and biogeochemical al., 2011); however, reductions have also been observed (Billings et al., cycles, that future climate change will amplify terrestrial N2O emissions 2002; Mosier et al., 2002), induced by changes in soil moisture, plant resulting from anthropogenic Nr additions, consistent with empirical productivity and nitrogen uptake, as well as activity and composition understanding (Butterbach-Bahl and Dannenmann, 2011). This result of soil microbial and fungal communities (Barnard et al., 2005; Singh suggests that the use of constant emission factors might underesti- et al., 2010). The effect of interacting climate and atmospheric CO2 mate future N2O emission trajectories. Significant uncertainty remains change modulates and potentially dampens the individual responses in the N2O climate feedback from land ecosystems, given the poorly to each driver (Brown et al., 2011). A terrestrial biosphere model that known response of emission processes to the changes in seasonal and integrates the interacting effects of temperature, moisture and CO2 1980 1990 2000 2010 1.2 1.0 0.8 (ppb yr-1) 0.6 0.4 0.2 0.0 90N 60N 45N 30N 15N Latitude EQ 15S 30S 45S 60S 90S 1980 1985 1990 1995 2000 2005 2010 Year 6 0.0 0.5 1.0 1.5 -1 (ppb yr ) Figure 6.19 | (Top) Globally averaged growth rate of atmospheric N2O in ppb yr 1 representative for the marine boundary layer. (Bottom) Atmospheric growth rate of N2O as a function of latitude. Sufficient observations are available only since the year 2002. Observations from the National Oceanic and Atmospheric Administration Earth System Research Laboratory (NOAA ESRL) network (Masarie and Tans, 1995; Dlugokencky and Tans, 2013b). 513 Chapter 6 Carbon and Other Biogeochemical Cycles changes is capable of qualitatively reproducing the observed sensitivi- coupled carbon climate models (Friedlingstein et al., 2006; Plattner et ties to these factors and their combinations (Xu-Ri et al., 2012). Thaw- al., 2008) and within each model parametrizations (Falloon et al., 2011; ing permafrost soils under particular hydrological settings may liberate Booth et al., 2012; Higgins and Harte, 2012). This uncertainty on the reactive nitrogen and turn into significant sources of N2O; however, coupling between carbon cycle and climate is of comparable magni- the global significance of this source is not established (Elberling et tude to the uncertainty caused by physical climate processes discussed al., 2010). in Chapter 12 of this Report (Denman et al., 2007; Gregory et al., 2009; Huntingford et al., 2009). 6.3.4.3 Global Nitrogen Budget Other biogeochemical cycles and feedbacks play an important role in For base year 2010, anthropogenic activities created ~210 (190 to 230) the future of the climate system, although the carbon cycle represents TgN of reactive nitrogen Nr from N2. This human-caused creation of the strongest of these. Natural CH4 emissions from wetland and fires reactive nitrogen in 2010 is at least 2 times larger than the rate of nat- are sensitive to climate change (Sections 6.2, 6.4.7 and 6.3.3.2). The ural terrestrial creation of ~58 TgN (50 to 100 TgN yr 1) (Table 6.9, Sec- fertilising effects of nitrogen deposition and rising CO2 also affect CH4 tion 1a). Note that the estimate of natural terrestrial biological fixation emissions by wetlands through increased plant productivity (Stocker (58 TgN yr 1) is lower than former estimates (100 TgN yr 1, Galloway et al., 2013). Changes in the nitrogen cycle, in addition to interactions et al., 2004), but the ranges overlap, 50 to 100 TgN yr 1, vs. 90 to 120 with CO2 sources and sinks, are very likely to affect the emissions of TgN yr 1, respectively). Of this created reactive nitrogen, NOx and NH3 N2O both on land and from the ocean (Sections 6.3.4.2 and 6.4.6) emissions from anthropogenic sources are about fourfold greater than and potentially on the rate of CH4 oxidation (Gärdenäs et al., 2011). A natural emissions (Table 6.9, Section 1b). A greater portion of the NH3 recent review highlighted the complexity of terrestrial biogeochemical emissions is deposited to the continents rather than to the oceans, rel- feedbacks on climate change (Arneth et al., 2010) and used the meth- ative to the deposition of NOy, due to the longer atmospheric residence odology of Gregory et al. (2009) to express their magnitude in common time of the latter. These deposition estimates are lower limits, as they units of W m 2 °C 1 (Figure 6.20). A similar degree of complexity exists do not include organic nitrogen species. New model and measurement in the ocean and in interactions between land, atmosphere and ocean information (Kanakidou et al., 2012) suggests that incomplete inclu- cycles. Many of these processes are not yet represented in coupled sion of emissions and atmospheric chemistry of reduced and oxidized climate biogeochemistry models. Leuzinger et al. (2011) observed a organic nitrogen components in current models may lead to system- trend from manipulation experiments for higher-order interactions atic underestimates of total global reactive nitrogen deposition by up between feedbacks to reduce the magnitude of response. Confidence to 35% (Table 6.9, Section 1c). Discharge of reactive nitrogen to the in the magnitude, and sometimes even the sign, of many of these feed- coastal oceans is ~45 TgN yr 1 (Table 6.9, Section 1d). Denitrification backs between climate and carbon and other biogeochemical cycles converts Nr back to atmospheric N2. The current estimate for the pro- is low. duction of atmospheric N2 is 110 TgN yr 1 (Bouwman et al., 2013). Thus of the ~280 TgN yr 1 of Nr from anthropogenic and natural sources, The response of land and ocean carbon storage to changes in climate, ~40% gets converted to N2 each year. The remaining 60% is stored in atmospheric CO2 and other anthropogenic activities (e.g., land use terrestrial ecosystems, transported by rivers and by atmospheric trans- change; Table 6.2) varies strongly on different time scales. This chapter port and deposition to the ocean, or emitted as N2O (a small fraction of has assessed carbon cycle changes across many time scales from mil- total Nr only despite the important forcing of increasing N2O emissions lennial (see Section 6.2) to interannual and seasonal (see Section 6.3), for climate change). For the oceans, denitrification producing atmos- and these are summarized in Table 6.10. A common result is that an pheric N2 is 200 to 400 TgN yr 1, which is larger than the current uptake increase in atmospheric CO2 will always lead to an increase in land and of atmospheric N2 by ocean biological fixation of 140 to 177 TgN yr 1 ocean carbon storage, all other things being held constant. Cox et al. (Table 6.9 Section 1e; Figure 6.4a). (2013) find an empirical relationship between short-term interannual variability and long-term land tropical carbon cycle sensitivity that may offer an observational constraint on the climate carbon cycle response 6.4 Projections of Future Carbon and Other over the next century. Generally, however, changes in climate on dif- Biogeochemical Cycles ferent time scales do not lead to a consistent sign and magnitude of the response in carbon storage change owing to the many different 6.4.1 Introduction mechanisms that operate. Thus, changes in carbon cycling on one time scale cannot be extrapolated to make projections on different time In this section, we assess coupled model projections of changes in the scales, but can provide valuable information on the processes at work 6 evolution of CO2, CH4 and N2O fluxes, and hence the role of carbon and and can be used to evaluate and improve models. other biogeochemical cycles in future climate under socioeconomic emission scenarios (see Box 6.4). AR4 reported how climate change 6.4.2 Carbon Cycle Feedbacks in Climate Modelling can affect the natural carbon cycle in a way that could feed back Intercomparison Project Phase 5 Models onto climate itself. A comparison of 11 coupled climate carbon cycle models of different complexity (Coupled Carbon Cycle Climate Model 6.4.2.1 Global Analysis Intercomparison Project (C4MIP); Friedlingstein et al., 2006) showed that all 11 models simulated a positive feedback. There is substantial The carbon cycle response to future climate and CO2 changes can be quantitative uncertainty in future CO2 and temperature, both across viewed as two strong and opposing feedbacks (Gregory et al., 2009). 514 Carbon and Other Biogeochemical Cycles Chapter 6 The climate carbon response ( ) determines changes in carbon storage Land and ocean carbon uptake may differ in sign between different due to changes in climate, and the concentration carbon response () regions and between models (Section 6.4.2.3). Inclusion of nitrogen determines changes in storage due to elevated CO2. Climate carbon cycle processes in two of the land carbon cycle model components out cycle feedback responses have been analyzed for eight CMIP5 ESMs of these eight reduces the magnitude of the sensitivity to both CO2 and that performed idealised simulations involving atmospheric CO2 climate (Section 6.4.6.3) and increases the spread across the CMIP5 increasing at a prescribed rate of 1% yr 1 (Arora et al., 2013; Box 6.4). ensemble. The CMIP5 spread in ocean sensitivity to CO2 and climate There is high confidence that increased atmospheric CO2 will lead to appears reduced compared with C4MIP. increased land and ocean carbon uptake but by an uncertain amount. Models agree on the sign of land and ocean response to rising CO2 but The role of the idealised experiment presented here is to study model show only medium and low agreement for the magnitude of ocean and processes and understand what causes the differences between models. land carbon uptake respectively (Figure 6.21). Future climate change Arora et al. (2013) assessed the global carbon budget from these ide- will decrease land and ocean carbon uptake compared to the case alised simulations and found that the CO2 contribution to changes in with constant climate (medium confidence). Models agree on the sign, land and ocean carbon storage sensitivity is typically four to five times globally, of land and ocean response to climate change but show low larger than the sensitivity to climate across the CMIP5 ESMs. The land agreement on the magnitude of this response, especially for the land. carbon-climate response ( ) is larger than the ocean carbon climate Land C response to CO2 (a,b) including N-cycle (a) Ocean C response to CO2 (b) Land C response to climate (a,b) including N-cycle (a) Ocean C response to climate (b) -1.5 -1 -0.5 0 0.5 1 1.5 ( W m-2 K-1) Permafrost CO2 (a,d,e) Wetlands CH4 (a,c,f) Climate CH4 lifetime (c,g) Climate on N2O (f) BVOC on ozone (a) fire (a) climate-aerosol (c) climate-ozone (c) climate-dust (c) climate-DMS (c) -0.4 -0.2 0 0.2 0.4 (W m-2 K-1) 6 Figure 6.20 | A synthesis of the magnitude of biogeochemical feedbacks on climate. Gregory et al. (2009) proposed a framework for expressing non-climate feedbacks in common units (W m 2 °C 1) with physical feedbacks, and Arneth et al. (2010) extended this beyond carbon cycle feedbacks to other terrestrial biogeochemical feedbacks. The figure shows the results compiled by Arneth et al. (2010), with ocean carbon feedbacks from the C4MIP coupled climate carbon models used for AR4 also added. Some further biogeochemical feedbacks are also shown but this list is not exhaustive. Black dots represent single estimates, and coloured bars denote the simple mean of the dots with no weighting or assess- ment being made to likelihood of any single estimate. There is low confidence in the magnitude of the feedbacks in the lower portion of the figure, especially for those with few, or only one, dot. The role of nitrogen limitation on terrestrial carbon sinks is also shown this is not a separate feedback, but rather a modulation to the climate carbon and concentra- tion carbon feedbacks. These feedback metrics are also to be state or scenario dependent and so cannot always be compared like-for-like (see Section 6.4.2.2). Results have been compiled from (a) Arneth et al. (2010), (b) Friedlingstein et al. (2006), (c) Hadley Centre Global Environmental Model 2-Earth System (HadGEM2-ES, Collins et al., 2011) simulations, (d) Burke et al. (2013), (e) von Deimling et al. (2012), (f) Stocker et al. (2013), (g) Stevenson et al. (2006). Note the different x-axis scale for the lower portion of the figure. 515 Chapter 6 Carbon and Other Biogeochemical Cycles Box 6.4 | Climate Carbon Cycle Models and Experimental Design What are coupled climate carbon cycle models and why do we need them? Atmosphere Ocean General Circulation Models (AOGCMs; see Glossary) have long been used for making climate projections, and have formed the core of previous IPCC climate projection chapters (e.g., Meehl et al. (2007); see also Chapters 1, 9 and 12). For the 5th Coupled Model Intercomparison Project (CMIP5), many models now have an interactive carbon cycle. What exactly does this mean, how do they work and how does their use differ from previous climate models? AOGCMs typically represent the physical behaviour of the atmosphere and oceans but atmospheric composition, such as the amount of CO2 in the atmosphere, is prescribed as an input to the model. This approach neglects the fact that changes in climate might affect the natural biogeochemical cycles, which control atmospheric composition, and so there is a need to represent these processes in climate projections. At the core of coupled climate carbon cycle models is the physical climate model, but additional components of land and ocean biogeochemistry respond to the changes in the climate conditions to influence in return the atmospheric CO2 concentration. Input to themodels comes in the form of anthropogenic CO2 emissions, which can increase the CO2 and then the natural carbon cycle exchanges CO2 between the atmosphere and land and ocean components. These climate carbon cycle models ( Earth System Models , ESMs; see Glossary) provide a predictive link between fossil fuel CO2 emissions and future CO2 concentrations and climate and are an important part of the CMIP5 experimental design (Hibbard et al., 2007; Taylor et al., 2012). Apart from Earth System GCMs, so-called Earth System Models of Intermediate Complexity (EMICs) are often used to perform similar experiments (Claussen et al., 2002; Plattner et al., 2008). EMICs have reduced resolution or complexity but run much more quickly and can be used for longer experiments or large ensembles. How are these models used? The capability of ESMs to simulate carbon cycle processes and feedbacks, and in some models other biogeochemical cycles, allows for a greater range of quantities to be simulated such as changes in natural carbon stores, fluxes or ecosystem functioning. There may also be applications where it is desirable for a user to predefine the pathway of atmospheric CO2 and prescribe it as a forcing to the ESMs. Thus, numerical simulations with ESM models can be either concentration driven or emissions driven . Concentration-driven simulations follow the traditional approach of prescribing the time-evolution of atmospheric CO2 as an input to the model. This is shown schematically in Box 6.4 Figure 1 (left-hand side). Atmospheric CO2 concentration is prescribed as input to the model from a given scenario and follows a predefined pathway regardless of changes in the climate or natural carbon cycle processes. The processes between the horizontal dashed lines in the figure represent the model components which are calculated during the concentration-driven simulation. Externally prescribed changes in atmospheric CO2 concentration, which drive climate change, affect land and ocean carbon storage. By construction, changes in land and ocean storage, however, do not feed back on the atmospheric CO2 concentration or on climate. The changes in natural carbon fluxes and stores are output by the model. So-called compatible fossil fuel emissions , E, can be diagnosed afterwards from mass conservation by calculating the residual between the prescribed CO2 pathway and the natural fluxes: dCO 2 (6.1) E = dt + (land_carbon_uptake + ocean_carbon_uptake) prescribed Land use change emissions cannot be diagnosed separately from a single simulation (see Section 6.4.3.2). Emissions-driven simulations allow the full range of interactions in the models to operate and determine the evolution of atmospheric CO2 and climate as an internal part of the simulation itself (Box 6.4, Figure 1, right-hand side). In this case emissions of CO2 are the externally prescribed input to the model and the subsequent changes in atmospheric CO2 concentration are simulated by it. 6 In emissions-driven experiments, the global atmospheric CO2 growth rate is calculated within the model as a result of the net balance between the anthropogenic emissions, E, and natural fluxes: dCO 2 (6.2) dt simulated = E (land_carbon_uptake + ocean_carbon_uptake) The effect of climate change on the natural carbon cycle will manifest itself either through changes in atmospheric CO2 in the emis- sions-driven experiments or in the compatible emissions in the concentration-driven experiments. (continued on next page) 516 Carbon and Other Biogeochemical Cycles Chapter 6 Box 6.4 (continued) Concentration Driven Emissions Driven CO2 emissions Atmospheric Atmospheric CO2 CO2 Internal model processes Internal model processes Climate System Climate System Land Ocean Land Ocean Carbon Carbon Carbon Carbon Cycle Cycle Cycle Cycle Output Output Box 6.4, Figure 1 | Schematic representation of carbon cycle numerical experimental design. Concentration-driven (left) and emissions-driven (right) simulation experiments make use of the same Earth System Models (ESMs), but configured differently. Concentration-driven simulations prescribe atmospheric CO2 as a pre- defined input to the climate and carbon cycle model components, but their output does not affect the CO2. Compatible emissions can be calculated from the output of the concentration-driven simulations. Emissions-driven simulations prescribe CO2 emissions as the input and atmospheric CO2 is an internally calculated element of the ESM. Concentration-driven simulation experiments have the advantage that they can also be performed by GCMs without an interactive- carbon cycle and have been used extensively in previous assessments (e.g., Prentice et al., 2001). For this reason, most of the Repre- sentative Concentration Pathway (RCP) simulations (see Chapter 1) presented later in this chapter with carbon cycle models and in Chapter 12 with models that do not all have an interactive carbon cycle are performed this way. Emissions-driven simulations have the advantage of representing the full range of interactions in the coupled climate carbon cycle models. The RCP8.5 pathway was repeated by many ESM models as an emissions-driven simulation (Chapter 12). Feedback Analysis The ESMs are made up of many components , corresponding to different processes or aspects of the system. To understand their behav- iour, techniques have been applied to assess different aspects of the models sensitivities (Friedlingstein et al., 2003, 2006; Arora et al., 2013). The two dominant emerging interactions are the sensitivity of the carbon cycle to changes in CO2 and its sensitivity to changes in climate. These can be measured using two metrics: beta () measures the strength of changes in carbon fluxes by land or ocean in 6 response to changes in atmospheric CO2; gamma ( ) measures the strength of changes in carbon fluxes by land or ocean in response to changes in climate. These metrics can be calculated as cumulative changes in carbon storage (as in Friedlingstein et al., 2006) or instantaneous rates of change (Arora et al., 2013). It is not possible to calculate these sensitivities in a single simulation, so it is necessary to perform decoupled simulations in which some processes in the models are artificially disabled in order to be able to evaluate the changes in other processes. See Table 1 in Box 6.4. (continued on next page) 517 Chapter 6 Carbon and Other Biogeochemical Cycles Box 6.4 (continued) A large positive value of denotes that a model responds to increasing CO2 by simulating large increases in natural carbon sinks. Negative values of denote that a model response to climate warming is to reduce CO2 uptake from the atmosphere, while a positive value means warming acts to increase CO2 uptake. and values are not specified in a model, but are properties that emerge from the suite of complex processes represented in the model. The values of the and metrics diagnosed from simulations can vary from place to place within the same model (see Section 6.4.2.3), although it is the average over the whole globe that determines the global extent of the climate carbon cycle feedback. Such an idealised analysis framework should be seen as a technique for assessing relative sensitivities of models and understand- ing their differences, rather than as absolute measures of invariant system properties. By design, these experiments exclude land use change. The complex ESMs have new components and new processes beyond conventional AO GCMs and thus require additional evaluation to assess their ability to make climate projections. Evaluation of the carbon cycle model components of ESMs is presented in Section 6.3.2.5.6 for ocean carbon models and Section 6.3.2.6.6 for land carbon models. Evaluation of the fully coupled ESMs is presented in Chapter 9. Box 6.4, Table 1 | Configurations of simulations designed for feedback analysis by allowing some carbon climate interactions to operate but holding others constant. The curves denote whether increasing or constant CO2 values are input to the radiation and carbon cycle model components. In a fully coupled simulation, the carbon cycle components of the models experience both changes in atmospheric CO2 (see Box 6.3 on fertilisation) and changes in climate. In biogeochemically coupled experiments, the atmospheric radiation experiences constant CO2 (i.e., the radiative forcing of increased CO2 is not activated in the simulation) whereas the carbon cycle model components experience increasing CO2. This experiment quantifies the strength of the effect of rising CO2 concentration alone on the carbon cycle (). In a radiatively coupled experiment, the climate model s radiation scheme experiences an increase in the radiative forcing of CO2 (and hence produces a change in climate) but CO2 concentration is kept fixed to pre-industrial value as input to the carbon cycle model components. This simulation quantifies the effect of climate change alone on the carbon cycle ( ). CO2 input to CO2 input to carbon- Reason radiation scheme cycle scheme Fully coupled Simulates the fully coupled system Biogeochemically Isolates the carbon-cycle response to CO2 coupled () for land and oceans esmFixClim Radiatively coupled Isolates carbon-cycle response to climate esmFdbk change ( ) for land and for oceans 6 518 Carbon and Other Biogeochemical Cycles Chapter 6 response in all models. Although land and ocean ­contribute equally to 6.4.2.3 Regional Feedback Analysis the total carbon concentration response (), the model spread in the land response is greater than for the ocean. The linear feedback analysis with the and metrics of Friedlingstein et al. (2006) has been applied at the regional scale to future carbon 6.4.2.2 Scenario Dependence of Feedbacks uptake by Roy et al. (2011) and Yoshikawa et al. (2008). Figure 6.22 shows this analysis extended to land and ocean points for the CMIP5 The values of carbon-cycle feedback metrics can vary markedly for models under the 1% yr 1 CO2 simulations. different scenarios and as such cannot be used to compare model s ­imulations over different time periods, nor to inter-compare model 6.4.2.3.1 Regional ocean response simulations with different scenarios (Arora et al., 2013). Gregory et al. (2009) demonstrated how sensitive the feedback metrics are to the Increased CO2 is projected by the CMIP5 models to increase oceanic rate of change of CO2 for two models: faster rates of CO2 increase lead CO2 sinks almost everywhere (positive ) (high confidence) with the to reduced values as the carbon uptake (especially in the ocean) lags exception of some very limited areas (Figure 6.22). The spatial distribu- further behind the forcing. is much less sensitive to the scenario, as tion of the CO2 ocean response, o, is consistent between the models both global temperature and carbon uptake lag the forcing. and with the Roy et al. (2011) analysis. On average, the regions with Table 6.10 | Comparison of the sign and magnitude of changes in carbon storage (PgC) by land and ocean over different time scales. These changes are shown as approximate numbers to allow a comparison across time scales. For more details see the indicated chapter section. An indication, where known, of what causes these changes (climate, CO2, land use change) is also given with an indication of the sign: + means that an increase in CO2 or global-mean temperature is associated with an increase in carbon storage (positive or ; see Section 6.4.2), and a means an increase in CO2 or global-mean temperature is associated with a decrease in carbon storage (negative or ). The processes that oper- ate to drive these changes can vary markedly, for example, from seasonal phenology of vegetation to long-term changes in ice sheet cover or ocean circulation impacting carbon reservoirs. Some of these processes are reversible in the context that they can increase and decrease cyclically, whereas some are irreversible in the context that changes in one sense might be much longer than in the opposite direction. Time Period Duration Land Ocean Section Climate CO2 Land Use Climate CO2 Seasonal cycle Weeks to months 3 8a 2 1 6.3.2.5.1 + + Interannual Months to years 2 4b 1 0.2 6.3.2.5.4 variability + + Historical Decades to 150c 180 2 155 6.3.2.5.3, Table 6.1 (1750 Present) centuries + ? + Decades to 21st Century 100 400d 100 to +100e 100 600d 6.4.3 centuries + + Little Ice Century +5 +2 to +30 6.2.3 Age (LIA) f + Holocene 10 kyr +300 50 to 150 +270 to 220g 6.2.2 + + Last Glacial Maximum/ >10 kyr +300 to +1000h 500 to 1200h 6.2.1 glacial cycles + + + Pulse i, 100 PgC 1 kyr +0 to +35 n/a +48 to +75 6.2.2 + + Notes: 6 a Dominated by northern mid to high latitudes. b Dominated by the tropics. c Residual land sink , Table 6.1. d Varies widely according to scenario. Climate effect estimated separately for RCP4.5 as 157 PgC (combined land and ocean), but not for other scenarios. e Future scenarios may increase or decrease area of anthropogenic land use. f Little Ice Age, 1500 1750. g Shown here are two competing drivers of Holocene ocean carbon changes: carbonate accumulation on shelves (coral growth) and carbonate compensation to pre-Holocene changes. These are discussed in Section 6.2.2. h Defined as positive if increasing from LGM to present, negative if decreasing. i Idealised simulations with models to assess the response of the global carbon cycle to a sudden release of 100 PgC. 519 Chapter 6 Carbon and Other Biogeochemical Cycles Climate response C4MIP to CO2 CMIP5 0.002 0.004 0.006 0.008 K ppm-1 Land C C4MIP response to CO2 CMIP5 Ocean C C4MIP response to CO2 CMIP5 0.5 1.0 1.5 2.0 2.5 3.0 PgC ppm-1 Land C C4MIP response to climate CMIP5 Ocean C C4MIP response to climate CMIP5 -200 -160 -120 -80 -40 0 PgC K-1 Figure 6.21 | Comparison of carbon cycle feedback metrics between the C4MIP ensemble of seven GCMs and four EMICs under the Special Report on Emission Scenario-A2 (SRES-A2) (Friedlingstein et al., 2006) and the eight CMIP5 models (Arora et al., 2013) under the 140-year 1% CO2 increase per year scenario. Black dots represent a single model simulation and coloured bars the mean of the multi-model results; grey dots are used for models with a coupled terrestrial nitrogen cycle. The comparison with C4MIP is for context, but these metrics are known to be variable across different scenarios and rates of change (see Section 6.4.2.2). Some of the CMIP5 models are derived from models that contributed to C4MIP and some are new to this analysis. Table 6.11 lists the main attributes of each CMIP5 model used in this analysis. The SRES A2 scenario is closer in rate of change to a 0.5% yr 1 scenario and as such it should be expected that the CMIP5 terms are comparable, but the terms are likely to be around 20% smaller for CMIP5 than for C4MIP due to lags in the ability of the land and ocean to respond to higher rates of CO2 increase (Gregory et al., 2009). This dependence on scenario (Section 6.4.2.2) reduces confidence in any quantitative statements of how CMIP5 carbon cycle feedbacks differ from C4MIP. CMIP5 models used: Max Planck Institute Earth System Model Low Resolution (MPI ESM LR), Beijing Climate Center Climate System Model 1 (BCC CSM1), Hadley Centre Global Environmental Model 2 Earth System (HadGEM2 ES), Institute Pierre Simon Laplace Coupled Model 5A Low Resolution (IPSL CM5A LR), Canadian Earth System Model 2 (CanESM2), Norwegian Earth System Model intermediate resolution with carbon cycle (NorESM ME), Community Earth System Model 1 Biogeochemical (CESM1 BGC), Model for Interdisciplinary Research On Climate Earth System Model (MIROC ESM). the strongest increase of oceanic CO2 sinks in response to higher Ocean have the largest negative o values. Reduced CO2 uptake in atmospheric CO2 are the North Atlantic and the Southern Oceans. The response to climate change in the sub-polar Southern Ocean and the magnitude and distribution of o in the ocean closely resemble the tropical regions has been attributed to warming induced decreased distribution of historical anthropogenic CO2 flux from inversion studies CO2 solubility, reduced CO2 uptake in the mid latitudes to decreased and forward modelling studies (Gruber et al., 2009), with the dominant CO2 solubility and decreased water mass formation which reduces the anthropogenic CO2 uptake in the Southern Ocean (Section 6.3.2.5). absorption of anthropogenic CO2 in intermediate and deep waters (Roy 6 et al., 2011). Increased uptake in the Arctic Ocean and the polar South- Climate warming is projected by the CMIP5 models to reduce oceanic ern Ocean is partly associated with a reduction in the fractional sea ice carbon uptake in most oceanic regions (negative ) (medium confi- coverage (Roy et al., 2011). dence) consistent with the Roy et al. (2011) analysis (Figure 6.22). This sensitivity of ocean CO2 sinks to climate, o, is mostly negative 6.4.2.3.2 Regional land response (i.e., a reduced regional ocean CO2 sink in response to climate change) but with regions of positive values in the Arctic, the Antarctic and in Increased CO2 is projected by the CMIP5 models to increase land CO2 the equatorial Pacific (i.e., climate change increases ocean CO2 sink in sinks everywhere (positive ) (medium confidence). This response, L, these regions). The North Atlantic Ocean and the mid-latitude ­ outhern S has the largest values over tropical land, in humid rather than arid 520 Table 6.11 | CMIP5 model descriptions in terms of carbon cycle attributes and processes. Model Modelling Atmos Ocean Land-Carbon Ocean Carbon Reference Centre Resolution Resolution Dynamic Model No. of Nitrogen- Model No. of Vegetation Incl. LUC? Fire Micronutrients? Name PFTs Cycle Name Plankton Types Cover? BCC-CSM1.1 BCC 2.8o, L26 0.3 1o, L40 BCC_AVIM1.0 N 15 N N OCMIP2 n/a n/a Wu et al. (2013) CanESM2 CCCma T63, L35 1.41° × CTEM N 9 Y N N CMOC 1 N Arora et al. (2011) 0.94°, L40 CESM1-BGC NSF-DOE-NCAR FV 0.9 × 1.25 1° CLM4 N 15 Y Y Y BEC 4 Y Long et al. (2013) GFDL-ESM2G NOAA GFDL o 2 × 2.5 , L24 o 1 , tri-polar, LM3 Y 5 Y N Y TOPAZ2 6 y Dunne et al. (2012); 1/3o at equa- Dunne et al. (2013) Carbon and Other Biogeochemical Cycles tor, L63. GFDL-ESM2M NOAA GFDL 2 × 2.5o, L24 1o, tri-polar, LM3 Y 5 Y N Y TOPAZ2 6 y Dunne et al. (2012); 1/3o at equa- Dunne et al. (2013) tor, L50. HadGEM2-ES MOHC N96 (~ 1o, 1/3 o at JULES Y 5 Y N N Diat- 3 Y Collins et al. (2011); 1.6°), L38 equator, L40 HadOCC Jones et al. (2011) INMCM4 INM IPSL-CM5A-LR IPSL 3.75 × 1.9 , L39 Zonal 2°, ORCHIDEE N 13 Y N Y PISCES 2 Y Dufresne et al. (2013) Meridional 2° 0.5° L31 MIROC-ESM MIROC T42, L80 Zonal: 1.4o, SEIB-DGVM Y 13 Y N N NPZD 2 (Phyoto- N Watanabe et al. (2011) Meridional: (Oschlies, plankton and 0.5 1.7o, 2001) Zoolo-plankton) Vertical: L43+BBL1 MPI-ESM-LR MPI-M T63 (~ ca.1.5°, L47 JSBACH Y 12 (8 Y N Y HAMOCC 2 Y Raddatz et al. 1.9°), L47 natural) (2007), Brovkin et al. (2009), Maier- Reimer et al. (2005) NorESM-ME NCC 1.9 × 2.5o, L26 1o, L53 CLM4 N 16 Y Y Y HAMOCC 2 N Iversen et al. (2013) Chapter 6 521 6 Chapter 6 Carbon and Other Biogeochemical Cycles a. Regional carbon-concentration feedback 0 0.10 0.20 6 (10 kgC m-1 ppm-1) -20 -12 -4 4 12 20 x 10-3 (kgC m-2 ppm-1) Land Ocean b. Regional carbon-climate feedback -10 0 10 6 (10 kgC m-1 K-1) -1 -0.5 0 0.5 1 6 (kgC m-2 K-1) Figure 6.22 | The spatial distributions of multi-model-mean land and ocean and for seven CMIP5 models using the concentration-driven idealised 1% yr 1 CO2 simulations. For land and ocean, and are defined from changes in terrestrial carbon storage and changes in air sea integrated fluxes respectively, from 1 × CO2 to 4 × CO2, relative to global (not local) CO2 and temperature change. In the zonal mean plots, the solid lines show the multi-model mean and shaded areas denote +/-1 standard deviation. Models used: Beijing Climate Center Climate System Model 1 (BCC CSM1), Canadian Earth System Model 2 (CanESM2), Community Earth System Model 1 Biogeochemical (CESM1 BGC), Hadley Centre Global Environmental Model 2 Earth System (HadGEM2 ES), Institute Pierre Simon Laplace Coupled Model 5A Low Resolution (IPSL CM5A-LR), Max Planck Institute Earth System Model Low Resolution (MPI ESM LR), Norwegian Earth System Model 1 (Emissions capable) (NorESM1 ME). The dashed lines show the models that include a land carbon component with an explicit representation of nitrogen cycle processes (CESM1-BGC, NorESM1-ME). 522 Carbon and Other Biogeochemical Cycles Chapter 6 regions, associated with enhanced carbon uptake in forested areas of Not all the CMIP5 ESMs used the full range of information available already high biomass. In the zonal totals, there is a secondary peak of from the land use change scenarios, such as wood harvest projections high L values over NH temperate and boreal ecosystems, partly due to or sub-grid scale shifting cultivation. Sensitivity studies indicated that a greater land area there but also coincident with large areas of forest. these processes, along with the start date of the simulation, all strongly Models agree on the sign of response but have low agreement on the affect estimated carbon fluxes (Hurtt et al., 2011; Sentman et al., 2011). magnitude. Land use has been in the past and will be in the future a significant The climate effect alone is projected by the CMIP5 models to reduce driver of forest land cover change and terrestrial carbon storage. Land land CO2 sinks in tropics and mid latitudes (negative ) (medium use trajectories in the RCPs show very distinct trends and cover a wide confidence). CMIP5 models show medium agreement that warming range of projections. These land use trajectories are very sensitive to may increase land carbon uptake in high latitudes but none of these assumptions made by each individual IAM regarding the amount of models include representation of permafrost carbon pools which are land needed for food production (Figure 6.23). The area of cropland projected to decrease in warmer conditions (Section 6.4.3.3); there- and pasture increases in RCP8.5 with the Model for Energy Supply fore confidence is low regarding the sign and magnitude of future Strategy Alternatives and their General Environmental Impact (MES- high-latitude land carbon response to climate change. Matthews et SAGE) IAM model, mostly driven by an increasing global population, al. (2005) showed that vegetation productivity is the major cause of but cropland area also increases in the RCP2.6 with the IMAGE IAM C4MIP model spread, but this manifests itself as changes in soil organ- model, as a result of bio-energy production and increased food demand ic matter (Jones and Falloon, 2009). as well. RCP6 with the AIM model shows an expansion of cropland but a decline in pasture land. RCP4.5 with the Global Change Assessment 6.4.3 Implications of the Future Projections for the Model (GCAM) IAM is the only scenario to show a decrease in global Carbon Cycle and Compatible Emissions cropland. Several studies (Wise et al., 2009; Thomson et al., 2010; Tilman et al., 2011) highlight the large sensitivity of future land use 6.4.3.1 The RCP Future Carbon Dioxide Concentration requirements to assumptions such as increases in crop yield, changes and Emissions Scenarios in diet, or how agricultural technology and intensification is applied. The CMIP5 simulations include four future scenarios referred to as Within the IAMs, land use change is translated into land use CO2 emis- Representative Concentration Pathways (RCPs; see Glossary) (Moss sions as shown in Figure 6.23(b). Cumulative emissions for the 21st et al., 2010): RCP2.6, RCP4.5, RCP6.0, RCP8.5 (see Chapter 1). These century (Figure 6.23c) vary markedly across RCPs, with increasing crop- future scenarios include CO2 concentration and emissions, and have land and pastureland areas in RCP2.6 and RCP8.5 giving rise to the been generated by four Integrated Assessment Models (IAMs) and are highest emissions from land use change, RCP4.5 to intermediate emis- labelled according to the approximate global radiative forcing level at sions and RCP6.0 to close to zero net emissions. All scenarios suggest 2100. These scenarios are described in more detail in Chapter 1 (Box that 21st century land use emissions will be less than half of those 1.1) and Section 12.3 and also documented in Annex II. from 1850 to the present day as rate of change of land conversion stabilises in future. van Vuuren et al. (2011) showed that the basic climate and carbon cycle responses of IAMs is generally consistent with the spread of climate and The adoption of widely differing approaches among ESMs for the treat- carbon cycle responses from ESMs. For the physical and biogeochemical ment and diagnosis of land use and land cover change (LULCC) pro- components of the RCP scenarios 4.5, 6.0 and 8.5, the underlying IAMs cesses in terrestrial carbon cycle models leads to substantial between- are closely related. Only the Integrated Model to Assess the Global Envi- model variation in the simulated impact on land carbon stocks. It is ronment (IMAGE) IAM, which created RCP2.6, differs markedly by using not yet possible to fully quantify LULCC fluxes from the CMIP5 model a more sophisticated carbon cycle sub-model for land and ocean. The simulations. The harmonization process applied to LULCC data sets for Model for the Assessment of Greenhouse-gas Induced Climate Change CMIP5 has been an important step toward consistency among IAMs; 6 (MAGICC6) simple climate model was subsequently used to generate however, among ESMs, and between IAMs and ESMs, assignment of the CO2 pathway for all four RCP scenarios using the CO2 emissions meaningful uncertainty ranges to present-day and future LULCC fluxes output by the four IAMs (Meinshausen et al., 2011). and states remains a critical knowledge gap with implications for com- patible emissions to achieve CO2 pathways (Section 6.4.3.3; Jones et 6.4.3.2 Land Use Changes in Future Scenarios al., 2013). ESMs and IAMs use a diversity of approaches for representing land 6.4.3.3 Projections of Future Carbon Cycle Response by 6 use changes, including different land use classifications, parameter Earth System Models Under the Representative settings, and geographical scales. To implement land use change in a Concentration Pathway Scenarios consistent manner across ESMs, a harmonized set of annual gridded land use change during the period 1500 2100 was developed for input Simulated changes in land and ocean carbon uptake and storage under to the CMIP5 ESMs (Hurtt et al., 2011). the four RCP scenarios are presented here using results from CMIP5 ESMs concentration-driven simulations (see Box 6.4). The implications of these changes on atmospheric CO2 and climate as simulated by CMIP5 emissions-driven simulations are presented in Chapter 12. 523 Chapter 6 Carbon and Other Biogeochemical Cycles a c 0.5 Global crop and pasture fraction Cumulative land-use CO2 emissions 140 0.4 Historical crop + pasture Historical RCP2.6 pasture RCP2.6 RCP4.5 RCP4.5 Fraction of land crop 0.3 RCP6.0 RCP6.0 RCP8.5 120 RCP8.5 0.2 100 0.1 0.0 1850 1900 1950 2000 2050 2100 80 Year PgC b 60 1.5 Land-use CO2 emissions 1.0 40 (PgC yr ) -1 0.5 0.0 20 Historical RCP2.6 RCP6.0 -0.5 RCP4.5 RCP8.5 0 -1.0 1850 1900 1950 2000 2050 2100 Hist RCP2.6 RCP4.5 RCP6.0 RCP8.5 Year Figure 6.23 | Land use trends and CO2 emissions according to the four different integrated assessment models (IAMs) used to define the RCP scenarios. Global changes in crop- lands and pasture from the historical record and the RCP scenarios (top left), and associated annual land use emissions of CO2 (bottom left). Bars (right panel) show cumulative land use emissions for the historical period (defined here as 1850 2005) and the four RCP scenarios from 2006 to 2100. The results of the concentration-driven CMIP5 ESMs simulations show from many or all CMIP5 land carbon cycles include the role of nutrient medium agreement on the magnitude of cumulative ocean carbon cycles, permafrost, fire and ecosystem acclimation to changing climate. uptake from 1850 to 2005 (Figure 6.24a): average 127 +/- 28 PgC (1 For this reason we assign low confidence to quantitative projections of standard deviation). The models show low agreement on the sign and future land uptake. magnitude of changes in land carbon storage (Figure 6.24a): average 2 +/- 74 PgC (1 standard deviation). These central estimates are very The concentration-driven ESM simulations can be used to quantify close to observational estimates of 125 +/- 25 PgC for the ocean and the compatible fossil fuel emissions required to follow the four RCP 5 +/- 40 PgC for the net cumulative land atmosphere flux respectively CO2 pathways (Jones et al., 2013; see Box 6.4, Figure 6.25, Table 6.12, (see Table 6.12), but show a large spread across models. With very Annex II, Table AII.2.1a). There is significant spread between ESMs, high confidence, for all four RCP scenarios, all models project contin- but general consistency between ESMs and compatible emissions ued ocean uptake throughout the 21st century, with higher uptake estimated by IAMs to define each RCP scenario. However, for RCP8.5 6 corresponding to higher concentration pathways. For RCP4.5, all the on average, the CMIP5 models project lower compatible emissions models also project an increase in land carbon uptake, but for RCP2.6, than the MESSAGE IAM. The IMAGE IAM predicts that global negative RCP6.0 and RCP8.5 a minority of models (4 out of 11 for RCP2.6, 1 emissions are required to achieve the RCP2.6 decline in radiative forc- out of 8 for RCP6.0 and 4 out of 15 for RCP8.5; Jones et al., 2013) ing from 3 W m 2 to 2.6 W m 2 by 2100. All models agree that strong project a decrease in land carbon storage at 2100 relative to 2005. emissions reductions are required to achieve this after about 2020 Model spread in land carbon projections is much greater than model (Jones et al., 2013). An average emission reduction of 50% (range 14 spread in ocean carbon projections, at least in part due to different to 96%) is required by 2050 relative to 1990 levels. There is disagree- treatment of land use change. Decade mean land and ocean fluxes are ment between those ESMs that performed this simulation over the documented in Annex II, Table AII.3.1a, b. Important processes missing necessity for global emissions in the RCP2.6 to become negative by 524 Carbon and Other Biogeochemical Cycles Chapter 6 a 200 Historical 150 Cumulative uptake (PgC) 100 ocean land 50 0 -50 -100 -150 1860 1880 1900 1920 1940 1960 1980 2000 Years b c 600 RCP2.6 600 RCP4.5 Cumulative uptake (PgC) Cumulative uptake (PgC) 400 ocean 400 ocean land land 200 200 0 0 -200 -200 2020 2040 2060 2080 2100 2020 2040 2060 2080 2100 Years Years d e 600 RCP6.0 600 RCP8.5 Cumulative uptake (PgC) Cumulative uptake (PgC) 400 ocean 400 ocean land land 200 200 0 0 -200 -200 2020 2040 2060 Years 2080 2100 2020 2040 2060 Years 2080 2100 6 Figure 6.24 | Cumulative land and ocean carbon uptake simulated for the historical period 1850 2005 (top) and for the four RCP scenarios up to 2100 (b e). Mean (thick line) and 1 standard deviation (shaded). Vertical bars on the right show the full model range as well as standard deviation. Black bars show observationally derived estimates for 2005. Models used: Canadian Earth System Model 2 (CanESM2), Geophysical Fluid Dynamics Laboratory Earth System Model 2G (GFDL ESM2G), Geophysical Fluid Dynamics Laboratory Earth System Model 2M (GFDL ESM2M), Hadley Centre Global Environmental Model 2 Carbon Cycle (HadGEM2-CC), Hadley Centre Global Environmental Model 2 Earth System (HadGEM2-ES), Institute Pierre Simon Laplace Coupled Model 5A Low Resolution (IPSL CM5A LR), Institute Pierre Simon Laplace Coupled Model 5A Medium Resolution (IPSL CM5A MR), Institute Pierre Simon Laplace Coupled Model 5B Low Resolution (IPSL CM5B LR), Model for Interdisciplinary Research On Climate Earth System Model (MIROC ESM CHEM), Model for Interdisciplinary Research On Climate Earth System Model (MIROC ESM), Max Planck Institute Earth System Model Low Resolution (MPI ESM LR), Norwegian Earth System Model 1 (Emissions capable) (NorESM1 ME), Institute for Numerical Mathematics Coupled Model 4 (INMCM4), Community Earth System Model 1 Biogeochemical (CESM1 BGC), Beijing Climate Center Climate System Model 1.1 (BCC CSM1.1). Not every model performed every scenario simulation. 525 Chapter 6 Carbon and Other Biogeochemical Cycles Table 6.12 | The range of compatible fossil fuel emissions (PgC) simulated by the CMIP5 models for the historical period and the four RCP scenarios, expressed as cumulative fossil fuel emission. To be consistent with Table 6.1 budgets are calculated up to 2011 for historical and 2012 2100 for future scenarios, and values are rounded to the nearest 5 PgC. Compatible Fossil Fuel Emissions Diagnosed from Land Carbon Changes Ocean Carbon Changes   Concentration-Driven CMIP5 Simulations   Historical / CMIP5 ESM CMIP5 ESM Historical / CMIP5 ESM CMIP5 ESM Historical / CMIP5 ESM CMIP5 ESM RCP Scenario Mean Range RCP Scenario Mean Range RCP Scenario Mean Range 1850 2011 375a 350 235 455 5 +/- 40b 10 125 to 160 140 +/- 25b 140 110 220 RCP2.6 275 270 140 410 c 65 50 to 195 c 150 105 185 RCP4.5 735 780 595 1005 230 55 to 450 250 185 400 RCP6.0 1165 1060 840 1250 200 80 to 370 295 265 335 RCP8.5 1855 1685 1415 1910 180 165 to 500 400 320 635 Notes: a Historical estimates of fossil fuel are as prescribed to all CMIP5 ESMs in the emissions-driven simulations (Andres et al., 2011). b Estimate of historical net land and ocean carbon uptake from Table 6.1 but over the shorter 1850 2011 time period. c IAM breakdown of future carbon changes by land and ocean are not available. the end of the 21st century to achieve this, with six ESMs simulat- under higher CO2 scenarios that exhibit a greater degree of climate ing negative compatible emissions and four ESM models simulating change (Jones et al., 2006). positive emissions from 2080 to 2100. The RCP2.6 scenario achieves this negative emission rate through use of large-scale bio-energy with 6.4.3.4 Permafrost Carbon ­carbon-capture and storage (BECCS). It is as likely as not that sustained globally negative emissions will be required to achieve the reductions Current estimates of permafrost soil carbon stocks are ~1700 PgC in atmospheric CO2 in the RCP2.6 scenario. This would be classed as a (Tarnocai et al., 2009), the single largest component of the terrestrial carbon dioxide removal (CDR) form of geoengineering under the defi- carbon pool. Terrestrial carbon models project a land CO2 sink with nition used in this IPCC report, and is discussed further in Section 6.5.2. warming at high northern latitudes; however none of the models The ESMs themselves make no assumptions about how the compatible participating in C4MIP or CMIP5 included explicit representation of emissions could or would be achieved, but merely compute the global permafrost soil carbon decomposition in response to future warming. total emission that is required to follow the CO2 concentration path- Including permafrost carbon processes into an ESM may change the way, accounting for the carbon cycle response to climate and CO2, and sign of the high northern latitude carbon cycle response to warm- for land use change CO2 emissions. ing from a sink to a source (Koven et al., 2011). Overall, there is high confidence that reductions in permafrost extent due to warming will The dominant cause of future changes in the airborne fraction of fossil cause thawing of some currently frozen carbon. However, there is low fuel emissions (see Section 6.3.2.4) is the emissions scenario and not confidence on the magnitude of carbon losses through CO2 and CH4 carbon cycle feedbacks (Jones et al., 2013; Figure 6.26). Models show emissions to the atmosphere. The magnitude of CO2 and CH4 emissions high agreement that 21st century cumulative airborne fraction will to the atmosphere is assessed to range from 50 to 250 PgC between increase under rapidly increasing CO2 in RCP8.5 and decreases under 2000 and 2100 for RCP8.5. The magnitude of the source of CO2 to the the peak-and-decline RCP2.6 scenarios. The airborne fraction declines atmosphere from decomposition of permafrost carbon in response to slightly under RCP4.5 and remains of similar magnitude in the RCP6.0 warming varies widely according to different techniques and scenarios. scenario. Between-model spread in changes in the land-fraction is Process models provide different estimates of the cumulative loss of greater than between-scenario spread. Models show high agreement permafrost carbon: 7 to 17 PgC (Zhuang et al., 2006) (not considered that the ocean fraction will increase under RCP2.6 and remain of simi- in the range given above because it corresponds only to contemporary lar magnitude in the other RCP scenarios. tundra soil carbon), 55 to 69 Pg (Koven et al., 2011), 126 to 254 PgC (Schaefer et al., 2011) and 68 to 508 PgC (MacDougall et al., 2012) Several studies (Jones et al., 2006; Matthews, 2006; Plattner et al., (not considered in the range given above because this estimate is not 2008; Miyama and Kawamiya, 2009) have shown that climate carbon obtained from a concentration driven, but for emission driven RCP sce- cycle feedbacks affect the compatible fossil fuel CO2 emissions that are nario and it is the only study of that type so far). Combining observed 6 consistent with a given CO2 concentration pathway. Using decoupled vertical soil carbon profiles with modeled thaw rates provides an esti- RCP4.5 simulations (see Box 6.4) five CMIP5 ESMs agree that the cli- mate that the total quantity of newly thawed soil carbon by 2100 will mate impact on carbon uptake by both land and oceans will reduce the be 246 PgC for RCP4.5 and 436 PgC for RCP8.5 (Harden et al., 2012), compatible fossil fuel CO2 emissions for that scenario by between 6% although not all of this amount will be released to the atmosphere on and 29% between 2006 and 2100 respectively (Figure 6.27), equating that time scale. Uncertainty estimates suggest the cumulative amount to an average of 157 +/- 76 PgC (1 standard deviation) less carbon that of thawed permafrost carbon could range from 33 to 114 PgC (68% can be emitted from fossil fuel use if climate feedback (see Glossary) is range) under RCP8.5 warming (Schneider von Deimling et al., 2012), included. Compatible emissions would be reduced by a greater degree or 50 to 270 PgC (5th to 95th percentile range) (Burke et al., 2013). 526 Carbon and Other Biogeochemical Cycles Chapter 6 30 Fossil-fuel emissions 25 1000 20 CMIP5 mean 800 RCP8.5 IAM scenario RCP6.0 (PgC yr-1) RCP4.5 15 600 RCP2.6 400 10 200 1850 1900 1950 2000 2050 2100 5 0 -5 1850 1900 1950 2000 2050 2100 Years 2000 Cumulative fossil-fuel emissions Historical emission inventories (1860-2005) RCP8.5 (2006-2100) 1500 RCP6.0 (2006-2100) RCP4.5 (2006-2100) RCP2.6 (2006-2100) (PgC) 1000 Historical IMAGE ESMs ESMs MESSAGE 500 GCAM ESMs ESMs ESMs AIM 0 Figure 6.25 | Compatible fossil fuel emissions simulated by the CMIP5 ESMs for the four RCP scenarios. Top: time series of compatible emission rate (PgC yr 1). Dashed lines represent the historical estimates and emissions calculated by the Integrated Assessment Models (IAMs) used to define the RCP scenarios, solid lines and plumes show results 6 from CMIP5 ESMs (model mean, with 1 standard deviation shaded). Bottom: cumulative emissions for the historical period (1860 2005) and 21st century (defined in CMIP5 as 2006 2100) for historical estimates and RCP scenarios. Dots denote individual ESM results, bars show the multi-model mean. In the CMIP5 model results, total carbon in the land atmosphere ocean system can be tracked and changes in this total must equal fossil fuel emissions to the system (see Box 6.4). Models used: Canadian Earth System Model 2 (CanESM2), Geophysical Fluid Dynamics Laboratory Earth System Model 2G (GFDL ESM2G), Geophysical Fluid Dynamics Laboratory Earth System Model 2M (GFDL ESM2M), Hadley Centre Global Environmental Model 2 Carbon Cycle(HadGEM2-CC), Hadley Centre Global Environmental Model 2 Earth System (HadGEM2-ES), Institute Pierre Simon Laplace Coupled Model 5A Low Resolution (IPSL CM5A LR), Institute Pierre Simon Laplace Coupled Model 5A Medium Resolution (IPSL CM5A MR), Institute Pierre Simon Laplace Coupled Model 5B Low Resolution (IPSL CM5B LR), Model for Interdisciplinary Research On Climate Earth System Model (MIROC ESM CHEM), Model for Interdis- ciplinary Research On Climate Earth System Model (MIROC ESM), Max Planck Institute Earth System Model Low Resolution (MPI ESM LR), Norwegian Earth System Model 1 (Emissions capable) (NorESM1 ME), Institute for Numerical Mathematics Coupled Model 4 (INMCM4), Community Earth System Model 1 Biogeochemical (CESM1 BGC), Beijing Climate Center Climate System Model 1.1 (BCC CSM1.1). Not every model performed every scenario simulation. 527 Chapter 6 Carbon and Other Biogeochemical Cycles Sources of uncertainty for the permafrost carbon feedback include the known with very high confidence. Overall, given evidence from Chap- physical thawing rates, the fraction of carbon that is released after ter 3 and model results from this chapter, it is virtually certain that the being thawed and the time scales of release, possible mitigating nutri- increased storage of carbon by the ocean will increase acidification in ent feedbacks and the role of fine-scale processes such as spatial the future, continuing the observed trends of the past decades. Expect- variability in permafrost degradation. It is also uncertain how much ed future changes are in line with what is measured at ocean time thawed carbon will decompose to CO2 or to CH4 (see Sections 6.4.7, series stations (see Chapter 3). Multi-model projections using ocean 12.5.5.4 and 12.4.8.1). process-based carbon cycle models discussed in AR4 demonstrate large decreases in pH and carbonate ion concentration [CO32 ] during the 6.4.4 Future Ocean Acidification 21st century throughout the world oceans (Orr et al., 2005). The largest decrease in surface [CO32 ] occur in the warmer low and mid-latitudes, A fraction of CO2 emitted to the atmosphere dissolves in the ocean, which are naturally rich in this ion (Feely et al., 2009). However, it is reducing surface ocean pH and carbonate ion concentrations. The asso- the low A waters in the high latitudes and in the upwelling regions ciated chemistry response to a given change in CO2 concentration is that first become undersaturated with respect to aragonite (i.e., A <1, Figure 6.26 | Changes in atmospheric, land and ocean fraction of fossil fuel carbon emissions. The fractions are defined as the changes in storage in each component (atmosphere, land, ocean) divided by the compatible fossil fuel emissions derived from each CMIP5 simulation for the four RCP scenarios. Solid circles show the observed estimate based on Table 6.1 for the 1990s. The coloured bars denote the cumulative uptake fractions for the 21st century under the different RCP scenarios for each model. Multi-model mean values are 6 shown as star symbols and the multi-model range (min-to-max) and standard deviation are shown by thin and thick vertical lines respectively. Owing to the difficulty of estimating land use emissions from the ESMs this figure uses a fossil fuel definition of airborne fraction, rather than the preferred definition of fossil and land use emissions discussed in Sec- tion 6.3.2.4. 21st century cumulative atmosphere, land and ocean fractions are shown here in preference to the more commonly shown instantaneous fractions because for RCP2.6 emissions reach and cross zero for some models and so an instantaneous definition of AF becomes singular at that point. Models used: Canadian Earth System Model 2 (CanESM2), Geophysical Fluid Dynamics Laboratory Earth System Model 2G (GFDL ESM2G), Geophysical Fluid Dynamics Laboratory Earth System Model 2M (GFDL ESM2M), Hadley Centre Global Environmental Model 2 Carbon Cycle (HadGEM2-CC), Hadley Centre Global Environmental Model 2 Earth System (HadGEM2-ES), Institute Pierre Simon Laplace Coupled Model 5A Low Resolution (IPSL CM5A LR), Institute Pierre Simon Laplace Coupled Model 5A Medium Resolution (IPSL CM5A MR), Institute Pierre Simon Laplace Coupled Model 5B Low Resolution (IPSL CM5B LR), Model for Interdisciplinary Research On Climate Earth System Model (MIROC ESM CHEM), Model for Interdisciplinary Research On Climate Earth System Model (MIROC ESM), Max Planck Institute Earth System Model Low Resolution (MPI ESM LR), Norwegian Earth System Model 1 (Emissions capable) (NorESM1 ME), Institute for Numerical Mathematics Coupled Model 4 (INMCM4), Community Earth System Model 1 Biogeochemical (CESM1 BGC). Not every model performed every scenario simulation. 528 Carbon and Other Biogeochemical Cycles Chapter 6 20 RCP4.5 compatible fossil-fuel emissions 15 without climate feedback with climate feedback 10 (PgC yr-1) 5 0 -5 1850 1900 1950 2000 2050 2100 Years Difference between simulations with and without feedbacks 2 0 (PgC yr-1) -2 -4 6 1850 1900 1950 2000 2050 2100 Years Figure 6.27 | Compatible fossil fuel emissions for the RCP4.5 scenario (top) in the presence (red) and absence (blue) of the climate feedback on the carbon cycle, and the dif- ference between them (bottom). Multi-model mean, 10-year smoothed values are shown, with 1 standard deviation shaded. This shows the impact of climate change on the compatible fossil fuel CO2 emissions to achieve the RCP4.5 CO2 concentration pathway. Models used: Canadian Earth System Model 2 (CanESM2), Geophysical Fluid Dynamics Laboratory Earth System Model 2M (GFDL-ESM2M), Hadley Centre Global Environmental Model 2 Earth System (HadGEM2-ES), Institute Pierre Simon Laplace Coupled Model 5A Low Resolution (IPSL-CM5A-LR) and Model for Interdisciplinary Research On Climate Earth System Model (MIROC ESM). 529 Chapter 6 Carbon and Other Biogeochemical Cycles Frequently Asked Questions FAQ 6.1 | Could Rapid Release of Methane and Carbon Dioxide from Thawing Permafrost or Ocean Warming Substantially Increase Warming? Permafrost is permanently frozen ground, mainly found in the high latitudes of the Arctic. Permafrost, including the sub-sea permafrost on the shallow shelves of the Arctic Ocean, contains old organic carbon deposits. Some are relicts from the last glaciation, and hold at least twice the amount of carbon currently present in the atmosphere as carbon dioxide (CO2). Should a sizeable fraction of this carbon be released as methane and CO2, it would increase atmospheric concentrations, which would lead to higher atmospheric temperatures. That in turn would cause yet more methane and CO2 to be released, creating a positive feedback, which would further amplify global warming. The Arctic domain presently represents a net sink of CO2 sequestering around 0.4 +/- 0.4 PgC yr 1 in growing vegeta- tion representing about 10% of the current global land sink. It is also a modest source of methane (CH4): between 15 and 50 Tg(CH4) yr 1 are emitted mostly from seasonally unfrozen wetlands corresponding to about 10% of the global wetland methane source. There is no clear evidence yet that thawing contributes significantly to the current global budgets of these two greenhouse gases. However, under sustained Arctic warming, modelling studies and expert judgments indicate with medium agreement that a potential combined release totalling up to 350 PgC as CO2 equivalent could occur by the year 2100. Permafrost soils on land, and in ocean shelves, con- tain large pools of organic carbon, which must be thawed and decomposed by microbes before it can CO2 uptake by land vegetation be released mostly as CO2. Where oxygen is limited, 0.3-0.6 PgC yr-1 as in waterlogged soils, some microbes also produce methane. CH4 from lakes and bogs 31-100 TgCH4 yr-1 On land, permafrost is overlain by a surface active CO2 uptake layer , which thaws during summer and forms part of 24-100 TgC yr-1 the tundra ecosystem. If spring and summer tempera- tures become warmer on average, the active layer will thicken, making more organic carbon available for CH4 outgassing microbial decomposition. However, warmer summers 1-12 TgCH4 yr-1 Permafrost soils C transport by rivers 1500-1850 PgC would also result in greater uptake of carbon diox- ~80 TgC yr-1 ide by Arctic vegetation through photosynthesis. That means the net Arctic carbon balance is a delicate one Flux to sediment CH4 hydrates ~8 TgC yr-1 3-130 PgCH4 between enhanced uptake and enhanced release of Arctic ocean shelves carbon. and shelf slopes Arctic ocean floor CH4 hydrates 2-65 PgCH4 CH4 hydrates Hydrological conditions during the summer thaw are 30-170 PgCH4 also important. The melting of bodies of excess ground Flux to sediment ~2 TgC yr-1 ice may create standing water conditions in pools and lakes, where lack of oxygen will induce methane pro- FAQ 6.1, Figure 1 | A simplified graph of current major carbon pools and flows duction. The complexity of Arctic landscapes under in the Arctic domain, including permafrost on land, continental shelves and ocean. climate warming means we have low confidence in (Adapted from McGuire et al., 2009; and Tarnocai et al., 2009.) TgC = 1012 gC, which of these different processes might dominate on and PgC = 1015 gC. a regional scale. Heat diffusion and permafrost melt- 6 ing takes time in fact, the deeper Arctic permafrost can be seen as a relict of the last glaciation, which is still slowly eroding so any significant loss of permafrost soil carbon will happen over long time scales. Given enough oxygen, decomposition of organic matter in soil is accompanied by the release of heat by microbes (similar to compost), which, during summer, might stimulate further permafrost thaw. Depending on carbon and ice content of the permafrost, and the hydrological regime, this mechanism could, under warming, trigger rela- tively fast local permafrost degradation. (continued on next page) 530 Carbon and Other Biogeochemical Cycles Chapter 6 FAQ 6.1 (continued) Modelling studies of permafrost dynamics and greenhouse gas emissions indicate a relatively slow positive feed- back, on time scales of hundreds of years. Until the year 2100, up to 250 PgC could be released as CO2, and up to 5 Pg as CH4. Given methane s stronger greenhouse warming potential, that corresponds to a further 100 PgC of equivalent CO2 released until the year 2100. These amounts are similar in magnitude to other biogeochemical feed- backs, for example, the additional CO2 released by the global warming of terrestrial soils. However, current models do not include the full complexity of Arctic processes that occur when permafrost thaws, such as the formation of lakes and ponds. Methane hydrates are another form of frozen carbon, occurring in deep permafrost soils, ocean shelves, shelf slopes and deeper ocean bottom sediments. They consist of methane and water molecule clusters, which are only stable in a specific window of low temperatures and high pressures. On land and in the ocean, most of these hydrates origi- nate from marine or terrestrial biogenic carbon, decomposed in the absence of oxygen and trapped in an aquatic environment under suitable temperature pressure conditions. Any warming of permafrost soils, ocean waters and sediments and/or changes in pressure could destabilise those hydrates, releasing their CH4 to the ocean. During larger, more sporadic releases, a fraction of that CH4 might also be outgassed to the atmosphere. There is a large pool of these hydrates: in the Arctic alone, the amount of CH4 stored as hydrates could be more than 10 times greater than the CH4 presently in the global atmosphere. Like permafrost thawing, liberating hydrates on land is a slow process, taking decades to centuries. The deeper ocean regions and bottom sediments will take still longer between centuries and millennia to warm enough to destabilise the hydrates within them. Furthermore, methane released in deeper waters has to reach the surface and atmosphere before it can become climatically active, but most is expected to be consumed by microorganisms before it gets there. Only the CH4 from hydrates in shallow shelves, such as in the Arctic Ocean north of Eastern Siberia, may actually reach the atmosphere to have a climate impact. Several recent studies have documented locally significant CH4 emissions over the Arctic Siberian shelf and from Siberian lakes. How much of this CH4 originates from decomposing organic carbon or from destabilizing hydrates is not known. There is also no evidence available to determine whether these sources have been stimulated by recent regional warming, or whether they have always existed it may be possible that these CH4 seepages have been present since the last deglaciation. In any event, these sources make a very small contribution to the global CH4 budget less than 5%. This is also confirmed by atmospheric methane concentration observations, which do not show any substantial increases over the Arctic. However modelling studies and expert judgment indicate that CH4 and CO2 emissions will increase under Arctic warming, and that they will provide a positive climate feedback. Over centuries, this feedback will be moderate: of a magnitude similar to other climate terrestrial ecosystem feedbacks. Over millennia and longer, however, CO2 and CH4 releases from permafrost and shelves/shelf slopes are much more important, because of the large carbon and methane hydrate pools involved. where A = [Ca+2][CO32 ]/Ksp, where Ksp is the solubility product for to calcite (Feely et al., 2009). Surface waters would then be corrosive the metastable form of CaCO3 known as aragonite; a value of A <1 to all CaCO3 minerals. These general trends are confirmed by the latest thus indicates aragonite undersaturation). This aragonite undersatura- projections from the CMIP5 Earth System models (Figure 6.28 and 6 tion in surface waters is reached before the end of the 21st century in 6.29). Between 1986 2005 and 2081 2100, decrease in global-mean the Southern Ocean as highlighted in AR4, but occurs sooner and is surface pH is 0.065 (0.06 to 0.07) for RCP2.6, 0.145 (0.14 to 0.15) for more intense in the Arctic (Steinacher et al., 2009). Ten percent of Arctic RCP4.5, 0.203 (0.20 to 0.21) for RCP6.0 and 0.31 (0.30 to 0.32) for surface waters are projected to become undersaturated when atmo- RCP8.5 (range from CMIP5 models spread). spheric CO2 reaches 428 ppm (by 2025 under all IPCC SRES scenarios). That proportion increases to 50% when atmospheric CO2 reaches 534 Surface CaCO3 saturation also varies seasonally, particularly in the ppm (Steinacher et al., 2009). By 2100 under the A2 scenario, much of high latitudes, where observed saturation is higher in summer and the Arctic surface is projected to become undersaturated with respect lower in winter (Feely et al., 1988; Merico et al., 2006; Findlay et al., 531 Chapter 6 Carbon and Other Biogeochemical Cycles 2008). Future projections using ocean carbon cycle models indicate subsurface saturation states decline, the horizon separating undersatu- that undersaturated conditions will be reached first in winter (Orr et rated waters below from supersaturated waters above is projected to al., 2005). In the Southern Ocean, it is projected that wintertime under- move upward (shoal). By 2100 under the RCP8.5 scenario, the median saturation with respect to aragonite will begin when atmospheric projection from 11 CMIP5 models is that this interface (aragonite sat- CO2 will reach 450 ppm, within 1-3 decades, which is about 100 ppm uration horizon) will shoal from 200 m up to 40 m in the subarctic sooner (~30 years under the IS92a scenario) than for the annual mean Pacific, from 1000 m up to the surface in the Southern Ocean, and from undersaturation (McNeil and Matear, 2008). As well, aragonite under- 2850 m to 150 m in the North Atlantic (Figure 6.29), consistent with saturation will be first reached during wintertime in parts (10%) of results from previous model comparison (Orr et al., 2005; Orr, 2011). the Arctic when atmospheric CO2 will reach 410 ppm, within a decade Under the SRES A2 scenario, the volume of ocean with supersaturated (Steinacher et al., 2009). Then, aragonite undersaturation will become waters is projected to decline from 42% in the preindustrial Era to widespread in these regions at atmospheric CO2 levels of 500 600 25% in 2100 (Steinacher et al., 2009). Yet even if atmospheric CO2 does ppm (Figure 6.28). not go above 450 ppm, most of the deep ocean volume is projected to become undersaturated with respect to both aragonite and calcite Although projected changes in pH are generally largest at the surface, after several centuries (Caldeira and Wickett, 2005). Nonetheless, the the greatest pH changes in the subtropics occur between 200 and 300 most recent projections under all RCPs scenarios but RCP8.5 illustrate m where subsurface increased loads of anthropogenic CO2 are similar that limiting atmospheric CO2 will greatly reduce the level of ocean to surface changes but the carbonate buffering capacity is lower (Orr, acidification that will be experienced (Joos et al., 2011). 2011). This more intense projected subsurface pH reduction is consis- tent with the observed subsurface changes in pH in the subtropical In the open ocean, future reductions in surface ocean pH and CaCO3 North Pacific (Dore et al., 2009; Byrne et al., 2010; Ishii et al., 2011). As (calcite and aragonite) saturation states are controlled mostly by the invasion of anthropogenic carbon. Other effects due to future climate change counteract less than 10% of the reductions in CaCO3 satura- a. Surface pH tion induced by the invasion of anthropogenic carbon (Orr et al., 2005; McNeil and Matear, 2006; Cao et al., 2007). Warming dominates other 8.2 effects from climate-change by reducing CO2 solubility and thus by 8.1 enhancing [CO32 ]. An exception is the Arctic Ocean where reductions 8.0 Arctic (>70°N) in pH and CaCO3 saturation states are projected to be exacerbated 7.9 S.Ocean (<60°S) by effects from increased freshwater input due to sea ice melt, more 7.8 Tropics (20°S-20°N) precipitation, and greater air sea CO2 fluxes due to less sea ice cover 7.7 (Steinacher et al., 2009; Yamamoto et al., 2012). The projected effect RCP8.5 RCP2.6 of freshening is consistent with current observations of lower satura- 7.6 tion states and lower pH values near river mouths and in areas under 1900 1950 2000 2050 2100 substantial fresh-water influence (Salisbury et al., 2008; Chierici and Fransson, 2009; Yamamoto-Kawai et al., 2009). b. Surface pH in 2090s (RCP8.5, changes from 1990s) Regional ocean carbon cycle models project that some nearshore sys- -0.2 tems are also highly vulnerable to future pH decrease. In the California Current System, an eastern boundary upwelling system, observations -0.3 and model results show that strong seasonal upwelling of carbon- rich waters (Feely et al., 2008) renders surface waters as vulnerable to future ocean acidification as those in the Southern Ocean (Gruber -0.4 et al., 2012). In the Northwestern European Shelf Seas, large spatio- temporal variability is enhanced by local effects from river input and -0.5 organic matter degradation, exacerbating acidification from anthropo- genic CO2 invasion (Artioli et al., 2012). In the Gulf of Mexico and East Figure 6.28 | Projected ocean acidification from 11 CMIP5 Earth System Models under China Sea, coastal eutrophication, another anthropogenic perturba- RCP8.5 (other RCP scenarios have also been run with the CMIP5 models): (a) Time tion, has been shown to enhance subsurface acidification as additional series of surface pH shown as the mean (solid line) and range of models (filled), given respired carbon accumulates at depth (Cai et al., 2011). 6 as area-weighted averages over the Arctic Ocean (green), the tropical oceans (red) and the Southern Ocean (blue). (b) Maps of the median model s change in surface pH from 1850 to 2100. Panel (a) also includes mean model results from RCP2.6 (dashed lines). 6.4.5 Future Ocean Oxygen Depletion Over most of the ocean, gridded data products of carbonate system variables (Key et al., 2004) are used to correct each model for its present-day bias by subtracting the It is very likely that global warming will lead to declines in dissolved O2 model-data difference at each grid cell following (Orr et al., 2005). Where gridded data products are unavailable (Arctic Ocean, all marginal seas, and the ocean near Indone- in the ocean interior through warming-induced reduction in O2 solubil- sia), the results are shown without bias correction. The bias correction reduces the range ity and increased ocean stratification. This will have implications for of model projections by up to a factor of 4, e.g., in panel (a) compare the large range nutrient and carbon cycling, ocean productivity and marine habitats of model projections for the Arctic (without bias correction) to the smaller range in the (Keeling et al., 2010). Southern Ocean (with bias correction). 532 Carbon and Other Biogeochemical Cycles Chapter 6 RCP8.5 a. Surface CO32- ( mol kg-1) RCP2.6 b. Surface Aragonite RCP8.5 2010 Arctic (>70°N) 200 S.Ocean (<60°S) Tropics (20°S-20°N) 100 Aragonite Saturation Calcite Saturation 0 1900 1950 2000 2050 2100 c. Aragonite RCP8.5 2100 d. Surface Aragonite RCP8.5 2050 3 0 2.5 2 1000 1.5 1.2 1 2000 0.9 0.8 0.7 3000 0.6 0.5 Depth (m) e. Aragonite RCP8.5 2100 f. Surface Aragonite RCP8.5 2100 0 1000 2000 3000 90°S 60°S 30°S 0° 30°N 60°N 90°N Figure 6.29 | Projected aragonite saturation state from 11 CMIP5 Earth System Models under RCP8.5 scenario: (a) time series of surface carbonate ion concentration shown as the mean (solid line) and range of models (filled), given as area-weighted averages over the Arctic Ocean (green), the tropical oceans (red), and the Southern Ocean (blue); maps of the median model s surface A in (b) 2010, (d) 2050 and (f) 2100; and zonal mean sections (latitude vs. depth) of A in 2100 over the (c) Atlantic and (e) Pacific, while the ASH is shown in 2010 (dotted line) as well as 2100 (solid line). Panel (a) also includes mean model results from RCP2.6 (dashed lines). As for Figure 6.28, gridded data products of carbonate system variables (Key et al., 2004) are used to correct each model for its present-day bias by subtracting the model-data difference at each grid cell following (Orr et al., 2005). Where gridded data products are unavailable (Arctic Ocean, all marginal seas, and the ocean near Indonesia), results are shown without bias correction. Future changes in dissolved O2 have been investigated using models of in O2 concentration from projected reductions in biological export various complexity (see references in Table 6.13). The global ocean dis- production production (Bopp et al., 2001; Steinacher et al., 2010) or solved oxygen will decline significantly under future scenarios (Cocco changes in ventilation age of the tropical thermocline (Gnanadesikan et al., 2013). Simulated declines in mean dissolved O2 concentration for et al., 2007). The largest regional decreases in oxygen concentration the global ocean range from 6 to 12 mol kg 1 by the year 2100 (Table (~20 to 100 mol kg 1) are projected for the intermediate (200 to 400 6.13), with a projection of 3 to 4 mol kg 1 in one model with low cli- m) to deep waters of the North Atlantic, North Pacific and Southern mate sensitivity (Frölicher et al., 2009). This general trend is confirmed Ocean for 2100 (Plattner et al., 2002; Matear and Hirst, 2003; Frölicher by the latest projections from the CMIP5 Earth System models, with et al., 2009; Matear et al., 2010; Cocco et al., 2013), which is confirmed 6 reductions in mean dissolved O2 concentrations from 1.5 to 4% (2.5 by the latest CMIP5 projections (Figure 6.30c and 6.30d). to 6.5 mol kg 1) in 2090s relative to 1990s for all RCPs (Figure 6.30a). It is as likely as not that the extent of open-ocean hypoxic (dissolved Most modelling studies (Table 6.13) explain the global decline in dis- oxygen <60 to 80 mol kg 1) and suboxic (dissolved oxygen <5 mol solved oxygen by enhanced surface ocean stratification leading to kg 1) waters will increase in the coming decades. Most models show reductions in convective mixing and deep water formation and by a even some increase in oxygen in most O2-poor waters and thus a slight contribution of 18 to 50% from ocean warming-induced reduction in decrease in the extent of suboxic waters under the SRES-A2 scenario solubility. These two effects are in part compensated by a small increase (Cocco et al., 2013), as well as under RCP8.5 scenario (see the model- 533 Chapter 6 Carbon and Other Biogeochemical Cycles Table 6.13 | Model configuration and projections for global marine O2 depletion by 2100 (adapted from Keeling et al. (2010). Ocean Carbon Mean [O2] Decrease Solubility Study Forcing Cycle Model ( mol kg 1)a,b Contribution (%) Sarmiento et al. (1998) GFDL   7c   Matear et al. (2000) CSIRO IS92a 18 Plattner et al. (2002) Bern 2D SRES A1 12 35 Bopp et al. (2002) IPSL SRES A2d 4 25 Matear and Hirst (2003) CSIRO IS92a 9 26 Schmittner et al. (2008) UVic SRES A2 9   Oschlies et al. (2008) UVic SRES A2 9     UVic-variable C:N SRES A2 12   Frölicher et al. (2009) NCAR CSM1.4-CCCM SRES A2 4 50     SRES B1 3   Shaffer et al. (2009) DCESS SRES A2 10e   Notes: a Assuming a total ocean mass of 1.48 × 1021 kg. b Relative to pre-industrial baseline in 1750. c Model simulation ends at 2065. d Radiative forcing of non-CO GHGs is excluded from this simulation. 2 e For simulations with reduced ocean exchange. CCCM = Coupled-Climate-Carbon Model; CSIRO = Commonwealth Scientific and Industrial Research Organisation; DCESS = Danish Center for Earth System Science; GFDL = Geophysical Fluid Dynamics Laboratory; IPSL = Institute Pierre Simon Laplace; NCAR = National Center for Atmospheric Research; IS92 = IPCC scenarios for 1992; SRES = Special Report on Emission Scenarios; UVic = University of Victoria. mean increase of sub-surface O2 in large parts of the tropical Indian rate than in the period 1951 1975, indicating a worsening of hypoxia and Atlantic Oceans, Figure 6.30d). This rise in oxygen in most sub- (Gilbert et al., 2010). Hypoxia in the shallow coastal ocean (apart from oxic waters has been shown to be caused in one model study by an continental shelves in Eastern Boundary Upwelling Systems) is largely increased supply of oxygen due to lateral diffusion (Gnanadesikan eutrophication driven and is controlled by the anthropogenic flux of et al., 2012). Given limitations of global ocean models in simulating nutrients (N and P) and organic matter from rivers. If continued indus- today s O2 distribution (Cocco et al., 2013), as well as reproducing the trialisation and intensification of agriculture yield larger nutrient loads measured changes in O2 concentrations over the past 50 years (see in the future, eutrophication should intensify (Rabalais et al., 2010), Chapter 3, and Stramma et al., 2012), the model projections are uncer- and further increase the coastal ocean deoxygenation. tain, especially concerning the evolution of O2 in and around oxygen minimum zones. On longer time scales beyond 2100, ocean deoxygenation is projected to increase with some models simulating a tripling in the volume of A number of biogeochemical ocean carbon cycle feedbacks, not yet suboxic waters by 2500 (Schmittner et al., 2008). Ocean deoxygen- included in most marine biogeochemical models (including CMIP5 ation and further expansion of suboxic waters could persist on millen- models, see Section 6.3.2.5.6), could also impact future trends of ocean nial time scales, with average dissolved O2 concentrations projected deoxygenation. For example, model experiments which include a pCO2- to reach minima of up to 56 mol kg 1 below pre-industrial levels sensitive C:N drawdown in primary production, as suggested by some in experiments with high CO2 emissions and high climate sensitivity mesocosm experiments (Riebesell et al., 2007), project future increases (Shaffer et al., 2009). of up to 50% in the volume of the suboxic waters by 2100 (Oschlies et al., 2008; Tagliabue et al., 2011). In addition, future marine hypoxia A potential expansion of hypoxic or suboxic water over large parts could be amplified by changes in the CaCO3 to organic matter rain of the ocean is likely to impact the marine cycling of important nutri- ratio in response to rising pCO2 (Hofmann and Schellnhuber, 2009). ents, particularly nitrogen. The intensification of low oxygen waters has Reduction in biogenic calcification due to ocean acidification would been suggested to lead to increases in water column denitrification 6 weaken the strength of CaCO3 mineral ballasting effect, which could and N2O emissions (e.g., Codispoti, 2010; Naqvi et al., 2010). Recent lead organic material to be remineralized at a shallower depth exacer- works, however, suggest that oceanic N2O production is dominated bating the future expansion of shallow hypoxic waters. by nitrification with a contribution of 7% by denitrification (Freing et al., 2012), Figure 6.4c) and that ocean deoxygenation in response to The modeled estimates do not take into account processes that are anthropogenic climate change could leave N2O production relatively specific to the coastal ocean and may amplify deoxygenation. Recent unchanged (Bianchi et al., 2012). observations for the period 1976 2000 have shown that dissolved O2 concentrations have declined at a faster rate in the coastal ocean ( 0.28 mol kg 1 yr 1) than the open ocean ( 0.02 mol kg 1 y 1, and a faster 534 Carbon and Other Biogeochemical Cycles Chapter 6 a. Ocean oxygen content change (%) b. Oxygen concentrations (200-600m) 1990s 1 320 0 280 240 -1 Historical 200 -2 RCP2.6 160 RCP4.5 120 -3 RCP6.0 80 RCP8.5 40 -4 0 1900 1950 2000 2050 2100 c. 2090s, changes from 1990s RCP2.6 d. 2090s, changes from 1990s RCP8.5 50 40 30 20 10 0 -10 -20 -30 -40 -50 (mmol m-3) Figure 6.30 | (a) Simulated changes in dissolved O2 (mean and model range as shading) relative to 1990s for RCP2.6, RCP4.5, RCP6.0 and RCP8.5. (b) Multi-model mean dis- solved O2 ( mol m 3) in the main thermocline (200 to 600 m depth average) for the 1990s, and changes in 2090s relative to 1990s for RCP2.6 (c) and RCP8.5 (d). To indicate consistency in the sign of change, regions are stippled where at least 80% of models agree on the sign of the mean change. These diagnostics are detailed in Cocco et al. (2013) in a previous model intercomparison using the SRES-A2 scenario and have been applied to CMIP5 models here. Models used: Community Earth System Model 1 Biogeochemical (CESM1-BGC), Geophysical Fluid Dynamics Laboratory Earth System Model 2G (GFDL-ESM2G), Geophysical Fluid Dynamics Laboratory Earth System Model 2M (GFDL-ESM2M), Hadley Centre Global Environmental Model 2 Earth System (HadGEM2-ES), Institute Pierre Simon Laplace Coupled Model 5A Low Resolution (IPSL-CM5A-LR), Institute Pierre Simon Laplace Coupled Model 5A Medium Resolution (IPSL-CM5A-MR), Max Planck Institute Earth System Model Low Resolution (MPI-ESM-LR), Max Planck Institute Earth System Model Medium Resolution (MPI-ESM-MR), Norwegian Earth System Model 1 (Emissions capable) (NorESM1). 6.4.6 Future Trends in the Nitrogen Cycle and Impact nitrogen. In particular, the A1 scenario which assumes a world with on Carbon Fluxes rapid economic growth, a global population that peaks mid-century and rapid introduction of new and more efficient technologies ends 6.4.6.1 Projections for Formation of Reactive Nitrogen by up as the potentially largest contributor to nitrogen use, as a result of Human Activity large amounts of biofuels required and the fertiliser used to produce it. This increase in nitrogen use is assumed to be largely in line with Since the 1970s, food production, industrial activity and fossil fuel the RCP2.6 scenario, where it appears to have rather limited adverse combustion have resulted in the creation of more reactive nitrogen effects like increasing N2O emissions (van Vuuren et al., 2011). (Nr) than natural terrestrial processes (Section 6.1; Box 6.2, Figure 1). Building on the general description of the set of AR4 Special Report on N2O emissions are projected to increase from increased anthropogen- Emission (SRES) scenarios, Erisman et al. (2008) estimated anthropo- ic Nr production. It is thus likely that N2O emissions from soils will genic nitrogen fertiliser consumption throughout the 21st century. Five increase due to the increased demand for feed/food and the reliance of driving parameters (population growth, consumption of animal pro- agriculture on nitrogen fertilisers. This is illustrated by the comparison tein, agricultural efficiency improvement and additional biofuel pro- of emissions from 1900 to those in 2000 and 2050, using the IAM duction) are used to project future nitrogen demands for four scenarios IMAGE model that served to define the RCP2.6 pathway (Figure 6.32). (A1, B1, A2 and B2) (Figure 6.31). Assigning these drivers to these four The anthropogenic N2O emission map IN 2050 shown in Figure 6.32 is 6 SRES scenarios, they estimated a production of Nr for agricultural use established from the RCP4.5 scenario; the RCP8.5 and RCP6 scenarios of 90 to 190 TgN yr 1 by 2100, a range that spans from slightly less to have much higher emissions, and RCP2.6 much lower (van Vuuren et almost twice as much current fertiliser consumption rates (Section 6.1, al., 2011). A spatially explicit inventory of soil nitrogen budgets in live- Figure 6.4a, Figure 1 in Box 6.2). stock and crop production systems using the IMAGE model (Bouwman et al., 2011) shows that between 1900 and 1950, the global soil Nr Despite the uncertainties and the non-inclusion of many important budget surplus almost doubled to 36 TgN yr 1, and further increased to drivers, three of the scenarios generated by the Erisman et al. (2008) 138 TgN yr 1 between 1950 and 2000. The IMAGE model scenario from model point towards an increase in future production of reactive Bouwman et al. (2011) shown in Figure 6.32 portrays a world with a 535 Chapter 6 Carbon and Other Biogeochemical Cycles population SRES A1 + biofuel 250 SRES A2 food equity 250 biofuels Global fertilizer Nr demand (Tg N yr-1) SRES B1 diet optimization SRES B2 efficiency increase 200 Tilman et al. 2001 200 FAO 2012 Bodirsky et al. 2012 Bouwman et al. 2009 150 150 100 100 50 50 0 0 1900 1950 2000 2050 2100 A1 A2 B1 B2 Year Figure 6.31 | Global nitrogen fertiliser consumption scenarios (left) and the impact of individual drivers on 2100 consumption (right). This resulting consumption is always the sum (denoted at the end points of the respective arrows) of elements increasing as well as decreasing nitrogen consumption. Other relevant estimates are presented for comparison. The A1, B1, A2 and B2 scenarios draw from the assumptions of the IPCC Special Report on Emission Scenarios (SRES) emission scenario storylines as explained in Erisman et al. (2008). further increasing global crop production (+82% for 2000 2050) and 2011). Modelling results (Stocker et al., 2013) suggest that the climate livestock production (+115%). Despite the assumed rapid increase and CO2-related amplification of terrestrial N2O emissions imply a in nitrogen use efficiency in crop (+35%) and livestock (+35%) pro- larger feedback of 0.03 to 0.05 W m 2 °C 1 by 2100. duction, global agricultural Nr surpluses are projected to continue to increase (+23%), and associated emissions of N2O to triple compared With the continuing increases in the formation of Nr from anthropo- to 1900 levels. genic activities will come increased Nr emissions and distribution of Nr by waters and the atmosphere. For the atmosphere, the main driver Regional to global scale model simulations suggest a strong effect of future global nitrogen deposition is the emission trajectories of NOy of climate variability on interannual variability of land N2O emissions and NH3. For all RCP scenarios except RCP2.6, nitrogen deposition is (Tian et al., 2010; Zaehle et al., 2011; Xu-Ri et al., 2012). Kesik et al. projected to remain relatively constant globally although there is a (2006) found for European forests that higher temperatures and lower projected increase in NHx deposition and decrease in NOy deposition. soil moisture will decrease future N2O emissions under scenarios of On a regional basis, future decreases of NHx and NOx are projected climate change, despite local increases of emission rates by up to 20%. in North America and northern Europe, and increases in Asia (Figure Xu-Ri et al. (2012) show that local climate trends result in a spatially 6.33). Spatially, projected changes in total nitrogen deposition driven diverse pattern of increases and decreases of N2O emissions, which primarily by increases in NHx emissions occur over large regions of the globally integrated result in a net climate response of N2O emissions world for all RCPs, with generally the largest in RCP8.5 and the small- of 1 TgN yr 1 per 1°C of land temperature warming. Using a further est in RCP2.6 (Figure 6.33) (Supplementary Material has RCP4.5 and development of this model, Stocker et al. (2013) estimate increases in RCP6.0). Previous IPCC scenarios (SRES A2 or IS92a) project a near terrestrial N2O from a pre-industrial terrestrial source of 6.9 TgN (N2O) doubling of atmospheric nitrogen deposition over some world biodi- yr 1 to 9.8 to 11.1 TgN (N2O) yr 1 (RCP 2.6) and 14.2 to 17.0 TgN (N2O) versity hotspots with half of these hotspots subjected to deposition yr 1 (RCP 8.5) by 2100. Of these increases, 1.1 to 2.4 TgN (N2O) yr 1 rates greater than 15 kgN ha 1 yr 1 (critical load threshold value) over 6 (RCP 2.6) or 4.7 to 7.7 TgN (N2O) yr 1 (RCP 8.5) are due to the inter- at least 10% of their total area (Dentener et al., 2005; Phoenix et al., acting effects of climate and CO2 on N2O emissions from natural and 2006; Bleeker et al., 2011). agricultural ecosystems. An independent modelling study suggested a climate change related increase of N2O emissions between 1860 and Large uncertainties remain in our understanding and modelling of 2100 by 3.1 TgN (N2O) yr 1 for the A2 SRES scenario (Zaehle, 2013) changes in Nr emissions, atmospheric transport and deposition pro- implying a slightly lower sensitivity of soil N2O emissions to climate of cesses, lead to low confidence in the projection of future Nr deposition 0.5 TgN (N2O) yr 1 per 1°C warming. While the present-day contribu- fluxes, particularly in regions remote from anthropogenic emissions tion of these climate-mediated effects on the radiative forcing from (Dentener et al., 2006). The large spread between atmospheric GCM N2O is likely to be small (0.016 W m 2 °C 1; Zaehle and Dalmonech, models associated with precipitation projections confounds ­ xtraction e 536 Carbon and Other Biogeochemical Cycles Chapter 6 of a climate signal in deposition projections (Langner et al., 2005; the discharge of dissolved inorganic nitrogen (DIN) to marine coastal Hedegaard et al., 2008). waters was >500 kg N km 2 of watershed area for most watershed systems downstream of either high population or extensive agricul- 6.4.6.2. Projected Changes in Sulphur Deposition tural activity (Mayorga et al., 2010; Seitzinger et al., 2010). Additional information and the supporting figure are found in the Supplementary Given the tight coupling between the atmospheric nitrogen and sul- Material. phur cycles, and the impact on climate (Section 7.3) this Chapter also presents scenarios for sulphur deposition. Deposition of SOx is pro- 6.4.6.3 Impact of Future Changes in Reactive Nitrogen on jected to decrease in all RCP pathways (Figures 6.33 and 6.34). By Carbon Uptake and Storage contrast, scenarios established prior to RCPs indicated decreases of sulphur deposition in North America and Europe, but increases in South Anthropogenic Nr addition and natural nitrogen-cycle responses to America, Africa, South and East Asia (Dentener et al., 2006; Tagaris et global changes will have an important impact on the global carbon al., 2008). In all RCPs, sulphur deposition is lower by 2100 than in 2000 cycle. As a principal nutrient for plant growth, nitrogen can both limit in all regions, with the largest decreases in North America, Europe and future carbon uptake and stimulate it depending on changes in Nr Asia (RCP2.6 and RCP 8.5 are seen in Figure 6.34; RCP4.5 and RCP6.0 availability. A range of global terrestrial carbon cycle models have are in the Supplementary Material) (Lamarque et al., 2011). Future hot been developed since AR4 that integrate nitrogen dynamics into the spots of deposition are still evident in East and South East Asia, espe- simulation of land carbon cycling (Thornton et al., 2007; Wang et al., cially for RCP6.0. 2007, 2010a; Sokolov et al., 2008; Xu-Ri and Prentice, 2008; Churkina et al., 2009; Jain et al., 2009; Fisher et al., 2010; Gerber et al., 2010; Projected future increase of Nr input into terrestrial ecosystems also Zaehle and Friend, 2010; Esser et al., 2011). However, only two ESMs in yields increased flux of Nr from rivers into coastal systems. As illus- CMIP5 (CESM1-BGC and NorESM1-ME) include a description of nitro- trated by the Global NEWS 2 model for 2050, by the base year 2000, gen carbon interactions. In response to climate warming, increased decomposition of soil N2O emissions (kgN km-2 y-1) organic matter increases nitrogen mineralisation, (high confidence) which can enhance Nr uptake and carbon storage by vegetation. Gen- a. 1900 erally, higher C:N ratio in woody vegetation compared to C:N ratio of soil organic matter causes increased ecosystem carbon storage as increased Nr uptake shifts nitrogen from soil to vegetation (Melillo et al., 2011). In two studies (Sokolov et al., 2008; Thornton et al., 2009), this effect was strong enough to turn the carbon climate interaction into a small negative feedback, that is, an increased land CO2 uptake in response to climate warming (positive L values in Figure 6.20), whereas in another study that described carbon nitrogen interactions (Zaehle et al., 2010b) the carbon climate interaction was reduced but b. 2000 4 remained positive, that is, decreased land CO2 uptake in response to climate change (negative L values in Figures 6.20, 6.21 and 6.22). The 3 two CMIP5 ESMs which include terrestrial carbon nitrogen interac- tions (Table 6.11) also simulate a small but positive climate carbon 2 feedback. 1 Consistent with the observational evidence (Finzi et al., 2006; Palmroth 0 et al., 2006; Norby et al., 2010), modelling studies have shown a strong effect of Nr availability in limiting the response of plant growth and c. 2050 land carbon storage to elevated atmospheric CO2 (e.g., Sokolov et al., 2008; Thornton et al., 2009; Zaehle and Friend, 2010). These analyses are affected by the projected future trajectories of anthropogenic Nr deposition. The effects of Nr deposition counteract the nitrogen limita- tion of CO2 fertilisation (Churkina et al., 2009; Zaehle et al., 2010a). 6 Estimates of the total net carbon storage on land due to Nr deposition between 1860 and 2100 range between 27 and 66 PgC (Thornton et al., 2009; Zaehle et al., 2010a). Figure 6.32 | N2O emissions in 1900, 2000 and projected to 2050 (Bouwman et al., It is very likely that, at the global scale, nutrient limitation will reduce 2011). This spatially explicit soil nutrient budget and nitrogen gas emission scenario was elaborated with the Integrated Model to Assess the Global Environment (IMAGE) the global land carbon storage projected by CMIP5 carbon-cycle only model on the basis of the International Assessment of Agricultural Knowledge, Science models. Only two of the current CMIP5 ESM models explicitly con- and Technology for Development (IAASTD) baseline scenario (McIntyre et al., 2009). sider carbon nitrogen interactions (CESM1-BGC and NorESM1-ME). 537 Chapter 6 Carbon and Other Biogeochemical Cycles The effect of the nitrogen limitations on terrestrial carbon sequestra- influence of nutrient addition for agriculture and pasture management tion in the results of the other CMIP5 models may be approximated is not addressed in this analysis. Results from the two CMIP5 models by comparing the implicit Nr requirement given plausible ranges of with explicit carbon nitrogen interactions show even lower land terrestrial C:N stoichiometry (Wang and Houlton, 2009) to plausible carbon sequestration than obtained by this approximation method increases in terrestrial Nr supply due to increased biological nitrogen (Figure 6.35). More models with explicit carbon nitrogen interactions fixation (Wang and Houlton, 2009) and anthropogenic Nr deposition are needed to understand between-model variation and construct an (Figure 6.35). For the ensemble of CMIP5 projections under the RCP ensemble response. 8.5 scenario, this implies a lack of available nitrogen of 1.3 to 13.1 PgN which would reduce terrestrial C sequestration by an average of The positive effect on land carbon storage due to climate-increased 137 PgC over the period 1860 2100, with a range of 41 to 273 PgC Nr mineralization is of comparable magnitude to the land carbon stor- among models. This represents an ensemble mean reduction in land age increase associated with increased anthropogenic Nr deposition. carbon sequestration of 55%, with a large spread across models (14 Models disagree, however, which of the two factors is more important, to 196%). Inferred reductions in ensemble-mean land carbon sink over with both effects dependent on the choice of scenario. Crucially, the the same period for RCPs 6.0, 4.5 and 2.6 are 109, 117 and 85 PgC, effect of nitrogen limitation on vegetation growth and carbon storage respectively. Between-model variation in these inferred reduced land under elevated CO2 is the strongest effect of the natural and disturbed carbon sinks is similar for all RCPs, with ranges of 57 to 162 PgC, 38 to nitrogen cycle on terrestrial carbon dynamics (Bonan and Levis, 2010; 208 PgC, and 32 to 171 PgC for RCPs 6.0, 4.5 and 2.6, respectively. The Zaehle et al., 2010a). In consequence, the projected atmospheric CO2 SOx Deposition NHx Deposition NOy Deposition 120 World 80 80 World World 100 60 60 1 1 1 80 TgN yr TgN yr TgS yr 60 40 40 40 20 20 20 0 0 0 12 8 15 North America North America North America 10 6 8 1 1 10 1 TgN yr TgN yr TgS yr 6 4 5 4 2 2 0 0 0 12 8 15 Europe Europe Europe 10 6 8 1 1 10 1 TgN yr TgN yr TgS yr 6 4 5 4 2 2 0 0 0 12 8 15 South Asia South Asia South Asia 10 6 8 1 1 10 1 TgN yr TgN yr TgS yr 6 4 5 4 2 2 0 0 0 12 8 15 East Asia East Asia East Asia Historical 10 6 RCP 2.6 6 8 1 1 10 1 RCP 4.5 TgN yr TgN yr TgS yr 6 4 RCP 6.0 5 4 RCP 8.0 2 2 CLE 0 0 0 A2 1850 1900 1950 2000 2050 2100 1850 1900 1950 2000 2050 2100 1850 1900 1950 2000 2050 2100 MFR Year Year Year Figure 6.33 | Deposition of SOx (left, TgS yr 1), NHx (middle, TgN yr 1) and NOy (right, TgN yr 1) from 1850 to 2000 and projections of deposition to 2100 under the four RCP emission scenarios (Lamarque et al., 2011; van Vuuren et al., 2011). Also shown are the 2030 scenarios using the SRES B1/A2 energy scenario with assumed current legislation and maximum technically feasible air pollutant reduction controls (Dentener et al., 2006). 538 Carbon and Other Biogeochemical Cycles Chapter 6 SOx deposition (kgS km-2 y-1) N deposition (kgN km-2 y-1) 1990s 1990s a. 5000 b. 5000 1000 1000 500 500 100 100 50 50 10 10 0 0 2090s, changes from 1990s 2090s, changes from 1990s c. d. 1000 1000 100 100 RCP8.5 RCP8.5 10 10 e. -10 f. -10 -100 -100 -1000 -1000 RCP2.6 RCP2.6 Figure 6.34 | Spatial variability of nitrogen and SOx deposition in 1990s with projections to the 2090s (shown as difference relative to the 1990s), using the RCP2.6 and RCP8.5 scenarios, kgN km 2 yr 1, adapted from Lamarque et al. (2011). Note that no information on the statistical significance of the shown differences is available. This is of particular relevance for areas with small changes. The plots for all four of the RCP scenarios are in the Supplementary Material. concentrations (and thus degree of climate change) in 2100 are higher this chapter and we assess here future changes in natural CH4 emis- in projections with models describing nitrogen limitations than in sions in response to climate change (e.g., O Connor et al., 2010; Figure those same models without these interactions. The influence of current 6.36). Projected increases in future fire occurrence (Section 6.4.8.1) and future nitrogen deposition on the ocean sink for anthropogenic suggest that CH4 from fires may increase (low confidence). Future carbon is estimated to be rather small, with less than 5% of the ocean changes in anthropogenic emissions due to anthropogenic alteration carbon sink in 2100 attributable to fertilisation from anthropogenic Nr of wetlands (e.g., peatland drainage) may also be important but are deposition over the oceans (Reay et al., 2008). not assessed here. None of the CMIP5 models include phosphorus as a limiting nutrient 6.4.7.1 Future Methane Emissions from Wetlands for land ecosystems, although this limitation and interactions with Nr availability are observed in many systems (Elser et al., 2007). Limita- Overall, there is medium confidence that emissions of CH4 from tion by Nr availability alone may act as a partial surrogate for com- wetlands are likely to increase in the future under elevated CO2 and bined nitrogen phosphorus limitation (Thornton et al., 2009; Section warmer climate. Wetland extent is determined by geomorphology and 6 6.4.8.2), but are likely to underestimate the overall nutrient limitation, soil moisture, which depends on precipitation, evapotranspiration, especially in lowland tropical forest. drainage and runoff. All of these may change in the future. Increas- ing temperature can lead to higher rates of evapotranspiration, reduc- 6.4.7 Future Changes in Methane Emissions ing soil moisture and therefore affect wetland extent, and temporary increasing aeration of existing wetlands with further consequences to Future atmospheric CH4 concentrations are sensitive to changes in both methane emissions. Regional projections of precipitation changes are emissions and OH oxidation. Atmospheric chemistry is not covered in especially uncertain (see Chapter 12). 539 Chapter 6 Carbon and Other Biogeochemical Cycles Direct effects on wetland CH4 emissions include: higher NPP under Since AR4, several modelling studies have attempted to quantify the higher temperature and higher atmospheric CO2 concentrations lead- sensitivity of global wetland CH4 emissions to environmental changes ing to more substrate for methanogenesis (White et al., 2008); higher (see Figure 6.37). The studies cover a wide range of simulation results CH4 production rates under higher temperature; and changes in CH4 but there is high agreement between model results that the com- oxidation through changed precipitation that alters water table posi- bined effect of CO2 increase and climate change by the end of the tion (Melton et al., 2013). Wetland CH4 emissions are also affected by 21st century will increase wetland CH4 emissions. Using a common changes in wetland area which may either increase (due to thawing experimental protocol with spatially uniform changes in precipita- permafrost or reduced evapotranspiration) or decrease (due to reduced tion, temperature and CO2 ( WETCHIMP ; Melton et al., 2013) seven precipitation or increased evaporation) regionally. In most models, ele- models predict that the effect of increased temperature alone (red bars vated CO2 has a stronger enhancement effect on CH4 emissions than in Figure 6.37) may cause an increase or decrease of wetland CH4 emis- climate change. However, large uncertainties exist concerning the lack sions, while the effect of increased precipitation alone (green bars in of wetland specific plant functional types in most models and the lack Figure 6.37) is always an increase, although generally small. The effect of understanding how wetland plants will react to CO2 fertilisation of increased atmospheric CO2 concentration (fertilisation of NPP; Box (e.g., Berendse et al., 2001; Boardman et al., 2011; Heijmans et al., 6.3; blue bars in Figure 6.37) always resulted in an increase of emis- 2001, 2002a, 2002b). sions (22 to 162%). Other studies assessed the effects of temperature and precipitation together (orange bars in Figure 6.37) and often found an increase in wetland CH4 emissions (Eliseev et al., 2008; Gedney et al., 2004; Shindell et al., 2004; Volodin, 2008) although Ringeval et al. 400 (2011) found a net decrease. The combined effect of climate and CO2 resulted in an increase of wetland CH4 emissions from 40% (Volodin Land C ssequestration, 1860-2100 (PgC) (2008); fixed wetland area) to 68% (Ringeval et al., 2011); variable wetland area). The models assessed here do not consider changes in soil hydrologi- 200 cal properties caused by changes in organic matter content. Positive feedbacks from increased drainage due to organic carbon loss may Emissions rate (Tg CH4 yr -1) 0 1000 Anthropogenic CMIP5 CMIP5 with inferred N constraint explicit C-N result 800 -200 600 Wetlands RCP2.6 RCP4.5 RCP6.0 RCP8.5 Figure 6.35 | Estimated influence of nitrogen availability on total land carbon seques- tration over the period 1860 2100 (based on analysis method of Wang and Houlton 400 (2009). Blue bars show, for each RCP scenario, the multi-model ensemble mean of land carbon sequestration, based on the carbon-only subset of CMIP5 models (Cana- dian Earth System Model 2 (CanESM2), Geophysical Fluid Dynamics Laboratory Earth 200 System Model 2G (GFDL-ESM2G), Geophysical Fluid Dynamics Laboratory Earth System Model 2M (GFDL-ESM2M), Hadley Centre Global Environmental Model 2 Carbon Terrestrial permafrost Wildfires Cycle(HadGEM2-CC), Hadley Centre Global Environmental Model 2 Earth System (Had- 0 Timescale (yr) GEM2-ES), Institute Pierre Simon Laplace Coupled Model 5A Low Resolution (IPSL- 1 10 100 1,000 CM5A-LR), Institute Pierre Simon Laplace Coupled Model 5A Medium Resolution (IPSL-CM5A-MR), Institute Pierre Simon Laplace Coupled Model 5B Low Resolution Figure 6.36 | Schematic synthesis of the magnitude and time scales associated with (IPSL-CM5B-LR), Max Planck Institute Earth System Model Low Resolution (MPI-ESM- possible future CH4 emissions (adapted from O Connor et al., 2010). Uncertainty in 6 LR): not all models produced results for all scenarios). Red bars show, for each scenario, these future changes is large, and so this figure demonstrates the relative magnitude the mean land carbon sequestration from the same ensemble of carbon-only models of possible future changes. Anthropogenic emissions starting at a present-day level of after correcting for inferred constraints on carbon uptake due to limited availability of 300 Tg(CH4) yr 1 (consistent with Table 6.8) and increasing or decreasing according to nitrogen. Black bars show +/- one standard deviation around the means. Black symbols RCP8.5 and RCP2.6 are shown for reference. Wetland emissions are taken as 140 to show individual model results from the two CMIP5 models with explicit carbon nitro- 280 Tg(CH4) yr 1 present day values (Table 6.8) and increasing by between 0 and 100% gen interactions (Community Earth System Model 1 Biogeochemical (CESM1-BGC) (Section 6.4.7.1; Figure 6.37). Permafrost emissions may become important during the and Norwegian Earth System Model 1 (Emissions capable) (NorESM1-ME)). These two 21st century. CH4 release from marine hydrates and subsea permafrost may also occur models have nearly identical representations of land carbon nitrogen dynamics, and but uncertainty is sufficient to prevent plotting emission rates here. Large CH4 hydrate differences between them here (for RCP4.5 and RCP8.5, where both models contrib- release to the atmosphere is not expected during the 21st century. No quantitative uted results) are due to differences in coupled system climate. All simulations shown estimates of future changes in CH4 emissions from wildfires exist, so plotted here are here used prescribed atmospheric CO2 concentrations. continued present-day emissions of 1 to 5 Tg(CH4) yr 1 (Table 6.8). 540 Carbon and Other Biogeochemical Cycles Chapter 6 accelerate peat decomposition rates (Ise et al., 2008). However, carbon et al., 2011) and has been projected to continue under future scenarios accumulation due to elevated NPP in wetland and permafrost regions (Avis et al., 2011). Alternatively, small lakes or ponds and wetland may to some extent offset CH4 emissions (Frolking and Roulet, 2007; growth may occur in continuous permafrost areas underlain by ice-rich Turetsky et al., 2007). None of the studies or models assessed here material subject to thermokarst (Christensen et al., 2004; Jorgenson et considers CH4 emissions from mangroves. al., 2006; Plug and West, 2009; Jones et al., 2011). The models also do not agree in their simulations of present day wet- There is high agreement between land surface models that permafrost land extent or CH4 emissions, and there are not adequate data sets to extent is expected to reduce during the 21st century, accompanying evaluate them thoroughly at the grid scale (typically 0.5°) (Melton et particularly rapid warming at high latitudes (Chapter 12). However, al., 2013). Hence despite high agreement between models of a strong estimates vary widely as to the pace of degradation (Lawrence and positive response of wetland CH4 emission rates to increasing atmo- Slater, 2005; Burn and Nelson, 2006; Lawrence et al., 2008). The LPJ- spheric CO2 we assign low confidence to quantitative projections of WHyMe model projected permafrost area loss of 30% (SRES B1) and future wetland CH4 emissions. 47% (SRES A2) by 2100 (Wania, 2007). Marchenko et al. (2008) calcu- late that by 2100, 57% of Alaska will lose permafrost within the top Soil CH4 oxidation of about 30 Tg(CH4) yr 1 (Table 6.8) represents the 2 m. For the RCP scenarios, the CMIP5 multi-model ensemble shows a smallest of the three sinks for atmospheric methane (see Table 6.8) but wide range of projections for permafrost loss: 15 to 87% under RCP4.5 is also sensitive to future environmental changes. Soil CH4 oxidation is and 30 to 99% under RCP8.5 (Koven et al., 2013). projected to increase by up to 23% under the SRES A1B due to rising atmospheric CH4 concentrations, higher soil temperature and lower soil Hydrological changes may lead to tradeoffs between the CO2 and CH4 moisture (Curry, 2007, 2009). balance of ecosystems underlain by permafrost, with methane produc- tion rates being roughly an order of magnitude less than rates of oxic 6.4.7.2 Future Methane Emissions from Permafrost Areas decomposition to CO2 but CH4 having a larger greenhouse warming potential (Frolking and Roulet, 2007). The extent of permafrost thaw Permafrost thaw may lead to increased drainage and a net reduction simulated by climate models has been used to estimate possible sub- in lakes and wetlands, a process that has already begun to be seen in sequent carbon release (Burke et al., 2013; Harden et al., 2012; ­ ection S lakes in the discontinuous permafrost zone (Smith et al., 2005; Jones 6.4.3.4) but few studies explicitly partition this into CO2 or CH4 release 6 Figure 6.37 | Relative changes of global CH4 emissions from either pre-industrial (a) or present-day (b) conditions and environmental changes that reflect potential conditions in 2100. The first seven models took part in the WETCHIMP intercomparison project and were run under a common protocol (Melton et al., 2013). Bars represent CH4 emission changes associated with temperature-only changes (T), precipitation only (P), CO2 only (CO2) or combinations of multiple factors. Other studies as listed in the figure used different future scenarios: Eliseev et al. (2008), Gedney et al. (2004), Ringeval et al. (2011), Shindell et al. (2004), Volodin (2008), Stocker et al. (2013). 541 Chapter 6 Carbon and Other Biogeochemical Cycles to the atmosphere. Schneider von Deimling et al. (2012) estimate cumu- slide of Norway 8200 years ago. Large methane hydrate release due to lative CH4 emissions by 2100 between 131 and 533 Tg(CH4) across marine landslides is unlikely as any given landslide could release only the 4 RCPs. CMIP5 projections of permafrost thaw do not consider a tiny fraction of the global inventory (Archer, 2007). changes in pond or lake formation. Thawing of unsaturated Yedoma carbon deposits (which contain large, but uncertain amounts of organ- There is low confidence in modelling abilities to simulate transient ic carbon in permafrost in northeast Siberia; Schirrmeister et al., 2011) changes in hydrate inventories, but large CH4 release to the atmo- was postulated to produce significant CH4 emissions (Khvorostyanov et sphere during this century is unlikely. al., 2008), however more recent estimates with Yedoma carbon lability constrained by incubation observations (Dutta et al., 2006) argue for 6.4.8 Other Drivers of Future Carbon Cycle Changes smaller emissions at 2100 (Koven et al., 2011). 6.4.8.1 Changes in Fire under Climate Change/Scenarios of 6.4.7.3 Future Methane Hydrate Emissions Anthropogenic Fire Changes Substantial quantities of methane are believed to be stored within sub- Regional studies for boreal regions suggest an increase in future fire marine hydrate deposits at continental margins (see also Section 6.1, risk (e.g., Amiro et al., 2009; Balshi et al., 2009; Flannigan et al., 2009a; FAQ 6.1). There is concern that warming of overlying waters may melt Spracklen et al., 2009; Tymstra et al., 2007; Westerling et al., 2011; these deposits, releasing CH4 into the ocean and atmosphere systems. Wotton et al., 2010) with implications for carbon and nutrient storage Overall, it is likely that subsequent emissions to the atmosphere caused (Certini, 2005). Kurz et al. (2008b) and Metsaranta et al. (2010) indi- by hydrate destabilisation would be in the form of CO2, due to CH4 cated that increased fire activity has the potential to turn the Canadi- oxidation in the water column. an forest from a sink to a source of atmospheric CO2. Models predict spatially variable responses in fire activity, including strong increases Considering a potential warming of bottom waters by 1°C, 3°C and and decreases, due to regional variations in the climate fire relation- 5°C during the next 100 years, Reagan and Moridis (2007) found ship, and anthropogenic interference (Scholze et al., 2006; Flannigan et that hydrates residing in a typical deep ocean setting (4°C and 1000 al., 2009b; Krawchuk et al., 2009; Pechony and Shindell, 2010; Kloster m depth) would be stable and in shallow low-latitude settings (6°C et al., 2012). Wetter conditions can reduce fire activity, but increased and 560 m) any sea floor CH4 fluxes would be oxidized within the biomass availability can increase fire emissions (Scholze et al., 2006; sediments. Only in cold-shallow Arctic settings (0.4°C and 320 m) Terrier et al., 2013). Using a land surface model and future climate would CH4 fluxes exceed rates of benthic sediment oxidation. Simula- projections from two GCMs, Kloster et al. (2012) projected fire carbon tions of heat penetration through the sediment by Fyke and Weaver emissions in 2075 2099 that exceed present-day emissions by 17 to (2006) suggest that changes in the gas hydrate stability zone will be 62% (0.3 to 1.0 PgC yr 1) depending on scenario. small on century time scales except in high-latitude regions of shal- low ocean shelves. In the longer term, Archer et al. (2009a) estimated Future fire activity will also depend on anthropogenic factors espe- that between 35 and 940 PgC could be released over several thousand cially related to land use change. For the Amazon it is estimated that years in the future following a 3°C seafloor warming. at present 58% of the area is too humid to allow deforestation fires but climate change might reduce this area to 37% by 2050 (Le Page Using multiple climate models (Lamarque, 2008), predicted an upper et al., 2010). Golding and Betts (2008) estimated that future Amazon estimate of the global sea floor flux of between 560 and 2140 Tg(CH4) forest vulnerability to fire may depend nonlinearly on combined cli- yr 1, mostly in the high latitudes. Hunter et al. (2013) also found 21st mate change and deforestation. century hydrate dissociation in shallow Arctic waters and comparable in magnitude to Biastoch et al. (2011), although maximum CH4 sea 6.4.8.2 Other Biogeochemical Cycles and Processes floor fluxes were smaller than Lamarque (2008), with emissions from Impacting Future Carbon Fluxes 330 to 450 Tg(CH4) yr 1 for RCP 4.5 to RCP8.5. Most of the sea floor flux of CH4 is expected to be oxidised in the water column into dissolved 6.4.8.2.1 Phosphorus CO2. Mau et al. (2007) suggest only 1% might be released to the atmo- sphere but this fraction depends on the depth of water and ocean con- On centennial time scales, the phosphoros (P) limitation of terrestrial ditions. Elliott et al. (2011) demonstrated significant impacts of such carbon uptake could become more severe than the nitrogen limitation sea floor release on marine hypoxia and acidity, although atmospheric because of limited phosphorus sources. Model simulations have shown CH4 release was small. a shift after 2100 from nitrogen to phosphorus limitation at high lati- 6 tudes (Goll et al., 2012). Observations of CH4 release along the Svalbard margin seafloor (West- brook et al., 2009) suggest observed regional warming of 1°C during 6.4.8.2.2 Elevated surface ozone the last 30 years is driving hydrate disassociation, an idea supported by modelling (Reagan and Moridis, 2009). However, these studies do not Plants are known to suffer damage due to exposure to levels of ozone consider subsea-permafrost hydrates suggested recently to be region- (O3) above about 40 ppb (Ashmore, 2005). Model simulations of plant ally significant sources of atmospheric CH4 (Shakhova et al., 2010). O3 damage on the carbon cycle have found a reduction in terrestrial There was no positive excursion in the methane concentration recorded carbon storage between 2005 and 2100 ranging from 4 to 140 PgC in ice cores from the largest known submarine landslide, the Storegga (Felzer et al., 2005) and up to 260 PgC (Sitch et al., 2007). 542 Carbon and Other Biogeochemical Cycles Chapter 6 6.4.8.2.3 Iron deposition to oceans 6.4.9 The Long-term Carbon Cycle and Commitments Changes in iron deposition may have affected ocean carbon uptake With very high confidence, the physical, biogeochemical carbon cycle in in the past (Section 6.2.1.1), but future projections of iron deposition the ocean and on land will continue to respond to climate change and from desert dust over the ocean are uncertain, even about the sign of rising atmospheric CO2 concentrations created during the 21st century. changes (Tegen et al., 2004; Mahowald et al., 2009). Tagliabue et al. Long-term changes in vegetation structure and induced carbon storage (2008) found relatively little impact of varying aeolian iron input on potentially show larger changes beyond 2100 than during the 21st ocean CO2 fluxes, but Mahowald et al. (2011) show projected changes century as the long time scale response of tree growth and ecosystem in ocean productivity as large as those due to CO2 increases and cli- migrations means that by 2100 only a part of the eventual commit- mate change. ted change will be realized (Jones et al., 2009). Holocene changes in tree-line lagged changes in climate by centuries (MacDonald et al., 6.4.8.2.4 Changes in the diffuse fraction of solar radiation 2008). Long-term commitments to ecosystems migration also carry at the surface long-term committed effects to changes in terrestrial carbon storage (Jones et al., 2010; Liddicoat et al., 2013) and permafrost (O Connor et Mercado et al. (2009) estimated that variations in the diffuse frac- al., 2010; Sections 6.4.3.3 and 6.4.7). tion, associated largely with the global dimming period (Stanhill and Cohen, 2001), enhanced the land carbon sink by approximately 25% Warming of high latitudes is common to most climate models (Chapter between 1960 and 1999. Under heavily polluted or dark cloudy skies, 12) and this may enable increased productivity and northward expan- plant productivity may decline as the diffuse effect is insufficient to sion of boreal forest ecosystems into present tundra regions depending offset decreased surface irradiance (UNEP, 2011). Under future scenar- on nutrient availability (Kellomäki et al., 2008; Kurz et al., 2008a; Mac- ios involving reductions in aerosol emissions (Figures 6.33 and 6.34), Donald et al., 2008). CMIP5 simulations by two ESMs with dynamic the diffuse-radiation enhancement of carbon uptake will decline. vegetation for extended RCP scenarios to 2300 (Meinshausen et al., 2011) allow analysis of this longer term response of the carbon cycle. Increases in tree cover and terrestrial carbon storage north of 60°N are shown in Figure 6.38. 6 ( ) ( ) Figure 6.38 | Maps of changes in woody cover fraction, %, (left) and terrestrial carbon storage, kg C m 2 (vegetation carbon, middle; soil carbon, right) between years 2100 and 2300 averaged for two models, Hadley Centre Global Environmental Model 2-Earth System (HadGEM2-ES) and Max Planck Institute Earth System Model (MPI-ESM), which simulate vegetation dynamics for three RCP extension scenarios 2.6 (top), 4.5 (middle), and 8.5 (bottom). Note the RCP6.0 extension was not a CMIP5 required simulation. Model results were interpolated on 1° × 1° grid; white colour indicates areas where models disagree in sign of changes. Anthropogenic land use in these extension scenarios is kept constant at 2100 levels, so these results show the response of natural ecosystems to the climate change. 543 Chapter 6 Carbon and Other Biogeochemical Cycles Frequently Asked Questions FAQ 6.2 | What Happens to Carbon Dioxide After It Is Emitted into the Atmosphere? Carbon dioxide (CO2), after it is emitted into the atmosphere, is firstly rapidly distributed between atmosphere, the upper ocean and vegetation. Subsequently, the carbon continues to be moved between the different reservoirs of the global carbon cycle, such as soils, the deeper ocean and rocks. Some of these exchanges occur very slowly. Depending on the amount of CO2 released, between 15% and 40% will remain in the atmosphere for up to 2000 years, after which a new balance is established between the atmosphere, the land biosphere and the ocean. Geo- logical processes will take anywhere from tens to hundreds of thousands of years perhaps longer to redistribute the carbon further among the geological reservoirs. Higher atmospheric CO2 concentrations, and associated climate impacts of present emissions, will, therefore, persist for a very long time into the future. CO2 is a largely non-reactive gas, which is rapidly mixed throughout the entire troposphere in less than a year. Unlike reactive chemical compounds in the atmosphere that are removed and broken down by sink processes, such as methane, carbon is instead redistributed among the different reservoirs of the global carbon cycle and ultimately recycled back to the atmosphere on a multitude of time scales. FAQ 6.2, Figure 1 shows a simplified diagram of the global carbon cycle. The open arrows indicate typical timeframes for carbon atoms to be transferred through the different reservoirs. Before the Industrial Era, the global carbon cycle was Atmosphere Volcanism roughly balanced. This can be inferred from ice core measurements, which show a near constant atmo- Weathering spheric concentration of CO2 over the last several Fossil fuel emissions Respiration thousand years prior to the Industrial Era. Anthro- Photosynthesis pogenic emissions of carbon dioxide into the atmo- Gas exchange sphere, however, have disturbed that equilibrium. As Vegetation global CO2 concentrations rise, the exchange process- from 1-100 yrs es between CO2 and the surface ocean and vegetation are altered, as are subsequent exchanges within and Surface ocean among the carbon reservoirs on land, in the ocean Soils from 10-500 yrs and eventually, the Earth crust. In this way, the added from 1-10 yrs Fossil fuel carbon is redistributed by the global carbon cycle, Deep sea reserves Rocks until the exchanges of carbon between the different Earth crust from carbon reservoirs have reached a new, approximate 100-2000 yrs balance. >10,000 yrs Sediments Over the ocean, CO2 molecules pass through the air-sea interface by gas exchange. In seawater, CO2 interacts with water molecules to form carbonic acid, FAQ 6.2, Figure 1 | Simplified schematic of the global carbon cycle showing the typical turnover time scales for carbon transfers through the major reservoirs. which reacts very quickly with the large reservoir of dissolved inorganic carbon bicarbonate and carbon- ate ions in the ocean. Currents and the formation of sinking dense waters transport the carbon between the surface and deeper layers of the ocean. The marine biota also redistribute carbon: marine organisms grow organic tissue and calcareous shells in surface waters, which, after their death, sink to deeper waters, where they are returned to the dissolved inorganic carbon reservoir by dissolu- tion and microbial decomposition. A small fraction reaches the sea floor, and is incorporated into the sediments. The extra carbon from anthropogenic emissions has the effect of increasing the atmospheric partial pressure of CO2, 6 which in turn increases the air-to-sea exchange of CO2 molecules. In the surface ocean, the carbonate chemistry quickly accommodates that extra CO2. As a result, shallow surface ocean waters reach balance with the atmosphere within 1 or 2 years. Movement of the carbon from the surface into the middle depths and deeper waters takes longer between decades and many centuries. On still longer time scales, acidification by the invading CO2 dis- solves carbonate sediments on the sea floor, which further enhances ocean uptake. However, current understand- ing suggests that, unless substantial ocean circulation changes occur, plankton growth remains roughly unchanged because it is limited mostly by environmental factors, such as nutrients and light, and not by the availability of inorganic carbon it does not contribute significantly to the ocean uptake of anthropogenic CO2. (continued on next page) 544 Carbon and Other Biogeochemical Cycles Chapter 6 FAQ 6.2 (continued) On land, vegetation absorbs CO2 by photosynthesis and converts it into organic matter. A fraction of this carbon is immediately returned to the atmosphere as CO2 by plant respiration. Plants use the remainder for growth. Dead plant material is incorporated into soils, eventually to be decomposed by microorganisms and then respired back into the atmosphere as CO2. In addition, carbon in vegetation and soils is also converted back into CO2 by fires, insects, herbivores, as well as by harvest of plants and subsequent consumption by livestock or humans. Some organic carbon is furthermore carried into the ocean by streams and rivers. An increase in atmospheric CO2 stimulates photosynthesis, and thus carbon uptake. In addition, elevated CO2 con- centrations help plants in dry areas to use ground water more efficiently. This in turn increases the biomass in veg- etation and soils and so fosters a carbon sink on land. The magnitude of this sink, however, also depends critically on other factors, such as water and nutrient availability. Coupled carbon-cycle climate models indicate that less carbon is taken up by the ocean and land as the climate warms constituting a positive climate feedback. Many different factors contribute to this effect: warmer seawater, for instance, has a lower CO2 solubility, so altered chemical carbon reactions result in less oceanic uptake of excess atmospheric CO2. On land, higher temperatures foster longer seasonal growth periods in temperate and higher latitudes, but also faster respiration of soil carbon. The time it takes to reach a new carbon distribution balance depends on the transfer times of carbon through the different reservoirs, and takes place over a multitude of time scales. Carbon is first exchanged among the fast carbon reservoirs, such as the atmosphere, surface ocean, land vegetation and soils, over time scales up to a few thousand years. Over longer time scales, very slow secondary geological processes dissolution of carbonate sedi- ments and sediment burial into the Earth s crust become important. FAQ 6.2, Figure 2 illustrates the decay of a large excess amount of CO2 (5000 PgC, or about 10 times the cumulative CO2 emitted so far since the beginning of the industrial Era) emitted into the atmosphere, and how it is redistrib- uted among land and the ocean over time. During the first 200 years, the ocean and land take up similar amounts of carbon. On longer time scales, the ocean uptake dominates mainly because of its larger reservoir size (~38,000 PgC) as compared to land (~4000 PgC) and atmosphere (589 PgC prior to the Industrial Era). Because of ocean chemistry the size of the initial input is important: higher emissions imply that a larger fraction of CO2 will remain in the atmosphere. After 2000 years, the atmosphere will still contain between 15% and 40% of those initial CO2 emissions. A further reduction by carbonate sediment dissolution, and reactions with igneous rocks, such as silicate weathering and sediment burial, will take anything from tens to hundreds of thousands of years, or even longer. Ocean invasion Ocean invasion Reaction with CaCO3 Land uptake 5000 4000 Ocean Land 3000 (PgC) 2000 Atmosphere Reactions with 1000 igneous rocks 0 0 50 100 150 200 500 1000 1500 2000 4000 6000 8000 10 000 6 Time (Years) FAQ 6.2, Figure 2 | Decay of a CO2 excess amount of 5000 PgC emitted at time zero into the atmosphere, and its subsequent redistribution into land and ocean as a function of time, computed by coupled carbon-cycle climate models. The sizes of the colour bands indicate the carbon uptake by the respective reservoir. The first two panels show the multi-model mean from a model intercomparison project (Joos et al., 2013). The last panel shows the longer term redistribution including ocean dissolution of carbonaceous sediments as computed with an Earth System Model of Intermediate Complexity (after Archer et al., 2009b). 545 Chapter 6 Carbon and Other Biogeochemical Cycles Increases in fire disturbance or insect damage may drive loss of forest remove CO2 from the atmosphere and is thus considered as a CDR in temperate regions (Kurz et al., 2008c), but this process is poorly method. The distinction between CDR and mitigation (see Glossary) is represented or not accounted at all in models. Recent evidence from not clear and there could be some overlap between the two. models (Huntingford et al., 2013) and studies on climate variability (Cox et al., 2013) suggests that large scale loss of tropical forest as pre- Insofar as the CDR-removed CO2 is sequestered in a permanent res- viously projected in some models (Cox et al., 2004; Scholze et al., 2006) ervoir, CDR methods could potentially reduce direct consequences is unlikely, but depends strongly on the predicted future changes in of high CO2 levels, including ocean acidification (see Section 6.4.4) regional temperature (Galbraith et al., 2010) and precipitation (Good (Matthews et al., 2009). However, the effects of CDR methods that et al., 2011, 2013), although both models here simulate reduced tree propose to manipulate carbon cycle processes are slow (see Box 6.1) cover and carbon storage for the RCP8.5 scenario. ESMs also poorly and hence the consequent climate effects would be slow. The climate simulate resilience of ecosystems to climate changes and usually do system has a less than 5-years relaxation (e-folding) time scale for an not account for possible existence of alternative ecosystem states such assumed instantaneous reduction in radiative forcing to preindustrial as tropical forest or savannah (Hirota et al., 2011). levels (Held et al., 2010). While the climate effect of SRM could be rapid (Shepherd et al., 2009) given this time scale, at present, there Regional specific changes in ecosystem composition and carbon stor- is no known CDR method, including industrial direct air capture that age are uncertain but it is very likely that ecosystems will continue to can feasibly reduce atmospheric CO2 to pre-industrial levels within a change for decades to centuries following stabilisation of GHGs and similar time scale. Therefore, CDR methods do not present an option for climate change. rapidly preventing climate change when compared to SRM. It is likely that CDR would have to be deployed at large-scale for at least one century to be able to significantly reduce atmospheric CO2. 6.5 Potential Effects of Carbon Dioxide Removal Methods and Solar Radiation Important carbon cycle science considerations for evaluating CDR Management on the Carbon Cycle methods include the associated carbon storage capacity, the perma- nence of carbon storage and potential adverse side effects (Shepherd 6.5.1 Introduction to Carbon Dioxide Removal Methods et al., 2009). Geological reservoirs could store several thousand PgC and the ocean may be able to store a few thousand PgC of anthropo- To slow or perhaps reverse projected increases in atmospheric CO2 genic carbon in the long-term (Metz et al., 2005; House et al., 2006; (Section 6.4), several methods have been proposed to increase the Orr, 2009) (see Box 6.1 and Archer et al., 2009b). The terrestrial bio- removal of atmospheric CO2 and enhance the storage of carbon in sphere may have a typical potential to store carbon equivalent to the land, ocean and geological reservoirs. These methods are categorized cumulative historical land use loss of 180 +/- 80 PgC (Table 6.1; Section as Carbon Dioxide Removal (CDR) methods (see Glossary). Another 6.5.2.1). class of methods involves the intentional manipulation of planetary solar absorption to counter climate change, and is called the Solar In this assessment, we use permanence to refer to time scales larger Radiation Management (SRM) (discussed in Chapter 7, Section 7.7; than tens of thousands of years. CDR methods associated with either see Glossary). In this section, CDR methods are discussed from the permanent or non-permanent carbon sequestration (see Table 6.14) aspect of the carbon cycle processes (Section 6.5.2) and their impacts have very different climate implications (Kirschbaum, 2003). Perma- and side effects on carbon cycle and climate (Section 6.5.3). A brief nent sequestration methods have the potential to reduce the radia- discussion on the indirect carbon cycle effects of SRM methods is given tive forcing of CO2 over time. By contrast, non-permanent sequestra- in Section 6.5.4. Most of the currently proposed CDR methods are sum- tion methods will release back the temporarily sequestered carbon as marized in Table 6.14 and some are illustrated schematically in Chapter CO2 to the atmosphere, after some delayed time interval (Herzog et 7 (Section 7.7; FAQ 7.3 Figure 1). Since some CDR methods might oper- al., 2003). As a consequence, elevated levels of atmospheric CO2 and ate on large spatial scales they are also called Geoengineering pro- climate warming will only be delayed and not avoided by the imple- posals (Keith, 2001). Removal of CH4 and N2O has also been proposed mentation of non-permanent CDR methods (Figure 6.39). Neverthe- to reduce climate change (Stolaroff et al., 2012). While the science of less, CDR methods that could create a temporary CO2 removal (Table geoengineering methods is assessed in this section (CDR) and Chapter 6.14) may still have value (Dornburg and Marland, 2008) by reducing 7 (SRM), the benefits and risks of SRM are planned to be assessed in the cumulative impact of higher temperature. Chapter 19 of AR5 WGII report. Further, Chapter 6 of AR5 WGIII report plans to assess the cost and socioeconomic implications of some CDR Another important carbon cycle consequence of CDR methods is the 6 and SRM methods for climate stabilization pathways. rebound effect (see Glossary). In the Industrial Era (since 1750) about half of the CO2 emitted into the atmosphere from fossil fuel emissions Large-scale industrial methods such as carbon capture and storage has been taken up by land and ocean carbon reservoirs (see Section 6.3 (CCS), biofuel energy production (without CCS) and reducing emis- and Table 6.1). As for current CO2 emissions and the consequent CO2 sions from deforestation and degradation (REDD) cannot be called rise, which are currently opposed by uptake of CO2 by natural reservoirs, CDR methods since they reduce fossil fuel use or land use change CO2 any removal of CO2 from the atmosphere by CDR will be opposed by emissions to the atmosphere but they do not involve a net removal of release of CO2 from natural reservoirs (Figure 6.40). It is thus virtually CO2 that is already in the atmosphere. However, direct air capture of certain that the removal of CO2 by CDR will be partially offset by out- CO2 using industrial methods (Table 6.14; and FAQ 7.3 Figure 1) will gassing of CO2 from the ocean and land ecosystems. Therefore, return- 546 Carbon and Other Biogeochemical Cycles Chapter 6 Table 6.14 | Examples of CDR methods and their implications for carbon cycle and climate. The list is non-exhaustive. A rebound effect and a thermal inertia of climate system are associated with all CDR methods. Carbon Cycle Nature of Process to Some Carbon Cycle and CDR Method Name CDR Removal Storage Location Storage Form be Modified Climate Implications Process Intentionally Afforestation / reforestationa Biological a,b,h Land (biomass, soils) Organic a,b,c,d,e,f,g,h,i Alters surface albedo a,b,c,d,e,f,g,h,i,j Improved forest managementb d Land/ocean floor Inorganic j and evapotranspiration a,b,c,e,f,g,h,i Lack of permanence Sequestration of wood in buildingsc e, f,g Land (soils) dPotentially permanent if buried on Biomass buriald i Land (wetland soils) the ocean floor Enhanced biological No till agriculturee j Ocean / geological formations jPermanent if stored in geological reservoir production and Biocharf storage on land Conservation agricultureg Fertilisation of land plantsh Creation of wetlandsi Biomass Energy with Carbon Capture and Storage (BECCS)j Ocean iron fertilisationk Biological Ocean k,n Inorganic kMay lead to expanded regions with Algae farming and buriall l,m Organic low oxygen concentration, increased Enhanced biological N2O production, deep ocean acidi- production and Blue carbon (mangrove, kelp farming)m fication and disruptions to marine storage in ocean Modifying ocean upwelling to bring ecosystems and regional carbon cycle nutrients from deep ocean to surface nDisruptions to regional carbon cycle oceann Enhanced weathering over lando Chemical o Soils and oceans o,p Inorganic o Permanent removal; likely to change Accelerated Enhanced weathering over oceanp p Ocean pH of soils, rivers, and ocean weathering pPermanent removal; likely to change pH of ocean Direct-air capture with storage Chemical Ocean/geological formations Inorganic Permanent removal if stored Others in geological reservoirs Notes Superscripts in column 2 refer to the corresponding superscripts in columns 4, 5 and 6 of the same row. ing to pre-industrial CO2 levels would require permanently sequester- achieve globally negative emissions after around 2080 (see Section ing an amount of carbon equal to total anthropogenic CO2 emissions 6.4.3). RCP4.5 also assumes some use of BECCS to stabilise CO2 con- that have been released before the time of CDR, roughly twice as much centration by 2100. Therefore it should be noted that potentials for the excess of atmospheric CO2 above pre-industrial level (Lenton and CDR assessed in this section cannot be seen as additional potential Vaughan, 2009; Cao and Caldeira, 2010b; Matthews, 2010). for CO2 removal from the low RCPs as this is already included in those scenarios. 6.5.2 Carbon Cycle Processes Involved in Carbon Dioxide Removal Methods 6.5.2.1 Enhanced Carbon Sequestration by Land Ecosystems The CDR methods listed in Table 6.14 rely primarily on human man- The key driver of these CDR methods is net primary productivity on agement of carbon cycle processes to remove CO2: (1) enhanced net land that currently produces biomass at a rate of approximately 50 biological uptake and subsequent sequestration by land ecosystems, to 60 PgC yr 1 (Nemani et al., 2003). The principle of these CDR meth- (2) enhanced biological production in ocean and subsequent seques- ods is to increase net primary productivity and/or store a larger frac- tration in the ocean and (3) accelerated chemical weathering reactions tion of the biomass produced into ecosystem carbon pools with long over land and ocean. The exceptional CDR method is industrial direct turnover times, for example, under the form of wood or refractory air capture of CO2, for example, relying on chemistry methods. CO2 organic matter in soils (Table 6.14). One variant is to harvest biomass removed by CDR is expected to be stored in organic form on land and for energy production and sequester the emitted CO2 (BECCS). BECCS in inorganic form in ocean and geological reservoirs (Table 6.14). This technology has not been tested at industrial scale, but is commonly management of the carbon cycle however has other implications on included in Integrated Assessment Models and future scenarios that 6 ecosystems and biogeochemical cycles. The principle of different CDR aim to achieve low CO2 concentrations. methods listed in Table 6.14 is described below and the characteristics of some CDR methods are summarized in Table 6.15. Estimates of the global potential for enhanced primary productivity over land are uncertain because the potential of any specific method Some of the RCP scenarios used as a basis for future projections in this will be severely constrained by competing land needs (e.g., agriculture, Assessment Report already include some CDR methods. To achieve biofuels, urbanization and conservation) and sociocultural consider- the RCP2.6 CO2 peak and decline the IMAGE integrated assessment ations. An order of magnitude of the upper potential of afforestation/ model simulates widespread implementation of BECCS technology to reforestation would be the restoration of all the carbon released by 547 Chapter 6 Carbon and Other Biogeochemical Cycles historical land use (180 +/- 80 PgC; Table 6.1; Section 6.3.2.2). House CDR-removed emissions et al. (2002) estimated that the atmospheric CO2 concentration by CO2 emissions (PgC yr-1) 2100 would be lowered by only about 40 to 70 ppm in that scenario (accounting for the rebound effect). Re-release of CDR-removed CO2 The capacity for enhancing the soil carbon content on agricultural and from non-permanent reservoirs degraded lands was estimated by one study at 50 to 60% of the his- torical soil carbon released, that is 42 to 78 PgC (Lal, 2004a). The pro- posed agricultural practices are the adoption of conservation tillage with cover crops and crop residue mulch, conversion of marginal lands into restorative land uses and nutrient cycling including the use of 500 Implied emissions Atmospheric CO2 Zero emissions Atmospheric CO2 (ppm) 400 (ppm) One time removal 300 Sustained removal (PgC) 1000 500 1000 PgC emitted; no CDR CDR & permanent storage of 380 PgC 0 Temperature change (°C) CDR & non-permanent storage of 380 PgC 2.0 Temperature 1.5 (C) 1.0 0.5 0.0 1850 1950 2050 2150 2250 2350 2450 Year Figure 6.40 | Idealised simulations with a simple global carbon cycle model (Cao and Figure 6.39 | Idealised model simulations (Matthews, 2010) to illustrate the effects of Caldeira, 2010b) to illustrate the rebound effect . Effects of an instantaneous cessa- CDR methods associated with either permanent or non-permanent carbon sequestra- tion of CO2 emissions in the year 2050 (amber line), one-time removal of the excess tion. There is an emission of 1000 PgC in the reference case (black line) between 1800 of atmospheric CO2 over pre-industrial levels (blue line) and removal of this excess of and 2100, corresponding approximately to RCP4.5 scenario (Section 6.4). Permanent atmospheric CO2 followed by continued removal of all the CO2 that degasses from the sequestration of 380 PgC, assuming no leakage of sequestered carbon would reduce cli- ocean (green line) are shown. For the years 1850 2010 observed atmospheric CO2 con- mate change (blue line, compared to black line). By contrast, a non-permanent seques- centrations are prescribed and CO2 emissions are calculated from CO2 concentrations 6 tration CDR method where carbon will be sequestered and later on returned to the and modeled carbon uptake. For the years 2011 2049, CO2 emissions are prescribed atmosphere in three centuries would not. In this idealised non-permanent sequestration following the SRES A2 scenario. Starting from year 2050, CO2 emission is either set to example scenario, climate change would only be delayed but the eventual magnitude of zero or calculated from modeled CO2 concentrations and CO2 uptake. To a first approxi- climate change will be equivalent to the no-sequestration case (green line, compared to mation, a cessation of emissions would prevent further warming but would not lead to black). Figure adapted from Figure 5 of Matthews (2010). significant cooling on the century time scale. A one-time removal of excess atmospheric CO2 would eliminate approximately only half of the warming experienced at the time of the removal because of CO2 that outgases from the ocean (the rebound effect). To bring atmospheric CO2 back to pre-industrial levels permanently, would require the removal of all previously emitted CO2, that is, an amount equivalent to approximately twice the excess atmospheric CO2 above pre-industrial level. (Figure adapted from Cao and Caldeira, 2010b.) 548 Carbon and Other Biogeochemical Cycles Chapter 6 Table 6.15 | Characteristics of some CDR methods from peer-reviewed literature. Note that a variety of economic, environmental, and other constraints could also limit their implementation and net potential. Means of Carbon Physical Potential Carbon Dioxide Removing Time Scale of Storage / of CO2 Removed Reference Unintended Side Effects Removal Method CO2 from Carbon Storage Form in a Centurya Atmosphere Biological Land /organic Decades to centuries 40 70 PgC House et al. (2002) Alters surface energy budget, depend- Afforestation and Canadell and ing on location; surface warming will reforestation Raupach (2008) be locally increased or decreased; hydrological cycle will be changed Bio-energy with car- Biological Geological or Effectively perma- 125 PgC See the footnoteb Same as above bon-capture and stor- ocean /inorganic nent for geologic, age (BECCS); biomass centuries for ocean energy with carbon capture and storage Biological Land /organic Decades to centuries 130 PgC Woolf et al. (2010) Same as above Biochar creation and storage in soils Biological Ocean / Centuries to millennia 15 60 PgC Aumont and Bopp (2006), Expanded regions with low oxygen inorganic Jin and Gruber (2003) concentration; enhanced N2O Ocean fertilisation Zeebe and Archer (2005) emissions; altered production of by adding nutrients 280 PgC Cao and Caldeira (2010a) dimethyl sulphide and non-CO2 to surface waters greenhouse gases; possible disruptions to marine ecosystems and regional carbon cycles Ocean-enhanced Biological Ocean / Centuries to millennia 90 PgC Oschlies et al. (2010a); Likely to cause changes to regional upwelling bringing inorganic 1 2 PgC Lenton and Vaughan ocean carbon cycle opposing CO2 more nutrients to (2009), Zhou and removal, e.g., compensatory surface waters Flynn (2005) downwelling in other regions Geochemical Ocean (and Centuries to mil- No determined limit Kelemen and Matter pH of soils and rivers will increase Land-based increased some soils) / lennia for carbon- (2008), Schuiling and locally, effects on terrestrial/ weathering inorganic ates, permanent for Krijgsman (2006) freshwater ecosystems silicate weathering 100 PgC Köhler et al. (2010) Geochemical Ocean / Centuries to mil- No determined limit Rau (2008), Increased alkalinity effects Ocean-based inorganic lennia for carbon- Kheshgi (1995) on marine ecosystems increased weathering ates, permanent for silicate weathering Chemical Geological or Effectively perma- No determined limit Keith et al. (2006), Not known Direct air capture ocean /inorganic nent for geologic, Shaffer (2010) centuries for ocean Notes: a Physical potential does not account for economic or environmental constraints of CDR methods; for example, the value of the physical potential for afforestation and reforestation does not consider the conflicts with land needed for agricultural production. Potentials for BECCS and biochar are highly speculative. b If 2.5 tC yr 1 per hectare can be harvested on a sustainable basis (Kraxner et al., 2003) on about 4% (~500 million hectares, about one tenth of global agricultural land area) of global land (13.4 billion hectares) for BECCS, approximately 1.25 PgC yr 1 could be removed or about 125 PgC in this century. Future CO2 concentration pathways, especially RCP2.6 and RCP4.5 include some CO2 removal by BECCS (Chapter 6 of AR5 WGIII) and hence the potentials estimated here cannot add on to existing model results (Section 6.4). compost and manure. Recent estimates suggest a cumulative potential organic carbon produced gets transported to the deep ocean. Some of 30 to 60 PgC of additional storage over 25 to 50 years (Lal, 2004b). of the inorganic carbon in the surface ocean that is removed by the export of net primary productivity below the surface layer will be sub- Finally, biochar and biomass burial methods aim to store organic sequently replaced by CO2 pumped from the atmosphere, thus remov- carbon into very long turnover time ecosystem carbon pools. The ing atmospheric CO2. Ocean primary productivity is limited by nutrients maximum sustainable technical potential of biochar cumulative (e.g., iron, nitrogen and phosphorus). Enhanced biological production s ­equestration is estimated at 130 PgC over a century by one study in ocean CDR methods (Table 6.14) is obtained by adding nutrients (Woolf et al., 2010). The residence time of carbon converted to biochar that would otherwise be limiting (Martin, 1990). The expected increase 6 and the additional effect of biochar on soil productivity are uncertain, in the downward flux of carbon can be partly sequestered as Dissolved and further research is required to assess the potential of this method Inorganic Carbon (DIC) after mineralization in the intermediate and (Shepherd et al., 2009). deep waters. In other ocean-based CDR methods, algae and kelp farm- ing and burial, carbon would be stored in organic form. 6.5.2.2 Enhanced Carbon Sequestration in the Ocean The effectiveness of ocean CDR through iron addition depends on the The principle here is to enhance the primary productivity of phyto- resulting increase of productivity and the fraction of this extra carbon plankton (biological pump; Section 6.1.1) so that a fraction of the extra exported to deep and intermediate waters, and its fate. Small-scale 549 Chapter 6 Carbon and Other Biogeochemical Cycles (~10 km2) experiments (Boyd et al., 2007) have shown only limited (century) time scale. For instance, large amounts of silicate minerals transient effects of iron addition in removing atmospheric CO2. An such as olivine ((Mg,Fe)2SiO4) could be mined, crushed, transported to increased productivity was indeed observed, but this effect was moder- and distributed on agricultural lands, to remove atmospheric CO2 and ated either by other limiting elements, or by compensatory respiration form carbonate minerals in soils and/or bicarbonate ions that would from increased zooplankton grazing. Most of the carbon produced by be transported to the ocean by rivers (Schuiling and Krijgsman, 2006)l. primary productivity is oxidized (remineralized into DIC) in the surface Alternatively, CO2 removal by weathering reactions might be enhanced layer, so that only a small fraction is exported to the intermediate and by exposing minerals such as basalt or olivine to elevated CO2, with deep ocean (Lampitt et al., 2008) although some studies indicate little potential CO2 removal rates exceeding 0.25 PgC yr 1 (Kelemen and remineralization in the surface layer (Jacquet et al., 2008). A recent Matter, 2008). In the idealised case where olivine could be spread as a study (Smetacek et al., 2012) finds that at least half the extra carbon in fine powder over all the humid tropics, potential removal rates of up to plankton biomass generated by artificial iron addition sank far below 1 PgC yr 1 have been estimated, despite limitations by the saturation a depth of 1000 m, and that a substantial portion is likely to have concentration of silicic acid (Köhler et al., 2010). For the United King- reached the sea floor. There are some indications that sustained natu- dom, the potential from silicate resources was estimated to be more ral iron fertilisation may have a higher efficiency in exporting carbon than 100 PgC (Renforth, 2012). from surface to intermediate and deep ocean than short term blooms induced by artificial addition of iron (Buesseler et al., 2004; Blain et al., Fossil fuel CO2 released to the atmosphere leads to the addition of 2007; Pollard et al., 2009). Thus, there is no consensus on the efficiency anthropogenic CO2 in the ocean (Section 6.3.2.5). This anthropogenic of iron fertilisation from available field experiments. CO2 will eventually dissolve ocean floor carbonate sediments to reach geochemical equilibrium on a 10 kyr time scale (Archer et al., 1997). Using ocean carbon models (see Section 6.3.2.5.6), the maximum The principle of ocean based weathering CDR methods is to accelerate drawdown of atmospheric CO2 have been estimated from 15 ppm this process. For instance, carbonate rocks could be crushed, reacted (Zeebe and Archer, 2005) to 33 ppm (Aumont and Bopp, 2006) for an with CO2 (e.g., captured at power plants) to produce bicarbonate idealised continuous (over 100 years) global ocean iron fertilisation, ions that would be released to the ocean (Rau and Caldeira, 1999; which is technically unrealistic. In other idealised simulations of ocean Caldeira and Rau, 2000; Rau, 2008). Alternatively, carbonate miner- fertilisation in the global ocean or only in the Southern Ocean (Joos als could be directly released into the ocean (Kheshgi, 1995; Harvey, et al., 1991; Peng and Broecker, 1991; Watson et al., 1994; Cao and 2008). Strong bases, derived from silicate rocks, could also be released Caldeira, 2010a), atmospheric CO2 was reduced by less than 100 ppm to ocean (House et al., 2007) to increase alkalinity and drawdown for ideal conditions. Jin and Gruber (2003) obtained an atmospheric of atmospheric CO2. Carbonate minerals such as limestone could be drawdown of more than 60 ppm over 100 years from an idealised iron heated to produce lime (Ca(OH)2); this lime could be added to the fertilisation scenario over the entire Southern Ocean. The radiative ocean to increase alkalinity as well (Kheshgi, 1995). While the level of benefit from lower CO2 could be offset by a few percent to more than confidence is very high for the scientific understanding of weathering 100% from an increase in N2O emissions (Jin and Gruber, 2003). All the chemical reactions, it is low for its effects and risks at planetary scale above estimates of maximum potential CO2 removal account for the (Section 6.5.3.3). rebound effect from oceans but not from the land (thus overestimate the atmospheric CO2 reduction). 6.5.2.4 Carbon Dioxide Removal by Direct Industrial Capture of Atmospheric Carbon Dioxide One ocean CDR variant is to artificially supply more nutrients to the surface ocean in upwelling areas (Lovelock and Rapley, 2007; Karl Direct Air Capture refers to the chemical process by which a pure CO2 and Letelier). The amount of carbon sequestered by these enhanced stream is produced by capturing CO2 from ambient air. The captured upwelling methods critically depends on their location (Yool et al., CO2 could be sequestered in geological reservoirs or the deep ocean. At 2009). Idealised simulations suggest an atmospheric CO2 removal at least three methods have been proposed to capture CO2 from the atmo- a net rate of about 0.9 PgC yr 1 (Oschlies et al., 2010b). This ocean- sphere: (1) adsorption on solids (Gray et al., 2008; Lackner, 2009, 2010; based CDR method has not been tested in the field, unlike iron addition Lackner et al., 2012); (2) absorption into highly alkaline solutions (Sto- experiments. laroff et al., 2008; Mahmoudkhani and Keith, 2009) and (3) absorption into moderate alkaline solution with a catalyst (Bao and Trachtenberg, 6.5.2.3 Accelerated Weathering 2006). The main limitation to direct air capture is the thermodynamic barrier due to the low concentration of CO2 in ambient air. The removal of CO2 by the weathering of silicate and carbonate miner- 6 als (Berner et al., 1983; Archer et al., 2009b) occurs on time scales from 6.5.3 Impacts of Carbon Dioxide Removal Methods thousands to tens of thousands of years (see Box 6.1) and at a rate on Carbon Cycle and Climate of ~ 0.3 PgC yr 1 (Figure 6.1; Gaillardet et al., 1999; Hartmann et al., 2009). This rate is currently much too small to offset the rate at which One impact common to all CDR methods is related to the thermal iner- fossil fuel CO2 is being emitted (Section 6.3). tia of the climate system. Climate warming will indeed continue for at least decades after CDR is applied. Therefore, temperature (and climate The principle of accelerated weathering CDR on land is to dissolve change) will lag a CDR-induced decrease in atmospheric CO2 (Boucher artificially silicate minerals so drawdown of atmospheric CO2 and et al., 2012). Modelling the impacts of CDR on climate change is still in geochemical equilibrium restoration could proceed on a much faster its infancy. Some of the first studies (Wu et al., 2010; Cao et al., 2011) 550 Carbon and Other Biogeochemical Cycles Chapter 6 showed that the global hydrological cycle could intensify in response rise rapidly because carbon removed from the atmosphere and stored to a reduction in atmospheric CO2 concentrations. in soils in the cooler climate caused by artificial upwelling could be rapidly released back (Oschlies et al., 2010b). The level of confidence 6.5.3.1 Impacts of Enhanced Land Carbon Sequestration on the impacts of the enhanced upwelling is low. In the case of land-based CDR, biomass in forests is a non-permanent 6.5.3.3 Impact of Enhanced Weathering ecosystem carbon pool and hence there is a risk that this carbon may return to the atmosphere, for example, by disturbances such as fire, or In the case of weathering-based CDR, the pH and carbonate mineral by future land use change. When considering afforestation/reforesta- saturation of soils, rivers and ocean surface waters will increase where tion, it is also important to account for biophysical effects on climate CDR is implemented. Köhler et al. (2010) simulated that the pH of the that come together with carbon sequestration because afforestation/ Amazon river would rise by 2.5 units if the dissolution of olivine in the reforestation changes the albedo (see Glossary), evapotranspiration entire Amazon basin was used to remove 0.5 PgC yr 1 from the atmo- and the roughness of the surface (Bonan, 2008; Bernier et al., 2011). sphere. In the marine environment, elevated pH and increased alka- Modelling studies show that afforestation in seasonally snow covered linity could potentially counteract the effects of ocean acidification, boreal and temperate regions will decrease the land surface albedo which is beneficial. Changes in alkalinity could also modify existing and have a net (biophysical plus biogeochemical) warming effect, ecosystems. There is uncertainty in our understanding of the net effect whereas afforestation in low latitudes (Tropics) is likely to enhance on ocean CO2 uptake but there will be a partial offset of the abiotic latent heat flux from evapotranspiration and have a net cooling effect effect by calcifying species. As for other CDR methods, the confidence (Bonan et al., 1992; Betts, 2000; Bala et al., 2007; Montenegro et al., level on the carbon cycle impacts of enhanced weathering is low. 2009; Bathiany et al., 2010). Consequently, the location of land eco- system based CDR methods needs to be considered carefully when 6.5.4 Impacts of Solar Radiation Management on the evaluating their effects on climate (Bala et al., 2007; Arora and Monte- Carbon Cycle negro, 2011; Lee et al., 2011; Pongratz et al., 2011b). In addition CDR in land ecosystems is likely to increase N2O emissions (Li et al., 2005). Solar radiation management (SRM) methods aim to reduce incoming Enhanced biomass production may also require more nutrients (fertilis- solar radiation at the surface (discussed in Section 7.7 and in AR5, ers) which are associated with fossil fuel CO2 emission from industrial WG2, Chapter 19). Balancing reduced outgoing radiation by reduced fertiliser production and Nr impacts. Biochar-based CDR could reduce incoming radiation may be able to cool global mean temperature but N2O emissions but may increase CO2 and CH4 emissions from agricul- may lead to a less intense global hydrological cycle (Bala et al., 2008) tural soils (Wang et al., 2012b). Addition of biochar could also promote with regionally different climate impacts (Govindasamy et al., 2003; a rapid loss of forest humus and soil carbon in some ecosystems during Matthews and Caldeira, 2007; Robock et al., 2008; Irvine et al., 2010; the first decades (Wardle et al., 2008). Ricke et al., 2010). Therefore, SRM will not prevent the effects of cli- mate change on the carbon and other biogeochemical cycles. 6.5.3.2 Impacts of Enhanced Carbon Sequestration in the Ocean SRM could reduce climate warming but will not interfere with the In the case of ocean-based CDR using fertilisation, adding macronu- direct biogeochemical effects of elevated CO2 on the carbon cycle. For trients such as nitrogen and phosphate in the fertilised region could example, ocean acidification caused by elevated CO2 (Section 6.4.4) lead to a decrease in production downstream of the fertilised region and the CO2 fertilisation of productivity (Box 6.3) will not be altered (Gnanadesikan et al., 2003; Gnanadesikan and Marinov, 2008; Watson by SRM (Govindasamy et al., 2002; Naik et al., 2003; Matthews and et al., 2008). Gnanadesikan et al. (2003) simulated a decline in export Caldeira, 2007). Similarly, SRM will not interfere with the stomatal production of 30 tC for every ton removed from the atmosphere. A response of plants to elevated CO2 (the CO2-physiological effect) that sustained global-ocean iron fertilisation for SRES A2 CO2 emission sce- leads to a decline in evapotranspiration, causing land temperatures to nario was also found to acidify the deep ocean (pH decrease of about warm and runoff to increase (Gedney et al., 2006; Betts et al., 2007; 0.1 to 0.2) while mitigating surface pH change by only 0.06 (Cao and Matthews and Caldeira, 2007; Piao et al., 2007; Cao et al., 2010; Fyfe Caldeira, 2010a). Other environmental risks associated with ocean et al., 2013). fertilisation include expanded regions with low oxygen concentration (Oschlies et al., 2010a), increased N2O emission (Jin and Gruber, 2003), However, due to carbon climate feedbacks (Section 6.4), the imple- increased production of dimethylsulphide (DMS), isoprene, CO, N2O, mentation of SRM could affect the carbon cycle. For instance, carbon CH4 and other non-CO2 GHGs (Oschlies et al., 2010a) and possible dis- uptake by land and ocean could increase in response to SRM by reduc- ruptions to marine ecosystems (Denman, 2008). ing the negative effects of climate change on carbon sinks (Matthews 6 and Caldeira, 2007). For instance, for the SRES A2 scenario with SRM, In the case of enhanced ocean upwelling CDR methods there could be a lower CO2 concentration of 110 ppm by year 2100 relative to a disturbance to the regional carbon balances, since the extra-upwelling baseline case without SRM has been simulated by Matthews and Cal- will be balanced by extra-downwelling at another location. Along with deira (2007). Land carbon sinks may be enhanced by increasing the growth-supporting nutrients, enhanced concentrations of DIC will also amount of diffuse relative to direct radiation (Mercado et al., 2009) if be brought to surface waters and partially offset the removal of CO2 SRM causes the fraction of diffuse light to increase (e.g., injection of by increased biological pump. Further, in case artificially enhanced a ­ erosols into the stratosphere). However, reduction of total incoming upwelling would be stopped, atmospheric CO2 concentrations could solar radiation could decrease terrestrial CO2 sinks as well. 551 Chapter 6 Carbon and Other Biogeochemical Cycles 6.5.5 Synthesis CDR methods are intentional large scale methods to remove atmo- spheric CO2 either by managing the carbon cycle or by direct industrial processes (Table 6.14). In contrast to SRM methods, CDR methods that manage the carbon cycle are unlikely to present an option for rap- idly preventing climate change. The maximum (idealised) potential for atmospheric CO2 removal by individual CDR methods is compiled in Table 6.15. In this compilation, note that unrealistic assumptions about the scale of deployment, such as fertilising the entire global ocean, are used, and hence large potentials are simulated. The rebound effect in the natural carbon cycle is likely to diminish the effectiveness of all the CDR methods (Figure 6.40). The level of confidence on the effects of both CDR and SRM methods on carbon and other biogeochemical cycles is very low. Acknowledgements We wish to acknowledge Anna Peregon (LSCE, France) for investing countless hours compiling and coordinating input from all of the Chap- ter 6 Lead Authors. She was involved in the production of every aspect of the chapter and we could not have completed our task on time without her help. We also thank Brett Hopwood (ORNL, USA) for skilful and artistic edits of several graphical figures representing the global biogeochemical cycles in the Chapter 6 Introduction. We also thank Silvana Schott (Max Planck Institute for Biogeochemistry, Germany) for graphics artwork for several of the figures in Chapter 6. 6 552 Carbon and Other Biogeochemical Cycles Chapter 6 References Achard, F., H. D. Eva, P. Mayaux, H.-J. Stibig, and A. Belward, 2004: Improved Arora, V. K., and A. Montenegro, 2011: Small temperature benefits provided by estimates of net carbon emissions from land cover change in the tropics for the realistic afforestation efforts. Nature Geosci., 4, 514 518. 1990s. Global Biogeochem. Cycles, 18, GB2008. Arora, V. K., et al., 2011: Carbon emission limits required to satisfy future Adair, E. C., P. B. Reich, S. E. Hobbie, and J. M. H. Knops, 2009: Interactive effects representative concentration pathways of greenhouse gases. Geophys. Res. of time, CO2, N, and diversity on total belowground carbon allocation and Lett., 38, L05805. ecosystem carbon storage in a grassland community. Ecosystems, 12, 1037 Arora, V. K., et al., 2013: Carbon-concentration and carbon-climate feedbacks in 1052. CMIP5 Earth system models. J. Clim., 26, 5289-5314. Adkins, J. F., K. McIntyre, and D. P. Schrag, 2002: The salinity, temperature and 18O Artioli, Y., et al., 2012: The carbonate system in the North Sea: Sensitivity and model of the glacial deep ocean. Science, 298, 1769 1773. validation. J. Mar. Syst., 102 104, 1 13. Ahn, J. and E. J. Brook, 2008: Atmospheric CO2 and climate on millennial time scales Ashmore, M. R., 2005: Assessing the future global impacts of ozone on vegetation. during the last glacial period. Science, 322, 83 85. Plant Cell Environ., 28, 949 964. Ahn, J., et al., 2012: Atmospheric CO2 over the last 1000 years: A high resolution Assmann, K. M., M. Bentsen, J. Segschneider, and C. Heinze, 2010: An isopycnic record from the West Antarctic Ice Sheet (WAIS) Divide ice core. Global ocean carbon cycle model. Geosci. Model Dev., 3, 143 167. Biogeochem. Cycles, 26, GB2027. Aufdenkampe, A. K., et al., 2011: Rivering coupling of biogeochemical cycles Ainsworth, E. A. and S. P. Long, 2004: What have we learned from 15 years of between land, oceans and atmosphere. Front. Ecol. Environ., 9, 23 60. free-air CO2 enrichment (FACE)? A meta-analytic review of the responses of Aumont, O., and L. Bopp, 2006: Globalizing results from ocean in situ iron fertilization photosynthesis, canopy properties and plant production to rising CO2. New studies. Global Biogeochem. Cycles, 20, GB2017. Phytologist, 165, 351 372. Avis, C. A., A. J. Weaver, and K. J. Meissner, 2011: Reduction in areal extent of high- Ainsworth, E. A., C. R. Yendrek, S. Sitch, W. J. Collins, and L. D. Emberson, 2012: The latitude wetlands in response to permafrost thaw. Nature Geosci., 4, 444 448. effects of tropospheric ozone on net primary productivity and implications for Ayres, R. U., W. H. Schlesinger, and R. H. Socolow, 1994: Human impacts on the climate change. Annu. Rev. Plant Biol., 63, 637 661. carbon and nitrogen cycles.In: Industrial Ecology and Global Change [R. H. Allan, W., H. Struthers, and D. C. Lowe, 2007: Methane carbon isotope effects Socolow, C. Andrews, F. Berkhout and V. Thomas (eds.)]. Cambridge University caused by atomic chlorine in the marine boundary layer: Global model results Press, Cambridge, United Kingdom, and New York, NY, USA, pp. 121 155. compared with Southern Hemisphere measurements. J. Geophys. Res. Atmos., Bacastow, R. B., and C. D. Keeling, 1979: Models to predict future atmospheric CO2 112, D04306. concentrations. In: Workshop on the Global Effects of Carbon Dioxide from Fossil Amiro, B. D., A. Cantin, M. D. Flannigan, and W. J. de Groot, 2009: Future emissions Fuels. United States Department of Energy, Washington, DC, pp. 72 90. from Canadian boreal forest fires. Can. J. Forest Res., 39, 383 395. Baccini, A., et al., 2012: Estimated carbon dioxide emissions from tropical Anderson, R. F., M. Q. Fleisher, Y. Lao, and G. Winckler, 2008: Modern CaCO3 deforestation improved by carbon-density maps. Nature Clim. Change, 2, 182 preservation in equatorial Pacific sediments in the context of late-Pleistocene 185. glacial cycles. Mar. Chem., 111, 30 46. Bader, M., E. Hiltbrunner, and C. Ko rner, 2009: Fine root responses of mature Andreae, M. O. and P. Merlet, 2001: Emission of trace gases and aerosols from deciduous forest trees to free air carbon dioxide enrichment (FACE). Funct. Ecol., biomass burning. Global Biogeochem. Cycles, 15, 955 966. 23, 913 921. Andres, R. J., J. S. Gregg, L. Losey, G. Marland, and T. A. Boden, 2011: Monthly, global Baker, A., S. Cumberland, and N. Hudson, 2008: Dissolved and total organic and emissions of carbon dioxide from fossil fuel consumption. Tellus B, 63, 309 327. inorganic carbon in some British rivers. Area, 40, 117 127. Andres, R. J., et al., 2012: A synthesis of carbon dioxide emissions from fossil-fuel Baker, D. F., et al., 2006: TransCom 3 inversion intercomparison: Impact of transport combustion. Biogeosciences, 9, 1845 1871. model errors on the interannual variability of regional CO2 fluxes, 1988 2003. Aranjuelo, I., et al., 2011: Maintenance of C sinks sustains enhanced C assimilation Global Biogeochem. Cycles, 20, GB1002. during long-term exposure to elevated [CO2] in Mojave Desert shrubs. Oecologia, Bala, G., P. B. Duffy, and K. E. Taylor, 2008: Impact of geoengineering schemes on the 167, 339 354. global hydrological cycle. Proc. Natl. Acad. Sci. U.S.A., 105, 7664 7669. Archer, D., 2007: Methane hydrate stability and anthropogenic climate change. Bala, G., K. Caldeira, M. Wickett, T. J. Phillips, D. B. Lobell, C. Delire, and A. Mirin, 2007: Biogeosciences, 4, 521 544. Combined climate and carbon-cycle effects of large-scale deforestation. Proc. Archer, D. and E. Maier-Reimer, 1994: Effect of deep-sea sedimentary calcite Natl. Acad. Sci. U.S.A., 104, 6550 6555. preservation on atmospheric CO2 concentration. Nature, 367, 260 263. Baldocchi, D. D., et al., 2001: FLUXNET: A new tool to study the temporal and spatial Archer, D. and V. Brovkin, 2008: The millennial atmospheric lifetime of anthropogenic variability of ecosystem-scale carbon dioxide, water vapor and energy flux CO2. Clim. Change, 90, 283 297. densities. Bull. Am. Meteorol. Soc., 82, 2415 2435. Archer, D., H. Kheshgi, and E. Maier-Reimer, 1997: Multiple timescales for Ballantyne, A. P., C. B. Alden, J. B. Miller, P. P. Tans, and J. W. C. White, 2012: Increase neutralization of fossil fuel CO2. Geophys. Res. Lett., 24, 405 408. in observed net carbon dioxide uptake by land and oceans during the last 50 Archer, D., H. Kheshgi, and E. Maier-Reimer, 1998: Dynamics of fossil fuel CO2 years. Nature, 488, 70 72. neutralization by marine CaCO3. Global Biogeochem. Cycles, 12, 259 276. Balshi, M. S., A. D. McGuire, P. Duffy, M. D. Flannigan, D. W. Kicklighter, and J. Melillo, Archer, D., B. Buffett, and V. Brovkin, 2009a: Ocean methane hydrates as a slow 2009: Vulnerability of carbon storage in North American boreal forests to tipping point in the global carbon cycle. Proc. Natl. Acad. Sci. U.S.A., 106, 20596 wildfires during the 21st century. Global Change Biol., 15, 1491 1510. 20601. Bao, L. H., and M. C. Trachtenberg, 2006: Facilitated transport of CO2 across a liquid Archer, D., A. Winguth, D. Lea, and N. Mahowald, 2000: What caused the glacial/ membrane: Comparing enzyme, amine, and alkaline. J. Membr. Sci., 280, 330 interglacial atmospheric pCO2 cycles? Rev. Geophys., 38, 159 189. 334. Archer, D., et al., 2009b: Atmospheric lifetime of fossil fuel carbon dioxide. Annu. Rev. Barnard, R., P. W. Leadley, and B. A. Hungate, 2005: Global change, nitrification, and 6 Earth Planet. Sci., 37, 117 134. denitrification: A review. Global Biogeochem. Cycles, 19, GB1007. Archer, D. E., P. A. Martin, J. Milovich, V. Brovkin, G.-K. Plattner, and C. Ashendel, Barnes, R. T., and P. A. Raymond, 2009: The contribution of agricultural and urban 2003: Model sensitivity in the effect of Antarctic sea ice and stratification on activities to inorganic carbon fluxes within temperate watersheds. Chem. Geol., atmospheric pCO2. Paleoceanography, 18, 1012. 266, 318 327. Archibald, S., D. P. Roy, B. W. van Wilgen, and R. J. Scholes, 2009: What limits fire? Barnosky, A. D., 2008: Colloquium Paper: Megafauna biomass tradeoff as a driver An examination of drivers of burnt area in Southern Africa. Global Change Biol., of Quaternary and future extinctions. Proc. Natl. Acad. Sci. U.S.A., 105, 11543 15, 613 630. 11548. Arneth, A., et al., 2010: Terrestrial biogeochemical feedbacks in the climate system. Bastviken, D., J. Cole, M. Pace, and L. Tranvik, 2004: Methane emissions from lakes: Nature Geosci., 3, 525 532. Dependence of lake characteristics, two regional assessments, and a global Arora, V. K., and G. J. Boer, 2010: Uncertainties in the 20th century carbon budget estimate. Global Biogeochem. Cycles, 18, GB4009. associated with land use change. Global Change Biol., 16, 3327 3348. 553 Chapter 6 Carbon and Other Biogeochemical Cycles Bastviken, D., L. J. Tranvik, J. A. Downing, P. M. Crill, and A. Enrich-Prast, 2011: Boardman, C. P., V. Gauci, J. S. Watson, S. Blake, and D. J. Beerling, 2011: Contrasting Freshwater methane emissions offset the continental carbon sink. Science, 331, wetland CH4 emission responses to simulated glacial atmospheric CO2 in 50. temperate bogs and fens. New Phytologist, 192, 898 911. Bathiany, S., M. Claussen, V. Brovkin, T. Raddatz, and V. Gayler, 2010: Combined Bock, M., J. Schmitt, L. Moller, R. Spahni, T. Blunier, and H. Fischer, 2010: Hydrogen biogeophysical and biogeochemical effects of large-scale forest cover changes isotopes preclude marine hydrate CH4 emissions at the onset of Dansgaard- in the MPI earth system model. Biogeosciences, 7, 1383 1399. Oeschger events. Science, 328, 1686 1689. Batjes, N. H., 1996: Total carbon and nitrogen in the soils of the world. Eur. J. Soil Boden, T., G. Marland, and R. Andres, 2011: Global CO2 emissions from fossil- Sci., 47, 151 163. fuel burning, cement manufacture, and gas flaring: 1751 2008 (accessed Battin, T. J., S. Luyssaert, L. A. Kaplan, A. K. Aufdenkampe, A. Richter, and L. J. Tranvik, at 2011.11.10). Oak Ridge National Laboratory, U. S. Department of Energy, 2009: The boundless carbon cycle Nature Geosci., 2, 598 600. Carbon Dioxide Information Analysis Center, Oak Ridge, TN, U.S.A., doi:10.3334/ Beaugrand, G., M. Edwards, and L. Legendre, 2010: Marine biodiversity, ecosystem CDIAC/00001_V2011, http://cdiac.ornl.gov/trends/emis/overview_2008.html. functioning, and carbon cycles. Proc. Natl. Acad. Sci. U.S.A., 107, 10120 10124. Bonan, G. B., 2008: Ecological Climatology: Concepts and Applications. Cambridge Beaulieu, J. J., et al., 2011: Nitrous oxide emission from denitrification in stream and University Press, New York, NY, USA. river networks. Proc. Natl. Acad. Sci. U.S.A., 108, 214 219. Bonan, G. B., and S. Levis, 2010: Quantifying carbon-nitrogen feedbacks in the Beer, C., et al., 2010: Terrestrial gross carbon dioxide uptake: Global distribution and Community Land Model (CLM4). Geophys. Res. Lett., 37, L07401. covariation with climate. Science, 329, 834-838. Bonan, G. B., D. Pollard, and S. L. Thompson, 1992: Effects of boreal forest vegetation Bellassen, V., G. Le Maire, J. F. Dhote, P. Ciais, and N. Viovy, 2010: Modelling forest on global climate. Nature, 359, 716 718. management within a global vegetation model. Part 1: Model structure and Bond-Lamberty, B., and A. Thomson, 2010: Temperature-associated increases in the general behaviour. Ecol. Model., 221, 2458 2474. global soil respiration record. Nature, 464, 579 U132. Bellassen, V., N. Viovy, S. Luyssaert, G. Le Maire, M.-J. Schelhaas, and P. Ciais, 2011: Booth, B. B. B., et al., 2012: High sensitivity of future global warming to land carbon Reconstruction and attribution of the carbon sink of European forests between cycle processes. Environ. Res. Lett., 7, 024002. 1950 and 2000. Global Change Biol., 17, 3274 3292. Bopp, L., K. E. Kohfeld, C. Le Quéré, and O. Aumont, 2003: Dust impact on marine Bennington, V., G. A. McKinley, S. Dutkiewicz, and D. Ullman, 2009: What does biota and atmospheric CO2 during glacial periods. Paleoceanography, 18, 1046. chlorophyll variability tell us about export and air-sea CO2 flux variability in the Bopp, L., C. Le Quéré, M. Heimann, A. C. Manning, and P. Monfray, 2002: Climate- North Atlantic? Global Biogeochem. Cycles, 23, GB3002. induced oceanic oxygen fluxes: Implications for the contemporary carbon Berendse, F., et al., 2001: Raised atmospheric CO2 levels and increased N deposition budget. Global Biogeochem. Cycles, 16, 1022. cause shifts in plant species composition and production in Sphagnum bogs. Bopp, L., et al., 2001: Potential impact of climate change on marine export Global Change Biol., 7, 591 598. production. Global Biogeochem. Cycles, 15, 81 99. Bergamaschi, P., et al., 2009: Inverse modeling of global and regional CH4 emissions Boucher, O., et al., 2012: Reversibility in an Earth System model in response to CO2 using SCIAMACHY satellite retrievals. J. Geophys. Res., 114, D22301. concentration changes. Environ. Res. Lett., 7, 024013. Berger, W. H., 1982: Increase of carbon dioxide in the atmosphere during deglaciation: Bousquet, P., D. A. Hauglustaine, P. Peylin, C. Carouge, and P. Ciais, 2005: Two The coral-reef hypothesis. Naturwissenschaften, 69, 87 88. decades of OH variability as inferred by an inversion of atmospheric transport Berner, R. A., 1992: Weathering, plants, and the long-term carbon-cycle. Geochim. and chemistry of methyl chloroform. Atmos. Chem. Phys., 5, 2635 2656. Cosmochim. Acta, 56, 3225 3231. Bousquet, P., P. Peylin, P. Ciais, C. Le Quéré, P. Friedlingstein, and P. P. Tans, 2000: Berner, R. A., A. C. Lasaga, and R. M. Garrels, 1983: The carbonate-silicate Regional changes in carbon dioxide fluxes of land and oceans since 1980. geochemical cycle and its effect on atmospheric carbon dioxide over the past Science, 290, 1342 1346. 100 million years. Am. J. Sci., 283, 641 683. Bousquet, P., et al., 2006: Contribution of anthropogenic and natural sources to Bernier, P. Y., R. L. Desjardins, Y. Karimi-Zindashty, D. E. Worth, A. Beaudoin, Y. Luo, atmospheric methane variability. Nature, 443, 439 443. and S. Wang, 2011: Boreal lichen woodlands: A possible negative feedback to Bousquet, P., et al., 2011: Source attribution of the changes in atmospheric methane climate change in Eastern North America. Agr. Forest Meteorol., 151, 521 528. for 2006 2008. Atmos. Chem. Phys., 11, 3689 3700. Betts, R. A., 2000: Offset of the potential carbon sink from boreal forestation by Bouttes, N., D. Paillard, D. M. Roche, V. Brovkin, and L. Bopp, 2011: Last Glacial decreases in surface albedo. Nature, 408, 187 190. Maximum CO2 and d13C successfully reconciled. Geophys. Res. Lett., 38, L02705. Betts, R. A., et al., 2007: Projected increase in continental runoff due to plant Bouttes, N., et al., 2012: Impact of oceanic processes on the carbon cycle during the responses to increasing carbon dioxide. Nature, 448, 1037 1041. last termination. Clim. Past, 8, 149 170. Bianchi, D., J. P. Dunne, J. L. Sarmiento, and E. D. Galbraith, 2012: Data-based Bouwman, A. F., et al., 2013: Global trends and uncertainties in terrestrial estimates of suboxia, denitrification and N2O production in the ocean and their denitrification and N2O emissions. Philos. Trans. R. Soc. London Ser. B, 368, sensitivity to dissolved O2. Global Biogeochem. Cycles, 26, GB2009. 20130112. Biastoch, A., et al., 2011: Rising Arctic Ocean temperatures cause gas hydrate Bouwman, L., et al., 2011: Exploring global changes in nitrogen and phosphorus destabilization and ocean acidification. Geophys. Res. Lett., 38, L08602. cycles in agriculture induced by livestock production over the 1900 2050 Billings, S. A., S. M. Schaeffer, and R. D. Evans, 2002: Trace N gas losses and N period. Proc. Natl. Acad. Sci. U.S.A., doi:10.1073/pnas.1012878108. mineralization in Mojave desert soils exposed to elevated CO2. Soil Biol. Boyd, P. W., et al., 2007: Mesoscale iron enrichment experiments 1993 2005: Biochem., 34, 1777 1784. Synthesis and future directions. Science, 315, 612 617. Bird, M. I., J. Lloyd, and G. D. Farquhar, 1996: Terrestrial carbon storage from the last Bozbiyik, A., M. Steinacher, F. Joos, T. F. Stocker, and L. Menviel, 2011: Fingerprints of glacial maximum to the present. Chemosphere, 33, 1675 1685. changes in the terrestrial carbon cycle in response to large reorganizations in Blain, S., et al., 2007: Effect of natural iron fertilization on carbon sequestration in ocean circulation. Clim. Past, 7, 319 338. the Southern Ocean. Nature, 446, 1070 1074. Brenninkmeijer, C. A. M., et al., 2007: Civil Aircraft for the regular investigation of Blake, D. R., E. W. Mayer, S. C. Tyler, Y. Makide, D. C. Montague, and F. S. Rowland, the atmosphere based on an instrumented container: The new CARIBIC system. 6 1982: Global increase in atmospheric methane concentrations between 1978 Atmos. Chem. Phys., 7, 4953 4976. and 1980. Geophys. Res. Lett., 9, 477 480. Bridgham, S. D., J. P. Megonigal, J. K. Keller, N. B. Bliss, and C. Trettin, 2006: The carbon Bleeker, A., W. K. Hicks, F. Dentener, and J. Galloway, 2011: Nitrogen deposition as balance of North American wetlands. Wetlands, 26, 889 916. a threat to the World s protected areas under the Convention on Biological Broecker, W. S., and T.-H. Peng, 1986: Carbon cycle: 1985 glacial to interglacial Diversity. Environ. Pollut., 159, 2280 2288. changes in the operation of the global carbon cycle. Radiocarbon, 28, 309 327. Bloom, A. A., J. Lee-Taylor, S. Madronich, D. J. Messenger, P. I. Palmer, D. S. Reay, Broecker, W. S., E. Clark, D. C. McCorkle, T.-H. Peng, I. Hajdas, and G. Bonani, 1999: and A. R. McLeod, 2010: Global methane emission estimates from ultraviolet Evidence for a reduction in the carbonate ion content of the deep sea during the irradiation of terrestrial plant foliage. New Phytologist, 187, 417 425. course of the Holocene. Paleoceanography, 14, 744 752. Blunier, T., J. Chappellaz, J. Schwander, B. Stauffer, and D. Raynaud, 1995: Variations Brovkin, V., A. Ganopolski, D. Archer, and S. Rahmstorf, 2007: Lowering of glacial in atmospheric methane concentration during the Holocene epoch. Nature, 374, atmospheric CO2 in response to changes in oceanic circulation and marine 46 49. biogeochemistry. Paleoceanography, 22, PA4202. 554 Carbon and Other Biogeochemical Cycles Chapter 6 Brovkin, V., J. H. Kim, M. Hofmann, and R. Schneider, 2008: A lowering effect of Chambers, J. Q., J. I. Fisher, H. Zeng, E. L. Chapman, D. B. Baker, and G. C. Hurtt, 2007: reconstructed Holocene changes in sea surface temperatures on the atmospheric Hurricane Katrina s carbon footprint on U.S. Gulf Coast forests. Science, 318, CO2 concentration. Global Biogeochem. Cycles, 22, GB1016. 1107. Brovkin, V., T. Raddatz, C. H. Reick, M. Claussen, and V. Gayler, 2009: Global Chantarel, A. M., J. M. G. Bloor, N. Deltroy, and J.-F. Soussana, 2011: Effects of climate biogeophysical interactions between forest and climate. Geophys. Res. Lett., 36, change drivers on nitrous oxide fluxes in an upland temperate grassland. L07405. Ecosystems, 14, 223 233. Brovkin, V., S. Sitch, W. von Bloh, M. Claussen, E. Bauer, and W. Cramer, 2004: Role of Chapuis-Lardy, L., N. Wrage, A. Metay, J. L. Chotte, and M. Bernoux, 2007: Soils, a land cover changes for atmospheric CO2 increase and climate change during the sink for N2O? A review. Global Change Biol., 13, 1 17. last 150 years. Global Change Biol., 10, 1253 1266. Chen, Y. H., and R. G. Prinn, 2006: Estimation of atmospheric methane emissions Brovkin, V., J. Bendtsen, M. Claussen, A. Ganopolski, C. Kubatzki, V. Petoukhov, and A. between 1996 and 2001 using a three-dimensional global chemical transport Andreev, 2002: Carbon cycle, vegetation, and climate dynamics in the Holocene: model. J. Geophys. Res. Atmos., 111, D10307. Experiments with the CLIMBER-2 model. Global Biogeochem. Cycles, 16, 1139. Chhabra, A., K. R. Manjunath, S. Panigrahy, and J. S. Parihar, 2013: Greenhouse gas Brovkin, V., et al., 2010: Sensitivity of a coupled climate-carbon cycle model to large emissions from Indian livestock. Clim. Change, 117, 329 344. volcanic eruptions during the last millennium. Tellus B, 62, 674 681. Chierici, M., and A. Fransson, 2009: Calcium carbonate saturation in the surface Brown, J. R., et al., 2011: Effects of multiple global change treatments on soil N2O water of the Arctic Ocean: Undersaturation in freshwater influenced shelves. fluxes. Biogeochemistry, 109, 85 100. Biogeosciences, 6, 2421 2431. Brzezinski, M. A., et al., 2002: A switch from Si(OH)4 to NO3 depletion in the glacial Christensen, T. R., et al., 2004: Thawing sub-arctic permafrost: Effects on vegetation Southern Ocean. Geophys. Res. Lett., 29, 1564. and methane emissions. Geophys. Res. Lett., 31, L04501. Buesseler, K. O., J. E. Andrews, S. M. Pike, and M. A. Charette, 2004: The effects of Churkina, G., V. Brovkin, W. von Bloh, K. Trusilova, M. Jung, and F. Dentener, 2009: iron fertilization on carbon sequestration in the Southern Ocean. Science, 304, Synergy of rising nitrogen depositions and atmospheric CO2 on land carbon 414 417. uptake moderately offsets global warming. Global Biogeochem. Cycles, 23, Burke, E. J., C. D. Jones, and C. D. Koven, 2013: Estimating the permafrost-carbon- GB4027. climate response in the CMIP5 climate models using a simplified approach. J. Ciais, P., P. Rayner, F. Chevallier, P. Bousquet, M. Logan, P. Peylin, and M. Ramonet, Clim., 26, 4897-4909. 2010: Atmospheric inversions for estimating CO2 fluxes: methods and Burn, C. R., and F. E. Nelson, 2006: Comment on A projection of severe near-surface perspectives. Clim. Change, 103, 69 92(24). permafrost degradation during the 21st century by David M. Lawrence and Ciais, P., et al., 2012: Large inert carbon pool in the terrestrial biosphere during the Andrew G. Slater. Geophys. Res. Lett., 33, L21503. Last Glacial Maximum. Nature Geosci., 5, 74 79. Butterbach-Bahl, K., and M. Dannenmann, 2011: Denitrification and associated soil Ciais, P., et al., 2008: Carbon accumulation in European forests. Nature Geosci., 1, N2O emissions due to agricultural activities in a changing climate. Curr. Opin. 425 429. Environ. Sustain., 3, 389 395. Ciais, P., et al., 2005: Europe-wide reduction in primary productivity caused by the Byrne, R. H., S. Mecking, R. A. Feely, and X. W. Liu, 2010: Direct observations of basin- heat and drought in 2003. Nature, 437, 529 533. wide acidification of the North Pacific Ocean. Geophys. Res. Lett., 37, L02601. Clark, D. B., D. A. Clark, and S. F. Oberbauer, 2010: Annual wood production in a Cadule, P., et al., 2010: Benchmarking coupled climate-carbon models against long- tropical rain forest in NE Costa Rica linked to climatic variation but not to term atmospheric CO2 measurements. Global Biogeochem. Cycles, 24, GB2016. increasing CO2. Global Change Biol., 16, 747 759. Cai, W.-J., et al., 2011: Acidification of subsurface coastal waters enhanced by Claussen, M., et al., 2002: Earth system models of intermediate complexity: Closing eutrophication. Nature Geosci, 4, 766 770. the gap in the spectrum of climate system models. Clim. Dyn., 18, 579 586. Caldeira, K., and G. H. Rau, 2000: Accelerating carbonate dissolution to sequester Cocco, V., et al., 2013: Oxygen and indicators of stress for marine life in multi-model carbon dioxide in the ocean: Geochemical implications. Geophys. Res. Lett., 27, global warming projections. Biogeosciences, 10, 1849 1868. 225 228. Codispoti, L. A., 2007: An oceanic fixed nitrogen sink exceeding 400 Tg N a 1 vs Caldeira, K., and M. E. Wickett, 2005: Ocean model predictions of chemistry changes the concept of homeostasis in the fixed-nitrogen inventory. Biogeosciences, 4, from carbon dioxide emissions to the atmosphere and ocean. J. Geophys. Res. 233 253. Oceans, 110, C09S04. Codispoti, L. A., 2010: Interesting Times for Marine N2O. Science, 327, 1339 1340. Canadell, J. G., and M. R. Raupach, 2008: Managing forests for climate change Cole, J. J., et al., 2007: Plumbing the global carbon cycle: Integrating inland waters mitigation. Science, 320, 1456 1457. into the terrestrial carbon budget. Ecosystems, 10, 171 184. Canadell, J. G., et al., 2007a: Saturation of the terrestrial carbon sink. In: Terrestrial Collins, W. J., et al., 2011: Development and evaluation of an Earth-System model Ecosystems in a Changing World. [J. G. Canadell, D. Pataki and L. Pitelka (eds.)]. HadGEM2. Geosci. Model Dev., 4, 1051 1075. The IGBP Series. Springer-Verlag, Berlin and Heidelberg, Germany, pp. 59 78, Conrad, R., 1996: Soil microorganisms as controllers of atmospheric trace gases (H2, Canadell, J. G., et al., 2007b: Contributions to accelerating atmospheric CO2 growth CO, CH4, OCS, N2O, and NO). Microbiol. Rev., 60, 609 640. from economic activity, carbon intensity, and efficiency of natural sinks. Proc. Conway, T., and P. Tans, 2011: Global CO2. National Oceanic and Atmospheric Natl. Acad. Sci. U.S.A., 104, 18,866 18,870. Administration, Earth System Research Library, Silver Spring, MD, USA. Canfield, D. E., A. N. Glazer, and P. G. Falkowski, 2010: The evolution and future of Conway, T. J., P. P. Tans, L. S. Waterman, K. W. Thoning, D. R. Kitzis, K. A. Masarie, and Earth s nitrogen cycle. Science, 330, 192 196. N. Zhang, 1994: Evidence for interannual variability of the carbon cycle from Cao, L., and K. Caldeira, 2010a: Can ocean iron fertilization mitigate ocean the National Oceanic and Atmospheric Administration Climate Monitoring and acidification? Clim. Change, 99, 303 311. Diagnostics Laboratory Global Air Sampling Network. J. Geophys. Res. Atmos., Cao, L., and K. Caldeira, 2010b: Atmospheric carbon dioxide removal: Long-term 99, 22831 22855. consequences and commitment. Environ. Res. Lett., 5, 024011. Corbiere, A., N. Metzl, G. Reverdin, C. Brunet, and A. Takahashi, 2007: Interannual Cao, L., K. Caldeira, and A. K. Jain, 2007: Effects of carbon dioxide and climate and decadal variability of the oceanic carbon sink in the North Atlantic subpolar change on ocean acidification and carbonate mineral saturation. Geophys. Res. gyre. Tellus B, 59, 168 178. 6 Lett., 34, L05607. Cox, P., and C. Jones, 2008: Climate change. Illuminating the modern dance of Cao, L., G. Bala, and K. Caldeira, 2011: Why is there a short-term increase in global climate and CO2. Science, 321, 1642 1644. precipitation in response to diminished CO2 forcing? Geophys. Res. Lett., 38, Cox, P. M., 2001: Description of the TRIFFID dynamic global vegetation model. L06703. Technical Note 24. Hadley Centre, Met Of ce, Exeter, Devon, UK. Cao, L., G. Bala, K. Caldeira, R. Nemani, and G. Ban-Weiss, 2010: Importance of Cox, P. M., R. A. Betts, M. Collins, P. P. Harris, C. Huntingford, and C. D. Jones, 2004: carbon dioxide physiological forcing to future climate change. Proc. Natl. Acad. Amazonian forest dieback under climate-carbon cycle projections for the 21st Sci. U.S.A., 107, 9513 9518. century. Theor. Appl. Climatol., 78, 137 156. Carcaillet, C., et al., 2002: Holocene biomass burning and global dynamics of the Cox, P. M., D. Pearson, B. B. Booth, P. Friedlingstein, C. Huntingford, C. D. Jones, and carbon cycle. Chemosphere, 49, 845 863. C. M. Luke, 2013: Sensitivity of tropical carbon to climate change constrained by Certini, G., 2005: Effects of fire on properties of forest soils: A review. Oecologia, carbon dioxide variability. Nature, 494, 341 344. 143, 1 10. 555 Chapter 6 Carbon and Other Biogeochemical Cycles Crévoisier, C., D. Nobileau, A. M. Fiore, R. Armante, A. Chédin, and N. A. Scott, 2009: Dlugokencky, E. J., S. Houweling, L. Bruhwiler, K. A. Masarie, P. M. Lang, J. B. Miller, Tropospheric methane in the tropics first year from IASI hyperspectral infrared and P. P. Tans, 2003: Atmospheric methane levels off: Temporary pause or a new observations. Atmos. Chem. Phys., 9, 6337 6350. steady-state? Geophys. Res. Lett., 30, 1992. Crutzen, P. J., A. R. Mosier, K. A. Smith, and W. Winiwarter, 2008: N2O release from Dlugokencky, E. J., et al., 2009: Observational constraints on recent increases in the agro-biofuel production negates global warming reduction by replacing fossil atmospheric CH4 burden. Geophys. Res. Lett., 36, L18803. fuels. Atmos. Chem. Phys., 8, 389 395. Dolman, A. J., et al., 2012: An estimate of the terrestrial carbon budget of Russia Cunnold, D. M., et al., 2002: In situ measurements of atmospheric methane at GAGE/ using inventory based, eddy covariance and inversion methods. Biogeosciences, AGAGE sites during 1985 2000 and resulting source inferences. J. Geophys. Res. 9, 5323 5340. Atmos., 107, ACH20 1, CiteID 4225. Doney, S. C., et al., 2009: Mechanisms governing interannual variability in upper- Curry, C. L., 2007: Modeling the soil consumption of methane at the global scale. ocean inorganic carbon system and air-sea CO2 fluxes: Physical climate and Global Biogeochem. Cycles, 21, GB4012. atmospheric dust. Deep-Sea Res. Pt. II, 56, 640 655. Curry, C. L., 2009: The consumption of atmospheric methane by soil in a simulated Dore, J. E., R. Lukas, D. W. Sadler, M. J. Church, and D. M. Karl, 2009: Physical and future climate. Biogeosciences, 6, 2355 2367. biogeochemical modulation of ocean acidification in the central North Pacific. Daniau, A. L., et al., 2012: Predictability of biomass burning in response to climate Proc. Natl. Acad. Sci. U.S.A., 106, 12235 12240. changes. Global Biogeochem. Cycles, 26, Gb4007. Dornburg, V., and G. Marland, 2008: Temporary storage of carbon in the biosphere Davidson, E. A., 2009: The contribution of manure and fertilizer nitrogen to does have value for climate change mitigation: A response to the paper by Miko atmospheric nitrous oxide since 1860. Nature Geosci., 2, 659 662. Kirschbaum. Mitigat. Adapt. Strat. Global Change, 13, 211 217. Davidson, E. A., et al., 2012: Excess nitrogen in the U.S. environnement: Trends, Duce, R. A., et al., 2008: Impacts of atmospheric anthropogenic nitrogen on the open risks, and solutions. Issues of Ecology, Report number 15. Ecological Society of ocean. Science, 320, 893 897. America, Washington, DC. Dueck, T. A., et al., 2007: No evidence for substantial aerobic methane emission by Dawes, M. A., S. Hättenschwiler, P. Bebi, F. Hagedorn, I. T. Handa, C. Körner, and C. terrestrial plants: A 13C-labelling approach. New Phytologist, 175, 29 35. Rixen, 2011: Species-specific tree growth responses to 9 years of CO2 enrichment Dufresne, J.-L., et al., 2013: Climate change projections using the IPSL-CM5 Earth at the alpine treeline. J. Ecol., 99, 383 394. System Model: From CMIP3 to CMIP5. Clim. Dyn., 40, 2123-2165. De Klein, C., et al., 2007: N2O emissions from managed soils, and CO2 emissions from Dukes, J. S., et al., 2005: Responses of grassland production to single and multiple lime and urea application. In: 2006 IPCC Guidelines for National Greenhouse global environmental changes. PloS Biol., 3, 1829 1837. Gas Inventories, Vol. 4 [M. Gytarsky, T. Higarashi, W. Irving, T. Krug and J. Penman Dunne, J. P., et al., 2012: GFDL s ESM2 global coupled climate-carbon Earth System (eds.)]. Intergovernmental Panel on Climate Change, Geneva, Switzerland, pp. Models Part I: Physical formulation and baseline simulation characteristics. J. 11.1 11.54. Clim., 25, 6646 6665. DeFries, R., and C. Rosenzweig, 2010: Toward a whole-landscape approach for Dunne, J. P., et al., 2013: GFDL s ESM2 global coupled climate-carbon Earth System sustainable land use in the tropics. Proc. Natl. Acad. Sci. U.S.A., 107, 19627 Models. Part II: Carbon system formation and baseline simulation characteristics. 19632. J. Clim., 26, 2247-2267. DeFries, R. S., R. A. Houghton, M. C. Hansen, C. B. Field, D. L. Skole, and J. Townshend, Dutaur, L., and L. V. Verchot, 2007: A global inventory of the soil CH4 sink. Global 2002: Carbon emissions from tropical deforestation and regrowth based on Biogeochem. Cycles, 21, GB4013. satellite observations for the 1980s and 1990s. Proc. Natl. Acad. Sci. U.S.A., 99, Dutta, K., E. A. G. Schuur, J. C. Neff, and S. A. Zimov, 2006: Potential carbon release 14256 14261. from permafrost soils of Northeastern Siberia. Global Change Biol., 12, 2336 Delmas, R. J., J.-M. Ascencio, and M. Legrand, 1980: Polar ice evidence that 2351. atmospheric CO2 20,000 yr BP was 50% of present. Nature, 284, 155 157. Eby, M., K. Zickfeld, A. Montenegro, D. Archer, K. J. Meissner, and A. J. Weaver, 2009: Denman, K. L., 2008: Climate change, ocean processes and ocean iron fertilization. Lifetime of anthropogenic climate change: Millennial time scales of potential Mar. Ecol. Prog. Ser., 364, 219 225. CO2 and surface temperature perturbations. J. Clim., 22, 2501 2511. Denman, K. L., et al., 2007: Couplings between changes in the climate system Eby, M., et al., 2013: Historical and idealized climate model experiments: An EMIC and biogeochemistry. In: Climate Change 2007: The Physical Science Basis. intercomparison. Clim. Past, 9, 1 30. Contribution of Working Group I to the Fourth Assessment Report of the EDGAR4 database, 2009: Emission Database for Global Atmospheric Research Intergovernmental Panel on Climate Change [Solomon, S., D. Qin, M. Manning, (EDGAR), release version 4.0. http://edgar.jrc.ec.europa.eu, 2009. European Z. Chen, M. Marquis, K. B. Averyt, M. Tignor and H. L. Miller (eds.)] Cambridge Commission. Joint Research Centre (JRC) / Netherlands Environmental University Press, Cambridge, United Kingdom and New York, NY, USA, 499-587. Assessment Agency (PBL). Dentener, F., W. Peters, M. Krol, M. van Weele, P. Bergamaschi, and J. Lelieveld, 2003: Elberling, B., H. H. Christiansen, and B. U. Hansen, 2010: High nitrous oxide Interannual variability and trend of CH4 lifetime as a measure for OH changes in production from thawing permafrost. Nature Geosci., 3, 332 335. the 1979 1993 time period. J. Geophys. Res. Atmos., 108, 4442. Eliseev, A. V., I. I. Mokhov, M. M. Arzhanov, P. F. Demchenko, and S. N. Denisov, 2008: Dentener, F., et al., 2005: The impact of air pollutant and methane emission controls Interaction of the methane cycle and processes in wetland ecosystems in a on tropospheric ozone and radiative forcing: CTM calculations for the period climate model of intermediate complexity. Izvestiya Atmos. Ocean. Phys., 44, 1990 2030. Atmos. Chem. Phys., 5, 1731 1755. 139 152. Dentener, F., et al., 2006: The global atmospheric environment for the next Elliott, S., M. Maltrud, M. Reagan, G. Moridis, and P. Cameron-Smith, 2011: Marine generation. Environ. Sci. Technol., 40, 3586 3594. methane cycle simulations for the period of early global warming. J. Geophys. Deutsch, C., J. L. Sarmiento, D. M. Sigman, N. Gruber, and J. P. Dunne, 2007: Spatial Res. Biogeosci., 116, G01010. coupling of nitrogen inputs and losses in the ocean. Nature, 445, 163 167. Elser, J. J., et al., 2007: Global analysis of nitrogen and phosphorus limitation of Dickens, G. R., 2003: A methane trigger for rapid warming? Science, 299, 1017. primary producers in freshwater, marine and terrestrial ecosystems. Ecol. Lett., Dlugokencky, E., and P. P. Tans, 2013a: Recent CO2, NOAA, ESRS. Retrieved from 10, 1135 1142. 6 www.esrl.noaa.gov/gmd/ccgg/trends/global.html, accessed 01-02-2013. Elsig, J., et al., 2009: Stable isotope constraints on Holocene carbon cycle changes Dlugokencky, E., and P. P. Tans, 2013b: Globally averaged marine surface annual from an Antarctic ice core. Nature, 461, 507 510. mean data, NOAA/ESRL. Retrieved from www.esrl.noaa.gov/gmd/ccgg/trends/, Emerson, S., and J. I. Hedges, 1988: Processes controlling the organic carbon content accessed 01-02-2013. of open ocean sediments. Paleoceanography, 3, 621 634. Dlugokencky, E. J., E. G. Nisbet, R. Fisher, and D. Lowry, 2011: Global atmospheric EPA, 2006: Global anthropogenic non-CO2 greenhouse gas emissions. United methane: Budget, changes and dangers. Philos. Trans. R. Soc. London Ser. A, 369, States Environmental Protection Agency (US EPA, Washington, DC) Report EPA- 2058 2072. 430-R-06-003. Retrieved from http://nepis.epa.gov/EPA/html/DLwait.htm?url=/ Dlugokencky, E. J., P. M. Lang, A. M. Crotwell, and K. A. Masarie, 2012: Atmospheric Adobe/PDF/2000ZL5G.PDF. methane dry air mole fractions from the NOAA ESRL Carbon Cycle Cooperative EPA, 2010: Methane and nitrous oxide emissions from natural sources. United States Global Air Sampling Network, 1983 2011, version 2012-09-24. Version 2010- Environmental Protection Agency (EPA) Report. Washington, DC. http://www. 08-12 ed. epa.gov/outreach/pdfs/Methane-and-Nitrous-Oxide-Emissions-From-Natural- Sources.pdf 556 Carbon and Other Biogeochemical Cycles Chapter 6 EPA, 2011a: Global anthropogenic non-CO2 greenhouse gas emissions: 1990 2030, Fisher, J. B., S. Sitch, Y. Malhi, R. A. Fisher, C. Huntingford, and S. Y. Tan, 2010: Carbon United States Environmental Protection Agency (US EPA) Report. Washington, cost of plant nitrogen acquisition: A mechanistic, globally applicable model of DC. http://www.epa.gov/climatechange/Downloads/EPAactivities/EPA_Global_ plant nitrogen uptake, retranslocation, and fixation. Global Biogeochem. Cycles, NonCO2_Projections_Dec2012.pdf 24, GB1014. EPA, 2011b: Reactive nitrogen in the United States: An analysis of inputs, flows, Flannigan, M. D., B. Stocks, M. Turetsky, and M. Wotton, 2009a: Impacts of climate consequences,and management options.Report EPA-SAB-11-013,Washington,DC, change on fire activity and fire management in the circumboreal forest. Global 140 pp. http://yosemite.epa.gov/sab/sabproduct.nsf/WebBOARD/INCFullReport/ Change Biol., 15, 549 560. $File/Final%20INC%20Report_8_19_11%28without%20signatures%29.pdf Flannigan, M. D., M. A. Krawchuk, W. J. de Groot, B. M. Wotton, and L. M. Gowman, Erisman, J. W., M. S. Sutton, J. N. Galloway, Z. Klimont, and W. Winiwarter, 2008: A 2009b: Implications of changing climate for global wildland fire. Int. J. Wildland century of ammonia synthesis. Nature Geosci., 1, 1 4. Fire, 18, 483 507. Erisman, J. W., J. Galloway, S. Seitzinger, A. Bleeker, and K. Butterbach-Bahl, 2011: Fleming, E. L., C. H. Jackman, R. S. Stolarski, and A. R. Douglass, 2011: A model study Reactive nitrogen in the environment and its effect on climate change. Curr. of the impact of source gas changes on the stratosphere for 1850 2100. Atmos. Opin. Environ. Sustain., 3, 281 290. Chem. Phys., 11, 8515 8541. Esser, G., J. Kattge, and A. Sakalli, 2011: Feedback of carbon and nitrogen cycles Flückiger, J., A. Dällenbach, T. Blunier, B. Stauffer, T. F. Stocker, D. Raynaud, and J.-M. enhances carbon sequestration in the terrestrial biosphere. Global Change Biol., Barnola, 1999: Variations in atmospheric N2O concentration during abrupt 17, 819 842. climate changes. Science, 285, 227 230. Etheridge, D. M., L. P. Steele, R. L. Langenfelds, R. J. Francey, J.-M. Barnola, and V. I. Flückiger, J., et al., 2002: High-resolution Holocene N2O ice core record and its Morgan, 1996: Natural and anthropogenic changes in atmospheric CO2 over relationship with CH4 and CO2. Global Biogeochem. Cycles, 16, 1 10. the last 1000 years from air in Antarctic ice and firn. J. Geophys. Res., 101, Flückiger, J., et al., 2004: N2O and CH4 variations during the last glacial epoch: Insight 4115 4128. into global processes. Global Biogeochem. Cycles, 18, GB1020. Etiope, G., K. R. Lassey, R. W. Klusman, and E. Boschi, 2008: Reappraisal of the fossil Foley, J. A., C. Monfreda, N. Ramankutty, and D. Zaks, 2007: Our share of the methane budget and related emission from geologic sources. Geophys. Res. planetary pie. Proc. Natl. Acad. Sci. U.S.A., 104, 12585 12586. Lett., 35, L09307. Foley, J. A., et al., 2011: Solutions for a cultivated planet. Nature, 478, 337 342. Evans, C. D., D. T. Monteith, and D. M. Cooper, 2005: Long-term increses in Fowler, D., et al., 2013: The global nitrogen cycle in the 21th century. Philos. Trans. R. surface water dissolved organic carbon: Observations, possible causes and Soc. London Ser. B, 368, 20130165. environmental impacts. Environ. Pollut., 137, 55 71. Francey, R. J., et al., 2013: Atmospheric verification of anthropogenic CO2 emission Falloon, P., C. Jones, M. Ades, and K. Paul, 2011: Direct soil moisture controls of trends. Nature Clim. Change, 3, 520-524. future global soil carbon changes: An important source of uncertainty. Global Frank, D. C., J. Esper, C. C. Raible, U. Buntgen, V. Trouet, B. Stocker, and F. Joos, 2010: Biogeochem. Cycles, 25, GB3010. Ensemble reconstruction constraints on the global carbon cycle sensitivity to Fan, S.-M., T. L. Blaine, and J. L. Sarmiento, 1999: Terrestrial carbon sink in the climate. Nature, 463, 527 U143. Northern Hemisphere estimated from the atmospheric CO2 difference between Frankenberg, C., et al., 2011: Global column-averaged methane mixing ratios from Manna Loa and the South Pole since 1959. Tellus B, 51, 863 870. 2003 to 2009 as derived from SCIAMACHY: Trends and variability. J. Geophys. FAO, 2005: Global Forest Resource Assessment 2005. Progress toward sustainable Res., 116, D04302. forest management. FAO Forestry Paper 147. Food and Agriculture Organization Frankenberg, C., et al., 2008: Tropical methane emissions: A revised view from of the United Nations, Rome, Italy, pp. 129 147. SCIAMACHY onboard ENVISAT. Geophys. Res. Lett., 35, L15811. FAO, 2010: Global Forest Resources Assessment 2010. Main report. FAO Forestry Freing, A., D. W. R. Wallace, and H. W. Bange, 2012: Global oceanic production of Paper 163, Food and Agriculture Organization of the United Nations, Rome, Italy, nitrous oxide. Philos. Trans. R. Soc. London Ser. B, 367, 1245 1255. 340 pp. Friedli, H., H. Lötscher, H. Oeschger, U. Siegenthaler, and B. Stauffer, 1986: Ice core Feely, R. A., S. C. Doney, and S. R. Cooley, 2009: Ocean acidification: Present record of the 13C/12C ratio of atmospheric CO2 in the past two centuries. Nature, conditions and future changes in a high-CO2 world. Oceanography, 22, 36 47. 324, 237 238. Feely, R. A., C. L. Sabine, J. M. Hernandez-Ayon, D. Ianson, and B. Hales, 2008: Friedlingstein, P., and I. C. Prentice, 2010: Carbon-climate feedbacks: A review of Evidence for upwelling of corrosive acidified water onto the continental shelf. model and observation based estimates. Curr. Opin. Environ. Sustain., 2, 251 Science, 320, 1490 1492. 257. Feely, R. A., R. H. Byrne, J. G. Acker, P. R. Betzer, C.-T. A. Chen, J. F. Gendron, and M. F. Friedlingstein, P., J. L. Dufresne, P. M. Cox, and P. Rayner, 2003: How positive is the Lamb, 1988: Winter-summer variations of calcite and aragonite saturation in the feedback between climate change and the carbon cycle? Tellus B, 55, 692 700. northeast Pacific. Mar. Chem., 25, 227 241. Friedlingstein, P., et al., 2010: Update on CO2 emissions. Nature Geosci., 3, 811 812. Feely, R. A., T. Takahashi, R. Wanninkhof, M. J. McPhaden, C. E. Cosca, S. C. Friedlingstein, P., et al., 2006: Climate-carbon cycle feedback analysis: Results from Sutherland, and M. E. Carr, 2006: Decadal variability of the air-sea CO2 fluxes in the C4MIP model intercomparison. J. Clim., 19, 3337 3353. the equatorial Pacific Ocean. J. Geophys. Res. Oceans, 111, C08S90. Friis, K., A. Körtzinger, J. Patsch, and D. W. R. Wallace, 2005: On the temporal increase Felzer, B., et al., 2005: Past and future effects of ozone on carbon sequestration and of anthropogenic CO2 in the subpolar North Atlantic. Deep-Sea Res. Pt. I, 52, climate change policy using a global biogeochemical model. Clim. Change, 73, 681 698. 345 373. Frölicher, T. L., F. Joos, and C. C. Raible, 2011: Sensitivity of atmospheric CO2 and Ferretti, D. F., et al., 2005: Unexpected changes to the global methane budget over climate to explosive volcanic eruptions. Biogeosciences, 8, 2317 2339. the past 2000 years. Science, 309, 1714 1717. Frölicher, T. L., F. Joos, C. C. Raible, and J. L. Sarmiento, 2013: Atmospheric CO2 Findlay, H. S., T. Tyrrell, R. G. J. Bellerby, A. Merico, and I. Skjelvan, 2008: Carbon response to volcanic eruptions: The role of ENSO, season, and variability. Global and nutrient mixed layer dynamics in the Norwegian Sea. Biogeosciences, 5, Biogeochem. Cycles, 27, 239-251. 1395 1410. Frölicher, T. L., F. Joos, G. K. Plattner, M. Steinacher, and S. C. Doney, 2009: Natural Findlay, S. E. G., 2005: Increased carbon transport in the Hudson River: Unexpected variability and anthropogenic trends in oceanic oxygen in a coupled carbon 6 consequence of nitrogen deposition? Front. Ecol. Environ., 3, 133 137. cycle climate model ensemble. Global Biogeochem. Cycles, 23, GB1003. Finzi, A. C., et al., 2006: Progressive nitrogen limitation of ecosystem processes under Frolking, S., and N. T. Roulet, 2007: Holocene radiative forcing impact of northern elevated CO2 in a warm-temperate forest. Ecology, 87, 15 25. peatland carbon accumulation and methane emissions. Global Change Biol., 13, Finzi, A. C., et al., 2007: Increases in nitrogen uptake rather than nitrogen-use 1079 1088. efficiency support higher rates of temperate forest productivity under elevated Fuller, D. Q., et al., 2011: The contribution of rice agriculture and livestock pastoralism CO2. Proc. Natl. Acad. Sci. U.S.A., 104, 14014 14019. to prehistoric methane levels: An archaeological assessment. The Holocene, 21, Fischer, H., et al., 2008: Changing boreal methane sources and constant biomass 743 759. burning during the last termination. Nature, 452, 864 867. Fung, I., M. Prather, J. John, J. Lerner, and E. Matthews, 1991: Three-dimensional model synthesis of the global methane cycle. J. Geophys. Res., 96, 13033 13065. Fyfe, J. C., and O. A. Saenko, 2006: Simulated changes in the extratropical Southern Hemisphere winds and currents. Geophys. Res. Lett., 33, L06701. 557 Chapter 6 Carbon and Other Biogeochemical Cycles Fyfe, J. C., J. N. S. Cole, V. K. Arora, and J. F. Scinocca, 2013: Biogeochemical carbon Golding, N., and R. Betts, 2008: Fire risk in Amazonia due to climate change in coupling influences global precipitation in geoengineering experiments. the HadCM3 climate model: Potential interactions with deforestation. Global Geophys. Res. Lett., 40, 651 655. Biogeochem. Cycles, 22, GB4007. Fyke, J. G., and A. J. Weaver, 2006: The effect of potential future climate change on Goll, D. S., et al., 2012: Nutrient limitation reduces land carbon uptake in the marine methane hydrate stability zone. J. Clim., 19, 5903 5917. simulations with a model of combined carbon, nitrogen and phosphorus cycling. Gaillard, M. J., et al., 2010: Holocene land-cover reconstructions for studies on land Biogeosciences, 9, 3547 3569. cover-climate feedbacks. Clim. Past, 6, 483 499. Good, P., C. Jones, J. Lowe, R. Betts, and N. Gedney, 2013: Comparing tropical Gaillardet, J., B. Dupre, P. Louvat, and C. J. Allegre, 1999: Global silicate weathering forest projections from two generations of Hadley Centre Earth System Models, and CO2 consumption rates deduced from the chemistry of large rivers. Chem. HadGEM2 ES and HadCM3LC. J. Clim., 26, 495 511. Geol., 159, 3 30. Good, P., C. Jones, J. Lowe, R. Betts, B. Booth, and C. Huntingford, 2011: Quantifying Galbraith, D., P. E. Levy, S. Sitch, C. Huntingford, P. Cox, M. Williams, and P. Meir, environmental drivers of future tropical forest extent. J. Clim., 24, 1337 1349. 2010: Multiple mechanisms of Amazonian forest biomass losses in three Govindasamy, B., K. Caldeira, and P. B. Duffy, 2003: Geoengineering Earth s radiation dynamic global vegetation models under climate change. New Phytologist, 187, balance to mitigate climate change from a quadrupling of CO2. Global Planet. 647 665. Change, 37, 157 168. Galloway, J. N., J. D. Aber, J. W. Erisman, S. P. Seitzinger, R. W. Howarth, E. B. Cowling, Govindasamy, B., S. Thompson, P. B. Duffy, K. Caldeira, and C. Delire, 2002: Impact and B. J. Cosby, 2003: The nitrogen cascade. BioScience, 53, 341 356. of geoengineering schemes on the terrestrial biosphere. Geophys. Res. Lett., 29, Galloway, J. N., et al., 2008: Transformation of the nitrogen cycle: Recent trends, 2061. questions, and potential solutions. Science, 320, 889. Graven, H. D., N. Gruber, R. Key, S. Khatiwala, and X. Giraud, 2012: Changing controls Galloway, J. N., et al., 2004: Nitrogen cycles: Past, present and future. Biogeochemistry, on oceanic radiocarbon: New insights on shallow-to-deep ocean exchange and 70, 153 226. anthropogenic CO2 uptake. J. Geophys. Res. Oceans, 117, C10005. Gao, H., et al., 2012: Intensive and extensive nitrogen loss from intertidal permeable Gray, M. L., K. J. Champagne, D. Fauth, J. P. Baltrus, and H. Pennline, 2008: Performance sediments of the Wadden Sea Limnol. Oceanogr., 57, 185 198. of immobilized tertiary amine solid sorbents for the capture of carbon dioxide. Gärdenäs, A. I., et al., 2011: Knowledge gaps in soil carbon and nitrogen Int. J. Greenh. Gas Control, 2, 3 8. interactions From molecular to global scale. Soil Biol. Biochem., 43, 702 717. Gregg, J. S., R. J. Andres, and G. Marland, 2008: China: Emissions pattern of the world GEA, 2006: Energy resources and potentials. In: Global Energy Assessment Toward leader in CO2 emissions from fossil fuel consumption and cement production. a Sustainable Future. Cambridge University Press, Cambridge, United Kingdom, Geophys. Res. Lett., 35, L08806. and New York, NY, USA, 425-512. Gregory, J. M., C. D. Jones, P. Cadule, and P. Friedlingstein, 2009: Quantifying carbon Gedalof, Z., and A. A. Berg, 2010: Tree ring evidence for limited direct CO2 fertilization cycle feedbacks. J. Clim., 22, 5232 5250. of forests over the 20th century. Global Biogeochem. Cycles, 24, Gb3027. Groszkopf, T., et al., 2012: Doubling of marine dinitrogen-fixation rates based on Gedney, N., P. M. Cox, and C. Huntingford, 2004: Climate feedback from wetland direct measurements. Nature, 488, 361 364. methane emissions. Geophys. Res. Lett., 31, L20503. Gruber, N., and J. N. Galloway, 2008: An Earth-system perspective of the global Gedney, N., P. M. Cox, R. A. Betts, O. Boucher, C. Huntingford, and P. A. Stott, 2006: nitrogen cycle. Nature, 451, 293 296. Detection of a direct carbon dioxide effect in continental river runoff records. Gruber, N., C. Hauri, Z. Lachkar, D. Loher, T. L. Frölicher, and G. K. Plattner, 2012: Rapid Nature, 439, 835 838. progression of ocean acidification in the California Current System. Science, Gerber, S., L. O. Hedin, M. Oppenheimer, S. W. Pacala, and E. Shevliakova, 2010: 337, 220 223. Nitrogen cycling and feedbacks in a global dynamic land model. Global Gruber, N., et al., 2009: Oceanic sources, sinks, and transport of atmospheric CO2. Biogeochem. Cycles, 24, GB1001. Global Biogeochem. Cycles, 23, GB1005. Gerber, S., F. Joos, P. Brugger, T. F. Stocker, M. E. Mann, S. Sitch, and M. Scholze, 2003: Gurney, K. R., and W. J. Eckels, 2011: Regional trends in terrestrial carbon exchange Constraining temperature variations over the last millennium by comparing and their seasonal signatures. Tellus B, 63, 328 339. simulated and observed atmospheric CO2. Clim. Dyn., 20, 281 299. Hamilton, S. K., A. L. Kurzman, C. Arango, L. Jin, and G. P. Robertson, 2007: Evidence Gervois, S., P. Ciais, N. de Noblet-Ducoudre, N. Brisson, N. Vuichard, and N. Viovy, for carbon sequestration by agricultural liming. Global Biogeochem. Cycles, 21, 2008: Carbon and water balance of European croplands throughout the 20th GB2021. century. Global Biogeochem. Cycles, 22, GB003018. Hansell, D. A., C. A. Carlson, D. J. Repeta, and R. Schlitzer, 2009: Dissolved organic Gilbert, D., N. N. Rabalais, R. J. Diaz, and J. Zhang, 2010: Evidence for greater oxygen matter in the ocean: A controversy stimulates new insights. Oceanography, 22, decline rates in the coastal ocean than in the open ocean. Biogeosciences, 7, 202 211. 2283 2296. Hansen, M. C., S. V. Stehman, and P. V. Potapov, 2010: Quantification of global gross Gloor, M., J. L. Sarmiento, and N. Gruber, 2010: What can be learned about carbon forest cover loss. Proc. Natl. Acad. Sci. U.S.A., 107, 8650 8655. cycle climate feedbacks from the CO2 airborne fraction? Atmos. Chem. Phys., Harden, J. W., et al., 2012: Field information links permafrost carbon to physical 10, 7739 7751. vulnerabilities of thawing. Geophys. Res. Lett., 39, L15704. Gloor, M., et al., 2012: The carbon balance of South America: A review of the status, Harris, N. L., et al., 2012: Baseline map of carbon emissions from deforestation in decadal trends and main determinants. Biogeosciences, 9, 5407 5430. tropical regions. Science, 336, 1573 1576. Gloor, M., et al., 2009: Does the disturbance hypothesis explain the biomass increase Harrison, K. G., 2000: Role of increased marine silica input on paleo-pCO2 levels. in basin-wide Amazon forest plot data? Global Change Biol., 15, 2418 2430. Paleoceanography, 15, 292 298. Gnanadesikan, A., and I. Marinov, 2008: Export is not enough: Nutrient cycling and Hartmann, J., N. Jansen, H. H. Dürr, S. Kempe, and P. Köhler, 2009: Global CO2 carbon sequestration. Mar. Ecol. Prog. Ser., 364, 289 294. consumption by chemical weathering: What is the contribution of highly active Gnanadesikan, A., J. L. Sarmiento, and R. D. Slater, 2003: Effects of patchy ocean weathering regions? Global Planet. Change, 69, 185 194. fertilization on atmospheric carbon dioxide and biological production. Global Harvey, L. D. D., 2008: Mitigating the atmospheric CO2 increase and ocean 6 Biogeochem. Cycles, 17, 1050. acidification by adding limestone powder to upwelling regions. J. Geophys. Res., Gnanadesikan, A., J. L. Russell, and F. Zeng, 2007: How does ocean ventilation 113, C04028. change under global warming? Ocean Sci., 3, 43 53. Haverd, V., et al., 2013: The Australian terrestrial carbon budget. Biogeosciences, Gnanadesikan, A., J. P. Dunne, and J. John, 2012: Understanding why the volume of 10, 851 869. suboxic waters does not increase over centuries of global warming in an Earth Hedegaard, G. B., J. Brandt, J. H. Christensen, L. M. Frohn, C. Geels, K. M. Hansen, System Model. Biogeosciences, 9, 1159 1172. and M. Stendel, 2008: Impacts of climate change on air pollution levels in the Goldewijk, K. K., 2001: Estimating global land use change over the past 300 years: Northern Hemisphere with special focus on Europe and the Arctic. Atmos. Chem. The HYDE Database. Global Biogeochem. Cycles, 15, 417 433. Phys., 8, 3337 3367. Goldewijk, K. K., A. Beusen, G. van Drecht, and M. de Vos, 2011: The HYDE 3.1 Heijmans, M. M. P. D., W. J. Arp, and F. Berendse, 2001: Effects of elevated CO2 and spatially explicit database of human-induced global land-use change over the vascular plants on evapotranspiration in bog vegetation. Global Change Biol., past 12,000 years. Global Ecol. Biogeogr., 20, 73 86. 7, 817 827. 558 Carbon and Other Biogeochemical Cycles Chapter 6 Heijmans, M. M. P. D., H. Klees, and F. Berendse, 2002a: Competition between Hsu, J., and M. J. Prather, 2010: Global long-lived chemical modes excited in a 3-D Sphagnum magellanicum and Eriophorum angustifolium as affected by raised chemistry transport model: Stratospheric N2O, NOy, O3 and CH4 chemistry. CO2 and increased N deposition. Oikos, 97, 415 425. Geophys. Res. Lett., 37, L07805. Heijmans, M. M. P. D., H. Klees, W. de Visser, and F. Berendse, 2002b: Response of Huang, J., et al., 2008: Estimation of regional emissions of nitrous oxide from 1997 a Sphagnum bog plant community to elevated CO2 and N supply. Plant Ecol., to 2005 using multinetwork measurements: A chemical transport model, and an 162, 123 134. inverse method. J. Geophys. Res., 113, D17313. Hein, R., P. J. Crutzen, and M. Heimann, 1997: An inverse modeling approach to Huber, C., et al., 2006: Isotope calibrated Greenland temperature record over Marine investigate the global atmospheric methane cycle. Global Biogeochem. Cycles, Isotope Stage 3 and its relation to CH4. Earth Planet. Sci. Lett., 243, 504 519. 11, 43 76. Hunter, S. J., A. M. Haywood, D. S. Goldobin, A. Ridgwell, and J. G. Rees, 2013: Held, I. M., M. Winton, K. Takahashi, T. Delworth, F. Zeng, and G. K. Vallis, 2010: Sensitivity of the global submarine hydrate inventory to scenarious of future Probing the fast and slow components of global warming by returning abruptly climate change. Earth Planet. Sci. Lett., 367, 105 115. to preindustrial forcing. J. Clim., 23, 2418 2427. Huntingford, C., J. A. Lowe, B. B. B. Booth, C. D. Jones, G. R. Harris, L. K. Gohar, Herridge, D. F., M. B. Peoples, and R. M. Boddey, 2008: Global inputs of biological and P. Meir, 2009: Contributions of carbon cycle uncertainty to future climate nitrogen fixation in agricultural systems. Plant Soil, 311, 1 18. projection spread. Tellus B, 61, 355 360. Herzog, H., K. Caldeira, and J. Reilly, 2003: An issue of permanence: Assessing the Huntingford, C., et al., 2013: Simulated resilience of tropical rainforests to CO2 effectiveness of temporary carbon storage. Clim. Change, 59, 293 310. induced climate change. Nature Geosci., 6, 268 273. Hibbard, K. A., G. A. Meehl, P. M. Cox, and P. Friedlingstein, 2007: A strategy for Hurtt, G. C., et al., 2011: Harmonization of land-use scenarios for the period 1500 climate change stabilization experiments. EOS Trans. Am. Geophys. Union, 88, 2100: 600 years of global gridded annual land-use transitions, wood harvest, 217-221. and resulting secondary lands. Clim. Change, 109, 117 161. Hickler, T., B. Smith, I. C. Prentice, K. Mjöfors, P. Miller, A. Arneth, and M. T. Sykes, Huybers, P., and C. Langmuir, 2009: Feedback between deglaciation, volcanism, and 2008: CO2 fertilization in temperate FACE experiments not representative of atmospheric CO2. Earth Planet. Sci. Lett., 286, 479 491. boreal and trophical forests. Global Change Biol., 14, 1531 1542. Indermühle, A., et al., 1999: Holocene carbon-cycle dynamics based on CO2 trapped Hietz, P., B. L. Turner, W. Wanek, A. Richter, C. A. Nock, and S. J. Wright, 2011: Long- in ice at Taylor Dome, Antarctica. Nature, 398, 121 126. term change in the nitrogen cycle of tropical forests. Science, 334, 664-666. Irvine, P. J., A. Ridgwell, and D. J. Lunt, 2010: Assessing the regional disparities in Higgins, P. A. T., and J. Harte, 2012: Carbon cycle uncertainty increases climate geoengineering impacts. Geophys. Res. Lett., 37, L18702. change risks and mitigation challenges. J. Clim., 25, 7660 7668. Ise, T., A. L. Dunn, S. C. Wofsy, and P. R. Moorcroft, 2008: High sensitivity of peat Hirota, M., M. Holmgren, E. H. Van Nes, and M. Scheffer, 2011: Global resilience of decomposition to climate change through water-table feedback. Nature Geosci., tropical forest and savanna to critical transitions. Science, 334, 232 235. 1, 763 766. Hirsch, A. I., A. M. Michalak, L. M. Bruhwiler, W. Peters, E. J. Dlugokencky, and P. P. Ishii, M., N. Kosugi, D. Sasano, S. Saito, T. Midorikawa, and H. Y. Inoue, 2011: Ocean Tans, 2006: Inverse modeling estimates of the global nitrous oxide surface flux acidification off the south coast of Japan: A result from time series observations from 1998 to 2001. Global Biogeochem. Cycles, 20, GB1008. of CO2 parameters from 1994 to 2008. J. Geophys. Res. Oceans, 116, C06022. Hodson, E. L., B. Poulter, N. E. Zimmermann, C. Prigent, and J. O. Kaplan, 2011: The Ishii, M., et al., 2009: Spatial variability and decadal trend of the oceanic CO2 in the El Nino-Southern Oscillation and wetland methane interannual variability. western equatorial Pacific warm/fresh water. Deep-Sea Res. Pt. II, 56, 591 606. Geophys. Res. Lett., 38, L08810. Ishijima, K., T. Nakazawa, and S. Aoki, 2009: Variations of atmospheric nitrous oxide Hoelzemann, J. J., M. G. Schultz, G. P. Brasseur, C. Granier, and M. Simon, 2004: concentration in the northern and western Pacific. Tellus B, 61, 408 415. Global Wildland Fire Emission Model (GWEM): Evaluating the use of global area Ito, A., and J. E. Penner, 2004: Global estimates of biomass burning emissions based burnt satellite data. J. Geophys. Res. Atmos., 109, D14S04. on satellite imagery for the year 2000. J. Geophys. Res., 109, D14S05. Hofmann, M., and H.-J. Schellnhuber, 2009: Oceanic acidification affects marine Ito, A., and M. Inatomi, 2012: Use of a process-based model for assessing the carbon pump and triggers extended marine oxygen holes. Proc. Natl. Acad. Sci. methane budgets of global terrestrial ecosystems and evaluation of uncertainty. U.S.A., 106, 3017 3022. Biogeosciences, 9, 759 773. Holland, E. A., J. Lee-Taylor, C. D. Nevison, and J. Sulzman, 2005: Global N cycle: Iudicone, D., et al., 2011: Water masses as a unifying framework for understanding Fluxes and N2O mixing ratios originating from human activity. Data set. Oak the Southern Ocean Carbon Cycle. Biogeosciences, 8, 1031 1052. Ridge National Laboratory Distributed Active Archive Center, Oak Ridge National Iversen, T., et al., 2013: The Norwegian Earth System Model, NorESM1 M. Part 2: Laboratory, Oak Ridge, TN. Retrieved from http://www.daac.ornl.gov Climate response and scenario projections. Geosci. Model Dev., 6, 389 415. Hönisch, B., N. G. Hemming, D. Archer, M. Siddall, and J. F. McManus, 2009: Jaccard, S. L., and E. D. Galbraith, 2012: Large climate-driven changes of oceanic Atmospheric carbon dioxide concentration across the mid-Pleistocene transition. oxygen concentrations during the last deglaciation. Nature Geosci., 5, 151-156. Science, 324, 1551 1554. Jaccard, S. L., G. H. Haug, D. M. Sigman, T. F. Pedersen, H. R. Thierstein, and U. R hl, Hooijer, A., S. Page, J. G. Canadell, M. Silvius, J. Kwadijk, H. Wösten, and J. Jauhiainen, 2005: Glacial/interglacial changes in subarctic North Pacific stratification. 2010: Current and future CO2 emissions from drained peatlands in Southeast Science, 308, 1003 1006. Asia. Biogeosciences, 7, 1505 1514. Jacobson, A. R., S. E. Mikaloff Fletcher, N. Gruber, J. L. Sarmiento, and M. Gloor, 2007: Hopcroft, P. O., P. J. Valdes, and D. J. Beerling, 2011: Simulating idealized Dansgaard- A joint atmosphere-ocean inversion for surface fluxes of carbon dioxide: 2. Oeschger events and their potential impacts on the global methane cycle. Quat. Regional results. Global Biogeochem. Cycles, 21, GB1020. Sci. Rev., 30, 3258 3268. Jacquet, S. H. M., N. Savoye, F. Dehairs, V. H. Strass, and D. Cardinal, 2008: Mesopelagic Houghton, R. A., 2003: Revised estimates of the annual net flux of carbon to the carbon remineralization during the European Iron Fertilization Experiment. atmosphere from changes in land use and land management 1850 2000. Tellus Global Biogeochem. Cycles, 22, 1 9. B, 55, 378 390. Jain, A., X. J. Yang, H. Kheshgi, A. D. McGuire, W. Post, and D. Kicklighter, 2009: Nitrogen Houghton, R. A., 2010: How well do we know the flux of CO2 from land-use change? attenuation of terrestrial carbon cycle response to global environmental factors. Tellus B, 62, 337 351. Global Biogeochem. Cycles, 23, GB4028. 6 Houghton, R. A., et al., 2012: Carbon emissions from land use and land-cover Jin, X., and N. Gruber, 2003: Offsetting the radiative benefit of ocean iron fertilization change. Biogeosciences, 9, 5125 5142. by enhancing N2O emissions. Geophys. Res. Lett., 30, 24, 2249. House, J. I., I. C. Prentice, and C. Le Quéré, 2002: Maximum impacts of future Jones, C., S. Liddicoat, and J. Lowe, 2010: Role of terrestrial ecosystems in determining reforestation or deforestation on atmospheric CO2. Global Change Biol., 8, CO2 stabilization and recovery behaviour. Tellus B, 62, 682 699. 1047 1052. Jones, C., M. Collins, P. M. Cox, and S. A. Spall, 2001: The carbon cycle response to House, K. Z., D. P. Schrag, C. F. Harvey, and K. S. Lackner, 2006: Permanent carbon ENSO: A coupled climate-carbon cycle model study. J. Clim., 14, 4113 4129. dioxide storage in deep-sea sediments. Proc. Natl. Acad. Sci. U.S.A., 103, 14255. Jones, C., J. Lowe, S. Liddicoat, and R. Betts, 2009: Committed terrestrial ecosystem House, K. Z., C. H. House, D. P. Schrag, and M. J. Aziz, 2007: Electrochemical changes due to climate change. Nature Geosci., 2, 484 487. acceleration of chemical weathering as an energetically feasible approach to Jones, C., et al., 2013: 21th Century compatible CO2 emissions and airborne fraction mitigating anthropogenic climate change. Environ. Sci. Technol., 41, 8464 8470. simulated by CMIP5 Earth System models under 4 Representative Concentration Pathways. J. Clim., 26, 4398-4413. 559 Chapter 6 Carbon and Other Biogeochemical Cycles Jones, C. D., and P. M. Cox, 2001: Modeling the volcanic signal in the atmospheric Keeling, C. D., S. C. Piper, R. B. Bacastow, M. Wahlen, T. P. Whorf, M. Heimann, and CO2 record. Global Biogeochem. Cycles, 15, 453 465. H. A. Meijer, 2005: Atmospheric CO2 and 13CO2 exchange with the terrestrial Jones, C. D., and P. Falloon, 2009: Sources of uncertainty in global modelling of biosphere and oceans from 1978 to 2000: Observations and carbon cycle future soil organic carbon storage. In: Uncertainties in Environmental Modelling implications. In: A History of Atmospheric CO2 and Its Effects on Plants, Animals, and Consequences for Policy Making [P. Bavaye, J. Mysiak and M. Laba (eds.)]. and Ecosystems [J. R. Ehleringer, T. E. Cerling and M. D. Dearing (eds.)]. Springer Springer Science+Business Media, New York, NY, USA and Heidelberg, Germany, Science+Business Media, New York, NY, USA, and Heidelberg, Germany, pp. pp. 283 315. 83 113. Jones, C. D., P. M. Cox, and C. Huntingford, 2006: Impact of climate carbon cycle Keeling, R. F., and S. R. Shertz, 1992: Seasonal and interannual variations in feedbacks on emission scenarios to achieve stabilization. In: Avoiding Dangerous atmospheric oxygen and implications for the global carbon cycle. Nature, 358, Climate Change [H. J. Schellnhuber, W. Cramer, N. Nakicenovic, T. Wigley and G. 723 727. Yohe (eds.)]. Cambridge University Press, Cambridge, United Kingdom, and New Keeling, R. F., S. C. Piper, and M. Heimann, 1996: Global and hemispheric CO2 sinks York, NY, USA, pp. 323 332. deduced from changes in atmospheric O2 concentration. Nature, 381, 218 221. Jones, C. D., et al., 2011: The HadGEM2 ES implementation of CMIP5 centennial Keeling, R. F., A. Körtzinger, and N. Gruber, 2010: Ocean deoxygenation in a warming simulations. Geosci. Model Dev., 4, 543 570. world. Annu. Rev. Mar. Sci., 2, 199 229. Joos, F., and R. Spahni, 2008: Rates of change in natural and anthropogenic radiative Keenan, T. F., et al., 2012: Terrestrial biosphere model performance for inter-annual forcing over the past 20,000 years. Proc. Natl. Acad. Sci. U.S.A., 105, 1425 1430. variability of land-atmosphere CO2 exchange. Global Change Biol., 18, 1971 Joos, F., J. L. Sarmiento, and U. Siegenthaler, 1991: Estimates of the effect of 1987. Southern-Ocean iron fertilization on atmospheric CO2 concentrations. Nature, Keith, D. W., 2001: Geoengineering. Nature, 409, 420. 349, 772 775. Keith, D. W., M. Ha-Duong, and J. K. Stolaroff, 2006: Climate strategy with CO2 Joos, F., T. L. Frölicher, M. Steinacher, and G.-K. Plattner, 2011: Impact of climate capture from the air. Clim. Change, 74, 17 45. change mitigation on ocean acidification projections. In: Ocean Acidification Kelemen, P. B., and J. Matter, 2008: In situ carbonation of peridotite for CO2 storage. [J. P. Gattuso and L. Hansson (eds.)]. Oxford University Press, Oxford, United Proc. Natl. Acad. Sci. U.S.A., 105, 17295 17300. Kingdom, and New York, NY, USA, pp. 273-289. Kellomäki, S., H. Peltola, T. Nuutinen, K. T. Korhonen, and H. Strandman, 2008: Joos, F., S. Gerber, I. C. Prentice, B. L. Otto-Bliesner, and P. J. Valdes, 2004: Transient Sensitivity of managed boreal forests in Finland to climate change, with simulations of Holocene atmospheric carbon dioxide and terrestrial carbon since implications for adaptive management. Philos. Trans. R. Soc. London Ser. B, 363, the Last Glacial Maximum. Global Biogeochem. Cycles, 18, Gb2002. 2341 2351. Joos, F., et al., 2013: Carbon dioxide and climate impulse response functions for the Keppler, F., J. T. G. Hamilton, M. Bra, and T. Röckmann, 2006: Methane emissions computation of greenhouse gas metrics: A multi-model analysis. Atmos. Chem. from terrestrial plants under aerobic conditions. Nature, 439, 187 191. Phys., 13, 2793 2825. Kesik, M., et al., 2006: Future scenarios of N2O and NO emissions from European Jorgenson, M. T., Y. L. Shur, and E. R. Pullman, 2006: Abrupt increase in permafrost forest soils. J. Geophys. Res. Biogeosci., 111, G02018. degradation in Arctic Alaska. Geophys. Res. Lett., 33, L02503. Key, R. M., et al., 2004: A global ocean carbon climatology: Results from Global Data Jung, M., et al., 2007: Assessing the ability of three land ecosystem models to Analysis Project (GLODAP). Global Biogeochem. Cycles, 18, GB4031. simulate gross carbon uptake of forests from boreal to Mediterranean climate in Khalil, M. A. K., and R. A. Rasmussen, 1989: Climate-induced feedbacks for the Europe. Biogeosciences, 4, 647 656. global cycles of methane and nitrous oxide. Tellus B, 41, 554 559. Jung, M., et al., 2011: Global patterns of land-atmosphere fluxes of carbon dioxide, Khalil, M. A. K., C. L. Butenhoff, and R. A. Rasmussen, 2007: Atmospheric methane: latent heat, and sensible heat derived from eddy covariance, satellite, and Trends and cycles of sources and sinks. Environ. Sci. Technol., 41, 2131 2137. meteorological observations. J. Geophys. Res. Biogeosci., 116, G00J07. Khatiwala, S., F. Primeau, and T. Hall, 2009: Reconstruction of the history of Jungclaus, J. H., et al., 2010: Climate and carbon-cycle variability over the last anthropogenic CO2 concentrations in the ocean. Nature, 462, 346 349. millennium. Clim. Past, 6, 723 737. Kheshgi, H. S., 1995: Sequestering atmospheric carbon-dioxide by increasing ocean Kai, F. M., S. C. Tyler, J. T. Randerson, and D. R. Blake, 2011: Reduced methane growth alkalinity. Energy, 20, 915 922. rate explained by decreased Northern Hemisphere microbial sources. Nature, Khvorostyanov, D., P. Ciais, G. Krinner, and S. Zimov, 2008: Vulnerability of east 476, 194 197. Siberia s frozen carbon stores to future warming. Geophys. Res. Lett., 35, L10703. Kanakidou, M., et al., 2012: Atmospheric fluxes of organic N and P to the global Kim, J. H., et al., 2004: North Pacific and North Atlantic sea-surface temperature ocean. Global Biogeochem. Cycles, 26, GB3026. variability during the Holocene. Quat. Sci. Rev., 23, 2141 2154. Kaplan, J. O., G. Folberth, and D. A. Hauglustaine, 2006: Role of methane and biogenic King, A. W., D. J. Hayes, D. N. Huntzinger, T. O. West, and W. M. Post, 2012: North volatile organic compound sources in late glacial and Holocene fluctuations of America carbon dioxide sources and sinks: Magnitude, attribution, and atmospheric methane concentrations. Global Biogeochem. Cycles, 20, Gb2016. uncertainty. Front. Ecol. Environ., 10, 512 519. Kaplan, J. O., I. C. Prentice, W. Knorr, and P. J. Valdes, 2002: Modeling the dynamics of Kirschbaum, M. U. F., 2003: Can trees buy time? An assessment of the role of terrestrial carbon storage since the Last Glacial Maximum. Geophys. Res. Lett., vegetation sinks as part of the global carbon cycle. Clim. Change, 58, 47 71. 29, 31-1-31-4. Kirschbaum, M. U. F., and A. Walcroft, 2008: No detectable aerobic methane efflux Kaplan, J. O., K. M. Krumhardt, E. C. Ellis, W. F. Ruddiman, C. Lemmen, and K. Klein from plant material, nor from adsorption/desorption processes. Biogeosciences, Goldewijk, 2011: Holocene carbon emissions as a result of anthropogenic land 5, 1551 1558. cover change. Holocene, 21, 775 791. Kleinen, T., V. Brovkin, and R. J. Schuldt, 2012: A dynamic model of wetland extent Karl, D. M., and R. M. Letelier, 2008: Nitrogen fixation-enhanced carbon sequestration and peat accumulation: Results for the Holocene. Biogeosciences, 9, 235 248. in low nitrate, low chlorophyll seascapes. Mar. Ecol. Prog. Ser., 364, 257 268. Kleinen, T., V. Brovkin, W. von Bloh, D. Archer, and G. Munhoven, 2010: Holocene Kato, E., T. Kinoshita, A. Ito, M. Kawamiya, and Y. Yamagata, 2013: Evaluation of carbon cycle dynamics. Geophys. Res. Lett., 37, L02705. spatially explicit emission scenario of land-use change and biomass burning Kloster, S., N. M. Mahowald, J. T. Randerson, and P. J. Lawrence, 2012: The impacts of 6 using a process-based biogeochemical model. J. Land Use Sci., 8, 104 122. climate, land use, and demography on fires during the 21st century simulated by Keeling, C. D., 1960: The concentration and isotopic abundances of carbon dioxide in CLM-CN. Biogeosciences, 9, 509 525. the atmosphere. Tellus B, 12, 200 203. Knorr, W., 2009: Is the airborne fraction of anthropogenic emissions increasing? Keeling, C. D., S. C. Piper, and M. Heimann, 1989: A three dimensional model of Geophys. Res. Lett., 36, L21710. atmospheric CO2 transport based on observed winds: 4. Mean annual gradients Kohfeld, K. E., and A. Ridgwell, 2009: Glacial-interglacial variability in atmospheric and interannual variations. In: Aspects of Climate Variability in the Pacific and CO2. In: Surface Ocean Lower Atmospheres Processes [C. Le Que re and E. S. the Western Americas [D. H. Peterson (ed.)]. Geophysical Monograph Series, Vol. Saltzman (eds.)]. American Geophysical Union, Washington, DC, pp. 251-286. 55. American Geophysical Union, Washington, DC, pp. 305 363. Köhler, P., J. Hartmann, and D. A. Wolf-Gladrow, 2010: Geoengineering potential of Keeling, C. D., R. B. Bacastow, A. E. Bainbridge, C. A. Ekdahl, P. R. Guenther, L. S. artificially enhanced silicate weathering of olivine. Proc. Natl. Acad. Sci. U.S.A., Waterman, and J. F. S. Chin, 1976: Atmospheric carbon-dioxide variations at 107, 20228 20233. Mauna-Loa Observatory, Hawaii. Tellus, 28, 538 551. 560 Carbon and Other Biogeochemical Cycles Chapter 6 Köhler, P., H. Fischer, G. Munhoven, and R. E. Zeebe, 2005: Quantitative interpretation Biogeochem. Cycles, 16, 1048. of atmospheric carbon records over the last glacial termination. Global Langner, J., R. Bergstrom, and V. Foltescu, 2005: Impact of climate change on surface Biogeochem. Cycles, 19, GB4020. ozone and deposition of sulphur and nitrogen in Europe. Atmos. Environ., 39, Konijnendijk, T. Y. M., S. L. Weber, E. Tuenter, and M. van Weele, 2011: Methane 1129 1141. variations on orbital timescales: A transient modeling experiment. Clim. Past, Larsen, K. S., et al., 2011: Reduced N cycling in response to elevated CO2, warming, 7, 635 648. and drought in a Danish heathland: Synthesizing results of the CLIMAITE project Koven, C. D., W. J. Riley, and A. Stern, 2013: Analysis of permafrost thermal dynamics after two years of treatments. Global Change Biol., 17, 1884 1899. and response to climate change in the CMIP5 Earth System Models. J. Clim., 26, Lassey, K. R., D. C. Lowe, and A. M. Smith, 2007: The atmospheric cycling of 1877-1900. radiomethane and the fossil fraction of the methane source. Atmos. Chem. Koven, C. D., et al., 2011: Permafrost carbon-climate feedbacks accelerate global Phys., 7, 2141 2149. warming. Proc. Natl. Acad. Sci. U.S.A., 108, 14769 14774. Law, R. M., R. J. Matear, and R. J. Francey, 2008: Comment on Saturation of the Krawchuk, M. A., M. A. Moritz, M.-A. Parisien, J. Van Dorn, and K. Hayhoe, 2009: Southern Ocean CO2 sink due to recent climate change . Science, 319, 570a. Global pyrogeography: The current and future distribution of wildfire. PLoS ONE, Lawrence, D., et al., 2011: Parameterization improvements and functional and 4, e5102. structural advances in version 4 of the Community Land Model. J. Adv. Model. Kraxner, F., S. Nilsson, and M. Obersteiner, 2003: Negative emissions from BioEnergy Earth Syst., 3, M03001, 27 pp. use, carbon capture and sequestration (BECS) the case of biomass production Lawrence, D. M., and A. G. Slater, 2005: A projection of severe near-surface permafrost by sustainable forest management from semi-natural temperate forests. degradation during the 21st century. Geophys. Res. Lett., 32, L24401. Biomass Bioenerg., 24, 285 296. Lawrence, D. M., A. G. Slater, V. E. Romanovsky, and D. J. Nicolsky, 2008: Sensitivity Krinner, G., et al., 2005: A dynamic global vegetation model for studies of the coupled of a model projection of near-surface permafrost degradation to soil column atmosphere-biosphere system. Global Biogeochem. Cycles, 19, GB1015. depth and representation of soil organic matter. J. Geophys. Res. Earth Surf., Krishnamurthy, A., J. K. Moore, N. Mahowald, C. Luo, S. C. Doney, K. Lindsay, and C. 113, F02011. S. Zender, 2009: Impacts of increasing anthropogenic soluble iron and nitrogen Le Page, Y., G. R. van der Werf, D. C. Morton, and J. M. C. Pereira, 2010: Modeling fire- deposition on ocean biogeochemistry. Global Biogeochem. Cycles, 23, GB3016. driven deforestation potential in Amazonia under current and projected climate Kroeze, C., A. Mosier, and L. Bouwman, 1999: Closing the global N2O budget: A conditions. J. Geophys. Res. Biogeosci., 115, G03012. retrospective analysis 1500 1994. Global Biogeochem. Cycles, 13, 1 8. Le Quéré, C., T. Takahashi, E. T. Buitenhuis, C. Rodenbeck, and S. C. Sutherland, 2010: Kroeze, C., L. Bouwman, and C. P. Slomp, 2007: Sinks for N2O at the Earth s surface. Impact of climate change and variability on the global oceanic sink of CO2. In: Greenhouse Gas Sinks [D. S. Raey , M. Hewitt, J. Grace and K. A. Smith (eds.)]. Global Biogeochem. Cycles, 24, GB4007. CAB International, pp. 227 243. Le Quéré, C., et al., 2007: Saturation of the southern ocean CO2 sink due to recent Kroeze, C., E. Dumont, and S. P. Seitzinger, 2010: Future trends in emissions of N2O climate change. Science, 316, 1735 1738. from rivers and estuaries. J. Integrat. Environ. Sci., 7, 71 78. Le Quéré, C., et al., 2009: Trends in the sources and sinks of carbon dioxide. Nature Kurahashi-Nakamura, T., A. Abe-Ouchi, Y. Yamanaka, and K. Misumi, 2007: Geosci., 2, 831 836. Compound effects of Antarctic sea ice on atmospheric pCO2 change during Le Quéré, C., et al., 2013: The global carbon budget 1959 2011. Earth Syst. Sci. Data, glacial-interglacial cycle. Geophys. Res. Lett., 34, L20708. 5, 165 186. Kurz, W. A., G. Stinson, and G. Rampley, 2008a: Could increased boreal forest LeBauer, D. S., and K. K. Treseder, 2008: Nitrogen limitation of net primary productivity ecosystem productivity offset carbon losses from increased disturbances? Philos. in terrestrial ecosystems is globally distributed. Ecology, 89, 371 379. Trans. R. Soc. London Ser. B, 363, 2261 2269. Lee, X., et al., 2011: Observed increase in local cooling effect of deforestation at Kurz, W. A., G. Stinson, G. J. Rampley, C. C. Dymond, and E. T. Neilson, 2008b: Risk of higher latitudes. Nature, 479, 384 387. natural disturbances makes future contribution of Canada s forests to the global Lemmen, C., 2009: World distribution of land cover changes during Pre- and carbon cycle highly uncertain. Proc. Natl. Acad. Sci. U.S.A., 105, 1551 1555. Protohistoric Times and estimation of induced carbon releases. Geomorphol. Kurz, W. A., et al., 2008c: Mountain pine beetle and forest carbon feedback to Relief Proc. Environ., 4, 303 312. climate change. Nature, 452, 987 990. Lenton, A., and R. J. Matear, 2007: Role of the Southern Annular Mode (SAM) in Kwon, E. Y., F. Primeau, and J. L. Sarmiento, 2009: The impact of remineralization Southern Ocean CO2 uptake. Global Biogeochem. Cycles, 21, Gb2016. depth on the air-sea carbon balance. Nature Geosci., 2, 630 635. Lenton, T. M., and C. Britton, 2006: Enhanced carbonate and silicate weathering Lackner, K. S., 2009: Capture of carbon dioxide from ambient air. Eur. Phys. J. Spec. accelerates recovery from fossil fuel CO2 perturbations. Global Biogeochem. Topics, 176, 93 106. Cycles, 20, Gb3009. Lackner, K. S., 2010: Washing carbon out of the air. Sci. Am., 302, 66 71. Lenton, T. M., and N. E. Vaughan, 2009: The radiative forcing potential of different Lackner, K. S., S. Brennan, J. M. Matter, A.-H. A. Park, A. Wright, and B. van der Zwaan, climate geoengineering options. Atmos. Chem. Phys., 9, 5539 5561. 2012: The urgency of the development of CO2 capture from ambient air. Proc. Lepistö, A., P. Kortelainen, and T. Mattsson, 2008: Increased organic C and N leaching Natl. Acad. Sci. U.S.A., 109, 13156 13162. in a northern boreal river basin in Finland. Global Biogeochem. Cycles, 22, Lal, R., 2004a: Soil carbon sequestration impacts on global climate change and food GB3029. security. Science, 304, 1623 1627. LeQuere, C., T. Takahashi, E. T. Buitenhuis, C. Rodenbeck, and S. C. Sutherland, 2010: Lal, R., 2004b: Soil carbon sequestration to mitigate climate change. Geoderma, Impact of climate change and variability on the global oceanic sink of CO2. 123, 1 22. Global Biogeochem. Cycles, 24. Lamarque, J.-F., 2008: Estimating the potential for methane clathrate instability in Leuzinger, S., Y. Q. Luo, C. Beier, W. Dieleman, S. Vicca, and C. Körner, 2011: Do global the 1%-CO2 IPCC AR-4 simulations. Geophys. Res. Lett., 35, L19806. change experiments overestimate impacts on terrestrial ecosystems? Trends Lamarque, J.-F., et al., 2010: Historical (1850 2000) gridded anthropogenic and Ecol. Evol., 26, 236 241. biomass burning emissions of reactive gases and aerosols: Methodology and Levin, I., et al., 2010: Observations and modelling of the global distribution and long- application. Atmos. Chem. Phys., 10, 7017 7039. term trend of atmospheric 14CO2. Tellus B, 62, 26 46. 6 Lamarque, J.-F., et al., 2013: The Atmospheric Chemistry and Climate Model Levin, I., et al., 2012: No inter-hemispheric 13CH4 trend observed. Nature, 486, E3 Intercomparison Project (ACCMIP): Overview and description of models, E4. simulations and climate diagnostics. Geosci. Model Dev., 6, 179 206. Levine, J. G., E. W. Wolff, P. O. Hopcroft, and P. J. Valdes, 2012: Controls on the Lamarque, J. F., et al., 2011: Global and regional evolution of short-lived radiatively- tropospheric oxidizing capacity during an idealized Dansgaard-Oeschger event, active gases and aerosols in the Representative Concentration Pathways. Clim. and their implications for the rapid rises in atmospheric methane during the last Change, 109, 191 212. glacial period. Geophys. Res. Lett., 39, L12805. Lampitt, R. S., et al., 2008: Ocean fertilization: A potential means of geoengineering? Levine, J. G., et al., 2011: Reconciling the changes in atmospheric methane sources Philos. Trans. R. Soc. London Ser. A, 366, 3919 3945. and sinks between the Last Glacial Maximum and the pre-industrial era. Langenfelds, R. L., R. J. Francey, B. C. Pak, L. P. Steele, J. Lloyd, C. M. Trudinger, and C. Geophys. Res. Lett., 38, L23804. E. Allison, 2002: Interannual growth rate variations of atmospheric CO2 and its d13C, H2, CH4, and CO between 1992 and 1999 linked to biomass burning. Global 561 Chapter 6 Carbon and Other Biogeochemical Cycles Levy, P. E., M. G. R. Cannell, and A. D. Friend, 2004: Modelling the impact of future Mahowald, N., et al., 1999: Dust sources and deposition during the last glacial changes in climate, CO2 concentration and land use on natural ecosystems and maximum and current climate: A comparison of model results with paleodata the terrestrial carbon sink. Global Environ. Change, 14, 21 30. from ice cores and marine sediments. J. Geophys. Res. Atmos, 104, 15895 Lewis, S. L., P. M. Brando, O. L. Phillips, G. M. van der Heijden, and D. Nepstad, 2011: 15916. The 2010 Amazon drought. Science, 331, 554. Mahowald, N., et al., 2011: Desert dust and anthropogenic aerosol interactions Lewis, S. L., et al., 2009: Increasing carbon storage in intact African tropical forests. in the Community Climate System Model coupled-carbon-climate model. Nature, 457, 1003 1006. Biogeosciences, 8, 387 414. Li, C., S. Frolking, and K. Butterbach-Bahl, 2005: Carbon sequestration can increase Mahowald, N. M., D. R. Muhs, S. Levis, P. J. Rasch, M. Yoshioka, C. S. Zender, and C. nitrous oxide emissions. Clim. Change, 72, 321 338. Luo, 2006: Change in atmospheric mineral aerosols in response to climate: Last Liberloo, M., et al., 2009: Coppicing shifts CO2 stimulation of poplar productivity glacial period, preindustrial, modern, and doubled carbon dioxide climates. J. to above-ground pools: A synthesis of leaf to stand level results from the POP/ Geophys. Res. Atmos., 111, D10202. EUROFACE experiment. New Phytologist, 182, 331 346. Mahowald, N. M., et al., 2009: Atmospheric iron deposition: Global ddistribution, Liddicoat, S., C. Jones, and E. Robertson, 2013: CO2 emissions determined by variability, and human perturbations. Annu. Rev. Mar. Sci., 1, 245 278. HadGEM2 ES to be compatible with the Representative Concentration Pathway Mahowald, N. M., et al., 2010: Observed 20th century desert dust variability: Impact scenarious and their extension. J. Clim., 26, 4381-4397. on climate and biogeochemistry. Atmos. Chem. Phys., 10, 10875 10893. Lohila, A., M. Aurela, J. Hatakka, M. Pihlatie, K. Minkkinen, T. Penttilä, and T. Laurila, Maier-Reimer, E., I. Kriest, J. Segschneider, and P. Wetzel, 2005: The HAMburg Ocean 2010: Responses of N2O fluxes to temperature, water table and N deposition in Carbon Cycle model HAMOCC 5.1 Technical description, Release 1.1. Max- a northern boreal fen. Eur. J. Soil Sci., 61, 651 661. Planck Institute for Meteorology, Hamburg, Germany. Long, M. C., K. Lindsay, S. Peacock, J. K. Moore, and S. C. Doney, 2013: Twentieth- Manning, A. C., and R. F. Keeling, 2006: Global oceanic and land biotic carbon sinks century oceanic carbon uptake and storage in CESM1(BGC). J. Clim., 26, 6775- from the Scripps atmospheric oxygen flask sampling network. Tellus B, 58, 6800. 95 116. Loose, B., and P. Schlosser, 2011: Sea ice and its effect on CO2 flux between the Marchenko, S. S., V. Romanovsky, and G. S. Tipenko, 2008: Numerical modeling of atmosphere and the Southern Ocean interior. J. Geophys. Res. Oceans, 116, C11. spatial permafrost dynamics in Alaska, Proceedings of the Ninth International Loulergue, L., et al., 2008: Orbital and millennial-scale features of atmospheric CH4 Conference on Permafrost, University of Alaska Fairbanks, June 29 July 3, 2008, over the past 800,000 years. Nature, 453, 383 386. 1125 1130. Lourantou, A., and N. Metzl, 2011: Decadal evolution of carbon sink within a strong Marland, G., and R. M. Rotty, 1984: Carbon dioxide emissions from fossil fuels: A bloom area in the subantarctic zone. Geophys. Res. Lett., 38, L23608. procedure for estimation and results for 1950 1982. Tellus B, 36, 232 261. Lourantou, A., J. Chappellaz, J.-M. Barnola, V. Masson-Delmotte, and D. Raynaud, Marlon, J. R., et al., 2008: Climate and human influences on global biomass burning 2010a: Changes in atmospheric CO2 and its carbon isotopic ratio during the over the past two millennia. Nature Geosci., 1, 697 702. penultimate deglaciation. Quat. Sci. Rev., 29, 1983 1992. Marlon, J. R., et al., 2012: Long-term perspective on wildfires in the western USA. Lourantou, A., et al., 2010b: Constraint of the CO2 rise by new atmospheric carbon Proc. Natl. Acad. Sci. U.S.A., 109, E535 E543. isotopic measurements during the last deglaciation. Global Biogeochem. Cycles, Martin, J. H., 1990: Glacial-interglacial CO2 change: The iron hypothesis. 24, GB2015. Paleoceanography, 5, 1 13. Lovelock, J. E., and C. G. Rapley, 2007: Ocean pipes could help the Earth to cure itself. Masarie, K. A., and P. P. Tans, 1995: Extension and integration of atmospheric carbon- Nature, 449, 403 403. dioxide data into a globally consistent measurement record. J. Geophys. Res. Lovenduski, N. S., N. Gruber, and S. C. Doney, 2008: Towards a mechanistic Atmos., 100, 11593 11610. understanding of the decadal trends in the Southern Ocean carbon sink. Global Mason Earles, J., S. Yeh, and K. E. Skog, 2012: Timing of carbon emissions from global Biogeochem. Cycles, 22, GB3016. forest clearance. Nature Clim. Change, 2, 682 685. Lovenduski, N. S., N. Gruber, S. C. Doney, and I. D. Lima, 2007: Enhanced CO2 Matear, R. J., and B. I. McNeil, 2003: Decadal accumulation of anthropogenic CO2 in outgassing in the Southern Ocean from a positive phase of the Southern Annular the Southern Ocean: A comparison of CFC-age derived estimates to multiple- Mode. Global Biogeochem. Cycles, 21, Gb2026. linear regression estimates. Global Biogeochem. Cycles, 17, 1113. Lucht, W., et al., 2002: Climatic control of the high-latitude vegetation greening Matear, R. J., and A. C. Hirst, 2003: Long-term changes in dissolved oxygen trend and Pinatubo effect. Science, 296, 1687 1689. concentrations in the ocean caused by protracted global warming. Global Luo, Y., D. Hui, and D. Zhang, 2006: Elevated carbon dioxide stimulates net Biogeochem. Cycles, 17, 1125. accumulations of carbon and nitrogen in terrestrial ecosystems: A meta-analysis. Matear, R. J., A. C. Hirst, and B. I. McNeil, 2000: Changes in dissolved oxygen in the Ecology, 87, 53 63. Southern Ocean with climate change. Geochem. Geophys. Geosyst., 1, 1050. Luo, Y., et al., 2004: Progressive nitrogen limitation of ecosystem responses to rising Matear, R. J., Y.-P. Wang, and A. Lenton, 2010: Land and ocean nutrient and carbon atmospheric carbon dioxide. BioScience, 54, 731 739. cycle interactions. Curr. Opin. Environ. Sustain., 2, 258 263. Lüthi, D., et al., 2008: High-resolution carbon dioxide concentration record 650,000 Matsumoto, K., 2007: Biology-mediated temperature control on atmospheric pCO2 800,000 years before present. Nature, 453, 379 382. and ocean biogeochemistry. Geophys. Res. Lett., 34, L20605. Luyssaert, S., et al., 2010: The European carbon balance. Part 3: Forests. Global Matsumoto, K., J. L. Sarmiento, and M. A. Brzezinski, 2002: Silicic acid leakage from Change Biol., 16, 1429 1450. the Southern Ocean: A possible explanation for glacial atmospheric pCO2. Global Luyssaert, S., et al., 2012: The European land and inland water CO2, CO, CH4 and N2O Biogeochem. Cycles, 16, 1031. balance between 2001 and 2005. Biogeosciences, 9, 3357 3380. Matsumoto, K., et al., 2004: Evaluation of ocean carbon cycle models with data- MacDonald, G. M., K. V. Kremenetski, and D. W. Beilman, 2008: Climate change and based metrics. Geophys. Res. Lett., 31, L007303. the northern Russian treeline zone. Philos. Trans. R. Soc. London Ser. B, 363, Matthews, H. D., 2006: Emissions targets for CO2 stabilization as modified by carbon 2285 2299. cycle feedbacks. Tellus B, 58, 591 602. 6 MacDougall, A. H., C. A. Avis, and A. J. Weaver, 2012: Significant contribution to Matthews, H. D., 2010: Can carbon cycle geoengineering be a useful complement to climate warming from the permafrost carbon feedback. Nature Geosci., 5, ambitious climate mitigation? Carbon Management, 1, 135 144. 719 721. Matthews, H. D., and K. Caldeira, 2007: Transient climate-carbon simulations of MacFarling-Meure, C., et al., 2006: Law Dome CO2, CH4 and N2O ice core records planetary geoengineering. Proc. Natl. Acad. Sci. U.S.A., 104, 9949 9954. extended to 2000 years BP. Geophys. Res. Lett., 33, L14810. Matthews, H. D., A. J. Weaver, and K. J. Meissner, 2005: Terrestrial carbon cycle Magnani, F., et al., 2007: The human footprint in the carbon cycle of temperate and dynamics under recent and future climate change. J. Clim., 18, 1609 1628. boreal forests. Nature, 447, 848 850. Matthews, H. D., L. Cao, and K. Caldeira, 2009: Sensitivity of ocean acidification to Mahmoudkhani, M., and D. W. Keith, 2009: Low-energy sodium hydroxide recovery geoengineered climate stabilization. Geophys. Res. Lett., 36, L10706. for CO2 capture from atmospheric air - Thermodynamic analysis. Int. J. Greenh. Mau, S., D. Valentine, J. Clark, J. Reed, R. Camilli, and L. Washburn, 2007: Dissolved Gas Cont., 3, 376 384. methane distributions and air-sea flux in the plume of a massive seep field, Coal Oil Point, California. Geophys. Res. Lett., 34, L22603. 562 Carbon and Other Biogeochemical Cycles Chapter 6 Mayorga, E., et al., 2010: Global nutrient export from WaterSheds 2 (NEWS 2): Model Metzl, N., et al., 2010: Recent acceleration of the sea surface fCO2 growth rate in development and implementation. Environ. Model. Software, 25, 837 853. the North Atlantic subpolar gyre (1993 2008) revealed by winter observations. McCarthy, H. R., et al., 2010: Re-assessment of plant carbon dynamics at the Duke Global Biogeochem. Cycles, 24, GB4004. free-air CO2 enrichment site: Interactions of atmospheric CO2 with nitrogen and Mieville, A., et al., 2010: Emissions of gases and particles from biomass burning water availability over stand development. New Phytologist, 185, 514 528. during the 20th century using satellite data and an historical reconstruction. McGuire, A. D., et al., 2009: Sensitivity of the carbon cycle in the Arctic to climate Atmos. Environ., 44, 1469 1477. change. Ecol. Monogr., 79, 523 555. Mikaloff-Fletcher, S. E., et al., 2006: Inverse estimates of anthropogenic CO2 uptake, McGuire, A. D., et al., 2012: An assessment of the carbon balance of Arctic tundra: transport, and storage by the ocean. Global Biogeochem. Cycles, 20, GB2002. Comparisons among observations, process models, and atmospheric inversions. Minschwaner, K., R. J. Salawitch, and M. B. McElroy, 1993: Absorption of solar Biogeosciences, 9, 3185 3204. radiation by O2: Implications for O3 and lifetimes of N2O, CFCl3, and CF2Cl2. J. McInerney, F. A., and S. L. Wing, 2011: The Paleocene-Eocene thermal maximum: A Geophys. Res. Atmos., 98, 10543 10561. perturbation of carbon cycle, climate, and biosphere with implications for the Mischler, J. A., et al., 2009: Carbon and hydrogen isotopic composition of methane future. Annu. Rev. Earth Planet. Sci., 39, 489 516. over the last 1000 years. Global Biogeochem. Cycles, 23, GB4024. McIntyre, B. D., H. R. Herren, J. Wakhungu, and R. T. Watson, 2009: International Mitchell, L. E., E. J. Brook, T. Sowers, J. R. McConnell, and K. Taylor, 2011: Multidecadal assessment of agricultural knowledge, science and technology for development variability of atmospheric methane, 1000 1800 C.E. J. Geophys. Res. Biogeosci., (IAASTD): Global report. International Assessment of Agricultural Knowledge, 116, G02007. Science and Technology for Development, 590 pp. Miyama, T., and M. Kawamiya, 2009: Estimating allowable carbon emission for CO2 McKinley, G. A., A. R. Fay, T. Takahashi, and N. Metzl, 2011: Convergence of concentration stabilization using a GCM-based Earth system model. Geophys. atmospheric and North Atlantic carbon dioxide trends on multidecadal Res. Lett., 36, L19709. timescales. Nature Geosci., 4, 606 610. Monnin, E., et al., 2001: Atmospheric CO2 concentrations over the last glacial McKinley, G. A., et al., 2006: North Pacific carbon cycle response to climate variability termination. Science, 291, 112 114. on seasonal to decadal timescales. J. Geophys. Res. Oceans, 111, C07s06. Monnin, E., et al., 2004: Evidence for substantial accumulation rate variability in McNeil, B. I., and R. J. Matear, 2006: Projected climate change impact on oceanic Antarctica during the Holocene through synchronization of CO2 in the Taylor acidification. Carbon Bal. Manag., 1. Dome, Dome C and DML ice cores. Earth Planet. Sci. Lett., 224, 45 54. McNeil, B. I., and R. J. Matear, 2008: Southern Ocean acidification: A tipping point Monteil, G., S. Houweling, E. J. Dlugockenky, G. Maenhout, B. H. Vaughn, J. W. C. at 450 ppm atmospheric CO2. Proc. Natl. Acad. Sci. U.S.A., 105, 18860 18864. White, and T. Rockmann, 2011: Interpreting methane variations in the past two McNeil, B. I., R. J. Matear, R. M. Key, J. L. Bullister, and J. L. Sarmiento, 2003: decades using measurements of CH4 mixing ratio and isotopic composition. Anthropogenic CO2 uptake by the ocean based on the global chlorofl uorocarbon Atmos. Chem. Phys., 11, 9141 9153. data set. Science, 299, 235 239. Monteith, D. T., et al., 2007: Dissolved organic carbon trends resulting from changes Medlyn, B. E., 2011: Comment on Drought-induced reductions in global terrestrial in atmospheric deposition chemistry. Nature, 450, 537 U539. net primary production from 2000 through 2009 . Science, 333, 1093. Montenegro, A., V. Brovkin, M. Eby, D. Archer, and A. J. Weaver, 2007: Long term fate Meehl, G. H., et al., 2007: Global Climate Projections. In: Climate Change 2007: The of anthropogenic carbon. Geophys. Res. Lett., 34, L19707. Physical Science Basis. Contribution of Working Group I to the Fourth Assessment Montenegro, A., M. Eby, Q. Z. Mu, M. Mulligan, A. J. Weaver, E. C. Wiebe, and M. S. Report of the Intergovernmental Panel on Climate Change [Solomon, S., D. Qin, Zhao, 2009: The net carbon drawdown of small scale afforestation from satellite M. Manning, Z. Chen, M. Marquis, K. B. Averyt, M. Tignor and H. L. Miller (eds.)] observations. Global Planet. Change, 69, 195 204. Cambridge University Press, Cambridge, United Kingdom and New York, NY, Montzka, S. A., M. Krol, E. Dlugokencky, B. Hall, P. Joeckel, and J. Lelieveld, 2011: Small USA, pp. 747 846. interannual variability of global atmospheric hydroxyl. Science, 331, 67 69. Meinshausen, M., et al., 2011: The RCP greenhouse gas concentrations and their Mooney, S. D., et al., 2011: Late Quaternary fire regimes of Australasia. Quat. Sci. extensions from 1765 to 2300. Clim. Change, 109, 213 241. Rev., 30, 28 46. Melillo, J. M., et al., 2011: Soil warming, carbon-nitrogen interactions, and forest Morford, S. L., B. Z. Houlton, and R. A. Dahlgren, 2011: Increased forest ecosystem carbon budgets. Proc. Natl. Acad. Sci. U.S.A., 108, 9508 9512. carbon and nitrogen storage from nitrogen rich bedrock. Nature, 477, 78 81. Melton, J. R., et al., 2013: Present state of global wetland extent and wetland Morino, I., et al., 2011: Preliminary validation of column-averaged volume mixing methane modelling: Conclusions from a model intercomparison project ratios of carbon dioxide and methane retrieved from GOSAT short-wavelength (WETCHIMP). Biogeosciences, 10, 753-788. infrared spectra. Atmos. Measure. Techn., 3, 5613 5643. Menviel, L., and F. Joos, 2012: Toward explaining the Holocene carbon dioxide and Mosier, A., C. Kroeze, C. Nevison, O. Oenema, S. Seitzinger, and O. van Cleemput, carbon isotope records: Results from transient ocean carbon cycle-climate 1998: Closing the global N2O budget: Nitrous oxide emissions through the simulations. Paleoceanography, 27, PA1207. agricultural nitrogen cycle - OECD/IPCC/IEA phase II development of IPCC Menviel, L., F. Joos, and S. P. Ritz, 2012: Simulating atmospheric CO2, C13 and the guidelines for national greenhouse gas inventory methodology. Nutr. Cycl. marine carbonate cycle during the last Glacial-Interglacial cycle: Possible role for Agroecosyst., 52, 225 248. a deepening of the mean remineralization depth and an increase in the oceanic Mosier, A. R., J. A. Morgan, J. Y. King, D. R. LeCain, and D. G. Milchunas, 2002: Soil- nutrient inventory. Quat. Sci. Rev., 56, 46 68. atmosphere exchange of CH4, CO2, NOx, and N2O in the Colorado shortgrass Menviel, L., A. Timmermann, A. Mouchet, and O. Timm, 2008: Meridional steppe under elevated CO2. Plant Soil, 240, 201 211. reorganizations of marine and terrestrial productivity during Heinrich events. Moss, R. H., et al., 2010: The next generation of scenarios for climate change research Paleoceanography, 23, PA1203. and assessment. Nature, 463, 747 756. Mercado, L. M., N. Bellouin, S. Sitch, O. Boucher, C. Huntingford, M. Wild, and P. M. Munhoven, G., 2002: Glacial-interglacial changes of continental weathering: Cox, 2009: Impact of changes in diffuse radiation on the global land carbon sink. Estimates of the related CO2 and HCO3 flux variations and their uncertainties. Nature, 458, 1014 1017. Global Planet. Change, 33, 155 176. Merico, A., T. Tyrrell, and T. Cokacar, 2006: Is there any relationship between Murata, A., Y. Kumamoto, S. Watanabe, and M. Fukasawa, 2007: Decadal increases 6 phytoplankton seasonal dynamics and the carbonate system? J. Mar. Syst., 59, of anthropogenic CO2 in the South Pacific subtropical ocean along 32 degrees S. 120 142. J. Geophys. Res. Oceans, 112, C05033. Metsaranta, J. M., W. A. Kurz, E. T. Neilson, and G. Stinson, 2010: Implications of Murata, A., Y. Kumamoto, K.-i. Sasaki, S. Watanabe, and M. Fukasawa, 2009: Decadal future disturbance regimes on the carbon balance of Canada s managed forest increases of anthropogenic CO2 along 149 degrees E in the western North (2010 2100). Tellus B, 62, 719 728. Pacific. J. Geophys. Res. Oceans, 114, C04018. Metz, B., O. Davidson, H. C. De Coninck, M. Loss, and L. A. Meyer, 2005: IPCC Special Murata, A., Y. Kumamoto, K. Sasaki, S. Watanabe, and M. Fukasawa, 2010: Decadal Report on Carbon Dioxide Capture and Storage Cambridge University Press, increases in anthropogenic CO2 along 20 degrees S in the South Indian Ocean. J. Cambridge, United Kingdom, and New York, NY, USA, 442 pp., Geophys. Res. Oceans, 115, C12055. Metzl, N., 2009: Decadal increase of oceanic carbon dioxide in Southern Indian Myneni, R. B., et al., 2001: A large carbon sink in the woody biomass of Northern Ocean surface waters (1991 2007). Deep-Sea Res. Pt. II, 56, 607 619. forests. Proc. Natl. Acad. Sci. U.S.A., 98, 14784 14789. 563 Chapter 6 Carbon and Other Biogeochemical Cycles Nabuurs, G. J., et al., 2008: Hotspots of the European forests carbon cycle. Forest Orr, J. C., et al., 2005: Anthropogenic ocean acidification over the twenty-first century Ecol. Manage., 256, 194 200. and its impact on calcifying organisms. Nature, 437, 681 686. Naegler, T., and I. Levin, 2009: Observation-based global biospheric excess Oschlies, A., 2001: Model-derived estimates of new production: New results point radiocarbon inventory 1963 2005. J. Geophys. Res., 114, D17302. towards lower values. Deep-Sea Res. Pt. II, 48, 2173 2197. Naik, V., D. J. Wuebbles, E. H. De Lucia, and J. A. Foley, 2003: Influence of geoengineered Oschlies, A., K. G. Schulz, U. Riebesell, and A. Schmittner, 2008: Simulated 21st climate on the terrestrial biosphere. Environ. Manage., 32, 373 381. century s increase in oceanic suboxia by CO2 enhanced biotic carbon export. Naqvi, S. W. A., H. W. Bange, L. Farias, P. M. S. Monteiro, M. I. Scranton, and J. Zhang, Global Biogeochem. Cycles, 22, GB4008. 2010: Coastal hypoxia/anoxia as a source of CH4 and N2O. Biogeosciences, 7, Oschlies, A., W. Koeve, W. Rickels, and K. Rehdanz, 2010a: Side effects and 2159 2190. accounting aspects of hypothetical large-scale Southern Ocean iron fertilization. Neef, L., M. van Weele, and P. van Velthoven, 2010: Optimal estimation of the Biogeosciences, 7, 4017 4035. present-day global methane budget. Global Biogeochem. Cycles, 24, GB4024. Oschlies, A., M. Pahlow, A. Yool, and R. J. Matear, 2010b: Climate engineering by Neftel, A., H. Oeschger, J. Schwander, B. Stauffer, and R. Zumbrunn, 1982: Ice core artificial ocean upwelling: Channelling the sorcerer s apprentice. Geophys. Res. sample measurements give atmospheric CO2 content during the past 40,000 yr. Lett., 37, L04701. Nature, 295, 220 223. Otto, D., D. Rasse, J. Kaplan, P. Warnant, and L. Francois, 2002: Biospheric carbon Nemani, R. R., et al., 2003: Climate-driven increases in global terrestrial net primary stocks reconstructed at the Last Glacial Maximum: Comparison between general production from 1982 to 1999. Science, 300, 1560 1563. circulation models using prescribed and computed sea surface temperatures. Nevison, C. D., et al., 2011: Exploring causes of interannual variability in the seasonal Global Planet. Change, 33, 117 138. cycles of tropospheric nitrous oxide. Atmos. Chem. Phys., 11, 3713 3730. Pacala, S. W., et al., 2001: Consistent land- and atmosphere-based U.S. carbon sink Nevle, R. J., and D. K. Bird, 2008: Effects of syn-pandemic fire reduction and estimates. Science, 292, 2316 2320. reforestation in the tropical Americas on atmospheric CO2 during European Page, S. E., J. O. Rieley, and C. J. Banks, 2011: Global and regional importance of the conquest. Palaeogeogr. Palaeoclimatol. Palaeoecol. 264, 25 38. tropical peatland carbon pool. Global Change Biol., 17, 798 818. Nevle, R. J., D. K. Bird, W. F. Ruddiman, and R. A. and Dull, 2011: Neotropical human Page, S. E., F. Siegert, J. O. Rieley, H.-D. V. Boehm, A. Jaya, and S. Limin, 2002: The landscape interactions, fire, and atmospheric CO2 during European conquest. amount of carbon released from peat and forest fires in Indonesia during 1997. Holocene, 21, 853 864. Nature, 420, 61 65. Newingham, B. A., C. H. Vanier, T. N. Charlet, K. Ogle, S. D. Smith, and R. S. Nowak, Palmroth, S., et al., 2006: Aboveground sink strength in forests controls the allocation 2013: No cumulative effect of ten years of elevated CO2 on perennial plant of carbon below ground and its [CO2]-induced enhancement. Proc. Natl. Acad. biomass components in the Mojave Desert. Global Change Biol., 19, 2168-2181.. Sci. U.S.A., 103, 19362 19367. Nisbet, R. E. R., et al., 2009: Emission of methane from plants. Proc. R. Soc. Ser. B, Pan, Y. D., et al., 2011: A large and persistent carbon sink in the world s forests. 276, 1347 1354. Science, 333, 988 993. Norby, R. J., 1998: Nitrogen deposition: A component of global change analysis. New Papa, F., C. Prigent, F. Aires, C. Jimenez, W. B. Rossow, and E. Matthews, 2010: Phytologist, 139, 189 200. Interannual variability of surface water extent at the global scale, 1993 2004. J. Norby, R. J., J. M. Warren, C. M. Iversen, B. E. Medlyn, and R. E. McMurtrie, 2010: CO2 Geophys. Res.Atmos., 115, D12111. enhancement of forest productivity constrained by limited nitrogen availability. Parekh, P., F. Joos, and S. A. Müller, 2008: A modeling assessment of the interplay Proc. Natl. Acad. Sci. U.S.A., 107, 19368 19373. between aeolian iron fluxes and iron-binding ligands in controlling carbon Norby, R. J., et al., 2005: Forest response to elevated CO2 is conserved across a broad dioxide fluctuations during Antarctic warm events. Paleoceanography, 23, range of productivity. Proc. Natl. Acad. Sci. U.S.A., 102, 18052 18056. PA4202. Nowak, R. S., D. S. Ellsworth, and S. D. Smith, 2004: Functional responses of plants to Park, G.-H., et al., 2010: Variability of global net air-sea CO2 fluxes over the last three elevated atmospheric CO2 do photosynthetic and productivity data from FACE decades using empirical relationships. Tellus B, 62, 352 368. experiments support early predictions? New Phytologist, 162, 253 280. Park, S., et al., 2012: Trends and seasonal cycles in the isotopic composition of O Connor, F. M., et al., 2010: Possible role of wetlands, permafrost, and methane nitrous oxide since 1940. Nature Geosci., 5, 261 265. hydrates in the methane cycle under future climate change: A review. Rev. Patra, P. K., et al., 2013: The carbon budget of South Asia. Biogeosciences, 10, 513 Geophys., 48, RG4005. 527. Oh, N.-H., and P. A. Raymond, 2006: Contribution of agricultural liming to riverine Patra, P. K., et al., 2011: TransCom model simulations of CH4 and related species: bicarbonate export and CO2 sequestration in the Ohio River basin. Global Linking transport, surface flux and chemical loss with CH4 variability in the Biogeochem. Cycles, 20, GB3012. troposphere and lower stratosphere. Atmos. Chem. Phys., 11, 12813 12837. Oleson, K. W., et al., 2010: Technical description of version 4.0 of the Community Pechony, O., and D. T. Shindell, 2010: Driving forces of global wildfires over the Land Model (CLM), NCAR Technical Note NCAR/TN-478+STR, National Center past millennium and the forthcoming century. Proc. Natl. Acad. Sci. U.S.A., 107, for Atmospheric Research, Boulder, CO, USA, 257 pp. 19167 19170. Olivier, J., J. Aardenne, F. Dentener, L. Ganzeveld, and J. Peters, 2005: Recent trends Peng, T.-H., and W. S. Broecker, 1991: Dynamic limitations on the Antarctic iron in global greenhouse emissions: Regional trends 1970 2000 and spatial fertilization strategy. Nature, 349, 227 229. distribution of key sources in 2000. Environ. Sci., 2, 81 99. Peng, T.-H., R. Wanninkhof, J. L. Bullister, R. A. Feely, and T. Takahashi, 1998: Olivier, J. G. J., and G. Janssens-Maenhout, 2012: Part III: Greenhouse gas emissions: Quantification of decadal anthropogenic CO2 uptake in the ocean based on 1. Shares and trends in greenhouse gas emissions; 2. Sources and Methods; dissolved inorganic carbon measurements. Nature, 396, 560 563. Total greenhouse gas emissions. In: CO2 Emissions from Fuel Combustion, 2012 Peng, T. H., R. Wanninkhof, and R. A. Feely, 2003: Increase of anthropogenic CO2 in Edition. International Energy Agency (IEA), Paris, France, III.1 III.51. the Pacific Ocean over the last two decades. Deep-Sea Res. Pt. II, 50, 3065 3082. Olofsson, J., and T. Hickler, 2008: Effects of human land-use on the global carbon Penuelas, J., J. G. Canadell, and R. Ogaya, 2011: Increased water-use-efficiency cycle during the last 6,000 years. Veget. Hist. Archaeobot., 17, 605 615. during the 20th century did not translate into enhanced tree growth. Global 6 Olsen, A., et al., 2006: Magnitude and origin of the anthropogenic CO2 increase and 13C Suess effect in the Nordic seas since 1981. Global Biogeochem. Cycles, 20, Ecol. Biogeogr., 20, 597 608. Pérez, F. F., M. Vázquez-Rodríguez, E. Louarn, X. A. Padin, H. Mercier, and A. F. Rios, GB3027. 2008: Temporal variability of the anthropogenic CO2 storage in the Irminger Sea. Opdyke, M. R., N. E. Ostrom, and P. H. Ostrom, 2009: Evidence for the predominance Biogeosciences, 5, 1669 1679. of denitrification as a source of N2O in temperate agricultural soils based on Perrin, A.-S., A. Probst, and J.-L. Probst, 2008: Impact of nitrogenous fertilizers isotopologue measurements. Global Biogeochem. Cycles, 23, Gb4018. on carbonate dissolution in small agricultural catchments: Implications for Orr, F. M. J., 2009: Onshore geologic storage of CO2. Science, 325, 1656 1658. weathering CO2 uptake at regional and global scales. Geochim. Cosmochim. Orr, J. C., 2011: Recent and future changes in ocean carbonate chemistry. In: Ocean Acta, 72, 3105 3123. Acidification [J.-P. Gattuso and L. Hansson (eds.)]. Oxford University Press, Pershing, A. J., L. B. Christensen, N. R. Record, G. D. Sherwood, and P. B. Stetson, 2010: Oxford, United Kingdom, and New York, NY, USA, pp. 41-66. The impact of whaling on the ocean carbon cycle: Why bigger was better. PLoS Orr, J. C., et al., 2001: Estimates of anthropogenic carbon uptake from four three- ONE, 5, e12444. dimensional global ocean models. Global Biogeochem. Cycles, 15, 43 60. 564 Carbon and Other Biogeochemical Cycles Chapter 6 Peters, G. P., et al., 2013: The challenge to keep global warming below 2°C. Nature Prentice, I. C., et al., 2001: The carbon cycle and atmospheric carbon dioxide. In: Clim. Change, 3, 4 6. Climate Change 2001: The Scientific Basis. Contribution of Working Group I to Petit, J. R., et al., 1999: Climate and atmospheric history of the past 420,000 years the Third Assessment Report of the Intergovernmental Panel on Climate Change from the Vostok ice core, Antarctica. Nature, 399, 429 436. [J. T. Houghton, Y. Ding, D. J. Griggs, M. Noquer, P. J. van der Linden, X. Dai, K. Petrenko, V. V., et al., 2009: 14CH4 Measurements in Greenland ice: Investigating Last Maskell and C. A. Johnson (eds.)]. Cambridge University Press, Cambridge, Glacial Termination CH4 sources. Science, 324, 506 508. United Kingdom and New York, NY, USA, pp. 183 237. Peylin, P., et al., 2005: Multiple constraints on regional CO2 flux variations over land Prinn, R. G., et al., 2001: Evidence for substantial variations of atmospheric hydroxyl and oceans. Global Biogeochem. Cycles, 19, GB1011. radicals in the past two decades. Science, 292, 1882 1888. Peylin, P., et al., 2013: Global atmospheric carbon budget: Results from an ensemble Prinn, R. G., et al., 2005: Evidence for variability of atmospheric hydroxyl radicals of atmospheric CO2 inversions. Biogeosci. Discuss., 10, 5301 5360. over the past quarter century. Geophys. Res. Lett., 32, L07809. Pfeil, G. B., et al., 2013: A uniform, quality controlled Surface Ocean CO2 Atlas Prinn, R. G., et al., 2000: A history of chemically and radiatively important gases in air (SOCAT). Earth Syst. Sci. Data, 5, 125 143. deduced from ALE/GAGE/AGAGE. J. Geophys. Res. Atmos., 105, 17751 17792. Phoenix, G. K., et al., 2006: Atmospheric nitrogen deposition in world biodiversity Quinton, J. N., G. Govers, K. Van Oost, and R. D. Bardgett, 2010: The impact of hotspots: The need for a greater global perspective in assessing N deposition agricultural soil erosion on biogeochemical cycling. Nature Geosci., 3, 311 314. impacts. Global Change Biol., 12, 470 476. Rabalais, N. N., R. J. Diaz, L. A. Levin, R. E. Turner, D. Gilbert, and J. Zhang, Piao, S., et al., 2011: Contribution of climate change and rising CO2 to terrestrial 2010: Dynamics and distribution of natural and human-caused hypoxia. carbon balance in East Asia: A multi-model analysis. Global Planet. Change, 75, Biogeosciences, 7, 585 619. 133 142. Raddatz, T. J., et al., 2007: Will the tropical land biosphere dominate the climate- Piao, S., et al., 2013: Evaluation of terrestrial carbon cycle models for their response carbon cycle feedback during the twenty-first century? Clim. Dyn., 29, 565 574. to climate variability and CO2 trends. Global Change Biol., 19, 2117-2132.. Rafelski, L. E., S. C. Piper, and R. F. Keeling, 2009: Climate effects on atmospheric Piao, S. L., P. Friedlingstein, P. Ciais, L. M. Zhou, and A. P. Chen, 2006: Effect of climate carbon dioxide over the last century. Tellus B, 61, 718 731. and CO2 changes on the greening of the Northern Hemisphere over the past two Ramankutty, N., and J. A. Foley, 1999: Estimating historical changes in global land decades. Geophys. Res. Lett., 33, L23402. cover: Croplands from 1700 to 1992. Global Biogeochem. Cycles, 13, 997 1027. Piao, S. L., P. Friedlingstein, P. Ciais, N. de Noblet-Ducoudré, D. Labat, and S. Zaehle, Ramankutty, N., C. Delire, and P. Snyder, 2006: Feedbacks between agriculture and 2007: Changes in climate and land use have a larger direct impact than rising climate: An illustration of the potential unintended consequences of human land CO2 on global river runoff trends. Proc. Natl. Acad. Sci. U.S.A., 104, 15242 15247. use activities. Global Planet. Change, 54, 79 93. Piao, S. L., P. Ciais, P. Friedlingstein, N. de Noblet-Ducoudré, P. Cadule, N. Viovy, and Randerson, J. T., et al., 2009: Systematic assessment of terrestrial biogeochemistry in T. Wang, 2009a: Spatiotemporal patterns of terrestrial carbon cycle during the coupled climate-carbon models. Global Change Biol., 15, 2462 2484. 20th century. Global Biogeochem. Cycles, 23, Gb4026. Rau, G. H., 2008: Electrochemical splitting of calcium carbonate to increase solution Piao, S. L., J. Y. Fang, P. Ciais, P. Peylin, Y. Huang, S. Sitch, and T. Wang, 2009b: The alkalinity: Implications for mitigation of carbon dioxide and ocean acidity. carbon balance of terrestrial ecosystems in China. Nature, 458, 1009 U82. Environ. Sci. Technol., 42, 8935 8940. Piao, S. L., et al., 2012: The carbon budget of terrestrial ecosystems in East Asia over Rau, G. H., and K. Caldeira, 1999: Enhanced carbonate dissolution: A means of the last two decades. Biogeosciences, 9, 3571 3586. sequestering waste CO2 as ocean bicarbonate. Energ. Conv. Manage., 40, 1803 Pison, I., P. Bousquet, F. Chevallier, S. Szopa, and D. Hauglustaine, 2009: Multi-species 1813. inversion of CH4, CO and H2 emissions from surface measurements. Atmos. Raupach, M. R., 2013: The exponential eigenmodes of the carbon-climate system, Chem. Phys., 9, 5281 5297. and their implications for ratios of responses to forcings Earth Syst. Dyn., 4, Plattner, G.-K., et al., 2008: Long-term climate commitments projected with climate- 31 49. carbon cycle models. J. Clim., 21, 2721-2751. Raupach, M. R., J. G. Canadell, and C. Le Quéré, 2008: Anthropogenic and biophysical Plattner, G. K., F. Joos, and T. Stocker, 2002: Revision of the global carbon budget due contributions to increasing atmospheric CO2 growth rate and airborne fraction. to changing air-sea oxygen fluxes. Global Biogeochem. Cycles, 16, 1096. Biogeosciences, 5, 1601 1613. Plug, L. J., and J. J. West, 2009: Thaw lake expansion in a two-dimensional coupled Ravishankara, A. R., J. S. Daniel, and R. W. Portmann, 2009: Nitrous oxide (N2O): The model of heat transfer, thaw subsidence, and mass movement. J. Geophys. Res., dominant ozone-depleting substance emitted in the 21st century. Science, 326, 114, F01002. 123 125. Pollard, R. T., et al., 2009: Southern Ocean deep-water carbon export enhanced by Raymond, P. A., and J. J. Cole, 2003: Increase in the export of alkalinity from North natural iron fertilization. Nature, 457, 577 580. America s largest river. Science, 301, 88 91. Pongratz, J., C. H. Reick, T. Raddatz, and M. Claussen, 2009: Effects of anthropogenic Raymond, P. A., N.-H. Oh, R. E. Turner, and W. Broussard, 2008: Anthropogenically land cover change on the carbon cycle of the last millennium. Global enhanced fluxes of water and carbon from the Mississippi River. Nature, 451, Biogeochem. Cycles, 23, Gb4001. 449 452. Pongratz, J., K. Caldeira, C. H. Reick, and M. Claussen, 2011a: Coupled climate- Rayner, P. J., R. M. Law, C. E. Allison, R. J. Francey, C. M. Trudinger, and C. Pickett- carbon simulations indicate minor global effects of wars and epidemics on Heaps, 2008: Interannual variability of the global carbon cycle (1992 2005) atmospheric CO2 between AD 800 and 1850. Holocene, 21, 843 851. inferred by inversion of atmospheric CO2 and 13 CO2 measurements. Global Pongratz, J., C. H. Reick, T. Raddatz, K. Caldeira, and M. Claussen, 2011b: Past land Biogeochem. Cycles, 22, GB3008. use decisions have increased mitigation potential of reforestation. Geophys. Res. Reagan, M. T., and G. J. Moridis, 2007: Oceanic gas hydrate instability and dissociation Lett., 38, L15701. under climate change scenarios. Geophys. Res. Lett., 34, L22709. Poulter, B., et al., 2010: Net biome production of the Amazon Basin in the 21st Reagan, M. T., and G. J. Moridis, 2009: Large-scale simulation of methane hydrate century. Global Change Biol., 16, 2062 2075. dissociation along the West Spitsbergen Margin. Geophys. Res. Lett., 36, L23612. Power, M. J., et al., 2013: Climatic control of the biomass-burning decline in the Reay, D. S., F. Dentener, P. Smith, J. Grace, and R. A. Feely, 2008: Global nitrogen Americas after AD 1500. Holocene, 23, 3 13. deposition and carbon sinks. Nature Geosci., 1, 430 437. 6 Power, M. J., et al., 2008: Changes in fire regimes since the Last Glacial Maximum: Renforth, P., 2012: The potential of enhanced weathering in the UK. Int. J. Greenh. An assessment based on a global synthesis and analysis of charcoal data. Clim. Gas Cont., 10, 229 243. Dyn., 30, 887 907. Revelle, R., and H. E. Suess, 1957: Carbon dioxide exchange between atmosphere Prather, M. J., C. D. Holmes, and J. Hsu, 2012: Reactive greenhouse gas scenarios: and ocean and the question of an increase of atmospheric CO2 during the past Systematic exploration of uncertainties and the role of atmospheric chemistry. decades. Tellus, 9, 18 27. Geophys. Res. Lett., 39, L09803. Rhee, T. S., A. J. Kettle, and M. O. Andreae, 2009: Methane and nitrous oxide emissions Prentice, I. C., and S. P. Harrison, 2009: Ecosystem effects of CO2 concentration: from the ocean: A reassessment using basin-wide observations in the Atlantic. J. Evidence from past climates. Clim. Past, 5, 297 307. Geophys. Res., 114, D12304. Ricke, K. L., M. G. Morgan, and M. R. Allen, 2010: Regional climate response to solar- radiation management. Nature Geosci., 3, 537 541. 565 Chapter 6 Carbon and Other Biogeochemical Cycles Ridgwell, A., and R. E. Zeebe, 2005: The role of the global carbonate cycle in the Sarmiento, J. L., T. M. C. Hughes, R. J. Stouffer, and S. Manabe, 1998: Simulated regulation and evolution of the Earth system. Earth Planet. Sci. Lett., 234, 299 response of the ocean carbon cycle to anthropogenic climate warming. Nature, 315. 393, 245 249. Ridgwell, A., and J. C. Hargreaves, 2007: Regulation of atmospheric CO2 by deep-sea Sarmiento, J. L., P. Monfray, E. Maier-Reimer, O. Aumont, R. Murnane, and J. C. Orr, sediments in an Earth system model. Global Biogeochem. Cycles, 21, Gb2008. 2000: Sea-air CO2 fluxes and carbon transport: A comparison of three ocean Ridgwell, A. J., 2001: Glacial-interglacial perturbations in the global carbon cycle. general circulation models. Global Biogeochem. Cycles, 14, 1267 1281. PhD Thesis, University of East Anglia, Norwich, United Kingdom, 134 pp. Sarmiento, J. L., et al., 2010: Trends and regional distributions of land and ocean Ridgwell, A. J., A. J. Watson, M. A. Maslin, and J. O. Kaplan, 2003: Implications of carbon sinks. Biogeosciences, 7, 2351 2367. coral reef buildup for the controls on atmospheric CO2 since the Last Glacial Savolainen, I., S. Monni, and S. Syri, 2009: The mitigation of methane emissions from Maximum. Paleoceanography, 18, 1083. the industrialised countries can explain the atmospheric concentration level-off. Riebesell, U., A. Körtzinger, and A. Oschlies, 2009: Sensitivities of marine carbon Int. J. Energ. Clean Environ., 10, 193 201. fluxes to ocean change. Proc. Natl. Acad. Sci. U.S.A., 106, 20602 20609. Schaefer, K., T. Zhang, L. Bruhwiler, and A. P. Barrett, 2011: Amount and timing of Riebesell, U., et al., 2007: Enhanced biological carbon consumption in a high CO2 permafrost carbon release in response to climate warming. Tellus B, 63, 165 ocean. Nature, 450, 545 548. 180. Rigby, M., et al., 2008: Renewed growth of atmospheric methane. Geophys. Res. Scheffer, M., V. Brovkin, and P. M. Cox, 2006: Positive feedback between global Lett., 35, L22805. warming and atmospheric CO2 concentration inferred from past climate change. Ringeval, B., P. Friedlingstein, C. Koven, P. Ciais, N. de Noblet-Ducoudre, B. Decharme, Geophys. Res. Lett., 33, L10702. and P. Cadule, 2011: Climate-CH4 feedback from wetlands and its interaction Schilt, A., M. Baumgartner, T. Blunier, J. Schwander, R. Spahni, H. Fischer, and T. F. with the climate-CO2 feedback. Biogeosciences, 8, 2137 2157. Stocker, 2010a: Glacial-interglacial and millennial-scale variations in the Robock, A., L. Oman, and G. L. Stenchikov, 2008: Regional climate responses to atmospheric nitrous oxide concentration during the last 800,000 years. Quat. geoengineering with tropical and Arctic SO2 injections. J. Geophys. Res., 113, Sci. Rev., 29, 182 192. D16101. Schilt, A., et al., 2010b: Atmospheric nitrous oxide during the last 140,000 years. Röckmann, T., and I. Levin, 2005: High-precision determination of the changing Earth Planet. Sci. Lett., 300, 33 43. isotopic composition of atmospheric N2O from 1990 to 2002. J. Geophys. Res. Schirrmeister, L., G. Grosse, S. Wetterich, P. P. Overduin, J. Strauss, E. A. G. Schuur, Atmos., 110, D21304. and H.-W. Hubberten, 2011: Fossil organic matter characteristics in permafrost Rödenbeck, C., S. Houweling, M. Gloor, and M. Heimann, 2003: CO2 flux history deposits of the northeast Siberian Arctic. J. Geophys. Res., 116, G00M02. 1982 2001 inferred from atmospheric data using a global inversion of Schmitt, J., et al., 2012: Carbon isotope constraints on the deglacial CO2 rise from ice atmospheric transport. Atmos. Chem. Phys., 3, 1919 1964. cores. Science, 336, 711 714. Rosamond, M. S., S. J. Thuss, and S. L. Schiff, 2012: Dependence of riverine nitrous Schmittner, A., and E. D. Galbraith, 2008: Glacial greenhouse-gas fluctuations oxide emissions on dissolved oxygen levels. Nature Geosci., 5, 715 718. controlled by ocean circulation changes. Nature, 456, 373 376. Roth, R., and F. Joos, 2012: Model limits on the role of volcanic carbon emissions in Schmittner, A., A. Oschlies, H. D. Matthews, and E. D. Galbraith, 2008: Future changes regulating glacial-interglacial CO2 variations. Earth Planet. Sci. Lett., 329 330, in climate, ocean circulation, ecosystems, and biogeochemical cycling simulated 141 149. for a business-as-usual CO2 emission scenario until year 4000 AD. Global Röthlisberger, R., M. Bigler, E. W. Wolff, F. Joos, E. Monnin, and M. A. Hutterli, 2004: Biogeochem. Cycles, 22, GB1013. Ice core evidence for the extent of past atmospheric CO2 change due to iron Schmittner, A., N. M. Urban, K. Keller, and D. Matthews, 2009: Using tracer fertilisation. Geophys. Res. Lett., 31, L16207. observations to reduce the uncertainty of ocean diapycnal mixing and climate- Rotty, R. M., 1983: Distribution of and changes in industrial carbon-cycle production. carbon cycle projections. Global Biogeochem. Cycles, 23, GB4009. J. Geophys. Res. Oceans, 88, 1301 1308. Schneider von Deimling, T. S., M. Meinshausen, A. Levermann, V. Huber, K. Frieler, Roy, T., et al., 2011: Regional impacts of climate change and atmospheric CO2 on D. M. Lawrence, and V. Brovkin, 2012: Estimating the near-surface permafrost- future ocean carbon uptake: A multimodel linear feedback analysis. J. Clim., 24, carbon feedback on global warming. Biogeosciences, 9, 649 665. 2300 2318. Scholze, M., W. Knorr, N. W. Arnell, and I. C. Prentice, 2006: A climate-change risk Rubasinghege, G., S. N. Spak, C. O. Stanier, G. R. Carmichael, and V. H. Grassian, analysis for world ecosystems. Proc. Natl. Acad. Sci. U.S.A., 103, 13116 13120. 2011: Abiotic mechanism for the formation of atmospheric nitrous oxide from Schuiling, R. D., and P. Krijgsman, 2006: Enhanced weathering: An effective and ammonium nitrate. Environ. Sci. Technol., 45, 2691 2697. cheap tool to sequester CO2. Clim. Change, 74, 349 354. Ruddiman, W. F., 2003: The anthropogenic greenhouse era began thousands of years Schultz, M. G., et al., 2007: Emission data sets and methodologies for estimating ago. Clim. Change, 61, 261 293. emissions. REanalysis of the TROpospheric chemical composition over the past Ruddiman, W. F., 2007: The early anthropogenic hypothesis: Challenges and 40 years. A long-term global modeling study of tropospheric chemistry funded responses. Rev. Geophys., 45, RG4001. under the 5th EU framework programme EU-Contract EVK2-CT-2002 00170. Sabine, C. L., R. A. Feely, F. J. Millero, A. G. Dickson, C. Langdon, S. Mecking, and D. Schulze, E. D., S. Luyssaert, P. Ciais, A. Freibauer, and I. A. Janssens, 2009: Importance Greeley, 2008: Decadal changes in Pacific carbon. J. Geophys. Res. Oceans, 113, of methane and nitrous oxide for Europe s terrestrial greenhouse-gas balance. C07021. Nature Geosci., 2, 842 850. Sabine, C. L., et al., 2004: The oceanic sink for anthropogenic CO2. Science, 305, Schulze, E. D., et al., 2010: The European carbon balance. Part 4: Integration of 367 371. carbon and other trace-gas fluxes. Global Change Biol., 16, 1451 1469. Salisbury, J., M. Green, C. Hunt, and J. Campbell, 2008: Coastal acidification by rivers: Schurgers, G., U. Mikolajewicz, M. Gröger, E. Maier-Reimer, M. Vizcaino, and A. A threat to shellfish? EOS Trans. AGU, 89, 513. Winguth, 2006: Dynamics of the terrestrial biosphere, climate and atmospheric Sallée, J.-B., R. J. Matear, S. R. Rintoul, and A. Lenton, 2012: Localized subduction CO2 concentration during interglacials: A comparison between Eemian and of anthropogenic carbon dioxide in the Southern Hemisphere oceans. Nature Holocene. Clim. Past, 2, 205 220. 6 Geosci., 5, 579 584. Schuster, U., and A. J. Watson, 2007: A variable and decreasing sink for atmospheric Samanta, A., M. H. Costa, E. L. Nunes, S. A. Viera, L. Xu, and R. B. Myneni, 2011: CO2 in the North Atlantic. J. Geophys. Res. Oceans, 112, C11006. Comment on Drought-induced reduction in global terrestrial net primary Schuster, U., et al., 2009: Trends in North Atlantic sea-surface fCO2 from 1990 to production from 2000 through 2009 . Science, 333, 1093. 2006. Deep-Sea Res. Pt. II, 56, 620 629. Sanderson, M. G., 1996: Biomass of termites and their emissions of methane and Schuster, U., et al., 2013: An assessment of the Atlantic and Arctic sea-air CO2 fluxes, carbon dioxide: A global database. Global Biogeochem. Cycles, 10, 543 557. 1990 2009. Biogeosciences, 10, 607 627. Sapart, C. J., et al., 2012: Natural and anthropogenic variations in methane sources Schwalm, C. R., et al., 2010: A model-data intercomparison of CO2 exchange across during the past two millennia. Nature, 490, 85 88. North America: Results from the North American Carbon Program site synthesis. Sarmiento, J. L., and N. Gruber, 2006: Ocean Biogeochemical Dynamics. Princeton J. Geophys. Res., 115, G00H05. University Press, Princeton, NJ, USA. Seitzinger, S. P., and C. Kroeze, 1998: Global distribution of nitrous oxide production Sarmiento, J. L., J. C. Orr, and U. Siegenthaler, 1992: A perturbation simulation of CO2 and N inputs in freshwater and coastal marine ecosystems. Global Biogeochem. uptake in an Ocean General Circulation Model. J. Geophys. Res., 97, 3621 3645. Cycles, 12, 93 113. 566 Carbon and Other Biogeochemical Cycles Chapter 6 Seitzinger, S. P., J. A. Harrison, E. Dumont, A. H. W. Beusen, and A. F. Bouwman, 2005: Smith, S. V., W. H. Renwick, R. W. Buddemeier, and C. J. Crossland, 2001b: Budgets Sources and delivery of carbon, nitrogen, and phosphorus to the coastal zone: of soil erosion and deposition for sediments and sedimentary organic carbon An overview of Global Nutrient Export from Watersheds (NEWS) models and across the conterminous United States. Global Biogeochem. Cycles, 15, 697 their application. Global Biogeochem. Cycles, 19, Gb4s01. 707. Seitzinger, S. P., et al., 2010: Global river nutrient export: A scenario analysis of past Sokolov, A. P., D. W. Kicklighter, J. M. Melillo, B. S. Felzer, C. A. Schlosser, and T. W. and future trends. Global Biogeochem. Cycles, 24, GB0A08. Cronin, 2008: Consequences of considering carbon-nitrogen interactions on Sentman, L. T., E. Shevliakova, R. J. Stouffer, and S. Malyshev, 2011: Time scales of the feedbacks between climate and the terrestrial carbon cycle. J. Clim., 21, terrestrial carbon response related to land-use application: Implications for 3776 3796. initializing an Earth System Model. Earth Interactions, 15, 1 16. Sowers, T., 2006: Late quaternary atmospheric CH4 isotope record suggests marine Shackleton, N. J., 2000: The 100,000 year ice-age cycle identified and found to lag clathrates are stable. Science, 311, 838 840. temperature, carbon dioxide, and orbital eccentricity. Science, 289, 1897 1902. Sowers, T., R. B. Alley, and J. Jubenville, 2003: Ice core records of atmospheric N2O Shaffer, G., 2010: Long-term effectiveness and consequences of carbon dioxide covering the last 106,000 years. Science, 301, 945 948. sequestration. Nature Geosci., 3, 464 467. Spahni, R., et al., 2011: Constraining global methane emissions and uptake by Shaffer, G., S. M. Olsen, and J. O. P. Pedersen, 2009: Long-term ocean oxygen ecosystems. Biogeosciences, 8, 1643 1665. depletion in response to carbon dioxide emission from fossil fuels. Nature Spracklen, D. V., L. J. Mickley, J. A. Logan, R. C. Hudman, R. Yevich, M. D. Flannigan, Geosci., 2, 105 109. and A. L. Westerling, 2009: Impacts of climate change from 2000 to 2050 on Shakhova, N., I. Semiletov, A. Salyuk, V. Yusupov, D. Kosmach, and O. Gustafsson, wildfire activity and carbonaceous aerosol concentrations in the western United 2010: Extensive methane venting to the atmosphere from sediments of the East States. J. Geophys. Res. Atmos., 114, D20301. Siberian Arctic shelf. Science, 327, 1246 1250. Stallard, R. F., 1998: Terrestrial sedimentation and the carbon cycle: Coupling Shallcross, D. E., M. A. K. Khalil, and C. L. Butenhoff, 2007: The atmospheric methane weathering and erosion to carbon burial. Global Biogeochem. Cycles, 12, 231 sink. In: Greenhouse Gas Sinks [D. Reay (ed.)] CAB International, pp. 171-183. 257. Shepherd, J., et al., 2009: Geoengineering the climate: Science, governance and Stanhill, G., and S. Cohen, 2001: Global dimming: A review of the evidence for a uncertainty. Report of the Royal Society, London, 98 pp. widespread and significant reduction in global radiation with discussion of its Shevliakova, E., et al., 2009: Carbon cycling under 300 years of land use change: probable causes and possible agricultural consequences. Agr. Forest Meteorol., Importance of the secondary vegetation sink. Global Biogeochem. Cycles, 23, 107, 255 278. GB2022. Steinacher, M., F. Joos, T. L. Frölicher, G.-K. Plattner, and S. C. Doney, 2009: Imminent Shindell, D. T., B. P. Walter, and G. Faluvegi, 2004: Impacts of climate change on ocean acidification in the Arctic projected with the NCAR global coupled carbon methane emissions from wetlands. Geophys. Res. Lett., 31, L21202. cycle-climate model. Biogeosciences, 6, 515 533. Siegenthaler, U., et al., 2005a: Supporting evidence from the EPICA Dronning Maud Steinacher, M., et al., 2010: Projected 21st century decrease in marine productivity: Land ice core for atmospheric CO2 changes during the past millennium. Tellus A multi-model analysis. Biogeosciences, 7, 979 1005. B, 57, 51 57. Stephens, B. B., and R. F. Keeling, 2000: The influence of Antarctic sea ice on glacial- Siegenthaler, U., et al., 2005b: Stable carbon cycle-climate relationship during the interglacial CO2 variations. Nature, 404, 171 174. late Pleistocene. Science, 310, 1313 1317. Stephens, B. B., et al., 2007: Weak northern and strong tropical land carbon uptake Sigman, D. M., M. P. Hain, and G. H. Haug, 2010: The polar ocean and glacial cycles from vertical profiles of atmospheric CO2. Science, 316, 1732 1735. in atmospheric CO2 concentration. Nature, 466, 47 55. Stevenson, D. S., et al., 2006: Multimodel ensemble simulations of present-day and Simpson, I. J., F. S. Rowland, S. Meinardi, and D. R. Blake, 2006: Influence of biomass near-future tropospheric ozone. J. Geophys. Res., 111, D08301. burning during recent fluctuations in the slow growth of global tropospheric Stocker, B. D., K. Strassmann, and F. Joos, 2011: Sensitivity of Holocene atmospheric methane. Geophys. Res. Lett., 33, L22808. CO2 and the modern carbon budget to early human land use: Analyses with a Simpson, I. J., et al., 2012: Long-term decline of global atmospheric ethane process-based model. Biogeosciences, 8, 69 88. concentrations and implications for methane. Nature, 488, 490 494. Stocker, B. D., et al., 2013: Multiple greenhouse gas feedbacks from the land Singarayer, J. S., P. J. Valdes, P. Friedlingstein, S. Nelson, and D. J. Beerling, 2011: biosphere under future climate change scenarious. Nature Clim. Change, 3, 666- Late Holocene methane rise caused by orbitally controlled increase in tropical 672. sources. Nature, 470, 82 85. Stöckli, R., et al., 2008: Use of FLUXNET in the Community Land Model development. Singh, B. K., R. D. Bardgett, P. Smith, and D. S. Reay, 2010: Microorganisms and J. Geophys. Res.Biogeosci., 113, G01025. climate change: Terrestrial feedbacks and mitigation options. Nature Rev. Stolaroff, J. K., D. W. Keith, and G. V. Lowry, 2008: Carbon dioxide capture from Microbiol., 8, 779 790. atmospheric air using sodium hydroxide spray. Environ. Sci. Technol., 42, 2728 Sitch, S., P. M. Cox, W. J. Collins, and C. Huntingford, 2007: Indirect radiative forcing 2735. of climate change through ozone effects on the land-carbon sink. Nature, 448, Stolaroff, J. K., S. Bhattacharyya, C. A. Smith, W. L. Bourcier, P. J. Cameron-Smith, and 791 794. R. D. Aines, 2012: Review of methane mitigation technologies with application to Sitch, S., et al., 2003: Evaluation of ecosystem dynamics, plant geography and rapid release of methane from the Arctic. Environ. Sci. Technol., 46, 6455 6469. terrestrial carbon cycling in the LPJ Dynamic Global Vegetation Model. Global Stramma, L., A. Oschlies, and S. Schmidtko, 2012: Mismatch between observed Change Biol., 9, 161 185. and modeled trends in dissolved upper-ocean oxygen over the last 50 years. Sitch, S., et al., 2008: Evaluation of the terrestrial carbon cycle, future plant Biogeosciences, 9, 4045 4057. geography and climate-carbon cycle feedbacks using five Dynamic Global Strassmann, K. M., F. Joos, and G. Fischer, 2008: Simulating effects of land use Vegetation Models (DGVMs). Global Change Biol., 14, 2015 2039. changes on carbon fluxes: Past contributions to atmospheric CO2 increases Skinner, L. C., S. Fallon, C. Waelbroeck, E. Michel, and S. Barker, 2010: Ventilation and future commitments due to losses of terrestrial sink capacity. Tellus B, 60, of the deep Southern ocean and deglacial CO2 rise. Science, 328, 1147 1151. 583 603. Smetacek, V., et al., 2012: Deep carbon export from a Southern Ocean iron-fertilized Stuiver, M., and P. D. Quay, 1981: Atmospheric 14C changes resulting from fossil-fuel 6 diatom bloom. Nature, 487, 313 319. CO2 release and cosmic-ray flux variability. Earth Planet. Sci. Lett., 53, 349 362. Smith, B., I. C. Prentice, and M. T. Sykes, 2001a: Representation of vegetation Suchet, P. A., and J. L. Probst, 1995: A Global model for present-day atmospheric soil dynamics in the modelling of terrestrial ecosystems: Comparing two contrasting CO2 consumption by chemical erosion of continental rocks (GEM-CO2). Tellus B, approaches within European climate space. Global Ecol. Biogeogr., 10, 621 637. 47, 273 280. Smith, K. A., A. R. Mosier, P. J. Crutzen, and W. Winiwarter, 2012: The role of N2O Sugimoto, A., T. Inoue, N. Kirtibutr, and T. Abe, 1998: Methane oxidation by termite derived from crop-based biofuels, and from agriculture in general, in Earth s mounds estimated by the carbon isotopic composition of methane. Global climate. Philos. Trans. R. Soc. London B, 367, 1169 1174. Biogeochem. Cycles, 12, 595 605. Smith, L. C., Y. Sheng, G. M. MacDonald, and L. D. Hinzman, 2005: Disappearing Sundquist, E. T., 1986: Geologic analogs: Their value and limitations in carbon Arctic lakes. Science, 308, 1429. dioxide research. In: The Changing Carbon Cycle [J. R. Trabalka and D. E. Reichle (eds.)], Springer-Verlag, New York, pp. 371 402. 567 Chapter 6 Carbon and Other Biogeochemical Cycles Sundquist, E. T, 1990: Influence of deep-sea benthic processes on atmospheric CO2. Thomas, H., et al., 2007: Rapid decline of the CO2 buffering capacity in the North Philos. Trans. R. Soc. London Series A, 331, 155 165. Sea and implications for the North Atlantic Ocean. Global Biogeochem. Cycles, Suntharalingam, P., et al., 2012: Quantifying the impact of anthropogenic nitrogen 21, GB4001. deposition on oceanic nitrous oxide. Geophys. Res. Lett., 39, L07605. Thompson, D. W. J., and S. Solomon, 2002: Interpretation of recent Southern Sussmann, R., F. Forster, M. Rettinger, and P. Bousquet, 2012: Renewed methane Hemisphere climate change. Science, 296, 895 899. increase for five years (2007 2011) observed by solar FTIR spectrometry. Atmos. Thomson, A. M., et al., 2010: Climate mitigation and food production in tropical Chem. Phys., 112, 4885 4891. landscapes. Special feature: Climate mitigation and the future of tropical Sutka, R. L., N. E. Ostrom, P. H. Ostrom, J. A. Breznak, H. Gandhi, A. J. Pitt, and F. Li, 2006: landscapes. Proc. Natl. Acad. Sci. U.S.A., 107, 19633 19638. Distinguishing nitrous oxide production from nitrification and denitrification on Thornton, P. E., J.-F. Lamarque, N. A. Rosenbloom, and N. M. Mahowald, 2007: the basis of isotopomer abundances. Appl. Environ. Microbiol., 72, 638 644. Influence of carbon-nitrogen cycle coupling on land model response to CO2 Sutton, M. A., D. D. Simpson, P. E. Levy, R. I. Smith, S. Reis, M. Van Oijen, and W. fertilization and climate variability. Global Biogeochem. Cycles, 21, Gb4018. De Vries, 2008: Uncertainties in the relationship between atmospheric nitrogen Thornton, P. E., et al., 2009: Carbon-nitrogen interactions regulate climate-carbon deposition and forest carbon sequestration. Global Change Biol., 14, 2057 cycle feedbacks: Results from an atmosphere-ocean general circulation model. 2063. Biogeosciences, 6, 2099 2120. Sutton, M. A., et al., 2011: The European Nitrogen Assessment Sources, Effects and Tian, H., X. Xu, M. Liu, W. Ren, C. Zhang, G. Chen, and C. Lu, 2010: Spatial and Policy Perspectives. Cambridge University Press, Cambridge, United Kingdom, temporal patterns of CH4 and N2O fluxes in terrestrial ecosystems of North and New York, NY, USA, 664 pp. America during 1979 2008: Application of a global biogeochemistry model. Syakila, A., and C. Kroeze, 2011: The global N2O budget revisited. Greenh. Gas Biogeosciences, 7, 2673 2694. Measure. Manage., 1, 17 26. Tilman, D., C. Balzer, J. Hill, and B. L. Befort, 2011: Global food demand and the Syakila, A., C. Kroeze, and C. P. Slomp, 2010: Neglecting sinks for N2O at the earth s sustainable intensification of agriculture. Proc. Natl. Acad. Sci. U.S.A., 108, surface: Does it matter? J. Integrat. Environ. Sci., 7, 79 87. 20260 20264. Syvitski, J. P. M., C. J. Vörösmarty, A. J. Kettner, and P. Green, 2005: Impact of humans Tjiputra, J. F., A. Olsen, K. Assmann, B. Pfeil, and C. Heinze, 2012: A model study of the on the flux of terrestrial sediment to the global coastal ocean. Science, 308, seasonal and long-term North Atlantic surface pCO2 variability. Biogeosciences, 376 380. 9, 907 923. Tagaris, E., K.-J. Liao, K. Manomaiphiboon, J.-H. Woo, S. He, P. Amar, and A. G. Russell, Todd-Brown, K., F. M. Hopkins, S. N. Kivlin, J. M. Talbot, and S. D. Allison, 2012: A 2008: Impacts of future climate change and emissions reductions on nitrogen framework for representing microbial decomposition in coupled climate models. and sulfur deposition over the United States. Geophys. Res. Lett., 35, L08811. Biogeoschemistry, 109, 19 33. Tagliabue, A., L. Bopp, and O. Aumont, 2008: Ocean biogeochemistry exhibits Toggweiler, J. R., 1999: Variation of atmospheric CO2 by ventilation of the ocean s contrasting responses to a large scale reduction in dust deposition. deepest water. Paleoceanography, 14, 571 588. Biogeosciences, 5, 11 24. Toggweiler, J. R., J. L. Russell, and S. R. Carson, 2006: Midlatitude westerlies, Tagliabue, A., L. Bopp, and M. Gehlen, 2011: The response of marine carbon atmospheric CO2, and climate change during the ice ages. Paleoceanography, and nutrient cycles to ocean acidification: Large uncertainties related to 21, PA2005. phytoplankton physiological assumptions. Global Biogeochem. Cycles, 25, Tranvik, L. J., et al., 2009: Lakes and reservoirs as regulators of carbon cycling and GB3017. climate. Limnol. Oceanogr., 54, 2298 2314. Takahashi, T., S. C. Sutherland, R. A. Feely, and R. Wanninkhof, 2006: Decadal change Trudinger, C. M., I. G. Enting, P. J. Rayner, and R. J. Francey, 2002: Kalman filter analysis of the surface water pCO2 in the North Pacific: A synthesis of 35 years of of ice core data 2. Double deconvolution of CO2 and 13C measurements. J. observations. J. Geophys. Res. Oceans, 111, C07s05. Geophys. Res., 107, D20. Takahashi, T., J. Olafsson, J. G. Goddard, D. W. Chipman, and S. C. Sutherland, 1993: Turetsky, M. R., R. K. Wieder, D. H. Vitt, R. J. Evans, and K. D. Scott, 2007: The Seasonal variation of CO2 and nutrients in the high-latitude surface oceans A disappearance of relict permafrost in boreal north America: Effects on peatland comparative study. Global Biogeochem. Cycles, 7, 843 878. carbon storage and fluxes. Global Change Biol., 13, 1922 1934. Takahashi, T., et al., 2009: Climatological mean and decadal change in surface ocean Tymstra, C., M. D. Flannigan, O. B. Armitage, and K. Logan, 2007: Impact of climate pCO2, and net sea-air CO2 flux over the global oceans. Deep-Sea Res. Pt. II, 56, change on area burned in Alberta s boreal forest. Int. J. Wildland Fire, 16, 153 554 577. 160. Tan, K., et al., 2010: Application of the ORCHIDEE global vegetation model to Tyrrell, T., J. G. Shepherd, and S. Castle, 2007: The long-term legacy of fossil fuels. evaluate biomass and soil carbon stocks of Qinghai-Tibetan grasslands. Global Tellus B, 59, 664 672. Biogeochem. Cycles, 24, GB1013. Ullman, D. J., G. A. McKinley, V. Bennington, and S. Dutkiewicz, 2009: Trends in the Tanhua, T., A. Körtzinger, K. Friis, D. W. Waugh, and D. W. R. Wallace, 2007: An North Atlantic carbon sink: 1992 2006. Global Biogeochem. Cycles, 23, Gb4011. estimate of anthropogenic CO2 inventory from decadal changes in oceanic UNEP, 2011: Integrated assessment of black carbon and tropospheric ozone: carbon content. Proc. Natl. Acad. Sci. U.S.A., 104, 3037 3042. Summary for decision makers. United Nations Environment Programme and Tans, P. P., T. J. Conway, and T. Nakazawa, 1989: Latitudinal distribution of the sources World Meterological Association, 38 pp. and sinks of atmospheric carbon dioxide derived from surface observations and Valsala, V., S. Maksyutov, M. Telszewski, S. Nakaoka, Y. Nojiri, M. Ikeda, and R. an atmospheric transport model. J. Geophys. Res. Atmos., 94, 5151 5172. Murtugudde, 2012: Climate impacts on the structures of the North Pacific air- Tarnocai, C., J. G. Canadell, E. A. G. Schuur, P. Kuhry, G. Mazhitova, and S. Zimov, 2009: sea CO2 flux variability. Biogeosciences, 9, 477 492. Soil organic carbon pools in the northern circumpolar permafrost region. Global van der Werf, G. R., et al., 2004: Continental-scale partitioning of fire emissions Biogeochem. Cycles, 23, Gb2023. during the 1997 to 2001 El Nino/La Nina period. Science, 303, 73 76. Taucher, J., and A. Oschlies, 2011: Can we predict the direction of marine primary van der Werf, G. R., et al., 2009: CO2 emissions from forest loss. Nature Geosci., 2, production change under global warming? Geophys. Res. Lett., 38, L02603. 737 738. 6 Taylor, K. E., R. J. Stouffer, and G. A. Meehl, 2012: An overview of CMIP5 and the van der Werf, G. R., et al., 2010: Global fire emissions and the contribution of experiment design. Bull. Am. Meteorol. Soc., 93, 485 498. deforestation, savanna, forest, agricultural, and peat fires (1997 2009). Atmos. Tegen, I., M. Werner, S. P. Harrison, and K. E. Kohfeld, 2004: Relative importance Chem. Phys., 10, 11707 11735. of climate and land use in determining present and future global soil dust van der Werf, G. R., et al., 2008: Climate regulation of fire emissions and deforestation emission. Geophys. Res. Lett., 31, L05105. in equatorial Asia Proc. Natl. Acad. Sci. U.S.A., 105, 20350 20355. Terazawa, K., S. Ishizuka, T. Sakata, K. Yamada, and M. Takahashi, 2007: Methane van Groenigen, K. J., C. W. Osenberg, and B. A. Hungate, 2011: Increased soil emissions from stems of Fraxinus mandshurica var. japonica trees in a floodplain emissions of potent greenhouse gases under increased atmospheric CO2. forest. Soil Biology and Biochemistry, 39, 2689 2692. Nature, 475, 214 216. Terrier, A., M. P. Girardin, C. Périé, P. Legendre, and Y. Bergeron, 2013: Potential van Huissteden, J., C. Berrittella, F. J. W. Parmentier, Y. Mi, T. C. Maximov, and A. J. changes in forest composition could reduce impacts of climate change on boreal Dolman, 2011: Methane emissions from permafrost thaw lakes limited by lake wildfires. Ecol. Appl., 23, 21 35. drainage. Nature Clim. Change, 1, 119 123. 568 Carbon and Other Biogeochemical Cycles Chapter 6 van Minnen, J. G., K. Klein Goldewijk, E. Stehfest, B. Eickhout, G. van Drecht, and R. Wania, R., 2007: Modelling Northern Peatland Land Surface Processes, Vegetation Leemans, 2009: The importance of three centuries of land-use change for the Dynamics and Methane Emissions. Ph.D. Thesis, Bristol, UK. global and regional terrestrial carbon cycle. Clim. Change, 97, 123 144. Wania, R., I. Ross, and I. C. Prentice, 2009: Integrating peatlands and permafrost into van Vuuren, D. P., L. F. Bouwman, S. J. Smith, and F. Dentener, 2011: Global projections a dynamic global vegetation model: 1. Evaluation and sensitivity of physical land for anthropogenic reactive nitrogen emissions to the atmosphere: An assessment surface processes. Global Biogeochem. Cycles, 23, GB3014. of scenarios in the scientific literature. Curr. Opin. Environ. Sustain., 3, 359 369. Wanninkhof, R., S. C. Doney, J. L. Bullister, N. M. Levine, M. Warner, and N. Gruber, Verdy, A., S. Dutkiewicz, M. J. Follows, J. Marshall, and A. Czaja, 2007: Carbon dioxide 2010: Detecting anthropogenic CO2 changes in the interior Atlantic Ocean and oxygen fluxes in the Southern Ocean: Mechanisms of interannual variability. between 1989 and 2005. J. Geophys. Res. Oceans, 115, C11028. Global Biogeochem. Cycles, 21, Gb2020. Wanninkhof, R., et al., 2013: Global ocean carbon uptake: Magnitude, variability and Vigano, I., H. van Weelden, R. Holzinger, F. Keppler, A. McLeod, and T. Röckmann, trends. Biogeosciences, 10, 1983 2000. 2008: Effect of UV radiation and temperature on the emission of methane from Wardle, D. A., M.-C. Nilsson, and O. Zackrisson, 2008: Fire-derived charcoal causes plant biomass and structural components. Biogeosciences, 5, 937 947. loss of forest humus. Science, 320, 629. Vitousek, P. M., S. Porder, B. Z. Houlton, and O. A. Chadwick, 2010: Terrestrial Watanabe, S., et al., 2011: MIROC-ESM 2010: Model description and basic results of phosphorus limitation: Mechanisms, implications, and nitrogen-phosphorus CMIP5 20c3m experiments. Geosci. Model Dev., 4, 845 872. interactions. Ecol. Appl., 20, 5 15. Watson, A. J., and A. C. N. Garabato, 2006: The role of Southern Ocean mixing and Vitousek, P. M., D. N. L. Menge, S. C. Reed, and C. C. Cleveland, 2013: Biological upwelling in glacial-interglacial atmospheric CO2 change. Tellus B, 58, 73 87. nitrogen fixation: Rates, patterns, and ecological controls in terrestrial Watson, A. J., D. C. E. Bakker, A. J. Ridgwell, P. W. Boyd, and C. S. Law, 2000: Effect ecosystems. Philos. Trans. R. Soc. London B,368, 20130119. of iron supply on Southern Ocean CO2 uptake and implications for glacial Volk, C. M., et al., 1997: Evaluation of source gas lifetimes from stratospheric atmospheric CO2. Nature, 407, 730 733. observations. J. Geophys. Res.: Atmos., 102, 25543 25564. Watson, A. J., P. W. Boyd, S. M. Turner, T. D. Jickells, and P. S. Liss, 2008: Designing the Volodin, E. M., 2008: Methane cycle in the INM RAS climate model. Izvestiya Atmos. next generation of ocean iron fertilization experiments. Mar. Ecol. Prog. Ser., Ocean. Phys., 44, 153 159. 364, 303 309. Voss, M., H. W. Bange, J. W. Dippner, J. J. Middelburg, J. P. Montoya, and B. Ward, 2013: Watson, A. J., et al., 1994: Minimal effect of iron fertilization on sea-surface carbon- The marine nitrogen cycle: Recent discoveries, uncertainties and the potential dioxide concentartions. Nature, 371, 143 145. relevance of climate change. Philos. Trans. R. Soc. London B, 368, 20130121. Watson, A. J., et al., 2009: Tracking the variable North Atlantic sink for atmospheric Voss, M., et al., 2011: Nitrogen processes in coastal and marine ecosystems. In: The CO2. Science, 326, 1391 1393. European Nitrogen Assessment: Sources, Effects, and Policy Perspectives. [M. A. Waugh, D. W., T. M. Hall, B. I. McNeil, R. Key, and R. J. Matear, 2006: Anthropogenic Sutton, C. M. Howard, J. W. Erisman, G. Billen, A. Bleeker, P. Grennfelt, H. van CO2 in the oceans estimated using transit time distributions. Tellus B, 58, 376 Grinsven and B. Grizetti (eds.)]. Cambridge University Press, Cambridge, United 389. Kingdom, and New York, NY, USA, pp. 147 176. Wecht, K. J., et al., 2012: Validation of TES methane with HIPPO aircraft observations: Voulgarakis, A., et al., 2013: Analysis of present day and future OH and methane Implications for inverse modeling of methane sources. Atmos. Chem. Phys., 12, lifetime in the ACCMIP simulations. Atmos. Chem. Phys., 13, 2563 2587. 1823 1832. Waelbroeck, C., et al., 2009: Constraints on the magnitude and patterns of ocean Westbrook, G. K., et al., 2009: Escape of methane gas from the seabed along the cooling at the Last Glacial Maximum. Nature Geosci., 2, 127 132. West Spitsbergen continental margin. Geophys. Res. Lett., 36, L15608. Wakita, M., S. Watanabe, A. Murata, N. Tsurushima, and M. Honda, 2010: Decadal Westerling, A. L., M. G. Turner, E. A. H. Smithwick, W. H. Romme, and M. G. Ryan, change of dissolved inorganic carbon in the subarctic western North Pacific 2011: Continued warming could transform Greater Yellowstone fire regimes by Ocean. Tellus B, 62, 608 620. mid-21st century. Proc. Natl. Acad. Sci. U.S.A., 108, 13165 13170. Walker, J. C. G., and J. F. Kasting, 1992: Effects of fuel and forest conservation White, J. R., R. D. Shannon, J. F. Weltzin, J. Pastor, and S. D. Bridgham, 2008: Effects of on future levels of atmospheric carbon dioxide. Palaeogeogr. Palaeoclimat. soil warming and drying on methane cycling in a northern peatland mesocosm Palaeoecol. (Global Planet. Change Sect.), 97, 151 189. study. J. Geophys. Res. Biogeosci., 113, G00A06. Walter Anthony, K. M., P. Anthony, G. Grosse, and J. Chanton, 2012: Geologic methane Wiedinmyer, C., S. K. Akagi, R. J. Yokelson, L. K. Emmons, J. A. Al-Saadi, J. J. Orlando, seeps along boundaries of Arctic permafrost thaw and melting glaciers. Nature and A. J. Soja, 2011: The Fire INventory from NCAR (FINN): A high resolution Geosci., 5, 419-426. global model to estimate the emissions from open burning. Geosci. Model Dev., Walter, K. M., L. C. Smith, and F. Stuart Chapin, 2007: Methane bubbling from 4, 625 641. northern lakes: Present and future contributions to the global methane budget. Williams, C. A., G. J. Collatz, J. Masek, and S. N. Goward, 2012a: Carbon consequences Philos. Trans. R. Soc. A, 365, 1657 1676. of forest disturbance and recovery across the conterminous United States. Walter, K. M., S. A. Zimov, J. P. Chanton, D. Verbyla, and F. S. I. Chapin, 2006: Methane Global Biogeochem. Cycles, 26, GB1005. bubbling from Siberian thaw lakes as a positive feedback to climate warming. Williams, J., and P. J. Crutzen, 2010: Nitrous oxide from aquaculture. Nature Geosci., Nature, 443, 71 75. 3, 143. Wang, D., S. A. Heckathorn, X. Wang, and S. M. Philpott, 2012a: A meta-analysis Williams, J. E., A. Strunk, V. Huijnen, and M. van Weele, 2012b: The application of of plant physiological and growth responses to temperature and elevated CO2. the Modified Band Approach for the calculation of on-line photodissociation Oecologia, 169, 1 13. rate constants in TM5: Implications for oxidative capacity. Geosci. Model Dev., Wang, J., X. Pan, Y. Liu, X. Zhang, and Z. Xiong, 2012b: Effects of biochar amendment 5, 15 35. in two soils on greenhouse gas emissions and crop production. Plant Soil, 360, Wise, M., et al., 2009: Implications of limiting CO2 concentrations for land Uue and 287 298. energy. Science, 324, 1183 1186. Wang, Y.-P., and B. Z. Houlton, 2009: Nitrogen constraints on terrestrial carbon Woodward, F. I., and M. R. Lomas, 2004: Simulating vegetation processes along the uptake: Implications for the global carbon-climate feedback. Geophys. Res. Lett., Kalahari transect. Global Change Biol., 10, 383 392. 36, L24403. Woolf, D., J. E. Amonette, F. A. Street-Perrott, J. Lehmann, and S. Joseph, 2010: 6 Wang, Y. P., B. Z. Houlton, and C. B. Field, 2007: A model of biogeochemical cycles Sustainable biochar to mitigate global climate change. Nature Commun., 1, 1 9. of carbon, nitrogen, and phosphorus including symbiotic nitrogen fixation and Worden, J., et al., 2012: Profiles of CH4, HDO, H2O, and N2O with improved lower phosphatase production. Global Biogeochem. Cycles, 21, GB1018. tropospheric vertical resolution from Aura TES radiances. Atmos. Measure. Wang, Y. P., R. M. Law, and B. Pak, 2010a: A global model of carbon, nitrogen and Techn., 5, 397 411 phosphorus cycles for the terrestrial biosphere. Biogeosciences, 7, 2261 2282. Worrall, F., T. Burt, and R. Shedden, 2003: Long term records of riverine dissolved Wang, Z., J. Chappellaz, K. Park, and J. E. Mak, 2010b: Large variations in Southern organic matter. Biogeochemistry, 64, 165 178. Hemisphere biomass burning during the last 650 years. Science, 330, 1663 Wotton, B. M., C. A. Nock, and M. D. Flannigan, 2010: Forest fire occurrence and 1666. climate change in Canada. Int. J. Wildland Fire, 19, 253 271. Wang, Z. P., X. G. Han, G. G. Wang, Y. Song, and J. Gulledge, 2008: Aerobic methane Wu, P. L., R. Wood, J. Ridley, and J. Lowe, 2010: Temporary acceleration of the emission from plants in the Inner Mongolia steppe. Environ. Sci. Technol., 42, hydrological cycle in response to a CO2 rampdown. Geophys. Res. Lett., 37, 62 68. L12705. 569 Chapter 6 Carbon and Other Biogeochemical Cycles Wu, T., et al., 2013: Global Carbon budgets simulated by the Beijing Climate Center Zeng, N., 2003: Glacial-interglacial atmospheric CO2 change The glacial burial Climate System Model for the last Century. J. Geophys. Res. Atmos., doi:10.1002/ hypothesis. Adv. Atmos. Sci., 20, 677 693. jgrd.50320, in press. Zhang, Q., Y. P. Wang, A. J. Pitman, and Y. J. Dai, 2011: Limitations of nitrogen and Wurzburger, N., J. P. Bellenger, A. M. L. Kraepiel, and L. O. Hedin, 2012: Molybdenum phosphorous on the terrestrial carbon uptake in the 20th century. Geophys. Res. and phosphorus interact to constrain asymbiotic nitrogen fixation in tropical Lett., 38, L22701. forests. PLoS ONE, 7, e33710. Zhao, M., and S. W. Running, 2010: Drought-induced reduction in global terrestrial Xu-Ri, and I. C. Prentice, 2008: Terrestrial nitrogen cycle simulation with a dynamic net primary production from 2000 through 2009. Science, 329, 940 943. global vegetation model. Global Change Biol., 14, 1745 1764. Zhou, S., and P. C. Flynn, 2005: Geoengineering downwelling ocean currents: A cost Xu-Ri, I. C. Prentice, R. Spahni, and H. S. Niu, 2012: Modelling terrestrial nitrous assessment. Clim. Change, 71, 203 220. oxide emissions and implications for climate feedback. New Phytologist, 196, Zhuang, Q. L., et al., 2006: CO2 and CH4 exchanges between land ecosystems and 472 488. the atmosphere in northern high latitudes over the 21st century. Geophys. Res. Yamamoto-Kawai, M., F. A. McLaughlin, E. C. Carmack, S. Nishino, and K. Shimada, Lett., 33, L17403. 2009: Aragonite undersaturation in the Arctic Ocean: Effects of ocean Zickfeld, K., M. Eby, H. D. Matthews, A. Schmittner, and A. J. Weaver, 2011: acidification and sea ice melt. Science, 326, 1098 1100. Nonlinearity of carbon cycle feedbacks. J. Clim., 24, 4255 4275. Yamamoto, A., M. Kawamiya, A. Ishida, Y. Yamanaka, and S. Watanabe, 2012: Impact Zimov, N. S., S. A. Zimov, A. E. Zimova, G. M. Zimova, V. I. Chuprynin, and F. S. Chapin, of rapid sea-ice reduction in the Arctic Ocean on the rate of ocean acidification. 2009: Carbon storage in pernafrost and soils of the mammoth tundra-steppe Biogeosciences, 9, 2365 2375. biome: Role in the global carbon budget. Geophys. Res. Lett., 36, L02502. Yan, X., H. Akiyama, K. Yagi, and H. Akimoto, 2009: Global estimations of the inventory and mitigation potential of methane emissions from rice cultivation conducted using the 2006 Intergovernmental Panel on Climate Change Guidelines. Global Biogeochem. Cycles, 23, GB2002. Yang, X., T. K. Richardson, and A. K. Jain, 2010: Contributions of secondary forest and nitrogen dynamics to terrestrial carbon uptake. Biogeosciences, 7, 3041-3050. Yevich, R., and J. A. Logan, 2003: An assessment of biofuel use and burning of agricultural waste in the developing world. Global Biogeochem. Cycles, 17, 1095. Yool, A., J. G. Shepherd, H. L. Bryden, and A. Oschlies, 2009: Low efficiency of nutrient translocation for enhancing oceanic uptake of carbon dioxide. J. Geophys. Res. Oceans, 114, C08009. Yoshikawa-Inoue, H. Y., and M. Ishii, 2005: Variations and trends of CO2 in the surface seawater in the Southern Ocean south of Australia between 1969 and 2002. Tellus B, 57, 58 69. Yoshikawa, C., M. Kawamiya, T. Kato, Y. Yamanaka, and T. Matsuno, 2008: Geographical distribution of the feedback between future climate change and the carbon cycle. J. Geophys. Res. Biogeosci., 113, G03002. Young, P., et al., 2013: Pre-industrial to end 21st century projections of tropospheric ozone from the Atmospheric Chemistry and Climate Model Intercomparison Project (ACCMIP). Atmos. Chem. Phys., 4, 2063 2090. Yu, J., W. S. Broecker, H. Elderfield, Z. Jin, J. McManus, and F. Zhang, 2010: Loss of carbon from the deep sea since the Last Glacial Maximum. Science, 330, 1084 1087. Yu, Z., 2011: Holocene carbon flux histories of the world s peatlands: Global carbon- cycle implications. Holocene, 21, 761 774. Zaehle, S., 2013: Terrestrial nitrogen-carbon cycle interactions at the global scale, Philos. Trans. R. Soc. London B, 368, 20130125. Zaehle, S., and A. D. Friend, 2010: Carbon and nitrogen cycle dynamics in the O-CN land surface model: 1. Model description, site-scale evaluation, and sensitivity to parameter estimates. Global Biogeochem. Cycles, 24, GB1005. Zaehle, S., and D. Dalmonech, 2011: Carbon-nitrogen interactions on land at global scales: Current understanding in modelling climate biosphere feedbacks. Curr. Opin. Environ. Sustain., 3, 311 320. Zaehle, S., P. Friedlingstein, and A. D. Friend, 2010a: Terrestrial nitrogen feedbacks may accelerate future climate change. Geophys. Res. Lett., 37, L01401. Zaehle, S., P. Ciais, A. D. Friend, and V. Prieur, 2011: Carbon benefits of anthropogenic reactive nitrogen offset by nitrous oxide emissions. Nature Geosci., 4, 601 605. Zaehle, S., A. D. Friend, P. Friedlingstein, F. Dentener, P. Peylin, and M. Schulz, 2010b: Carbon and nitrogen cycle dynamics in the O-CN land surface model: 6 2. Role of the nitrogen cycle in the historical terrestrial carbon balance. Global Biogeochem. Cycles, 24, GB1006. Zak, D. R., K. S. Pregitzer, M. E. Kubiske, and A. J. Burton, 2011: Forest productivity under elevated CO2 and O3: Positive feedbacks to soil N cycling sustain decade- long net primary productivity enhancement by CO2. Ecol. Lett., 14, 1220 1226. Zech, R., Y. Huang, M. Zech, R. Tarozo, and W. Zech, 2011: High carbon sequestration in Siberian permafrost loess-paleosoils during glacials. Clim. Past, 7, 501 509. Zeebe, R. E., and D. Wolf-Gladrow, 2001: CO2 in Seawater: Equilibrium, Kinetics, Isotopes. Elsevier Science, Amsterdam, Netherlands, and Philadelphia, PA, USA. Zeebe, R. E., and D. Archer, 2005: Feasibility of ocean fertilization and its impact on future atmospheric CO2 levels. Geophys. Res. Lett., 32, L09703. 570 7 Clouds and Aerosols Coordinating Lead Authors: Olivier Boucher (France), David Randall (USA) Lead Authors: Paulo Artaxo (Brazil), Christopher Bretherton (USA), Graham Feingold (USA), Piers Forster (UK), Veli-Matti Kerminen (Finland), Yutaka Kondo (Japan), Hong Liao (China), Ulrike Lohmann (Switzerland), Philip Rasch (USA), S.K. Satheesh (India), Steven Sherwood (Australia), Bjorn Stevens (Germany), Xiao-Ye Zhang (China) Contributing Authors: Govindasamy Bala (India), Nicolas Bellouin (UK), Angela Benedetti (UK), Sandrine Bony (France), Ken Caldeira (USA), Anthony Del Genio (USA), Maria Cristina Facchini (Italy), Mark Flanner (USA), Steven Ghan (USA), Claire Granier (France), Corinna Hoose (Germany), Andy Jones (UK), Makoto Koike (Japan), Ben Kravitz (USA), Benjamin Laken (Spain), Matthew Lebsock (USA), Natalie Mahowald (USA), Gunnar Myhre (Norway), Colin O Dowd (Ireland), Alan Robock (USA), Bjrn Samset (Norway), Hauke Schmidt (Germany), Michael Schulz (Norway), Graeme Stephens (USA), Philip Stier (UK), Trude Storelvmo (USA), Dave Winker (USA), Matthew Wyant (USA) Review Editors: Sandro Fuzzi (Italy), Joyce Penner (USA), Venkatachalam Ramaswamy (USA), Claudia Stubenrauch (France) This chapter should be cited as: Boucher, O., D. Randall, P. Artaxo, C. Bretherton, G. Feingold, P. Forster, V.-M. Kerminen, Y. Kondo, H. Liao, U. Lohmann, P. Rasch, S.K. Satheesh, S. Sherwood, B. Stevens and X.Y. Zhang, 2013: Clouds and Aerosols. In: Climate Change 2013: The Physical Science Basis. Contribution of Working Group I to the Fifth Assessment Report of the Intergovernmental Panel on Climate Change [Stocker, T.F., D. Qin, G.-K. Plattner, M. Tignor, S.K. Allen, J. Boschung, A. Nauels, Y. Xia, V. Bex and P.M. Midgley (eds.)]. Cambridge University Press, Cambridge, United Kingdom and New York, NY, USA. 571 Table of Contents Executive Summary...................................................................... 573 7.5 Radiative Forcing and Effective Radiative Forcing by Anthropogenic Aerosols............................ 614 7.1 Introduction....................................................................... 576 7.5.1 Introduction and Summary of AR4............................. 614 7.1.1 Clouds and Aerosols in the Atmosphere..................... 576 7.5.2 Estimates of Radiative Forcing and Effective Radiative Forcing from Aerosol Radiation Interactions............. 614 7.1.2 Rationale for Assessing Clouds, Aerosols and Their Interactions....................................................... 576 7.5.3 Estimate of Effective Radiative Forcing from Combined Aerosol Radiation and Aerosol Cloud 7.1.3 Forcing, Rapid Adjustments and Feedbacks............... 576 Interactions................................................................ 618 7.1.4 Chapter Roadmap...................................................... 578 7.5.4 Estimate of Effective Radiative Forcing from Aerosol Cloud Interactions Alone............................................ 620 7.2 Clouds.................................................................................. 578 7.2.1 Clouds in the Present-Day Climate System................ 578 7.6 Processes Underlying Precipitation Changes.......... 624 7.2.2 Cloud Process Modelling............................................ 582 7.6.1 Introduction............................................................... 624 7.2.3 Parameterization of Clouds in Climate Models.......... 584 7.6.2 The Effects of Global Warming on Large-Scale Precipitation Trends.................................................... 624 7.2.4 Water Vapour and Lapse Rate Feedbacks................... 586 7.6.3 Radiative Forcing of the Hydrological Cycle............... 624 7.2.5 Cloud Feedbacks and Rapid Adjustments to Carbon Dioxide.......................................................... 587 7.6.4 Effects of Aerosol Cloud Interactions on Precipitation............................................................... 625 7.2.6 Feedback Synthesis.................................................... 591 7.6.5 The Physical Basis for Changes in 7.2.7 Anthropogenic Sources of Moisture Precipitation Extremes............................................... 626 and Cloudiness........................................................... 592 7.7 Solar Radiation Management and Related 7.3 Aerosols.............................................................................. 595 Methods.............................................................................. 627 7.3.1 Aerosols in the Present-Day Climate System.............. 595 7.7.1 Introduction............................................................... 627 7.3.2 Aerosol Sources and Processes.................................. 599 7.7.2 Assessment of Proposed Solar Radiation 7.3.3 Progress and Gaps in Understanding Climate Management Methods............................................... 627 Relevant Aerosol Properties....................................... 602 7.7.3 Climate Response to Solar Radiation 7.3.4 Aerosol Radiation Interactions.................................. 604 Management Methods............................................... 629 7.3.5 Aerosol Responses to Climate Change 7.7.4 Synthesis on Solar Radiation Management and Feedback............................................................. 605 Methods..................................................................... 635 7.4 Aerosol Cloud Interactions.......................................... 606 References .................................................................................. 636 7.4.1 Introduction and Overview of Progress Since AR4..... 606 Frequently Asked Questions 7.4.2 Microphysical Underpinnings of Aerosol Cloud Interactions................................................................ 609 FAQ 7.1 How Do Clouds Affect Climate and Climate Change?.................................................................... 593 7.4.3 Forcing Associated with Adjustments in Liquid Clouds............................................................. 609 FAQ 7.2 How Do Aerosols Affect Climate and Climate Change?.................................................................... 622 7.4.4 Adjustments in Cold Clouds....................................... 611 FAQ 7.3 Could Geoengineering Counteract Climate 7.4.5 Synthesis on Aerosol Cloud Interactions................... 612 Change and What Side Effects Might Occur?...... 632 7.4.6 Impact of Cosmic Rays on Aerosols and Clouds......... 613 Supplementary Material Supplementary Material is available in online versions of the report. 7 572 Clouds and Aerosols Chapter 7 Executive Summary that also includes rapid adjustments. For aerosols one can further dis- tinguish forcing processes arising from aerosol radiation interactions Clouds and aerosols continue to contribute the largest uncertainty to (ari) and aerosol cloud interactions (aci). {7.1, Figures 7.1 to 7.3} estimates and interpretations of the Earth s changing energy budget. This chapter focuses on process understanding and considers observa- The quantification of cloud and convective effects in models, tions, theory and models to assess how clouds and aerosols contribute and of aerosol cloud interactions, continues to be a challenge. and respond to climate change. The following conclusions are drawn. Climate models are incorporating more of the relevant process- es than at the time of AR4, but confidence in the representation Progress in Understanding of these processes remains weak. Cloud and aerosol properties vary at scales significantly smaller than those resolved in climate models, Many of the cloudiness and humidity changes simulated and cloud-scale processes respond to aerosol in nuanced ways at these by climate models in warmer climates are now  understood scales. Until sub-grid scale parameterizations of clouds and aerosol as  responses to large-scale circulation changes that do not cloud interactions are able to address these issues, model estimates of appear to depend strongly on sub-grid scale model processes, aerosol cloud interactions and their radiative effects will carry large increasing confidence in these changes. For example, multiple lines uncertainties. Satellite-based estimates of aerosol cloud interactions of evidence now indicate positive feedback contributions from circula- remain sensitive to the treatment of meteorological influences on tion-driven changes in both the height of high clouds and the latitudi- clouds and assumptions on what constitutes pre-industrial conditions. nal distribution of clouds (medium to high confidence1). However, some {7.3, 7.4, 7.5.3, 7.5.4, 7.6.4, Figures 7.8, 7.12, 7.16} aspects of the overall cloud response vary substantially among models, and these appear to depend strongly on sub-grid scale processes in Precipitation and evaporation are expected to increase on aver- which there is less confidence. {7.2.4, 7.2.5, 7.2.6, Figure 7.11} age in a warmer climate, but also undergo global and regional adjustments to carbon dioxide (CO2) and other forcings that Climate-relevant aerosol processes are better understood, and differ from their warming responses. Moreover, there is high climate-relevant aerosol properties better observed, than at the confidence that, as climate warms, extreme precipitation rates time of AR4. However, the representation of relevant processes varies on for example, daily time scales will increase faster than the greatly in global aerosol and climate models and it remains unclear time average. Changes in average precipitation must remain consis- what level of sophistication is required to model their effect on climate. tent with changes in the net rate of cooling of the troposphere, which Globally, between 20 and 40% of aerosol optical depth (medium confi- is affected by its temperature but also by greenhouse gases (GHGs) dence) and between one quarter and two thirds of cloud condensation and aerosols. Consequently, while the increase in global mean pre- nucleus concentrations (low confidence) are of anthropogenic origin. cipitation would be 1.5 to 3.5% °C 1 due to surface temperature {7.3, Figures 7.12 to 7.15} change alone, warming caused by CO2 or absorbing aerosols results in a smaller sensitivity, even more so if it is partially offset by albedo Cosmic rays enhance new particle formation in the free tropo- increases. The complexity  of land surface and atmospheric process- sphere, but the effect on the concentration of cloud condensa- es  limits confidence in regional projections  of  precipitation change, tion nuclei is too weak to have any detectable climatic influence especially over land, although there is a component of a wet-get-wet- during a solar cycle or over the last century (medium evidence, ter and dry-get-drier response over oceans at the large scale. Chang- high agreement). No robust association between changes in cosmic es in  local  extremes on daily and sub-daily time scales are strongly rays and cloudiness has been identified. In the event that such an asso- influenced by lower-tropospheric water vapour concentrations, and on ciation existed, a mechanism other than cosmic ray-induced nucleation average will increase by roughly 5 to 10% per degree Celsius of warm- of new aerosol particles would be needed to explain it. {7.4.6} ing (medium confidence). Aerosol cloud interactions can influence the character of individual storms, but evidence for a systematic aerosol Recent research has clarified the importance of distinguishing effect on storm or precipitation intensity is more limited and ambigu- forcing (instantaneous change in the radiative budget) and ous. {7.2.4, 7.4, 7.6, Figures 7.20, 7.21} rapid adjustments (which modify the radiative budget indirectly through fast atmospheric and surface changes) from feedbacks (which operate through changes in climate variables that are mediated by a change in surface temperature). Furthermore, one can distinguish between the traditional concept of radiative forcing (RF) and the relatively new concept of effective radiative forcing (ERF) In this Report, the following summary terms are used to describe the available evidence: limited, medium, or robust; and for the degree of agreement: low, medium, or high. 1 A level of confidence is expressed using five qualifiers: very low, low, medium, high, and very high, and typeset in italics, e.g., medium confidence. For a given evidence and agreement statement, different confidence levels can be assigned, but increasing levels of evidence and degrees of agreement are correlated with increasing confidence (see Section 1.4 and Box TS.1 for more details). 7 573 Chapter 7 Clouds and Aerosols Water Vapour, Cloud and Aerosol Feedbacks emissions5: +0.0 ( 0.2 to +0.2) W m 2, nitrate aerosol: 0.11 ( 0.3 to 0.03) W m 2, and mineral dust: 0.1 ( 0.3 to +0.1) W m 2 although The net feedback from water vapour and lapse rate changes the latter may not be entirely of anthropogenic origin. While there combined, as traditionally defined, is extremely likely2 positive is robust evidence for the existence of rapid adjustment of clouds in (amplifying global climate changes). The sign of the net radia- response to aerosol absorption, these effects are multiple and not well tive feedback due to all cloud types is less certain but likely represented in climate models, leading to large uncertainty. Unlike in positive. Uncertainty in the sign and magnitude of the cloud the last IPCC assessment, the RF from BC on snow and ice includes the feedback is due primarily to continuing uncertainty in the effects on sea ice, accounts for more physical processes and incorpo- impact of warming on low clouds. We estimate the water vapour rates evidence from both models and observations. This RF has a 2 to 4 plus lapse rate feedback3 to be +1.1 (+0.9 to +1.3) W m 2 °C 1 and times larger global mean surface temperature change per unit forcing the cloud feedback from all cloud types to be +0.6 ( 0.2 to +2.0) W than a change in CO2. {7.3.4, 7.5.2, Figures 7.17, 7.18} m 2 °C 1. These ranges are broader than those of climate models to account for additional uncertainty associated with processes that may The total ERF due to aerosols (ERFari+aci, excluding the effect not have been accounted for in those models. The mean values and of absorbing aerosol on snow and ice) is assessed to be 0.9 ranges in climate models are essentially unchanged since AR4, but are ( 1.9 to 0.1) W m 2 with medium confidence. The ERFari+aci esti- now supported by stronger indirect observational evidence and better mate includes rapid adjustments, such as changes to the cloud lifetime process understanding, especially for water vapour. Low clouds con- and aerosol microphysical effects on mixed-phase, ice and convective tribute positive feedback in most models, but that behaviour is not well clouds. This range was obtained from expert judgement guided by cli- understood, nor effectively constrained by observations, so we are not mate models that include aerosol effects on mixed-phase and convec- confident that it is realistic. {7.2.4, 7.2.5, 7.2.6, Figures 7.9 to 7.11}. tive clouds in addition to liquid clouds, satellite studies and models that allow cloud-scale responses. This forcing can be much larger Aerosol climate feedbacks occur mainly through changes in the regionally but the global mean value is consistent with several new source strength of natural aerosols or changes in the sink effi- lines of evidence suggesting less negative estimates for the ERF due to ciency of natural and anthropogenic aerosols; a limited number aerosol cloud interactions than in AR4. {7.4, 7.5.3, 7.5.4, Figure 7.19} of modelling studies have bracketed the feedback parameter within +/-0.2 W m 2 °C 1 with low confidence. There is medium con- Persistent contrails from aviation contribute a RF of +0.01 fidence for a weak dimethylsulphide cloud condensation nuclei cloud (+0.005 to +0.03) W m 2 for year 2011, and the combined con- albedo feedback due to a weak sensitivity of cloud condensation nuclei trail and contrail-cirrus ERF from aviation is assessed to be population to changes in dimethylsulphide emissions. {7.3.5} +0.05 (+0.02 to +0.15) W m 2. This forcing can be much larger regionally but there is now medium confidence that it does not pro- Quantification of climate forcings4 due to aerosols duce observable regional effects on either the mean or diurnal range and clouds of surface temperature. {7.2.7} The ERF due to aerosol radiation interactions that takes rapid Geoengineering Using Solar Radiation Management adjustments into account (ERFari) is assessed to be 0.45 ( 0.95 Methods to +0.05) W m 2. The RF from absorbing aerosol on snow and ice is assessed separately to be +0.04 (+0.02 to +0.09) W m 2. Theory, model studies and observations suggest that some Solar Prior to adjustments taking place, the RF due to aerosol radiation Radiation Management (SRM) methods, if practicable, could sub- interactions (RFari) is assessed to be 0.35 ( 0.85 to +0.15) W m 2. The stantially offset a global temperature rise and partially offset assessment for RFari is less negative than reported in AR4 because of a some other impacts of global warming, but the compensation re-evaluation of aerosol absorption. The uncertainty estimate is wider for the climate change caused by GHGs would be imprecise but more robust, based on multiple lines of evidence from models, (high confidence). SRM methods are unimplemented and untested. remotely sensed data, and ground-based measurements. Fossil fuel Research on SRM is in its infancy, though it leverages understanding and biofuel emissions4 contribute to RFari via sulphate aerosol: 0.4 of how the climate responds to forcing more generally. The efficacy of ( 0.6 to 0.2) W m 2, black carbon (BC) aerosol: +0.4 (+0.05 to +0.8) a number of SRM strategies was assessed, and there is medium con- W m 2, and primary and secondary organic aerosol: 0.12 ( 0.4 to fidence that stratospheric aerosol SRM is scalable to counter the RF +0.1) W m 2. Additional RFari contributions occur via biomass burning ­ from increasing GHGs at least up to approximately 4 W m 2; however, 2 In this Report, the following terms have been used to indicate the assessed likelihood of an outcome or a result: Virtually certain 99 100% probability, Very likely 90 100%, Likely 66 100%, About as likely as not 33 66%, Unlikely 0 33%, Very unlikely 0 10%, Exceptionally unlikely 0 1%. Additional terms (Extremely likely: 95 100%, More likely than not >50 100%, and Extremely unlikely 0 5%) may also be used when appropriate. Assessed likelihood is typeset in italics, e.g., very likely (see Section 1.4 and Box TS.1 for more details). 3 This and all subsequent ranges given with this format are 90% uncertainty ranges unless otherwise specified. 4 All climate forcings (RFs and ERFs) are anthropogenic and relate to the period 1750 2010 unless otherwise specified. 7 5 This species breakdown is less certain than the total RFari and does not sum to the total exactly. 574 Clouds and Aerosols Chapter 7 the required injection rate of aerosol precursors remains very uncertain. There is no consensus on whether a similarly large RF could be achieved from cloud brightening SRM owing to uncertainties in understanding and representation of aerosol cloud interactions. It does not appear that land albedo change SRM can produce a large RF. Limited literature on other SRM methods precludes their assessment. Models consistently suggest that SRM would generally reduce climate differences compared to a world with elevated GHG concentrations and no SRM; however, there would also be residual regional differences in climate (e.g., tem- perature and rainfall) when compared to a climate without elevated GHGs. {7.4.3, 7.7} Numerous side effects, risks and shortcomings from SRM have been identified. Several lines of evidence indicate that SRM would produce a small but significant decrease in global precipitation (with larger differences on regional scales) if the global surface tempera- ture were maintained. A number of side effects have been identified. One that is relatively well characterized is the likelihood of modest polar stratospheric ozone depletion associated with stratospheric aerosol SRM. There could also be other as yet unanticipated conse- quences. As long as  GHG concentrations continued to increase, the SRM would require commensurate increase, exacerbating side effects. In addition, scaling SRM to substantial levels would carry the risk that if the SRM were terminated for any reason, there is high confidence that surface temperatures would increase rapidly (within a decade or two)  to values consistent with the GHG  forcing, which would stress systems sensitive to the rate of climate change. Finally, SRM would not compensate for ocean acidification from increasing CO2. {7.6.3, 7.7, Figures 7.22 to 7.24} 7 575 Chapter 7 Clouds and Aerosols 7.1 Introduction inter-model differences in cloud feedbacks constitute by far the prima- ry source of spread of both equilibrium and transient climate responses 7.1.1 Clouds and Aerosols in the Atmosphere simulated by climate models (Dufresne and Bony, 2008) despite the fact that most models agree that the feedback is positive (Randall et The atmosphere is composed mostly of gases, but also contains liquid al., 2007; Section 7.2). Thus confidence in climate projections requires a and solid matter in the form of particles. It is usual to distinguish these thorough assessment of how cloud processes have been accounted for. particles according to their size, chemical composition, water content and fall velocity into atmospheric aerosol particles, cloud particles and Aerosols of anthropogenic origin are responsible for a radiative forcing falling hydrometeors. Despite their small mass or volume fraction, (RF) of climate change through their interaction with radiation, and particles in the atmosphere strongly influence the transfer of radi- also as a result of their interaction with clouds. Quantification of this ant energy and the spatial distribution of latent heating through the forcing is fraught with uncertainties (Haywood and Boucher, 2000; atmosphere, thereby influencing the weather and climate. Lohmann and Feichter, 2005) and aerosols dominate the uncertain- ty in the total anthropogenic RF (Forster et al., 2007; Haywood and Cloud formation usually takes place in rising air, which expands and Schulz, 2007; Chapter 8). Furthermore, our inability to better quantify cools, thus permitting the activation of aerosol particles into cloud non-greenhouse gas RFs, and primarily those that result from aerosol droplets and ice crystals in supersaturated air. Cloud particles are gen- cloud interactions, underlie difficulties in constraining climate sensitiv- erally larger than aerosol particles and composed mostly of liquid water ity from observations even if we had a perfect knowledge of the tem- or ice. The evolution of a cloud is governed by the balance between perature record (Andreae et al., 2005). Thus a complete understanding a number of dynamical, radiative and microphysical processes. Cloud of past and future climate change requires a thorough assessment of particles of sufficient size become falling hydrometeors, which are cat- aerosol cloud radiation interactions. egorized as drizzle drops, raindrops, snow crystals, graupel and hail- stones. Precipitation is an important and complex climate variable that 7.1.3 Forcing, Rapid Adjustments and Feedbacks is influenced by the distribution of moisture and cloudiness, and to a lesser extent by the concentrations and properties of aerosol particles. Figure 7.1 illustrates key aspects of how clouds and aerosols contribute to climate change, and provides an overview of important terminolog- Aerosol particles interact with solar radiation through absorption and ical distinctions. Forcings associated with agents such as greenhouse scattering and, to a lesser extent with terrestrial radiation through gases (GHGs) and aerosols act on global mean surface temperature absorption, scattering and emission. Aerosols6 can serve as cloud through the global radiative (energy) budget. Rapid adjustments condensation nuclei (CCN) and ice nuclei (IN) upon which cloud drop- (sometimes called rapid responses) arise when forcing agents, by alter- lets and ice crystals form. They also play a wider role in atmospheric ing flows of energy internal to the system, affect cloud cover or other chemistry and biogeochemical cycles in the Earth system, for instance, components of the climate system and thereby alter the global budget by carrying nutrients to ocean ecosystems. They can be of natural or indirectly. Because these adjustments do not operate through changes anthropogenic origin. in the global mean surface temperature (DT), which are slowed by the massive heat capacity of the oceans, they are generally rapid and most Cloud and aerosol amounts7 and properties are extremely variable in are thought to occur within a few weeks. Feedbacks are associated space and time. The short lifetime of cloud particles in subsaturated air with changes in climate variables that are mediated by a change in creates relatively sharp cloud edges and fine-scale variations in cloud global mean surface temperature; they contribute to amplify or damp properties, which is less typical of aerosol layers. While the distinction global temperature changes via their impact on the radiative budget. between aerosols and clouds is generally appropriate and useful, it is not always unambiguous, which can cause interpretational difficulties In this report, following an emerging consensus in the literature, the (e.g., Charlson et al., 2007; Koren et al., 2007). traditional concept of radiative forcing (RF, defined as the instanta- neous radiative forcing with stratospheric adjustment only) is de-em- 7.1.2 Rationale for Assessing Clouds, Aerosols and phasized in favour of an absolute measure of the radiative effects of Their Interactions all responses triggered by the forcing agent that are independent of surface temperature change (see also Section 8.1). This new measure The representation of cloud processes in climate models has been rec- of the forcing includes rapid adjustments and the net forcing with ognized for decades as a dominant source of uncertainty in our under- these adjustments included is termed the effective radiative forcing standing of changes in the climate system (e.g., Arakawa, 1975, 2004; (ERF). The climate sensitivity to ERF will differ somewhat from tradi- Charney et al., 1979; Cess et al., 1989; Randall et al., 2003; Bony et al., tional equilibrium climate sensitivity, as the latter include adjustment 2006), but has never been systematically assessed by the IPCC before. effects. As shown in Figure 7.1, adjustments can occur through geo- Clouds respond to climate forcing mechanisms in multiple ways, and graphic temperature variations, lapse rate changes, cloud changes 6 For convenience the term aerosol , which includes both the particles and the suspending gas, is often used in its plural form to mean aerosol particles both in this chapter and the rest of this Report. 7 7 In this chapter, we use cloud amount as an inexact term to refer to the quantity of clouds, both in the horizontal and vertical directions. The term cloud cover is used in its usual sense and refers to the horizontal cloud cover. 576 Clouds and Aerosols Chapter 7 Aerosol Feedbacks Cloud Feedbacks Clouds and Other Feedbacks Precipitation Additional state variables Moisture and Winds Adjustments Temperature Profile Aerosol Cloud Regional Variability Interactions (aci) Biosphere Aerosol Radiation Radiation Interactions (ari) Aerosols Effective Radiative Anthropogenic Forcing (ERF) and Sources Feedbacks Global Surface Greenhouse Temperature Gases Radiative Forcing Figure 7.1 | Overview of forcing and feedback pathways involving greenhouse gases, aerosols and clouds. Forcing agents are in the green and dark blue boxes, with forcing mechanisms indicated by the straight green and dark blue arrows. The forcing is modified by rapid adjustments whose pathways are independent of changes in the globally aver- aged surface temperature and are denoted by brown dashed arrows. Feedback loops, which are ultimately rooted in changes ensuing from changes in the surface temperature, are represented by curving arrows (blue denotes cloud feedbacks; green denotes aerosol feedbacks; and orange denotes other feedback loops such as those involving the lapse rate, water vapour and surface albedo). The final temperature response depends on the effective radiative forcing (ERF) that is felt by the system, that is, after accounting for rapid adjustments, and the feedbacks. and ­ egetation effects. Measures of ERF and rapid adjustments have v RF stratospherically adjusted existed in the literature for more than a decade, with a number of 8 different terminologies and calculation methods adopted. These were principally aimed to help quantify the effects of aerosols on clouds (Rotstayn and Penner, 2001; Lohmann et al., 2010) and understand ERF xed sea surface temperature 6 different forcing agent responses (Hansen et al., 2005), but it is now realized that there are rapid adjustments in response to the CO2 forcing itself (Section 7.2.5.6). N (W m-2) 4 In principle rapid adjustments are independent of DT, while feedbacks ERF regression operate purely through DT. Thus, within this framework adjustments are not another type of feedback but rather a non-feedback phenom- 2 enon, required in the analysis by the fact that a single scalar DT cannot fully characterize the system. This framework brings most efficacies close to unity although they are not necessarily exactly 1 (Hansen et al., 0 2005; Bond et al., 2013). There is also no clean separation in time scale between rapid adjustments and warming. Although the former occur mostly within a few days of applying a forcing (Dong et al., 2009), -2 some adjustments such as those that occur within the stratosphere 0 1 2 3 4 5 6 and snowpack can take several months or longer. Meanwhile the land T (C) surface warms quickly so that a small part of DT occurs within days to Figure 7.2 | Radiative forcing (RF) and effective radiative forcing (ERF) estimates weeks of an applied forcing. This makes the two phenomena difficult to derived by two methods, for the example of 4 × CO2 experiments in one climate model. isolate in model runs. Other drawbacks are that adjustments are diffi- N is the net energy imbalance at the top of the atmosphere and DT the global mean cult to observe, and typically more model-dependent than RF. However, surface temperature change. The fixed sea surface temperature ERF estimate is from an recent work is beginning to meet the challenges of quantifying the atmosphere land model averaged over 30 years. The regression estimate is from 150 adjustments, and has noted advantages of the new framework (e.g., years of a coupled model simulation after an instantaneous quadrupling of CO2, with the N from individual years in this regression shown as black diamonds. The strato- Vial et al., 2013; Zelinka et al., 2013). spherically adjusted RF is the tropopause energy imbalance from otherwise identical radiation calculations at 1 × and 4 × CO2 concentrations. (Figure follows Andrews et al., There is no perfect method to determine ERF. Two common meth- 2012.) See also Figure 8.1. ods are to regress the net energy imbalance onto DT in a transient 7 577 Chapter 7 Clouds and Aerosols Irradiance Changes from Irradiance Changes from Aerosol-Radiation Interactions (ari) Aerosol-Cloud Interactions (aci) Direct Effect Semi-Direct Effects Cloud Albedo Effect Lifetime (including glaciation AR4 & thermodynamic) Effects Radiative Forcing (RFari) Adjustments Radiative Forcing (RFaci) Adjustments AR5 Effective Radiative Forcing (ERFari) Effective Radiative Forcing (ERFaci) Figure 7.3 | Schematic of the new terminology used in this Assessment Report (AR5) for aerosol radiation and aerosol cloud interactions and how they relate to the terminology used in AR4. The blue arrows depict solar radiation, the grey arrows terrestrial radiation and the brown arrow symbolizes the importance of couplings between the surface and the cloud layer for rapid adjustments. See text for further details. warming simulation (Gregory et al., 2004; Figure 7.2), or to simulate clouds and precipitation and their relevance for climate and climate the climate response with sea surface temperatures (SSTs) held fixed change. This chapter assesses the climatic roles and feedbacks of water (Hansen et al., 2005). The former can be complicated by natural var- vapour, lapse rate and clouds (Section 7.2), discusses aerosol radiation iability or time-varying feedbacks, while the non-zero DT from land (Section 7.3) and aerosol cloud (Section 7.4) interactions and quanti- warming complicates the latter. Both methods are used in this chapter. fies the resulting aerosol RF on climate (Section 7.5). It also introduc- es the physical basis for the precipitation responses to aerosols and Figure 7.3 links the former terminology of aerosol direct, semi-direct climate changes (Section 7.6) noted later in the Report, and assesses and indirect effects with the new terminology used in this chapter and geoengineering methods based on solar radiation management (Sec- in Chapter 8. The RF from aerosol radiation interactions (abbreviat- tion 7.7). ed RFari) encompasses radiative effects from anthropogenic aerosols before any adjustment takes place and corresponds to what is usually referred to as the aerosol direct effect. Rapid adjustments induced by 7.2 Clouds aerosol radiative effects on the surface energy budget, the atmospheric profile and cloudiness contribute to the ERF from aerosol radiation This section summarizes our understanding of clouds in the current interactions (abbreviated ERFari). They include what has earlier been climate from observations and process models; advances in the rep- referred to as the semi-direct effect. The RF from aerosol cloud inter- resentation of cloud processes in climate models since AR4; assessment actions (abbreviated RFaci) refers to the instantaneous effect on cloud of cloud, water vapour and lapse rate feedbacks and adjustments; and albedo due to changing concentrations of cloud condensation and ice the RF due to clouds induced by moisture released by two anthropo- nuclei, also known as the Twomey effect. All subsequent changes to genic processes (air traffic and irrigation). Aerosol cloud interactions the cloud lifetime and thermodynamics are rapid adjustments, which are assessed in Section 7.4. The fidelity of climate model simulations of contribute to the ERF from aerosol cloud interactions (abbreviated clouds in the current climate is assessed in Chapter 9. ERFaci). RFaci is a theoretical construct that is not easy to separate from other aerosol cloud interactions and is therefore not quantified 7.2.1 Clouds in the Present-Day Climate System in this chapter. 7.2.1.1 Cloud Formation, Cloud Types and Cloud Climatology 7.1.4 Chapter Roadmap To form a cloud, air must cool or moisten until it is sufficiently super- For the first time in the IPCC WGI assessment reports, clouds and aer- saturated to activate some of the available condensation or freezing osols are discussed together in a single chapter. Doing so allows us nuclei. Clouds may be composed of liquid water (possibly supercooled), to assess, and place in context, recent developments in a large and ice or both (mixed phase). The nucleated cloud particles are initially growing area of climate change research. In addition to assessing very small, but grow by vapour deposition. Other microphysical mecha- cloud feedbacks and aerosol forcings, which were covered in previ- nisms dependent on the cloud phase (e.g., droplet collision and coales- ous assessment reports in a less unified manner, it becomes possible cence for liquid clouds, riming and Wegener Bergeron Findeisen pro- 7 to assess understanding of the multiple interactions among aerosols, cesses for mixed-phase clouds and crystal aggregation in ice clouds) 578 Clouds and Aerosols Chapter 7 can produce a broader spectrum of particle sizes and types; turbulent is the launch in 2006 of two coordinated, active sensors, the Cloud mixing produces further variations in cloud properties on scales from Profiling Radar (CPR) on the CloudSat satellite (Stephens et al., 2002) kilometres to less than a centimetre (Davis et al., 1999; Bodenschatz and the Cloud Aerosol Lidar with Orthogonal Polarization (CALIOP) on et al., 2010). If and when some of the droplets or ice particles become board the Cloud Aerosol Lidar and Infrared Pathfinder Satellite Obser- large enough, these will fall out of the cloud as precipitation. vations (CALIPSO) satellite (Winker et al., 2009). These sensors have significantly improved our ability to quantify vertical profiles of cloud Atmospheric flows often organize convection and associated clouds occurrence and water content (see Figures 7.5 and 7.6), and comple- into coherent systems having scales from tens to thousands of kilo- ment the detection capabilities of passive multispectral sensors (e.g., metres, such as cyclones or frontal systems. These represent a signifi- Stubenrauch et al., 2010; Chan and Comiso, 2011). Satellite cloud-ob- cant modelling and theoretical challenge, as they are usually too large serving capacities are reviewed by Stubenrauch et al. (2013). to represent within the limited domains of cloud-resolving models (Section 7.2.2.1), but are also not well resolved nor parameterized by Clouds cover roughly two thirds of the globe (Figure 7.5a, c), with a most climate models; this gap, however, is beginning to close (Sec- more precise value depending on both the optical depth threshold tion 7.2.2.2). Finally, clouds and cloud systems are organized by larg- used to define cloud and the spatial scale of measurement (Wielicki er-scale circulations into different regimes such as deep convection and Parker, 1992; Stubenrauch et al., 2013). The mid-latitude ocean- near the equator, subtropical marine stratocumulus, or mid-latitude ic storm tracks and tropical precipitation belts are particularly cloudy, storm tracks guided by the tropospheric westerly jets. Figure 7.4 shows while continental desert regions and the central subtropical oceans are a selection of widely occurring cloud regimes schematically and as they relatively cloud-free. Clouds are composed of liquid at temperatures might appear in a typical geostationary satellite image. above 0°C, ice below about 38°C (e.g., Koop et al., 2000), and either or both phases at intermediate temperatures (Figure 7.5b). Throughout New satellite sensors and new analysis of previous data sets have given most of the troposphere, temperatures at any given altitude are usually us a clearer picture of the Earth s clouds since AR4. A notable example warmer in the tropics, but clouds also extend higher there such that ice (a) (b) Cirrus Altostratus Nimbostratus 10 km Stratus COLD Polar (mixed phase) Stratus (a ) (a) WARM Mid-Latitudes High Latitudes (c) Thin Cirrus Convective Anvils Large-scale Subsidence 17 km Land/Sea Circulation Melting Level WARM SUBSIDING REGIONS Shallow Cumulus Stratocumulus Trade Winds WARM OCEAN COLD OCEAN Deep Tropics Subtropics Figure 7.4 | Diverse cloud regimes reflect diverse meteorology. (a) A visible-wavelength geostationary satellite image shows (from top to bottom) expanses and long arcs of cloud associated with extratropical cyclones, subtropical coastal stratocumulus near Baja California breaking up into shallow cumulus clouds in the central Pacific and mesoscale convec- tive systems outlining the Pacific Intertropical Convergence Zone (ITCZ). (b) A schematic section along the dashed line from the orange star to the orange circle in (a), through a typical warm front of an extratropical cyclone. It shows (from right to left) multiple layers of upper-tropospheric ice (cirrus) and mid-tropospheric water (altostratus) cloud in the upper-tropospheric outflow from the frontal zone, an extensive region of nimbostratus associated with frontal uplift and turbulence-driven boundary layer cloud in the warm sector. (c) A schematic section along the dashed line from the red star to the red circle in (a), along the low-level trade wind flow from a subtropical west coast of a continent to the ITCZ. It shows (from right to left) typical low-latitude cloud mixtures, shallow stratocumulus trapped under a strong subsidence inversion above the cool waters of the oceanic upwelling zone near the coast and shallow cumulus over warmer waters further offshore transitioning to precipitating cumulonimbus cloud systems with extensive cirrus anvils associated with rising air motions in the ITCZ. 7 579 Chapter 7 Clouds and Aerosols cloud amounts are no less than those at high latitudes. At any given over high-latitude continents and subtropical oceans (Naud et al., time, most clouds are not precipitating (Figure 7.5d). 2008; Mace et al., 2009), and the common assumption that the radi- ative effects of precipitating ice can be neglected is not necessarily In this chapter cloud above the 440 hPa pressure level is considered warranted (Waliser et al., 2011). New observations have led to revised high , that below the 680 hPa level low , and that in-between is con- treatments of overlap in some models, which significantly affects cloud sidered mid-level . Most high cloud (mainly cirrus and deep cumulus radiative effects (Pincus et al., 2006; Shonk et al., 2012). Active sensors outflows) occurs near the equator and over tropical continents, but can have also been useful in detecting low-lying Arctic clouds over sea ice also be seen in the mid-latitude storm track regions and over mid-lati- (Kay et al., 2008), improving our ability to test climate model simula- tude continents in summer (Figure 7.6a, e); it is produced by the storms tions of the interaction between sea ice loss and cloud cover (Kay et generating most of the global rainfall in regions where tropospheric air al., 2011). motion is upward, such that dynamical, rainfall and high-cloud fields closely resemble one another (Figure 7.6d, h). Mid-level cloud (Figure 7.2.1.2 Effects of Clouds on the Earth s Radiation Budget 7.6b, f), comprising a variety of types, is prominent in the storm tracks and some occurs in the Intertropical Convergence Zone (ITCZ). Low The effect of clouds on the Earth s present-day top of the atmosphere cloud (Figure 7.6c, g), including shallow cumulus and stratiform cloud, (TOA) radiation budget, or cloud radiative effect (CRE), can be inferred occurs over essentially all oceans but is most prevalent over cooler from satellite data by comparing upwelling radiation in cloudy and subtropical oceans and in polar regions. It is less common over land, non-cloudy conditions (Ramanathan et al., 1989). By enhancing the except at night and in winter. planetary albedo, cloudy conditions exert a global and annual short- wave cloud radiative effect (SWCRE) of approximately 50 W m 2 and, Overlap between cloud layers has long been an issue both for sat- by contributing to the greenhouse effect, exert a mean longwave effect ellite (or ground-based) detection and for calculating cloud radiative (LWCRE) of approximately +30 W m 2, with a range of 10% or less effects. Active sensors show more clearly that low clouds are preva- between published satellite estimates (Loeb et al., 2009). Some of the lent in nearly all types of convective systems, and are often under- apparent LWCRE comes from the enhanced water vapour coinciding estimated by models (Chepfer et al., 2008; Naud et al., 2010; Haynes with the natural cloud fluctuations used to measure the effect, so the et al., 2011). Cloud layers at different levels overlap less often than true cloud LWCRE is about 10% smaller (Sohn et al., 2010). The net typically assumed in General Circulation Models (GCMs), ­ specially e global mean CRE of approximately 20 W m 2 implies a net cooling a) Cloud Fraction c) Cloud Occurrence 15 Height (km) Ice 9 3 Liquid 0 b) Condensate Path d) Precipitation Occurrence (x2) (kg m-2) 15 Height (km) -38C 0.2 9 Ice Water Path 0C 0.1 3 Liquid Water Path 0 0 60S 30 Eq 30 60N 60S 30 Eq 30 60N Fraction (or Occurrence Frequency) 0 0.25 0.5 0.75 1 Figure 7.5 | (a) Annual mean cloud fractional occurrence (CloudSat/CALIPSO 2B-GEOPROF-LIDAR data set for 2006 2011; Mace et al., 2009). (b) Annual zonal mean liquid water path (blue shading, microwave radiometer data set for 1988 2005 from O Dell et al. (2008)) and total water path (ice path shown with grey shading, from CloudSat 2C-ICE data set for 2006 2011 from Deng et al. (2010) over oceans). The 90% uncertainty ranges, assessed to be approximately 60 to 140% of the mean for the liquid and total water paths, are schematically indicated by the error bars. (c d) latitude-height sections of annual zonal mean cloud (including precipitation falling from cloud) occurrence and precipitation 7 (attenuation-corrected radar reflectivity >0 dBZ) occurrence; the latter has been doubled to make use of a common colour scale (2B-GEOPROF-LIDAR data set). The dashed curves show the annual mean 0°C and 38°C isotherms. 580 Clouds and Aerosols Chapter 7 December January February June July August (a) (e) High Cloud (b) (f) Middle Cloud (c) (g) Low Cloud Fraction 0 0.2 0.4 0.6 0.8 1 (d) (h) Mid-troposphere Vertical Pressure Velocity (hPa day-1) -50 -50 0 25 50 Figure 7.6 | (a d) December January February mean high, middle and low cloud cover from CloudSat/CALIPSO 2B-GEOPROF R04 and 2B-GEOPROF-LIDAR P1.R04 data sets for 2006 2011 (Mace et al., 2009), 500 hPa vertical pressure velocity (colours, from ERA-Interim for 1979 2010; Dee et al., 2011), and Global Precipitation Climatology Project (GPCP) version 2.2 precipitation rate (1981 2010, grey contours at 3 mm day 1 in dash and 7 mm day 1 in solid); (e h) same as (a d), except for June July August. For low clouds, the GCM-Oriented CALIPSO Cloud Product (GOCCP) data set for 2007 2010 (Chepfer et al., 2010) is used at locations where it indicates a larger fractional cloud cover, because the GEOPROF data set removes some clouds with tops at altitudes below 750 m. Low cloud amounts are probably underrepresented in regions of high cloud (Chepfer et al., 2008), 7 although not as severely as with earlier satellite instruments. 581 Chapter 7 Clouds and Aerosols (a) Shortwave (global mean = 47.3 W m-2) effect of clouds on the current climate. Owing to the large magnitudes of the SWCRE and LWCRE, clouds have the potential to cause signifi- cant climate feedback (Section 7.2.5). The sign of this feedback on cli- mate change cannot be determined from the sign of CRE in the current climate, but depends instead on how climate-sensitive the properties are that govern the LWCRE and SWCRE. The regional patterns of annual-mean TOA CRE (Figure 7.7a, b) reflect those of the altitude-dependent cloud distributions. High clouds, which are cold compared to the clear-sky radiating temperature, dominate patterns of LWCRE, while the SWCRE is sensitive to optically thick (b) clouds at all altitudes. SWCRE also depends on the available sunlight, Longwave (global mean = 26.2 W m-2) so for example is sensitive to the diurnal and seasonal cycles of cloud- iness. Regions of deep, thick cloud with large positive LWCRE and large negative SWCRE tend to accompany precipitation (Figure 7.7d), showing their intimate connection with the hydrological cycle. The net CRE is negative over most of the globe and most negative in regions of very extensive low-lying reflective stratus and stratocumulus cloud such as the mid-latitude and eastern subtropical oceans, where SWCRE is strong but LWCRE is weak (Figure 7.7c). In these regions, the spatial distribution of net CRE on seasonal time scales correlates strongly with measures of low-level stability or inversion strength (Klein and Hart- (c) mann, 1993; Williams et al., 2006; Wood and Bretherton, 2006; Zhang Net (global mean = 21.1 W m-2) et al., 2010). Clouds also exert a CRE at the surface and within the troposphere, thus affecting the hydrological cycle and circulation (Section 7.6), though this aspect of CRE has received less attention. The net downward flux of radiation at the surface is sensitive to the vertical and horizontal distribution of clouds. It has been estimated more accurately through radiation budget measurements and cloud profiling (Kato et al., 2011). Based on these observations, the global mean surface downward long- wave flux is about 10 W m 2 larger than the average in climate models, probably due to insufficient model-simulated cloud cover or lower Cloud Radiative Effect (W m-2) tropospheric moisture (Stephens et al., 2012). This is consistent with a global mean precipitation rate in the real world somewhat larger than -100 -50 0 50 100 current observational estimates. (d) Precipitation (global mean = 2.7 mm day-1) 7.2.2 Cloud Process Modelling Cloud formation processes span scales from the sub-micrometre scale of CCN, to cloud-system scales of up to thousands of kilometres. This range of scales is impossible to resolve with numerical simulations on computers, and this is not expected to change in the foreseeable future. Nonetheless progress has been made through a variety of mod- elling strategies, which are outlined briefly in this section, followed by a discussion in Section 7.2.3 of developments in representing clouds in global models. The implications of these discussions are synthesized in Section 7.2.3.5. (mm day-1) 7.2.2.1 Explicit Simulations in Small Domains 0 5 10 High-resolution models in small domains have been widely used to Figure 7.7 | Distribution of annual-mean top of the atmosphere (a) shortwave, (b) simulate interactions of turbulence with various types of clouds. The longwave, (c) net cloud radiative effects averaged over the period 2001 2011 from grid spacing is chosen to be small enough to resolve explicitly the dom- the Clouds and the Earth s Radiant Energy System (CERES) Energy Balanced and Filled inant turbulent eddies that drive cloud heterogeneity, with the effects (EBAF) Ed2.6r data set (Loeb et al., 2009) and (d) precipitation rate (1981 2000 aver- 7 age from the GPCP version 2.2 data set; Adler et al., 2003). of smaller-scale phenomena parameterized. Such models can be run in 582 Clouds and Aerosols Chapter 7 idealized settings, or with boundary conditions for specific observed ­systems (Grabowski et al., 1998). Modern high-order turbulence clo- cases. This strategy is typically called large-eddy simulation (LES) when sure schemes may allow some statistics of boundary-layer cloud distri- boundary-layer eddies are resolved, and cloud-resolving model (CRM) butions, including cloud fractions and fluxes of moisture and energy, to when only deep cumulus motions are well resolved. It is useful not be reasonably simulated even at horizontal resolution of 1 km or larger only in simulating cloud and precipitation characteristics, but also in (Cheng and Xu, 2006, 2008). Finer grids (down to hundreds of metres) understanding how turbulent circulations within clouds transport and better resolve individual storm characteristics such as vertical velocity process aerosols and chemical constituents. It can be applied to any or tracer transport. Some cloud ensemble properties remain sensitive type of cloud system, on any part of the Earth. Direct numerical simula- to CRM microphysical parameterization assumptions regardless of res- tion (DNS) can be used to study turbulence and cloud microphysics on olution, particularly the vertical distribution and optical depth of clouds scales of a few metres or less (e.g., Andrejczuk et al., 2006) but cannot containing ice. span crucial meteorological scales and is not further considered here. Because of these requirements, it is computationally demanding to run Cloud microphysics, precipitation and aerosol interactions are treated a CRM in a domain large enough to capture convective organisation or with varying levels of sophistication, and remain a weak point in all perform regional forecasts. Some studies have created smaller regions models regardless of resolution. For example, recent comparisons to of CRM-like resolution within realistically forced regional-scale models satellite data show that liquid water clouds in CRMs generally begin to (e.g., Zhu et al., 2010; Boutle and Abel, 2012; Zhu et al., 2012), a spe- rain too early in the day (Suzuki et al., 2011). Especially for ice clouds, cial case of the common nesting approach for regional downscaling and for interactions between aerosols and clouds, our understanding of (see Section 9.6). One application has been to orographic precipitation, the basic micro-scale physics is not yet adequate, although it is improv- associated both with extratropical cyclones (e.g., Garvert et al., 2005) ing. Moreover, microphysical effects are quite sensitive to co-variations and with explicitly simulated cumulus convection (e.g., Hohenegger of velocity and composition down to very small scales. High-resolution et al., 2008); better resolution of the orography improves the simula- models, such as those used for LES, explicitly calculate most of these tion of precipitation initiation and wind drift of falling rain and snow variations, and so provide much more of the information needed for between watersheds. microphysical calculations, whereas in a GCM they are not explicitly available. For these reasons, low-resolution (e.g., climate) models will LES of shallow cumulus cloud fields with horizontal grid spacing of have even more trouble representing local aerosol cloud interactions about 100 m and vertical grid spacing of about 40 m produces vertical than will high-resolution models. Parameterizations are under develop- profiles of cloud fraction, temperature, moisture and turbulent fluxes ment that could account for the small-scale variations statistically (e.g., that agree well with available observations (Siebesma et al., 2003), Larson and Golaz, 2005) but have not been used in the Coupled Model though the simulated precipitation efficiency still shows some sensi- Intercomparison Project Phase 5 (CMIP5) simulations. tivity to microphysical parameterizations (vanZanten et al., 2011). LES of stratocumulus-topped boundary layers reproduces the turbulence High-resolution models have enhanced our understanding of cloud statistics and vertical thermodynamic structure well (e.g., Stevens et processes in several ways. First, they can help interpret in situ and al., 2005b; Ackerman et al., 2009), and has been used to study the high-resolution remote sensing observations (e.g., Stevens et al., sensitivity of stratocumulus properties to aerosols (e.g., Savic-Jovcic 2005b; Blossey et al., 2007; Fridlind et al., 2007). Second, they have and Stevens, 2008; Xue et al., 2008) and meteorological conditions. revealed important influences of small-scale interactions, turbulence, However, the simulated entrainment rate and cloud liquid water path entrainment and precipitation on cloud dynamics that must eventu- are sensitive to the underlying numerical algorithms, even with vertical ally be accounted for in parameterizations (e.g., Krueger et al., 1995; grid spacings as small as 5 m, due to poor resolution of the sharp cap- Derbyshire et al., 2004; Kuang and Bretherton, 2006; Ackerman et al., ping inversion (Stevens et al., 2005a). 2009). Third, they can be used to predict how cloud system properties (such as cloud cover, depth, or radiative effect) may respond to cli- These grid requirements mean that low-cloud processes dominating mate changes (e.g., Tompkins and Craig, 1998; Bretherton et al., 2013). the known uncertainty in cloud feedback cannot be explicitly simulat- Fourth, they have become an important tool in testing and improv- ed except in very small domains. Thus, notwithstanding all of the above ing parameterizations of cloud-controlling processes such as cumulus benefits of explicit cloud modeling, these models cannot on their own convection, turbulent mixing, small-scale horizontal cloud variability quantify global cloud feedbacks or aerosol cloud interactions defini- and aerosol cloud interactions (Randall et al., 2003; Rio and Hourdin, tively. They are important, however, in suggesting and testing feedback 2008; Stevens and Seifert, 2008; Lock, 2009; Del Genio and Wu, 2010; and adjustment mechanisms (see Sections 7.2.5 and 7.4). Fletcher and Bretherton, 2010), as well as the interplay between con- vection and large-scale circulations (Kuang, 2008). 7.2.2.2 Global Models with Explicit Clouds Different aspects of clouds, and cloud types, require different grid reso- Since AR4, increasing computer power has led to three types of devel- lutions. CRMs of deep convective cloud systems with horizontal resolu- opments in global atmospheric models. First, models have been run tions of 2 km or finer (Bryan et al., 2003) can represent some statistical with resolution that is higher than in the past, but not sufficiently high properties of the cloud system, including fractional area coverage of that cumulus clouds can be resolved explicitly. Second, models have cloud (Xu et al., 2002), vertical thermodynamic structure (Blossey et been run with resolution high enough to resolve (or permit ) large al., 2007), the distribution of updraughts and downdraughts (Khair- individual cumulus clouds over the entire globe. In a third approach, outdinov et al., 2009) and organization into mesoscale convective the parameterizations of global models have been replaced by 7 583 Chapter 7 Clouds and Aerosols e ­ mbedded CRMs. The first approach is assessed in Chapter 9. The other 105 Climate System Time scale (day) two approaches are discussed below. General Circulation Model (GCM) 104 Global Cloud-Resolving Models (GCRMs) have been run with grid spac- ings as small as 3.5 km (Tomita et al., 2005; Putman and Suarez, 2011). Cloud Processes Super Parameterization (MMF) At present GCRMs can be used only for relatively short simulations of a 103 & Global Cloud-Resolving Model (GCRM) few simulated months to a year or two on the fastest supercomputers, but in the not-too distant future they may provide climate projections. 102 Cloud-Resolving Model (CRM) GCRMs provide a consistent way to couple convective circulations to & large-scale dynamics, but must still parameterize the effects of individ- 101 Large-Eddy Simulation (LES) ual clouds, microphysics and boundary-layer circulations. 101 102 103 104 105 106 107 Spatial scale (m) Because they avoid the use of uncertain cumulus parameterizations, GCRMs better simulate many properties of convective circulations that Figure 7.8 | Model and simulation strategy for representing the climate system and climate processes at different  space and time  scales. Also shown are the ranges of are very challenging for many current conventional GCMs, including space and time scales usually associated with cloud processes (orange, lower left) and the diurnal cycles of precipitation (Sato et al., 2009) and the Asian the climate system (blue, upper right). Classes of models are usually defined based on summer monsoon (Oouchi et al., 2009). Inoue et al. (2010) showed the range of spatial scales they represent, which in the figure is roughly spanned by the that the cloudiness simulated by a GCRM is in good agreement with text for each model class. The temporal scales simulated by a particular type of model observations from CloudSat and CALIPSO, but the results are sensitive vary more widely. For instance, climate models are often run for a few time steps for diagnostic studies, or can simulate millennia. Hence the figure indicates the typical time to the parameterizations of turbulence and cloud microphysics (Satoh scales for which a given model is used. Computational power prevents one model from et al., 2010; Iga et al., 2011; Kodama et al., 2012). covering all time and space scales. Since the AR4, the  development of  Global Cloud Resolving Models (GCRMs), and hybrid approaches such as General Circulation Models Heterogeneous multiscale methods, in which CRMs are embedded in (GCMs) using the super-parameterization approach (sometimes called the Multiscale each grid cell of a larger scale model (Grabowski and Smolarkiewicz, Modelling Framework (MMF)), have  helped fill the gap between climate  system and cloud process models. 1999), have also been further developed as a way to realize some of the advantages of GCRMs but at less cost. This approach has come to be known as super-parameterization, because the CRM effectively affects many aspects of a model s overall simulated climate including replaces some of the existing GCM parameterizations (e.g., Khairoutdi- the Hadley circulation, precipitation patterns, and tropical variability. nov and Randall, 2001; Tao et al., 2009). Super-parameterized models, Therefore continuing weakness in these parameterizations affects not which are sometimes called multiscale modeling frameworks, occupy a only modeled climate sensitivity, but also the fidelity with which these middle ground between high-resolution process models and climate other variables can be simulated or projected. models (see Figure 7.8), in terms of both advantages and cost. Most CMIP5 climate model simulations use horizontal resolutions of Like GCRMs, super-parameterized models give more realistic simula- 100 to 200 km in the atmosphere, with vertical layers varying between tions of the diurnal cycle of precipitation (Khairoutdinov et al., 2005; 100 m near the surface to more than 1000 m aloft. Within regions Pritchard and Somerville, 2010) and the Madden-Julian Oscillation of this size in the real world, there is usually enormous small-scale (Benedict and Randall, 2009) than most conventional GCMs; they can variability in cloud properties, associated with variability in humidity, also improve aspects of the Asian monsoon and the El Nino Southern temperature and vertical motion (Figure 7.16). This variability must Oscillation (ENSO; Stan et al., 2010; DeMott et al., 2011). Moreover, be accounted for to accurately simulate cloud radiation interaction, because they also begin to resolve cloud-scale circulations, both strat- condensation, evaporation and precipitation and other cloud processes egies provide a framework for studying aerosol cloud interactions that crucially depend on how cloud condensate is distributed across that conventional GCMs lack (Wang et al., 2011b). Thus both types of each grid box (Cahalan et al., 1994; Pincus and Klein, 2000; Larson et global model provide important insights, but because neither of them al., 2001; Barker et al., 2003). fully resolves cloud processes, especially for low clouds (see Section 7.2.2.1), their results must be treated with caution just as with con- The simulation of clouds in modern climate models involves several ventional GCMs. parameterizations that must work in unison. These include parame- terization of turbulence, cumulus convection, microphysical processes, 7.2.3 Parameterization of Clouds in Climate Models radiative transfer and the resulting cloud amount (including the ver- tical overlap between different grid levels), as well as sub-grid scale 7.2.3.1 Challenges of Parameterization transport of aerosol and chemical species. The system of parameter- izations must balance simplicity, realism, computational stability and The representation of cloud microphysical processes in climate models efficiency. Many cloud processes are unrealistic in current GCMs, and is particularly challenging, in part because some of the fundamen- as such their cloud response to climate change remains uncertain. tal details of these microphysical processes are poorly understood (particularly for ice- and mixed-phase clouds), and because spatial Cloud processes and/or turbulence parameterization are important not heterogeneity of key atmospheric properties occurs at scales signif- only for the GCMs used in climate projections but also for special- 7 icantly smaller than a GCM grid box. Such representation, however, ized chemistry aerosol climate models (see review by Zhang, 2008), 584 Clouds and Aerosols Chapter 7 for regional climate models, and indeed for the cloud process models than typical CMIP3 parameterizations (Kay et al., 2012). However described in Section 7.2.2 which must still parameterize small-scale new observations reveal complexities not correctly captured by even and microphysical effects. The nature of the parameterization problem, relatively advanced schemes (Ma et al., 2012a). New representations however, shifts as model scale decreases. Section 7.2.3.2 briefly assess- of the Wegener Bergeron Findeisen process in mixed-phase clouds es recent developments relevant to GCMs. (Storelvmo et al., 2008b; Lohmann and Hoose, 2009) compare the rate at which the pre-existing ice crystals deplete the water vapour with the 7.2.3.2 Recent Advances in Representing Cloud condensation rate for liquid water driven by vertical updraught speed Microphysical Processes (Korolev, 2007); these are not yet included in CMIP5 models. Climate models are increasingly representing detailed microphysics, including 7.2.3.2.1 Liquid clouds mixed-phase processes, inside convective clouds (Fowler and Randall, 2002; Lohmann, 2008; Song and Zhang, 2011). Such processes can Recent development efforts have been focused on the introduction of influence storm characteristics like strength and electrification, and are more complex representations of microphysical processes, with the crucial for fully representing aerosol cloud interactions, but are still dual goals of coupling them better to atmospheric aerosols and link- not included in most climate models; their representation is moreover ing them more consistently to the sub-grid variability assumed by the subject to all the caveats noted in Section 7.2.3.1. model for other calculations. For example, most CMIP3 climate models predicted the average cloud and rain water mass in each grid cell only 7.2.3.3 Recent Advances in Parameterizing Moist Turbulence at a given time, diagnosing the droplet concentration using empiri- and Convection cal relationships based on aerosol mass (e.g., Boucher and Lohmann, 1995; Menon et al., 2002), or altitude and proximity to land. Many Both the mean state and variability in climate models are sensitive to were forced to employ an arbitrary lower bound on droplet concentra- the parameterization of cumulus convection. Since AR4, the develop- tion to reduce the aerosol RF (Hoose et al., 2009). Such formulations ment of convective parameterization has been driven largely by rapidly oversimplify microphysically mediated cloud variations. growing use of process models, in particular LES and CRMs, to inform parameterization development (e.g., Hourdin et al., 2013). By contrast, more models participating in CMIP5 predict both mass and number mixing ratios for liquid stratiform cloud. Some determine rain Accounting for greater or more state-dependent entrainment of air into and snow number concentrations and mixing ratios (e.g., Morrison and deep cumulus updraughts has improved simulations of the Madden Gettelman, 2008; Salzmann et al., 2010), allowing treatment of aerosol Julian Oscillation, tropical convectively coupled waves and mean rain- scavenging and the radiative effect of snow. Some models explicitly fall patterns in some models (Bechtold et al., 2008; Song and Zhang, treat sub-grid cloud water variability for calculating microphysical pro- 2009; Chikira and Sugiyama, 2010; Hohenegger and Bretherton, 2011; cess rates (e.g., Morrison and Gettelman, 2008). Cloud droplet activa- Mapes and Neale, 2011; Del Genio et al., 2012; Kim et al., 2012) but tion schemes now account more realistically for particle composition, usually at the expense of a degraded simulation of the mean state. In mixing and size (Abdul-Razzak and Ghan, 2000; Ghan et al., 2011; another model, revised criteria for convective initiation and parame- Liu et al., 2012). Despite such advances in internal consistency, a con- terizations of cumulus momentum fluxes improved ENSO and tropical tinuing weakness in GCMs (and to a much lesser extent GCRMs and vertical temperature profiles (Neale et al., 2008; Richter and Rasch, super-parameterized models) is their inability to fully represent turbu- 2008). Since AR4, more climate models have adopted cumulus param- lent motions to which microphysical processes are highly sensitive. eterizations that diagnose the expected vertical velocity in cumulus updraughts (e.g., Del Genio et al., 2007; Park and Bretherton, 2009; 7.2.3.2.2 Mixed-phase and ice clouds Chikira and Sugiyama, 2010; Donner et al., 2011), in principle allowing more complete representations of aerosol activation, cloud microphys- Ice treatments are following a path similar to those for liquid water, ical evolution and gravity wave generation by the convection. and face similar but greater challenges because of the greater com- plexity of ice processes. Many CMIP3 models predicted the condensed Several new parameterizations couple shallow cumulus convection water amount in just two categories cloud and precipitation with more closely to moist boundary layer turbulence (Siebesma et al., a temperature-dependent partitioning between liquid and ice within 2007; Neggers, 2009; Neggers et al., 2009; Couvreux et al., 2010) either category. Although supersaturation with respect to ice is com- including cold pools generated by nearby deep convection (Grandpeix monly observed at low temperatures, only one CMIP3 GCM (ECHAM) and Lafore, 2010). Many of these efforts have led to more accurate allowed ice supersaturation (Lohmann and Kärcher, 2002). simulations of boundary-layer cloud radiative properties and vertical structure (e.g., Park and Bretherton, 2009; Köhler et al., 2011), and Many climate models now include separate, physically based equations have ameliorated the common problem of premature deep convective for cloud liquid versus cloud ice, and for rain versus snow, allowing a initiation over land in one CMIP5 GCM (Rio et al., 2009). more realistic treatment of mixed-phase processes and ice supersatu- ration (Liu et al., 2007; Tompkins et al., 2007; Gettelman et al., 2010; 7.2.3.4 Recent Advances in Parameterizing Salzmann et al., 2010; see also Section 7.4.4). These new schemes are Cloud Radiative Effects tested in a single-column model against cases observed in field cam- paigns (e.g., Klein et al., 2009) or against satellite observations (e.g., Some models have improved representation of sub-grid scale cloud Kay et al., 2012), and provide superior simulations of cloud structure variability, which has important effects on grid-mean radiative fluxes 7 585 Chapter 7 Clouds and Aerosols and precipitation fluxes, for example, based on the use of probability Because global temperatures have been rising, the above arguments density functions of thermodynamic variables (Sommeria and Dear- imply WVMR should be rising accordingly, and multiple observing sys- dorff, 1977; Watanabe et al., 2009). Stochastic approaches for radi- tems indeed show this (Sections 2.5.4 and 2.5.5). A study challenging ative transfer can account for this variability in a computationally the water vapour increase (Paltridge et al., 2009) used an old reanalysis efficient way (Barker et al., 2008). New treatments of cloud overlap product, whose trends are contradicted by newer ones (Dessler and have been motivated by new observations (Section 7.2.1.1). Despite Davis, 2010) and by actual observations (Chapter 2). The study also these advances, the CMIP5 models continue to exhibit the too few, too reported decreasing relative humidity in data from Australian radio- bright low-cloud problem (Nam et al., 2012), with a systematic over- sondes, but more complete studies show Australia to be exceptional estimation of cloud optical depth and underestimation of cloud cover. in this respect (Dai et al., 2011). Thus data remain consistent with the expected global feedback. 7.2.3.5 Cloud Modelling Synthesis Some studies have proposed that the response of upper-level humid- Global climate models used in CMIP5 have improved their represen- ity to natural fluctuations in the global mean surface temperature is tation of cloud processes relative to CMIP3, but still face challenges informative about the feedback. However, small changes to the global and uncertainties, especially regarding details of small-scale variability mean (primarily from ENSO) involve geographically heterogeneous that are crucial for aerosol cloud interactions (see Section 7.4). Finer- temperature change patterns, the responses to which may be a poor scale LES and CRM models are much better able to represent this vari- analogue for global warming (Hurley and Galewsky, 2010a). Most ability and are an important research tool, but still suffer from imper- climate models reproduce these natural responses reasonably well fect representations of aerosol and cloud microphysics and known (Gettelman and Fu, 2008; Dessler and Wong, 2009), providing addi- biases. Most CRM and LES studies do not span the large  space and tional evidence that they at least represent the key processes. time scales needed to fully determine the interactions among differ- ent cloud regimes and the resulting net planetary radiative effects. Thus The last-saturation concept approximates the WVMR of air by its sat- our assessments in this chapter do not regard any model type on its uration value when it was last in a cloud (see Sherwood et al., 2010a own as definitive, but weigh the implications of process model studies for a review), which can be inferred from trajectory analysis. Studies in assessing the quantitative results of the global models. since the AR4 using a variety of models and observations (including concentrations of water vapour isotopes) support this concept (Sher- 7.2.4 Water Vapour and Lapse Rate Feedbacks wood and Meyer, 2006; Galewsky and Hurley, 2010). The concept has clarified what determines relative humidity in the subtropical upper Climate feedbacks determine the sensitivity of global surface temper- troposphere and placed the water vapour feedback on firmer theo- ature to external forcing agents. Water vapour, lapse rate and cloud retical footing by directly linking actual and saturation WVMR values feedbacks each involve moist atmospheric processes closely linked to (Hurley and Galewsky, 2010b). CRMs show that convection can adopt clouds, and in combination, produce most of the simulated climate varying degrees of self-aggregation (e.g., Muller and Held, 2012), feedback and most of its inter-model spread (Section 9.7). The radia- which could modify the water vapour or other feedbacks if this were tive feedback from a given constituent can be quantified as its impact climate sensitive, although observations do not suggest aggregation (other constituents remaining equal) on the TOA net downward radi- changes have a large net radiative effect (Tobin et al., 2012). ative flux per degree of global surface (or near-surface) temperature increase, and may be compared with the basic black-body response In a warmer climate, an upward shift of the tropopause and poleward of 3.4 W m 2 °C 1 (Hansen et al., 1984). This definition assigns posi- shift of the jets and associated climate zones are expected (Sections tive values to positive feedbacks, in keeping with the literature on this 2.7.4 and 2.7.5) and simulated by most GCMs (Section 10.3.3). These topic but contradictory to the conventions sometimes adopted in other changes account, at least qualitatively, for robust regional changes in climate research. the relative humidity simulated in warmer climate by GCMs, includ- ing decreases in the subtropical troposphere and tropical uppermost 7.2.4.1 Water Vapour Response and Feedback troposphere, and increases near the extratropical tropopause and high latitudes (Sherwood et al., 2010b). This pattern may be amplified, As pointed out in previous reports (Section 8.6.3.1 in Randall et al., however, by non-uniform atmospheric temperature or wind changes 2007), physical arguments and models of all types suggest global (Hurley and Galewsky, 2010b). It is also the apparent cause of most water vapour amounts increase in a warmer climate, leading to a model-predicted changes in mid- and upper-level cloudiness patterns positive feedback via its enhanced greenhouse effect. The saturated (Wetherald and Manabe, 1980; Sherwood et al., 2010b; see also Sec- water vapour mixing ratio (WVMR) increases nearly exponentially and tion 7.2.5.2). Idealized CRM simulations of warming climates also very rapidly with temperature, at 6 to 10% °C 1 near the surface, and show upward shifts of the humidity patterns with little change in the even more steeply aloft (up to 17% °C 1) where air is colder. Mounting mean (e.g., Kuang and Hartmann, 2007; Romps, 2011). evidence indicates that any changes in relative humidity in warmer climates would have much less impact on specific humidity than the It remains unclear whether stratospheric water vapour contributes above increases, at least in a global and statistical sense. Hence the significantly to climate feedback. Observations have shown decadal overall WVMR is expected to increase at a rate similar to the saturated variations in stratospheric water vapour, which may have affected the WVMR. planetary radiation budget somewhat (Solomon et al., 2010) but are 7 not clearly linked to global temperature (Section 3.4.2.4 in Trenberth et 586 Clouds and Aerosols Chapter 7 al., 2007). A strong positive feedback from stratospheric water vapour rather than specific humidity, is the feedback variable. Analysed in that was reported in one GCM, but with parameter settings that produced framework the inherent stabilization by the Planck response is weaker, an unrealistic present climate (Joshi et al., 2010). but the water vapour and lapse rate feedbacks are also very small; thus the traditional view of large and partially compensating feedbacks has, 7.2.4.2 Relationship Between Water Vapour and Lapse arguably, arisen from arbitrary choices made when the analysis frame- Rate Feedbacks work was originally set out, rather than being an intrinsic feature of climate or climate models. The lapse rate (decrease of temperature with altitude) should, in the tropics, change roughly as predicted by a moist adiabat, due to the There is some observational evidence (Section 2.4.4) suggesting trop- strong restoring influence of convective heating. This restoring influ- ical lapse rates might have increased in recent decades in a way not ence has now been directly inferred from satellite data (Lebsock et simulated by models (Section 9.4.1.4.2). Because the combined lapse al., 2010), and the near-constancy of tropical atmospheric stability and rate and water vapour feedback depends on relative humidity change, deep-convective thresholds over recent decades is also now observ- however, the imputed lapse rate variations would have little influence able in SST and deep convective data (Johnson and Xie, 2010). The on the total feedback or climate sensitivity even if they were a real stronger warming of the atmosphere relative to the surface produces a warming response (Ingram, 2013b). In summary, there is increased negative feedback on global temperature because the warmed system evidence for a strong, positive feedback (measured in the tradition- radiates more thermal emission to space for a given increase in surface al framework) from the combination of water vapour and lapse rate temperature than in the reference case where the lapse rate is fixed. changes since AR4, with no reliable contradictory evidence. This feedback varies somewhat among models because lapse rates in middle and high latitudes, which decrease less than in the tropics, do 7.2.5 Cloud Feedbacks and Rapid Adjustments to so differently among models (Dessler and Wong, 2009). Carbon Dioxide As shown by Cess (1975) and discussed in the AR4 (Randall et al., The dominant source of spread among GCM climate sensitivities in AR4 2007), models with a more negative lapse rate feedback tend to have was due to diverging cloud feedbacks, particularly due to low clouds, a more positive water vapour feedback. Cancellation between these and this continues to be true (Section 9.7). All global models continue to is close enough that their sum has a 90% range in CMIP3 models of produce a near-zero to moderately strong positive net cloud feedback. only +0.96 to +1.22 W m 2 °C 1 (based on a Gaussian fit to the data of Progress has been made since the AR4 in understanding the reasons Held and Shell (2012), see Figure 7.9) with essentially the same range for positive feedbacks in models and providing a stronger theoretical in CMIP5 (Section 9.7). The physical reason for this cancellation is that and observational basis for some mechanisms contributing to them. as long as water vapour infrared absorption bands are nearly saturat- There has also been progress in quantifying feedbacks including ed, outgoing longwave radiation is determined by relative humidity separating the effects of different cloud types, using radiative-kernel (Ingram, 2010) which exhibits little global systematic change in any residual methods (Soden et al., 2008) and by computing cloud effects model (Section 7.2.4.1). In fact, Held and Shell (2012) and Ingram directly (e.g., Zelinka et al., 2012a) and in distinguishing between (2013a) argue that it makes more sense physically to redefine feed- feedback and adjustment responses (Section 7.2.5.6). backs in a different analysis framework in which relative humidity, Until very recently cloud feedbacks have been diagnosed in models by differencing cloud radiative effects in doubled CO2 and control cli- 3 mates, normalized by the change in global mean surface temperature. Standard Decomposition RH-based Decomposition 2 Different diagnosis methods do not always agree, and some simple Feedback (W m-2 C-1) methods can make positive cloud feedbacks look negative by failing to 1 account for the nonlinear interaction between cloud and water vapour (Soden and Held, 2006). Moreover, it is now recognized that some of 0 the cloud changes are induced directly by the atmospheric radiative -1 effects of CO2 independently of surface warming, and are therefore rapid adjustments rather than feedbacks (Section 7.2.5.6). Most of the -2 published studies available for this assessment did not separate these effects, and only the total response is assessed here unless otherwise -3 noted. It appears that the adjustments are sufficiently small in most Total Planck Lapse WVMR PlanckRH LapseRH RH models that general conclusions regarding feedbacks are not signifi- cantly affected. Figure 7.9 | Feedback parameters associated with water vapour or the lapse rate predicted by CMIP3 GCMs, with boxes showing interquartile range and whiskers show- ing extreme values. At left is shown the total radiative response including the Planck Cloud changes cause both longwave (greenhouse warming) and short- response. In the darker shaded region is shown the traditional breakdown of this into wave (reflective cooling) effects, which combine to give the overall a Planck response and individual feedbacks from water vapour (labelled WVMR ) and cloud feedback or forcing adjustment. Cloud feedback studies point lapse rate (labelled Lapse ). In the lighter-shaded region at right are the equivalent to five aspects of the cloud response to climate change which are three parameters calculated in an alternative, relative humidity-based framework. In distinguished here: changes in high-level cloud altitude, effects of this framework all three components are both weaker and more consistent among the hydrological cycle and storm track changes on cloud systems, changes ­ 7 models. (Data are from Held and Shell, 2012.) 587 Chapter 7 Clouds and Aerosols in low-level cloud amount, microphysically induced opacity (optical compensation between their longwave and shortwave cloud radiative depth) changes and changes in high-latitude clouds. Finally, recent effects (Harrison et al., 1990; Figure 7.7). Similar compensation can research on the rapid cloud adjustments to CO2 is assessed. Feedbacks be seen in the opposing variations of these two components of the involving aerosols (Section 7.3.5) are not considered here, and the high-cloud feedback across GCMs (Figure 7.10). This might suggest discussion focuses only on mechanisms affecting the TOA radiation that the altitude feedback could be similarly compensated. However, budget. GCMs can reproduce the observed compensation in the present cli- mate (Sherwood et al., 1994) without producing one under global 7.2.5.1 Feedback Mechanisms Involving the Altitude of warming. In the above-noted cloud-resolving simulations, the entire High-Level Cloud cloud field (including the typical base) moved upward, in accord with a general upward shift of tropospheric fields (Singh and O Gorman, A dominant contributor of positive cloud feedback in models is the 2012) and with drying at levels near cloud base (Minschwaner et al., increase in the height of deep convective outflows tentatively attribut- 2006; Sherwood et al., 2010b). This supports the prediction of GCMs ed in AR4 to the so-called fixed anvil-temperature mechanism (Hart- that the altitude feedback is not compensated by an increase in high- mann and Larson, 2002). According to this mechanism, the average cloud thickness or albedo. outflow level from tropical deep convective systems is determined in steady state by the highest point at which water vapour cools the The observational record offers limited further support for the altitude atmosphere significantly through infrared emission; this occurs at a increase. The global tropopause is rising as expected (Section 2.7.4). particular water vapour partial pressure, therefore at a similar temper- Observed cloud heights change roughly as predicted with regional, ature (higher altitude) as climate warms. A positive feedback results seasonal and interannual changes in near-tropopause temperature because, since the cloud top temperature does not keep pace with that structure (Xu et al., 2007; Eitzen et al., 2009; Chae and Sherwood, of the troposphere, its emission to space does not increase at the rate 2010; Zelinka and Hartmann, 2011), although these tests may not be expected for the no-feedback system. This occurs at all latitudes and good analogues for global warming. Davies and Molloy (2012) report has long been noted in model simulations (Hansen et al., 1984; Cess an apparent recent downward mean cloud height trend but this is et al., 1990). This mechanism, with a small modification to account for probably an artefact (Evan and Norris, 2012); observed cloud height lapse rate changes, predicts roughly +0.5 W m 2 °C 1 of positive long- trends do not appear sufficiently reliable to test this cloud-height feed- wave feedback in GCMs (Zelinka and Hartmann, 2010), compared to back mechanism (Section 2.5.6). an overall cloud-height feedback of +0.35 (+0.09 to +0.58) W m 2 °C 1 (Figure 7.10). Importantly, CRMs also reproduce this increase in cloud In summary, the consistency of GCM responses, basic understanding, height (Tompkins and Craig, 1998; Kuang and Hartmann, 2007; Romps, strong support from process models, and weak further support from 2011; Harrop and Hartmann, 2012). observations give us high confidence in a positive feedback contribu- tion from increases in high-cloud altitude. On average, natural fluctuations in tropical high cloud amount exert little net TOA radiative effect in the current climate due to near-­ 7.2.5.2 Feedback Mechanisms Involving the Amount of Middle and High Cloud CMIP5 CMIP3 CFMIP LW SW Net As noted in Section 7.2.5.1, models simulate a range of nearly compen- 1.5 CFMIP & CMIP3 Models CFMIP Models sating differences in shortwave and longwave high-cloud feedbacks, (by cloud level) (by cloud property) consistent with different changes in high-cloud amount, but also show Feedback (W m-2 C-1) 1.0 a net positive offset consistent with higher cloud altitude (Figure 7.10). However, there is a tendency in most GCMs toward reduced middle 0.5 and high cloud amount in warmer climates in low- and mid-latitudes, especially in the subtropics (Trenberth and Fasullo, 2009; Zelinka and 0.0 Hartmann, 2010). This loss of cloud amount adds a positive shortwave and negative longwave feedback to the model average, which causes -0.5 the average net positive feedback to appear to come from the short- wave part of the spectrum. The net effect of changes in amount of all -1.0 Total High Middle Low Amount Height Opacity cloud types averaged over models is a positive feedback of about +0.2 W m 2 °C 1, but this roughly matches the contribution from low clouds Figure 7.10 | Cloud feedback parameters as predicted by GCMs for responses to CO2 (see the following section), implying a near-cancellation of longwave increase including rapid adjustments. Total feedback shown at left, with centre light- and shortwave effects for the mid- and high-level amount changes. shaded section showing components attributable to clouds in specific height ranges (see Section 7.2.1.1), and right dark-shaded panel those attributable to specific cloud property changes where available. The net feedback parameters are broken down in Changes in predicted cloud cover geographically correlate with sim- their longwave (LW) and shortwave (SW) components. Type attribution reported for ulated subtropical drying (Meehl et al., 2007), suggesting that they CMIP3 does not conform exactly to the definition used in the Cloud Feedback Model are partly tied to large-scale circulation changes including the pole- Intercomparison Project (CFMIP) but is shown for comparison, with their mixed cat- ward shifts found in most models (Wetherald and Manabe, 1980; Sher- egory assigned to middle cloud. CFMIP data (original and CFMIP2) are from Zelinka wood et al., 2010b; Section 2.7). Bender et al. (2012) and Eastman 7 et al. (2012a, 2012b; 2013); CMIP3 from Soden and Vecchi (2011); and CMIP5 from and Warren (2013) report poleward shifts in cloud since the 1970s Tomassini et al. (2013). 588 Clouds and Aerosols Chapter 7 c ­ onsistent with those reported in other observables (Section 2.5.6) and 2013), or conditioned on a particular dynamical state (Bony et al., simulated by most GCMs, albeit with weaker amplitude (Yin, 2005). 2004), and is similar in equilibrium or transient simulations (Yokohata This shift of clouds to latitudes of weaker sunlight decreases the plan- et al., 2008), it appears to be attributable to how cloud, convective and etary albedo and would imply a strong positive feedback if it were due boundary layer processes are parameterized in GCMs. to global warming (Bender et al., 2012), although it is probably partly driven by other factors (Section 10.3). The true amount of positive feed- The modelled response of low clouds does not appear to be dominated back coming from poleward shifts therefore remains highly uncertain, by a single feedback mechanism, but rather the net effect of sever- but is underestimated by GCMs if, as suggested by observational com- al potentially competing mechanisms as elucidated in LES and GCM parisons, the shifts are underestimated (Johanson and Fu, 2009; Allen sensitivity studies (e.g., Zhang and Bretherton, 2008; Blossey et al., et al., 2012). 2013; Bretherton et al., 2013). Starting with some proposed negative feedback mechanisms, it has been argued that in a warmer climate, The upward mass flux in deep clouds should decrease in a warmer low clouds will be: (1) horizontally more extensive, because changes climate (Section 7.6.2), which might contribute to cloudiness decreases in the lapse rate of temperature also modify the lower-tropospheric in storm tracks or the ITCZ (Chou and Neelin, 2004; Held and Soden, stability (Miller, 1997); (2) optically thicker, because adiabatic ascent 2006). Tselioudis and Rossow (2006) predict this within the storm is accompanied by a larger condensation rate (Somerville and Remer, tracks based on observed present-day relationships with meteorologi- 1984); and (3) vertically more extensive, in response to a weakening cal variables combined with model-simulated changes to those driving of the tropical overturning circulation (Caldwell and Bretherton, 2009). variables but do not infer a large feedback. Most CMIP3 GCMs produce While these mechanisms may play some role in subtropical low cloud too little storm-track cloud cover in the southern hemisphere compared feedbacks, none of them appears dominant. Regarding (1), dry static to nearly overcast conditions in reality, but clouds are also too bright. stability alone is a misleading predictor with respect to climate chang- Arguments have been advanced that such biases could imply either es, as models with comparably good simulations of the current region- model overestimation or underestimation of feedbacks (Trenberth and al distribution and/or relationship to stability of low cloud can produce Fasullo, 2010; Brient and Bony, 2012). a broad range of cloud responses to climate perturbations (Wyant et al., 2006). Mechanism (2), discussed briefly in the next section, appears The role of thin cirrus clouds for cloud feedback is not known and to have a small effect. Mechanism (3) cannot yet be ruled out but does remains a source of possible systematic bias. Unlike high-cloud sys- not appear to be the dominant factor in determining subtropical cloud tems overall, these particular clouds exert a clear net warming effect changes in GCMs (Bony and Dufresne, 2005; Zhang and Bretherton, (Jensen et al., 1994; Chen et al., 2000), making a significant cloud-­ 2008). cover feedback possible in principle (e.g., Rondanelli and Lindzen, 2010). While this does not seem to be important in recent GCMs Since the AR4, several new positive feedback mechanisms have been (Zelinka et al., 2012b), and no specific mechanism has been suggested, proposed, most associated with the marine boundary layer clouds the representation of cirrus in GCMs appears to be poor (Eliasson et thought to be at the core of the spread in responses. These include the al., 2011) and such clouds are microphysically complex (Section 7.4.4). ideas that: warming-induced changes in the absolute humidity lapse This implies significant feedback uncertainty in addition to that already rate change the energetics of mixing in ways that demand a reduction evident from model spread. in cloud amount or thickness (Webb and Lock, 2013; Bretherton et al., 2013; Brient and Bony, 2013); energetic constraints prevent the sur- Model simulations, physical understanding and observations thus pro- face evaporation from increasing with warming at a rate sufficient to vide medium confidence that poleward shifts of cloud distributions balance expected changes in dry air entrainment, thereby reducing the will contribute to positive feedback, but by an uncertain amount. Feed- supply of moisture to form clouds (Rieck et al., 2012; Webb and Lock, backs from thin cirrus amount cannot be ruled out and are an impor- 2013); and that increased concentrations of GHGs reduce the radia- tant source of uncertainty. tive cooling that drives stratiform cloud layers and thereby the cloud amount (Caldwell and Bretherton, 2009; Stevens and Brenguier, 2009; 7.2.5.3 Feedback Mechanisms Involving Low Cloud Bretherton et al., 2013). These mechanisms, crudely operating through parameterized representations of cloud processes, could explain why Differences in the response of low clouds to a warming are responsible climate models consistently produce positive low-cloud feedbacks. for most of the spread in model-based estimates of equilibrium climate Among CFMIP GCMs, the low-cloud feedback ranges from 0.09 to sensitivity (Randall et al., 2007). Since the AR4 this finding has with- +0.63 W m 2 °C 1 (Figure 7.10), and is largely associated with a reduc- stood further scrutiny (e.g., Soden and Vecchi, 2011; Webb et al., 2013), tion in low-cloud amount, albeit with considerable spatial variabili- holds in CMIP5 models (Vial et al., 2013) and has been shown to apply ty (e.g., Webb et al., 2013). One super-parameterized GCM (Section also to the transient climate response (e.g., Dufresne and Bony, 2008). 7.2.2.2) simulates a negative low-cloud feedback (Wyant et al., 2006, This discrepancy in responses occurs over most oceans and cannot 2009), but that model s representation of low clouds was worse than be clearly confined to any single region (Trenberth and Fasullo, 2010; some conventional GCMs. Webb et al., 2013), but is usually associated with the representation of shallow cumulus or stratocumulus clouds (Williams and Tselioudis, The tendency of both GCMs and process models to produce these 2007; Williams and Webb, 2009; Xu et al., 2010). Because the spread positive feedback effects suggests that the feedback contribution of responses emerges in a variety of idealized model formulations from changes in low clouds is positive. However, deficient representa- (Medeiros et al., 2008; Zhang and Bretherton, 2008; Brient and Bony, tion of low clouds in GCMs, diverse model results, a lack of reliable 7 589 Chapter 7 Clouds and Aerosols observational constraints, and the tentative nature of the suggested stimulates cloud formation by boundary-layer convection (Kay and mechanisms leave us with low confidence in the sign of the low-cloud Gettelman, 2009; Vavrus et al., 2011). Kay et al. (2011) show that a feedback contribution. GCM can represent this seasonal sensitivity of low cloud to open water, but doing so depends on the details of how boundary-layer clouds are 7.2.5.4 Feedbacks Involving Changes in Cloud Opacity parameterized. Vavrus et al. (2009) show that in a global warming sce- nario, GCMs simulate more Arctic low cloud in all seasons, but espe- It has long been suggested that cloud water content could increase in cially during autumn and the onset of winter when open water and a warmer climate simply due to the availability of more vapour for con- very thin sea ice increase considerably, increasing upward moisture densation in a warmer atmosphere, yielding a negative feedback (Pal- transport to the clouds. tridge, 1980; Somerville and Remer, 1984), but this argument ignores the physics of crucial cloud-regulating processes such as precipitation A few studies in the literature suggest negative feedbacks from Arctic formation and turbulence. Observational evidence discounting a large clouds, based on spatial correlations of observed warming and cloud- effect of this kind was reported in AR4 (Randall et al., 2007). iness (Liu et al., 2008) or tree-ring proxies of cloud shortwave effects over the last millenium (Gagen et al., 2011). However, the spatial cor- The global mean net feedback from cloud opacity changes in CFMIP relations are not reliable indicators of feedback (Section 7.2.5.7), and models (Figure 7.10) is approximately zero. Optical depths tend to the tree-ring evidence (assuming it is a good proxy) applies only to the reduce slightly at low and middle latitudes, but increase poleward shortwave effect of summertime cloud cover. The GCM studies would of 50°, yielding a positive longwave feedback that roughly offsets be consistent with warmer climates being cloudier, but have opposite the negative shortwave feedback. These latitude-dependent opacity radiative effects and positive feedback during the rest of the year. Even changes may be attributed to phase changes at high latitudes and though a small positive feedback is suggested by models, there is over- greater poleward moisture transport (Vavrus et al., 2009), and possibly all little evidence for significant feedbacks from Arctic cloud. to poleward shifts of the circulation. 7.2.5.6 Rapid Adjustments of Clouds to a Carbon Studies have reported warming-related changes in cloud opacity tied Dioxide Change to cloud phase (e.g., Senior and Mitchell, 1993; Tsushima et al., 2006). This might be expected to cause negative feedback, because at mixed- It is possible to partition the response of TOA radiation in GCMs to phase temperatures of 38 to 0°C, cloud ice particles have typical an instantaneous doubling of CO2 into a rapid adjustment in which diameters of 10 to 100 um (e.g., Figure 8 in Donovan, 2003), sever- the land surface, atmospheric circulations and clouds respond to the al-fold larger than cloud water drops, so a given mass of cloud water radiative effect of the CO2 increase, and an SST-mediated response would have less surface area and reflect less sunlight in ice form than that develops more slowly as the oceans warm (see Section 7.1.3). in liquid form. As climate warms, a shift from ice to liquid at a given This distinction is important not only to help understand model pro- location could increase cloud opacity. An offsetting factor that may cesses, but because the presence of rapid adjustments would cause explain the absence of this in CFMIP, however, is that mixed-phase clouds to respond slightly differently to a transient climate change (in clouds may form at higher altitudes, and similar local temperatures, in which SST changes have not caught up to CO2 changes) or to a climate warmer climates (Section 7.2.5.1). The key physics is in any case not change caused by other forcings, than they would to the same warm- adequately represented in climate models. Thus this particular feed- ing at equilibrium driven by CO2. There is also a rapid adjustment of the back mechanism is highly uncertain. hydrological cycle and precipitation field, discussed in Section 7.6.3. 7.2.5.5 Feedback from Arctic Cloud Interactions with Sea Ice Gregory and Webb (2008) reported that in some climate models, rapid adjustment of clouds can have TOA radiative effects comparable to Arctic clouds, despite their low altitude, have a net heating effect at those of the ensuing SST-mediated cloud changes, though Andrews the surface in the present climate because their downward emission of and Forster (2008) found a smaller effect. Subsequent studies using infrared radiation over the year outweighs their reflection of sunlight more accurate kernel-based techniques find a cloud adjustment during the short summer season. Their net effect on the atmosphere is of roughly +0.4 to +0.5 W m 2 per doubling of CO2, with standard cooling, however, so their effect on the energy balance of the whole deviation of about 0.3 W m 2 across models (Vial et al., 2013; Zelinka system is ambiguous and depends on the details of the vertical cloud et al., 2013). This would account for about 20% of the overall cloud distribution and the impact of cloud radiative interactions on ice cover response in a model with average sensitivity; and because it is not (Palm et al., 2010). strongly correlated with model sensitivity, it contributes perhaps 20% of the inter-model response spread (Andrews et al., 2012; Webb et al., Low-cloud amount over the Arctic oceans varies inversely with sea ice 2013), which therefore remains dominated by feedbacks. The response amount (open water producing more cloud) as now confirmed since occurs due to a general decrease in cloud cover caused by the slight AR4 by visual cloud reports (Eastman and Warren, 2010) and lidar and stratification driven by CO2 warming of the troposphere, which espe- radar observations (Kay and Gettelman, 2009; Palm et al., 2010). The cially for middle and low clouds has a net warming effect (Colman and observed effect is weak in boreal summer, when the melting sea ice McAvaney, 2011; Zelinka et al., 2013). As explained at the beginning is at a similar temperature to open water and stable boundary layers of Section 7.2.5, feedback numbers given in this report already account with extensive low cloud are common over both surfaces, and strong- for these rapid adjustments. 7 est in boreal autumn when cold air flowing over regions of open water 590 Clouds and Aerosols Chapter 7 7.2.5.7 Observational Constraints on Global Cloud Feedback For a putative observational constraint on climate sensitivity to be accepted, it should have a sound physical basis and its assumptions A number of studies since AR4 have attempted to constrain overall should be tested appropriately in climate models. No method yet pro- cloud feedback (or climate sensitivity) from observations of natural cli- posed meets both conditions. Moreover, cloud responses to warming mate variability; here we discuss those using modern cloud, radiation can be sensitive to relatively subtle details in the geographic warm- or other measurements (see a complementary discussion in Section ing pattern, such as the slight hemispheric asymmetry due to the lag 12.5 based on past temperature data and forcing proxies). of southern ocean warming relative to northern latitudes (Senior and Mitchell, 2000; Yokohata et al., 2008). Cloud responses to specified One approach is to seek observable aspects of present-day cloud uniform ocean warming without CO2 increases are not the same as behaviour that reveal cloud feedback or some component thereof. those to CO2-induced global warming simulated with more realis- Varying parameters in a GCM sometimes produces changes in cloud tic oceans (Ringer et al., 2006), partly because of rapid adjustments feedback that correlate with the properties of cloud simulated for (Section 7.2.5.6) and because low clouds also feed back tightly to the the present day, but this depends on the GCM (Yokohata et al., 2010; underlying surface (Caldwell and Bretherton, 2009). Simulated cloud Gettelman et al., 2013), and the resulting relationships do not hold feedbacks also differ significantly between colder and warmer climates across multiple models such as those from CMIP3 (Collins et al., 2011). in some models (Crucifix, 2006; Yoshimori et al., 2009) and between Among the AR4 models, net cloud feedback correlates strongly with volcanic and other forcings (Yokohata et al., 2005). These sensitivities mid-latitude relative humidity (Volodin, 2008), with TOA radiation at highlight the challenges facing any attempt to infer long-term cloud high southern latitudes (Trenberth and Fasullo, 2010), and with humid- feedbacks from simple data analyses. ity at certain latitudes during boreal summer (Fasullo and Trenberth, 2012); if valid each of these regression relations would imply a rela- 7.2.6 Feedback Synthesis tively strong positive cloud feedback in reality, but no mechanism has been proposed to explain or validate them and such apparent skill can Together, the water vapour, lapse rate and cloud feedbacks are the prin- arise fortuitously (Klocke et al., 2011). Likewise, Clement et al. (2009) cipal determinants of equilibrium climate sensitivity. The water vapour found realistic decadal variations of cloud cover over the North Pacific and lapse rate feedbacks, as traditionally defined, should be thought of in only one model (HadGEM1) and argued that the relatively strong as a single phenomenon rather than in isolation (see Section 7.2.4.2). cloud feedback in this model should therefore be regarded as more To estimate a 90% probability range for that feedback, we double the likely, but this finding lacks a mechanistic explanation and may depend variance of GCM results about the mean to account for the possibility on how model output is used (Broccoli and Klein, 2010). Chang and of errors common to all models, to arrive at +1.1 (+0.9 to +1.3) W m 2 Coakley (2007) examined mid-latitude maritime clouds and found °C 1. Values in this range are supported by a steadily growing body cloud thinning with increasing temperature, consistent with a positive of observational evidence, model tests, and physical reasoning. As a feedback, whereas Gordon and Norris (2010) found the opposite result corollary, the net feedback from water vapour and lapse rate changes following a methodology that tried to isolate thermal and advective combined is extremely likely positive, allowing for the possibility of effects. In summary, there is no evidence of a robust link between any deep uncertainties or a fat-tailed error distribution. Key aspects of the of the noted observables and the global feedback, though some appar- responses of water vapour and clouds to climate warming now appear ent connections are tantalizing and are being studied further. to be constrained by large-scale dynamical mechanisms that are not sensitive to poorly represented small-scale processes, and as such, are Several studies have attempted to derive global climate sensitivity more credible. This feedback is thus known to be positive with high from interannual relationships between global mean observations of confidence, and contributes only a small part of the spread in GCM TOA radiation and surface temperature (see also Section 10.8.2.2). climate sensitivity (Section 9.7). An alternative framework has recently One problem with this is the different spatial character of interannu- been proposed in which these feedbacks, and stabilization via ther- al and long-term warming; another is that the methodology can be mal emission, are all significantly smaller and more consistent among confounded by cloud variations not caused by those of surface tem- models; thus the range given above may overstate the true uncertainty. perature (Spencer and Braswell, 2008). A range of climate sensitivities has been inferred based on such analyses (Forster and Gregory, 2006; Several cloud feedback mechanisms now appear consistently in GCMs, Lindzen and Choi, 2011). Crucially, however, among different GCMs summarized in Figure 7.11, most supported by other lines of evidence. there is no correlation between the interannual and long-term cloud Nearly all act in a positive direction. First, high clouds are expected temperature relationships (Dessler, 2010; Colman and Hanson, 2012), to rise in altitude and thereby exert a stronger greenhouse effect in contradicting the basic assumption of these methods. Many but not warmer climates. This altitude feedback mechanism is well understood, all atmosphere ocean GCMs predict relationships that are consistent has theoretical and observational support, occurs consistently in GCMs with observations (Dessler, 2010, 2013). More recently there is interest and CRMs and explains about half of the mean positive cloud feedback in relating the time-lagged correlations of cloud and temperature to in GCMs. Second, middle and high-level cloud cover tends to decrease feedback processes (Spencer and Braswell, 2010) but again these rela- in warmer climates even within the ITCZ, although the feedback effect tionships appear to reveal only a model s ability to simulate ENSO or of this is ambiguous and it cannot yet be tested observationally. Third, other modes of interannual variability properly, which are not obvious- observations and most models suggest storm tracks shift poleward in ly informative about the cloud feedback on long-term global warming a warmer climate, drying the subtropics and moistening the high lat- (Dessler, 2011). itudes, which causes further positive feedback via a net shift of cloud cover to latitudes that receive less sunshine. Finally, most GCMs also 7 591 Chapter 7 Clouds and Aerosols Rising High Clouds Broadening of the Hadley Cell Narrowing of Tropical Ocean Rainfall Zones Rising High Clouds Rising of the Melting Level Poleward Shift of Storms Less Low Clouds More Polar Clouds Equator 30 60 Pole Figure 7.11 | Robust cloud responses to greenhouse warming (those simulated by most models and possessing some kind of independent support or understanding). The tro- popause and melting level are shown by the thick solid and thin grey dashed lines, respectively. Changes anticipated in a warmer climate are shown by arrows, with red colour indicating those making a robust positive feedback contribution and grey indicating those where the feedback contribution is small and/or highly uncertain. No robust mechanisms contribute negative feedback. Changes include rising high cloud tops and melting level, and increased polar cloud cover and/or optical thickness (high confidence); broadening of the Hadley Cell and/or poleward migration of storm tracks, and narrowing of rainfall zones such as the Intertropical Convergence Zone (medium confidence); and reduced low-cloud amount and/or optical thickness (low confidence). Confidence assessments are based on degree of GCM consensus, strength of independent lines of evidence from observations or process models and degree of basic understanding. predict that low cloud amount decreases, especially in the subtropics, the feedback must be broader than its spread in GCMs. We estimate another source of positive feedback though one that differs signifi- a  probability distribution  for this feedback by doubling the spread cantly among models and lacks a well-accepted theoretical basis. Over about the mean of all model values in Figure 7.10 (in effect assuming middle and high latitudes, GCMs suggest warming-induced transitions an additional uncertainty about 1.7 times as large as  that encapsu- from ice to water clouds may cause clouds to become more opaque, lated in the GCM range, added to it in quadrature). This yields a 90% but this appears to have a small systematic net radiative effect in (very likely) range of 0.2 to +2.0 W m 2 °C 1, with a 17% probability models, possibly because it is offset by cloud altitude changes. of a negative feedback. Currently, neither cloud process models (CRMs and LES) nor observa- Note that the assessment of feedbacks in this chapter is independent tions provide clear evidence to contradict or confirm feedback mecha- of constraints on climate sensitivity from observed trends or palaeocli- nisms involving low clouds. In some cases these models show stronger mate information discussed in Box 12.2. low-cloud feedbacks than GCMs, but each model type has limitations, and some studies suggest stronger positive feedbacks are more realistic 7.2.7 Anthropogenic Sources of Moisture and Cloudiness (Section 7.2.5.7). Cloud process models suggest a variety of potentially opposing response mechanisms that may account for the current spread Human activity can be a source of additional cloudiness through spe- of GCM feedbacks. In summary we find no evidence to contradict either cific processes involving a source of water vapour in the atmosphere. the cloud or water vapour lapse rate feedback ranges shown by current We discuss here the impact of aviation and irrigation on water vapour GCMs, although the many uncertainties mean that true feedback could and cloudiness. The impact of water vapour sources from combustion still lie outside these ranges. In particular, microphysical mechanisms at the Earth s surface is thought to be negligible. Changes to the hydro- affecting cloud opacity or cirrus amount may well be missing from logical cycle because of land use change are briefly discussed in Sec- GCMs. Missing feedbacks, if any, could act in either direction. tion 12.4.8. Based on the preceding synthesis of cloud behaviour, the net radia- 7.2.7.1 Contrails and Contrail-Induced Cirrus tive feedback due to all cloud types is judged likely to be positive. This is reasoned probabilistically as follows. First, because evidence from Aviation jet engines emit hot moist air, which can form line shaped observations and process models is mixed as to whether GCM cloud persistent condensation trails (contrails) in environments that are feedback is too strong or too weak overall, and because the positive supersaturated with respect to ice and colder than about 40°C. The feedback found in GCMs comes mostly from mechanisms now sup- contrails are composed of ice crystals that are typically smaller than ported by other lines of evidence, the central (most likely) estimate of those of background cirrus (Heymsfield et al., 2010; Frömming et al., the total cloud feedback is taken as the mean from GCMs (+0.6 W m 2 2011). Their effect on longwave radiation dominates over their short- °C 1). Second, because there is no accepted basis to discredit individual wave effect (Stuber and Forster, 2007; Rap et al., 2010b; Burkhardt and GCMs a priori, the probability distribution of the true feedback cannot Kärcher, 2011) but models disagree on the relative importance of the be any narrower than the distribution of GCM results. Third, since feed- two effects. Contrails have been observed to spread into large cirrus back mechanisms are probably missing from GCMs and some CRMs sheets that may persist for several hours, and observational studies 7 suggest feedbacks outside the range in GCMs, the probable range of confirm their overall positive net RF impact (Haywood et al., 2009). 592 Clouds and Aerosols Chapter 7 Frequently Asked Questions FAQ 7.1 | How Do Clouds Affect Climate and Climate Change? Clouds strongly affect the current climate, but observations alone cannot yet tell us how they will affect a future, warmer climate. Comprehensive prediction of changes in cloudiness requires a global climate model. Such models simulate cloud fields that roughly resemble those observed, but important errors and uncertainties remain. Dif- ferent climate models produce different projections of how clouds will change in a warmer climate. Based on all available evidence, it seems likely that the net cloud climate feedback amplifies global warming. If so, the strength of this amplification remains uncertain. Since the 1970s, scientists have recognized the critical importance of clouds for the climate system, and for climate change. Clouds affect the climate system in a variety of ways. They produce precipitation (rain and snow) that is necessary for most life on land. They warm the atmosphere as water vapour condenses. Although some of the con- densed water re-evaporates, the precipitation that reaches the surface represents a net warming of the air. Clouds strongly affect the flows of both sunlight (warming the planet) and infrared light (cooling the planet as it is radi- ated to space) through the atmosphere. Finally, clouds contain powerful updraughts that can rapidly carry air from near the surface to great heights. The updraughts carry energy, moisture, momentum, trace gases, and aerosol particles. For decades, climate scientists have been using both observations and models to study how clouds change with the daily weather, with the seasonal cycle, and with year-to-year changes such as those associated with El Nino. All cloud processes have the potential to change as the climate state changes. Cloud feedbacks are of intense inter- est in the context of climate change. Any change in a cloud process that is caused by climate change and in turn influences climate represents a cloud climate feedback. Because clouds interact so strongly with both sunlight and infrared light, small changes in cloudiness can have a potent effect on the climate system. Many possible types of cloud climate feedbacks have been suggested, involving changes in cloud amount, cloud- top height and/or cloud reflectivity (see FAQ7.1, Figure 1). The literature shows consistently that high clouds amplify global warming as they interact with infrared light emitted by the atmosphere and surface. There is more uncer- tainty, however, about the feedbacks associated with low-altitude clouds, and about cloud feedbacks associated with amount and reflectivity in general. Thick high clouds efficiently reflect sunlight, and both thick and thin high clouds strongly reduce the amount of infrared light that the atmosphere and surface emit to space. The compensation between these two effects makes (continued on next page) Tropics Mid-latitudes Greenhouse Warming High clouds rise as troposphere Reduction in mid- and low-level cloudiness (left). Cloud deepens, increasing difference Shift of cloudy storm tracks poleward into Response between cloud top and surface regions with less sunlight (right). temperature. Feedback High clouds more effectively trap Less sunlight reflected by clouds back to space, infrared radiation, increasing increasing surface warming. Mechanism surface warming. FAQ 7.1, Figure 1 | Schematic of important cloud feedback mechanisms. 7 593 Chapter 7 Clouds and Aerosols FAQ 7.1 (continued) the surface temperature somewhat less sensitive to changes in high cloud amount than to changes in low cloud amount. This compensation could be disturbed if there were a systematic shift from thick high cloud to thin cirrus cloud or vice versa; while this possibility cannot be ruled out, it is not currently supported by any evidence. On the other hand, changes in the altitude of high clouds (for a given high-cloud amount) can strongly affect surface temperature. An upward shift in high clouds reduces the infrared light that the surface and atmosphere emit to space, but has little effect on the reflected sunlight. There is strong evidence of such a shift in a warmer climate. This amplifies global warming by preventing some of the additional infrared light emitted by the atmosphere and surface from leaving the climate system. Low clouds reflect a lot of sunlight back to space but, for a given state of the atmosphere and surface, they have only a weak effect on the infrared light that is emitted to space by the Earth. As a result, they have a net cooling effect on the present climate; to a lesser extent, the same holds for mid-level clouds. In a future climate warmed by increasing greenhouse gases, most IPCC-assessed climate models simulate a decrease in low and mid-level cloud amount, which would increase the absorption of sunlight and so tend to increase the warming. The extent of this decrease is quite model-dependent, however. There are also other ways that clouds may change in a warmer climate. Changes in wind patterns and storm tracks could affect the regional and seasonal patterns of cloudiness and precipitation. Some studies suggest that the signal of one such trend seen in climate models a poleward migration of the clouds associated with mid-latitude storm tracks is already detectable in the observational record. By shifting clouds into regions receiving less sunlight, this could also amplify global warming. More clouds may be made of liquid drops, which are small but numerous and reflect more sunlight back to space than a cloud composed of the same mass of larger ice crystals. Thin cirrus cloud, which exerts a net warming effect and is very hard for climate models to simulate, could change in ways not simu- lated by models although there is no evidence for this. Other processes may be regionally important, for example, interactions between clouds and the surface can change over the ocean where sea ice melts, and over land where plant transpiration is reduced. There is as yet no broadly accepted way to infer global cloud feedbacks from observations of long-term cloud trends or shorter-time scale variability. Nevertheless, all the models used for the current assessment (and the preceding two IPCC assessments) produce net cloud feedbacks that either enhance anthropogenic greenhouse warming or have little overall effect. Feedbacks are not put into the models, but emerge from the functioning of the clouds in the simulated atmosphere and their effects on the flows and transformations of energy in the climate system. The differences in the strengths of the cloud feedbacks produced by the various models largely account for the different sensitivities of the models to changes in greenhouse gas concentrations. Aerosol emitted within the aircraft exhaust may also affect high-level Forster et al. (2007) quoted Sausen et al. (2005) to update the 2000 cloudiness. This last effect is classified as an aerosol cloud interaction forcing for aviation-induced cirrus (including linear contrails) to +0.03 and is deemed too uncertain to be further assessed here (see also Sec- (+0.01 to +0.08) W m 2 but did not consider this to be a best esti- tion 7.4.4). Climate model experiments (Rap et al., 2010a) confirm ear- mate because of large uncertainties. Schumann and Graf (2013) con- lier results (Kalkstein and Balling Jr, 2004; Ponater et al., 2005) that avi- strained their model with observations of the diurnal cycle of contrails ation contrails do not have, at current levels of coverage, an observable and cirrus in a region with high air traffic relative to a region with effect on the mean or diurnal range of surface temperature (medium little air traffic, and estimated a RF of +0.05 (+0.04 to +0.08) W m 2 confidence). for contrails and contrail-induced cirrus in 2006, but their model has a large shortwave contribution, and larger estimates are possible. An Estimates of the RF from persistent (linear) contrails often correspond alternative approach was taken by Burkhardt and Kärcher (2011), who to different years and need to be corrected for the continuous increase estimated a global RF for 2002 of +0.03 W m 2 from contrails and in air traffic. More recent estimates tend to indicate somewhat smaller contrail cirrus within a climate model (Burkhardt and Kärcher, 2009), RF than assessed in the AR4 (see Table 7.SM.1 and text in Supplemen- after compensating for reduced background cirrus cloudiness in the tary Material). We adopt an RF estimate of +0.01 (+0.005 to +0.03) W main traffic areas. Based on these two studies we assess the combined m 2 for persistent (linear) contrails for 2011, with a medium confidence contrail and contrail-induced cirrus ERF for the year 2011 to be +0.05 attached to this estimate. An additional RF of +0.003 W m 2 is due to (+0.02 to +0.15) W m 2 to take into uncertainties on spreading rate, emissions of water vapour in the stratosphere by aviation as estimated optical depth, ice particle shape and radiative transfer and the ongoing 7 by Lee et al. (2009). increase in air traffic (see also Supplementary Material). A low confi- dence is attached to this estimate. 594 Clouds and Aerosols Chapter 7 7.2.7.2 Irrigation-Induced Cloudiness and formation of secondary particulate matter from gaseous precur- sors (Figure 7.12). The main constituents of the atmospheric aerosol Boucher et al. (2004) estimated a global ERF due to water vapour from are inorganic species (such as sulphate, nitrate, ammonium, sea salt), irrigation in the range of +0.03 to +0.10 W m 2 but the net climate organic species (also termed organic aerosol or OA), black carbon (BC, effect was dominated by the evaporative cooling at the surface and by a distinct type of carbonaceous material formed from the incomplete atmospheric thermal responses to low-level humidification. Regional combustion of fossil and biomass based fuels under certain condi- surface cooling was confirmed by a number of more recent region- tions), mineral species (mostly desert dust) and primary biological al and global studies (Kueppers et al., 2007; Lobell et al., 2009). The aerosol particles (PBAPs). Mineral dust, sea salt, BC and PBAPs are resulting increase in water vapour may induce a small enhancement in introduced into the atmosphere as primary particles, whereas non-sea- precipitation downwind of the major irrigation areas (Puma and Cook, salt sulphate, nitrate and ammonium are predominantly from second- 2010), as well as some regional circulation patterns (Kueppers et al., ary aerosol formation processes. The OA has both significant primary 2007). Sacks et al. (2009) reported a 0.001 increase in cloud fraction and secondary sources. In the present-day atmosphere, the majority over land (0.002 over irrigated land). This suggests an ERF no more of BC, sulphate, nitrate and ammonium come from anthropogenic negative than 0.1 W m 2 with very low confidence. sources, whereas sea salt, most mineral dust and PBAPs are predomi- nantly of natural origin. Primary and secondary organic aerosols (POA and SOA) are influenced by both natural and anthropogenic sources. 7.3 Aerosols Emission rates of aerosols and aerosol precursors are summarized in Table 7.1. The characteristics and role of the main aerosol species are The section assesses the role of aerosols in the climate system, focus- listed in Table 7.2. ing on aerosol processes and properties, as well as other factors, that influence aerosol radiation and aerosol cloud interactions. Processes 7.3.1.2 Aerosol Observations and Climatology directly relevant to aerosol cloud interactions are discussed in Section 7.4, and estimates of aerosol RFs and ERFs are assessed in Section New and improved observational aerosol data sets have emerged since 7.5. The time evolution of aerosols and their forcings are discussed AR4. A number of field experiments have taken place such as the Inter- in Chapters 2 and 8, with Chapter 8 also covering changes in natural continental Chemical Transport Experiment (INTEX, Bergstrom et al., volcanic aerosols. 2010; Logan et al., 2010), African Monsoon Multidisciplinary Analysis (AMMA; Jeong et al., 2008; Hansell et al., 2010), Integrated Campaign 7.3.1 Aerosols in the Present-Day Climate System for Aerosols, gases and Radiation Budget (ICARB; Moorthy et al., 2008 and references therein), Megacity Impact on Regional and Global Envi- 7.3.1.1 Aerosol Formation and Aerosol Types ronments field experiment (MILAGRO; Paredes-Miranda et al., 2009), Geostationary Earth Radiation Budget Inter-comparisons of Longwave Atmospheric aerosols, whether natural or anthropogenic, originate and Shortwave (GERBILS, Christopher et al., 2009), Arctic Research from two different pathways: emissions of primary particulate matter of the Composition of the Troposphere from Aircraft and Satellites ­ Optical Properties ERFari (optical depth, single scattering albedo, asymmetry factor) Gas Phase Condensed Phase Reactions & Low volatility gases Nucleation (sulphuric & nitric acid, Secondary Particles ammonia, organics) (inorganics, SOA) Coagulation Atmospheric state, Cloud distribution, Chemical Reactions Condensation & Cloud Processing Aged Aerosols Surface properties, Sun-earth geometry High volatility gases Primary Particles (SO2, NOx, VOCs) (POA, BC, sea-salt, dust) Emissions Emissions Deposition Deposition Cloud Activity Surface (cloud condensation nuclei, ice nuclei) ERFaci Figure 7.12 | Overview of atmospheric aerosol and environmental variables and processes influencing aerosol radiation and aerosol cloud interactions. Gas-phase variables and processes are highlighted in red while particulate-phase variables and processes appear in green. Although this figure shows a linear chain of processes from aerosols to forcings 7 (ERFari and ERFaci), it is increasingly recognized that aerosols and clouds form a coupled system with two-way interactions (see Figure 7.16). 595 Chapter 7 Clouds and Aerosols Table 7.1 | (a) Global and regional anthropogenic emissions of aerosols and aerosol precursors. The average, minimum and maximum values are from a range of available inven- tories (Cao et al., 2006; European Commission et al., 2009; Sofiev et al., 2009; Lu et al., 2010, 2011; Granier et al., 2011 and references therein; Knorr et al., 2012). It should be noted that the minimum to maximum range is not a measure of uncertainties which are often difficult to quantify. Units are Tg yr 1 and TgS yr 1 for sulphur dioxide (SO2). NMVOCs stand for non-methane volatile organic compounds. (b) Global natural emissions of aerosols and aerosol precursors. Dust and sea-spray estimates span the range in the historical CMIP5 simulations. The ranges for monoterpenes and isoprene are from Arneth et al. (2008). There are other biogenic volatile organic compounds (BVOCs) such as sesquiterpenes, alcohols and aldehydes which are not listed here. Marine primary organic aerosol (POA) and terrestrial primary biological aerosol particle (PBAP) emission ranges are from Gantt et al. (2011) and Burrows et al. (2009), respectively. Note that emission fluxes from mineral dust, sea spray and terrestrial PBAPs are highly sensitive to the cut-off radius. The conver- sion rate of BVOCs to secondary organic aerosol (SOA) is also indicated using the range from Spracklen et al. (2011) and a lower bound from Kanakidou et al. (2005). Units are Tg yr 1 except for BVOCs (monoterpenes and isoprene), in TgC yr 1, and dimethysulphide (DMS), in TgS yr 1. (a) Year 2000 Anthropogenic Anthropogenic Anthropogenic Anthropogenic Anthropogenic Biomass Burning Emissions NMVOCs Black Carbon POA SO2 NH3 Aerosols Tg yr 1 or TgS yr 1 Avg Min Max Avg Min Max Avg Min Max Avg Min Max Avg Min Max Avg Min Max Total 126.9 98.2 157.9 4.8 3.6 6.0 10.5 6.3 15.3 55.2 43.3 77.9 41.6 34.5 49.6 49.1 29.0 85.3 Western Europe 11.0 9.2 14.3 0.4 0.3 0.4 0.4 0.3 0.4 4.0 3.0 7.0 4.2 3.4 4.5 0.4 0.1 0.8 Central Europe 2.9 2.3 3.5 0.1 0.1 0.2 0.3 0.2 0.4 3.0 2.3 5.0 1.2 1.1 1.2 0.3 0.1 0.4 Former Soviet Union 9.8 6.5 15.2 0.3 0.2 0.4 0.7 0.5 0.9 5.2 3.0 7.0 1.7 1.5 2.0 5.4 3.0 7.9 Middle East 13.0 9.9 15.0 0.1 0.1 0.2 0.2 0.2 0.3 3.6 3.2 4.1 1.4 1.4 1.4 0.3 0.0 1.3 North America 17.8 14.5 20.9 0.4 0.3 0.4 0.5 0.4 0.6 8.7 7.8 10.4 4.6 3.8 5.5 2.0 0.8 4.4 Central America 3.8 2.9 4.4 0.1 0.1 0.1 0.3 0.2 0.3 2.1 1.9 2.8 1.1 1.1 1.2 1.44 0.3 2.7 South America 8.6 8.2 9.2 0.3 0.2 0.3 0.6 0.3 0.8 2.5 1.9 3.6 3.4 3.4 3.5 5.9 2.6 10.9 Africa 13.2 9.9 15.0 0.5 0.4 0.6 1.4 1.0 1.9 3.1 2.6 4.4 2.4 2.3 2.4 23.9 18.5 35.3 China 16.4 11.5 24.5 1.2 0.7 1.5 2.4 1.1 3.1 11.7 9.6 17.0 10.9 8.9 12.7 1.1 0.3 2.3 India 8.9 7.3 10.8 0.7 0.5 1.0 1.9 1.0 3.3 2.9 2.6 3.9 5.8 3.7 8.5 0.5 0.1 0.9 Rest of Asia 18.1 14.1 23.9 0.6 0.5 0.7 1.7 0.8 3.0 3.9 2.2 5.7 4.1 3.2 5.9 2.0 0.4 3.4 Oceania 1.2 1.0 1.5 0.03 0.03 0.04 0.05 0.04 0.08 1.2 0.9 1.4 0.7 0.7 0.7 5.8 2.7 16.8 International Shipping 2.1 1.3 3.0 0.1 0.1 0.1 0.1 0.1 0.1 3.3 2.1 5.5 (b) Natural Global of the main aerosol types can be constructed from such measurements Source Min Max (e.g., Jimenez et al., 2009; Zhang et al., 2012b; Figure 7.13). Such analy- Sea spray 1400 6800 ses show a wide spatial variability in aerosol mass concentration, dom- including marine POA 2 20 inant aerosol type, and aerosol composition. Mineral dust dominates Mineral dust 1000 4000 the aerosol mass over some continental regions with relatively higher Terrestrial PBAPs 50 1000 concentrations especially in urban South Asia and China, accounting for about 35% of the total aerosol mass with diameter smaller than including spores 28 10 mm. In the urban North America and South America, organic carbon Dimethylsulphide (DMS) 10 40 (OC) contributes the largest mass fraction to the atmospheric aerosol Monoterpenes 30 120 (i.e., 20% or more), while in other areas of the world the OC fraction Isoprene 410 600 ranks second or third with a mean of about 16%. Sulphate normally SOA production from all BVOCs 20 380 accounts for about 10 to 30% by mass, except for the areas in rural Africa, urban Oceania and South America, where it is less than about 10%. The mass fractions of nitrate and ammonium are only around 6% (ARCTAS, Lyapustin et al., 2010), the Amazonian Aerosol Character- and 4% on average, respectively. In most areas, elemental carbon (EC, ization Experiment 2008 (AMAZE-08, Martin et al., 2010b), the Inte- which refers to a particular way of measuring BC) represents less than grated project on Aerosol Cloud Climate and Air Quality interactions 5% of the aerosol mass, although this percentage may be larger (about (EUCAARI, Kulmala et al., 2011) and Atmospheric Brown Clouds (ABC, 12%) in South America, urban Africa, urban Europe, South, Southeast Nakajima et al., 2007), which have improved our understanding of and East Asia and urban Oceania due to the larger impact of combus- regional aerosol properties. tion sources. Sea salt can be dominant at oceanic remote sites with 50 to 70% of aerosol mass. Long-term aerosol mass concentrations are also measured more sys- tematically at the surface by global and regional networks (see Section Aerosol optical depth (AOD), which is related to the column-inte- 2.2.3), and there are institutional efforts to improve the coordination grated aerosol amount, is measured by the Aerosol Robotic Network 7 and quality assurance of the measurements (e.g., GAW, 2011). A survey (AERONET, Holben et al., 1998), other ground-based networks (e.g., 596 Clouds and Aerosols Chapter 7 Table 7.2 | Key aerosol properties of the main aerosol species in the troposphere. Terrestrial primary biological aerosol particles (PBAPs), brown carbon and marine primary organic aerosols (POA) are particular types of organic aerosols (OA) but are treated here as separate components because of their specific properties. The estimated lifetimes in the troposphere are based on the AeroCom models, except for terrestrial PBAPs which are treated by analogy to other coarse mode aerosol types. Tropospheric Key Climate Aerosol Species Size Distribution Main Sources Main Sinks Lifetime Relevant Properties Sulphate Primary: Aitken, accumulation Primary: marine and volcanic emissions. Wet deposition ~ 1 week Light scattering. Very and coarse modes Secondary: oxidation of SO2 and other S gases Dry deposition ­hygroscopic. Enhances Secondary: Nucleation, Aitken, from natural and anthropogenic sources absorption when deposited and accumulation modes as a coating on black carbon. Cloud condensation nuclei (CCN) active. Nitrate Accumulation and coarse modes Oxidation of NOx Wet deposition ~ 1 week Light scattering. Dry deposition H ­ ygroscopic. CCN active. Black carbon Freshly emitted: <100 nm Combustion of fossil fuels, biofuels and biomass Wet deposition 1 week to 10 days Large mass absorption Aged: accumulation mode Dry deposition efficiency in the shortwave. CCN active when coated. May be ice nuclei (IN) active. Organic aerosol POA: Aitken and accumulation Combustion of fossil fuel, biofuel and biomass. Wet deposition ~ 1 week Light scattering. Enhances modes. SOA: nucleation, Aitken Continental and marine ecosystems. Dry deposition absorption when deposited and mostly accumulation modes. Some anthropogenic and biogenic as a coating on black Aged OA: accumulation mode non-combustion sources carbon. CCN active (depending on aging time and size). ... of which Freshly emitted: 100 400 nm Combustion of biofuels and biomass. Natural Wet deposition ~ 1 week Medium mass absorption brown carbon Aged: accumulation mode humic-like substances from the biosphere Dry deposition efficiency in the UV and visible. Light scattering. ... of which Mostly coarse mode Terrestrial ecosystems Sedimentation 1 day to 1 week May be IN active. May ­terrestrial PBAP Wet deposition depending on size form giant CCN Dry deposition Mineral dust Coarse and super-coarse modes, Wind erosion, soil resuspension. Sedimentation 1 day to 1 week IN active. Light scattering with a small accumulation mode Some agricultural practices and ­ Dry deposition depending on size and absorption. industrial activities (cement) Wet deposition Greenhouse effect. Sea spray Coarse and accumulation modes Breaking of air bubbles induced e.g., by Sedimentation 1 day to 1 week Light scattering. Very wave breaking. Wind erosion. Wet deposition depending on size hygroscopic. CCN active. Dry deposition Can include primary organic compounds in smaller size range ... of which Preferentially Aitken and Emitted with sea spray in biologically Sedimentation ~ 1 week CCN active. marine POA accumulation modes active oceanic regions Wet deposition Dry deposition Bokoye et al., 2001; Che et al., 2009) and a number of satellite-based can be combined with information from global aerosol models through sensors. Instruments designed for aerosol retrievals such as Moderate data assimilation techniques (e.g., Benedetti et al., 2009; Figure 7.14a). Resolution Imaging Spectrometer (MODIS; Remer et al., 2005; Levy et Owing to the heterogeneity in their sources, their short lifetime and the al., 2010; Kleidman et al., 2012), Multi-angle Imaging Spectro-Radi- dependence of sinks on the meteorology, aerosol distributions show ometer (MISR; Kahn et al., 2005; Kahn et al., 2007) and Polarization large variations on daily, seasonal and interannual scales. and Directionality of the Earth s Reflectances (POLDER)/Polarization and Anisotropy of Reflectances for Atmospheric Sciences Coupled with The CALIPSO spaceborne lidar (Winker et al., 2009) complements Observations from Lidar (PARASOL) (Tanré et al., 2011) are used pref- existing ground-based lidars. It now provides a climatology of the erentially to less specialized instruments such as Advanced Very High aerosol extinction coefficient (Figure 7.14b e), highlighting that over Resolution Radiometer (AVHRR; e.g., Zhao et al., 2008a; Mishchenko most regions the majority of the optically active aerosol resides in the et al., 2012), Total Ozone Mapping Spectrometer (TOMS; Torres et al., lowest 1 to 2 km. Yu et al. (2010) and Koffi et al. (2012) found that 2002) and Along Track Scanning Radiometer (ATSR)/Advanced Along global aerosol models tend to have a positive bias in the aerosol extin- Track Scanning Radiometer (AATSR) (Thomas et al., 2010) although ction scale height in some (but not all) regions, due to an overesti- the latter are useful for building aerosol climatologies because of their mate of aerosol concentrations above 6 km. There is less information long measurement records (see Section 2.2.3). Although each AOD available on the vertical profile of aerosol number and mass concen- retrieval by satellite sensors shows some skill against more accurate trations, although a number of field experiments involving research sunphotometer measurements such as those of AERONET, there are and commercial aircraft have measured aerosol concentrations (e.g., still large differences among satellite products in regional and seasonal Heintzenberg et al., 2011). In particular vertical profiles of BC mixing patterns because of differences and uncertainties in calibration, sam- ratios have been measured during the Aerosol Radiative Forcing over pling, cloud screening, treatment of the surface reflectivity and aero- India (ARFI) aircraft/high altitude balloon campaigns (Satheesh et al., sol microphysical properties (e.g., Li et al., 2009; Kokhanovsky et al., 2008), Arctic Research of the Composition of the Troposphere from Air- 2010). The global but incomplete sampling of satellite measurements craft and Satellites (ARCTAS; Jacob et al., 2010), Aerosol, Radiation, 7 597 Chapter 7 Clouds and Aerosols (1) S. America (2) N. America (3) Europe (4) Oceania urban rural urban rural urban concentration ( g m-3) concentration ( g m-3) concentration ( g m-3) concentration ( g m-3) 100 100 100 100 1 1 1 1 0.01 0.01 0.01 0.01 123456 123456 123456 123456 123456 123456 1: SO42- 2: OC 3: NO3- 4: NH4+ 5: EC 6: Mineral or Sea Salt (5) Marine (6) Marine Indian Ocean N. Atlantic Ocean concentration ( g m-3) concentration ( g m-3) (Amsterdam Is.) (Mace Head) 100 100 1 1 0.01 0.01 123456 123456 (7) Africa (8) Asia urban rural High Asia urban concentration ( g m-3) concentration ( g m-3) S.E.-E. Asia 100 100 1 1 urban urban rural S. Asia China China 0.01 0.01 123456 123456 123456 123456 123456 123456 123456 Figure 7.13 | Bar chart plots summarizing the mass concentration ( g m 3) of seven major aerosol components for particles with diameter smaller than 10 m, from various rural and urban sites (dots on the central world map) in six continental areas of the world with at least an entire year of data and two marine sites. The density of the sites is a qualitative measure of the spatial representativeness of the values for each area. The North Atlantic and Indian Oceans panels correspond to measurements from single sites (Mace Head and Amsterdam Island, respectively) that are not necessarily representative. The relative abundances of different aerosol compounds are considered to reflect the relative importance of emissions of these compounds or their precursors, either anthropogenic or natural, in the different areas. For consistency the mass of organic aerosol (OA) has been converted to that of organic carbon (OC), according to a conversion factor (typically 1.4 to 1.6), as provided in each study. For each area, the panels represent the median, the 25th to 75th percentiles (box), and the 10th to 90th percentiles (whiskers) for each aerosol component. These include: (1) South America (Artaxo et al., 1998; Morales et al., 1998; Artaxo et al., 2002; Celis et al., 2004; Bourotte et al., 2007; Fuzzi et al., 2007; Mariani and Mello, 2007; de Souza et al., 2010; Martin et al., 2010a; Gioda et al., 2011); (2) North America with urban United States (Chow et al., 1993; Kim et al., 2000; Ito et al., 2004; Malm and Schichtel, 2004; Sawant et al., 2004; Liu et al., 2005); and rural United States (Chow et al., 1993; Malm et al., 1994; Malm and Schichtel, 2004; Liu et al., 2005); (3) Europe with urban Europe (Lenschow et al., 2001; Querol et al., 2001, 2004, 2006, 2008; Roosli et al., 2001; Rodriguez et al., 2002, 2004; Putaud et al., 2004; Hueglin et al., 2005; Lonati et al., 2005; Viana et al., 2006, 2007; Perez et al., 2008; Yin and Harrison, 2008; Lodhi et al., 2009); and rural Europe (Gullu et al., 2000; Querol et al., 2001, 2004, 2009; Rodriguez et al., 2002; Putaud et al., 2004; Puxbaum et al., 2004;Rodr guez et al., 2004; Hueglin et al., 2005; Kocak et al., 2007; Salvador et al., 2007; Yttri, 2007; Viana et al., 2008; Yin and Harrison, 2008;Theodosi et al., 2010); (4) urban Oceania (Chan et al., 1997; Maenhaut et al., 2000; Wang and Shooter, 2001; Wang et al., 2005a; Radhi et al., 2010); (5) marine northern Atlantic Ocean (Rinaldi et al., 2009; Ovadnevaite et al., 2011); (6) marine Indian Ocean (Sciare et al., 2009; Rinaldi et al., 2011); (7) Africa with urban Africa (Favez et al., 2008; Mkoma, 2008; Mkoma et al., 2009a); and rural Africa (Maenhaut et al., 1996; Nyanganyura et al., 2007; Mkoma, 2008, 2009a, 2009b; Weinstein et al., 2010); (8) Asia with high Asia, with altitude larger than 1680 m (Shresth et al., 2000; Zhang et al., 2001, 2008, 2012b; Carrico et al., 2003; Rastogi and Sarin, 2005; Ming et al., 2007a; Rengarajan et al., 2007; Qu et al., 2008; Decesari et al., 2010; Ram et al., 2010); urban Southeast and East Asia (Lee and Kang, 2001; Oanh et al., 2006; Kim et al., 2007; Han et al., 2008; Khan et al., 2010); urban South Asia (Rastogi and Sarin, 2005; Kumar et al., 2007; Lodhi et al., 2009; Chakraborty and Gupta, 2010; Khare and Baruah, 2010; Raman et al., 2010; Safai et al., 2010; Stone et al., 2010; Sahu et al., 2011); urban China 7 (Cheng et al., 2000; Yao et al., 2002; Zhang et al., 2002; Wang et al., 2003, 2005b, 2006; Ye et al., 2003; Xiao and Liu, 2004; Hagler et al., 2006; Oanh et al., 2006; Zhang et al., 2011, 2012b); and rural China (Hu et al., 2002; Zhang et al., 2002; Hagler et al., 2006; Zhang et al., 2012b). 598 Clouds and Aerosols Chapter 7 (a) Optical Depth (550 nm) 0.8 0.6 0.4 0.2 0.0 b c d e (km) (b) 180W - 120W (c) 120W - 60W 4 3 2 1 0 (km) (d) 20W - 40E (e) 60E - 120E 4 3 2 1 0 82S 60S 30S Eq 30N 60N 82N 82S 60S 30S Eq 30N 60N 82N 10-5 10-4 10-3 10-2 10-1 Extinction coefficient (km-1) Figure 7.14 | (a) Spatial distribution of the 550 nm aerosol optical depth (AOD, unitless) from the European Centre for Medium Range Weather Forecasts (ECMWF) Integrated Forecast System model with assimilation of Moderate Resolution Imaging Spectrometer (MODIS) aerosol optical depth (Benedetti et al., 2009; Morcrette et al., 2009) averaged over the period 2003 2010; (b e) latitudinal vertical cross sections of the 532 nm aerosol extinction coefficient (km 1) for four longitudinal bands (180°W to 120°W, 120°W to 60°W, 20°W to 40°E, and 60°E to 120°E, respectively) from the Cloud Aerosol Lidar with Orthogonal Polarization (CALIOP) instrument for the year 2010 (nighttime all-sky data, version 3; Winker et al., 2013). and Cloud Processes affecting Arctic Climate (ARCPAC; Warneke et is larger than about 0.4. Koch et al. (2009b) and Bond et al. (2013) al., 2010), Aerosol Radiative Forcing in East Asia (A-FORCE; Oshima used AERONET-based retrievals of absorption AOD to show that most et al., 2012) and HIAPER Pole-to-Pole Observations (HIPPO1; Schwarz AeroCom models underestimate absorption in many regions, but there et al., 2010) campaigns. Comparison between models and observa- remain representativeness issues when comparing point observations tions have shown that aerosol models tend to underestimate BC mass to a model climatology. concentrations in some outflow regions, especially in Asia, but overes- timate concentrations in remote regions, especially at altitudes (Koch 7.3.2 Aerosol Sources and Processes et al., 2009b; Figure 7.15), which make estimates of their RFari uncer- tain (see Section 7.5.2) given the large dependence of RFari on the 7.3.2.1 Aerosol Sources vertical distribution of BC (Ban-Weiss et al., 2012). Absorption AOD can be retrieved from sun photometer measurements (Dubovik et al., Sea spray is produced at the sea surface by bubble bursting induced 2002) or a combination of ground-based transmittance and satellite mostly, but not exclusively, by breaking waves. The effective emission reflectance measurements (Lee et al., 2007) in situations where AOD flux of sea spray particles to the atmosphere depends on the surface 7 599 Chapter 7 Clouds and Aerosols BCC GISS-MATRIX IMPACT OsloCTM2 GMI CAM4-Oslo GISS-modelE INCA SPRINTARS GOCART CAM5.1 HadGEM2 ECHAM5-HAM TM5 200 HIPPO-1 (Jan 2009) 20N - 60N Pressure (hPa) 600 HIPPO-1 (Jan 2009) 20S - 20N 1000 200 Pressure (hPa) HIPPO-1 (Jan 2009) A-FORCE (Mar-Apr. 2009) 60S - 20S 26N - 38N 600 1000 0.01 0.1 1 10 100 1000 0.01 0.1 1 10 100 1000 BC MMR (ng kg-1) BC MMR (ng kg-1) Figure 7.15 | Comparison of vertical profiles of black carbon (BC) mass mixing ratios (MMR, in ng kg 1) as measured by airborne single particle soot photometer (SP2) instruments during the HIAPER Pole-to-Pole Observations (HIPPO1; Schwarz et al., 2010) and Aerosol Radiative Forcing in East Asia (A-FORCE; Oshima et al., 2012) aircraft campaigns and simulated by a range of AeroCom II models (Schulz et al., 2009). The black solid lines are averages of a number of vertical profiles in each latitude zone with the horizontal lines representing the standard deviation of the measurements at particular height ranges. Each HIPPO1 profile is the average of about 20 vertical profiles over the mid-Pacific in a two- week period in January 2009. The A-FORCE profile is the average of 120 vertical profiles measured over the East China Sea and Yellow Sea downstream of the Asian continent in March to April 2009. The model values (colour lines) are monthly averages corresponding to the measurement location and month, using meteorology and emissions corresponding to the year 2006. wind speed, sea state and atmospheric stability, and to a lesser extent Mineral dust particles are produced mainly by disintegration of aggre- on the temperature and composition of the sea water. Our understand- gates following creeping and saltation of larger soil particles over ing of sea spray emissions has increased substantially since AR4; how- desert and other arid surfaces (e.g., Zhao et al., 2006; Kok, 2011). ever, process-based estimates of the total mass and size distribution The magnitude of dust emissions to the atmosphere depends on the of emitted sea spray particles continue to have large uncertainties (de surface wind speed and many soil-related factors such as its texture, Leeuw et al., 2011; Table 7.1). Sea spray particles are composed of sea moisture and vegetation cover. The range of estimates for the global salt and marine primary organic matter, the latter being found prefer- dust emissions spans a factor of about five (Huneeus et al., 2011; Table entially in particles smaller than 200 nm in diameter (Leck and Bigg, 7.1). Anthropogenic sources, including road dust and mineral dust due 2008; Russell et al., 2010). The emission rate of marine POA depends to human land use change, remain ill quantified although some recent on biological activity in ocean waters (Facchini et al., 2008) and its satellite observations suggest the fraction of mineral dust due to the global emission rate has been estimated to be in the range 2 to 20 latter source could be 20 to 25% of the total (Ginoux et al., 2012a, Tg yr 1 (Gantt et al., 2011). Uncertainty in the source and composi- 2012b). tion of sea spray translates into a significant uncertainty in the aerosol number concentration in the marine atmosphere that, unlike aerosol The sources of biomass burning aerosols at the global scale are usually optical depth and mass concentrations, can only be constrained by in inferred from satellite retrieval of burned areas and/or active fires, but 7 situ observations (Heintzenberg et al., 2000; Jaeglé et al., 2011). inventories continue to suffer from the lack of sensitivity of ­ atellite s 600 Clouds and Aerosols Chapter 7 data to small fires (Randerson et al., 2012) and uncertainties in emis- aerosol particles. Since AR4, substantial progress in our understanding sion factors. Terrestrial sources of PBAPs include bacteria, pollen, fungal of atmospheric nucleation and new particle formation has been made spores, lichen, viruses and fragments of animals and plants (Després (e.g., Zhang et al., 2012a). Multiple lines of evidence indicate that et al., 2012). Most of these particles are emitted in the coarse mode while sulphuric acid is the main driver of nucleation (Kerminen et al., (Pöschl et al., 2010) and the contribution to the accumulation mode is 2010; Sipilä et al., 2010), the nucleation rate is affected by ammonia thought to be small. There are only a few estimates of the global flux and amines (Kurten et al., 2008; Smith et al., 2010; Kirkby et al., 2011; of PBAPs and these are poorly constrained (Burrows et al., 2009; Heald Yu et al., 2012) as well as low-volatility organic vapours (Metzger et al., and Spracklen, 2009; see Table 7.1). 2010; Paasonen et al., 2010; Wang et al., 2010a). Nucleation pathways involving only uncharged molecules are expected to dominate over The main natural aerosol precursors are dimethylsulphide (DMS) emit- nucleation induced by ionization of atmospheric molecules in conti- ted by the oceans and biogenic volatile organic compounds (BVOC) nental boundary layers, but the situation might be different in the free emitted mainly by the terrestrial biosphere. BVOC emissions depend on atmosphere (Kazil et al., 2010; Hirsikko et al., 2011). the amount and type of vegetation, temperature, radiation, the ambi- ent CO2 concentration and soil humidity (Grote and Niinemets, 2008; Condensation is the main process transferring low-volatility vapours to Pacifico et al., 2009; Penuelas and Staudt, 2010). While speciated BVOC aerosol particles, and also usually the dominant process for growth to emission inventories have been derived for some continental regions, larger sizes. The growth of the smallest particles depends crucially on global emission inventories or schemes are available only for isoprene, the condensation of organic vapours (Donahue et al., 2011b; Riipinen et monoterpenes and a few other compounds (Müller et al., 2008; Guen- al., 2011; Yu, 2011) and is therefore tied strongly with atmospheric SOA ther et al., 2012). The total global BVOC emissions have large uncer- formation discussed in Section 7.3.3.1. The treatment of condensation tainties, despite the apparent convergence in different model-based of semi-volatile compounds, such as ammonia, nitric acid and most estimates (Arneth et al., 2008). organic vapours, remains a challenge in climate modeling. In addition, small aerosol particles collide with one another and stick (coagulate), The ratio of secondary to primary organic aerosol is larger than previ- one of the processes contributing to aerosol internal mixing. Coagula- ously thought, but has remained somewhat ambiguous due to atmo- tion is an important sink for sub-micrometre size particles, typically spheric transformation processes affecting both these components under high concentrations near sources and at lower concentrations in (Robinson et al., 2007; Jimenez et al., 2009; Pye and Seinfeld, 2010). locations where the aerosol lifetime is long and amount of condens- Globally, most of the atmospheric SOA is expected to originate from able vapours is low. It is the main sink for the smallest aerosol particles biogenic sources, even though anthropogenic sources could be equally (Pierce and Adams, 2007). important at northern mid-latitudes (de Gouw and Jimenez, 2009; Lin et al., 2012). Recent studies suggest that the SOA formation from Since AR4, observations of atmospheric nucleation and subsequent BVOCs may be enhanced substantially by anthropogenic pollution due growth of nucleated particles to larger sizes have been increasingly to (1) high concentrations of nitrogen oxides (NOx) enhancing BVOC reported in different atmospheric environments (Kulmala and Ker- oxidation, and (2) high anthropogenic POA concentrations that facili- minen, 2008; Manninen et al., 2010; O Dowd et al., 2010). Nucleation tate transformation of oxidized volatile organic compounds (VOCs) to and growth enhance atmospheric CCN concentrations (Spracklen et the particle phase (Carlton et al., 2010; Heald et al., 2011; Hoyle et al., 2008; Merikanto et al., 2009; Pierce and Adams, 2009a; Yu and al., 2011). The uncertainty range of atmospheric SOA formation is still Luo, 2009) and potentially affect aerosol cloud interactions (Wang large and estimated to be approximately 20 to 380 Tg yr 1 (Hallquist et and Penner, 2009; Kazil et al., 2010; Makkonen et al., 2012a). However, al., 2009; Farina et al., 2010; Heald et al., 2010; Spracklen et al., 2011; CCN concentrations may be fairly insensitive to changes in nucleation Table 7.1). rate because the growth of nucleated particles to larger sizes is limited by coagulation (see Sections 7.3.3.3 and 7.4.6.2). Anthropogenic sources of aerosol particles (BC, POA) and aerosol precursors (sulphur dioxide, ammonia, NOx and NMVOCs, hereafter Aerosols also evolve due to cloud processing, followed by the aerosol also referred to as VOCs for simplicity) can be inferred from a priori release upon evaporation of cloud particles, affecting the number con- emission inventories (Table 7.1). They are generally better constrained centration, composition, size and mixing state of atmospheric aerosol than natural sources, exceptions being anthropogenic sources of BC, particles. This occurs via aqueous-phase chemistry taking place inside which could be underestimated (Bond et al., 2013), and anthropogenic clouds, via altering aerosol precursor chemistry around and below emissions of some VOCs, fly-ash and dust which are still poorly known. clouds, and via different aerosol hydrometeor interactions. These pro- Since AR4, remote sensing by satellites has been increasingly used to cesses are discussed further in Section 7.4.1.2. constrain natural and anthropogenic aerosol and aerosol precursor emissions (e.g., Dubovik et al., 2008; Jaeglé et al., 2011; Huneeus et The understanding and modelling of aerosol sinks has seen less prog- al., 2012). ress since AR4 in comparison to other aerosol processes. Improved dry deposition models, which depend on the particle size as well as the 7.3.2.2 Aerosol Processes characteristics of the Earth s surface, have been developed and are increasingly being used in global aerosol models (Kerkweg et al., 2006; New particle formation is the process by which low-volatility vapours Feng, 2008; Petroff and Zhang, 2010). Sedimentation throughout the nucleate into stable molecular clusters, which under certain condens- atmosphere and its role in dry deposition at the surface are impor- able vapour regimes can grow rapidly to produce nanometre-sized tant for the largest particles in the coarse mode. The uncertainty in 7 601 Chapter 7 Clouds and Aerosols the estimate of wet deposition by nucleation and impaction scaveng- Formation processes of OA still remain highly uncertain, which is a ing is controlled by the uncertainties in the prediction of the amount, major weakness in the present understanding and model representa- frequency and areal extent of precipitation, as well as the size and tion of atmospheric aerosols (Kanakidou et al., 2005; Hallquist et al., chemical composition of aerosol particles. For insoluble primary parti- 2009; Ziemann and Atkinson, 2012). Measurements by aerosol mass cles like BC and dust, nucleation scavenging also depends strongly on spectrometers have provided some insights into sources and atmo- their degree of mixing with soluble compounds. Parameterization of spheric processing of OA (Zhang et al., 2005b; Lanz et al., 2007; Ulbrich aerosol wet deposition remains a key source of uncertainty in aerosol et al., 2009). Observations at continental mid-latitudes including urban models, which affects the vertical and horizontal distributions of aer- and rural/remote air suggest that the majority of SOA is probably osols (Prospero et al., 2010; Vignati et al., 2010; Lee et al., 2011), with oxygenated OA (Zhang et al., 2005a, 2007a). Experiments within and further impact on model estimates of aerosol forcings. downstream of urban air indicate that under most circumstances SOA substantially contributes to the total OA mass (de Gouw et al., 2005; 7.3.3 Progress and Gaps in Understanding Climate Volkamer et al., 2006; Zhang et al., 2007a). Relevant Aerosol Properties There is a large range in the complexity with which OA is represent- The climate effects of atmospheric aerosol particles depend on their ed in global aerosol models. Some complex, yet still parameterized, atmospheric distribution, along with their hygroscopicity, optical prop- chemical schemes have been developed recently that account for mul- erties and ability to act as CCN and IN. Key quantities for aerosol optical tigenerational oxidation (Robinson et al., 2007; Jimenez et al., 2009; and cloud forming properties are the particle number size distribution, Donahue et al., 2011a). Since AR4, some regional and global model chemical composition, mixing state and morphology. These properties have used a new scheme based on lumping VOCs into volatility bins are determined by a complex interplay between their sources, atmo- (Robinson et al., 2007), which is an improved representation of the spheric transformation processes and their removal from the atmo- two-product absorptive partitioning scheme (Kroll and Seinfeld, 2008) sphere (Section 7.3.2, Figures 7.12 and 7.16). Since AR4, measurement for the formation and aging of SOA. This new framework includes of some of the key aerosol properties has been greatly improved in lab- organic compounds of different volatility, produced from parent VOCs oratory and field experiments using advanced instrumentation, which by multi-generation oxidation processes and partitioned between the allows for instance the analysis of individual particles. These experi- aerosol and gas phases (Farina et al., 2010; Tsimpidi et al., 2010; Yu, mental studies have in turn stimulated improvement in the model rep- 2011), which improves the agreement between observed and modeled resentations of the aerosol physical, chemical and optical properties SOA in urban areas (Hodzic et al., 2010; Shrivastava et al., 2011). Field (Ghan and Schwartz, 2007). We focus our assessment on some of the observations and laboratory studies suggest that OA is also formed key issues where there has been progress since AR4. efficiently in aerosol and cloud and liquid water contributing a sub- stantial fraction of the organic aerosol mass (Sorooshian et al., 2007; 7.3.3.1 Chemical Composition and Mixing State Miyazaki et al., 2009; Lim et al., 2010; Ervens et al., 2011a). Chemical reactions in the aerosol phase (e.g., oligomerization) also make OA Research on the climate impacts of aerosols has moved beyond the less volatile and more hygroscopic, influencing aerosol radiation and simple cases of externally mixed sulphate, BC emitted from fossil fuel aerosol cloud interactions (Jimenez et al., 2009). As a consequence, combustion and biomass burning aerosols. Although the role of inor- OA concentrations are probably underestimated in many global aero- ganic aerosols as an important anthropogenic contributor to aerosol sol models that do not include these chemical processes (Hallquist et radiation and aerosol cloud interactions has not been questioned, BC al., 2009). has received increasing attention because of its high absorption as has SOA because of its ubiquitous nature and ability to mix with other Some of the OA is light absorbing and can be referred to as brown aerosol types. carbon (BrC; Kirchstetter et al., 2004; Andreae and Gelencser, 2006). A fraction of the SOA formed in cloud and aerosol water is light- The physical properties of BC (strongly light-absorbing, refractory with absorbing in the visible (e.g., Shapiro et al., 2009), while SOA produced a vaporization temperature near 4000 K, aggregate in morphology, from gas-phase oxidation of VOCs absorbs ultraviolet radiation (e.g., insoluble in most organic solvents) allow a strict definition in principle Nakayama et al., 2010). (Bond et al., 2013). Direct measurement of individual BC-containing particles is possible with laser-induced incandescence (single particle Multiple observations show co-existence of external and internal mix- soot photometer, also called SP2; Gao et al., 2007; Schwarz et al., 2008a; tures relatively soon after emission (e.g., Hara et al., 2003; Schwarz Moteki and Kondo, 2010), which has enabled accurate measurements et al., 2008b; Twohy and Anderson, 2008). In biomass burning aero- of the size of BC cores, as well as total BC mass concentrations. Con- sol, organic compounds and BC are frequently internally mixed with densation of gas-phase compounds on BC and coagulation with other ammonium, nitrate, and sulphate (Deboudt et al., 2010; Pratt and particles alter the mixing state of BC (e.g., Li et al., 2003; Pósfai et al., Prather, 2010). Over urban locations, as much as 90% of the particles 2003; Adachi et al., 2010), which can produce internally mixed BC in are internally mixed with secondary inorganic species (Bi et al., 2011). polluted urban air on a time scale of about 12 hours (Moteki et al., Likewise mineral dust and biomass burning aerosols can become inter- 2007; McMeeking et al., 2010). The resulting BC-containing particles nally mixed when these aerosol types age together (Hand et al., 2010). can become hygroscopic, which can lead to reduced lifetime and atmo- The aerosol mixing state can alter particle size distribution and hygro- spheric loading (Stier et al., 2006). scopicity and hence the aerosol optical properties and ability to act as 7 CCN (Wex et al., 2010). Global aerosol models can now approximate 602 Clouds and Aerosols Chapter 7 the aerosol mixing state using size-resolving bin or modal schemes is medium confidence that between 20 and 40% of the global mean (e.g., Stier et al., 2005; Kim et al., 2008; Mann et al., 2010). AOD is of anthropogenic origin. There is agreement that the anthropo- genic aerosol is smaller in size and more absorbing than the natural 7.3.3.2 Size Distribution and Optical Properties aerosol (Myhre, 2009; Loeb and Su, 2010), but there is disagreement on the anthropogenic absorption AOD and its contribution to the total Aerosol size distribution is a key parameter determining both the aero- absorption AOD, that is, 0.0015 +/- 0.0007 (one standard deviation) rel- sol optical and CCN properties. Since the AR4, much effort has been ative to 1850 in Myhre et al. (2013) but about 0.004 and half of the put into measuring and simulating the aerosol number rather than total absorption AOD in Bellouin et al. (2013). volume size distribution. For instance, number size distributions in the submicron range (30 to 500 nm) were measured at 24 sites in Europe 7.3.3.3 Cloud Condensation Nuclei for two years (Asmi et al., 2011), although systematic measurements are still limited in other regions. Although validation studies show A subset of aerosol particles acts as CCN (see Table 7.2). The ability of agreement between column-averaged volume size distribution from an aerosol particle to take up water and subsequently activate, thereby sunphotometer measurements and direct in situ (surface as well as acting as a CCN at a given supersaturation, is determined by its size aircraft-based) measurements at some locations (Gerasopoulos et al., and composition. Common CCN in the atmosphere are composed of 2007; Radhi et al., 2010; Haywood et al., 2011), these inversion prod- sea salt, sulphates and sulphuric acid, nitrate and nitric acid and some ucts have not been systematically validated. Satellite measurements organics. The uptake of water vapour by hygroscopic aerosols strongly produce valuable but more limited information on aerosol size. affects their RFari. The aerosol scattering, absorption and extinction coefficients depend CCN activity of inorganic aerosols is relatively well understood, and on the aerosol size distribution, aerosol refractive index and mixing lately most attention has been paid to the CCN activity of mixed state. The humidification of internally mixed aerosols further influenc- organic/inorganic aerosols (e.g., King et al., 2010; Prisle et al., 2010). es their light scattering and absorption properties, through changes Uncertainties in our current understanding of CCN properties are in particle shape, size and refractive index (Freney et al., 2010). Aer- due primarily to SOA (Good et al., 2010), mainly because OA is still osol absorption is a key climate-relevant aerosol property and earlier poorly characterized (Jimenez et al., 2009). The important effect of the in situ methods to measure it suffered from significant uncertainties formation of SOA is that internally mixed SOA contributes to the mass (Moosmüller et al., 2009), partly due to the lack of proper reference of aerosol particles, and therefore to their sizes. The size of the CCN material for instrument calibration and development (Baumgardner et has been found to be more important than their chemical composi- al., 2012). Recent measurements using photo-acoustic methods and tion at two continental locations as larger particles are more readily laser-induced incandescence methods are more accurate but remain activated than smaller particles because they require a lower critical sparse. The mass absorption cross sections for freshly generated BC supersaturation (Dusek et al., 2006; Ervens et al., 2007). However, the were measured to be 7.5 +/- 1.2 m2 g 1 at 550 nm (Bond et al., 2013). chemical composition may be important in other locations such as Laboratory measurements conducted under well controlled conditions the marine environment, where primary organic particles (hydrogels) show that thick coating of soluble material over BC cores enhance the have been shown to be exceptionally good CCN (Orellana et al., 2011; mass absorption cross section by a factor of 1.8 to 2 (Cross et al., 2010; Ovadnevaite et al., 2011). For SOA it is not clear how important surface Shiraiwa et al., 2010). It is more difficult to measure the enhance- tension effects and bulk-to-surface partitioning of surfactants are, and ment factor in mass absorption cross sections for ambient BC, partly if the water activity coefficient changes significantly as a function of owing to the necessity of removing coatings of BC. Knox et al. (2009) the solute concentration (Prisle et al., 2008; Good et al., 2010). observed enhancement by a factor 1.2 to 1.6 near source regions. A much lower enhancement factor was observed by Cappa et al. (2012) The bulk hygroscopicity parameter k has been introduced as a concise by a new measurement technique, in contradiction to the laboratory measure of how effectively an aerosol particle acts as a CCN (Rissler experiments and theoretical calculations. These results may not be rep- et al., 2004, 2010; Petters and Kreidenweis, 2007). It can be measured resentative and would require confirmation by independent measure- experimentally and is increasingly being used as a way to character- ment methods. ize aerosol properties. Pringle et al. (2010) used surface and aircraft measurements to evaluate the k distributions simulated by a global As discussed in Section 7.3.1.2, the global mean AOD is not well con- aerosol model, and found generally good agreement. When the aerosol strained from satellite-based measurements and remains a significant is dominated by organics, discrepancies between values of k obtained source of uncertainty when estimating aerosol radiation interactions directly from both CCN activity measurements and sub-saturated par- (Su et al., 2013). This is also true of the anthropogenic fraction of AOD ticle water uptake measurements have been observed in some instanc- which is more difficult to constrain from observations. AeroCom phase es (e.g., Prenni et al., 2007; Irwin et al., 2010; Roberts et al., 2010), II models simulate an anthropogenic AOD at 550 nm of 0.03 +/- 0.01 whereas in other studies closure has been obtained (e.g., Duplissy et (with the range corresponding to one standard deviation) relative al., 2008; Kammermann et al., 2010; Rose et al., 2011). Adsorption to 1850, which represents 24 +/- 6% of the total AOD (Myhre et al., theory (Kumar et al., 2011) replaces k-theory for CCN activation for 2013). This is less than suggested by some satellite-based studies, i.e., insoluble particles (e.g., mineral dust) while alternative theories are 0.03 over the ocean only in Kaufman et al. (2005), and about 0.06 still required for explanation of marine POA that seem to have peculiar as a global average in Loeb and Su (2010) and Bellouin et al. (2013), gel-like properties (Ovadnevaite et al., 2011). but more than in the CMIP5 models (see Figure 9.29). Overall there 7 603 Chapter 7 Clouds and Aerosols Available modelling studies (Pierce and Adams, 2009a; Wang and 7.3.4 Aerosol Radiation Interactions Penner, 2009; Schmidt et al., 2012a) disagree on the anthropogen- ic fraction of CCN (taken here at 0.2% supersaturation). Based on 7.3.4.1 Radiative Effects due to Aerosol Radiation Interactions these studies we assess this fraction to be between one fourth and two thirds in the global mean with low confidence, and highlight The radiative effect due to aerosol radiation interactions (REari), large interhemispheric and regional variations. Models that neglect formerly known as direct radiative effect, is the change in radiative or underestimate volcanic and natural organic aerosols would over- flux caused by the combined scattering and absorption of radiation estimate this fraction. by anthropogenic and natural aerosols. The REari results from well- understood physics and is close to being an observable quantity, yet 7.3.3.4 Ice Nuclei our knowledge of aerosol and environmental characteristics needed to quantify the REari at the global scale remains incomplete (Ander- Aerosols that act as IN are solid substances at atmospheric temper- son et al., 2005; Satheesh and Moorthy, 2005; Jaeglé et al., 2011). The atures and supersaturations. Mineral dust, volcanic ash and primary REari requires knowledge of the spectrally varying aerosol extinction bioaerosols such as bacteria, fungal spores and pollen, are typically coefficient, single scattering albedo, and phase function, which can in known as good IN (Vali, 1985; Hoose and Möhler, 2012). Conflicting principle be estimated from the aerosol size distribution, shape, chemi- evidence has been presented for the ability of BC, organic, organic cal composition and mixing state (Figure 7.12). Radiative properties semi-solid/glassy organic and biomass burning particles to act as IN of the surface, atmospheric trace gases and clouds also influence the (Hoose and Möhler, 2012; Murray et al., 2012). The importance of bio- REari. In the solar spectrum under cloud-free conditions the REari is logical particles acting as IN is unclear. A new study finds evidence of typically negative at the TOA, but it weakens and can become posi- a large fraction of submicron particles in the middle-to-upper tropo- tive with increasing aerosol absorption, decreasing upscatter fraction sphere to be composed of biological particles (DeLeon-Rodriguez et or increasing albedo of the underlying surface. REari is weaker in al., 2013); however global modelling studies suggest that their concen- cloudy conditions, except when the cloud layer is thin or when absorb- trations are not sufficient to play an important role for ice formation ing aerosols are located above or between clouds (e.g., Chand et al., (Hoose et al., 2010a; Sesartic et al., 2012). Because BC has anthropo- 2009). The REari at the surface is negative and can be much stronger genic sources, its increase since pre-industrial times may have caused than the REari at the TOA over regions where aerosols are absorbing changes to the lifetime of mixed-phase clouds (Section 7.4.4) and thus (Li et al., 2010). In the longwave part of the spectrum, TOA REari is to RF (Lohmann, 2002b; Section 7.5). generally positive and mainly exerted by coarse-mode aerosols, such as sea spray and desert dust (Reddy et al., 2005), and by stratospheric Four heterogeneous ice-nucleation modes are distinguished in the aerosols (McCormick et al., 1995). literature: immersion freezing (initiated from within a cloud droplet), condensation freezing (freezing during droplet formation), contact There have been many measurement-based estimates of shortwave freezing (due to collision with an IN) and deposition nucleation (that REari (e.g., Yu et al., 2006; Bergamo et al., 2008; Di Biagio et al., 2010; refers to the direct deposition of vapour onto IN). Lidar observations Bauer et al., 2011) although some studies involve some degree of mod- reveal that liquid cloud droplets are present before ice crystals form via elling. In contrast, estimates of longwave REari remain limited (e.g., heterogeneous freezing mechanisms (Ansmann et al., 2008; de Boer et Bharmal et al., 2009). Observed and calculated shortwave radiative al., 2011), indicating that deposition nucleation does not seem to be fluxes agree within measurement uncertainty when aerosol properties important for mixed-phase clouds. IN can either be bare or mixed with are known (e.g., Osborne et al., 2011). Global observational estimates other substances. As bare particles age in the atmosphere, they acquire of the REari rely on satellite remote sensing of aerosol properties and/ liquid surface coatings by condensing soluble species and water or measurements of the Earth s radiative budget (Chen et al., 2011; vapour or by coagulating with soluble particles, which may transform Kahn, 2012). Estimates of shortwave TOA REari annually averaged over IN from deposition or contact nuclei into possible immersion nuclei. A cloud-free oceans range from 4 to 6 W m 2, mainly contributed by change from contact to immersion freezing implies activation at colder sea spray (Bellouin et al., 2005; Loeb and Manalo-Smith, 2005; Yu et temperatures, with consequences for the lifetime and radiative effect al., 2006; Myhre et al., 2007). However, REari can reach tens of W m 2 of mixed-phase clouds (Sections 7.4.4 and 7.5.3). locally. Estimates over land are more difficult as the surface is less well characterized (Chen et al., 2009; Jethva et al., 2009) despite recent The atmospheric concentrations of IN are very uncertain because of progress in aerosol inversion algorithms (e.g., Dubovik et al., 2011). the aforementioned uncertainties in freezing mechanisms and the dif- Attempts to estimate the REari in cloudy sky remain elusive (e.g., ficulty of measuring IN in the upper troposphere. The anthropogenic Peters et al., 2011b), although passive and active remote sensing of fraction cannot be estimated at this point because of a lack of knowl- aerosols over clouds is now possible (Torres et al., 2007; Omar et al., edge about the anthropogenic fractions of BC and mineral dust acting 2009; Waquet et al., 2009; de Graaf et al., 2012). Notable areas of posi- as IN and the contributions of PBAPs, other organic aerosols and other tive TOA REari exerted by absorbing aerosols include the Arctic over ice aerosols acting as IN. surfaces (Stone et al., 2008) and seasonally over southeastern Atlantic stratocumulus clouds (Chand et al., 2009; de Graaf et al., 2012). While AOD and aerosol size are relatively well constrained, uncertainties in the aerosol single-scattering albedo (McComiskey et al., 2008; Loeb and Su, 2010) and vertical profile (e.g., Zarzycki and Bond, 2010) con- 7 tribute significantly to the overall uncertainties in REari. Consequently, 604 Clouds and Aerosols Chapter 7 diversity in large-scale numerical model estimates of REari increases 7.3.5 Aerosol Responses to Climate Change and with aerosol absorption and between cloud-free and cloudy conditions Feedback (Stier et al., 2013). The climate drivers of changes in aerosols can be split into physical 7.3.4.2 Rapid Adjustments to Aerosol Radiation Interactions changes (temperature, humidity, precipitation, soil moisture, solar radi- ation, wind speed, sea ice extent, etc.), chemical changes (availability Aerosol radiation interactions give rise to rapid adjustments (see of oxidants) and biological changes (vegetation cover and properties, Section 7.1), which are particularly pronounced for absorbing aero- plankton abundance and speciation, etc). The response of aerosols sols such as BC. Associated cloud changes are often referred to as the to climate change may constitute a feedback loop whereby climate semi-direct aerosol effect (see Figure 7.3). The ERF from aerosol radia- processes amplify or dampen the initial perturbation (Carslaw et al., tion interactions is quantified in Section 7.5.2; only the corresponding 2010; Raes et al., 2010). We assess here the relevance and strength processes governing rapid adjustments are discussed here. Impacts on of aerosol climate feedbacks in the context of future climate change precipitation are discussed in Section 7.6.3. scenarios. Since AR4, additional observational studies have found correlations 7.3.5.1 Changes in Sea Spray and Mineral Dust between cloud cover and absorbing aerosols (e.g., Brioude et al., 2009; Wilcox, 2010), and eddy-resolving, regional and global scale modelling Concentrations of sea spray will respond to changes in surface wind studies have helped confirm a causal link. Relationships between cloud speed, atmospheric stability, precipitation and sea ice cover (Struthers and aerosol reveal a more complicated picture than initially anticipat- et al., 2011). Climate models disagree about the balance of effects, ed (e.g., Hill and Dobbie, 2008; Koch and Del Genio, 2010; Zhuang et with estimates ranging from an overall 19% reduction in global sea al., 2010; Sakaeda et al., 2011; Ghan et al., 2012). salt burden from the present-day to year 2100 (Liao et al., 2006), to little sensitivity (Mahowald et al., 2006a), to a sizeable increase (Jones Absorbing aerosols modify atmospheric stability in the boundary layer et al., 2007; Bellouin et al., 2011). In particular there is little under- and free troposphere (e.g., Wendisch et al., 2008; Babu et al., 2011). standing of how surface wind speed may change over the ocean in a The effect of this on cloud cover depends on the height of the aerosol warmer climate, and observed recent changes (e.g., Young et al., 2011; relative to the cloud and the type of cloud (e.g., Yoshimori and Broccoli, Section 2.7.2) may not be indicative of future changes. Given that sea 2008; Allen and Sherwood, 2010; Koch and Del Genio, 2010; Persad et spray particles comprise a significant fraction of CCN concentrations al., 2012). Aerosol also reduces the downwelling solar radiation at the over the oceans, such large changes will feed back on climate through surface. Together the changes in atmospheric stability and reduction changes in cloud droplet number (Korhonen et al., 2010b). in surface fluxes provide a means for aerosols to significantly modify the fraction of surface-forced clouds (Feingold et al., 2005; Sakaeda et Studies of the effects of climate change on dust loadings also give al., 2011). These changes may also affect precipitation as discussed in a wide range of results. Woodward et al. (2005) found a tripling of Section 7.6.3. the dust burden in 2100 relative to present-day because of a large increase in bare soil fraction. A few studies projected moderate (10 Cloud cover is expected to decrease if absorbing aerosol is embedded to 20%) increases, or decreases (e.g., Tegen et al., 2004; Jacobson and in the cloud layer. This has been observed (Koren et al., 2004) and sim- Streets, 2009; Liao et al., 2009). Mahowald et al. (2006b) found a 60% ulated (e.g., Feingold et al., 2005) for clouds over the Amazon forest in decrease under a doubled CO2 concentration due to the effect of CO2 the presence of smoke aerosols. In the stratocumulus regime, absorb- fertilization on vegetation. The large range reflects different responses ing aerosol above cloud top strengthens the temperature inversion, of the atmosphere and vegetation cover to climate change forcings, reduces entrainment and tends to enhance cloudiness. Satellite obser- and results in low confidence in these predictions. vations (Wilcox, 2010) and modelling (Johnson et al., 2004) of marine stratocumulus show a thickening of the cloud layer beneath layers of 7.3.5.2 Changes in Sulphate, Ammonium and Nitrate Aerosols absorbing smoke aerosol, which induces a local negative forcing. The responses of other cloud types, such as those associated with deep The DMS sulphate cloud climate feedback loop could operate in convection, are not well determined. numerous ways through changes in temperature, absorbed solar radia- tion, ocean mixed layer depth and nutrient recycling, sea ice extent, Absorbing aerosols embedded in cloud drops enhance their absorp- wind speed, shift in marine ecosystems due to ocean acidification tion, which can affect the dissipation of cloud. The contribution to RFari and climate change, and atmospheric processing of DMS into CCN. is small (Stier et al., 2007; Ghan et al., 2012), and there is contradictory Although no study has included all the relevant effects, two decades of evidence regarding the magnitude of the cloud dissipation effect influ- research have questioned the original formulation of the feedback loop encing ERFari (Feingold et al., 2005; Ghan et al., 2012; Jacobson, 2012; (Leck and Bigg, 2007) and have provided important insights into this Bond et al., 2013). Global forcing estimates are necessarily based on complex, coupled system (Ayers and Cainey, 2007; Kloster et al., 2007; global models (see Section 7.5.2), although the accuracy of GCMs in Carslaw et al., 2010). There is now medium confidence for a weak feed- this regard is limited by their ability to represent low cloud processes back due to a weak sensitivity of the CCN population to changes in accurately. This is an area of concern as discussed in Section 7.2 and DMS emissions, based on converging evidence from observations and limits confidence in these estimates. Earth System model simulations (Carslaw et al., 2010; Woodhouse et al., 2010; Quinn and Bates, 2011). Parameterizations of oceanic DMS 7 605 Chapter 7 Clouds and Aerosols production nevertheless lack robust mechanistic justification (Halloran to future global emissions (Makkonen et al., 2012b). Future changes et al., 2010) and as a result the sensitivity to ocean acidification and in vegetation cover, whether they are natural or anthropogenic, also climate change remains uncertain (Bopp et al., 2004; Kim et al., 2010; introduce large uncertainty in emissions (Lathiere et al., 2010; Wu et Cameron-Smith et al., 2011). al., 2012). There is little understanding on how the marine source of organic aerosol may change with climate, notwithstanding the large Chemical production of sulphate increases with atmospheric tempera- range of emission estimates for the present day (Carslaw et al., 2010). ture (Aw and Kleeman, 2003; Dawson et al., 2007; Kleeman, 2008), but future changes in sulphate are found to be more sensitive to simulated 7.3.5.4 Synthesis future changes in precipitation removal. Under fixed anthropogenic emissions, most studies to date predict a small (0 to 9%) reduction in The emissions, properties and concentrations of aerosols or aerosol pre- global sulphate burden mainly because of future increases in precipita- cursors could respond significantly to climate change, but there is little tion (Liao et al., 2006; Racherla and Adams, 2006; Unger et al., 2006; consistency across studies in the magnitude or sign of this response. Pye et al., 2009). However, Rae et al. (2007) found a small increase The lack of consistency arises mostly from our limited understanding in global sulphate burden from 2000 to 2100 because the simulated of processes governing the source of natural aerosols and the complex future precipitation was reduced in regions of high sulphate abun- interplay of aerosols with the hydrological cycle. The feedback param- dance. eter as a result of the future changes in emissions of natural aerosols is mostly bracketed within +/-0.1 W m 2 °C 1 (Carslaw et al., 2010). With Changes in temperature have a large impact on nitrate aerosol forma- respect to anthropogenic aerosols, Liao et al. (2009) showed a signifi- tion through shifting gas particle equilibria. There is some agreement cant positive feedback (feedback parameter of +0.04 to +0.15 W m 2 among global aerosol models that climate change alone will contrib- °C 1 on a global mean basis) while Bellouin et al. (2011) simulated a ute to a decrease in the nitrate concentrations (Liao et al., 2006; Rach- smaller negative feedback of 0.08 to 0.02 W m 2 °C 1. Overall we erla and Adams, 2006; Pye et al., 2009; Bellouin et al., 2011) with the assess that models simulate relatively small feedback parameters (i.e., exception of Bauer et al. (2007) who found little change in nitrate for within +/-0.2 W m 2 °C 1) with low confidence, however regional effects year 2030. It should be noted however that these modeling studies on the aerosol may be important. have reported that changes in precursor emissions are expected to increase nitrate concentrations in the future (Section 11.3.5). Besides the changes in meteorological parameters, climate change can also 7.4 Aerosol Cloud Interactions influence ammonium formation by changing concentrations of sul- phate and nitrate, but the effect of climate change alone was found to 7.4.1 Introduction and Overview of Progress Since AR4 be small (Pye et al., 2009). This section assesses our understanding of aerosol cloud interac- 7.3.5.3 Changes in Carbonaceous Aerosols tions, emphasizing the ways in which anthropogenic aerosol may be affecting the distribution and radiative properties of non- and weakly There is evidence that future climate change could lead to increases precipitating clouds. The idea that anthropogenic aerosol is changing in the occurrence of wildfires because of changes in fuel availabili- cloud properties, thus contributing a substantial forcing to the climate ty, readiness of the fuel to burn and ignition sources (Mouillot et al., system, has been addressed to varying degrees in all of the previous 2006; Marlon et al., 2008; Spracklen et al., 2009; Kloster et al., 2010; IPCC assessment reports. Pechony and Shindell, 2010). However, vegetation dynamics may also play a role that is not well understood. Increased fire occurrence would Since AR4, research has continued to articulate new pathways through increase aerosol emissions, but decrease BVOC emissions. This could which the aerosol may affect the radiative properties of clouds, as well lead to a small positive or negative net radiative effect and feedback as the intensity and spatial patterns of precipitation (e.g., Rosenfeld (Carslaw et al., 2010). et al., 2008). Progress can be identified on four fronts: (1) global-scale modelling now represents a greater diversity of aerosol cloud interac- A large fraction of SOA forms from the oxidation of isoprene, mono- tions, and with greater internal consistency; (2) observational studies terpenes and sesquiterpenes from biogenic sources (Section 7.3.3.1). continue to document strong local correlations between aerosol and Emissions from vegetation can increase in a warmer atmosphere, cloud properties or precipitation, but have become more quantitative everything else being constant (Guenther et al., 2006). Global aerosol and are increasingly identifying and addressing the methodological models simulate an increase in isoprene emissions of 22 to 55% by challenges associated with such correlations; (3) regional-scale mod- 2100 in response to temperature change (Sanderson et al., 2003; Liao elling is increasingly being used to assess regional influences of aer- et al., 2006; Heald et al., 2008) and a change in global SOA burden osol on cloud field properties and precipitation; (4) fine-scale process of 6% to +100% through climate-induced changes in aerosol pro- models have begun to be used more widely, and among other things cesses and removal rates (Liao et al., 2006; Tsigaridis and Kanakidou, have shown how turbulent mixing, cloud and meso-scale circulations 2007; Heald et al., 2008). An observationally based study suggest a may buffer the effects of aerosol perturbations. small global feedback parameter of 0.01 W m 2 °C 1 despite larger regional effects (Paasonen et al., 2013). Increasing CO2 concentrations, This section focuses on the microphysics of aerosol cloud interactions drought and surface ozone also affect BVOC emissions (Arneth et al., in liquid, mixed-phase and pure ice clouds. Their radiative implications 7 2007; Penuelas and Staudt, 2010), which adds significant uncertainty are quantified in Section 7.5. This section also includes a discussion of 606 Clouds and Aerosols Chapter 7 aerosol influences on light precipitation in shallow clouds but defers 7.4.1.2 Advances and Challenges in Observing Aerosol Cloud discussion of aerosol effects on more substantial precipitation from Interactions mixed-phase clouds to Section 7.6.4. Since AR4, numerous field studies (e.g., Rauber et al., 2007; Wood et 7.4.1.1 Classification of Hypothesized Aerosol Cloud al., 2011b; Vogelmann et al., 2012) and laboratory investigations (e.g., Interactions Stratmann et al., 2009) of aerosol cloud interactions have highlighted the numerous ways that the aerosol impacts cloud processes, and how Denman et al. (2007) catalogued several possible pathways via which clouds in turn modify the aerosol. The latter occurs along a number of the aerosol might affect clouds. Given the number of possible aero- pathways including aqueous chemistry, which adds aerosol mass to sol cloud interactions, and the difficulty of isolating them individually, droplets (e.g., Schwartz and Freiberg, 1981; Ervens et al., 2011a); coa- there is little value in attempting to assess each effect in isolation, lescence scavenging, whereby drop collision coalescence diminishes especially because modelling studies suggest that the effects may the droplet (and aerosol) number concentration (Hudson, 1993) and interact and compensate (Stevens and Feingold, 2009; Morrison and changes the mixing state of the aerosol; new particle formation in the Grabowski, 2011). Instead, all radiative consequences of aerosol vicinity of clouds (Clarke et al., 1999); and aerosol removal by precipi- cloud interactions are grouped into an effective radiative forcing due tation (see also Section 7.3.2.2). to aerosol cloud interactions , or ERFaci (Figure 7.3). ERFaci accounts for aerosol-related microphysical modifications to the cloud albedo Satellite-based remote sensing continues to be the primary source of (Twomey, 1977), as well as any secondary effects that result from global data for aerosol cloud interactions but concerns persist regard- clouds adjusting rapidly to changes in their environment (i.e., lifetime ing how measurement artefacts affect retrievals of both aerosol (Tanré effects ; Albrecht, 1989; Liou and Ou, 1989; Pincus and Baker, 1994). et al., 1996; Tanré et al., 1997; Kahn et al., 2005; Jeong and Li, 2010) We do assess the physical underpinnings of the cloud albedo effect, and cloud properties (Platnick et al., 2003; Yuekui and Di Girolamo, but in contrast to previous assessments, no longer distinguish the 2008) in broken cloud fields. Two key issues are that measurements resultant forcing. Note that ERFaci includes potential radiative adjust- classified as cloud-free may not be, and that aerosol measured in the ments to the cloud system associated with aerosol cloud interactions vicinity of clouds is significantly different than it would be were the but does not include adjustments originating from aerosol radiation cloud field, and its proximate cause (high humidity), not present (e.g., interactions (ERFari). Possible contributions to ERFaci from warm Loeb and Schuster, 2008). The latter results from humidification effects (liquid) clouds are discussed in Section 7.4.3, separately from those on aerosol optical properties (Charlson et al., 2007; Su et al., 2008; associated with adjustments by cold (ice or mixed-phase) clouds (Sec- Tackett and Di Girolamo, 2009; Twohy et al., 2009; Chand et al., 2012), tion 7.4.4). Figure 7.16 shows a schematic of many of the processes to contamination by undetectable cloud fragments (Koren et al., 2007) be discussed in Sections 7.4, 7.5 and 7.6. and the remote effects of radiation scattered by cloud edges on aerosol retrieval (Wen et al., 2007; Várnai and Marshak, 2009). Shortwave Irradiance Longwave Irradiance Stratiform Convective Interactions Ice Nucleation Ice Formation & Precipitation Initiation Scavenging 15 km Clear-sky Re ectance New Particle Production Mesoscale Downdraughts Mixing Scavenging by Precipitation Aerosol Activation Surface fluxes Droplet Coalesence Cold Pool Convective Initiation 50 100 km Figure 7.16 | Schematic depicting the myriad aerosol cloud precipitation related processes occurring within a typical GCM grid box. The schematic conveys the importance of considering aerosol cloud precipitation processes as part of an interactive system encompassing a large range of spatiotemporal scales. Cloud types include low-level stratocumu- lus and cumulus where research focuses on aerosol activation, mixing between cloudy and environmental air, droplet coalescence and scavenging which results in cloud processing of aerosol particles, and new particle production near clouds; cirrus clouds where a key issue is ice nucleation through homogeneous and heterogeneous freezing; and deep convec- tive clouds where some of the key questions relate to aerosol influences on liquid, ice, and liquid ice pathways for precipitation formation, cold pool formation and scavenging. These processes influence the shortwave and longwave cloud radiative effect and hence climate. Primary processes that affect aerosol cloud interactions are labelled in blue while 7 secondary processes that result from and influence aerosol cloud interactions are in grey. 607 Chapter 7 Clouds and Aerosols While most passive satellite retrievals are unable to distinguish aerosol be less sensitive to aerosol perturbations in nature than in large-scale layers above or below clouds from those intermingling with the cloud models, which do not represent all of these compensating processes. field, active space-based remote sensing (L Ecuyer and Jiang, 2010) has Hints of similar behaviour in mixed-phase (liquid and ice) stratus are begun to address this problem (Stephens et al., 2002; Anderson et al., beginning to be documented (Section 7.4.4.3) but process-level under- 2005; Huffman et al., 2007; Chand et al., 2008; Winker et al., 2010). standing and representation in models are less advanced. Spectral polarization and multi-angular measurements can discrimi- nate between cloud droplets and aerosol particles and thus improve Regional models include realism in the form of non-idealized meteor- estimates of aerosol loading and absorption (Deuzé et al., 2001; Chow- ology, synoptic scale forcing, variability in land surface, and diurnal/ dhary et al., 2005; Mishchenko et al., 2007; Hasekamp, 2010). monthly cycles (e.g., Iguchi et al., 2008; Bangert et al., 2011; Seifert et al., 2012; Tao et al., 2012), however, at the expense of resolving fine- Use of active remote sensing, both from monitoring ground stations scale cloud processes. Regional models have brought to light the pos- (e.g., McComiskey et al., 2009; Li et al., 2011) and satellites (Costantino sibility of aerosol spatial inhomogeneity causing changes in circulation and Bréon, 2010), as well as aerosol proxies not influenced by cloud patterns via numerous mechanisms including changes in the radiative contamination of retrievals (Jiang et al., 2008; Berg et al., 2011) have properties of cloud anvils (van den Heever et al., 2011), changes in the emerged as a particular effective way of identifying whether aerosol spatial distribution of precipitation (Lee, 2012; Section 7.6.2) or gra- and cloud perturbations are intermingled. dients in heating rates associated with aerosol radiation interactions (Lau et al., 2006; Section 7.3.4.2). Because the aerosol is a strong function of air-mass history and origin, and is strongly influenced by cloud and precipitation processes (Clarke GCMs, our primary tool for quantifying global mean forcings, now rep- et al., 1999; Petters et al., 2006; Anderson et al., 2009), and both are resent an increasing number of hypothesized aerosol cloud interac- affected by meteorology (Engström and Ekman, 2010; Boucher and tions, but at poor resolution. GCMs are being more closely scrutinized Quaas, 2013), correlations between the aerosol and cloud, or precipi- through comparisons to observations and to other models (Quaas et tation, should not be taken as generally indicating a cloud response to al., 2009; Penner et al., 2012). Historically, aerosol cloud interactions the aerosol (e.g., Painemal and Zuidema, 2010). Furthermore, attempts in GCMs have been based on simple constructs (e.g., Twomey, 1977; to control for other important factors (air-mass history or cloud dynam- Albrecht, 1989; Pincus and Baker, 1994). There has been significant ical processes) are limited by a lack of understanding of large-scale progress on parameterizing aerosol activation (e.g., Ghan et al., 2011) cloud controlling factors in the first place (Anderson et al., 2009; Siebe- and ice nucleation (Liu and Penner, 2005; Barahona and Nenes, 2008; sma et al., 2009; Stevens and Brenguier, 2009). These problems are DeMott et al., 2010; Hoose et al., 2010b); however, these still depend increasingly being considered in observationally based inferences of heavily on unresolved quantities such as updraught velocity. Similarly, aerosol effects on clouds and precipitation, but ascribing changes in parameterizations of aerosol influences on cloud usually do not account cloud properties to changes in the aerosol remains a fundamental for known non-monotonic responses of cloud amount and properties ­challenge. to aerosols (Section 7.4.3.2). Global models are now beginning to rep- resent aerosol effects in convective, ice and mixed-phase clouds (e.g., 7.4.1.3 Advances and Challenges in Modelling Lohmann, 2008; Song and Zhang, 2011; Section 7.6.4). Nevertheless, Aerosol Cloud Interactions for both liquid-only and mixed-phase clouds, these parameterizations are severely limited by the need to parameterize cloud-scale motions Modelling of aerosol cloud interactions must contend with the fact over a huge range of spatio-temporal scales (Section 7.2.3). that the key physical processes are fundamentally occurring at the fine scale and cannot be represented adequately based on large-scale Although advances have been considerable, the challenges remain fields. There exist two distinct challenges: fundamental understanding daunting. The response of cloud systems to aerosol is nuanced (e.g., of processes and their representation in large-scale models. vanZanten et al., 2011) and the representation of both clouds and aerosol cloud interactions in large-scale models remains primitive Fine-scale LES and CRM models (Section 7.2.2.1) have greatly advanced (Section 7.2.3). Thus it is not surprising that large-scale models exhibit as a tool for testing the physical mechanisms proposed to govern aer- a range of manifestations of aerosol cloud interactions, which limits osol cloud precipitation interactions (e.g., Ackerman et al., 2009; van- quantitative inference (Quaas et al., 2009). This highlights the need to Zanten et al., 2011). Their main limitation is that they are idealized, for incorporate into GCMs the lessons learned from cloud-scale models, in example, they do not resolve synoptic scale circulations or allow for a physically-consistent way. New super-parameterization and prob- representation of orography. A general finding from explicit numerical ability distribution function approaches (Golaz et al., 2002; Rio and simulations of warm (liquid) clouds is that various aerosol impact mech- Hourdin, 2008; Section 7.2.2.2) hold promise, with recent results sup- anisms tend to be mediated (and often buffered) by interactions across porting the notion that aerosol forcing is smaller than simulated by scales not included in the idealized albedo and lifetime effects (Stevens standard climate models (Wang et al., 2011b; see Section 7.5.3). and Feingold, 2009). Specific examples involve the interplay between the drop-size distribution and mixing processes that determine cloud 7.4.1.4 Combined Modelling and Observational Approaches macrostructure (Stevens et al., 1998; Ackerman et al., 2004; Brether- ton et al., 2007; Wood, 2007; Small et al., 2009), or the dependence of Combined approaches, which attempt to maximize the respective precipitation development in stratiform clouds on details of the vertical advantage of models and observations, are beginning to add to 7 structure of the cloud (Wood, 2007). Thus warm clouds may typically u ­ nderstanding of aerosol cloud interactions. These include inversions 608 Clouds and Aerosols Chapter 7 of the observed historical record using simplified climate models (e.g., and is therefore part of ERFari (Section 7.3.4.2). Negative correlation Forest et al., 2006; Aldrin et al., 2012) but also the use of reanalysis between AOD and ice particle size has also been documented in deep and chemical transport models to help interpret satellite records (Cha- convective clouds (e.g., Sherwood, 2002; Jiang et al., 2008). meides et al., 2002; Koren et al., 2010a; Mauger and Norris, 2010), field study data to help constrain fine-scale modelling studies (e.g., Acker- 7.4.2.3 Advances in Process Level Understanding man et al., 2009; vanZanten et al., 2011), or satellite/surface-based climatologies to constrain large-scale modelling (Wang et al., 2012). At the heart of the albedo effect lie two fundamental issues. The first is aerosol activation and its sensitivity to aerosol and dynamical param- 7.4.2 Microphysical Underpinnings of Aerosol Cloud eters. The primary controls on droplet concentration are the aerosol Interactions number concentration (particularly at diameters greater than about 60 nm) and cooling rate (proportional to updraught velocity). Aerosol size 7.4.2.1 The Physical Basis distribution can play an important role under high aerosol loadings, whereas aerosol composition tends to be much less important, except The cloud albedo effect (Twomey, 1977) is the mechanism by which perhaps under very polluted conditions and low updraught veloci- an increase in aerosol number concentration leads to an increase in ties (e.g., Ervens et al., 2005; McFiggans et al., 2006). This is partially the albedo of liquid clouds (reflectance of incoming solar radiation) because aging tends to make particles more hygroscopic regardless of by increasing the cloud droplet number concentration, decreasing the their initial composition, but also because more hygroscopic particles droplet size, and hence increasing total droplet surface area, with the lead to faster water vapour uptake, which then lowers supersaturation, liquid water content and cloud geometrical thickness hypothetically limiting the initial increase in activation. held fixed. Although only the change in the droplet concentration is considered in the original concept of the cloud albedo effect, a change The second issue is that the amount of energy reflected by a cloud in the shape of the droplet size distribution that is directly induced system is a strong function of the amount of condensate. Simple argu- by the aerosols may also play a role (e.g., Feingold et al., 1997; Liu ments show that in a relative sense the amount of reflected energy is and Daum, 2002). In the Arctic, anthropogenic aerosol may influence approximately two-and-a-half times more sensitive to changes in the the longwave emissivity of optically thin liquid clouds and generate liquid water path than to changes in droplet concentration (Boers and a positive forcing at the surface (Garrett and Zhao, 2006; Lubin and Mitchell, 1994). Because both of these parameters experience similar Vogelmann, 2006; Mauritsen et al., 2011), but TOA forcing is thought ranges of relative variability, the magnitude of aerosol cloud related to be negligible. forcing rests mostly on dynamical factors such as turbulent strength and entrainment that control cloud condensate, and a few key aerosol 7.4.2.2 Observational Evidence for Aerosol Cloud Interactions parameters such as aerosol number concentration and size distribu- tion, and to a much lesser extent, composition. The physical basis of the albedo effect is fairly well understood, with research since AR4 generally reinforcing earlier work. Detailed in situ 7.4.3 Forcing Associated with Adjustments in aircraft observations show that droplet concentrations observed just Liquid Clouds above the cloud base generally agree with those predicted based on the aerosol properties and updraught velocity observed below the 7.4.3.1 The Physical Basis for Adjustments in Liquid Clouds cloud (e.g., Fountoukis et al., 2007). Vertical profiles of cloud droplet effective radius also agree with those predicted by models that take The adjustments giving rise to ERFaci are multi-faceted and are asso- into account the effect of entrainment (Lu et al., 2008), although uncer- ciated with both albedo and so-called lifetime effects (Figure 7.3). tainties still remain in estimating the shape of the droplet size distri- However, this old nomenclature is misleading because it assumes a bution (Brenguier et al., 2011), and the degree of entrainment mixing relationship between cloud lifetime and cloud amount or water con- within clouds. tent. Moreover, the effect of the aerosol on cloud amount may have nothing to do with cloud lifetime per se (e.g., Pincus and Baker, 1994). At relatively low aerosol loading (AOD less than about 0.3) there is ample observational evidence for increases in aerosol resulting in an The traditional view (Albrecht, 1989; Liou and Ou, 1989) has been increase in droplet concentration and decrease in droplet size (for that adjustment effects associated with aerosol cloud precipitation constant liquid water) but uncertainties remain regarding the magni- interactions will add to the initial albedo increase by increasing cloud tude of this effect, and its sensitivity to spatial averaging. Based on amount. The chain of reasoning involves three steps: that droplet con- simple metrics, there is a large range of physically plausible responses, centrations depend on the number of available CCN; that precipita- with aircraft measurements (e.g., Twohy et al., 2005; Lu et al., 2007, tion development is regulated by the droplet concentration; and that 2008;; Hegg et al., 2012) tending to show stronger responses than sat- the development of precipitation reduces cloud amount (Stevens and ellite-derived responses (McComiskey and Feingold, 2008; Nakajima Feingold, 2009). Of the three steps, the first has ample support in both and Schulz, 2009; Grandey and Stier, 2010). At high AOD and high observations and theory (Section 7.4.2.2). More problematic are the aerosol concentration, droplet concentration tends to saturate (e.g., last two links in the chain of reasoning. Although increased droplet Verheggen et al., 2007) and, if the aerosol is absorbing, there may be concentrations inhibit the initial development of precipitation (see reductions in droplet concentration and cloudiness (Koren et al., 2008). Section 7.4.3.2.1), it is not clear that such an effect is sustained in This ­ bsorbing effect originates from aerosol radiation interactions a an evolving cloud field. In the trade-cumulus regime, some modelling 7 609 Chapter 7 Clouds and Aerosols studies suggest the opposite, with increased aerosol concentrations The development of precipitation in stratocumulus, whether due to actually promoting the development of deeper clouds and invigorat- aerosol or meteorological influence can, in some instances, change ing precipitation (Stevens and Seifert, 2008; see discussion of similar a highly reflective closed-cellular cloud field to a weakly reflective responses in deep convective clouds in Section 7.6.4). Others have broken open-cellular field (Comstock et al., 2005; Stevens et al., 2005a; shown alternating cycles of larger and smaller cloud water in both vanZanten et al., 2005; Sharon et al., 2006; Savic-Jovcic and Stevens, aerosol-perturbed stratocumulus (Sandu et al., 2008) and trade cumu- 2008; Wang and Feingold, 2009a). In some cases, compact regions lus (Lee et al., 2012), pointing to the important role of environmental (pockets) of open-cellular convection become surrounded by regions adjustment. There exists limited unambiguous observational evidence of closed-cellular convection. It is, however, noteworthy that observed (exceptions to be given below) to support the original hypothesised precipitation rates can be similar in both open and closed-cell envi- cloud-amount effects, which are often assumed to hold universally and ronments (Wood et al., 2011a). The lack of any apparent difference in have dominated GCM parameterization of aerosol cloud interactions. the large-scale environment of the open cells, versus the surrounding GCMs lack the more nuanced responses suggested by recent work, closed cellular convection, suggests the potential for multiple equilib- which influences their ERFaci estimates. ria (Baker and Charlson, 1990; Feingold et al., 2010). Therefore in the stratocumulus regime, the onset of precipitation due to a dearth of 7.4.3.2 Observational Evidence of Adjustments in Liquid Clouds aerosol may lead to a chain of events that leads to a large-scale reduc- tion of cloudiness in agreement with Liou and Ou (1989) and Albrecht Since observed effects generally include both the albedo effect and (1989). The transition may be bidirectional: ship tracks passing through the adjustments, with few if any means of observing only one or the open-cell regions also appear to revert the cloud field to a closed-cell other in isolation, in this section we discuss and interpret observation- regime inducing a potentially strong ERFaci locally (Christensen and al findings that reflect both effects. Stratocumulus and trade cumulus Stephens, 2011; Wang et al., 2011a; Goren and Rosenfeld, 2012). regimes are discussed separately. 7.4.3.2.2 Trade-cumulus 7.4.3.2.1 Stratocumulus Precipitation from trade cumuli proves difficult to observe, as the The cloud albedo effect is best manifested in so-called ship tracks, clouds are small, and not easily observed by space-based remote sens- which are bright lines of clouds behind ships. Many ship tracks are ing techniques (Stephens et al., 2008). Satellite remote sensing of trade characterized by an increase in the droplet concentration resulting cumuli influenced by aerosol associated with slow volcanic degas- from the increase in aerosol number concentration and an absence of sing points to smaller droplet size, decreased precipitation efficiency, drizzle size drops, which leads to a decrease in the droplet radius and increased cloud amount and higher cloud tops (Yuan et al., 2011). an increase in the cloud albedo (Durkee et al., 2000), all else equal. Other studies show that in the trade cumulus regime cloud amount However, liquid water changes are the primary determinant of albedo tends to increase with precipitation amount: for example, processes changes (Section 7.4.2.3; Chen et al., 2012), therefore adjustments are that favour precipitation development also favour cloud development key to understanding radiative response. Coakley and Walsh (2002) (Nuijens et al., 2009); precipitation-driven colliding outflows tend to showed that cloud water responses can be either positive or nega- regenerate clouds; and trade cumuli that support precipitation reach tive. This is supported by more recent shiptrack analyses based on new heights where wind shear increases cloud fraction (Zuidema et al., satellite sensors (Christensen and Stephens, 2011): aerosol intrusions 2012). result in weak decreases in liquid water ( 6%) in overcast clouds, but significant increases in liquid water (+39%) and increases in cloud frac- While observationally based study of the microphysical aspects of aer- tion in precipitating, broken stratocumulus clouds. The global ERFaci of osol cloud interactions has a long history, more recent assessment of visible ship tracks has been estimated from satellite and found to be the ability of detailed models to reproduce the associated radiative insignificant at about 0.5 mW m 2 (Schreier et al., 2007), although effect in cumulus cloud fields is beginning to provide the important link this analysis may not have identified all shiptracks. Some observational between aerosol cloud interactions and total RF (Schmidt et al., 2009). studies downwind of ship tracks have been unable to distinguish aero- sol influences from meteorological influences on cloud microphysical 7.4.3.3 Advances in Process Level Understanding or macrophysical properties (Peters et al., 2011a), although it is not clear whether their methodology had sufficient sensitivity to detect the Central to ERFaci is the question of how susceptible is precipitation aerosol effects. Notwithstanding evidence of shiptracks locally increas- to droplet concentration, and by inference, to the available aerosol. ing the cloud fraction and albedo of broken cloud scenes quite sig- Some studies point to the droplet effective radius as a threshold indi- nificantly (e.g., Christensen and Stephens, 2011; Goren and Rosenfeld, cator of the onset of drizzle (Rosenfeld and Gutman, 1994; Gerber, 2012), their contribution to global ERFaci is thought to be small. These 1996; Rosenfeld et al., 2012). Others focus on the sensitivity of the ship track results are consistent with satellite studies of the influence conversion of cloud water to rain water (i.e., autoconversion) to droplet of long-term degassing of low-lying volcanic aerosol on stratocumulus, concentration, which is usually in the form of (droplet concentration) which point to smaller droplet sizes but ambiguous changes in cloud to the power -a. Both approaches indicate that from the microphysical fraction and cloud water (Gasso, 2008). The lack of clear evidence for standpoint, an increase in the aerosol suppresses rainfall. Models and a global increase in cloud albedo from shiptracks and volcanic plumes theory show a ranging from 1/2 (Kostinski, 2008; Seifert and Stevens, should be borne in mind when considering geoengineering methods 2010) to 2 (Khairoutdinov and Kogan, 2000), while observational stud- 7 that rely on cloud modification (Section 7.7.2.2). ies suggest a = 1 (approximately the inverse of drop concentration; 610 Clouds and Aerosols Chapter 7 Pawlowska and Brenguier, 2003; Comstock et al., 2005; vanZanten et depending on the parameterization. Elimination of the sensitivity of al., 2005). Note that thicker liquid clouds amplify rain via accretion rain formation to the autoconversion process has begun to be consid- of cloud droplets by raindrops, a process that is relatively insensitive ered in GCMs (Posselt and Lohmann, 2009). Wang et al. (2012) have to droplet concentration, and therefore to aerosol perturbations (e.g., used satellite observations to constrain autoconversion and find a Khairoutdinov and Kogan, 2000). The balance of evidence suggests reduction in ERFaci of about 33% relative to a standard GCM autocon- that a = 1/2 is more likely and that liquid water path (or cloud depth) version parameterization. It is worth reiterating that these uncertain- has significantly more leverage over precipitation than does droplet ties are not necessarily associated with uncertainties in the physical concentration. Many GCMs assume a much stronger relationship process itself, but more so by the ability of a GCM to resolve the pro- between precipitation and cloud droplet number concentration (i.e., a cesses (see Section 7.4.1.3). = 2) (Quaas et al., 2009). 7.4.4 Adjustments in Cold Clouds Small-scale studies (Ackerman et al., 2004; Xue et al., 2008; Small et al., 2009) and satellite observations (Lebsock et al., 2008; Christensen 7.4.4.1 The Physical Basis for Adjustments in Cold Clouds and Stephens, 2011) tend to confirm two responses of the cloud liquid water to increasing aerosol. Under clean conditions when clouds are Mixed-phase clouds, containing both liquid water and ice particles, prone to precipitation, an increase in the aerosol tends to increase exist at temperatures between 0°C and 38°C. At warmer tempera- cloud amount as a result of aerosol suppression of precipitation. tures ice melts rapidly, whereas at colder temperatures liquid water Under non-precipitating conditions, clouds tend to thin in response freezes homogeneously. The formation of ice in mixed-phase clouds to increasing aerosol through a combination of droplet sedimentation depends on heterogeneous freezing, initiated by IN (Section 7.3.3.4), (Bretherton et al., 2007) and evaporation entrainment adjustments which are typically solid or crystalline aerosol particles. In spite of their (e.g., Hill et al., 2009). Treatment of the subtlety of these responses and very low concentrations (on the order of 1 per litre), IN have an impor- associated detail in small-scale cloud processes is not currently feasi- tant influence on mixed-phase clouds. Mineral dust particles have been ble in GCMs, although probability distribution function approaches are identified as good IN but far less is known about the IN ability of other promising (Guo et al., 2010). aerosol types, and their preferred modes of nucleation. For example, the ice nucleating ability of BC particles remains controversial (Hoose Since AR4, cloud resolving model simulation has begun to stress the and Möhler, 2012). Soluble matter can hinder glaciation by depress- importance of scale interactions when addressing aerosol cloud inter- ing the freezing temperature of supercooled drops to the point where actions. Model domains on the order of 100 km allow mesoscale circu- homogeneous freezing occurs (e.g., Girard et al., 2004; Baker and lations to develop in response to changes in the aerosol. These dynam- Peter, 2008). Hence anthropogenic perturbations to the aerosol have ical responses may have a significant impact on cloud morphology and the potential to affect glaciation, water and ice optical properties, and RF. Examples include the significant changes in cloud albedo asso- their radiative effect. ciated with transitions between closed and open cellular states dis- cussed above, and the cloud-free, downdraught shadows that appear Because the equilibrium vapour pressure with respect to ice is lower alongside ship tracks (Wang and Feingold, 2009b). Similar examples than that with respect to liquid, the initiation of ice in a supercooled of large-scale changes in circulation associated with aerosol and asso- liquid cloud will cause vapour to diffuse rapidly toward ice particles ciated influence on precipitation are discussed in Section 7.6.4. These at the expense of the liquid water (Wegener Bergeron Findeisen pro- underscore the large gap between our process level understanding of cess; e.g., Schwarzenbock et al., 2001; Verheggen et al., 2007; Hudson aerosol cloud precipitation interactions and the ability of GCMs to et al., 2010). This favours the depositional growth of ice crystals, the represent them. largest of which may sediment away from the water-saturated region of the atmosphere, influencing the subsequent evolution of the cloud. 7.4.3.4 Advances in and Insights Gained from Large-Scale Hence anthropogenic perturbations to the IN can influence the rate at Modelling Studies which ice forms, which in turn may regulate cloud amount (Lohmann, 2002b; Storelvmo et al., 2011; see also Section 7.2.3.2.2), cloud optical Regional models are increasingly including representation of aerosol properties and humidity near the tropopause. cloud interactions using sophisticated microphysical models (Bangert et al., 2011; Yang et al., 2011; Seifert et al., 2012). Some of these Finally, formation of the ice phase releases latent heat to the environ- regional models are operational weather forecast models that under- ment (influencing cloud dynamics), and provides alternate, complex go routine evaluation. Yang et al. (2011) show improved simulations of pathways for precipitation to develop (e.g., Zubler et al., 2011, and stratocumulus fields when aerosol cloud interactions are introduced. Section 7.6.4). Regional models are increasingly being used to provide the meteoro- logical context for satellite observations of aerosol cloud interactions 7.4.4.2 Observations of ERFaci in Deep Convective Clouds (see Section 7.4.1.4), with some (e.g., Painemal and Zuidema, 2010) suggesting that droplet concentration differences are driven primarily As noted in Section 7.4.2.2, observations have demonstrated corre- by synoptic scale influences rather than aerosol. lations between aerosol loading and ice crystal size but influence on cloud optical depth is unclear (e.g., Koren et al., 2005). Satellite remote GCM studies that have explored sensitivity to autoconversion param- sensing suggests that aerosol-related invigoration of deep convec- eterization (Golaz et al., 2011) show that ERFaci can vary by 1 W m 2 tive clouds may generate more extensive anvils that radiate at cooler 7 611 Chapter 7 Clouds and Aerosols t ­emperatures, are optically thinner, and generate a positive contribu- Gettelman et al., 2010; Salzmann et al., 2010), whereas others attempt tion to ERFaci (Koren et al., 2010b). The global influence on ERFaci is to represent the processes explicitly (Jacobson, 2003) or ground the unclear. development of parameterizations in concepts derived from classical nucleation theory (Chen et al., 2008; Hoose et al., 2010b). The details 7.4.4.3 Observations of Aerosol Effects on Arctic Ice and of how these processes are treated have important implications for Mixed-Phase Stratiform Clouds tropical anvils (Ekman et al., 2007; Fan et al., 2010). Arctic mixed-phase clouds have received a great deal of attention Homogeneous ice nucleation in cirrus clouds (at temperatures lower since AR4, with major field programs conducted in 2004 (Verlinde et than about 38°C) depends crucially on the cloud updraught velocity al., 2007) and 2008 (Jacob et al., 2010; Brock et al., 2011; McFarqu- and hence the supersaturation with respect to ice. The onset relative har et al., 2011), in addition to long-term monitoring at high north- humidities for nucleation have been parameterized using results of ern latitude stations (e.g., Shupe et al., 2008) and analysis of earlier parcel model simulations (e.g., Sassen and Dodd, 1988; Barahona and field experiments (Uttal et al., 2002). Mixed-phase Arctic clouds persist Nenes, 2009), airborne measurements in cirrus or wave clouds (Heyms- for extended periods of time (days and even weeks; Zuidema et al., field and Miloshevich, 1995; Heymsfield et al., 1998), extensions of clas- 2005), in spite of the inherent instability of the ice water mix (see sical homogeneous ice nucleation theory (Khvorostyanov and Sassen, also Section 7.2.3.2.2). In spite of their low concentrations, IN have 1998; Khvorostyanov and Curry, 2009) and data from laboratory meas- an important influence on cloud persistence, with clouds tending to urements (e.g., Bertram et al., 2000; Koop et al., 2000; Mohler et al., glaciate and disappear rapidly when IN concentrations are relatively 2003; Magee et al., 2006; Friedman et al., 2011). There is new evidence high and/or updraught velocities too small to sustain a liquid water that although ice nucleation in cirrus has traditionally been regarded as layer (e.g., Ovchinnikov et al., 2011). The details of the heterogeneous homogeneous, the preferred freezing pathway may be heterogeneous ice-nucleation mechanism remain controversial but there is increasing because it occurs at lower onset relative humidities (or higher onset evidence that ice forms in Arctic stratus via the liquid phase (immersion temperatures) than homogeneous nucleation (Jensen et al., 2010). The freezing) so that the CCN population also plays an important role (de onset relative humidities (or temperatures) for heterogeneous nuclea- Boer et al., 2011; Lance et al., 2011). If ice indeed forms via the liquid tion depend on the type and size of the IN (Section 7.3.3.4). phase this represents a self-regulating feedback that helps sustain the mixed-phase clouds: as ice forms, liquid water (the source of the ice) Cloud resolving modeling of deep convective clouds points to the is depleted, which restricts further ice formation and competition for potential for aerosol-related changes in cirrus anvils (e.g., Morrison water vapour via the Wegener Bergeron Findeisen process (Morrison and Grabowski, 2011; van den Heever et al., 2011; Storer and van den et al., 2012). Heever, 2013), but the physical mechanisms involved and their influence on ERFaci are poorly understood, and their global impact is unclear. 7.4.4.4 Advances in Process Level Understanding 7.4.4.5 Advances in and Insights Gained from Large-Scale Since AR4, research on ice-microphysical processes has been very Modelling Studies active as evidenced by the abovementioned field experiments (Sec- tion 7.4.4.3). The persistence of some mixed-phase stratiform clouds Since the AR4, mixed-phase and ice clouds have received significant has prompted efforts to explain this phenomenon in a theoretical attention, with effort on representation of both heterogeneous (mixed- framework (Korolev and Field, 2008). Predicting cloud persistence may phase clouds) and homogeneous (cirrus) freezing processes in GCMs require a high level of understanding of very detailed processes. For (e.g., Lohmann and Kärcher, 2002; Storelvmo et al., 2008a). In GCMs example, ice particle growth by vapour diffusion depends strongly on the physics of cirrus clouds usually involves only ice-phase microphys- crystal shape (Harrington et al., 2009), the details of which may have ical processes and is somewhat simpler than that of mixed-phase similar relative influence on glaciation times to the representation of clouds. Nevertheless, representation of aerosol cloud interactions in ice nucleation mechanism (Ervens et al., 2011b). A recent review (Mor- mixed-phase and ice clouds is considerably less advanced than that rison et al., 2012) discusses the myriad processes that create a resilient involving liquid-only clouds. mixed-phase cloud system, invoking the ideas of buffering seen in liquid clouds (Stevens and Feingold, 2009). Importantly, the Wegener Our poor understanding of the climatology and lifecycle of aerosol par- Bergeron Findeisen process does not necessarily destabilize the cloud ticles that can serve as IN complicates attempts to assess what consti- system, unless sufficient ice exists (Korolev, 2007). Bistability has also tutes an anthropogenic perturbation to the IN population, let alone the been observed in the mixed-phase Arctic cloud system; the resilient effect of such a perturbation. BC can impact background (i.e., non con- cloud state is sometimes interrupted by a cloud-free state (Stramler trail) cirrus by affecting ice nucleation properties but the effect remains et al., 2011), but there is much uncertainty regarding the meteorolog- uncertain (Kärcher et al., 2007). The numerous GCM studies that have ical and microphysical conditions determining which of these states is evaluated ERFaci for ice clouds are summarised in Section 7.5.4. preferred. 7.4.5 Synthesis on Aerosol Cloud Interactions Significant effort has been expended on heterogeneous freezing param- eterizations employed in cloud or larger-scale models. Some parame- Earlier assessments considered the radiative implications of aerosol terizations are empirical (e.g., Lohmann and Diehl, 2006; Hoose et al., cloud interactions as two complementary processes albedo and life- 7 2008; Phillips et al., 2008; Storelvmo et al., 2008a; DeMott et al., 2010; time effects that together amplify forcing. Since then the complexity 612 Clouds and Aerosols Chapter 7 of cloud system responses to aerosol perturbations has become more 1999) or locations (Udelhofen and Cess, 2001; Usoskin and Kovaltsov, fully appreciated. Recent work at the process scale has identified com- 2008). The purported correlations have also been attributed to ENSO pensating adjustments that make the system less susceptible to pertur- variability (Farrar, 2000; Laken et al., 2012) and artefacts of the sat- bation than might have been expected based on the earlier albedo and ellite data cannot be ruled out (Pallé, 2005). Statistically significant lifetime effects. Increases in the aerosol can therefore result in either (but weak) correlations between the diffuse fraction of surface solar an increase or a decrease in aerosol cloud related forcing depending radiation and the cosmic ray flux have been found at some locations on the particular environmental conditions. Because many current in the UK over the 1951 2000 period (Harrison and Stephenson, GCMs do not include the possibility of compensating effects that are 2006). Harrison (2008) also found a unique 1.68-year periodicity in not mediated by the large-scale state, there are grounds for expecting surface radiation for two different UK sites between 1978 and 1990, these models to overestimate the magnitude of ERFaci. Nevertheless it potentially indicative of a cosmic ray effect of the same periodicity. is also possible that poorly understood and unrepresented interactions Svensmark et al. (2009) found large global reductions in the aerosol could cause real ERFaci to differ in either direction from that predicted Angström exponent, liquid water path, and cloud cover after large by current models. Forcing estimates are discussed in Section 7.5.3. Forbush decreases, but these results were not corroborated by other studies that found no statistically significant links between the cosmic 7.4.6 Impact of Cosmic Rays on Aerosols and Clouds ray flux and clouds at the global scale (Èalogoviæ et al., 2010; Laken and Èalogoviæ, 2011). Although some studies found statistically signif- Many studies have reported observations that link solar activity to par- icant correlations between the cosmic ray flux and cloudiness at the ticular aspects of the climate system (e.g., Bond et al., 2001; Dengel et regional scale (Laken et al., 2010; Rohs et al., 2010), these correlations al., 2009; Eichler et al., 2009). Various mechanisms have been proposed were generally weak, cloud changes were small, and the results were that could amplify relatively small variations in total solar irradiance, sensitive to how the Forbush events were selected and composited such as changes in stratospheric and tropospheric circulation induced (Kristjánsson et al., 2008; Laken et al., 2009). by changes in the spectral solar irradiance or an effect of the flux of cosmic rays on clouds. We focus in this subsection on the latter hypoth- 7.4.6.2 Physical Mechanisms Linking Cosmic Rays to Cloudiness esis while Box 10.2 discusses solar influences on the climate system more generally. The most widely studied mechanism proposed to explain the possible influence of the cosmic ray flux on cloudiness is the ion-aerosol clear Solar activity variations influence the strength and three-dimensional air mechanism, in which atmospheric ions produced by cosmic rays structure of the heliosphere. High solar activity increases the deflec- facilitate aerosol nucleation and growth ultimately impacting CCN tion of low energy cosmic rays, which reduces the flux of cosmic rays concentrations and cloud properties (Carslaw et al., 2002; Usoskin and impinging upon the Earth s atmosphere. It has been suggested that the Kovaltsov, 2008). The variability in atmospheric ionization rates due to ionization caused by cosmic rays in the troposphere has an impact on changes in cosmic ray flux can be considered relatively well quanti- aerosols and clouds (e.g., Dickinson, 1975; Kirkby, 2007). This subsec- fied (Bazilevskaya et al., 2008), whereas resulting changes in aerosol tion assesses studies that either seek to establish a causal relationship nucleation rates are very poorly known (Enghoff and Svensmark, 2008; between cosmic rays and aerosols or clouds by examining empirical Kazil et al., 2008). Laboratory experiments indicate that ionization correlations, or test one of the physical mechanisms that have been put induced by cosmic rays enhances nucleation rates under middle and forward to account for such a relationship. upper tropospheric conditions, but not necessarily so in the continental boundary layer (Kirkby et al., 2011). Field measurements qualitative- 7.4.6.1 Observed Correlations Between Cosmic Rays and ly support this view but cannot provide any firm conclusion due to Properties of Aerosols and Clouds the scarcity and other limitations of free-troposphere measurements (Arnold, 2006; Mirme et al., 2010), and due to difficulties in separating Correlation between the cosmic ray flux and cloud properties has been nucleation induced by cosmic rays from other nucleation pathways in examined for decadal variations induced by the 11-year solar cycle, the continental boundary layer (Hirsikko et al., 2011). Based on surface shorter variations associated with the quasi-periodic oscillation in solar aerosol measurements at one site, Kulmala et al. (2010) found no con- activity centred on 1.68 years, or sudden and large variations known as nection between the cosmic ray flux and new particle formation or any Forbush decrease events. It should be noted that long-term variations other aerosol property over a solar cycle (1996 2008), although parti- in cloud properties are difficult to detect (Section 2.5.6) while short- cles nucleated in the free troposphere are known to contribute to par- term variations may be difficult to attribute to a particular cause. More- ticle number and CCN concentrations in the boundary layer (Merikanto over, the cosmic ray flux co-varies with other solar parameters such as et al., 2009). Our understanding of the ion-aerosol clear air mecha- solar and UV irradiance. This makes any attribution of cloud changes to nism as a whole relies on a few model investigations that simulate the cosmic ray flux problematic (Laken et al., 2011). changes in cosmic ray flux over a solar cycle (Pierce and Adams, 2009b; Snow-Kropla et al., 2011; Kazil et al., 2012) or during strong Forbush Some studies have shown co-variation between the cosmic ray flux decreases (Bondo et al., 2010; Snow-Kropla et al., 2011; Dunne et al., and low-level cloud cover using global satellite data over periods of 2012). Changes in CCN concentrations due to variations in the cosmic typically 5 to 10 years (Marsh and Svensmark, 2000; Pallé Bagó and ray flux appear too weak to cause a significant radiative effect because Butler, 2000). Such correlations have not proved to be robust when the aerosol system is insensitive to a small change in the nucleation extending the time period under consideration (Agee et al., 2012), rate in the presence of pre-existing aerosols (see also Section 7.3.2.2). and restricting the analysis to particular cloud types (Kernthaler et al., 7 613 Chapter 7 Clouds and Aerosols A second pathway linking the cosmic ray flux to cloudiness has been was given as two distinct ranges: 2.3 to 0.2 W m 2 from models and proposed through the global electric circuit. A small direct current is a 1.7 to 0.1 W m 2 range from inverse estimates. able to flow vertically between the ionosphere and the Earth s surface over fair-weather regions because of cosmic-ray-induced atmospher- As discussed in Section 7.4, it is inherently difficult to separate RFaci ic ionization. Charge can accumulate at the upper and lower cloud from subsequent rapid cloud adjustments either in observations or boundaries as a result of the effective scavenging of ions by cloud model calculations (e.g., George and Wood, 2010; Lohmann et al., droplets (Tinsley, 2008). This creates conductivity gradients at the cloud 2010; Mauger and Norris, 2010; Painemal and Zuidema, 2010). For this edges (Nicoll and Harrison, 2010), and may influence droplet droplet reason estimates of RFaci are of limited interest and are not assessed in collisions (Khain et al., 2004), cloud droplet particle collisions (Tinsley, this report. This chapter estimates RFari, ERFari, and ERFari+aci based 2008) and cloud droplet formation processes (Harrison and Ambaum, purely on a priori approaches, and calculates ERFaci as the residual 2008). These microphysical effects may potentially influence cloud between ERFari+aci and ERFari assuming the two effects are additive. properties both directly and indirectly. Although Harrison and Ambaum Inverse studies that estimate ERFari+aci from the observed rate of (2010) observed a small reduction in downward longwave radiation planetary energy uptake and estimates of climate feedbacks and other that they associated with variations in surface current density, sup- RFs are discussed in Section 10.8. porting observations are extremely limited. Our current understanding of the relationship between cloud properties and the global electric For consistency with AR4 and Chapter 8 of this Report, all quoted circuit remains very low, and there is no evidence yet that associated ranges represent a 5 to 95% uncertainty range unless otherwise stated, cloud processes could be of climatic significance. and we evaluate the forcings between 1750 and approximately 2010. The reference year of 1750 is chosen to represent pre-industrial times, 7.4.6.3 Synthesis so changes since then broadly represent the anthropogenic effect on climate, although for several aerosol species (such as biomass burning) Correlations between cosmic ray flux and observed aerosol or cloud this does not quite equate to the anthropogenic effect as emissions properties are weak and local at best, and do not prove to be robust started to be influenced by humans before the Industrial Revolution. on the regional or global scale. Although there is some evidence that Many studies estimate aerosol forcings between 1850 and the present ionization from cosmic rays may enhance aerosol nucleation in the day and any conversion to a forcing between 1750 and the present day free troposphere, there is medium evidence and high agreement that increases the uncertainty (Bellouin et al., 2008). This section principally the cosmic ray-ionization mechanism is too weak to influence global discusses global forcing estimates and attributes them to aerosol spe- concentrations of CCN or droplets or their change over the last century cies. Chapter 8 discusses regional forcings and additionally attributes or during a solar cycle in any climatically significant way. aerosol forcing to emission sources. 7.5.2 Estimates of Radiative Forcing and Effective 7.5 Radiative Forcing and Effective Radiative Radiative Forcing from Aerosol Radiation Forcing by Anthropogenic Aerosols Interactions 7.5.1 Introduction and Summary of AR4 Building on our understanding of aerosol processes and their radiative effects (Section 7.3), this subsection assesses RFari and ERFari, but also In this section, aerosol forcing estimates are synthesized and updated the forcings from absorbing aerosol (BC and dust) on snow and ice. from AR4. As depicted in Figure 7.3, RF refers to the radiative forcing due to either aerosol radiation interactions (ari), formerly known as 7.5.2.1 Radiative Forcing and Effective Radiative Forcing the direct aerosol forcing, or aerosol cloud interactions (aci), formerly from All Aerosols known as the first indirect aerosol forcing or cloud albedo effect in AR4. ERF refers to the effective radiative forcing and is typically esti- Observations can give useful constraints to aspects of the global RFari mated from experiments with fixed SSTs (see Sections 7.1.3 and 8.1). but cannot measure it directly (Section 7.3.4; Anderson et al., 2005; It includes rapid adjustments, such as changes to the cloud lifetime, Kahn, 2012). Remote sensing observations, in situ measurements of cloud altitude, changes in lapse rate due to absorbing aerosols and fine-mode aerosol properties and a better knowledge of bulk aerosol aerosol microphysical effects on mixed-phase, ice and convective optical properties make the estimate of total RFari more robust than clouds. the RF for individual species (see Forster et al., 2007). Estimates of RFari are either taken from global aerosol models directly (Schulz et al., Chapter 2 of AR4 (Forster et al., 2007) assessed RFari to be 0.5 +/- 2009; Myhre et al., 2013) or based mostly on observations, but using 0.4 W m 2 and broke this down into components associated with sev- supplemental information from models (e.g., Myhre, 2009; Loeb and eral species. Land albedo changes associated with BC on snow were Su, 2010; Su et al., 2013). A number of studies (Bellouin et al., 2008; assessed to be +0.1 +/- 0.1 W m 2. The RFaci was assessed to be 0.70 W Zhao et al., 2008b, 2011; Myhre, 2009) have improved aspects of the m 2 with a 1.8 to 0.3 W m 2 uncertainty range. These uncertainty esti- satellite-based RFari estimate over those quoted in AR4. Of these, only mates were based on a combination of model results and observations Myhre (2009) make the necessary adjustments to the observations to from remote sensing. The semi-direct effect and other aerosol indirect account for forcing in cloudy regions and pre-industrial concentrations effects were assessed in Chapter 7 of AR4 (Denman et al., 2007) to to estimate a RFari of 0.3 +/- 0.2 W m 2. 7 contribute additional uncertainty. The combined total aerosol forcing 614 Clouds and Aerosols Chapter 7 A second phase of AeroCom model results gives an RFari estimate of is partly based on models, and to account for this aspect, it is com- 0.35 W m 2, with a model range of about 0.60 to 0.13 W m 2, after bined in quadrature with a +/-0.1 W m 2 uncertainty from non-aerosol their forcings for 1850 2000 have been scaled by emissions to repre- related parameters following Stier et al. (2013). This gives an assessed sent 1750 2010 changes (Myhre et al., 2013). Figure 7.17 shows the RFari of 0.35 +/- 0.5 W m 2. This is a larger range than that exhibited zonal mean total RFari for AeroCom phase II models for 1850 2000. by the AeroCom II models. It is also a smaller magnitude but slightly Robust features are the maximum negative RF around 10°N to 50°N, larger range than in AR4, with a more positive upper bound. This more at latitudes of highest aerosol concentrations, and a positive RF at positive upper bound can be justified by the sensitivity to BC aerosol higher latitudes due to the higher surface albedo there. (Ma et al., 2012b and Section 7.5.2.3). Despite the larger range, there is increased confidence in this assessment due to dedicated modelling For observationally based estimates, a variety of factors are important sensitivity studies, more robust observationally based estimates and in constraining the radiative effect of aerosols (McComiskey et al., their better agreement with models. 2008; Loeb and Su, 2010; Kahn, 2012). Particularly important are the single scattering albedo (especially over land or above clouds) and the ERFari adds the radiative effects from rapid adjustments onto RFari. AOD (see Section 7.3.4.1). Errors in remotely sensed, retrieved AOD Studies have evaluated the rapid adjustments separately as a semi-di- can be 0.05 or larger over land (Remer et al., 2005; Kahn et al., 2010; rect effect (see Section 7.3.4.2) and/or the ERFari has been directly Levy et al., 2010; Kahn, 2012). Loeb and Su (2010) found that the total evaluated. Rapid adjustments are caused principally by cloud changes. uncertainty in forcing was dominated by the uncertainty in single There is high confidence that the local heating caused by absorbing scattering albedo, using single scattering albedo errors of +/- 0.06 over aerosols can alter clouds. However, there is low confidence in deter- ocean and +/- 0.03 over land from Dubovik et al. (2000), and assum- mining the sign and magnitude of the rapid adjustments at the global ing errors can be added in quadrature. These retrieval uncertainties scale as current models differ in their responses and are known to could lead to a 0.5 to 1.0 W m 2 uncertainty in RFari (Loeb and Su, inadequately represent some of the important relevant cloud processes 2010). However, model sensitivity studies and reanalyses can provide (see Section 7.3.4). Existing estimates of ERFari nevertheless rely on additional constraints leading to a reduced error estimate. Ma et al. such global models. Five GCMs were analysed for RFari and ERFari in (2012b) performed a sensitivity study in one model, finding a best Lohmann et al. (2010). Their rapid adjustments ranged from 0.3 to estimate of RFari of 0.41 W m 2 with an asymmetrical uncertainty +0.1 W m 2. In a further study, Takemura and Uchida (2011) found range of 0.61 to 0.08 W m 2, with BC particle size and mixing state a rapid adjustment of +0.06 W m 2. The sensitivity analysis of Ghan having the largest effect of the parameters investigated. In models, et al. (2012) found a 0.1 to +0.1 W m 2 range over model variants, assumptions about surface albedo, background cloud distribution where an improved aging of the mixing state led to small negative and radiative transfer contribute a relative standard deviation of 39% rapid adjustment of around 0.1 W m 2. Bond et al. (2013) assessed (Stier et al., 2013). Bellouin et al. (2013) quantified uncertainties in scaled RF and efficacy estimates from seven earlier studies focusing on RFari using reanalysis data that combined MODIS satellite data over oceans with the global coverage of their model. This approach broke 2 AeroCom mean down the uncertainty in aerosol properties into a local and a regional error to find a RFari standard deviation of 0.3 W m 2, not accounting AeroCom 5% 95% range for uncertainty in the pre-industrial reference. When cloudy-sky and Bellouin et al. (2013) pre-industrial corrections were applied an RFari best estimate of 0.4 1 Su et al. (2013) W m 2 was suggested. RFari (W m-2) The overall forcing uncertainty in RFari consists of the uncertainty in the distribution of aerosol amount, composition and radiative prop- 0 erties (Loeb and Su, 2010; Myhre et al., 2013), the uncertainty in radi- ative transfer (Randles et al., 2013) and the uncertainty owing to the dependence of the forcing calculation on other uncertain parameters, such as clouds or surface albedos (Stier et al., 2013). To derive a best -1 estimate and range for RFari we combine modelling and observation- ally based studies. The best estimate is taken as 0.35 W m 2. This is the same as the AeroCom II model estimate, and also the average of -2 the Myhre (2009) observationally based estimate ( 0.3 W m 2) and 60S 30S Eq 30N 60N the Bellouin et al. (2013) reanalysis estimate ( 0.4 W m 2). Models Latitude probably underestimate the positive RFari from BC and the negative forcing from OA aerosol (see Section 7.5.2.2), and currently there is Figure 7.17 | Annual zonal mean top of the atmosphere radiative forcing due to aero- no evidence that one of these opposing biases dominates over the sol radiation interactions (RFari, in W m 2) due to all anthropogenic aerosols from the other. The 5 to 95% range of RFari adopted in this assessment employs different AeroCom II models. No adjustment for missing species in certain models has the Bellouin et al. (2013) uncertainty to account for retrieval error in been applied. The multi-model mean and 5th to 95th percentile range from AeroCom II models (Myhre et al., 2013) are shown with a black solid line and grey envelope. The observational quantities when constrained by global models, giving an estimates from Bellouin et al. (2013) and Su et al. (2013) are shown with dotted and uncertainty estimate of +/-0.49 W m 2. This is at the low end of the dashed lines, respectively. The forcings are for the 1850 to 2000 period. See Supplemen- uncertainty analysis of Loeb and Su (2010). However, our uncertainty tary Material for a figure with labelled individual AeroCom II model estimates. 7 615 Chapter 7 Clouds and Aerosols BC and found a range of rapid adjustments between 0.2 and 0.01 mated in remote regions and at altitude (Figure 7.15). Models also W m 2. There is a potential additional rapid adjustment term from the probably underestimate the mass absorption cross-section probably effect of cloud drop inclusions (see Section 7.3.4.2). Based on Ghan et because enhanced absorption due to internal mixing is insufficiently al. (2012) and Jacobson (2012), Bond et al. (2013) estimate an addi- accounted for (see Section 7.3.3.2). Together these biases are expected tional ERFari term of +0.2 W m 2, with an uncertainty range of 0.1 to to cause the modelled BC RF to be underestimated. The Bond et al. +0.9 W m 2; however there is very low confidence in the sign or magni- estimate accounted for these biases by scaling model results. However, tude of this effect and we do not include it in our assessment. Overall a there are a number of methodological difficulties associated with the best estimate for the rapid adjustment is taken to be 0.1 W m 2, with absorption AOD retrieval from sunphotometer retrievals (see Section a 5 to 95% uncertainty range of 0.3 to +0.1 W m 2. The best estimate 7.3.3.2), the attribution of absorption AOD to BC, and the distribution is based on Ghan et al. (2012) and the range on Lohmann et al. (2010). and representativeness of AERONET stations for constraining global The uncertainties are added in quadrature to the estimate of RFari and and relatively coarse-resolution models. Absorption by OA (see Section rounded to give an overall assessment for ERFari of 0.45 +/- 0.5 W m 2. 7.3.3), which may amount to 20% of fine-mode aerosol absorption (Chung et al., 2012), is included into the BC RF estimate in Bond et 7.5.2.2 Radiative Forcing by Species al. but is now treated separately in most AeroCom II models, some of which have a global absorption AOD close to the Bond et al. estimate. AeroCom II studies have calculated aerosol distributions using 1850 We use our expert judgement here to adopt a BC RF estimate that is and 2000 simulations with the same meteorology to isolate RFari halfway between the two estimates and has a wider uncertainty range for individual aerosol types (sulphate, BC fossil-fuel plus biofuel, OA from combining distributions. This gives a BC RF estimate from fossil fossil-fuel and biofuel, biomass burning or BB, SOA, nitrate). Many of fuel and biofuel of +0.4 (+0.05 to +0.8) W m 2. these models account for internal mixing, so that partitioning RFari by species is not straightforward, and different modelling groups adopt The AeroCom II estimate of the SOA RFari is 0.03 ( 0.27 to 0.02) W different techniques (Myhre et al., 2013). Note also that due to internal m 2 and the primary OA from fossil fuel and biofuel estimate is 0.05 mixing of aerosol types the total RFari is not necessarily the sum of the ( 0.09 to 0.02) W m 2. An intercomparison of current chemistry cli- RFari from different types (Ocko et al., 2012). Unless otherwise noted mate models found two models outside of this range for SOA RFari, in the text below, the best estimate and 5 to 95% ranges for individual with one model exhibiting a significant positive forcing from land types quoted in Figure 7.18 are solely based on the AeroCom II range use and cover changes influencing biogenic emissions (Shindell et al., (Myhre et al., 2013) and the estimates have been scaled by emissions 2013). We therefore adjust the upper end of the range to account for to derive 1750 2010 RFari values. Note that although global numbers this, giving an SOA RFari estimate of 0.03 ( 0.27 to +0.20) W m 2. Our are presented here, these RF estimates all exhibit large regional var- assessment also scales the AeroComII estimate of the primary OA from iations, and individual aerosol species can contribute significantly to fossil fuel and biofuel by 1.74 to 0.09 ( 0.16 to 0.03) W m 2 to allow regional climate change despite rather small RF estimates (e.g., Wang for the underestimate of emissions identified in Bond et al. (2013). For et al., 2010b). OA from natural burning, and for SOA, the natural radiative effects can be an order of magnitude larger than the RF (see Sections 7.3.2 and For sulphate, AeroCom II models give a RF median and 5 to 95% uncer- 7.3.4, and O Donnell et al., 2011) and they could thus contribute to tainty range of 0.31 ( 0.58 to 0.11) W m 2 for the 1850 2000 period, climate feedback (see Section 7.3.5). and 0.34 ( 0.61 to 0.13) W m 2 for the 1750 2010 period. This esti- mate and uncertainty range are consistent with the AR4 estimate of The RFari from biomass burning includes both BC and OA species that 0.4 +/- 0.2 W m 2, which is retained as the best estimate for AR5. contribute RFari of opposite sign, giving a net RFari close to zero (Bond et al., 2013; Myhre et al., 2013). The AeroCom II models give a 1750 RF from BC is evaluated in different ways in the literature. The BC RF 2010 RFari of 0.00 ( 0.08 to +0.07) W m 2, and an estimate of +0.0 in this report is from fossil fuel and biofuel sources, while open burn- ( 0.2 to +0.2) W m 2 is adopted in this assessment, doubling the model ing sources are attributed separately to the biomass-burning aerosol, uncertainty range to account for a probable underestimate of their which also includes other organic species (see Section 7.3.2). BC can emissions (Bond et al., 2013). Combining information in Samset et al. also affect clouds and surface albedo (see Sections 7.5.2.3 and Chapter (2013) and Myhre et al. (2013) would give a BC RFari contribution from 8). Here we only isolate the fossil fuel and biofuel RFari attributable biomass burning of slightly less than +0.1 W m 2 over 1850 2000 from to BC over 1750 2010. Two comprehensive studies have quantified the models. However, this ignores a significant contribution expected the BC RFari and derive different central estimates and uncertainty before 1850 and the probable underestimate in emissions. Our assess- ranges. Myhre et al. (2013) quantify RF over 1850 2000 in the Aer- ment therefore solely relies on Bond et al. (2013), giving an estimate oCom II generation of models and scale these up using emissions to of +0.2 (+0.03 to +0.4) W m 2 for the 1750 2010 BC contribution to derive an RF estimate over 1750 2010 of +0.23 (+0.06 to +0.48) W the biomass burning RFari. This is a 50% larger forcing than the earlier m 2 for fossil fuel and biofuel emissions. Bond et al. (2013) employ an generation of AeroCom models found (Schulz et al., 2006). Note that observationally weighted scaling of an earlier generation of AeroCom we also expect an OA RFari of the same magnitude with opposite sign. models, regionally scaling BC absorption to match absorption AOD as retrieved at available AERONET sites. They derive a RF of +0.51 (+0.06 The AeroCom II RF estimate for nitrate aerosol gives an RFari of 0.11 to +0.91) W m 2 for fossil fuel and biofuel sources. There are known ( 0.17 to 0.03) W m 2, but comprises a relatively large 1850 to 1750 biases in BC RF estimates from aerosol models. BC concentrations are correction term. In these models ammonium aerosol is included within 7 underestimated near source regions, especially in Asia, but overesti- the sulphate and nitrate estimates. An intercomparison of current 616 Clouds and Aerosols Chapter 7 chemistry climate models found an RF range of 0.41 to 0.03 W m 2 from these species agrees with, but is slightly weaker than, the best over 1850 2000. Some of the models with strong RF did not exhibit estimate of the better-constrained total RFari. obvious biases, whereas others did (Shindell et al., 2013). These sets of estimates are in good agreement with earlier estimates (e.g., Adams et 7.5.2.3 Absorbing Aerosol on Snow and Sea Ice al., 2001; Bauer et al., 2007; Myhre et al., 2009). Our assessment of the RFari from nitrate aerosol is 0.11 ( 0.3 to 0.03) W m 2. This is based Forster et al. (2007) estimated the RF for surface albedo changes from on AeroCom II with an increased lower bound. BC deposited on snow to be +0.10 +/- 0.10 W m 2, with a low level of understanding, based largely on studies from Hansen and Nazarenko Anthropogenic sources of mineral aerosols can result from changes in (2004) and Jacobson (2004). Since AR4, observations of BC in snow land use and water use or climate change. Estimates of the RF from have been conducted using several different measurement techniques anthropogenic mineral aerosols are highly uncertain, because natural (e.g., McConnell et al., 2007; Forsström et al., 2009; Ming et al., 2009; and anthropogenic sources of mineral aerosols are often located close Xu et al., 2009; Doherty et al., 2010; Huang et al., 2011; Kaspari et to each other (Mahowald et al., 2009; Ginoux et al., 2012b). Using al., 2011), providing data with which to constrain models. Laboratory a compilation of observations of dust records over the 20th century measurements have confirmed the albedo reduction due to BC in snow with model simulations, Mahowald et al. (2010) deduced a 1750 2000 (Hadley and Kirchstetter, 2012). The albedo effects of non-BC constitu- change in mineral aerosol RFari including both natural and anthropo- ents have also been investigated but not rigorously quantified. Remote genic changes of 0.14 +/- 0.11 W m 2. This is consistent within the AR4 sensing can inform on snow impurity content in some highly polluted estimate of 0.1 +/- 0.2 W m 2 (Forster et al., 2007) which is retained regions. However, it cannot be used to infer global anthropogenic RF here. Note that part of the dust RF could be due to feedback processes because of numerous detection challenges (Warren, 2013). (see Section 7.3.5). Global modelling studies since AR4 have quantified present-day radi- Overall the species breakdown of RFari is less certain than the total ative effects from BC on snow of +0.01 to +0.08 W m 2 (Flanner et al., RFari. Fossil fuel and biofuel emissions contribute to RFari via sulphate 2007, 2009; Hansen et al., 2007; Koch et al., 2009a; Rypdal et al., 2009; aerosol 0.4 ( 0.6 to 0.2) W m 2; black carbon aerosol +0.4 (+0.05 to Skeie et al., 2011; Wang et al., 2011c; Lee et al., 2013). These studies +0.8) W m 2; and primary and secondary organic aerosol 0.12 ( 0.4 apply different BC emission inventories and atmospheric aerosol rep- to +0.1) W m 2 (adding uncertainties in quadrature). Additional RFari resentations, include forcing from different combinations of terrestrial contributions are via biomass burning emissions, where black carbon snow, sea ice, and snow on sea ice, and some include different rapid and organic aerosol changes offset each other to give an estimate of adjustment effects such as snow grain size evolution and melt-induced +0.0 ( 0.2 to +0.2) W m 2; nitrate aerosol 0.11 ( 0.3 to 0.03) W accumulation of impurities at the snow surface, observed on Tibetan m 2; and a contribution from mineral dust of 0.1 ( 0.3 to +0.1) W m 2 glaciers (Xu et al., 2012) and in Arctic snow (Doherty et al., 2013). The that may not be entirely anthropogenic in origin. The sum of the RFari forcing operates mostly on terrestrial snow and is largest during March to May, when boreal snow and ice are exposed to strong insolation (Flanner et al., 2007). BC FF All climate modelling studies find that the Arctic warms in response 0.5 to snow and sea ice forcing. In addition, estimates of the change in global mean surface temperature per unit forcing are 1.7 to 4.5 times SOA greater for snow and sea ice forcing than for CO2 forcing (Hansen and Mineral RFari (W m-2) Nazarenko, 2004; Hansen et al., 2005; Flanner et al., 2007; Flanner et al., 2009; Bellouin and Boucher, 2010). The Koch et al. (2009a) estimate 0.0 is not included in this range owing to the lack of a clear signal in their study. The greater response of global mean temperature occurs primar- POA FF ily because all of the forcing energy is deposited directly into the cry- BB Nitrate osphere, whose evolution drives a positive albedo feedback on climate. Key sources of forcing uncertainty include BC concentrations in snow 0.5 and ice, BC mixing state and optical properties, snow and ice coverage Sulphate and patchiness, co-presence of other light-absorbing particles in the snow pack, snow effective grain size and its influence on albedo per- Total turbation, the masking of snow surfaces by clouds and vegetation and the accumulation of BC at the top of snowpack caused by melting and sublimation. Bond et al. (2013) derive a 1750 2010 snow and sea ice Figure 7.18 | Annual mean top of the atmosphere radiative forcing due to aerosol radiation interactions (RFari, in W m 2) due to different anthropogenic aerosol types, RF estimate of +0.046 (+0.015 to +0.094) W m 2 for BC by (1) consid- for the 1750 2010 period. Hatched whisker boxes show median (line), 5th to 95th ering forcing ranges from all relevant global studies, (2) accounting for percentile ranges (box) and min/max values (whiskers) from AeroCom II models (Myhre biases caused by (a) modelled Arctic BC-in-snow concentrations using et al., 2013) corrected for the 1750 2010 period. Solid coloured boxes show the AR5 measurements from Doherty et al. (2010), and (b) excluding mineral best estimates and 90% uncertainty ranges. BC FF is for black carbon from fossil fuel dust, which reduces BC forcing by approximately 20%, (3) combining and biofuel, POA FF is for primary organic aerosol from fossil fuel and biofuel, BB is for in quadrature individual uncertainty terms from Flanner et al. (2007) 7 biomass burning aerosols and SOA is for secondary organic aerosols. 617 Chapter 7 Clouds and Aerosols plus that originating from the co-presence of dust, and (4) scaling the magnitude of the ERF is that some aerosols also act as IN causing present-day radiative contributions from BB, biofuel and fossil fuel BC supercooled clouds to glaciate and precipitate more readily. This reduc- emissions according to their 1750 2010 changes. Note that this RF tion in cloud cover leads to less reflected shortwave radiation and a estimate allows for some rapid adjustments in the snowpack but is not less negative ERFari+aci. This effect can however be offset if the IN a full ERF as it does not account for adjustments in the atmosphere. become coated with soluble material, making them less effective at For this RF, we adopt an estimate of +0.04 (+0.02 to +0.09) W m 2 and nucleating ice, leading to less efficient precipitation production and note that the surface temperature change is roughly three (two to four) more reflected shortwave radiation (Hoose et al., 2008; Storelvmo times more responsive to this RF relative to CO2. et al., 2008a). Models that have begun to incorporate aerosol cloud interactions in convective clouds also have a tendency to reduce the 7.5.3 Estimate of Effective Radiative Forcing from magnitude of the ERF, but this effect is less systematic (Jacobson, 2003; Combined Aerosol Radiation and Aerosol Cloud Lohmann, 2008; Suzuki et al., 2008) and reasons for differences among Interactions the models in this category are less well understood. In addition to ERFari, there are changes due to aerosol cloud interac- For our expert judgment of ERFari+aci a subset of GCM studies, which tions (ERFaci). Because of nonlinearities in forcings and rapid adjust- strived for a more complete and consistent treatment of aerosol cloud ments, the total effective forcing ERFari+aci does not necessarily equal interactions (by incorporating either convective or mixed-phase process- the sum of the ERFari and ERFaci calculated separately. Moreover a es) was identified and scrutinized. The ERFari+aci derived from these strict separation is often difficult in either state of the art models or models is somewhat less negative than in the full suite of models, and observations. Therefore we first assess ERFari+aci and postpone our ranges from 1.68 and 0.81 W m 2 with a median value of 1.38 W assessment of ERFaci to Section 7.5.4. For similar reasons, we focus m 2. Because in some cases a number of studies have been performed primarily on ERF rather than RF. with the same GCM, in what might be described as an evolving effort, our assessment is further restricted to the best (usually most recent) ERFari+aci is defined as the change in the net radiation at the TOA from estimate by each modelling group (see black symbols in Figure 7.19 and pre-industrial to present day. Climate model estimates of ERFari+aci in Table 7.4). This ensures that no single GCM is given a disproportionate the literature differ for a number of reasons. (1) The reference years weight. Further, we consider only simulations not constrained by the for pre-industrial and present-day conditions vary between estimates. historical temperature rise, motivated by the desire to emphasize a pro- Studies can use 1750, 1850, or 1890 for pre-industrial; early estimates cess-based estimate. Although it may be argued that greater uncertain- of ERFari+aci used present-day emissions for 1985, whereas most ty is introduced by giving special weight to models that only incorporate newer estimates use emissions for the year 2000. (2) The processes more comprehensive treatments of aerosol cloud interactions, and for they include also differ: aerosol cloud interactions in large-scale liquid processes that (as Section 7.4 emphasizes) are on the frontier of under- stratiform clouds are typically included, but studies can also include standing, it should be remembered that aerosol cloud interactions for aerosol cloud interactions for mixed-phase, convective clouds and/or liquid-phase clouds remain very uncertain. Although the understanding cirrus clouds. (3) The way in which ERFari+aci is calculated can also and treatment of aerosol cloud interactions in  convective or  mixed- differ between models, with some earlier studies only reporting the phase clouds are also very uncertain, as discussed in Section 7.4.4, we change in shortwave radiation. Changes in longwave radiation arise exercise our best judgment of their influence. from rapid adjustments, or from aerosol cloud interactions involving mixed-phase or ice clouds (e.g., Storelvmo et al., 2008b, 2010; Ghan et A less negative ERFari+aci ( 0.93 to 0.45 W m 2 with a median of al., 2012), and tend to partially offset changes in shortwave radiation. 0.85 W m 2, Figure 7.19 and Table 7.4) is found in studies that use vari- In the estimates discussed below and those shown in Figure 7.19, we ability in the present day satellite record to infer aerosol cloud inter- refer to estimates of the change in net (shortwave plus longwave) TOA actions, or that constrain GCM parameterizations to optimize agree- radiation whenever possible, but report changes in shortwave radia- ment with satellite observations. Because some groups have published tion when changes in net radiation are not available. While this mostly multiple estimates as better information became available, only their affects earlier studies, the subset of models that we concentrate on latest study was incorporated into this assessment. Moreover, if a study all include both shortwave and longwave radiative effects. However, did not report ERFari+aci but only evaluated changes in ERFaci, their for the sake of comparison, the satellite studies must be adjusted to individual estimate was combined with the average ERFari of 0.45 W account for missing longwave contributions as explained below. m 2 from Section 7.5.2. Likewise, those (all but one) studies that only accounted for changes in shortwave radiation when computing ERFaci Early GCM estimates of ERFari+aci only included aerosol cloud inter- were corrected by adding a constant factor of +0.2 W m 2, taken from actions in liquid phase stratiform clouds; some of these were already the lower range of the modeled longwave effects which varied from considered in AR4. Grouping these early estimates with similar (liquid +0.2 to +0.6 W m 2 in the assessed models. These procedures result in phase only) estimates from publications since the AR4 yields a median the final estimates of ERFari+aci shown as black symbols in Figure 7.19 value of ERFari+aci of 1.5 W m 2 with a 5 to 95% range between 2.4 and in Table 7.4. This resulted in a median ERFari+aci of 0.85 W m 2 and 0.6 W m 2 (Figure 7.19). In those studies that attempt a more for satellite-based ERFari+aci estimates. Results of pure satellite-based complete representation of aerosol cloud interactions, by including studies are sensitive to the spatial scale of measurements (Grandey aerosol cloud interactions in mixed-phase and/or convective cloud, and Stier, 2010; McComiskey and Feingold, 2012; Section 7.4.2.2), as the magnitude of the ERF tends to be somewhat smaller (see Figure well as to how pre-industrial conditions and variations between pre- 7 7.19). The physical explanation for the mixed-phase reduction in the industrial and present-day conditions are inferred from the observed 618 Clouds and Aerosols Chapter 7 (a) 0 Aerosol Forcing (W m-2) -1 -2 -3 RFari ERFaci ERFari+aci with mixed-phase ACI CMIP5/ACCMIP AR4 AR5 with convective ACI satellites (b) 0 Aerosol Forcing (W m-2) -1 -2 Highlighted Highlighted Expert CMIP5 AR4, AR5 All GCMs Satellites Judgement -3 Figure 7.19 | (a) GCM studies and studies involving satellite estimates of RFari (red), ERFaci (green) and ERFari+aci (blue in grey-shaded box). Each symbol represents the best estimate per model and paper (see Table 7.3 for references). The values for RFari are obtained from the CMIP5 models. ERFaci and ERFari+aci studies from GCMs on liquid phase stratiform clouds are divided into those published prior to and included in AR4 (labelled AR4, triangles up), studies published after AR4 (labelled AR5, triangles down) and from the CMIP5/ACCMIP models (filled circles). GCM estimates that include adjustments beyond aerosol cloud interactions in liquid phase stratiform clouds are divided into those includ- ing aerosol cloud interactions in mixed-phase clouds (stars) and those including aerosol cloud interactions in convective clouds (diamonds). Studies that take satellite data into account are labelled as satellites . Studies highlighted in black are considered for our expert judgement of ERFari+aci. (b) Whisker boxes from GCM studies and studies involving satellite data of RFari, ERFaci and ERFari+aci. They are grouped into RFari from CMIP5/ACCMIP GCMs (labelled CMIP5 in red), ERFaci from GCMs (labelled AR4, AR5 in green), all estimates of ERFari+aci shown in the upper panel (labelled All in blue), ERFari+aci from GCMs highlighted in the upper panel (labelled Highlighted GCMs in blue), ERFari+aci from satellites highlighted in the upper panel (labelled Highlighted Satellites in blue), and our expert judgement based on estimates of ERFari+aci from these GCM and satellite studies (labelled Expert Judgement in blue). Displayed are the averages (cross sign), median values (middle line), 17th and 83th percentiles (likely range shown as box boundaries) and 5th and 95th percentiles (whiskers). References for the individual estimates are provided in Table 7.3. Table 7.4 includes the values of the GCM and satellite studies considered for the expert judgement of ERFari+aci that are highlighted in black. Table 7.3 | List of references for each category of estimates displayed in Figure 7.19. Estimate Acronym References Effective radiative forcing due to aerosol cloud AR4 Lohmann and Feichter (1997); Rotstayn (1999); Lohmann et al., (2000); Ghan et al. (2001); Jones interactions (ERFaci) published prior to and considered et al. (2001); Rotstayn and Penner (2001); Williams et al. (2001); Kristjánsson (2002); Lohmann in AR4 (2002a); Menon et al. (2002); Peng and Lohmann (2003); Penner et al. (2003); Easter et al. (2004); Kristjánsson et al. (2005); Ming et al. (2005); Rotstayn and Liu (2005); Takemura et al. (2005); Johns et al. (2006); Penner et al. (2006); Quaas et al. (2006); Storelvmo et al. (2006) ERFaci published since AR4 AR5 Menon and Del Genio (2007); Ming et al. (2007b); Kirkevag et al. (2008); Seland et al. (2008); Storelvmo et al. (2008a); Hoose et al. (2009); Quaas et al. (2009); Rotstayn and Liu (2009); Chen et al. (2010); Ghan et al. (2011); Penner et al. (2011); Makkonen et al. (2012a); Takemura (2012); Kirkevag et al. (2013) Effective radiative forcing due to aerosol radiation AR4 Lohmann and Feichter (2001); Quaas et al. (2004); Menon and Rotstayn (2006); Quaas et al. (2006) and aerosol cloud interactions (ERFari+aci) in liquid phase stratiform clouds published prior to AR4 ERFari+aci in liquid phase stratiform clouds AR5 Lohmann et al. (2007); Rotstayn et al. (2007); Posselt and Lohmann (2008); Posselt and Lohmann published since AR4 (2009); Quaas et al. (2009); Salzmann et al. (2010); Bauer and Menon (2012); Gettelman et al. (2012); Ghan et al. (2012); Makkonen et al. (2012a); Takemura (2012); Kirkevag et al. (2013) ERFari+aci in liquid and mixed-phase stratiform clouds with mixed- Lohmann (2004); Jacobson (2006); Lohmann and Diehl (2006); Hoose et al. (2008); Storelvmo et al. (2008a); phase clouds Lohmann and Hoose (2009); Hoose et al. (2010b); Lohmann and Ferrachat (2010); Salzmann et al. (2010); Storelvmo et al. (2010) ERFari+aci in stratiform and convective clouds with ­convective Menon and Rotstayn (2006); Menon and Del Genio (2007); Lohmann (2008); clouds Koch et al. (2009a); Unger et al. (2009); Wang et al. (2011b) ERFari+aci including satellite observations Satellites Lohmann and Lesins (2002); Sekiguchi et al. (2003); Quaas et al. (2006); Lebsock et al. (2008); 7 Quaas et al. (2008); Quaas et al. (2009); Bellouin et al. (2013) 619 Chapter 7 Clouds and Aerosols Table 7.4 | List of ERFari+aci values (W m 2) considered for the expert judgement of ERFari+aci (black symbols in Figure 7.19). For the GCM studies only the best estimate per modelling group is used. For satellite studies the estimates are corrected for the ERFari and for the longwave component of ERFari+aci when these are not included (see text). Category Best Estimate Climate Model and/or Satellite Instrument Reference with mixed-phase clouds 1.55 CAM Oslo Hoose et al. (2010b) with mixed-phase clouds 1.02 ECHAM Lohmann and Ferrachat (2010) with mixed-phase clouds 1.68 GFDL Salzmann et al. (2010) with mixed-phase clouds 0.81 CAM Oslo Storelvmo et al. (2008b; 2010) with convective clouds 1.50 ECHAM Lohmann (2008) with convective clouds 1.38 GISS Koch et al. (2009a) with convective clouds 1.05 PNNL-MMF Wang et al. (2011b) Satellite-based 0.85 ECHAM + POLDER Lohmann and Lesins (2002) Satellite-based 0.93 AVHRR Sekiguchi et al. (2003) Satellite-based 0.67 CERES / MODIS Lebsock et al. (2008) Satellite-based 0.45 CERES / MODIS Quaas et al. (2008) Satellite-based 0.95 Model mean + MODIS Quaas et al. (2009) Satellite-based 0.85 MACC + MODIS Bellouin et al. (2013) AVHRR = Advanced Very High Resolution Radiometer. MACC = Monitoring Atmospheric Composition and Climate. POLDER = Polarization and Directionality of the Earth s Reflectances. CERES = Clouds and the Earth s Radiant Energy System. MODIS = Moderate Resolution Imaging Spectrometer. variability in present-day aerosol and cloud properties (Quaas et al., changes in aerosol, consistent with other fine-scale modelling studies 2011; Penner et al., 2012). In addition, all (model- and satellite-based) discussed in Section 7.4. estimates of ERFari+aci are very sensitive to the assumed pre-industrial or natural cloud droplet concentration (Hoose et al., 2009). The large Based on the above considerations, we assess ERFari+aci using expert spatial scales of satellite measurements relative to in situ measure- judgement to be 0.9 W m 2 with a 5 to 95% uncertainty range of ments generally suggest smaller responses in cloud droplet number 1.9 to 0.1 W m 2 (medium confidence), and a likely range of 1.5 to increases for a given aerosol increase (Section 7.4.2.2). Satellite studies, 0.4 W m 2. These ranges account for the GCM results by allowing for however, show a strong effect of aerosol on cloud amount, which could an ERFari+aci somewhat stronger than what is estimated by the sat- be a methodological artefact as GCMs associate clouds with humidity ellite studies with a longer tail in the direction of stronger effects, but and aerosol swelling (Quaas et al., 2010). There are thus possible biases (for reasons given above) give less weight to the early GCM estimates in both directions, so the sign and magnitude of any net bias is not clear. shown in Figure 7.19. The ERFari+aci can be much larger regionally but the global value is consistent with several new lines of evidence In large-scale models for which cloud-scale circulations are not explic- suggesting less negative estimates of aerosol cloud interactions than itly represented, it is difficult to capture all relevant cloud controlling the corresponding estimate in Chapter 7 of AR4 of 1.2 W m 2. The processes (Section 7.2.2). Because the response of clouds to aerosol AR4 estimate was based mainly on GCM studies that did not take perturbations depends critically on the interplay of poorly understood secondary processes (such as aerosol effects on mixed-phase and/or physical processes, global model-based estimates of aerosol cloud convective clouds) into account, did not benefit as much from the use interactions remain uncertain (Section 7.4). Moreover, the connection of the recent satellite record, and did not account for the effect of between the aerosol amount and cloud properties is too direct in the rapid adjustments on the longwave radiative budget. This uncertainty large-scale modelling studies (as it relies heavily on the autoconversion range is slightly smaller than the 2.3 to 0.2 W m 2 in AR4, with a rate). Because of this, GCMs tend to overestimate the magnitude of the less negative upper bound due to the reasons outlined above. The best aerosol effect on cloud properties (Section 7.4.5; see also discussion in estimate of ERFari+aci is not only consistent with the studies allowing Section 7.5.4). This view has some support from studies that begin to cloud-scale responses (Wang et al., 2011b; Khairoutdinov and Yang, incorporate some cloud, or cloud-system scale responses to aerosol 2013) but also is in line with the average ERFari+aci from the CMIP5/ cloud interactions. For instance, in an attempt to circumvent some of ACCMIP models (about 1 W m 2, see Table 7.5), which as a whole difficulties of parameterizing clouds, some groups (e.g., Wang et al., reproduce the observed warming of the 20th century (see Chapter 10). 2011b) have begun developing modelling frameworks that can explic- Studies that infer ERFari+aci from the historical temperature rise are itly represent cloud-scale circulations, and hence the spatio-temporal discussed in Section 10.8. covariances of cloud-controlling processes. Another group (Khairout- dinov and Yang, 2013) has used the same cloud-resolving model in a 7.5.4 Estimate of Effective Radiative Forcing from radiative convective equilibrium approach, and compared the relative ­ Aerosol Cloud Interactions Alone contribution of aerosol cloud interactions to warming from the dou- bling of atmospheric CO2. In both studies a smaller ( 1.1 and 0.8 W ERFaci refers to changes in TOA radiation since pre-industrial times m 2, respectively) ERFari+aci than for the average GCM was found. due only to aerosol cloud interactions, i.e., albedo effects augmented Furthermore, the study best resolving the cloud-scale circulations by possible changes in cloud amount and lifetime. As stated in Sec- 7 (Wang et al., 2011b) found little change in cloud amount in response to tion 7.5.1, we do not discuss RFaci by itself because it is an academic 620 Clouds and Aerosols Chapter 7 Table 7.5 | Estimates of aerosol 1850 2000 effective radiative forcing (ERF, in W m 2) in some of the CMIP5 and ACCMIP models. The ERFs are estimated from fixed-sea-surface ­ temperature (SST) experiments using atmosphere-only version of the models listed. Different models include different aerosol effects. The CMIP5 and ACCMIP protocols differ, hence differences in forcing estimates for one model. Modelling Group Model Name ERFari+aci from All Anthropogenic Aerosols ERFari+aci from Sulphate Aerosols Only CCCma CanESM2 0.87 0.90 CSIRO-QCCCE CSIRO-Mk3-6-0b 1.41 1.10 GFDL GFDL-AM3 1.60 ( 1.44a) 1.62 GISS GISS-E2-R b 1.10a 0.61 GISS GISS-E2-R-TOMASb 0.76a IPSL IPSL-CM5A-LR 0.72 0.71 LASG-IAP FGOALS-s2c 0.38 0.34 MIROC MIROC-CHEM b 1.24a MIROC MIROC5 1.28 1.05 MOHC HadGEM2-A 1.22 1.16 MRI MRI-CGM3 1.10 0.48 NCAR NCAR-CAM5.1b 1.44a NCC NorESM1-M 0.99 Ensemble mean 1.08 Standard deviation +0.32 Notes: a From ACCMIP (Shindell et al., 2013). ACCMIP = Atmospheric Chemistry and Climate Model Intercomparison Project. b These models include the black carbon on snow effect. CMIP5 = Coupled Model Intercomparison Project Phase 5. c This model does not include the ERF from aerosol cloud interactions. c ­ onstruct. However, processes in GCMs that tend to affect RFaci such There is conflicting evidence for the importance of ERFaci associated as changes to the droplet size distribution breadth (e.g., Rotstayn with cirrus, ranging from a statistically significant impact on cirrus cov- and Liu, 2005) will also affect ERFaci. Early studies evaluated just the erage (Hendricks et al., 2005) to a very small effect (Liu et al., 2009). change in shortwave radiation or cloud radiative effect for ERFaci, but Penner et al. (2009) obtained a rather large negative RFaci of anthro- lately the emphasis has changed to report changes in net TOA radia- pogenic ice-forming aerosol on upper tropospheric clouds of 0.67 to tion for ERFaci. As discussed in Section 7.5.3, evaluating ERFaci from 0.53 W m 2; however, they ignored potential compensating effects changes in net TOA radiation is the only correct method, and therefore on lower lying clouds. A new study based on two GCMs and different this is used whenever possible also in this section. However some ear- ways to deduce ERFaci on cirrus clouds estimates ERFaci to be +0.27 lier estimates of ERFaci only reported changes in cloud radiative effect, +/- 0.1 W m 2 (Gettelman et al., 2012), thus rendering aerosol effects on which we show in Figure 7.19 as the last resort. However, estimates cirrus clouds smaller than previously estimated and of opposite sign. of changes in cloud radiative effect can differ quite substantially from those in net radiation if rapid adjustments to aerosol cloud interac- One reason for having switched to providing an expert judgment esti- tions induce changes in clear-sky radiation. mate of ERFari+aci rather than of ERFaci is that the individual con- tributions are very difficult to disentangle. The individual components Cloud amount and lifetime effects manifest themselves in GCMs via can be isolated only if linearity of ERFari and ERFaci is assumed but their representation of autoconversion of cloud droplets to rain, a pro- there is no a priori reason why the ERFs should be additive because cess that is inversely dependent on droplet concentration. Thus, ERFaci by definition they occur in a system that is constantly readjusting to and ERFari+aci have been found to be very sensitive to the autoconver- multiple nonlinear forcings. Nevertheless assuming additivity, ERFaci sion parameterization (Rotstayn, 2000; Golaz et al., 2011; Wang et al., could be obtained as the difference between ERFari+aci and ERFari. 2012). GCMs probably underestimate the extent to which precipitation This yields an ERFaci estimate of 0.45 W m 2, that is, much smaller is formed via raindrop accretion of cloud droplets (Wood, 2005), a pro- than the median ERFaci value of 1.4 W m 2 (see above and Figure cess that is insensitive to aerosol and droplet concentration. Indeed, 7.19). This discrepancy arises because the GCM estimates of ERFaci do models that remedy this imbalance in precipitation formation between not consider secondary processes and because these studies are not autoconversion and accretion (Posselt and Lohmann, 2009; Wang et necessarily conducted with the same GCMs that estimate ERFari+aci. al., 2012) exhibit weaker ERFaci in agreement with small-scale stud- This difference could also be a measure of the non-linearity of the ERFs. ies that typically do not show a systematic increase in cloud lifetime A 90% uncertainty range of 1.2 to 0 W m 2 is adopted for ERFaci, because of entrainment and because smaller droplets also evaporate which accounts for the error covariance between ERFari and ERFaci more readily (Jiang et al., 2006; Bretherton et al., 2007). Bottom-up and the larger uncertainty on the lower bound. In summary, there is estimates of ERFaci are shown in Figure 7.19. Their median estimate of much less confidence associated with the estimate of ERFaci than with 1.4 W m 2 is more negative than our expert judgement of ERFari+aci the estimate of ERFari+aci. because of the limitations of these studies discussed above. 7 621 Chapter 7 Clouds and Aerosols Frequently Asked Questions FAQ 7.2 | How Do Aerosols Affect Climate and Climate Change? Atmospheric aerosols are composed of small liquid or solid particles suspended in the atmosphere, other than larger cloud and precipitation particles. They come from natural and anthropogenic sources, and can affect the climate in multiple and complex ways through their interactions with radiation and clouds. Overall, models and observations indicate that anthropogenic aerosols have exerted a cooling influence on the Earth since pre-industrial times, which has masked some of the global mean warming from greenhouse gases that would have occurred in their absence. The projected decrease in emissions of anthropogenic aerosols in the future, in response to air quality policies, would eventually unmask this warming. Atmospheric aerosols have a typical lifetime of one day to two weeks in the troposphere, and about one year in the stratosphere. They vary greatly in size, chemical composition and shape. Some aerosols, such as dust and sea spray, are mostly or entirely of natural origin, while other aerosols, such as sulphates and smoke, come from both natural and anthropogenic sources. Aerosols affect climate in many ways. First, they scatter and absorb sunlight, which modifies the Earth s radiative balance (see FAQ.7.2, Figure 1). Aerosol scattering generally makes the planet more reflective, and tends to cool the climate, while aerosol absorption has the opposite effect, and tends to warm the climate system. The balance between cooling and warming depends on aerosol properties and environmental conditions. Many observational studies have quantified local radiative effects from anthropogenic and natural aerosols, but determining their (continued on next page) Aerosol-radiation interactions Scattering aerosols (a) (a) (b) Cooling Aerosols scatter solar radiation. Less solar radiation The atmospheric circulation and mixing processes spread reaches the surface, which leads to a localised cooling. the cooling regionally and in the vertical. Absorbing aerosols (c) (d) (c) Warming Aerosols absorb solar radiation. This heats the aerosol At the larger scale there is a net warming of the surface and layer but the surface, which receives less solar radiation, atmosphere because the atmospheric circulation and can cool locally. mixing processes redistribute the thermal energy. FAQ 7.2, Figure 1 | Overview of interactions between aerosols and solar radiation and their impact on climate. The left panels show the instantaneous radiative effects of aerosols, while the right panels show their overall impact after the climate system has responded to their radiative effects. 7 622 Clouds and Aerosols Chapter 7 FAQ 7.2 (continued) global impact requires satellite data and models. Aerosol-cloud interactions One of the remaining uncertainties comes from black carbon, an absorbing aerosol that not only is more difficult to measure than scattering (a) aerosols, but also induces a complicated cloud response. Most studies agree, however, that the overall radiative effect from anthropogenic aerosols is to cool the planet. Aerosols also serve as condensation and ice nucleation sites, on which cloud droplets and ice particles can form (see FAQ.7.2, Figure 2). When influenced by more aerosol particles, clouds of liquid water droplets tend to have more, but smaller droplets, which causes these clouds to reflect more solar radiation. There are however many other pathways for aerosol cloud inter- actions, particularly in ice or mixed liquid and Aerosols serve as cloud condensation nuclei upon which ice clouds, where phase changes between liquid droplets can form. liquid and ice water are sensitive to aerosol con- centrations and properties. The initial view that an increase in aerosol concentration will also (b) increase the amount of low clouds has been challenged because a number of counteracting processes come into play. Quantifying the overall impact of aerosols on cloud amounts and proper- ties is understandably difficult. Available studies, based on climate models and satellite observa- tions, generally indicate that the net effect of anthropogenic aerosols on clouds is to cool the climate system. Because aerosols are distributed unevenly in the atmosphere, they can heat and cool the climate system in patterns that can drive changes in the More aerosols result in a larger concentration of smaller weather. These effects are complex, and hard to droplets, leading to a brighter cloud. However there are simulate with current models, but several stud- many other possible aerosol cloud precipitation ies suggest significant effects on precipitation in processes which may amplify or dampen this effect. certain regions. FAQ 7.2, Figure 2 | Overview of aerosol cloud interactions and their impact Because of their short lifetime, the abundance of on climate. Panels (a) and (b) represent a clean and a polluted low-level cloud, aerosols and their climate effects have varied respectively. over time, in rough concert with anthropogenic emissions of aerosols and their precursors in the gas phase such as sulphur dioxide (SO2) and some volatile organic compounds. Because anthropogenic aerosol emissions have increased substantially over the industrial period, this has counteracted some of the warming that would otherwise have occurred from increased concentrations of well mixed greenhouse gases. Aerosols from large volcanic eruptions that enter the stratosphere, such as those of El Chichón and Pinatubo, have also caused cooling periods that typically last a year or two. Over the last two decades, anthropogenic aerosol emissions have decreased in some developed countries, but increased in many developing countries. The impact of aerosols on the global mean surface temperature over this particular period is therefore thought to be small. It is projected, however, that emissions of anthropogenic aero- sols will ultimately decrease in response to air quality policies, which would suppress their cooling influence on the Earth s surface, thus leading to increased warming. 7 623 Chapter 7 Clouds and Aerosols 7.6 Processes Underlying Precipitation is some evidence that the sub-tropical dry zones are expanding (Sec- Changes tion 7.2.5.2 and Section 2.7.5), both as a result of the tropical conver- gence zones narrowing (Neelin et al., 2006; Chou et al., 2009), and the 7.6.1 Introduction storm tracks moving poleward (Allen et al., 2012) and strengthening (O Gorman and Schneider, 2008). In this section we outline some of the main processes thought to con- trol the climatological distribution of precipitation and precipitation The wet-get-wetter and dry-get-drier response that is evident at extremes. Emphasis is placed on large-scale constraints that relate large scales over oceans can be understood as a simple consequence to processes, such as changes in the water vapour mixing ratio that of a change in the water vapour content carried by circulations, which accompany warming, or changes in atmospheric heating rates that otherwise are little changed (Mitchell et al., 1987; Held and Soden, accompany changing GHG and aerosol concentrations, which are dis- 2006). Wet regions are wet because they import moisture from dry cussed earlier in this chapter. The fidelity with which large-scale models regions, increasingly so with warmer temperatures. These ideas have represent different aspects of precipitation, ranging from the diurnal withstood additional analysis and scrutiny since AR4 (Chou et al., cycle to extremes, is discussed in Section 9.4.1. Building on, and adding 2009; Seager et al., 2010; Muller and O Gorman, 2011), are evident to, concepts developed here, Section 11.3.2 presents near term pro- in 20th century precipitation trends (Allan and Soden, 2007; Zhang et jections of changes in regional precipitation features. Projections of al., 2007b; see Section 2.5.1) and are assessed on different time scales changes on longer time scales, again with more emphasis on regionally in Chapters 11 and 12. Because the wet-get-wetter argument implies specific features and the coupling to the land surface, are presented that precipitation changes associated with warming correlate with the in Section 12.4.5. The effect of processes discussed in this section on present-day pattern of precipitation, biases in the simulation of pres- specific precipitation systems, such as the monsoon, the intertropical ent-day precipitation will lead to biases in the projections of future convergence zones, or tropical cyclones are presented in Chapter 14. precipitation change (Bony et al., 2013). Precipitation is sustained by the availability of moisture and energy. The wet-get-wetter and dry-get-drier response pattern is mitigated, In a globally averaged sense the oceans provide an unlimited supply particularly in the dry regions, by the anticipated slowdown of the of moisture, so that precipitation formation is energetically limited atmospheric circulation (as also discussed in Section 7.2.5.3), as well (Mitchell et al., 1987). Locally precipitation can be greatly modified by as by gains from local surface evaporation. The slowdown within the limitations in the availability of moisture (for instance over land) and descent regions can be partly understood as a consequence of the the effect of circulation systems, although these too are subject to local change in the dry static stability of the atmosphere with warming. And energetic constraints (Neelin and Held, 1987; Raymond et al., 2009). although this line of argument is most effective for explaining changes There are many ways to satisfy these constraints, and climate models over the ocean (Chou et al., 2009; Bony et al., 2013), it can also be still exhibit substantial biases in their representation of the spatio-tem- used to understand the GCM land responses to some extent (Muller poral distribution of precipitation (Stephens et al., 2010; Liepert and and O Gorman, 2011). Previdi, 2012; Section 9.5). Nonetheless, through careful analysis, it is possible to identify robust features in the simulated response of precip- The non-uniform nature of surface warming induces regional circula- itation to changes in precipitation drivers. In almost every case these tion shifts that affect precipitation trends. In the tropics SSTs warm can be related to well understood processes, as described below. more where winds are weak and thus are less effective in damping surface temperature anomalies, and precipitation systematically shifts 7.6.2 The Effects of Global Warming on Large-Scale to regions that warm more (Xie et al., 2010). The greater warming over Precipitation Trends land, and its regional variations, also affect the regional distributions of precipitation (Joshi et al., 2008). However, low understanding of soil The atmospheric water vapour mixing ratio is expected to increase with moisture precipitation feedbacks complicates interpretations of local temperature roughly following the saturation value (e.g., with increas- responses to warming over land (Hohenegger et al., 2009), so that the es in surface values ranging from 6 to 10% °C 1 and larger increases effect of warming on precipitation at the scale of individual catch- aloft, see Section 7.2.4.1). Increases in global mean precipitation are, ments is not well understood. Some broad-scale responses, particularly however, constrained by changes in the net radiative cooling rate of over the ocean, are more robust and relatively well understood. the troposphere. GCMs, whose detailed treatment of radiative trans- fer provides a basis for calculating these energetic limitations, sug- 7.6.3 Radiative Forcing of the Hydrological Cycle gest that for the CO2 forcing, globally-averaged precipitation increases with global mean surface temperature at about 1 to 3% °C 1 (Mitch- In the absence of a compensating temperature change, an increase in ell et al., 1987; Held and Soden, 2006; Richter and Xie, 2008). Pre- well-mixed GHG concentrations tends to reduce the net radiative cool- cipitation changes evince considerable regional variability about the ing of the troposphere. This reduces the rainfall rate and the strength globally averaged value; generally speaking precipitation is expected of the overturning circulation (Andrews et al., 2009; Bony et al., 2013), to increase in the wettest latitudes, whereas dry latitudes may even such that the increase in global mean precipitation would be 1.5 to see a precipitation decrease (Mitchell et al., 1987; Allen and Ingram, 3.5% °C 1 due to temperature alone but is reduced by about 0.5% °C 1 2002; Held and Soden, 2006). On smaller scales, or near precipitation due to the effect of CO2 (Lambert and Webb, 2008). The dynamic effects margins, the response is less clear due to model-specific, and less well are similar to those that result from the effect of atmospheric warming 7 understood, regional circulation shifts (Neelin et al., 2006), but there on the lapse rate, which also reduces the strength of the atmospheric 624 Clouds and Aerosols Chapter 7 overturning circulation (e.g., Section 7.6.2), and are robustly evident prominently, absorption of solar radiation by atmospheric aerosols over a wide range of models and model configurations (Bony et al., is understood to reduce the globally averaged precipitation. But this 2013; see also Figure 7.20). These circulation changes influence the effect may be offset by the tendency of absorbing aerosols to reduce regional response, and are more pronounced over the ocean, because the planetary albedo, thereby raising surface temperature, leading to asymmetries in the land-sea response to changing concentrations of more precipitation (Andrews et al., 2009). Heterogeneously distributed GHGs (Joshi et al., 2008) amplify the maritime and dampen or even precipitation drivers such as clouds, aerosols and tropospheric ozone reverse the terrestrial signal (Wyant et al., 2012; Bony et al., 2013). will also induce circulations that may amplify or dampen their local impact on the hydrological cycle (Ming et al., 2010; Allen et al., 2012; The dependence of the intensity of the hydrological cycle on the tro- Shindell et al., 2012). Such regional effects are discussed further in pospheric cooling rate helps to explain why perturbations having Chapter 14 for the case of aerosols. the same RF do not produce the same precipitation responses. Apart from the relatively small increase in absorption by atmospheric water 7.6.4 Effects of Aerosol Cloud Interactions on vapour, increased solar forcing does not directly affect the net tropo- Precipitation spheric cooling rate. As a result the hydrological cycle mostly feels the subsequent warming through its influence on the rate of tropospheric Aerosol cloud interactions directly influence the cloud microphysical cooling (Takahashi, 2009). This is why modeling studies suggest that structure, and only indirectly (if at all) the net atmospheric heating solar radiation management (geoengineering) methods that maintain rate, and for this reason have mostly been explored in terms of their a constant surface temperature will lead to a reduction in globally effect on the character and spatio-temporal distribution of precipita- averaged precipitation as well as different regional distributions of tion, rather than on the globally-averaged amount of precipitation. precipitation (Schmidt et al., 2012b; Section 7.7.3). The sensitivity of simulated clouds to their microphysical development Changes in cloud radiative effects, and aerosol RF can also be effec- (e.g., Fovell et al., 2009; Parodi and Emanuel, 2009) suggests that they tive in changing the net radiative heating rate within the troposphere may be susceptible to the availability of CCN and IN. For instance, an (Lambert and Webb, 2008; Pendergrass and Hartmann, 2012). Most increase in CCN favours smaller cloud droplets, which delays the onset of precipitation and the formation of ice particles in convective clouds (Rosenfeld and Woodley, 2001; Khain et al., 2005). It has been hypoth- Abrupt 4xCO2 in CMIP5 Coupled Model esized that such changes may affect the vertical distribution and total RCP 8.5 scenario at 4xCO2 amount of latent heating in ways that would intensify or invigorate 4xCO2 in AGCMs ( xed SST) convective storms, as measured by the strength and vertical extent 5 4xCO2 in aqua-planet AGCMs ( xed SST) of the convective updraughts (Andreae et al., 2004; Rosenfeld et al., Change in overturning strength (%) 2008; Rosenfeld and Bell, 2011; Tao et al., 2012). Support for the idea that the availability of CCN influences the vigour of convective systems 0 can be found in some modelling studies, but the strength, and even sign, of such an effect has been shown to be contingent on a variety of environmental factors (Seifert and Beheng, 2006; Fan et al., 2009; Khain, 2009; Seifert et al., 2012) as well as on modelling assumptions -5 (Ekman et al., 2011). Observational studies, based on large data sets that sample many -10 land global convective systems, report systematic correlations between aerosol ocean amount and cloud-top temperatures (Devasthale et al., 2005; Koren et al., 2010a; Li et al., 2011). Weekly cycles in cloud properties and 0 2 4 6 precipitation, wherein convective intensity, cloud cover or precipita- Tropical TS (C) tion increases during that part of the week when aerosol concentra- tions are largest, have also been reported (Bäumer and Vogel, 2007; Figure 7.20 | Illustration of the response of the large-scale atmospheric overturning Bell et al., 2008; Rosenfeld and Bell, 2011). Both types of studies have to warming (adapted from Bony et al., 2013). The overturning intensity is shown on the been interpreted in terms of an aerosol influence on convective cloud y-axis and is measured by the difference between the mean motion in upward moving systems. However, whether or not these findings demonstrate that a air and the mean motion in downward moving air. The warming is shown on the x-axis and is measured by the change in surface temperature averaged over the Tropics, Ts, greater availability of CCN systematically invigorates, or otherwise after an abrupt quadrupling of atmospheric CO2. The grey region delineates responses affects, convection remains controversial. Many of the weekly cycle for which Ts is zero by definition. Nearly one half of the final reduction in the intensity studies are disputed on statistical or other methodological grounds of the overturning is evident before any warming is felt, and can be associated with (Barmet et al., 2009; Stjern, 2011; Tuttle and Carbone, 2011; Sanchez- a rapid adjustment of the hydrological cycle to changes in the atmospheric cooling Lorenzo et al., 2012; Yuter et al., 2013). Even in cases where relation- rate accompanying a change in CO2. With warming the circulation intensity is further reduced. The rapid adjustment, as measured by the change in circulation intensity for ships between aerosol amount and some measure of convective inten- zero warming, is different over land and ocean. Over land the increase in CO2 initially sity appear to be unambiguous, the interpretation that this reflects an causes an intensification of the circulation. The result is robust in the sense that it is aerosol effect on the convection is less clear, as both aerosol properties apparent in all of the 15 CMIP5 models analysed, irrespective of the details of their and convection are strongly influenced by meteorological factors that 7 configuration. 625 Chapter 7 Clouds and Aerosols are not well controlled for (e.g., Boucher and Quaas, 2013). Studies tropics A. GCM only that have used CRMs to consider the net effect of aerosol cloud inter- B. Observed Trends actions integrated over many storms, or in more of a climate context 12 C. GCM Constrained by Obs D. CRM RCE wherein convective heating must balance radiative cooling within the Change in precipitation (% C-1) E. LES RCE atmosphere, also do not support a strong and systematic invigoration 10 C effect resulting from very large (many fold) changes in the ambient aerosol (Morrison and Grabowski, 2011; van den Heever et al., 2011; 8 Seifert et al., 2012; Khairoutdinov and Yang, 2013). Locally in space or E time, however, radiative processes are less constraining, leaving open B D D the possibility of stronger effects from localized or transient aerosol 6 perturbations. A temperature 4 only Because precipitation development in clouds is a time-dependent extratropics process, which proceeds at rates that are partly determined by the A 2 only A cloud microphysical structure (Seifert and Zängl, 2010), aerosol cloud interactions may lead to shifts in topographic precipitation to the lee- Increasing CO2 0 ward side of mountains when precipitation is suppressed, or to the Time average 24 hr Extreme 1hr Extreme windward side in cases when it is more readily initiated. Orographic clouds show a reduction in the annual precipitation over topographical Figure 7.21 | Estimate (5 to 95% range) of the increase in precipitation amount per degree Celsius of global mean surface temperature change. At left (blue) are climate barriers downwind of major urban areas in some studies (Givati and model predictions of changes in time-averaged global precipitation; at centre and right Rosenfeld, 2004; Jirak and Cotton, 2006) but not in others (Halfon et (orange) are predictions or estimates of the typical or average increase in local 99.9th al., 2009). Even in cases where effects are reported, the results have percentile extremes, over 24 hours (centre) and over one hour or less (right). Data proven sensitive to how the data are analysed (Alpert et al., 2008; are adapted from (A) GCM studies (Allen and Ingram, 2002; and Lambert and Webb, Levin and Cotton, 2009). 2008, for time average; O Gorman and Schneider, 2009 for extremes), (B) long-term trends at many sites globally (Westra et al., 2013), (C) GCMs constrained by present- day observations of extremes (O Gorman, 2012), (D, E) cloud-resolving model (CRM) In summary, it is unclear whether changes in aerosol cloud interac- and large-eddy simulation (LES) studies of radiative convective equilibrium (Muller et tions arising from changes in the availability of CCN or IN can affect, al., 2011; Romps, 2011). and possibly intensify, the evolution of individual precipitating cloud systems. Some observational and modelling studies suggest such an effect, but are undermined by alternative interpretations of the obser- daily rainfall to global temperature from this study were 10% °C 1 in vational evidence, and a lack of robustness in the modelling studies. the tropics (O Gorman, 2012), compared to 5% °C 1 predicted by the The evidence for systematic effects over larger areas and long time models in the extratropics, where they may be more reliable (O Gorman periods is, if anything, more limited and ambiguous. and Schneider, 2009). How precipitation extremes depend on temper- ature has also been explored using cloud resolving simulations (but 7.6.5 The Physical Basis for Changes in Precipitation only for tropical conditions) which produce similar increases in extreme Extremes instantaneous rain rate (Romps, 2011) and daily or hourly rain totals (Muller et al., 2011) within storms (Figure 7.21). Because these latter The physical basis for aerosol microphysical effects on convective studies are confined to small domains, they may exclude important intensity was discussed in the previous section. Here we briefly dis- synoptic or larger-scale dynamical changes such as increases in flow cuss process understanding of the effect of warming on precipitation convergence (Chou et al., 2009; Sugiyama et al., 2010). extremes; observed trends supporting these conclusions are presented in Section 2.6.2. By taking advantage of natural variability in the present day climate, a number of studies have correlated observed rainfall extremes with Precipitation within individual storms is expected to increase with the local temperature variations. In the extratropics, these studies docu- available moisture content in the atmosphere or near the surface rather ment sensitivities of extreme precipitation to temperature much higher than with the global precipitation (Allen and Ingram, 2002; Held and than those reported above (Lenderink and Van Meijgaard, 2008), but Soden, 2006), which leads to a 6 to 10% °C 1 increase, but with longer sensitivities vary with temperature (Lenderink et al., 2011), are often intervals between storms (O Gorman and Schneider, 2009). Because negative in the tropics (Hardwick Jones et al., 2010) and usually GCMs are generally poor at simulating precipitation extremes (Ste- strengthen at the shortest (e.g., hourly or less) time scales (e.g., Haerter phens et al., 2010) and predicted changes in a warmer climate vary et al., 2010; Hardwick Jones et al., 2010). However, local temperature (Kharin et al., 2007; Sugiyama et al., 2010), they are not usually thought changes may not be a good proxy for global warming because they of as a source of reliable information regarding extremes. Howev- tend to co-vary with other meteorological factors (such as humidity, er, a recent study (O Gorman, 2012) shows that GCM predictions of atmospheric stability, or wind direction) in ways that are uncharac- extremes can be constrained by observable relationships in the pres- teristic of changes in the mean temperature (see Section 7.2.5.7), ent day climate, and upon doing so become broadly consistent with and these other meteorological factors may dominate the observed the idea that extreme precipitation increases by 6 to 10% per °C of signal (e.g., Haerter and Berg, 2009). Thus, the idea that precipitation 7 warming. Central estimates of sensitivity of extreme (99.9th ­ ercentile) p extremes depend much more strongly on temperature than the 5 to 626 Clouds and Aerosols Chapter 7 10% increase per degree Celsius attributable to water vapour changes, 7.7.2.1 Stratospheric Aerosols remains controversial. Some SRM methods propose increasing the amount of stratospher- Following the AR4, studies have also continued to show that extremes ic aerosol to produce a cooling effect like that observed after strong in precipitation are associated with the coincidence of particular explosive volcanic eruptions (Budyko, 1974; Crutzen, 2006). Recent weather patterns (e.g., Lavers et al., 2011). We currently lack an ade- studies have used numerical simulations and/or natural analogues to quate understanding of what controls the return time and persistence explore the possibility of forming sulphuric acid aerosols by injecting of such rare events. sulphur-containing gases into the stratosphere (Rasch et al., 2008b). Because aerosols eventually sediment out of the stratosphere (within From the aforementioned model and observational evidence, there is roughly a year or less), these methods require replenishment to main- high confidence that the intensity of extreme precipitation events will tain a given level of RF. Research has also begun to explore the efficacy increase with warming, at a rate well exceeding that of the mean pre- of other types of aerosol particles (Crutzen, 2006; Keith, 2010; Ferraro cipitation. There is medium confidence that the increase is roughly 5 to et al., 2011; Kravitz et al., 2012) but the literature is much more limited 10% °C 1 warming but may vary with time scale, location and season. and not assessed here. The RF depends on the choice of chemical species (gaseous sulphur 7.7 Solar Radiation Management and dioxide (SO2), sulphuric acid (H2SO4) or sprayed aerosols), location(s), Related Methods rate and frequency of injection. The injection strategy affects particle size (Rasch et al., 2008a; Heckendorn et al., 2009; Pierce et al., 2010; 7.7.1 Introduction English et al., 2012), with larger particles producing less RF (per unit mass) and more rapid sedimentation than smaller particles, affecting Geoengineering also called climate engineering is defined as a the efficacy of the method. The aerosol size distribution is controlled broad set of methods and technologies that aim to deliberately alter by an evolving balance between new particle formation, condensation the climate system in order to alleviate impacts of climate change of vapour on pre-existing particles, evaporation of particles, coagula- (Keith, 2000; Izrael et al., 2009; Royal Society, 2009; IPCC, 2011). Two tion and sedimentation. Models that more fully account for aerosol main classes of geoengineering are often considered. Solar Radiation processes (Heckendorn et al., 2009; Pierce et al., 2010; English et al., Management (SRM) proposes to counter the warming associated with 2012) found smaller aerosol burdens, larger particles and weaker RF increasing GHG concentrations by reducing the amount of sunlight than earlier studies that prescribed the particle size over the particle absorbed at the surface. A related method seeks to alter high-altitude lifetime. Current modeling studies indicate that injection of sulphate cirrus clouds to reduce their greenhouse effect. Another class of geoen- aerosol precursors of at least 10 Mt S (approximately the amount of gineering called Carbon Dioxide Removal (CDR) is discussed in Section sulphur injected by the Mount Pinatubo eruption) would be needed 6.5. This section assesses how the climate system might respond to annually to maintain a RF of 4 W m 2, roughly equal but opposite to some proposed SRM methods and related methods thought to have that associated with a doubling of atmospheric CO2 (Heckendorn et al., the potential to influence the global energy budget by at least a few 2009; Pierce et al., 2010; Niemeier et al., 2011). Stratospheric aerosols tenths of a W m 2 but it does not assess technological or economi- may affect high clouds in the tropopause region, and one study (Kueb- cal feasibility, or consider methods targeting specific climate impacts beler et al., 2012) suggests significant negative forcing would result, (MacCracken, 2009). Geoengineering is quite a new field of research, but this is uncertain given limited understanding of ice nucleation in and there are relatively few studies focussed on it. Assessment of SRM high clouds (Section 7.4.4.4). is limited by (1) gaps in understanding of some important processes; (2) a relative scarcity of studies; and (3) a scarcity of studies using Along with its potential to mitigate some aspects of global warming, similar experimental design. This section discusses some aspects of the potential side effects of SRM must also be considered. Tilmes et al. SRM potential to mitigate global warming, outlines robust conclusions (2008; 2009) estimated that stratospheric aerosols SRM might increase where they are apparent, and evaluates uncertainties and potential chemical ozone loss at high latitudes and delay recovery of the Antarc- side effects. Additional impacts of SRM are assessed in Section 19.5.4 tic ozone hole (expected at the end of this century) by 30 to 70 years, of the WGII report, while some of the socio-economic issues are with changes in column ozone of 3 to 10% in polar latitudes and assessed in Chapters 3, 6 and 13 of the WGIII report. +3 to +5% in the tropics. A high latitude ozone loss is expected to increase UV radiation reaching the surface there, although the effect 7.7.2 Assessment of Proposed Solar Radiation would be partially compensated by the increase in attenuation by the Management Methods aerosol itself (Vogelmann et al., 1992; Tilmes et al., 2012). A decrease in direct radiation and increase in diffuse radiation reaching the Earth s A number of studies have suggested reducing the amount of sunlight surface would occur and would be expected to increase photosynthesis reaching the Earth by placing solid or refractive disks, or dust particles, in terrestrial ecosystems (Mercado et al., 2009; see Section 6.5.4) and in outer space (Early, 1989; Mautner, 1991; Angel, 2006; Bewick et al., decrease the efficiency of some solar energy technologies (see WGII 2012). Although we do not assess the feasibility of these methods, they AR5, Section 19.5.4). Models indicate that stratospheric aerosol SRM provide an easily described mechanism for reducing sunlight reach- would not pose a surface acidification threat with maximum acid dep- ing the planet, and motivate the idealized studies discussed in Section osition rates estimated to be at least 500 times less than the threshold 7.7.3. of concern for the most sensitive land ecosystems (Kuylenstierna et al., 7 627 Chapter 7 Clouds and Aerosols 2001; Kravitz et al., 2009); contributions to ocean acidification are also ERFari) also found an increase in the amplitude of the ERF by 30 to estimated to represent a very small fraction of that induced by anthro- 50% (Hill and Ming, 2012; Partanen et al., 2012), thereby making the pogenic CO2 emissions (Kravitz et al., 2009). There are other known aerosol seeding more effective than previous estimates that neglected side effects that remain unquantified, and limited understanding (and that effect. limited study) make additional impacts difficult to anticipate. In summary, evidence that cloud brightening methods are effective 7.7.2.2 Cloud Brightening and feasible in changing cloud reflectivity is ambiguous and subject to many of the uncertainties associated with aerosol cloud interactions Boundary layer clouds act to cool the planet, and relatively small more broadly. If cloud brightening were to produce large local chang- changes in albedo or areal extent of low cloud can have profound es in ERF, those changes would affect the local energy budget, with effects on the Earth s radiation budget (Section 7.2.1). Theoretical, further impacts on larger-scale oceanic and atmospheric circulations. modelling and observational studies show that the albedo of these Possible side effects accompanying such large and spatially heteroge- types of cloud systems are susceptible to changes in their droplet con- neous changes in ERF have not been systematically studied. centrations, but the detection and quantification of RF attributable to such effects is difficult to separate from meteorological variability (Sec- 7.7.2.3 Surface Albedo Changes tion 7.4.3.2). Nonetheless, by systematically introducing CCN into the marine boundary layer, it should be possible to locally increase bound- A few studies have explored how planetary albedo might be increased ary layer cloud albedo as discussed in Section 7.4.2. These ideas under- by engineering local changes to the albedo of urban areas, croplands, pin the method of cloud brightening, for instance through the direct grasslands, deserts and the ocean surface. Effects from whitening of injection (seeding) of sea-spray particles into cloud-forming air masses urban areas have been estimated to yield a potential RF of 0.17 W m 2 (Latham, 1990). An indirect cloud brightening mechanism through (Hamwey, 2007) although subsequent studies (Lenton and Vaughan, enhanced DMS production has also been proposed (Wingenter et al., 2009; Oleson et al., 2010; Jacobson and Ten Hoeve, 2012) suggest that 2007) but the efficacy of the DMS mechanism is disputed (Vogt et al., this estimate may be at the upper end of what is achievable. Larger 2008; Woodhouse et al., 2008). effects might be achievable by replacing native grassland or cropland with natural or bioengineered species with a larger albedo. A hypo- The seeding of cloud layers with a propensity to precipitate may thetical 25% increase in grassland albedo could yield a RF as large as change cloud structure (e.g., from open to closed cells) and/or increase 0.5 W m 2 (Lenton and Vaughan, 2009), with the maximum effect in liquid water content (Section 7.4.3.2.1), in either case changing albedo the mid-latitudes during summer (Ridgwell et al., 2009; Doughty et al., and producing strong negative forcing. A variety of methods have been 2011). The feasibility of increasing crop and grassland albedo remains used to identify which cloud regions are most susceptible to an aerosol unknown, and there could be side effects on photosynthetic activity, change (Oreopoulos and Platnick, 2008; Salter et al., 2008; Alterskjaer carbon uptake and biodiversity. The low albedo and large extent of et al., 2012). Marine stratocumulus clouds with relatively weak precipi- oceanic surfaces mean that only a small increase in albedo, for exam- tation are thought to be an optimal cloud type for brightening because ple, by increasing the concentration of microbubbles in the surface of their relatively low droplet concentrations, their expected increase in layer of the ocean (Evans et al., 2010; Seitz, 2011), could be sufficient cloud water in response to seeding (Section 7.4.3.2.1), and the longer to offset several W m 2 of RF by GHGs. Neither the extent of micro- lifetime of sea salt particles in non- or weakly precipitating environ- bubble generation and persistence required for a significant climate ments. Relatively strong local ERFaci ( 30 to 100 W m 2) would be impact, nor the potential side effects on the ocean circulation, air-sea required to produce a global forcing of 1 to 5 W m 2 if only the more fluxes and marine ecosystems have been assessed. susceptible clouds were seeded. 7.7.2.4 Cirrus Thinning Simple modelling studies suggest that increasing droplet concentra- tions in marine boundary layer clouds by a factor of five or so (to con- Although not strictly a form of SRM, proposals have been made to cool centrations of 375 to 1000 cm 3) could produce an ERFaci of about 1 the planet by reducing the coverage or longwave opacity of high thin W m 2 if 5% of the ocean surface area were seeded, and an ERFaci as cirrus clouds, which act to warm the surface through their greenhouse strong as 4 W m 2 if that fraction were increased to 75% (Latham et effect (see Section 7.2.1.2). A proposal for doing so involves adding al., 2008; Jones et al., 2009; Rasch et al., 2009). Subsequent studies efficient IN in regions prone to forming thin cirrus cloud (Mitchell and with more complete treatments of aerosol cloud interactions have pro- Finnegan, 2009). To the extent such a proposal is feasible, one mod- duced both stronger (Alterskjaer et al., 2012; Partanen et al., 2012) and elling study suggests that an ERFaci of as strong as 2 W m 2 could weaker (Korhonen et al., 2010a) changes. Because the initial response be achieved (Storelvmo et al., 2013), with further negative forcing to cloud seeding is local, high-resolution, limited-domain simulations caused by a reduction in humidity of the upper troposphere associat- are especially useful to explore the efficacy of seeding. One recent ed with the cloud changes. However, lack of understanding of cirrus study of this type (Wang et al., 2011a) found that cloud brightening cloud formation processes, as well as ice microphysical processes (Sec- is sensitive to cloud dynamical adjustments that are difficult to treat tion 7.4.4), makes it difficult to judge the feasibility of such a method, in current GCMs (Sections 7.4.3 and 7.5.3) and concluded that the particularly in light of the fact that increasing ice nucleation can also seeding rates initially proposed for cloud seeding may be insufficient increase cirrus opacity, under some circumstances producing an oppo- to produce the desired cloud brightening. Recent studies accounting site, positive forcing (Storelvmo et al., 2013). Side effects specific to the 7 for clear-sky brightening from increased aerosol concentrations (i.e., cirrus thinning method have not been investigated. 628 Clouds and Aerosols Chapter 7 7.7.3 Climate Response to Solar Radiation ­irradiance, which could approximate the radiative impact of space Management Methods reflectors. Reductions in solar irradiance in particular regions (over land or ocean, or in polar or tropical regions) could also provide useful As discussed elsewhere in this and other chapters of this assessment, information. Idealized simulations often focus on the effects of a com- significant gaps remain in our understanding of the climate response plete cancellation of the warming from GHGs, but the rate of warming to forcing mechanisms. Geoengineering is also a relatively new field of is occasionally explored by producing a negative RF that partially can- research. The gaps in understanding, scarcity of studies and diversity cels the anthropogenic forcing (e.g., Eliseev et al., 2009). They can also in the model experimental design make quantitative model evaluation provide insight into the climate response to other SRM methods, and and intercomparison difficult, hindering an assessment of the efficacy can provide a simple baseline for examining other SRM techniques. and side effects of SRM. This motivates dividing the discussion into two sections, one that assesses idealized studies that focus on conceptual The most comprehensive and systematic evaluation of idealized SRM issues and searches for robust responses to simple changes in the bal- to date is the Geoengineering Model Intercomparison Project (Kravitz ance between solar irradiance and CO2 forcing, and another discussing et al., 2011). Together with earlier model studies, this project found studies that more closely emulate specific SRM methods. robust surface temperature reductions when the total solar irradiance is reduced: when this reduction compensates for CO2 RF, residual effects 7.7.3.1 Climate Response in Idealized Studies appear regionally, but they are much smaller than the warming due to the CO2 RF alone (Kravitz et al., 2011; Schmidt et al., 2012b; Figures Perhaps the simplest SRM experiment that can be performed in a 7.22a, b and 7.23a d) The substantial warming from 4 × CO2 at high climate model consists of a specified reduction of the total solar latitudes (4°C to 18°C) is reduced to a warming of 0°C to 3°C near Quadrupling CO2 Quadrupling CO2 and Reducing Solar Input (a) 20 (b) 20 18 18 BNU-ESM HadCM3 CanESM2 HadGEM2-ES 16 16 CCSM4 IPSL-CM5A-LR Change in temperature (°C) 14 14 CESM-CAM5.1-FV MIROC-ESM EC-Earth MPI-ESM-LR 12 12 GISS-E2-R NorESM1-M 10 10 Ensemble Mean 8 8 6 6 4 4 2 2 0 0 90°S 60°S 30°S Eq 30°N 60°N 90°N 90°S 60°S 30°S Eq 30°N 60°N 90°N (c) 2 (d) 2 Change in precipitation (mm day-1) 1.5 1.5 1 1 0.5 0.5 0 0 0.5 0.5 1 1 90°S 60°S 30°S Eq 30°N 60°N 90°N 90°S 60°S 30°S Eq 30°N 60°N 90°N Latitude Latitude Figure 7.22 | Zonally and annually averaged change in surface air temperature (°C) for (a) an abrupt 4 × CO2 experiment and (b) an experiment where the 4 × CO2 forcing is balanced by a reduction in the total solar irradiance to produce a global top of the atmosphere flux imbalance of less than +/-0.1 W m 2 during the first 10 years of the simulation (Geoengineering Model Intercomparison Project (GeoMIP) G1 experiment; Kravitz et al., 2011). (c, d) Same as (a) and (b) but for the change in precipitation (mm day 1). The multi- model ensemble mean is shown with a thick black solid line. All changes are relative to the pre-industrial control experiment and averaged over years 11 to 50. The figure extends 7 the results from Schmidt et al. (2012b) and shows the results from an ensemble of 12 coupled ocean atmosphere general circulation models. 629 Chapter 7 Clouds and Aerosols the winter pole. The residual surface temperature changes are gener- For example, SRM will change heating rates only during daytime, but ally positive at mid- and high-latitudes, especially over continents, and increasing greenhouse effect changes temperatures during both day generally negative in the tropics (Bala et al., 2008; Lunt et al., 2008; and night, influencing the diurnal cycle of surface temperature even if Schmidt et al., 2012b). These anomalies can be understood in terms of compensation for the diurnally averaged surface temperature is correct. the difference between the more uniform longwave forcing associated with changes in long-lived GHGs and the less uniform shortwave forc- Although increasing CO2 concentrations lead to a positive RF that ing from SRM that has a stronger variation in latitude and season. The warms the entire troposphere, SRM produces a negative RF that tends compensation between SRM and CO2 forcing is inexact in other ways. to cool the surface. The combination of RFs produces an increase in Figure 7.23 | Multi-model mean of the change in surface air temperature (°C) averaged over December, January and February (DJF) for (a) an abrupt 4 × CO2 simulation and (b) an experiment where the 4 × CO2 forcing is balanced by a reduction in the total solar irradiance to produce a global top of the atmosphere flux imbalance of less than +/-0.1 W m 2 during the first 10 years of the simulation (Geoengineering Model Intercomparison Project (GeoMIP) G1 experiment; Kravitz et al., 2011). (c, d) Same as (a-b) but for June, July and August (JJA). (e h) same as (a d) but for the change in precipitation (mm day 1). All changes are relative to the pre-industrial control experiment and averaged over years 11 to 50. The figure extends the results from Schmidt et al. (2012b) and shows the results from an ensemble of 12 coupled ocean atmosphere general circulation models. Stippling denotes 7 agreement on the sign of the anomaly in at least 9 out of the 12 models. 630 Clouds and Aerosols Chapter 7 BNU-ESM HadCM3 High CO2 concentrations from anthropogenic emissions will persist CanESM2 HadGEM2-ES in the atmosphere for more than a thousand years in the absence of CCSM4 IPSL-CM5A-LR active efforts to remove atmospheric CO2 (see Chapter 6). If SRM were (a) 3.0 CESM-CAM5.1-FV MIROC-ESM used to counter positive forcing, it would be needed as long as the EC-Earth MPI-ESM-LR GISS-E2-R NorESM1-M CO2 concentrations remained high (Boucher et al., 2009). If GHG con- 2.5 Ensemble Mean centrations continued to increase, then the scale of SRM to offset the Change in temperature (°C) resulting warming would need to increase proportionally, amplifying 2.0 residual effects from increasingly imperfect compensation. Figure 7.24 shows projections of the globally averaged surface temperature and 1.5 precipitation changes associated with a 1% yr 1 CO2 increase, with and 1.0 without SRM. The scenario includes a hypothetical, abrupt termination of SRM at year 50, which could happen due to any number of unfore- 0.5 seeable circumstances. After this event, all the simulations predict a return to temperature levels consistent with the CO2 forcing within one 0.0 to two decades (high confidence), and with a large rate of temperature change (see also Irvine et al., 2012). Precipitation, which drops by 1% -0.5 over the SRM period, rapidly returns to levels consistent with the CO2 0 10 20 30 40 50 60 70 forcing upon SRM termination. The very rapid warming would prob- (b) ably affect ecosystem and human adaptation, and would also weaken 0.10 carbon sinks, accelerating atmospheric CO2 accumulation and contrib- Change in precipitation (mm day-1) 0.08 uting to further warming (Matthews and Caldeira, 2007). 0.06 Research suggests that this termination effect might be avoided if 0.04 SRM were used at a modest level and for a relatively short period of 0.02 time (less than a century) when combined with aggressive CO2 removal efforts to minimize the probability that the global mean temperature 0 might exceed some threshold (Matthews, 2010; Smith and Rasch, -0.02 2012). -0.04 7.7.3.2 Climate Response to Specific Solar Radiation -0.06 Management Methods -0.08 0 10 20 30 40 50 60 70 Year Several studies examined the model response to more realistic strat- ospheric aerosol SRM (Rasch et al., 2008b; Robock et al., 2008; Jones Figure 7.24 | Time series of globally averaged (a) surface temperature (°C) and (b) et al., 2010; Fyfe et al., 2013). These studies produced varying aerosol precipitation (mm day 1) changes relative to each model s 1 × CO2 reference simula- burdens, and RF and model responses also varied more strongly than tion. Solid lines are for simulations using solar radiation management (SRM) through in idealized experiments. Although these studies differ in details, their an increasing reduction of the total solar irradiance to balance a 1% yr 1 increase in climate responses were generally consistent with the idealized experi- CO2 concentration until year 50, after which SRM is stopped for the next 20 years ments described in Section 7.7.3.1. (Geoengineering Model Intercomparison Project (GeoMIP) G2 experiment; Kravitz et al., 2011). Dashed lines are for 1% CO2 increase simulations with no SRM. The multi-model ensemble mean is shown with thick black lines. Studies treating the interaction between the carbon cycle, the hydro- logic cycle, and SRM indicate that SRM could affect the temperature- driven suppression of some carbon sinks, and that the increased sto- stability that leads to less global precipitation as seen in Figures 7.22c, matal resistance with increased CO2 concentrations combined with d and 7.23e g (Bala et al., 2008; Andrews et al., 2010; Schmidt et al., less warming, may further affect the hydrological cycle over land (Mat- 2012b) and discussed in Section 7.6.3. The reduction in precipitation thews and Caldeira, 2007; Fyfe et al., 2013), with larger impacts on shows similarities to the climate response induced by the Pinatubo precipitation for stratospheric aerosol SRM than for a uniform reduc- eruption (Trenberth and Dai, 2007). Although the impact of changes in tion in incoming sunlight. the total solar irradiance on global mean precipitation is well under- stood and robust in models, there is less understanding and agree- Coupled ocean atmosphere sea ice models have also been used to ment among models in the spatial pattern of the precipitation changes. assess the climate impacts of cloud brightening due to droplet concen- Modelling studies suggest that some residual patterns may be robust tration changes (Jones et al., 2009; Rasch et al., 2009; Baughman et al., (e.g., approximately 5% reduction in precipitation over Southeast Asia 2012; Hill and Ming, 2012). The patterns of temperature and precipita- and the Pacific Warm Pool in June, July and August), but a physical tion change differ substantially between models. These studies showed explanation for these changes is lacking. Some model results indicate larger residual temperature changes than the idealized SRM studies, that an asymmetric hemispheric SRM forcing would induce changes in with more pronounced cooling over the regions of enhanced albedo. some regional precipitation patterns (Haywood et al., 2013). The cooling over the seeded regions (the marine stratocumulus regions) 7 631 Chapter 7 Clouds and Aerosols Frequently Asked Questions FAQ 7.3 | Could Geoengineering Counteract Climate Change and What Side Effects Might Occur? Geoengineering also called climate engineering is defined as a broad set of methods and technologies that aim to deliberately alter the climate system in order to alleviate impacts of climate change. Two distinct categories of geoengineering methods are usually considered: Solar Radiation Management (SRM, assessed in Section 7.7) aims to offset the warming from anthropogenic greenhouse gases by making the planet more reflective while Carbon Dioxide Removal (CDR, assessed in Section 6.5) aims at reducing the atmospheric CO2 concentration. The two cat- egories operate on different physical principles and on different time scales. Models suggest that if SRM methods were realizable they would be effective in countering increasing temperatures, and would be less, but still, effective in countering some other climate changes. SRM would not counter all effects of climate change, and all proposed geoengineering methods also carry risks and side effects. Additional consequences cannot yet be anticipated as the level of scientific understanding about both SRM and CDR is low. There are also many (political, ethical, and practi- cal) issues involving geoengineering that are beyond the scope of this report. Carbon Dioxide Removal Methods CDR methods aim at removing CO2 from the atmosphere by deliberately modifying carbon cycle processes, or by industrial (e.g., chemical) approaches. The carbon withdrawn from the atmosphere would then be stored in land, ocean or in geological reservoirs. Some CDR methods rely on biological processes, such as large-scale afforestation/ reforestation, carbon sequestration in soils through biochar, bioenergy with carbon capture and storage (BECCS) and ocean fertilization. Others would rely on geological processes, such as accelerated weathering of silicate and carbonate rocks on land or in the ocean (see FAQ.7.3, Figure 1). The CO2 removed from the atmosphere would (continued on next page) CARBON DIOXIDE REMOVAL Ocean A Fertilisation D Direct Air Capture G Alkalinity Biomass Energy B Addition With Carbon E To The Capture Ocean And Storage H F Accelerated C Weathering F Afforestation L K E I SOLAR RADIATION MANAGEMENT D Deployment Ocean Brightening G Of Space J With Microbubbles J Mirrors A B C Stratospheric Crop H Aerosol K Brightening Injection Marine Cloud Whitening I Brightening L Rooftops FAQ 7.3, Figure 1 | Overview of some proposed geoengineering methods as they have been suggested. Carbon Dioxide Removal methods (see Section 6.5 for details): (A) nutrients are added to the ocean (ocean fertilization), which increases oceanic productivity in the surface ocean and transports a fraction of the resulting biogenic carbon downward; (B) alkalinity from solid minerals is added to the ocean, which causes more atmospheric CO2 to dissolve in the ocean; (C) the weathering rate of silicate rocks is increased, and the dissolved carbonate minerals are transported to the ocean; (D) atmospheric CO2 is captured chemically, and stored either underground or in the ocean; (E) biomass is burned at an electric power plant with carbon capture, and the captured CO2 is stored either underground or in the ocean; and (F) CO2 is captured through afforestation and reforestation to be stored in land ecosystems. Solar Radiation Management methods (see Section 7.7 for details): (G) reflectors are placed in space to reflect solar radiation; (H) aerosols are injected in the stratosphere; (I) marine clouds are seeded in order to be made more reflective; (J) 7 microbubbles are produced at the ocean surface to make it more reflective; (K) more reflective crops are grown; and (L) roofs and other built structures are whitened. 632 Clouds and Aerosols Chapter 7 FAQ 7.3 (continued) then be stored in organic form in land reservoirs, or in inorganic form in oceanic and geological reservoirs, where it would have to be stored for at least hundreds of years for CDR to be effective. CDR methods would reduce the radiative forcing of CO2 inasmuch as they are effective at removing CO2 from the atmosphere and keeping the removed carbon away from the atmosphere. Some methods would also reduce ocean acidification (see FAQ 3.2), but other methods involving oceanic storage might instead increase ocean acidification if the carbon is sequestered as dissolved CO2. A major uncertainty related to the effectiveness of CDR methods is the storage capacity and the permanence of stored carbon. Permanent carbon removal and storage by CDR would decrease climate warming in the long term. However, non-permanent storage strategies would allow CO2 to return back to the atmosphere where it would once again contribute to warming. An intentional removal of CO2 by CDR methods will be partially offset by the response of the oceanic and terrestrial carbon reservoirs if the CO2 atmospheric concentration is reduced. This is because some oceanic and terrestrial carbon reservoirs will outgas to the atmosphere the anthropogenic CO2 that had previously been stored. To completely offset past anthropogenic CO2 emissions, CDR techniques would therefore need to remove not just the CO2 that has accumulated in the atmosphere since pre-industrial times, but also the anthropogenic carbon previously taken up by the terrestrial biosphere and the ocean. Biological and most chemical weathering CDR methods cannot be scaled up indefinitely and are necessarily limited by various physical or environmental constraints such as competing demands for land. Assuming a maximum CDR sequestration rate of 200 PgC per century from a combination of CDR methods, it would take about one and half centuries to remove the CO2 emitted in the last 50 years, making it difficult even for a suite of additive CDR meth- ods to mitigate climate change rapidly. Direct air capture methods could in principle operate much more rapidly, but may be limited by large-scale implementation, including energy use and environmental constraints. CDR could also have climatic and environmental side effects. For instance, enhanced vegetation productivity may increase emissions of N2O, which is a more potent greenhouse gas than CO2. A large-scale increase in vegetation coverage, for instance through afforestation or energy crops, could alter surface characteristics, such as surface reflectivity and turbulent fluxes. Some modelling studies have shown that afforestation in seasonally snow-covered boreal regions could in fact accelerate global warming, whereas afforestation in the tropics may be more effective at slowing global warming. Ocean-based CDR methods that rely on biological production (i.e., ocean fertilization) would have numerous side effects on ocean ecosystems, ocean acidity and may produce emissions of non-CO2 greenhouse gases. Solar Radiation Management Methods The globally averaged surface temperature of the planet is strongly influenced by the amount of sunlight absorbed by the Earth s atmosphere and surface, which warms the planet, and by the existence of the greenhouse effect, the process by which greenhouse gases and clouds affect the way energy is eventually radiated back to space. An increase in the greenhouse effect leads to a surface temperature rise until a new equilibrium is found. If less incom- ing sunlight is absorbed because the planet has been made more reflective, or if energy can be emitted to space more effectively because the greenhouse effect is reduced, the average global surface temperature will be reduced. Suggested geoengineering methods that aim at managing the Earth s incoming and outgoing energy flows are based on this fundamental physical principle. Most of these methods propose to either reduce sunlight reaching the Earth or increase the reflectivity of the planet by making the atmosphere, clouds or the surface brighter (see FAQ 7.3, Figure 1). Another technique proposes to suppress high-level clouds called cirrus, as these clouds have a strong greenhouse effect. Basic physics tells us that if any of these methods change energy flows as expected, then the planet will cool. The picture is complicated, however, because of the many and complex physical processes which govern the interactions between the flow of energy, the atmospheric circulation, weather and the resulting climate. While the globally averaged surface temperature of the planet will respond to a change in the amount of sunlight reaching the surface or a change in the greenhouse effect, the temperature at any given location and time is influ- enced by many other factors and the amount of cooling from SRM will not in general equal the amount of warm- ing caused by greenhouse gases. For example, SRM will change heating rates only during daytime, but increasing greenhouse gases can change temperatures during both day and night. This inexact compensation can influence (continued on next page) 7 633 Chapter 7 Clouds and Aerosols FAQ 7.3 (continued) the diurnal cycle of surface temperature, even if the average surface temperature is unchanged. As another exam- ple, model calculations suggest that a uniform decrease in sunlight reaching the surface might offset global mean CO2-induced warming, but some regions will cool less than others. Models suggest that if anthropogenic green- house warming were completely compensated by stratospheric aerosols, then polar regions would be left with a small residual warming, while tropical regions would become a little cooler than in pre-industrial times. SRM could theoretically counteract anthropogenic climate change rapidly, cooling the Earth to pre-industrial levels within one or two decades. This is known from climate models but also from the climate records of large volcanic eruptions. The well-observed eruption of Mt Pinatubo in 1991 caused a temporary increase in stratospheric aerosols and a rapid decrease in surface temperature of about 0.5°C. Climate consists of many factors besides surface temperature. Consequences for other climate features, such as rainfall, soil moisture, river flow, snowpack and sea ice, and ecosystems may also be important. Both models and theory show that compensating an increased greenhouse effect with SRM to stabilize surface temperature would somewhat lower the globally averaged rainfall (see FAQ 7.3, Figure 2 for an idealized model result), and there also could be regional changes. Such imprecise compensation in 3 regional and global climate patterns makes it improbable that SRM (a) Change in temperature (°C) will produce a future climate that is just like the one we experi- 2.5 ence today, or have experienced in the past. However, available 2 climate models indicate that a geoengineered climate with SRM and high atmospheric CO2 levels would be generally closer to 20th 1.5 century climate than a future climate with elevated CO2 concentra- 1 tions and no SRM. 0.5 SRM techniques would probably have other side effects. For exam- ple, theory, observation and models suggest that stratospheric 0 sulphate aerosols from volcanic eruptions and natural emissions -0.5 deplete stratospheric ozone, especially while chlorine from chlo- 0 10 20 30 40 50 60 70 rofluorocarbon emissions resides in the atmosphere. Stratospheric 4 aerosols introduced for SRM are expected to have the same effect. (b) Change in precipitation (%) Ozone depletion would increase the amount of ultraviolet light 3 reaching the surface damaging terrestrial and marine ecosystems. 2 Stratospheric aerosols would also increase the ratio of direct to dif- fuse sunlight reaching the surface, which generally increases plant 1 productivity. There has also been some concern that sulphate aero- sol SRM would increase acid rain, but model studies suggest that 0 acid rain is probably not a major concern since the rate of acid rain -1 production from stratospheric aerosol SRM would be much smaller than values currently produced by pollution sources. SRM will also -2 not address the ocean acidification associated with increasing atmo- 0 10 20 30 40 50 60 70 spheric CO2 concentrations and its impacts on marine ecosystems. Year Without conventional mitigation efforts or potential CDR meth- FAQ 7.3, Figure 2 | Change in globally averaged (a) sur- ods, high CO2 concentrations from anthropogenic emissions will face temperature (°C) and (b) precipitation (%) in two ideal- ized experiments. Solid lines are for simulations using Solar persist in the atmosphere for as long as a thousand years, and SRM Radiation Management (SRM) to balance a 1% yr 1 increase in would have to be maintained as long as CO2 concentrations were CO2 concentration until year 50, after which SRM is stopped. high. Stopping SRM while CO2 concentrations are still high would Dashed lines are for simulations with a 1% yr 1 increase in lead to a very rapid warming over one or two decades (see FAQ7.3, CO2 concentration and no SRM. The yellow and grey envelopes Figure 2), severely stressing ecosystem and human adaptation. show the 25th to 75th percentiles from eight different models. If SRM were used to avoid some consequences of increasing CO2 concentrations, the risks, side effects and short- comings would clearly increase as the scale of SRM increase. Approaches have been proposed to use a time-limited amount of SRM along with aggressive strategies for reducing CO2 concentrations to help avoid transitions across climate thresholds or tipping points that would be unavoidable otherwise; assessment of such approaches would require a very careful risk benefit analysis that goes much beyond this Report. 7 634 Clouds and Aerosols Chapter 7 and a warmer North Pacific adjacent to a cooler northwestern Canada, by the imprecise compensation between SRM and CO2 forcing would produced a SST response with a La Nina-like pattern. One study has also increase. If SRM were terminated for any reason, a rapid increase noted regional shifts in the potential hurricane intensity and hurricane in surface temperatures (within a decade or two) to values consistent genesis potential index in the Atlantic Ocean and South China Sea in with the high GHG forcing would result (high confidence). This rate of response to cloud brightening (Baughman et al., 2012), due primarily climate change would far exceed what would have occurred without to decreases in vertical wind shear, but overall the investigation and geoengineering, causing any impacts related to the rate of change to identification of robust side effects has not been extensively explored. be correspondingly greater than they would have been without geoen- gineering. In contrast, SRM in concert with aggressive CO2 mitigation Irvine et al. (2011) tested the impact of increasing desert albedo up might conceivably help avoid transitions across climate thresholds or to 0.80 in a climate model. This cooled surface temperature by 1.1°C tipping points that would be unavoidable otherwise. (versus 0.22°C and 0.11°C for their largest crop and urban albedo change) and produced very significant changes in regional precipita- tion patterns. Acknowledgements 7.7.4 Synthesis on Solar Radiation Management Methods Thanks go to Anne-Lise Barbanes (IPSL/CNRS, Paris), Bénédicte Fisset (IPSL/CNRS, Paris) and Edwina Berry for their help in assembling the list Theory, model studies and observations suggest that some SRM meth- of references. Sylvaine Ferrachat (ETH Zürich) is acknowledged for her ods may be able to counteract a portion of global warming effects (on contribution to drafting Figure 7.19. temperature, sea ice and precipitation) due to high concentrations of anthropogenic GHGs (high confidence). But the level of understanding about SRM is low, and it is difficult to assess feasibility and efficacy because of remaining uncertainties in important climate processes and the interactions among those processes. Although SRM research is still in its infancy, enough is known to identify some potential benefits, which must be weighed against known side effects (there could also be side effects that have not yet been identified). All studies suggest there would be a small but measurable decrease in global precipita- tion from SRM. Other side effects are specific to specific methods, and a number of research areas remain largely unexplored. There are also features that develop as a consequence of the combination of high CO2 and SRM (e.g., effects on evapotranspiration and precipitation). SRM counters only some consequences of elevated CO2 concentrations; it does not in particular address ocean acidification. Many model studies indicate that stratospheric aerosol SRM could counteract some changes resulting from GHG increases that produce a RF as strong as 4 W m 2 (medium confidence), but they disagree on details. Marine cloud brightening SRM has received less attention, and there is no consensus on its efficacy, in large part due to the high level of uncertainty about cloud radiative responses to aerosol changes. There have been fewer studies and much less attention focused on all other SRM methods, and it is not currently possible to provide a general assessment of their specific efficacy, scalability, side effects and risks. There is robust agreement among models and high confidence that the compensation between GHG warming and SRM cooling is impre- cise. SRM would not produce a future climate identical to the present (or pre-industrial) climate. Nonetheless, although models disagree on details, they consistently suggest that a climate with SRM and high atmospheric CO2 levels would be closer to that of the last century than a world with elevated CO2 concentrations and no SRM (Lunt et al., 2008; Ricke et al., 2010; Moreno-Cruz et al., 2011), as long as the SRM could be continuously sustained and calibrated to offset the forcing by GHGs. Aerosol-based methods would, however, require a continuous program of replenishment to achieve this. If CO2 concentrations and SRM were increased in concert, the risks and residual climate change produced 7 635 Chapter 7 Clouds and Aerosols References Abdul-Razzak, H., and S. Ghan, 2000: A parameterization of aerosol activation 2. Angel, R., 2006: Feasibility of cooling the Earth with a cloud of small spacecraft near Multiple aerosol types. J. Geophys. Res., 105, 6837 6844. the inner Lagrange Point (L1). Proc. Natl. Acad. Sci. U.S.A., 103, 17184 17189. Ackerman, A. S., M. P. Kirkpatrick, D. E. Stevens, and O. B. Toon, 2004: The impact of Ansmann, A., et al., 2008: Influence of Saharan dust on cloud glaciation in southern humidity above stratiform clouds on indirect aerosol climate forcing. Nature, Morocco during the Saharan Mineral Dust Experiment. J. Geophys. Res., 113, 432, 1014 1017. D04210. Ackerman, A. S., et al., 2009: Large-eddy simulations of a drizzling, stratocumulus- Arakawa, A., 1975: Modeling clouds and cloud processes for use in climate models. topped marine boundary layer. Mon. Weather Rev., 137, 1083 1110. In: The Physical Basis of Climate and Climate Modelling. ICSU/WMO, GARP Adachi, K., S. H. Chung, and P. R. Buseck, 2010: Shapes of soot aerosol particles and Publications Series N° 16, Geneva, Switzerland, pp. 181 197. implications for their effects on climate. J. Geophys. Res., 115, D15206. Arakawa, A., 2004: The cumulus parameterization problem: Past, present, and future. Adams, P. J., J. H. Seinfeld, D. Koch, L. Mickley, and D. Jacob, 2001: General circulation J. Clim., 17, 2493 2525. model assessment of direct radiative forcing by the sulfate-nitrate-ammonium- Arneth, A., R. K. Monson, G. Schurgers, U. Niinemets, and P. I. Palmer, 2008: Why are water inorganic aerosol system. J. Geophys. Res., 106, 1097 1111. estimates of global terrestrial isoprene emissions so similar (and why is this not Adler, R. F., et al., 2003: The Version 2 Global Precipitation Climatology Project (GPCP) so for monoterpenes)? Atmos. Chem. Phys., 8, 4605 4620. Monthly Precipitation Analysis (1979 Present). J. Hydrometeor., 4, 1147 1167. Arneth, A., P. A. Miller, M. Scholze, T. Hickler, G. Schurgers, B. Smith, and I. C. Agee, E. M., K. Kiefer, and E. Cornett, 2012: Relationship of lower troposphere cloud Prentice, 2007: CO2 inhibition of global terrestrial isoprene emissions: Potential cover and cosmic rays: An updated perspective. J. Clim., 25, 1057 1060. implications for atmospheric chemistry. Geophys. Res. Lett., 34, L18813. Albrecht, B. A., 1989: Aerosols, cloud microphysics, and fractional cloudiness. Arnold, F., 2006: Atmospheric aerosol and cloud condensation nuclei formation: A Science, 245, 1227 1230. possible influence of cosmic rays? Space Sci. Rev., 125, 169 186. Aldrin, M., M. Holden, P. Guttorp, R. B. Skeie, G. Myhre, and T. K. Berntsen, 2012: Artaxo, P., et al., 1998: Large-scale aerosol source apportionment in Amazonia. J. Bayesian estimation of climate sensitivity based on a simple climate model Geophys. Res., 103, 31837 31847. fitted to observations of hemispheric temperatures and global ocean heat Artaxo, P., et al., 2002: Physical and chemical properties of aerosols in the wet and content. Environmetrics, 23, 253 271. dry seasons in Rondônia, Amazonia. J. Geophys. Res., 107, 8081. Allan, R. P., and B. J. Soden, 2007: Large discrepancy between observed and Asmi, A., et al., 2011: Number size distributions and seasonality of submicron simulated precipitation trends in the ascending and descending branches of the particles in Europe 2008 2009. Atmos. Chem. Phys., 11, 5505 5538. tropical circulation. Geophys. Res. Lett., 34, L18705. Aw, J., and M. J. Kleeman, 2003: Evaluating the first-order effect of intraannual Allen, M. R., and W. J. Ingram, 2002: Constraints on future changes in climate and temperature variability on urban air pollution. J. Geophys. Res., 108, 4365. the hydrologic cycle. Nature, 419, 224 232. Ayers, G. P., and J. M. Cainey, 2007: The CLAW hypothesis: A review of the major Allen, R. J., and S. C. Sherwood, 2010: Aerosol-cloud semi-direct effect and land-sea developments. Environ. Chem., 4, 366 374. temperature contrast in a GCM. Geophys. Res. Lett., 37, L07702. Babu, S. S., et al., 2011: Free tropospheric black carbon aerosol measurements Allen, R. J., S. C. Sherwood, J. R. Norris, and C. S. Zender, 2012: Recent Northern using high altitude balloon: Do BC layers build their own homes up in the Hemisphere tropical expansion primarily driven by black carbon and tropospheric atmosphere? Geophys. Res. Lett., 38, L08803. ozone. Nature, 485, 350 354. Baker, M. B., and R. J. Charlson, 1990: Bistability of CCN concentrations and Alpert, P., N. Halfon, and Z. Levin, 2008: Does air pollution really suppress thermodynamics in the cloud-topped boundary-layer. Nature, 345, 142 145. precipitation in Israel? J. Appl. Meteor. Climatol., 47, 933 943. Baker, M. B., and T. Peter, 2008: Small-scale cloud processes and climate. Nature, Alterskjaer, K., J. E. Kristjánsson, and O. Seland, 2012: Sensitivity to deliberate sea 451, 299 300. salt seeding of marine clouds - observations and model simulations. Atmos. Bala, G., P. B. Duffy, and K. E. Taylor, 2008: Impact of geoengineering schemes on the Chem. Phys., 12, 2795 2807. global hydrological cycle. Proc. Natl. Acad. Sci. U.S.A., 105, 7664 7669. Anderson, T. L., et al., 2005: An A-Train strategy for quantifying direct climate Ban-Weiss, G., L. Cao, G. Bala, and K. Caldeira, 2012: Dependence of climate forcing forcing by anthropogenic aerosols. Bull. Am. Meteor. Soc., 86, 1795 1809. and response on the altitude of black carbon aerosols. Clim. Dyn., 38, 897 911. Anderson, T. L., et al., 2009: Temporal and spatial variability of clouds and related Bangert, M., C. Kottmeier, B. Vogel, and H. Vogel, 2011: Regional scale effects of aerosol. In: Clouds in the Perturbed Climate System: Their Relationship to the aerosol cloud interaction simulated with an online coupled comprehensive Energy Balance, Atmospheric Dynamics, and Precipitation [R. J. Charlson, and J. chemistry model. Atmos. Chem. Phys., 11, 4411 4423. Heintzenberg (eds.)]. MIT Press, Cambridge, MA, USA, pp. 127 148. Barahona, D., and A. Nenes, 2008: Parameterization of cirrus cloud formation in Andreae, M. O., and A. Gelencser, 2006: Black carbon or brown carbon? The nature large-scale models: Homogeneous nucleation. J. Geophys. Res., 113, D11211. of light-absorbing carbonaceous aerosols. Atmos. Chem. Phys., 6, 3131 3148. Barahona, D., and A. Nenes, 2009: Parameterizing the competition between Andreae, M. O., C. D. Jones, and P. M. Cox, 2005: Strong present-day aerosol cooling homogeneous and heterogeneous freezing in ice cloud formation polydisperse implies a hot future. Nature, 435, 1187 1190. ice nuclei. Atmos. Chem. Phys., 9, 5933 5948. Andreae, M. O., D. Rosenfeld, P. Artaxo, A. A. Costa, G. P. Frank, K. M. Longo, and Barker, H. W., J. N. S. Cole, J.-J. Morcrette, R. Pincus, P. Raisaenen, K. von Salzen, and M. A. F. Silva-Dias, 2004: Smoking rain clouds over the Amazon. Science, 303, P. A. Vaillancourt, 2008: The Monte Carlo Independent Column Approximation: 1337 1342. An assessment using several global atmospheric models. Q. J. R. Meteorol. Soc., Andrejczuk, M., W. W. Grabowski, S. P. Malinowski, and P. K. Smolarkiewicz, 2006: 134, 1463 1478. Numerical simulation of cloud-clear air interfacial mixing: Effects on cloud Barker, H. W., et al., 2003: Assessing 1D atmospheric solar radiative transfer models: microphysics. J. Atmos. Sci., 63, 3204 3225. Interpretation and handling of unresolved clouds. J. Clim., 16, 2676 2699. Andrews, T., and P. M. Forster, 2008: CO2 forcing induces semi-direct effects with Barmet, P., T. Kuster, A. Muhlbauer, and U. Lohmann, 2009: Weekly cycle in particulate consequences for climate feedback interpretations. Geophys. Res. Lett., 35, matter versus weekly cycle in precipitation over Switzerland. J. Geophys. Res., L04802. 114, D05206. Andrews, T., P. M. Forster, and J. M. Gregory, 2009: A surface energy perspective on Bauer, S., E. Bierwirth, M. Esselborn, A. Petzold, A. Macke, T. Trautmann, and M. climate change. J. Clim., 22, 2557 2570. Wendisch, 2011: Airborne spectral radiation measurements to derive solar Andrews, T., J. M. Gregory, M. J. Webb, and K. E. Taylor, 2012: Forcing, feedbacks radiative forcing of Saharan dust mixed with biomass burning smoke particles. and climate sensitivity in CMIP5 coupled atmosphere-ocean climate models. Tellus B, 63, 742 750. Geophys. Res. Lett., 39, L09712. Bauer, S. E., and S. Menon, 2012: Aerosol direct, indirect, semidirect, and surface Andrews, T., P. M. Forster, O. Boucher, N. Bellouin, and A. Jones, 2010: Precipitation, albedo effects from sector contributions based on the IPCC AR5 emissions for radiative forcing and global temperature change. Geophys. Res. Lett., 37, preindustrial and present-day conditions. J. Geophys. Res., 117, D01206. L14701. Bauer, S. E., D. Koch, N. Unger, S. M. Metzger, D. T. Shindell, and D. G. Streets, 2007: 7 Nitrate aerosols today and in 2030: |A global simulation including aerosols and tropospheric ozone. Atmos. Chem. Phys., 7, 5043 5059. 636 Clouds and Aerosols Chapter 7 Baughman, E., A. Gnanadesikan, A. Degaetano, and A. Adcroft, 2012: Investigation Bokoye, A. I., A. Royer, N. T. O Neil, P. Cliche, G. Fedosejevs, P. M. Teillet, and L. J. B. of the surface and circulation impacts of cloud-brightening geoengineering. J. McArthur, 2001: Characterization of atmospheric aerosols across Canada from a Clim., 25, 7527 7543. ground-based sunphotometer network: AEROCAN. Atmos. Ocean, 39, 429 456. Bäumer, D., and B. Vogel, 2007: An unexpected pattern of distinct weekly periodicities Bond, G., et al., 2001: Persistent solar influence on North Atlantic climate during the in climatological variables in Germany. Geophys. Res. Lett., 34, L03819. Holocene. Science, 294, 2130 2136. Baumgardner, D., et al., 2012: Soot reference materials for instrument calibration Bond, T. C., et al., 2013: Bounding the role of black carbon in the climate system: A and intercomparisons: A workshop summary with recommendations. Atmos. scientific assessment. J. Geophys. Res. Atmos., 118, 5380 5552. Meas. Tech., 5, 1869 1887. Bondo, T., M. B. Enghoff, and H. Svensmark, 2010: Model of optical response of Bazilevskaya, G. A., et al., 2008: Cosmic ray induced ion production in the marine aerosols to Forbush decreases. Atmos. Chem. Phys., 10, 2765 2776. atmosphere. Space Sci. Rev., 137, 149 173. Bony, S., and J.-L. Dufresne, 2005: Marine boundary layer clouds at the heart of Bechtold, P., et al., 2008: Advances in simulating atmospheric variability with the tropical cloud feedback uncertainties in climate models. Geophys. Res. Lett., 32, ECMWF model: From synoptic to decadal time-scales. Q. J. R. Meteorol. Soc., L20806. 134, 1337 1351. Bony, S., J.-L. Dufresne, H. Le Treut, J.-J. Morcrette, and C. Senior, 2004: On dynamic Bell, T. L., D. Rosenfeld, K.-M. Kim, J.-M. Yoo, M.-I. Lee, and M. Hahnenberger, 2008: and thermodynamic components of cloud changes. Clim. Dyn., 22, 71 86. Midweek increase in US summer rain and storm heights suggests air pollution Bony, S., G. Bellon, D. Klocke, S. Sherwood, S. Fermapin, and S. Denvil, 2013: Robust invigorates rainstorms. J. Geophys. Res., 113, D02209. direct effect of carbon dioxide on tropical circulation and regional precipitation. Bellouin, N., and O. Boucher, 2010: Climate response and efficacy of snow forcing Nature Geosci., 6, 447 451. in the HadGEM2 AML climate model. Hadley Centre Technical Note N°82. Met Bony, S., et al., 2006: How well do we understand and evaluate climate change Office, Exeter, Devon, UK. feedback processes? J. Clim., 19, 3445 3482. Bellouin, N., O. Boucher, J. Haywood, and M. S. Reddy, 2005: Global estimate of Bopp, L., O. Boucher, O. Aumont, S. Belviso, J.-L. Dufresne, M. Pham, and P. Monfray, aerosol direct radiative forcing from satellite measurements. Nature, 438, 2004: Will marine dimethylsulfide emissions amplify or alleviate global 1138 1141. warming? A model study. Can. J. Fish. Aquat. Sci., 61, 826 835. Bellouin, N., A. Jones, J. Haywood, and S. A. Christopher, 2008: Updated estimate Boucher, O., and U. Lohmann, 1995: The sulfate-CCN-cloud albedo effect A of aerosol direct radiative forcing from satellite observations and comparison sensitivity study with two general circulation models. Tellus B, 47, 281 300. against the Hadley Centre climate model. J. Geophys. Res., 113, D10205. Boucher, O., and J. Quaas, 2013: Water vapour affects both rain and aerosol optical Bellouin, N., J. Quaas, J.-J. Morcrette, and O. Boucher, 2013: Estimates of aerosol depth. Nature Geosci., 6, 4 5. radiative forcing from the MACC re-analysis. Atmos. Chem. Phys., 13, 2045 Boucher, O., G. Myhre, and A. Myhre, 2004: Direct influence of irrigation on 2062. atmospheric water vapour and climate. Clim. Dyn., 22, 597 603. Bellouin, N., J. Rae, C. Johnson, J. Haywood, A. Jones, and O. Boucher, 2011: Aerosol Boucher, O., J. Lowe, and C. D. Jones, 2009: Constraints of the carbon cycle on forcing in the Climate Model Intercomparison Project (CMIP5) simulations by timescales of climate-engineering options. Clim. Change, 92, 261 273. HadGEM2 ES and the role of ammonium nitrate. J. Geophys. Res., 116, D20206. Bourotte, C., A.-P. Curi-Amarante, M.-C. Forti, L. A. A. Pereira, A. L. Braga, and P. A. Bender, F. A.-M., V. Ramanathan, and G. Tselioudis, 2012: Changes in extratropical Lotufo, 2007: Association between ionic composition of fine and coarse aerosol storm track cloudiness 1983 2008: Observational support for a poleward shift. soluble fraction and peak expiratory flow of asthmatic patients in Sao Paulo city Clim. Dyn., 38, 2037 2053. (Brazil). Atmos. Environ., 41, 2036 2048. Benedetti, A., et al., 2009: Aerosol analysis and forecast in the ECMWF Integrated Boutle, I. A., and S. J. Abel, 2012: Microphysical controls on the stratocumulus topped Forecast System. Part II : Data assimilation. J. Geophys. Res., 114, D13205. boundary-layer structure during VOCALS-REx. Atmos. Chem. Phys., 12, 2849 Benedict, J. J., and D. A. Randall, 2009: Structure of the Madden-Julian Oscillation in 2863. the Superparameterized CAM. J. Atmos. Sci., 66, 3277 3296. Brenguier, J.-L., F. Burnet, and O. Geoffroy, 2011: Cloud optical thickness and liquid Berg, L. K., C. M. Berkowitz, J. C. Barnard, G. Senum, and S. R. Springston, 2011: water path does the k coefficient vary with droplet concentration? Atmos. Observations of the first aerosol indirect effect in shallow cumuli. Geophys. Res. Chem. Phys., 11, 9771 9786. Lett., 38, L03809. Bretherton, C. S., P. N. Blossey, and J. Uchida, 2007: Cloud droplet sedimentation, Bergamo, A., A. M. Tafuro, S. Kinne, F. De Tomasi, and M. R. Perrone, 2008: Monthly- entrainment efficiency, and subtropical stratocumulus albedo. Geophys. Res. averaged anthropogenic aerosol direct radiative forcing over the Mediterranian Lett., 34, L03813. based on AERONET aerosol properties. Atmos. Chem. Phys., 8, 6995 7014. Bretherton, C. S., P. N. Blossey, and C. R. Jones, 2013: A large-eddy simulation of Bergstrom, R. W., et al., 2010: Aerosol spectral absorption in the Mexico City area: mechanisms of boundary layer cloud response to climate change. J. Adv. Model. Results from airborne measurements during MILAGRO/INTEX B. Atmos. Chem. Earth Syst., 5, 316 337. Phys., 10, 6333 6343. Brient, F., and S. Bony, 2012: How may low-cloud radiative properties simulated Bertram, A. K., T. Koop, L. T. Molina, and M. J. Molina, 2000: Ice formation in in the current climate influence low-cloud feedbacks under global warming? (NH4)2SO4 H2O particles. J. Phys. Chem. A, 104, 584 588. Geophys. Res. Lett., 39, L20807. Bewick, R., J. P. Sanchez, and C. R. McInnes, 2012: Gravitationally bound Brient, F., and S. Bony, 2013: Interpretation of the positive low-cloud feedback geoengineering dust shade at the inner Lagrange point. Adv. Space Res., 50, predicted by a climate model under global warming. Clim. Dyn., 40, 2415 2431. 1405 1410. Brioude, J., et al., 2009: Effect of biomass burning on marine stratocumulus clouds Bharmal, N. A., A. Slingo, G. J. Robinson, and J. J. Settle, 2009: Simulation of surface off the California coast. Atmos. Chem. Phys., 9, 8841 8856. and top of atmosphere thermal fluxes and radiances from the radiative Broccoli, A. J., and S. A. Klein, 2010: Comment on Observational and model evidence atmospheric divergence using the ARM Mobile Facility, GERB data, and AMMA for positive low-level cloud feedback . Science, 329, 277 a. Stations experiment. J. Geophys. Res., 114, D00E07. Brock, C. A., et al., 2011: Characteristics, sources, and transport of aerosols measured Bi, X., et al., 2011: Mixing state of biomass burning particles by single particle in spring 2008 during the aerosol, radiation, and cloud processes affecting Arctic aerosol mass spectrometer in the urban area of PRD, China. Atmos. Environ., Climate (ARCPAC) Project. Atmos. Chem. Phys., 11, 2423 2453. 45, 3447 3453. Bryan, G. H., J. C. Wyngaard, and J. M. Fritsch, 2003: Resolution requirements for Blossey, P. N., C. S. Bretherton, J. Cetrone, and M. Kharoutdinov, 2007: Cloud- the simulation of deep moist convection. Mon. Weather Rev., 131, 2394 2416. resolving model simulations of KWAJEX: Model sensitivities and comparisons Budyko, M. I., 1974: Izmeniya Klimata. Gidrometeoroizdat, Leningrad. with satellite and radar observations. J. Atmos. Sci., 64, 1488 1508. Burkhardt, U., and B. Kärcher, 2009: Process-based simulation of contrail cirrus in a Blossey, P. N., et al., 2013: Marine low cloud sensitivity to an idealized climate global climate model. J. Geophys. Res., 114, D16201. change: The CGILS LES intercomparison. J. Adv. Model. Earth Syst.,5, 234 258. Burkhardt, U., and B. Kärcher, 2011: Global radiative forcing from contrail cirrus. Bodenschatz, E., S. P. Malinowski, R. A. Shaw, and F. Stratmann, 2010: Can we Nature Clim. Change, 1, 54 58. understand clouds without turbulence? Science, 327, 970 971. Burrows, S. M., T. Butler, P. Jöckel, H. Tost, A. Kerkweg, U. Pöschl, and M. G. Lawrence, Boers, R., and R. M. Mitchell, 1994: Absorption feedback in stratocumulus clouds 2009: Bacteria in the global atmosphere Part 2: Modeling of emissions and Influence on cloud-top albedo. Tellus A, 46, 229 241. transport between different ecosystems. Atmos. Chem. Phys., 9, 9281 9297. 7 637 Chapter 7 Clouds and Aerosols Cahalan, R. F., W. Ridgway, W. J. Wiscombe, T. L. Bell, and J. B. Snider, 1994: The Chen, J. P., A. Hazra, and Z. Levin, 2008: Parameterizing ice nucleation rates using albedo of fractal stratocumulus clouds. J. Atmos. Sci., 51, 2434 2455. contact angle and activation energy derived from laboratory data. Atmos. Chem. Caldwell, P., and C. S. Bretherton, 2009: Response of a subtropical stratocumulus- Phys., 8, 7431 7449. capped mixed layer to climate and aerosol changes. J. Clim., 22, 20 38  Chen, L., G. Shi, S. Qin, S. Yang, and P. Zhang, 2011: Direct radiative forcing of Èalogoviæ, J., C. Albert, F. Arnold, J. Beer, L. Desorgher, and E. O. Flueckiger, 2010: anthropogenic aerosols over oceans from satellite observations. Adv. Atmos. Sudden cosmic ray decreases: No change of global cloud cover. Geophys. Res. Sci., 28, 973 984. Lett., 37, L03802. Chen, T., W. B. Rossow, and Y. Zhang, 2000: Radiative effects of cloud-type variations. Cameron-Smith, P., S. Elliott, M. Maltrud, D. Erickson, and O. Wingenter, 2011: J. Clim., 13, 264 286. Changes in dimethyl sulfide oceanic distribution due to climate change. Chen, W. T., Y. H. Lee, P. J. Adams, A. Nenes, and J. H. Seinfeld, 2010: Will black carbon Geophys. Res. Lett., 38, L07704. mitigation dampen aerosol indirect forcing? Geophys. Res. Lett., 37, L09801. Cao, G., X. Zhang, and F. Zheng, 2006: Inventory of black carbon and organic carbon Chen, Y., Q. Li, R. A. Kahn, J. T. Randerson, and D. J. Diner, 2009: Quantifying aerosol emissions from China. Atmos. Environ., 40, 6516 6527. direct radiative effect with Multiangle Imaging Spectroradiometer observations: Cappa, C. D., et al., 2012: Radiative absorption enhancements due to the mixing Top-of-atmosphere albedo change by aerosols based on land surface types. J. state of atmospheric black carbon. Science, 337, 1078 1081. Geophys. Res., 114, D02109. Carlton, A. G., R. W. Pinder, P. V. Bhave, and G. A. Pouliot, 2010: To what extent can Chen, Y. C., M. W. Christensen, L. Xue, A. Sorooshian, G. L. Stephens, R. M. Rasmussen, biogenic SOA be controlled? Environ. Sci. Technol., 44, 3376 3380. and J. H. Seinfeld, 2012: Occurrence of lower cloud albedo in ship tracks. Atmos. Carrico, C. M., M. H. Bergin, A. B. Shrestha, J. E. Dibb, L. Gomes, and J. M. Harris, 2003: Chem. Phys., 12, 8223 8235. The importance of carbon and mineral dust to seasonal aerosol properties in the Cheng, A., and K. M. Xu, 2006: Simulation of shallow cumuli and their transition Nepal Himalaya. Atmos. Environ., 37, 2811 2824. to deep convective clouds by cloud-resolving models with different third-order Carslaw, K. S., R. G. Harrison, and J. Kirkby, 2002: Cosmic rays, clouds, and climate. turbulence closures. Q. J. R. Meteorol. Soc., 132, 359 382. Science, 298, 1732 1737. Cheng, A., and K.-M. Xu, 2008: Simulation of boundary-layer cumulus and Carslaw, K. S., O. Boucher, D. V. Spracklen, G. W. Mann, J. G. L. Rae, S. Woodward, and stratocumulus clouds using a cloud-resolving model with low- and third-order M. Kulmala, 2010: A review of natural aerosol interactions and feedbacks within turbulence closures. J. Meteorol. Soc. Jpn, 86, 67 86. the Earth system. Atmos. Chem. Phys., 10, 1701 1737. Cheng, Z. L., K. S. Lam, L. Y. Chan, and K. K. Cheng, 2000: Chemical characteristics Celis, J. E., J. R. Morales, C. A. Zarorc, and J. C. Inzunza, 2004: A study of the particulate of aerosols at coastal station in Hong Kong. I. Seasonal variation of major ions, matter PM10 composition in the atmosphere of Chillán, Chile. Chemosphere, 54, halogens and mineral dusts between 1995 and 1996. Atmos. Environ., 34, 541 550. 2771 2783. Cess, R. D., 1975: Global climate change Investigation of atmospheric feedback Chepfer, H., S. Bony, D. Winker, M. Chiriaco, J.-L. Dufresne, and G. Seze, 2008: Use of mechanisms. Tellus, 27, 193 198. CALIPSO lidar observations to evaluate the cloudiness simulated by a climate Cess, R. D., et al., 1989: Interpretation of cloud-climate feedbacks as produced by 14 model. Geophys. Res. Lett., 35, L15704. atmospheric general circulation models. Science, 245, 513 516. Chepfer, H., et al., 2010: The GCM-Oriented CALIPSO Cloud Product (CALIPSO- Cess, R. D., et al., 1990: Intercomparison and interpretation of climate feedback GOCCP). J. Geophys. Res., 115, D00H16. processes in 19 atmospheric general circulation models. J. Geophys. Res., 95, Chikira, M., and M. Sugiyama, 2010: A cumulus parameterization with state- 16601 16615. dependent entrainment rate. Part I: Description and sensitivity to temperature Chae, J. H., and S. C. Sherwood, 2010: Insights into cloud-top height and dynamics and humidity profiles. J. Atmos. Sci., 67, 2171 2193. from the seasonal cycle of cloud-top heights observed by MISR in the West Chou, C., and J. D. Neelin, 2004: Mechanisms of global warming impacts on regional Pacific region. J. Atmos. Sci., 67, 248 261. tropical precipitation. J. Clim., 17, 2688 2701. Chakraborty, A., and T. Gupta, 2010: Chemical characterization and source Chou, C., J. D. Neelin, C. A. Chen, and J. Y. Tu, 2009: Evaluating the Rich-Get-Richer apportionment of submicron (PM1) aerosol in Kanpur region, India. Aeros. Air mechanism in tropical precipitation change under global warming. J. Clim., 22, Qual. Res., 10, 433 445. 1982 2005. Chameides, W. L., C. Luo, R. Saylor, D. Streets, Y. Huang, M. Bergin, and F. Giorgi, 2002: Chow, J. C., J. G. Waston, D. H. Lowenthal, P. A. Solomon, K. L. Magliano, S. D. Ziman, Correlation between model-calculated anthropogenic aerosols and satellite- and L. W. Richards, 1993: PM10 and PM2.5 compositions in California s San derived cloud optical depths: Indication of indirect effect? J. Geophys. Res., 107, Joaquin Valley. Aer. Sci. Technol., 18, 105 128. 4085. Chowdhary, J., et al., 2005: Retrieval of aerosol scattering and absorption properties Chan, M. A., and J. C. Comiso, 2011: Cloud features detected by MODIS but not by from photopolarimetric observations over the ocean during the CLAMS CloudSat and CALIOP. Geophys. Res. Lett., 38, L24813. experiment. J. Atmos. Sci., 62, 1093 1117. Chan, Y. C., R. W. Simpson, G. H. Mctainsh, P. D. Vowles, D. D. Cohen, and G. M. Christensen, M. W., and G. L. Stephens, 2011: Microphysical and macrophysical Bailey, 1997: Characterisation of chemical species in PM2.5 and PM10 aerosols in responses of marine stratocumulus polluted by underlying ships: Evidence of Brisbane, Australia. Atmos. Environ., 31, 3773 3785. cloud deepening. J. Geophys. Res., 116, D03201. Chand, D., R. Wood, T. L. Anderson, S. K. Satheesh, and R. J. Charlson, 2009: Satellite- Christopher, S. A., B. Johnson, T. A. Jones, and J. Haywood, 2009: Vertical and spatial derived direct radiative effect of aerosols dependent on cloud cover. Nature distribution of dust from aircraft and satellite measurements during the GERBILS Geosci., 2, 181 184. field campaign. Geophys. Res. Lett., 36, L06806. Chand, D., T. L. Anderson, R. Wood, R. J. Charlson, Y. Hu, Z. Liu, and M. Vaughan, Chung, C. E., V. Ramanathan, and D. Decremer, 2012: Observationally constrained 2008: Quantifying above-cloud aerosol using spaceborne lidar for improved estimates of carbonaceous aerosol radiative forcing. Proc. Natl. Acad. Sci. U.S.A., understanding of cloudy-sky direct climate forcing. J. Geophys. Res., 113, 109, 11624 11629. D13206. Clarke, A. D., V. N. Kapustin, F. L. Eisele, R. J. Weber, and P. H. McMurry, 1999: Particle Chand, D., et al., 2012: Aerosol optical depth increase in partly cloudy conditions. J. production near marine clouds: Sulfuric acid and predictions from classical Geophys. Res., 117, D17207. binary nucleation. Geophys. Res. Lett., 26, 2425 2428. Chang, F. L., and J. A. Coakley, 2007: Relationships between marine stratus cloud Clement, A. C., R. Burgman, and J. R. Norris, 2009: Observational and model evidence optical depth and temperature: Inferences from AVHRR observations. J. Clim., for positive low-level cloud feedback. Science, 325, 460 464. 20, 2022 2036. Coakley, J. A., and C. D. Walsh, 2002: Limits to the aerosol indirect radiative effect Charlson, R. J., A. S. Ackerman, F. A. M. Bender, T. L. Anderson, and Z. Liu, 2007: On derived from observations of ship tracks. J. Atmos. Sci., 59, 668 680. the climate forcing consequences of the albedo continuum between cloudy and Collins, M., B. B. Booth, B. Bhaskaran, G. R. Harris, J. M. Murphy, D. M. H. Sexton, and clear air. Tellus B, 59, 715 727. M. J. Webb, 2011: Climate model errors, feedbacks and forcings: A comparison Charney, J. G., et al., 1979: Carbon dioxide and climate: A scientific assessment. of perturbed physics and multi-model ensembles. Clim. Dyn., 36, 1737 1766. Report of an Ad-Hoc Group on Carbon Dioxide and Climate, National Academy Colman, R. A., and B. J. McAvaney, 2011: On tropospheric adjustment to forcing and of Sciences, Washington D.C., USA, 33 pp. climate feedbacks. Clim. Dyn., 36, 1649 1658. 7 Che, H., et al., 2009: Instrument calibration and aerosol optical depth validation Colman, R. A., and L. I. Hanson, 2012: On atmospheric radiative feedbacks associated of the China Aerosol Remote Sensing Network. J. Geophys. Res., 114, D03206. with climate variability and change. Clim. Dyn., 40, 475 492. 638 Clouds and Aerosols Chapter 7 Comstock, K. K., C. S. Bretherton, and S. E. Yuter, 2005: Mesoscale variability and Denman, K. L., et al., 2007: Couplings between changes in the climate system drizzle in Southeast Pacific stratocumulus. J. Atmos. Sci., 62, 3792 3807. and biogeochemistry. In: Climate Change 2007: The Physical Science Basis. Costantino, L., and F.-M. Bréon, 2010: Analysis of aerosol-cloud interaction from Contribution of Working Group I to the Fourth Assessment Report of the multi-sensor satellite observations. Geophys. Res. Lett., 37, L11801. Intergovernmental Panel on Climate Change [Solomon, S., D. Qin, M. Manning, Couvreux, F., F. Hourdin, and C. Rio, 2010: Resolved versus parametrized boundary- Z. Chen, M. Marquis, K. B. Averyt, M. Tignor and H. L. Miller (eds.)] Cambridge layer plumes. Part I: A parametrization-oriented conditional sampling in large- University Press, Cambridge, United Kingdom and New York, NY, USA, pp. 499- eddy simulations. Bound. Layer Meteor., 134, 441 458. 587. Cross, E. S., et al., 2010: Soot Particle Studies Instrument Inter-Comparison Derbyshire, S. H., I. Beau, P. Bechtold, J.-Y. Grandpeix, J.-M. Piriou, J.-L. Redelsperger, Project Overview. Aer. Sci. Technol., 44, 592 611. and P. M. M. Soares, 2004: Sensitivity of moist convection to environmental Crucifix, M., 2006: Does the Last Glacial Maximum constrain climate sensitivity? humidity. Q. J. R. Meteorol. Soc., 130, 3055 3079. Geophys. Res. Lett., 33, L18701. Després, V. R., et al., 2012: Primary biological aerosol particles in the atmosphere: A Crutzen, P. J., 2006: Albedo enhancement by stratospheric sulfur injections: A review. Tellus B, 64, 15598. contribution to resolve a policy dilemma? Clim. Change, 77, 211 220. Dessler, A. E., 2010: A determination of the cloud feedback from climate variations Dai, A. G., J. H. Wang, P. W. Thorne, D. E. Parker, L. Haimberger, and X. L. L. Wang, over the past decade. Science, 330, 1523 1527. 2011: A new approach to homogenize daily radiosonde humidity data. J. Clim., Dessler, A. E., 2011: Cloud variations and the Earth s energy budget. Geophys. Res. 24, 965 991. Lett., 38, L19701. Davies, R., and M. Molloy, 2012: Global cloud height fluctuations measured by MISR Dessler, A. E., 2013: Observations of climate feedbacks over 2000 10 and on Terra from 2000 to 2010. Geophys. Res. Lett., 39, L03701. comparisons to climate models. J. Clim., 26, 333 342. Davis, A. B., A. Marshak, H. Gerber, and W. J. Wiscombe, 1999: Horizontal structure Dessler, A. E., and S. Wong, 2009: Estimates of the water vapor climate feedback of marine boundary layer clouds from centimeter to kilometer scales. J. Geophys. during El Nino-Southern Oscillation. J. Clim., 22, 6404 6412. Res., 104, 6123 6144. Dessler, A. E., and S. M. Davis, 2010: Trends in tropospheric humidity from reanalysis Dawson, J. P., P. J. Adams, and S. N. Pandis, 2007: Sensitivity of PM2.5 to climate systems. J. Geophys. Res., 115, D19127. in the Eastern US: A modeling case study. Atmos. Chem. Phys., 7, 4295 4309. Deuzé, J.-L., et al., 2001: Remote sensing of aerosols over land surfaces from de Boer, G., H. Morrison, M. D. Shupe, and R. Hildner, 2011: Evidence of liquid POLDER-ADEOS-1 polarized measurements. J. Geophys. Res., 106, 4913 4926. dependent ice nucleation in high-latitude stratiform clouds from surface remote Devasthale, A., O. Kruger, and H. Graßl, 2005: Change in cloud-top temperatures over sensors. Geophys. Res. Lett., 38, L01803. Europe. IEEE Geosci. Remote Sens. Lett., 2, 333 336. de Gouw, J., and J. L. Jimenez, 2009: Organic aerosols in the Earth s atmosphere. Di Biagio, C., A. di Sarra, and D. Meloni, 2010: Large atmospheric shortwave radiative Environ. Sci. Technol., 43, 7614 7618. forcing by Mediterranean aerosols derived from simultaneous ground-based de Gouw, J. A., et al., 2005: Budget of organic carbon in a polluted atmosphere: and spaceborne observations and dependence on the aerosol type and single Results from the New England Air Quality Study in 2002. J. Geophys. Res., 110, scattering albedo. J. Geophys. Res., 115, D10209. D16305. Dickinson, R., 1975: Solar variability and the lower atmosphere. Bull. Am. Meteor. de Graaf, M., L. G. Tilstra, P. Wang, and P. Stammes, 2012: Retrieval of the aerosol Soc., 56, 1240 1248. direct radiative effect over clouds from spaceborne spectrometry. J. Geophys. Doherty, S. J., S. G. Warren, T. C. Grenfell, A. D. Clarke, and R. E. Brandt, 2010: Light- Res., 117, D07207. absorbing impurities in Arctic snow. Atmos. Chem. Phys., 10, 11647 11680. de Leeuw, G., et al., 2011: Production flux of sea spray aerosol. Rev. Geophys., 49, Doherty, S. J., T. C. Grenfell, S. Forsström, D. L. Hegg, R. E. Brandt, and S. G. Warren, RG2001. 2013: Observed vertical redistribution of black carbon and other insoluble light- de Souza, P. A., W. Z. de Mello, L. M. Rauda, and S. M. Sella, 2010: Caracterizaçao do absorbing particles in melting snow. J. Geophys. Res. Atmos., 118, 5553 5569. material particulado fino e grosso e composiçao da fraçao inorgânica solúvel em Donahue, N. M., S. A. Epstein, S. N. Pandis, and A. L. Robinson, 2011a: A two- água em Sao José Dos Campos (SP). Química Nova, 33, 1247 1253. dimensional volatility basis set: 1. Organic-aerosol mixing thermodynamics. Deboudt, K., P. Flament, M. Choel, A. Gloter, S. Sobanska, and C. Colliex, 2010: Mixing Atmos. Chem. Phys., 11, 3303 3318. state of aerosols and direct observation of carbonaceous and marine coatings Donahue, N. M., E. R. Trump, J. R. Pierce, and I. Riipinen, 2011b: Theoretical constraints on African dust by individual particle analysis. J. Geophys. Res., 115, D24207. on pure vapor-pressure driven condensation of organics to ultrafine particles. Decesari, S., et al., 2010: Chemical composition of PM10 and PM1 at the high-altitude Geophys. Res. Lett., 38, L16801. Himalayan station Nepal Climate Observatory-Pyramid (NCO-P) (5079 m.a.s.l.). Dong, B.-W., J. M. Gregory, and R. T. Sutton, 2009: Understanding land-sea warming Atmos. Chem. Phys., 10, 4583 4596. contrast in response to increasing greenhouse gases. Part I: Transient adjustment. Dee, D. P., et al., 2011: The ERA-Interim reanalysis: Configuration and performance of J. Clim., 22, 3079 3097. the data assimilation system. Q. J. R. Meteorol. Soc., 137, 553 597. Donner, L. J., et al., 2011: The dynamical core, physical parameterizations, and basic Del Genio, A. D., and J. B. Wu, 2010: The role of entrainment in the diurnal cycle of simulation characteristics of the atmospheric component AM3 of the GFDL continental convection. J. Clim., 23, 2722 2738. global coupled model CM3. J. Clim., 24, 3484 3519  Del Genio, A. D., M.-S. Yao, and J. Jonas, 2007: Will moist convection be stronger in a Donovan, D. P., 2003: Ice-cloud effective particle size parameterization based on warmer climate? Geophys. Res. Lett., 34, L16703. combined lidar, radar reflectivity, and mean Doppler velocity measurements. J. Del Genio, A. D., Y.-H. Chen, D. Kim, and M.-S. Yao, 2012: The MJO transition Geophys. Res., 108, 4573. from shallow to deep convection in CloudSat/CALIPSO data and GISS GCM Doughty, C. E., C. B. Field, and A. M. S. McMillan, 2011: Can crop albedo be increased simulations. J. Clim., 25, 3755 3770. through the modification of leaf trichomes, and could this cool regional climate? DeLeon-Rodriguez, N., et al., 2013: Microbiome of the upper troposphere: Species Clim. Change, 104, 379 387. composition and prevalence, effects of tropical storms, and atmospheric Dubovik, O., A. Smirnov, B. N. Holben, M. D. King, Y. J. Kaufman, T. F. Eck, and I. implications. Proc. Natl. Acad. Sci. U.S.A., 110, 2575 2580. Slutsker, 2000: Accuracy assessments of aerosol optical properties retrieved DeMott, C. A., C. Stan, D. A. Randall, J. L. Kinter III, and M. Khairoutdinov, 2011: The from Aerosol Robotic Network (AERONET) Sun and sky radiance measurements. Asian Monsoon in the super-parameterized CCSM and its relation to tropical J. Geophys. Res., 105, 9791 9806. wave activity. J. Clim., 24, 5134 5156. Dubovik, O., T. Lapyonok, Y. J. Kaufman, M. Chin, P. Ginoux, R. A. Kahn, and A. Sinyuk, DeMott, P. J., et al., 2010: Predicting global atmospheric ice nuclei distributions and 2008: Retrieving global aerosol sources from satellites using inverse modeling. their impacts on climate. Proc. Natl. Acad. Sci. U.S.A., 107, 11217 11222. Atmos. Chem. Phys., 8, 209 250. Deng, M., G. G. Mace, Z. E. Wang, and H. Okamoto, 2010: Tropical Composition, Dubovik, O., et al., 2002: Variability of absorption and optical properties of key Cloud and Climate Coupling Experiment validation for cirrus cloud profiling aerosol types observed in worldwide locations. J. Atmos. Sci., 59, 590 608. retrieval using CloudSat radar and CALIPSO lidar. J. Geophys. Res., 115, D00J15. Dubovik, O., et al., 2011: Statistically optimized inversion algorithm for enhanced Dengel, S., D. Aeby, and J. Grace, 2009: A relationship between galactic cosmic retrieval of aerosol properties from spectral multi-angle polarimetric satellite radiation and tree rings. New Phytologist, 184, 545 551. observations. Atmos. Meas. Tech., 4, 975 1018. 7 639 Chapter 7 Clouds and Aerosols Dufresne, J.-L., and S. Bony, 2008: An assessment of the primary sources of spread Facchini, M. C., et al., 2008: Primary submicron marine aerosol dominated by of global warming estimates from coupled atmosphere-ocean models. J. Clim., insoluble organic colloids and aggregates. Geophys. Res. Lett., 35, L17814. 21, 5135 5144. Fan, J., et al., 2009: Dominant role by vertical wind shear in regulating aerosol effects Dunne, E. M., L. A. Lee, C. L. Reddington, and K. S. Carslaw, 2012: No statistically on deep convective clouds. J. Geophys. Res., 114, D22206. significant effect of a short-term decrease in the nucleation rate on atmospheric Fan, J. W., J. M. Comstock, and M. Ovchinnikov, 2010: The cloud condensation nuclei aerosols. Atmos. Chem. Phys., 12, 11573 11587. and ice nuclei effects on tropical anvil characteristics and water vapor of the Duplissy, J., et al., 2008: Cloud forming potential of secondary organic aerosol under tropical tropopause layer. Environ. Res. Lett., 5, 6. near atmospheric conditions. Geophys. Res. Lett., 35, L03818. Farina, S. C., P. J. Adams, and S. N. Pandis, 2010: Modeling global secondary organic Durkee, P. A., K. J. Noone, and R. T. Bluth, 2000: The Monterey Area Ship Track aerosol formation and processing with the volatility basis set: Implications for experiment. J. Atmos. Sci., 57, 2523 2541. anthropogenic secondary organic aerosol. J. Geophys. Res., 115, D09202. Dusek, U., et al., 2006: Size matters more than chemistry for cloud-nucleating ability Farrar, P. D., 2000: Are cosmic rays influencing oceanic cloud coverage or is it only of aerosol particles. Science, 312, 1375 1378. El Nino? Clim. Change, 47, 7 15. Early, J. T., 1989: Space-based solar shield to offset greenhouse effect. J. Br. Fasullo, J. T., and K. E. Trenberth, 2012: A less cloudy future: The role of subtropical Interplanet. Soc., 42, 567 569. subsidence in climate sensitivity. Science, 338, 792 794. Easter, R. C., et al., 2004: MIRAGE: Model description and evaluation of aerosols and Favez, O., H. Cachier, J. Sciarea, S. C. Alfaro, T. M. El-Araby, M. A. Harhash, and trace gases. J. Geophys. Res., 109, D20210. Magdy M. Abdelwahab, 2008: Seasonality of major aerosol species and their Eastman, R., and S. G. Warren, 2010: Interannual variations of Arctic cloud types in transformations in Cairo megacity. Atmos. Environ., 42, 1503 1516. relation to sea ice. J. Clim., 23, 4216 4232. Feingold, G., H. L. Jiang, and J. Y. Harrington, 2005: On smoke suppression of clouds Eastman, R., and S. G. Warren, 2013: A 39 yr survey of cloud changes from land in Amazonia. Geophys. Res. Lett., 32, L02804. stations worldwide 1971 2009: Long-term trends, relation to aerosols, and Feingold, G., R. Boers, B. Stevens, and W. R. Cotton, 1997: A modeling study of the expansion of the tropical belt. J. Clim., 26, 1286 1303. effect of drizzle on cloud optical depth and susceptibility. J. Geophys. Res., 102, Eichler, A., et al., 2009: Temperature response in the Altai region lags solar forcing. 13527 13534. Geophys. Res. Lett., 36, L01808. Feingold, G., I. Koren, H. Wang, H. Xue, and W. A. Brewer, 2010: Precipitation- Eitzen, Z. A., K. M. Xu, and T. Wong, 2009: Cloud and radiative characteristics of generated oscillations in open cellular cloud fields. Nature, 466, 849 852. tropical deep convective systems in extended cloud objects from CERES Feng, J., 2008: A size-resolved model and a four-mode parameterization of dry observations. J. Clim., 22, 5983 6000. deposition of atmospheric aerosols. J. Geophys. Res., 113, D12201. Ekman, A. M. L., A. Engström, and C. Wang, 2007: The effect of aerosol composition Ferraro, A. J., E. J. Highwood, and A. J. Charlton-Perez, 2011: Stratospheric heating by and concentration on the development and anvil properties of a continental potential geoengineering aerosols. Geophys. Res. Lett., 38, L24706. deep convective cloud. Q. J. R. Meteorol. Soc., 133, 1439 1452. Flanner, M. G., C. S. Zender, J. T. Randerson, and P. J. Rasch, 2007: Present-day climate Ekman, A. M. L., A. Engström, and A. Söderberg, 2011: Impact of two-way aerosol forcing and response from black carbon in snow. J. Geophys. Res., 112, D11202. cloud interaction and changes in aerosol size distribution on simulated aerosol- Flanner, M. G., C. S. Zender, P. G. Hess, N. M. Mahowald, T. H. Painter, V. Ramanathan, induced deep convective cloud sensitivity. J. Atmos. Sci., 68, 685 698. and P. J. Rasch, 2009: Springtime warming and reduced snow cover from Eliasson, S., S. A. Buehler, M. Milz, P. Eriksson, and V. O. John, 2011: Assessing carbonaceous particles. Atmos. Chem. Phys., 9, 2481 2497. observed and modelled spatial distributions of ice water path using satellite Fletcher, J. K., and C. S. Bretherton, 2010: Evaluating boundary layer-based mass flux data. Atmos. Chem. Phys., 11, 375 391. closures using cloud-resolving model simulations of deep convection. J. Atmos. Eliseev, A. V., A. V. Chernokulsky, A. A. Karpenko, and I. I. Mokhov, 2009: Global Sci., 67, 2212 2225. warming mitigation by sulphur loading in the stratosphere: Dependence of Forest, C. E., P. H. Stone, and A. P. Sokolov, 2006: Estimated PDFs of climate system required emissions on allowable residual warming rate. Theor. Appl. Climatol., properties including natural and anthropogenic forcings. Geophys. Res. Lett., 33, 101, 67 81. L01705. Enghoff, M. B., and H. Svensmark, 2008: The role of atmospheric ions in aerosol Forsström, S., J. Ström, C. A. Pedersen, E. Isaksson, and S. Gerland, 2009: Elemental nucleation a review. Atmos. Chem. Phys., 8, 4911 4923. carbon distribution in Svalbard snow. J. Geophys. Res., 114, D19112. English, J. T., O. B. Toon, and M. J. Mills, 2012: Microphysical simulations of sulfur Forster, P., et al., 2007: Changes in Atmospheric Constituents and in Radiative Forcing. burdens from stratospheric sulfur geoengineering. Atmos. Phys. Chem., 12, In: Climate Change 2007: The Physical Science Basis. Contribution of Working 4775 4793. Group I to the Fourth Assessment Report of the Intergovernmental Panel on Engström, A., and A. M. L. Ekman, 2010: Impact of meteorological factors on the Climate Change [Solomon, S., D. Qin, M. Manning, Z. Chen, M. Marquis, K. B. correlation between aerosol optical depth and cloud fraction. Geophys. Res. Averyt, M. Tignor and H. L. Miller (eds.)] Cambridge University Press, Cambridge, Lett., 37, L18814. United Kingdom and New York, NY, USA, pp. 129 234. Ervens, B., G. Feingold, and S. M. Kreidenweis, 2005: Influence of water-soluble Forster, P. M. D., and J. M. Gregory, 2006: The climate sensitivity and its components organic carbon on cloud drop number concentration. J. Geophys. Res., 110, diagnosed from Earth Radiation Budget data. J. Clim., 19, 39 52. D18211. Fountoukis, C., et al., 2007: Aerosol-cloud drop concentration closure for clouds Ervens, B., B. J. Turpin, and R. J. Weber, 2011a: Secondary organic aerosol formation in sampled during the International Consortium for Atmospheric Research on cloud droplets and aqueous particles (aqSOA): A review of laboratory, field and Transport and Transformation 2004 campaign. J. Geophys. Res., 112, D10S30. model studies. Atmos. Chem. Phys., 11, 11069 11102. Fovell, R. G., K. L. Corbosiero, and H.-C. Kuo, 2009: Cloud microphysics impact on Ervens, B., G. Feingold, K. Sulia, and J. Harrington, 2011b: The impact of microphysical hurricane track as revealed in idealized experiments. J. Atmos. Sci., 66, 1764 parameters, ice nucleation mode, and habit growth on the ice/liquid partitioning 1778. in mixed-phase Arctic clouds. J. Geophys. Res., 116, D17205. Fowler, L. D., and D. A. Randall, 2002: Interactions between cloud microphysics and Ervens, B., et al., 2007: Prediction of cloud condensation nucleus number cumulus convection in a general circulation model. J. Atmos. Sci., 59, 3074 3098. concentration using measurements of aerosol size distributions and composition Freney, E. J., K. Adachi, and P. R. Buseck, 2010: Internally mixed atmospheric aerosol and light scattering enhancement due to humidity. J. Geophys. Res., 112, particles: Hygroscopic growth and light scattering. J. Geophys. Res., 115, D10S32. D19210. European Commission, Joint Research Centre, and Netherlands Environmental Fridlind, A. M., et al., 2007: Ice properties of single-layer stratocumulus during the Assessment Agency (PBL), 2009: Emission Database for Global Atmospheric Mixed-Phase Arctic Cloud Experiment: 2. Model results. J. Geophys. Res., 112, Research (EDGAR), release version 4.0. http://edgar.jrc.ec.europa.eu, last D24202. accessed 7 June 2013. Friedman, B., G. Kulkarni, J. Beranek, A. Zelenyuk, J. A. Thornton, and D. J. Cziczo, Evan, A. T., and J. R. Norris, 2012: On global changes in effective cloud height. 2011: Ice nucleation and droplet formation by bare and coated soot particles. J. Geophys. Res. Lett., 39, L19710. Geophys. Res., 116, D17203. Evans, J. R. G., E. P. J. Stride, M. J. Edirisinghe, D. J. Andrews, and R. R. Simons, 2010: Frömming, C., M. Ponater, U. Burkhardt, A. Stenke, S. Pechtl, and R. Sausen, 2011: 7 Can oceanic foams limit global warming? Clim. Res., 42, 155 160. Sensitivity of contrail coverage and contrail radiative forcing to selected key parameters. Atmos. Environ., 45, 1483 1490. 640 Clouds and Aerosols Chapter 7 Fuzzi, S., et al., 2007: Overview of the inorganic and organic composition of size- Givati, A., and D. Rosenfeld, 2004: Quantifying precipitation suppression due to air segregated aerosol in Rondonia, Brazil, from the biomass-burning period to the pollution. J. Appl. Meteorol., 43, 1038 1056. onset of the wet season. J. Geophys. Res., 112, D01201. Golaz, J. C., V. E. Larson, and W. R. Cotton, 2002: A PDF-based model for boundary Fyfe, J. C., J. N. S. Cole, V. K. Arora, and J. F. Scinocca, 2013: Biogeochemical carbon layer clouds. Part I: Method and model description. J. Atmos. Sci., 59, 3540 3551. coupling influences global precipitation in geoengineering experiments. Golaz, J. C., M. Salzmann, L. J. Donner, L. W. Horowitz, Y. Ming, and M. Zhao, 2011: Geophys. Res. Lett., 40, 651 655. Sensitivity of the aerosol indirect effect to subgrid variability in the cloud Gagen, M., et al., 2011: Cloud response to summer temperatures in Fennoscandia parameterization of the GFDL atmosphere general circulation model AM3. J. over the last thousand years. Geophys. Res. Lett., 38, L05701. Clim., 24, 3145 3160. Galewsky, J., and J. V. Hurley, 2010: An advection-condensation model for subtropical Good, N., et al., 2010: Consistency between parameterisations of aerosol water vapor isotopic ratios. J. Geophys. Res., 115, D16116. hygroscopicity and CCN activity during the RHaMBLe discovery cruise. Atmos. Gantt, B., N. Meskhidze, M. C. Facchini, M. Rinaldi, D. Ceburnis, and C. D. O Dowd, Chem. Phys., 10, 3189 3203. 2011: Wind speed dependent size-resolved parameterization for the organic Gordon, N. D., and J. R. Norris, 2010: Cluster analysis of midlatitude oceanic cloud mass fraction of sea spray aerosol. Atmos. Chem. Phys., 11, 8777 8790. regimes: Mean properties and temperature sensitivity. Atmos. Chem. Phys., 10, Gao, R. S., et al., 2007: A novel method for estimating light-scattering properties of 6435 6459. soot aerosols using a modified single-particle soot photometer. Aer. Sci. Technol., Goren, T., and D. Rosenfeld, 2012: Satellite observations of ship emission induced 41, 125 135. transitions from broken to closed cell marine stratocumulus over large areas. J. Garrett, T. J., and C. F. Zhao, 2006: Increased Arctic cloud longwave emissivity Geophys. Res., 117, D17206. associated with pollution from mid-latitudes. Nature, 440, 787 789. Grabowski, W. W., and P. K. Smolarkiewicz, 1999: CRCP: A Cloud Resolving Garvert, M. F., C. P. Woods, B. A. Colle, C. F. Mass, P. V. Hobbs, M. T. Stoelinga, and J. B. Convection Parameterization for modeling the tropical convecting atmosphere. Wolfe, 2005: The 13 14 December 2001 IMPROVE-2 event. Part II: Comparisons Physica D, 133, 171 178. of MM5 model simulations of clouds and precipitation with observations. J. Grabowski, W. W., X. Wu, M. W. Moncrieff, and W. D. Hall, 1998: Cloud-resolving Atmos. Sci., 62, 3520 3534. modeling of cloud systems during Phase III of GATE. Part II: Effects of resolution Gasso, S., 2008: Satellite observations of the impact of weak volcanic activity on and the third spatial dimension. J. Atmos. Sci., 55, 3264 3282. marine clouds. J. Geophys. Res., 113, D14S19. Grandey, B. S., and P. Stier, 2010: A critical look at spatial scale choices in satellite- GAW, 2011: WMO/GAW Standard Operating Procedures for In-situ Measurements based aerosol indirect effect studies. Atmos. Chem. Phys., 10, 11459 11470. of Aerosol Mass Concentration, Light Scattering and Light Absorption. GAW Grandpeix, J.-Y., and J.-P. Lafore, 2010: A density current parameterization coupled Report No. 200, World Meteorological Organization, Geneva, Switzerland, 130 with Emanuel s convection scheme. Part I: The models. J. Atmos. Sci., 67, 881 pp. 897. George, R. C., and R. Wood, 2010: Subseasonal variability of low cloud radiative Granier, C., et al., 2011: Evolution of anthropogenic and biomass burning emissions properties over the southeast Pacific Ocean. Atmos. Chem. Phys., 10, 4047 of air pollutants at global and regional scales during the 1980 2010 period. 4063. Clim. Change, 109, 163 190. Gerasopoulos, E., et al., 2007: Size-segregated mass distributions of aerosols over Gregory, J., and M. Webb, 2008: Tropospheric adjustment induces a cloud component Eastern Mediterranean: Seasonal variability and comparison with AERONET in CO2 forcing. J. Clim., 21, 58 71. columnar size-distributions. Atmos. Chem. Phys., 7, 2551 2561. Gregory, J. M., et al., 2004: A new method for diagnosing radiative forcing and Gerber, H., 1996: Microphysics of marine stratocumulus clouds with two drizzle climate sensitivity. Geophys. Res. Lett., 31, L03205. modes. J. Atmos. Sci., 53, 1649 1662. Grote, R., and U. Niinemets, 2008: Modeling volatile isoprenoid emissions - a story Gettelman, A., and Q. Fu, 2008: Observed and simulated upper-tropospheric water with split ends. Plant Biology, 10, 8 28. vapor feedback. J. Clim., 21, 3282 3289. Guenther, A., T. Karl, P. Harley, C. Wiedinmyer, P. I. Palmer, and C. Geron, 2006: Gettelman, A., J. E. Kay, and J. T. Fasullo, 2013: Spatial decomposition of climate Estimates of global terrestrial isoprene emissions using MEGAN (Model of feedbacks in the Community Earth System Model. J. Clim., 26, 3544 3561. Emissions of Gases and Aerosols from Nature). Atmos. Chem. Phys., 6, 3181 Gettelman, A., X. Liu, D. Barahona, U. Lohmann, and C.-C. Chen, 2012: Climate 3210. impacts of ice nucleation. J. Geophys. Res., 117, D20201. Guenther, A. B., X. Jiang, C. L. Heald, T. Sakulyanontvittaya, T. Duhl, L. K. Emmons, Gettelman, A., et al., 2010: Global simulations of ice nucleation and ice and X. Wang, 2012: The Model of Emissions of Gases and Aerosols from Nature supersaturation with an improved cloud scheme in the Community Atmosphere version 2.1 (MEGAN2.1): An extended and updated framework for modeling Model. J. Geophys. Res., 115, D18216. biogenic emissions. Geosci. Model Dev., 5, 1471 1492. Ghan, S., R. Easter, J. Hudson, and F.-M. Bréon, 2001: Evaluation of aerosol indirect Gullu, H. G., I. Ölmez, and G. Tuncel, 2000: Temporal variability of atmospheric trace radiative forcing in MIRAGE. J. Geophys. Res., 106, 5317 5334. element concentrations over the eastern Mediterranean Sea. Spectrochim. Acta, Ghan, S. J., and S. E. Schwartz, 2007: Aerosol properties and processes - A path from B55, 1135 1150. field and laboratory measurements to global climate models. Bull. Am. Meteor. Guo, H., J. C. Golaz, L. J. Donner, V. E. Larson, D. P. Schanen, and B. M. Griffin, 2010: Soc., 88, 1059 1083. Multi-variate probability density functions with dynamics for cloud droplet Ghan, S. J., H. Abdul-Razzak, A. Nenes, Y. Ming, X. Liu, and M. Ovchinnikov, 2011: activation in large-scale models: Single column tests. Geosci. Model Dev., 3, Droplet nucleation: Physically-based parameterizations and comparative 475 486. evaluation. J. Adv. Model. Earth Syst., 3, M10001. Hadley, O. L., and T. W. Kirchstetter, 2012: Black-carbon reduction of snow albedo. Ghan, S. J., X. Liu, R. C. Easter, R. Zaveri, P. J. Rasch, J.-H. Yoon, and B. Eaton, 2012: Nature Clim. Change, 2, 437 440. Toward a minimal representation of aerosols in climate models: Comparative Haerter, J. O., and P. Berg, 2009: Unexpected rise in extreme precipitation caused by decomposition of aerosol direct, semi-direct, and indirect radiative forcing. J. a shift in rain type? Nature Geosci., 2, 372 373. Clim., 25, 6461 6476. Haerter, J. O., P. Berg, and S. Hagemann, 2010: Heavy rain intensity distributions on Ginoux, P., J. M. Prospero, T. E. Gill, N. C. Hsu, and M. Zhao, 2012a: Global-scale varying time scales and at different temperatures. J. Geophys. Res., 115, D17102. attribution of anthropogenic and natural dust sources and their emission rates Hagler, G. S. W., et al., 2006: Source areas and chemical composition of fine based on MODIS Deep Blue aerosol products. Rev. Geophys., 50, RG3005. particulate matter in the Pearl River Delta region of China. Atmos. Environ., 40, Ginoux, P., L. Clarisse, C. Clerbaux, P.-F. Coheur, O. Dubovik, N. C. Hsu, and 3802 3815.. M. Van Damme, 2012b: Mixing of dust and NH3 observed globally over Halfon, N., Z. Levin, and P. Alpert, 2009: Temporal rainfall fluctuations in Israel anthropogenic dust sources Atmos. Chem. Phys., 12, 7351 7363. and their possible link to urban and air pollution effects. Environ. Res. Lett., 4, Gioda, A., B. S. Amaral, I. L. G. Monteiro, and T. D. Saint Pierre, 2011: Chemical 025001. composition, sources, solubility, and transport of aerosol trace elements in a Halloran, P. R., T. G. Bell, and I. J. Totterdell, 2010: Can we trust empirical marine tropical region. J. Environ. Monit., 13, 2134 2142. DMS parameterisations within projections of future climate? Biogeosciences, Girard, E., J.-P. Blanchet, and Y. Dubois, 2004: Effects of arctic sulphuric acid aerosols 7, 1645 1656. on wintertime low-level atmospheric ice crystals, humidity and temperature at Hallquist, M., et al., 2009: The formation, properties and impact of secondary organic 7 Alert, Nunavut. Atmos. Res., 73, 131 148. aerosol: Current and emerging issues. Atmos. Chem. Phys., 9, 5155 5236. 641 Chapter 7 Clouds and Aerosols Hamwey, R. M., 2007: Active amplification of the terrestrial albedo to mitigate Heald, C. L., et al., 2008: Predicted change in global secondary organic aerosol climate change: An exploratory study. Mitigat. Adapt. Strat. Global Change, 12, concentrations in response to future climate, emissions, and land use change. J. 419 439. Geophys. Res., 113, D05211. Han, Y.-J., T.-S. Kim, and H. Kim, 2008: Ionic constituents and source analysis of PM2.5 Heald, C. L., et al., 2011: Exploring the vertical profile of atmospheric organic aerosol: in three Korean cities. Atmos. Environ., 42, 4735 4746. Comparing 17 aircraft field campaigns with a global model. Atmos. Chem. Phys., Hand, V. L., G. Capes, D. J. Vaughan, P. Formenti, J. M. Haywood, and H. Coe, 2010: 11, 12673 12696. Evidence of internal mixing of African dust and biomass burning particles by Heckendorn, P., et al., 2009: The impact of geoengineering aerosols on stratospheric individual particle analysis using electron beam techniques. J. Geophys. Res., temperature and ozone. Environ. Res. Lett., 4, 045108. 115, D13301. Hegg, D. A., D. S. Covert, H. H. Jonsson, and R. K. Woods, 2012: A simple relationship Hansell, R. A., et al., 2010: An assessment of the surface longwave direct radiative between cloud drop number concentration and precursor aerosol concentration effect of airborne Saharan dust during the NAMMA field campaign. J. Atmos. for the regions of Earth s large marine stratocumulus decks. Atmos. Chem. Phys., Sci., 67, 1048 1065. 12, 1229 1238. Hansen, J., and L. Nazarenko, 2004: Soot climate forcing via snow and ice albedos. Heintzenberg, J., D. C. Covert, and R. Van Dingenen, 2000: Size distribution and Proc. Natl. Acad. Sci. U.S.A., 101, 423 428. chemical composition of marine aerosols: A compilation and review. Tellus, 52, Hansen, J., M. Sato, P. Kharecha, G. Russell, D. W. Lea, and M. Siddall, 2007: Climate 1104 1122. change and trace gases. Philos. Trans. R. Soc. London A, 365, 1925 1954. Heintzenberg, J., et al., 2011: Near-global aerosol mapping in the upper troposphere Hansen, J., et al., 1984: Climate sensitivity: Analysis of feedback mechanisms. In: and lowermost stratosphere with data from the CARIBIC project. Tellus B, 63, Climate Processes and Climate Sensitivity, Geophysical Monograph Series, 875 890. Vol. 29 [J. E. Hansen and T. Takahashi (eds.)]. American Geophysical Union, Held, I. M., and B. J. Soden, 2006: Robust responses of the hydrological cycle to Washington, DC, USA, pp. 130 163. global warming. J. Clim., 19, 5686 5699. Hansen, J., et al., 2005: Efficacy of climate forcings. J. Geophys. Res., 110, D18104. Held, I. M., and K. M. Shell, 2012: Using relative humidity as a state variable in Hara, K., et al., 2003: Mixing states of individual aerosol particles in spring Arctic climate feedback analysis. J. Clim., 25, 2578 2582. troposphere during ASTAR 2000 campaign. J. Geophys. Res., 108, 4209. Hendricks, J., B. Karcher, U. Lohmann, and M. Ponater, 2005: Do aircraft black carbon Hardwick Jones, R., S. Westra, and A. Sharma, 2010: Observed relationships between emissions affect cirrus clouds on the global scale? Geophys. Res. Lett., 32, extreme sub-daily precipitation, surface temperature, and relative humidity. L12814. Geophys. Res. Lett., 37, L22805. Heymsfield, A., D. Baumgardner, P. DeMott, P. Forster, K. Gierens, and B. Kärcher, Harrington, J. Y., D. Lamb, and R. Carver, 2009: Parameterization of surface kinetic 2010: Contrail microphysics. Bull. Am. Meteor. Soc., 91, 465 472. effects for bulk microphysical models: Influences on simulated cirrus dynamics Heymsfield, A. J., and L. M. Miloshevich, 1995: Relative humidity and temperature and structure. J. Geophys. Res., 114, D06212. influences on cirrus formation and evolution: Observations from wave clouds Harrison, E. F., P. Minnis, B. R. Barkstrom, V. Ramanathan, R. D. Cess, and G. G. Gibson, and FIRE II. J. Atmos. Sci., 52, 4302 4326. 1990: Seasonal variation of cloud radiative forcing derrived from the Earth Heymsfield, A. J., et al., 1998: Cloud properties leading to highly reflective tropical Radiation Budget Experiment. J. Geophys. Res., 95, 18687 18703. cirrus: Interpretations from CEPEX, TOGA COARE, and Kwajalein, Marshall Harrison, R., and M. Ambaum, 2008: Enhancement of cloud formation by droplet Islands. J. Geophys. Res., 103, 8805 8812. charging. Proc. R. Soc. London A, 464, 2561 2573. Hill, A. A., and S. Dobbie, 2008: The impact of aerosols on non-precipitating marine Harrison, R. G., 2008: Discrimination between cosmic ray and solar irradiance effects stratocumulus. II: The semi-direct effect. Q. J. R. Meteorol. Soc., 134, 1155 1165. on clouds, and evidence for geophysical modulation of cloud thickness. Proc. R. Hill, A. A., G. Feingold, and H. Jiang, 2009: The influence of entrainment and mixing Soc. London A, 464, 2575 2590. assumption on aerosol-cloud interactions in marine stratocumulus. J. Atmos. Sci., Harrison, R. G., and D. B. Stephenson, 2006: Empirical evidence for a nonlinear effect 66, 1450 1464. of galactic cosmic rays on clouds. Proc. R. Soc. London A, 462, 1221 1233. Hill, S., and Y. Ming, 2012: Nonlinear climate response to regional brightening of Harrison, R. G., and M. H. P. Ambaum, 2010: Observing Forbush decreases in cloud at tropical marine stratocumulus. Geophys. Res. Lett., 39, L15707. Shetland. J. Atmos. Sol. Terres. Phys., 72, 1408 1414. Hirsikko, A., et al., 2011: Atmospheric ions and nucleation: A review of observations. Harrop, B. E., and D. L. Hartmann, 2012: Testing the role of radiation in determining Atmos. Chem. Phys., 11, 767 798. tropical cloud-top temperature. J. Clim., 25, 5731 5747. Hodzic, A., J. L. Jimenez, S. Madronich, M. R. Canagaratna, P. F. DeCarlo, L. Kleinman, Hartmann, D. L., and K. Larson, 2002: An important constraint on tropical cloud- and J. Fast, 2010: Modeling organic aerosols in a megacity: Potential contribution climate feedback. Geophys. Res. Lett., 29, 1951. of semi-volatile and intermediate volatility primary organic compounds to Hasekamp, O. P., 2010: Capability of multi-viewing-angle photo-polarimetric secondary organic aerosol formation. Atmos. Chem. Phys., 10, 5491 5514. measurements for the simultaneous retrieval of aerosol and cloud properties. Hohenegger, C., and C. S. Bretherton, 2011: Simulating deep convection with a Atmos. Meas. Tech., 3, 839 851. shallow convection scheme. Atmos. Chem. Phys., 11, 10389 10406. Haynes, J. M., C. Jakob, W. B. Rossow, G. Tselioudis, and J. Brown, 2011: Major Hohenegger, C., P. Brockhaus, and C. Schar, 2008: Towards climate simulations at characteristics of Southern Ocean cloud regimes and their effects on the energy cloud-resolving scales. Meteorol. Z., 17, 383 394. budget. J. Clim., 24, 5061 5080. Hohenegger, C., P. Brockhaus, C. S. Bretherton, and C. Schär, 2009: The soil Haywood, J., and O. Boucher, 2000: Estimates of the direct and indirect radiative moisture precipitation feedback in simulations with explicit and parameterized forcing due to tropospheric aerosols: A review. Rev. Geophys., 38, 513 543. convection. J. Clim., 22, 5003 5020. Haywood, J., and M. Schulz, 2007: Causes of the reduction in uncertainty in the Holben, B. N., et al., 1998: AERONET - A federated instrument network and data anthropogenic radiative forcing of climate between IPCC (2001) and IPCC archive for aerosol characterization. Remote Sens. Environ., 66, 1 16. (2007). Geophys. Res. Lett., 34, L20701. Hoose, C., and O. Möhler, 2012: Heterogeneous ice nucleation on atmospheric Haywood, J. M., A. Jones, N. Bellouin, and D. Stephenson, 2013: Asymmetric forcing aerosols: A review of results from laboratory experiments. Atmos. Chem. Phys., from stratospheric aerosols impacts Sahelian rainfall. Nature Clim. Change, 3, 12, 9817 9854. 660 665. Hoose, C., J. E. Kristjánsson, and S. M. Burrows, 2010a: How important is biological Haywood, J. M., et al., 2009: A case study of the radiative forcing of persistent ice nucleation in clouds on a global scale? Environ. Res. Lett., 5, 024009. contrails evolving into contrail-induced cirrus. J. Geophys. Res., 114, D24201. Hoose, C., U. Lohmann, R. Erdin, and I. Tegen, 2008: The global influence of dust Haywood, J. M., et al., 2011: Motivation, rationale and key results from the GERBILS mineralogical composition on heterogeneous ice nucleation in mixed-phase Saharan dust measurement campaign. Q. J. R. Meteorol. Soc., 137, 1106 1116. clouds. Environ. Res. Lett., 3, 025003. Heald, C. L., and D. V. Spracklen, 2009: Atmospheric budget of primary biological Hoose, C., J. E. Kristjánsson, J. P. Chen, and A. Hazra, 2010b: A classical-theory-based aerosol particles from fungal spores. Geophys. Res. Lett., 36, L09806. parameterization of heterogeneous ice nucleation by mineral dust, soot, and Heald, C. L., D. A. Ridley, S. M. Kreidenweis, and E. E. Drury, 2010: Satellite biological particles in a global climate model. J. Atmos. Sci., 67, 2483 2503. observations cap the atmospheric organic aerosol budget. Geophys. Res. Lett., Hoose, C., J. E. Kristjánsson, T. Iversen, A. Kirkevag, O. Seland, and A. Gettelman, 7 37, L24808. 2009: Constraining cloud droplet number concentration in GCMs suppresses the aerosol indirect effect. Geophys. Res. Lett., 36, L12807. 642 Clouds and Aerosols Chapter 7 Hourdin, F., et al., 2013: LMDZ5B: The atmospheric component of the IPSL climate Jacobson, M. Z., 2003: Development of mixed-phase clouds from multiple aerosol model with revisited parameterizations for clouds and convection. Clim. Dyn., size distributions and the effect of the clouds on aerosol removal. J. Geophys. 40, 2193 2222. Res., 108, 4245. Hoyle, C. R., et al., 2011: A review of the anthropogenic influence on biogenic Jacobson, M. Z., 2004: Climate response of fossil fuel and biofuel soot, accounting secondary organic aerosol. Atmos. Chem. Phys., 11, 321 343. for soot s feedback to snow and sea ice albedo and emissivity. J. Geophys. Res., Hu, M., L. Y. He, Y. H. Zhang, M. Wang, Y. Pyo Kim, and K. C. Moon, 2002: Seasonal 109, D21201. variation of ionic species in fine particles at Qingdao. Atmos. Environ., 36, Jacobson, M. Z., 2006: Effects of externally-through-internally-mixed soot inclusions 5853 5859. within clouds and precipitation on global climate. J. Phys. Chem. A, 110, 6860 Huang, J., Q. Fu, W. Zhang, X. Wang, R. Zhang, H. Ye, and S. G. Warren, 2011: Dust 6873. and black carbon in seasonal snow across Northern China. Bull. Am. Meteor. Jacobson, M. Z., 2012: Investigating cloud absorption effects: Global absorption Soc., 92, 175 181. properties of black carbon, tar balls, and soil dust in clouds and aerosols. J. Hudson, J. G., 1993: Cloud condensation nuclei near marine cumulus. J. Geophys. Geophys. Res., 117, D06205. Res., 98, 2693 2702. Jacobson, M. Z., and D. G. Streets, 2009: Influence of future anthropogenic emissions Hudson, J. G., S. Noble, and V. Jha, 2010: Comparisons of CCN with supercooled on climate, natural emissions, and air quality. J. Geophys. Res., 114, D08118. clouds. J. Atmos. Sci., 67, 3006 3018. Jacobson, M. Z., and J. E. Ten Hoeve, 2012: Effects of urban surfaces and white roofs Hueglin, C., R. Gehrig, U. Baltensperger, M. Gysel, C. Monn, and H. Vonmont, 2005: on global and regional climate. J. Clim., 25, 1028 1044. Chemical characterisation of PM2.5, PM10 and coarse particles at urban, near-city Jaeglé, L., P. K. Quinn, T. S. Bates, B. Alexander, and J. T. Lin, 2011: Global distribution of and rural sites in Switzerland. Atmos. Environ., 39, 637 651. sea salt aerosols: New constraints from in situ and remote sensing observations. Huffman, G. J., et al., 2007: The TRMM multisatellite precipitation analysis (TMPA): Atmos. Chem. Phys., 11, 3137 3157. Quasi-global, multiyear, combined-sensor precipitation estimates at fine scales. Jensen, E. J., S. Kinne, and O. B. Toon, 1994: Tropical cirrus cloud radiative forcing: J. Hydrometeor., 8, 38 55. Sensitivity studies. Geophys. Res. Lett., 21, 2023 2026. Huneeus, N., F. Chevallier, and O. Boucher, 2012: Estimating aerosol emissions by Jensen, E. J., L. Pfister, T. P. Bui, P. Lawson, and D. Baumgardner, 2010: Ice nucleation assimilating observed aerosol optical depth in a global aerosol model. Atmos. and cloud microphysical properties in tropical tropopause layer cirrus. Atmos. Chem. Phys., 12, 4585 4606. Chem. Phys., 10, 1369 1384. Huneeus, N., et al., 2011: Global dust model intercomparison in AeroCom phase I. Jeong, M.-J., and Z. Li, 2010: Separating real and apparent effects of cloud, humidity, Atmos. Chem. Phys., 11, 7781 7816. and dynamics on aerosol optical thickness near cloud edges. J. Geophys. Res., Hurley, J. V., and J. Galewsky, 2010a: A last saturation analysis of ENSO humidity 115, D00K32. variability in the Subtropical Pacific. J. Clim., 23, 918 931. Jeong, M. J., S. C. Tsay, Q. Ji, N. C. Hsu, R. A. Hansell, and J. Lee, 2008: Ground- Hurley, J. V., and J. Galewsky, 2010b: A last-saturation diagnosis of subtropical water based measurements of airborne Saharan dust in marine environment during vapor response to global warming. Geophys. Res. Lett., 37, L06702. the NAMMA field experiment. Geophys. Res. Lett., 35, L20805. Iga, S., H. Tomita, Y. Tsushima, and M. Satoh, 2011: Sensitivity of Hadley Circulation Jethva, H., S. K. Satheesh, J. Srinivasan, and K. K. Moorthy, 2009: How good is the to physical parameters and resolution through changing upper-tropospheric ice assumption about visible surface reflectance in MODIS aerosol retrieval over clouds using a global cloud-system resolving model. J. Clim., 24, 2666 2679. land? A comparison with aircraft measurements over an urban site in India. IEEE Iguchi, T., T. Nakajima, A. P. Khain, K. Saito, T. Takemura, and K. Suzuki, 2008: Modeling Trans. Geosci. Remote Sens., 47, 1990 1998. the influence of aerosols on cloud microphysical properties in the east Asia Jiang, H. L., H. W. Xue, A. Teller, G. Feingold, and Z. Levin, 2006: Aerosol effects on the region using a mesoscale model coupled with a bin-based cloud microphysics lifetime of shallow cumulus. Geophys. Res. Lett., 33, L14806. scheme. J. Geophys. Res., 113, D14215. Jiang, J. H., H. Su, M. Schoeberl, S. T. Massie, P. Colarco, S. Platnick, and N. J. Livesey, Ingram, W., 2010: A very simple model for the water vapour feedback on climate 2008: Clean and polluted clouds: Relationships among pollution, ice cloud and change. Q. J. R. Meteorol. Soc., 136, 30 40. precipitation in South America. Geophys. Res. Lett., 35, L14804. Ingram, W., 2013a: A new way of quantifying GCM water vapour feedback. Clim. Jimenez, J. L., et al., 2009: Evolution of organic aerosols in the atmosphere. Science, Dyn., 40, 913 924. 326, 1525 1529. Ingram, W., 2013b: Some implications of a new approach to the water vapour Jirak, I. L., and W. R. Cotton, 2006: Effect of air pollution on precipitation along the feedback. Clim. Dyn., 40, 925 933. front range of the Rocky Mountains. J. Appl. Meteor. Climatol., 45, 236 245. Inoue, T., M. Satoh, Y. Hagihara, H. Miura, and J. Schmetz, 2010: Comparison of Johanson, C. M., and Q. Fu, 2009: Hadley cell widening: Model simulations versus high-level clouds represented in a global cloud system-resolving model with observations. J. Clim., 22, 2713 2725. CALIPSO/CloudSat and geostationary satellite observations. J. Geophys. Res., Johns, T. C., et al., 2006: The new Hadley Centre Climate Model (HadGEM1): 115, D00H22. Evaluation of coupled simulations. J. Clim., 19, 1327 1353. IPCC, 2011: IPCC Expert Meeting Report on Geoengineering, [Edenhofer O, Field C, Johnson, B. T., K. P. Shine, and P. M. Forster, 2004: The semi-direct aerosol effect: Pichs-Madruga R, Sokona Y, Stocker T, Barros V, Dahe Q, Minx J, Mach K, Plattner Impact of absorbing aerosols on marine stratocumulus. Q. J. R. Meteorol. Soc., GK, Schlomer S, Hansen G, Mastrandrea M (eds.)]. IPCC Working Group III 130, 1407 1422. Technical Support Unit, Potsdam Institute for Climate Impact Research. Johnson, N. C., and S. P. Xie, 2010: Changes in the sea surface temperature threshold Irvine, P. J., A. Ridgwell, and D. J. Lunt, 2011: Climatic effects of surface albedo for tropical convection. Nature Geosci., 3, 842 845. geoengineering. J. Geophys. Res., 116, D24112. Jones, A., J. M. Haywood, and O. Boucher, 2007: Aerosol forcing, climate response Irvine, P. J., R. L. Sriver, and K. Keller, 2012: Tension between reducing sea-level rise and climate sensitivity in the Hadley Centre climate model. J. Geophys. Res., and global warming through solar-radiation management. Nature Clim. Change, 112, D20211. 2, 97 100. Jones, A., J. Haywood, and O. Boucher, 2009: Climate impacts of geoengineering Irwin, M., N. Good, J. Crosier, T. W. Choularton, and G. McFiggans, 2010: Reconciliation marine stratocumulus clouds. J. Geophys. Res., 114, D10106. of measurements of hygroscopic growth and critical supersaturation of aerosol Jones, A., D. L. Roberts, M. J. Woodage, and C. E. Johnson, 2001: Indirect sulphate particles in central Germany. Atmos. Chem. Phys., 10, 11737 11752. aerosol forcing in a climate model with an interactive sulphur cycle. J. Geophys. Ito, K., N. Xue, and G. Thurston, 2004: Spatial variation of PM2.5 chemical species and Res., 106, 20293 20310. source-apportioned mass concentrations in New York City. Atmos. Environ., 38, Jones, A., J. Haywood, O. Boucher, B. Kravitz, and A. Robock, 2010: Geoengineering 5269 5282. by stratospheric SO2 injection: Results from the Met Office HadGEM2 climate Izrael, Y. A., A. G. Ryaboshapko, and N. N. Petrov, 2009: Comparative analysis of model and comparison with the Goddard Institute for Space Studies ModelE. geo-engineering approaches to climate stabilization. Russ. Meteorol. Hydrol., Atmos. Chem. Phys., 10, 5999 6006. 34, 335 347. Joshi, M. M., M. J. Webb, A. C. Maycock, and M. Collins, 2010: Stratospheric water Jacob, D. J., et al., 2010: The Arctic Research of the Composition of the Troposphere vapour and high climate sensitivity in a version of the HadSM3 climate model. from Aircraft and Satellites (ARCTAS) mission: Design, execution, and first Atmos. Chem. Phys., 10, 7161 7167. results. Atmos. Chem. Phys., 10, 5191 5212. 7 643 Chapter 7 Clouds and Aerosols Joshi, M. M., J. M. Gregory, M. J. Webb, D. M. H. Sexton, and T. C. Johns, 2008: Khain, A., M. Arkhipov, M. Pinsky, Y. Feldman, and Y. Ryabov, 2004: Rain enhancement Mechanisms for the land/sea warming contrast exhibited by simulations of and fog elimination by seeding with charged droplets. Part 1: Theory and climate change. Clim. Dyn., 30, 455 465. numerical simulations. J. Appl. Meteorol., 43, 1513 1529. Kahn, R., 2012: Reducing the uncertainties in direct aerosol radiative forcing. Surv. Khain, A. P., 2009: Notes on state-of-the-art investigations of aerosol effects on Geophys., 33, 701 721. precipitation: A critical review. Environ. Res. Lett., 4, 015004. Kahn, R. A., B. J. Gaitley, J. V. Martonchik, D. J. Diner, K. A. Crean, and B. Holben, 2005: Khairoutdinov, M., and Y. Kogan, 2000: A new cloud physics parameterization in a Multiangle Imaging Spectroradiometer (MISR) global aerosol optical depth large-eddy simulation model of marine stratocumulus. Mon. Weather Rev., 128, validation based on 2 years of coincident Aerosol Robotic Network (AERONET) 229 243. observations. J. Geophys. Res., 110, D10S04. Khairoutdinov, M., D. Randall, and C. DeMott, 2005: Simulations of the atmospheric Kahn, R. A., B. J. Gaitley, M. J. Garay, D. J. Diner, T. F. Eck, A. Smirnov, and B. N. general circulation using a cloud-resolving model as a superparameterization of Holben, 2010: Multiangle Imaging SpectroRadiometer global aerosol product physical processes. J. Atmos. Sci., 62, 2136 2154. assessment by comparison with the Aerosol Robotic Network. J. Geophys. Res., Khairoutdinov, M. F., and D. A. Randall, 2001: A cloud resolving model as a cloud 115, D23209. parameterization in the NCAR Community Climate System Model: Preliminary Kahn, R. A., et al., 2007: Satellite-derived aerosol optical depth over dark water results. Geophys. Res. Lett., 28, 3617 3620. from MISR and MODIS: Comparisons with AERONET and implications Khairoutdinov, M. F., and C.-E. Yang, 2013: Cloud-resolving modelling of aerosol for climatological studies. J. Geophys. Res., 112, D18205. indirect effects in idealised radiative-convective equilibrium with interactive and Kalkstein, A. J., and R. C. Balling Jr, 2004: Impact of unusually clear weather on fixed sea surface temperature. Atmos. Chem. Phys., 13, 4133 4144. United States daily temperature range following 9/11/2001 Clim. Res., 26, 1 4. Khairoutdinov, M. F., S. K. Krueger, C.-H. Moeng, P. A. Bogenschutz, and D. A. Randall, Kammermann, L., et al., 2010: Subarctic atmospheric aerosol composition: 3. 2009: Large-eddy simulation of maritime deep tropical convection. J. Adv. Model. Measured and modeled properties of cloud condensation nuclei. J. Geophys. Earth Syst., 1, 15. Res., 115, D04202. Khan, M. F., Y. Shirasuna, K. Hirano, and S. Masunaga, 2010: Characterization of Kanakidou, M., et al., 2005: Organic aerosol and global climate modelling: A review. PM2.5, PM2.5 10 and PMN10 in ambient air, Yokohama, Japan. Atmos. Res., Atmos. Chem. Phys., 5, 1053 1123. 96, 159 172. Kärcher, B., O. Mohler, P. J. DeMott, S. Pechtl, and F. Yu, 2007: Insights into the role Khare, P., and B. P. Baruah, 2010: Elemental characterization and source identification of soot aerosols in cirrus cloud formation. Atmos. Chem. Phys., 7, 4203 4227. of PM2.5 using multivariate analysis at the suburban site of North-East India. Kaspari, S. D., M. Schwikowski, M. Gysel, M. G. Flanner, S. Kang, S. Hou, and P. A. Atmos. Res., 98, 148 162. Mayewski, 2011: Recent increase in black carbon concentrations from a Mt. Kharin, V. V., F. W. Zwiers, X. B. Zhang, and G. C. Hegerl, 2007: Changes in temperature Everest ice core spanning 1860 2000 AD. Geophys. Res. Lett., 38, L04703. and precipitation extremes in the IPCC ensemble of global coupled model Kato, S., et al., 2011: Improvements of top-of-atmosphere and surface irradiance simulations. J. Clim., 20, 1419 1444. computations with CALIPSO-, CloudSat-, and MODIS-derived cloud and aerosol Khvorostyanov, V., and K. Sassen, 1998: Toward the theory of homogeneous properties. J. Geophys. Res., 116, D19209. nucleation and its parameterization for cloud models. Geophys. Res. Lett., 25, Kaufman, Y. J., O. Boucher, D. Tanré, M. Chin, L. A. Remer, and T. Takemura, 2005: 3155 3158. Aerosol anthropogenic component estimated from satellite data. Geophys. Res. Khvorostyanov, V. I., and J. A. Curry, 2009: Critical humidities of homogeneous and Lett., 32, L17804. heterogeneous ice nucleation: Inferences from extended classical nucleation Kay, J. E., and A. Gettelman, 2009: Cloud influence on and response to seasonal theory. J. Geophys. Res., 114, D04207. Arctic sea ice loss. J. Geophys. Res., 114, D18204. Kim, B. M., S. Teffera, and M. D. Zeldin, 2000: Characterization of PM2.5 and PM10 in Kay, J. E., K. Raeder, A. Gettelman, and J. Anderson, 2011: The boundary layer the South Coast air basin of Southern California: Part 1 Spatial variations. J. Air response to recent Arctic sea ice loss and implications for high-latitude climate Waste Manag. Assoc., 50, 2034 2044. feedbacks. J. Clim., 24, 428 447. Kim, D., C. Wang, A. M. L. Ekman, M. C. Barth, and P. J. Rasch, 2008: Distribution and Kay, J. E., T. L Ecuyer, A. Gettelman, G. Stephens, and C. O Dell, 2008: The contribution direct radiative forcing of carbonaceous and sulfate aerosols in an interactive of cloud and radiation anomalies to the 2007 Arctic sea ice extent minimum. size-resolving aerosol climate model. J. Geophys. Res., 113, D16309. Geophys. Res. Lett., 35, L08503. Kim, D., et al., 2012: The tropical subseasonal variability simulated in the NASA GISS Kay, J. E., et al., 2012: Exposing global cloud biases in the Community Atmosphere General Circulation Model. J. Clim., 25, 4641 4659. Model (CAM) using satellite observations and their corresponding instrument Kim, H.-S., J.-B. Huh, P. K. Hopke, T. M. Holsen, and S.-M. Yi, 2007: Characteristics of simulators. J. Clim., 25, 5190 5207. the major chemical constituents of PM2.5 and smog events in Seoul, Korea in Kazil, J., R. G. Harrison, and E. R. Lovejoy, 2008: Tropospheric new particle formation 2003 and 2004. Atmos. Environ., 41, 6762 6770. and the role of ions. Space Sci. Rev., 137, 241 255. Kim, J. M., et al., 2010: Enhanced production of oceanic dimethylsulfide resulting Kazil, J., K. Zhang, P. Stier, J. Feichter, U. Lohmann, and K. O Brien, 2012: The present- from CO2 induced grazing activity in a high CO2 world. Environ. Sci. Technol., day decadal solar cycle modulation of Earth s radiative forcing via charged 44, 8140 8143. H2SO4/H2O aerosol nucleation. Geophys. Res. Lett., 39, L02805. King, S. M., et al., 2010: Cloud droplet activation of mixed organic-sulfate particles Kazil, J., et al., 2010: Aerosol nucleation and its role for clouds and Earth s radiative produced by the photooxidation of isoprene. Atmos. Chem. Phys., 10, 3953 forcing in the aerosol-climate model ECHAM5 HAM. Atmos. Chem. Phys., 10, 3964. 10733 10752. Kirchstetter, T. W., T. Novakov, and P. V. Hobbs, 2004: Evidence that the spectral Keith, D. W., 2000: Geoengineering the climate: History and prospect. Annu. Rev. dependence of light absorption by aerosols is affected by organic carbon. J. Energ. Environ., 25, 245 284. Geophys. Res., 109, D21208. Keith, D. W., 2010: Photophoretic levitation of engineered aerosols for Kirkby, J., 2007: Cosmic rays and climate. Surv. Geophys. 28, 333 375. geoengineering. Proc. Natl. Acad. Sci. U.S.A., 107, 16428 16431. Kirkby, J., et al., 2011: Role of sulphuric acid, ammonia and galactic cosmic rays in Kerkweg, A., J. Buchholz, L. Ganzeveld, A. Pozzer, H. Tost, and P. Jöckel, 2006: Technical atmospheric aerosol nucleation. Nature, 476, 429 433. Note: An implementation of the dry removal processes DRY DEPositionand Kirkevag, A., T. Iversen, O. Seland, J. B. Debernard, T. Storelvmo, and J. E. Kristjánsson, SEDImentation in the Modular Earth Submodel System (MESSy). Atmos. Chem. 2008: Aerosol-cloud-climate interactions in the climate model CAM-Oslo. Tellus Phys., 6, 4617 4632. A, 60, 492 512. Kerminen, V. M., et al., 2010: Atmospheric nucleation: Highlights of the EUCAARI Kirkevag, A., et al., 2013: Aerosol climate interactions in the Norwegian Earth project and future directions. Atmos. Chem. Phys., 10, 10829 10848. System Model NorESM1 M. Geoophys. Model Dev., 6, 207 244. Kernthaler, S. C., R. Toumi, and J. D. Haigh, 1999: Some doubts concerning a link Kleeman, M. J., 2008: A preliminary assessment of the sensitivity of air quality in between cosmic ray fluxes and global cloudiness. Geophys. Res. Lett., 26, 863 California to global change. Clim. Change, 87, S273 S292. 865. Kleidman, R. G., A. Smirnov, R. C. Levy, S. Mattoo, and D. Tanré, 2012: Evaluation Khain, A., D. Rosenfeld, and A. Pokrovsky, 2005: Aerosol impact on the dynamics and and wind speed dependence of MODIS aerosol retrievals over open ocean. IEEE 7 microphysics of deep convective clouds. Q. J. R. Meteorol. Soc., 131, 2639 2663. Trans. Geosci. Remote Sens., 50, 429 435. 644 Clouds and Aerosols Chapter 7 Klein, S. A., and D. L. Hartmann, 1993: The seasonal cycle of low stratiform clouds. J. Kostinski, A. B., 2008: Drizzle rates versus cloud depths for marine stratocumuli. Clim., 6, 1587 1606. Environ. Res. Lett., 3, 045019. Klein, S. A., et al., 2009: Intercomparison of model simulations of mixed-phase clouds Kravitz, B., A. Robock, D. Shindell, and M. Miller, 2012: Sensitivity of stratospheric observed during the ARM Mixed-Phase Arctic Cloud Experiment. I: Single-layer geoengineering with black carbon to aerosol size and altitude of injection. J. cloud. Q. J. R. Meteorol. Soc., 135, 979 1002. Geophys. Res., 117, D09203. Klocke, D., R. Pincus, and J. Quaas, 2011: On constraining estimates of climate Kravitz, B., A. Robock, L. Oman, G. Stenchikov, and A. B. Marquardt, 2009: Sulfuric sensitivity with present-day observations through model weighting. J. Clim., 24, acid deposition from stratospheric geoengineering with sulfate aerosols. J. 6092 6099. Geophys. Res., 114, D14109. Kloster, S., et al., 2007: Response of dimethylsulfide (DMS) in the ocean and Kravitz, B., A. Robock, O. Boucher, H. Schmidt, K. Taylor, G. Stenchikov, and M. Schulz, atmosphere to global warming. J. Geophys. Res., 112, G03005. 2011: The Geoengineering Model Intercomparison Project (GeoMIP). Atmos. Sci. Kloster, S., et al., 2010: Fire dynamics during the 20th century simulated by the Lett., 12, 162 167. Community Land Model. Biogeosciences, 7, 1877 1902. Kristjánsson, J. E., 2002: Studies of the aerosol indirect effect from sulfate and black Knorr, W., V. Lehsten, and A. Arneth, 2012: Determinants and predictability of global carbon aerosols. J. Geophys. Res., 107, 4246. wildfire emissions. Atmos. Chem. Phys., 12, 6845 6861. Kristjánsson, J. E., T. Iversen, A. Kirkevag, O. Seland, and J. Debernard, 2005: Knox, A., et al., 2009: Mass absorption cross-section of ambient black carbon aerosol Response of the climate system to aerosol direct and indirect forcing: Role of in relation to chemical age. Aer. Sci. Technol., 43, 522 532. cloud feedbacks. J. Geophys. Res., 110, D24206. Kocak, M., N. Mihalopoulos, and N. Kubilay, 2007: Chemical composition of the Kristjánsson, J. E., C. W. Stjern, F. Stordal, A. M. Fjaeraa, G. Myhre, and K. Jónasson, fine and coarse fraction of aerosols in the northeastern Mediterranean. Atmos. 2008: Cosmic rays, cloud condensation nuclei and clouds a reassessment using Environ., 41, 7351 7368. MODIS data. Atmos. Chem. Phys., 8, 7373 7387. Koch, D., and A. D. Del Genio, 2010: Black carbon semi-direct effects on cloud cover: Kroll, J. H., and J. H. Seinfeld, 2008: Chemistry of secondary organic aerosol: Review and synthesis. Atmos. Chem. Phys., 10, 7685 7696. Formation and evolution of low-volatility organics in the atmosphere. Atmos. Koch, D., S. Menon, A. Del Genio, R. Ruedy, I. Alienov, and G. A. Schmidt, 2009a: Environ., 42, 3593 3624. Distinguishing aerosol impacts on climate over the past century. J. Clim., 22, Krueger, S. K., G. T. McLean, and Q. Fu, 1995: Numerical simulation of the stratus-to- 2659 2677. cumulus transition in the subtropical marine boundary layer. 1. Boundary-layer Koch, D., et al., 2009b: Evaluation of black carbon estimations in global aerosol structure. J. Atmos. Sci., 52, 2839 2850. models. Atmos. Chem. Phys., 9, 9001 9026. Kuang, Z. M., 2008: Modeling the interaction between cumulus convection and Kodama, C., A. T. Noda, and M. Satoh, 2012: An assessment of the cloud signals linear gravity waves using a limited-domain cloud system-resolving model. J. simulated by NICAM using ISCCP, CALIPSO, and CloudSat satellite simulators. J. Atmos. Sci., 65, 576 591. Geophys. Res., 117, D12210. Kuang, Z. M., and C. S. Bretherton, 2006: A mass-flux scheme view of a high- Koffi, B., et al., 2012: Application of the CALIOP layer product to evaluate the vertical resolution simulation of a transition from shallow to deep cumulus convection. distribution of aerosols estimated by global models: Part 1. AeroCom phase I J. Atmos. Sci., 63, 1895 1909. results. J. Geophys. Res., 117, D10201. Kuang, Z. M., and D. L. Hartmann, 2007: Testing the fixed anvil temperature Köhler, M., M. Ahlgrimm, and A. Beljaars, 2011: Unified treatment of dry convective hypothesis in a cloud-resolving model. J. Clim., 20, 2051 2057. and stratocumulus-topped boundary layers in the ECMWF model. Q. J. R. Kuebbeler, M., U. Lohmann, and J. Feichter, 2012: Effects of stratospheric sulfate Meteorol. Soc., 137, 43 57. aerosol geo-engineering on cirrus clouds. Geophys. Res. Lett., 39, L23803. Kok, J. F., 2011: A scaling theory for the size distribution of emitted dust aerosols Kueppers, L. M., M. A. Snyder, and L. C. Sloan, 2007: Irrigation cooling effect: suggests climate models underestimate the size of the global dust cycle. Proc. Regional climate forcing by land-use change. Geophys. Res. Lett., 34, L03703. Natl. Acad. Sci. U.S.A., 108, 1016 1021. Kulmala, M., and V. M. Kerminen, 2008: On the formation and growth of atmospheric Kokhanovsky, A. A., et al., 2010: The inter-comparison of major satellite aerosol nanoparticles. Atmos. Res., 90, 132 150. retrieval algorithms using simulated intensity and polarization characteristics of Kulmala, M., et al., 2010: Atmospheric data over a solar cycle: No connection reflected light. Atmos. Meas. Tech., 3, 909 932. between galactic cosmic rays and new particle formation. Atmos. Chem. Phys., Koop, T., B. P. Luo, A. Tsias, and T. Peter, 2000: Water activity as the determinant for 10, 1885 1898. homogeneous ice nucleation in aqueous solutions. Nature, 406, 611 614. Kulmala, M., et al., 2011: General overview: European Integrated project on Aerosol Koren, I., G. Feingold, and L. A. Remer, 2010a: The invigoration of deep convective Cloud Climate and Air Quality interactions (EUCAARI) integrating aerosol clouds over the Atlantic: Aerosol effect, meteorology or retrieval artifact? Atmos. research from nano to global scales. Atmos. Chem. Phys., 11, 13061 13143. Chem. Phys., 10, 8855 8872. Kumar, P., I. N. Sokolik, and A. Nenes, 2011: Measurements of cloud condensation Koren, I., Y. Kaufman, L. Remer, and J. Martins, 2004: Measurement of the effect nuclei activity and droplet activation kinetics of fresh unprocessed regional dust of Amazon smoke on inhibition of cloud formation. Science, 303, 1342 1345. samples and minerals. Atmos. Chem. Phys., 11, 3527 3541. Koren, I., J. V. Martins, L. A. Remer, and H. Afargan, 2008: Smoke invigoration versus Kumar, R., S. S. Srivastava, and K. M. Kumari, 2007: Characteristics of aerosols over inhibition of clouds over the Amazon. Science, 321, 946 949. suburban and urban site of semiarid region in India: Seasonal and spatial Koren, I., Y. J. Kaufman, D. Rosenfeld, L. A. Remer, and Y. Rudich, 2005: Aerosol variations. Aer. Air Qual. Res., 7, 531 549. invigoration and restructuring of Atlantic convective clouds. Geophys. Res. Lett., Kurten, T., V. Loukonen, H. Vehkamaki, and M. Kulmala, 2008: Amines are likely 32, L14828. to enhance neutral and ion-induced sulfuric acid-water nucleation in the Koren, I., L. A. Remer, Y. J. Kaufman, Y. Rudich, and J. V. Martins, 2007: On the twilight atmosphere more effectively than ammonia. Atmos. Chem. Phys., 8, 4095 4103. zone between clouds and aerosols. Geophys. Res. Lett., 34, L08805. Kuylenstierna, J. C. I., H. Rodhe, S. Cinderby, and K. Hicks, 2001: Acidification in Koren, I., L. A. Remer, O. Altaratz, J. V. Martins, and A. Davidi, 2010b: Aerosol-induced developing countries: Ecosystem sensitivity and the critical load approach on a changes of convective cloud anvils produce strong climate warming. Atmos. global scale. Ambio, 30, 20 28. Chem. Phys., 10, 5001 5010. L Ecuyer, T. S., and J. H. Jiang, 2010: Touring the atmosphere aboard the A-Train. Korhonen, H., K. S. Carslaw, and S. Romakkaniemi, 2010a: Enhancement of marine Physics Today, 63, 36 41. cloud albedo via controlled sea spray injections: A global model study of the Laken, B., A. Wolfendale, and D. Kniveton, 2009: Cosmic ray decreases and changes in influence of emission rates, microphysics and transport. Atmos. Chem. Phys., the liquid water cloud fraction over the oceans. Geophys. Res. Lett., 36, L23803. 10, 4133 4143. Laken, B., D. Kniveton, and A. Wolfendale, 2011: Forbush decreases, solar irradiance Korhonen, H., K. S. Carslaw, P. M. Forster, S. Mikkonen, N. D. Gordon, and H. variations, and anomalous cloud changes. J. Geophys. Res., 116, D09201. Kokkola, 2010b: Aerosol climate feedback due to decadal increases in Southern Laken, B., E. Pallé, and H. Miyahara, 2012: A decade of the Moderate Resolution Hemisphere wind speeds. Geophys. Res. Lett., 37, L02805. Imaging Spectroradiometer: is a solar cloud link detectable? J. Clim., 25, 4430 Korolev, A., 2007: Limitations of the Wegener-Bergeron-Findeisen mechanism in the 4440. evolution of mixed-phase clouds. J. Atmos. Sci., 64, 3372 3375. Laken, B. A., and J. Èalogoviæ, 2011: Solar irradiance, cosmic rays and cloudiness over Korolev, A., and P. R. Field, 2008: The effect of dynamics on mixed-phase clouds: daily timescales. Geophys. Res. Lett., 38, L24811. 7 Theoretical considerations. J. Atmos. Sci., 65, 66 86. 645 Chapter 7 Clouds and Aerosols Laken, B. A., D. R. Kniveton, and M. R. Frogley, 2010: Cosmic rays linked to rapid mid- Li, J., M. Pósfai, P. V. Hobbs, and P. R. Buseck, 2003: Individual aerosol particles from latitude cloud changes. Atmos. Chem. Phys., 10, 10941 10948. biomass burning in southern Africa: 2, Compositions and aging of inorganic Lambert, F. H., and M. J. Webb, 2008: Dependency of global mean precipitation on particles. J. Geophys. Res., 108, 8484. surface temperature. Geophys. Res. Lett., 35, L16706. Li, Z., K.-H. Lee, Y. Wang, J. Xin, and W.-M. Hao, 2010: First observation-based Lance, S., et al., 2011: Cloud condensation nuclei as a modulator of ice processes in estimates of cloud-free aerosol radiative forcing across China. J. Geophys. Res., Arctic mixed-phase clouds. Atmos. Chem. Phys., 11, 8003 8015. 115, D00K18. Lanz, V. A., M. R. Alfarra, U. Baltensperger, B. Buchmann, C. Hueglin, and A. S. H. Li, Z., F. Niu, J. Fan, Y. Liu, D. Rosenfeld, and Y. Ding, 2011: Long-term impacts of Prévôt, 2007: Source apportionment of submicron organic aerosols at an urban aerosols on the vertical development of clouds and precipitation Nature Geosci., site by factor analytical modelling of aerosol mass spectra. Atmos. Chem. Phys., 4, 888 894. 7, 1503 1522. Li, Z., et al., 2009: Uncertainties in satellite remote sensing of aerosols and impact Larson, V. E., and J. C. Golaz, 2005: Using probability density functions to derive on monitoring its long-term trend: A review and perspective. Annal. Geophys., consistent closure relationships among higher-order moments. Mon. Weather 27, 2755 2770. Rev., 133, 1023 1042. Liao, H., W. T. Chen, and J. H. Seinfeld, 2006: Role of climate change in global Larson, V. E., R. Wood, P. R. Field, J.-C. Golaz, T. H. Vonder Haar, and W. R. Cotton, predictions of future tropospheric ozone and aerosols. J. Geophys. Res., 111, 2001: Systematic biases in the microphysics and thermodynamics of numerical D12304. models that ignore subgrid-scale variability. J. Atmos. Sci., 58, 1117 1128. Liao, H., Y. Zhang, W. T. Chen, F. Raes, and J. H. Seinfeld, 2009: Effect of chemistry- Latham, J., 1990: Control of global warming? Nature, 347, 339 340. aerosol-climate coupling on predictions of future climate and future levels of Latham, J., et al., 2008: Global temperature stabilization via controlled albedo tropospheric ozone and aerosols. J. Geophys. Res., 114, D10306. enhancement of low-level maritime clouds. Philos. Trans. R. Soc. London A, 366, Liepert, B. G., and M. Previdi, 2012: Inter-model variability and biases of the global 3969 3987. water cycle in CMIP3 coupled climate models. Environ. Res. Lett., 7, 014006. Lathiere, J., C. N. Hewitt, and D. J. Beerling, 2010: Sensitivity of isoprene emissions Lim, Y. B., Y. Tan, M. J. Perri, S. P. Seitzinger, and B. J. Turpin, 2010: Aqueous chemistry from the terrestrial biosphere to 20th century changes in atmospheric CO2 and its role in secondary organic aerosol (SOA) formation. Atmos. Chem. Phys., concentration, climate, and land use. Global Biogeochem. Cycles, 24, GB1004. 10, 10521 10539. Lau, K. M., M. K. Kim, and K. M. Kim, 2006: Asian summer monsoon anomalies Lin, G., J. E. Penner, S. Sillman, D. Taraborrelli, and J. Lelieveld, 2012: Global modeling induced by aerosol direct forcing: Tthe role of the Tibetan Plateau. Clim. Dyn., of SOA formation from dicarbonyls, epoxides, organic nitrates and peroxides. 26, 855 864. Atmos. Chem. Phys., 12, 4743 4774. Lavers, D. A., R. P. Allan, E. F. Wood, G. Villarini, D. J. Brayshaw, and A. J. Wade, 2011: Lindzen, R. S., and Y.-S. Choi, 2011: On the observational determination of climate Winter floods in Britain are connected to atmospheric rivers. Geophys. Res. Lett., sensitivity and its implications. Asia Pac. J. Atmos. Sci., 47, 377 390. 38, L23803. Liou, K. N., and S. C. Ou, 1989: The role of cloud microphysical processes in climate Lebsock, M. D., G. L. Stephens, and C. Kummerow, 2008: Multisensor satellite An assessment from a one-dimensional perspective. J. Geophys. Res., 94, 8599 observations of aerosol effects on warm clouds. J. Geophys. Res., 113, D15205. 8607. Lebsock, M. D., C. Kummerow, and G. L. Stephens, 2010: An observed tropical Liu, W., Y. Wang, A. Russell, and E. S. Edgerton, 2005: Atmospheric aerosol over two oceanic radiative-convective cloud feedback. J. Clim., 23, 2065 2078. urban-rural pairs in the southeastern United States: Chemical composition and Leck, C., and E. K. Bigg, 2007: A modi ed aerosol cloud climate feedback possible sources. Atmos. Environ., 39, 4453 4470. hypothesis. Environ. Chem., 4, 400 403. Liu, X., J. Penner, S. Ghan, and M. Wang, 2007: Inclusion of ice microphysics in the Leck, C., and E. K. Bigg, 2008: Comparison of sources and nature of the tropical NCAR community atmospheric model version 3 (CAM3). J. Clim., 20, 4526 4547. aerosol with the summer high Arctic aerosol. Tellus B, 60, 118 126. Liu, X., et al., 2012: Toward a minimal representation of aerosols in climate models: Lee, D. S., et al., 2009: Aviation and global climate change in the 21st century. Atmos. Description and evaluation in the Community Atmosphere Model CAM5. Geosci. Environ., 43, 3520 3537. Model Dev., 5, 709 739. Lee, H. S., and B. W. Kang, 2001: Chemical characteristics of principal PM2.5 species Liu, X. H., and J. E. Penner, 2005: Ice nucleation parameterization for global models. in Chongju, South Korea. Atmos. Environ., 35, 739 749. Meteorol. Z., 14, 499 514. Lee, K. H., Z. Li, M. S. Wong, J. Xin, Y. Wang, W.-M. Hao, and F. Zhao, 2007: Aerosol Liu, X. H., J. E. Penner, and M. H. Wang, 2009: Influence of anthropogenic sulfate and single scattering albedo estimated across China from a combination of ground black carbon on upper tropospheric clouds in the NCAR CAM3 model coupled to and satellite measurements. J. Geophys. Res., 112, D22S15. the IMPACT global aerosol model. J. Geophys. Res., 114, D03204. Lee, L. A., K. S. Carslaw, K. Pringle, G. W. Mann, and D. V. Spracklen, 2011: Emulation of Liu, Y., and P. H. Daum, 2002: Anthropogenic aerosols: Indirect warming effect from a complex global aerosol model to quantify sensitivity to uncertain parameters. dispersion forcing. Nature, 419, 580 581. Atmos. Chem. Phys., 11, 12253 12273. Liu, Y., J. R. Key, and X. Wang, 2008: The influence of changes in cloud cover on recent Lee, S.-S., G. Feingold, and P. Y. Chuang, 2012: Effect of aerosol on cloud-environment surface temperature trends in the Arctic. J. Clim., 21, 705 715. interactions in trade cumulus. J. Atmos. Sci., 69, 3607 3632. Lobell, D., G. Bala, A. Mirin, T. Phillips, R. Maxwell, and D. Rotman, 2009: Regional Lee, S. S., 2012: Effect of aerosol on circulations and precipitation in deep convective differences in the influence of irrigation on climate. J. Clim., 22, 2248 2255. clouds. J. Atmos. Sci., 69, 1957 1974. Lock, A. P., 2009: Factors influencing cloud area at the capping inversion for shallow Lee, Y. H., et al., 2013: Evaluation of preindustrial to present-day black carbon and its cumulus clouds. Q. J. R. Meteorol. Soc., 135, 941 952. albedo forcing from Atmospheric Chemistry and Climate Model Intercomparison Lodhi, A., B. Ghauri, M. R. Khan, S. Rahmana, and S. Shafiquea, 2009: Particulate Project (ACCMIP). Atmos. Chem. Phys., 13, 2607 2634. matter (PM2.5) concentration and source apportionment in Lahore. J. Brazil. Lenderink, G., and E. Van Meijgaard, 2008: Increase in hourly precipitation extremes Chem. Soc., 20, 1811 1820. beyond expectations from temperature changes. Nature Geosci., 1, 511 514. Loeb, N. G., and N. Manalo-Smith, 2005: Top-of-atmosphere direct radiative effect Lenderink, G., H. Y. Mok, T. C. Lee, and G. J. van Oldenborgh, 2011: Scaling and trends of aerosols over global oceans from merged CERES and MODIS observations. J. of hourly precipitation extremes in two different climate zones - Hong Kong and Clim., 18, 3506 3526. the Netherlands. Hydrol. Earth Syst. Sci., 15, 3033 3041. Loeb, N. G., and G. L. Schuster, 2008: An observational study of the relationship Lenschow, P., H. J. Abraham, K. Kutzner, M. Lutz, J. D. Preu, and W. Reichenbacher, between cloud, aerosol and meteorology in broken low-level cloud conditions. J. 2001: Some ideas about the sources of PM10. Atmos. Environ., 35, 23 33. Geophys. Res., 113, D14214. Lenton, T. M., and N. E. Vaughan, 2009: The radiative forcing potential of different Loeb, N. G., and W. Y. Su, 2010: Direct aerosol radiative forcing uncertainty based on climate geoengineering options. Atmos. Chem. Phys., 9, 5539 5561. a radiative perturbation analysis. J. Clim., 23, 5288 5293. Levin, Z., and W. R. Cotton, 2009: Aerosol Pollution Impact on Precipitation: A Loeb, N. G., et al., 2009: Toward optimal closure of the Earth s top-of-atmosphere Scientific Review. Springer Science+Business Media, New York, NY, USA, and radiation budget. J. Clim., 22, 748 766. Heidelberg, Germany, 386 pp. Logan, T., B. K. Xi, X. Q. Dong, R. Obrecht, Z. Q. Li, and M. Cribb, 2010: A study of Levy, R. C., L. A. Remer, R. G. Kleidman, S. Mattoo, C. Ichoku, R. Kahn, and T. F. Eck, Asian dust plumes using satellite, surface, and aircraft measurements during the 7 2010: Global evaluation of the Collection 5 MODIS dark-target aerosol products INTEX-B field experiment. J. Geophys. Res., 115, D00K25. over land. Atmos. Chem. Phys., 10, 10399 10420. 646 Clouds and Aerosols Chapter 7 Lohmann, U., 2002a: Possible aerosol effects on ice clouds via contact nucleation. J. Maenhaut, W., I. Salma, and J. Cafrneyer, 1996: Regional atmospheric aerosol Atmos. Sci., 59, 647 656. composition and sources in the eastern Transvaal, South Africa, and impact of Lohmann, U., 2002b: A glaciation indirect aerosol effect caused by soot aerosols. biomass burning. J. Geophys. Res., 101, 23613 23650. Geophys. Res. Lett., 29, 1052. Maenhaut, W., M.-T. Fernandez-Jimenez, J. L. Vanderzalm, B. Hooper, M. A. Hooper, Lohmann, U., 2004: Can anthropogenic aerosols decrease the snowfall rate? J. and N. J. Tapper, 2000: Aerosol composition at Jabiru, Australia, and impact of Atmos. Sci., 61, 2457 2468. biomass burning. J. Aer. Sci., 31, 745 746. Lohmann, U., 2008: Global anthropogenic aerosol effects on convective clouds in Magee, N., A. M. Moyle, and D. Lamb, 2006: Experimental determination of the ECHAM5 HAM. Atmos. Chem. Phys., 8, 2115 2131. deposition coefficient of small cirrus-like ice crystals near  50°C. Geophys. Res. Lohmann, U., and J. Feichter, 1997: Impact of sulfate aerosols on albedo and lifetime Lett., 33, L17813. of clouds: A sensitivity study with the ECHAM4 GCM. J. Geophys. Res., 102, Mahowald, N. M., J. F. Lamarque, X. X. Tie, and E. Wolff, 2006a: Sea-salt aerosol 13685 13700. response to climate change: Last Glacial Maximum, preindustrial, and doubled Lohmann, U., and J. Feichter, 2001: Can the direct and semi-direct aerosol effect carbon dioxide climates. J. Geophys. Res., 111, D05303. compete with the indirect effect on a global scale? Geophys. Res. Lett., 28, Mahowald, N. M., D. R. Muhs, S. Levis, P. J. Rasch, M. Yoshioka, C. S. Zender, and C. 159 161. Luo, 2006b: Change in atmospheric mineral aerosols in response to climate: Last Lohmann, U., and B. Kärcher, 2002: First interactive simulations of cirrus clouds glacial period, preindustrial, modern, and doubled carbon dioxide climates. J. formed by homogeneous freezing in the ECHAM general circulation model. J. Geophys. Res., 111, D10202. Geophys. Res., 107, 4105. Mahowald, N. M., et al., 2009: Atmospheric iron deposition: Global distribution, Lohmann, U., and G. Lesins, 2002: Stronger constraints on the anthropogenic indirect variability, and human perturbations. Annu. Rev. Mar. Sci., 1, 245 278. aerosol effect. Science, 298, 1012 1015. Mahowald, N. M., et al., 2010: Observed 20th century desert dust variability: impact Lohmann, U., and J. Feichter, 2005: Global indirect aerosol effects: A review. Atmos. on climate and biogeochemistry. Atmos. Chem. Phys., 10, 10875 10893. Chem. Phys., 5, 715 737. Makkonen, R., A. Asmi, V. Kerminen, M. Boy, A. Arneth, P. Hari, and M. Kulmala, Lohmann, U., and K. Diehl, 2006: Sensitivity studies of the importance of dust ice 2012a: Air pollution control and decreasing new particle formation lead to nuclei for the indirect aerosol effect on stratiform mixed-phase clouds. J. Atmos. strong climate warming. Atmos. Chem. Phys., 12, 1515 1524. Sci., 63, 968 982. Makkonen, R., A. Asmi, V. M. Kerminen, M. Boy, A. Arneth, A. Guenther, and M. Lohmann, U., and C. Hoose, 2009: Sensitivity studies of different aerosol indirect Kulmala, 2012b: BVOC-aerosol-climate interactions in the global aerosol- effects in mixed-phase clouds. Atmos. Chem. Phys., 9, 8917 8934. climate model ECHAM5.5 HAM2. Atmos. Chem. Phys., 12, 10077 10096. Lohmann, U., and S. Ferrachat, 2010: Impact of parametric uncertainties on the Malm, W. C., and B. A. Schichtel, 2004: Spatial and monthly trends in speciated fine present-day climate and on the anthropogenic aerosol effect. Atmos. Chem. particle concentration in the United States. J. Geophys. Res., 109, D03306. Phys., 10, 11373 11383. Malm, W. C., J. F. Sisler, D. Huffman, R. A. Eldred, and T. A. Cahill, 1994: Spatial and Lohmann, U., J. Feichter, J. Penner, and R. Leaitch, 2000: Indirect effect of sulfate seasonal trends in particle concentration and optical extinction in the United and carbonaceous aerosols: A mechanistic treatment. J. Geophys. Res., 105, States. J. Geophys. Res., 99, 1347 1370. 12193 12206. Mann, G. W., et al., 2010: Description and evaluation of GLOMAP-mode: A modal Lohmann, U., P. Stier, C. Hoose, S. Ferrachat, S. Kloster, E. Roeckner, and J. Zhang, global aerosol microphysics model for the UKCA composition-climate model. 2007: Cloud microphysics and aerosol indirect effects in the global climate Geosci. Model Dev., 3, 519 551. model ECHAM5 HAM. Atmos. Chem. Phys., 7, 3425 3446. Manninen, H. E., et al., 2010: EUCAARI ion spectrometer measurements at 12 Lohmann, U., et al., 2010: Total aerosol effect: Radiative forcing or radiative flux European sites analysis of new particle formation events. Atmos. Chem. Phys., perturbation? Atmos. Chem. Phys., 10, 3235 3246. 10, 7907 7927. Lonati, G., M. Giugliano, P. Butelli, L. Romele, and R. Tardivo, 2005: Major chemical Mapes, B., and R. Neale, 2011: Parameterizing convective organization to escape the components of PM2.5 in Milan (Italy). Atmos. Environ., 39, 1925 1934. entrainment dilemma. J. Adv. Model. Earth Syst., 3, M06004. Lu, M.-L., W. C. Conant, H. H. Jonsson, V. Varutbangkul, R. C. Flagan, and J. H. Mariani, R. L., and W. Z. d. Mello, 2007: PM2.5 10, PM2.5 and associated water- Seinfeld, 2007: The marine stratus/stratocumulus experiment (MASE): Aerosol- soluble inorganic species at a coastal urban site in the metropolitan region of cloud relationships in marine stratocumulus. J. Geophys. Res., 112, D10209. Rio de Janeiro. Atmos. Environ., 41, 2887 2892. Lu, M.-L., G. Feingold, H. H. Jonsson, P. Y. Chuang, H. Gates, R. C. Flagan, and J. H. Marlon, J. R., et al., 2008: Climate and human influences on global biomass burning Seinfeld, 2008: Aerosol-cloud relationships in continental shallow cumulus. J. over the past two millennia. Nature Geosci., 1, 697 702. Geophys. Res., 113, D15201. Marsh, N. D., and H. Svensmark, 2000: Low cloud properties influenced by cosmic Lu, Z., Q. Zhang, and D. G. Streets, 2011: Sulfur dioxide and primary carbonaceous rays. Phys. Rev. Lett., 85, 5004 5007. aerosol emissions in China and India, 1996 2010. Atmos. Chem. Phys., 11, Martin, S. T., et al., 2010a: Sources and properties of Amazonian aerosol particles. 9839 9864. Rev. Geophys., 48, RG2002. Lu, Z., et al., 2010: Sulfur dioxide emissions in China and sulfur trends in East Asia Martin, S. T., et al., 2010b: An overview of the Amazonian Aerosol Characterization since 2000. Atmos. Chem. Phys., 10, 6311 6331. Experiment 2008 (AMAZE-08). Atmos. Chem. Phys., 10, 11415 11438. Lubin, D., and A. M. Vogelmann, 2006: A climatologically significant aerosol Matthews, H. D., 2010: Can carbon cycle geoengineering be a useful complement to longwave indirect effect in the Arctic. Nature, 439, 453 456. ambitious climate mitigation? Carbon Manag., 1, 135 144. Lunt, D. J., A. Ridgwell, P. J. Valdes, and A. Seale, 2008: Sunshade World : A fully Matthews, H. D., and K. Caldeira, 2007: Transient climate carbon simulations of coupled GCM evaluation of the climatic impacts of geoengineering. Geophys. planetary geoengineering. Proc. Natl. Acad. Sci. U.S.A., 104, 9949 9954. Res. Lett., 35, L12710. Mauger, G. S., and J. R. Norris, 2010: Assessing the impact of meteorological history Lyapustin, A., et al., 2010: Analysis of snow bidirectional reflectance from ARCTAS on subtropical cloud fraction. J. Clim., 23, 2926 2940. Spring-2008 Campaign. Atmos. Chem. Phys., 10, 4359 4375. Mauritsen, T., et al., 2011: An Arctic CCN-limited cloud-aerosol regime. Atmos. Chem. Ma, H. Y., M. Kohler, J. L. F. Li, J. D. Farrara, C. R. Mechoso, R. M. Forbes, and D. Phys., 11, 165 173. E. Waliser, 2012a: Evaluation of an ice cloud parameterization based on a Mautner, M., 1991: A space-based solar screen against climate warming. J. Br. dynamical-microphysical lifetime concept using CloudSat observations and the Interplanet. Soc., 44, 135 138. ERA-Interim reanalysis. J. Geophys. Res., 117, D05210. McComiskey, A., and G. Feingold, 2008: Quantifying error in the radiative forcing of Ma, X., F. Yu, and G. Luo, 2012b: Aerosol direct radiative forcing based on GEOS- the first aerosol indirect effect. Geophys. Res. Lett., 35, L02810. Chem-APM and uncertainties. Atmos. Chem. Phys., 12, 5563 5581. McComiskey, A., and G. Feingold, 2012: The scale problem in quantifying aerosol MacCracken, M. C., 2009: On the possible use of geoengineering to moderate indirect effects. Atmos. Chem. Phys., 12, 1031 1049. specific climate change impacts. Environ. Res. Lett., 4, 045107. McComiskey, A., S. Schwartz, B. Schmid, H. Guan, E. Lewis, P. Ricchiazzi, and J. Ogren, Mace, G. G., Q. Q. Zhang, M. Vaughan, R. Marchand, G. Stephens, C. Trepte, and D. 2008: Direct aerosol forcing: Calculation from observables and sensitivities to Winker, 2009: A description of hydrometeor layer occurrence statistics derived inputs. J. Geophys. Res., 113, D09202. from the first year of merged Cloudsat and CALIPSO data. J. Geophys. Res., 114, McComiskey, A., et al., 2009: An assessment of aerosol-cloud interactions in marine 7 D00A26. stratus clouds based on surface remote sensing. J. Geophys. Res., 114, D09203. 647 Chapter 7 Clouds and Aerosols McConnell, J. R., et al., 2007: 20th-century industrial black carbon emissions altered Mitchell, J. F., C. A. Wilson, and W. M. Cunnington, 1987: On CO2 climate sensitivity Arctic climate forcing. Science, 317, 1381 1384. and model dependence of results. Q. J. R. Meteorol. Soc., 113, 293 322. McCormick, M. P., L. W. Thomason, and C. R. Trepte, 1995: Atmospheric effects of the Miyazaki, Y., S. G. Aggarwal, K. Singh, P. K. Gupta, and K. Kawamura, 2009: Mt Pinatubo eruption. Nature, 373, 399 404. Dicarboxylic acids and water-soluble organic carbon in aerosols in New Delhi, McFarquhar, G. M., et al., 2011: Indirect and semi-direct aerosol campaign: The India, in winter: Characteristics and formation processes. J. Geophys. Res., 114, impact of Arctic aerosols on clouds. Bull. Am. Meteor. Soc., 92, 183 201. D19206. McFiggans, G., et al., 2006: The effect of physical and chemical aerosol properties on Mkoma, S. L., 2008: Physico-chemical characterisation of atmospheric aerosol in warm cloud droplet activation. Atmos. Chem. Phys., 6, 2593 2649. Tanzania, with emphasis on the carbonaceous aerosol components and on McMeeking, G. R., et al., 2010: Black carbon measurements in the boundary layer chemical mass closure. Ph.D. Ghent University, Ghent, Belgium. over western and northern Europe. Atmos. Chem. Phys., 10, 9393 9414. Mkoma, S. L., W. Maenhaut, X. G. Chi, W. Wang, and N. Raes, 2009a: Characterisation Medeiros, B., B. Stevens, I. M. Held, M. Zhao, D. L. Williamson, J. G. Olson, and C. S. of PM10 atmospheric aerosols for the wet season 2005 at two sites in East Africa. Bretherton, 2008: Aquaplanets, climate sensitivity, and low clouds. J. Clim., 21, Atmos. Environ., 43, 631 639. 4974 4991. Mkoma, S. L., W. Maenhaut, X. Chi, W. Wang, and N. Raes, 2009b: Chemical Meehl, G. A., et al., 2007: Global climate projections. In: Climate Change 2007: The composition and mass closure for PM10 aerosols during the 2005 dry season at Physical Science Basis. Contribution of Working Group I to the Fourth Assessment a rural site in Morogoro, Tanzania. X-Ray Spectrom., 38, 293 300. Report of the Intergovernmental Panel on Climate Change [Solomon, S., D. Qin, Mohler, O., et al., 2003: Experimental investigation of homogeneous freezing of M. Manning, Z. Chen, M. Marquis, K. B. Averyt, M. Tignor and H. L. Miller (eds.)] sulphuric acid particles in the aerosol chamber AIDA. Atmos. Chem. Phys., 3, Cambridge University Press, Cambridge, United Kingdom and New York, NY, 211 223. USA, pp. 747 843. Moorthy, K. K., S. K. Satheesh, S. S. Babu, and C. B. S. Dutt, 2008: Integrated Campaign Menon, S., and L. Rotstayn, 2006: The radiative influence of aerosol effects on liquid- for Aerosols, gases and Radiation Budget (ICARB): An overview. J. Earth Syst. phase cumulus and stratiform clouds based on sensitivity studies with two Sci., 117, 243 262. climate models. Clim. Dyn., 27, 345 356. Moosmüller, H., R. Chakrabarty, and W. Arnott, 2009: Aerosol light absorption and Menon, S., and A. Del Genio, 2007: Evaluating the impacts of carbonaceous aerosols its measurement: A review. J. Quant. Spectrosc. Radiat. Transfer, 110, 844 878. on clouds and climate. In: Human-Induced Climate Change: An Interdisciplinary Morales, J. A., D. Pirela, M. G. d. Nava, B. S. d. Borrego, H. Velasquez, and J. Duran, Assessment [M. E. Schlesinger, H. S. Kheshgi, J. Smith, F. C. de la Chesnaye, J. M. 1998: Inorganic water soluble ions in atmospheric particles over Maracaibo Lake Reilly, T. Wilson and C. Kolstad (eds.)]. Cambridge University Press, Cambridge, Basin in the western region of Venezuela. Atmos. Res., 46, 307 320. United Kingdom, and New York, NY, USA, pp. 34 48. Morcrette, J.-J., et al., 2009: Aerosol analysis and forecast in the ECMWF Integrated Menon, S., A. D. Del Genio, D. Koch, and G. Tselioudis, 2002: GCM Simulations of the Forecast System. Part I: Forward modelling. J. Geophys. Res., 114, D06206. aerosol indirect effect: Sensitivity to cloud parameterization and aerosol burden. Moreno-Cruz, J. B., K. W. Ricke, and D. W. Keith, 2011: A simple model to account for J. Atmos. Sci., 59, 692 713. regional inequalities in the effectiveness of solar radation management. Clim. Mercado, L. M., N. Bellouin, S. Sitch, O. Boucher, C. Huntingford, M. Wild, and P. M. Change, 110, 649 668. Cox, 2009: Impact of changes in diffuse radiation on the global land carbon sink. Morrison, H., and A. Gettelman, 2008: A new two-moment bulk stratiform cloud Nature, 458, 1014 1017. microphysics scheme in the community atmosphere model, version 3 (CAM3). Merikanto, J., D. V. Spracklen, G. W. Mann, S. J. Pickering, and K. S. Carslaw, 2009: Part I: Description and numerical tests. J. Clim., 21, 3642 3659. Impact of nucleation on global CCN. Atmos. Chem. Phys., 9, 8601 8616. Morrison, H., and W. W. Grabowski, 2011: Cloud-system resolving model simulations Metzger, A., et al., 2010: Evidence for the role of organics in aerosol particle formation of aerosol indirect effects on tropical deep convection and its thermodynamic under atmospheric conditions. Proc. Natl. Acad. Sci. U.S.A., 107, 6646 6651. environment. Atmos. Chem. Phys., 11, 10503 10523. Miller, R. L., 1997: Tropical thermostats and low cloud cover. J. Clim., 10, 409 440. Morrison, H., G. de Boer, G. Feingold, J. Harrington, M. D. Shupe, and K. Sulia, 2012: Ming, J., D. Zhang, S. Kang, and W. Tian, 2007a: Aerosol and fresh snow chemistry in Resilience of persistent Arctic mixed-phase clouds. Nature Geosci., 5, 11 17. the East Rongbuk Glacier on the northern slope of Mt. Qomolangma (Everest). Moteki, N., and Y. Kondo, 2010: Dependence of laser-induced incandescence on J. Geophys. Res., 112, D15307. physical properties of black carbon aerosols: Measurements and theoretical Ming, J., C. D. Xiao, H. Cachier, D. H. Qin, X. Qin, Z. Q. Li, and J. C. Pu, 2009: Black interpretation. Aer. Sci. Technol., 44, 663 675. Carbon (BC) in the snow of glaciers in west China and its potential effects on Moteki, N., et al., 2007: Evolution of mixing state of black carbon particles: Aircraft albedos. Atmos. Res., 92, 114 123. measurements over the western Pacific in March 2004. Geophys. Res. Lett., 34, Ming, Y., V. Ramaswamy, and G. Persad, 2010: Two opposing effects of absorbing L11803. aerosols on global-mean precipitation. Geophys. Res. Lett., 37, L13701. Mouillot, F., A. Narasimha, Y. Balkanski, J. F. Lamarque, and C. B. Field, 2006: Global Ming, Y., V. Ramaswamy, P. A. Ginoux, L. W. Horowitz, and L. M. Russell, 2005: carbon emissions from biomass burning in the 20th century. Geophys. Res. Lett., Geophysical Fluid Dynamics Laboratory general circulation model investigation 33, L01801. of the indirect radiative effects of anthropogenic sulfate aerosol. J. Geophys. Muller, C. J., and P. A. O Gorman, 2011: An energetic perspective on the regional Res., 110, D22206. response of precipitation to climate change. Nature Clim. Change, 1, 266 271. Ming, Y., V. Ramaswamy, L. J. Donner, V. T. J. Phillips, S. A. Klein, P. A. Ginoux, and L. W. Muller, C. J., and I. M. Held, 2012: Detailed investigation of the self-aggregation of Horowitz, 2007b: Modeling the interactions between aerosols and liquid water convection in cloud-resolving simulations. J. Atmos. Sci., 69, 2551 2565. clouds with a self-consistent cloud scheme in a general circulation model. J. Muller, C. J., P. A. O Gorman, and L. E. Back, 2011: Intensification of precipitation Atmos. Sci., 64, 1189 1209. extremes with warming in a cloud-resolving model. J. Clim., 24, 2784 2800. Minschwaner, K., A. E. Dessler, and P. Sawaengphokhai, 2006: Multimodel analysis Müller, J.-F., et al., 2008: Global isoprene emissions estimated using MEGAN, of the water vapor feedback in the tropical upper troposphere. J. Clim., 19, ECMWF analyses and a detailed canopy environment model. Atmos. Chem. 5455 5464. Phys., 8, 1329 1341. Mirme, S., A. Mirme, A. Minikin, A. Petzold, U. Horrak, V. M. Kerminen, and M. Murray, B. J., D. O Sullivan, J. D. Atkinson, and M. E. Webb, 2012: Ice nucleation by Kulmala, 2010: Atmospheric sub-3 nm particles at high altitudes. Atmos. Chem. particles immersed in supercooled cloud droplets. Chem. Soc. Rev., 41, 6519 Phys., 10, 437 451. 6554. Mishchenko, M. I., et al., 2007: Accurate monitoring of terrestrial aerosols and total Myhre, G., 2009: Consistency between satellite-derived and modeled estimates of solar irradiance Introducing the Glory mission. Bull. Am. Meteor. Soc., 88, the direct aerosol effect. Science, 325, 187 190. 677 691. Myhre, G., et al., 2007: Comparison of the radiative properties and direct radiative Mishchenko, M. I., et al., 2012: Aerosol retrievals from channel-1 and -2 AVHRR effect of aerosols from a global aerosol model and remote sensing data over radiances: Long-term trends updated and revisited. J. Quant. Spectrosc. Radiat. ocean. Tellus B, 59, 115 129. Transfer, 113, 1974 1980. Myhre, G., et al., 2009: Modelled radiative forcing of the direct aerosol effect with Mitchell, D. L., and W. Finnegan, 2009: Modification of cirrus clouds to reduce global multi-observation evaluation. Atmos. Chem. Phys., 9, 1365 1392. 7 warming. Environ. Res. Lett., 4, 045102. Myhre, G., et al., 2013: Radiative forcing of the direct aerosol effect from AeroCom Phase II simulations. Atmos. Chem. Phys., 13, 1853 1877. 648 Clouds and Aerosols Chapter 7 Nakajima, T., and M. Schulz, 2009: What do we know about large scale changes of Orellana, M. V., P. A. Matrai, C. Leck, C. D. Rauschenberg, A. M. Lee, and E. Coz, 2011: aerosols, clouds, and the radiative budget? In: Clouds in the Perturbed Climate Marine microgels as a source of cloud condensation nuclei in the high Arctic. System: Their Relationship to Energy Balance, Atmospheric Dynamics, and Proc. Natl. Acad. Sci. U.S.A., 108, 13612 13617. Precipitation [R. J. Charlson and J. Heintzenberg (eds.)], MIT Press, Cambridge, Oreopoulos, L., and S. Platnick, 2008: Radiative susceptibility of cloudy atmospheres MA, USA, pp. 401 430. to droplet number perturbations: 2. Global analysis from MODIS. J. Geophys. Nakajima, T., et al., 2007: Overview of the Atmospheric Brown Cloud East Asian Res., 113, D14S21. Regional Experiment 2005 and a study of the aerosol direct radiative forcing in Osborne, S. R., A. J. Barana, B. T. Johnson, J. M. Haywood, E. Hesse, and S. Newman, East Asia. J. Geophys. Res., 112, D24S91. 2011: Short-wave and long-wave radiative properties of Saharan dust aerosol. Nakayama, T., Y. Matsumi, K. Sato, T. Imamura, A. Yamazaki, and A. Uchiyama, Q. J. R. Meteorol. Soc., 137, 1149 1167. 2010: Laboratory studies on optical properties of secondary organic aerosols Oshima, N., et al., 2012: Wet removal of black carbon in Asian outflow: Aerosol generated during the photooxidation of toluene and the ozonolysis of -pinene. Radiative Forcing in East Asia (A-FORCE) aircraft campaign. J. Geophys. Res., J. Geophys. Res., 115, D24204. 117, D03204. Nam, C., S. Bony, J.-L. Dufresne, and H. Chepfer, 2012: The  too few, too bright  tropical Ovadnevaite, J., et al., 2011: Primary marine organic aerosol: A dichotomy of low low-cloud problem in CMIP5 models. Geophys. Res. Lett., 39, L21801. hygroscopicity and high CCN activity. Geophys. Res. Lett., 38, L21806. Naud, C. M., A. D. Del Genio, M. Bauer, and W. Kovari, 2010: Cloud vertical Ovchinnikov, M., A. Korolev, and J. Fan, 2011: Effects of ice number concentration distribution across warm and cold fronts in CloudSat-CALIPSO data and a on dynamics of a shallow mixed-phase stratiform cloud. J. Geophys. Res., 116, General Circulation Model. J. Clim., 23, 3397 3415. D00T06. Naud, C. M., A. Del Genio, G. G. Mace, S. Benson, E. E. Clothiaux, and P. Kollias, 2008: Paasonen, P., et al., 2010: On the roles of sulphuric acid and low-volatility organic Impact of dynamics and atmospheric state on cloud vertical overlap. J. Clim., vapours in the initial steps of atmospheric new particle formation. Atmos. Chem. 21, 1758 1770. Phys., 10, 11223 11242. Neale, R. B., J. H. Richter, and M. Jochum, 2008: The impact of convection on ENSO: Paasonen, P., et al., 2013: Warming-induced increase in aerosol number concentration From a delayed oscillator to a series of events. J. Clim., 21, 5904 5924. likely to moderate climate change. Nature Geosci., 6, 438 442. Neelin, J. D., and I. M. Held, 1987: Modeling tropical convergence based on the moist Pacifico, F., S. P. Harrison, C. D. Jones, and S. Sitch, 2009: Isoprene emissions and static energy budget. Mon. Weather Rev., 115, 3 12. climate. Atmos. Environ., 43, 6121 6135. Neelin, J. D., M. Münnich, H. Su, J. E. Meyerson, and C. E. Holloway, 2006: Tropical Painemal, D., and P. Zuidema, 2010: Microphysical variability in southeast Pacific drying trends in global warming models and observations. Proc. Natl. Acad. Sci. Stratocumulus clouds: Synoptic conditions and radiative response. Atmos. Chem. U.S.A., 103, 6110 6115. Phys., 10, 6255 6269. Neggers, R. A. J., 2009: A dual mass flux framework for boundary layer convection. Pallé Bagó, E., and C. J. Butler, 2000: The influence of cosmic rays on terrestrial clouds Part II: Clouds. J. Atmos. Sci., 66, 1489 1506. and global warming. Astron. Geophys., 41, 4.18 4.22. Neggers, R. A. J., M. Kohler, and A. C. M. Beljaars, 2009: A dual mass flux framework Pallé, E., 2005: Possible satellite perspective effects on the reported correlations for boundary layer convection. Part I: Transport. J. Atmos. Sci., 66, 1465 1487. between solar activity and clouds. Geophys. Res. Lett., 32, L03802. Nicoll, K., and R. G. Harrison, 2010: Experimental determination of layer cloud edge Palm, S. P., S. T. Strey, J. Spinhirne, and T. Markus, 2010: Influence of Arctic sea charging from cosmic ray ionization. Geophys. Res. Lett., 37, L13802. ice extent on polar cloud fraction and vertical structure and implications for Niemeier, U., H. Schmidt, and C. Timmreck, 2011: The dependency of geoengineered regional climate. J. Geophys. Res., 115, D21209. sulfate aerosol on the emission strategy. Atmos. Sci. Lett., 12, 189 194. Paltridge, G., A. Arking, and M. Pook, 2009: Trends in middle- and upper-level Nuijens, L., B. Stevens, and A. P. Siebesma, 2009: The environment of precipitating tropospheric humidity from NCEP reanalysis data. Theor. Appl. Climatol., 98, shallow cumulus convection. J. Atmos. Sci., 66, 1962 1979. 351 359. Nyanganyura, D., W. Maenhaut, M. Mathuthu, A. Makarau, and F. X. Meixner, 2007: Paltridge, G. W., 1980: Cloud-radiation feedback to climate. Q. J. R. Meteorol. Soc., The chemical composition of tropospheric aerosols and their contributing 106, 895 899. sources to a continental background site in northern Zimbabwe from 1994 to Paredes-Miranda, G., W. P. Arnott, J. L. Jimenez, A. C. Aiken, J. S. Gaffney, and N. A. 2000. Atmos. Environ., 41, 2644 2659. Marley, 2009: Primary and secondary contributions to aerosol light scattering O Dell, C. W., F. J. Wentz, and R. Bennartz, 2008: Cloud liquid water path from and absorption in Mexico City during the MILAGRO 2006 campaign. Atmos. satellite-based passive microwave observations: A new climatology over the Chem. Phys., 9, 3721 3730. global oceans. J. Clim., 21, 1721 1739. Park, S., and C. S. Bretherton, 2009: The University of Washington shallow convection O Donnell, D., K. Tsigaridis, and J. Feichter, 2011: Estimating the direct and indirect and moist turbulence schemes and their impact on climate simulations with the effects of secondary organic aerosols using ECHAM5 HAM. Atmos. Chem. Phys., Community Atmosphere Model. J. Clim., 22, 3449 3469. 11, 8635 8659. Parodi, A., and K. Emanuel, 2009: A theory for buoyancy and velocity scales in deep O Dowd, C., C. Monahan, and M. Dall Osto, 2010: On the occurrence of open ocean moist convection. J. Atmos. Sci., 66, 3449 3463. particle production and growth events. Geophys. Res. Lett., 37, L19805. Partanen, A.-I., et al., 2012: Direct and indirect effects of sea spray geoengineering O Gorman, P. A., 2012: Sensitivity of tropical precipitation extremes to climate and the role of injected particle size. J. Geophys. Res., 117, D02203. change. Nature Geosci., 5, 697 700. Pawlowska, H., and J. L. Brenguier, 2003: An observational study of drizzle formation O Gorman, P. A., and T. Schneider, 2008: The hydrological cycle over a wide range of in stratocumulus clouds for general circulation model (GCM) parameterizations. climates simulated with an idealized GCM. J. Clim., 21, 3815 3832. J. Geophys. Res., 108, 8630. O Gorman, P. A., and T. Schneider, 2009: The physical basis for increases in Pechony, O., and D. T. Shindell, 2010: Driving forces of global wildfires over the precipitation extremes in simulations of 21st-century climate change. Proc. Natl. past millennium and the forthcoming century. Proc. Natl. Acad. Sci. U.S.A., 107, Acad. Sci. U.S.A., 106, 14773 14777. 19167 19170. Oanh, N. T. K., et al., 2006: Particulate air pollution in six Asian cities: Spatial and Pendergrass, A. G., and D. L. Hartmann, 2012: Global-mean precipitation and black temporal distributions, and associated sources. Atmos. Environ., 40, 3367 3380. carbon in AR4 simulations. Geophys. Res. Lett., 39, L01703. Ocko, I. B., V. Ramaswamy, P. Ginoux, Y. Ming, and L. W. Horowitz, 2012: Sensitivity Peng, Y. R., and U. Lohmann, 2003: Sensitivity study of the spectral dispersion of of scattering and absorbing aerosol direct radiative forcing to physical climate the cloud droplet size distribution on the indirect aerosol effect. Geophys. Res. factors. J. Geophys. Res., 117, D20203. Lett., 30, 1507. Oleson, K. W., G. B. Bonan, and J. Feddema, 2010: Effects of white roofs on urban Penner, J. E., S. Y. Zhang, and C. C. Chuang, 2003: Soot and smoke aerosol may not temperature in a global climate model. Geophys. Res. Lett., 37, L03701. warm climate. J. Geophys. Res., 108, 4657. Omar, A. H., et al., 2009: The CALIPSO automated aerosol classification and lidar Penner, J. E., L. Xu, and M. H. Wang, 2011: Satellite methods underestimate indirect ratio selection algorithm. J. Atmos. Ocean. Technol., 26, 1994 2014. climate forcing by aerosols. Proc. Natl. Acad. Sci. U.S.A., 108, 13404 13408. Oouchi, K., A. T. Noda, M. Satoh, B. Wang, S. P. Xie, H. G. Takahashi, and T. Yasunari, Penner, J. E., C. Zhou, and L. Xu, 2012: Consistent estimates from satellites and 2009: Asian summer monsoon simulated by a global cloud-system-resolving models for the first aerosol indirect forcing. Geophys. Res. Lett., 39, L13810. model: Diurnal to intra-seasonal variability. Geophys. Res. Lett., 36, L11815. 7 649 Chapter 7 Clouds and Aerosols Penner, J. E., Y. Chen, M. Wang, and X. Liu, 2009: Possible influence of anthropogenic Prisle, N. L., T. Raatikainen, A. Laaksonen, and M. Bilde, 2010: Surfactants in cloud aerosols on cirrus clouds and anthropogenic forcing. Atmos. Chem. Phys., 9, droplet activation: Mixed organic-inorganic particles. Atmos. Chem. Phys., 10, 879 896. 5663 5683. Penner, J. E., et al., 2006: Model intercomparison of indirect aerosol effects. Atmos. Prisle, N. L., T. Raatikainen, R. Sorjamaa, B. Svenningsson, A. Laaksonen, and M. Bilde, Chem. Phys., 6, 3391 3405. 2008: Surfactant partitioning in cloud droplet activation: A study of C8, C10, C12 Penuelas, J., and M. Staudt, 2010: BVOCs and global change. Trends Plant Sci., 15, and C14 normal fatty acid sodium salts. Tellus B, 60, 416 431. 133 144. Pritchard, M. S., and R. C. J. Somerville, 2010: Assessing the diurnal cycle of Perez, N., J. Pey, X. Querol, A. Alastuey, J. M. Lopez, and M. Viana, 2008: Partitioning precipitation in a multi-scale climate model. J. Adv. Model. Earth Syst., 1, 12. of major and trace components in PM10 PM2.5 PM1 at an urban site in Prospero, J. M., W. M. Landing, and M. Schulz, 2010: African dust deposition to Southern Europe. Atmos. Environ., 42, 1677 1691. Florida: Temporal and spatial variability and comparisons to models. J. Geophys. Persad, G. G., Y. Ming, and V. Ramaswamy, 2012: Tropical tropospheric-only responses Res., 115, D13304. to absorbing aerosols. J. Clim., 25, 2471 2480. Puma, M. J., and B. I. Cook, 2010: Effects of irrigation on global climate during the Peters, K., J. Quaas, and H. Grassl, 2011a: A search for large-scale effects of ship 20th century. J. Geophys. Res., 115, D16120. emissions on clouds and radiation in satellite data. J. Geophys. Res., 116, Putaud, J.-P., et al., 2004: European aerosol phenomenology-2: Chemical D24205. characteristics of particulate matter at kerbside, urban, rural and background Peters, K., J. Quaas, and N. Bellouin, 2011b: Effects of absorbing aerosols in cloudy sites in Europe. Atmos. Environ., 38, 2579 2595. skies: A satellite study over the Atlantic Ocean. Atmos. Chem. Phys., 11, 1393 Putman, W. M., and M. Suarez, 2011: Cloud-system resolving simulations with the 1404. NASA Goddard Earth Observing System global atmospheric model (GEOS-5). Petroff, A., and L. Zhang, 2010: Development and validation of a size-resolved Geophys. Res. Lett., 38, L16809. particle dry deposition scheme for application in aerosol transport models. Puxbaum, H., et al., 2004: A dual site study of PM2.5 and PM10 aerosol chemistry in Geosci. Model Dev., 3, 753 769. the larger region of Vienna, Austria. Atmos. Environ., 38, 3949 3958. Petters, M. D., and S. M. Kreidenweis, 2007: A single parameter representation of Pye, H. O. T., and J. H. Seinfeld, 2010: A global perspective on aerosol from low- hygroscopic growth and cloud condensation nucleus activity. Atmos. Chem. volatility organic compounds. Atmos. Chem. Phys., 10, 4377 4401. Phys., 7, 1961 1971. Pye, H. O. T., H. Liao, S. Wu, L. J. Mickley, D. J. Jacob, D. K. Henze, and J. H. Seinfeld, Petters, M. D., J. R. Snider, B. Stevens, G. Vali, I. Faloona, and L. M. Russell, 2006: 2009: Effect of changes in climate and emissions on future sulfate-nitrate- Accumulation mode aerosol, pockets of open cells, and particle nucleation in the ammonium aerosol levels in the United States. J. Geophys. Res., 114, D01205. remote subtropical Pacific marine boundary layer. J. Geophys. Res., 111, D02206. Qu, W. J., X. Y. Zhang, R. Arimoto, D. Wang, Y. Q. Wang, L. W. Yan, and Y. Li, 2008: Phillips, V. T. J., P. J. DeMott, and C. Andronache, 2008: An empirical parameterization Chemical composition of the background aerosol at two sites in southwestern of heterogeneous ice nucleation for multiple chemical species of aerosol. J. and northwestern China: Potential influences of regional transport. Tellus B, 60, Atmos. Sci., 65, 2757 2783. 657 673. Pierce, J. R., and P. J. Adams, 2007: Efficiency of cloud condensation nuclei formation Quaas, J., O. Boucher, and F. M. Bréon, 2004: Aerosol indirect effects in POLDER from ultrafine particles. Atmos. Chem. Phys., 7, 1367 1379. satellite data and the Laboratoire de Météorologie Dynamique-Zoom (LMDZ) Pierce, J. R., and P. J. Adams, 2009a: Uncertainty in global CCN concentrations from general circulation model. J. Geophys. Res., 109, D08205. uncertain aerosol nucleation and primary emission rates. Atmos. Chem. Phys., Quaas, J., O. Boucher, and U. Lohmann, 2006: Constraining the total aerosol indirect 9, 1339 1356. effect in the LMDZ and ECHAM4 GCMs using MODIS satellite data. Atmos. Pierce, J. R., and P. J. Adams, 2009b: Can cosmic rays affect cloud condensation nuclei Chem. Phys., 6, 947 955. by altering new particle formation rates? Geophys. Res. Lett., 36, L09820. Quaas, J., N. Bellouin, and O. Boucher, 2011: Which of satellite-based or model-based Pierce, J. R., D. K. Weisenstein, P. Heckendorn, T. Peter, and D. W. Keith, 2010: Efficient estimates are closer to reality for aerosol indirect forcing? Reply to Penner et al. formation of stratospheric aerosol for climate engineering by emission of Proc. Natl. Acad. Sci. U.S.A., 108, E1099. condensible vapor from aircraft. Geophys. Res. Lett., 37, L18805. Quaas, J., O. Boucher, N. Bellouin, and S. Kinne, 2008: Satellite-based estimate of Pincus, R., and M. B. Baker, 1994: Effect of precipitation on the albedo susceptibility the direct and indirect aerosol climate forcing. J. Geophys. Res., 113, D05204. of clouds in the marine boundary-layer. Nature, 372, 250 252. Quaas, J., B. Stevens, P. Stier, and U. Lohmann, 2010: Interpreting the cloud cover Pincus, R., and S. A. Klein, 2000: Unresolved spatial variability and microphysical aerosol optical depth relationship found in satellite data using a general process rates in large-scale models. J. Geophys. Res., 105, 27059 27065. circulation model. Atmos. Chem. Phys., 10, 6129 6135. Pincus, R., R. Hemler, and S. A. Klein, 2006: Using stochastically generated Quaas, J., et al., 2009: Aerosol indirect effects general circulation model subcolumns to represent cloud structure in a large-scale model. Mon. Weather intercomparison and evaluation with satellite data. Atmos. Chem. Phys., 9, Rev., 134, 3644 3656. 8697 8717. Platnick, S., M. D. King, S. A. Ackerman, W. P. Menzel, B. A. Baum, J. C. Riedi, and R. Querol, X., et al., 2001: PM10 and PM2.5 source apportionment in the Barcelona A. Frey, 2003: The MODIS cloud products: Algorithms and examples from Terra. Metropolitan Area, Catalonia, Spain. Atmos. Environ., 35/36, 6407 6419. IEEE Trans. Geosci. Remote Sens., 41, 459 473. Querol, X., et al., 2009: Variability in regional background aerosols within the Ponater, M., S. Marquart, R. Sausen, and U. Schumann, 2005: On contrail climate Mediterranean. Atmos. Chem. Phys., 9, 4575 4591. sensitivity. Geophys. Res. Lett., 32, L10706. Querol, X., et al., 2004: Speciation and origin of PM10 and PM2.5 in selected Pöschl, U., et al., 2010: Rainforest aerosols as biogenic nuclei of clouds and European cities. Atmos. Environ., 38, 6547 6555. precipitation in the Amazon. Science, 329, 1513 1516. Querol, X., et al., 2006: Atmospheric particulate matter in Spain: Levels, composition Pósfai, M., R. Simonics, J. Li, P. V. Hobbs, and P. R. Buseck, 2003: Individual aerosol and source origin. CSIC and Ministerio de Medioambiente, Madrid, Spain, 39 pp. particles from biomass burning in southern Africa: 1. Compositions and size Querol, X., et al., 2008: Spatial and temporal variations in airborne particulate matter distributions of carbonaceous particles. J. Geophys. Res., 108, 8483. (PM10 and PM2.5) across Spain 1999 2005. Atmos. Environ., 42, 3964 3979. Posselt, R., and U. Lohmann, 2008: Influence of giant CCN on warm rain processes in Quinn, P. K., and T. S. Bates, 2011: The case against climate regulation via oceanic the ECHAM5 GCM. Atmos. Chem. Phys., 8, 3769 3788. phytoplankton sulphur emissions. Nature, 480, 51 56. Posselt, R., and U. Lohmann, 2009: Sensitivity of the total anthropogenic aerosol Racherla, P. N., and P. J. Adams, 2006: Sensitivity of global tropospheric ozone and effect to the treatment of rain in a global climate model. Geophys. Res. Lett., fine particulate matter concentrations to climate change. J. Geophys. Res., 111, 36, L02805. D24103. Pratt, K. A., and K. A. Prather, 2010: Aircraft measurements of vertical profiles of Radhi, M., M. A. Box, G. P. Box, R. M. Mitchell, D. D. Cohen, E. Stelcer, and M. D. aerosol mixing states. J. Geophys. Res., 115, D11305. Keywood, 2010: Optical, physical and chemical characteristics of Australian Prenni, A. J., et al., 2007: Can ice-nucleating aerosols affect arctic seasonal climate? continental aerosols: Results from a field experiment. Atmos. Chem. Phys., 10, Bull. Am. Meteor. Soc., 88, 541 550. 5925 5942. Pringle, K. J., H. Tost, A. Pozzer, U. Pöschl, and J. Lelieveld, 2010: Global distribution of Rae, J. G. L., C. E. Johnson, N. Bellouin, O. Boucher, J. M. Haywood, and A. Jones, 7 the effective aerosol hygroscopicity parameter for CCN activation. Atmos. Chem. 2007: Sensitivity of global sulphate aerosol production to changes in oxidant Phys., 10, 5241 5255. concentrations and climate. J. Geophys. Res., 112, D10312. 650 Clouds and Aerosols Chapter 7 Raes, F., H. Liao, W.-T. Chen, and J. H. Seinfeld, 2010: Atmospheric chemistry-climate Rinaldi, M., et al., 2009: On the representativeness of coastal aerosol studies to open feedbacks. J. Geophys. Res., 115, D12121. ocean studies: Mace Head a case study. Atmos. Chem. Phys., 9, 9635 9646. Ram, K., M. M. Sarin, and P. Hegde, 2010: Long-term record of aerosol optical Rinaldi, M., et al., 2011: Evidence of a natural marine source of oxalic acid and a properties and chemical composition from a high-altitude site (Manora Peak) in possible link to glyoxal. J. Geophys. Res., 116, D16204. Central Himalaya. Atmos. Chem. Phys., 10, 11791 11803. Ringer, M. A., et al., 2006: Global mean cloud feedbacks in idealized climate change Raman, R. S., S. Ramachandran, and N. Rastogi, 2010: Source identification of experiments. Geophys. Res. Lett., 33, L07718. ambient aerosols over an urban region in western India. J. Environ. Monit., 12, Rio, C., and F. Hourdin, 2008: A thermal plume model for the convective boundary 1330 1340. layer: Representation of cumulus clouds. J. Atmos. Sci., 65, 407 425. Ramanathan, V., R. D. Cess, E. F. Harrison, P. Minnis, B. R. Barkstrom, E. Ahmad, and Rio, C., F. Hourdin, J.-Y. Grandpeix, and J.-P. Lafore, 2009: Shifting the diurnal cycle D. Hartmann, 1989: Cloud-radiative forcing and climate: Results from the Earth of parameterized deep convection over land. Geophys. Res. Lett., 36, L07809. Radiation Budget Experiment. Science, 243, 57 63. Rissler, J., B. Svenningsson, E. O. Fors, M. Bilde, and E. Swietlicki, 2010: An evaluation Randall, D., M. Khairoutdinov, A. Arakawa, and W. Grabowski, 2003: Breaking the and comparison of cloud condensation nucleus activity models: Predicting cloud parameterization deadlock. Bull. Am. Meteor. Soc., 84, 1547 1564. particle critical saturation from growth at subsaturation. J. Geophys. Res., 115, Randall, D. A., et al., 2007: Climate models and their evaluation. In: Climate Change D22208. 2007: The Physical Science Basis. Contribution of Working Group I to the Rissler, J., E. Swietlicki, J. Zhou, G. Roberts, M. O. Andreae, L. V. Gatti, and P. Artaxo, Fourth Assessment Report of the Intergovernmental Panel on Climate Change 2004: Physical properties of the sub-micrometer aerosol over the Amazon rain [Solomon, S., D. Qin, M. Manning, Z. Chen, M. Marquis, K. B. Averyt, M. Tignor forest during the wet-to-dry season transition comparison of modeled and and H. L. Miller (eds.)] Cambridge University Press, Cambridge, United Kingdom measured CCN concentrations. Atmos. Chem. Phys., 4, 2119 2143. and New York, NY, USA, pp. 589 662. Roberts, G. C., et al., 2010: Characterization of particle cloud droplet activity and Randerson, J. T., Y. Chen, G. R. van der Werf, B. M. Rogers, and D. C. Morton, 2012: composition in the free troposphere and the boundary layer during INTEX-B. Global burned area and biomass burning emissions from small fires. J. Geophys. Atmos. Chem. Phys., 10, 6627 6644. Res., 117, G04012. Robinson, A. L., et al., 2007: Rethinking organic aerosols: Semivolatile emissions and Randles, C. A., et al., 2013: Intercomparison of shortwave radiative transfer schemes photochemical aging. Science, 315, 1259 1262. in global aerosol modeling: Results from the AeroCom Radiative Transfer Robock, A., L. Oman, and G. Stenchikov, 2008: Regional climate responses to Experiment. Atmos. Chem. Phys., 13, 2347 2379. geoengineering with tropical and Arctic SO2 injections. J. Geophys. Res., 113, Rap, A., P. M. Forster, J. M. Haywood, A. Jones, and O. Boucher, 2010a: Estimating D16101. the climate impact of linear contrails using the UK Met Office climate model. Rodriguez, S., X. Querol, A. Alastuey, and F. Plana, 2002: Sources and processes Geophys. Res. Lett., 37, L20703. affecting levels and composition of atmospheric aerosol in the western Rap, A., P. Forster, A. Jones, O. Boucher, J. Haywood, N. Bellouin, and R. De Leon, Mediterranean. J. Geophys. Res., 107, 4777. 2010b: Parameterization of contrails in the UK Met Office Climate Model. J. Rodr guez, S., X. Querol, A. Alastuey, M. M. Viana, M. Alarcon, E. Mantilla, and C. R. Geophys. Res., 115, D10205. Ruiz, 2004: Comparative PM10 PM2.5 source contribution study at rural, urban Rasch, P. J., P. J. Crutzen, and D. B. Coleman, 2008a: Exploring the geoengineering of and industrial sites during PM episodes in Eastern Spain. Sci. Tot. Environ., 328, climate using stratospheric sulfate aerosols: The role of particle size. Geophys. 95 113. Res. Lett., 35, L02809. Rohs, S., R. Spang, F. Rohrer, C. Schiller, and H. Vos, 2010: A correlation study of high- Rasch, P. J., C. C. Chen, and J. L. Latham, 2009: Geo-engineering by cloud seeding: altitude and midaltitude clouds and galactic cosmic rays by MIPAS-Envisat. J. influence on sea-ice and the climate system. Environ. Res. Lett., 4, 045112. Geophys. Res., 115, D14212. Rasch, P. J., et al., 2008b: An overview of geoengineering of climate using Romps, D. M., 2011: Response of tropical precipitation to global warming. J. Atmos. stratospheric sulphate aerosols. Philos. Trans. R. Soc. London A, 366, 4007 4037. Sci., 68, 123 138. Rastogi, N., and M. M. Sarin, 2005: Long-term characterization of ionic species in Rondanelli, R., and R. S. Lindzen, 2010: Can thin cirrus clouds in the tropics provide a aerosols from urban and high-altitude sites in western India: Role of mineral solution to the faint young Sun paradox? J. Geophys. Res., 115, D02108. dust and anthropogenic sources. Atmos. Environ., 39, 5541 5554. Roosli, M., et al., 2001: Temporal and spatial variation of the chemical composition Rauber, R. M., et al., 2007: Rain in shallow cumulus over the ocean - The RICO of PM10 at urban and rural sites in the Basel area, Switzerland. Atmos. Environ., campaign. Bull. Am. Meteor. Soc., 88, 1912 1928. 35, 3701 3713. Raymond, D. J., S. L. Sessions, A. H. Sobel, and Z. Fuchs, 2009: The mechanics of gross Rose, D., et al., 2011: Cloud condensation nuclei in polluted air and biomass burning moist stability. J. Adv. Model. Earth Syst., 1, 9. smoke near the mega-city Guangzhou, China -Part 2: Size-resolved aerosol Reddy, M. S., O. Boucher, Y. Balkanski, and M. Schulz, 2005: Aerosol optical depths chemical composition, diurnal cycles, and externally mixed weakly CCN-active and direct radiative perturbations by species and source type. Geophys. Res. soot particles. Atmos. Chem. Phys., 11, 2817 2836. Lett., 32, L12803. Rosenfeld, D., and G. Gutman, 1994: Retrieving microphysical properties near the Remer, L. A., et al., 2005: The MODIS aerosol algorithm, products, and validation. J. tops of potential rain clouds by multi spectral analysis of AVHRR data. Atmos. Atmos. Sci., 62, 947 973. Res., 34, 259 283. Rengarajan, R., M. M. Sarin, and A. K. Sudheer, 2007: Carbonaceous and inorganic Rosenfeld, D., and W. L. Woodley, 2001: Pollution and clouds. Physics World, 14, species in atmospheric aerosols during wintertime over urban and high-altitude 33 37. sites in North India. J. Geophys. Res., 112, D21307. Rosenfeld, D., and T. L. Bell, 2011: Why do tornados and hailstorms rest on weekends? Richter, I., and S. P. Xie, 2008: Muted precipitation increase in global warming J. Geophys. Res., 116, D20211. simulations: A surface evaporation perspective. J. Geophys. Res., 113, D24118. Rosenfeld, D., H. Wang, and P. J. Rasch, 2012: The roles of cloud drop effective radius Richter, J. H., and P. J. Rasch, 2008: Effects of convective momentum transport on and LWP in determining rain properties in marine stratocumulus. Geophys. Res. the atmospheric circulation in the community atmosphere model, version 3. J. Lett., 39, L13801. Clim., 21, 1487 1499. Rosenfeld, D., et al., 2008: Flood or drought: How do aerosols affect precipitation? Ricke, K. L., G. Morgan, and M. R. Allen, 2010: Regional climate response to solar- Science, 321, 1309 1313. radiation management. Nature Geosci., 3, 537 541. Rotstayn, L. D., 1999: Indirect forcing by anthropogenic aerosols: A global climate Ridgwell, A., J. S. Singarayer, A. M. Hetherington, and P. J. Valdes, 2009: Tackling model calculation of the effective-radius and cloud-lifetime effects. J. Geophys. regional climate change by leaf albedo bio-geoengineering. Curr. Biol., 19, Res., 104, 9369 9380. 146 150. Rotstayn, L. D., 2000: On the tuning of autoconversion parameterizations in Rieck, M., L. Nuijens, and B. Stevens, 2012: Marine boundary-layer cloud feedbacks climate models. J. Geophys. Res., 105, 15495 15508. in a constant relative humidity atmosphere. J. Atmos. Sci., 69, 2538 2550. Rotstayn, L. D., and J. E. Penner, 2001: Indirect aerosol forcing, quasi forcing, and Riipinen, I., et al., 2011: Organic condensation: A vital link connecting aerosol climate response. J. Clim., 14, 2960 2975. formation to cloud condensation nuclei (CCN) concentrations. Atmos. Chem. Rotstayn, L. D., and Y. G. Liu, 2005: A smaller global estimate of the second indirect Phys., 11, 3865 3878. aerosol effect. Geophys. Res. Lett., 32, L05708. 7 651 Chapter 7 Clouds and Aerosols Rotstayn, L. D., and Y. G. Liu, 2009: Cloud droplet spectral dispersion and the indirect Schmidt, H., et al., 2012b: Solar irradiance reduction to counteract radiative forcing aerosol effect: Comparison of two treatments in a GCM. Geophys. Res. Lett., from a quadrupling of CO2: Climate responses simulated by four earth system 36, L10801. models. Earth Syst. Dyn., 3, 63 78. Rotstayn, L. D., et al., 2007: Have Australian rainfall and cloudiness increased due Schmidt, K. S., G. Feingold, P. Pilewskie, H. Jiang, O. Coddington, and M. Wendisch, to the remote effects of Asian anthropogenic aerosols? J. Geophys. Res., 112, 2009: Irradiance in polluted cumulus fields: Measured and modeled cloud- D09202. aerosol effects. Geophys. Res. Lett., 36, L07804. Royal Society, 2009: Geoengineering  the climate,  Science, governance and Schreier, M., H. Mannstein, V. Eyring, and H. Bovensmann, 2007: Global ship track uncertainty. Report 10/09, Royal Society, London, United Kingdom, 82 pp. distribution and radiative forcing from 1 year of AATSR data. Geophys. Res. Lett., Russell, L. M., L. N. Hawkins, A. A. Frossard, P. K. Quinn, and T. S. Bates, 2010: 34, L17814. Carbohydrate-like composition of submicron atmospheric particles and their Schulz, M., M. Chin, and S. Kinne, 2009: The Aerosol Model Comparison Project, production from ocean bubble bursting. Proc. Natl. Acad. Sci. U.S.A., 107, 6652 AeroCom, phase II: Clearing up diversity. IGAC Newsletter N° 41, 2 11. 6657. Schulz, M., et al., 2006: Radiative forcing by aerosols as derived from the AeroCom Rypdal, K., N. Rive, T. K. Berntsen, Z. Klimont, T. K. Mideksa, G. Myhre, and R. B. Skeie, present-day and pre-industrial simulations. Atmos. Chem. Phys., 6, 5225 5246. 2009: Costs and global impacts of black carbon abatement strategies. Tellus B, Schumann, U., and K. Graf, 2013: Aviation-induced cirrus and radiation changes at 61, 625 641. diurnal timescales. J. Geophys. Res., 118, 2404 2421. Sacks, W. J., B. I. Cook, N. Buenning, S. Levis, and J. H. Helkowski, 2009: Effects of Schwartz, S. E., and J. E. Freiberg, 1981: Mass-transport limitation to the rate of global irrigation on the near-surface climate. Clim. Dyn., 33, 159 175. reaction of gases in liquid droplets - Application to oxidation of SO2 in aqueous Safai, P. D., K. B. Budhavant, P. S. P. Rao, K. Ali, and A. Sinha, 2010: Source solutions. Atmos. Environ., 15, 1129 1144. characterization for aerosol constituents and changing roles of calcium and Schwarz, J. P., et al., 2008a: Coatings and their enhancement of black carbon light ammonium aerosols in the neutralization of aerosol acidity at a semi-urban site absorption in the tropical atmosphere. J. Geophys. Res., 113, D03203. in SW India. Atmos. Res., 98, 78 88. Schwarz, J. P., et al., 2008b: Measurement of the mixing state, mass, and optical size Sahu, L. K., Y. Kondo, Y. Miyazaki, P. Pongkiatkul, and N. T. Kim Oanh, 2011: Seasonal of individual black carbon particles in urban and biomass burning emissions. and diurnal variations of black carbon and organic carbon aerosols in Bangkok. Geophys. Res. Lett., 35, L13810. J. Geophys. Res., 116, D15302. Schwarz, J. P., et al., 2010: Global-scale black carbon profiles observed in the remote Sakaeda, N., R. Wood, and P. J. Rasch, 2011: Direct and semidirect aerosol effects of atmosphere and compared to models. Geophys. Res. Lett., 37, L18812. southern African biomass burning aerosol. J. Geophys. Res., 116, D12205. Schwarzenbock, A., S. Mertes, J. Heintzenberg, W. Wobrock, and P. Laj, 2001: Impact Salter, S., G. Sortino, and J. Latham, 2008: Sea-going hardware for the cloud albedo of the Bergeron-Findeisen process on the release of aerosol particles during the method of reversing global warming. Philos. Trans. R. Soc. London A, 366, 3989 evolution of cloud ice. Atmos. Res., 58, 295 313. 4006. Sciare, J., O. Favez, K. Oikonomou, R. Sarda-Esteve, H. Cachier, and V. Kazan, 2009: Salvador, P., B. Art nano, X. Querol, A. Alastuey, and M. Costoya, 2007: Long-term observation of carbonaceous aerosols in the Austral Ocean: Evidence Characterisation of local and external contributions of atmospheric particulate of a marine biogenic origin. J. Geophys. Res., 114, D15302. matter at a background coastal site. Atmos. Environ., 41, 1 17. Seager, R., N. Naik, and G. A. Vecchi, 2010: Thermodynamic and dynamic mechanisms Salzmann, M., et al., 2010: Two-moment bulk stratiform cloud microphysics in the for large-scale changes in the hydrological cycle in response to global warming. GFDL AM3 GCM: Description, evaluation, and sensitivity tests. Atmos. Chem. J. Clim., 23, 4651 4668. Phys., 10, 8037 8064. Seifert, A., and K. Beheng, 2006: A two-moment cloud microphysics parameterization Samset, B. H., et al., 2013: Black carbon vertical profiles strongly affect its radiative for mixed-phase clouds, Part II: Maritime versus continental deep convective forcing uncertainty. Atmos. Chem. Phys., 13, 2423 2434. storms. Meteorol. Atmos. Phys., 92, 67 88. Sanchez-Lorenzo, A., P. Laux, H. J. Hendricks Franssen, J. Calbó, S. Vogl, A. K. Seifert, A., and B. Stevens, 2010: Microphysical scaling relations in a kinematic Georgoulias, and J. Quaas, 2012: Assessing large-scale weekly cycles in model of isolated shallow cumulus clouds. J. Atmos. Sci., 67, 1575 1590. meteorological variables: A review. Atmos. Chem. Phys., 12, 5755 5771. Seifert, A., and G. Zängl, 2010: Scaling relations in warm-rain orographic Sanderson, M. G., C. D. Jones, W. J. Collins, C. E. Johnson, and R. G. Derwent, 2003: precipitation. Meteorol. Z., 19, 417 426. Effect of climate change on isoprene emissions and surface ozone levels. Seifert, A., C. Köhler, and K. D. Beheng, 2012: Aerosol-cloud-precipitation effects Geophys. Res. Lett., 30, 1936. over Germany as simulated by a convective-scale numerical weather prediction Sandu, I., J.-L. Brenguier, O. Geoffroy, O. Thouron, and V. Masson, 2008: Aerosol model. Atmos. Chem. Phys., 12, 709 725. impacts on the diurnal cycle of marine stratocumulus. J. Atmos. Sci., 65, 2705 Seitz, R., 2011: Bright water: Hydrosols, water conservation and climate change. 2718. Clim. Change, 105, 365 381. Sassen, K., and G. C. Dodd, 1988: Homogeneous nucleation rate for highly Sekiguchi, M., et al., 2003: A study of the direct and indirect effects of aerosols using supercooled cirrus cloud droplets. J. Atmos. Sci., 45, 1357 1369. global satellite data sets of aerosol and cloud parameters. J. Geophys. Res., 108, Satheesh, S. K., and K. K. Moorthy, 2005: Radiative effects of natural aerosols: A 4699. review. Atmos. Environ., 39, 2089 2110. Seland, O., T. Iversen, A. Kirkevag, and T. Storelvmo, 2008: Aerosol-climate Satheesh, S. K., K. K. Moorthy, S. S. Babu, V. Vinoj, and C. B. S. Dutt, 2008: Climate interactions in the CAM-Oslo atmospheric GCM and investigation of associated implications of large warming by elevated aerosol over India. Geophys. Res. basic shortcomings. Tellus A, 60, 459 491. Lett., 35, L19809. Senior, C. A., and J. F. B. Mitchell, 1993: Carbon dioxide and climate: The impact of Sato, T., H. Miura, M. Satoh, Y. N. Takayabu, and Y. Q. Wang, 2009: Diurnal cycle of cloud parameterization. J. Clim., 6, 393 418. precipitation in the Tropics simulated in a global cloud-resolving model. J. Clim., Senior, C. A., and J. F. B. Mitchell, 2000: The time-dependence of climate sensitivity. 22, 4809 4826. Geophys. Res. Lett., 27, 2685 2688. Satoh, M., T. Inoue, and H. Miura, 2010: Evaluations of cloud properties of global and Sesartic, A., U. Lohmann, and T. Storelvmo, 2012: Bacteria in the ECHAM5 HAM local cloud system resolving models using CALIPSO and CloudSat simulators. J. global climate model. Atmos. Chem. Phys., 12, 8645 8661. Geophys. Res., 115, D00H14. Shapiro, E. L., J. Szprengiel, N. Sareen, C. N. Jen, M. R. Giordano, and V. F. McNeill, Sausen, R., et al., 2005: Aviation radiative forcing in 2000: An update on IPCC (1999). 2009: Light-absorbing secondary organic material formed by glyoxal in aqueous Meteorol. Z., 14, 555 561. aerosol mimics. Atmos. Chem. Phys., 9, 2289 2300. Savic-Jovcic, V., and B. Stevens, 2008: The structure and mesoscale organization of Sharon, T. M., et al., 2006: Aerosol and cloud microphysical characteristics of rifts precipitating stratocumulus. J. Atmos. Sci., 65, 1587 1605. and gradients in maritime stratocumulus clouds. J. Atmos. Sci., 63, 983 997. Sawant, A. A., K. Na, X. Zhu, and D. R. Cocker III, 2004: Chemical characterization Sherwood, S. C., 2002: Aerosols and ice particle size in tropical cumulonimbus. J. of outdoor PM2.5 and gas-phase compounds in Mira Loma, California. Atmos. Clim., 15, 1051 1063. Environ., 38, 5517 5528. Sherwood, S. C., and C. L. Meyer, 2006: The general circulation and robust relative Schmidt, A., et al., 2012a: Importance of tropospheric volcanic aerosol for indirect humidity. J. Clim., 19, 6278 6290. 7 radiative forcing of climate. Atmos. Chem. Phys., 12, 7321 7339. Sherwood, S. C., R. Roca, T. M. Weckwerth, and N. G. Andronova, 2010a: Tropospheric water vapor, convection, and climate. Rev. Geophys., 48, RG2001. 652 Clouds and Aerosols Chapter 7 Sherwood, S. C., V. Ramanathan, T. P. Barnett, M. K. Tyree, and E. Roeckner, 1994: Song, X., and G. J. Zhang, 2011: Microphysics parameterization for convective Response of an atmospheric general circulation model to radiative forcing of clouds in a global climate model: Description and single-column model tests. J. tropical clouds. J. Geophys. Res., 99, 20829 20845. Geophys. Res., 116, D02201. Sherwood, S. C., W. Ingram, Y. Tsushima, M. Satoh, M. Roberts, P. L. Vidale, and P. A. Song, X. L., and G. J. Zhang, 2009: Convection parameterization, tropical Pacific O Gorman, 2010b: Relative humidity changes in a warmer climate. J. Geophys. double ITCZ, and upper-ocean biases in the NCAR CCSM3. Part I: Climatology Res., 115, D09104. and atmospheric feedback. J. Clim., 22, 4299 4315. Shindell, D. T., A. Voulgarakis, G. Faluvegi, and G. Milly, 2012: Precipitation response Sorooshian, A., N. L. Ng, A. W. H. Chan, G. Feingold, R. C. Flagan, and J. H. Seinfeld, to regional radiative forcing. Atmos. Chem. Phys., 12, 6969 6982. 2007: Particulate organic acids and overall water-soluble aerosol composition Shindell, D. T., et al., 2013: Radiative forcing in the ACCMIP historical and future measurements from the 2006 Gulf of Mexico Atmospheric Composition and climate simulations. Atmos. Chem. Phys., 13, 2939 2974. Climate Study (GoMACCS). J. Geophys. Res., 112, D13201. Shiraiwa, M., Y. Kondo, T. Iwamoto, and K. Kita, 2010: Amplification of light Spencer, R. W., and W. D. Braswell, 2008: Potential biases in feedback diagnosis from absorption of black carbon by organic coating. Aer. Sci. Technol., 44, 46 54. observational data: A simple model demonstration. J. Clim., 21, 5624 5628. Shonk, J. K. P., R. J. Hogan, and J. Manners, 2012: Impact of improved representation Spencer, R. W., and W. D. Braswell, 2010: On the diagnosis of radiative feedback in of horizontal and vertical cloud structure in a climate model. Clim. Dyn., 38, the presence of unknown radiative forcing. J. Geophys. Res., 115, D16109. 2365 2376. Spracklen, D. V., L. J. Mickley, J. A. Logan, R. C. Hudman, R. Yevich, M. D. Flannigan, Shresth, A. B., C. P. Wake, J. E. Dibb, P. A. Mayewski, S. I. Whitlow, G. R. Carmichael, and and A. L. Westerling, 2009: Impacts of climate change from 2000 to 2050 on M. Ferm, 2000: Seasonal variations in aerosol concentrations and compositions wildfire activity and carbonaceous aerosol concentrations in the western United in the Nepal Himalaya. Atmos. Environ., 34, 3349 3363. States. J. Geophys. Res., 114, D20301. Shrivastava, M., et al., 2011: Modeling organic aerosols in a megacity: Comparison Spracklen, D. V., et al., 2008: Contribution of particle formation to global cloud of simple and complex representations of the volatility basis set approach. condensation nuclei concentrations. Geophys. Res. Lett., 35, L06808. Atmos. Chem. Phys., 11, 6639 6662. Spracklen, D. V., et al., 2011: Aerosol mass spectrometer constraint on the global Shupe, M. D., et al., 2008: A focus on mixed-phase clouds. The status of ground- secondary organic aerosol budget. Atmos. Chem. Phys., 11, 12109 12136. based observational methods. Bull. Am. Meteor. Soc., 89, 1549 1562. Stan, C., et al., 2010: An ocean-atmosphere climate simulation with an embedded Siebesma, A. P., P. M. M. Soares, and J. Teixeira, 2007: A combined eddy-diffusivity cloud resolving model. Geophys. Res. Lett., 37, L01702. mass-flux approach for the convective boundary layer. J. Atmos. Sci., 64, 1230 Stephens, G. L., M. Wild, P. W. Stackhouse, T. L Ecuyer, S. Kato, and D. S. Henderson, 1248. 2012: The global character of the flux of downward longwave radiation. J. Clim., Siebesma, A. P., et al., 2003: A large eddy simulation intercomparison study of 25, 2329 2340. shallow cumulus convection. J. Atmos. Sci., 60, 1201 1219. Stephens, G. L., et al., 2002: The Cloudsat mission and the A-train A new dimension Siebesma, A. P., et al., 2009: Cloud-controlling factors. In: Clouds in the Perturbed of space-based observations of clouds and precipitation. Bull. Am. Meteor. Soc., Climate System: Their Relationship to Energy Balance, Atmospheric Dynamics, 83, 1771 1790. and Precipitation [J. Heintzenberg and R. J. Charlson (eds.)]. MIT Press, Stephens, G. L., et al., 2008: CloudSat mission: Performance and early science after Cambridge, MA, USA, pp. 269 290. the first year of operation. J. Geophys. Res., 113, D00A18. Singh, M. S., and P. A. O Gorman, 2012: Upward shift of the atmospheric general Stephens, G. L., et al., 2010: Dreary state of precipitation in global models. J. Geophys. circulation under global warming: Theory and simulations. J. Clim., 25, 8259 Res., 115, D24211. 8276. Stevens, B., and A. Seifert, 2008: Understanding macrophysical outcomes of Sipilä, M., et al., 2010: The role of sulfuric acid in atmospheric nucleation. Science, microphysical choices in simulations of shallow cumulus convection. J. Meteorol. 327, 1243 1246. Soc. Jpn., 86, 143 162. Skeie, R. B., T. Berntsen, G. Myhre, C. A. Pedersen, J. Ström, S. Gerland, and J. A. Ogren, Stevens, B., and G. Feingold, 2009: Untangling aerosol effects on clouds and 2011: Black carbon in the atmosphere and snow, from pre-industrial times until precipitation in a buffered system. Nature, 461, 607 613. present. Atmos. Chem. Phys., 11, 6809 6836. Stevens, B., and J.-L. Brenguier, 2009: Cloud-controlling factors: Low clouds. In: Small, J. D., P. Y. Chuang, G. Feingold, and H. Jiang, 2009: Can aerosol decrease cloud Clouds in the Perturbed Climate System: Their Relationship to Energy Balance, lifetime? Geophys. Res. Lett., 36, L16806. Atmospheric Dynamics, and Precipitation [J. Heintzenberg and R. J. Charlson Smith, J. N., et al., 2010: Observations of aminium salts in atmospheric nanoparticles (eds.)]. MIT Press, Cambridge, MA, USA, pp. 173 196. and possible climatic implications. Proc. Natl. Acad. Sci. U.S.A., 107, 6634 6639. Stevens, B., W. R. Cotton, G. Feingold, and C.-H. Moeng, 1998: Large-eddy simulations Smith, S. J., and P. J. Rasch, 2012: The long-term policy context for solar radiation of strongly precipitating, shallow, stratocumulus-topped boundary layers. J. management. Clim. Change, doi: 10.1007/s10584-012-0577-3. Atmos. Sci., 55, 3616 3638. Snow-Kropla, E. J., J. R. Pierce, D. M. Westervelt, and W. Trivitayanurak, 2011: Cosmic Stevens, B., et al., 2005a: Pockets of open cells and drizzle in marine stratocumulus. rays, aerosol formation and cloud-condensation nuclei: Sensitivities to model Bull. Am. Meteor. Soc., 86, 51 57. uncertainties. Atmos. Chem. Phys., 11, 4001 4013. Stevens, B., et al., 2005b: Evaluation of large-eddy simulations via observations of Soden, B. J., and I. M. Held, 2006: An assessment of climate feedbacks in coupled nocturnal marine stratocumulus. Mon. Weather Rev., 133, 1443 1462. ocean-atmosphere models. J. Clim., 19, 3354 3360. Stier, P., J. H. Seinfeld, S. Kinne, and O. Boucher, 2007: Aerosol absorption and Soden, B. J., and G. A. Vecchi, 2011: The vertical distribution of cloud feedback in radiative forcing. Atmos. Chem. Phys., 7, 5237 5261. coupled ocean-atmosphere models. Geophys. Res. Lett., 38, L12704. Stier, P., J. H. Seinfeld, S. Kinne, J. Feichter, and O. Boucher, 2006: Impact of Soden, B. J., I. M. Held, R. Colman, K. M. Shell, J. T. Kiehl, and C. A. Shields, 2008: nonabsorbing anthropogenic aerosols on clear-sky atmospheric absorption. J. Quantifying climate feedbacks using radiative kernels. J. Clim., 21, 3504 3520. Geophys. Res., 111, D18201. Sofiev, M., et al., 2009: An operational system for the assimilation of the satellite Stier, P., et al., 2005: The Aerosol-Climate Model ECHAM5 HAM. Atmos. Chem. information on wild-land fires for the needs of air quality modelling and Phys., 5, 1125 1156. forecasting. Atmos. Chem. Phys., 9, 6833 6847. Stier, P., et al., 2013: Host model uncertainties in aerosol forcing estimates: Results Sohn, B. J., T. Nakajima, M. Satoh, and H. S. Jang, 2010: Impact of different definitions from the AeroCom Prescribed intercomparison study. Atmos. Chem. Phys., 13, of clear-sky flux on the determination of longwave cloud radiative forcing: 3245 3270. NICAM simulation results. Atmos. Chem. Phys., 10, 11641 11646. Stjern, C. W., 2011: Weekly cycles in precipitation and other meteorological variables Solomon, S., K. H. Rosenlof, R. W. Portmann, J. S. Daniel, S. M. Davis, T. J. Sanford, in a polluted region of Europe. Atmos. Chem. Phys., 11, 4095 4104. and G.-K. Plattner, 2010: Contributions of stratospheric water vapor to decadal Stone, E., J. Schauer, T. A. Quraishi, and A. Mahmood, 2010: Chemical characterization changes in the rate of global warming. Science, 327, 1219 1223. and source apportionment of fine and coarse particulate matter in Lahore, Somerville, R. C. J., and L. A. Remer, 1984: Cloud optical-thickness feedbacks in the Pakistan. Atmos. Environ., 44, 1062 1070. CO2 climate problem. J. Geophys. Res., 89, 9668 9672. Stone, R. S., et al., 2008: Radiative impact of boreal smoke in the Arctic: Observed Sommeria, G., and J. W. Deardorff, 1977: Subgrid-scale condensation in models of and modeled. J. Geophys. Res., 113, D14S16. nonprecipitating clouds. J. Atmos. Sci., 34, 344 355. Storelvmo, T., J. E. Kristjánsson, and U. Lohmann, 2008a: Aerosol influence on mixed- 7 phase clouds in CAM-Oslo. J. Atmos. Sci., 65, 3214 3230. 653 Chapter 7 Clouds and Aerosols Storelvmo, T., C. Hoose, and P. Eriksson, 2011: Global modeling of mixed-phase Tanré, D., Y. J. Kaufman, M. Herman, and S. Mattoo, 1997: Remote sensing of aerosol clouds: The albedo and lifetime effects of aerosols. J. Geophys. Res., 116, D05207. properties over oceans using the MODIS/EOS spectral radiances. J. Geophys. Storelvmo, T., J. E. Kristjánsson, S. J. Ghan, A. Kirkevag, O. Seland, and T. Iversen, Res., 102, 16971 16988. 2006: Predicting cloud droplet number concentration in Community Atmosphere Tanré, D., et al., 2011: Remote sensing of aerosols by using polarized, directional and Model (CAM)-Oslo. J. Geophys. Res., 111, D24208. spectral measurements within the A-Train: The PARASOL mission. Atmos. Meas. Storelvmo, T., J. E. Kristjánsson, U. Lohmann, T. Iversen, A. Kirkevag, and O. Seland, Tech., 4, 1383 1395. 2008b: Modeling of the Wegener Bergeron Findeisen process implications for Tao, W.-K., J.-P. Chen, Z. Li, C. Wang, and C. Zhang, 2012: Impact of aerosols on aerosol indirect effects. Environ. Res. Lett., 3, 045001. convective clouds and precipitation. Rev. Geophys., 50, RG2001. Storelvmo, T., J. E. Kristjánsson, U. Lohmann, T. Iversen, A. Kirkevag, and O. Seland, Tao, W.-K., et al., 2009: A Multiscale Modeling System: Developments, applications, 2010: Corrigendum: Modeling of the Wegener Bergeron Findeisen process and critical issues. Bull. Am. Meteor. Soc., 90, 515 534. implications for aerosol indirect effects. Environ. Res. Lett., 5, 019801 Tegen, I., M. Werner, S. P. Harrison, and K. E. Kohfeld, 2004: Relative importance Storelvmo, T., J. E. Kristjánsson, H. Muri, M. Pfeffer, D. Barahona, and A. Nenes, 2013: of climate and land use in determining present and future global soil dust Cirrus cloud seeding has potential to cool climate Geophys. Res. Lett., 40, 178 emission. Geophys. Res. Lett., 31, L05105. 182. Theodosi, C., U. Im, A. Bougiatioti, P. Zarmpas, O. Yenigun, and N. Mihalopoulos, 2010: Storer, R. L., and S. C. van den Heever, 2013: Microphysical processes evident in Aerosol chemical composition over Istanbul. Sci. Tot. Environ., 408, 2482 2491. aerosol forcing of tropical deep convective clouds. J. Atmos. Sci., 70, 430 446. Thomas, G. E., et al., 2010: Validation of the GRAPE single view aerosol retrieval for Stramler, K., A. D. Del Genio, and W. B. Rossow, 2011: Synoptically driven Arctic ATSR-2 and insights into the long term global AOD trend over the ocean. Atmos. winter states. J. Clim., 24, 1747 1762. Chem. Phys., 10, 4849 4866. Stratmann, F., O. Moehler, R. Shaw, and W. Heike, 2009: Laboratory cloud simulation: Tilmes, S., R. Müller, and R. Salawitch, 2008: The sensitivity of polar ozone depletion Capabilities and future directions. In: Clouds in the Perturbed Climate System: to proposed geoengineering schemes. Science, 320, 1201 1204. Their Relationship to Energy Balance, Atmospheric Dynamics, and Precipitation Tilmes, S., R. R. Garcia, E. D. Kinnison, A. Gettelman, and P. J. Rasch, 2009: Impact [J. Heintzenberg and R. J. Charlson (eds.)]. MIT Press, Cambridge, MA, USA, pp. of geo-engineered aerosols on troposphere and stratosphere. J. Geophys. Res., 149 172. 114, D12305. Struthers, H., et al., 2011: The effect of sea ice loss on sea salt aerosol concentrations Tilmes, S., et al., 2012: Impact of very short-lived halogens on stratospheric ozone and the radiative balance in the Arctic. Atmos. Chem. Phys., 11, 3459 3477. abundance and UV radiation in a geo-engineered atmosphere. Atmos. Chem. Stubenrauch, C. J., S. Cros, A. Guignard, and N. Lamquin, 2010: A 6 year global Phys., 12, 10945 10955. cloud climatology from the Atmospheric InfraRed Sounder AIRS and a statistical Tinsley, B. A., 2008: The global atmospheric electric circuit and its effects on cloud analysis in synergy with CALIPSO and CloudSat. Atmos. Chem. Phys., 10, 7197 microphysics. Rep. Prog. Phys., 71, 066801. 7214. Tobin, I., S. Bony, and R. Roca, 2012: Observational evidence for relationships Stubenrauch, C. J., et al., 2013: Assessment of global cloud datasets from between the degree of aggregation of deep convection, water vapor, surface satellites:  Project and database initiated by the GEWEX Radiation Panel. Bull. fluxes, and radiation. J. Clim., 25, 6885 6904. Am. Meteor. Soc., 94, 1031 1049. Tomassini, L., et al., 2013: The respective roles of surface temperature driven feedbacks Stuber, N., and P. Forster, 2007: The impact of diurnal variations of air traffic on and tropospheric adjustment to CO2 in  CMIP5 transient climate simulations. contrail radiative forcing. Atmos. Chem. Phys., 7, 3153 3162. Clim. Dyn., doi:10.1007/s00382-013-1682-3. Su, W., N. G. Loeb, G. L. Schuster, M. Chin, and F. G. Rose, 2013: Global all-sky Tomita, H., H. Miura, S. Iga, T. Nasuno, and M. Satoh, 2005: A global cloud-resolving shortwave direct radiative forcing of anthropogenic aerosols from combined simulation: Preliminary results from an aqua planet experiment. Geophys. Res. satellite observations and GOCART simulations. J. Geophys. Res., 118, 655 669. Lett., 32, L08805. Su, W. Y., et al., 2008: Aerosol and cloud interaction observed from high spectral Tompkins, A. M., and G. C. Craig, 1998: Radiative convective equilibrium in a three- resolution lidar data. J. Geophys. Res., 113, D24202. dimensional cloud-ensemble model. Q. J. R. Meteorol. Soc., 124, 2073 2097. Sugiyama, M., H. Shiogama, and S. Emori, 2010: Precipitation extreme changes Tompkins, A. M., K. Gierens, and G. Radel, 2007: Ice supersaturation in the ECMWF exceeding moisture content increases in MIROC and IPCC climate models. Proc. integrated forecast system. Q. J. R. Meteorol. Soc., 133, 53 63. Natl. Acad. Sci. U.S.A., 107, 571 575. Torres, O., P. K. Bhartia, J. R. Herman, A. Sinyuk, P. Ginoux, and B. Holben, 2002: Suzuki, K., G. L. Stephens, S. C. van den Heever, and T. Y. Nakajima, 2011: Diagnosis A long-term record of aerosol optical depth from TOMS observations and of the warm rain process in cloud-resolving models using joint CloudSat and comparison to AERONET measurements. J. Atmos. Sci., 59, 398 413. MODIS observations. J. Atmos. Sci., 68, 2655 2670. Torres, O., et al., 2007: Aerosols and surface UV products from Ozone Monitoring Suzuki, K., T. Nakajima, M. Satoh, H. Tomita, T. Takemura, T. Y. Nakajima, and G. L. Instrument observations: An overview. J. Geophys. Res., 112, D24S47. Stephens, 2008: Global cloud-system-resolving simulation of aerosol effect on Trenberth, K. E., and A. Dai, 2007: Effects of Mount Pinatubo volcanic eruption on warm clouds. Geophys. Res. Lett., 35, L19817. the hydrological cycle as an analog of geoengineering. Geophys. Res. Lett., 34, Svensmark, H., T. Bondo, and J. Svensmark, 2009: Cosmic ray decreases affect L15702. atmospheric aerosols and clouds. Geophys. Res. Lett., 36, L15101. Trenberth, K. E., and J. T. Fasullo, 2009: Global warming due to increasing absorbed Tackett, J. L., and L. Di Girolamo, 2009: Enhanced aerosol backscatter adjacent to solar radiation. Geophys. Res. Lett., 36, L07706. tropical trade wind clouds revealed by satellite-based lidar. Geophys. Res. Lett., Trenberth, K. E., and J. T. Fasullo, 2010: Simulation of present-day and twenty-first- 36, L14804. century energy budgets of the Southern Oceans. J. Clim., 23, 440 454. Takahashi, K., 2009: The global hydrological cycle and atmospheric shortwave Trenberth, K. E., et al., 2007: Observations: Surface and atmospheric climate change. absorption in climate models under CO2 forcing. J. Clim., 22, 5667 5675. In: Climate Change 2007: The Physical Science Basis. Contribution of Working Takemura, T., 2012: Distributions and climate effects of atmospheric aerosols from Group I to the Fourth Assessment Report of the Intergovernmental Panel on the preindustrial era to 2100 along Representative Concentration Pathways Climate Change [Solomon, S., D. Qin, M. Manning, Z. Chen, M. Marquis, K. B. (RCPs) simulated using the global aerosol model SPRINTARS. Atmos. Chem. Averyt, M. Tignor and H. L. Miller (eds.)] Cambridge University Press, Cambridge, Phys., 12, 11555 11572. United Kingdom and New York, NY, USA, pp. 235 336. Takemura, T., and T. Uchida, 2011: Global climate modeling of regional changes in Tselioudis, G., and W. B. Rossow, 2006: Climate feedback implied by observed cloud, precipitation, and radiation budget due to the aerosol semi-direct effect radiation and precipitation changes with midlatitude storm strength and of black carbon. Sola, 7, 181 184. frequency. Geophys. Res. Lett., 33, L02704. Takemura, T., T. Nozawa, S. Emori, T. Y. Nakajima, and T. Nakajima, 2005: Simulation Tsigaridis, K., and M. Kanakidou, 2007: Secondary organic aerosol importance in the of climate response to aerosol direct and indirect effects with aerosol transport- future atmosphere. Atmos. Environ., 41, 4682 4692. radiation model. J. Geophys. Res., 110, D02202. Tsushima, Y., et al., 2006: Importance of the mixed-phase cloud distribution in the Tanré, D., M. Herman, and Y. J. Kaufman, 1996: Information on aerosol size control climatefor assessing the response of clouds to carbon dioxide increase:A distribution contained in solar reflected spectral radiances. J. Geophys. Res., multi-model study. Clim. Dyn., 27, 113 126. 7 101, 19043 19060. 654 Clouds and Aerosols Chapter 7 Tsimpidi, A. P., et al., 2010: Evaluation of the volatility basis-set approach for the Vogt, M., S. Vallina, and R. von Glasow, 2008: Correspondence on Enhancing the simulation of organic aerosol formation in the Mexico City metropolitan area. natural cycle to slow global warming . Atmos. Environ., 42, 4803 4805. Atmos. Chem. Phys., 10, 525 546. Volkamer, R., et al., 2006: Secondary organic aerosol formation from anthropogenic Tuttle, J. D., and R. E. Carbone, 2011: Inferences of weekly cycles in summertime air pollution: Rapid and higher than expected. Geophys. Res. Lett., 33, L17811. rainfall. J. Geophys. Res., 116, D20213. Volodin, E. M., 2008: Relation between temperature sensitivity to doubled carbon Twohy, C. H., and J. R. Anderson, 2008: Droplet nuclei in non-precipitating clouds: dioxide and the distribution of clouds in current climate models. Izvestiya Atmos. Composition and size matter. Environ. Res. Lett., 3, 045002. Ocean. Phys., 44, 288 299. Twohy, C. H., J. A. Coakley, Jr., and W. R. Tahnk, 2009: Effect of changes in relative Waliser, D. E., J. L. F. Li, T. S. L Ecuyer, and W. T. Chen, 2011: The impact of precipitating humidity on aerosol scattering near clouds. J. Geophys. Res., 114, D05205. ice and snow on the radiation balance in global climate models. Geophys. Res. Twohy, C. H., et al., 2005: Evaluation of the aerosol indirect effect in marine Lett., 38, L06802. stratocumulus clouds: Droplet number, size, liquid water path, and radiative Wang, G., H. Wang, Y. Yu, S. Gao, J. Feng, S. Gao, and L. Wang, 2003: Chemical impact. J. Geophys. Res., 110, D08203. characterization of water-soluble components of PM10 and PM2.5 atmospheric Twomey, S., 1977: Influence of pollution on shortwave albedo of clouds. J. Atmos. aerosols in five locations of Nanjing, China. Atmos. Environ., 37, 2893 2902. Sci., 34, 1149 1152. Wang, H., and D. Shooter, 2001: Water soluble ions of atmospheric aerosols in three Udelhofen, P. M., and R. D. Cess, 2001: Cloud cover variations over the United States: New Zealand cities: Seasonal changes and sources. Atmos. Environ., 35, 6031 An influence of cosmic rays or solar variability? Geophys. Res. Lett., 28, 2617 6040. 2620. Wang, H., K. Kawamuraa, and D. Shooter, 2005a: Carbonaceous and ionic Ulbrich, I. M., M. R. Canagaratna, Q. Zhang, D. R. Worsnop, and J. L. Jimenez, 2009: components in wintertime atmospheric aerosols from two New Zealand cities: Interpretation of organic components from Positive Matrix Factorization of Implications for solid fuel combustion. Atmos. Environ., 39, 5865 5875. aerosol mass spectrometric data. Atmos. Chem. Phys., 9, 2891 2918. Wang, H., P. J. Rasch, and G. Feingold, 2011a: Manipulating marine stratocumulus Unger, N., S. Menon, D. M. Koch, and D. T. Shindell, 2009: Impacts of aerosol-cloud cloud amount and albedo: A process-modelling study of aerosol-cloud- interactions on past and future changes in tropospheric composition. Atmos. precipitation interactions in response to injection of cloud condensation nuclei. Chem. Phys., 9, 4115 4129. Atmos. Chem. Phys., 11, 4237 4249. Unger, N., D. T. Shindell, D. M. Koch, M. Amann, J. Cofala, and D. G. Streets, 2006: Wang, H. L., and G. Feingold, 2009a: Modeling mesoscale cellular structures and Influences of man-made emissions and climate changes on tropospheric ozone, drizzle in marine stratocumulus. Part I: Impact of drizzle on the formation and methane, and sulfate at 2030 from a broad range of possible futures. J. Geophys. evolution of open cells. J. Atmos. Sci., 66, 3237 3256. Res., 111, D12313. Wang, H. L., and G. Feingold, 2009b: Modeling mesoscale cellular structures and Usoskin, I. G., and G. A. Kovaltsov, 2008: Cosmic rays and climate of the Earth: drizzle in marine stratocumulus. Part II: The microphysics and dynamics of the Possible connection. C. R. Geosci., 340, 441 450. boundary region between open and closed cells. J. Atmos. Sci., 66, 3257 3275. Uttal, T., et al., 2002: Surface heat budget of the Arctic Ocean. Bull. Am. Meteor. Soc., Wang, L., A. F. Khalizov, J. Zheng, W. Xu, Y. Ma, V. Lal, and R. Y. Zhang, 2010a: 83, 255 275. Atmospheric nanoparticles formed from heterogeneous reactions of organics. Vali, G., 1985: Atmospheric ice nucleation A review. J. Rech. Atmos., 19, 105 115. Nature Geosci., 3, 238 242. van den Heever, S. C., G. L. Stephens, and N. B. Wood, 2011: Aerosol indirect effects Wang, M., and J. Penner, 2009: Aerosol indirect forcing in a global model with on tropical convection characteristics under conditions of radiative-convective particle nucleation. Atmos. Chem. Phys., 9, 239 260. equilibrium. J. Atmos. Sci., 68, 699 718. Wang, M., et al., 2011b: Aerosol indirect effects in a multi-scale aerosol-climate vanZanten, M. C., B. Stevens, G. Vali, and D. H. Lenschow, 2005: Observations of model PNNL-MMF. Atmos. Chem. Phys., 11, 5431 5455. drizzle in nocturnal marine stratocumulus. J. Atmos. Sci., 62, 88 106. Wang, M., et al., 2012: Constraining cloud lifetime effects of aerosols using A-Train vanZanten, M. C., et al., 2011: Controls on precipitation and cloudiness in simulations satellite observations. Geophys. Res. Lett., 39, L15709. of trade-wind cumulus as observed during RICO. J. Adv. Model. Earth Syst., 3, Wang, T., S. Li, Y. Shen, J. Deng, and M. Xie, 2010b: Investigations on direct and M06001. indirect effect of nitrate on temperature and precipitation in China using a Várnai, T., and A. Marshak, 2009: MODIS observations of enhanced clear sky regional climate chemistry modeling system. J. Geophys. Res., 115, D00K26. reflectance near clouds. Geophys. Res. Lett., 36, L06807. Wang, Y., G. Zhuang, A. Tang, H. Yuan, Y. Sun, S. Chen, and A. Zheng, 2005b: The Vavrus, S., M. M. Holland, and D. A. Bailey, 2011: Changes in Arctic clouds during ion chemistry and the source of PM 2.5 aerosol in Beijing. Atmos. Environ. 39, intervals of rapid sea ice loss. Clim. Dyn., 36, 1475 1489. 3771 3784. Vavrus, S., D. Waliser, A. Schweiger, and J. Francis, 2009: Simulations of 20th and 21st Wang, Y., et al., 2006: The ion chemistry, seasonal cycle, and sources of PM2.5 and century Arctic cloud amount in the global climate models assessed in the IPCC TSP aerosol in Shanghai. Atmos. Environ., 40, 2935 2952. AR4. Clim. Dyn., 33, 1099 1115. Wang, Z. L., H. Zhang, and X. S. Shen, 2011c: Radiative forcing and climate response Verheggen, B., et al., 2007: Aerosol partitioning between the interstitial and the due to black carbon in snow and ice. Adv. Atmos. Sci., 28, 1336 1344. condensed phase in mixed-phase clouds. J. Geophys. Res., 112, D23202. Waquet, F., J. Riedi, L. C. Labonnote, P. Goloub, B. Cairns, J.-L. Deuzé, and D. Tanré, Verlinde, J., et al., 2007: The mixed-phase Arctic cloud experiment. Bull. Am. Meteor. 2009: Aerosol remote sensing over clouds using A-Train observations. J. Atmos. Soc., 88, 205 221. Sci., 66, 2468 2480. Vial, J., J.-L. Dufresne, and S. Bony, 2013: On the interpretation of inter-model spread Warneke, C., et al., 2010: An important contribution to springtime Arctic aerosol in CMIP5 climate sensitivity estimates. Climate Dynamics, doi:10.1007/s00382- from biomass burning in Russia. Geophys. Res. Lett., 37, L01801. 013-1725-9. Warren, S. G., 2013: Can black carbon in snow be detected by remote sensing? J. Viana, M., W. Maenhaut, X. Chi, X. Querol, and A. lastuey, 2007: Comparative Geophys. Res., 118, 779 786. chemical mass closure of fine and coarse aerosols at two sites in South and West Watanabe, M., S. Emori, M. Satoh, and H. Miura, 2009: A PDF-based hybrid prognostic Europe: Implications for EU air pollution policies. Atmos. Environ., 41, 315 326. cloud scheme for general circulation models. Clim. Dyn., 33, 795 816. Viana, M., X. Chi, W. Maenhaut, X. Querol, A. Alastuey, P. Mikuska, and Z. Vecera, Webb, M. J., and A. Lock, 2013: Coupling between subtropical cloud feedback and 2006: Organic and elemental carbon concentrations during summer and winter the local hydrological cycle in a climate model. Clim. Dyn., 41, 1923 1939. sampling campaigns in Barcelona, Spain. Atmos. Environ., 40, 2180 2193. Webb, M. J., F. H. Lambert, and J. M. Gregory, 2013: Origins of differences in climate Viana, M., et al., 2008: Characterising exposure to PM aerosols for an epidemiological sensitivity, forcing and feedback in climate models. Clim. Dyn., 40, 677 707. study. Atmos. Environ., 42, 1552 1568. Weinstein, J. P., S. R. Hedges, and S. Kimbrough, 2010: Characterization and aerosol Vignati, E., M. Karl, M. Krol, J. Wilson, P. Stier, and F. Cavalli, 2010: Sources of mass balance of PM2.5 and PM10 collected in Conakry, Guinea during the 2004 uncertainties in modelling black carbon at the global scale. Atmos. Chem. Phys., Harmattan period. Chemosphere, 78, 980 988. 10, 2595 2611. Wen, G., A. Marshak, R. F. Cahalan, L. A. Remer, and R. G. Kleidman, 2007: 3 D Vogelmann, A. M., T. P. Ackerman, and R. P. Turco, 1992: Enhancements in biologically aerosol-cloud radiative interaction observed in collocated MODIS and ASTER effective ultraviolet radiation following volcanic eruptions. Nature, 359, 47 49. images of cumulus cloud fields. J. Geophys. Res., 112, D13204. Vogelmann, A. M., et al., 2012: RACORO extended-term aircraft observations of Wendisch, M., et al., 2008: Radiative and dynamic effects of absorbing aerosol 7 boundary layer clouds. Bull. Am. Meteor. Soc., 93, 861 878. particles over the Pearl River Delta, China. Atmos. Environ., 42, 6405 6416. 655 Chapter 7 Clouds and Aerosols Westra, S., L. V. Alexander, and F. W. Zwiers, 2013: Global increasing trends in annual Xie, S.-P., C. Deser, G. A. Vecchi, J. Ma, H. Teng, and A. T. Wittenberg, 2010: Global maximum daily precipitation. J. Clim., 26, 3904 3918. warming pattern formation: Sea surface temperature and rainfall. J. Clim., 23, Wetherald, R. T., and S. Manabe, 1980: Cloud cover and climate sensitivity. J. Atmos. 966 986. Sci., 37, 1485 1510. Xu, B., J. Cao, D. R. Joswiak, X. Liu, H. Zhao, and J. He, 2012: Post-depositional Wex, H., G. McFiggans, S. Henning, and F. Stratmann, 2010: Influence of the external enrichment of black soot in snow-pack and accelerated melting of Tibetan mixing state of atmospheric aerosol on derived CCN number concentrations. glaciers. Environ. Res. Lett., 7, 014022. Geophys. Res. Lett., 37, L10805. Xu, B. Q., et al., 2009: Black soot and the survival of Tibetan glaciers. Proc. Natl. Acad. Wielicki, B. A., and L. Parker, 1992: On the determination of cloud cover from satellite Sci. U.S.A., 106, 22114 22118. sensors: The effect of sensor spatial resolution. J. Geophys. Res., 97, 12799 Xu, K. M., A. N. Cheng, and M. H. Zhang, 2010: Cloud-resolving simulation of low- 12823. cloud feedback to an increase in sea surface temperature. J. Atmos. Sci., 67, Wilcox, E. M., 2010: Stratocumulus cloud thickening beneath layers of absorbing 730 748. smoke aerosol. Atmos. Chem. Phys., 10, 11769 11777. Xu, K. M., T. Wong, B. A. Wielicki, L. Parker, B. Lin, Z. A. Eitzen, and M. Branson, 2007: Williams, K. D., and G. Tselioudis, 2007: GCM intercomparison of global cloud Statistical analyses of satellite cloud object data from CERES. Part II: Tropical regimes: Present-day evaluation and climate change response. Clim. Dyn., 29, convective cloud objects during 1998 El Nino and evidence for supporting the 231 250. fixed anvil temperature hypothesis. J. Clim., 20, 819 842. Williams, K. D., and M. J. Webb, 2009: A quantitative performance assessment of Xu, K. M., et al., 2002: An intercomparison of cloud-resolving models with the cloud regimes in climate models. Clim. Dyn., 33, 141 157. atmospheric radiation measurement summer 1997 intensive observation period Williams, K. D., A. Jones, D. L. Roberts, C. A. Senior, and M. J. Woodage, 2001: The data. Q. J. R. Meteorol. Soc., 128, 593 624. response of the climate system to the indirect effects of anthropogenic sulfate Xue, H., G. Feingold, and B. Stevens, 2008: Aerosol effects on clouds, precipitation, aerosol. Clim. Dyn., 17, 845 856. and the organization of shallow cumulus convection. J. Atmos. Sci., 65, 392 406. Williams, K. D., et al., 2006: Evaluation of a component of the cloud response to Yang, Q., et al., 2011: Assessing regional scale predictions of aerosols, marine climate change in an intercomparison of climate models. Clim. Dyn., 26, 145 stratocumulus, and their interactions during VOCALS-REx using WRF-Chem. 165. Atmos. Chem. Phys., 11, 11951 11975. Wingenter, O. W., S. M. Elliot, and D. R. Blake, 2007: Enhancing the natural sulfur Yao, X., et al., 2002: The water-soluble ionic composition of PM2.5 in Shanghai and cycle to slow global warming Atmos. Environ., 41, 7373 7375. Beijing, China. Atmos. Environ., 36, 4223 4234. Winker, D. M., J. L. Tackett, B. J. Getzewich, Z. Liu, M. A. Vaughan, and R. R. Rogers, Ye, B., et al., 2003: Concentration and chemical composition of PM2.5 in Shanghai 2013: The global 3 D distribution of tropospheric aerosols as characterized by for a 1 year period. Atmos. Environ., 37, 499 510. CALIOP. Atmos. Chem. Phys., 13, 3345 3361. Yin, J., and R. M. Harrison, 2008: Pragmatic mass closure study for PM1.0, PM2.5 Winker, D. M., et al., 2009: Overview of the CALIPSO mission and CALIOP data and PM10 at roadside, urban background and rural sites. Atmos. Environ., 42, processing algorithms. J. Atmos. Ocean. Technol., 26, 2310 2323. 980 988. Winker, D. M., et al., 2010: The CALIPSO mission: A global 3D view of aerosols and Yin, J. H., 2005: A consistent poleward shift of the storm tracks in simulations of 21st clouds. Bull. Am. Meteor. Soc., 91, 1211 1229. century climate. Geophys. Res. Lett., 32, L18701. Wood, R., 2005: Drizzle in stratiform boundary layer clouds. Part II: Microphysical Yokohata, T., S. Emori, T. Nozawa, Y. Tsushima, T. Ogura, and M. Kimoto, 2005: Climate aspects. J. Atmos. Sci., 62, 3034 3050. response to volcanic forcing: Validation of climate sensitivity of a coupled Wood, R., 2007: Cancellation of aerosol indirect effects in marine stratocumulus atmosphere-ocean general circulation model. Geophys. Res. Lett., 32, L21710. through cloud thinning. J. Atmos. Sci., 64, 2657 2669. Yokohata, T., M. J. Webb, M. Collins, K. D. Williams, M. Yoshimori, J. C. Hargreaves, and Wood, R., and C. S. Bretherton, 2006: On the relationship between stratiform low J. D. Annan, 2010: Structural similarities and differences in climate responses cloud cover and lower-tropospheric stability. J. Clim., 19, 6425 6432. to CO2 increase between two perturbed physics ensembles. J. Clim., 23, 1392 Wood, R., C. S. Bretherton, D. Leon, A. D. Clarke, P. Zuidema, G. Allen, and H. Coe, 1410. 2011a: An aircraft case study of the spatial transition from closed to open Yokohata, T., et al., 2008: Comparison of equilibrium and transient responses to CO2 mesoscale cellular convection over the Southeast Pacific. Atmos. Chem. Phys., increase in eight state-of-the-art climate models. Tellus A, 60, 946 961. 11, 2341 2370. Yoshimori, M., and A. J. Broccoli, 2008: Equilibrium response of an atmosphere- Wood, R., et al., 2011b: The VAMOS Ocean-Cloud-Atmosphere-Land Study Regional mixed layer ocean model to different radiative forcing agents: Global and zonal Experiment (VOCALS-REx): Goals, platforms, and field operations. Atmos. Chem. mean response. J. Clim., 21, 4399 4423. Phys., 11, 627 654. Yoshimori, M., T. Yokohata, and A. Abe-Ouchi, 2009: A comparison of climate Woodhouse, M. T., G. W. Mann, K. S. Carslaw, and O. Boucher, 2008: The impact feedback strength between CO2 doubling and LGM experiments. J. Clim., 22, of oceanic iron fertilisation on cloud condensation nuclei. Atmos. Environ., 42, 3374 3395. 5728 5730. Young, I. R., S. Zieger, and A. V. Babanin, 2011: Global trends in wind speed and wave Woodhouse, M. T., K. S. Carslaw, G. W. Mann, S. M. Vallina, M. Vogt, P. R. Halloran, height. Science, 332, 451 455. and O. Boucher, 2010: Low sensitivity of cloud condensation nuclei to changes Yttri, K. E., 2007: Concentrations of particulate matter (PM10, PM2.5) in Norway. in the sea-air flux of dimethyl-sulphide. Atmos. Chem. Phys., 10, 7545 7559. Annual and seasonal trends and spatial variability. In: EMEP Particulate Matter Woodward, S., D. L. Roberts, and R. A. Betts, 2005: A simulation of the effect of Assessment Report, Part B, report EMEP/CCC-Report 8/2007, Norwegian climate change-induced desertification on mineral dust aerosol. Geophys. Res. Institute for Air Research, Oslo, Norway, pp. 292 307. Lett., 32, L18810. Yu, F., 2011: A secondary organic aerosol formation model considering successive Wu, S., L. J. Mickley, J. O. Kaplan, and D. J. Jacob, 2012: Impacts of changes in land oxidation aging and kinetic condensation of organic compounds: Global scale use and land cover on atmospheric chemistry and air quality over the 21st implications. Atmos. Chem. Phys., 11, 1083 1099. century. Atmos. Chem. Phys., 12, 1597 1609. Yu, F., and G. Luo, 2009: Simulation of particle size distribution with a global aerosol Wyant, M. C., C. S. Bretherton, and P. N. Blossey, 2009: Subtropical low cloud model: Contribution of nucleation to aerosol and CCN number concentrations. response to a warmer climate in a superparameterized climate model. Part I: Atmos. Chem. Phys., 9, 7691 7710. Regime sorting and physical mechanisms. J. Adv. Model. Earth Syst., 1, 7. Yu, H., R. McGraw, and S. Lee, 2012: Effects of amines on formation of sub-3 nm Wyant, M. C., C. S. Bretherton, P. N. Blossey, and M. Khairoutdinov, 2012: Fast cloud particles and their subsequent growth. Geophys. Res. Lett., 39, L02807. adjustment to increasing CO2 in a superparameterized climate model. J. Adv. Yu, H., et al., 2006: A review of measurement-based assessments of the aerosol Model. Earth Syst., 4, M05001. direct radiative effect and forcing. Atmos. Chem. Phys., 6, 613 666. Wyant, M. C., et al., 2006: A comparison of low-latitude cloud properties and their Yu, H. B., M. Chin, D. M. Winker, A. H. Omar, Z. Y. Liu, C. Kittaka, and T. Diehl, 2010: response to climate change in three AGCMs sorted into regimes using mid- Global view of aerosol vertical distributions from CALIPSO lidar measurements tropospheric vertical velocity. Clim. Dyn., 27, 261 279. and GOCART simulations: Regional and seasonal variations. J. Geophys. Res., Xiao, H.-Y., and C.-Q. Liu, 2004: Chemical characteristics of water-soluble components 115, D00H30. 7 in TSP over Guiyang, SW China, 2003. Atmos. Environ., 38, 6297 6306. 656 Clouds and Aerosols Chapter 7 Yuan, T., L. A. Remer, and H. Yu, 2011: Microphysical, macrophysical and radiative Zhao, T. X. P., N. G. Loeb, I. Laszlo, and M. Zhou, 2011: Global component aerosol signatures of volcanic aerosols in trade wind cumulus observed by the A-Train. direct radiative effect at the top of atmosphere. Int. J. Remote Sens., 32, 633 Atmos. Chem. Phys., 11, 7119 7132. 655. Yuekui, Y., and L. Di Girolamo, 2008: Impacts of 3 D radiative effects on satellite Zhao, T. X. P., H. B. Yu, I. Laszlo, M. Chin, and W. C. Conant, 2008b: Derivation of cloud detection and their consequences on cloud fraction and aerosol optical component aerosol direct radiative forcing at the top of atmosphere for clear- depth retrievals. J. Geophys. Res., 113, D04213. sky oceans. J. Quant. Spectrosc. Radiat. Transfer, 109, 1162 1186. Yuter, S. E., M. A. Miller, M. D. Parker, P. M. Markowski, Y. Richardson, H. Brooks, Zhu, P., B. A. Albrecht, V. P. Ghate, and Z. D. Zhu, 2010: Multiple-scale simulations of and J. M. Straka, 2013: Comment on Why do tornados and hailstorms rest on stratocumulus clouds. J. Geophys. Res., 115, D23201. weekends?   by D. Rosenfeld and T. Bell. J. Geophys. Res. Atmos., 118, 7332 Zhu, P., et al., 2012: A limited area model (LAM) intercomparison study of a TWP- 7338. ICE active monsoon mesoscale convective event. J. Geophys. Res., 117, D11208. Zarzycki, C. M., and T. C. Bond, 2010: How much can the vertical distribution of black Zhuang, B. L., L. Liu, F. H. Shen, T. J. Wang, and Y. Han, 2010: Semidirect radiative carbon affect its global direct radiative forcing? Geophys. Res. Lett., 37, L20807. forcing of internal mixed black carbon cloud droplet and its regional climatic Zelinka, M. D., and D. L. Hartmann, 2010: Why is longwave cloud feedback positive? effect over China. J. Geophys. Res., 115, D00K19. J. Geophys. Res., 115, D16117. Ziemann, P. J., and R. Atkinson, 2012: Kinetics, products, and mechanisms of Zelinka, M. D., and D. L. Hartmann, 2011: The observed sensitivity of high clouds secondary organic aerosol formation. Chem. Soc. Rev., 41, 6582 6605. to mean surface temperature anomalies in the tropics. J. Geophys. Res., 116, Zubler, E. M., U. Lohmann, D. Lüthi, C. Schär, and A. Muhlbauer, 2011: Statistical D23103. analysis of aerosol effects on simulated mixed-phase clouds and precipitation in Zelinka, M. D., S. A. Klein, and D. L. Hartmann, 2012a: Computing and partitioning the Alps. J. Atmos. Sci., 68, 1474 1492. cloud feedbacks using cloud property histograms. Part I: Cloud radiative kernels. Zuidema, P., et al., 2005: An arctic springtime mixed-phase cloudy boundary layer J. Clim., 25, 3715 3735. observed during SHEBA. J. Atmos. Sci., 62, 160 176. Zelinka, M. D., S. A. Klein, and D. L. Hartmann, 2012b: Computing and partitioning Zuidema, P., et al., 2012: On trade wind cumulus cold pools. J. Atmos. Sci., 69, 258 cloud feedbacks using cloud property histograms. Part II: Attribution to changes 280. in cloud amount, altitude, and optical depth. J. Clim., 25, 3736 3754. Zelinka, M. D., S. A. Klein, K. E. Taylor, T. Andrews, M. J. Webb, J. M. Gregory, and P. M. Forster, 2013: Contributions of different cloud types to feedbacks and rapid adjustments in CMIP5. J. Clim., 26, 5007 5027. Zhang, G. J., A. M. Vogelmann, M. P. Jensen, W. D. Collins, and E. P. Luke, 2010: Relating satellite-observed cloud properties from MODIS to meteorological conditions for marine boundary layer clouds. J. Clim., 23, 1374 1391. Zhang, M. H., and C. Bretherton, 2008: Mechanisms of low cloud-climate feedback in idealized single-column simulations with the Community Atmospheric Model, version 3 (CAM3). J. Clim., 21, 4859 4878. Zhang, Q., D. R. Worsnop, M. R. Canagaratna, and J. L. Jimenez, 2005a: Hydrocarbon- like and oxygenated organic aerosols in Pittsburgh: Insights into sources and processes of organic aerosols. Atmos. Chem. Phys., 5, 3289 3311. Zhang, Q., M. R. Alfarra, D. R. Worsnop, J. D. Allan, H. Coe, M. R. Canagaratna, and J. L. Jimenez, 2005b: Deconvolution and quantification of hydrocarbon-like and oxygenated organic aerosols based on aerosol mass spectrometry. Environ. Sci. Technol., 39, 4938 4952. Zhang, Q., et al., 2007a: Ubiquity and dominance of oxygenated species in organic aerosols in anthropogenically-influenced Northern Hemisphere midlatitudes. Geophys. Res. Lett., 34, L13801. Zhang, R. Y., A. Khalizov, L. Wang, M. Hu, and W. Xu, 2012a: Nucleation and growth of nanoparticles in the atmosphere. Chem. Rev., 112, 1957 2011. Zhang, X. B., et al., 2007b: Detection of human influence on twentieth-century precipitation trends. Nature, 448, 461 465. Zhang, X. Y., R. Arimoto, Z. S. An, J. J. Cao, and D. Wang, 2001: Atmospheric dust aerosol over the Tibetian Plateau. J. Geophys. Res., 106, 18471 18476. Zhang, X. Y., Y. Q. Wang, X. C. Zhang, W. Guo, and S. L. Gong, 2008: Carbonaceous aerosol composition over various regions of China during 2006. J. Geophys. Res., 113, D14111. Zhang, X. Y., J. J. Cao, L. M. Li, R. Arimoto, Y. Cheng, B. Huebert, and D. Wang, 2002: Characterization of atmospheric aerosol over Xian in the south margin of the loess plateau, China. Atmos. Environ., 36, 4189 4199. Zhang, X. Y., Y. Q. Wang, T. Niu, X. C. Zhang, S. L. Gong, Y. M. Zhang, and J. Y. Sun, 2012b: Atmospheric aerosol compositions in China: Spatial/temporal variability, chemical signature, regional haze distribution and  comparisons with global aerosols. Atmos. Chem. Phys., 12, 779 799. Zhang, Y., 2008: Online-coupled meteorology and chemistry models: History, current status, and outlook. Atmos. Chem. Phys., 8, 2895 2932. Zhang, Y. M., X. Y. Zhang, J. Y. Sun, W. L. Lin, S. L. Gong, X. J. Shen, and S. Yang, 2011: Characterization of new particle and secondary aerosol formation during summertime in Beijing, China. Tellus B, 63, 382 394. Zhao, T. L., S. L. Gong, X. Y. Zhang, A. A. Mawgoud, and Y. P. Shao, 2006: An assessment of dust emission schemes in modeling east Asian dust storms. J. Geophys. Res., 111, D05S90. Zhao, T. X.-P., et al., 2008a: Study of long-term trend in aerosol optical thickness observed from operational AVHRR satellite instrument. J. Geophys. Res., 113, 7 D07201. 657 8 Anthropogenic and Natural Radiative Forcing Coordinating Lead Authors: Gunnar Myhre (Norway), Drew Shindell (USA) Lead Authors: François-Marie Bréon (France), William Collins (UK), Jan Fuglestvedt (Norway), Jianping Huang (China), Dorothy Koch (USA), Jean-François Lamarque (USA), David Lee (UK), Blanca Mendoza (Mexico), Teruyuki Nakajima (Japan), Alan Robock (USA), Graeme Stephens (USA), Toshihiko Takemura (Japan), Hua Zhang (China) Contributing Authors: Borgar Aamaas (Norway), Olivier Boucher (France), Stig B. Dalsren (Norway), John S. Daniel (USA), Piers Forster (UK), Claire Granier (France), Joanna Haigh (UK), Oivind Hodnebrog (Norway), Jed O. Kaplan (Switzerland/Belgium/USA), George Marston (UK), Claus J. Nielsen (Norway), Brian C. O Neill (USA), Glen P. Peters (Norway), Julia Pongratz (Germany), Michael Prather (USA), Venkatachalam Ramaswamy (USA), Raphael Roth (Switzerland), Leon Rotstayn (Australia), Steven J. Smith (USA), David Stevenson (UK), Jean-Paul Vernier (USA), Oliver Wild (UK), Paul Young (UK) Review Editors: Daniel Jacob (USA), A.R. Ravishankara (USA), Keith Shine (UK) This chapter should be cited as: Myhre, G., D. Shindell, F.-M. Bréon, W. Collins, J. Fuglestvedt, J. Huang, D. Koch, J.-F. Lamarque, D. Lee, B. Mendoza, T. Nakajima, A. Robock, G. Stephens, T. Takemura and H. Zhang, 2013: Anthropogenic and Natural Radiative Forc- ing. In: Climate Change 2013: The Physical Science Basis. Contribution of Working Group I to the Fifth Assessment Report of the Intergovernmental Panel on Climate Change [Stocker, T.F., D. Qin, G.-K. Plattner, M. Tignor, S.K. Allen, J. Boschung, A. Nauels, Y. Xia, V. Bex and P.M. Midgley (eds.)]. Cambridge University Press, Cambridge, United Kingdom and New York, NY, USA. 659 Table of Contents Executive Summary...................................................................... 661 8.7 Emission Metrics.............................................................. 710 8.7.1 Metric Concepts......................................................... 710 8 8.1 Radiative Forcing............................................................. 664 Box 8.4: Choices Required When Using Emission Metrics..... 711 8.1.1 The Radiative Forcing Concept................................... 664 8.7.2 Application of Metrics................................................ 716 Box 8.1: Definition of Radiative Forcing and Effective Radiative Forcing........................................................................ 665 References .................................................................................. 721 Box 8.2: Grouping Forcing Compounds by Common Properties .................................................................................. 668 Appendix 8.A: Lifetimes, Radiative Efficiencies and 8.1.2 Calculation of Radiative Forcing due to Metric Values.................................................................................. 731 Concentration or Emission Changes.......................... 668 Frequently Asked Questions 8.2 Atmospheric Chemistry.................................................. 669 FAQ 8.1 How Important Is Water Vapour to 8.2.1 Introduction............................................................... 669 Climate Change?..................................................... 666 8.2.2 Global Chemistry Modelling in Coupled Model FAQ 8.2 Do Improvements in Air Quality Have an Intercomparison Project Phase 5................................ 670 Effect on Climate Change?.................................... 684 8.2.3 Chemical Processes and Trace Gas Budgets............... 670 Supplementary Material 8.3 Present-Day Anthropogenic Radiative Forcing....... 675 Supplementary Material is available in online versions of the report. 8.3.1 Updated Understanding of the Spectral Properties of Greenhouse Gases and Radiative Transfer Codes....... 675 8.3.2 Well-mixed Greenhouse Gases.................................. 676 8.3.3 Ozone and Stratospheric Water Vapour...................... 679 8.3.4 Aerosols and Cloud Effects........................................ 682 8.3.5 Land Surface Changes................................................ 686 8.4 Natural Radiative Forcing Changes: Solar and Volcanic....................................................................... 688 8.4.1 Solar Irradiance.......................................................... 688 8.4.2 Volcanic Radiative Forcing......................................... 691 Box 8.3: Volcanic Eruptions as Analogues............................... 693 8.5 Synthesis of Global Mean Radiative Forcing, Past and Future................................................................. 693 8.5.1 Summary of Radiative Forcing by Species and Uncertainties.............................................................. 694 8.5.2 Time Evolution of Historical Forcing........................... 698 8.5.3 Future Radiative Forcing............................................ 700 8.6 Geographic Distribution of Radiative Forcing........ 702 8.6.1 Spatial Distribution of Current Radiative Forcing....... 702 8.6.2 Spatial Evolution of Radiative Forcing and Response over the Industrial Era................................................ 705 8.6.3 Spatial Evolution of Radiative Forcing and Response for the Future............................................................. 708 660 Anthropogenic and Natural Radiative Forcing Chapter 8 Executive Summary WMGHG is the same as the RF but with a larger uncertainty (+/-20%). {8.3.2, 8.5.2, Figures 8.6, 8.18} It is unequivocal that anthropogenic increases in the well-mixed greenhouse gases (WMGHGs) have substantially enhanced The net forcing by WMGHGs other than CO2 shows a small the greenhouse effect, and the resulting forcing continues to increase since the AR4 estimate for the year 2005. A small growth increase. Aerosols partially offset the forcing of the WMGHGs and in the CH4 concentration has increased its RF by 2% to an AR5 value dominate the uncertainty associated with the total anthropogenic of 0.48 (0.43 to 0.53) W m 2. RF of nitrous oxide (N2O) has increased 8 driving of climate change. by 6% since AR4 and is now 0.17 (0.14 to 0.20) W m 2. N2O concen- trations continue to rise while those of dichlorodifluoromethane (CFC- As in previous IPCC assessments, AR5 uses the radiative forcing1 12), the third largest WMGHG contributor to RF for several decades, is (RF) concept, but it also introduces effective radiative forcing2 falling due to its phase-out under the Montreal Protocol and amend- (ERF). The RF concept has been used for many years and in previous ments. Since 2011 N2O has become the third largest WMGHG contrib- IPCC assessments for evaluating and comparing the strength of the utor to RF. The RF from all halocarbons (0.36 W m 2) is very similar to various mechanisms affecting the Earth s radiation balance and thus the value in AR4, with a reduced RF from chlorofluorocarbons (CFCs) causing climate change. Whereas in the RF concept all surface and but increases from many of their substitutes. Four of the halocarbons tropospheric conditions are kept fixed, the ERF calculations presented (trichlorofluoromethane (CFC-11), CFC-12, trichlorotrifluoroethane here allow all physical variables to respond to perturbations except (CFC-113) and chlorodifluoromethane (HCFC-22)) account for around for those concerning the ocean and sea ice. The inclusion of these 85% of the total halocarbon RF. The first three of these compounds adjustments makes ERF a better indicator of the eventual temperature have declining RF over the last 5 years but their combined decrease response. ERF and RF values are significantly different for anthropo- is compensated for by the increased RF from HCFC-22. Since AR4, the genic aerosols owing to their influence on clouds and on snow cover. RF from all HFCs has nearly doubled but still only amounts to 0.02 These changes to clouds are rapid adjustments and occur on a time W  m 2. There is high confidence4 that the overall growth rate in RF scale much faster than responses of the ocean (even the upper layer) to from all WMGHG is smaller over the last decade than in the 1970s and forcing. RF and ERF are estimated over the Industrial Era from 1750 to 1980s owing to a reduced rate of increase in the combined non-CO2 2011 if other periods are not explicitly stated. {8.1, Box 8.1, Figure 8.1} RF. {8.3.2; Figure 8.6} Industrial-Era Anthropogenic Forcing Ozone and stratospheric water vapour contribute substantially to RF. The total RF estimated from modelled ozone changes is 0.35 The total anthropogenic ERF over the Industrial Era is 2.3 (1.1 to (0.15 to 0.55) W m 2, with RF due to tropospheric ozone changes of 3.3) W m 2.3 It is certain that the total anthropogenic ERF is positive. 0.40 (0.20 to 0.60) W m 2 and due to stratospheric ozone changes of Total anthropogenic ERF has increased more rapidly since 1970 than 0.05 ( 0.15 to +0.05) W m 2. Ozone is not emitted directly into the during prior decades. The total anthropogenic ERF estimate for 2011 is atmosphere but is formed by photochemical reactions. Tropospheric 43% higher compared to the AR4 RF estimate for the year 2005 owing ozone RF is largely attributed to anthropogenic emissions of methane to reductions in estimated forcing due to aerosols but also to contin- (CH4), nitrogen oxides (NOx), carbon monoxide (CO) and non-methane ued growth in greenhouse gas RF. {8.5.1, Figures 8.15, 8.16} volatile organic compounds (NMVOCs), while stratospheric ozone RF results primarily from ozone depletion by halocarbons. Estimates are Due to increased concentrations, RF from WMGHGs has also provided attributing RF to emitted compounds. Ozone-depleting increased by 0.20 (0.18 to 0.22) W m 2 (8%) since the AR4 esti- substances (ODS) cause ozone RF of 0.15 ( 0.30 to 0.0) W m 2, some mate for the year 2005. The RF of WMGHG is 2.83 (2.54 to 3.12) of which is in the troposphere. Tropospheric ozone precursors cause W m 2. The majority of this change since AR4 is due to increases in the ozone RF of 0.50 (0.30 to 0.70) W m 2, some of which is in the strato- carbon dioxide (CO2) RF of nearly 10%. The Industrial Era RF for CO2 sphere; this value is larger than that in AR4. There is robust evidence alone is 1.82 (1.63 to 2.01) W m 2, and CO2 is the component with the that tropospheric ozone also has a detrimental impact on vegetation largest global mean RF. Over the last decade RF of CO2 has an average physiology, and therefore on its CO2 uptake, but there is a low confi- growth rate of 0.27 (0.24 to 0.30) W m 2 per decade. Emissions of CO2 dence on quantitative estimates of the RF owing to this indirect effect. have made the largest contribution to the increased anthropogenic RF for stratospheric water vapour produced by CH4 oxidation is 0.07 forcing in every decade since the 1960s. The best estimate for ERF of (0.02 to 0.12) W m 2. The RF best estimates for ozone and stratospheric Change in net downward radiative flux at the tropopause after allowing for stratospheric temperatures to readjust to radiative equilibrium, while holding surface and tropo- 1 spheric temperatures and state variables fixed at the unperturbed values. Change in net downward radiative flux at the top of the atmosphere (TOA) after allowing for atmospheric temperatures, water vapour, clouds and land albedo to adjust, but 2 with global mean surface temperature or ocean and sea ice conditions unchanged (calculations presented in this chapter use the fixed ocean conditions method). Uncertainties are given associated with best estimates of forcing. The uncertainty values represent the 5 95% (90%) confidence range. 3 In this Report, the following summary terms are used to describe the available evidence: limited, medium, or robust; and for the degree of agreement: low, medium, or high. 4 A level of confidence is expressed using five qualifiers: very low, low, medium, high, and very high, and typeset in italics, e.g., medium confidence. For a given evidence and agreement statement, different confidence levels can be assigned, but increasing levels of evidence and degrees of agreement are correlated with increasing confidence (see Section 1.4 and Box TS.1 for more details). 661 Chapter 8 Anthropogenic and Natural Radiative Forcing water vapour are either identical or consistent with the range in AR4. 20th century, extending to Asia, South America and central Africa by {8.2, 8.3.3, Figure 8.7} 1980. Emission controls have since reduced aerosol pollution in North America and Europe, but not in much of Asia. Ozone forcing increased The magnitude of the aerosol forcing is reduced relative to AR4. throughout the 20th century, with peak positive amplitudes around The RF due to aerosol radiation interactions, sometimes referred to as 15°N to 30°N due to tropospheric pollution but negative values over direct aerosol effect, is given a best estimate of 0.35 ( 0.85 to +0.15) Antarctica due to stratospheric loss late in the century. The pattern W m 2, and black carbon (BC) on snow and ice is 0.04 (0.02 to 0.09) and spatial gradients of forcing affect global and regional temperature 8 W m 2. The ERF due to aerosol radiation interactions is 0.45 ( 0.95 to responses as well as other aspects of climate response such as the +0.05) W m 2. A total aerosol cloud interaction5 is quantified in terms hydrologic cycle. {8.6.2, Figure 8.25} of the ERF concept with an estimate of 0.45 ( 1.2 to 0.0) W m 2. The total aerosol effect (excluding BC on snow and ice) is estimated as ERF Natural Forcing of 0.9 ( 1.9 to 0.1) W m 2. The large uncertainty in aerosol ERF is the dominant contributor to overall net Industrial Era forcing uncertain- Satellite observations of total solar irradiance (TSI) changes ty. Since AR4, more aerosol processes have been included in models, from 1978 to 2011 show that the most recent solar cycle min- and differences between models and observations persist, resulting in imum was lower than the prior two. This very likely led to a small similar uncertainty in the aerosol forcing as in AR4. Despite the large negative RF of 0.04 ( 0.08 to 0.00) W m 2 between 1986 and 2008. uncertainty range, there is a high confidence that aerosols have offset The best estimate of RF due to TSI changes representative for the 1750 a substantial portion of WMGHG global mean forcing. {8.3.4, 8.5.1, to 2011 period is 0.05 (to 0.10) W  m 2. This is substantially smaller Figures 8.15, 8.16} than the AR4 estimate due to the addition of the latest solar cycle and inconsistencies in how solar RF has been estimated in earlier IPCC There is robust evidence that anthropogenic land use change assessments. There is very low confidence concerning future solar forc- has increased the land surface albedo, which leads to an RF of ing estimates, but there is high confidence that the TSI RF variations 0.15 +/- 0.10 W m 2. There is still a large spread of estimates owing to will be much smaller than the projected increased forcing due to GHG different assumptions for the albedo of natural and managed surfaces during the forthcoming decades. {8.4.1, Figures 8.10, 8.11} and the fraction of land use changes before 1750. Land use change causes additional modifications that are not radiative, but impact the The RF of volcanic aerosols is well understood and is greatest surface temperature, in particular through the hydrologic cycle. These for a short period (~2 years) following volcanic eruptions. There are more uncertain and they are difficult to quantify, but tend to offset have been no major volcanic eruptions since Mt Pinatubo in 1991, but the impact of albedo changes. As a consequence, there is low agree- several smaller eruptions have caused a RF for the years 2008 2011 of ment on the sign of the net change in global mean temperature as a 0.11 ( 0.15 to 0.08) W m 2 as compared to 1750 and 0.06 ( 0.08 result of land use change. {8.3.5} to 0.04) W m 2 as compared to 1999 2002. Emissions of CO2 from volcanic eruptions since 1750 have been at least 100 times smaller Attributing forcing to emissions provides a more direct link than anthropogenic emissions. {8.4.2, 8.5.2, Figures 8.12, 8.13, 8.18} from human activities to forcing. The RF attributed to methane emissions is very likely6 to be much larger (~1.0 W  m 2) than that There is very high confidence that industrial-era natural forcing attributed to methane concentration increases (~0.5 W m 2) as concen- is a small fraction of the anthropogenic forcing except for brief tration changes result from the partially offsetting impact of emissions periods following large volcanic eruptions. In particular, robust of multiple species and subsequent chemical reactions. In addition, evidence from satellite observations of the solar irradiance and volcan- emissions of CO are virtually certain to have had a positive RF, while ic aerosols demonstrates a near-zero ( 0.1 to +0.1 W m 2) change in emissions of NOX are likely to have had a net negative RF at the global the natural forcing compared to the anthropogenic ERF increase of 1.0 scale. Emissions of ozone-depleting halocarbons are very likely to have (0.7 to 1.3) W m 2 from 1980 to 2011. The natural forcing over the last caused a net positive RF as their own positive RF has outweighed the 15 years has likely offset a substantial fraction (at least 30%) of the negative RF from the stratospheric ozone depletion that they have anthropogenic forcing. {8.5.2; Figures 8.18, 8.19, 8.20} induced. {8.3.3, 8.5.1, Figure 8.17, FAQ 8.2} Future Anthropogenic Forcing and Emission Metrics Forcing agents such as aerosols, ozone and land albedo changes are highly heterogeneous spatially and temporally. These pat- Differences in RF between the emission scenarios considered terns generally track economic development; strong negative aerosol here7 are relatively small for year 2030 but become very large by forcing appeared in eastern North America and Europe during the early 2100 and are dominated by CO2. The scenarios show a substantial ­ 5 The aerosol cloud interaction represents the portion of rapid adjustments to aerosols initiated by aerosol-cloud interactions, and is defined here as the total aerosol ERF minus the ERF due to aerosol-radiation-interactions (the latter includes cloud responses to the aerosol radiation interaction RF) 6 In this Report, the following terms have been used to indicate the assessed likelihood of an outcome or a result: Virtually certain 99 100% probability, Very likely 90 100%, Likely 66 100%, About as likely as not 33 66%, Unlikely 0 33%, Very unlikely 0 10%, Exceptionally unlikely 0 1%. Additional terms (Extremely likely: 95 100%, More likely than not >50 100%, and Extremely unlikely 0 5%) may also be used when appropriate. Assessed likelihood is typeset in italics, e.g., very likely (see Section 1.4 and Box TS.1 for more details). 7 Chapter 1 describes the Representative Concentration Pathways (RCPs) that are the primary scenarios discussed in this report. 662 Anthropogenic and Natural Radiative Forcing Chapter 8 weakening of the negative total aerosol ERF. Nitrate aerosols are an exception to this reduction, with a substantial increase, which is a robust feature among the few available models for these scenarios. The scenarios emphasized in this assessment do not span the range of future emissions in the literature, however, particularly for near-term climate forcers. {8.2.2, 8.5.3, Figures 8.2, 8.21, 8.22} 8 Emission metrics such as Global Warming Potential (GWP) and Global Temperature change Potential (GTP) can be used to quantify and communicate the relative and absolute contribu- tions to climate change of emissions of different substances, and of emissions from regions/countries or sources/sectors. The metric that has been used in policies is the GWP, which integrates the RF of a substance over a chosen time horizon, relative to that of CO2. The GTP is the ratio of change in global mean surface temperature at a chosen point in time from the substance of interest relative to that from CO2. There are significant uncertainties related to both GWP and GTP, and the relative uncertainties are larger for GTP. There are also limitations and inconsistencies related to their treatment of indirect effects and feedbacks. The values are very dependent on metric type and time horizon. The choice of metric and time horizon depends on the particular application and which aspects of climate change are considered relevant in a given context. Metrics do not define policies or goals but facilitate evaluation and implementation of multi-com- ponent policies to meet particular goals. All choices of metric contain implicit value-related judgements such as type of effect considered and weighting of effects over time. This assessment provides updated values of both GWP and GTP for many compounds. {8.7.1, 8.7.2, Table 8.7, Table 8.A.1, Supplementary Material Table 8.SM.16} Forcing and temperature response can also be attributed to sec- tors. From this perspective and with the GTP metric, a single year s worth of current global emissions from the energy and industrial sec- tors have the largest contributions to global mean warming over the next approximately 50 to 100 years. Household fossil fuel and biofuel, biomass burning and on-road transportation are also relatively large contributors to warming over these time scales, while current emis- sions from sectors that emit large amounts of CH4 (animal husbandry, waste/landfills and agriculture) are also important over shorter time horizons (up to 20 years). {8.7.2, Figure 8.34} 663 Chapter 8 Anthropogenic and Natural Radiative Forcing 8.1 Radiative Forcing excluded from this definition of forcing. The assumed relation between a sustained RF and the equilibrium global mean surface temperature There are a variety of ways to examine how various drivers contribute response (DT) is DT = lRF where l is the climate sensitivity parameter. to climate change. In principle, observations of the climate response The relationship between RF and DT is an expression of the energy to a single factor could directly show the impact of that factor, or cli- b ­ alance of the climate system and a simple reminder that the steady- mate models could be used to study the impact of any single factor. state global mean climate response to a given forcing is determined In practice, however, it is usually difficult to find measurements that both by the forcing and the responses inherent in l. 8 are influenced by only a single cause, and it is computationally pro- hibitive to simulate the response to every individual factor of interest. Implicit in the concept of RF is the proposition that the change in net Hence various metrics intermediate between cause and effect are used irradiance in response to the imposed forcing alone can be separat- to provide estimates of the climate impact of individual factors, with ed from all subsequent responses to the forcing. These are not in fact applications both in science and policy. Radiative forcing (RF) is one always clearly separable and thus some ambiguity exists in what may of the most widely used metrics, with most other metrics based on RF. be considered a forcing versus what is part of the climate response. In this chapter, we discuss RF from natural and anthropogenic compo- nents during the industrial period, presenting values for 2011 relative In both the Third Assessment Report (TAR) and AR4, the term radiative to 1750 unless otherwise stated, and projected values through 2100 forcing (RF, also called stratospherically adjusted RF, as distinct from (see also Annex II). In this section, we present the various definitions instantaneous RF) was defined as the change in net irradiance at the of RF used in this chapter, and discuss the utility and limitations of tropopause after allowing for stratospheric temperatures to readjust to RF. These definitions are used in the subsequent sections quantifying radiative equilibrium, while holding surface and tropospheric tempera- the RF due to specific anthropogenic (Section 8.3) and natural (Sec- tures and state variables such as water vapour and cloud cover fixed at tion 8.4) causes and integrating RF due to all causes (Sections 8.5 and the unperturbed values8. RF is generally more indicative of the surface 8.6). Atmospheric chemistry relevant for RF is discussed in Section 8.2 and tropospheric temperature responses than instantaneous RF, espe- and used throughout the chapter. Emission metrics using RF that are cially for agents such as carbon dioxide (CO2) or ozone (O3) change designed to facilitate rapid evaluation and comparison of the climate that substantially alter stratospheric temperatures. To be consistent effects of emissions are discussed in Section 8.7. with TAR and AR4, RF is hereafter taken to mean the stratospherically adjusted RF. 8.1.1 The Radiative Forcing Concept 8.1.1.2 Defining Effective Radiative Forcing RF is the net change in the energy balance of the Earth system due to some imposed perturbation. It is usually expressed in watts per square For many forcing agents the RF gives a very useful and appropriate way meter averaged over a particular period of time and quantifies the to compare the relative importance of their potential climate effect. energy imbalance that occurs when the imposed change takes place. Instantaneous RF or RF is not an accurate indicator of the temper- Though usually difficult to observe, calculated RF provides a simple ature response for all forcing agents, however. Rapid adjustments in quantitative basis for comparing some aspects of the potential climate the troposphere can either enhance or reduce the flux perturbations, response to different imposed agents, especially global mean temper- leading to substantial differences in the forcing driving long-term cli- ature, and hence is widely used in the scientific community. Forcing is mate change. In much the same way that allowing for the relatively often presented as the value due to changes between two particular rapid adjustment of stratospheric temperatures provides a more useful times, such as pre-industrial to present-day, while its time evolution characterization of the forcing due to stratospheric constituent chang- provides a more complete picture. es, inclusion of rapid tropospheric adjustments has the potential to provide more useful characterization for drivers in the troposphere (see 8.1.1.1 Defining Radiative Forcing also Section 7.1.3). Alternative definitions of RF have been developed, each with its own Many of the rapid adjustments affect clouds and are not readily includ- advantages and limitations. The instantaneous RF refers to an instan- ed into the RF concept. For example, for aerosols, especially absorbing taneous change in net (down minus up) radiative flux (shortwave plus ones, changes in the temperature distribution above the surface occur longwave; in W m 2) due to an imposed change. This forcing is usually due to a variety of effects, including cloud response to changing atmos- defined in terms of flux changes at the top of the atmosphere (TOA) pheric stability (Hansen et al., 2005; see Section 7.3.4.2) and cloud or at the climatological tropopause, with the latter being a better indi- absorption effects (Jacobson, 2012), which affect fluxes but are not cator of the global mean surface temperature response in cases when strictly part of RF. Similar adjustments take place for many forcings, they differ. including CO2 (see Section 7.2.5.6). Climate change takes place when the system responds in order to Aerosols also alter cloud properties via microphysical interactions counteract the flux changes, and all such responses are explicitly leading to indirect forcings (referred to as aerosol cloud interactions; 8 Tropospheric variables were fixed except for the impact of aerosols on cloud albedo due to changes in droplet size with constant cloud liquid water which was considered an RF in AR4 but is part of ERF in AR5. 664 Anthropogenic and Natural Radiative Forcing Chapter 8 see Section 7.4). Although these adjustments are complex and not fully the initial ERF (Gregory et al., 2004; Gregory and Webb, 2008). The quantified, they occur both on the microphysical scale of the cloud ERF calculated using the regression technique has an uncertainty of particles as well as on a more macroscopic scale involving whole cloud about 10% (for the 5 to 95% confidence interval) for a single 4 × CO2 systems (e.g., Shine et al., 2003; Penner et al., 2006; Quaas et al., 2009). simulation (ERF ~7 W m 2) due to internal variability in the transient A portion of these adjustments occurs over a short period, on cloud life climate (Andrews et al., 2012a), while given a similar length simulation cycle time scales, and is not part of a feedback arising from the sur- the uncertainty due to internal variability in ERF calculated using the face temperature changes. Previously these type of adjustments were fixed-SST technique is much smaller and hence the latter may be more 8 sometimes termed fast feedbacks (e.g., Gregory et al., 2004; Hansen suitable for very small forcings. Analysis of both techniques shows that et al., 2005), whereas in AR5 they are denoted rapid adjustments to the fixed-SST method yields a smaller spread across models, even in emphasize their distinction from feedbacks involving surface temper- calculations neglecting the uncertainty in the regression fitting proce- ature changes. Atmospheric chemistry responses have typically been dure (Andrews et al., 2012a). As a portion of land area responses are included under the RF framework, and hence could also be included in included in the fixed-SST technique, however, that ERF is slightly less a forcing encompassing rapid adjustments, which is important when than it would be with surface temperature held fixed everywhere. It is evaluating forcing attributable to emissions changes (Section 8.1.2) possible to adjust for this in the global mean forcing, though we do not and in the calculation of emission metrics (Section 8.7). include such a correction here as we examine regional as well as global ERF, but the land response will also introduce artificial gradients in Studies have demonstrated the utility of including rapid adjustment in land sea temperatures that could cause small local climate responses. comparison of forcing agents, especially in allowing quantification of In contrast, there is no global mean temperature response included in forcing due to aerosol-induced changes in clouds (e.g., effects previ- the regression method. Despite the low bias in fixed-SST ERF due to ously denoted as cloud lifetime or semi-direct effects; see Figure 7.3) land responses, results from a multi-model analysis of the forcing due that are not amenable to characterization by RF (e.g., Rotstayn and to CO2 are 7% greater using this method than using the regression Penner, 2001; Shine et al., 2003; Hansen et al., 2005; Lohmann et al., technique (Andrews et al., 2012a) though this is within the uncertainty 2010; Ban-Weiss et al., 2012). Several measures of forcing have been range of the calculations. Although each technique has advantages, introduced that include rapid adjustments. We term a forcing that forcing diagnosed using the fixed-SST method is available for many accounts for rapid adjustments the effective radiative forcing (ERF). more forcing agents in the current generation of climate models than Conceptually, ERF represents the change in net TOA downward radi- forcing diagnosed using the regression method. Hence for practical ative flux after allowing for atmospheric temperatures, water vapour purposes, ERF is hereafter used for results from the fixed-SST technique and clouds to adjust, but with global mean surface temperature or a unless otherwise stated (see also Box 8.1). portion of surface conditions unchanged. The primary methods in use for such calculations are (1) fixing sea surface temperatures (SSTs) and The conceptual relation between instantaneous RF, RF and ERF is illus- sea ice cover at climatological values while allowing all other parts of trated in Figure 8.1. It implies the adjustments to the instantaneous RF the system to respond until reaching steady state (e.g., Hansen et al., involve effects of processes that occur more rapidly than the time scale 2005) or (2) analyzing the transient global mean surface temperature of the response of the global mean surface temperature to the forcing. response to an instantaneous perturbation and using the regression of However, there is no a priori time scale defined for adjustments to be the response extrapolated back to the start of the simulation to derive rapid with the fixed-SST method. The majority take place on time scales Box 8.1 | Definition of Radiative Forcing and Effective Radiative Forcing The two most commonly used measures of radiative forcing in this chapter are the radiative forcing (RF) and the effective radiative forcing (ERF). RF is defined, as it was in AR4, as the change in net downward radiative flux at the tropopause after allowing for strato- spheric temperatures to readjust to radiative equilibrium, while holding surface and tropospheric temperatures and state variables such as water vapor and cloud cover fixed at the unperturbed values. ERF is the change in net TOA downward radiative flux after allowing for atmospheric temperatures, water vapour and clouds to adjust, but with surface temperature or a portion of surface conditions unchanged. Although there are multiple methods to calculate ERF, we take ERF to mean the method in which sea surface temperatures and sea ice cover are fixed at climatological values unless otherwise specified. Land surface properties (temperature, snow and ice cover and vegetation) are allowed to adjust in this method. Hence ERF includes both the effects of the forcing agent itself and the rapid adjustments to that agent (as does RF, though stratospheric tem- perature is the only adjustment for the latter). In the case of aerosols, the rapid adjustments of clouds encompass effects that have been referred to as indirect or semi-direct forcings (see Figure 7.3 and Section 7.5), with some of these same cloud responses also taking place for other forcing agents (see Section 7.2). Calculation of ERF requires longer simulations with more complex models than calculation of RF, but the inclusion of the additional rapid adjustments makes ERF a better indicator of the eventual global mean tem- perature response, especially for aerosols. When forcing is attributed to emissions or used for calculation of emission metrics, additional responses including atmospheric chemistry and the carbon cycle are also included in both RF and ERF (see Section 8.1.2). The general term forcing is used to refer to both RF and ERF. 665 Chapter 8 Anthropogenic and Natural Radiative Forcing Frequently Asked Questions FAQ 8.1 | How Important Is Water Vapour to Climate Change? As the largest contributor to the natural greenhouse effect, water vapour plays an essential role in the Earth s climate. However, the amount of water vapour in the atmosphere is controlled mostly by air temperature, rather 8 than by emissions. For that reason, scientists consider it a feedback agent, rather than a forcing to climate change. Anthropogenic emissions of water vapour through irrigation or power plant cooling have a negligible impact on the global climate. Water vapour is the primary greenhouse gas in the Earth s atmosphere. The contribution of water vapour to the natural greenhouse effect relative to that of carbon dioxide (CO2) depends on the accounting method, but can be considered to be approximately two to three times greater. Additional water vapour is injected into the atmo- sphere from anthropogenic activities, mostly through increased evaporation from irrigated crops, but also through power plant cooling, and marginally through the combustion of fossil fuel. One may therefore question why there is so much focus on CO2, and not on water vapour, as a forcing to climate change. Water vapour behaves differently from CO2 in one fundamental way: it can condense and precipitate. When air with high humidity cools, some of the vapour condenses into water droplets or ice particles and precipitates. The typical residence time of water vapour in the atmosphere is ten days. The flux of water vapour into the atmosphere from anthropogenic sources is considerably less than from natural evaporation. Therefore, it has a negligible impact on overall concentrations, and does not contribute significantly to the long-term greenhouse effect. This is the main reason why tropospheric water vapour (typically below 10 km altitude) is not considered to be an anthro- pogenic gas contributing to radiative forcing. Anthropogenic emissions do have a significant impact on water vapour in the stratosphere, which is the part of the atmosphere above about 10 km. Increased concentrations of methane (CH4) due to human activities lead to an additional source of water, through oxidation, which partly explains the observed changes in that atmospheric layer. That stratospheric water change has a radiative impact, is considered a forcing, and can be evaluated. Strato- spheric concentrations of water have varied significantly in past decades. The full extent of these variations is not well understood and is probably less a forcing  than a feedback process added to natural variability. The T -4 T -2 T 0 0 T +2 T +4 Temperature change 0 0 0 contribution of stratospheric water vapour to warm- ing, both forcing and feedback, is much smaller than 1.4 from CH4 or CO2. 1.2 1.0 The maximum amount of water vapour in the air 0.8 is controlled by temperature. A typical column of 0.6 air extending from the surface to the stratosphere Water vapour in polar regions may contain only a few kilograms of water vapour per square metre, while a simi- lar column of air in the tropics may contain up to 70 kg. With every extra degree of air temperature, the atmosphere can retain around 7% more water vapour (see upper-left insert in the FAQ 8.1, Figure 1). This increase in concentration amplifies the green- house effect, and therefore leads to more warming. This process, referred to as the water vapour feed- back, is well understood and quantified. It occurs in all models used to estimate climate change, where its strength is consistent with observations. Although an increase in atmospheric water vapour has been FAQ 8.1, Figure 1 | Illustration of the water cycle and its interaction with the greenhouse effect. The upper-left insert indicates the relative increase of poten- observed, this change is recognized as a climate feed- tial water vapour content in the air with an increase of temperature (roughly back (from increased atmospheric temperature) and 7% per degree). The white curls illustrate evaporation, which is compensated by should not be interpreted as a radiative forcing from precipitation to close the water budget. The red arrows illustrate the outgoing anthropogenic emissions. (continued on next page) infrared radiation that is partly absorbed by water vapour and other gases, a pro- cess that is one component of the greenhouse effect. The stratospheric processes are not included in this figure. 666 Anthropogenic and Natural Radiative Forcing Chapter 8 FAQ 8.1 (continued) Currently, water vapour has the largest greenhouse effect in the Earth s atmosphere. However, other greenhouse gases, primarily CO2, are necessary to sustain the presence of water vapour in the atmosphere. Indeed, if these other gases were removed from the atmosphere, its temperature would drop sufficiently to induce a decrease of water vapour, leading to a runaway drop of the greenhouse effect that would plunge the Earth into a frozen state. So 8 greenhouse gases other than water vapour provide the temperature structure that sustains current levels of atmo- spheric water vapour. Therefore, although CO2 is the main anthropogenic control knob on climate, water vapour is a strong and fast feedback that amplifies any initial forcing by a typical factor between two and three. Water vapour is not a significant initial forcing, but is nevertheless a fundamental agent of climate change. of seasons or less, but there is a spectrum of adjustment times. Chang- isolate the ERF of small forcings that are easily isolated in the pair of es in land ice and snow cover, for instance, may take place over many radiative transfer calculations performed for RF (Figure 8.1). For RF, on years. The ERF thus represents that part of the instantaneous RF that is the other hand, a definition of the tropopause is required, which can maintained over long time scales and more directly contributes to the be ambiguous. steady-state climate response. The RF can be considered a more limited version of ERF. Because the atmospheric temperature has been allowed In many cases, however, ERF and RF are nearly equal. Analysis of 11 to adjust, ERF would be nearly identical if calculated at the tropopause models from the current Coupled Model Intercomparison Project Phase instead of the TOA for tropospheric forcing agents, as would RF. Recent 5 (CMIP5) generation finds that the rapid adjustments to CO2 cause work has noted likely advantages of the ERF framework for under- fixed-SST-based ERF to be 2% less than RF, with an intermodel stand- standing model responses to CO2 as well as to more complex forcing ard deviation of 7% (Vial et al., 2013). This is consistent with an earlier agents (see Section 7.2.5.6). study of six GCMs that found a substantial inter-model variation in the rapid tropospheric adjustment to CO2 using regression analysis in The climate sensitivity parameter derived with respect to RF can vary slab ocean models, though the ensemble mean adjustment was less substantially across different forcing agents (Forster et al., 2007). The than 5% (Andrews and Forster, 2008). Part of the large uncertainty response to RF from a particular agent relative to the response to RF range arises from the greater noise inherent in regression analyses of from CO2 has been termed the efficacy (Hansen et al., 2005). By includ- single runs in comparison with fixed-SST experiments. Using fixed-SST ing many of the rapid adjustments that differ across forcing agents, simulations, Hansen et al. (2005) found that ERF is virtually identical the ERF concept includes much of their relative efficacy and therefore to RF for increased CO2, tropospheric ozone and solar irradiance, and leads to more uniform climate sensitivity across agents. For example, within 6% for methane (CH4), nitrous oxide (N2O), stratospheric aer- the influence of clouds on the interaction of aerosols with sunlight and osols and for the aerosol radiation interaction of reflective aerosols. the effect of aerosol heating on cloud formation can lead to very large Shindell et al. (2013b) also found that RF and ERF are statistically equal differences in the response per unit RF from black carbon (BC) located for tropospheric ozone. Lohmann et al. (2010) report a small increase at different altitudes, but the response per unit ERF is nearly uniform in the forcing from CO2 using ERF instead of RF based on the fixed-SST with altitude (Hansen et al., 2005; Ming et al., 2010; Ban-Weiss et al., technique, while finding no substantial difference for CH4, RF due to 2012). Hence as we use ERF in this chapter when it differs significantly aerosol radiation interactions or aerosol effects on cloud albedo. In from RF, efficacy is not used hereinafter. For inhomogeneous forcings, the fixed-SST simulations of Hansen et al. (2005), ERF was about 20% we note that the climate sensitivity parameter may also depend on the less than RF for the atmospheric effects of BC aerosols (not including horizontal forcing distribution, especially with latitude (Shindell and microphysical aerosol cloud interactions), and nearly 300% greater Faluvegi, 2009; Section 8.6.2). for the forcing due to BC snow albedo forcing (Hansen et al., 2007). ERF was slightly greater than RF for stratospheric ozone in Hansen A combination of RF and ERF will be used in this chapter with RF pro- et al. (2005), but the opposite is true for more recent analyses (Shin- vided to keep consistency with TAR and AR4, and ERF used to allow dell et al., 2013b), and hence it seems most appropriate at present to quantification of more complex forcing agents and, in some cases, pro- use RF for this small forcing. The various studies demonstrate that RF vide a more useful metric than RF. provides a good estimate of ERF in most cases, as the differences are very small, with the notable exceptions of BC-related forcings (Bond 8.1.1.3 Limitations of Radiative Forcing et al., 2013). ERF provides better characterization of those effects, as well as allowing quantification of a broader range of effects including Both the RF and ERF concepts have strengths and weaknesses in all aerosol cloud interactions. Hence while RF and ERF are generally addition to those discussed previously. Dedicated climate model sim- quite similar for WMGHGs, ERF typically provides a more useful indica- ulations that are required to diagnose the ERF can be more compu- tion of climate response for near-term climate forcers (see Box 8.2). As tationally demanding than those for instantaneous RF or RF because the rapid adjustments included in ERF differ in strength across climate many years are required to reduce the influence of climate variability. models, the uncertainty range for ERF estimates tends to be larger than The presence of meteorological variability can also make it difficult to the range for RF estimates. 667 Chapter 8 Anthropogenic and Natural Radiative Forcing Box 8.2 | Grouping Forcing Compounds by Common Properties As many compounds cause RF when their atmospheric concentration is changed, it can be useful to refer to groups of compounds with similar properties. Here we discuss two primary groupings: well-mixed greenhouse gases (WMGHGs) and near-term climate forcers (NTCFs). 8 We define as well-mixed those greenhouse gases that are sufficiently mixed throughout the troposphere that concentration measure- ments from a few remote surface sites can characterize the climate-relevant atmospheric burden; although these gases may still have local variation near sources and sinks and even small hemispheric gradients. Global forcing per unit emission and emission metrics for these gases thus do not depend on the geographic location of the emission, and forcing calculations can assume even horizontal distributions. These gases, or a subset of them, have sometimes been referred to as long-lived greenhouse gases as they are well mixed because their atmospheric lifetimes are much greater than the time scale of a few years for atmospheric mixing, but the physical property that causes the aforementioned common characteristics is more directly associated with their mixing within the atmosphere. WMGHGs include CO2, N2O, CH4, SF6, and many halogenated species. Conversely, ozone is not a WMGHG. We define near-term climate forcers (NTCFs) as those compounds whose impact on climate occurs primarily within the first decade after their emission. This set of compounds is composed primarily of those with short lifetimes in the atmosphere compared to WMGHGs, and has been sometimes referred to as short-lived climate forcers or short-lived climate pollutants. However, the common property that is of greatest interest to a climate assessment is the time scale over which their impact on climate is felt. This set of compounds includes methane, which is also a WMGHG, as well as ozone and aerosols, or their precursors, and some halogenated species that are not WMGHGs. These compounds do not accumulate in the atmosphere at decadal to centennial time scales, and so their effect on climate is predominantly in the near term following their emission. Whereas the global mean ERF provides a useful indication of the even- temperatures, which can differ greatly from the global mean. Hence tual change in global mean surface temperature, it does not reflect although they are quite useful for understanding the factors driving regional climate changes. This is true for all forcing agents, but is espe- global mean temperature change, they provide only an imperfect and cially the case for the inhomogeneously distributed forcings because limited perspective on the factors driving broader climate change. In they activate climate feedbacks based on their regional distribution. addition, a metric based solely on radiative perturbations does not For example, forcings over Northern Hemisphere (NH) middle and high allow comparison of non-RFs, such as effects of land cover change latitudes induce snow and ice albedo feedbacks more than forcings at on evapotranspiration or physiological impacts of CO2 and O3 except lower latitudes or in the Southern Hemisphere (SH) (e.g., Shindell and where these cause further impacts on radiation such as through cloud Faluvegi, 2009). cover changes (e.g., Andrews et al., 2012b). In the case of agents that strongly absorb incoming solar radiation 8.1.2 Calculation of Radiative Forcing due to (such as BC, and to a lesser extent organic carbon (OC) and ozone) the Concentration or Emission Changes TOA forcing provides little indication of the change in solar radiation reaching the surface which can force local changes in evaporation and Analysis of forcing due to observed or modelled concentration changes alter regional and general circulation patterns (e.g., Ramanathan and between pre-industrial, defined here as 1750, and a chosen later year Carmichael, 2008; Wang et al., 2009). Hence the forcing at the surface, provides an indication of the importance of different forcing agents to or the atmospheric heating, defined as the difference between sur- climate change during that period. Such analyses have been a main- face and tropopause/TOA forcing, might also be useful metrics. Global stay of climate assessments. This perspective has the advantage that mean precipitation changes can be related separately to ERF within observational data are available to accurately quantify the concentra- the atmosphere and to a slower response to global mean temperature tion changes for several of the largest forcing components. Atmospher- changes (Andrews et al., 2010; Ming et al., 2010; Ban-Weiss et al., ic concentration changes, however, are the net result of variations in 2012). Relationships between surface forcing and localized aspects of emissions of multiple compounds and any climate changes that have climate response have not yet been clearly quantified, however. influenced processes such as wet removal, atmospheric chemistry or the carbon cycle. Characterizing forcing according to concentration In general, most widely used definitions of forcing and most forc- changes thus mixes multiple root causes along with climate feedbacks. ing-based metrics are intended to be proportional to the eventual Policy decisions are better informed by analysis of forcing attributable temperature response, and most analyses to date have explored the to emissions, which the IPCC first presented in AR4. These analyses can global mean temperature response only. These metrics do not explic- be applied to historical emissions changes in a backward-looking per- itly include impacts such as changes in precipitation, surface sunlight spective, as done for example, for major WMGHGs (den Elzen et al., available for photosynthesis, extreme events, and so forth, or regional 2005; Hohne et al., 2011) and NTCFs (Shindell et al., 2009), or to current 668 Anthropogenic and Natural Radiative Forcing Chapter 8 a Stratospheric b c d e T adjust Net Flux RF: Net Flux ERF: Net Flux Climatological IRF: Net change at change at Tropopause change at Net Flux = 0 Flux TOA TOA tropopause change Temperature Radiative adjusts forcing tries Temperature Tropospheric Ground everywhere to modify xed temperature temperature xed Ocean 8 original everywhere xed xed temperature T0 Ts Calculation Methodology Online or of ine pair of Difference between Difference between Difference between Difference between radiative transfer two of ine radiative two full atmospheric two full atmospheric two full coupled calculations within one transfer calculations model simulations model simulations atmosphere-ocean simulation with prescribed surface with prescribed with prescribed ocean model simulations and tropospheric surface conditions conditions (SSTs and conditions allowing everywhere or sea ice) stratospheric estimate based on temperature to adjust regression of response in full coupled atmosphere- ocean simulation Figure 8.1 | Cartoon comparing (a) instantaneous RF, (b) RF, which allows stratospheric temperature to adjust, (c) flux change when the surface temperature is fixed over the whole Earth (a method of calculating ERF), (d) the ERF calculated allowing atmospheric and land temperature to adjust while ocean conditions are fixed and (e) the equilibrium response to the climate forcing agent. The methodology for calculation of each type of forcing is also outlined. DTo represents the land temperature response, while DTs is the full surface temperature response. (Updated from Hansen et al., 2005.) or projected future emissions in a forward-looking view (see Section have been included in the various estimates of forcing attributed to 8.7). Emissions estimates through time typically come from the scientific emissions (Sections 8.3 and 8.7). community, often making use of national reporting for recent decades. RF or ERF estimates based on either historical emissions or concen- With the greater use of emission-driven models, for example, in CMIP5, trations provide valuable insight into the relative and absolute con- it is becoming more natural to estimate ERF resulting from emissions tribution of various drivers to historical climate change. Scenarios of of a particular species rather than concentration-based forcing. Such changing future emissions and land use are also developed based on calculations typically necessitate model simulations with chemical various assumptions about socioeconomic trends and societal choices. transport models or chemistry climate models, however, and require The forcing resulting from such scenarios is used to understand the careful consideration of which processes are included, especially when drivers of potential future climate changes (Sections 8.5.3 and 8.6). comparing results to concentration-based forcings. In particular, simu- As with historical forcings, the actual impact on climate depends on lation of concentration responses to emissions changes requires incor- both the temporal and spatial structure of the forcings and the rate of porating models of the carbon cycle and atmospheric chemistry (gas response of various portions of the climate system. and aerosol phases). The requisite expansion of the modelling realm for emissions-based forcing or emission metrics should in principle be con- sistent for all drivers. For example, as the response to aerosol or ozone 8.2 Atmospheric Chemistry precursor emissions includes atmospheric chemistry, the response to CO2 emissions should as well. In addition, if the CO2 concentration 8.2.1 Introduction responses to CO2 emissions include the impact of CO2-induced climate changes on carbon uptake, then the effect of climate changes caused Most radiatively active compounds in the Earth s atmosphere are by any other emission on carbon uptake should also be included. Simi- chemically active, meaning that atmospheric chemistry plays a large larly, if the effects of atmospheric CO2 concentration change on carbon role in determining their burden and residence time. In the atmosphere, uptake are included, the effects of other atmospheric composition or a gaseous chemically active compound can be affected by (1) interac- deposition changes on carbon uptake should be included as well (see tion with other species (including aerosols and water) in its immediate also Section 6.4.1). Comparable issues are present for other forcing vicinity and (2) interaction with solar radiation (photolysis). Physical agents. In practice, the modelling realm used in studies of forcing processes (wet removal and dry deposition) act on some chemical attributable to emissions has not always been consistent. Furthermore, compounds (gas or aerosols) to further define their residence time climate feedbacks have sometimes been included in the calculation in the atmosphere. Atmospheric chemistry is characterized by many of forcing due to ozone or aerosol changes, as when concentrations interactions and patterns of temporal or spatial variability, leading to from a historical transient climate simulation are imposed for an ERF significant nonlinearities (Kleinman et al., 2001) and a wide range of calculation. In this chapter, we endeavour to clarify which processes time scales of importance (Isaksen et al., 2009). 669 Chapter 8 Anthropogenic and Natural Radiative Forcing This section assesses updates in understanding of processes, modelling 8.2.3 Chemical Processes and Trace Gas Budgets and observations since AR4 (see Section 2.3) on key reactive species contributing to RF. Note that aerosols, including processes responsible 8.2.3.1 Tropospheric Ozone for the formation of aerosols, are extensively described in Section 7.3. The RF from tropospheric ozone is strongly height- and latitude-de- 8.2.2 Global Chemistry Modelling in Coupled Model pendent through coupling of ozone change with temperature, water 8 Intercomparison Project Phase 5 vapour and clouds (Lacis et al., 1990; Berntsen et al., 1997; Worden et al., 2008, 2011; Bowman et al., 2013). Consequently, it is necessary Because the distribution of NTCFs cannot be estimated from obser- to accurately estimate the change in the ozone spatio-temporal struc- vations alone, coupled chemistry-climate simulations are required to ture using global models and observations. It is also well established define their evolution and associated RF. While several CMIP5 mode- that surface ozone detrimentally affects plant productivity (Ashmore, ling groups performed simulations with interactive chemistry (i.e., com- 2005; Fishman et al., 2010), albeit estimating this impact on climate, puted simultaneously within the climate model), many models used although possibly significant, is still limited to a few studies (Sitch et as input pre-computed distributions of radiatively active gases and/ al., 2007; UNEP, 2011). or aerosols. To assess the distributions of chemical species and their respective RF, many research groups participated in the Atmospheric Tropospheric ozone is a by-product of the oxidation of carbon monox- Chemistry and Climate Model Intercomparison Project (ACCMIP). ide (CO), CH4, and non-CH4 hydrocarbons in the presence of nitrogen oxides (NOx). As emissions of these precursors have increased (Figure The ACCMIP simulations (Lamarque et al., 2013) were defined to pro- 8.2), tropospheric ozone has increased since pre-industrial times (Volz vide information on the long-term changes in atmospheric composi- and Kley, 1988; Marenco et al., 1994) and over the last decades (Parrish tion with a few, well-defined atmospheric simulations. Because of the et al., 2009; Cooper et al., 2010; Logan et al., 2012), but with important nature of the simulations (pre-industrial, present-day and future cli- regional variations (Section 2.2). Ozone production is usually limited mates), only a limited number of chemistry-transport models (models by the supply of HOx (OH + HO2) and NOX (NO + NO2) (Levy, 1971; which require a full definition of the meteorological fields needed to Logan et al., 1981). Ozone s major chemical loss pathways in the trop- simulate physical processes and transport) participated in the ACCMIP osphere are through (1) photolysis (to O(1D), followed by reaction with project, which instead drew primarily from the same General Circu- water vapour) and (2) reaction with HO2 (Seinfeld and Pandis, 2006). lation Models (GCMs) as CMIP5 (see Lamarque et al., 2013 for a list The former pathway leads to couplings between stratospheric ozone of the participating models and their configurations), with extensive (photolysis rate being a function of the overhead ozone column) and model evaluation against observations (Bowman et al., 2013; Lee et climate change (through water vapour). Observed surface ozone abun- al., 2013; Shindell et al., 2013c; Voulgarakis et al., 2013; Young et al., dances typically range from less than 10 ppb over the tropical Pacific 2013). Ocean to more than 100 ppb downwind of highly emitting regions. The lifetime of ozone in the troposphere varies strongly with season and In all CMIP5/ACCMIP chemistry simulations, anthropogenic and bio- location: it may be as little as a few days in the tropical boundary layer, mass burning emissions are specified. More specifically, a single set of or as much as 1 year in the upper troposphere. Two recent studies give historical anthropogenic and biomass burning emissions (Lamarque et similar global mean lifetime of ozone: 22.3 +/- 2 days (Stevenson et al., al., 2010) and one set of emissions for each of the RCPs (van Vuuren 2006) and 23.4 +/- 2.2 days (Young et al., 2013). et al., 2011) was defined (Figure 8.2). This was designed to increase the comparability of simulations. However, these uniform emission For present (about 2000) conditions, the various components of the specifications mask the existing uncertainty (e.g., Bond et al., 2007; Lu budget of global mean tropospheric ozone are estimated from the et al., 2011), so that there is in fact a considerable range in the esti- ACCMIP simulations and other model simulations since AR4 (Table mates and time evolution of recent anthropogenic emissions (Granier 8.1). In particular, most recent models define a globally and annually et al., 2011). Historical reconstructions of biomass burning (wildfires averaged tropospheric ozone burden of (337 +/- 23 Tg, 1-). Differences and deforestation) also exhibit quite large uncertainties (Kasischke and in the definition of the tropopause lead to inter-model variations of Penner, 2004; Ito and Penner, 2005; Schultz et al., 2008; van der Werf approximately 10% (Wild, 2007). This multi-model mean estimate of et al., 2010). In addition, the RCP biomass burning projections do not global annual tropospheric ozone burden has not significantly changed include the feedback between climate change and fires discussed in since the Stevenson et al. (2006) estimates (344 +/- 39 Tg, 1-), and Bowman et al. (2009), Pechony and Shindell (2010) and Thonicke et al. is consistent with the most recent satellite-based Ozone Monitoring (2010). Finally, the RCP anthropogenic precursor emissions of NTCFs Instrument Microwave Limb Sounder (OMI-MLS; Ziemke et al., 2011) tend to span a smaller range than available from existing scenarios and Tropospheric Emission Spectrometer (TES; Osterman et al., 2008) (van Vuuren et al., 2011). The ACCMIP simulations therefore provide an climatologies. estimate of the uncertainty due to range of representation of physical and chemical processes in models, but do not incorporate uncertainty Estimates of the ozone chemical sources and sinks (uncertainty esti- in emissions. mates are quoted here as 1-) are less robust, with a net chemical production (production minus loss) of 618 +/- 275 Tg yr 1 (Table 8.1), larger than the Atmospheric Composition Change: a European Net- work (ACCENT) results (442 +/- 309 Tg yr 1; Stevenson et al., 2006). Esti- mates of ozone deposition (1094 +/- 264 Tg yr 1) are slightly increased 670 Anthropogenic and Natural Radiative Forcing Chapter 8 Black Carbon CH4 (TgCH4 year-1) CO (TgC year ) -1 (TgCO year-1) Historical SRES A2 RCP2.6 SRES B1 RCP4.5 IS92a RCP6.0 GAINS-CLE RCP8.5 GAINS-MFR 8 NH3 NMVOC NOx (TgNH3 year ) -1 (TgNMVOC year ) -1 (TgNO2 year-1) Organic Carbon SO2 Post-SRES scenarios CH4: Rogelj et al. (2011) (TgC year-1) (TgSO2 year-1) Others: van Vuuren et al. (2008) policy (upper bound) reference (upper bound) policy (lower bound) reference (lower bound) Asia Modeling Exercise (Calvin et al., 2012) 2.6 W m-2 (mean) reference (mean) Figure 8.2 | Time evolution of global anthropogenic and biomass burning emissions 1850 2100 used in CMIP5/ACCMIP following each RCP. Historical (1850 2000) values are from Lamarque et al. (2010). RCP values are from van Vuuren et al. (2011). Emissions estimates from Special Report on Emission Scenarios (SRES) are discussed in Annex II; note that black carbon and organic carbon estimates were not part of the SRES and are shown here only for completeness. The Maximum Feasible Reduction (MFR) and Current Legislation (CLE) are discussed in Cofala et al. (2007); as biomass burning emissions are not included in that publication, a fixed amount, equivalent to the value in 2000 from the RCP esti- mates, is added (see Annex II for more details; Dentener et al., 2006). The post-SRES scenarios are discussed in Van Vuuren et al. (2008) and Rogelj et al. (2011). For those, only the range (minimum to maximum) is shown. Global emissions from the Asian Modelling Exercise are discussed in Calvin et al. (2012). Regional estimates are shown in Supplementary Material Figure 8.SM.1 and Figure 8.SM.2 for the historical and RCPs. since ACCENT (1003 +/- 200 Tg yr 1) while estimates of the net influx of indicate 10 to 20% negative bias at 250 hPa in the SH tropical region, ozone from the stratosphere to the troposphere (477 +/- 96 Tg yr 1) have and a slight underestimate in NH tropical region. Comparison with slightly decreased since ACCENT (552 +/- 168 Tg yr 1). Additional model satellite-based estimates of tropospheric ozone column (Ziemke et al., estimates of this influx (Hegglin and Shepherd, 2009; Hsu and Prather, 2011) indicates an annual mean bias of 4.3 +/- 29 Tg (with a spatial 2009) fall within both ranges, as do estimates based on observations correlation of 0.87 +/- 0.07, 1-) for the ACCMIP simulations (Young et (Murphy and Fahey, 1994; Gettelman et al., 1997; Olsen et al., 2002), al., 2013). Overall, our ability to simulate tropospheric ozone burden all estimates being sensitive to their choice of tropopause definition for present (about 2000) has not substantially changed since AR4. and interannual variability. Evaluation (using a subset of two ACCMIP models) of simulated trends (1960s to present or shorter) in surface ozone against observations at Model simulations for present-day conditions or the recent past are remote surface sites (see Section 2.2) indicates an underestimation, evaluated (Figure 8.3) against frequent ozonesonde measurements especially in the NH (Lamarque et al., 2010). Although this limits the (Logan, 1999; Tilmes et al., 2012) and additional surface, aircraft and ability to represent recent ozone changes, it is unclear how this trans- satellite measurements. The ACCMIP model simulations (Figure 8.3) lates into an uncertainty on changes since pre-industrial times. 671 Chapter 8 Anthropogenic and Natural Radiative Forcing Table 8.1 | Summary of tropospheric ozone global budget model and observation estimates for present (about 2000) conditions. Focus is on modelling studies published since AR4. STE stands for stratosphere troposphere exchange. All uncertainties quoted as 1 standard deviation (68% confidence interval). Burden Production Loss Deposition STE Reference Tg Tg yr 1 Tg yr 1 Tg yr 1 Tg yr 1 Modelling Studies 8 337 +/- 23 4877 +/- 853 4260 +/- 645 1094 +/- 264 477 +/- 96 Young et al. (2013); ACCMIP 323 N/A N/A N/A N/A Archibald et al. (2011) 330 4876 4520 916 560 Kawase et al. (2011) 312 4289 3881 829 421 Huijnen et al. (2010) 334 3826 3373 1286 662 Zeng et al. (2010) 324 4870 4570 801 502 Wild and Palmer (2008) 314 N/A N/A 1035 452 Zeng et al. (2008) 319 4487 3999 N/A 500 Wu et al. (2007) 372 5042 4507 884 345 Horowitz (2006) 349 4384 3972 808 401 Liao et al. (2006) 344 +/- 39 5110 +/- 606 4668 +/- 727 1003 +/- 200 552 +/- 168 Stevenson et al. (2006); ACCENT 314 +/- 33 4465 +/- 514 4114 +/- 409 949 +/- 222 529 +/- 105 Wild (2007) (post-2000 studies) N/A N/A N/A N/A 515 Hsu and Prather (2009) N/A N/A N/A N/A 655 Hegglin and Shepherd (2009) N/A N/A N/A N/A 383 451 Clark et al. (2007) Observational Studies 333 N/A N/A N/A N/A Fortuin and Kelder (1998) 327 N/A N/A N/A N/A Logan (1999) 325 N/A N/A N/A N/A Ziemke et al. (2011); 60S 60N 319 351 N/A N/A N/A N/A Osterman et al. (2008); 60S 60N N/A N/A N/A N/A 449 (192 872) Murphy and Fahey (1994) N/A N/A N/A N/A 510 (450 590) Gettelman et al. (1997) N/A N/A N/A N/A 500 +/- 140 Olsen et al. (2001) In most studies pre-industrial does not identify a specific year but is (Stevenson et al., 2013) indicate unequivocally that anthropogenic usually assumed to correspond to 1850s levels; no observational infor- changes in ozone precursor emissions are responsible for the increase mation on ozone is available for that time period. Using the Lamarque between 1850 and present or into the future. et al. (2010) emissions, the ACCMIP models (Young et al., 2013) are unable to reproduce the low levels of ozone observed at Montsouris 8.2.3.2 Stratospheric Ozone and Water Vapour 1876 1886 (Volz and Kley, 1988). The other early ozone measurements using the Schönbein paper are controversial (Marenco et al., 1994) Stratospheric ozone has experienced significant depletion since the and assessed to be of qualitative use only. The main uncertainty in 1960s due to bromine and chlorine-containing compounds (Solomon, estimating the pre-industrial to present-day change in ozone there- 1999), leading to an estimated global decrease of stratospheric ozone fore remains the lack of constraint on emission trends because of the of 5% between the 1970s and the mid-1990s, the decrease being very incomplete knowledge of pre-industrial ozone concentrations, of largest over Antarctica (Fioletov et al., 2002). Most of the ozone loss which no new information is available. The uncertainty on pre-indus- is associated with the long-lived bromine and chlorine-containing trial conditions is not confined to ozone but applies to aerosols as well compounds (chlorofluorocarbons and substitutes) released by human (e.g., Schmidt et al., 2012), although ice and lake core records provide activities, in addition to N2O. This is in addition to a background level some constraint on pre-industrial aerosol concentrations. of natural emissions of short-lived halogens from oceanic and volcanic sources. The ACCMIP results provide an estimated tropospheric ozone increase (Figure 8.4) from 1850 to 2000 of 98 +/- 17 Tg (model range), similar With the advent of the Montreal Protocol and its amendments, emis- to AR4 estimates. Skeie et al. (2011a) found an additional 5% increase sions of chlorofluorocarbons (CFCs) and replacements have strongly in the anthropogenic contribution to the ozone burden between 2000 declined (Montzka et al., 2011), and signs of ozone stabilization and and 2010, which translates into an approximately 1.5% increase in even possibly recovery have already occurred (Mader et al., 2010; Salby tropospheric ozone burden. A best estimate of the change in ozone et al., 2012). A further consequence is that N2O emissions (Section since 1850 is assessed at 100 +/- 25 Tg (1-). Attribution simulations 8.2.3.4) likely dominate all other emissions in terms of ozone-depleting 672 Anthropogenic and Natural Radiative Forcing Chapter 8 100 r = 0.90, 0.96 r = 0.61, 0.45 mnbe = 12.2%, 3.0% mnbe = 1.1%, 19.3% 80 250 hPa 80 60 60 40 40 Ozonesondes 8 ACCMIP mean ACCMIP models 20 20 ACCENT mean 100 100 100 J F M A M J J A S O N D J J F M A M J J A S O N D J r = 0.95, 0.97 r = 0.94, 0.91 r = 0.71, 0.59 mnbe = 2.2%, 10.9% mnbe = 10.0%, 1.5% mnbe = 5.0%, 15.0% 80 80 80 80 500 hPa Ozone volume mixing ratio (ppb) 60 60 60 60 40 40 40 40 r = 0.99, 0.84 20 20 20 20 mnbe = 12.8%, 9.9% 100 100 100 J F M A M J J A S O N D J J F M A M J J A S O N D J J F M A M J J A S O N D J J F M A M J J A S O N D J r = 0.97, 0.98 r = 0.97, 0.89 r = 0.89, 0.81 r = 0.95, 0.84 mnbe = 2.6%, 8.5% mnbe = 8.6%, 3.1% mnbe = 7.8%, 10.8% mnbe = 13.2%, 3.0% 80 80 80 80 750 hPa 60 60 60 60 40 40 40 40 20 20 20 20 J FMAMJ J ASOND J J FMAMJ J ASOND J J FMAMJ J ASOND J J FMAMJ J ASOND J 90°S - 30°S 30°S - EQ EQ - 30°N 30°N - 90°N Figure 8.3 | Comparisons between observations and simulations for the monthly mean ozone for ACCMIP results (Young et al., 2013). ACCENT refers to the model results in Stevenson et al. (2006). For each box, the correlation of the seasonal cycle is indicated by the r value, while the mean normalized bias estimated is indicated by mnbe value. Modeled Past Observations Modeled Projections 600 Historical RCP2.6 RCP4.5 RCP6.0 RCP8.5 500 Osterman et al. ACCENT 400 Logan Burden (Tg) 2000 mean 300 Fortuin and Kelder Ziemke et al. 200 100 0 1850 1930 1980 2000 2000 2030 2100 2030 2100 2030 2100 2030 2100 Figure 8.4 | Time evolution of global tropospheric ozone burden (in Tg(O3)) from 1850 to 2100 from ACCMIP results, ACCENT results (2000 only), and observational estimates (see Table 8.1). The box, whiskers, line and dot show the interquartile range, full range, median and mean burdens and differences, respectively. The dashed line indicates the 2000 ACCMIP mean. (Adapted from Young et al., 2013.) 673 Chapter 8 Anthropogenic and Natural Radiative Forcing potential (Ravishankara et al., 2009). Chemistry-climate models with other main source of OH is through secondary reactions (Lelieveld et resolved stratospheric chemistry and dynamics recently predicted an al., 2008), although some of those reactions are still poorly understood estimated global mean total ozone column recovery to 1980 levels to (Paulot et al., 2009; Peeters et al., 2009; Taraborrelli et al., 2012). A occur in 2032 (multi-model mean value, with a range of 2024 to 2042) recent estimate of the CH4 tropospheric chemical lifetime with respect under the A1B scenario (Eyring et al., 2010a). Increases in the strato- to OH constrained by methyl chloroform surface observations is 11.2 spheric burden and acceleration of the stratospheric circulation leads +/- 1.3 years (Prather et al., 2012). In addition, bacterial uptake in soils to an increase in the stratosphere troposphere flux of ozone (Shindell provides an additional small, less constrained loss (Fung et al., 1991); 8 et al., 2006c; Grewe, 2007; Hegglin and Shepherd, 2009; Zeng et al., estimated lifetime = 120 +/- 24 years (Prather et al., 2012), with another 2010). This is also seen in recent RCP8.5 simulations, with the impact small loss in the stratosphere (Ehhalt and Heidt, 1973); estimated life- of increasing tropospheric burden (Kawase et al., 2011; Lamarque et time = 150 +/- 50 years (Prather et al., 2012). Halogen chemistry in the al., 2011). However, observationally based estimates of recent trends troposphere also contributes to some tropospheric CH4 loss (Allan et in age of air (Engel et al., 2009; Stiller et al., 2012) do not appear al., 2007), estimated lifetime = 200 +/- 100 years (Prather et al., 2012). to be consistent with the acceleration of the stratospheric circulation found in model simulations, possibly owing to inherent difficulties with The ACCMIP estimate for present CH4 lifetime with respect to trop- extracting trends from SF6 observations (Garcia et al., 2011). ospheric OH varies quite widely (9.8 +/- 1.6 years (Voulgarakis et al., 2013)), slightly shorter than the 10.2 +/- 1.7 years in (Fiore et al. (2009), Oxidation of CH4 in the stratosphere (see Section 8.2.3.3) is a signifi- but much shorter than the methyl chloroform-based estimate of 11.2 cant source of water vapour and hence the long-term increase in CH4 +/- 1.3 years (Prather et al., 2012). A partial explanation for the range in leads to an anthropogenic forcing (see Section 8.3) in the stratosphere. CH4 lifetime changes can be found in the degree of representation of Stratospheric water vapour abundance increased by an average of 1.0 chemistry in chemistry climate models. Indeed, Archibald et al. (2010) +/- 0.2 (1-) ppm during 1980 2010, with CH4 oxidation explaining showed that the response of OH to increasing nitrogen oxides strongly approximately 25% of this increase (Hurst et al., 2011). Other factors depends on the treatment of hydrocarbon chemistry in a model. The contributing to the long-term change in water vapour include changes impact on CH4 distribution in the ACCMIP simulations is, however, in tropical tropopause temperatures (see Section 2.2.2.1). rather limited because most models prescribed CH4 as a time-varying lower-boundary mixing ratio (Lamarque et al., 2013). 8.2.3.3 Methane The chemical coupling between OH and CH4 leads to a significant The surface mixing ratio of CH4 has increased by 150% since pre-indus- amplification of an emission impact; that is, increasing CH4 emissions trial times (Sections 2.2.1.1.2 and 8.3.2.2), with some projections indi- decreases tropospheric OH which in turn increases the CH4 lifetime cating a further doubling by 2100 (Figure 8.5). Bottom-up estimates of and therefore its burden. The OH-lifetime sensitivity for CH4, s_OH = present CH4 emissions range from 542 to 852 TgCH4 yr 1 (see Table 6.8), ln(OH)/ln(CH4), was estimated in Chapter 4 of TAR to be 0.32, while a recent top-down estimate with uncertainty analysis is 554 +/- implying a 0.32% decrease in tropospheric mean OH (as weighted by 56 TgCH4 yr 1 (Prather et al., 2012). All quoted uncertainties in Section CH4 loss) for a 1% increase in CH4. The Fiore et al. (2009) multi-mod- 8.2.3.3 are defined as 1-. el (12 models) study provides a slightly smaller value (0.28 +/- 0.03). Holmes et al. (2013) gives a range 0.31 +/- 0.04 by combining Fiore et al. The main sink of CH4 is through its reaction with the hydroxyl radical (2009), Holmes et al. (2011) and three new model results (0.36, 0.31, (OH) in the troposphere (Ehhalt and Heidt, 1973). A primary source 0.27). Only two ACCMIP models reported values (0.19 and 0.26; Voul- of tropospheric OH is initiated by the photodissociation of ozone, fol- garakis et al., 2013). The projections of future CH4 in Chapter 11 use lowed by reaction with water vapour (creating sensitivity to humid- the Holmes et al. (2013) range and uncertainty, which at the 2- level ity, cloud cover and solar radiation) (Levy, 1971; Crutzen, 1973). The covers all but one model result. The feedback factor f, the ratio of the CH4 (ppm) CO2 (ppm) N2O (ppm) SRES A2 SRES B1 IS92a Historical RCP2.6 RCP4.5 RCP6.0 RCP8.5 Figure 8.5 | Time evolution of global-averaged mixing ratio of long-lived species1850 2100 following each RCP; blue (RCP2.6), light blue (RCP4.5), orange (RCP6.0) and red (RCP8.5). (Based on Meinshausen et al., 2011b.) 674 Anthropogenic and Natural Radiative Forcing Chapter 8 lifetime of a CH4 perturbation to the lifetime of the total CH4 burden, is of non-methane hydrocarbons (and their products) with the hydroxyl calculated as f = 1/(1-s). Other CH4 losses, which are relatively insensi- radical (OH), ozone, nitrate (NO3) or photolysis (Hallquist et al., 2009). tive to CH4 burden, must be included so that f = 1.34 +/- 0.06, (slightly Thus although many hydrocarbons in the atmosphere are of biogenic larger but within the range of the Stevenson et al. (2006) estimate of origin, anthropogenic pollutants can have impacts on their conversion 1.29 +/- 0.04, based on six models), leading to an overall perturbation to SOAs. There is tremendous complexity and still much uncertainty in lifetime of 12.4 +/- 1.4 years, which is used in calculations of metrics the processes involved in the formation of SOAs (Hallquist et al., 2009; in Section 8.7. Additional details are provided in the Supplementary Carslaw et al., 2010). Additional information can be found in Section 8 Material Section 8.SM.2. 7.3.2. 8.2.3.4 Nitrous Oxide Once generated, the size and composition of aerosol particles can be modified by additional chemical reactions, condensation or evapora- Nitrous oxide (N2O) in 2011 has a surface concentration 19% above tion of gaseous species and coagulation (Seinfeld and Pandis, 2006). its 1750 level (Sections 2.2.1.1.3 and 8.3.2.3). Increases in N2O lead to It is this set of processes that defines their physical, chemical and opti- depletion of mid- to upper-stratospheric ozone and increase in mid-lat- cal properties, and hence their impact on radiation and clouds, with itude lower stratospheric ozone (as a result of increased photolysis large regional and global differences (see Section 7.3.3). Furthermore, rate from decreased ozone above). This impacts tropospheric chemistry their distribution is affected by transport and deposition, defining a through increase in stratosphere troposphere exchange of ozone and residence time in the troposphere of usually a few days (Textor et al., odd nitrogen species and increase in tropospheric photolysis rates and 2006). OH formation (Prather and Hsu, 2010). Anthropogenic emissions repre- sent around 30 to 45% of the present-day global total, and are mostly from agricultural and soil sources (Fowler et al., 2009) and fossil-fuel 8.3 Present-Day Anthropogenic Radiative activities. Natural emissions come mostly from microbial activity in the Forcing soil. The main sink for N2O is through photolysis and oxidation reac- tions in the stratosphere, leading to an estimated lifetime of 131 +/- Human activity has caused a variety of changes in different forcing 10 years (Prather et al., 2012), slightly larger than previous estimates agents in the atmosphere or land surface. A large number of GHGs (Prather and Hsu, 2010; Montzka et al., 2011). The addition of N2O have had a substantial increase over the Industrial Era and some of to the atmosphere changes its own lifetime through feedbacks that these gases are entirely of anthropogenic origin. Atmospheric aerosols couple N2O to stratospheric NOy and ozone depletion (Prather, 1998; have diverse and complex influences on the climate. Human activity Ravishankara et al., 2009; Prather and Hsu, 2010), so that the lifetime has modified the land cover and changed the surface albedo. Some of of a perturbation is less than that of the total burden, 121 +/- 10 years the gases and aerosols are directly emitted to the atmosphere where- (1-; Prather et al., 2012) and is used in calculations of metrics (Sec- as others are secondary products from chemical reactions of emitted tion 8.7). species. The lifetimes of these different forcing agents vary substan- tially. This section discusses all known anthropogenic forcing agents 8.2.3.5 Halogenated Species of non-negligible importance and their quantification in terms of RF or ERF based on changes in abundance over the 1750 2011 period. Halogenated species can be powerful greenhouse gases (GHGs). Those containing chlorine and bromine also deplete stratospheric ozone In this section we determine the RFs for WMGHGs and heterogene- and are referred to as ozone-depleting substances (ODSs). Most of ously distributed species in fundamentally different ways. As described those compounds do not have natural emissions and, because of the in Box 8.2, the concentrations of WMGHGs can be determined from implementation of the Montreal Protocol and its amendments, total observations at a few surface sites. For the pre-industrial concentra- emissions of ODSs have sharply decreased since the 1990s (Montzka tions these are typically from trapped air in polar ice or firn (see Sec- et al., 2011). For CFCs, perfluorocarbons (PFCs) and SF6 the main loss tion 2.2.1). Thus the RFs from WMGHGs are determined entirely from is through photolysis in the stratosphere. The CFC substitutes (hydro- observations (Section 8.3.2). In contrast, we do not have sufficient chlorofluorocarbons (HCFCs) and hydrofluorocarbons (HFCs)) are pre-industrial or present-day observations of heterogeneously distrib- destroyed by OH oxidation in the troposphere. Their global concen- uted forcing agents (e.g., ozone and aerosols) to be able to character- tration has steadily risen over the recent past (see Section 2.2.1.1.4). ize their RF; therefore we instead have to rely on chemistry climate models (Sections 8.3.3 and 8.3.4). 8.2.3.6 Aerosols 8.3.1 Updated Understanding of the Spectral Properties Aerosol particles are present in the atmosphere with size ranges of Greenhouse Gases and Radiative Transfer Codes from a few nanometres to tens of micrometres. They are the results of direct emission (primary aerosols: BC, OC, sea salt, dust) into the RF estimates are performed with a combination of radiative transfer atmosphere or as products of chemical reactions (secondary inorganic codes typical for GCMs as well as more detailed radiative transfer aerosols: sulphate, nitrate, ammonium; and secondary organic aero- codes. Physical properties are needed in the radiative transfer codes sols (SOAs)) occurring in the atmosphere. Secondary inorganic aero- such as spectral properties for gases. The HITRAN (HIgh Resolution sols are the products of reactions involving sulphur dioxide, ammonia TRANsmission molecular absorption) database (Rothman, 2010) is and nitric oxide emissions. SOAs are the result of chemical reactions widely used in radiative transfer models. Some researchers studied 675 Chapter 8 Anthropogenic and Natural Radiative Forcing the difference among different editions of HITRAN databases for t ­ransfer codes compared and validated against LBL models, and the diverse uses (Feng et al., 2007; Kratz, 2008; Feng and Zhao, 2009; uncertainty range from AR4 in the RF of GHG of 10% is retained. We Fomin and Falaleeva, 2009; Lu et al., 2012). Model calculations have underscore that uncertainty in RF calculations in many GCMs is sub- shown that modifications of the spectroscopic characteristics tend to stantially higher owing both to radiative transfer codes and meteoro- have a modest effect on the determination of RF estimates of order logical data such as clouds adopted in the simulations. 2 to 3% of the calculated RF attributed to the combined doubling of 8 CO2, N2O and CH4. These results showed that even the largest overall 8.3.2 Well-mixed Greenhouse Gases RF induced by differences among the HITRAN databases is consider- ably smaller than the range reported for the modelled RF estimates; AR4 assessed the RF from 1750 to 2005 of the WMGHGs to be 2.63 thus the line parameter updates to the HITRAN database are not a W m 2. The four most important gases were CO2, CH4, dichlorodifluo- significant source for discrepancies in the RF calculations appearing romethane (CFC-12) and N2O in that order. Halocarbons, comprising in the IPCC reports. However, the more recent HITRAN data set is CFCs, HCFCs, HFCs, PFCs and SF6, contributed 0.337 W m 2 to the total. still recommended, as the HITRAN process offers internal verification Uncertainties (90% confidence ranges) were assessed to be approxi- and tends to progress closer to the best laboratory measurements. mately 10% for the WMGHGs. The major changes to the science since It is found that the differences among the water vapour continuum AR4 are the updating of the atmospheric concentrations, the inclusion absorption formulations tend to be comparable to the differences of new species (NF3 and SO2F2) and discussion of ERF for CO2. Since among the various HITRAN databases (Paynter and Ramaswamy, AR4 N2O has overtaken CFC-12 as the third largest contributor to RF. 2011); but use of the older Robert continuum formula produces signif- The total WMGHG RF is now 2.83 (2.54 to 3.12) W m 2. icantly larger flux differences, thus, replacement of the older continu- um is warranted (Kratz, 2008) and there are still numerous unresolved The RFs in this section are derived from the observed differences in issues left in the continuum expression, especially related to short- concentrations of the WMGHGs between 1750 and 2011. The con- wave radiative transfer (Shine et al., 2012). Differences in absorption centrations of CO2, CH4 and N2O vary throughout the pre-industrial data from various HITRAN versions are very likely a small contributor era, mostly due to varying climate, with a possible small contribution to the uncertainty in RF of GHGs. from anthropogenic emissions (MacFarling Meure et al., 2006). These variations do not contribute to uncertainty in the RF as strictly defined Line-by-line (LBL) models using the HITRAN data set as an input are here, but do affect the RF attribution to anthropogenic emissions. On the benchmark of radiative transfer models for GHGs. Some research- centennial time scales, variations in late Holocene concentrations of ers compared different LBL models (Zhang et al., 2005; Collins et al., CO2 are around 10 ppm (see note to Table 2.1), much larger than 2006) and line-wing cutoff, line-shape function and gas continuum the uncertainty in the 1750 concentration. This would equate to a absorption treatment effects on LBL calculations (Zhang et al., 2008; variation in the RF of 10%. For CH4 and N2O the centennial variations Fomin and Falaleeva, 2009). The agreement between LBL codes has are comparable to the uncertainties in the 1750 concentrations and been investigated in many studies and found to generally be within so do not significantly affect the estimate of the 1750 value used in a few percent (e.g., Collins et al., 2006; Iacono et al., 2008; Forster et calculating RF. al., 2011a) and to compare well to observed radiative fluxes under controlled situations (Oreopoulos et al., 2012). Forster et al. (2011a) 8.3.2.1 Carbon Dioxide evaluated global mean radiatively important properties of chemistry climate models (CCMs) and found that the combined WMGHG global The tropospheric mixing ratio of CO2 has increased globally from 278 annual mean instantaneous RF at the tropopause is within 30% of LBL (276 280) ppm in 1750 to 390.5 (390.3 to 390.7) ppm in 2011 (see models for all CCM radiation codes tested. The accuracies of the LW RF Section 2.2.1.1.1). Here we assess the RF due to changes in atmos- due to CO2 and tropospheric ozone increase are generally very good pheric concentration rather than attributing it to anthropogenic emis- and within 10% for most of the participation models, but problems sions. Section 6.3.2.6 describes how only a fraction of the historical remained in simulating RF for stratospheric water vapour and ozone CO2 emissions have remained in the atmosphere. The impact of land changes with errors between 3% and 200% compared to LBL models. use change on CO2 from 1850 to 2000 was assessed in AR4 to be 12 to Whereas the differences in the results from CCM radiation codes were 35 ppm (0.17 to 0.51 W m 2). large, the agreement among the LW LBL codes was within 5%, except for stratospheric water vapour changes. Using the formula from Table 3 of Myhre et al. (1998), and see Supple- mentary Material Table 8.SM.1, the CO2 RF (as defined in Section 8.1) Most intercomparison studies of the RF of GHGs are for clear-sky and from 1750 to 2011 is 1.82 (1.63 to 2.01) W m 2. The uncertainty is dom- aerosol-free conditions; the introduction of clouds would greatly com- inated by the radiative transfer modelling which is assessed to be 10% plicate the targets of research and are usually omitted in the intercom- (Section 8.3.1). The uncertainty in the knowledge of 1750 concentra- parison exercises of GCM radiation codes and LBL codes (e.g., Collins tions contributes only 2% (see Supplementary Material Table 8.SM.2) et al., 2006; Iacono et al., 2008). It is shown that clouds can reduce the magnitude of RF due to GHGs by about 25% (Forster et al., 2005; Table 8.2 shows the concentrations and RF in AR4 (2005) and 2011 for Worden et al., 2011; Zhang et al., 2011), but the influence of clouds the most important WMGHGs. Figure 8.6 shows the time evolution of on the diversity in RF is found to be within 5% in four detailed radi- RF and its rate of change. Since AR4, the RF of CO2 has increased by ative transfer schemes with realistic cloud distributions (Forster et al., 0.16 W m 2 and continues the rate noted in AR4 of almost 0.3 W m 2 2005). Estimates of GHG RF are based on the LBL codes or the radiative ­ per decade. As shown in Figure 8.6(d) the rate of increase in the RF 676 Anthropogenic and Natural Radiative Forcing Chapter 8 from the WMGHGs over the last 15 years has been dominated by CO2. As described in Section 8.1.1.3, CO2 can also affect climate through Since AR4, CO2 has accounted for more than 80% of the WMGHG RF physical effects on lapse rates and clouds, leading to an ERF that will increase. The interannual variability in the rate of increase in the CO2 be different from the RF. Analysis of CMIP5 models (Vial et al., 2013) RF is due largely to variation in the natural land uptake whereas the found a large negative contribution to the ERF (20%) from the increase trend is driven by increasing anthropogenic emissions (see Figure 6.8 in land surface temperatures which was compensated for by positive in Section 6.3.1). contributions from the combined effects on water vapour, lapse rate, albedo and clouds. It is therefore not possible to conclude with the 8 current information whether the ERF for CO2 is higher or lower than the RF. Therefore we assess the ratio ERF/RF to be 1.0 and assess our uncertainty in the CO2 ERF to be ( 20% to 20%). We have medium confidence in this based on our understanding that the physical pro- cesses responsible for the differences between ERF and RF are small enough to be covered within the 20% uncertainty. There are additional effects mediated through plant physiology, reduc- ing the conductance of the plant stomata and hence the transpiration of water. Andrews et al. (2012b) find a physiological enhancement of the adjusted forcing by 3.5% due mainly to reductions in low cloud. This is smaller than a study with an earlier model by Doutriaux-Bouch- er et al. (2009) which found a 10% effect. Longer-term impacts of CO2 on vegetation distributions also affect climate (O ishi et al., 2009; Andrews et al., 2012b) but because of the longer time scale we choose to class these as feedbacks rather than rapid adjustments. 8.3.2.2 Methane Globally averaged surface CH4 concentrations have risen from 722 +/- 25 ppb in 1750 to 1803 +/- 2 ppb by 2011 (see Section 2.2.1.1.2). Over that time scale the rise has been due predominantly to changes in anthropogenic-related CH4. Anthropogenic emissions of other com- pounds have also affected CH4 concentrations by changing its remov- al rate (Section 8.2.3.3). Using the formula from Myhre et al. (1998) (see Supplementary Material Table 8.SM.1) the RF for CH4 from 1750 to 2011 is 0.48 +/- 0.05 W m 2, with an uncertainty dominated by the radiative transfer calculation. This increase of 0.01 W m 2 since AR4 is due to the 29 ppb increase in the CH4 mixing ratio. This is much larger than the 11 ppb increase between TAR and AR4, and has been driven by increases in net natural and anthropogenic emissions, but the rel- ative contributions are not well quantified. Recent trends in CH4 and their causes are discussed in Sections 2.2.1.1.2 and 6.3.3.1. CH4 con- centrations do vary with latitude and decrease above the tropopause; however, this variation contributes only 2% to the uncertainty in RF (Freckleton et al., 1998). In this section only the direct forcing from changing CH4 concentrations is addressed. CH4 emissions can also have indirect effects on climate through impacts on CO2, stratospheric water vapour, ozone, sulphate aerosol and lifetimes of HFCs and HCFCs (Boucher et al., 2009; Shindell et al., 2009; Collins et al., 2010). Some of these are discussed further in Sections 8.3.3, 8.5.1 and 8.7.2. 8.3.2.3 Nitrous Oxide Figure 8.6 | (a) Radiative forcing (RF) from the major well-mixed greenhouse gases (WMGHGs) and groups of halocarbons from 1850 to 2011 (data from Tables A.II.1.1 Concentrations of nitrous oxide have risen from 270 +/- 7 ppb in 1750 to and A.II.4.16), (b) as (a) but with a logarithmic scale, (c) RF from the minor WMGHGs 324.2 +/- 0.1 ppb in 2011, an increase of 5 ppb since 2005 (see Section from 1850 to 2011 (logarithmic scale). (d) Rate of change in forcing from the major WMGHGs and groups of halocarbons from 1850 to 2011. 2.2.1.1.3). N2O now has the third largest forcing of the anthropogenic gases, at 0.17 +/- 0.03 W m 2 an increase of 6% since 2005 (see Table 677 Chapter 8 Anthropogenic and Natural Radiative Forcing 8.2) where the uncertainty is due approximately equally to the pre-in- of WMGHG RF is shown in Figure 8.6 (d). Between 1970 and 1990 dustrial concentration and radiative transfer. Only the direct RF from halocarbons made a significant contribution to the rate of change of changing nitrous oxide concentrations is included. Indirect effects of RF. The rate of change in the total WMGHG RF was higher in 1970 to N2O emissions on stratospheric ozone are not taken into account here 1990 with high confidence compared to the present owing to higher but are discussed briefly in Section 8.7.2. contribution from non-CO2 gases especially the halocarbons. Since the Montreal Protocol and its amendments, the rate of change of RF from 8 8.3.2.4 Other Well-mixed Greenhouse Gases halocarbons and related compounds has been much less, but still just positive (total RF of 0.360 W m 2 in 2011 compared to 0.351 W m 2 in RFs of the other WMGHG are shown in Figure 8.6 (b and c) and Table 2005) as the growth of HCFCs, HFCs, PFCs and other halogens (SF6, 8.2. The contribution of groups of halocarbons to the rate of change SO2F2, NF3) RFs (total 0.022 W m 2 since 2005) more than compensates Table 8.2 | Present-day mole fractions (in ppt(pmol mol 1) except where specified) and RF (in W m 2) for the WMGHGs. Concentration data are averages of National Oceanic and Atmospheric Administration (NOAA) and Advanced Global Atmospheric Gases Experiment (AGAGE) observations where available. CO2 concentrations are the average of NOAA and SIO. See Table 2.1 for more details of the data sources. The data for 2005 (the time of the AR4 estimates) are also shown. Some of the concentrations vary slightly from those reported in AR4 owing to averaging different data sources. Radiative efficiencies for the minor gases are given in Table 8.A.1. Uncertainties in the RF for all gases are dominated by the uncertainties in the radiative efficiencies. We assume the uncertainties in the radiative efficiencies to be perfectly correlated between the gases, and the uncertainties in the present day and 1750 concentrations to be uncorrelated. Concentrations (ppt) Radiative forcinga (W m 2) Species 2011 2005 2011 2005 CO2 (ppm) 391 +/- 0.2 379 1.82 +/- 0.19 1.66 CH4 (ppb) 1803 +/- 2 1774 0.48 +/- 0.05 0.47e N2O (ppb) 324 +/- 0.1 319 0.17 +/- 0.03 0.16 CFC-11 238 +/- 0.8 251 0.062 0.065 CFC-12 528 +/- 1 542 0.17 0.17 CFC-13 2.7 0.0007 CFC-113 74.3 +/- 0.1 78.6 0.022 0.024 CFC-115 8.37 8.36 0.0017 0.0017 HCFC-22 213 +/- 0.1 169 0.0447 0.0355 HCFC-141b 21.4 +/- 0.1 17.7 0.0034 0.0028 HCFC-142b 21.2 +/- 0.2 15.5 0.0040 0.0029 HFC-23 24.0 +/- 0.3 18.8 0.0043 0.0034 HFC-32 4.92 1.15 0.0005 0.0001 HFC-125 9.58 +/- 0.04 3.69 0.0022 0.0008 HFC-134a 62.7 +/- 0.3 34.3 0.0100 0.0055 HFC-143a 12.0 +/- 0.1 5.6 0.0019 0.0009 HFC-152a 6.4 +/- 0.1 3.4 0.0006 0.0003 SF6 7.28 +/- 0.03 5.64 0.0041 0.0032 SO2F2 1.71 1.35 0.0003 0.0003 NF3 0.9 0.4 0.0002 0.0001 CF4 79.0 +/- 0.1 75.0 0.0040 0.0036 C2F6 4.16 +/- 0.02 3.66 0.0010 0.0009 CH3CCl3 6.32 +/- 0.07 18.32 0.0004 0.0013 CCl4 85.8 +/- 0.8 93.1 0.0146 0.0158 CFCs 0.263 +/- 0.026b 0.273c HCFCs 0.052 +/- 0.005 0.041 Montreal gases d 0.330 +/- 0.033 0.331 Total halogens 0.360 +/- 0.036 0.351f Total 2.83 +/- 0.029 2.64 Notes: a Pre-industrial values are zero except for CO2 (278 ppm), CH4 (722 ppb), N2O (270 ppb) and CF4 (35 ppt). b Total includes 0.007 W m 2 to account for CFC-114, Halon-1211 and Halon-1301. c Total includes 0.009 W m 2 forcing (as in AR4) to account for CFC-13, CFC-114, CFC-115, Halon-1211 and Halon-1301. d Defined here as CFCs + HCFCs + CH3CCl3 + CCl4. e The value for the 1750 methane concentrations has been updated from AR4 in this report, thus the 2005 methane RF is slightly lower than reported in AR4. f Estimates for halocarbons given in the table may have changed from estimates reported in AR4 owing to updates in radiative efficiencies and concentrations. 678 Anthropogenic and Natural Radiative Forcing Chapter 8 for the decline in the CFCs, CH3CCl3 and CCl4 RFs ( 0.013 W m 2 since Since AR4, there have been a few individual studies of tropospheric 2005). The total halocarbon RF is dominated by four gases, namely or stratospheric ozone forcing (Shindell et al., 2006a, 2006c, 2013a; CFC-12, trichlorofluoromethane (CFC-11), chlorodifluoromethane Skeie et al., 2011a; Svde et al., 2011), a multi-model study of strat- (HCFC-22) and trichlorofluoroeethane (CFC-113) in that order, which ospheric ozone RF in the 2010 WMO stratospheric ozone assessment account for about 85% of the total halocarbon RF (see Table 8.2) . The (Forster et al., 2011b), and the ACCMIP multi-model study of tropo- indirect RF from the impacts of ODSs is discussed in Section 8.3.3.2. spheric and tropospheric + stratospheric chemistry models (Conley et al., 2013; Stevenson et al., 2013). There is now greater understanding 8 8.3.2.4.1 Chlorofluorocarbons and hydrochlorofluorocarbons of how tropospheric ozone precursors can affect stratospheric ozone, and how ODSs can affect tropospheric ozone (Shindell et al., 2013a). The CFCs and HCFCs contribute approximately 11% of the WMGHG We assess the total ozone RF to be +0.35 (0.15 to 0.55) W m 2. This RF. Although emissions have been drastically reduced for CFCs, their can be split according to altitude or by emitted species (Shindell et long lifetimes mean that reductions take substantial time to affect their al., 2013a). We assess these contributions to be 0.40 (0.20 to 0.60) W concentrations. The RF from CFCs has declined since 2005 (mainly due m 2 for ozone in the troposphere and 0.05 +/- 0.10 W m 2 for ozone in to a reduction in the concentrations of CFC-11 and CFC-12), whereas the stratosphere based on the studies presented in Table 8.3. Alterna- the RF from HCFCs is still rising (mainly due to HCFC-22). tively, the contributions to the total ozone forcing can be attributed as 0.50 (0.30 to 0.70) W m 2 from ozone precursors and 0.15 ( 0.3 8.3.2.4.2 Hydrofluorocarbons to 0.0) W m 2 from the effect of ODSs. The value attributed to ODSs is assessed to be slightly smaller in magnitude than in the two studies The RF of HFCs is 0.02 W m 2 and has close to doubled since AR4 (2005 quoted in Table 8.3 (Svde et al., 2011; Shindell et al., 2013a) because concentrations). HFC-134a is the dominant contributor to RF of the the models used for these had stratospheric ozone RFs with higher HFCs, with an RF of 0.01 W m 2. magnitudes than the ACCMIP mean (Conley et al., 2013). Differences between the ERFs and RFs for tropospheric and stratospheric ozone 8.3.2.4.3 Perfluorocarbons and sulphur hexafluoride are likely to be small compared to the uncertainties in the RFs (Shin- dell et al., 2013b), so the assessed values for the ERFs are the same These gases have lifetimes of thousands to tens of thousands of years as those for the RFs. (Table 8.A.1); therefore emissions essentially accumulate in the atmos- phere on the time scales considered here. CF4 has a natural source and The influence of climate change is typically included in ozone RF esti- a 1750 concentration of 35 ppt (see Section 2.2.1.1.4). These gases mates as those are based on modelled concentration changes, but the currently contribute 0.01 W m 2 of the total WMGHG RF. available literature provides insufficient evidence for the sign and mag- nitude of the impact and we therefore refrain from giving an estimate 8.3.2.4.4 New species except to assess that it is very likely to be smaller than the overall uncertainty in the total RF. Unlike the WMGHGs, there are significant Nitrogen trifluoride (NF3) is used in the electronics industry and sulfuryl latitudinal variations in the RFs from changes in tropospheric and strat- fluoride (SO2F2) is used as a fumigant. Both have rapidly increasing ospheric ozone. The implications of inhomogeneous RFs are explored emissions and high GWPs, but currently contribute only around 0.0002 in more detail in Section 8.6. W m 2 and 0.0003 W m 2 to anthropogenic RF, respectively (Weiss et al., 2008; Andersen et al., 2009; Muhle et al., 2009; Arnold et al., 2013). There has been one study since AR4 (Myhre et al., 2007) on the RF from water vapour formed from the stratospheric oxidation of CH4 (Section 8.3.3 Ozone and Stratospheric Water Vapour 8.3.3.3). This is consistent with the AR4 value and so has not led to any change in the recommended value of 0.07 (0.02 to 0.12) W m 2 Unlike for the WMGHGs, the estimate of the tropospheric and strato- since AR4. spheric ozone concentration changes are almost entirely model based for the full pre-industrial to present-day interval (though, especially for 8.3.3.1 Tropospheric Ozone the stratosphere, more robust observational evidence on changes is available for recent decades; see Section 2.2). Ozone is formed in the troposphere by photochemical reactions of nat- ural and anthropogenic precursor species (Section 8.2.3.1). Changes in AR4 assessed the RF (for 1750 2005) from tropospheric ozone to be ozone above the tropopause due to emissions of stratospheric ODSs 0.35 W m 2 from multi-model studies with a high 95th percentile of can also affect ozone in the troposphere either by transport across 0.65 W m 2 to allow for the possibility of model overestimates of the the tropopause or modification of photolysis rates. Changes in climate pre-industrial tropospheric ozone levels. The stratospheric ozone RF have also affected tropospheric ozone concentrations (medium evi- was assessed from observational trends from 1979 to 1998 to be 0.05 dence, low agreement) through changes in chemistry, natural emis- +/- 0.1 W m 2, with the 90% confidence range increased to reflect uncer- sions and transport from the stratosphere (Isaksen et al., 2009). tainty in the trend prior to 1979 and since 1998. In AR4 the RF from stratospheric water vapour generated by CH4 oxidation was assessed The most recent estimates of tropospheric ozone RF come from to be +0.07 +/- 0.05 W m 2 based on Hansen et al. (2005). m ­ ulti-model studies under ACCMIP (Conley et al., 2013; Lamarque et al., 2013; Stevenson et al., 2013). The model ensemble reported only 1850 2000 RFs (0.34 W m 2) so the single-model results from Skeie et 679 Chapter 8 Anthropogenic and Natural Radiative Forcing al. (2011a) were used to expand the timespan to 1750 2010, adding non-methane volatile organic compounds (NMVOCs). These results 0.04 W m 2, and 0.02 W m 2 to account for the periods 1750 1850 and were calculated by reducing the precursor emissions individually from 2000 2010 respectively. The best estimate of tropospheric ozone RF 2000 to pre-industrial levels. The results were scaled by the total ozone taking into account the ACCMIP models and the Svde et al. (2011) RFs attributed to ozone precursors (0.50 W m 2) to give the contri- results (the Skeie et al. (2011a) and Shindell et al. (2013a) models are butions to the full 1750 2010 RF. Because of the nonlinearity of the included in ACCMIP) is 0.40 (0.20 to 0.60) W m 2. The quantifiable chemistry an alternative method of starting from pre-industrial con- uncertainties come from the inter-model spread ( 0.11 to 0.11 W m 2) ditions and increasing precursor emissions singly may give a different 8 and the differences between radiative transfer models ( 0.07 to 0.07 result. Note that as well as inducing an ozone RF, these ozone pre- W m 2); all 5 to 95% confidence interval. Additional uncertainties arise cursor species can also strongly affect the concentrations of CH4 and from the lack of knowledge of pre-industrial emissions and the rep- aerosols, adding extra terms (both positive and negative) to their total resentation of chemical and physical processes beyond those included indirect forcings. The contributions to the 1750 2010 CH4 RF are again in the current models. The tropospheric ozone RF is sensitive to the based on Stevenson et al. (2013) and Shindell et al. (2009). The Steven- assumed pre-industrial levels. As described in Section 8.2.3.1, very son et al. (2013) values are for 1850 2000 rather than 1750 to 2011 limited late 19th and early 20th century observations of surface ozone so for these we distribute the CH4 RF for 1750 1850 and 2000 2011 concentrations are lower than the ACCMIP models for the same period; (0.06 W m 2) by scaling the CH4 and CO contributions (assuming these however, we assess that those observations are very uncertain. Skeie were the most significant contributors over those time periods). This et al. (2011a) and Stevenson et al. (2013) increase their uncertainty gives contributions of 0.58 +/- 0.08, 0.29 +/- 0.18, 0.07 +/- 0.02 and 0.02 ranges to 30% for 1 standard deviation which is equivalent to ( 50% +/- 0.02 W m 2 for changes from historical to present day emissions of to +50%) for the 5 to 95% confidence range and we adopt this for CH4 (inferred emissions), NOX, CO and VOCs respectively (uncertainties AR5. The overall confidence in the tropospheric ozone RF is assessed are 5 to 95% confidence intervals). The difference between the total as high. CH4 RF attributed to ozone precursors here (0.38 W m 2) and the value calculated from CH4 concentration changes in Table 8.2 (0.48 W m 2) Because we have low confidence in the pre-industrial ozone observa- is due to nonlinearities in the CH4 chemistry because large single-step tions, and these were extremely limited in spatial coverage, it is not changes were used. To allow an easier comparison between the con- possible to calculate a purely observationally based ozone RF. However, centration-based and emission-based approaches in Section 8.5.1 the modern observations can be used to assess the performance of the nonlinear term (+0.1 W m 2) is distributed between the four emitted chemistry models. Bowman et al. (2013) used satellite retrievals from species according to their absolute magnitude so that they total 0.48 the TES instrument to constrain the RF from the ACCMIP models. This W m 2. The scaled results still lie within the uncertainty bounds of the reduced the inter-model uncertainty by 30%; however, we still main- values quoted above. The impact of climate change over the historical tain overall the ( 50% to +50%) 5 to 95% confidence range for AR5. period on CH4 oxidation is not accounted for in these calculations. The time evolution of the tropospheric ozone forcing is shown in Figure Tropospheric ozone can also affect the natural uptake of CO2 by 8.7. There is a noticeable acceleration in the forcing after 1950 and a decreasing plant productivity (see Sections 6.4.8.2 and 8.2.3.1) and deceleration in the 1990s reflecting the time evolution of anthropo- it is found that this indirect effect could have contributed to the total genic precursor emissions. Observational evidence for trends in ozone CO2 RF (Section 8.3.2.1; Sitch et al., 2007), roughly doubling the over- concentrations is discussed in Section 2.2.2.3. all RF attributed to ozone precursors. Although we assess there to be It can be useful to calculate a normalized radiative forcing (NRF) which is an RF per change in ozone column in W m 2 DU 1 or W mol 1. This is 0.6 only an approximation as the NRF is sensitive to the vertical profile Ozone Radiative Forcing ( W m-2 ) Tropospheric of the ozone change and to the latitudinal profile to a smaller extent. From Table 8.3 we assess the NRF to be 0.042 (0.037 to 0.047) W m 2 0.4 Total DU 1 (94 (83 to 105) W mol 1) similar to the value of 0.042 W m 2 DU 1 (94 W mol 1) in TAR (Ramaswamy et al., 2001). Stratospheric 0.2 A small number of studies have looked at attributing the ozone chang- es among the anthropogenically emitted species. Svde et al. (2011) report a tropospheric ozone RF of 0.38 W m 2, 0.44 W m 2 from ozone 0.0 precursors and 0.06 W m 2 from the impact of stratospheric ozone depletion on the troposphere. Shindell et al. (2013a) also calculate that ODSs are responsible for about 0.06 W m 2 of the tropospher- -0.2 ic ozone RF, and ozone precursors for about 0.41 W m 2. Six of the 1750 1800 1850 1900 1950 2000 models in Stevenson et al. (2013) and Shindell et al. (2009) performed experiments to attribute the ozone RF to the individual precursor emis- Figure 8.7 | Time evolution of the radiative forcing from tropospheric and stratospheric ozone from 1750 to 2010. Tropospheric ozone data are from Stevenson et al. (2013) sions. An average of these seven model results leads to attributions of scaled to give 0.40 W m 2 at 2010. The stratospheric ozone RF follow the functional 0.24 +/- 0.13 W m 2 due to CH4 emissions, 0.14 +/- 0.09 W m 2 from NOX shape of the Effective Equivalent Stratospheric Chlorine assuming a 3-year age of air emissions, 0.07 +/- 0.03 W m 2 from CO, and 0.04 +/- 0.03W m 2 from (Daniel et al., 2010) scaled to give 0.05 W m 2 at 2010. 680 Anthropogenic and Natural Radiative Forcing Chapter 8 Table 8.3 | Contributions of tropospheric and stratospheric ozone changes to radiative forcing (W m 2) from 1750 to 2011. Troposphere Stratosphere Normalized Longwave Shortwave Total Radiative Forcing Longwave Shortwave Total m W m 2 DU 1 AR4 (Forster 0.35 0.05 et al. (2007) (0.25 to 0.65) ( 0.15 to 0.05) 8 Shindell et al. 0.33 0.08 (2013a)f (0.31 to 0.35) ( 0.10 to 0.06) 0.03a WMO (Forster ( 0.23 to +0.17) et al., 2011b) +0.03b 0.45c 40 0.12 Svde et al. (2011) 0.38 d 39 0.12 0.41 38 Skeie et al. (2011a) (0.21 to 0.61) 0.33 0.08 0.41 42 0.13 0.11 0.02 ACCMIPe (0.24 to 0.42) (0.06 to 0.10) (0.21 to 0.61) (37 to 47) ( 0.26 to 0) (0.03 to 0.19) ( 0.09 to 0.05) 0.40 42 0.05 AR5 (0.20 to 0.60) (37 to 47) (-0.15 to 0.05) Notes: a From multi-model results. b From Randel and Wu (2007) observation-based data set. c Using the REF chemistry, see Svde et al. (2011). d Using the R2 chemistry. e The Atmospheric Chemistry and Climate Model Intercomparison Project (ACCMIP) tropospheric ozone RFs are from Stevenson et al. (2013). The stratospheric ozone values are from Conley et al. (2013) calculations for 1850 2005 disregarding the Modele de Chimie Atmosphérique a Grande Echelle (MOCAGE) model which showed excessive ozone depletion. f Only the Goddard Institute for Space Studies (GISS)-E2-R results (including bias correction) from the Shindell et al. (2013a) study are shown here rather than the multi-model result presented in that paper. robust evidence of an effect, we make no assessment of the magnitude 2005. The RF from stratospheric ozone due to changes in emissions because of lack of further corroborating studies. of ozone precursors and ODSs is here assessed to be 0.05 ( 0.15 to 0.05) taking into account all the studies listed in Table 8.3. This is in 8.3.3.2 Stratospheric Ozone agreement with AR4, although derived from different data. The time- line of stratospheric ozone forcing is shown in Figure 8.7, making the The decreases in stratospheric ozone due to anthropogenic emissions assumption that it follows the trajectory of the changes in EESC. It of ODSs have a positive RF in the shortwave (increasing the flux into reaches a minimum in the late 1990s and starts to recover after that. the troposphere) and a negative RF in the longwave. This leaves a residual forcing that is the difference of two larger terms. In the lower The net global RF from ODSs taking into account the compensating stratosphere the longwave effect tends to be larger, whereas in the effects on ozone and their direct effects as WMGHGs is 0.18 (0.03 to upper stratosphere the shortwave dominates. Thus whether strat- 0.33) W m 2. The patterns of RF for these two effects are different so ospheric ozone depletion has contributed an overall net positive or the small net global RF comprises areas of positive and negative RF. negative forcing depends on the vertical profile of the change (Forster and Shine, 1997). WMO (2011) assessed the RF from 1979 to 2005 8.3.3.3 Stratospheric Water Vapour from observed ozone changes (Randel and Wu, 2007) and results from 16 models for the 1970s average to 2004. The observed and modelled Stratospheric water vapour is dependent on the amount entering from mean ozone changes gave RF values of different signs (see Table 8.3). the tropical troposphere and from direct injection by volcanic plumes Negative net RFs arise from models with ozone decline in the lower- (Joshi and Jones, 2009) and aircraft, and the in situ chemical pro- most stratosphere, particularly at or near the tropopause. duction from the oxidation of CH4 and hydrogen. This contrasts with tropospheric water vapour which is almost entirely controlled by the The ACCMIP study also included some models with stratospheric balance between evaporation and precipitation (see FAQ 8.1). We con- chemistry (Conley et al., 2013). One model in that study stood out as sider trends in the transport (for instance, due to the Brewer Dobson having excessive ozone depletion. Removing that model leaves a strat- circulation or tropopause temperature changes) to be climate feedback ospheric ozone RF of 0.02 ( 0.09 to 0.05) W m 2. These results are in rather than a forcing so the anthropogenic RFs come from oxidation of good agreement with the model studies from WMO (2011). Forster et CH4 and hydrogen, and emissions from stratospheric aircraft. al. (2007) in AR4 calculated a forcing of 0.05 W m 2 from observa- tions over the period 1979 1998 and increased the uncertainty to 0.10 Myhre et al. (2007) used observations of the vertical profile of CH4 to W m 2 to encompass changes between the pre-industrial period and deduce a contribution from oxidation of anthropogenic CH4 of 0.083 681 Chapter 8 Anthropogenic and Natural Radiative Forcing W m 2 which compares with the value of 0.07 W m 2 from calcula- were provided in AR4 (see Section 8.1). However, the ERF of aerosol tions in a 2D model in Hansen et al. (2005). Both of these values are cloud and aerosol radiation interactions were included in the discus- consistent with AR4 which obtained the stratospheric water vapour sion of total aerosol effect in Chapter 7 in AR4 (Denman et al., 2007). forcing by scaling the CH4 direct forcing by 15%. Thus the time evolu- The mechanisms influenced by anthropogenic aerosol including the tion of this forcing is also obtained by scaling the CH4 forcing by 15%. aerosol cloud interactions are discussed in detail in this assessment in The best estimate and uncertainty range from AR4 of 0.07 (0.02 to Section 7.5 and summarized in the subsections that follow. 0.12) W m 2 remain unchanged and the large uncertainty range is due 8 to large differences found in the intercomparison studies of radiative 8.3.4.2 Radiation Forcing of the Aerosol Radiation transfer modelling for changes in stratospheric water vapour (see Sec- Interaction by Component tion 8.3.1). Based on a combination of global aerosol models and observa- RF from the current aircraft fleet through stratospheric water vapour tion-based methods, the best RF estimate of the aerosol radiation emissions is very small. Wilcox et al. (2012) estimate a contribution interaction in AR5 is 0.35 ( 0.85 to +0.15) W m 2 (see Section 7.5). from civilian aircraft in 2005 of 0.0009 (0.0003 to 0.0013) W m 2 This estimate is thus smaller in magnitude than in AR4, however; with with high confidence in the upper limit. Water vapour emissions from larger uncertainty range. Overall, the estimate compared to AR4 is aircraft in the troposphere also contribute to contrails which are dis- more robust because the agreement between estimates from models cussed in Section 8.3.4.5. and observation-based methods is much greater (see Section 7.5). The larger range arises primarily from analysis by observation-based meth- 8.3.4 Aerosols and Cloud Effects ods (see Section 7.5). 8.3.4.1 Introduction and Summary of AR4 The main source of the model estimate is based on updated simula- tions in AeroCom (Myhre et al., 2013), which is an intercomparison In AR4 (Forster et al., 2007), RF estimates were provided for three aer- exercise of a large set of global aerosol models that includes extensive osol effects. These were the RF of aerosol radiation interaction (previ- evaluation against measurements. The assessment in Chapter 7 relies ously denoted as direct aerosol effect), RF of the aerosol cloud inter- to a large extent on this study for the separation in the various aerosol action (previously denoted as the cloud albedo effect), and the impact components, except for BC where the assessment in Chapter 7 relies in of BC on snow and ice surface albedo. See Chapter 7 and Figure 7.3 for addition on Bond et al. (2013). The RF of aerosol radiation interaction an explanation of the change in terminology between AR4 and AR5. is separated into seven components in this report; namely sulphate, The RF due to aerosol radiation interaction is scattering and absorp- BC from fossil fuel and biofuel, OA from fossil fuel and biofuel, BC and tion of shortwave and longwave radiation by atmospheric aerosols. OA combined from biomass burning (BB), nitrate, SOA and mineral Several different aerosol types from various sources are present in the dust. BC and OA from biomass burning are combined due to the joint atmosphere (see Section 8.2). Most of the aerosols primarily scatter sources, whereas treated separately for fossil fuel and biofuel because solar radiation, but some components absorb solar radiation to various there is larger variability in the ratio of BC to OA in the fossil fuel extents with BC as the most absorbing component. RF of aerosols in and biofuel emissions. This approach is consistent with TAR and AR4. the troposphere is often calculated at the TOA because it is similar to Table 8.4 compares the best estimates of RF due to aerosol radiation tropopause values (Forster et al., 2007). A best estimate RF of 0.5 +/- interaction for various components in this report with values in SAR, 0.4 W m 2 was given in AR4 for the change in the net aerosol radia- TAR and AR4. In magnitude the sulphate and BC from use of fossil fuel tion interaction between 1750 and 2005 and a medium to low level of and biofuel dominate. It is important to note that the BB RF is small in scientific understanding (LOSU). magnitude but consists of larger, offsetting terms in magnitude from OA and BC (see Section 7.5.2). Changes in the estimates of RF due to An increase in the hygroscopic aerosol abundance may enhance the aerosol radiation interaction of the various components have been concentration of cloud condensation nuclei (CCN). This may increase rather modest compared to AR4, except for BC from fossil fuel and the cloud albedo and under the assumption of fixed cloud water con- biofuel (see Section 7.5). SOA is a new component compared to AR4. tent this effect was given a best estimate of 0.7 W m 2 (range from Anthropogenic SOA precursors contribute only modestly to the anthro- 1.8 to 0.3) in AR4 and a low LOSU. pogenic change in SOA. The increase in SOA is mostly from biogenic precursors and enhanced partitioning of SOA into existing particles BC in the snow or ice can lead to a decrease of the surface albedo. from anthropogenic sources and changes in the atmospheric oxidation This leads to a positive RF. In AR4 this mechanism was given a best RF (Carlton et al., 2010). This change in SOA is therefore of anthropogenic estimate of 0.1 +/- 0.1 W m 2 and a low LOSU. origin, but natural emission of SOA precursors is important (Hoyle et al., 2011). Impacts on clouds from the ERF of aerosol cloud interaction (includ- ing both effects previously denoted as cloud lifetime and cloud albedo Note that the best estimate and the uncertainty for the total is not effect) and the ERF of aerosol radiation interaction (including both equal to the sum of the aerosol components because the total is effects previously denoted as direct aerosol effect and semi-direct estimated based on a combination of methods (models and observa- effect) were not strictly in accordance with the RF concept, because tion-based methods), whereas the estimates for the components rely they involve tropospheric changes in variables other than the forcing mostly on model estimates. agent at least in the available model estimates, so no best RF estimates ­ 682 Anthropogenic and Natural Radiative Forcing Chapter 8 Table 8.4 | Global and annual mean RF (W m 2) due to aerosol radiation interaction between 1750 and 2011 of seven aerosol components for AR5. Values and uncertainties from SAR, TAR, AR4 and AR5 are provided when available. Note that for SAR, TAR and AR4 the end year is somewhat different than for AR5 with 1993, 1998 and 2005, respectively. Global Mean Radiative Forcing (W m 2) SAR TAR AR4 AR5 Sulphate aerosol 0.40 ( 0.80 to 0.20) 0.40 ( 0.80 to 0.20) 0.40 ( 0.60 to 0.20) 0.40 ( 0.60 to 0.20) Black carbon aerosol from fossil fuel and biofuel +0.10 (+0.03 to +0.30) +0.20 (+0.10 to +0.40) +0.20 (+0.05 to +0.35) +0.40 (+0.05 to +0.80) 8 Primary organic aerosol Not estimated 0.10 ( 0.30 to 0.03) 0.05 (0.00 to 0.10) 0.09 ( 0.16 to 0.03) from fossil fuel and biofuel Biomass burning 0.20 ( 0.60 to 0.07) 0.20 ( 0.60 to 0.07) +0.03( 0.09 to +0.15) 0.0 ( 0.20 to +0.20) Secondary organic aerosol Not estimated Not estimated Not estimated 0.03 ( 0.27 to +0.20) Nitrate Not estimated Not estimated 0.10 ( 0.20 to 0.00) 0.11 ( 0.30 to 0.03) Dust Not estimated 0.60 to +0.40 0.10 ( 0.30 to +0.10) 0.10 ( 0.30 to +0.10) Total Not estimated Not estimated 0.50 ( 0.90 to 0.10) 0.35 ( 0.85 to +0.15) The RF due to aerosol radiation interaction during some time periods is lake core records, and uncertainties in the historical emission of aero- more uncertain than the current RF. Improvements in the observations sols and their precursors used in the global aerosol modeling are larger of aerosols have been substantial with availability of remote sensing than for current conditions. Emissions of carbonaceous aerosols are from the ground-based optical observational network AErosol RObotic particularly uncertain in the 1800s due to a significant biofuel source in NETwork (AERONET) and the launch of the Moderate Resolution Imag- this period, in contrast to the SO2 emissions which were very small until ing Spectrometer (MODIS) and Multi-angle Imaging Spectro-Radiom- the end of the 1800s. The uncertainty in the biomass burning emissions eter (MISR) instruments (starting in 2000) as well as other satellite also increases backward in time. Note that, for 1850, the biomass burn- data. This has contributed to constraining the current RF using aerosol ing emissions from Lamarque et al. (2010) are quite different from the observations. The aerosol observations are very limited backward in previous estimates, but RF due to aerosol radiation interaction is close time, although there is growing constraint coming from new ice and to zero for this component. Figure 8.8 shows an example of the time evolution of the RF due to aerosol radiation interaction as a total and separated into six aerosol components. From 1950 to 1990 there was 0.80 a strengthening of the total RF due to aerosol radiation interaction, 0.4 mainly due to a strong enhancement of the sulphate RF. After 1990 the change has been small with even a weakening of the RF due to aero- Radiative Forcing (W m-2) BC sol radiation interaction, mainly due to a stronger BC RF as a result of 0.2 increased emissions in East and Southeast Asia. BC on snow BioBurn 8.3.4.3 Aerosol Cloud Interactions 0.0 SOA OC The RF by aerosol effects on cloud albedo was previously referred to Nitrate -0.2 Sulfate as the Twomey or cloud albedo effect (see Section 7.1). Although this Total Aerosols RF can be calculated, no estimate of this forcing is given because it has heuristic value only and does not simply translate to the ERF due -0.4 to aerosol cloud interaction. The total aerosol ERF, namely ERF due 1850 1900 1950 2000 -0.60 -0.81 to aerosol radiation and aerosol cloud interactions (excluding BC on snow and ice) provided in Chapter 7 is estimated with a 5 to 95% Figure 8.8 | Time evolution of RF due to aerosol radiation interaction and BC on uncertainty between 1.9 and 0.1 W m 2 with a best estimate value snow and ice. Multi-model results for 1850, 1930, 1980 and 2000 from ACCMIP for of 0.9 W m 2 (medium confidence). The likely range of this forcing aerosol radiation interaction (Shindell et al., 2013c) and BC on snow and ice (Lee et al., is between 1.5 and 0.4 W m 2. The estimate of ERF due to aero- 2013) are combined with higher temporal-resolution results from the Goddard Institute sol radiation and aerosol-cloud interaction is lower (i.e., less negative) for Space Studies (GISS)-E2 and Oslo-Chemical Transport Model 2 (OsloCTM2) models than the corresponding AR4 RF estimate of 1.2 W m 2 because the (aerosol radiation interaction) and OsloCTM2 (BC on snow and ice). Uncertainty ranges (5 to 95%) for year 2010 are shown with vertical lines. Values next to the uncertainty latter was based mainly on GCM studies that did not take secondary lines are for cases where uncertainties go beyond the scale. The total includes the RF processes (such as aerosol effects on mixed-phase and/or convective due to aerosol radiation interaction for six aerosol components and RF due to BC on clouds and effects on longwave radiation) into account. This new best snow and ice. All values have been scaled to the best estimates for 2011 given in Table estimate of ERF due to aerosol radiation and aerosol cloud interac- 8.4. Note that time evolution for mineral dust is not included and the total RF due to tion is also consistent with the studies allowing cloud-scale process- aerosol radiation interaction is estimated based on simulations of the six other aerosol components. es and related responses and with the lower estimates of this forcing inferred from satellite observations. 683 Chapter 8 Anthropogenic and Natural Radiative Forcing Frequently Asked Questions FAQ 8.2 | Do Improvements in Air Quality Have an Effect on Climate Change? Yes they do, but depending on which pollutant(s) they limit, they can either cool or warm the climate. For example, whereas a reduction in sulphur dioxide (SO2) emissions leads to more warming, nitrogen oxide (NOx) emission 8 control has both a cooling (through reducing of tropospheric ozone) and a warming effect (due to its impact on methane lifetime and aerosol production). Air pollution can also affect precipitation patterns. Air quality is nominally a measure of airborne surface pollutants, such as ozone, carbon monoxide, NOx and aerosols (solid or liquid particulate matter). Exposure to such pollutants exacerbates respiratory and cardiovascular diseases, harms plants and damages buildings. For these reasons, most major urban centres try to control discharges of air- borne pollutants. Unlike carbon dioxide (CO2) and other well-mixed greenhouse gases, tropospheric ozone and aerosols may last in the atmosphere only for a few days to a few weeks, though indirect couplings within the Earth system can prolong their impact. These pollutants are usually most potent near their area of emission or formation, where they can force local or regional perturbations to climate, even if their globally averaged effect is small. Air pollutants affect climate differently according to their physical and chemical characteristics. Pollution-generated greenhouse gases will impact climate primarily through shortwave and longwave radiation, while aerosols can in addition affect climate through cloud aerosol interactions. Controls on anthropogenic emissions of methane (FAQ 8.2, Figure 1) to lower surface ozone have been identified as win win situations. Consequences of controlling other ozone precursors are not always as clear. NOx emission con- trols, for instance, might be expected to have a cooling effect as they reduce tropospheric ozone, but their impact on CH4 lifetime and aerosol formation is more likely instead to cause overall warming. Satellite observations have identified increasing atmospheric concentrations of SO2 (the primary precursor to scat- tering sulphate aerosols) from coal-burning power plants over eastern Asia during the last few decades. The most recent power plants use scrubbers to reduce such emissions (albeit not the concurrent CO2 emissions and associated long-term climate warming). This improves air quality, but also reduces the cooling effect of sulphate aerosols and therefore exacerbates warming. Aerosol cooling occurs through aerosol radiation and aerosol cloud interactions and is estimated at 0.9 W m 2 (all aerosols combined, Section 8.3.4.3) since pre-industrial, having grown especially during the second half of the 20th century when anthropogenic emissions rose sharply. (continued on next page) Ozone pollution Particulate controls matter controls Carbon Nitrogen VOCs Ammonia Black Organic Sulfur Methane monoxide oxides carbon carbon dioxide Cooling Warming FAQ 8.2, Figure 1 | Schematic diagram of the impact of pollution controls on specific emissions and climate impact. Solid black line indicates known impact; dashed line indicates uncertain impact. 684 Anthropogenic and Natural Radiative Forcing Chapter 8 FAQ 8.2 (continued) Black carbon or soot, on the other hand, absorbs heat in the atmosphere (leading to a 0.4 W m 2 radiative forcing from anthropogenic fossil and biofuel emissions) and, when deposited on snow, reduces its albedo, or ability to reflect sunlight. Reductions of black carbon emissions can therefore have a cooling effect, but the additional inter- 8 action of black carbon with clouds is uncertain and could lead to some counteracting warming. Air quality controls might also target a specific anthropogenic activity sector, such as transportation or energy pro- duction. In that case, co-emitted species within the targeted sector lead to a complex mix of chemistry and climate perturbations. For example, smoke from biofuel combustion contains a mixture of both absorbing and scattering particles as well as ozone precursors, for which the combined climate impact can be difficult to ascertain. Thus, surface air quality controls will have some consequences on climate. Some couplings between the targeted emissions and climate are still poorly understood or identified, including the effects of air pollutants on precipi- tation patterns, making it difficult to fully quantify these consequences. There is an important twist, too, in the potential effect of climate change on air quality. In particular, an observed correlation between surface ozone and temperature in polluted regions indicates that higher temperatures from climate change alone could worsen summertime pollution, suggesting a climate penalty . This penalty implies stricter surface ozone controls will be required to achieve a specific target. In addition, projected changes in the frequency and duration of stagnation events could impact air quality conditions. These features will be regionally variable and difficult to assess, but better understanding, quantification and modelling of these processes will clarify the overall interaction between air pollutants and climate. One reason an expert judgment estimate of ERF due to aerosol radi- ther description in Section 7.5.2.3). This RF has a two to four times ation and aerosol cloud interaction is provided rather than ERF due larger global mean surface temperature change per unit forcing than to aerosol cloud interaction specifically is that the individual contri- a change in CO2. butions are very difficult to disentangle. These contributions are the response of processes that are the outputs from a system that is con- In Figure 8.8, the time evolution of global mean RF due to BC on snow stantly readjusting to multiple nonlinear forcings. Assumptions of inde- and ice is shown based on multi-model simulations in ACCMIP (Lee et pendence and linearity are required to deduce ERF due to aerosol radi- al., 2013) for 1850, 1930, 1980 and 2000. The results show a maximum ation interaction and ERF due to aerosol cloud interaction (although in the RF in 1980 with a small increase since 1850 and a 20% lower there is no a priori reason why the individual ERFs should be simply RF in 2000 compared to 1980. Those results are supported by obser- additive). Under these assumptions, ERF due to aerosol cloud interac- vations. The BC concentration in the Arctic atmosphere is observed to tion is deduced as the difference between ERF due to aerosol radia- be declining since 1990, at least in the Western Hemisphere portion tion and aerosol cloud interaction and ERF due to aerosol radiation (Sharma et al., 2004), which should lead to less deposition of BC on interaction alone. This yields an ERF due to aerosol cloud interaction the snow surface. Surveys across Arctic during 1998 and 2005 to 2009 estimate of 0.45 W m 2 which is much smaller in magnitude than the showed that the BC content of Arctic snow appears to be lower than 1.4 W m 2 median forcing value of the models summarized in Figure in 1984 (Doherty et al., 2010) and found BC concentrations in Canada, 7.19 and is also smaller in magnitude than the AR4 estimates of 0.7 Alaska and the Arctic Ocean (e.g., Hegg et al., 2009), about a factor of W m 2 for RF due to aerosol cloud interaction. 2 lower than measured in the 1980s (e.g., Clarke and Noone, 1985). Large-area field campaigns (Huang et al., 2011; Ye et al., 2012) found 8.3.4.4 Black Carbon Deposition in Snow and Ice that the BC content of snow in northeast China is comparable to values found in Europe. The steep drop off in BC content of snow with latitude Because absorption by ice is very weak at visible and ultraviolet (UV) in northeast China may indicate that there is not much BC in the Arctic wavelengths, BC in snow makes the snow darker and increases absorp- coming from China (Huang et al., 2011; Ye et al., 2012; Wang et al., tion. This is not enough darkening to be seen by eye, but it is enough 2013). The change in the spatial pattern of emission of BC is a main to be important for climate (Warren and Wiscombe, 1980; Clarke and cause for the difference in the temporal development of RF due to BC Noone, 1985). Several studies since AR4 have re-examined this issue on snow and ice compared to the BC from RF due to aerosol radiation and find that the RF may be weaker than the estimates of Hansen interaction over the last decades. and Nazarenko (2004) in AR4 (Flanner et al., 2007; Koch et al., 2009a; Rypdal et al., 2009; Lee et al., 2013). The anthropogenic BC on snow/ ice is assessed to have a positive global and annual mean RF of +0.04 W m 2, with a 0.02 0.09 W m 2 5 to 95% uncertainty range (see fur- 685 Chapter 8 Anthropogenic and Natural Radiative Forcing 8.3.4.5 Contrails and Contrail-Induced Cirrus experienced in the early part of the second millennium. There is still significant uncertainty in the anthropogenic land cover change, and in AR4 assessed the RF of contrails (persistent linear contrails) as +0.01 particular its time evolution (Gaillard et al., 2010). ( 0.007 to +0.02) W m 2 and provided no estimate for contrail induced cirrus. In AR5, Chapter 7 gives a best estimate of RF due to contrails of 8.3.5.3 Surface Albedo and Radiative Forcing +0.01 (+0.005 to +0.03) W m 2 and an ERF estimate of the combined contrails and contrail-induced cirrus of +0.05 (+0.02 to +0.15) W Surface albedo is the ratio between reflected and incident solar flux 8 m 2. Since AR4, the evidence for contrail-induced cirrus has increased at the surface. It varies with the surface cover. Most forests are darker because of observational studies (for further details see Section 7.2.7). (i.e., lower albedo) than grasses and croplands, which are darker than barren land and desert. As a consequence, deforestation tends 8.3.5 Land Surface Changes to increase the Earth albedo (negative RF) while cultivation of some bright surfaces may have the opposite effect. Deforestation also leads 8.3.5.1 Introduction to a large increase in surface albedo in case of snow cover as low vege- tation accumulates continuous snow cover more readily in early winter Anthropogenic land cover change has a direct impact on the Earth radi- allowing it to persist longer in spring. This causes average winter ation budget through a change in the surface albedo. It also impacts albedo in deforested areas to be generally much higher than that of a the climate through modifications in the surface roughness, latent heat tree-covered landscape (Bernier et al., 2011). flux and river runoff. In addition, human activity may change the water cycle through irrigation and power plant cooling, and also generate The pre-industrial impact of the Earth albedo increase due to land use direct input of heat to the atmosphere by consuming energy. Land use change, including the reduced snow masking by tall vegetation, is esti- change, and in particular deforestation, also has significant impacts on mated to be on the order of 0.05 W m 2 (Pongratz et al., 2009). Since WMGHG concentration, which are discussed in Section 6.3.2.2. Poten- then, the increase in world population and agriculture development tial geo-engineering techniques that aim at increasing the surface led to additional forcing. Based on reconstruction of land use since the albedo are discussed in Section 7.7.2.3. beginning of the Industrial Era, Betts et al. (2007) and Pongratz et al. (2009) computed spatially and temporally distributed estimates of the AR4 referenced a large number of RF estimates resulting from a change land use RF. They estimate that the shortwave flux change induced by in land cover albedo. It discussed the uncertainties due to the recon- the albedo variation, from fully natural vegetation state to 1992, is on struction of historical vegetation, the characterization of present-day the order of 0.2 W m 2 (range 0.24 to 0.21W m 2). The RF, defined vegetation and the surface radiation processes. On this basis, AR4 gave with respect to 1750, is in the range 0.17 to 0.18 W m 2. A slightly a best estimate of RF relative to 1750 due to land use related surface stronger value ( 0.22 W m 2) was found by Davin et al. (2007) for the albedo at 0.2 +/- 0.2 W m 2 with a level of scientific understanding at period 1860 1992. medium-low. In recent years, the availability of global scale MODIS data (Schaaf et 8.3.5.2 Land Cover Changes al., 2002) has improved surface albedo estimates (Rechid et al., 2009). These data have been used by Myhre et al (2005a) and Kvalevag et al. Hurtt et al. (2006) estimates that 42 to 68% of the global land sur- (2010). They argue that the observed albedo difference between nat- face was impacted by land use activities (crop, pasture, wood harvest) ural vegetation and croplands is less than usually assumed in climate during the 1700 2000 period. Until the mid-20th century most land simulations, so that the RF due to land use change is weaker than in use change took place over the temperate regions of the NH. Since estimates that do not use the satellite data. On the other hand, Nair et then, reforestation is observed in Western Europe, North America and al. (2007) show observational evidence of an underestimate of the sur- China as a result of land abandonment and afforestation efforts, while face albedo change in land use analysis in southwest Australia. Overall, deforestation is concentrated in the tropics. After a rapid increase of there is still a significant range of RF estimates for the albedo com- the rate of deforestation during the 1980s and 1990s, satellite data ponent of land use forcing. This is mostly due to the range of albedo indicate a slowdown in the past decade (FAO, 2012). change as a result of land use change, as shown in an inter-comparison of seven atmosphere land models (de Noblet-Ducoudre et al., 2012). Since AR4, Pongratz et al. (2008) and Kaplan et al. (2011) extended existing reconstructions on land use back in time to the past millenni- Deforestation has a direct impact on the atmospheric CO2 concen- um, accounting for the progress of agriculture technique and historical tration and therefore contributes to the WMGHG RF as quantified in events such as the black death or war invasions. As agriculture was Section 8.3.2. Conversely, afforestation is a climate mitigation strate- already widespread over Europe and South Asia by 1750, the RF, which gy to limit the CO2 concentration increase. Several authors have com- is defined with respect to this date, is weaker than the radiative flux pared the radiative impact of deforestation/afforestation that results change from the state of natural vegetation cover (see Figure 8.9). from the albedo change with the greenhouse effect of CO2 released/ Deforestation in Europe and Asia during the last millennium led to a sequestered. Pongratz et al. (2010) shows that the historic land use significant regional negative forcing. Betts et al. (2007) and Goosse change has had a warming impact (i.e., greenhouse effect dominates) et al. (2006) argue that it probably contributed to the Little Ice Age , at the global scale and over most regions with the exception of Europe together with natural solar and volcanic activity components, before and India. Bala et al. (2007) results show latitudinal contrast where the increase in GHG concentration led to temperatures similar to those the greenhouse effect dominates for low-latitude deforestation while 686 Anthropogenic and Natural Radiative Forcing Chapter 8 pears within one to a few years (Jin et al., 2012). Myhre et al. (2005b) e ­ stimates a global albedo-related radiative effect due to African fires of 0.015 W m 2. Over semi-arid areas, the development of agriculture favours the gen- eration of dust. Mulitza et al. (2010) demonstrates a very large increase of dust emission and deposition in the Sahel concomitant with the 8 1750 development of agriculture in this area. This, together with the anal- ysis of dust sources (Ginoux et al., 2010), suggests that a significant fraction of the dust that is transported over the Atlantic has an anthro- pogenic origin and impacts the Earth albedo. There is no full estimate of the resulting RF, however. The dust RF estimate in Section 8.3.4.2 includes both land use contributions and change in wind-driven emis- sions. Both dust and biomass burning aerosol may impact the Earth surface albedo as these particles can be deposed on snow, which has a large impact on its absorption, in particular for soot. This is discussed 1900 in Section 8.3.4.4. Urban areas have an albedo that is 0.01 to 0.02 smaller than adjacent croplands (Jin et al., 2005). There is the potential for a strong increase through white roof coating with the objective of mitigating the heat island effect (Oleson et al., 2010). Although the global scale impact is small, local effects can be very large, as shown by Campra et al. (2008) that reports a regional (260 km2) 0.09 increase in albedo and 20 W m 2 RF as a consequence of greenhouse horticulture development. 1992 8.3.5.5 Impacts of Surface Change on Climate Rad. forc. (W m-2) 0.0 SW flux change 0.0 (W m-2) -0.1 Davin et al. (2007) argues that the climate sensitivity to land use forc- -0.1 1750 -4.5 -3 -1 0 1 3 4.5 -0.2 ing is lower than that for other forcings, due to its spatial distribution (W m-2) 1400 1600 1800 2000 but also the role of non-radiative processes. Indeed, in addition to the impact on the surface albedo, land use change also modifies the evap- Figure 8.9 | Change in top of the atmosphere (TOA) shortwave (SW) flux (W m 2) following the change in albedo as a result of anthropogenic Land Use Change for three oration and surface roughness, with counterbalancing consequences periods (1750, 1900 and 1992 from top to bottom). By definition, the RF is with respect on the lower atmosphere temperature. There is increasing evidence to 1750, but some anthropogenic changes had already occurred in 1750. The lower that the impact of land use on evapotranspiration a non-RF on cli- right inset shows the globally averaged impact of the surface albedo change to the TOA mate is comparable to, but of opposite sign than, the albedo effect, SW flux (left scale) as well as the corresponding RF (right scale) after normalization to so that RF is not as useful a metric as it is for gases and aerosols. For the 1750 value. Based on simulations by Pongratz et al. (2009). instance, Findell et al. (2007) climate simulations show a negligible impact of land use change on the global mean temperature, although the combined effect of albedo and evapotranspiration impact does at there are some significant regional changes. high latitude. These results are also supported by Bathiany et al. (2010). Similarly, Lohila et al. (2010) shows that the afforestation of boreal Numerical climate experiments demonstrate that the impact of land use peatlands results in a balanced RF between the albedo and green- on climate is much more complex than just the RF. This is due in part house effect. Overall, because of the opposite impacts, the potential of to the very heterogeneous nature of land use change (Barnes and Roy, afforestation to mitigate climate change is limited (Arora and Monte- 2008), but mostly due to the impact on the hydrological cycle through negro, 2011) while it may have undesired impacts on the atmospheric evapotranspiration, root depth and cloudiness (van der Molen et al., circulation, shifting precipitation patterns (Swann et al., 2012). 2011). As a consequence, the forcing on climate is not purely radiative and the net impact on the surface temperature may be either positive 8.3.5.4 Other Impacts of Land Cover Change on the or negative depending on the latitude (Bala et al., 2007). Davin and de Earth s Albedo Noblet-Ducoudre (2010) analyses the impact on climate of large-scale deforestation; the albedo cooling effect dominates for high latitude Burn scars resulting from agriculture practices, uncontrolled fires or whereas reduced evapotranspiration dominates in the tropics. This lat- deforestation (Bowman et al., 2009) have a lower albedo than unper- itudinal trend is confirmed by observations of the temperature differ- turbed vegetation (Jin and Roy, 2005). On the other hand, at high lat- ence between open land and nearby forested land (Lee et al., 2011). itude, burnt areas are more easily covered by snow, which may result in an overall increase of the surface albedo. Surface blackening of nat- Irrigated areas have continuously increased during the 20th century ural vegetation due to fire is relatively short lived and typically disap- although a slowdown has been observed in recent decades (Bonfils ­ 687 Chapter 8 Anthropogenic and Natural Radiative Forcing and Lobell, 2007). There is clear evidence that irrigation leads to local RF while numbers quoted from AR4 will be provided both as RF and cooling of several degrees (Kueppers et al., 2007). Irrigation also instantaneous RF at TOA. affects cloudiness and precipitation (Puma and Cook, 2010). In the United States, DeAngelis et al. (2010) found that irrigation in the Great 8.4.1.1 Satellite Measurements of Total Solar Irradiance Plains in the summer produced enhanced precipitation in the Midwest 1000 km to the northeast. Total solar irradiance (TSI) measured by the Total Irradiance Monitor (TIM) on the spaceborne Solar Radiation and Climate Experiment 8 8.3.5.6 Conclusions (SORCE) is 1360.8 +/- 0.5 W m 2 during 2008 (Kopp and Lean, 2011) which is ~4.5 W m 2 lower than the Physikalisch-Meteorologisches There is still a rather wide range of estimates of the albedo change Observatorium Davos (PMOD) TSI composite during 2008 (Frohlich, due to anthropogenic land use change, and its RF. Although most 2009).The difference is probably due to instrumental biases in meas- published studies provide an estimate close to 0.2 W  m 2, there is urements prior to TIM. Measurements with the PREcision MOnitor convincing evidence that it may be somewhat weaker as the albedo Sensor (PREMOS) instrument support the TIM absolute values (Kopp difference between natural and anthropogenic land cover may have and Lean, 2011). The TIM calibration is also better linked to national been overestimated. In addition, non-radiative impact of land use have standards which provides further support that it is the most accurate a similar magnitude, and may be of opposite sign, as the albedo effect (see Supplementary Material Section 8.SM.6). Given the lower TIM TSI (though these are not part of RF). A comparison of the impact of land values relative to currently used standards, most general circulation use change according to seven climate models showed a wide range of models are calibrated to incorrectly high values. However, the few results (Pitman et al., 2009), partly due to difference in the implemen- tenths of a percent bias in the absolute TSI value has minimal con- tation of land cover change, but mostly due to different assumptions sequences for climate simulations because the larger uncertainties in on ecosystem albedo, plant phenology and evapotranspiration. There cloud properties have a greater effect on the radiative balance. As the is no agreement on the sign of the temperature change induced by maximum-to-minimum TSI relative change is well-constrained from anthropogenic land use change. It is very likely that land use change observations, and historical variations are calculated as changes rela- led to an increase of the Earth albedo with a RF of 0.15 +/- 0.10 W m 2, tive to modern values, a revision of the absolute value of TSI affects RF but a net cooling of the surface accounting for processes that are not by the same fraction as it affects TSI. The downward revision of TIM TSI limited to the albedo is about as likely as not. with respect to PMOD, being 0.3%, thus has a negligible impact on RF, which is given with a relative uncertainty of several tenths of a percent. 8.4 Natural Radiative Forcing Changes: Since 1978, several independent space-based instruments have direct- Solar and Volcanic ly measured the TSI. Three main composite series were constructed, referred to as the Active Cavity Radiometer Irradiance Monitor (ACRIM) Several natural drivers of climate change operate on multiple time (Willson and Mordvinov, 2003), the Royal Meteorological Institute of scales. Solar variability takes place at many time scales that include Belgium (RMIB) (Dewitte et al., 2004) and the PMOD (Frohlich, 2006) centennial and millennial scales (Helama et al., 2010), as the radiant series. There are two major differences between ACRIM and PMOD. energy output of the Sun changes. Also, variations in the astronomical The first is the rapid drift in calibration between PMOD and ACRIM alignment of the Sun and the Earth (Milankovitch cycles) induce cycli- before 1981. This arises because both composites employ the Hickey cal changes in RF, but this is substantial only at millennial and longer Frieden (HF) radiometer data for this interval, while a re-evaluation of time scales (see Section 5.2.1.1). Volcanic forcing is highly episodic, the early HF degradation has been implemented by PMOD but not by but can have dramatic, rapid impacts on climate. No major asteroid ACRIM. The second one, involving also RMIB, is the bridging of the gap impacts occurred during the reference period (1750 2012) and thus between the end of ACRIM I (mid-1989) and the beginning of ACRIM this effect is not considered here. This section discusses solar and II (late 1991) observations, as it is possible that a change in HF data volcanic forcings, the two dominant natural contributors of climate occurred during this gap. This possibility is neglected in ACRIM and change since the pre-industrial time. thus its TSI increases by more than 0.5 W m 2 during solar cycle (SC) 22. These differences lead to different long-term TSI trends in the three 8.4.1 Solar Irradiance composites (see Figure 8.10): ACRIM rises until 1996 and subsequently declines, RMIB has an upward trend through 2008 and PMOD shows a In earlier IPCC reports the forcing was estimated as the instantaneous decline since 1986 which unlike the other two composites, follows the RF at TOA. However, due to wavelength-albedo dependence, solar radi- solar-cycle-averaged sunspot number (Lockwood, 2010). Moreover, the ation-wavelength dependence and absorption within the stratosphere ACRIM trend implies that the TSI on time scales longer than the SC is and the resulting stratospheric adjustment, the RF is reduced to about positively correlated with the cosmic ray variation indicating a decline 78% of the TOA instantaneous RF (Gray et al., 2009). There is low con- in TSI throughout most of the 20th century (the opposite to most TSI fidence in the exact value of this number, which can be model and time reconstructions produced to date; see Section 8.4.1.2). Furthermore, scale dependent (Gregory et al., 2004; Hansen et al., 2005). AR4 gives extrapolating the ACRIM TSI long-term drift would imply a brighter an 11-year running mean instantaneous TOA RF between 1750 and Sun in the Maunder minimum (MM) than now, again opposite to most the present of 0.12 W m 2 with a range of estimates of 0.06 to 0.30 W TSI reconstructions (Lockwood and Frohlich, 2008). Finally, analysis m 2, equivalent to a RF of 0.09 W m 2 with a range of 0.05 to 0.23 W of instrument degradation and pointing issues (Lee et al., 1995) and m 2. For a consistent treatment of all forcing agents, hereafter we use independent modeling based on solar magnetograms (Wenzler et al., 688 Anthropogenic and Natural Radiative Forcing Chapter 8 on physical modeling of the evolution of solar surface magnetic flux, and its relationship with sunspot group number (before 1974) and sunspot umbra and penumbra and faculae afterwards. This provides ) a more detailed reconstruction than other models (see the time series in Supplementary Material Table 8.SM.3). The best estimate from our ( assessment of the most reliable TSI reconstruction gives a 7-year run- ning mean RF between the minima of 1745 and 2008 of 0.05 W m 2. 8 Our assessment of the range of RF from TSI changes is 0.0 to 0.10 W m 2 which covers several updated reconstructions using the same 7-year running mean past-to-present minima years (Wang et al., 2005; Steinhilber et al., 2009; Delaygue and Bard, 2011), see Supplementa- ry Material Table 8.SM.4. All reconstructions rely on indirect proxies that inherently do not give consistent results. There are relatively large discrepancies among the models (see Figure 8.11).With these consid- erations, we adopt this value and range for AR5. This RF is almost half Figure 8.10 | Annual average composites of measured total solar irradiance: The of that in AR4, in part because the AR4 estimate was based on the Active Cavity Radiometer Irradiance Monitor (ACRIM) (Willson and Mordvinov, 2003), previous solar cycle minimum while the AR5 estimate includes the drop the Physikalisch-Meteorologisches Observatorium Davos (PMOD) (Frohlich, 2006) and of TSI in 2008 compared to the previous two SC minima (see 8.4.1). the Royal Meteorological Institute of Belgium (RMIB) (Dewitte et al., 2004).These com- Concerning the uncertainty range, in AR4 the upper limit corresponded posites are standardized to the annual average (2003 2012) Total Irradiance Monitor to the reconstruction of Lean (2000), based on the reduced brightness (TIM) (Kopp and Lean, 2011) measurements that are also shown. of non-cycling Sun-like stars assumed typical of a Maunder minimum (MM) state. The use of such stellar analogues was based on the work 2009; Ball et al., 2012), confirm the need for correction of HF data, and of Baliunas and Jastrow (1990), but more recent surveys have not we conclude that PMOD is more accurate than the other composites. reproduced their results and suggest that the selection of the original set was flawed (Hall and Lockwood, 2004; Wright, 2004); the lower TSI variations of approximately 0.1% were observed between the limit from 1750 to present in AR4 was due to the assumed increase maximum and minimum of the 11-year SC in the three composites in the amplitude of the 11-year cycle only. Thus the RF and uncertain- mentioned above (Kopp and Lean, 2011). This variation is mainly due ty range have been obtained in a different way in AR5 compared to to an interplay between relatively dark sunspots, bright faculae and AR4. Maxima to maxima RF give a higher estimate than minima to bright network elements (Foukal and Lean, 1988; see Section 5.2.1.2). minima RF, but the latter is more relevant for changes in solar activity. A declining trend since 1986 in PMOD solar minima is evidenced in Given the medium agreement and medium evidence, this RF value has Figure 8.10. Considering the PMOD solar minima values of 1986 and a medium confidence level (although confidence is higher for the last 2008, the RF is 0.04 W m 2. Our assessment of the uncertainty range three decades). Figure 8.11 shows several TSI reconstructions modelled of changes in TSI between 1986 and 2008 is 0.08 to 0.0 W m 2 and using sunspot group numbers (Wang et al., 2005; Krivova et al., 2010; thus very likely negative, and includes the uncertainty in the PMOD data (Frohlich, 2009; see Supplementary Material Section 8.SM.6) but is extended to also take into account the uncertainty of combining the satellite data. ) For incorporation of TIM data with the previous and overlapping data, ( in Figure 8.10 we have standardized the composite time series to the TIM series (over 2003 2012, the procedure is explained in Supplemen- tary Material Section 8.SM.6. Moreover as we consider annual averag- es, ACRIM and PMOD start at 1979 because for 1978 both composites have only two months of data. 8.4.1.2 Total Solar Irradiance Variations Since Preindustrial Time The year 1750, which is used as the preindustrial reference for estimat- ing RF, corresponds to a maximum of the 11-year SC. Trend analysis are usually performed over the minima of the solar cycles that are more Figure 8.11 | Reconstructions of total solar irradiance since1745; annual resolution stable. For such trend estimates, it is then better to use the closest series from Wang et al. (2005) with and without an independent change in the back- SC minimum, which is in 1745. To avoid trends caused by compar- ground level of irradiance, Krivova et al. (2010) combined with Ball et al. (2012) and ing different portions of the solar cycle, we analyze TSI changes using 5-year time resolution series from Steinhilber et al. (2009) and Delaygue and Bard multi-year running means. For the best estimate we use a recent TSI (2011). The series are standardized to the Physikalisch-Meteorologisches Observato- rium Davos (PMOD) measurements of solar cycle 23 (1996 2008) (PMOD is already reconstruction by Krivova et al. (2010) between 1745 and 1973 and standardized to Total Irradiance Monitor). from 1974 to 2012 by Ball et al. (2012). The reconstruction is based 689 Chapter 8 Anthropogenic and Natural Radiative Forcing Ball et al., 2012) and sunspot umbra and penumbra and faculae (Ball ing of the background ozone is dominant and over twice as large as et al., 2012), or cosmogenic isotopes (Steinhilber et al., 2009; Delaygue the ozone heating in the upper stratosphere and above, while indirect and Bard, 2011). These reconstructions are standardized to PMOD SC solar and terrestrial radiation through the SC-induced ozone change 23 (1996 2008) (see also Supplementary Material Section 8.SM.6). is dominant below about 5 hPa (Shibata and Kodera, 2005). The RF due to solar-induced ozone changes is a small fraction of the solar RF For the MM-to-present AR4 gives a TOA instantaneous RF range of discussed in Section 8.4.1.1 (Gray et al., 2009). 0.1 to 0.28 W m 2, equivalent to 0.08 to 0.22 W m 2 with the RF defi- 8 nition used here. The reconstructions in Schmidt et al. (2011) indicate 8.4.1.4.2 Measurements of spectral irradiance a MM-to-present RF range of 0.08 to 0.18 W  m 2, which is within the AR4 range although narrower. As discussed above, the estimates Solar spectral irradiance (SSI) variations in the far (120 to 200 nm) based on irradiance changes in Sun-like stars are not included in this and middle (200 to 300 nm) ultraviolet (UV) are the primary driver for range because the methodology has been shown to be flawed. A more heating, composition, and dynamic changes of the stratosphere, and detailed explanation of this is found in Supplementary Material Section although these wavelengths compose a small portion of the incoming 8.SM.6. For details about TSI reconstructions on millennia time scales radiation they show large relative variations between the maximum see Section 5.2.1.2. and minimum of the SC compared to the corresponding TSI chang- es. As UV heating of the stratosphere over a SC has the potential to 8.4.1.3 Attempts to Estimate Future Centennial Trends of influence the troposphere indirectly, through dynamic coupling, and Total Solar Irradiance therefore climate (Haigh, 1996; Gray et al., 2010), the UV may have a more significant impact on climate than changes in TSI alone would Cosmogenic isotope and sunspot data (Rigozo et al., 2001; Solanki and suggest. Although this indicates that metrics based only on TSI are not Krivova, 2004; Abreu et al., 2008) reveal that currently the Sun is in a appropriate, UV measurements present several controversial issues grand activity maximum that began about 1920 (20th century grand and modelling is not yet robust. maximum). However, SC 23 showed an activity decline not previous- ly seen in the satellite era (McComas et al., 2008; Smith and Balogh, Multiple space-based measurements made in the past 30 years indi- 2008; Russell et al., 2010). Most current estimations suggest that the cated that UV variations account for about 30% of the SC TSI varia- forthcoming solar cycles will have lower TSI than those for the past 30 tions, while about 70% were produced within the visible and infrared years (Abreu et al., 2008; Lockwood et al., 2009; Rigozo et al., 2010; (Rottman, 2006). However, current models and data provide the range Russell et al., 2010). Also there are indications that the mean magnetic of 30 to 90% for the contribution of the UV variability below 400 nm to field in sunspots may be diminishing on decadal level. A linear expan- TSI changes (Ermolli et al., 2013), with a more probable value of ~60% sion of the current trend may indicate that of the order of half the (Morrill et al., 2011; Ermolli et al., 2013). The Spectral Irradiance Mon- sunspot activity may disappear by about 2015 (Penn and Livingston, itor (SIM) on board SORCE (Harder et al., 2009) shows, over the SC 23 2006). These studies only suggest that the Sun may have left the 20th declining phase, measurements that are rather inconsistent with prior century grand maximum and not that it is entering another grand min- understanding, indicating that additional validation and uncertainty imum. But other works propose a grand minimum during the 21st cen- estimates are needed (DeLand and Cebula, 2012; Lean and Deland, tury, estimating an RF within a range of -0.16 to 0.12 W m 2 between 2012). A wider exposition can be found in Supplementary Material this future minimum and the present-day TSI (Jones et al., 2012). How- Section 8.SM.6. ever, much more evidence is needed and at present there is very low confidence concerning future solar forcing estimates. 8.4.1.4.3 Reconstructions of preindustrial ultraviolet variations Nevertheless, even if there is such decrease in the solar activity, there The Krivova et al. (2010) reconstruction is based on what is known is a high confidence that the TSI RF variations will be much smaller about spectral contrasts of different surface magnetic features and the in magnitude than the projected increased forcing due to GHG (see relationship between TSI and magnetic fields. The authors interpolated Section 12.3.1). backwards to the year 1610 based on sunspot group numbers and magnetic information. The Lean (2000) model is based on historical 8.4.1.4 Variations in Spectral Irradiance sunspot number and area and is scaled in the UV using measurements from the Solar Stellar Irradiance Comparison Experiment (SOLSTICE) 8.4.1.4.1 Impacts of ultraviolet variations on the stratosphere on board the Upper Atmosphere Research Satellite (UARS). The results show smoothed 11-year UV SSI changes between 1750 and the pres- Ozone is the main gas involved in stratospheric radiative heating. ent of about 25% at about 120 nm, about 8% at 130 to 175 nm, ~4% Ozone production rate variations are largely due to solar UV irradi- at 175 to 200 nm, and about 0.5% at 200 to 350 nm. Thus, the UV SSI ance changes (HAIGH, 1994), with observations showing statistical- appears to have generally increased over the past four centuries, with ly significant variations in the upper stratosphere of 2 to 4% along larger trends at shorter wavelengths. As few reconstructions are avail- the SC (Soukharev and Hood, 2006). UV variations may also produce able, and recent measurements suggest a poor understanding of UV transport-induced ozone changes due to indirect effects on circulation variations and their relationship with solar activity, there is very low (Shindell et al., 2006b). In addition, statistically significant evidence for confidence in these values. an 11-year variation in stratospheric temperature and zonal winds is attributed to UV radiation (Frame and Gray, 2010). The direct UV heat- 690 Anthropogenic and Natural Radiative Forcing Chapter 8 8.4.1.5 The Effects of Cosmic Rays on Clouds twice the magnitude of the 1999 2002 RF of 0.06 ( 0.08 to 0.04) W m 2, consistent with the trends noted in Solomon et al. (2011). Changing cloud amount or properties modify the Earth s albedo and However, the CMIP5 simulations discussed elsewhere in this report therefore affect climate. It has been hypothesized that cosmic ray flux did not include the recent small volcanic forcing in their calculations. create atmospheric ions which facilitates aerosol nucleation and new New work has also produced a better understanding of high latitude particle formation with a further impact on cloud formation (Dickinson, eruptions, the hydrological response to volcanic eruptions (Trenberth 1975; Kirkby, 2007). High solar activity means a stronger heliospheric and Dai, 2007; Anchukaitis et al., 2010), better long-term records of 8 magnetic field and thus a more efficient screen against cosmic rays. past volcanism and better understanding of the effects of very large Under the hypothesis underlined above, the reduced cosmic ray flux eruptions. would promote fewer clouds amplifying the warming effect expected from high solar activity. There is evidence from laboratory, field and There are several ways to measure both the SO2 precursor and sul- modelling studies that ionization from cosmic ray flux may enhance phate aerosols in the stratosphere, using balloons, airplanes, and both aerosol nucleation in the free troposphere (Merikanto et al., 2009; ground- and satellite-based remote sensing. Both the infrared and Mirme et al., 2010; Kirkby et al., 2011). However, there is high con- ultraviolet signals sensed by satellite instruments can measure SO2, fidence (medium evidence and high agreement) that the cosmic ray and stratospheric aerosol measurements by space-based sensors have ionization mechanism is too weak to influence global concentrations been made on a continuous basis since 1978 by a number of instru- of cloud condensation nuclei or their change over the last century or ments employing solar and stellar occultation, limb scattering, limb during a SC in a climatically significant way (Harrison and Ambaum, emission, and lidar strategies (Thomason and Peter, 2006; Kravitz et al., 2010; Erlykin and Wolfendale, 2011; Snow-Kropla et al., 2011). A 2011; Solomon et al., 2011). detailed exposition is found in Section 7.4.6. Forster et al. (2007) described four mechanisms by which volcanic 8.4.2 Volcanic Radiative Forcing forcing influences climate: RF due to aerosol radiation interaction; differential (vertical or horizontal) heating, producing gradients and 8.4.2.1 Introduction changes in circulation; interactions with other modes of circulation, such as El Nino-Southern Oscillation (ENSO); and ozone depletion with Volcanic eruptions that inject substantial amounts of SO2 gas into its effects on stratospheric heating, which depends on anthropogenic the stratosphere are the dominant natural cause of externally forced chlorine (stratospheric ozone would increase with a volcanic eruption climate change on the annual and multi-decadal time scales, both under low-chlorine conditions). In addition, the enhanced diffuse light because of the multi-decadal variability of eruptions and the time from volcanic aerosol clouds impacts vegetation and hence the carbon scale of the climate system response, and can explain much of the cycle (Mercado et al., 2009) and aerosol cloud interaction of sulphate pre-industrial climate change of the last millennium (Schneider et aerosols on clouds in the troposphere can also be important (Schmidt al., 2009; Brovkin et al., 2010; Legras et al., 2010; Miller et al., 2012). et al., 2010), though Frolicher et al. (2011) showed that the impacts of Although volcanic eruptions inject both mineral particles (called ash the 1991 Mt Pinatubo eruption on the carbon cycle were small. or tephra) and sulphate aerosol precursor gases (predominantly SO2) into the atmosphere, it is the sulphate aerosols, which because of their 8.4.2.2 Recent Eruptions small size are effective scatterers of sunlight and have long lifetimes, that are responsible for RF important for climate. Global annually aver- The background stratospheric aerosol concentration was affected by aged emissions of CO2 from volcanic eruptions since 1750 have been several small eruptions in the first decade of the 21st century (Nagai et at least 100 times smaller than anthropogenic emissions and incon- al., 2010; Vernier et al., 2011; Neely et al., 2013; see also Figure 8.13), sequential for climate on millennial and shorter time scales (Gerlach, with a very small contribution from tropospheric pollution (Siddaway 2011). To be important for climate change, sulphur must be injected and Petelina, 2011; Vernier et al., 2011), and had a small impact on RF into the stratosphere, as the lifetime of aerosols in the troposphere is (Solomon et al., 2011). Two recent high-latitude eruptions, of Kasa- only about one week, whereas sulphate aerosols in the stratosphere tochi Volcano (52.1°N, 175.3°W) on August 8, 2008 and of Sarychev from tropical eruptions have a lifetime of about one year, and those Volcano (48.1°N, 153.2°E) on June 12 16, 2009, each injected ~1.5 from high-latitude eruptions last several months. Most stratospheric Tg(SO2) into the stratosphere, but did not produce detectable climate aerosols are from explosive eruptions that directly put sulphur into the response. Their eruptions, however, led to better understanding of the stratosphere, but Bourassa et al. (2012, 2013) showed that sulphur dependence of the amount of material and time of year of high-lat- injected into the upper troposphere can then be lifted into the strato- itude injections to produce climate impacts (Haywood et al., 2010; sphere over the next month or two by deep convection and large scale Kravitz et al., 2010, 2011). The RF from high-latitude eruptions is a Asian summer monsoon circulation, although Vernier et al. (2013) and function of seasonal distribution of insolation and the 3- to 4-month Fromm et al. (2013) suggested that direct injection was also important. lifetime of high-latitude volcanic aerosols. Kravitz and Robock (2011) Robock (2000), AR4 (Forster et al., 2007) and Timmreck (2012) provide showed that high-latitude eruptions must inject at least 5 Tg(SO2) into summaries of this relatively well understood forcing agent. the lower stratosphere in the spring or summer, and much more in fall or winter, to have a detectible climatic response. There have been no major volcanic eruptions since Mt Pinatubo in 1991 (Figure 8.12), but several smaller eruptions have caused a RF for On April 14, 2010 the Eyjafjallajökull volcano in Iceland (63.6°N, the years 2008 2011 of 0.11 ( 0.15 to 0.08) W m 2, approximately 19.6°W) began an explosive eruption phase that shut down air traffic 691 Chapter 8 Anthropogenic and Natural Radiative Forcing in Europe for 6 days and continued to disrupt it for another month. The climatic impact of Eyjafjallajökull was about 10,000 times less than that of Mt Pinatubo; however, because it emitted less than 50 ktonnes SO2 and its lifetime in the troposphere was 50 times less than if it had been injected into the stratosphere, and was therefore undetectable amidst the chaotic weather noise in the atmosphere (Robock, 2010). 2011 saw the continuation of a number of small eruptions with signif- 8 icant tropospheric SO2 and ash injections, including Puyehue-Cordón Caulle in Chile, Nabro in Eritrea, and Grimsvötn in Iceland. None have been shown to have produced an important RF, but the June 13, 2011 Nabro eruption resulted in the largest stratospheric aerosol cloud since the 1991 Mt Pinatubo eruption (Bourassa et al., 2012), more than 1.5 Tg(SO2). Figure 8.12 shows reconstructions of volcanic aerosol optical depth since 1750. Figure 8.13 shows details of the vertical distribution of Figure 8.12 | Volcanic reconstructions of global mean aerosol optical depth (at 550 nm). Gao et al. (2008) and Crowley and Unterman (2013) are from ice core data, and stratospheric aerosols in the tropics since 1985. The numerous small end in 2000 for Gao et al. (2008) and 1996 for Crowley and Unterman (2013). Sato et eruptions in the past decade are evident, but some of them were at al. (1993) includes data from surface and satellite observations, and has been updated higher latitudes and their full extent is not captured in this plot. through 2011. (Updated from Schmidt et al., 2011.) Figure 8.13 | (Top) Monthly mean extinction ratio (525 nm) profile evolution in the tropics [20°N to 20°S] from January 1985 through December 2012 derived from Stratospheric Aerosol and Gas Experiment (SAGE) II extinction in 1985 2005 and Cloud-Aerosol Lidar and Infrared Pathfinder Satellite Observation (CALIPSO) scattering ratio in 2006 2012, after removing clouds below 18 km based on their wavelength dependence (SAGE II) and depolarization properties (CALIPSO) compared to aerosols. Black contours represent the extinction ratio in log-scale from 0.1 to 100. The position of each volcanic eruption occurring during the period is displayed with its first two letters on the horizontal axis, where tropical eruptions are noted in red. The eruptions were Nevado del Ruiz (Ne), Augustine (Au), Chikurachki (Ch), Kliuchevskoi (Kl), Kelut (Ke), Pinatubo (Pi), Cerro Hudson (Ce), Spur (Sp), Lascar (La), Rabaul (Ra), Ulawun (Ul), Shiveluch (Sh), Ruang (Ru), Reventador (Re), Manam (Ma), Soufriere Hills (So), Tavurvur (Ta), Okmok (Ok), Kasatochi (Ka), Victoria (Vi* forest fires with stratospheric aerosol injection), Sarychev (Sa), Merapi (Me), Nabro (Na). (Updated from Figure 1 from Vernier et al., 2011.) (Bottom) Mean stratospheric aerosol optical depth (AOD) in the tropics [20°N to 20°S] between the tropopause and 40 km since 1985 from the SAGE II (black line), the Global Ozone Monitoring by Occultation of Stars (GOMOS) (red line), and CALIPSO (blue line). (Updated from Figure 5 from Vernier et al., 2011.) 692 Anthropogenic and Natural Radiative Forcing Chapter 8 Box 8.3 | Volcanic Eruptions as Analogues Volcanic eruptions provide a natural experiment of a stratospheric aerosol cloud that can serve to inform us of the impacts of the pro- posed production of such a cloud as a means to control the climate, which is one method of geoengineering (Rasch et al., 2008); see Section 7.7. For example, Trenberth and Dai (2007) showed that the Asian and African summer monsoon, as well as the global hydro- logical cycle, was weaker for the year following the 1991 Mt Pinatubo eruption, which is consistent with climate model simulations 8 (Robock et al., 2008). MacMynowski et al. (2011) showed that because the climate system response of the hydrological cycle is rapid, forcing from volcanic eruptions, which typically last about a year, can serve as good analogues for longer-lived forcing. The formation of sulphate aerosols, their transport and removal, their impacts on ozone chemistry, their RF, and the impacts on whitening skies all also serve as good analogues for geoengineering proposals. Volcanic impacts on the carbon cycle because of more diffuse radiation (Mercado et al., 2009) and on remote sensing can also be useful analogues, and the impacts of contrail-generated sub-visual cirrus (Long et al., 2009) can be used to test the long-term impacts of a permanent stratospheric cloud. Smoke from fires generated by nuclear explosions on cities and industrial areas, which could be lofted into the stratosphere, would cause surface cooling and a reduction of stratospheric ozone (Mills et al., 2008). Volcanic eruptions that produce substantial strato- spheric aerosol clouds also serve as an analogue that supports climate model simulations of the transport and removal of stratospheric aerosols, their impacts on ozone chemistry, their RF, and the climate response. The use of the current global nuclear arsenal still has the potential to produce nuclear winter, with continental temperatures below freezing in summer (Robock et al., 2007a; Toon et al., 2008), and the use of only 100 nuclear weapons could produce climate change unprecedented in recorded human history (Robock et al., 2007b), with significant impacts on global agriculture (Özdo an et al., 2013; Xia and Robock, 2013). 8.4.2.3 Records of Past Volcanism and Effects of Very 8.4.2.4 Future Effects Large Eruptions We expect large eruptions over the next century but cannot predict Although the effects of volcanic eruptions on climate are largest in when. Ammann and Naveau (2003) and Stothers (2007) suggested an the 2 years following a large stratospheric injection, and the winter 80-year periodicity in past eruptions, but the data record is quite short warming effect in the NH has been supported by long-term records and imperfect, and there is no mechanism proposed that would cause (Fischer et al., 2007), there is new work indicating extended volcanic this. While the period 1912 1963 was unusual for the past 500 years in impacts via long-term memory in the ocean heat content and sea level having no large volcanic eruptions, and the period 1250 1300 had the (Stenchikov et al., 2009; Gregory, 2010; Otterä et al., 2010). Zanchet- most globally climatically significant eruptions in the past 1500 years tin et al. (2012) found changes in the North Atlantic Ocean circulation (Gao et al., 2008), current knowledge only allows us to predict such that imply strengthened northward oceanic heat transport a decade periods on a statistical basis, assuming that the recent past distribu- after major eruptions, which contributes to the emergence of extensive tions are stationary. Ammann and Naveau (2003), Gusev (2008), and winter warming over the continental NH along with persistent cooling Deligne et al. (2010) studied these statistical properties and Ammann over Arctic regions on decadal time scales, in agreement with Zhong et and Naveau (2010) showed how they could be used to produce a sta- al. (2011) and Miller et al. (2012). tistical distribution for future simulations. Although the future forcing from volcanic eruptions will depend only on the stratospheric aerosol New work on the mechanisms by which a supereruption (Self and loading for most forcing mechanisms, the future effects on reducing Blake, 2008) could force climate has focused on the 74,000 BP eruption ozone will diminish as ozone depleting substances diminish in the of the Toba volcano (2.5°N, 99.0°E). Robock et al. (2009) used simu- future (Eyring et al., 2010b). lations of up to 900 times the 1991 Mt Pinatubo sulphate injection to show that the forcing is weaker than that predicted based on a linear relationship with the sulphate aerosol injection. The results agreed 8.5 Synthesis of Global Mean Radiative with a previous simulation by Jones et al. (2005). They also showed Forcing, Past and Future that chemical interactions with ozone had small impacts on the forcing and that the idea of Bekki et al. (1996) that water vapour would limit The RF can be used to quantify the various agents that drive climate and prolong the growth of aerosols was not supported. Timmreck et al. change over the Industrial Era or the various contributions to future (2010) however, incorporating the idea of Pinto et al. (1989) that aer- climate change. There are multiple ways in which RF can be attribut- osols would grow and therefore both have less RF per unit mass and ed to underlying causes, each providing various perspectives on the fall out of the atmosphere more quickly, found much less of a radiative importance of the different factors driving climate change. This section impact from such a large stratospheric input. evaluates the RF with respect to emitted component and with respect to the ultimate atmospheric concentrations. The uncertainties in the RF 693 Chapter 8 Anthropogenic and Natural Radiative Forcing agents vary and the confidence levels for these are presented in this value within the estimated range. Some of the RF agents have robust section. Finally, this section shows historical and scenarios of future e ­ vidence such as WMGHG with well documented increases based on time evolution of RF. high precision measurements as well as contrails as additional clouds which can be seen by direct observations. However, for some forcing 8.5.1 Summary of Radiative Forcing by Species and agents the evidence is more limited regarding their existence such as Uncertainties aerosol influence on cloud cover. The consistency in the findings for a particular forcing agent determines the evaluation of the evidence. 8 Table 8.5 has an overview of the RF agents considered here and each of A combination of different methods, for example, observations and them is given a confidence level for the change in RF over the Industrial modeling, and thus the understanding of the processes causing the Era to the present day. The confidence level is based on the evidence forcing is important for this evaluation. The agreement is a qualitative (robust, medium, and limited) and the agreement (high, medium, and judgment of the difference between the various estimates for a par- low; see further description in Chapter 1). The confidence level of the ticular RF agent. Figure 1.11 shows how the combined evidence and forcing agents goes beyond the numerical values available in estimates agreement results in five levels for the confidence level. and is an assessment for a particular forcing agent to have a real Table 8.5 | Confidence level for the forcing estimate associated with each forcing agent for the 1750 2011 period. The confidence level is based on the evidence and the agree- ment as given in the table. The basis for the confidence level and change since AR4 is provided. See Figure 1.11 for further description of the evidence, agreement and confidence level. The colours are adopted based on the evidence and agreement shown in Figure 1.11. Dark green is High agreement and Robust evidence , light green is either High agreement and Medium evidence or Medium agreement and Robust evidence , yellow is either High agreement and limited evidence or Medium agreement and Medium evidence or Low agreement and Robust evidence , orange is either Medium agreement and Limited evidence or Low agreement and Medium evidence and finally red is Low agreement and Limited evidence . Note, that the confidence levels given in Table 8.5 are for 2011 relative to 1750 and for some of the agents the confidence level may be different for certain portions of the Industrial Era. Confidence Basis for Uncertainty Estimates Change in Under- Evidence Agreement Level (more certain / less certain) standing Since AR4 Well-mixed Measured trends from different observed data sets and differences No major change Robust High Very high greenhouse gases between radiative transfer models Tropospheric Observed trends of ozone in the troposphere and model results for No major change Robust Medium High ozone the industrial era/Differences between model estimates of RF Stratospheric Observed trends in stratospheric and total ozone and model- No major change Robust Medium High ozone ling of ozone depletion/Differences between estimates of RF Stratospheric Elevated owing to more studies Similarities in results of independent methods to estimate the water vapour Robust Low Medium RF/Known uncertainty in RF calculations from CH4 Aerosol radiation A large set of observations and converging independent estimates of Elevated owing to more robust esti- Robust Medium High interactions RF/Differences between model estimates of RF mates from independent methods Variety of different observational evidence and modelling activities/ ERF in AR5 has a similar Aerosol cloud Medium Low Low Spread in model estimates of ERF and differences between confidence level to RF in AR4 interactions observations and model results Rapid adjustment Elevated owing to Observational evidence combined with results from different types of aerosol radiation Medium Low Low increased evidence models/Large spread in model estimates interactions A large set of observations and model results, independent methods Not provided previously Total aerosol Medium Medium Medium to derive ERF estimates/Aerosol cloud interaction processes effect and anthropogenic fraction of CCN still fairly uncertain Surface albedo Estimates of deforestation for agricultural purposes and well known Elevated owing to the availability Robust Medium High (land use) physical processes/Spread in model estimates of RF of high-quality satellite data Surface albedo No major change Observations of snow samples and the link between BC content (BC aerosol on Medium Low Low in snow and albedo/Large spread in model estimates of RF snow and ice) Contrails observations , large number of model estimates/Spread in Elevated owing to more studies Contrails Robust Low Medium model estimates of RF and uncertainties in contrail optical properties Contrail- induced Observations of a few events of contrail induced cirrus/Extent of Elevated owing to additional Medium Low Low cirrus events uncertain and large spread in estimates of ERF studies increasing the evidence Satellite information over recent decades and small uncertainty Elevated owing to better Solar irradiance Medium Medium Medium in radiative transfer calculations/Large relative spread in agreement of a weak RF reconstructions based on proxy data Observations of recent volcanic eruptions/Reconstructions of Elevated owing to improved Volcanic aerosol Robust Medium High past eruptions understanding Notes: The confidence level for aerosol cloud interactions includes rapid adjustments (which include what was previously denoted as cloud lifetime effect or second indirect aerosol effect). The separate confidence level for the rapid adjustment for aerosol cloud interactions is very low. For aerosol radiation interaction the table provides separate confidence levels for RF due to aerosol radiation interaction and rapid adjustment associated with aerosol radiation interaction. 694 Anthropogenic and Natural Radiative Forcing Chapter 8 Evidence is robust for several of the RF agents because of long term used instead of confidence level. For comparison with previous IPCC observations of trends over the industrial era and well defined links assessments the LOSU is converted approximately to confidence level. between atmospheric or land surfaced changes and their radiative Note that LOSU and confidence level use different terms for their rank- effects. Evidence is medium for a few agents where the anthropogenic ings. The figure shows generally increasing confidence levels but also changes or the link between the forcing agent and its radiative effect that more RF mechanisms have been included over time. The confi- are less certain. Medium evidence can be assigned in cases where dence levels for the RF due to aerosol radiation interactions, surface observations or modelling provide a diversity of information and thus albedo due to land use and volcanic aerosols have been raised and 8 not a consistent picture for a given forcing agent. We assess the evi- are now at the same ranking as those for change in stratospheric and dence to be limited only for rapid adjusment associated with aerosol tropospheric ozone. This is due to an increased understanding of key cloud interaction where model studies in some cases indicate changes parameteres and their uncertainties for the elevated RF agents. For but direct observations of cloud alterations are scarce. High agreement tropospheric and stratospheric ozone changes, research has shown fur- is given only for the WMGHG where the relative uncertainties in the RF ther complexities with changes primarily influencing the troposphere estimates are much smaller than for the other RF agents. Low agree- or the stratosphere being linked to some extent (see Section 8.3.3). The ment can either be due to large diversity in estimates of the magnitude rapid adjustment associated with aerosol cloud interactions is given of the forcing or from the fact that the method to estimate the forcing the confidence level very low and had a similar level in AR4. For rapid has a large uncertainty. Stratospheric water vapour is an example of adjustment associated with aerosol radiation interactions (previously the latter with modest difference in the few available estimates but denoted as semi-direct effect) the confidence level is low and is raised a known large uncertainty in the radiative transfer calculations (see compared to AR4, as the evidence is improved and is now medium (see further description in Section 8.3.1). Section 7.5.2). Figure 8.14 shows the development of the confidence level over the Table 8.6 shows the best estimate of the RF and ERF (for AR5 only) last four IPCC assessments for the various RF mechanisms. In the pre- for the various RF agents from the various IPCC assessments. The RF vious IPCC reports level of scientific understanding (LOSU) has been due to WMGHG has increased by 16% and 8% since TAR and AR4, Figure 8.14 | Confidence level of the forcing mechanisms in the 4 last IPCC assessments. In the previous IPCC assessments the level of scientific understanding (LOSU) has been adopted instead of confidence level, but for comparison with previous IPCC assessments the LOSU is converted approximately to confidence level. The thickness of the bars repre- sents the relative magnitude of the current forcing (with a minimum value for clarity of presentation). LOSU for the RF mechanisms was not available in the first IPCC Assessment (Houghton et al., 1990). Rapid adjustments associated with aerosol cloud interactions (shown as RA aero. cloud interac.) which include what was previously referred to as the second indirect aerosol effect or cloud lifetime effect whereas rapid adjustments associated with aerosol radiation interactions (shown as RA aero.-rad. interac.) were previously referred to as the semi-direct effect (see Figure 7.3). In AR4 the confidence level for aerosol cloud interaction was given both for RF due to aerosol cloud interaction and rapid adjustment associated with aerosol cloud interaction. Generally the aerosol cloud interaction is not separated into various components in AR5, hence the confidence levels for ERF due to aerosol cloud interaction in AR5 and for RF due to aerosol cloud interaction from previous IPCC reports are compared. The confidence level for the rapid adjustment associated with aerosol cloud interaction is comparable for AR4 and AR5. The colours are adopted based on the evidence and agreement shown in Figure 1.11. Dark green is High agreement and Robust evidence , light green is either High agreement and Medium evidence or Medium agreement and Robust evidence , yellow is either High agreement and limited evidence or Medium agreement and Medium evidence or Low agreement and Robust evidence , orange is either Medium agreement and Limited evidence or Low agreement and Medium evidence and finally red is Low agreement and Limited evidence . 695 Chapter 8 Anthropogenic and Natural Radiative Forcing r ­espectively. This is due mainly to increased concentrations (see Sec- H ­ owever, for these forcing mechanisms the RF uncertainties are larger tion 8.3.2), whereas the other changes for the anthropogenic RF than for the WMGHG and thus it is unlikely that rapid adjustments agents compared to AR4 are due to re-evaluations and in some cases change the uncertainties substantially. from improved understanding. An increased number of studies, addi- tional observational data and better agreement between models and Figure 8.15 shows the RF for agents listed in Table 8.6 over the observations can be the causes for such re-evaluations. The best esti- 1750 2011 period. The methods for calculation of forcing estimates mates for RF due to aerosol radiation interactions, BC on snow and are described in Section 8.3 and 8.4. For some of the components the 8 solar irradiance are all substantially decreased in magnitude compared forcing estimates are based on observed abundance whereas some to AR4; otherwise the modifications to the best estimates are rather are estimated from a combination of model simulations and observa- small. For the RF due to aerosol radiation interaction and BC on snow tions and for others are purely model based. Solid bars are given for the changes in the estimates are based on additional new studies since ERF, whereas RF values are given as (additional) hatched bars. Similarly AR4 (see Section 8.3.4 and Section 7.5). For the change in the estimate the uncertainties are given for ERF in solid lines and dotted lines for of the solar irradiance it is a combination on how the RF is calculated, RF. An important assumption is that different forcing mechanisms can new evidence showing some larger earlier estimates were incorrect, be treated additively to calculate the total forcing (see Boucher and and a downward trend observed during recent years in the solar activ- Haywood, 2001; Forster et al., 2007; Haywood and Schulz, 2007). Total ity that has been taken into account (see Section 8.4.1). The estimate ERF over the Industrial Era calculated from Monte Carlo simulations for ERF due to to aerosol cloud interaction includes rapid adjustment are shown in Figure 8.16, with a best estimate of 2.29 W m 2. For each but still this ERF is smaller in magnitude than the AR4 RF estimate due of the forcing agents a probability density function (PDF) is generated to aerosol cloud interactions without rapid adjustments (a theoreti- based on uncertainties provided in Table 8.6. The combination of the cal construct not quantified in AR5). The uncertainties for ERF due to individual RF agents to derive total forcing follows the same approach CO2 increase when compared to RF (see Section 8.3.2). We assume the as in AR4 (Forster et al., 2007) which is based on the method in Bouch- relative ERF uncertainties for CO2 apply to all WMGHG. For the short- er and Haywood (2001). The PDF of the GHGs (sum of WMGHG, ozone lived GHG we do not have sufficient information to include separate and stratospheric water vapour) has a more narrow shape than the ERF uncertainty to each of these forcing agents (see Section 8.1.1.3). PDF for the aerosols owing to the much lower relative uncertainty. Table 8.6 | Summary table of RF estimates for AR5 and comparison with the three previous IPCC assessment reports. ERF values for AR5 are included. For AR5 the values are given for the period 1750 2011, whereas earlier final years have been adopted in the previous IPCC assessment reports. Global Mean Radiative Forcing (W m 2) ERF (W m 2) SAR TAR AR4 AR5 Comment AR5 (1750 1993) (1750 1998) (1750 2005) (1750 2011) Well-mixed 2.45 (2.08 to 2.82) 2.43 (2.19 to 2.67) 2.63 (2.37 to 2.89) 2.83 (2.54 to 3.12) Change due to increase 2.83 (2.26 to 3.40) greenhouse gases in concentrations (CO2, CH4, N2O, and halocarbons) Tropospheric ozone +0.40 (0.20 to 0.60) +0.35 (0.20 to 0.50) +0.35 (0.25 to 0.65) +0.40 (0.20 to 0.60) Slightly modified estimate Stratospheric ozone 0.1 ( 0.2 to 0.05) 0.15 ( 0.25 to 0.05) 0.05 ( 0.15 to +0.05) 0.05 ( 0.15 to +0.05) Estimate unchanged Stratospheric water Not estimated +0.01 to +0.03 +0.07 (+0.02, +0.12) +0.07 (+0.02 to +0.12) Estimate unchanged vapour from CH4 Aerosol radia- Not estimated Not estimated 0.50 ( 0.90 to 0.10) 0.35 ( 0.85 to +0.15) Re-evaluated to be 0.45 ( 0.95 to +0.05) tion interactions smaller in magnitude Aerosol cloud 0 to 1.5 0 to 2.0 0.70 ( 1.80 to 0.30) Not estimated Replaced by ERF and re-evaluated 0.45 ( 1.2 to 0.0) interactions (sulphate only) (all aerosols) (all aerosols) to be smaller in magnitude Surface albedo Not estimated 0.20 ( 0.40 to 0.0) 0.20 ( 0.40 to 0.0) 0.15 ( 0.25 to 0.05) Re-evaluated to be slightly (land use) smaller in magnitude Surface albedo Not estimated Not estimated +0.10 (0.0 to +0.20) +0.04 (+0.02 to +0.09) Re-evaluated to be weaker (black carbon aero- sol on snow and ice) Not estimated +0.02 (+0.006 +0.01 (+0.003 +0.01 (+0.005 No major change Contrails to +0.07) to +0.03) to +0.03) Combined contrails Not estimated 0 to +0.04 Not estimated Not estimated 0.05 (0.02 to 0.15) and contrail- induced cirrus Not estimated Not estimated 1.6 (0.6 to 2.4) Not estimated Stronger positive due to changes 2.3 (1.1 to 3.3) Total anthropogenic in various forcing agents +0.30 (+0.10 +0.30 (+0.10 +0.12 (+0.06 +0.05 (0.0 to +0.10) Re-evaluated to be weaker Solar irradiance to +0.50) to +0.50) to +0.30) Notes: Volcanic RF is not added to the table due to the periodic nature of volcanic eruptions, which makes it difficult to compare to the other forcing mechanisms. 696 Anthropogenic and Natural Radiative Forcing Chapter 8 Radiative forcing of climate between 1750 and 2011 Forcing agent CO2 Well Mixed Halocarbons Greenhouse Gases Other WMGHG CH4 N2O 8 Ozone Stratospheric Tropospheric Anthropogenic Stratospheric water vapour from CH4 Surface Albedo Land Use Black carbon on snow Contrails Contrail induced cirrus Aerosol-Radiation Interac. Aerosol-Cloud Interac. Total anthropogenic Natural Solar irradiance -1 0 1 2 3 Radiative Forcing (W m-2) Figure 8.15 | Bar chart for RF (hatched) and ERF (solid) for the period 1750 2011, where the total ERF is derived from Figure 8.16. Uncertainties (5 to 95% confidence range) are given for RF (dotted lines) and ERF (solid lines). AR4 RF Therefore, the large uncertainty in the aerosol forcing is the main 1.2 cause of the large uncertainty in the total anthropogenic ERF. The total Greenhouse anthropogenic forcing is virtually certain to be positive with the prob- 1.0 gases ability for a negative value less than 0.1%. Compared to AR4 the total Probability density function anthropogenic ERF is more strongly positive with an increase of 43%. 0.8 Aerosols This is caused by a combination of growth in GHG concentration, and Total anthropogenic thus strengthening in forcing of WMGHG, and weaker ERF estimates of 0.6 aerosols (aerosol radiation and aerosol cloud interactions) as a result of new assessments of these effects. 0.4 Figure 8.17 shows the forcing over the Industrial Era by emitted com- 0.2 pounds (see Supplementary Material Tables 8.SM.6 and 8.SM.7 for 0.0 actual numbers and references). It is more complex to view the RF -2 0 2 4 by emitted species than by change in atmospheric abundance (Figure Effective radiative forcing (Wm-2) 8.15) since the number of emitted compounds and changes leading to RF is larger than the number of compounds causing RF directly (see Figure 8.16 | Probability density function (PDF) of ERF due to total GHG, aerosol forcing and total anthropogenic forcing. The GHG consists of WMGHG, ozone and Section 8.3.3). The main reason for this is the indirect effect of sever- stratospheric water vapour. The PDFs are generated based on uncertainties provided in al compounds and in particular components involved in atmospheric Table 8.6. The combination of the individual RF agents to derive total forcing over the chemistry (see Section 8.2). To estimate the RF by the emitted com- Industrial Era are done by Monte Carlo simulations and based on the method in Boucher pounds in some cases the emission over the entire Industrial Era is and Haywood (2001). PDF of the ERF from surface albedo changes and combined con- needed (e.g., for CO2) whereas for other compounds (such as ozone trails and contrail-induced cirrus are included in the total anthropogenic forcing, but not shown as a separate PDF. We currently do not have ERF estimates for some forcing and CH4) quite complex simulations are required (see Section 8.3.3). mechanisms: ozone, land use, solar, etc. For these forcings we assume that the RF is CO2 is the dominant positive forcing both by abundance and by emit- representative of the ERF and for the ERF uncertainty an additional uncertainty of 17% ted compound. Emissions of CH4, CO, and NMVOC all lead to excess has been included in quadrature to the RF uncertainty. See Supplementary Material Sec- CO2 as one end product if the carbon is of fossil origin and is the reason tion 8.SM.7 and Table 8.SM.4 for further description on method and values used in the why the RF of direct CO2 emissions is slightly lower than the RF of calculations. Lines at the top of the figure compare the best estimates and uncertainty ranges (5 to 95% confidence range) with RF estimates from AR4. abundance change of CO2. For CH4 the contribution from emission is estimated to be almost twice as large as that from the CH4 concen- 697 Chapter 8 Anthropogenic and Natural Radiative Forcing tration change, 0.97 (0.80 to 1.14) W m 2 versus 0.48 (0.43 to 0.53) reduction of CH4 lifetime and thus its concentration, and through con- ­ W m 2, respectively. This is because emission of CH4 leads to ozone tributions to nitrate aerosol formation. The best estimate of the overall production, stratospheric water vapour, CO2 (as mentioned above), and effect of anthropogenic emissions of NOX is a negative RF (-0.15 (-0.34 importantly affects its own lifetime (Section 8.2). Actually, emissions of to +0.02) W m 2). Emissions of ammonia also contribute to nitrate aer- CH4 would lead to a stronger RF via the direct CH4 greenhouse effect osol formation, with a small offset due to compensating changes in (0.64 W m 2) than the RF from abundance change of CH4 (0.48 W m 2). sulphate aerosols. Additionally indirect effects from sulphate on atmos- This is because other compounds have influenced the lifetime of CH4 pheric compounds are not included here as models typically simulate 8 and reduced the abundance of CH4, most notably NOx. Emissions of CO a small effect, but there are large relative differences in the response (0.23 (0.18 to 0.29) W m 2) and NMVOC (0.10 (0.06 to 0.14) W m 2) between models. Impacts of emissions other than CO2 on the carbon have only indirect effects on RF through ozone production, CH4 and cycle via changes in atmospheric composition (ozone or aerosols) are CO2 and thus contribute an overall positive RF. Emissions of NOX, on also not shown owing to the limited amount of available information. the other hand, have indirect effects that lead to positive RF through ozone production and also effects that lead to negative RF through For the WMGHG, the ERF best estimate is the same as the RF. The uncertainty range is slightly larger, however. The total emission-based ERF of WMGHG is 3.00 (2.22 to 3.78) W m 2. That of CO2 is 1.68 (1.33 to 2.03) W m 2; that of CH4 is 0.97 (0.74 to 1.20) W m 2; that of strat- ospheric ozone-depleting halocarbons is 0.18 (0.01 to 0.35) W m 2. Emissions of BC have a positive RF through aerosol radiation interac- tions and BC on snow (0.64 W m 2, see Section 8.3.4 and Section 7.5). The emissions from the various compounds are co-emitted; this is in particular the case for BC and OC from biomass burning aerosols. The net RF of biomass burning emissions for aerosol radiation interactions is close to zero, but with rather strong positive RF from BC and negative RF from OC (see Sections 8.3.4 and 7.5). The ERF due to aerosol cloud interactions is caused by primary anthropogenic emissions of BC, OC and dust as well as secondary aerosol from anthropogenic emissions of SO2, NOX and NH3. However, quantification of the contribution from the various components to the ERF due to aerosol cloud interactions has not been attempted in this assessment. 8.5.2 Time Evolution of Historical Forcing The time evolution of global mean forcing is shown in Figure 8.18 for the Industrial Era. Over all time periods during the Industrial Era CO2 and other WMGHG have been the dominant term, except for short- er periods with strong volcanic eruptions. The time evolution shows an almost continuous increase in the magnitude of anthropogenic ERF. This is the case both for CO2 and other WMGHGs as well as sev- eral individual aerosol components. The forcing from CO2 and other WMGHGs has increased somewhat faster since the 1960s. Emissions ( ) of CO2 have made the largest contribution to the increased anthropo- genic forcing in every decade since the 1960s. The total aerosol ERF Figure 8.17 | RF bar chart for the period 1750 2011 based on emitted compounds (aerosol radiation interaction and aerosol cloud interaction) has the (gases, aerosols or aerosol precursors) or other changes. Numerical values and their strongest negative forcing (except for brief periods with large volcanic uncertainties are shown in Supplementary Material Tables 8.SM.6 and 8.SM.7. Note forcing), with a strengthening in the magnitude similar to many of the that a certain part of CH4 attribution is not straightforward and discussed further in other anthropogenic forcing mechanisms with time. The global mean Section 8.3.3. Red (positive RF) and blue (negative forcing) are used for emitted com- ponents which affect few forcing agents, whereas for emitted components affecting forcing of aerosol radiation interactions was rather weak until 1950 many compounds several colours are used as indicated in the inset at the upper part but strengthened in the latter half of the last century and in particular the figure. The vertical bars indicate the relative uncertainty of the RF induced by each in the period between 1950 and 1980. The RF due to aerosol radiation component. Their length is proportional to the thickness of the bar, that is, the full length interaction by aerosol component is shown in Section 8.3.4 (Figure 8.8). is equal to the bar thickness for a +/-50% uncertainty. The net impact of the individual contributions is shown by a diamond symbol and its uncertainty (5 to 95% confidence range) is given by the horizontal error bar. ERFaci is ERF due to aerosol cloud interac- Although there is high confidence for a substantial enhancement in the tion. BC and OC are co-emitted, especially for biomass burning emissions (given as negative aerosol forcing in the period 1950 1980, there is much more Biomass Burning in the figure) and to a large extent also for fossil and biofuel emissions uncertainty in the relative change in global mean aerosol forcing over (given as Fossil and Biofuel in the figure where biofuel refers to solid biomass fuels). the last two decades (1990 2010). Over the last two decades there SOA have not been included because the formation depends on a variety of factors not has been a strong geographic shift in aerosol and aerosol precursor currently sufficiently quantified. 698 Anthropogenic and Natural Radiative Forcing Chapter 8 ­emissions (see Section 2.2.3), and there are some uncertainties in these emissions (Granier et al., 2011). In addition to the regional changes in ) the aerosol forcing there is also likely a competition between various aerosol effects. Emission data indicate a small increase in the BC emis- ( sions (Granier et al., 2011) but model studies also indicate a weak enhancement of other aerosol types. Therefore, the net aerosol forc- ing depends on the balance between absorbing and scattering aero- 8 sols for aerosol radiation interaction as well as balance between the changes in aerosol radiation and aerosol cloud interactions. In the ACCMIP models, for example, the RF due to aerosol radiation inter- action becomes less negative during 1980 to 2000, but total aerosol ERF becomes more negative (Shindell et al., 2013c). There is a very low confidence for the trend in the total aerosol forcing during the past two to three decades, even the sign; however, there is high confidence that the offset from aerosol forcing to WMGHG forcing during this period Figure 8.18 | Time evolution of forcing for anthropogenic and natural forcing mecha- was much smaller than over the 1950 1980 period. nisms. Bars with the forcing and uncertainty ranges (5 to 95% confidence range) at present are given in the right part of the figure. For aerosol the ERF due to aerosol The volcanic RF has a very irregular temporal pattern and for certain radiation interaction and total aerosol ERF are shown. The uncertainty ranges are for present (2011 versus 1750) and are given in Table 8.6. For aerosols, only the uncertainty years has a strongly negative RF. There has not been a major volcanic in the total aerosol ERF is given. For several of the forcing agents the relative uncertainty eruption in the past decade, but some weaker eruptions give a current may be larger for certain time periods compared to present. See Supplementary Material RF that is slightly negative relative to 1750 and slightly stronger in Table 8.SM.8 for further information on the forcing time evolutions. Forcing numbers magnitude compared to 1999 2002 (see Section 8.4.2). provided in Annex II. The total antropogenic forcing was 0.57 (0.29 to 0.85) W m 2 in 1950, 1.25 (0.64 to 1.86) W m 2 in 1980 and 2.29 (1.13 to 3.33) W m 2 in 2011. Figure 8.19 shows linear trends in forcing (anthropogenic, natural and total) over four different time periods. Three of the periods are the is the larger domination of WMGHG forcing and smaller contribution same as chosen in Box 9.2 (1984 1998, 1998 2011 and 1951 2011) from aerosol forcing compared to previous periods. Similar to the and the period 1970 2011 is shown in Box 13.1. Monte Carlo sim- results for 1970 2011 in Figure 8.19, Box 13.1 shows that the global ulations are performed to derive uncertainties in the forcing based energy budget is dominated by anthropogenic forcing compared to on ranges given in Table 8.6 and the derived linear trends. Further, the natural forcing, except for the two major volcanic eruption in this these uncertainties are combined with uncertainties derived from period as can be easily seen in Figure 8.18. shifting time periods +/-2 years and the full 90% confidence range is shown in Figure 8.19 (in Box 9.2 only the total forcing is shown with Figure 8.20 shows the forcing between 1980 and 2011. Compared uncertainties derived from the forcing uncertainty without sensitivity to the whole Industrial Era the dominance of the CO2 is larger for to time period). For the anthropogenic forcing sensitivity to the selec- this recent period both with respect to other WMGHG and the total tion of time periods is very small with a maximum contribution to the a ­ nthropogenic RF. The forcing due to aerosols is rather weak leading uncertainties shown in Figure 8.19 of 2%. However, for the natural forcing the sensitivity to time periods is the dominant contributor to the overall uncertainty (see Supplementary Material Figure 8.SM.3) for the relatively short periods 1998 2011 and 1984 1998, whereas this is not the case for the longer periods. For the 1998 2011 period the natural forcing is very likely negative and has offset 2 to 89% of the anthropogenic forcing. It is likely that the natural forcing change has offset at least 30% of the anthropogenic forcing increase and very likely that it has offset at least 10% of the anthropogenic increase. For the 1998 2011 period both the volcanic and solar forcings contribute to this negative natural forcing, with the latter dominating. For the other periods shown in Figure 8.19 the best estimate of the natural is much smaller in magnitude than the anthropogenic forcing, but note that the natural forcing is very dependent on the selection of time period near the 1984 1998 interval. Over the period 1951 2011 the trend in anthropogenic forcing is almost 0.3 W m 2 per decade and ( ) thus anthropogenic forcing over this period is more than 1.5 W m 2. The anthropogenic forcing for 1998 2011 is 30% higher and with smaller Figure 8.19 | Linear trend in anthropogenic, natural and total forcing for the indicated time periods. The uncertainty ranges (5 to 95% confidence range) are combined from uncertainty than for the 1951 2011 period. Note that due to large uncertainties in the forcing values (from Table 8.6) and the uncertainties in selection WMGHG forcing (Section 8.3.2) the anthropogenic forcing was similar of time period. Monte Carlo simulations were performed to derive uncertainties in the in the late 1970s and early 1980s to the 1998 2011 period. The reason forcing based on ranges given in Table 8.6 and linear trends in forcing. The sensitivity to for the reduced uncertainty in the 1998 2011 anthropogenic forcing time periods has been derived from changing the time periods by +/-2 years. 699 Chapter 8 Anthropogenic and Natural Radiative Forcing Radiative forcing of climate between 1980 and 2011 of atmospheric chemistry and the carbon cycle, along with further dis- Forcing agent cussion on the representativeness of the RCP projections in context with the broader set of scenarios in the literature, is presented in Sec- CO2 tion 11.3.5 and Section 12.3 (also see Section 8.2). As the ACCMIP Well Mixed project provided projected forcings primarily at 2030 and 2100, we Greenhouse Gases N2O Other WMGHG CH4 hereafter highlight those times. Although understanding the relative Halocarbons contributions of various processes to the overall effect of aerosols on 8 Ozone Stratospheric Tropospheric forcing is useful, we emphasize the total aerosol ERF, which includes Anthropogenic all aerosol radiation and aerosol cloud interactions, as this is the Stratospheric water most indicative of the aerosol forcing driving climate change. We also vapour from CH4 present traditional RF due to aerosol radiation interaction (previously BC on snow Surface Albedo called direct aerosol effect) but do not examine further the various + Land Use components of aerosol ERF. Aerosol forcing estimates, both mean and Contrails uncertainty ranges, are derived from the 10 ACCMIP models, 8 of which Contrail induced cirrus are also CMIP5 models. We analyze forcing during the 21st century Aero.-Rad. Interac. (relative to 2000), and hence the WMGHG forcing changes are in addi- tion to persistent forcing from historical WMGHG increases. Aero.-Cloud Interac. Analysis of forcing at 2030 relative to 2000 shows that under RCP2.6, Total anthropogenic total ozone (tropospheric and stratospheric) forcing is near zero, RF due to aerosol radiation interaction is positive but small, and hence Natural Solar irradiance WMGHG forcing dominates changes over this time period (Figure 8.21). WMGHG forcing is dominated by increasing CO2, as declining CH4 and -0.5 0.0 0.5 1.0 increasing N2O have nearly offsetting small contributions to forcing. Radiative Forcing (W m-2) Aerosol ERF was not evaluated for this RCP under ACCMIP, and values Figure 8.20 | Bar chart for RF (hatched) and ERF (solid) for the period 1980 2011, cannot be readily inferred from RF due to aerosol radiation interaction where the total anthropogenic ERF are derived from Monte-Carlo simulations similar to as these are not directly proportional. Under RCP8.5, RF due to aerosol Figure 8.16. Uncertainties (5 to 95% confidence range) are given for RF (dotted lines) radiation interaction in 2030 is weakly negative, aerosol ERF is positive and ERF (solid lines). with a fairly small value and large uncertainty range, total ozone forc- ing is positive but small (~0.1 W m 2), and thus WMGHG forcing again to a very strong net positive ERF for the 1980 2011 period. More than dominates with a value exceeding 1 W m 2. As with RCP2.6, WMGHG 40% of the total anthropogenic ERF has occurred over the 1980 2011 forcing is dominated by CO2, but under this scenario the other WMGHGs period with a value close to 1.0 (0.7 to 1.3) W m 2. The major contri- all contribute additional positive forcing. Going to 2100, ozone forcing bution to the uncertainties in the time evolution of the anthropogenic diverges in sign between the two scenarios, consistent with changes in forcing is associated with the aerosols (see Section 8.5.1). Despite this, the tropospheric ozone burden (Figure 8.4) which are largely attribut- anthropogenic ERF is very likely considerably more positive than the able to projected CH4 emissions, but is small in either case. Ozone RF natural RF over the decadal time periods since 1950. This is in par- is the net impact of a positive forcing from stratospheric ozone recov- ticular the case after 1980, where satellite data are available that pro- ery owing to reductions in anthropogenic ozone-depleting halocarbon vide important measurements to constrain the natural RF mechanisms emissions in both scenarios and a larger impact from changes in tropo- (e.g., the volcanic RF change between 2007 2011 and 1978 1982 is spheric precursors (Shindell et al., 2013c) which have a negative forcing 0.06 W m 2 and the representative change in solar irradiance over the in RCP2.6 and a positive forcing in RCP8.5. 1980 2011 period is 0.06 W m 2) with total natural RF of 0.0 (-0.1 to +0.1) W m 2. The two scenarios are fairly consistent in their trends in RF due to aero- sol radiation interaction by component (Figure 8.21). There is positive 8.5.3 Future Radiative Forcing RF due to aerosol radiation interaction due to reductions in sulfate aerosol. This is largely offset by negative RF due to aerosol radiation Projections of global mean RF are assessed based on results from mul- interaction by primary carbonaceous aerosols and especially by nitrate tiple sources examining the RF due to RCP emissions: the ACCMIP ini- (though nearly all CMIP5 models did not include nitrate), leaving net tiative (see Section 8.2) provides analysis of the RF or ERF due to aer- aerosol RF due to aerosol radiation interaction values that are very osols and ozone (Shindell et al., 2013c), while WMGHG, land use and small, 0.1 W m 2 or less in magnitude, in either scenario at 2030 and stratospheric water RFs are taken from the results of calculations with 2100. Nitrate aerosols continue to increase through 2100 as ammo- the reduced-complexity Model for the Assessment of Greenhouse-gas nia emissions rise steadily due to increased use of agricultural ferti- Induced Climate Change 6 (MAGICC6) driven by the RCP emissions lizer even as all other aerosol precursor emissions decrease (Figure and land use (Meinshausen et al., 2011a). While MAGICC6 also esti- 8.2), including sulphur dioxide which drives the reduction in sulphate mated ozone and aerosol RF, those values differ substantially from the ­aerosol that also contributes to additional formation of nitrate aerosols ACCMIP values and are considered less realistic. Additional discussion in the future (Bauer et al., 2007; Bellouin et al., 2011). Aerosol ERF is of biases in the MAGICC6 results due to the simplified representations likely similar at this time in all scenarios given that they all have greatly 700 Anthropogenic and Natural Radiative Forcing Chapter 8 reduced emissions of all aerosols and aerosol precursors other than 8 0.6 ammonia. Aerosol ERF shows a large positive value at 2100 relative RCP8.5 RCP2.6 } 2100 vs 2000 0.4 } Effective radiative forcing (W m-2) RCP8.5 2030 vs 2000 to 2000, nearly returning to its 1850 levels (the 2100 versus 1850 ERF 0.2 RCP2.6 represents a decrease in ERF of 91% relative to the 2000 versus 1850 6 0.0 value), as is expected given the RCP emissions. Thus although some -0.2 models project large increases in nitrate RF in the future, the reduc- 4 -0.4 tion in overall aerosol loading appears to lead to such a strong reduc- Sulphate Carbonaceous Nitrate SOA 8 tion in aerosol ERF that the impact of aerosols becomes very small under these RCPs. Of course the projections of drastic reductions in 2 primary aerosol as well as aerosol and ozone precursor emissions may be overly optimistic as they assume virtually all nations in the world become wealthy and that emissions reductions are directly dependent 0 on wealth. The RCPs also contain substantially lower projected growth CO2 O3+Strat H2O Aerosol ERF in HFC emissions than in some studies (e.g., Velders et al., 2009). Other WMGHG Total RFari Total Although aerosol ERF becomes less negative by nearly 1 W m 2 from Figure 8.21 | Radiative forcing relative to 2000 due to anthropogenic composition 2000 to 2100, this change is still small compared with the increased changes based on ACCMIP models for aerosols (with aerosol ERF scaled to match WMGHG forcing under RCP8.5, which is roughly 6 W m 2 during this the best estimate of present-day forcing) and total ozone and RCP WMGHG forcings. Ranges are one standard deviation in the ACCMIP models and assessed relative uncer- time (Figure 8.21). Roughly 5 W m 2 of this WMGHG forcing comes tainty for WMGHGs and stratospheric water vapor. Carbonaceous aerosols refer to pri- from CO2, with substantial additional forcing from increases in both mary carbonaceous, while SOA are secondary organic aerosols. Note that 2030 ERF for CH4 and nitrous oxide and only a very small negative forcing from RCP2.6 was not available, and hence the total shown for that scenario is not perfectly reductions in halocarbons. Under RCP2.6, the WMGHG forcing is comparable to the other total values. RFari is RF due to aerosol radiation interaction. only about 0.5 W m 2 during this time, as relatively strong decreases in CH4 and halocarbon forcing offset roughly 40% of the increased CO2 forcing, which is itself far less than under RCP8.5. Hence under analyzed, indicating that the discrepancy between the methods is not this scenario, the projected future forcing due to aerosol reductions is related to analysis of a different set of models. Instead, it may reflect actually stronger than the WMGHG forcing. Viewing the timeseries of nonlinearities in the response to forcing that are not represented by the various forcings, however, indicates that aerosol ERF is returning the regression analysis of the response to abrupt CO2 increase experi- to its pre-industrial levels, so that net forcing becomes increasingly ments (Long and Collins, 2013) or differences in the response to other dominated by WMGHGs regardless of scenario during the 21st cen- forcing agents relative to the response to CO2 used in deriving the tury (Figure 8.22). As the forcing is so heavily dominated by WMGHGs CMIP5 estimates (see also 12.3.3). at 2100, and the WMGHG concentrations (CO2) or emissions (others) were chosen to match forcing targets, all the scenarios show net forc- Natural forcings will also change in the future. The magnitudes cannot ing values at that time that are fairly close to the scenarios target be reliably projected, but are likely to be small at multi-decadal scales values. The reduced aerosol forcing, with its large uncertainty, leads (see Section 8.4). Brief episodic volcanic forcing could be large, ­however. to a pronounced decrease in the uncertainty of the total net forcing by 2100. Based on the spread across ACCMIP models (using ERF for aerosols and converting to ERF for GHGs), the 90% confidence interval 8 RCP8.5 Effective radiative forcing (W m -2) (CI) is about 20% for the 2100 net forcing, versus 26% for 2030 under RCP8.5 and 45 61% for 1980 and 2000 (Shindell et al., 2013c). The RCP6.0 total ERF due to all causes has been independently estimated based 6 on the transient response in the CMIP5 models and a linear forc- RCP4.5 ing-response relationship derived through regression of the modelled RCP2.6 4 response to an instantaneous increase in CO2 (Forster et al., 2013). Uncertainties based on model spread behave similarly, with the 90% WMGHG CI for net total ERF decreasing from 53% for 2003 to only 24 to 34% 2 Net for 2100. Forcing relative to 2000 due to land use (via albedo only) and Ozone stratospheric water vapor changes are not shown separately as their 0 projected values under the four RCPs are quite small: 0.09 to 0.00 and Aerosol 0.03 to 0.10 W m 2, respectively. 1850 1900 1950 2000 2050 2100 The CMIP5 forcing estimates (Forster et al., 2013) for the total project- Figure 8.22 | Global mean anthropogenic forcing with symbols indicating the times at ed 2030 and 2100 ERF are slightly smaller than the results obtained which ACCMIP simulations were performed (solid lines with circles are net; long dashes from the ACCMIP models (or the RCP targets; see Section 12.3.3). with squares are ozone; short dashes with diamonds are aerosol; dash-dot are WMGHG; Examining the subset of models included in both this regression anal- colours indicate the RCPs with red for RCP8.5, orange RCP6.0, light blue RCP4.5, and ysis and in ACCMIP shows that the ACCMIP subset show forcings on dark blue RCP2.6). RCPs 2.6, 4.5 and 6.0 net forcings at 2100 are approximate values using aerosol ERF projected for RCP8.5 (modified from Shindell et al., 2013c). Some the low side of the mean value obtained from the full set of CMIP5 individual components are omitted for some RCPs for visual clarity. 701 Chapter 8 Anthropogenic and Natural Radiative Forcing 8.6 Geographic Distribution of Radiative SH (e.g., by nearly a factor of 3; Ming et al., 2007). Rapid adjustment Forcing associated with aerosol radiation and aerosol cloud interactions may enhance or reduce cloud cover depending on the region, cloud dynam- The forcing spatial pattern of the various RF mechanisms varies sub- ics and aerosol loading (e.g., Randles and Ramaswamy, 2008; Koch stantially in space and in time, especially for the NTCFs. The spatial pat- and Del Genio, 2010; Persad et al., 2012). In general, the ocean-land tern is of interest to the extent that it may influence climate response forcing pattern differs from that reported in AR4, where the forcing due (Section 8.6.2.2) as is being particularly investigated in the ACCMIP to aerosol cloud interaction were larger over land than ocean (Forster 8 simulations. et al., 2007), and this continues to be a source of uncertainty. Since AR4, Quaas et al. (2009) showed using satellite retrievals that the cor- 8.6.1 Spatial Distribution of Current Radiative Forcing relation between AOD changes and droplet number changes is strong- er over oceans than over land and that models tend to overestimate The WMGHGs such as CO2 have the largest forcing in the subtropics, the strength of the relation over land. Penner et al. (2011) showed decreasing toward the poles, with the largest forcing in warm and dry that satellite retrievals, due to their dependence on present-day condi- regions and smaller values in moist regions and in high-altitude regions tions, may underestimate the forcing due to aerosol cloud interaction, (Taylor et al., 2011). For the NTCFs (Box 8.2) their concentration spatial especially over land, although this model analysis may overestimate pattern and therefore their RF pattern are highly inhomogeneous, and the cloud condensation nucleus to AOD relation (Quaas et al., 2011). again meteorological factors such as temperature, humidity, clouds, Wang and Penner (2009) also showed that if models include boundary and surface albedo influence how concentration translates to RF. layer nucleation and increase the fraction of sulphur emitted as a pri- mary particle, the effect over land is increased relative to over ocean Figure 8.23 shows the RF spatial distribution of the major NTCFs togeth- (see also Section 7.5.3). The aerosol ERF standard deviation is large er with standard deviation among the ACCMIP models (Shindell et al., in biomass burning regions, as for the RF, and in regions where cloud 2013c) the net anthropogenic composition (WMGHG+ozone+aerosol) effects differ among models (e.g., northern North America, northeast forcing is also shown (lower left panel). These models used unified Asia, Amazonia). The spread in aerosol ERF is much larger than for the anthropogenic emissions of aerosol and ozone precursors (Supplemen- RF alone, although the relative standard deviation is no larger (Shindell tary Material Figure 8.SM.2), so that the model diversity in RF is due et al., 2013c). only to differences in model chemical and climate features and natu- ral emissions, and would be larger if uncertainty in the anthropogenic For components that primarily scatter radiation, the radiative effect at emissions were also included. In general, the confidence in geograph- the surface is similar to the RF (according to the definition in Section ical distribution is lower than for global mean, due to uncertainties in 8.1.1). However for components that absorb radiation in the atmos- chemistry, transport and removal of species. phere the radiation reaching the surface is reduced (Forster et al., 2007; Ramanathan and Carmichael, 2008; Andrews et al., 2010). This absorp- The negative RF due to aerosol radiation interaction (first row; defined tion of incoming solar radiation alters the vertical temperature profile in Figure 7.3) is greatest in the NH and near populated and biomass in the atmospheric column and can thus change atmospheric circula- burning regions. The standard deviation for the net RF due to aerosol tion and cloud formation. The aerosol atmospheric absorption (Figure radiation interaction is typically largest over regions where vegetation 8.23, bottom right), or the difference between ERF and the analogous changes are largest (e.g., South Asia and central Africa), due to uncer- radiative flux reaching the surface including rapid adjustments, has a tainties in biomass burning aerosol optical properties and in treatment spatial pattern that to lowest order tracks the carbonaceous aerosol of secondary organic aerosols. Carbonaceous aerosol forcing (second forcing, but is also affected by cloud changes, where e.g., cloud loss row) is greatest in South and East Asia and can be negative in biomass could enhance atmospheric absorption. Atmospheric aerosol absorp- burning regions due to large weakly absorbing organic components. tion patterns thus mirror the ERF due to aerosol cloud interaction pat- Absorbing aerosols also have enhanced positive forcing when they tern, with larger forcing over continents. overlie high albedo surfaces such as cryosphere, desert or clouds, with as much as 50% of BC RF resulting from BC above clouds (Zarzycki Ozone RF is calculated using the methodology described in Shindell et and Bond, 2010). al. (2013c), but applied to the larger set of models in ACCMIP (Steven- son et al., 2013). The net ozone RF (Figure 8.23; fourth row) is largest Figure 8.24 compares the aerosol RFs for ACCMIP (Shindell et al., in subtropical latitudes, and is more positive in the NH than the SH. 2013c), which are representative of the CMIP5 experiments, with those Pollution in the NH accounts for positive tropospheric forcing; strato- from the AeroCom model intercomparison (Myhre et al., 2013) which spheric ozone loss has caused negative SH polar forcing. Model stand- includes sixteen models that used unified meteorology and are more ard deviation is largest in the polar regions where lower stratosphere/ extensively compared to measurements (e.g., Koch et al., 2009b; Koffi upper troposphere changes differ in the models (Young et al., 2013). et al., 2012). The forcing results are very similar, establishing the repre- sentativeness and validity of the ACCMIP aerosol simulations. Overall, the confidence in aerosol and ozone RF spatial patterns is medium and lower than that for the global mean due to the large The net aerosol ERF (Figure 8.23; third row), includes both aerosol regional standard deviations (Figure 8.23), and is exacerbated in aero- radiation and aerosol cloud interactions. The spatial pattern corre- sol ERF patterns due to uncertainty in cloud responses. lates with the RF (first row), except with stronger effect in the out- flow regions over oceans. The flux change is larger in the NH than the 702 Anthropogenic and Natural Radiative Forcing Chapter 8 Preindustrial to Present-Day Forcing Multi-model mean Standard deviation -0.26 W m-2 0.14 (0.30) W m-2 All aerosol RF 8 -.88 -.62 -.38 -.12 .12 .38 .62 .88 0 .35 .70 1.05 1.40 1.75 2.10 2.45 0.21 W m-2 0.08 (0.18) W m-2 Carbonaceous aerosol RF -.88 -.62 -.38 -.12 .12 .38 .62 .88 0 .35 .70 1.05 1.40 1.75 2.10 2.45 -1.17 W m-2 0.29 (1.27) W m-2 All aerosol ERF -3.5 -2.5 -1.5 -.5 .25 .75 1.25 1.75 0 .35 .70 1.05 1.40 1.75 2.10 2.45 0.29 W m-2 0.16 (0.19) W m-2 Ozone RF -.88 -.62 -.38 -.12 .12 .38 .62 .88 0 .06 .12 .18 .24 .30 .36 .42 Multi-model mean 1.46 W m-2 Multi-model mean 1.42 W m-2 Total anthropogenic composition forcing atmospheric forcing All aerosol effective -3.0 -2.0 -1.0 -0.5 0.5 1.0 2.0 3.0 -3.5 -2.5 -1.5 -.5 .5 1.5 2.5 3.5 Figure 8.23 | Spatial pattern of ACCMIP models 1850 to 2000 forcings, mean values (left) and standard deviation (right) for aerosols and ozone (top four rows). Values above are the average of the area-weighted global means, with the area weighted mean of the standard deviation of models at each point provided in parenthesis. Shown are net aerosol RF due to aerosol radiation interaction (top, 10 models), carbonaceous aerosol RF due to aerosol-radiation interaction (2nd row, 7 models), aerosol ERF (3rd row, 8 models), ozone (4th row, 11 models), total anthropogenic composition forcing (WMGHG+ozone+aerosols; bottom left), aerosol atmospheric absorption including rapid adjustment (bottom right, 6 models). Note that RF and ERF means are shown with different colour scales, and standard deviation colour scales vary among rows. 703 Chapter 8 Anthropogenic and Natural Radiative Forcing Preindustrial to present-day forcing Multi-model mean Standard deviation -0.26 W m-2 0.14 (0.30) W m-2 8 ACCMIP All aerosol RF -0.27 W m-2 0.15 (0.27) W m-2 AeroCom 0.25 W m-2 0.10 (0.12) W m-2 Fossil & biofuel Black Carbon RF ACCMIP 0.18 W m-2 0.07 (0.12) W m-2 AeroCom -.88 -.62 -.38 -.12 .12 .38 .62 .88 0 .35 .70 1.05 1.40 1.75 2.10 2.45 Figure 8.24 | Spatial pattern of ACCMIP and 16 AeroCom models 1850 to 2000 RF due to aerosol radiation interaction, mean values (left) and standard deviation (right). Note that different carbonaceous aerosol diagnostics are used here compared to Figure 8.23, due to available AeroCom fields. Values above are the average of the area-weighted global means, with the area weighted mean of the standard deviation of models at each point provided in parentheses. 704 Anthropogenic and Natural Radiative Forcing Chapter 8 8.6.2 Spatial Evolution of Radiative Forcing and regions (Figure 8.25, left). Between 1950 and 1970, coal burning for Response over the Industrial Era power generation increased while coal burning for other purposes was replaced by oil and natural gas and motor vehicle usage grew rapidly in 8.6.2.1 Regional Forcing Changes During the Industrial Era these regions, leading to more sulphate and less BC. Peak aerosol forc- ing in North America and Europe occurred around 1970 1980 (Figure The spatial distribution of the WMGHG RF has shifted only slightly over 8.25, second column), while Asian development led to increased bio- the industrial period; however the RF spatial distributions for NTCFs fuel and fossil fuel sources of aerosols and ozone precursors toward 8 has shifted with emissions, due to the timing of regional development the end of the century. During the final decades of the century, des- and implementation of pollution standards (Supplementary Material ulphurization controls reduced sulphur emissions from North America Figures 8.SM.1 and 8.SM.2 show regional trends and emissions maps; and Europe, resulting in reduced negative forcing in these regions and Lamarque et al., 2013). Figure 8.25 shows how the distributions of positive Arctic aerosol forcing. The SH ozone hole developed during the aerosol and ozone forcings are modelled to have changed up to 1930, final three decades, with negative forcing over high latitudes. Biomass 1980 and 2000. Substantial industrial coal-burning in the early part of burning generated ozone and carbonaceous aerosols in NH high-lati- the 20th century occurred in the northeastern United States and West- tudes early in the century, with increased tropical burning from mid to ern Europe, leading to stronger sulphate and BC forcing near those late century. 1930 vs 1850 1980 vs 1850 2000 vs 1850 -0.06 W m-2 -0.29 W m-2 -0.26 W m-2 All aerosol RF 0.07 W m-2 0.17 W m-2 0.21 W m-2 Carbonaceous aerosol RF 0.09 W m-2 0.27 W m-2 0.29 W m-2 Ozone RF -.88 -.62 -.38 -.12 .12 .38 .62 .88 -0.27 W m-2 -1.03 W m-2 -1.17 W m-2 All aerosol ERF -3.5 -2.5 -1.5 -.5 .25 .75 1.25 1.75 Figure 8.25 | Multi-model mean RF due to aerosol radiation interaction of all aerosols, carbonaceous aerosols, ozone, and aerosol ERF (W m 2) for the indicated times based on the ACCMIP simulations. Global area-weighted means are given in the upper right. 705 Chapter 8 Anthropogenic and Natural Radiative Forcing Aerosol ERF grew rapidly from 1930 to 1980, as did RF due to aero- response. Yet Crook and Forster (2011) showed that both the spatial sol radiation interaction, with a spatial structure reflecting both the distribution of climate feedbacks and of heterogeneous forcing played influence of aerosol radiation and aerosol cloud interactions that are important roles in the patterns of 20th century temperature changes. especially strong over pollution outflow regions and over areas with Other studies since AR4 have probed relationships between forcing high surface albedo. From 1980 to 2000, aerosol ERF continued to patterns and climate responses. become more negative even as negative RF due to aerosol radiation interaction grew weaker, with the spatial pattern showing strengthen- Broad links between forcing and climate response have been identi- 8 ing of aerosol ERF over Asia and weakening of aerosol ERF over North fied. Shindell et al. (2010) used multiple models to show that surface America and Europe. temperature changes are much more sensitive to latitudinal than longi- tudinal variations in forcing. Shindell and Faluvegi (2009) used a model Soil dust has changed since the pre-industrial due to land disturbance inverse approach to infer that NH aerosol reduction was associated and resulting desertification (a forcing) and to changes in climate (a with more than 70% of Arctic warming from the 1970s to the 2000s, feedback). Mahowald et al. (2010) showed approximate doubling in and that Arctic and much of the SH surface temperature changes are dust loading over the 20th century ( 0.1 W m 2; consistent with the strongly affected by remote forcing changes (alsoSection 10.3.1.1.4). best estimate in Section 7.5.2; Section 8.3.4.2), primarily from the Voulgarakis and Shindell (2010) defined a regional transient tempera- Saharan and Middle Eastern Deserts, with largest increase from the ture sensitivity parameter, or temperature response per unit forcing for 1950s to the 1980s ( 0.3 W m 2), followed by a leveling. The increased each 4-degree latitude band. Using observed surface air temperature dustiness reduces model precipitation within the Saharan source changes they showed that the parameter is best constrained from 50°S region, improving agreement with observed precipitation. to 25°N, where the value is 0.35°C (W m 2) 1, smaller than at northern higher latitudes, and 35% smaller than in AR4 models. Aerosol loading changes during the past century have impacted radi- ation at the surface (Section 2.3.3), with peak radiation reductions Some aerosol model studies have demonstrated highly localized cli- in North America and Europe in the 1980s, and ongoing reduction in mate response to regional forcing. Significant regional cooling and South and East Asia (Wild, 2009). The AR4 and CMIP5 models simu- hydrological shifts in the eastern USA and in Eastern Asia during the lated these trends but underestimated their magnitude, the decadal last half of the 20th century were modelled and attributed to local temperature variations and the diurnal temperature range over land aerosols (Leibensperger et al., 2008, 2012a, 2012b; Chang et al., 2009) (Wild, 2009; see Chapter 9). and localized warming projected for aerosol reductions (Mickley et al., 2012). Observations have also linked historical trends in aerosols and Changes in spatial patterns of species and their forcing over the cen- temperature (Ruckstuhl et al., 2008; Philipona et al., 2009). tury are difficult to validate due to sparse observations of short-lived species. Some constraint comes from limited historical observations Since AR4, there has been new research on aerosol influences on the in ice core records and from shorter trends beginning in late century hydrologic cycle (also Sections 7.4, 7.6.4, 10.3.3.1 and 11.3.2.4.3). from satellite and surface-based site measurements. The emissions Increased aerosol loading, with greater surface energy flux reduction estimates for historical species are very uncertain, especially for car- in the NH, has been implicated in the observed southward shift of the bonaceous aerosols and dust. Therefore, the confidence in the histor- Intertropical Convergence Zone (ITCZ) towards the hemisphere with ical forcing pattern changes is low for RF due to aerosol radiation smaller surface energy reduction: southward up to the 1980s with a interaction and ozone, and very low for ERF, carbonaceous aerosols reversal since (e.g., Denman et al., 2007; Zhang et al., 2007). Several and dust. studies have modelled an associated reduction in NH precipitation and associated shifts in the Hadley circulation (e.g., Rotstayn et al., 8.6.2.2 Relationship Between Regional Forcing Patterns and 2000; Williams et al., 2001; Ming et al., 2011). The ITCZ shift may in Climate Response During the Industrial Era turn be responsible for broad regional precipitation changes, includ- ing drying of the Sahel (e.g., Rotstayn and Lohmann, 2002; Biasutti An increasing body of research considers how spatial variations in RF and Giannini, 2006; Kawase et al., 2010; Ackerley et al., 2011) and affect climate response. Detection and attribution methods have had northwestern Brazil (Cox et al., 2008), both of which peaked in the limited success in discerning statistically significant regional climate 1980s. These hemispheric asymmetric ITCZ effects are overlaid on signals from regional forcing, due to large internal climate variability thermodynamic aerosol effects which moisten subtropical regions, at regional scales, uncertainty in model processes and sparse region- countering GHG-induced drying of these regions (Ming et al., 2011). al observational records (Chapter 10). Meanwhile, research including Studies indicate that aerosols are more effective than an equivalent model sensitivity studies for NTCFs, which vary strongly in space in WMGHG forcing for shifting precipitation, and that historical trends time, explores climate response patterns. in several areas cannot be explained without including aerosol forcing (Bollasina et al., 2011; Booth et al., 2012; Shindell et al., 2012a; Shin- In AR4 (Forster et al., 2007; Knutti et al., 2008) it was argued that the dell et al., 2012b). However, confidence in attribution of any human spatial pattern of forcing is not indicative of the pattern of climate influence on zonal shifts in precipitation distribution is only medium response. Rather, the response is linked more closely to TOA flux result- (Section 10.3.2.2). ing from the climate feedback spatial patterns (Boer and Yu, 2003; Taylor et al., 2011; Ming and Ramaswamy, 2012), with the lapse rate, There is increasing evidence but limited agreement that absorbing surface albedo and cloud feedbacks explaining most of the ­temperature a ­erosols influence cloud distributions (Section 7.3.4.2). Absorbing 706 Anthropogenic and Natural Radiative Forcing Chapter 8 a ­ erosols apparently have complex influences on precipitation in mon- albedo feedback. These forcings can also have non-local impacts that soon regions. Model studies of Stephens et al. (2004) and Miller et result from enhanced land-ocean temperature contrast, increasing sur- al. (2004) showed that dust absorption over Africa enhances low-lev- face convergence over land and divergence over oceans. A poleward el convergence, vertical velocities and therefore local monsoon cir- intensification of the high pressure patterns and subtropical jet may culation and precipitation. On the other hand, Kawase et al. (2010) also result (Fletcher et al., 2009). BC contributions to snow darken- showed that biomass burning BC may cause the decreasing precipita- ing reduces snow cover, however the magnitude of the effect is very tion trend seen in tropical Africa during austral summer, due to reduc- uncertain (see Sections 7.5.2.3 and 8.3.4.4). A model study calculated 8 tion in evaporation and enhanced subsidence. The aerosol effects on BC-albedo reduction to cause about 20% Arctic snow/ice cover reduc- the Indian monsoon are similarly complex, and have been the sub- tion and 20% of Arctic warming over the previous century (Koch et al., ject of numerous studies (e.g., Ramanathan et al., 2005; Chung and 2011). However, reductions in Arctic soot during the past two decades Ramanathan, 2006; Lau et al., 2006; Wang et al., 2009; Bollasina et (e.g., Hegg et al., 2009) have likely reversed that trend (e.g., Koch et al., al., (2011), but a clear picture of how the regional aerosol forcing 2011; Skeie et al., 2011b; Lee et al., 2013). Cryospheric feedbacks and correlates with responses has not yet fully emerged. Attribution of atmospheric dynamical responses in models have an associated pole- changes in monsoon to human influence generally has low confidence ward shift in the temperature response to aerosol cloud interactions (Section 10.3). (Kristjansson et al., 2005; Koch et al., 2009a; Chen et al., 2010). Stratospheric ozone loss modelling has demonstrated an effect on the Solar spectral (UV) irradiance variations along the solar cycle induce SH stratosphere similar to increased GHGs, cooling stratospheric tem- ozone responses by modifying the ozone production rate through pho- peratures, strengthening the polar vortex and shifting the westerly jet tolysis of molecular oxygen (Section 8.4.1.4.1), and the resulting dif- poleward; however causing cooler Antarctic surface temperatures, with ferential heating can drive circulation anomalies that lead to regional larger influence on austral summer conditions (Son et al., 2009; McLan- temperature and precipitation changes (Haigh, 1999; Shindell et al., dress et al., 2011; Thompson et al., 2011; see also Sections 10.3.3 and 2006b; Frame and Gray, 2010; Gray et al., 2010). Such solar forcing 11.3.2.4.3.) In the troposphere, models indicate that increased tropo- may influence natural modes of circulation such as the Northern Annu- spheric ozone has caused warming, proportionally more in the NH and lar Mode (e.g., Shindell et al., 2001; de la Torre et al., 2006; Ineson et al., notably to the Arctic during winter, mainly during the second half of the 2011), the South Asian Summer Monsoon (Fan et al., 2009), the South- 20th century (Shindell et al., 2006a). ern Annular Mode (Kuroda and Kodera, 2005; Roscoe and Haigh, 2007) or the ENSO (Mann et al., 2005). The pattern of temperature response Albedo changes due to land use and land cover changes exert a heter- is less uniform than the forcing, for example, warming in the NH, but ogeneous climate forcing (Figure 8.9). The surface albedo brightened little response in the SH due to temperature moderation by wind speed on the one hand due to a shift from forest to brighter croplands, caus- enhancement effects on ocean circulation (Swingedouw et al., 2011). ing local cooling (e.g., Eliseev and Mokhov, 2011; Lee et al., 2011), but Regional responses to solar forcing are mediated by the stratosphere, also darkened due to the re-expansion of forests to higher latitudes so that reproducing such change requires spectrally varying solar forc- (Esper and Schweingruber, 2004) and increased vegetation height in ing rather than TSI forcing (Lee et al., 2009; Section 8.4.1.4). snowy regions (Bonfils et al., 2012; also Section 8.3.5). Model studies have shown cooling from land use and land cover changes, especial- Stratospheric aerosol clouds (also Section 8.4.2.2) from tropical erup- ly over NH continents, although without demonstrating a detectable tions spread poleward and can cover an entire hemisphere or the signal in observations (Matthews et al., 2004). globe, depending on the initial latitudinal spread. The aerosol eruption cloud from the 1963 Agung was confined mainly to the SH; the 1982 El In addition to land use and climate-induced vegetation changes, CO2 Chichón mainly to the NH; and the 1991 Pinatubo covered the globe, affects vegetation forcing indirectly, reducing transpiration from plants all with an e-folding lifetime of about 1 year (e.g., Antuna et al., 2003). as stomata open less with increasing CO2, resulting in localized atmos- High-latitude eruptions typically stay confined to the high-latitude pheric drying and warming (Section 11.3.2.3.1; Joshi and Gregory, regions with shorter lifetimes of 2 to 4 months (Kravitz and Robock, 2008). These are not included in the standard RF (Section 8.1) and may 2011). Volcanic aerosols primarily scatter solar radiation back to space, be considered feedbacks (Section 8.3.2). This is modelled to be largest but also absorb longwave radiation with the former larger by an order over the Amazon, the central African forest, and to some extent over of magnitude. Stratospheric aerosol absorption heats the layer where boreal and temperate forests (Andrews et al., 2011). In the coupled they reside and produces distinct vertical and horizontal distributions climate modelling study of Lawrence and Chase (2010), the vegetation of the heating rate. The temperature and chemical effects of the aer- changes caused significant reduction in evapotranspiration, drying and osols also enhance ozone destruction, which somewhat counteracts warming in tropical and subtropical regions, with insignificant cool- the radiative heating (Stenchikov et al., 2002). For tropical eruptions, ing at higher latitudes. Overall, vegetation changes may have caused this may affect atmospheric dynamics, with a stronger polar vortex, a modest cooling at high latitudes and warming at low latitudes, but the positive mode of the Arctic Oscillation, and winter warming over NH uncertainties are large and confidence is very low. continents (Robock, 2000). Climate responses to solar and volcanic forcings are further discussed in the context of detection and attribu- Deposition of BC on snow and ice, and loss of snow and ice darken tion of millennial climate change (see Section 10.7). the surface, reduces albedo, and enhances climate warming. Substan- tial snow-cover reduction of North America leads to warmer North The study of how climate responds to regionally varying patterns of American summertime temperature in models having a strong snow forcing is critical for understanding how local activities impact regional 707 Chapter 8 Anthropogenic and Natural Radiative Forcing climate; however, the studies are exploratory and generally evoke very in sea ice cover leading to increased emission of high-latitude sea low confidence. However there is medium to high confidence in some salt (Struthers et al., 2011; Takemura, 2012) and SOA from vegetation qualitative but robust features, such as the damped warming of the changes (Tsigaridis and Kanakidou, 2007). NH and shifting of the ITCZ from aerosols, and positive feedbacks from high-latitude snow and ice albedo changes. The simulations applying the RCPs indicate that the latitude of max- imum emission of NTCFs, and therefore of maximum RF, is projected 8 8.6.3 Spatial Evolution of Radiative Forcing and to shift somewhat southward for the next few decades (in 2030 of Response for the Future Figure 8.26). The shift of peak aerosol loading southward is expected to cause the ITCZ to continue to shift northward. This, in combination with Most components of aerosols and ozone precursors are estimated to warming and drying over tropical land, has been modelled to lead to decrease toward the end of this century in the RCPs except CH4 in greatly enhanced drought conditions in the Amazon (Cox et al., 2008). RCP8.5 (Figure 8.2) and nitrate aerosols, though some species reach On the other hand, if the low-latitude aerosol is sufficiently absorbing, the maximum amounts of emissions around the mid-21st century broadening of the ITCZ convergence region and enhanced cloud cover (Figure 8.2). The RCPs therefore contrast with the emission scenarios could result, as modelled for dust (Perlwitz and Miller, 2010). for TAR and AR4, which were based on Special Report on Emissions Scenarios (SRES) and have future projections of larger increase in the Reductions in high-latitude BC are expected to contribute to reducing near-term climate forcers (NTCFs). It has been questioned whether Arctic forcing (e.g., Koch et al., 2011), due to reduction in BC deposi- such low emission of NTCFs is possible in the future given the current tion on snow as well as in absorption of sunlight over bright surface. policies (Pozzer et al., 2012). This section surveys spatial differences in On the other hand, reduction in mid-high-latitude scattering aerosols the RF of aerosols and ozone for the future based on the RCPs. may offset all or part of the impact of the local Arctic forcing change (Shindell et al., 2010; Koch et al., 2011). Figure 8.26 shows the global distributions of changes in aerosol and ozone forcings in 2030 and 2100 relative to 2000 for RCP2.6 and 8.5 Figure 8.26 also shows the ozone RF in 2030 and 2100 relative to 2000, (Shindell et al., 2013c). Both scenarios indicate reduced aerosol load- which includes changes both in tropospheric and stratospheric ozone. ing, and thus positive forcing over Europe, North America and Asia by Recovery of ozone in the stratosphere in the 21st century will result in 2100 where RF is above +0.5 W m 2 because of substantial reduction positive forcing in the SH high latitudes in comparison with the year of scattering aerosols. The global mean RF due to aerosol radiation 2000 for both the pathways. This is because of the reduced emissions interaction is estimated to be +0.12 and +0.08 W m 2 for RCP2.6 and of ozone-depleting substances controlled under the Montreal Protocol, 8.5, respectively, in 2100. Though the RF by total anthropogenic aer- with a small additional effect from a feedback of changes in temper- osols is positive, reduced BC contributes substantial negative forcing ature and in the vertical circulation due to changes in stratospheric especially over the similar regions. The global mean carbonaceous RF compositions (Kawase et al., 2011; Lamarque et al., 2011). In the trop- including both the effects of BC and OC is estimated to be 0.20 and osphere, on the other hand, a large difference in the CH4 emissions 0.11 W m 2 for RCP2.6 and 8.5, respectively, in 2100. Early in the between RCP8.5 and the other pathways shown in Figure 8.2 leads century, on the other hand, both scenarios indicate increased negative to a different RF trend outside the SH high latitudes. Ozone recovery aerosol forcing over South Asia, with reversal between 2030 and 2100. in the stratosphere and ozone increase in the troposphere leads to a Emissions of BC, OC and SO2 will reach their maximums early and positive RF all over the globe in RCP8.5 with a mean of +0.26 W m 2 middle in the century for RCP2.6 and 8.5, respectively in India. In RCP6, in 2100. The cancellation between positive RF due to ozone increase in high emission levels of SO2 in China persist until the mid-21st century the stratosphere and negative RF due to ozone decrease in the tropo- (Supplementary Material Figure 8.SM.1), and then it is predicted to sphere results in a global mean RF of 0.12 W m 2 in RCP2.6. keep a high negative RF due to aerosol radiation interaction over East Asia. The RF due to aerosol radiation interaction for carbonaceous aer- Figure 8.26 also shows the global distributions of changes in ERF due osol is positive over East and South Asia in 2030 relative to 2000 for to both aerosol radiation and aerosol cloud interactions in 2030 and RCP8.5 because BC emission is also larger in 2030. Over central and 2100 relative to 2000 for RCP8.5. Although the ERF includes rapid southern Africa, a change in the future RF due to aerosol radiation adjustments and therefore its magnitude is much larger than that of interaction based on RCPs is not clear mainly because of uncertainties RF due to aerosol radiation interaction, the spatial pattern is generally in the wildfires emissions (see Section 7.3.5.3). The global mean total similar to RF. The ERF in 2100 shows positive values relative to 2000 RF due to aerosol radiation interaction in the future is rather small due in North America, Europe and Asia even with RCP8.5, which indicates to offsetting effects, with reductions in BC, increases in nitrate aero- the aerosol forcing is projected to approach to the pre-industrial level. sols, and reductions in scattering aerosols each causing substantially more forcing than the net. Emissions and atmospheric loadings of natural aerosols are affected by climate change. There is, however, no consensus among studies on future trends of their changes for major natural aerosols, mineral dust and sea salt, as indicated in Section 7.3.5.1. The spatial pattern of the aerosol forcing may be influenced by natural aerosols due to ­ eduction r 708 Anthropogenic and Natural Radiative Forcing Chapter 8 2030 0.09 W m-2 2100 0.12 W m-2 All aerosol RF RCP 2.6 8 0.01 W m-2 0.08 W m-2 All aerosol RF RCP 8.5 -0.03 W m-2 -0.20 W m-2 Carbonaceous aerosol RF Carbonaceous aerosol RF RCP 2.6 -0.02 W m-2 -0.11 W m-2 RCP 8.5 0.00 W m-2 -0.12 W m-2 Ozone RF RCP 2.6 0.11 W m-2 0.26 W m-2 Ozone RF RCP 8.5 -.88 -.62 -.38 -.12 .12 .38 .62 .88 0.27 W m-2 0.90 W m-2 All aerosol ERF RCP 8.5 -1.75 -1.25 -.75 -.25 .5 1.5 2.5 3.5 Figure 8.26 | Multi-model mean RF (W m ) due to aerosol radiation interaction of all anthropogenic aerosols (first and second rows) and anthropogenic carbonaceous (BC+OC) 2 aerosols (third and fourth rows), and total ozone (fifth and sixth rows) in 2030 (left) and 2100 (right) relative to 2000 for RCP2.6 (top each) and RCP8.5 (bottom each) based on the ACCMIP simulations. The seventh row shows multi-model mean ERF (W m 2) by all anthropogenic aerosols in 2030 (left) and 2100 (right) relative to 2000 for RCP8.5. Global area-weighted means are given in the upper right of each panel. 709 Chapter 8 Anthropogenic and Natural Radiative Forcing 8.7 Emission Metrics Metrics do not define goals and policy they are tools that enable evaluation and implementation of multi-component policies (i.e., 8.7.1 Metric Concepts which emissions to abate). The most appropriate metric will depend on which aspects of climate change are most important to a particu- 8.7.1.1 Introduction lar application, and different climate policy goals may lead to differ- ent conclusions about what is the most suitable metric with which To quantify and compare the climate impacts of various emissions, to implement that policy, for example, Plattner et al. (2009); Tol et al. 8 it is necessary to choose a climate parameter by which to measure (2012). Metrics that have been proposed include physical metrics as the effects; that is, RF, temperature response, and so forth. Thus, var- well as more comprehensive metrics that account for both physical and ious choices are needed for the steps down the cause effect chain economic dimensions (see 8.7.1.5 and WGIII, Chapter 3). from emissions to climate change and impacts (Figure 8.27 and Box 8.4). Each step in the cause effect chain requires a modelling frame- This section provides an assessment that focuses on the scientific work. For assessments and evaluation one may as an alternative to aspects and utility of emission metrics. Extending such an assessment models that explicitly include physical processes resulting in forcing to include more policy-oriented aspects of their performance and and responses apply simpler measures or metrics that are based on usage such as simplicity, transparency, continuity, economic implica- results from complex models. Metrics are used to quantify the contri- tions of usage of one metric over another, and so forth, is not given butions to climate change of emissions of different substances and can here as this is beyond the scope of WGI. However, consideration of thus act as exchange rates in multi-component policies or compar- such aspects is vital for user-assessments. In the following, the focus is isons of emissions from regions/countries or sources/sectors. Metrics on the more well-known Global Warming Potential (GWP) and Global are also used in areas such as Life Cycle Assessments and Integrated Temperature change Potential (GTP), though other concepts are also Assessment Modelling (e.g., by IPCC WGIII). briefly discussed. Metrics can be given in absolute terms (e.g., K kg 1) or in relative terms 8.7.1.2 The Global Warming Potential Concept by normalizing to a reference gas usually CO2. To transform the effects of different emissions to a common scale often called CO2 The Global Warming Potential (GWP) is defined as the time-integrat- equivalent emissions the emission (Ei) of component i can be mul- ed RF due to a pulse emission of a given component, relative to a tiplied with the adopted normalized metric (Mi): Mi × Ei = CO2-eqi. pulse emission of an equal mass of CO2 (Figure 8.28a and formula). Ideally, the climate effects of the calculated CO2 equivalent emissions The GWP was presented in the First IPCC Assessment (Houghton et al., should be the same regardless of the mix of components emitted. 1990), stating It must be stressed that there is no universally accepted However, different components have different physical properties, and methodology for combining all the relevant factors into a single global a metric that establishes equivalence with regard to one effect cannot warming potential for greenhouse gas emissions. A simple approach guarantee equivalence with regard to other effects and over extended has been adopted here to illustrate the difficulties inherent in the time periods, for example, Lauder et al. (2013), O Neill (2000), Smith concept, ... . Further, the First IPCC Assessment gave no clear physical and Wigley (2000), Fuglestvedt et al. (2003). interpretation of the GWP. Figure 8.27 | The cause effect chain from emissions to climate change and impacts showing how metrics can be defined to estimate responses to emissions (left) and for develop- ment of multi-component mitigation (right). The relevance of the various effects increases downwards but at the same time the uncertainty also increases. The dotted line on the left indicates that effects and impacts can be estimated directly from emissions, while the arrows on the right side indicate how these estimates can be used in development of strategies for reducing emissions. (Adapted from Fuglestvedt et al., 2003, and Plattner et al., 2009.) 710 Anthropogenic and Natural Radiative Forcing Chapter 8 Box 8.4 | Choices Required When Using Emission Metrics Time frames: One can apply a backward-looking (i.e., historical) or a forward-looking perspective on the responses to emissions. In the forward-looking case one may use pulses of emissions, sustained emissions or emission scenarios. All choices of emission perturba- tions are somewhat artificial and idealized, and different choices serve different purposes. One may use the level (e.g., degrees Celsius) or rate of change (e.g., degrees Celsius per decade). Furthermore, the effects of emissions may be estimated at a particular time or be 8 integrated over time up to a chosen time horizon. Alternatively, discounting of future effects may be introduced (i.e., a weighting of effects over time). Type of effect or end-point: Radiative forcing, temperature change or sea level change, for example, could be examined (Figure 8.27). Metrics may also include eco/biological or socioeconomic damages. The choice of climate impact parameters is related to which aspects of climate change are considered relevant for interpretation of dangerous anthropogenic interference with the climate system (UNFCCC Article 2). Spatial dimension for emission and response: Equal-mass emissions of NTCFs from different regions can induce varying global mean climate responses, and the climate response also has a regional component irrespective of the regional variation in emissions. Thus, metrics may be given for region of emission as well as region of response. Some of the choices involved in metrics are scientific (e.g., type of model, and how processes are included or parameterized in the models). Choices of time frames and climate impact are policy-related and cannot be based on science alone, but scientific studies can be used to analyse different approaches and policy choices. A direct interpretation is that the GWP is an index of the total energy added to the climate system by a component in question relative to that added by CO2. However, the GWP does not lead to equivalence with temperature or other climate variables (Fuglestvedt et al., 2000, 2003; O Neill, 2000; Daniel et al., 2012; Smith and Wigley, 2000; Tanaka et al., 2009). Thus, the name Global Warming Potential may be somewhat misleading, and relative cumulative forcing index would be more appropriate. It can be shown that the GWP is approximately equal to the ratio (normalizing by the similar expression for CO2) of the equilibrium temperature response due to a sustained emission of the species or to the integrated temperature response for a pulse emission (assuming efficacies are equal for the gases that are compared; O Neill, 2000; Prather, 2002; Shine et al., 2005a; Peters et al., 2011a; Azar and Johansson, 2012). The GWP has become the default metric for transferring emissions of different gases to a common scale; often called CO2 equivalent emis- sions (e.g., Shine, 2009). It has usually been integrated over 20, 100 or 500 years consistent with Houghton et al. (1990). Note, however that Houghton et al. presented these time horizons as candidates for discussion [that] should not be considered as having any special sig- nificance . The GWP for a time horizon of 100 years was later adopted as a metric to implement the multi-gas approach embedded in the United Nations Framework Convention on Climate Change (UNFCCC) Figure 8.28 | (a) The Absolute Global Warming Potential (AGWP) is calculated by and made operational in the 1997 Kyoto Protocol. The choice of time integrating the RF due to emission pulses over a chosen time horizon; for example, 20 and 100 years (vertical lines). The GWP is the ratio of AGWP for component i over AGWP horizon has a strong effect on the GWP values and thus also on the for the reference gas CO2. The blue hatched field represents the integrated RF from a calculated contributions of CO2 equivalent emissions by component, pulse of CO2, while the green and red fields represent example gases with 1.5 and 13 sector or nation. There is no scientific argument for selecting 100 years years lifetimes, respectively. (b) The Global Temperature change Potential (GTP) is based compared with other choices (Fuglestvedt et al., 2003; Shine, 2009). on the temperature response at a selected year after pulse emission of the same gases; The choice of time horizon is a value judgement because it depends e.g., 20 or 100 years (vertical lines). See Supplementary Material Section 8.SM.11 for equations for calculations of GWP and GTP. 711 Chapter 8 Anthropogenic and Natural Radiative Forcing t ­ emperature effects of emissions relative to that of CO2 for the chosen time horizon. As for GWP, the choice of time horizon has a strong effect on the metric values and the calculated contributions to warming. In addition, the AGTP can be used to calculate the global mean temper- ature change due to any given emission scenario (assuming linearity) using a convolution of the emission scenarios and AGTPi: 8 = ! ! ! ! ! (8.1) where i is component, t is time, and s is time of emission (Berntsen and Fuglestvedt, 2008; Peters et al., 2011b; Shindell et al., 2011). ( ) By accounting for the climate sensitivity and the exchange of heat between the atmosphere and the ocean, the GTP includes physical pro- Figure 8.29 | Development of AGWP-CO2, AGWP-CH4 and GWP-CH4 with time hori- cesses that the GWP does not. The GTP accounts for the slow response zon. The yellow and blue curves show how the AGWPs changes with increasing time of the (deep) ocean, thereby prolonging the response to emissions horizon. Because of the integrative nature the AGWP for CH4 (yellow curve) reaches a constant level after about five decades. The AGWP for CO2 continues to increase for cen- beyond what is controlled by the decay time of the atmospheric con- turies. Thus the ratio which is the GWP (black curve) falls with increasing time horizon. centration. Thus the GTP includes both the atmospheric adjustment time scale of the component considered and the response time scale of the climate system. on the relative weight assigned to effects at different times. Other important choices include the background atmosphere on which the The GWP and GTP are fundamentally different by construction and dif- GWP calculations are superimposed, and the way indirect effects and ferent numerical values can be expected. In particular, the GWPs for feedbacks are included (see Section 8.7.1.4). NTCFs, over the same time frames, are higher than GTPs due to the integrative nature of the metric. The GTP values can be significantly For some gases the variation in GWP with time horizon mainly reflects affected by assumptions about the climate sensitivity and heat uptake properties of the reference gas, not the gas for which the GWP is cal- by the ocean. Thus, the relative uncertainty ranges are wider for the culated. The GWP for NTCFs decreases with increasing time horizon, as GTP compared to GWP (see Section 8.7.1.4). The additional uncertainty GWP is defined with the integrated RF of CO2 in the denominator. As is a typical trade-off when moving along the cause effect chain to an shown in Figure 8.29, after about five decades the development in the effect of greater societal relevance (Figure 8.27). The formulation of the GWP for CH4 is almost entirely determined by CO2. However, for long- ocean response in the GTP has a substantial effect on the values; thus lived gases (e.g., SF6) the development in GWP is controlled by both the its characterization also represents a trade-off between simplicity and increasing integrals of RF from the long-lived gas and CO2. accuracy. As for GWP, the GTP is also influenced by the background atmosphere, and the way indirect effects and feedbacks are included 8.7.1.3 The Global Temperature change Potential Concept (see Section 8.7.1.4). Compared to the GWP, the Global Temperature change Potential (GTP; 8.7.1.4 Uncertainties and Limitations related to Global Warming Shine et al., 2005a) goes one step further down the cause effect Potential and Global Temperature change Potential chain (Figure 8.27) and is defined as the change in global mean sur- face temperature at a chosen point in time in response to an emission The uncertainty in the numerator of GWP; that is, the AGWPi (see for- pulse relative to that of CO2. Whereas GWP is integrated in time mula in Figure 8.28a) is determined by uncertainties in lifetimes (or (Figure 8.28a), GTP is an end-point metric that is based on tempera- perturbation lifetimes) and radiative efficiency. Inclusion of indirect ture change for a selected year, t, (see Figure 8.28b with formula). Like effects increases uncertainties (see below). For the reference gas CO2, for the GWP, the impact from CO2 is normally used as reference, hence, the uncertainty is dominated by uncertainties in the impulse response for a component i, GTP(t)i = AGTP(t)i / AGTP(t)CO2 = T((t)i / T(t)CO2, function (IRF) that describes the development in atmospheric concen- where AGTP is the absolute GTP giving temperature change per unit tration that follows from an emission pulse (Joos et al., 2013); see Box emission (see Supplementary Material Section 8.SM.11 for equations 6.2 and Supplementary Material Section 8.SM.12. The IRF is sensitive and parameter values). Shine et al. (2005a) presented the GTP for both to model representation of the carbon cycle, pulse size and background pulse and sustained emission changes based on an energy balance CO2 concentrations and climate. model as well as analytical equations. A modification was later intro- duced (Shine et al., 2007) in which the time horizon is determined by Based on a multi-model study, Joos et al. (2013) estimate uncertain- the proximity to a target year as calculated by using scenarios and ty ranges for the time-integrated IRF for CO2 to be +/-15% and +/-25% climate models (see Section 8.7.1.5). (5 to 95% uncertainty range) for 20- and 100-year time horizons, respectively. Assuming quadratic error propagation, and +/-10% uncer- Like GWP, the GTP values can be used for weighting the emissions tainty in radiative efficiency, the uncertainty ranges in AGWP for CO2 to obtain CO2 equivalents (see Section 8.7.1.1). This gives the were estimated to be +/-18% and +/-26% for 20 and 100 years. These 712 Anthropogenic and Natural Radiative Forcing Chapter 8 u ­ ncertainties affect all metrics that use CO2 as reference. Reisinger et including CH4, had an additional substantial climate effect because al. (2010) and Joos et al. (2013) show that these uncertainties increase they increased or decreased the rate of oxidation of SO2 to sulphate with time horizon. aerosol. Studies with different sulphur cycle formulations have found lower sensitivity (Collins et al., 2010; Fry et al., 2012). Collins et al. The same factors contribute to uncertainties in the GTP, with an addi- (2010) postulated an additional component to their GWPs and GTPs tional contribution from the parameters describing the ocean heat for ozone precursors due to the decreased productivity of plants under uptake and climate sensitivity. In the first presentation of the GTP, higher levels of surface ozone. This was estimated to have the same 8 Shine et al. (2005a) used one time constant for the climate response in magnitude as the ozone and CH4 effects. This effect, however, has their analytical expression. Improved approaches were used by Bouch- so far only been examined with one model. In a complex and inter- er and Reddy (2008), Collins et al. (2010) and Berntsen and Fuglestvedt connected system, feedbacks can become increasingly complex, and (2008) that include more explicit representations of the deep ocean uncertainty of the magnitude and even direction of feedback increases that increased the long-term response to a pulse forcing. Over the the further one departs from the primary perturbation, resulting in a range of climate sensitivities from AR4, GTP50 for BC was found to vary trade-off between completeness and robustness, and hence utility for by a factor of 2, the CH4 GTP50 varied by about 50%, while for N2O decision-making. essentially no dependence was found (Fuglestvedt et al., 2010). AGTPs for CO2 were also calculated in the multi-model study by Joos et al. Gillett and Matthews (2010) included climate carbon feedbacks in (2013). They found uncertainty ranges in AGTP that are much larger calculations of GWP for CH4 and N2O and found that this increased than for AGWP; +/-45% and +/-90% for 20 and 100 years (5 to 95% the values by about 20% for 100 years. For GTP of CH4 they found uncertainty range). These uncertainty ranges also reflect the signal-to- an increase of ~80%. They used numerical models for their studies noise ratio, and not only uncertainty in the physical mechanisms. and suggest that climate carbon feedbacks should be considered and parameterized when used in simple models to derive metrics. Col- There are studies combining uncertainties in various input parameters. lins et al. (2013) parameterize the climate-carbon feedback based on Reisinger et al. (2011) estimated the uncertainty in the GWP for CH4 Friedlingstein et al. (2006) and Arora et al. (2013) and find that this and found an uncertainty of 30 to +40% for the GWP100 and 50 to more than doubles the GTP100 for CH4. Enhancement of the GTP for +75% for GTP100 of CH4 (for 5 to 95% of the range). Boucher (2012) CH4 due to carbon climate feedbacks may also explain the higher GTP performed a Monte Carlo analysis with uncertainties in perturbation values found by Reisinger et al. (2010). lifetime and radiative efficiency, and for GWP100 for CH4 (assuming a constant background atmosphere) he found +/-20%, and 40 to +65 for The inclusion of indirect effects and feedbacks in metric values has GTP100 (for 5 to 95% uncertainty range). been inconsistent in the IPCC reports. In SAR and TAR, a carbon model without a coupling to a climate model was used for calculation of IRF Here we estimate uncertainties in GWP values based on the uncer- for CO2 (Joos et al., 1996), while in AR4 climate-carbon feedbacks were tainties given for radiative efficiencies (Section 8.3.1), perturbation included for the CO2 IRF (Plattner et al., 2008). For the time horizons lifetimes, indirect effects and in the AGWP for the reference gas CO2 20 and 100 years, the AGWPCO2 calculated with the Bern3D-LPJ model (see Supplementary Material Section 8.SM.12). For CH4 GWP we esti- is, depending on the pulse size, 4 to 5% and 13 to 15% lower, respec- mate an uncertainty of +/-30% and +/-40% for 20- and 100-year time tively, when carbon cycle climate feedbacks are not included (Joos horizons, respectively (for 5 to 95% uncertainty range). The uncertainty et al., 2013). While the AGWP for the reference gas CO2 included cli- is dominated by AGWP for CO2 and indirect effects. For gases with life- mate carbon feedbacks, this is not the case for the non-CO2 gas in the times of a century or more the uncertainties are of the order of +/-20% numerator of GWP, as recognized by Gillett and Matthews (2010), Joos and +/-30% for 20- and 100-year horizons. The uncertainty in GWPs for et al. (2013), Collins et al. (2013) and Sarofim (2012). This means that gases with lifetimes of a few decades is estimated to be of the order the GWPs presented in AR4 may underestimate the relative impacts of +/-25% and +/-35% for 20 and 100 years. For shorter-lived gases, the of non-CO2 gases. The different inclusions of feedbacks partially repre- uncertainties in GWPs will be larger (see Supplementary Material Sec- sent the current state of knowledge, but also reflect inconsistent and tion 8.SM.12 for a discussion of contributions to the total uncertainty.) ambiguous definitions. In calculations of AGWP for CO2 in AR5 we use For GTP, few uncertainty estimates are available in the literature. Based the IRF for CO2 from Joos et al. (2013) which includes climate carbon on the results from Joos et al. (2013), Reisinger et al. (2010) and Bou- feedbacks. Metric values in AR5 are presented both with and without cher (2012) we assess the uncertainty to be of the order of +/-75% for including climate carbon feedbacks for non-CO2 gases. This feedback the CH4 GTP100. is based on the carbon-cycle response in a similar set of models (Arora et al., 2013) as used for the reference gas (Collins et al., 2013). The metric values are also strongly dependent on which processes are included in the definition of a metric. Ideally all indirect effects The effect of including this feedback for the non-reference gas increas- (Sections 8.2 and 8.3) should be taken into account in the calculation es with time horizon due to the long-lived nature of the initiated CO2 of metrics. The indirect effects of CH4 on its own lifetime, tropospher- perturbation (Table 8.7). The relative importance also increases with ic ozone and stratospheric water have been traditionally included in decreasing lifetime of the component, and is larger for GTP than GWP its GWP. Boucher et al. (2009) have quantified an indirect effect on due to the integrative nature of GWP. We calculate an increase in the CO2 when fossil fuel CH4 is oxidized in the atmosphere. Shindell et CH4 GWP100 of 20%. For GTP100, however, the changes are much larger; al. (2009) estimated the impact of reactive species emissions on both of the order of 160%. For the shorter time horizons (e.g., 20 years) gaseous and aerosol forcing species and found that ozone precursors, the effect of including this feedback is small (<5%) for both GWP 713 Chapter 8 Anthropogenic and Natural Radiative Forcing Table 8.7 | GWP and GTP with and without inclusion of climate carbon feedbacks (cc fb) in response to emissions of the indicated non-CO2 gases (climate-carbon feedbacks in response to the reference gas CO2 are always included). Lifetime (years) GWP20 GWP100 GTP20 GTP100 CH4b 12.4 a No cc fb 84 28 67 4 With cc fb 86 34 70 11 HFC-134a 13.4 No cc fb 3710 1300 3050 201 8 With cc fb 3790 1550 3170 530 CFC-11 45.0 No cc fb 6900 4660 6890 2340 With cc fb 7020 5350 7080 3490 N2O 121.0a No cc fb 264 265 277 234 With cc fb 268 298 284 297 CF4 50,000.0 No cc fb 4880 6630 5270 8040 With cc fb 4950 7350 5400 9560 Notes: Uncertainties related to the climate carbon feedback are large, comparable in magnitude to the strength of the feedback for a single gas. a Perturbation lifetime is used in the calculation of metrics. b These values do not include CO from methane oxidation. Values for fossil methane are higher by 1 and 2 for the 20 and 100 year metrics, respectively (Table 8.A.1). 2 and GTP. For the more long-lived gases the GWP100 values increase r (for the weighting function e rt) must be chosen instead. The choice of by 10 to 12%, while for GTP100 the increase is 20 to 30%. Table 8.A.1 discount rate is also value based (see WGIII, Chapter 3). gives metric values including the climate carbon feedback for CO2 only, while Supplementary Material Table 8.SM.16 gives values for all For NTCFs the metric values also depend on the location and timing halocarbons that include the climate carbon feedback. Though uncer- of emission and whether regional or global metrics are used for these tainties in the carbon cycle are substantial, it is likely that including gases is also a choice for the users. Metrics are usually calculated for the climate carbon feedback for non-CO2 gases as well as for CO2 pulses, but some studies also give metric values that assume constant provides a better estimate of the metric value than including it only emissions over the full time horizon (e.g., Shine et al., 2005a; Jacobson, for CO2. 2010). It is important to be aware of the idealized assumption about constant future emissions (or change in emissions) of the compound Emission metrics can be estimated based on a constant or variable being considered if metrics for sustained emissions are used. background climate and this influences both the adjustment times and the concentration forcing temperature relationships. Thus, all metric 8.7.1.5 New Metric Concepts values will need updating due to changing atmospheric conditions as well as improved input data. In AR5 we define the metric values New metric concepts have been developed both to modify physical with respect to a constant present-day condition of concentrations and metrics to address shortcomings as well as to replace them with met- climate. However, under non-constant background, Joos et al. (2013) rics that account for economic dimensions of problems to which met- found decreasing CO2 AGWP100 for increasing background levels (up to rics are applied. Modifications to physical metrics have been proposed 23% for RCP8.5). This means that GWP for all non-CO2 gases (except to better represent CO2 emissions from bioenergy, regional patterns of CH4 and N2O) would increase by roughly the same magnitude. Reising- response, and for peak temperature limits. er et al. (2011) found a reduction in AGWP for CO2 of 36% for RCP8.5 from 2000 to 2100 and that the CH4 radiative efficiency and AGWP Emissions of CO2 from the combustion of biomass for energy in nation- also decrease with increasing CH4 concentration. Accounting for both al emission inventories are currently assumed to have no net RF, based effects, the GWP100 for CH4 would increase by 10 to 20% under low on the assumption that these emissions are compensated by biomass and mid-range RCPs by 2100, but would decrease by up to 10% by regrowth (IPCC, 1996). However, there is a time lag between combus- mid-century under the highest RCP. While these studies have focused tion and regrowth, and while the CO2 is resident in the atmosphere on the background levels of GHGs, the same issues apply for tempera- it leads to an additional RF. Modifications of the GWP and GTP for ture. Olivié et al. (2012) find different temperature IRFs depending on bioenergy (GWPbio, GTPbio) have been developed (Cherubini et al., 2011; the background climate (and experimental set up). Cherubini et al., 2012). The GWP­bio give values generally between zero (current default for bioenergy) and one (current for fossil fuel emissions) User related choices (see Box 8.4) such as the time horizon can greatly (Cherubini et al., 2011), and negative values are possible for GTPbio affect the numerical values obtained for CO2 equivalents. For a change due to the fast time scale of atmospheric ocean CO2 exchange relative in time horizon from 20 to 100 years, the GWP for CH4 decreases by to the growth cycle of biomass (Cherubini et al., 2012). GWPbio and a factor of approximately 3 and its GTP by more than a factor of 10. GTPbio have been used in only a few applications, and more research is Short-lived species are most sensitive to this choice. Some approaches needed to assess their robustness and applicability. Metrics for bioge- have removed the time horizon from the metrics (e.g., Boucher, 2012), ophysical effects, such as albedo changes, have been proposed (Betts, but discounting is usually introduced which means that a discount rate 2000; Rotenberg and Yakir, 2010) , but as for NTCFs regional variations 714 Anthropogenic and Natural Radiative Forcing Chapter 8 are important (Claussen et al., 2001) and the RF concept may not be If other indexes, such as the GWP, are used instead of an economic adequate (Davin et al., 2007). cost-minimizing index, costs to society will increase. Cost implications at the project or country level could be substantial under some cir- New concepts have also been developed to capture information cumstances (Godal and Fuglestvedt, 2002; Shine, 2009; Reisinger et about regional patterns of responses and cancelling effects that are al., 2013). However, under idealized conditions of full participation in lost when global mean metrics are used. The use of nonlinear damage mitigation policy, the increase is relatively small at the global level, functions to capture information on the spatial pattern of responses particularly when compared to the cost savings resulting from a multi- 8 has been explored (Shine et al., 2005b; Lund et al., 2012). In addi- (as opposed to single-) gas mitigation strategy even when based on tion, the Absolute Regional Temperature Potential (ARTP) (Shindell, an imperfect metric (O Neill, 2003; Aaheim et al., 2006; Johansson et 2012; Collins et al., 2013) has been developed to provide estimates al., 2006; Johansson, 2012; Reisinger et al., 2013; Smith et al., 2013). of impacts at a sub-global scale. ARTP gives the time-dependent tem- perature response in four latitude bands as a function of the regional Purely physical metrics continue to be used in many contexts due at forcing imposed in all bands. These metrics, as well as new regional least in part to the added uncertainties in mitigation and damage precipitation metrics (Shindell et al., 2012b), require additional studies costs, and therefore in the values of economic metrics (Boucher, 2012). to determine their robustness. Efforts have been made to view purely physical metrics such as GWPs and GTPs as approximations of economic indexes. GTPs, for example, Alternatives to the single basket approach adopted by the Kyoto Pro- can be interpreted as an approximation of a Global Cost Potential tocol are a component-by-component approach or a multi-basket designed for use in a cost-effectiveness setting (Shine et al., 2007; Tol approach (Rypdal et al., 2005; Daniel et al., 2012; Sarofim, 2012; Jack- et al., 2012). Quantitative values for time-dependent GTPs reproduce son, 2009). Smith et al. (2012) show how peak temperature change is in broad terms several features of the Global Cost Potential such as the constrained by cumulative emissions (see 12.5.4) for gases with long rising value of metrics for short-lived gases as a climate policy target is lifetimes and emissions rates for shorter-lived gases (including CH4). approached (Tanaka et al., 2013). Figure 8.30 shows how contributions Thus, they divide gases into two baskets and present two metrics that of N2O, CH4 and BC to warming in the target year changes over time. can be used for estimating peak temperature for various emission sce- The contributions are given relative to CO2 and show the effects of narios. This division of gases into the two baskets is sensitive to the emission occurring at various times. Similarly, GWPs can be interpret- time of peak temperature in the different scenarios. The approach uses ed as approximations of the Global Damage Potential designed for a time invariant metrics that do not account for the timing of emissions cost benefit framework (Tol et al., 2012). These interpretations of the relative to the target year. The choice of time horizon is implicit in the GTP and GWP imply that using even a purely physical metric in an eco- scenario assumed and this approach works only for a peak scenario. nomic policy context involves an implicit economic valuation. A number of new metrics have been developed to add economic In both cases, a number of simplifying assumptions must be made dimensions to purely physically based metrics such as the GWP and for these approximations to hold (Tol et al., 2012). For example, in GTP. The use of physical metrics in policy contexts has been criticized the case of the GWP, the influence of emissions on RF, and therefore by economists (Reilly and Richards, 1993; Schmalensee, 1993; Hammitt implicitly on costs to society, beyond the time horizon is not taken et al., 1996; Reilly et al., 1999; Bradford, 2001; De Cara et al., 2008). A into account, and there are substantial numerical differences between prominent use of metrics is to set relative prices of gases when imple- GWP and GDP values (Marten and Newbold, 2012). In the case of the menting a multi-gas policy. Once a particular policy has been agreed GTP, the influence of emissions on temperature change (and costs) is on, economic metrics can address policy goals more directly than phys- ical metrics by accounting not only for physical dimensions but also for economic dimensions such as mitigation costs, damage costs and discount rates (see WGIII, Chapter 3; Deuber et al., 2013). For example, if mitigation policy is set within a cost-effectiveness framework with the aim of making the least cost mix of emissions reductions across components to meet a global temperature target, the price ratio (Manne and Richels, 2001), also called the Global Cost Potential (GCP) (Tol et al., 2012), most directly addresses the goal. The choice of target is a policy decision; metric values can then be calcu- lated based on an agreed upon target. Similarly, if policy is set within a cost benefit framework, the metric that directly addresses the policy goal is the ratio of the marginal damages from the emission of a gas (i.e., the damage costs to society resulting from an incremental increase in emissions) relative to the marginal damages of an emission of CO2, known as the Global Damage Potential (GDP) (Kandlikar, 1995). Both Figure 8.30 | Global Temperature change Potential (GTP(t)) for CH4, nitrous oxide types of metrics are typically determined within an integrated climate and BC for each year from year of emission to the time at which the temperature economy model, since they are affected both by the response of the change target is reached. The (time-invariant) GWP100 is also shown for N2O and CH4 climate system as well as by economic factors. for ­comparison. 715 Chapter 8 Anthropogenic and Natural Radiative Forcing i ­ncluded only at the time the target is reached, but not before nor chosen year with no weight on years before or after. The most appro- after. Other metrics have been developed to more closely approximate priate metric depends on the particular application and which aspect GCPs or GDPs. The Cost-Effective Temperature Potential (CETP) repro- of ­ limate change is considered relevant in a given context. The GWP c duces values of the GCP more closely than does the GTP (Johansson, is not directly related to a temperature limit such as the 2°C target 2012). It is similar to the GTP but accounts for post-target temperature (Manne and Richels, 2001; Shine et al., 2007; Manning and Reisinger, effects based on an assumption about how to value costs beyond the 2011; Smith et al., 2012; Tol et al., 2012; Tanaka et al., 2013), whereas time the target is reached. Metrics have also been proposed that take some economic metrics and physical end-point metrics like the GTP 8 into account forcing or temperature effects that result from emissions may be more suitable for this purpose. trajectories over broad time spans, and that behave similarly to GCP and GTP (Tanaka et al., 2009; Manning and Reisinger, 2011) or to GWP To provide metrics that can be useful to the users and policymakers (e.g., O Neill, 2000; Peters et al., 2011a; Gillett and Matthews, 2010; a more effective dialog and discussion on three topics is needed: (1) Azar and Johansson, 2012). which applications particular metrics are meant to serve; (2) how com- prehensive metrics need to be in terms of indirect effects and feed- 8.7.1.6 Synthesis backs, and economic dimensions; and related to this (3) how impor- tant it is to have simple and transparent metrics (given by analytical In the application and evaluation of metrics, it is important to distin- formulations) versus more complex model-based and thus model-de- guish between two main sources of variation in metric values. While pendent metrics. These issues are also important to consider in a wider scientific choices of input data have to be made, there are also choic- disciplinary context (e.g., across the IPCC Working Groups). Finally, it es involving value judgements. For some metrics such choices are not is important to be aware that all metric choices, even traditional or always explicit and transparent. The choice of metric type and time widely used metrics, contain implicit value judgements as well as horizon will for many components have a much larger effect than large uncertainties. improved estimates of input parameters and can have strong effects on perceived impacts of emissions and abatement strategies. 8.7.2 Application of Metrics In addition to progress in understanding of GWP, new concepts have 8.7.2.1 Metrics for Carbon Dioxide, Methane, Nitrous Oxide, been introduced or further explored since AR4. Time variant metrics Halocarbons and Related Compounds introduce more dynamical views of the temporal contributions that accounts for the proximity to a prescribed target (in contrast to the tra- Updated (A)GWP and (A)GTP values for CO2, CH4, N2O, CFCs, HCFCs, ditional static GWP). Time variant metrics can be presented in a format bromofluorocarbons, halons, HFCs, PFCs, SF6, NF3, and related halogen- that makes changing metric values over time predictable. containing compounds are given for some illustrative and tentative time horizons in Tables 8.7, 8.A.1 and Supplementary Material Table As metrics use parameters further down the cause effect chain the met- 8.SM.16. The input data and methods for calculations of GWPs and rics become in general more policy relevant, but at the same time the GTPs are documented in the Supplementary Material Section 8.SM.13. uncertainties increase. Furthermore, metrics that account for regional Indirect GWPs that account for the RF caused by depletion of strat- variations in sensitivity to emissions or regional variation in response ospheric ozone (consistent with Section 8.3.3) are given for selected could give a very different emphasis to various emissions. Many spe- gases in Table 8.A.2. cies, especially NTCFs, produce distinctly regionally heterogeneous RF and climate response patterns. These aspects are not accounted for in The confidence in the ability to provide useful metrics at time scales of the commonly used global scale metrics. several centuries is very low due to nonlinear effects, large uncertain- ties for multi-century processes and strong assumptions of constant The GWPs and GTPs have had inconsistent treatment of indirect effects background conditions. Thus, we do not give metric values for longer and feedbacks. The GWPs reported in AR4 include climate carbon time scales than 100 years (see discussion in Supplementary Material feedbacks for the reference gas CO2 but not for the non-CO2 gases. Section 8.SM.11). However, these time scales are important to consider Such feedbacks may have significant impacts on metrics and should be for gases such as CO2, SF6 and PFCs. For CO2, as much as 20 to 40% of treated consistently. More studies are needed to assess the importance the initial increase in concentration remains after 500 years. For PFC- of consistent treatment of indirect effects/feedbacks in metrics. 14, 99% of an emission is still in the atmosphere after 500 years. The effects of emissions on these time scales are discussed in Chapter 12. The weighting of effects over time choice of time horizon in the case of GWP and GTP is value based. Discounting is an alternative, The GWP values have changed from previous assessments due to which also includes value judgements and is equally controversial. The new estimates of lifetimes, impulse response functions and radiative weighting used in the GWP is a weight equal to one up to the time hori- efficiencies. These are updated due to improved knowledge and/or zon and zero thereafter, which is not in line with common approaches changed background levels. Because CO2 is used as reference, any for evaluation of future effects in economics (e.g., as in WGIII, Chapter changes for this gas will affect all metric values via AGWP changes. 3). Adoption of a fixed horizon of e.g., 20, 100 or 500 years will inev- Figure 8.31 shows how the values of radiative efficiency (RE), integrat- itably put no weight on the long-term effect of CO2 beyond the time ed impulse response function (IRF) and consequentially AGWP for CO2 horizon (Figure 8.28 and Box 6.1). While GWP integrates the effects up have changed from earlier assessments relative to AR5 values. The net to a chosen time horizon the GTP gives the temperature just for one effect of change in RE and IRF is an increase of approximately 1% and 716 Anthropogenic and Natural Radiative Forcing Chapter 8 is reduced by one third. Among the hydrofluoroethers (HFEs) there are also several large changes in lifetimes. In addition, substantial updates of radiative efficiencies are made for several important gases; CFC- 11, CFC-115, HCFC-124, HCFC-225cb, HFC-143a, HFC-245fa, CCl4, CHCl3, and SF6. The radiative efficiency for carbon tetrachloride (CCl4) is higher now and the GWP100 has increased by almost 25% from AR4. Uncertainties in metric values are given in Section 8.7.1.4. See 8 also Supplementary Material Section 8.SM.12 and footnote to Table 8.A.1. As can be seen from Table 8.A.2, some ODS have strong indi- rect effects through stratospheric ozone forcing, which for some of the gases reduce their net GWP100 values substantially (and for the halons, to large negative values). Note that, consistent with Section 8.3.3, the uncertainties are large; +/-100% for this indirect effect. When climate-carbon feedbacks are included for both the non-CO2 and Figure 8.31 | Changes in the radiative efficiency (RE), integrated impulse response reference gases, all metric values increase relative to the methodolo- function (IRF) and Absolute Global Warming Potential (AGWP) for CO2 for 100 years gy used in AR4, sometimes greatly (Table 8.7, Supplementary Material from earlier IPCC Assessment Reports normalized relative to the values given in AR5. Table 8.SM.16). Though the uncertainties range for these metric values The original values are calculated based on the methods explained or value reported in each IPCC Assessment Report. The updated values are calculated based on the is greater, as uncertainties in climate-carbon feedbacks are substantial, methods used in AR5, but the input values from each Assessment Report. The differ- these calculations provide a more consistent methodology. ence is primarily in the formula for the RE, which was updated in TAR. The different integrated IRF in TAR relates to a different parameterisation of the same IRF (WMO, 8.7.2.2 Metrics for Near-Term Climate Forcers 1999). Changes represent both changes in scientific understanding and a changing background atmospheric CO2 concentration (note that y-axis starts from 0.8). The lines connecting individual points are meant as a visual guide and not to represent the values The GWP concept was initially used for the WMGHGs, but later for between different Assessment Reports. NTCFs as well. There are, however, substantial challenges related to calculations of GWP (and GTP) values for these components, which is reflected in the large ranges of values in the literature. Below we 6% from AR4 to AR5 in AGWP for CO2 for 20 and 100 years, respective- present and assess the current status of knowledge and quantification ly (see Supplementary Material Section 8.SM.12). These increases in of metrics for various NTCFs. the AGWP of the reference gas lead to corresponding decreases in the GWPs for all non-CO2 gases. Continued increases in the atmospheric 8.7.2.2.1 Nitrogen oxides levels of CO2 will lead to further changes in GWPs (and GTPs) in the future. Metric values for NOX usually include the short-lived ozone effect, CH4 changes and the CH4-controlled O3 response. NOX also causes RF To understand the factors contributing to changes relative to AR4, through nitrate formation, and via CH4 it affects stratospheric H2O and comparisons are made here using the AR5 values that include climate through ozone it influences CO2. In addition, NOx affects CO2 through carbon feedbacks for CO2 only. Relative to AR4 the CH4 AGWP has nitrogen deposition (fertilization effect). Due to high reactivity and changed due to changes in perturbation lifetime, a minor change in RE the many nonlinear chemical interactions operating on different time due to an increase in background concentration, and changes in the scales, as well as heterogeneous emission patterns, calculation of net estimates of indirect effects. The indirect effects on O3 and stratospheric climate effects of NOX is difficult. The net effect is a balance of large H2O are accounted for by increasing the effect of CH4 by 50% and 15%, opposing effects with very different temporal behaviours. There is also respectively (see Supplementary Material Table 8.SM.12). The ozone a large spread in values among the regions due to variations in chem- effect has doubled since AR4 taking into account more recent studies ical and physical characteristics of the atmosphere. as detailed in Sections 8.3.3 and 8.5.1. Together with the changes in AGWP for CO2 the net effect is increased GWP values of CH4. As shown in Table 8.A.3 the GTP and GWP values are very different. This is due to the fundamentally different nature of these two metrics The GWPs for N2O are lower here compared to AR4. A longer perturba- (see Figure 8.28) and the way they capture the temporal behaviour of tion lifetime is used in AR5, while the radiative efficiency is lower due responses to NOx emissions. Time variation of GTP for NOX is complex, to increased abundances of CH4 and N2O. In addition, the reduction in which is not directly seen by the somewhat arbitrary choices of time CH4 via stratospheric O3, UV fluxes and OH levels due to increased N2O horizon, and the net GTP is a fine balance between the contributing abundance is included in GWPs and GTP. Owing to large uncertainties terms. The general pattern for NOX is that the short-lived ozone forc- related to altitude of changes, we do not include the RF from strato- ing is always positive, while the CH4-induced ozone forcing and CH4 spheric ozone changes as an indirect effect of N2O. forcing are always negative (see Section 8.5.1). Nitrate aerosols from NOx emission are not included in Table 8.A.3. For the GTP, all estimates Lifetimes for most of the halocarbons are taken from WMO (2011) and for NOX from surface sources give a negative net effect. As discussed many of these have changed from AR4. The lifetimes of CFC-114, CFC- in Section 8.7.1.4 Collins et al. (2010) and Shindell et al. (2009) imple- 115 and HCF-161 are reduced by approximately 40%, while HFC-152 mented further indirect effects, but these are not included in Table 717 Chapter 8 Anthropogenic and Natural Radiative Forcing 8.A.3 due to large uncertainties. The metric estimates for NOX reflect the region where BC is emitted by about +/-30% . For larger regions the level of knowledge, but they also depend on experimental design, of emissions, Collins et al. (2013) calculated GWPs and GTPs for the treatment of transport processes, and modelling of background levels. direct effect of BC and found somewhat lower variations among the The multi-model study by Fry et al. (2012) shows the gaseous chemistry regions. response to NOX is relatively robust for European emissions, but that the uncertainty is so large that for some regions of emissions it is not Several studies have focused on the effects of emissions of BC and possible to conclude whether NOX causes cooling or warming. OC from different regions (Bauer et al., 2007; Koch et al., 2007; Naik 8 et al., 2007; Reddy and Boucher, 2007; Rypdal et al., 2009). However, 8.7.2.2.2 Carbon monoxide and volatile organic compounds examination of results from these models (Fuglestvedt et al., 2010) reveals that there is not a robust relationship between the region of Emissions of carbon monoxide (CO) and volatile organic compounds emission and the metric value hence, regions that yield the highest (VOCs) lead to production of ozone on short time scales. By affecting metric value in one study, do not, in general, do so in the other studies. OH and thereby the levels of CH4 they also initiate a positive long-term ozone effect. With its lifetime of 2 to 3 months, the effect of CO emis- The metric values for OC are quite consistent across studies, but fewer sions is less dependent on location than is the case for NOX (see Table studies are available (see Table 8.A.6). A brief overview of metric 8.A.4). There is also less variation across models. However, Collins et values for other components is given in the Supplementary Material al. (2010) found that inclusion of vegetation effects of O3 increased the Section 8.SM.14. GTP values for CO by 20 to 50%. By including aerosol responses Shin- dell et al. (2009) found an increase in GWP100 by a factor of ~2.5. CO of 8.7.2.2.4 Summary of status of metrics for near-term climate forcers fossil origin will also have a forcing effect by contributing to CO2 levels. This effect adds 1.4 to 1.6 to the GWP100 for CO (Daniel and Solomon, The metrics provide a format for comparing the magnitudes of the 1998; Derwent et al., 2001). (The vegetation and aerosol effects are not various emissions as well as for comparing effects of emissions from included in the numbers in Table 8.A.4.) different regions. They can also be used for comparing results from different studies. Much of the spread in results is due to differences in VOC is not a well-defined group of hydrocarbons. This group of gases experimental design and how the models treat physical and chemical with different lifetimes is treated differently across models by lump- processes. Unlike most of the WMGHGs, many of the NTCFs are tightly ing or using representative key species. However, the spread in metric coupled to the hydrologic cycle and atmospheric chemistry, leading to values in Table 8.A.5 is moderate across regions, with highest values a much larger spread in results as these are highly complex processes for emissions in South Asia (of the four regions studied). The effects that are difficult to validate on the requisite small spatial and short via ozone and CH4 cause warming, and the additional effects via inter- temporal scales. The confidence level is lower for many of the NTCF actions with aerosols and via the O3 CO2 link increase the warming compared to WMGHG and much lower where aerosol cloud interac- effect further. Thus, the net effects of CO and VOC are less uncertain tions are important (see Section 8.5.1). There are particular difficulties than for NOX for which the net is a residual between larger terms of for NOX, because the net impact is a small residual of opposing effects opposite sign. However, the formation of SOAs is usually not included with quite different spatial distributions and temporal behaviour. in metric calculations for VOC, which introduces a cooling effect and Although climate carbon feedbacks for non-CO2 emissions have not increased uncertainty. been included in the NTCF metrics (other than CH4) presented here, they can greatly increase those values (Collins et al., 2013) and likely 8.7.2.2.3 Black carbon and organic carbon provide more realistic results. Most of the metric values for BC in the literature include the aero- 8.7.2.3 Impact by Emitted Component sol radiation interaction and the snow/ice albedo effect of BC, though whether external or internal mixing is used varies between the studies. We now use the metrics evaluated here to estimate climate impacts Bond et al. (2011) calculate GWPs and find that when the albedo effect of various components (in a forward looking perspective). Figure 8.32 is included the values increase by 5 to 15%. Studies have shown, how- shows global anthropogenic emissions of some selected components ever, that the climate response per unit forcing to this mechanism is weighted by the GWP and GTP. The time horizons are chosen as exam- stronger than for WMGHG (see Section 7.5). ples and illustrate how the perceived impacts of components relative to the impact of the reference gas vary strongly as function of impact Bond et al. (2013) assessed the current understanding of BC effects parameter (integrated RF in GWP or end-point temperature in GTP) and calculated GWP and GTP for BC that includes aerosol radiation and with time horizon. interaction, aerosol cloud interactions and albedo. As shown in Table 8.A.6 the uncertainties are wide for both metrics (for 90% uncertain- We may also calculate the temporal development of the temperature ty range) reflecting the current challenges related to understanding responses to pulse or sustained emissions using the AGTP metric. and quantifying the various effects (see Sections 7.5, 8.3.4 and 8.5.1). Figure 8.33 shows that for a one-year pulse the impacts of NTCF decay Their aerosol radiation interaction effect is about 65% of the total quickly owing to their atmospheric adjustment times even if effects are effect while the albedo effect is approximately 20% of the aerosol prolonged due to climate response time (in the case of constant emis- radiation interaction effect. Based on two studies (Rypdal et al., 2009; sions the effects reach approximately constant levels since the emis- Bond et al., 2011), the GWP and GTP metrics were found to vary with sions are replenished each year, except for CO2, which has a ­raction f 718 Anthropogenic and Natural Radiative Forcing Chapter 8 GWP GTP 10 CO2 equivalent emissions (Pg CO2-eq) CO2 equivalent emissions (PgC-eq) 20 5 8 0 0 CO2 CH4 N2O NOX CO -5 -20 SO2 BC OC 10 yrs 20 yrs 100 yrs 10 yrs 20 yrs 100 yrs Figure 8.32 | Global anthropogenic emissions weighted by GWP and GTP for chosen time horizons (aerosol cloud interactions are not included). Emission data for 2008 are taken from the EDGAR database. For BC and OC emissions for 2005 are from Shindell et al. (2012a). The units are CO2 equivalents which reflects equivalence only in the impact parameter of the chosen metric (integrated RF over the chosen time horizon for GWP; temperature change at the chosen point in time for GTP), given as Pg(CO2)eq (left axis) and given as PgCeq (right axis). There are large uncertainties related to the metric values and consequentially also to the calculated CO2 equivalents (see text). remaining in the atmosphere on time scales of centuries). Figure 8.33 also shows how some components have strong short-lived effects of 20 both signs while CO2 has a weaker initial effect but one that persists Temperature impact (10-3 K) to create a long-lived warming effect. Note that there are large uncer- 10 tainties related to the metric values (as discussed in Section 8.7.1.4); especially for the NTCFs. 0 These examples show that the outcome of comparisons of effects of -10 emissions depends strongly on choice of time horizon and metric type. Such end-user choices will have a strong influence on the calculat- -20 ed contributions from NTCFs versus WMGHGs or non-CO2 versus CO2 emissions. Thus, each specific analysis should use a design chosen in -30 light of the context and questions being asked. 0 20 40 60 80 Time Horizon (yr) 8.7.2.4 Metrics and Impacts by Sector Figure 8.33 | Temperature response by component for total anthropogenic emissions While the emissions of WMGHGs vary strongly between sectors, the cli- for a 1-year pulse. Emission data for 2008 are taken from the EDGAR database and for mate impacts of these gases are independent of sector. The latter is not BC and OC for 2005 from Shindell et al. (2012a). There are large uncertainties related to the AGTP values and consequentially also to the calculated temperature responses the case for chemically active and short-lived components, due to the (see text). dependence of their impact on the emission location. Since most sectors have multiple co-emissions, and for NTCFs some of these are warm- ing while others are cooling, the net impact of a given sector requires to using integrated RF up to the chosen times for pulse emissions (as explicit calculations. Since AR4, there has been significant progress in in GWPs). Such studies are relevant for policymaking that focuses on the understanding and quantification of climate impacts of NTCFs from regulating the total activity of a sector or for understanding the con- sectors such as transportation, power production and biomass burning tribution from a sector to climate change. On the other hand, the fixed (Berntsen and Fuglestvedt, 2008; Skeie et al., 2009; Stevenson and Der- mix of emissions makes it less general and relevant for emission sce- went, 2009; Lee et al., 2010; Unger et al., 2010; Dahlmann et al., 2011). narios. Alternatively, one may adopt a component-by-component view Supplementary Material Table 8.SM.18 gives an overview of recent pub- which is relevant for policies directed towards specific components (or lished metric values for various components by sector. sets of components, as controlling an individual pollutant in isolation is usually not practical). But this view will not capture interactions and The impact from sectors depends on choice of metric, time horizon, non-linearities within the suite of components emitted by most sectors. pulse versus sustained emissions and forward versus backward looking The effects of specific emission control technologies or policies or pro- perspective (see Section 8.7.1 and Box 8.4). Unger et al. (2010) calcu- jected societal changes on the mix of emissions is probably the most lated RF for a set of components emitted from each sector. RF at chosen relevant type of analysis, but there are an enormous number of possi- points in time (20 and 100 years) for sustained emissions was used by ble actions and regional details that could be investigated. Henze et al. Unger et al. (2010) as the metric for comparison. This is ­ omparable c (2012) demonstrate a method for providing highly spatially resolved 719 Chapter 8 Anthropogenic and Natural Radiative Forcing estimates of forcing per component, and caution that RF aggregated in terms of temperature change. The AGTP concept can be used to over regions or sectors may not represent the impacts of emissions study the effects of the various components for chosen time horizons. changes on finer scales. A single year s worth of current global emissions from the energy and industrial sectors have the largest contributions to warming after 100 Metrics for individual land-based sectors are often similar to the global years (see Figure 8.34a). Household fossil fuel and biofuel, biomass mean metric values (Shindell et al., 2008). In contrast, metrics for emis- burning and on-road transportation are also relatively large contribu- sions from aviation and shipping usually show large differences from tors to warming over 100-year time scales. Those same sectors, along 8 global mean metric values (Table 8.A.3 versus Table 8.SM.18). Though with sectors that emit large amounts of CH4 (animal husbandry, waste/ there can sometimes be substantial variation in the impact of land- landfills and agriculture), are most important over shorter time hori- based sectors across regions, and for a particular region even from one zons (about 20 years; see Figure 8.34b). sector to another, variability between different land-based sources is generally smaller than between land, sea and air emissions. Analysing climate change impacts by using the net effect of particular activities or sectors may compared to other perspectives provide NOx from aviation is one example where the metric type is especial- more insight into how societal actions influence climate. Owing to ly important. GWP20 values are positive due to the strong response large variations in mix of short- and long-lived components, as well of short-lived ozone. Reported GWP100 and GTP100 values are of either as cooling and warming effects, the results will also in these cases sign, however, due to the differences in balance between the individ- depend strongly on choice of time horizon and climate impact param- ual effects modelled. Even if the models agree on the net effect of eter. Improved understanding of aerosol cloud interactions, and how NOX, the individual contributions can differ significantly, with large those are attributed to individual components is clearly necessary to uncertainties stemming from the relative magnitudes of the CH4 and refine estimates of sectoral or emitted component impacts. O3 responses (Myhre et al., 2011) and the background tropospheric concentrations of NOX (Holmes et al., 2011; Stevenson and Derwent, (a) 2009). Köhler et al. (2013), find strong regional sensitivity of ozone and CH4 to NOX particularly at cruise altitude. Generally, they find the strongest effects at low latitudes. For the aviation sector contrails and contrail induced cirrus are also important. Based on detailed studies in the literature, Fuglestvedt et al. (2010) produced GWP and GTP for contrails, water vapor and contrail-induced cirrus. The GWP and GTPs for NOX from shipping are strongly negative for all time horizons. The strong positive effect via O3 due to the low-NOX environment into which ships generally emit NOX is outweighed by the stronger effect on CH4 destruction due to the relatively lower latitudes of these emissions compared to land-based sources. In addition to having large emissions of NOX the shipping sector has ( ) large emission of SO2. The direct GWP100 for shipping ranges from 11 to 43 (see Supplementary Material Table 8.SM.18). Lauer et al. (2007) (b) reported detailed calculations of the indirect forcing specifically for this sector and found a wide spread of values depending on the emission inventory. Righi et al. (2011) and Peters et al. (2012) calculate indirect effects that are 30 to 50% lower than the indirect forcing reported by Lauer et al. (2007). The values from Shindell and Faluvegi (2010) for SO2 from power generation are similar to those for shipping. Although the various land transport sectors often are treated as one aggregate (e.g., road transport) there are important subdivisions. For instance, Bond et al. (2013) points out that among the BC-rich sec- tors they examined, diesel vehicles have the most clearly positive net impact on forcing. Studies delving even further have shown substantial differences between trucks and cars, gasoline and diesel vehicles, and ( ) low-sulphur versus high-sulphur fuels. Similarly, for power production Figure 8.34 | Net global mean temperature change by source sector after (a) 100 there are important differences depending on fuel type (coal, oil, gas; and (b) 20 years (for 1-year pulse emissions). Emission data for 2008 are taken from e.g., Shindell and Faluvegi, 2010). the EDGAR database. For BC and OC anthropogenic emissions are from Shindell et al. (2012a) and biomass burning emissions are from Lamarque et al. (2010), see Supple- In the assessment of climate impacts of current emissions by sectors mentary Material Section 8.SM.17. There are large uncertainties related to the AGTP we give examples and apply a forward-looking perspective on effects values and consequentially also to the calculated temperature responses (see text). 720 Anthropogenic and Natural Radiative Forcing Chapter 8 References Aaheim, A., J. Fuglestvedt, and O. Godal, 2006: Costs savings of a flexible multi-gas Bala, G., K. Caldeira, M. Wickett, T. J. Phillips, D. B. Lobell, C. Delire, and A. Mirin, 2007: climate policy. Energy J. (Special Issue No. 3), 485 501. Combined climate and carbon-cycle effects of large-scale deforestation. Proc. Abreu, J., J. Beer, F. Steinhilber, S. Tobias, and N. Weiss, 2008: For how long will the Natl. Acad. Sci. U.S.A., 104, 6550 6555. current grand maximum of solar activity persist? Geophys. Res. Lett., 35, L20109. Baliunas, S., and R. Jastrow, 1990: Evidence for long-term brightness changes of Ackerley, D., B. B. B. Booth, S. H. E. Knight, E. J. Highwood, D. J. Frame, M. R. Allen, solar-type stars. Nature, 348, 520 523. and D. P. Rowell, 2011: Sensitivity of twentieth-century Sahel rainfall to sulfate Ball, W., Y. Unruh, N. Krivova, S. Solanki, T. Wenzler, D. Mortlock, and A. Jaffe, 2012: 8 aerosol and CO2 forcing. J. Clim., 24, 4999 5014. Reconstruction of total solar irradiance 1974 2009. Astron. Astrophys., 541, Allan, W., H. Struthers, and D. C. Lowe, 2007: Methane carbon isotope effects A27. caused by atomic chlorine in the marine boundary layer: Global model results Ban-Weiss, G., L. Cao, G. Bala, and K. Caldeira, 2012: Dependence of climate forcing compared with Southern Hemisphere measurements. J. Geophys. Res. Atmos., and response on the altitude of black carbon aerosols. Clim. Dyn., 38, 897 911. 112, D04306. Barnes, C. A., and D. P. Roy, 2008: Radiative forcing over the conterminous United Ammann, C. M., and P. Naveau, 2003: Statistical analysis of tropical explosive States due to contemporary land cover land use albedo change. Geophys. Res. volcanism occurrences over the last 6 centuries. Geophys. Res. Lett., 30, 1210. Lett., 35, L09706. Ammann, C. M., and P. Naveau, 2010: A statistical volcanic forcing scenario Bathiany, S., M. Claussen, V. Brovkin, T. Raddatz, and V. Gayler, 2010: Combined generator for climate simulations. J. Geophys. Res. Atmos., 115, D05107. biogeophysical and biogeochemical effects of large-scale forest cover changes Anchukaitis, K. J., B. M. Buckley, E. R. Cook, B. I. Cook, R. D. D Arrigo, and C. M. in the MPI earth system model. Biogeosciences, 7, 1383 1399. Ammann, 2010: Influence of volcanic eruptions on the climate of the Asian Bauer, S., D. Koch, N. Unger, S. Metzger, D. Shindell, and D. Streets, 2007: Nitrate monsoon region. Geophys. Res. Lett., 37, L22703. aerosols today and in 2030: A global simulation including aerosols and Andersen, M., D. Blake, F. Rowland, M. Hurley, and T. Wallington, 2009: Atmospheric tropospheric ozone. Atmos. Chem. Phys., 7, 5043 5059. chemistry of sulfuryl fluoride: Reaction with OH radicals, CI atoms and O3, Bekki, S., J. A. Pyle, W. Zhong, R. Toumi, J. D. Haigh, and D. M. Pyle, 1996: The role of atmospheric lifetime, IR spectrum, and global warming potential. Environ. Sci. microphysical and chemical processes in prolonging the climate forcing of the Technol., 43, 1067 1070. Toba eruption. Geophys. Res. Lett., 23, 2669 2672. Andersen, M., V. Andersen, O. Nielsen, S. Sander, and T. Wallington, 2010: Atmospheric Bellouin, N., J. Rae, A. Jones, C. Johnson, J. Haywood, and O. Boucher, 2011: Aerosol chemistry of HCF2O(CF2CF2O)(x)CF2H (x=2 4): Kinetics and mechanisms of the forcing in the Climate Model Intercomparison Project (CMIP5) simulations by chlorine-atom-initiated oxidation. Chemphyschem, 11, 4035 4041. HadGEM2 ES and the role of ammonium nitrate. J. Geophys. Res. Atmos., 116, Andrews, T., and P. M. Forster, 2008: CO2 forcing induces semi-direct effects with D20206. consequences for climate feedback interpretations. Geophys. Res. Lett., 35, Bernier, P. Y., R. L. Desjardins, Y. Karimi-Zindashty, D. Worth, A. Beaudoin, Y. Luo, and S. L04802. Wang, 2011: Boreal lichen woodlands: A possible negative feedback to climate Andrews, T., M. Doutriaux-Boucher, O. Boucher, and P. M. Forster, 2011: A regional change in eastern North America. Agr. Forest Meteorol., 151, 521 528. and global analysis of carbon dioxide physiological forcing and its impact on Berntsen, T., and J. Fuglestvedt, 2008: Global temperature responses to current climate. Clim. Dyn., 36, 783 792. emissions from the transport sectors. Proc. Natl. Acad. Sci. U.S.A., 105, 19154 Andrews, T., J. Gregory, M. Webb, and K. Taylor, 2012a: Forcing, feedbacks and climate 19159. sensitivity in CMIP5 coupled atmosphere-ocean climate models. Geophys. Res. Berntsen, T. K., et al., 1997: Effects of anthropogenic emissions on tropospheric Lett., 39, L09712. ozone and its radiative forcing. J. Geophys. Res. Atmos., 102, 28101 28126. Andrews, T., P. Forster, O. Boucher, N. Bellouin, and A. Jones, 2010: Precipitation, Betts, R., 2000: Offset of the potential carbon sink from boreal forestation by radiative forcing and global temperature change. Geophys. Res. Lett., 37, decreases in surface albedo. Nature, 408, 187 190. doi:10.1029/2010GL043991, L14701. Betts, R. A., P. D. Falloon, K. K. Goldewijk, and N. Ramankutty, 2007: Biogeophysical Andrews, T., M. Ringer, M. Doutriaux-Boucher, M. Webb, and W. Collins, 2012b: effects of land use on climate: Model simulations of radiative forcing and large- Sensitivity of an Earth system climate model to idealized radiative forcing. scale temperature change. Agr. Forest Meteorol., 142, 216 233. Geophys. Res. Lett., 39, L10702. Biasutti, M., and A. Giannini, 2006: Robust Sahel drying in response to late 20th Antuna, J. C., A. Robock, G. Stenchikov, J. Zhou, C. David, J. Barnes, and L. Thomason, century forcings. Geophys. Res. Lett., 33, L11706. 2003: Spatial and temporal variability of the stratospheric aerosol cloud Blowers, P., K. F. Tetrault, and Y. Trujillo-Morehead, 2008: Global warming potential produced by the 1991 Mount Pinatubo eruption. J. Geophys. Res. Atmos., 108, predictions for hydrofluoroethers with two carbon atoms. Theor. Chem. Acc., 4624. 119, 369 381. Archibald, A. T., M. E. Jenkin, and D. E. Shallcross, 2010: An isoprene mechanism Blowers, P., D. Moline, K. Tetrault, R. Wheeler, and S. Tuchawena, 2007: Prediction of intercomparison. Atmos. Environ., 44, 5356 5364. radiative forcing values for hydrofluoroethers using density functional theory Archibald, A. T., et al., 2011: Impacts of HO(x) regeneration and recycling in the methods. J. Geophys. Res. Atmos., 112, D15108. oxidation of isoprene: Consequences for the composition of past, present and Boer, G. J., and B. Yu, 2003: Climate sensitivity and response. Clim. Dyn., 20, 415 429. future atmospheres. Geophys. Res. Lett., 38, L05804. Bollasina, M. A., Y. Ming, and V. Ramaswamy, 2011: Anthropogenic aerosols and the Arnold, T., et al., 2013: Nitrogen trifluoride global emissions estimated from updated weakening of the South Asian summer monsoon. Science, 334, 502 505. atmospheric measurements, Proc. Natl. Acad. Sci. U.S.A, 110, 2029-2034. Bond, T., C. Zarzycki, M. Flanner, and D. Koch, 2011: Quantifying immediate radiative Arora, V. K., and A. Montenegro, 2011: Small temperature benefits provided by forcing by black carbon and organic matter with the Specific Forcing Pulse. realistic afforestation efforts. Nature Geosci., 4, 514 518. Atmos. Chem. Phys., 11, 1505 1525. Arora, V. K., et al., 2013: Carbon-concentration and carbon-climate feedbacks in Bond, T. C., et al., 2007: Historical emissions of black and organic carbon aerosol CMIP5 Earth system models. J. Clim., 26, 5289-5314. from energy-related combustion, 1850 2000. Global Biogeochem. Cycles, 21, Ashmore, M. R., 2005: Assessing the future global impacts of ozone on vegetation. Gb2018. Plant Cell Environ., 28, 949 964. Bond, T. C., et al., 2013: Bounding the role of black carbon in the climate system: Azar, C., and D. J. A. Johansson, 2012: On the relationship between metrics to A scientific assessment. J. Geophys. Res. Atmos., 118, doi:10.1002/jgrd.50171, compare greenhouse gases the case of IGTP, GWP and SGTP. Earth Syst. 5380-5552. Dynam., 3, 139 147. Bonfils, C., and D. Lobell, 2007: Empirical evidence for a recent slowdown in Baasandorj, M., A. R. Ravishankara, and J. B. Burkholder, 2011: Atmospheric irrigation-induced cooling. Proc. Natl. Acad. Sci. U.S.A., 104, 13582 13587. chemistry of (Z)-CF3CH=CHCF3: OH radical reaction rate coefficient and global Bonfils, C. J. W., T. J. Phillips, D. M. Lawrence, P. Cameron-Smith, W. J. Riley, and Z. M. warming potential. J. Phys. Chem. A, 115, 10539 10549. Subin, 2012: On the influence of shrub height and expansion on northern high Baasandorj, M., G. Knight, V. Papadimitriou, R. Talukdar, A. Ravishankara, and J. latitude climate. Environ. Res. Lett., 7, 015503. Burkholder, 2010: Rate coefficients for the gas-phase reaction of the hydroxyl radical with CH2 = CHF and CH2 = CF2. J. Phys. Chem. A, 114, 4619 4633. 721 Chapter 8 Anthropogenic and Natural Radiative Forcing Booth, B., N. Dunstone, P. Halloran, T. Andrews, and N. Bellouin, 2012: Aerosols Collins, W. D., et al., 2006: Radiative forcing by well-mixed greenhouse gases: implicated as a prime driver of twentieth-century North Atlantic climate Estimates from climate models in the Intergovernmental Panel on Climate variability. Nature, 485, 534 534. Change (IPCC) Fourth Assessment Report (AR4). J. Geophys. Res. Atmos., 111, Boucher, O., 2012: Comparison of physically- and economically-based CO2 D14317. equivalences for methane. Earth Syst. Dyn., 3, 49 61. Collins, W. J., S. Sitch, and O. Boucher, 2010: How vegetation impacts affect climate Boucher, O., and J. Haywood, 2001: On summing the components of radiative forcing metrics for ozone precursors. J. Geophys. Res. Atmos., 115, D23308. of climate change. Clim. Dyn., 18, 297 302. Collins, W. J., M. M. Fry, H. Yu, J. S. Fuglestvedt, D. T. Shindell, and J. J. West, 2013: Boucher, O., and M. Reddy, 2008: Climate trade-off between black carbon and Global and regional temperature-change potentials for near-term climate 8 carbon dioxide emissions. Energy Policy, 36, 193 200. forcers. Atmos. Chem. Phys., 13, 2471 2485. Boucher, O., P. Friedlingstein, B. Collins, and K. P. Shine, 2009: The indirect global Conley, A. J., J. F. Lamarque, F. Vitt, W. D. Collins, and J. Kiehl, 2013: PORT, a CESM tool warming potential and global temperature change potential due to methane for the diagnosis of radiative forcing. Geosci. Model Dev., 6, 469 476. oxidation. Environ. Res. Lett., 4, 044007. Cooper, O. R., et al., 2010: Increasing springtime ozone mixing ratios in the free Bourassa, A. E., et al., 2012: Large volcanic aerosol load in the stratosphere linked to troposphere over western North America. Nature, 463, 344 348. Asian monsoon transport. Science, 337, 78 81. Cox, P. M., et al., 2008: Increasing risk of Amazonian drought due to decreasing Bourassa, A. E., et al., 2013: Response to Comments on Large Volcanic Aerosol Load aerosol pollution. Nature, 453, 212 215. in the Stratosphere Linked to Asian Monsoon Transport . Science, 339, 6120. Crook, J., and P. Forster, 2011: A balance between radiative forcing and climate Bowman, D., et al., 2009: Fire in the Earth System. Science, 324, 481 484. feedback in the modeled 20th century temperature response. J. Geophys. Res. Bowman, K. W., et al., 2013: Evaluation of ACCMIP outgoing longwave radiation Atmos., 116, D17108. from tropospheric ozone using TES satellite observations. Atmos. Chem. Phys., Crowley, T. J., and M. B. Unterman, 2013: Technical details concerning development 13, 4057 4072. of a 1200 yr proxy index for global volcanism. Earth Syst. Sci. Data, 5, 187-197. Bradford, D., 2001: Global change Time, money and tradeoffs. Nature, 410, 649 Crutzen, P., 1973: Discussion of chemistry of some minor constituents in stratosphere 650. and troposhere. Pure Appl. Geophys., 106, 1385 1399. Bravo, I., et al., 2010: Infrared absorption spectra, radiative efficiencies, and global Dahlmann, K., V. Grewe, M. Ponater, and S. Matthes, 2011: Quantifying the warming potentials of perfluorocarbons: Comparison between experiment and contributions of individual NOx sources to the trend in ozone radiative forcing. theory. J. Geophys. Res. Atmos., 115, D24317. Atmos. Environ., 45, 2860 2868. Brovkin, V., et al., 2010: Sensitivity of a coupled climate-carbon cycle model to large Daniel, J., and S. Solomon, 1998: On the climate forcing of carbon monoxide. J. volcanic eruptions during the last millennium. Tellus B, 62, 674 681. Geophys. Res. Atmos., 103, 13249 13260. Calvin, K., et al., 2012: The role of Asia in mitigating climate change: Results from the Daniel, J., S. Solomon, and D. Abritton, 1995: On the evaluation of halocarbon Asia modeling exercise. Energ. Econ., 34, S251 S260. radiative forcing and global warming potentials. J. Geophys. Res. Atmos., 100, Campra, P., M. Garcia, Y. Canton, and A. Palacios-Orueta, 2008: Surface temperature 1271 1285. cooling trends and negative radiative forcing due to land use change toward Daniel, J., E. Fleming, R. Portmann, G. Velders, C. Jackman, and A. Ravishankara, greenhouse farming in southeastern Spain. J. Geophys. Res. Atmos., 113, 2010: Options to accelerate ozone recovery: Ozone and climate benefits. Atmos. D18109. Chem. Phys., 10, 7697 7707. Carlton, A. G., R. W. Pinder, P. V. Bhave, and G. A. Pouliot, 2010: To what extent can Daniel, J., S. Solomon, T. Sanford, M. McFarland, J. Fuglestvedt, and P. Friedlingstein, biogenic SOA be controlled? Environ. Sci. Technol., 44, 3376 3380. 2012: Limitations of single-basket trading: Lessons from the Montreal Protocol Carslaw, K. S., O. Boucher, D. V. Spracklen, G. W. Mann, J. G. L. Rae, S. Woodward, and for climate policy. Clim. Change, 111, 241 248. M. Kulmala, 2010: A review of natural aerosol interactions and feedbacks within Davin, E., N. de Noblet-Ducoudre, and P. Friedlingstein, 2007: Impact of land cover the Earth system. Atmos. Chem. Phys., 10, 1701 1737. change on surface climate: Relevance of the radiative forcing concept. Geophys. Chang, W. Y., H. Liao, and H. J. Wang, 2009: Climate responses to direct radiative Res. Lett., 34, L13702. forcing of anthropogenic aerosols, tropospheric ozone, and long-lived Davin, E. L., and N. de Noblet-Ducoudre, 2010: Climatic impact of global-scale greenhouse gases in Eastern China over 1951 2000. Adv. Atmos. Sci., 26, 748 deforestation: Radiative versus nonradiative orocesses. J. Clim., 23, 97 112. 762. De Cara, S., E. Galko, and P. Jayet, 2008: The global warming potential paradox: Chen, W. T., A. Nenes, H. Liao, P. J. Adams, J. L. F. Li, and J. H. Seinfeld, 2010: Global Implications for the design of climate policy. In: Design of Climate Policy [R. climate response to anthropogenic aerosol indirect effects: Present day and year Guesnerie and H. Tulkens (eds.)]. The MIT Press, Cambridge, MA, USA, pp. 359 2100. J. Geophys. Res. Atmos., 115, D12207. 384. Cherubini, F., G. Guest, and A. Strmman, 2012: Application of probablity distributions de la Torre, L., et al., 2006: Solar influence on Northern Annular Mode spatial to the modelleing of biogenic CO2 fluxes in life cycle assessment. Global Change structure and QBO modulation. Part. Accel. Space Plasma Phys. Sol. Radiat. Biol. , 4, doi:10.1111/j.1757 1707.2011.01156.x, 784-798. Earth. Atmos. Clim., 37, 1635 1639. Cherubini, F., G. Peters, T. Berntsen, A. Stromman, and E. Hertwich, 2011: CO2 de Noblet-Ducoudre, N., et al., 2012: Determining robust impacts of land-use- emissions from biomass combustion for bioenergy: Atmospheric decay and induced land cover changes on surface climate over North America and Eurasia: contribution to global warming. Global Change Biol. Bioenerg., 3, 413 426. Results from the first set of LUCID experiments. J. Clim., 25, 3261 3281. Chung, C. E., and V. Ramanathan, 2006: Weakening of North Indian SST gradients DeAngelis, A., F. Dominguez, Y. Fan, A. Robock, M. D. Kustu, and D. Robinson, 2010: and the monsoon rainfall in India and the Sahel. J. Clim., 19, 2036 2045. Evidence of enhanced precipitation due to irrigation over the Great Plains of the Clark, H. L., M. L. Cathala, H. Teyssedre, J. P. Cammas, and V. H. Peuch, 2007: Cross- United States. J. Geophys. Res. Atmos., 115, D15115. tropopause fluxes of ozone using assimilation of MOZAIC observations in a DeLand, M., and R. Cebula, 2012: Solar UV variations during the decline of Cycle 23. global CTM. Tellus B, 59, 39 49. J. Atmos. Sol. Terres. Phys., 77, 225 234. Clarke, A. D., and K. J. Noone, 1985: Soot in the Arctic Snowpack A cause for Delaygue, G., and E. Bard, 2011: An Antarctic view of Beryllium-10 and solar activity peturbations in radiative-transfer. Atmos. Environ., 19, 2045 2053. for the past millennium. Clim. Dyn., 36, 2201 2218. Claussen, M., V. Brovkin, and A. Ganopolski, 2001: Biogeophysical versus Deligne, N. I., S. G. Coles, and R. S. J. Sparks, 2010: Recurrence rates of large explosive biogeochemical feedbacks of large-scale land cover change. Geophys. Res. Lett., volcanic eruptions. J. Geophys. Res. Sol. Earth, 115, B06203. 28, 1011 1014. den Elzen, M., et al., 2005: Analysing countries contribution to climate change: Cofala, J., M. Amann, Z. Klimont, K. Kupiainen, and L. Hoglund-Isaksson, 2007: Scientific and policy-related choices. Environ. Sci. Policy, 8, 614 636. Scenarios of global anthropogenic emissions of air pollutants and methane until Denman, K. L., et al., 2007: Couplings between changes in the climate system 2030. Atmos. Environ., 41, 8486 8499. and biogeochemistry. In: Climate Change 2007: The Physical Science Basis. Collins, W., R. Derwent, C. Johnson, and D. Stevenson, 2002: The oxidation of organic Contribution of Working Group I to the Fourth Assessment Report of the compounds in the troposphere and their global warming potentials. Clim. Intergovernmental Panel on Climate Change [Solomon, S., D. Qin, M. Manning, Change, 52, 453 479. Z. Chen, M. Marquis, K. B. Averyt, M. Tignor and H. L. Miller (eds.)] Cambridge University Press, Cambridge, United Kingdom and New York, NY, USA, 499-587. 722 Anthropogenic and Natural Radiative Forcing Chapter 8 Dentener, F., et al., 2006: The global atmospheric environment for the next Forster, P., and K. Shine, 1997: Radiative forcing and temperature trends from generation. Environ. Sci. Technol., 40, 3586 3594. stratospheric ozone changes. J. Geophys. Res. Atmos., 102, 10841 10855. Derwent, R., W. Collins, C. Johnson, and D. Stevenson, 2001: Transient behaviour of Forster, P., et al., 2005: Resolution of the uncertainties in the radiative forcing of tropospheric ozone precursors in a global 3-D CTM and their indirect greenhouse HFC-134a. J. Quant. Spectrosc. Radiat. Transfer, 93, 447 460. effects. Clim. Change, 49, 463 487. Forster, P., et al., 2007: Changes in Atmospheric Constituents and in Radiative Forcing. Deuber, O., G. Luderer, and O. Edenhofer, 2013: Physico-economic evaluation of In: Climate Change 2007: The Physical Science Basis. Contribution of Working climate metrics: A conceptual framework. Environ. Sci. Policy, 29, 37 45. Group I to the Fourth Assessment Report of the Intergovernmental Panel on Dewitte, S., D. Crommelynck, S. Mekaoui, and A. Joukoff, 2004: Measurement and Climate Change [Solomon, S., D. Qin, M. Manning, Z. Chen, M. Marquis, K. B. uncertainty of the long-term total solar irradiance trend. Solar Phys., 224, 209 Averyt, M. Tignor and H. L. Miller (eds.)] Cambridge University Press, Cambridge, 8 216. United Kingdom and New York, NY, USA, 129-234. Dickinson, R., 1975: Solar variability and lower atmosphere. Bull. Am. Meteorol. Soc., Forster, P., et al., 2011a: Evaluation of radiation scheme performance within 56, 1240 1248. chemistry climate models. J. Geophys. Res. Atmos., 116, D10302. Doherty, S. J., S. G. Warren, T. C. Grenfell, A. D. Clarke, and R. E. Brandt, 2010: Light- Forster, P. M., T. Andrews, P. Good, J. M. Gregory, L. S. Jackson, and M. Zelinka, 2013: absorbing impurities in Arctic snow. Atmos. Chem. Phys., 10, 11647 11680. Evaluating adjusted forcing and model spread for historical and future scenarios Doutriaux-Boucher, M., M. Webb, J. Gregory, and O. Boucher, 2009: Carbon dioxide in the CMIP5 generation of climate models. J. Geophys. Res. Atmos., 118, 1139 induced stomatal closure increases radiative forcing via a rapid reduction in low 1150. cloud. Geophys. Res. Lett., 36, doi:10.1029/2008GL036273, L02703. Forster, P. M., et al., 2011b: Stratospheric changes and climate. In: Scientific Ehhalt, D. H., and L. E. Heidt, 1973: Vertical profiles of CH4 in troposphere and Assessment of Ozone Depletion: 2010. Global Ozone Research and stratosphere. J. Geophys. Res., 78, 5265 5271. Monitoring Project Report No. 52, World Meteorological Organization, Geneva, Eliseev, A. V., and I.I. Mokhov, 2011: Effect of including land-use driven radiative Switzerland, 516 pp. forcing of the surface albedo of land on climate response in the 16th-21st Fortuin, J. P. F., and H. Kelder, 1998: An ozone climatology based on ozonesonde and centuries. Izvestiya Atmos. Ocean. Phys., 47, 15 30. satellite measurements. J. Geophys. Res. Atmos., 103, 31709 31734. Engel, A., et al., 2009: Age of stratospheric air unchanged within uncertainties over Foukal, P., and J. Lean, 1988: Magnetic modulation of solar luminosity by phtospheric the past 30 years. Nature Geosci., 2, 28 31. activity. Astrophys. J., 328, 347 357. Erlykin, A., and A. Wolfendale, 2011: Cosmic ray effects on cloud cover and their Fowler, D., et al., 2009: Atmospheric composition change: Ecosystems-atmosphere relevance to climate change. J. Atmos. Sol. Terres. Phys., 73, 1681 1686. interactions. Atmos. Environ., 43, 5193 5267. Ermolli, I., K. Matthes, T. Dudok de Wit, N. A. Krivova, K. Tourpali, M. Weber, Y. C. Frame, T., and L. Gray, 2010: The 11-yr solar cycle in ERA-40 data: An update to 2008. Unruh, L. Gray, U. Langematz, P. Pilewskie, E. Rozanov, W. Schmutz, A. Shapiro, J. Clim., 23, 2213 2222. S. K. Solanki, and T. N. Woods, 2013: Recent variability of the solar spectral Freckleton, R., E. Highwood, K. Shine, O. Wild, K. Law, and M. Sanderson, 1998: irradiance and its impact on climate modelling, Atmospheric Chemistry and Greenhouse gas radiative forcing: Effects of averaging and inhomogeneities in Physics, 13, 3945-3977. trace gas distribution. Q. J. R. Meteorol. Soc., 124, 2099 2127. Esper, J., and F. H. Schweingruber, 2004: Large-scale treeline changes recorded in Friedlingstein, P., et al., 2006: Climate-carbon cycle feedback analysis: Results from Siberia. Geophys. Res. Lett., 31, L06202. the C(4)MIP model intercomparison. J. Clim., 19, 3337 3353. Eyring, V., et al., 2010a: Sensitivity of 21st century stratospheric ozone to greenhouse Frohlich, C., 2006: Solar irradiance variability since 1978 Revision of the PMOD gas scenarios. Geophys. Res. Lett., 37, L16807. composite during solar cycle 21. Space Sci. Rev., 125, 53 65. Eyring, V., et al., 2010b: Multi-model assessment of stratospheric ozone return dates Frohlich, C., 2009: Evidence of a long-term trend in total solar irradiance. Astron. and ozone recovery in CCMVal-2 models. Atmos. Chem. Phys., 10, 9451 9472. Astrophys., 501, L27 L30. Fan, F. X., M. E. Mann, and C. M. Ammann, 2009: Understanding changes in the Frolicher, T. L., F. Joos, and C. C. Raible, 2011: Sensitivity of atmospheric CO2 and Asian summer monsoon over the past millennium: Insights from a long-term climate to explosive volcanic eruptions. Biogeosciences, 8, 2317 2339. coupled model simulation. J. Clim., 22, 1736 1748. Fromm, M., G. Nedoluha, and Z. Charvat, 2013: Comment on Large Volcanic FAO, 2012: State of the world s forests. Food and Agriculture Organization of the Aerosol Load in the Stratosphere Linked to Asian Monsoon Transport . Science, United Nations, Rome, Italy, 60 pp. 339, 647 c. Feng, X., and F. Zhao, 2009: Effect of changes of the HITRAN database on Fry, M., et al., 2012: The influence of ozone precursor emissions from four world transmittance calculations in the near-infrared region. J. Quant. Spectrosc. regions on tropospheric composition and radiative climate forcing. J. Geophys. Radiat. Transfer, 110, 247 255. Res. Atmos., 117, D07306. Feng, X., F. Zhao, and W. Gao, 2007: Effect of the improvement of the HITIRAN Fuglestvedt, J., T. Berntsen, O. Godal, and T. Skodvin, 2000: Climate implications of database on the radiative transfer calculation. J. Quant. Spectrosc. Radiat. GWP-based reductions in greenhouse gas emissions. Geophys. Res. Lett., 27, Transfer, 108, 308 318. 409 412. Findell, K. L., E. Shevliakova, P. C. D. Milly, and R. J. Stouffer, 2007: Modeled impact of Fuglestvedt, J., T. Berntsen, O. Godal, R. Sausen, K. Shine, and T. Skodvin, 2003: anthropogenic land cover change on climate. J. Clim., 20, 3621 3634. Metrics of climate change: Assessing radiative forcing and emission indices. Fioletov, V. E., G. E. Bodeker, A. J. Miller, R. D. McPeters, and R. Stolarski, 2002: Global Clim. Change, 58, 267 331. and zonal total ozone variations estimated from ground-based and satellite Fuglestvedt, J. S., et al., 2010: Transport impacts on atmosphere and climate: Metrics. measurements: 1964 2000. J. Geophys. Res. Atmos., 107, 4647. Atmos. Environ., 44, 4648 4677. Fiore, A. M., et al., 2009: Multimodel estimates of intercontinental source-receptor Fung, I., J. John, J. Lerner, E. Matthews, M. Prather, L. P. Steele, and P. J. Fraser, 1991: relationships for ozone pollution. J. Geophys. Res. Atmos., 114, D04301. 3-Dimensional model synthesis of the global methane cycle. J. Geophys. Res. Fischer, E. M., J. Luterbacher, E. Zorita, S. F. B. Tett, C. Casty, and H. Wanner, 2007: Atmos., 96, 13033 13065. European climate response to tropical volcanic eruptions over the last half Gaillard, M. J., et al., 2010: Holocene land-cover reconstructions for studies on land millennium. Geophys. Res. Lett., 34, L05707. cover-climate feedbacks. Clim. Past, 6, 483 499. Fishman, J., et al., 2010: An investigation of widespread ozone damage to the Gao, C. C., A. Robock, and C. Ammann, 2008: Volcanic forcing of climate over soybean crop in the upper Midwest determined from ground-based and satellite the past 1500 years: An improved ice core-based index for climate models. J. measurements. Atmos. Environ., 44, 2248 2256. Geophys. Res. Atmos., 113, D23111. Flanner, M. G., C. S. Zender, J. T. Randerson, and P. J. Rasch, 2007: Present-day climate Garcia, R. R., W. J. Randel, and D. E. Kinnison, 2011: On the determination of age of forcing and response from black carbon in snow. J. Geophys. Res. Atmos., 112, air trends from atmospheric trace species. J. Atmos. Sci., 68, 139 154. D11202. Gerlach, T., 2011: Volcanic versus anthropogenic carbon dioxide. Eos, 92, 201 202. Fletcher, C. G., P. J. Kushner, A. Hall, and X. Qu, 2009: Circulation responses to snow Gettelman, A., J. Holton, and K. Rosenlof, 1997: Mass fluxes of O3, CH4, N2O and albedo feedback in climate change. Geophys. Res. Lett., 36, L09702. CF2Cl2 in the lower stratosphere calculated from observational data. J. Geophys. Fomin, B. A., and V. A. Falaleeva, 2009: Recent progress in spectroscopy and its effect Res. Atmos., 102, 19149 19159. on line-by-line calculations for the validation of radiation codes for climate models. Atmos. Oceanic Opt., 22, 626 629. 723 Chapter 8 Anthropogenic and Natural Radiative Forcing Gillett, N., and H. Matthews, 2010: Accounting for carbon cycle feedbacks in a Henze, D. K., et al., 2012: Spatially refined aerosol direct radiative forcing efficiencies. comparison of the global warming effects of greenhouse gases. Environ. Res. Environ. Sci. Technol., 46, 9511 9518. Lett., 5, 034011. Hohne, N., et al., 2011: Contributions of individual countries emissions to climate Ginoux, P., D. Garbuzov, and N. C. Hsu, 2010: Identification of anthropogenic and change and their uncertainty. Clim. Change, 106, 359 391. natural dust sources using Moderate Resolution Imaging Spectroradiometer Holmes, C., Q. Tang, and M. Prather, 2011: Uncertainties in climate assessment for (MODIS) Deep Blue level 2 data. J. Geophys. Res. Atmos., 115, D05204. the case of aviation NO. Proc. Natl. Acad. Sci. U.S.A., 108, 10997 11002. Godal, O., and J. Fuglestvedt, 2002: Testing 100-year global warming potentials: Holmes, C. D., M. J. Prather, O. A. Sovde, and G. Myhre, 2013: Future methane, Impacts on compliance costs and abatement profile. Clim. Change, 52, 93 127. hydroxyl, and their uncertainties: Key climate and emission parameters for future 8 Goosse, H., et al., 2006: The origin of the European Medieval Warm Period . Clim. predictions. Atmos. Chem. Phys., 13, 285 302. Past, 2, 99 113. Horowitz, L. W., 2006: Past, present, and future concentrations of tropospheric ozone Granier, C., et al., 2011: Evolution of anthropogenic and biomass burning emissions and aerosols: Methodology, ozone evaluation, and sensitivity to aerosol wet of air pollutants at global and regional scales during the 1980 2010 period. removal. J. Geophys. Res. Atmos., 111, D22211. Clim. Change, 109, 163 190. Houghton, J. T., G. J. Jenkins, and J. J. Ephraums (eds.), 1990: Climate Change. The Gray, L., S. Rumbold, and K. Shine, 2009: Stratospheric temperature and radiative IPCC Scientific Assessment. Cambridge University Press, Cambridge, United forcing response to 11-year solar cycle changes in irradiance and ozone. J. Kingdom and New York, NY, USA, 364 pp. Atmos. Sci., 66, 2402 2417. Hoyle, C., et al., 2011: A review of the anthropogenic influence on biogenic secondary Gray, L., et al., 2010: Solar influences on climate. Rev. Geophys., 48, RG4001. organic aerosol. Atmos. Chem. Phys., 11, 321 343. Gregory, J., and M. Webb, 2008: Tropospheric adjustment induces a cloud component Hsu, J., and M. J. Prather, 2009: Stratospheric variability and tropospheric ozone. J. in CO2 forcing. J. Clim., 21, 58 71. Geophys. Res. Atmos., 114, D06102. Gregory, J., et al., 2004: A new method for diagnosing radiative forcing and climate Huang, J., Q. Fu, W. Zhang, X. Wang, R. Zhang, H. Ye, and S. Warren, 2011: Dust and sensitivity. Geophys. Res. Lett., 31, L03205. black carbon in seasonal snow across northern China. Bull. Am. Meteorol. Soc., Gregory, J. M., 2010: Long-term effect of volcanic forcing on ocean heat content. 92, 175 181. Geophys. Res. Lett., 37, L22701. Huijnen, V., et al., 2010: The global chemistry transport model TM5: Description and Grewe, V., 2007: Impact of climate variability on tropospheric ozone. Sci. Tot. evaluation of the tropospheric chemistry version 3.0. Geosci. Model Dev., 3, Environ., 374, 167 181. 445 473. Gusev, A. A., 2008: Temporal structure of the global sequence of volcanic eruptions: Hurst, D. F., et al., 2011: Stratospheric water vapor trends over Boulder, Colorado: Order clustering and intermittent discharge rate. Phys. Earth Planet. Inter., 166, Analysis of the 30 year Boulder record. J. Geophys. Res. Atmos., 116, D02306. 203 218. Hurtt, G. C., et al., 2006: The underpinnings of land-use history: Three centuries Haigh, J., 1994: The role of stratospheric ozone in modulating the solar radiative of global gridded land-use transitions, wood-harvest activity, and resulting forcing of climate. Nature, 370, 544 546. secondary lands. Global Change Biol., 12, 1208 1229. Haigh, J., 1999: A GCM study of climate change in response to the 11-year solar Iacono, M. J., J. S. Delamere, E. J. Mlawer, M. W. Shephard, S. A. Clough, and W. D. cycle. Q. J. R. Meteorol. Soc., 125, 871 892. Collins, 2008: Radiative forcing by long-lived greenhouse gases: Calculations Haigh, J. D., 1996: The impact of solar variability on climate. Science, 272, 981 984. with the AER radiative transfer models. J. Geophys. Res. Atmos., 113, D13103. Hall, J., and G. Lockwood, 2004: The chromospheric activity and variability of cycling Ineson, S., A. A. Scaife, J. R. Knight, J. C. Manners, N. J. Dunstone, L. J. Gray, and and flat activity solar-analog stars. Astrophys. J., 614, 942 946. J. D. Haigh, 2011: Solar forcing of winter climate variability in the Northern Hallquist, M., et al., 2009: The formation, properties and impact of secondary organic Hemisphere. Nature Geosci., 4, 753 757. aerosol: Current and emerging issues. Atmos. Chem. Phys., 9, 5155 5236. IPCC, 1996: Revised 1996 IPCC Guidelines for National Greenhouse Gas Inventories. Hammitt, J., A. Jain, J. Adams, and D. Wuebbles, 1996: A welfare-based index for Intergovernmental Panel of Climate Change. assessing environmental effects of greenhouse-gas emissions. Nature, 381, Isaksen, I., et al., 2009: Atmospheric composition change: Climate-chemistry 301 303. interactions. Atmos. Environ., 43, 5138 5192. Hansen, J., and L. Nazarenko, 2004: Soot climate forcing via snow and ice albedos. Ito, A., and J. E. Penner, 2005: Historical emissions of carbonaceous aerosols from Proc. Natl. Acad. Sci. U.S.A., 101, 423 428. biomass and fossil fuel burning for the period 1870 2000. Global Biogeochem. Hansen, J., et al., 2005: Efficacy of climate forcings. J. Geophys. Res. Atmos., 110, Cycles, 19, Gb2028. D18104. Jackson, S., 2009: Parallel pursuit of near-term and long-term climate mitigation. Hansen, J., et al., 2007: Climate simulations for 1880 2003 with GISS modelE. Clim. Science, 326, 526 527. Dyn., 29, 661 696. Jacobson, M., 2010: Short-term effects of controlling fossil-fuel soot, biofuel soot Harder, J., J. Fontenla, P. Pilewskie, E. Richard, and T. Woods, 2009: Trends in solar and gases, and methane on climate, Arctic ice, and air pollution health. J. spectral irradiance variability in the visible and infrared. Geophys. Res. Lett., Geophys. Res. Atmos., 115, D14209. 36, L07801. Jacobson, M., 2012: Investigating cloud absorption effects: Global absorption Harrison, R., and M. Ambaum, 2010: Observing Forbush decreases in cloud at properties of black carbon, tar balls, and soil dust in clouds and aerosols. J. Shetland. J. Atmos. Sol. Terres. Phys., 72, 1408 1414. Geophys. Res. Atmos., 117, D06205. Haywood, J., and M. Schulz, 2007: Causes of the reduction in uncertainty in the Javadi, M., O. Nielsen, T. Wallington, M. Hurley, and J. Owens, 2007: Atmospheric anthropogenic radiative forcing of climate between IPCC (2001) and IPCC chemistry of 2-ethoxy-3,3,4,4,5 pentafluorotetra-hydro-2,5-bis[1,2,2,2- (2007). Geophys. Res. Lett., 34, L20701. tetrafluoro-1-(trifluoromethyl)ethyl]-furan: Kinetics, mechanisms, and products Haywood, J. M., et al., 2010: Observations of the eruption of the Sarychev volcano of CL atom and OH radical initiated oxidation. Environ. Sci. Technol., 41, 7389 and simulations using the HadGEM2 climate model. J. Geophys. Res. Atmos., 7395. 115, D21212. Jin, M. L., R. E. Dickinson, and D. L. Zhang, 2005: The footprint of urban areas on Heathfield, A., C. Anastasi, A. McCulloch, and F. Nicolaisen, 1998: Integrated infrared global climate as characterized by MODIS. J. Clim., 18, 1551 1565. absorption coefficients of several partially fluorinated ether compounds: Jin, Y., and D. P. Roy, 2005: Fire-induced albedo change and its radiative forcing at CF3OCF2H, CF2HOCF2H, CH3OCF2CF2H, CH3OCF2CFClH, CH3CH2OCF2CF2H, the surface in northern Australia. Geophys. Res. Lett., 32, L13401. CF3CH2OCF2CF2H AND CH2=CHCH2OCF2CF2H. Atmos. Environ., 32, 2825 2833. Jin, Y. F., J. T. Randerson, M. L. Goulden, and S. J. Goetz, 2012: Post-fire changes in Hegg, D. A., S. G. Warren, T. C. Grenfell, S. J. Doherty, T. V. Larson, and A. D. Clarke, net shortwave radiation along a latitudinal gradient in boreal North America. 2009: Source attribution of black carbon in Arctic snow. Environ. Sci. Technol., Geophys. Res. Lett., 39, L13403. 43, 4016 4021. Johansson, D., 2012: Economics- and physical-based metrics for comparing Hegglin, M. I., and T. G. Shepherd, 2009: Large climate-induced changes in ultraviolet greenhouse gases. Clim. Change, 110, 123 141. index and stratosphere-to-troposphere ozone flux. Nature Geosci., 2, 687 691. Johansson, D., U. Persson, and C. Azar, 2006: The cost of using global warming Helama, S., M. Fauria, K. Mielikainen, M. Timonen, and M. Eronen, 2010: Sub- potentials: Analysing the trade off between CO2, CH4 and N2O. Clim. Change, 77, Milankovitch solar forcing of past climates: Mid and late Holocene perspectives. doi:10.1007/s10584-006-9054-1, 291 309. Geol. Soc. Am. Bull., 122, 1981 1988. 724 Anthropogenic and Natural Radiative Forcing Chapter 8 Jones, G., M. Lockwood, and P. Stott, 2012: What influence will future solar Kristjansson, J. E., T. Iversen, A. Kirkevag, O. Seland, and J. Debernard, 2005: activity changes over the 21st century have on projected global near-surface Response of the climate system to aerosol direct and indirect forcing: Role of temperature changes? J. Geophys. Res. Atmos., 117, D05103. cloud feedbacks. J. Geophys. Res. Atmos., 110, D24206. Jones, G. S., J. M. Gregory, P. A. Stott, S. F. B. Tett, and R. B. Thorpe, 2005: An AOGCM Krivova, N., L. Vieira, and S. Solanki, 2010: Reconstruction of solar spectral irradiance simulation of the climate response to a volcanic super-eruption. Clim. Dyn., 25, since the Maunder minimum. J. Geophys. Res. Space Phys., 115, A12112. 725 738. Kueppers, L. M., M. A. Snyder, and L. C. Sloan, 2007: Irrigation cooling effect: Joos, F., M. Bruno, R. Fink, U. Siegenthaler, T. Stocker, and C. LeQuere, 1996: An Regional climate forcing by land-use change. Geophys. Res. Lett., 34, L03703. efficient and accurate representation of complex oceanic and biospheric models Kuroda, Y., and K. Kodera, 2005: Solar cycle modulation of the southern annular of anthropogenic carbon uptake. Tellus B, 48, 397 417. mode. Geophys. Res. Lett., 32, L13802. 8 Joos, F., et al., 2013: Carbon dioxide and climate impulse response functions for the Kvalevag, M. M., G. Myhre, G. Bonan, and S. Levis, 2010: Anthropogenic land cover computation of greenhouse gas metrics: A multi-model analysis. Atmos. Chem. changes in a GCM with surface albedo changes based on MODIS data. Int. J. Phys., 13, 2793 2825. Climatol., 30, 2105 2117. Joshi, M., and J. Gregory, 2008: Dependence of the land-sea contrast in surface Lacis, A. A., D. J. Wuebbles, J. A. Logan, 1990: Radiative forcing of climate by changes climate response on the nature of the forcing. Geophys. Res. Lett., 35, L24802. in the vertical-distribution of ozone, J. Geophys. Res., 95, 9971-9981. Joshi, M. M., and G. S. Jones, 2009: The climatic effects of the direct injection of Lamarque, J., et al., 2011: Global and regional evolution of short-lived radiatively- water vapour into the stratosphere by large volcanic eruptions. Atmos. Chem. active gases and aerosols in the Representative Concentration Pathways. Clim. Phys., 9, 6109 6118. Change, 109, 191 212. Kandlikar, M., 1995: The relative role of trace gas emissions in greenhouse abatement Lamarque, J., et al., 2010: Historical (1850 2000) gridded anthropogenic and policies. Energ. Policy, 23, 879 883. biomass burning emissions of reactive gases and aerosols: Methodology and Kaplan, J. O., K. M. Krumhardt, E. C. Ellis, W. F. Ruddiman, C. Lemmen, and K. K. application. Atmos. Chem. Phys., 10, 7017 7039. Goldewijk, 2011: Holocene carbon emissions as a result of anthropogenic land Lamarque, J. F., et al., 2013: The Atmospheric Chemistry and Climate Model cover change. Holocene, 21, 775 791. Intercomparison Project (ACCMIP): Overview and description of models, Kasischke, E. S., and J. E. Penner, 2004: Improving global estimates of atmospheric simulations and climate diagnostics. Geosci. Model Dev., 6, 179 206. emissions from biomass burning. J. Geophys. Res. Atmos., 109, D14S01. Lau, K. M., M. K. Kim, and K. M. Kim, 2006: Asian summer monsoon anomalies Kawase, H., T. Nagashima, K. Sudo, and T. Nozawa, 2011: Future changes in induced by aerosol direct forcing: The role of the Tibetan Plateau. Clim. Dyn., tropospheric ozone under Representative Concentration Pathways (RCPs). 26, 855 864. Geophys. Res. Lett., 38, L05801. Lauder, A. R., I. G. Enting, J. O. Carter, N. Clisby, A. L. Cowie, B. K. Henry, and M. Kawase, H., M. Abe, Y. Yamada, T. Takemura, T. Yokohata, and T. Nozawa, 2010: R. Raupach, 2013: Offsetting methane emissions An alternative to emission Physical mechanism of long-term drying trend over tropical North Africa. equivalence metrics. Int. J. Greenh. Gas Control, 12, 419 429. Geophys. Res. Lett., 37, L09706. Lauer, A., V. Eyring, J. Hendricks, P. Jockel, and U. Lohmann, 2007: Global model Kirkby, J., 2007: Cosmic rays and climate. Surv. Geophys., 28, 333 375. simulations of the impact of ocean-going ships on aerosols, clouds, and the Kirkby, J., et al., 2011: Role of sulphuric acid, ammonia and galactic cosmic rays in radiation budget. Atmos. Chem. Phys., 7, 5061 5079. atmospheric aerosol nucleation. Nature, 476, 429 433. Lawrence, P. J., and T. N. Chase, 2010: Investigating the climate impacts of global Kleinman, L. I., P. H. Daum, Y. N. Lee, L. J. Nunnermacker, S. R. Springston, J. Weinstein- land cover change in the community climate system model. Int. J. Climatol., 30, Lloyd, and J. Rudolph, 2001: Sensitivity of ozone production rate to ozone 2066 2087. precursors. Geophys. Res. Lett., 28, 2903 2906. Lean, J., 2000: Evolution of the sun s spectral irradiance since the Maunder Minimum. Knutti, R., et al., 2008: A review of uncertainties in global temperature projections Geophys. Res. Lett., 27, 2425 2428. over the twenty-first century. J. Clim., 21, 2651 2663. Lean, J., and M. Deland, 2012: How does the sun s spectrum vary? J. Clim., 25, Koch, D., and A. D. Del Genio, 2010: Black carbon semi-direct effects on cloud cover: 2555 2560. Review and synthesis. Atmos. Chem. Phys., 10, 7685 7696. Lee, D. S., et al., 2010: Transport impacts on atmosphere and climate: Aviation. Koch, D., T. Bond, D. Streets, N. Unger, and G. van der Werf, 2007: Global impacts Atmos. Environ., 44, 4678 4734. of aerosols from particular source regions and sectors. J. Geophys. Res. Atmos., Lee, J., D. Shindell, and S. Hameed, 2009: The influence of solar forcing on tropical 112, D02205. circulation. J. Clim., 22, 5870 5885. Koch, D., S. Menon, A. Del Genio, R. Ruedy, I. Alienov, and G. A. Schmidt, 2009a: Lee, R., M. Gibson, R. Wilson, and S. Thomas, 1995: Long-term total solar irradiance Distinguishing aerosol impacts on climate over the past century. J. Clim., 22, variability during sunspot cycle-22. J. Geophys. Res. Space Phys., 100, 1667 2659 2677. 1675. Koch, D., et al., 2011: Coupled aerosol-chemistry-climate twentieth-century transient Lee, X., et al., 2011: Observed increase in local cooling effect of deforestation at model investigation: Trends in short-lived species and climate responses. J. Clim., higher latitudes. Nature, 479, 384 387. 24, 2693 2714. Lee, Y. H., et al., 2013: Evaluation of preindustrial to present-day black carbon and its Koch, D., et al., 2009b: Evaluation of black carbon estimations in global aerosol albedo forcing from Atmospheric Chemistry and Climate Model Intercomparison models. Atmos. Chem. Phys., 9, 9001 9026. Project (ACCMIP). Atmos. Chem. Phys., 13, 2607 2634. Koehler, M. O., G. Raedel, K. P. Shine, H. L. Rogers, and J. A. Pyle, 2013: Latitudinal Legras, B., O. Mestre, E. Bard, and P. Yiou, 2010: A critical look at solar-climate variation of the effect of aviation NOx emissions on atmospheric ozone and relationships from long temperature series. Clim. Past, 6, 745 758. methane and related climate metrics. Atmos. Environ., 64, 1 9. Leibensperger, E. M., L. J. Mickley, and D. J. Jacob, 2008: Sensitivity of US air quality Koffi, B., et al., 2012: Application of the CALIOP layer product to evaluate the vertical to mid-latitude cyclone frequency and implications of 1980 2006 climate distribution of aerosols estimated by global models: AeroCom phase I results. J. change. Atmos. Chem. Phys., 8, 7075 7086. Geophys. Res. Atmos., 117, D10201. Leibensperger, E. M., et al., 2012a: Climatic effects of 1950 2050 changes in US Kopp, G., and J. Lean, 2011: A new, lower value of total solar irradiance: Evidence anthropogenic aerosols Part 1: Aerosol trends and radiative forcing. Atmos. and climate significance. Geophys. Res. Lett., 38, L01706. Chem. Phys., 12, 3333 3348. Kratz, D., 2008: The sensitivity of radiative transfer calculations to the changes in Leibensperger, E. M., et al., 2012b: Climatic effects of 1950 2050 changes in US the HITRAN database from 1982 to 2004. J. Quant. Spectrosc. Radiat. Transfer, anthropogenic aerosols Part 2: Climate response. Atmos. Chem. Phys., 12, 109, 1060 1080. 3349 3362. Kravitz, B., and A. Robock, 2011: Climate effects of high-latitude volcanic eruptions: Lelieveld, J., et al., 2008: Atmospheric oxidation capacity sustained by a tropical Role of the time of year. J. Geophys. Res. Atmos., 116, D01105. forest. Nature, 452, 737 740. Kravitz, B., A. Robock, and A. Bourassa, 2010: Negligible climatic effects from the Levy, H., 1971: Normal atmosphere Large radical and formaldehyde concentrations 2008 Okmok and Kasatochi volcanic eruptions. J. Geophys. Res. Atmos., 115, predicted. Science, 173, 141 143. D00L05. Liao, H., W. T. Chen, and J. H. Seinfeld, 2006: Role of climate change in global Kravitz, B., et al., 2011: Simulation and observations of stratospheric aerosols from predictions of future tropospheric ozone and aerosols. J. Geophys. Res. Atmos., the 2009 Sarychev volcanic eruption. J. Geophys. Res. Atmos., 116, D18211. 111, D12304. 725 Chapter 8 Anthropogenic and Natural Radiative Forcing Lockwood, M., 2010: Solar change and climate: An update in the light of the current Meinshausen, M., T. Wigley, and S. Raper, 2011a: Emulating atmosphere-ocean exceptional solar minimum. Proc. R. Soc. London A, 466, 303 329. and carbon cycle models with a simpler model, MAGICC6 Part 2: Applications. Lockwood, M., and C. Frohlich, 2008: Recent oppositely directed trends in solar Atmos. Chem. Phys., 11, 1457 1471. climate forcings and the global mean surface air temperature. II. Different Meinshausen, M., et al., 2011b: The RCP greenhouse gas concentrations and their reconstructions of the total solar irradiance variation and dependence on extensions from 1765 to 2300. Clim. Change, 109, 213 241. response time scale. Proc. R. Soc. London A, 464, 1367 1385. Mercado, L. M., N. Bellouin, S. Sitch, O. Boucher, C. Huntingford, M. Wild, and P. M. Lockwood, M., A. Rouillard, and I. Finch, 2009: The rise and fall of open solar flux Cox, 2009: Impact of changes in diffuse radiation on the global land carbon sink. during the current grand solar maximum. Astrophys. J., 700, 937 944. Nature, 458, 1014 1017. 8 Logan, J. A., 1999: An analysis of ozonesonde data for the troposphere: Merikanto, J., D. Spracklen, G. Mann, S. Pickering, and K. Carslaw, 2009: Impact of Recommendations for testing 3-D models and development of a gridded nucleation on global CCN. Atmos. Chem. Phys., 9, 8601 8616. climatology for tropospheric ozone. J. Geophys. Res. Atmos., 104, 16115 16149. Mickley, L. J., E. M. Leibensperger, D. J. Jacob, and D. Rind, 2012: Regional warming Logan, J. A., M. J. Prather, S. C. Wofsy, and M. B. McElroy, 1981: Tropospheric from aerosol removal over the United States: Results from a transient 2010 chemistry A global perspective. J. Geophys. Res. Oceans Atmos., 86, 7210 2050 climate simulation. Atmos. Environ., 46, 545 553. 7254. Miller, G. H., et al., 2012: Abrupt onset of the Little Ice Age triggered by volcanism Logan, J. A., et al., 2012: Changes in ozone over Europe: Analysis of ozone and sustained by sea-ice/ocean feedbacks. Geophys. Res. Lett., 39, L02708. measurements from sondes, regular aircraft (MOZAIC) and alpine surface sites. Miller, R. L., I. Tegen, and J. Perlwitz, 2004: Surface radiative forcing by soil dust J. Geophys. Res. Atmos., 117, D09301. aerosols and the hydrologic cycle. J. Geophys. Res. Atmos., 109, D04203. Lohila, A., et al., 2010: Forestation of boreal peatlands: Impacts of changing albedo Mills, M. J., O. B. Toon, R. P. Turco, D. E. Kinnison, and R. R. Garcia, 2008: Massive and greenhouse gas fluxes on radiative forcing. J. Geophys. Res. Biogeosci., 115, global ozone loss predicted following regional nuclear conflict. Proc. Natl. Acad. G04011. Sci. U.S.A., 105, 5307 5312. Lohmann, U., et al., 2010: Total aerosol effect: Radiative forcing or radiative flux Ming, Y., and V. Ramaswamy, 2012: Nonlocal component of radiative flux perturbation? Atmos. Chem. Phys., 10, 3235 3246. perturbation. Geophys. Res. Lett., 39, L22706. Long, C. N., E. G. Dutton, J. A. Augustine, W. Wiscombe, M. Wild, S. A. McFarlane, and Ming, Y., V. Ramaswamy, and G. Persad, 2010: Two opposing effects of absorbing C. J. Flynn, 2009: Significant decadal brightening of downwelling shortwave in aerosols on global-mean precipitation. Geophys. Res. Lett., 37, L13701. the continental United States. J. Geophys. Res. Atmos., 114, D00D06. Ming, Y., V. Ramaswamy, and G. Chen, 2011: A model investigation of aerosol- Long, D., and M. Collins, 2013: Quantifying global climate feedbacks, responses and induced changes in boreal winter extratropical circulation. J. Clim., 24, 6077 forcing under abrupt and gradual CO2 forcing. Clim. Dyn., 41, 2471-2479. 6091. Lu, P., H. Zhang, and X. Jing, 2012: The effects of different HITRAN versions on Ming, Y., V. Ramaswamy, L. J. Donner, V. T. J. Phillips, S. A. Klein, P. A. Ginoux, and L. W. calculated long-wave radiation and uncertainty evaluation. Acta Meteorol. Sin., Horowitz, 2007: Modeling the interactions between aerosols and liquid water 26, 389 398. clouds with a self-consistent cloud scheme in a general circulation model. J. Lu, Z., Q. Zhang, and D. G. Streets, 2011: Sulfur dioxide and primary carbonaceous Atmos. Sci., 64, 1189 1209. aerosol emissions in China and India, 1996 2010. Atmos. Chem. Phys., 11, Mirme, S., A. Mirme, A. Minikin, A. Petzold, U. Horrak, V.-M. Kerminen, and M. 9839 9864. Kulmala, 2010: Atmospheric sub-3 nm particles at high altitudes. Atm. Chem. Lund, M., T. Berntsen, J. Fuglestvedt, M. Ponater, and K. Shine, 2012: How much Phys., 10, 437 451. information is lost by using global-mean climate metrics? an example using the Montzka, S. A., E. J. Dlugokencky, and J. H. Butler, 2011: Non-CO2 greenhouse gases transport sector. Clim. Change, 113, 949 963. and climate change. Nature, 476, 43 50. MacFarling Meure, C., et al., 2006: Law Dome CO2, CH4 and N2O ice core records Morrill, J., L. Floyd, and D. McMullin, 2011: The solar ultraviolet spectrum estimated extended to 2000 years BP. Geophys. Res. Lett., 33, L14810. using the Mg II Index and Ca II K disk activity. Solar Physics, 269, 253 267. MacMynowski, D., H. Shin, and K. Caldeira, 2011: The frequency response of Muhle, J., et al., 2009: Sulfuryl fluoride in the global atmosphere. J. Geophys. Res. temperature and precipitation in a climate model. Geophys. Res. Lett., 38, Atmos., 114, D05306. L16711. Mulitza, S., et al., 2010: Increase in African dust flux at the onset of commercial Mader, J. A., J. Staehelin, T. Peter, D. Brunner, H. E. Rieder, and W. A. Stahel, 2010: agriculture in the Sahel region. Nature, 466, 226 228. Evidence for the effectiveness of the Montreal Protocol to protect the ozone Murphy, D., and D. Fahey, 1994: An estimate of the flux of stratospheric reactive layer. Atmos. Chem. Phys., 10, 12161 12171. nitrogen and ozone into the troposphere. J. Geophys. Res. Atmos., 99, 5325 Mahowald, N. M., et al., 2010: Observed 20th century desert dust variability: Impact 5332. on climate and biogeochemistry. Atmos. Chem. Phys., 10, 10875 10893. Myhre, G., M. M. Kvalevag, and C. B. Schaaf, 2005a: Radiative forcing due to Mann, M., M. Cane, S. Zebiak, and A. Clement, 2005: Volcanic and solar forcing of anthropogenic vegetation change based on MODIS surface albedo data. the tropical Pacific over the past 1000 years. J. Clim., 18, 447 456. Geophys. Res. Lett., 32, L21410. Manne, A., and R. Richels, 2001: An alternative approach to establishing trade-offs Myhre, G., E. J. Highwood, K. P. Shine, and F. Stordal, 1998: New estimates of among greenhouse gases. Nature, 410, 675 677. radiative forcing due to well mixed greenhouse gases. Geophys. Res. Lett., 25, Manning, M., and A. Reisinger, 2011: Broader perspectives for comparing different 2715 2718. greenhouse gases. Philos. Trans. R. Soc. London A, 369, 1891 1905. Myhre, G., Y. Govaerts, J. M. Haywood, T. K. Berntsen, and A. Lattanzio, 2005b: Marenco, A., H. Gouget, P. Nedelec, J. P. Pages, and F. Karcher, 1994: Evidence of Radiative effect of surface albedo change from biomass burning. Geophys. Res. a long-term increase in troposheric ozone from PIC Du Midi Data Series Lett., 32, L20812. Consequences Positive radiative forcing. J. Geophys. Res. Atmos., 99, 16617 Myhre, G., J. Nilsen, L. Gulstad, K. Shine, B. Rognerud, and I. Isaksen, 2007: Radiative 16632. forcing due to stratospheric water vapour from CH4 oxidation. Geophys. Res. Marten, A. L., and S. C. Newbold, 2012: Estimating the social cost of non-CO2 GHG Lett., 34, L01807. emissions: Methane and nitrous oxide. Energ. Policy, 51, 957 972. Myhre, G., et al., 2011: Radiative forcing due to changes in ozone and methane Matthews, H. D., A. J. Weaver, K. J. Meissner, N. P. Gillett, and M. Eby, 2004: Natural caused by the transport sector. Atmos. Environ., 45, 387 394. and anthropogenic climate change: Incorporating historical land cover change, Myhre, G., et al., 2013: Radiative forcing of the direct aerosol effect from AeroCom vegetation dynamics and the global carbon cycle. Clim. Dyn., 22, 461 479. Phase II simulations. Atmos. Chem. Phys., 13, 1853 1877. McComas, D., R. Ebert, H. Elliott, B. Goldstein, J. Gosling, N. Schwadron, and R. Nagai, T., B. Liley, T. Sakai, T. Shibata, and O. Uchino, 2010: Post-Pinatubo evolution Skoug, 2008: Weaker solar wind from the polar coronal holes and the whole and subsequent trend of the stratospheric aerosol layer observed by mid- Sun. Geophys. Res. Lett., 35, L18103. latitude lidars in both hemispheres. Sola, 6, 69 72. McLandress, C., T. G. Shepherd, J. F. Scinocca, D. A. Plummer, M. Sigmond, A. I. Naik, V., D. L. Mauzerall, L. W. Horowitz, M. D. Schwarzkopf, V. Ramaswamy, and M. Jonsson, and M. C. Reader, 2011: Separating the dynamical effects of climate Oppenheimer, 2007: On the sensitivity of radiative forcing from biomass burning change and ozone depletion. Part II. Southern Hemisphere troposphere. J. Clim., aerosols and ozone to emission location. Geophys. Res. Lett., 34, L03818. 24, 1850 1868. 726 Anthropogenic and Natural Radiative Forcing Chapter 8 Nair, U. S., D. K. Ray, J. Wang, S. A. Christopher, T. J. Lyons, R. M. Welch, and R. A. Pielke, Pitman, A. J., et al., 2009: Uncertainties in climate responses to past land cover 2007: Observational estimates of radiative forcing due to land use change in change: First results from the LUCID intercomparison study. Geophys. Res. Lett., southwest Australia. J. Geophys. Res. Atmos., 112, D09117. 36, L14814. Neely, R. R., et al., 2013: Recent anthropogenic increases in SO2 from Asia have Plattner, G.-K., T. Stocker, P. Midgley, and M. Tignor, 2009: IPCC Expert Meeting minimal impact on stratospheric aerosol. Geophys. Res. Lett., 40, 999-1004. on the Science of Alternative Metrics: Meeting Report. IPCC Working Group I, O ishi, R., A. Abe-Ouchi, I. Prentice, and S. Sitch, 2009: Vegetation dynamics and plant Technical Support Unit. CO2 responses as positive feedbacks in a greenhouse world. Geophys. Res. Lett., Plattner, G. K., et al., 2008: Long-term climate commitments projected with climate- 36, L11706. carbon cycle models. J. Clim., 21, 2721 2751. O Neill, B., 2000: The jury is still out on global warming potentials. Clim. Change, Pongratz, J., C. Reick, T. Raddatz, and M. Claussen, 2008: A reconstruction of global 8 44, 427 443. agricultural areas and land cover for the last millennium. Global Biogeochem. O Neill, B., 2003: Economics, natural science, and the costs of global warming Cycles, 22, Gb3018. potentials An editorial comment. Clim. Change, 58, 251 260. Pongratz, J., C. H. Reick, T. Raddatz, and M. Claussen, 2010: Biogeophysical versus Oleson, K. W., G. B. Bonan, and J. Feddema, 2010: Effects of white roofs on urban biogeochemical climate response to historical anthropogenic land cover change. temperature in a global climate model. Geophys. Res. Lett., 37, L03701. Geophys. Res. Lett., 37, L08702. Olivié, D. J. L., G. Peters, and D. Saint-Martin, 2012: Atmosphere response time scales Pongratz, J., T. Raddatz, C. H. Reick, M. Esch, and M. Claussen, 2009: Radiative estimated from AOGCM experiments. J. Climate, 25, 7956 7972. forcing from anthropogenic land cover change since AD 800. Geophys. Res. Lett., Olsen, S. C., C. A. McLinden, and M. J. Prather, 2001: Stratospheric N2O NOy system: 36, L02709. Testing uncertainties in a three-dimensional framework, J. Geophys. Res., 106, Pozzer, A., et al., 2012: Effects of business-as-usual anthropogenic emissions on air 28771. quality. Atmos. Chem. Phys., 12, 6915 6937. Oreopoulos, L., et al., 2012: The Continual Intercomparison of Radiation Codes: Prather, M., 2002: Lifetimes of atmospheric species: Integrating environmental Results from Phase I. J. Geophys. Res. Atmos., 117, D06118. impacts. Geophys. Res. Lett., 29, 2063. Osterman, G. B., et al., 2008: Validation of Tropospheric Emission Spectrometer (TES) Prather, M., and J. Hsu, 2010: Coupling of nitrous oxide and methane by global measurements of the total, stratospheric, and tropospheric column abundance atmospheric chemistry. Science, 330, 952 954. of ozone. J. Geophys. Res. Atmos., 113, D15S16. Prather, M. J., 1998: Time scales in atmospheric chemistry: Coupled perturbations to Otterä, O. H., M. Bentsen, H. Drange, and L. L. Suo, 2010: External forcing as a N2O, NOy, and O3. Science, 279, 1339 1341. metronome for Atlantic multidecadal variability. Nature Geosci., 3, 688 694. Prather, M. J., C. D. Holmes, and J. Hsu, 2012: Reactive greenhouse gas scenarios: Özdo an, M., A. Robock, and C. J. Kucharik, 2013: Impacts of a nuclear war in South Systematic exploration of uncertainties and the role of atmospheric chemistry. Asia on soybean and maize production in the Midwest United States. Clim. Geophys. Res. Lett., 39, L09803. Change, 116, 373 387. Puma, M. J., and B. I. Cook, 2010: Effects of irrigation on global climate during the Parrish, D. D., D. B. Millet, and A. H. Goldstein, 2009: Increasing ozone in marine 20th century. J. Geophys. Res. Atmos., 115, D16120. boundary layer inflow at the west coasts of North America and Europe. Atmos. Quaas, J., O. Boucher, N. Bellouin, and S. Kinne, 2011: Which of satellite- or model- Chem. Phys., 9, 1303 1323. based estimates is closer to reality for aerosol indirect forcing? Proc. Natl. Acad. Paulot, F., J. D. Crounse, H. G. Kjaergaard, A. Kurten, J. M. St Clair, J. H. Seinfeld, Sci. U.S.A., 108, E1099. and P. O. Wennberg, 2009: Unexpected epoxide formation in the gas-phase Quaas, J., et al., 2009: Aerosol indirect effects general circulation model photooxidation of isoprene. Science, 325, 730 733. intercomparison and evaluation with satellite data. Atmos. Chem. Phys., 9, Paynter, D., and V. Ramaswamy, 2011: An assessment of recent water vapor 8697 8717. continuum measurements upon longwave and shortwave radiative transfer. J. Rajakumar, B., R. Portmann, J. Burkholder, and A. Ravishankara, 2006: Rate Geophys. Res. Atmos., 116, D20302. coefficients for the reactions of OH with CF3CH2CH3 (HFC-263fb), CF3CHFCH2F Pechony, O., and D. Shindell, 2010: Driving forces of global wildfires over the past (HFC-245eb), and CHF2CHFCHF2 (HFC-245ea) between 238 and 375 K. J. Phys. millennium and the forthcoming century. Proc. Natl. Acad. Sci. U.S.A., 107, Chem. A, 110, 6724 6731. 19167 19170. Ramanathan, V., and G. Carmichael, 2008: Global and regional climate changes due Peeters, J., T. L. Nguyen, and L. Vereecken, 2009: HO(x) radical regeneration in the to black carbon. Nature Geosci., 1, 221 227. oxidation of isoprene. Phys. Chem. Chem. Phys., 11, 5935 5939. Ramanathan, V., et al., 2005: Atmospheric brown clouds: Impacts on South Asian Penn, M., and W. Livingston, 2006: Temporal changes in sunspot umbral magnetic climate and hydrological cycle. Proc. Natl. Acad. Sci. U.S.A., 102, 5326 5333. fields and temperatures. Astrophys. J., 649, L45 L48. Ramaswamy, V., et al., 2001: Radiative forcing of climate change. In: Climate Penner, J., L. Xu, and M. Wang, 2011: Satellite methods underestimate indirect Change 2001: The Scientific Basis. Contribution of Working Group I to the climate forcing by aerosols. Proc. Natl. Acad. Sci. U.S.A., 108, 13404 13408. Third Assessment Report of the Intergovernmntal Panel on Climate Change [J. Penner, J., et al., 2006: Model intercomparison of indirect aerosol effects. Atmos. T. Houghton, Y. Ding, D. J. Griggs, M. Noquer, P. J. van der Linden, X. Dai, K. Chem. Phys., 6, 3391 3405. Maskell and C. A. Johnson (eds.)]. Cambride University Press, Cambridge, United Perlwitz, J., and R. L. Miller, 2010: Cloud cover increase with increasing aerosol Kingdom and New York, NY, USA, 349-416. absorptivity: A counterexample to the conventional semidirect aerosol effect. J. Randel, W., and F. Wu, 2007: A stratospheric ozone profile data set for 1979 2005: Geophys. Res. Atmos., 115, D08203. Variability, trends, and comparisons with column ozone data. J. Geophys. Res. Persad, G. G., Y. Ming, and V. Ramaswamy, 2012: Tropical tropospheric-only responses Atmos., 112, D06313. to absorbing aerosols. J. Clim., 25, 2471 2480. Randles, C. A., and V. Ramaswamy, 2008: Absorbing aerosols over Asia: A Geophysical Peters, G., B. Aamaas, T. Berntsen, and J. Fuglestvedt, 2011a: The integrated global Fluid Dynamics Laboratory general circulation model sensitivity study of model temperature change potential (iGTP) and relationships between emission response to aerosol optical depth and aerosol absorption. J. Geophys. Res. metrics. Environ. Res. Lett., 6, 044021. Atmos., 113, D21203. Peters, G. P., B. Aamaas, M. T. Lund, C. Solli, and J. S. Fuglestvedt, 2011b: Alternative Rasch, P. J., et al., 2008: An overview of geoengineering of climate using stratospheric Global Warming metrics in life cycle assessment: A case study with existing sulphate aerosols. Philos. Trans. R. Soc. A, 366, 4007 4037. transportation data. Environ. Sci. Technol., 45, 8633 8641. Ravishankara, A. R., J. S. Daniel, and R. W. Portmann, 2009: Nitrous oxide (N2O): The Peters, K., P. Stier, J. Quaas, and H. Grassl, 2012: Aerosol indirect effects from shipping dominant ozone-depleting substance emitted in the 21st century. Science, 326, emissions: Sensitivity studies with the global aerosol-climate model ECHAM- 123 125. HAM. Atmos. Chem. Phys., 12, 5985 6007. Rechid, D., T. Raddatz, and D. Jacob, 2009: Parameterization of snow-free land Philipona, R., K. Behrens, and C. Ruckstuhl, 2009: How declining aerosols and rising surface albedo as a function of vegetation phenology based on MODIS data and greenhouse gases forced rapid warming in Europe since the 1980s. Geophys. applied in climate modelling. Theor. Appl. Climatol., 95, 245 255. Res. Lett., 36, L02806. Reddy, M., and O. Boucher, 2007: Climate impact of black carbon emitted from Pinto, J. P., R. P. Turco, and O. B. Toon, 1989: Self-limiting physical and chemical energy consumption in the world s regions. Geophys. Res. Lett., 34, L11802. effects in volcanic-eruption clouds. J. Geophys. Res. Atmos., 94, 11165 11174. Reilly, J., et al., 1999: Multi-gas assessment of the Kyoto Protocol. Nature, 401, 549 555. 727 Chapter 8 Anthropogenic and Natural Radiative Forcing Reilly, J. M., and K. R. Richards, 1993: Climate-change damage and the trace-gas- Schaaf, C., et al., 2002: First operational BRDF, albedo nadir reflectance products index issue. Environ. Resour. Econ., 3, 41 61. from MODIS. Remote Sens. Environ., 83, 135 148. Reisinger, A., M. Meinshausen, and M. Manning, 2011: Future changes in global Schmalensee, R., 1993: Comparing greenhouse gases for policy purposes. Energy J., warming potentials under representative concentration pathways. Environ. Res. 14, 245 256. Lett., 6, 024020. Schmidt, A., K. S. Carslaw, G. W. Mann, M. Wilson, T. J. Breider, S. J. Pickering, and Reisinger, A., M. Meinshausen, M. Manning, and G. Bodeker, 2010: Uncertainties of T. Thordarson, 2010: The impact of the 1783 1784 AD Laki eruption on global global warming metrics: CO2 and CH4. Geophys. Res. Lett., 37, L14707. aerosol formation processes and cloud condensation nuclei. Atmos. Chem. Phys., Reisinger, A., P. Havlik, K. Riahi, O. Vliet, M. Obersteiner, and M. Herrero, 2013: 10, 6025 6041. 8 Implications of alternative metrics for global mitigation costs and greenhouse Schmidt, A., et al., 2012: Importance of tropospheric volcanic aerosol for indirect gas emissions from agriculture. Clim. Change, 117, 677-690. radiative forcing of climate. Atmos. Chem. Phys., 12, 7321 7339. Righi, M., C. Klinger, V. Eyring, J. Hendricks, A. Lauer, and A. Petzold, 2011: Climate Schmidt, G., et al., 2011: Climate forcing reconstructions for use in PMIP simulations impact of biofuels in shipping: global model studies of the aerosol indirect of the last millennium (v1.0). Geosci. Model Dev., 4, 33 45. effect. Environ. Sci. Technol., 45, 3519 3525. Schneider, D. P., C. M. Ammann, B. L. Otto-Bliesner, and D. S. Kaufman, 2009: Climate Rigozo, N., E. Echer, L. Vieira, and D. Nordemann, 2001: Reconstruction of Wolf response to large, high-latitude and low-latitude volcanic eruptions in the sunspot numbers on the basis of spectral characteristics and estimates of Community Climate System Model. J. Geophys. Res. Atmos., 114, D15101. associated radio flux and solar wind parameters for the last millennium. Sol. Schultz, M. G., et al., 2008: Global wildland fire emissions from 1960 to 2000. Global Phys., 203, 179 191. Biogeochem. Cycles, 22, Gb2002. Rigozo, N., D. Nordemann, E. Echer, M. Echer, and H. Silva, 2010: Prediction of solar Seinfeld, J. H., and S. N. Pandis, 2006: Atmospheric Chemistry and Physics: From Air minimum and maximum epochs on the basis of spectral characteristics for the Pollution to Climate Change. John Wiley & Sons, Hoboken, NJ, USA. next millennium. Planet. Space Sci., 58, 1971 1976. Self, S., and S. Blake, 2008: Consequences of explosive supereruptions. Elements, Robock, A., 2000: Volcanic eruptions and climate. Rev. Geophys., 38, 191 219. 4, 41 46. Robock, A., 2010: New START, Eyjafjallajökull, and Nuclear Winter. Eos, 91, 444 445. Sharma, S., D. Lavoue, H. Cachier, L. Barrie, and S. Gong, 2004: Long-term trends of Robock, A., L. Oman, and G. L. Stenchikov, 2007a: Nuclear winter revisited with the black carbon concentrations in the Canadian Arctic. J. Geophys. Res. Atmos., a modern climate model and current nuclear arsenals: Still catastrophic 109, D15203. consequences. J. Geophys. Res. Atmos., 112, D13107. Shibata, K., and K. Kodera, 2005: Simulation of radiative and dynamical responses Robock, A., L. Oman, and G. L. Stenchikov, 2008: Regional climate responses to of the middle atmosphere to the 11-year solar cycle. J. Atmos. Sol. Terres. Phys., geoengineering with tropical and Arctic SO2 injections. J. Geophys. Res. Atmos., 67, 125 143. 113, D16101. Shindell, D., and G. Faluvegi, 2009: Climate response to regional radiative forcing Robock, A., L. Oman, G. L. Stenchikov, O. B. Toon, C. Bardeen, and R. P. Turco, 2007b: during the twentieth century. Nature Geosci., 2, 294 300. Climatic consequences of regional nuclear conflicts. Atmos. Chem. Phys., 7, Shindell, D., and G. Faluvegi, 2010: The net climate impact of coal-fired power plant 2003 2012. emissions. Atmos. Chem. Phys., 10, 3247 3260. Robock, A., C. M. Ammann, L. Oman, D. Shindell, S. Levis, and G. Stenchikov, 2009: Shindell, D., G. Schmidt, M. Mann, D. Rind, and A. Waple, 2001: Solar forcing of Did the Toba volcanic eruption of similar to 74 ka BP produce widespread regional climate change during the maunder minimum. Science, 294, 2149 glaciation? J. Geophys. Res. Atmos., 114, D10107. 2152. Rogelj, J., et al., 2011: Emission pathways consistent with a 2 degrees C global Shindell, D., G. Faluvegi, A. Lacis, J. Hansen, R. Ruedy, and E. Aguilar, 2006a: Role of temperature limit. Nature Clim. Change, 1, 413 418. tropospheric ozone increases in 20th-century climate change. J. Geophys. Res. Roscoe, H. K., and J. D. Haigh, 2007: Influences of ozone depletion, the solar cycle Atmos., 111, D08302. and the QBO on the Southern Annular Mode. Q. J. R. Meteorol. Soc., 133, 1855 Shindell, D., G. Faluvegi, R. Miller, G. Schmidt, J. Hansen, and S. Sun, 2006b: Solar and 1864. anthropogenic forcing of tropical hydrology. Geophys. Res. Lett., 33, L24706. Rotenberg, E., and D. Yakir, 2010: Contribution of semi-arid forests to the climate Shindell, D., M. Schulz, Y. Ming, T. Takemura, G. Faluvegi, and V. Ramaswamy, 2010: system. Science, 327, 451 454. Spatial scales of climate response to inhomogeneous radiative forcing. J. Rothman, L., 2010: The evolution and impact of the HITRAN molecular spectroscopic Geophys. Res. Atmos., 115, D19110. database. J. Quant. Spectrosc. Radiat. Transfer, 111, 1565 1567. Shindell, D., et al., 2008: Climate forcing and air quality change due to regional Rotstayn, L., and J. Penner, 2001: Indirect aerosol forcing, quasi forcing, and climate emissions reductions by economic sector. Atmos. Chem. Phys., 8, 7101 7113. response. J. Clim., 14, 2960 2975. Shindell, D., et al., 2011: Climate, health, agricultural and economic impacts of Rotstayn, L. D., and U. Lohmann, 2002: Tropical rainfall trends and the indirect tighter vehicle-emission standards. Nature Clim. Change, 1, 59 66. aerosol effect. J. Clim., 15, 2103 2116. Shindell, D., et al., 2013a: Attribution of historical ozone forcing to anthropogenic Rotstayn, L. D., B. F. Ryan, and J. E. Penner, 2000: Precipitation changes in a GCM emissions. Nature Clim. Change, 3, 567-570. resulting from the indirect effects of anthropogenic aerosols. Geophys. Res. Lett., Shindell, D., et al., 2012a: Simultaneously mitigating near-term climate change and 27, 3045 3048. improving human health and food Security. Science, 335, 183 189. Rottman, G., 2006: Measurement of total and spectral solar irradiance. Space Sci. Shindell, D. T., 2012: Evaluation of the absolute regional temperature potential. Rev., 125, 39 51. Atmos. Chem. Phys., 12, 7955 7960. Ruckstuhl, C., et al., 2008: Aerosol and cloud effects on solar brightening and the Shindell, D. T., A. Voulgarakis, G. Faluvegi, and G. Milly, 2012b: Precipitation response recent rapid warming. Geophys. Res. Lett., 35, L12708. to regional radiative forcing. Atmos. Chem. Phys., 12, 6969 6982. Russell, C., J. Luhmann, and L. Jian, 2010: How unprecedented a solar minimum? Shindell, D. T., G. Faluvegi, D. M. Koch, G. A. Schmidt, N. Unger, and S. E. Bauer, 2009: Rev. Geophys., 48, RG2004. Improved attribution of climate forcing to emissions. Science, 326, 716 718. Rypdal, K., N. Rive, T. Berntsen, Z. Klimont, T. Mideksa, G. Myhre, and R. Skeie, 2009: Shindell, D. T., et al., 2006c: Simulations of preindustrial, present-day, and 2100 Costs and global impacts of black carbon abatement strategies. Tellus B., 61, conditions in the NASA GISS composition and climate model G-PUCCINI. Atmos. 625 641. Chem. Phys., 6, 4427 4459. Rypdal, K., et al., 2005: Tropospheric ozone and aerosols in climate agreements: Shindell, D. T., et al., 2013b: Interactive ozone and methane chemistry in GISS-E2 Scientific and political challenges. Environ. Sci. Policy, 8, 29 43. historical and future climate simulations. Atmos. Chem. Phys., 13, 2653 2689. Salby, M. L., E. A. Titova, and L. Deschamps, 2012: Changes of the Antarctic ozone Shindell, D. T., et al., 2013c: Radiative forcing in the ACCMIP historical and future hole: Controlling mechanisms, seasonal predictability, and evolution. J. Geophys. climate simulations. Atmos. Chem. Phys., 13, 2939 2974. Res. Atmos., 117, D10111. Shine, K., 2009: The global warming potential-the need for an interdisciplinary Sarofim, M., 2012: The GTP of methane: Modeling analysis of temperature impacts of retrial. Clim. Change, 96, 467 472. methane and carbon dioxide reductions. Environ. Model. Assess., 17, 231 239. Shine, K., J. Cook, E. Highwood, and M. Joshi, 2003: An alternative to radiative Sato, M., J. E. Hansen, M. P. McCormick, and J. B. Pollack, 1993: Stratospheric aerosol forcing for estimating the relative importance of climate change mechanisms. optical depths, 1850 1990. J. Geophys. Res. Atmos., 98, 22987 22994. Geophys. Res. Lett., 30, 2047. 728 Anthropogenic and Natural Radiative Forcing Chapter 8 Shine, K., J. Fuglestvedt, K. Hailemariam, and N. Stuber, 2005a: Alternatives to Stevenson, D. S., et al., 2006: Multimodel ensemble simulations of present-day and the global warming potential for comparing climate impacts of emissions of near-future tropospheric ozone. J. Geophys. Res. Atmos., 111, D08301. greenhouse gases. Clim. Change, 68, 281 302. Stiller, G. P., et al., 2012: Observed temporal evolution of global mean age of Shine, K., T. Berntsen, J. Fuglestvedt, and R. Sausen, 2005b: Scientific issues in the stratospheric air for the 2002 to 2010 period. Atmos. Chem. Phys., 12, 3311 design of metrics for inclusion of oxides of nitrogen in global climate agreements. 3331. Proc. Natl. Acad. Sci. U.S.A., 102, 15768 15773. Stothers, R. B., 2007: Three centuries of observation of stratospheric transparency. Shine, K., T. Berntsen, J. Fuglestvedt, R. Skeie, and N. Stuber, 2007: Comparing the Clim. Change, 83, 515 521. climate effect of emissions of short- and long-lived climate agents. Philos. Trans. Struthers, H., et al., 2011: The effect of sea ice loss on sea salt aerosol concentrations R. Soc. A, 365, 1903 1914. and the radiative balance in the Arctic. Atmos. Chem. Phys., 11, 3459 3477. 8 Shine, K. P., I. V. Ptashnik, and G. Raedel, 2012: The water vapour continuum: Brief Swann, A. L. S., I. Y. Fung, and J. C. H. Chiang, 2012: Mid-latitude afforestation shifts history and recent developments. Surv. Geophys., 33, 535 555. general circulation and tropical precipitation. Proc. Natl. Acad. Sci. U.S.A., 109, Siddaway, J. M., and S. V. Petelina, 2011: Transport and evolution of the 2009 712 716. Australian Black Saturday bushfire smoke in the lower stratosphere observed by Swingedouw, D., L. Terray, C. Cassou, A. Voldoire, D. Salas-Melia, and J. Servonnat, OSIRIS on Odin. J. Geophys. Res. Atmos., 116, D06203. 2011: Natural forcing of climate during the last millennium: Fingerprint of solar Sitch, S., P. M. Cox, W. J. Collins, and C. Huntingford, 2007: Indirect radiative forcing variability. Clim. Dyn., 36, 1349 1364. of climate change through ozone effects on the land-carbon sink. Nature, 448, Takemura, T., 2012: Distributions and climate effects of atmospheric aerosols from 791 794. the preindustrial era to 2100 along Representative Concentration Pathways Skeie, R., T. Berntsen, G. Myhre, K. Tanaka, M. Kvalevag, and C. Hoyle, 2011a: (RCPs) simulated using the global aerosol model SPRINTARS. Atmos. Chem. Anthropogenic radiative forcing time series from pre-industrial times until 2010. Phys., 12, 11555 11572. Atmos. Chem. Phys., 11, 11827 11857. Tanaka, K., D. Johansson, B. O Neill, and J. Fuglestvedt, 2013: Emission metrics under Skeie, R., T. Berntsen, G. Myhre, C. Pedersen, J. Strom, S. Gerland, and J. Ogren, the 2°C climate stabilization. Clim. Change Lett. , 117, 933-941. 2011b: Black carbon in the atmosphere and snow, from pre-industrial times Tanaka, K., B. O Neill, D. Rokityanskiy, M. Obersteiner, and R. Tol, 2009: Evaluating until present. Atmos. Chem. Phys., 11, 6809 6836. Global Warming Potentials with historical temperature. Clim. Change, 96, 443 Skeie, R. B., J. Fuglestvedt, T. Berntsen, M. T. Lund, G. Myhre, and K. Rypdal, 2009: 466. Global temperature change from the transport sectors: Historical development Taraborrelli, D., et al., 2012: Hydroxyl radical buffered by isoprene oxidation over and future scenarios. Atmos. Environ., 43, 6260 6270. tropical forests. Nature Geosci., 5, 190 193. Smith, E., and A. Balogh, 2008: Decrease in heliospheric magnetic flux in this solar Taylor, P. C., R. G. Ellingson, and M. Cai, 2011: Seasonal variations of climate minimum: Recent Ulysses magnetic field observations. Geophys. Res. Lett., 35, feedbacks in the NCAR CCSM3. J. Clim., 24, 3433 3444. L22103. Textor, C., et al., 2006: Analysis and quantification of the diversities of aerosol life Smith, S., and M. Wigley, 2000: Global warming potentials: 1. Climatic implications cycles within AeroCom. Atmos. Chem. Phys., 6, 1777 1813. of emissions reductions. Clim. Change, 44, 445 457. Thomason, L., and T. Peter, 2006: Assessment of Stratospheric Aerosol Properties Smith, S., J. Karas, J. Edmonds, J. Eom, and A. Mizrahi, 2013: Sensitivity of multi-gas (ASAP). SPARC ReportsWCRP-124, WMO/TD- No. 1295, SPARC Report No. 4. climate policy to emission metrics. Clim. Change, 117, 663 675. Thompson, D. W. J., S. Solomon, P. J. Kushner, M. H. England, K. M. Grise, and D. J. Smith, S. M., J. A. Lowe, N. H. A. Bowerman, L. K. Gohar, C. Huntingford, and M. Karoly, 2011: Signatures of the Antarctic ozone hole in Southern Hemisphere R. Allen, 2012: Equivalence of greenhouse-gas emissions for peak temperature surface climate change. Nature Geosci., 4, 741 749. limits. Nature Clim. Change, 2, 535 538. Thonicke, K., A. Spessa, I. C. Prentice, S. P. Harrison, L. Dong, and C. Carmona- Snow-Kropla, E., J. Pierce, D. Westervelt, and W. Trivitayanurak, 2011: Cosmic Moreno, 2010: The influence of vegetation, fire spread and fire behaviour on rays, aerosol formation and cloud-condensation nuclei: Sensitivities to model biomass burning and trace gas emissions: Results from a process-based model. uncertainties. Atmos. Chem. Phys., 11, 4001 4013. Biogeosciences, 7, 1991 2011. Solanki, S., and N. Krivova, 2004: Solar irradiance variations: From current Tilmes, S., et al., 2012: Technical Note: Ozonesonde climatology between 1995 and measurements to long-term estimates. Sol. Phys., 224, 197 208. 2011: Description, evaluation and applications. Atmos. Chem. Phys., 12, 7475 Solomon, S., 1999: Stratospheric ozone depletion: A review of concepts and history. 7497. Rev. Geophys., 37, 275 316. Timmreck, C., 2012: Modeling the climatic effects of large explosive volcanic Solomon, S., J. S. Daniel, R. R. Neely, J. P. Vernier, E. G. Dutton, and L. W. Thomason, eruptions. Climate Change, 3, 545 564. 2011: The persistently variable background stratospheric aerosol layer and Timmreck, C., et al., 2010: Aerosol size confines climate response to volcanic super- global climate change. Science, 333, 866 870. eruptions. Geophys. Res. Lett., 37, L24705. Son, S. W., N. F. Tandon, L. M. Polvani, and D. W. Waugh, 2009: Ozone hole and Tol, R., T. Berntsen, B. O Neill, J. Fuglestvedt, and K. Shine, 2012: A unifying framework Southern Hemisphere climate change. Geophys. Res. Lett., 36, L15705. for metrics for aggregating the climate effect of different emissions. Environ. Soukharev, B., and L. Hood, 2006: Solar cycle variation of stratospheric ozone: Res. Lett., 7, 044006. Multiple regression analysis of long-term satellite data sets and comparisons Toon, O. B., A. Robock, and R. P. Turco, 2008: Environmental consequences of nuclear with models. J. Geophys. Res. Atmos., 111, D20314. war. Physics Today, 61, 37 42. Svde, O., C. Hoyle, G. Myhre, and I. Isaksen, 2011: The HNO3 forming branch of the Trenberth, K. E., and A. Dai, 2007: Effects of Mount Pinatubo volcanic eruption on HO2 + NO reaction: Pre-industrial-to-present trends in atmospheric species and the hydrological cycle as an analog of geoengineering. Geophys. Res. Lett., 34, radiative forcings. Atmos. Chem. Phys., 11, 8929 8943. L15702. Steinhilber, F., J. Beer, and C. Frohlich, 2009: Total solar irradiance during the Tsigaridis, K., and M. Kanakidou, 2007: Secondary organic aerosol importance in the Holocene. Geophys. Res. Lett., 36, L19704. future atmosphere. Atmos. Environ., 41, 4682 4692. Stenchikov, G., A. Robock, V. Ramaswamy, M. D. Schwarzkopf, K. Hamilton, and S. UNEP, 2011: Near-term Climate Protection and Clean Air Benefits: Actions Ramachandran, 2002: Arctic Oscillation response to the 1991 Mount Pinatubo for Controlling Short-Lived Climate Forcers. United Nations Environment eruption: Effects of volcanic aerosols and ozone depletion. J. Geophys. Res. Programme (UNEP), 78 pp. Atmos., 107, 4803. Unger, N., T. C. Bond, J. S. Wang, D. M. Koch, S. Menon, D. T. Shindell, and S. Bauer, Stenchikov, G., T. L. Delworth, V. Ramaswamy, R. J. Stouffer, A. Wittenberg, and F. R. 2010: Attribution of climate forcing to economic sectors. Proc. Natl. Acad. Sci. Zeng, 2009: Volcanic signals in oceans. J. Geophys. Res. Atmos., 114, D16104. U.S.A., 107, 3382 3387. Stephens, G. L., N. B. Wood, and L. A. Pakula, 2004: On the radiative effects of dust van der Molen, M. K., B. J. J. M. van den Hurk, and W. Hazeleger, 2011: A dampened on tropical convection. Geophys. Res. Lett., 31, L23112. land use change climate response towards the tropics. Clim. Dyn., 37, 2035 Stevenson, D., and R. Derwent, 2009: Does the location of aircraft nitrogen oxide 2043. emissions affect their climate impact? Geophys. Res. Lett., 36, L17810. van der Werf, G. R., et al., 2010: Global fire emissions and the contribution of Stevenson, D. S., et al., 2013: Tropospheric ozone changes, radiative forcing and deforestation, savanna, forest, agricultural, and peat fires (1997 2009). Atmos. attribution to emissions in the Atmospheric Chemistry and Climate Model Chem. Phys., 10, 11707 11735. Intercomparison Project (ACCMIP). Atmos. Chem. Phys., 13, 3063 3085. 729 Chapter 8 Anthropogenic and Natural Radiative Forcing van Vuuren, D., J. Edmonds, M. Kainuma, K. Riahi, and J. Weyant, 2011: A special Worden, H., K. Bowman, J. Worden, A. Eldering, and R. Beer, 2008: Satellite issue on the RCPs. Clim. Change, 109, 1 4. measurements of the clear-sky greenhouse effect from tropospheric ozone. Van Vuuren, D. P., et al., 2008: Temperature increase of 21st century mitigation Nature Geosci., 1, 305 308. scenarios. Proc. Natl. Acad. Sci. U.S.A., 105, 15258 15262. Wright, J., 2004: Do we know of any Maunder minimum stars? Astron. J., 128, Vasekova, E., E. Drage, K. Smith, and N. Mason, 2006: FTIR spectroscopy and 1273 1278. radiative forcing of octafluorocyclobutane and octofluorocyclopentene. J. Quant. Wu, S. L., L. J. Mickley, D. J. Jacob, J. A. Logan, R. M. Yantosca, and D. Rind, 2007: Why Spectrosc. Radiat. Transfer, 102, 418 424. are there large differences between models in global budgets of tropospheric Velders, G. J. M., D. W. Fahey, J. S. Daniel, M. McFarland, and S. O. Andersen, 2009: ozone? J. Geophys. Res. Atmos., 112, D05302. 8 The large contribution of projected HFC emissions to future climate forcing. Proc. Xia, L. L., and A. Robock, 2013: Impacts of a nuclear war in South Asia on rice Natl. Acad. Sci. U.S.A., 106, 10949 10954. production in Mainland China. Clim. Change, 116, 357 372. Vernier, J. P., L. W. Thomason, T. D. Fairlie, P. Minnis, R. Palikonda, and K. M. Bedka, Ye, H., R. Zhang, J. Shi, J. Huang, S. G. Warren, and Q. Fu, 2012: Black carbon in 2013: Comment on Large Volcanic Aerosol Load in the Stratosphere Linked to seasonal snow across northern Xinjiang in northwestern China. Environ. Res. Asian Monsoon Transport . Science, 339, 647-d. Lett., 7, 044002. Vernier, J. P., et al., 2011: Major influence of tropical volcanic eruptions on the Young, P. J., et al., 2013: Pre-industrial to end 21st century projections of tropospheric stratospheric aerosol layer during the last decade. Geophys. Res. Lett., 38, ozone from the Atmospheric Chemistry and Climate Model Intercomparison L12807. Project (ACCMIP). Atmos. Chem. Phys., 13, 2063 2090. Vial, J., J.-L. Dufresne, and S. Bony, 2013: On the interpretation of inter-model spread Zanchettin, D., et al., 2012: Bi-decadal variability excited in the coupled ocean- in CMIP5 climate sensitivity estimates. Clim. Dyn., doi:10.1007/s00382-013- atmosphere system by strong tropical volcanic eruptions. Clim. Dyn., 39, 419 1725-9, in press. 444. Volz, A., and D. Kley, 1988: Evaluation of the Montsouris Series of ozone Zarzycki, C. M., and T. C. Bond, 2010: How much can the vertical distribution of black measurements made in the 19th century. Nature, 332, 240 242. carbon affect its global direct radiative forcing? Geophys. Res. Lett., 37, L20807. Voulgarakis, A., and D. T. Shindell, 2010: Constraining the sensitivity of regional Zeng, G., J. A. Pyle, and P. J. Young, 2008: Impact of climate change on tropospheric climate with the use of historical observations. J. Clim., 23, 6068 6073. ozone and its global budgets. Atmos. Chem. Phys., 8, 369 387. Voulgarakis, A., et al., 2013: Analysis of present day and future OH and methane Zeng, G., O. Morgenstern, P. Braesicke, and J. A. Pyle, 2010: Impact of stratospheric lifetime in the ACCMIP simulations. Atmos. Chem. Phys., 13, 2563 2587. ozone recovery on tropospheric ozone and its budget. Geophys. Res. Lett., 37, Wang, C., D. Kim, A. Ekman, M. Barth, and P. Rasch, 2009: Impact of anthropogenic L09805. aerosols on Indian summer monsoon. Geophys. Res. Lett., 36, L21704. Zhang, H., G. Y. Shi, and Y. Liu, 2005: A comparison between the two line-by-line Wang, M., and J. E. Penner, 2009: Aerosol indirect forcing in a global model with integration algorithms. Chin. J. Atmos. Sci., 29, 581 593. particle nucleation. Atmos. Chem. Phys., 9, 239 260. Zhang, H., G. Shi, and Y. Liu, 2008: The effects of line-wing cutoff in LBL integration Wang, X., S. J. Doherty, and J. Huang, 2013: Black carbon and other light-absorbing on radiation calculations. Acta Meteorol. Sin., 22, 248 255. impurities in snow across Northern China. J. Geophys. Res. Atmos., 118, 1471 Zhang, H., J. Wu, and P. Luc, 2011: A study of the radiative forcing and global 1492. warming potentials of hydrofluorocarbons. J. Quant. Spectrosc. Radiat. Transfer, Wang, Y., J. Lean, and N. Sheeley, 2005: Modeling the sun s magnetic field and 112, 220 229. irradiance since 1713. Astrophys. J., 625, 522 538. Zhang, X. B., et al., 2007: Detection of human influence on twentieth-century Warren, S., and W. Wiscombe, 1980: A model for the spectral albedo of snow. 2. precipitation trends. Nature, 448, 461 465. Snow containing atmospheric aerosols. J. Atmos. Sci., 37, 2734 2745. Zhong, Y., G. H. Miller, B. L. Otto-Bliesner, M. M. Holland, D. A. Bailey, D. P. Schneider, Weiss, R., J. Muhle, P. Salameh, and C. Harth, 2008: Nitrogen trifluoride in the global and A. Geirsdottir, 2011: Centennial-scale climate change from decadally-paced atmosphere. Geophys. Res. Lett., 35, L20821. explosive volcanism: A coupled sea ice-ocean mechanism. Clim. Dyn., 37, 2373 Wenzler, T., S. Solanki, and N. Krivova, 2009: Reconstructed and measured total solar 2387. irradiance: Is there a secular trend between 1978 and 2003? Geophys. Res. Lett., Ziemke, J. R., S. Chandra, G. J. Labow, P. K. Bhartia, L. Froidevaux, and J. C. Witte, 36, L11102. 2011: A global climatology of tropospheric and stratospheric ozone derived from Wilcox, L., K. Shine, and B. Hoskins, 2012: Radiative forcing due to aviation water Aura OMI and MLS measurements. Atmos. Chem. Phys., 11, 9237 9251. vapour emissions. Atmos. Environ., 63, 1 13. Wild, M., 2009: How well do IPCC-AR4/CMIP3 climate models simulate global dimming/brightening and twentieth-century daytime and nighttime warming? J. Geophys. Res. Atmos., 114, D00D11. Wild, O., 2007: Modelling the global tropospheric ozone budget: Exploring the variability in current models. Atmos. Chem. Phys., 7, 2643 2660. Wild, O., and P. I. Palmer, 2008: How sensitive is tropospheric oxidation to anthropogenic emissions? Geophys. Res. Lett., 35, L22802. Wild, O., M. Prather, and H. Akimoto, 2001: Indirect long-term global radiative cooling from NOx emissions. Geophys. Res. Lett., 28, 1719 1722. Williams, K. D., A. Jones, D. L. Roberts, C. A. Senior, and M. J. Woodage, 2001: The response of the climate system to the indirect effects of anthropogenic sulfate aerosol. Clim. Dyn., 17, 845 856. Willson, R., and A. Mordvinov, 2003: Secular total solar irradiance trend during solar cycles 21 23. Geophys. Res. Lett., 30, 1199. WMO, 1999: Scientific Assessment of Ozone Depletion: 1998. Global Ozone Research and Monitoring Project. Report No. 44.World Meteorological Organization, Geneva, Switzerland. WMO, 2011: Scientific Assessment of Ozone Depletion: 2010. Global Ozone Research and Monitoring Project-Report. World Meteorological Organisation, Geneva, Switzerland, 516 pp. Worden, H., K. Bowman, S. Kulawik, and A. Aghedo, 2011: Sensitivity of outgoing longwave radiative flux to the global vertical distribution of ozone characterized by instantaneous radiative kernels from Aura-TES. J. Geophys. Res. Atmos., 116, D14115. 730 Appendix 8.A: Lifetimes, Radiative Efficiencies and Metric Values Table 8.A.1 | Radiative efficiencies (REs), lifetimes/adjustment times, AGWP and GWP values for 20 and 100 years, and AGTP and GTP values for 20, 50 and 100 years. Climate carbon feedbacks are included for CO2 while no climate feedbacks are included for the other components (see discussion in Sections 8.7.1.4 and 8.7.2.1, Supplementary Material and notes below the table; Supplementary Material Table 8.SM.16 gives analogous values including climate carbon feedbacks for non-CO2 emissions). For a complete list of chemical names and CAS numbers, and for accurate replications of metric values, see Supplementary Material Section 8.SM.13 and references therein. Radiative AGWP AGWP Acronym, Common AGTP AGTP AGTP Chemical Lifetime Efficiency 20-year GWP 100-year GWP GTP GTP GTP Name or Chemi- 20-year 50-year 100-year Formula (Years) (W m 2 (W m 2 20-year (W m 2 100-year 20-year 50-year 100-year cal Name (K kg 1) (K kg 1) (K kg 1) ppb 1) yr kg 1) yr kg 1) Carbon dioxide CO2 see* 1.37e-5 2.49e-14 1 9.17e-14 1 6.84e-16 1 6.17e-16 1 5.47e-16 1 Methane CH4 12.4 3.63e-4 2.09e-12 84 2.61e-12 28 4.62e-14 67 8.69e-15 14 2.34e-15 4 Fossil methane CH4 12.4 3.63e-4 2.11e-12 85 2.73e-12 30 4.68e-14 68 9.55e-15 15 3.11e-15 6 Nitrous Oxide N2O 121 3.00e-3 6.58e-12 264 2.43e-11 265 1.89e-13 277 1.74e-13 282 1.28e-13 234 Chlorofluorocarbons Anthropogenic and Natural Radiative Forcing CFC-11 CCl3F 45.0 0.26 1.72e-10 6900 4.28e-10 4660 4.71e-12 6890 3.01e-12 4890 1.28e-12 2340 CFC-12 CCl2F2 100.0 0.32 2.69e-10 10,800 9.39e-10 10,200 7.71e-12 11,300 6.75e-12 11,000 4.62e-12 8450 CFC-13 CClF3 640.0 0.25 2.71e-10 10,900 1.27e-09 13,900 7.99e-12 11,700 8.77e-12 14,200 8.71e-12 15,900 CFC-113 CCl2FCClF2 85.0 0.30 1.62e-10 6490 5.34e-10 5820 4.60e-12 6730 3.85e-12 6250 2.45e-12 4470 CFC-114 CClF2CClF2 190.0 0.31 1.92e-10 7710 7.88e-10 8590 5.60e-12 8190 5.56e-12 9020 4.68e-12 8550 CFC-115 CClF2CF3 1,020.0 0.20 1.46e-10 5860 7.03e-10 7670 4.32e-12 6310 4.81e-12 7810 4.91e-12 8980 Hydrochlorofluorocarbons HCFC-21 CHCl2F 1.7 0.15 1.35e-11 543 1.35e-11 148 1.31e-13 192 1.59e-14 26 1.12e-14 20 HCFC-22 CHClF2 11.9 0.21 1.32e-10 5280 1.62e-10 1760 2.87e-12 4200 5.13e-13 832 1.43e-13 262 HCFC-122 CHCl2CF2Cl 1.0 0.17 5.43e-12 218 5.43e-12 59 4.81e-14 70 6.25e-15 10 4.47e-15 8 HCFC-122a CHFClCFCl2 3.4 0.21 2.36e-11 945 2.37e-11 258 2.91e-13 426 2.99e-14 48 1.96e-14 36 HCFC-123 CHCl2CF3 1.3 0.15 7.28e-12 292 7.28e-12 79 6.71e-14 98 8.45e-15 14 6.00e-15 11 HCFC-123a CHClFCF2Cl 4.0 0.23 3.37e-11 1350 3.39e-11 370 4.51e-13 659 4.44e-14 72 2.81e-14 51 HCFC-124 CHClFCF3 5.9 0.20 4.67e-11 1870 4.83e-11 527 7.63e-13 1120 7.46e-14 121 4.03e-14 74 HCFC-132c CH2FCFCl2 4.3 0.17 3.07e-11 1230 3.10e-11 338 4.27e-13 624 4.14e-14 67 2.58e-14 47 HCFC-141b CH3CCl2F 9.2 0.16 6.36e-11 2550 7.17e-11 782 1.27e-12 1850 1.67e-13 271 6.09e-14 111 HCFC-142b CH3CClF2 17.2 0.19 1.25e-10 5020 1.82e-10 1980 3.01e-12 4390 8.46e-13 1370 1.95e-13 356 HCFC-225ca CHCl2CF2CF3 1.9 0.22 1.17e-11 469 1.17e-11 127 1.17e-13 170 1.38e-14 22 9.65e-15 18 HCFC-225cb CHClFCF2CClF2 5.9 0.29 4.65e-11 1860 4.81e-11 525 7.61e-13 1110 7.43e-14 120 4.01e-14 73 (E)-1-Chloro-3,3,3- trans-CF3CH=CHCl 26.0 days 0.04 1.37e-13 5 1.37e-13 1 1.09e-15 2 1.54e-16 <1 1.12e-16 <1 trifluoroprop-1-ene (continued on next page) Chapter 8 731 8 8 Table 8.A.1 (continued) Radiative AGWP AGWP 732 AGTP AGTP AGTP Acronym, Common Name Chemical Lifetime Efficiency 20-year GWP 100-year GWP GTP GTP GTP 20-year 50-year 100-year or Chemical Name Formula (Years) (W m 2 (W m 2 20-year (W m 2 100-year 20-year 50-year 100-year (K kg 1) (K kg 1) (K kg 1) Chapter 8 ppb 1) yr kg 1) yr kg 1) Hydrofluorocarbons HFC-23 CHF3 222.0 0.18 2.70e-10 10,800 1.14e-09 12,400 7.88e-12 11,500 7.99e-12 13,000 6.95e-12 12,700 HFC-32 CH2F2 5.2 0.11 6.07e-11 2430 6.21e-11 677 9.32e-13 1360 8.93e-14 145 5.17e-14 94 HFC-41 CH3F 2.8 0.02 1.07e-11 427 1.07e-11 116 1.21e-13 177 1.31e-14 21 8.82e-15 16 HFC-125 CHF2CF3 28.2 0.23 1.52e-10 6090 2.91e-10 3170 3.97e-12 5800 1.84e-12 2980 5.29e-13 967 HFC-134 CHF2CHF2 9.7 0.19 8.93e-11 3580 1.02e-10 1120 1.82e-12 2660 2.54e-13 412 8.73e-14 160 HFC-134a CH2FCF3 13.4 0.16 9.26e-11 3710 1.19e-10 1300 2.09e-12 3050 4.33e-13 703 1.10e-13 201 HFC-143 CH2FCHF2 3.5 0.13 3.00e-11 1200 3.01e-11 328 3.76e-13 549 3.82e-14 62 2.49e-14 46 HFC-143a CH3CF3 47.1 0.16 1.73e-10 6940 4.41e-10 4800 4.76e-12 6960 3.12e-12 5060 1.37e-12 2500 HFC-152 CH2FCH2F 0.4 0.04 1.51e-12 60 1.51e-12 16 1.25e-14 18 1.71e-15 3 1.24e-15 2 HFC-152a CH3CHF2 1.5 0.10 1.26e-11 506 1.26e-11 138 1.19e-13 174 1.47e-14 24 1.04e-14 19 HFC-161 CH3CH2F 66.0 days 0.02 3.33e-13 13 3.33e-13 4 2.70e-15 4 3.76e-16 <1 2.74e-16 <1 HFC-227ca CF3CF2CHF2 28.2 0.27 1.27e-10 5080 2.42e-10 2640 3.31e-12 4830 1.53e-12 2480 4.41e-13 806 HFC-227ea CF3CHFCF3 38.9 0.26 1.34e-10 5360 3.07e-10 3350 3.61e-12 5280 2.12e-12 3440 7.98e-13 1460 HFC-236cb CH2FCF2CF3 13.1 0.23 8.67e-11 3480 1.11e-10 1210 1.94e-12 2840 3.92e-13 636 1.01e-13 185 HFC-236ea CHF2CHFCF3 11.0 0.30a 1.03e-10 4110 1.22e-10 1330 2.18e-12 3190 3.53e-13 573 1.06e-13 195 HFC-236fa CF3CH2CF3 242.0 0.24 1.73e-10 6940 7.39e-10 8060 5.06e-12 7400 5.18e-12 8400 4.58e-12 8380 HFC-245ca CH2FCF2CHF2 6.5 0.24b 6.26e-11 2510 6.56e-11 716 1.07e-12 1570 1.09e-13 176 5.49e-14 100 HFC-245cb CF3CF2CH3 47.1 0.24 1.67e-10 6680 4.24e-10 4620 4.58e-12 6690 3.00e-12 4870 1.32e-12 2410 HFC-245ea CHF2CHFCHF2 3.2 0.16c 2.15e-11 863 2.16e-11 235 2.59e-13 378 2.70e-14 44 1.79e-14 33 HFC-245eb CH2FCHFCF3 3.1 0.20c 2.66e-11 1070 2.66e-11 290 3.15e-13 460 3.31e-14 54 2.20e-14 40 HFC-245fa CHF2CH2CF3 7.7 0.24 7.29e-11 2920 7.87e-11 858 1.35e-12 1970 1.51e-13 245 6.62e-14 121 HFC-263fb CH3CH2CF3 1.2 0.10c 6.93e-12 278 6.93e-12 76 6.31e-14 92 8.02e-15 13 5.70e-15 10 HFC-272ca CH3CF2CH3 2.6 0.07 1.32e-11 530 1.32e-11 144 1.46e-13 213 1.61e-14 26 1.09e-14 20 HFC-329p CHF2CF2CF2CF3 28.4 0.31 1.13e-10 4510 2.16e-10 2360 2.94e-12 4290 1.37e-12 2220 3.96e-13 725 HFC-365mfc CH3CF2CH2CF3 8.7 0.22 6.64e-11 2660 7.38e-11 804 1.30e-12 1890 1.62e-13 262 6.24e-14 114 HFC-43-10mee CF3CHFCHFCF2CF3 16.1 0.42b 1.08e-10 4310 1.51e-10 1650 2.54e-12 3720 6.62e-13 1070 1.54e-13 281 HFC-1132a CH2=CF2 4.0 days 0.004 d 3.87e-15 <1 3.87e-15 <1 3.08e-17 <1 4.35e-18 <1 3.18e-18 <1 HFC-1141 CH2=CHF 2.1 days 0.002d 1.54e-15 <1 1.54e-15 <1 1.23e-17 <1 1.73e-18 <1 1.27e-18 <1 (Z)-HFC-1225ye CF3CF=CHF(Z) 8.5 days 0.02 2.14e-14 <1 2.14e-14 <1 1.70e-16 <1 2.40e-17 <1 1.76e-17 <1 (E)-HFC-1225ye CF3CF=CHF(E) 4.9 days 0.01 7.25e-15 <1 7.25e-15 <1 5.77e-17 <1 8.14e-18 <1 5.95e-18 <1 (Z)-HFC-1234ze CF3CH=CHF(Z) 10.0 days 0.02 2.61e-14 1 2.61e-14 <1 2.08e-16 <1 2.93e-17 <1 2.14e-17 <1 HFC-1234yf CF3CF=CH2 10.5 days 0.02 3.22e-14 1 3.22e-14 <1 2.57e-16 <1 3.62e-17 <1 2.65e-17 <1 (E)-HFC-1234ze trans-CF3CH=CHF 16.4 days 0.04 8.74e-14 4 8.74e-14 <1 6.98e-16 <1 9.82e-17 <1 7.18e-17 <1 d (Z)-HFC-1336 CF3CH=CHCF3(Z) 22.0 days 0.07 1.54e-13 6 1.54e-13 2 1.23e-15 2 1.73e-16 <1 1.26e-16 <1 Anthropogenic and Natural Radiative Forcing (continued on next page) Table 8.A.1 (continued) Radia- tive AGWP AGWP AGTP AGTP AGTP Lifetime Effi- 20-year GWP 100-year GWP GTP GTP GTP Acronym, Common Name or Chemical Name Chemical Formula 20-year 50-year 100-year (Years) ciency (W m 2 20-year (W m 2 100-year 20-year 50-year 100-year (K kg 1) (K kg 1) (K kg 1) (W m 2 yr kg 1) yr kg 1) ppb 1) HFC-1243zf CF3CH=CH2 7.0 days 0.01 1.37e-14 1 1.37e-14 <1 1.09e-16 <1 1.53e-17 <1 1.12e-17 <1 HFC-1345zfc C2F5CH=CH2 7.6 days 0.01 1.15e-14 <1 1.15e-14 <1 9.19e-17 <1 1.30e-17 <1 9.48e-18 <1 3,3,4,4,5,5,6,6,6-Nonafluorohex-1-ene C4F9CH=CH2 7.6 days 0.03 1.25e-14 <1 1.25e-14 <1 9.92e-17 <1 1.40e-17 <1 1.02e-17 <1 3,3,4,4,5,5,6,6,7,7,8,8,8-Tridecafluorooct-1-ene C6F13CH=CH2 7.6 days 0.03 9.89e-15 <1 9.89e-15 <1 7.87e-17 <1 1.11e-17 <1 8.12e-18 <1 3,3,4,4,5,5,6,6,7,7,8,8,9,9,10,10,10-Hep- C8F17CH=CH2 7.6 days 0.03 8.52e-15 <1 8.52e-15 <1 6.79e-17 <1 9.57e-18 <1 7.00e-18 <1 tadecafluorodec-1-ene Chlorocarbons and Hydrochlorocarbons Methyl chloroform CH3CCl3 5.0 0.07 1.44e-11 578 1.47e-11 160 2.17e-13 317 2.07e-14 34 1.22e-14 22 Anthropogenic and Natural Radiative Forcing Carbon tetrachloride CCl4 26.0 0.17 8.69e-11 3480 1.59e-10 1730 2.24e-12 3280 9.68e-13 1570 2.62e-13 479 Methyl chloride CH3Cl 1.0 0.01a 1.12e-12 45 1.12e-12 12 9.93e-15 15 1.29e-15 2 9.20e-16 2 Methylene chloride CH2Cl2 0.4 0.03b 8.18e-13 33 8.18e-13 9 6.78e-15 10 9.26e-16 2 6.72e-16 1 Chloroform CHCl3 0.4 0.08 1.50e-12 60 1.50e-12 16 1.25e-14 18 1.70e-15 3 1.24e-15 2 1,2-Dichloroethane CH2ClCH2Cl 65.0 days 0.01 8.24e-14 3 8.24e-14 <1 6.67e-16 <1 9.29e-17 <1 6.77e-17 <1 Bromocarbons, Hydrobromocarbons and Halons Methyl bromide CH3Br 0.8 0.004 2.16e-13 9 2.16e-13 2 1.87e-15 3 2.47e-16 <1 1.78e-16 <1 Methylene bromide CH2Br2 0.3 0.01 9.31e-14 4 9.31e-14 1 7.66e-16 1 1.05e-16 <1 7.65e-17 <1 Halon-1201 CHBrF2 5.2 0.15 3.37e-11 1350 3.45e-11 376 5.17e-13 756 4.96e-14 80 2.87e-14 52 Halon-1202 CBr2F2 2.9 0.27 2.12e-11 848 2.12e-11 231 2.43e-13 356 2.61e-14 42 1.75e-14 32 Halon-1211 CBrClF2 16.0 0.29 1.15e-10 4590 1.60e-10 1750 2.70e-12 3950 6.98e-13 1130 1.62e-13 297 Halon-1301 CBrF3 65.0 0.30 1.95e-10 7800 5.77e-10 6290 5.46e-12 7990 4.16e-12 6750 2.28e-12 4170 Halon-2301 CH2BrCF3 3.4 0.14 1.59e-11 635 1.59e-11 173 1.96e-13 286 2.01e-14 33 1.32e-14 24 Halon-2311 / Halothane CHBrClCF3 1.0 0.13 3.77e-12 151 3.77e-12 41 3.35e-14 49 4.34e-15 7 3.10e-15 6 Halon-2401 CHFBrCF3 2.9 0.19 1.68e-11 674 1.68e-11 184 1.94e-13 283 2.07e-14 34 1.39e-14 25 Halon-2402 CBrF2CBrF2 20.0 0.31 8.59e-11 3440 1.35e-10 1470 2.12e-12 3100 7.08e-13 1150 1.66e-13 304 Fully Fluorinated Species Nitrogen trifluoride NF3 500.0 0.20 3.19e-10 12,800 1.47e-09 16,100 9.39e-12 13,700 1.02e-11 16,500 9.91e-12 18,100 Sulphur hexafluoride SF6 3,200.0 0.57 4.37e-10 17,500 2.16e-09 23,500 1.29e-11 18,900 1.47e-11 23,800 1.54e-11 28,200 (Trifluoromethyl) sulphur pentafluoride SF5CF3 800.0 0.59 3.36e-10 13,500 1.60e-09 17,400 9.93e-12 14,500 1.10e-11 17,800 1.11e-11 20,200 Sulphuryl fluoride SO2F2 36.0 0.20 1.71e-10 6840 3.76e-10 4090 4.58e-12 6690 2.55e-12 4140 9.01e-13 1650 PFC-14 CF4 50,000.0 0.09 1.22e-10 4880 6.08e-10 6630 3.61e-12 5270 4.12e-12 6690 4.40e-12 8040 PFC-116 C2F6 10,000.0 0.25 2.05e-10 8210 1.02e-09 11,100 6.07e-12 8880 6.93e-12 11,200 7.36e-12 13,500 PFC-c216 c-C3F6 3,000.0 0.23e 1.71e-10 6850 8.44e-10 9200 5.06e-12 7400 5.74e-12 9310 6.03e-12 11,000 PFC-218 C3F8 2,600.0 0.28 1.66e-10 6640 8.16e-10 8900 4.91e-12 7180 5.56e-12 9010 5.83e-12 10,700 PFC-318 c-C4F8 3,200.0 0.32 1.77e-10 7110 8.75e-10 9540 5.25e-12 7680 5.96e-12 9660 6.27e-12 11,500 Chapter 8 733 (continued on next page) 8 8 Table 8.A.1 (continued) Radia- 734 tive AGWP AGWP AGTP AGTP AGTP Lifetime Effi- 20-year GWP 100-year GWP GTP GTP GTP Acronym, Common Name or Chemical Name Chemical Formula 20-year 50-year 100-year Chapter 8 (Years) ciency (W m 2 20-year (W m 2 100-year 20-year 50-year 100-year (K kg 1) (K kg 1) (K kg 1) (W m 2 yr kg 1) yr kg 1) ppb 1) PFC-31-10 C4F10 2,600.0 0.36 1.71e-10 6870 8.44e-10 9200 5.08e-12 7420 5.75e-12 9320 6.02e-12 11,000 Perfluorocyclopentene c-C5F8 31.0 days 0.08f 1.71e-13 7 1.71e-13 2 1.37e-15 2 1.92e-16 <1 1.40e-16 <1 PFC-41-12 n-C5F12 4,100.0 0.41 1.58e-10 6350 7.84e-10 8550 4.69e-12 6860 5.33e-12 8650 5.62e-12 10,300 PFC-51-14 n-C6F14 3,100.0 0.44 1.47e-10 5890 7.26e-10 7910 4.35e-12 6370 4.94e-12 8010 5.19e-12 9490 PFC-61-16 n-C7F16 3,000.0 0.50 1.45e-10 5830 7.17e-10 7820 4.31e-12 6290 4.88e-12 7920 5.13e-12 9380 PFC-71-18 C8F18 3,000.0 0.55 1.42e-10 5680 6.99e-10 7620 4.20e-12 6130 4.76e-12 7710 5.00e-12 9140 PFC-91-18 C10F18 2,000.0 0.55 1.34e-10 5390 6.59e-10 7190 3.98e-12 5820 4.49e-12 7290 4.68e-12 8570 Perfluorodecalin (cis) Z-C10F18 2,000.0 0.56 1.35e-10 5430 6.64e-10 7240 4.01e-12 5860 4.52e-12 7340 4.72e-12 8630 Perfluorodecalin (trans) E-C10F18 2,000.0 0.48 1.18e-10 4720 5.77e-10 6290 3.48e-12 5090 3.93e-12 6380 4.10e-12 7500 PFC-1114 CF2=CF2 1.1 days 0.002 2.68e-16 <1 2.68e-16 <1 2.13e-18 <1 3.00e-19 <1 2.20e-19 <1 PFC-1216 CF3CF=CF2 4.9 days 0.01 6.42e-15 <1 6.42e-15 <1 5.11e-17 <1 7.21e-18 <1 5.27e-18 <1 Perfluorobuta-1,3-diene CF2=CFCF=CF2 1.1 days 0.003 3.29e-16 <1 3.29e-16 <1 2.61e-18 <1 3.69e-19 <1 2.70e-19 <1 Perfluorobut-1-ene CF3CF2CF=CF2 6.0 days 0.02 8.38e-15 <1 8.38e-15 <1 6.67e-17 <1 9.41e-18 <1 6.88e-18 <1 Perfluorobut-2-ene CF3CF=CFCF3 31.0 days 0.07 1.62e-13 6 1.62e-13 2 1.30e-15 2 1.82e-16 <1 1.33e-16 <1 Halogenated Alcohols and Ethers HFE-125 CHF2OCF3 119.0 0.41 3.10e-10 12,400 1.14e-09 12,400 8.91e-12 13,000 8.14e-12 13,200 5.97e-12 10,900 HFE-134 (HG-00) CHF2OCHF2 24.4 0.44 2.90e-10 11,600 5.10e-10 5560 7.42e-12 10,800 3.02e-12 4900 7.83e-13 1430 HFE-143a CH3OCF3 4.8 0.18 4.72e-11 1890 4.80e-11 523 6.95e-13 1020 6.66e-14 108 3.99e-14 73 HFE-227ea CF3CHFOCF3 51.6 0.44 2.22e-10 8900 5.92e-10 6450 6.15e-12 8980 4.22e-12 6850 1.98e-12 3630 HCFE-235ca2 (enflurane) CHF2OCF2CHFCl 4.3 0.41 5.30e-11 2120 5.35e-11 583 7.36e-13 1080 7.14e-14 116 4.44e-14 81 HCFE-235da2 (isoflurane) CHF2OCHClCF3 3.5 0.42 4.49e-11 1800 4.50e-11 491 5.62e-13 822 5.72e-14 93 3.73e-14 68 g HFE-236ca CHF2OCF2CHF2 20.8 0.56 2.42e-10 9710 3.89e-10 4240 6.03e-12 8820 2.10e-12 3400 4.98e-13 912 HFE-236ea2 (desflurane) CHF2OCHFCF3 10.8 0.45 1.39e-10 5550 1.64e-10 1790 2.93e-12 4280 4.64e-13 753 1.42e-13 260 HFE-236fa CF3CH2OCF3 7.5 0.36 8.35e-11 3350 8.98e-11 979 1.53e-12 2240 1.68e-13 273 7.54e-14 138 HFE-245cb2 CF3CF2OCH3 4.9 0.33 5.90e-11 2360 6.00e-11 654 8.77e-13 1280 8.40e-14 136 4.99e-14 91 HFE-245fa1 CHF2CH2OCF3 6.6 0.31 7.22e-11 2900 7.59e-11 828 1.25e-12 1820 1.27e-13 206 6.35e-14 116 HFE-245fa2 CHF2OCH2CF3 5.5 0.36 7.25e-11 2910 7.45e-11 812 1.15e-12 1670 1.10e-13 179 6.21e-14 114 2,2,3,3,3-Pentafluoropropan-1-ol CF3CF2CH2OH 0.3 0.14 1.72e-12 69 1.72e-12 19 1.42e-14 21 1.95e-15 3 1.42e-15 3 HFE-254cb1 CH3OCF2CHF2 2.5 0.26 2.76e-11 1110 2.76e-11 301 2.99e-13 438 3.34e-14 54 2.28e-14 42 HFE-263fb2 CF3CH2OCH3 23.0 days 0.04 1.22e-13 5 1.22e-13 1 9.72e-16 1 1.37e-16 <1 9.98e-17 <1 HFE-263m1 CF3OCH2CH3 0.4 0.13 2.70e-12 108 2.70e-12 29 2.25e-14 33 3.06e-15 5 2.22e-15 4 3,3,3-Trifluoropropan-1-ol CF3CH2CH2OH 12.0 days 0.02 3.57e-14 1 3.57e-14 <1 2.85e-16 <1 4.01e-17 <1 2.93e-17 <1 HFE-329mcc2 CHF2CF2OCF2CF3 22.5 0.53 1.68e-10 6720 2.81e-10 3070 4.23e-12 6180 1.59e-12 2580 3.93e-13 718 HFE-338mmz1 (CF3)2CHOCHF2 21.2 0.44 1.48e-10 5940 2.40e-10 2620 3.70e-12 5410 1.31e-12 2130 3.14e-13 575 Anthropogenic and Natural Radiative Forcing (continued on next page) Table 8.A.1 (continued) Radia- tive AGWP AGWP AGTP AGTP AGTP Lifetime Effi- 20-year GWP 100-year GWP GTP GTP GTP Acronym, Common Name or Chemical Name Chemical Formula 20-year 50-year 100-year (Years) ciency (W m 2 20-year (W m 2 100-year 20-year 50-year 100-year (K kg 1) (K kg 1) (K kg 1) (W m 2 yr kg 1) yr kg 1) ppb 1) HFE-338mcf2 CF3CH2OCF2CF3 7.5 0.44 7.93e-11 3180 8.52e-11 929 1.45e-12 2120 1.60e-13 259 7.16e-14 131 Sevoflurane (HFE-347mmz1) (CF3)2CHOCH2F 2.2 0.32 1.98e-11 795 1.98e-11 216 2.06e-13 302 2.37e-14 38 1.64e-14 30 HFE-347mcc3 (HFE-7000) CH3OCF2CF2CF3 5.0 0.35 4.78e-11 1910 4.86e-11 530 7.18e-13 1050 6.87e-14 111 4.05e-14 74 HFE-347mcf2 CHF2CH2OCF2CF3 6.6 0.42 7.45e-11 2990 7.83e-11 854 1.29e-12 1880 1.31e-13 212 6.55e-14 120 HFE-347pcf2 CHF2CF2OCH2CF3 6.0 0.48h 7.86e-11 3150 8.15e-11 889 1.30e-12 1900 1.27e-13 206 6.81e-14 124 HFE-347mmy1 (CF3)2CFOCH3 3.7 0.32 3.32e-11 1330 3.33e-11 363 4.27e-13 624 4.28e-14 69 2.76e-14 51 HFE-356mec3 CH3OCF2CHFCF3 3.8 0.30 3.53e-11 1410 3.55e-11 387 4.60e-13 673 4.58e-14 74 2.94e-14 54 105.0 HFE-356mff2 CF3CH2OCH2CF3 0.17 1.54e-12 62 1.54e-12 17 1.26e-14 18 1.74e-15 3 1.26e-15 2 Anthropogenic and Natural Radiative Forcing days HFE-356pcf2 CHF2CH2OCF2CHF2 5.7 0.37 6.40e-11 2560 6.59e-11 719 1.03e-12 1500 9.97e-14 162 5.50e-14 101 HFE-356pcf3 CHF2OCH2CF2CHF2 3.5 0.38 4.08e-11 1640 4.09e-11 446 5.11e-13 747 5.20e-14 84 3.39e-14 62 HFE-356pcc3 CH3OCF2CF2CHF2 3.8 0.32 3.77e-11 1510 3.79e-11 413 4.91e-13 718 4.89e-14 79 3.14e-14 57 HFE-356mmz1 (CF3)2CHOCH3 97.1 days 0.15 1.25e-12 50 1.25e-12 14 1.02e-14 15 1.41e-15 2 1.02e-15 2 HFE-365mcf3 CF3CF2CH2OCH3 19.3 days 0.05 8.51e-14 3 8.51e-14 <1 6.80e-16 <1 9.56e-17 <1 6.99e-17 <1 HFE-365mcf2 CF3CF2OCH2CH3 0.6 0.26i 5.35e-12 215 5.35e-12 58 4.53e-14 66 6.10e-15 10 4.40e-15 8 HFE-374pc2 CHF2CF2OCH2CH3 5.0 0.30 5.65e-11 2260 5.75e-11 627 8.48e-13 1240 8.12e-14 132 4.79e-14 88 4,4,4-Trifluorobutan-1-ol CF3(CH2)2CH2OH 4.0 days 0.01 1.73e-15 <1 1.73e-15 <1 1.38e-17 <1 1.94e-18 <1 1.42e-18 <1 2,2,3,3,4,4,5,5-Octafluorocyclopentanol -(CF2)4CH(OH)- 0.3 0.16 1.18e-12 47 1.18e-12 13 9.67e-15 14 1.33e-15 2 9.69e-16 2 HFE-43-10pccc124 (H-Galden 1040x, HG-11) CHF2OCF2OC2F4OCHF2 13.5 1.02 2.00e-10 8010 2.58e-10 2820 4.52e-12 6600 9.46e-13 1530 2.38e-13 436 HFE-449s1 (HFE-7100) C4F9OCH3 4.7 0.36 3.80e-11 1530 3.86e-11 421 5.54e-13 809 5.32e-14 86 3.21e-14 59 n-HFE-7100 n-C4F9OCH3 4.7 0.42 4.39e-11 1760 4.45e-11 486 6.39e-13 934 6.14e-14 99 3.70e-14 68 i-HFE-7100 i-C4F9OCH3 4.7 0.35 3.68e-11 1480 3.73e-11 407 5.35e-13 783 5.14e-14 83 3.10e-14 57 HFE-569sf2 (HFE-7200) C4F9OC2H5 0.8 0.30 5.21e-12 209 5.21e-12 57 4.52e-14 66 5.97e-15 10 4.29e-15 8 i n-HFE-7200 n-C4F9OC2H5 0.8 0.35 5.92e-12 237 5.92e-12 65 5.14e-14 75 6.78e-15 11 4.87e-15 9 i-HFE-7200 i-C4F9OC2H5 0.8 0.24 4.06e-12 163 4.06e-12 44 3.52e-14 52 4.65e-15 8 3.34e-15 6 HFE-236ca12 (HG-10) CHF2OCF2OCHF2 25.0 0.65 2.75e-10 11,000 4.91e-10 5350 7.06e-12 10,300 2.94e-12 4770 7.75e-13 1420 HFE-338pcc13 (HG-01) CHF2OCF2CF2OCHF2 12.9 0.86 2.10e-10 8430 2.67e-10 2910 4.69e-12 6860 9.28e-13 1500 2.42e-13 442 1,1,1,3,3,3-Hexafluoropropan-2-ol (CF3)2CHOH 1.9 0.26 1.67e-11 668 1.67e-11 182 1.66e-13 243 1.97e-14 32 1.38e-14 25 HF2C (OCF2CF2)2 HG-02 12.9 1.24i 1.97e-10 7900 2.50e-10 2730 4.40e-12 6430 8.70e-13 1410 2.27e-13 415 OCF2H HF2C (OCF2CF2)3 HG-03 12.9 1.76i 2.06e-10 8270 2.62e-10 2850 4.60e-12 6730 9.10e-13 1480 2.37e-13 434 OCF2H HG-20 HF2C (OCF2)2 OCF2H 25.0 0.92i 2.73e-10 10,900 4.86e-10 5300 7.00e-12 10,200 2.91e-12 4730 7.68e-13 1400 HF2C OCF2CF2OC- HG-21 13.5 1.71i 2.76e-10 11,100 3.57e-10 3890 6.23e-12 9110 1.31e-12 2120 3.29e-13 602 F2OCF2O CF2H Chapter 8 735 (continued on next page) 8 8 Table 8.A.1 (continued) Radia- 736 tive AGWP AGWP AGTP AGTP AGTP Lifetime Effi- 20-year GWP 100-year GWP GTP GTP GTP Acronym, Common Name or Chemical Name Chemical Formula 20-year 50-year 100-year Chapter 8 (Years) ciency (W m 2 20-year (W m 2 100-year 20-year 50-year 100-year (K kg 1) (K kg 1) (K kg 1) (W m 2 yr kg 1) yr kg 1) ppb 1) HG-30 HF2C (OCF2)3 OCF2H 25.0 1.65i 3.77e-10 15,100 6.73e-10 7330 9.68e-12 14,100 4.03e-12 6530 1.06e-12 1940 1-Ethoxy-1,1,2,2,3,3,3-heptafluoropropane CF3CF2CF2OCH2CH3 0.8 0.28i 5.56e-12 223 5.56e-12 61 4.80e-14 70 6.36e-15 10 4.57e-15 8 i Fluoroxene CF3CH2OCH=CH2 3.6 days 0.01 4.97e-15 <1 4.97e-15 <1 3.95e-17 <1 5.58e-18 <1 4.08e-18 <1 1,1,2,2-Tetrafluoro-1-(fluoromethoxy)ethane CH2FOCF2CF2H 6.2 0.34i 7.68e-11 3080 7.99e-11 871 1.29e-12 1880 1.28e-13 207 6.68e-14 122 2-Ethoxy-3,3,4,4,5-pentafluorotetrahydro-2,5-bis[1,2,2,2- C12H5F19O2 1.0 0.49j 5.09e-12 204 5.09e-12 56 4.53e-14 66 5.86e-15 10 4.19e-15 8 tetrafluoro-1-(trifluoromethyl)ethyl]-furan Fluoro(methoxy)methane CH3OCH2F 73.0 days 0.07g 1.15e-12 46 1.15e-12 13 9.34e-15 14 1.30e-15 2 9.46e-16 2 Difluoro(methoxy)methane CH3OCHF2 1.1 0.17g 1.32e-11 528 1.32e-11 144 1.18e-13 173 1.52e-14 25 1.08e-14 20 Fluoro(fluoromethoxy)methane CH2FOCH2F 0.9 0.19g 1.20e-11 479 1.20e-11 130 1.05e-13 153 1.37e-14 22 9.84e-15 18 Difluoro(fluoromethoxy)methane CH2FOCHF2 3.3 0.30g 5.65e-11 2260 5.66e-11 617 6.88e-13 1010 7.11e-14 115 4.69e-14 86 Trifluoro(fluoromethoxy)methane CH2FOCF3 4.4 0.33g 6.82e-11 2730 6.89e-11 751 9.59e-13 1400 9.27e-14 150 5.72e-14 105 HG -01 CH3OCF2CF2OCH3 2.0 0.29 2.03e-11 815 2.03e-11 222 2.06e-13 301 2.42e-14 39 1.68e-14 31 HG -02 CH3O(CF2CF2O)2CH3 2.0 0.56 2.16e-11 868 2.16e-11 236 2.19e-13 320 2.57e-14 42 1.79e-14 33 HG -03 CH3O(CF2CF2O)3CH3 2.0 0.76 2.03e-11 812 2.03e-11 221 2.05e-13 299 2.41e-14 39 1.67e-14 31 HFE-329me3 CF3CFHCF2OCF3 40.0 0.48 1.79e-10 7170 4.17e-10 4550 4.85e-12 7090 2.89e-12 4690 1.12e-12 2040 3,3,4,4,5,5,6,6,7,7,7-Undecafluoroheptan-1-ol CF3(CF2)4CH2CH2OH 20.0 days 0.06 3.91e-14 2 3.91e-14 <1 3.12e-16 <1 4.39e-17 <1 3.21e-17 <1 3,3,4,4,5,5,6,6,7,7,8,8,9,9,9-Pentadecafluorononan-1-ol CF3(CF2)6CH2CH2OH 20.0 days 0.07 3.00e-14 1 3.00e-14 <1 2.40e-16 <1 3.37e-17 <1 2.46e-17 <1 3,3,4,4,5,5,6,6,7,7,8,8,9,9,10,10,11,11,11-Non- CF3(CF2)8CH2CH2OH 20.0 days 0.05 1.72e-14 <1 1.72e-14 <1 1.37e-16 <1 1.93e-17 <1 1.41e-17 <1 adecafluoroundecan-1-ol 2-Chloro-1,1,2-trifluoro-1-methoxyethane CH3OCF2CHFCl 1.4 0.21 1.12e-11 449 1.12e-11 122 1.05e-13 153 1.31e-14 21 9.24e-15 17 CF3OCF(CF3) PFPMIE (perfluoropolymethylisopropyl ether) 800.0 0.65 1.87e-10 7500 8.90e-10 9710 5.52e-12 8070 6.11e-12 9910 6.15e-12 11,300 CF2OCF2OCF3 HFE-216 CF3OCF=CF2 8.4 days 0.02 1.92e-14 <1 1.92e-14 <1 1.53e-16 <1 2.15e-17 <1 1.58e-17 <1 i Trifluoromethyl formate HCOOCF3 3.5 0.31 5.37e-11 2150 5.39e-11 588 6.73e-13 984 6.85e-14 111 4.47e-14 82 Perfluoroethyl formate HCOOCF2CF3 3.5 0.44i 5.30e-11 2130 5.32e-11 580 6.64e-13 971 6.76e-14 110 4.41e-14 81 Perfluoropropyl formate HCOOCF2CF2CF3 2.6 0.50i 3.45e-11 1380 3.45e-11 376 3.80e-13 555 4.19e-14 68 2.85e-14 52 Perfluorobutyl formate HCOOCF2CF2CF2CF3 3.0 0.56 i 3.59e-11 1440 3.59e-11 392 4.19e-13 613 4.45e-14 72 2.97e-14 54 2,2,2-Trifluoroethyl formate HCOOCH2CF3 0.4 0.16i 3.07e-12 123 3.07e-12 33 2.55e-14 37 3.48e-15 6 2.52e-15 5 3,3,3-Trifluoropropyl formate HCOOCH2CH2CF3 0.3 0.13i 1.60e-12 64 1.60e-12 17 1.31e-14 19 1.80e-15 3 1.31e-15 2 i 1,2,2,2-Tetrafluoroethyl formate HCOOCHFCF3 3.2 0.35 4.30e-11 1720 4.31e-11 470 5.17e-13 755 5.39e-14 87 3.57e-14 65 1,1,1,3,3,3-Hexafluoropropan-2-yl formate HCOOCH(CF3)2 3.2 0.33i 3.05e-11 1220 3.05e-11 333 3.66e-13 535 3.81e-14 62 2.53e-14 46 Perfluorobutyl acetate CH3COOCF2CF2CF2CF3 21.9 days 0.12i 1.52e-13 6 1.52e-13 2 1.21e-15 2 1.71e-16 <1 1.25e-16 <1 Perfluoropropyl acetate CH3COOCF2CF2CF3 21.9 days 0.11i 1.59e-13 6 1.59e-13 2 1.27e-15 2 1.78e-16 <1 1.30e-16 <1 i Perfluoroethyl acetate CH3COOCF2CF3 21.9 days 0.10 1.89e-13 8 1.89e-13 2 1.51e-15 2 2.12e-16 <1 1.55e-16 <1 Trifluoromethyl acetate CH3COOCF3 21.9 days 0.07i 1.90e-13 8 1.90e-13 2 1.52e-15 2 2.14e-16 <1 1.56e-16 <1 Anthropogenic and Natural Radiative Forcing (continued on next page) Table 8.A.1 (continued) Radia- tive AGWP AGWP AGTP AGTP AGTP Lifetime Effi- 20-year GWP 100-year GWP GTP GTP GTP Acronym, Common Name or Chemical Name Chemical Formula 20-year 50-year 100-year (Years) ciency (W m 2 20-year (W m 2 100-year 20-year 50-year 100-year (K kg 1) (K kg 1) (K kg 1) (W m 2 yr kg 1) yr kg 1) ppb 1) Methyl carbonofluoridate FCOOCH3 1.8 0.07i 8.74e-12 350 8.74e-12 95 8.60e-14 126 1.03e-14 17 7.21e-15 13 1,1-Difluoroethyl carbonofluoridate FCOOCF2CH3 0.3 0.17i 2.46e-12 99 2.46e-12 27 2.02e-14 30 2.78e-15 5 2.02e-15 4 1,1-Difluoroethyl 2,2,2-trifluoroacetate CF3COOCF2CH3 0.3 0.27i 2.83e-12 113 2.83e-12 31 2.33e-14 34 3.20e-15 5 2.32e-15 4 Ethyl 2,2,2-trifluoroacetate CF3COOCH2CH3 21.9 days 0.05i 1.26e-13 5 1.26e-13 1 1.00e-15 1 1.41e-16 <1 1.03e-16 <1 2,2,2-Trifluoroethyl 2,2,2-trifluoroacetate CF3COOCH2CF3 54.8 days 0.15i 6.27e-13 25 6.27e-13 7 5.06e-15 7 7.07e-16 1 5.15e-16 <1 Methyl 2,2,2-trifluoroacetate CF3COOCH3 0.6 0.18i 4.80e-12 192 4.80e-12 52 4.08e-14 60 5.47e-15 9 3.95e-15 7 Methyl 2,2-difluoroacetate HCF2COOCH3 40.1 days 0.05i 3.00e-13 12 3.00e-13 3 2.41e-15 4 3.38e-16 <1 2.47e-16 <1 Difluoromethyl 2,2,2-trifluoroacetate CF3COOCHF2 0.3 0.24i 2.48e-12 99 2.48e-12 27 2.04e-14 30 2.81e-15 5 2.04e-15 4 Anthropogenic and Natural Radiative Forcing 2,2,3,3,4,4,4-Heptafluorobutan-1-ol C3F7CH2OH 0.6 0.20 3.10e-12 124 3.10e-12 34 2.61e-14 38 3.52e-15 6 2.55e-15 5 1,1,2-Trifluoro-2-(trifluoromethoxy)-ethane CHF2CHFOCF3 9.8 0.35 9.91e-11 3970 1.14e-10 1240 2.03e-12 2960 2.88e-13 467 9.74e-14 178 1-Ethoxy-1,1,2,3,3,3-hexafluoropropane CF3CHFCF2OCH2CH3 0.4 0.19 2.14e-12 86 2.14e-12 23 1.77e-14 26 2.43e-15 4 1.76e-15 3 1,1,1,2,2,3,3-Heptafluoro-3-(1,2,2,2- CF3CF2CF2OCHFCF3 67.0 0.58 1.98e-10 7940 5.95e-10 6490 5.57e-12 8140 4.29e-12 6960 2.39e-12 4380 tetrafluoroethoxy)-propane 2,2,3,3-Tetrafluoro-1-propanol CHF2CF2CH2OH 91.3 days 0.11 1.19e-12 48 1.19e-12 13 9.72e-15 14 1.35e-15 2 9.79e-16 2 2,2,3,4,4,4-Hexafluoro-1-butanol CF3CHFCF2CH2OH 94.9 days 0.19 1.56e-12 63 1.56e-12 17 1.27e-14 19 1.76e-15 3 1.28e-15 2 2,2,3,3,4,4,4-Heptafluoro-1-butanol CF3CF2CF2CH2OH 0.3 0.16 1.49e-12 60 1.49e-12 16 1.23e-14 18 1.69e-15 3 1.23e-15 2 1,1,2,2-Tetrafluoro-3-methoxy-propane CHF2CF2CH2OCH3 14.2 days 0.03 4.82e-14 2 4.82e-14 <1 3.84e-16 <1 5.41e-17 <1 3.96e-17 <1 perfluoro-2-methyl-3-pentanone CF3CF2C(O)CF(CF3)2 7.0 days 0.03 9.14e-15 <1 9.14e-15 <1 7.27e-17 <1 1.03e-17 <1 7.51e-18 <1 3,3,3-Trifluoro-propanal CF3CH2CHO 2.0 days 0.004 9.86e-16 <1 9.86e-16 <1 7.84e-18 <1 1.11e-18 <1 8.10e-19 <1 2-Fluoroethanol CH2FCH2OH 20.4 days 0.02 8.07e-14 3 8.07e-14 <1 6.45e-16 <1 9.07e-17 <1 6.63e-17 <1 2,2-Difluoroethanol CHF2CH2OH 40.0 days 0.04 2.78e-13 11 2.78e-13 3 2.23e-15 3 3.12e-16 <1 2.28e-16 <1 2,2,2-Trifluoroethanol CF3CH2OH 0.3 0.10 1.83e-12 73 1.83e-12 20 1.50e-14 22 2.07e-15 3 1.50e-15 3 1,1 -Oxybis[2-(difluoromethoxy)-1,1,2,2-tetrafluoroethane HCF2O(CF2CF2O)2CF2H 26.0 1.15k 2.47e-10 9910 4.51e-10 4920 6.38e-12 9320 2.75e-12 4460 7.45e-13 1360 1,1,3,3,4,4,6,6,7,7,9,9,10,10,12,12-hexa- HCF2O(CF2CF2O)3CF2H 26.0 1.43k 2.26e-10 9050 4.12e-10 4490 5.83e-12 8520 2.51e-12 4080 6.81e-13 1250 decafluoro-2,5,8,11-Tetraoxadodecane 1,1,3,3,4,4,6,6,7,7,9,9,10,10,12,12,13,13,15,15-eico- HCF2O(CF2CF2O)4CF2H 26.0 1.46k 1.83e-10 7320 3.33e-10 3630 4.71e-12 6880 2.03e-12 3300 5.50e-13 1010 safluoro-2,5,8,11,14-Pentaoxapentadecane Notes: For CH4 we estimate an uncertainty of +/-30% and +/-40% for 20- and 100-year time horizon, respectively (for 90% uncertainty range). The uncertainty is dominated by AGWP for CO2 and indirect effects. The uncertainty in GWP for N2O is estimated to +/-20% and +/-30% for 20- and 100-year time horizon, with the largest contributions from CO2. The uncertainty in GWP for HFC-134a is estimated to +/-25% and +/-35% for 20- and 100-year time horizons while for CFC-11 the GWP the corresponding numbers are approximately +/-20% and +/-35% (not accounting for the indirect effects). For CFC-12 the corresponding numbers are +/-20 and +/-30. The uncertainties estimated for HFC-134a and CFC-11 are assessed as representative for most other gases with similar or longer lifetimes. For shorter-lived gases, the uncertainties will be larger. For GTP, few estimates are available in the literature. The uncertainty is assessed to be of the order of +/-75% for the methane GTP100. * No single lifetime can be given. The impulse response function for CO2 from Joos et al. (2013) has been used. See also Supplementary Material Section 8.SM.11. Perturbation lifetime is used in calculation of metrics, not the lifetime of the atmospheric burden. (continued on next page) Chapter 8 737 8 8 Table 8.A.1 Notes (continued) Metric values for CH4 of fossil origin include the oxidation to CO2 (based on Boucher et al., 2009). In applications of these values, inclusion of the CO2 effect of fossil methane must be done with caution to avoid any double-counting because CO2 emissions 738 numbers are often based on total carbon content. Methane values without the CO2 effect from fossil methane are thus appropriate for fossil methane sources for which the carbon has been accounted for elsewhere, or for biospheric methane sources for which there is abalance between CO2 taken up by the biosphere and CO2 produced from CH4 oxidization. The addition effect on GWP and GTP represents lower limits from Boucher et al. (2009) and assume 50% of the carbon is deposited as formaldehyde to the surface and is then lost. The upper limit in Boucher et al. (2009) made the assumption that this deposited formaldehyde was subsequently further oxidized to CO2 . Chapter 8 a RE is unchanged since AR4. b RE is unchanged since AR4 except the absolute forcing is increased by a factor of 1.04 to account for the change in the recommended RE of CFC-11. c Based on Rajakumar et al. (2006) (lifetime correction factor has been applied to account for non-homogeneous horizontal and vertical mixing). d Based on instantaneous RE from Baasandorj et al. (2010); Baasandorj et al. (2011) (correction factors have been applied to account for stratospheric temperature adjustment and non-homogeneous horizontal and vertical mixing). e Based on instantaneous RE from ab initio study of Bravo et al. (2010) (a factor 1.10 has been applied to account for stratospheric temperature adjustment). f Based on average instantaneous RE reported in literature (Vasekova et al., 2006; Bravo et al., 2010) (correction factors have been applied to account for stratospheric temperature adjustment and non-homogeneous horizontal and vertical mixing). g Based on instantaneous RE from ab initio studies of Blowers et al. (2007, 2008)(correction factors have been applied to account for stratospheric temperature adjustment and non-homogeneous horizontal and vertical mixing). h Based on instantaneous RE from Heathfield et al. (1998) (correction factors have been applied to account for stratospheric temperature adjustment and non-homogeneous horizontal and vertical mixing). i Note that calculation of RE is based on calculated (ab initio) absorption cross-section and uncertainties are therefore larger than for calculations using experimental absorption cross section. j Based on instantaneous RE from Javadi et al. (2007) (correction factors have been applied to account for stratospheric temperature adjustment and non-homogeneous horizontal and vertical mixing). k Based on instantaneous RE from Andersen et al. (2010) (correction factors have been applied to account for stratospheric temperature adjustment and non-homogeneous horizontal and vertical mixing). The GTP values are calculated with a temperature impulse response function taken from Boucher and Reddy (2008). See also Supplementary Material Section 8.SM.11. Anthropogenic and Natural Radiative Forcing Anthropogenic and Natural Radiative Forcing Chapter 8 Table 8.A.2 | Halocarbon indirect GWPs from ozone depletion using the EESC-based method described in WMO (2011), adapted from Daniel et al. (1995). A radiative forcing in year 2011 of 0.15 ( 0.30 to 0.0) W m 2 relative to preindustrial times is used (see Section 8.3.3). Uncertainty on the indirect AGWPs due to the ozone forcing uncertainty is +/-100%. Gas GWP100 CFC-11 2640 8 CFC-12 2100 CFC-113 2150 CFC-114 914 CFC-115 223 HCFC-22 98 HCFC-123 37 HCFC-124 46 HCFC-141b 261 HCFC-142b 152 CH3CCl3 319 CCl4 2110 CH3Br 1250 Halon-1211 19,000 Halon-1301 44,500 Halon-2402 32,000 HCFC-225ca 40 HCFC-225cb 60 Table 8.A.3 | GWP and GTP for NOX from surface sources for time horizons of 20 and 100 years from the literature. All values are on a per kilogram of nitrogen basis. Uncertainty for numbers from Fry et al. (2012) and Collins et al. (2013) refer to 1-s. For the reference gas CO2, RE and IRF from AR4 are used in the calculations. The GWP100 and GTP100 values can be scaled by 0.94 and 0.92, respectively, to account for updated values for the reference gas CO2. For 20 years the changes are negligible. GWP GTP H = 20 H = 100 H = 20 H = 100 NOX East Asiaa 6.4 (+/-38.1) 5.3 (+/-11.5) 55.6 (+/-23.8) 1.3 (+/-2.1) NOX EU + North Africaa 39.4 (+/-17.5) 15.6 (+/-5.8) 48.0 (+/-14.9) 2.5 (+/-1.3) NOX North Americaa 2.4 (+/-30.3) 8.2 (+/-10.3) 61.9 (+/-27.8) 1.7 (+/-2.1) NOX South Asia a 40.7 (+/-88.3) 25.3 (+/-29.0) 124.6 (+/-67.4) 4.6 (+/-5.1) NOX four above regionsa 15.9 (+/-32.7) 11.6 (+/-10.7) 62.1 (+/-26.2) 2.2 (+/-2.1) Mid-latitude NOxc 43 to +23 18 to +1.6 55 to 37 2.9 to 0.02 Tropical NOxc 43 to 130 28 to 10 260 to 220 6.6 to 5.4 NOX globalb 19 11 87 2.9 108 +/- 35 31 +/- 10 NOX globald 335 +/- 110 95 +/- 31 560 +/- 279 159 +/- 79 Notes: a Fry et al. (2012) (updated by including stratospheric H2O) and Collins et al. (2013). b Fuglestvedt et al. (2010); based on Wild et al. (2001). c Fuglestvedt et al. (2010). d Shindell et al. (2009). Three values are given: First, without aerosols, second, direct aerosol effect included (sulfate and nitrate), third, direct and indirect aerosol effects included. Uncertainty ranges from Shindell et al. (2009) are given for 95% confidence levels. 739 Chapter 8 Anthropogenic and Natural Radiative Forcing Table 8.A.4 | GWP and GTP for CO for time horizons of 20 and 100 years from the literature. Uncertainty for numbers from Fry et al. (2012) and Collins et al. (2013) refer to 1-s. For the reference gas CO2, RE and IRF from AR4 are used in the calculations. The GWP100 and GTP100 values can be scaled by 0.94 and 0.92, respectively, to account for updated values for the reference gas CO2. For 20 years the changes are negligible. GWP GTP H = 20 H = 100 H = 20 H = 100 CO East Asiaa 5.4 (+/-1.7) 1.8 (+/-0.6) 3.5 (+/-1.3) 0.26 (+/-0.12) 8 CO EU + North Africaa 4.9 (+/-1.5) 1.6 (+/-0.5) 3.2 (+/-1.2) 0.24 (+/-0.11) CO North Americaa 5.6 (+/-1.8) 1.8 (+/-0.6) 3.7 (+/-1.3) 0.27 (+/-0.12) CO South Asiaa 5.7 (+/-1.3) 1.8 (+/-0.4) 3.4 (+/-1.0) 0.27 (+/-0.10) CO four regions abovea 5.4 (+/-1.6) 1.8 (+/-0.5) 3.5 (+/-1.2) 0.26 (+/-0.11) CO globalb 6 to 9.3 2 to 3.3 3.7 to 6.1 0.29 to 0.55 7.8 +/- 2.0 2.2 +/- 0.6 CO globalc 11.4 +/- 2.9 3.3 +/- 0.8 18.6 +/- 8.3 5.3 +/- 2.3 Notes: a Fry et al. (2012) (updated by including stratospheric H2O) and Collins et al. (2013). b Fuglestvedt et al. (2010). c Shindell et al. (2009). Three values are given: First, without aerosols, second, direct aerosol effect included, third, direct and indirect aerosol effects included. Uncertainty ranges from Shindell et al. (2009) are given for 95% confidence levels. Table 8.A.5 | GWP and GTP for VOCs for time horizons of 20 and 100 years from the literature. Uncertainty for numbers from Fry et al. (2012) and Collins et al. (2013) refer to 1-s. For the reference gas CO2, RE and IRF from AR4 are used in the calculations. The GWP100 and GTP100 values can be scaled by 0.94 and 0.92, respectively, to account for updated values for the reference gas CO2. For 20 years the changes are negligible. GWP GTP H = 20 H = 100 H = 20 H = 100 VOC East Asiaa 16.3 (+/-6.4) 5.0 (+/-2.1) 8.4 (+/-4.6) 0.7 (+/-0.4) VOC EU + North Africa a 18.0 (+/-8.5) 5.6 (+/-2.8) 9.5 (+/-6.5) 0.8 (+/-0.5) VOC North Americaa 16.2 (+/-9.2) 5.0 (+/-3.0) 8.6 (+/-6.4) 0.7 (+/-0.5) VOC South Asiaa 27.8 (+/-5.6) 8.8 (+/-1.9) 15.7 (+/-5.0) 1.3 (+/-0.5) VOC four regions above 18.7 (+/-7.5) 5.8 (+/-2.5) 10.0 (+/-5.7) 0.9 (+/-0.5) VOC global b 14 4.5 7.5 0.66 Notes: Fry et al. (2012) (updated by including stratospheric H2O) and Collins et al. (2013). a b Fuglestvedt et al. (2010) based on Collins et al. (2002). The values are given on a per kilogram of C basis. Table 8.A.6 | GWP and GTP from the literature for BC and OC for time horizons of 20 and 100 years. For the reference gas CO2, RE and IRF from AR4 are used in the calculations. The GWP100 and GTP100 values can be scaled by 0.94 and 0.92, respectively, to account for updated values for the reference gas CO2. For 20 years the changes are negligible. GWP GTP H = 20 H = 100 H = 20 H = 100 BC total, globalc 3200 (270 to 6200) 900 (100 to 1700) 920 (95 to 2400) 130 (5 to 340) BC (four regions)d 1200 +/- 720 345 +/- 207 420 +/- 190 56 +/- 25 BC global a 1600 460 470 64 BC aerosol radiation interaction +albedo, globalb 2900 +/- 1500 830 +/- 440 OC globala 240 69 71 10 OC global b 160 ( 60 to 320) 46 ( 18 to 19) OC (4 regions)d 160 +/- 68 46 +/- 20 55 +/- 16 7.3+/-2.1 Notes: a Fuglestvedt et al. (2010). b Bond et al. (2011). Uncertainties for OC are asymmetric and are presented as ranges. c Bond et al. (2013). Metric values are given for total effect. d Collins et al. (2013). The four regions are East Asia, EU + North Africa, North America and South Asia (as also given in Fry et al., 2012). Only aerosol-radiation interaction is included. 740 Evaluation of Climate Models 9 Coordinating Lead Authors: Gregory Flato (Canada), Jochem Marotzke (Germany) Lead Authors: Babatunde Abiodun (South Africa), Pascale Braconnot (France), Sin Chan Chou (Brazil), William Collins (USA), Peter Cox (UK), Fatima Driouech (Morocco), Seita Emori (Japan), Veronika Eyring (Germany), Chris Forest (USA), Peter Gleckler (USA), Eric Guilyardi (France), Christian Jakob (Australia), Vladimir Kattsov (Russian Federation), Chris Reason (South Africa), Markku Rummukainen (Sweden) Contributing Authors: Krishna AchutaRao (India), Alessandro Anav (UK), Timothy Andrews (UK), Johanna Baehr (Germany), Nathaniel L. Bindoff (Australia), Alejandro Bodas-Salcedo (UK), Jennifer Catto (Australia), Don Chambers (USA), Ping Chang (USA), Aiguo Dai (USA), Clara Deser (USA), Francisco Doblas-Reyes (Spain), Paul J. Durack (USA/Australia), Michael Eby (Canada), Ramon de Elia (Canada), Thierry Fichefet (Belgium), Piers Forster (UK), David Frame (UK/New Zealand), John Fyfe (Canada), Emiola Gbobaniyi (Sweden/Nigeria), Nathan Gillett (Canada), Jesus Fidel González-Rouco (Spain), Clare Goodess (UK), Stephen Griffies (USA), Alex Hall (USA), Sandy Harrison (Australia), Andreas Hense (Germany), Elizabeth Hunke (USA), Tatiana Ilyina (Germany), Detelina Ivanova (USA), Gregory Johnson (USA), Masa Kageyama (France), Viatcheslav Kharin (Canada), Stephen A. Klein (USA), Jeff Knight (UK), Reto Knutti (Switzerland), Felix Landerer (USA), Tong Lee (USA), Hongmei Li (Germany/China), Natalie Mahowald (USA), Carl Mears (USA), Gerald Meehl (USA), Colin Morice (UK), Rym Msadek (USA), Gunnar Myhre (Norway), J. David Neelin (USA), Jeff Painter (USA), Tatiana Pavlova (Russian Federation), Judith Perlwitz (USA), Jean-Yves Peterschmitt (France), Jouni Räisänen (Finland), Florian Rauser (Germany), Jeffrey Reid (USA), Mark Rodwell (UK), Benjamin Santer (USA), Adam A. Scaife (UK), Jörg Schulz (Germany), John Scinocca (Canada), David Sexton (UK), Drew Shindell (USA), Hideo Shiogama (Japan), Jana Sillmann (Canada), Adrian Simmons (UK), Kenneth Sperber (USA), David Stephenson (UK), Bjorn Stevens (Germany), Peter Stott (UK), Rowan Sutton (UK), Peter W. Thorne (USA/Norway/UK), Geert Jan van Oldenborgh (Netherlands), Gabriel Vecchi (USA), Mark Webb (UK), Keith Williams (UK), Tim Woollings (UK), Shang-Ping Xie (USA), Jianglong Zhang (USA) Review Editors: Isaac Held (USA), Andy Pitman (Australia), Serge Planton (France), Zong-Ci Zhao (China) This chapter should be cited as: Flato, G., J. Marotzke, B. Abiodun, P. Braconnot, S.C. Chou, W. Collins, P. Cox, F. Driouech, S. Emori, V. Eyring, C. Forest, P. Gleckler, E. Guilyardi, C. Jakob, V. Kattsov, C. Reason and M. Rummukainen, 2013: Evaluation of Climate Models. In: Climate Change 2013: The Physical Science Basis. Contribution of Working Group I to the Fifth Assess- ment Report of the Intergovernmental Panel on Climate Change [Stocker, T.F., D. Qin, G.-K. Plattner, M. Tignor, S.K. Allen, J. Boschung, A. Nauels, Y. Xia, V. Bex and P.M. Midgley (eds.)]. Cambridge University Press, Cambridge, United Kingdom and New York, NY, USA. 741 Table of Contents Executive Summary...................................................................... 743 9.6 Downscaling and Simulation of Regional-Scale Climate................................................................................ 810 9.1 Climate Models and Their Characteristics................ 746 9.6.1 Global Models............................................................ 810 9.1.1 Scope and Overview of this Chapter.......................... 746 9.6.2 Regional Climate Downscaling.................................. 814 9.1.2 Overview of Model Types to Be Evaluated................. 746 9.6.3 Skill of Downscaling Methods.................................... 814 9.1.3 Model Improvements................................................. 748 9.6.4 Value Added through RCMs....................................... 815 Box 9.1: Climate Model Development and Tuning................. 749 9.6.5 Sources of Model Errors and Uncertainties................ 815 9.6.6 Relating Downscaling Performance to Credibility 9.2 Techniques for Assessing Model Performance........ 753 of Regional Climate Information................................ 816 9 9.2.1 New Developments in Model Evaluation Approaches................................................................ 753 9.7 Climate Sensitivity and Climate Feedbacks............. 817 9.2.2 Ensemble Approaches for Model Evaluation.............. 754 9.7.1 Equilibrium Climate Sensitivity, Idealized Radiative Forcing, and Transient Climate Response in the 9.2.3 The Model Evaluation Approach Used in this Coupled Model Intercomparison Project Chapter and Its Limitations........................................ 755 Phase 5 Ensemble...................................................... 817 9.7.2 Understanding the Range in Model Climate 9.3 Experimental Strategies in Support of Climate Sensitivity: Climate Feedbacks................................... 819 Model Evaluation............................................................. 759 9.7.3 Climate Sensitivity and Model Performance............... 820 9.3.1 The Role of Model Intercomparisons.......................... 759 9.3.2 Experimental Strategy for Coupled Model 9.8 Relating Model Performance to Credibility of Intercomparison Project Phase 5................................ 759 Model Applications.......................................................... 821 9.8.1 Synthesis Assessment of Model Performance............. 821 9.4 Simulation of Recent and Longer-Term Records in Global Models.............................................................. 760 9.8.2 Implications of Model Evaluation for Climate Change Detection and Attribution............................. 825 9.4.1 Atmosphere............................................................... 760 9.8.3 Implications of Model Evaluation for Model Box 9.2: Climate Models and the Hiatus in Global Mean Projections of Future Climate..................................... 825 Surface Warming of the Past 15 Years..................................... 769 9.4.2 Ocean......................................................................... 777 References .................................................................................. 828 9.4.3 Sea Ice....................................................................... 787 9.4.4 Land Surface, Fluxes and Hydrology........................... 790 Appendix 9.A: Climate Models Assessed in Chapter 9 .................................................................................. 854 9.4.5 Carbon Cycle.............................................................. 792 9.4.6 Aerosol Burdens and Effects on Insolation................. 794 Frequently Asked Questions FAQ 9.1 Are Climate Models Getting Better, and How 9.5 Simulation of Variability and Extremes..................... 795 Would We Know?.................................................... 824 9.5.1 Importance of Simulating Climate Variability............. 795 9.5.2 Diurnal-to-Seasonal Variability................................... 796 9.5.3 Interannual-to-Centennial Variability......................... 799 9.5.4 Extreme Events.......................................................... 806 Box 9.3: Understanding Model Performance.......................... 809 742 Evaluation of Climate Models Chapter 9 Executive Summary internal variability, with possible contributions from forcing error and some models overestimating the response to increasing greenhouse Climate models have continued to be developed and improved gas (GHG) forcing. Most, though not all, models overestimate the since the AR4, and many models have been extended into Earth observed warming trend in the tropical troposphere over the last 30 System models by including the representation of biogeochem- years, and tend to underestimate the long-term lower stratospheric ical cycles important to climate change. These models allow for cooling trend. {9.4.1, Box 9.2, Figure 9.8} policy-relevant calculations such as the carbon dioxide (CO2) emissions compatible with a specified climate stabilization target. In addition, the The simulation of large-scale patterns of precipitation has range of climate variables and processes that have been evaluated has improved somewhat since the AR4, although models continue greatly expanded, and differences between models and observations to perform less well for precipitation than for surface tempera- are increasingly quantified using performance metrics . In this chapter, ture. The spatial pattern correlation between modelled and observed model evaluation covers simulation of the mean climate, of historical annual mean precipitation has increased from 0.77 for models availa- climate change, of variability on multiple time scales and of regional ble at the time of the AR4 to 0.82 for current models. At regional scales, modes of variability. This evaluation is based on recent internationally precipitation is not simulated as well, and the assessment remains dif- coordinated model experiments, including simulations of historic and ficult owing to observational uncertainties. {9.4.1, 9.6.1, Figure 9.6} paleo climate, specialized experiments designed to provide insight into 9 key climate processes and feedbacks and regional climate downscal- The simulation of clouds in climate models remains challeng- ing. Figure 9.44 provides an overview of model capabilities as assessed ing. There is very high confidence that uncertainties in cloud processes in this chapter, including improvements, or lack thereof, relative to explain much of the spread in modelled climate sensitivity. However, models assessed in the AR4. The chapter concludes with an assessment the simulation of clouds in climate models has shown modest improve- of recent work connecting model performance to the detection and ment relative to models available at the time of the AR4, and this has attribution of climate change as well as to future projections. {9.1.2, been aided by new evaluation techniques and new observations for 9.8.1, Table 9.1, Figure 9.44} clouds. Nevertheless, biases in cloud simulation lead to regional errors on cloud radiative effect of several tens of watts per square meter. The ability of climate models to simulate surface temperature {9.2.1, 9.4.1, 9.7.2, Figures 9.5, 9.43} has improved in many, though not all, important aspects rel- ative to the generation of models assessed in the AR4. There Models are able to capture the general characteristics of storm continues to be very high confidence1 that models reproduce observed tracks and extratropical cyclones, and there is some evidence of large-scale mean surface temperature patterns (pattern correlation of improvement since the AR4. Storm track biases in the North Atlantic ~0.99), though systematic errors of several degrees are found in some have improved slightly, but models still produce a storm track that is regions, particularly over high topography, near the ice edge in the too zonal and underestimate cyclone intensity. {9.4.1} North Atlantic, and over regions of ocean upwelling near the equa- tor. On regional scales (sub-continental and smaller), the confidence Many models are able to reproduce the observed changes in in model capability to simulate surface temperature is less than for upper ocean heat content from 1961 to 2005 with the mul- the larger scales; however, regional biases are near zero on average, ti-model mean time series falling within the range of the avail- with intermodel spread of roughly +/-3°C. There is high confidence that able observational estimates for most of the period. The ability regional-scale surface temperature is better simulated than at the time of models to simulate ocean heat uptake, including variations imposed of the AR4. Current models are also able to reproduce the large-scale by large volcanic eruptions, adds confidence to their use in assessing patterns of temperature during the Last Glacial Maximum (LGM), indi- the global energy budget and simulating the thermal component of cating an ability to simulate a climate state much different from the sea level rise. {9.4.2, Figure 9.17} present. {9.4.1, 9.6.1, Figures 9.2, 9.6, 9.39, 9.40} The simulation of the tropical Pacific Ocean mean state has There is very high confidence that models reproduce the gener- improved since the AR4, with a 30% reduction in the spurious al features of the global-scale annual mean surface temperature westward extension of the cold tongue near the equator, a per- increase over the historical period, including the more rapid vasive bias of coupled models. The simulation of the tropical Atlan- warming in the second half of the 20th century, and the cooling tic remains deficient with many models unable to reproduce the basic immediately following large volcanic eruptions. Most simulations east west temperature gradient. {9.4.2, Figure 9.14} of the historical period do not reproduce the observed reduction in global mean surface warming trend over the last 10 to 15 years. There is medium confidence that the trend difference between models and observations during 1998 2012 is to a substantial degree caused by In this Report, the following summary terms are used to describe the available evidence: limited, medium, or robust; and for the degree of agreement: low, medium, or high. 1 A level of confidence is expressed using five qualifiers: very low, low, medium, high, and very high, and typeset in italics, e.g., medium confidence. For a given evidence and agreement statement, different confidence levels can be assigned, but increasing levels of evidence and degrees of agreement are correlated with increasing confidence (see Section 1.4 and Box TS.1 for more details). 743 Chapter 9 Evaluation of Climate Models Current climate models reproduce the seasonal cycle of Arctic observed sea surface temperatures, though so far only a few studies of sea ice extent with a multi-model mean error of less than about this kind are available. {9.5.4, Figure 9.37} 10% for any given month. There is robust evidence that the downward trend in Arctic summer sea ice extent is better sim- An important development since the AR4 is the more wide- ulated than at the time of the AR4, with about one quarter of spread use of Earth System models, which include an interac- the simulations showing a trend as strong as, or stronger, than tive carbon cycle. In the majority of these models, the simulated in observations over the satellite era (since 1979). There is a ten- global land and ocean carbon sinks over the latter part of the dency for models to slightly overestimate sea ice extent in the Arctic 20th century fall within the range of observational estimates. (by about 10%) in winter and spring. In the Antarctic, the multi-model However, the regional patterns of carbon uptake and release are less mean seasonal cycle agrees well with observations, but inter-model well reproduced, especially for NH land where models systematically spread is roughly double that for the Arctic. Most models simulate a underestimate the sink implied by atmospheric inversion techniques small decreasing trend in Antarctic sea ice extent, albeit with large The ability of models to simulate carbon fluxes is important because inter-model spread, in contrast to the small increasing trend in obser- these models are used to estimate compatible emissions (carbon vations. {9.4.3, Figures 9.22, 9.24} dioxide emission pathways compatible with a particular climate change target; see Chapter 6). {9.4.5, Figure 9.27} 9 Models are able to reproduce many features of the observed global and Northern Hemispher (NH) mean temperature vari- The majority of Earth System models now include an interac- ance on interannual to centennial time scales (high confidence), tive representation of aerosols, and make use of a consistent and most models are now able to reproduce the observed peak specification of anthropogenic sulphur dioxide emissions. How- in variability associated with the El Nino (2- to 7-year period) ever, uncertainties in sulphur cycle processes and natural sources and in the Tropical Pacific. The ability to assess variability from millennial sinks remain and so, for example, the simulated aerosol optical depth simulations is new since the AR4 and allows quantitative evaluation of over oceans ranges from 0.08 to 0.22 with roughly equal numbers of model estimates of low-frequency climate variability. This is important models over- and under-estimating the satellite-estimated value of when using climate models to separate signal and noise in detection 0.12. {9.1.2, 9.4.6, Table 9.1, Figure 9.29} and attribution studies (Chapter 10). {9.5.3, Figures 9.33, 9.35} Time-varying ozone is now included in the latest suite of models, Many important modes of climate variability and intraseasonal either prescribed or calculated interactively. Although in some to seasonal phenomena are reproduced by models, with some models there is only medium agreement with observed changes in improvements evident since the AR4. The statistics of the global total column ozone, the inclusion of time-varying stratospheric ozone monsoon, the North Atlantic Oscillation, the El Nino-Southern Oscilla- constitutes a substantial improvement since the AR4 where half of the tion (ENSO), the Indian Ocean Dipole and the Quasi-Biennial Oscilla- models prescribed a constant climatology. As a result, there is robust tion are simulated well by several models, although this assessment is evidence that the representation of climate forcing by stratospheric tempered by the limited scope of analysis published so far, or by limited ozone has improved since the AR4. {9.4.1, Figure 9.10} observations. There are also modes of variability that are not simulated well. These include modes of Atlantic Ocean variability of relevance Regional downscaling methods are used to provide climate to near term projections in Chapter 11 and ENSO teleconnections information at the smaller scales needed for many climate outside the tropical Pacific, of relevance to Chapter 14. There is high impact studies, and there is high confidence that downscaling confidence that the multi-model statistics of monsoon and ENSO have adds value both in regions with highly variable topography and improved since the AR4. However, this improvement does not occur in for various small-scale phenomena. Regional models necessar- all models, and process-based analysis shows that biases remain in the ily inherit biases from the global models used to provide boundary background state and in the strength of associated feedbacks. {9.5.3, conditions. Furthermore, the ability to systematically evaluate region- Figures 9.32, 9.35, 9.36} al climate models, and statistical downscaling schemes, is hampered because coordinated intercomparison studies are still emerging. How- There has been substantial progress since the AR4 in the assess- ever, several studies have demonstrated that added value arises from ment of model simulations of extreme events. Based on assess- higher resolution of stationary features like topography and coastlines, ment of a suite of indices, the inter-model range of simulated climate and from improved representation of small-scale processes like con- extremes is similar to the spread amongst observationally based esti- vective precipitation. {9.6.4} mates in most regions. In addition, changes in the frequency of extreme warm and cold days and nights over the second half of the 20th centu- Earth system Models of Intermediate Complexity (EMICs) pro- ry are consistent between models and observations, with the ensemble vide simulations of millennial time-scale climate change, and are global mean time series generally falling within the range of observa- used as tools to interpret and expand upon the results of more tional estimates. The majority of models underestimate the sensitivity comprehensive models. Although they are limited in the scope and of extreme precipitation to temperature variability or trends, especially resolution of information provided, EMIC simulations of global mean in the tropics, which implies that models may underestimate the pro- surface temperature, ocean heat content and carbon cycle response jected increase in extreme precipitation in the future. Some high-res- over the 20th century are consistent with the historical records and olution atmospheric models have been shown to reproduce observed with more comprehensive models, suggesting that they can be used to year-to-year variability of Atlantic hurricane counts when forced with provide calibrated projections of long-term transient climate response 744 Evaluation of Climate Models Chapter 9 and stabilization, as well as large ensembles and alternative, policy-rel- evant, scenarios. {9.4.1, 9.4.2, 9.4.5, Figures 9.8, 9.17, 9.27} The Coupled Model Intercomparison Project Phase 5 (CMIP5) model spread in equilibrium climate sensitivity ranges from 2.1°C to 4.7°C and is very similar to the assessment in the AR4. No correlation is found between biases in global mean surface tem- perature and equilibrium climate sensitivity, and so mean temperature biases do not obviously affect the modelled response to GHG forcing. There is very high confidence that the primary factor contributing to the spread in equilibrium climate sensitivity continues to be the cloud feedback. This applies to both the modern climate and the LGM. There is likewise very high confidence that, consistent with observations, models show a strong positive correlation between tropospheric tem- perature and water vapour on regional to global scales, implying a pos- itive water vapour feedback in both models and observations. {9.4.1, 9 9.7.2, Figures 9.9, 9.42, 9.43} Climate and Earth System models are based on physical princi- ples, and they reproduce many important aspects of observed climate. Both aspects contribute to our confidence in the models suitability for their application in detection and attri- bution studies (Chapter 10) and for quantitative future predic- tions and projections (Chapters 11 to 14). In general, there is no direct means of translating quantitative measures of past performance into confident statements about fidelity of future climate projections. However, there is increasing evidence that some aspects of observed variability or trends are well correlated with inter-model differences in model projections for quantities such as Arctic summertime sea ice trends, snow albedo feedback, and the carbon loss from tropical land. These relationships provide a way, in principle, to transform an observable quantity into a constraint on future projections, but the application of such constraints remains an area of emerging research. There has been substantial progress since the AR4 in the methodol- ogy to assess the reliability of a multi-model ensemble, and various approaches to improve the precision of multi-model projections are being explored. However, there is still no universal strategy for weight- ing the projections from different models based on their historical per- formance. {9.8.3, Figure 9.45} 745 Chapter 9 Evaluation of Climate Models 9.1 Climate Models and Their Characteristics 2006d). Applications include simulating palaeo or historical climate, sensitivity and process studies for attribution and physical understand- 9.1.1 Scope and Overview of this Chapter ing, predicting near-term climate variability and change on seasonal to decadal time scales, making projections of future climate change Climate models are the primary tools available for investigating the over the coming century or more and downscaling such projections response of the climate system to various forcings, for making climate to provide more detail at the regional and local scale. Computational predictions on seasonal to decadal time scales and for making projec- cost is a factor in all of these, and so simplified models (with reduced tions of future climate over the coming century and beyond. It is crucial complexity or spatial resolution) can be used when larger ensembles therefore to evaluate the performance of these models, both individu- or longer integrations are required. Examples include exploration of ally and collectively. The focus of this chapter is primarily on the models parameter sensitivity or simulations of climate change on the millenni- whose results will be used in the detection and attribution Chapter 10 al or longer time scale. Here, we provide a brief overview of the climate and the chapters that present and assess projections (Chapters 11 to models evaluated in this chapter. 14; Annex I), and so this is necessarily an incomplete evaluation. In particular, this chapter draws heavily on model results collected as part 9.1.2.1 Atmosphere Ocean General Circulation Models of the Coupled Model Intercomparison Projects (CMIP3 and CMIP5) 9 (Meehl et al., 2007; Taylor et al., 2012b), as these constitute a set of Atmosphere Ocean General Circulation Models (AOGCMs) were the coordinated and thus consistent and increasingly well-documented cli- standard climate models assessed in the AR4. Their primary function mate model experiments. Other intercomparison efforts, such as those is to understand the dynamics of the physical components of the cli- dealing with Regional Climate Models (RCMs) and those dealing with mate system (atmosphere, ocean, land and sea ice), and for making Earth System Models of Intermediate Complexity (EMICs) are also used. projections based on future greenhouse gas (GHG) and aerosol forcing. It should be noted that the CMIP3 model archive has been extensively These models continue to be extensively used, and in particular are run evaluated, and much of that evaluation has taken place subsequent to (sometimes at higher resolution) for seasonal to decadal climate pre- the AR4. By comparison, the CMIP5 models are only now being evalu- diction applications in which biogeochemical feedbacks are not critical ated and so there is less published literature available. Where possible (see Chapter 11). In addition, high-resolution or variable-resolution we show results from both CMIP3 and CMIP5 models so as to illustrate AOGCMs are often used in process studies or applications with a focus changes in model performance over time; however, where only CMIP3 on a particular region. An overview of the AOGCMs assessed in this results are available, they still constitute a useful evaluation of model chapter can be found in Table 9.1 and the details in Table 9.A.1. For performance in that for many quantities, the CMIP3 and CMIP5 model some specific applications, an atmospheric component of such a model performances are broadly similar. is used on its own. The direct approach to model evaluation is to compare model output 9.1.2.2 Earth System Models with observations and analyze the resulting difference. This requires knowledge of the errors and uncertainties in the observations, which ESMs are the current state-of-the-art models, and they expand on have been discussed in Chapters 2 through 6. Where possible, aver- AOGCMs to include representation of various biogeochemical cycles ages over the same time period in both models and observations are such as those involved in the carbon cycle, the sulphur cycle, or ozone compared, although for many quantities the observational record is (Flato, 2011). These models provide the most comprehensive tools rather short, or only observationally based estimates of the climato- available for simulating past and future response of the climate system logical mean are available. In cases where observations are lacking, to external forcing, in which biogeochemical feedbacks play an impor- we resort to intercomparison of model results to provide at least some tant role. An overview of the ESMs assessed in this chapter can be quantification of model uncertainty via inter-model spread. found in Table 9.1 and details in Table 9.A.1. After a more thorough discussion of the climate models and meth- 9.1.2.3 Earth System Models of Intermediate Complexity ods for evaluation in Sections 9.1 and 9.2, we describe climate model experiments in Section 9.3, evaluate recent and longer-term records as EMICs attempt to include relevant components of the Earth system, simulated by climate models in Section 9.4, variability and extremes but often in an idealized manner or at lower resolution than the in Section 9.5, and regional-scale climate simulation including down- models described above. These models are applied to certain scientific scaling in Section 9.6. We conclude with a discussion of model perfor- questions such as understanding climate feedbacks on millennial time mance and climate sensitivity in Section 9.7, and the relation between scales or exploring sensitivities in which long model integrations or model performance and the credibility of future climate projections in large ensembles are required (Claussen et al., 2002; Petoukhov et al., Section 9.8. 2005). This class of models often includes Earth system components not yet included in all ESMs (e.g., ice sheets). As computing power 9.1.2 Overview of Model Types to Be Evaluated increases, this model class has continued to advance in terms of reso- lution and complexity. An overview of EMICs assessed in this chapter The models used in climate research range from simple energy balance and in the AR5 WG1 is provided in Table 9.2 with additional details in models to complex Earth System Models (ESMs) requiring state of the Table 9.A.2. art high-performance computing. The choice of model depends directly on the scientific question being addressed (Held, 2005; Collins et al., 746 Evaluation of Climate Models Chapter 9 Table 9.1 | Main features of the Atmosphere Ocean General Circulation Models (AOGCMs) and Earth System Models (ESMs) participating in Coupled Model Intercomparison Project Phase 5 (CMIP5), and a comparison with Coupled Model Intercomparison Project Phase 3 (CMIP3), including components and resolution of the atmosphere and the ocean models. Detailed CMIP5 model description can be found in Table 9.A.1 (* refers to Table 9.A.1 for more details). Official CMIP model names are used. HT stands for High-Top atmosphere, which has a fully resolved stratosphere with a model top above the stratopause. AMIP stands for models with atmosphere and land surface only, using observed sea surface temperature and sea ice extent. A component is coloured when it includes at least a physically based prognostic equation and at least a two-way coupling with another component, allowing climate feedbacks. For aerosols, lighter shading means semi-interactive and darker shading means fully interactive . The resolution of the land surface usually follows that of the atmosphere, and the resolution of the sea ice follows that of the ocean. In moving from CMIP3 to CMIP5, note the increased complexity and resolution as well as the absence of artificial flux correction (FC) used in some CMIP3 models. 9 747 Chapter 9 Evaluation of Climate Models Table 9.2 | Main features of the EMICs assessed in the AR5, including components and complexity of the models. Model complexity for four components is indicated by colour shading. Further detailed descriptions of the models are contained in Table 9.A.2. 9 Significant advances in EMIC capabilities are inclusion of ice sheets The overall approach to model development and tuning is summarized (UVic 2.9, Weaver et al., 2001; CLIMBER-2.4, Petoukhov et al., 2000; in Box 9.1. LOVECLIM, Goosse et al., 2010) and ocean sediment models (DCESS, Shaffer et al., 2008; UVic 2.9, Weaver et al., 2001; Bern3D-LPJ, Ritz 9.1.3.1 Parameterizations et al., 2011). These additional interactive components provide criti- cal feedbacks involved in sea level rise estimates and carbon cycle Parameterizations are included in all model components to represent response on millennial time scales (Zickfeld et al., 2013). Further, the processes that cannot be explicitly resolved; they are evaluated both flexibility and efficiency of EMICs allow calibration to specific climate in isolation and in the context of the full model. The purpose of this change events to remove potential biases. section is to highlight recent developments in the parameterizations employed in each model component. Some details for individual 9.1.2.4 Regional Climate Models models are listed in Table 9.1. RCMs are limited-area models with representations of climate process- 9.1.3.1.1 Atmosphere es comparable to those in the atmospheric and land surface compo- nents of AOGCMs, though typically run without interactive ocean and Atmospheric models must parameterize a wide range of processes, sea ice. RCMs are often used to dynamically downscale global model including those associated with atmospheric convection and clouds, simulations for some particular geographical region to provide more cloud-microphysical and aerosol processes and their interaction, detailed information (Laprise, 2008; Rummukainen, 2010). By contrast, boundary layer processes, as well as radiation and the treatment of empirical and statistical downscaling methods constitute a range of unresolved gravity waves. Advances made in the representation of techniques to provide similar regional or local detail. cloud processes, including aerosol cloud and cloud radiation interac- tions, and atmospheric convection are described in Sections 7.2.3 and 9.1.3 Model Improvements 7.4. The climate models assessed in this report have seen a number of Improvements in representing the atmospheric boundary layer since improvements since the AR4. Model development is a complex and the AR4 have focussed on basic boundary layer processes, the rep- iterative task: improved physical process descriptions are developed, resentation of the stable boundary layer, and boundary layer clouds new model components are introduced and the resolution of the (Teixeira et al., 2008). Several global models have successfully adopt- models is improved. After assembly of all model components, model ed new approaches to the parameterization of shallow cumulus con- parameters are adjusted, or tuned, to provide a stable model climate. vection and moist boundary layer turbulence that acknowledge their 748 Evaluation of Climate Models Chapter 9 Box 9.1 | Climate Model Development and Tuning The Atmosphere Ocean General Circulation Models, Earth System Models and Regional Climate Models evaluated here are based on fundamental laws of nature (e.g., energy, mass and momentum conservation). The development of climate models involves several principal steps: 1. Expressing the system s physical laws in mathematical terms. This requires theoretical and observational work in deriving and sim- plifying mathematical expressions that best describe the system. 2. Implementing these mathematical expressions on a computer. This requires developing numerical methods that allow the solution of the discretized mathematical expressions, usually implemented on some form of grid such as the latitude longitude height grid for atmospheric or oceanic models. 3. Building and implementing conceptual models (usually referred to as parameterizations) for those processes that cannot be rep- 9 resented explicitly, either because of their complexity (e.g., biochemical processes in vegetation) or because the spatial and/or temporal scales on which they occur are not resolved by the discretized model equations (e.g., cloud processes and turbulence). The development of parameterizations has become very complex (e.g., Jakob, 2010) and is often achieved by developing conceptual models of the process of interest in isolation using observations and comprehensive process models. The complexity of each process representation is constrained by observations, computational resources and current knowledge (e.g., Randall et al., 2007). The application of state-of-the-art climate models requires significant supercomputing resources. Limitations in those resources lead to additional constraints. Even when using the most powerful computers, compromises need to be made in three main areas: 1. Numerical implementations allow for a choice of grid spacing and time step, usually referred to as model resolution . Higher model resolution generally leads to mathematically more accurate models (although not necessarily more reliable simulations) but also to higher computational costs. The finite resolution of climate models implies that the effects of certain processes must be represented through parameterizations (e.g., the carbon cycle or cloud and precipitation processes; see Chapters 6 and 7). 2. The climate system contains many processes, the relative importance of which varies with the time scale of interest (e.g., the carbon cycle). Hence compromises to include or exclude certain processes or components in a model must be made, recognizing that an increase in complexity generally leads to an increase in computational cost (Hurrell et al., 2009). 3. Owing to uncertainties in the model formulation and the initial state, any individual simulation represents only one of the possible pathways the climate system might follow. To allow some evaluation of these uncertainties, it is necessary to carry out a number of simulations either with several models or by using an ensemble of simulations with a single model, both of which increase compu- tational cost. Trade-offs amongst the various considerations outlined above are guided by the intended model application and lead to the several classes of models introduced in Section 9.1.2. Individual model components (e.g., the atmosphere, the ocean, etc.) are typically first evaluated in isolation as part of the model devel- opment process. For instance, the atmospheric component can be evaluated by prescribing sea surface temperature (SST) (Gates et al., 1999) or the ocean and land components by prescribing atmospheric conditions (Barnier et al., 2006; Griffies et al., 2009). Subsequently, the various components are assembled into a comprehensive model, which then undergoes a systematic evaluation. At this stage, a small subset of model parameters remains to be adjusted so that the model adheres to large-scale observational constraints (often global aver- ages). This final parameter adjustment procedure is usually referred to as model tuning . Model tuning aims to match observed climate system behaviour and so is connected to judgements as to what constitutes a skilful representation of the Earth s climate. For instance, maintaining the global mean top of the atmosphere (TOA) energy balance in a simulation of pre-industrial climate is essential to prevent the climate system from drifting to an unrealistic state. The models used in this report almost universally contain adjustments to param- eters in their treatment of clouds to fulfil this important constraint of the climate system (Watanabe et al., 2010; Donner et al., 2011; Gent et al., 2011; Golaz et al., 2011; Martin et al., 2011; Hazeleger et al., 2012; Mauritsen et al., 2012; Hourdin et al., 2013). With very few exceptions (Mauritsen et al., 2012; Hourdin et al., 2013) modelling centres do not routinely describe in detail how they tune their models. Therefore the complete list of observational constraints toward which a particular model is tuned is generally not (continued on next page) 749 Chapter 9 Evaluation of Climate Models Box 9.1 (continued) available. However, it is clear that tuning involves trade-offs; this keeps the number of constraints that can be used small and usually focuses on global mean measures related to budgets of energy, mass and momentum. It has been shown for at least one model that the tuning process does not necessarily lead to a single, unique set of parameters for a given model, but that different combinations of parameters can yield equally plausible models (Mauritsen et al., 2012). Hence the need for model tuning may increase model uncer- tainty. There have been recent efforts to develop systematic parameter optimization methods, but owing to model complexity they cannot yet be applied to fully coupled climate models (Neelin et al., 2010). Model tuning directly influences the evaluation of climate models, as the quantities that are tuned cannot be used in model evalua- tion. Quantities closely related to those tuned will provide only weak tests of model performance. Nonetheless, by focusing on those quantities not generally involved in model tuning while discounting metrics clearly related to it, it is possible to gain insight into model performance. Model quality is tested most rigorously through the concurrent use of many model quantities, evaluation techniques, and performance metrics that together cover a wide range of emergent (or un-tuned) model behaviour. 9 The requirement for model tuning raises the question of whether climate models are reliable for future climate projections. Models are not tuned to match a particular future; they are tuned to reproduce a small subset of global mean observationally based constraints. What emerges is that the models that plausibly reproduce the past, universally display significant warming under increasing green- house gas concentrations, consistent with our physical understanding. close mutual coupling. One new development is the Eddy-Diffusivi- 2007; Danabasoglu et al., 2008; Ferrari et al., 2008, 2010). Another ty-Mass-Flux (EDMF) approach (Siebesma et al., 2007; Rio and Hour- focus concerns eddy diffusivity, with many CMIP5 models employing din, 2008; Neggers, 2009; Neggers et al., 2009; Rio et al., 2010). The flow-dependent schemes. Both of these refinements are important for EDMF approach, like the shallow cumulus scheme of Park and Breth- the mean state and the response to changing forcing, especially in erton (2009), determines the cumulus-base mass flux from the statis- the Southern Ocean (Hallberg and Gnanadesikan, 2006; Boning et al., tical distribution of boundary layer updraft properties, a conceptual 2008; Farneti et al., 2010; Farneti and Gent, 2011; Gent and Danabaso- advance over the ad hoc closure assumptions used in the past. Realistic glu, 2011; Hofmann and Morales Maqueda, 2011). treatment of the stable boundary layer remains difficult (Beare et al., 2006; Cuxart et al., 2006; Svensson and Holtslag, 2009) with implica- In addition to mesoscale eddies, there has been a growing awareness tions for modelling of the diurnal cycle of temperature even under clear of the role that sub-mesoscale eddies and fronts play in restratifying skies (Svensson et al., 2011). the mixed layer (Boccaletti et al., 2007; Fox-Kemper et al., 2008; Klein and Lapeyre, 2009), and the parameterization of Fox-Kemper et al. Parameterizations of unresolved orographic and non-orographic gravi- (2011) is now used in some CMIP5 models. ty-wave drag (GWD) have seen only a few changes since the AR4 (e.g., Richter et al., 2010; Geller et al., 2011). In addition to new formula- There is an active research effort on the representation of dianeutral tions, the estimation of the parameters used in the GWD schemes has mixing associated with breaking gravity waves (MacKinnon et al., recently been advanced through the availability of satellite and ground- 2009), with this work adding rigour to the prototype energetically con- based observations of gravity-wave momentum fluxes, high-resolution sistent abyssal tidal mixing parameterization of Simmons et al. (2004) numerical modelling, and focussed process studies (Alexander et al., now used in several climate models (e.g., Jayne, 2009; Danabasoglu et 2010). Evidence from the Numerical Weather Prediction community al., 2012). The transport of dense water down-slope by gravity currents that important terrain-generated features of the atmospheric circu- (e.g., Legg et al., 2008, 2009) has also been the subject of focussed lation are better represented at higher model resolution has recently efforts, with associated parameterizations making their way into some been confirmed (Watanabe et al., 2008; Jung et al., 2012). CMIP5 models (Jackson et al., 2008b; Legg et al., 2009; Danabasoglu et al., 2010). 9.1.3.1.2 Ocean 9.1.3.1.3 Land Ocean components in contemporary climate models generally have horizontal resolutions that are too coarse to admit mesoscale eddies. Land surface properties such as vegetation, soil type and the amount Consequently, such models typically employ some version of the Redi of water stored on the land as soil moisture, snow and groundwa- (Redi, 1982) neutral diffusion and Gent and McWilliams (Gent and ter all strongly influence climate, particularly through their effects on McWilliams, 1990) eddy advection parameterization (see also Gent surface albedo and evapotranspiration. These climatic effects can be et al., 1995; McDougall and McIntosh, 2001). Since the AR4, a focus profound; for example, it has been suggested that changes in the state has been on how parameterized mesoscale eddy fluxes in the ocean of the land surface may have played an important part in the severity interior interact with boundary layer turbulence (Gnanadesikan et al., and length of the 2003 European drought (Fischer et al., 2007), and 750 Evaluation of Climate Models Chapter 9 that more than 60% of the projected increase in interannual summer the inclusion of chemistry and biogeochemistry (Piot and von Glasow, temperature variability in Europe is due to soil moisture temperature 2008; Zhao et al., 2008; Vancoppenolle et al., 2010; Hunke et al., 2011), feedbacks (Seneviratne et al., 2006). with dependencies on the ice microstructure and salinity profile. Land surface schemes calculate the fluxes of heat, water and momen- Melt ponds can drain through interconnected brine channels when tum between the land and the atmosphere. At the time of the AR4, the ice becomes warm and permeable. This flushing can effectively even the more advanced land surface schemes suffered from obvious clean the ice of salt, nutrients, and other inclusions, which affect the simplifications, such as the need to prescribe rather than simulate the albedo, conductivity and biogeochemical processes and thereby play a vegetation cover and a tendency to ignore lateral flows of water and role in climate change. Advanced parameterizations for melt ponds are sub-gridscale heterogeneity in soil moisture (Pitman, 2003). Since the making their way into sea ice components of global climate models AR4, a number of climate models have included some representation (e.g., Flocco et al., 2012; Hunke et al., 2013). of vegetation dynamics (see Sections 9.1.3.2.5 and 9.4.4.3), land atmosphere CO2 exchange (see Section 9.4.5), sub-gridscale hydrology 9.1.3.2 New Components and Couplings: Emergence of (Oleson et al., 2008b) and changes in land use (see Section 9.4.4.4). Earth System Modelling 9.1.3.1.4 Sea ice 9.1.3.2.1 Carbon cycle 9 Most large-scale sea ice processes, such as basic thermodynamics and The omission of internally consistent feedbacks among the physical, dynamics, are well understood and well represented in models (Hunke chemical and biogeochemical processes in the Earth s climate system et al., 2010). However, important details of sea ice dynamics and defor- is a limitation of AOGCMs. The conceptual issue is that the physical mation are not captured, especially at small scales (Coon et al., 2007; climate influences natural sources and sinks of CO2 and methane (CH4), Girard et al., 2009; Hutchings et al., 2011). Currently, sea ice model the two most important long-lived GHGs. ESMs incorporate many of development is focussed mainly on (1) more precise descriptions of the important biogeochemical processes, making it possible to sim- physical processes such as microstructure evolution and anisotropy ulate the evolution of these radiatively active species based on their and (2) including biological and chemical species. Many models now emissions from natural and anthropogenic sources together with their include some representation of sub-grid-scale thickness variations, interactions with the rest of the Earth system. Alternatively, when along with a description of mechanical redistribution that converts forced with specified concentrations, a model can be used to diagnose thinner ice to thicker ice under deformation (Hunke et al., 2010). these sources with feedbacks included (Hibbard et al., 2007). Given the large natural sources and sinks of CO2 relative to anthropogenic emis- Sea ice albedo has long been recognized as a critical aspect of the sions, and given the primacy of CO2 among anthropogenic GHGs, some global heat balance. The average ice surface albedo on the scale of a of the most important enhancements are the addition of terrestrial climate model grid cell is (as on land) the result of a mixture of surface and oceanic carbon cycles. These cycles have been incorporated into types: bare ice, melting ice, snow-covered ice, open water, etc. Many many models (Christian et al., 2010; Tjiputra et al., 2010) used to make sea ice models use a relatively simple albedo parameterization that projections of climate change (Schurgers et al., 2008; Jungclaus et al., specifies four albedo values: cold snow; warm, melting snow; cold, 2010). Several ESMs now include coupled carbon and nitrogen cycles bare ice; and warm, melting ice, and the specific values may be subject (Thornton et al., 2007; Gerber et al., 2010; Zaehle and Dalmonech, to tuning (e.g., Losch et al., 2010). Some parameterizations take into 2011) in order to simulate the interactions of nitrogen compounds with account the ice and snow thickness, spectral band and surface melt ecosystem productivity, GHGs including nitrous oxide (N2O) and ozone (e.g., Pedersen et al., 2009; Vancoppenolle et al., 2009). Solar radiation (O3), and global carbon sequestration (Zaehle and Dalmonech, 2011). may be distributed within the ice column assuming exponential decay or via a more complex multiple-scattering radiative transfer scheme Oceanic uptake of CO2 is highly variable in space and time, and is deter- (Briegleb and Light, 2007). mined by the interplay between the biogeochemical and physical pro- cesses in the ocean. About half of CMIP5 models make use of schemes Snow model development for sea ice has lagged behind terrestrial that partition marine ecosystems into nutrients, plankton, zooplankton snow models. Lecomte et al. (2011) introduced vertically varying snow and detritus (hence called NPZD-type models) while others use a more temperature, density and conductivity to improve vertical heat con- simplified representation of ocean biogeochemistry (see Table 9.A.1). duction and melting in a 1D model intended for climate simulation, These NPZD-type models allow simulation of some of the important but many physical processes affecting the evolution of the snow pack, feedbacks between climate and oceanic CO2 uptake, but are limited by such as redistribution by wind, moisture transport (including flooding the lack of marine ecosystem dynamics. Some efforts have been made and snow ice formation) and snow grain size evolution, still are not to include more plankton groups or plankton functional types in the included in most climate models. models (Le Quere et al., 2005) with as-yet uncertain implications for Earth system response. Salinity affects the thermodynamic properties of sea ice, and is used in the calculation of fresh water and salt exchanges at the ice ocean Ocean acidification and the associated decrease in calcification in interface (Hunke et al., 2011). Some models allow the salinity to vary many marine organisms provides a negative feedback on atmospheric in time (Schramm et al., 1997), while others assume a salinity profile CO2 increase (Ridgwell et al., 2007a). New-generation models there- that is constant (e.g., Bitz and Lipscomb, 1999). Another new thrust is fore include various parameterizations of calcium carbonate (CaCO3) 751 Chapter 9 Evaluation of Climate Models production as a function of the saturation state of seawater with ecosystems and ecological feedbacks on further climate change. The respect to calcite (Gehlen et al., 2007; Ridgwell et al., 2007a; Ilyina et incorporation of DGVMs has required considerable improvement in the al., 2009) or partial pressure CO2 (pCO2; Heinze, 2004). On centennial physics of coupled models to produce stable and realistic distributions of to multi-millennial scales, deep-sea carbonate sediments neutralize flora (Oleson et al., 2008b). The improvements include better treatments atmospheric CO2. Some CMIP5 models include the sediment carbon of surface, subsurface and soil hydrological processes; the exchange of reservoir, and progress has been made toward refined sediment rep- water with the atmosphere; and the discharge of water into rivers and resentation in the models (Heinze et al., 2009). streams. Whereas the first DGVMs have been coupled primarily to the carbon cycle, the current generation of DGVMs are being extended 9.1.3.2.2 Aerosol particles to include ecological sources and sinks of other non-CO2 trace gases including CH4, N2O, biogenic volatile organic compounds (BVOCs) and The treatment of aerosol particles has advanced since the AR4. Many nitrogen oxides collectively known as NOx (Arneth et al., 2010). BVOCs AOGCMs and ESMs now include the basic features of the sulphur and NOx can alter the lifetime of some GHGs and act as precursors cycle and so represent both the direct effect of sulphate aerosol, along for secondary organic aerosols (SOAs) and ozone. Disturbance of the with some of the more complex indirect effects involving cloud drop- natural landscape by fire has significant climatic effects through its let number and size. Further, several AOGCMs and ESMs are currently impact on vegetation and through its emissions of GHGs, aerosols and 9 capable of simulating the mass, number, size distribution and mixing aerosol precursors. Because the frequency of wildland fires increases state of interacting multi-component aerosol particles (Bauer et al., rapidly with increases in ambient temperature (Westerling et al., 2006), 2008b; Liu et al., 2012b). The incorporation of more physically com- the effects of fires are projected to grow over the 21st century (Kloster plete representations of aerosol often improves the simulated climate et al., 2012). The interactions of fires with the rest of the climate system under historical and present-day conditions, including the mean pat- are now being introduced into ESMs (Arora and Boer, 2005; Pechony tern and interannual variability in continental rainfall (Rotstayn et al., and Shindell, 2009; Shevliakova et al., 2009). 2010, 2011). However, despite the addition of aerosol cloud interac- tions to many AOGCMs and ESMs since the AR4, the representation 9.1.3.2.5 Land use/land cover change of aerosol particles and their interaction with clouds and radiative transfer remains an important source of uncertainty (see Sections 7.3.5 The impacts of land use and land cover change on the environment and 7.4). Additional aerosol-related topics that have received attention and climate are explicitly included as part of the Representative Con- include the connection between dust aerosol and ocean biogeochemis- centration Pathways (RCPs; cf. Chapters  1 and  12) used for climate try, the production of oceanic dimethylsulphide (DMS, a natural source projections to be assessed in later chapters (Moss et al., 2010). Several of sulphate aerosol), and vegetation interactions with organic atmos- important types of land use and land cover change include effects of pheric chemistry (Collins et al., 2011). agriculture and changing agricultural practices, including the poten- tial for widespread introduction of biofuel crops; the management of 9.1.3.2.3 Methane cycle and permafrost forests for preservation, wood harvest and production of woody bio- fuel stock; and the global trends toward greater urbanization. ESMs In addition to CO2, an increasing number of ESMs and EMICs are also include increasingly detailed treatments of crops and their interaction incorporating components of the CH4 cycle, for example, atmospheric with the landscape (Arora and Boer, 2010; Smith et al., 2010a, 2010b), CH4 chemistry and wetland emissions, to quantify some of the feed- forest management (Bellassen et al., 2010, 2011) and the interactions backs from changes in CH4 sources and sinks under a warming climate between urban areas and the surrounding climate systems (Oleson et (Stocker et al., 2012). Some models now simulate the evolution of the al., 2008a). permafrost carbon stock (Khvorostyanov et al., 2008a, 2008b), and in some cases this is integrated with the representation of terrestrial and 9.1.3.2.6 Chemistry climate interactions and stratosphere oceanic CH4 cycles (Volodin, 2008b; Volodin et al., 2010). troposphere coupling 9.1.3.2.4 Dynamic global vegetation models and wildland fires Important chemistry climate interactions such as the impact of the ozone hole and recovery on Southern Hemisphere (SH) climate or the One of the potentially more significant effects of climate change is the radiative effects of stratospheric water vapour changes on surface alteration of the distribution, speciation and life cycle of vegetated temperature have been confirmed in multiple studies (SPARC-CCMVal, ecosystems (Bergengren et al., 2001, 2011). Vegetation has a signifi- 2010; WMO, 2011). In the majority of the CMIP5 simulations strato- cant influence on the surface energy balance, exchanges of non-CO2 spheric ozone is prescribed. The main advance since the AR4 is that GHGs and the terrestrial carbon sink. Systematic shifts in vegetation, for time-varying rather than constant stratospheric ozone is now generally example, northward migration of boreal forests, would therefore impose used. In addition, several CMIP5 models treat stratospheric chemistry biogeophysical feedbacks on the physical climate system (Clark et al., interactively, thus prognostically calculating stratospheric ozone and 2011). In order to include these effects in projections of climate change, other chemical constituents. Important chemistry climate interactions several dynamic global vegetation models (DGVMs) have been devel- such as an increased influx of stratospheric ozone in a warmer climate oped and deployed in ESMs (Cramer et al., 2001; Sitch et al., 2008; Ostle that results in higher ozone burdens in the troposphere have also been et al., 2009). Although agriculture and managed forests are not yet gen- identified (Young et al., 2013). Ten of the CMIP5 models simulate trop- erally incorporated, DGVMs can simulate the interactions among natu- ospheric chemistry interactively whereas it is prescribed in the remain- ral and anthropogenic drivers of global warming, the state of terrestrial ­ ing models (see Table 9.1 and Eyring et al. (2013)). 752 Evaluation of Climate Models Chapter 9 It is now widely accepted that in addition to the influence of trop- s ­ubstantially due to the trade-off against higher complexity in such ospheric circulation and climate change on the stratosphere, strato- models. Since the AR4, typical regional climate model resolution has spheric dynamics can in turn influence the tropospheric circulation and increased from around 50 km to around 25 km (see Section 9.6.2.2), its variability (SPARC-CCMVal, 2010; WMO, 2011). As a result, many and the impact of this has been explored with multi-decadal regional climate models now have the ability to include a fully resolved strato- simulations (e.g., Christensen et al., 2010). In some cases, RCMs are sphere with a model top above the stratopause, located at around 50 being run at 10 km resolution or higher (e.g., Kanada et al., 2008; km. The subset of CMIP5 models with high-top configurations is com- Kusaka et al., 2010; van Roosmalen et al., 2010; Kendon et al., 2012). pared to the set of low-top models with a model top below the strat- opause in several studies (Charlton-Perez et al., 2012; Hardiman et al., Higher resolution can sometimes lead to a stepwise, rather than incre- 2012; Wilcox et al., 2012), although other factors such as differences mental, improvement in model performance (e.g., Roberts et al., 2004; in tropospheric warming or ozone could affect the two sub-ensembles. Shaffrey et al., 2009). For example, ocean models undergo a transition from laminar to eddy-permitting when the computational grid contains 9.1.3.2.7 Land ice sheets more than one or two grid points per first baroclinic Rossby radius (i.e., finer than 50 km at low latitudes and 10 km at high latitudes) (Smith The rate of melt water release from the Greenland and Antarctic ice et al., 2000; McWilliams, 2008). Such mesoscale eddy-permitting ocean sheets in response to climate change remains a major source of uncer- models better capture the large amount of energy contained in fronts, 9 tainty in projections of sea level rise (see Sections 13.4.3 and 13.4.4). boundary currents, and time dependent eddy features (e.g., McClean Until recently, the long-term response of these ice sheets to alterations et al., 2006). Models run at such resolution have been used for some in the surrounding atmosphere and ocean has been simulated using climate simulations, though much work remains before they are as offline models. Several ESMs currently have the capability to have ice mature as the coarser models currently in use (Bryan et al., 2007; Bryan sheet component models coupled to the rest of the climate system et al., 2010; Farneti et al., 2010; McClean et al., 2011; Delworth et al., (Driesschaert et al., 2007; Charbit et al., 2008; Vizcaino et al., 2008; 2012). Huybrechts et al., 2011; Robinson et al., 2012) although these capabil- ities are not exercised for CMIP5. Similarly, atmospheric models with grids that allow the explicit rep- resentation of convective cloud systems (i.e., finer than a few kilo- 9.1.3.2.8 Additional features in ocean atmosphere coupling metres) avoid employing a parameterization of their effects a long- standing source of uncertainty in climate models. For example, Kendon Several features in the coupling between the ocean and the atmos- et al. (2012) simulated the climate of the UK region over a 20-year phere have become more widespread since the AR4. The bulk formulae period at 1.5 km resolution, and demonstrated several improvements used to compute the turbulent fluxes of heat, water and momentum of errors typical of coarser resolution models. Further discussion of this at the air sea interface, have been revised. A number of models now is provided in Section 7.2.2. consider the ocean surface current when calculating wind stress (e.g., Luo et al., 2005; Jungclaus et al., 2006). The coupling frequency has been increased in some cases to include the diurnal cycle, which was 9.2 Techniques for Assessing Model shown to reduce the SST bias in the tropical Pacific (Schmidt et al., Performance 2006; Bernie et al., 2008; Ham et al., 2010). Several models now repre- sent the coupling between the penetration of the solar radiation into Systematic evaluation of models through comparisons with observa- the ocean and light-absorbing chlorophyll, with some implications on tions is a prerequisite to applying them confidently. Several significant the representation of the mean climate and climate variability (Murtu- developments in model evaluation have occurred since the AR4 and gudde et al., 2002; Wetzel et al., 2006). This coupling is achieved either are assessed in this section. This is followed by a description of the by prescribing the chlorophyll distribution from observations, or by overall approach to evaluation taken in this chapter and a discussion computing the chlorophyll distribution with an ocean biogeochemical of its known limitations. model (e.g., Arora et al., 2009). 9.2.1 New Developments in Model Evaluation 9.1.3.3 Resolution Approaches The typical horizontal resolution (defined here as horizontal grid spac- 9.2.1.1 Evaluating the Overall Model Results ing) for current AOGCMs and ESMs is roughly 1 to 2 degrees for the atmospheric component and around 1 degree for the ocean (Table 9.1). The most straightforward approach to evaluate models is to compare The typical number of vertical layers is around 30 to 40 for the atmos- simulated quantities (e.g., global distributions of temperature, precip- phere and around 30 to 60 for the ocean (note that some high-top itation, radiation etc.) with corresponding observationally based esti- models may have 80 or more vertical levels in the atmosphere). There mates (e.g., Gleckler et al., 2008; Pincus et al., 2008; Reichler and Kim, has been some modest increase in model resolution since the AR4, 2008). A significant development since the AR4 is the increased use of especially for the near-term simulations (e.g., around 0.5 degree for quantitative statistical measures, referred to as performance metrics. the atmosphere in some cases), based on increased availability of more The use of such metrics simplifies synthesis and visualization of model powerful computers. For the models used in long-term simulations performance (Gleckler et al., 2008; Pincus et al., 2008; Waugh and with interactive biogeochemistry, the resolution has not increased Eyring, 2008; Cadule et al., 2010; Sahany et al., 2012) and enables the 753 Chapter 9 Evaluation of Climate Models quantitative assessment of model improvements over time (Reichler Project (ISCCP) (Yu et al., 1996; Klein and Jakob, 1999; Webb et al., and Kim, 2008). Recent work has addressed redundancy of multiple 2001) has been widely used for model evaluation since the AR4 (Chen performance metrics through methods such as cluster analysis (Yokoi and Del Genio, 2009; Marchand et al., 2009; Wyant et al., 2009; Yoko- et al., 2011; Nishii et al., 2012). hata et al., 2010), often in conjunction with statistical techniques to separate model clouds into cloud regimes (e.g., Field et al., 2008; Wil- 9.2.1.2 Isolating Processes liams and Brooks, 2008; Williams and Webb, 2009). New simulators for other satellite products have also been developed and are increasingly To understand the cause of model errors it is necessary to evaluate the applied for model evaluation (Bodas-Salcedo et al., 2011). Although representation of processes both in the context of the full model and often focussed on clouds and precipitation, the simulator approach has in isolation. A number of evaluation techniques to achieve both pro- also been used successfully for other variables such as upper tropo- cess and component isolation have been developed. One involves the spheric humidity (Allan et al., 2003; Iacono et al., 2003; Ringer et al., so-called regime-oriented approach to process evaluation. Instead of 2003; Brogniez et al., 2005; Brogniez and Pierrehumbert, 2007; Zhang averaging model results in time (e.g., seasonal averages) or space (e.g., et al., 2008b; Bodas-Salcedo et al., 2011). Although providing an alter- global averages), results are averaged within categories that describe native to the use of model-equivalents from observations, instrument physically distinct regimes of the system. Applications of this approach simulators have limitations (Pincus et al., 2012) and are best applied in 9 since the AR4 include the use of circulation regimes (Bellucci et al., combination with other model evaluation techniques. 2010; Brown et al., 2010b; Brient and Bony, 2012; Ichikawa et al., 2012), cloud regimes (Williams and Brooks, 2008; Chen and Del Genio, 9.2.1.4 Initial Value Techniques 2009; Williams and Webb, 2009; Tsushima et al., 2013) and thermody- namic states (Sahany et al., 2012; Su, 2012). The application of new To be able to forecast the weather a few days ahead, knowledge of the observations, such as vertically resolved cloud and water vapour infor- present state of the atmosphere is of primary importance. In contrast, mation from satellites (Jiang et al., 2012a; Konsta et al., 2012; Quaas, climate predictions and projections simulate the statistics of weather 2012) and water isotopes (Risi et al., 2012a; Risi et al., 2012b), has also seasons to centuries in advance. Despite their differences, both weath- enhanced the ability to evaluate processes in climate models. er predictions and projections of future climate are performed with very similar atmospheric model components. The atmospheric com- Another approach involves the isolation of model components or ponent of climate models can be integrated as a weather prediction parameterizations in off-line simulations, such as Single Column model if initialized appropriately (Phillips et al., 2004). This allows Models of the atmosphere. Results of such simulations are compared testing parameterized sub-grid scale processes without the compli- to measurements from field studies or to results of more detailed pro- cation of feedbacks substantially altering the underlying state of the cess models (Randall et al., 2003). Numerous process evaluation data ­atmosphere. sets have been collected since the AR4 (Redelsperger et al., 2006; Ill- ingworth et al., 2007; Verlinde et al., 2007; May et al., 2008; Wood et The application of these techniques since the AR4 has led to some al., 2011) and have been applied to the evaluation of climate model new insights. For example, many of the systematic errors in the mod- processes (Xie et al., 2008; Boone et al., 2009; Boyle and Klein, 2010; elled climate develop within a few days of simulation, highlighting Hourdin et al., 2010). These studies are crucial to test the realism of the the important role of fast, parameterized processes (Klein et al., 2006; process formulations that underpin climate models. Boyle et al., 2008; Xie et al., 2012). Errors in cloud properties for exam- ple were shown to be present within a few days in a forecast in at least 9.2.1.3 Instrument Simulators some models (Williams and Brooks, 2008), although this was not the case in another model (Boyle and Klein, 2010; Zhang et al., 2010b). Satellites provide nearly global coverage, sampling across many mete- Other studies have highlighted the advantage of such methodologies orological conditions. This makes them powerful tools for model eval- for the detailed evaluation of model processes using observations that uation. The conventional approach has been to convert satellite-ob- are available only for limited locations and times (Williamson and served radiation information to model-equivalents (Stephens and Olson, 2007; Bodas-Salcedo et al., 2008; Xie et al., 2008; Hannay et al., Kummerow, 2007), and these have been used in numerous studies 2009; Boyle and Klein, 2010), an approach that is difficult to apply to (Allan et al., 2007; Gleckler et al., 2008; Li et al., 2008; Pincus et al., long-term climate simulations. 2008; Waliser et al., 2009b; Li et al., 2011a, 2012a; Jiang et al., 2012a). A challenge is that limitations of the satellite sensors demand various 9.2.2 Ensemble Approaches for Model Evaluation assumptions in order to convert a satellite measurement into a model equivalent climate variable. Ensemble methods are used to explore the uncertainty in climate model simulations that arise from internal variability, boundary con- An alternative approach is to calculate observation-equivalents from ditions, parameter values for a given model structure or structural models using radiative transfer calculations to simulate what the uncertainty due to different model formulations (Tebaldi and Knutti, satellite would provide if the satellite system were observing the 2007; Hawkins and Sutton, 2009; Knutti et al., 2010a). Since the AR4, model. This approach is usually referred to as an instrument simula- techniques have been designed to specifically evaluate model per- tor . Microphysical assumptions (which differ from model to model) formance of individual ensemble members. Although this is typically can be included in the simulators, avoiding inconsistencies. A simulator done to better characterize uncertainties, the methods and insights are for cloud properties from the International Cloud Satellite Climatology applicable to model evaluation in general. The ensembles are generally 754 Evaluation of Climate Models Chapter 9 of two types: Multi-model Ensembles (MMEs) and Perturbed Parameter i ­ndividual models, by diagnosing whether observations can be con- (or Physics) Ensembles (PPEs). sidered statistically indistinguishable from a model ensemble. Studies based on this approach have suggested that MMEs (CMIP3/5) are reli- 9.2.2.1 Multi-Model Ensembles able in that they are not too narrow or too dispersive as a sample of possible models, but existing single-model-based ensembles tend to The MME is created from existing model simulations from multiple be too narrow (Yokohata et al., 2012, 2013). Although initial work has climate modelling centres. MMEs sample structural uncertainty and analysed the current mean climate state, further work is required to internal variability. However, the sample size of MMEs is small, and study the relationships between simulation errors and uncertainties in is confounded because some climate models have been developed ensembles of future projections (Collins et al., 2012). by sharing model components leading to shared biases (Masson and Knutti, 2011a). Thus, MME members cannot be treated as purely inde- Bayesian methods offer insights into how to account for model inad- pendent, which implies a reduction in the effective number of inde- equacies and combine information from several metrics in both MME pendent models (Tebaldi and Knutti, 2007; Jun et al., 2008; Knutti, and PPE approaches (Sexton and Murphy, 2012; Sexton et al., 2012), 2010; Knutti et al., 2010a; Pennell and Reichler, 2011). but they are complex. A simpler strategy of screening out some model variants on the basis of some observational comparison has been used 9.2.2.2 Perturbed-Parameter Ensembles with some PPEs (Lambert et al., 2012; Shiogama et al., 2012). Edwards 9 et al. (2011) provided a statistical framework for pre-calibrating out In contrast, PPEs are created to assess uncertainty based on a single such poor model variants. Screening techniques have also been used model and benefit from the explicit control on parameter perturba- with MMEs (Santer et al., 2009). tions. This allows statistical methods to determine which parameters are the main drivers of uncertainty across the ensemble (e.g., Rougier Additional Bayesian methods are applied to the MMEs so that past et al., 2009). PPEs have been used frequently in simpler models such model performance is combined with prior distributions to estimate as EMICs (Xiao et al., 1998; Forest et al., 2002, 2006, 2008; Stott and uncertainty from the MME (Furrer et al., 2007; Tebaldi and Knutti, 2007; Forest, 2007; Knutti and Tomassini, 2008; Sokolov et al., 2009; Loutre et Milliff et al., 2011). Similar to Bayesian PPE methods, common biases al., 2011) and are now being applied to more complex models (Murphy can be assessed within the MME to determine effective independence et al., 2004; Annan et al., 2005; Stainforth et al., 2005; Collins et al., of the climate models (Knutti et al., 2013) (see Section 12.2.2 for a 2006a, 2007; Jackson et al., 2008a; Brierley et al., 2010; Klocke et al., discussion of the assumptions in the Bayesian approaches). 2011; Lambert et al., 2012). The disadvantage of PPEs is that they do not explore structural uncertainty, and thus the estimated uncertainty 9.2.3 The Model Evaluation Approach Used in this will depend on the underlying model that is perturbed (Yokohata et al., Chapter and Its Limitations 2010) and may be too narrow (Sakaguchi et al., 2012). Several stud- ies (Sexton et al., 2012; Sanderson, 2013) recognize the importance This chapter applies a variety of evaluation techniques ranging from of sampling both parametric and structural uncertainty by combining visual comparison of observations and the multi-model ensemble information from both MMEs and PPEs. However, even these approach- and its mean, to application of quantitative performance metrics (see es cannot account for the effect on uncertainty of systematic errors. Section 9.2.2). No individual evaluation technique or performance measure is considered superior; rather, it is the combined use of many 9.2.2.3 Statistical Methods Applied to Ensembles techniques and measures that provides a comprehensive overview of model performance. The most common approach to characterize MME results is to calcu- late the arithmetic mean of the individual model results, referred to Although crucial, the evaluation of climate models based on past cli- as an unweighted multi-model mean. This approach of one vote per mate observations has some important limitations. By necessity, it model gives equal weight to each climate model regardless of (1) how is limited to those variables and phenomena for which observations many simulations each model has contributed, (2) how interdependent exist. Table 9.3 provides an overview of the observations used in this the models are or (3) how well each model has fared in objective eval- chapter. In many cases, the lack or insufficient quality of long-term uation. The multi-model mean will be used often in this chapter. Some observations, be it a specific variable, an important processes, or a climate models share a common lineage and so share common biases particular region (e.g., polar areas, the upper troposphere/lower strat- (Frame et al., 2005; Tebaldi and Knutti, 2007; Jun et al., 2008; Knutti, osphere (UTLS), and the deep ocean), remains an impediment. In addi- 2010; Knutti et al., 2010a, 2013; Annan and Hargreaves, 2011; Pennell tion, owing to observational uncertainties and the presence of internal and Reichler, 2011; Knutti and Sedlácek, 2013). As a result, collections variability, the observational record against which models are assessed such as the CMIP5 MME cannot be considered a random sample of is imperfect . These limitations can be reduced, but not entirely elim- independent models. This complexity creates challenges for how best inated, through the use of multiple independent observations of the to make quantitative inferences of future climate as discussed further same variable as well as the use of model ensembles. in Chapter 12 (Knutti et al., 2010a; Collins et al., 2012; Stephenson et al., 2012; Sansom et al., 2013). The approach to model evaluation taken in the chapter reflects the need for climate models to represent the observed behaviour of Annan and Hargreaves (2010) have proposed a rank histogram past climate as a necessary condition to be considered a viable tool approach to evaluate model ensembles as a whole, rather than for future projections. This does not, however, provide an answer to 755 Chapter 9 Evaluation of Climate Models the much more difficult question of determining how well a model ensemble projections. These examples, which are discussed further in must agree with observations before projections made with it can be Section 9.8.3, remain part of an area of active and as yet inconclusive deemed reliable. Since the AR4, there are a few examples of emer- research. gent constraints where observations are used to constrain multi-model Table 9.3 | Overview of observations that are used to evaluate climate models in this chapter. The quantity and CMIP5 output variable name are given along with references for the observations. Superscript (1) indicates this observations-based data set is obtained from atmospheric reanalysis. Superscript (D) indicates default reference; superscript (A) alternate reference. CMIP5 Output Observations Quantity Reference Figure and Section Number(s) Variable Name (Default/Alternates) ATMOSPHERE Surface (2 m) Air Tas ERA-Interim1 Dee et al. (2011) Figures 9.2, 9.3, 9.6D, 9.7D, Section 9.4.1; Temperature (°C) (2 m) Figures 9.38, 9.40, Section 9.6.1 NCEP-NCAR1 Kalnay et al. (1996) Figures 9.6A, 9.7A, Section 9.4.1 9 ERA401 Uppala et al. (2005) Figure 9.38, Section 9.6.1 CRU TS 3.10 Mitchell and Jones (2005) Figures 9.38, 9.39, Section 9.6.1 HadCRUT4 Morice et al. (2012) Figure 9.8, Section 9.4.1 GISTEMP Hansen et al. (2010) Figure 9.8, Section 9.4.1 MLOST Vose et al. (2012) Figure 9.8, Section 9.4.1 Temperature (C) Ta ERA-Interim1 Dee et al. (2011) Figure 9.9D Section 9.4. (200, 850 hPa) NCEP-NCAR1 Kalnay et al. (1996) Figure 9.9A Section 9.4.1 Zonal mean Ua ERA-Interim1 Dee et al. (2011) Figure 9.7D, Section 9.4.1 wind ( m s 1) (200, 850 hPa) NCEP-NCAR1 Kalnay et al. (1996) Figure 9.7A, Section 9.4.1 Zonal wind Tauu QuikSCAT satellite measurements Risien and Chelton (2008) Figures 9.19 9.20, Section 9.4.2 stress ( m s 1) NCEP/NCAR reanalysis Kalnay et al. (1996) Figures 9.19 9.20, Section 9.4.2 ERA-Interim Dee et al. (2011) Figures 9.19 9.20, Section 9.4.2 Meridional wind (m s ) 1 Va ERA-Interim 1 Dee et al. (2011) Figure 9.7D, Section 9.4.1 (200, 850 hPa) NCEP-NCAR1 Kalnay et al. (1996) Figure 9.7A, Section 9.4.1 Geopotential Zg ERA-Interim1 Dee et al. (2011) Figure 9.7D, Section 9.4.1 height (m) (500 hPa) NCEP-NCAR1 Kalnay et al. (1996) Figure 9.7A, Section 9.4.1 TOA reflected short- Rsut CERES EBAF 2.6 Loeb et al. (2009) Figure 9.9D Section 9.4.1 wave radiation (W m 2) ERBE Barkstrom (1984) Figure 9.9A, Section 9.4.1 TOA longwave Rlut CERES EBAF 2.6 Loeb et al. (2009) Figure 9.9D Section 9.4.1 radiation (W m 2) ERBE Barkstrom (1984) Figure 9.9A, Section 9.4.1 Clear sky TOA short- SW CRE CERES EBAF 2.6 Loeb et al. (2009) Figures 9.5D, 9.6D, 9.7D, Section 9.4.1 wave cloud radiative effect (W m 2) Derived from CMIP5 CERES ES-4 ERBE Loeb et al. (2009) Figure 9.5A, Section 9.4.1 rsut and rsutcs Barkstrom (1984) Figure 9.7A, Section 9.4.1 Clear sky TOA long- LW CRE CERES EBAF 2.6 Loeb et al. (2009) Figure 9.9D, Section 9.4.1 wave cloud radiative effect (W m 2) Derived from CMIP5 CERES ES-4 ERBE Loeb et al. (2009) Figure 9.5A, Section 9.4.1 rsut and rsutcs Total precipitation Pr GPCP Adler et al. (2003) Figures 9.4, 9.6D, 9.7D, Section 9.4.1; (mm day 1) Figures 9.38, 9.40, Section 9.6.1 CMAP Xie and Arkin (1997) Figures 9.6A, 9.7A, Section 9.4.1; Figures 9.38, 9.40, Section 9.6.1 CRU TS3.10.1 Mitchell and Jones (2005) Figures 9.38, 9.39, Section 9.6.1 (continued on next page) 756 Evaluation of Climate Models Chapter 9 Table 9.3 (continued) CMIP5 Output Observations Quantity Variable Name (Default/Alternates) Reference Figure and Section Number(s) ATMOSPHERE (continued) Precipitable water PRW RSS V7 SSM/I Wentz et al. (2007) Figure 9.9, Section 9.4.1 ERA-INTERIM Dee et al. (2011) MERRA Rienecker et al. (2011) JRA-25 Onogi et al. (2007) Lower-tropospheric TLT RSS V3.3 MSU/AMSU Mears et al. (2011) Figure 9.9, Section 9.4.1 temperature UAH V5.4 MSU/AMSU Christy et al. (2007) ERA-INTERIM Dee et al. (2011) MERRA Rienecker et al. (2011) 9 JRA-25 Onogi et al. (2007) Snow albedo tas, rsds, rsus Advanced Very High Resolu- Hall and Qu (2006); Fern- Figure 9.43, Section 9.8.3 feedback (%/K) tion Radiometer (AVHRR), Polar andes et al. (2009) Pathfinder-x (APP-x), all-sky albedo and ERA40 surface air temperature Reconstruction of bio- Tas, pr,, tcold, twarm, Bartlein et al. (2010) Figure 9.11, Section 9.4.1 climatic variables for GDD5, alpha Figure 9.12, Section 9.4.1 the mid-Holocene and the Last Glacial Maximum OZONE and AEROSOLS Aerosol optical depth aod MODIS Shi et al. (2011) Figures 9.28, 9.29, Section 9.4.6 MISR Zhang and Reid (2010); Ste- Figure 9.29, Section 9.4.6 vens and Schwartz (2012) Total column ozone tro3 Ground-based measurements updated from Fioletov et al. (2002) Figure 9.10, Section 9.4.1 (DU) NASA TOMS/OMI/SBUV(/2) Stolarski and Frith (2006) merged satellite data NIWA combined total Bodeker et al. (2005) column ozone database Solar Backscatter Ultraviolet Updated from Miller et al. (2002) (SBUV, SBUV/2) retrievals DLR GOME/SCIA/GOME-2 Loyola et al. (2009); Loyola and Coldewey-Egbers (2012) CARBON CYCLE Atmospheric CO2 Masarie and Tans (1995); co2 Figure 9.45, Section 9.8.3 (ppmv) Meinshausen et al. (2011) Global Land Carbon NBP GCP Le Quere et al. (2009) Figure 9.26, 9.27, Section 9.4.5 Sink (PgC yr 1) Global Ocean Carbon fgCO2 GCP Le Quere et al. (2009) Figure 9.26, 9.27, Section 9.4.5 Sink (PgC yr 1) Regional Land Sinks NBP JAM Gurney et al. (2003) Figure 9.27, Section 9.4.5 (PgC yr 1) Regional Ocean Gurney et al. (2003); fgCO2 JAM Figure 9.27, Section 9.4.5 Sinks (PgC yr 1) Takahashi et al. (2009) (continued on next page) 757 Chapter 9 Evaluation of Climate Models Table 9.3 (continued) Quantity CMIP5 Output Observations Reference Figure and Section Number(s) Variable Name (Default/Alternates) OCEAN Annual mean thetao Levitus et al. (2009) Figure 9.13, Section 9.4.2 temperature Annual mean salinity so Antonov et al. (2010) Figure 9.13, Section 9.4.2 Sea Surface tos HadISST1.1 Rayner et al. (2003) Figure 9.14, Section 9.4.2 Temperature HadCRU 4 Jones et al. (2012) Figure 9.35, Section 9.5.3 ERA40 Uppala et al. (2005) Figure 9.36, Section 9.5.3 Global ocean heat OHC Levitus Levitus et al. (2009) Figure 9.17, Section 9.4.2 content (0 to 700 m) 9 Ishii Domingues Ishii and Kimoto, 2009) Domingues et al. (2008) Dynamic Sea SSH AVISO AVISO Figure 9.16, Section 9.4.2 surface height Meridional heat hfnorth (1) Using surface and TOA heat fluxes: Trenberth and Fasullo (2008) Figure 9.21, Section 9.4.2 transport Large and Yeager (2009) NCEP/NCAR Kalnay et al. (1996) ERA40 Uppala et al. (2005) Updated NCEP reanalysis Kistler et al. (2001) (2) Direct estimates using Ganachaud and Wunsch (2003) WOCE and inverse models Annual mean tem- Palaeoclimate reconstruction Adkins et al. (2002) Figure 9.18, Section 9.4.2 perature and salinity of temperature and salinity Total area (km2) of grid HadISST Rayner et al. (2003) Figure 9.22, Section 9.4.3 cells where Sea Ice Area Fraction (%) is NSIDC Fetterer et al. (2002) Figure 9.23, Section 9.4.3 >15%. Boundary of sea ice where Sea Ice Area NASA Comiso and Nishio (2008) Figure 9.24, Section 9.4.3 Fraction (%) is >15% MISC Total area (km2) of grid Robinson and Frei (2000) Figure 9.25, Section 9.4.4 cells where Surface Snow Area Fraction (%) is 15% or Surface Snow Amount (kg m 2) is >5 kg m 2 15,000 stations and cor- Dai (2006) Figure 9.30, Section 9.5.2 3-hour precipitation rected Ta from COADS (Dai fields and Deser, 1999; Dai, 2001 Absolute value of GPCP Wang et al. (2011a) Figure 9.32, Section 9.5.2 MJJAS minus NDJFM (Adler et al., 2003) precipitation exceeding 375 mm EXTREMES tas, precip ERA40 Uppala et al. (2005) Figure 9.37, Section 9.5.4 Daily maximum and minimum surface air ERA-Interim, Dee et al. (2011) temperature fields (C) Daily precipitation NCEP/NCAR Reanalysis 1, Kistler et al. (2001) fields (mm day 1) NCEP-DOE, Reanalysis 2 Kanamitsu et al. (2002) for calculating extremes indices Calculation of indices is based on Sillmann et al. (2013) Temperature extremes HadEX2 Donat et al. (2013) Figure 9.37, Section 9.5.4 indices based on station observations Notes: 1 This observationally constrained data set is obtained from atmospheric reanalysis. D Default reference. A Alternate reference. 758 Evaluation of Climate Models Chapter 9 9.3 Experimental Strategies in Support of of these experiments is prescribed as a time series of either global Climate Model Evaluation mean concentrations or spatially resolved anthropogenic emissions (Section 9.3.2.2). The analyses of model performance in this chapter 9.3.1 The Role of Model Intercomparisons are based on the concentration-based experiments with the exception of the evaluation of the carbon cycle (see Section 9.4.5). Systematic model evaluation requires a coordinated and well-doc- umented suite of model simulations. Organized Model Intercompar- Most of the model diagnostics are derived from the historical simula- ison Projects (MIPs) provide this via standard or benchmark experi- tions that span the period 1850 2005. In some cases, these histori- ments that represent critical tests of a model s ability to simulate cal simulations are augmented by results from a scenario run, either the observed climate. When modelling centres perform a common RCP4.5 or RCP8.5 (see Section 9.3.2.2), so as to facilitate comparison experiment, it offers the possibility to compare their results not just with more recent observations. CMIP5/Paleoclimate Modelling Inter- with observations, but with other models as well. This intercompari- comparison Project version 3 (PMIP3) simulations for the mid-Holo- son enables researchers to explore the range of model behaviours, to cene and last glacial maximum are used to evaluate model response isolate the various strengths and weaknesses of different models in a to palaeoclimatic conditions. Historical emissions-driven simulations controlled setting, and to interpret, through idealized experiments, the are used to evaluate the prognostic carbon cycle. The analysis of global inter-model differences. Benchmark MIP experiments offer a way to surface temperature variability is based in part on long pre-industrial 9 distinguish between errors particular to an individual model and those control runs to facilitate calculation of variability on decadal to cen- that might be more universal and should become priority targets for tennial time scales. Idealized simulations with 1% per year increases model improvement. in CO2 are utilized to derive transient climate response. Equilibrium cli- mate sensitivities are derived using results of specialized experiments, 9.3.2 Experimental Strategy for Coupled Model with fourfold CO2 increase, designed specifically for this purpose. Intercomparison Project Phase 5 9.3.2.2 Forcing of the Historical Experiments 9.3.2.1 Experiments Utilized for Model Evaluation Under the protocols adopted for CMIP5 and previous assessments, the CMIP5 includes a much more comprehensive suite of model experi- transient climate experiments are conducted in three phases. The first ments than was available in the preceding CMIP3 results assessed in phase covers the start of the modern industrial period through to the the AR4 (Meehl et al., 2007). In addition to a better constrained speci- present day, years 1850 2005 (van Vuuren et al., 2011). The second fication of historical forcing, the CMIP5 collection also includes initial- phase covers the future, 2006 2100, and is described by a collection ized decadal-length predictions and long-term experiments using both of RCPs (Moss et al., 2010). As detailed in Chapter 12, the third phase ESMs and AOGCMs (Taylor et al., 2012b) (Figure 9.1). The CO2 forcing is described by a corresponding collection of Extension Concentration additional predictions Initialized in all years from 1960-present AMIP & historical ensembles Control, 10-year hindcast & prediction AMIP & RCP4.5, ensembles: initialized 1960, historical RCP8.5 1965, , 2005 E-driven Control E-driven prediction with alternative & historical RCP8.5 2010 Pinatubo- initialization like eruption strategies 30-year hindcast & prediction 1%/yr CO2 (140 yrs) ensembles: initialized 1960, abrupt 4xCO2 (150 yrs) 1980 & 2005 fixed SST with 1x & 4x CO2 AMIP Figure 9.1 | Left: Schematic summary of CMIP5 short-term experiments with tier 1 experiments (yellow background) organized around a central core (pink background). (From Taylor et al., 2012b, their Figure 2). Right: Schematic summary of CMIP5 long-term experiments with tier 1 experiments (yellow background) and tier 2 experiments (green back- ground) organized around a central core (pink background). Green font indicates simulations to be performed only by models with carbon cycle representations, and E-driven means emission-driven . Experiments in the upper semicircle either are suitable for comparison with observations or provide projections, whereas those in the lower semicircle are either idealized or diagnostic in nature, and aim to provide better understanding of the climate system and model behaviour. (From Taylor et al., 2012b, their Figure 3.) 759 Chapter 9 Evaluation of Climate Models Pathways (Meinshausen et al., 2011). The forcings for the historical one of two prescribed volcanic aerosol data sets (Sato et al., 1993) simulations evaluated in this section and are described briefly here or (Ammann et al., 2003) but at least one ESM employed interactive (with more details in Annex II). aerosol injection (Driscoll et al., 2012). The prescribed data sets did not incorporate injection from explosive volcanoes after 2000. In the CMIP3 20th century experiments, the forcings from radiatively active species other than long-lived GHGs and sulphate aerosols were 9.3.2.3 Relationship of Decadal and Longer-Term Simulations left to the discretion of the individual modelling groups (IPCC, 2007). By contrast, a comprehensive set of historical anthropogenic emissions The CMIP5 archive also includes a new class of decadal-prediction and land use and land cover change data have been assembled for experiments (Meehl et al., 2009, 2013b) (Figure 9.1). The goal is to the CMIP5 experiments in order to produce a relatively homogeneous understand the relative roles of forced changes and internal variability ensemble of historical simulations with common time series of forcing in historical and near-term climate variables, and to assess the predict- agents. Emissions of natural aerosols including soil dust, sea salt and ability that might be realized on decadal time scales. These experiments volcanic species are still left to the discretion of the individual model- comprise two sets of hindcast and prediction ensembles with initial ling groups. conditions spanning 1960 through 2005. The set of 10-year ensembles are initialized starting at 1960 in 1-year increments through the year 9 For AOGCMs without chemical and biogeochemical cycles, the forcing 2005 while the 30-year ensembles are initialized at 1960, 1980 and agents are prescribed as a set of concentrations. The concentrations 2005. The same physical models are often used for both the short-term for GHGs and related compounds include CO2, CH4, N2O, all fluori- and long-term experiments (Figure  9.1) despite the different initiali- nated gases controlled under the Kyoto Protocol (hydrofluorocarbons zation of these two sets of simulations. Results from the short-term (HFCs), perfluorocarbons (PFCs), and sulphur hexafluoride (SF6)), and experiments are described in detail in Chapter 11. ozone-depleting substances controlled under the Montreal Proto- col (chlorofluorocarbons (CFCs), hydrochlorofluorocarbons (HCFCs), Halons, carbon tetrachloride (CCl4), methyl bromide (CH3Br), methyl 9.4 Simulation of Recent and Longer-Term chloride (CH3Cl)). The concentrations for aerosol species include sul- Records in Global Models phate (SO4), ammonium nitrate (NH4NO3), hydrophobic and hydrophilic black carbon, hydrophobic and hydrophilic organic carbon, secondary 9.4.1 Atmosphere organic aerosols (SOAs) and four size categories of dust and sea salt. For ESMs that include chemical and biogeochemical cycles, the forc- Many aspects of the atmosphere have been more extensively evaluat- ing agents are prescribed both as a set of concentrations and as a ed than other climate model components. One reason is the availability set of emissions with provisions to separate the forcing by natural of near-global observationally based data for energy fluxes at the TOA, and anthropogenic CO2 (Hibbard et al., 2007). The emissions include cloud cover and cloud condensate, temperature, winds, moisture, ozone time-dependent spatially resolved fluxes of CH4, NOX, CO, NH3, black and other important properties. As discussed in Box 2.3, atmospheric and organic carbon, and volatile organic compounds (VOCs). For reanalyses have also enabled integrating independent observations in models that treat the chemical processes associated with biomass a physically consistent manner. In this section we use this diversity of burning, emissions of additional species such as C2H4O (acetaldehyde), data (see Table 9.3) to evaluate the large-scale atmospheric behaviour. C2H5OH (ethanol), C2H6S (dimethylsulphide) and C3H6O (acetone) are also prescribed. Historical land use and land cover change is described 9.4.1.1 Temperature and Precipitation Spatial Patterns of in terms of the time-evolving partitioning of land surface area among the Mean State cropland, pasture, primary land and secondary (recovering) land, including the effects of wood harvest and shifting cultivation, as well Surface temperature is perhaps the most routinely examined quantity as land use changes and transitions from/to urban land (Hurtt et al., in atmospheric models. Many processes must be adequately represent- 2009). These emissions data are aggregated from empirical recon- ed in order for a model to realistically capture the observed temper- structions of grassland and forest fires (Schultz et al., 2008; Mieville ature distribution. The dominant external influence is incoming solar et al., 2010); international shipping (Eyring et al., 2010); aviation (Lee radiation, but many aspects of the simulated climate play an important et al., 2009), sulphur (Smith et al., 2011b), black and organic carbon role in modulating regional temperature such as the presence of clouds (Bond et al., 2007); and NOX, CO, CH4 and non methane volatile organic and the complex interactions between the atmosphere and the under- compounds (NMVOCs) (Lamarque et al., 2010) contributed by all other lying land, ocean, snow, ice and biosphere. sectors. The annual mean surface air temperature (at 2 m) is shown in Figure For the natural forcings a recommended monthly averaged total solar 9.2(a) for the mean of all available CMIP5 models, and the error, rela- irradiance time series was given, but there was no recommended treat- tive to an observationally constrained reanalysis (ECMWF reanalysis of ment of volcanic forcing. Both integrated solar irradiance and its spec- the global atmosphere and surface conditions (ERA)-Interim; Dee et al., trum were available, but not all CMIP5 models used the spectral data. 2011) is shown in Figure 9.2(b). In most areas the multi-model mean The data employed an 1850-2008 reconstruction of the solar cycle and agrees with the reanalysis to within 2°C, but there are several loca- its secular trend using observations of sunspots and faculae, the 10.7 tions where the biases are much larger, particularly at high elevations cm solar irradiance measurements and satellite observations (Frohlich over the Himalayas and parts of both Greenland and Antarctica, near and Lean, 2004).For volcanic forcing CMIP5 models typically employed the ice edge in the North Atlantic, and over ocean upwelling regions 760 Evaluation of Climate Models Chapter 9 off the west coasts of South America and Africa. Averaging the abso- and it is generally larger at higher latitudes as a result of the larger lute error of the individual CMIP5 models (Figure 9.2c) yields similar seasonal amplitude in insolation. Figures 9.3(c) and (d) show the mean magnitude as the multi-model mean bias (Figure 9.2b), implying that model bias of the seasonal cycle relative to the ERA-Interim reanaly- compensating errors across models is limited. The inconsistency across sis (Dee et al., 2011). The largest biases correspond to areas of large the three available global reanalyses (Figure 9.2d) that have assimilat- seasonal amplitude, notably high latitudes over land, but relatively ed temperature data at two metres (Onogi et al., 2007; Simmons et al., large biases are also evident in some lower latitude regions such as 2010) provides an indication of observational uncertainty. Although over northern India. Over most land areas the amplitude of the mod- the reanalysis inconsistency is smaller than the mean absolute bias in elled seasonal cycle is larger than observed, whereas over much of the almost all regions, areas where inconsistency is largest (typically where extratropical oceans the modelled amplitude is too small. observations are sparse) tend to be the same regions where the CMIP5 models show largest mean absolute error. The simulation of precipitation is a more stringent test for models as it depends heavily on processes that must be parameterized. Challenges Seasonal performance of models can be evaluated by examining the are compounded by the link to surface fields (topography, coastline, difference between means for December January February (DJF) and vegetation) that lead to much greater spatial heterogeneity at regional June July August (JJA). Figures 9.3(a) and (b) show the CMIP5 mean scales. Figure 9.4 shows the mean precipitation rate simulated by the model seasonal cycle amplitude in surface air temperature (as meas- CMIP5 multi-model ensemble, along with measures of error relative to 9 ured by the difference between the DJF and JJA and the absolute value precipitation analyses from the Global Precipitation Climatology Pro- of this difference). The seasonal cycle amplitude is much larger over ject (Adler et al., 2003). The magnitude of observational uncertainty for land where the thermal inertia is much smaller than over the oceans, precipitation varies with region, which is why many studies make use ( ) ( ) ( ) ( ) Figure 9.2 | Annual-mean surface (2 m) air temperature (°C) for the period 1980 2005. (a) Multi-model (ensemble) mean constructed with one realization of all available models used in the CMIP5 historical experiment. (b) Multi-model-mean bias as the difference between the CMIP5 multi-model mean and the climatology from ECMWF reanalysis of the global atmosphere and surface conditions (ERA)-Interim (Dee et al., 2011); see Table 9.3. (c) Mean absolute model error with respect to the climatology from ERA-Interim. (d) Mean inconsistency between ERA-Interim, ERA 40-year reanalysis (ERA40) and Japanese 25-year ReAnalysis (JRA-25) products as the mean of the absolute pairwise differences between those fields for their common period (1979 2001). 761 Chapter 9 Evaluation of Climate Models ( ) ( ) 9 ( ) ( ) Figure 9.3 | Seasonality (December January February minus June July August ) of surface (2 m) air temperature (°C) for the period 1980 2005. (a) Multi-model mean, calcu- lated from one realization of all available CMIP5 models for the historical experiment. (b) Multi-model mean of absolute seasonality. (c) Difference between the multi-model mean and the ECMWF reanalysis of the global atmosphere and surface conditions (ERA)-Interim seasonality. (d) Difference between the multi-model mean and the ERA-Interim absolute seasonality. of several estimates of precipitation. Known large-scale features are 2008). Judged by similarity with the spatial pattern of observations, reproduced by the multi-model mean, such as a maximum precipita- the overall quality of the simulation of the mean state of precipitation tion just north of the equator in the central and eastern tropical Pacific, in the CMIP5 ensemble is slightly better than in the CMIP3 ensemble dry areas over the eastern subtropical ocean basins, and the minimum (see FAQ 9.1 and Figure 9.6). rainfall in Northern Africa (Dai, 2006). While many large-scale fea- tures of the tropical circulation are reasonably well simulated, there In summary, there is high confidence that large-scale patterns of sur- are persistent biases. These include too low precipitation along the face temperature are well simulated by the CMIP5 models. In certain equator in the Western Pacific associated with ocean atmosphere regions this agreement with observations is limited, particularly at feedbacks maintaining the equatorial cold tongue (Collins et al., elevations over the Himalayas and parts of both Greenland and Ant- 2010) and excessive precipitation in tropical convergence zones arctica. The broad-scale features of precipitation as simulated by the south of the equator in the Atlantic and the Eastern Pacific (Lin, 2007; CMIP5 models are in modest agreement with observations, but there Pincus et al., 2008). Other errors occurring in several models include are systematic errors in the Tropics. an overly zonal orientation of the South-Pacific Convergence Zone (Brown et al., 2013) as well as an overestimate of the frequency of 9.4.1.2 Atmospheric Moisture, Clouds and Radiation occurrence of light rain events (Stephens et al., 2010). Regional-scale precipitation simulation has strong parameter dependence (Rougi- The global annual mean precipitable water is a measure of the total er et al., 2009; Chen et al., 2010; Neelin et al., 2010), and in some moisture content of the atmosphere. For the CMIP3 ensemble, the models substantial improvements have been shown through increas- values of precipitable water agreed with one another and with multi- es in resolution (Delworth et al., 2012) and improved representa- ple estimates from the National Centers for Environmental Prediction/ tions of sub-gridscale processes, particularly convection (Neale et al., National Center for Atmospheric Research (NCEP/NCAR) and ECMWF 762 Evaluation of Climate Models Chapter 9 ( ) ( ) 9 ( ) ( ) Figure 9.4 | Annual-mean precipitation rate (mm day 1) for the period 1980 2005. (a) Multi-model-mean constructed with one realization of all available AOGCMs used in the CMIP5 historical experiment. (b) Difference between multi-model mean and precipitation analyses from the Global Precipitation Climatology Project (Adler et al., 2003). (c) Multi- model-mean absolute error with respect to observations. (d) Multi-model-mean error relative to the multi-model-mean precipitation itself. ERA40 meteorological reanalyses to within approximately 10% (Walis- Jiang et al. (2012a) show that the largest biases occur in the upper er et al., 2007). Initial analysis of the CMIP5 ensemble shows the model troposphere, with model values up to twice that observed, while in the results are within the uncertainties of the observations (Jiang et al., middle and lower troposphere models simulate water vapour to within 2012a). 10% of the observations. Modelling the vertical structure of water vapour is subject to great- The spatial patterns and seasonal cycle of the radiative fluxes at the er uncertainty since the humidity profile is governed by a variety of TOA are fundamental energy balance quantities. Both the CMIP3 and processes. The CMIP3 models exhibited a significant dry bias of up to CMIP5 model ensembles reproduce these patterns with considerable 25% in the boundary layer and a significant moist bias in the free fidelity relative to the National Aeronautics and Space Adminsitration troposphere of up to 100% (John and Soden, 2007). Upper tropospher- (NASA) Clouds and the Earth s Radiant Energy System (CERES) data ic water vapour varied by a factor of three across the multi-model sets (Pincus et al., 2008; Wang and Su, 2013). Globally averaged TOA ensemble (Su et al., 2006). Many models have large biases in lower shortwave and longwave components of the radiative fluxes in 12 stratospheric water vapour (Gettelman et al., 2010), which could have atmosphere-only versions of the CMIP5 models were within 2.5 W m 2 implications for surface temperature change (Solomon et al., 2010). of the observed values (Wang and Su, 2013). The limited number of studies available for the CMIP5 model ensem- ble broadly confirms the results from the earlier model generation. In Comparisons against surface components of radiative fluxes show tropical regions, the models are too dry in the lower troposphere and that, on average, the CMIP5 models overestimate the global mean too moist in the upper troposphere, whereas in the extratropics they downward all-sky shortwave flux at the surface by 2 +/- 6 W m 2 (1 +/- are too moist throughout the troposphere (Tian et al., 2013). However, 3%) and underestimate the global downward longwave flux by 6 +/- 9 many of the model values lie within the observational uncertainties. W m 2 (2 +/- 2%) (Stephens et al., 2012). Although in tropical regions 763 Chapter 9 Evaluation of Climate Models between 1 and 3 W m 2 of the bias may be due to systematic omission is consistent with an analysis of the global annual mean estimates of precipitating and/or convective core ice hydrometeors (Waliser et al., of clear-sky atmospheric absorption from the CMIP3 ensemble and 2011), the correlation between the biases in the all-sky and clear-sky the systematic underestimation of clear-sky solar absorption by radi- downwelling fluxes suggests that systematic errors in clear-sky radia- ative transfer codes (Oreopoulos et al., 2012). The underestimation of tive transfer calculations may be a primary cause for these biases. This absorption can be attributed to the omission or underestimation of 9 ( ) Figure 9.5 | Annual-mean cloud radiative effects of the CMIP5 models compared against the Clouds and the Earth s Radiant Energy System Energy Balanced and Filled 2.6 (CERES EBAF 2.6) data set (in W m 2; top row: shortwave effect; middle row: longwave effect; bottom row: net effect). On the left are the global distributions of the multi-model-mean biases, and on the right are the zonal averages of the cloud radiative effects from observations (solid black: CERES EBAF 2.6; dashed black: CERES ES-4), individual models (thin grey lines), and the multi-model mean (thick red line). Model results are for the period 1985 2005, while the available CERES data are for 2001 2011. For a definition and maps of cloud radiative effect, see Section 7.2.1.2 and Figure 7.7. 764 Evaluation of Climate Models Chapter 9 absorbing aerosols, in particular carbonaceous species (Kim and Ram- changes in the observational estimates of CRE, in particular at polar anathan, 2008), or to the omission of weak-line (Collins et al., 2006b) and subpolar as well as subtropical latitudes (Loeb et al., 2009). or continuum (Ptashnik et al., 2011) absorption by water vapour (Wild et al., 2006). Understanding the biases in CRE in models requires a more in-depth analysis of the biases in cloud properties, including the fractional cov- One of the major influences on radiative fluxes in the atmosphere is erage of clouds, their vertical distribution as well as their liquid water the presence of clouds and their radiative properties. To measure the and ice content. Major progress in this area has resulted from both the influence of clouds on model deficiencies in the TOA radiation budget, availability of new observational data sets and improved diagnostic Figure 9.5 shows maps of deviations from observations in annual mean techniques, including the increased use of instrument simulators (e.g., shortwave (top left), longwave (middle left) and net (bottom left) cloud Cesana and Chepfer, 2012; Jiang et al., 2012a). Many models have radiative effect (CRE) for the CMIP5 multi-model mean. The figure (right particular difficulties simulating upper tropospheric clouds (Jiang et al., panels) also shows zonal averages of the same quantities from two 2012a), and low and mid-level cloud occurrence are frequently under- sets of observations, the individual CMIP5 models, and the ­ ulti-model m estimated (Cesana and Chepfer, 2012; Nam et al., 2012; Tsushima et average. The definition of CRE and observed mean fields for these quan- al., 2013). Global mean values of both simulated ice and liquid water tities can be found in Chapter 7 (Section 7.2.1.2, Figure 7.7). path vary by factors of 2 to 10 between models (Jiang et al., 2012a; Li et al., 2012a). The global mean fraction of clouds that can be detected 9 Models show large regional biases in CRE in the shortwave component, with confidence from satellites (optical thickness >1.3, Pincus et al. and these are particularly pronounced in the subtropics with too weak (2012)) is underestimated by 5 to 10 % (Klein et al., 2013). Some of the an effect (positive error) of model clouds on shortwave radiation in above errors in clouds compensate to provide the global mean balance the stratocumulus regions and too strong an effect (negative error) in in radiation required by model tuning (Tsushima et al., 2013; Wang and the trade cumulus regions. This error has been shown to largely result Su, 2013; Box 9.1). from an overestimation of cloud reflectance, rather than cloud cover (Nam et al., 2012). A too weak cloud influence on shortwave radia- In-depth analysis of several global and regional models (Karlsson et al., tion is evident over the subpolar oceans of both hemispheres and the 2008; Teixeira et al., 2011) has shown that the interaction of boundary Northern Hemisphere (NH) land areas. It is evident in the zonal mean layer and cloud processes with the larger scale circulation systems that graphs that there is a wide range in both longwave and shortwave CRE ultimately drive the observed subtropical cloud distribution remains between individual models. As is also evident, a significant reduction poorly simulated. Large errors in subtropical clouds have been shown in the difference between models and observations has resulted from to negatively affect SST patterns in coupled model simulations (Hu 1 __ _ _ _ _ __ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 0.9 _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ __ _ _ _ _ _ _ _ _ _ _ _ _ _ Correlation 0.8 _ _ _ _ _ _ _ _ _ _ _ _ __ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 0.7 _ _ CMIP3 _ _ _ _ _ _ _ _ _ CMIP5 _ _ _ _ OBS _ _ _ _ 0.6 0.5 TAS RLUT PR SW CRE Figure 9.6 | Centred pattern correlations between models and observations for the annual mean climatology over the period 1980 1999. Results are shown for individual CMIP3 (black) and CMIP5 (blue) models as thin dashes, along with the corresponding ensemble average (thick dash) and median (open circle). The four variables shown are surface air temperature (TAS), top of the atmosphere (TOA) outgoing longwave radiation (RLUT), precipitation (PR) and TOA shortwave cloud radiative effect (SW CRE). The observations used for each variable are the default products and climatological periods identified in Table 9.3. The correlations between the default and alternate (Table 9.3) observations are also shown (solid green circles). To ensure a fair comparison across a range of model resolutions, the pattern correlations are computed at a resolution of 4 in longitude and 5 in latitude. Only one realization is used from each model from the CMIP3 20C3M and CMIP5 historical simulations. 765 Chapter 9 Evaluation of Climate Models 9 Figure 9.7 | Relative error measures of CMIP5 model performance, based on the global seasonal-cycle climatology (1980 2005) computed from the historical experiments. Rows and columns represent individual variables and models, respectively. The error measure is a space time root-mean-square error (RMSE), which, treating each variable separately, is portrayed as a relative error by normalizing the result by the median error of all model results (Gleckler et al., 2008). For example, a value of 0.20 indicates that a model s RMSE is 20% larger than the median CMIP5 error for that variable, whereas a value of 0.20 means the error is 20% smaller than the median error. No colour (white) indicates that model results are currently unavailable. A diagonal split of a grid square shows the relative error with respect to both the default reference data set (upper left triangle) and the alternate (lower right triangle). The relative errors are calculated independently for the default and alternate data sets. All reference data used in the diagram are summarized in Table 9.3. et al., 2011; Wahl et al., 2011). Several studies have highlighted the In summary, despite modest improvements there remain significant potential importance and poor simulation of subpolar clouds in the errors in the model simulation of clouds. There is very high confidence Arctic and Southern Oceans (Karlsson and Svensson, 2010; Trenberth that these errors contribute significantly to the uncertainties in esti- and Fasullo, 2010b; Haynes et al., 2011; Bodas-Salcedo et al., 2012). A mates of cloud feedbacks (see Section 9.7.2.3; Section 7.2.5, Figure particular challenge for models is the simulation of the correct phase 7.10) and hence the spread in climate change projections reported in of the cloud condensate, although very few observations are available Chapter 12. to evaluate models particularly with respect to their representation of cloud ice (Waliser et al., 2009b; Li et al., 2012a). Regime-oriented 9.4.1.3 Quantifying Model Performance with Metrics approaches to the evaluation of model clouds (see Section 9.2.1) have identified that compensating errors in the CRE are largely a result of Performance metrics were used to some extent in the Third Assessment misrepresentations of the frequency of occurrence of key observed Report (TAR) and the Fourth Assessment Report (AR4), and are expand- cloud regimes, while the radiative properties of the individual regimes ed upon here because of their increased appearance in the recent lit- contribute less to the overall model deficiencies (Tsushima et al., 2013). erature. As a simple example, Figure 9.6 illustrates how the pattern correlation between the observed and simulated climatological annual Several studies have identified progress in the simulation of clouds in mean spatial patterns depends very much on the quantity examined. the CMIP5 models compared to their CMIP3 counterparts. Particular All CMIP3 and CMIP5 models capture the mean surface temperature examples include the improved simulation of vertically integrated ice distribution quite well, with correlations above 0.95, which are large- water path (Jiang et al., 2012a; Li et al., 2012a) as well as a reduction ly determined by the meridional temperature gradient. Correlations of overabundant optically thick clouds in the mid-latitudes (Klein et al., for outgoing longwave radiation are somewhat lower. For precipita- 2013; Tsushima et al., 2013). tion and the TOA shortwave cloud radiative effect, the correlations ­ 766 Evaluation of Climate Models Chapter 9 between models and observations are below 0.90, and there is con- largely arbitrary, combining the results of multiple metrics can reduce siderable scatter among model results. This example quantifies how the chance that a poorer performing model will score well for the some aspects of the simulated large-scale climate agree with observa- wrong reasons. Recent work (Nishii et al., 2012) has demonstrated that tions better than others. Some of these differences are attributable to different methods used to produce a multi-variate skill measure for the smoothly varying fields (e.g., temperature, water vapour) often agree- CMIP3 models did not substantially alter the conclusions about the ing better with observations than fields that exhibit fine structure (e.g., better and lesser performing models. precipitation) (see also Section 9.6.1.1). Incremental improvement in each field is also evident in Figure 9.6, as gauged by the mean and Large scale performance metrics are a typical first-step toward quan- median results in the CMIP5 ensemble having higher correlations than tifying model agreement with observations, and summarizing broad CMIP3. This multi-variate quantification of model improvement across characteristics of model performance that are not focussed on a par- development cycles is evident in several studies (e.g., Reichler and Kim, ticular application. More specialized performance tests target aspects 2008; Knutti et al., 2013) of a simulation believed to be especially important for constraining model projections, although to date the connections between particu- Figure 9.7 (following Gleckler et al., 2008) depicts the space time root- lar performance metrics and reliability of future projections are not mean-square error (RMSE) for the 1980 2005 climatological season- well established. This important topic is addressed in Section 9.8.3, al cycle of the historically forced CMIP5 simulations. For each of the which highlights several identified relationships between model per- 9 fields examined, this portrait plot depicts relative performance, with formance and projection responses. blue shading indicating performance being better, and red shading worse, than the median of all model results. In each case, two obser- 9.4.1.4 Long-Term Global-Scale Changes vations-based estimates are used to demonstrate the impact of the selection of reference data on the results. Some models consistently The comparison of observed and simulated climate change is compli- compare better with observations than others, some exhibit mixed cated by the fact that the simulation results depend on both model performance and some stand out with relatively poor agreement with formulation and the time-varying external forcings imposed on the observations. For most fields, the choice of the observational data set models (Allen et al., 2000; Santer et al., 2007). De-convolving the does not substantially change the result for global error measures (e.g., importance of model and forcing differences in the historical simula- between a state-of-the-art and an older-generation reanalysis), indi- tions is an important topic that is addressed in Chapter 10; however, cating that inter-model differences are substantially larger than the in this section a direct comparison is made to illustrate the ability of differences between the two reference data sets or the impact of two models to reproduce past changes. different climatological periods (e.g., for radiation fields: Earth Radia- tion Budget Experiment (ERBE) 1984 1988; CERES EBAF, 2001 2011). 9.4.1.4.1 Global surface temperature Nevertheless, it is important to recognize that different data sets often rely on the same source of measurements, and that the results in this Figure 9.8 compares the observational record of 20th century changes figure can have some sensitivity to a variety of factors such as instru- in global surface temperature to that simulated by each CMIP5 and ment uncertainty, sampling errors (e.g., limited record length of obser- EMIC model and the respective multi-model means. The inset on the vations), the spatial scale of comparison, the domain considered and right of the figure shows the climatological mean temperature for each the choice of metric. model, averaged over the 1961 1990 reference period. Although biases in mean temperature are apparent, there is less confidence in observa- Another notable feature of Figure 9.7 is that in most cases the mul- tional estimates of climatological temperature than in variations about ti-model mean agrees more favourably with observations than any this mean (Jones et al. (1999). For the CMIP5 models, interannual varia- individual model. This has been long recognized to hold for surface bility in most of the simulations is qualitatively similar to that observed temperature and precipitation (e.g., Lambert and Boer, 2001). However, although there are several exceptions. The magnitude of interannual since the AR4, it has become clear that this holds for a broad range of variations in the observations is noticeably larger than the multi-mod- climatological fields (Gleckler et al., 2008; Pincus et al., 2008; Knutti el mean because the averaging of multiple model results acts to filter et al., 2010a) and is theoretically better understood (Annan and Harg- much of the simulated variability. On the other hand, the episodic reaves, 2011). It is worth noting that when most models suffer from a volcanic forcing that is applied to most models (see Section 9.3.2.2) common error, such as the cold bias at high latitudes in the upper trop- is evident in the multi-model agreement with the observed cooling osphere (see TA 200 hPa of Figure 9.7), individual models can agree particularly noticeable after the 1991 Pinatubo eruption. The gradual better with observations than the multi-model mean. warming evident in the observational record, particularly in the more recent decades, is also evident in the simulations, with the multi-model Correlations between the relative errors for different quantities in mean tracking the observed value closely over most of the century, and Figure 9.7 are known to exist, reflecting physical relationships in the individual model results departing by less than about 0.5oC. Because model formulations and in the real world. Cluster analysis methods the interpretation of differences in model behaviour can be confounded have recently been used in an attempt to reduce this redundancy (e.g., by internal variability and forcing, some studies have attempted to iden- Yokoi et al., 2011; Nishii et al., 2012), thereby providing more succinct tify and remove dominant factors such as El Nino-Southern Oscillation summaries of model performance. Some studies have attempted an (ENSO) and the impacts of volcanic eruptions (e.g., Fyfe et al., 2010). overall skill score by averaging together the results from multiple met- Figure 9.8 shows the similar capability for EMICs to simulate the glob- rics (e.g., Reichler and Kim, 2008). Although this averaging ­ rocess is p al-scale response to the 20th century forcings (Eby et al. 2013). These 767 Chapter 9 Evaluation of Climate Models results demonstrate a level of consistency between the EMICs with both increase over the historical period, including the more rapid warming the observations and the CMIP5 ensemble. in the second half of the 20th century, and the cooling immediately following large volcanic eruptions. The disagreement apparent over the In summary, there is very high confidence that models reproduce the most recent 10 to 15 years is discussed in detail in Box 9.2. general features of the global-scale annual mean surface temperature 9 Figure 9.8 | Observed and simulated time series of the anomalies in annual and global mean surface temperature. All anomalies are differences from the 1961 1990 time-mean of each individual time series. The reference period 1961 1990 is indicated by yellow shading; vertical dashed grey lines represent times of major volcanic eruptions. (a) Single simulations for CMIP5 models (thin lines); multi-model mean (thick red line); different observations (thick black lines). Observational data (see Chapter 2) are Hadley Centre/Climatic Research Unit gridded surface temperature data set 4 (HadCRUT4; Morice et al., 2012), Goddard Institute for Space Studies Surface Temperature Analysis (GISTEMP; Hansen et al., 2010) and Merged Land Ocean Surface Temperature Analysis (MLOST; Vose et al., 2012) and are merged surface temperature (2 m height over land and surface temperature over the ocean). All model results have been sub-sampled using the HadCRUT4 observational data mask (see Chapter 10). Following the CMIP5 protocol (Taylor et al., 2012b), all simulations use specified historical forcings up to and including 2005 and use RCP4.5 after 2005 (see Figure 10.1 and note different reference period used there; results will differ slightly when using alternative RCP scenarios for the post-2005 period). (a) Inset: the global mean surface temperature for the reference period 1961 1990, for each individual model (colours), the CMIP5 multi-model mean (thick red), and the observations (thick black: Jones et al., 1999). (Bottom) Single simulations from available EMIC simulations (thin lines), from Eby et al. (2013). Observational data are the same as in (a). All EMIC simulations ended in 2005 and use the CMIP5 historical forcing scenario. (b) Inset: Same as in (a) but for the EMICs. 768 Evaluation of Climate Models Chapter 9 Box 9.2 | Climate Models and the Hiatus in Global Mean Surface Warming of the Past 15 Years The observed global mean surface temperature (GMST) has shown a much smaller increasing linear trend over the past 15 years than over the past 30 to 60 years (Section 2.4.3, Figure 2.20, Table 2.7; Figure 9.8; Box 9.2 Figure 1a, c). Depending on the observational data set, the GMST trend over 1998 2012 is estimated to be around one-third to one-half of the trend over 1951 2012 (Section 2.4.3, Table 2.7; Box 9.2 Figure 1a, c). For example, in HadCRUT4 the trend is 0.04C per decade over 1998 2012, compared to 0.11C per decade over 1951 2012. The reduction in observed GMST trend is most marked in Northern Hemisphere winter (Section 2.4.3; Cohen et al., 2012). Even with this hiatus in GMST trend, the decade of the 2000s has been the warmest in the instrumental record of GMST (Section 2.4.3, Figure 2.19). Nevertheless, the occurrence of the hiatus in GMST trend during the past 15 years raises the two related questions of what has caused it and whether climate models are able to reproduce it. Figure 9.8 demonstrates that 15-year-long hiatus periods are common in both the observed and CMIP5 historical GMST time series (see also Section 2.4.3, Figure 2.20; Easterling and Wehner, 2009; Liebmann et al., 2010). However, an analysis of the full suite of CMIP5 historical simulations (augmented for the period 2006 2012 by RCP4.5 simulations, Section 9.3.2) reveals that 111 out of 9 114 realizations show a GMST trend over 1998 2012 that is higher than the entire HadCRUT4 trend ensemble (Box 9.2 Figure 1a; CMIP5 ensemble mean trend is 0.21C per decade). This difference between simulated and observed trends could be caused by some combination of (a) internal climate variability, (b) missing or incorrect radiative forcing and (c) model response error. These potential sources of the difference, which are not mutually exclusive, are assessed below, as is the cause of the observed GMST trend hiatus. Internal Climate Variability Hiatus periods of 10 to 15 years can arise as a manifestation of internal decadal climate variability, which sometimes enhances and sometimes counteracts the long-term externally forced trend. Internal variability thus diminishes the relevance of trends over periods as short as 10 to 15 years for long-term climate change (Box 2.2, Section 2.4.3). Furthermore, the timing of internal decadal climate variability is not expected to be matched by the CMIP5 historical simulations, owing to the predictability horizon of at most 10 to 20 years (Section 11.2.2; CMIP5 historical simulations are typically started around nominally 1850 from a control run). However, climate models exhibit individual decades of GMST trend hiatus even during a prolonged phase of energy uptake of the climate system (e.g., Figure 9.8; Easterling and Wehner, 2009; Knight et al., 2009), in which case the energy budget would be balanced by increasing subsurface ocean heat uptake (Meehl et al., 2011, 2013a; Guemas et al., 2013). Owing to sampling limitations, it is uncertain whether an increase in the rate of subsurface ocean heat uptake occurred during the past 15 years (Section 3.2.4). However, it is very likely2 that the climate system, including the ocean below 700 m depth, has continued to accumulate energy over the period 1998 2010 (Section 3.2.4, Box 3.1). Consistent with this energy accumulation, global mean sea level has continued to rise during 1998 2012, at a rate only slightly and insignificantly lower than during 1993 2012 (Section 3.7). The consistency between observed heat-content and sea level changes yields high confidence in the assessment of continued ocean energy accumulation, which is in turn consistent with the positive radiative imbalance of the climate system (Section 8.5.1; Section 13.3, Box 13.1). By contrast, there is limited evidence that the hiatus in GMST trend has been accompanied by a slower rate of increase in ocean heat content over the depth range 0 to 700 m, when comparing the period 2003 2010 against 1971 2010. There is low agreement on this slowdown, since three of five analyses show a slowdown in the rate of increase while the other two show the increase continuing unabated (Section 3.2.3, Figure 3.2). During the 15-year period beginning in 1998, the ensemble of HadCRUT4 GMST trends lies below almost all model-simulated trends (Box 9.2 Figure 1a), whereas during the 15-year period ending in 1998, it lies above 93 out of 114 modelled trends (Box 9.2 Figure 1b; HadCRUT4 ensemble-mean trend 0.26°C per decade, CMIP5 ensemble-mean trend 0.16°C per decade). Over the 62-year period 1951 2012, observed and CMIP5 ensemble-mean trends agree to within 0.02C per decade (Box 9.2 Figure 1c; CMIP5 ensemble-mean trend 0.13°C per decade). There is hence very high confidence that the CMIP5 models show long-term GMST trends consistent with observations, despite the disagreement over the most recent 15-year period. Due to internal climate variability, in any given 15-year period the observed GMST trend sometimes lies near one end of a model ensemble (Box 9.2, Figure 1a, b; Easterling and Wehner, 2009), an effect that is pronounced in Box 9.2, Figure 1a, b because GMST was influenced by a very strong El Nino event in 1998. (continued on next page) 2 In this Report, the following terms have been used to indicate the assessed likelihood of an outcome or a result: Virtually certain 99 100% probability, Very likely 90 100%, Likely 66 100%, About as likely as not 33 66%, Unlikely 0 33%, Very unlikely 0 10%, Exceptionally unlikely 0 1%. Additional terms (Extremely likely: 95 100%, More likely than not >50 100%, and Extremely unlikely 0 5%) may also be used when appropriate. Assessed likelihood is typeset in italics, e.g., very likely (see Section 1.4 and Box TS.1 for more details). 769 Chapter 9 Evaluation of Climate Models Box 9.2 (continued) Unlike the CMIP5 historical simulations referred to above, some CMIP5 predictions were initialized from the observed climate state during the late 1990s and the early 21st century (Section 11.1, Box 11.1; Section 11.2). There is medium evidence that these initialized predictions show a GMST lower by about 0.05C to 0.1C compared to the historical (uninitialized) simulations and maintain this lower GMST during the first few years of the simulation (Section 11.2.3.4, Figure 11.3 top left; Doblas-Reyes et al., 2013; Guemas et al., 2013). In some initialized models this lower GMST occurs in part because they correctly simulate a shift, around 2000, from a positive to a negative phase of the Interdecadal Pacific Oscillation (IPO, Box 2.5; e.g., Meehl and Teng, 2012; Meehl et al., 2013a). However, the improvement of this phasing of the IPO through initialization is not universal across the CMIP5 predictions (cf. Section 11.2.3.4). Moreover, while part of the GMST reduction through initialization indeed results from initializing at the correct phase of internal variability, another part may result from correcting a model bias that was caused by incorrect past forcing or incorrect model response to past forcing, especially in the ocean. The relative magnitudes of these effects are at present unknown (Meehl and Teng, 2012); moreover, the quality of a forecasting system cannot be evaluated from a single prediction (here, a 10-year prediction within the period 1998 2012; Section 11.2.3). Overall, there is medium confidence that initialization leads to simulations of GMST during 1998 2012 9 that are more consistent with the observed trend hiatus than are the uninitialized CMIP5 historical simulations, and that the hiatus is in part a consequence of internal variability that is predictable on the multi-year time scale. Radiative Forcing On decadal to interdecadal time scales and under continually increasing effective radiative forcing (ERF), the forced component of the GMST trend responds to the ERF trend relatively rapidly and almost linearly (medium confidence, e.g., Gregory and Forster, 2008; Held et al., 2010; Forster et al., 2013). The expected forced-response GMST trend is related to the ERF trend by a factor that has been estimated for the 1% per year CO2 increases in the CMIP5 ensemble as 2.0 [1.3 to 2.7] W m 2 °C 1 (90% uncertainty range; Forster et al., 2013). Hence, an ERF trend can be approximately converted to a forced-response GMST trend, permitting an assessment of how much of the change in the GMST trends shown in Box 9.2 Figure 1 is due to a change in ERF trend. The AR5 best-estimate ERF trend over 1998 2011 is 0.22 [0.10 to 0.34] W m 2 per decade (90% uncertainty range), which is substantially lower than the trend over 1984 1998 (0.32 [0.22 to 0.42] W m 2 per decade; note that there was a strong volcanic eruption in 1982) and the trend over 1951 2011 (0.31 [0.19 to 0.40] W m 2 per decade; Box 9.2, Figure 1d f; numbers based on Section 8.5.2, Figure 8.18; the end year 2011 is chosen because data availability is more limited than for GMST). The resulting forced-response GMST trend would approximately be 0.12 [0.05 to 0.29] °C per decade, 0.19 [0.09 to 0.39] °C per decade, and 0.18 [0.08 to 0.37] °C per decade for the periods 1998 2011, 1984 1998 and 1951 2011, respectively (the uncertainty ranges assume that the range of the conversion factor to GMST trend and the range of ERF trend itself are independent). The AR5 best-estimate ERF forcing trend difference between 1998 2011 and 1951 2011 thus might explain about one-half (0.05°C per decade) of the observed GMST trend difference between these periods (0.06 to 0.08°C per decade, depending on observational data set). The reduction in AR5 best-estimate ERF trend over 1998 2011 compared to both 1984 1998 and 1951 2011 is mostly due to decreasing trends in the natural forcings, 0.16 [ 0.27 to 0.06] W m 2 per decade over 1998 2011 compared to 0.01 [ 0.00 to 0.01] W m 2 per decade over 1951 2011 (Section 8.5.2, Figure 8.19). Solar forcing went from a relative maximum in 2000 to a relative minimum in 2009, with a peak-to-peak difference of around 0.15 W m 2 and a linear trend over 1998 2011 of around 0.10 W m 2 per decade (cf. Section 10.3.1, Box 10.2). Furthermore, a series of small volcanic eruptions has increased the observed stratospheric aerosol loading after 2000, leading to an additional negative ERF linear-trend contribution of around 0.06 W m 2 per decade over 1998 2011 (cf. Section 8.4.2.2, Section 8.5.2, Figure 8.19; Box 9.2 Figure 1d, f). By contrast, satellite-derived estimates of tropospheric aerosol optical depth (AOD) suggests little overall trend in global mean AOD over the last 10 years, implying little change in ERF due to aerosol-radiative interaction (low confidence because of low confidence in AOD trend itself, Section 2.2.3; Section 8.5.1; Murphy, 2013). Moreover, because there is only low confidence in estimates of ERF due to aerosol cloud interaction (Section 8.5.1, Table 8.5), there is likewise low confidence in its trend over the last 15 years. For the periods 1984 1998 and 1951 2011, the CMIP5 ensemble-mean ERF trend deviates from the AR5 best-estimate ERF trend by only 0.01 W m 2 per decade (Box 9.2 Figure 1e, f). After 1998, however, some contributions to a decreasing ERF trend are missing in the CMIP5 models, such as the increasing stratospheric aerosol loading after 2000 and the unusually low solar minimum in 2009. Nonetheless, over 1998 2011 the CMIP5 ensemble-mean ERF trend is lower than the AR5 best-estimate ERF trend by 0.03 W m 2 per decade (Box 9.2 Figure 1d). Furthermore, global mean AOD in the CMIP5 models shows little trend over 1998 2012, similar to the observations (Figure 9.29). Although the forcing uncertainties are substantial, there are no apparent incorrect or missing global mean forcings in the CMIP5 models over the last 15 years that could explain the model observations difference during the warming hiatus. (continued on next page) 770 Evaluation of Climate Models Chapter 9 Box 9.2 (continued) Model Response Error The discrepancy between simulated and observed GMST trends during 1998 2012 could be explained in part by a tendency for some CMIP5 models to simulate stronger warming in response to increases in greenhouse gas (GHG) concentration than is consistent with observations (Section 10.3.1.1.3, Figure 10.4). Averaged over the ensembles of models assessed in Section 10.3.1.1.3, the best- estimate GHG and other anthropogenic (OA) scaling factors are less than one (though not significantly so, Figure 10.4), indicating that the model-mean GHG and OA responses should be scaled down to best match observations. This finding provides evidence that some CMIP5 models show a larger response to GHGs and other anthropogenic factors (dominated by the effects of aerosols) than the real world (medium confidence). As a consequence, it is argued in Chapter 11 that near-term model projections of GMST increase should be scaled down by about 10% (Section 11.3.6.3). This downward scaling is, however, not sufficient to explain the model-mean overestimate of GMST trend over the hiatus period. Another possible source of model error is the poor representation of water vapour in the upper atmosphere (Section 9.4.1.2). It has been suggested that a reduction in stratospheric water vapour after 2000 caused a reduction in downward longwave radiation and 9 hence a surface-cooling contribution (Solomon et al., 2010), possibly missed by the models, However, this effect is assessed here to be small, because there was a recovery in stratospheric water vapour after 2005 (Section 2.2.2.1, Figure 2.5). (continued on next page) (a) 1998-2012 (b) 1984-1998 (c) 1951-2012 8 HadCRUT4 Normalized density 6 4 CMIP5 2 0 0.0 0.2 0.4 0.6 0.0 0.2 0.4 0.6 0.0 0.2 0.4 0.6 (°C per decade) (°C per decade) (°C per decade) (d) 1998-2011 (e) 1984-1998 (f) 1951-2011 5 4 Normalized density 3 2 1 0 -0.3 0.0 0.3 0.6 0.9 -0.3 0.0 0.3 0.6 0.9 -0.3 0.0 0.3 0.6 0.9 (W m-2 per decade) (W m-2 per decade) (W m-2 per decade) Box 9.2, Figure 1 | (Top) Observed and simulated global mean surface temperature (GMST) trends in degrees Celsius per decade, over the periods 1998 2012 (a), 1984 1998 (b), and 1951 2012 (c). For the observations, 100 realizations of the Hadley Centre/Climatic Research Unit gridded surface temperature data set 4 (HadCRUT4) ensemble are shown (red, hatched: Morice et al., 2012). The uncertainty displayed by the ensemble width is that of the statistical construction of the global average only, in contrast to the trend uncertainties quoted in Section 2.4.3, which include an estimate of internal climate variability. Here, by contrast, internal variability is characterized through the width of the model ensemble. For the models, all 114 available CMIP5 historical realizations are shown, extended after 2005 with the RCP4.5 scenario and through 2012 (grey, shaded: after Fyfe et al., 2010). (Bottom) Trends in effective radiative forcing (ERF, in W m 2 per decade) over the periods 1998 2011 (d), 1984 1998 (e), and 1951 2011 (f). The figure shows AR5 best-estimate ERF trends (red, hatched; Section 8.5.2, Figure 8.18) and CMIP5 ERF (grey, shaded: from Forster et al., 2013). Black lines are smoothed versions of the histograms. Each histogram is normalized so that its area sums up to one. 771 Chapter 9 Evaluation of Climate Models Box 9.2 (continued) In summary, the observed recent warming hiatus, defined as the reduction in GMST trend during 1998 2012 as compared to the trend during 1951 2012, is attributable in roughly equal measure to a cooling contribution from internal variability and a reduced trend in external forcing (expert judgment, medium confidence). The forcing trend reduction is primarily due to a negative forcing trend from both volcanic eruptions and the downward phase of the solar cycle. However, there is low confidence in quantifying the role of forcing trend in causing the hiatus, because of uncertainty in the magnitude of the volcanic forcing trend and low confidence in the aerosol forcing trend. Almost all CMIP5 historical simulations do not reproduce the observed recent warming hiatus. There is medium confidence that the GMST trend difference between models and observations during 1998 2012 is to a substantial degree caused by internal variability, with possible contributions from forcing error and some CMIP5 models overestimating the response to increasing GHG and other anthropogenic forcing. The CMIP5 model trend in ERF shows no apparent bias against the AR5 best estimate over 1998 2012. However, confidence in this assessment of CMIP5 ERF trend is low, primarily because of the uncertainties in model aerosol forcing and processes, 9 which through spatial heterogeneity might well cause an undetected global mean ERF trend error even in the absence of a trend in the global mean aerosol loading. The causes of both the observed GMST trend hiatus and of the model observation GMST trend difference during 1998 2012 imply that, barring a major volcanic eruption, most 15-year GMST trends in the near-term future will be larger than during 1998 2012 (high confidence; see 11.3.6.3. for a full assessment of near-term projections of GMST). The reasons for this implication are fourfold: first, anthropogenic greenhouse-gas concentrations are expected to rise further in all RCP scenarios; second, anthropogenic aerosol concentration is expected to decline in all RCP scenarios, and so is the resulting cooling effect; third, the trend in solar forcing is expected to be larger over most near-term 15-year periods than over 1998 2012 (medium confidence), because 1998 2012 contained the full downward phase of the solar cycle; and fourth, it is more likely than not that internal climate variability in the near-term will enhance and not counteract the surface warming expected to arise from the increasing anthropogenic forcing. 9.4.1.4.2 Tropical tropospheric temperature trends of ­emperature changes in the tropical troposphere, and there is only t low confidence in the rate of change and its vertical structure (Section Most climate model simulations show a larger warming in the tropical 2.4.4). troposphere than is found in observational data sets (e.g., McKitrick et al., 2010; Santer et al., 2013). There has been an extensive and some- For the 30-year period 1979 2009 (sometimes updated through 2010 times controversial debate in the published literature as to whether or 2011), the CMIP3 models simulate a tropical warming trend ranging this difference is statistically significant, once observational uncertain- from 0.1°C to somewhat above 0.4°C per decade for both LT and MT ties and natural variability are taken into account (e.g., Douglass et (McKitrick et al., 2010), while the CMIP5 models simulate a tropical al., 2008; Santer et al., 2008, 2013; Christy et al., 2010; McKitrick et warming trend ranging from slightly below 0.15°C to somewhat above al., 2010; Bengtsson and Hodges, 2011; Fu et al., 2011; McKitrick et 0.4°C per decade for both LT and MT (Santer et al., 2013; see also Po- al., 2011; Thorne et al., 2011). For the period 1979 2012, the various Chedley and Fu, 2012, who considered the period 1979 2005). Both observational data sets find, in the tropical lower troposphere (LT), model ensembles show trends that on average are higher than in the a linear warming trend ranging from 0.06°C to 0.13°C per decade observational estimates, although both model ensembles overlap the (Section 2.4.4, Figure 2.27). In the tropical middle troposphere (MT), observational ensemble. Because the differences between the various the linear warming trend ranges from 0.02°C to 0.12°C per decade observational estimates are largely systematic and structural (Section (Section 2.4.4, Figure 2.27). Uncertainty in these trend values arises 2.4.4; Mears et al., 2011), the uncertainty in the observed trends cannot from different methodological choices made by the groups deriving be reduced by averaging the observations as if the differences between satellite products (Mears et al., 2011) and radiosonde compilations the data sets were purely random. Likewise, to properly represent inter- (Thorne et al., 2011), and from fitting a linear trend to a time series nal climate variability, the full model ensemble spread must be used in containing substantial interannual and decadal variability (Box 2.2; a comparison against the observations (e.g., Box 9.2; Section 11.2.3.2; Section 2.4.4; (Santer et al., 2008; McKitrick et al., 2010)). Although Raftery et al., 2005; Wilks, 2006; Jolliffe and Stephenson, 2011). The there have been substantial methodological debates about the calcu- very high significance levels of model observation discrepancies in LT lation of trends and their uncertainty, a 95% confidence interval of and MT trends that were obtained in some studies (e.g., Douglass et around +/-0.1°C per decade has been obtained consistently for both LT al., 2008; McKitrick et al., 2010) thus arose to a substantial degree from and MT (e.g., Section 2.4.4; McKitrick et al., 2010). In summary, despite using the standard error of the model ensemble mean as a measure unanimous agreement on the sign of the observed trends, there exists of uncertainty, instead of the ensemble standard deviation or some substantial disagreement between available estimates as to the rate other appropriate measure for uncertainty arising from internal climate 772 Evaluation of Climate Models Chapter 9 variability (e.g., Box 9.2; Section 11.2.3.2; Raftery et al., 2005; Wilks, still produce too zonal a storm track in this region and most models 2006; Jolliffe and Stephenson, 2011). Nevertheless, almost all model underestimate cyclone intensity (Colle et al., 2013; Zappa et al., 2013). ensemble members show a warming trend in both LT and MT larger Chang et al. (2012) also find the storm tracks in the CMIP5 models than observational estimates (McKitrick et al., 2010; Po-Chedley and to be too weak and too equatorwards in their position, similar to the Fu, 2012; Santer et al., 2013). CMIP3 models. The performance of the CMIP5 models in representing North Atlantic cyclones was found to be strongly dependent on model The CMIP3 models show a 1979 2010 tropical SST trend of 0.19°C resolution (Colle et al., 2013). Studies based on individual models typi- per decade in the multi-model mean, significantly larger than the cally find that models capture the general characteristics of storm tracks various observational trend estimates ranging from 0.10°C to 0.14°C and extratropical cyclones (Ulbrich et al., 2008; Catto et al., 2010) and per decade (including the 95% confidence interval; Fu et al., 2011). their associated fronts (Catto et al., 2013) and show improvements over As a consequence, simulated tropospheric temperature trends are earlier model versions (Loptien et al., 2008). However, some models also too large because models attempt to maintain static stability. By have deficiencies in capturing the location of storm tracks (Greeves et contrast, atmospheric models that are forced with the observed SST al., 2007; Catto et al., 2011), in part owing to problems related to the are in better agreement with observations, as was found in the CMIP3 location of warm waters such as the Gulf Stream and Kuroshio Current model ECHAM5 (Bengtsson and Hodges, 2011) and the CMIP5 atmos- (Greeves et al., 2007; Keeley et al., 2012). This is an important issue phere-only runs. In the latter, the LT trend range for the period 1981 because future projections of storm tracks are sensitive to changes in 9 2008 is 0.13 to 0.19C per decade less than in the CMIP5 coupled SSTs (Catto et al., 2011; Laine et al., 2011; McDonald, 2011; Woollings models, but still an overestimate (Po-Chedley and Fu, 2012). The influ- et al., 2012). Some studies find that storm track and cyclone biases are ence of SST trend errors on the analysis can be reduced by considering strongly related to atmospheric processes and parameterizations (Bauer trends in tropospheric static stability, measured by the amplification et al., 2008a; Boer and Lambert, 2008; Zappa et al., 2013). Representa- of MT trends against LT trends; another approach is to consider the tion of the Mediterranean storm track has been shown to be particularly amplification of tropospheric trends against SST trends. The results of dependent on model resolution (Pinto et al., 2006; Raible et al., 2007; such analyses strongly depend on the time scale considered. Month- Bengtsson et al., 2009; Ulbrich et al., 2009), as is the representation to-month variations are consistent between observations and models of storm intensity and associated extremes in this area (Champion et concerning amplification aloft against SST variations (Santer et al., al., 2011). Most studies have focussed on NH storm tracks. However, 2005) and concerning amplification of MT against LT variations (Po- recently two CMIP3 models were found to differ significantly in their Chedley and Fu, 2012). By contrast, the 30-year trend in tropical static simulation of extratropical cyclones affecting Australia (Dowdy et al., stability has been found to be larger than in the satellite observations 2013) and only about a third of the CMIP3 models were able to capture for almost all ensemble members in both CMIP3 (Fu et al., 2011) and the observed changes and trends in Southern Hemisphere (SH) baro- CMIP5 (Po-Chedley and Fu, 2012). However, if the radiosonde compi- clinicity responsible for a reduction in the growth rate of the leading lations are used for the comparison, the trends in static stability in the winter storm track modes (Frederiksen et al., 2011). There is still a lack CMIP3 models agree much better with the observations, and inconsist- of information on SH storm track evaluation for the CMIP5 models. ency cannot be diagnosed unambiguously (Seidel et al., 2012) . What caused the remaining trend overestimate in static stability is not clear 9.4.1.4.4 Tropical circulation but has been argued recently to result from an upward propagation of bias in the model climatology (O Gorman and Singh, 2013). Earlier assessments of a weakening Walker circulation (Vecchi et al., 2006; Vecchi and Soden, 2007; DiNezio et al., 2009) from models and In summary, most, though not all, CMIP3 and CMIP5 models overesti- reanalyses (Yu and Zwiers, 2010) have been tempered by subsequent mate the observed warming trend in the tropical troposphere during evidence that tropical Pacific Trade winds may have strengthened the satellite period 1979 2012. Roughly one-half to two-thirds of this since the early 1990s (e.g., Merrifield and Maltrud, 2011). Models difference from the observed trend is due to an overestimate of the suggest that the width of the Hadley cell should increase (Frierson SST trend, which is propagated upward because models attempt to et al., 2007; Lu et al., 2007), and there are indications that this has maintain static stability. There is low confidence in these assessments, been observed over the past 25 years (Seidel et al., 2008) but at an however, due to the low confidence in observed tropical tropospheric apparent rate (2 to 5 degrees of latitude since 1979) that is faster trend rates and vertical structure (Section 2.4.4). than in the CMIP3 models (Johanson and Fu, 2009). 9.4.1.4.3 Extratropical circulation The tendency in a warming climate for wet areas to receive more precipitation and subtropical dry areas to receive less, often termed The AR4 concluded that models, when forced with observed SSTs, the rich-get richer mechanism (Chou et al., 2006; Held and Soden, are capable of producing the spatial distribution of storm tracks, but 2006) is simulated in CMIP3 models (Chou and Tu, 2008), and obser- generally show deficiencies in the numbers and depth of cyclones and vational support for this is found from ocean salinity observations the exact locations of the storm tracks. The ability to represent extra- (Durack et al., 2012) and precipitation gauge data over land (Zhang tropical cyclones in climate models has been improving, partly due to et al., 2007). There is medium confidence that models are correct in increases in horizontal resolution. simulating precipitation increases in wet areas and decreases in dry areas on broad spatial scales in a warming climate based on agree- Storm track biases over the North Atlantic have decreased in CMIP5 ment among models and some evidence that this has been detected in models compared to CMIP3 (Zappa et al., 2013) although models observed trends (see Section 2.5.1). 773 Chapter 9 Evaluation of Climate Models Several recent studies have examined the co-variability of tropical cli- substances (see also Section 2.2.2.2 and Figure 2.6). Since the AR4, mate variables as a further means of evaluating climate models. Spe- there is increasing evidence that the ozone hole has led to a poleward cifically, there are observed relationships between lower tropospheric shift and strengthening of the SH mid-latitude tropospheric jet during temperature and total column precipitable water (Mears et al., 2007), summer (Perlwitz et al., 2008; Son et al., 2008, 2010; SPARC-CCMVal, and between surface temperature and relative humidity (Willett et al., 2010; McLandress et al., 2011; Polvani et al., 2011; WMO, 2011; Swart 2010). Figure 9.9 (updated from Mears et al., 2007) shows the relation- and Fyfe, 2012b). These trends are well captured in both chemistry cli- ship between 25-year (1988 2012) linear trends in tropical precipita- mate models (CCMs) with interactive stratospheric chemistry and in ble water and lower tropospheric temperature for individual historical CMIP3 models with prescribed time-varying ozone (Son et al., 2010; simulations (extended by appending RCP8.5 simulations after 2005, SPARC-CCMVal, 2010). However, around half of the CMIP3 models pre- see Santer et al., 2013). As described by Mears et al. (2007), the ratio scribe ozone as a fixed climatological value, and so these models are between changes in these two quantities is fairly tightly constrained not able to simulate trends in surface climate attributable to changing in the model simulations and similar across a range of time scales, stratospheric ozone amount (Karpechko et al., 2008; Son et al., 2008, indicating that relative humidity is close to invariant in each model. In 2010; Fogt et al., 2009). For CMIP5, a new time-varying ozone data the updated figure, the Remote Sensing System (RSS) observations are set (Cionni et al., 2011) was developed and prescribed in the majority in fairly good agreement with model expectations, and the University of models without interactive chemistry. This zonal mean data set is 9 of Alabama in Huntsville (UAH) observations less so. The points asso- based on observations by Randel and Wu (2007) and CCM projections ciated with two of the reanalyses are also relatively far from the line, in the future (SPARC-CCMVal, 2010). Further, nine of the CMIP5 models consistent with long-term changes in relative humidity. It is not known include interactive chemistry and so compute their own ozone evolu- whether these  discrepancies  are due to remaining inhomogeneity in tion. As a result, all CMIP5 models consider stratospheric ozone deple- the observational data and/or  reanalysis results, or due to problems tion and capture associated effects on SH surface climate, a significant with the climate simulations. All of the observational and reanalysis advance over CMIP3. Figure 9.10 shows the global annual mean and points lie at the lower end of the model distribution, consistent with Antarctic October mean of total column ozone in the CMIP5 models. the findings of (Santer et al., 2013). The simulated trends in total column ozone are in medium agreement with observations, noting that some models that calculate ozone inter- 9.4.1.4.5 Ozone and lower stratospheric temperature trends actively show significant deviations from observation (Eyring et al., 2013). The multi-model mean agrees well with observations, and there Stratospheric ozone has been subject to a major perturbation since is robust evidence that this constitutes a significant improvement over the late 1970s due to anthropogenic emissions of ozone-depleting CMIP3, where around half of the models did not include stratospheric Trend in precitable water (% per decade) Slope = 5.7 (% °C-1) Trend in TLT (°C per decade) Figure 9.9 | Scatter plot of decadal trends in tropical (20S to 20N) precipitable water as a function of trends in lower tropospheric temperature (TLT) over the world s oceans. Coloured symbols are from CMIP5 models; black symbols are from satellite observations or from reanalysis output. Trends are calculated over the 1988 2012 period, so CMIP5 historical runs, which typically end in December 2005, were extended using RCP8.5 simulations initialized using these historical runs. Figure updated from Mears et al. (2007). 774 Evaluation of Climate Models Chapter 9 9 Figure 9.10 | Time series of area-weighted total column ozone from 1960 to 2005 for (a) annual and global mean (90°S to 90°N) and (b) Antarctic October mean (60°S to 90°S). Individual CMIP5 models with interactive or semi-interactive chemistry are shown in thin coloured lines, their multi-model mean (CMIP5Chem) in thick red and their standard deviation as the blue shaded area. Further shown are the multi-model mean of the CMIP5 models that prescribe ozone (CMIP5noChem, thick green), the International Global Atmospheric Chemistry/Stratospheric Processes and their Role in Climate (IGAC/SPARC) ozone database (thick pink), the Chemistry Climate Model Validation-2 (CCMVal-2) multi- model mean (thick orange), and observations from five different sources (black symbols). These sources include ground-based measurements (updated from Fioletov et al., 2002), National Aeronautics and Space Administration (NASA) Total Ozone Mapping Spectrometer/Ozone Monitoring Instrument/Solar Backscatter Ultraviolet(/2) (TOMS/OMI/SBUV(/2)) merged satellite data (Stolarski and Frith, 2006), the National Institute of Water and Atmospheric Research (NIWA) combined total column ozone database (Bodeker et al., 2005), Solar Backscatter Ultraviolet (SBUV, SBUV/2) retrievals (updated from Miller et al. 2002), and Deutsches Zentrum für Luft- und Raumfahrt/ Global Ozone Monitoring Experiment/ SCanning Imaging Absorption spectrometer for atmospheric chartography /GOME-2 (DLR GOME/SCIA/GOME-2; Loyola et al., 2009; Loyola and Coldewey-Egbers, 2012). Note that the IGAC/SPARC database over Antarctica (and thus the majority of the CMIP5noChem models) is based on ozonesonde measurements at the vortex edge (69°S) and as a result underestimates Antarctic ozone depletion compared to the observations shown. Ozone depletion was more pronounced after 1960 as equivalent stratospheric chlorine values steadily increased throughout the stratosphere. (Adapted from Figure 2 of Eyring et al., 2013.) ozone trends. Correspondingly, there is high confidence that the rep- timate the long-term cooling trend (Charlton-Perez et al., 2012; Eyring resentation of associated effects on high-latitude surface climate and et al., 2013; Santer et al., 2013) (see Chapter 10). lower stratospheric cooling trends has improved compared to CMIP3. Tropospheric ozone is an important GHG and as such needs to be Lower stratospheric temperature change is affected by ozone, and well represented in climate simulations. In the historical period it has since 1958 the change is characterized by a long-term global cooling increased due to increases in ozone precursor emissions from anthro- trend interrupted by three 2-year warming episodes following large pogenic activities (see Chapters 2 and 8). Since the AR4, a new emis- volcanic eruptions (Figure 2.24). During the satellite era (since 1979) sion data set has been developed (Lamarque et al., 2010), which has the cooling occurred mainly in two step-like transitions in the after- led to some differences in tropospheric ozone burden compared to pre- math of the El Chichón eruption in 1982 and the Mt Pinatubo eruption vious studies, mainly due to biomass burning emissions (Lamarque et in 1991, with each cooling transition followed by a period of relatively al., 2010; Cionni et al., 2011; Young et al., 2013). Climatological mean steady temperatures (Randel et al., 2009; Seidel et al., 2011). This spe- tropospheric ozone in the CMIP5 simulations generally agrees well cific evolution of global lower stratosphere temperatures since 1979 is with satellite observations and ozonesondes, although as in the strato- well captured in the CMIP5 models when forced with both natural and sphere, biases exist for individual models (Eyring et al., 2013; Young et anthropogenic climate forcings, although the models tend to underes- al., 2013) (see also Chapter 8). 775 Chapter 9 Evaluation of Climate Models 9.4.1.5 Model Simulations of the Last Glacial Maximum and In addition the CMIP5/PMIP3 simulations can compared to previous the Mid-Holocene palaeoclimate intercomparisons (Joussaume and Taylor, 1995; Bracon- not et al., 2007c). Simulations of past climate can be used to test a model s response to forcings larger than those of the 20th century (see Chapter 5), and Figure 9.11 compares model results to palaeoclimate reconstructions the CMIP5 protocol includes palaeoclimate simulations referred to as for both LGM (left) and MH (right). For most models the simulated LGM PMIP3 (Paleoclimate Model Intercomparison Project, version 3) (Taylor cooling is within the range of the climate reconstructions (Braconnot et al., 2012b). Specifically, the Last Glacial Maximum (LGM, 21000 et al., 2007c; Izumi et al., 2013), however Hargreaves et al. (2011) find years BP) allows testing of the modelled climate response to the pres- a global mean model warm bias over the ocean of about 1°C for this ence of a large ice sheet in the NH and to lower concentrations of period (Hargreaves et al., 2011). LGM simulations tend to overestimate radiatively active trace gases, whereas the mid-Holocene (MH, 6000 tropical cooling and underestimate mid-latitude cooling (Kageyama et years BP) tests the response to changes in seasonality of insolation in al., 2006; Otto-Bliesner et al., 2009). They thus underestimate polar the NH (see Chapter 5). For these periods, palaeoclimate reconstruc- amplification which is a feature also found for the mid-Holocene (Mas- tions allow quantitative model assessment (Braconnot et al., 2012). son-Delmotte et al., 2006; Zhang et al., 2010a) and other climatic con- 9 a b ( ) (mm year-1) c d (mm year-1) Figure 9.11 | Reconstructed and simulated conditions for the Last Glacial Maximum (LGM, 21,000 years BP, left) and the mid-Holocene (MH, 6000 years BP, right). (a) LGM change in annual mean surface temperature (°C) over land as shown by palaeo-environmental climate reconstructions from pollen, macrofossils, and ice cores (Bartlein et al., 2010; Bracon- not et al., 2012), and in annual mean sea surface temperature (°C) over the ocean from different type of marine records (Waelbroeck et al., 2009). (b) MH change in annual mean precipitation (mm yr 1) over land (Bartlein et al., 2010). In (a) and (b), the size of the dots is proportional to the uncertainties at the different sites as provided in the reconstructions. (c) Annual mean temperature changes over land against changes over the ocean, in the tropics (downward triangles) and over the North Atlantic and Europe (upward triangles). The mean and range of the reconstructions are shown in black, the Paleoclimate Modelling Intercomparison Project version 2 (PMIP2) simulations as grey triangles, and the CMIP5/ PMIP3 simulations as coloured triangles. The 5 to 95% model ranges are in red for the tropics and in blue for the North Atlantic/Europe. (d) Changes in annual mean precipitation in different data-rich regions. Box plots for reconstructions provide the range of reconstructed values for the region. For models, the individual model average over the region is plotted for PMIP2 (small grey circle) and CMIP5/PMIP3 simulations (coloured circles). Note that in PMIP2, ESM indicates that vegetation is computed using a dynamical vegetation model, whereas in CMIP5/PMIP3 it indicates that models have an interactive carbon cycle with different complexity in dynamical vegetation (see Table 9.A.1). The limits of the boxes are as follows: Western Europe (40°N to 50°N, 10°W to 30°E); northeast America (35°N to 60°N, 95°W to 60°W); North Africa (10°N to 25°N, 20°W to 30°W), and East Asia (25°N to 40°N, 75°E to 105°E). (Adapted from Braconnot et al., 2012.) 776 Evaluation of Climate Models Chapter 9 a b 9 Figure 9.12 | Relative model performance for the Last Glacial Maximum (LGM, about 21,000 yr BP) and the mid-Holocene (MH, about 6000 yr BP) for seven bioclimatic variables: annual mean sea surface temperature, mean annual temperature (over land), mean temperature of the coldest month, mean temperature of the warmest month, growing degree days above a threshold of 5°C, and ratio of actual to equilibrium evapotranspiration. Model output is compared to the Bartlein et al. (2010) data set over land, including ice core data over Greenland and Antarctica (Braconnot et al., 2012) and the Margo data set (Waelbroeck et al., 2009) over the ocean. The CMIP5/Paleoclimate Modelling Intercomparison Project version 3 (PMIP3) ensemble of Ocean Atmosphere (OA) and Earth System Model (ESM) simulations are compared to the respective PMIP2 ensembles in the first four columns of each panel. A diagonal divides each cell in two parts to show in the upper triangle a measure of the distance between model and data, taking into account the uncertainties in the palaeoclimate reconstructions (Guiot et al., 1999), and in the lower triangle the normalized mean-square error (NMSE) that indicates how well the spatial pattern is represented. In this graph all the values have been normalized following (Gleckler et al., 2008) using the median of the CMIP5/PMIP3 ensemble. The colour scale is such that blue colours mean that the result is better than the median CMIP5 model and red means that it is worse. texts (Masson-Delmotte et al., 2010). Part of this can be attributed to LGM, because the forcing is weaker and involves smaller scale respons- uncertainties in the representation of sea ice and vegetation feedbacks es over the continent (Hargreaves et al., 2013). As is the case for the that have been shown to amplify the response at the LGM and the MH simulations of present day climate, there is only modest improvement in these latitudes (Braconnot et al., 2007b; Otto et al., 2009; O ishi and between the results of the more recent models (CMIP5/PMIP3) and Abe-Ouchi, 2011). Biases in the representation of the coupling between those of earlier model versions (PMIP2) despite higher resolution and vegetation and soil moisture are also responsible for excessive conti- sophistication. nental drying at the LGM (Wohlfahrt et al., 2008) and uncertainties in ­ vegetation feedback in monsoon regions (Wang et al., 2008; Dallmeyer 9.4.2 Ocean et al., 2010). Nevertheless, the ratio between the simulated change in temperature over land and over the ocean (Figure 9.11c) is rather Accurate simulation of the ocean in climate models is essential for the similar in different models, resulting mainly from simulation of the correct estimation of transient ocean heat uptake and transient climate hydrological cycle over land and ocean (Sutton et al., 2007; Laine et al., response, ocean CO2 uptake, sea level rise, and coupled climate modes 2009). At a regional scale, models tend to underestimate the changes such as ENSO. In this section model performance is assessed for the in the north-south temperature gradient over Europe both at the LGM mean state of ocean properties, surface fluxes and their impact on the (Ramstein et al., 2007) and at the mid-Holocene (Brewer et al., 2007; simulation of ocean heat content and sea level, and aspects of impor- Davis and Brewer, 2009). tance for climate variability. Simulations of both the recent and more distant past are evaluated against available data. Following Chapter 3, The large-scale pattern of precipitation change during the MH (Figure ocean reanalyses are not used for model evaluation as many of their 9.11d) is reproduced, but models tend to underestimate the magnitude properties depend on the model used to build the reanalysis. of precipitation change in most regions. In the SH (not shown in the figure), the simulated change in atmospheric circulation is consistent 9.4.2.1 Simulation of Mean Temperature and Salinity Structure with precipitation records in Patagonia and New Zealand, even though the differences between model results are large and the reconstruc- Potential temperature and salinity are the main ocean state variables tions have large uncertainties (Rojas et al., 2009; Rojas and Moreno, and their zonal distribution offers an evaluation of climate models in 2011). different parts of the ocean (upper ocean, thermocline, deep ocean). Over most latitudes, at depths ranging from 200 m to 2000 m, the A wider range of model performance metrics is provided in Figure 9.12 CMIP5 multi-model mean zonally averaged ocean temperature is too (Guiot et al., 1999; Brewer et al., 2007; Annan and Hargreaves, 2011; warm (Figure 9.13a), albeit with a cooler deep ocean. Similar biases Izumi et al., 2013). Results for the MH are less reliable than for the were evident in the CMIP3 multi-model mean. Above 200 m, however, 777 Chapter 9 Evaluation of Climate Models 0 32 33 25 34 35 5 35 5 10 34 20 34 .5 35.5 5 35 34. 200 35 0 0 10 15 5 400 34.5 35 600 34.5 34.5 0 5 800 Depth (m) 5 1000 5 34.5 0 2000 3000 4000 5000 Temperature A Salinity B 0 90S 60S 30S EQU 30N 60N 90N 90S 60S 30S EQU 30N 60N 90N 9 Latitude Latitude 3 2 1 0 1 2 3 1 0.75 0.5 0.25 0 0.25 0.5 0.75 1 Figure 9.13 | (a) Potential temperature (oC) and (b) salinity (PSS-78); shown in colour are the time-mean differences between the CMIP5 ensemble mean and observations, zonally averaged for the global ocean (excluding marginal and regional seas). The observed climatological values are sourced from the World Ocean Atlas 2009 (WOA09; Prepared by the Ocean Climate Laboratory, National Oceanographic Data Center, Silver Spring, MD, USA), and are shown as labelled black contours. White contours show regions in (a) where poten- tial temperature differences exceed positive or negative 1, 2 or 3°C, and in (b) where salinity differences exceed positive or negative 0.25, 0.5, 0.75 or 1 (PSS-78). The simulated annual mean climatologies are obtained for 1975 to 2005 from available historical simulations, whereas WOA09 synthesizes observed data from 1874 to 2008 in calculations of the annual mean; however, the median time for gridded observations most closely resembles the 1980 2010 period (Durack and Wijffels, 2012). Multiple realizations from individual models are first averaged to form a single-model climatology, before the construction of the multi-model ensemble mean. A total of 43 available CMIP5 models have contributed to the temperature panel (a) and 41 models to the salinity panel (b). the CMIP5 (and CMIP3) multi-model mean is too cold, with maximum sea ice formation/melt and river runoff) are only loosely related to the cold bias (more than 1°C) near the surface at mid-latitudes of the NH SSS itself, allowing errors to develop unchecked in coupled models. An and near 200 m at 15°S. Zonal salinity errors (Figure 9.13b) exhibit a analysis of CMIP3 models showed that, whereas the historical trend in different pattern from those of the potential temperature indicating global mean SSS is well captured by the models, regional SSS biases that most do not occur via density compensation. Some near surface are as high as +/-2.5 psu (Terray et al., 2012). Comparisons of modelled structures in the tropics and in the northern mid-latitude are ­ndicative i versus observed estimates of evaporation minus precipitation suggest of density compensation and are presumably due to surface fluxes that model biases in surface freshwater flux play a role in some regions errors. At intermediate depths, errors in water mass formation translate (e.g., double Intertropical Convergence Zone (ITCZ) in the East Pacific; into errors in both salinity and potential temperature. Lin, 2007) or over the Indian ocean (Pokhrel et al., 2012). In the AR4 it was noted that the largest errors in SST in CMIP3 were The performance of coupled climate models in simulating hydrograph- found in mid and high latitudes. While this is still the case in CMIP5, ic structure and variability were assessed in two important regions, there is marginal improvement with fewer individual models exhibiting the Labrador and Irminger Seas and the Southern Ocean (de Jong et serious bias the inter-model zonal mean SST error standard deviation al., 2009) and (Sloyan and Kamenkovich, 2007). Eight CMIP3 models is significantly reduced at all latitudes north of 40oS even though the produce simulations of the intermediate and deep layers in the Lab- multi-model mean is only slightly improved (Figure 9.14a, c). Near the rador and Irminger Seas that are generally too warm and saline, with equator, the cold tongue error in the Pacific (see Section 9.4.2.5.1) is biases up to 0.7 psu and 2.9°C. The biases arise because the convective reduced by 30% in CMIP5; the Atlantic still exhibits serious errors and regime is restricted to the upper 500 m; thus, intermediate water that the Indian is still well simulated (Figure 9.14b,d). In the Tropics, Li and in reality is formed by convection is, in the models, partly replaced Xie (2012) have shown that SST errors could be classified into those by warmer water from the south. In the Southern Ocean, Subantarctic exhibiting broad meridional structures that are due to cloud errors, and Mode Water (SAMW) and Antarctic Intermediate Water (AAIW), two those associated with Pacific and Atlantic cold tongue errors that are water masses indicating very efficient ocean ventilation, are found to due to thermocline depth errors. be well simulated in some CMIP3 and CMIP5 models but not in others, some having a significant fresh bias (Sloyan and Kamenkovich, 2007; Sea surface salinity (SSS) is more challenging to observe, even though Salle et al., 2013). McClean and Carman (2011) found biases in the the last decade has seen substantial improvements in the development properties of the North Atlantic mode waters and their formation rates of global salinity observations, such as those from the Array for Real- in the CMIP3 models. Errors in Subtropical Mode Water (STMW) forma- time Geostrophic Oceanography (ARGO) network (see Chapter 3). tion rate and volume produce a turnover time of 1 to 2 years, approx- Whereas SST is strongly constrained by air sea interactions, the sources imately half of that observed. Bottom water properties assessment in of SSS variations (surface forcing via evaporation minus ­ recipitation, p CMIP5 shows that about half of the models create dense water on 778 Evaluation of Climate Models Chapter 9 9 Figure 9.14 | (a) Zonally averaged sea surface temperature (SST) error in CMIP5 models. (b) Equatorial SST error in CMIP5 models. (c) Zonally averaged multi-model mean SST error for CMIP5 (red curve) and CMIP3 (blue curve), together with inter-model standard deviation (shading). (d) Equatorial multi-model mean SST in CMIP5 (red curve), CMIP3 (blue curve) together with inter-model standard deviation (shading) and observations (black). Model climatologies are derived from the 1979 1999 mean of the historical simulations. The Hadley Centre Sea Ice and Sea Surface Temperature (HadISST) (Rayner et al., 2003) observational climatology for 1979 1999 is used as reference for the error calculation (a), (b), and (c); and for observations in (d). the Antarctic shelf, but it mixes with lighter water and is not export- 9.4.2.2 Simulation of Sea Level and Ocean Heat Content ed as bottom water. Instead most models create deep water by open ocean deep convection, a process occurring rarely in reality (Heuzé et Steric and dynamic components of the mean dynamic topography (MDT) al., 2013) which leads to errors in deep water formation and properties and sea surface height (SSH) patterns can be compared to observations in the Southern Ocean as shown in Figure 9.15. (Maximenko et al., 2009). Pattern correlations between simulated and observed MDT are above 0.95 for all of the CMIP5 models (Figure Few studies have assessed the performance of models in simulating 9.16), an improvement compared to CMIP3. MDT biases over tropical Mixed Layer Depth (MLD). In the North East Pacific region, Jang et al. ocean regions are consistent with surface wind stress biases (Lee et al., (2011) found that the CMIP3 models exhibit the observed deep MLD 2013). Over the Antarctic Circumpolar Current, the parameterization of in the Kuroshio Extension, though with a deep bias and only one large eddy-induced transports is essential for the models density structure deep MLD region, rather than the observed two localized maxima. and thus MDT (Kuhlbrodt et al., 2012). High-resolution eddy resolving Other studies have noted MLD biases near sea ice edges (Capotondi ocean models show improved SSH simulations over coarser resolution et al., 2012). versions (McClean et al., 2006). Chapter 13 provides a more extensive 779 Chapter 9 Evaluation of Climate Models assessment of sea level changes in CMIP5 simulations, including com- from large ice sheets discussed in Section 9.1.3.2.7). However, glob- parisons with century-scale historical records. al-scale changes in OHC are highly correlated with the thermosteric contribution to global SSH changes (Domingues et al., 2008). Approx- Ocean heat content (OHC) depends only on ocean temperature, where- imately half of the historical CMIP3 simulations did not include the as absolute changes in sea level are also influenced by processes that effects of volcanic eruptions, resulting in substantially greater than are only now being incorporated into global models (e.g., mass loss observed ocean heat uptake during the late 20th century (Gleckler et a) b) c) d) Clim. CSIRO Obs. CanESM2 CNRM-CM5 Mk3-6-0 0.54 1.90 0.72 9 e) f) g) h) GFDL ESM2M HiGEM GISS-E2-R HadGM2-ES 1.52 0.67 1.24 0.62 i) j) k) l) IPSL MPI MRI CM5A-LR ESM-LR CGCM3 NorESM1-M 0.66 0.94 1.34 0.73 m) n) o) p) GFDL MIROC ESM2G INMCM4 MIROC4h ESM-CHEM 0.72 1.30 1.14 0.52 (°C) -2 -1 0 1 2 3 -2 -1 0 1 2 Figure 9.15 | Time-mean bottom potential temperature in the Southern Ocean, observed (a) and the differences between individual CMIP5 models and observations (b p); left colour bar corresponds to the observations, right colour bar to the differences between model and observations (same unit). Thick dashed black line is the mean August sea ice extent (concentration >15%); thick continuous black line is the mean February sea ice extent (concentration >15%). Numbers indicate the area-weighted root-mean-square (RMS) error for all depths between the model and the climatology (unit °C); mean RMS error = 0.97 °C. (After Heuzé et al., 2013.) 780 Evaluation of Climate Models Chapter 9 0.9 Observations GFDL ESM2M CMIP5 mean GISS E2 R CMIP3 mean HadGEM2 CC 0.8 ACCESS1.0 HadGEM2 ES C o ACCESS1.3 INM CM4 rr BCC CSM1.1 IPSL CM5A LR e CCSM4 IPSL CM5A MR la 0.7 CESM1(BGC) IPSL CM5B LR t CESM1(WACCM) MIROC ESM CHEM io CMCC CM MIROC ESM Standard deviation n CNRM CM5 MIROC4h 0.57 CSIRO Mk3.6.0 MIROC5 0.8 CanCM4 MPI ESM LR 0.5 CanESM2 MPI ESM MR EC EARTH MPI ESM P GFDL CM2p1 MRI CGCM3 GFDL CM3 NorESM1 ME 0.9 0.4 GFDL ESM2G NorESM1 M 9 0.6 0.95 0 .5 0.4 SD RM 0 .3 0.99 0.2 CMIP5 mean Observations 0.1 0 CMIP3 mean 0 0.4 0.5 0.57 0.7 0.8 0.9 Figure 9.16 | Taylor diagram for the dynamic sea surface height climatology (1987 2000). The radial coordinate shows the standard deviation of the spatial pattern, normalized by the observed standard deviation. The azimuthal variable shows the correlation of the modelled spatial pattern with the observed spatial pattern. The root-mean square error with bias removed is indicated by the dashed grey circles about the observational point. Analysis is for the global ocean, 50°S to 50°N. The reference data set is Archiving, Validation and Interpretation of Satellite Oceanographic data (AVISO), a merged satellite product (Ducet et al., 2000), which is described in Chapter 3. One realization per model is shown for each CMIP5 and CMIP3 model result. Grey filled circles are for individual CMIP3 models; other symbols as in legend. al., 2006; Domingues et al., 2008). Figure 9.17 shows observed and the 20th century. This will result in biased thermosteric sea level rise simulated global 0 to 700 m and total OHC changes during the overlap for millennial projections. Calibrated EMICs (Meinshausen et al., 2009; period of the observational record and the CMIP5 historical experiment Sokolov et al., 2010) would remove such biases. (1961 2005). Three upper-ocean observational estimates, assessed in Chapter 3, are also shown to indicate observational uncertainty. The In idealized CMIP5 experiments (CO2 increasing 1% yr 1), the heat CMIP5 multi-model mean falls within the range of observations for uptake efficiency of the CMIP5 models varies by a factor of two, most of the period, and the intermodel spread is reduced relative to explaining about 50% of the model spread (Kuhlbrodt and Gregory, CMIP3 (Gleckler et al., 2006; Domingues et al., 2008). This may result 2012). Despite observational uncertainties, this recent work also pro- from most CMIP5 models including volcanic forcings. When the deep vides limited evidence that in the upper 2000 m, most CMIP5 models ocean is included, the CMIP5 multi-model mean also agrees well with are less stratified (in the global mean) than is observed, which sug- the observations, although the deeper ocean estimates are much more gests that these models transport heat downwards more efficiently uncertain (Chapter 3). There is  high confidence  that  many CMIP5 than the real ocean. These results are consistent with earlier studies models reproduce the observed increase in ocean heat content since (Forest et al., 2006, 2008; Boe et al., 2009a; Sokolov et al., 2010) that 1960. conclude the CMIP3 models may overestimate oceanic mixing efficien- cy and therefore underestimate the Transient Climate Response (TCR) EMIC results for changes in total OHC are also compared with obser- and its impact on future surface warming. However, Kuhlbrodt and vations in Figure 9.17. (Note: results in this figure are based on Eby Gregory (2012) also find that this apparent bias explains very little of et al. (2013) who show OHC changes for 0 to 2000 m, whereas here the model spread in TCR. Although some progress has been made in the time-integrated net heat flux into the ocean surface is shown to understanding mixing deficiencies in ocean models (Griffies and Great- compare with CMIP5 results (Figure 9.17b)). There is a tendency for batch, 2012; Ilicak et al., 2012), this remains a key challenge in improv- the EMICs to overestimate total OHC changes and this could alter the ing the representation of physical processes that impact the evolution temperature related feedbacks on the oceanic carbon cycle, and affect of ocean heat content and thermal expansion. the long-term millennium projections in Chapter 12. However, it should be noted that high OHC changes can compensate for biases in climate sensitivity or RF so as to reproduce surface temperature changes over 781 Chapter 9 Evaluation of Climate Models 9 Figure 9.17 | Time series of simulated and observed global ocean heat content anomalies (with respect to 1971). CMIP5 historical simulations and observations for both the upper 700 meters of the ocean (a) as well as for the total ocean heat content (b). Total ocean heat content results are also shown for EMICs and observations (c). EMIC estimates are based on time-integrated surface heat flux into the ocean. The 0 to 700 m and total heat content observational estimates (thick lines) are respectively described in Figure 3.2 and Box 3.1, Figure 1. Simulation drift has been removed from all CMIP5 runs with a contemporaneous portion of a quadratic fit to each corresponding pre-industrial control run (Gleckler et al., 2012). Units are 1022 Joules. 9.4.2.3 Simulation of Circulation Features Important for of the northward oceanic heat transport. Long-term AMOC estimates Climate Response have had to be inferred from hydrographic measurements sporadically available over the last decades (e.g., Bryden et al., 2005; Lumpkin et 9.4.2.3.1 Simulation of recent ocean circulation al., 2008, Chapter 3.6.3). Continuous AMOC monitoring at 26.5°N was started in 2004 (Cunningham et al., 2007) and now provides a 5-year Atlantic Meridional Overturning Circulation mean value of 18.5 Sv with annual means having a standard devia- The Atlantic Meridional Overturning Circulation (AMOC) consists of tion of 1 Sv (McCarthy et al., 2012). The ability of models to simulate northward transport of shallow warm water overlying a southward this important circulation feature is tied to the credibility of simulated transport of deep cold water and is responsible for a considerable part AMOC weakening during the 21st century because the magnitude of 782 Evaluation of Climate Models Chapter 9 the weakening is correlated with the initial AMOC strength (Gregory range in the zonal mean ACC position is smaller than in the CMIP3 et al., 2005). The mean AMOC strength in CMIP5 models ranges from ensemble (in CMIP5, the mean transport is 148 Sv and the standard 15 to 30 Sv for the historical period which is comparable to the CMIP3 deviation is 50 Sv across an ensemble of 21 models).  models (Weaver et al., 2012; see Figure 12.35). The variability of the AMOC is assessed in Section 9.5.3.3.1. Simulation of glacial ocean conditions Reconstructions of the last glacial maximum from sediment cores dis- Southern Ocean circulation cussed in Chapter 5 indicate that the regions of deep water forma- The Southern Ocean is an important driver for the meridional over- tion in the North Atlantic were shifted southward, that the boundary turning circulation and is closely linked to the zonally continuous between North Atlantic Deep Water (NADW) and Antarctic Bottom Antarctic Circumpolar Current (ACC). Gupta et al. (2009) noted that Water (AABW) was substantially shallower than today, and that relatively small deficiencies in the position of the ACC lead to more NADW formation was less intense (Duplessy et al., 1988; Dokken obvious biases in the SST in the models. The ability of CMIP3 models and Jansen, 1999; McManus et al., 2004; Curry and Oppo, 2005). This to adequately represent Southern Ocean circulation and water masses signal, although estimated from a limited number of sites, is robust seems to be affected by several factors (Russell et al., 2006). The most (see Chapter 5). The AR4 reported that model simulations showed a important are the strength of the westerlies at the latitude of the Drake wide range of AMOC response to LGM forcing (Weber et al., 2007), Passage, the heat flux gradient over this region, and the change in with some models exhibiting reduced strength of the AMOC and its 9 salinity with depth across the ACC. Kuhlbrodt et al. (2012) found that extension at depth and other showing no change or an increase. Figure the strongest influence on ACC transport in the CMIP3 models was the 9.18 provides an update of the diagnosis proposed by Otto-Bliesner Gent-McWilliams thickness diffusivity. The ACC has a typical transport et al. (2007) to compare model results with the deep ocean data from through the Drake Passage of about 135 Sv (e.g., Cunningham et al., Adkins et al. (2002) using PMIP2 and CMIP5/PMIP3 pre-industrial and 2003). A comparison of CMIP5 models (Meijers et al., 2012) shows LGM simulations (Braconnot et al., 2012). These models reproduce the that, firstly, the ACC transport through Drake Passage is improved as modern deep ocean temperature salinity (T S) structure in the Atlan- compared to the CMIP3 models, and secondly, that the inter-model tic basin, but most of them do not capture the cold and salty bottom Present LGM N. Atl. N. Atl. = Site 981, 55.5N 14.5W 2184m N. Atl. SO SO = Site 1093, 50S, 6E 3626m SO Observed Black circles 44.8 6 CMIP5/PMIP3 Big colored triangles 45.2 CCSM4 CNRM-CM5 45.6 Potential temperature (°C) FGOALS-g2 4 GISS-E2-R_p150 GISS-E2-R_p151 46 IPSL-CM5A-LR MIROC-ESM 46.4 46.8 47.2 MPI-ESM-P_p1 2 MPI-ESM-P_p2 MRI-CGCM3 47.6 PMIP2 48 Small grey triangles 0 CCSM ECBILT 48.4 ECHAM_oav FGOALS HadCM -2 48. 8 HadCM_oav IPSL MIROC 34.0 34.5 35.0 35.5 36.0 36. 5 37. 0 37. 5 Salinity (psu) Figure 9.18 | Temperature and salinity for the modern period (open symbols) and the Last Glacial Maximum (LGM, filled symbols) as estimated from proxy data at Ocean Drilling Program (ODP) sites (black symbols, from Adkins et al., 2002) and simulated by the Paleoclimate Modelling Intercomparison Project version 2 (PMIP2, small triangles) and PMIP3/ CMIP5 (big triangles) models. The isolines represent lines of equal density. Site 981 (triangles) is located in the North Atlantic (Feni Drift, 55N, 15W, 2184 m). Site 1093 (upside- down triangles) is located in the South Atlantic (Shona Rise, 50S, 6E, 3626 m). In PMIP2, only Community Climate System Model (CCSM) included a 1 psu adjustment of ocean salinity at initialization to account for freshwater frozen into LGM ice sheets; the other PMIP2 model-simulated salinities have been adjusted to allow a comparison. In PMIP3, all simulations include the 1 psu adjustment as required in the PMIP2/CMIP5 protocol (Braconnot et al., 2012). The dotted lines allow a comparison of the values at the NH and SH sites for a same model. This figure is adapted from Otto-Bliesner et al. (2007). 783 Chapter 9 Evaluation of Climate Models water suggested by the LGM reconstructions, providing evidence that allow useful evaluation of models. This is still the case and so the focus processes responsible for such palaeoclimate changes may not be well here is on an integrated quantity, meridional heat transport, which is reproduced in contemporary climate models. This is expected to also less prone to errors. Surface wind stress is better observed and models affect projected changes in deep ocean properties. are evaluated against observed products below. 9.4.2.4 Simulation of Surface Fluxes and Meridional Transports The zonal component of wind stress is particularly important in driv- ing ocean surface currents; modelled and observed values are shown Surface fluxes play a large part in determining the fidelity of ocean in Figure 9.19. At middle to high latitudes, the model-simulated wind simulations. As noted in the AR4, large uncertainties in surface heat stress maximum lies 5 to 10 degree equatorward of that in the obser- and fresh water flux observations (usually obtained indirectly) do not vationally based estimates, and so mid-latitude westerly winds are 9 Figure 9.19 | Zonal-mean zonal wind stress over the oceans in (a) CMIP5 models and (b) multi-model mean comparison with CMIP3. Shown is the time-mean of the period 1970 1999 from the historical simulations. The black solid, dashed, and dotted curves represent ECMWF reanalysis of the global atmosphere and surface conditions (ERA)-Interim (Dee et al., 2011), National Centers for Environmental Prediction/National Center for Atmospheric Research (NCEP/NCAR) reanalysis I (Kalnay et al., 1996), and QuikSCAT satellite measurements (Risien and Chelton, 2008), respectively. In (b) the shading indicates the inter-model standard deviation. 784 Evaluation of Climate Models Chapter 9 too strong in models. This equatorward shift in the southern ocean strong in the western Pacific, with no major improvement from CMIP3 is slightly reduced in CMIP5 relative to CMIP3. At these latitudes, the to CMIP5. largest near surface wind speed biases in CMIP5 are located over the Pacific sector and the smallest are in the Atlantic sector (Bracegirdle et The CMIP5 model simulations qualitatively agree with the various al., 2013). Such wind stress errors may adversely affect oceanic heat observational estimates on the most important features of ocean heat and carbon uptake (Swart and Fyfe, 2012a). At middle to low latitudes, transport (Figure 9.21) and, in a multi-model sense, no major change the CMIP3 and CMIP5 model spreads are smaller than at high lati- from CMIP3 can be seen. All CMIP5 models are able to the represent the tudes, although near the equator this can occur through compensating strong north-south asymmetry, with the largest values in the NH, con- errors (Figure 9.20). The simulated multi-model mean equatorial zonal sistent with the observational estimates. At most latitudes the majority wind stress is too weak in the Atlantic and Indian Oceans and too of CMIP5 model results fall within the range of observational estimates, 9 Figure 9.20 | Equatorial (2°S to 2°N averaged) zonal wind stress for the Indian, Pacific, and Atlantic oceans in (a) CMIP5 models and (b) multi-model mean comparison with CMIP3. Shown is the time-mean of the period 1970 1999 from the historical simulations. The black solid, dashed, and dotted curves represent ERA-Interim (Dee et al., 2011), National Centers for Environmental Prediction/National Center for Atmospheric Research (NCEP/NCAR) reanalysis I (Kalnay et al., 1996) and QuikSCAT satellite measurements (Risien and Chelton, 2008), respectively. In (b) the shading indicates the inter-model standard deviation. 785 Chapter 9 Evaluation of Climate Models 9 Figure 9.21 | Annual- and zonal-mean oceanic heat transport implied by net heat flux imbalances at the sea surface for CMIP5 simulations, under an assumption of negligible changes in oceanic heat content. Observational estimates include: the data set from Trenberth and Caron (2001) for the period February 1985 to April 1989, derived from reanalysis products from the National Centers for Environmental Prediction/National Center for Atmospheric Research (NCEP/NCAR; Kalnay et al., 1996; dash-dotted black) and European Centre for Medium Range Weather Forecasts 40-year reanalysis (ERA40; Uppala et al., 2005; short-dashed black), an updated version by Trenberth and Fasullo (2008) with improved top of the atmosphere (TOA) radiation data from the Clouds and Earth s Radiant Energy System (CERES) for March 2000 to May 2004, and updated NCEP reanalysis (Kistler et al., 2001) up to 2006 (solid black), the Large and Yeager (2009) analysis based on the range of annual mean transport estimated over the years 1984 2006, computed from air sea surface fluxes adjusted to agree in the mean with a variety of satellite and in situ measurements (long-dashed black), and direct estimates by Ganachaud and Wunsch (2003) obtained from hydrographic sections during the World Ocean Circulation Experiment combined with inverse models (black diamonds). The model climatologies are derived from the years 1986 to 2005 in the historical simulations in CMIP5. The multi-model mean is shown as a thick red line. The CMIP3 multi-model mean is added as a thick blue line. although there is some suggestion of modest underestimate between along the equator, the structure of the equatorial current system, and 15°N and 25°N and south of about 60°S. Some models show an equa- the excessive equatorial cold tongue (Reichler and Kim, 2008; Brown torward transport at Southern-Hemisphere mid-latitudes that is also et al., 2010a; Zheng et al., 2012). Many reasons for these biases have featured in the observation estimate of Large and Yeager (2009). This been proposed, such as: too strong trade winds; a too diffusive ther- highlights the difficulties in representing large-scale energy processes mocline; deficient horizontally isotropic mixing coefficients; insufficient in the Southern ocean as discussed by Trenberth and Fasullo (2010b). penetration of solar radiation; and too weak tropical instability waves Note that climate models should exhibit a vanishing net energy balance (Meehl et al., 2001; Wittenberg et al., 2006; Lin, 2007). It is noteworthy when long time averages are considered but unphysical sources and that CMIP5 models exhibit some improvements in the western equato- sinks lead to energy biases (Trenberth and Fasullo, 2009, 2010a; Luca- rial Pacific when compared to CMIP3, with reduced SST and trade wind rini and Ragone, 2011) that are also found in reanalysis constrained by errors (Figures 9.14 and 9.20). Because of strong interactions between observations (Trenberth et al., 2009). When correcting for the imperfect the processes involved, it is difficult to identify the ultimate source of closure of the energy cycle, as done here, comparison between models these errors, although new approaches using the rapid adjustment of and observational estimates become possible. initialized simulations hold promise (Vanniere et al., 2011). 9.4.2.5 Simulation of Tropical Mean State A particular problem in simulating the seasonal cycle in the tropical Pacific arises from the double ITCZ , defined as the appearance of a 9.4.2.5.1 Tropical Pacific Ocean spurious ITCZ in the SH associated with excessive tropical precipita- tion. Further problems are too strong a seasonal cycle in simulated Although the basic east west structure of the tropical Pacific is well SST and winds in the eastern Pacific and the appearance of a spurious captured, models have shown persistent biases in important proper- semi-annual cycle. The latter has been attributed to meridional asym- ties of the mean state (AchutaRao and Sperber, 2002; Randall et al., metry in the background state that is too weak, possibly in conjunc- 2007; Guilyardi et al., 2009b) with severe local impacts (Brown et al., tion with incorrect regional water vapour feedbacks (Li and Philander, 2012). Among these biases are the mean thermocline depth and slope 1996; Guilyardi, 2006; Timmermann et al., 2007; De Szoeke and Xie, 2008; Wu et al., 2008a; Hirota et al., 2011). 786 Evaluation of Climate Models Chapter 9 A further persistent problem is insufficient marine stratocumulus cloud ocean heat uptake, sea level rise, and coupled modes of variability. in the eastern tropical Pacific, caused presumably by weak coastal There is little evidence that CMIP5 models differ significantly from upwelling off South America leading to a warm SST bias (Lin, 2007). CMIP3, although there is some evidence of modest improvement. Many Although the problem persists, improvements are being made (Achuta- improvements are seen in individual CMIP5 ocean components (some Rao and Sperber, 2006). now including interactive ocean biogeochemistry) and the number of relatively poor-performing models has been reduced (thereby reducing 9.4.2.5.2 Tropical Atlantic Ocean inter-model spread). New since the AR4, process-based model evalua- tion is now helping identify the cause of some specific biases, helping CMIP3 and CMIP5 models exhibit severe biases in the tropical Atlantic to overcome the limits set by the short observational records available. Ocean, so severe that some of the most fundamental features the east west SST gradient and the eastward shoaling thermocline along 9.4.3 Sea Ice the equator cannot be reproduced (Figure 9.14; (Chang et al., 2007; Chang et al., 2008; Richter and Xie, 2008; Richter et al., 2013). In many Evaluation of sea ice performance requires accurate information on ice models, the warm SST bias along the Benguela coast is in excess of concentration, thickness, velocity, salinity, snow cover and other fac- 5°C and the Atlantic warm pool in the western basin is grossly under- tors. The most reliably measured characteristic of sea ice remains sea estimated (Liu et al., 2013a). As in the Pacific, CMIP3 models suffer the ice extent (usually understood as the area covered by ice with a con- 9 double ITCZ error in the Atlantic. Hypotheses for the complex Atlantic centration above 15%). Caveats, however, exist related to the uneven bias problem tend to draw on the fact that the Atlantic Ocean has a far reliability of different sources of sea ice extent estimates (e.g., satellite smaller basin, and thus encourages a tighter and more complex land vs. pre-satellite observations; see Chapter 4), as well as to limitations of atmosphere ocean interaction. A recent study using a high-resolution this characteristic as a metric of model performance (Notz et al., 2013). coupled model suggests that the warm eastern equatorial Atlantic SST bias is more sensitive to the local rather than basin-wide trade wind bias and to a wet Congo basin instead of a dry Amazon a finding that differs from previous studies (Patricola et al., 2012). Recent ocean model studies show that a warm subsurface temperature bias in the Sea ice extent (106 km2) eastern equatorial Atlantic is common to virtually all ocean models forced with best estimated surface momentum and heat fluxes, owing to problems in parameterization of vertical mixing (Hazeleger and Haarsma, 2005). Toniazzo and Woolnough (2013) show that among a variety of causes for the initial bias development, ocean atmosphere coupling is key for their maintenance. 9.4.2.5.3 Tropical Indian Ocean CMIP3 and CMIP5 models simulate equatorial Indian Ocean climate reasonably well (e.g., Figure 9.14), though most models produce weak westerly winds and a flat thermocline on the equator. The models show a large spread in the modelled depth of the 20°C isotherm in the east- ern equatorial Indian Ocean (Saji et al., 2006). The reasons are unclear Sea ice extent (106 km2) but may be related to differences in the various parameterizations of vertical mixing as well as the wind structure (Schott et al., 2009). CMIP3 models generally simulate the Seychelles Chagos thermocline ridge in the Southwest Indian Ocean, a feature important for the Indian monsoon and tropical cyclone activity in this basin (Xie et al., 2002). The models, however, have significant problems in accurately representing its seasonal cycle because of the difficulty in capturing the asymmetric nature of the monsoonal winds over the basin, resulting in too weak a semi-annual harmonic in the local Ekman pumping over the ridge region compared to observations (Yokoi et al., 2009b). In about half of the models, the thermocline ridge is displaced eastward associated Figure 9.22 | Mean (1980 1999) seasonal cycle of sea ice extent (the ocean area with a sea ice concentration of at least 15%) in the Northern Hemisphere (upper) and the with the easterly wind biases on the equator (Nagura et al., 2013). Southern Hemisphere (lower) as simulated by 42 CMIP5 and 17 CMIP3 models. Each model is represented with a single simulation. The observed seasonal cycles (1980 9.4.2.6 Summary 1999) are based on the Hadley Centre Sea Ice and Sea Surface Temperature (HadISST; Rayner et al., 2003), National Aeronautics and Space Administration (NASA; Comiso There is high confidence that the CMIP3 and CMIP5 models simulate and Nishio, 2008) and the National Snow and Ice Data Center (NSIDC; Fetterer et al., 2002) data sets. The shaded areas show the inter-model standard deviation for each the main physical and dynamical processes at play during transient ensemble. (Adapted from Pavlova et al., 2011.) 787 Chapter 9 Evaluation of Climate Models et al., 2012) that in some cases model improvements, such as new sea ice albedo parameterization schemes (e.g., Pedersen et al., 2009; Hol- land et al., 2012), have been responsible. (Holland et al., 2010) show that models with initially thicker ice generally retain more extensive ice throughout the 21st century, and indeed several of the CMIP5 models start the 20th century with rather thin winter ice cover promoting more rapid melt (Stroeve et al., 2012). Notz et al. (2013) caution, however, against direct comparison of modelled trends with observations unless the models internal variability is carefully taken into account. Their analysis of the MPI-ESM ensemble shows that internal variability in the Arctic can result in individual model realizations exhibiting a range of trends (negative, or even positive) for the 29-year-long period start- ing in 1979, even if the background climate is warming. According to the distribution of sea ice extent trends over the period 1979 2010 obtained in an ensemble of simulations with individual CMIP5 models 9 (Figure 9.24) about one quarter of the simulations shows a September trend in the Arctic as strong as, or stronger, than in observations. The majority of CMIP5 (and CMIP3) models exhibit a decreasing trend in SH austral summer sea ice extent over the satellite era, in contrast to the observed weak but significant increase (see Chapter 4). A large spread in the modelled trends is present, and a comparison of multi- ple ensemble members from the same model suggests large internal Figure 9.23 | Sea ice distribution (1986 2005) in the Northern Hemisphere (upper panels) and the Southern Hemisphere (lower panels) for February (left) and Septem- variability during the late 20th century and the first decade of the 21st ber (right). AR5 baseline climate (1986 2005) simulated by 42 CMIP5 AOGCMs. Each century (e.g., Landrum et al., 2012; Zunz et al., 2013). Compared to model is represented with a single simulation. For each 1° × 1° longitude-latitude grid observations, CMIP5 models strongly overestimate the variability of cell, the figure indicates the number of models that simulate at least 15% of the area sea ice extent, at least in austral winter (Zunz et al., 2013).Therefore, covered by sea ice. The observed 15% concentration boundaries (red line) are based on using the models to assess the potential role of the internal variability the Hadley Centre Sea Ice and Sea Surface Temperature (HadISST) data set (Rayner et al., 2003). (Adapted from Pavlova et al., 2011.) in the trend of sea ice extent in the Southern Ocean over the last three decades presents a significant challenge. The CMIP5 multi-model ensemble exhibits improvements over CMIP3 Sea ice is a product of atmosphere ocean interaction. There are a in simulation of sea ice extent in the both hemispheres (Figure 9.22). number of ways in which sea ice is influenced by and interacts with the In the Arctic, the multi-model mean error do not exceed 10% of the atmosphere and ocean, and some of these feedbacks are still poorly observationally based estimates for any given month. In the Antarc- quantified. As noted in the AR4, among the primary causes of biases tic, the corresponding multi-model mean error exceeds 10% (but is in simulated sea ice extent, especially its geographical distribution, are less than 20%) near the annual minimum of sea ice extent; around problems with simulating high-latitude winds, ocean heat advection the annual maximum, the CMIP5 multi-model mean shows a clear and mixing. For example, Koldunov et al. (2010) have shown, for a par- improvement over CMIP3. ticular CMIP3 model, that significant ice thickness errors originate from biases in the atmospheric component. Similarly, Melsom et al. (2009) In many models the regional distribution of sea ice concentration is note sea ice improvements associated with improved description of poorly simulated, even if the hemispheric extent is approximately cor- heat transport by ocean currents. Biases imparted on modelled sea ice, rect. In Figure 9.23, however, one can see that the median ice edge common to many models, may also be related to representation of position (indicated by the colour at which half of the models have ice high-latitude processes (e.g., polar clouds) or processes not yet com- of 15% concentration) agrees reasonably well with observations in monly included in models (e.g., deposition of carbonaceous aerosols both hemispheres (except austral summer in Antarctica), as was the on snow and ice). Some CMIP5 models show improvements in simula- case for the CMIP3 models. tion of sea ice that are connected to improvements in simulation of the atmosphere (e.g., Notz et al., 2013). A widely discussed feature of the CMIP3 models as a group is a pro- nounced underestimation of the trend in the September (annual min- 9.4.3.1 Summary imum) sea ice extent in the Arctic over the past several decades (e.g., Stroeve et al., 2007; Zhang, 2010; Rampal et al., 2011; Winton, 2011). CMIP5 models reproduce the seasonal cycle of sea ice extent in both Possible reasons for the discrepancy include variability inherent to high hemispheres. There is robust evidence that the downward trend in latitudes, model shortcomings, and observational uncertainties (e.g., Arctic summer sea ice extent is better simulated than at the time of Kattsov et al., 2010; Kay et al., 2011; Day et al., 2012). Compared to the AR4, with about one quarter of the simulations showing a trend CMIP3, the CMIP5 models better simulate the observed trend of Sep- as strong as, or stronger than, that observed over the satellite era. The tember Arctic ice extent (Figure 9.24). It has been suggested (Stroeve performance improvements are not only a result of improvements in 788 Evaluation of Climate Models Chapter 9 Sea ice extent (106 km2) 9 Sea ice extent (106 km2) (c) CMIP5 Arctic ice extent September trends (1979 - 2010) (d) CMIP5 Antarctic ice extent February trends (1979 - 2010) (106 km2 per decade) (106 km2 per decade) Figure 9.24 | (Top and middle rows) Time series of sea ice extent from 1900 to 2012 for (a) the Arctic in September and (b) the Antarctic in February, as modelled in CMIP5 (coloured lines) and observations-based (NASA; Comiso and Nishio, 2008) and NSIDC; (Fetterer et al., 2002), solid and dashed thick black lines, respectively). The CMIP5 multi- model ensemble mean (thick red line) is based on 37 CMIP5 models (historical simulations extended after 2005 with RCP4.5 projections). Each model is represented with a single simulation. The dotted black line for the Arctic in (a) relates to the pre-satellite period of observation-based time series (Stroeve et al., 2012). In (a) and (b) the panels on the right are based on the corresponding 37-member ensemble means from CMIP5 (thick red lines) and 12-model ensemble means from CMIP3 (thick blue lines). The CMIP3 12-model means are based on CMIP3 historical simulations extended after 1999 with Special Report on Emission Scenarios (SRES) A2 projections. The pink and light blue shadings denote the 5 to 95 percentile range for the corresponding ensembles. Note that these are monthly means, not yearly minima. (Adapted from Pavlova et al., 2011.) (Bottom row) CMIP5 sea ice extent trend distributions over the period 1979 2010 for (c) the Arctic in September and (d) the Antarctic in February. Altogether 66 realizations are shown from 26 different models (historical simulations extended after 2005 with RCP4.5 projections). They are compared against the observations-based estimates of the trends (green vertical lines in (c) and (d) from Comiso and Nishio (2008); blue vertical line in (d) from Parkinson and Cavalieri (2012)). In (c), the observations-based estimates (Cavalieri and Parkinson, 2012; Comiso and Nishio, 2008) coincide. 789 Chapter 9 Evaluation of Climate Models sea ice components themselves but also in atmospheric circulation. Most CMIP5 models simulate a decrease in Antarctic sea ice extent over the past few decades compared to the small but significant increase observed. 9.4.4 Land Surface, Fluxes and Hydrology The land surface determines the partitioning of precipitation into evap- otranspiration and runoff, and the partitioning of surface net radiation into sensible and latent heat fluxes. Land surface processes therefore impact strongly on both the climate and hydrological resources. This subsection summarizes recent studies on the evaluation of land sur- face models, wherever possible emphasizing their performance in CMIP3 and CMIP5 climate models. 9 9.4.4.1 Snow Cover and Near-Surface Permafrost The modelling of snow and near-surface permafrost (NSP) processes has received increased attention since the AR4, in part because of the recognition that these processes can provide significant feedbacks on climate change (e.g., Koven et al., 2011; Lawrence et al., 2011). The SnowMIP2 project compared results from 33 snowpack models of vary- ing complexity, including some snow models that are used in AOGCMs, using driving data from five NH locations (Rutter et al., 2009). Most Figure 9.25 | Terrestrial snow cover distribution (1986 2005) in the Northern Hemi- snow models were found to be consistent with observations at open sphere (NH) as simulated by 30 CMIP5 models for February, updated for CMIP5 from Pavlova et al. (2007). For each 1° × 1° longitude-latitude grid cell, the figure indicates sites, but there was much greater discrepancy at forested sites due the number of models that simulate at least 5 kg m 2 of snow-water equivalent. The to the complex interactions between plant canopy and snow cover. observations-based boundaries (red line) mark the territory with at least 20% of the Despite these difficulties, the CMIP5 multi-model ensemble reproduces days per month with snow cover (Robinson and Frei, 2000) over the period 1986 2005. key features of the large-scale snow cover (Figure 9.25). In the NH, The annual mean 0°C isotherm at 3.3 m depth averaged across 24 CMIP5 models models are able to simulate the seasonal cycle of snow cover over (yellow line) is a proxy for the near-surface permafrost boundary. Observed permafrost extent in the NH (magenta line) is based on Brown et al. (1997, 1998). the northern parts of continents, with more disagreement in south- erly regions where snow cover is sparse, particularly over China and Mongolia (Brutel-Vuilmet et al., 2013). The latter weaknesses are asso- ciated with incorrect timing of the snow onset and melt, and possibly to differences in simulated surface climate and to varying abilities of with the choice of thresholds for diagnosing snow cover in the model the underlying land surface models. Even though many CMIP5 models output. In spite of the good performance of the multi-model mean, include some representation of soil freezing in mineral soils, very few there is a significant inter-model scatter of spring snow cover extent include key processes necessary to accurately model NSP changes, in some regions. There is a strong linear correlation between North- such as the distinct properties of organic soils, the existence of local ern-Hemisphere spring snow cover extent and annual mean surface water tables and the heat released by microbial respiration (Nicolsky et air temperature in the models, consistent with available observations. al., 2007; Wania et al., 2009; Koven et al., 2011, 2013). The recent negative trend in spring snow cover is underestimated by the CMIP5 (and CMIP3) models (Derksen and Brown, 2012), which is Despite large differences in the absolute NSP area, the relationship associated with an underestimate of the boreal land surface warming between the decrease in NSP area and the warming air temperature (Brutel-Vuilmet et al., 2013). over the present-day NSP region is similar, and approximately linear, in many models (Slater and Lawrence, 2013). Some CMIP5 models now represent NSP and frozen soil process- es (Koven et al., 2013), but this is not generally the case. Therefore 9.4.4.2 Soil Moisture and Surface Hydrology it is difficult to make a direct quantitative evaluation of most CMIP5 models against permafrost observations. A less direct but more inclu- The partitioning of precipitation into evapotranspiration and runoff is sive approach is to diagnose NSP extent using snow depths and skin highly dependent on the moisture status of the land surface, especially temperatures generated by climate models to drive a stand-alone mul- the amount of soil moisture available for evapotranspiration, which in ti-layer permafrost model (Pavlova et al., 2007). Figure 9.25 shows the turn depends on properties of the land cover such as the rooting depth result of using this approach on the CMIP5 ensemble. The multi-model of plants. mean is able to simulate the approximate location of the NSP bound- ary (as indicated by the 0°C soil temperature isotherm). However, the There has been a long history of off-line evaluation of land surface range of present-day (1986 2005) NSP area inferred from individu- schemes, aided more recently by the increasing availability of site-spe- al models spans a factor of more than six (~4 to 25 × 106 km2) due cific data (Friend et al., 2007; Blyth et al., 2010). Throughout this time, 790 Evaluation of Climate Models Chapter 9 representations of the land surface have significantly increased in DGVMs are designed to simulate the large-scale geographical distri- complexity, allowing the representation of key processes such as links bution of plant functional types and how these patterns will change between stomatal conductance and photosynthesis, but at the cost of in response to climate change, CO2 increases, and other forcing factors increasing the number of poorly known internal model parameters. (Cramer et al., 2001). These models typically include rather detailed These more sophisticated land surface models are based on physical representations of plant photosynthesis but less sophisticated treat- principles that should make them more appropriate for projections of ments of soil carbon, with a varying number of soil carbon pools. In future climate and increased CO2. However for specific data-rich sites, the absence of nitrogen limitations on CO2 fertilization, offline DGVMs current land surface models still struggle to perform as well as statis- agree qualitatively that CO2 increase alone will tend to enhance tical models in predicting year-to-year variations in latent and sensible carbon uptake on the land while the associated climate change will heat fluxes (Abramowitz et al., 2008) and runoff (Materia et al., 2010). tend to reduce it. There is also good agreement on the degree of CO2 fertilization in the case of no nutrient limitation (Sitch et al., 2008). There are few evaluations of the performance of land surface schemes However, under more extreme emissions scenarios the responses of in coupled climate models, but those that have been undertaken find the DGVMs diverge markedly. Large uncertainties are associated with major limitations associated with the atmospheric forcing rather than the responses of tropical and boreal ecosystems to elevated tempera- the land surface schemes themselves. For example, an evaluation of tures and changing soil moisture status. Particular areas of uncertainty the soil moisture simulations of CMIP3 models found that long-term are the high-temperature response of photosynthesis (Galbraith et al., 9 soil moisture trends could only be reproduced in models that simu- 2010), and the extent of CO2 fertilization (Rammig et al., 2010) in the lated the reduction in solar radiation at the surface associated with Amazonian rainforest. global dimming (Li et al., 2007). A comparison of simulated evapo- transpiration fluxes from CMIP3 against large-scale observation-based Most of the land surface models and DGVMs used in the CMIP5 estimates, showed underestimates in India and parts of eastern South models continue to neglect nutrient limitations on plant growth (see America, and overestimates in the western USA, Australia and China Section 6.4.6.2), even though these may significantly moderate the (Mueller et al., 2011). response of photosynthesis to CO2 (Wang and Houlton, 2009). Recent extensions of two land surface models to include nitrogen limitations Land atmosphere coupling determines the ability of climate models improve the fit to Free-Air CO2 Enrichment Experiments , and suggest to simulate the influence of soil moisture anomalies on rainfall, that models without these limitations are expected to overestimate droughts and high-temperature extremes (Fischer et al., 2007; Lorenz the land carbon sink in the nitrogen-limited mid and high latitudes et al., 2012). The coupling strength depends both on the sensitivity of (Thornton et al., 2007; Zaehle et al., 2010). evapotranspiration to soil moisture, which is determined by the land surface scheme, and the sensitivity of precipitation to evapotranspi- 9.4.4.4 Land Use Change ration, which is determined by the atmospheric model (Koster et al., 2004; Seneviratne et al., 2010). Comparison of climate model simu- A major innovation in the land component of ESMs since the AR4 is the lations to observations suggests that the models correctly represent inclusion of the effects of land use change associated with the spread the soil-moisture impacts on temperature extremes in southeastern of agriculture, urbanization and deforestation. These affect climate Europe, but overestimate them in central Europe (Hirschi et al., 2011). by altering the biophysical properties of the land surface, such as its The influence of soil moisture on rainfall varies significantly with region, albedo, aerodynamic roughness and water-holding capacity (Bondeau and with the lead-time between a soil moisture anomaly and a rainfall et al., 2007; Bonan, 2008; Bathiany et al., 2010; Levis, 2010). Land event (Seneviratne et al., 2010). In some regions, such as the Sahel, use change has also contributed almost 30% of total anthropogen- enhanced precipitation can even be induced by dry anomalies (Taylor ic CO2 emissions since 1850 (see Table 6.1), and affects emissions of et al., 2011). Recent analyses of CMIP5 models reveals considerable trace gases, and VOCs such as isoprene. The latest ESMs used in CMIP5 spread in the ability of the models to reproduce observed correlations attempt to model the CO2 emissions implied by prescribed land use between precipitation and soil moisture in the tropics (Williams et al., change and many also simulate the associated changes in the biophys- 2012), and a systematic failure to simulate the positive impact of dry ical properties of the land surface. This represents a major advance on soil moisture anomalies on rainfall in the Sahel (Taylor et al., 2012a). the CMIP3 models which typically neglected land use change, aside from its assumed contribution to anthropogenic CO2 emissions. 9.4.4.3 Dynamic Global Vegetation and Nitrogen Cycling However, the increasing sophistication of the modelling of the impacts At the time of the AR4 very few climate models included dynamic veg- of land use change has introduced additional spread in climate model etation, with vegetation being prescribed and fixed in all but a handful projections. The first systematic model intercomparison demonstrated of coupled climate carbon cycle models (Friedlingstein et al., 2006). that large-scale land cover change can significantly affect regional cli- Dynamic Global Vegetation Models (DGVMs) certainly existed at the mate (Pitman et al., 2009) and showed a large spread in the response time of the AR4 (Cramer et al., 2001) but these were not typically of different models to the same imposed land cover change (de Nob- incorporated in climate models. Since the AR4 there has been continual let-Ducoudre et al., 2012). This uncertainty arises from the often coun- development of offline DGVMs, and some climate models incorporate teracting effects of evapotranspiration and albedo changes (Boisier et dynamic vegetation in at least a subset of the runs submitted to CMIP5 al., 2012) and has consequences for the simulation of temperature and (also see Section 9.1.3.2.4), with likely consequences for climate model rainfall extremes (Pitman et al., 2012b). biases and regional climate projection (Martin and Levine, 2012). 791 Chapter 9 Evaluation of Climate Models 9.4.5 Carbon Cycle of models are within 50% of the uncertain observational estimates (Anav et al., 2013). An important development since the AR4 is the more widespread implementation of ESMs that include an interactive carbon cycle. Large-scale land atmosphere and global atmosphere fluxes are not Coupled climate-carbon cycle models are used extensively for the pro- directly measured, but global estimates can be made from the carbon jections presented in Chapter 12. The evaluation of the carbon cycle balance, and large-scale regional fluxes can be estimated from the within coupled models is discussed here, while the performance of the inversion of atmospheric CO2 measurements (see Section 6.3.2). Figure individual land and ocean carbon models, together with the detailed 9.26 shows modelled annual mean ocean atmosphere and net land analysis of climate carbon cycle feedbacks, is presented in Chapter 6 atmosphere CO2 fluxes from the historical simulations in the CMIP5 (Section 6.4 and Box 6.4). archive (Anav et al., 2013). Also shown are estimates derived from offline ocean carbon cycle models, measurements of atmospheric CO2, The transition from AOGCMs to ESMs was motivated in part by the and best estimates of the CO2 fluxes from fossil fuels and land use results from the first generation coupled climate carbon cycle models, change (Le Quere et al., 2009). Uncertainties in these latter annual which suggested that feedbacks between the climate and the carbon estimates are approximately +/-0.5 PgC yr 1, arising predominantly from cycle were uncertain but potentially very important in the context of the uncertainty in the model-derived ocean CO2 uptake. The confidence 9 21st century climate change (Cox et al., 2000; Friedlingstein et al., limits for the ensemble mean are derived by assuming that the CMIP5 2001). The first-generation models used in the Coupled Climate Carbon models form a t-distribution centred on the ensemble mean (Anav et Cycle Model Intercomparison Project (C4MIP) included both extended al., 2013). AOGCMS and EMICs. The C4MIP experimental design involved running each model under a common emission scenario (SRES A2) and cal- The evolution of the global ocean carbon sink is shown in the top culating the evolution of the global atmospheric CO2 concentration panel of Figure 9.26. The CMIP5 ensemble mean global ocean uptake interactively within the model. The impacts of climate carbon cycle (+/- standard deviation of the multi-model ensemble), computed using feedbacks were diagnosed by carrying out parallel uncoupled sim- all the 23 models that reported ocean CO2 fluxes, increases from 0.47 ulations in which increases in atmospheric CO2 did not influence cli- +/- 0.32 PgC yr 1 over the period 1901 1930 to 1.53 +/- 0.36 PgC yr 1 mate. Analysis of the C4MIP runs highlighted a greater than 200 ppmv for the period 1960 2005. For comparison, the Global Carbon Project range in the CO2 concentration by 2100 due to uncertainties in cli- (GCP) estimates a stronger ocean carbon sink of 1.92 +/- 0.3 PgC yr 1 for mate carbon cycle feedbacks, and that the largest uncertainties were 1960 2005 (Anav et al., 2013). The bottom panel of Figure 9.26 shows associated with the response of land ecosystems to climate and CO2 the variability in global land carbon uptake evident in the GCP esti- (Friedlingstein et al., 2006). mates, with the global land carbon sink being strongest during La Nina years and after volcanoes, and turning into a source during El Nino For CMIP5 a different experimental design was proposed in which the years. The CMIP5 models cannot be expected to precisely reproduce core simulations use prescribed RCPs of atmospheric CO2 and other this year-to-year variability as these models will naturally simulate cha- GHGs (Moss et al., 2010). Under a prescribed CO2 scenario, ESMs calcu- otic ENSO variability that is out of phase with the historical variability. late land and ocean carbon fluxes interactively, but these fluxes do not However, the ensemble mean does successfully simulate a strengthen- affect the evolution of atmospheric CO2. Instead the modelled land and ing global land carbon sink during the 1990s, especially after the Mt ocean fluxes, along with the prescribed increase in atmospheric CO2, Pinatubo eruption in 1991. The CMIP5 ensemble mean land atmos- can be used to diagnose the compatible emissions of CO2 consistent phere flux (+/- standard deviation of the multi-model ensemble) evolves with the simulation (see Section 6.3; Miyama and Kawamiya, 2009; from a small source of 0.34 +/- 0.49 PgC yr 1 over the period 1901 Arora et al., 2011). The compatible emissions for each model can then 1930, predominantly due to land use change, to a sink of 0.47 +/- 0.72 be evaluated against the best estimates of the actual historical CO2 PgC yr 1 in the period 1960 2005. The GCP estimates give a weaker emissions. Parallel model experiments in which the carbon cycle does sink of 0.36 +/- 1 PgC yr 1 for the 1960 2005 period. not respond to the simulated climate change (which are equivalent to the uncoupled simulations in C4MIP) provide a means to diagnose Figure 9.27 shows the ocean atmosphere fluxes (top panel) and mean climate carbon cycle feedbacks in terms of their impact on the com- land atmosphere fluxes (bottom panel) simulated by ESMs and EMICs patible emissions of CO2 (Hibbard et al., 2007). (Eby et al., 2013) for the period 1986 2005, and compares these to observation-based estimates from GCP and Atmospheric Tracer Carbon cycle model evaluation is limited by the availability of direct Transport Model Intercomparison Project (TRANSCOM3) atmospheric observations at appropriately large spatial scales. Field studies and inversions (Gurney et al., 2003). Unlike Figure 9.26, only models that eddy-covariance flux measurements provide detailed information on reported both land and ocean carbon fluxes are included in this figure. the land carbon cycle over short-time scales and for specific locations, The atmospheric inversions results are taken from the Japanese Meteo- and ocean inventories are able to constrain the long-term uptake of rological Agency (JMA) as this was the only TRANSCOM3 model that anthropogenic CO2 by the ocean (Sabine et al., 2004; Takahashi et al., reported results for all years of the 1986 2005 reference period. The 2009). However the stores of carbon on the land are less well-known, error bars on the observational estimates (red triangles) and the ESM even though these are important determinants of the CO2 fluxes from simulations (black diamonds) represent the interannual variation in land use change. ESM simulations vary by a factor of at least six in the form of the standard deviation of the annual fluxes. EMICs do global soil carbon (Anav et al., 2013; Todd-Brown et al., 2013) and by not typically simulate interannual variability, so only mean values are a factor of four in global vegetation carbon, although about two thirds shown for these models (green boxes). Here, as in Figure 9.26, the net 792 Evaluation of Climate Models Chapter 9 Atmosphere Ocean CO2 Flux (Pg C yr -1) 4 Sink 10 20 30 40 50 60 70 80 90 3 Confidence (%) 2 1 0 CMIP5 GCP Source 1 1900 1915 1930 1945 1960 1975 1990 2005 9 Year Atmosphere Land CO Flux (Pg C yr -1) 9 Sink 7 10 20 30 40 50 60 70 80 90 Confidence (%) 5 3 2 1 1 3 5 7 CMIP5 Source GCP 9 1900 1915 1930 1945 1960 1975 1990 2005 Year Figure 9.26 | Ensemble-mean global ocean carbon uptake (top) and global land carbon uptake (bottom) in the CMIP5 ESMs for the historical period 1900 2005. For comparison, the observation-based estimates provided by the Global Carbon Project (Le Quere et al., 2009) are also shown (thick black line). The confidence limits on the ensemble mean are derived by assuming that the CMIP5 models come from a t-distribution. The grey areas show the range of annual mean fluxes simulated across the model ensemble. This figure includes results from all CMIP5 models that reported land CO2 fluxes, ocean CO2 fluxes, or both (Anav et al., 2013). land atmosphere flux is Net Biome Productivity (NBP) which includes systematically underestimate the contemporary land carbon sink (Eby the net CO2 emissions from land use change as well as the changing et al., 2013). Some ESMs (notably GFDL-ESM2M and GFDL-ESM2G) carbon balance of undisturbed ecosystems. significantly overestimate the interannual variation in the global land atmosphere CO2 flux, with a possible consequence being an overes- For the period 1986 2005 the observation-based estimates of the timate of the vulnerability of tropical ecosystems to future climate global ocean carbon sink are 1.71 PgC yr 1 (JMA), 2.19 PgC yr 1 (GCP) change (Cox et al., 2013), and see Figure 9.45). All ESMs qualitatively and 2.33 PgC yr 1 (Takahashi et al., 2009). Taking into account the simulate the expected pattern of ocean CO2 fluxes, with outgassing in uncertainties in the mean values of these fluxes associated with inter- the tropics and uptake in the mid and high latitudes (Anav et al., 2013). annual variability, the observationally constrained range is approxi- However, there are systematic differences between the ESMs and the mately 1.4 to 2.4 PgC yr 1. All of the ESMs, and all but one of the JMA inversion estimates for the zonal land CO2 fluxes, with the ESMs EMICs, simulate ocean sinks within this range. The observation-based tending to produce weaker uptake in the NH, and simulating a net land estimates of GCP and JMA agree well on the mean global land carbon carbon sink rather than a source in the tropics. sink over the period 1986 2005, and most ESMs fit within the uncer- tainty bounds of these estimates (i.e., 1.17 +/- 1.06 PgC yr 1 for JMA). In summary, there is high confidence that CMIP5 ESMs can simu- The exceptions are two ESMs sharing common atmosphere and land late the global mean land and ocean carbon sinks within the range components (CESM1-BGC and NorESM1-ME) which model a net land of observation-based estimates. Overall, EMICs reproduce the recent carbon source rather than a sink over this period. The EMICs tend to global ocean CO2 fluxes uptake as well as ESMs, but estimate a lower 793 Chapter 9 Evaluation of Climate Models a) Global Atmosphere-Ocean CO2 Flux between 2000 and 2005 (Smith et al., 2011b). For the period 2001 to 2.5 2.3 2005, CMIP5 models underestimate the mean AOD at 550 nm relative (Pg C yr-1) 2.1 to satellite-retrieved AOD by at least 20% over virtually all land surfac- 1.9 es (Figure 9.28). The differences between the modelled and measured 1.7 1.5 AODs exceed the errors in the Multi-angle Imaging Spectro-Radiome- 1.3 ter (MISR) retrievals over land of +/-0.05 or 0.2×AOD (Kahn et al., 2005) b) Global Atmosphere-Land CO2 Flux and the RMS errors in the corrected Moderate Resolution Imaging 4.0 3.0 Spectrometer (MODIS) retrievals over ocean of 0.061(Shi et al., 2011). (Pg C yr-1) 2.0 1.0 0.0 The effects of sulphate and other aerosol species on surface insolation -1.0 through direct and indirect forcing appear to be one of the principal -2.0 causes of the global dimming between the 1960s and 1980s and DCESS HadGEM2-CC GCP CESM1-BGC HadGEM2-ES IPSL-CM5A-LR MPI-ESM-LR IPSL-CM5B-LR CanESM2 MIROC-ESM-CHEM Bern3D GENIE GFDL-ESM2G JMA UVic BNU-ESM MIROC-ESM UMD MRI -ESM1 NorESM1-ME IGSM MPI-ESM-MR IPSL-CM5A-MR GFDL-ESM2M MESMO subsequent global brightening in the last two decades (see Section 2.3.3.1). This inference is supported by trends in aerosol optical depth and trends in surface insolation under cloud-free conditions. Thirteen 9 out of 14 CMIP3 models examined by Ruckstuhl and Norris (2009) pro- duce a transition from dimming to brightening that is consistent Figure 9.27 | Simulation of global mean (a) atmosphere ocean CO2 fluxes ( fgCO2 ) with the timing of the transition from increasing to decreasing global and (b) net atmosphere land CO2 fluxes ( NBP ), by ESMs (black diamonds) and EMICs anthropogenic aerosol emissions. The transition from dimming to (green boxes), for the period 1986 2005. For comparison, the observation-based esti- mates provided by Global Carbon Project (GCP; Le Quere et al., 2009), and the Japa- brightening in both Europe and North America is well simulated with nese Meteorological Agency (JMA) atmospheric inversion (Gurney et al., 2003) are also the HadGEM2 model (Haywood et al., 2011). shown as the red triangles. The error bars for the ESMs and observations represent interannual variability in the fluxes, calculated as the standard deviation of the annual means over the period 1986 2005. land carbon sink compared with most ESMs while remaining consist- ent with the observations (Eby et al., 2013). With few exceptions, the CMIP5 ESMs also reproduce the large-scale pattern of ocean atmos- phere CO2 fluxes, with uptake in the Southern Ocean and northern mid-latitudes, and outgassing in the tropics. However, the geographical pattern of simulated land atmosphere fluxes agrees much less well with inversion estimates, which suggest a larger sink in the northern mid-latitudes, and a net source rather than a sink in the tropics. While there are also inherent uncertainties in atmospheric inversions, dis- crepancies like this might be expected from known deficiencies in the CMIP5 generation of ESMs namely the failure to correctly simulate nitrogen fertilization in the mid-latitudes, and a rudimentary treat- ment of the net CO2 emissions arising from land use change and forest regrowth. 9.4.6 Aerosol Burdens and Effects on Insolation 9.4.6.1 Recent Trends in Global Aerosol Burdens and Effects on Insolation The ability of CMIP5 models to simulate the current burden of tropo- spheric aerosol and the decadal trends in this burden can be assessed using observations of aerosol optical depth (AOD, see Section 7.3.1.2). The historical data used to drive the CMIP5 20th century simulations reflect recent trends in anthropogenic SO2 emissions, and hence these trends should be manifested in the modelled and observed AOD. During Figure 9.28 | (a): Annual mean visible aerosol optical depth (AOD) for 2001 through the last three decades, anthropogenic emissions of SO2 from North 2005 using the Moderate Resolution Imaging Spectrometer (MODIS) version 5 satellite America and Europe have declined due to the imposition of emission retrievals for ocean regions (Remer et al., 2008) with corrections (Zhang et al., 2008a; controls, while the emissions from Asia have increased. The combina- Shi et al., 2011) and version 31 of MISR retrievals over land (Zhang and Reid, 2010; Stevens and Schwartz, 2012). (b): The absolute error in visible AOD from the median of a tion of the European, North American, and Asians trends has yielded a subset of CMIP5 models historical simulations relative to the satellite retrievals of AOD global reduction in SO2 emissions of 20 Gg(SO2), or 15% between 1970 shown in (a). The model outputs for 2001 through 2005 are from 21 CMIP5 models with and 2000 although emissions subsequently increased by 9 Gg(SO2) interactive or semi-interactive aerosol representation. 794 Evaluation of Climate Models Chapter 9 9 Figure 9.29 | Time series of global oceanic mean aerosol optical depth (AOD) from individual CMIP5 models historical (1850 2005) and RCP4.5 (2006 2010) simulations, cor- rected Moderate Resolution Imaging Spectrometer (MODIS) satellite observations by Shi et al. (2011) and Zhang et al. (2008a), and the Atmospheric Chemistry and Climate Model Intercomparison Project (ACCMIP) simulations for the 1850s by Shindell et al. (2013b). These recent trends are superimposed on a general upward trend in emissions. The use of a single set of emissions removes an important, aerosol loading since 1850 reflected by an increase in global mean but not dominant, source of uncertainty in the AR5 simulations of the oceanic AODs from the CMIP5 historical and RCP 4.5 simulations from sulphur cycle. In experiments based on a single chemistry climate 1850 to 2010 (Figure 9.29). Despite the use of common anthropogenic model with perturbations to both emissions and sulphur-cycle process- aerosol emissions for the historical simulations (Lamarque et al., 2010), es, uncertainties in emissions accounted for 53.3% of the ensemble the simulated oceanic AODs for 2010 range from 0.08 to 0.215, with variance (Ackerley et al., 2009). The next largest source of uncertain- nearly equal numbers of models over and underestimating the satellite ty was associated with the wet scavenging of sulphate (see Section retrieved AOD of 0.12 (Figure 9.29). This range in AODs results from 7.3.2), which accounted for 29.5% of the intra-ensemble variance and differing estimates of the trends and of the initial global mean oceanic represents the source/sink term with the largest relative range in the AOD at 1850 across the CMIP5 ensemble (Figure 9.29). aerosol models evaluated by Faloona (2009). Similarly, simulations run with heterogeneous or harmonized emissions data sets yielded approx- 9.4.6.2 Principal Sources of Uncertainty in Projections of imately the same intermodel standard deviation in sulphate burden of Sulphate Burdens 25 Tg. These results show that a dominant source of the spread among the sulphate burdens is associated with differences in the treatment Natural sources of sulphate from oxidation of dimethylsulphide (DMS) of chemical production, transport, and removal from the atmosphere emissions from the ocean surface are not specified under the RCP pro- (Liu et al., 2007; Textor et al., 2007). Errors in modelled aerosol burden tocol and therefore represent a source of uncertainty in the sulphur systematically affect anthropogenic RF (Shindell et al., 2013b). cycle simulated by the CMIP5 ensemble. In simulations of present-day conditions, DMS emissions span a 5 to 95% confidence interval of 10.7 to 28.1 TgS yr 1 (Faloona, 2009). After chemical processing, DMS 9.5 Simulation of Variability and Extremes contributes between 18 and 42% of the global atmospheric sulphate burden and up to 80% of the sulphate burden over most the SH (Car- 9.5.1 Importance of Simulating Climate Variability slaw et al., 2010). Several CMIP5 models include prognostic calculation of the biogenic DMS source; however, the effects from differences in The ability of a model to simulate the mean climate, and the slow, DMS emissions on modelled sulphate burdens remain to be quantified. externally forced change in that mean state, was evaluated in the pre- vious section. However, the ability to simulate climate variability, both In contrast to CMIP3, the models in the CMIP5 ensemble are provid- unforced internal variability and forced variability (e.g., diurnal and ed with a single internally consistent set of future anthropogenic SO2 seasonal cycles) is also important. This has implications for the signal- 795 Chapter 9 Evaluation of Climate Models to-noise estimates inherent in climate change detection and attribu- understood. Other changes such as the representation of entrainment tion studies where low-frequency climate variability must be estimat- in deep convection (Stratton and Stirling, 2012), improved coupling ed, at least in part, from long control integrations of climate models between shallow and deep convection, and inclusion of density cur- (Section 10.2). It also has implications for the ability of models to rents (Peterson et al., 2009) have been shown to greatly improve the make quantitative projections of changes in climate variability and the diurnal cycle of convection over tropical land and provide a good rep- statistics of extreme events under a warming climate. In many cases, resentation of the timing of convection over land in coupled ocean the impacts of climate change will be experienced more profoundly in atmosphere simulations (Hourdin et al., 2013). Thanks to improve- terms of the frequency, intensity or duration of extreme events (e.g., ments like this, the best performing models in Figure 9.30 appear now heat waves, droughts, extreme rainfall events; see Section 12.4). The to be able to capture the land and ocean diurnal phase and amplitude ability to simulate climate variability is also central to achieving skill quite well. in climate prediction by initializing models from the observed climate state (Sections 11.1 and 11.2). 9.5.2.2 Blocking Evaluating model simulations of climate variability also provides a In the mid latitudes, climate is often characterized by weather regimes means to explore the representation of certain processes, such as the (see Chapter 2), amongst which blocking regimes play a role in the 9 coupled processes underlying the ENSO and other important modes of occurrence of extreme weather events (Buehler et al., 2011; Sillmann variability. A model s representation of the diurnal or seasonal cycle et al., 2011; Pfahl and Wernli, 2012). During blocking, the prevailing both of which represent responses to external (rotational or orbital) mid-latitude westerly winds and storm systems are interrupted by a forcing may also provide some insight into a model s sensitivity and local reversal of the zonal flow. Climate models in the past have uni- by extension, the ability to respond correctly to GHG, aerosol, volcanic versally underestimated the occurrence of blocking, in particular in the and solar forcing. Euro-Atlantic sector (Scaife et al., 2010). 9.5.2 Diurnal-to-Seasonal Variability There are important differences in methods used to identify blocking (Barriopedro et al., 2010a), and the diagnosed blocking frequency can 9.5.2.1 Diurnal Cycles of Temperature and Precipitation be very sensitive to details such as the choice of latitude (Barnes et al., 2012). Blocking indices can be sensitive to biases in the representa- The diurnally varying solar radiation received at a given location tion of mean state (Scaife et al., 2010) or in variability (Barriopedro drives, through complex interactions with the atmosphere, land sur- et al., 2010b; Vial and Osborn, 2012). When blocking is measured via face and upper ocean, easily observable diurnal variations in surface anomaly fields, rather than reversed absolute fields, model skill can and near-surface temperature, precipitation, level stability and winds. be high even in relatively low-resolution models (e.g., Sillmann and The AR4 noted that climate models simulated the global pattern of Croci-Maspoli, 2009). the diurnal temperature range, zonally and annually averaged over the continent, but tended to underestimate its magnitude in many regions Recent work has shown that models with high horizontal (Matsueda, (Randall et al., 2007). New analyses over land indicate that model 2009; Matsueda et al., 2009, 2010) or vertical resolution (Anstey et al., deficiencies in surface atmosphere interactions and the planetary 2013) are better able to simulate blocking. These improvement arise boundary layer are also expected to contribute to some of the diurnal from increased representation of orography and atmospheric dynamics cycle errors and that model agreement with observations depends on (Woollings et al., 2010b; Jung et al., 2012; Berckmans et al., 2013), as region, vegetation type and season (Lindvall et al., 2012). Analyses of well as reduced ocean surface temperature errors in the extra tropics CMIP3 simulations show that the diurnal amplitude of precipitation is (Scaife et al., 2011). Improved physical parameterizations have also realistic, but most models tend to start moist convection prematurely been shown to improve simulations of blocking (Jung et al., 2010). over land (Dai, 2006; Wang et al., 2011a). Many CMIP5 models also However, as in CMIP3 (Scaife et al., 2010; Barnes et al., 2012), most of have peak precipitation several hours too early compared to surface the CMIP5 models still significantly underestimate winter Euro-Atlantic observations and TRMM satellite observations (Figure 9.30). This blocking (Anstey et al., 2013; Masato et al., 2012; Dunn-Sigouin and and the so-called drizzling bias (Dai, 2006) can have large adverse Son, 2013). These new results show that the representation of blocking impacts on surface evaporation and runoff (Qian et al., 2006). Over events is improving in models, even though the overall quality of CMIP5 the ocean, models often rain too frequently and underestimated the ensemble is medium. There is high confidence that model representa- diurnal amplitude (Stephens et al., 2010). It has also been suggested tion of blocking is improved through increases in model resolution. that a weak diurnal cycle of surface air temperature is produced over the ocean because of a lack of diurnal variations in SST (Bernie et al., 9.5.2.3 Madden Julian Oscillation 2008), and most models have difficulty with this due to coarse vertical resolution and coupling frequency (Dai and Trenberth, 2004; Danaba- During the boreal winter the eastward propagating feature known soglu et al., 2006). as the Madden Julian Oscillation (MJO; (Madden and Julian, 1972, 1994) predominantly affects the deep tropics, while during the boreal Improved representation of the diurnal cycle has been found with summer there is also northward propagation over much of southern increased atmospheric resolution (Sato et al., 2009; Ploshay and Lau, Asia (Annamalai and Sperber, 2005). The MJO has received much 2010) or with improved representation of cloud physics (Khairoutdinov attention given the prominent role it plays in tropical climate variabil- et al., 2005), but the reasons for these improvements remain poorly ity (e.g., monsoons, ENSO, and mid-latitudes; Lau and Waliser, 2011) 796 Evaluation of Climate Models Chapter 9 9 Figure 9.30 | Composite diurnal cycle of precipitation averaged over land (left) and ocean (right) for three different latitude bands at each local time and season (June July August (JJA), December January February (DJF), or their sum). For most of the CMIP5 models, data from 1980 2005 from the historical runs were averaged to derived the composite cycle; however, a few models had the required 3-hourly data only for 1990 2005 or 1996 2005. For comparison with the model results, a similar diagnosis from observations are shown (black solid line: surface-observed precipitation frequency; black dashed line: TRMM 3B42 data set, 1998 2003 mean). (Update of Figure 17 of Dai, 2006.) Phenomenological diagnostics (Waliser et al., 2009a) and process-ori- improved diurnal cycle do not necessarily produce an improved MJO ented diagnostics (e.g., Xavier, 2012) have been used to evaluate MJO (Mizuta et al., 2012). in climate models. An important reason for model errors in repre- senting the MJO is that convection parameterizations do not provide Most models underestimate the strength and the coherence of convec- sufficient build-up of moisture in the atmosphere for the large scale tion and wind variability (Lin et al., 2006; Lin and Li, 2008). The sim- organized convection to occur (Kim et al., 2012; Mizuta et al., 2012). plified metric shown in Figure 9.31 provides a synthesis of CMIP3 and Biases in the model mean state also contribute to poor MJO simulation CMIP5 model results (Sperber and Kim, 2012). It shows that simulation (Inness et al., 2003). High-frequency coupling with the ocean is also of the MJO is still a challenge for climate models (Lin et al., 2006; Kim an important factor (Bernie et al., 2008). While new parameterizations et al., 2009; Xavier et al., 2010). Most models have weak coherence in of convection may improve the MJO (Hourdin et al., 2013), this some- their MJO propagation (smaller maximum positive correlation). Even times occurs at the expense of a good simulation of the mean tropical so, relative to CMIP3 there has been improvement in CMIP5 in simu- climate (Kim et al., 2012). In addition, high resolution models with an lating the eastward propagation of boreal winter MJO convection from 797 Chapter 9 Evaluation of Climate Models 9 Figure 9.31 | (a, b) The two leading Empirical Orthogonal Functions (EOFs) of outgoing longwave radiation (OLR) from years of strong Madden Julian Oscillation (MJO) variability computed following Sperber (2003). The 20- to 100-day filtered OLR from observations and each of the CMIP5 historical simulations and the CMIP3 simulations of 20th century climate is projected on these two leading EOFs to obtain MJO Principal Component time series. The scatterplot (c) shows the maximum positive correlation between the resulting MJO Principal Components and the time lag at which it occurred for all winters (November to March). The maximum positive correlation is an indication of the coherence with which the MJO convection propagates from the Indian Ocean to the Maritime Continent/western Pacific, and the time lag is approximately one fourth of the period of the MJO. (Constructed following Sperber and Kim, 2012.) the Indian Ocean into the western Pacific (Hung et al., 2013) and north- The different monsoon systems are connected through the large-scale ward propagation during boreal summer (Sperber et al., 2012). In addi- tropical circulation, offering the possibility to evaluate a models rep- tion there is evidence that models reproduce MJO characteristics in the resentation of monsoon domain and intensity through the global mon- east Pacific (Jiang et al., 2012b), and that, overall, there is improvement soon concept (Wang and Ding, 2008; Wang et al., 2011a). The CMIP5 compared to previous generations of climate models (Waliser et al., multi-model ensemble generally reproduces the observed spatial pat- 2003; Lin et al., 2006; Sperber and Annamalai, 2008). terns but somewhat underestimates the extent and intensity, especial- ly over Asia and North America (Figure 9.32). The best model has simi- 9.5.2.4 Large-Scale Monsoon Rainfall and Circulation lar performance to the multi-model mean, whereas the poorest models fail to capture the monsoon precipitation domain and intensity over Monsoons are the dominant modes of annual variation in the tropics Asia and the western Pacific, Central America, and Australia. Fan et al. (Trenberth et al., 2000; Wang and Ding, 2008), and affect weather and (2010) also show that CMIP3 simulations capture the observed trend climate in numerous regions (Chapter 14). High-fidelity simulation of of weakening of the South Asian summer circulation over the past half the mean monsoon and its variability is of great importance for simulat- century, but are unable to reproduce the magnitude of the observed ing future climate impacts (Wang, 2006; Sperber et al., 2010; Colman et trend in precipitation. On longer time scales, mid-Holocene simulations al., 2011; Turner and Annamalai, 2012). The monsoon is characterized by show that even though models capture the sign of the monsoon pre- an annual reversal of the low level winds and well defined dry and wet cipitation changes, they tend to underestimate its magnitude (Bracon- seasons (Wang and Ding, 2008), and its variability is primarily connect- not et al., 2007b; Zhao and Harrison, 2012) ed to the MJO and ENSO (Section 9.5.3). The AR4 reported that most CMIP3 models poorly represent the characteristics of the monsoon and Poor simulation of the monsoon has been attributed to cold SST biases monsoon teleconnections (Randall et al., 2007), though improvement in over the Arabian Sea (Levine and Turner, 2012), a weak meridional CMIP5 has been noted for the mean climate, seasonal cycle, intrasea- temperature gradient (Joseph et al., 2012), unrealistic development sonal and interannual variability (Sperber et al., 2012). of the Indian Ocean dipole (Achuthavarier et al., 2012; Boschat et al., 798 Evaluation of Climate Models Chapter 9 9 Figure 9.32 | Monsoon precipitation intensity (shading, dimensionless) and monsoon precipitation domain (lines) are shown for (a) observation-based estimates from Global Pre- cipitation Climatology Project (GPCP), (b) the CMIP5 multi-model mean, (c) the best model and (d) the worst model in terms of the threat score for this diagnostic. These measures are based on the seasonal range of precipitation using hemispheric summer (May through September in the Northern Hemisphere (NH)) minus winter (November through March in the NH) values. The monsoon precipitation domain is defined where the annual range is >2.5 mm day 1, and the monsoon precipitation intensity is the seasonal range divided by the annual mean. The threat scores (Wilks, 1995) indicate how well the models represent the monsoon precipitation domain compared to the GPCP data. The threat score in panel (a) is between GPCP and CMAP rainfall to indicate observational uncertainty, whereas in the other panel it is between the simulations and the GPCP observational data set. A threat score of 1.0 would indicate perfect agreement between the two data sets. See Wang and Ding (2008),Wang et al. (2011a), and Kim et al. (2011) for details of the calculations. 2012) and changes to the circulation through excessive precipitation than that of human-induced climate change. The observational record over the southwest equatorial Indian Ocean (Bollasina and Ming, is usually too short to fully evaluate the representation of variability 2013). These biases lead to too weak inland moisture transport and an in models and this motivates the use of reanalysis or proxies, even underestimate of monsoon precipitation over India. Similar SST biases though these have their own limitations. contribute to model-data mismatch in the simulation of the mid-Hol- ocene Asian monsoon (Ohgaito and Abe-Ouchi, 2009), even though 9.5.3.1 Global Surface Temperature Multi-Decadal Variability the representation of atmospheric processes such as convection seems to dominate the model spread in this region (Ohgaito and Abe-Ouchi, The AR4 concluded that modelled global temperature variance at dec- 2009) or over Africa (Zheng and Braconnot, 2013). Factors that have adal to inter-decadal time scales was consistent with 20th century contributed to improved representation of the monsoon in some observations. In addition, results from the last millennium suggest that CMIP5 models include better simulation of topography-related mon- simulated variability is consistent with indirect estimates (Hegerl et soon precipitation due to higher horizontal resolution (Mizuta et al., al., 2007). 2012), a more realistic ENSO monsoon teleconnection (Meehl et al., 2012) and improved propagation of intraseasonal variations (Sperber Figure 9.33a shows simulated internal variability of mean surface tem- and Kim, 2012). The impact of aerosols on monsoon precipitation and perature from CMIP5 pre-industrial control simulations. Model spread its variability is the subject of ongoing investigation (Lau et al., 2008). is largest in the tropics and mid to high latitudes (Jones et al., 2012), where variability is also large; however, compared to CMIP3, the spread These results provide robust evidence that CMIP5 models simulate is smaller in the tropics owing to improved representation of ENSO var- more realistic monsoon climatology and variability than their CMIP3 iability (Jones et al., 2012). The power spectral density of global mean predecessors, but they still suffer from biases in the representation of temperature variance in the historical simulations is shown in Figure the monsoon domain and intensity leading to medium model quality at 9.33b and is generally consistent with the observational estimates. the global scale and declining quality at the regional scale. At longer time scale of the spectra estimated from last millennium 9.5.3 Interannual-to-Centennial Variability simulations, performed with a subset of the CMIP5 models, can be assessed by comparison with different NH temperature proxy records In addition to the annual, intra-seasonal and diurnal cycles described (Figure 9.33c; see Chapter 5 for details). The CMIP5 millennium sim- above, a number of other modes of variability arise on multi-annual to ulations include natural and anthropogenic forcings (solar, volcanic, multi-decadal time scales (see also Box 2.5). Most of these modes have GHGs, land use) (Schmidt et al., 2012). Significant differences between a particular regional manifestation whose amplitude can be larger unforced and forced simulations are seen for time scale larger than 799 Chapter 9 Evaluation of Climate Models 9 Figure 9.33 | Global climate variability as represented by: (a) Standard deviation of zonal-mean surface temperature of the CMIP5 pre-industrial control simulations (after Jones et al., 2012). (b) Power spectral density for 1901 2010 global mean surface temperature for both historical CMIP5 simulations and the observations (after Jones et al., 2012). The grey shading provides the 5 to 95% range of the simulations. (c) Power spectral density for Northern Hemisphere surface temperature from the CMIP5/ Paleoclimate Modelling Intercomparison Project version 3 (PMIP3) last-millennium simulations (colour, solid) using common external forcing configurations (Schmidt et al., 2012), together with the cor- responding pre-industrial simulations (colour, dashed), previous last-millennium AOGCM simulations (black: Fernandez-Donado et al., 2013), and temperature reconstructions from different proxy records (see Section 5.3.5). For comparison between model results and proxy records, the spectra in (c) have been computed from normalized Northern Hemisphere time series. The small panel included in the bottom panel shows for the different models and reconstructions the percentage of spectral density cumulated for periods longer than 50 years, to highlight the differences between unforced (pre-industrial control) and forced (PMIP3 and pre-PMIP3) simulations, compared to temperature reconstruction for the longer time scales. In (b) and (c) the spectra have been computed using a Tukey Hanning filter of width 97 and 100 years, respectively. The model outputs were not detrended, except for the MIROC-ESM millennium simulation. The 5 to 95% intervals (vertical lines) provide the accuracy of the power spectra estimated given a typical length of 110 years for (b) and 1150 years for (c). 800 Evaluation of Climate Models Chapter 9 50 years, indicating the importance of forced variability at these time 9.5.3.3 Atlantic Modes scales (Fernandez-Donado et al., 2013). It should be noted that a few models exhibit slow background climate drift which increases the 9.5.3.3.1 Atlantic Meridional Overturning Circulation variability spread in variance estimates at multi-century time scales. Nevertheless, the lines of evidence above suggest with high confidence that models Previous comparisons of the observed and simulated AMOC were reproduce global and NH temperature variability on a wide range of restricted to its mean strength, as it had only been sporadically time scales. observed (see Chapter 3 and Section 9.4.2.3.1). Continuous AMOC time series now exist for latitudes 41°N (reconstructions since 1993) 9.5.3.2 Extratropical Circulation, North Atlantic Oscillation and 26.5°N (estimate based on direct observations since 2004) (Cun- and Other Related Dipolar and Annular Modes ningham et al., 2010; Willis, 2010). At 26.5°N, CMIP3 and CMIP5 model simulations show total AMOC variability that is within the observa- Based on CMIP3 models, Gerber et al. (2008) confirmed the AR4 assess- tional uncertainty (Baehr et al., 2009; Marsh et al., 2009; Balan Saro- ment that climate models are able to capture the broad spatial and jini et al., 2011; Msadek et al., 2013). However, the total AMOC is the temporal features of the North Atlantic Oscillation (NAO), but there sum of a wind-driven component and a mid-ocean geostrophic com- are substantial differences in the spatial patterns amongst individual ponent. While both CMIP3 and CMIP5 models tend to overestimate models (Casado and Pastor, 2012; Handorf and Dethloff, 2012). Climate the wind-driven variability, they tend to underestimate the mid-ocean 9 models tend to have patterns of variability that are more annular in geostrophic variability (Baehr et al., 2009; Balan Sarojini et al., 2011; character than observed (Xin et al., 2008). Models substantially overes- Msadek et al., 2013). The latter is suggested to result from deficien- timate persistence on sub-seasonal and seasonal time scales, and have cies in the simulation of the hydrographic characteristics (Baehr et al., difficulty simulating the seasonal cycle of annular mode time scales 2009), specifically the Nordic Seas overflows (Yeager and Danaba- found in reanalyses (Gerber et al., 2008). The unrealistically long time soglu, 2012; Msadek et al., 2013). scale of variability is worse in models with particularly strong equator- ward biases in the mean jet location, a result which has been found to 9.5.3.3.2 Atlantic multi-decadal variability/Atlantic hold in the North Atlantic and in the SH (Barnes and Hartmann, 2010; Multi-decadal Oscillation Kidston and Gerber, 2010). The Atlantic Multi-decadal Variability (AMV), also known as Atlantic As described in the AR4, climate models have generally been unable Multi-decadal Oscillation (AMO), is a mode of climate variability with to simulate changes as strong as the observed NAO trend over the an apparent period of about 70 years, and a pattern centred in the period 1965 1995, either in coupled mode (Gillett, 2005; Stephenson North Atlantic Ocean (see Section 14.7.6). In the AR4, it was shown et al., 2006; Stoner et al., 2009) or forced with observed boundary con- that a number of climate models produced AMO-like multidecadal var- ditions (Scaife et al. (2009). However, there are a few exceptions to iability in the North Atlantic linked to variability in the strength of the this (e.g., Selten et al., 2004; Semenov et al., 2008), so it is unclear to AMOC. Subsequent analyses has confirmed this, however simulated what extent the underestimation of late 20th century trends reflects time scales range from 40 to 60 years (Frankcombe et al., 2010; Park model shortcomings versus internal variability. Further evidence has and Latif, 2010; Kavvada et al., 2013), to a century or more (Msadek emerged of the coupling of NAO variability between the troposphere and Frankignoul, 2009; Menary et al., 2012). In addition, the spatial and the stratosphere, and even models with improved stratospheric patterns of variability related to the AMOC differ in many respects from resolution appear to underestimate the vertical coupling (Morgenstern one model to another as shown in Figure 9.34. et al., 2010) with consequences for the NAO response to anthropogen- ic forcing (Sigmond and Scinocca, 2010; Karpechko and Manzini, 2012; The presence of AMO-like variability in unforced simulations, and the Scaife et al., 2012). fact that forced 20th century simulations in the CMIP3 multi-mod- el ensemble produce AMO variability that is not in phase with that The Pacific basin analogue of the NAO, the North Pacific Oscillation observed, implies the AMO is not predominantly a result of the forc- (NPO) is a prominent pattern of wintertime atmospheric circulation ings imposed on the models (Kravtsov and Spannagle, 2008; Knight, variability characterized by a north south dipole in sea level pressure 2009; Ting et al., 2009). Results from the CMIP5 models also show a (Linkin and Nigam, 2008). Although climate models simulate the main key role for internal variability, alongside a contribution from external spatial features of the NPO, many of them are unable to capture the forcings in recent decades (Terray, 2012). Historical AMO fluctuations observed linkages with tropical variability and the ocean (Furtado et have been better reproduced in a model with a more sophisticated aer- al., 2011). osol treatment than was typically used in CMIP3 (Booth et al., 2012a), albeit at the expense of introducing other observational inconsisten- Raphael and Holland (2006) showed that coupled models produce a cies (Zhang et al., 2013). This could suggest that at least part of the clear Southern Annular Mode (SAM) but that there are relatively large AMO may in fact be forced, and that aerosols play a role. In addition differences between models in terms of the exact shape and orien- to tropospheric aerosols, Ottera et al. (2010) showed the potential for tation of this pattern. Karpechko et al. (2009) found that the CMIP3 simulated volcanic forcing to have influenced AMO fluctuations over models have problems representing linkages between the SAM and the last 600 years. SST, surface air temperature, precipitation and particularly sea ice in the Antarctic region. 801 Chapter 9 Evaluation of Climate Models ( ) ( ) 9 ( ) Figure 9.34 | Sequence of physical links postulated to connect Atlantic Meridional Overturning Circulation (AMOC) and Atlantic Multi-decadal Variability (AMV), and how they are represented in three climate models. Shown are regression patters for the following quantities (from top to bottom): sea surface temperature (SST) composites using AMOC time series; precipitation composites using cross-equatorial SST difference time series; equatorial salinity composites using Intertropical Convergence Zone (ITCZ)-strength time series; subpolar-gyre depth-averaged salinity (top 800 to 1000 m) using equatorial salinity time series; subpolar gyre depth averaged density using subpolar gyre depth averaged salinity time series. From left to right: the two CMIP3 models HadCM3 and ECHAM/MPI-OM (MPI), and the non-CMIP model KCM. Black outlining signifies areas statistically significant at the 5% level for a two-tailed t test using the moving-blocks bootstrapping technique (Wilks, 1995). (Figure 3 from Menary et al., 2012.) 9.5.3.3.3 Tropical zonal and meridional modes most models underestimate the SST variance associated with the AMM, and position the north tropical Atlantic SST anomaly too far The Atlantic Meridional Mode (AMM) is the dominant mode of inter- equatorward. More problematic is the fact that the development of annual variability in the tropical Atlantic, is characterized by an anom- the AMM in many models is led by a zonal mode during boreal win- alous meridional shift in the ITCZ (Chiang and Vimont, 2004), and has ter a feature that is not observed in nature (Breugem et al., 2006). impacts on hurricane tracks over the North Atlantic (Xie et al., 2005; This spurious AMM behaviour in the models is expected to be associ- Smirnov and Vimont, 2011). Virtually all CMIP models simulate AMM- ated with the severe model biases in simulating the ITCZ (see Section like SST variability in their 20th century climate simulations. However, ­ 9.4.2.5.2). 802 Evaluation of Climate Models Chapter 9 Atlantic Nino 9.5.3.4 Indo-Pacific Modes CMIP3 models have considerable difficulty simulating Atlantic Nino in their 20th century climate simulations. For many models the so- 9.5.3.4.1 El Nino-Southern Oscillation called Atl-3 SST index (20°W to 0°W, 3°S to 3°N) displays the wrong seasonality, with the maximum value in either DJF or SON instead of The ENSO phenomenon is the dominant mode of climate variability on JJA as is observed (Breugem et al., 2006). Despite large biases in the seasonal to interannual time scales (see Wang and Picaut (2004) and simulated climatology (Section 9.4.2.5.2), about one third of CMIP5 Chapter 14). The representation of ENSO in climate models has stead- models capture some aspects of Atlantic Nino variability, including ily improved and now bears considerable similarity to observed ENSO amplitude, spatial pattern and seasonality (Richter et al., 2013). This properties (AchutaRao and Sperber, 2002; Randall et al., 2007; Guil- represents an improvement over CMIP3. yardi et al., 2009b). However, as was the case in the AR4, simulations 9 Figure 9.35 | Maximum entropy power spectra of surface air temperature averaged over the NINO3 region (5°N to 5°S, 150°W to 90°W) for (a) the CMIP5 models and (b) the CMIP3 models. ECMWF reanalysis in (a) refers to the European Centre for Medium Range Weather Forecasts (ECMWF) 15-year reanalysis (ERA15). The vertical lines correspond to periods of two and seven years. The power spectra from the reanalyses and for SST from the Hadley Centre Sea Ice and Sea Surface Temperature (HadISST) version 1.1, Hadley Centre/Climatic Research Unit gridded surface temperature data set 4 (HadCRU 4), ECMWF 40-year reanalysis (ERA40) and National Centers for Environmental Prediction/National Center for Atmospheric Research (NCEP/NCAR) data set are given by the series of black curves. (Adapted from AchutaRao and Sperber, 2006.) 803 Chapter 9 Evaluation of Climate Models (°C) Obs. (°C) 9 Obs. Obs. ) per ( OE DL SL IN P AR BC 3 I NC I F C CC U CN C CM P5 BC C CR CM a RM O O GV M MI C MO B HC SS MP MR IP Cm IA U NC BN C RO FI IR IN IP I -D GF GI CM CS MI NS Figure 9.36 | ENSO metrics for pre-industrial control simulations in CMIP3 and CMIP5. (a) and (b): SST anomaly standard deviation (°C) in Nino 3 and Nino 4, respectively, (c) precipitation response (standard deviation, mm/day) in Nino4. Reference data sets, shown as dashed lines: Hadley Centre Sea Ice and Sea Surface Temperature (HadISST) version 1.1 for (a) and (b), CPC Merged Analysis of Precipitation (CMAP) for (c). The CMIP5 and CMIP3 multi-model means are shown as squares on the left of each panel with the whiskers representing the model standard deviation. Individual CMIP3 models shown as filled grey circles, and individual CMIP5 models are identified in the legend. of both background climate (time mean and seasonal cycle, see Section 2007; Leloup et al., 2008; Guilyardi et al., 2009b; Ohba et al., 2010; Yu 9.4.2.5.1) and internal variability exhibit serious systematic errors (van and Kim, 2011; Su and Jiang, 2012) or teleconnections (Watanabe et Oldenborgh et al., 2005; Capotondi et al., 2006; Guilyardi, 2006; Wit- al., 2012; Weller and Cai, 2013a). tenberg et al., 2006; Watanabe et al., 2011; Stevenson et al., 2012; Yeh et al., 2012), many of which can be traced to the representation of Since AR4, new analysis methods have emerged and are now being deep convection, trade wind strength and cloud feedbacks, with little applied. For example, Jin et al. (2006) and Kim and Jin (2011a) iden- improvement from CMIP3 to CMIP5 (Braconnot et al., 2007a; L Ecuyer tified five different feedbacks affecting the Bjerknes (or BJ) index, and Stephens, 2007; Guilyardi et al., 2009a; Lloyd et al., 2009, 2010; which in turn characterizes ENSO stability. Kim and Jin (2011b) applied Sun et al., 2009; Zhang and Jin, 2012). this process-based analysis to the CMIP3 multi-model ensemble and demonstrated a significant positive correlation between ENSO ampli- While a number of CMIP3 models do not exhibit an ENSO variability tude and the BJ index. When respective components of the BJ index maximum at the observed 2- to 7- year time scale, most CMIP5 models obtained from the coupled models were compared with those from do have a maximum near the observed range and fewer models have observations, it was shown that most coupled models underestimated the tendency for biennial oscillations (Figure 9.35; see also Stevenson, the negative thermal damping feedback (Lloyd et al., 2012; Chen et 2012). In CMIP3 the amplitude of El Nino ranged from less than half al., 2013) and the positive zonal advective and thermocline feedbacks. to more than double the observed amplitude (van Oldenborgh et al., 2005; AchutaRao and Sperber, 2006; Guilyardi, 2006; Guilyardi et al., Detailed quantitative evaluation of ENSO performance is hampered by 2009b). By contrast, the CMIP5 models show less inter-model spread the short observational record of key processes (Wittenberg, 2009; Li (Figure 9.36; Kim and Yu, 2012). The CMIP5 models still exhibit errors et al., 2011b; Deser et al., 2012) and the complexity and diversity of in ENSO amplitude, period, irregularity, skewness, spatial patterns (Lin, the processes involved (Wang and Picaut, 2004). While shortcomings 804 Evaluation of Climate Models Chapter 9 remain (Guilyardi et al., 2009b), the CMIP5 model ensemble shows Sardeshmukh, 2009; Shin et al., 2010) as well as zonal wind variability some improvement compared to CMIP3, but there has been no major patterns (Handorf and Dethloff, 2012). Teleconnections hence play a breakthrough and the multi-model improvement is mostly due to a central role in regional climate change (see Chapter 14). reduced number of poor-performing models. 9.5.3.5.1 Teleconnections affecting North America 9.5.3.4.2 Indian Ocean basin and dipole modes The Pacific North American (PNA) pattern is a wavetrain-like pattern in Indian Ocean SST displays a basin-wide warming following El Nino mid-level geopotential heights. The majority of CMIP3 models simulate (Klein et al., 1999). This Indian Ocean basin (IOB) mode peaks in boreal the spatial structure of the PNA pattern in wintertime (Stoner et al., spring and persists through the following summer. Most CMIP5 models 2009). The PNA pattern has contributions from both internal atmos- capture this IOB mode, an improvement over CMIP3 (Du et al., 2013). pheric variability (Johnson and Feldstein, 2010) and ENSO and PDO However, only about half the CMIP5 models capture its long tempo- teleconnections (Deser et al., 2004). The power spectrum of this tempo- ral persistence, and these models tend to simulate ENSO-forced ocean ral behaviour is generally captured by the CMIP3 models, although the Rossby waves in the tropical south Indian Ocean (Zheng et al., 2011). level of year-to-year autocorrelation varies according to the strength of the simulated ENSO and PDO (Stoner et al., 2009). The Indian Ocean zonal dipole mode (IOD) (Saji et al., 1999; Webster 9 et al., 1999) appears to be part of a hemispheric response to tropi- 9.5.3.5.2 Tropical ENSO teleconnections cal atmospheric forcing (Fauchereau et al., 2003; Hermes and Reason, 2005). Most CMIP3 models are able to reproduce the general features These moist teleconnection pathways involve mechanisms related to of the IOD, including its phase lock onto the July to November season those at play in the precipitation response to global warming (Chiang (Saji et al., 2006). The modelled SST anomalies, however, tend to show and Sobel, 2002; Neelin et al., 2003) and provide challenging test sta- too strong a westward extension along the equator in the eastern tistics for model precipitation response. Compared to earlier genera- Indian Ocean. CMIP3 models exhibit considerable spread in IOD ampli- tion climate models, CMIP3 and CMIP5 models tend to do somewhat tude, some of which can be explained by differences in the strength better (Neelin, 2007; Cai et al., 2009; Coelho and Goddard, 2009; Lan- of the simulated Bjerknes feedback (Liu et al., 2011; Cai and Cowan, genbrunner and Neelin, 2013) at precipitation reductions associated 2013). No substantial change is seen in CMIP5 (Weller and Cai, 2013a). with El Nino over equatorial South America and the Western Pacific, although CMIP5 offers little further improvement over CMIP3 (see for Many models simulate the observed correlation between IOD and instance the standard deviation of precipitation in the western Pacific ENSO. The magnitude of this correlation varies substantially between in Figure 9.36). CMIP5 models simulate the sign of the precipitation models, but is apparently not tied to the amplitude of ENSO (Saji et change over broad regions, and do well at predicting the amplitude of al., 2006). A subset of CMIP3 models show a spurious correlation with the change (for a given SST forcing) (Langenbrunner and Neelin, 2013). ENSO following the decay of ENSO events, instead of during the ENSO developing phase, possibly due to erroneous representation of oceanic A regression of the West African monsoon precipitation index with pathways connecting the equatorial Pacific and Indian Oceans (Cai et global SSTs reveals two major teleconnections (Fontaine and Janicot, al., 2011). 1996). The first highlights the strong influence of ENSO, while the second reveals a relationship between the SST in the Gulf of Guinea 9.5.3.4.3 Tropospheric biennial oscillation and the northward migration of the monsoon rain belt over West Africa. Most CMIP3 models show a single dominant Pacific telecon- The tropospheric biennial oscillation (TBO, Section 14.7.4) is a bien- nection, which is, however, of the wrong sign for half of the models nial tendency of many phenomena in the Indo-Pacific region that (Joly et al., 2007). Only one model shows a significant second mode, affects droughts and floods over large areas of south Asia and Aus- emphasizing the difficulty in simulating the response of the African tralia (e.g., Chang and Li, 2000; Li et al., 2001; Meehl et al., 2003). rain belt to Atlantic SST anomalies that are not synchronous with Pacif- The IOD involves regional patterns of SST anomalies in the TBO in the ic anomalies. Indian Ocean during the northern fall season following the south Asian monsoon (Loschnigg et al., 2003). The TBO has been simulated in a Both CMIP3 and CMIP5 models have been evaluated and found to vary number of global coupled climate model simulations (e.g., Ogasawara in their abilities to represent both the seasonal cycle of correlations et al., 1999; Loschnigg et al., 2003; Nanjundiah et al., 2005; Turner et between the Nino 3.4 and North Australian SSTs (Catto et al., 2012a, al., 2007; Meehl and Arblaster, 2011). 2012b) with little change in quality from CMIP3 to CMIP5. Generally the models do not capture the strength of the negative correlations 9.5.3.5 Indo-Pacific Teleconnections during the second half of the year. The models also still struggle to capture the SST evolution in the North Australian region during El Nino Tropical SST variability provides a significant forcing of atmospheric and La Nina. Teleconnection patterns from both ENSO and the Indian teleconnections and drives a large portion of the climate variability Ocean Dipole to precipitation over Australia are reasonably well simu- over land (Goddard and Mason, 2002; Shin et al., 2010). Although lated in the key September-November season (Cai et al., 2009; Weller local forcings and feedbacks can play an important role (Pitman et and Cai, 2013b) in the CMIP3 and CMIP5 multi-model mean. al., 2012a), the simulation of land surface temperatures and precip- itation requires accurate predictions of SST patterns (Compo and 805 Chapter 9 Evaluation of Climate Models 9.5.3.6 Pacific Decadal Oscillation and Interdecadal HadGEM2, MPI-ESM-LR, MIROC). Many features of the QBO such as Pacific Oscillation its width and phase asymmetry also appear spontaneously in these simulations due to internal dynamics (Dunkerton, 1991; Scaife et al., The Pacific Decadal Oscillation (PDO) refers to a mode of variabil- 2002; Haynes, 2006). Some of the QBO effects on the extratropical cli- ity involving SST anomalies over the North Pacific (north of 20°N) mate (Holton and Tan, 1980; Hamilton, 1998; Naoe and Shibata, 2010) (Mantua et al., 1997). Although the PDO time series exhibits consider- as well as ozone (Butchart et al., 2003; Shibata and Deushi, 2005) are able decadal variability, it is difficult to ascertain whether there are any also reproduced in models. robust spectral peaks given the relatively short observational record (Minobe, 1997, 1999; Pierce, 2001; Deser et al., 2004). The ability of 9.5.3.8 Summary climate models to represent the PDO has been assessed by Stoner et al. (2009) and Furtado et al. (2011). Their results indicate that approxi- In summary, most modes of interannual to interdecadal variability are mately half of the CMIP3 models simulate the observed spatial pattern now present in climate models. As in AR4, their assessment presents and temporal behaviour (e.g., enhanced variance at low frequencies); a varied picture and CMIP5 models only show a modest improvement however, spectral peaks are consistently higher in frequency than over CMIP3, mostly due to fewer poor-performing models. New since those suggested by the short observational record. The modelled PDO the AR4, process-based model evaluation is now helping identify 9 correlation with SST anomalies in the tropical Indo-Pacific are strongly sources of specific biases, although the observational record is often underestimated by the CMIP3 models (Wang et al., 2010; Deser et al., too short or inaccurate to offer strong constraints. The assessment of 2011; Furtado et al., 2011; Lienert et al., 2011). Climate models have modes and patterns is summarized in Table 9.4. been shown to simulate features of the closely related Interdecadal Pacific Oscillation (IPO, based on SSTs over the entire Pacific basin; see 9.5.4 Extreme Events Section 14.7.3; Power and Colman, 2006; Power et al., 2006; Meehl et al., 2009), although deficiencies remain in the strength of the tropical Extreme events are realizations of the tail of the probability distribu- extratropical connections. tion of weather and climate variability. They are higher-order statistics and thus generally more difficult to realistically represent in climate 9.5.3.7 The Quasi-Biennial Oscillation models. Shorter time scale extreme events are often associated with smaller scale spatial structure, which may be better represented as Significant progress has been made in recent years to model and model resolution increases. In the AR4, it was concluded that models understand the impacts of the Quasi-Biennial Oscillation (QBO; Bald- could simulate the statistics of extreme events better than expected win et al., 2001). Many climate models have now increased their from the generally coarse resolution of the models at that time, espe- vertical domain and/or improved their physical parameterizations cially for temperature extremes (Randall et al., 2007). (see Tables 9.1 and 9.A.1), and some of these reproduce a QBO (e.g., Table 9.4 | Summary of assessment of interannual to interdecadal variability in climate models. See also Figure 9.44. Level of Difference with AR4 Short Level of Degree of Model Evidence for (including CMIP5 Section Name Confidence Agreement Quality Evaluation vs. CMIP3) Global sea surface tem- SST-var High Robust Medium Medium Slight improvement in the tropics 9.5.3.1 perature (SST) variability North Atlantic Oscillation NAO Medium Medium Medium High No assessment 9.5.3.2 and Northern Annular Mode Southern Annular Mode SAM Low Limited Medium Medium No assessment 9.5.3.2 Atlantic Meridional Overturn- AMOC-var Low Limited Medium Medium No improvement 9.5.3.3 ing Circulation Variability Atlantic Multi-decadal AMO Low Limited Medium Medium No improvement 9.5.3.3 Variability Atlantic Meridional Mode AMM High Medium High Low No assessment 9.5.3.3 Atlantic Nino AN Low Limited Medium Low Slight improvement 9.5.3.3 El Nino Southern Oscillation ENSO High Medium High Medium Slight improvement 9.5.3.4 Indian Ocean Basin mode IOB Medium Medium Medium High Slight improvement 9.5.3.4 Indian Ocean Dipole IOD Medium Medium Medium Medium No improvement 9.5.3.4 Pacific North American PNA High Medium High Medium Slight improvement 9.5.3.5 Tropical ENSO tele- ENSOtele High Robust Medium Medium No improvement 9.5.3.5 connections Pacific Decadal Oscillation PDO Low Limited Medium Medium No assessment 9.5.3.6 Interdecadal Pacific IPO Low Limited Medium High No assessment 9.5.3.6 Oscillation Quasi-Biennial Oscillation QBO Medium Medium Medium High No assessment 9.5.3.7 806 Evaluation of Climate Models Chapter 9 The IPCC has conducted an assessment of extreme events in the con- for five precipitation-related indices. Darker grey shadings in the RMSE text of climate change the Special Report on Managing the Risks columns for precipitation indicate larger discrepancies between models of Extreme Events and Disasters to Advance Climate Change Adap- and reanalyses for precipitation extremes in general. Sillmann et al. tation (SREX) (IPCC, 2012). Although there is no comprehensive cli- (2013) found that the CMIP5 models tend to simulate more intense mate model evaluation with respect to extreme events in SREX, there precipitation and fewer consecutive wet days than the CMIP3, and is some consideration of model performance taken into account in thus are closer to the observationally based indices. assessing uncertainties in projections. It is known from sensitivity studies that simulated extreme precipita- 9.5.4.1 Extreme Temperature tion is strongly dependent on model resolution. Growing evidence has shown that high-resolution models (50 km or finer in the atmosphere) Since the AR4, evaluation of CMIP3 and CMIP5 models has been can reproduce the observed intensity of extreme precipitation (Wehner undertaken with respect to temperature extremes. Both model ensem- et al., 2010; Endo et al., 2012; Sakamoto et al., 2012), though some of bles simulate present-day warm extremes, in terms of 20-year return these results are based on models with observationally constrained values, reasonably well, with errors typically within a few degrees Cel- surface or lateral boundary conditions (i.e., Atmospheric General Circu- sius over most of the globe (Kharin et al., 2007; Kharin et al., 2012). The lation Models (AGCMs) or Regional Climate Models (RCMs)). CMIP5 and CMIP3 models perform comparably for various tempera- 9 ture extreme indices, but with smaller inter-model spread in CMIP5.The In terms of historical trends, a detection and attribution study by Min et inter-model range of simulated indices is similar to the spread amongst al. (2011) found consistency in sign between the observed increase in observationally based estimates in most regions (Sillmann et al., 2013). heavy precipitation over NH land areas in the second half of the 20th Figure 9.37 shows relative error estimates of available CMIP5 models century and that simulated by CMIP3 models, but they found that the for various extreme indices based on Sillmann et al. (2013). Although models tend to underestimate the magnitude of the trend (see also the relative performance of an individual model may depend on the Chapter 10). Related to this, it has been pointed out from comparisons choice of the reference data set (four different reanalyses are used), to satellite-based data sets that the majority of models underestimate the mean and median models tend to outperform individual models. the sensitivity of extreme precipitation intensity to temperature in the According to the standardized multi-model median errors (RMSEstd) for tropics (Allan and Soden, 2008; Allan et al., 2010; O Gorman, 2012) CMIP3 and CMIP5 shown on the right side of Figure 9.37, the perfor- and globally (Liu et al., 2009; Shiu et al., 2012). O Gorman (2012) mance of the two ensembles is similar. showed that this implies possible underestimation of the projected future increase in extreme precipitation in the tropics. In terms of historical trends, CMIP3 and CMIP5 models generally cap- ture observed trends in temperature extremes in the second half of 9.5.4.3 Tropical Cyclones the 20th century (Sillmann et al., 2013), as illustrated in Figure 9.37. The modelled trends are consistent with both reanalyses and sta- It was concluded in the AR4 that high-resolution AGCMs generally tion-based estimates. It is also clear in the figure that model-based reproduced the frequency and distribution, but underestimated inten- indices respond coherently to major volcanic eruptions. Detection and sity of tropical cyclones (Randall et al., 2007). Since then, Mizuta et attribution studies based on CMIP3 models suggest that models tend al. (2012) have shown that a newer version of the MRI-AGCM with to overestimate the observed warming of warm temperature extremes improved parameterizations (at 20 km horizontal resolution) simulates and underestimate the warming of cold extremes in the second half tropical cyclones as intense as those observed with improved distri- of 20th century (Christidis et al., 2011; Zwiers et al., 2011) as noted bution as well. Another remarkable finding since the AR4 is that the in SREX (Seneviratne et al., 2012). See also Chapter 10. This is not as observed year-to-year count variability of Atlantic hurricanes can be obvious in the CMIP5 model evaluation (Figure 9.37 and Sillmann et al. well simulated by modestly high resolution (100 km or finer) AGCMs (2013)) and needs further investigation. forced by observed SST, though with less skill in other basins (Larow et al., 2008; Zhao et al., 2009; Strachan et al., 2013). Vortices that have 9.5.4.2 Extreme Precipitation some characteristics of tropical cyclones can also be detected and tracked in AOGCMs in CMIP3 and 5, but their intensities are generally For extreme precipitation, observational uncertainty is much larger too weak (Yokoi et al., 2009a; Yokoi et al., 2012; Tory et al., 2013; Walsh than for temperature, making model evaluation more challenging. Dis- et al., 2013). crepancies between different reanalyses for extreme precipitation are substantial, whereas station-based observations have limited spatial 9.5.4.4 Droughts coverage (Kharin et al., 2007, 2012; Sillmann et al., 2013). Moreover, a station-based observational data set, which is interpolated from sta- Drought is caused by long time scale (months or longer) variability of tion measurements, has a potential mismatch of spatial scale when both precipitation and evaporation. Sheffield and Wood (2008) found compared to model results or reanalyses (Chen and Knutson, 2008). that models in the CMIP3 ensemble simulated large-scale droughts in Uncertainties are especially large in the tropics. In the extratropics, the 20th century reasonably well, in the sense that multi-model spread precipitation extremes in terms of 20-year return values simulated by includes the observational estimate in each of several regions. Howev- CMIP3 and CMIP5 models compared relatively well with the observa- er, it should be noted that there are various definitions of drought (see tional data sets, with typical discrepancies in the 20% range (Kharin Chapter 2 and Seneviratne et al., 2012) and the performance of simu- et al., 2007, 2012). Figure 9.37 shows relative errors of CMIP5 models lated drought can depend on the definition. Moreover, different models 807 Chapter 9 Evaluation of Climate Models CMIP5 global land 1981 2000 (a) SDII 0.5 1.1 R95p 0.4 1 RX5day 0.3 0.9 RX1day 0.2 0.8 CDD 0.1 0.7 TR 0 0.6 FD 0.1 0.5 TXn 0.2 0.4 TXx 0.3 0.3 0.4 0.2 TNn 0.5 0.1 TNx ENSMEAN ENSMEDIAN BCC CSM1 1 BCC CSM1 1 M BNU ESM CanCM4 CanESM2 CCSM4 CESM1 BGC CMCC CM CNRM CM5 GFDL CM3 GFDL ESM2G GFDL ESM2M GISS E2 R HadCM3 HadGEM2 CC HadGEM2 ES INMCM4 IPSL CM5A LR IPSL CM5B LR IPSL CM5A MR MPI ESM P MPI ESM LR MPI ESM MR MRI CGCM3 NorESM1 M CMIP5 RMSEstd CMIP3 RMSEstd CSIRO Mk3 6 0 MIROC4h MIROC5 MIROC ESM MIROC ESM CHEM ACCESS1 0 EC EARTH 9 Cold nights Cold days (b) CMIP5 ERA 40 HadEX2 (c) CMIP5 ERA 40 HadEX2 20 CMIP3 NCEP/NCAR 20 20 CMIP3 NCEP/NCAR 20 Exceedance rate (%) Exceedance rate (%) 15 15 15 15 10 10 10 10 5 5 5 5 1950 1960 1970 1980 1990 2000 2010 1950 1960 1970 1980 1990 2000 2010 Year Year Warm nights Warm days (d) CMIP5 ERA 40 HadEX2 (e) CMIP5 ERA 40 HadEX2 20 CMIP3 NCEP/NCAR 20 20 CMIP3 NCEP/NCAR 20 Exceedance rate (%) Exceedance rate (%) 15 15 15 15 10 10 10 10 5 5 5 5 1950 1960 1970 1980 1990 2000 2010 1950 1960 1970 1980 1990 2000 2010 Year Year Figure 9.37 | (a) Portrait plot of relative error metrics for the CMIP5 temperature and precipitation extreme indices based on Sillmann et al. (2013). (b) (e) Time series of global mean temperature extreme indices over land from 1948 to 2010 for CMIP3 (blue) and CMIP5 (red) models, ECMWF 40-year reanalysis (ERA40, green dashed) and National Centers for Environmental Prediction/National Center for Atmospheric Research (NCEP/NCAR, green dotted) reanalyses and HadEX2 station-based observational data set (black) based on Sillmann et al. (2013). In (a), reddish and bluish colours indicate, respectively, larger and smaller root-mean-square (RMS) errors for an individual model relative to the median model. The relative error is calculated for each observational data set separately. The grey-shaded columns on the right side indicate the RMS error for the multi-model median standardized by the spatial standard deviation of the index climatology in the reanalysis, representing absolute errors for CMIP3 and CMIP5 ensembles. Results for four different reference data sets, ERA-interim (top), ERA40 (left), NCEP/NCAR (right) and NCEP- Department of Energy (DOE) (bottom) reanalyses, are shown in each box. The analysis period is 1981 2000, and only land areas are considered. The indices shown are simple daily precipitation intensity index (SDII), very wet days (R95p), annual maximum 5-day/1-day precipitation (RX5day/ RX1day), consecutive dry days (CDD), tropical nights (TR), frost days (FD), annual minimum/maximum daily maximum surface air temperature (TXn/TXx), and annual minimum/ maximum daily minimum surface air temperature (TNn/TNx). See Box 2.4 for the definitions of indices. Note that only a small selection of the indices analysed in Sillmann et al. (2013) is shown, preferentially those that appear in Chapters 2, 10, 11, 12, 14. Also note that the NCEP/NCAR reanalysis has a known defect for TXx (Sillmann et al., 2013), but its impact on this figure is small. In (b) (e), shading for model results indicates the 25th to 75th quantile range of inter-model spread. Grey shading along the horizontal axis indicates the evolution of globally averaged volcanic forcing according to Sato et al. (1993). The indices shown are the frequency of daily minimum/maximum surface air temperature below the 10th percentile (b: Cold nights/c: Cold days) and that above 90th percentile (d: Warm nights/e: Warm days) of the 1961 1990 base period. Note that, as these indices essentially represent changes relative to the base period, they are particularly suitable for being shown in time series and not straightforward for being shown in (a). 808 Evaluation of Climate Models Chapter 9 can simulate drought with different mechanisms (McCrary and Ran- tend to simulate more intense and thus more realistic precipitation dall, 2010; Taylor et al., 2012a). A comprehensive evaluation of CMIP5 extremes than CMIP3, which could be partly due to generally higher models for drought is currently not available, although Sillmann et al. horizontal resolution. There is medium evidence and high agreement (2013) found that consecutive dry days simulated by CMIP5 models are that CMIP3 models tend to underestimate the sensitivity of extreme comparable to observations in magnitude and distribution. precipitation intensity to temperature. There is medium evidence and high agreement that high resolution (50 km or finer) AGCMs tend to 9.5.4.5 Summary simulate the intensity of extreme precipitation comparable to observa- tional estimates. There is medium evidence (i.e., a few multi-model studies) and high agreement that the global distribution of temperature extremes are There is medium evidence and high agreement that year-to-year count represented well by CMIP3 and CMIP5 models. The observed global variability of Atlantic hurricanes can be well simulated by modestly warming trend of temperature extremes in the second half of the 20th high resolution (100 km or finer) AGCMs forced by observed SSTs. century is reproduced in models, but there is medium evidence (a few There is medium evidence and medium agreement (as inter-model CMIP3 studies) and medium agreement (not evident in a preliminary difference is large) that the intensity of tropical cyclones is too weak look at CMIP5) that models tend to overestimate the warming of warm in CMIP3 and CMIP5 models. Finally, there is medium evidence (a temperature extremes and underestimate the warming of cold temper- few multi-model studies) and medium agreement (as it might depend 9 ature extremes. on definitions of drought) that models can simulate aspects of large- scale drought. There is medium evidence (single multi-model study) and medium agreement (as inter-model difference is large) that CMIP5 models Box 9.3 | Understanding Model Performance This Box provides a synthesis of findings on understanding model performance based on the model evaluations discussed in this chapter. Uncertainty in Process Representation Some model errors can be traced to uncertainty in representation of processes (parameterizations). Some of these are long-standing issues in climate modelling, reflecting our limited, though gradually increasing, understanding of very complex processes and the inherent challenges in mathematically representing them. For the atmosphere, cloud processes, including convection and its interaction with boundary layer and larger-scale circulation, remain major sources of uncertainty (Section 9.4.1). These in turn cause errors or uncertainties in radiation which propagate through the coupled climate system. Distribution of aerosols is also a source of uncertainty arising from modelled microphysical processes and transport (Sections 9.4.1 and 9.4.6). Ocean models are subject to uncertainty in parameterizations of vertical and horizontal mixing and convection (Sections 9.4.2, 9.5.2 and 9.5.3), and ocean errors in turn affect the atmosphere through resulting SST biases. Simulation of sea ice is also affected by errors in both the atmosphere and the ocean as well as the parameterization of sea ice itself (Section 9.4.3). With respect to biogeochemical components in Earth System Models (ESMs), parameterizations of nitrogen limitation and forest fires are thought to be important for simulating the carbon cycle, but very few ESMs incorporate these so far (Sections 9.4.4 and 9.4.5). Error Propagation Causes of one model bias can sometimes be associated with another. Although the root cause of those biases is often unclear, knowl- edge of the causal chain or a set of interrelated biases can provide a key to further understanding and improvement of model per- formance. For example, biases in storm track position are partly due to a SST biases in the Gulf Stream and Kuroshio Current (Section 9.4.1). Some biases in variability or trend can be partly traced back to biases in mean states. The decreasing trend in September Arctic ice extent tends to be underestimated when sea ice thickness is overestimated (Section 9.4.3). In such cases, improvement of the mean state may improve simulated variability or trend. Sensitivity to Resolution Some phenomena or aspects of climate are found to be better simulated with models run at higher horizontal and/or vertical resolution. In particular, increased resolution in the atmosphere has improved, at least in some models, storm track and extratropical cyclones (Section 9.4.1), diurnal variation of precipitation over land (Section 9.5.2), extreme precipitation, and tropical cyclone intensity and structure (Section 9.5.4). Similarly, increased horizontal resolution in the ocean is shown to improve sea surface height variability, western boundary currents, tropical instability waves and coastal upwelling (Section 9.4.2), and variability of Atlantic meridional over- turning circulation (Section 9.5.3). High vertical resolution and a high model top, as well as high horizontal resolution, are important for simulating lower stratospheric climate variability (Section 9.4.1), blocking (Section 9.5.2), the Quasi-Biennial Oscillation and the North Atlantic Oscillation (Section 9.5.3). (continued on next page) 809 Chapter 9 Evaluation of Climate Models Box 9.3 (continued) Uncertainty in Observational Data In some cases, insufficient length or quality of observational data makes model evaluation challenging, and is a frequent problem in the evaluation of simulated variability or trends. This is evident for evaluation of upper tropical tropospheric temperature, tropical atmos- pheric circulation (Section 9.4.1), the Atlantic meridional overturning circulation, the North Atlantic Oscillation and the Pacific Decadal Oscillation (Section 9.5.3). Data quality has been pointed out as an issue for arctic cloud properties (Section 9.4.1), ocean heat content, heat and fresh water fluxes over the ocean (Section 9.4.2) and extreme precipitation (Section 9.5.4). Palaeoclimate reconstructions also have large inherent uncertainties (Section 9.5.2). It is clear therefore that updated or newly available data affect model evaluation conclusions. Other Factors Model evaluation can be affected by how models are forced. Uncertainties in specified greenhouse gases, aerosols emissions, land use change, etc. will all affect model results and hence evaluation of model quality. Different statistical methods used in model evaluation 9 can also lead to subtle or substantive differences in the assessment of model quality. 9.6 Downscaling and Simulation of regions in Figure 9.39, and for polar and oceanic regions in Figure Regional-Scale Climate 9.40. The CMIP5 median temperature biases range from about 3°C to 1.5°C. Substantial cold biases over NH regions are more prevalent Regional-scale climate information can be obtained directly from in winter (December to February) than summer (June to August). The global models; however, their horizontal resolution is often too low to median biases appear in most cases slightly less negative for CMIP5 resolve features that are important at regional scales. High-resolution than CMIP3. The spread amongst models, as characterized by the 25 AGCMs, variable-resolution global models, and statistical and dynam- to 75% and 5 to 95% ranges, is slightly reduced from CMIP3 to CMIP5 ical downscaling (i.e., regional climate modelling) are used to comple- in a majority of the regions and is roughly +/-3C. The RMS error of ment AOGCMs, and to generate region-specific climate information. individual CMIP5 models is smaller than that for CMIP3 in 24 of the 26 These approaches are evaluated in the following. regions in Figure 9.39 in DJF, JJA and the annual mean. The absolute value of the ensemble mean bias has also been reduced in most cases. 9.6.1 Global Models The inter-model spread remains large, particularly in high-latitude regions in winter and in regions with steep orography (such as CAS, 9.6.1.1 Regional-Scale Simulation by Atmosphere Ocean SAS, TIB and WSA). The inter-model temperature spread has decreased General Circulation Models from CMIP3 to CMIP5 over most of the oceans and over the Arctic and Antarctic land regions. The cold winter bias over the Arctic has been A comparison of CMIP3 and CMIP5 seasonal cycles of temperature reduced. There is little systematic inter-ensemble difference in temper- and precipitation for different regions (Figure 9.38) shows that tem- ature over lower latitude oceans. perature is generally better simulated than precipitation in terms of the amplitude and phase of the seasonal cycle. The multi-model mean Biases in seasonal total precipitation are shown in the right column is closer to observations than most of the individual models. The sys- of Figures 9.39 and 9.40 for the NH winter (October to March) and tematic difference between the CMIP5 and CMIP3 ensembles is small summer (April to September) half years as well as the annual mean. in most regions, although there is evident improvement in South Asia The largest systematic biases over land regions occur in ALA, WSA and (SAS) and Tropical South America (TSA) in the rainy seasons. In some TIB, where the annual precipitation exceeds that observed in all CMIP5 cases the spread amongst observational estimates can be of compa- models, with a median bias on the order of 100%. All these regions are rable magnitude to the model spread, e.g., winter in the Europe and characterized by high orography and / or a large fraction of solid pre- Mediterranean (EUM) region. cipitation, both of which are expected to introduce a negative bias in gauge-based precipitation (Yang and Ohata, 2001; Adam et al., 2006) There are as yet rather few published studies in which regional behav- that may amplify the model-observation discrepancy. A large negative iour of the CMIP5 models is evaluated in great detail. Cattiaux et al. relative bias in SAH occurs in October to March, but it is of negligible (2013) obtained results for Europe similar to those discussed above. magnitude in absolute terms. In nearly all other seasonal and region- Joetzjer et al. (2013) considered 13 models that participated in both al cases over land, the observational estimate falls within the range CMIP3 and in CMIP5 and found that the seasonal cycle of precipitation of the CMIP5 simulations. Compared with CMIP3, the CMIP5 median over the Amazon improved in the latter. precipitation is slightly higher in most regions. In contrast with tem- perature, the seasonal and annual mean ensemble mean and the root- Based on the CMIP archives, regional biases in seasonal and annual mean square precipitation biases are larger for CMIP5 than for CMIP3 mean temperature and precipitation are shown for several land in a slight majority of land regions (Figure 9.39) and in most of the 810 Evaluation of Climate Models Chapter 9 9 Figure 9.38 | Mean seasonal cycle of (a) temperature (C) and (b) precipitation (mm day 1). The average is taken over land areas within the indicated regions, and over the period 1980 1999. The red line is the average over 45 CMIP5 models; the blue line is the average over 22 CMIP3 models. The standard deviation of the respective data set is indicated with shading. The different line styles in black refer to observational and reanalysis data: Climatic Research Unit (CRU) TS3.10, ECMWF 40-year reanalysis (ERA40) and ERA-Interim for temperature; CRU TS3.10.1, Global Precipitation Climatology Project (GPCP), and CPC Merged Analysis of Precipitation (CMAP) for precipitation. Note the different axis-ranges for some of the sub-plots. The 15 regions shown are: Western North America (WNA), Eastern North America (ENA), Central America (CAM), Tropical South America (TSA), Southern South America (SSA), Europe and Mediterranean (EUM), North Africa (NAF), Central Africa (CAF), South Africa (SAF), North Asia (NAS), Central Asia (CAS), East Asia (EAS), South Asia (SAS), Southeast Asia (SEA) and Australia (AUS). 811 Chapter 9 Evaluation of Climate Models 14 other regions (Figure 9.40). However, considering the observational Especially over the oceans and polar regions (Figure 9.40), the scarci- uncertainty, the performance of the CMIP3 and CMIP5 ensembles is ty of observations and their uncertainty complicates the evaluation of assessed to be broadly similar. The inter-model spreads are similar and simulated precipitation. Of two commonly used data sets, CMAP indi- typically largest in arid areas when expressed in relative terms. cates systematically more precipitation than GPCP over low-latitude 9 Figure 9.39 | Seasonal- and annual mean biases of (left) temperature (°C) and (right) precipitation (%) in the IPCC Special Report on Managing the Risks of Extreme Events and Disasters to Advance Climate Change Adaptation (SREX) land regions (cf. Seneviratne et al., 2012, p. 12. The region s coordinates can be found from their online Appendix 3.A). The 5th, 25th, 50th, 75th and 95th percentiles of the biases in 42 CMIP5 models are shown in box-and-whisker format, and corresponding values for 23 CMIP3 models with crosses. The CMIP3 20C3M simulations are complemented with the corresponding A1B runs for the 2001 2005 period. The biases are calculated over 1986 2005, using Climatic Research Unit (CRU) T3.10 as the reference for temperature and CRU TS 3.10.01 for precipitation. The regions are labelled with red when the root-mean-square error for the individual CMIP5 models is larger than that for CMIP3 and blue when it is smaller. The regions are: Alaska/NW Canada (ALA), Eastern Canada/Greenland/Iceland (CGI), Western North America (WNA), Central North America (CNA), Eastern North America (ENA), Central America/Mexico (CAM), Amazon (AMZ), NE Brazil (NEB), West Coast South America (WSA), South- Eastern South America (SSA), Northern Europe (NEU), Central Europe (CEU), Southern Europe/the Mediterranean (MED), Sahara (SAH), Western Africa (WAF), Eastern Africa (EAF), Southern Africa (SAF), Northern Asia (NAS), Western Asia (WAS), Central Asia (CAS), Tibetan Plateau (TIB), Eastern Asia (EAS), Southern Asia (SAS), Southeast Asia (SEA), Northern Australia (NAS) and Southern Australia/New Zealand (SAU). Note that the region WSA is poorly resolved in the models. 812 Evaluation of Climate Models Chapter 9 oceans and less over many high-latitude regions (Yin et al., 2004; Shin et al., 2011). Over most low-latitude ocean regions, annual precipita- tion in most CMIP3 and CMIP5 models exceeds GPCP. The difference relative to CMAP is smaller although mostly of the same sign. In Arctic and Antarctic Ocean areas, simulated precipitation is much above CMAP, but more similar to GPCP. Over Antarctic land, precipitation in most models is below CMAP, but close to or above GPCP. Continental to sub-continental mean values may not be representative for smaller-scale biases, as biases generally increase with decreasing spatial averaging (Masson and Knutti, 2011b; Raisanen and Ylhaisi, 2011). A typical order of magnitude for grid-box-scale annual mean biases in individual CMIP3 models was 2°C for temperature and 1 mm day 1 for precipitation (Raisanen, 2007; Masson and Knutti, 2011b), with some geographical variation. This has been noted also in studies on how much spatial averaging would be needed in order to filter out 9 the most unreliable small-scale features (e.g., Räisänen and Ylhäisi, 2011). In order to reduce such errors while still retaining information on small scales, Masson and Knutti (2011b) found, depending on the variable and the region, that smoothing needed to vary from the grid- point scale to around 2000 km. On the whole, based on analysis of both ensemble means and inter-model spread, there is high confidence that the CMIP5 models simulate regional-scale temperature distributions somewhat better than the CMIP3 models did. This improvement is evident for most regions. For precipitation, there is medium confidence that there is no systematic change in model performance. In many regions, precipita- tion biases relative to CRU TS 3.10.01 and GPCP (and CMAP) are larger for CMIP5 than for CMIP3, but given observational uncertainty, the two ensembles are broadly similar. 9.6.1.2 Regional-Scale Simulation by Atmospheric General Circulation Models Stand-alone global atmospheric models (AGCMs) run at higher res- olution than AOGCMs provide complementary regional-scale climate information, sometimes referred to as global downscaling . One important example of this is the simulation of tropical cyclones (e.g., Zhao et al., 2009, 2012; Murakami and Sugi, 2010; Murakami et al., 2012). A number of advantages of high-resolution AGCMs have been identified, including improved regional precipitation (Zhao et al., 2009; Kusunoki et al., 2011) and blocking (Matsueda et al., 2009, 2010). As AGCMs do not simulate interactions with the ocean, their ability to capture some high-resolution phenomena, such as the cold wake in the surface ocean after a tropical cyclone, is limited (e.g., Hasegawa and Emori, 2007). As in lower-resolution models, performance is affected by the quality of physical parameterizations (Lin et al., 2012; Mizuta et al., Figure 9.40 | As Figure 9.39, but for polar and ocean regions, with ECMWF reanalysis 2012; Zhao et al., 2012). of the global atmosphere and surface conditions (ERA)-Interim reanalysis as the refer- ence for temperature and Global Precipitation Climatology Project (GPCP) for precipi- 9.6.1.3 Regional-Scale Simulation by Variable-Resolution tation. Global land, ocean and overall means are also shown. The regions are: Arctic: Global Climate Models 67.5 to 90°N, Caribbean (area defined by the following coordinates): 68.8°W, 11.4°N; 85.8°W, 25°N; 60°W, 25°N, 60°W, 11.44°N; Western Indian Ocean: 25°S to 5°N, 52°E to 75°E; Northern Indian Ocean: 5°N to 30°N, 60°E to 95°E; Northern Tropical Pacific: An alternative to global high resolution is the use of variable reso- 5°N to 25°N, 155°E to 150°W; Equatorial Tropical Pacific: 5°S to 5°N, 155°E to 130°W; lution (so-called stretched grid ) models with higher resolution over Southern Tropical Pacific: 5°S to 25°S, 155°E to 130°W; Antarctic: 50°S to 90°S. The the region of interest. Some examples are Abiodun et al. (2011) who normalized difference between CPC Merged Analysis of Precipitation (CMAP) and GPCP showed that such simulations improve the simulation of West African precipitation is shown with dotted lines. 813 Chapter 9 Evaluation of Climate Models monsoon systems and African easterly jets, and White et al. (2013) Doscher et al., 2010). Smith et al. (2011a) added vegetation dynamics who demonstrated improvements in temperature and precipitation ecosystem biogeochemistry in an RCM. related extreme indices. Fox-Rabinovitz et al. (2008) showed that regional biases in the high-resolution portion of a stretched grid model At the time of the AR4, RCMs were typically used for time-slice experi- were similar to that of a global model with the same high resolution ments. Since then, multi-decadal and centennial RCM simulations have everywhere. Markovic et al. (2010) and Déqué (2010) reported similar emerged in larger numbers (e.g., Diffenbaugh et al., 2011; Kjellstrom results. Although not widely used, such methods can complement more et al., 2011; de Elia et al., 2013). Coordinated RCM experiments and conventional climate models. ensembles have also become much more common and today, with domains covering Europe (e.g., Christensen et al., 2010; Vautard et 9.6.2 Regional Climate Downscaling al., 2013), North America (e.g., Gutowski et al., 2010; Lucas-Picher et al., 2012a; Mearns et al., 2012), South America (e.g., Menendez et al., Regional Climate Models (RCMs) are applied over a limited-area 2010; Chou et al., 2012; Krüger et al., 2012), Africa (e.g., Druyan et al., domain with boundary conditions either from global reanalyses or 2010; Ruti et al., 2011; Nikulin et al., 2012; Paeth et al., 2012; Hernán- global climate model output. The use of RCMs for dynamical down- dez-Díaz et al., 2013), the Arctic (e.g., Inoue et al., 2006) and Asian scaling has grown since the AR4, their resolution has increased, regions (e.g., Feng and Fu, 2006; Shkolnik et al., 2007; Feng et al., 2011; 9 process-descriptions have developed further, new components have Ozturk et al., 2012; Suh et al., 2012). been added, and coordinated experimentation has become more widespread (Laprise, 2008; Rummukainen, 2010). Statistical downscal- 9.6.3 Skill of Downscaling Methods ing (SD) involves deriving empirical relationships linking large-scale atmospheric variables (predictors) and local/regional climate variables Downscaling skill varies with location, season, parameter and bounda- (predictands). These relationships may then be applied to equivalent ry conditions (see Section 9.6.5) (e.g., Schmidli et al., 2007; Maurer and predictors from global models. SD methods have also been applied Hidalgo, 2008). Although there are indications that model skill increas- to RCM output (e.g., Boe et al., 2007; Déqué, 2007; Segui et al., 2010; es with higher resolution, it does not do so linearly. Rojas (2006) found Paeth, 2011; van Vliet et al., 2011). A significant constraint in a com- more improvement when increasing resolution from 135 km to 45 km prehensive evaluation of regional downscaling is that available studies than from 45 km to 15 km. Walther et al. (2013) found that the diurnal often involve different methods, regions, periods and observational precipitation cycle and light precipitation improved more when going data for evaluation. Thus, evaluation results are difficult to generalize. from 12 km to 6 km resolution than when going from 50 km to 25 km or from 25 km to 12 km. Higher resolution does enable better sim- 9.6.2.1 Recent Developments of Statistical Methods ulation of extremes (Seneviratne et al., 2012). For example, Pryor et al. (2012) noted that an increase in RCM resolution from 50 km to 6 The development of SD since the AR4 has been quite vigorous (e.g., km increased extreme wind speeds more than the mean wind speed. Fowler et al., 2007; Maraun et al., 2010b), and many state-of-the-art Kawazoe and Gutowski (2013) compared six RCMs and the two GCMs approaches combine different methods (e.g., Vrac and Naveau, 2008; to high resolution observations, concluding that precipitation extremes van Vliet et al., 2011). There is an increasing number of studies on were more representative in the RCMs than in the GCMs. Vautard et al. extremes (e.g., Vrac and Naveau, 2008; Wang and Zhang, 2008), and (2013) found that warm extremes in Europe were generally better sim- on features such as hurricanes (Emanuel et al., 2008), river flow and ulated in RCMs with 12 km resolution compared to 50 km. Kendon et discharge, sediment, soil erosion and crop yields (e.g., Zhang, 2007; al. (2012) and Chan et al. (2012) found mixed results in daily precipita- Prudhomme and Davies, 2009; Lewis and Lamoureux, 2010). Tech- tion simulated at 12 km and 1.5 km resolution, although the latter had niques have also been developed to consider multiple climatic vari- improved sub-daily features, perhaps as convection could be explicitly ables simultaneously in order to preserve some physical consistency resolved. (e.g., Zhang and Georgakakos, 2011). The methods used to evaluate SD approaches vary with the downscaled variable and include metrics Coupled RCMs, with an interactive ocean, offer further improvements. related to intensities (e.g., Ning et al., 2011; Tryhorn and DeGaetano, Döscher et al. (2010) reproduced empirical relationships between 2011), temporal behaviour (e.g., May, 2007; Timbal and Jones, 2008; Arctic sea ice extent and sea ice thickness and NAO in a coupled RCM. Maraun et al., 2010a; Brands et al., 2011), and physical processes (Len- Zou and Zhou (2013) found that a regional ocean atmosphere model derink and Van Meijgaard, 2008; Maraun et al., 2010a). SD capabilities improved the simulation of precipitation over the western North are also examined through secondary variables like runoff, river dis- Pacific compared to an uncoupled model. Samuelsson et al. (2010) charge and stream flow (e.g., Boe et al., 2007; Teutschbein et al., 2011). showed that coupling a lake model with an RCM captured the effect of lakes on the air temperature over adjacent land. Lenaerts et al. 9.6.2.2 Recent Developments of Dynamical Methods (2012) added drifting snow in an RCM run for the Antarctica, which increased the area of ablation and improved the fit to observations. Since the AR4, typical RCM resolution has increased from around 50 Smith et al. (2011a) added vegetation dynamics ecosystem biogeo- km to around 25 km (e.g., Christensen et al., 2010). Long RCM runs chemistry into an RCM, and found some evidence of local feedback at very high resolution are still, however, rather few (e.g., Yasutaka et to air temperature. al., 2008; Chan et al., 2012; Kendon et al., 2012). Coupled RCMs, with interactive ocean and, when appropriate, also sea ice have also been Applying an RCM developed for a specific region to other regions developed (Somot et al., 2008; Dorn et al., 2009; Artale et al., 2010; exposes it to a wider range of conditions and therefore provides an 814 Evaluation of Climate Models Chapter 9 opportunity for more rigorous evaluation. Transferability experiments In summary, there is high confidence that downscaling adds value to target this by running RCMs for different regions while holding their the simulation of spatial climate detail in regions with highly varia- process-descriptions constant (cf. Takle et al., 2007; Gbobaniyi et al., ble topography (e.g., distinct orography, coastlines) and for mesoscale 2011; Jacob et al., 2012). Suh et al. (2012) noted that 10 RCMs run phenomena and extremes. Regional downscaling is therefore comple- for Africa did well overall for average and maximum temperature, but mentary to results obtained directly from global climate models. These systematically overestimated the daily minimum temperature. Precip- results are from a variety of distinct studies with different RCMs. itation was generally simulated betted for wet regions than for dry regions. Similarly, Nikulin et al. (2012) reported on 10 RCMs over 9.6.5 Sources of Model Errors and Uncertainties Africa, run with boundary conditions from ERA-Interim, and evaluated against different observational data sets. Many of the RCMs simulated In addition to issues related to resolution and model complexity (see precipitation better than the ERA-Interim reanalysis itself. Section 9.6.3), errors and uncertainties arise from observational uncer- tainty in evaluation data and parameterizations (see Box 9.3), choice of Christensen et al. (2010) examined a range metrics related to simula- model domain and application of boundary conditions (driving data). tion of extremes, mesoscale features, trends, aspects of variability and consistency with the driving boundary conditions. Only one of these In the case of SD, sources of model errors and uncertainties depend on metrics led to clear differentiation among RCMs (Lenderink, 2010). This the choice of method, including the choice of the predictors, the esti- 9 may imply a general skilfulness of models, but may also simply indicate mation of empirical relationships between predictors and predictands that the metrics were not very informative. Nevertheless, using some of from limited data sets, and also the data used to estimate the predic- these metrics, Coppola et al. (2010) and Kjellström et al. (2010) found tors (Frost et al., 2011). There are numerous different SD methods, and that weighted sets of RCMs outperformed sets without weighting for the findings are difficult to generalize. both temperature and precipitation. Sobolowski and Pavelsky (2012) demonstrated a similar impact. Small domains allow less freedom for RCMs to generate the small-scale features that give rise to added value (e.g., Leduc and Laprise, 2009). 9.6.4 Value Added through RCMs Therefore large domains covering entire continents have become more common. Kltzow et al. (2008) found improvements with the RCMs are regularly tested to evaluate whether they show improve- use of a larger domain, but the RCM solution can become increasingly ments over global models (Laprise et al., 2008), that is, whether they decoupled from the driving data (e.g., Rockel et al., 2008), which can do indeed add value . In essence, added value is a measure of the introduce inconsistencies. Large domains also introduce large internal extent to which the downscaled climate is closer to observations than variability, which can significantly contaminate interannual variability the model from which the boundary conditions were obtained. Differ- of seasonal means (Kanamitsu et al., 2010). Techniques such as spec- ences between RCM and GCM simulations are not always very obvious tral nudging (Misra, 2007; Separovic et al., 2012) can be used to con- for time-averaged quantities on larger scales or in fairly homogeneous strain such inconsistencies (Feser et al., 2011). Winterfeldt and Weisse regions. RCM fields are, however, richer in spatial and temporal detail. (2009) concluded that nudging improved the simulation of marine Indeed, the added value of RCMs is mainly expected in the simulation wind climate, while Otte et al. (2012) demonstrated improvements in of topography-influenced phenomena and extremes with relatively temperature and precipitation. Nudging may, however, also lead to small spatial or short temporal character (e.g., Feser et al., 2011; Feser deterioration of features such as precipitation extremes (Alexandru et and Barcikowska, 2012; Shkol nik et al., 2012). As an example, RCM al., 2009; Kawazoe and Gutowski, 2013). Veljovic et al. (2010) showed downscaling led to better large-scale monsoon precipitation patterns that an RCM can in fact improve the large scales with respect to those (Gao et al., 2012) for East Asia than in the global models used for inherent in the boundary conditions, and argued that nudging may be boundary conditions. In the few instances where RCMs have been undesirable. interactively coupled to global models (i.e., two-way coupling), the effects of improved small scales propagate to larger scales and this The quality of RCM results may vary according to the synoptic situation, has been found to improve the simulation of larger scale phenomena season, and the geographic location of the lateral boundaries (Alexan- (Lorenz and Jacob, 2005; Inatsu and Kimoto, 2009; Inatsu et al., 2012). dru et al., 2007; Xue et al., 2007; Laprise et al., 2008; Separovic et al., 2008; Leduc and Laprise, 2009; Nikiema and Laprise, 2010; Rapaiæ et Other examples include improved simulation of convective precipita- al., 2010). In addition to lateral boundary conditions, RCMs also need tion (Rauscher et al., 2010), near-surface temperature (Feser, 2006), sea surface information. Few studies have explored the dependency near-surface temperature and wind (Kanamaru and Kanamitsu, 2007), of RCM results on the treatment of the SSTs and sea ice, although temperature and precipitation (Lucas-Picher et al., 2012b), extreme Koltzow et al. (2011) found that the specification of SSTs was less influ- precipitation (Kanada et al., 2008), coastal climate features (Winter- ential than was the domain or the lateral boundaries. Woollings et al. feldt and Weisse, 2009; Winterfeldt et al., 2011; Kawazoe and Gutows- (2010a) investigated the effect of specified SST on the simulation of ki, 2013; Vautard et al., 2013), Atlantic hurricanes (Bender et al., the Atlantic storm track and found that it was better simulated with 2010), European storm damage (Donat et al., 2010), strong mesoscale high-resolution SSTs, whereas increasing temporal resolution gave cyclones (Cavicchia and Storch, 2011), cutoff lows (Grose et al., 2012), mixed results. polar lows (Zahn and von Storch, 2008) and higher statistical moments of the water budget (e.g., Bresson and Laprise, 2011). As is the case in global models, RCM errors are directly related to shortcomings in process parameterizations. Examples include the 815 Chapter 9 Evaluation of Climate Models representation of clouds, convection and land surface atmosphere using RCM outputs (e.g., Vrac and Naveau, 2008; Driouech et al., 2010) interactions, the planetary boundary layer, horizontal diffusion, and or long series of observations (e.g. Schmith, 2008). microphysics (Tjernstrom et al., 2008; Wyser et al., 2008; Lynn et al., 2009; Pfeiffer and Zängl, 2010; Axelsson et al., 2011; Crétat et al., 2012; Giorgi and Coppola (2010) argued that regional-scale climate pro- Evans et al., 2012; Roy et al., 2012; Solman and Pessacg, 2012). The jections over land in the CMIP3 models were not sensitive to their representation of land surface and atmosphere coupling is also impor- temperature biases. For precipitation, the same was found for about tant, particularly for simulating monsoon regions (Cha et al., 2008; two thirds of the global land area. However, there is some recent evi- Yhang and Hong, 2008; Boone et al., 2010; Druyan et al., 2010; van dence that regional biases may be nonlinear for temperature extremes den Hurk and van Meijgaard, 2010). (Christensen et al., 2008; Boberg and Christensen, 2012; Christensen and Boberg, 2013) in both global and regional models. A mechanism 9.6.6 Relating Downscaling Performance to Credibility at play may be that models tend to dry out the soil too effectively at of Regional Climate Information high temperatures, which can lead to systematic biases in projected warm summertime conditions (Christensen et al., 2008; Kostopoulou A fundamental issue is how the performance of a downscaling method et al., 2009). This is illustrated in Figure 9.41 for the Mediterranean relates to its ability to provide credible future projections (Raisanen, region, which suggests a tendency in RCMs, CMIP3 and CMIP5 models 9 2007). This subject is discussed further in Section 9.8. The credibility of towards an enhanced warm bias in the warmer months. The implica- downscaled information of course depends on the quality of the down- tion is that the typically large warming signal in these regions could scaling method itself (e.g., Dawson et al., 2012; Déqué et al., 2012; be biased (Boberg and Christensen, 2012; Mearns et al., 2012). Find- Eum et al., 2012), and on the quality of the global climate models pro- ings such as these stress the importance of a thorough assessment of viding the large-scale boundary conditions (e.g., van Oldenborgh et al., models biases when they are applied for projections (e.g., de Elia and 2009; Diaconescu and Laprise, 2013). Cote, 2010; Boberg and Christensen, 2012; Christensen and Boberg, 2013). Specific to SD is the statistical stationarity hypothesis, that is, that the relationships inferred from historical data remain valid under a chang- Di Luca et al. (2012) analysed downscaled climate change projections ing climate (Maraun, 2012). Vecchi et al. (2008) note that a statistical from six RCMs run over North America. The climate change signals for method that captures interannual hurricane variability gives very dif- seasonal precipitation and temperature were similar to those in the ferent results for projections compared to RCMs. Such results suggest driving AOGCMs, and the spatial detail gained by downscaling was that good performance of statistical downscaling as assessed against comparable in both present and future climate. Déqué et al. (2012) observations does not guarantee credible regional climate information. studied projections with several combinations of AOGCM and RCM Some recent studies have proposed ways to evaluate SD approaches for Europe. A larger part of the spread in winter temperature and Figure 9.41 | Ranked modelled versus observed monthly mean temperature for the Mediterranean region for the 1961 2000 period. The Regional Climate Model (RCM) data (a) are from Christensen et al. (2008) and are adjusted to get a zero mean in model temperature with respect to the diagonal. The smaller insert shows uncentred data. The General Circulation Model (GCM) data (b) are from CMIP5 and CMIP3 and adjusted in the same way. (After Boberg and Christensen, 2012.) 816 Evaluation of Climate Models Chapter 9 precipitation projections was explained by the differences in global model from that used for the historical simulations and climate projec- model boundary conditions, although much of the spread in project- tions. However, in the few comparisons that were made, the resulting ed summer precipitation was explained by RCM. This underlines the disagreement in ECS was less than about 10% (Boer and Yu, 2003; importance of both the quality of the boundary conditions and the Williams et al., 2008; Danabasoglu and Gent, 2009; Li et al., 2013a). downscaling method. In CMIP5, climate sensitivity is diagnosed directly from the AOGCMs following the approach of Gregory et al. (2004). In this case the CO2 concentration is instantaneously quadrupled and kept constant for 150 9.7 Climate Sensitivity and Climate Feedbacks years of simulation, and both equilibrium climate sensitivity and RF are diagnosed from a linear fit of perturbations in global mean surface An overall assessment of climate sensitivity and transient climate temperature to the instantaneous radiative imbalance at the TOA. response is given in Box 12.2. Observational constraints based on observed warming over the last century are discussed in Section 10.8.2 The transient climate response (TCR) is the change in global and annual and shown in Box 12.2, Figure 2. mean surface temperature from an experiment in which the CO2 con- centration is increased by 1% yr 1, and calculated using the difference 9.7.1 Equilibrium Climate Sensitivity, Idealized Radiative between the start of the experiment and a 20-year period centred on Forcing, and Transient Climate Response in the the time of CO2 doubling. TCR is smaller than ECS because ocean heat 9 Coupled Model Intercomparison Project Phase 5 uptake delays surface warming. TCR is linearly correlated with ECS in Ensemble the CMIP5 ensemble (Figure 9.42), although the relationship may be nonlinear outside the range spanned in Table 9.5 (Knutti et al., 2005). Equilibrium climate sensitivity (ECS) is the equilibrium change in global and annual mean surface air temperature after doubling the atmos- Based on the methods outlined above and explained in Section pheric concentration of CO2 relative to pre-industrial levels. In the AR4, 9.7.2 below, Table 9.5 shows effective ERF, ECS, TCR and feedback the range in equilibrium climate sensitivity of the CMIP3 models was strengths for the CMIP5 ensemble. The two estimates of ERF agree 2.1°C to 4.4°C, and the single largest contributor to this spread was with each other to within 5% for six models (CanESM2, INM-CM4, differences among modelled cloud feedbacks. These assessments carry IPSL-CM5A-LR, MIROC5, MPI-ESM-LR and MPI-ESM-P), although the over to the CMIP5 ensemble without any substantial change (Table deviation exceeds 10% for four models (CCSM4, CSIRO-Mk3-6-0, 9.5). HadGEM2-ES, and MRI-CGCM3) and is indicative of deviations from the basic assumptions underlying one or both ERF estimation methods. The method of diagnosing climate sensitivity in CMIP5 differs funda- However, the mean difference of 0.3 W m 2 between the two meth- mentally from the method employed in CMIP3 and assessed in the ods for diagnosing ERF is only about half of the ensemble standard AR4 (Randall et al., 2007). In CMIP3, an AGCM was coupled to a deviation of 0.5 W m 2, or 15% of the mean value for ERF by CO2 non-dynamic mixed-layer (slab) ocean model with prescribed ocean using fixed SSTs. ECS and TCR vary across the ensemble by a factor heat transport convergence. CO2 concentration was then instantane- of approximately 2. The multi-model ensemble mean in ECS is 3.2°C, ously doubled, and the model was integrated to a new equilibrium a value nearly identical to that for CMIP3, while the CMIP5 ensemble with unchanged implied ocean heat transport. While computationally range is 2.1°C to 4.7°C, a spread which is also nearly indistinguishable efficient, this method had the disadvantage of employing a different from that for CMIP3. While every CMIP5 model whose heritage can Figure 9.42 | (a) Equilibrium climate sensitivity (ECS) against the global mean surface temperature of CMIP5 models, both for the period 1961 1990 (larger symbols, cf. Figure 9.8, Table 9.5) and for the pre-industrial control runs (smaller symbols). (b) Equilibrium climate sensitivity against transient climate response (TCR). The ECS and TCR information are based on Andrews et al. (2012) and Forster et al. (2013) and updated from the CMIP5 archive. 817 9 Table 9.5 | Effective radiative forcing (ERF), climate sensitivity and climate feedbacks estimated for the CMIP5 AOGCMs (see Table 9.1 for model details). ERF, equilibrium climate sensitivity (ECS) and transient climate response (TCR) are based 818 on Andrews et al. ( 2012) and Forster et al. (2013) and updated from the CMIP5 archive. The ERF entries are calculated according to Hansen et al. (2005) using fixed sea surface temperatures (SSTs) and Gregory et al. (2004) using regression. ECS is calculated using regressions following Gregory et al. (2004). TCR is calculated from the CMIP5 simulations with 1% CO2 increase per year (Taylor et al., 2012b), using the 20-year mean centred on the year of CO2 doubling. The climate sensitivity parameter and its inverse, the climate feedback parameter, are calculated from the regression-based ERF and the ECS. Strengths of the individual feedbacks are taken from Vial et al. (2013), following Soden et al. (2008) and using radiative kernel Chapter 9 methods with two different kernels. The sign convention is such that a positive entry for an individual feedback marks a positive feedback; the sum of individual feedback strengths must hence be multiplied by 1 to make it comparable to the climate feedback parameter. The entries for radiative forcing and equilibrium climate sensitivity were obtained by dividing by two the original results, which were obtained for CO2 quadrupling. ERF and ECS for BNU-ESM are from Vial et al. (2013). Effective Radiative Forcing Equilibrium Transient Climate 2 × CO2 (W m 2) Climate Feed- Water Vapour Lapse Rate Surface Albedo Climate Climate Sensitivity Planck Feedback Cloud Feedback Model back Parameter Feedback Feedback Feedback Sensitivity Response Parameter (W m 2 °C 1) (W m 2 °C 1) Fixed SST Regression (W m 2 °C 1) (W m 2 °C 1) (W m 2 °C 1) (W m 2 °C 1) (°C) (°C) (°C (W m 2) 1) ACCESS1.0 n.a. 3.0 3.8 2.0 1.3 0.8 n.a. n.a. n.a. n.a. n.a. ACCESS1.3 n.a. n.a. n.a. 1.7 n.a. n.a. n.a. n.a. n.a. n.a. n.a. BCC CSM1.1 n.a. 3.2 2.8 1.7 0.9 1.1 n.a. n.a. n.a. n.a. n.a. BCC CSM1.1(m) n.a. 3.6 2.9 2.1 0.8 1.2 n.a. n.a. n.a. n.a. n.a. BNU ESM n.a. 3.9 4.1 2.6 1.1 1.0 3.1 1.4 0.2 0.4 0.1 CanESM2 3.7 3.8 3.7 2.4 1.0 1.0 3.2 1.7 0.6 0.3 0.5 CCSM4 4.4 3.6 2.9 1.8 0.8 1.2 3.2 1.5 0.4 0.4 0.4 CESM1(BGC) n.a. n.a. n.a. 1.7 n.a. n.a. n.a. n.a. n.a. n.a. n.a. CESM1(CAM5) n.a. n.a. n.a. 2.3 n.a. n.a. n.a. n.a. n.a. n.a. n.a. CNRM CM5 n.a. 3.7 3.3 2.1 0.9 1.1 n.a. n.a. n.a. n.a. n.a. CSIRO Mk3.6.0 3.1 2.6 4.1 1.8 1.6 0.6 n.a. n.a. n.a. n.a. n.a. FGOALS g2 n.a. n.a. n.a. 1.4 n.a. n.a. n.a. n.a. n.a. n.a. n.a. GFDL CM3 n.a. 3.0 4.0 2.0 1.3 0.8 n.a. n.a. n.a. n.a. n.a. GFDL ESM2G n.a. 3.1 2.4 1.1 0.8 1.3 n.a. n.a. n.a. n.a. n.a. GFDL ESM2M n.a. 3.4 2.4 1.3 0.7 1.4 n.a. n.a. n.a. n.a. n.a. GISS E2 H n.a. 3.8 2.3 1.7 0.6 1.7 n.a. n.a. n.a. n.a. n.a. GISS E2 R n.a. 3.8 2.1 1.5 0.6 1.8 n.a. n.a. n.a. n.a. n.a. HadGEM2 ES 3.5 2.9 4.6 2.5 1.6 0.6 3.2 1.4 0.5 0.3 0.4 INM CM4 3.1 3.0 2.1 1.3 0.7 1.4 3.2 1.7 0.7 0.3 0 IPSL CM5A LR 3.2 3.1 4.1 2.0 1.3 0.8 3.3 1.9 1 0.2 1.2 IPSL CM5A MR n.a. n.a. n.a. 2.0 n.a. n.a. n.a. n.a. n.a. n.a. n.a. IPSL CM5B LR n.a. 2.7 2.6 1.5 1.0 1.0 n.a. n.a. n.a. n.a. n.a. MIROC5 4.0 4.1 2.7 1.5 0.7 1.5 3.2 1.7 0.6 0.3 0.1 MIROC ESM n.a. 4.3 4.7 2.2 1.1 0.9 n.a. n.a. n.a. n.a. n.a. MPI ESM LR 4.3 4.1 3.6 2.0 0.9 1.1 3.3 1.8 0.9 0.3 0.5 MPI ESM MR n.a. n.a. n.a. 2.0 n.a. n.a. n.a. n.a. n.a. n.a. n.a. MPI ESM P 4.3 4.3 3.5 2.0 0.8 1.2 n.a. n.a. n.a. n.a. n.a. MRI CGCM3 3.6 3.2 2.6 1.6 0.8 1.2 3.2 1.6 0.6 0.3 0.2 NorESM1 M n.a. 3.1 2.8 1.4 0.9 1.1 3.2 1.6 0.5 0.3 0.2 NorESM1 ME n.a. n.a. n.a. 1.6 n.a. n.a. n.a. n.a. n.a. n.a. n.a. Model mean 3.7 3.4 3.2 1.8 1.0 1.1 3.2 1.6 0.6 0.3 0.3 Evaluation of Climate Models 90% uncertainty +/-0.8 +/-0.8 +/-1.3 +/-0.6 +/-0.5 +/-0.5 +/-0.1 +/-0.3 +/-0.4 +/-0.1 +/-0.7 Evaluation of Climate Models Chapter 9 9 Figure 9.43 | (a) Strengths of individual feedbacks for CMIP3 and CMIP5 models (left and right columns of symbols) for Planck (P), water vapour (WV), clouds (C), albedo (A), lapse rate (LR), combination of water vapour and lapse rate (WV+LR) and sum of all feedbacks except Planck (ALL), from Soden and Held (2006) and Vial et al. (2013), following Soden et al. (2008). CMIP5 feedbacks are derived from CMIP5 simulations for abrupt fourfold increases in CO2 concentrations (4 × CO2). (b) ECS obtained using regression techniques by Andrews et al. (2012) against ECS estimated from the ratio of CO2 ERF to the sum of all feedbacks. The CO2 ERF is one-half the 4 × CO2 forcings from Andrews et al. (2012), and the total feedback (ALL + Planck) is from Vial et al. (2013). be traced to CMIP3 shows some change in ECS, there is no discernible 9.7.2.1 Role of Humidity and Lapse Rate Feedbacks in systematic tendency. This broad similarity between CMIP3 and CMIP5 Climate Sensitivity and the good agreement between different methods where they were applied to the same atmospheric GCM indicate that the uncertainty The compensation between the water vapour and lapse-rate feed- in methodology is minor compared to the overall spread in ECS. The backs noted in the CMIP3 models is still present in the CMIP5 models, change in TCR from CMIP3 to CMIP5 is generally of the same sign but and possible explanations of the compensation have been developed of smaller magnitude compared to the change in ECS. The relationship (Ingram, 2010; Ingram, 2013). New formulations of the feedbacks, between ECS and an estimates derived from total feedbacks are dis- replacing specific with relative humidity, eliminate most of the cancel- cussed in Section 9.7.2. lation between the water vapour and lapse rate feedbacks and reduce the inter-model scatter in the individual feedback terms (Held and Although ECS can vary with global mean surface temperature owing to Shell, 2012). the temperature dependencies of the various feedbacks (Colman and McAvaney, 2009; cf. Section 9.7.2), Figure 9.42 shows no discernible 9.7.2.2 Role of Surface Albedo in Climate Sensitivity correlation for the CMIP5 historical temperature ranges, a fact that suggests that ECS is less sensitive to errors in the current climate than Analysis of observed declines in sea ice and snow coverage from 1979 to other sources of uncertainty. to 2008 suggests that the NH albedo feedback is between 0.3 and 1.1 W m 2 °C 1 (Flanner et al., 2011). This range is substantially above the 9.7.2 Understanding the Range in Model Climate global feedback of 0.3 +/- 0.1 W m 2 °C 1 of the CMIP5 models ana- Sensitivity: Climate Feedbacks lysed in Table 9.5. One possible explanation is that the CMIP5 models underestimate the strength of the feedback as did the CMIP3 models The strengths of individual feedbacks for the CMIP3 and CMIP5 based upon the systematic errors in simulated sea ice coverage decline models are compared in Figure 9.43. The feedbacks are generally relative to observed rates (Boe et al., 2009b). similar between CMIP3 and CMIP5, and the water vapour, lapse rate, and cloud feedbacks are assessed in detail in Chapter 7. The surface 9.7.2.3 Role of Cloud Feedbacks in Climate Sensitivity albedo feedback is assessed here to be likely positive. There is high confidence that the sum of all feedbacks (excluding the Planck feed- Cloud feedbacks represent the main cause for the range in modelled back) is positive. Advances in estimating and understanding each of climate sensitivity (Chapter 7). The spread due to inter-model differenc- the feedback parameters in Table 9.5 are described in detail below (see es in cloud feedbacks is approximately 3 times larger than the spread also ­ hapters 7 and 8). C contributed by feedbacks due to variations in water vapour and lapse 819 Chapter 9 Evaluation of Climate Models rate combined (Dufresne and Bony, 2008), and is a primary factor same time (Lemoine, 2010). Following Schlesinger and Mitchell (1987), governing the range of climate sensitivity across the CMIP3 ensem- Roe and Baker (2007) suggest that symmetrically distributed uncer- ble (Volodin, 2008a). Differences in equilibrium and effective climate tainties in feedbacks lead to inherently asymmetrical uncertainties in sensitivity are due primarily to differences in the shortwave cloud feed- climate sensitivity with increased probability in extreme positive values back (Yokohata et al., 2008). of the sensitivity. Roe and Baker (2007) conclude that this relationship makes it extremely difficult to reduce uncertainties in climate sensitiv- In perturbed ensembles of three different models, the primary cloud-re- ity through incremental improvements in the specification of feedback lated factor contributing to the spread in equilibrium climate sensitivity parameters. While subsequent analysis has suggested that this finding is the low-level shortwave cloud feedback (Yokohata et al., 2010; Klocke could be an artifact of the statistical formulation (Hannart et al., 2009) et al., 2011). Changes in the high-altitude clouds also induce climate and linearization (Zaliapin and Ghil, 2010) of the relationship between feedbacks due to the large areal extent and significant longwave cloud feedback and sensitivity adopted by (Roe and Baker, 2007), these radiative effects of tropical convective cloud systems. In experiments issues remain unsettled (Roe and Armour, 2011; Roe and Baker, 2011). with perturbed physics ensembles of AOGCMs, the parameterization of ice fall speed also emerges as one of the most important determi- 9.7.3 Climate Sensitivity and Model Performance nants of climate sensitivity (Sanderson et al., 2008a, 2010; Sexton et 9 al., 2012). Other non-microphysical feedback mechanisms are detailed Despite the range in equilibrium sensitivity of 2.1°C to 4.4°C for CMIP3 in Chapter 7. models, they reproduce the global surface air temperature anomaly of 0.76°C over 1850 2005 to within 25% relative error. The relative- Cloud feedbacks in AOGCMs are generally positive or near neutral ly small range of historical climate response suggests that there is (Shell et al., 2008; Soden et al., 2008), as evidenced by the net positive another mechanism, for example a compensating non-GHG forcing, or neutral cloud feedbacks in all of the models examined in a mul- present in the historical simulations that counteracts the relatively ti-thousand member ensemble of AOGCMs constructed by parameter large range in sensitivity obtained from idealized experiments forced perturbations (Sanderson et al., 2010). The sign of cloud feedbacks in only by increasing CO2. One possible mechanism is a systematic neg- the current climate deduced from observed relationships between SSTs ative correlation across the multi-model ensemble between ECS and and TOA radiative fluxes are discussed further in Section 7.2.5.7. anthropogenic aerosol forcing (Kiehl, 2007; Knutti, 2008; Anderson et al., 2010). A second possible mechanism is a systematic overestimate 9.7.2.4 Relationship of Feedbacks to Modelled Climate of the mixing between the oceanic mixed layer and the full depth Sensitivity ocean underneath (Hansen et al., 2011). However, despite the same range of ECS in the CMIP5 models as in the CMIP3 models, there is The ECS can be estimated from the ratio of forcing to the total cli- no significant relationship across the CMIP5 ensemble between ECS mate feedback parameter. This approach is applicable to simulations and the 20th-century ERF applied to each individual model (Forster et in which the net radiative balance is much smaller than the forcing al., 2013). This indicates a lesser role of compensating ERF trends from and hence the modelled climate system is essentially in equilibrium. GHGs and aerosols in CMIP5 historical simulations than in CMIP3. Dif- This approach can also serve to check the internal consistency of esti- ferences in ocean heat uptake also do not appreciably affect the spread mates of the ECS, forcing, and feedback parameters obtained using in projected changes in global mean temperature by 2095 (Forster et independent methods. The relationship between ECS from Andrews et al., 2013). al. (2012) and estimates of ECS obtained from the ratio of forcings to feedbacks is shown in Figure 9.43b. The forcings are estimated using 9.7.3.1 Constraints on Climate Sensitivity from Earth System both regression and fixed SST techniques (Gregory et al., 2004; Hansen Models of Intermediate Complexity et al., 2005) by Andrews et al. (2012) and the feedbacks are calculated using radiative kernels (Soden et al., 2008). On average, the ECS from An EMIC intercomparison (Eby et al., 2013; Zickfeld et al., 2013) allows forcing to feedback ratios underestimate the ECS from Andrews et al. an assessment of model response characteristics, including ECS, TCR, (2012) by 25% and 35%, or up to 50% for individual models, using and heat uptake efficiency (Table 9.6). In addition, Bayesian methods fixed-SST and regression forcings, respectively. applied to PPE experiments using EMICs have estimated uncertainty in model response characteristics (see Box 12.2) based on simulated 9.7.2.5 Relationship of Feedbacks to Uncertainty in climate change in 20th century, past millennia, and LGM scenarios. Modelled Climate Sensitivity Here, the range of response metrics (Table 9.6) described for default model configurations (Eby et al., 2013) indicates consistency with the Objective methods for perturbing uncertain model parameters to CMIP5 ensemble. optimize performance relative to a set of observational metrics have shown a tendency toward an increase in the mean and a narrowing 9.7.3.2 Climate Sensitivity During the Last Glacial Maximum of the spread of estimated climate sensitivity (Jackson et al., 2008a). This tendency is opposed by the effects of structural biases related Climate sensitivity can also be explored in another climatic context. to incomplete process representations in GCMs. If common structur- The AR4 assessed attempts to relate simulated LGM changes in trop- al biases are replicated across models in a MME (cf. Section 9.2.2.7), ical SST to global climate sensitivity (Hegerl et al., 2007; Knutti and the most likely sensitivity for the MME tends to shift towards lower Hegerl, 2008). LGM temperature changes in the tropics (Hargreaves sensitivities while the possibility of larger sensitivities increases at the et al., 2007), but not in Antarctica (Hargreaves et al., 2012), have been 820 Evaluation of Climate Models Chapter 9 Table 9.6 | Model response metrics for EMICs in Table 9.2. TCR2X, TCR4X and ECS4X are the changes in global average model surface air temperature from the decades centred at years 70, 140 and 995 respectively, from the idealized 1% increase to 4 × CO2 experiment. The ocean heat uptake efficiency, 4X, is calculated from the global average heat flux divided by TCR4X for the decade centred at year 140, from the same idealized experiment. ECS2x was calculated from the decade centred about year 995 from a 2 × CO2 pulse experiment. (Data from Eby et al., 2013.) Model TCR2X (°C) ECS2x(°C) TCR4X (°C) ECS4X (°C) 4X (W m 2 °C 1) Bern3D 2.0 3.3 4.6 6.8 0.58 CLIMBER2 2.1 3.0 4.7 5.8 0.84 CLIMBER3 1.9 3.2 4.5 5.9 0.93 DCESS 2.1 2.8 3.9 4.8 0.72 FAMOUS 2.3 3.5 5.2 8.0 0.55 GENIE 2.5 4.0 5.4 7.0 0.51 IAP RAS CM 1.6 3.7 4.3 IGSM2 1.5 1.9 3.7 4.5 9 LOVECLIM1.2 1.2 2.0 2.1 3.5 1.17 MESMO 2.4 3.7 5.3 6.9 0.55 MIROC-lite 1.6 2.4 3.6 4.6 0.66 MIROC-lite-LCM 1.6 2.8 3.7 5.5 1.00 SPEEDO 0.8 3.6 2.9 5.2 0.84 UMD 1.6 2.2 3.2 4.3 Uvic 1.9 3.5 4.3 6.6 0.92 EMIC mean 1.8 3.0 4.0 5.6 0.8 EMIC range 0.8 2.5 1.9 4.0 2.1 5.4 3.5 8.0 0.5 1.2 shown to scale well with climate sensitivity because the signal is mostly are therefore of limited value to further constrain climate sensitivity dominated by CO2 forcing in these regions (Braconnot et al., 2007b; or TCR. The assessed literature suggests that the range of climate Jansen et al., 2007). The analogy between the LGM climate sensitivity sensitivities and transient responses covered by CMIP3/5 cannot be and future climate sensitivity is, however, not perfect (Crucifix, 2006). narrowed significantly by constraining the models with observations In a single-model ensemble of simulations, the magnitudes of the LGM of the mean climate and variability, consistent with the difficulty of cooling and the warming induced by a doubling of CO2 are nonline- constraining the cloud feedbacks from observations (see Chapter 7). ar in the forcings applied (Hargreaves et al., 2007). Differences in the Studies based on PPE and CMIP3 support the conclusion that a credi- cloud radiative feedback are at the origin of this asymmetric response ble representation of the mean climate and variability is very difficult to equivalent positive and negative forcings (Yoshimori et al., 2009). to achieve with equilibrium climate sensitivities below 2°C (Piani et al., There is thus still low confidence that the regional LGM model-data 2005; Stainforth et al., 2005; Sanderson et al., 2008a, 2008b; Huber et comparisons can be used to evaluate model climate sensitivity. How- al., 2011; Klocke et al., 2011; Fasullo and Trenberth, 2012). High climate ever, even if the results do not scale perfectly with equilibrium or tran- sensitivity values above 5°C (in some cases above 10°C) are found in sient climate sensitivity, the LGM simulations allow the identification the PPE based on HadAM/HadCM3. Several recent studies find that of the different feedback factors that contributed to the LGM global such high values cannot be excluded based on climatological con- cooling (Yoshimori et al., 2011) and model spread in these feedbacks. straints, but comparison with observations shows the smallest errors The largest spread in LGM model feedbacks is found for the shortwave for many fields if ECS is between 3 and 4°C (Piani et al., 2005; Knutti cloud feedback, just as for the modern climate. This correspondence et al., 2006; Rodwell and Palmer, 2007; Sanderson et al., 2008a, 2008b, between LGM and modern climates adds to the high confidence that 2010; Sanderson, 2011, 2013). the shortwave cloud feedback is the dominant source of model spread in climate sensitivity (cf. Section 5.3.3). 9.8 Relating Model Performance to 9.7.3.3 Constraints on Equilibrium Climate Sensitivity from Credibility of Model Applications Climate-Model Ensembles and Observations 9.8.1 Synthesis Assessment of Model Performance The large scale climatological information available has so far been insufficient to constrain model behaviour to a range tighter than This chapter has assessed the performance of individual climate CMIP3, at least on a global scale. Sanderson and Knutti (2012) sug- models as well as the multi-model mean. In addition, changes between gest that much of the available and commonly used large scale obser- models available now and those that were available at the time of the vations have already been used to develop and evaluate models and AR4 have been documented. The models display a range of abilities to 821 Chapter 9 Evaluation of Climate Models simulate climate characteristics, underlying processes, and phenome- evidence includes the number of studies and quality of observation- na. No model scores high or low in all performance metrics, but some al data. Generally, evidence is most robust when there are multiple, models perform substantially better than others for specific climate independent studies that evaluate multiple models using high-quality variables or phenomena. For a few climate characteristics, the assess- observations. The degree of agreement measures whether different ment has shown that some classes of models, for example, those with studies come to the same conclusions or not. The figure shows that higher horizontal resolution, higher model top or a more complete several important aspects of the climate are simulated well by con- representation of the carbon cycle, aerosols or chemistry, agree better temporary models, with varying levels of confidence. The colour coding with observations, although this is not universally true. provides an indication of how model quality has changed from CMIP3 to CMIP5. For example, there is high confidence that the model perfor- Figure 9.44 provides a synthesis of key model evaluation results for mance for global mean surface air temperature (TAS) is high, and it is AOGCMs and ESMs. The figure makes use of the calibrated language shown in green because there is robust evidence of improvement since as defined in Mastrandrea et al. (2011). The x-axis refers to the level CMIP3. By contrast, the diurnal cycle of global mean surface air tem- of confidence which increases towards the right as suggested by the perature (TAS-diur) is simulated with medium performance, but there increasing strength of shading. The level of confidence is a combina- is low confidence in this assessment owing to as yet limited analy- tion of the level of evidence and the degree of agreement. The level of ses. It should be noted that there are no instances in the figure for 9 (a) Mean State (b) Trends -t TAS SST TAS-RS TrInOcean ArcSIE fgCO2-t ArcSIE-t TAS-t High High MHT fgCO2 ZTaux PR Monsoon SSS AMOC EqTaux NBP Medium Medium NBP-t OHC-t Model Performance Model Performance TAS-diur fgCO2-sp TrPacific AntSIE LST-t TotalO3-t VAR-diur TropO3 TrSST SNC Blocking NBP-sp SSS-RS CRE TrAtlantic AOD TTT-t AntSIE-t SMO PR-diur SAF Low Low PR-RS Very low Low Medium High Very high Very low Low Medium High Very high Confidence in Assessment Confidence in Assessment (c) Variability (d) Extremes QBO IPO NAO Hurric-hr TAS-ext High High IOB PDO SST-var TC-hr Medium Medium SAM ENSO PR-ext PR-ext-hr Model Performance Model Performance IOD AMOC-var ENSOtele TAS-ext-t PR-ext-t AMO PNA Droughts AMM CO2-iav AN TC dCO2-iav MJO Low Low Very low Low Medium High Very high Very low Low Medium High Very high Confidence in Assessment Confidence in Assessment Degradation since CMIP3 No changes since CMIP3 Improvements since CMIP3 No relative assessment CMIP3 vs. CMIP5 Figure 9.44 | Summary of the findings of Chapter 9 with respect to how well the CMIP5 models simulate important features of the climate of the 20th century. Confidence in the assessment increases towards the right as suggested by the increasing strength of shading. Model performance improves from bottom to top. The colour coding indicates changes since CMIP3 (or models of that generation) to CMIP5. The assessment of model performance is expert judgment based on the agreement with observations of the multi-model mean and distribution of individual models around the mean, taking into account internal climate variability. Note that assessed model performance is simplified for representation in the figure and it is referred to the text for details of each assessment. The figure highlights the following key features, with the sections that back up the assessment added in parentheses: 822 Evaluation of Climate Models Chapter 9 PANEL a: PANEL c (Variability): AMOC Atlantic Meridional Overturning Circulation mean AMM Atlantic Meridional Mode (Section 9.5.3.3) (Section 9.4.2.3) AMO Atlantic Multi-decadal Variability (Section 9.5.3.3) AntSIE Seasonal cycle Antarctic sea ice extent (Section 9.4.3) AMOC-var Atlantic Meridional Overturning Circulation (Section 9.5.3.3) AOD Aerosol Optical Depth (Section 9.4.6) AN Atlantic Nino (Section 9.5.3.3) ArctSIE Seasonal cycle Arctic sea ice extent (Section 9.4.3) CO2-iav Interannual variability of atmospheric CO2 (Section 9.8.3) Blocking Blocking events (Section 9.5.2.2) dCO2-iav Sensitivity of CO2 growth rate to tropical temperature CRE Cloud radiative effects (Section 9.4.1.2) (Section 9.8.3) EqTaux Equatorial zonal wind stress (Section 9.4.2.4) ENSO El Nino Southern Oscillation (Section 9.5.3.4) fgCO2 Global ocean carbon sink (Section 9.4.5) ENSOtele Tropical ENSO teleconnections (Section 9.5.3.5) fgCO2-sp Spatial pattern of ocean atmosphere CO2 fluxes (Section 9.4.5) IOB Indian Ocean basin mode (Section 9.5.3.4) MHT Meridional heat transport (Section 9.4.2.4) IOD Indian Ocean dipole (Section 9.5.3.4) Monsoon Global monsoon (Section 9.5.2.4) IPO Interdecadal Pacific Oscillation (Section 9.5.3.6) NBP Global land carbon sink (Section 9.4.5) MJO Madden-Julian Oscillation (Section 9.5.2.3) NBP-sp Spatial pattern of land atmosphere CO2 fluxes (Section 9.4.5) NAO North Atlantic Oscillation and Northern annular mode PR Large scale precipitation (Sections 9.4.1.1, 9.4.1.3) (Section 9.5.3.2) 9 PR-diur Diurnal cycle precipitation (Section 9.5.2.1) PDO Pacific Decadal Oscillation (Section 9.5.3.6) PR-RS Regional scale precipitation (Section 9.6.1.1) PNA Pacific North American (Section 9.5.3.5) SAF Snow albedo feedbacks (Section 9.8.3) QBO Quasi-Biennial Oscillation (Section 9.5.3.7) SMO Soil moisture (Section 9.4.4) SAM Southern Annular Mode (Section 9.5.3.2) SNC Snow cover (Section 9.4.4) SST-var Global sea surface temperature variability (Section 9.5.3.1) SSS Sea surface salinity (Section 9.4.2.1) PANEL d (Extremes): SSS-RS Regional Sea surface salinity (Section 9.4.2.1) Hurric-hr Year-to-year counts of Atlantic hurricanes in high-resolution SST Sea surface temperature (Section 9.4.2.1) AGCMs (Section 9.5.4.3) TAS Large scale surface air temperature (Sections 9.4.1.1, 9.4.1.3) PR-ext Global distributions of precipitation extremes (Section 9.5.4.2) TAS-diur Diurnal cycle surface air temperature (Section 9.5.2.1) PR-ext-hr Global distribution of precipitation extremes in high-resolution TAS-RS Regional scale surface air temperature (Section 9.6.1.1) AGCMs (Section 9.5.4.2) TrSST Tropical sea surface temperature (Section 9.4.2.1) PR-ext-t Global trends in precipitation extremes (Section 9.5.4.2) TropO3 Tropospheric column ozone climatology (Section 9.4.1.4.5) TAS-ext Global distributions of surface air temperature extremes TrAtlantic Tropical Atlantic mean state (Section 9.4.2.5) (Section 9.5.4.1) TrInOcean Tropical Indian Ocean mean state (Section 9.4.2.5) TAS-ext-t Global trends in surface air temperature extremes TrPacific Tropical Pacific mean state (Section 9.4.2.5) (Section 9.5.4.1) VAR-diur Diurnal cycle other variables (Section 9.5.2.1) TC Tropical cyclone tracks and intensity (Section 9.5.4.3) ZTaux Zonal mean zonal wind stress (Section 9.4.2.4) TC-hr Tropical cyclone tracks and intensity in high-resolution AGCMs (Section 9.5.4.3) PANEL b (Trends) Droughts Droughts (Section 9.5.4.4) AntSIE-t Trend in Antarctic sea ice extent (Section 9.4.3) ArctSIE-t Trend in Arctic sea ice extent (Section 9.4.3) fgCO2-t Global ocean carbon sink trends (Section 9.4.5) LST-t Lower stratospheric temperature trends (Section 9.4.1.4.5) NBP-t Global land carbon sink trends (Section 9.4.5) OHC-t Global ocean heat content trends (Section 9.4.2.2) TotalO3-t Total column ozone trends (Section 9.4.1.4.5) TAS-t Surface air temperature trends (Section 9.4.1.4.1) TTT-t Tropical tropospheric temperature trends (Section 9.4.1.4.2) which CMIP5 models perform worse than CMIP3 models (something from the EMIC intercomparison project (Eby et al., 2013; Zickfeld et al., that would have been indicated by the red colour). A description that 2013) illustrate the ability to reproduce the large-scale climate chang- explains the expert judgment for each of the results presented in Figure es in GMST (Figure 9.8) and OHC (Figure 9.17) during the 20th century. 9.44 can be found in the body of this chapter, with a link to the specific The models also estimate CO2 fluxes for land and oceans, which are as sections given in the figure caption. consistent with observations as are fluxes estimated by ESMs (Figure 9.27). This gives confidence that the EMICs, albeit limited in the scope EMICs have also been evaluated to some extent in this chapter as they and resolution of information they can provide, can be used for long- are used to provide long-term projections (in Chapter 12) beyond year term projections compatible with those of ESMs (Plattner et al., 2008; 2300, and to provide large ensembles emulating the response of more Eby et al., 2013). Overall, these studies imply that EMICs are well suited comprehensive ESMs and allowing probabilistic estimates. Results for simulations extending beyond the CMIP5 ensemble. 823 Chapter 9 Evaluation of Climate Models Frequently Asked Questions FAQ 9.1 | Are Climate Models Getting Better, and How Would We Know? Climate models are extremely sophisticated computer programs that encapsulate our understanding of the climate system and simulate, with as much fidelity as currently feasible, the complex interactions between the atmosphere, ocean, land surface, snow and ice, the global ecosystem and a variety of chemical and biological processes. The complexity of climate models the representation of physical processes like clouds, land surface interactions and the representation of the global carbon and sulphur cycles in many models has increased substantially since the IPCC First Assessment Report in 1990, so in that sense, current Earth System Models are vastly better than the models of that era. This development has continued since the Fourth Assessment, while other factors have also contributed to model improvement. More powerful supercomputers allow current models to resolve finer spatial detail. Today s models also reflect improved understanding of how climate processes work understanding that has 9 come from ongoing research and analysis, along with new and improved observations. Climate models of today are, in principle, better than their predecessors. However, every bit of added complexity, while intended to improve some aspect of simulated climate, also introduces new sources of possible error (e.g., via uncertain parameters) and new interactions between model components that may, if only temporarily, degrade a model s simulation of other aspects of the climate system. Furthermore, despite the progress that has been made, scientific uncertainty regarding the details of many processes remains. An important consideration is that model performance Surface Temperature can be evaluated only relative to past observations, 1 taking into account natural internal variability. To have confidence in the future projections of such models, his- 0.99 Pattern correlation torical climate and its variability and change must be well simulated. The scope of model evaluation, in terms 0.98 of the kind and quantity of observations available, the availability of better coordinated model experiments, 0.97 and the expanded use of various performance met- 0.96 rics, has provided much more quantitative information about model performance. But this alone may not be 0.95 sufficient. Whereas weather and seasonal climate pre- CMIP2 CMIP3 CMIP5 dictions can be regularly verified, climate projections Precipitation spanning a century or more cannot. This is particularly 1 the case as anthropogenic forcing is driving the climate system toward conditions not previously observed in the 0.9 Pattern correlation instrumental record, and it will always be a limitation. 0.8 Quantifying model performance is a topic that has fea- tured in all previous IPCC Working Group I Reports. 0.7 Reading back over these earlier assessments provides a general sense of the improvements that have been 0.6 made. Past reports have typically provided a rather 0.5 broad survey of model performance, showing differenc- CMIP2 CMIP3 CMIP5 es between model-calculated versions of various climate quantities and corresponding observational estimates. FAQ 9.1, Figure 1 | Model capability in simulating annual mean temperature and precipitation patterns as illustrated by results of three recent phases of Inevitably, some models perform better than others for the Coupled Model Intercomparison Project (CMIP2, models from about year certain climate variables, but no individual model clear- 2000; CMIP3, models from about 2005; and CMIP5, the current generation ly emerges as the best overall. Recently, there has been of models). The figure shows the correlation (a measure of pattern similarity) progress in computing various performance metrics, between observed and modelled temperature (upper panel) and precipitation (lower panel). Larger values indicate better correspondence between modelled which synthesize model performance relative to a range and observed spatial patterns. The black symbols indicate correlation coefficient of different observations according to a simple numeri- for individual models, and the large green symbols indicate the median value cal score. Of course, the definition of such a score, how (i.e., half of the model results lie above and the other half below this value). it is computed, the observations used (which have their Improvement in model performance is evident by the increase in correlation for (continued on next page) successive model generations. 824 Evaluation of Climate Models Chapter 9 FAQ 9.1 (continued) own uncertainties), and the manner in which various scores are combined are all important, and will affect the end result. Nevertheless, if the metric is computed consistently, one can compare different generations of models. Results of such comparisons generally show that, although each generation exhibits a range in performance, the aver- age model performance index has improved steadily between each generation. An example of changes in model performance over time is shown in FAQ 9.1, Figure 1, and illustrates the ongoing, albeit modest, improvement. It is interesting to note that both the poorest and best performing models demonstrate improvement, and that this improvement comes in parallel with increasing model complexity and an elimination of artificial adjustments to atmosphere and ocean coupling (so-called flux adjustment ). Some of the reasons for this improvement include increased understanding of various climate processes and better representation of these processes in climate models. More comprehensive Earth observations are also driving improvements. So, yes, climate models are getting better, and we can demonstrate this with quantitative performance metrics 9 based on historical observations. Although future climate projections cannot be directly evaluated, climate models are based, to a large extent, on verifiable physical principles and are able to reproduce many important aspects of past response to external forcing. In this way, they provide a scientifically sound preview of the climate response to different scenarios of anthropogenic forcing. 9.8.2 Implications of Model Evaluation for Climate of future climate (Section 9.8.3), because D&A research is focussed on Change Detection and Attribution historical and control-run simulations which can be directly evaluated against observations. The evaluation of model simulations of historical climate is of direct relevance to detection and attribution (D&A) studies (Chapter 10) 9.8.3 Implications of Model Evaluation for Model since these rely on model-derived patterns (or fingerprints ) of climate Projections of Future Climate response to external forcing, and on the ability of models to simulate decadal and longer-time scale internal variability (Hegerl and Zwiers, Confidence in climate model projections is based on physical under- 2011). Conversely, D&A research contributes to model evaluation standing of the climate system and its representation in climate through estimation of the amplitude of modeled response to vari- models, and on a demonstration of how well models represent a wide ous forcings (Section 10.3.1.1.3). The estimated fingerprint for some range of processes and climate characteristics on various spatial and variables such as water vapor is governed by basic physical process- temporal scales (Knutti et al., 2010b). A climate model s credibility is es that are well represented in models and are rather insensitive to increased if the model is able to simulate past variations in climate, model uncertainties (Santer et al., 2009). Figure 9.44 illustrates slight such as trends over the 20th century and palaeoclimatic changes. Pro- improvements in the representation of some of the modes of variability jections from previous IPCC assessments can also be directly compared and climate phenomena discussed in Sections 9.5.2 and 9.5.3, sug- to observations (see Figures 1.4 and 1.5), with the caveat that these gesting with medium confidence that models now better reproduce projections were not intended to be predictions over the short time internal variability. On the other hand, biases that affect D&A studies scales for which observations are available to date. Unlike shorter lead remain. An example is the warm bias of lower-stratosphere tempera- forecasts, longer-term climate change projections push models into ture trends during the satellite period (Section 9.4.1.4.5) that can be conditions outside the range observed in the historical period used for linked to uncertainties in stratospheric ozone forcing (Solomon et al., evaluation. 2012; Santer et al., 2013). Recent studies of climate extremes (Sec- tion 9.5.4) also provide evidence that models have reasonable skill in In some cases, the spread in climate projections can be reduced by these important attributes of a changing climate; however, there is an weighting of models according to their ability to reproduce past indication that models have difficulties in reproducing the right bal- observed climate. Several studies have explored the use of unequally ance between historical changes in cold and warm extremes. They also weighted means, with the weights based on the models performance confirm that resolution affects the confidence that can be placed in the in simulating past variations in climate, typically using some perfor- analyses of extreme in precipitation. D&A studies focussed on extreme mance metric or collection of metrics (Connolley and Bracegirdle, events are therefore constrained by current model limitations. Lastly, 2007; Murphy et al., 2007; Waugh and Eyring, 2008; Pierce et al., 2009; some D&A studies have incorporated model quality results by repeat- Reifen and Toumi, 2009; Christensen et al., 2010; Knutti et al., 2010b; ing a multi-model analysis with only the models that agree best with Raisanen et al., 2010; Abe et al., 2011; Shiogama et al., 2011; Wat- observations (Santer et al., 2009). This model discrimination or weight- terson and Whetton, 2011; Tsushima et al., 2013). When applied to ing is less problematic for D&A analysis than it is for model projections projections of Arctic sea ice, averages in which extra weight is given 825 Chapter 9 Evaluation of Climate Models to models with the most realistic historical sea ice do give different variation in sunshine or anthropogenic forcing. Comparison with an results than the unweighted mean (Stroeve et al., 2007, 2012; Scher- observational estimate of snow albedo feedback reveals a large spread rer, 2011; Massonnet et al., 2012; Wang and Overland, 2012; Overland with both high and low biases. and Wang, 2013). Another frequently used approach is the re-calibra- tion of model outputs to a given observed value (Boe et al., 2009b; The right panel of Figure 9.45 shows another example of an emergent Mahlstein and Knutti, 2012; Wang and Overland, 2012), see further constraint, where the sensitivity of tropical land carbon to warming discussion in Section 12.4.6.1. Some studies explicitly formulate a (i.e., without CO2 fertilization effects) is related to the sensitivity of the statistical frameworks that relate future observables to climate model annual CO2 growth rate to tropical temperature anomalies (Cox et al., output (reviewed in Knutti et al. (2010b) and Stephenson et al. (2012)). 2013) ). The horizontal axis is the regression of the atmospheric CO2 Such frameworks not only provide weights for the mean response but growth rate on the tropical temperature anomaly for each model. The also allow the uncertainty in the predicted response to be quantified strong statistical relationship between these two variables is consist- (Bracegirdle and Stephenson, 2012). ent with the fact that interannual variability in the CO2 growth-rate is known to be dominated by the response of tropical land to climatic There are several encouraging examples of emergent constraints , anomalies, associated particularly with ENSO. Thus the relationship has which are relationships across an ensemble of models between some a physical as well as a statistical basis. The interannual sensitivity of 9 aspect of Earth System sensitivity and an observable trend or varia- the CO2 growth rate to tropical temperature can be estimated from tion in the contemporary climate (Allen and Ingram, 2002; Hall and observational data. Like the snow albedo feedback example, this inter- Qu, 2006; Eyring et al., 2007; Boe et al., 2009a, 2009b; Mahlstein model relationship provides a credible means to reduce model spread and Knutti, 2010; Son et al., 2010; Huber et al., 2011; Schaller et al., in the sensitivity of tropical land carbon to tropical climate change. 2011; Bracegirdle and Stephenson, 2012; Fasullo and Trenberth, 2012; O Gorman, 2012). For example, analyzing the CMIP3 ensemble, Hall On the other hand, many studies have failed to find strong relation- and Qu (2006) showed that inter-model variations of snow albedo ships between observables and projections. Whetton et al. (2007) and feedback in the contemporary seasonal cycle strongly correlate with Knutti et al. (2010a) found that correlations between local to region- comparably large inter-model variations in this feedback under future al climatological values and projected changes are small except for climate change. An update of this analysis with CMIP5 models added a few regions. Scherrer (2011) finds no robust relationship between is shown in Figure 9.45 (left panel). This relationship presumably arises the ability of the CMIP3 models to represent interannual variability from the fact that surface albedo values in areas covered by snow vary of near-surface air temperature and the amplitude of future warm- widely across the models, particularly in the heavily vegetated boreal ing.Raisanen et al. (2010) report only small (10 20%) reductions in forest zone. Models with higher surface albedos in these areas have a cross-validation error of simulated 21st century temperature changes larger contrast between snow-covered and snow-free areas, and hence when weighting the CMIP3 models based on their simulation of the a stronger snow albedo feedback whether the context is the seasonal present-day climatology. The main difficulties are sparse coverage in Figure 9.45 | (Left) Scatterplot of simulated springtime snow albedo feedback (s/Ts) values in climate change (y-axis) versus simulated springtime s/Ts values in the seasonal cycle (x-axis) in transient climate change experiments from 17 CMIP3 (blue) and 24 CMIP5 models (s and Ts are surface albedo and surface air temperature, respectively). (Adapted from Hall and Qu, 2006.) (Right) Constraint on the climate sensitivity of land carbon in the tropics (30°N to 30°S) from interannual variability in the growth rate of global atmospheric CO2 (Cox et al., 2013). This is based on results from Earth System Models (ESMs) with free-running CO2; Coupled Climate Carbon Cycle Model Intercomparison Project General Circulation Models (C4MIP GCMs, black labels; Friedlingstein et al., 2006), and three land carbon physics ensembles with HadCM3 (red labels; Booth et al., 2012b) . The values on the y-axis are calculated over the period 1960 2099 inclusive, and those on the x-axis over the period 1960 2010 inclusive. In both cases the temperature used is the mean (land+ocean) temperature over 30°N to 30°S. The width of the vertical yellow bands in both (a) and (b) shows the observation-based estimate of the variable on the x-axis. 826 Evaluation of Climate Models Chapter 9 many observed variables, short time series for observed trends, lack of correlation between observed quantities and projected past or future trends, and systematic errors in the models (Tebaldi and Knutti, 2007; Jun et al., 2008; Knutti, 2010; Knutti et al., 2010a), the ambiguity of possible performance metrics and the difficulty of associating them with predictive skill (Parker et al., 2007; Gleckler et al., 2008; Pincus et al., 2008; Reichler and Kim, 2008; Pierce et al., 2009; Knutti et al., 2010a). Emergent constraints can be difficult to identify if climate models are structurally similar and share common biases, thereby reducing the effective ensemble size. Comparison of emergent constraints in MMEs from different modelling experiments can help reveal which constraints are robust (Massonnet et al., 2012; Bracegirdle and Stephenson, 2013). Another issue is that testing of large numbers of predictors will find statistically significant correlations that do not remain significant in 9 a different ensemble. This is particularly important if many predictors are tested using only small ensembles like CMIP3 (DelSole and Shukla, 2009; Raisanen et al., 2010; Huber et al., 2011; Masson and Knutti, 2013). All of these potential pitfalls underscore the need for analysis of the mechanism underpinning the statistical relationship between current and future climate parameters for any proposed emergent ­constraint. 827 Chapter 9 Evaluation of Climate Models References Abe, M., H. Shiogama, T. Nozawa, and S. Emori, 2011: Estimation of future surface Anderson, B. T., J. R. Knight, M. A. Ringer, C. Deser, A. S. Phillips, J. H. Yoon, and temperature changes constrained using the future-present correlated modes A. Cherchi, 2010: Climate forcings and climate sensitivities diagnosed from in inter-model variability of CMIP3 multimodel simulations. J. Geophys. Res. atmospheric global circulation models. Clim. Dyn., 35, 1461 1475. Atmos., 116, D18104. Andrews, T., J. M. Gregory, M. J. Webb, and K. E. Taylor, 2012: Forcing, feedbacks Abiodun, B., W. Gutowski, A. Abatan, and J. Prusa, 2011: CAM-EULAG: A non- and climate sensitivity in CMIP5 coupled atmosphere-ocean climate models. hydrostatic atmospheric climate model with grid stretching. Acta Geophys., 59, Geophys. Res. Lett., 39, L09712. 1158 1167. Annamalai, H., and K. R. Sperber, 2005: Regional heat sources and the active and Abramowitz, G., R. Leuning, M. Clark, and A. Pitman, 2008: Evaluating the break phases of boreal summer intraseasonal (30 50 day) variability. J. Atmos. performance of land surface models. J. Clim., 21, 5468 5481. Sci., 62, 2726 2748. AchutaRao, K., and K. Sperber, 2002: Simulation of the El Nino Southern Oscillation: Annan, J., and J. Hargreaves, 2011: Understanding the CMIP3 Multimodel Ensemble. Results from the coupled model intercomparison project. Clim. Dyn., 19, 191 J. Clim., 24, 4529 4538. 209. Annan, J. D., and J. C. Hargreaves, 2010: Reliability of the CMIP3 ensemble. Geophys. AchutaRao, K., and K. Sperber, 2006: ENSO simulations in coupled ocean-atmosphere Res. Lett., 37, L02703. models: Are the current models better? Clim. Dyn., 27, 1 16. Annan, J. D., D. J. Lunt, J. C. Hargreaves, and P. J. Valdes, 2005: Parameter estimation in 9 Achuthavarier, D., V. Krishnamurthy, B. P. Kirtman, and B. H. Huang, 2012: Role of the an atmospheric GCM using the Ensemble Kalman Filter. Nonlin. Proc. Geophys., Indian Ocean in the ENSO-Indian Summer Monsoon Teleconnection in the NCEP 12, 363 371. Climate Forecast System. J. Clim., 25, 2490 2508. Anstey, J. A., et al., 2013: Multi-model analysis of Northern Hemisphere winter Ackerley, D., E. J. Highwood, and D. J. Frame, 2009: Quantifying the effects of blocking and its relation to the stratosphere. J. Geophys. Res. Atmos., 118, perturbing the physics of an interactive sulfur scheme using an ensemble of 3956 3971. GCMs on the climateprediction.net platform. J. Geophys. Res. Atmos., 114, Antonov, J. I., et al., 2010: World Ocean Atlas 2009, Vol. 2: Salinity. [S. Levitus (eds.)]. D01203 NOAA Atlas NESDIS 69, U.S. Gov. Printing Office, Washington, D.C., 184 pp. Adachi, Y., et al., 2013: Basic performance of a new earth system model of the Archer, D. E., G. Eshel, A. Winguth, W. Broecker, R. Pierrehumbert, M. Tobis, and R. Meteorological Research Institute (MRI-ESM1). Papers Meteorol. Geophys., Jacob, 2000: Atmospheric pCO(2) sensitivity to the biological pump in the ocean. doi:10.2467/mripapers.64.1. Global Biogeochem. Cycles, 14, 1219 1230. Adam, J., E. Clark, D. Lettenmaier, and E. Wood, 2006: Correction of global Arneth, A., et al., 2010: From biota to chemistry and climate: Towards a precipitation products for orographic effects. J. Clim., 19, 15 38. comprehensive description of trace gas exchange between the biosphere and Adkins, J. F., K. McIntyre, and D. P. Schrag, 2002: The salinity, temperature, and delta atmosphere. Biogeosciences, 7, 121 149. O-18 of the glacial deep ocean. Science, 298, 1769 1773. Arora, V. K., and G. J. Boer, 2005: Fire as an interactive component of dynamic Adler, R. F., et al., 2003: The Version 2 Global Precipitation Climatology Project (GPCP) vegetation models. J. Geophys. Res.-Biogeosciences, 110, G02008. Monthly Precipitation Analysis (1979 Present). J. Hydrometeor., 4, 1147 1167. Arora, V. K., and G. J. Boer, 2010: Uncertainties in the 20th century carbon budget Alekseev, V. A., E. M. Volodin, V. Y. Galin, V. P. Dymnikov, and V. N. Lykossov, 1998: associated with land use change. Global Change Biol., 16, 3327 3348. Modeling of the present-day climate by the atmospheric model of INM RAS Arora, V. K., et al., 2011: Carbon emission limits required to satisfy future DNM GCM. Description of the model version A5421 and results of AMIP2 representative concentration pathways of greenhouse gases. Geophys. Res. simulations. Institute of Numerical Mathematics, Moscow, Russia, 200 pp. Lett., 38, L05805. Alessandri, A., P. G. Fogli, M. Vichi, and N. Zeng, 2012: Strengthening of the Arora, V. K., et al., 2009: The effect of terrestrial photosynthesis down regulation on hydrological cycle in future scenarios: Atmospheric energy and water balance the twentieth-century carbon budget simulated with the CCCma Earth System perspective. Earth Syst. Dyn., 3, 199 212. Model. J. Clim., 22, 6066 6088. Alexander, M. J., et al., 2010: Recent developments in gravity-wave effects in climate Artale, V., et al., 2010: An atmosphere ocean regional climate model for the models and the global distribution of gravity-wave momentum flux from Mediterranean area: Assessment of a present climate simulation. Clim. Dyn., observations and models. Q. J. R. Meteorol. Soc., 136, 1103 1124. 35, 721 740. Alexandru, A., R. de Elia, and R. Laprise, 2007: Internal variability in regional climate Arzhanov, M. M., P. F. Demchenko, A. V. Eliseev, and I. I. Mokhov, 2008: Simulation of downscaling at the seasonal scale. Mon. Weather Rev., 135 3221 3238. characteristics of thermal and hydrologic soil regimes in equilibrium numerical Alexandru, A., R. de Elia, R. Laprise, L. Separovic, and S. Biner, 2009: Sensitivity study experiments with a Climate Model of Intermediate Complexity. Izvestiya Atmos. of regional climate model simulations to large-scale nudging parameters. Mon. Ocean. Phys., 44, 548 566. Weather Rev., 137, 1666 1686. Assmann, K. M., M. Bentsen, J. Segschneider, and C. Heinze, 2010: An isopycnic Allan, R. P., and B. J. Soden, 2008: Atmospheric warming and the amplification of ocean carbon cycle model. Geosci. Model Dev., 3, 143 167. precipitation extremes. Science, 321, 1481 1484. Aumont, O., and L. Bopp, 2006: Globalizing results from ocean in situ iron fertilization Allan, R. P., M. A. Ringer, and A. Slingo, 2003: Evaluation of moisture in the Hadley studies. Global Biogeochem. Cycles, 20, Gb2017. Centre climate model using simulations of HIRS water-vapour channel radiances. Aumont, O., E. Maier-Reimer, S. Blain, and P. Monfray, 2003: An ecosystem model Q. J. R. Meteorol. Soc., 129, 3371 3389. of the global ocean including Fe, Si, P colimitations. Global Biogeochem. Cycles, Allan, R. P., A. Slingo, S. F. Milton, and M. E. Brooks, 2007: Evaluation of the Met 17, 1060. Office global forecast model using Geostationary Earth Radiation Budget Austin, J., and R. J. Wilson, 2006: Ensemble simulations of the decline and recovery (GERB) data. Q. J. R. Meteorol. Soc., 133, 1993 2010. of stratospheric ozone. J. Geophys. Res. Atmos., 111, D16314. Allan, R. P., B. J. Soden, V. O. John, W. Ingram, and P. Good, 2010: Current changes in Axelsson, P., M. Tjernström, S. Söderberg, and G. Svensson, 2011: An ensemble of tropical precipitation. Environ. Res. Lett., 5, 025205. Arctic simulations of the AOE-2001 field experiment. Atmosphere, 2, 146 170. Allen, M., P. Stott, J. Mitchell, R. Schnur, and T. Delworth, 2000: Quantifying the Baehr, J., S. Cunnningham, H. Haak, P. Heimbach, T. Kanzow, and J. Marotzke, 2009: uncertainty in forecasts of anthropogenic climate change. Nature, 407, 617 Observed and simulated estimates of the meridional overturning circulation at 620. 26.5 N in the Atlantic. Ocean Sci., 5, 575 589. Allen, M. R., and W. J. Ingram, 2002: Constraints on future changes in climate and Balan Sarojini, B., et al., 2011: High frequency variability of the Atlantic meridional the hydrologic cycle. Nature, 419, 224 232. overturning circulation. Ocean Science, 7, 471 486. Ammann, C. M., G. A. Meehl, W. M. Washington, and C. S. Zender, 2003: A monthly Baldwin, M. P., et al., 2001: The quasi-biennial oscillation. Rev. Geophys., 39, 179 and latitudinally varying volcanic forcing dataset in simulations of 20th century 229. climate. Geophys. Res. Lett., 30, 1657. Balsamo, G., P. Viterbo, A. Beljaars, B. van den Hurk, M. Hirschi, A. K. Betts, and K. Anav, A., et al., 2013: Evaluating the land and ocean components of the global Scipal, 2009: A revised hydrology for the ECMWF Model: Verification from field carbon cycle in the CMIP5 Earth System Models. J. Clim., 26, 6801 6843. site to terrestrial water storage and impact in the Integrated Forecast System. J. Hydrometeorol., 10, 623 643. 828 Evaluation of Climate Models Chapter 9 Bao, Q., G. Wu, Y. Liu, J. Yang, Z. Wang, and T. Zhou, 2010: An introduction to the Bernie, D. J., E. Guilyardi, G. Madec, J. M. Slingo, S. Woolnough, and J. Cole, 2008: coupled model FGOALS1.1-s and its performance in East Asia. Adv. Atmos. Sci., Impact of resolving the diurnal cycle in an ocean-atmosphere GCM. Part 2: A 27, 1131 1142. diurnally coupled CGCM. Clim. Dyn., 31, 909 925. Bao, Q., et al., 2013: The Flexible Global Ocean-Atmosphere-Land System model Bi, D., et al., 2013a: ACCESS-OM: The Ocean and Sea ice Core of the ACCESS Coupled Version: FGOALS-s2. Adv. Atmos. Sci., doi:10.1007/s00376-012-2113-9. Model. Aust. Meteorol. Oceanogr. J., 63, 213 232. Bao, Y., F. L. Qiao, and Z. Y. Song, 2012: Historical simulation and twenty-first century Bi, D., et al., 2013b: The ACCESS Coupled Model: Description, control climate and prediction of oceanic CO2 sink and pH change. Acta Ocean. Sin., 31, 87 97. evaluation. Aust. Meteorol. Oceanogr. J., 63, 41 64. Barkstrom, B. R., 1984: The Earth Radiation Budget Experiment (ERBE). Bull. Am. Bitz, C. M., and W. H. Lipscomb, 1999: An energy-conserving thermodynamic sea ice Meteorol. Soc., 65, 1170 1185. model for climate study. J. Geophys. Res.. Oceans, 104, 15669 15677. Barnes, E. A., and D. L. Hartmann, 2010: Influence of eddy-driven jet latitude on Blyth, E., J. Gash, A. Lloyd, M. Pryor, G. P. Weedon, and J. Shuttleworth, 2010: North Atlantic jet persistence and blocking frequency in CMIP3 integrations. Evaluating the JULES Land Surface Model Energy Fluxes Using FLUXNET Data. J. Geophys. Res. Lett., 37, L23802. Hydrometeorol., 11, 509 519. Barnes, E. A., J. Slingo, and T. Woollings, 2012: A methodology for the comparison of Boberg, F., and J. H. Christensen, 2012: Overestimation of Mediterranean summer blocking climatologies across indices, models and climate scenarios. Clim. Dyn., temperature projections due to model deficiencies. Nature Clim. Change, 2, 38, 2467 2481. 433 436. Barnier, B., et al., 2006: Impact of partial steps and momentum advection schemes Boccaletti, G., R. Ferrari, and B. Fox-Kemper, 2007: Mixed layer instabilities and in a global ocean circulation model at eddy-permitting resolution. Ocean Dyn., restratification. J. Phys. Oceanogr., 37, 2228 2250. 56, 543 567. Bodas-Salcedo, A., K. D. Williams, P. R. Field, and A. P. Lock, 2012: The surface Barriopedro, D., R. Garcia-Herrera, and R. M. Trigo, 2010a: Application of blocking downwelling solar radiation surplus over the Southern Ocean in the Met Office 9 diagnosis methods to General Circulation Models. Part I: A novel detection Model: The role of midlatitude cyclone clouds. J. Clim., 25, 7467 7486. scheme. Clim. Dyn., 35, 1373 1391. Bodas-Salcedo, A., M. Webb, M. Brooks, M. Ringer, K. Williams, S. Milton, and D. Barriopedro, D., R. Garcia-Herrera, J. F. Gonzalez-Rouco, and R. M. Trigo, 2010b: Wilson, 2008: Evaluating cloud systems in the Met Office global forecast model Application of blocking diagnosis methods to General Circulation Models. Part using simulated CloudSat radar reflectivities. J. Geophys. Res. Atmos., 113, II: Model simulations. Clim. Dyn., 35, 1393 1409. D00A13. Bartlein, P. J., et al., 2010: Pollen-based continental climate reconstructions at 6 and Bodas-Salcedo, A., et al., 2011: COSP: Satellite simulation software for model 21 ka: A global synthesis. Clim. Dyn., 37, 775 802. assessment. Bull. Am. Meteorol. Soc., 92, 1023 1043. Bathiany, S., M. Claussen, V. Brovkin, T. Raddatz, and V. Gayler, 2010: Combined Bodeker, G., H. Shiona, and H. Eskes, 2005: Indicators of Antarctic ozone depletion. biogeophysical and biogeochemical effects of large-scale forest cover changes Atmos. Chem. Phys., 5, 2603 2615. in the MPI earth system model. Biogeosciences, 7, 1383 1399. Boe, J., A. Hall, and X. Qu, 2009a: Deep ocean heat uptake as a major source of Bauer, H. S., V. Wulfmeyer, and L. Bengtsson, 2008a: The representation of a synoptic- spread in transient climate change simulations. Geophys. Res. Lett., 36, L22701. scale weather system in a thermodynamically adjusted version of the ECHAM4 Boe, J., L. Terray, F. Habets, and E. Martin, 2007: Statistical and dynamical downscaling general circulation model. Meteorol. Atmos. Phys., 99, 129 153. of the Seine basin climate for hydro-meteorological studies. Int. J. Climatol. , 27, Bauer, S. E., D. Koch, N. Unger, S. M. Metzger, D. T. Shindell, and D. G. Streets, 2007: 1643 1655. Nitrate aerosols today and in 2030: A global simulation including aerosols and Boe, J. L., A. Hall, and X. Qu, 2009b: September sea-ice cover in the Arctic Ocean tropospheric ozone. Atmos. Chem. Phys., 7, 5043 5059. projected to vanish by 2100. Nature Geosci., 2, 341 343. Bauer, S. E., et al., 2008b: MATRIX (Multiconfiguration Aerosol TRacker of mIXing Boer, G., and S. Lambert, 2008: The energy cycle in atmospheric models. Clim. Dyn., state): An aerosol microphysical module for global atmospheric models. Atmos. 30, 371 390. Chem. Phys., 8, 6003 6035. Boer, G. J., and B. Yu, 2003: Climate sensitivity and climate state. Clim. Dyn., 21, Beare, R., et al., 2006: An intercomparison of large-eddy simulations of the Stable 167 176. Boundary Layer. Boundary-Layer Meteorol., 118, 247 272. Boisier, J.-P., et al., 2012: Attributing the biogeophysical impacts of land-use induced Bellassen, V., G. Le Maire, J. F. Dhote, P. Ciais, and N. Viovy, 2010: Modelling forest Land-Cover Changes on surface climate to specific causes. Results from the first management within a global vegetation model Part 1: Model structure and LUCID set of simulations. J. Geophys. Res., 117, D12116. general behaviour. Ecol. Model., 221, 2458 2474. Bollasina, M. A., and Y. Ming, 2013: The general circulation model precipitation Bellassen, V., G. le Maire, O. Guin, J. F. Dhote, P. Ciais, and N. Viovy, 2011: Modelling bias over the southwestern equatorial Indian Ocean and its implications for forest management within a global vegetation model-Part 2: Model validation simulating the South Asian monsoon. Clim. Dyn., 40, 823 838. from a tree to a continental scale. Ecol. Model., 222, 57 75. Bonan, G. B., 2008: Forests and climate change: Forcings, feedbacks, and the climate Bellouin, N., J. Rae, A. Jones, C. Johnson, J. Haywood, and O. Boucher, 2011: Aerosol benefits of forests. Science, 320, 1444 1449. forcing in the Climate Model Intercomparison Project (CMIP5) simulations by Bond, T. C., et al., 2007: Historical emissions of black and organic carbon aerosol HadGEM2 ES and the role of ammonium nitrate. J. Geophys. Res., 116, 1 25. from energy-related combustion, 1850 2000. Global Biogeochem. Cycles, 21, Bellucci, A., S. Gualdi, and A. Navarra, 2010: The Double-ITCZ Syndrome in Coupled GB2018. General Circulation Models: The role of large-scale vertical circulation regimes. Bondeau, A., P. C. Smith, S. Zaehle, S. Schaphoff, W. Lucht, W. Cramer, and D. Gerten, J. Clim., 23, 1127 1145. 2007: Modelling the role of agriculture for the 20th century global terrestrial Bender, M. A., T. R. Knutson, R. E. Tuleya, J. J. Sirutis, G. A. Vecchi, S. T. Garner, and I. carbon balance. Global Change Biol., 13, 679 706. M. Held, 2010: Modeled impact of anthropogenic warming on the frequency of Boning, C. W., A. Dispert, M. Visbeck, S. R. Rintoul, and F. U. Schwarzkopf, 2008: The intense Atlantic hurricanes. Science, 327, 454 458. response of the Antarctic Circumpolar Current to recent climate change. Nature Bengtsson, L., and K. Hodges, 2011: On the evaluation of temperature trends in the Geosci., 1, 864 869. tropical troposphere. Clim. Dyn., 36, 419 430. Boone, A., et al., 2009: THE AMMA Land Surface Model Intercomparison Project Bengtsson, L., K. I. Hodges, and N. Keenlyside, 2009: Will extratropical storms (ALMIP). Bull. Am. Meteorol. Soc., 90, 1865 1880. intensify in a warmer climate? J. Clim., 22, 2276 2301. Boone, A. A., I. Poccard-Leclercq, Y. K. Xue, J. M. Feng, and P. de Rosnay, 2010: Berckmans, J., T. Woollings, M.-E. Demory, P.-L. Vidal, and M. Roberts, 2013: Evaluation of the WAMME model surface fluxes using results from the AMMA Atmospheric blocking in a high resolution climate model: Influences of mean land-surface model intercomparison project. Clim. Dyn., 35, 127 142. state, orography and eddy forcing. Atmos. Sci. Lett., 14, 34 40. Booth, B. B. B., N. J. Dunstone, P. R. Halloran, T. Andrews, and N. Bellouin, 2012a: Bergengren, J., D. Waliser, and Y. Yung, 2011: Ecological sensitivity: A biospheric view Aerosols implicated as a prime driver of twentieth-century North Atlantic of climate change. Clim. Change, 107, 433 457. climate variability. Nature, 484, 228 232. Bergengren, J., S. Thompson, D. Pollard, and R. DeConto, 2001: Modeling global Booth, B. B. B., et al., 2012b: High sensitivity of future global warming to land carbon climate-vegetation interactions in a doubled CO2 world. Clim. Change, 50, cycle processes. Environ. Res. Lett., 7, 024002. 31 75. Boschat, G., P. Terray, and S. Masson, 2012: Robustness of SST teleconnections and precursory patterns associated with the Indian summer monsoon. Clim. Dyn., 38, 2143 2165. 829 Chapter 9 Evaluation of Climate Models Boyle, J., and S. A. Klein, 2010: Impact of horizontal resolution on climate model Brown, J., O. J. Ferrians, J. A. Heginbottom, and E. S. E.S. Melnikov, 1998: Digital forecasts of tropical precipitation and diabatic heating for the TWP-ICE period. circum-arctic map of permafrost and ground ice conditions. In: Circumpolar J. Geophys. Res., 115, D23113. Active-Layer Permafrost System (CAPS). CD-ROM. 1.0 ed., University of Colorado Boyle, J., S. Klein, G. Zhang, S. Xie, and X. Wei, 2008: Climate Model Forecast at Boulder National Snow and Ice Data Center. Boulder, CO, USA. Experiments for TOGA COARE. Mon. Weather Rev., 136, 808 832. Brown, J. R., C. Jakob, and J. M. Haynes, 2010b: An evaluation of rainfall frequency Bracegirdle, T., et al., 2013: Assessment of surface winds over the Atlantic, Indian and intensity over the Australian region in a Global Climate Model. J. Clim., 23, and Pacific Ocean sectors of the Southern Hemisphere in CMIP5 models: 6504 6525. Historical bias, forcing response, and state dependence. J. Geophys. Res. Atmos., Brown, J. R., A. F. Moise, and R. A. Colman, 2013: The South Pacific Convergence Zone doi:10.1002/jgrd.50153. in CMIP5 simulations of historical and future climate. Clim. Dyn., doi:10.1007/ Bracegirdle, T. J., and D. B. Stephenson, 2012: Higher precision estimates of regional s00382-012-1591 x. polar warming by ensemble regression of climate model projections. Clim. Dyn., Brutel-Vuilmet, C., M. Menegoz, and G. Krinner, 2013: An analysis of present and 39, 2805 2821. future seasonal Northern Hemisphere land snow cover simulated by CMIP5 Bracegirdle, T. J., and D. B. Stephenson, 2013: On the robustness of emergent coupled climate models. Cryosphere, 7, 67 80. constraints used in multi-model climate change projections of Arctic warming. Bryan, F. O., M. W. Hecht, and R. D. Smith, 2007: Resolution convergence and J. Clim., 26, 669 678. sensitivity studies with North Atlantic circulation models. Part I: The western Braconnot, P., F. Hourdin, S. Bony, J. Dufresne, J. Grandpeix, and O. Marti, 2007a: boundary current system. Ocean Model., 16, 141 159. Impact of different convective cloud schemes on the simulation of the tropical Bryan, F. O., R. Tomas, J. M. Dennis, D. B. Chelton, N. G. Loeb, and J. L. McClean, 2010: seasonal cycle in a coupled ocean-atmosphere model. Clim. Dyn., 29, 501 520. Frontal scale air-sea interaction in high-resolution coupled climate models. J. 9 Braconnot, P., et al., 2012: Evaluation of climate models using palaeoclimatic data. Clim., 23, 6277 6291. Nature Clim. Change, 2, 417 424. Bryan, K., and L. J. Lewis, 1979: Water mass model of the world ocean. J. Geophys. Braconnot, P., et al., 2007b: Results of PMIP2 coupled simulations of the Mid- Res. Oceans Atmos., 84, 2503 2517. Holocene and Last Glacial Maximum - Part 2: Feedbacks with emphasis on the Bryden, H. L., H. R. Longworth, and S. A. Cunningham, 2005: Slowing of the Atlantic location of the ITCZ and mid- and high latitudes heat budget. Clim. Past, 3, meridional overturning circulation at 25° N. Nature, 438, 655 657. 279 296. Buehler, T., C. C. Raible, and T. F. Stocker, 2011: The relationship of winter season Braconnot, P., et al., 2007c: Results of PMIP2 coupled simulations of the Mid- North Atlantic blocking frequencies to extreme cold or dry spells in the ERA-40. Holocene and Last Glacial Maximum - Part 1: Experiments and large-scale Tellus A, 63, 212 222. features. Clim. Past, 3, 261 277. Butchart, N., A. A. Scaife, J. Austin, S. H. E. Hare, and J. R. Knight, 2003: Quasi-biennial Brands, S., J. Taboada, A. Cofino, T. Sauter, and C. Schneider, 2011: Statistical oscillation in ozone in a coupled chemistry-climate model. J. Geophys. Res., 108, downscaling of daily temperatures in the NW Iberian Peninsula from global 4486. climate models: Validation and future scenarios. Clim. Res., 48, 163 176. Cadule, P., et al., 2010: Benchmarking coupled climate-carbon models against long- Bresson, R., and R. Laprise, 2011: Scale-decomposed atmospheric water budget over term atmospheric CO2 measurements. Global Biogeochem. Cycles, 24, Gb2016. North America as simulated by the Canadian Regional Climate Model for current Cai, W., and T. Cowan, 2013: Why is the amplitude of the Indian Ocean Dipole overly and future climates. Clim. Dyn., 36, 365 384. large in CMIP3 and CMIP5 climate models? . Geophys. Res. Lett., doi:10.1002/ Breugem, W. P., W. Hazeleger, and R. J. Haarsma, 2006: Multimodel study of tropical grl.50208. Atlantic variability and change. Geophys. Res. Lett., 33, L23706. Cai, W., A. Sullivan, and T. Cowan, 2011: Interactions of ENSO, the IOD, and the SAM Brewer, S., J. Guiot, and F. Torre, 2007: Mid-Holocene climate change in Europe: A in CMIP3 Models. J. Clim., 24, 1688 1704. data-model comparison. Clim. Past, 3, 499 512. Cai, W. J., A. Sullivan, and T. Cowan, 2009: Rainfall teleconnections with Indo-Pacific Briegleb, B. P., and B. Light, 2007: A Delta-Eddington multiple scattering variability in the WCRP CMIP3 models. J. Clim., 22, 5046 5071. parameterization for solar radiation in the sea ice component of the Community Calov, R., A. Ganopolski, V. Petoukhov, M. Claussen, and R. Greve, 2002: Large-scale Climate System Model. NCAR Technical Note, National Center for Atmospheric instabilities of the Laurentide ice sheet simulated in a fully coupled climate- Research, 100 pp. system model. Geophys. Res. Lett., 29, 2216. Briegleb, B. P., C. M. Blitz, E. C. Hunke, W. H. Lipscomb, M. M. Holland, J. L. Schramm, Cameron-Smith, P., J. F. Lamarque, P. Connell, C. Chuang, and F. Vitt, 2006: Toward an and R. E. Moritz, 2004: Scientific description of the sea ice component in the Earth system model: Atmospheric chemistry, coupling, and petascale computing. Community Climate System Model, Version 3. NCAR Technical Note, National Scidac 2006: Scientific Discovery through Advanced Computing [W. M. Tang Center for Atmospheric Research, 70 pp. (ed.)]. Journal of Physics: Conference Series, Vol. 46, Denver, Colorado, USA. Brient, F., and S. Bony, 2012: Interpretation of the positive low-cloud feedback Capotondi, A., A. Wittenberg, and S. Masina, 2006: Spatial and temporal structure predicted by a climate model under global warming. Clim. Dyn., doi:10.1007/ of Tropical Pacific interannual variability in 20th century coupled simulations. s00382 011 1279 7. Ocean Model., 15, 274 298. Brierley, C. M., M. Collins, and A. J. Thorpe, 2010: The impact of perturbations to Capotondi, A., M. A. Alexander, N. A. Bond, E. N. Curchitser, and J. D. Scott, 2012: ocean-model parameters on climate and climate change in a coupled model. Enhanced upper ocean stratification with climate change in the CMIP3 models. Clim. Dyn., 34, 325 343. J. Geophys. Res. Oceans 117, C04031. Brogniez, H., and R. T. Pierrehumbert, 2007: Intercomparison of tropical tropospheric Cariolle, D., and H. Teyssedre, 2007: A revised linear ozone photochemistry humidity in GCMs with AMSU-B water vapor data. Geophys. Res. Lett., 34, parameterization for use in transport and general circulation models: Multi- L17812 annual simulations. Atmos. Chem. Phys., 7, 2183 2196. Brogniez, H., R. Roca, and L. Picon, 2005: Evaluation of the distribution of subtropical Carslaw, K. S., O. Boucher, D. V. Spracklen, G. W. Mann, J. G. L. Rae, S. Woodward, and free tropospheric humidity in AMIP-2 simulations using METEOSAT water vapor M. Kulmala, 2010: A review of natural aerosol interactions and feedbacks within channel data. Geophys. Res. Lett., 32, L19708. the Earth system. Atmos. Chem. Phys., 10, 1701 1737. Brovkin, V., J. Bendtsen, M. Claussen, A. Ganopolski, C. Kubatzki, V. Petoukhov, and A. Casado, M. J., and M. A. Pastor, 2012: Use of variability modes to evaluate AR4 Andreev, 2002: Carbon cycle, vegetation, and climate dynamics in the Holocene: climate models over the Euro-Atlantic region. Clim. Dyn., 38, 225 237. Experiments with the CLIMBER-2 model. Global Biogeochem. Cycles, 16, 1139. Cattiaux, J., H. Douville, and Y. Peings, 2013: European temperatures in CMIP5: Brown, A., S. Milton, M. Cullen, B. Golding, J. Mitchell, and A. Shelly, 2012: Unified Origins of present-day biases and future uncertainties. Clim. Dyn., doi:10.1007/ modeling and prediction of weather and climate: A 25-year journey. Bull. Am. s00382-013-1731-y. Meteorol. Soc., 93, 1865 1877. Catto, J., N. Nicholls, and C. Jakob, 2012a: North Australian sea surface temperatures Brown, J., A. Fedorov, and E. Guilyardi, 2010a: How well do coupled models replicate and the El Nin o Southern Oscillation in observations and models. J. Clim., 25, ocean energetics relevant to ENSO? Clim. Dyn., 36, 2147 2158. 5011 5029. Brown, J., O. J. Ferrians, J. A. Heginbottom, and E. S. E.S. Melnikov, 1997: International Catto, J., N. Nicholls, and C. Jakob, 2012b: North Australian sea surface temperatures Permafrost Association Circum-Arctic Map of Permafrost and Ground Ice and the El Nin o Southern Oscillation in the CMIP5 models. J. Clim., 25, 6375 Conditions. Geological Survey (U.S.), Denver, CO, USA. 6382. 830 Evaluation of Climate Models Chapter 9 Catto, J. L., L. C. Shaffrey, and K. I. Hodges, 2010: Can climate models capture the Christian, J. R., et al., 2010: The global carbon cycle in the Canadian Earth system structure of extratropical cyclones? J. Clim., 23, 1621 1635. model (CanESM1): Preindustrial control simulation. J. Geophys. Res. Biogeosci., Catto, J. L., L. C. Shaffrey, and K. I. Hodges, 2011: Northern Hemisphere Extratropical 115, G03014 cyclones in a warming climate in the HiGEM high-resolution climate Model. J. Christidis, N., P. A. Stott, and S. J. Brown, 2011: The role of human activity in the recent Clim., 24, 5336 5352. warming of extremely warm daytime temperatures. J. Clim., 24, 1922 1930. Catto, J. L., C. Jakob, and N. Nicholls, 2013: A global evaluation of fronts and Christy, J. R., W. B. Norris, R. W. Spencer, and J. J. Hnilo, 2007: Tropospheric temperature precipitation in the ACCESS model. Aust. Meteorol. Oceanogr. J., 63,191-203. change since 1979 from tropical radiosonde and satellite measurements. J. Cavalieri, D. J., and C. L. Parkinson, 2012: Arctic sea ice variability and trends, 1979 Geophys. Res. Atmos., 112, D06102. 2010. Cryosphere, 6, 881 889. Christy, J. R., et al., 2010: What do observational datasets say about modeled Cavicchia, L., and H. von Storch, 2011: The simulation of medicanes in a high- tropospheric temperature trends since 1979? Remote Sens., 2, 2148 2169. resolution regional climate model. Clim. Dyn., 39 2273 2290. Cimatoribus, A. A., S. S. Drijfhout, and H. A. Dijkstra, 2012: A global hybrid coupled Cesana, G., and H. Chepfer, 2012: How well do climate models simulate cloud vertical model based on atmosphere-SST feedbacks. Clim. Dyn., 38, 745 760. structure? A comparison between CALIPSO-GOCCP satellite observations and Cionni, I., et al., 2011: Ozone database in support of CMIP5 simulations: Results and CMIP5 models. Geophys. Res. Lett., 39, L20803. corresponding radiative forcing. Atmos. Chem. Phys., 11, 11267 11292. Cha, D., D. Lee, and S. Hong, 2008: Impact of boundary layer processes on seasonal Clark, D. B., et al., 2011: The Joint UK Land Environment Simulator (JULES), model simulation of the East Asian summer monsoon using a Regional Climate Model. description - Part 2: Carbon fluxes and vegetation dynamics. Geosci. Model Dev., Meteorol. Atmos. Phys., 100, 53 72. 4, 701 722. Champion, A. J., K. I. Hodges, L. O. Bengtsson, N. S. Keenlyside, and M. Esch, 2011: Claussen, M., et al., 2002: Earth system models of intermediate complexity: Closing Impact of increasing resolution and a warmer climate on extreme weather from the gap in the spectrum of climate system models. Clim. Dyn., 18, 579 586. 9 Northern Hemisphere extratropical cyclones. Tellus A, 63, 893 890. Coelho, C. A. S., and L. Goddard, 2009: El Nino-induced tropical droughts in climate Chan, S. C., E. J. Kendon, H. J. Fowler, S. Blenkinsop, C. A. T. Ferro, and D. B. Stephenson, change projections. J. Clim., 22, 6456 6476. 2012: Does increasing resolution improve the simulation of United Kingdom Cohen, J. L., J. C. Furtado, M. Barlow, V. A. Alexeev, and J. E. Cherry, 2012: Asymmetric daily precipitation in a regional climate model? Clim. Dyn., doi:10.1007/s00382- seasonal temperature trends. Geophys. Res. Lett., 39, L04705. 012-1568-9. Collatz, G. J., M. Ribas-Carbo, and J. A. Berry, 1992: Coupled photosynthesis-stomatal Chang, C. P., and T. Li, 2000: A theory for the tropical tropospheric biennial oscillation. conductance model for leaves of C4 Plants. Aust. J. Plant Physiol., 19, 519 538. J. Atmos. Sci., 57, 2209 2224. Collatz, G. J., J. T. Ball, C. Grivet, and J. A. Berry, 1991: Physiological and environmental Chang, C. Y., S. Nigam, and J. A. Carton, 2008: Origin of the springtime westerly regulation of stomatal conductance, photosynthesis and transpiration: A model bias in equatorial Atlantic surface winds in the Community Atmosphere Model that includes a laminar boundary layer. Agr. Forest Meteorol., 54, 107 136. version 3 (CAM3) simulation. J. Clim., 21, 4766 4778. Colle, B. A., Z. Zhang, K. A. Lombardo, E. Chang, P. Liu, and M. Zhang, 2013: Historical Chang, C. Y., J. A. Carton, S. A. Grodsky, and S. Nigam, 2007: Seasonal climate of the evaluation and future prediction of eastern North America and western Atlantic tropical Atlantic sector in the NCAR community climate system model 3: Error extratropical cyclones in the CMIP5 models during the cool season. J. Clim., structure and probable causes of errors. J. Clim., 20, 1053 1070. doi:10.1175/JCLI-D-12 00498.1. Chang, E. K. M., Y. Guo, and X. Xia, 2012: CMIP5 multi-model ensemble projection Collins, M., S. Tett, and C. Cooper, 2001: The internal climate variability of HadCM3, of storm track change under global warming. J. Geophys. Res., 117, D23118. a version of the Hadley Centre coupled model without flux adjustments. Clim. Charbit, S., D. Paillard, and G. Ramstein, 2008: Amount of CO2 emissions irreversibly Dyn., 17, 61 81. leading to the total melting of Greenland. Geophys. Res. Lett., 35, L12503. Collins, M., C. M. Brierley, M. MacVean, B. B. B. Booth, and G. R. Harris, 2007: The Charlton-Perez, A. J., et al., 2012: Mean climate and variability of the stratosphere in sensitivity of the rate of transient climate change to ocean physics perturbations. CMIP5 models. J. Geophys. Res., doi:10.1002/jgrd.50125. J. Clim., 20, 2315 2320. Chen, C. T., and T. Knutson, 2008: On the verification and comparison of extreme Collins, M., B. B. B. Booth, G. R. Harris, J. M. Murphy, D. M. H. Sexton, and M. J. Webb, rainfall indices from climate models. J. Clim., 21, 1605 1621. 2006a: Towards quantifying uncertainty in transient climate change. Clim. Dyn., Chen, H. M., T. J. Zhou, R. B. Neale, X. Q. Wu, and G. J. Zhang, 2010: Performance of 27, 127 147. the New NCAR CAM3.5 in East Asian summer monsoon simulations: Sensitivity Collins, M., R. Chandler, P. Cox, J. Huthnance, J. Rougier, and D. Stephenson, 2012: to modifications of the Convection Scheme. J. Clim., 23, 3657 3675. Quantifying future climate change. Nature Clim. Change, 2, 403 409. Chen, L., Y. Yu, and D. Sun, 2013: Cloud and water vapor feedbacks to the El Nino Collins, M., B. Booth, B. Bhaskaran, G. Harris, J. Murphy, D. Sexton, and M. Webb, warming: Are they still biased in CMIP5 models? J. Clim., doi:10.1175/JCLI-D- 2010: Climate model errors, feedbacks and forcings: A comparison of perturbed 12-00575.1. physics and multi-model ensembles. Clim. Dyn., 36, 1737 1766. Chen, Y. H., and A. D. Del Genio, 2009: Evaluation of tropical cloud regimes in Collins, W. D., J. M. Lee-Taylor, D. P. Edwards, and G. L. Francis, 2006b: Effects of observations and a general circulation model. Clim. Dyn., 32, 355 369. increased near-infrared absorption by water vapor on the climate system. J. Chiang, J. C. H., and A. H. Sobel, 2002: Tropical tropospheric temperature variations Geophys. Res. Atmos., 111, D18109. caused by ENSO and their influence on the remote tropical climate. J. Clim., 15, Collins, W. D., et al., 2006c: The formulation and atmospheric simulation of the 2616 2631. Community Atmosphere Model version 3 (CAM3). J. Clim., 19, 2144 2161. Chiang, J. C. H., and D. J. Vimont, 2004: Analogous Pacific and Atlantic meridional Collins, W. D., et al., 2006d: The Community Climate System Model version 3 modes of tropical atmosphere-ocean variability. J. Clim., 17, 4143 4158. (CCSM3). J. Clim., 19, 2122 2143. Chou, C., and J. Y. Tu, 2008: Hemispherical asymmetry of tropical precipitation in Collins, W. J., et al., 2011: Development and evaluation of an Earth-System model- ECHAM5/MPI-OM during El Nino and under global warming. J. Clim., 21, 1309 HadGEM2. Geosci. Model Dev., 4, 1051 1075. 1332. Colman, R., and B. McAvaney, 2009: Climate feedbacks under a very broad range of Chou, C., J. D. Neelin, J. Y. Tu, and C. T. Chen, 2006: Regional tropical precipitation forcing. Geophys. Res. Lett., 36, L01702. change mechanisms in ECHAM4/OPYC3 under global warming. J. Clim., 19, Colman, R. A., A. F. Moise, and L. I. Hanson, 2011: Tropical Australian climate and the 4207 4223. Australian monsoon as simulated by 23 CMIP3 models. J. Geophys. Res. Atmos., Chou, S., et al., 2012: Downscaling of South America present climate driven by 116, D10116. 4-member HadCM3 runs. Clim. Dyn., 38, 635 653. Comiso, J. C., and F. Nishio, 2008: Trends in the sea ice cover using enhanced and Christensen, J., F. Boberg, O. Christensen, and P. Lucas-Picher, 2008: On the need compatible AMSR-E, SSM/I, and SMMR data. J. Geophys. Res. Oceans, 113, for bias correction of regional climate change projections of temperature and C02s07. precipitation. Geophys. Res. Lett., 35, L20709. Compo, G. P., and P. D. Sardeshmukh, 2009: Oceanic influences on recent continental Christensen, J., E. Kjellstrom, F. Giorgi, G. Lenderink, and M. Rummukainen, 2010: warming. Clim. Dyn., 32, 333 342. Weight assignment in regional climate models. Clim. Res., 44, 179 194. Connolley, W., and T. Bracegirdle, 2007: An Antarctic assessment of IPCC AR4 Christensen, J. H., and F. Boberg, 2013: Temperature dependent climate projection coupled models. Geophys. Res. Lett., 34 L22505. deficiencies in CMIP5 models. Geophys. Res. Lett., 39, L24705. 831 Chapter 9 Evaluation of Climate Models Coon, M., R. Kwok, G. Levy, M. Pruis, H. Schreyer, and D. Sulsky, 2007: Arctic Danabasoglu, G., et al., 2012: The CCSM4 Ocean Component. J. Clim., 25, 1361 Ice Dynamics Joint Experiment (AIDJEX) assumptions revisited and found 1389. inadequate. J. Geophys. Res., 112, C11S90. Davies, T., M. J. P. Cullen, A. J. Malcolm, M. H. Mawson, A. Staniforth, A. A. White, and Coppola, E., F. Giorgi, S. Rauscher, and C. Piani, 2010: Model weighting based N. Wood, 2005: A new dynamical core for the Met Office s global and regional on mesoscale structures in precipitation and temperature in an ensemble of modelling of the atmosphere. Q. J. R. Meteorol. Soc., 131, 1759 1782. regional climate models. Clim. Res., 44 121 134. Davis, B. A. S., and S. Brewer, 2009: Orbital forcing and role of the latitudinal Cox, P., 2001: Description of the TRIFFID Dynamic Global Vegetation Model Hadley insolation/temperature gradient. Clim. Dyn., 32, 143 165. Centre, Met Office Hadley Centre, Berks, United Kingdom, 16 pp. Dawson, A., T. N. Palmer, and S. Corti, 2012: Simulating regime structures in weather Cox, P. M., R. A. Betts, C. D. Jones, S. A. Spall, and I. J. Totterdell, 2000: Acceleration and climate prediction models. Geophys. Res. Lett., 39, L21805. of global warming due to carbon-cycle feedbacks in a coupled climate model. Day, J. J., J. C. Hargreaves, J. D. Annan, and A. Abe-Ouchi, 2012: Sources of multi- Nature, 408, 184 187. decadal variability in Arctic sea ice extent. Environ. Res. Lett., 7, 034011. Cox, P. M., R. A. Betts, C. B. Bunton, R. L. H. Essery, P. R. Rowntree, and J. Smith, 1999: de Elia, R., and H. Cote, 2010: Climate and climate change sensitivity to model The impact of new land surface physics on the GCM simulation of climate and configuration in the Canadian RCM over North America. Meteorol. Z., 19, 325 climate sensitivity. Clim. Dyn., 15, 183 203. 339. Cox, P. M., D. Pearson, B. B. B. Booth, P. Friedlingstein, C. Huntingford, C. D. Jones, and de Elia, R., S. Biner, and A. Frigon, 2013: Interannual variability and expected regional C. M. Luke, 2013: Sensitivity of tropical carbon to climate change constrained by climate change over North America. Clim. Dyn., doi:10.1007/s00382-013-1717- carbon dioxide variability. Nature, 494, 341 344. 9. Cramer, W., et al., 2001: Global response of terrestrial ecosystem structure and de Jong, M. F., S. S. Drijfhout, W. Hazeleger, H. M. van Aken, and C. A. Severijns, 9 function to CO2 and climate change: Results from six dynamic global vegetation 2009: Simulations of hydrographic properties in the Northwestern North Atlantic models. Global Change Biol., 7, 357 373. Ocean in Coupled Climate Models. J. Clim., 22, 1767 1786. Crétat, J., B. Pohl, Y. Richard, and P. Drobinski, 2012: Uncertainties in simulating de Noblet-Ducoudre, N., et al., 2012: Determining robust impacts of land-use regional climate of Southern Africa: Sensitivity to physical parameterizations induced land-cover changes on surface climate over North America and Eurasia; using WRF. Clim. Dyn., 38, 613 634. Results from the first set of LUCID experiments. J. Clim., 25, 3261 3281. Croft, B., U. Lohmann, and K. von Salzen, 2005: Black carbon ageing in the Canadian De Szoeke, S. P., and S. P. Xie, 2008: The tropical eastern Pacific seasonal cycle: Centre for Climate modelling and analysis atmospheric general circulation Assessment of errors and mechanisms in IPCC AR4 coupled ocean - atmosphere model. Atmos. Chem. Phys., 5, 1931 1949. general circulation models. J. Clim., 21, 2573 2590. Crucifix, M., 2006: Does the Last Glacial Maximum constrain climate sensitivity? Dee, D. P., et al., 2011: The ERA-Interim reanalysis: Configuration and performance of Geophys. Res. Lett., 33, L18701. the data assimilation system. Q. J. R. Meteorol. Soc., 137, 553 597. Cunningham, S., et al., 2010: The present and future system for measuring the DelSole, T., and J. Shukla, 2009: Artificial skill due to predictor screening. J. Clim., Atlantic meridional overturning circulation and heat transport. In: Proceedings 22, 331 345. of OceanObs 09: Sustained Ocean Observations and Information for Society (Vol. Delworth, T. L., et al., 2012: Simulated climate and climate change in the GFDL 2), Venice, Italy, 21 25 September 2009, ESA Publication. CM2.5 High-Resolution Coupled Climate Model. J. Clim., 25, 2755 2781. Cunningham, S. A., S. G. Alderson, B. A. King, and M. A. Brandon, 2003: Transport and Delworth, T. L., et al., 2006: GFDL s CM2 global coupled climate models. Part I: variability of the Antarctic Circumpolar Current in Drake Passage. J. Geophys. Formulation and simulation characteristics. J. Clim., 19, 643 674. Res.-Oceans, 108, 8084. Déqué, M., 2007: Frequency of precipitation and temperature extremes over France Cunningham, S. A., et al., 2007: Temporal variability of the Atlantic meridional in an anthropogenic scenario: Model results and statistical correction according overturning circulation at 26.5°N. Science, 317, 935 938. to observed values. Global Planet. Change, 57, 16 26. Curry, W. B., and D. W. Oppo, 2005: Glacial water mass geometry and the distribution Déqué, M., 2010: Regional climate simulation with a mosaic of RCMs. Meteorol. Z., of delta C-13 of sigma CO2 in the western Atlantic Ocean. Paleoceanography, 19, 259 266. 20, Pa1017. Déqué, M., C. Dreveton, A. Braun, and D. Cariolle, 1994: The ARPEGE/IFS atmosphere Cuxart, J., et al., 2006: Single-column model intercomparison for a stably stratified model: A contribution to the French community climate modelling. Clim. Dyn., atmospheric boundary layer. Boundary-Layer Meteorol., 118, 273 303. 10, 249 266. Dai, A., 2001: Global precipitation and thunderstorm frequencies. Part II: Diurnal Déqué, M., S. Somot, E. Sanchez-Gomez, C. Goodess, D. Jacob, G. Lenderink, and variations. J. Clim., 14, 1112 1128. O. Christensen, 2012: The spread amongst ENSEMBLES regional scenarios: Dai, A., 2006: Precipitation characteristics in eighteen coupled climate models. J. Regional climate models, driving general circulation models and interannual Clim., 19, 4605 4630. variability. Clim. Dyn., 38, 951 964. Dai, A., and C. Deser, 1999: Diurnal and semidiurnal variations in global surface wind Derksen, C., and R. Brown, 2012: Spring snow cover extent reductions in the and divergence fields. J. Geophys. Res. Atmos., 104, 31109 31125. 2008 2012 period exceeding climate model projections. Geophys. Res. Lett., 39, Dai, A., and K. E. Trenberth, 2004: The diurnal cycle and its depiction in the Community L19504. Climate System Model. J. Clim., 17, 930 951. Deser, C., A. S. Phillips, and J. W. Hurrell, 2004: Pacific interdecadal climate variability: Dai, Y. J., R. E. Dickinson, and Y. P. Wang, 2004: A two-big-leaf model for canopy Linkages between the tropics and the North Pacific during boreal winter since temperature, photosynthesis, and stomatal conductance. J. Clim., 17, 2281 1900. J. Clim., 17, 3109 3124. 2299. Deser, C., A. S. Phillips, V. Bourdette, and H. Teng, 2011: Uncertainty in climate change Dai, Y. J., et al., 2003: The Common Land Model. Bull. Am. Meteorol. Soc., 84, 1013 projections: The role of internal variability. Clim. Dyn., 38, 527 546. 1023. Deser, C., et al., 2012: ENSO and Pacific decadal variability in Community Climate Dallmeyer, A., M. Claussen, and J. Otto, 2010: Contribution of oceanic and vegetation System Model Version 4. J. Clim., 25, 2622 2651. feedbacks to Holocene climate change in monsoonal Asia. Clim. Past, 6, 195 Deushi, M., and K. Shibata, 2011: Development of a Meteorological Research 218. Institute Chemistry-Climate Model version 2 for the Study of Tropospheric and Danabasoglu, G., and P. R. Gent, 2009: Equilibrium climate sensitivity: Is it accurate Stratospheric Chemistry. Papers Meteorol. Geophys., 62, 1 46. to use a Slab Ocean Model? J. Clim., 22, 2494 2499. Di Luca, A., R. Elía, and R. Laprise, 2012: Potential for small scale added value of Danabasoglu, G., R. Ferrari, and J. C. McWilliams, 2008: Sensitivity of an ocean RCM s downscaled climate change signal. Clim. Dyn., 40, 601 618. general circulation model to a parameterization of near-surface eddy fluxes. J. Diaconescu, E. P., and R. Laprise, 2013: Can added value be expected in RCM- Clim., 21, 1192 1208. simulated large scales? Clim. Dyn., doi:10.1007/s00382-012-1649-9. Danabasoglu, G., W. G. Large, and B. P. Briegleb, 2010: Climate impacts of Diffenbaugh, N., M. Ashfaq, and M. Scherer, 2011: Transient regional climate parameterized Nordic Sea overflows. J. Geophys. Res. Oceans, 115, C11005. change: Analysis of the summer climate response in a high-resolution, century- Danabasoglu, G., W. G. Large, J. J. Tribbia, P. R. Gent, B. P. Briegleb, and J. C. scale ensemble experiment over the continental United States. J. Geophys. Res. McWilliams, 2006: Diurnal coupling in the tropical oceans of CCSM3. J. Clim., Atmos., 116, D24111. 19, 2347 2365. DiNezio, P. N., A. C. Clement, G. A. Vecchi, B. J. Soden, and B. P. Kirtman, 2009: Climate response of the equatorial Pacific to global warming. J. Clim., 22, 4873 4892. 832 Evaluation of Climate Models Chapter 9 Dix, M., et al., 2013: The ACCESS Coupled Model:  Documentation of core CMIP5 Easterling, D. R., and M. F. Wehner, 2009: Is the climate warming or cooling? simulations and initial results. Aust. Meteorol. Oceanogr. J., 63, 83 99. Geophys. Res. Lett., 36, L08706. Doblas-Reyes, F. J., et al., 2013: Initialized near-term regional climate change Eby, M., K. Zickfeld, A. Montenegro, D. Archer, K. J. Meissner, and A. J. Weaver, 2009: prediction. Nature Commun., 4, 1715. Lifetime of anthropogenic climate change: Millennial time scales of potential Dokken, T. M., and E. Jansen, 1999: Rapid changes in the mechanism of ocean CO2 and surface temperature perturbations. J. Clim., 22, 2501 2511. convection during the last glacial period. Nature, 401, 458 461. Eby, M., et al., 2013: Historical and idealized climate model experiments: An EMIC Domingues, C., J. Church, N. White, P. Gleckler, S. Wijffels, P. Barker, and J. Dunn, 2008: intercomparison. Clim. Past, 9, 1111 1140. Improved estimates of upper-ocean warming and multi-decadal sea-level rise. Edwards, N., and R. Marsh, 2005: Uncertainties due to transport-parameter Nature, 453, 1090 1093. sensitivity in an efficient 3-D ocean-climate model. Clim. Dyn., 24, 415 433. Donat, M., G. Leckebusch, S. Wild, and U. Ulbrich, 2010: Benefits and limitations Edwards, N. R., D. Cameron, and J. Rougier, 2011: Precalibrating an intermediate of regional multi-model ensembles for storm loss estimations. Clim. Res., 44, complexity climate model. Clim. Dyn., 37, 1469 1482. 211 225. Ek, M. B., et al., 2003: Implementation of Noah land surface model advances in the Donat, M. G., et al., 2013: Updated analyses of temperature and precipitation National Centers for Environmental Prediction operational mesoscale Eta model. extreme indices since the beginning of the twentieth century: The HadEX2 J. Geophys. Res. Atmos., 108, 8851. dataset. J. Geophys. Res., doi:10.1002/2012JD018606. Eliseev, A. V., and I. I. Mokhov, 2011: Uncertainty of climate response to natural and Donner, L. J., et al., 2011: The dynamical core, physical parameterizations, and basic anthropogenic forcings due to different land use scenarios. Adv. Atmos. Sci., 28, simulation characteristics of the atmospheric component AM3 of the GFDL 1215 1232. Global Coupled Model CM3. J. Clim., 24, 3484 3519. Emanuel, K., R. Sundararajan, and J. Williams, 2008: Hurricanes and global Dorn, W., K. Dethloff, and A. Rinke, 2009: Improved simulation of feedbacks between warming Results from downscaling IPCC AR4 simulations. Bull. Am. Meteorol. 9 atmosphere and sea ice over the Arctic Ocean in a coupled regional climate Soc., 89, 347 367. model. Ocean Model., 29, 103 114. Endo, H., A. Kitoh, T. Ose, R. Mizuta, and S. Kusunoki, 2012: Future changes and Doscher, R., K. Wyser, H. E. M. Meier, M. W. Qian, and R. Redler, 2010: Quantifying uncertainties in Asian precipitation simulated by multiphysics and multi-sea Arctic contributions to climate predictability in a regional coupled ocean-ice- surface temperature ensemble experiments with high-resolution Meteorological atmosphere model. Clim. Dyn., 34, 1157 1176. Research Institute atmospheric general circulation models (MRI-AGCMs). J. Douglass, D., J. Christy, B. Pearson, and S. Singer, 2008: A comparison of tropical Geophys. Res. Atmos., 117, D16118. temperature trends with model predictions. Int. J. Climatol. , 28, 1693 1701. Essery, R. L. H., M. J. Best, R. A. Betts, P. M. Cox, and C. M. Taylor, 2003: Explicit Dowdy, A. J., G. A. Mills, B. Timbal, and Y. Wang, 2013: Changes in the risk of representation of subgrid heterogeneity in a GCM land surface scheme. J. extratropical cyclones in Eastern Australia. J. Clim., 26, 1403 1417. Hydrometeorol., 4, 530 543. Driesschaert, E., et al., 2007: Modeling the influence of Greenland ice sheet melting Eum, H., P. Gachon, R. Laprise, and T. Ouarda, 2012: Evaluation of regional climate on the Atlantic meridional overturning circulation during the next millennia. model simulations versus gridded observed and regional reanalysis products Geophys. Res. Lett., 34, L10707. using a combined weighting scheme. Clim. Dyn., 38, 1433 1457. Driouech, F., M. Deque, and E. Sanchez-Gomez, 2010: Weather regimes-Moroccan Evans, J. P., M. Ekstroem, and F. Ji, 2012: Evaluating the performance of a WRF precipitation link in a regional climate change simulation. Global Planet. physics ensemble over South-East Australia. Clim. Dyn., 39, 1241 1258. Change, 72, 1 10. Eyring, V., et al., 2010: Transport impacts on atmosphere and climate: Shipping. Driscoll, S., A. Bozzo, L. J. Gray, A. Robock, and G. Stenchikov, 2012: Coupled Model Atmos. Environ., 44, 4735 4771. Intercomparison Project 5 (CMIP5) simulations of climate following volcanic Eyring, V., et al., 2013: Long-term ozone changes and associated climate impacts in eruptions. J. Geophys. Res. Atmos., 117, D17105. CMIP5 simulations. J. Geophys. Res., doi:10.1002/jgrd.50316. Druyan, L. M., et al., 2010: The WAMME regional model intercomparison study. Clim. Eyring, V., et al., 2007: Multimodel projections of stratospheric ozone in the 21st Dyn., 35, 175 192. century. J. Geophys. Res. Atmos., 112, D16303. Du, Y., S.-P. Xie, Y.-L. Yang, X.-T. Zheng, L. Liu, and G. Huang, 2013: Indian Ocean Faloona, I., 2009: Sulfur processing in the marine atmospheric boundary layer: A variability in the CMIP5 multi-model ensemble: The basin mode. J. Clim., 26, review and critical assessment of modeling uncertainties. Atmos. Environ., 43, 7240 7266. 2841 2854. Ducet, N., P. Y. Le Traon, and G. Reverdin, 2000: Global high-resolution mapping Fan, F. X., M. E. Mann, S. Lee, and J. L. Evans, 2010: Observed and modeled changes of ocean circulation from TOPEX/Poseidon and ERS-1 and-2. J. Geophys. Res. in the South Asian summer monsoon over the Historical Period. J. Clim., 23, Oceans, 105, 19477 19498. 5193 5205. Dufresne, J.-L., et al., 2012: Climate change projections using the IPSL-CM5 Earth Fanning, A. F., and A. J. Weaver, 1996: An atmospheric energy-moisture balance System Model: From CMIP3 to CMIP5. Clim. Dyn., doi:10.1007/s00382-012- model: Climatology, interpentadal climate change, and coupling to an ocean 1636-1. general circulation model. J. Geophys. Res. Atmos., 101, 15111 15128. Dufresne, J. L., and S. Bony, 2008: An assessment of the primary sources of spread Farneti, R., and P. R. Gent, 2011: The effects of the eddy-induced advection coefficient of global warming estimates from coupled atmosphere-ocean models. J. Clim., in a coarse-resolution coupled climate model. Ocean Model., 39, 135 145. 21, 5135 5144. Farneti, R., T. L. Delworth, A. J. Rosati, S. M. Griffies, and F. R. Zeng, 2010: The role of Dunkerton, T. J., 1991: Nonlinear propagation of zonal winds in an atmosphere with mesoscale eddies in the rectification of the Southern Ocean response to climate Newtonian cooling and equatorial wavedriving. J. Atmos. Sci., 48, 236 263. change. J. Phys. Oceanogr., 40, 1539 1557. Dunn-Sigouin, E., and S.-W. Son, 2013: Northern Hemisphere blocking frequency and Fasullo, J., and K. E. Trenberth, 2012: A less cloudy future: The role of subtropical duration in the CMIP5 models. J. Geophys. Res., 118, 1179 1188. subsidence in climate sensitivity. Science, 338, 792 794. Dunne, J. P., et al., 2013: GFDL s ESM2 global coupled climate-carbon Earth Fauchereau, N., S. Trzaska, Y. Richard, P. Roucou, and P. Camberlin, 2003: Sea-surface System Models Part II: Carbon system formulation and baseline simulation temperature co-variability in the southern Atlantic and Indian Oceans and its characteristics. J. Clim., doi:10.1175/JCLI-D-12-00150.1. connections with the atmospheric circulation in the Southern Hemisphere. Int. J. Dunne, J. P., et al., 2012: GFDL s ESM2 Global coupled climate-carbon Earth System Climatol. , 23, 663 677. models. Part I: Physical formulation and baseline simulation characteristics. J. Felzer, B., D. Kicklighter, J. Melillo, C. Wang, Q. Zhuang, and R. Prinn, 2004: Effects of Clim., 25, 6646 6665. ozone on net primary production and carbon sequestration in the conterminous Duplessy, J. C., N. J. Shackleton, R. Fairbanks, L. Labeyrie, D. Oppo, and N. Kallel, 1988: United States using a biogeochemistry model. Tellus B, 56, 230 248. Deep water source variation during the last climatic cycle and their impact on th Feng, J., and C. Fu, 2006: Inter-comparison of 10 year precipitation simulated by global deep water circulation. Paleoceanography, 3, 343 360. several RCMs for Asia. Adv. Atmos. Sci., 23 531 542. Durack, P. J., and S. E. Wijffels, 2010: Fifty-year trends in global ocean salinities and Feng, J., et al., 2011: Comparison of four ensemble methods combining regional their relationship to broad-scale warming. J. Clim., 23, 4342-4362. climate simulations over Asia. Meteorol. Atmos. Phys., 111, 41 53. Durack, P. J., S. E. Wijffels, and R. J. Matear, 2012: Ocean salinities reveal strong Fernandes, R., H. X. Zhao, X. J. Wang, J. Key, X. Qu, and A. Hall, 2009: Controls on global water cycle intensification during 1950 to 2000. Science, 336, 455 458. Northern Hemisphere snow albedo feedback quantified using satellite Earth observations. Geophys. Res. Lett., 36, L21702. 833 Chapter 9 Evaluation of Climate Models Fernandez-Donado, L., et al., 2013: Large-scale temperature response to external Fox-Rabinovitz, M., J. Cote, B. Dugas, M. Deque, J. McGregor, and A. Belochitski, forcing in simulations and reconstructions of the last millennium. Clim. Past, 9, 2008: Stretched-grid Model Intercomparison Project: Decadal regional climate 393 421. simulations with enhanced variable and uniform-resolution GCMs. Meteorol. Ferrari, R., J. C. McWilliams, V. M. Canuto, and M. Dubovikov, 2008: Parameterization Atmos. Phys., 100, 159 177. of eddy fluxes near oceanic boundaries. J. Clim., 21, 2770 2789. Frame, D., B. Booth, J. Kettleborough, D. Stainforth, J. Gregory, M. Collins, and M. Ferrari, R., S. M. Griffies, A. J. G. Nurser, and G. K. Vallis, 2010: A boundary-value Allen, 2005: Constraining climate forecasts: The role of prior assumptions. problem for the parameterized mesoscale eddy transport. Ocean Model., 32, Geophys. Res. Lett., 32, L09702. 143 156. Frankcombe, L. M., A. von der Heydt, and H. A. Dijkstra, 2010: North Atlantic Feser, F., 2006: Enhanced detectability of added value in limited-area model results multidecadal climate variability: An investigation of dominant time scales and separated into different spatial scales. Mon. Weather Rev., 134, 2180 2190. processes. J. Clim., 23, 3626 3638. Feser, F., and M. Barcikowska, 2012: The influence of spectral nudging on typhoon Frederiksen, C. S., J. S. Frederiksen, J. M. Sisson, and S. L. Osbrough, 2011: Australian formation in regional climate models. Environ. Res. Lett., 7, 014024. winter circulation and rainfall changes and projections. Int. J. Clim. Change Strat. Feser, F., B. Rockel, H. von Storch, J. Winterfeldt, and M. Zahn, 2011: Regional climate Manage., 3, 170 188. models add value to global model data: A review and selected examples. Bull. Friedlingstein, P., et al., 2001: Positive feedback between future climate change and Am. Meteorol. Soc., 92, 1181 1192. the carbon cycle. Geophys. Res. Lett., 28, 1543 1546. Fetterer, F., K. Knowles, W. Meier, and M. Savoie, 2002: Sea Ice Index. National Snow Friedlingstein, P., et al., 2006: Climate-carbon cycle feedback analysis: Results from and Ice Data Center. Boulder, CO, USA. the (CMIP)-M-4 model intercomparison. J. Clim., 19, 3337 3353. Fichefet, T., and M. A. M. Maqueda, 1997: Sensitivity of a global sea ice model to Friend, A. D., et al., 2007: FLUXNET and modelling the global carbon cycle. Global 9 the treatment of ice thermodynamics and dynamics. J. Geophys. Res., 102, Change Biol., 13, 610 633. 12609 12646. Frierson, D. M. W., J. Lu, and G. Chen, 2007: Width of the Hadley cell in simple and Fichefet, T., and M. A. M. Maqueda, 1999: Modelling the influence of snow comprehensive general circulation models. Geophys. Res. Lett., 34, L18804. accumulation and snow-ice formation on the seasonal cycle of the Antarctic Frohlich, C., and J. Lean, 2004: Solar radiative output and its variability: Evidence and sea-ice cover. Clim. Dyn., 15, 251 268. mechanisms. Astron. Astrophys. Rev., 12, 273 320. Field, P. R., A. Gettelman, R. B. Neale, R. Wood, P. J. Rasch, and H. Morrison, 2008: Frost, A. J., et al., 2011: A comparison of multi-site daily rainfall downscaling Midlatitude cyclone compositing to constrain climate model behavior using techniques under Australian conditions. J. Hydrol., 408, 1 18. satellite observations. J. Clim., 21, 5887 5903. Fu, Q., S. Manabe, and C. M. Johanson, 2011: On the warming in the tropical upper Fioletov, V., G. Bodeker, A. Miller, R. McPeters, and R. Stolarski, 2002: Global and troposphere: Models versus observations. Geophys. Res. Lett., 38, L15704. zonal total ozone variations estimated from ground-based and satellite Furrer, R., R. Knutti, S. Sain, D. Nychka, and G. Meehl, 2007: Spatial patterns of measurements: 1964 2000. J. Geophys. Res. Atmos., 107, 4647. probabilistic temperature change projections from a multivariate Bayesian Fischer, E. M., S. I. Seneviratne, D. Lüthi, and C. Schär, 2007: Contribution of land- analysis. Geophys. Res. Lett., 34, L06711. atmosphere coupling to recent European summer heat waves. Geophys. Res. Furtado, J., E. Di Lorenzo, N. Schneider, and N. A. Bond, 2011: North Pacific decadal Lett., 34, L06707. variability and climate change in the IPCC AR4 models. J. Clim., 24, 3049 3067 Flanner, M. G., K. M. Shell, M. Barlage, D. K. Perovich, and M. A. Tschudi, 2011: Fyfe, J. C., N. P. Gillett, and D. W. J. Thompson, 2010: Comparing variability and trends Radiative forcing and albedo feedback from the Northern Hemisphere in observed and modelled global-mean surface temperature. Geophys. Res. Lett., cryosphere between 1979 and 2008. Nature Geosci., 4, 151 155. 37, L16802. Flato, G., 2011: Earth system models: an overview. Wiley Interdisciplinary Reviews, Fyke, J. G., A. J. Weaver, D. Pollard, M. Eby, L. Carter, and A. Mackintosh, 2011: A new Climate Change, 2, 783 800. coupled ice sheet/climate model: Description and sensitivity to model physics Flocco, D., D. Schroeder, D. L. Feltham, and E. C. Hunke, 2012: Impact of melt ponds under Eemian, Last Glacial Maximum, late Holocene and modern climate on Arctic sea ice simulations from 1990 to 2007. J. Geophys. Res.Oceans, 117, conditions. Geosci. Model Dev., 4, 117 136. C09032. Galbraith, D., P. E. Levy, S. Sitch, C. Huntingford, P. Cox, M. Williams, and P. Meir, Fogli, P. G., et al., 2009: INGV-CMCC Carbon (ICC): A Carbon Cycle Earth System 2010: Multiple mechanisms of Amazonian forest biomass losses in three Model. CMCC Res. Papers. Euro-Mediterranean Center on Climate Change, dynamic global vegetation models under climate change. New Phytologist, 187, Bologna, Italy, 31 pp. 647 665. Fogt, R. L., J. Perlwitz, A. J. Monaghan, D. H. Bromwich, J. M. Jones, and G. J. Marshall, Ganachaud, A., and C. Wunsch, 2003: Large-scale ocean heat and freshwater 2009: Historical SAM variability. Part II: Twentieth-century variability and trends transports during the World Ocean Circulation Experiment. J. Clim., 16, 696 705. from reconstructions, observations, and the IPCC AR4 models. J. Clim., 22, Gangsto, R., F. Joos, and M. Gehlen, 2011: Sensitivity of pelagic calcification to ocean 5346 5365. acidification. Biogeosciences, 8, 433 458. Fontaine, B., and S. Janicot, 1996: Sea surface temperature fields associated with Gao, X., Y. Shi, D. Zhang, J. Wu, F. Giorgi, Z. Ji, and Y. Wang, 2012: Uncertainties in West African rainfall anomaly types. J. Clim., 9, 2935 2940. monsoon precipitation projections over China: Results from two high-resolution Forest, C. E., P. H. Stone, and A. P. Sokolov, 2006: Estimated PDFs of climate system RCM simulations. Clim. Res., 52, 213 226. properties including natural and anthropogenic forcings. Geophys. Res. Lett., 33, Gates, W. L., et al., 1999: An overview of the results of the Atmospheric Model L01705. Intercomparison Project (AMIP I). Bull. Am. Meteorol. Soc., 80, 29 55. Forest, C. E., P. H. Stone, and A. P. Sokolov, 2008: Constraining climate model Gbobaniyi, E. O., B. J. Abiodun, M. A. Tadross, B. C. Hewitson, and W. J. Gutowski, parameters from observed 20th century changes. Tellus A, 60, 911 920. 2011: The coupling of cloud base height and surface fluxes: A transferability Forest, C. E., P. H. Stone, A. P. Sokolov, M. R. Allen, and M. D. Webster, 2002: intercomparison. Theor. Appl. Climatol., 106, 189 210. Quantifying uncertainties in climate system properties with the use of recent Gehlen, M., R. Gangsto, B. Schneider, L. Bopp, O. Aumont, and C. Ethe, 2007: The fate climate observations. Science, 295, 113 117. of pelagic CaCO3 production in a high CO2 ocean: A model study. Biogeosciences, Forster, P. M., T. Andrews, P. Good, J. M. Gregory, L. S. Jackson, and M. Zelinka, 2013: 4, 505 519. Evaluating adjusted forcing and model spread for historical and future scenarios Geller, M. A., et al., 2011: New gravity wave treatments for GISS climate models. J. in the CMIP5 generation of climate models. J. Geophys. Res. Atmos., 118, 1139 Clim., 24, 3989 4002. 1150. Gent, P. R., and J. C. McWilliams, 1990: Isopycnal mixing in ocean circulation models. Fowler, H., S. Blenkinsop, and C. Tebaldi, 2007: Linking climate change modelling to J. Phys. Oceanogr., 20, 150 155. impacts studies: Recent advances in downscaling techniques for hydrological Gent, P. R., and G. Danabasoglu, 2011: Response to increasing Southern Hemisphere modelling. Int. J. Climatol., 27, 1547 1578. winds in CCSM4. J. Clim., 24, 4992 4998. Fox-Kemper, B., R. Ferrari, and R. Hallberg, 2008: Parameterization of mixed layer Gent, P. R., J. Willebrand, T. J. McDougall, and J. C. McWilliams, 1995: Parameterizing eddies. Part I: Theory and diagnosis. J. Phys. Oceanogr., 38, 1145 1165. eddy-induced tracer transports in ocean circulation models. J. Phys. Oceanogr., Fox-Kemper, B., et al., 2011: Parameterization of mixed layer eddies. III: 25, 463 474. Implementation and impact in global ocean climate simulations. Ocean Model., Gent, P. R., et al., 2011: The Community Climate System Model Version 4. J. Clim., 39, 61 78. 24, 4973 4991. 834 Evaluation of Climate Models Chapter 9 Gerber, E. P., L. M. Polvani, and D. Ancukiewicz, 2008: Annular mode time scales Grose, M., M. Pook, P. McIntosh, J. Risbey, and N. Bindoff, 2012: The simulation of in the Intergovernmental Panel on Climate Change Fourth Assessment Report cutoff lows in a regional climate model: Reliability and future trends. Clim. Dyn., models. Geophys. Res. Lett., 35, L22707. 39, 445 459. Gerber, S., L. O. Hedin, M. Oppenheimer, S. W. Pacala, and E. Shevliakova, 2010: Guemas, V., F. J. Doblas-Reyes, I. Andreu-Burillo, and M. Asif, 2013: Retrospective Nitrogen cycling and feedbacks in a global dynamic land model. Global prediction of the global warming slowdown in the last decade. Nature Clim. Biogeochem. Cycles, 24, Gb1001. Change, doi:10.1038/nclimate1863. Gettelman, A., et al., 2010: Multimodel assessment of the upper troposphere and Guilyardi, E., 2006: El Nino - mean state - seasonal cycle interactions in a multi- lower stratosphere: Tropics and global trends. J. Geophys. Res. Atmos., 115, model ensemble. Clim. Dyn., 26, 229 348. D00m08. Guilyardi, E., P. Braconnot, F. F. Jin, S. T. Kim, M. Kolasinski, T. Li, and I. Musat, Ghan, S., X. Liu, R. Easter, P. Rasch, J. Yoon, and B. Eaton, 2012: Toward a minimal 2009a: Atmosphere feedbacks during ENSO in a coupled GCM with a modified representation of aerosols in climate models: Comparative decomposition of atmospheric convection scheme. J. Clim., 22, 5698 5718. aerosol direct, semi-direct and indirect radiative forcing. J. Clim., doi:10.1175/ Guilyardi, E., et al., 2009b: Understanding El Nino in ocean atmosphere general JCLI-D-11-00650.1. circulation models: Progress and challenges. Bull. Am. Meteorol. Soc., 90, 325 Gillett, N. P., 2005: Climate modelling Northern Hemisphere circulation. Nature, 340. 437, 496 496. Guiot, J., J. J. Boreux, P. Braconnot, F. Torre, and P. Participants, 1999: Data-model Giorgi, F., and E. Coppola, 2010: Does the model regional bias affect the projected comparison using fuzzy logic in paleoclimatology. Clim. Dyn., 15, 569 581. regional climate change? An analysis of global model projections. Clim. Change, Gupta, A. S., A. Santoso, A. S. Taschetto, C. C. Ummenhofer, J. Trevena, and M. H. 100, 787 795. England, 2009: Projected changes to the Southern Hemisphere ocean and sea Girard, L., J. Weiss, J. M. Molines, B. Barnier, and S. Bouillon, 2009: Evaluation of ice in the IPCC AR4 climate models. J. Clim., 22, 3047 3078. 9 high-resolution sea ice models on the basis of statistical and scaling properties Gurney, K. R., et al., 2003: TransCom 3 CO2 inversion intercomparison: 1. Annual of Arctic sea ice drift and deformation. J. Geophys. Res., 114, C08015. mean control results and sensitivity to transport and prior flux information. Gleckler, P., K. Taylor, and C. Doutriaux, 2008: Performance metrics for climate Tellus B, 55, 555 579. models. J. Geophys. Res. Atmos., 113, D06104. Gutowski, W., et al., 2010: Regional extreme monthly precipitation simulated by Gleckler, P., K. AchutaRao, J. Gregory, B. Santer, K. Taylor, and T. Wigley, 2006: NARCCAP RCMs. J. Hydrometeorol., 11, 1373 1379. Krakatoa lives: The effect of volcanic eruptions on ocean heat content and Hall, A., and X. Qu, 2006: Using the current seasonal cycle to constrain snow albedo thermal expansion. Geophys. Res. Lett., 33, L17702. feedback in future climate change. Geophys. Res. Lett., 33, L03502. Gleckler, P. J., et al., 2012: Human-induced global ocean warming on multidecadal Hallberg, R., and A. Gnanadesikan, 2006: The role of eddies in determining the timescales, Nature Climate Change, 2, 524 529. structure and response of the wind-driven southern hemisphere overturning: Gnanadesikan, A., S. M. Griffies, and B. L. Samuels, 2007: Effects in a climate model Results from the Modeling Eddies in the Southern Ocean (MESO) project. J. Phys. of slope tapering in neutral physics schemes. Ocean Model., 16, 1 16. Oceanogr., 36, 2232 2252. Goddard, L., and S. J. Mason, 2002: Sensitivity of seasonal climate forecasts to Hallberg, R., and A. Adcroft, 2009: Reconciling estimates of the free surface height persisted SST anomalies. Clim. Dyn., 19, 619 631. in Lagrangian vertical coordinate ocean models with mode-split time stepping. Golaz, J.-C., M. Salzmann, L. J. Donner, L. W. Horowitz, Y. Ming, and M. Zhao, 2011: Ocean Model., 29, 15 26. Sensitivity of the aerosol indirect effect to subgrid variability in the cloud Halloran, P. R., 2012: Does atmospheric CO2 seasonality play an important role in parameterization of the GFDL Atmosphere General Circulation Model AM3. J. governing the air-sea flux of CO2? Biogeosciences, 9, 2311 2323. Clim., 24, 3145 3160. Ham, Y.-G., J. S. Kug, I. S. Kang, F. F. Jin, and A. Timmermann, 2010: Impact of diurnal Goosse, H., and T. Fichefet, 1999: Importance of ice-ocean interactions for the global atmospher-ocean coupling on tropical climate simulations using a coupled ocean circulation: A model study. J. Geophys. Res. Oceans, 104, 23337 23355. GCM. Clim. Dyn., 34, 905 917. Goosse, H., et al., 2010: Description of the Earth system model of intermediate Hamilton, K., 1998: Effects of an imposed Quasi-Biennial Oscillation in complexity LOVECLIM version 1.2. Geosci. Model Dev., 3, 603 633. a  comprehensive troposphere-stratosphere-mesosphere general Gordon, C., et al., 2000: The simulation of SST, sea ice extents and ocean heat circulation model. J. Atmos. Sci., 55, 2393 2418. transports in a version of the Hadley Centre coupled model without flux Handorf, D., and K. Dethloff, 2012: How well do state-of-the-art atmosphere-ocean adjustments. Clim. Dyn., 16, 147 168. general circulation models reproduce atmospheric teleconnection patterns? Gordon, H., et al., 2010: The CSIRO Mk3.5 Climate Model. CAWCR Technical Tellus A, 64, 19777. Report, 21, 1 74. Hannart, A., J. L. Dufresne, and P. Naveau, 2009: Why climate sensitivity may not be Gordon, H. B., et al., 2002: The CSIRO Mk3 Climate System Model. Technical Paper so unpredictable. Geophys. Res. Lett., 36, L16707. No. 60. CSIRO Atmospheric Research,  Aspendale, Vic., Australia. Hannay, C., et al., 2009: Evaluation of Forecasted Southeast Pacific Stratocumulus in Greeves, C. Z., V. D. Pope, R. A. Stratton, and G. M. Martin, 2007: Representation the NCAR, GFDL, and ECMWF Models. J. Clim., 22, 2871 2889. of Northern Hemisphere winter storm tracks in climate models. Clim. Dyn., 28, Hansen, J., R. Ruedy, M. Sato, and K. Lo, 2010: Global surface temperature change. 683 702. Rev. Geophys., 48, Rg4004. Gregory, J. M., and P. M. Forster, 2008: Transient climate response estimated from Hansen, J., M. Sato, P. Kharecha, and K. von Schuckmann, 2011: Earth s energy radiative forcing and observed temperature change. J. Geophys. Res. Atmos., imbalance and implications. Atmos. Chem. Phys., 11, 13421 13449. 113, D23105. Hansen, J., et al., 1983: Efficient Three-Dimensional Global Models for Climate Gregory, J. M., et al., 2004: A new method for diagnosing radiative forcing and Studies: Models I and II. Mon. Weath. Rev., 111, 609 662. climate sensitivity. Geophys. Res. Lett., 31, L03205. Hansen, J., et al., 1984: Climate Sensitivity: Analysis of Feedback Mechanisms. Clim. Gregory, J. M., et al., 2005: A model intercomparison of changes in the Atlantic Proc. Clim. Sens. Geophys. Monogr., 29, 130 163. thermohaline circulation in response to increasing atmospheric CO2 Hansen, J., et al., 2005: Efficacy of climate forcings. J. Geophys. Res. Atmos., 110, concentration. Geophys. Res. Lett., 32, L12703. D18104. Griffies, S. M., 2009: Elements of MOM4p1. GFDL Ocean Group Technical Report No. Hardiman, S. C., N. Butchart, T. J. Hinton, S. M. Osprey, and L. J. Gray, 2012: The effect 6. NOAA/GFDL. Princeton, USA, 371 pp. of a well-resolved stratosphere on surface climate: Differences between CMIP5 Griffies, S. M., and R. J. Greatbatch, 2012: Physical processes that impact the simulations with high and low top versions of the Met Office Climate Model. J. evolution of global mean sea level in ocean climate models. Ocean Model., 51, Clim., 25, 7083 7099. 37 72. Hargreaves, J. C., A. Abe-Ouchi, and J. D. Annan, 2007: Linking glacial and future Griffies, S. M., M. J. Harrison, R. C. Pacanowski, and A. Rosati, 2004: A Technical Guide climates through an ensemble of GCM simulations. Clim. Past, 3, 77 87. to MOM4. GFDL Ocean Group Technical Report No. 5, Princeton, USA, 337 pp. Hargreaves, J. C., J. D. Annan, M. Yoshimori, and A. Abe-Ouchi, 2012: Can the Last Griffies, S. M., et al., 2005: Formulation of an ocean model for global climate Glacial Maximum constrain climate sensitivity? Geophys. Res. Lett., 39, L24702. simulations. Ocean Sci., 1, 45 79. Hargreaves, J. C., A. Paul, R. Ohgaito, A. Abe-Ouchi, and J. D. Annan, 2011: Are Griffies, S. M., et al., 2009: Coordinated Ocean-ice Reference Experiments (COREs). paleoclimate model ensembles consistent with the MARGO data synthesis? Ocean Model., 26, 1 46. Clim. Past, 7, 917 933. 835 Chapter 9 Evaluation of Climate Models Hargreaves, J. C., J. D. Annan, R. Ohgaito, A. Paul, and A. Abe-Ouchi, 2013: Skill and Hirota, N., Y. N. Takayabu, M. Watanabe, and M. Kimoto, 2011: Precipitation reliability of climate model ensembles at the Last Glacial Maximum and mid reproducibility over tropical oceans and its relationship to the double ITCZ Holocene. Clim. Past, 9, 811 823. problem in CMIP3 and MIROC5 climate models. J. Clim., 24, 4859 4873. Hasegawa, A., and S. Emori, 2007: Effect of air-sea coupling in the assessment of Hirschi, M., et al., 2011: Observational evidence for soil-moisture impact on hot CO2-induced intensification of tropical cyclone activity. Geophys. Res. Lett., 34, extremes in southeastern Europe. Nature Geosci., 4, 17 21. L05701. Hofmann, M., and M. A. Morales Maqueda, 2011: The response of Southern Ocean Hasumi, H., 2006: CCSR Ocean Component Model (COCO) Version 4.0. CCSR Report. eddies to increased midlatitude westerlies: A non eddy resolving model study. Centre for Climate System Research, University of Tokyo, Tokyo, Japan, 68 pp. Geophys. Res. Lett., 38, L03605. Hasumi, H., and S. Emori, 2004: K-1 Coupled GCM (MIROC) Description. Center for Holden, P. B., N. R. Edwards, D. Gerten, and S. Schaphoff, 2013: A model based Climate System Research, University of Tokyo, Tokyo, Japan, 34 pp. constraint of CO2 fertilisation. Biogeosciences, 10, 339 355. Hawkins, E., and R. Sutton, 2009: The potential to narrow uncertainty in regional Holian, G. L., A. P. Sokolov, and R. G. Prinn, 2001: Uncertainty in atmospheric CO2 climate predictions. Bull. Am. Meteorol. Soc., 90, 1095 1107. predictions from a parametric uncertainty analysis of a Global Ocean Carbon Haynes, J. M., C. Jakob, W. B. Rossow, G. Tselioudis, and J. Brown, 2011: Major Cycle Model. Joint Program Report Series. MIT Joint Program on the Science and characteristics of Southern Ocean cloud regimes and their effects on the energy Policy of Global Change, Cambridge, MA, USA, 25 pp. budget. J. Clim., 24, 5061 5080. Holland, M., D. Bailey, B. Briegleb, B. Light, and E. Hunke, 2012: Improved sea ice Haynes, P. H., 2006: The latitudinal structure of the QBO. Q. J. R. Meteorol. Soc., 124, shortwave radiation physics in CCSM4: The impact of melt ponds and aerosols 2645 2670. on arctic aea ice. J. Clim., 25, 1413 1430. Haywood, J. M., N. Bellouin, A. Jones, O. Boucher, M. Wild, and K. P. Shine, 2011: The Holland, M. M., M. C. Serreze, and J. Stroeve, 2010: The sea ice mass budget of the 9 roles of aerosol, water vapor and cloud in future global dimming/brightening. J. Arctic and its future change as simulated by coupled climate models. Clim. Dyn., Geophys. Res. Atmos., 116, D20203. doi:10.1007/s00382-008-0493-4. Hazeleger, W., and R. J. Haarsma, 2005: Sensitivity of tropical Atlantic climate to Holton, J. R., and H. C. Tan, 1980: The influence of the equatorial Quasi-Biennial mixing in a coupled ocean-atmosphere model. Clim. Dyn., 25, 387 399. Oscillation on the global circulation at 50 mb. J. Atmos. Sci., 37, 2200 2208. Hazeleger, W., et al., 2012: EC-Earth V2.2: Description and validation of a new Horowitz, L. W., et al., 2003: A global simulation of tropospheric ozone and related seamless earth system prediction model. Clim. Dyn., 39, 2611 2629. tracers: Description and evaluation of MOZART, version 2. J. Geophys. Res. Hegerl, G., and F. Zwiers, 2011: Use of models in detection and attribution of climate Atmos., 108, 4784. change. Clim. Change, 2, 570 591. Hourdin, F., et al., 2012: Impact of the LMDZ atmospheric grid configuration on the Hegerl, G. C., et al., 2007: Understanding and attributing climate change. In: Climate climate and sensitivity of the IPSL-CM5A coupled model. Clim. Dyn., doi:10.1007/ Change 2007: The Physical Science Basis. Contribution of Working Group I to the s00382 012 1411 3. Fourth Assessment Report of the Intergovernmental Panel on Climate Change Hourdin, F., et al., 2013: LMDZ5B: The atmospheric component of the IPSL climate [Solomon, S., D. Qin, M. Manning, Z. Chen, M. Marquis, K. B. Averyt, M. Tignor model with revisited parameterizations for clouds and convection. Clim. Dyn., and H. L. Miller (eds.)] Cambridge University Press, Cambridge, United Kingdom 40, 2193 2222. and New York, NY, USA, pp. 665 775. Hourdin, F., et al., 2010: AMMA-Model Intercomparison Project. Bull. Am. Meteorol. Heinze, C., 2004: Simulating oceanic CaCO3 export production in the greenhouse. Soc., 91, 95 104. Geophys. Res. Lett., 31, L16308. Hu, Z.-Z., B. Huang, Y.-T. Hou, W. Wang, F. Yang, C. Stan, and E. Schneider, 2011: Heinze, C., I. Kriest, and E. Maier-Reimer, 2009: Age offsets among different biogenic Sensitivity of tropical climate to low-level clouds in the NCEP climate forecast and lithogenic components of sediment cores revealed by numerical modeling. system. Clim. Dyn., 36, 1795 1811. Paleoceanography, 24, PA4214. Huang, C. J., F. Qiao, Q. Shu, and Z. Song, 2012: Evaluating austral summer mixed- Held, I. M., 2005: The gap between simulation and understanding in climate layer response to surface wave-induced mixing in the Southern Ocean. J. modeling. Bull. Am. Meteorol. Soc., 86, 1609 1614. Geophys. Res.-Oceans, 117, C00j18. Held, I. M., and B. J. Soden, 2006: Robust responses of the hydrological cycle to Huber, M., I. Mahlstein, M. Wild, J. Fasullo, and R. Knutti, 2011: Constraints on climate global warming. J. Clim., 19, 5686 5699. sensitivity from radiation patterns in climate models. J. Clim., 24, 1034 1052. Held, I. M., and K. M. Shell, 2012: Using Relative Humidity as a State Variable in Hung, M., J. Lin, W. Wang, D. Kim, T. Shinoda, and S. Weaver, 2013: MJO and Climate Feedback Analysis. J. Clim., 25, 2578 2582. convectively coupled equatorial waves simulated by CMIP5 climate models. J. Held, I. M., M. Winton, K. Takahashi, T. Delworth, F. R. Zeng, and G. K. Vallis, 2010: Clim., doi:10.1175/JCLI-D-12-00541.1. Probing the fast and slow components of global warming by returning abruptly Hunke, E. C., and J. K. Dukowicz, 1997: An elastic-viscous-plastic model for sea ice to preindustrial forcing. J. Clim., 23, 2418 2427. dynamics. J. Phys. Oceanogr., 27, 1849 1867. Henson, S. A., D. Raitsos, J. P. Dunne, and A. McQuatters-Gollop, 2009: Decadal Hunke, E. C., and W. H. Lipscomb, 2008: CICE: The Los Alamos Sea Ice variability in biogeochemical models: Comparison with a 50-year ocean colour ModelDocumentation and Software User s ManualVersion 4.1. Los Alamos dataset. Geophys. Res. Lett., 36, L21601. National Laboratory, Los Alamos, NM, USA, 76 pp. Hermes, J. C., and C. J. C. Reason, 2005: Ocean model diagnosis of interannual Hunke, E. C., W. H. Lipscomb, and A. K. Turner, 2010: Sea ice models for climate study: coevolving SST variability in the South Indian and South Atlantic Oceans. J. Clim., Retorspective and new directions. J. Glaciol., 56, 1162 1172. 18, 2864 2882. Hunke, E. C., D. A. Hebert, and O. Lecomte, 2013: Level-ice melt ponds in the Los Hernández-Díaz, L., R. Laprise, L. Sushama, A. Martynov, K. Winger, and B. Dugas, Alamos sea ice model, CICE. Ocean Model., doi:10.1016/j.ocemod.2012.11.008. 2013: Climate simulation over CORDEX Africa domain using the fifth-generation Hunke, E. C., D. Notz, A. K. Turner, and M. Vancoppenolle, 2011: The multiphase Canadian Regional Climate Model (CRCM5). Clim. Dyn., 40, 1415 1433. physics of sea ice : A review for model developers. Cryosphere, 5, 989 1009. Heuzé, C., K. J. Heywood, D. P. Stevens, and J. K. Ridley, 2013: Southern Ocean Hurrell, J., G. A. Meehl, D. Bader, T. L. Delworth, B. Kirtman, and B. Wielicki, 2009: bottom water characteristics in CMIP5 models. Geophys. Res. Lett., doi:10.1002/ A unified modeling approach to climate system prediction. Bull. Am. Meteorol. grl.50287. Soc., 90, 1819 1832. Hewitt, H. T., et al., 2011: Design and implementation of the infrastructure of Hurrell, J., et al., 2013: The Community Earth System Model: A framework for HadGEM3: The next-generation Met Office climate modelling system. Geosci. collaborative research. Bull. Am. Meteorol. Soc., doi:10.1175/BAMS-D-12 Model Dev., 4, 223 253. 00121. Hibbard, K. A., G. A. Meehl, P. M. Cox, and P. Friedlingstein, 2007: A strategy for Hurtt, G. C., et al., 2009: Harmonization of global land-use scenarios for the period climate change stabilization experiments. Eos Trans. Am. Geophys. Union, 88, 1500 2100 for IPCC-AR5. iLEAPS Newsl., 7, 6 8. 217 221. Hutchings, J. K., A. Roberts, C. A. Geiger, and J. Richter-Menge, 2011: Spatial and Hirai, M., T. Sakashita, H. Kitagawa, T. Tsuyuki, M. Hosaka, and M. Oh Izumi, 2007: temporal characterization of sea-ice deformation. Ann. Glaciol., 52, 360 368. Development and validation of a new land surface model for JMA s operational Huybrechts, P., 2002: Sea-level changes at the LGM from ice-dynamic reconstructions global model using the CEOP observation dataset. J. Meteorol. Soc. Jpn., 85A, of the Greenland and Antarctic ice sheets during the glacial cycles. Quat. Sci. 1 24. Rev., 21, 203 231. 836 Evaluation of Climate Models Chapter 9 Huybrechts, P., H. Goelzer, I. Janssens, E. Driesschaert, T. Fichefet, H. Goosse, and M. Jang, C. J., J. Park, T. Park, and S. Yoo, 2011: Response of the ocean mixed layer depth F. Loutre, 2011: Response of the Greenland and Antarctic ice sheets to multi- to global warming and its impact on primary production: A case for the North millennial greenhouse warming in the Earth System Model of Intermediate Pacific Ocean. Ices J. Mar. Sci., 68, 996 1007. Complexity LOVECLIM. Surv. Geophys., 32, 397 416. Jansen, E., et al., 2007: Paleoclimate. In: Climate Change 2007: The Physical Science Iacono, M. J., J. S. Delamere, E. J. Mlawer, and S. A. Clough, 2003: Evaluation of upper Basis. Contribution of Working Group I to the Fourth Assessment Report of the tropospheric water vapor in the NCAR Community Climate Model (CCM3) using Intergovernmental Panel on Climate Change [Solomon, S., D. Qin, M. Manning, modeled and observed HIRS radiances. J. Geophys. Res., 108, 4037. Z. Chen, M. Marquis, K. B. Averyt, M. Tignor and H. L. Miller (eds.)] Cambridge Ichikawa, H., H. Masunaga, Y. Tsushima, and H. Kanzawa, 2012: Reproducibility by University Press, Cambridge, United Kingdom and New York, NY, USA, pp. 433 climate models of cloud radiative forcing associated with tropical convection. J. 498. Clim., 25, 1247 1262. Jayne, S. R., 2009: The impact of abyssal mixing parameterizations in an ocean Ilicak, M., A. J. Adcroft, S. M. Griffies, and R. W. Hallberg, 2012: Spurious dianeutral General Circulation Model. J. Phys. Oceanogr., 39, 1756 1775. mixing and the role of momentum closure. Ocean Model., 45 46, 37 58. Ji, J., M. Huang, and K. Li, 2008: Prediction of carbon exchanges between China Illingworth, A. J., et al., 2007: Cloudnet. Bull. Am. Meteor. Soc., 88, 883 898. terrestrial ecosystem and atmosphere in 21st century. Sci. China D, 51, 885 898. Ilyina, T., R. E. Zeebe, E. Maier-Reimer, and C. Heinze, 2009: Early detection of ocean Ji, J. J., 1995: A climate-vegetation interaction model: Simulating physical and acidification effects on marine calcification. Global Biogeochem. Cycles, 23, biological processes at the surface. J. Biogeogr., 22, 445 451. Gb1008. Jiang, J. H., et al., 2012a: Evaluation of cloud and water vapor simulations in CMIP5 Ilyina, T., K. Six, J. Segschneider, J. Maier-Reimer, H. Li, and I. Nunez-Riboni, 2013: climate models using NASA A-Train satellite observations. J. Geophys. Res., The global ocean biogeochemistry model HAMOCC: Model architecture 117, D14105. and performance as component of the MPI-Earth System Model in different Jiang, X., et al., 2012b: Simulation of the intraseasonal variability over the Eastern 9 CMIP5 experimental realizations. J. Adv. Model. Earth Syst., 5, 287 315. Pacific ITCZ in climate models. Clim. Dyn., 39, 617 636. Inatsu, M., and M. Kimoto, 2009: A scale interaction study on East Asian cyclogenesis Jin, F. F., S. T. Kim, and L. Bejarano, 2006: A coupled-stability index for ENSO. Geophys. using a General Circulation Model coupled with an Interactively Nested Regional Res. Let., 33, L23708. Model. Mon. Weather Rev., 137, 2851 2868. Joetzjer, E., H. Douville, C. Delire, and P. Ciais, 2013: Present-day and future Inatsu, M., Y. Satake, M. Kimoto, and N. Yasutomi, 2012: GCM bias of the western Amazonian precipitation in global climate models: CMIP5 versus CMIP3. Clim. Pacific summer monsoon and its correction by two-way nesting system. J. Dyn., doi: 10.1007/s00382 012-1644-1. Meteorol. Soc. Jpn., 90B, 1 10. Johanson, C. M., and Q. Fu, 2009: Hadley Cell Widening: Model Simulations versus Ingram, W., 2010: A very simple model for the water vapour feedback on climate Observations. J. Clim., 22, 2713 2725. change. Q. J. R. Meteorol. Soc., 136, 30 40. John, V., and B. Soden, 2007: Temperature and humidity biases in global climate Ingram, W., 2013: Some implications of a new approach to the water vapour models and their impact on climate feedbacks. Geophys. Res. Lett., 34, L18704. feedback. Clim. Dyn., 40, 925 933. Johns, T. C., et al., 2003: Anthropogenic climate change for 1860 to 2100 simulated Inness, P. M., J. M. Slingo, E. Guilyardi, and J. Cole, 2003: Simulation of the Madden- with the HadCM3 model under updated emissions scenarios. Clim. Dyn., 20, Julian oscillation in a coupled general circulation model. Part II: The role of the 583 612. basic state. J. Clim., 16, 365 382. Johns, T. C., et al., 2006: The new Hadley Centre Climate Model (HadGEM1): Inoue, J., J. P. Liu, J. O. Pinto, and J. A. Curry, 2006: Intercomparison of Arctic Regional Evaluation of coupled simulations. J. Clim., 19, 1327 1353 Climate Models: Modeling clouds and radiation for SHEBA in May 1998. J. Clim., Johnson, N. C., and S. B. Feldstein, 2010: The continuum of north Pacific sea level 19, 4167 4178. pressure patterns: Intraseasonal, interannual, and interdecadal variability. J. IPCC, 2007: Climate Change 2007: The Physical Science Basis. Contribution of Clim., 23, 851 867. Working Group I to the Fourth Assessment Report of the Intergovernmental Jolliffe, I. T., and D. B. Stephenson, 2011: Forecast Verification: A Practitioner s Guide Panel on Climate Change [Solomon, S., D. Qin, M. Manning, Z. Chen, M. Marquis, in Atmospheric Science. 2nd ed. John Wiley & Sons, Hoboken, NJ, 292 pp. K. B. Averyt, M. Tignor and H. L. Miller (eds.)] Cambridge University Press, Joly, M., A. Voldoire, H. Douville, P. Terray, and J. F. Royer, 2007: African monsoon Cambridge, United Kingdom and New York, NY, USA, 996 pp. teleconnections with tropical SSTs: Validation and evolution in a set of IPCC4 IPCC, 2012: IPCC WGI/WGII Special Report on Managing the Risks of Extreme simulations. Clim. Dyn., 29, 1 20. Events  and Disasters to Advance Climate Change Adaptation (SREX). [Field, Jones, A., D. L. Roberts, M. J. Woodage, and C. E. Johnson, 2001: Indirect sulphate C.B., V. Barros, T.F. Stocker, D. Qin, D.J. Dokken, K.L. Ebi, M.D. Mastrandrea, K.J. aerosol forcing in a climate model with an interactive sulphur cycle. J. Geophys. Mach, G.-K. Plattner, S.K. Allen, M. Tignor, and P.M. Midgley (Eds.)]. Cambridge Res. Atmos., 106, 20293 20310. University Press, The Edinburgh Building, Shaftesbury Road, Cambridge CB2 8RU Jones, G. S., P. A. Stott, and N. Christidis, 2012: Attribution of observed historical ENGLAND, 582 pp. near surface temperature variations to anthropogenic and natural causes using Ishii, M., and M. Kimoto, 2009: Reevaluation of historical ocean heat content CMIP5 simulations. J. Geophys. Res., doi:10.1002/jgrd.50239. variations with time-varying XBT and MBT depth bias corrections. J. Oceanogr., Jones, P. D., M. New, D. E. Parker, S. Martin, and I. G. Rigor, 1999: Surface air 65, 287 299. temperature and its changes over the past 150 years. Rev. Geophys., 37, 173 Ito, A., and T. Oikawa, 2002: A simulation model of the carbon cycle in land 199. ecosystems (Sim-CYCLE): A description based on dry-matter production theory Joseph, S., A. K. Sahai, B. N. Goswami, P. Terray, S. Masson, and J. J. Luo, 2012: Possible and plot-scale validation. Ecol. Model., 151, 143 176. role of warm SST bias in the simulation of boreal summer monsoon in SINTEX-F2 Iversen, T., et al., 2013: The Norwegian Earth System Model, NorESM1 M. Part coupled model. Clim. Dyn., 38, 1561 1576. 2: Climate response and scenario projections. Geosci. Model Dev., 6, 1 27. Joussaume, S., and K. E. Taylor, 1995: Status of the Paleoclimate Modeling Izumi, K., P. J. Bartlein, and S. P. Harrison, 2013: Consistent large-scale temperature Intercomparison Project. In: Proceedings of the first international AMIP scientific responses in warm and cold climates. Geophys. Res. Lett., doi:2013GL055097. conference, WCRP-92, Monterey, USA, 425 430. Jackson, C. S., M. K. Sen, G. Huerta, Y. Deng, and K. P. Bowman, 2008a: Error reduction Jun, M., R. Knutti, and D. Nychka, 2008: Spatial analysis to quantify numerical model and convergence in climate prediction. J. Clim., 21, 6698 6709. bias and dependence: How many climate models are there? J. Am. Stat. Assoc., Jackson, L., R. Hallberg, and S. Legg, 2008b: A parameterization of shear-driven 103, 934 947. turbulence for ocean climate models. J. Phys. Oceanogr., 38, 1033 1053. Jung, T., et al., 2010: The ECMWF model climate: Recent progress through improved Jacob, D., et al., 2012: Assessing the transferability of the Regional Climate Model physical parametrizations. Q. J. R. Meteorol. Soc., 136, 1145 1160. REMO to Different COordinated Regional Climate Downscaling EXperiment Jung, T., et al., 2012: High-resolution global climate simulations with the ECMWF (CORDEX) regions. Atmosphere, 3, 181 199. Model in Project Athena: Experimental design, model climate, and seasonal Jakob, C., 2010: Accelerating progress in Global Atmospheric Model development forecast skill. J. Clim., 25, 3155 3172. through improved parameterizations: Challenges, opportunities, and strategies. Jungclaus, J. H., et al., 2006: Ocean circulation and tropical variability in the coupled Bull. Am. Meteorol. Soc., 91, 869 875. model ECHAM5/MPI-OM. J. Clim., 19, 3952 3972. 837 Chapter 9 Evaluation of Climate Models Jungclaus, J. H., et al., 2013: Characteristics of the ocean simulations in MPIOM, Khvorostyanov, D. V., P. Ciais, G. Krinner, S. A. Zimov, C. Corradi, and G. Guggenberger, the ocean componentof the MPI-Earth System Model. J. Adv. Model. Earth Syst., 2008b: Vulnerability of permafrost carbon to global warming. Part II: Sensitivity doi:10.1002/jame.20023. of permafrost carbon stock to global warming. Tellus B, 60, 265 275. Jungclaus, J. H., et al., 2010: Climate and carbon-cycle variability over the last Kidston, J., and E. P. Gerber, 2010: Intermodel variability of the poleward shift of the millennium. Clim. Past, 6, 723 737. austral jet stream in the CMIP3 integrations linked to biases in 20th century Kageyama, M., et al., 2006: Last Glacial Maximum temperatures over the North climatology. Geophys. Res. Lett., 37, L09708. Atlantic, Europe and western Siberia: A comparison between PMIP models, Kiehl, J. T., 2007: Twentieth century climate model response and climate sensitivity. MARGO sea-surface temperatures and pollen-based reconstructions. Quat. Sci. Geophys. Res. Lett., 34. Rev., 25, 2082 2102. Kim, D., and V. Ramanathan, 2008: Solar radiation budget and radiative forcing due Kahn, R. A., B. J. Gaitley, J. V. Martonchik, D. J. Diner, K. A. Crean, and B. Holben, 2005: to aerosols and clouds. J. Geophys. Res. Atmos., 113, D02203. Multiangle Imaging Spectroradiometer (MISR) global aerosol optical depth Kim, D., et al., 2012: The tropical subseasonal variability simulated in the NASA GISS validation based on 2 years of coincident Aerosol Robotic Network (AERONET) general circulation model. J. Clim., 25, 4641 4659. observations. J. Geophys. Res. Atmos., 110, D10s04. Kim, D., et al., 2009: Application of MJO simulation diagnostics to climate models. J. Kalnay, E., et al., 1996: The NCEP/NCAR 40-year reanalysis project. Bull. Am. Clim., 22, 6413 6436. Meteorol. Soc., 77, 437 471. Kim, H.-J., K. Takata, B. Wang, M. Watanabe, M. Kimoto, T. Yokohata, and T. Yasunari, Kanada, S., M. Nakano, S. Hayashi, T. Kato, M. Nakamura, K. Kurihara, and A. Kitoh, 2011: Global monsoon, El Nino, and their interannual linkage simulated by 2008: Reproducibility of Maximum Daily Precipitation Amount over Japan by a MIROC5 and the CMIP3 CGCMs. J. Clim., 24, 5604 5618. High-resolution Non-hydrostatic Model. Sola, 4, 105 108. Kim, S., and F.-F. Jin, 2011a: An ENSO stability analysis. Part I: Results from a hybrid 9 Kanamaru, H., and M. Kanamitsu, 2007: Fifty-seven-year California reanalysis coupled model. Clim. Dyn., 36, 1593 1607. downscaling at 10 km (CaRD10). Part II: Comparison with North American Kim, S., and F.-F. Jin, 2011b: An ENSO stability analysis. Part II: Results from the regional reanalysis. J. Clim., 20, 5572 5592. twentieth and twenty-first century simulations of the CMIP3 models. Clim. Dyn., Kanamitsu, M., K. Yoshimura, Y. B. Yhang, and S. Y. Hong, 2010: Errors of interannual 36, 1609 1627. variability and trend in dynamical downscaling of reanalysis. J. Geophys. Res. Kim, S. T., and J.-Y. Yu, 2012: The two types of ENSO in CMIP5 models. Geophys. Res. Atmos., 115, D17115. Lett., 39, L11704. Kanamitsu, M., W. Ebisuzaki, J. Woollen, S. K. Yang, J. J. Hnilo, M. Fiorino, and G. L. Kirkevag, K., et al., 2013: Aerosol-climate interactions in the Norwegian Earth Potter, 2002: NCEP-DOE AMIP-II reanalysis (R-2). Bull. Am. Meteorol. Soc., 83, System Model NorESM1 M. Geophys. Model Dev., 6, 207 244. 1631 1643. Kistler, R., et al., 2001: The NCEP-NCAR 50 year reanalysis: Monthly means CD-ROM Karlsson, J., and G. Svensson, 2010: The simulation of Arctic clouds and their and documentation. Bull. Am. Meteorol. Soc., 82, 247 267. influence on the winter surface temperature in present-day climate in the CMIP3 Kjellstrom, E., G. Nikulin, U. Hansson, G. Strandberg, and A. Ullerstig, 2011: 21st multi-model dataset. Clim. Dyn., 36, 623 635. century changes in the European climate: Uncertainties derived from an Karlsson, J., G. Svensson, and H. Rodhe, 2008: Cloud radiative forcing of subtropical ensemble of regional climate model simulations. Tellus A, 63, 24 40. low level clouds in global models. Clim. Dyn., 30, 779 788. Kjellstrom, E., F. Boberg, M. Castro, J. Christensen, G. Nikulin, and E. Sanchez, 2010: Karpechko, A., N. Gillett, G. Marshall, and A. Scaife, 2008: Stratospheric influence on Daily and monthly temperature and precipitation statistics as performance circulation changes in the Southern Hemisphere troposphere in coupled climate indicators for regional climate models. Clim. Res., 44 135 150. models. Geophys. Res. Lett., 35, L20806. Klein, P., and G. Lapeyre, 2009: The oceanic vertical pump induced by mesoscale and Karpechko, A. Y., and E. Manzini, 2012: Stratospheric influence on tropospheric submesoscale turbulence. Annu. Rev. Mar. Sci., 1, 351 375. climate change in the Northern Hemisphere. J. Geophys. Res. Atmos., 117, Klein, S. A., and C. Jakob, 1999: Validation and sensitivities of frontal clouds D05133. simulated by the ECMWF model. Mon. Weather Rev., 127, 2514 2531. Karpechko, A. Y., N. P. Gillett, G. J. Marshall, and J. A. Screen, 2009: Climate impacts Klein, S. A., B. J. Soden, and N. C. Lau, 1999: Remote sea surface temperature of the southern annular mode simulated by the CMIP3 models. J. Clim., 22, variations during ENSO: Evidence for a tropical atmospheric bridge. J. Clim., 12, 6149 6150. 917 932. Kattsov, V. M., et al., 2010: Arctic sea-ice change: A grand challenge of climate Klein, S. A., X. Jiang, J. Boyle, S. Malyshev, and S. Xie, 2006: Diagnosis of the science. J. Glaciol., 56, 1115 1121. summertime warm and dry bias over the U.S. Southern Great Plains in the GFDL Kavvada, A., A. Ruiz-Barradas, and S. Nigam, 2013: AMO s structure and climate climate model using a weather forecasting approach. Geophys. Res. Lett., 33, footprint in observations and IPCC AR5 climate simulations. Clim. Dyn., L18805. doi:10.1007/s00382 013 1712 1. Klein, S. A., Y. Zhang, M. D. Zelinka, R. Pincus, J. S. Boyle, and P. J. Glecker, 2013: Are Kawazoe, S., and W. Gutowski, 2013: Regional, very heavy daily precipitation in climate model simulations of clouds improving? An evaluation using the ISCCP NARCCAP simulations. J. Hydrometeorol., doi:10.1175/JHM-D-12-068.1. simulator. J. Geophys. Res., doi:10.1002/jgrd.50141. Kay, J. E., M. M. Holland, and A. Jahn, 2011: Inter-annual to multi-decadal Arctic sea Klocke, D., R. Pincus, and J. Quaas, 2011: On constraining estimates of climate ice extent trends in a warming world. Geophys. Res. Lett., 38, L15708. sensitivity with present-day observations through model weighting. J. Clim., 24, Keeley, S. P. E., R. T. Sutton, and L. C. Shaffrey, 2012: The impact of North Atlantic sea 6092 6099. surface temperature errors on the simulation of North Atlantic European region Kloster, S., N. M. Mahowald, J. T. Randerson, and P. J. Lawrence, 2012: The impacts of climate. Q. J. R. Meteorol. Soc., doi:10.1002/qj.1912. climate, land use, and demography on fires during the 21st century simulated by Kendon, E. J., N. M. Roberts, C. A. Senior, and M. J. Roberts, 2012: Realism of rainfall CLM-CN. Biogeosciences, 9, 509 525. in a very high resolution regional climate model. J. Clim., 25, 5791 5806. Knight, J., et al., 2009: Do global temperature trends over the last decade falsify Khairoutdinov, M. F., D. A. Randall, and C. DeMott, 2005: Simulations of the climate predictions? [In: State of the Climate in 2008]. Bull. Am. Meteorol. Soc., Atmospheric general circulation using a cloud-resolving model as a 90, S22 S23. superparameterization of physical processes. J. Atmos. Sci., 62, 2136 2154. Knight, J. R., 2009: The Atlantic Multidecadal Oscillation inferred from the forced Kharin, V. V., F. W. Zwiers, X. B. Zhang, and G. C. Hegerl, 2007: Changes in temperature climate response in Coupled General Circulation Models. J. Clim., 22, 1610 and precipitation extremes in the IPCC ensemble of global coupled model 1625. simulations. J. Clim., 20, 1419 1444. Knutti, R., 2008: Why are climate models reproducing the observed global surface Kharin, V. V., F. W. Zwiers, X. Zhang, and M. Wehner, 2012: Changes in temperature warming so well? Geophys. Res. Lett., 35, L18704 and precipitation extremes in the CMIP5 ensemble. Clim. Change, doi:10.1007/ Knutti, R., 2010: The end of model democracy? Clim. Change, 102, 395 404. s10584-013-0705-8. Knutti, R., and G. C. Hegerl, 2008: The equilibrium sensitivity of the Earth s Khvorostyanov, D. V., G. Krinner, P. Ciais, M. Heimann, and S. A. Zimov, 2008a: temperature to radiation changes. Nature Geosci., 1, 735 743. Vulnerability of permafrost carbon to global warming. Part I: Model description Knutti, R., and L. Tomassini, 2008: Constraints on the transient climate response and role of heat generated by organic matter decomposition. Tellus B, 60, 250 from observed global temperature and ocean heat uptake. Geophys. Res. Lett., 264. 35, L09701. 838 Evaluation of Climate Models Chapter 9 Knutti, R., and J. Sedlácek, 2013: Robustness and uncertainties in the new CMIP5 Kusaka, H., T. Takata, and Y. Takane, 2010: Reproducibility of regional climate in climate model projections. Nature Clim. Change, 3, 369 373. central Japan using the 4-km Resolution WRF Model. Sola, 6, 113 116. Knutti, R., D. Masson, and A. Gettelman, 2013: Climate model genealogy: Generation Kusunoki, S., R. Mizuta, and M. Matsueda, 2011: Future changes in the East Asian CMIP5 and how we got there. Geophys. Res. Lett., 40, 1194 1199. rain band projected by global atmospheric models with 20-km and 60-km grid Knutti, R., G. A. Meehl, M. R. Allen, and D. A. Stainforth, 2006: Constraining climate size. Clim. Dyn., 37, 2481 2493. sensitivity from the seasonal cycle in surface temperature. J. Clim., 19, 4224 L Ecuyer, T., and G. Stephens, 2007: The tropical atmospheric energy budget from the 4233. TRMM perspective. Part II: Evaluating GCM representations of the sensitivity Knutti, R., F. Joos, S. A. Muller, G. K. Plattner, and T. F. Stocker, 2005: Probabilistic of regional energy and water cycles to the 1998 99 ENSO Cycle. J. Clim., 20, climate change projections for CO2 stabilization profiles. Geophys. Res. Lett., 4548 4571. 32, L20707. Laine, A., G. Lapeyre, and G. Riviere, 2011: A quasigeostrophic model for moist storm Knutti, R., R. Furrer, C. Tebaldi, J. Cermak, and G. A. Meehl, 2010a: Challenges in tracks. J. Atmos. Sci., 68, 1306 1322. combining projections from multiple climate models. J. Clim., 23, 2739 2758. Laine, A., M. Kageyama, P. Braconnot, and R. Alkama, 2009: Impact of greenhouse Knutti, R., G. Abramowitz, M. Collins, V. Eyring, P. J. Gleckler, B. Hewitson, and L. gas concentration changes on surface energetics in IPSL-CM4: Regional Mearns, 2010b: Good practice guidance paper on assessing and combining warming patterns, land-sea warming ratios, and glacial-interglacial differences. multi model climate projections. In: Meeting Report of the Intergovernmental J. Clim., 22, 4621 4635. Panel on Climate Change Expert Meeting on Assessing and Combining Multi Lamarque, J. F., et al., 2012: CAM-chem: Description and evaluation of interactive Model Climate Projections [T. F. Stocker, T.F., D. Qin, G.-K. Plattner, M. Tignor, and atmospheric chemistry in the Community Earth System Model. Geosci. Model P.M. Midgley (eds.)]. IPCC Working Group I Technical Support Unit, University of Dev., 5, 369 411. Bern, Bern, Switzerland. Lamarque, J. F., et al., 2010: Historical (1850 2000) gridded anthropogenic and 9 Koch, D., et al., 2011: Coupled Aerosol-Chemistry-Climate Twentieth-Century biomass burning emissions of reactive gases and aerosols: Methodology and Transient Model investigation: Trends in short-lived species and climate application. Atmos. Chem. Phys., 10, 7017 7039. responses. J. Clim., 24, 2693 2714. Lambert, F. H., G. R. Harris, M. Collins, J. M. Murphy, D. M. H. Sexton, and B. B. Koldunov, N. V., D. Stammer, and J. Marotzke, 2010: Present-day Arctic sea ice B. Booth, 2012: Interactions between perturbations to different Earth system variability in the coupled ECHAM5/MPI-OM model. J. Clim., 23, 2520 2543. components simulated by a fully-coupled climate model. Clim. Dyn., doi:10.1007/ Koltzow, M., T. Iversen, and J. Haugen, 2008: Extended Big-Brother experiments: The s00382-012-1618-3. role of lateral boundary data quality and size of integration domain in regional Lambert, S., and G. Boer, 2001: CMIP1 evaluation and intercomparison of coupled climate modelling. Tellus A, 60, 398 410. climate models. Clim. Dyn., 17, 83 106. Koltzow, M. A. O., T. Iversen, and J. E. Haugen, 2011: The importance of lateral Landrum, L., M. M. Holland, D. P. Schneider, and E. Hunke, 2012: Antarctic sea ice boundaries, surface forcing and choice of domain size for dynamical downscaling climatology, variability and late 20th century change in CCSM4. J. Clim., 25, of global climate simulations. Atmosphere, 2, 67 95. 4817 4838. Komuro, Y., et al., 2012: Sea-ice in twentieth-century simulations by new MIROC Langenbrunner, B., and J. D. Neelin, 2013: Analyzing ENSO teleconnections in CMIP Coupled Models: A comparison between models with high resolution and with models as a measure of model fidelity in simulating precipitation. J. Clim., ice thickness distribution. J. Meteorol. Soc. Jpn,, 90A, 213 232. doi:10.1175/JCLI-D-12-00542.1. Konsta, D., H. Chepfer, and J.-L. Dufresne, 2012: A process oriented characterization Laprise, R., 2008: Regional climate modelling. J. Comput. Phys., 227, 3641 3666. of tropical oceanic clouds for climate model evaluation, based on a statistical Laprise, R., et al., 2008: Challenging some tenets of regional climate modelling. analysis of daytime A-train observations. Clim. Dyn., 39, 2091 2108. Meteorol. Atmos. Phys., 100, 3 22. Koster, R., et al., 2004: Regions of strong coupling between soil moisture and Large, W., and S. Yeager, 2009: The global climatology of an interannually varying precipitation. Science, 305, 1138 1140. air-sea flux data set. Clim. Dyn., 33, 341 364. Kostopoulou, E., K. Tolika, I. Tegoulias, C. Giannakopoulos, S. Somot, C. Larow, T. E., Y. K. Lim, D. W. Shin, E. P. Chassignet, and S. Cocke, 2008: Atlantic basin Anagnostopoulou, and P. Maheras, 2009: Evaluation of a regional climate model seasonal hurricane simulations. J. Clim., 21, 3191 3206. using in situ temperature observations over the Balkan Peninsula. Tellus A, 61, Lau, K. M., et al., 2008: The Joint Aerosol-Monsoon Experiment A new challenge 357 370. for monsoon climate research. Bull. Am. Meteorol. Soc., 89, 369 383. Koven, C. D., W. J. Riley, and A. Stern, 2013: Analysis of permafrost thermal dynamics Lau, W. K. M., and D. E. Waliser, 2011: Intraseasonal Variability of the Atmosphere- and response to climate change in the CMIP5 Earth System Models. J. Clim., 26, Ocean Climate System. Springer Science+Business Media, New York, NY, USA, 1877 1900. and Heidelberg, Germany. Koven, C. J., et al., 2011: Permafrost carbon-climate feedbacks accelerate global Lawrence, D. M., et al., 2012: The CCSM4 Land Simulation, 1850 2005: Assessment warming. Proc. Natl. Acad. Sci. U.S.A., 108, 14769 14774. of surface climate and new capabilities. J. Clim., 25, 2240 2260. Kowalczyk, E. A., Y. P. Wang, R. M. Law, H. L. Davies, J. L. McGregor, and G. Abramowitz Lawrence, D. M., et al., 2011: Parameterization improvements and functional and 2006: The CSIRO Atmosphere Biosphere Land Exchange (CABLE) model for use structural advances in version 4 of the Community Land Model. J. Adv. Model. in  climate models and as an offline model.  CSIRO Marine and Atmospheric Earth Syst., 3, 2011MS000045. Research paper 013, Victoria, Australia, 37 pp. Le Quere, C., et al., 2005: Ecosystem dynamics based on plankton functional types Kowalczyk, E. A., et al., 2013: The land surface model component of ACCESS: for global ocean biogeochemistry models. Global Change Biol., 11, 2016 2040. Description and impact on the simulated surface climatology. Aust. Meteorol. Le Quere, C., et al., 2009: Trends in the sources and sinks of carbon dioxide. Nature Oceanogr. J., 63, 65 82. Geosci., 2, 831 836. Kravtsov, S., and C. Spannagle, 2008: Multidecadal climate variability in observed Lecomte, O., T. Fichefet, M. Vancoppenolle, and M. Nicolaus, 2011: A new snow and modeled surface temperatures. J. Clim., 21, 1104 1121. thermodynamic scheme for large-scale sea-ice models. Ann. Glaciol., 52, 337 Krinner, G., et al., 2005: A dynamic global vegetation model for studies of the coupled 346. atmosphere-biosphere system. Global Biogeochem. Cycles, 19, GB1015. Leduc, M., and R. Laprise, 2009: Regional climate model sensitivity to domain size. Krüger, L., R. da Rocha, M. Reboita, and T. Ambrizzi, 2012: RegCM3 nested in Clim. Dyn., 32, 833 854. HadAM3 scenarios A2 and B2: Projected changes in extratropical cyclogenesis, Lee, D. S., et al., 2009: Aviation and global climate change in the 21st century. Atmos. temperature and precipitation over the South Atlantic Ocean. Clim. Change, Environ., 43, 3520 3537. 113, 599 621. Lee, T., D. E. Waliser, J.-L. F. Li, F. W. Landerer, and M. M. Gierach, 2013: Evaluation of Kuhlbrodt, T., and J. Gregory, 2012: Ocean heat uptake and its consequences CMIP3 and CMIP5 wind stress climatology using satellite measurements and for the magnitude of sea level rise and climate change. Geophys. Res. Lett., atmospheric reanalysis products. J. Clim., doi:10.1175/JCLI-D-12-00591.1. doi:10.1029/2012GL052952. Legg, S., L. Jackson, and R. W. Hallberg, 2008: Eddy-resolving modeling of overflows. Kuhlbrodt, T., R. S. Smith, Z. Wang, and J. M. Gregory, 2012: The influence of eddy In: Eddy Resolving Ocean Models, 177 ed. [M. Hecht, and H. Hasumi (eds.)]. parameterizations on the transport of the Antarctic Circumpolar Current in American Geophysical Union, Washington, DC, pp. 63 82. coupled climate models. Ocean Model., 52 53, 1 8. 839 Chapter 9 Evaluation of Climate Models Legg, S., et al., 2009: Improving oceanic overflow representation in climate models: Lin, Y., et al., 2012: TWP-ICE global atmospheric model intercomparison: Convection The Gravity Current Entrainment Climate Process Team. Bull. Am. Meteorol. Soc., responsiveness and resolution impact. J. Geophys. Res., 117, D09111. 90, 657 670. Lindvall, J., G. Svensson, and C. Hannay, 2012: Evaluation of near-surface parameters Leloup, J., M. Lengaigne, and J.-P. Boulanger, 2008: Twentieth century ENSO in the two versions of the atmospheric model in CESM1 using flux station characteristics in the IPCC database. Clim. Dyn., 30, 277 291. observations. J. Clim., 26 26 44. Lemoine, D. M., 2010: Climate sensitivity distributions dependence on the possibility Linkin, M., and S. Nigam, 2008: The north pacific oscillation-west Pacific that models share biases. J. Clim., 23, 4395 4415. teleconnection pattern: Mature-phase structure and winter impacts. J. Clim., 21, Lenaerts, J., M. van den Broeke, S. Dery, E. van Meijgaard, W. van de Berg, S. Palm, and 1979 1997. J. Rodrigo, 2012: Modeling drifting snow in Antarctica with a regional climate Liu, H., C. Wang, S. K. Lee, and D. Enfield, 2013a: Atlantic Warm Pool Variability in the model: 1. Methods and model evaluation. J. Geophys. Res. Atmos., 117, D05108. CMIP5 Simulations. J. Clim., doi:10.1175/JCLI-D-12 00556.1. Lenderink, G., 2010: Exploring metrics of extreme daily precipitation in a large Liu, H. L., P. F. Lin, Y. Q. Yu, and X. H. Zhang, 2012a: The baseline evaluation of LASG/ ensemble of regional climate model simulations. Clim. Res., 44 151 166. IAP Climate system Ocean Model (LICOM) version 2.0. Acta Meteorol. Sin., 26, Lenderink, G., and E. Van Meijgaard, 2008: Increase in hourly precipitation extremes 318 329. beyond expectations from temperature changes. Nature Geosci., 1, 511 514. Liu, J., 2010: Sensitivity of sea ice and ocean simulations to sea ice salinity in a Levine, R. C., and A. G. Turner, 2012: Dependence of Indian monsoon rainfall on coupled global climate model. Science China Earth Sci., 53, 911 918. moisture fluxes across the Arabian Sea and the impact of coupled model sea Liu, L., W. Yu, and T. Li, 2011: Dynamic and thermodynamic air sea coupling surface temperature biases. Clim. Dyn., 38, 2167 2190. associated with the Indian Ocean dipole diagnosed from 23 WCRP CMIP3 Levis, S., 2010: Modeling vegetation and land use in models of the Earth System. Models. J. Clim., 24, 4941 4958. 9 Clim. Change, 1, 840 856. Liu, S. C., C. B. Fu, C. J. Shiu, J. P. Chen, and F. T. Wu, 2009: Temperature dependence Levitus, S., J. I. Antonov, T. P. Boyer, R. A. Locarnini, H. E. Garcia, and A. V. Mishonov, of global precipitation extremes. Geophys. Res. Lett., 36, L17702. 2009: Global ocean heat content 1955 2008 in light of recently revealed Liu, X., et al., 2012b: Toward a minimal representation of aerosols in climate models: instrumentation problems. Geophys. Res. Lett., 36, L07608 Description and evaluation in the Community Atmosphere Model CAM5. Levy, H., L. W. Horowitz, M. D. Schwarzkopf, Y. Ming, J.-C. Golaz, V. Naik, and V. Geophys. Model Dev., 5, 709 739. Ramaswamy, 2013: The roles of aerosol direct and indirect effects in past and Liu, X. H., et al., 2007: Uncertainties in global aerosol simulations: Assessment using future climate change. J. Geophys. Res., doi:10.1002/jgrd.50192. three meteorological data sets. J. Geophys. Res. Atmos., 112, D11212 Lewis, T., and S. Lamoureux, 2010: Twenty-first century discharge and sediment yield Liu, Y., 1996: Modeling the emissions of nitrous oxide and methane from the predictions in a small high Arctic watershed. Global Planet. Change, 71, 27 41. terrestrial biosphere to the atmosphere. In: Joint Program Report Series. MIT Li, C., J.-S. von Storch, and J. Marotzke, 2013a: Deep-ocean heat uptake and Joint Program on the Science and Policy of Global Change, Cambridge, MA, equilibrium climate response. Clim. Dyn., 40, 1071 1086. USA, 219 pp. Li, G., and S.-P. Xie, 2012: Origins of tropical-wide SST biases in CMIP multi-model Liu, Y., J. Hu, B. He, Q. Bao, A. Duan, and G. X. Wu, 2013b: Seasonal evolution of ensembles. Geophys. Res. Lett., 39, L22703. subtropical anticyclones in the Climate System Model FGOALS-s2. Adv. Atmos. Li, H. B., A. Robock, and M. Wild, 2007: Evaluation of Intergovernmental Panel on Sci., 30, 593 606. Climate Change Fourth Assessment soil moisture simulations for the second half Lloyd, J., E. Guilyardi, and H. Weller, 2010: The role of atmosphere feedbacks during of the twentieth century. J. Geophys. Res. Atmos., 112, D06106 ENSO in the CMIP3 models. Part II: Using AMIP runs to understand the heat flux Li, J.-L. F., D. E. Waliser, and J. H. Jiang, 2011a: Correction to Comparisons of satellites feedback mechanisms. Clim. Dyn., 37, 1271 1292. liquid water estimates to ECMWF and GMAO analyses, 20th century IPCC AR4 Lloyd, J., E. Guilyardi, and H. Weller, 2012: The role of atmosphere feedbacks during climate simulations, and GCM simulations . Geophys. Res. Lett., 38, L24807. ENSO in the CMIP3 Models. Part III: The Shortwave Flux Feedback. J. Clim., 25, Li, J.-L. F., et al., 2008: Comparisons of satellites liquid water estimates to ECMWF 4275 4293. and GMAO analyses, 20th century IPCC AR4 climate simulations, and GCM Lloyd, J., E. Guilyardi, H. Weller, and J. Slingo, 2009: The role of atmosphere feedbacks simulations. Geophys. Res. Lett., 35, L19710. during ENSO in the CMIP3 models. Atmos. Sci. Lett., 10, 170 176. Li, J., S.-P. X. and A. Mestas-Nunez, E. R. C. and Gang Huang, R. D Arrigo, F. Liu, J. Ma, Loeb, N. G., et al., 2009: Toward optimal closure of the Earth s top-of-atmosphere and X. Zheng, 2011b: Interdecadal modulation of ENSO amplitude during the radiation budget. J. Clim., 22, 748 766. last millennium. Nature Clim. Change, 1, 114 118. Lohmann, U., K. von Salzen, N. McFarlane, H. G. Leighton, and J. Feichter, 1999: Li, J. L. F., et al., 2012a: An observationally-based evaluation of cloud ice water in Tropospheric sulfur cycle in the Canadian general circulation model. J. Geophys. CMIP3 and CMIP5 GCMs and contemporary reanalyses using contemporary Res. Atmos., 104, 26833 26858. satellite data. J. Geophys. Res., 117, D16105. Long, M. C., K. Lindsay, S. Peacock, J. K. Moore, and S. C. Doney, 2012: Twentieth- Li, L., et al., 2013b: Development and Evaluation of Grid-point Atmospheric Model of century oceanic carbon uptake and storage in CESM1(BGC). J. Clim., doi:10.1175/ IAP LASG, Version 2.0 (GAMIL 2.0). Adv. Atmos. Sci., 30, 855 867. JCLI-D-12-00184.1. Li, L., et al., 2012b: The Flexible Global Ocean-Atmosphere-Land System Model: Grid- Loptien, U., O. Zolina, S. Gulev, M. Latif, and V. Soloviov, 2008: Cyclone life cycle point Version 2: FGOALS-g2. Adv. Atmos. Sci., doi:10.1007/s00376 012 2140 6. characteristics over the Northern Hemisphere in coupled GCMs. Clim. Dyn., 31, Li, T., and G. H. Philander, 1996: On the annual cycle in the eastern equatorial Pacific. 507 532. J. Clim., 9, 2986 2998. Lorenz, P., and D. Jacob, 2005: Influence of regional scale information on the global Li, T., C. W. Tham, and C. P. Chang, 2001: A coupled air-sea-monsoon oscillator for the circulation: A two-way nesting climate simulation. Geophys. Res. Lett., 32, tropospheric biennial oscillation. J. Clim., 14, 752 764. L18706. Liebmann, B., R. M. Dole, C. Jones, I. Blade, and D. Allured, 2010: Influence of Lorenz, R., E. L. Davin, and S. I. Seneviratne, 2012: Modeling land-climate coupling choice of time period on global surface temperature trend wstimates. Bull. Am. in Europe: Impact of land surface representation on climate variability and Meteorol. Soc., 91, 1485 1491. extremes. J. Geophys. Res., 117, doi:10.1029/2012JD017755. Lienert, f., J. C. Fyfe, and W. J. Merryfield, 2011: Do climate models capture the Losch, M., D. Menemenlis, J.-M. Campin, P. Heimbach, and C. Hill, 2010: On the tropical influences on North Pacific sea surface temperature variability? J. Clim., formulation of sea-ice models. Part 1: Effects of different solver implementations 24, 6203 6209. and parameterizations. Ocean Model., 33, 129 144. Lin, A. L., and T. Li, 2008: Energy spectrum characteristics of Boreal Summer Loschnigg, J., G. A. Meehl, P. J. Webster, J. M. Arblaster, and G. P. Compo, 2003: The Intraseasonal Oscillations: Climatology and variations during the ENSO Asian monsoon, the tropospheric biennial oscillation, and the Indian Ocean developing and decaying phases. J. Clim., 21, 6304 6320. zonal mode in the NCAR CSM. J. Clim., 16, 1617 1642. Lin, J.-L., 2007: The double-ITCZ problem in IPCC AR4 Coupled GCMs: Ocean- Loutre, M. F., A. Mouchet, T. Fichefet, H. Goosse, H. Goelzer, and P. Huybrechts, atmosphere feedback analysis. J. Clim., 20, 4497 4525. 2011: Evaluating climate model performance with various parameter sets using Lin, J. L., et al., 2006: Tropical intraseasonal variability in 14 IPCC AR4 climate models. observations over the recent past. Clim. Past, 7, 511 526. Part I: Convective signals. J. Clim., 19, 2665 2690. Loyola, D., and M. Coldewey-Egbers, 2012: Multi-sensor data merging with stacked Lin, P., Y. Yongqiang, and H. Liu, 2013: Long-term stability and oceanic mean state neural networks for the creation of satellite long-term climate data records. simulated by the coupled model FGOALS-s2. Adv. Atmos. Sci., 30, 175 192. Eurasip J. Adv. Signal Proc., doi:10.1186/1687 6180 2012 91. 840 Evaluation of Climate Models Chapter 9 Loyola, D., et al., 2009: Global long-term monitoring of the ozone layer a Marsland, S. J., et al., 2013: Evaluation of ACCESS Climate Model ocean diagnostics prerequisite for predictions. Int. J. Remote Sens., 30, 4295 4318. in CMIP5 simulations. Aust. Meteorol. and Oceanogr. J., 63,101 119. Lu, J., G. A. Vecchi, and T. Reichler, 2007: Expansion of the Hadley cell under global Martin, G. M., and R. C. Levine, 2012: The influence of dynamic vegetation on warming. Geophys. Res. Lett., 34, L06805. the present-day simulation and future projectons of the South Asian summer Lu, J. H., and J. J. Ji, 2006: A simulation and mechanism analysis of long-term monsoon in the HadGEM2 family. Earth Syst. Dyn., 2, 245 261. variations at land surface over arid/semi-arid area in north China. J. Geophys. Martin, G. M., et al., 2011: The HadGEM2 family of Met Office Unified Model climate Res. Atmos., 111, D09306. configurations. Geophys. Model Dev., 4, 723 757. Lucarini, V., and F. Ragone, 2011: Energetics of climate models: Net energy balance Masarie, K. A., and P. P. Tans, 1995: Extension and integration of atmospheric carbon and meridional enthalpy transport. Rev. Geophys., 49, RG1001. dioxide data into a globally consistent measurement record. J. Geophys. Res. Lucas-Picher, P., S. Somot, M. Déqué, B. Decharme, and A. Alias, 2012a: Evaluation of Atmos., 100, 11593 11610. the regional climate model ALADIN to simulate the climate over North America Masato, G., B. Hoskins, and T. Woollings, 2012: Winter and summer Northern in the CORDEX framework. Clim. Dyn., doi:10.1007/s00382-012-1613-8. Hemisphere blocking in CMIP5 models. J. Clim., doi:10.1175/JCLI-D-12-00466.1. Lucas-Picher, P., M. Wulff-Nielsen, J. Christensen, G. Adalgeirsdottir, R. Mottram, and Masson-Delmotte, V., et al., 2010: EPICA Dome C record of glacial and interglacial S. Simonsen, 2012b: Very high resolution regional climate model simulations intensities. Quat. Sci. Rev., 29, 113 128. over Greenland: Identifying added value. J. Geophys. Res. Atmos., 117, D02108. Masson-Delmotte, V., et al., 2006: Past and future polar amplification of climate Lumpkin, R., K. G. Speer, and K. P. Koltermann, 2008: Transport across 48°N in the change: Climate model intercomparisons and ice-core constraints. Clim. Dyn., Atlantic Ocean. J. Phys. Oceanogr., 38, 733 752. 27, 437 440. Luo, J. J., S. Masson, E. Roeckner, G. Madec, and T. Yamagata, 2005: Reducing Masson, D., and R. Knutti, 2011a: Climate model genealogy. Geophys. Res. Lett., climatology bias in an ocean-atmosphere CGCM with improved coupling 38, L08703. 9 physics. J. Clim., 18, 2344 2360. Masson, D., and R. Knutti, 2011b: Spatial-scale dependence of climate model Lynn, B., R. Healy, and L. Druyan, 2009: Quantifying the sensitivity of simulated performance in the CMIP3 ensemble. J. Clim., 24, 2680 2692. climate change to model configuration. Clim. Change, 92, 275 298. Masson, D., and R. Knutti, 2013: Predictor screening, calibration and observational MacKinnon, J., et al., 2009: Using global arrays to investigate internal-waves and constraints in climate model ensembles: An illustration using climate sensitivity. mixing. In: OceanObs09:  Sustained Ocean  Observations and Information for J. Clim., 26, 887 898. Society, Venice, Italy, ESA. Massonnet, F., T. Fichefet, H. Goosse, C. M. Bitz, G. Philippon-Berthier, M. M. Holland, Madden, R. A., and P. R. Julian, 1972: Description of global-scale circulation ells in and P.-Y. Barriat, 2012: Constraining projections of summer Arctic sea ice. tropics with a 40 50 day period. J. Atmos. Sci., 29, 1109 1123. Cryosphere, 6, 1383 1394. Madden, R. A., and P. R. Julian, 1994: Observations of the 40 50-Day Tropical Mastrandrea, M. D., et al., 2011: Guidance Note for Lead Authors of the IPCC Fifth Oscillation a Review. Mon. Weather Rev., 122, 814 837. Assessment Report on Consistent Treatment of Uncertainties. Intergovernmental Madec, G., 2008: NEMO ocean engine. Technical Note. Institut Pierre-Simon Laplace Panel on Climate Change (IPCC). IPCC guidance note, Jasper Ridge, CA, USA, 7 (IPSL), France, 300pp. pp. Madec, G., P. Delecluse, M. Imbard, and C. Levy, 1998: OPA 8.1 ocean general Materia, S., P. A. Dirmeyer, Z. C. Guo, A. Alessandri, and A. Navarra, 2010: The circulation model reference manual. IPSL Note du Pole de Modelisation, Institut sensitivity of simulated river discharge to land surface representation and Pierre-Simon Laplace (IPSL), France, 91 pp. meteorological forcings. J. Hydrometeorol., 11, 334 351. Mahlstein, I., and R. Knutti, 2010: Regional climate change patterns identified by Matsueda, M., 2009: Blocking predictability in operational medium-range ensemble cluster analysis. Clim. Dyn., 35, 587 600. forecasts. Sola, 5, 113 116. Mahlstein, I., and R. Knutti, 2012: September Arctic sea ice predicted to disappear Matsueda, M., R. Mizuta, and S. Kusunoki, 2009: Future change in wintertime near 2C global warming above present. J. Geophys. Res., 117, D06104. atmospheric blocking simulated using a 20-km-mesh atmospheric global Maier-Reimer, E., I. Kriest, J. Segschneider, and P. Wetze, 2005: The HAMburg Ocean circulation model. J. Geophys. Res. Atmos., 114, D12114. Carbon Cycle Model HAMOCC 5.1-Technical Description Release 1.1. Tech. Matsueda, M., H. Endo, and R. Mizuta, 2010: Future change in Southern Hemisphere Rep. 14, Rep. Earth Syst. Sci., Max Planck Institute for Meteorology, Hamburg, summertime and wintertime atmospheric blockings simulated using a Germany, 50 pp. 20-km-mesh AGCM. Geophys. Res. Lett., 37, L02803. Mantua, N. J., S. R. Hare, Y. Zhang, J. M. Wallace, and R. C. Francis, 1997: A Pacific Matsumoto, K., K. S. Tokos, A. R. Price, and S. J. Cox, 2008: First description of the interdecadal climate oscillation with impacts on salmon production. Bull. Am. Minnesota Earth System Model for Ocean biogeochemistry (MESMO 1.0). Meteorol. Soc., 78, 1069 1079. Geophys. Model Dev., 1, 1 15. Manzini, E., C. Cagnazzo, P. G. Fogli, A. Bellucci, and W. A. Muller, 2012: Stratosphere- Maurer, E., and H. Hidalgo, 2008: Utility of daily vs. monthly large-scale climate data: troposphere coupling at inter-decadal time scales: Implications for the North An intercomparison of two statistical downscaling methods. Hydrol. Earth Syst. Atlantic Ocean. Geophys. Res. Lett., 39, L05801. Sci., 12, 551 563. Maraun, D., 2012: Nonstationarities of regional climate model biases in European Mauritsen, T., et al., 2012: Tuning the climate of a global model. J. Adv. Model. Earth seasonal mean temperature and precipitation sums. Geophys. Res. Lett., 39, Syst., 4, M00A01. L06706. Maximenko, N., et al., 2009: Mean dynamic topography of the ocean derived from Maraun, D., H. Rust, and T. Osborn, 2010a: Synoptic airflow and UK daily precipitation satellite and drifting buoy data using three different techniques. J. Atmos. Ocean. extremes: Development and validation of a vector generalised linear model. Technol., 26, 1910 1919. Extremes, 13, 133 153. May, P. T., J. H. Mather, G. Vaughan, K. N. Bower, C. Jakob, G. M. McFarquhar, and G. Maraun, D., et al., 2010b: Precipitation downscaling under climate change: Recent G. Mace, 2008: The Tropical Warm Pool International Cloud Experiment. Bull. Am. developments to bridge the gap between dynamical models and the end user. Meteorol. Soc., 89, 629 645. Rev. Geophys., 48, RG3003. May, W., 2007: The simulation of the variability and extremes of daily precipitation Marchand, R., N. Beagley, and T. P. Ackerman, 2009: Evaluation of hydrometeor over Europe by the HIRHAM regional climate model. Global Planet. Change, occurrence profiles in the Multiscale Modeling Framework Climate Model using 57, 59 82. atmospheric classification. J. Clim., 22, 4557 4573. McCarthy, G., et al., 2012: Observed interannual variability of the Atlantic meridional Markovic, M., H. Lin, and K. Winger, 2010: Simulating global and North American overturning circulation at 26.5 degrees N. Geophys. Res. Lett., 39, L19609. climate using the Global Environmental Multiscale Model with a Variable- McClean, J. L., and J. C. Carman, 2011: Investigation of IPCC AR4 coupled climate Resolution Modeling Approach. Mon. Weather Rev., 138, 3967 3987. model North Atlantic modewater formation. Ocean Model., 40, 14 34. Marsh, R., S. A. Mueller, A. Yool, and N. R. Edwards, 2011: Incorporation of the McClean, J. L., M. E. Maltrud, and F. O. Bryan, 2006: Measures of the fidelity of C-GOLDSTEIN efficient climate model into the GENIE framework: eb_go_gs eddying ocean models. Oceanography, 19, 104 117. configurations of GENIE. Geophys. Model Dev., 4, 957 992. McClean, J. L., et al., 2011: A prototype two-decade fully-coupled fine-resolution Marsh, R., et al., 2009: Recent changes in the North Atlantic circulation simulated CCSM simulation. Ocean Model. 39, 10 30. with eddy-permitting and eddy-resolving ocean models. Ocean Model., 28, 226 239. 841 Chapter 9 Evaluation of Climate Models McCormack, J. P., S. D. Eckermann, D. E. Siskind, and T. J. McGee, 2006: CHEM2D-OPP: Meissner, K. J., A. J. Weaver, H. D. Matthews, and P. M. Cox, 2003: The role of land A new linearized gas-phase ozone photochemistry parameterization for high- surface dynamics in glacial inception: a study with the UVic Earth System Model. altitude NWP and climate models. Atmos. Chem. Phys., 6, 4943 4972. Clim. Dyn., 21, 515 537. McCrary, R. R., and D. A. Randall, 2010: Great plains drought in simulations of the Melillo, J. M., A. D. McGuire, D. W. Kicklighter, B. Moore, C. J. Vorosmarty, and A. twentieth century. J. Clim., 23, 2178 2196. L. Schloss, 1993: Global climate-change and terrestrial net primary production. McDonald, R. E., 2011: Understanding the impact of climate change on Northern Nature, 363, 234 240. Hemisphere extra-tropical cyclones. Clim. Dyn., 37, 1399 1425. Melsom, A., V. Lien, and W. P. Budgell, 2009: Using the Regional Ocean Modeling McDougall, T. J., and P. C. McIntosh, 2001: The temporal-residual-mean velocity. Part System (ROMS) to improve the ocean circulation from a GCM 20th century II: Isopycnal interpretation and the tracer and momentum equations. J. Phys. simulation. Ocean Dyn., 59, 969 981. Oceanogr., 31, 1222 1246. Menary, M., W. Park, K. Lohmann, M. Vellinga, D. Palmer, M. Latif, and J. H. Jungclaus, McKitrick, R., S. McIntyre, and C. Herman, 2010: Panel and multivariate methods for 2012: A multimodel comparison of centennial Atlantic meridional overturning tests of trend equivalence in climate data series. Atmos. Sci. Lett., 11, 270 277. circulation variability. Clim. Dyn., 38, 2377 2388. McKitrick, R., S. McIntyre, and C. Herman, 2011: Panel and multivariate methods for Menendez, C., M. de Castro, A. Sorensson, J. Boulanger, and C. M. Grp, 2010: CLARIS tests of trend equivalence in climate data series. Atmos. Sci. Lett., 12, 386 388. Project: Towards climate downscaling in South America. Meteorol. Z., 19, 357 McLandress, C., T. Shepherd, J. Scinocca, D. Plummer, M. Sigmond, A. Jonsson, and 362. M. Reader, 2011: Separating the dynamical effects of climate change and ozone Menon, S., D. Koch, G. Beig, S. Sahu, J. Fasullo, and D. Orlikowski, 2010: Black carbon depletion. Part II Southern Hemisphere troposphere. J. Clim., 24, 1850 1868. aerosols and the third polar ice cap. Atmos. Chem. Phys., 10, 4559 4571. McLaren, A. J., et al., 2006: Evaluation of the sea ice simulation in a new coupled Mercado, L. M., C. Huntingford, J. H. C. Gash, P. M. Cox, and V. Jogireddy, 2007: 9 atmosphere-ocean climate model (HadGEM1). J. Geophys. Res. Oceans, 111, Improving the representation of radiation interception and photosynthesis for C12014. climate model applications. Tellus B, 59, 553 565. McManus, J. F., R. Francois, J. M. Gherardi, L. D. Keigwin, and S. Brown-Leger, 2004: Merrifield, M. A., and M. E. Maltrud, 2011: Regional sea level trends due to a Pacific Collapse and rapid resumption of Atlantic meridional circulation linked to trade wind intensification. Geophys. Res. Lett., 38, L21605. deglacial climate changes. Nature, 428, 834 837. Merryfield, W. J., et al., 2013: The Canadian Seasonal to Interannual Prediction McWilliams, J. C., 2008: The nature and consequences of oceanic eddies. In: Ocean System. Part I: Models and Initialization. Mon. Weather Rev., doi:10.1175/MWR- Modeling in an Eddying Regime [M. Hecht and H. Hasumi (eds.)]. American D-12-00216.1. Geophysical Union, Washington, DC, pp. 5 15. Mieville, A., et al., 2010: Emissions of gases and particles from biomass burning Mearns, L. O., et al., 2012: The North American Regional Climate Change Assessment during the 20th century using satellite data and an historical reconstruction. Program: Overview of Phase I Results. Bull. Am. Meteorol. Soc., 93, 1337 1362. Atmos. Environ., 44, 1469 1477. Mears, C. A., F. J. Wentz, P. Thorne, and D. Bernie, 2011: Assessing uncertainty in Miller, A., et al., 2002: A cohesive total ozone data set from the SBUV(/2) satellite estimates of atmospheric temperature changes from MSU and AMSU using a system. J. Geophys. Res. Atmos., 107, 4701. Monte-Carlo estimation technique. J. Geophys. Res., 116, D08112. Milliff, R., A. Bonazzi, C. Wikle, N. Pinardi, and L. Berliner, 2011: Ocean ensemble Mears, C. A., B. D. Santer, F. J. Wentz, K. E. Taylor, and M. F. Wehner, 2007: Relationship forecasting. Part I: Ensemble Mediterranean winds from a Bayesian hierarchical between temperature and precipitable water changes over tropical oceans. model. Q. J. R. Meteorol. Soc., 137, 858 878. Geophys. Res. Lett., 34, L24709. Milly, P. C. D., and A. B. Shmakin, 2002: Global modeling of land water and energy Meehl, G. A., and J. M. Arblaster, 2011: Decadal variability of Asian-Australian balances. Part I: the land dynamics (LaD) model. J. Hydrometeorol., 3, 283 299. Monsoon-ENSO-TBO relationships. J. Clim., 24, 4925 4940. Min, S. K., X. B. Zhang, F. W. Zwiers, and G. C. Hegerl, 2011: Human contribution to Meehl, G. A., and H. Teng, 2012: Case studies for initialized decadal hindcasts and more-intense precipitation extremes. Nature, 470, 376 379. predictions for the Pacific region. Geophys. Res. Lett., 39, L22705. Minobe, S., 1997: A 50 70 year climatic oscillation over the North Pacific and North Meehl, G. A., J. M. Arblaster, and J. Loschnigg, 2003: Coupled ocean-atmosphere America. Geophys. Res. Lett., 24, 683 686. dynamical processes in the tropical Indian and Pacific Oceans and the TBO. J. Minobe, S., 1999: Resonance in bidecadal and pentadecadal climate oscillations Clim., 16, 2138 2158. over the North Pacific: Role in climatic regime shifts. Geophys. Res. Lett., 26, Meehl, G. A., J. M. Arblaster, J. T. Fasullo, A. X. Hu, and K. E. Trenberth, 2011: Model- 855 858. based evidence of deep-ocean heat uptake during surface-temperature hiatus Misra, V., 2007: Addressing the issue of systematic errors in a regional climate periods. Nature Clim. Change, 1, 360 364. model. J. Clim., 20, 801 818. Meehl, G. A., A. Hu, J. Arblaster, J. Fasullo, and K. E. Trenberth, 2013a: Externally Mitchell, T. D., and P. D. Jones, 2005: An improved method of constructing a database forced and internally generated decadal climate variability associated with the of monthly climate observations and associated high-resolution grids. Int. J. Interdecadal Pacific Oscillation. J. Clim., doi:10.1175/JCLI-D-12-00548.1. Climatol. , 25, 693 712. Meehl, G. A., P. R. Gent, J. M. Arblaster, B. L. Otto-Bliesner, E. C. Brady, and A. Craig, Miyama, T., and M. Kawamiya, 2009: Estimating allowable carbon emission for 2001: Factors that affect the amplitude of El Nino in global coupled climate CO(2) concentration stabilization using a GCM-based Earth system model. models. Clim. Dyn., 17, 515 526. Geophys. Res. Lett., 36, L19709. Meehl, G. A., J. M. Arblaster, J. M. Caron, H. Annamalai, M. Jochum, A. Chakraborty, Mizuta, R., et al., 2012: Climate simulations using MRI-AGCM3.2 with 20-km grid. J. and R. Murtugudde, 2012: Monsoon regimes and processes in CCSM4. Part I: The Meteorol. Soc. Jpn., 90A, 233 258. Asian-Australian Monsoon. J. Clim., 25, 2583 2608. Molteni, F., 2003: Atmospheric simulations using a GCM with simplified physical Meehl, G. A., et al., 2007: The WCRP CMIP3 multimodel dataset A new era in parameterizations. I: Model climatology and variability in multi-decadal climate change research. Bull. Am. Meteorol. Soc., 88, 1383 1394. experiments. Clim. Dyn., 20, 175 191. Meehl, G. A., et al., 2009: Decadal prediction: Can it be skillful? Bull. Am. Meteorol. Montoya, M., A. Griesel, A. Levermann, J. Mignot, M. Hofmann, A. Ganopolski, and S. Soc., 90, 1467 1485. Rahmstorf, 2005: The earth system model of intermediate complexity CLIMBER-3 Meehl, G. A., et al., 2013b: Decadal climate prediction: An update from the trenches. alpha. Part 1: description and performance for present-day conditions. Clim. Bull. Am. Meteorol. Soc., doi:10.1175/BAMS-D-12-00241.1. Dyn., 25, 237 263. Meijers, A., E. Shuckburgh, N. Bruneau, J.-B. Sallee, T. Bracegirdle, and Z. Wang, Morgenstern, O., et al., 2010: Anthropogenic forcing of the Northern Annular Mode 2012: Representation of the Antarctic Circumpolar Current in the CMIP5 climate in CCMVal-2 models. J. Geophys. Res., 115, D00M03. models and future changes under warming scenarios. J. Geophys. Res. Oceans, Morice, C. P., J. J. Kennedy, N. A. Rayner, and P. D. Jones, 2012: Quantifying 117, C12008. uncertainties in global and regional temperature change using an ensemble of Meinshausen, M., et al., 2009: Greenhouse-gas emission targets for limiting global observational estimates: The HadCRUT4 data set. J. Geophys. Res. Atmos., 117, warming to 2 degrees C. Nature, 458, 1158 1162. D08101. Meinshausen, M., et al., 2011: The RCP greenhouse gas concentrations and their Moss, R. H., et al., 2010: The next generation of scenarios for climate change research extensions from 1765 to 2300. Clim. Change, 109, 213 241. and assessment. Nature, 463, 747 756. 842 Evaluation of Climate Models Chapter 9 Mouchet, A., and L. M. François, 1996: Sensitivity of a global oceanic carbon cycle Nicolsky, D. J., V. E. Romanovsky, V. A. Alexeev, and D. M. Lawrence, 2007: Improved model to the circulation and the fate of organic matter: Preliminary results. Phys. modeling of permafrost dynamics in a GCM land-surface scheme. J. Geophys. Chem. Earth, 21, 511 516. Res., 34, L08501. Msadek, R., and C. Frankignoul, 2009: Atlantic multidecadal oceanic variability and Nikiema, O., and R. Laprise, 2010: Diagnostic budget study of the internal variability its influence on the atmosphere in a climate model. Clim. Dyn., 33, 45 62. in ensemble simulations of the Canadian RCM. Clim. Dyn., 36 2313 2337. Msadek, R., W. E. Johns, S. G. Yeager, G. Danabasoglu, T. Delworth, and T. Rosati, Nikulin, G., et al., 2012: Precipitation climatology in an ensemble of CORDEX-Africa 2013: The Atlantic meridional heat transport at 26.5N and its relationship with regional climate simulations. J. Clim., doi:10.1175/jcli-d-11 00375.1. the MOC in the RAPID-array and GFDL and NCAR coupled models. J. Clim., Ning, L., M. E. Mann, R. Crane, and T. Wagener, 2011: Probabilistic projections of doi:10.1175/JCLI-D-12 00081.1. climate change for the Mid-Atlantic region of the United States Validation of Mueller, B., et al., 2011: Evaluation of global observations-based evapotranspiraion precipitation downscaling during the Historical Era. J. Clim., 25, 509 526. datasets and IPCC AR4 simulations. Geophys. Res. Lett., 38, L06402. Nishii, K., et al., 2012: Relationship of the reproducibility of multiple variables among Muller, S. A., F. Joos, N. R. Edwards, and T. F. Stocker, 2006: Water mass distribution Global Climate Models. J. Meteorol. Soc. Jpn., 90A, 87 100. and ventilation time scales in a cost-efficient, three-dimensional ocean model. Notz, D., F. A. Haumann, H. Haak, J. H. Jungclaus, and J. Marotzke, 2013: Sea-ice J. Clim., 19, 5479 5499. evolution in the Arctic as modeled by MPI-ESM. J. Adv. Model. Earth Syst., Murakami, H., and M. Sugi, 2010: Effect of model resolution on tropical cyclone doi:10.1002/jame.20016. climate projections. Sola, 6, 73 76. O Connor, F. M., C. E. Johnson, O. Morgenstern, and W. J. Collins, 2009: Interactions Murakami, H., et al., 2012: Future changes in tropical cyclone activity projected by between tropospheric chemistry and climate model temperature and humidity the new high-resolution MRI-AGCM. J. Clim., 25, 3237 3260. biases. Geophys. Res. Lett., 36, L16801. Murphy, D. M., 2013: Little net clear-sky radiative forcing from recent regional O Farrell, S. P., 1998: Investigation of the dynamic sea ice component of a coupled 9 redistribution of aerosols. Nature Geosci., 6, 258 262. atmosphere sea ice general circulation model. J. Geophys. Res.-Oceans, 103, Murphy, J., B. Booth, M. Collins, G. Harris, D. Sexton, and M. Webb, 2007: A 15751 15782. methodology for probabilistic predictions of regional climate change from O Gorman, P. A., 2012: Sensitivity of tropical precipitation extremes to climate perturbed physics ensembles. Philos.Trans. R. Soc. London A, 365 1993 2028. change. Nature Geosci., 5, 697 700. Murphy, J. M., D. M. H. Sexton, D. N. Barnett, G. S. Jones, M. J. Webb, M. Collins, O Gorman, P. A., and M. S. Singh, 2013: Vertical structure of warming consistent and D. A. Stainforth, 2004: Quantification of modelling uncertainties in a large with an upward shift in the middle and upper troposphere. Geophys. Res. Lett., ensemble of climate change simulations. Nature, 430, 768 772. doi:10.1002/grl.50328. Murtugudde, R., J. Beauchamp, C. R. McClain, M. Lewis, and A. J. Busalacchi, 2002: O ishi, R., and A. Abe-Ouchi, 2011: Polar amplification in the mid-Holocene derived Effects of penetrative radiation on the upper tropical ocean circulation. J. Clim., from dynamical vegetation change with a GCM. Geophys. Res. Lett., 38, L14702. 15, 470 486. Ogasawara, N., A. Kitoh, T. Yasunari, and A. Noda, 1999: Tropospheric biennial Muryshev, K. E., A. V. Eliseev, I. I. Mokhov, and N. A. Diansky, 2009: Validating and oscillation of ENSO-monsoon system in the MRI coupled GCM. J. Meteorol. Soc. assessing the sensitivity of the climate model with an ocean general circulation Jpn., 77, 1247 1270. model developed at the Institute of Atmospheric Physics, Russian Academy of Ohba, M., D. Nohara, and H. Ueda, 2010: Simulation of asymmetric ENSO transition Sciences. Izvestiya Atmos. Ocean. Phys., 45, 416 433. in WCRP CMIP3 Multimodel Experiments. J. Clim., 23, 6051 6067. Nagura, M., W. Sasaki, T. Tozuka, J.-J. Luo, S. K. Behera, and T. Yamagata, 2013: Ohgaito, R., and A. Abe-Ouchi, 2009: The effect of sea surface temperature bias in Longitudinal biases in the Seychelles Dome simulated by 35 ocean-atmosphere the PMIP2 AOGCMs on mid-Holocene Asian monsoon enhancement. Clim. Dyn., coupled general circulation models. J. Geophys. Res., doi:10.1029/2012JC008352. 33, 975 983. Nakano, H., H. Tsujino, M. Hirabara, T. Yasuda, T. Motoi, M. Ishii, and G. Yamanaka, Oka, A., E. Tajika, A. Abe-Ouchi, and K. Kubota, 2011: Role of the ocean in controlling 2011: Uptake mechanism of anthropogenic CO2 in the Kuroshio Extension atmospheric CO2 concentration in the course of global glaciations. Clim. Dyn., region in an ocean general circulation model. J. Oceanogr., 67, 765 783. 37, 1755 1770. Nam, C., S. Bony, J. L. Dufresne, and H. Chepfer, 2012: The too few, too bright Oleson, K. W., 2004: Technical description of the Community Land Model (CLM). tropical low-cloud problem in CMIP5 models. Geophys. Res. Lett., 39, L21801. NCAR Technical Note NCAR/TN-461+STR, National Center for Atmospheric Nanjundiah, R. S., V. Vidyunmala, and J. Srinivasan, 2005: The impact of increase Research, Boulder, CO, 174 pp. in CO2 on the simulation of tropical biennial oscillations (TBO) in 12 coupled Oleson, K. W., G. B. Bonan, J. Feddema, M. Vertenstein, and C. S. B. Grimmond, 2008a: general circulation models. Atmos. Sci. Lett., 6, 183 191. An urban parameterization for a global climate model. Part I: Formulation and Naoe, H., and K. Shibata, 2010: Equatorial quasi-biennial oscillation influence on evaluation for two cities. J. Appl. Meteorol. Climatol., 47, 1038 1060. northern winter extratropical circulation. J. Geophys. Res. Atmos., 115, D19102. Oleson, K. W., et al., 2010: Technical Description of version 4.0 of the Community Neale, R. B., J. H. Richter, and M. Jochum, 2008: The Impact of Convection on ENSO: Land Model (CLM)  NCAR Technical Note NCAR/TN-478+STR, National Center From a delayed oscillator to a series of events. J. Clim., 21, 5904 5924. for Atmospheric Research, Boulder, CO, 257 pp. Neale, R. B., J. Richter, S. Park, P. H. Lauritzen, S. J. Vavrus, P. J. Rasch, and M. Zhang, Oleson, K. W., et al., 2008b: Improvements to the Community Land Model and their 2013: The Mean Climate of the Community Atmosphere Model (CAM4) in forced impact on the hydrological cycle. J. Geophys. Res. Biogeosci., 113, G01021 SST and fully coupled experiments. J. Clim., doi:10.1175/JCLI-D-12-00236.1. Onogi, K., et al., 2007: The JRA-25 reanalysis. J. Meteorol. Soc. Jpn., 85, 369 432. Neale, R. B., et al., 2010: Description of the NCAR Community Atmosphere Model Opsteegh, J. D., R. J. Haarsma, F. M. Selten, and A. Kattenberg, 1998: ECBILT: A (CAM 4.0). NCAR Technical Note NCAR/TN-486+STR, National Center for dynamic alternative to mixed boundary conditions in ocean models. Tellus A, Atmopsheric Research, Boulder, CO, 268 pp. 50, 348 367. Neelin, J. D., 2007: Moist dynamics of tropical convection zones in monsoons, Oreopoulos, L., et al., 2012: The continual intercomparison of radiation codes: teleconnections and global warming. In: The Global Circulation of the Results from Phase I. J. Geophys. Res. Atmos., 117, D06118. Atmosphere [T. Schneider and A. Sobel (eds.)]. Princeton University Press, Ostle, N. J., et al., 2009: Integrating plant-soil interactions into global carbon cycle Princeton, NJ. 385 pp. models. J. Ecol., 97, 851 863. Neelin, J. D., and N. Zeng, 2000: A quasi-equilibrium tropical circulation model Otte, T. L., C. G. Nolte, M. J. Otte, and J. H. Bowden, 2012: Does nudging squelch the Formulation. J. Atmos. Sci., 57, 1741 1766. extremes in Regional Climate Modeling? J. Clim., 25, 7046 7066. Neelin, J. D., C. Chou, and H. Su, 2003: Tropical drought regions in global warming Ottera, O. H., M. Bentsen, H. Drange, and L. Suo, 2010: External forcing as a and El Nino teleconnections. Geophys. Res. Lett., 30, 2275. metronome for Atlantic multidecadal variability. Nature Geosci., 3, 688 694. Neelin, J. D., A. Bracco, H. Luo, J. C. McWilliams, and J. E. Meyerson, 2010: Otto-Bliesner, B. L., et al., 2007: Last Glacial Maximum ocean thermohaline Considerations for parameter optimization and sensitivity in climate models. circulation: PMIP2 model intercomparisons and data constraints. Geophys. Res. Proc. Nat. Acad. Sci. U.S.A., 107, 21349 21354. Lett., 34, L12706. Neggers, R. A. J., 2009: A dual mass flux framework for boundary layer convection. Otto-Bliesner, B. L., et al., 2009: A comparison of PMIP2 model simulations and Part II: Clouds. J. Atmos. Sci., 66, 1489 1506. the MARGO proxy reconstruction for tropical sea surface temperatures at last Neggers, R. A. J., M. Kohler, and A. C. M. Beljaars, 2009: A dual mass flux framework glacial maximum. Clim. Dyn., 32, 799 815. for boundary layer convection. Part I: Transport. J. Atmos. Sci., 66, 1465 1487. 843 Chapter 9 Evaluation of Climate Models Otto, J., T. Raddatz, M. Claussen, V. Brovkin, and V. Gayler, 2009: Separation of Pierce, D. W., 2001: Distinguishing coupled ocean-atmosphere interactions from atmosphere-ocean-vegetation feedbacks and synergies for mid-Holocene background noise in the North Pacific. Prog. Oceanogr., 49, 331 352. climate. Global Biogeochem. Cycles, 23, L09701. Pierce, D. W., T. P. Barnett, B. D. Santer, and P. J. Gleckler, 2009: Selecting global Overland, J. E., and M. Wang, 2013: When will the summer Arctic be nearly sea ice climate models for regional climate change studies. Proc. Natl. Acad. Sci. U.S.A., free? Geophys. Res. Lett., doi:10.1002/grl.50316, doi:10.1002/grl.50316. 106, 8441 8446. Ozturk, T., H. Altinsoy, M. Turkes, and M. Kurnaz, 2012: Simulation of temperature Pincus, R., C. P. Batstone, R. J. P. Hofmann, K. E. Taylor, and P. J. Glecker, 2008: and precipitation climatology for the central Asia CORDEX domain using RegCM Evaluating the present-day simulation of clouds, precipitation, and radiation in 4.0. Clim. Res., 52, 63 76. climate models. J. Geophys. Res. Atmos., 113, D14209 Paeth, H., 2011: Postprocessing of simulated precipitation for impact research in Pincus, R., S. Platnick, S. A. Ackerman, R. S. Hemler, and R. J. P. Hofmann, 2012: West Africa. Part I: Model output statistics for monthly data. Clim. Dyn., 36, Reconciling simulated and observed views of clouds: MODIS, ISCCP, and the 1321 1336. limits of instrument simulators. J. Clim., 25, 4699 4720. Paeth, H., et al., 2012: Progress in regional downscaling of west African precipitation. Pinto, J. G., T. Spangehl, U. Ulbrich, and P. Speth, 2006: Assessment of winter cyclone Atmos. Sci. Lett., 12, 75 82. activity in a transient ECHAM4 OPYC3 GHG experiment. Meteorol. Z., 15, Palmer, J. R., and I. J. Totterdell, 2001: Production and export in a global ocean 279 291. ecosystem model. Deep-Sea R. Pt. I, 48, 1169 1198. Piot, M., and R. von Glasow, 2008: The potential importance of frost flowers, Parekh, P., F. Joos, and S. A. Muller, 2008: A modeling assessment of the interplay recycling on snow, and open leads for ozone depletion events. Atmos. Chem. between aeolian iron fluxes and iron-binding ligands in controlling carbon Phys., 8, 2437 2467. dioxide fluctuations during Antarctic warm events. Paleoceanography, 23, Pitman, A., A. Arneth, and L. Ganzeveld, 2012a: Regionalizing global climate models. 9 Pa4202. Int. J. Climatol. , 32, 321 337. Park, S., and C. S. Bretherton, 2009: The University of Washington Shallow Convection Pitman, A. J., 2003: The evolution of, and revolution in, land surface schemes and Moist Turbulence schemes and their impact on climate simulations with the designed for climate models. Int. J. Climatol. , 23, 479 510. Community Atmosphere Model. J. Clim., 22, 3449 3469. Pitman, A. J., et al., 2012b: Effects of land cover change on temperature and rainfall Park, W., and M. Latif, 2010: Pacific and Atlantic multidecadal variability in the Kiel extremes in multi-model ensemble simulations. Earth Syst. Dyn., 13, 213 231. Climate Model. Geophys. Res. Lett., 37, L24702. Pitman, A. J., et al., 2009: Uncertainties in climate responses to past land cover Parker, D., C. Folland, A. Scaife, J. Knight, A. Colman, P. Baines, and B. W. Dong, 2007: change: First results from the LUCID intercomparison study. Geophys. Res. Lett., Decadal to multidecadal variability and the climate change background. J. 36, L14814. Geophys. Res. Atmos., 112, D18115. Plattner, G. K., et al., 2008: Long-term climate commitments projected with climate- Parkinson, C. L., and D. J. Cavalieri, 2012: Antarctic sea ice variability and trends, carbon cycle models. J. Clim., 21, 2721 2751. 1979 2010. Cryosphere, 6, 871 880. Ploshay, J. J., and N.-C. Lau, 2010: Simulation of the diurnal cycle in tropical rainfall Patricola, C. M., M. Li, Z. Xu, P. Chang, R. Saravanan, and J.-S. Hsieh, 2012: An and circulation during Boreal Summer with a high-resolution GCM. Mon. investigation of tropical Atlantic bias in a high-resolution Coupled Regional Weather Rev., 138, 3434 3453. Climate Model. Clim. Dyn., doi:10.1007/s00382-012-1320-5. Po-Chedley, S., and Q. Fu, 2012: Discrepancies in tropical upper tropospheric Pavlova, T. V., V. M. Kattsov, and V. A. Govorkova, 2011: Sea ice in CMIP5 models: warming between atmospheric circulation models and satellites. Environ. Res. Closer to reality? Trudy GGO (MGO Proc., in Russian), 564, 7 18. Lett., 7, 044018. Pavlova, T. V., V. M. Kattsov, Y. D. Nadyozhina, P. V. Sporyshev, and V. A. Govorkova, Pokhrel, S., H. Rahaman, A. Parekh, S. K. Saha, A. Dhakate, H. S. Chaudhari, and R. 2007: Terrestrial cryosphere evolution through the 20th and 21st centuries as M. Gairola, 2012: Evaporation-precipitation variability over Indian Ocean and simulated with the new generation of global climate models. Earth Cryosphere its assessment in NCEP Climate Forecast System (CFSv2). Clim. Dyn., 39, 2585 (in Russian), 11, 3 13. 2608. Pechony, O., and D. T. Shindell, 2009: Fire parameterization on a global scale. J. Polvani, L., D. Waugh, G. Correa, and S. Son, 2011: Stratospheric ozone depletion: Geophys. Res. Atmos., 114, D16115 The main driver of twentieth-century atmospheric circulation changes in the Pedersen, C. A., E. Roeckner, M. Lüthje, and J. Winther, 2009: A new sea ice albedo Southern Hemisphere. J. Clim., 24, 795 812. scheme including melt ponds for ECHAM5 general circulation model. J. Geophys. Pope, V. D., M. L. Gallani, P. R. Rowntree, and R. A. Stratton, 2000: The impact of new Res., 114, D08101. physical parametrizations in the Hadley Centre climate model: HadAM3. Clim. Pennell, C., and T. Reichler, 2011: On the effective number of climate models. J. Clim., Dyn., 16, 123 146. 24 2358 2367 Power, S., and R. Colman, 2006: Multi-year predictability in a coupled general Perlwitz, J., S. Pawson, R. Fogt, J. Nielsen, and W. Neff, 2008: Impact of stratospheric circulation model. Clim. Dyn., 26, 247 272 ozone hole recovery on Antarctic climate. Geophys. Res. Lett., 35, L08714. Power, S., M. Haylock, R. Colman, and X. D. Wang, 2006: The predictability of Peterson, T. C., et al., 2009: State of the Climate in 2008. Bull. Am. Meteorol. Soc., interdecadal changes in ENSO activity and ENSO teleconnections. J. Clim., 19, 90, S1 S196. 4755 4771. Petoukhov, V., I. I. Mokhov, A. V. Eliseev, and V. A. Semenov, 1998: The IAP RAS global Prudhomme, C., and H. Davies, 2009: Assessing uncertainties in climate change climate model. Dialogue-MSU, Moscow, Russia. impact analyses on the river flow regimes in the UK. Part 1: Baseline climate. Petoukhov, V., A. Ganopolski, V. Brovkin, M. Claussen, A. Eliseev, C. Kubatzki, and Clim. Change, 93, 177 195. S. Rahmstorf, 2000: CLIMBER-2: a climate system model of intermediate Pryor, S., G. Nikulin, and C. Jones, 2012: Influence of spatial resolution on regional complexity. Part I: Model description and performance for present climate. Clim. climate model derived wind climates. J. Geophys. Res. Atmos., 117, D03117. Dyn., 16, 1 17. Ptashnik, I. V., R. A. McPheat, K. P. Shine, K. M. Smith, and R. G. Williams, 2011: Petoukhov, V., et al., 2005: EMIC Intercomparison Project (EMIP-CO2): Comparative Water vapor self-continuum absorption in near-infrared windows derived from analysis of EMIC simulations of climate, and of equilibrium and transient laboratory measurements. J. Geophys. Res. Atmos., 116, D16305. responses to atmospheric CO2 doubling. Clim. Dyn., 25, 363 385. Qian, T. T., A. Dai, K. E. Trenberth, and K. W. Oleson, 2006: Simulation of global land Pfahl, S., and H. Wernli, 2012: Quantifying the relevance of atmospheric blocking surface conditions from 1948 to 2004. Part I: Forcing data and evaluations. J. for co-located temperature extremes in the Northern Hemisphere on (sub-)daily Hydrometeorol., 7, 953 975. time scales. Geophys. Res. Lett., 39, L12807. Qiao, F., Y. Yuan, Y. Yang, Q. Zheng, C. Xia, and J. Ma, 2004: Wave-induced mixing Pfeiffer, A., and G. Zängl, 2010: Validation of climate-mode MM5 simulations for the in the upper ocean: Distribution and application to a global ocean circulation European Alpine Region. Theor. Appl. Climatol., 101, 93 108. model. Geophys. Res. Lett., 31, L11303. Phillips, T. J., et al., 2004: Evaluating parameterizations in General Circulation Quaas, J., 2012: Evaluating the critical relative humidity as a measure of Models: Climate simulation meets weather prediction. Bull. Am. Meteorol. Soc., subgrid-scale variability of humidity in general circulation model cloud cover 85, 1903 1915. parameterizations using satellite data. J. Geophys. Res. Atmos., 117, D09208. Piani, C., D. J. Frame, D. A. Stainforth, and M. R. Allen, 2005: Constraints on climate Raftery, A. E., T. Gneiting, F. Balabdaoui, and M. Polakowski, 2005: Using Bayesian change from a multi-thousand member ensemble of simulations. Geophys. Res. model averaging to calibrate forecast ensembles. Mon. Weather Rev., 133, Lett., 32, L23825. 1155 1174. 844 Evaluation of Climate Models Chapter 9 Raible, C. C., M. Yoshimori, T. F. Stocker, and C. Casty, 2007: Extreme midlatitude Ridgwell, A., and J. C. Hargreaves, 2007: Regulation of atmospheric CO(2) by deep- cyclones and their implications for precipitation and wind speed extremes in sea sediments in an Earth system model. Global Biogeochem. Cycles, 21, simulations of the Maunder Minimum versus present day conditions. Clim. Dyn., Gb2008. 28, 409 423. Ridgwell, A., I. Zondervan, J. C. Hargreaves, J. Bijma, and T. M. Lenton, 2007a: Raisanen, J., 2007: How reliable are climate models? Tellus A, 59, 2 29. Assessing the potential long-term increase of oceanic fossil fuel CO2 uptake due Raisanen, J., and J. S. Ylhaisi, 2011: How much should climate model output be to CO2 calcification feedback. Biogeosciences, 4, 481 492. smoothed in space? J. Clim., 24, 867 880. Ridgwell, A., et al., 2007b: Marine geochemical data assimilation in an efficient Earth Raisanen, J., L. Ruokolainen, and J. Ylhaisi, 2010: Weighting of model results for System Model of global biogeochemical cycling. Biogeosciences, 4, 87 104. improving best estimates of climate change. Clim. Dyn., 35, 407 422. Rienecker, M. M., et al., 2011: MERRA: NASA s modern-era retrospective analysis for Rammig, A., et al., 2010: Estimating the risk of Amazonian forest dieback. New research and applications. J. Clim., 24, 3624 3648. Phytologist, 187, 694 706. Ringer, M. A., J. M. Edwards, and A. Slingo, 2003: Simulation of satellite channel Rampal, P., J. Weiss, C. Dubois, and J. M. Campin, 2011: IPCC climate models do not radiances in the Met Office Unified Model. Q. J. R. Meteorol. Soc., 129, 1169 capture Arctic sea ice drift acceleration: Consequences in terms of projected sea 1190. ice thinning and decline. J. Geophys. Res. Oceans, 116, C00d07. Rio, C., and F. Hourdin, 2008: A thermal plume model for the convective boundary Ramstein, G., M. Kageyama, J. Guiot, H. Wu, C. Hely, G. Krinner, and S. Brewer, 2007: layer: Representation of cumulus clouds. J. Atmos. Sci., 65, 407 425. How cold was Europe at the Last Glacial Maximum? A synthesis of the progress Rio, C., F. Hourdin, F. Couvreux, and A. Jam, 2010: Resolved versus parametrized achieved since the first PMIP model-data comparison. Clim. Past, 3, 331 339. boundary-layer plumes. Part II: Continuous formulations of mixing rates for Randall, D. A., M. F. Khairoutdinov, A. Arakawa, and W. W. Grabowski, 2003: Breaking mass-flux schemes. Boundary-Layer Meteorol., 135, 469 483. the cloud parameterization deadlock. Bull. Am. Meteorol. Soc., 84, 1547 1564. Risi, C., et al., 2012a: Process-evaluation of tropospheric humidity simulated by 9 Randall, D. A., et al., 2007: Climate models and their evaluation. In: Climate Change general circulation models using water vapor isotopic observations: 2. Using 2007: The Physical Science Basis. Contribution of Working Group I to the isotopic diagnostics to understand the mid and upper tropospheric moist bias in Fourth Assessment Report of the Intergovernmental Panel on Climate Change the tropics and subtropics. J. Geophys. Res. Atmos., 117, D05304. [Solomon, S., D. Qin, M. Manning, Z. Chen, M. Marquis, K. B. Averyt, M. Tignor Risi, C., et al., 2012b: Process-evaluation of tropospheric humidity simulated by and H. L. Miller (eds.)] Cambridge University Press, Cambridge, United Kingdom general circulation models using water vapor isotopologues: 1. Comparison and New York, NY, USA, pp. 589 662. between models and observations. J. Geophys. Res. Atmos., 117, D05303. Randel, W., and F. Wu, 2007: A stratospheric ozone profile data set for 1979 2005: Risien, C. M., and D. B. Chelton, 2008: A global climatology of surface wind and Variability, trends, and comparisons with column ozone data. J. Geophys. Res. wind stress fields from eight years of QuikSCAT Scatterometer data. J. Phys. Atmos., 12, D06313. Oceanogr., 38, 2379 2413. Randel, W. J., et al., 2009: An update of observed stratospheric temperature trends. J. Ritz, S. P., T. F. Stocker, and F. Joos, 2011: A coupled dynamical ocean-energy balance Geophys. Res. Atmos., 114, D02107. atmosphere model for paleoclimate studies. J. Clim., 24, 349 375. Rapaiæ, M., M. Leduc, and R. Laprise, 2010: Evaluation of the internal variability and Roberts, M. J., et al., 2004: Impact of an eddy-permitting ocean resolution on control estimation of the downscaling ability of the Canadian Regional Climate Model and climate change simulations with a global coupled GCM. J. Clim., 17, 3 20. for different domain sizes over the north Atlantic region using the Big-Brother Robinson, A., R. Calov, and A. Ganopolski, 2012: Multistability and critical thresholds experimental approach. Clim. Dyn., 36 1979 2001. of the Greenland ice sheet. Nature Clim. Change, 2, 429 432. Raphael, M. N., and M. M. Holland, 2006: Twentieth century simulation of the Robinson, D. A., and A. Frei, 2000: Seasonal variability of northern hemisphere snow Southern Hemisphere climate in coupled models. Part 1: Large scale circulation extent using visible satellite data. Prof. Geograph., 51, 307 314. variability. Clim. Dyn., 26, 217 228. Rockel, B., C. L. Castro, R. A. Pielke, H. von Storch, and G. Leoncini, 2008: Dynamical Rashid, H. A., A. C. Hirst, and M. Dix, 2013: Atmospheric circulation features in downscaling: Assessment of model system dependent retained and added the ACCESS model simulations for CMIP5: Historical simulation and future variability for two different regional climate models. J. Geophys. Res. Atmos., projections Aust. Meteorol. Oceanogr. J., 63, 145 160. 113, D21107. Rauscher, S. A., E. Coppola, C. Piani, and F. Giorgi, 2010: Resolution effects on Rodwell, M., and T. Palmer, 2007: Using numerical weather prediction to assess regional climate model simulations of seasonal precipitation over Europe. Clim. climate models. Q. J. R. Meteorol. Soc., 133, 129 146. Dyn., 35, 685 711. Roe, G. H., and M. B. Baker, 2007: Why is climate sensitivity so unpredictable? Rayner, N. A., et al., 2003: Global analysis of sea surface temperature, sea ice, and Science, 318, 629 632. night marine air temperature since the late ninteeth century. J. Geophys. Res., Roe, G. H., and K. C. Armour, 2011: How sensitive is climate sensitivity? Geophys. 108, 4407. Res. Lett., 38, L14708. Redelsperger, J.-L., C. D. Thorncroft, A. Diedhiou, T. Lebel, D. J. Parker, and J. Polcher, Roe, G. H., and M. B. Baker, 2011: Comment on Another look at climate sensitivity 2006: African Monsoon Multidisciplinary Analysis: An international research by Zaliapin and Ghil (2010). Nonlin.Proc. Geophys., 18, 125 127. project and field campaign. Bull. Am. Meteorol. Soc., 87, 1739 1746. Roeckner, E., et al., 2006: Sensitivity of simulated climate to horizontal and vertical Redi, M. H., 1982: Oceanic isopycnal mixing by coordinate rotation. J. Phys. resolution in the ECHAM5 atmosphere model. J. Clim., 19, 3771 3791. Oceanogr., 12, 1154 1158. Rojas, M., 2006: Multiply nested regional climate simulation for southern South Reichler, T., and J. Kim, 2008: How well do coupled models simulate today s climate? America: Sensitivity to model resolution. Mon. Weather Rev., 134, 2208 2223. Bull. Am. Meteorol. Soc., 89, 303 311. Rojas, M., and P. I. Moreno, 2011: Atmospheric circulation changes and neoglacial Reick, C. H., T. Raddatz, V. Brovkin, and V. Gayler, 2013: The representation of natural conditions in the Southern Hemisphere mid-latitudes: Insights from PMIP2 and anthropogenic land cover change in MPI-ESM. J. Adv. Model. Earth Syst., simulations at 6 kyr. Clim. Dyn., 37, 357 375. doi:10.1002/jame.20022. Rojas, M., et al., 2009: The Southern Westerlies during the last glacial maximum in Reifen, C., and R. Toumi, 2009: Climate projections: Past performance no guarantee PMIP2 simulations. Clim. Dyn., 32, 525 548. of future skill? Geophys. Res. Lett., 36, L13704. Romanou, A., et al., 2013: Natural air sea flux of CO2 in simulations of the NASA- Remer, L. A., et al., 2008: Global aerosol climatology from the MODIS satellite GISS climate model: Sensitivity to the physical ocean model formulation. Ocean sensors. J. Geophys. Res. Atmos., 113, D14s07. Model., doi:10.1016/j.ocemod.2013.01.008. Richter, I., and S.-P. Xie, 2008: On the origin of equatorial Atlantic biases in coupled Rotstayn, L. D., and U. Lohmann, 2002: Simulation of the tropospheric sulfur cycle general circulation models. Clim. Dyn., 31, 587 598. in a global model with a physically based cloud scheme. J. Geophys. Res., 107, Richter, I., S.-P. Xie, S. K. Behera, T. Doi, and Y. Masumoto, 2013: Equatorial 4592. Atlantic variability and its relation to mean state biases in CMIP5. Clim. Dyn., Rotstayn, L. D., M. A. Collier, R. M. Mitchell, Y. Qin, S. K. Campbell, and S. M. Dravitzki, doi:10.1007/s00382-012-1624-5. 2011: Simulated enhancement of ENSO-related rainfall variability due to Richter, J. H., F. Sassi, and R. R. Garcia, 2010: Toward a physically based gravity Australian dust. Atmos. Chem. Phys., 11, 6575 6592. wave source parameterization in a General Circulation Model. J. Atmos. Sci., 67, 136 156. 845 Chapter 9 Evaluation of Climate Models Rotstayn, L. D., S. J. Jeffrey, M. A. Collier, S. M. Dravitzki, A. C. Hirst, J. I. Syktus, and Sansom, P. G., D. B. Stephenson, C. A. T. Ferro, G. Zappa, and L. Shaffrey, 2013: Simple K. K. Wong, 2012: Aerosol- and greenhouse gas-induced changes in summer uncertainty frameworks for selecting weighting schemes and interpreting multi- rainfall and circulation in the Australasian region: A study using single-forcing model ensemble climate change experiments doi:10.1175/JCLI-D-12-00462.1. climate simulations. Atmos. Chem. Phys., 12, 6377 6404. Santer, B., et al., 2009: Incorporating model quality information in climate change Rotstayn, L. D., et al., 2010: Improved simulation of Australian climate and ENSO- detection and attribution studies. Proc. Natl. Acad. Sci. U.S.A., 106, 14778 related rainfall variability in a global climate model with an interactive aerosol 14783. treatment. Int. J. Climatol. , 30, 1067 1088. Santer, B., et al., 2007: Identification of human-induced changes in atmospheric Rougier, J., D. M. H. Sexton, J. M. Murphy, and D. Stainforth, 2009: Analyzing the moisture content. Proc. Natl. Acad. Sci. U.S.A., 104, 15248 15253. climate sensitivity of the HadSM3 climate model using ensembles from different Santer, B., et al., 2008: Consistency of modelled and observed temperature trends in but related experiments. J. Clim., 22, 3540 3557. the tropical troposphere. Int. J. Climatol. , 28, 1703 1722. Roy, P., P. Gachon, and R. Laprise, 2012: Assessment of summer extremes and climate Santer, B., et al., 2005: Amplification of surface temperature trends and variability in variability over the north-east of North America as simulated by the Canadian the tropical atmosphere. Science, 309, 1551 1556. Regional Climate Model. Int. J. Climatol. , 32 1615 1627. Santer, B. D., et al., 2013: Identifying human influences on atmospheric temperature. Ruckstuhl, C., and J. R. Norris, 2009: How do aerosol histories affect solar dimming Proc. Natl. Acad. Sci. U.S.A, 110, 26 33. and brightening over Europe?: IPCC-AR4 models versus observations. J. Sato, M., J. E. Hansen, M. P. McCormick, and J. B. Pollack, 1993: Stratospheric aerosol Geophys. Res. Atmos., 114, D00d04. optical depth, 1850 1990. J. Geophys. Res. Atmos., 98, 22987 22994  Rummukainen, M., 2010: State-of-the-art with regional climate models. Clim. Sato, T., H. Miura, M. Satoh, Y. N. Takayabu, and Y. Q. Wang, 2009: Diurnal cycle of Change, 1, 82 96. precipitation in the Tropics simulated in a global cloud-resolving model. J. Clim., 9 Russell, J. L., R. J. Stouffer, and K. W. Dixon, 2006: Intercomparison of the Southern 22, 4809 4826. Ocean circulations in IPCC coupled model control simulations. J. Clim., 19, Scaife, A. A., N. Butchart, C. D. Warner, and R. Swinbank, 2002: Impact of a spectral 4560 4575. gravity wave parameterization on the stratosphere in the met office unified Ruti, P. M., et al., 2011: The West African climate system: A review of the AMMA model. J. Atmos. Sci., 59, 1473 1489. model inter-comparison initiatives. Atmos. Sci. Lett., 12 116 122 Scaife, A. A., T. Woollings, J. Knight, G. Martin, and T. Hinton, 2010: Atmospheric Rutter, N., et al., 2009: Evaluation of forest snow processes models (SnowMIP2). J. blocking and mean biases in climate models. J. Clim., 23, 6143 6152. Geophys. Res. Atmos., 114, D06111. Scaife, A. A., et al., 2011: Improved Atlantic winter blocking in a climate model. Sabine, C. L., et al., 2004: The oceanic sink for anthropogenic CO2. Science, 305, Geophys. Res. Lett., 38, L23703. 367 371. Scaife, A. A., et al., 2012: Climate change and stratosphere-troposphere interaction. Saha, S., et al., 2010: The NCEP Climate Forecast System Reanalysis. Bull. Am. Clim. Dyn., 38, 2089 2097. Meteorol. Soc., 91, 1015 105. Scaife, A. A., et al., 2009: The CLIVAR C20C project: Selected twentieth century Sahany, S., J. D. Neelin, K. Hales, and R. B. Neale, 2012: Temperature moisture climate events. Clim. Dyn., 33, 603 614. dependence of the Deep Convective Transition as a constraint on entrainment in Schaller, N., I. Mahlstein, J. Cermak, and R. Knutti, 2011: Analyzing precipitation climate models. J. Atmos. Sci., 69, 1340 1358. projections: A comparison of different approaches to climate model evaluation. Saji, N. H., S. P. Xie, and T. Yamagata, 2006: Tropical Indian Ocean variability in the J. Geophys. Res. Atmos., 116, D10118. IPCC twentieth-century climate simulations. J. Clim., 19, 4397 4417. Scherrer, S., 2011: Present-day interannual variability of surface climate in CMIP3 Saji, N. H., B. N. Goswami, P. N. Vinayachandran, and T. Yamagata, 1999: A dipole models and its relation to future warming. Int. J. Climatol. , 31, 1518 1529. mode in the tropical Indian Ocean. Nature, 401, 360 363. Schlesinger, M. E., and J. F. B. Mitchell, 1987: Climate model simulations of the Sakaguchi, K., X. B. Zeng, and M. A. Brunke, 2012: The hindcast skill of the CMIP equilibrium climatic response to increased carbon-dioxide. Rev. Geophys., 25, ensembles for the surface air temperature trend. J. Geophys. Res. Atmos., 117, 760 798. D16113. Schmidli, J., C. Goodess, C. Frei, M. Haylock, Y. Hundecha, J. Ribalaygua, and T. Sakamoto, T. T., et al., 2012: MIROC4h a new high-resolution atmosphere-ocean Schmith, 2007: Statistical and dynamical downscaling of precipitation: An coupled general circulation model. J. Meteorol. Soc. Jpn., 90, 325 359. evaluation and comparison of scenarios for the European Alps. J. Geophys. Res. Salas-Melia, D., 2002: A global coupled sea ice-ocean model. Ocean Model., 4, Atmos., 112, D04105. 137 172 Schmidt, G. A., et al., 2012: Climate forcing reconstructions for use in PMIP Salle, J. B., E. Shuckburgh, N. Bruneau, A. J. S. Meijers, T. J. Bracegirdle, Z. Wang, simulations of the Last Millennium (v1.1). Geophys. Model Dev., 5, 185 191. and T. Roy, 2013: Assessment of Southern Ocean water mass circulation and Schmidt, G. A., et al., 2006: Present day atmospheric simulations using GISS ModelE: characteristics in CMIP5 models: Historical bias and forcing response. J. Geophys. Comparison to in-situ, satellite and reanalysis data. J. Clim., 19, 153 192. Res. Oceans, doi:10.1002/jgrc.20135. Schmith, T., 2008: Stationarity of regression relationships: Application to empirical Samuelsson, P., E. Kourzeneva, and D. Mironov, 2010: The impact of lakes on the downscaling. J. Clim., 21, 4529 4537. European climate as simulated by a regional climate model. Boreal Environ. Res., Schmittner, A., A. Oschlies, X. Giraud, M. Eby, and H. L. Simmons, 2005: A global 15, 113 129. model of the marine ecosystem for long-term simulations: Sensitivity to ocean Sander, S. P., 2006: Chemical Kinetics and Photochemical Data for Use in Atmospheric mixing, buoyancy forcing, particle sinking, and dissolved organic matter cycling. Studies. Evaluation 15. JPL Publications, Pasadena, CA, USA, 523 pp. Global Biogeochem. Cycles, 19, Gb3004. Sanderson, B. M., 2011: A multimodel study of parametric uncertainty in predictions Schott, F. A., S.-P. Xie, and J. P. McCreary, Jr., 2009: Indian Ocean circulation and of climate response to rising greenhouse gas concentrations. J. Clim., 25, 1362 climate variability. Rev. Geophys., 47, RG1002. 1377. Schramm, J. L., M. M. Holland, J. A. Curry, and E. E. Ebert, 1997: Modeling the Sanderson, B. M., 2013: On the estimation of systematic error in regression- thermodynamics of a sea ice thickness 1. Sensitivity to ice thickness resolution. based predictions of climate sensitivity. Clim. Change, doi:10.1007/s10584-012- J. Geophys. Res., 102, 23079 23091. 0671-6. Schultz, M. G., et al., 2008: Global wildland fire emissions from 1960 to 2000. Global Sanderson, B. M., and R. Knutti, 2012: On the interpretation of constrained climate Biogeochem. Cycles, 22, GB2002. model ensembles. Geophys. Res. Lett., 39, L16708. Schurgers, G., U. Mikolajewicz, M. Groger, E. Maier-Reimer, M. Vizcaino, and A. Sanderson, B. M., K. M. Shell, and W. Ingram, 2010: Climate feedbacks determined Winguth, 2008: Long-term effects of biogeophysical and biogeochemical using radiative kernels in a multi-thousand member ensemble of AOGCMs. Clim. interactions between terrestrial biosphere and climate under anthropogenic Dyn., 35, 1219 1236. climate change. Global Planet. Change, 64, 26 37. Sanderson, B. M., C. Piani, W. J. Ingram, D. A. Stone, and M. R. Allen, 2008a: Towards Scoccimarro, E., et al., 2011: Effects of tropical cyclones on ocean heat transport in constraining climate sensitivity by linear analysis of feedback patterns in a high resolution Coupled General Circulation Model. J. Clim., 24, 4368 4384. thousands of perturbed-physics GCM simulations. Clim. Dyn., 30, 175 190. Séférian, R., et al., 2013: Skill assessment of three earth system models with common Sanderson, B. M., et al., 2008b: Constraints on model response to greenhouse gas marine biogeochemistry. Clim. Dyn., 40, 2549 2573. forcing and the role of subgrid-scale processes. J. Clim., 21, 2384 2400. 846 Evaluation of Climate Models Chapter 9 Segui, P., A. Ribes, E. Martin, F. Habets, and J. Boe, 2010: Comparison of three Shin, S. I., D. Sardeshmukh, and K. Pegion, 2010: Realism of local and remote downscaling methods in simulating the impact of climate change on the feedbacks on tropical sea surface temperatures in climate models. J. Geophys. hydrology of Mediterranean basins. J. Hydrol., 383, 111 124. Res. Atmos., 115, D21110. Seidel, D. J., M. Free, and J. S. Wang, 2012: Reexamining the warming in the tropical Shindell, D. T., et al., 2013a: Interactive ozone and methane chemistry in GISS- upper troposphere: Models versus radiosonde observations. Geophys. Res. Lett., E2 historical and future climate simulations Atmos. Chem. Phys., 13, 2653 2689. 39, L22701. Shindell, D. T., et al., 2013b: Radiative forcing in the ACCMIP historical and future Seidel, D. J., Q. Fu, W. J. Randel, and T. J. Reichler, 2008: Widening of the tropical belt climate simulations. Atmos. Chem. Phys., 13, 2939 2974. in a changing climate. Nature Geosci., 1, 21 24. Shiogama, H., S. Emori, N. Hanasaki, M. Abe, Y. Masutomi, K. Takahashi, and T. Seidel, D. J., N. P. Gillett, J. R. Lanzante, K. P. Shine, and P. W. Thorne, 2011: Stratospheric Nozawa, 2011: Observational constraints indicate risk of drying in the Amazon temperature trends: Our evolving understanding. Clim. Change, 2, 592 616. basin. Nature Commun., 2, 253. Selten, F. M., G. W. Branstator, H. A. Dijkstra, and M. Kliphuis, 2004: Tropical origins Shiogama, H., et al., 2012: Perturbed physics ensemble using the MIROC5 coupled for recent and future Northern Hemisphere climate change. Geophys. Res. Lett., atmosphere ocean GCM without flux corrections: Experimental design and 31, L21205. results. Clim. Dyn., 39, 3041 3056. Semenov, V. A., M. Latif, J. H. Jungclaus, and W. Park, 2008: Is the observed NAO Shiu, C.-J., S. C. Liu, C. Fu, A. Dai, and Y. Sun, 2012: How much do precipitation variability during the instrumental record unusual? Geophys. Res. Lett., 35, extremes change in a warming climate? Geophys. Res. Lett., 39, L17707. L11701. Shkol nik, I., V. Meleshko, S. Efimov, and E. Stafeeva, 2012: Changes in climate Seneviratne, S., et al., 2012: Changes in climate extremes and their impacts extremes on the territory of Siberia by the middle of the 21st century: An on the  natural physical environment. In: IPCC WGI/WGII Special Report on ensemble forecast based on the MGO regional climate model. Russ. Meteorol. Managing the Risks of Extreme Events  and Disasters to Advance Climate Hydrol., 37, 71 84. 9 Change Adaptation (SREX), [Field, C.B., V. Barros, T.F. Stocker, D. Qin, D.J. Dokken, Shkolnik, I., V. Meleshko, and V. Kattsov, 2007: The MGO climate model for Siberia. K.L. Ebi, M.D. Mastrandrea, K.J. Mach, G.-K. Plattner, S.K. Allen, M. Tignor, and Russ. Meteorol. Hydrol., 32, 351 359. P.M. Midgley (Eds.)]. Cambridge University Press, The Edinburgh Building, Siebesma, A. P., P. M. M. Soares, and J. Teixeira, 2007: A combined eddy-diffusivity Shaftesbury Road, Cambridge CB2 8RU ENGLAND, pp. 109 230. mass-flux approach for the convective boundary layer. J. Atmos. Sci., 64, 1230 Seneviratne, S. I., D. Luethi, M. Litschi, and C. Schaer, 2006: Land-atmosphere 1248. coupling and climate change in Europe. Nature, 443, 205 209. Sigmond, M., and J. F. Scinocca, 2010: The influence of the basic state on the Northern Seneviratne, S. I., et al., 2010: Investigating soil moisture-climate interactions in a Hemisphere circulation response to climate change. J. Clim., 23, 1434 1446. changing climate: A review. Earth Sci. Rev., 99, 125 161. Sillmann, J., and M. Croci-Maspoli, 2009: Present and future atmospheric blocking Separovic, L., R. De Elia, and R. Laprise, 2008: Reproducible and irreproducible and its impact on European mean and extreme climate. Geophys. Res. Lett., 36, components in ensemble simulations with a Regional Climate Model. Mon. L10702. Weather Rev., 136, 4942 4961. Sillmann, J., M. Croci-Maspoli, M. Kallache, and R. W. Katz, 2011: Extreme cold winter Separovic, L., R. Elía, and R. Laprise, 2012: Impact of spectral nudging and domain temperatures in Europe under the influence of North Atlantic atmospheric size in studies of RCM response to parameter modification. Clim. Dyn., 38, blocking. J. Clim., 24, 5899 5913. 1325 1343. Sillmann, J., V. V. Kharin, X. Zhang, and F. W. Zwiers, 2013: Climate extreme indices Severijns, C. A., and W. Hazeleger, 2010: The efficient global primitive equation in the CMIP5 multi-model ensemble. Part 1: Model evaluation in the present climate model SPEEDO V2.0. Geophys. Model Dev., 3, 105 122. climate. J. Geophys. Res., doi:10.1029/2012JD018390. Sexton, D. M. H., and J. M. Murphy, 2012: Multivariate probabilistic projections using Simmons, A. J., K. M. Willett, P. D. Jones, P. W. Thorne, and D. P. Dee, 2010: Low- imperfect climate models. Part II: robustness of methodological choices and frequency variations in surface atmospheric humidity, temperature, and consequences for climate sensitivity. Clim. Dyn., 38, 2543 2558. precipitation: Inferences from reanalyses and monthly gridded observational Sexton, D. M. H., J. M. Murphy, M. Collins, and M. J. Webb, 2012: Multivariate data sets. J. Geophys. Res. Atmos., 115, D01110. probabilistic projections using imperfect climate models part I: Outline of Simmons, H. L., S. R. Jayne, L. C. St Laurent, and A. J. Weaver, 2004: Tidally driven methodology. Clim. Dyn., 38, 2513 2542. mixing in a numerical model of the ocean general circulation. Ocean Model., Shaffer, G., and J. L. Sarmiento, 1995: Biogeochemical cycling in the global ocean. 6, 245 263. 1. A new, analytical model with continuous vertical resolution and high-latitude Sitch, S., et al., 2003: Evaluation of ecosystem dynamics, plant geography and dynamics. J. Geophys. Res. Oceans, 100, 2659 2672. terrestrial carbon cycling in the LPJ dynamic global vegetation model. Global Shaffer, G., S. M. Olsen, and J. O. P. Pedersen, 2008: Presentation, calibration and Change Biol., 9, 161 185. validation of the low-order, DCESS Earth System Model (Version 1). Geophys. Sitch, S., et al., 2008: Evaluation of the terrestrial carbon cycle, future plant Model Dev., 1, 17 51. geography and climate-carbon cycle feedbacks using five Dynamic Global Shaffrey, L. C., et al., 2009: UK HiGEM: The new UK High-Resolution Global Vegetation Models (DGVMs). Global Change Biol., 14, 2015 2039. Environment Model Model description and basic evaluation. J. Clim., 22, Six, K. D., and E. Maier-Reimer, 1996: Effects of plankton dynamics on seasonal 1861 1896. carbon fluxes in an Ocean General Circulation Model. Global Biogeochem. Sheffield, J., and E. F. Wood, 2008: Projected changes in drought occurrence under Cycles, 10, 559 583. future global warming from multi-model, multi-scenario, IPCC AR4 simulations. Slater, A. G., and D. M. Lawrence, 2013: Diagnosing present and future permafrost Clim. Dyn., 31, 79 105. from climate models. J. Clim., doi:10.1175/JCLI-D-12-00341.1. Shell, K. M., J. T. Kiehl, and C. A. Shields, 2008: Using the radiative kernel technique to Sloyan, B. M., and I. V. Kamenkovich, 2007: Simulation of Subantarctic Mode and calculate climate feedbacks in NCAR s Community Atmospheric Model. J. Clim., Antarctic Intermediate Waters in climate models. J. Clim., 20, 5061 5080. 21, 2269 2282. Smirnov, D., and D. J. Vimont, 2011: Variability of the Atlantic Meridional Mode Shevliakova, E., et al., 2009: Carbon cycling under 300 years of land use change: during the Atlantic Hurricane Season. J. Clim., 24, 1409 1424. Importance of the secondary vegetation sink. Global Biogeochem. Cycles, 23, Smith, B., P. Samuelsson, A. Wramneby, and M. Rummukainen, 2011a: A model GB2022 of the coupled dynamics of climate, vegetation and terrestrial ecosystem Shi, Y., J. Zhang, J. S. Reid, B. Holben, E. J. Hyer, and C. Curtis, 2011: An analysis of the biogeochemistry for regional applications. Tellus A, 63, 87 106. collection 5 MODIS over-ocean aerosol optical depth product for its implication Smith, P. C., N. De Noblet-Ducoudre, P. Ciais, P. Peylin, N. Viovy, Y. Meurdesoif, and in aerosol assimilation. Atmos. Chem. Phys., 11, 557 565. A. Bondeau, 2010a: European-wide simulations of croplands using an improved Shibata, K., and M. Deushi, 2005: Radiative effect of ozone on the quasi-biennial terrestrial biosphere model: Phenology and productivity. J. Geophys. Res. oscillation in the equatorial stratosphere. Geophys. Res. Lett., 32, L24802. Biogeosci., 115, G01014. Shin, D., J. Kim, and H. Park, 2011: Agreement between monthly precipitation Smith, P. C., P. Ciais, P. Peylin, N. De Noblet-Ducoudre, N. Viovy, Y. Meurdesoif, and estimates from TRMM satellite, NCEP reanalysis, and merged gauge-satellite A. Bondeau, 2010b: European-wide simulations of croplands using an improved analysis. J. Geophys. Res. Atmos., 116, D16105. terrestrial biosphere model: 2. Interannual yields and anomalous CO2 fluxes in 2003. J. Geophys. Res. Biogeosci., 115, G04028 847 Chapter 9 Evaluation of Climate Models Smith, R. D., M. E. Maltrud, F. O. Bryan, and M. W. Hecht, 2000: Numerical simulation Stephenson, D. B., M. Collins, J. C. Rougier, and R. E. Chandler, 2012: Statistical of the North Atlantic Ocean at 1/10 degrees. J. Phys. Oceanogr., 30, 1532 1561. problems in the probabilistic prediction of climate change. Environmetrics, 23, Smith, R. S., J. M. Gregory, and A. Osprey, 2008: A description of the FAMOUS 364 372. (version XDBUA) climate model and control run. Geophys. Model Dev., 1, 53 68. Stephenson, D. B., V. Pavan, M. Collins, M. M. Junge, R. Quadrelli, and C. M. G. Smith, S. J., J. van Aardenne, Z. Klimont, R. J. Andres, A. Volke, and S. D. Arias, 2011b: Participating, 2006: North Atlantic Oscillation response to transient greenhouse Anthropogenic sulfur dioxide emissions: 1850 2005. Atmos. Chem. Phys., 11, gas forcing and the impact on European winter climate: A CMIP2 multi-model 1101 1116. assessment. Clim. Dyn., 27, 401 420. Sobolowski, S., and T. Pavelsky, 2012: Evaluation of present and future North Stevens, B., and S. E. Schwartz, 2012: Observing and modeling Earth s energy flows. American Regional Climate Change Assessment Program (NARCCAP) regional Surv. Geophys., 33, 779 816. climate simulations over the southeast United States. J. Geophys. Res. Atmos., Stevens, B., et al., 2012: The atmospheric component of the MPI-M Earth  System 117, D01101. Model: ECHAM6. J. Adv. Model. Earth Syst., doi:10.1002/jame.20015. Soden, B. J., and I. M. Held, 2006: An assessment of climate feedbacks in coupled Stevenson, S., 2012: Significant changes to ENSO strength and impacts ocean-atmosphere models. J. Clim., 19, 3354 3360. in the twenty-first century: Results from CMIP5. Geophys. Res. Lett., Soden, B. J., I. M. Held, R. Colman, K. M. Shell, J. T. Kiehl, and C. A. Shields, 2008: doi:10.1029/2012GL052759. Quantifying climate feedbacks using radiative kernels. J. Clim., 21, 3504 3520. Stevenson, S., B. Fox-Kemper, M. Jochum, R. Neale, C. Deser, and G. Meehl, 2012: Sokolov, A. P., and P. H. Stone, 1998: A flexible climate model for use in integrated Will there be a significant change to El Nino in the twenty-first century? J. Clim., assessments. Clim. Dyn., 14, 291 303. 25, 2129 2145. Sokolov, A. P., C. E. Forest, and P. H. Stone, 2010: Sensitivity of climate change Stocker, B. D., K. Strassmann, and F. Joos, 2011: Sensitivity of Holocene atmospheric 9 projections to uncertainties in the estimates of observed changes in deep-ocean CO2 and the modern carbon budget to early human land use: Analyses with a heat content. Clim. Dyn., 34, 735 745. process-based model. Biogeosciences, 8, 69 88. Sokolov, A. P., et al., 2009: Probabilistic forecast for twenty-first-century climate Stocker, B. D., et al., 2012: Multiple greenhouse gas feedbacks from the land based on uncertainties in emissions (without policy) and climate parameters. J. biosphere under future climate change scenarios. Nature Clim. Change, Clim., 22, 5175 5204. doi:10.1038/nclimate1864. Sokolov, A. P., et al., 2005: The MIT Integrated Global System Model (IGSM) Version 2: Stolarski, R., and S. Frith, 2006: Search for evidence of trend slow-down in the long- Model description and baseline evaluation. MIT JP Report 124. MIT, Cambridge, term TOMS/SBUV total ozone data record: The importance of instrument drift MA. uncertainty. Atmos. Chem. Phys., 6, 4057 4065. Solman, S., and N. Pessacg, 2012: Regional climate simulations over South America: Stoner, A. M. K., K. Hayhoe, and D. J. Wuebbles, 2009: Assessing General Circulation Sensitivity to model physics and to the treatment of lateral boundary conditions Model simulations of atmospheric teleconnection patterns. J. Clim., 22, 4348 using the MM5 model. Clim. Dyn., 38, 281 300. 4372. Solomon, S., P. J. Young, and B. Hassler, 2012: Uncertainties in the evolution of Stott, P. A., and C. E. Forest, 2007: Ensemble climate predictions using climate models stratospheric ozone and implications for recent temperature changes in the and observational constraints. Philos. R. Soc. London A, 365, 2029 2052. tropical lower stratosphere. Geophys. Res. Lett., 39 L17706. Strachan, J., P. L. Vidale, K. Hodges, M. Roberts, and M.-E. Demory, 2013: Investigating Solomon, S., K. H. Rosenlof, R. W. Portmann, J. S. Daniel, S. M. Davis, T. J. Sanford, global tropical cyclone activity with a hierarchy of AGCMs: The role of model and G. K. Plattner, 2010: Contributions of stratospheric water vapor to decadal resolution. J. Clim., 26, 133 152. changes in the rate of global warming. Science, 327, 1219 1223. Strassmann, K. M., F. Joos, and G. Fischer, 2008: Simulating effects of land use Somot, S., F. Sevault, M. Deque, and M. Crepon, 2008: 21st century climate change changes on carbon fluxes: Past contributions to atmospheric CO2 increases scenario for the Mediterranean using a coupled atmosphere-ocean regional and future commitments due to losses of terrestrial sink capacity. Tellus B, 60, climate model. Global Planet. Change, 63 112 126. 583 603. Son, S., et al., 2008: The impact of stratospheric ozone recovery on the Southern Stratton, R. A., and A. J. Stirling, 2012: Improving the diurnal cycle of convection in Hemisphere westerly jet. Science, 320, 1486 1489. GCMs. Q. J. R. Meteorol. Soc., 138, 1121 1134. Son, S., et al., 2010: Impact of stratospheric ozone on Southern Hemisphere Stroeve, J., M. Holland, W. Meier, T. Scambos, and M. Serreze, 2007: Arctic sea ice circulation change: A multimodel assessment. J. Geophys. Res. Atmos., 115, decline: Faster than forecast. Geophys. Res. Lett., 34, L09501. D00M07. Stroeve, J. C., V. Kattsov, A. Barrett, M. Serreze, T. Pavlova, M. Holland, and W. N. Meier, Song, Z., F. Qiao, and Y. Song, 2012: Response of the equatorial basin-wide SST to 2012: Trends   in Arctic sea ice extent from CMIP5, CMIP3 and observations. non-breakingsurface wave-induced mixing in a climate model: An amendment Geophys. Res. Lett., 39, L16502. to tropical bias. J. Geophys. Res., doi:10.1029/2012JC007931. Su, H., and J. H. Jiang, 2012: Tropical clouds and circulation changes during the SPARC-CCMVal, 2010: SPARC Report on the Evaluation of Chemistry-Climate Models 2006 07 and 2009 10 El Ninos. J. Clim., doi:10.1175/JCLI-D-1200152.1. [V. Eyring, T.G. Shepherd, D.W. Waugh (eds.)], SPARC Report No. 5, WCRP-132, Su, H., D. E. Waliser, J. H. Jiang, J. L. Li, W. G. Read, J. W. Waters, and A. M. Tompkins, WMO/TD-No. 1526. 2006: Relationships of upper tropospheric water vapor, clouds and SST: MLS Sperber, K., and D. Kim, 2012: Simplified metrics for the identification of the Madden- observations, ECMWF analyses and GCM simulations. Geophys. Res. Lett., 33, Julian oscillation in models. Atmos. Sci. Let., doi:10.1002/asl.378. L22802 Sperber, K., et al., 2012: The Asian summer monsoon: An intercomparison of CMIP-5 Su, H., et al. , 2012: Diagnosis of regime-dependent cloud simulation errors in CMIP5 vs. CMIP-3 simulations of the late 20th century. Clim. Dyn., doi:10.1007/s00382- models using A-Train satellite observations and reanalysis data. J. Geophys. 012-1607-6. Res., doi:10.1029/2012JD018575. Sperber, K. R., 2003: Propagation and the vertical structure of the Madden-Julian Sudo, K., M. Takahashi, J. Kurokawa, and H. Akimoto, 2002: CHASER: A global oscillation. Mon. Weather Rev., 131, 3018 3037. chemical model of the troposphere - 1. Model description. J. Geophys. Res. Sperber, K. R., and H. Annamalai, 2008: Coupled model simulations of boreal summer Atmos., 107, 4339. intraseasonal (30 50 day) variability, Part 1: Systematic errors and caution on Suh, M., S. Oh, D. Lee, D. Cha, S. Choi, C. Jin, and S. Hong, 2012: Development of new use of metrics. Clim. Dyn., 31, 345 372. ensemble methods based on the performance skills of regional climate models Sperber, K. R., et al., 2010: Monsoon Fact Sheet: CLIVAR Asian-Australian Monsoon over South Korea. J. Clim., 25, 7067 7082. Panel. Sun, D.-Z., Y. Yu, and T. Zhang, 2009: Tropical water vapor and cloud feedbacks in Stainforth, D. A., et al., 2005: Uncertainty in predictions of the climate response to climate models: A further assessment using coupled simulations. J. Clim., 22, rising levels of greenhouse gases. Nature, 433, 403 406. 1287 1304. Stephens, G. L., and C. D. Kummerow, 2007: The remote sensing of clouds and Sutton, R. T., B. W. Dong, and J. M. Gregory, 2007: Land/sea warming ratio in response precipitation from space: A review. J. Atmos. Sci., 64, 3742 3765. to climate change: IPCC AR4 model results and comparison with observations. Stephens, G. L., et al., 2010: Dreary state of precipitation in global models. J. Geophys. Geophys. Res. Lett., 34, L02701 Res., 115, D24211. Svensson, G., and A. Holtslag, 2009: Analysis of model results for the turning of the Stephens, G. L., et al., 2012: An Update on the Earth s energy balance in light of new wind and related momentum fluxes in the stable boundary layer. Boundary- global observations. Nature Geosci., 5, 691 696. Layer Meteorol., 132, 261 277. 848 Evaluation of Climate Models Chapter 9 Svensson, G., et al., 2011: Evaluation of the diurnal cycle in the atmospheric Timmermann, A., S. Lorenz, S.-I. An, A. Clement, and S.-P. Xie, 2007: The effect of boundary layer over land as represented by a variety of single-column models: orbital forcing on the mean climate and variability of the tropical Pacific. J. Clim., The Second GABLS Experiment. Boundary-Layer Meteorol., 140, 177 206. 20, 4147 4159. Swart, N. C., and J. C. Fyfe, 2012a: Ocean carbon uptake and storage influenced by Timmermann, R., H. Goosse, G. Madec, T. Fichefet, C. Ethe, and V. Duliere, 2005: On wind bias in global climate models. Nature Clim. Change, 2, 47 52. the representation of high latitude processes in the ORCA-LIM global coupled Swart, N. C., and J. C. Fyfe, 2012b: Observed and simulated changes in the sea ice-ocean model. Ocean Model., 8, 175 201. Southern Hemisphere surface westerly wind-stress. Geophys. Res. Lett., Ting, M., Y. Kushnir, R. Seager, and C. Li, 2009: Forced and internal twentieth-century doi:10.1029/2012GL052810. SST trends in the north Atlantic. J. Clim., 22, 1469 1481. Tachiiri, K., J. C. Hargreaves, J. D. Annan, A. Oka, A. Abe-Ouchi, and M. Kawamiya, Tjernstrom, M., J. Sedlar, and M. Shupe, 2008: How well do regional climate 2010: Development of a system emulating the global carbon cycle in Earth models reproduce radiation and clouds in the Arctic? An evaluation of ARCMIP system models. Geophys. Model Dev., 3, 365 376. simulations. J. Appl. Meteorol. Climatol., 47, 2405 2422. Takahashi, T., et al., 2009: Climatological mean and decadal change in surface ocean Tjiputra, J. F., K. Assmann, M. Bentsen, I. Bethke, O. H. Ottera, C. Sturm, and C. Heinze, pCO(2), and net sea-air CO2 flux over the global oceans. Deep-Sea Res. Pt., 56, 2010: Bergen Earth system model (BCM-C): Model description and regional 554 577. climate-carbon cycle feedbacks assessment. Geophys. Model Dev., 3, 123 141. Takata, K., S. Emori, and T. Watanabe, 2003: Development of the minimal advanced Tjiputra, J. F., et al., 2013: Evaluation of the carbon cycle components inthe treatments of surface interaction and runoff. Global Planet. Change, 38, 209 Norwegian Earth System Model (NorESM). Geophys. Model Dev., 6, 301 325. 222. Todd-Brown, K. E. O., J. T. Randerson, W. M. Post, F. M. Hoffman, C. Tarnocai, E. A. G. Takemura, T., T. Nakajima, O. Dubovik, B. N. Holben, and S. Kinne, 2002: Single- Schuur, and S. D. Allison, 2013: Causes of variation in soil carbon simulations from scattering albedo and radiative forcing of various aerosol species with a global CMIP5 Earth system models and comparison with observations. Biogeosciences, 9 three-dimensional model. J. Clim., 15, 333 352. 10, 1717 1736. Takemura, T., T. Nozawa, S. Emori, T. Y. Nakajima, and T. Nakajima, 2005: Simulation Toniazzo, T., and S. Woolnough, 2013: Development of warm SST errors in the of climate response to aerosol direct and indirect effects with aerosol transport- southern tropical Atlantic in CMIP5 decadal hindcasts. Clim. Dyn., doi:10.1007/ radiation model. J. Geophys. Res. Atmos., 110, D02202. s00382-013-1691-2. Takemura, T., H. Okamoto, Y. Maruyama, A. Numaguti, A. Higurashi, and T. Nakajima, Tory, K., S. Chand, R. Dare, and J. McBride, 2013: An assessment of a model-, grid- 2000: Global three-dimensional simulation of aerosol optical thickness and basin-independent tropical cyclone detection scheme in selected CMIP3 distribution of various origins. J. Geophys. Res. Atmos., 105, 17853 17873. global climate models. J. Clim., doi:10.1175/JCLI-D-12-00511.1. Takemura, T., M. Egashira, K. Matsuzawa, H. Ichijo, R. O Ishi, and A. Abe-Ouchi, 2009: Trenberth, K. E., and J. M. Caron, 2001: Estimates of meridional atmosphere and A simulation of the global distribution and radiative forcing of soil dust aerosols ocean heat transports. J. Clim., 14, 3433 3443. at the Last Glacial Maximum. Atmos. Chem. Phys., 9, 3061 3073. Trenberth, K. E., and J. T. Fasullo, 2008: An observational estimate of inferred ocean Takle, E. S., et al., 2007: Transferability intercomparison An opportunity for new energy divergence. J. Phys. Oceanogr., 38, 984 999. insight on the global water cycle and energy budget. Bull. Am. Meteorol. Soc., Trenberth, K. E., and J. T. Fasullo, 2009: Global warming due to increasing absorbed 88, 375 384. solar radiation. Geophys. Res. Lett., 36, L07706. Taylor, C. M., R. A. M. de Jeu, F. Guichard, P. P. Harris, and W. A. Dorigo, 2012a: Trenberth, K. E., and J. T. Fasullo, 2010a: Climate change: Tracking Earth s energy. Afternoon rain more likely over drier soils. Nature, 489, 423 426. Science, 328, 316 317. Taylor, C. M., A. Gounou, F. Guichard, P. P. Harris, R. J. Ellis, F. Couvreux, and M. De Trenberth, K. E., and J. T. Fasullo, 2010b: Simulation of present-day and twenty-first- Kauwe, 2011: Frequency of Sahelian storm initiation enhanced over mesoscale century energy budgets of the Southern Oceans. J. Clim., 23, 440 454. soil-moisture patterns. Nature Geosci., 4, 430 433. Trenberth, K. E., D. P. Stepaniak, and J. M. Caron, 2000: The global monsoon as seen Taylor, K. E., R. J. Stouffer, and G. A. Meehl, 2012b: An overview of CMIP5 and the through the divergent atmospheric circulation. J. Clim., 13, 3969 3993. experiment design. Bull. Am. Meteorol. Soc., 93, 485 498. Trenberth, K. E., J. T. Fasullo, and J. Kiehl, 2009: Earth s global energy budget. Bull. Tebaldi, C., and R. Knutti, 2007: The use of the multi-model ensemble in probabilistic Am. Meteorol. Soc., 90, 311 323. climate projections. Philos. Trans. R. Soc. London A, 365 2053 2075. Tryhorn, L., and A. DeGaetano, 2011: A comparison of techniques for downscaling Teixeira, J., et al., 2008: Parameterization of the atmospheric boundary layer. Bull. extreme precipitation over the northeastern United States. Int. J. Climatol. , 31, Am. Meteorol. Soc., 89, 453 458. 1975 1989. Teixeira, J., et al., 2011: Tropical and subtropical cloud transitions in weather Tschumi, T., F. Joos, and P. Parekh, 2008: How important are Southern Hemisphere and climate prediction models: The GCSS/WGNE Pacific Cross-Section wind changes for low glacial carbon dioxide? A model study. Paleoceanography, Intercomparison (GPCI). J. Clim., 24, 5223 5256. 23, PA4208. Terray, L., 2012: Evidence for multiple drivers of North Atlantic multi-decadal climate Tschumi, T., F. Joos, M. Gehlen, and C. Heinze, 2011: Deep ocean ventilation, carbon variability. Geophys. Res. Lett., 39, L19712. isotopes, marine sedimentation and the deglacial CO(2) rise. Clim. Past, 7, Terray, L., L. Corre, S. Cravatte, T. Delcroix, G. Reverdin, and A. Ribes, 2012: Near- 771 800. surface salinity as nature s rain gauge to detect human influence on the tropical Tsigaridis, K., and M. Kanakidou, 2007: Secondary organic aerosol importance in the water cycle. J. Clim., 25, 958 977. future atmosphere. Atmos. Environ., 41, 4682 4692. Teutschbein, C., F. Wetterhall, and J. Seibert, 2011: Evaluation of different Tsujino, H., M. Hirabara, H. Nakano, T. Yasuda, T. Motoi, and G. Yamanaka, downscaling techniques for hydrological climate-change impact studies at the 2011: Simulating present climate of the global ocean ice system using the catchment scale. Clim. Dyn., 37, 2087 2105. Meteorological Research Institute Community Ocean Model (MRI. COM): Textor, C., et al., 2007: The effect of harmonized emissions on aerosol properties in Simulation characteristics and variability in the Pacific sector. J. Oceanogr., 67, global models an AeroCom experiment. Atmos. Chem. Phys., 7, 4489 4501. 449 479. Thorndike, A. S., D. A. Rothrock, G. A. Maykut, and R. Colony, 1975: Thickness Tsushima, Y., M. Ringer, M. Webb, and K. Williams, 2013: Quantitative evaluation of distribution of sea ice J. Geophys. Res. Oceans Atmos., 80, 4501 4513. the seasonal variations in climate model cloud regimes. Clim. Dyn., doi:10.1007/ Thorne, P. W., et al., 2011: A quantification of uncertainties in historical tropical s00382-012-1609-4. tropospheric temperature trends from radiosondes. J. Geophys. Res., 116, Turner, A. G., and H. Annamalai, 2012: Climate change and the south Asian summer D12116. monsoon. Nature Clim. Change, 2, 1 9. Thornton, P. E., J. F. Lamarque, N. A. Rosenbloom, and N. M. Mahowald, 2007: Turner, A. G., P. M. Inness, and J. M. Slingo, 2007: The effect of doubled CO2 and Influence of carbon-nitrogen cycle coupling on land model response to CO2 model basic state biases on the monsoon-ENSO system. II: Changing ENSO fertilization and climate variability. Global Biogeochem. Cycles, 21, GB4018. regimes. Q. J. R. Meteorol. Soc., 133, 1159 1173. Tian, B., E. J. Fetzer, B. H. Kahn, J. Teixeira, E. Manning, and T. Hearty, 2013: Evaluating Ulbrich, U., G. C. Leckebusch, and J. G. Pinto, 2009: Extra-tropical cyclones in the CMIP5 models using AIRS tropospheric air temperature and specific humidity present and future climate: a review. Theor. Appl. Climatol., 96, 117 131. climatology. J. Geophys. Res. Atmos., 118, 114 134. Ulbrich, U., J. G. Pinto, H. Kupfer, G. C. Leckebusch, T. Spangehl, and M. Reyers, 2008: Timbal, B., and D. Jones, 2008: Future projections of winter rainfall in southeast Changing northern hemisphere storm tracks in an ensemble of IPCC climate Australia using a statistical downscaling technique. Clim. Change, 86, 165 187. change simulations. J. Clim., 21, 1669 1679. 849 Chapter 9 Evaluation of Climate Models UNESCO, 1981: Tenth report of the joint panel on oceanographic tables and Volodin, E. M., 2007: Atmosphere-ocean general circulation model with the carbon standards UNESCO. cycle. Izvestiya Atmos. Ocean. Phys., 43, 298 313. Uotila, P., S. O Farrell, S. J. Marsland, and D. Bi, 2012: A sea-ice sensitivity study with Volodin, E. M., 2008a: Relation between temperature sensitivity to doubled carbon a global ocean-ice model. Ocean Model., 51, 1 18. dioxide and the distribution of clouds in current climate models. Izvestiya Atmos. Uotila, P., S. O Farrell, S. J. Marsland, and D. Bi, 2013: The sea-ice performance of the Ocean. Phys., 44, 288 299. Australian climate models participating in the CMIP5. Aust. Meteorol. Oceanogr. Volodin, E. M, 2008b: Methane cycle in the INM RAS climate model. Izvestiya Atmos. J., 63, 121 143. Ocean. Phys., 44, 153 159. Uppala, S. M., et al., 2005: The ERA-40 re-analysis. Q. J. R. Meteorol. Soc., 131, Volodin, E. M., and V. N. Lykosov, 1998: Parametrization of heat and moisture transfer 2961 3012. in the soil-vegetation system for use in atmospheric general circulation models: van den Hurk, B., and E. van Meijgaard, 2010: Diagnosing land-atmosphere 1. Formulation and simulations based on local observational data. Izvestiya interaction from a regional climate model simulation over West Africa. J. Akad. Nauk Fizik. Atmosf. Okean., 34, 453 465. Hydrometeorol., 11, 467 481. Volodin, E. M., N. A. Dianskii, and A. V. Gusev, 2010: Simulating present-day climate van Oldenborgh, G., et al., 2009: Western Europe is warming much faster than with the INMCM4.0 coupled model of the atmospheric and oceanic general expected. Clim. Past, 5, 1 12. circulations. Izvestiya Atmos. Ocean. Phys., 46, 414 431. van Oldenborgh, G. J., S. Y. Philip, and M. Collins, 2005: El Nino in a changing climate: von Salzen, K., et al., 2013: The Canadian Fourth Generation Atmospheric Global A multi-model study. Ocean Sci., 1, 81 95. Climate Model (CanAM4). Part I: Representation of physical processes. Atmos. van Roosmalen, L., J. H. Christensen, M. B. Butts, K. H. Jensen, and J. C. Refsgaard, Ocean, 51, 104 125. 2010: An intercomparison of regional climate model data for hydrological Vose, R. S., et al., 2012: NOAA S merged land-ocean surface temperature analysis. 9 impact studies in Denmark. J. Hydrol., 380, 406 419. Bull. Am. Meteorol. Soc., 93, 1677 1685. van Vliet, M., S. Blenkinsop, A. Burton, C. Harpham, H. Broers, and H. Fowler, 2011: A Vrac, M., and P. Naveau, 2008: Stochastic downscaling of precipitation: From dry multi-model ensemble of downscaled spatial climate change scenarios for the events to heavy rainfall Water Resour. Res., 43, W07402. Dommel catchment, Western Europe. Clim. Change, 111, 249 277. Waelbroeck, C., et al., 2009: Constraints on the magnitude and patterns of ocean van Vuuren, D., et al., 2011: The representative concentration pathways: An overview. cooling at the Last Glacial Maximum. Nature Geosci., 2, 127 132. Clim. Change, 109, 5 31. Wahl, S., M. Latif, W. Park, and N. Keenlyside, 2011: On the Tropical Atlantic SST warm Vancoppenolle, M., T. Fichefet, H. Goosse, S. Bouillon, G. Madec, and M. A. M. bias in the Kiel Climate Model. Clim. Dyn., 36, 891 906. Maqueda, 2009: Simulating the mass balance and salinity of Arctic and Antarctic Waliser, D., K. W. Seo, S. Schubert, and E. Njoku, 2007: Global water cycle agreement sea ice. 1. Model description and validation. Ocean Model., 27, 33 53. in the climate models assessed in the IPCC AR4. Geophys. Res. Lett., 34, L16705 Vancoppenolle, M., H. Goosse, A. de Montety, T. Fichefet, B. Tremblay, and J. L. Tison, Waliser, D., et al., 2009a: MJO simulation diagnostics. J. Clim., 22, 3006 3030. 2010: Interactions between brine motion, nutrients and primary production in Waliser, D. E., J. L. F. Li, T. S. L Ecuyer, and W. T. Chen, 2011: The impact of precipitating sea ice. J. Geophys. Res., 115, C02005. ice and snow on the radiation balance in global climate models. Geophys. Res. Vanniere, B., E. Guilyardi, G. Madec, F. J. Doblas-Reyes, and S. Woolnough, 2011: Lett., 38, L06802. Using seasonal hindcasts to understand the origin of the equatorial cold tongue Waliser, D. E., et al., 2003: AGCM simulations of intraseasonal variability associated bias in CGCMs and its impact on ENSO. Clim. Dyn., 40, 963 981. with the Asian summer monsoon. Clim. Dyn., 21, 423 446. Vautard, R., et al., 2013: The simulation of European heat waves from an ensemble Waliser, D. E., et al., 2009b: Cloud ice: A climate model challenge with signs and of regional climate models within the EURO-CORDEX project. Clim. Dyn., expectations of progress. J. Geophys. Res., 114, D00A21. doi:10.1007/s00382-013-1714-z. Walsh, K., S. Lavender, E. Scoccimarro, and H. Murakami, 2013: Resolution Vecchi, G. A., and B. J. Soden, 2007: Global warming and the weakening of the dependence of tropical cyclone formation in CMIP3 and finer resolution models. tropical circulation. J. Clim., 20, 4316 4340. Clim. Dyn., 40, 585 599. Vecchi, G. A., K. L. Swanson, and B. J. Soden, 2008: Climate Change: Whither hurricane Walther, A., J.-H. Jeong, G. Nikulin, C. Jones, and D. Chen, 2013: Evaluation of the activity? Science, 322, 687 689. warm season diurnal cycle of precipitation over Sweden simulated by the Rossby Vecchi, G. A., B. J. Soden, A. T. Wittenberg, I. M. Held, A. Leetmaa, and M. J. Harrison, Centre regional climate model RCA3. Atmos. Res., 119, 131 139. 2006: Weakening of tropical Pacific atmospheric circulation due to anthropogenic Wang, B., 2006: The Asian Monsoon. Springer Science+Business Media, Praxis, New forcing. Nature, 327, 216 -219. York, NY, USA, 787 pp. Veljovic, K., B. Rajkovic, M. J. Fennessy, E. L. Altshuler, and F. Mesinger, 2010: Regional Wang, B., and Q. H. Ding, 2008: Global monsoon: Dominant mode of annual variation climate modeling: Should one attempt improving on the large scales? Lateral in the tropics. Dyn. Atmos. Oceans, 44, 165 183. boundary condition scheme: Any impact? Meteorol. Z., 19, 237 246. Wang, B., H. J. Kim, K. Kikuchi, and A. Kitoh, 2011a: Diagnostic metrics for evaluation Verlinde, J., et al., 2007: The Mixed-Phase Arctic Cloud Experiment. Bull. Am. of annual and diurnal cycles. Clim. Dyn., 37, 941 955. Meteorol. Soc., 88, 205 221. Wang, B., H. Wan, Z. Z. Ji, X. Zhang, R. C. Yu, Y. Q. Yu, and H. T. Liu, 2004: Design of Verseghy, D. L., 2000: The Canadian Land Surface Scheme (CLASS): Its history and a new dynamical core for global atmospheric models based on some efficient future. Atmos. Ocean, 38, 1 13. numerical methods. Sci. China A, 47, 4 21. Vial, J., and T. J. Osborn, 2012: Assessment of atmosphere-ocean general circulation Wang, C., and J. Picaut, 2004: Understanding ENSO physics A review. In: Earth s model simulations of winter northern hemisphere atmospheric blocking. Clim. Climate: The Ocean-Atmosphere Interaction [C. Wang, S.-P. Xie and J.A. Carton Dyn., 39, 95 112. (eds.)]. American Geophysical Union, Washington, DC, pp. 21 48. Vial, J., J.-L. Dufresne, and S. Bony, 2013: On the interpretation of inter-model spread Wang, H., and W. Su, 2013: Evaluating and understanding top of the atmosphere in CMIP5 climate sensitivity estimates. Clim. Dyn., doi:10.1007/s00382-013- cloud radiative effects. In Intergovernmental Panel on Climate Change (IPCC) 1725-9. Fifth Assessment Report (AR5) Coupled Model Intercomparison Project Phase Vichi, M., S. Masina, and A. Navarra, 2007: A generalized model of pelagic 5 (CMIP5) models using satellite observations. J. Geophys. Res. Atmos., 118, biogeochemistry for the global ocean ecosystem. Part II: Numerical simulations. 683 699. J. Mar. Syst., 64, 110 134. Wang, J., Q. Bao, N. Zeng, Y. Liu, G. Wu, and D. Ji, 2013: The Earth System Model Vichi, M., et al., 2011: Global and regional ocean carbon uptake and climate change: FGOALS-s2: Coupling a dynamic global vegetation and terrestrial carbon model Sensitivity to a substantial mitigation scenario. Clim. Dyn., 37, 1929 1947. with the physical climate. Adv. Atmos. Sci., doi:10.1007/s00376 013-2169-1. Vizcaino, M., U. Mikolajewicz, M. Groger, E. Maier-Reimer, G. Schurgers, and A. M. E. Wang, J. F., and X. B. Zhang, 2008: Downscaling and projection of winter extreme Winguth, 2008: Long-term ice sheet-climate interactions under anthropogenic daily precipitation over North America. J. Clim., 21, 923 937. greenhouse forcing simulated with a complex Earth System Model. Clim. Dyn., Wang, M., and J. E. Overland, 2012: A sea ice free summer Arctic within 30 years: An 31, 665 690. update from CMIP5 models. Geophys. Res. Lett., 39, L18501. Voldoire, A., et al., 2013: The CNRM-CM5.1 global climate model : Description and Wang, M., J. E. Overland, and N. A. Bond, 2010: Climate projections for selected large basic evaluation. Clim. Dyn., 40, 2091 2121. marine ecosystems. J. Mar. Syst., 79, 258 266. 850 Evaluation of Climate Models Chapter 9 Wang, Y., M. Notaro, Z. Liu, R. Gallimore, S. Levis, and J. E. Kutzbach, 2008: Detecting Wilks, D. S., 1995: Statistical Methods in the Atmospheric Sciences. Vol. 59, Academic vegetation-precipitation feedbacks in mid-Holocene North Africa from two Press, San Diego, CA, USA, 467 pp. climate models. Clim. Past, 4, 59 67. Wilks, D. S., 2006: Statistical Methods in the Atmospheric Sciences. Vol. 91, Academic Wang, Y. P., and B. Z. Houlton, 2009: Nitrogen constraints on terrestrial carbon Press, Elsevier, San Diego, CA, USA, 627 pp. uptake: Implications for the global carbon-climate feedback. Geophys. Res. Lett., Willett, K., P. Jones, P. Thorne, and N. Gillett, 2010: A comparison of large scale 36, L24403. changes in surface humidity over land in observations and CMIP3 general Wang, Y. P., et al., 2011b: Diagnosing errors in a land surface model (CABLE) in the circulation models. Environ. Res. Lett., 5, 025210. time and frequency domains. J. Geophys. Res. Biogeosci., 116, G01034. Williams, C. J. R., R. P. Allan, and D. R. Kniveton, 2012: Diagnosing atmosphere-land Wania, R., I. Ross, and I. C. Prentice, 2009: Integrating peatlands and permafrost into feedbacks in CMIP5 climate models. Environ. Res. Lett., 7, 044003. a dynamic global vegetation model: 1. Evaluation and sensitivity of physical land Williams, K., and M. Webb, 2009: A quantitative performance assessment of cloud surface processes. Global Biogeochem. Cycles, 23, Gb3014. regimes in climate models. Clim. Dyn., 33 141 157. Watanabe, M., 2008: Two regimes of the equatorial warm pool. Part I: A simple Williams, K. D., and M. E. Brooks, 2008: Initial tendencies of cloud regimes in the Met tropical climate model. J. Clim., 21, 3533 3544. Office unified model. J. Clim., 21, 833 840. Watanabe, M., M. Chikira, Y. Imada, and M. Kimoto, 2011: Convective control of Williams, K. D., W. J. Ingram, and J. M. Gregory, 2008: Time variation of effective ENSO simulated in MIROC. J. Clim., 24, 543 562. climate sensitivity in GCMs. J. Clim., 21, 5076 5090. Watanabe, M., J. S. Kug, F. F. Jin, M. Collins, M. Ohba, and A. T. Wittenberg, 2012: Williamson, D. L., and J. G. Olson, 2007: A comparison of forecast errors in CAM2 and Uncertainty in the ENSO amplitude change from the past to the future. Geophys. CAM3 at the ARM Southern Great Plains site. J. Clim., 20, 4572 4585. Res. Lett., 39, L20703. Williamson, M. S., T. M. Lenton, J. G. Shepherd, and N. R. Edwards, 2006: An efficient Watanabe, M., et al., 2010: Improved climate simulation by MIROC5: Mean states, numerical terrestrial scheme (ENTS) for Earth system modelling. Ecol. Model., 9 variability, and climate sensitivity. J. Clim., 23, 6312 6335. 198, 362 374. Watanabe, S., Y. Kawatani, Y. Tomikawa, K. Miyazaki, M. Takahashi, and K. Sato, 2008: Willis, J. K., 2010: Can in situ floats and satellite altimeters detect long-term changes General aspects of a T213L256 middle atmosphere general circulation model. J. in Atlantic Ocean overturning? Geophys. Res. Lett., 37, L06602. Geophys. Res. Atmos., 113, D12110. Winterfeldt, J., and R. Weisse, 2009: Assessment of value added for surface marine Watterson, I., and P. Whetton, 2011: Distributions of decadal means of temperature wind speed obtained from two regional climate models. Mon. Weather Rev., and precipitation change under global warming. J. Geophys. Res. Atmos., 116, 137, 2955 2965. D07101. Winterfeldt, J., B. Geyer, and R. Weisse, 2011: Using QuikSCAT in the added value Waugh, D., and V. Eyring, 2008: Quantitative performance metrics for stratospheric- assessment of dynamically downscaled wind speed. Int. J. Climatol. , 31, 1028 resolving chemistry-climate models. Atmos. Chem. Phys., 8, 5699 5713. 1039. Weaver, A. J., et al., 2001: The UVic Earth System Climate Model: Model description, Winton, M., 2000: A reformulated three-layer sea ice model. J. Atmos. Ocean. climatology, and applications to past, present and future climates. Atmos. Technol., 17, 525 531. Ocean, 39, 361 428. Winton, M., 2011: Do climate models underestimate the sensitivity of Northern Weaver, A. J., et al., 2012: Stability of the Atlantic meridional overturning circulation: Hemisphere sea ice cover? J. Clim., 24, 3924 3934. A model intercomparison. Geophys. Res. Lett., 39, L20709. Wittenberg, A. T., 2009: Are historical records sufficient to constrain ENSO Webb, M., C. Senior, S. Bony, and J. J. Morcrette, 2001: Combining ERBE and ISCCP simulations? Geophys. Res. Lett., 36, L12702. data to assess clouds in the Hadley Centre, ECMWF and LMD atmospheric Wittenberg, A. T., A. Rosati, N. C. Lau, and J. J. Ploshay, 2006: GFDL s CM2 Global climate models. Clim. Dyn., 17, 905 922. Coupled Climate Models. Part III: Tropical Pacific climate and ENSO. J. Clim., 19, Weber, S. L., et al., 2007: The modern and glacial overturning circulation in the 698 722. Atlantic Ocean in PMIP coupled model simulations. Clim. Past, 3, 51 64. WMO, 2011: Scientific Assessment of Ozone Depletion: 2010. Global Ozone Webster, P. J., A. M. Moore, J. P. Loschnigg, and R. R. Leben, 1999: Coupled ocean- Research and Monitoring Project Report. World Meteorological Organisation, atmosphere dynamics in the Indian Ocean during 1997 98. Nature, 401, 356 Geneva, Switzerland. 360. Wohlfahrt, J., et al., 2008: Evaluation of coupled ocean-atmosphere simulations of Wehner, M. F., R. L. Smith, G. Bala, and P. Duffy, 2010: The effect of horizontal the mid-Holocene using palaeovegetation data from the northern hemisphere resolution on simulation of very extreme US precipitation events in a global extratropics. Clim. Dyn., 31, 871 890. atmosphere model. Clim. Dyn., 34, 241 247. Wood, R., et al., 2011: The VAMOS Ocean-Cloud-Atmosphere-Land Study Regional Weller, E., and W. Cai, 2013a: Realism of the Indian Ocean Dipole in CMIP5 Experiment (VOCALS-REx): Goals, platforms, and field operations. Atmos. Chem. models: The implication for 1 climate projections. J. Clim., doi:10.1175/JCLI-D- Phys., 11, 627 654. 12-00807.1. Woollings, T., B. Hoskins, M. Blackburn, D. Hassell, and K. Hodges, 2010a: Storm Weller, E., and W. Cai, 2013b: Asymmetry in the IOD and ENSO teleconnection track sensitivity to sea surface temperature resolution in a regional atmosphere in a CMIP5 model ensemble and its relevance to regional rainfall. J. Clim., model. Clim. Dyn., 35, 341 353. doi:10.1175/JCLI-D-12-00789.1. Woollings, T., A. Charlton-Perez, S. Ineson, A. G. Marshall, and G. Masato, 2010b: Wentz, F. J., L. Ricciardulli, K. Hilburn, and C. Mears, 2007: How much more rain will Associations between stratospheric variability and tropospheric blocking. J. global warming bring? Science, 317, 233 235. Geophys. Res. Atmos., 115, D06108. Westerling, A. L., H. G. Hidalgo, D. R. Cayan, and T. W. Swetnam, 2006: Warming Woollings, T., J. M. Gregory, J. G. Pinto, M. Reyers, and D. J. Brayshaw, 2012: Response and earlier spring increase western US forest wildfire activity. Science, 313, of the North Atlantic storm track to climate change shaped by ocean-atmosphere 940 943. coupling. Nature Geosci., 5, 313 317. Wetzel, P., E. Maier-Reimer, M. Botzet, J. H. Jungclaus, N. Keenlyside, and M. Latif, Wright, D. G., and T. F. Stocker, 1992: Sensitivities of a zonally averaged Global Ocean 2006: Effects of ocean biology on the penetrative radiation in a Coupled Climate Circulation Model.. J. Geophys. Res. Oceans, 97, 12707 12730. Model. J. Clim., 19, 3973 3987. Wu, Q. G., D. J. Karoly, and G. R. North, 2008a: Role of water vapor feedback on the Whetton, P., I. Macadam, J. Bathols, and J. O Grady, 2007: Assessment of the use of amplitude of season cycle in the global mean surface air temperature. Geophys. current climate patterns to evaluate regional enhanced greenhouse response Res. Lett., 35, L08711. patterns of climate models. Geophys. Res. Lett., 34, L14701. Wu, T., 2012: A mass-flux cumulus parameterization scheme for large-scale models: White, C. J., et al., 2013: On regional dynamical downscaling for the assessment Description and test with observations. Clim. Dyn., 38, 725 744. and projection of temperature and precipitation extremes across Tasmania, Wu, T., R. Yu, and F. Zhang, 2008b: A modified dynamic framework for the atmospheric Australia. Clim. Dyn., doi:10.1007/s00382-013-1718-8. spectral model and its application. J. Atmos. Sci., 65, 2235 2253. Wilcox, L. J., A. J. Charlton-Perez, and L. J. Gray, 2012: Trends in Austral jet position in Wu, T., et al., 2010a: Erratum The Beijing Climate Center atmospheric general ensembles of high- and low-top CMIP5 models. J. Geophys. Res., 117, D13115. circulation model: Description and its performance for the present-day climate. Wild, M., C. N. Long, and A. Ohmura, 2006: Evaluation of clear-sky solar fluxes Clim. Dyn., 34, 149 150. in GCMs participating in AMIP and IPCC-AR4 from a surface perspective. J. Geophys. Res. Atmos., 111, D01104 851 Chapter 9 Evaluation of Climate Models Wu, T., et al., 2010b: The Beijing Climate Center atmospheric general circulation Yokohata, T., J. Annan, M. Collins, C. Jackson, M. Tobis, M. Webb, and J. Hargreaves, model: Description and its performance for the present-day climate. Clim. Dyn., 2012: Reliability of multi-model and structurally different single-model 34, 123 147. ensembles. Clim. Dyn., 39, 599 616. Wyant, M. C., C. S. Bretherton, and P. N. Blossey, 2009: Subtropical low cloud Yokohata, T., et al., 2013: Reliability and importance of structural diversity of climate response to a warmer climate in a superparameterized climate model. Part I: model ensembles. Clim. Dyn., doi:10.1007/s00382-013-1733 9. Regime sorting and physical mechanisms. J. Adv. Model. Earth Syst., 1, 7. Yokohata, T., et al., 2008: Comparison of equilibrium and transient responses to CO2 Wyser, K., et al., 2008: An evaluation of Arctic cloud and radiation processes during increase in eight state-of-the-art climate models. Tellus A, 60, 946 961. the SHEBA year: Simulation results from eight Arctic regional climate models. Yokoi, S., Y. N. Takayabu, and J. C. L. Chan, 2009a: Tropical cyclone genesis frequency Clim. Dyn., 30, 203 223. over the western North Pacific simulated in medium-resolution coupled general Xavier, P. K., 2012: Intraseasonal convective moistening in CMIP3 models. J. Clim., circulation models. Clim. Dyn., 33, 665 683. 25, 2569 2577. Yokoi, S., C. Takahashi, K. Yasunaga, and R. Shirooka, 2012: Multi-model projection of Xavier, P. K., J. P. Duvel, P. Braconnot, and F. J. Doblas-Reyes, 2010: An evaluation tropical cyclone genesis frequency over the western North Pacific: CMIP5 results. metric for intraseasonal variability and its application to CMIP3 twentieth- Sola, 8, 137 140. century simulations. J. Clim., 23, 3497 3508. Yokoi, S., et al., 2011: Application of cluster analysis to climate model performance Xiao, X., et al., 1998: Transient climate change and net ecosystem production of the metrics. J. Appl. Meteorol. Climatol., 50, 1666 1675. terrestrial biosphere. Global Biogeochem. Cycles, 12, 345 360. Yokoi, T., T. Tozuka, and T. Yamagata, 2009b: Seasonal variations of the Seychelles Xie, L., T. Z. Yan, L. J. Pietrafesa, J. M. Morrison, and T. Karl, 2005: Climatology and Dome simulated in the CMIP3 models. J. Phys. Oceanogr., 39, 449 457. interannual variability of North Atlantic hurricane tracks. J. Clim., 18, 5370 5381. Yoshimori, M., T. Yokohata, and A. Abe-Ouchi, 2009: A comparison of climate 9 Xie, P., and P. A. Arkin, 1997: Global Precipitation: A 17-year monthly analysis based feedback strength between CO2 doubling and LGM experiments. J. Clim., 22, on gauge observations, satellite estimates, and numerical model outputs. Bull. 3374 3395. Am. Meteorol. Soc., 78, 2539 2558. Yoshimori, M., J. C. Hargreaves, J. D. Annan, T. Yokohata, and A. Abe-Ouchi, 2011: Xie, S., J. Boyle, S. A. Klein, X. Liu, and S. Ghan, 2008: Simulations of Arctic mixed- Dependency of feedbacks on forcing and climate state in physics parameter phase clouds in forecasts with CAM3 and AM2 for M-PACE. J. Geophys. Res., ensembles. J. Clim., 24, 6440 6455. 113, D04211. Young, P. J., et al., 2013: Pre-industrial to end 21st century projections of tropospheric Xie, S., H.-Y. Ma, J. S. Boyle, S. A. Klein, and Y. Zhang, 2012: On the correspondence ozone from the Atmospheric Chemistry and Climate Model Intercomparison between short- and long- timescale systematic errors in CAM4/CAM5 for the Project (ACCMIP). Atmos. Chem. Phys., 13, 2063 2090. years of tropical convection. J. Clim., 25, 7937 7955. Yu, B., and F. W. Zwiers, 2010: Changes in equatorial atmospheric zonal circulations Xie, S. P., H. Annamalai, F. A. Schott, and J. P. McCreary, 2002: Structure and in recent decades. Geophys. Res. Lett., 37, L05701. mechanisms of South Indian Ocean climate variability. J. Clim., 15, 864 878. Yu, J.-Y., and S. T. Kim, 2011: Reversed spatial asymmetries between El Nino and La Xin, X.-G., T.-J. Zhou, and R.-C. Yu, 2008: The Arctic Oscillation in coupled climate Nina and their linkage to decadal ENSO modulation in CMIP3 models. J. Clim., models. Chin. J. Geophys. Chinese Edition, 51, 337 351. 24, 5423 5434. Xin, X., L. Zhang, J. Zhang, T. Wu, and Y. Fang, 2013: Climate change projections over Yu, W., M. Doutriaux, G. Seze, H. LeTreut, and M. Desbois, 1996: A methodology East Asia with BCC_CSM1.1 climate model under RCP scenarios. J. Meteorol. study of the validation of clouds in GCMs using ISCCP satellite observations. Soc. Jpn., 91, 413 429. Clim. Dyn., 12, 389 401. Xin, X., T. Wu, J. Li, Z. Wang, W. Li, and F. Wu, 2012: How well does BCC_CSM1.1 Yukimoto, S., et al., 2011: Meteorological Research Institute-Earth System Model v1 reproduce the 20th  century climate change over China?  . Atmos. Ocean. Sci. (MRI-ESM1) Model Description. Technical Report of MRI. Ibaraki, Japan, 88 pp. Lett., 6, 21 26. Yukimoto, S., et al., 2012: A new global climate model of the Meteorological Xu, Y. F., Y. Huang, and Y. C. Li, 2012: Summary of recent climate change studies on Research Institute: MRI-CGCM3 Model description and basic performance. J. the carbon and nitrogen cycles in the terrestrial ecosystem and ocean in China. Meteorol. Soc. Jpn., 90A, 23 64. Adv. Atmos. Sci., 29, 1027 1047. Zaehle, S., and D. Dalmonech, 2011: Carbon-nitrogen interactions on land at global Xue, Y. K., R. Vasic, Z. Janjic, F. Mesinger, and K. E. Mitchell, 2007: Assessment of scales: Current understanding in modelling climate biosphere feedbacks. Curr. dynamic downscaling of the continental US regional climate using the Eta/SSiB Opin. Environ. Sustain., 3, 311 320. regional climate model. J. Clim., 20, 4172 4193. Zaehle, S., P. Friedlingstein, and A. D. Friend, 2010: Terrestrial nitrogen feedbacks may Yakovlev, N. G., 2009: Reproduction of the large-scale state of water and sea ice in accelerate future climate change. Geophys. Res. Lett., 37, L01401. the Arctic Ocean in 1948 2002: Part I. Numerical model. Izvestiya Atmos. Ocean. Zahn, M., and H. von Storch, 2008: A long-term climatology of North Atlantic polar Phys., 45, 628 641. lows. Geophys. Res. Lett., 35, L22702. Yang, D., and T. Ohata, 2001: A bias-corrected Siberian regional precipitation Zalesny, V. B., et al., 2010: Numerical simulation of large-scale ocean circulation climatology. J. Hydrometeorol., 2, 122 139. based on the multicomponent splitting method. Russ. J. Numer. Anal. Math. Yasutaka, W., N. Masaomi, K. Sachie, and M. Chiashi, 2008: Climatological Model., 25, 581 609. reproducibility evaluation and future climate projection of extreme precipitation Zaliapin, I., and M. Ghil, 2010: Another look at climate sensitivity. Nonlin. Proc. events in the Baiu season using a high-resolution non-hydrostatic RCM in Geophys., 17, 113 122. comparison with an AGCM. J. Meteorol. Soc. Jpn., 86, 951 967. Zappa, G., L. C. Shaffrey, and K. I. Hodges, 2013: The ability of CMIP5 models to Yeager, S., and G. Danabasoglu, 2012: Sensitivity of Atlantic Meridional Overturning simulate North Atlantic extratropical cyclones. J. Clim., doi:10.1175/JCLI-D-12- Circulation variability to parameterized Nordic Sea overflows in CCSM4. J. Clim., 00501.1. 25, 2077 2103. Zeng, N., 2003: Glacial-interglacial atmospheric CO2 change The glacial burial Yeh, S. W., Y. G. Ham, and J. Y. Lee, 2012: Changes in the tropical Pacific SST trend hypothesis. Adv. Atmos. Sci., 20, 677 693. from CMIP3 to CMIP5 and its implication of ENSO. J. Clim., 25, 7764 7771. Zeng, N., 2006: Quasi-100ky glacial-interglacial cycles triggered by subglacial burial Yhang, Y. B., and S. Y. Hong, 2008: Improved physical processes in a regional climate carbon release. Clim. Past, 2, 371 397. model and their impact on the simulated summer monsoon circulations over Zeng, N., J. D. Neelin, and C. Chou, 2000: A quasi-equilibrium tropical circulation East Asia. J. Clim., 21, 963 979. model Implementation and simulation. J. Atmos. Sci., 57, 1767 1796. Yin, X., A. Gruber, and P. Arkin, 2004: Comparison of the GPCP and CMAP merged Zeng, N., A. Mariotti, and P. Wetzel, 2005: Terrestrial mechanisms of interannual CO2 gauge-satellite monthly precipitation products for the period 1979 2001. J. variability. Global Biogeochem. Cycles, 19, GB1016. Hydrometeorol., 5, 1207 1222. Zeng, N., H. F. Qian, E. Munoz, and R. Iacono, 2004: How strong is carbon cycle- Yokohata, T., M. J. Webb, M. Collins, K. D. Williams, M. Yoshimori, J. C. Hargreaves, and climate feedback under global warming? Geophys. Res. Lett., 31, L20203. J. D. Annan, 2010: Structural similarities and differences in climate responses Zhang, F., and A. Georgakakos, 2011: Joint variable spatial downscaling. Clim. to CO2 increase between two perturbed physics ensembles. J. Clim., 23, 1392 Change, 111, 945 972 1410. Zhang, J., and J. S. Reid, 2010: A decadal regional and global trend analysis of the aerosol optical depth using a data-assimilation grade over-water MODIS and Level 2 MISR aerosol products. Atmos. Chem. Phys., 10, 10949 10963. 852 Evaluation of Climate Models Chapter 9 Zhang, J., J. S. Reid, D. L. Westphal, N. L. Baker, and E. J. Hyer, 2008a: A system for operational aerosol optical depth data assimilation over global oceans. J. Geophys. Res. Atmos., 113, D10208. Zhang, Q., H. S. Sundqvist, A. Moberg, H. Kornich, J. Nilsson, and K. Holmgren, 2010a: Climate change between the mid and late Holocene in northern high latitudes Part 2: Model-data comparisons. Clim. Past, 6, 609 626. Zhang, R., et al., 2013: Have aerosols caused the observed Atlantic multidecadal variability? J. Atmos. Sci., doi:10.1175/JAS-D-12-0331.1. Zhang, W., and F.-F. Jin, 2012: Improvements in the CMIP5 simulations of ENSO-SSTA meridional width. Geophys. Res. Lett., 39, L23704. Zhang, X., 2007: A comparison of explicit and implicit spatial downscaling of GCM output for soil erosion and crop production assessments. Clim. Change, 84, 337 363. Zhang, X., 2010: Sensitivity of arctic summer sea ice coverage to global warming forcing: towards reducing uncertainty in arctic climate change projections. Tellus A, 62, 220 227. Zhang, X., et al., 2007: Detection of human influence on twentieth-century precipitation trends. Nature, 448, 461 465. Zhang, Y., S. A. Klein, J. Boyle, and G. G. Mace, 2010b: Evaluation of tropical cloud 9 and precipitation statistics of Community Atmosphere Model version 3 using CloudSat and CALIPSO data. J. Geophys. Res., 115, D12205. Zhang, Y., et al., 2008b: On the diurnal cycle of deep convection, high-level cloud, and upper troposphere water vapor in the Multiscale Modeling Framework. J. Geophys. Res., 113, D16105. Zhao, M., I. M. Held, and S.-J. Lin, 2012: Some counterintuitive dependencies of tropical cyclone frequency on parameters in a GCM. J. Atmos. Sci., 69, 2272 2283. Zhao, M., I. M. Held, S. J. Lin, and G. A. Vecchi, 2009: Simulations of global hurricane climatology, interannual variability, and response to global warming using a 50-km resolution GCM. J. Clim., 22, 6653-6678. Zhao, T. L., et al., 2008: A three-dimensional model study on the production of BrO and Arctic boundary layer ozone depletion. J. Geophys. Res., 113, D24304. Zhao, Y., and S. P. Harrison, 2012: Mid-Holocene monsoons: A multi-model analysis of the inter-hemispheric differences in the responses to orbital forcing and ocean feedbacks. Clim. Dyn., 39, 1457-1487. Zheng, W. P., and P. Braconnot, 2013: Characterization of model spread in PMIP2 Mid-Holocene simulations of the African monsoon. J. Clim., 26, 1192-1210. Zheng, X.-T., S.-P. Xie, and Q. Liu, 2011: Response of the Indian Ocean basin mode and its capacitor effect to global warming. J. Clim., 24, 6146-6164. Zheng, Y., J.-L. Lin, and T. Shinoda, 2012: The equatorial Pacific cold tongue simulated by IPCC AR4 coupled GCMs: Upper ocean heat budget and feedback analysis. J. Geophys. Res. Oceans, 117, C05024. Zickfeld, K., et al., 2013: Long-term climate change commitment and reversibility: An EMIC intercomparison. J. Clim., doi:10.1175/JCLI-D-12-00584.1. Zou, L., and T. Zhou, 2013: Can a regional ocean-atmosphere coupled model improve the simulation of the interannual variability of the western North Pacific summer monsoon? J. Clim., 26, 2353 2367. Zunz, V., H. Goosse, and F. Massonnet, 2013: How does internal variability influence the ability of CMIP5 models to reproduce the recent trend in Southern Ocean sea ice extent? Cryosphere, 7, 451-468. Zwiers, F. W., X. Zhang, and Y. Feng, 2011: Anthropogenic influence on long return period daily temperature extremes at regional scales. J. Clim., 24, 881-892. 853 9 Appendix 9.A: Climate Models Assessed in Chapter 9 854 Table 9.A.1 | Salient features of the Atmosphere Ocean General Circulation Models (AOGCMs) and Earth System Models (ESMs) participating in CMIP5 (see also Table 9.1). Column 1: Official CMIP5 model name along with the calendar year ( vintage ) of the first publication for each model; Column 2: sponsoring institution(s), main reference(s); subsequent columns for each of the model components, with names and main component reference(s). In addition, there are standard Chapter 9 entries for the atmosphere component: horizontal grid resolution, number of vertical levels, grid top (low or high top); and for the ocean component: horizontal grid resolution, number of vertical levels, top level, vertical coordinate type, ocean free surface type ( Top BC ). This table information was initially extracted from the CMIP5 online questionnaire (http://q.cmip5.ceda.ac.uk/) as of January 2013. A blank entry indicates that information was not available. (1) Model Name (1) Institution Atmosphere Aerosol Atmos Chemistry Land Surface Ocean Ocean Biogeo- Sea Ice (2) Vintage (2) Main Reference(s) (1) Component Name (1) Component (1) Component Name (1) Component Name (1) Component Name chemistry (1) Component (2) Horizontal Grid Name or type (2) References (2) References (2) Horizontal Resolution (1) Component Name Name (3) Number of Vert (2) References (3) Number of Vertical Levels (2) References (2) References Levels (4) Top Level (4) Grid Top (5) z Co-ord (5) References (6) Top BC (7) References (1) ACCESS1.0 (1) Commonwealth (1) Included (as in (1) CLASSIC Not implemented (1) MOSES2.2 (1) ACCESS-OM (MOM4p1) Not implemented (1) CICE4.1 (2) 2011 Scientific and Industrial HadGEM2 (r1.1)) (2) (Bellouin et al., (2) (Cox et al., 1999; (2) primarily 1° latitude/longitude (2) (Uotila et al., Research Organization (2) 192 × 145 N96 2011; Dix et al., 2013) Essery et al., 2003; tripolar with enhanced resolution 2012; Bi et al., (CSIRO) and Bureau of (3) 38 Kowalczyk et al., 2013) near equator and at high latitudes 2013a; Uotila Meteorology (BOM), (4) 39,255 m (3) 50 et al., 2013) Australia (5) (Martin et al., (4) 0 10 m (2) (Bi et al., 2013b; 2011; Bi et al., 2013b; (5) z* Dix et al., 2013) Rashid et al., 2013) (6) nonlinear split-explicit (7) (Bi et al., 2013a; Marsland et al., 2013) (1) ACCESS1.3 (1) Commonwealth (1) Included (similar to (1) CLASSIC Not implemented (1) CABLE (1) ACCESS-OM (MOM4p1) Not implemented (1) CICE4.1 (2) 2011 Scientific and Industrial UK Met Office Global (2) (Bellouin et al., (2) (Kowalczyk et al., (2) primarily 1° latitude/longitude (2) (Uotila et al., Research Organization Atmosphere 1.0) 2011; Dix et al., 2013) 2006; Wang et al., 2011b; tripolar with enhanced resolution 2012; Bi et al., (CSIRO) and Bureau of (2) 192 × 145 N96 Kowalczyk et al., 2013) near equator and at high latitudes 2013a; Uotila Meteorology (BOM), (3) 38 (3) 50 et al., 2013) Australia (4) 39,255 m (4) 0 10 m (2) (Bi et al., 2013b; (5) (5) z* Dix et al., 2013) (Hewitt et al., 2011; Bi (6) nonlinear split-explicit et al., 2013b; Rashid et (7) (Bi et al., 2013a; Marsland et al., 2013) al., 2013) (1) BCC-CSM1.1 (1) Beijing Climate Center, (1) BCC_AGCM2.1 Prescribed Not implemented (1) BCC-AVIM1.0 (1) MOM4-L40 (1) Included (1) GFDL Sea Ice (2) 2011 China Meteorological (2) T42 T42L26 (2) (Ji, 1995; Lu and (2) 1° with enhanced resolution (2) Based on the Simulator (SIS) Administration (3) 26 Ji, 2006; Ji et al., in the meridional direction in the protocols from the Ocean (2) (Winton, 2000) (2) (Wu, 2012; Xin et al., (4) 2.917 hPa 2008; Wu, 2012) tropics (1/3° meridional resolution Carbon Cycle Model 2012; Xin et al., 2013) (5) (Wu et al., 2008b; at the equator) tripolar Intercomparison Proj- Wu et al., 2010b, (3) 40 ect Phase 2 (OCMIP2, 2010a; Wu, 2012) (4) 25 m http://www.ipsl.jussieu. (5) z fr/OCMIP/ phase2/) (6) linear split-explicit (7) (Griffies et al., 2005) (1) BCC-CSM1.1(m) (1) Beijing Climate Center, (1) BCC_AGCM2.1 Prescribed Not implemented (1) BCC-AVIM1.0 (1) MOM4-L40 (1) Included (1) GFDL Sea Ice (2) 2011 China Meteorological (2) T106 (2) (Ji, 1995; Lu and (2) Tri-polar: 1° with enhanced (2) Based on the Simulator (SIS) Administration (3) 26 Ji, 2006; Ji et al., resolution in the meridional direc- protocols from the Ocean (2) (Winton, 2000) (2) (Wu, 2012; Xin et al., (4) 2.917 hPa 2008; Wu, 2012) tion in the tropics (1/3° meridional Carbon Cycle Model 2012; Xin et al., 2013) (5) (Wu et al., 2008b; resolution at the equator) Intercomparison Proj- Wu et al., 2010b, (3) 40 ect Phase 2 (OCMIP2, 2010a; Wu, 2012) (4) 25 m http://www.ipsl.jussieu. (5) z fr/OCMIP/ phase2/) (6) implicit Evaluation of Climate Models (7) (Griffies et al., 2005) (continued on next page) Table 9.A.1 (continued) (1) Model Name (1) Institution Atmosphere Aerosol Atmos Chemistry Land Surface Ocean Ocean Biogeo- Sea Ice (2) Vintage (2) Main Reference(s) (1) Component Name (1) Component (1) Component Name (1) Component Name (1) Component Name chemistry (1) Component (2) Horizontal Grid Name or type (2) References (2) References (2) Horizontal Resolution (1) Component Name Name (3) Number of Vert (2) References (3) Number of Vertical Levels (2) References (2) References Levels (4) Top Level (4) Grid Top (5) z Co-ord (5) References (6) Top BC (7) References (1) BNU-ESM (1) Beijing Normal (1) CAM3.5 Semi-interactive Not implemented (1)CoLM+B- (1) MOM4p1 IBGC CICE4.1 (2) 2011 University (2) T42 NUDGVM(C/N) (2) 200(lat) × 360(lon) (2) (3) 26 (2) (Dai et al., 2003; (3) 50 Evaluation of Climate Models (4) 2.194 hPa Dai et al., 2004) (1) CanCM4 (1) Canadian Center for (1) Included (1) Interactive (1) Included (1) CLASS 2.7 (2) (1) Included Not implemented (1) Included (2) 2010 Climate Modelling and (2) Spectral T63 (2) (Lohmann et al., (2) (von Salzen (Verseghy, 2000; von (2) 256 × 192 (2) (Merryfield Analysis (3) 35 levels 1999; Croft et al., 2005; et al., 2013) Salzen et al., 2013) (3) 40 et al., 2013) (2) (von Salzen et al., 2013) (4) 0.5 hPa von Salzen et al., 2013) (4) 0 m (5) (von Salzen (5) depth et al., 2013) (6) rigid lid (7) (Merryfield et al., 2013) (1) CanESM2 (1) Canadian Center for (1) Included (1) Interactive (1) Included (1) CLASS 2.7; CTEM (1) Included (1) CMOC (1) Included (2) 2010 Climate Modelling and (2) Spectral T63 (2) (Lohmann et al., (2) (von Salzen (2) (Verseghy, 2000) (2) 256 × 192 (2) (Arora et al., 2009; (2) (Merryfield Analysis (3) 35 levels 1999; Croft et al., 2005; et al., 2013) (Arora et al., 2009; von (3) 40 Christian et al., 2010) et al., 2013) (2) (Arora et al., 2011; (4) 0.5 hPa von Salzen et al., 2013) Salzen et al., 2013) (4) 0 m von Salzen et al., 2013) (5) (von Salzen (5) depth et al., 2013) (6) rigid lid (7) (Merryfield et al., 2013) (1) CCSM4 (1) US National Centre for (1) CAM4 (1) Interactive Not implemented (1) Community Land (1) POP2 with modifications Not implemented (1) CICE4 with (2) 2010 Atmospheric Research (2) 0.9 ×1.25 (2) (Neale et al., 2010; Model 4 (CLM4) (2) Nominal 1° (1.125° in longitude, modifications (2) (Gent et al., 2011) (3) 27 Oleson et al., 2010; (2) (Oleson et al., 2010; 0.27 0.64° variable in latitude) (2) (Hunke and (4) 2.194067 hPa Holland et al., 2012) Lawrence et al., 2011; (3) 60 Lipscomb, 2008; (5) (Neale et al., 2010; Lawrence et al., 2012) (4) 10 m thick with sur- Holland et al., 2012) Neale et al., 2013) face variables at 5 m (5) depth (level) (6) linearized, implicit free surface with constant-volume ocean (7) (Danabasoglu et al., 2012) (1) CESM1(BGC) (1) NSF-DOE-NCAR (1) CAM4 (1) Semi-interactive Not implemented (1) CLM4 (1) POP2 with modifications (1) Biogeochemical (1) CICE4 with (2) 2010 (2) (Long et al., 2012; (2) 0.9 ×1.25 (2) (Neale et al., 2010; (2) (Oleson et al., 2010; (2) Nominal 1° (1.125° in longitude, Elemental Cycling (BEC) modifications Hurrell et al., 2013) (3) 27 Oleson et al., 2010; Lawrence et al., 2011; 0.27 0.64° variable in latitude) (2) (Hunke and (4) 2.194067 hPa Holland et al., 2012) Lawrence et al., 2012) (3) 60 Lipscomb, 2008; (5) (Neale et al., 2010; (4) 10 m with surface Holland et al., 2012) Neale et al., 2013) variables at 5 m (5) depth (level) (6) linearized, implicit free surface with constant-volume ocean (7) (Danabasoglu et al., 2012) (continued on next page) Chapter 9 855 9 9 Table 9.A.1 (continued) (1) Model Name (1) Institution Atmosphere Aerosol Atmos Chemistry Land Surface Ocean Ocean Biogeo- Sea Ice 856 (2) Vintage (2) Main Reference(s) (1) Component Name (1) Component (1) Component Name (1) Component Name (1) Component Name chemistry (1) Component (2) Horizontal Grid Name or type (2) References (2) References (2) Horizontal Resolution (1) Component Name Name Chapter 9 (3) Number of Vert (2) References (3) Number of Vertical Levels (2) References (2) References Levels (4) Top Level (4) Grid Top (5) z Co-ord (5) References (6) Top BC (7) References (1) CESM1(CAM5) (1) NSF-DOE-NCAR (1) Community Atmo- (1) Semi-interactive Not implemented (1) CLM4 Same as CESM1 (BGC) Not implemented (1) CICE4 with (2) 2010 (2) (Hurrell et al., 2013) sphere Model 5 (CAM5) (2) (Neale et al., 2010; (2) (Oleson et al., 2010) modifications (2) 0.9 × 1.25 Oleson et al., 2010; (Lawrence et al., 2011; (2) (Hunke and (3) 27 Holland et al., 2012) Lawrence et al., 2012) Lipscomb, 2008; (4) 2.194067 hPa Holland et al., 2012) (5) (Neale et al., 2010; Neale et al., 2013) (1) (1) NSF-DOE-NCAR (1) Community Atmo- (1) Modal Aerosol Not implemented (1) Community Land Same as CESM1 (BGC) Not implemented (1) CICE4 with CESM1(CAM5.1.FV2) (2) (Hurrell et al., 2013) sphere Model (CAM5.1) Module (MAM3) Model (CLM4) modifications (2) 2012 (2) 1.9 × 2.0 (2) (Ghan et al., 2012; (2) (Oleson et al., 2010; (2) (Hunke and (3) 30 Liu et al., 2012b) Lawrence et al., 2011) Lipscomb, 2008; (4) 10 hPa Holland et al., 2012) (5) (Neale et al., 2013) (1) CESM1(WACCM) (1) NSF-DOE-NCAR (1) WACCM4 Semi-interactive Included (1) CLM4 Same as CESM1 (BGC) Not implemented (1) CICE4 with (2) 2010 (2) (Hurrell et al., 2013) (2) 1.9o × 2.5o (2) (Oleson et al., 2010; modifications (3) 66 Lawrence et al., 2011; (2) (Hunke and (4) 5.1 × 10 6 hPa Lawrence et al., 2012) Lipscomb, 2008; Holland et al., 2012) (1) CESM1(FASTCHEM) (1) NSF-DOE-NCAR (1) Included, (1) Interactive (1) Included, CAM-CHEM (1) Community Land Same as CESM1 (BGC) Not implemented (1) CICE4 with (2) 2010 (2) (Cameron-Smith et al., CAM4-CHEM (2) (Neale et al., 2010; (2) (Lamarque Model 4 (CLM4) modifications 2006; Eyring et al., 2013; (2) 0.9 × 1.25 Oleson et al., 2010; et al., 2012) (2) (Oleson et al., 2010; (2) (Hunke and Hurrell et al., 2013) (3) 27 Holland et al., 2012; Lawrence et al., 2011; Lipscomb, 2008; (4) 2.194067 hPa Lamarque et al., 2012) Lawrence et al., 2012) Holland et al., 2012) (5) (Neale et al., 2010; Lamarque et al., 2012; Neale et al., 2013) (1) CMCC-CESM (1) Centro Euro-Mediter- (1) ECHAM5 Semi-interactive Not implemented (1) SILVA Same as CMCC-CM (1) PELAGOS (1) LIM2 (2) 2009 raneo per I Cambiamenti (2) 3.75° × 3.75° (T31) (2) (Alessandri (2) (Vichi et al., 2007) (2) (Timmermann Climatici (3) 39 et al., 2012) et al., 2005) (2) (Fogli et al., 2009; (4) 0.01 hPa Vichi et al., 2011) (5) (Roeckner et al., 2006; Manzini et al., 2012) (1) CMCC-CM (1) Centro Euro-Mediter- (1) ECHAM5 Semi-interactive Not implemented Not implemented (1) OPA8.2 Not implemented (1) LIM2 (2) 2009 raneo per I Cambiamenti (2) 0.75° × 0.75° (T159) (2) 2° average, 0.5° at the equator (2) (Timmermann Climatici (3) 31 (ORCA2) et al., 2005) (2) (Fogli et al., 2009; (4) 10 hPa (3) 31 Scoccimarro et al., 2011) (5) (Roeckner et al., 2006) (4) 5 m (5) depth (z-level) (6) linear implicit (7) (Madec et al., 1998) (1) CMCC-CMS (1) Centro Euro-Mediter- (1) ECHAM5 Semi-interactive Not implemented Not implemented Same as CMCC-CM Not implemented (1) LIM2 (2) 2009 raneo per I Cambiamenti (2) 1.875° × 1.875° (T63) (2) (Timmermann Climatici (3) 95 et al., 2005) (2) (Fogli et al., 2009) (4) 0.01 hPa (5) (Roeckner et al., 2006; Manzini et al., 2012) Evaluation of Climate Models (continued on next page) Table 9.A.1 (continued) (1) Model Name (1) Institution Atmosphere Aerosol Atmos Chemistry Land Surface Ocean Ocean Biogeo- Sea Ice (2) Vintage (2) Main Reference(s) (1) Component Name (1) Component (1) Component Name (1) Component Name (1) Component Name chemistry (1) Component (2) Horizontal Grid Name or type (2) References (2) References (2) Horizontal Resolution (1) Component Name Name (3) Number of Vert (2) References (3) Number of Vertical Levels (2) References (2) References Levels (4) Top Level (4) Grid Top (5) z Co-ord (5) References (6) Top BC (7) References (1) CNRM-CM51 (1) Centre National de (1) ARPEGE-Climat Prescribed (1) (3-D linear ozone (1) SURFEX (Land and (1) NEMO (1) PISCES (1) Gelato5 (Sea (2) 2010 Recherches Meteoro- (2) TL127 chemistry model) Ocean Surface) (2) 0.7° on average ORCA1 (2) (Aumont and Ice) logiques and Centre (3) 31 (2) (Cariolle and (2) (Voldoire et al., 2013) (3) 42 Bopp, 2006; Séfé- (2) (Salas-Melia, Evaluation of Climate Models Europeen de Recherche (4) 10 hPa Teyssedre, 2007) (4) 5 m rian et al., 2013) 2002; Voldoire et Formation Avancees en (5) (Déqué et al., 1994; (5) z-coordinate et al., 2013) Calcul Scientifique. Voldoire et al., 2013) (6) linear filtered (2) (Voldoire et al., 2013) (7) (Madec, 2008) (1) CSIRO-Mk3.6.0 (1) Queensland (1) Included (1) Interactive Not implemented (1) Included (1) Modified MOM2.2 Not implemented (1) Included (2) 2009 Climate Change (2) ~1.875 × 1.875 (2) (Rotstayn and (2) (Gordon et al., 2002; (2) ~0.9 × 1.875 (2) (O Farrell, 1998; Centre of (spectral T63) Lohmann, 2002; Gordon et al., 2010) (3) 31 Gordon et al., 2010) Excellence and (3) 18 Rotstayn et al., 2011; (4) 5 m Commonwealth (4) ~4.5 hPa Rotstayn et al., 2012) (5) depth Scientific and (5) (Gordon et al., 2002; (6) rigid lid Industrial Gordon et al., 2010; (7) (Gordon et al., 2002; Research Rotstayn et al., 2012) Gordon et al., 2010) Organisation (2) (Rotstayn et al., 2012) (1) EC-EARTH (1) Europe (1) IFS c31r1 Prescribed Not implemented (1) HTESSEL (1) NEMO_ecmwf Not implemented (1) LIM2 (2) 2010 (2) (Hazeleger et al., 2012) (2) 1.125 longitudinal (2) (Balsamo et al., 2009) (2) The grid is a tripolar curvilinear (2) (Fichefet and spacing, Gaussian grid grid with a 1° resolution. ORCA1 Maqueda, 1999) T159L62 (3) 31 (3) 62 (4) 1 m (4) 1 hPa (5) z (5) (Hazeleger (6) free surface linear filtered et al., 2012) (7) (Hazeleger et al., 2012) (1) FGOALS-g2 (1) LASG (Institute of (1) GAMIL2 Semi-interactive Not implemented (1) CLM3 (1) LICOM2 Not implemented (1) CICE4-LASG (2) 2011 Atmospheric Physics)- (2) 2.8125° × 2.8125° (2) (Oleson et al., 2010) (2) 1 × 1° with 0.5 meridional (2) (Wang and CESS(Tsinghua University) (3) 26 layers degree in the tropical region Houlton, 2009; (2) (Li et al., 2012b) (4) 2.194 hPa (3) 30 Liu, 2010) (5) (Wang et al., 2004; (4) 10 m Li et al., 2013b) (5) eta co-ordinate (6) (7) (Liu et al., 2012a) (1) FGOALS-s2 (1) The State Key Labora- (1) SAMIL2.4.7 Semi-interactive Not implemented (1) CLM3.0 (1) LICOM (1) IAP-OBM (1) CSIM5 (2) 2011 tory of Numerical Modeling (2)R42 (2.81° × 1.66°) (2) (Oleson, 2004; (2) The zonal resolution is 1°. (2) (Xu et al., 2012) (2) (Briegleb for Atmospheric Sciences (3) 26 Zeng et al., 2005; The meridional resolution is 0.5° et al., 2004) and Geophysical Fluid (4) 2.19hPa Wang et al., 2013) between 10°S and 10°N and Dynamics, The Institute (5) (Bao et al., 2010; increases from 0.5° to 1° from 10° of Atmospheric Physics Liu et al., 2013b) (3) 30 layers (4) 10 m (for vertical velocity (2) (Bao et al., 2010; Bao et and pressure) and 5 meter (for al., 2013) Temperature and salinity, zonal and meridional velocity) (5) depth (6) linear split-explicit (7) (Lin et al., 2013) (continued on next page) 1 Chapter 9 857 A CNRM-CM5-2 version exists that only differs from CNRM-CM5 in the treatment of volcanoes 9 9 Table 9.A.1 (continued) (1) Model Name (1) Institution Atmosphere Aerosol Atmos Chemistry Land Surface Ocean Ocean Biogeo- Sea Ice 858 (2) Vintage (2) Main Reference(s) (1) Component Name (1) Component (1) Component Name (1) Component Name (1) Component Name chemistry (1) Component (2) Horizontal Grid Name or type (2) References (2) References (2) Horizontal Resolution (1) Component Name Name Chapter 9 (3) Number of Vert (2) References (3) Number of Vertical Levels (2) References (2) References Levels (4) Top Level (4) Grid Top (5) z Co-ord (5) References (6) Top BC (7) References (1) FIO-ESM v1.0 (1) The First Institute of (1) CAM3.0 Prescribed Not implemented (1) CLM3.5 (1) Modified POP2.0 through (1) Improved OCMIP-2 (1) CICE4.0 (2) 2011 Oceanography, State Ocea- (2) T42 (2) (Oleson et al., 2008b) incorporating the non-breaking biogeochemical model (2) (Hunke and nic Administration, China (3) 26 surface wave-induced mixing (2) (Bao et al., 2012) Lipscomb, 2008) (4) 3.545 hPa (2) 1.125° in longitude, (5) (Collins et al., 2006c) 0.27 0.64° variable in latitude (3) 40 (4) 10 m with surface variables at 5 m (5) depth (6) linear implicit (7) (Huang et al., 2012) (1) GFDL-CM2.1 (2) (Qiao et al., 2004; (1) Included Semi-interactive Not implemented Included (1) Included Not implemented (1) SIS (2) 2006 Song et al., 2012) (2) 2.5° longitude, 2° (2) 1° tripolar360 × 200L50 (2) (Winton, latitude M45L24 (3) 50 2000; Delworth (3) 24 (4) 0 m et al., 2006) (4) midpoint of top (5) depth box is 3.65 hPa (6) nonlinear split-explicit (5) (Delworth et al., 2006) (7) (1) GFDL-CM3 (1) NOAA Geophysical (1) Included (1) Interactive (1) Atmospheric (1) Included (1) MOM4.1 Not implemented (1) SIS (2) 2011 Fluid Dynamics Laboratory (2) ~200 km C48L48 (2) (Levy et al., 2013) Chemistry (2) (Milly and Shmakin, (2) 1° tripolar360 × 200L50 (2) (Griffies and (2) (Delworth et al., 2006; (3) 48 (2) (Horowitz et al., 2002; Shevliakova (3) 50 Greatbatch, 2012) Donner et al., 2011) (4) 0.01 hPa 2003; Austin and Wilson, et al., 2009) (4) 0 m (5) (Donner et al., 2011) 2006; Sander, 2006) (5) z* (6) non-linear split-explicit (7) (Griffies and Greatbatch, 2012) (1) GFDL-ESM2G (1) NOAA Geophysical (1) Included Semi-interactive Not implemented (1) Included (1) GOLD (1) TOPAZ (1) SIS (2) 2012 Fluid Dynamics Laboratory (2) 2.5° longitude, 2° (2) (Milly and Shmakin, (2) 1° tripolar 360 × 2 10L63 (2) (Henson et al., 2009; (2) (Winton, (2) (Dunne et al., 2012; latitude M45L24 2002; Shevliakova (3) 63 Dunne et al., 2013) 2000; Delworth Dunne et al., 2013) (3) 24 et al., 2009; Donner (4) 0 m et al., 2006) (4) midpoint of top box is et al., 2011) (5) Isopycnic 3.65 hPa (6) nonlinear split-explicit (5) (Delworth et al., 2006) (7) (Hallberg and Adcroft, 2009; Dunne et al., 2012) (1) GFDL-ESM2M (1) NOAA Geophysical (1) Included Semi-interactive Not implemented (1) Included (1) MOM4.1 (1) TOPAZ (1) SIS (2) 2011 Fluid Dynamics Laboratory (2) 2.5° longitude, 2° (2) (Milly and Shmakin, (2) 1° tripolar 360 × 200L50 (2) (Henson et al., 2009; (2) (Winton, (2) (Dunne et al., 2012; latitude M45L24 2002; Shevliakova (3) 50 Dunne et al., 2013) 2000; Delworth Dunne et al., 2013) (3) 24 et al., 2009; Donner (4) 0 m et al., 2006) (4) midpoint of top et al., 2011) (5) z* box is 3.65 hPa (6) nonlinear split-explicit (5) (Delworth et al., 2006) (7) (Griffies, 2009; Dunne et al., 2012) (1) GFDL-HIRAM-C180 (1) NOAA Geophysical (1) Included Prescribed Not implemented (1) Included Not implemented Not implemented Not implemented (2) 2011 Fluid Dynamics Laboratory (2) Averaged cell size: (2) (Milly and Shmakin, (2) (Delworth et al., 2006; approximately 50 × 50 2002; Shevliakova Donner et al., 2011) km. C180L32 et al., 2009) (3) 32 (4) 2.164 hPa (5) (Donner et al., 2011) Evaluation of Climate Models (continued on next page) Table 9.A.1 (continued) (1) Model Name (1) Institution Atmosphere Aerosol Atmos Chemistry Land Surface Ocean Ocean Biogeo- Sea Ice (2) Vintage (2) Main Reference(s) (1) Component Name (1) Component (1) Component Name (1) Component Name (1) Component Name chemistry (1) Component (2) Horizontal Grid Name or type (2) References (2) References (2) Horizontal Resolution (1) Component Name Name (3) Number of Vert (2) References (3) Number of Vertical Levels (2) References (2) References Levels (4) Top Level (4) Grid Top (5) z Co-ord (5) References (6) Top BC (7) References (1) GFDL-HIRAM-C360 (1) NOAA Geophysical (1) Included Prescribed Not implemented (1) Included Not implemented Not implemented Not implemented (2) Fluid Dynamics Laboratory (2) Averaged cell size: (2) (Milly and Shmakin, (2) (Delworth et al., 2006; approximately 25 × 25 2002; Shevliakova Evaluation of Climate Models Donner et al., 2011) km. C360L32 et al., 2009) (3) 32 (4) 2.164 hPa (5) (Donner et al., 2011) (1) GISS-E2-H (1) NASA Goddard Institute (1) Included (1) Interactive (1) G-PUCCINI Included (1) HYCOM Ocean Not implemented Included (2) 2004 for Space Studies USA (2) 2° latitude × 2.5°lon- (2) (Bauer et al., (2) (Shindell et al., 2013a) (2) 0.2 to 1° latitude × 1° longitude (2) (Schmidt et al., 2006) gitude F 2007; Tsigaridis and Note: Atmos Chem HYCOM Note: all GISS models come (3) 40 Kanakidou, 2007; is fully interactive (3) 26 in three flavours: p1 = non- (4) 0.1 hPa Menon et al., 2010; for p2 and p3, semi (4) 0 m interactive composition, Koch et al., 2011) interactive for p1 (5) hybrid z isopycnic p2= interactive composi- Note: Aerosol is (6) nonlinear split-explicit tion, p3 = interactive com- fully interactive (7) position + interactive AIE for p2 and p3, semi interactive for p1 (1) GISS-E2-H-CC (1) NASA Goddard Institute (1) Included (1) Interactive (p1 only) (1) G-PUCCINI Included (1) HYCOM Ocean (1) Included Included (2) 2011 for Space Studies USA (2) Nominally 1° (2) (Bauer et al., (2) (Shindell et al., 2013a) (2) 0.2 to 1° latitude × 1° longitude (2) (Romanou (2) (Schmidt et al., 2006) (3) 40 2007; Tsigaridis and HYCOM et al., 2013) Note: p1 only (4) 0.1 hPa Kanakidou, 2007; (3) 26 Menon et al., 2010; (4) 0 m Koch et al., 2011) (5) hybrid z isopycnic (6) nonlinear split-explicit (7) (1) GISS-E2-R (1) NASA Goddard Institute (1) Included (1) Interactive (1) G-PUCCINI Included (1) Russell Ocean Not implemented Included (2) 2011 for Space Studies USA (2) 2° latitude × 2.5° (2) (Bauer et al., (2) (Shindell et al., 2013a) (2) 1° latitude × 1.25° longitude (2) (Schmidt et al., 2006) longitude F 2007; Tsigaridis and Note: Atmos Chem Russell 1 × 1Q See note for GISS-E2-H (3) 40 Kanakidou, 2007; is fully interactive (3) 32 (4) 0.1 hPa Menon et al., 2010; for p2 and p3, semi (4) 0 m Koch et al., 2011) interactive for p1 (5) z*-coordinate Note: Aerosol is (6) other fully interactive (7) for p2 and p3, semi interactive for p1 (1) GISS-E2-R-CC (1) NASA Goddard Institute (1) Included (1) Interactive (p1 only) (1) G-PUCCINI Included (1) Russell Ocean (1) Included Included (2) 2011 for Space Studies USA (2) Nominally 1° (2) (Bauer et al., (2) (Shindell et al., 2013a) (2) 1° latitude × 1.25° longitude (2) (Romanou (2) (Schmidt et al., 2006) (3) 40 2007; Tsigaridis and Russell 1×1Q et al., 2013) Note: p1 only (4) 0.1 hPa Kanakidou, 2007; (3) 32 Menon et al., 2010; (4) 0 m Koch et al., 2011) (5) z*-coordinate (6) other (7) (continued on next page) Chapter 9 859 9 9 Table 9.A.1 (continued) (1) Model Name (1) Institution Atmosphere Aerosol Atmos Chemistry Land Surface Ocean Ocean Biogeo- Sea Ice 860 (2) Vintage (2) Main Reference(s) (1) Component Name (1) Component (1) Component Name (1) Component Name (1) Component Name chemistry (1) Component (2) Horizontal Grid Name or type (2) References (2) References (2) Horizontal Resolution (1) Component Name Name Chapter 9 (3) Number of Vert (2) References (3) Number of Vertical Levels (2) References (2) References Levels (4) Top Level (4) Grid Top (5) z Co-ord (5) References (6) Top BC (7) References (1) HadCM3 (1) UK Met Office Hadley (1) HadAM3 (1) Interactive Not implemented (1) Included (1) HadOM (lat: 1.25 lon: 1.25 L20) Not implemented Included (2) 1998 Centre (2) N48L19 (2) (Jones et al., 2001) (2) (Collatz et al., 1991; (2) 1.25° in longitude by 1.25° in (2) (Gordon et al., 3.75 × 2.5° Collatz et al., 1992; Cox latitude N144 2000; Pope et al., 2000; (3) 19 et al., 1999; Cox, 2001; (3) 20 Collins et al., 2001; (4) 0.005 hPa Mercado et al., 2007) (4) 5.0 m Johns et al., 2003) (5) (Pope et al., 2000) (5) depth (6) linear implicit (7) (UNESCO, 1981) (1) HadGEM2-AO (1) National Institute of (1) HadGAM2 (1) Interactive Not implemented (1) Included (1) Included Not implemented (1) Included (2) 2009 Meteorological Research/ (2) 1.875° in longitude by (2) (Bellouin et (2) (Cox et al., 1999; (2) 1.875° in longitude by 1.25° in (2) (Thorndike et Korea Meteorological 1.25° in latitude N96 al., 2011) Essery et al., 2003) latitude N96 al., 1975; McLaren Administration (3) 60 (3) et al., 2006) (2) ( Collins et al., 2011; (4) 84132.439 m (4) Martin et al., 2011) (5) (Davies et al., 2005) (5) z (6) linear implicit (7) (Bryan and Lewis, 1979; Johns et al., 2006); (1) HadGEM2-CC (1) UK Met Office Hadley (1) HadGAM2 (1) Interactive (1) Atmospheric (1) Included (1) Included (1) Included (1) Included (2) 2010 Centre (2) 1.875° in longitude by (2) (Bellouin et Chemistry (2) (Cox et al., 1999; (2) 1.875° in longitude by 1.25° in (2) (Palmer and Totterdell, (2) (Thorndike et (2) ( Collins et al., 2011; 1.25°in latitude N96 al., 2011) (2) (Jones et al., 2001; Essery et al., 2003) latitude N96 2001; Halloran, 2012) al., 1975; McLaren Martin et al., 2011) (3) 60 Martin et al., 2011) (3) et al., 2006) (4) 84132.439 m (4) (5) (Davies et al., 2005) (5) z (6) linear implicit (7) (Bryan and Lewis, 1979; Johns et al., 2006) (1) HadGEM2-ES (1) UK Met Office Hadley (1) HadGAM2 (1) Interactive (1) Atmospheric (1) Included (1) Included (1) Included (1) Included (2) 2009 Centre (2) 1.875° in longitude by (2) (Bellouin et Chemistry (2) (Cox et al., 1999; (2) 1° by 1° between 30 N/S and (2) (Palmer and Totterdell, (2) (Thorndike et (2) ( Collins et al., 2011; 1.25° in latitude N96 al., 2011) (2) (O Connor Essery et al., 2003) the poles; meridional resolution 2001; Halloran, 2012) al., 1975; McLaren Martin et al., 2011) (3) 38 et al., 2009) increases to 1/3° at the equator et al., 2006) (4) 39254.8 m N180 (5) (Davies et al., 2005) (3) 40 (4) 5.0 m (5) z (6) linear implicit (7) (Bryan and Lewis, 1979; Johns et al., 2006) (1) INM-CM4 (1) Russian Institute for (1) Included Prescribed Not implemented (1) Included (1) Included (1) Included (1) Included (2) 2009 Numerical Mathematics (2) 2 ×1.5° in longitude (2) (Alekseev et al., (2) 1 × 0.5° in longitude and (2) (Volodin, 2007) (2) (Yakovlev, 2009) (2) (Volodin et al., 2010) and latitude latitude- 1998; Volodin and latitude generalized spherical longitude Lykosov, 1998) coordinates with poles displaced (3) 21 outside ocean (4) sigma = 0.01 (3) 40 (4) sigma = 0.0010426 (5) sigma (6) linear implicit (7) (Volodin et al., 2010; Zalesny et al., 2010) Evaluation of Climate Models (continued on next page) Table 9.A.1 (continued) (1) Model Name (1) Institution Atmosphere Aerosol Atmos Chemistry Land Surface Ocean Ocean Biogeo- Sea Ice (2) Vintage (2) Main Reference(s) (1) Component Name (1) Component (1) Component Name (1) Component Name (1) Component Name chemistry (1) Component (2) Horizontal Grid Name or type (2) References (2) References (2) Horizontal Resolution (1) Component Name Name (3) Number of Vert (2) References (3) Number of Vertical Levels (2) References (2) References Levels (4) Top Level (4) Grid Top (5) z Co-ord (5) References (6) Top BC (7) References (1) IPSL-CM5A-LR (1) Institut Pierre Simon (1) LMDZ5 Semi-interactive Not implemented (1) Included (1) Included (1) PISCES (1) LIM2 (2) 2010 Laplace (2) 96 × 95 equivalent (2) (Krinner et al., 2005) (2) 2 × 2-0.5° ORCA2 (2) (Aumont et al., 2003; (2) (Fichefet and (2) (Dufresne et al., 2012) to 1.9° × 3.75° LMDZ96 (3) 31 Aumont and Bopp, 2006) Maqueda, 1999) Evaluation of Climate Models × 95 (4) 0m (3) 39 (5) depth (4) 0.04 hPa (6) linear filtered (5)(Hourdin et al., 2012) (7) (Madec, 2008) (1) IPSL-CM5A-MR (1) Institut Pierre Simon (1) LMDZ5 Semi-interactive Not implemented (1) Included (1) Included (1) PISCES (1) Included (2) 2009 Laplace (2) 144 × 143 equivalent (2) (Krinner et al., 2005) (2) 2 × 2-0.5° ORCA2 (2) (Aumont et al., 2003; (2) (Fichefet and (2) (Dufresne et al., 2012) to 1,25° × 2.5° LMDZ144 (3) 31 Aumont and Bopp, 2006) Maqueda, 1999) × 143 (4) 0 m (3) 39 (5) depth (4) 0.04 hPa (6) linear filtered (5) (Hourdin et al., 2012) (7) (Madec, 2008) (1) IPSL-CM5B-LR (1) Institut Pierre Simon (1) LMDZ5 Semi-interactive Not implemented (1) Included (1) Included (1) PISCES (1) Included (2) 2010 Laplace (2) 96 × 95 equivalent (2) (Krinner et al., 2005) (2) 2 × 2-0.5° ORCA2 (2) (Aumont et al., 2003; (2) (Fichefet and (2) (Dufresne et al., 2012) to 1.9° × 3.75° LMDZ96 (3) 31 Aumont and Bopp, 2006) Maqueda, 1999) × 95 (4) 0 m (3) 39 (5) depth (4) 0.04 hPa (6) linear filtered (5)(Hourdin et al., 2013) (7) (Madec, 2008) (1) MIROC4h (1) University of Tokyo, (1) CCSR / NIES / FRCGC (1) SPRINTARS Not implemented (1) MATSIRO (1) COCO3.4 Not implemented Included (2) 2009 National Institute for AGCM5.7 (2) (Takemura et (2) (Takata et al., 2003) (2) 1/4° by 1/6° (average grid spac- Environmental Studies, (2) 0.5625 × 0.5625° al., 2000; Takemura ing is 0.28° and 0.19° for zonal and and Japan Agency for T213 et al., 2002) meridional directions) Marine-Earth Science and (3) 56 (3) 48 Technology (4) about 0.9 hPa (4) 1.25 m (2) (Sakamoto et al., 2012) (5) hybrid z-s (6) nonlinear split-explicit (7) (Hasumi and Emori, 2004) (1) MIROC5 (1) University of Tokyo, (1) CCSR/NIES/ FRCGC (1) SPRINTARS Not implemented (1) MATSIRO (1) COCO4.5 Not implemented (1) Included (2) 2010 National Institute for AGCM6 (2) (Takemura et (2) (Takata et al., 2003) (2) 1.4° (zonally) × 0.5 1.4° (2) (Komuro et Environmental Studies, (2) 1.40625 × 1.40625° al., 2005; Takemura (meridionally) al., 2012) and Japan Agency for T85 et al., 2009) (3) 50 Marine-Earth Science and (3) 40 (4) 1.25 m Technology (4) about 2.9 hPa (5) hybrid z-s (2) (Watanabe et al., 2010) (6) linear split-explicit (7) (Hasumi and Emori, 2004) (1) MIROC-ESM (1) University of Tokyo, (1) MIROC-AGCM (1) SPRINTARS Not implemented (1) MATSIRO (1) COCO3.4 (1) NPZD-type Included (2) 2010 National Institute for (2) 2.8125 × 2.8125° T42 (2) (Takemura et (2) (Takata et al., 2003) (2) 1.4° (zonally) × 0.5 1.4° (2) (Schmittner Environmental Studies, (3) 80 al., 2005; Takemura (meridionally) et al., 2005) and Japan Agency for (4) 0.003 hPa et al., 2009) (3) 44 Marine-Earth Science and (5) (Watanabe, 2008) (4) 1.25 m Technology (5) hybrid z-s (2) (Watanabe et al., 2011) (6) linear split-explicit (7) (Hasumi and Emori, 2004) (continued on next page) Chapter 9 861 9 9 Table 9.A.1 (continued) (1) Model Name (1) Institution Atmosphere Aerosol Atmos Chemistry Land Surface Ocean Ocean Biogeo- Sea Ice 862 (2) Vintage (2) Main Reference(s) (1) Component Name (1) Component (1) Component Name (1) Component Name (1) Component Name chemistry (1) Component (2) Horizontal Grid Name or type (2) References (2) References (2) Horizontal Resolution (1) Component Name Name Chapter 9 (3) Number of Vert (2) References (3) Number of Vertical Levels (2) References (2) References Levels (4) Top Level (4) Grid Top (5) z Co-ord (5) References (6) Top BC (7) References (1) MIROC-ESM-CHEM (1) University of Tokyo, (1) MIROC-AGCM (1) SPRINTARS (1) CHASER (1) MATSIRO (1) COCO3.4 (1) NPZD-type Included (2) 2010 National Institute for (2) 2.8125 × 2.8125° T42 (2) (Takemura et (2) (Sudo et al., 2002) (2) (Takata et al., 2003) (2) 1.4° (zonally) × 0.5 1.4° (2) (Schmittner et al., Environmental Studies, (3) 80 al., 2005; Takemura (meridionally) 2005) and Japan Agency for (4) 0.003 hPa et al., 2009) (3) 44 Marine-Earth Science and (5) (Watanabe, 2008) (4) 1.25 m Technology (5) hybrid z-s (2) (Watanabe et al., 2011) (6) linear split-explicit (7) (Hasumi and Emori, 2004) (1) MPI-ESM-LR (1) Max Planck Institute for (1) ECHAM6 Prescribed Not implemented (1) JSBACH (1) MPIOM (1) HAMOCC (1) Included (2) 2009 Meteorology (2) approx. 1.8° T63 (2) (Reick et al., 2013) (2) average 1.5° GR15 (2) (Maier-Reimer et al., (2) (Notz et al., (2) (3) 47 (3) 40 2005; Ilyina et al., 2013) 2013) (4) 0.01 hPa (4) 6 m (5) (Stevens et al., 2012) (5) depth (6) linear implicit (7) (Jungclaus et al., 2013) (1) MPI-ESM-MR (1) Max Planck Institute for (1) ECHAM6 Prescribed Not implemented (1) JSBACH (1) MPIOM (1) HAMOCC (1) Included (2) 2009 Meteorology (2) approx. 1.8° T63 (2) (Reick et al., 2013) (2) approx. 0.4° TP04 (2) (Maier-Reimer et al., (2) (Notz et al., (2) (3) 95 (3) 40 2005; Ilyina et al., 2013) 2013) (4) 0.01 hPa (4) 6 m (5) (Stevens et al., 2012) (5) depth (6) linear implicit (7) (Jungclaus et al., 2013) (1) MPI-ESM-P (1) Max Planck Institute for (1) ECHAM6 Prescribed Not implemented (1) JSBACH (1) MPIOM (1) HAMOCC (1) Included (2) 2009 Meteorology (2) approx. 1.8° T63 (2) (Reick et al., 2013) (2) average 1.5° GR15 (2) (Maier-Reimer et al., (2) (Notz et al., (2) (3) 47 (3) 40 2005; Ilyina et al., 2013) 2013) (4) 0.01 hPa (4) 6 m (5) (Stevens et al., 2012) (5) depth (6) linear implicit (7) (Jungclaus et al., 2013) (1) MRI-AGCM3.2H (1) Meteorological (1) Included Prescribed Not implemented (1) SiB0109 Not implemented Not implemented Not implemented (2) 2009 Research Institute (2) 640 × 320 TL319 (2) (Hirai et al., 2007; (2) (Mizuta et al., 2012) (3) 64 Yukimoto et al., 2011; (4) 0.01 hPa Yukimoto et al., 2012) (1) MRI-AGCM3.2S (1) Meteorological (1) Included Prescribed Not implemented (1) SiB0109 Not implemented Not implemented Not implemented (2) 2009 Research Institute (2) (2) 1920 × 960 TL959 (2) (Hirai et al., 2007; (Mizuta et al., 2012) (3) 64 Yukimoto et al., 2011; (4) 0.01 hPa Yukimoto et al., 2012) (5) (Mizuta et al., 2012) (1) MRI-CGCM3 (1) Meteorological (1) MRI-AGCM3.3 (1) MASINGAR mk-2 Not implemented (1) HAL (1) MRI.COM3 Not implemented (1) Included (MRI. (2) 2011 Research Institute (2) 320 × 160 TL159 (2) (Yukimoto et al., (2) (Yukimoto et al., 2011; (2) 1 × 0.5 COM3) (2) (Yukimoto et al., 2011; (3) 48 2011; Yukimoto et Yukimoto et al., 2012) (3) 50 + 1 Bottom Boundary Layer (2) (Tsujino et al., Yukimoto et al., 2012) (4) 0.01 hPa al., 2012; Adachi (4) 0 m 2011; Yukimoto et (5) (Yukimoto et al., 2011; et al., 2013) (5) hybrid sigma-z al., 2011; Yukimoto Yukimoto et al., 2012) (6) nonlinear split-explicit et al., 2012) (7) (Tsujino et al., 2011; Yukimoto et al., 2011; Yukimoto et al., 2012) Evaluation of Climate Models (continued on next page) Table 9.A.1 (continued) (1) Model Name (1) Institution Atmosphere Aerosol Atmos Chemistry Land Surface Ocean Ocean Biogeo- Sea Ice (2) Vintage (2) Main Reference(s) (1) Component Name (1) Component (1) Component Name (1) Component Name (1) Component Name chemistry (1) Component (2) Horizontal Grid Name or type (2) References (2) References (2) Horizontal Resolution (1) Component Name Name (3) Number of Vert (2) References (3) Number of Vertical Levels (2) References (2) References Levels (4) Top Level (4) Grid Top (5) z Co-ord (5) References (6) Top BC (7) References (1) MRI-ESM1 (1) Meteorological (1) MRI-AGCM3.3 (1) MASINGAR mk-2 (1) MRI-CCM2 (1) HAL (1) MRI.COM3 (1) Included (MRI.COM3) (1) Included (MRI. (2) 2011 Research Institute (2) TL159(320 × 160) (2) (Yukimoto et al., (2) (Deushi and Shibata, (2) (Yukimoto et al., 2011; (2) 1x0.5 (2) (Nakano et al., 2011; COM3) (2) (Yukimoto et al., 2011; (3) 48 2011; Yukimoto et 2011; Yukimoto et al., Yukimoto et al., 2012) (3) 50 + 1 Bottom Boundary Layer Adachi et al., 2013) (2) (Tsujino et al., Evaluation of Climate Models Yukimoto et al., 2012; (4) 0.01 hPa al., 2012; Adachi 2011; Adachi et al., 2013) (4) 0m 2011; Yukimoto et Adachi et al., 2013) (5) (Yukimoto et al., 2011; et al., 2013) (5) hybrid sigma-z al., 2011; Yukimoto Yukimoto et al., 2012; (6) non-linear split-explicit et al., 2012) Adachi et al., 2013) (7)(Tsujino et al., 2011; Yukimoto et al., 2011; Yukimoto et al., 2012) (1) NCEP-CFSv2 (1) National Centers for (1) Global Forecast Model Semi-interactive (1) Ozone chemistry (1) Noah Land Surface (1) MOM4 Not implemented (1) SIS (2) 2011 Environmental Prediction (2) 0.9375 T126 (2) (McCormack Model (2) 0.5° zonal resolution, meridional (2) (Hunke and (3) 64 et al., 2006) (2) (Ek et al., 2003) resolution varying from 0.25° at Dukowicz, 1997; (4) 0.03 hPa the equator to 0.5° north/south of Winton, 2000) (5) (Saha et al., 2010) 10N/10S. Tripolar. (3) 40 (4) 5.0 m (5) depth (6) nonlinear split explicit (7) (Griffies et al., 2004) (1) NorESM1-M (1) Norwegian Climate (1) CAM4-Oslo (1) CAM4-Oslo (1) CAM4-Oslo (1) CLM4 (1) NorESM-Ocean Not implemented (1)CICE4 (2) 2011 Centre (2) Finite Volume 1.9° (2) (Kirkevag et (2) (Kirkevag et al., 2013) (2) (Oleson et al., 2010; (2) 1.125° along the equator (2)(Hunke and (2) (Iversen et al., 2013) latitude, 2.5° longitude al., 2013) Lawrence et al., 2011) (3) 53 Lipscomb, 2008; (3) 26 (4) 1 m Holland et al., 2012) (4) 2.194067 hPa (5) hybrid z isopycnic (5) (Neale et al., 2010; (6) nonlinear split-explicit Kirkevag et al., 2013) (7) (1) NorESM1-ME (1) Norwegian Climate (1) CAM4-Oslo (1) CAM4-Oslo (1) CAM4-Oslo (1) CLM4 (1) NorESM-Ocean (1) HAMOCC5 (1) CICE4 (2) 2012 Centre (2) Finite Volume 1.9° (2) (Kirkevag et (2) (Kirkevag et al., 2013) (2) (Oleson et al., 2010; (2) 1.125° along the equator (2) (Maier-Reimer et (2) (Hunke and (2) (Tjiputra et al., 2013) latitude, 2.5° longitude al., 2013) Lawrence et al., 2011) (3) 53 al., 2005; Assmann Lipscomb, 2008; (3) 26 (4) 1 m et al., 2010; Tjipu- Holland et al., 2012) (4) 2.194067 hPa (5) hybrid z isopycnic tra et al., 2013) (5) (Neale et al., 2010; (6) nonlinear split-explicit Kirkevag et al., 2013) (7) Chapter 9 863 9 9 Table 9.A.2 | Salient features of the Earth system Models of Intermediate Complexity (EMICs) assessed in the AR5 (see also Table 9.2). Column 1: Model name used in WG1 and the official model version along with the first publication for 864 each model; subsequent columns for each of the eight component models with specific information and the related references are provided. This information was initially gathered for the EMIC intercomparison project in Eby et al. (2013). (1) Model name Atmospherea Oceanb Sea Icec Couplingd Land Surfacee Biospheref Ice Sheetsg Sediment and Chapter 9 (2) Model version (1) Model type (1) Model type (1) Schemes (1) Flux adjustment (1) Soil schemes (1) Ocean and references (1) Model type Weatheringh (3) Main reference (2) Dimensions (2) Dimensions (2) References (2) References (2) References (2) Land and references (2) Dimensions (1) Model type (3) Resolution (3) Resolution (3) Vegetation and references (3) Resolution (2) References (4) Radiation (4) Parametrizations (4) References and cloudiness (5) References (5) References (1) Bern3D (1) EMBM (1) FG with parameterized (1) 0-LT, DOC, 2-LIT (1) PM, NH, RW (1) Bern3D: 1-LST, (1) BO (Parekh et al., 2008; Tschumi N/A (1) CS, SW (2) Bern3D-LPJ (2) 2-D(, ) zonal pressure gradient NSM, RIV et al., 2008; Gangsto et al., 2011) (2) (Tschumi et (3) (Ritz et al., 2011) (3) 10° × (3 19)° (2) 3-D LPJ: 8-LST, CSM with (2) BT (Sitch et al., 2003; Strassmann al., 2011) (4) NCL (3) 10° × (3 19)°, L32 uncoupled hydrology et al., 2008; Stocker et al., 2011) (5) (4) RL, ISO, MESO (2) (Wania et al., 2009) (3) BV (Sitch et al., 2003) (5) (Muller et al., 2006) (1) CLIMBER2 (1) SD (1) FG, (1) 1-LT, PD, 2-LIT (1) NM, NH, NW (1) 1-LST, CSM, RIV (1,2,3) BO, BT, BV (Brovkin et al., 2002) (1) TM N/A (2) CLIMBER-2.4 (2) 3-D (2) 2-D(,z) (2) (Petoukhov (2) (Petoukhov (2) (Petoukhov et al., (2) 3-D (3) (Petoukhov et al., (3) 10° × 51°, L10 (3) 2.5°, L21 et al., 2000) et al., 2000) 2000) (3) 0.75° × 1.5°, L20 2000) (4) CRAD, ICL (4) RL (4) (Calov et al., (5) (5) (Wright and Stocker, 2002) 1992) (1) CLIMBER3 (1) SD (1) PE (1) 2-LT, R, 2-LIT (1) AM, NH, RW (1) 1-LST, CSM, RIV (1) BO (Six and Maier-Reimer, 1996) N/A N/A (2) CLIMBER-3 (2) 3-D (2) 3-D (2) (Fichefet and (2) (Petoukhov et al., (2,3) BT, BV (Brovkin et al., 2002) (3) (Montoya et al., (3) 7.5° × 22.5°, L10 (3) 3.75° × 3.75°, L24 Morales Maqueda, 2000) 2005) (4) CRAD, ICL (4) FS, ISO, MESO, TCS, DC 1997) (5) (Petoukhov et al., 2000) (1) DCESS (1) EMBM (1) 2-box in (1) Parameter- (1) NH, NW (1) NST, NSM (1,2) BO, BT (Shaffer et al., 2008) N/A (1) CS, SW (2) DCESS (2) 2-box in , (2) ized from surface (2) (Shaffer et (2) (Shaffer et al., 2008) (2) (Shaffer et (3) (Shaffer et al., 2008) (3) (3) L55 temperature al., 2008) al., 2008) (4) LRAD, CHEM (4) parameterized circulation (2) (Shaffer et (5) (Shaffer et al., 2008) and exchange, MESO al., 2008) (5) (Shaffer and Sarmiento, 1995) (1) FAMOUS (1) PE (1) PE (1) 0-LT, DOC, 2-LIT (1) NM, NH, NW (1) 4-LST, CSM, RIV (1) BO (Palmer and Totterdell, 2001) N/A N/A (2) FAMOUS XDBUA (2) 3-D (2) 3-D (2) (Cox et al., 1999) (3) (Smith et al., 2008) (3) 5° × 7.5°, L11 (3) 2.5° × 3.75°, L20 (4) CRAD, ICL (4) RL, ISO, MESO (5) (Pope et al., 2000) (5) (Gordon et al., 2000) (1) GENIE (1) EMBM (1) FG (1) 1-LT, DOC, 2-LIT (1) PM, NH, RW (1) 1-LST, BSM, RIV (1,2) BO, BT (Williamson et al., 2006; N/A (1) CS, SW (2) GENIE (2) 2-D(, ) (2) 3-D (2) (Marsh et al., (2) (Marsh et al., (2) (Williamson et al., Ridgwell et al., 2007b; Holden et al., 2013) (2) (Ridgwell and (3) (Holden et al., 2013) (3) 10° × (3 19)° (3) 10° × (3 19) °, L16 2011) 2011) 2006) Hargreaves, 2007) (4) NCL (4) RL, ISO, MESO (5) (Marsh et al., 2011) (5) (Marsh et al., 2011) (1) IAP RAS CM (1) SD (1) PE (1) 0-LT, 2-LIT (1) NM, NH, NW (1) 240-LST, CSM (2) BT (Eliseev and Mokhov, 2011) N/A N/A (2) IAP RAS CM (2) 3-D (2) 3-D (2) (Muryshev et al., (2) (Muryshev et al., (2) (Arzhanov et al., 2008) (3) (Eliseev and (3) 4.5° × 6°, L8 (3) 3.5° × 3.5°, L32 2009) 2009) Mokhov, 2011) (4) CRAD, ICL (4) RL, ISO, TCS (5) (Petoukhov et al., (5) (Muryshev et al., 2009) 1998) (continued on next page) Evaluation of Climate Models Table 9.A.2 (continued) (1) Model name Atmospherea Oceanb Sea Icec Couplingd Land Surfacee Biospheref Ice Sheetsg Sediment and (2) Model version (1) Model type (1) Model type (1) Schemes (1) Flux adjustment (1) Soil schemes (1) Ocean and references (1) Model type Weatheringh (3) Main reference (2) Dimensions (2) Dimensions (2) References (2) References (2) References (2) Land and references (2) Dimensions (1) Model type (3) Resolution (3) Resolution (3) Vegetation and references (3) Resolution (2) References (4) Radiation (4) Parametrizations (4) References and cloudiness (5) References (5) References (1) IGSM2 (1) SD (1) Q-flux mixed-layer, (1) 2-LT (1) Q-flux (1) CSM (1) BO (Holian et al., 2001) N/A N/A (2) IGSM 2.2 (2) 2-D(, Z) anomaly diffusing, (2) (Hansen et (2) (Sokolov et (2) (Oleson et al., 2008b) (2) BT (Melillo et al., 1993; Liu, 1996; (3) (Sokolov et (3) 4° × 360° , L11 (2) 3-D al., 1984) al., 2005) Felzer et al., 2004) al., 2005) (4) ICL, CHEM (3) 4° × 5°, L11 Evaluation of Climate Models (5) (Sokolov and (4) Stone, 1998) (5) (Hansen et al., 1984) (1) LOVECLIM1.2 (1) QG (1) PE (1) 3-LT, R, 2-LIT (1) NM, NH, RW (1) 1-LST, BSM, RIV (1) BO (Mouchet and François, 1996) (1) TM N/A (2) LOVECLIM1.2 (2) 3-D (2) 3-D (2) (Fichefet and (2) (Goosse et al., (2) (Goosse et al., 2010) (2,3) BT, BV (Brovkin et al., 2002) (2) 3-D (3) (Goosse et al., (3) 5.6° × 5.6°, L3 (3) 3° × 3°, L30 Morales Maqueda, 2010) (3) 10 km × 10 km, 2010) (4) LRAD, NCL (4) FS, ISO, MESO, TCS, DC 1997) L30 (5) (Opsteegh et al., (5) (Goosse and (4) (Huybrechts, 1998) Fichefet, 1999) 2002) (1) MESMO (1) EMBM (1) FG (1) 0-LT, DOC, 2-LIT (1) PM, NH, RW (1) NST, NSM, RIV (1) BO (Matsumoto et al., 2008) N/A N/A (2) MESMO 1.0 (2) 2-D(, ) (2) 3-D (2) (Edwards and (2) (Edwards and (3) (Matsumoto (3) 10° × (3 19)° (3) 10° × (3 19)°, L16 Marsh, 2005) Marsh, 2005) et al., 2008) (4) NCL, (4) RL, ISO, MESO (5) (Fanning and (5) (Edwards and Weaver, 1996) Marsh, 2005) (1) MIROC-lite (1) EMBM (1) PE (1) 0-LT, R, 2-LIT (1) PM, NH, NW (1) 1-LST, BSM N/A N/A N/A (2) MIROC-lite (2) 2-D(, ) (2) 3-D (2) (Hasumi, 2006) (2) (Oka et al., 2011) (2) (Oka et al., 2011) (3) (Oka et al., 2011) (3) 4° × 4° (3) 4° × 4° (4) NCL (4) FS, ISO, MESO, TCS (5) (Oka et al., 2011) (5) (Hasumi, 2006) (1) MIROC-lite-LCM (1) EMBM, tuned (1) PE (1) 0-LT, R, 2-LIT (1) NM, NH RW (1) 1-LST, BSM (1) BO (Palmer and Totterdell, 2001) N/A N/A (2) MIROC-lite-LCM for 3 K equilibrium (2) 3-D (2) (Hasumi, 2006) (2) (Oka et al., 2011) (2) (Oka et al., 2011) (2) loosely coupled BT (Ito and Oikawa, (3) (Tachiiri et al., 2010) climate sensitivity (3) 6° × 6°, L15 (Tachiiri et al., 2010) 2002) (2) 2-D(, ) (4) FS, ISO, MESO, TCS (3) 6° × 6° (5) (Hasumi, 2006) (4) NCL (5) (Oka et al., 2011) (1) SPEEDO (1) PE (1) PE (1) 3-LT, R, 2-LIT (1)NM, NH, NW (1) 1-LST, BSM, RIV N/A N/A N/A (2) SPEEDO V2.0 (2) 3-D (2) 3-D (2) (Fichefet and (2) (Cimatoribus (2) (Opsteegh et al., 1998) (3) (Severijns and (3) T30, L8 (3) 3° × 3°, L20 Morales Maqueda, et al., 2012) Hazeleger, 2010) (4) LRAD, IDL, (4) FS, ISO, MESO, TCS, DC 1997) (5) (Molteni, 2003) (5) (Goosse and Fichefet, 1999) (1) UMD (1) QG (1) Q-flux mixed-layer N/A (1) Energy and water (1) 2-LST with 2-layer (1) BO (Archer et al., 2000) N/A N/A (2) UMD 2.0 (2) 3-D (2) 2-D surface, deep exchange only soil moisture (2,3) BT, BV (Zeng, 2003; Zeng et al., (3) (Zeng et al., 2004) (3) 3.75° × 5.625°, L2 ocean box model (2) (Zeng et al., 2004) (2) (Zeng et al., 2000) 2005; Zeng, 2006) (4) LRAD, ICL (3) 3.75° × 5.625° (5) (Neelin and Zeng, (5) (Hansen et al., 1983), 2000; Zeng et al., 2000) (continued on next page) Chapter 9 865 9 9 Table 9.A.2 (continued) (1) Model name Atmospherea Oceanb Sea Icec Couplingd Land Surfacee Biospheref Ice Sheetsg Sediment and 866 (2) Model version (1) Model type (1) Model type (1) Schemes (1) Flux adjustment (1) Soil schemes (1) Ocean and references (1) Model type Weatheringh (3) Main reference (2) Dimensions (2) Dimensions (2) References (2) References (2) References (2) Land and references (2) Dimensions (1) Model type Chapter 9 (3) Resolution (3) Resolution (3) Vegetation and references (3) Resolution (2) References (4) Radiation (4) Parametrizations (4) References and cloudiness (5) References (5) References (1) Uvic (1) DEMBM (1) PE (1) 0-LT, R, 2-LIT (1) AM, NH, NW (1) 1-LST, CSM, RIV (1) BO (Schmittner et al., 2005) (1) TM (1) CS, SW (2) UVic 2.9 (2) 2-D(, ) (2) 3-D (2) (Weaver et al., (2) (Weaver et al., (2) (Meissner et al., 2003) (2,3) BT, BV (Cox, 2001) (2) 3-D (2) (Eby et al., 2009) (3) (Weaver et al., (3) 1.8° × 3.6° (3) 1.8° × 3.6°, L19 2001) 2001) (3) 20 km × 20 km, 2001) (4) NCL (4) RL, ISO, MESO L10 (5) (Weaver et al., 2001) (5) (Weaver et al., 2001) (4) (Fyke et al., 2011) Notes: (a) EMBM = energy moisture balance model; DEMBM = energy moisture balance model including some dynamics; SD = statistical-dynamical model; QG = quasi-geostrophic model; 2-D(, ) = vertically averaged; 3-D = three- dimensional; LRAD = linearized radiation scheme; CRAD = comprehensive radiation scheme; NCL = non-interactive cloudiness; ICL = interactive cloudiness; CHEM = chemistry module; n° × m° = n degrees latitude by m degrees longitude horizontal resolution; Lp = p vertical levels. (b) FG = frictional geostrophic model; PE = primitive equation model; 2-D(, z) = zonally averaged; 3-D = three-dimensional; RL = rigid lid; FS = free surface; ISO = isopycnal diffusion; MESO = parameterization of the effect of mesoscale eddies on tracer distribution; TCS = complex turbulence closure scheme; DC = parameterization of density-driven downward-sloping currents; n° × m° = n degrees latitude by m degrees longitude horizontal resolution; Lp = p vertical levels. (c) n-LT = n-layer thermodynamic scheme; PD = prescribed drift; DOC = drift with oceanic currents; R = viscous-plastic or elastic-viscous-plastic rheology; 2-LIT = two-level ice thickness distribution (level ice and leads). (d) PM = prescribed momentum flux; AM = momentum flux anomalies relative to the control run are computed and added to climatological data; NM = no momentum flux adjustment; NH = no heat flux adjustment; RW = regional freshwater flux adjustment; NW = no freshwater flux adjustment. (e) NST = no explicit computation of soil temperature; n-LST = n-layer soil temperature scheme; NSM = no moisture storage in soil; BSM = bucket model for soil moisture; CSM = complex model for soil moisture; RIV = river routing scheme. (f) BO = model of oceanic carbon dynamics; BT = model of terrestrial carbon dynamics; BV = dynamical vegetation model. (g) TM = thermomechanical model; 3-D = three-dimensional; n° × m° = n degrees latitude by m degrees longitude horizontal resolution; n km × m km = horizontal resolution in kilometres; Lp = p vertical levels. (h) CS = complex ocean sediment model; SW = simple, specified or diagnostic weathering model. Evaluation of Climate Models Detection and Attribution of Climate Change: 10 from Global to Regional Coordinating Lead Authors: Nathaniel L. Bindoff (Australia), Peter A. Stott (UK) Lead Authors: Krishna Mirle AchutaRao (India), Myles R. Allen (UK), Nathan Gillett (Canada), David Gutzler (USA), Kabumbwe Hansingo (Zambia), Gabriele Hegerl (UK/Germany), Yongyun Hu (China), Suman Jain (Zambia), Igor I. Mokhov (Russian Federation), James Overland (USA), Judith Perlwitz (USA), Rachid Sebbari (Morocco), Xuebin Zhang (Canada) Contributing Authors: Magne Aldrin (Norway), Beena Balan Sarojini (UK/India), Jürg Beer (Switzerland), Olivier Boucher (France), Pascale Braconnot (France), Oliver Browne (UK), Ping Chang (USA), Nikolaos Christidis (UK), Tim DelSole (USA), Catia M. Domingues (Australia/Brazil), Paul J. Durack (USA/ Australia), Alexey Eliseev (Russian Federation), Kerry Emanuel (USA), Graham Feingold (USA), Chris Forest (USA), Jesus Fidel González Rouco (Spain), Hugues Goosse (Belgium), Lesley Gray (UK), Jonathan Gregory (UK), Isaac Held (USA), Greg Holland (USA), Jara Imbers Quintana (UK), William Ingram (UK), Johann Jungclaus (Germany), Georg Kaser (Austria), Veli-Matti Kerminen (Finland), Thomas Knutson (USA), Reto Knutti (Switzerland), James Kossin (USA), Mike Lockwood (UK), Ulrike Lohmann (Switzerland), Fraser Lott (UK), Jian Lu (USA/Canada), Irina Mahlstein (Switzerland), Valérie Masson-Delmotte (France), Damon Matthews (Canada), Gerald Meehl (USA), Blanca Mendoza (Mexico), Viviane Vasconcellos de Menezes (Australia/ Brazil), Seung-Ki Min (Republic of Korea), Daniel Mitchell (UK), Thomas Mölg (Germany/ Austria), Simone Morak (UK), Timothy Osborn (UK), Alexander Otto (UK), Friederike Otto (UK), David Pierce (USA), Debbie Polson (UK), Aurélien Ribes (France), Joeri Rogelj (Switzerland/ Belgium), Andrew Schurer (UK), Vladimir Semenov (Russian Federation), Drew Shindell (USA), Dmitry Smirnov (Russian Federation), Peter W. Thorne (USA/Norway/UK), Muyin Wang (USA), Martin Wild (Switzerland), Rong Zhang (USA) Review Editors: Judit Bartholy (Hungary), Robert Vautard (France), Tetsuzo Yasunari (Japan) This chapter should be cited as: Bindoff, N.L., P.A. Stott, K.M. AchutaRao, M.R. Allen, N. Gillett, D. Gutzler, K. Hansingo, G. Hegerl, Y. Hu, S. Jain, I.I. Mokhov, J. Overland, J. Perlwitz, R. Sebbari and X. Zhang, 2013: Detection and Attribution of Climate Change: from Global to Regional. In: Climate Change 2013: The Physical Science Basis. Contribution of Working Group I to the Fifth Assessment Report of the Intergovernmental Panel on Climate Change [Stocker, T.F., D. Qin, G.-K. Plattner, M. Tignor, S.K. Allen, J. Boschung, A. Nauels, Y. Xia, V. Bex and P.M. Midgley (eds.)]. Cambridge University Press, Cambridge, United Kingdom and New York, NY, USA. 867 Table of Contents Executive Summary...................................................................... 869 10.7 Multi-century to Millennia Perspective..................... 917 10.7.1 Causes of Change in Large-Scale Temperature over 10.1 Introduction....................................................................... 872 the Past Millennium................................................... 917 10.7.2 Changes of Past Regional Temperature...................... 919 10.2 Evaluation of Detection and Attribution Methodologies.................................................................. 872 10.7.3 Summary: Lessons from the Past................................ 919 10.2.1 The Context of Detection and Attribution.................. 872 10.8 Implications for Climate System Properties 10.2.2 Time Series Methods, Causality and Separating and Projections................................................................. 920 Signal from Noise....................................................... 874 10.8.1 Transient Climate Response....................................... 920 Box 10.1: How Attribution Studies Work................................. 875 10.8.2 Constraints on Long-Term Climate Change and the 10.2.3 Methods Based on General Circulation Models Equilibrium Climate Sensitivity................................... 921 and Optimal Fingerprinting........................................ 877 10.8.3 Consequences for Aerosol Forcing and Ocean 10.2.4 Single-Step and Multi-Step Attribution and the Heat Uptake............................................................... 926 Role of the Null Hypothesis........................................ 878 10.8.4 Earth System Properties............................................. 926 10.3 Atmosphere and Surface............................................... 878 10.9 Synthesis............................................................................. 927 10.3.1 Temperature............................................................... 878 10 10.9.1 Multi-variable Approaches......................................... 927 Box 10.2: The Sun s Influence on the Earth s Climate............ 885 10.9.2 Whole Climate System............................................... 927 10.3.2 Water Cycle................................................................ 895 10.3.3 Atmospheric Circulation and Patterns of References .................................................................................. 940 Variability................................................................... 899 Frequently Asked Questions 10.4 Changes in Ocean Properties....................................... 901 FAQ 10.1 Climate Is Always Changing. How Do We 10.4.1 Ocean Temperature and Heat Content....................... 901 Determine the Causes of Observed Changes?.................................................................. 894 10.4.2 Ocean Salinity and Freshwater Fluxes........................ 903 FAQ 10.2 When Will Human Influences on Climate 10.4.3 Sea Level.................................................................... 905 Become Obvious on Local Scales?........................ 928 10.4.4 Oxygen and Ocean Acidity......................................... 905 Supplementary Material 10.5 Cryosphere......................................................................... 906 Supplementary Material is available in online versions of the report. 10.5.1 Sea Ice....................................................................... 906 10.5.2 Ice Sheets, Ice Shelves and Glaciers........................... 909 10.5.3 Snow Cover................................................................ 910 10.6 Extremes............................................................................. 910 10.6.1 Attribution of Changes in Frequency/Occurrence and Intensity of Extremes.......................................... 910 10.6.2 Attribution of Weather and Climate Events................ 914 868 Detection and Attribution of Climate Change: from Global to Regional Chapter 10 Executive Summary Northern Hemisphere (NH) warming over the same period is far out- side the range of any similar length trends in residuals from reconstruc- Atmospheric Temperatures tions of the past millennium. The spatial pattern of observed warming differs from those associated with internal variability. The model-based More than half of the observed increase in global mean surface simulations of internal variability are assessed to be adequate to make temperature (GMST) from 1951 to 2010 is very likely1 due to the this assessment. {9.5.3, 10.3.1, 10.7.5, Table 10.1} observed anthropogenic increase in greenhouse gas (GHG) con- centrations. The consistency of observed and modeled changes across It is likely that anthropogenic forcings, dominated by GHGs, the climate system, including warming of the atmosphere and ocean, have contributed to the warming of the troposphere since 1961 sea level rise, ocean acidification and changes in the water cycle, the and very likely that anthropogenic forcings, dominated by the cryosphere and climate extremes points to a large-scale warming depletion of the ozone layer due to ozone-depleting substanc- resulting primarily from anthropogenic increases in GHG concentra- es, have contributed to the cooling of the lower stratosphere tions. Solar forcing is the only known natural forcing acting to warm since 1979. Observational uncertainties in estimates of tropospheric the climate over this period but it has increased much less than GHG temperatures have now been assessed more thoroughly than at the forcing, and the observed pattern of long-term tropospheric warming time of AR4. The structure of stratospheric temperature trends and and stratospheric cooling is not consistent with the expected response multi-year to decadal variations are well represented by models and to solar irradiance variations. The Atlantic Multi-decadal Oscillation physical understanding is consistent with the observed and modelled (AMO) could be a confounding influence but studies that find a signif- evolution of stratospheric temperatures. Uncertainties in radiosonde icant role for the AMO show that this does not project strongly onto and satellite records make assessment of causes of observed trends in 1951 2010 temperature trends. {10.3.1, Table 10.1} the upper troposphere less confident than an assessment of the overall atmospheric temperature changes. {2.4.4, 9.4.1, 10.3.1, Table 10.1} It is extremely likely that human activities caused more than half of the observed increase in GMST from 1951 to 2010. This Further evidence has accumulated of the detection and attri- assessment is supported by robust evidence from multiple studies bution of anthropogenic influence on temperature change in 10 using different methods. Observational uncertainty has been explored different parts of the world. Over every continental region, except much more thoroughly than previously and the assessment now con- Antarctica, it is likely that anthropogenic influence has made a sub- siders observations from the first decade of the 21st century and sim- stantial contribution to surface temperature increases since the mid- ulations from a new generation of climate models whose ability to 20th century. The robust detection of human influence on continental simulate historical climate has improved in many respects relative to scales is consistent with the global attribution of widespread warming the previous generation of models considered in AR4. Uncertainties in over land to human influence. It is likely that there has been an anthro- forcings and in climate models temperature responses to individual pogenic contribution to the very substantial Arctic warming over the forcings and difficulty in distinguishing the patterns of temperature past 50 years. For Antarctica large observational uncertainties result response due to GHGs and other anthropogenic forcings prevent a in low confidence2 that anthropogenic influence has contributed to more precise quantification of the temperature changes attributable to the observed warming averaged over available stations. Anthropo- GHGs. {9.4.1, 9.5.3, 10.3.1, Figure 10.5, Table 10.1} genic influence has likely contributed to temperature change in many sub-continental regions. {2.4.1, 10.3.1, Table 10.1} GHGs contributed a global mean surface warming likely to be between 0.5°C and 1.3°C over the period 1951 2010, with the Robustness of detection and attribution of global-scale warm- contributions from other anthropogenic forcings likely to be ing is subject to models correctly simulating internal variabili- between 0.6°C and 0.1°C, from natural forcings likely to be ty. Although estimates of multi-decadal internal variability of GMST between 0.1°C and 0.1°C, and from internal variability likely need to be obtained indirectly from the observational record because to be between 0.1°C and 0.1°C. Together these assessed contri- the observed record contains the effects of external forcings (meaning butions are consistent with the observed warming of approximately the combination of natural and anthropogenic forcings), the standard 0.6°C over this period. {10.3.1, Figure 10.5} deviation of internal variability would have to be underestimated in climate models by a factor of at least three to account for the observed It is virtually certain that internal variability alone cannot warming in the absence of anthropogenic influence. Comparison with account for the observed global warming since 1951. The observations provides no indication of such a large difference between observed global-scale warming since 1951 is large compared to cli- climate models and observations. {9.5.3, Figures 9.33, 10.2, 10.3.1, mate model estimates of internal variability on 60-year time scales. The Table 10.1} In this Report, the following terms have been used to indicate the assessed likelihood of an outcome or a result: Virtually certain 99 100% probability, Very likely 90 100%, 1 Likely 66 100%, About as likely as not 33 66%, Unlikely 0 33%, Very unlikely 0-10%, Exceptionally unlikely 0 1%. Additional terms (Extremely likely: 95 100%, More likely than not >50 100%, and Extremely unlikely 0 5%) may also be used when appropriate. Assessed likelihood is typeset in italics, e.g., very likely (see Section 1.4 and Box TS.1 for more details). In this Report, the following summary terms are used to describe the available evidence: limited, medium, or robust; and for the degree of agreement: low, medium, or high. 2 A level of confidence is expressed using five qualifiers: very low, low, medium, high, and very high, and typeset in italics, e.g., medium confidence. For a given evidence and agreement statement, different confidence levels can be assigned, but increasing levels of evidence and degrees of agreement are correlated with increasing confidence (see Section 1.4 and Box TS.1 for more details). 869 Chapter 10 Detection and Attribution of Climate Change: from Global to Regional The observed recent warming hiatus, defined as the reduction humidity. There is medium confidence that there is an ­ nthropogenic a in GMST trend during 1998 2012 as compared to the trend c ­ontribution to observed increases in atmospheric specific humidi- during 1951 2012, is attributable in roughly equal measure to ty since 1973 and to global scale changes in precipitation patterns a cooling contribution from internal variability and a reduced over land since 1950, including increases in NH mid to high latitudes. trend in external forcing (expert judgement, medium confi- Remaining observational and modelling uncertainties, and the large dence). The forcing trend reduction is primarily due to a negative forc- internal variability in precipitation, preclude a more confident assess- ing trend from both volcanic eruptions and the downward phase of the ment at this stage. {2.5.1, 2.5.4, 10.3.2, Table 10.1} solar cycle. However, there is low confidence in quantifying the role of forcing trend in causing the hiatus because of uncertainty in the mag- It is very likely that anthropogenic forcings have made a dis- nitude of the volcanic forcing trends and low confidence in the aerosol cernible contribution to surface and subsurface oceanic salini- forcing trend. Many factors, in addition to GHGs, including changes ty changes since the 1960s. More than 40 studies of regional and in tropospheric and stratospheric aerosols, stratospheric water vapour, global surface and subsurface salinity show patterns consistent with and solar output, as well as internal modes of variability, contribute to understanding of anthropogenic changes in the water cycle and ocean the year-to-year and decade- to-decade variability of GMST. {Box 9.2, circulation. The expected pattern of anthropogenic amplification of cli- 10.3.1, Figure 10.6} matological salinity patterns derived from climate models is detected in the observations although there remains incomplete understanding Ocean Temperatures and Sea Level Rise of the observed internal variability of the surface and sub-surface salin- ity fields. {3.3.2, 10.4.2, Table 10.1} It is very likely that anthropogenic forcings have made a sub- stantial contribution to upper ocean warming (above 700 m) It is likely that human influence has affected the global water observed since the 1970s. This anthropogenic ocean warming has cycle since 1960. This assessment is based on the combined evidence contributed to global sea level rise over this period through thermal from the atmosphere and oceans of observed systematic changes that expansion. New understanding since AR4 of measurement errors and are attributed to human influence in terrestrial precipitation, atmos- 10 their correction in the temperature data sets have increased the agree- pheric humidity and oceanic surface salinity through its connection ment in estimates of ocean warming. Observations of ocean warming to precipitation and evaporation. This is a major advance since AR4. are consistent with climate model simulations that include anthropo- {3.3.2, 10.3.2, 10.4.2, Table 10.1} genic and volcanic forcings but are inconsistent with simulations that exclude anthropogenic forcings. Simulations that include both anthro- Cryosphere pogenic and natural forcings have decadal variability that is consistent with observations. These results are a major advance on AR4. {3.2.3, Anthropogenic forcings are very likely to have contributed to 10.4.1, Table 10.1} Arctic sea ice loss since 1979. There is a robust set of results from simulations that show the observed decline in sea ice extent is simu- It is very likely that there is a substantial contribution from lated only when models include anthropogenic forcings. There is low anthropogenic forcings to the global mean sea level rise since confidence in the scientific understanding of the observed increase in the 1970s. It is likely that sea level rise has an anthropogenic con- Antarctic sea ice extent since 1979 owing to the incomplete and com- tribution from Greenland melt since 1990 and from glacier mass loss peting scientific explanations for the causes of change and low confi- since 1960s. Observations since 1971 indicate with high confidence dence in estimates of internal variability. {10.5.1, Table 10.1} that thermal expansion and glaciers (excluding the glaciers in Antarc- tica) explain 75% of the observed rise. {10.4.1, 10.4.3, 10.5.2, Table Ice sheets and glaciers are melting, and anthropogenic influ- 10.1, 13.3.6} ences are likely to have contributed to the surface melting of Greenland since 1993 and to the retreat of glaciers since the Ocean Acidification and Oxygen Change 1960s. Since 2007, internal variability is likely to have further enhanced the melt over Greenland. For glaciers there is a high level of scientific It is very likely that oceanic uptake of anthropogenic carbon understanding from robust estimates of observed mass loss, internal dioxide has resulted in acidification of surface waters which variability and glacier response to climatic drivers. Owing to a low level is observed to be between 0.0014 and 0.0024 pH units per of scientific understanding there is low confidence in attributing the year. There is medium confidence that the observed global pattern causes of the observed loss of mass from the Antarctic ice sheet since of decrease in oxygen dissolved in the oceans from the 1960s to the 1993. {4.3.3, 10.5.2, Table 10.1} 1990s can be attributed in part to human influences. {3.8.2, Box 3.2, 10.4.4, Table 10.1} It is likely that there has been an anthropogenic component to observed reductions in NH snow cover since 1970. There is high The Water Cycle agreement across observations studies and attribution studies find a human influence at both continental and regional scales. {10.5.3, Table New evidence is emerging for an anthropogenic influence on 10.1} global land precipitation changes, on precipitation increases in high northern latitudes, and on increases in atmospheric 870 Detection and Attribution of Climate Change: from Global to Regional Chapter 10 Climate Extremes A Millennia to Multi-Century Perspective There has been a strengthening of the evidence for human influ- Taking a longer term perspective shows the substantial role ence on temperature extremes since the AR4 and IPCC Special played by anthropogenic and natural forcings in driving climate Report on Managing the Risks of Extreme Events and Disasters variability on hemispheric scales prior to the twentieth century. to Advance Climate Change Adaptation (SREX) reports. It is very It is very unlikely that NH temperature variations from 1400 to 1850 likely that anthropogenic forcing has contributed to the observed can be explained by internal variability alone. There is medium confi- changes in the frequency and intensity of daily temperature extremes dence that external forcing contributed to NH temperature variability on the global scale since the mid-20th century. Attribution of changes from 850 to 1400 and that external forcing contributed to European in temperature extremes to anthropogenic influence is robustly seen in temperature variations over the last five centuries. {10.7.2, 10.7.5, independent analyses using different methods and different data sets. Table 10.1} It is likely that human influence has substantially increased the prob- ability of occurrence of heatwaves in some locations. {10.6.1, 10.6.2, Climate System Properties Table 10.1} The extended record of observed climate change has allowed In land regions where observational coverage is sufficient for a better characterization of the basic properties of the climate assessment, there is medium confidence that anthropogen- system that have implications for future warming. New evidence ic forcing has contributed to a global-scale intensification of from 21st century observations and stronger evidence from a wider heavy precipitation over the second half of the 20th century. range of studies have strengthened the constraint on the transient There is low confidence in attributing changes in drought over global climate response (TCR) which is estimated with high confidence to land areas since the mid-20th century to human influence owing to be likely between 1°C and 2.5°C and extremely unlikely to be greater observational uncertainties and difficulties in distinguishing decad- than 3°C. The Transient Climate Response to Cumulative CO2 Emissions al-scale variability in drought from long-term trends. {10.6.1, Table (TCRE) is estimated with high confidence to be likely between 0.8°C 10.1} and 2.5°C per 1000 PgC for cumulative CO2 emissions less than about 10 2000 PgC until the time at which temperatures peak. Estimates of the There is low confidence in attribution of changes in tropical Equilibrium Climate Sensitivity (ECS) based on multiple and partly cyclone activity to human influence owing to insufficient obser- independent lines of evidence from observed climate change indicate vational evidence, lack of physical understanding of the links that there is high confidence that ECS is extremely unlikely to be less between anthropogenic drivers of climate and tropical cyclone than 1°C and medium confidence that the ECS is likely to be between activity and the low level of agreement between studies as to 1.5°C and 4.5°C and very unlikely greater than 6°C. These assessments the relative importance of internal variability, and anthropo- are consistent with the overall assessment in Chapter 12, where the genic and natural forcings. This assessment is consistent with that inclusion of additional lines of evidence increases confidence in the of SREX. {10.6.1, Table 10.1} assessed likely range for ECS. {10.8.1, 10.8.2, 10.8.4, Box 12.2} Atmospheric Circulation Combination of Evidence It is likely that human influence has altered sea level pressure Human influence has been detected in the major assessed com- patterns globally. Detectable anthropogenic influence on changes ponents of the climate system. Taken together, the combined in sea level pressure patterns is found in several studies. Changes in evidence increases the level of confidence in the attribution of atmospheric circulation are important for local climate change since observed climate change, and reduces the uncertainties associ- they could lead to greater or smaller changes in climate in a particular ated with assessment based on a single climate variable. From region than elsewhere. There is medium confidence that stratospheric this combined evidence it is virtually certain that human influ- ozone depletion has contributed to the observed poleward shift of the ence has warmed the global climate system. Anthropogenic influ- southern Hadley Cell border during austral summer. There are large ence has been identified in changes in temperature near the surface uncertainties in the magnitude of this poleward shift. It is likely that of the Earth, in the atmosphere and in the oceans, as well as changes stratospheric ozone depletion has contributed to the positive trend in the cryosphere, the water cycle and some extremes. There is strong in the Southern Annular Mode seen in austral summer since the mid- evidence that excludes solar forcing, volcanoes and internal variability 20th century which corresponds to sea level pressure reductions over as the strongest drivers of warming since 1950. {10.9.2, Table 10.1} the high latitudes and an increase in the subtropics. There is medium confidence that GHGs have also played a role in these trends of the southern Hadley Cell border and the Southern Annular Mode in Austral summer. {10.3.3, Table 10.1} 871 Chapter 10 Detection and Attribution of Climate Change: from Global to Regional 10.1 Introduction bution, in which it is necessary to partition the response of the climate system to different forcings, most CMIP5 models include simulations of This chapter assesses the causes of observed changes assessed in the response to natural forcings only, and the response to increases in Chapters 2 to 5 and uses understanding of physical processes, climate well mixed GHGs only (Taylor et al., 2012). models and statistical approaches. The chapter adopts the terminolo- gy for detection and attribution proposed by the IPCC good practice The advances enabled by this greater wealth of observational and guidance paper on detection and attribution (Hegerl et al., 2010) and model data are assessed in this chapter. In this assessment, there is for uncertainty Mastrandrea et al. (2011). Detection and attribution of increased focus on the extent to which the climate system as a whole impacts of climate changes are assessed by Working Group II, where is responding in a coherent way across a suite of climate variables Chapter 18 assesses the extent to which atmospheric and oceanic such as surface mean temperature, temperature extremes, ocean heat changes influence ecosystems, infrastructure, human health and activ- content, ocean salinity and precipitation change. There is also a global ities in economic sectors. to regional perspective, assessing the extent to which not just global mean changes but also spatial patterns of change across the globe can Evidence of a human influence on climate has grown stronger over be attributed to anthropogenic and natural forcings. the period of the four previous assessment reports of the IPCC. There was little observational evidence for a detectable human influence on climate at the time of the First IPCC Assessment Report. By the time 10.2 Evaluation of Detection and Attribution of the second report there was sufficient additional evidence for it to Methodologies conclude that the balance of evidence suggests a discernible human influence on global climate . The Third Assessment Report found that Detection and attribution methods have been discussed in previous a distinct greenhouse gas (GHG) signal was robustly detected in the assessment reports (Hegerl et al., 2007b) and the IPCC Good Practice observed temperature record and that most of the observed warming Guidance Paper (Hegerl et al., 2010), to which we refer. This section over the last fifty years is likely to have been due to the increase in reiterates key points and discusses new developments and challenges. 10 greenhouse gas concentrations. 10.2.1 The Context of Detection and Attribution With the additional evidence available by the time of the Fourth Assess- ment Report, the conclusions were further strengthened. This evidence In IPCC Assessments, detection and attribution involve quantifying the included a wider range of observational data, a greater variety of more evidence for a causal link between external drivers of climate change sophisticated climate models including improved representations of and observed changes in climatic variables. It provides the central, forcings and processes and a wider variety of analysis techniques. although not the only (see Section 1.2.3) line of evidence that has This enabled the AR4 report to conclude that most of the observed supported statements such as the balance of evidence suggests a dis- increase in global average temperatures since the mid-20th century is cernible human influence on global climate or most of the observed very likely due to the observed increase in anthropogenic greenhouse increase in global average temperatures since the mid-20th century is gas concentrations . The AR4 also concluded that discernible human very likely due to the observed increase in anthropogenic greenhouse influences now extend to other aspects of climate, including ocean gas concentrations. warming, continental-average temperatures, temperature extremes and wind patterns. The definition of detection and attribution used here follows the ter- minology in the IPCC guidance paper (Hegerl et al., 2010). Detection A number of uncertainties remained at the time of AR4. For example, of change is defined as the process of demonstrating that climate or the observed variability of ocean temperatures appeared inconsist- a system affected by climate has changed in some defined statistical ent with climate models, thereby reducing the confidence with which sense without providing a reason for that change. An identified change observed ocean warming could be attributed to human influence. Also, is detected in observations if its likelihood of occurrence by chance although observed changes in global rainfall patterns and increases due to internal variability alone is determined to be small (Hegerl in heavy precipitation were assessed to be qualitatively consistent et al., 2010). Attribution is defined as the process of evaluating the with expectations of the response to anthropogenic forcings, detec- relative contributions of multiple causal factors to a change or event tion and attribution studies had not been carried out. Since the AR4, with an assignment of statistical confidence . As this wording implies, improvements have been made to observational data sets, taking more attribution is more complex than detection, combining statistical anal- complete account of systematic biases and inhomogeneities in obser- ysis with physical understanding (Allen et al., 2006; Hegerl and Zwiers, vational systems, further developing uncertainty estimates, and cor- 2011). In general, a component of an observed change is attributed to recting detected data problems (Chapters 2 and 3). A new set of sim- a specific causal factor if the observations can be shown to be consist- ulations from a greater number of AOGCMs have been performed as ent with results from a process-based model that includes the causal part of the Coupled Model Intercomparison Project Phase 5 (CMIP5). factor in question, and inconsistent with an alternate, otherwise iden- These new simulations have several advantages over the CMIP3 sim- tical, model that excludes this factor. The evaluation of this consistency ulations assessed in the AR4 (Hegerl et al., 2007b). They incorporate in both of these cases takes into account internal chaotic variability some moderate increases in resolution, improved parameterizations, and known uncertainties in the observations and responses to external and better representation of aerosols (Chapter 9). Importantly for attri- causal factors. 872 Detection and Attribution of Climate Change: from Global to Regional Chapter 10 Attribution does not require, and nor does it imply, that every aspect et al., 2010), the new guidance recognized that it may be possible, in of the response to the causal factor in question is simulated correct- some instances, to attribute a change in a particular variable to some ly. Suppose, for example, the global cooling following a large volcano external factor before that change could actually be detected in the matches the cooling simulated by a model, but the model underes- variable itself, provided there is a strong body of knowledge that links timates the magnitude of this cooling: the observed global cooling a change in that variable to some other variable in which a change can can still be attributed to that volcano, although the error in magni- be detected and attributed. For example, it is impossible in principle to tude would suggest that details of the model response may be unre- detect a trend in the frequency of 1-in-100-year events in a 100-year liable. Physical understanding is required to assess what constitutes record, yet if the probability of occurrence of these events is physically a plausible discrepancy above that expected from internal variability. related to large-scale temperature changes, and we detect and attrib- Even with complete consistency between models and data, attribution ute a large-scale warming, then the new guidance allows attribution statements can never be made with 100% certainty because of the of a change in probability of occurrence before such a change can be presence of internal variability. detected in observations of these events alone. This was introduced to draw on the strength of attribution statements from, for example, This definition of attribution can be extended to include antecedent time-averaged temperatures, to attribute changes in closely related conditions and internal variability among the multiple causal factors variables. contributing to an observed change or event. Understanding the rela- tive importance of internal versus external factors is important in the Attribution of observed changes is not possible without some kind of analysis of individual weather events (Section 10.6.2), but the primary model of the relationship between external climate drivers and observ- focus of this chapter will be on attribution to factors external to the able variables. We cannot observe a world in which either anthropo- climate system, like rising GHG levels, solar variability and volcanic genic or natural forcing is absent, so some kind of model is needed activity. to set up and evaluate quantitative hypotheses: to provide estimates of how we would expect such a world to behave and to respond to There are four core elements to any detection and attribution study: anthropogenic and natural forcings (Hegerl and Zwiers, 2011). Models may be very simple, just a set of statistical assumptions, or very com- 10 1. Observations of one or more climate variables, such as surface plex, complete global climate models: it is not necessary, or possible, temperature, that are understood, on physical grounds, to be rel- for them to be correct in all respects, but they must provide a physically evant to the process in question consistent representation of processes and scales relevant to the attri- bution problem in question. 2. An estimate of how external drivers of climate change have evolved before and during the period under investigation, includ- One of the simplest approaches to detection and attribution is to com- ing both the driver whose influence is being investigated (such as pare observations with model simulations driven with natural forc- rising GHG levels) and potential confounding influences (such as ings alone, and with simulations driven with all relevant natural and solar activity) anthropogenic forcings. If observed changes are consistent with simu- lations that include human influence, and inconsistent with those that 3. A quantitative physically based understanding, normally encapsu- do not, this would be sufficient for attribution providing there were no lated in a model, of how these external drivers are thought to have other confounding influences and it is assumed that models are sim- affected these observed climate variables ulating the responses to all external forcings correctly. This is a strong assumption, and most attribution studies avoid relying on it. Instead, 4. An estimate, often but not always derived from a physically they typically assume that models simulate the shape of the response based model, of the characteristics of variability expected in these to external forcings (meaning the large-scale pattern in space and/or observed climate variables due to random, quasi-periodic and cha- time) correctly, but do not assume that models simulate the magnitude otic fluctuations generated in the climate system that are not due of the response correctly. This is justified by our fundamental under- to externally driven climate change standing of the origins of errors in climate modelling. Although there is uncertainty in the size of key forcings and the climate response, the A climate model driven with external forcing alone is not expected to overall shape of the response is better known: it is set in time by the replicate the observed evolution of internal variability, because of the timing of emissions and set in space (in the case of surface tempera- chaotic nature of the climate system, but it should be able to capture tures) by the geography of the continents and differential responses of the statistics of this variability (often referred to as noise ). The relia- land and ocean (see Section 10.3.1.1.2). bility of forecasts of short-term variability is also a useful test of the representation of relevant processes in the models used for attribution, So-called fingerprint detection and attribution studies characterize but forecast skill is not necessary for attribution: attribution focuses on their results in terms of a best estimate and uncertainty range for scal- changes in the underlying moments of the weather attractor , mean- ing factors by which the model-simulated responses to individual forc- ing the expected weather and its variability, while prediction focuses ings can be scaled up or scaled down while still remaining consistent on the actual trajectory of the weather around this attractor. with the observations, accounting for similarities between the patterns of response to different forcings and uncertainty due to internal climate In proposing that the process of attribution requires the detection of a variability. If a scaling factor is significantly larger than zero (at some change in the observed variable or closely associated variables (Hegerl significance level), then the response to that forcing, as simulated by 873 Chapter 10 Detection and Attribution of Climate Change: from Global to Regional that model and given that estimate of internal variability and other (Section 10.7.1) (Esper et al., 2012) or derived from control simula- potentially confounding responses, is detectable in these observations, tions of coupled models (Section 10.2.3). The majority of studies use whereas if the scaling factor is consistent with unity, then that mod- modelled variability and routinely check that the residual variability el-simulated response is consistent with observed changes. Studies do from observations is consistent with modelled internal variability used not require scaling factors to be consistent with unity for attribution, over time scales shorter than the length of the instrumental record but any discrepancy from unity should be understandable in terms of (Allen and Tett, 1999). Assessing the accuracy of model-simulated known uncertainties in forcing or response: a scaling factor of 10, for variability on longer time scales using paleoclimate reconstructions is example, might suggest the presence of a confounding factor, calling complicated by the fact that some reconstructions may not capture into question any attribution claim. Scaling factors are estimated by fit- the full spectrum of variability because of limitations of proxies and ting model-simulated responses to observations, so results are unaffect- reconstruction methods, and by the unknown role of external forcing in ed, at least to first order, if the model has a transient climate response, the pre-instrumental record. In general, however, paleoclimate recon- or aerosol forcing, that is too low or high. Conversely, if the spatial or structions provide no clear evidence either way whether models are temporal pattern of forcing or response is wrong, results can be affect- over- or underestimating internal variability on time scales relevant for ed: see Box 10.1 and further discussion in Section 10.3.1.1 and Hegerl attribution (Esper et al., 2012; Schurer et al., 2013). and Zwiers (2011) and Hegerl et al. (2011b). Sensitivity of results to the pattern of forcing or response can be assessed by comparing results 10.2.2 Time Series Methods, Causality and across multiple models or by representing pattern uncertainty explicitly Separating Signal from Noise (Huntingford et al., 2006), but errors that are common to all models (through limited vertical resolution, for example) will not be addressed Some studies attempt to distinguish between externally driven climate in this way and are accounted for in this assessment by downgrading change and changes due to internal variability minimizing the use of overall assessed likelihoods to be generally more conservative than the climate models, for example, by separating signal and noise by time quantitative likelihoods provided by individual studies. scale (Schneider and Held, 2001), spatial pattern (Thompson et al., 2009) or both. Other studies use model control simulations to identify 10 Attribution studies must compromise between estimating responses patterns of maximum predictability and contrast these with the forced to different forcings separately, which allows for the possibility of dif- component in climate model simulations (DelSole et al., 2011): see ferent errors affecting different responses (errors in aerosol forcing Section 10.3.1. Conclusions of most studies are consistent with those that do not affect the response to GHGs, for example), and estimating based on fingerprint detection and attribution, while using a different responses to combined forcings, which typically gives smaller uncer- set of assumptions (see review in Hegerl and Zwiers, 2011). tainties because it avoids the issue of degeneracy : if two responses have very similar shapes in space and time, then it may be impossible A number of studies have applied methods developed in the econo- to estimate the magnitude of both from a single set of observations metrics literature (Engle and Granger, 1987) to assess the evidence because amplification of one may be almost exactly compensated for for a causal link between external drivers of climate and observed by amplification or diminution of the other (Allen et al., 2006). Many climate change, using the observations themselves to estimate the studies find it is possible to estimate the magnitude of the responses expected properties of internal climate variability (e.g., Kaufmann to GHG and other anthropogenic forcings separately, particularly when and Stern, 1997). The advantage of these approaches is that they do spatial information is included. This is important, because it means the not depend on the accuracy of any complex global climate model, but estimated response to GHG increase is not dependent on the uncer- they nevertheless have to assume some kind of model, or restricted tain magnitude of forcing and response due to aerosols (Hegerl et al., class of models, of the properties of the variables under investigation. 2011b). Attribution is impossible without a model: although this model may be implicit in the statistical framework used, it is important to assess The simplest way of fitting model-simulated responses to observations its physical consistency (Kaufmann et al., 2013). Many of these time is to assume that the responses to different forcings add linearly, so series methods can be cast in the overall framework of co-integration the response to any one forcing can be scaled up or down without and error correction (Kaufmann et al., 2011), which is an approach affecting any of the others and that internal climate variability is inde- to analysing relationships between stationary and non-stationary time pendent of the response to external forcing. Under these conditions, series. If there is a consistent causal relationship between two or more attribution can be expressed as a variant of linear regression (see Box possibly non-stationary time series, then it should be possible to find 10.1). The additivity assumption has been tested and found to hold a linear combination such that the residual is stationary (contains no for large-scale temperature changes (Meehl et al., 2003; Gillett et al., stochastic trend) over time (Kaufmann and Stern, 2002; Kaufmann 2004) but it might not hold for other variables like precipitation (Hegerl et al., 2006; Mills, 2009). Co-integration methods are thus similar in et al., 2007b; Hegerl and Zwiers, 2011; Shiogama et al., 2012), nor for overall principle to regression-based approaches (e.g., Douglass et al., regional temperature changes (Terray, 2012). In principle, additivity is 2004; Stone and Allen, 2005; Lean, 2006) to the extent that regression not required for detection and attribution, but to date non-additive studies take into account the expected time series properties of the approaches have not been widely adopted. data the example described in Box 10.1 might be characterized as looking for a linear combination of anthropogenic and natural forcings The estimated properties of internal climate variability play a central such that the observed residuals were consistent with internal climate role in this assessment. These are either estimated empirically from variability as simulated by the CMIP5 models. Co-integration and error the observations (Section 10.2.2) or from paleoclimate reconstructions correction methods, however, generally make more explicit use of time 874 Detection and Attribution of Climate Change: from Global to Regional Chapter 10 Box 10.1 | How Attribution Studies Work This box presents an idealized demonstration of the concepts underlying most current approaches to detection and attribution of cli- mate change and how these relate to conventional linear regression. The coloured dots in Box 10.1a, Figure 1 show observed annual GMST from 1861 to 2012, with warmer years coloured red and colder years coloured blue. Observations alone indicate, unequivocally, that the Earth has warmed, but to quantify how different external factors have contributed to this warming, studies must compare such observations with the expected responses to these external factors. The orange line shows an estimate of the GMST response to anthropogenic (GHG and aerosol) forcing obtained from the mean of the CMIP3 and CMIP5 ensembles, while the blue line shows the CMIP3/CMIP5 ensemble mean response to natural (solar and volcanic) forcing. In statistical terms, attribution involves finding the combination of these anthropogenic and natural responses that best fits these observations: this is shown by the black line in panel (a). To show how this fit is obtained in non-technical terms, the data are plotted against model-simulated anthropogenic warming, instead of time, in panel (b). There is a strong correlation between observed temper- atures and model-simulated anthropogenic warming, but because of the presence of natural factors and internal climate variability, correlation alone is not enough for attribution. To quantify how much of the observed warming is attributable to human influence, panel (c) shows observed temperatures plotted against the model-simulated response to anthropogenic forcings in one direction and natural forcings in the other. Observed tempera- tures increase with both natural and anthropogenic model-simulated warming: the warmest years are in the far corner of the box. A flat surface through these points (here obtained by an ordinary least-squares fit), indicated by the coloured mesh, slopes up away from the viewer. 10 The orientation of this surface indicates how model-simulated responses to natural and anthropogenic forcing need to be scaled to reproduce the observations. The best-fit gradient in the direction of anthropogenic warming (visible on the rear left face of the box) is 0.9, indicating the CMIP3/CMIP5 ensemble average overestimates the magnitude of the observed response to anthropogenic forcing by about 10%. The best-fit gradient in the direction of natural changes (visible on the rear right face) is 0.7, indicating that the observed response to natural forcing is 70% of the average model-simulated response. The black line shows the points on this flat surface that are directly above or below the observations: each pin corresponds to a different year. When re-plotted against time, indicated by the years on the rear left face of the box, this black line gives the black line previously seen in panel (a). The length of the pins indicates residual temperature fluctuations due to internal variability. The timing of these residual temperature fluctuations is unpredictable, representing an inescapable source of uncertainty. We can quantify this uncertainty by asking how the gradients of the best-fit surface might vary if El Nino events, for example, had occurred in different years in the observed temperature record. To do this, we repeat the analysis in panel (c), replacing observed temperatures with samples of simulated internal climate variability from control runs of coupled climate models. Grey diamonds in panel (d) show the results: these gradients cluster around zero, because control runs have no anthropogenic or natural forcing, but there is still some scatter. Assuming that internal variability in global temperature simply adds to the response to external forcing, this scatter provides an estimate of uncertainty in the gradients, or scaling factors, required to reproduce the observations, shown by the red cross and ellipse. The red cross and ellipse are clearly separated from the origin, which means that the slope of the best-fit surface through the obser- vations cannot be accounted for by internal variability: some climate change is detected in these observations. Moreover, it is also separated from both the vertical and horizontal axes, which means that the responses to both anthropogenic and natural factors are individually detectable. The magnitude of observed temperature change is consistent with the CMIP3/CMIP5 ensemble average response to anthropogenic forcing (uncertainty in this scaling factor spans unity) but is significantly lower than the model-average response to natural forcing (this 5 to 95% confidence interval excludes unity). There are, however, reasons why these models may be underestimating the response to volcanic forcing (e.g., Driscoll et al, 2012), so this discrepancy does not preclude detection and attribution of both anthropogenic and natural influence, as simulated by the CMIP3/CMIP5 ensemble average, in the observed GMST record. The top axis in panel (d) indicates the attributable anthropogenic warming over 1951 2010, estimated from the anthropogenic warm- ing in the CMIP3/CMIP5 ensemble average, or the gradient of the orange line in panel (a) over this period. Because the model-simulat- ed responses have been scaled to fit the observations, the attributable anthropogenic warming in this example is 0.6°C to 0.9°C and does not depend on the magnitude of the raw model-simulated changes. Hence an attribution statement based on such an analysis, (continued on next page) 875 Chapter 10 Detection and Attribution of Climate Change: from Global to Regional Box 10.1 (continued) such as most of the warming over the past 50 years is attributable to anthropogenic drivers , depends only on the shape, or time his- tory, not the size, of the model-simulated warming, and hence does not depend on the models sensitivity to rising GHG levels. Formal attribution studies like this example provide objective estimates of how much recent warming is attributable to human influ- ence. Attribution is not, however, a purely statistical exercise. It also requires an assessment that there are no confounding factors that could have caused a large part of the attributed change. Statistical tests can be used to check that observed residual temperature fluctuations (the lengths and clustering of the pins in panel (c)) are consistent with internal variability expected from coupled models, but ultimately these tests must complement physical arguments that the combination of responses to anthropogenic and natural forc- ing is the only available consistent explanation of recent observed temperature change. This demonstration assumes, for visualization purposes, that there are only two candidate contributors to the observed warming, anthropogenic and natural, and that only GMST is available. More complex attribution problems can be undertaken using the same principles, such as aiming to separate the response to GHGs from other anthropogenic factors by also including spatial information. These require, in effect, an extension of panel (c), with additional dimensions corresponding to additional causal factors, and additional points corresponding to temperatures in different regions. 10 Box 10.1, Figure 1 | Example of a simplified detection and attribution study. (a) Observed global annual mean temperatures relative to 1880 1920 (coloured dots) compared with CMIP3/CMIP5 ensemble-mean response to anthropogenic forcing (orange), natural forcing (blue) and best-fit linear combination (black). (b) As (a) but all data plotted against model-simulated anthropogenic warming in place of time. Selected years (increasing nonlinearly) shown on top axis. (c) Observed temperatures versus model-simulated anthropogenic and natural temperature changes, with best-fit plane shown by coloured mesh. (d) Gradient of best-fit plane in (c), or scaling on model-simulated responses required to fit observations (red diamond) with uncertainty estimate (red ellipse and cross) based on CMIP5 control integrations (grey dia- monds). Implied attributable anthropogenic warming over the period 1951 2010 is indicated by the top axis. Anthropogenic and natural responses are noise-reduced with 5-point running means, with no smoothing over years with major volcanoes. 876 Detection and Attribution of Climate Change: from Global to Regional Chapter 10 series properties (notice how date information is effectively discarded e ­ xternal drivers, including spatial information, and the properties of in panel (b) of Box 10.1, Figure 1) and require fewer assumptions about internal climate variability. This can help to separate patterns of forced the stationarity of the input series. change both from each other and from internal variability. The price, however, is that results depend to some degree on the accuracy of the All of these approaches are subject to the issue of confounding fac- shape of model-simulated responses to external factors (e.g., North tors identified by Hegerl and Zwiers (2011). For example, Beenstock et and Stevens, 1998), which is assessed by comparing results obtained al. (2012) fail to find a consistent co-integrating relationship between with expected responses estimated from different climate models. atmospheric carbon dioxide (CO2) concentrations and GMST using pol- When the signal-to-noise (S/N) ratio is low, as can be the case for ynomial cointegration tests, but the fact that CO2 concentrations are some regional indicators and some variables other than temperature, derived from different sources in different periods (ice cores prior to the the accuracy of the specification of variability becomes a central factor mid-20th-century, atmospheric observations thereafter) makes it diffi- in the reliability of any detection and attribution study. Many studies cult to assess the physical significance of their result, particularly in the of such variables inflate the variability estimate from models to deter- light of evidence for co-integration between temperature and radiative mine if results are sensitive to, for example, doubling of variance in the forcing (RF) reported by Kaufmann et al. (2011) using tests of linear control (e.g., Zhang et al., 2007), although Imbers et al. (2013) note cointegration, and also the results of Gay-Garcia et al. (2009), who find that errors in the spectral properties of simulated variability may also evidence for external forcing of climate using time series properties. be important. The assumptions of the statistical model employed can also influence A full description of optimal fingerprinting is provided in Appendix 9.A results. For example, Schlesinger and Ramankutty (1994) and Zhou of Hegerl et al. (2007b) and further discussion is to be found in Hassel- and Tung (2013a) show that GMST are consistent with a linear anthro- mann (1997), Allen and Tett (1999), Allen et al. (2006), and Hegerl and pogenic trend, enhanced variability due to an approximately 70-year Zwiers (2011). Box 10.1 provides a simple example of fingerprinting Atlantic Meridional Oscillation (AMO) and shorter-term variability. If, based on GMST alone. In a typical fingerprint analysis, model-simu- however, there are physical grounds to expect a nonlinear anthropo- lated spatio-temporal patterns of response to different combinations genic trend (see Box 10.1 Figure 1a), the assumption of a linear trend of external forcings, including segments of control integrations with 10 can itself enhance the variance assigned to a low-frequency oscillation. no forcing, are observed in a similar manner to the historical record The fact that the AMO index is estimated from detrended historical tem- (masking out times and regions where observations are absent). The perature observations further increases the risk that its variance may magnitudes of the model-simulated responses are then estimated in be overestimated, because regressors and regressands are not inde- the observations using a variant of linear regression, possibly allowing pendent. Folland et al. (2013), using a physically based estimate of the for signals being contaminated by internal variability (Allen and Stott, anthropogenic trend, find a smaller role for the AMO in recent warming. 2003) and structural model uncertainty (Huntingford et al., 2006). Time series methods ultimately depend on the structural adequacy of In optimal fingerprinting, model-simulated responses and observa- the statistical model employed. Many such studies, for example, use tions are normalized by internal variability to improve the S/N ratio. models that assume a single exponential decay time for the response This requires an estimate of the inverse noise covariance estimated to both external forcing and stochastic fluctuations. This can lead to from the sample covariance matrix of a set of unforced (control) sim- an overemphasis on short-term fluctuations, and is not consistent with ulations (Hasselmann, 1997), or from variations within an initial-con- the response of more complex models (Knutti et al., 2008). Smirnov and dition ensemble. Because these control runs are generally too short Mokhov (2009) propose an alternative characterization that allows to estimate the full covariance matrix, a truncated version is used, them to distinguish a long-term causality that focuses on low-fre- retaining only a small number, typically of order 10 to 20, of high-vari- quency changes. Trends that appear significant when tested against ance principal components. Sensitivity analyses are essential to ensure an AR(1) model may not be significant when tested against a process results are robust to this, relatively arbitrary, choice of truncation (Allen that supports this long-range dependence (Franzke, 2010). Although and Tett, 1999; Ribes and Terray, 2013; Jones et al., 2013 ). Ribes et the evidence for long-range dependence in global temperature data al. (2009) use a regularized estimate of the covariance matrix, mean- remains a topic of debate (Mann, 2011; Rea et al., 2011) , it is generally ing a linear combination of the sample covariance matrix and a unit desirable to explore sensitivity of results to the specification of the sta- matrix that has been shown (Ledoit and Wolf, 2004) to provide a more tistical model, and also to other methods of estimating the properties accurate estimate of the true covariance, thereby avoiding dependence of internal variability, such as more complex climate models, discussed on truncation. Optimization of S/N ratio is not, however, essential for next. For example, Imbers et al. (2013) demonstrate that the detection many attribution results (see, e.g., Box 10.1) and uncertainty analysis of the influence of increasing GHGs in the global temperature record in conventional optimal fingerprinting does not require the covariance is robust to the assumption of a Fractional Differencing (FD) model of matrix to be inverted, so although regularization may help in some internal variability, which supports long-range dependence. cases, it is not essential. Ribes et al. (2010) also propose a hybrid of the model-based optimal fingerprinting and time series approaches, 10.2.3 Methods Based on General Circulation Models referred to as temporal optimal detection , under which each signal is and Optimal Fingerprinting assumed to consist of a single spatial pattern modulated by a smoothly varying time series estimated from a climate model (see also Santer et Fingerprinting methods use climate model simulations to provide al., 1994). more complete information about the expected response to different 877 Chapter 10 Detection and Attribution of Climate Change: from Global to Regional The final statistical step in an attribution study is to check that the reported that the response to anthropogenic GHG increase is very likely residual variability, after the responses to external drivers have been greater than half the total observed warming, it means that the null estimated and removed, is consistent with the expected properties of hypothesis that the GHG-induced warming is less than half the total internal climate variability, to ensure that the variability used for uncer- can be rejected with the data available at the 10% significance level. tainty analysis is realistic, and that there is no evidence that a potential- Expert judgment is required in frequentist attribution assessments, but ly confounding factor has been omitted. Many studies use a standard its role is limited to the assessment of whether internal variability and F-test of residual consistency for this purpose (Allen and Tett, 1999). potential confounding factors have been adequately accounted for, Ribes et al. (2013) raise some issues with this test, but key results are and to downgrade nominal significance levels to account for remaining not found to be sensitive to different formulations. A more important uncertainties. Uncertainties may, in some cases, be further reduced if issue is that the F-test is relatively weak (Berliner et al., 2000; Allen et prior expectations regarding attribution results themselves are incor- al., 2006; Terray, 2012), so passing this test is not a safeguard against porated, using a Bayesian approach, but this not currently the usual unrealistic variability, which is why estimates of internal variability are practice. discussed in detail in this chapter and in Chapter 9. This traditional emphasis on single-step studies and placing lower A further consistency check often used in optimal fingerprinting is bounds on the magnitude of signals under investigation means that, whether the estimated magnitude of the externally driven responses very often, the communication of attribution results tends to be con- are consistent between model and observations (scaling factors con- servative, with attention focussing on whether or not human influence sistent with unity in Box 10.1): if they are not, attribution is still possi- in a particular variable might be zero, rather than the upper end of the ble provided the discrepancy is explicable in terms of known uncertain- confidence interval, which might suggest a possible response much ties in the magnitude of either forcing or response. As is emphasized bigger than current model-simulated changes. Consistent with previous in Section 10.2.1 and Box 10.1, attribution is not a purely statistical Assessments and the majority of the literature, this chapter adopts this assessment: physical judgment is required to assess whether the com- conservative emphasis. It should, however, be borne in mind that this bination of responses considered allows for all major potential con- means that positive attribution results will tend to be biased towards 10 founding factors and whether any remaining discrepancies are consist- well-observed, well-modelled variables and regions, which should be ent with a physically based understanding of the responses to external taken into account in the compilation of global impact assessments forcing and internal climate variability. (Allen, 2011; Trenberth, 2011a). 10.2.4 Single-Step and Multi-Step Attribution and the Role of the Null Hypothesis 10.3 Atmosphere and Surface Attribution studies have traditionally involved explicit simulation of This section assesses causes of change in the atmosphere and at the the response to external forcing of an observable variable, such as sur- surface over land and ocean. face temperature, and comparison with corresponding observations of that variable. This so-called single-step attribution has the advantage 10.3.1 Temperature of simplicity, but restricts attention to variables for which long and consistent time series of observations are available and that can be Temperature is first assessed near the surface of the Earth in Section simulated explicitly in current models driven solely with external cli- 10.3.1.1 and then in the free atmosphere in Section 10.3.1.2. mate forcing. 10.3.1.1 Surface (Air Temperature and Sea Surface Temperature) To address attribution questions for variables for which these condi- tions are not satisfied, Hegerl et al. (2010) introduced the notation of 10.3.1.1.1 Observations of surface temperature change multi-step attribution , formalizing existing practice (e.g., Stott et al., 2004). In a multi-step attribution study, the attributable change in a GMST warmed strongly over the period 1900 1940, followed by a variable such as large-scale surface temperature is estimated with a period with little trend, and strong warming since the mid-1970s (Sec- single-step procedure, along with its associated uncertainty, and the tion 2.4.3, Figure 10.1). Almost all observed locations have warmed implications of this change are then explored in a further (physically since 1901 whereas over the satellite period since 1979 most regions or statistically based) modelling step. Overall conclusions can only be have warmed while a few regions have cooled (Section 2.4.3; Figure as robust as the least certain link in the multi-step procedure. As the 10.2). Although this picture is supported by all available global focus shifts towards more noisy regional changes, it can be difficult near-surface temperature data sets, there are some differences in to separate the effect of different external forcings. In such cases, it detail between them, but these are much smaller than both interan- can be useful to detect the response to all external forcings, and then nual variability and the long-term trend (Section 2.4.3). Since 1998 determine the most important factors underlying the attribution results the trend in GMST has been small (see Section 2.4.3, Box 9.2). Urban- by reference to a closely related variable for which a full attribution ization is unlikely to have caused more than 10% of the measured analysis is available (e.g., Morak et al., 2011). centennial trend in land mean surface temperature, though it may have contributed substantially more to regional mean surface temperature Attribution results are typically expressed in terms of conventional fre- trends in rapidly developing regions (Section 2.4.1.3). quentist confidence intervals or results of hypothesis tests: when it is 878 Detection and Attribution of Climate Change: from Global to Regional Chapter 10 10.3.1.1.2 Simulations of surface temperature change ulated GMST anomalies spans the observational estimates of GMST anomaly in almost every year whereas this is not the case for simu- As discussed in Section 10.1, the CMIP5 simulations have several lations in which only natural forcings are included (Figure 10.1b) (see advantages compared to the CMIP3 simulations assessed by (Hegerl et also Jones et al., 2013; Knutson et al., 2013). Anomalies are shown al., 2007b) for the detection and attribution of climate change. Figure relative to 1880 1919 rather than absolute temperatures. Showing 10.1a shows that when the effects of anthropogenic and natural exter- anomalies is necessary to prevent changes in observational cover- nal forcings are included in the CMIP5 simulations the spread of sim- age being reflected in the calculated global mean and is reasonable 10 Figure 10.1 | (Left-hand column) Three observational estimates of global mean surface temperature (GMST, black lines) from Hadley Centre/Climatic Research Unit gridded surface temperature data set 4 (HadCRUT4), Goddard Institute of Space Studies Surface Temperature Analysis (GISTEMP), and Merged Land Ocean Surface Temperature Analysis (MLOST), compared to model simulations [CMIP3 models thin blue lines and CMIP5 models thin yellow lines] with anthropogenic and natural forcings (a), natural forcings only (b) and greenhouse gas (GHG) forcing only (c). Thick red and blue lines are averages across all available CMIP5 and CMIP3 simulations respectively. CMIP3 simulations were not avail- able for GHG forcing only (c). All simulated and observed data were masked using the HadCRUT4 coverage (as this data set has the most restricted spatial coverage), and global average anomalies are shown with respect to 1880 1919, where all data are first calculated as anomalies relative to 1961 1990 in each grid box. Inset to (b) shows the three observational data sets distinguished by different colours. (Adapted from Jones et al., 2013.) (Right-hand column) Net adjusted forcing in CMIP5 models due to anthropogenic and natural forcings (d), natural forcings only (e) and GHGs only (f). (From Forster et al., 2013.) Individual ensemble members are shown by thin yellow lines, and CMIP5 multi-model means are shown as thick red lines. 879 Chapter 10 Detection and Attribution of Climate Change: from Global to Regional because climate sensitivity is not a strong function of the bias in GMST may explain the wider spread of the CMIP5 ensemble compared to in the CMIP5 models (Section 9.7.1; Figure 9.42). Simulations with GHG the CMIP3 ensemble (Figure 10.1a). Climate model parameters are changes only, and no changes in aerosols or other forcings, tend to sim- typically chosen primarily to reproduce features of the mean climate ulate more warming than observed (Figure 10.1c), as expected. Better and variability (Box 9.1), and CMIP5 aerosol emissions are standard- agreement between models and observations when the models include ized across models and based on historical emissions (Lamarque et anthropogenic forcings is also seen in the CMIP3 simulations (Figure al., 2010; Section 8.2.2), rather than being chosen by each modelling 10.1, thin blue lines). RF in the simulations including anthropogenic group independently (Curry and Webster, 2011; Hegerl et al., 2011c). and natural forcings differs considerably among models (Figure 10.1d), and forcing differences explain much of the differences in temperature Figure 10.2a shows the pattern of annual mean surface temperature response between models over the historical period (Forster et al., 2013 trends observed over the period 1901 2010, based on Hadley Centre/ ). Differences between observed GMST based on three observational Climatic Research Unit gridded surface temperature data set 4 (Had- data sets are small compared to forced changes (Figure 10.1). CRUT4). Warming has been observed at almost all locations with suffi- cient observations available since 1901. Rates of warming are general- As discussed in Section 10.2, detection and attribution assessments ly higher over land areas compared to oceans, as is also apparent over are more robust if they consider more than simple consistency argu- the 1951 2010 period (Figure 10.2c), which simulations indicate is ments. Analyses that allow for the possibility that models might be due mainly to differences in local feedbacks and a net anomalous heat consistently over- or underestimating the magnitude of the response transport from oceans to land under GHG forcing, rather than differ- to climate forcings are assessed in Section 10.3.1.1.3, the conclusions ences in thermal inertia (e.g., Boer, 2011). Figure 10.2e demonstrates from which are not affected by evidence that model spread in GMST that a similar pattern of warming is simulated in the CMIP5 simula- in CMIP3, is smaller than implied by the uncertainty in RF (Schwartz tions with natural and anthropogenic forcing over the 1901 2010 et al., 2007). Although there is evidence that CMIP3 models with a period. Over most regions, observed trends fall between the 5th and higher climate sensitivity tend to have a smaller increase in RF over 95th percentiles of simulated trends, and van Oldenborgh et al. (2013) the historical period (Kiehl, 2007; Knutti, 2008; Huybers, 2010), no find that over the 1950 2011 period the pattern of observed grid cell 10 such ­elationship was found in CMIP5 (Forster et al., 2013 ) which r trends agrees with CMIP5 simulated trends to within a combination of 1901-2010 1901-1950 1951-2010 1979-2010 -180 -90 0 90 180 -90 0 90 180 -90 0 90 180 -90 0 90 180 90 HadCRUT4 45 0 -45 -90 a b c d 90 45 historical 0 -45 -90 e 14% f 32% g 15% h 21% 90 historicalGHG historicalNat 45 0 -45 -90 i 89% j 44% k 69% l 48% 90 45 0 -45 -90 m 50% n 46% o 43% p 22% -2 -1 0 1 2 Trend (°C per period) Figure 10.2 | Trends in observed and simulated temperatures (K over the period shown) over the 1901 2010 (a, e, i, m), 1901 1950 (b, f, j, n), 1951 2010 (c, g, k, o) and 1979 2010 (d, h, l, p) periods. Trends in observed temperatures from the Hadley Centre/Climatic Research Unit gridded surface temperature data set 4 (HadCRUT4) (a d), CMIP3 and CMIP5 model simulations including anthropogenic and natural forcings (e h), CMIP3 and CMIP5 model simulations including natural forcings only (i l) and CMIP3 and CMIP5 model simulations including greenhouse gas forcing only (m p). Trends are shown only where sufficient observational data are available in the HadCRUT4 data set, and grid cells with insufficient observations to derive trends are shown in grey. Boxes in (e p) show where the observed trend lies outside the 5 to 95th percentile range of simulated trends, and the ratio of the number of such grid cells to the total number of grid cells with sufficient data is shown as a percentage in the lower right of each panel. (Adapted from Jones et al., 2013.) 880 Detection and Attribution of Climate Change: from Global to Regional Chapter 10 model spread and internal variability. Areas of disagreement over the 90S 60S 30S 0 30N 60N 90N 1901 2010 period include parts of Asia and the Southern Hemisphere (a) 5 (°C per 50 years) (°C per 110 years) 4 1901-2010 (SH) mid-latitudes, where the simulations warm less than the obser- 3 vations, and parts of the tropical Pacific, where the simulations warm 2 more than the observations (Jones et al., 2013; Knutson et al., 2013). 1 Stronger warming in observations than models over parts of East Asia 0 -1 could in part be explained by uncorrected urbanization influence in the -2 observations (Section 2.4.1.3), or by an overestimate of the response (b) 5 4 HadCRUT4 1901-1950 to aerosol increases. Trends simulated in response to natural forcings GISTEMP 3 only are generally close to zero, and inconsistent with observed trends MLOST 2 in most locations (Figure 10.2i) (see also Knutson et al., 2013). Trends 1 simulated in response to GHG changes only over the 1901 2010 0 period are larger than those observed at most locations, and in many -1 -2 cases significantly so (Figure 10.2m). This is expected because these (c) 5 1951-2010 (°C per 60 years) simulations do not include the cooling effects of aerosols. Differenc- 4 3 es in patterns of simulated and observed seasonal mean temperature 2 trends and possible causes are considered in more detail in Box 11.2. 1 0 Over the period 1979 2010 most observed regions exhibited warming -1 -2 (Figure 10.2d), but much of the eastern Pacific and Southern Oceans (d) 5 1979-2010 (°C per 32 years) cooled. These regions of cooling are not seen in the simulated trends 4 3 historical 5-95% range over this period in response to anthropogenic and natural forcing historicalNat 5-95% range 2 (Figure 10.2h), which show significantly more warming in much of 1 these regions (Jones et al., 2013; Knutson et al., 2013). This cooling 0 10 and reduced warming in observations over the Southern Hemisphere -1 -2 mid-latitudes over the 1979 2010 period can also be seen in the zonal 90S 60S 30S 0 30N 60N 90N mean trends (Figure 10.3d), which also shows that the models tend to Latitude warm too much in this region over this period. However, there is no dis- crepancy in zonal mean temperature trends over the longer 1901 2010 Figure 10.3 | Zonal mean temperature trends over the 1901 2010 (a), 1901 1950 period in this region (Figure 10.3a), suggesting that the discrepancy (b), 1951 2010 (c) and 1979 2010 (d) periods. Solid lines show Hadley Centre/Cli- matic Research Unit gridded surface temperature data set 4 (HadCRUT4, red), God- over the 1979 2010 period either may be an unusually strong manifes- dard Institute of Space Studies Surface Temperature Analysis (GISTEMP, brown) and tation of internal variability in the observations or relate to regionally Merged Land Ocean Surface Temperature Analysis (MLOST, green) observational data important forcings over the past three decades which are not included sets, orange hatching represents the 90% central range of CMIP3 and CMIP5 simula- in most CMIP5 simulations, such as sea salt aerosol increases due to tions with anthropogenic and natural forcings, and blue hatching represents the 90% strengthened high latitude winds (Korhonen et al., 2010), or sea ice central range of CMIP3 and CMIP5 simulations with natural forcings only. All model and observations data are masked to have the same coverage as HadCRUT4. (Adapted extent increases driven by freshwater input from ice shelf melting (Bin- from Jones et al., 2013.) tanja et al., 2013). Except at high latitudes, zonal mean trends over the 1901 2010 period in all three data sets are inconsistent with natural- ly forced trends, indicating a detectable anthropogenic signal in most trends in GMST with a combination of simulated internal variability zonal means over this period (Figure 10.3a). McKitrick and Tole (2012) and the response to natural forcings and find that the observed trend find that few CMIP3 models have significant explanatory power when would still be detected for trends over this period even if the magni- fitting the spatial pattern of 1979 2002 trends in surface temperature tude of the simulated natural variability (i.e., the standard deviation of over land, by which they mean that these models add little or no skill trends) were tripled. to a fit including the spatial pattern of tropospheric temperature trends as well as the major atmospheric oscillations. This is to be expected, 10.3.1.1.3 Attribution of observed global-scale temperature as temperatures in the troposphere are well correlated in the vertical, changes and local temperature trends over so short a period are dominated by internal variability. The evolution of temperature since the start of the global instrumental record CMIP5 models generally exhibit realistic variability in GMST on decadal Since the AR4, detection and attribution studies have been carried out to multi-decadal time scales (Jones et al., 2013; Knutson et al., 2013; using new model simulations with more realistic forcings, and new Section 9.5.3.1, Figure 9.33), although it is difficult to evaluate internal observational data sets with improved representation of uncertainty variability on multi-decadal time scales in observations given the short- (Christidis et al., 2010; Jones et al., 2011, 2013; Gillett et al., 2012, ness of the observational record and the presence of external forcing. 2013; Stott and Jones, 2012; Knutson et al., 2013; Ribes and Terray, The observed trend in GMST since the 1950s is very large compared to 2013). Although some inconsistencies between the simulated and model estimates of internal variability (Stott et al., 2010; Drost et al., observed responses to forcings in individual models were identified 2012; Drost and Karoly, 2012). Knutson et al. (2013) compare observed ( Gillett et al., 2013; Jones et al., 2013; Ribes and Terray, 2013) over- 881 Chapter 10 Detection and Attribution of Climate Change: from Global to Regional all these results support the AR4 assessment that GHG increases very et al., 2013; Jones et al., 2013; Ribes and Terray, 2013), the period over likely caused most (>50%) of the observed GMST increase since the which the analysis is applied (Figure 10.4; Gillett et al., 2013; Jones et mid-20th century (Hegerl et al., 2007b). al., 2013), and the Empirical Orthogonal Function (EOF) truncation or degree of spatial filtering (Jones et al., 2013; Ribes and Terray, 2013). The results of multiple regression analyses of observed temperature In some cases the GHG response is not detectable in regressions using changes onto the simulated responses to GHG, other anthropogen- individual models (Figure 10.4; Gillett et al., 2013; Jones et al., 2013; ic and natural forcings are shown in Figure 10.4 (Gillett et al., 2013; Ribes and Terray, 2013), or a residual test is failed (Gillett et al., 2013; Jones et al., 2013; Ribes and Terray, 2013). The results, based on Had- Jones et al., 2013; Ribes and Terray, 2013), indicating a poor fit between CRUT4 and a multi-model average, show robustly detected responses the simulated response and observed changes. Such cases are proba- to GHG in the observational record whether data from 1861 2010 or bly due largely to errors in the spatio-temporal pattern of responses only from 1951 2010 are analysed (Figure 10.4b). The advantage of to forcings simulated in individual models (Ribes and Terray, 2013), analysing the longer period is that more information on observed and although observational error and internal variability errors could also modelled changes is included, while a disadvantage is that it is difficult play a role. Nonetheless, analyses in which responses are averaged to validate climate models estimates of internal variability over such across multiple models generally show much less ­ ensitivity to period s a long period. Individual model results exhibit considerable spread and EOF trucation (Gillett et al., 2013; Jones et al., 2013), and more among scaling factors, with estimates of warming attributable to each consistent residuals (Gillett et al., 2013), which may be because model forcing sensitive to the model used for the analsys (Figure 10.4; Gillett response errors are smaller in a multi-model mean. 1951-2010 trend Scaling factor 1951-2010 trend Scaling factor multi (a) (b) (c) (d) 10 NorESM1-M IPSL-CM5A-LR HadGEM2-ES GISS-E2-R GISS-E2-H CSIRO-Mk3-6-0 CNRM-CM5 CanESM2 BCC-CSM1-1 -1 0 1 -0.5 0 0.5 1 1.5 -1 0 1 -0.5 0 0.5 1 1.5 (°C per 60 years) Scaling factor (°C per 60 years) Scaling factor Figure 10.4 | (a) Estimated contributions of greenhouse gas (GHG, green), other anthropogenic (yellow) and natural (blue) forcing components to observed global mean surface temperature (GMST) changes over the 1951 2010 period. (b) Corresponding scaling factors by which simulated responses to GHG (green), other anthropogenic (yellow) and natural forcings (blue) must be multiplied to obtain the best fit to Hadley Centre/Climatic Research Unit gridded surface temperature data set 4 (HadCRUT4; Morice et al., 2012) observations based on multiple regressions using response patterns from nine climate models individually and multi-model averages (multi). Results are shown based on an analysis over the 1901 2010 period (squares, Ribes and Terray, 2013), an analysis over the 1861 2010 period (triangles, Gillett et al., 2013) and an analysis over the 1951 2010 period (diamonds, Jones et al., 2013). (c, d) As for (a) and (b) but based on multiple regressions estimating the contributions of total anthropogenic forcings (brown) and natural forcings (blue) based on an analysis over 1901 2010 period (squares, Ribes and Terray, 2013) and an analysis over the 1861 2010 period (triangles, Gillett et al., 2013). Coloured bars show best estimates of the attributable trends (a and c) and 5 to 95% confidence ranges of scaling factors (b and d). Vertical dashed lines in (a) and (c) show the best estimate HadCRUT4 observed trend over the period concerned. Vertical dotted lines in (b) and d) denote a scaling factor of unity. 882 Detection and Attribution of Climate Change: from Global to Regional Chapter 10 We derive assessed ranges for the attributable contributions of GHGs, GHG-attributable warming and aerosol-attributable cooling (Jones and other anthropogenic forcings and natural forcings by taking the small- Stott, 2011; Gillett et al., 2013; Knutson et al., 2013). The response to est ranges with a precision of one decimal place that span the 5 to GHGs was detected using Hadley Centre new Global Environmental 95% ranges of attributable trends over the 1951 2010 period from Model 2-Earth System (HadGEM2-ES; Stott and Jones, 2012), Canadian the Jones et al. (2013) weighted multi-model analysis and the Gillett Earth System Model 2 (CanESM2; Gillett et al., 2012) and other CMIP5 et al. (2013) multi-model analysis considering observational uncer- models except for Goddard Institute for Space Studies-E2-H (GISS- tainty (Figure 10.4a). The assessed range for the attributable contri- E2-H; Gillett et al., 2013; Jones et al., 2013) (Figure 10.4). However, the bution of combined anthropogenic forcings was derived in the same influence of other anthropogenic forcings was detected only in some way from the Gillett et al. (2013) multi-model attributable trend and CMIP5 models (Figure 10.4). This lack of detection of other anthro- shown in Figure 10.4c. We moderate our likelihood assessment and pogenic forcings compared to detection of an aerosol response using report likely ranges rather than the very likely ranges directly implied four CMIP3 models over the period 1900 1999 (Hegerl et al., 2007b) by these studies in order to account for residual sources of uncertainty does not only relate to the use of data to 2010 rather than 2000 (Stott including sensitivity to EOF truncation and analysis period (e.g., Ribes and Jones, 2012), although this could play a role (Gillett et al., 2013; and Terray, 2013). In this context, GHGs means well-mixed greenhouse Ribes and Terray, 2013). Whether it is associated with a cancellation of gases (WMGHGs), other anthropogenic forcings means aerosol chang- aerosol cooling by ozone and black carbon (BC) warming in the CMIP5 es, and in most models ozone changes and land use changes, and nat- simulations, making the signal harder to detect, or by some aspect of ural forcings means solar irradiance changes and volcanic aerosols. the response to other anthropogenic forcings that is less realistic in Over the 1951 2010 period, the observed GMST increased by approx- these models is not clear. imately 0.6°C. GHG increases likely contributed 0.5°C to 1.3°C, other anthropogenic forcings likely contributed 0.6°C to 0.1°C and natural Although closely constraining the GHG and other anthropogenic con- forcings likely contributed 0.1°C to 0.1°C to observed GMST trends tributions to observed warming remains challenging owing to their over this period. Internal variability likely contributed 0.1°C to 0.1°C degeneracy and sensitivity to methodological choices (Jones et al., to observed trends over this period (Knutson et al., 2013). This assess- 2013; Ribes and Terray, 2013), a total anthropogenic contribution to ment is shown schematically in Figure 10.5. The assessment is support- warming can be much more robustly constrained by a regression of 10 ed additionally by a complementary analysis in which the parameters observed temperature changes onto the simulated responses to all of an Earth System Model of Intermediate Complexity (EMIC) were anthropogenic forcings and natural forcings (Figure 10.4; Gillett et constrained using observations of near-surface temperature and ocean al., 2013; Ribes and Terray, 2013). Robust detection of anthropogenic heat content, as well as prior information on the magnitudes of forc- influence is also found if a new optimal detection methodology, the ings, and which concluded that GHGs have caused 0.6°C to 1.1°C (5 Regularised Optimal Fingerprint approach (see Section 10.2; Ribes et to 95% uncertainty) warming since the mid-20th century (Huber and al., 2013), is applied (Ribes and Terray, 2013). A better constrained Knutti, 2011); an analysis by Wigley and Santer (2013), who used an estimate of the total anthropogenic contribution to warming since the energy balance model and RF and climate sensitivity estimates from mid-20th century than the GHG contribution is also found by Wigley AR4, and they concluded that there was about a 93% chance that and Santer (2013). Knutson et al. (2013) demonstrate that observed GHGs caused a warming greater than observed over the 1950 2005 trends in GMST are inconsistent with the simulated response to natural period; and earlier detection and attribution studies assessed in the forcings alone, but consistent with the simulated response to natural AR4 (Hegerl et al., 2007b). and anthropogenic forcings for all periods beginning between 1880 and 1990 and ending in 2010, which they interpret as evidence that The inclusion of additional data to 2010 (AR4 analyses stopped at warming is in part attributable to anthropogenic influence over these 1999; Hegerl et al. (2007b)) helps to better constrain the magnitude of periods. Based on the well-constrained attributable anthropogenic the GHG-attributable warming (Drost et al., 2012; Gillett et al., 2012; trends shown in Figure 10.4 we assess that anthropogenic forcings Stott and Jones, 2012; Gillett et al., 2013), as does the inclusion of likely contributed 0.6°C to 0.8°C to the observed warming over the spatial information (Stott et al., 2006; Gillett et al., 2013), though Ribes 1951 2010 period (Figure 10.5). and Terray (2013) caution that in some cases there are inconsistencies between observed spatial patterns of response and those simulated in There are some inconsistencies in the simulated and observed magni- indvidual models. While Hegerl et al. (2007b) assessed that a significant tudes of responses to forcing for some CMIP5 models (Figure 10.4); for cooling of about 0.2 °C was attributable to natural forcings over the example, CanESM2 has a GHG regression coefficient significantly less 1950 1999 period, the temperature trend attributable to natural forc- than 1 and a regression coefficient for other anthropogenic forcings ings over the 1951 2010 period is very small (<0.1°C). This is because, also significantly less than 1 (Gillett et al., 2012; Gillett et al., 2013; while Mt Pinatubo cooled global temperatures in the early 1990s, Jones et al., 2013; Ribes and Terray, 2013), indicating that this model there have been no large volcanic eruptions since, resulting in small overestimates the magnitude of the response to GHGs and to other simulated trends in response to natural forcings over the 1951 2010 anthropogenic forcings. Averaged over the ensembles of models con- period (Figure 10.1b). Regression coefficients for natural forcings tend sidered by Gillett et al. (2013) and Jones et al. (2013), the best-estimate to be smaller than one, suggesting that the response to natural forc- GHG and OA scaling factors are less than 1 (Figure 10.4), indicating ings may be overestimated by the CMIP5 models on average (Figure that the model mean GHG and OA responses should be scaled down 10.4; Gillett et al., 2013; Knutson et al., 2013). Attribution of observed to best match observations. The best-estimate GHG scaling factors are changes is robust to observational uncertainty which is comparably larger than the best-estimate OA scaling factors, although the discrep- important to internal climate variability as a source of uncertainty in ancy from 1 is not significant in either case and the ranges of the GHG 883 Chapter 10 Detection and Attribution of Climate Change: from Global to Regional Figure 10.5 | Assessed likely ranges (whiskers) and their mid-points (bars) for attributable warming trends over the 1951 2010 period due to well-mixed greenhouse gases, other anthropogenic forcings (OA), natural forcings (NAT), combined anthropogenic forcings (ANT) and internal variability. The Hadley Centre/Climatic Research Unit gridded surface temperature data set 4 (HadCRUT4) observations are shown in black with the 5 to 95% uncertainty range due to observational uncertainty in this record (Morice et al., 2012). 10 and OA scaling factors are overlapping. Overall there is some evidence s ­ imulated mainly over the Northern Hemisphere (NH) with a sufficient- that some CMIP5 models have a higher transient response to GHGs ly distinct spatio-temporal pattern that it could be separated from the and a larger response to other anthropogenic forcings (dominated by response to other forcings in this study. the effects of aerosols) than the real world (medium confidence). Incon- sistencies between simulated and observed trends in GMST were also Several recent studies have used techniques other than regres- identified in several CMIP3 models by Fyfe et al. (2010) after remov- sion-based detection and attribution analyses to address the causes ing volcanic, El Nino-Southern Oscillation (ENSO), and Cold Ocean/ of recent global temperature changes. Drost and Karoly (2012) Warm Land pattern (COWL) signals from GMST, although uncertainties demonstrated that observed GMST, land ocean temperature con- may have been underestimated because residuals were modelled by trast, meridional temperature gradient and annual cycle amplitude a first-order autoregressive processes. A longer observational record exhibited trends over the period 1956 2005 that were outside the 5 and a better understanding of the temporal changes in forcing should to 95% range of simulated internal variability in eight CMIP5 models, make it easier to identify discrepancies between the magnitude of the based on three different observational data sets. They also found that observed response to a forcing, and the magnitude of the response observed trends in GMST and land ocean temperature contrast were simulated in individual models. To the extent that inconsistencies larger than those simulated in any of 36 CMIP5 simulations with nat- between simulated and observed changes are independent between ural forcing only. Drost et al. (2012) found that 1961 2010 trends in models, this issue may be addressed by basing our assessment on attri- GMST and land ocean temperature contrast were significantly larger bution analyses using the mean response from multiple models, and than simulated internal variability in eight CMIP3 models. By compar- by accounting for model uncertainty when making such assessments. ing observed GMST with simple statistical models, Zorita et al. (2008) concluded that there is a very low probability that observed clustering In conclusion, although some inconsistencies in the forced respons- of very warm years in the last decade occurred by chance. Smirnov and es of individual models and observations have been identified, the Mokhov (2009), adopting an approach that allowed them to distin- detection of the global temperature response to GHG increases using guish between conventional Granger causality and a long-term cau- average responses from multiple models is robust to observational sality that focuses on low-frequency changes (see Section 10.2), found uncertainty and methodological choices. It is supported by basic phys- that increasing CO2 concentrations are the principal determining factor ical arguments. We conclude, consistent with Hegerl et al. (2007b), in the rise of GMST over recent decades. Sedlacek and Knutti (2012) that more than half of the observed increase in GMST from 1951 to found that the spatial patterns of sea surface temperature (SST) trends 2010 is very likely due to the observed anthropogenic increase in GHG from simulations forced with increases in GHGs and other anthropo- ­concentrations. genic forcings agree well with observations but differ from warming patterns associated with internal variability. The influence of BC aerosols (from fossil and biofuel sources) has been detected in the recent global temperature record in one analy- Several studies that have aimed to separate forced surface temper- sis, although the warming attributable to BC by Jones et al. (2011) is ature variations from those associated with internal variability have small compared to that attributable to GHG increases. This warming is identified the North Atlantic as a dominant centre of multi-decadal 884 Detection and Attribution of Climate Change: from Global to Regional Chapter 10 internal variability, and in particular modes of variability related to Some studies implicate tropospheric aerosols in driving decadal var- the Atlantic Multi-decadal Oscillation (AMO; Section 14.7.6). The AMO iations in Atlantic SST (Evan et al., 2011; Booth et al., 2012; Terray, index is defined as an area average of North Atlantic SSTs, and it has 2012), and temperature variations in eastern North America (Leibens- an apparent period of around 70 years, which is long compared to perger et al., 2012). Booth et al. (2012) find that most multi-decadal the length of observational record making it difficult to deduce robust variability in North Atlantic SSTs is simulated in one model mainly in conclusions about the role of the AMO from only two cycles. Never- response to aerosol variations, although its simulated changes in North theless, several studies claim a role for internal variability associated Atlantic ocean heat content and salinity have been shown to be incon- with the AMO in driving enhanced warming in the 1980s and 1990s sistent with observations (Zhang et al., 2012). To the extent that cli- as well as the recent slow down in warming (Box 9.2), while attribut- mate models simulate realistic internal variability in the AMO (Section ing long-term warming to anthropogenically forced variations either 9.5.3.3.2), AMO variability is accounted for in uncertainty estimates by analysing time series of GMST, forcings and indices of the AMO from regression-based detection and attribution studies (e.g., Figure (Rohde et al., 2013; Tung and Zhou, 2013; Zhou and Tung, 2013a) or by 10.4). analysing both spatial and temporal patterns of temperature (Swan- son et al., 2009; DelSole et al., 2011; Wu et al., 2011). Studies based To summarize, recent studies using spatial features of observed tem- on global mean time series could risk falsely attributing variability to perature variations to separate AMO variability from externally forced the AMO when variations in external forcings, for example, associated changes find that detection of external influence on global tempera- with aerosols, could also cause similar variability. In contrast, studies tures is not compromised by accounting for AMO-congruent variability using space time patterns seek to distinguish the spatial structure of (high confidence). There remains some uncertainty about how much temperature anomalies associated with the AMO from those associat- decadal variability of GMST that is attributed to AMO in some studies ed with forced variability. Unforced climate simulations indicate that is actually related to forcing, notably from aerosols. There is agree- internal multi-decadal variability in the Atlantic  is characterized by ment among studies that the contribution of the AMO to global warm- surface anomalies of the same sign from the equator to the high lat- ing since 1951 is very small (considerably less than 0.1°C; see also itudes, with maximum amplitudes in subpolar regions (Delworth and Figure 10.6) and given that observed warming since 1951 is very large Mann, 2000; Latif et al., 2004; Knight et al., 2005; DelSole et al., 2011) compared to climate model estimates of internal variability (Section 10 while the net response to anthropogenic and natural forcing over the 10.3.1.1.2), which are assessed to be adequate at global scale (Section 20th century, such as observed temperature change, is characterized 9.5.3.1), we conclude that it is virtually certain that internal variability by warming nearly everywhere on the globe, but with minimum warm- alone cannot account for the observed global warming since 1951. ing or even cooling in the subpolar regions of the North Atlantic (Figure 10.2; Ting et al., 2009; DelSole et al., 2011). Box 10.2 | The Sun s Influence on the Earth s Climate A number of studies since AR4 have addressed the possible influences of long-term fluctuations of solar irradiance on past climates, particularly related to the relative warmth of the Medieval Climate Anomaly (MCA) and the relative coolness in the Little Ice Age (LIA). There is medium confidence that both external solar and volcanic forcing, and internal variability, contributed substantially to the spa- tial patterns of surface temperature changes between the MCA and the LIA, but very low confidence in quantitative estimates of their relative contributions (Sections 5.3.5.3 and 5.5.1). The combined influence of volcanism, solar forcing and a small drop in greenhouse gases (GHGs) likely contributed to Northern Hemisphere cooling during the LIA (Section 10.7.2). Solar radiative forcing (RF) from the Maunder Minimum (1745) to the satellite era (average of 1976 2006) has been estimated to be +0.08 to +0.18 W m 2 (low confidence, Section 8.4.1.2). This may have contributed to early 20th century warming (low confidence, Section 10.3.1). More recently, it is extremely unlikely that the contribution from solar forcing to the observed global warming since 1950 was larger than that from GHGs (Section 10.3.1.1.3). It is very likely that there has been a small decrease in solar forcing of 0.04 [ 0.08 to 0.00] W m 2 over a period with direct satellite measurements of solar output from 1986 to 2008 (Section 8.4.1.1). There is high confidence that changes in total solar irradiance have not contributed to global warming during that period. Since AR4, there has been considerable new research that has connected solar forcing to climate. The effect of solar forcing on GMST trends has been found to be small, with less than 0.1°C warming attributable to combined solar and volcanic forcing over the 1951 2010 period (Section 10.3.1), although the 11-year cycle of solar variability has been found to have some influence on GMST variability over the 20th century. GMST changes between solar maxima and minima are estimated to be of order 0.1°C from some regression studies of GMST and forcing estimates (Figure 10.6), although several studies have suggested these results may be too large owing to issues including degeneracy between forcing and with internal variability, overfitting of forcing indices and underestimated uncertain- ties in responses (Ingram, 2007; Benestad and Schmidt, 2009; Stott and Jones, 2009). Climate models generally show less than half this variability (Jones et al., 2012). (continued on next page) 885 Chapter 10 Detection and Attribution of Climate Change: from Global to Regional Box 10.2 (continued) Variability associated with the 11-year solar cycle has also been shown to produce measurable short-term regional and seasonal climate anomalies (Miyazaki and Yasunari, 2008; Gray et al., 2010; Lockwood, 2012; National Research Council, 2012) particularly in the Indo-Pacific, Northern Asia and North Atlantic regions (medium evidence). For example, studies have suggested an 11-year solar response in the Indo-Pacific region in which the equatorial eastern Pacific sea surface temperatures (SSTs) tend to be below normal, the sea level pressure (SLP) in the Gulf of Alaska and the South Pacific above normal, and the tropical convergence zones on both hemispheres strengthened and displaced polewards under solar maximum conditions, although it can be difficult to discriminate the solar-forced signal from the El Nino-Southern Oscillation (ENSO) signal (van Loon et al., 2007; van Loon and Meehl, 2008; White and Liu, 2008; Meehl and Arblaster, 2009; Roy and Haigh, 2010, 2012; Tung and Zhou, 2010; Bal et al., 2011; Haam and Tung, 2012; Hood and Soukharev, 2012; Misios and Schmidt, 2012). For northern summer, there is evidence that for peaks in the 11-year solar cycle, the Indian monsoon is intensified (Kodera, 2004; van Loon and Meehl, 2012), with solar variability affecting interannual connections between the Indian and Pacific sectors due to a shift in the location of the descending branch of the Walker Circulation (Kodera et al., 2007). In addition, model sensitivity experiments (Ineson et al., 2011) suggest that the negative phase of the North Atlantic Oscillation (NAO) is more prevalent during solar minima and there is some evidence of this in observations, including an indication of increased frequency of high-pressure blocking events over Europe in winter (Barriopedro et al., 2008; Lockwood et al., 2010; Woollings et al., 2010). Two mechanisms have been identified in observations and simulated with climate models that could explain these low amplitude regional responses (Gray et al., 2010; medium evidence). These mechanisms are additive and may reinforce one another so that the response to an initial small change in solar irradiance is amplified regionally (Meehl et al., 2009). The first mechanism is a top-down mechanism first noted by Haigh (1996) where greater solar ultraviolet radiation (UV) in peak solar years warms the stratosphere direct- ly via increased radiation and indirectly via increased ozone production. This can result in a chain of processes that influences deep 10 tropical convection (Balachandran et al., 1999; Shindell et al., 1999; Kodera and Kuroda, 2002; Haigh et al., 2005; Kodera, 2006; Matthes et al., 2006). In addition, there is less heating than average in the tropical upper stratosphere under solar minimum conditions which weakens the equator-to-pole temperature gradient. This signal can propagate downward to weaken the tropospheric mid-latitude westerlies, thus favoring a negative phase of the Arctic Oscillation (AO) or NAO. This response has been shown in several models (e.g., Shindell et al., 2001; Ineson et al., 2011) though there is no significant AO or NAO response to solar irradiance variations on average in the CMIP5 models (Gillett and Fyfe, 2013). The second mechanism is a bottom-up mechanism that involves coupled air sea radiative processes in the tropical and subtropical Pacif- ic that also influence convection in the deep tropics (Meehl et al., 2003, 2008; Rind et al., 2008; Bal et al., 2011; Cai and Tung, 2012; Zhou and Tung, 2013b). Such mechanisms have also been shown to influence regional temperatures over longer time scales (decades to cen- turies), and can help explain patterns of regional temperature changes seen in paleoclimate data (e.g., Section 10.7.2; Mann et al., 2009; Goosse et al., 2012b) although they have little effect on global or hemispheric mean temperatures at either short or long time scales. A possible amplifying mechanism linking solar variability and the Earth s climate system via cosmic rays has been postulated. It is proposed that variations in the cosmic ray flux associated with changes in solar magnetic activity affect ion-induced aerosol nucleation and cloud condensation nuclei (CCN) production in the troposphere (Section 7.4.6). A strong solar magnetic field would deflect cosmic rays and lead to fewer CCN and less cloudiness, thereby allowing for more solar energy into the system. Since AR4, there has been further evidence to disprove the importance of this amplifying mechanism. Correlations between cosmic ray flux and observed aerosol or cloud properties are weak and local at best, and do not prove to be robust on the regional or global scale (Section 7.4.6). Although there is some evidence that ionization from cosmic rays may enhance aerosol nucleation in the free troposphere, there is medium evi- dence and high agreement that the cosmic ray ionization mechanism is too weak to influence global concentrations of CCN or their change over the last century or during a solar cycle in any climatically significant way (Sections 7.4.6 and 8.4.1.5). The lack of trend in cosmic ray intensity over the 1960 2005 period (McCracken and Beer, 2007) provides another argument against the hypothesis of a major contribution of cosmic ray variations to the observed warming over that period given the existence of short time scales in the climate system response. Thus, although there is medium confidence that solar variability has made contributions to past climate fluctuations, since the mid- 20th century there has been little trend in solar forcing. There are at least two amplifying mechanisms that have been proposed and simulated in some models that could explain small observed regional and seasonal climate anomalies associated with the 11-year solar cycle, mostly in the Indo-Pacific region and northern mid to high latitudes. Regarding possible future influences of the sun on the Earth s climate, there is very low confidence in our ability to predict future solar output, but there is high confidence that the effects from solar irradiance variations will be much smaller than the projected climate changes from increased RF due to GHGs (Sections 8.4.1.3 and 11.3.6.2.2). 886 Detection and Attribution of Climate Change: from Global to Regional Chapter 10 Based on a range of detection and attribution analyses using multi- biases in SST observations leads to a higher estimate of 1950s temper- ple solar irradiance reconstructions and models, Hegerl et al. (2007b) atures, but does not substantially change the warming between 1900 concluded that it is very likely that GHGs caused more global warming and 1940 (Morice et al., 2012). The AR4 concluded that the early 20th than solar irradiance variations over the 1950 1999 period. Detection century warming is very likely in part due to external forcing (Hegerl and attribution analyses applied to the CMIP5 simulations (Figure et al., 2007b), and that it is likely that anthropogenic forcing contrib- 10.4) indicate less than 0.1°C temperature change attributable to com- uted to this warming. This assessment was based on studies including bined solar and volcanic forcing over the 1951 2010 period. Based on Shiogama et al. (2006) who find a contribution from solar and volcanic a regression of paleo temperatures onto the response to solar forc- forcing to observed warming to 1949, and Min and Hense (2006), who ing simulated by an energy balance model, Scafetta and West (2007) find strong evidence for a forced (either natural or combined natu- find that up to 50% of the warming since 1900 may be solar-induced, ral and anthropogenic) contribution to global warming from 1900 to but Benestad and Schmidt (2009) show this conclusion is not robust, 1949. Ring et al. (2012) estimate, based on time series analysis, that being based on disregarding forcings other than solar in the prein- part of the early 20th century warming was due to GHG increases (see dustrial period, and assuming a high and precisely known value for also Figure 10.6), but find a dominant contribution by internal varia- climate sensitivity. Despite claims that more than half the warming bility. CMIP5 model simulations of the historical period show forced since 1970 can be ascribed to solar variability (Loehle and Scaffetta, warming over the early 20th century (Figure 10.1a), consistent with 2011) , a conclusion based on an incorrect assumption of no anthro- earlier detection and attribution analyses highlighted in the AR4 and pogenic influence before 1950 and a 60-year solar cycle influence on TAR. The early 20th century contributes to the detection of external global temperature (see also Mazzarella and Scafetta, 2012), several forcings over the 20th century estimated by detection and attribution studies show that solar variations cannot explain global mean surface results (Figure 10.4; Gillett et al., 2013; Ribes and Terray, 2013) and warming over the past 25 years, because solar irradiance has declined to the detected change over the last millennium to 1950 (see Figure over this period (Lockwood and Fro hlich, 2007, 2008; Lockwood, 2008, 10.19; Schurer et al., 2013). 2012 ). Lean and Rind (2008) conclude that solar forcing explains only 10% of the warming over the past 100 years, while contributing a The pattern of warming and residual differences between models and small cooling over the past 25 years. Thus while there is some evidence observations indicate a role for circulation changes as a contributor to 10 for solar influences on regional climate variability (Box 10.2) solar forc- early 20th cenury warming (Figure 10.2), and the contribution of internal ing has only had a small effect on GMST. Overall, we conclude that it variability to the early 20th century warming has been analysed in sev- is extremely unlikely that the contribution from solar forcing to the eral publications since the AR4. Crook and Forster (2011) find that the warming since 1950 was larger than that from GHGs. observed 1918 1940 warming was significantly greater than that simu- lated by most of the CMIP3 models. A distinguishing feature of the early A range of studies have used statistical methods to separate out the 20th century warming is its pattern (Brönnimann, 2009) which shows influence of known sources of internal variability, including ENSO the most pronounced warming in the Arctic during the cold season, fol- and, in some cases, the AMO, from the response to external drivers, lowed by North America during the warm season, the North Atlantic including volcanoes, solar variability and anthropogenic influence, Ocean and the tropics. In contrast, there was no unusual warming in in the recent GMST record: see, for example, Lockwood (2008), Lean Australia among other regions (see Figure 10.2b). Such a pronounced and Rind (2009), Folland et al. (2013 ), Foster and Rahmstorf (2011) pattern points to a role for circulation change as a contributing factor and Kaufmann et al. (2011). Representative results, as summarized in to the regional anomalies contributing to this warming. Some studies Imbers et al. (2013), are shown in Figure 10.6. These consistently attrib- have suggested that the warming is a response to the AMO (Schlesinger ute most of the warming over the past 50 years to anthropogenic influ- and Ramankutty, 1994; Polyakov et al., 2005; Knight et al., 2006; Tung ence, even allowing for potential confounding factors like the AMO. and Zhou, 2013), or a large but random expression of internal variability While results of such statistical approaches are sensitive to assump- (Bengtsson et al., 2006; Wood and Overland, 2010). Knight et al. (2009) tions regarding the properties of both responses to external drivers and diagnose a shift from the negative to the positive phase of the AMO internal variability (Imbers et al., 2013), they provide a complementary from 1910 to 1940, a mode of circulation that is estimated to contribute approach to attribution studies based on global climate models. approximately 0.1°C, trough to peak, to GMST (Knight et al., 2005). Nonetheless, these studies do not challenge the AR4 assessment that Overall, given that the anthropogenic increase in GHGs likely caused external forcing very likely made a contribution to the warming over this 0.5°C to 1.3°C warming over 1951 2010, with other anthropogenic period. In conclusion, the early 20th century warming is very unlikely to forcings probably contributing counteracting cooling, that the effects be due to internal variability alone. It remains difficult to quantify the of natural forcings and natural internal variability are estimated to be contribution to this warming from internal variability, natural forcing small, and that well-constrained and robust estimates of net anthropo- and anthropogenic forcing, due to forcing and response uncertainties genic warming are substantially more than half the observed warming and incomplete observational coverage. (Figure 10.4) we conclude that it is extremely likely that human activ- ities caused more than half of the observed increase in GMST from Year-to-year and decade-to-decade variability of global mean 1951 to 2010. surface temperature Time series analyses, such as those shown in Figure 10.6, seek to par- The early 20th century warming tition the variability of GMST into components attributable to anthro- The instrumental GMST record shows a pronounced warming during pogenic and natural forcings and modes of internal variability such the first half of the 20th century (Figure 10.1a). Correction of residual as ENSO and the AMO. Although such time series analyses support 887 Chapter 10 Detection and Attribution of Climate Change: from Global to Regional the major role of anthropogenic forcings, particularly due to increasing 10.3.1.1.4 Attribution of regional surface temperature change GHG concentrations, in contributing to the overall warming over the last 60 years, many factors, in addition to GHGs, including changes Anthropogenic influence on climate has been robustly detected on in tropospheric and stratospheric aerosols, stratospheric water vapour the global scale, but for many applications an estimate of the anthro- and solar output, as well as internal modes of variability, contribute pogenic contribution to recent temperature trends over a particular to the year-to-year and decade-to-decade variability of GMST (Figure region is more useful. However, detection and attribution of climate 10.6). Detailed discussion of the evolution of GMST of the past 15 change at continental and smaller scales is more difficult than on the years since 1998 is contained in Box 9.2. global scale for several reasons (Hegerl et al., 2007b; Stott et al., 2010). Estimated contributions to global mean temperature change 1 a) HadCRUT3 observations Obs. & reconstructions (°C) Folland 0.5 Lean Kaufmann 0 Lockwood 0.5 All temperatures relative to 1980 2000 1 0.5 b) 10 ENSO (°C) 0 0.5 0.5 c) Volcanoes (°C) 0 0.5 0.5 d) Solar (°C) 0 0.5 0.5 e) AMO and other (°C) Anthropogenic (°C) 0 0.5 0.5 f) 0 0.5 1890 1900 1910 1920 1930 1940 1950 1960 1970 1980 1990 2000 2010 Figure 10.6 | (Top) The variations of the observed global mean surface temperature (GMST) anomaly from Hadley Centre/Climatic Research Unit gridded surface temperature data set version 3 (HadCRUT3, black line) and the best multivariate fits using the method of Lean (red line), Lockwood (pink line), Folland (green line) and Kaufmann (blue line). (Below) The contributions to the fit from (a) El Nino-Southern Oscillation (ENSO), (b) volcanoes, (c) solar forcing, (d) anthropogenic forcing and (e) other factors (Atlantic Multi-decadal Oscillation (AMO) for Folland and a 17.5-year cycle, semi-annual oscillation (SAO), and Arctic Oscillation (AO) from Lean). (From Lockwood (2008), Lean and Rind (2009), Folland et al. (2013 ) and Kaufmann et al. (2011), as summarized in Imbers et al. (2013).) 888 Detection and Attribution of Climate Change: from Global to Regional Chapter 10 1880 1920 1960 2000 1880 1920 1960 2000 1880 1920 1960 2000 2.0 2.0 Global Global Land Global ocean 1.5 1.5 1.0 1.0 0.5 0.5 0.0 0.0 -0.5 -0.5 -1.0 -1.0 Temperature anomaly (°C) 2.0 2.0 North America South America Europe 1.5 1.5 1.0 1.0 0.5 0.5 0.0 0.0 -0.5 -0.5 -1.0 -1.0 2.0 2.0 Africa Asia Australasia 1.5 1.5 1.0 1.0 0.5 0.5 0.0 0.0 -0.5 -0.5 10 -1.0 -1.0 1880 1920 1960 2000 1880 1920 1960 2000 2.0 Antarctica 1.5 historical 5-95% 1.0 historicalNat 5-95% HadCRUT4 0.5 0.0 -0.5 -1.0 1880 1920 1960 2000 Figure 10.7 | Global, land, ocean and continental annual mean temperatures for CMIP3 and CMIP5 historical (red) and historicalNat (blue) simulations (multi-model means shown as thick lines, and 5 to 95% ranges shown as thin light lines) and for Hadley Centre/Climatic Research Unit gridded surface temperature data set 4 (HadCRUT4, black). Mean tem- peratures are shown for Antarctica and six continental regions formed by combining the sub-continental scale regions defined by Seneviratne et al. (2012). Temperatures are shown with respect to 1880 1919 for all regions apart from Antarctica where temperatures are shown with respect to 1950 2010. (Adapted from Jones et al., 2013.) First, the relative contribution of internal variability compared to the to surface temperature increases in every continent except Antarcti- forced response to observed changes tends to be larger on smaller ca since the middle of the 20th century . Figure 10.7 shows compari- scales, as spatial differences in internal variations are averaged out in sons of observed continental scale temperatures (Morice et al., 2012) large-scale means. Second, because the patterns of response to climate with CMIP5 simulations including both anthropogenic and natural forcings tend to be large scale, there is less spatial information to help forcings (red lines) and including just natural forcings (blue lines). distinguish between the responses to different forcings when attention Observed temperatures are largely within the range of simulations is restricted to a sub-global area. Third, forcings omitted in some global with ­ nthropogenic forcings for all regions and outside the range of a climate model simulations may be important on regional scales, such simulations with only natural forcings for all regions except Antarctica as land use change or BC aerosol. Lastly, simulated internal variability (Jones et al., 2013 ). Averaging over all observed locations, Antarcti- and responses to forcings may be less reliable on smaller scales than ca has warmed over the 1950 2008 period (Section 2.4.1.1; Gillett et on the global scale. Knutson et al. (2013) find a tendency for CMIP5 al., 2008b; Jones et al., 2013 ), even though some individual locations models to overestimate decadal variability in the NH extratropics in have cooled, particularly in summer and autumn, and over the shorter individual grid cells and underestimate it elsewhere, although Karoly 1960 1999 period (Thompson and Solomon, 2002; Turner et al., 2005). and Wu (2005) and Wu and Karoly (2007) find that variability is not When temperature changes associated with changes in the South- generally underestimated in earlier generation models. ern Annular Mode are removed by regression, both observations and model simulations indicate warming at all observed locations except Based on several studies, Hegerl et al. (2007b) concluded that it is the South Pole over the 1950 1999 period (Gillett et al., 2008b). An likely that there has been a substantial anthropogenic contribution analysis of Antarctic land temperatures over the period 1950 1999 889 Chapter 10 Detection and Attribution of Climate Change: from Global to Regional detected separate natural and anthropogenic responses of consist- ­ regions, the response to anthropogenic forcings is detected when the ent magnitude in simulations and observations (Gillett et al., 2008b). response to natural forcings is also included in the analysis (Gillett et al., Thus anthropogenic influence on climate has now been detected on all 2008a). Knutson et al. (2013) detect an anthropogenic influence over seven continents. However the evidence for human influence on Ant- Canada, but not over the continental USA, Alaska or Mexico. arctic temperature is much weaker than for the other six continental regions. There is only one attribution study for this region, and there Gillett et al. (2008b) detect anthropogenic influence on near-surface is greater observational uncertainty than the other regions, with very Arctic temperatures over land, with a consistent magnitude in simu- few data before 1950, and sparse coverage that is mainly limited to lations and observations. Wang et al. (2007) also find that observed the coast and the Antarctic Peninsula. As a result of the observational Arctic warming is inconsistent with simulated internal variability. Both uncertainties, there is low confidence in Antarctic region land surface studies ascribe Arctic warmth in the 1930s and 1940s largely to inter- air temperatures changes (Section 2.4.1.1) and we conclude for Ant- nal variability. Shindell and Faluvegi (2009) infer a large contribution arctica there is low confidence that anthropogenic influence has con- to both mid-century Arctic cooling and late century warming from tributed to the observed warming averaged over available stations. aerosol forcing changes, with GHGs the dominant driver of long-term warming, though they infer aerosol forcing changes from temperature Since the publication of the AR4 several other studies have applied changes using an inverse approach which may lead to some changes attribution analyses to continental and sub-continental scale regions. associated with internal variability being attributed to aerosol forc- Min and Hense (2007) applied a Bayesian decision analysis technique ing. We therefore conclude that despite the uncertainties introduced to continental-scale temperatures using the CMIP3 multi-model ensem- by limited observational coverage, high internal variability, modelling ble and concluded that forcing combinations including GHG increases uncertainties (Crook et al., 2011) and poorly understood local forcings, provide the best explanation of 20th century observed changes in tem- such as the effect of BC on snow, there is sufficiently strong evidence perature on every inhabited continent except Europe, where the obser- to conclude that it is likely that there has been an anthropogenic con- vational evidence is not decisive in their analysis. Jones et al. (2008) tribution to the very substantial warming in Arctic land surface temper- detected anthropogenic influence on summer temperatures over all atures over the past 50 years. 10 NH continents and in many subcontinental NH land regions in an optimal detection analysis that considered the temperature responses Some attribution analyses have considered temperature trends at the to anthropogenic and natural forcings. Christidis et al. (2010) used a climate model grid box scale. At these spatial scales robust attribu- multi-model ensemble constrained by global-scale observed tempera- tion is difficult to obtain, since climate models often lack the processes ture changes to estimate the changes in probability of occurrence of needed to simulate regional details realistically, regionally important warming or cooling trends over the 1950 1997 period over various forcings may be missing in some models and observational uncertain- sub-continental scale regions. They concluded that the probability of ties are very large for some regions of the world at grid box scale occurrence of warming trends had been at least doubled by anthro- (Hegerl et al., 2007b; Stott et al., 2010). Nevertheless an attribution pogenic forcing over all such regions except Central North America. analysis has been carried out on Central England temperature, a record The estimated distribution of warming trends over the Central North that extends back to 1659 and is sufficiently long to demonstrate that America region was approximately centred on the observed trend, so the representation of multi-decadal variability in the single grid box in no inconsistency between simulated and observed trends was identi- the model used, Hadley Centre climate prediction model 3 (HadCM3) fied there. Knutson et al. (2013) demonstrated that observed temper- is adequate for detection (Karoly and Stott, 2006). The observed trend ature trends from the beginning of the observational record to 2010 in Central England Temperature is inconsistent with either internal var- averaged over Europe, Africa, Northern Asia, Southern Asia, Australia iability or the simulated response to natural forcings, but is consistent and South America are all inconsistent with the simulated response to with the simulated response when anthropogenic forcings are included natural forcings alone, and consistent with the simulated response to (Karoly and Stott, 2006). combined natural and anthropogenic forcings in the CMIP5 models. They reached a similar conclusion for the major ocean basins with the Observed 20th century grid cell trends from Hadley Centre/Climatic exception of the North Atlantic, where variability is high. Research Unit gridded surface temperature data set 2v (HadCRUT2v; Jones et al., 2001) are inconsistent with simulated internal variability Several recent studies have applied attribution analyses to specific at the 10% significance level in around 80% of grid cells even using sub-continental regions. Anthropogenic influence has been found in HadCM2 which was found to overestimate variability in 5-year mean winter minimum temperature over the western USA (Bonfils et al., 2008; temperatures at most latitudes (Karoly and Wu, 2005). Sixty percent of Pierce et al., 2009), a conclusion that is found to be robust to weighting grid cells were found to exhibit significant warming trends since 1951, models according to various aspects of their climatology (Pierce et al., a much larger number than expected by chance (Karoly and Wu, 2005; 2009); anthropogenic influence has been found in temperature trends Wu and Karoly, 2007), and similar results apply when circulation-relat- over New Zealand (Dean and Stott, 2009) after circulation-related var- ed variability is first regressed out (Wu and Karoly, 2007). However, as iability is removed as in Gillett et al. (2000); and anthropogenic influ- discussed in the AR4 (Hegerl et al., 2007b), when a global field signifi- ence has been found in temperature trends over France, using a first-or- cance test is applied, this becomes a global detection study; since not der autoregressive model of internal variability (Ribes et al., 2010). all grid cells exhibit significant warming trends the overall interpreta- Increases in anthropogenic GHGs were found to be the main driver tion of the results in terms of attribution at individual locations remains of the 20th-century SST increases in both Atlantic and Pacific tropical problematic. Mahlstein et al. (2012) find significant changes in summer cyclogenesis regions (Santer et al., 2006; Gillett et al., 2008a). Over both season temperatures in about 40% of low-latitude and about 20% of 890 Detection and Attribution of Climate Change: from Global to Regional Chapter 10 extratropical land grid cells with sufficient observations, when testing irradiance increases cause a general warming of the atmosphere and against the null hypothesis of no change in the distribution of summer volcanic aerosol ejected into the stratosphere causes tropospheric temperatures. Observed grid cell trends are compared with CMIP5 sim- cooling and stratospheric warming (Hegerl et al., 2007b). ulated trends in Figure 10.2i, which shows that in the great majority (89%) of grid cells with sufficient observational coverage, observed 10.3.1.2.1 Tropospheric temperature change trends over the 1901 2010 period are inconsistent with a combination of simulated internal variability and the response to natural forcings Chapter 2 concludes that it is virtually certain that globally the tropo- (Jones et al., 2013). Knutson et al. (2013) find some deficiencies in the sphere has warmed since the mid-twentieth century with only medium simulation of multi-decadal variability at the grid cell scale in CMIP5 (NH extratropics) to low confidence (tropics and SH extratropics) in the models, but demonstrate that trends at more than 75% of individu- rate and vertical structure of these changes. During the satellite era al grid cells with sufficient observational coverage in HadCRUT4 are CMIP3 and CMIP5 models tend to warm faster than observations spe- inconsistent with the simulated response to natural forcings alone, and cifically in the tropics (McKitrick et al., 2010; Fu et al., 2011; Po-Chedley consistent or larger than the simulated response to combined anthro- and Fu, 2012; Santer et al., 2013); however, because of the large uncer- pogenic and natural forcings in CMIP5 models. tainties in observed tropical temperature trends (Section 2.4.4; Seidel et al. (2012); Figures 2.26 and Figure 2.27) there is only low confidence In summary, it is likely that anthropogenic forcing has made a substan- in this assessment (Section 9.4.1.4.2). Outside the tropics, and over tial contribution to the warming of each of the inhabited continents the period of the radiosonde record beginning in 1961, the discrep- since 1950. For Antarctica large observational uncertainties result in ancy between simulated and observed trends is smaller (Thorne et al., low confidence that anthropogenic influence has contributed to the 2011; Lott et al., 2013; Santer et al., 2013). Specifically there is better observed warming averaged over available stations. Anthropogen- agreement between observed trends and CMIP5 model trends for the ic influence has likely contributed to temperature change in many NH extratropics (Lott et al., 2013). Factors other than observational sub-continental regions. Detection and attribution of climate change at uncertainties that contribute to inconsistencies between observed and continental and smaller scales is more difficult than at the global scale simulated free troposphere warming include specific manifestation of due to the greater contribution of internal variability, the greater dif- natural variability in the observed coupled atmosphere ocean system, 10 ficulty of distinguishing between different causal factors, and greater forcing errors incorporated in the historical simulations and model errors in climate models representation of regional details. Neverthe- response errors (Santer et al., 2013). less, statistically significant warming trends are observed at a majority of grid cells, and the observed warming is inconsistent with estimates Utilizing a subset of CMIP5 models with single forcing experiments of possible warming due to natural causes at the great majority of grid extending until 2010, Lott et al. (2013) detect influences of both cells with sufficient observational coverage. human induced GHG increase and other anthropogenic forcings (e.g., ozone and aerosols) in the spatio-temporal changes in tropospheric 10.3.1.2 Atmosphere temperatures from 1961 to 2010 estimated from radiosonde observa- tions. Figure 10.8 illustrates that a subsample of CMIP5 models (see This section presents an assessment of the causes of global and region- Supplementary Material for model selection) forced with both anthro- al temperature changes in the free atmosphere. In AR4, Hegerl et al. pogenic and natural climate drivers (red profiles) exhibit trends that (2007b) concluded that the observed pattern of tropospheric warming are consistent with radiosonde records in the troposphere up to about and stratospheric cooling is very likely due to the influence of anthro- 300 hPa, albeit with a tendency for this subset of models to warm more pogenic forcing, particularly greenhouse gases and stratospheric ozone than the observations. This finding is seen in near-globally averaged depletion. Since AR4, insight has been gained into regional aspects of data (where there is sufficient observational coverage to make a mean- free tropospheric trends and the causes of observed changes in strat- ingful comparison: 60°S to 60°N) (right panel), as well as in latitudinal ospheric temperature. bands of the SH extratropics (Figure 10.8, first panel), tropics (Figure 10.8, second panel) and the NH extratropics (Figure 10.8, third panel). Atmospheric temperature trends through the depth of the atmos- Figure 10.8 also illustrates that it is very unlikely that natural forc- phere offer the possibility of separating the effects of multiple climate ings alone could have caused the observed warming of tropospheric forcings, as climate model simulations indicate that each external temperatures (blue profiles). The ensembles with both anthropogen- forcing produces a different characteristic vertical and zonal pattern ic and natural forcings (red) and with GHG forcings only (green) are of ­emperature response (Hansen et al., 2005b; Hegerl et al., 2007b; t not clearly separated. This could be due to cancellation of the effects Penner et al., 2007; Yoshimori and Broccoli, 2008). GHG forcing is of increases in reflecting aerosols, which cool the troposphere, and expected to warm the troposphere and cool the stratosphere. Strat- absorbing aerosol (Penner et al., 2007) and tropospheric ozone, which ospheric ozone depletion cools the stratosphere, with the cooling both warm the troposphere. Above 300 hPa the three radiosonde data being most pronounced in the polar regions. Its effect on tropospheric sets exhibit a larger spread as a result of larger uncertainties in the temperatures is small, which is consistent with a small estimated RF observational record (Thorne et al., 2011; Section 2.4.4). In this region of stratospheric ozone changes (SPARC CCMVal, 2010; McLandress of the upper troposphere simulated CMIP5 temperature trends tend et al., 2012). Tropospheric ozone increase, on the other hand, causes to be more positive than observed trends (Figure 10.8). Further, an tropospheric warming. Reflective aerosols like sulphate cool the trop- assessment of causes of observed trends in the upper troposphere is osphere while absorbing aerosols like BC have a warming effect. Free less confident than an assessment of overall atmospheric temperature atmosphere temperatures are also affected by natural forcings: solar changes because of observational uncertainties and potential remain- 891 Chapter 10 Detection and Attribution of Climate Change: from Global to Regional 10 Figure 10.8 | Observed and simulated zonal mean temperatures trends from 1961 to 2010 for CMIP5 simulations containing both anthropogenic and natural forcings (red), natural forcings only (blue) and greenhouse gas forcing only (green) where the 5 to 95th percentile ranges of the ensembles are shown. Three radiosonde observations are shown (thick black line: Hadley Centre Atmospheric Temperature data set 2 (HadAT2), thin black line: RAdiosone OBservation COrrection using REanalyses 1.5 (RAOBCORE 1.5), dark grey band: Radiosonde Innovation Composite Homogenization (RICH)-obs 1.5 ensemble and light grey: RICH- 1.5 ensemble. (After Lott et al., 2013.) ing systematic biases in observational data sets in this region (Thorne subsample of CMIP5 models that also suggest that the warming effect et al., 2011; Haimberger et al., 2012). An analysis of contributions of of well mixed GHGs is partly offset by the combined effects of reflect- natural and anthropogenic forcings to more recent trends from 1979 to ing aerosols and other forcings. Our understanding has been increased 2010 (Supplementary Material, Figure S.A.1) is less robust because of regarding the time scale of detectability of global scale troposphere increased uncertainty in observed trends (consistent with Seidel et al. temperature. Taken together with increased understanding of the (2012)) as well as decreased capability to separate between individual uncertainties in observational records of tropospheric temperatures forcings ensembles. (including residual systematic biases; Section 2.4.4) the assessment remains as it was for AR4 that it is likely that anthropogenic forcing has One approach to identify a climate change signal in a time series is the led to a detectable warming of tropospheric temperatures since 1961. analysis of the ratio between the amplitude of the observed signal of change divided by the magnitude of internal variability, in other words 10.3.1.2.2 Stratospheric temperature change the S/N ratio of the data record. The S/N ratio represents the result of a non-optimal fingerprint analysis (in contrast to optimal finger- Lower stratospheric temperatures have not evolved uniformly over print analyses where model-simulated responses and observations are the period since 1958 when the stratosphere has been observed with n ­ ormalized by internal variability to improve the S/N ratio (see Section sufficient regularity and spatial coverage. A long-term global cooling 10.2.3). For changes in the lower stratospheric temperature between trend is interrupted by three 2-year warming episodes following large 1979 and 2011, S/N ratios vary from 26 to 36, depending on the choice volcanic eruptions (Section 2.4.4). During the satellite period the cool- of observational data set. In the lower troposphere, the fingerprint ing evolved mainly in two steps occurring in the aftermath of the El strength in observations is smaller, but S/N ratios are still significant at Chichón eruption in 1982 and the Mt Pinatubo eruption of 1991, with the 1% level or better, and range from 3 to 8. There is no evidence that each cooling transition being followed by a period of relatively steady these ratios are spuriously inflated by model variability errors. After all temperatures (Randel et al., 2009; Seidel et al., 2011). Since the mid- global mean signals are removed, model fingerprints remain identifi- 1990s little net change has occurred in lower stratospheric tempera- able in 70% of the tests involving tropospheric temperature changes tures (Section 2.4.4). (Santer et al., 2013). Since AR4, progress has been made in simulating the observed evo- Hegerl et al. (2007a) concluded that increasing GHGs are the main lution of global mean lower stratospheric temperature. On the one cause for warming of the troposphere. This result is supported by a hand, this has been achieved by using models with an improved 892 Detection and Attribution of Climate Change: from Global to Regional Chapter 10 r ­epresentation of stratospheric processes (chemistry climate models and some CMIP5 models). It is found that in these models which have an upper boundary above the stratopause with an altitude of about 50 km (so-called high-top models) and improved stratospheric physics, variability of lower stratosphere climate in general is well simulated (Butchart et al., 2011; Gillett et al., 2011; Charlton-Perez et al., 2013) whereas in so-called low-top models (including models participating in CMIP3) it is generally underestimated (Cordero and Forster, 2006; Charlton-Perez et al., 2013). On the other hand, CMIP5 models all include changes in stratospheric ozone (Eyring et al., 2013) whereas only about half of the models participating in CMIP3 include strato- spheric ozone changes (Section 9.4.1.4.5). A comparison of a low-top Figure 10.9 | Time series (1979 2010) of observed (black) and simulated global mean and high-top version of the HadGEM2 model shows detectable dif- (82.5°S to 82.5°N) Microwave Sounding Unit (MSU) lower stratosphere temperature anomalies in a subset of CMIP5 simulations (simulations with both anthropogenic and ferences in modelled temperature changes, particularly in the lower natural forcings (red), simulations with well-mixed greenhouse gases (green), simula- tropical stratosphere, with the high-top version s simulation of tem- tions with natural forcings (blue)). Anomalies are calculated relative to 1996 2010. perature trends in the tropical troposphere in better agreement with (Adapted from Ramaswamy et al., 2006.) radiosondes and reanalyses over 1981 2010 (Mitchell et al., 2013). CMIP5 models forced with changes in WMGHGs and stratospheric Models disagree with observations for seasonally varying changes in ozone as well as with changes in solar irradiance and volcanic aerosol the strength of the Brewer Dobson circulation in the lower strato- forcings simulate the evolution of observed global mean lower strat- sphere (Ray et al., 2010) which has been linked to zonal and seasonal ospheric temperatures over the satellite era reasonably well although patterns of changes in lower stratospheric temperatures (Thompson they tend to underestimate the long-term cooling trend (Charlton-Per- and Solomon, 2009; Fu et al., 2010; Lin et al., 2010b; Forster et al., ez et al., 2013; Santer et al., 2013). Compared with radiosonde data the 2011; Free, 2011). One robust feature is the observed cooling in spring 10 cooling trend is also underestimated in a subset of CMIP5 simulations over the Antarctic, which is simulated in response to stratospheric over the period 1961 2010 (Figure 10.8) and in CMIP3 models over ozone depletion in climate models (Young et al., 2012), although this the 1958 1999 period (Cordero and Forster, 2006). Potential causes has not been the subject of a formal detection and attribution study. for biases in lower stratosphere temperature trends are observational uncertainties (Section 2.4.4) and forcing errors related to prescribed Since AR4, progress has been made in simulating the response of stratospheric aerosol loadings and stratospheric ozone changes affect- global mean lower stratosphere temperatures to natural and anthro- ing the tropical lower stratosphere (Free and Lanzante, 2009; Solomon pogenic forcings by improving the representation of climate forcings et al., 2012; Santer et al., 2013). and utilizing models that include more stratospheric processes. New detection and attribution studies of lower stratospheric temperature Since AR4, attribution studies have improved our knowledge of the changes made since AR4 support an assessment that it is very likely role of anthropogenic and natural forcings in observed lower strato- that anthropogenic forcing, dominated by stratospheric ozone deple- spheric temperature change. Gillett et al. (2011) use the suite of chem- tion due to ozone-depleting substances, has led to a detectable cooling istry climate model simulations carried out as part of the Chemistry of the lower stratosphere since 1979. Climate Model Validation (CCMVal) activity phase 2 for an attribution study of observed changes in stratospheric zonal mean temperatures. 10.3.1.2.3 Overall atmospheric temperature change Chemistry climate models prescribe changes in ozone-depleting sub- stances (ODS) and ozone changes are calculated interactively. Gillett et When temperature trends from the troposphere and stratosphere al. (2011) partition 1979 2005 Microwave Sounding Unit (MSU) lower are analysed together, detection and attribution studies using CMIP5 stratospheric temperature trends into ODS-induced and GHG-induced models show robust detections of the effects of GHGs and other changes and find that both ODSs and natural forcing contributed to anthropogenic forcings on the distinctive fingerprint of tropospheric the observed stratospheric cooling in the lower stratosphere with the warming and stratospheric cooling seen since 1961 in radiosonde data impact of ODS dominating. The influence of GHGs on stratospheric (Lott et al., 2013; Mitchell et al., 2013). Combining the evidence from temperature could not be detected independently of ODSs. free atmosphere changes from both troposphere and stratosphere shows an increased confidence in the attribution of free atmosphere The step-like cooling of the lower stratosphere can only be explained temperature changes compared to AR4 owing to improved under- by the combined effects of changes in both anthropogenic and natu- standing of stratospheric temperature changes. There is therefore ral factors (Figure 10.9; Eyring et al., 2006; Ramaswamy et al., 2006). stronger evidence than at the time of AR4 to support the conclusion Although the anthropogenic factors (ozone depletion and increases in that it is very likely that anthropogenic forcing, particularly GHGs and WMGHGs) cause the overall cooling, the natural factors (solar irradi- stratospheric ozone depletion, has led to a detectable observed pattern ance variations and volcanic aerosols) modulate the evolution of the of tropospheric warming and lower stratospheric cooling since 1961. cooling (Figure 10.9; Ramaswamy et al., 2006; Dall Amico et al., 2010) with temporal variability of global mean ozone contributing to the step-like temperature evolution (Thompson and Solomon, 2009). 893 Chapter 10 Detection and Attribution of Climate Change: from Global to Regional Frequently Asked Questions FAQ 10.1 | Climate Is Always Changing. How Do We Determine the Causes of Observed Changes? The causes of observed long-term changes in climate (on time scales longer than a decade) are assessed by determin- ing whether the expected fingerprints of different causes of climate change are present in the historical record. These fingerprints are derived from computer model simulations of the different patterns of climate change caused by individual climate forcings. On multi-decade time scales, these forcings include processes such as greenhouse gas increases or changes in solar brightness. By comparing the simulated fingerprint patterns with observed climate changes, we can determine whether observed changes are best explained by those fingerprint patterns, or by natu- ral variability, which occurs without any forcing. The fingerprint of human-caused greenhouse gas increases is clearly apparent in the pattern of observed 20th cen- tury climate change. The observed change cannot be otherwise explained by the fingerprints of natural forcings or natural variability simulated by climate models. Attribution studies therefore support the conclusion that it is extremely likely that human activities have caused more than half of the observed increase in global mean surface temperatures from 1951 to 2010. The Earth s climate is always changing, and that can occur for many reasons. To determine the principal causes of observed changes, we must first ascertain whether an observed change in climate is different from other fluctua- tions that occur without any forcing at all. Climate variability without forcing called internal variability is the consequence of processes within the climate system. Large-scale oceanic variability, such as El Nino-Southern Oscil- 10 lation (ENSO) fluctuations in the Pacific Ocean, is the dominant source of internal climate variability on decadal to centennial time scales. Climate change can also result from natural forcings external to the climate system, such as volcanic eruptions, or changes in the brightness of the sun. Forcings such as these are responsible for the huge changes in climate that are clearly documented in the geological record. Human-caused forcings include greenhouse gas emissions or atmo- spheric particulate pollution. Any of these forcings, natural or human caused, could affect internal variability as well as causing a change in average climate. Attribution studies attempt to determine the causes of a detected change in observed climate. Over the past century we know that global average temperature has increased, so if the observed change is forced then the principal forcing must be one that causes warming, not cooling. Formal climate change attribution studies are carried out using controlled experiments with climate models. The model-simulated responses to specific climate forcings are often called the fingerprints of those forcings. A climate model must reliably simulate the fingerprint patterns associated with individual forcings, as well as the patterns of unforced internal variability, in order to yield a meaningful climate change attribution assessment. No model can perfectly reproduce all features of climate, but many detailed studies indicate that simulations using current models are indeed sufficiently reliable to carry out attribution assessments. FAQ 10.1, Figure 1 illustrates part of a fingerprint assessment of global temperature change at the surface during the late 20th century. The observed change in the latter half of the 20th century, shown by the black time series in the left panels, is larger than expected from just internal variability. Simulations driven only by natural forcings (yellow and blue lines in the upper left panel) fail to reproduce late 20th century global warming at the surface with a spatial pattern of change (upper right) completely different from the observed pattern of change (middle right). Simulations including both natural and human-caused forcings provide a much better representation of the time rate of change (lower left) and spatial pattern (lower right) of observed surface temperature change. Both panels on the left show that computer models reproduce the naturally forced surface cooling observed for a year or two after major volcanic eruptions, such as occurred in 1982 and 1991. Natural forcing simulations capture the short-lived temperature changes following eruptions, but only the natural + human caused forcing simulations simulate the longer-lived warming trend. A more complete attribution assessment would examine temperature above the surface, and possibly other climate variables, in addition to the surface temperature results shown in FAQ 10.1, Figure 1. The fingerprint patterns asso- ciated with individual forcings become easier to distinguish when more variables are considered in the assessment. (continued on next page) 894 Detection and Attribution of Climate Change: from Global to Regional Chapter 10 FAQ 10.1 (continued) Overall, FAQ 10.1, Figure 1 shows that the pattern of observed temperature change is significantly different than the pattern of response to natural forcings alone. The simulated response to all forcings, including human-caused forcings, provides a good match to the observed changes at the surface. We cannot correctly simulate recent observed climate change without including the response to human-caused forcings, including greenhouse gases, stratospheric ozone, and aerosols. Natural causes of change are still at work in the climate system, but recent trends in temperature are largely attributable to human-caused forcing. Natural forcing Natural forcing 90N Temperature anomaly (°C) 1.5 45N CMIP3 1.0 CMIP5 0 observations 45S 0.5 90S 180 90W 0 90E 180 0.0 Observed trend 1951-2010 90N -0.5 45N 1860 1880 1900 1920 1940 1960 1980 2000 Year 0 45S 10 Natural and Human forcing Temperature anomaly (°C) 1.5 90S 180 90W 0 90E 180 CMIP3 1.0 CMIP5 Natural and Human forcing observations 90N 0.5 45N 0 0.0 45S -0.5 90S 180 90W 0 90E 180 1860 1880 1900 1920 1940 1960 1980 2000 Year -2 -1 0 1 2 Trend (°C per period) FAQ 10.1, Figure 1 | (Left) Time series of global and annual-averaged surface temperature change from 1860 to 2010. The top left panel shows results from two ensemble of climate models driven with just natural forcings, shown as thin blue and yellow lines; ensemble average temperature changes are thick blue and red lines. Three different observed estimates are shown as black lines. The lower left panel shows simulations by the same models, but driven with both natural forcing and human-induced changes in greenhouse gases and aerosols. (Right) Spatial patterns of local surface temperature trends from 1951 to 2010. The upper panel shows the pattern of trends from a large ensemble of Coupled Model Intercomparison Project Phase 5 (CMIP5) simulations driven with just natural forcings. The bottom panel shows trends from a corresponding ensemble of simulations driven with natural + human forcings. The middle panel shows the pattern of observed trends from the Hadley Centre/Climatic Research Unit gridded surface temperature data set 4 (HadCRUT4) during this period. 10.3.2 Water Cycle cited in this section, use less formal detection and attribution criteria than are often used for assessments of temperature change, owing Detection and attribution studies of anthropogenic change in hydro- to difficulties defining large-scale fingerprint patterns of hydrologic logic variables are challenged by the length and quality of observed change in models and isolating those fingerprints in data. For example, data sets, and by the difficulty in simulating hydrologic variables in correlations between observed hydrologic changes and the patterns of dynamical models. AR4 cautiously noted that the observed increase change in models forced by increasing GHGs can provide suggestive in atmospheric water vapour over oceans was consistent with warm- evidence towards attribution of change. ing of SSTs attributed to anthropogenic influence, and that observed changes in the latitudinal distribution of precipitation, and increased Since the publication of AR4, in situ hydrologic data sets have been incidence of drought, were suggestive of a possible human influence. reanalysed with more stringent quality control. Satellite-derived data Many of the published studies cited in AR4, and some of the studies records of worldwide water vapour and precipitation variations have 895 Chapter 10 Detection and Attribution of Climate Change: from Global to Regional lengthened. Formal detection and attribution studies have been car- identified from CMIP3 models (e.g., Polson et al., 2013). The AR4 con- ried out with newer models that potentially offer better simulations cluded that the latitudinal pattern of change in land precipitation and of natural variability. Reviews of detection and attribution of trends in observed increases in heavy precipitation over the 20th century appear various components of the water cycle have been published by Stott et to be consistent with the anticipated response to anthropogenic forc- al. (2010) and Trenberth (2011b). ing . Detection and attribution of regional precipitation changes gen- erally focuses on continental areas using in situ data because observa- 10.3.2.1 Changes in Atmospheric Water Vapour tional coverage over oceans is limited to a few island stations (Arkin et al., 2010; Liu et al., 2012; Noake et al., 2012) , although model-data In situ surface humidity measurements have been reprocessed since comparisons over continents also illustrate large observational uncer- AR4 to create new gridded analyses for climatic research, as discussed tainties (Tapiador, 2010; Noake et al., 2012; Balan Sarojini et al., 2012; in Chapter 2. The HadCRUH Surface Humidity data set (Willett et Polson et al., 2013). Available satellite data sets that could supplement al., 2008) indicates significant increases in surface specific humidity oceanic studies are short and their long-term homogeneity is still between 1973 and 2003 averaged over the globe, the tropics, and the unclear (Chapter 2); hence they have not yet been used for detection NH, with consistently larger trends in the tropics and in the NH during and attribution of changes. Continuing uncertainties in climate model summer, and negative or non significant trends in relative humidity. simulations of precipitation make quantitative model/data compari- These results are consistent with the hypothesis that the distribution sons difficult (e.g., Stephens et al., 2010), which also limits confidence of relative humidity should remain roughly constant under climate in detection and attribution. Furthermore, sparse observational cover- change (see Section 2.5). Simulations of the response to historical age of precipitation across much of the planet makes the fingerprint anthropogenic and natural forcings robustly generate an increase in of precipitation change challenging to isolate in observational records atmospheric humidity consistent with observations (Santer et al., 2007; (Balan Sarojini et al., 2012; Wan et al., 2013). Willett et al., 2007; Figure 9.9). A recent cessation of the upward trend in specific humidity is observed over multiple continental areas in Had- Considering just land regions with sufficient observations, the largest CRUH and is also found in the European Centre for Medium range signal of differences between models with and without anthropogenic 10 Weather Forecast (ECMWF) interim reanalysis of the global atmos- forcings is in the high latitudes of the NH, where increases in precip- phere and surface conditions (ERA-Interim; Simmons et al. 2010). This itation are a robust feature of climate model simulations (Scheff and change in the specific humidity trend is temporally correlated with a Frierson, 2012a, 2012b). Such increases have been observed (Figure levelling off of global ocean temperatures following the 1997 1998 El 10.10) in several different observational data sets (Min et al., 2008a; Nino event (Simmons et al., 2010). Noake et al., 2012; Polson et al., 2013), although high-latitude trends vary between data sets and with coverage (e.g., Polson et al., 2013). The anthropogenic water vapour fingerprint simulated by an ensemble of 22 climate models has been identified in lower tropospheric mois- Attribution of zonally averaged precipitation trends has been attempt- ture content estimates derived from Special Sensor Microwave/Imager ed using different observational products and ensembles of forced (SSM/I) data covering the period 1988 2006 (Santer et al., 2007). simulations from both the CMIP3 and CMIP5 archives, for annu- Santer et al. (2009) find that detection of an anthropogenic response al-averaged (Zhang et al., 2007; Min et al., 2008a) and season-spe- in column water vapour is insensitive to the set of models used. They cific (Noake et al., 2012; Polson et al., 2013) results (Figure 10.11). rank models based on their ability to simulate the observed mean total Zhang et al. (2007) identify the fingerprint of anthropogenic chang- column water vapour, and its annual cycle and variability associated es in observed annual zonal mean precipitation averaged over the with ENSO. They report no appreciable differences between the finger- periods 1925 1999 and 1950 1999, and separate the anthropogenic prints or detection results derived from the best or worst performing fingerprint from the influence of natural forcing. The fingerprint of models, and so conclude that attribution of water vapour changes to external forcing is also detected in seasonal means for boreal spring anthropogenic forcing is not sensitive to the choice of models used for in all data sets assessed by Noake et al. (2012), and in all but one the assessment. data set assessed by Polson et al. (2013) (Figure 10.11), and in boreal winter in all but one data set (Noake et al., 2012), over the period In summary, an anthropogenic contribution to increases in specific 1951 1999 and to 2005. The fingerprint features increasing high-lati- humidity at and near the Earth s surface is found with medium con- tude precipitation, and decreasing precipitation trends in parts of the fidence. Evidence of a recent levelling off of the long-term surface tropics that are reasonably robustly observed in all four data sets con- atmospheric moistening trend over land needs to be better understood sidered albeit with large observational uncertainties north of 60°N and simulated as a prerequisite to increased confidence in attribution (Figure 10.11). Detection of seasonal-average precipitation change is studies of water vapour changes. Length and quality of observation- less convincing for June, July, August (JJA) and September, October, al humidity data sets, especially above the surface, continue to limit November (SON) and results vary with observation data set (Noake detection and attribution studies of atmospheric water vapour. et al., 2012; Polson et al., 2013). Although Zhang et al. (2007) detect anthropogenic changes even if a separate fingerprint for natural forc- 10.3.2.2 Changes in Precipitation ings is considered, Polson et al. (2013) find that this result is sensi- tive to the data set used and that the fingerprints can be separated Analysis of CMIP5 model simulations yields clear global and region- robustly only for the data set most closely constrained by station data. al scale changes associated with anthropogenic forcing (e.g., Scheff The analysis also finds that model simulated precipitation variability and Frierson, 2012a, 2012b), with patterns broadly similar to those is smaller than observed variability in the tropics (Zhang et al., 2007; 896 Detection and Attribution of Climate Change: from Global to Regional Chapter 10 Polson et al., 2013) which is addressed by increasing the estimate of are expressed as a percentage of climatological precipitation and that variance from models (Figure 10.11). the observed and simulated changes are largely consistent between CMIP5 models and observations given data uncertainty. Use of addi- Another detection and attribution study focussed on precipitation in tional data sets illustrates remaining observational uncertainty in high the NH high latitudes and found an attributable human influence (Min latitudes of the NH (Figure 10.11). Regional-scale attribution of pre- et al., 2008a). Both Min et al. (2008a) and Zhang et al. (2007) find that cipitation change is still problematic although regional climate models the observed changes are significantly larger than the model simulated have yielded simulations consistent with observed wintertime changes changes. However, Noake et al. (2012) and Polson et al. (2013) find that for northern Europe (Bhend and von Storch, 2008; Tapiador, 2010). the difference between models and observations decreases if changes Precipitation change over ocean has been attributed to human influ- ence by Fyfe et al. (2012) for the high-latitude SH in austral summer, Land masked by Obs where zonally averaged precipitation has declined around 45°S and 0.25 increased around 60°S since 1957, consistent with CMIP5 historical simulations, with the magnitude of the half-century trend outside the range of simulated natural variability. Confidence in this attribution 0.00 result, despite limitations in precipitation observations, is enhanced by its consistency with trends in large-scale sea level pressure data (see Global Section 10.3.3). -0.25 1950 1970 1990 2010 In summary, there is medium confidence that human influence has con- 0.25 tributed to large-scale changes in precipitation patterns over land. The expected anthropogenic fingerprints of change in zonal mean precip- 0.00 itation reductions in low latitudes and increases in NH mid to high latitudes have been detected in annual and some seasonal data. 10 60N-90N Observational uncertainties including limited global coverage and large -0.25 natural variability, in addition to challenges in precipitation modeling, 1950 1970 1990 2010 limit confidence in assessment of climatic changes in precipitation. 0.25 10.3.2.3 Changes in Surface Hydrologic Variables 0.00 This subsection assesses recent research on detection and attribu- tion of long-term changes in continental surface hydrologic variables, 30N-60N -0.25 including soil moisture, evapotranspiration and streamflow. Stream- 1950 1970 1990 2010 flows are often subject to large non-climatic human influence, such as 0.25 diversions and land use changes, that must be accounted for in order to attribute detected hydrologic changes to climate change. Cryospher- ic aspects of surface hydrology are discussed in Section 10.5; extremes 0.00 in surface hydrology (such as drought) and precipitation are covered in Section 10.6.1. The variables discussed here are subject to large mod- 30S-30N eling uncertainties (Chapter 9) and observational challenges (Chapter -0.25 2), which in combination place severe limits on climate change detec- 1950 1970 1990 2010 tion and attribution. Obs Nat Direct observational records of soil moisture and surface fluxes tend All to be sparse and/or short, thus limiting recent assessments of change in these variables (Jung et al., 2010). Assimilated land surface data Figure 10.10 | Global and zonal average changes in annual mean precipitation (mm sets and new satellite observations (Chapter 2) are promising tools, day 1) over areas of land where there are observations, expressed relative to the base- but assessment of past and future climate change of these variables line period of 1961 1990, simulated by CMIP5 models forced with both anthropogenic (Hoekema and Sridhar, 2011) is still generally carried out on derived and natural forcings (red lines) and natural forcings only (blue lines) for the global mean quantities such as the Palmer Drought Severity Index, as discussed and for three latitude bands. Multi-model means are shown in thick solid lines. Observa- more fully in Section 10.6.1. Recent observations (Jung et al., 2010) tions (gridded values derived from Global Historical Climatology Network station data, updated from Zhang et al. (2007) are shown as a black solid line. An 11-year smoothing show regional trends towards drier soils. An optimal detection analysis is applied to both simulations and observations. Green stars show statistically significant of reconstructed evapotranspiration identifies the effects of anthro- changes at 5% level (p value <0.05) between the ensemble of runs with both anthropo- pogenic forcing on evapotranspiration, with the Centre National de genic and natural forcings (red lines) and the ensemble of runs with just natural forcings Recherches Météorologiques (CNRM)-CM5 model simulating chang- (blue lines) using a two-sample two-tailed t-test for the last 30 years of the time series. es consistent with those estimated to have occurred (Douville et al., (From Balan Sarojini et al., 2012.) Results for the Climate Research Unit (CRU) TS3.1 data set are shown in Figure 10.A.2. 2013). 897 Chapter 10 Detection and Attribution of Climate Change: from Global to Regional ALL 6 10 (a) (b) (c) (d) (e) ANNUAL Precipitation trend (% per decade) ANT NAT 8 4 6 DJF MAM JJA SON 2 Scaling factor 4 0 2 2 Obs(V) 0 1 sig 2 sig Obs(Z) 4 Obs(C) 2 Obs(G) MM 6 4 ZZ C Z V G CZVG CZVG CZVG CZVG Z Z HH 40 20 0 20 40 60 Observation dataset Latitude 6 6 (f) JJA (g) DJF Precipitation trend (% per decade) Precipitation trend (% per decade) 4 4 2 2 10 0 0 2 2 4 4 6 6 40 20 0 20 40 60 40 20 0 20 40 60 Latitude Latitude Figure 10.11 | Detection and attribution results for zonal land precipitation trends in the second half of the 20th century. (Top left) Scaling factors for precipitation changes. (Top right and bottom) Zonally averaged precipitation changes over continents from models and observations. (a) Crosses show the best-guess scaling factor derived from multi-model means. Thick bars show the 5 to 95% uncertainty range derived from model-simulated variability, and thin bars show the uncertainty range if doubling the multi-model variance. Red bars indicate scaling factors for the estimated response to all forcings, blue bars for natural-only forcing and brown bars for anthropogenic-only forcing. Labels on the x-axis identify results from four different observational data sets (Z is Zhang et al. (2007), C is Climate Research Unit (CRU), V is Variability Analyses of Surface Climate Observations (Vas- ClimO), G is Global Precipitation Climatology Centre (GPCC), H is Hadley Centre gridded data set of temperature and precipitation extremes (HadEX)). (a) Detection and attribution results for annual averages, both single fingerprint ( 1-sig ; 1950 1999) and two fingerprint results ( 2-sig ; Z, C, G (1951 2005), V (1952 2000)). (b) Scaling factors resulting from single-fingerprint analyses for seasonally averaged precipitation (Z, C, G (1951 2005), V (1952 2000); the latter in pink as not designed for long-term homogeneity) for four different seasons. (c) Scaling factors for spatial pattern of Arctic precipitation trends (1951 1999). (d) Scaling factors for changes in large-scale intense precipitation (1951 1999). (e) Thick solid lines show observed zonally and annually averaged trends (% per decade) for four different observed data sets. Corresponding results from individual simulations from 33 different climate models are shown as thin solid lines, with the multimodel mean shown as a red dashed line. Model results are masked to match the spatial and temporal coverage of the GPCC data set (denoted G in the seasonal scaling factor panel). Grey shading indicates latitude bands within which >75% of simulations yield positive or negative trends. (f, g) Like (e) but showing zonally averaged precipitation changes for (f) June, July, August (JJA) and (g) December, January, February (DJF) seasons. Scaling factors (c) and (d) adapted from Min et al. (2008a) and Min et al. (2011), respectively; other results adapted from Zhang et al. (2007) and Polson et al. (2013). Trends towards earlier timing of snowmelt-driven streamflows in west- discharge, which could be a good integrator for monitoring changes ern North America since 1950 have been demonstrated to be differ- in precipitation in high latitudes, are found to be explainable only if ent from natural variability (Hidalgo et al., 2009). Similarly, internal model simulations include anthropogenic forcings (Min et al., 2008a). variability associated with natural decade-scale fluctuations could not account for recent observed declines of northern Rocky Mountain Barnett et al. (2008) analysed changes in the surface hydrology of streamflow (St Jacques et al., 2010). Statistical analyses of stream- the western USA, considering snow pack (measured as snow water flows demonstrate regionally varying changes that are consistent with equivalent), the seasonal timing of streamflow in major rivers, and changes expected from increasing temperature, in Scandinavia (Wilson average January to March daily minimum temperature over the region, et al., 2010), Europe (Stahl et al., 2010) and the USA (Krakauer and the two hydrological variables they studied being closely related to Fung, 2008; Wang and Hejazi, 2011). Observed increases in Arctic river temperature. Observed changes were compared with the output of a 898 Detection and Attribution of Climate Change: from Global to Regional Chapter 10 regional hydrologic model forced by the Parallel Climate Model (PCM) is greater than determined from CMIP3 and CMIP5 simulations (Seidel and Model for Interdisciplinary Research On Climate (MIROC) climate et al., 2008; Johanson and Fu, 2009; Hu et al., 2013; Figure 10.12). The models. They derived a fingerprint of anthropogenic changes from the causes as to why models underestimate the observed poleward expan- two climate models and found that the observations, when projected sion of the tropical belt are not fully understood. Potential factors are onto the fingerprint of anthropogenic changes, show a positive signal lack of understanding of the magnitude of natural variability as well strength consistent with the model simulations that falls outside as changes in observing systems that also affect reanalysis products the range expected from internal variability as estimated from 1600 (Thorne and Vose, 2010; Lucas et al., 2012; Box 2.3). years of downscaled climate model data. They conclude that there is a detectable and attributable anthropogenic signature on the hydrology Climate model simulations suggest that Antarctic ozone depletion is of this region. a major factor in causing poleward expansion of the southern Hadley cell during austral summer over the last three to five decades with In summary, there is medium confidence that human influence on GHGs also playing a role (Son et al., 2008, 2009, 2010; McLandress climate has affected stream flow and evapotranspiration in limited et al., 2011; Polvani et al., 2011; Hu et al., 2013). In reanalysis data regions of middle and high latitudes of the NH. Detection and attribu- a detectable signal of ozone forcing is separable from other external tion studies have been applied only to limited regions and using a few forcing including GHGs when utilizing both CMIP5 and CMIP3 simu- models. Observational uncertainties are large and in the case of evap- lations combined (Min and Son, 2013). An analysis of CMIP3 simula- otranspiration depend on reconstructions using land surface models. tions suggests that BC aerosols and tropospheric ozone were the main drivers of the observed poleward expansion of the northern Hadley 10.3.3 Atmospheric Circulation and Patterns of Variability cell in boreal summer (Allen et al., 2012). It is found that global green- house warming causes increase in static stability, such that the onset The atmospheric circulation is driven by various processes including of baroclinicity is shifted poleward, leading to poleward expansion of the uneven heating of the Earth s surface by solar radiation, land sea the Hadley circulation (Frierson, 2006; Frierson et al., 2007; Hu and contrast and orography. The circulation transports heat from warm to Fu, 2007; Lu et al., 2007, 2008). Tropical SST increase may also con- cold regions and thereby acts to reduce temperature contrasts. Thus, tribute to a widening of the Hadley circulation (Hu et al., 2011; Staten 10 changes in circulation and in patterns of variability are of critical impor- et al., 2012). Althoughe some Atmospheric General Circulation Model tance for the climate system, influencing regional climate and regional (AGCM) simulations forced by observed time-varying SSTs yield a wid- climate variability. Any such changes are important for local climate ening by about 1° in latitude over 1979 2002 (Hu et al., 2011), other change because they could act to reinforce or counteract the effects of simulations suggest that SST changes have little effect on the tropical external forcings on climate in a particular region. Observed changes expansion when based on the tropopause metric of the tropical width in atmospheric circulation and patterns of variability are assessed in (Lu et al., 2009). However, it is found that the tropopause metric is not Section 2.7.5. Although new and improved data sets are now available, changes in patterns of variability remain difficult to detect because of Poleward expansion (°C per decade) large variability on interannual to decadal time scales (Section 2.7). Since AR4, progress has been made in understanding the causes of changes in circulation-related climate phenomena and modes of var- iability such as the width of the tropical circulation, and the Southern Annular Mode (SAM). For other climate phenomena, such as ENSO, Indian Ocean Dipole (IOD), Pacific Decadal Oscillation (PDO), and mon- soons, there are large observational and modelling uncertainties (see Section 9.5 and Chapter 14), and there is low confidence that changes in these phenomena, if observed, can be attributed to human-induced influence. 10.3.3.1 Tropical Circulation Various indicators of the width of the tropical belt based on independ- ent data sets suggest that the tropical belt as a whole has widened since 1979; however, the magnitude of this change is very uncertain Figure 10.12 | December to February mean change of southern border of the Hadley (Fu et al., 2006; Hudson et al., 2006; Hu and Fu, 2007; Seidel and circulation. Unit is degree in latitude per decade. Reanalysis data sets (see also Box 2.3) Randel, 2007; Seidel et al., 2008; Lu et al., 2009; Fu and Lin, 2011; are marked with different colours. Trends are all calculated over the period of 1979 Hu et al., 2011; Davis and Rosenlof, 2012; Lucas et al., 2012; Wilcox 2005. The terms historicalNAT, historicalGHG, and historical denote CMIP5 simulations et al., 2012; Nguyen et al., 2013) (Section 2.7.5). CMIP3 and CMIP5 with natural forcing, with greenhouse gas forcing and with both anthropogenic and natural forcings, respectively. For each reanalysis data set, the error bars indicate the simulations suggest that anthropogenic forcings have contributed to 95% confidence level of the standard t-test. For CMIP5 simulations, trends are first the observed widening of the tropical belt since 1979 (Johanson and calculated for each model, and all ensemble members of simulations are used. Then, Fu, 2009; Hu et al., 2013). On average the poleward expansion of the trends are averaged for multi-model ensembles. Trend uncertainty is estimated from Hadley circulation and other indicators of the width of the tropical belt multi-model ensembles, as twice the standard error. (Updated from Hu et al., 2013.) 899 Chapter 10 Detection and Attribution of Climate Change: from Global to Regional very reliable because of the use of arbitrary thresholds (Birner, 2010; (a) Davis and Rosenlof, 2012). historical NAM trend (hPa per decade) 1.5 historicalGHG historicalAer In summary, there are multiple lines of evidence that the Hadley cell historicalOz and the tropical belt as a whole have widened since at least 1979; historicalNat 1.0 control however, the magnitude of the widening is very uncertain. Based on HadSLP2 modelling studies there is medium confidence that stratospheric ozone 20CR depletion has contributed to the observed poleward shift of the south- 0.5 ern Hadley cell border during austral summer, with GHGs also playing a role. The contribution of internal climate variability to the observed 0.0 poleward expansion of the Hadley circulation remains very uncertain. -0.5 10.3.3.2 Northern Annular Mode/North Atlantic Oscillation MAM JJA SON DJF The NAO, which exhibited a positive trend from the 1960s to the 1990s, Season (b) has since exhibited lower values, with exceptionally low anomalies in SAM trend (hPa per decade) the winters of 2009/2010 and 2010/2011 (Section 2.7.8). This means 1.5 that the positive trend in the NAO discussed in the AR4 has considera- bly weakened when evaluated up to 2011. Similar results apply to the 1.0 closely related Northern Annular Mode (NAM), with its upward trend over the past 60 years in the 20th Century Reanalysis (Compo et al., 2011) and in Hadley Centre Sea Level Pressure data set 2r (HadSLP2r; 0.5 Allan and Ansell, 2006) not being significant compared to internal var- 10 iability (Figure 10.13). An analysis of CMIP5 models shows that they 0.0 simulate positive trends in NAM in the DJF season over this period, albeit not as large as those observed which are still within the range of -0.5 natural internal variability (Figure 10.13). MAM JJA SON DJF Other work (Woollings, 2008) demonstrates that while the NAM is Season largely barotropic in structure, the simulated response to anthropogen- Figure 10.13 | Simulated and observed 1951 2011 trends in the Northern Annular ic forcing has a strong baroclinic component, with an opposite geopo- Mode (NAM) index (a) and Southern Annular Mode (SAM) index (b) by season. The NAM tential height trends in the mid-troposphere compared to the surface is a Li and Wang (2003) index based on the difference between zonal mean seal level in many models. Thus while the circulation response to anthropogenic pressure (SLP) at 35°N and 65°N. and the, and the SAM index is a difference between forcing may project onto the NAM, it is not entirely captured by the zonal mean SLP at 40°S and 65°S (Gong and Wang, 1999). Both indices are defined without normalization, so that the magnitudes of simulated and observed trends can NAM index. be compared. Black lines show observed trends from the HadSLP2r data set (Allan and Ansell, 2006) (solid), and the 20th Century Reanalysis (Compo et al., 2011) (dotted). Consistent with previous findings (Hegerl et al., 2007b), Gillett and Grey bars and red boxes show 5 to 95% ranges of trends in CMIP5 control and histori- Fyfe (2013) find that GHGs tend to drive a positive NAM response in cal simulations respectively. Ensemble mean trends and their 5 to 95% uncertainties the CMIP5 models. Recent modelling work also indicates that ozone are shown for the response to greenhouse gases (light green), aerosols (dark green), ozone (magenta) and natural (blue) forcing changes, based on CMIP5 individual forcing changes drive a small positive NAM response in spring (Morgenstern simulations. (Adapted from Gillett and Fyfe, 2013.) et al., 2010; Gillett and Fyfe, 2013). 10.3.3.3 Southern Annular Mode et al., 2011; Polvani et al., 2011; Sigmond et al., 2011; Gillett and Fyfe, 2013). Sigmond et al. (2011) find approximately equal contributions The Southern Annular Mode (SAM) index has remained mainly positive to simulated annual mean SAM trends from GHGs and stratospher- since the publication of the AR4, although it has not been as strongly ic ozone depletion up to the present. Fogt et al. (2009) demonstrate positive as in the late 1990s. Nonetheless, an index of the SAM shows that observed SAM trends over the period 1957 2005 are positive in a significant positive trend in most seasons and data sets over the all seasons, but only statistically significant in DJF and March, April, 1951 2011 period (Figure 10.13; Table 2.14). Recent modelling studies May (MAM), based on simulated internal variability. Roscoe and Haigh confirm earlier findings that the increase in GHG concentrations tends (2007) apply a regression-based approach and find that stratospheric to lead to a strengthening and poleward shift of the SH eddy-driven ozone changes are the primary driver of observed trends in the SAM. polar jet (Karpechko et al., 2008; Son et al., 2008, 2010; Sigmond et al., Observed trends are also consistent with CMIP3 simulations including 2011; Staten et al., 2012; Swart and Fyfe, 2012; Eyring et al., 2013; Gil- stratospheric ozone changes in all seasons, though in MAM observed lett and Fyfe, 2013) which projects onto the positive phase of the SAM. trends are roughly twice as large as those simulated (Miller et al., 2006). Stratospheric ozone depletion also induces a strengthening and pole- Broadly consistent results are found when comparing observed trends ward shift of the polar jet in models, with the largest response in aus- and CMIP5 simulations (Figure 10.13), with a station-based SAM index tral summer (Karpechko et al., 2008; Son et al., 2008, 2010; McLandress showing a significant positive trend in MAM, JJA and DJF, compared 900 Detection and Attribution of Climate Change: from Global to Regional Chapter 10 to simulated internal variability over the 1951 2010 period. Fogt et change (Gregory et al., 2004; AchutaRao et al., 2006). After the IPCC al. (2009) find that the largest forced response has likely occurred in AR4 report in 2007, time-and depth-dependent systematic errors in DJF, the season in which stratospheric ozone depletion has been the bathythermograph temperatures were discovered (Gouretski and dominant contributor to the observed trends. Koltermann, 2007; Section 3.2). Bathythermograph data account for a large fraction of the historical temperature observations and are there- Taking these findings together, it is likely that the positive trend in fore a source of bias in ocean heat content studies. Bias corrections the SAM seen in austral summer since the mid-20th century is due in were then developed and applied to observations. With the newer part to stratospheric ozone depletion. There is medium confidence that bias-corrected estimates (Domingues et al., 2008; Wijffels et al., 2008; GHGs have also played a role. Ishii and Kimoto, 2009; Levitus et al., 2009), it became obvious that the large decadal variability in earlier estimates of global upper-ocean heat 10.3.3.4 Change in Global Sea Level Pressure Patterns content was an observational artefact (Section 3.2). A number of studies have applied formal detection and attribution The interannual to decadal variability of ocean temperature simulat- studies to global fields of atmospheric SLP finding detection of human ed by the CMIP3 models agrees better with observations when the influence on global patterns of SLP (Gillett et al., 2003, 2005; Gil- model data is sampled using the observational data mask (AchutaRao lett and Stott, 2009). Analysing the contributions of different forcings et al., 2007). In the upper 700 m, CMIP3 model simulations agreed to observed changes in SLP, Gillett and Stott (2009) find separately more closely with observational estimates of global ocean heat con- detectable influences of anthropogenic and natural forcings in zonal tent based on bias-corrected ocean temperature data, both in terms of mean seasonal mean SLP, strengthening evidence for a human influ- the decadal variability and multi-decadal trend (Figure 10.14a) when ence on SLP. Based on the robustness of the evidence from multiple forced with the most complete set of natural and anthropogenic forc- models we conclude that it is likely that human influence has altered ings (Domingues et al., 2008). For the simulations with the most com- SLP patterns globally since 1951. plete set of forcings, the multi-model ensemble mean trend was only 10% smaller than observed for 1961 1999. Model simulations that included only anthropogenic forcing (i.e., no solar or volcanic forcing) 10 10.4 Changes in Ocean Properties significantly overestimate the multi-decadal trend and underestimate decadal variability. This overestimate of the trend is partially caused This section assesses the causes of oceanic changes in the main prop- by the ocean s response to volcanic eruptions, which results in rapid erties of interest for climate change: ocean heat content, ocean salinity cooling followed by decadal or longer time variations during the recov- and freshwater fluxes, sea level, oxygen and ocean acidification. ery phase. Although it has been suggested (Gregory, 2010) that the cooling trend from successive volcanic events is an artefact because 10.4.1 Ocean Temperature and Heat Content models were not spun up with volcanic forcing, this discrepancy is not expected to be as significant in the upper ocean as in the deeper layers The oceans are a key part of the Earth s energy balance (Boxes 3.1 and where longer term adjustments take place (Gregory et al., 2012 ). Thus 13.1). Observational studies continue to demonstrate that the ocean for the upper ocean, there is high confidence that the more frequent heat content has increased in the upper layers of the ocean during eruptions during the second half of the 20th century have caused a the second half of the 20th century and early 21st century (Section multi-decadal cooling that partially offsets the anthropogenic warm- 3.2; Bindoff et al., 2007), and that this increase is consistent with a ing and contributes to the apparent decadal variability (Church et al., net positive radiative imbalance in the climate system. It is of signifi- 2005; Delworth et al., 2005; Fyfe, 2006; Gleckler et al., 2006; Gregory cance that this heat content increase is an order of magnitude larger et al., 2006; AchutaRao et al., 2007; Domingues et al., 2008; Palmer et than the increase in energy content of any other component of the al., 2009; Stenchikov et al., 2009). Earth s ocean atmosphere cryosphere system and accounts for more than 90% of the Earth s energy increase between 1971 and 2010 (e.g., Gleckler et al. (2012) examined the detection and attribution of upper- Boxes 3.1 and 13.1; Bindoff et al., 2007; Church et al., 2011; Hansen ocean warming in the context of uncertainties in the underlying et al., 2011). observational data sets, models and methods. Using three bias-cor- rected observational estimates of upper-ocean temperature changes Despite the evidence for anthropogenic warming of the ocean, the (Domingues et al., 2008; Ishii and Kimoto, 2009; Levitus et al., 2009) level of confidence in the conclusions of the AR4 report that the and models from the CMIP3 multi-model archive, they found that mul- warming of the upper several hundred meters of the ocean during the ti-decadal trends in the observations were best understood by includ- second half of the 20th century was likely to be due to anthropogenic ing contributions from both natural and anthropogenic forcings. The forcing reflected the level of uncertainties at that time. The major anthropogenic fingerprint in observed upper-ocean warming, driven by uncertainty was an apparently large decadal variability (warming in global mean and basin-scale pattern changes, was also detected. The the 1970s and cooling in the early 1980s) in the observational esti- strength of this signal (estimated from successively longer trend peri- mates that was not simulated by climate models (Hegerl et al., 2007b, ods of ocean heat content starting from 1970) crossed the 5% and 1% see their Table 9.4). The large decadal variability in observations raised significance threshold in 1980 and progressively becomes more strong- concerns about the capacity of climate models to simulate observed ly detected for longer trend periods (Figure 10.14b), for all ocean heat variability. There were also lingering concerns about the presence of content time series. This stronger detection for longer periods occurs non-climate related biases in the observations of ocean heat content because the noise (standard deviation of trends in the unforced chang- 901 Chapter 10 Detection and Attribution of Climate Change: from Global to Regional es in pattern similarity from model control runs) tends to decrease for An analysis of upper-ocean (0 to 700 m) temperature changes for longer trend lengths. On decadal time scales, there is limited evidence 1955 2004, using bias-corrected observations and 20 global climate that basin scale space-time variability structure of CMIP3 models is models from CMIP5 (Pierce et al., 2012) builds on previous detection approximately 25% lower than the (poorly constrained) observations, and attribution studies of ocean temperature (Barnett et al., 2001, this underestimate is far less than the factor of 2 needed to throw 2005; Pierce et al., 2006). This analysis found that observed tempera- the anthropogenic fingerprint into question. This result is robust to a ture changes during the above period are inconsistent with the effects number of known observational, model, methodological and structural of natural climate variability. That is signal strengths are separated uncertainties. from zero at the 5% significance level, and the probability that the 10 Figure 10.14 | (A) Comparison of observed global ocean heat content for the upper 700 m (updated from Domingues et al. 2008) with simulations from ten CMIP5 models that included only natural forcings ( HistoricalNat runs shown in blue lines) and simulations that included natural and anthropogenic forcings ( Historical runs in pink lines). Grey shad- ing shows observational uncertainty. The global mean stratospheric optical depth (Sato et al., 1993) in beige at the bottom indicates the major volcanic eruptions and the brown curve is a 3-year running average of these values. (B) Signal-to-noise (S/N) ratio (plotted as a function of increasing trend length L) of basin-scale changes in volume averaged temperature of newer, expendable bathythermograph (XBT)-corrected data (solid red, purple and blue lines), older, uncorrected data (dashed red and blue lines); the average of the three corrected observational sets (AveObs; dashed cyan line); and simulations that include volcanic (V) or exclude volcanic eruptions (NoV) (black solid and grey dashed lines respectively). The start date for the calculation of signal trends is 1970 and the initial trend length is 10 years. The 1% and 5% significance thresholds are shown (as horizontal grey lines) and assume a Gaussian distribution of noise trends in the V-models control-run pseudo-principal components. The detection time is defined as the year at which S/N exceeds and remains above 1% or 5% significance threshold (Gleckler et al., 2012). 902 Detection and Attribution of Climate Change: from Global to Regional Chapter 10 null hypothesis of observed changes being consistent with natural var- the increasing number of the Array for Real-time Geostrophic Ocean- iability is less than 0.05 from variability either internal to the climate ography (ARGO) profile data, and historical data have extended the system alone, or externally forced by solar fluctuations and volcanic observational salinity data sets allowing the examination of the long- eruptions. However, the observed ocean changes are consistent with term changes at the surface and in the interior of the ocean (Section those expected from anthropogenically induced atmospheric changes 3.3) and supporting analyses of precipitation changes over land (see from GHGs and aerosol concentrations. Sections 10.3.2.2 and 2.5.1). Attribution to anthropogenic warming from recent detection and attri- Patterns of subsurface salinity changes largely follow the existing bution studies (Gleckler et al., 2012; Pierce et al., 2012) have made use mean salinity pattern at the surface and within the ocean. For example, of new bias-corrected observations and have systematically explored the inter-basin contrast between the Atlantic (salty) and Pacific Oceans methodological uncertainties, yielding more confidence in the results. (fresh) has intensified over the observed record (Boyer et al., 2005; With greater consistency and agreement across observational data Hosoda et al., 2009; Roemmich and Gilson, 2009; von Schuckmann sets and resolution of structural issues, the major uncertainties at the et al., 2009; Durack and Wijffels, 2010). In the Southern Ocean, many time of AR4 have now largely been resolved. The high levels of confi- studies show a coherent freshening of Antarctic Intermediate Water dence and the increased understanding of the contributions from both that is subducted at about 50°S (Johnson and Orsi, 1997; Wong et al., natural and anthropogenic sources across the many studies mean that 1999; Bindoff and McDougall, 2000; Curry et al., 2003; Boyer et al., it is very likely that the increase in global ocean heat content observed 2005; Roemmich and Gilson, 2009; Durack and Wijffels, 2010; Helm et in the upper 700 m since the 1970s has a substantial contribution from al., 2010; Kobayashi et al., 2012). There is also a clear increase in salin- anthropogenic forcing. ity of the high-salinity subtropical waters (Durack and Wijffels, 2010; Helm et al., 2010). Although there is high confidence in understanding the causes of global heat content increases, attribution of regional heat content changes The 50-year trends in surface salinity show that there is a strong pos- are less certain. Earlier regional studies used a fixed depth data and itive correlation between the mean climate of the surface salinity and only considered basin-scale averages (Barnett et al., 2005). At regional its temporal changes from 1950 to 2000 (see Figures 3.4 and 10.15b 10 scales, however, changes in advection of ocean heat are important and ocean obs point). The correlation between the climate and the trends need to be isolated from changes due to air sea heat fluxes (Palmer in surface salinity of 0.7 implies that fresh surface waters get fresh- et al., 2009; Grist et al., 2010). Their fixed isotherm (rather than fixed er, and salty waters get saltier (Durack et al., 2012). Such patterns of depth) approach to optimal detection analysis, in addition to being surface salinity change are also found in Atmosphere Ocean General largely insensitive to observational biases, is designed to separate the Circulation Models (AOGCM) simulations both for the 20th century ocean s response to air sea flux changes from advective changes. Air and projected future changes into the 21st century (Figure 10.15b). sea fluxes are the primary mechanism by which the oceans are expect- The pattern of temporal change in observations from CMIP3 simula- ed to respond to externally forced anthropogenic and natural volcanic tions is particularly strong for those projections using Special Report on influences. The finer temporal resolution of the analysis allowed Palmer Emission Scenarios (SRES) with larger global warming changes (Figure et al. (2009) to attribute distinct short-lived cooling episodes to major 10.15b). For the period 1950 2000 the observed amplification of the volcanic eruptions while, at multi-decadal time scales, a more spatially surface salinity is 16 +/- 10% per °C of warming and is twice the simu- uniform near-surface (~ upper 200 m) warming pattern was detected lated surface salinity change in CMIP3 models. This difference between across all ocean basins (except in high latitudes where the isotherm the surface salinity amplification is plausibly caused by the tendency approach has limitations due to outcropping of isotherms at the ocean of CMIP3 ocean models mixing surface salinity into deeper layers and surface) and attributed to anthropogenic causes at the 5% significance consequently surface salinity increases at a slower rate than observed level. Considering that individual ocean basins are affected by different (Durack et al., 2012). observational and modelling uncertainties and that internal variabili- ty is larger at smaller scales, detection of significant anthropogenic Although there are now many established observed long-term trends forcing through space and time studies (Palmer et al., 2009; Pierce et of salinity change at the ocean surface and within the interior ocean al., 2012) provides more compelling evidence of human influence at at regional and global scales (Section 3.3), there are relatively few regional scales of near-surface ocean warming observed during the studies that attribute these changes formally to anthropogenic forcing. second half of the 20th century. Analysis at the regional scale of the observed recent surface salinity increases in the North Atlantic (20°N to 50°N) show a small signal that 10.4.2 Ocean Salinity and Freshwater Fluxes could be attributed to anthropogenic forcings but for this ocean is not significant compared with internal variability (Stott et al., 2008a; Terray There is increasing recognition of the importance of ocean salinity as et al., 2012; and Figure 10.15c). On a larger spatial scale, the surface an essential climate variable (Doherty et al., 2009), particularly for salinity patterns in the band from 30°S to 50°N show anthropogenic understanding the hydrological cycle. In the IPCC Fourth Assessment contributions that are larger than the 5 to 95% uncertainty range Report observed ocean salinity change indicated that there was a sys- (Terray et al., 2012). The strongest signals that can be attributed to tematic pattern of increased salinity in the shallow subtropics and a anthropogenic forcing are in the tropics (TRO, 30°S to 30°N) and the tendency to freshening of waters that originate in the polar regions western Pacific. These results also show the salinity contrast between (Bindoff et al., 2007; Hegerl et al., 2007b) (Figure 10.15a, upper and the Pacific and Atlantic oceans is also enhanced with significant lower panels). New atlases and revisions of the earlier work based on contributions from anthropogenic forcing. 903 Chapter 10 Detection and Attribution of Climate Change: from Global to Regional ) ( ( ) 10 Figure 10.15 | Ocean salinity change and hydrologic cycle. (A) Ocean salinity change observed in the interior of the ocean (A, lower panel in practical salinity units or psu, and white lines are surfaces of constant density) and comparison with ten CMIP3 model projections of precipitation minus evaporation (P E) in mm yr 1 for the same period as the observed changes (1970 to 1990s) (A, top panel, red line is the mean of the simulations and error bars are the simulated range). (B) The amplification of the current surface salinity pattern over a 50-year period as a function of global temperature change. Ocean surface salinity pattern amplification has a 16% increase for the 1950 2000 period (red diamond, see text and Section 3.3). Also on this panel CMIP3 simulations from Special Report on Emission Scenarios (SRES) (yellow squares) and from 20th century simulations (blue circles). A total of 93 simulations have been used. (C) Regional detection and attribution in the equatorial Pacific and Atlantic Oceans for 1970 to 2002. Scaling factors for all forcings (anthropogenic) fingerprint are shown (see Box 10.1) with their 5 to 95% uncertainty range, estimated using the total least square approach. Full domain (FDO, 30°S to 50°N), Tropics (TRO, 30°S to 30°N), Pacific (PAC, 30°S to 30°N), west Pacific (WPAC, 120°E to 160°W), east Pacific (EPAC, 160°W to 80°W), Atlantic (ATL, 30°S to 50°N), subtropical north Atlantic (NATL, 20°N to 40°N) and equatorial Atlantic (EATL, 20°S to 20°N) factors are shown. Black filled dots indicate when the residual consistency test passes with a truncation of 16 whereas empty circles indicate a higher truncation was needed to pass the consistency test. Horizontal dashed lines indicate scaling factor of 0 or 1. (A, B and C are adapted from Helm et al. (2010), Durack et al. (2012) and Terray et al. (2012), respectively.) 904 Detection and Attribution of Climate Change: from Global to Regional Chapter 10 On a global scale surface and subsurface salinity changes (1955 2004) The observed contribution from thermal expansion is well captured over the upper 250 m of the water column cannot be explained by in climate model simulations with historical forcings as are contribu- natural variability (probability is <0.05) (Pierce et al., 2012). However, tions from glacier melt when simulated by glacier models driven by the observed salinity changes match the model distribution of forced climate model simulations of historical climate (Church et al., 2013; changes (GHG and tropospheric aerosols), with the observations Table 13.1). The model results indicate that most of the variation in the typically falling between the 25th and 75th percentile of the model contributions of thermal expansion and glacier melt to global mean distribution at all depth levels for salinity (and temperature). Natural sea level is in response to natural and anthropogenic RFs (Domingues external variability taken from the simulations with just solar and vol- et al., 2008; Palmer et al., 2009; Church et al., 2013). canic variations in forcing do not match the observations at all, thus excluding the hypothesis that observed trends can be explained by just The strong physical relationship between thermosteric sea level and solar or volcanic variations. ocean heat content (through the equation of state for seawater) means that the anthropogenic ocean warming (Section 10.4.1) has contribut- The results from surface salinity trends and changes are consistent ed to global sea level rise over this period through thermal expansion. with the results from studies of precipitation over the tropical ocean As Section 10.5.2 concludes, it is likely that the observed substantial from the shorter satellite record (Wentz et al., 2007; Allan et al., 2010). mass loss of glaciers is due to human influence and that it is likely These surface salinity results are also consistent with our understand- that anthropogenic forcing and internal variability are both contribu- ing of the thermodynamic response of the atmosphere to warming tors to recent observed changes on the Greenland ice sheet. The causes (Held and Soden, 2006; Stephens and Hu, 2010) and the amplification of recently observed Antarctic ice sheet contribution to sea level are of the water cycle. The large number of studies showing patterns of less clear due to the short observational record and incomplete under- change consistent with amplification of the water cycle, and the detec- standing of natural variability. Taking the causes of Greenland ice sheet tion and attribution studies for the tropical oceans (Terray et al., 2012) melt and glacier mass loss together (see Section 10.5.2), it is concluded and the global pattern of ocean salinity change (Pierce et al., 2012), with high confidence that it is likely that anthropogenic forcing has when combined with our understanding of the physics of the water contributed to sea level rise from melting glaciers and ice sheets. Com- cycle and estimates of internal climate variability, give high confidence bining the evidence from ocean warming and mass loss of glaciers we 10 in our understanding of the drivers of surface and near surface salinity conclude that it is very likely that there is a substantial contribution changes. It is very likely that these salinity changes have a discernable from anthropogenic forcing to the global mean sea level rise since the contribution from anthropogenic forcing since the 1960s. 1970s. 10.4.3 Sea Level On ocean basin scales, detection and attribution studies do show the emergence of detectable signals in the thermosteric component of sea At the time of the AR4, the historical sea level rise budget had not been level that can be largely attributed to human influence (Barnett et al., closed (within uncertainties), and there were few studies quantifying 2005; Pierce et al., 2012). Regional changes in sea level at the sub- the contribution of anthropogenic forcing to the observed sea level ocean basin scales and finer exhibit more complex variations asso- rise and glacier melting. Relying on expert assessment, the AR4 had ciated with natural (dynamical) modes of climate variability (Section concluded based on modelling and ocean heat content studies that 13.6). In some regions, sea level trends have been observed to differ ocean warming and glacier mass loss had very likely contributed to significantly from global mean trends. These have been related to sea level rise during the latter half of the 20th century. The AR4 had thermosteric changes in some areas and in others to changing wind reported that climate models that included anthropogenic and natural fields and resulting changes in the ocean circulation (Han et al., 2010; forcings simulated the observed thermal expansion since 1961 reason- Timmermann et al., 2010; Merrifield and Maltrud, 2011). The regional ably well, and that it is very unlikely that the warming during the past variability on decadal and longer time scales can be quite large (and half century is due only to known natural causes (Hegerl et al., 2007b). is not well quantified in currently available observations) compared to secular changes in the winds that influence sea level. Detection of Since the AR4, corrections applied to instrumental errors in ocean human influences on sea level at the regional scale (that is smaller temperature measurements have considerably improved estimates of than sub-ocean basin scales) is currently limited by the relatively small upper-ocean heat content (see Sections 3.2 and 10.4.1), and there- anthropogenic contributions compared to natural variability (Meyssig- fore ocean thermal expansion. Closure of the global mean sea level nac et al., 2012) and the need for more sophisticated approaches than rise budget as an evolving time series since the early1970s (Church currently available. et al., 2011) indicates that the two major contributions to the rate of global mean sea level rise have been thermal expansion and glacier 10.4.4 Oxygen and Ocean Acidity melting with additional contributions from Greenland and Antarctic ice sheets. Observations since 1971 indicate with high confidence that Oxygen is an important physical and biological tracer in the ocean thermal expansion and glaciers (excluding the glaciers in Antarctica) (Section 3.8.3) and is projected to decline by 3 to 6% by 2100 in explain 75% of the observed rise (see Section 13.3.6). Ice sheet con- response to surface warming (see Section 6.4.5). Oxygen decreases are tributions remain the greatest source of uncertainty over this period also observed in the atmosphere and linked to burning of fossil fuels and on longer time scales. Over the 20th century, the global mean sea (Section 6.1.3.2). Despite the relatively few observational studies of level rise budget (Gregory et al., 2012 ) has been another important oxygen change in the oceans (Bindoff and McDougall, 2000; Ono et al., step in understanding the relative contributions of different drivers. 2001; Keeling and Garcia, 2002; Emerson et al., 2004; Aoki et al., 2005; 905 Chapter 10 Detection and Attribution of Climate Change: from Global to Regional Mecking et al., 2006; Nakanowatari et al., 2007; Brandt et al., 2010) 2008; Deser and Teng, 2008; Zhang et al., 2008; Alekseev et al., 2009; they all show a pattern of change consistent with the known ocean Comiso, 2012; Polyakov et al., 2012). Based on a sea ice reanalysis circulation and surface ventilation. A recent global analysis of oxygen and verified by ice thickness estimates from satellite sensors, it is data from the 1960s to 1990s for change confirm these earlier results estimated that three quarters of summer Arctic sea ice volume has and extends the spatial coverage from local to global scales (Helm et been lost since the 1980s (Schweiger et al., 2011; Maslowski et al., al., 2011). The strongest decreases in oxygen occur in the mid-latitudes 2012; Laxon et al., 2013; Overland and Wang, 2013). There was also of both hemispheres, near regions where there is strong water renew- a rapid reduction in ice extent, to 37% less in September 2007 and to al and exchange between the ocean interior and surface waters. The 49% less in September 2012 relative to the 1979 2000 climatology attribution study of oxygen decreases using two Earth System Models (Figure 4.11, Section 4.2.2). Unlike the loss record set in 2007 that (ESMs) concluded that observed changes for the Atlantic Ocean are was dominated by a major shift in climatological winds, sea ice loss indistinguishable from natural internal variability ; however, the in 2012 was more due to a general thinning of the sea ice (Lindsay changes of the global zonal mean to external forcing (all forcings et al., 2009; Wang et al., 2009a; Zhang et al., 2013). All recent years including GHGs) has a detectable influence at the 10% significance have ice extents that fall at least two standard deviations below the level (Andrews et al., 2013). The chief sources of uncertainty are the long-term sea ice trend. paucity of oxygen observations, particularly in time, the precise role of the biological pump and changes in ocean productivity in the models The amount of old, thick multi-year sea ice in the Arctic has decreased (see Sections 3.8.3 and 6.4.5), and model circulation biases particularly by 50% from 2005 through 2012 (Giles et al., 2008; Kwok et al., 2009; near the oxygen minimum zone in tropical waters (Brandt et al., 2010; Kwok and Untersteiner, 2011 and Figures 4.13 and 4.14). Sea ice has Keeling et al., 2010; Stramma et al., 2010). These results of observed also become more mobile (Gascard et al., 2008). We now have seven changes in oxygen and the attribution studies of oxygen changes years of data that show sea ice conditions are substantially different (Andrews et al., 2013), along with the attribution of human influences to that observed prior to 2006. The relatively large increase in the per- on the physical factors that affect oxygen in the oceans such as surface centage of first year sea ice across the Arctic basin can be considered temperatures changes (Section 10.3.2), increased ocean heat content a new normal. 10 (Section 10.4.1) and observed increased in ocean stratification (Section 3.2.2) provides evidence for human influence on oxygen. When these Confidence in detection of change comes in part from the consistency lines of evidence are taken together it is concluded that with medium of multiple lines of evidence. Since AR4, evidence has continued to confidence or about as likely as not that the observed oxygen decreas- accumulate from a range of observational studies that systematic es can be attributed in part to human influences. changes are occurring in the Arctic. Persistent trends in many Arctic variables, including sea ice, the timing of spring snow melt, increased The observed trends (since the 1980s) for ocean acidification and its shrubbiness in tundra regions, changes in permafrost, increased area of cause from rising CO2 concentrations is discussed in Section 3.8.2 (Box forest fires, changes in ecosystems, as well as Arctic-wide increases in 3.2 and Table 10.1). There is very high confidence that anthropogen- air temperatures, can no longer be associated solely with the dominant ic CO2 has resulted in the acidification of surface waters of between climate variability patterns such as the Arctic Oscillation, Pacific North 0.0015 and 0.0024 pH units per year. American pattern or Atlantic Meridional Oscillation (AMO) (Quadrelli and Wallace, 2004; Vorosmarty et al., 2008; Overland, 2009; Brown and Robinson, 2011; Mahajan et al., 2011; Oza et al., 2011a; Wassmann et 10.5 Cryosphere al., 2011; Nagato and Tanaka, 2012). Duarte et al. (2012) completed a meta-analysis showing evidence from multiple indicators of detectable This section considers changes in sea ice, ice sheets and ice shelves, climate change signals in the Arctic. glaciers, snow cover. The assessment of attribution of human influenc- es on temperature over the Arctic and Antarctica is in Section 10.3.1. The increase in the magnitude of recent Arctic temperature and decrease in sea ice volume and extent are hypothesized to be due to 10.5.1 Sea Ice coupled Arctic amplification mechanisms (Serreze and Francis, 2006; Miller et al., 2010). These feedbacks in the Arctic climate system sug- 10.5.1.1 Arctic and Antarctic Sea Ice gest that the Arctic is sensitive to external forcing (Mahlstein and Knutti, 2012 ). Historically, changes were damped by the rapid forma- The Arctic cryosphere shows large observed changes over the last tion of sea ice in autumn causing a negative feedback and a rapid decade as noted in Chapter 4 and many of these shifts are indicators seasonal cooling. But recently, the increased mobility and loss of multi- of major regional and global feedback processes (Kattsov et al., 2010). year sea ice, combined with enhanced heat storage in the sea ice-free An assessment of sea ice models capacity to simulate Arctic and Ant- regions of the Arctic Ocean form a connected set of processes with arctic sea ice extent is given in Section 9.4.3. Of principal importance is positive feedbacks causing an increase in Arctic temperatures and a Arctic Amplification (see Box 5.1) where surface temperatures in the decrease in sea ice extent (Manabe and Wetherald, 1975; Gascard et Arctic are increasing faster than elsewhere in the world. al., 2008; Serreze et al., 2009; Stroeve et al., 2012a, 2012b) . In addition to the well known ice albedo feedback where decreased sea ice cover The rate of decline of Arctic sea ice thickness and September sea ice decreases the amount of insolation reflected from the surface, there extent has increased considerably in the first decade of the 21st cen- is a late summer/early autumn positive ice insulation feedback due to tury (Maslanik et al., 2007; Nghiem et al., 2007; Comiso and Nishio, additional ocean heat storage in areas previously covered by sea ice 906 Detection and Attribution of Climate Change: from Global to Regional Chapter 10 (Jackson et al., 2010). Arctic amplification may also have a contribution A question as recently as 6 years ago was whether the recent Arctic from poleward heat transport in the atmosphere and ocean (Langen warming and sea ice loss was unique in the instrumental record and and Alexeev, 2007; Graversen and Wang, 2009; Doscher et al., 2010; whether the observed trend would continue (Serreze et al., 2007). Yang et al., 2010). Arctic temperature anomalies in the 1930s were apparently as large as those in the 1990s and 2000s. There is still considerable discussion of It appears that recent Arctic changes are in response to a combination the ultimate causes of the warm temperature anomalies that occurred of global-scale warming, from warm anomalies from internal climate in the Arctic in the 1920s and 1930s (Ahlmann, 1948; Veryard, 1963; variability on different time scales, and are amplified from the mul- Hegerl et al., 2007a, 2007b). The early 20th century warm period, while tiple feedbacks described above. For example, when the 2007 sea ice reflected in the hemispheric average air temperature record (Brohan et minimum occurred, Arctic temperatures had been rising and sea ice al., 2006), did not appear consistently in the mid-latitudes nor on the extent had been decreasing over the previous two decades (Stroeve et Pacific side of the Arctic (Johannessen et al., 2004; Wood and Overland, al., 2008; Screen and Simmonds, 2010). Nevertheless, it took unusually 2010). Polyakov et al. (2003) argued that the Arctic air temperature persistent southerly winds along the dateline over the summer months records reflected a natural cycle of about 50 to 80 years. However, to initiate the sea ice loss event in 2007 (Zhang et al., 2008; Wang et many authors (Bengtsson et al., 2004; Grant et al., 2009; Wood and al., 2009b). Similar southerly wind patterns in previous years did not Overland, 2010; Brönnimann et al., 2012) instead link the 1930s tem- initiate major reductions in sea ice extent because the sea ice was peratures to internal variability in the North Atlantic atmospheric and too thick to respond (Overland et al., 2008). Increased oceanic heat ocean circulation as a single episode that was sustained by ocean transport through the Barents Sea in the first decade of the 21st cen- and sea ice processes in the Arctic and north Atlantic. The Arctic-wide tury and the AMO on longer time scales may also have played a role increases of temperature in the last decade contrast with the episodic in determining sea ice anomalies in the Atlantic Arctic (Dickson et al., regional increases in the early 20th century, suggesting that it is unlike- 2000; Semenov, 2008; Zhang et al., 2008; Day et al., 2012) . Based ly that recent increases are due to the same primary climate process as on the evidence in the previous paragraphs there is high confidence the early 20th century. that these Arctic amplification mechanisms are currently affecting regional Arctic climate. But it also suggests that the timing of future In the case of the Arctic we have high confidence in observations since 10 major sea ice loss events will be difficult to project. There is evidence 1979, from models (see Section 9.4.3 and from simulations comparing therefore that internal variability of climate, long-term warming, and with and without anthropogenic forcing), and from physical under- Arctic Amplification feedbacks have all contributed to recent decreases standing of the dominant processes; taking these three factors togeth- in Arctic sea ice (Kay et al., 2011b; Kinnard et al., 2011; Overland et al., er it is very likely that anthropogenic forcing has contributed to the 2011; Notz and Marotzke, 2012). observed decreases in Arctic sea ice since 1979. Turning to model-based attribution studies, Min et al. (2008b) com- Whereas sea ice extent in the Arctic has decreased, sea ice extent in the pared the seasonal evolution of Arctic sea ice extent from observations Antarctic has very likely increased (Section 4.2.3). Sea ice extent across with those simulated by multiple General Circulation Models (GCMs) the SH over the year as a whole increased by 1.3 to 1.67% per decade for 1953 2006. Comparing changes in both the amplitude and shape from 1979 to 2012, with the largest increase in the Ross Sea during of the annual cycle of the sea ice extent reduces the chance of spuri- the autumn, while sea ice extent decreased in the Amundsen-Belling- ous detection due to coincidental agreement between the response shausen Sea (Comiso and Nishio, 2008; Turner et al., 2009, 2013; Sec- to anthropogenic forcing and other factors, such as slow internal vari- tion 4.2.3; Oza et al., 2011b). The observed upward trend in Antarctic ability. They found that human influence on the sea ice extent changes sea ice extent is found to be inconsistent with internal variability based has been robustly detected since the early 1990s. The anthropogenic on the residuals from a linear trend fitted to the observations, though signal is also detectable for individual months from May to December, this approach could underestimate multi-decadal variability (Section suggesting that human influence, strongest in late summer, now also 4.2.3; Turner et al., 2013; Section 4.2.3; Zunz et al., 2013). The CMIP5 extends into colder seasons. Kay et al. (2011b), Jahn et al. (2012) and simulations on average simulate a decrease in Antarctic sea ice extent Schweiger et al. (2011) used the Community Climate System Model 4 (Turner et al., 2013; Zunz et al., 2013; Figure 10.16b), though Turner et (CCSM4) to investigate the influence of anthropogenic forcing on late al. (2013) find that approximately 10% of CMIP5 simulations exhibit 20th century and early 21st century Arctic sea ice extent and volume an increasing trend in Antarctic sea ice extent larger than observed trends. On all time scales examined (2 to 50+ years), the most extreme over the 1979 2005 period. However, Antarctic sea ice extent varia- negative extent trends observed in the late 20th century cannot be bility appears on average to be too large in the CMIP5 models (Turner explained by modeled internal variability alone. Comparing trends et al., 2013; Zunz et al., 2013). Overall, the shortness of the observed from the CCSM4 ensemble to observed trends suggests that inter- record and differences in simulated and observed variability preclude nal variability could account for approximately half of the observed an assessment of whether or not the observed increase in Antarctic 1979 2005 September Arctic sea ice extent loss. Attribution of anthro- sea ice extent is inconsistent with internal variability. Based on Figure pogenic forcing is also shown by comparing September sea ice extent 10.16b and Meehl et al. (2007b), the trend of Antarctic sea ice loss in as projected by seven models from the set of CMIP5 models hindcasts simulations due to changes in forcing is weak (relative to the Arctic) to control runs without anthropogenic forcing (Figure 10.16a; Wang and the internal variability is high, and thus the time necessary for and Overland, 2009). The mean of sea ice extents in seven models detection is longer than in the Arctic. ensemble members are below the level of their control runs by about 1995, similar to the result of Min et al. (2008b). 907 Chapter 10 Detection and Attribution of Climate Change: from Global to Regional Several recent studies have investigated the possible causes of Antarctic from ice shelf melting, have made the high-latitude Southern Ocean sea ice trends. Early studies suggested that stratospheric ozone deple- fresher (see Section 3.3) and more stratified, decreasing the upward tion may have driven increasing trends in Antarctic ice extent (Goosse heat flux and driving more sea ice formation (Zhang, 2007; Goosse et et al., 2009; Turner et al., 2009; WMO (World Meteorological Organi- al., 2009; Bintanja et al., 2013). An idealized simulation of the response zation), 2011), but recent studies demonstrate that simulated sea ice to freshwater input similar to that estimate due to ice shelf melting extent decreases in response to prescribed changes in stratospheric exhibited an increase in sea ice extent (Bintanja et al., 2013), but this ozone (Sigmond and Fyfe, 2010; Bitz and Polvani, 2012). An alternative result has yet to be reproduced with other models. Overall we con- explanation for the lack of melting of Antarctic sea ice is that sub-sur- clude that there is low confidence in the scientific understanding of face ocean warming, and enhanced freshwater input possibly in part ­ the observed increase in Antarctic sea ice extent since 1979, owing to NH40 90N September ice extent 11 10 9 8 7 (106 km2) 6 10 5 4 3 2 1950 1960 1970 1980 1990 2000 2010 Year SH40 90S September ice extent 20 19 18 17 (106 km2) 16 15 14 13 12 11 1950 1960 1970 1980 1990 2000 2010 Year Figure 10.16 | September sea ice extent for Arctic (top) and Antarctic (bottom) adapted from (Wang and Overland, 2012). Only CMIP5 models that simulated seasonal mean and magnitude of seasonal cycle in reasonable agreement with observations are included in the plot. The grey lines are the runs from the pre-industrial control runs, and the red lines are from Historical simulations runs patched with RCP8.5 runs for the period of 2005 2012. The black line is based on data from National Snow and Ice Data Center (NSIDC). There are 24 ensemble members from 11 models for the Arctic and 21 members from 6 models for the Antarctic plot. See Supplementary Material for the precise models used in the top and bottom panel. 908 Detection and Attribution of Climate Change: from Global to Regional Chapter 10 the larger differences between sea ice simulations from CMIP5 models melt over Greenland. Mass loss and melt is also occurring in Greenland and to the incomplete and competing scientific explanations for the through the intrusion of warm water into the major glaciers such as causes of change and low confidence in estimates of internal variabil- Jacobshaven Glacier (Holland et al., 2008; Walker et al., 2009). ity (Section 9.4.3). Hanna et al. (2008) attribute increased Greenland runoff and melt since 10.5.2 Ice Sheets, Ice Shelves and Glaciers 1990 to global warming; southern Greenland coastal and NH summer temperatures were uncorrelated between the 1960s and early 1990s 10.5.2.1 Greenland and Antarctic Ice Sheets but correlated significantly positively thereafter. This relationship was modulated by the NAO, whose summer index significantly negatively The Greenland and Antarctic ice sheets are important to regional and correlated with southern Greenland summer temperatures until the global climate because (along with other cryospheric elements) they early 1990s but not thereafter. Regional modelling and observations cause a polar amplification of surface temperatures, a source of fresh tell a consistent story of the response of Greenland temperatures and water to the ocean, and represent a source of potentially irrevers- ice sheet runoff to shifts in recent regional atmospheric circulation ible change to the state of the Earth system (Hansen and Lebedeff, associated with larger scale flow patterns and global temperature 1987). These two ice sheets are important contributors to sea level increases. It is likely that anthropogenic forcing has contributed to sur- rise representing two-thirds of the contributions from all ice covered face melting of the Greenland ice sheet since 1993. regions (Jacob et al., 2012; Pritchard et al., 2012; see Sections 4.4 and 13.3.3). Observations of surface mass balance (increased ablation There is clear evidence that the West Antarctic ice sheet is contribut- versus increased snowfall) are dealt with in Section 4.4.3 and ice sheet ing to sea level rise (Bromwich et al., 2013). Estimates of ice mass in models are discussed in Sections 13.3 and 13.5. Antarctic since 2000 show that the greatest losses are at the edges (see Section 4.4). An analysis of observations underneath a floating ice Attribution of change is difficult as ice sheet and glacier changes shelf off West Antarctica shows that ocean warming and more trans- are local and ice sheet processes are not generally well represented port of heat by ocean circulation are largely responsible for increasing in climate models thus precluding formal single-step detection and melt rates (Jacobs et al., 2011; Joughin and Alley, 2011; Mankoff et al., 10 attribution studies. However, Greenland observational records show 2012; Pritchard et al., 2012). large recent changes. Section 13.3 concludes that regional models for Greenland can reproduce trends in the surface mass balance loss quite Antarctica has regionally dependent decadal variability in surface tem- well if they are forced with the observed meteorological record, but perature with variations in these trends depending on the strength of not with forcings from a Global Climate Model. Regional model simula- the SAM climate pattern. Recent warming in continental west Antarc- tions (Fettweis et al., 2013) show that Greenland surface melt increas- tica has been linked to SST changes in the tropical Pacific (Ding et al., es nonlinearly with rising temperatures due to the positive feedback 2011). As with Antarctic sea ice, changes in Antarctic ice sheets have between surface albedo and melt. complex causes (Section 4.4.3). The observational record of Antarctic mass loss is short and the internal variability of the ice sheet is poorly There have been exceptional changes in Greenland since 2007 marked understood. Due to a low level of scientific understanding there is low by record-setting high air temperatures, ice loss by melting and confidence in attributing the causes of the observed loss of mass from marine-terminating glacier area loss (Hanna et al., 2013; Section 4.4. the Antarctic ice sheet since 1993. Possible future instabilities in the 4). Along Greenland s west coast temperatures in 2010 and 2011were west Antarctic ice sheet cannot be ruled out, but projection of future the warmest since record keeping began in 1873 resulting in the high- climate changes over West Antarctica remains subject to considerable est observed melt rates in this region since 1958 (Fettweis et al., 2011). uncertainty (Steig and Orsi, 2013). The annual rate of area loss in marine-terminating glaciers was 3.4 times that of the previous 8 years, when regular observations became 10.5.2.2 Glaciers available. In 2012, a new record for summertime ice mass loss was two standard deviations below the 2003 2012 mean, as estimated from In the 20th century, there is robust evidence that large-scale internal the Gravity Recovery and Climate Experiment (GRACE) satellite (Tedes- climate variability governs interannual to decadal variability in glacier co et al., 2012). The trend of summer mass change during 2003 2012 mass (Hodge et al., 1998; Nesje et al., 2000; Vuille et al., 2008; Huss et is rather uniform over this period at 29 +/- 11 Gt yr 1. al., 2010; Marzeion and Nesje, 2012) and, along with glacier dynamics, impacts glacier length as well (Chinn et al., 2005). On time periods Record surface melts during 2007 2012 summers are linked to per- longer than years and decades, there is now evidence of recent ice sistent atmospheric circulation that favored warm air advection over loss (see Section 4.3.3) due to increased ambient temperatures and Greenland. These persistent events have changed in frequency since the associated regional moisture changes. However, few studies evaluate beginning of the 2000s (L Heureux et al., 2010; Fettweis et al., 2011). the direct attribution of the current observed mass loss to anthropo- Hanna et al. (2013) show a weak relation of Greenland temperatures genic forcing, owing to the difficulty associated with contrasting scales and ice sheet runoff with the AMO; they more strongly correlate with between glaciers and the large-scale atmospheric circulation (Mölg et a Greenland atmospheric blocking index. Overland et al. (2012) and al., 2012). Reichert et al. (2002) show for two sample sites at mid and Francis and Vavrus (2012) suggest that the increased frequency of the high latitude that internal climate variability over multiple millennia as Greenland blocking pattern is related to broader scale Arctic changes. represented in a GCM would not result in such short glacier lengths as Since 2007, internal variability is likely to have further enhanced the observed in the 20th century. For a sample site at low latitude using 909 Chapter 10 Detection and Attribution of Climate Change: from Global to Regional multi-step attribution, Mölg et al. (2009) (and references therein) found those extremes. SREX assessed causes of changes in different types a close relation between glacier mass loss and the externally forced of extremes including temperature and precipitation, phenomena that atmosphere ocean circulation in the Indian Ocean since the late 19th influence the occurrence of extremes (e.g., storms, tropical cyclones), century. A second, larger group of studies makes use of century-scale and impacts on the natural physical environment such as drought (Sen- glacier records (mostly glacier length but mass balance as well) to eviratne et al., 2012). This section assesses current understanding of extract evidence for external drivers. These include local and regional causes of changes in weather and climate extremes, using AR4 as a changes in precipitation and air temperature, and related parameters starting point. Any changes or modifications to SREX assessment are (such as melt factors and solid/liquid precipitation ratio) estimated highlighted. from the observed change in glaciers. In general these studies show that the glacier changes reveal unique departures since the 1970s, and 10.6.1 Attribution of Changes in Frequency/ that the inferred climatic drivers in the 20th century and particularly in Occurrence and Intensity of Extremes most recent decades, exceed the variability of the earlier parts of the records (Oerlemans, 2005; Yamaguchi et al., 2008; Huss and Bauder, This sub-section assesses attribution of changes in the characteristics 2009; Huss et al., 2010; Leclercq and Oerlemans, 2011). These results of extremes including frequency and intensity of extremes. Many of the underline the contrast to former centuries where observed glacier extremes discussed in this sub-section are moderate extreme events fluctuations can be explained by internal climate variability (Reichert that occur more than once in a year (see Box 2.4 for detailed discus- et al., 2002; Roe and O Neal, 2009; Nussbaumer and Zumbühl, 2012). sion). Attribution of changes in the risk of specific extreme events, Anthropogenic land cover change is an unresolved forcing, but a first which are also very rare in general, is assessed in the next sub-section. assessment suggests that it does not confound the impacts of recent temperature and precipitation changes if the land cover changes are 10.6.1.1 Temperature Extremes of local nature (Mölg et al., 2012). The robustness of the estimates of observed mass loss since the 1960s (Section 4.3, Figure 4.11), the AR4 concluded that surface temperature extremes have likely been confidence we have in estimates of natural variations and internal vari- affected by anthropogenic forcing . Many indicators of climate 10 ability from long-term glacier records, and our understanding of glacier extremes and variability showed changes consistent with warming, response to climatic drivers provides robust evidence and, therefore, including a widespread reduction in number of frost days in mid-lat- high confidence that a substantial part of the mass loss of glaciers is itude regions and evidence that in many regions warm extremes had likely due to human influence. become warmer and cold extremes had become less cold. We next assess new studies made since AR4. 10.5.3 Snow Cover Relatively warm seasonal mean temperatures (e.g., those that have Both satellite and in situ observations show significant reductions in a recurrence once in 10 years) have seen a rapid increase in frequen- the NH snow cover extent (SCE) over the past 90 years, with most cy for many regions worldwide (Jones et al., 2008; Stott et al., 2011; reduction occurring in the 1980s (see Section 4.5). Formal detection Hansen et al., 2012) and an increase in the occurrence frequencies of and attribution studies have indicated anthropogenic influence on NH unusually warm seasonal and annual mean temperatures has been SCE (Rupp et al., 2013) and western USA snow water equivalent (SWE, attributed in part to human influence (Stott et al., 2011; Christidis et Pierce et al., 2008). Pierce et al. (2008) detected anthropogenic influ- al., 2012a, 2012b). ence in the ratio of 1 April SWE over October to March precipitation over the period 1950 1999. These reductions could not be explained A large amount of evidence supports changes in daily data based tem- by natural internal climate variability alone, nor by changes in solar perature extreme indices consistent with warming, despite different and volcanic forcing. In their analysis of NH SCE using 13 CMIP5 sim- data sets or different methods for data processing having been used ulations over the 1922 2005 period, Rupp et al. (2013) showed that (Section 2.6). The effects of human influence on daily temperature some CMIP5 simulations with natural external and anthropogenic extremes is suggested by both qualitative and quantitative compar- forcings could explain the observed decrease in spring SEC though the isons between observed and CMIP3 based modelled values of warm CMIP5 simulations with all forcing as a whole could only explain half days and warm nights (the number of days exceeding the 90th percen- of the magnitude of decrease, and that volcanic and solar variations tile of daily maximum and daily minimum temperatures referred to as (from four CMIP5 simulations) were inconsistent with observations. TX90p and TN90p, see also Section 2.7) and cold days and cold nights We conclude with high confidence in the observational and modelling (the number of days with daily maximum and daily minimum tem- evidence that the decrease in NH snow extent since the 1970s is likely peratures below the 10th percentile referred to as TX10p and TN10p; to be caused by all external forcings and has an anthropogenic contri- see also Section 2.7). Trends in temperature extreme indices comput- bution (see Table 10.1). ed for Australia (Alexander and Arblaster, 2009) and the USA (Meehl et al., 2007a) using observations and simulations of the 20th century with nine GCMs that include both anthropogenic and natural forcings 10.6 Extremes are found to be consistent. Both observations and model simulations show a decrease in the number of frost days, and an increase in the Because many of the impacts of climate changes may manifest them- growing season length, heatwave duration and TN90p in the second selves through weather and climate extremes, there is increasing inter- half of the 20th century. Two of the models (PCM and CCSM3) with est in quantifying the role of human and other external influences on simulations that include only anthropogenic or only natural forcings 910 Detection and Attribution of Climate Change: from Global to Regional Chapter 10 indicate that the observed changes are simulated with anthropogenic daily maximum and minimum temperatures, referred to as TXx, TNx) forcings, but not with natural forcings (even though there are some and coldest daily maximum and minimum temperatures (annual differences in the details of the forcings). Morak et al. (2011) found minimum daily maximum and minimum temperatures, referred to as that over many sub-continental regions, the number of warm nights TXn, TNn) from observations and from simulations with anthropogen- (TN90p) shows detectable changes over the second half of the 20th ic forcing or anthropogenic and natural external forcings from seven century that are consistent with model simulated changes in response GCMs. They consider these extreme daily temperatures to follow gen- to historical external forcings. They also found detectable changes in eralized extreme value (GEV) distributions with location, shape and indices of temperature extremes when the data were analysed over scale parameters. They fit GEV distributions to the observed extreme the globe as a whole. As much of the long-term change in TN90p temperatures with location parameters as linear functions of signals can be predicted based on the interannual correlation of TN90p with obtained from the model simulation. They found that both anthropo- mean temperature, Morak et al. (2013) conclude that the detectable genic influence and combined influence of anthropogenic and natural changes are attributed in a multi-step approach (see Section 10.2.4) forcing can be detected in all four extreme temperature variables at in part to GHG increases. Morak et al. (2013) have extended this anal- the global scale over the land, and also regionally over many large ysis to TX10p, TN10p, TX90p as well as TN90p, using fingerprints from land areas (Figure 10.17). In a complementary study, Christidis et al. HadGEM1 and find detectable changes on global scales and in many (2011) used an optimal fingerprint method to compare observed and regions (Figure 10.17). modelled time-varying location parameters of extreme temperature distributions. They detected the effects of anthropogenic forcing on Human influence has also been detected in two different measures warmest daily temperatures in a single fingerprint analysis, and were of the intensity of extreme daily temperatures in a year. Zwiers et al. able to separate the effects of natural from anthropogenic forcings in (2011) compared four extreme temperature variables including warm- a two fingerprint analysis. est daily maximum and minimum temperatures (annual maximum ­ 10 Figure 10.17 | Detection results for changes in intensity and frequency of extreme events. The left side of each panel shows scaling factors and their 90% confidence intervals for intensity of annual extreme temperatures in response to external forcings for the period 1951 2000. TNn and TXn represent coldest daily minimum and maximum temperatures, respectively, while TNx and TXx represent warmest daily minimum and maximum temperatures (updated from Zwiers et al., 2011). Fingerprints are based on simulations of climate models with both anthropogenic and natural forcings. Right-hand sides of each panel show scaling factors and their 90% confidence intervals for changes in the frequency of temperature extremes for winter (October to March for the Northern Hemisphere and April to September for the Southern Hemisphere), and summer half years. TN10p, TX10p are respectively the frequency of cold nights and days (daily minimum and daily maximum temperatures falling below their 10th percentiles for the base period 1961 1990). TN90p and TX90p are the frequency of warm nights and days (daily minimum and daily maximum temperatures above their respective 90th percentiles calculated for the 1961 1990 base period (Morak et al., 2013) with fingerprints based on simulations of Hadley Centre Global Environmental Model 1 (HadGEM1) with both anthropogenic and natural forcings. Detection is claimed at the 5% significance level if the 90% confidence interval of a scaling factor is entirely above the zero line. Grey represents regions with insufficient data. 911 Chapter 10 Detection and Attribution of Climate Change: from Global to Regional Human influence on annual extremes of daily temperatures may be aspects of the global hydrological cycle (Stott et al., 2010), which is detected separately from natural forcing at the global scale (Christidis directly relevant to extreme precipitation changes. An anthropogen- et al., 2011) and also at continental and sub-continental scales (Min ic influence on increasing atmospheric moisture content has been et al., 2013). Over China, Wen et al. (2013) showed that anthropo- detected (see Section 10.3.2). A higher moisture content in the atmos- genic influence may be separately detected from that of natural forc- phere would be expected to lead to stronger extreme precipitation as ing in daily extreme temperatures (TNn, TNx, TXn and TXx), although extreme precipitation typically scales with total column moisture if cir- the influence of natural forcing is not detected, and they also showed culation does not change. An observational analysis shows that winter that the influence of GHGs in these indices may be separately detect- maximum daily precipitation in North America has statistically signifi- ed from other anthropogenic forcings. Christidis et al. (2013) found cant positive correlations with local atmospheric moisture (Wang and that on a quasi-global scale, the cooling effect due to the decrease Zhang, 2008). in tree cover and increase in grass cover since pre-industrial times as simulated by one ESM is detectable in the observed change of warm There is only a modest body of direct evidence that natural or anthro- extremes. Urbanization may have also affected extreme temperatures pogenic forcing has affected global mean precipitation (see Section in some regions; for example Zhou and Ren (2011) found that extreme 10.3.2 and Figure 10.10), despite a robust expectation of increased temperature warms more in rural stations than in urban sites in China. precipitation (Balan Sarojini et al., 2012 ) and precipitation extremes The effect of land use change and urban heat Island is found to be (see Section 7.6.5). However, mean precipitation is expected to small in GMST (Section 2.4.1.3). Consequently, this effect on extreme increase less than extreme precipitation because of energy constraints temperature is also expected to be small in the global average. (e.g., Allen and Ingram, 2002). A perfect model analysis with an ensem- ble of GCM simulations shows that anthropogenic influence should These new studies show that there is stronger evidence for anthropo- be detectable in precipitation extremes in the second half of the 20th genic forcing on changes in extreme temperatures than at the time of century at global and hemispheric scales, and at continental scale as the SREX assessment. New evidence since SREX includes the separation well but less robustly (Min et al., 2008c), see also Hegerl et al. (2004). of the influence of anthropogenic forcings from that of natural forcings One study has also linked the observed intensification of precipitation 10 on extreme daily temperatures at the global scale and to some extent extremes (including RX1day and annual maximum 5-day precipitation at continental and sub-continental scales in some regions. These new (RX5day)) over NH land areas to human influence using a limited set results suggest more clearly the role of anthropogenic forcing on tem- of climate models and observations (Min et al., 2011). However, the perature extremes compared to results at the time of the SREX assess- detection was less robust if using the fingerprint for combined anthro- ment. We assess that it is very likely that human influence has contrib- pogenic and natural influences compared to that for anthropogenic uted to the observed changes in the frequency and intensity of daily influences only, possibly due to a number of factors including weak temperature extremes on the global scale since the mid-20th century. S/N ratio and uncertainties in observation and model simulations. Also, models still have difficulties in simulating extreme daily precipitation 10.6.1.2 Precipitation Extremes directly comparable with those observed at the station level, which has been addressed to some extent by Min et al. (2011) by independently Observations have showed a general increase in heavy precipitation transforming annual precipitation extremes in models and observations at the global scale. This appears to be consistent with the expected onto a dimensionless scale that may be more comparable between the response to anthropogenic forcing as a result of an enhanced moisture two. Detection of anthropogenic influence on smaller spatial scales content in the atmosphere but a direct cause-and-effect relationship is more difficult due to the increased level of noise and uncertainties between changes in external forcing and extreme precipitation had and confounding factors on local scales. Fowler and Wilby (2010) sug- not been established at the time of the AR4. As a result, the AR4 con- gested that there may have only been a 50% likelihood of detecting cluded that increases in heavy precipitation were more likely than not anthropogenic influence on UK extreme precipitation in winter at that consistent with anthropogenic influence during the latter half of the time, and a very small likelihood of detecting it in other seasons. 20th century (Hegerl et al., 2007b). Given the evidence of anthropogenic influence on various aspects of Extreme precipitation is expected to increase with warming. A com- the global hydrological cycle that implies that extreme precipitation bination of evidence leads to this conclusion though by how much would be expected to have increased and some limited direct evidence remains uncertain and may vary with time scale (Section 7.6.5). Obser- of anthropogenic influence on extreme precipitation, but given also the vations and model projected future changes both indicate increase in difficulties in simulating extreme precipitation by climate models and extreme precipitation associated with warming. Analysis of observed limited observational coverage, we assess, consistent with SREX (Sen- annual maximum 1-day precipitation (RX1day) over global land areas eviratne et al., 2012) that there is medium confidence that anthropo- with sufficient data smaples indicates a significant increase in extreme genic forcing has contributed to a global scale intensification of heavy percipitation globally, with a median increase about 7% °C 1 GMST precipitation over the second half of the 20th century in land regions increase (Westra et al., 2013). CMIP3 and CMIP5 simulations project where observational coverage is sufficient for assessment. an increase in the globally averaged 20-year return values of annual maximum 24-hour precipitation amounts of about 6 to 7% with each 10.6.1.3 Drought degree Celsius of global mean warming, with the bulk of models sim- ulating values in the range of 4 to 10% °C 1(Kharin et al., 2007; Kharin AR4 concluded that that an increased risk of drought was more likely et al., 2013). Anthropogenic influence has been detected on various than not due to anthropogenic forcing during the second half of the 912 Detection and Attribution of Climate Change: from Global to Regional Chapter 10 20th century. This assessment was based on one detection study that bility, indicates that the AR4 conclusions regarding global increasing identified an anthropogenic fingerprint in a global Palmer Drought trends in droughts since the 1970s should be tempered. There is not Severity Index (PDSI) data set (Burke et al., 2006) and studies of some enough evidence to support medium or high confidence of attribution regions which indicated that droughts in those regions were linked of increasing trends to anthropogenic forcings as a result of observa- to SST changes or to a circulation response to anthropogenic forcing. tional uncertainties and variable results from region to region (Section SREX (Seneviratne et al., 2012) assessed that there was medium confi- 2.6.2.3). Combined with difficulties described above in distinguishing dence that anthropogenic influence has contributed to some changes decadal scale variability in drought from long-term climate change we in the drought patterns observed in the second half of the 20th century conclude consistent with SREX that there is low confidence in detec- based on attributed impact of anthropogenic forcing on precipitation tion and attribution of changes in drought over global land areas since and temperature changes, and that there was low confidence in the the mid-20th century. assessment of changes in drought at the level of single regions. 10.6.1.4 Extratropical Cyclones Drought is a complex phenomenon that is affected by precipitation predominantly, as well as by other climate variables including temper- AR4 concluded that an anthropogenic influence on extratropical ature, wind speed and solar radiation (e.g., Seneviratne, 2012; Shef- cyclones was not formally detected, owing to large internal variability field et al., 2012). It is also affected by non-atmospheric conditions and problems due to changes in observing systems. Although there such as antecedent soil moisture and land surface conditions. Trends is evidence that there has been a poleward shift in the storm tracks in two important drought-related climate variables (precipitation and (see Section 2.6.4), various causal factors have been cited including temperature) are consistent with the expected responses to anthro- oceanic heating (Butler et al., 2010) and changes in large-scale cir- pogenic forcing over the globe. However, there is large uncertainty culation due to effects of external forcings (Section 10.3.3). Increases in observed changes in drought (Section 2.6.2.3) and its attribution in mid-latitude SST gradients generally lead to stronger storm tracks to causes globally. The evidence for changes in soil moisture indices that are shifted poleward and increases in subtropical SST gradients and drought indices over the period since 1950 globally is conflicting may lead to storm tracks shifting towards the equator (Brayshaw et (Hoerling et al., 2012; Sheffield et al., 2012; Dai, 2013), possibly due to al., 2008; Semmler et al., 2008; Kodama and Iwasaki, 2009; Graff and 10 the examination of different time periods, different forcing fields used LaCasce, 2012). However, changes in storm-track intensity are much to drive land surface models and uncertainties in land surface models more complicated, as they are sensitive to the competing effects of (Pitman et al., 2009; Seneviratne et al., 2010; Sheffield et al., 2012). changes in temperature gradients and static stability at different levels In a recent study, Sheffield et al. (2012) identify the representation and are thus not linked to GMST in a simple way (Ulbrich et al., 2009; of potential evaporation as solely dependent on temperature (using O Gorman, 2010). Overall global average cyclone activity is expected the Thornthwaite-based formulation) as a possible explanation for to change little under moderate GHG forcing (O Gorman and Schnei- their finding that PDSI-based estimates might overestimate historical der, 2008; Ulbrich et al., 2009; Bengtsson and Hodges, 2011), although drought trends. This stands in partial contradiction to previous assess- in one study, human influence has been detected in geostrophic wind ments suggesting that using a more sophisticated formulation (Pen- energy and ocean wave heights derived from sea level pressure data man-Monteith) for potential evaporation did not affect the results of (Wang et al., 2009b). respective PDSI trends (Dai, 2011; van der Schrier et al., 2011). Sheffield et al. (2012) argue that issues with the treatment of spurious trends in 10.6.1.5 Tropical Cyclones atmospheric forcing data sets and/or the choice of calibration periods explain these conflicting results. These conflicting results point out the AR4 concluded that anthropogenic factors more likely than not have challenges in quantitatively defining and detecting long-term changes contributed to an increase in tropical cyclone intensity (Hegerl et al., in a multivariable phenomenon such as drought. 2007b). Evidence that supports this assessment was the strong correla- tion between the Power Dissipation Index (PDI, an index of the destruc- Recent long-term droughts in western North America cannot defini- tiveness of tropical cyclones) and tropical Atlantic SSTs (Emanuel, tively be shown to lie outside the very large envelope of natural precip- 2005; Elsner, 2006) and the association between Atlantic warming and itation variability in this region (Cayan et al., 2010; Seager et al., 2010), the increase in GMST (Mann and Emanuel, 2006; Trenberth and Shea, particularly given new evidence of the history of high-magnitude nat- 2006). Observations suggest an increase globally in the intensities of ural drought and pluvial episodes suggested by palaeoclimatic recon- the strongest tropical cyclones (Elsner et al., 2008) but it is difficult structions (see Chapter 5). Low-frequency tropical ocean temperature to attribute such changes to particular causes (Knutson et al., 2010). anomalies in all ocean basins appear to force circulation changes that The US Climate Change Science Program (CCSP; Kunkel et al., 2008) promote regional drought (Hoerling and Kumar, 2003; Seager et al., discussed human contributions to recent hurricane activity based on 2005; Dai, 2011). Uniform increases in SST are not particularly effective a two-step attribution approach. They concluded merely that it is very in this regard (Schubert et al., 2009; Hoerling et al., 2012). Therefore, likely (Knutson et al., 2010) that human-induced increase in GHGs has the reliable separation of natural variability and forced climate change contributed to the increase in SSTs in the hurricane formation regions will require simulations that accurately reproduce changes in large- and that over the past 50 years there has been a strong statistical scale SST gradients at all time scales. connection between tropical Atlantic SSTs and Atlantic hurricane activ- ity as measured by the PDI. Knutson et al. (2010), assessed that it In summary, assessment of new observational evidence, in conjunc- remains uncertain whether past changes in tropical cyclone activity tion with updated simulations of natural and forced climate varia- have exceeded the variability expected from natural causes. Senevi- 913 Chapter 10 Detection and Attribution of Climate Change: from Global to Regional ratne et al. (2012) concurred with this finding. Section 14.6.1 gives a 10.6.2 Attribution of Weather and Climate Events detailed account of past and future changes in tropical cyclones. This section assesses causes of observed changes. Since many of the impacts of climate change are likely to manifest themselves through extreme weather, there is increasing interest in Studies that directly attribute tropical cyclone activity changes to quantifying the role of human and other external influences on climate anthropogenic GHG emission are lacking. Among many factors that in specific weather events. This presents particular challenges for both may affect tropical cyclone activity, tropical SSTs have increased and science and the communication of results. It has so far been attempted this increase has been attributed at least in part to anthropogen- for a relatively small number of specific events (e.g., Stott et al., 2004; ic forcing (Gillett et al., 2008a). However, there are diverse views on Pall et al., 2011) although Peterson et al. (2012) attempt, for the first the connection between tropical cyclone activity and SST (see Section time, a coordinated assessment to place different high-impact weather 14.6.1 for details). Strong correlation between the PDI and tropical events of the previous year in a climate perspective. In this assessment, Atlantic SSTs (Emanuel, 2005; Elsner, 2006) would suggest an anthro- selected studies are used to illustrate the essential principles of event pogenic influence on tropical cyclone activity. However, recent stud- attribution: see Stott et al. (2013) for a more exhaustive review. ies also suggest that regional potential intensity correlates with the difference between regional SSTs and spatially averaged SSTs in the Two distinct ways have emerged of framing the question of how an tropics (Vecchi and Soden, 2007; Xie et al., 2010; Ramsay and Sobel, external climate driver like increased GHG levels may have contributed 2011) and projections are uncertain on whether the relative SST will to an observed weather event. First, the attributable risk approach increase over the 21st century under GHG forcing (Vecchi et al., 2008; considers the event as a whole, and asks how the external driver Xie et al., 2010; Villarini and Vecchi, 2012, 2013) . Analyses of CMIP5 may have increased or decreased the probability of occurrence of an simulations suggest that while PDI over the North Atlantic is project- event of comparable magnitude. Second, the attributable magnitude ed to increase towards late 21st century no detectable change in PDI approach considers how different external factors contributed to the should be present in the 20th century (Villarini and Vecchi, 2013) . On event or, more specifically, how the external driver may have increased the other hand, Emanuel et al. (2013) point out that while GCM hind- the magnitude of an event of comparable occurrence probability. Hoer- 10 casts indeed predict little change over the 20th century, downscaling ling et al. (2013) uses both methods to infer changes in magnitude and driving by reanalysis data that incorporate historical observations are likelihood of the 2011 Texas heat wave. in much better accord with observations and do indicate a late 20th century increase. Quantifying the absolute risk or probability of an extreme weather event in the absence of human influence on climate is particularly Some recent studies suggest that the reduction in the aerosol forcing challenging. Many of the most extreme events occur because a self-re- (both anthropogenic and natural) over the Atlantic since the 1970s inforcing process that occurs only under extreme conditions amplifies may have contributed to the increase in tropical cyclone activity in the an initial anomaly (e.g., Fischer et al., 2007). Hence the probability of region (see Section 14.6.1 for details), and similarly that aerosols may occurrence of such events cannot, in general, be estimated simply by have acted to reduce tropical cyclone activity in the Atlantic in ear- extrapolating from the distribution of less extreme events that are lier years when aerosol forcing was increasing (Villarini and Vecchi, sampled in the historical record. Proxy records of pre-industrial climate 2013). However, there are different views on the relative contribution generally do not resolve high-frequency weather, so inferring changes of aerosols and decadal natural variability of the climate system to in probabilities requires a combination of hard-to-test distributional the observed changes in Atlantic tropical cyclone activity among these assumptions and extreme value theory. Quantifying absolute probabil- studies. Some studies indicate that aerosol changes have been the ities with climate models is also difficult because of known biases in main driver (Mann and Emanuel, 2006; Evan et al., 2009; Booth et al., their simulation of extreme events. Hence, with only a couple of excep- 2012; Villarini and Vecchi, 2012, 2013). Other studies infer the influ- tions (e.g., Hansen et al., 2012), studies have focussed on how risks ence of natural variability to be as large as or larger than that from have changed or how different factors have contributed to an observed aerosols (Zhang and Delworth, 2009; Villarini and Vecchi, 2012, 2013). event, rather than claiming that the absolute probability of occurrence of that event would have been extremely low in the absence of human Globally, there is low confidence in any long-term increases in tropical influence on climate. cyclone activity (Section 2.6.3) and we assess that there is low con- fidence in attributing global changes to any particular cause. In the Even without considering absolute probabilities, there remain con- North Atlantic region there is medium confidence that a reduction in siderable uncertainties in quantifying changes in probabilities. The aerosol forcing over the North Atlantic has contributed at least in part assessment of such changes will depend on the selected indicator, time to the observed increase in tropical cyclone activity since the 1970s. period and spatial scale on which the event is analysed, and the way in There remains substantial disagreement on the relative importance of which the event-attribution question is framed can substantially affect internal variability, GHG forcing and aerosols for this observed trend. apparent conclusions . If an event occurs in the tail of the distribution, It remains uncertain whether past changes in tropical cyclone activity then a small shift in the distribution as a whole can result in a large are outside the range of natural internal variability. increase in the probability of an event of a given magnitude: hence it is possible for the same event to be both mostly natural in terms of attributable magnitude (if the shift in the distribution due to human influence is small compared to the anomaly in the natural variability that was the primary cause) and mostly anthropogenic in terms of 914 Detection and Attribution of Climate Change: from Global to Regional Chapter 10 a) Autumn runoff, England and Wales b) Spring flow, River Don, UK c) July temperatures, Western Russia 0.65 300 10% 1% 29 10% 1% 10% 1% Monthly temperature equivalent in (°C) Chance of exceeding 0.6 27 threshold in a given year 250 Daily runoff in (mm day-1) Daily peak flow in (m3 s-1) 0.55 25 200 0.5 23 0.45 150 21 0.4 19 100 0.35 17 50 0.3 Autumn 2000 Spring 2001 15 2000 2009 Non industrial Non industrial 1960 1969 0.25 0 13 1 10 100 1 10 100 1 10 100 Return time (yr) Return time (yr) Return time (yr) Figure 10.18 | Return times for precipitation-induced floods aggregated over England and Wales for (a) conditions corresponding to September to November 2000 with bound- ary conditions as observed (blue) and under a range of simulations of the conditions that would have obtained in the absence of anthropogenic greenhouse warming over the 20th century (green) with different AOGCMs used to define the greenhouse signal, black horizontal line corresponds to the threshold exceeded in autumn 2000 (from Pall et al., 2011); (b) corresponding to January to March 2001 with boundary conditions as observed (blue) and under a range of simulations of the condition that would have obtained in the absence of anthropogenic greenhouse warming over the 20th century (green) adapted from Kay et al. (2011a); (c) return periods of temperature-geopotential height conditions in the model simulations for the 1960s (green) and the 2000s (blue). The vertical black arrow shows the anomaly of the 2010 Russian heat wave (black horizontal line) compared to the July mean temperatures of the 1960s (dashed line). The vertical red arrow gives the increase in temperature for the event whereas the horizontal red arrow shows the change 10 in the return period (from Otto et al., 2012). attributable risk (if human influence has increased its probability of attributable trend is identified in some other variable, such as surface occurrence by more than a factor of 2). These issues are discussed fur- temperature, and a physically based weather model is used to assess ther using the example of the 2010 Russian heat wave below. the implications for extreme weather risk. Neither approach is free of assumptions: no atmospheric model is perfect, but statistical extrapo- The majority of studies have focussed on quantifying attributable risk. lation may also be misleading for reasons given above. Formally, risk is a function of both hazard and vulnerability (IPCC, 2012), although most studies attempting to quantify risk in the con- Pall et al. (2011) provide an example of multi-step assessment of text of extreme weather do not explicitly use this definition, which is attributable risk using a physically based model, applied to the floods discussed further in Chapter 19 of WGII, but use the term as a short- that occurred in the UK in the autumn of 2000, the wettest autumn hand for the probability of the occurrence of an event of a given mag- to have occurred in England and Wales since records began. To assess nitude. Any assessment of change in risk depends on an assumption the contribution of the anthropogenic increase in GHGs to the risk of of all other things being equal , including natural drivers of climate these floods, a several thousand member ensemble of atmospheric change and vulnerability. Given this assumption, the change in hazard models with realistic atmospheric composition, SST and sea ice bound- is proportional to the change in risk, so we will follow the published ary conditions imposed was compared with a second ensemble with literature and continue to refer to Fraction Attributable Risk, defined composition and surface temperatures and sea ice boundary condi- as FAR = 1 P0/P1, P0 being the probability of an event occurring in tions modified to simulate conditions that would have occurred had the absence of human influence on climate, and P1 the corresponding there been no anthropogenic increase in GHGs since 1900. Simulated probability in a world in which human influence is included. FAR is daily precipitation from these two ensembles was fed into an empirical thus the fraction of the risk that is attributable to human influence (or, rainfall-runoff model and daily England and Wales runoff used as a potentially, any other external driver of climate change) and does not proxy for flood risk. Results (Figure 10.18a) show that including the require knowledge of absolute values of P0 and P1, only their ratio. influence of anthropogenic greenhouse warming increases flood risk at the threshold relevant to autumn 2000 by around a factor of two in For individual events with return times greater than the time scale the majority of cases, but with a broad range of uncertainty: in 10% of over which the signal of human influence is emerging (30 to 50 years, cases the increase in risk is less than 20%. meaning P0 and P1 less than 2 to 3% in any given year), it is impossi- ble to observe a change in occurrence frequency directly because of the Kay et al. (2011a), analysing the same ensembles but using a more shortness of the observed record, so attribution is necessarily a mul- sophisticated hydrological model found a reduction in the risk of snow ti-step procedure. Either a trend in occurrence frequency of more fre- melt induced flooding in the spring season (Figure 10.18b) which, quent events is attributed to human influence and a statistical model aggregated over the entire year, largely compensated for the increased is then used to extrapolate to the implications for P0 and P1; or an risk of precipitation-induced flooding in autumn. This illustrates an 915 Chapter 10 Detection and Attribution of Climate Change: from Global to Regional important general point: even if a particular flood event may have similar conclusions about the 2011 Texas heat wave, both noting the been made more likely by human influence on climate, there is no cer- importance of La Nina conditions in the Pacific, with anthropogenic tainty that all kinds of flood events in that location, country or region warming making a relatively small contribution to the magnitude of have been made more likely. the event, but a more substantial contribution to the risk of temper- atures exceeding a high threshold. This shows that the quantification Rahmstorf and Coumou (2011) provide an example of an empirical of attributable risks and and changes in magnitude are affected by approach to the estimation of attributable risk applied to the 2010 modelling error (e.g., Visser and Petersen, 2012) as they depend on the Russian heat wave. They fit a nonlinear trend to central Russian tem- atmospheric model s ability to simulate the observed anomalies in the peratures and show that the warming that has occurred in this region general circulation (Chapter 9). since the 1960s has increased the risk of a heat wave of the mag- nitude observed in 2010 by around a factor of 5, corresponding to Because much of the magnitude of these two heat waves is attrib- an FAR of 0.8. They do not address what has caused the trend since utable to atmospheric flow anomalies, any evidence of a causal link 1960, although they note that other studies have attributed most of between rising GHGs and the occurrence or persistence of flow anom- the large-scale warming over this period to the anthropogenic increase alies such as blocking would have a very substantial impact on attri- in GHG concentrations. bution claims. Pall et al. (2011) argue that, although flow anomalies played a substantial role in the autumn 2000 floods in the UK, thermo- Dole et al. (2011) take a different approach to the 2010 Russian heat dynamic mechanisms were primarily responsible for the change in risk wave, focussing on attributable magnitude, analysing contributions between their ensembles. Regardless of whether the statistics of flow from various external factors, and conclude that this event was mainly regimes themselves have changed, observed temperatures in recent natural in origin . First, observations show no evidence of a trend in years in Europe are distinctly warmer than would be expected for anal- occurrence frequency of hot Julys in western Russia, and despite the ogous atmospheric flow regimes in the past, affecting both warm and warming that has occurred since the 1960s, mean July temperatures in cold extremes (Yiou et al., 2007; Cattiaux et al., 2010). that region actually display a (statistically insignificant) cooling trend 10 over the century as a whole, in contrast to the case for central and In summary, increasing numbers of studies are finding that the prob- southern European summer temperatures (Stott et al., 2004). Mem- ability of occurrence of events associated with extremely high tem- bers of the CMIP3 multi-model ensemble likewise show no evidence peratures has increased substantially due to the large-scale warming of a trend towards warming summers in central Russia. Second, Dole since the mid-20th century. Because most of this large-scale warming et al. (2011) note that the 2010 Russian event was associated with is very likely due to the increase in atmospheric GHG concentrations, it a strong blocking atmospheric flow anomaly, and even the complete is possible to attribute, via a multi-step procedure, some of the increase 2010 boundary conditions are insufficient to increase the probability in probability of these regional events to human influence on climate. of a prolonged blocking event in this region, in contrast again to the Such an increase in probability is consistent with the implications of situation in Europe in 2003. This anomaly in the large-scale atmos- single-step attribution studies looking at the overall implications of pheric flow led to low-pressure systems being redirected around the increasing mean temperatures for the probabilities of exceeding tem- blocking over Russia causing severe flooding in Pakistan which could perature thresholds in some regions. We conclude that it is likely that so far not be attributed to anthropogenic causes (van Oldenborgh et human influence has substantially increased the probability of occur- al., 2012), highlighting that a global perspective is necessary to unravel rence of heat waves in some locations. It is expected that attributable the different factors influencing individual extreme events (Trenberth risks for extreme precipitation events are generally smaller and more and Fasullo, 2012). uncertain, consistent with the findings in Kay et al. (2011a) and Pall et al. (2011). The science of event attribution is still confined to case Otto et al. (2012) argue that it is possible to reconcile the results of studies, often using a single model, and typically focussing on high-im- Rahmstorf and Coumou (2011) with those of Dole et al. (2011) by pact events for which the issue of human influence has already arisen. relating the attributable risk and attributable magnitude approaches While the increasing risk of heat waves measured as the occurrence of to framing the event attribution question. This is illustrated in Figure a previous temperature record being exceeded can simply be explained 10.18c, which shows return times of July temperatures in western by natural variability superimposed by globally increasing temperature, Russia in a large ensemble of atmospheric model simulations for the conclusions for holistic events including general circulation patterns 1960s (in green) and 2000s (in blue). The threshold exceeded in 2010 are specific to the events that have been considered so far and rely on is shown by the solid horizontal line which is almost 6°C above 1960s the representation of relevant processes in the model. mean July temperatures, shown by the dashed line. The difference between the green and blue lines could be characterized as a 1.5°C Anthropogenic warming remains a relatively small contributor to the increase in the magnitude of a 30-year event (the vertical red arrow, overall magnitude of any individual short-term event because its mag- which is substantially smaller than the size of the anomaly itself, sup- nitude is small relative to natural random weather variability on short porting the assertion that the event was mainly natural in terms of time scales (Dole et al., 2011; Hoerling et al., 2013). Because of this attributable magnitude. Alternatively, it could be characterized as a random variability, weather events continue to occur that have been threefold increase in the risk of the 2010 threshold being exceeded, made less likely by human influence on climate, such as extreme winter supporting the assertion that risk of the event occurring was mainly cold events (Massey et al., 2012), or whose probability of occurrence attributable to the external trend, consistent with Rahmstorf and has not been significantly affected either way. Quantifying how dif- Coumou (2011). Rupp et al. (2012) and Hoerling et al. (2013) reach ferent external factors contribute to current risks, and how risks are 916 Detection and Attribution of Climate Change: from Global to Regional Chapter 10 changing, is possible with much higher confidence than quantifying in the 17th century (Section 6.2.3, Figure 6.7), followed by increases in absolute risk. Biases in climate models, uncertainty in the probability GHG concentrations with industrialization since the middle of the 18th distribution of the most extreme events and the ambiguity of paleocli- century (middle of the 19th century for N2O, Figure 6.11). matic records for short-term events mean that it is not yet possible to quantify the absolute probability of occurrence of any observed weath- When interpreting reconstructions of past climate change with the help er event in a hypothetical pristine climate. At present, therefore, the of climate models driven with estimates of past forcing, it helps that evidence does not support the claim that we are observing weather the uncertainties in reconstructions and forcing are independent from events that would, individually, have been extremely unlikely in the each other. Thus, uncertainties in forcing and reconstructions combined absence of human-induced climate change, although observed trends should lead to less, rather than more similarity between fingerprints in the concurrence of large numbers of events (see Section 10.6.1) of forced climate change and reconstructions, making it improbable may be more easily attributable to external factors. The most impor- that the response to external drivers is spuriously detected. Howev- tant development since AR4 is an emerging consensus that the role of er, this is the case only if all relevant forcings and their uncertainties external drivers of climate change in specific extreme weather events, are considered, reducing the risk of misattribution due to spurious including events that might have occurred in a pre-industrial climate, correlations between external forcings, and if the data are homoge- can be quantified using a probabilistic approach. neous and statistical tests properly applied (e.g., Legras et al., 2010). Hence this section focuses on work that considers all relevant forcings ­simultaneously. 10.7 Multi-century to Millennia Perspective 10.7.1 Causes of Change in Large-Scale Temperature Evaluating the causes of climate change before the 20th century is over the Past Millennium important to test and improve our understanding of the role of inter- nal and forced natural climate variability for the recent past. This sec- Despite the uncertainties in reconstructions of past NH mean temper- tion draws on assessment of temperature reconstructions of climate atures, there are well-defined climatic episodes in the last millennium change over the past millennium and their uncertainty in Chapter 5 that can be robustly identified (Chapter 5, see also Figure 10.19). Chap- 10 (Table 5.A.1; Sections 5.3.5 and 5.5.1 for regional records), and on ter 5 concludes that in response to solar, volcanic and anthropogenic comparisons of models and data over the pre-instrumental period in RFs, climate models simulate temperature changes in the NH which Chapters 5 and 9 (Sections 5.3.5, 5.5.1 and 9.5.3), and focuses on the are generally consistent in magnitude and timing with reconstructions, evidence for the contribution by radiatively forced climate change to within their broad uncertainty ranges (Section 5.3.5). reconstructions and early instrumental records. In addition, the residual variability that is not explained by forcing from palaeoclimatic records 10.7.1.1 Role of External Forcing in the Last Millennium provides a useful comparison to estimates of climate model internal variability. The model dependence of estimates of internal variability is The AR4 concluded that A substantial fraction of the reconstructed an important uncertainty in detection and attribution results. NH inter-decadal temperature variability of the seven centuries prior to 1950 is very likely attributable to natural external forcing . The lit- The inputs for detection and attribution studies for periods covered by erature since the AR4, and the availability of more simulations of the indirect, or proxy, data are affected by more uncertainty than those last millennium with more complete forcing (see Schmidt et al., 2012), from the instrumental period (see Chapter 5), owing to the sparse data including solar, volcanic and GHG influences, and generally also land coverage, particularly further back in time, and uncertainty in the link use change and orbital forcing) and more sophisticated models, to a between proxy data and, for example, temperature. Records of past much larger extent coupled climate or coupled ESMs (Chapter 9), some radiative influences on climate are also uncertain (Section 5.2; see of them with interactive carbon cycle, strengthens these conclusions. Schmidt et al., 2011; Schmidt et al., 2012). For the preindustrial part of the last millennium changes in solar, volcanic, GHG forcing, and land Most reconstructions show correlations with external forcing that are use change, along with a small orbital forcing are potentially important similar to those found between pre-Paleoclimate Modelling Intercom- external drivers of climate change. Estimates of solar forcing (Figure parison Project Phase 3 (PMIP3) simulations of the last millennium 5.1a; Box 10.2) are uncertain, particularly in their amplitude, as well as and forcing, suggesting an influence by external forcing (Fernández- in modelling, for example, of the influence of solar forcing on atmos- Donado et al., 2013). From a global scale average of new regional pheric circulation involving stratospheric dynamics (see Box 10.2; Gray reconstructions, Past Global Changes 2k (PAGES 2k) Consortium et al., 2010). Estimates of past volcanism are reasonably well estab- (2013) find that periods with strong volcanic and solar forcing com- lished in their timing, but the magnitude of the RF of individual erup- bined occurring over the last millennium show significantly cooler tions is uncertain (Figure 5.1a). It is possible that large eruptions had a conditions than randomly selected periods from the last two millen- more moderated climate effect than simulated by many climate models nia. Detection analyses based on PMIP3 and CMIP5 model simulations due to faster fallout associated with larger particle size (Timmreck et for the years from 850 to 1950 and also from 850 to 1850 find that al., 2009), or increased amounts of injected water vapour (Joshi and the fingerprint of external forcing is detectable in all reconstructions Jones, 2009). Reconstructed changes in land cover and its effect on of NH mean temperature considered (Schurer et al., 2013; see Figure climate are also uncertain (Kaplan et al., 2009; Pongratz et al., 2009). 10.19), but only in about half the cases considered does detection also Forcing of WMGHGs shows only very subtle variations over the last occur prior to 1400. The authors find a smaller response to forcing in millennium up to 1750. This includes a small drop and partial recovery reconstructions than simulated, but this discrepancy is consistent with 917 Chapter 10 Detection and Attribution of Climate Change: from Global to Regional uncertainties in forcing or proxy response to it, particularly associated Detection and attribution studies support results from modelling stud- with volcanism. The discrepancy is reduced when using more strongly ies that infer a strong role of external forcing in the cooling of NH tem- smoothed data or omitting major volcanic eruptions from the analysis. peratures during the Little Ice Age (LIA; see Chapter 5 and Glossary). The level of agreement between fingerprints from multiple models in Both model simulations (Jungclaus et al., 2010) and results from detec- response to forcing and reconstructions decreases earlier in time, and tion and attribution studies (Hegerl et al., 2007a; Schurer et al., 2013) the forced signal is detected only in about half the cases considered suggest that a small drop in GHG concentrations may have contributed when analysing the period 851 to 1401. This may be partly due to to the cool conditions during the 16th and 17th centuries. Note, how- weaker forcing and larger forcing uncertainty early in the millennium ever, that centennial variations of GHG during the late Holocene are and partly due to increased uncertainty in reconstructions. Detection very small relative to their increases since pre-industrial times (Section results indicate a contribution by external drivers to the warm con- 6.2.3). The role of solar forcing is less clear except for decreased agree- ditions in the 11th to 12th century, but cannot explain the warmth ment if using very large solar forcing (e.g., Ammann et al., 2007; Feul- around the 10th century in some of the reconstructions (Figure 10.19). ner, 2011). Palastanga et al. (2011) demonstrate that neither a slow- This detection of a role of external forcing extends work reported in down of the thermohaline circulation nor a persistently negative NAO AR4 back into to the 9th century CE. alone can explain the reconstructed temperature pattern over Europe during the periods 1675 1715 and 1790 1820. 10 Figure 10.19 | The top panel compares the mean annual Northern Hemisphere (NH) surface air temperature from a multi-model ensemble to several NH temperature reconstruc- tions. These reconstructions are: CH-blend from Hegerl et al. (2007a) in purple, which is a reconstruction of 30°N to 90°N land only (Mann et al., 2009), plotted for the region 30°N to 90°N land and sea (green) and D Arrigo et al. (2006) in red, which is a reconstruction of 20°N to 90°N land only. The dotted coloured lines show the corresponding instrumental data. The multi-model mean for the reconstructed domain is scaled to fit each reconstruction in turn, using a total least squares (TLS) method. The best estimate of the detected forced signal is shown in orange (as an individual line for each reconstruction; lines overlap closely) with light orange shading indicating the range expected if accounting for internal variability. The best fit scaling values for each reconstruction are given in the insert as well as the detection results for six other reconstructions (M8; M9 (Mann et al., 2008, 2009); AW (Ammann and Wahl, 2007); Mo (Moberg et al., 2005); Ju (Juckes et al., 2007); CH (Hegerl et al., 2007a); CL (Christiansen and Ljungqvist, 2011) and inverse regressed onto the instrumental record CS; DA (D Arrigo et al., 2006); Fr (Frank et al., 2007). An asterisk next to the reconstruction name indicates that the residuals (over the more robustly reconstructed period 1401 1950) are inconsistent with the internal variability generated by the combined control simulations of all climate models investigated (for details see Schurer et al., 2013). The ensemble average of a data-assimilation simulation (Goosse et al., 2012b) is plotted in blue, for the region 30°N to 90°N land and sea, with the error range shown in light blue shading. The bottom panel is similar to the top panel, but showing the European region, following Hegerl et al. (2011a) but using the simulations and method in Schurer et al. (2013). The detection analysis is performed for the period 1500 1950 for two reconstructions: Luterbacher et al. (2004)(representing the region 35°N to 70°N,25°W to 40°E, land only, labelled Lu in the insert ) shown in red, and Mann et al. (2009) (averaged over the region 25°N to 65°N, 0° to 60°E, land and sea, labelled M9 in the insert), shown in green. As in the top panel, best fit estimates are shown in dark orange with uncertainty range due to internal variability shown in light orange. The data assimilation from Goosse et al. (2012a), constrained by the Mann et al. (2009) reconstruction is shown in blue, with error range in light blue. All data are shown with respect to the mean of the period covered by the white part of the figure (850 1950 for the NH, 1500 1950 for European mean data). 918 Detection and Attribution of Climate Change: from Global to Regional Chapter 10 Data assimilation studies support the conclusion that external forcing, estimate of internal variability for NH mean temperature that is not together with internal climate variability, provides a consistent expla- directly derived from climate model simulation. This residual variability nation of climate change over the last millennium. Goosse et al. (2010, is somewhat larger than control simulation variability for some recon- 2012a, 2012b) select, from a very large ensemble with an EMIC, the structions if the comparison is extended to the full period since 850 individual simulations that are closest to the spatial reconstructions of CE (Schurer et al., 2013), However, when extracting 50- and 60-year temperature between 30°N and 60°N by Mann et al. (2009) account- trends from this residual variability, the distribution of these trends is ing for reconstruction uncertainties. The method also varies the exter- similar to the multi-model control simulation ensemble used in Schurer nal forcing within uncertainties, determining a combined realization of et al. (2013). In all cases considered, the most recent 50-and 60-year the forced response and internal variability that best matches the data. trend from instrumental data is far outside the range of any 50-year Results (Figure 10.19) show that simulations reproduce the target trend in residuals from reconstructions of NH mean temperature of the reconstruction within the uncertainty range, increasing confidence past millennium. in the consistency of the reconstruction and the forcing. The results suggest that long-term circulation anomalies may help to explain the 10.7.2 Changes of Past Regional Temperature hemispheric warmth early in the millennium, although results vary dependent on input parameters of the method. Several reconstructions of European regional temperature variability are available (Section 5.5). While Bengtsson et al. (2006) emphasized 10.7.1.2 Role of Individual Forcings the role of internal variability in pre-industrial European climate as reconstructed by Luterbacher et al. (2004), Hegerl et al. (2011a) find Volcanic forcing shows a detectable influence on large-scale tempera- a detectable response to external forcing in summer temperatures in ture (see AR4; Chapter 5), and volcanic forcing plays an important role the period 1500 1900, for winter temperatures during 1500 1950 and in explaining past cool episodes, for example, in the late 17th and early 1500 2000; and throughout the record for spring. The fingerprint of 19th centuries (see Chapter 5 and 9; Hegerl et al., 2007b; Jungclaus et the forced response shows coherent time evolution between models al., 2010; Miller et al., 2012) . Schurer et al. (2013) separately detect the and reconstructed temperatures over the entire analysed period (com- response to GHG variations between 1400 and 1900 in most NH recon- pare to annual results in Figure 10.19, using a larger multi-model 10 structions considered, and that of solar and volcanic forcing combined ensemble). This suggests that the cold European winter conditions in in all reconstructions considered. the late 17th and early 19th century and the warming in between were at least partly externally driven. Even the multi-century perspective makes it difficult to distinguish century-scale variations in NH temperature due to solar forcing alone Data assimilation results focussing on the European sector suggests from the response to other forcings, due to the few degrees of freedom that the explanation of forced response combined with internal varia- constraining this forcing (see Box 10.2). Hegerl et al. (2003, 2007a) bility is self-consistent (Goosse et al., 2012a, Figure 10.19). The assim- found solar forcing detectable in some cases. Simulations with higher ilated simulations reproduce the warmth of the MCA better than the than best guess solar forcing may reproduce the warm period around forced only simulations do. The response to individual forcings is diffi- 1000 more closely, but the peak warming occurs about a century ear- cult to distinguish from each other in noisier regional reconstructions. lier in reconstructions than in solar forcing and with it model simu- An epoch analysis of years immediately following strong, largely tropi- lations (Jungclaus et al., 2010; Figure 5.8; Fernández-Donado et al., cal, volcanic eruptions shows that European summers show detectable 2013). Even if solar forcing were on the high end of estimates for the fingerprints of volcanic response , while winters show a noisy response last millennium, it would not be able to explain the recent warming of warming in northern Europe and cooling in southern Europe (Hegerl according both to model simulations (Ammann et al., 2007; Tett et al., et al., 2011a). Landrum et al. (2013) suggest similar volcanic responses 2007; Feulner, 2011) and detection and attribution approaches that for North America, with warming in the north of the continent and scale the temporal fingerprint of solar forcing to best match the data cooling in the south. There is also evidence for a decrease in SSTs fol- (Hegerl et al., 2007a; Schurer et al., 2013; Figure 10.19). Some studies lowing tropical volcanic forcing in tropical reconstructions over the suggest that particularly for millennial and multi-millenial time scales past 450 years (D Arrigo et al., 2009). There is also substantial liter- orbital forcing may be important globally (Marcott et al., 2013) and for ature suggesting solar influences on regional climate reconstructions, high-latitude trends (Kaufman et al., 2009) based on a comparison of possibly due to circulation changes, for example, changes in Northern the correspondence between long-term Arctic cooling in models and Annular Modes (e.g., Kobashi et al., 2013; see Box 10.2). data though the last millennium up to about 1750 (see also PAGES 2k Consortium, 2013). 10.7.3 Summary: Lessons from the Past 10.7.1.3 Estimates of Internal Climate Variability Detection and attribution studies strengthen results from AR4 that external forcing contributed to past climate variability and change prior The interdecadal and longer-term variability in large-scale temper- to the 20th century. Ocean Atmosphere General Circulation Models atures in climate model simulations with and without past external (OAGCMs) simulate similar changes on hemispheric and annual scales forcing is quite different (Tett et al., 2007; Jungclaus et al., 2010), con- as those by simpler models used earlier, and enable detection of sistent with the finding that a large fraction of temperature variance in regional and seasonal changes. Results suggest that volcanic forcing the last millennium has been externally driven. The residual variability and GHG forcing in particular are important for explaining past chang- in past climate that is not explained by changes in RF provides an es in NH temperatures. Results from data assimilation runs confirm 919 Chapter 10 Detection and Attribution of Climate Change: from Global to Regional that the combination of internal variability and external forcing pro- after 70 years) in a 1% yr 1 increasing CO2 experiment (see Hegerl et vides a consistent explanation of the last millennium and suggest that al., 2007b), but like ECS, it can also be thought of as a generic property changes in circulation may have further contributed to climate anoma- of the climate system that determines the global temperature response lies. The role of external forcing extends to regional records, for exam- T to any gradual increase in RF, F, taking place over an approximate- ple, European seasonal temperatures. In summary, it is very unlikely ly 70-year time scale, normalized by the ratio of the forcing change to that NH temperature variations from 1400 to 1850 can be explained the forcing due to doubling CO2, F2×CO2: TCR = F2×CO2 T/F (Frame et by internal variability alone. There is medium confidence that external al., 2006; Gregory and Forster, 2008; Held et al., 2010; Otto et al., 2013). forcing contributed to NH temperature variability from 850 to 1400. This generic definition of the TCR has also been called the Transient There is medium confidence that external forcing (anthropogenic and Climate Sensitivity (Held et al., 2010). TCR is related to ECS and the natural forcings together) contributed to European temperatures of the global energy budget as follows: ECS = F2×CO2/, where is the sensi- last five centuries. tivity parameter representing the net increase in energy flux to space per degree of warming given all feedbacks operating on these time scales. Hence, by conservation of energy, ECS = F2×CO2 T/(F Q), 10.8 Implications for Climate System where Q is the change in the rate of increase of climate system heat Properties and Projections content in response to the forcing F. On these time scales, deep ocean heat exchange affects the surface temperature response as if it were Detection and Attribution results can be used to constrain predictions an enhanced radiative damping, introducing a slow, or recalcitrant , of future climate change (see Chapters 11 and 12) and key climate component of the response which would not be reversed for many system properties. These properties include: the Equilibrium Climate decades even if it were possible to return RF to pre-industrial values Sensitivity (ECS), which determines the long-term equilibrium warming (Held et al., 2010): hence the difficulty of placing an upper bound on response to stable atmospheric composition, but not accounting for ECS from observed surface warming alone (Forest et al., 2002; Frame vegetation or ice sheet changes (Section 12.5.3; see Box 12.2); the et al., 2006). Because Q is always positive at the end of a period of transient climate response (TCR), which is a measure of the magni- increasing forcing, before the climate system has re-equilibrated, TCR 10 tude of transient warming while the climate system, particularly the is always less than ECS, and since Q is uncertain, TCR is generally deep ocean, is not in equilibrium; and the transient climate response to better constrained by observations of recent climate change than ECS. cumulative CO2 emissions (TCRE), which is a measure of the transient warming response to a given mass of CO2 injected into the atmos- Because TCR focuses on the short- and medium-term response, con- phere, and combines information on both the carbon cycle and cli- straining TCR with observations is a key step in narrowing estimates of mate response. TCR is more tightly constrained by the observations future global temperature change in the relatively short term and under of transient warming than ECS. The observational constraints on TCR, scenarios where forcing continues to increase or peaks and declines ECS and TCRE assessed here focus on information provided by recent (Frame et al., 2006). After stabilization, the ECS eventually becomes the observed climate change, complementing analysis of feedbacks and relevant climate system property. Based on observational constraints climate modelling information, which are assessed in Chapter 9. The alone, the AR4 concluded that TCR is very likely to be larger than 1°C assessment in this chapter also incorporates observational constraints and very unlikely to be greater than 3.5°C (Hegerl et al., 2007b). This based on palaeoclimatic information, building on Chapter 5, and con- supported the overall assessment that the transient climate response is tributes to the overall synthesis assessment in Chapter 12 (Box 12.2). very unlikely greater than 3°C and very likely greater than 1°C (Meehl et al., 2007a). New estimates of the TCR are now available. Because neither ECS nor TCR is directly observed, any inference about them requires some form of climate model, ranging in complexity from Scaling factors derived from detection and attribution studies (see Sec- a simple zero-dimensional energy balance box model to OAGCMs tion 10.2) express how model responses to GHGs and aerosols need to (Hegerl and Zwiers, 2011). Constraints on estimates of long-term be scaled to match the observations over the historical period. These climate change and equilibrium climate change from recent warm- scaled responses were used in AR4 to provide probabilistic projections ing hinge on the rate at which the ocean has taken up heat (Section of both TCR and future changes in global temperature in response to 3.2), and by the extent to which recent warming has been reduced by these forcings under various scenarios (Allen et al., 2000; Stott and Ket- cooling from aerosol forcing. Therefore, attempts to estimate climate tleborough, 2002; Stott et al., 2006, 2008b; Kettleborough et al., 2007; sensitivity (transient or equilibrium) often also estimate the total aer- Meehl et al., 2007b; Stott and Forest, 2007). Allen et al. (2000), Frame osol forcing and the rate of ocean heat uptake, which are discussed in et al. (2006) and Kettleborough et al. (2007) demonstrate a near linear Section 10.8.3. The AR4 contained a detailed discussion on estimat- relationship between 20th century warming, TCR and warming by the ing quantities relevant for projections, and included an appendix with mid-21st century as parameters are varied in Energy Balance Models, the relevant estimation methods. Here, we build on this assessment, justifying this approach. Forster et al. (2013 ) show how the ratio T/ repeating information and discussion only where necessary to provide F does depend on the forcing history, with very rapid increases in context. forcing giving lower values: hence any inference from past attributable warming to future warming or TCR depends on a model (which may be 10.8.1 Transient Climate Response simple or complex, but ideally physically based) to relate these quanti- ties. Such inferences also depend on forcing estimates and projections. The AR4 discussed for the first time estimates of the TCR. TCR was Recent revisions to RF (see Chapter 8) suggest higher net anthropo- originally defined as the warming at the time of CO2 doubling (i.e., genic forcing over the 20th century, and hence a smaller estimated 920 Detection and Attribution of Climate Change: from Global to Regional Chapter 10 TCR. Stott et al. (2008b) demonstrated that optimal detection analy- They note, however, the danger of overinterpreting a single, possibly sis of 20th century temperature changes (using HadCM3) are able to anomalous, decade, and report a larger TCR range of 0.7°C to 2.5°C exclude the very high and low temperature responses to aerosol forc- replacing the 2000s with the 40 years 1970 2009. ing. Consequently, projected 21st century warming may be more close- ly constrained than if the full range of aerosol forcings is used (Andreae Tung et al. (2008) examine the response to the 11-year solar cycle using et al., 2005). Stott and Forest (2007) demonstrate that projections discriminant analysis, and find a high range for TCR: >2.5°C to 3.6°C obtained from such an approach are similar to those obtained by con- However, this estimate may be affected by different mechanisms by straining EMIC parameters from observations. Stott et al. (2011), using which solar forcing affects climate (see Box 10.2). The authors attempt HadGEM2-ES, and Gillett et al. (2012), using CanESM2, both show that to minimize possible aliasing with the response to other forcings in the inclusion of observations between 2000 and 2010 in such an anal- the 20th century and with internal climate variability, although some ysis reduces the uncertainties in projected warming in the 21st century, influence by them cannot be ruled out. and tends to constrain the maximum projected warming to below that projected using data to 2000 only (Stott et al, 2006). Such an improve- Rogelj et al. (2012) take a somewhat different approach, using a simple ment is consistent with prior expectations of how additional data will climate model to match the distribution of TCR to observational con- narrow uncertainties (Stott and Kettleborough, 2002). straints and a consensus distribution of ECS (which will itself have been informed by recent climate change), following Meinshausen et TCR estimates have been derived using a variety of methods (Figure al. (2009). Harris et al. (2013) estimate a distribution for TCR based on 10.20a). Knutti and Tomassini (2008) compare EMIC simulations with a large sample of emulated GCM equilibrium responses, constrained 20th century surface and ocean temperatures to derive a probability by multiannual mean observations of recent climate and adjusted to density function for TCR skewed slightly towards lower values with a account for additional uncertainty associated with model structural 5 to 95% range of 1.1°C to 2.3°C. Libardoni and Forest (2011) take a deficiencies (Sexton et al., 2012). The equilibrium responses are scaled similar approach with a different EMIC and include atmospheric data by global temperature changes associated with the sampled model and, under a variety of assumptions, obtain 5 to 95% ranges for TCR variants, reweighting the projections based on the likelihood that they spanning 0.9°C to 2.4°C. Updating this study to include data to 2004 correctly replicate observed historical changes in surface temperature, 10 gives results that are essentially unchanged. Using a single model and to predict the TCR distribution. Both of these studies represent a com- observations from 1851 to 2010 Gillett et al. (2012) derive a 5 to 95% bination of multiple lines of evidence, although still strongly informed range of 1.3°C to 1.8°C and using a single model, but using multiple by recent observed climate change, and hence are assessed here for sets of observations and analysis periods ending in 2010 and begin- completeness. ning in 1910 or earlier, Stott et al. (2011) derive 5 to 95% ranges that were generally between 1°C and 3°C. Both Stott et al. (2011) and Gil- Based on this evidence, including the new 21st century observations lett et al. (2012) find that the inclusion of data between 2000 and that were not yet available to AR4, we conclude that, on the basis of 2010 helps to constrain the upper bound of TCR. Gillett et al. (2012) constraints provided by recent observed climate change, TCR is likely to find that the inclusion of data prior to 1900 also helps to constrain lie in the range 1°C to 2.5°C and extremely unlikely to be greater than TCR, though Stott et al. (2011) do not. Gillett et al. (2013 ) account for 3°C. This range for TCR is smaller than given at the time of AR4, due a broader range of model and observational uncertainties, in particular to the stronger observational constraints and the wider range of stud- addressing the efficacy of non-CO2 gases, and find a range of 0.9°C to ies now available. Our greater confidence in excluding high values of 2.3°C. Several of the estimates of TCR that were cited by Hegerl et al. TCR arises primarily from higher and more confident estimates of past (2007b) may have underestimated non-CO2 efficacies relative to the forcing: estimates of TCR are not strongly dependent on observations more recent estimates in Forster et al. (2007). Because observational- of ocean heat uptake. ly constrained estimates of TCR are based on the ratio between past attributable warming and past forcing, this could account for a high 10.8.2 Constraints on Long-Term Climate Change and the bias in some of the inputs used for the AR4 TCR estimate. Equilibrium Climate Sensitivity Held et al. (2010) show that a two-box model originally proposed by The equilibrium climate sensitivity (ECS) is defined as the warming in Gregory (2000), distinguishing the fast and recalcitrant responses, response to a sustained doubling of carbon dioxide in the atmosphere fits both historical simulations and instantaneous doubled CO2 sim- relative to pre-industrial levels (see AR4). The equilibrium to which ulations of the GFDL coupled model CM2.1. The fast response has a the ECS refers to is generally assumed to be an equilibrium involving relaxation time of 3 to 5 years, and the historical simulation is almost the ocean atmosphere system, which does not include Earth system completely described by this fast component of warming. Padilla et al. feedbacks such as long-term melting of ice sheets and ice caps, dust (2011) use this simple model to derive an observationally constrained forcing or vegetation changes (see Chapter 5 and Section 12.5.3). The estimate of the TCR of 1.3°C to 2.6°C. Schwartz (2012) uses this two- ECS cannot be directly deduced from transient warming attributable to time scale formulation to obtain TCR estimates ranging from 0.9°C to GHGs, or from TCR, as the role of ocean heat uptake has to be taken 1.9°C, the lower values arising from higher estimates of forcing over into account (see Forest et al., 2000; Frame et al., 2005; Knutti and the 20th century. Otto et al. (2013) update the analysis of Gregory et Hegerl, 2008). Estimating the ECS generally relies on the paradigm of al. (2002) and Gregory and Forster (2008) using forcing estimates from a comparison of observed change with results from a physically based Forster et al. (2013 ) to obtain a 5 to 95% range for TCR of 0.9°C to climate model, sometimes a very simple one, given uncertainty in the 2.0°C comparing the decade 2000 2009 with the period 1860 1879. model, data, RF and due to internal variability. 921 Chapter 10 Detection and Attribution of Climate Change: from Global to Regional For example, estimates can be based on the simple box model intro- 10.8.2.1 Estimates from Recent Temperature Change duced in Section 10.8.1, ECS = F2×CO2 T/(F Q). Simple energy balance calculations rely on a very limited representation of climate As estimates of ECS based on recent temperature change can only response time scales, and cannot account for nonlinearities in the cli- sample atmospheric feedbacks that occur with presently evolving cli- mate system that may lead to changes in feedbacks for different forc- mate change, they provide information on the effective climate sen- ings (see Chapter 9). Alternative approaches are estimates that use sitivity (e.g., Forest et al., 2008). As discussed in AR4, analyses based climate model ensembles with varying parameters that evaluate the on global scale data find that within data uncertainties, a strong aer- ECS of individual models and then infer the probability density function osol forcing or a large ocean heat uptake might have masked a strong (PDF) for the ECS from the model data agreement or by using optimi- greenhouse warming (see, e.g., Forest et al., 2002; Frame et al., 2005; zation methods (Tanaka et al., 2009). Stern, 2006; Roe and Baker, 2007; Hannart et al., 2009; Urban and Keller, 2009; Church et al., 2011). This is consistent with the finding that As discussed in the AR4, the probabilistic estimates available in the a set of models with a large range of ECS and aerosol forcing could literature for climate system parameters, such as ECS and TCR have all be consistent with the observed warming (Kiehl, 2007). Consequent- been based, implicitly or explicitly, on adopting a Bayesian approach ly, such analyses find that constraints on aerosol forcing are essen- and therefore, even if it is not explicitly stated, involve using some tial to provide tighter constraints on future warming (Tanaka et al., kind of prior information. The shape of the prior has been derived from 2009; Schwartz et al., 2010). Aldrin et al. (2012) analyse the observed expert judgement in some studies, observational or experimental evi- record from 1850 to 2007 for hemispheric means of surface temper- dence in others or from the distribution of the sample of models avail- ature, and upper 700 m ocean heat content since 1955. The authors able. In all cases the constraint by data, for example, from transient use a simple climate model and a Markov Chain Monte Carlo Bayesian warming, or observations related to feedbacks is fairly weak on the technique for analysis. The authors find a quite narrow range of ECS, upper tail of ECS (e.g., Frame et al., 2005). Therefore, results are sensi- which narrows further if using a uniform prior in 1/ECS rather than tive to the prior assumptions (Tomassini et al., 2007; Knutti and Hegerl, ECS (Figure 10.20). If observations are updated to 2010 and forcing 2008; Sanso and Forest, 2009; Aldrin et al., 2012). When the prior distri- estimates including further indirect aerosol effects are used (following 10 bution fails to taper off for high sensitivities, as is the case for uniform Skeie et al., 2011), this yields a reduced upper tail (see Figure 10.20b, priors (Frame et al., 2005), this leads to long tails (Frame et al., 2005; dash dotted). However, this estimate involves a rather simple model for Annan and Hargreaves, 2011; Lewis, 2013). Uniform priors have been internal variability, hence may underestimate uncertainties. Olson et criticized (e.g., Annan and Hargreaves, 2011; Pueyo, 2012; Lewis, 2013) al. (2012) use similar global scale constraints and surface temperature since results assuming a uniform prior in ECS translates instead into a to 2006, and ocean data to 2003 and arrive at a wide range if using a strongly structured prior on climate feedback parameter and vice versa uniform prior in ECS, and a quite well constrained range if using a prior (Frame et al., 2005; Pueyo, 2012). Objective Bayesian analyses attempt derived from current mean climate and Last Glacial Maximum (LGM) to avoid this paradox by using a prior distribution that is invariant constraints (see Figure 10.20b). Some of the differences between Olson to parameter transforms and rescaling, for example, a Jeffreys prior et al. (2012) and Aldrin et al. (2012) may be due to structural differences (Lewis, 2013). Estimated probability densities based on priors that are in the model used (Aldrin et al. use a simple EBM while Olson use the strongly non-uniform in the vicinity of the best fit to the data, as is typi- UVIC EMIC), some due to different statistical methods and some due to cally the case for the Jeffreys prior in this instance, can peak at values use of global rather than hemispheric temperatures in the latter work. very different from the location of the best fit, and hence need to be An approach based on regressing forcing histories used in 20th century interpreted carefully. To what extent results are sensitive to priors can simulations on observed surface temperatures (Schwartz, 2012) esti- be evaluated by using different priors, and this has been done more mates ranges of ECS that encompass the AR4 ranges if accounting for consistently in studies than at the time of AR4 (see Figure 10.20b) and data uncertainty (Figure 10.20). Otto et al. (2013) updated the Greg- is assessed where available, illustrated in Figure 10.20. Results will also ory et al. (2002) global energy balance analysis (see equation above), be sensitive to the extent to which uncertainties in forcing (Tanaka et using temperature and ocean heat content data to 2009 and estimates al., 2009), models and observations and internal climate variability are of RF that are approximately consistent with estimates from Chapters taken into account, and can be acutely sensitive to relatively arbitrary 7 and 8, and ocean heat uptake estimates that are consistent with choices of observation period, choice of truncation in estimated covar- Chapter 3 and find that inclusion of recent deep ocean heat uptake and iance matrices and so forth (Lewis, 2013), illustrating the importance temperature data considerably narrow estimates of ECS compared to of sensitivity studies. Analyses that make a more complete effort to results using data to the less recent past. estimate all uncertainties affecting the model data comparison lead to more trustworthy results, but end up with larger uncertainties (Knutti Estimates of ECS and TCR that make use of both spatial and tempo- and Hegerl, 2008). ral information, or separate the GHG attributable warming using fin- gerprint methods, can yield tighter estimates (e.g., Frame et al., 2005; The detection and attribution chapter in AR4 (Hegerl et al., 2007b) con- Forest et al., 2008; Libardoni and Forest, 2011). The resulting GHG cluded that Estimates based on observational constraints indicate that attributable warming tends to be reasonably robust to uncertainties in it is very likely that the equilibrium climate sensitivity is larger than 1.5°C aerosol forcing (Section 10.3.1.1.3). Forest et al. (2008) have updated with a most likely value between 2°C and 3°C . The following sections their earlier study using a newer version of the MIT model and five discuss evidence since AR4 from several lines of evidence, followed by different surface temperature data sets (Libardoni and Forest, 2011). an overall assessment of ECS based on observed climate changes, and a Correction of statistical errors in estimation procedure pointed out by subset of available new estimates is shown in Figure 10.20b. Lewis (see Lewis, 2013) changes their result only slightly (Libardoni 922 Detection and Attribution of Climate Change: from Global to Regional Chapter 10 and Forest, 2013). The overarching 5 to 95% range of effective cli- ­radiation compared to Earth Radiation Budget Experiment (ERBE) data, mate sensitivity widens to 1.2°C to 5.3°C when all five data sets are but this result was found unreliable owing to use of a limited sample used, and constraints on effective ocean diffusivity become very weak of periods and of a domain limited to low latitudes (Murphy and For- (Forest et al., 2008). Uncertainties would likely further increase if esti- ster, 2010). Lindzen and Choi (2011) address some of these criticisms mates of forcing uncertainty, for example, due to natural forcings, are (Chung et al., 2010; Trenberth et al., 2010), but the results remains also included (Forest et al., 2006). Lewis (2013) reanalysed the data uncertain. For example, the lag-lead relationship between TOA balance used in Forest et al. (2006) using an objective Bayesian method (see and SST (Lindzen and Choi, 2011) is replicated by Atmospheric Model discussion at top of section). The author finds that use of a Jeffreys Intercomparison Project (AMIP) simulations where SST cannot respond prior narrows the upper tail considerably, to 3.6°C for the 95th percen- (Dessler, 2011). Hence, as discussed in Section 7.2.5.7, the influence of tile. When revising the method, omitting upper air data, and adding 6 internal temperature variations on short time scales seriously affects more years of data a much reduced 5 to 95% range of 1.2°C to 2.2°C such estimates of feedbacks. In addition, the energy budget changes results (see Figure 10.20), similar to estimates by Ring et al. (2012) that are used to derive feedbacks are also affected by RF, which Lin- using data to 2008. Lewis s upper limit extends to 3.0°C if accounting dzen and Choi (2009) do not account for. Murphy and Forster (2010) for forcing and surface temperature uncertainty (Lewis, 2013). Lewis further question if estimates of the feedback parameter are suitable (2013) also reports a range of 1.1°C to 2.9°C using his revised diag- to estimate the ECS, as multiple time scales are involved in feedbacks nostics and the Forest et al. (2006) statistical method, whereas adding that contribute to climate sensitivity (Knutti and Hegerl, 2008; Dessler, 9 more years to the Libardoni and Forest (2013) corrected diagnostic 2010). Lin et al. (2010a) use data over the 20th century combined with (after Libardoni and Forest, 2011; Figure 10.20; using an expert prior an estimate of present TOA imbalance based on modelling (Hansen et in both cases), does not change results much (Figure 10.20b). The dif- al., 2005a) to estimate the energy budget of the planet and give a best ferences between results reported in Forest et al. (2008); Libardoni and estimate of ECS of 3.1°C, but do not attempt to estimate a distribution Forest (2011); Lewis (2013); Libardoni and Forest (2013) are still not that accounts fully for uncertainties. In conclusion, measurement and fully understood, but appear to be due to a combination of sensitivity methodological uncertainties in estimates of the feedback parameter of results to the choice of analysis period as well as differences in diag- and the ECS from short-term variations in the satellite period preclude nostics and statistical approach. strong constraints on ECS. When accounting for these uncertainties, 10 estimates of ECS based on the TOA radiation budget appear consistent In summary, analyses that use the most recent decade find a tighten- with those from other lines of evidence within large uncertainties (e.g., ing of the range of ECS based on a combination of recent heat uptake Forster and Gregory, 2006; Figure 10.20b). and surface temperature data. Results consistently give low probability to ECS values under 1.0°C (Figure 10.20). The mode of the PDFs varies 10.8.2.3 Estimates Based on Response to Volcanic Forcing or considerably with period considered as expected from the influence of Internal Variability internal variability on the single realization of observed climate change. Estimates including the most recent data tend to have reduced upper Some analyses used in AR4 were based on the well observed forcing tails (Libardoni and Forest, 2011; Aldrin et al., 2012 and update; Ring and responses to major volcanic eruptions during the 20th century. et al., 2012 and update cf. Figure 10.20; Lewis, 2013; Otto et al., 2013), The constraint is fairly weak because the peak response to short-term although further uncertainty in statistical assumptions and structural volcanic forcing depends nonlinearly on ECS (Wigley et al., 2005; Boer uncertainties in simple models used, as well as neglected uncertainties, et al., 2007). Recently, Bender et al. (2010) re-evaluated the constraint for example, in forcings, increase assessed uncertainty. and found a close relationship in 9 out of 10 AR4 models between the shortwave TOA imbalance, the simulated response to the eruption of 10.8.2.2 Estimates Based on Top of the Atmosphere Radiative Mt Pinatubo and the ECS. Applying the constraint from observations Balance suggests a range of ECS of 1.7°C to 4.1°C. This range for ECS is subject to observational uncertainty and uncertainty due to internal climate With the satellite era, measurements are now long enough to allow variability, and is derived from a limited sample of models. Schwartz direct estimates of variations in the energy budget of the planet, (2007) tried to relate the ECS to the strength of natural variability using although the measurements are not sufficiently accurate to determine the fluctuation dissipation theorem but studies suggest that the obser- absolute top of the atmosphere (TOA) fluxes or trends (see Section 2.3 vations are too short to support a well constrained and reliable esti- and Box 13.1). Using a simple energy balance relationship between mate and would yield an underestimate of sensitivity (Kirk-Davidoff, net energy flow towards the Earth, net forcing and a climate feedback 2009); and that assuming single time scales is too simplistic for the parameter and the satellite measurements Murphy et al. (2009) made climate system (Knutti and Hegerl, 2008) . Thus, credible estimates of direct estimates of the climate feedback parameter as the regression ECS from the response to natural and internal variability do not disa- coefficient of radiative response against GMST. The feedback parame- gree with other estimates, but at present cannot provide more reliable ter in turn is inversely proportional to the ECS (see above, also Forster estimates of ECS. and Gregory, 2006). Such regression based estimates are, however, subject to uncertainties (see Section 7.2.5.7; see also, Gregory and For- 10.8.2.4 Paleoclimatic Evidence ster, 2008; Murphy and Forster, 2010). Lindzen and Choi (2009) used data from the radiative budget and simple energy balance models over Palaeoclimatic evidence is promising for estimating ECS (Edwards the tropics to investigate feedbacks in climate models. Their result et al., 2007). This section reports on probabilistic estimates of ECS suggests that climate models overestimate the outgoing shortwave derived from paleoclimatic data by drawing on Chapter 5 information ­ 923 Chapter 10 Detection and Attribution of Climate Change: from Global to Regional on ­ orcing and temperature changes. For periods of past climate, which f d ­ ifferent from today and as climate sensitivity can be state depend- were close to radiative balance or when climate was changing slowly, ent, as discussed above. Also, the response on very long time scales is for example, the LGM, radiative imbalance and with it ocean heat determined by the Earth System Sensitivity, which includes very slow uptake is less important than for the present (Sections 5.3.3.1 and feedbacks by ice sheets and vegetation (see Section 12.5.3). Paleosens 5.3.3.2). Treating the RF due to ice sheets, dust and CO2 as forcings Members (2012) reanalysed the relationship between RF and temper- rather than feedbacks implies that the corresponding RF contributions ature response from paleoclimatic studies, considering Earth system are associated with considerable uncertainties (see Section 5.2.2.3). feedbacks as forcings in order to derive an estimate of ECS that is limit- Koehler et al. (2010) used an estimate of LGM cooling along with its ed to atmospheric feecbacks (sometimes referred to as Charney sensi- uncertainties together with estimates of LGM RF and its uncertainty to tivity and directly comparable to ECS), and find that resulting estimates derive an overall estimate of climate sensitivity. This method accounts are reasonably consistent over the past 65 million years (see detailed for the effect of changes in feedbacks for this very different climatic discussion in Section 5.3.1). They estimate a 95% range of 1.1°C to state using published estimates of changes in feedback factors (see 7.0°C, largely based on the past 800,000 years. However, uncertain- Section 5.3.3.2; Hargreaves et al., 2007; Otto-Bliesner et al., 2009). The ties in paleoclimate estimates of ECS are likely to be larger than from authors find a best estimate of 2.4°C and a 5 to 95% range of ECS the instrumental record, for example, due to changes in feedbacks from 1.4°C to 5.2°C, with sensitivities beyond 6°C difficult to reconcile between different climatic states. In conclusion, estimates of ECS have with the data. In contrast, Chylek and Lohmann (2008b) estimate the continued to emerge from palaeoclimatic periods that indicate that ECS to be 1.3°C to 2.3°C based on data for the transition from the ECS is very likely less than 6°C and very likely greater than 1.0°C (see LGM to the Holocene. However, the true uncertainties are likely larger Section 5.3.3). due to uncertainties in relating local proxies to large-scale temperature change observed over a limited time (Ganopolski and von Deimling, 10.8.2.5 Combining Evidence and Overall Assessment 2008; Hargreaves and Annan, 2009). The authors also use an aerosol RF estimate that may be high (see response by Chylek and Lohmann, Most studies find a lower 5% limit for ECS between 1°C and 2°C (Figure 2008a; Ganopolski and von Deimling, 2008). 10.20). The combined evidence thus indicates that the net feedbacks to 10 RF are significantly positive. At present, there is no credible individual At the time of the AR4, several studies were assessed in which param- line of evidence that yields very high or very low climate sensitivity as eters in climate models had been perturbed systematically in order to best estimate. Some recent studies suggest a low climate sensitivity estimate ECS, and further studies have been published since, some (Chylek et al., 2007; Schwartz et al., 2007; Lindzen and Choi, 2009). making use of expanded data for LGM climate change (see Section However, these are based on problematic assumptions, for example, 5.3.3.2, Table 5.3). Sometimes substantial differences between esti- about the climate s response time, the cause of climate fluctuations, mates based on similar data reflect not only differences in assumptions or neglect uncertainty in forcing, observations and internal variability on forcing and use of data, but also structural model uncertainties, for (as discussed in Foster et al., 2008; Knutti and Hegerl, 2008; Murphy example, in how feedbacks change between different climatic states and Forster, 2010). In some cases the estimates of the ECS have been (e.g., Schneider von Deimling et al., 2006; Hargreaves et al., 2007; (see refuted by testing the method of estimation with a climate model of also Otto-Bliesner et al., 2009). Holden et al. (2010) analysed which known sensitivity (e.g., Kirk-Davidoff, 2009). versions of the EMIC Genie are consistent with LGM tropical SSTs and find a 90% range of 2.0°C to 5.0°C. Recently, new data synthesis prod- Several authors (Annan and Hargreaves, 2006; Hegerl et al., 2006; ucts have become available for assessment with climate model simu- Annan and Hargreaves, 2010) had proposed combining estimates lations of the LGM which together with further data cover much more of climate sensitivity from different lines of evidence by the time of of the LGM ocean and land areas, although there are still substantial AR4; these and recent work is shown in the panel combined in Figure gaps and substantial data uncertainty (Section 5.3.3). An analysis of 10.20. Aldrin et al. (2012) combined the Hegerl et al. (2006) estimate the recent SST and land temperature reconstructions for the LGM com- based on the last millennium with their estimate based on the 20th pared to simulations with an EMIC suggests a 90% range of 1.4°C to century; and Olson et al. (2012) combined weak constraints from cli- 2.8°C for ECS, with SST data providing a narrower range and lower matology and the LGM in their prior, updated by data on temperature values than land data only (see Figure 10.20; Schmittner et al., 2011). changes. This approach is robust only if the lines of evidence used are However, structural model uncertainty as well as data uncertainty may truly independent. The latter is hard to evaluate when using prior distri- increase this range substantially (Fyke and Eby, 2012; Schmittner et butions based on expert knowledge (e.g., Libardoni and Forest, 2011). al., 2012). Hargreaves et al. (2012) derived a relationship between ECS If lines of evidence are not independent, overly confident assessments and LGM response for seven model simulations from PMIP2 simula- of equilibrium climate sensitivity may result (Henriksson et al., 2010; tions and found a linear relationship between tropical cooling and ECS Annan and Hargreaves, 2011). (see Section 5.3.3.2) which has been used to derive an estimate of ECS (Figure 10.20); and has been updated using PMIP3 simulations In conclusion, estimates of the Equilibrium Climate Sensitivity (ECS) (Section 5.3.3.2). However, uncertainties remain as the relationship is based on multiple and partly independent lines of evidence from dependent on the ensemble of models used. observed climate change, including estimates using longer records of surface temperature change and new palaeoclimatic evidence, indi- Estimates of ECS from other, more distant paleoclimate periods (e.g., cate that there is high confidence that ECS is extremely unlikely less Royer et al., 2007; Royer, 2008; Pagani et al., 2009; Lunt et al., 2010) than 1°C and medium confidence that the ECS is likely between 1.5°C are difficult to directly compare, as climatic conditions were very and 4.5°C and very unlikely greater than 6°C. They complement the 924 Detection and Attribution of Climate Change: from Global to Regional Chapter 10 a) Schwartz (2012) Libardoni & Forest (2011) Padilla et al (2011) Gregory & Forster (2008) Stott & Forest (2007) Gillett et al (2013) Probability / Relative Frequency (°C-1) Tung et al (2008) Otto et al (2013) 1970 2009 budget Otto et al (2013) 2000 2009 budget Rogelj et al (2012) Harris et al (2013) Meinshausen et al (2009) Knutti & Tomassini (2008) expert ECS prior Knutti & Tomassini (2008) uniform ECS prior 1.5 1 0.5 Dashed lines AR4 studies 0 0 1 2 3 4 5 6 Transient Climate Response (°C) b) Un ce Aldrin et al. (2012) r ta scie 1.2 Bender et al. (2010) Lewis (2013) Si m Si m Cl os e int nti ies c Ove 10 Lin et al. (2010) ila ila to ac und rall 0.8 Lindzen & Choi (2011) ba r cli rf ee eq uil for coun erst leve Murphy et al. (2009) se ma db ibr /k t an l o Olson et al. (2012) sta te ac ium now ed din f 0.4 Otto et al. (2013) te ks n g Schwartz (2012) Tomassini et al. (2007) 0.0 Instrumental Probability / Relative Frequency (°C-1) 1.2 Chylek & Lohmann (2008) Hargreaves et al. (2012) 0.8 Holden et al. (2010) ¨ Kohler et al. (2010) Palaeosens (2012) 0.4 Schmittner et al. (2012) 0.0 Palaeoclimate 0.8 Aldrin et al. (2012) Libardoni & Forest (2013) 0.4 Olson et al. (2012) 0.0 Combination 0 1 2 3 4 5 6 7 8 9 10 Equilibrium Climate Sensitivity (°C) Figure 10.20 | (a) Examples of distributions of the transient climate response (TCR, top) and the equilibrium climate sensitivity (ECS, bottom) estimated from observational con- straints. Probability density functions (PDFs), and ranges (5 to 95%) for the TCR estimated by different studies (see text). The grey shaded range marks the very likely range of 1°C to 2.5°C for TCR and the grey solid line represents the extremely unlikely <3°C upper bound as assessed in this section. Representative distributions from AR4 shown as dashed lines and open bar. (b) Estimates of ECS are compared to overall assessed likely range (solid grey), with solid line at 1°C and a dashed line at 6°C. The figure compares some selected old estimates used in AR4 (no labels, thin lines; for references see Supplementary Material) with new estimates available since AR4 (labelled, thicker lines). Distributions are shown where available, together with 5 to 95% ranges and median values (circles). Ranges that are assessed as being incomplete are marked by arrows; note that in contrast to the other estimates Schwartz (2012), shows a sampling range and Chylek and Lohmann a 95% range. Estimates are based on changes over the instrumental period (top row); and changes from palaeoclimatic data (2nd row). Studies that combine multiple lines of evidence are shown in the bottom panel. The boxes on the right-hand side indicate limitations and strengths of each line of evidence, for example, if a period has a similar climatic base state, if feedbacks are similar to those operating under CO2 doubling, if the observed change is close to equilibrium, if, between all lines of evidence plotted, uncertainty is accounted for relatively completely, and summarizes the level of scientific understanding of this line of evidence overall. A blue box indicates an overall line of evidence that is well understood, has small uncertainty, or many studies and overall high confidence. Pale yellow indicates medium, and dark red low, confidence (i.e., poorly understood,very few studies, poor agreement, unknown limitations, after Knutti and Hegerl, 2008). Where available, results are shown using several different prior distributions; for example for Aldrin et al. (2012) solid shows the result using a uniform prior in ECS, which is shown as updated to 2010 in dash-dots; dashed: uniform prior in 1/ECS; and in bottom panel, result combining with Hegerl et al. (2006) prior, For Lewis (2013), dashed shows results using the Forest et al. (2006) diagnostic and an objective Bayesian prior, solid a revised diagnostic. For Otto et al. (2013), solid is an estimate using change to 1979 2009, dashed using the change to 2000 2009. Palaeoclimate: Hargreaves et al. (2012) is shown in solid, with dashed showing an update based on PMIP3 simulations (see Chapter 5); For Schmittner et al. (2011), solid is land-and-ocean, dashed land-only, and dash-dotted is ocean-only diagnostic. 925 Chapter 10 Detection and Attribution of Climate Change: from Global to Regional e ­ valuation in Chapter 9 and support the overall assessment in Chapter only a small contribution to uncertainty in TCR (Knutti and Tomassini, 12 that concludes between all lines of evidence with high confidence 2008; Kuhlbrodt and Gregory, 2012; see Section 13.4.1). Nonetheless, that ECS is likely in the range 1.5°C to 4.5°C. Earth system feedbacks ocean thermal expansion and heat content change simulated in CMIP5 can lead to different, probably larger, warming than indicated by ECS models show relatively good agreement with observations, although on very long time scales. this might also be due to a compensation between ocean heat uptake efficiency and atmospheric feedbacks (Kuhlbrodt and Gregory, 2012). 10.8.3 Consequences for Aerosol Forcing and Ocean In summary, constraints on effective ocean diffusivity are presently not Heat Uptake conclusive. Some estimates of ECS also yield estimates of aerosol forcing that are 10.8.4 Earth System Properties consistent with observational records, which we briefly mention here. Note that the estimate will reflect any forcings with a time or time A number of papers have found the global warming response to CO2 space pattern resembling aerosol forcing that is not explicitly includ- emissions to be determined primarily by total cumulative emissions of ed in the overall estimate (see discussion in Olson et al., 2012), for CO2, irrespective of the timing of those emissions over a broad range example, BC on snow; and should hence be interpreted as an estimate of scenarios (Allen et al., 2009; Matthews et al., 2009; Zickfeld et al., of aerosol plus neglected forcings. Estimates will also vary with the 2009; Section 12.5.4.2), although Bowerman et al. (2011) find that, method applied and diagnostics used (e.g., analyses including spatial when scenarios with persistent emission floors are included, the information will yield stronger results). Murphy et al. (2009) use cor- strongest predictor of peak warming is cumulative emissions to 2200. relations between surface temperature and outgoing shortwave and Moreover, the ratio of global warming to cumulative carbon emissions, longwave flux over the satellite period to estimate how much of the known variously as the Absolute Global Temperature Change Poten- total recent forcing has been reduced by aerosol total reflection, which tial (AGTP; defined for an infinitesimal pulse emission) (Shine et al., they estimate as 1.1 +/- 0.4 W m 2 from 1970 to 2000 (1 standard devi- 2005), the Cumulative Warming Commitment (defined based on peak ation), while Libardoni and Forest (2011), see also Forest et al. (2008), warming in response to a finite injection; CWC) (Allen et al., 2009) or 10 based on the 20th century, find somewhat lower estimates, namely a the Carbon Climate Response (CCR) (Matthews et al., 2009), is approx- 90% bound of 0.83 to 0.19 W m 2 for the 1980s relative to preindus- imately scenario-independent and constant in time. trial. Lewis (2013), using similar diagnostics but an objective Bayesi- an method, estimates a total aerosol forcing of about 0.6 to 0.1W The ratio of CO2-induced warming realized by a given year to cumula- m 2 or 0.6 to 0.0 W m 2 dependent on diagnostic used. The range of tive carbon emissions to that year is known as the Transient Climate the aerosol forcing estimates that are based on the observed climate Response to cumulative CO2 Emissions (TCRE, see Chapter 12). TCRE change are in-line with the expert judgement of the effective RF by depends on TCR and the Cumulative Airborne Fraction (CAF), which is aerosol radiation and aerosol cloud interactions combined (ERFaci+ari; the ratio of the increased mass of CO2 in the atmosphere to cumula- Chapter 7) of 0.9 W m 2 with a range from 1.9 to 0.1 W m 2 that tive CO2 emissions (not including natural fluxes and those arising from has been guided by climate models that include aerosol effects on Earth system feedbacks) over a long period, typically since pre-indus- mixed-phase and convective clouds in addition to liquid clouds, satel- trial times (Gregory et al., 2009): TCRE = TCR × CAF/C0, where C0 is the lite studies and models that allow cloud-scale responses (see Section mass of carbon (in the form of CO2) in the pre-industrial atmosphere 7.5.2). (590 PgC). Given estimates of CAF to the time of CO2 doubling of 0.4 to 0.7 (Zickfeld et al., 2013), we therefore expect values of TCRE, if Several estimates of ECS also estimate a parameter that describe the expressed in units of °C per 1000 PgC, to be similar to or slightly lower efficiency with which the ocean takes up heat, e.g., effective global than, and more uncertain than, values of TCR (Gillett et al., 2013 ). vertical ocean diffusivity (e.g., Tomassini et al., 2007; Forest et al., 2008; Olson et al., 2012; Lewis, 2013). Forest and Reynolds (2008) find that TCRE may be estimated from observations by dividing an estimate of the effective global ocean diffusivity Kv in many of the CMIP3 models warming to date attributable to CO2 by historical cumulative carbon lies above the median value based on observational constraints, result- emissions, which gives a 5 to 95% range of 0.7°C to 2.0°C per 1000 ing in a positive bias in their ocean heat uptake. Lewis (2013) similarly PgC (Gillett et al., 2013 ), 1.0°C to 2.1°C per 1000 PgC (Matthews et finds better agreement for small values of effective ocean diffusivity. al., 2009) or 1.4°C to 2.5°C per 1000 PgC (Allen et al., 2009), the higher However, such a finding was very sensitive to data sets used for sur- range in the latter study reflecting a higher estimate of CO2-attribut- face temperature (Libardoni and Forest, 2011) and ocean data (Sokolov able warming to 2000. The peak warming induced by a given total et al., 2010), is somewhat sensitive to the diagnostic applied (Lewis, cumulative carbon emission (Peak Response to Cumulative Emissions 2013), and limited by difficulties observing heat uptake in the deep (PRCE)) is less well constrained, since warming may continue even ocean (see, e.g., Chapters 3 and 13). Olson et al. (2012) and Tomassini after a complete cessation of CO2 emissions, particularly in high-re- et al. (2007) find that data over the historical period provide only a sponse models or scenarios. Using a combination of observations and weak constraint on background ocean effective diffusivity. Compari- models to constrain temperature and carbon cycle parameters in a son of the vertical profiles of temperature and of historical warming simple climate-carbon-cycle model, (Allen et al., 2009), obtain a PRCE in models and observations suggests that the ocean heat uptake effi- 5 to 95% confidence interval of 1.3°C to 3.9°C per 1000 PgC. They ciency may be typically too large (Kuhlbrodt and Gregory, 2012; Sec- also report that (Meinshausen et al., 2009) obtain a 5 to 95% range tion 13.4.1; see also Sections 9.4.2, 10.4.1, 10.4.3). If effective diffu- in PRCE of 1.1°C to 2.7°C per 1000 PgC using a Bayesian approach sivity were high in models this might lead to a tendency to bias ocean with a different simple model, with climate parameters constrained warming high relative to surface warming; but this uncertainty makes 926 Detection and Attribution of Climate Change: from Global to Regional Chapter 10 by observed warming and carbon cycle parameters constrained by the depends on the ability of the models to represent the co-variability of C4MIP simulations (Friedlingstein et al., 2006). the variables concerned. Multi-variable attribution studies potentially provide a stronger test of climate models than single variable attribu- The ratio of warming to cumulative emissions, the TCRE, is assessed tion studies although there can be sensitivity to weighting of different to be likely between 0.8°C and 2.5C per 1000 PgC based on observa- components of the multi-variable fingerprint. In an analysis of ocean tional constraints. This implies that, for warming due to CO2 emissions variables, Pierce et al. (2012) found that the joint analysis of tempera- alone to be likely less than 2°C at the time CO2 emissions cease, total ture and salinity changes yielded a stronger signal of climate change cumulative emissions from all anthropogenic sources over the entire than either salinity or temperature alone . industrial era would need to be limited to about 1000 PgC, or one trillion tonnes of carbon (see Section 12.5.4). Further insights can be gained by considering a synthesis of evidence across the climate system. This is the subject of the next subsection. 10.9 Synthesis 10.9.2 Whole Climate System The evidence has grown since the Fourth Assessment Report that wide- To demonstrate how observed changes across the climate system can spread changes observed in the climate system since the 1950s are be understood in terms of natural and anthropogenic causes Figure attributable to anthropogenic influences. This evidence is document- 10.21 compares observed and modelled changes in the atmosphere, ed in the preceding sections of this chapter, including for near sur- ocean and cryosphere. The instrumental records associated with each face temperatures (Section 10.3.1.1), free atmosphere temperatures element of the climate system are generally independent (see FAQ 2.1), (Section 10.3.1.2), atmospheric moisture content (Section 10.3.2.1), and consequently joint interpretations across observations from the precipitation over land (Section 10.3.2.2), ocean heat content (Sec- main components of the climate system increases the confidence to tion 10.4.1), ocean salinity (Section 10.4.2), sea level (Section 10.4.3), higher levels than from any single study or component of the climate Arctic sea ice (Section 10.5.1), climate extremes (Section 10.6) and evi- system. The ability of climate models to replicate observed changes dence from the last millenium (Section 10.7). These results strengthen (to within internal variability) across a wide suite of climate indicators 10 the conclusion that human influence on climate has played the domi- also builds confidence in the capacity of the models to simulate the nant role in observed warming since the 1950s. However, the approach Earth s climate. taken so far in this chapter has been to examine each aspect of the climate system the atmosphere, oceans, cryosphere, extremes, and The coherence of observed changes for the variables shown in Figure from paleoclimate archives separately in each section and sub-sec- 10.21 with climate model simulations that include anthropogenic and tion. In this section we look across the whole climate system to assess natural forcing is remarkable. Surface temperatures over land, SSTs the extent that a consistent picture emerges across sub-systems and and ocean heat content changes show emerging anthropogenic and climate variables. natural signals with a clear separation between the observed changes and the alternative hypothesis of just natural variations (Figure 10.21, 10.9.1 Multi-variable Approaches Global panels). These signals appear not just in the global means, but also at continental and ocean basin scales in these variables. Sea ice Multi-variable studies provide one approach to gain a more compre- emerges strongly from the range expected from natural variability for hensive view across the climate system, although there have been rela- the Arctic and Antarctica remains broadly within the range of natural tively few applications of multi-variable detection and attribution stud- variability consistent with expectations from model simulations includ- ies in the literature. A combined analysis of near-surface temperature ing anthropogenic forcings. from weather stations and free atmosphere temperatures from radio- sondes detected an anthropogenic influence on the joint changes in Table 10.1 illustrates a larger suite of detection and attribution results temperatures near the surface and aloft (Jones et al., 2003). In a Bayes- across the climate system than summarized in Figure 10.21. These ian application of detection and attribution Schnur and Hasselmann results include observations from both the instrumental record and (2005) combined surface temperature, diurnal temperature range and paleo-reconstructions on a range of time scales ranging from daily precipitation into a single analysis and showed strong net evidence extreme precipitation events to variability over millennium time scales. for detection of anthropogenic forcings despite low likelihood ratios for diurnal temperature range and precipitation on their own. Barnett From up in the stratosphere, down through the troposphere to the sur- et al. (2008) applied a multi-variable approach in analysing changes in face of the Earth and into the depths of the oceans there are detectable the hydrology of the Western United States (see also Section 10.3.2.3). signals of change such that the assessed likelihood of a detectable, and often quantifiable, human contribution ranges from likely to extremely The potential for a multi-variable analysis to have greater power to likely for many climate variables (Table 10.1). Indeed to successfully discriminate between forced changes and internal variability has been describe the observed warming trends in the atmosphere, ocean and demonstrated by Stott and Jones (2009) and Pierce et al. (2012). In the at the surface over the past 50 years, contributions from both anthro- former case, they showed that a multi-variable fingerprint consisting of pogenic and natural forcings are required (e.g., results 1, 2, 3, 4, 5, 7, 9 the responses of GMST and sub-tropical Atlantic salinity has a higher in Table 10.1). This is consistent with anthropogenic forcings warming S/N ratio than the fingerprints of each variable separately. They found the surface of the Earth, troposphere and oceans superimposed with reduced detection times as a result of low correlations between the cooling events caused by the three large explosive volcanic eruptions two variables in the control simulation although the detection result since the 1960 s. These two effects (anthropogenic warming and vol- 927 Chapter 10 Detection and Attribution of Climate Change: from Global to Regional Frequently Asked Questions FAQ 10.2 | When Will Human Influences on Climate Become Obvious on Local Scales? Human-caused warming is already becoming locally obvious on land in some tropical regions, especially during the warm part of the year. Warming should become obvious in middle latitudes during summer at first within the next several decades. The trend is expected to emerge more slowly there, especially during winter, because natural climate variability increases with distance from the equator and during the cold season. Temperature trends already detected in many regions have been attributed to human influence. Temperature-sensitive climate variables, such as Arctic sea ice, also show detected trends attributable to human influence. Warming trends associated with global change are generally more evident in averages of global temperature than in time series of local temperature ( local here refers generally to individual locations, or small regional averages). This is because most of the local variability of local climate is averaged away in the global mean. Multi-decadal warming trends detected in many regions are considered to be outside the range of trends one might expect from natural internal variability of the climate system, but such trends will only become obvious when the local mean cli- mate emerges from the noise of year-to-year variability. How quickly this happens depends on both the rate of the warming trend and the amount of local variability. Future warming trends cannot be predicted precisely, especially at local scales, so estimates of the future time of emergence of a warming trend cannot be made with precision. In some tropical regions, the warming trend has already emerged from local variability (FAQ 10.2, Figure 1). This happens more quickly in the tropics because there is less temperature variability there than in other parts of the globe. Projected warming may not emerge in middle latitudes until the mid-21st century even though warming 10 trends there are larger because local temperature variability is substantially greater there than in the tropics. On a seasonal basis, local temperature variability tends to be smaller in summer than in winter. Warming therefore tends to emerge first in the warm part of the year, even in regions where the warming trend is larger in winter, such as in central Eurasia in FAQ 10.2, Figure 1. Variables other than land surface temperature, including some oceanic regions, also show rates of long-term change different from natural variability. For example, Arctic sea ice extent is declining very rapidly, and already shows a human influence. On the other hand, local precipitation trends are very hard to detect because at most locations the variability in precipitation is quite large. The probability of record-setting warm summer temperatures has increased throughout much of the Northern Hemisphere . High temperatures presently considered extreme are projected to become closer to the norm over the coming decades. The probabilities of other extreme events, includ- ing some cold spells, have lessened. In the present climate, individual extreme weather events cannot be unambiguously ascribed to climate change, since such events could have happened in an unchanged climate. However the probability of occurrence of such events could have changed significantly at a particular location. Human-induced increases in greenhouse gases are estimated to have contributed substantially to the probability of some heatwaves. Similarly, climate model studies suggest that increased greenhouse gases have contributed to the observed intensification of heavy precipitation events found over parts of the Northern Hemisphere. However, the probability of many other extreme weather events may not have changed substantially. Therefore, it is incorrect to ascribe every new weather record to climate change. The date of future emergence of projected warming trends also depends on local climate variability, which can temporarily increase or decrease temperatures. Furthermore, the projected local temperature curves shown in FAQ 10.2, Figure 1 are based on multiple climate model simulations forced by the same assumed future emissions sce- nario. A different rate of atmospheric greenhouse gas accumulation would cause a different warming trend, so the spread of model warming projections (the coloured shading in FAQ 10.2, Figure 1) would be wider if the figure included a spread of greenhouse gas emissions scenarios. The increase required for summer temperature change to emerge from 20th century local variability (regardless of the rate of change) is depicted on the central map in FAQ 10.2, Figure 1. A full answer to the question of when human influence on local climate will become obvious depends on the strength of evidence one considers sufficient to render something obvious . The most convincing scientific evidence for the effect of climate change on local scales comes from analysing the global picture, and from the wealth of evidence from across the climate system linking many observed changes to human influence. (continued on next page) 928 Detection and Attribution of Climate Change: from Global to Regional Chapter 10 FAQ 10.2 (continued) DJF temperature anomaly (°C) JJA temperature anomaly (°C) 12 12 JJA temperature anomaly (°C) DJF temperature anomaly (°C) 8 8 4 4 12 0 12 0 8 -4 8 -4 4 -8 4 -8 0 0 -4 -4 -8 -8 1900 1940 1980 2020 2060 2100 1900 1940 1980 2020 2060 2100 Year Year 1.4 Global temperature increase (°C) needed for temperatures in summer at individual locations to emerge from the 1.3 1.2 envelope of early 20th century variability 1.1 1 0.9 10 0.8 0.7 0.6 0.5 0.4 0.3 0.2 JJA temperature anomaly (°C) JJA temperature anomaly (°C) DJF temperature anomaly (°C) DJF temperature anomaly (°C) 8 8 4 4 0 0 8 -4 8 -4 4 -8 4 -8 0 0 -4 -4 -8 -8 1900 1940 1980 2020 2060 2100 1900 1940 1980 2020 2060 2100 Year Year FAQ 10.2, Figure 1 | Time series of projected temperature change shown at four representative locations for summer (red curves, representing June, July and August at sites in the tropics and Northern Hemisphere or December, January and February in the Southern Hemisphere) and winter (blue curves). Each time series is surrounded by an envelope of projected changes (pink for the local warm season, blue for the local cold season) yielded by 24 different model simulations, emerging from a grey envelope of natural local variability simulated by the models using early 20th century conditions. The warming signal emerges first in the tropics during summer. The central map shows the global temperature increase (°C) needed for temperatures in summer at individual locations to emerge from the envelope of early 20th century variability. Note that warm colours denote the smallest needed temperature increase, hence earliest time of emergence. All calculations are based on Coupled Model Intercomparison Project Phase 5 (CMIP5) global climate model simulations forced by the Representative Concentration Pathway 8.5 (RCP8.5) emissions scenario. Envelopes of projected change and natural variability are defined as +/-2 standard deviations. (Adapted and updated from Mahlstein et al., 2011.) 929 Chapter 10 Detection and Attribution of Climate Change: from Global to Regional 10 Figure 10.21 | Detection and attribution signals in some elements of the climate system, at regional scales (top panels) and global scales (bottom four panels). Brown panels are land surface temperature time series, green panels are precipitation time series, blue panels are ocean heat content time series and white panels are sea ice time series. Observa- tions are shown on each panel in black or black and shades of grey. Blue shading is the model time series for natural forcing simulations and pink shading is the combined natural and anthropogenic forcings. The dark blue and dark red lines are the ensemble means from the model simulations. All panels show the 5 to 95% intervals of the natural forcing simulations, and the natural and anthropogenic forcing simulations. For surface temperature the results are from Jones et al. (2013 ) (and Figure 10.1). The observed surface tem- perature is from Hadley Centre/Climatic Research Unit gridded surface temperature data set 4 (HadCRUT4). Observed precipitation is from Zhang et al. (2007) (black line) and CRU TS 3.0 updated (grey line). Three observed records of ocean heat content (OHC) are shown. Sea ice anomalies (rather than absolute values) are plotted and based on models in Figure 10.16. The green horizontal lines indicate quality of the observations and estimates. For land and ocean surface temperatures panels and precipitation panels, solid green lines at bottom of panels indicate where data spatial coverage being examined is above 50% coverage and dashed green lines where coverage is below 50%. For example, data coverage of Antarctica never goes above 50% of the land area of the continent. For ocean heat content and sea ice panels the solid green line is where the coverage of data is good and higher in quality, and the dashed green line is where the data coverage is only adequate. More details of the sources of model simulations and observations are given in the Supplementary Material (10.SM.1). 930 Detection and Attribution of Climate Change: from Global to Regional Chapter 10 canic eruptions) cause much of the observed response (see also Figures that human influence has substantially increased the probability of 10.5, 10.6, 10.9, 10.14a and 10.21). Both natural and anthropogenic occurrence of heat waves in some locations (result 33, likely, Table forcings are required to understand fully the variability of the Earth 10.1). system during the past 50 years. An analysis of these results (from Table 10.1) shows that there is high Water in the free atmosphere is expected to increase, as a consequence confidence in attributing many aspects of changes in the climate of warming of the atmosphere (Section 10.6.1), and atmospheric circu- system to human influence including from atmospheric measurements lation controls the global distribution of precipitation and evaporation. of temperature. Synthesizing the results in Table 10.1 shows that the Simulations show that GHGs increase moisture in the atmosphere and combined evidence from across the climate system increases the level change its transport in such a way as to produce patterns of precipi- of confidence in the attribution of observed climate change to human tation and evaporation that are quite distinct from the observed pat- influence and reduces the uncertainties associated with assessments terns of warming. Our assessment shows that anthropogenic forcings based on a single variable. From this combined evidence, it is virtually have contributed to observed increases in moisture content in the certain that human influence has warmed the global climate system. atmosphere (result 16, medium confidence, Table 10.1), to global scale changes in precipitation patterns over land (result 14, medium confi- dence), to a global scale intensification of heavy precipitation in land Acknowledgements regions where there observational coverage is sufficient to make an assessment (result 15, medium confidence), and to changes in surface We acknowledge the major contributions of the following scientists and sub-surface ocean salinity (result 11, very likely). Combining evi- who took a substantial part in the production of key figures: Beena dence from both atmosphere and ocean that systematic changes in Balan Sarojini, Oliver Browne, Jara Imbers Quintana, Gareth Jones, precipitation over land and ocean salinity can be attributed to human Fraser Lott, Irina Mahlstein, Alexander Otto, Debbie Polson, Andrew influence supports an assessment that it is likely that human influence Schurer, Lijun Tao, and Muyin Wang. We also acknowledge the contri- has affected the global water cycle since 1960. butions of Viviane Vasconcellos de Menezes for her work on the pro- duction of figures and for her meticulous management of the bibliog- 10 Warming of the atmosphere and the oceans affects the cryosphere, and raphy database used for this chapter. in the case of snow and sea ice warming leads to positive feedbacks that amplify the warming response in the atmosphere and oceans. Retreat of mountain glaciers has been observed with an anthropo- genic influence detected (result 17, likely, Table 10.1), Greenland ice sheet has melted at the edges and accumulating snow at the higher elevations is consistent with GHG warming supporting an assessment for an anthropogenic influence on the negative surface mass balance of Greenland s ice sheet (result 18, likely, Table 10.1). Our level of sci- entific understanding is too low to provide a quantifiable explanation of the observed mass loss of the Antarctic ice sheet (low confidence, result 19, Table 10.1). Sea ice in the Arctic is decreasing rapidly and the changes now exceed internal variability and with an anthropogenic contribution detected (result 20, very likely, Table 10.1). Antarctic sea ice extent has grown overall over the last 30 years but there is low sci- entific understanding of the spatial variability and changes in Antarctic sea ice extent (result 21, Table 10.1). There is evidence for an anthro- pogenic component to observed reductions in NH snow cover since the 1970s (likely, result 22, Table 10.1). Anthropogenic forcing has also affected temperature on continental scales, with human influences having made a substantial contribution to warming in each of the inhabited continents (results 28, likely, Table 10.1), and having contributed to the very substantial Arctic warming over the past 50 years (result 29, likely, Table 10.1) while because of large observational uncertainties there is low confidence in attribution of warming averaged over available stations over Antarctica (result 30, Table 10.1). There is also evidence that anthropogenic forcings have contributed to temperature change in many sub-continental regions (result 32, likely, Table 10.1) and that anthropogenic forcings have c ­ontributed to the observed changes in the frequency and intensity of daily temperature extremes on the global scale since the mid-20th century (result 8, very likely, Table 10.1). Furthermore there is evidence 931 10 Table 10.1 | Synthesis of detection and attribution results across the climate system from this chapter. Note that we follow the guidance note for lead authors of the IPCC AR5 on consistent treatment of uncertainties (Mastrandrea et al., 932 2011). Where the confidence is medium or less there is no assessment given of the quantified likelihood measure, and the table cell is marked not applicable (N/A). Result (1) Statement about (2) Confidence (3) Quantified measure of uncer- (4) Data sources (5) Type, amount, quality, consistency (6) Factors contributing to the assessments variable or property: (Very high, High, tainty where the probability of Observational evidence (Chapters 2 of evidence from attribution studies including physical understanding, observational Chapter 10 time, season medium or low, the outcome can be quantified to 5); Models (Chapter 9) and degree of agreement of studies. and modelling uncertainty, and caveats. very low) (Likelihood given generally only if high or very high confidence) Global Scale Atmospheric Temperature Changes 1 More than half of the High Very likely Four global surface temperature series . Many formal attribution studies, The observed warming is well understood in terms observed increase in global (HadCRUT3, HadCRUT4, MLOST, GISTEMP). including optimal fingerprint time-space of contributions of anthropogenic forcings such mean surface temperatures CMIP3 and CMIP5 models. studies and time series based studies. as greenhouse gases (GHGs) and tropospheric from 1951 to 2010 is due to . Robust evidence. Attribution of more aerosols and natural forcings from volcanic erup- the observed anthropogenic than half of warming since 1950 to tions. Solar forcing is the only other forcing that increase in greenhouse gas GHGs seen in multiple independent could explain long-term warming but pattern of (GHG) concentrations. analyses using different observational warming is not consistent with observed pattern data sets and climate models. of change in time, vertical change and estimated . High agreement. Studies agree in to be small. AMO could be confounding influ- robust detection of GHG contribu- ence but studies that find significant role for AMO tion to observed warming that show this does not project strongly onto 60-year is larger than any other factor trends. (Section 10.3.1.1, Figures 10.4 and 10.5) including internal variability. 2 More than half of the High Extremely likely Mutliple CMIP5 models and . Formal attribution studies including The observed warming is well understood in observed increase in global multiple methodologies. different optimal detection methodolo- terms of contributions of anthropogenic and mean surface temperatures gies and time series based studies. natural forcings. Solar forcing and AMO could be from 1951 to 2010 is due to . Robust evidence of well-constrained confounding influence but are estimated to be human influence on climate. estimates of net anthropogenic warming smaller than the net effects of human influence. estimated in optimal detection studies. (Section 10.3.1.1, Figures 10.4, 10.5, 10.6) . High agreement. Both optimal detec- tion and time series studies agree in robust detection of anthropogenic influence that is substantially more than half of the observed warming. 3 Early 20th century warming is High Very likely Instrumental global surface temperature . Formal detection and attribution studies Modelling studies show contribution from external due in part to external forcing. series and reconstructions of the last looking at early century warming and forcings to early century warming. Residual differ- millenium. CMIP3 and CMIP5 models. studies for the last few hundred years. ences between models and observations indicate role . High agreement across a number for circulation changes as contributor. of studies in detecting external (Section 10.3.1.1, Figures 10.1, 10.2, 10.6) forcings when including early 20th century period although they vary in contributions from different forcings. 4 Warming since 1950 cannot High Virtually certain Estimates of internal variability from CMIP3 . Many, including optimal fingerprint Based on all evidence above combined. Observed be explained without external and CMIP5 models, observation based time-space studies, observation warming since 1950 is very large compared to climate forcing. time series and space pattern analyses, based time series and space pattern model estimates of internal variability, which are and estimating residuals of the non-forced analyses and paleo data studies. assessed to be adequate at global scale. The Northern component from paleo data. . Robust evidence and high agreement. Hemisphere (NH) mean warming since 1950 is far out- . Detection of anthropogenic finger- side the range of any similar length trend in residuals print robustly seen in independent from reconstructions of NH mean temperature of analyses using different observa- the past millennium. The spatial pattern of observed tional data sets, climate models, warming differs from those associated with internal and methodological approaches. variability. (Sections 9.5.3.1, 10.3.1.1, 10.7.1) (continued on next page) Detection and Attribution of Climate Change: from Global to Regional Table 10.1 (continued) Result (1) Statement about (2) Confidence (3) Quantified measure of uncer- (4) Data sources (5) Type, amount, quality, consistency (6) Factors contributing to the assessments variable or property: (Very high, High, tainty where the probability of Observational evidence (Chapters 2 of evidence from attribution studies including physical understanding, observational time, season medium or low, the outcome can be quantified to 5); Models (Chapter 9) and degree of agreement of studies. and modelling uncertainty, and caveats. very low) (Likelihood given generally only if high or very high confidence) 5 Anthropogenic forcing High Likely Multiple radiosonde data sets from . Formal attribution studies with CMIP3 Observational uncertainties in radiosondes are has led to a detectable 1958 and satellite data sets from 1979 models (assessed in AR4) and CMIP5 now much better documented than at time of warming of troposphere to present. CMIP3 and CMIP5 models. models. AR4. It is virtually certain that the troposphere has temperatures since 1961. . Robust detection and attribution of warmed since the mid-20th century but there is anthropogenic influence on only medium confidence in the rate and vertical tropospheric warming with large structure of those changes in the NH extratropi- signal-to-noise (S/N) ratios estimated. cal troposphere and low confidence elsewhere. . Studies agree in detecting an anthropo- Most, though not all, CMIP3 and CMIP5 models genic influence on tropospheric overestimate the observed warming trend in the warming trends. tropical troposphere during the satellite period although observational uncertainties are large and outside the tropics and over the period of the radiosonde record beginning in 1961 there is better agreement between simulated and observed trends. (Sections 2.4.4, 9.4.1.4.2, 10.3.1.2, Figure 10.8) 6 Anthropogenic forcing High Very Likely Radiosonde data from 1958 and satellite . A formal optimal detection attribution New generation of stratosphere resolving models dominated by the depletion data from 1979 to present. CCMVal, study using stratosphere resolving appear to have adequate representation of lower of the ozone layer due to CMIP3 and CMIP5 simulations. chemistry climate models and a stratospheric variability. Structure of stratospheric ozone depleting substances, detection study analysing the S/N temperature trends and variations is reasonably has led to a detectable ratio of the data record together well represented by models. CMIP5 models all cooling of lower stratosphere with many separate modelling include changes in stratospheric ozone while only temperatures since 1979. studies and observational studies. about half of the models participating in CMIP3 . Physical reasoning and model studies include stratospheric ozone changes. (Sections show very consistent understanding 9.4.1.4.5, 10.3.1.2.2, Figures10.8 and 10.9) of observed evolution of stratospheric Detection and Attribution of Climate Change: from Global to Regional temperatures, consistent with formal detection and attribution results. . Studies agree in showing very strong cooling in stratosphere that can be explained only by anthropogenic forcings dominated by ozone depleting substances. 7 Anthropogenic forcing, particu- High Very likely Radiosonde data from 1958 and . Attribution studies using CMIP3 and Fingerprint of changes expected from physical under- larly GHGs and stratospheric satellite data from 1979 to present. CMIP5 models. standing and as simulated by models is detected in ozone depletion has led to a . Physical reasoning and modelling sup- observations. Understanding of stratospheric changes detectable observed pattern ports robust expectation of fingerprint has improved since AR4. Understanding of obser- of tropospheric warming of anthropogenic influence of tropo- vational uncertainty has improved although uncer- and lower stratospheric spheric warming and lower stratospheric tainties remain particularly in the tropical upper tropo- cooling since 1961. cooling which is robustly detected sphere. (Sections 2.4.4, 10.3.1.2.3, Figures 10.8, 10.9) in multiple observational records. . Fingerprint of anthropogenic influence is detected in different measures of free atmosphere temperature changes including tropospheric warming, and a very clear identification of stratospheric cooling in models that include anthropogenic forcings. (continued on next page) Chapter 10 933 10 10 Table 10.1 (continued) (1) Statement about (2) Confidence (3) Quantified measure of uncer- (4) Data sources (5) Type, amount, quality, consistency (6) Factors contributing to the assessments 934 Result variable or property: (Very high, High, tainty where the probability of Observational evidence (Chapters 2 of evidence from attribution studies including physical understanding, observational time, season medium or low, the outcome can be quantified to 5); Models (Chapter 9) and degree of agreement of studies. and modelling uncertainty, and caveats. very low) (Likelihood given generally only Chapter 10 if high or very high confidence) 8 Anthropogenic forcing has High Very Likely Indices for frequency and intensity of . Several studies including fingerprint Expected from physical principles that changes in contributed to the observed extreme temperatures including annual time space studies. mean temperature should bring changes in extremes, changes in the frequency and maximum and annual minimum daily . Detection of anthropogenic influence confirmed by detection and attribution studies. intensity of daily temperatures temperatures, over land areas of the robustly seen in independent analysis New evidence since AR4 for detection of human extremes on the global scale World except parts of Africa, South using different statistical methods and influence on extremely warm daytime maximum since the mid-20th century. America and Antarctica. CMIP3 and different indices. temperatures and new evidence that influence of CMIP5 simulations, 1950 2005. anthropogenic forcing can be separately detected from natural forcing. More limited observational data and greater observational uncertainties than for mean temperatures. (Section 10.6.1.1, Figure 10.17) Oceans 9 Anthropogenic forcings High Very likely Several observational data sets since . Several new attribution studies detect New understanding of the structural errors in the have made a substantial the 1970s. CMIP3 and CMIP5 models. role of anthropogenic forcing on temperature data sets has led to their correction contribution to upper ocean observed increase in ocean s global which means that the unexplained multi-decadal warming (above 700 m) heat content with volcanic forcing also scale variability reported in AR4 has largely been observed since the 1970s. contributing to observed variability. resolved as being spurious. The observations and . The evidence is very robust, and tested climate simulations have similar trends (including This anthropogenic ocean against known structural deficiencies anthropogenic and volcanic forcings) and similar warming has contrib- in the observations, and in the models. decadal variability. The detection is well above S/N uted to global sea level rise . High levels of agreement across attribu- levels required at 1 and 5% significance levels. over this period through tion studies and observation and model The new results show the conclusions to be very thermal expansion. comparison studies. The strong physical robust to structural uncertainties in observational relationship between thermosteric sea data sets and transient climate simulations. (Sec- level and ocean heat content means that tions 3.2.5, 10.4.1, 10.4.3, 13.3.6, Figure 10.14) the anthropogenic ocean warming has contributed to global sea level rise over this period through thermal expansion. 10 Anthropogenic forcing has High Likely Observational evidence of melting glaciers . Several new mass balance studies Strong observational evidence of contribution from contributed to sea level rise (Section 4.3) and ice sheets (Section 4.4). quantifying glacier and ice sheet melt melting glaciers and high confidence in attribution of through melting glaciers Global mean sea level budget closure to rates (Section 10.5.2) and their contribu- glacier melt to human influence. Increasing rates of and Greenland ice sheet. within uncertainties. (Section13.3.6) tions to sea level rise. (Section 13.3) ice sheet contributions albeit from short observa- tional record (especially of Antarctic mass loss). Current climate models do not represent glacier and ice sheet processes. Natural vari- ability of glaciers and ice sheets not fully understood. (Sections 10.4.3, 10.5.2) (continued on next page) Detection and Attribution of Climate Change: from Global to Regional Table 10.1 (continued) Result (1) Statement about (2) Confidence (3) Quantified measure of uncer- (4) Data sources (5) Type, amount, quality, consistency (6) Factors contributing to the assessments variable or property: (Very high, High, tainty where the probability of Observational evidence (Chapters 2 of evidence from attribution studies including physical understanding, observational time, season medium or low, the outcome can be quantified to 5); Models (Chapter 9) and degree of agreement of studies. and modelling uncertainty, and caveats. very low) (Likelihood given generally only if high or very high confidence) 11 The observed ocean surface High Very likely Oceans chapter (Section 3.3) and . Robust observational evidence for More than 40 studies of regional, global surface and and sub-surface salinity chang- attribution studies in Section 10.4.2. amplification of climatological patterns subsurface salinity observations show patterns of es since the 1960s are due, in of surface salinity. change consistent with acceleration of hydrological part, to anthropogenic forcing. . CMIP3 simulations show patterns of water cycle. Climate models that include anthropgenic salinity change consistent with observa- forcings show the same consistent pattern of surface tions, but there are only a few formal salinity change. (Sections 3.3.5, 10.4.2, Figure 10.15) attributions studies that include a full characterization of internal variability. . Physical understanding of expected patterns of change in salinity due to changes in water cycle support results from detection and attriution studies. 12 Observed increase in Very high Very likely Evidence from Section 3.8.2 . Based on ocean chemistry, expert Very high confidence, based on the number of studies, surface ocean acidification and Box 3.2, Figure 3.18. judgement, and many analyses of time the updates to earlier results in AR4, and the very well since 1980s is a resulted of series and other indirect measurements established physical understanding of gas exchange rising atmospheric CO2 . Robust evidence from time series between atmosphere and surface ocean, and the measurements. Measurements sources of excess carbon dioxide in the atmosphere. have a high degree of certainty (see Alternative processes and hypotheses can be Table 3.2) and instrumental records excluded. (Section 3.8.2, Box 3.2, Section 10.4.4) show increase in ocean acidity. . High agreement of the observed trends. 13 Observed pattern of Medium About as likely as not Evidence from Section 3.8.3 and . Qualitative expert judgement based on Physical understanding of ocean circulation and decrease in oxygen content attribution studies in Section 10.4.4. comparison of observed and expected ventilation, and from the global carbon cycle, and is, in part, attributable to changes in response to increasing CO2. from simulations of ocean oxygen concentrations Detection and Attribution of Climate Change: from Global to Regional anthropogenic forcing. . Medium evidence. One specific global from coupled bio-geochemical models with OAGCMs. To correctly read: Observed ocean study, many studies of hydro- Main uncertainty is observed decadal variability which pattern of decrease in oxygen graphic sections and repeat station is not well understood in global and regional content from the 1960s to the data, high agreement across inventories of dissolved oxygen in the oceans. 1990s is, in part, attributable observational studies. (Section 10.4.4) to anthropogenic forcing. . Medium agreement. One attri- bution study, and only limited regional and large-scale modelling and observation comparisons. Water Cycle 14 Global scale precipitation Medium N/A Multiple observational data sets . Several land precipitation studies exam- Increases of precipitation at high latitudes of the NH patterns over land have based on rain gauges over land, ining annual and seasonal precipitation. are a robust feature of climate model simulations changed due to anthropogenic with coverage dominated by the . Evidence for consistency between and are expected from process understanding. forcings including increases NH. CMIP3 and CMIP5 models. observed and modelled changes in Global-land average long-term changes small at in NH mid to high latitudes. global precipitation patterns over land present time, whereas decadal variability over some regions with sufficient observations. land areas is large. Observations are very uncertain . Medium degree of agreement of studies. and poor coverage of precipitation expected to Expected anthropogenic fingerprints make fingerprint of changes much more indistinct. of changes in zonal mean precipitation (Sections 2.5.1, 10.3.2.2, Figures 10.10 and 10.11) found in annual and some seasonal data with some sensitivity of attribution results to observational data set used. (continued on next page) Chapter 10 935 10 10 Table 10.1 (continued) Result (1) Statement about (2) Confidence (3) Quantified measure of uncer- (4) Data sources (5) Type, amount, quality, consistency (6) Factors contributing to the assessments 936 variable or property: (Very high, High, tainty where the probability of Observational evidence (Chapters 2 of evidence from attribution studies including physical understanding, observational time, season medium or low, the outcome can be quantified to 5); Models (Chapter 9) and degree of agreement of studies. and modelling uncertainty, and caveats. very low) (Likelihood given generally only Chapter 10 if high or very high confidence) 15 In land regions where Medium N/A Wettest 1-day and 5-day precipitation . Only one detection and attribution Evidence for anthropogenic influence on vari- observational coverage is in a year obtained from rain guage study restricted to NH land where ous aspects of the hydrological cycle that implies sufficient for assessment, observations, CMIP3 simulations. observations were available. extreme precipitation would be expected to anthropogenic forcing has . Study found stronger detectability increase. There are large observational uncertainties contributed to global-scale for models with natural forcings but and poor global coverage which makes assess- intensification of heavy not able to differentiate anthro- ment difficult. (Section 10.6.1.2, Figure 10.11) precipitation over the second pogenic from natural forcings. half of the 20th century. . Although only one formal detection and attribution study, observa- tions of a general increase in heavy precipitation at the global scale agree with physical expectations. 16 Anthropogenic contribu- Medium N/A Observations of atmospheric moisture . Detection and attribution studies of Recent reductions in relative humidity over land and tion to atmospheric specific content over ocean from satellite; observa- both surface humidity from weather sta- levelling off of specific humidity not fully under- humidity since 1973. tions of surface humidity from weather tions over land and atmospheric mois- stood. Length and quality of observational data stations and radiosondes over land. ture content over oceans from satellites. sets limit detection and attribution and assimi- . Detection of anthropogenic influence lated analyses not judged sufficiently reliable for on atmospheric moisture content over detection and attribution. (Section 10.3.2.1) oceans robust to choice of models. . Studies looking at different variables agree in detecting specific humidity changes. Hemispheric Scale Changes; Basin Scale Changes Cryosphere 17 A substantial part of glaciers High Likely Robust agreement from . Several new recent studies since last Well established records of glacier length, and better mass loss since the 1960 s long-term glacier records. (Section 4.3.3) assessment. methods of estimating glacier volumes and is due to human influence. . High agreement across a limited mass loss. Better characterization of internal variability, number of studies. and better understanding of the response to natural variability, and local land cover change. (Sections 4.3.3, 10.5.2) 18 Anthropogenic forcing High Likely Robust agreement across in situ and . Several new studies since last Documented evidence of surface mass loss. Uncer- has contributed to surface satellite derived estimates of surface assessment. tainty caused by poor characterization of the internal melting of the Greenland mass balance (Section 4.4). Nested or . Robust evidence from different sources. variability of the surface mass balance (strong depen- ice sheet since 1993. downscaled model simulations show pat- . High agreement across a limited number dence on atmospheric variability) that is not well tern of change consistent with warming. of studies. represented in CMIP5 models. (Section 4.4.2, 10.5.2.1) 19 Antarctic ice sheet mass bal- Low N/A Observational evidence for Antarctic mass . No formal studies exist. Processes for Low confidence assessment based on low scientific ance loss has a contribution sheet loss is well established across a mass loss for Antarctica are not well understanding. (Sections 4.4.2, 13.4, 10.5.2) from anthropogenic forcing. broad range of studies. (Section 4.4) understood. Regional warming and changed wind patterns (increased westerlies, increase in the Southern Annular Mode (SAM)) could contribute to enhanced melt of Antarctica. High agreement in observational studies. 20 Anthropogenic forcing has High Very likely Robust agreement across all . Multiple detection and attribution High confidence based on documented observa- contributed to the Arctic observations. (Section 4.2) studies, large number of model simula- tions of ice extent loss, and also good evidence sea ice loss since 1979. tions and data comparisons for for a significant reduction in sea ice volume. The instrumental record. physics of Arctic sea ice is well understood and . Robust set of studies of simulations of consistent with the observed warming in the region, sea ice and observed sea ice extent. and from simulations of Arctic sea ice extent with . High agreement between stud- anthropogenic forcing. (Sections 9.4.3, 10.5.1) ies of sea ice simulations and Detection and Attribution of Climate Change: from Global to Regional observed sea ice extent. (continued on next page) Table 10.1 (continued) Result (1) Statement about (2) Confidence (3) Quantified measure of uncer- (4) Data sources (5) Type, amount, quality, consistency (6) Factors contributing to the assessments variable or property: (Very high, High, tainty where the probability of Observational evidence (Chapters 2 of evidence from attribution studies including physical understanding, observational time, season medium or low, the outcome can be quantified to 5); Models (Chapter 9) and degree of agreement of studies. and modelling uncertainty, and caveats. very low) (Likelihood given generally only if high or very high confidence) 21 Incomplete scientific explana- N/A N/A The increase in sea ice extent in . No formal attribution studies. Low confidence based on low scientific understand- tions of the observed increase observations is robust, based on . Estimates of internal variability from ing of the spatial variability and changes in the in Antarctic sea ice extent pre- satellite measurements and ship-based CMIP5 simulations exceed observed Antarctic sea ice. (Sections 4.5.2, 10.5.1, 9.4.3) cludes attribution at this time. measurements. (Section 4.5.2) sea ice variability. . Modelling studies have a low level of agreement for observed increase, and there are competing scientific explanations. 22 There is an anthropogenic com- High Likely Observations show decrease . Two snow cover attribution studies. Expert judgement and attribution studies sup- ponent to observed reductions in NH snow cover. . Decrease in snow cover in the port the human influence on reduction in snow in NH snow cover since 1970 observations are consistent among cover extent. (Sections 4.5.2, 4.5.3, 10.5.3) many studies. (Section 4.5.2, 4.5.3) . Reductions in observed snow cover inconsistent with internal variability and can be explained only by climate models that include anthropogenic forcings. Atmospheric Circulation and Patterns of Variability 23 Human influence has altered High Likely An observational gridded data set and . A number of studies find detectable Detectable anthropogenic influence on changes sea level pressure patterns reanalyses. Multiple climate models. anthropogenic influence on sea level in sea level pressure patterns is found in several globally since 1951. pressure patterns. attribution studies that sample observational and . Detection of anthropogenic influence is modelling uncertainty. Observational uncertainties found to be robust to currently sampled not fully sampled as results based largely on variants modelling and observational uncertainty. of one gridded data set although analyses based on Detection and Attribution of Climate Change: from Global to Regional reanalyses also support the finding of a detectable anthropogenic influence. (Section 10.3.3.4) 24 The positive trend in the High Likely Measurements since 1957. Clear . Many studies comparing consistency Consistent result of modelling studies is that SAM seen in austral summer signal of SAM trend in December, of observed and modelled trends, and the main aspect of the anthropogenically forced since 1951 is due in part to January and February (DJF) is robust consistency of observed trend with response on the DJF SAM is the impact of ozone stratospheric ozone depletion. to observational uncertainty. simulated internal variability. depletion. The observational record is relatively . Observed trends are consistent with short, observational uncertainties remain, and CMIP3 and CMIP5 simulations that the DJF SAM trend since 1951 is only margin- include stratospheric ozone depletion. ally inconsistent with internal variability in some . Several studies show that the data sets. (Section 10.3.3.3, Figure 10.13) observed increase in the DJF SAM is inconsistent with simulated internal variability. High agreement of model- ling studies that ozone depletion drives an increase in the DJF SAM index. There is medium confidence that GHGs have also played a role. 25 Stratospheric ozone deple- Medium N/A Multiple observational lines of . Consistent evidence for effects of The observed magnitude of the tropical belt tion has contributed to the evidence for widening but large stratospheric ozone depletion. widening is uncertain. The contribution of internal observed poleward shift of spread in the magnitude. . Evidence from modelling studies is climate variability to the observed poleward the Southern Hadley cell Reanalysis suggest a southward robust that stratospheric ozone drives expansion of the Hadley circulation remains very during austral summer. shift of southern Hadley cell border a poleward shift of the southern Hadley uncertain. (Section 10.3.3.1, Figure 10.12) during DJF which is also seen in Cell border during austral summer. The CMIP3 and CMIP5 models. magnitude of the shift is very uncertain and appears to be underestimated by models. There is medium confidence that GHGs have also played a role. Chapter 10 937 (continued on next page) 10 10 Table 10.1 (continued) Result (1) Statement about (2) Confidence (3) Quantified measure of uncer- (4) Data sources (5) Type, amount, quality, consistency (6) Factors contributing to the assessments 938 variable or property: (Very high, High, tainty where the probability of Observational evidence (Chapters 2 of evidence from attribution studies including physical understanding, observational time, season medium or low, the outcome can be quantified to 5); Models (Chapter 9) and degree of agreement of studies. and modelling uncertainty, and caveats. very low) (Likelihood given generally only Chapter 10 if high or very high confidence) 26 Attribution of changes in Low N/A Incomplete and short observa- . Formal attribution studies on SSTs Insufficient observational evidence of multi-decadal tropical cyclone activity tional records in most basins. in tropics. However, mechanisms scale variability. Physical understanding lacking. to human influence. linking anthropogenically induced There remains substantial disagreement on the SST increases to changes in tropical relative importance of internal variability, GHG cyclone activity poorly understood. forcing, and aerosols. (Sections 10.6.1.5, 14.6.1) . Attribution assessments depend on multi-step attribution linking anthro- pogenic influence to large-scale drivers and thence to tropical cyclone activity. . Low agreement between studies, medium evidence. Millennium Time Scale 27 External forcing contrib- High for period Very likely for period See Chapter 5 for reconstructions; . A small number of detection and Large uncertainty in reconstructions particularly for uted to NH temperature from 1400 to 1850; from 1400 to 1850. simulations from PMIP3 and CMIP5 attribution studies and further evidence the first half of the millennium but good agreement variability from 1400 to 1850, medium for period models, with more robust detec- from climate modelling studies; com- between reconstructed and simulated large scale fea- and from 850 to 1400. from 850 to 1400. tion results for 1400 onwards. parison of models with reconstructions tures from 1400. Detection of forced influence robust and results from data assimilation. for a large range of reconstructions. Difficult to sepa- . Robust agreement from a number of rate role of individual forcings. Results prior to 1400 studies using a range of reconstruc- much more uncertain, partly due to larger data and tions and models (EBMs to ESMs) forcing uncertainty. (Sections 10.7.1, 10.7.2, 10.7.3) that models are able to reproduce key features of last seven centuries. . Detection results and simulations indicate a contribution by external drivers to the warm conditions in the 11th to 12th century, but cannot explain the warmth around the 10th century in some reconstructions. Continental to Regional Scale Changes 28 Anthropogenic forcing has High Likely Robust observational evidence except . New studies since AR4 detect Anthropogenic pattern of warming widespread made a substantial contribu- for Africa due to poor sampling. anthropogenic warming on continental across all inhabited continents. Lower S/N ratios at tion to warming to each of Detection and attribution studies and sub-continental scales. continental scales than global scales. Separation of the inhabited continents. with CMIP3 and CMIP5 models. . Robust detection of human influence on response to forcings more difficult at these scales. continental scales agrees with global Models have greater errors in representation of attribution of widespread warming over regional details. (Section 10.3.1.1,4, Box 11.2) land to human influence. . Studies agree in detecting human influence on continental scales. 29 Anthropogenic contribution to High Likely Adequate observational coverage since . Multiple models show amplification of Large temperature signal relative to mid-latitudes but very substantial Arctic warm- 1950s. Arctic temperatures from anthropogenic also larger internal variability and poorer obser- ing over the past 50 years. Detection and attribution analy- forcing. vational coverage than at lower latitudes. Known sis with CMIP3 models. . Large positive Arctic-wide temperature multiple processes including albedo shifts and added anomalies in observations over last heat storage contribute to faster warming than at decade and models are consistent only lower latitudes. (Sections 10.3.1.1.4. 10.5.1.1) when they include external forcing. (continued on next page) Detection and Attribution of Climate Change: from Global to Regional Table 10.1 (continued) Result (1) Statement about (2) Confidence (3) Quantified measure of uncer- (4) Data sources (5) Type, amount, quality, consistency (6) Factors contributing to the assessments variable or property: (Very high, High, tainty where the probability of Observational evidence (Chapters 2 of evidence from attribution studies including physical understanding, observational time, season medium or low, the outcome can be quantified to 5); Models (Chapter 9) and degree of agreement of studies. and modelling uncertainty, and caveats. very low) (Likelihood given generally only if high or very high confidence) 30 Human contribution Low N/A Poor observational coverage of Antarc- . One optimal detection study, and some Possible contribution to changes from SAM increase. to observed warming tica with most observations around the modelling studies. Residual when SAM induced changes are removed averaged over available coast. Detection and attribution studies . Clear detection in one optimal detection shows warming consistent with expectation due to stations over Antarctica. with CMIP3 and CMIP5 models. study. anthropogenic forcing. High observational uncertainty and sparse data coverage (individual stations only mostly around the coast). (Sections 10.3.1.1.4, 2.4.1.1) 31 Contribution by forcing to Medium N/A European seasonal tempera- . One detection and attribution study and Robust volcanic response detected in Epoch analyses reconstructed European tures from 1500 onwards. several modelling studies. in several studies. Models reproduce low-frequency temperature variability over . Clear detection of external forcings in evolution when include external forcings. Uncertainty last five centuries. one study; robust volcanic signal seen in overall level of variability, uncertainty in in several studies (see also Chapter 5). reconstruction particularly prior to late 17th century. (Sections 10.7.2, 5.5.1) 32 Anthropogenic forcing has High Likely Good observational coverage for . A number of detection and attribution Larger role of internal variability at smaller scales contributed to temperature many regions (e.g., Europe) and studies have analysed temperatures relative to signal of climate change. In some regions change in many sub-conti- poor for others (e.g., Africa, Arctic). on scales from Giorgi regions to observational coverage is poor. Local forcings nental regions of the world. Detection and attribution studies climate model grid box scale. and feedbacks as well as circulation changes are with CMIP3 and CMIP5 models. . Many studies agree in showing that important in many regions and may not be well an anthropogenic signal is appar- simulated in all regions. (Section 10.3.1.1.4, Box 11.2) ent in many sub-continental scale regions. In some sub-continental- scale regions circulation changes may have played a bigger role. 33 Human influence has High Likely Good observational coverage for some . Multi-step attribution studies of some In some instances, circulation changes could be more substantially increased the regions and poor for others (thus biasing events including the Europe 2003, important than thermodynamic changes. This could Detection and Attribution of Climate Change: from Global to Regional probability of occurrence of studies to regions where observational Western Russia 2010, and Texas 2011 be a possible confounding influence since much of heat waves in some locations. coverage is good). Coupled modeling stud- heatwaves have shown an anthropo- the magnitude (as opposed to the probability of ies examining the effects of anthropogonic genic contribution to their occurrence occurrence) of many heat waves is attributable to warming and the probability of occurrence probability, backed up by studies atmospheric flow anomalies. (Sections 10.6.1, 10.6.2) of very warm seasonal temperatures and looking at the overall implications of targeted experiments with models forced increasing mean temperatures for the with prescribed sea surface temperatures. probability of exceeding seasonal mean temperature thresholds in some regions. . To infer the probability of a heatwave, extrapolation has to be made from the scales on which most attribution studies have been carried out to the spatial and temporal scales of heatwaves. . Studies agree in finding robust evidence for anthropogenic influence on increase in probability of occurrence of extreme seasonal mean temperatures in many regions. Chapter 10 939 10 Chapter 10 Detection and Attribution of Climate Change: from Global to Regional References AchutaRao, K. M., B. D. Santer, P. J. Gleckler, K. E. Taylor, D. W. Pierce, T. P. Barnett, and Balachandran, N. K., D. Rind, P. Lonergan, and D. T. Shindell, 1999: Effects of solar T. M. L. Wigley, 2006: Variability of ocean heat uptake: Reconciling observations cycle variability on the lower stratosphere and the troposphere. J. Geophys. Res. and models. J. Geophys. Res. Oceans, 111, C05019. Atmos., 104, 27321 27339. AchutaRao, K. M., et al., 2007: Simulated and observed variability in ocean Balan Sarojini, B., P. Stott, E. Black, and D. Polson, 2012 Fingerprints of changes in temperature and heat content. Proc. Natl. Acad. Sci. U.S.A., 104, 10768 10773. annual and seasonal precipitation from CMIP5 models over land and ocean. Ahlmann, H. W., 1948: The present climatic fluctuation. Geogr.J., 112, 165 195. Geophys. Res. Lett., 39, L23706. Aldrin, M., M. Holden, P. Guttorp, R. B. Skeie, G. Myhre, and T. K. Berntsen, 2012: Barnett, T. P., D. W. Pierce, and R. Schnur, 2001: Detection of anthropogenic climate Bayesian estimation of climate sensitivity based on a simple climate model change in the world s oceans. Science, 292, 270 274. fitted to observations of hemispheric temperatures and global ocean heat Barnett, T. P., D. W. Pierce, K. Achutarao, P. Gleckler, B. Santer, J. Gregory, and W. content. Environmetrics, 23, 253 271. Washington, 2005: Penetration of human-induced warming into the world s Alekseev, G. V., A. I. Danilov, V. M. Kattsov, S. I. Kuz mina, and N. E. Ivanov, 2009: oceans. Science, 309, 284 287. Changes in the climate and sea ice of the Northern Hemisphere in the 20th and Barnett, T. P., et al., 2008: Human-induced changes in the hydrology of the western 21st centuries from data of observations and modeling. Izvestiya Atmospheric United States. Science, 319, 1080 1083. and Oceanic Physics, 45, 675 686. Barriopedro, D., R. Garcia-Herrera, and R. Huth, 2008: Solar modulation of Northern Alexander, L. V., and J. M. Arblaster, 2009: Assessing trends in observed and modelled Hemisphere blocking. J. Geophys. Res. Atmos., 113, D14118. climate extremes over Australia in relation to future projections. Int. J. Climatol., Beenstock, M., Y. Reingewertz, and N. Paldor, 2012: Polynomial cointegration tests of 29, 417 435. anthropogenic impact on global warming. Earth Syst. Dyn. Discuss., 3, 561 596. Allan, R., and T. Ansell, 2006: A new globally complete monthly historical gridded Bender, F. A. M., A. M. L. Ekman, and H. Rodhe, 2010: Response to the eruption of mean sea level pressure dataset (HadSLP2): 1850 2004. J. Clim., 19, 5816 5842. Mount Pinatubo in relation to climate sensitivity in the CMIP3 models. Clim. Allan, R. P., B. J. Soden, V. O. John, W. Ingram, and P. Good, 2010: Current changes in Dyn., 35, 875 886. tropical precipitation. Environ. Res. Lett., 5, 025205. Benestad, R. E., and G. A. Schmidt, 2009: Solar trends and global warming. J. Allen, M., 2011: In defense of the traditional null hypothesis: Remarks on the Geophys. Res. Atmos., 114, D14101. Trenberth and Curry WIREs opinion articles. WIREs Clim. Change, 2, 931 934. Bengtsson, L., and K. I. Hodges, 2011: On the evaluation of temperature trends in the Allen, M. R., and S. F. B. Tett, 1999: Checking for model consistency in optimal tropical troposphere. Clim. Dyn., 36 419 430. 10 fingerprinting. Clim. Dyn., 15, 419 434. Bengtsson, L., V. A. Semenov, and O. M. Johannessen, 2004: The early twentieth- Allen, M. R., and W. J. Ingram, 2002: Constraints on future changes in climate and century warming in the Arctic A possible mechanism. J. Clim., 17, 4045 4057. the hydrologic cycle. Nature, 419, 224 232. Bengtsson, L., K. I. Hodges, E. Roeckner, and R. Brokopf, 2006: On the natural Allen, M. R., and P. A. Stott, 2003: Estimating signal amplitudes in optimal variability of the pre-industrial European climate. Clim. Dyn., 27, 743 760. fingerprinting. Part I: Theory. Clim. Dyn., 21, 477 491. Berliner, L. M., A. L. Richard, and J. S. Dennis, 2000: Bayesian climate change Allen, M. R., P. A. Stott, J. F. B. Mitchell, R. Schnur, and T. L. Delworth, 2000: Quantifying assessment. J. Clim., 13, 3805 3820. the uncertainty in forecasts of anthropogenic climate change. Nature, 407, Bhend, J., and H. von Storch, 2008: Consistency of observed winter precipitation 617 620. trends in northern Europe with regional climate change projections. Clim. Dyn., Allen, M. R., D. J. Frame, C. Huntingford, C. D. Jones, J. A. Lowe, M. Meinshausen, 31, 17 28. and N. Meinshausen, 2009: Warming caused by cumulative carbon emissions Bindoff, N. L., and T. J. McDougall, 2000: Decadal changes along an Indian ocean towards the trillionth tonne. Nature, 458, 1163 1166. section at 32 degrees S and their interpretation. J. Phys. Oceanogr., 30, 1207 Allen, M. R., et al., 2006: Quantifying anthropogenic influence on recent near-surface 1222. temperature change. Surv. Geophys., 27, 491 544. Bindoff, N. L., et al., 2007: Observations: Oceanic climate change and sea level. Allen, R. J., S. C. Sherwood, J. R. Norris, and C. S. Zender, 2012: Recent Northern In: Climate Change 2007: The Physical Science Basis. Contribution of Working Hemisphere tropical expansion primarily driven by black carbon and tropospheric Group I to the Fourth Assessment Report of the Intergovernmental Panel on ozone. Nature, 485, 350 354. Climate Change [Solomon, S., D. Qin, M. Manning, Z. Chen, M. Marquis, K. B. Ammann, C. M., and E. R. Wahl, 2007: The importance of the geophysical context in Averyt, M. Tignor and H. L. Miller (eds.)] Cambridge University Press, Cambridge, statistical evaluations of climate reconstruction procedures. Clim. Change, 85, United Kingdom and New York, NY, USA, pp. 385 432. 71 88. Bintanja, R., G. J. van Oldenborgh, S. S. Drijfhout, B. Wouters, and C. A. Katsman, Ammann, C. M., F. Joos, D. S. Schimel, B. L. Otto-Bliesner, and R. A. Tomas, 2007: 2013: Important role for ocean warming and increased ice-shelf melt in Antarctic Solar influence on climate during the past millennium: Results from transient sea-ice expansion. Nature Geosci., 6, 376 379. simulations with the NCAR Climate System Model. Proc. Natl. Acad. Sci. U.S.A., Birner, T., 2010: Recent widening of the tropical belt from global tropopause 104, 3713 3718. statistics: Sensitivities. J. Geophys. Res. Atmos., 115, D23109. Andreae, M. O., C. D. Jones, and P. M. Cox, 2005: Strong present-day aerosol cooling Bitz, C. M., and L. M. Polvani, 2012: Antarctic climate response to stratospheric implies a hot future. Nature, 435, 1187 1190. ozone depletion in a fine resolution ocean climate model. Geophys. Res. Lett., Andrews, O. D., N. L. Bindoff, P. R. Halloran, T. Ilyina, and C. Le Quéré, 2013: Detecting 39, L20705. an external influence on recent changes in oceanic oxygen using an optimal Boer, G. J., 2011: The ratio of land to ocean temperature change under fingerprinting method. Biogeosciences, 10, 1799 1813. global warming Clim. Dyn., 37, 2253 2270. Annan, J. D., and J. C. Hargreaves, 2006: Using multiple observationally-based Boer, G. J., M. Stowasser, and K. Hamilton, 2007: Inferring climate sensitivity from constraints to estimate climate sensitivity. Geophys. Res. Lett., 33, L06704. volcanic events. Clim. Dyn., 28, 481 502. Annan, J. D., and J. C. Hargreaves, 2010: Reliability of the CMIP3 ensemble. Geophys. Bonfils, C., P. B. Duffy, B. D. Santer, T. M. L. Wigley, D. B. Lobell, T. J. Phillips, and Res. Lett., 37, L02703. C. Doutriaux, 2008: Identification of external influences on temperatures in Annan, J. D., and J. C. Hargreaves, 2011: On the generation and interpretation of California. Clim. Change, 87, S43 S55. probabilistic estimates of climate sensitivity. Clim. Change, 104, 423 436. Booth, B. B. B., N. J. Dunstone, P. R. Halloran, T. Andrews, and N. Bellouin, 2012: Aoki, S., N. L. Bindoff, and J. A. Church, 2005: Interdecadal water mass changes in Aerosols implicated as a prime driver of twentieth-century North Atlantic the Southern Ocean between 30 degrees E and 160 degrees E. Geophys. Res. climate variability. Nature, 484, 228 232. Lett., 32, L07607. Bowerman, N. H. A., D. J. Frame, C. Huntingford, J. A. Lowe, and M. R. Allen, 2011: Arkin, P. A., T. M. Smith, M. R. P. Sapiano, and J. Janowiak, 2010: The observed Cumulative carbon emissions, emissions floors and short-term rates of warming: sensitivity of the global hydrological cycle to changes in surface temperature. Implications for policy. Philos. Trans. R. Soc. A, 369, 45 66. Environ. Res. Lett., 5, 035201. Boyer, T. P., S. Levitus, J. I. Antonov, R. A. Locarnini, and H. E. Garcia, 2005: Linear Bal, S., S. Schimanke, T. Spangehl, and U. Cubasch, 2011: On the robustness of the trends in salinity for the World Ocean, 1955 1998. Geophys. Res. Lett., 32, solar cycle signal in the Pacific region. Geophys. Res. Lett., 38, L14809. L01604. 940 Detection and Attribution of Climate Change: from Global to Regional Chapter 10 Brandt, P., et al., 2010: Changes in the ventilation of the oxygen minimum zone of Chylek, P., and U. Lohmann, 2008a: Reply to comment by Andrey Ganopolski and the tropical North Atlantic. J. Phys. Oceanogr., 40, 1784 1801. Thomas Schneider von Deimling on Aerosol radiative forcing and climate Brayshaw, D. J., B. Hoskins, and M. Blackburn, 2008: The storm-track response to sensitivity deduced from the Last Glacial Maximum to Holocene transition . idealized SST perturbations in an aquaplanet GCM. J. Atmos. Sci., 65, 2842 Geophys. Res. Lett., 35, L23704. 2860. Chylek, P., and U. Lohmann, 2008b: Aerosol radiative forcing and climate sensitivity Brohan, P., J. J. Kennedy, I. Harris, S. F. B. Tett, and P. D. Jones, 2006: Uncertainty deduced from the last glacial maximum to Holocene transition. Geophys. Res. estimates in regional and global observed temperature changes: A new data set Lett., 35, L04804. from 1850. J. Geophys. Res. Atmos., 111, D12106. Chylek, P., U. Lohmann, M. Dubey, M. Mishchenko, R. Kahn, and A. Ohmura, Bromwich, D. H., J. P. Nicolas, A. J. Monaghan, M. A. Lazzara, L. M. Keller, G. A. 2007: Limits on climate sensitivity derived from recent satellite and surface Weidner, and A. B. Wilson, 2013: Central West Antarctica among the most rapidly observations. J. Geophys. Res. Atmos., 112, D24S04. warming regions on Earth. Nature Geosci., 6, 139 145. Comiso, J. C., 2012: Large decadal decline in Arctic multiyear ice cover. J. Clim., 25, Brönnimann, S., 2009: Early twentieth-century warming. Nature Geosci., 2, 735 736. 1176 1193. Brönnimann, S., et al., 2012: A multi-data set comparison of the vertical structure Comiso, J. C., and F. Nishio, 2008: Trends in the sea ice cover using enhanced and of temperature variability and change over the Arctic during the past 100 year. compatible AMSR-E, SSM/I, and SMMR data. J. Geophys. Res. Oceans, 113, Clim. Dyn., 39 1577 1598. C02S07. Brown, R. D., and D. A. Robinson, 2011: Northern Hemisphere spring snow cover Compo, G. P., et al., 2011: The twentieth century reanalysis project. Q. J. R. Meteorol. variability and change over 1922 2010 including an assessment of uncertainty. Soc., 137, 1 28. Cryosphere, 5, 219 229. Cordero, E. C., and P. M. D. Forster, 2006: Stratospheric variability and trends in Burke, E. J., S. J. Brown, and N. Christidis, 2006: Modeling the recent evolution of models used for the IPCC AR4. Atmos. Chem. Phys., 6, 5369 5380. global drought and projections for the twenty-first century with the Hadley Crook, J. A., and P. M. Forster, 2011: A balance between radiative forcing and climate Centre climate model. J. Hydrometeorol., 7 1113 1125. feedback in the modeled 20th century temperature response. J. Geophys. Res. Butchart, N., et al., 2011: Multimodel climate and variability of the stratosphere. J. Atmos., 116, D17108. Geophys. Res. Atmos., 116, D05102. Crook, J. A., P. M. Forster, and N. Stuber, 2011: Spatial patterns of modeled climate Butler, A. H., D. W. Thompson, and R. Heikes, 2010: The steady-state atmospheric feedback and contributions to temperature response and polar amplification. J. circulation response to climate change like thermal forcings in a simple general Clim., 24, 3575 3592. circulation model. J. Clim., 23, 3474 3496. Curry, J. A., and P. J. Webster, 2011: Climate science and the uncertainty monster. Bull. Cai, M., and K.-K. Tung, 2012: Robustness of dynamical feedbacks from radiative Am. Meteorol. Soc., 92, 1667 1682. forcing: 2% solar versus 2XCO2 experiments in an idealized GCM. J. Atmos. Sci., Curry, R., B. Dickson, and I. Yashayaev, 2003: A change in the freshwater balance of 69, 2256 2271. the Atlantic Ocean over the past four decades. Nature, 426, 826 829 10 Cattiaux, J., R. Vautard, C. Cassou, P. Yiou, V. Masson-Delmotte, and F. Codron, 2010: D Arrigo, R., R. Wilson, and G. Jacoby, 2006: On the long-term context for late Winter 2010 in Europe: A cold extreme in a warming climate. Geophys. Res. twentieth century warming. J. Geophys. Res. Atmos., 111 D03103. Lett., 37, L20704. D Arrigo, R., R. Wilson, and A. Tudhope, 2009: The impact of volcanic forcing on Cayan, D. R., T. Das, D. W. Pierce, T. P. Barnett, M. Tyree, and A. Gershunov, 2010: tropical temperatures during the past four centuries. Nature Geosci., 2, 51 56. Future dryness in the southwest US and the hydrology of the early 21st century Dai, A., 2011: Drought under global warming: A review. WIREs Clim. Change, 2, drought. Proc. Natl. Acad. Sci. U.S.A., 107, 21271 21276. 45 65. Charlton-Perez, A. J., et al., 2013: On the lack of stratospheric dynamical variability in Dai, A., 2013: Increasing drought under global warming in observations and models. low-top versions of the CMIP5 models. J. Geophys. Res. Atmos., 118, 2494 2505. Nature Clim. Change, 3, 52 58. Chinn, T., S. Winkler, M. J. Salinger, and N. Haakensen, 2005: Recent glacier Dall Amico, M., L. J. Gray, K. H. Rosenlof, A. A. Scaife, K. P. Shine, and P. A. Stott, 2010: advances in Norway and New Zealand: A comparison of their glaciological and Stratospheric temperature trends: Impact of ozone variability and the QBO. Clim. meteorological causes. Geograf. Annal. A, 87, 141 157. Dyn., 34, 381 398. Christiansen, B., and F. C. Ljungqvist, 2011: Reconstruction of the extratropical NH Davis, S. M., and K. H. Rosenlof, 2012: A multidiagnostic intercomparison of tropical- mean temperature over the last millennium with a method that preserves low- width time series using reanalyses and satellite observations. J. Clim., 25, 1061 frequency variability. J. Clim., 24, 6013 6034. 1078. Christidis, N., P. A. Stott, and S. J. Brown, 2011: The role of human activity in the recent Day, J. J., J. C. Hargreaves, J. D. Annan, and A. Abe-Ouchi, 2012: Sources of multi- warming of extremely warm daytime temperatures. J. Clim., 24, 1922 1930. decadal variability in Arctic sea ice extent. Environ. Res. Lett., 7, 034011. Christidis, N., P. A. Stott, G. C. Hegerl, and R. A. Betts, 2013: The role of land use Dean, S. M., and P. A. Stott, 2009: The effect of local circulation variability on the change in the recent warming of daily extreme temperatures. Geophys. Res. detection and attribution of New Zealand temperature trends. J. Clim., 22, Lett., 40, 589 594. 6217 6229. Christidis, N., P. A. Stott, F. W. Zwiers, H. Shiogama, and T. Nozawa, 2010: Probabilistic DelSole, T., M. K. Tippett, and J. Shukla, 2011: A significant component of unforced estimates of recent changes in temperature: A multi-scale attribution analysis. multidecadal variability in the recent acceleration of global warming. J. Clim., Clim. Dyn., 34, 1139 1156. 24, 909 926. Christidis, N., P. A. Stott, F. W. Zwiers, H. Shiogama, and T. Nozawa, 2012a: The Delworth, T., V. Ramaswamy, and G. Stenchikov, 2005: The impact of aerosols on contribution of anthropogenic forcings to regional changes in temperature simulated ocean temperature and heat content in the 20th century. Geophys. during the last decade. Clim. Dyn., 39, 1259 1274. Res. Lett., 32, L24709. Christidis, N., P. A. Stott, G. S. Jones, H. Shiogama, T. Nozawa, and J. Luterbacher, Delworth, T. L., and M. E. Mann, 2000: Observed and simulated multidecadal 2012b: Human activity and anomalously warm seasons in Europe. Int. J. variability in the Northern Hemisphere. Clim. Dyn., 16, 661 676. Climatol., 32, 225 239. Deser, C., and H. Teng, 2008: Evolution of Arctic sea ice concentration trends and Chung, E. S., B. J. Soden, and B. J. Sohn, 2010: Revisiting the determination of climate the role of atmospheric circulation forcing, 1979 2007. Geophys. Res. Lett., 35, sensitivity from relationships between surface temperature and radiative fluxes. L02504. Geophys. Res. Lett., 37, L10703. Dessler, A. E., 2010: A determination of the cloud feedback from climate variations Church, J., N. White, and J. Arblaster, 2005: Significant decadal-scale impact of over the past decade. Science, 330, 1523 1527. volcanic eruptions on sea level and ocean heat content. Nature, 438, 74 77. Dessler, A. E., 2011: Cloud variations and the Earth s energy budget. Geophys. Res. Church, J. A., D. Monselesan, J. M. Gregory, and B. Marzeion, 2013: Evaluating the Lett., 38, L19701. ability of process based models to project sealevel change. Environ. Res. Lett., Dickson, R. R., et al., 2000: The Arctic Ocean response to the North Atlantic oscillation. 8, 014051. J. Clim., 13, 2671 2696. Church, J. A., et al., 2011: Revisiting the Earth s sea-level and energy budgets from Ding, Q. H., E. J. Steig, D. S. Battisti, and M. Kuttel, 2011: Winter warming in West 1961 to 2008. Geophys. Res. Lett., 38, L18601. Antarctica caused by central tropical Pacific warming. Nature Geosci., 4, 398 403. 941 Chapter 10 Detection and Attribution of Climate Change: from Global to Regional Doherty, S. J., et al., 2009: Lessons learned from IPCC AR4 scientific developments Fettweis, X., B. Franco, M. Tedesco, J. H. van Angelen, J. T. M. Lenaerts, M. R. van den needed to understand, predict, and respond to climate change. Bull. Am. Broeke, and H. Gallée, 2013: Estimating the Greenland ice sheet surface mass Meteorol. Soc., 90, 497 513. balance contribution to future sea level rise using the regional atmospheric Dole, R., et al., 2011: Was there a basis for anticipating the 2010 Russian heat wave? climate model MAR. Cryosphere, 7, 469 489. Geophys. Res. Lett., 38, L06702. Feulner, G., 2011: Are the most recent estimates for Maunder Minimum solar Domingues, C., J. Church, N. White, P. Gleckler, S. Wijffels, P. Barker, and J. Dunn, 2008: irradiance in agreement with temperature reconstructions? Geophys. Res. Lett., Improved estimates of upper-ocean warming and multi-decadal sea-level rise. 38, L16706. Nature, 453, 1090 1093. Fischer, E. M., S. I. Seneviratne, P. L. Vidale, D. Luthi, and C. Schar, 2007: Soil moisture Doscher, R., K. Wyser, H. E. M. Meier, M. W. Qian, and R. Redler, 2010: Quantifying atmosphere interactions during the 2003 European summer heat wave. J. Arctic contributions to climate predictability in a regional coupled ocean-ice- Clim., 20, 5081 5099. atmosphere model. Clim. Dyn., 34, 1157 1176. Fogt, R. L., J. Perlwitz, A. J. Monaghan, D. H. Bromwich, J. M. Jones, and G. J. Marshall, Douglass, D. H., E. G. Blackman, and R. S. Knox, 2004: Corrigendum to: Temperature 2009: Historical SAM variability. Part II: Twentieth-century variability and trends response of Earth to the annual solar irradiance cycle [Phys. Lett. A 323 (2004) from reconstructions, observations, and the IPCC AR4 models. J. Clim., 22, 315]. Phys. Lett. A, 325, 175 176. 5346 5365. Douville, H., A. Ribes, B. Decharme, R. Alkama, and J. Sheffield, 2013: Anthropogenic Folland, C. K., et al., 2013 High predictive skill of global surface temperature a year influence on multidecadal changes in reconstructed global evapotranspiration. ahead. Geophys. Res. Lett., 40, 761 767. Nature Clim. Change, 3, 59 62. Forest, C. E., and R. W. Reynolds, 2008: Climate change Hot questions of Driscoll, S., A. Bozzo, L. J. Gray, A. Robock, and G. Stenchikov, 2012: Coupled Model temperature bias. Nature, 453, 601 602. Intercomparison Project 5 (CMIP5) simulations of climate following volcanic Forest, C. E., P. H. Stone, and A. P. Sokolov, 2006: Estimated PDFs of climate system eruptions. J. Geophys. Res., 117, D17105. properties including natural and anthropogenic forcings. Geophys. Res. Lett., 33, Drost, F., and D. Karoly, 2012 Evaluating global climate responses to different L01705. forcings using simple indices. Geophys. Res. Lett., 39, L16701. Forest, C. E., P. H. Stone, and A. P. Sokolov, 2008: Constraining climate model Drost, F., D. Karoly, and K. Braganza, 2012: Communicating global climate change parameters from observed 20th century changes. Tellus A, 60, 911 920. using simple indices: An update Clim. Dyn., 39, 989 999. Forest, C. E., M. R. Allen, P. H. Stone, and A. P. Sokolov, 2000: Constraining Duarte, C. M., T. M. Lenton, P. Wadhams, and P. Wassmann, 2012: Abrupt climate uncertainties in climate models using climate change detection techniques. change in the Arctic. Nature Clim. Change, 2, 60 62. Geophys. Res. Lett., 27, 569 572. Durack, P., S. Wijffels, and R. Matear, 2012: Ocean salinities reveal strong global Forest, C. E., P. H. Stone, A. P. Sokolov, M. R. Allen, and M. D. Webster, 2002: water cycle intensification during 1950 to 2000. Science, 336, 455 458. Quantifying uncertainties in climate system properties with the use of recent 10 Durack, P. J., and S. E. Wijffels, 2010: Fifty-year trends in global ocean salinities and climate observations. Science, 295, 113 117. their relationship to broad-scale warming. J. Clim., 23, 4342 4362. Forster, P., et al., 2007: Changes in atmospheric constituents and in radiative forcing. Edwards, T. L., M. Crucifix, and S. P. Harrison, 2007: Using the past to constrain the In: Climate Change 2007: The Physical Science Basis. Contribution of Working future: How the palaeorecord can improve estimates of global warming. Prog. Group I to the Fourth Assessment Report of the Intergovernmental Panel on Phys. Geogr., 31, 481 500. Climate Change [Solomon, S., D. Qin, M. Manning, Z. Chen, M. Marquis, K. B. Elsner, J. B., 2006: Evidence in support of the climate change Atlantic hurricane Averyt, M. Tignor and H. L. Miller (eds.)] Cambridge University Press, Cambridge, hypothesis. Geophys. Res. Lett., 33, L16705. United Kingdom and New York, NY, USA, pp. 129 234. Elsner, J. B., J. P. Kossin, and T. H. Jagger, 2008: The increasing intensity of the Forster, P. M., T. Andrews, P. Good, J. M. Gregory, L. S. Jackson, and M. Zelinka, 2013 strongest tropical cyclones. Nature, 455, 92 95. Evaluating adjusted forcing and model spread for historical and future scenarios Emanuel, K., 2005: Increasing destructiveness of tropical cyclones over the past 30 in the CMIP5 generation of climate models. J. Geophys. Res. Atmos., 118, 1139 years. Nature, 436, 686 688. 1150. Emanuel, K., S. Solomon, D. Folini, S. Davis, and C. Cagnazzo, 2013: Influence of Forster, P. M., et al., 2011: Stratospheric changes and climate. In: Scientific Assessment tropical tropopause layer cooling on Atlantic hurricane activity. J. Clim., 26, of Ozone Depletion: 2010. Global Ozone Research and Monitoring Project- 2288 2301. Report No. 52 [P. M. Forster and D. W. J. Thompson (eds.)]. World Meteorological Emerson, S., Y. W. Watanabe, T. Ono, and S. Mecking, 2004: Temporal trends in Organization, Geneva, Switzerland, 516 pp. apparent oxygen utilization in the upper pycnocline of the North Pacific: 1980 Forster, P. M. D., and J. M. Gregory, 2006: The climate sensitivity and its components 2000. J. Oceanogr., 60, 139 147. diagnosed from Earth Radiation Budget data. J. Clim., 19, 39 52. Engle, R. F., and C. W. J. Granger, 1987: Co-integration and error correction: Foster, G., and S. Rahmstorf, 2011: Global temperature evolution 1979 2010. Representation, estimation, and testing. Econometrica, 55, 251 276. Environ. Res. Lett., 6, 044022. Esper, J., et al., 2012: Orbital forcing of tree-ring data. Nature Clim. Change, 2, Foster, G., J. D. Annan, G. A. Schmidt, and M. E. Mann, 2008: Comment on Heat 862 866. capacity, time constant, and sensitivity of Earth s climate system by S. E. Evan, A. T., G. R. Foltz, D. X. Zhang, and D. J. Vimont, 2011: Influence of African dust Schwartz. J. Geophys. Res. Atmos., 113, D15102. on ocean-atmosphere variability in the tropical Atlantic. Nature Geosci., 4, Fowler, H. J., and R. L. Wilby, 2010: Detecting changes in seasonal precipitation 762 765. extremes using regional climate model projections: Implications for managing Evan, A. T., D. J. Vimont, A. K. Heidinger, J. P. Kossin, and R. Bennartz, 2009: The fluvial flood risk. Water Resour. Res., 46, W03525. role of aerosols in the evolution of tropical North Atlantic Ocean temperature Frame, D. J., D. A. Stone, P. A. Stott, and M. R. Allen, 2006: Alternatives to stabilization anomalies. Science, 324, 778 781. scenarios. Geophys. Res. Lett., 33, L14707. Eyring, V., et al., 2013: Long-term changes in tropospheric and stratospheric ozone Frame, D. J., B. B. B. Booth, J. A. Kettleborough, D. A. Stainforth, J. M. Gregory, M. and associated climate impacts in CMIP5 simulations. J. Geophys. Res. Atmos., Collins, and M. R. Allen, 2005: Constraining climate forecasts: The role of prior doi:10.1002/jgrd.50316. assumptions. Geophys. Res. Lett., 32, L09702. Eyring, V., et al., 2006: Assessment of temperature, trace species, and ozone in Francis, J. A., and S. J. Vavrus 2012: Evidence linking Arctic amplification to extreme chemistry-climate model simulations of the recent past. J. Geophys. Res. Atmos., weather in mid-latitudes. Geophys. Res. Lett., 39, L06801. 111, D22308. Frank, D., J. Esper, and E. R. Cook, 2007: Adjustment for proxy number and coherence Fernández-Donado, L., et al., 2013: Large-scale temperature response to external in a large-scale temperature reconstruction. Geophys. Res. Lett., 34, L16709. forcing in simulations and reconstructions of the last millennium. Clim. Past, 9, Frank, D. C., J. Esper, C. C. Raible, U. Buntgen, V. Trouet, B. Stocker, and F. Joos, 2010: 393 421. Ensemble reconstruction constraints on the global carbon cycle sensitivity to Fettweis, X., G. Mabille, M. Erpicum, S. Nicolay, and M. Van den Broeke, 2011: climate. Nature, 463, 527 530. The 1958 2009 Greenland ice sheet surface melt and the mid-tropospheric Franzke, C., 2010: Long-range dependence and climate noise characteristics of atmospheric circulation. Clim. Dyn., 36, 139 159. Antarctic temperature data. J. Clim., 23, 6074 6081. Free, M., 2011: The seasonal structure of temperature trends in the tropical lower stratosphere. J. Clim., 24, 859 866. 942 Detection and Attribution of Climate Change: from Global to Regional Chapter 10 Free, M., and J. Lanzante, 2009: Effect of volcanic eruptions on the vertical Goosse, H., W. Lefebvre, A. de Montety, E. Crespin, and A. H. Orsi, 2009: Consistent temperature profile in radiosonde data and climate models. J. Clim., 22, 2925 past half-century trends in the atmosphere, the sea ice and the ocean at high 2939. southern latitudes. Clim. Dyn., 33, 999 1016. Friedlingstein, P., et al., 2006: Climate carbon cycle feedback analysis: Results from Goosse, H., J. Guiot, M. E. Mann, S. Dubinkina, and Y. Sallaz-Damaz, 2012a: The the C4MIP model intercomparison. J. Clim., 19, 3337 3353. medieval climate anomaly in Europe: Comparison of the summer and annual Frierson, D. M. W., 2006: Robust increases in midlatitude static stability in simulations mean signals in two reconstructions and in simulations with data assimilation. of global warming. Geophys. Res. Lett., 33, L24816. Global Planet. Change, 84 85, 35 47. Frierson, D. M. W., J. Lu, and G. Chen, 2007: Width of the Hadley cell in simple and Goosse, H., E. Crespin, A. de Montety, M. E. Mann, H. Renssen, and A. Timmermann, comprehensive general circulation models. Geophys. Res. Lett., 34, L18804. 2010: Reconstructing surface temperature changes over the past 600 years Fu, Q., and P. Lin, 2011: Poleward shift of subtropical jets inferred from satellite- using climate model simulations with data assimilation. J. Geophys. Res. Atmos., observed lower stratospheric temperatures. J. Clim., 24, 5597 5603. 115, D09108. Fu, Q., S. Solomon, and P. Lin, 2010: On the seasonal dependence of tropical lower- Goosse, H., et al., 2012b: The role of forcing and internal dynamics in explaining the stratospheric temperature trends. Atmos. Chem. Phys., 10, 2643 2653. Medieval Climate Anomaly . Clim. Dyn., 39, 2847 2866. Fu, Q., S. Manabe, and C. M. Johanson, 2011: On the warming in the tropical upper Gouretski, V., and K. Koltermann, 2007: How much is the ocean really warming? troposphere: Models versus observations. Geophys. Res. Lett., 38, L15704. Geophys. Res. Lett., 34, L01610. Fu, Q., C. M. Johanson, J. M. Wallace, and T. Reichler, 2006: Enhanced mid-latitude Graff, L. S., and J. H. LaCasce, 2012: Changes in the extratropical storm tracks in tropospheric warming in satellite measurements. Science, 312, 1179 1179. response to changes in SST in an GCM. J. Clim., 25, 1854 1870. Fyfe, J. C., 2006: Southern Ocean warming due to human influence. Geophys. Res. Grant, A. N., S. Bronnimann, T. Ewen, T. Griesser, and A. Stickler, 2009: The early Lett., 33, L19701. twentieth century warm period in the European Arctic. Meteorol. Z., 18 425 Fyfe, J. C., N. P. Gillett, and D. W. J. Thompson, 2010: Comparing variability and trends 432. in observed and modelled global-mean surface temperature. Geophys. Res. Lett., Graversen, R. G., and M. H. Wang, 2009: Polar amplification in a coupled climate 37, L16802. model with locked albedo. Clim. Dyn., 33, 629 643. Fyfe, J. C., N. P. Gillett, and G. J. Marshal, 2012: Human influence on extratropical Gray, L. J., et al., 2010: Solar influences on climate. Rev. Geophys., 48 RG4001. Southern Hemisphere summer precipitation. Geophys. Res. Lett., 39, L23711. Gregory, J. M., 2000: Vertical heat transports in the ocean and their effect an time- Fyke, J., and M. Eby, 2012: Comment on Climate sensitivity estimated from dependent climate change. Clim. Dyn., 16, 501 515. temperature reconstructions of the Last Glacial Maximum . Science, 337, 1294. Gregory, J. M., 2010: Long-term effect of volcanic forcing on ocean heat content. Ganopolski, A., and T. S. von Deimling, 2008: Comment on Aerosol radiative forcing Geophys. Res. Lett., 37, L22701. and climate sensitivity deduced from the Last Glacial Maximum to Holocene Gregory, J. M., and P. M. Forster, 2008: Transient climate response estimated from transition by Petr Chylek and Ulrike Lohmann. Geophys. Res. Lett., 35, L23703. radiative forcing and observed temperature change. J. Geophys. Res. Atmos., 10 Gascard, J. C., et al., 2008: Exploring Arctic transpolar drift during dramatic sea ice 113, D23105. retreat. Eos Trans. Am. Geophys. Union, 89, 21 22. Gregory, J. M., J. A. Lowe, and S. F. B. Tett, 2006: Simulated global-mean sea level Gay-Garcia, C., F. Estrada, and A. Sanchez, 2009: Global and hemispheric temperature changes over the last half-millennium. J. Clim., 19, 4576 4591. revisited. Clim. Change, 94, 333 349 Gregory, J. M., C. D. Jones, P. Cadule, and P. Friedlingstein, 2009: Quantifying carbon Giles, K. A., S. W. Laxon, and A. L. Ridout, 2008: Circumpolar thinning of Arctic sea ice cycle feedbacks. J. Clim., 22, 5232 5250. following the 2007 record ice extent minimum. Geophys. Res. Lett., 35, L22502. Gregory, J. M., R. J. Stouffer, S. C. B. Raper, P. A. Stott, and N. A. Rayner, 2002: An Gillett, N. P., and P. A. Stott, 2009: Attribution of anthropogenic influence on seasonal observationally based estimate of the climate sensitivity. J. Clim., 15, 3117 sea level pressure. Geophys. Res. Lett., 36, L23709. 3121. Gillett, N. A., and J. C. Fyfe, 2013: Annular Mode change in the CMIP5 simulations. Gregory, J. M., H. T. Banks, P. A. Stott, J. A. Lowe, and M. D. Palmer, 2004: Simulated Geophys. Res. Lett., 40, 1189 1193. and observed decadal variability in ocean heat content. Geophys. Res. Lett., 31, Gillett, N. P., R. J. Allan, and T. J. Ansell, 2005: Detection of external influence on sea L15312. level pressure with a multi-model ensemble. Geophys. Res. Lett., 32, L19714. Gregory, J. M., et al., 2012 Twentieth-century global-mean sea-level rise: Is the whole Gillett, N. P., P. A. Stott, and B. D. Santer, 2008a: Attribution of cyclogenesis region greater than the sum of the parts? J. Clim., doi:10.1175/JCLI-D-12-00319.1. sea surface temperature change to anthropogenic influence. Geophys. Res. Lett., Grist, J., et al., 2010: The roles of surface heat flux and ocean heat transport 35, L09707. convergence in determining Atlantic Ocean temperature variability. Ocean Dyn., Gillett, N. P., G. C. Hegerl, M. R. Allen, and P. A. Stott, 2000: Implications of changes in 60, 771 790. the Northern Hemisphere circulation for the detection of anthropogenic climate Haam, E., and K. K. Tung, 2012: Statistics of solar cycle-La Nina connection: change. Geophys. Res. Lett., 27, 993 996. Correlation of two autocorrelated time series. J. Atmos. Sci., 69 2934 2939. Gillett, N. P., F. W. Zwiers, A. J. Weaver, and P. A. Stott, 2003: Detection of human Haigh, J., M. Blackburn, and R. Day, 2005: The response of tropospheric circulation influence on sea-level pressure. Nature, 422, 292 294. to perturbations in lower-stratospheric temperature. J. Clim., 18, 3672 3685. Gillett, N. P., M. F. Wehner, S. F. B. Tett, and A. J. Weaver, 2004: Testing the linearity Haigh, J. D., 1996: The impact of solar variability on climate. Science, 272 981 984. of the response to combined greenhouse and sulfate aerosol forcing. Geophys. Haimberger, L., C. Tavolato, and S. Sperka, 2012: Homogenization of the global Res. Lett., 31, L14201. radiosonde temperature dataset through combined comparison with reanalysis Gillett, N. P., V. K. Arora, G. M. Flato, J. F. Scinocca, and K. von Salzen, 2012: Improved background series and neighboring stations. J. Clim., 25, 8108 8131. constraints on 21st-century warming derived using 160 years of temperature Han, W., et al., 2010: Patterns of Indian Ocean sea-level change in a warming observations. Geophys. Res. Lett., 39, L01704. climate. Nature Geosci., 3, 546 550. Gillett, N. P., V. K. Arora, D. Matthews, P. A. Stott, and M. R. Allen, 2013 Constraining Hanna, E., J. M. Jones, J. Cappelen, S. H. Mernild, L. Wood, K. Steffen, and P. the ratio of global warming to cumulative CO2 emissions using CMIP5 Huybrechts, 2013: The influence of North Atlantic atmospheric and oceanic simulations. J. Clim., doi:10.1175/JCLI-D-12 00476.1. forcing effects on 1900 2010 Greenland summer climate and ice melt/runoff. Gillett, N. P., et al., 2008b: Attribution of polar warming to human influence. Nature Int. J. Climatol., 33, 862 880. Geosci., 1, 750 754. Hanna, E., et al., 2008: Increased runoff from melt from the Greenland ice sheet: A Gillett, N. P., et al., 2011: Attribution of observed changes in stratospheric ozone and response to global warming. J. Clim., 21, 331 341. temperature. Atmos. Chem. Phys., 11 599 609. Hannart, A., J. L. Dufresne, and P. Naveau, 2009: Why climate sensitivity may not be Gleckler, P. J., T. M. L. Wigley, B. D. Santer, J. M. Gregory, K. AchutaRao, and K. E. so unpredictable. Geophys. Res. Lett., 36, L16707. Taylor, 2006: Volcanoes and climate: Krakatoa s signature persists in the ocean. Hansen, J., and S. Lebedeff, 1987: Global trends of measured surface air-temperature. Nature, 439, 675 675. J. Geophys. Res. Atmos., 92, 13345 13372. Gleckler, P. J., et al., 2012: Human-induced global ocean warming on multidecadal Hansen, J., M. Sato, and R. Ruedy, 2012: Perception of climate change. Proc. Natl. timescales. Nature Clim. Change, 2, 524 529. Acad. Sci. U.S.A., 109, 14726 14727. Gong, D., and S. Wang, 1999: Definition of Antarctic oscillation index. Geophys. Res. Hansen, J., M. Sato, P. Kharecha, and K. von Schuckmann, 2011: Earth s energy Lett., 26, 459 462. imbalance and implications. Atmos. Chem. Phys., 11, 13421 13449. 943 Chapter 10 Detection and Attribution of Climate Change: from Global to Regional Hansen, J., et al., 2005a: Earth s energy imbalance: Confirmation and implications. Hoekema, D. J., and V. Sridhar, 2011: Relating climatic attributes and water resources Science, 308, 1431 1435. allocation: A study using surface water supply and soil moisture indices in the Hansen, J., et al., 2005b: Efficacy of climate forcings. J. Geophys. Res. Atmos., 110, Snake River basin, Idaho. Water Resour. Res., 47, W07536. D18104. Hoerling, M., and A. Kumar, 2003: The perfect ocean for drought. Science, 299, Hargreaves, J. C., and J. D. Annan, 2009: Comment on Aerosol radiative 691 694. forcing and climate sensitivity deduced from the Last Glacial Maximum Hoerling, M., et al., 2013: Anatomy of an extreme event. J. Clim., 26, 2811 2832. to Holocene transition , by P. Chylek and U. Lohmann, Geophys. Res. Lett., Hoerling, M. P., J. K. Eischeid, X.-W. Quan, H. F. Diaz, R. S. Webb, R. M. Dole, and D. R. doi:10.1029/2007GL032759., 2008. Clim. Past, 5, 143 145. Easterling, 2012: Is a transition to semipermanent drought conditions imminent Hargreaves, J. C., A. Abe-Ouchi, and J. D. Annan, 2007: Linking glacial and future in the U.S. great plains? J. Clim., 25, 8380 8386. climates through an ensemble of GCM simulations. Clim. Past, 3, 77 87. Holden, P. B., N. R. Edwards, K. I. C. Oliver, T. M. Lenton, and R. D. Wilkinson, 2010: Hargreaves, J. C., J. D. Annan, M. Yoshimori, and A. Abe-Ouchi, 2012: Can the Last A probabilistic calibration of climate sensitivity and terrestrial carbon change in Glacial Maximum constrain climate sensitivity? Geophys. Res. Lett., 39, L24702. GENIE-1. Clim. Dyn., 35, 785 806. Harris, G. R., D. M. H. Sexton, B. B. B. Booth, M. Collins, and J. M. Murphy, 2013: Holland, D. M., R. H. Thomas, B. De Young, M. H. Ribergaard, and B. Lyberth, 2008: Probabilistic projections of transient climate change. Clim. Dyn., doi:10.1007/ Acceleration of Jakobshavn Isbrae triggered by warm subsurface ocean waters. s00382-012-1647-y. Nature Geosci., 1, 659 664. Hasselmann, K., 1997: Multi-pattern fingerprint method for detection and attribution Hood, L. L., and R. E. Soukharev, 2012: The lower-stratospheric response to 11-yr of climate change. Clim. Dyn., 13, 601 611. solar forcing: Coupling to the troposphere-ocean response. J. Atmos. Sci., 69, Hegerl, G., and F. Zwiers, 2011: Use of models in detection and attribution of climate 1841 1864. change. WIREs Clim. Change, 2, 570 591. Hosoda, S., T. Suga, N. Shikama, and K. Mizuno, 2009: Global surface layer salinity Hegerl, G., J. Luterbacher, F. Gonzalez-Rouco, S. F. B. Tett, T. Crowley, and E. change detected by Argo and its implication for hydrological cycle intensification. Xoplaki, 2011a: Influence of human and natural forcing on European seasonal J. Oceanogr., 65, 579 586. temperatures. Nature Geosci., 4, 99 103. Hu, Y., and Q. Fu, 2007: Observed poleward expansion of the Hadley circulation since Hegerl, G. C., F. W. Zwiers, and C. Tebaldi, 2011b: Patterns of change: Whose 1979. Atmos. Chem. Phys., 7, 5229 5236. fingerprint is seen in global warming? Environ. Res. Lett., 6, 044025. Hu, Y. Y., C. Zhou, and J. P. Liu, 2011: Observational evidence for poleward expansion Hegerl, G. C., F. W. Zwiers, P. A. Stott, and V. V. Kharin, 2004: Detectability of of the Hadley circulation. Adv. Atmos. Sci., 28, 33 44. anthropogenic changes in annual temperature and precipitation extremes. J. Hu, Y. Y., L. J. Tao, and J. P. Liu, 2013: Poleward expansion of the Hadley circulation in Clim., 17, 3683 3700. CMIP5 simulations. Adv. Atmos. Sci., 30, 790 795. Hegerl, G. C., T. J. Crowley, W. T. Hyde, and D. J. Frame, 2006: Climate sensitivity Huber, M., and R. Knutti, 2011: Anthropogenic and natural warming inferred from 10 constrained by temperature reconstructions over the past seven centuries. changes in Earth/ s energy balance. Nature Geosci., 5, 31 36. Nature, 440, 1029 1032. Hudson, R. D., M. F. Andrade, M. B. Follette, and A. D. Frolov, 2006: The total ozone Hegerl, G. C., P. Stott, S. Solomon, and F. W. Zwiers, 2011c: Comment on Climate field separated into meteorological regimes Part II: Northern Hemisphere mid- science and the uncertainty monster by J.A. Curry and P.J. Webster . Bull. Am. latitude total ozone trends. Atmos. Chem. Phys., 6, 5183 5191. Meteorol. Soc., 92, 1683 1685. Huntingford, C., P. A. Stott, M. R. Allen, and F. H. Lambert, 2006: Incorporating model Hegerl, G. C., T. J. Crowley, S. K. Baum, K. Y. Kim, and W. T. Hyde, 2003: Detection of uncertainty into attribution of observed temperature change. Geophys. Res. volcanic, solar and greenhouse gas signals in paleo-reconstructions of Northern Lett., 33, L05710. Hemispheric temperature. Geophys. Res. Lett., 30, 1242. Huss, M., and A. Bauder, 2009: 20th-century climate change inferred from four long- Hegerl, G. C., T. J. Crowley, M. Allen, W. T. Hyde, H. N. Pollack, J. Smerdon, and E. term point observations of seasonal mass balance. Ann. Glaciol., 50, 207 214. Zorita, 2007a: Detection of human influence on a new, validated 1500-year Huss, M., R. Hock, A. Bauder, and M. Funk, 2010: 100-year mass changes in the temperature reconstruction. J. Clim., 20, 650 666. Swiss Alps linked to the Atlantic Multidecadal Oscillation. Geophys. Res. Lett., Hegerl, G. C., et al., 2010: Good practice guidance paper on detection and 37, L10501. attribution related to anthropogenic climate change. In: Meeting Report of the Huybers, P., 2010: Compensation between model feedbacks and curtailment of Intergovernmental Panel on Climate Change Expert Meeting on Detection and climate sensitivity. J. Clim., 23, 3009 3018. Attribution of Anthropogenic Climate Change [T. F. Stocker, et al. (eds.)]. IPCC Imbers, J., A. Lopez, C. Huntingford, and M. R. Allen, 2013: Testing the robustness Working Group I Technical Support Unit, University of Bern, Bern, Switzerland, of the anthropogenic climate change detection statements using different 8 pp. empirical models. J. Geophys. Res. Atmos., doi:10.1002/jgrd.50296. Hegerl, G. C., et al., 2007b: Understanding and attributing climate change. In: Climate Ineson, S., A. A. Scaife, J. R. Knight, J. C. Manners, N. M. Dunstone, L. J. Gray, and Change 2007: The Physical Science Basis. Contribution of Working Group I to the J. D. Haigh, 2011: Solar forcing of winter climate variability in the Northern Fourth Assessment Report of the Intergovernmental Panel on Climate Change Hemisphere. Nature Geosci., 4, 753 757. [Solomon, S., D. Qin, M. Manning, Z. Chen, M. Marquis, K. B. Averyt, M. Tignor Ingram, W. J., 2007: Detection and attribution of climate change, and understanding and H. L. Miller (eds.)] Cambridge University Press, Cambridge, United Kingdom solar influence on climate. In: Solar Variability and Planetary Climates [Y. and New York, NY, USA, pp. 663 745. Calisesi, R.-M. Bonnet , L. Gray , J. Langen, and M. Lockwood (eds.)]. Springer Held, I. M., and B. J. Soden, 2006: Robust responses of the hydrological cycle to Science+Business Media, New York, NY, USA, and Heidelberg, Germany, pp. global warming. J. Clim., 19, 5686 5699. 199 211. Held, I. M., M. Winton, K. Takahashi, T. Delworth, F. R. Zeng, and G. K. Vallis, 2010: IPCC, 2012: Managing the risks of extreme events and disasters to advance Probing the fast and slow components of global warming by returning abruptly climate change adaptation. A Special Report of Working Groups I and II of the to preindustrial forcing. J. Clim., 23, 2418 2427. Intergovernmental Panel on Climate Change [C. B. Field et al. (eds.)]. Cambridge Helm, K. P., N. L. Bindoff, and J. A. Church, 2010: Changes in the global hydrological- University Press, Cambridge, UK, and New York, NY, USA, 582. cycle inferred from ocean salinity. Geophys. Res. Lett., 37, L18701. Ishii, M., and M. Kimoto, 2009: Reevaluation of historical ocean heat content Helm, K. P., N. L. Bindoff, and J. A. Church, 2011: Observed decreases in oxygen variations with time-varying XBT and MBT depth bias corrections. J. Oceanogr., content of the global ocean. Geophys. Res. Lett., 38, L23602. 65, 287 299. Henriksson, S. V., E. Arjas, M. Laine, J. Tamminen, and A. Laaksonen, 2010: Jackson, J. M., E. C. Carmack, F. A. McLaughlin, S. E. Allen, and R. G. Ingram, 2010: Comment on Using multiple observationally-based constraints to estimate Identification, characterization, and change of the near-surface temperature climate sensitivity by J. D. Annan and J. C. Hargreaves, Geophys. Res. Lett., maximum in the Canada Basin, 1993 2008. J. Geophys. Res.J. Geophys. Res. doi:10.1029/2005GL025259, 2006. Clim. Past, 6, 411 414. Oceans, 115, C05021. Hidalgo, H. G., et al., 2009: Detection and attribution of streamflow timing changes Jacob, T., J. Wahr, W. T. Pfeffer, and S. Swenson, 2012: Recent contributions of glaciers to climate change in the Western United States. J. Clim., 22, 3838 3855. and ice caps to sea level rise. Nature, 482 514 518. Hodge, S. M., D. C. Trabant, R. M. Krimmel, T. A. Heinrichs, R. S. March, and E. G. Jacobs, S. S., A. Jenkins, C. F. Giulivi, and P. Dutrieux, 2011: Stronger ocean circulation Josberger, 1998: Climate variations and changes in mass of three glaciers in and increased melting under Pine Island Glacier ice shelf. Nature Geosci., 4, western North America. J. Clim., 11, 2161 2179. 519 523. 944 Detection and Attribution of Climate Change: from Global to Regional Chapter 10 Jahn, A., et al., 2012: Late-twentieth-century simulation of Arctic sea-ice and ocean Keeling, R. F., and H. E. Garcia, 2002: The change in oceanic O2 inventory associated properties in the CCSM4. J. Clim., 25, 1431 1452. with recent global warming. Proc. Natl. Acad. Sci. U.S.A., 99, 7848 7853. Johannessen, O. M., et al., 2004: Arctic climate change: Observed and modelled Keeling, R. F., A. Kortzinger, and N. Gruber, 2010: Ocean deoxygenation in a warming temperature and sea-ice variability Tellus A, 56, 559 560. world. Annu. Rev. Mar. Sci., 2, 199 229. Johanson, C. M., and Q. Fu, 2009: Hadley cell widening: Model simulations versus Kettleborough, J. A., B. B. B. Booth, P. A. Stott, and M. R. Allen, 2007: Estimates of observations. J. Clim., 22, 2713 2725. uncertainty in predictions of global mean surface temperature. J. Clim., 20, Johnson, G. C., and A. H. Orsi, 1997: Southwest Pacific Ocean water-mass changes 843 855. between 1968/69 and 1990/91. J. Clim., 10, 306 316. Kharin, V. V., F. W. Zwiers, X. Zhang, and G. C. Hegerl, 2007: Changes in temperature Jones, G. S., and P. A. Stott, 2011: Sensitivity of the attribution of near surface and precipitation extremes in the IPCC ensemble of global coupled model temperature warming to the choice of observational dataset. Geophys. Res. simulations. J. Clim., 20, 1419 1444. Lett., 38, L21702. Kharin, V. V., F. W. Zwiers, X. Zhang, and M. Wehner, 2013: Changes in temperature Jones, G. S., S. F. B. Tett, and P. A. Stott, 2003: Causes of atmospheric temperature and precipitation extremes in the CMIP5 ensemble. Clim. Change, doi:10.1007/ change 1960 2000: A combined attribution analysis. Geophys. Res. Lett., 30, s10584-013-0705-8. 1228. Kiehl, J. T., 2007: Twentieth century climate model response and climate sensitivity. Jones, G. S., P. A. Stott, and N. Christidis, 2008: Human contribution to rapidly Geophys. Res. Lett., 34, L22710. increasing frequency of very warm Northern Hemisphere summers. J. Geophys. Kinnard, C., C. M. Zdanowicz, D. A. Fisher, E. Isaksson, A. Vernal, and L. G. Thompson, Res. Atmos., 113, D02109. 2011: Reconstructed changes in Arctic sea ice cover over the past 1450 years. Jones, G. S., N. Christidis, and P. A. Stott, 2011: Detecting the influence of fossil fuel Nature, 479, 509 513. and bio-fuel black carbon aerosols on near surface temperature changes. Atmos. Kirk-Davidoff, D. B., 2009: On the diagnosis of climate sensitivity using observations Chem. Phys., 11 799 816. of fluctuations. Atmos. Chem. Phys., 9, 813 822. Jones, G. S., M. Lockwood, and P. A. Stott, 2012: What influence will future solar Knight, J., et al., 2009: Do global temperature trends over the last decade falsify activity changes over the 21st century have on projected global near surface climate predictions? In: State of the Climate in 2008. Bull. Am. Meteorol. Soc., temperature changes ? J. Geophys. Res. Atmos., 117, D05103. 90, S22 S23. Jones, G. S., P. A. Stott, and N. Christidis, 2013 Attribution of observed historical Knight, J. R., C. K. Folland, and A. A. Scaife, 2006: Climate impacts of the Atlantic near surface temperature variations to anthropogenic and natural causes using Multidecadal Oscillation. Geophys. Res. Lett., 33, L17706. CMIP5 simulations. J. Geophys. Res. Atmos., doi:10.1002/jgrd.50239. Knight, J. R., R. J. Allan, C. K. Folland, M. Vellinga, and M. E. Mann, 2005: A signature Jones, P. D., et al., 2001: Adjusting for sampling density in grid box land and ocean of persistent natural thermohaline circulation cycles in observed climate. surface temperature time series. J. Geophys. Res. Atmos., 106, 3371 3380. Geophys. Res. Lett., 32, L20708. Joshi, M. M., and G. S. Jones, 2009: The climatic effects of the direct injection of Knutson, T. R., F. Zeng, and A. T. Wittenberg, 2013: Multi-model assessment of 10 water vapour into the stratosphere by large volcanic eruptions. Atmos. Chem. regional surface temperature trends. J. Clim., doi:10.1175/JCLI-D-12-00567.1. Phys., 9, 6109 6118. Knutson, T. R., et al., 2010: Tropical cyclones and climate change. Nature Geosci., 3, Joughin, I., and R. B. Alley, 2011: Stability of the West Antarctic ice sheet in a 157 163. warming world. Nature Geosci., 4, 506 513. Knutti, R., 2008: Why are climate models reproducing the observed global surface Juckes, M. N., et al., 2007: Millennial temperature reconstruction intercomparison warming so well? Geophys. Res. Lett., 35, L18704. and evaluation. Clim. Past, 3, 591 609. Knutti, R., and G. C. Hegerl, 2008: The equilibrium sensitivity of the Earth s Jung, M., et al., 2010: Recent decline in the global land evapotranspiration trend due temperature to radiation changes. Nature Geosci., 1, 735 743. to limited moisture supply. Nature, 467, 951 954. Knutti, R., and L. Tomassini, 2008: Constraints on the transient climate response Jungclaus, J. H., et al., 2010: Climate and carbon-cycle variability over the last from observed global temperature and ocean heat uptake. Geophys. Res. Lett., millennium. Clim. Past, 6, 723 737. 35, L09701. Kaplan, J. O., K. M. Krumhardt, and N. Zimmermann, 2009: The prehistoric and Knutti, R., S. Krähenmann, D. J. Frame, and M. R. Allen, 2008: Comment on Heat preindustrial deforestation of Europe. Quat. Sci. Rev., 28, 3016 3034. capacity, time constant, and sensitivity of Earth s climate system by S. E. Karoly, D. J., and Q. G. Wu, 2005: Detection of regional surface temperature trends. Schwartz. J. Geophys. Res. Atmos., 113, D15103. J. Clim., 18, 4337 4343. Kobashi, T., D. T. Shindell, K. Kodera, J. E. Box, T. Nakaegawa, and K. Kawamura, 2013: Karoly, D. J., and P. A. Stott, 2006: Anthropogenic warming of central England On the origin of multidecadal to centennial Greenland temperature anomalies temperature. Atmos. Sci. Lett., 7 81 85. over the past 800 yr. Clim. Past, 9, 583 596. Karpechko, A. Y., N. P. Gillett, G. J. Marshall, and A. A. Scaife, 2008: Stratospheric Kobayashi, T., K. Mizuno, and T. Suga, 2012: Long-term variations of surface and influence on circulation changes in the Southern Hemisphere troposphere in intermediate waters in the southern Indian Ocean along 32°S. J. Oceanogr., 68, coupled climate models. Geophys. Res. Lett., 35, L20806. 243 265. Kattsov, V. M., et al., 2010: Arctic sea-ice change: A grand challenge of climate Kodama, C., and T. Iwasaki, 2009: Influence of the SST rise on baroclinic instability science. J. Glaciol., 56, 1115 1121. wave activity under an aquaplanet condition. J. Atmos. Sci., 66, 2272 2287. Kaufman, D. S., et al., 2009: Recent warming reverses long-term arctic cooling. Kodera, K., 2004: Solar influence on the Indian Ocean monsoon through dynamical Science, 325, 1236 1239. processes. Geophys. Res. Lett., 31, L24209. Kaufmann, R. K., and D. I. Stern, 1997: Evidence for human influence on climate from Kodera, K., 2006: The role of dynamics in solar forcing. Space Sci. Rev., 23 319 330. hemispheric temperature relations. Nature, 388, 39 44. Kodera, K., and Y. Kuroda, 2002: Dynamical response to the solar cycle. J. Geophys. Kaufmann, R. K., and D. I. Stern, 2002: Cointegration analysis of hemispheric Res. Atmos., 107, 4749. temperature relations. J. Geophys. Res. Atmos., 107, 4012. Kodera, K., K. Coughlin, and O. Arakawa, 2007: Possible modulation of the Kaufmann, R. K., H. Kauppi, and J. H. Stock, 2006: Emission, concentrations, & connection between the Pacific and Indian Ocean variability by the solar cycle. temperature: A time series analysis. Clim. Change, 77, 249 278. Geophys. Res. Lett., 34, L03710. Kaufmann, R. K., H. Kauppi, M. L. Mann, and J. H. Stock, 2011: Reconciling Koehler, P., R. Bintanja, H. Fischer, F. Joos, R. Knutti, G. Lohmann, and V. Masson- anthropogenic climate change with observed temperature 1998 2008. Proc. Delmotte, 2010: What caused Earth s temperature variations during the last Natl. Acad. Sci. U.S.A., 108, 11790 11793. 800,000 years? Data-based evidence on radiative forcing and constraints on Kaufmann, R. K., H. Kauppi, M. L. Mann, and J. H. Stock, 2013: Does temperature climate sensitivity. Quat. Sci. Rev., 29, 129 145. contain a stochastic trend: Linking statistical results to physical mechanisms. Korhonen, H., K. S. Carslaw, P. M. Forster, S. Mikkonen, N. D. Gordon, and H. Clim. Change, doi:10.1007/s10584 012 0683 2. Kokkola, 2010: Aerosol climate feedback due to decadal increases in Southern Kay, A. L., S. M. Crooks, P. Pall, and D. A. Stone, 2011a: Attribution of Autumn/Winter Hemisphere wind speeds. Geophys. Res. Lett., 37, L02805. 2000 flood risk in England to anthropogenic climate change: A catchment-based Krakauer, N. Y., and I. Fung, 2008: Mapping and attribution of change in streamflow study. J. Hydrol., 406, 97 112. in the coterminous United States. Hydrol. Earth Syst. Sci., 12, 1111 1120. Kay, J. E., M. M. Holland, and A. Jahn, 2011b: Inter-annual to multi-decadal Arctic sea ice extent trends in a warming world. Geophys. Res. Lett., 38, L15708. 945 Chapter 10 Detection and Attribution of Climate Change: from Global to Regional Kuhlbrodt, T., and J. M. Gregory, 2012: Ocean heat uptake and its consequences for Lindzen, R. S., and Y. S. Choi, 2011: On the observational determination of climate the magnitude of sea level rise and climate change. Geophys. Res. Lett., 39, sensitivity and its implications. Asia-Pacific J. Atmos. Sci., 47, 377 390. L18608. Liu, C., R. P. Allan, and G. J. Huffman, 2012: Co-variation of temperature and Kunkel, K. E., et al., 2008: Observed changes in weather and climate extremes. In: precipitation in CMIP5 models and satellite observations. Geophys. Res. Lett., Weather and Climate Extremes in a Changing Climate. Regions of Focus: North 39, L13803. America, Hawaii, Caribbean, and U.S. Pacific Islands [G. A. M. T. R. Karl, C. D. Lockwood, M., 2008: Recent changes in solar outputs and the global mean surface Miller, S. J. Hassol, A. M. Waple, and W. L. Murray (eds.)]. A Report by the U.S. temperature. III. Analysis of contributions to global mean air surface temperature Climate Change Science Program and the Subcommittee on Global Change rise. Proc. R. Soc. London A, 464, 1387 1404. Research, Washington, DC, pp. 35 80. Lockwood, M., 2012: Solar influence on global and regional climates. Surv. Geophys., Kwok, R., and N. Untersteiner, 2011: The thinning of Arctic sea ice. Physics Today, 33, 503 534. 64, 36 41. Lockwood, M., and C. Fro hlich, 2007: Recent oppositely directed trends in solar Kwok, R., G. F. Cunningham, M. Wensnahan, I. Rigor, H. J. Zwally, and D. Yi, 2009: climate forcings and the global mean surface air temperature Proc. R. Soc. Thinning and volume loss of the Arctic Ocean sea ice cover: 2003 2008. J. London A, 463, 2447 2460. Geophys. Res.J. Geophys. Res. Oceans, 114, C07005. Lockwood, M., and C. Fro hlich, 2008: Recent oppositely directed trends in solar L Heureux, M., A. H. Butler, B. Jha, A. Kumar, and W. Q. Wang, 2010: Unusual extremes climate forcings and the global mean surface air temperature: II. Different in the negative phase of the Arctic Oscillation during 2009. Geophys. Res. Lett., reconstructions of the total solar irradiance variation and dependence on 37, L10704. response time scale. Proc. R. Soc. London A, 464, 1367 1385. Lamarque, J.-F., et al., 2010: Historical (1850 2000) gridded anthropogenic and Lockwood, M., R. G. Harrison, T. Woollings, and S. K. Solanki, 2010: Are cold winters biomass burning emissions of reactive gases and aerosols: Methodology and in Europe associated with low solar activity? Environ. Res. Lett., 5, 024001. application. Atmos. Chem. Phys., 10, 7017 7039. Loehle, C., and N. Scaffetta, 2011: Climate change attribution using empirical Landrum, L., B. L. Otto-Bliesner, E. R. Wahl, A. Conley, P. J. Lawrence, N. Rosenbloom, decomposition of climatic data. Open Atmos. Sci. J., 5, 74 86. and H. Teng, 2013: Last millennium climate and its variability in CCSM4. J. Clim., Lott, F. C., et al., 2013: Models versus radiosondes in the free atmosphere: A new 26, 1085 1111. detection and attribution analysis of temperature. J. Geophys. Res. Atmos., 118, Langen, P. L., and V. A. Alexeev, 2007: Polar amplification as a preferred response in 2609 2619. an idealized aquaplanet GCM. Clim. Dyn., 29, 305 317. Lu, J., G. A. Vecchi, and T. Reichler, 2007: Expansion of the Hadley cell under global Latif, M., et al., 2004: Reconstructing, monitoring, and predicting multidecadal- warming. Geophys. Res. Lett., 34, L06805. scale changes in the North Atlantic thermohaline circulation with sea surface Lu, J., G. Chen, and D. M. W. Frierson, 2008: Response of the zonal mean atmospheric temperature. J. Clim., 17, 1605 1614. circulation to El Nino versus global warming. J. Clim., 21, 5835 5851. 10 Laxon, S. W., et al., 2013: CryoSat-2 estimates of Arctic sea ice thickness and volume. Lu, J., C. Deser, and T. Reichler, 2009: Cause of the widening of the tropical belt since Geophys. Res. Lett., 40, 732 737. 1958. Geophys. Res. Lett., 36, L03803. Lean, J. L., 2006: Comment on Estimated solar contribution to the global surface Lucas, C., H. Nguyen, and B. Timbal, 2012: An observational analysis of Southern warming using the ACRIM TSI satellite composite by N. Scafetta and B. J. West. Hemisphere tropical expansion. J. Geophys. Res. Atmos., 117, D17112. Geophys. Res. Lett., 33, L15701. Lunt, D. J., A. M. Haywood, G. A. Schmidt, U. Salzmann, P. J. Valdes, and H. J. Dowsett, Lean, J. L., and D. H. Rind, 2008: How natural and anthropogenic influences alter 2010: Earth system sensitivity inferred from Pliocene modelling and data. global and regional surface temperatures: 1889 to 2006. Geophys. Res. Lett., Nature Geosci., 3, 60 64. 35, L18701. Luterbacher, J., D. Dietrich, E. Xoplaki, M. Grosjean, and H. Wanner, 2004: European Lean, J. L., and D. H. Rind, 2009: How will Earth s surface temperature change in seasonal and annual temperature variability, trend, and extremes since 1500. future decades? Geophys. Res. Lett., 36, L15708. Science, 303, 1499 1503. Leclercq, P. W., and J. Oerlemans, 2011: Global and hemispheric temperature Mahajan, S., R. Zhang, and T. L. Delworth, 2011: Impact of the Atlantic Meridional reconstruction from glacier length fluctuations. Clim. Dyn., 38, 1065 1079. Overturning Circulation (AMOC) on Arctic surface air temperature and sea ice Ledoit, O., and M. Wolf, 2004: A well-conditioned estimator for large-dimensional variability. J. Clim., 24, 6573 6581. covariance matrices. J. Multivar. Anal., 88, 365 411. Mahlstein, I., and R. Knutti, 2012 September Arctic sea ice predicted to disappear Legras, B., O. Mestre, E. Bard, and P. Yiou, 2010: A critical look at solar-climate for 2oC global warming above present. J. Geophys. Res. Atmos., 117, D06104. relationships from long temperature series. Clim. Past, 6, 745 758. Mahlstein, I., G. Hegerl, and S. Solomon, 2012: Emerging local warming signals in Leibensperger, E. M., et al., 2012: Climatic effects of 1950 2050 changes in US observational data. Geophys. Res. Lett., 39, L21711. anthropogenic aerosols Part 1: Aerosol trends and radiative forcing. Atmos. Mahlstein, I., R. Knutti, S. Solomon, and R. W. Portmann, 2011: Early onset of Chem. Phys., 12, 3333 3348. significant local warming in low latitude countries. Environ. Res. Lett., 6, 034009. Levitus, S., J. I. Antonov, T. P. Boyer, R. A. Locarnini, H. E. Garcia, and A. V. Mishonov, Manabe, S., and R. T. Wetherald, 1975: The effects of doubling the CO2 concentration 2009: Global ocean heat content 1955 2008 in light of recently revealed on the climate of a General Circulation Model. J. Atmos. Sci., 32, 3 15. instrumentation problems. Geophys. Res. Lett., 36, L07608. Mankoff, K. D., S. S. Jacobs, S. M. Tulaczyk, and S. E. Stammerjohn, 2012: The role of Lewis, N., 2013: An objective Bayesian, improved approach for applying optimal Pine Island Glacier ice shelf basal channels in deep water upwelling, polynyas fingerprint techniques to estimate climate sensitivity. J. Clim., doi:10.1175/JCLI- and ocean circulation in Pine Island Bay, Antarctica. Ann. Glaciol., 53, 123 128. D-12-00473.1. Mann, M. E., 2011: On long range temperature dependence in global surface Li, J. P., and J. L. X. L. Wang, 2003: A modified zonal index and its physical sense. temperature series. Clim. Change, 107, 267 276. Geophys. Res. Lett., 30, 1632. Mann, M. E., and K. A. Emanuel, 2006: Atlantic hurricane trends linked to climate Libardoni, A. G., and C. E. Forest, 2011: Sensitivity of distributions of climate system change. Eos Trans. Am. Geophys. Union, 87, 233 238. properties to the surface temperature dataset. Geophys. Res. Lett., 38, L22705. Mann, M. E., Z. H. Zhang, M. K. Hughes, R. S. Bradley, S. K. Miller, S. Rutherford, and Libardoni, A. G., and C. E. Forest, 2013: Correction to Sensitivity of distributions of F. B. Ni, 2008: Proxy-based reconstructions of hemispheric and global surface climate system properties to the surface temperature dataset . Geophys. Res. temperature variations over the past two millennia. Proc. Natl. Acad. Sci. U.S.A., Lett., doi:10.1002/grl.50480. 105, 13252 13257. Lin, B., et al., 2010a: Estimations of climate sensitivity based on top-of-atmosphere Mann, M. E., et al., 2009: Global signatures and dynamical origins of the Little Ice radiation imbalance. Atmos. Chem. Phys., 10, 1923 1930. age and medieval climate anomaly. Science, 326, 1256 1260. Lin, P., Q. A. Fu, S. Solomon, and J. M. Wallace, 2010b: Temperature trend patterns in Marcott, S. A., J. D. Shakun, P. U. Clark, and A. C. Mix, 2013: A reconstruction of Southern Hemisphere high latitudes: Novel indicators of stratospheric change J. regional and global temperature for the past 11,300 years. Science, 339, 1198 Clim., 22, 6325 6341. 1201 Lindsay, R. W., J. Zhang, A. Schweiger, M. Steele, and H. Stern, 2009: Arctic sea ice Marzeion, B., and A. Nesje, 2012: Spatial patterns of North Atlantic Oscillation retreat in 2007 follows thinning trend. J. Clim., 22, 165 176. influence on mass balance variability of European glaciers. Cryosphere, 6, Lindzen, R. S., and Y. S. Choi, 2009: On the determination of climate feedbacks from 661 673. ERBE data. Geophys. Res. Lett., 36, L16705. 946 Detection and Attribution of Climate Change: from Global to Regional Chapter 10 Maslanik, J. A., C. Fowler, J. Stroeve, S. Drobot, J. Zwally, D. Yi, and W. Emery, 2007: A Miller, R. L., G. A. Schmidt, and D. T. Shindell, 2006: Forced annular variations in younger, thinner Arctic ice cover: Increased potential for rapid, extensive sea-ice the 20th century intergovernmental panel on climate change fourth assessment loss. Geophys. Res. Lett., 34, L24501. report models. J. Geophys. Res. Atmos., 111, D18101. Maslowski, W., J. C. Kinney, M. Higgins, and A. Roberts, 2012: The future of Arctic sea Mills, T. C., 2009: How robust is the long-run relationship between temperature and ice. Annu. Rev. Earth Planet. Sci., 40, 625 654. radiative forcing? Clim. Change, 94, 351 361. Massey, N., T. Anna, R. C., F. E. L. Otto, S. Wilson, R. G. Jones, and M. R. Allen, Min, S.-K., and A. Hense, 2006: A Bayesian assessment of climate change using 2012: Have the odds of warm November temperatures and of cold December multimodel ensembles. Part I: Global mean surface temperature. J. Clim., 19, temperatures in central england changed? Bull. Am. Meteorol. Soc., 93, 1057 3237 3256. 1059. Min, S.-K., and A. Hense, 2007: A Bayesian assessment of climate change Mastrandrea, M. D., et al., 2011: Guidance note for lead authors of the IPCC Fifth using multimodel ensembles. Part II: Regional and seasonal mean surface Assessment Report on consistent treatment of uncertainties. Intergovernmental temperatures. J. Clim., 20, 2769 2790. Panel on Climate Change (IPCC), Geneva, Switzerland. Min, S.-K., and S.-W. Son, 2013: Multi-model attribution of the Southern Hemisphere Matthes, K., Y. Kuroda, K. Kodera, and U. Langematz, 2006: Transfer of the solar Hadley cell widening: Major role of ozone depletion. J. Geophys. Res. Atmos., signal from the stratosphere to the troposphere: Northern winter. J. Geophys. 118, 3007 3015. Res. Atmos., 111 D06108. Min, S.-K., X. B. Zhang, and F. Zwiers, 2008a: Human-induced arctic moistening. Matthews, H. D., N. P. Gillett, P. A. Stott, and K. Zickfeld, 2009: The proportionality of Science, 320, 518 520. global warming to cumulative carbon emissions. Nature, 459, 829 U3. Min, S.-K., X. B. Zhang, F. W. Zwiers, and T. Agnew, 2008b: Human influence on Arctic Mazzarella, A., and N. Scafetta, 2012: Evidences for a quasi 60-year North Atlantic sea ice detectable from early 1990s onwards. Geophys. Res. Lett., 35, L21701. Oscillation since 1700 and its meaning for global climate change. Theor. Appl. Min, S.-K., X. Zhang, F. W. Zwiers, and G. C. Hegerl, 2011: Human contribution to Climatol., 107, 599 609. more intense precipitation extremes. Nature, 470, 378 381. McCracken, K. G., and J. Beer, 2007: Long-term changes in the cosmic ray intensity at Min, S.-K., X. Zhang, F. W. Zwiers, P. Friederichs, and A. Hense, 2008c: Signal Earth, 1428 2005. J. Geophys. Res. Space Physics, 112, A10101. detectability in extreme precipitation changes assessed from twentieth century McKitrick, R., and L. Tole, 2012: Evaluating explanatory models of the spatial climate simulations. Clim. Dyn., 32, 95 111. pattern of surface climate trends using model selection and Bayesian averaging Min, S.-K., X. Zhang, F. Zwiers, H. Shiogama, Y.-S. Tung, and M. Wehner, 2013: Multi- methods. Clim. Dyn., 39, 2867 2882. model detection and attribution of extreme temperature changes. J. Clim., McKitrick, R., S. McIntyre, and C. Herman, 2010: Panel and multivariate methods for doi:10.1175/JCLI-D-12-00551.w. tests of trend equivalence in climate data series. Atmos. Sci. Lett., 11, 270 277. Misios, S., and H. Schmidt, 2012: Mechanisms involved in the amplification of the McLandress, C., J. Perlwitz, and T. G. Shepherd, 2012: Comment on Tropospheric 11-yr solar cycle signal in the Tropical Pacific ocean. J. Clim., 25, 5102 5118. temperature response to stratospheric ozone recovery in the 21st century by Mitchell, D. M., P. A. Stott, L. J. Gray, F. C. Lott, N. Butchart, S. C. Hardiman, and S. 10 Hu et al. , 2011. Atmos. Chem. Phys., 12, 2533 2540. M. Osprey, 2013: The impact of stratospheric resolution on the detectability of McLandress, C., T. G. Shepherd, J. F. Scinocca, D. A. Plummer, M. Sigmond, A. I. climate change signals in the free atmosphere. Geophys. Res. Lett., 40, 937 942. Jonsson, and M. C. Reader, 2011: Separating the dynamical effects of climate Miyazaki, C., and T. Yasunari, 2008: Dominant interannual and decadal variability of change and ozone depletion. Part II: Southern Hemisphere troposphere. J. Clim., winter surface air temperature over Asia and the surrounding oceans. J. Clim., 24, 1850 1868. 21, 1371 1386. Mecking, S., M. J. Warner, and J. L. Bullister, 2006: Temporal changes in pCFC-12 Moberg, A., D. M. Sonechkin, K. Holmgren, N. M. Datsenko, and W. Karlen, 2005: ages and AOU along two hydrographic sections in the eastern subtropical North Highly variable Northern Hemisphere temperatures reconstructed from low- and Pacific. Deep-Sea Res. Pt. I, 53, 169 187. high-resolution proxy data. Nature, 433, 613 617. Meehl, G. A., and J. M. Arblaster, 2009: A lagged warm event-like response to peaks Mölg, T., N. J. Cullen, D. R. Hardy, M. Winkler, and G. Kaser, 2009: Quantifying climate in solar forcing in the Pacific region. J. Clim., 22, 3647 3660. change in the tropical midtroposphere over East Africa from glacier shrinkage on Meehl, G. A., J. M. Arblaster, and C. Tebaldi, 2007a: Contributions of natural and Kilimanjaro. J. Clim., 22, 4162 4181. anthropogenic forcing to changes in temperature extremes over the U.S. Mölg, T., M. Großhauser, A. Hemp, M. Hofer, and B. Marzeion, 2012: Limited forcing Geophys. Res. Lett., 34, L19709. of glacier loss through land-cover change on Kilimanjaro. Nature Clim. Change, Meehl, G. A., J. M. Arblaster, G. Branstator, and H. van Loon, 2008: A coupled air-sea 2, 254 258. response mechanism to solar forcing in the Pacific region. J. Clim., 21 2883 Morak, S., G. C. Hegerl, and J. Kenyon, 2011: Detectable regional changes in the 2897. number of warm nights. Geophys. Res. Lett., 38, L17703. Meehl, G. A., W. M. Washington, T. M. L. Wigley, J. M. Arblaster, and A. Dai, 2003: Morak, S., G. C. Hegerl, and N. Christidis, 2013: Detectable changes in the frequency Solar and greenhouse gas forcing and climate response in the 20th century. J. of temperature extremes. J. Clim., 26, 1561 1574. Clim., 16 426 444. Morgenstern, O., et al., 2010: Anthropogenic forcing of the Northern Annular Mode Meehl, G. A., J. M. Arblaster, K. Matthes, F. Sassi, and H. van Loon, 2009: Amplifying in CCMVal-2 models. J. Geophys. Res. Atmos., 115, D00M03. the Pacific climate system response to a small 11-747year solar cycle forcing. Morice, C. P., J. J. Kennedy, N. A. Rayner, and P. D. Jones, 2012: Quantifying Science, 325 1114 1118. uncertainties in global and regional temperature change using an ensemble of Meehl, G. A., et al., 2007b: Global climate projections. In: Climate Change 2007: The observational estimates: The HadCRUT4 data set. J. Geophys. Res. Atmos., 117, Physical Science Basis. Contribution of Working Group I to the Fourth Assessment D08101. Report of the Intergovernmental Panel on Climate Change [Solomon, S., D. Qin, Murphy, D. M., and P. M. Forster, 2010: On the accuracy of deriving climate feedback M. Manning, Z. Chen, M. Marquis, K. B. Averyt, M. Tignor and H. L. Miller (eds.)] parameters from correlations between surface temperature and outgoing Cambridge University Press, Cambridge, United Kingdom and New York, NY, radiation. J. Clim., 23 4983 4988. USA, pp. 747 846. Murphy, D. M., S. Solomon, R. W. Portmann, K. H. Rosenlof, P. M. Forster, and T. Wong, Meinshausen, M., et al., 2009: Greenhouse-gas emission targets for limiting global 2009: An observationally based energy balance for the Earth since 1950. J. warming to 2 °C. Nature, 458, 1158 1162. Geophys. Res. Atmos., 114, D17107. Merrifield, M., and M. Maltrud, 2011: Regional sea level trends due to a Pacific trade Nagato, Y., and H. L. Tanaka, 2012: Global warming trend without the contribution wind intensification. Geophys. Res. Lett., 38, L21605. from decadal variability of the Arctic oscillation. Polar Sci., 6, 15 22. Meyssignac, B., D. Salas y Melia, M. Becker, W. Llovel, and A. Cazenave, 2012: Tropical Nakanowatari, T., K. I. Ohshima, and M. Wakatsuchi, 2007: Warming and oxygen Pacific spatial trend patterns in observed sea level: Internal variability and/or decrease of intermediate water in the northwestern North Pacific, originating anthropogenic signature? Clim. Past, 8, 787 802. from the Sea of Okhotsk, 1955 2004. Geophys. Res. Lett., 34, L04602. Miller, G. H., R. B. Alley, J. Brigham-Grette, J. J. Fitzpatrick, L. Polyak, M. C. Serreze, National Research Council, 2012: The Effects of Solar Variability on Earth s Climate: and J. W. C. White, 2010: Arctic amplification: Can the past constrain the future? A Workshop Report. The National Academies Press, Washington, DC, 70 pp. Quat. Sci. Rev., 29, 1779 1790. Nesje, A., O. Lie, and S. O. Dahl, 2000: Is the North Atlantic Oscillation reflected in Miller, G. H., et al., 2012: Abrupt onset of the Little Ice Age triggered by volcanism Scandinavian glacier mass balance records? J. Quat. Sci., 15, 587 601. and sustained by sea-ice/ocean feedbacks. Geophys. Res. Lett., 39, L02708. 947 Chapter 10 Detection and Attribution of Climate Change: from Global to Regional Nghiem, S. V., I. G. Rigor, D. K. Perovich, P. Clemente-Colon, J. W. Weatherly, and G. Palmer, M. D., S. A. Good, K. Haines, N. A. Rayner, and P. A. Stott, 2009: A new Neumann, 2007: Rapid reduction of Arctic perennial sea ice. Geophys. Res. Lett., perspective on warming of the global oceans. Geophys. Res. Lett., 36, L20709. 34, L19504. Penner, J. E., M. Wang, A. Kumar, L. Rotstayn, and B. Santer, 2007: Effect of black Nguyen, H., B. Timbal, A. Evans, C. Lucas, and I. Smith, 2013: The Hadley circulation in carbon on mid-troposphere and surface temperature trends. In: Human-Induced reanalyses: Climatology, variability and change. J. Clim., 26, 3357 3376. Climate Change: An Interdisciplinary Assessment [M. Schlesinger, et al. (ed.)], Noake, K., D. Polson, G. Hegerl, and X. Zhang, 2012: Changes in seasonal land Cambridge University Press, Cambridge, United Kingdom, and New York, NY, precipitation during the latter 20th Century. Geophys. Res. Lett., 39, L03706. USA, pp. 18 33. North, G. R., and M. J. Stevens, 1998: Detecting climate signals in the surface Peterson, T. C., P. A. Stott, and S. Herring, 2012: Explaining extreme events of 2011 temperature record. J. Clim., 11, 563 577. from a climate perspective. Bull. Am. Meteorol. Soc., 93, 1041 1067. Notz, D., and J. Marotzke, 2012: Observations reveal external driver for Arctic sea-ice Pierce, D. W., T. P. Barnett, B. D. Santer, and P. J. Gleckler, 2009: Selecting global retreat. Geophys. Res. Lett., 39, L08502. climate models for regional climate change studies. Proc. Natl. Acad. Sci. U.S.A., Nussbaumer, S. U., and H. J. Zumbühl, 2012: The Little Ice Age history of the Glacier 106, 8441 8446. des Bossons (Mont Blanc massif, France): A new high-resolution glacier length Pierce, D. W., P. J. Gleckler, T. P. Barnett, B. D. Santer, and P. J. Durack, 2012: The curve based on historical documents. Clim. Change, 111, 301 334. fingerprint of human-induced changes in the ocean s salinity and temperature O Gorman, P. A., and T. Schneider, 2008: Energy of midlatitude transient eddies in fields. Geophys. Res. Lett., 39, L21704. idealized simulations of changed climates. J. Clim., 21, 5797 5806. Pierce, D. W., T. P. Barnett, K. AchutaRao, P. Gleckler, J. Gregory, and W. Washington, O Gorman, P. A., 2010: Understanding the varied response of the extratropical storm 2006: Anthropogenic warming of the oceans: Observations and model results. J. tracks to climate change. Proc. Natl. Acad. Sci. U.S.A., 107, 19176 19180. Clim., 19, 1873 1900. Oerlemans, J., 2005: Extracting a climate signal from 169 glacier records. Science, Pierce, D. W., et al., 2008: Attribution of declining Western U.S. snowpack to human 308, 675 677. effects. J. Clim., 21, 6425 6444. Olson, R., R. Sriver, M. Goes, N. M. Urban, H. D. Matthews, M. Haran, and K. Keller, Pitman, A. J., et al., 2009: Uncertainties in climate responses to past land cover 2012: A climate sensitivity estimate using Bayesian fusion of instrumental change: First results from the LUCID intercomparison study. Geophys. Res. Lett., observations and an Earth System model. J. Geophys. Res. Atmos., 117, D04103. 36, L14814. Ono, T., T. Midorikawa, Y. W. Watanabe, K. Tadokoro, and T. Saino, 2001: Temporal Po-Chedley, S., and Q. Fu, 2012: Discrepancies in tropical upper tropospheric increases of phosphate and apparent oxygen utilization in the subsurface warming between atmospheric circulation models and satellites. Environ. Res. waters of western subarctic Pacific from 1968 to 1998. Geophys. Res. Lett., 28, Lett., 7, 044018. 3285 3288. Polson, D., G. C. Hegerl, X. Zhang, and T. J. Osborn, 2013: Causes of robust seasonal Otto-Bliesner, B. L., et al., 2009: A comparison of PMIP2 model simulations and land precipitation changes. J. Clim., doi:10.1175/JCLI-D-12-00474.1. 10 the MARGO proxy reconstruction for tropical sea surface temperatures at last Polvani, L. M., D. W. Waugh, G. J. P. Correa, and S. W. Son, 2011: Stratospheric ozone glacial maximum. Clim. Dyn., 32, 799 815. depletion: The main driver of twentieth-century atmospheric circulation changes Otto, A., et al., 2013: Energy budget constraints on climate response. Nature Geosci., in the southern hemisphere. J. Clim., 24, 795 812. 6, 415 416. Polyakov, I. V., J. E. Walsh, and R. Kwok, 2012: Recent changes of Arctic multiyear Otto, F. E. L., N. Massey, G. J. van Oldenborgh, R. G. Jones, and M. R. Allen, 2012: sea ice coverage and the likely causes. Bull. Am. Meteorol. Soc., 93, 145 151. Reconciling two approaches to attribution of the 2010 Russian heat wave. Polyakov, I. V., U. S. Bhatt, H. L. Simmons, D. Walsh, J. E. Walsh, and X. Zhang, 2005: Geophys. Res. Lett., 39, L04702. Multidecadal variability of North Atlantic temperature and salinity during the Overland, J. E., 2009: The case for global warming in the Arctic. In: Influence of twentieth century. J. Clim., 18, 4562 4581. Climate Change on the Changing Arctic and Sub-Arctic Conditions. NATO Polyakov, I. V., et al., 2003: Variability and trends of air temperature and pressure in Science for Peace and Security Series C: Environmental Security [J. C. J. Nihoul the maritime Arctic, 1875 2000. J. Clim., 16, 2067 2077. and A. G. Kostianoy (eds.)]. Springer Science+Business Media, Dordrecht, Pongratz, J., C. H. Reick, T. Raddatz, and M. Claussen, 2009: Effects of anthropogenic Netherlands, pp. 13 23. land cover change on the carbon cycle of the last millennium. Global Overland, J. E., and M. Wang, 2013: When will the summer arctic be nearly sea ice Biogeochem. Cycles, 23, GB4001. free? Geophys. Res. Lett., doi:10.1002/grl.50316. Pritchard, H. D., S. R. M. Ligtenberg, H. A. Fricker, D. G. Vaughan, M. R. van den Broeke, Overland, J. E., M. Wang, and S. Salo, 2008: The recent Arctic warm period. Tellus A, and L. Padman, 2012: Antarctic ice sheet loss driven by basal melting of ice 60, 589 597. shelves. Nature, 484, 502 505. Overland, J. E., K. R. Wood, and M. Wang, 2011: Warm Arctic-cold continents: Climate Pueyo, S., 2012: Solution to the paradox of climate sensitivity. Clim. Change, 113, impacts of the newley open Arctic sea. Polar Res., 30, 15787. 163 179 Overland, J. E., J. A. Francis, E. Hanna, and W. M., 2012: The recent shift in early Quadrelli, R., and J. M. Wallace, 2004: A simplified linear framework for interpreting summer Arctic atmospheric circulation. Geophys. Res. Lett., 39, L19804. patterns of Northern Hemisphere wintertime climate variability. J. Clim., 17, Oza, S. R., R. K. K. Singh, N. K. Vyas, and A. Sarkar, 2011a: Spatio-Temporal analysis of 3728 3744. melting onset dates of sea ice in the Arctic. Indian J. Geo-Mar. Sci., 40, 497 501. Rahmstorf, S., and D. Coumou, 2011: Increase of extreme events in a warming world. Oza, S. R., R. K. K. Singh, A. Srivastava, M. K. Dash, I. M. L. Das, and N. K. Vyas, 2011b: Proc. Natl. Acad. Sci. U.S.A., 108, 17905 17909. Inter-annual variations observed in spring and summer antarctic sea ice extent Ramaswamy, V., M. D. Schwarzkopf, W. J. Randel, B. D. Santer, B. J. Soden, and G. in recent decade. Mausam, 62, 633 640. L. Stenchikov, 2006: Anthropogenic and natural influences in the evolution of Padilla, L. E., G. K. Vallis, and C. W. Rowley, 2011: Probabilistic estimates of transient lower stratospheric cooling. Science, 311, 1138 1141. climate sensitivity subject to uncertainty in forcing and natural variability. J. Ramsay, H. A., and A. H. Sobel, 2011: The effects of relative and absolute sea surface Clim., 24, 5521 5537. temperature on tropical cyclone potential intensity using a single column model. Pagani, M., K. Caldeira, R. Berner, and D. J. Beerling, 2009: The role of terrestrial J. Clim., 24, 183 193. plants in limiting atmospheric CO2 decline over the past 24 million years. Nature, Randel, W. J., et al., 2009: An update of observed stratospheric temperature trends. J. 460, 85 88. Geophys. Res. Atmos., 114, D02107. PAGES 2k Consortium, 2013: Continental-scale temperature variability during the Ray, E. A., et al., 2010: Evidence for changes in stratospheric transport and mixing past two millennia. Nature Geosci., 6, 339 346. over the past three decades based on multiple data sets and tropical leaky pipe Palastanga, V., G. van der Schrier, S. L. Weber, T. Kleinen, K. R. Briffa, and T. J. Osborn, analysis. J. Geophys. Res. Atmos., 115, D21304. 2011: Atmosphere and ocean dynamics: Contributors to the Little Ice Age and Rea, W., M. Reale, and J. Brown, 2011: Long memory in temperature reconstructions. Medieval Climate Anomaly. Clim. Dyn., 36, 973 987. Clim. Change, 107, 247 265. Paleosens Members, 2012: Making sense of palaeoclimate sensitivity. Nature, 491, Reichert, B. K., L. Bengtsson, and J. Oerlemans, 2002: Recent glacier retreat exceeds 683 691. internal variability. J. Clim., 15, 3069 3081. Pall, P., et al., 2011: Anthropogenic greenhouse gas contribution to UK autumn flood Ribes, A., and L. Terray, 2013: Application of regularised optimal fingerprint analysis risk. Nature, 470, 382 385. for attribution. Part II: Application to global near-surface temperature Clim. Dyn., doi:10.1007/s00382-013-1736-6. 948 Detection and Attribution of Climate Change: from Global to Regional Chapter 10 Ribes, A., J. M. Azais, and S. Planton, 2009: Adaptation of the optimal fingerprint Schlesinger, M. E., and N. Ramankutty, 1994: An oscillation in the global climate method for climate change detection using a well-conditioned covariance system of period 65 70 years. Nature, 367, 723 726. matrix estimate. Clim. Dyn., 33, 707 722. Schmidt, G., et al., 2012: Climate forcing reconstructions for use in PMIP simulations Ribes, A., J. M. Azais, and S. Planton, 2010: A method for regional climate change of the last millennium (v1.1). Geosci. Model Dev., 5, 185 191. detection using smooth temporal patterns. Clim. Dyn., 35, 391 406. Schmidt, G. A., et al., 2011: Climate forcing reconstructions for use in PMIP Ribes, A., S. Planton, and L. Terray, 2013: Application of regularised optimal simulations of the last millennium (v1.0). Geosci. Model Dev., 4, 33 45. fingerprint for attribution. Part I: Method, properties and idealised analysis. Clim. Schmittner, A., et al., 2011: Climate sensitivity estimated from temperature Dyn., doi:10.1007/s00382-013-1735-7. reconstructions of the last glacial maximum. Science, 334, 1385 1388. Rind, D., J. Lean, J. Lerner, P. Lonergan, and A. Leboisitier, 2008: Exploring the Schmittner, A., et al., 2012: Response to comment on Climate sensitivity estimated stratospheric/troposheric response to solar forcing. J. Geophys. Res. Atmos., 113 from temperature reconstructions of the Last Glacial Maximum . Science, 337 D24103. 1294 Ring, M. J., D. Lindner, E. F. Cross, and M. E. Schlesinger, 2012: Causes of the global Schneider, T., and I. M. Held, 2001: Discriminants of twentieth-century changes in warming observed since the 19th century. atmospheric and climate sciences earth surface temperatures. J. Clim., 14 249 254. Atmos. Clim. Sci., 2, 401 415. Schneider von Deimling, T., H. Held, A. Ganopolski, and S. Rahmstorf, 2006: Climate Roe, G. H., and M. B. Baker, 2007: Why is climate sensitivity so unpredictable? sensitivity estimated from ensemble simulations of glacial climate. Clim. Dyn., Science, 318, 629 632. 27, 149 163. Roe, G. H., and M. A. O Neal, 2009: The response of glaciers to intrinsic climate Schnur, R., and K. I. Hasselmann, 2005: Optimal filtering for Bayesian detection and variability: Observations and models of late-Holocene variations in the Pacific attribution of climate change. Clim. Dyn., 24, 45 55. Northwest. J. Glaciol., 55, 839 854. Schubert, S., et al., 2009: A US CLIVAR project to assess and compare the responses Roemmich, D., and J. Gilson, 2009: The 2004 2008 mean and annual cycle of of global climate models to drought-related sst forcing patterns: Overview and temperature, salinity, and steric height in the global ocean from the Argo results. J. Clim., 22, 5251 5272. Program. Prog. Oceanogr., 82, 81 100. Schurer, A., G. Hegerl, M. E. Mann, S. F. B. Tett, and S. J. Phipps, 2013: Separating Rogelj, J., M. Meinshausen, and R. Knutti, 2012: Global warming under old and new forced from chaotic climate variability over the past millennium. J. Clim., scenarios using IPCC climate sensitivity range estimates. Nature Clim. Change, doi:10.1175/JCLI-D-12-00826.1. 2, 248 253. Schwartz, S. E., 2007: Heat capacity, time constant, and sensitivity of Earth s climate Rohde, R., et al., 2013: A new estimate of the average Earth surface land temperature system. J. Geophys. Res. Atmos., 112, D24S05. spanning 1753 to 2011. Geoinf. Geostat. Overview, 1, 1. Schwartz, S. E., 2012: Determination of Earth s transient and equilibrium climate Roscoe, H. K., and J. D. Haigh, 2007: Influences of ozone depletion, the solar cycle sensitivities from observations over the twentieth century: Strong dependence and the QBO on the Southern Annular Mode. Q. J. R. Meteorol. Soc., 133, 1855 on assumed forcing. Surv. Geophys., 33 745 777. 10 1864. Schwartz, S. E., R. J. Charlson, and H. Rodhe, 2007: Quantifying climate change too Roy, I., and J. D. Haigh, 2010: Solar cycle signals in sea level pressure and sea surface rosy a picture? Nature Rep. Clim. Change, doi:10.1038/climate.2007.22, 23 24. temperature. Atmos. Chem. Phys., 10 3147 3153. Schwartz, S. E., R. J. Charlson, R. A. Kahn, J. A. Ogren, and H. Rodhe, 2010: Why hasn t Roy, I., and J. D. Haigh, 2012: Solar cycle signals in the Pacific and the issue of Earth warmed as much as expected? J. Clim., 23, 2453 2464. timings. J. Atmos. Sci., 69 1446 1451. Schweiger, A., R. Lindsay, J. Zhang, M. Steele, H. Stern, and R. Kwok, 2011: Royer, D. L., 2008: Linkages between CO2, climate, and evolution in deep time. Proc. Uncertainty in modeled Arctic sea ice volume. J. Geophys. Res.J. Geophys. Res. Natl. Acad. Sci. U.S.A., 105, 407 408. Oceans, 116, C00D06. Royer, D. L., R. A. Berner, and J. Park, 2007: Climate sensitivity constrained by CO2 Screen, J. A., and I. Simmonds, 2010: Increasing fall-winter energy loss from the concentrations over the past 420 million years. Nature, 446, 530 532. Arctic Ocean and its role in Arctic temperature amplification. Geophys. Res. Lett., Rupp, D. E., P. W. Mote, N. L. Bindoff, P. A. Stott, and D. A. Robinson, 2013: Detection 37, L16707. and attribution of observed changes in Northern Hemisphere spring snow cover. Seager, R., N. Naik, and G. A. Vecchi, 2010: Thermodynamic and dynamic mechanisms J. Clim., doi:10.1175/JCLI-D-12-00563.1. for large-scale changes in the hydrological cycle in response to global warming. Rupp, D. E., P. W. Mote, N. Massey, J. R. Cameron, R. Jones, and M. R. Allen, 2012: Did J. Clim., 23, 4651 4668. human influence on climate make the 2011 Texas drought more probable? Bull. Seager, R., Y. Kushnir, C. Herweijer, N. Naik, and J. Velez, 2005: Modeling of tropical Am. Meteorol. Soc., 93, 1052 1054. forcing of persistent droughts and pluvials over western North America: 1856 Sanso, B., and C. Forest, 2009: Statistical calibration of climate system properties. J. 2000. J. Clim., 18, 4065 4088. R. Stat. Soc. C, 58, 485 503. Sedlacek, K., and R. Knutti, 2012: Evidence for external forcing on 20th-century Santer, B. D., W. Bruggemann, U. Cubasch, K. Hasselmann, H. Hock, E. Maierreimer, climate from combined ocean atmosphere warming patterns. Geophys. Res. and U. Mikolajewicz, 1994: Signal-to-noise analysis of time-dependent Lett., 39, L20708. greenhouse warming experiments: 1. Pattern-analysis. Clim. Dyn., 9, 267 285. Seidel, D. J., and W. J. Randel, 2007: Recent widening of the tropical belt: Evidence Santer, B. D., et al., 2009: Incorporating model quality information in climate change from tropopause observations. J. Geophys. Res. Atmos., 112, D20113. detection and attribution studies. Proc. Natl. Acad. Sci. U.S.A., 106 14778 14783. Seidel, D. J., Q. Fu, W. J. Randel, and T. J. Reichler, 2008: Widening of the tropical belt Santer, B. D., et al., 2007: Identification of human-induced changes in atmospheric in a changing climate. Nature Geosci., 1, 21 24. moisture content. Proc. Natl. Acad. Sci. U.S.A., 104, 15248 15253. Seidel, D. J., N. P. Gillett, J. R. Lanzante, K. P. Shine, and P. W. Thorne, 2011: Santer, B. D., et al., 2006: Forced and unforced ocean temperature changes in Stratospheric temperature trends: Our evolving understanding. WIREs Clim. Atlantic and Pacific tropical cyclogenesis regions. Proc. Natl. Acad. Sci. U.S.A., Change, 2, 592 616. 103, 13905 13910. Seidel, D. J., Y. Zhang, A. Beljaars, J.-C. Golaz, A. R. Jacobson, and B. Medeiros, 2012: Santer, B. D., et al., 2013: Identifying human influences on atmospheric temperature. Climatology of the planetary boundary layer over the continental United States Proc. Natl. Acad. Sci. U.S.A., 110, 26 33. and Europe. J. Geophys. Res. Atmos., 117, D17106. Sato, M., J. E. Hansen, M. P. McCormick, and J. B. Pollack, 1993: Stratospheric aerosol Semenov, V. A., 2008: Influence of oceanic inflow to the Barents Sea on climate optical depth, 1850 1990. J. Geophys. Res. Atmos., 98, 22987 22994. variability in the Arctic region. Doklady Earth Sci., 418, 91 94. Scafetta, N., and B. J. West, 2007: Phenomenological reconstructions of the solar Semmler, T., S. Varghese, R. McGrath, P. Nolan, S. L. Wang, P., and C. O Dowd, 2008: signature in the Northern Hemisphere surface temperature records since 1600. Regional climate model simulations of NorthAtlantic cyclones: Frequency and J. Geophys. Res. Atmos., 112, D24S03. intensity changes. Clim. Res, 36, 1 16. Scheff, J., and D. M. W. Frierson, 2012a: Robust future precipitation declines in CMIP5 Seneviratne, S. I., 2012: Historical drought trends revisited. Nature, 491, 338 339. largely reflect the poleward explansion of the model subtropical dry zones. Seneviratne, S. I., et al., 2010: Investigating soil moisture-climate interactions in a Geophys. Res. Lett., 39, L18704. changing climate: A review. Earth Sci. Rev., 99, 125 161. Scheff, J., and D. M. W. Frierson, 2012b: Twenty-first-century multimodel subtropical precipitation declines are mostly midlatitude shifts. J. Clim., 25, 4330 434. 949 Chapter 10 Detection and Attribution of Climate Change: from Global to Regional Seneviratne, S. I., et al., 2012: Changes in climate extremes and their impacts on the SPARC CCMVal, 2010: SPARC Report on the Evaluation of Chemistry-Climate natural physical environment. In: Managing the Risks of Extreme Events and Models. SPARC Report No. 5, WCRP-132, WMO/TD-No. 1526, [V. Eyring, T. G. Disasters to Advance Climate Change Adaptation. A Special Report of Working Shepherd and D. W. Waugh (eds.)]. Stratospheric Processes And their Role in Groups I and II of the Intergovernmental Panel on Climate Change (IPCC) [C. B. Climate. Available at: http://www.atmosp.physics.utoronto.ca/SPARC. Field et al. (eds.)]. Cambridge University Press, Cambridge, United Kingdom, and St Jacques, J. M., D. J. Sauchyn, and Y. Zhao, 2010: Northern Rocky Mountain New York, NY, USA, pp. 109 230. streamflow records: Global warming trends, human impacts or natural Serreze, M. C., and J. A. Francis, 2006: The arctic amplification debate. Clim. Change, variability? Geophys. Res. Lett., 37, L06407. 76, 241 264. Stahl, K., et al., 2010: Streamflow trends in Europe: Evidence from a dataset of near- Serreze, M. C., M. M. Holland, and J. Stroeve, 2007: Perspectives on the Arctic s natural catchments. Hydrol. Earth Syst. Sci., 14, 2367 2382. shrinking sea-ice cover. Science, 315, 1533 1536. Staten, P. W., J. J. Rutz, T. Reichler, and J. Lu, 2012: Breaking down the tropospheric Serreze, M. C., A. P. Barrett, J. C. Stroeve, D. N. Kindig, and M. M. Holland, 2009: The circulation response by forcing. Clim. Dyn., 39 2361 2375. emergence of surface-based Arctic amplification. Cryosphere, 3, 9. Steig, E. J., and A. J. Orsi, 2013: The heat is on in Antarctica. Nature Geosci., 6 87 88. Sexton, D. M. H., J. M. Murphy, M. Collins, and M. J. Webb, 2012: Multivariate Stenchikov, G., T. L. Delworth, V. Ramaswamy, R. J. Stouffer, A. Wittenberg, and F. probabilistic projections using imperfect climate models part I: Outline of Zeng, 2009: Volcanic signals in oceans. J. Geophys. Res. Atmos., 114, D16104. methodology. Clim. Dyn., 38, 2513 2542. Stephens, G. L., and Y. X. Hu, 2010: Are climate-related changes to the character of Sheffield, J., E. F. Wood, and M. Roderick, 2012: Little change in global drought over global-mean precipitation predictable? Environ. Res. Lett., 5, 025209. the past 60 years Nature, 491, 435 438. Stephens, G. L., et al., 2010: Dreary state of precipitation in global models. J. Geophys. Shindell, D., and G. Faluvegi, 2009: Climate response to regional radiative forcing Res. Atmos., 115, D24211. during the twentieth century. Nature Geosci., 2, 294 300. Stern, D. I., 2006: An atmosphere-ocean time series model of global climate change. Shindell, D., D. Rind, N. Balachandran, J. Lean, and J. Lonergan, 1999: Solar cycle Comput. Stat. Data Anal., 51, 1330 1346. variability, ozone, and climate. Science, 284, 305 308. Stone, D. A., and M. R. Allen, 2005: Attribution of global surface warming without Shindell, D. T., G. A. Schmidt, R. L. Miller, and D. Rind, 2001: Northern Hemisphere dynamical models. Geophys. Res. Lett., 32, L18711. winter climate response to greenhouse gas, ozone, solar, and volcanic forcing. J. Stott, P. A., and J. Kettleborough, 2002: Origins and estimates of uncertainty in Geophys. Res. Atmos., 106, 7193 7210. predictions of twenty-first century temperature rise Nature, 416, 723 726. Shine, K. P., J. S. Fuglestvedt, K. Hailemariam, and N. Stuber, 2005: Alternatives to Stott, P. A., and C. E. Forest, 2007: Ensemble climate predictions using climate the global warming potential for comparing climate impacts of emissions of models and observational constraints. Philos. Trans. R. Soc. A, 365, 2029 2052. greenhouse gases. Clim. Change, 68, 281 302. Stott, P. A., and G. S. Jones, 2009: Variability of high latitude amplification of Shiogama, H., T. Nagashima, T. Yokohata, S. A. Crooks, and T. Nozawa, 2006: Influence anthropogenic warming. Geophys. Res. Lett., 36, L10701. 10 of volcanic activity and changes in solar irradiance on surface air temperatures Stott, P. A., and G. S. Jones, 2012: Observed 21st century temperatures further in the early twentieth century. Geophys. Res. Lett., 33, L09702. constrain decadal predictions of future warming. Atmos. Sci. Lett., 13, 151 156. Shiogama, H., D. A. Stone, T. Nagashima, T. Nozawa, and S. Emori, 2012: On the linear Stott, P. A., D. A. Stone, and M. R. Allen, 2004: Human contribution to the European additivity of climate forcing-response relationships at global and continental heatwave of 2003. Nature, 432, 610 614. scales. Int. J. Climatol., doi:10.1002/joc.3607. Stott, P. A., R. T. Sutton, and D. M. Smith, 2008a: Detection and attribution of Atlantic Sigmond, M., and J. C. Fyfe, 2010: Has the ozone hole contributed to increased salinity changes. Geophys. Res. Lett., 35, L21702. Antarctic sea ice extent? Geophys. Rese. Lett., 37, L18502. Stott, P. A., C. Huntingford, C. D. Jones, and J. A. Kettleborough, 2008b: Observed Sigmond, M., M. C. Reader, J. C. Fyfe, and N. P. Gillett, 2011: Drivers of past and climate change constrains the likelihood of extreme future global warming. future Southern Ocean change: Stratospheric ozone versus greenhouse gas Tellus B, 60, 76 81. impacts. Geophys. Res. Lett., 38, L12601. Stott, P. A., G. S. Jones, N. Christidis, F. W. Zwiers, G. Hegerl, and H. Shiogama, Simmons, A. J., K. M. Willett, P. D. Jones, P. W. Thorne, and D. P. Dee, 2010: Low- 2011: Single-step attribution of increasing frequencies of very warm regional frequency variations in surface atmospheric humidity, temperature, and temperatures to human influence. Atmos. Sci. Lett., 12, 220 227. precipitation: Inferences from reanalyses and monthly gridded observational Stott, P. A., J. F. B. Mitchell, M. R. Allen, T. L. Delworth, J. M. Gregory, G. A. Meehl, and data sets. J. Geophys. Res. Atmos., 115, D01110 B. D. Santer, 2006: Observational constraints on past attributable warming and Skeie, R. B., T. K. Berntsen, G. Myhre, K. Tanaka, M. M. Kvalevag, and C. R. Hoyle, predictions of future global warming. J. Clim., 19, 3055 3069. 2011: Anthropogenic radiative forcing time series from pre-industrial times until Stott, P. A., N. P. Gillett, G. C. Hegerl, D. J. Karoly, D. A. Stone, X. Zhang, and F. Zwiers, 2010. Atmos. Chem. Phys., 11, 11827 11857. 2010: Detection and attribution of climate change: A regional perspective. Smirnov, D. A., and I. I. Mokhov, 2009: From Granger causality to long-term causality: WIREs Clim. Change, 1, 192 211. Application to climatic data. Phys. Rev. E, 80, 016208. Stott, P. A., et al., 2013: Attribution of weather and climate-related events. In: Climate Sokolov, A., C. Forest, and P. Stone, 2010: Sensitivity of climate change projections to Science for Serving Society: Research, Modelling and Prediction Priorities [G. uncertainties in the estimates of observed changes in deep-ocean heat content. R. Asrar and J. W. Hurrell (eds.)]. Springer Science+Business Media, Dordrecht, Clim. Dyn., 34, 735 745. Netherlands, 477 pp. Solomon, S., P. J. Young, and B. Hassler, 2012: Uncertainties in the evolution of Stramma, L., S. Schmidtko, L. Levin, and G. Johnson, 2010: Ocean oxygen minima stratospheric ozone and implications for recent temperature changes in the expansions and their biological impacts. Deep-Sea Res. Pt. I, 57, 587 595. tropical lower stratosphere. Geophys. Res. Lett., 39, L17706. Stroeve, J., et al., 2008: Arctic Sea ice extent plumments in 2007. Eos Trans. Am. Solomon, S., et al., 2007: Technical Summary. In: Climate Change 2007: The Physical Geophys. Union, 89, 13 14. Science Basis. Contribution of Working Group I to the Fourth Assessment Report Stroeve, J. C., M. C. Serreze, M. M. Holland, J. E. Kay, W. Meier, and A. P. Barrett, of the Intergovernmental Panel on Climate Change [Solomon, S., D. Qin, M. 2012a: The Arctic s rapidly shrinking sea ice cover: A research synthesis. Clim. Manning, Z. Chen, M. Marquis, K. B. Averyt, M. Tignor and H. L. Miller (eds.)] Change, 110 1005 1027. Cambridge University Press, Cambridge, United Kingdom and New York, NY, Stroeve, J. C., V. Kattsov, A. Barrett, M. Serreze, T. Pavlova, M. Holland, and W. N. Meier, USA, pp. 19 92. 2012b: Trends in Arctic sea ice extent from CMIP5, CMIP3 and observations. Son, S. W., N. F. Tandon, L. M. Polvani, and D. W. Waugh, 2009: Ozone hole and Geophys. Res. Lett., 39, L16502. Southern Hemisphere climate change. Geophys. Res. Lett., 36, L15705. Swanson, K. L., G. Sugihara, and A. A. Tsonis, 2009: Long-term natural variability and Son, S. W., et al., 2008: The impact of stratospheric ozone recovery on the Southern 20th century climate change. Proc. Natl. Acad. Sci. U.S.A., 106, 16120 16123. Hemisphere westerly jet. Science, 320, 1486 1489. Swart, N. C., and J. C. Fyfe, 2012: Observed and simulated changes in the Southern Son, S. W., et al., 2010: Impact of stratospheric ozone on Southern Hemisphere Hemisphere surface westerly wind-stress. Geophys. Res. Lett., 39, L16711. circulation change: A multimodel assessment. J. Geophys. Res. Atmos., 115, Tanaka, K., T. Raddatz, B. C. O Neill, and C. H. Reick, 2009: Insufficient forcing D00M07. uncertainty underestimates the risk of high climate sensitivity. Geophys. Res. Lett., 36 L16709. 950 Detection and Attribution of Climate Change: from Global to Regional Chapter 10 Tapiador, F. J., 2010: A joint estimate of the precipitation climate signal in Europe van der Schrier, G., P. D. Jones, and K. R. Briff, 2011: The sensitivity of the PDSI using eight regional models and five observational datasets. J. Clim., 23, 1719 to the Thornthwaite and Penman-Monteith parameterizations for potential 1738. evapotranspiration. J. Geophys. Res. Atmos., 116, D03106. Taylor, K. E., R. J. Stouffer, and G. A. Meehl, 2012: An overview of CMIP5 and the van Loon, H., and G. A. Meehl, 2008: The response in the Pacific to the sun s decadal experiment design. Bull. Am. Meteorol. Soc., 93, 485 498. peaks and contrasts to cold events in the Southern Oscillation. J. Atmos. Sol. Tedesco, M., J. E. Box, J. Cappellen, T. Mote, R. S. W. van der Wal, and J. Wahr, 2012: Terres. Phys., 70 1046 1055. Greenland ice sheet. In State of the Climate in 2011. Bull. Am. Meteorol. Soc., van Loon, H., and G. A. Meehl, 2012: The Indian summer monsoon during peaks in 93, S150 S153. the 11 year sunspot cycle. Geophys. Res. Lett., 39 L13701. Terray, L., 2012: Evidence for multiple drivers of North Atlantic multi decadal climate van Loon, H., G. A. Meehl, and D. J. Shea, 2007: Coupled air-sea response to solar variability. Geophys. Res. Lett., 39, L19712. forcing in the Pacific region during northern winter. J. Geophys. Res. Atmos., 112, Terray, L., L. Corre, S. Cravatte, T. Delcroix, G. Reverdin, and A. Ribes, 2012: Near- D02108. Surface salinity as nature s rain gauge to detect human influence on the tropical van Oldenborgh, G. J., A. van Urk, and M. Allen, 2012: The absence of a role of climate water cycle. J. Clim., 25, 958 977. change in the 2011 Thailand floods. Bull. Am. Meteorol. Soc., 93, 1047 1049. Tett, S. F. B., et al., 2007: The impact of natural and anthropogenic forcings on climate van Oldenborgh, G. J., F. J. Doblas Reyes, S. S. Drijfhout, and E. Hawkins, 2013: and hydrology since 1550. Clim. Dyn., 28, 3 34. Reliability of regional climate model trends. Environ. Res. Lett., 8, 014055. Thompson, D. W. J., and S. Solomon, 2002: Interpretation of recent Southern Vecchi, G. A., and B. J. Soden, 2007: Global warming and the weakening of the Hemisphere climate change. Science, 296, 895 899. tropical circulation. J. Clim., 20, 4316 4340. Thompson, D. W. J., and S. Solomon, 2009: Understanding recent stratospheric Vecchi, G. A., K. L. Swanson, and B. J. Soden, 2008: Whither hurricane activity. climate change. J. Clim., 22, 1934 1943. Science, 322, 687 689 Thompson, D. W. J., J. M. Wallace, P. D. Jones, and J. J. Kennedy, 2009: Identifying Veryard, H. G., 1963: A review of studies on climate fluctuations during the period of signatures of natural climate variability in time series of global-mean surface the meteorological. Changes of Climate: Proceedings of the Rome Symposium temperature: Methodology and insights. J. Clim., 22, 6120 6141. Organised by UNESCO and WMO, pp. 3 15. Thorne, P. W., and R. S. Vose, 2010: Reanalyses suitable for characterizing long-term Villarini, G., and G. A. Vecchi, 2012: Twenty-first-century projections of North Atlantic trends Bull. Am. Meteorol. Soc., 91, 353 361. tropical storms from CMIP5 models. Nature Clim. Change, 2, 604 607. Thorne, P. W., et al., 2011: A quantification of uncertainties in historical tropical Villarini, G., and G. A. Vecchi, 2013: Projected increases in North Atlantic tropical tropospheric temperature trends from radiosondes. J. Geophys. Res. Atmos., cyclone intensity from CMIP5 models. J. Clim., 26, 3231 3240. 116, D12116. Visser, H., and A. C. Petersen, 2012: Inference on weather extremes and weather Timmermann, A., S. McGregor, and F. F. Jin, 2010: Wind effects on past and future related disasters: A review of statistical methods. Clim. Past, 8 265 286. regional sea level trends in the southern Indo-Pacific. J. Clim., 23, 4429 4437. von Schuckmann, K., F. Gaillard, and P. Y. Le Traon, 2009: Global hydrographic 10 Timmreck, C., S. J. Lorenz, T. J. Crowley, S. Kinne, T. J. Raddatz, M. A. Thomas, and J. variability patterns during 2003 2008. J. Geophys. Res.J. Geophys. Res. Oceans, H. Jungclaus, 2009: Limited temperature response to the very large AD 1258 114, C09007. volcanic eruption. Geophys. Res. Lett., 36, L21708. Vorosmarty, C., L. Hinzman, and J. Pundsack, 2008: Introduction to special section on Ting, M. F., Y. Kushnir, R. Seager, and C. H. Li, 2009: Forced and internal twentieth- changes in the arctic freshwater system: Identification, attribution, and impacts century sst trends in the North Atlantic. J. Clim., 22 1469 1481. at local and global scales. J. Geophys. Res. Biogeosci., 113, G01S91. Tomassini, L., P. Reichert, R. Knutti, T. F. Stocker, and M. E. Borsuk, 2007: Robust Vuille, M., G. Kaser, and I. Juen, 2008: Glacier mass balance variability in the bayesian uncertainty analysis of climate system properties using Markov chain Cordillera Blanca, Peru and its relationship with climate and the large-scale Monte Carlo methods. J. Clim., 20, 1239 1254. circulation. Global Planet. Change, 62, 14 28. Trenberth, K., 2011a: Attribution of climate variations and trends to human Walker, R. T., T. K. Dupont, D. M. Holland, B. R. Parizek, and R. B. Alley, 2009: Initial influences and natural variability. WIREs Clim. Change, 2, 925 930. effects of oceanic warming on a coupled ocean-ice shelf-ice stream system. Trenberth, K., 2011b: Changes in precipitation with climate change. Clim. Research, Earth Planet. Sci. Lett., 287, 483 487. 47, 123 138. Wan, H., X. Zhang, F. W. Zwiers, and H. Shiogama, 2013: Effect of data coverage on Trenberth, K. E., and D. J. Shea, 2006: Atlantic hurricanes and natural variability in the estimation of mean and variability of precipitation at global and regional 2005. Geophys. Res. Lett., 33, L12704. scales. J. Geophys. Res. Atmos., 118, 534 546. Trenberth, K. E., and J. T. Fasullo, 2012: Climate extremes and climate change: The Wang, D. B., and M. Hejazi, 2011: Quantifying the relative contribution of the climate Russian heat wave and other climate extremes of 2010. J. Geophys. Res. Atmos., and direct human impacts on mean annual streamflow in the contiguous United 117, D17103. States. Water Resour. Res., 47, W00J12. Trenberth, K. E., J. T. Fasullo, C. O Dell, and T. Wong, 2010: Relationships between Wang, J., and X. Zhang, 2008: Downscaling and projection of winter extreme daily tropical sea surface temperature and top-of-atmosphere radiation. Geophys. precipitation over North America. J. Clim., 21, 923 937. Res. Lett., 37, L03702. Wang, J., et al., 2009a: Is the Dipole Anomaly a major driver to record lows in Arctic Tung, K.-K., and J. Zhou, 2010: The Pacific s response to surface heating in 130 yr of summer sea ice extent? Geophys. Res. Lett., 36, L05706. SST: La Nina-like or El Nino-like? J. Atmos. Sci., 67, 2649 2657. Wang, M., and J. E. Overland, 2012: A sea ice free summer Arctic within 30 years: An Tung, K.-K., and J. Zhou, 2013: Using data to attribute episodes of warming and update from CMIP5 models. Geophys. Res. Lett., 39, L18501. cooling in instrumental records. Proc. Natl. Acad. Sci. U.S.A., 110 2058 2063. Wang, M. Y., and J. E. Overland, 2009: A sea ice free summer Arctic within 30 years? Tung, K. K., J. S. Zhou, and C. D. Camp, 2008: Constraining model transient climate Geophys. Res. Lett., 36, L07502. response using independent observations of solar-cycle forcing and response. Wang, M. Y., J. E. Overland, V. Kattsov, J. E. Walsh, X. D. Zhang, and T. Pavlova, 2007: Geophys. Res. Lett., 35 L17707. Intrinsic versus forced variation in coupled climate model simulations over the Turner, J., T. J. Bracegirdle, T. Phillips, G. J. Marshall, and J. S. Hosking, 2013: An initial Arctic during the twentieth century. J. Clim., 20 1093 1107. assessment of Antarctic sea ice extent in the CMIP5 models. J. Clim., 26, 1473 Wang, X. L., V. R. Swail, F. W. Zwiers, X. Zhang, and Y. Feng, 2009b: Detection of 1484. external influence on trends of atmospheric storminess and northern oceans Turner, J., et al., 2005: Antarctic change during the last 50 years. Int. J. Climatol., 25, wave heights. Clim. Dyn., 32, 189 203. 1147 1148. Wassmann, P., C. M. Duarte, S. Agusti, and M. K. Sejr, 2011: Footprints of climate Turner, J., et al., 2009: Non-annular atmospheric circulation change induced by change in the Arctic marine ecosystem. Global Change Biol., 17, 1235 1249. stratospheric ozone depletion and its role in the recent increase of Antarctic sea Wen, Q. H., X. Zhang, Y. Xu, and B. Wang, 2013: Detecting human influence on ice extent. Geophys. Res. Lett., 36, L08502. extreme temperatures in China. Geophys. Res. Lett., 40, 1171 1176. Ulbrich, U., G. C. Leckebusch, and J. G. Pinto, 2009: Extra-tropical cyclones in the Wentz, F. J., L. Ricciardulli, K. Hilburn, and C. Mears, 2007: How much more rain will present and future climate: A review. Theor. Appl. Climatol., 96, 117 131. global warming bring? Science, 317 233 235. Urban, N. M., and K. Keller, 2009: Complementary observational constraints on Westra, S., L. V. Alexander, and F. W. Zwiers, 2013: Global increasing trends in annual climate sensitivity. Geophys. Res. Lett., 36, L04708. maximum daily precipitation. J. Clim., doi:10.1175/JCLI-D-12-00502.1. 951 Chapter 10 Detection and Attribution of Climate Change: from Global to Regional White, W. B., and Z. Y. Liu, 2008: Non-linear alignment of El Nino to the 11-yr solar Zhou, J., and K.-K. Tung, 2013a: Deducing multidecadal anthropogenic global cycle. Geophys. Res. Lett., 35, L19607. warming trends using multiple regression analysis. J. Atmos. Sci., 70, 3 8. Wigley, T. M. L., and B. D. Santer, 2013: A probabilistic quantification of the Zhou, J., and K.-K. Tung, 2013b: Observed tropospheric temperature response to anthropogenic component of twentieth century global warming. Clim. Dyn., 40, 11-yr solar cycle and what it reveals about mechanisms. J. Atmos. Sci., 70, 9 14. 1087 1102. Zhou, Y., and G. Ren, 2011: Change in extreme temperature event frequency over Wigley, T. M. L., C. M. Ammann, B. D. Santer, and K. E. Taylor, 2005: Comment on mainland China, 1961 2008. Climate Research, 50, 125 139. Climate forcing by the volcanic eruption of Mount Pinatubo by David H. Zickfeld, K., M. Eby, H. D. Matthews, and A. J. Weaver, 2009: Setting cumulative Douglass and Robert S. Knox. Geophys. Res. Lett., 32, L20709. emissions targets to reduce the risk of dangerous climate change. Proc. Natl. Wijffels, S., et al., 2008: Changing expendable bathythermograph fall rates and their Acad. Sci. U.S.A., 106, 16129 16134. impact on estimates of thermosteric sea level rise. J. Clim., 21, 5657 5672. Zickfeld, K., et al., 2013: Long-term climate change commitment and reversibility: An Wilcox, L. J., B. J. Hoskins, and K. P. Shine, 2012: A global blended tropopause based EMIC intercomparison. J. Clim., doi:10.1175/JCLI-D-12 00584.1. on ERA data. Part II: Trends and tropical broadening. Q. J. R. Meteorol. Soc., 138, Zorita, E., T. F. Stocker, and H. von Storch, 2008: How unusual is the recent series of 576 584. warm years? Geophys. Res. Lett., 35, L24706. Willett, K. M., N. P. Gillett, P. D. Jones, and P. W. Thorne, 2007: Attribution of observed Zunz, V., H. Goosse, and F. Massonnet, 2013: How does internal variability influence surface humidity changes to human influence. Nature, 449, 710 712 the ability of CMIP5 models to reproduce the recent trend in Southern Ocean sea Willett, K. M., P. D. Jones, N. P. Gillett, and P. W. Thorne, 2008: Recent changes in ice extent? Cryosphere, 7, 451 468. surface humidity: Development of the HadCRUH dataset. J. Clim., 21, 5364 Zwiers, F. W., X. Zhang, and Y. Feng, 2011: Anthropogenic influence on long return 5383. period daily temperature extremes at regional scales. J. Clim., 24, 881 892. Wilson, D., H. Hisdal, and D. Lawrence, 2010: Has streamflow changed in the Nordic countries? Recent trends and comparisons to hydrological projections. J. Hydrol., 394, 334 346. WMO (World Meteorological Organization), 2011: Scientific Assessment of Ozone Depletion: 2010. Global Ozone Research and Monitoring Project Report No. 52, World Meterological Organization, Geneva, Switzerland, 516 pp. Wong, A. P. S., N. L. Bindoff, and J. A. Church, 1999: Large-scale freshening of intermediate waters in the Pacific and Indian oceans. Nature, 400, 440 443. Wood, K. R., and J. E. Overland, 2010: Early 20th century Arctic warming in retrospect. Int. J. Climatol., 30, 1269 1279. 10 Woollings, T., 2008: Vertical structure of anthropogenic zonal-mean atmospheric circulation change. Geophys. Res. Lett., 35, L19702. Woollings, T., M. Lockwood, G. Masato, C. Bell, and L. J. Gray, 2010: Enhanced signatures of solar variability in Eurasian winter climate. Geophys. Res. Lett., 37 L20805. Wu, Q. G., and D. J. Karoly, 2007: Implications of changes in the atmospheric circulation on the detection of regional surface air temperature trends. Geophys. Res. Lett., 34, L08703. Wu, Z. H., N. E. Huang, J. M. Wallace, B. V. Smoliak, and X. Y. Chen, 2011: On the time-varying trend in global-mean surface temperature. Clim. Dyn., 37 759 773. Xie, S.-P., C. Deser, G. A. Vecchi, J. Ma, H. Teng, and A. T. Wittenberg, 2010: Global warming pattern formation: Sea surface temperature and rainfall. J. Clim., 23, 966 986. Yamaguchi, S., R. Naruse, and T. Shiraiwa, 2008: Climate reconstruction since the Little Ice Age by modelling Koryto glacier, Kamchatka Peninsula, Russia. J. Glaciol., 54, 125 130. Yang, X., J. C. Fyfe, and G. M. Flato, 2010: The role of poleward energy transport in Arctic temperature. Geophys. Res. Lett., 37, L14803. Yiou, P., R. Vautard, P. Naveau, and C. Cassou, 2007: Inconsistency between atmospheric dynamics and temperatures during the exceptional 2006/2007 fall/ winter and recent warming in Europe. Geophys. Res. Lett., 34, L21808. Yoshimori, M., and A. J. Broccoli, 2008: Equilibrium response of an atmosphere- mixed layer ocean model to different radiative forcing agents: Global and zonal mean response. J. Clim., 21, 4399 4423. Young, P. J., et al., 2012: Agreement in late twentieth century Southern Hemisphere stratospheric temperature trends in observations and CCMVal-2, CMIP3 and CMIP5 models. J. Geophys. Res. Atmos., 118 605 613. Zhang, J., R. Lindsay, A. Schweiger, and M. Steele, 2013: The impact of an intense summer cyclone on 2012 Arctic sea ice retreat. Geophys. Res. Lett., 40, 720 726. Zhang, J. L., 2007: Increasing Antarctic sea ice under warming atmospheric and oceanic conditions. J. Clim., 20, 2515 2529. Zhang, R., and T. L. Delworth, 2009: A new method for attributing climate variations over the Atlantic Hurricane Basin s main development region. Geophys. Res. Lett., 36, L06701. Zhang, R., et al., 2012: Have aerosols caused the observed Atlantic Multidecadal Variability? J. Atmos. Sci., 70, 1135 1144. Zhang, X. B., et al., 2007: Detection of human influence on twentieth-century precipitation trends. Nature, 448, 461 465. Zhang, X. D., A. Sorteberg, J. Zhang, R. Gerdes, and J. C. Comiso, 2008: Recent radical shifts of atmospheric circulations and rapid changes in Arctic climate system. Geophys. Res. Lett., 35, L22701. 952 11 Near-term Climate Change: Projections and Predictability Coordinating Lead Authors: Ben Kirtman (USA), Scott B. Power (Australia) Lead Authors: Akintayo John Adedoyin (Botswana), George J. Boer (Canada), Roxana Bojariu (Romania), Ines Camilloni (Argentina), Francisco Doblas-Reyes (Spain), Arlene M. Fiore (USA), Masahide Kimoto (Japan), Gerald Meehl (USA), Michael Prather (USA), Abdoulaye Sarr (Senegal), Christoph Schär (Switzerland), Rowan Sutton (UK), Geert Jan van Oldenborgh (Netherlands), Gabriel Vecchi (USA), Hui-Jun Wang (China) Contributing Authors: Nathaniel L. Bindoff (Australia), Philip Cameron-Smith (USA/New Zealand), Yoshimitsu Chikamoto (USA/Japan), Olivia Clifton (USA), Susanna Corti (Italy), Paul J. Durack (USA/ Australia), Thierry Fichefet (Belgium), Javier García-Serrano (Spain), Paul Ginoux (USA), Lesley Gray (UK), Virginie Guemas (Spain/France), Ed Hawkins (UK), Marika Holland (USA), Christopher Holmes (USA), Johnna Infanti (USA), Masayoshi Ishii (Japan), Daniel Jacob (USA), Jasmin John (USA), Zbigniew Klimont (Austria/Poland), Thomas Knutson (USA), Gerhard Krinner (France), David Lawrence (USA), Jian Lu (USA/Canada), Daniel Murphy (USA), Vaishali Naik (USA/India), Alan Robock (USA), Luis Rodrigues (Spain/Brazil), Jan Sedláèek (Switzerland), Andrew Slater (USA/Australia), Doug Smith (UK), David S. Stevenson (UK), Bart van den Hurk (Netherlands), Twan van Noije (Netherlands), Steve Vavrus (USA), Apostolos Voulgarakis (UK/Greece), Antje Weisheimer (UK/Germany), Oliver Wild (UK), Tim Woollings (UK), Paul Young (UK) Review Editors: Pascale Delecluse (France), Tim Palmer (UK), Theodore Shepherd (Canada), Francis Zwiers (Canada) This chapter should be cited as: Kirtman, B., S.B. Power, J.A. Adedoyin, G.J. Boer, R. Bojariu, I. Camilloni, F.J. Doblas-Reyes, A.M. Fiore, M. Kimoto, G.A. Meehl, M. Prather, A. Sarr, C. Schär, R. Sutton, G.J. van Oldenborgh, G. Vecchi and H.J. Wang, 2013: Near-term Climate Change: Projections and Predictability. In: Climate Change 2013: The Physical Science Basis. Contribution of Working Group I to the Fifth Assessment Report of the Intergovernmental Panel on Climate Change [Stocker, T.F., D. Qin, G.-K. Plattner, M. Tignor, S.K. Allen, J. Boschung, A. Nauels, Y. Xia, V. Bex and P.M. Midgley (eds.)]. Cambridge University Press, Cambridge, United Kingdom and New York, NY, USA. 953 Table of Contents Executive Summary...................................................................... 955 11.1 Introduction....................................................................... 958 Box 11.1: Climate Simulation, Projection, Predictability and Prediction...................................................................... 959 11.2 Near-term Predictions..................................................... 962 11.2.1 Introduction............................................................... 962 11.2.2 Climate Prediction on Decadal Time Scales................ 965 11.2.3 Prediction Quality...................................................... 966 11.3 Near-term Projections..................................................... 978 11.3.1 Introduction............................................................... 978 11.3.2 Near-term Projected Changes in the Atmosphere and Land Surface....................................................... 980 11.3.3 Near-term Projected Changes in the Ocean............... 993 11.3.4 Near-term Projected Changes in the Cryosphere........ 995 11.3.5 Projections for Atmospheric Composition and Air Quality to 2100.................................................... 996 11.3.6 Additional Uncertainties in Projections of Near-term Climate................................................... 1004 Box 11.2: Ability of Climate Models to Simulate Observed Regional Trends................................................ 1013 11 References ................................................................................ 1015 Frequently Asked Questions FAQ 11.1 If You Cannot Predict the Weather Next Month, How Can You Predict Climate for the Coming Decade?..................................................... 964 FAQ 11.2 How Do Volcanic Eruptions Affect Climate and Our Ability to Predict Climate?........................... 1008 954 Near-term Climate Change: Projections and Predictability Chapter 11 Executive Summary maximum technically feasible emission reductions (factors of 2). In the near term, the SRES Coupled Model Intercomparison Project Phase 3 This chapter assesses the scientific literature describing expectations (CMIP3) results, which did not incorporate current legislation on air for near-term climate (present through mid-century). Unless otherwise pollutants, include up to three times more anthropogenic aerosols stated, near-term change and the projected changes below are for the than RCP CMIP5 results (high confidence), and thus the CMIP5 global period 2016 2035 relative to the reference period 1986 2005. Atmos- mean temperatures may be up to 0.2°C warmer than if forced with pheric composition (apart from CO2; see Chapter 12) and air quality SRES aerosol scenarios (medium confidence). {10.3.1.1.3, Figure 10.4, projections through to 2100 are also assessed. 11.3.1.1, 11.3.5.1, 11.3.6.1, Figure 11.25, Tables AII.2.16 to AII.2.22 and AII.6.8} Decadal Prediction Including uncertainties for the chemically reactive GHG meth- The nonlinear and chaotic nature of the climate system imposes natu- ane gives a range in concentration that is 30% wider than the ral limits on the extent to which skilful predictions of climate statistics spread in RCP concentrations used in CMIP5 models (likely2). By may be made. Model-based predictability studies, which probe these 2100 this range extends 520 ppb above RCP8.5 and 230 ppb below limits and investigate the physical mechanisms involved, support the RCP2.6 (likely), reflecting uncertainties in emissions from agricultural, potential for the skilful prediction of annual to decadal average tem- forestry and land use sources, in atmospheric lifetimes, and in chemical perature and, to a lesser extent precipitation. feedbacks, but not in natural emissions. {11.3.5} Predictions for averages of temperature, over large regions of Emission reductions aimed at decreasing local air pollution the planet and for the global mean, exhibit positive skill when could have a near-term impact on climate (high confidence). verified against observations for forecast periods up to ten Short-lived air pollutants have opposing effects: cooling from sulphate years (high confidence1). Predictions of precipitation over some land and nitrate; warming from black carbon (BC) aerosol, carbon monox- areas also exhibit positive skill. Decadal prediction is a new endeavour ide (CO) and methane (CH4). Anthropogenic CH4 emission reductions in climate science. The level of quality for climate predictions of annual (25%) phased in by 2030 would decrease surface ozone and reduce to decadal average quantities is assessed from the past performance of warming averaged over 2036 2045 by about 0.2°C (medium confi- initialized predictions and non-initialized simulations. {11.2.3, Figures dence). Combined reductions of BC and co-emitted species (78%) 11.3 and 11.4} on top of methane reductions (24%) would further reduce warming (low confidence), but uncertainties increase. {Section 7.6, Chapter 8, In current results, observation-based initialization is the dominant con- 11.3.6.1, Figure 11.24a, 8.7.2.2.2, Table AII.7.5a} tributor to the skill of predictions of annual mean temperature for the first few years and to the skill of predictions of the global mean surface Projected Changes in Near-term Climate temperature and the temperature over the North Atlantic, regions of 11 the South Pacific and the tropical Indian Ocean for longer periods (high Projections of near-term climate show modest sensitivity to confidence). Beyond the first few years the skill for annual and multi- alternative RCP scenarios on global scales, but aerosols are an annual averages of temperature and precipitation is due mainly to the important source of uncertainty on both global and regional specified radiative forcing (high confidence). {Section 11.2.3, Figures scales. {11.3.1, 11.3.6.1} 11.3 to 11.5} Projected Changes in Near-term Temperature Projected Changes in Radiative Forcing of Climate The projected change in global mean surface air temperature For greenhouse gas (GHG) forcing, the new Representative Con- will likely be in the range 0.3 to 0.7°C (medium confidence). This centration Pathway (RCP) scenarios are similar in magnitude and projection is valid for the four RCP scenarios and assumes there will be range to the AR4 Special Report on Emission Scenarios (SRES) no major volcanic eruptions or secular changes in total solar irradiance scenarios in the near term, but for aerosol and ozone precursor before 2035. A future volcanic eruption similar to the 1991 eruption emissions the RCPs are much lower than SRES by factors of 1.2 of Mt Pinatubo would cause a rapid drop in global mean surface air to 3. For these emissions the spread across RCPs by 2030 is much nar- temperature of several tenths °C in the following year, with recovery rower than between scenarios that considered current legislation and over the next few years. Possible future changes in solar irradiance In this Report, the following summary terms are used to describe the available evidence: limited, medium, or robust; and for the degree of agreement: low, medium, or high. 1 A level of confidence is expressed using five qualifiers: very low, low, medium, high, and very high, and typeset in italics, e.g., medium confidence. For a given evidence and agreement statement, different confidence levels can be assigned, but increasing levels of evidence and degrees of agreement are correlated with increasing confidence (see Section 1.4 and Box TS.1 for more details). In this Report, the following terms have been used to indicate the assessed likelihood of an outcome or a result: Virtually certain 99 100% probability, Very likely 90 100%, 2 Likely 66 100%, About as likely as not 33 66%, Unlikely 0 33%, Very unlikely 0 10%, Exceptionally unlikely 0 1%. Additional terms (Extremely likely: 95 100%, More likely than not >50 10 0%, and Extremely unlikely 0 5%) may also be used when appropriate. Assessed likelihood is typeset in italics, e.g., very likely (see Section 1.4 and Box TS.1 for more details). 955 Chapter 11 Near-term Climate Change: Projections and Predictability could influence the rate at which global mean surface air temperature There is medium confidence in near-term projections of a north- increases, but there is high confidence that this influence will be small ward shift of Northern Hemisphere storm tracks and westerlies. in comparison to the influence of increasing concentrations of GHGs in {11.3.2} the atmosphere. {11.3.6, Figure 11.25} Projected Changes in the Ocean and Cryosphere It is more likely than not that the mean global mean surface air temperature for the period 2016 2035 will be more than It is very likely that globally averaged surface and vertically 1°C above the mean for 1850 1900, and very unlikely that it averaged ocean temperatures will increase in the near term. will be more than 1.5°C above the 1850 1900 mean (medium It is likely that there will be increases in salinity in the tropical and confidence). {11.3.6.3} (especially) subtropical Atlantic, and decreases in the western tropical Pacific over the next few decades. The Atlantic Meridional Overturning In the near term, differences in global mean surface air temper- Circulation is likely to decline by 2050 (medium confidence). However, ature change across RCP scenarios for a single climate model the rate and magnitude of weakening is very uncertain and, due to are typically smaller than differences between climate models large internal variability, there may be decades when increases occur. under a single RCP scenario. In 2030, the CMIP5 ensemble median {11.3.3} values differ by at most 0.2C between RCP scenarios, whereas the model spread (17 to 83% range) for each RCP is about 0.4C. The It is very likely that there will be further shrinking and thinning inter-scenario spread increases in time: by 2050 it is 0.8C, whereas of Arctic sea ice cover, and decreases of northern high-latitude the model spread for each scenario is only 0.6C. Regionally, the spring time snow cover and near surface permafrost (see glos- largest differences in surface air temperature between RCP2.6 and sary) as global mean surface temperature rises. For high GHG RCP8.5 are found in the Arctic. {11.3.2.1.1, 11.3.6.1, 11.3.6.3, Figure emissions such as those corresponding to RCP8.5, a nearly ice-free 11.24a,b, Table AII.7.5} Arctic Ocean (sea ice extent less than 1 × 106 km2 for at least 5 con- secutive years) in September is likely before mid-century (medium con- It is very likely that anthropogenic warming of surface air tem- fidence). This assessment is based on a subset of models that most perature will proceed more rapidly over land areas than over closely reproduce the climatological mean state and 1979 to 2012 oceans, and that anthropogenic warming over the Arctic in trend of Arctic sea ice cover. There is low confidence in projected near- winter will be greater than the global mean warming over the term decreases in the Antarctic sea ice extent and volume. {11.3.4} same period, consistent with the AR4. Relative to natural internal variability, near-term increases in seasonal mean and annual mean Projected Changes in Extremes temperatures are expected to be larger in the tropics and subtropics than in mid-latitudes (high confidence). {11.3.2, Figures 11.10 and In most land regions the frequency of warm days and warm 11 11.11} nights will likely increase in the next decades, while that of cold days and cold nights will decrease. Models project near-term Projected Changes in the Water Cycle and Atmospheric increases in the duration, intensity and spatial extent of heat waves Circulation and warm spells. These changes may proceed at a different rate than the mean warming. For example, several studies project that European Zonal mean precipitation will very likely increase in high and high-percentile summer temperatures warm faster than mean temper- some of the mid latitudes, and will more likely than not decrease atures. {11.3.2.5.1, Figures 11.17 and 11.18} in the subtropics. At more regional scales precipitation changes may be influenced by anthropogenic aerosol emissions and will be strongly The frequency and intensity of heavy precipitation events over influenced by natural internal variability. {11.3.2, Figures 11.12 and land will likely increase on average in the near term. However, 11.13} this trend will not be apparent in all regions because of natural vari- ability and possible influences of anthropogenic aerosols. {11.3.2.5.2, Increases in near-surface specific humidity over land are very Figures 11.17 and 11.18} likely. Increases in evaporation over land are likely in many regions. There is low confidence in projected changes in soil moisture There is low confidence in basin-scale projections of changes and surface run off. {11.3.2, Figure 11.14} in the intensity and frequency of tropical cyclones (TCs) in all basins to the mid-21st century. This low confidence reflects the It is likely that the descending branch of the Hadley Circulation small number of studies exploring near-term TC activity, the differences and the Southern Hemisphere (SH) mid-latitude westerlies will across published projections of TC activity, and the large role for nat- shift poleward. It is likely that in austral summer the projected recov- ural variability and non-GHG forcing of TC activity up to the mid-21st ery of stratospheric ozone and increases in GHG concentrations will century. There is low confidence in near-term projections for increased have counteracting impacts on the width of the Hadley Circulation and TC intensity in the North Atlantic, which is in part due to projected the meridional position of the SH storm track. Therefore, it is likely that reductions in North Atlantic aerosols loading. {11.3.2.5.3} in the near term the poleward expansion of the descending southern branch of the Hadley Circulation and the SH mid-­atitude westerlies in l austral summer will be less rapid than in recent decades. {11.3.2} 956 Near-term Climate Change: Projections and Predictability Chapter 11 Projected Changes in Air Quality  The range in projections of air quality (O3 and PM2.5 in near- surface air) is driven primarily by emissions (including CH4), rather than by physical climate change (medium confidence). The response of air quality to climate-driven changes is more uncertain than the response to emission-driven changes (high confidence). Globally, warming decreases background surface O3 (high confidence). High CH4 levels (RCP8.5, SRES A2) can offset this decrease, raising 2100 background surface O3 on average by about 8 ppb (25% of current levels) relative to scenarios with small CH4 chang- es (RCP4.5, RCP6.0) (high confidence). On a continental scale, pro- jected air pollution levels are lower under the new RCP scenarios than under the SRES scenarios because the SRES did not incorporate air quality legislation (high confidence). {11.3.5, 11.3.5.2; Figures 11.22 and 11.23ab, AII.4.2, AII.7.1 AII.7.4} Observational and modelling evidence indicates that, all else being equal, locally higher surface temperatures in polluted regions will trigger regional feedbacks in chemistry and local emissions that will increase peak levels of O3 and PM2.5 (medium confidence). Local emissions combined with background levels and with meteorological conditions conducive to the formation and accu- mulation of pollution are known to produce extreme pollution epi- sodes on local and regional scales. There is low confidence in project- ing changes in meteorological blocking associated with these extreme episodes. For PM2.5, climate change may alter natural aerosol sources (wildfires, wind-lofted dust, biogenic precursors) as well as precipi- tation scavenging, but no confidence level is attached to the overall impact of climate change on PM2.5 distributions. {11.3.5, 11.3.5.2, Box 14.2} 11 957 Chapter 11 Near-term Climate Change: Projections and Predictability 11.1 Introduction p ­ rovide valuable information on externally forced changes to near- ­ term climate, however, and are an important source of information This chapter describes current scientific expectations for near-term cli- that complements information from the predictions. Projections are mate. Here near term refers to the period from the present to mid-cen- also assessed in this chapter. tury, during which the climate response to different emissions scenar- ios is generally similar. Greatest emphasis in this chapter is given to The objectives of this chapter are to assess the state of the science con- the period 2016 2035, though some information on projected changes cerning both near-term predictions and near-term projections. CMIP5 before and after this period (up to mid-century) is also assessed. An results are considered for the near term as are other published near- assessment of the scientific literature relating to atmospheric compo- term predictions and projections. The chapter consists of four major sition (except carbon dioxide (CO2), which is addressed in Chapter 12) assessments: and air quality for the near-term and beyond to 2100 is also provided. 1. The scientific basis for near-term prediction as reflected in esti- This emphasis on near-term climate arises from (1) a recognition of mates of predictability (see Box 11.1), and the dynamical and its importance to decision makers in government and industry; (2) an physical mechanisms underpinning predictability, and the process- increase in the international research effort aimed at improving our es that limit predictability (see Section 11.2). understanding of near-term climate; and (3) a recognition that near- term projections are generally less sensitive to differences between 2. The current state of knowledge in near-term prediction (see Sec- future emissions scenarios than are long-term projections. Climate tion 11.2). Here the emphasis is placed on the results from the prediction on seasonal to multi-annual time scales require accurate decadal (10-year) multi-model prediction experiments in the estimates of the initial climate state with less dependence on chang- CMIP5 database. es in external forcing3 over the period. On longer time scales climate projections rely on projections of external forcing with little reliance on 3. The current state of knowledge in near-term projection (see Sec- the initial state of internal variability. Estimates of near-term climate tion 11.3). Here the emphasis is on what the climate in next few depend partly on the committed change (caused by the inertia of the decades may look like relative to 1986 2005, based on near-term oceans as they respond to historical external forcing), the time evo- projections (i.e., the forced climatic response). The focus is on the lution of internally generated climate variability and the future path core near-term period (2016 2035), but some information prior of external forcing. Near-term climate is sensitive to rapid changes in to this period and out to mid-century is also discussed. A key issue some short-lived climate forcing agents (Jacobson and Streets, 2009; is when, where and how the signal of externally forced climate Wigley et al., 2009; UNEP and WMO, 2011; Shindell et al., 2012b). change is expected to emerge from the background of natural cli- mate variability. The need for near-term climate information has spawned a new field of 11 climate science: decadal climate prediction (Smith et al., 2007; Meehl 4. Projected changes in atmospheric composition and air quality, and et al., 2009b, 2013d). The Coupled Model Intercomparison Project their interactions with climate change during the near term and Phase 5 (CMIP5) experimental protocol includes a sequence of near- beyond, including new findings from the Atmospheric Chemistry term predictions (1 to 10 years) where observation-based information and Climate Model Intercomparison (ACCMIP) initiative. is used to initialize the models used to produce the forecasts. The goal is to exploit the predictability of internally generated climate variability as well as that of the externally forced component. The result depends on the ability of current models to reproduce the observed variability as well as on the accurate depiction of the initial state (see Box 11.1). Skilful multi-annual to decadal climate predictions (in the technical sense of skilful as outlined in 11.2.3.2 and FAQ 11.1) are being pro- duced although technical challenges remain that need to be overcome in order to improve skill. These challenges are now being addressed by the scientific community. Climate change experiments with models that do not depend on initial condition but on the history and projection of climate forcings (often referred to as uninitialized or non-initialized projections or simply as projections ) are another component of CMIP5. Such projections have been the main focus of assessments of future climate in previ- ous IPCC assessments and are considered in Chapters 12 to 14. The main focus of attention in past assessments has been on the properties of projections for the late 21st century and beyond. Projections also 3 Seasonal-to-interannual predictions typically include the impact of external forcing. 958 Near-term Climate Change: Projections and Predictability Chapter 11 Box 11.1 | Climate Simulation, Projection, Predictability and Prediction This section outlines some of the ideas and the terminology used in this chapter. Internally generated and externally forced climate variability It is useful for purposes of analysis and description to consider the pre-industrial climate system as being in a state of climatic equilib- rium with a fixed atmospheric composition and an unchanging Sun. In this idealized state, naturally occurring processes and interac- tions within the climate system give rise to internally generated climate variability on many time scales (as discussed in Chapter 1). Variations in climate may also result due to features external to this idealized system. Forcing factors, such as volcanic eruptions, solar variations, anthropogenic changes in the composition of the atmosphere, land use change etc., give rise to externally forced climate variations. In this sense climate system variables such as annual mean temperatures (as in Box 11.1, Figure 1 for instance) may be characterized as a combination of externally forced and internally generated components with T(t) = Tf(t) + Ti(t). This separation of T, and other climate variables, into components is useful when analysing climate behaviour but does not, of course, mean that the climate system is linear or that externally forced and internally generated components do not interact. Climate simulation A climate simulation is a model-based representation of the temporal behaviour of the climate system under specified external forcing and boundary conditions. The result is the modelled response to the imposed external forcing combined with internally generated var- iability. The thin yellow lines in Box 11.1, Figure 1 represent an ensemble of climate simulations begun from pre-industrial conditions with imposed historical external forcing. The imposed external conditions are the same for each ensemble member and differences among the simulations reflect differences in the evolutions of the internally generated component. Simulations are not intended to be forecasts of the observed evolution of the system (the black line in Box 11.1, Figure 1) but to be possible evolutions that are consistent with the external forcings. In practice, and in Box 11.1, Figure 1, the forced component of the temperature variation is estimated by averaging over the different simulations of T(t) with Tf(t) the component that survives ensemble averaging (the red curve) while Ti(t) averages to near zero for a large enough ensemble. The spread among individual ensemble members (from these or pre-industrial simulations) and their behaviour with time provides some information on the statistics of the internally generated variability. (continued on next page) 1.0 Global mean temperature 11 Individual forecasts Temperature anomaly (°C) Forecast 0.5 start time Observed Individual simulations 0.0 Ensemble mean forecast 0.5 Ensemble mean simulation 1960 1970 1980 1990 2000 2010 Year Box 11.1, Figure 1 | The evolution of observation-based global mean temperature T (the black line) as the difference from the 1986 2005 average together with an ensemble of externally forced simulations to 2005 and projections based on the RCP4.5 scenario thereafter (the yellow lines). The model-based estimate of the externally forced component Tf (the red line) is the average over the ensemble of simulations. To the extent that the red line correctly estimates the forced component, the difference between the black and red lines is the internally generated component Ti for global mean temperature. An ensemble of forecasts of global annual mean temperature, initialized in 1998, is plotted as thin purple lines and their average, the ensemble mean forecast, as the thick green line. The grey areas along the axis indicate the presence of external forcing associated with volcanoes. 959 Chapter 11 Near-term Climate Change: Projections and Predictability Box 11.1 (continued) Climate projection A climate projection is a climate simulation that extends into the future based on a scenario of future external forcing. The simulations in Box 11.1, Figure 1 become climate projections for the period beyond 2005 where the results are based on the RCP4.5 forcing scenario (see Chapters 1 and 8 for a discussion of forcing scenarios). Climate prediction, climate forecast A climate prediction or climate forecast is a statement about the future evolution of some aspect of the climate system encompassing both forced and internally generated components. Climate predictions do not attempt to forecast the actual day-to-day progression of the system but instead the evolution of some climate statistic such as seasonal, annual or decadal averages or extremes, which may be for a particular location, or a regional or global average. Climate predictions are often made with models that are the same as, or similar to, those used to produce climate simulations and projections (assessed in Chapter 9). A climate prediction typically proceeds by integrating the governing equations forward in time from observation-based initial conditions. A decadal climate prediction com- bines aspects of both a forced and an initial condition problem as illustrated in Box 11.1, Figure 2. At short time scales the evolution is largely dominated by the initial state while at longer time scales the influence of the initial conditions decreases and the importance of the forcing increases as illustrated in Box 11.1, Figure 4. Climate predictions may also be made using statistical methods which relate current to future conditions using statistical relationships derived from past system behaviour. Because of the chaotic and nonlinear nature of the climate system small differences, in initial conditions or in the formulation of the forecast model, result in different evolutions of forecasts with time. This is illustrated in Box 11.1, Figure 1, which displays an ensemble of forecasts of global annual mean temperature (the thin purple lines) initiated in 1998. The individual forecasts are begun from slightly different initial conditions, which are observation-based estimates of the state of the climate system. The thick green line is the average of these forecasts and is an attempt to predict the most probable outcome and to maximize forecast skill. In this schematic example, the 1998 initial conditions for the forecasts are warmer than the average of the simulations. The individual and ensemble mean forecasts exhibit a decline in global temperature before beginning to rise again. In this case, initialization has resulted in more realistic values for the forecasts than for the corresponding simulation, at least for short lead times in the forecast. As the individual forecasts evolve they diverge from one another and begin to resemble the projection results. A probabilistic view of forecast behaviour is depicted schematically in Box 11.1, Figure 3. The probability distribution associated with 11 the climate simulation of temperature evolves in response to external forcing. By contrast, the probability distribution associated with a climate forecast has a sharply peaked initial distribution representing the comparatively small uncertainty in the observation-based initial state. The forecast probability distribution broadens with time until, ultimately, it becomes indistinguishable from that of an uninitialized climate projection. Climate predictability The term predictability , as used here, indicates the extent to which even minor imperfections in the knowledge of the current state or of the representation of the system limits knowledge of subsequent states. The rate of separation or divergence of initially close states of the climate system with time (as for the light purple lines in Box 11.1, Figure 1), or the rate of displacement and broadening of its (continued on next page) Box 11.1, Figure 2 | A schematic illustrating the progression from an initial-value based prediction at short time scales to the forced boundary-value problem of climate projection at long time scales. Decadal prediction occupies the middle ground between the two. (Based on Meehl et al., 2009b.) 960 Near-term Climate Change: Projections and Predictability Chapter 11 Box 11.1 (continued) probability distribution (as in Box 11.1, Figure 3) are indications of the system s predictability. If initially close states separate rapidly (or the probability dis- tribution broadens quickly towards the climatological distribution), the predictability of the system is low and vice versa. Formally, predictability in climate sci- ence is a feature of the physical system itself, rather than of our ability to make skilful predictions in prac- tice . The latter depends on the accuracy of models and initial conditions and on the correctness with which the external forcing can be treated over the forecast period. Forecast quality, forecast skill p[X  |  forcing] Forecast (or prediction) quality measures the success of a prediction against observation-based informa- p[X  |  forcing,initialization] tion. Forecasts made for past cases, termed retrospec- tive forecasts or hindcasts, may be analysed to give an indication of the quality that may be expected for t future forecasts for a particular variable at a particular location. Box 11.1, Figure 3 | A schematic representation of prediction in terms of probability. The probability distribution corresponding to a forced simulation is in red, with the deeper shades The relative importance of initial conditions and of indicating higher probability. The probabilistic forecast is in blue. The sharply peaked forecast distribution based on initial conditions broadens with time as the influence of the initial condi- external forcing for climate prediction, as depicted tions fades until the probability distribution of the initialized prediction approaches that of an schematically in Box 11.1, Figure 2, is further illustrat- uninitialized projection. (Based on Branstator and Teng, 2010.) ed in the example of Box 11.1, Figure 4 which plots correlation measures of both forecast skill and predictability for temperature averages over the globe ranging from a month to a decade. Initialized forecasts exhibit enhanced values compared to uninitialized simulations for shorter time averages but the advantage declines as averaging time increases and the forced component grows in importance. 11 1.0 Globally averaged correlation skill 0.9 0.8 0.7 0.6 0.5 0.4 0.3 0.2 0.1 Initialized Actual skill Unitialized Potential skill 1m 2m 3m 6m 1y 2y 3y 4y 6y 8y Time averaging Box 11.1, Figure 4 | An example of the relative importance of initial conditions and external forcing for climate prediction and predictability. The global average of the correlation skill score of ensemble mean initialized forecasts are plotted as solid orange lines and the corresponding model-based predictability measure as dashed orange lines. The green lines are the same quantities but for uninitialized climate simulations. Results are for temperature averaged over periods from a month to a decade. Values plotted for the monthly average correspond to the first month, those for the annual average to the first year and so on up to the decadal average. (Based on Boer et al., 2013.) 961 Chapter 11 Near-term Climate Change: Projections and Predictability 11.2 Near-term Predictions p ­ redictability of 19-year filtered Pacific SSTs in terms of low order EOFs and find predictability on these long time scales. 11.2.1 Introduction Hermanson and Sutton (2010) report that predictable signals in dif- 11.2.1.1 Predictability Studies ferent regions and for different variables may arise from differing ini- tial conditions and that ocean heat content is more predictable than The innate behaviour of the climate system imposes limits on the abil- atmospheric and surface variables. Branstator and Teng (2010) ana- ity to predict its evolution. Small differences in initial conditions, exter- lyse upper ocean temperatures, and some SSTs, for averages over the nal forcing and/or in the representation of the behaviour of the system North Atlantic, North Pacific and the tropical Atlantic and Pacific in the produce differences in results that limit useful prediction. Predictability National Center for Atmospheric Research (NCAR) model. Predictabil- studies estimate predictability limits for different variables and regions. ity associated with the initial state of the system decreases whereas that due to external forcing increases with time. The cross-over time, 11.2.1.2 Prognostic Predictability Studies when the two contributions are equal, is longer in extratropical (7 to 11 years) compared to tropical (2 years) regions and in the North Atlantic Prognostic predictability studies analyse the behaviour of models inte- compared to the North Pacific. Boer et al. (2013) estimate surface air grated forward in time from perturbed initial conditions. The study of (rather than upper ocean) temperature predictability in the Canadian Griffies and Bryan (1997) is one of the earliest studies of the predict- Centre for Climate Modelling and Analysis (CCCma) model and find ability of internally generated decadal variability in a coupled atmos- a cross-over time (using a different measure) on the order of 3 years phere ocean climate model. The study concentrates on the North when averaged over the globe. Atlantic and the subsurface ocean temperature while the subsequent studies of Boer (2000) and Collins (2002) deal mainly with surface 11.2.1.3 Diagnostic Predictability Studies t ­emperature. Long time scale temperature variability in the North Atlantic has received considerable attention together with its possible ­ Diagnostic predictability studies are based on analyses of the observed connection to the variability of the Atlantic Meridional Overturn- record or the output of climate models. Because long data records are ing Circulation (AMOC) in predictability studies by Collins and Sinha needed, diagnostic multi-annual to decadal predictability studies based (2003), Collins et al. (2006), Dunstone and Smith (2010), Dunstone et on observational data are comparatively few. Newman (2007) and al. (2011), Grotzner et al. (1999), Hawkins and Sutton (2009), Latif et al. Alexander et al. (2008) develop multivariate empirical Linear Inverse (2006, 2007), )Msadek et al. (2010), Persechino et al. (2012), Pohlmann Models (LIMs) from observation-based SSTs and find predictability for et al. (2004, 2013), Swingedouw et al. (2013), and Teng et al. (2011). ENSO and PDV type patterns that are generally limited to the order of The predictability of the AMOC varies among models and, to some a year although exceeding this in some areas. Zanna (2012) develops extent, with initial model states, ranging from several to 10 or more a LIM based on Atlantic SSTs and infers the possibility of decadal scale 11 years. The predictability values are model-based and the realism of the predictability. Hoerling et al. (2011) appeal to forced climate change simulated AMOC in the models cannot be easily judged in the absence relative to the 1971 2000 period together with the statistics of natural of a sufficiently long record of observation-based AMOC values. Many variability to infer the potential for the prediction of temperature over predictability studies are based on perturbations to surface quantities North America for 2011 2020. but Sevellec and A. Fedorov (2012) and Zanna (2012) note that small perturbations to deep ocean quantities may also affect upper ocean Tziperman et al. (2008) apply LIM-based methods to Geophysical Fluid values. The predictability of the North Atlantic sea surface temperature Dynamics Laboratory (GFDL) model output, as do Hawkins and Sutton (SST) is typically weaker than that of the AMOC and the connection (2009) and Hawkins et al. (2011) to Hadley Centre model output and between the predictability of the AMOC, and the SST is inconsistent find predictability up to a decade or more for the AMOC and North among models. Atlantic SST. Branstator et al. (2012) use analog and multivariate linear regression methods to quantify the predictability of the internally gen- Prognostic predictability studies of the Pacific are less plentiful erated component of upper ocean temperature in results from six cou- although Pacific Decadal Variability (PDV) mechanisms (including the pled models. Results differ considerably across models but offer some Pacific Decadal Oscillation (PDO) and the Inter-decadal Pacific Oscilla- areas of commonality. Basin-average estimates indicate predictability tion (IPO) have received considerable study (see Chapters 2 and 12). for up to a decade in the North Atlantic and somewhat less in the North Power and Colman (2006) find predictability on multi-year time scales Pacific. Branstator and Teng (2012) assess the predictability of both the in SST and on decadal time-scales in the sub-surface ocean temper- internally generated and forced component of upper ocean temperature ature in the off-equatorial South Pacific in their model. Power et al. in results from 12 coupled models participating in CMIP5. They infer (2006) find no evidence for the predictability of inter-decadal changes potential predictability from initializing the internally generated com- in the nature of El Nino-Southern Oscillation (ENSO) impacts on Aus- ponent for 5 years in the North Pacific and 9 years in the North Atlantic tralian rainfall. Sun and Wang (2006) suggest that some of the tem- while the forced component dominates after 6.5 and 8 years in the two perature variability linked to PDV can be predicted approximately 7 basins. Results vary among models, although with some agreement for years in advance. Teng et al. (2011) investigate the predictability of the internal component predictability in subpolar gyre regions. first two Empirical Orthogonal Functions (EOFs) of annual mean SST and upper ocean temperature identified with PDV and find predict- Studies of potential predictability take a number of forms but broad- ability of the order of 6 to 10 years. Meehl et al. (2010) consider the ly assume that overall variability may be separated into a long time 962 Near-term Climate Change: Projections and Predictability Chapter 11 scale component of interest and shorter time scale components that a number of approaches to estimating potential predictability each are unpredictable on these long time scales, written symbolically as with its statistical difficulties (e.g., DelSole and Feng, 2013). At mul- s2X = s2v + s2e. The fraction p = s2v / s2X is a measure of potentially ti-annual time scales the potential predictability of the internally gen- predictable variance provided that hypothesis that s2v is zero may be erated component of temperature is studied in Boer (2000), Collins rejected. Small p indicates either a lack of long time scale variability (2002), Pohlmann et al. (2004), Power and Colman (2006) and, in a or its smallness as a fraction of the total. Predictability is potential in multi-model context, in Boer (2004) and Boer and Lambert (2008). the sense that the existence of appreciable long time scale ­ ariability v Power and Colman (2006) report that potential predictability in the is not a direct indication that it may be skilfully predicted. There are ocean tends to increase with latitude and depth. Multi-model results 11 Figure 11.1 | The potential predictability of 5-year means of temperature (lower), the contribution from the forced component (middle) and from the internally generated compo- nent (upper). These are multi-model results from CMIP5 RCP4.5 scenario simulations from 17 coupled climate models following the methodology of Boer (2011). The results apply to the early 21st century. 963 Chapter 11 Near-term Climate Change: Projections and Predictability Frequently Asked Questions FAQ 11.1 | If You Cannot Predict the Weather Next Month, How Can You Predict Climate for the Coming Decade? Although weather and climate are intertwined, they are in fact different things. Weather is defined as the state of the atmosphere at a given time and place, and can change from hour to hour and day to day. Climate, on the other hand, generally refers to the statistics of weather conditions over a decade or more. An ability to predict future climate without the need to accurately predict weather is more commonplace that it might first seem. For example, at the end of spring, it can be accurately predicted that the average air temperature over the coming summer in Melbourne (for example) will very likely be higher than the average temperature during the most recent spring even though the day-to-day weather during the coming summer cannot be predicted with accuracy beyond a week or so. This simple example illustrates that factors exist in this case the seasonal cycle in solar radiation reaching the Southern Hemisphere that can underpin skill in predicting changes in climate over a coming period that does not depend on accuracy in predicting weather over the same period. The statistics of weather conditions used to define climate include long-term averages of air temperature and rainfall, as well as statistics of their variability, such as the standard deviation of year-to-year rainfall variability from the long-term average, or the frequency of days below 5°C. Averages of climate variables over long periods of time are called climatological averages. They can apply to individual months, seasons or the year as a whole. A climate prediction will address questions like: How likely will it be that the average temperature during the coming summer will be higher than the long-term average of past summers? or: How likely will it be that the next decade will be warmer than past decades? More specifically, a climate prediction might provide an answer to the question: What is the probability that temperature (in China, for instance) averaged over the next ten years will exceed the temperature in China averaged over the past 30 years? Climate predictions do not provide forecasts of the detailed day-to-day evolution of future weather. Instead, they provide probabilities of long-term changes to the statistics of future climatic variables. Weather forecasts, on the other hand, provide predictions of day-to-day weather for specific times in the future. They help to address questions like: Will it rain tomorrow? Sometimes, weather forecasts are given in terms of prob- abilities. For example, the weather forecast might state that: the likelihood of rainfall in Apia tomorrow is 75% . 11 To make accurate weather predictions, forecasters need highly detailed information about the current state of the atmosphere. The chaotic nature of the atmosphere means that even the tiniest error in the depiction of initial con- ditions typically leads to inaccurate forecasts beyond a week or so. This is the so-called butterfly effect . Climate scientists do not attempt or claim to predict the detailed future evolution of the weather over coming seasons, years or decades. There is, on the other hand, a sound scientific basis for supposing that aspects of climate can be predicted, albeit imprecisely, despite the butterfly effect. For example, increases in long-lived atmospheric greenhouse gas concentrations tend to increase surface temperature in future decades. Thus, information from the past can and does help predict future climate. Some types of naturally occurring so-called internal variability can in theory at least extend the capacity to predict future climate. Internal climatic variability arises from natural instabilities in the climate system. If such variability includes or causes extensive, long-lived, upper ocean temperature anomalies, this will drive changes in the overlying atmosphere, both locally and remotely. The El Nino-Southern Oscillation phenomenon is probably the most famous example of this kind of internal variability. Variability linked to the El Nino-Southern Oscillation unfolds in a partially predictable fashion. The butterfly effect is present, but it takes longer to strongly influence some of the variability linked to the El Nino-Southern Oscillation. Meteorological services and other agencies have exploited this. They have developed seasonal-to-interannual pre- diction systems that enable them to routinely predict seasonal climate anomalies with demonstrable predictive skill. The skill varies markedly from place to place and variable to variable. Skill tends to diminish the further the predic- tion delves into the future and in some locations there is no skill at all. Skill is used here in its technical sense: it is a measure of how much greater the accuracy of a prediction is, compared with the accuracy of some typically simple prediction method like assuming that recent anomalies will persist during the period being predicted. Weather, seasonal-to-interannual and decadal prediction systems are similar in many ways (e.g., they all incorpo- rate the same mathematical equations for the atmosphere, they all need to specify initial conditions to kick-start (continued on next page) 964 Near-term Climate Change: Projections and Predictability Chapter 11 FAQ 11.1 (continued) predictions, and they are all subject to limits on forecast accuracy imposed by the butterfly effect). However, decadal prediction, unlike weather and seasonal-to-interannual prediction, is still in its infancy. Decadal prediction systems nevertheless exhibit a degree of skill in hindcasting near-surface temperature over much of the globe out to at least nine years. A hindcast is a prediction of a past event in which only observations prior to the event are fed into the prediction system used to make the prediction. The bulk of this skill is thought to arise from external forcing. External forcing is a term used by climate scientists to refer to a forcing agent outside the climate system causing a change in the climate system. This includes increases in the concentration of long-lived greenhouse gases. Theory indicates that skill in predicting decadal precipitation should be less than the skill in predicting decadal sur- face temperature, and hindcast performance is consistent with this expectation. Current research is aimed at improving decadal prediction systems, and increasing the understanding of the reasons for any apparent skill. Ascertaining the degree to which the extra information from internal variability actually translates to increased skill is a key issue. While prediction systems are expected to improve over coming decades, the chaotic nature of the climate system and the resulting butterfly effect will always impose unavoidable limits on predictive skill. Other sources of uncertainty exist. For example, as volcanic eruptions can influence climate but their timing and magnitude cannot be predicted, future eruptions provide one of a number of other sources of uncertainty. Additionally, the shortness of the period with enough oceanic data to initialize and assess decadal predictions presents a major challenge. Finally, note that decadal prediction systems are designed to exploit both externally forced and internally generat- ed sources of predictability. Climate scientists distinguish between decadal predictions and decadal projections. Pro- jections exploit only the predictive capacity arising from external forcing. While previous IPCC Assessment Reports focussed exclusively on projections, this report also assesses decadal prediction research and its scientific basis. for both externally forced and internally generated components of the a few years to a decade, than is available from uninitialized climate potential predictability of decadal means of surface air temperature in simulations alone. Predictability results are, however, based mainly on 11 simulations of 21st century climate in CMIP3 model data are analysed climate model results and depend on the verisimilitude with which the in Boer (2011) and results based on CMIP5 model data are shown models reproduce climate system behaviour (Chapter 9). There is evi- in Figure 11.2. Potential predictability of 5-year means for internally dence of multi-year predictability for both the internally generated and generated variability is found over extratropical oceans but is generally externally forced components of temperature over considerable por- weak over land while that associated with the decadal change in the tions of the globe with the first dominating at shorter and the second forced component is found in tropical areas and over some land areas. at longer time scales. Predictability for precipitation is based on fewer studies, is more modest than for temperature, and appears to be asso- Predictability studies of precipitation on long time scales are com- ciated mainly with the forced component at longer time scales. Predict- paratively few. Jai and DelSole (2012) identify optimally predictable ability can also vary from location to location. fractions of internally generated temperature and precipitation vari- ance over land on multi-year time scales in the control simulations of 11.2.2 Climate Prediction on Decadal Time Scales 10 models participating in CMIP5, with results that vary considerably from model to model. Boer and Lambert (2008) find little potential 11.2.2.1 Initial Conditions predictability for decadal means of precipitation in the internally gen- erated variability of a collection of CMIP3 model control simulations A dynamical prediction consists of an ensemble of forecasts pro- other than over parts of the North Atlantic. This is also the case for the duced by integrating a climate model forward in time from a set of internally generated component of CMIP3 precipitation in 21st century observation-based initial conditions. As the forecast range increases, climate change simulations in Boer (2011) although there is evidence p ­ rocesses in the ocean become increasingly important and the sparse- of potential predictability for the forced component of precipitation ness, non-uniformity and secular change in sub-surface ocean obser- mainly at higher latitudes and for longer time scales. vations is a challenge to analysis and prediction (Meehl et al., 2009b, 2013d; Murphy et al., 2010) and can lead to differences among ocean 11.2.1.4 Summary analyses, that is, quantified descriptions of ocean initial conditions (Stammer, 2006; Keenlyside and Ba, 2010). Approaches to ocean ini- Predictability studies suggest that initialized climate forecasts should tialization include (as listed in Table 11.1): assimilation only of SSTs be able to provide more detailed information on climate evolution, over to initialize the sub-surface ocean indirectly (Keenlyside et al., 2008; 965 Chapter 11 Near-term Climate Change: Projections and Predictability Dunstone, 2010; Swingedouw et al., 2013); the forcing of the ocean and Miller, 2001). Both global and regional predictions of surface model with atmospheric observations (e.g., Du et al., 2012; Matei et al., temperature have been made based on projected changes in external 2012b; Yeager et al., 2012) and more sophisticated alternatives based forcing and the observed state of the natural variability at the start on fully coupled data assimilation schemes (e.g., Zhang et al., 2007a; date (Lean and Rind, 2009; Krueger and von Storch, 2011; Ho et al., Sugiura et al., 2009). 2012a; Newman, 2013). Some of these forecast systems are also used as benchmarks to compare with the dynamical systems under devel- Dunstone and Smith (2010) and Zhang et al. (2010a) found an expected opment. Comparisons (Newman (2013) have shown that there is simi- improvement in skill when sub-surface information was used as part of larity in the temperature skill between a linear inverse method and the the initialization. Assimilation of atmospheric data, on the other hand, CMIP5 hindcasts, pointing at a similarity in their sources of skill. In the is expected to have little impact after the first few months (Balmaseda ­ future, the combination of information from empirical and dynamical and Anderson, 2009). The initialization of sea ice, snow cover, frozen predictions might be explored to provide a unified and more skilful soil and soil moisture can potentially contribute to seasonal and sub- source of information. seasonal skill (e.g., Koster et al., 2010; Toyoda et al., 2011; Chevallier and Salas-Melia, 2012; Paolino et al., 2012), although an assessment of Evidence for skilful interannual to decadal temperatures using dynam- their benefit at longer time scales has not yet been determined. ical models forced only by previous and projected changes in anthro- pogenic greenhouse gases (GHGs) and aerosols and natural varia- 11.2.2.2 Ensemble Generation tions in volcanic aerosols and solar irradiance is reported by Lee et al. (2006b), Räisänen and Ruokolainen (2006) and Laepple et al. (2008). An ensemble can be generated in many different ways and a wide range Some attempts to predict the 10-year climate over regions have been of methods have been explored in seasonal prediction (e.g., Stockdale done using this approach, and include assessments of the role of the et al., 1998; Stan and Kirtman, 2008) but not yet fully investigated for internal decadal variability (Hoerling et al., 2011). To be clear, in the decadal prediction (Corti et al., 2012). Methods being investigated context of this report these studies are viewed as projections because include adding random perturbations to initial conditions, using atmos- no attempt is made to use observational estimates for the initial con- pheric states displaced in time, using parallel assimilation runs (Doblas- ditions. Essentially, an uninitialized prediction is synonymous with a Reyes et al., 2011; Du et al., 2012) and perturbing ocean initial condi- projection. These projections or uninitialized predictions are referred tions (Zhang et al., 2007a; Mochizuki et al., 2010). Perturbations leading to synonymously in the literature as NoInit, or NoAssim , referring to to rapidly growing modes, common in weather forecasting, have also the fact that no assimilated observations are used for the specification been investigated (Kleeman et al., 2003; Vikhliaev et al., 2007; Hawkins of the initial conditions. and Sutton, 2009, 2011; Du et al., 2012). The uncertainty associated with the limitations of a model s representation of the climate system Additional skill can be realized by initializing the models with obser- may be partially represented by perturbed physics (Stainforth et al., vations in order to predict the evolution of the internally generated 11 2005; Murphy et al., 2007) or stochastic physics (Berner et al., 2008), component and to correct the model s response to previously imposed and applied to multi-annual and decadal predictions (Doblas-Reyes et forcing (Smith et al., 2010; Fyfe et al., 2011; Kharin et al., 2012; Smith al., 2009; Smith et al., 2010). Weisheimer et al. (2011) compare these et al., 2012). Again, to be clear, the assessment provided here distin- three approaches in a seasonal prediction context. guishes between predictions in which attempts are made to initialize the models with observations, and projections. See Box 11.1 and FAQ The multi-model approach, which is used widely and most common- 11.1 for further details. ly, combines ensembles of predictions from a collection of models, thereby increasing the sampling of both initial conditions and model The ENSEMBLES project (van Oldenborgh et al., 2012), for example, properties. Multi-model approaches are used across time scales rang- has conducted a multi-model decadal retrospective prediction study, ing from seasonal interannual (e.g., DEMETER; Palmer et al. (2004), and the Coupled Model Intercomparison Project phase 5 (CMIP5) pro- to seasonal-decadal (e.g., Weisheimer et al., 2011; van Oldenborgh et posed a coordinated experiment that focuses on decadal, or near-term, al., 2012), in climate change simulation (e.g., IPCC, 2007, Chapter 10; climate prediction (Meehl et al., 2009b; Taylor et al., 2012). Prior to Meehl et al., 2007b) and in the ENSEMBLES and CMIP5-based decadal these initiatives, several pioneering attempts at initialized decadal pre- predictions assessed in Section 11.2.3. A problem with the multi-model diction were made (Pierce et al., 2004; Smith et al., 2007; Troccoli and approach is tha inter-dependence of the climate models used in current Palmer, 2007; Keenlyside et al., 2008; Pohlmann et al., 2009; Mochizuki forecast systems (Power et al. 2012; Knutti et al. 2013) is expected to et al., 2010). Results from the CMIP5 coordinated experiment (Taylor et lead to co-dependence of forecast error. al., 2012) are the basis for the assessment reported here. 11.2.3 Prediction Quality Because the practice of decadal prediction is in its infancy, details of how to initialize the models included in the CMIP5 near-term exper- 11.2.3.1 Decadal Prediction Experiments iment were left to the discretion of the modelling groups and are described in Meehl et al. (2013d) and Table 11.1. In CMIP5 experi- Decadal predictions for specific variables can be made by exploiting ments, volcanic aerosol and solar cycle variability are prescribed empirical relationships based on past observations and expected phys- along the integration using observation-based values up to 2005, and ical relationships. Predictions of North Pacific Ocean temperatures assuming a climatological 11-year solar cycle and a background vol- have been achieved using prior wind stress observations (Schneider canic aerosol load in the future. These forcings are shared with CMIP5 966 Near-term Climate Change: Projections and Predictability Chapter 11 historical runs (i.e., unintialized projections) started from pre-industrial When initialized with states close to the observations, models drift control simulations, enabling an assessment of the impact of initial- towards their imperfect climatology (an estimate of the mean climate), ization. The specification of the volcanic aerosol load and the solar leading to biases in the simulations that depend on the forecast time. irradiance in the hindcasts gives an optimistic estimate of the forecast The time scale of the drift in the atmosphere and upper ocean is, in quality with respect to an operational prediction system, where no most cases, a few years (Hazeleger et al., 2013a). Biases can be largely such future information can be used. Table 11.1 summarizes forecast removed using empirical techniques a posteriori (Garcia-Serrano and systems contributing to, and the initialization methods used in, the Doblas-Reyes, 2012; Kharin et al., 2012). The bias correction or adjust- CMIP5 near-term experiment. ment linearly corrects for model drift (e.g., Stockdale, 1997; Garcia-Ser- rano et al., 2012; Gangst et al., 2013). The approach assumes that the The coordinated nature of the ENSEMBLES and CMIP5 experiments model bias is stable over the prediction period (from 1960 onward in also offers a good opportunity to study multi-model ensembles (Gar- the CMIP5 experiment). This might not be the case if, for instance, the cia-Serrano and Doblas-Reyes, 2012; van Oldenborgh et al., 2012) as predicted temperature trend differs from the observed trend (Fyfe et a means of sampling model uncertainty while some modelling groups al., 2011; Kharin et al., 2012). Figure 11.2 is an illustration of the time have also investigated this using perturbed parameter approaches scale of the global SST drift, while at the same time showing the sys- (Smith et al., 2010). The relative merit of the different approaches for tematic error of several of the forecast systems contributing to CMIP5. decadal predictions has yet to be assessed. It is important to note that the systematic errors illustrated here are SST 60S-60N CMIP5 Init / rm=12months 21 20 (C) 19 11 MRI-CGCM3 HadCM3 CNRM-CM5 CanCM4 ERSST MIROC4h EC-Earth2.3 IPSL-CM5 MPI-M HadISST 18 MIROC5 CMCC-CM GFDL-CM2 1960 1970 1980 1990 2000 2010 0.5 Anomaly (°C) 0.0 -0.5 MRI-CGCM3 HadCM3 CNRM-CM5 CanCM4 ERSST MIROC4h EC-Earth2.3 IPSL-CM5 MPI-M HadISST MIROC5 CMCC-CM GFDL-CM2 1960 1970 1980 1990 2000 2010 Figure 11.2 | Time series of global mean sea surface temperature from the (a) direct model output and (b) anomalies of the CMIP5 multi-model initialized hindcasts. Results for each forecast system are plotted with a different colour, with each line representing an individual member of the ensemble. Results for the start dates 1961, 1971, 1981, 1991 and 2001 are shown, while the model and observed climatologies to obtain the anomalies in (b) have been estimated using data from start dates every five years. The reference data (ERSST) is drawn in black. All time series have been smoothed with a 24-month centred moving average that filters out the seasonal cycle and removes data for the first and last years of each time series. 967 11 Table 11.1 | Initialization methods used in models that entered CMIP5 near-term experiments. (Figures 11.3 to 11.7 have been prepared using those contributions with asterisk on top of the modelling centre s name.). 968 CMIP5 Near- Initialization Perturbation Aerosol term Players CMIP5 Chapter 11 official AGCM OGCM Reference Direct(D)/ Name of modeling model id Anomaly Concentration Atmosphere/Land Ocean Sea Ice Atmosphere Ocean Indirect centre (or group) Assimilation? (C) /Emission (E) (I1,I2) (*) Beijing Climate Center, China Meteoro- BCC-CSM Perturbed atmosphere/ Xin et al. 2.8°L26 1°L40 No SST, T&S (SODA) No No C D logical Administration 1.1 ocean/land/sea ice (2013) (BCC) China (*) Canadian Centre for Climate Model- 1.4° × SST (ERSST&OISST), Merryfield et CanCM4 2.8°L35 ERA40/Interim HadISST1.1 No Ensemble assimilation E D, I1 ling and Analysis 0.9°L40 T&S (SODA & GODAS) al. (2013) (CCCMA) Canada (*) Centro Euro- Mediterraneo per I 0.5 ° 2° SST, T&S (INGV CMCC-CM Bellucci et CMCC-CM 0.75°L31 No No Ensemble assimilation C D, I1 Cambiamenti Climatici L31 ocean analysis) climatology al. (2013) (CMCC-CM) Italy (*) Centre National de Recherches Metéoro- logiques, and Centre 1st day T&S (NEMOVAR- Meehl et al. Européen de Recherche CNRM-CM5 1.4°L31 1°L42 No No No atmospheric No C D, I1 COMBINE) (2013d) et Formation Avancées conditions en Calcul Scientifique (CNRM-CERFACS) France National Centers for NCEP CFSR Environmental ocean analysis Prediction and Center 0.25 (NCEP runs) NCEP CFSR Saha et al. CFSv2-2011 0.9°L64 NCEP CFSR reanalysis No No No C D, I1 for Ocean-Land- 0.5°L40 reanalysis (2010) Atmosphere Studies (NCEP and COLA) USA NEMOVAR-S4 ocean analysis (COLA runs) Ensemble (*) EC-EARTH consor- NEMO3.2- ocean Du et al. (2012) Ocean assimilation No (KNMI & IC3) Start dates and tium (EC-EARTH) EC-EARTH 1.1°L62 1°L42 ERA40/Interim LIM2 forced assim C D Hazeleger et (ORAS4/NEMOVAR S4) yes (SMHI) singular vectors Europe with DFS4.3 (NEM- al. (2013a) OVAR) White (*) Institut Pierre-Simon IPSL- 1.9 × SST anomalies (Reyn- Swingedouw 2°L31 No No Yes No noise C D, I1 Laplace (IPSL) France CM5A-LR 3.8o L39 olds observations) et al. (2013) on SST (*) AORI/NIES/JAMSTEC, MIROC4h 0.6°L56 0.3°L48 SST, T&S (Ishii and Start dates and ensemble Tatebe et No No Yes E D,I1,I2 Japan Kimoto, 2009) assimilation al. (2012) MIROC5 1.4°L40 1.4°L50 (*) Met Office Hadley ERA40/ECMWF SST, T&S (Smith and SST per- Smith et al. HadCM3 3.8°L19 1.3°L20 HADISST Yes, also full field No E D Centre (MOHC) UK operational analysis Murphy, 2007) turbation (2013a) (continued on next page) Near-term Climate Change: Projections and Predictability (Table 11.1 continued) CMIP5 Near- Initialization Perturbation Aerosol term Players CMIP5 Official AGCM OGCM Reference Direct(D)/ Name of Modeling Model ID Anomaly Concentration Atmosphere/Land Ocean Sea Ice Atmosphere Ocean Indirect Centre (or group) Assimilation? (C) /Emission (E) (I1,I2) MPI-ESM (*) Max Planck Institute 1.9°L47 1.5°L40 -LR Matei et al. for Meteorology No T&S from forced OGCM No Yes 1-day lagged C D (2012b) (MPI-M) Germany MPI-ESM 1.9°L95 0.4°L40 -MR (*) Meteorological SST, T&S (Ishii and Start dates and ensemble Tatebe et Research Institute MRI-CGCM3 1.1°L48 1°L51 No No Yes E D,I1,I2 Kimoto, 2009) assimilation al. (2012) (MRI) Japan Global Modeling 2.5 °×2o T&S from ocean assimi- GEOS iODAS and Assimilation GEOS-5 1°L50 MERRA No Two-sided breeding method E D L72 lation (GEOS iODAS) reanalysis Office, (NASA) USA Ice state Ensemble Ocean assimila- Single atm from from forced assimila- tion (POPDART) AMIP run (*) National Center ocean-ice tion Near-term Climate Change: Projections and Predictability for Atmospheric CCSM4 1.3°L26 1.0°L60 No GCM (strong No E D Research (NCAR) USA salinity Staggered atm Single Ocean state from Yeager et restoring for start dates from member forced ocean-ice GCM al. (2012) POPDART) uninitialized run ocean GFDL-CM (*) Geophysical Fluid Ocean observations Yang et al. Dynamics Labora- 2.5°L24 1°L50 NCEP reanalysis No No Coupled EnKF C D tory (GFDL) USA of 3-D T & S & SST (2013) 2.1 LASG, Institute of Atmo- spheric Physics, Chinese SST, T&S (Ishii Wang et al. Academy of Sciences; FGOALS-g2 2.8°L26 1°L30 No No No A simplified scheme of 3DVar C D, I1 et al., 2006) (2013) and CESS, Tsinghua University China LASG, Institute of Atmo- spheric Physics, Chinese Incremental Analysis Wu and Zhou Academy of Sciences FGOALS-s2 2.8°L26 1°L30 No T&S (EN3_v2a) No Yes C D Updates (IAU) scheme (2012) China, Tsinghua University China Chapter 11 969 11 Chapter 11 Near-term Climate Change: Projections and Predictability common to both decadal prediction systems and climate-change posteriori corrections to model spread. Forecast quality can also be projections. The bias adjustment itself is another important source of improved by unequal weighting (Weigel et al., 2010; DelSole et al., uncertainty in climate predictions (e.g., Ho et al., 2012b). There may be 2013), although this option has not been explored in decadal pre- nonlinear relationships between the mean state and the anomalies, diction to date, because a long training sample is required to obtain that are neglected in linear bias adjustment techniques. There are also robust weights. difficulties in estimating the drift in the presence of volcanic eruptions. The assessment of forecast quality depends on the quantities of great- It has been recognized that including as many initial states as possible est interest to those who use the information. World Meteorological in computing the drift and adjusting the bias is more desirable than a Organization (WMO) s Standard Verification System (SVS) for Long- greater number of ensemble members per initial state (Meehl et al., Range Forecasts (LRF) (WMO, 2002) outlines specifications for long- 2013d), although increasing both is desirable to obtain robust fore- range (sub-seasonal to seasonal) forecast quality assessment. These cast quality estimates. A procedure for bias adjustment following the measures are also described in Jolliffe and Stephenson (2011) and technique outlined above has been recommended for CMIP5 (ICPO, Wilks (2006). A recommendation for a deterministic metric for dec- 2011). A suitable adjustment depends also on there being a sufficient adal climate predictions is the mean square skill score (MSSS), and number of hindcasts for statistical robustness (Garcia-Serrano et al., for a probabilistic metric, the continuous ranked probability skill score 2012; Kharin et al., 2012). (CRPSS) as described in Goddard et al. (2013) and Meehl et al. (2013d). For dynamical ensemble systems, a useful measure of the characteris- To reduce the impact of the drift many of the early attempts at decadal tics of an ensemble forecast system is spread. The relative spread can prediction (Smith et al., 2007; Keenlyside et al., 2008; Pohlmann et al., be described in terms of the ratio between the mean spread around the 2009; Mochizuki et al., 2010) use an approach called anomaly initial- ensemble mean and the root mean square error (RMSE) of the ensem- ization (Schneider et al., 1999; Pierce et al., 2004; Smith et al., 2007). ble-mean prediction, or spread-to-RMSE ratio. A ratio of 1 is considered The anomaly initialization approach attempts to circumvent model drift a desirable feature for a Gaussian-distributed variable of a well-cali- and the need for a time-varying bias correction. The models are initial- brated (i.e., reliable) prediction system (Palmer et al., 2006). The impor- ized by adding observed anomalies to an estimate of the model mean tance of using statistical inference in forecast quality assessments has climate. The mean model climate is subsequently subtracted from the been recently emphasized (Garcia-Serrano and Doblas-Reyes, 2012; predictions to obtain forecast anomalies. Sampling error in the estima- Goddard et al., 2013). This is even more important when there are only tion of the mean climatology affects the success of this approach. This small samples available (Kumar, 2009) and a small number of degrees is also the case for full-field initialization, although as anomaly initial- of freedom (Gangst et al., 2013). Confidence intervals for the scores isation is affected to a smaller degree by the drift, the sampling error are typically computed using either parametric or bootstrap methods is assumed to be smaller (Hazeleger et al., 2013a). The relative merits (Lanzante, 2005; Jolliffe, 2007; Hanlon et al., 2013). of anomaly versus full initialization are being quantified (Hazeleger et 11 al., 2013a; Magnusson et al., 2013; Smith et al., 2013a), although no The skill of seasonal predictions can vary from generation to genera- initialization method was found to be definitely better in terms of fore- tion (Power et al. 1999) and from one generation of forecast systems cast quality. Another less widely explored alternative is dynamic bias to the next (Balmaseda et al., 1995). This highlights the possibility correction in which multi-year monthly mean analysis increments are that the skill of decadal predictions might also vary from one period added during the integration of the ocean model (Wang et al., 2013). to another. Certain initial conditions might precede more predictable Figure 11.2 includes predictions performed with both full and anomaly near-term states than other initial conditions, and this has the poten- initialization systems. tial to be reflected in predictive skill assessments. However, the short length of the period available to initialize and verify the predictions 11.2.3.2 Forecast Quality Assessment makes the analysis of the variations in skill very difficult. The quality of a forecast system is assessed by estimating, among 11.2.3.3 Pre-CMIP5 Decadal Prediction Experiments others, the accuracy, skill and reliability of a set of hindcasts (Jolliffe and Stephenson, 2011). These three terms accuracy, skill and reli- Early decadal prediction studies found little additional predictive skill ability are used here in a strict technical sense. A suite of meas- from initialization, over that due to changes in radiative forcing (RF), ures needs to be considered, particularly when a forecast system are on global (Pierce et al., 2004) and regional scales (Troccoli and Palmer, compared. The accuracy of a forecast system refers to the average 2007). However, neither of these studies considered more than two distance/error between forecasts and observations. The skill score is start dates. More comprehensive tests, which considered at least nine a relative measure of the quality of the forecasting system compared different start dates indicated temperature skill (Smith et al., 2007; to some benchmark or reference forecast (e.g., climatology or per- Keenlyside et al., 2008; Pohlmann et al., 2009; Sugiura et al., 2009; sistence). The reliability, which is a property of the specific forecast Mochizuki et al., 2010; Smith et al., 2010; Doblas-Reyes et al., 2011; system, measures the trustworthiness of the predictions. Reliability Garcia-Serrano and Doblas-Reyes, 2012; Garcia-Serrano et al., 2012; measures how well the predicted probability distribution matches the Kroger et al., 2012; Matei et al., 2012b; van Oldenborgh et al., 2012; observed relative frequency of the forecast event. Accuracy and relia- Wu and Zhou, 2012; MacLeod et al., 2013). Moreover, this skill was bility are aspects of forecast quality that can be improved by improv- enhanced by initialization (local increase in correlation of 0.1 to 0.3, ing the individual forecast systems or by combining several of them depending on the system) mostly over the ocean, in particular over into a multi-model prediction. The reliability can be improved by a the North Atlantic and subtropical Pacific oceans. Regions with skill 970 Near-term Climate Change: Projections and Predictability Chapter 11 improvements from initialization for precipitation are small and rarely tralia (Power et al., 1999; Deser et al., 2004; Seager et al., 2008; Zhu statistically significant (Goddard et al., 2013). et al., 2011; Li et al., 2012). The combination of Pacific and Atlantic variability and climate change is an important driver of multi-decadal 11.2.3.4 Coupled Model Intercomparison Project Phase 5 USA drought (McCabe et al., 2004; Burgman et al., 2010) including key Decadal Prediction Experiments events like the American dustbowl of the 1930s (Schubert et al., 2004). van Oldenborgh et al. (2012) reported weak skill in hindcasting the IPO Indices of global mean temperature, the Atlantic Multi-decadal Varia- in the ENSEMBLES multi-model. Doblas-Reyes et al. (2013) show that bility (AMV; (Trenberth and Shea, 2006)) and the Inter-decadal Pacific the ensemble-mean skill of the ENSEMBLES multi-model IPO is not Oscillation (IPO; Power et al., 1999) or Pacific Decadal Oscillation (PDO) statistically significant at the 95% level and shows no clear impact of are used as benchmarks to assess the ability of decadal forecast sys- the initialization, in agreement with the predictability study of Meehl tems to predict multi-annual averages of climate variability (Kim et al., et al. (2010). On the other hand, case studies suggest that there might 2012; van Oldenborgh et al., 2012; Doblas-Reyes et al., 2013; Goddard be some initial states that can produce skill in predicting IPO-related et al., 2013; see also Figure 11.3). Initialized predictions of global mean decadal variability for some time periods (e.g., Chikamoto et al., 2012b; surface air temperature (GMST) for the following year are now being Meehl and Arblaster, 2012; Meehl et al., 2013a). performed in almost-real time (Folland et al., 2013). The higher AMV and global mean temperature skill of the CMIP5 pre- Non-initialized predictions (or projections) of the global mean tem- dictions with respect to the ENSEMBLES hindcasts (van Oldenborgh perature are statistically significantly skilful for most of the forecast et al., 2012; Goddard et al., 2013) might be partly due to the CMIP5 ranges considered (high confidence), due to the almost monotonic multi-model using specified instead of projected aerosol loading (espe- increase in temperature, pointing to the importance of the time-var- cially the volcanic aerosol) and solar irradiance variations during the ying RF (Murphy et al., 2010; Kim et al., 2012). This leads to a high simulations. As these forcings cannot be specified in a real forecast set- (above 0.9) correlation of the ensemble mean prediction that varies ting, ENSEMBLES offers an estimate of the skill closer to what could be very as a function of forecast lead time. This holds whether the changes expected from a real-time forecast system such as the one described in the external forcing (i.e., changes in natural and/or anthropogenic in (Smith et al., 2013a). The use of correct forcings nevertheless allows atmospheric composition) are specified (i.e., CMIP5) or are projected a more powerful test of the effect of initialization on the ability of (ENSEMBLES). The skill of the multi-annual global mean surface tem- models to reproduce past observations. perature improves with initialization, although this is mainly evidenced when the accuracy is measured in terms of the RMSE (Doblas-Reyes et Near-term prediction systems have significant skill for temperature al., 2013). An improved prediction of global mean surface temperature over large regions (Figure 11.4), especially over the oceans (Smith et al., is evidenced by the closer fit of the initialized predictions during the 2010; Doblas-Reyes et al., 2011; Kim et al., 2012; Matei et al., 2012b; 21st century (Figure 11.3; Meehl and Teng, 2012; Doblas-Reyes et al., van Oldenborgh et al., 2012; Hanlon et al., 2013). It has been shown 2013; Guemas et al., 2013; Box 9.2). The impact of initialization is seen that a large part of the skill corresponds to the correct representation 11 as a better representation of the phase of the internal variability, in of the long-term trend (high confidence) as the skill decreases substan- particular in increasing the upper ocean heat content (Meehl et al., tially after an estimate of the long-term trend is removed from both 2011) and in terms of a correction of the model s forced response. the predictions and the observations (e.g., Corti et al., 2012; van Old- enborgh et al., 2012; MacLeod et al., 2013). Robust skill increase due The AMV (Chapter 14) has important impacts on temperature and pre- to initialization (Figure 11.4) is limited to areas of the North Atlantic, cipitation over land (Li and Bates, 2007; Li et al., 2008; Semenov et al., the Indian Ocean and the southeast Pacific (high confidence) (Doblas- 2010). The AMV index shows a large fraction of its variability on dec- Reyes et al., 2013), in agreement with previous results (Pohlmann et adal time scales and has multi-year predictability (Murphy et al., 2010; al., 2009; Smith et al., 2010; Mochizuki et al., 2012) and predictability Garcia-Serrano and Doblas-Reyes, 2012). The AMV has been connected estimates (Branstator and Teng, 2012). Similar results have been found to multi-decadal variability of Atlantic tropical cyclones (Goldenberg et in several individual forecast systems (e.g., Muller et al., 2012; Bel- al., 2001; Zhang and Delworth, 2006; Smith et al., 2010; Dunstone et al., lucci et al., 2013). However, the impact of initialization on the skill in 2011). Figure 11.3 shows that the CMIP5 multi-model ensemble mean those regions, though robust (as shown by the agreement between the has skill on multi-annual time scales, the skill being generally larger different CMIP5 systems) is small and not statistically significant with than for the single-model forecast systems (Garcia-Serrano and Doblas- 90% confidence. Reyes, 2012; Kim et al., 2012). The skill of the AMV index improves with initialization (high confidence) for the early forecast ranges. In particu- The improvement in retrospective North Atlantic variability predictions lar, the RMSE is substantially reduced (indicating improved skill) with from initialization (Smith et al., 2010; Dunstone et al., 2011; Garcia- initialization for the AMV. The positive correlation of the non-initialized Serrano et al., 2012; Hazeleger et al., 2013b) suggests that internal var- AMV predictions is consistent with the view that part of the recent iability was important to North Atlantic variability during the past few variability is due to external forcings (Evan et al., 2009; Ottera et al., decades. However, the interpretation of the results is complicated by 2010; Chang et al., 2011; Booth et al., 2012; Garcia-Serrano et al., 2012; the fact that the impact on skill varies slightly with the forecast quality Terray, 2012; Villarini and Vecchi, 2012; Doblas-Reyes et al., 2013). measure used (Figure 11.3; Doblas-Reyes et al., 2013). This has been attributed to, among other things, the different impact of the predicted Pacific decadal variability is associated with potentially important local trends on the scores used (Goddard et al., 2013). Skill in hindcasts climate impacts, including rainfall over America, Asia, Africa and Aus- of subpolar Atlantic temperature, which is evident in Figure 11.4, is 971 Chapter 11 Near-term Climate Change: Projections and Predictability GMST AMV -0.4 -0.2 0.0 0.2 0.4 0.6 -0.2 -0.1 0.0 0.1 0.2 (C) 1960 1970 1980 1990 2000 2010 1960 1970 1980 1990 2000 2010 0.9 0.9 correlation 0.6 0.3 0.6 0.0 1-4 2-5 3-6 4-7 5-8 6-9 1-4 2-5 3-6 4-7 5-8 6-9 Forecast time (yr) Forecast time (yr) 0.15 0.15 11 0.10 0.10 rmse (C) 0.05 0.05 0.00 0.00 1-4 2-5 3-6 4-7 5-8 6-9 1-4 2-5 3-6 4-7 5-8 6-9 Forecast time (yr) Forecast time (yr) CMIP5 Init CMIP5 NoInit Figure 11.3 | Decadal prediction forecast quality of two climate indices. (Top row) Time series of the 2- to 5-year average ensemble-mean initialized hindcast anomalies and the corresponding non-initialized experiments for two climate indices: global mean surface temperature (GMST, left) and the Atlantic multi-decadal variability (AMV, right). The obser- vational time series, Goddard Institute of Space Studies (GISS) GMST and Extended Reconstructed Sea Surface Temperature (ERSST) for the AMV, are represented with dark grey (positive anomalies) and light grey (negative anomalies) vertical bars, where a 4-year running mean has been applied for consistency with the time averaging of the predictions. Predicted time series are shown for the CMIP5 Init (solid) and NoInit (dotted) simulations with hindcasts started every 5 years over the period 1960 2005. The lower and upper quartile of the multi-model ensemble are plotted using thin lines. The AMV index was computed as the SST anomalies averaged over the region Equator to 60N and 80W to 0W minus the SST anomalies averaged over 60S to 60N. Note that the vertical axes are different for each time series. (Middle row) Correlation of the ensemble mean prediction with the observational reference along the forecast time for 4-year averages of the three sets of CMIP5 hindcasts for Init (solid) and NoInit (dashed). The one-sided 95% confidence level with a t distribution is represented in grey. The effective sample size has been computed taking into account the autocorrelation of the observational time series. A two-sided t test (where the effective sample size has been computed taking into account the autocorrelation of the observational time series) has been used to test the differences between the correlation of the initialized and non-initialized experiments, but no differences where found statistically significant with a confidence equal or higher than 90%. (Bottom row) Root mean square error (RMSE) of the ensemble mean prediction along the forecast time for 4-year averages of the CMIP5 hindcasts for Init (solid) and NoInit (dashed). A two- sided F test (where the effective sample size has been computed taking into account the autocorrelation of the observational time series) has been used to test the ratio between the RMSE of the Init and NoInit, and those forecast times with differences statistically significant with a confidence equal or higher than 90% are indicated with an open square. (Adapted from Doblas-Reyes et al., 2013.) 972 Near-term Climate Change: Projections and Predictability Chapter 11 improved more by initialization than is skill in hindcasting sub-tropical tercile threshold has been estimated separately for the predictions and Atlantic temperature (Garcia-Serrano et al., 2012; Robson et al., 2012; the observations). The diagrams are constructed using predictions for Hazeleger et al., 2013b). This is relevant because the sub-polar branch each grid point over the corresponding area. For perfect reliability the of the AMV is a source of skill for multi-year North Atlantic tropical forecast probability and the frequency of occurrence should be equal, storm frequency predictions (Smith et al., 2010). Vecchi et al. (2013) and the plotted points should lie on the diagonal (solid black line in argued that the nominal improvement in multi-year forecasts of North the figure). When the line joining the bullets (the reliability curve) has Atlantic hurricane frequency was mainly due to persistence. positive slope it indicates that as the forecast probability of the event increases, so does the chance of observing the event. The predictions Sugiura et al. (2009) reported on skill in hindcasting the Pacific Decadal therefore can be considered as moderately reliable. However, if the Oscillation (PDO) in their forecast system. They ascribed the skill to slope of the curve is less than the slope of the diagonal, then the fore- the interplay between Rossby waves and a clockwise propagation of cast system is overconfident. If the reliability curve is mainly horizontal, ocean heat content anomalies along the Kuroshio Oyashio extension then the frequency of occurrence of the event does not depend on the and subtropical subduction pathway. However, as Figure 11.4 shows, forecast probabilities and the predictions contain no more information the Pacific Ocean has the lowest temperature skill overall, with no con- than a random guess. An ideal forecast should have a good resolution sistent impact from initialization. The central North Pacific has zero or whilst retaining reliability, that is, probability forecasts should be both negative skill, which may be due to the relatively large amplitude of sharp and reliable. the interannual variability when compared to the long-term trend; the overall failure to predict the largest warming events (Guémas et al., In agreement with Corti et al. (2012), CMIP5 multi-model surface tem- 2012) beyond a few months; and differences (compared to AMV) in perature predictions are more reliable for the North Atlantic than when how surface temperature and upper ocean heat content interact for considered over the global oceans, and have a tendency to be over- the PDO (Mochizuki et al., 2010; Chikamoto et al., 2012a; Mochizuki confident particularly for the global oceans (medium confidence). This et al., 2012). There is a robust loss of skill due to initialization in the means that the multi-model ensemble spread should not be considered CMIP5 predictions over the equatorial Pacific (Doblas-Reyes et al., as a robust measure of the actual uncertainty, at least for multi-an- 2013) that has not been adequately explained. nual averages. The attributes diagrams already take into account the s ­ ystematic error in the simulated variability by estimating separately The AMV is thought to be related to the AMOC (Knight et al., 2005). An the event thresholds for the predictions and the observational refer- assessment of the impact of observing systems on AMOC predictability ence. For the North Atlantic, initialization improves the reliability of the indicates that the recent dense observations of oceanic temperature predictions, which translates into an increase of the Brier skill score, the and salinity are crucial to constraining the AMOC in one model Zhang probabilistic skill measure with respect to a naive climatological pre- et al. (2007a). The observing system representative of the pre-2000s diction (which is reliable, but not skilful) used to aggregate the infor- was not as effective, indicating that inadequate observations in the mation in the attributes diagram. However, the uncertainty associated past might also limit the impact of initialization on the predictions. with these estimates is not negligible. This is due mainly to the small 11 This has been confirmed by Pohlmann et al. (2013) using decadal pre- sample of start dates, which has the consequence that the number of dictions, where they also find a positive impact from initialization that predictions with a given probability is small to give a robust estimate agrees with Hazeleger et al. (2013b). Assessments of the skill of pre- of the observed relative frequency (Brocker and Smith, 2007). In addi- diction systems to hindcast past variability in the AMOC have been tion to this, there are biases in the reliability diagram itself (Ferro and attempted (Pohlmann et al., 2013; Swingedouw et al., 2013) although Fricker, 2012). These results suggest that the multi-model ensemble direct measures of the AMOC are far too short to underpin a relia- should be used with care when estimating probability forecasts or the ble estimate of skill, and longer histories are poorly known (Matei et uncertainty of the mean predictions. Given that the models used for the al., 2012a; Vecchi et al., 2012). There is very low confidence in current dynamical predictions are the same as those used for the projections, estimates of the skill of the AMOC hindcasts. Sustained ocean observa- this verification also provides useful information for the assessment of tions, such as Argo, a broad global array of temperature/salinity profil- the projections (cf. Box 11.2). ing floats, and Rapid Climate Change-Meridional Overturning Circula- tion and Heatflux Array (RAPID-MOCHA), will be needed to build a The skill in hindcasting precipitation over land (Figure 11.6) is much capability to reliably predict the AMOC (Srokosz et al., 2012). lower than the skill in hindcasting temperature over land. This is con- sistent with predictability studies discussed previously (e.g., Box 11.1) Climate prediction is, by nature, probabilistic. Probabilistic predictions (high confidence). Several regions, especially in the Northern Hemi- are expected to be skilful, but also reliable. Decadal predictions should sphere (NH) and West Africa (Gaetani and Mohino, 2013), have skill be evaluated on the basis of whether they give an accurate estimation but these regions are not statistically significant with a 95% confi- of the relative frequency of the predicted outcome. This question can dence level. The positive skill in hindcasting precipitation can be attrib- be addressed using, among other tools, attributes diagrams (Mason, uted mostly to variable RF (high confidence) as initialization improves 2004). They measure how closely the forecast probabilities of an event the skill very little (Goddard et al., 2013). The areas with positive skill correspond to the mean probability of observing the event. They are agree with those where the precipitation trends of multi-annual aver- based on a discrete binning of many forecast probabilities taken over ages are the largest (Doblas-Reyes et al., 2013). The skill in areas like a given geographical region. Figure 11.5 illustrates the CMIP5 mul- West Africa might be associated with the positive AMV skill, as the ti-model Init and NoInit attributes diagrams for predictions of both the AMV drives interannual variability in precipitation over this region (van global and North Atlantic SSTs to be in the lower tercile (where the Oldenborgh et al., 2012). 973 Chapter 11 Near-term Climate Change: Projections and Predictability The small amount of statistically significant differences found between Serrano et al., 2012) suggests that, although a five-year interval sam- the initialized and non-initialized experiments does not necessarily pling allows an estimate of the level of skill, local maxima as a function mean that the impact of the initialization does not have a physical of forecast time might well be due to poor sampling of the start dates basis. A comparison of the global mean temperature and AMV fore- (Garcia-Serrano and Doblas-Reyes, 2012; Kharin et al., 2012; Doblas- cast quality using 1- and 5-year intervals between start dates (Garcia-­ Reyes et al., 2013; Goddard et al., 2013). Several signals, such as the RMSSS Init for tas at forecast time 2 5yrs ................ .............. Inf .................. .......... .... . .. ............... .............. ........... ... ..... .............. ...... ........ . .. . .. . 40 . ....... ... . .. ........... . ...... ..... .. ..... . . ... ......... .. . ... ............. ....... .......... ......... .......... . .. ... ...... .... 30 .. . ..... . . .. ...... .... ......... . ........................ . .. . . ... .. .... . ... . ..................... .. . .. .. . ....... .. .. . ............. .. ................ ... ... . . . .. ........ . ... . ...... . . ...................... ... ..................... ... .. . . . . . . . ... ............. ....... ... . .. . .... . 20 . .......... ............ ........... . .. .... ..... .. . . ........ ...... ... . . ..... . ...... .... .... .... ........... . . ........... ...... ................ .... .. ................ .. ............... ...... ....... ....... ...... . .......... ......... . .......... . .................. ................. ...... 10 ................ .. ... ..... ................ .... .. .... . .. .. .. . .............. ....... .. .. ...... ..... . .... . ..... .. . . . .. ........... .... ................ .. ......... . .... .. ... .......... 10 .. . . .... .. . . . .. . . . ... ...... .... . . .. .. 20 . .. . . ...... .... .. . .. ..... ... .... . 30 40 Inf RMSE Init/RMSE NoInit for tas at forecast time 2 5yrs 3 7 .. 1.4 11 5 . .... 1.3 1.2 . 75 1.1 5 7 0.9 0.8 .. ... . 75 0.7 75 75 0.6 75 75 75 0 Figure 11.4 | (a) Root mean square skill score of the near surface air temperature forecast quality for the forecast time 2 to 5 years from the multi-model ensemble mean of the CMIP5 Init experiment with 5-year interval between start dates over the period 1960 2005. A combination of temperatures from Global Historical Climatology Network/Climate Anomaly Monitoring System (GHCN/CAMS) air temperature over land, Extended Reconstructed Sea Surface Temperature (ERSST) and Goddard Institute of Space Studies Surface Temperature Analysis (GISTEMP) 1200 over the polar areas is used as a reference. Black dots correspond to the points where the skill score is statistically significant with 95% confidence using a one-sided F-test taking into account the autocorrelation of the observation minus prediction time series. (b) Ratio between the root mean square error of the ensemble mean of Init and NoInit. Dots are used for the points where the ratio is significantly above or below 1, with 90% confidence using a two-sided F-test taking into account the autocorrelation of the observation minus prediction time series. Contours are used for areas where the ratio of at least 75% of the single forecast systems is either above or below one agreeing with the value of the ratio in the multi-model ensemble. Poorly observationally sampled areas are masked in grey. The original model data have been bilinearly interpolated to the observational grid. The ensemble mean of each forecast system has been estimated before computing the multi-model ensemble mean. (Adapted from Doblas- Reyes et al., 2013.) 974 Near-term Climate Change: Projections and Predictability Chapter 11 skill improvement for temperature over the North Atlantic, are robust impact are both slightly reduced in the results with yearly start dates, in the sense that it is found in more than 75% of forecast system. How- but at the same time the spatial variability is substantially reduced. ever, it is difficult to obtain statistical significance with these limited samples. The low start date sampling frequency is one of the limita- The CMIP5 multi-model overestimates the spread of the multi-annual tions of the core CMIP5 near-term prediction experiment, the other one average temperature (Doblas-Reyes et al., 2013). Figure 11.7 shows being the short length of the period of study, limited by the availability the ratio of the spread around the ensemble mean prediction and the of observational data. Results estimated with yearly start dates are RMSE of the ensemble mean prediction of Init and NoInit, which in more robust than with a 5-year start date frequency. However, even a well-calibrated system is expected to be close to 1. However, the with 1-year start date frequency, the impact of the initialization is sim- ratio is overestimated over the North Atlantic, the Indian Ocean and ilar. The spatial distribution of the skill does not change substantially the Arctic, and underestimated over the North Pacific and most conti- with the different start date frequency. The skill and the initialization nental areas, suggesting that the CMIP5 systems do not discriminate 1 1 0.8 0.8 Observed frequency Observed frequency 0.6 0.6 0.4 0.4 0.2 0.2 0 0 0 0.2 0.4 0.6 0.8 1 0 0.2 0.4 0.6 0.8 1 Forecast probability Forecast probability 11 1 1 0.8 0.8 Observed frequency Observed frequency 0.6 0.6 0.4 0.4 0.2 0.2 0 0 0 0.2 0.4 0.6 0.8 1 0 0.2 0.4 0.6 0.8 1 Forecast probability Forecast probability Figure 11.5 | Attributes diagram for the CMIP5 multi-model decadal initialized (a and c) and non-initialized (b and d) hindcasts for the event surface air temperature anomalies below the lower tercile over (a) and (b) the global oceans (60N to 60S) and (c) and (d) the North Atlantic (87.5N to 30N, 80W to 10W) for the forecast time 2 to 5 years. The red bullets in the figure correspond to the number of probability bins (10 in this case) used to estimate forecast probabilities. The size of the bullets represents the number of forecasts in a specific probability category and is a measure of the sharpness (or variance of the forecast probabilities) of the predictions. The blue horizontal and vertical lines indicate the climatological frequency of the event in the observations and the mean forecast probability, respectively. Grey vertical bars indicate the uncertainty in the observed frequency for each probability category estimated at 95% level of confidence with a bootstrap resampling procedure based on 1000 samples. The longer the bars, the more the vertical position of the bullets may change as new hindcasts become available. The black dashed line separates skilful from unskilled regions in the diagram in the Brier skill score sense. The Brier skill score with respect to the climatological forecast is drawn in the top left corner of each panel. (Adapted from Corti et al., 2012.) 975 Chapter 11 Near-term Climate Change: Projections and Predictability between the regions where the spread should be reduced according to points to the need for a careful interpretation of current ensemble and the RMSE level in the area. These results are found for both the Init and probabilistic climate information for climate adaptation and services. NoInit ensembles and agree with the overconfidence of the probability forecasts shown in Figure 11.6 (Corti et al., 2012). The spread overes- The skill of extreme daily temperature and precipitation in multi-annu- timation also agrees with the results found for the indices illustrate al time scales has also been assessed (Eade et al., 2012; Hanlon et al., in Figure 11.3 (Doblas-Reyes et al., 2013). The spread overestimation 2013). There is little improvement in skill with the initialization beyond RMSSS Init for prlr at forecast time 2 5yrs Inf .. 20 . 15 10 5 5 10 15 20 Inf RMSE Init/RMSE NoInit for prlr at forecast time 2 5yrs 3 11 . 75 1.4 75 1.2 1.1 1.05 0.95 0.9 0.8 0.6 0 Figure 11.6 | (a) Root mean square skill score for precipitation hindcasts for the forecast time 2 to 5 years from the multi-model ensemble mean of the CMIP5 Init experiment with 5-year interval between start dates over the period 1960 2005. Global Precipitation Climatology Centre (GPCC) precipitation is used as a reference. Black dots correspond to the points where the skill score is statistically significant with 95% confidence using a one-sided F-test taking into account the autocorrelation of the observation minus prediction time series. (b) Ratio between the root mean square error of the ensemble mean of Init and NoInit. Dots are used for the points where the ratio is significantly above or below one with 90% confidence using a two-sided F-test taking into account the autocorrelation of the observation minus prediction time series. Contours are used for areas where the ratio of at least 75% of the single forecast systems is either above or below 1, agreeing with the value of the ratio in the multi-model ensemble. The model original data have been bilinearly interpolated to the observational grid. The ensemble mean of each forecast system has been estimated before computing the multi-model ensemble mean. (Adapted from Doblas-Reyes et al., 2013.) 976 Near-term Climate Change: Projections and Predictability Chapter 11 Spread/RMSE Init for tas at forecast time 2 5yrs 6 4 2 1.5 1.2 0.9 0.8 0.6 0.3 0 Figure 11.7 | Ratio between the surface temperature spread around the ensemble mean and the root mean square error (RMSE) of the ensemble-mean prediction of Init and NoInit for the forecast time 2 to 5 years with 5-year interval between start dates over the period 1960 2005. A combination of temperatures from Global Historical Climatology Network/Climate Anomaly Monitoring System (GHCN/CAMS) air temperature over land, Extended Reconstructed Sea Surface Temperature (ERSST) v3b over sea and Goddard Insti- tute of Space Studies Surface Temperature Analysis (GISTEMP) 1200 over the polar areas is used as a reference to compute the RMSE. (Adapted from Doblas-Reyes et al., 2013.) the first year, suggesting that skill then arises largely from the varying dict ocean heat and density anomalies (Zhang et al., 2007a; Dunstone external forcing. The skill for extremes is generally similar to, but slight- and Smith, 2010). Another important advancement is the availabili- ly lower than, that for the mean. ty of highly accurate altimetry data, made especially useful after the launching of TOPography EXperiment (TOPEX)/Poseidon in 1992. Argo Responding to the increases in decadal skill in certain regions due to and altimeter data became available only in 2000 and 1992 respec- initialization, a coordinated quasi-operational decadal prediction ini- tively, so an accurate estimate of their impact on real forecasts has to 11 tiative has been organized (Smith et al., 2013b). The forecast systems wait (Dunstone and Smith, 2010). In all cases, both the length of the participating in the initiative are based on those of CMIP5 and have observational data sets and the reduced coverage of the data avail- been evaluated for forecast quality. Statistical predictions are also able, especially before 2000, are serious limitations to obtain robust included in the initiative. The most recent forecast shows (compared estimates of forecast quality. to the projections) substantial warming of the north Atlantic subpo- lar gyre, cooling of the north Pacific throughout the next decade and Improved initialization of other aspects such as sea ice, snow cover, cooling over most land and ocean regions and in the global average frozen soil and soil moisture, may also have potential to contribute to out to several years ahead. However, in the absence of explosive or predictive skill beyond the seasonal time scale. This could be investi- frequent volcanic eruptions, global surface temperature is predicted to gated, for example by using measurements of soil moisture from the continue to rise and, to a certain degree, recover from the reduced rate Soil Moisture and Ocean Salinity (SMOS) satellite launched in 2009, or of warming (see Box 9.2). by initializing sea ice thickness with observations from the CryoSat-2 satellite launched in 2010. Along the same line, understanding the 11.2.3.5 Realizing Potential links between the initialization and the correct prediction of both the internal and external variability should help improving forecast quality Although idealized model experiments show considerable promise for (Solomon et al., 2011). predicting internal variability, realizing this potential is a challenging task. There are three main hurdles: (1) the limited availability of data Many of the current decadal prediction systems use relatively simple to initialize and verify predictions, (2) limited progress in initialization initialization schemes and do not adopt fully coupled initialization/ techniques for decadal predictions and (3) dynamical model shortcom- ensemble generation schemes. Assimilation schemes offer opportuni- ings that require validating how the simulated variance compares with ties for fully coupled initialization including assimilation of variables the observed variance. such as sea ice, snow cover and soil moisture, although they present technically and scientifically challenging problems. This approach has It is expected that the availability of temperature and salinity data in been tested in schemes like four-dimensional variational data assim- the top 2 km of the ocean through the enhanced global deployment of ilation (4DVAR; Sugiura et al., 2008) and the ensemble Kalman filter Argo floats will give a step change in our ability to initialize and pre- (Keppenne et al., 2005; Zhang et al., 2007a). 977 Chapter 11 Near-term Climate Change: Projections and Predictability Bias correction is used to reduce the effects of model drift, but the forcing? Note finally that a great deal of additional information on nonlinearity in the climate system (e.g., Power (1995) might limit the near-term projections is provided in Annex I. effectiveness of bias correction and thereby reduce forecast quality. Understanding and reducing both drift and systematic errors is impor- 11.3.1.1 Uncertainty in Near-term Climate Projections tant (Palmer and Weisheimer, 2011), as it is also for seasonal-to-inter- annual climate prediction and for climate change projections. While As discussed in Chapters 1 (Section 1.4) and 12 (Section 12.2), climate improving models is the highest priority, efforts to quantify the degree projections are subject to several sources of uncertainty. Here three of interference between model bias and predictive signals should not main sources are distinguished. The first arises from natural internal be overlooked. variability, which is intrinsic to the climate system, and includes phe- nomena such as variability in the mid-latitude storm tracks and the ENSO. The existence of internal variability places fundamental limits 11.3 Near-term Projections on the precision with which future climate variables can be project- ed. The second is uncertainty concerning the past, present and future 11.3.1 Introduction forcing of the climate system by natural and anthropogenic forcing agents such as GHGs, aerosols, solar forcing and land use change. Forc- In this section the outlook for global and regional climate up to ing agents may be specified in various ways, for example, as emissions mid-century is assessed, based on climate model projections. In con- or as concentrations (see Section 12.2). The third is uncertainty related trast to the predictions discussed in Section 11.2, these projections are to the response of the climate system to the specified forcing agents. not initialized using observations; instead, they are initialized from historical simulations of the evolution of climate from pre-industrial Quantifying the uncertainty that arises from each of the three sources is conditions up to the present. The historical simulations are forced by an important challenge. For projections, no attempt is made to predict estimates of past anthropogenic and natural climate forcing agents, the evolution of the internal variability. Instead, the statistics of this and the projections are obtained by forcing the models with scenari- variability are included as a component of the uncertainty associated os for future climate forcing agents. Major use is made of the CMIP5 with a projection. The magnitude of internal variability can be estimat- model experiments forced by the Representative Concentration Path- ed from observations (Chapters 2, 3 and 4) or from climate models way (RCP) scenarios discussed in Chapters 1 and 8. Projections of cli- (Chapter 9). Challenges arise in estimating the variability on decadal mate change in this and subsequent chapters are expressed relative and longer time scales, and for rare events such as extremes, as obser- to the reference period: 1986 2005. In this chapter most emphasis is vational records are often too short to provide robust estimates. given to the period 2016 2035, but some information on changes pro- jected before and after this period (up to mid-century) is also provided. Uncertainty concerning the past forcing of the climate system arises Longer-term projections are assessed in Chapters 12 and 13. from a lack of direct or proxy observations, and from observational 11 errors. This uncertainty can influence future projections of some vari- Key assessment questions addressed in this section are: What is the ables (particularly large-scale ocean variables) for years or even dec- externally forced signal of near-term climate change, and how large ades ahead (e.g., Meehl and Hu, 2006; Stenchikov et al., 2009; Gregory, is it compared to natural internal variability? From the point of view 2010). Uncertainty about future forcing arises from the inability to pre- of climate impacts, the absolute magnitude of climate change may dict future anthropogenic emissions and land use change, and natural in some instances be less important than the magnitude relative to forcings (e.g., volcanoes), and from uncertainties concerning carbon the local level of natural internal variability. Because many systems cycle and other biogeochemical feedbacks (Chapters 6, 12 and Annex are naturally adapted to a background level of variability, it may be II.4.1). The uncertainties in future anthropogenic forcing are typically changes that move outside of this range that are most likely to trigger investigated through the development of specific scenarios (e.g., for impacts that are unprecedented in the recent past (e.g., Lobell and emissions or concentrations), such as the RCP scenarios (Chapters 1 Burke (2008) for crops). and 8). Different scenarios give rise to different climate projections, and the spread of such projections is commonly described as scenario An important conclusion of the AR4 (Section 10.3.1) was that near- uncertainty. The sensitivity of climate projections to alternative sce- term climate projections are not very sensitive to plausible alternative narios for future anthropogenic emissions is discussed especially in non-mitigation scenarios for GHG concentrations (specifically the Spe- Section 11.3.6.1 cial Report on Emission Scenarios (SRES) scenarios; comparison with RCP scenarios is discussed in Chapter 1), that is, in the near term, dif- To project the climate response to specified forcing agents, climate ferent scenarios give rise to similar magnitudes and patterns of climate models are required. The term model uncertainty describes uncertainty change. (Note, however, that some impacts may be more sensitive.) For about the extent to which any particular climate model provides an this reason, most of the projections presented in this chapter are based accurate representation of the real climate system. This uncertainty on one specific RCP scenario, RCP4.5. RCP4.5 was chosen because of arises from approximations required in the development of models. its intermediate GHG forcing. However, there is greater sensitivity to Such approximations affect the representation of all aspects of the other forcing agents, in particular anthropogenic aerosols (e.g., Chalm- climate including natural internal variability and the response to exter- ers et al., 2012). Consequently, a further question addressed in this nal forcings. As discussed in Chapter 1 (Section 1.4.2), the term model section (especially in Section 11.3.6.1) is: To what extent are near-term uncertainty is sometimes used in a narrower sense to describe the climate projections sensitive to alternative scenarios for anthropogenic spread between projections generated using different models or model 978 Near-term Climate Change: Projections and Predictability Chapter 11 (a) (b) Sources of uncertainty in projected global mean temperature Regional decadal mean temperature 5 Observations (3 datasets) Temperature change relative to 1986 2005 [K] 4.5 2 Internal variability 4 Model spread RCP scenario spread Signal to uncertainty ratio 3.5 Historical model spread 1.5 3 2.5 2 1 1.5 Global 1 Europe 0.5 Australasia 0.5 North America 0 South America 0.5 Africa East Asia 1 0 1960 1980 2000 2020 2040 2060 2080 2100 2020 2040 2060 2080 2100 Year Year (c) (d) Uncertainty in Global decadal mean ANN temperature Uncertainty in Europe decadal mean DJF temperature 100 100 90 90 80 80 Fraction of total variance [%] Fraction of total variance [%] 70 70 60 60 50 50 40 40 30 30 20 20 10 10 11 0 0 2020 2040 2060 2080 2100 2020 2040 2060 2080 2100 Year Year (e) (f) Uncertainty in East Asia decadal mean JJA precipitation Uncertainty in Europe decadal mean DJF precipitation 100 100 90 90 80 80 Fraction of total variance [%] Fraction of total variance [%] 70 70 60 60 50 50 40 40 30 30 20 20 10 10 0 0 2020 2040 2060 2080 2100 2020 2040 2060 2080 2100 Year Year Figure 11.8 | Sources of uncertainty in climate projections as a function of lead time based on an analysis of CMIP5 results. (a) Projections of global mean decadal mean surface air temperature to 2100 together with a quantification of the uncertainty arising from internal variability (orange), model spread (blue) and RCP scenario spread (green). (b) Signal- to-uncertainty ratio for various global and regional averages. The signal is defined as the simulated multi-model mean change in surface air temperature relative to the simulated mean surface air temperature in the period 1986 2005, and the uncertainty is defined as the total uncertainty. (c f) The fraction of variance explained by each source of uncertainty for: global mean decadal and annual mean temperature (c), European (30°N to 75°N, 10°W to 40°E) decadal mean boreal winter (December to February) temperature (d) and precipitation (f), and East Asian (5°N to 45°N, 67.5°E to 130°E) decadal mean boreal summer (June to August) precipitation (e). See text and Hawkins and Sutton (2009) and Hawkins and Sutton (2011) for further details. 979 Chapter 11 Near-term Climate Change: Projections and Predictability versions; however, such a measure is crude as it takes no account of 11.3.2 Near-term Projected Changes in the Atmosphere factors such as model quality (Chapter 9) or model independence. The and Land Surface term model response uncertainty is used here to describe the dimen- sion of model uncertainty that is directly related to the response to 11.3.2.1 Surface Temperature external forcings. To obtain projections of extreme events such as trop- ical cyclones, or regional phenomena such as orographic rainfall, it is 11.3.2.1.1 Global mean surface air temperature sometimes necessary to employ a dynamical or statistical downscaling procedure. Such downscaling introduces an additional dimension of Figure 11.9 (a) and (b) show CMIP5 projections of global mean surface model uncertainty (e.g., Alexandru et al., 2007). air temperature under RCP4.5. The 5 to 95% range for the projected anomaly for the period 2016 2035, relative to the reference period The relative importance of the different sources of uncertainty depends 1986 2005, is 0.47°C to 1.00°C (see also Table 12.2). However, as on the variable of interest, the space and time scales involved (Sec- discussed in Section 11.3.1.1, this range provides only a very crude tion 10.5.4.3 of Meehl et al. (2007b)), and the lead-time of the projec- measure of uncertainty, and there is no guarantee that the real world tion. Figure 11.8 provides an illustration of these dependencies based must lie within this range. Obtaining better estimates is an important on an analysis of CMIP5 projections (following Hawkins and Sutton, challenge. One approach involves initializing climate models using 2009, 2011;Yip et al., 2011). In this example, the forcing-related uncer- observations, as discussed in Section 11.2. Figure 11.9 (b) compares tainty is estimated using the spread of projections for different RCP multi-model initialized climate predictions (8 models from Smith et al., scenarios (i.e., scenario uncertainty), while the spread among differ- 2013b), initialized in 2011; 14 CMIP5 decadal prediction experiment ent models for individual RCP scenarios is used as a measure of the models following the methodology of Meehl and Teng (2012), initial- model response uncertainty. Internal variability is estimated from the ized in 2006 with the raw uninitialized CMIP5 projections. The 5 to models as in Hawkins and Sutton (2009). Key points are: (1) the uncer- 95% range for both sets of initialized predictions is cooler (by about tainty in near-term projections is dominated by internal variability and 15% for the median values) than the corresponding range for the raw model spread. This finding provides some of the rationale for consid- projections, particularly at the upper end. The differences are partly a ering near-term projections separately from long-term projections. consequence of initializing the models in a state that is cool (in com- Note, however, that the RCP scenarios do not sample the full range parison to the median of the raw projections) as a result of the recent of uncertainty in future anthropogenic forcing, and that uncertainty in hiatus in global mean surface temperature rise (see Box 9.2). However, aerosol forcings in particular may be more important than is suggested it is not yet possible to attribute all of the reasons with confidence by Figure 11.8 (see Section 11.3.6.1); (2) internal variability becomes because the raw projections are based on a different, and larger, set increasingly important on smaller space and time scales; (3) for pro- of models than the initialized predictions, and because of uncertainties jections of precipitation, scenario uncertainty is less important and (on related to the bias adjustment of the initialized predictions (Goddard regional scales) internal variability is generally more important than for et al., 2013; Meehl et al., 2013d) 11 projections of surface air temperature; (4) the full model uncertainty may well be larger or smaller than the model spread due to common Another approach to making projections involves weighting models errors or unrealistic models. according to some measure of their quality (see Chapter 9). A specific approach of this type, known as Allen, Stott and Kettleborough (ASK) A key quantity for any climate projection is the signal-to-noise (S/N) (Allen et al., 2000; Stott and Kettleborough, 2002), is based on the use ratio (Christensen et al., 2007), where the signal is a measure of the of results from detection and attribution studies (Chapter 10), in which amplitude of the projected climate change, and the noise is a measure the fit between observations and model simulations of the past is used of the uncertainty in the projection. Higher S/N ratios indicate more to scale projections of the future. ASK requires specific simulations to robust projections of change and/or changes that are large relative be carried out with individual forcings (e.g., anthropogenic GHG forc- to background levels of variability. Depending on the purpose, it may ing alone), and only some of the centres participating in CMIP5 have be useful to identify the noise with the total uncertainty, or with a carried out the necessary integrations. Biases in ASK-derived projec- specific component such as the internal variability. The evolution of the tions may arise from errors in the specified forcings, or in the simulated S/N ratio with lead time depends on whether the signal grows more patterns of response, and/or from nonlinearities in the responses to rapidly than the noise, or vice versa. Figure 11.8 (top right) shows that, forcings. when the noise is identified with the total uncertainty, the S/N ratio for surface air temperature is typically higher at lower latitudes and Figure 11.9c shows the projected range of global mean surface air tem- has a maximum at a lead time of a few decades (Cox and Stephenson, perature change derived using the ASK approach for RCP4.5 (Stott and 2007; Hawkins and Sutton, 2009). The former feature is primarily a G. Jones, 2012; Stott et al., 2013) applied to six models and compares consequence of the greater amplitude of internal variability in mid-lat- this with the range derived from the 42 CMIP5 models. In this case itudes. The latter feature arises because over the first few decades, decadal means are shown. The 5 to 95% confidence interval for the when scenario uncertainty is small, the signal grows most rapidly, but projected temperature anomaly for the period 2016 2035, based on subsequently, the contribution from scenario uncertainty grows more the ASK method, is 0.39°C to 0.87°C. As for the initialized predictions rapidly than does the signal, so the S/N ratio falls. See Hawkins and shown in Figure 11.9b, both the lower and upper values are below the Sutton (2009, 2011) for further details. corresponding values obtained from the raw CMIP5 results, although there is substantial overlap between the two ranges. The relative cooling of the ASK results is directly related to evidence presented in 980 Near-term Climate Change: Projections and Predictability Chapter 11 Global mean temperature projections (RCP 4.5), relative to 1986 2005 2.5 Historical (42 models, 1 ensemble member per model) (a) Temperature anomaly [oC] RCP 4.5 (42 models, 1 ensemble member per model) 2 Observations (4 datasets) Observational uncertainty (HadCRUT4) 1.5 1 All ranges are 5 95% 0.5 0 Annual means 0.5 Historical RCP 4.5 1990 2000 2010 2020 2030 2040 2050 2.5 RCP 4.5 (42 models, 1 ensemble member per model) (b) Temperature anomaly [oC] RCP 4.5 (min max, 107 ensemble members) 2 Smith et al. (2012) forecast 1.5 Meehl & Teng (2012, updated) Observations 1 0.5 0 Annual means 0.5 Historical RCP 4.5 1990 2000 2010 2020 2030 2040 2050 2.5 RCP 4.5 (42 models, 1 ensemble member per model) (c) 11 Temperature anomaly [oC] RCP 4.5 (min max, 107 ensemble members) 2 Stott et al. (2013) constrained projections Lean & Rind (2009, updated) 1.5 Observations 1 0.5 0 Decadal means 0.5 Historical RCP 4.5 1990 2000 2010 2020 2030 2040 2050 Year Figure 11.9 | (a) Projections of global mean, annual mean surface air temperature 1986 2050 (anomalies relative to 1986 2005) under RCP4.5 from CMIP5 models (blue lines, one ensemble member per model), with four observational estimates: Hadley Centre/Climate Research Unit gridded surface temperature data set 3 (HadCRUT3: Brohan et al., 2006); European Centre for Medium range Weather Forecast (ECMWF) interim reanalysis of the global atmosphere and surface conditions (ERA-Interim: Simmons et al., 2010); Goddard Institute of Space Studies Surface Temperature Analysis (GISTEMP: Hansen et al., 2010); National Oceanic and Atmospheric Administration (NOAA: Smith et al. (2008) for the period 1986 2011 (black lines). (b) As in (a) but showing the 5 to 95% range (grey and blue shades, with the multi-model median in white) of annual mean CMIP5 projections using one ensemble member per model from RCP4.5 scenario, and annual mean observational estimates (solid black line). The maximum and minimum values from CMIP5 are shown by the grey lines. Red hatching shows 5 to 95% range for predictions initialized in 2006 for 14 CMIP5 models applying the Meehl and Teng (2012) methodology. Black hatching shows the 5 to 95% range for predictions initialized in 2011 for eight models from Smith et al. (2013b). (c) As (a) but showing the 5 to 95% range (grey and blue shades, with the multi-model median in white) of decadal mean CMIP5 projections using one ensemble member per model from RCP4.5 scenario, and decadal mean observational estimates (solid black line). The maximum and minimum values from CMIP5 are shown by the grey lines. The dashed black lines show an estimate of the projected 5 to 95% range for decadal mean global mean surface air temperature for the period 2016 2040 derived using the ASK methodology applied to six CMIP5 GCMs. (From Stott et al., 2013.) The red line shows a statistical prediction based on the method of Lean and Rind (2009), updated for RCP4.5. 981 Chapter 11 Near-term Climate Change: Projections and Predictability Chapter 10 (Section 10.3.1) that This provides evidence that some The extent of agreement between the CMIP5 projections provides CMIP5 models have a higher transient response to GHGs and a larger rough guidance about the likelihood of a particular outcome. But as response to other anthropogenic forcings (dominated by the effects of partly illustrated by the discussion above it must be kept firmly in aerosols) than the real world (medium confidence). The ASK results mind that the real world could fall outside of the range spanned by and the initialised predictions both suggest that those CMIP5 models these particular models. See Section 11.3.6 for further discussion. that warm most rapidly over the period (1986 2005) to (2016 2035) may be inconsistent with the observations. This possibility is also sug- 11.3.2.1.2 Regional and seasonal patterns of surface warming gested by comparing the models with the observed rate of warming since 1986 see Box 9.2 for a full discussion of this comparison. Lastly, The geographical pattern of near-term surface warming simulated Figure 11.9 also shows a statistical prediction for global mean surface by the CMIP5 models (Figure 11.10) is consistent with previous IPCC air temperature, using the method of Lean and Rind (2009), which uses reports in a number of key aspects, although weaknesses in the ability multiple linear regression to decompose observed temperature vari- of current models to capture observed regional trends (Box 11.2) must ations into distinct components. This prediction is very similar to the be kept in mind. First, temperatures over land increase more rapidly CMIP5 multi-model median. than over sea (e.g., Manabe et al., 1991; Sutton et al., 2007). Process- es that contribute to this land sea warming contrast include differ- The projections shown in Figure 11.9 assume the RCP4.5 scenario ent local feedbacks over ocean and land and changes in atmospheric and use the 1986 2005 reference period. In Section 11.3.6 addition- energy transport from ocean to land regions (e.g., Lambert and Chiang, al uncertainties associated with future forcing, climate responses and 2007; Vidale et al., 2007; Shimpo and Kanamitsu, 2009; Fasullo, 2010; sensitivity to the choice of reference period, are discussed. An overall Boer, 2011; Joshi et al., 2011). assessment of the likely range for future global mean surface air tem- perature is provided in Section 11.3.6.3. Second, the projected warming in wintertime shows a pronounced polar amplification in the NH (see Box 5.1). This feature is found in For the remaining projections in this chapter the spread among the virtually all coupled model projections, but the CMIP3 simulations CMIP5 models is used as a simple, but crude, measure of ­ ncertainty. u g ­enerally appeared to underestimate this effect in comparison to Seasonal mean air temperature change (RCP4.5: 2016-2035) 11 Figure 11.10 | CMIP5 multi-model ensemble mean of projected changes in December, January and February and June, July and August surface air temperature for the period 2016 2035 relative to 1986 2005 under RCP4.5 scenario (left panels). The right panels show an estimate of the model-estimated internal variability (standard deviation of 20-year means). Hatching in left-hand panels indicates areas where projected changes are small compared to the internal variability (i.e., smaller than one standard deviation of estimated internal variability), and stippling indicates regions where the multi-model mean projections deviate significantly from the simulated 1986 2005 period (by at least two standard deviations of internal variability) and where at least 90% of the models agree on the sign of change. The number of models considered in the analysis is listed in the top-right portion of the panels; from each model one ensemble member is used. See Box 12.1 in Chapter 12 for further details and discussion. Technical details are in Annex I. 982 Near-term Climate Change: Projections and Predictability Chapter 11 observations (Stroeve et al., 2007; Screen and Simmonds, 2010). Sever- Figure 11.11 quantifies the Time of Emergence (ToE) of the mean al studies have isolated mechanisms behind this amplification, which warming signal relative to the recent past (1986 2005), based on the include reductions in snow cover and retreat of sea ice (e.g., Serreze CMIP5 RCP4.5 projections, using a spatial resolution of 2.5° latitude et al., 2007; Comiso et al., 2008); changes in atmospheric and ocean- × 2.5° longitude, the standard deviation of interannual variations as ic circulations (Chylek et al., 2009, 2010; Simmonds and Keay, 2009); the measure of internal variability, and a signal-to-noise threshold of 1. presence of anthropogenic soot in the Arctic environment (Flanner et Because of the dependence on user-driven choices, the most important al., 2007; Quinn et al., 2008; Jacobson, 2010; Ramana et al., 2010); and information in Figure 11.11 is the geographical and seasonal variation increases in cloud cover and water vapour (Francis, 2007; Schweiger et in ToE, seen in the maps, and the variation in ToE between models, al., 2008). Most studies argue that changes in sea ice are central to the shown in the histograms. Consistent with Mahlstein et al. (2011), the polar amplification see Section 11.3.4.1 for further discussion. Fur- earliest ToE is found in the tropics, with ToE in mid-latitudes typically a ther information about the regional changes in surface air temperature decade or so later. Over North Africa and Asia, earlier ToE is found for projected by the CMIP5 models is presented in Annex I. the warm half-year (April to September) than for the cool half-year. Earlier ToE is generally found for larger space and time scales, because As discussed in Sections 11.1 and 11.3.1, the signal of climate change the variance of natural internal variability decreases with averaging is emerging against a background of natural internal variability. The (Section 11.3.1.1 and AR4, Section 10.5.4.3). This tendency can be seen concept of emergence describes the magnitude of the climate change in Figure 11.11 by comparing the median value of the histograms for signal relative to this background variability, and may be useful for area averages with the area average of the median ToE inferred from some climate impact assessments (e.g., AR4, Chapter 11, Table 11.1; the maps (e.g., for Region 2). The large range of values for ToE implied Mahlstein et al., 2011; Hawkins and Sutton, 2012; see also FAQ 10.2). by different CMIP5 models, which can be as much as 30 years, is a con- However, it is important to recognize that there is no single metric of sequence of differences in both the magnitude of the warming signal emergence. It depends on user-driven choices of variable, space and simulated by the models (i.e., uncertainty in the climate response, see time scale, of the baseline relative to which changes are measured Section 11.3.1.1) and in the amplitude of simulated natural internal (e.g., pre-industrial versus recent climate) and of the threshold at variability (Hawkins and Sutton, 2012). which emergence is defined. NUMBER OF MODELS 25 Region 1 25 ONDJFM Region 2 25 Region 3 20 20 20 15 15 15 10 10 10 11 5 5 5 0 0 0 2000 2010 2020 2030 2040 2050 2000 2010 2020 2030 2040 2050 2000 2010 2020 2030 2040 2050 ONDJFM AMJJAS Time of 2 2 2040 Emergence 1 1 2030 S/N > 1 3 3 2020 37 models 2010 NUMBER OF MODELS 25 Region 1 25 AMJJAS Region 2 25 Region 3 20 20 20 15 15 15 10 10 10 5 5 5 0 0 0 2000 2010 2020 2030 2040 2050 2000 2010 2020 2030 2040 2050 2000 2010 2020 2030 2040 2050 Figure 11.11 | Time of Emergence (ToE) of significant local warming derived from 37 CMIP5 models under the RCP4.5 scenario. Warming is quantified as the half-year mean temperature anomaly relative to 1986 2005, and the noise as the standard deviation of half-year mean temperature derived from a control simulation of the relevant model. Central panels show the median time at which the signal-to-noise ratio exceeds a threshold value of 1 for (left) the October to March half year and (right) the April to September half year, using a spatial resolution of 2.5° × 2.5°. Histograms show the distribution of ToE for area averages over the regions indicated obtained from the different CMIP5 models. Full details of the methodology may be found in Hawkins and Sutton (2012). 983 Chapter 11 Near-term Climate Change: Projections and Predictability In summary, it is very likely that anthropogenic warming of surface air the AR4 (Power et al., 2012). Information on the reliability of near- temperature over the next few decades will proceed more rapidly over term projections can also be obtained from verification of past regional land areas than over oceans, and that the warming over the Arctic in trends (Räisänen (2007); Box 11.2) winter will be greater than the global mean warming over the same period. Relative to background levels of natural internal variability, Since the AR4 there has also been considerable progress in under- near-term increases in seasonal mean and annual mean temperatures standing the factors that govern the spatial pattern of change in pre- are expected to occur more rapidly in the tropics and subtropics than cipitation (P), precipitation minus evaporation (P E), and inter-model in mid-latitudes (high confidence). differences in these patterns. The general pattern of wet-get-wetter (also referred to as rich-get-richer , e.g., Held and Soden, 2006; Chou 11.3.2.2 Free Atmospheric Temperature et al., 2009; Allan et al., 2010) and dry-get-drier has been confirmed, although with deviations in some dry regions at present that are pro- Changes in zonal mean temperature for the near-term period (2016 jected to become wetter by some models, e.g., Northeast Brazil in 2035 compared to the base period 1986 2005) for the multi-model austral summer and East Africa (see Annex I). It has been demon- CMIP5 ensemble show a pattern similar to that in the CMIP3, with strated that the wet-get-wetter pattern implies an enhanced season- warming in the troposphere and cooling in the stratosphere of a couple al precipitation range between wet and dry seasons in the tropics, of degrees that is significant even in the near term period. There is and enhanced inter-hemispheric precipitation gradients (Chou et al., relatively greater warming in the tropical upper troposphere and ­ 2007). northern high latitudes. A more detailed assessment of observed and simulated changes in free atmospheric temperatures can be found in It has recently been proposed that analysis of the energy budget, pre- Sections 10.3.1.2.1 and 12.4.3.2. viously applied only to the global mean, may provide further insights into the controls on regional changes in precipitation (Levermann et 11.3.2.3 The Water Cycle al., 2009; Muller and O Gorman, 2011; O Gorman et al., 2012). Muller and O Gorman (2011) argue in particular that changes in radiative and As discussed in the AR4 (Section 10.3.6; Meehl et al., 2007b), the IPCC surface sensible heat fluxes provide a guide to the local precipitation Technical Paper on Climate Change and Water (Bates et al., 2008) response over land. Projected and observed patterns of oceanic pre- and the Special Report on Managing the Risks of Extreme Events and cipitation change in the tropics tend to follow patterns of SST change Disasters to Advance Climate Change Adaptation (Seneviratne et al., because of local changes in atmospheric stability, such that regions 2012), a general intensification of the global hydrological cycle, and warming more than the tropics as a whole tend to exhibit an increase of precipitation extremes, are expected for a future warmer climate in local precipitation, while regions warming less tend to exhibit (e.g., (Huntington, 2006; Williams et al., 2007; Wild et al., 2008; Chou reduced precipitation (Johnson and Xie, 2010; Xie et al., 2010). et al., 2009; Dery et al., 2009; O Gorman and Schneider, 2009; Lu and 11 Fu, 2010; Seager et al., 2010; Wu et al., 2010; Kao and Ganguly, 2011; AR4 (Section 10.3.2 and Chapter 11) showed that, especially in Muller et al., 2011; Durack et al., 2012). In this section, projected the near term, and on regional or smaller scales, the magnitude of changes in the time-mean hydrological cycle are discussed; changes in p ­ rojected changes in mean precipitation was small compared to the extremes, are presented in Section 11.3.2.5 while processes underlying magnitude of natural internal variability (Christensen et al., 2007). precipitation changes are treated in Chapter 7. Recent work has confirmed this result, and provided more quantifi- cation (e.g., Hawkins and Sutton, 2011; Hoerling et al., 2011; Rowell, 11.3.2.3.1 Changes in precipitation 2011; Deser et al., 2012; Power et al., 2012). Hawkins and Sutton (2011) presented further analysis of CMIP3 results and found that, on AR4 projections of the spatial patterns of precipitation change in spatial scales of the order of 1000 km, internal variability contributes response to GHG forcing (Chapter 10, Section 10.3.2) showed consist- 50 to 90% of the total uncertainty in all regions for projections of dec- ency between models on the largest scales (i.e., zonal means) but large adal and seasonal mean precipitation change for the next decade, and uncertainty on smaller scales. The consistent pattern was characterized is the most important source of uncertainty for many regions for lead by increases at high latitudes and in wet regions (including the maxima times up to three decades ahead (Figure 11.8). Thereafter, response in mean precipitation found in the tropics), and decreases in dry regions uncertainty is generally dominant. Forcing uncertainty (except for that (including large parts of the subtropics). Large uncertainties in the sign relating to aerosols, see Section 11.4.7) is generally negligible for near- of projected change were seen especially in regions located on the term projections. The S/N ratio for projected changes in seasonal mean borders between regions of increases and regions of decreases. More precipitation is highest in the subtropics and at high latitudes. Rowell recent research has highlighted the fact that if models agree that the (2011) found that the contribution of response uncertainty to the total projected change is small in some sense relative to internal variability, uncertainty (response plus internal variability) in local precipitation then agreement on the sign of the change is not expected (Tebaldi et change is highest in the deep tropics, particularly over South Amer- al., 2011; Power et al., 2012). This recognition led to the identifica- ica, Africa, the east and central Pacific, and the Atlantic. Over trop- tion of subregions within the border regions, where models agree that ical land and summer mid-latitude continents the representation of projected changes are either zero or small (Power et al., 2012). This, SST changes, ­ tmospheric processes, land surface processes, and the ­ a and other considerations, also led to the realization that the consensus terrestrial carbon cycle all contribute to the uncertainty in projected among models on precipitation projections is more widespread than changes in rainfall. might have been inferred on the basis of the projections described in 984 Near-term Climate Change: Projections and Predictability Chapter 11 Seasonal mean percentage precipitation change (RCP4.5: 2016-2035) Figure 11.12 | CMIP5 multi-model ensemble mean of projected changes (%) in precipitation for 2016 2035 relative to 1986 2005 under RCP4.5 for the four seasons. The number of CMIP5 models used is indicated in the upper right corner. Hatching and stippling as in Figure 11.10. In addition to the response to GHG forcing, forcing from natural and further large uncertainties arise in assessing the responses to these anthropogenic aerosols may exert significant impacts on regional pat- emissions. These issues are discussed in Section 11.3.6. terns of precipitation change as well as on global mean temperature 11 (Bollasina et al., 2011; Yue et al., 2011; Fyfe et al., 2012). Precipitation Figures 11.12 and 11.13a present projections of near-term changes changes may arise as a consequence of temperature and stratification in precipitation from CMIP5. Regional maps and time series are pre- changes driven by aerosol-induced radiative effects, and/or as indirect sented in Annex I. The basic pattern of wet regions tending to get aerosol effects on cloud microphysics (Chapter 7). Future emissions of wetter and dry regions tending to get dryer is apparent, although aerosols and aerosol precursors are subject to large uncertainty, and with some regional deviations as mentioned previously. However, the (a) (b) 25 0.4 Precipitation minus evaporation change (mm day-1) 20 0.3 15 Precipitation change (%) 0.2 10 0.1 5 0 0 -0.1 -5 -0.2 -10 90S 60S 30S EQ 30N 60N 90N 90S 60S 30S EQ 30N 60N 90N Latitude Latitude Figure 11.13 | CMIP5 multi-model projections of changes in annual and zonal mean (a) precipitation (%) and (b) precipitation minus evaporation (mm day 1) for the period 2016 2035 relative to 1986 2005 under RCP4.5. The light blue denotes the 5 to 95% range, the dark blue the 17 to 83% range of model spread. The grey indicates the 1 range of natural variability derived from the pre-industrial control runs (see Annex I for details). 985 Chapter 11 Near-term Climate Change: Projections and Predictability large response uncertainty is evident in the substantial spread in the in latent heat fluxes from the surface (e.g., Fischer et al., 2007; Sen- magnitude of projected change simulated by different climate models eviratne et al., 2010). Jung et al. (2010) use a mixture of observations (Figure 11.13a). In addition, it is important to recognize as discussed and models to illustrate a recent global mean decline in land surface in previous sections that models may agree and still be in error (e.g., evaporation due to soil-moisture limitations. Accompanying precipita- Power et al. 2012). In particular, there is some evidence from com- tion effects are more subtle, as there are significant uncertainties and paring observations with simulations of the recent past that climate large geographical variations regarding the soil-moisture precipitation models might be underestimating the magnitude of changes in precip- feedback (Hohenegger et al., 2009; Taylor et al., 2011). AR4 projec- itation in many regions (Pincus et al., 2008; Liepert and Previdi, 2009; tions (Meehl et al. (2007b) of annual mean soil moisture changes for Schaller et al., 2011; Joetzjer et al., 2012) This evidence is discussed the 21st century showed a tendency for decreases in the subtropics, in detail in Chapter 9 (Section 9.4.1) and Box 11.2, and could imply southern South America and the Mediterranean region, and increases that projected changes in precipitation are underestimated by current in limited areas of east Africa and central Asia. Changes seen in other models. However, the magnitude of any underestimation has yet to be regions were mostly not consistent or statistically significant. quantified, and is subject to considerable uncertainty. AR4 projections of 21st century runoff changes (Meehl et al., 2007b) Figures 11.12 and 11.13a also highlight the large amplitude of the nat- showed consistency in sign among models indicating annual mean ural internal variability of mean precipitation. On regional scales, mean reductions in southern Europe and increases in Southeast Asia and at projected changes are almost everywhere smaller than the estimated high northern latitudes. Projected changes in global mean runoff asso- standard deviation of natural internal variability. The only exceptions ciated with the physiological effects of doubled CO2 concentrations are the northern high latitudes and the equatorial Pacific Ocean (Figure show increases of 6 to 8% relative to pre-industrial levels, an increase 11.12). For zonal means (Figure 11.13a) and at high latitudes only, that is comparable to that simulated in response to RF changes (11% the projected changes relative to the recent past exceed the estimated +/- 6%) (Betts et al., 2007; Cao et al., 2010). Gosling et al. (2011) assess standard deviation of internal variability. the projected impacts of climate change on river runoff from global and basin-scale hydrological models obtaining increased runoff with Overall, zonal mean precipitation will very likely increase in high and global warming in the Liard (Canada), Rio Grande (Brazil) and Xiangxi some of the mid latitudes, and will more likely than not decrease in (China) basins and decrease for the Okavango (southwest Africa). the subtropics. At more regional scales precipitation changes may be influenced by anthropogenic aerosol emissions and will be strongly Consideration of hydrological drought conditions employs a range of influenced by natural internal variability. different dryness indicators, such as soil moisture or other drought indi- ces that integrate precipitation and evaporation effects (Seneviratne et 11.3.2.3.2 Changes in evaporation, evaporation minus precipitation, al., 2012). There are large uncertainties in regional drought projections runoff, soil moisture, relative humidity and specific (Burke and Brown, 2008), and very few studies have addressed the 11 humidity near-term future (Sheffield and Wood, 2008; Dai, 2011). In order to provide an indication of future changes of water availability, Figure Because the variability of the atmospheric moisture storage is negli- 11.13b presents zonal mean changes in precipitation minus evapora- gible, global mean increases in evaporation are required to balance tion (P E) from CMIP5. As in the case of precipitation (Figure 11.13a), increases in precipitation in response to anthropogenic forcing (Meehl the uncertainty is dominated by model differences as opposed to et al., 2007a; Trenberth et al., 2007; Bates et al., 2008; Lu and M. Cai, natural variability (compare blue versus grey shading). The results are 2009). The global atmospheric water content is constrained by the ­consistent with the wet-get-wetter and dry-get-drier pattern (e.g., Held Clausius Clapeyron equation to increase at around 7% K 1; howev- and Soden 2006): In the high latitudes and the tropics, most of the er, both the global precipitation and evaporation in global warming models project zonal-mean increases in P E, which over land would simulations increase at 1 to 3% K 1 (Lambert and Webb, 2008; Lu and need to be compensated by increases in runoff (see next paragraph). M.Cai, 2009). In contrast, zonal mean projected changes in the subtropics are nega- tive, indicating decreases in water availability. Although this pattern Changes in evapotranspiration over land are influenced not only by the is evident in most or all of the models, and although several studies response to RF, but also by the vegetation response to elevated CO2 project drought increases in the near term future (Sheffield and Wood, concentrations. Physiological effects of CO2 may involve both the sto- 2008; Dai, 2011), the assessment is debated in the literature based on matal response, which acts to restrict transpiration (Field et al., 1995; discrepancies in the recent past and due to natural variability (Senevi- Hungate et al., 2002; Cao et al., 2009, 2010; Lammertsma et al., 2011), ratne et al., 2012; Sheffield et al., 2012). and an increase in plant growth and leaf area, which acts to increase evapotranspiration (El Nadi, 1974; Bounoua et al., 2010). Simulation of The global distribution of the 2016 2035 changes in annual mean the latter process requires the inclusion of vegetation models that allow evaporation, evaporation minus precipitation (E P), surface runoff, soil spatial and temporal variability in the amount of active biomass, either moisture, relative humidity and surface-level specific humidity from the by changes in the phenological cycle or changes in the biome structure. CMIP5 multi-model ensemble under RCP4.5 are shown in Figure 11.14. Changes in evaporation over land (Figure11.14a), are mostly positive In response to GHG forcing, dry land areas tend to show a reduction with the largest values at northern high latitudes, in agreement with of evaporation and often precipitation, accompanied by a drying of the projected temperature increases (Figure 11.10). Over the oceans, evap- soil and an increase of surface temperature, in response to decreases oration is also projected to increase in most regions. Projected changes 986 Near-term Climate Change: Projections and Predictability Chapter 11 are larger than the estimated standard deviation of internal variability North Atlantic, although the model agreement is low in that region. only at high latitudes and over the tropical oceans. Decreases in evap- Projected changes in (E P) over land (Figure 11.14b) are generally oration over land (i.e., Australia, southern Africa, northeastern South consistent with the zonal mean changes shown in Figure 11.13b. In the America and Mexico) and oceans are smaller than the estimated stand- high northern latitudes and the tropics, (E P) changes are mostly neg- ard deviation of internal variability; the only ­ xception is the western e ative as dominated by precipitation increases (Figure 11.12), while in 11 Figure 11.14 | CMIP5 multi-model annual mean projected changes for the period 2016 2035 relative to 1986 2005 under RCP4.5 for: (a) evaporation (%), (b) evaporation minus precipitation (E P, mm day 1), (c) total runoff (%), (d) soil moisture in the top 10 cm (%), (e) relative change in specific humidity (%), and (f) absolute change in relative humidity (%). The number of CMIP5 models used is indicated in the upper right corner of each panel. Hatching and stippling as in Figure 11.10. 987 Chapter 11 Near-term Climate Change: Projections and Predictability the subtropics several areas exhibit increases in (E P), in particular in climate models. These features include high- and low-latitude physics Europe, western Australia and central-western USA. However, in most (Rind, 2008; Woollings, 2010), ocean circulation (Woollings and Black- locations changes are smaller than internal variability. burn, 2012), tropical circulation (Haarsma and Selten, 2012) and strat- ospheric dynamics (Huebener et al., 2007; Morgenstern et al., 2010; Annual mean shallow soil moisture (Figure 11.14d) shows decreases in Scaife et al., 2012). As a result, there is considerable model uncertainty most subtropical regions (except La Plata basin in South America) and in the response of the NH storm track position (Ulbrich et al., 2008), in central Europe, and increases in northern mid-to-high latitudes. Pro- stationary waves (Brandefelt and Kornich, 2008) and the jet streams jected changes are larger than the estimated internal variability only (Miller et al., 2006; Ihara and Kushnir, 2009; Woollings and Blackburn, in southern Africa, the Amazon region and Europe. Projected changes 2012). Further, CMIP5 models show that the response of NH extratrop- in runoff (Figure 11.14c) show decreases in northern Africa, western ical circulation to even strong GHG forcing remains weak compared to Australia, southern Europe and southwestern USA and increases larger recent multidecadal variability and a recent detection and attribution than the internal variability in northwestern Africa, southern Arabia study suggests that tropospheric ozone and aerosol changes may have and southeastern South America associated to the projected changes been a key driver to NH extratropical circulation changes (Gillett et al., in precipitation (Figure 11.12). Owing to the simplified hydrological 2013). Some AOGCMs simulate multi-decadal NAO variability as large models in many CMIP5 climate models, the projections of soil moisture as that recently observed with no external forcing (Selten et al., 2004; and runoff have large model uncertainties. Semenov et al., 2008). This suggests that internal variability could dom- inate the anthropogenically forced response in the near term (Deser et Changes in near-surface specific humidity are positive, with the larg- al., 2012). est values at northern high latitudes when expressed in percentage terms (Figure 11.14e). This is consistent with the projected increases in Some studies have predicted a shift to the negative phase of the Atlan- temperature when assuming constant relative humidity. These changes tic Multi-decadal Oscillation (AMO) over the coming few decades, with are larger than the estimated standard deviation of internal variabil- potential impacts on atmospheric circulation around the Atlantic sector ity almost everywhere: the only exceptions are oceanic regions such (Knight et al., 2005; Sutton and Hodson, 2005; Folland et al., 2009). It as the northern North Atlantic and around Antarctica. In comparison, has also been suggested that there may be significant changes in solar absolute changes in near-surface relative humidity (Figure 11.14f) are forcing over the next few decades, which could have an influence on much smaller, on the order of a few percent, with general decreases NAO-related atmospheric circulation (Lockwood et al., 2011), although over most land areas, and small increases over the oceans. Significant these predictions are highly uncertain (see Section 11.3.6.2.2). decreases relative to natural variability are projected in the Amazonia, southern Africa and Europe, although the model agreement in these There is only medium confidence in near-term projections of a north- regions is low. ward shift of NH storm track and westerlies, and an increase of the NAO/NAM because of the large response uncertainty and the poten- 11 Over the next few decades projected increases in near-surface specific tially large influence of internal variability. humidity are very likely, and projected increases in evaporation are likely in many land regions. There is low confidence in projected chang- 11.3.2.4.2 Southern Hemisphere extratropical circulation es in soil moisture and surface runoff. Increases in GHGs, and related dynamical processes, are projected 11.3.2.4 Atmospheric Circulation to lead to poleward shifts in the annual mean position of Southern H ­ emisphere (SH) extratropical storm tracks and winds (Figure 11.17; 11.3.2.4.1 Northern Hemisphere extratropical circulation Chapters 10 and 12). A key issue in projections of near-term SH extra- tropical circulation change is the extent to which changes driven by In the NH extratropics, some Atmosphere Ocean General Circulation stratospheric ozone recovery will counteract changes driven by increas- Models (AOGCMs) indicate changes to atmospheric circulation from ing GHGs. Several observational and modeling studies (Gillett and anthropogenic forcing by the mid-21st century, including a pole- Thompson, 2003; Shindell and Schmidt, 2004; Arblaster and Meehl, ward shift of the jet streams and associated zonal mean storm tracks 2006; Roscoe and Haigh, 2007; Fogt et al., 2009; Polvani et al., 2011a; (Miller et al., 2006; Pinto et al., 2007; Paeth and Pollinger, 2010) and Gillett et al., 2013) indicate that, over the late 20th and early 21st cen- a strengthening of the Atlantic storm track (Pinto et al., 2007), Figure turies, the observed summertime poleward shift of the westerly jet (a 11.15. Consistent with this, the CMIP5 AOGCMs exhibit an ensemble positive Southern Annular Mode (SAM)) has been caused primarily by mean increase in the North Atlantic Oscillation (NAO) and Northern the depletion of stratospheric ozone, with increasing GHGs contributing Annular Model (NAM) indices by 2050, especially in autumn and only a smaller fraction to the observed trends. The latest generation winter (Gillett et al., 2013). of climate models project substantially smaller poleward trends in SH atmospheric circulation in austral summer over the coming half century However, there are reasons to be cautious over these near-term projec- compared to those over the late 20th century, as the recovery of strat- tions. Although models simulate the broad features of the large-scale ospheric ozone will oppose the effects of continually increasing GHGs circulation well, there remain quite significant biases in many models (Arblaster et al., 2011; McLandress et al., 2011; Polvani et al., 2011a; (see Sections 9.4.1.4.3 and 9.5.3.2). The response of the NH circulation Eyring et al., 2013). Locally, internal variability may be a dominant con- can be sensitive to small changes in model formulation (Sigmond et al., tributor to near-term changes in lower-tropospheric zonal winds (Figure 2007), and to features that are known to be poorly simulated in many 11.17). The average 2016 2035 SH extratropical storm tracks and zonal 988 Near-term Climate Change: Projections and Predictability Chapter 11 winds are likely to shift poleward relative to 1986 2005. However, even though a full recovery of the ozone hole is not expected until the 2060s to 2070s (Table 5.4; WMO, 2010; see Chapter 12), it is likely that over the near term there will be a reduced rate in the austral summertime poleward shift of the SH circumpolar trough, SH extratropical storm tracks and winds compared to its movement over the past 30 years, including the possibility of no detectable shift. 11.3.2.4.3 Tropical circulation Increases in GHGs are expected to lead to a poleward shift of the Hadley Circulation (Lu et al., 2007; Chapter 12, Figure 11.18). Relative to the late 20th century, the tendency towards a poleward expansion of the Hadley Circulation will start to emerge by the mid-2030s, with certain intra-model consensus in the SH expansion, despite the coun- Figure 11.15 | CMIP5 multi-model ensemble mean of projected changes (m s 1) in teracting effect of ozone recovery (Figure 11.18). As with near-term zonal (west-to-east) wind at 850 hPa for 2016 2035 relative to 1986 2005 under changes in SH extratropical circulation, a key for near-term projections RCP4.5. The number of CMIP5 models used is indicated in the upper right corner. Hatch- of the structure of the SH Hadley Circulation is the extent to which ing and stippling as in Figure 11.10. future stratospheric ozone recovery will counteract the impact of GHGs. The poleward expansion of the Hadley Circulation, particularly poleward expansion of the Hadley Circulation driven by the response of the SH branch during austral summer, during the later decades of of the atmosphere to increasing GHGs (Lu et al., 2007; Kang et al., the 20th century has been largely attributed to the combined impact 2011; Staten et al., 2011; Butler et al., 2012) would be counteract- of stratospheric ozone depletion (Thompson and Solomon, 2002; Son ed in the SH by reduced stratospheric ozone depletion but depends et al., 2008, 2009a, 2009b; Polvani et al., 2011a, 2011b; Min and Son, on the rate of ozone recovery (UNEP and WMO, 2011). Increases in 2013) and the concurrent increase in GHGs (Arblaster and Meehl, the incoming solar radiation can lead to a widening of the Hadley 2006; Arblaster et al., 2011) as discussed in the previous section. The Cell (Haigh, 1996; Haigh et al., 2005) and large volcanic eruption to RCP4.5(2016 2035) Historical(1986 2005) northward 1 1 CanESM2 11 MIROC ESM CHEM 2 CCSM4 12 MIROC ESM 0.8 11 3 CNRM CM5 13 MPI ESM LR 5 4 FGOALS g2 14 MRI CGCM3 0.6 4 5 GFDL CM3 15 NorESM1 M 9 Displacement of dry zone (°lat) 6 6 GFDL ESM2G 0.4 7 GFDL ESM2M 11 8 HadGEM2 ES 0.2 9 IPSL CM5A LR 7 8 10 IPSL CM5A MR 15 2 14 12 0 3 14 411 15 10 0.2 12 3 7 2 1 9 8 6 1 0.4 10 13 5 13 0.6 0.8 southward 1 0.8 0.6 0.4 0.2 0 0.2 0.4 0.6 0.8 southward Displacement of the Hadely Cell boundaries (°lat) northward Figure 11.16 | Projected changes in the annual averaged poleward edge of the Hadley Circulation (horizontal axis) and sub-tropical dry zones (vertical axis) based on 15 Atmo- sphere Ocean General Circulation Models (AOGCMs) from the CMIP5 (Taylor et al., 2012) multi-model ensemble, under 21st century RCP4.5. Orange symbols show the change in the northern edge of the Hadley Circulation/dry zones, while blue symbols show the change in the southern edge of the Hadley Circulation/dry zones. Open circles indicate the multi-model average, while horizontal and vertical coloured lines indicate the +/-1 standard deviation range for internal climate variability estimated from each model. Values refer- enced to the 1986 2005 climatology. (Figure based on the methodology of Lu et al., 2007.) 989 Chapter 11 Near-term Climate Change: Projections and Predictability contraction of the tropics and the tropical circulation (Lu et al., 2007; Meehl and Arblaster, 2012; Meehl et al., 2013a). Even on time scales Birner, 2010). So future solar variations and volcanic activities could of 30 to 100 years, substantial variations in the strength of the Pacific also lead to variations in the width of the Hadley Cell. The poleward Walker Circulation in the absence of changes in RF are possible (Power extent of the Hadley Circulation and associated dry zones can exhibit et al., 2006; Vecchi et al., 2006). Estimated near-term weakening of substantial internal variability (e.g., Birner, 2010; Davis and Rosenlof, the Walker Circulation from CMIP3 models under the A1B scenario 2012) that can be as large as its near-term projected changes (Figure (Vecchi and Soden, 2007; Power and Kociuba, 2011a) are very likely to 11.16). There is also considerable uncertainty in the amplitude of the be smaller than the impact of internal climate variations over 50-year poleward shift of the Hadley Circulation in response to GHGs across time scales (Vecchi et al., 2006). There is also considerable response multiple AOGCMs (Lu et al., 2007; Figure 11.16). It is likely that the uncertainty in the amplitude of the weakening of Walker Circulation in poleward extent of the Hadley Circulation will increase through the response to GHG increase across multiple AOGCMs (Vecchi and Soden, mid-21st century. However, because of the counteracting impacts of 2007; DiNezio et al., 2009; Power and Kociuba, 2011a, 2011b). Thus, future changes in stratospheric ozone and GHG concentrations, it is there is low confidence in projected near-term changes to the Walker unlikely that it will continue to expand poleward in the SH as rapidly Circulation. It is very likely that there will be decades in which the as it did in recent decades. Walker Circulation strengthens and weakens due to internal variability through the mid-century as the externally forced change is small com- The Hadley Cell expansion in the NH has been largely attributed to the pared to internally generated decadal variability. low-frequency variability of the SST (Hu et al., 2013), the increase of black carbon (BC) and tropospheric ozone (Allen and Sherwood, 2011). 11.3.2.5 Atmospheric Extremes Internal variability in the poleward edge of the NH Hadley Circulation is large relative the radiatively forced signal (Figure 11.16. Given the Extreme events in a changing climate are the subject of Chapter 3 (Sen- complexity in the forcing mechanism of the NH expansion and the eviratne et al., 2012) of the IPCC Special Report on Extremes (SREX). uncertainties in future concentrations of tropospheric pollutants, there This previous IPCC chapter provides an assessment of more than 1000 is low confidence in the character of near-term changes to the struc- studies. Here the focus is on near-term aspects and an assessment of ture of the NH Hadley Circulation. more recent studies is provided. Global climate models and theoretical considerations suggest that 11.3.2.5.1 Temperature extremes a warming of the tropics should lead to a weakening of the zonally asymmetric or Walker Circulation (Knutson and Manabe, 1995; Held In the AR4 (Meehl et al., 2007b), cold episodes were projected to and Soden, 2006; Vecchi and Soden, 2007; Gastineau et al., 2009). decrease significantly in a future warmer climate and it was ­considered Aerosol forcing can modify both Hadley and Walker Circulations, very likely that heat waves would be more intense, more frequent and which depending on the details of the aerosol forcing may lead to last longer towards the end of the 21st century. These conclusions 11 temporary reversals or enhancements in any GHG-driven weakening have generally been confirmed in subsequent studies addressing both of the Walker Circulation (Sohn and Park, 2010; Bollasina et al., 2011; global scales (Clark et al., 2010; Diffenbaugh and Scherer, 2011; Caesar Merrifield, 2011; DiNezio et al., 2013). Meanwhile, the strength and and Lowe, 2012; Orlowsky and Seneviratne, 2012; Sillmann et al., structure of the Walker Circulation are impacted by internal climate 2013) and regional scales (e.g., Gutowski et al., 2008; Alexander and variations, such as the ENSO (e.g., Battistiand Sarachik, 1995), the PDO Arblaster, 2009; Fischer and Schar, 2009; Marengo et al., 2009; Meehl (e.g., Zhang et al. 1997) and the IPO (Power et al., 1999, 2006; Meehl et al., 2009a; Diffenbaugh and Ashfaq, 2010; Fischer and Schar, 2010; and Hu, 2006; Meehl and Arblaster, 2011; Power and Kociuba, 2011b; Cattiaux et al., 2012; Wang et al., 2012). In the SREX assessment it is (a) Warm days (TX90p) (b) Cold Days (TX10p) (c) Very Wet Days (R95p) historical 12 12 historical 70 RCP2.5 70 RCP2.5 60 60 RCP4.5 RCP4.5 RCP8.5 10 10 RCP8.5 60 60 Exceedance rate (%) Exceedance rate (%) Relative change (%) 50 50 8 8 40 40 40 40 6 6 30 30 20 20 4 4 20 20 historical 2 RCP2.5 2 RCP4.5 0 0 10 10 RCP8.5 0 0 1960 1980 2000 2020 2040 2060 2080 2100 1960 1980 2000 2020 2040 2060 2080 2100 1960 1980 2000 2020 2040 2060 2080 2100 Year Year Year Figure 11.17 | Global projections of the occurrence of (a) warm days (TX90p), (b) cold days (TX10p) and (c) precipitation amount from very wet days (R95p). Results are shown from CMIP5 for the RCP2.6, RCP4.5 and RCP8.5 scenarios. Solid lines indicate the ensemble median and shading indicates the interquartile spread between individual projections (25th and 75th percentiles). The specific definitions of the indices shown are (a) percentage of days annually with daily maximum surface air temperature (Tmax) exceeding the 90th percentile of Tmax for 1961 1990, (b) percentage of days with Tmax below the 10th percentile and (c) percentage change relative to 1986 2005 of the annual precipitation amount from daily events above the 95th percentile. (From Sillmann et al., 2013.) 990 Near-term Climate Change: Projections and Predictability Chapter 11 concluded that increases in the number of warm days and nights and Near-term projections from General Circulation Model Regional Cli- decreases in the number of cold days and nights are virtually certain mate Model (GCM RCM) model chains (van der Linden and ­ itchell, M on the global scale. 2009) for Europe are shown in Figure 11.18, displaying near-term changes in mean and extreme temperature (left-hand panels) and None of the aforementioned studies specifically addressed the near precipitation (right-hand panels) relative to the reference period 1986 term. However, detection and attribution studies (see also Chapter 2005. In terms of mean June, July and August (JJA) temperatures (Figure 10) show that temperature extremes have already increased in many 11.18a), projections show a warming of 0.6°C to 1.5°C, with highest regions, consistent with climate change projections, and analyses changes over the land portion of the Mediterranean. The north south of CMIP5 global projections show that this trend will continue and gradient in the projections is consistent with the AR4. Daytime extreme become more notable. The CMIP5 model ensemble exhibits a signifi- summer temperatures in southern and central Europe are projected to cant decrease in the frequency of cold nights, an increase in the fre- warm substantially faster than mean temperatures (compare Figure quency of warm days and nights and an increase in the duration of 11.18a and b). This difference between changes in mean and extremes warm spells (Sillmann et al., 2013). These changes are particularly evi- can be explained by increases in interannual and/or synoptic variability, dent in global mean projections (see Figure 11.17). Figure 11.17 shows or increases in diurnal temperature range (Gregory and Mitchell, 1995; that for the next few decades as discussed in the introduction to Schar et al., 2004; Fischer and Schar, 2010; Hansen, 2012; Quesada et the current chapter these changes are remarkably insensitive to the al., 2012; Seneviratne et al., 2012). There is some evidence, however, emission scenario considered (Caesar and Lowe, 2012). In most land that this effect is overestimated in some of the models (Fischer et al., regions and in the near-term, the frequency of warm days and warm 2012; Stegehuis et al., 2012), leading to a potential overestimation of nights will thus likely continue to increase, while that of cold days and the projected Mediterranean summer mean warming (Buser et al., 2009; cold nights will likely continue to decrease. Boberg and Christensen, 2012). With regard to near-term projections of 11 (°C) (%) Figure 11.18 | European-scale projections from the ENSEMBLES regional climate modelling project for 2016 2035 relative to 1986 2005, with top and bottom panels applicable to June, July and August (JJA) and December, January, February (DJF), respectively. For temperature, projected changes (°C) are displayed in terms of ensemble mean changes of (a, c) mean seasonal surface temperature, and (b, d) the 90th percentile of daily maximum temperatures. For precipitation, projected changes (%) are displayed in terms of ensemble mean changes of (e, g) mean seasonal precipitation and (f, h) the 95th percentile of daily precipitation. The stippling in (e h) highlights regions where 80% of the models agree in the sign of the change (for temperature all models agree on the sign of the change). The analysis includes the following 10 GCM-RCM simulation chains for the SRES A1B scenario (naming includes RCM group and GCM simulation): HadRM3Q0-HadCM3Q0, ETHZ-HadCM3Q0, HadRM3Q3-HadCM3Q3, SMHI-HadCM3Q3, HadRM3Q16-HadCM3Q16, SMHI- BCM, DMI-ARPEGE, KNMI-ECHAM5, MPI-ECHAM5, DMI-ECHAM5. (Rajczak et al., 2013.) 991 Chapter 11 Near-term Climate Change: Projections and Predictability record heat compared to record cold (Meehl et al., 2009b) show, for one Previous work reviewed in AR4 has established that extreme model, that over the USA the ratio of daily record high temperatures to p ­ recipitation events may increase substantially stronger than mean daily record low temperatures could increase from an early 2000s value precipitation amounts. More specifically, extreme events may increase of roughly 2 to 1 to a mid-century value of about 20 to 1. with the atmospheric water vapour content, that is, up to the rate of the Clausius Clapeyron (CC) relationship (e.g., Allen and Ingram, In terms of December, January and February (DJF) temperatures (Figure 2002). More recent work suggests that increases beyond this threshold 11.18c), projections show a warming of 0.3°C to 1.8°C, with the larg- may occur for short-term events associated with thunderstorms (Len- est changes in the N NE part of Europe. This characteristic pattern of derink and Van Meijgaard, 2008; Lenderink and Meijgaard, 2010) and changes tends to persist to the end of century (van der Linden and tropical convection (O Gorman, 2012). A number of studies showed Mitchell, 2009). In contrast to JJA temperatures, daytime high-percen- strong dependencies on location and season, but confirm the exist- tile (i.e., warm) winter temperatures are projected to warm slower than ence of significant deviations from the CC scaling (e.g., Lenderink et mean temperatures (compare Figure 11.18c and Figure 11.18d), while al., 2011; Mishra et al., 2012; Berg et al., 2013). Studies with cloud-re- low-percentile (i.e., cold) winter temperatures warm faster than the solving models generally support the existence of temperature-precip- mean. This behaviour is indicative of reductions in internal variability, itation relations that are close to or above (up to about twice) the CC which may be linked to changes in storm track activity, reductions in relation (Muller et al., 2011; Singleton and Toumi, 2012). diurnal temperature range and changes in snow cover (e.g., Colle et al. 2013; Dutra et al., 2011). 11.3.2.5.3 Tropical cyclones 11.3.2.5.2 Heavy precipitation events The projected response of tropical cyclones (TCs) at the end of the 21st century is summarized in Section 14.6.1 and the IPCC Special Report For the 21st century, the AR4 and the SREX concluded that heavy pre- on Extremes (SREX) (Seneviratne et al., 2012). Relative to the number cipitation events were likely to increase in many areas of the globe of studies focussing on projections of TC activity at the end of the 21st (IPCC, 2007). Since AR4, a larger number of additional studies have century (Section 14.6.1; Knutson et al., 2010; Seneviratne et al., 2012 been published using global and regional climate models (Fowler et al., there are fewer studies that have explored near-term projections of TC 2007; Gutowski et al., 2007; Sun et al., 2007; Im et al., 2008; O Gorman activity (Table 11.2); the North Atlantic (NA) stands out as the basin and Schneider, 2009; Xu et al., 2009; Hanel and Buishand, 2011; Hein- with most studies. In the NA, there are mixed projections for basin- rich and Gobiet, 2011; Meehl et al., 2012b). For the near term, CMIP5 wide TC frequency, suggesting significant decreases (Knutson et al., global projections (Figure 11.17c) confirm a clear tendency for increas- 2013a) or non-significant changes (Villarini et al., 2011; Villarini and es in heavy precipitation events in the global mean, but there are sig- Vecchi, 2012). Multi-model mean projected NA TC frequency chang- nificant variations across regions (Sillmann et al., 2013). Past observa- es based on CMIP3 and CMIP5 over the first half of the 21st century tions have also shown that interannual and decadal variability in mean were smaller than the overall uncertainty estimated from the Coupled 11 and heavy precipitation are large, and are in addition strongly affected General Circulation Models (CGCMs), with internal climate variability by internal variability (e.g., El Nino), volcanic forcing and anthropogen- being a leading source of uncertainty through the mid-21st century ic aerosol loads (see Section 2.3.1). In general models have difficulties (Villarini et al., 2011; Villarini and Vecchi, 2012). Therefore, based on in representing these variations, particularly in the tropics (see Section the limited literature available, the conflicting near-term projections 9.5.4.2). Thus the frequency and intensity of heavy precipitation events in basins with more than one study, the large influence of internal will likely increase over many land areas in the near term, but this trend variability, the lack of confidently detected/attributed changes in TC will not be apparent in all regions, because of natural variability and activity (Chapter 10) and the conflicting projections for basin-wide TC possible influences of anthropogenic aerosols. frequency even at the end of the 21st century (Chapter 14), there is currently low confidence in basin-scale and global projections of trends Simulations with regional climate models demonstrate that the in tropical cyclone frequency to the mid-21st century. response in terms of heavy precipitation events to anthropogenic cli- mate change may become evident in some but not all regions in the Exploring different hurricane intensity measures, two studies project near term. For instance, ENSEMBLES projections for Europe (see Figure near-term increases of NA hurricane intensity (Knutson et al., 2013a; 11.18e h) confirm the previous IPCC results that changes in mean Villarini and Vecchi, 2013), driven in large part by projected reductions precipitation as well as heavy precipitation events are characterized in NA tropospheric aerosols in CMIP5 future forcing scenarios. Studies by a pronounced north south gradient in the extratropics, especially project near-term increases in the frequency Category 4 5 TCs in the in the winter season, with precipitation increases in the higher lati- NA (Knutson et al., 2013a) and southwest Pacific (Leslie et al., 2007). tudes and decreases in the subtropics. Although this pattern starts to Published studies agree in the sign of projected mid-century intensity emerge in the near term, the projected changes are statistically signif- change (intensification), but the only basin with more than one study icant only in a fraction of the domain. The results are affected by both exploring intensity is the NA. For the NA, an estimate of the time scale changes in water vapour content as induced by large-scale warming of emergence of projected changes in intense TC frequency exceeds 60 and large-scale circulation changes. Figure 11.18e h also shows that years (Bender et al., 2010), although that estimate depends crucially mid- and high-latitude projections for changes in DJF extremes and on the amplitude of internal climate variations of intense hurricane fre- means are qualitatively similar in the near term, at least for the event quency (e.g., Emanuel, 2011), which remains poorly constrained at the size considered. moment. Therefore, there is low confidence in near-term TC intensity projections in all TC basins. 992 Near-term Climate Change: Projections and Predictability Chapter 11 Table 11.2 | Summary of studies exploring near-term projections of tropical cyclone (TC) activity. First column lists the TC basin explored, the second column summarizes the changes in TC activity reported in each study, the third column presents notes on the methodology and the fourth column provides a reference to the study. TC Basin Projected Change in TC Activity Reported Notes Reference Explored Reduced global, Northern Hemisphere and Southern Hemisphere frequency High-resolution atmospheric model forced by CMIP3 Sugi and Yoshimura Global 2016 2035 relative to 1986 2005. SRES A1B multi-model SST change 2004 2099. (2012) Over first half of 21st century: Reduced Activity over South China Sea, Statistical downscale of five CMIP3 Wang et al. (2011) N.W. Pacific Increased Activity near subtropical Asia models under SRES A1B. Over 2001 2040, a decrease in TC frequency in the East China Sea, and a frequen- Statistical downscaling of CGCM forced Orlowsky and N.W. Pacific cy decrease and increase in intensity of Yangze River Basin landfalling typhoons. by CMIP3 SRES A1B scenario. Seneviratne (2012) Differences of 2000 2050 with 1970 2000. Negligible change in overall Dynamical regional downscale of coupled AOGCM Leslie et al. (2007) S.W. Pacific frequency. Significant (~15%) increase in number of Category 4 5 TCs. forced with IPCC IS92a increasing CO2 scenario. Linear trend in TC frequency 2001 2050: Ensemble-mean non-significant Statistical downscaling of CMIP3 Villarini et al. (2011) N. Atlantic decrease in TC frequency ( 5%). Ensemble range of 50% to +30%. models under A1B scenario. TC frequency averaged 2016 2035 minus 1986 2005: Ensemble-mean non- Statistical downscaling of CMIP5 Villarini and N. Atlantic significant increase for RCP2.6 (4%), non-significant decrease for RCP4.5 ( 2%) RCP2.6, RCP4.5 and RCP8.5 Vecchi (2012) and RCP8.5 ( 1%). Ensemble range of 30% to 27% across all scenarios/models. Power Dissipation Index averaged 2016 2035 minus 1986 2005: Ensemble mean Statistical downscaling of CMIP5 Villarini and N. Atlantic significant increase for RCP2.6 (23%) and RCP8.5 (17%), non-significant increase RCP2.6, RCP4.5 and RCP4.5 Vecchi (2013) for RCP4.5 (10%). Ensemble range of 43% to 78% across all scenarios/models. Difference 2016 2035 minus 1986 2005 averages: Significant decrease Double dynamical refinement of CMIP5 RCP4.5 Knutson et al. (2013a) ( 20%) to overall TC and hurricane frequency. Significant increase multi-model ensemble projections. N. Atlantic (+45%) in number of Category 4 5 TCs. Significant increase in pre- cipitation of hurricanes (11%) and tropical storms (18%). Modes of climate variability that in the past have led to variations in impact the radiative balance of the planet for 2 to 3 years after their the intensity, frequency and structure of tropical cyclones across the eruption and act to reduce oceanic temperature for decades into the globe such as the ENSO (e.g., Zhang and Delworth, 2006; Wang et future (Delworth et al., 2005; Stenchikov et al., 2009; Gregory, 2010). al., 2007; Callaghan and Power, 2011; Chapter 14) are very likely to An estimate using the GFDL-CM2.1 coupled AOGCM (Stenchikov et al., continue influencing TC activity through the mid-21st century. There- 2009) suggests that a single Tambora (1815)-like volcano could erase fore, it is very likely that over the next few decades tropical cyclone the projected global ocean depth-averaged temperature increase for 11 frequency, intensity and spatial distribution globally, and in individual many years to a decade. A Pinatubo (1991)-like volcano could erase basins, will vary from year to year and decade to decade. the projected increase for 2 to 10 years. See Section 11.3.6 for further discussion. 11.3.3 Near-term Projected Changes in the Ocean Global sea surface temperature change 11.3.3.1 Temperature Globally averaged surface and near-surface ocean temperatures are projected by AOGCMs to warm over the early 21st century, in response to both present day atmospheric concentrations of GHGs ( committed warming ; e.g., Meehl et al., 2006) and projected future changes in RF (°C) (Figure 11.19). Globally averaged SST shows substantial year-to-year and decade-to-decade variability (e.g., Knutson et al., 2006; Meehl et al., 2011), whereas the variability of depth-averaged ocean tempera- tures is much less (e.g., Meehl et al., 2011; Palmer et al., 2011). The rate at which globally averaged surface and depth-averaged temperatures rise in response to a given scenario for RF shows a considerable spread between models (an example of response uncertainty; see Section 11.2), due to differences in climate sensitivity and ocean heat uptake (e.g., Gregory and Forster, 2008). In the CMIP5 models under all RCP Figure 11.19 | Projected changes in annual averaged, globally averaged, surface forcing scenarios, globally averaged SSTs are projected to be warmer ocean temperature based on 12 Atmosphere Ocean General Circulation Models over the near term relative to 1986 2005 (Figure 11.20). (AOGCMs) from the CMIP5 (Meehl et al., 2007b) multi-model ensemble, under 21st century scenarios RCP2.6, RCP4.5, RCP6.0 and RCP8.5. Shading indicates the 90% A key uncertainty in the future evolution of globally averaged oceanic range of projected annual global mean surface temperature anomalies. Anomalies com- puted against the 1986 2005 average from the historical simulations of each model. temperature are possible future large volcanic eruptions, which could 993 Chapter 11 Near-term Climate Change: Projections and Predictability Figure 11.20 | CMIP5 multi-model ensemble mean of projected changes in sea surface temperature (right panel; °C) and sea surface salinity (left panel; practical salinity units) for 2016 2035 relative to 1986 2005 under RCP4.5. The number of CMIP5 models used is indicated in the upper right corner. Hatching and stippling as in Figure 11.10. In the absence of multiple major volcanic eruptions (see Section in the subtropical North Atlantic, and decreases in the west Pacific 11.3.6.2), it is very likely that globally averaged surface and depth-av- warm pool region (Stott et al., 2008; Cravatte et al., 2009; Durack and eraged temperatures averaged 2016 2035 will be warmer than those Wijffels, 2010; Durack et al., 2012; Pierce et al., 2012; Terray et al., averaged over 1986 2005. 2012). Models generally predict increases in salinity in the tropical and (especially) subtropical Atlantic, and decreases in the western tropical There are regional variations in the projected amplitude of ocean tem- Pacific over the next few decades (Figure 11.20) (Durack et al., 2012; perature change (Figure 11.20) which are influenced by ocean circula- Terray et al., 2012). These projected decreases in the Atlantic and in the tion as well as surface heating (Timmermann et al., 2007; Vecchi and western tropical Pacific are considered likely. Soden, 2007; DiNezio et al., 2009; Yin et al., 2009; Xie et al., 2010; Yin et al., 2010), including changes in tropospheric aerosol concentrations Projected near-term increases in freshwater flux into the Arctic Ocean (e.g., Booth et al., 2012; Villarini and Vecchi, 2012). Inter-decadal vari- produce a fresher surface layer and increased transport of fresh water 11 ability of upper ocean temperatures is larger in mid-latitudes, particu- into the North Atlantic (Holland et al., 2006; Holland et al., 2007; Vavrus larly in the NH, than in the tropics. A consequence of this contrast is et al., 2012). Such contributions to decreased density of the ocean sur- that it will take longer in the mid-latitudes than in the tropics for the face layer in the North Atlantic could act to reduce deep ocean con- anthropogenic warming signal to emerge from the noise of internal vection there and contribute to a near-term reduction of strength of variability (Wang et al., 2010). Atlantic Meridional Ocean Circulation (AMOC). However, the strength of the AMOC can also be modulated by changes in temperature, such Projected changes to thermal structure of the tropical Indo-Pacific as those from changing RF (Delworth and Dixon, 2006). are strongly dependent on the future behaviour of the Walker Circu- lation (Vecchi and Soden, 2007; DiNezio et al., 2009; Timmermann et 11.3.3.3 Circulation al., 2010), in addition to changes in heat transport and changes in surface heat fluxes. It is likely that internal climate variability will be a As discussed in previous assessment reports, the AMOC is generally dominant contributor to changes in the depth and tilt of the equatorial projected to weaken over the next century in response to increase in thermocline, and the strength of the east west gradient of SST across atmospheric GHG. However, the rate and magnitude of weakening the Pacific through the mid-21st century; thus it is likely there will be is very uncertain. Response uncertainty is a major contributor in the multi-year periods with increases or decreases in these measures. near term, but the influence of anthropogenic aerosols and natural RFs (solar, volcanic) cannot be neglected, and could be as important as the 11.3.3.2 Salinity influence of GHGs (e.g., Delworth and Dixon, 2006; Stenchikov et al., 2009). For example, the rate of weakening of the AMOC in two models Changes in sea surface salinity are expected in response to changes with different climate sensitivities is quite different, with the less in precipitation, evaporation and runoff (see Section 11.3.2.3), as well sensitive model (CCSM4) showing less weakening and a more rapid as ocean circulation; in general (but not in every region), salty regions recovery than the more sensitive model (Community Earth System are expected to become saltier and fresh regions fresher (e.g., Durack Model 1/Community Atmosphere Model 5 (CESM1/CAM5; Meehl et et al. 2012; Terray et al. 2012; Figure 11.20). As discussed in Chapter al., 2013c). In addition, the natural variability of the AMOC on dec- 10 (Section 10.4.2), observation-based and attribution studies have adal time scales is poorly known and poorly understood, and could found some evidence of an emerging anthropogenic signal in salin- dominate any anthropogenic response in the near term (Drijfhout and ity change (Section 10.4.2), in particular increases in surface ­ alinity s Hazeleger, 2007). The AMOC is known to play an important role in the 994 Near-term Climate Change: Projections and Predictability Chapter 11 decadal variability of the North Atlantic Ocean, but climate models it would take only until about 2016 to reach a nearly ice-free Arctic show large differences in their simulation of both the amplitude and Ocean in summer. However, such an approach not only neglects the spectrum of AMOC variability (e.g., Bryan et al., 2006; Msadek et al., effect of year-to-year or longer-term variability (Overland and Wang, 2010). In some AOGCMs changes in SH surface winds influence the 2013) but also ignores the negative feedbacks that can occur when evolution of the AMOC on time scales of many decades (Delworth and the sea ice cover becomes thin (Notz, 2009). Mahlstein and Knutti Zeng, 2008), so the delayed response to SH wind changes, driven by (2012) estimated the annual mean global surface warming threshold the historical reduction in stratospheric ozone along with its projected for nearly ice-free Arctic conditions in September to be ~2°C above the recovery, could be an additional confounding issue (Section 11.3.2.3). present derived from both CMIP3 models and observations. Overall, it is likely that there will be some decline in the AMOC by 2050, but decades during which the AMOC increases are also to be expect- An analysis of CMIP3 model simulations indicates that for near-term ed. There is low confidence in projections of when an anthropogenic predictions the dominant factor for decreasing sea ice is increased ice influence on the AMOC might be detected (Baehr et al., 2008; Roberts melt, and reductions in ice growth play a secondary role (Holland et and Palmer, 2012). al., 2010). Arctic sea ice has larger volume loss when there is thicker ice initially across the CMIP3 models, with a projected accumulated Projected changes to oceanic circulation in the Indo-Pacific are strongly mass loss of about 0.5 m by 2020, and roughly 1.0 m by 2050, with dependent on future response of the Walker Circulation (Vecchi and considerable model spread (Holland et al., 2010). The CMIP3 models Soden, 2007; DiNezio et al., 2009), the near-term projected weaken- tended to under-estimate the observed rapid decline of summer Arctic ing of which is smaller than the expected variability on time scales of sea ice during the satellite era, but these recent trends are more accu- decades to years (Section 11.3.2.4.3). Taking variability into account, rately simulated in the CMIP5 models (see Section 12.4.6.1). For CMIP3 there is medium confidence in a weakening of equatorial Pacific cir- models, results indicate that the changes in Arctic sea ice mass budget culation, including equatorial upwelling and the shallow subtropical over the 21st century are related to the late 20th century mean sea overturning in the Pacific, and the Indonesian Throughflow over the ice thickness distribution (Holland et al., 2010), average sea ice thick- coming decades. ness (Bitz, 2008; Hodson et al., 2012), fraction of thin ice cover (Boe et al., 2009) and oceanic heat transport to the Arctic (Mahlstein et al., 11.3.4 Near-term Projected Changes in the Cryosphere 2011). Acceleration of sea ice drift observed over the last three dec- ades, underestimated in CMIP3 projections (Rampal et al., 2011), and This section assesses projected near-term changes of elements of the the presence of fossil-fuel and biofuel soot in the Arctic environment cryosphere. These consist of sea ice, snow cover and near-surface per- (Jacobson, 2010), could also contribute to ice-free late summer condi- mafrost (frozen ground), changes to the Arctic Ocean and possible tions over the Arctic in the near term. Details on the transition to an abrupt changes involving the cryosphere. Glaciers and ice sheets are ice-free summer over the Arctic are presented in Chapter 12 (Sections addressed in Chapter 13. Here near-term changes in the geographi- 12.4.6.1 and 12.5.5.7). cal coverage of sea ice, snow cover and near-surface permafrost are 11 assessed. The discussion in Section 12.4.6.1 makes the case for assessing near- term projections of Arctic sea ice by weighting/recalibrating the models Trends due to changes in external forcing exist alongside consider- based on their present-day Arctic sea ice simulations, with a credible able interannual and decadal variability. This complicates our ability underlying physical basis in order to increase confidence in the results, to make specific, precise short-term projections, and delays the emer- and accounting for the potentially large imprint of natural variabili- gence of a forced signal above the noise. ty on both observations and model simulations (see Section 9.8.3). A subselection of a set of CMIP5 models that fits those criteria, following 11.3.4.1 Sea Ice the methodology proposed by Massonnet et al. (2012), is applied in Chapter 12 (Section 12.4.6.1) to the full set of models that provid- Though most of the CMIP5 models project a nearly ice-free Arctic (sea ed the CMIP5 database with sea ice output. Among the five selected ice extent less than 1 × 106 km2 for at least 5 consecutive years) at the models, four project a nearly ice-free Arctic Ocean in September (sea end of summer by 2100 in the RCP8.5 scenario (see Section 12.4.6.1), ice extent less than 1 × 106 km2 for at least 5 consecutive years) before some show large changes in the near term as well. Some previous 2050 for RCP8.5, the earliest and latest years of near disappearance of models project an ice-free summer period in the Arctic Ocean by 2040 the sea ice pack being about 2040 and about 2060, respectively. The (Holland et al., 2006), and even as early as the late 2030s using a potential irreversibility of the Arctic sea ice loss and the possibility of criterion of 80% sea ice area loss (e.g., Zhang, 2010). By scaling six an abrupt transition toward an ice-free Arctic Ocean are discussed in CMIP3 models to recent observed September sea ice changes, a nearly Section 12.5.5.7. ice-free Arctic in September is projected to occur by 2037, reaching the first quartile of the distribution for timing of September sea ice loss by In light of all these results and others discussed in greater detail in Sec- 2028 (Wang and Overland, 2009). However, a number of models that tion 12.4.6.1, it is very likely that the Arctic sea ice cover will continue have fairly thick Arctic sea ice produce a slower near-term decrease in to shrink and thin all year round during the 21st century as the annual sea ice extent compared to observations (Stroeve et al., 2007). Based mean global surface temperature rises. It is also likely that the Arctic on a linear extrapolation into the future of the recent sea ice volume Ocean will become nearly ice-free in September before the middle of trend from a hindcast simulation conducted with a regional model of the century for high GHG emissions such as those corresponding to the Arctic sea ice ocean system (Maslowski et al., 2012) projected that RCP8.5 (medium confidence). 995 Chapter 11 Near-term Climate Change: Projections and Predictability In early 21st century simulations, Antarctic sea ice cover is projected 2 to 3 m; see Callaghan et al. (2011) and see glossary for detailed defi- to decrease in the CMIP5 models, though CMIP3 and CMIP5 models nition), and thaw depth deepening over much of the permafrost area simulate recent decreases in Antarctic sea ice extent compared to (Sushama et al., 2006; Lawrence et al., 2008; Guo and Wang, 2012). slight increases in the observations (Section 12.4.6.1). However, there As discussed in more detail in Section 12.4.6.2, these projections have is the possibility that melting of the Antarctic ice sheet could be chang- increased credibility compared to the previous generation of models ing the vertical ocean temperature stratification around Antarctica assessed in the AR4 because current climate models represent perma- and encourage sea ice growth (Bintanja et al., 2013). This and other frost more accurately (Alexeev et al., 2007; Nicolsky et al., 2007; Law- evidence discussed in Section 12.4.6.1 leads to the assessment that rence et al., 2008). The reduction in annual mean near-surface perma- there is low confidence in Antarctic sea ice model projections that frost area for the 2016 2035 time period compared to the 1986 2005 show near-term decreases of sea ice cover because of the wide range reference period for the CMIP5 models (Slater and Lawrence, 2013) for of model responses and the inability of almost all of the models to the NH for the four RCP scenarios is 21% +/- 5% (RCP2.6), 18% +/- 6% reproduce the mean seasonal cycle, interannual variability and overall (RCP4.5), 18% +/- 3% (RCP6.0) and 20% +/- 5% (RCP8.5). increase of the Antarctic sea ice areal coverage observed during the satellite era (see Section 9.4.3). 11.3.5 Projections for Atmospheric Composition and Air Quality to 2100 11.3.4.2 Snow Cover The future evolution of atmospheric composition is determined by Decreases of snow cover extent (SCE, defined over ice-free land areas) the chemical physical processes in the atmosphere, forced primarily are strongly connected to a shortening of seasonal snow cover dura- by anthropogenic and natural emissions and by interactions with the tion (Brown and Mote, 2009) and are related to both precipitation and biosphere and ocean (Chapters 2, 6, 7, 8 and 12). Twenty-first century temperature changes (see Section 12.4.6.2). This has implications for projections of the chemically reactive GHGs, including methane (CH4), snow on sea ice where loss of sea ice area in autumn delays snowfall nitrous oxide (N2O) and ozone (O3), as well as aerosols, are assessed accumulation, with CMIP5 multi-model mean values of snow depth in here (Section 11.3.5.1). Future air pollution, specifically ground-level April north of 70°N reduced from about 28 cm to roughly 18 cm for O3 and PM2.5 (particulate matter with a diameter of less than 2.5 m, the 2031 2050 period compared to the 1981 2000 average (Hezel et a measure of aerosol concentration), is also assessed here (Section al., 2012). The snow accumulation season by mid-century in one model 11.3.5.2). The impact of changes in natural emissions and deposition is projected to begin later in autumn, with the melt season initiated through altered land use (Heald et al., 2008; Chen et al., 2009a; Cook earlier in the spring (Lawrence and Slater, 2010). As discussed in great- et al., 2009; Wu et al., 2012) and production of food or biofuels (Chap- er detail in Section 12.4.6.2, projected increases in snowfall across ter 6) on atmospheric composition and air quality are not assessed much of the northern high latitudes act to increase snow amounts, here. Projected CO2 abundances are discussed in Chapters 6 and 12. but warming reduces the fraction of precipitation that falls as snow. 11 In addition, the reduction of Arctic sea ice also provides an increased Projections for the 21st century are based predominantly on the CMIP5 moisture source for snowfall (Liu et al., 2012). Whether the average models that included atmospheric chemistry and the related ACCMIP SCE decreases or increases by mid-century depends on the balance (Atmospheric Chemistry and Climate Model Intercomparison Project) between these competing factors. The dividing line where models tran- models, driven by the RCP emission and climate scenarios. These and sition from simulating increasing or decreasing maximum snow water the earlier SRES scenarios include only direct anthropogenic emis- equivalent roughly coincides with the 20°C isotherm in the mid-20th sions. Natural emissions may also change with biosphere feedbacks century November to March mean surface air temperature (Raisanen, in response to climate or land use change (Chapters 6, 8). Emphasis is 2008). The projected change of SCE over some regions is inconsistent placed on evaluating the 21st-century RCP scenarios from emissions with that of extreme snowfall, a major contributor to SCE. For instance, to abundance, summarized in tables in Annex II. For the well-mixed SCE is projected to decrease over northern China by the mid-21st greenhouse gases (WMGHGs), the effective radiative forcing (ERF) in century (Shi et al., 2011), while the extreme snowfall events over the both RCP and SRES scenarios increases similarly before 2040 with little region are projected to increase (Sun et al., 2010). spread (+/-16% in ERF; see Tables AII.6.1 to AII.6.10), but by 2050 the RCP2.6 scenario diverges, falling well below the envelope containing Time series of projected changes in relative SCE (for NH ice-free land both the SRES and other RCP scenarios. areas) are shown in Figure 12.32. Multi-model averages from the CMIP5 archive (Brutel-Vuilmet et al., 2013) show percentage decreas- National and regional regulations implemented on emissions con- es of NH SCE +/- 1 standard deviation for the 2016 2035 time period tributing to ground-level ozone and PM2.5 pollution influence global for a March to April average using a 15% extent threshold for the four atmospheric chemistry and climate (NRC, 2009; HTAP, 2010a), as was RCP scenarios as follows: RCP2.6: 5.2% +/- 1.9% (21 models); RCP4.5: recognized in the TAR (Jacob et al., 1993; Penner et al., 1993; Johnson 5.3% +/- 1.5% (24 models); RCP6.0: 4.5% +/- 1.2% (16 models); et al., 1999; Prather et al., 2001). Ozone and aerosols are radiatively RCP8.5: 6.0% +/- 2.0% (24 models). active species (Chapters 7 and 8) and many of their precursors serve as indirect GHGs (e.g., nitrogen oxides (NOx), carbon monoxide (CO), 11.3.4.3 Near Surface Permafrost Non Methane Volatile Organic Compounds (NMVOC)) by changing the atmospheric oxidative capacity, and thereby the lifetimes and abun- Virtually all near-term projections indicate a substantial amount of dances of CH4, hydrofluorocarbons (HFCs) and tropospheric O3 (Chap- near-surface permafrost degradation (typically taking place in the upper ter 8). Consequently their evolution can influence near-term climate 996 Near-term Climate Change: Projections and Predictability Chapter 11 both regionally and globally (Section 11.3.6.1 and FAQ 8.2). The RCP the current best understanding of natural and anthropogenic emis- and SRES scenarios differ greatly in terms of the short-lived air pollut- sions, atmospheric chemistry and biogeochemistry and RF of climate ants and aerosol climate forcing. The CMIP3 climate simulations driven (Chapters 2, 6 and 8) (see, e.g., Dlugokencky et al., 2011; Prather et al., by the SRES scenarios projected a wide range of future air pollutant 2012; Lamarque et al., 2013; Stevenson et al., 2013; Voulgarakis et al., trajectories, including unconstrained growth that resulted in very 2013; Young et al., 2013). Rather, the best estimates of atmospheric large tropospheric O3 increases (Prather et al., 2003). Subsequently, abundances and associated RF include a more complete atmospheric the near-term projections of current legislation (CLE) and maximum chemistry description and a fuller set of uncertainties than considered feasible reductions (MFR) emissions illustrated the impacts of air pollu- in the RCPs provided to the CMIP5 models. While this widens the range tion control strategies on air quality, global atmospheric chemistry and of climate forcing for each individual scenario, this uncertainty general- near-term climate (Dentener et al., 2005, 2006; Stevenson et al., 2006). ly remains smaller than the range across the four RCP scenarios. The RCP scenarios applied in the CMIP5 climate models all assume a continuation of current trends in air pollution policies (van Vuuren et 11.3.5.1.1 Methane, nitrous oxide and the fluorinated gases al., 2011) and thus do not cover the range of future pollutant emissions found in the literature, specifically those with higher pollutant emis- Kyoto GHG abundances projected to year 2100 are given in Annex II sions (Dentener et al., 2005; Kloster et al., 2008; Pozzer et al., 2012); (Tables AII.4.1 AII.4.15) as both RCP published values (Meinshausen see Chapter 8. et al., 2011b) and derived from the RCP anthropogenic emissions path- ways. The latter includes current best estimates of atmospheric chem- The new RCP emissions are compared to the older SRES and other istry and natural sources, with uncertainties (denoted RCP&). Emissions published emission scenarios in Annex II (Tables AII.2.1 to AII.2.22) and of CH4 and N2O, primarily from the agriculture, forestry and other land Figures 8.2 and 8.SM.1. By 2030 the RCP aerosol and ozone precursor use sectors (AFOLU) are uncertain, typically by 25% or more (Prather emissions are smaller than SRES by factors of 1.2 to 3. For these short- et al., 2009; NRC, 2010). Following the method of Prather et al. (2012) lived air pollutants, the spread across RCPs by 2030 is much smaller a best estimate and uncertainty range for the year 2011 anthropogenic than the range between the CLE and MFR scenarios: +/-12% vs. +/-31% and natural emissions of CH4 and N2O are derived using updated AR5 for nitrogen oxides; +/-17% vs. +/-60% for sulphate; +/-5% vs. +/-11% values (see Chapters 2, 5 and 6). The re-scaled RCP& anthropogenic-on- for carbon monoxide. BC aerosol emissions also vary little across the ly emissions of CH4 and N2O are given in Tables AII.2.2 and AII.2.3 and RCPs: +/-4% range in 2030; +/-15% in 2100. Most of this spread is due to differ from the published RCPs by a single scale factor for each species. uncertain projections for the rapidly industrializing nations. From 2000 An uncertainty range for 2011 values (likely, +/-1 standard deviation to 2030, sulphur dioxide (SO2) emissions decline in the RCPs by 15% in %, based on Prather et al. 2012) is applied to all subsequent years. to 8% per decade, within the range of the MFR and CLE scenarios Abundances are then integrated using these rescaled RCP& anthropo- ( 23% to +2% per decade), but far below the SRES range (+4% to genic emissions, the best estimate for natural emissions, and a model +21% per decade). Evaluation of recent trends in SO2 emissions shows projecting changes in tropospheric OH (see Holmes et al., 2013; for a trend similar to the near-term RCP projections (Smith et al., 2011; details). Similar scaling to match current observational constraints 11 Klimont et al., 2013), but independent estimates for recent trends in (harmonization) was done for the SRES emissions (Prather et al., 2001) other aerosol species are not available. The RCP trend in NOx emissions and the RCPs (Meinshausen et al., 2011b). However, these earlier har- ( 5% to +2% per decade) is likewise within the CLE-MFR range, but monizations used older values for lifetimes and natural sources, and far below the SRES trends (+10% to +30% per decade). For OC and BC did not provide estimates of uncertainty. emissions, the RCP trend lies between the SRES B1/A2 range. A simple sum of the main four aerosol emissions (N, S, OC, BC; Tables AII.2.18 Combining CH4 observations, lifetime estimates for the present day, to AII.2.22) in the SRES vs. RCP scenarios indicates that the CMIP3 the ACCMIP studies, plus estimated limits on changing natural sourc- simulations driven by the SRES scenarios have about 40% more aero- es, gives a year 2011 total anthropogenic CH4 emission of 354 +/- 45 sols in 2000 than the CMIP5 simulations driven by the RCP scenarios. Tg(CH4) yr 1 (Montzka et al., 2011; Prather et al., 2012) (Chapters 2, On average, these aerosols increase by 9% per decade in the SRES 6 and 8). The RCP total emission lies within 10% of this value, and scenarios but decrease by 5% per decade in the RCP scenarios over thus the scaling factor between the RCP& and RCP total emission, is the near term. By 2030, the CMIP3 models thus include up to three small (Table AII.2.2). Projection of the tropospheric OH lifetime of CH4 times more anthropogenic aerosols under the SRES scenarios than the (AII.5.8) is based on the ACCMIP simulations of the RCPs for 2100 time CMIP5 models driven by the RCP scenarios (high confidence). slice simulations (Voulgarakis et al., 2013), other modelling studies (Stevenson et al., 2006; John et al., 2012) and multi-model sensitiv- 11.3.5.1 Reactive Greenhouse Gases and Aerosols ity analyses of key factors (Holmes et al., 2013) that includes uncer- tainties in emissions from agricultural, forest and land use sources, in The IPCC has assessed previous emission-based scenarios for future atmospheric lifetimes, and in chemical feedbacks and loss. Lifetimes, GHGs and aerosols in the SAR (IS92) and TAR/AR4 (SRES). The new and thus future CH4 abundances, decrease slowly under RCP2.6 and RCP scenarios are different in that they embed a simple, parametric RCP4.5, remain almost constant under RCP6.0 and increase slowly model of atmospheric chemistry and biogeochemistry that maps emis- under RCP8.5. Future changes in natural sources of CH4 due to land sions onto atmospheric abundances (the concentration pathways ) use and climate change are included in a few CMIP5 models and may (Lamarque et al., 2011; Meinshausen et al., 2011a, 2011b; van Vuuren alter future CH4 abundances (Chapter 6), but there is limited evidence, et al., 2011). As an integrated product, the RCP-prescribed emissions, and thus these changes are not included in the RCP& projections. abundances and RF used in the CMIP5 model ensembles do not reflect 997 Chapter 11 Near-term Climate Change: Projections and Predictability 1100 (CCMVal) project a more vigorous stratospheric overturning by 2100 (a) CH4 anthropogenic emissions (Mt CH4 yr-1) 1000 that is expected to shorten the N2O lifetime (Oman et al., 2010; Strahan et al., 2011), but no evaluation of the lifetime is reported. Here we com- 900 bine observations of N2O (pre-industrial, present, and present trends; 800 Chapter 2), with two modern studies of the lifetime (Hsu and Prather, 700 2010; Fleming et al., 2011), and a Monte Carlo method (Prather et al., 600 2012) to estimate a year 2011 total anthropogenic emission of 6.7 +/- 1.3 TgN(N2O) yr 1 (Table AII.2.3). All RCP N2O (anthropogenic) emis- 500 sions are reduced by 20% so that year 2011 values are consistent with 400 an observationally constrained budget using a longer lifetime than 300 adopted by the RCPs (Table AII.2.3). The N2O lifetime (Table AII.5.9) 200 is projected to decrease by 2 to 4% by year 2100, due to changing c ­ irculation and chemistry in the stratosphere (Fleming et al., 2011) and 100 2010 2020 2030 2040 2050 2060 2070 2080 2090 2100 to the negative chemical feedback on its own lifetime (Prather and Hsu, 2010). In the near term, the spread in N2O across RCP&s is small: 4500 (b) 330 to 332 +/- 4 ppb in year 2020; 346 to 365 +/- 11 ppb in year 2050. By RCP2.0 4000 RCP4.5 year 2100, the range of best-estimate N2O concentrations across the RCP6.0 RCP&s (354 425 ppb) is 20% smaller than that across the RCPs (344 3500 RCP8.5 CH4 abundance (ppb) RCP2.6& 435 ppb), but the likely range in RCP&s encompasses the RCP range. RCP4.5& 3000 RCP6.0& RCP8.5& Recent measurements show some discrepancies with bottom-up 2500 inventories of the industrially produced, synthetic fluorinated (F) gases (AII.2.4 to AII.2.15). European HFC-23 emissions are greatly under-re- 2000 ported (Keller et al., 2011) while HFC-125 and 152a are roughly con- 1500 sistent with emissions inventories (Brunner et al., 2012). Globally, HFC- 365mfc and HFC-245fa emissions are overestimated (Vollmer et al., 1000 2011) while SF6 appears to be under-reported (Levin et al., 2010). For 500 HFC-134a, combining current measurements and lifetimes (Table 2.1, 2010 2020 2030 2040 2050 2060 2070 2080 2090 2100 Chapter 8; WMO, 2010; Prather et al., 2012) gives an estimate of 2010 emissions (~150 Gg yr 1) that is consistent with the RCP range (139 to Figure 11.21 | Projections for CH4 (a) anthropogenic emissions (MtCH4 yr 1) and (b) 153 Gg yr 1). Without clear guidance on how to correct or place uncer- atmospheric abundances (ppb) for the four RCP scenarios (2010 2100). Natural emis- 11 sions in 2010 are estimated to be 202 +/- 35 MtCH4 yr 1 (see Chapter 8). The thick solid tainty on the RCP F-gas emissions, the RCP emissions are reported lines show the published RCP2.6 (light blue), RCP4.5 (dark blue), RCP6.0 (orange) and without uncertainty estimates in Annex II Tables AII.2.4 to AII.2.15. For RCP8.5 (red) values. Thin lines with markers show values from this assessment (denot- the very long-lived SF6 and perfluorocarbons (CF4, C2F6, C6F10) uncer- ed as RCPn.n&, following methods of Prather et al. (2012) and Holmes et al. (2013): tainty in lifetimes does not significantly affect the projected abundanc- red plus, RCP8.5; orange square, RCP6.0; light blue circle, RCP4.5; dark blue asterisk, es over the 21st century (AII.4.4 to AII.4.7). Projected HFC abundances RCP2.6. The shaded region shows the likely range from the Monte Carlo calculations depend on the changes in tropospheric OH, which determines their that consider uncertainties, including in current anthropogenic emissions. atmospheric lifetime (Chapter 8). The relative change in hydroxyl rad- ical (OH), as indicated by the projected OH lifetime of CH4 (AII.5.8), is The resulting best estimates of total CH4 anthropogenic emissions and used to project HFCs including uncertainties (likely range) (AII.4.8 to abundances (RCP&) are compared with RCP values in Figure 11.21. AII.4.15) (Prather et al., 2012). For RCP2.6, the CH4 abundance is projected to decline continuously over the century by about 30%, whereas in RCP 4.5 and 6.0 it peaks Scenarios for the ozone-depleting GHG under control of the Montreal mid-century and then declines to below the year 2011 abundance by Protocol (chlorofluorocarbons (CFCs), HCFCs, halons in AII.4.16) follow the end of the century. Throughout the century, the uncertainty in CH4 scenario A1 of the 2010 WMO Ozone Assessment (WMO, 2010; Table abundance for an individual scenario is less than range from RCP2.6 5-A3). All CFC abundances decline throughout the century, but some to RCP8.5. For example, by year 2020 the spread in CH4 abundance HCFC abundances increase to 2030 before their phase-out and decline. across the RCPs is already large, 1720 to 1920 ppb, with uncertainty in The summed ERF of all these F-gases is approximately constant (0.35 each scenario estimated at only +/-20 ppb. The likely range for RCP& CH4 to 0.39 W m 2) up to year 2040 for all RCPs but declines thereafter. In is 30% wider than that in the RCP CH4 abundances used to force the RCP8.5, the drop in ERF from the Montreal Protocol gases is nearly CMIP5 models (Figure 11.21): by year 2100 the likely range of RCP8.5& made up by the growth in HFCs (Tables AII.6.4 to AII.6.6, Chapter 8). CH4 abundance extends 520 ppb above the single-valued RCP8.5 CH4 abundance, and RCP2.6& CH4 extends 230 ppb below RCP2.6 CH4. 11.3.5.1.2 Tropospheric and stratospheric O3 Substantial effort has gone into identifying and quantifying individual Projected O3 changes are broken into tropospheric and stratospheric sources of N2O (see Chapter 6) but less into evaluating its lifetime and columns (Dobson Unit (DU); see AII.5.1 and AII.5.2) because each has chemical feedbacks. Recent multi-model, chemistry climate studies different driving factors and RF efficiencies (Chapter 8). Tropospheric 998 Near-term Climate Change: Projections and Predictability Chapter 11 O3 changes are driven by anthropogenic emissions of CH4, NOx, CO, aerosol optical depth (AAOD) is primarily of anthropogenic origin NMVOC (AII.2.2.16 to AII.2.2.18). Small changes (<10%) are project- (Chapter 7). Uniformly, anthropogenic aerosols decrease under RCPs ed over the next few decades. By 2100 tropospheric O3 decreases in as expected from the declining emissions (11.3.5, Figure 8.2, AII.2.17 RCP2.6, 4.5 and 6.0 but increases in RCP8.5 due to CH4 increases. to AII.2.22). From years 2010 to 2030 the aerosol burdens decrease Higher tropospheric temperatures and humidity drive a decline in trop- across the RCPs but at varied rates: for sulphate from 6% (RCP8.5) ospheric O3, but stratospheric O3 recovery and increased stratosphere to 23% (RCP2.6); for BC from 5% (RCP4.5) to 15% (RCP2.6), and for troposphere exchange can counter that (Shindell et al., 2006; Zeng et OC from 0% (RCP6.0) to 11% (RCP4.5). The summed aerosol load- al., 2008, 2010; Kawase et al., 2011; Lamarque et al., 2011). The latter ing of these three anthropogenic components drop from year 2010 to effect is difficult to quantify but it is included in some of the ACCMIP year 2030 by 5% to 12% (across RCPs), and by year 2100 this drop is and CMIP5 models used to project tropospheric O3. Changes in natu- 24% to 39% (Tables AII.5.5 to AII.5.7). These evolving aerosol loadings ral emissions of NOx, particularly soil and lightning NOx, and biogenic reduce the magnitude of the negative aerosol forcing (Chapter 8; Table NMVOC may also alter tropospheric O3 abundances (Wild, 2007; Wu et AII.6.9) even in the near term (11.3.6.1). al., 2007). However, global estimates of their change with climate (e.g., Kesik et al., 2006; Monson et al., 2007; Butterbach-Bahl et al., 2009; 11.3.5.2 Projections of Air Quality for the 21st Century Price, 2013) remain highly uncertain. Future air quality depends on anthropogenic emissions (local, regional Best estimates for projected tropospheric O3 change following the RCP and global), natural biogenic emissions and the physical climate (e.g., scenarios (Table AII.5.2) are based on ACCMIP time slice simulations Steiner et al., 2006, 2010; Meleux et al., 2007; Tao et al., 2007; Wu et al., for 2030 and 2100 with chemistry climate models (Young et al., 2013) 2008; Doherty et al., 2009; Carlton et al., 2010; Tai et al., 2010; Hoyle and the CMIP5 simulations (Eyring et al., 2013). There is high confi- et al., 2011). This assessment focuses on O3 and PM2.5 in surface air, dence in these results because similar estimates are obtained when reflecting the preponderance of published literature and multi-model projections are made using the response of tropospheric O3 to key assessments for these air pollutants (e.g., HTAP, 2010a) plus the chem- forcing factors that vary across scenarios (Prather et al., 2001; Steven- istry climate CMIP5 and ACCMIP model simulations. Nitrogen and acid son et al., 2006; Oman et al., 2010; Wild et al., 2012). The ACCMIP deposition is addressed in Chapter 6. Toxic atmospheric species such as models show a wide range in tropospheric O3 burden changes from mercury and persistent organic pollutants are outside this assessment 2000 to 2100: 5 DU ( 15%) in RCP2.6 to +5 DU in RCP8.5. The CMIP5 (Jacob and Winner, 2009; NRC, 2009; HTAP, 2010b, 2010c). results are similar but not identical: 3 DU ( 9%) to +10 DU (+30%). The 2030 and 2100 multi-model mean estimates are more robust for The global and continental-scale surface O3 and PM2.5 changes assessed ACCMIP which includes 5 to 11 models (range depends on time slice here include (1) the impact of climate change (Section 11.3.5.2.1), and and scenario) than for CMIP5 (4 models). Tropospheric O3 changes in (2) the impact of changing global and regional anthropogenic emis- the near term (2030 2040) are small (+/-2 DU), except for RCP8.5 (>3 sions (Section 11.3.5.2.2). Changes in local emissions within a met- DU), which shows continued growth through to 2100 driven primarily ropolitan region or surrounding air basin on local air quality projec- 11 by CH4 increases. The ERF from tropospheric O3 changes (AII.6.7b) par- tions are not assessed here. Anthropogenic emissions of O3 precursors allels the O3 burden change (Stevenson et al., 2013). include NOx, CH4, CO, and NMVOC; PM2.5 is both directly emitted (OC, BC) and produced photochemically from precursor emissions (NOx, Stratospheric O3 is being driven by declining chlorine levels, changing NH3, SO2, NMVOC) (see Tables AII.2.2,16-22). Recent reviews describe N2O and CH4, cooler temperatures from increased CO2, and a more the impact of temperature-driven processes on O3 and PM2.5 air qual- vigorous overturning circulation in the stratosphere driven by more ity from observational and modelling evidence (Isaksen et al., 2009; wave propagation under climate change (Butchart et al., 2006; Eyring Jacob and Winner, 2009; Fiore et al., 2012). Projecting future air quality et al., 2010; Oman et al., 2010). Overall stratospheric O3 is expected empirically from a mean surface warming using the observed correla- to increase in the coming decades, reversing the majority of the loss tion with temperature is problematic, as there is little evidence that that occurred between 1980 and 2000. Best estimates for global mean future pollution episodes can be simply modelled as all else being stratospheric O3 change under the RCP scenarios (Table AII.5.1) are equal except for a uniform temperature shift. Air quality relationships taken from the CMIP5 results (Eyring et al., 2013). By 2100 stratospher- with synoptic conditions may be more robust (e.g., Dharshana et al., ic O3 columns show a 5 to 7% increase above 2000 levels for all RCPs, 2010; Appelhans et al., 2012; Tai et al., 2012a, 2012b), but require the recovering to within 1% of the pre-ozone hole 1980 levels by 2050, but ability to project changes in key conditions such as blocking and stag- with latitudinal differences. nation episodes. The response of blocking frequency to global warming is complex, with summertime increases possible over some regions, 11.3.5.1.3 Aerosols but models are generally biased compared to observed blocking sta- tistics, and indicate even larger uncertainty in projecting changes in Aerosol species can be emitted directly (mineral dust, sea salt, BC blocking intensity and persistence (Box 14.2). and some organic carbon (OC)) or indirectly through precursor gases (SO2, ammonia, nitrogen oxides, hydrocarbons); see Chapter 7. CMIP5 11.3.5.2.1 Climate-driven changes models (Lamarque et al., 2011; Shindell et al., 2013) have projected changes in aerosol burden (Tg) and aerosol optical depth (AOD) to year Projecting regional air quality faces the challenge of simulating 2100 using RCP emissions for anthropogenic source (Tables AII.5.3 to first the changes in regional climate and then the feedbacks from AII.5.8). Total AOD is dominated by dust and sea salt, but absorbing atmospheric chemistry and the biosphere. The air pollution response 999 Chapter 11 Near-term Climate Change: Projections and Predictability to ­limate-driven changes in the biosphere is uncertain as to sign c tral Europe by 2030; green dashed line for Europe in Figure 11.22). because of competing effects: for example, plants currently emit more Regional models projecting summer daytime statistics tend to simu- NMVOC with warmer temperatures; with higher CO2 and water stress late a wider range of climate-driven changes (e.g., Zhang et al., 2008; plants may emit less; with a warmer climate the vegetation types may Avise et al., 2012; Kelly et al., 2012), with most studies focusing on shift to emit either more or less NMVOC; shifting vegetation types 2050 (Fiore et al., 2012) or beyond. For example, summer tempera- may also alter surface uptake of ozone and aerosols; and our under- ture extremes over parts of Europe are projected to warm more than standing of chemical oxidation pathways for biogenic emissions is the corresponding mean local temperatures due to enhanced variabil- incomplete (e.g., Monson et al., 2007; Carlton et al., 2009; Hallquist ity at interannual to intraseasonal time scales (see Section 12.4.3.3). et al., 2009; Ito et al., 2009; Pacifico et al., 2009, 2012; Paulot et al., Several modelling studies note a longer season for O3 pollution in a 2009). Although studies have split the cause of air quality changes warmer world (Nolte et al., 2008; Racherla and Adams, 2008). For some into climate versus emissions, these attributions are difficult to assess regions, models agree on the sign of the O3 response to a warming for several reasons: the global-to-regional down-scaling of meteorol- climate (e.g., increases in northeastern USA and southern Europe; ogy that is model dependent (see Chapters 9 and 14; also Manders decreases in northern Europe), but they often disagree (e.g., the mid- et al., 2012), the brief simulations that preclude clear separation of west, southeast, and western USA (Jacob and Winner, 2009; Weaver et climate change from climate variability (Nolte et al., 2008; Fiore et al., al., 2009; Langner et al., 2012a; Langner et al., 2012b; Manders et al., 2012; Langner et al., 2012a), and the lack of systematically explored 2012)). Several studies have suggested a role for changing synoptic standard scenarios for local anthropogenic emissions, land use change meteorology on future air pollution levels (Leibensperger et al., 2008; and biogenic emissions. Jacob and Winner, 2009; Weaver et al., 2009; Lang and Waugh, 2011; Tai et al., 2012a, 2012b; Turner et al., 2013), but projected regional Ozone changes in synoptic conditions are uncertain (see Sections 11.3.2.4, Globally, a warming climate decreases baseline surface O3 almost every- 12.4.3.3 and Box 14.2). Observational and modelling evidence togeth- where but increases O3 levels in some polluted regions and seasons. er indicate that, all else being equal, a warming climate is expected to The surface ozone response to climate change alone between 2000 increase surface O3 in polluted regions (medium confidence), although and 2030 is shown in Figure 11.22 (CLIMATE), where the ranges reflect a systematic evaluation of all the factors driving extreme pollution epi- multi-model differences in spatial averages (solid green lines) and spa- sodes is lacking. tial variability within a single model (dashed green lines). There is high confidence that in unpolluted regions, higher water vapour abundances Aerosols and temperatures enhance O3 destruction, leading to lower baseline O3 Evaluations as to whether climate change will worsen or improve levels in a warmer climate (e.g., global average in Figure 11.22). Higher aerosol pollution are model-dependent. Assessments are confound- CH4 levels such as in RCP8.5 can offset this climate-driven decrease in ed by opposing influences on the individual species contributing to baseline O3. Other large-scale factors that could increase baseline O3 in total PM2.5 and large interannual variability caused by the small-scale 11 a warming climate include increased lightning NOx and stratospheric meteorology (e.g., convection and precipitation) that controls aerosol influx of O3 (see Section 11.3.5.1). Evidence and agreement are lim- concentrations (Mahmud et al., 2010). For a full discussion, see Chap- ited regarding the impact of climate change on long-range transport ter 7. Higher temperatures generally decrease nitrate aerosol through of pollutants (Wu et al., 2008; HTAP, 2010a; Doherty et al., 2013). The enhanced volatility but increase sulphate aerosol through faster pro- global chemistry-climate models assessed here (Figures 11.22, 11.23ab) duction, although observed PM2.5 temperature correlations also reflect include most of these feedback processes, but a systematic evaluation humidity and synoptic meteorology (e.g., Aw and Kleeman, 2003; Liao of their relative impacts is lacking. et al., 2006; Racherla and Adams, 2006; Unger et al., 2006a; Hedegaard In polluted regions, observations show that high-O3 episodes correlate et al., 2008; Jacobson, 2008; Kleeman, 2008; Pye et al., 2009; Tai et with high temperatures (e.g., Lin et al., 2001; Bloomer et al., 2009; Ras- al., 2012b). Natural aerosols may increase with temperature, particu- mussen et al., 2012), but these episodes also coincide with cloud-free larly carbonaceous aerosol from wildfires, mineral dust, and biogenic enhanced photochemistry and with air stagnation that concentrates secondary organic aerosol (SOA; Section 7.3.5; Mahowald and Luo, pollution near the surface (e.g., AR4 Box 7.4). Other temperature-re- 2003; Tegen et al., 2004; Jickells et al., 2005; Woodward et al., 2005; lated factors, such as biogenic emissions from vegetation and soils, Mahowald et al., 2006; Liao et al., 2007; Mahowald, 2007; Tagaris et volatilization of NMVOC, thermal decomposition of organic nitrates to al., 2007; Heald et al., 2008; Spracklen et al., 2009; Jiang et al., 2010; NOx and wildfire frequency may increase with a warming climate and Yue et al., 2010; Carvalho et al., 2011; Fiore et al., 2012). SOA formation are expected to increase surface O3 (e.g., Doherty et al., 2013; Skjth also depends on anthropogenic emissions and atmospheric oxidizing and Geels, 2013; and as reviewed by Isaksen et al. (2009), Jacob and capacity (Carlton et al., 2010; Jiang et al., 2010). Winner (2009) and Fiore et al. (2012)), although some of these process- es are known to have optimal temperature ranges (e.g., Sillman and Aerosols are scavenged from the atmosphere by precipitation and Samson, 1995; Guenther et al., 2006; Steiner et al., 2010). Overall, the direct deposition (see Chapter 7). Hence most components of PM2.5 are integrated effect of these processes on O3 remains poorly understood, anti-correlated with precipitation (Tai et al., 2010), and aerosol bur- and they have been implemented with varying levels of complexity in dens are expected to decrease on average where precipitation increas- the models assessed here. es (Racherla and Adams, 2006; Liao et al., 2007; Tagaris et al., 2007; Zhang et al., 2008; Avise et al., 2009; Pye et al., 2009). However, a shift Models show that a warmer atmosphere can lead to local O3 increas- in the frequency and type of precipitation may be as important as the es during the peak pollution season (e.g., by 2 to 6 ppb within Cen- change in mean precipitation (see Chapter 7). Seasonal and regional 1000 Near-term Climate Change: Projections and Predictability Chapter 11 The largest surface O3 changes under the RCP scenarios are much 15 2030 - 2000 smaller than those projected under the older SRES scenarios (Figures surface O3 change (ppb) CLIMATE CLE MFR SRES RCP 11.22 and 11.23a; Table AII.7; Lamarque et al., 2011; Wild et al., 2012). 10 By 2100, global annual multi-model mean surface O3 rises by 12 ppb in SRES A2, but by only 3 ppb in RCP8.5. Much larger O3 decreases 5 are projected to occur by 2030 under the MFR scenario (Figure 11.22), which assumes that existing control technologies are applied uniform- 0 ly across the globe (Dentener et al., 2006). -5 For RCP2.6, RCP4.5 and RCP6.0, the CMIP5/ACCMIP models pro- ject that continental-scale spatially averaged near-term surface O3 -10 decreases or changes little ( 4 to +1 ppb) from 2000 to 2030 for all Global N.Am./USA Europe E.Asia S.Asia regions except South Asia, whereas the long-term change to 2100 is a consistent decrease ( 14 to 3 ppb) for all regions (Figure 11.23a; Figure 11.22 | Changes in surface O3 (ppb) between year 2000 and 2030 driven by and Table AII.7.3). For RCP8.5, the CMIP5/ACCMIP models project ­ climate alone (CLIMATE, green) or driven by emissions alone, following current legisla- tion (CLE, black), maximum feasible reductions (MFR, grey), SRES (blue) and RCP (red) c ­ ontinental-scale spatial average surface O3 increases of up to +5 ppb emission scenarios. Results are reported globally and for the four northern mid-latitude for both 2030 and 2100 (Figure 11.23a; Table AII.7.3). The increas- source regions used by the Task Force on Hemispheric Transport of Air Pollution (HTAP, es under RCP8.5 reflect the prominent rise in methane abundances 2010a). Where two vertical bars are shown (CLE, MFR, SRES ), they represent the multi- (Kawase et al., 2011; Lamarque et al., 2011; Wild et al., 2012), which model standard deviation of the annual mean based on (left bar; SRES includes A2 by 2100 raise background O3 levels by 5 to 14 ppb over continen- only) the Atmospheric Composition Change: a European Network (ACCENT)/Photocomp study (Dentener et al., 2006) and (right bar) the parametric HTAP ensemble (Wild et tal-scale regions, and on average by about 8 ppb (25% above current al., 2012; four SRES and RCP scenarios included). Under Global, the leftmost (dashed levels) above RCP4.5 and RCP6.0 which include more stable methane green) vertical bar denotes the spatial range in climate-only changes from one model pathways over the 21st century (high confidence). Earlier studies have (Stevenson et al., 2005) while the green square shows global annual mean climate-only shown that rising CH4 abundances (and global NOx emissions) increase changes in another model (Unger et al., 2006b). Under Europe, the dashed green bar baseline O3, and can offset aggressive local emission reductions and denotes the range of climate-only changes in summer daily maximum O3 in one model (Forkel and Knoche 2006). (Adapted from Figure 3 of Fiore et al., 2012.) lengthen the O3 pollution season (Jacob et al., 1999; Prather et al., 2001, 2003; Fiore et al., 2002, 2009; Hogrefe et al., 2004; Granier et al., 2006; Szopa et al., 2006; Tao et al., 2007; Huang et al., 2008; Lin et differences in aerosol burdens versus precipitation further preclude a al., 2008; Wu et al., 2008; Avise et al., 2009; Chen et al., 2009b;HTAP, simple scaling of aerosol response to precipitation changes (Kloster et 2010a; Wild et al., 2012; Lei et al., 2013). al., 2010; Fang et al., 2011). Climate-driven changes in the frequency of drizzle and the mixing depths or ventilation of the surface layer also The O3 changes driven by the RCP emissions scenarios with fixed, 11 influence projected changes in PM2.5 (e.g., Kleeman, 2008; Dawson et present-day climate (Figure 11.22; Wild et al., 2012) are similar to the al., 2009; Jacob and Winner, 2009; Mahmud et al., 2010), and aerosols changes estimated with the full chemistry climate models (Figure in turn can influence locally clouds, precipitation and scavenging (e.g., 11.23a). Although the regions considered are not identical, the evi- Zhang et al., 2010b; see Section 7.6). dence supports a major role for global emissions in determining near- term O3 concentrations. Overall, the multi-model ranges associated While PM2.5 is expected to decrease in regions where precipitation with the influence of near-term climate change on global and regional increases, the climate variability at these scales results in only low con- O3 air quality are smaller than those across emission scenarios (Figure fidence for projections at best. Further, consensus is lacking on the 11.22; HTAP, 2010a; Wild et al., 2012). other factors including climate-driven changes in biogenic and mineral dust aerosols, leading to no confidence level being attached to the Aerosol changes driven by anthropogenic emissions depend somewhat overall impact of climate change on PM2.5 distributions. on oxidant levels (e.g., Unger et al., 2006a; Kleeman, 2008; Leibens- perger et al., 2011a), but generally sulphate follows SO2 emissions and 11.3.5.2.2 Changes driven by regional and global anthropogenic carbonaceous aerosols follow the primary elemental and OC emis- pollutant emissions sions. Competition between sulphate and nitrate for ammonium (see Chapter 7) means that reducing SO2 emissions while increasing NH3 Projections for annual-mean surface O3 and PM2.5 for 2000 through emissions as in the RCPs (Tables AII.2.19 and AII.2.20) would lead to 2100 are shown in Figures 11.23a and 11.25b, respectively. Changes are near-term nitrate aerosol levels equal to or higher than those of sul- spatially averaged over selected world (land-only) regions and include phate in some regions; see Section 7.3.5.2 (Bauer et al., 2007; Pye et the combined effects of emission and climate changes under the RCPs. al., 2009; Bellouin et al., 2011; Henze et al., 2012). Results are taken from the ACCMIP models and a subset of the CMIP5 models that included atmospheric chemistry. Large interannual varia- Regional PM2.5 in the CMIP5 and ACCMIP chemistry climate models tions are evident in the CMIP5 transient simulations, and large regional following the RCP scenarios generally declines over the 21st centu- variations occur in both the CMIP5 and the ACCMIP decadal time slice ry, with little difference across the individual scenarios except for the simulations (see Lamarque et al., (2013) for ACCMIP overview). South and East Asia regions (Figure 11.23b). The noisy projections over Africa, the Middle East and to some extent Australia, reflect dust 1001 Chapter 11 Near-term Climate Change: Projections and Predictability sources and their strong dependence on interannual meteorological ­climate-driven changes for PM2.5 will vary regionally with future chang- variability. Over the two Asian regions, different PM2.5 levels between es in precipitation, wildfires, dust and biogenic emissions. the RCPs are due to (1) OC emission trajectories over South Asia and (2) combined changes in carbonaceous aerosol and SO2 over East Asia In summary, lower air pollution levels are projected following the (Fiore et al., 2012) (Figure 8.SM.1). RCP emissions as compared to the SRES emissions in the TAR and AR4, reflecting implementation of air pollution control measures (high Global emissions of aerosols and precursors can contribute to high-PM confidence). The range in projections of air quality is driven primarily events. For example, dust trans-oceanic transport events are observed by emissions (including CH4) rather than by physical climate change to increase aerosols in downwind regions (Prospero, 1999; Grousset (medium confidence). The total emission-driven range in air quality et al., 2003; Chin et al., 2007; Fairlie et al., 2007; Huang et al., 2008; including the CLE and MFR scenarios is larger than that spanned by Liu et al., 2009; Ramanathan and Feng, 2009; HTAP, 2010a). The bal- the RCPs (see Section 11.3.5.1 for comparison of RCPs and SRES). ance between regional and global anthropogenic emissions versus 11 Figure 11.23a | Projected changes in annual mean surface O3 (ppb mole fraction) from 2000 to 2100 following the RCP scenarios (8.5, red; 6.0, orange; 4.5, light blue; 2.6, dark blue). Results in each box are averaged over the designated coloured land regions. Continuous coloured lines and shading denote the average and full range of four chemistry cli- mate models (GFDL-CM3, GISS-E2-R, and NCAR-CAM3.5 from CMIP5 plus LMDz-ORINCA). Coloured dots and vertical black bars denote the average and full range of the ACCMIP models (CESM-CAM-superfast, CICERO-OsloCTM2, CMAM, EMAC-DLR, GEOSCCM, GFDL-AM3, HadGEM2, MIROC-CHEM, MOCAGE, NCAR-CAM3.5, STOC-HadAM3, UM-CAM) for decadal time slices centred on 2010, 2030, 2050 and 2100. Participation in the decadal slices ranges from 2 to 12 models (see (Lamarque et al., 2013)). Changes are relative to the 1986 2005 reference period for the CMIP5 transient simulations, and relative to the average of the 1980 and 2000 decadal time slices for the ACCMIP ensemble. The average value and model standard deviation for the reference period is shown in the top of each panel for CMIP5 models (left) and ACCMIP models (right). In cases where multiple ensemble members are available from a single model, they are averaged prior to inclusion in the multi-model mean. (Adapted from Fiore et al., 2012.) 1002 Near-term Climate Change: Projections and Predictability Chapter 11 11 Figure 11.23b | Projected changes in annual mean surface PM2.5 (micrograms per cubic metre of aerosols with diameter less than 2.5 m) from 2000 to 2100 following the RCP scenarios (8.5 red, 6.0 orange, 4.5 light blue, 2.6 dark blue). PM2.5 values are calculated as the sum of individual aerosol components (black carbon + organic carbon + sulphate + secondary organic aerosol + 0.1*dust + 0.25*sea salt). Nitrate was not reported for most models and is not included here. See Figure 11.23a for details, but note that fewer models contribute: GISS-E2-R and GFDL-CM3 from CMIP5; CICERO-OsloCTM2, GEOSCCM, GFDL-AM3, HadGEM2, MIROC-CHEM, and NCAR-CAM3.5 from ACCMIP. (Adapted from Fiore et al., 2012.) 11.3.5.2.3 Extreme weather and air pollution p ­ rojected to decrease in a warming climate but increases may occur in some regions, and projected changes in their intensity and duration Extreme air quality episodes are associated with changing weather remain uncertain (Chapters 9 and 14; Box 14.2). Projections in regional patterns, such as heat waves and stagnation episodes (Logan, 1989; air pollution extremes are necessarily conditioned on projected chang- Vukovich, 1995; Cox and Chu, 1996; Mickley et al., 2004; Stott et es in these weather patterns. The severity of extreme pollution events al., 2004). Heat waves are generally associated with poor air quality also depends on local emissions (see references in Fiore et al., 2012). (Ordónez et al., 2005; Vautard et al., 2005; Lee et al., 2006b; Struzewska Feedbacks from vegetation (higher biogenic NMVOC emissions, lower and Kaminski, 2008; Tressol et al., 2008; Vieno et al., 2010; Hodnebrog stomatal uptake of O3 with higher temperatures) can combine with et al., 2012). Although anthropogenic climate change has increased similar positive feedbacks via dust and wildfires to worsen air pollution the near-term risk of such heat waves (Stott et al., 2004; Clark et al., and its impacts during heat waves (Lee et al., 2006a; Jiang et al., 2008; 2010; Diffenbaugh and Ashfaq, 2010; Chapter 10; Section 11.3.2.5.1), Royal Society, 2008; Flannigan et al., 2009; Andersson and Engardt, projected changes in the frequency of regional air stagnation events, 2010; Vieno et al., 2010; Hodnebrog et al., 2012; Jaffe and Wigder, which are largely driven by blocking events, remain difficult to assess: 2012; Mues et al., 2012). the frequency of blocking events with persistent high pressure is 1003 Chapter 11 Near-term Climate Change: Projections and Predictability There is high agreement across numerous modelling studies projecting concern for projections are mechanisms that could lead to major sur- increases in extreme O3 pollution events over the USA and Europe, prises such as an abrupt or rapid change that affects global-to-con- but the projections do not consistently agree at the regional level tinental scale climate. Several such mechanisms are discussed in this (Kleeman, 2008; Jacob and Winner, 2009; Jacobson and Streets, 2009; assessment report; these include: rapid changes in the Arctic (Section Weaver et al., 2009; Huszar et al., 2011; Katragkou et al., 2011; Langner 11.3.4 and Chapter 12), rapid changes in the ocean s overturning cir- et al., 2012b) because they depend on accurate projections of local culation (Chapter 12), rapid change of ice sheets (Chapter 13) and emissions, regional climate and poorly understood biospheric feed- rapid changes in regional monsoon systems and hydrological climate backs. Although observational evidence clearly demonstrates a strong (Chapter 14). Additional mechanisms may also exist as synthesized in statistical correlation between extreme temperatures (heat waves) and Chapter 12. These mechanisms have the potential to influence climate pollution events, this temperature correlation reflects in part the coin- in the near term as well as in the long term, albeit the likelihood of cident occurrence of stagnation events and clear skies that also drive substantial impacts increases with global warming and is generally extreme pollution. Mechanistic understanding of biogenic emissions, lower for the near term. Section 11.3.6.3 provides an overall assess- deposition and atmospheric chemistry is consistent with a tempera- ment of projections for global mean surface air temperature, taking ture-driven increase in pollution extremes in already polluted regions, into account all known quantifiable uncertainties. although these processes may not scale simply with mean tempera- ture under a changing climate (see Section 11.3.5.2.1), and better pro- 11.3.6.1 Uncertainties in Future Anthropogenic Forcing jections of the changing meteorology at regional scales are needed. and the Consequences for Near-term Climate Assuming all else is equal (e.g., local anthropogenic emissions) this collective evidence indicates that uniformly higher temperatures in Climate projections for periods prior to year 2050 are not very sensi- polluted environments will trigger regional feedbacks during air stag- tive to available alternative scenarios for anthropogenic CO2 emissions nation episodes that will increase peak pollution (medium confidence). (see Section 11.3.2.1.1; Stott and Kettleborough, 2002; Meehl et al., 2007b). Near-term projections, however, may be sensitive to changes 11.3.6 Additional Uncertainties in Projections of in emissions of climate forcing agents with lifetimes shorter than CO2, Near-term Climate particularly the GHGs CH4 (lifetime of a decade), tropospheric O3 (life- time of weeks), and tropospheric aerosols (lifetime of days). Although As discussed in Section 11.3.1, most of the projections presented in the RCPs and SRES scenarios span a similar range of total effective Sections 11.3.2 to 11.3.4 are based on the RCP4.5 scenario and rely radiative forcing (ERF, see Section 7.5, Figure 7.3, Chapter 8), they on the spread among the CMIP5 ensemble of opportunity as an ad hoc include different ranges of ERF from aerosol, CH4, and tropospheric O3 measure of uncertainty. It is possible that the real world might follow (see Section 11.3.5.1, Tables AII.6.2 and AII.6.7 to AII.6.10). From years a path outside (above or below) the range projected by the CMIP5 2000 to 2030 the change in ERF across the RCPs ranges from 0.05 to models. Such an eventuality could arise if there are processes operating +0.14 W m 2 for CH4 and from 0.04 to +0.08 W m 2 for tropospheric O3 11 in the real world that are missing from, or inadequately represented in, (Tables AII.6.2 and AII.6.7; Stevenson et al., 2013). From years 2000 to the models. Two main possibilities must be considered: (1) Future radi- 2030 the total aerosol ERF becomes less negative, increasing by +0.26 ative and other forcings may diverge from the RCP4.5 scenario and, W m 2 for RCP8.5 (only RCP evaluated; for ACCMIP results see Table more generally, could fall outside the range of all the RCP scenarios; (2) AII.6.9; Shindell et al., 2013). Total ERF change across scenarios derived The response of the real climate system to radiative and other forcing from the CMIP5 ensemble can be compared only beginning in 2010. may differ from that projected by the CMIP5 models. A third possibility For the period 2010 to 2030, total ERF in the CMIP5 decadal averages is that internal fluctuations in the real climate system are inadequately increases by +0.5 to +1.0 W m 2 (RCP2.6 and RCP6.0 to RCP8.5; Table simulated in the models. The fidelity of the CMIP5 models in simulating AII.6.10) while total ERF from the published RCPs increases by +0.7 internal climate variability is discussed in Chapter 9. to +1.1 W m 2 (RCP2.6 and RCP6.0 to RCP8.5, Table AII.6.8). Here we re-examine the near-term temperature increases projected from the Future changes in RF will be caused by anthropogenic and natural RCPs (see Section 11.3.2.1.1) and assess the potential for changes in processes. The consequences for near-term climate of uncertainties near-term anthropogenic forcing to induce climate responses that fall in anthropogenic emissions and land use are discussed in Section outside these scenarios. 11.3.6.1. The uncertainties in natural RF that are most important for near-term climate are those associated with future volcanic eruptions For the different RCP pathways the increase in global mean surface and variations in the radiation received from the Sun (solar output), temperature by 2026 2035 relative to the reference period 1986-2005 and are discussed in Section 11.3.6.2. In addition, carbon cycle and ranges from 0.74°C (RCP2.6 and RCP6.0) to 0.94°C (RCP8.5) (median other biogeochemical feedbacks in a warming climate could poten- of CMIP5 models, see Figure 11.24, Table AII.7.5). This inter-scenario tially lead to abundances of CO2 and CH4 (and hence RF) outside the range of 0.20°C is smaller than the inter-model spread for an indi- range of the RCP scenarios, but these feedbacks are not expected to vidual scenario: 0.33°C to 0.52°C (defined as the 17 to 83% range play a major role in near term climate see Chapters 6 and 12 for of the decadal means of the models). This RCP inter-scenario spread further discussion. may be too narrow as discussed in Section 11.3.5.1. The temperature increase of the most rapidly warming scenario (RCP8.5) emerges from The response of the climate system to radiative and other forcing is inter-model spread (i.e., becomes greater than two times the 17 to influenced by a very wide range of processes, not all of which are 83% range) by about 2040, due primarily to increasing CH4 and CO2. a ­ dequately simulated in the CMIP5 models (Chapter 9). Of particular By 2050 the inter-scenario spread is 0.8C whereas the model spread 1004 Near-term Climate Change: Projections and Predictability Chapter 11 Temperature change (°C w.r.t. 1986-2005) 2.5 Temperature change (°C w.r.t. 1850-1900) SRES A1b RCP 2.6 3.0 RCP 4.5 RCP 6.0 RCP 8.5 2.0 UNEP-ref -CH4 2.5 1.5 2.0 1.0 1.5 0.5 1.0 0.0 2020 2030 2040 2050 Figure 11.24a | Near-term increase in global mean surface air temperatures (°C) across scenarios. Increases in 10-year mean (2016 2025, 2026 2035, 2036 2045 and 2046 2055) relative to the reference period (1986 2005) of the globally averaged surface air temperatures. Results are shown for the CMIP5 model ensembles (see Annex I for listing of models included) for RCP2.6 (dark blue), RCP4.5 (light blue), RCP6.0 (orange), and RCP8.5 (red) and the CMIP3 model ensemble (22 models) for SRES A1b (black). The multi-model median (square), 17 to 83% range (wide boxes), 5 to 95% range (whiskers) across all models are shown for each decade and scenario. Values are provided in Table AII.7.5. Also shown are best estimates for a UNEP scenario (UNEP-ref, grey upward triangles) and one that implements technological controls on methane emissions (UNEP CH4, red downward-pointing triangles) (UNEP and WMO, 2011; Shindell et al., 2012a). Both UNEP scenarios are adjusted to reflect the 1986 2005 reference period. The right-hand floating axis shows increases in global mean surface air temperature relative to the early instrumental period (0.61°C), defined from the difference between 1850 1900 and 1986 2005 in the Hadley Centre/Climate Research Unit gridded surface temperature data set 4 (HadCRUT4) global mean temperature analysis (Chapter 2 and Table AII.1.3). Note that uncertainty remains on how to match the 1986 2005 reference period in observations with that in CMIP5 results. See discussion of Figure 11.25. for each scenario is only 0.6C. At 2040 the ERF in the published RCPs Streets, 2009; Raes and Seinfeld, 2009; Wigley et al., 2009; Kloster et ranges from 2.6 (RCP2.6) to 3.6 (RCP8.5) W m 2, and about 40% of this al., 2010; Makkonen et al., 2012). difference is due to the steady increases in CH4 and tropospheric O3 found only in RCP8.5. RCP6.0 has the lowest ERF and thus warms less Because global mean aerosol forcing decreases in all RCP scenarios rapidly than other RCPs up to 2030 (Table AII.6.8). (AII.5.3 to AII.5.7, AII.6.9; see Section 11.3.5), the potential exists for 11 a systematic difference between the CMIP3 models forced with the In terms of geographic patterns of warming, differences between SRES scenarios and the CMIP5 models forced with the RCP scenarios. RCP8.5 and RCP2.6 are within +/-0.5°C over most of the globe for both One study directly addressed the impacts of aerosols on climate under summer and winter seasons for 2016 2035 (Figure 11.24b), but by the RCP4.5 scenario, and found that the aerosol emission reductions 2036 2055 RCP8.5 is projected to be warmer than RCP2.6 by 0.5°C induce about a 0.2°C warming in the near term compared with fixed to 1.0°C over most continents, and by more than 1.0°C over the Arctic 2005 aerosol levels (more indicative of the SRES CMIP3 aerosols) (Levy in winter. Although studies suggest that the Arctic response is complex et al., 2013). The cooling over the period 1951 2010 that is attribut- and particularly sensitive to BC aerosols (Flanner et al., 2007; Quinn ed to non-WMGHG anthropogenic forcing in the CMIP5 models (Fig- et al., 2008; Jacobson, 2010; Ramana et al., 2010; Bond et al., 2013; ures 10.4 and 10.5) has a likely range of 0.25°C +/- 0.35°C compared Sand et al., 2013), the difference in ERF between RCP2.6 and RCP8.5 to +0.9°C +/- 0.4°C for WMGHG. The non-WMGHG forcing generally is dominated by the GHGs, as the BC atmospheric burden is decreas- includes the influence of non-aerosol warming agents over the histor- ing through the century with little difference across the RCPs (Table ical period such as tropospheric ozone, and a simple correction would AII.5.7). give an aerosol-only cooling that is about 50% larger in magnitude (see ERF components, Chapter 8). The near-term reductions in total Large changes in emissions of the well-mixed greenhouse gases aerosol emissions, however, even under the MFR scenario, are at most (WMGHGs) produce only modest changes in the near term because about 50% (AII.2.17 to AII.2.22), indicating a maximum near-term these gases are long lived: For example, a 50% cut in Kyoto-gas emis- temperature response of about half that induced by the addition of sions beginning in 1990 offsets the warming that otherwise would aerosols over the last century. Hence, the evidence indicates that dif- have occurred by only 0.11°C +/- 0.03°C after 12 years (Prather et al., ferences in aerosol loading from the SRES (conservatively assuming 2009). In contrast, many studies have noted the large potential for air roughly constant aerosols) to the RCP scenarios can increase warming pollutant emission reductions to influence near-term climate because in the CMIP5 models relative to the CMIP3 models by up to 0.2°C in RF from these species responds almost immediately to changes in the near term for the same WMGHG forcing (medium confidence). emissions. Decreases in sulphate aerosol have occurred through miti- gation of both air pollution and fossil-fuel emissions, and are expected Many studies show that air pollutants influence climate and identi- to produce a near-term rise in surface temperatures (e.g., Jacobson and fy approaches to mitigate both air pollution and global warming by 1005 Chapter 11 Near-term Climate Change: Projections and Predictability (°C) Figure 11.24b | Global maps of near-term differences in surface air temperature across the RCP scenarios. Differences between (RCP8.5) and low (RCP2.6) scenarios for the CMIP5 model ensemble (31 models) are shown for averages over 2016 2035 (left) and 2036 2055 (right) in boreal winter (December, January and February; top row) and summer (June, July and August; bottom row). decreasing CH4, tropospheric O3 and absorbing aerosols, particularly al., 2012; Rotstayn et al., 2012; Shindell et al., 2012b; Teng et al., 2012; BC (e.g., Hansen et al., 2000; Fiore et al., 2002, 2008, 2009; Dentener Bond et al., 2013). Recent trends in aerosol fog interactions and snow- et al., 2005; West et al., 2006; Royal Society, 2008; Jacobson, 2010; pack decline are implicated in more rapid regional warming in Europe Penner et al., 2010; UNEP and WMO, 2011; Anenberg et al., 2012; Shin- (van Oldenborgh et al., 2010; Ceppi et al., 2012; Scherrer et al., 2012), dell et al., 2012b; Unger, 2012; Bond et al., 2013). An alternative set of and coupling of aerosols and soil moisture could increase near-term technologically based scenarios (UNEP and WMO, 2011) that examined local warming in the eastern USA (Mickley et al., 2011). Major changes controls on CH4 and BC emissions designed to reduce tropospheric CH4, in the tropical circulation and rainfall have been attributed to increas- 11 O3 and BC also included reductions of co-emitted species (e.g., CO, OC, ing aerosols, but studies often disagree in sign (see Section 11.3.2.4.3, NOx). These reductions were applied in two CMIP5 models, and then Chapters 10 and 14). The lack of standardization (e.g., different those model responses were combined with the AR4 best estimates regions, different mixtures of reflecting and absorbing aerosols) and for the range of climate sensitivity and for uncertainty estimates for agreement across studies prevents generalization of these findings to each component of RF (Shindell et al., 2012a). This approach provided a project aerosol-induced changes in regional atmospheric circulation or near-term best estimate and range of global mean temperature change precipitation in the near term. for the reference (UNEP-ref) and CH4-mitigation (UNEP-CH4) scenarios (Figure 11.24a, adjusted to reflect the 1986 2005 reference period). Land use and land cover change (LULCC; see Chapter 6), including Under UNEP-CH4, anthropogenic CH4 emissions decrease by 24% from deforestation, forest degradation and agricultural expansion for bioen- 2010 to 2030, and global warming is reduced by 0.16°C (best estimate) ergy (Georgescu et al., 2009; Anderson-Teixeira et al., 2012), can alter at 2030 and by 0.28°C at 2050. A third UNEP scenario (UNEP-BC+CH4; global climate forcing through changing surface albedo (assessed as not shown) adds reductions in BC by 78% onto CH4 mitigation and ERF; Chapter 8), the hydrological cycle, GHGs (for CO2, see Chapters 6 reduces warming by an additional 0.12°C (best estimate) at 2030. How- and 12), or aerosols. The shift from forest to grassland in many places ever, it greatly increases the uncertainty owing to poor understanding since the pre-industrial era has been formally attributed as a cause of associated cloud adjustments (i.e., semi-direct and indirect effects) of regionally lower mean and extreme temperatures (Christidis et al., as well as of the ratio of BC to co-emitted reflective OC aerosols, their 2013). RCP CO2 and CH4 anthropogenic emissions include land use size distributions and mixing states (see Chapter 7, Section 7.5). Corre- changes (Hurtt et al., 2011) that vary with the underlying storylines sponding BC reductions in the RCPs are only 4 to 11%. and differ across RCPs. These global-scale changes in crop and pasture land projected over the near term (+2% for RCP2.6 and RCP8.5; 4% Beyond global mean temperature, shifting magnitudes and geographic for RCP4.5and RCP6.0) are smaller in magnitude than the 1950 2000 patterns of emissions may induce aerosol-specific changes in region- change (+6%) (see Figure 6.23). Overall LULCC has had small impact al atmospheric circulation and precipitation. See Chapter 7, especially on ERF ( 0.15 W m 2; see AII.1.2) and thus as projected is not a major Sections 7.6.2 and 7.6.4, for assessment of this work (Roeckner et al., factor in near-term climate change on global scales. 2006; Menon and et al., 2008; Ming et al., 2010, 2011; Ott et al., 2010; Randles and Ramaswamy, 2010; Allen and Sherwood, 2011; Bollasina Land use changes can also lead to sustained near-term changes in et al., 2011; Leibensperger et al., 2011b;Fyfe et al., 2012; Ganguly et regional climate through modification of the biogeophysical proper- 1006 Near-term Climate Change: Projections and Predictability Chapter 11 ties that alter the water and energy cycles. Local- and regional-scale Although it is possible to detect when various existing volcanoes climate responses to LULCC can exceed those associated with global become more active, or are more likely to erupt, the precise timing of mean warming (Baidya Roy and Avissar, 2002; Findell et al., 2007; an eruption, the amount of SO2 emitted and its distribution in the strat- Pitman et al., 2009, 2012; Pielke et al., 2011; Boisier et al., 2012; osphere are not predictable until after the eruption. Eruptions compa- de Noblet-Ducoudre et al., 2012; Lee and Berbery, 2012). Examples rable to Mt Pinatubo can be expected to cause a short-term cooling of LULCC-driven changes include: Brazilian conversion to sugarcane of the climate with related effects on surface climate that persist for a induces seasonal shifts of 1 to 2°C (Georgescu et al., 2013); European few years before a return to warming trajectories discussed in Section forested areas experience less severe heat waves (Teuling et al., 2010); 11.3.2. Larger eruptions, or several eruptions occurring close together and deforested regions over the Amazon lack deep convective clouds in time, would lead to larger and/or more persistent effects. (Wang et al., 2009). Systematic assessment of near-term, local-to-re- gional climate change is beyond the scope here. 11.3.6.2.2 The effects of future changes in solar forcing In summary, climate projections for the near term are not very sensitive Some of the future CMIP5 climate simulations using the RCP scenarios to the range in anthropogenic emissions of CO2 and other WMGHGs. By include an 11-year variation in total solar irradiance (TSI) but no under- the 2040s the CMIP5 median for global mean temperature ranges from lying trend beyond 2005. Chapter 10 noted that there has been little a low of +0.9°C (RCP2.6 and RCP6.0) to a high of +1.3°C (RCP8.5) observed trend in TSI during a time period of rapid global warming above the CMIP5 reference period (Figure 11.24a; Table AII.7.5). See since the late 1970s, but that the 11-year solar cycle does introduce discussion below regarding possible offsets between the observed and a significant and measurable pattern of response in the troposphere CMIP5 reference periods. Alternative CH4 scenarios incorporating large (Section 10.3.1.1.3). As discussed in Chapter 8 (Section 8.4.1.3), the emission reductions outside the RCP range would offset near-term Sun has been in a grand solar maximum of magnetic activity on the warming by 0.2°C (medium confidence). Aerosols remain a major multi-decadal time scale. However, the most recent solar minimum was source of uncertainty in near-term projections, on both global and the lowest and longest since 1920, and some studies (e.g., Lockwood, regional scales. Removal of half of the sulphate aerosol, as projected 2010) suggest there could be a continued decline towards a much qui- before 2030 in the MFR scenario and by 2050 in most RCPs, would eter period in the coming decades, but there is low confidence in these increase warming by up to +0.2°C (medium confidence). Actions to projections (Section 8.4.1.3). Nevertheless, if there is such a reduction reduce BC aerosol could reduce warming, but the magnitude is highly in solar activity, there is high confidence that the variations in TSI RF uncertain, depending on co-emitted (reflective) aerosols and aero- will be much smaller than the projected increased forcing due to GHGs sol-cloud interactions (Chapter 7; Section 7.5). In addition, near-term (Section 8.4.1.3). In addition, studies that have investigated the effect climate change, including extremes and precipitation, may be driven of a possible decline in TSI on future climate have shown that the asso- locally by land use change and shifting geographic patterns of aero- ciated decrease in global mean surface temperature is much smaller sols; and these regional climatic effects may exceed those induced by than the warming expected from increases in anthropogenic GHGs the global ERF. (Feulner and Rahmstorf, 2010; Jones et al., 2012; Meehl et al., 2013b) 11 However, regional impacts could be more significant (Xoplaki et al., 11.3.6.2 Uncertainties in Future Natural Radiative Forcing and 2001; Mann et al., 2009; Gray et al., 2010; Ineson et al., 2011). the Consequences for Near-term Climate As discussed in Section 8.4.1, a recent satellite measurement (Harder 11.3.6.2.1 The effects of future volcanic eruptions et al., 2009) found much greater than expected reduction at ultraviolet (UV) wavelengths in the recent declining solar cycle phase. Changes As discussed in Chapters 8 and 10, explosive volcanic eruptions are the in solar UV drive stratospheric O3 chemistry and can change RF. Haigh major cause of natural variations in RF on interannual to decadal time et al. (2010) show that if these observations are correct, they imply scales. Most important are large tropical and subtropical eruptions the opposite relationship between solar RF and solar activity over that that inject substantial amounts of SO2 directly into the stratosphere. period than has hitherto been assumed. These new measurements The subsequent formation of sulphate aerosols leads to a negative RF therefore increase uncertainty in estimates of the sign of solar RF, but of several watts per metre squared, with a typical lifetime of a year they are not expected to alter estimates of the maximum absolute (Robock, 2000). The eruption of Mt Pinatubo in 1991 was one of the magnitude of the solar contribution to RF, which remains small (Chap- largest in recent times, with a return period of about three times per ter 8). However, they do suggest the possibility of a much larger impact century, but dwarfed by Tambora in 1815 (Gao et al., 2008). Mt Pina- of solar variations on the stratosphere than previously thought, and tubo caused a rapid drop in a global mean surface air temperature some studies have suggested that this may lead to significant regional of several tenths of a degree Celsius over the following year, but this impacts on climate (as discussed in Section 10.3.1.1.3) that are not signal disappeared over the next five years (Hansen et al., 1992; Soden necessarily reflected by the RF metric (see Section 8.4.1). et al., 2002; Bender et al., 2010). In addition to global mean cooling, there are effects on the hydrological cycle (e.g., Trenberth and Dai, In summary, possible future changes in solar irradiance could influence 2007), atmosphere and ocean circulation (e.g., Stenchikov et al., 2006; the rate at which global mean surface air temperature increases, but Ottera et al., 2010). The surface climate response typically persists for there is high confidence that this influence will be small in comparison a few years, but the subsurface ocean response can persist for dec- to the influence of increasing concentrations of GHGs in the atmos- ades or centuries, with consequences for sea level rise (Delworth et al., phere. Understanding of the impacts of changes in solar irradiance on 2005; Stenchikov et al., 2009; Gregory, 2010; Timmreck, 2012). continental and sub-continental scale climate remains low. 1007 Chapter 11 Near-term Climate Change: Projections and Predictability Frequently Asked Questions FAQ 11.2 | How Do Volcanic Eruptions Affect Climate and Our Ability to Predict Climate? Large volcanic eruptions affect the climate by injecting sulphur dioxide gas into the upper atmosphere (also called stratosphere), which reacts with water to form clouds of sulphuric acid droplets. These clouds reflect sunlight back to space, preventing its energy from reaching the Earth s surface, thus cooling it, along with the lower atmosphere. These upper atmospheric sulphuric acid clouds also locally absorb energy from the Sun, the Earth and the lower atmosphere, which heats the upper atmosphere (see FAQ 11.2, Figure 1). In terms of surface cooling, the 1991 Mt Pinatubo eruption in the Philippines, for example, injected about 20 million tons of sulphur dioxide (SO2) into the stratosphere, cooling the Earth by about 0.5°C for up to a year. Globally, eruptions also reduce precipitation, because the reduced incoming shortwave at the surface is compensated by a reduction in latent heating (i.e., in evaporation and hence rainfall). For the purposes of predicting climate, an eruption causing significant global surface cooling and upper atmo- spheric heating for the next year or so can be expected. The problem is that, while a volcano that has become more active can be detected, the precise timing of an eruption, or the amount of SO2 injected into the upper atmosphere and how it might disperse cannot be predicted. This is a source of uncertainty in climate predictions. Large volcanic eruptions produce lots of particles, called ash or tephra. However, these particles fall out of the atmosphere quickly, within days or weeks, so they do not affect the global climate. For example, the 1980 Mount St. Helens eruption affected surface temperatures in the northwest USA for several days but, because it emitted little SO2 into the stratosphere, it had no detectable global climate impacts. If large, high-latitude eruptions inject sulphur into the stratosphere, they will have an effect only in the hemisphere where they erupted, and the effects will only last a year at most, as the stratospheric cloud they produce only has a lifetime of a few months. Tropical or subtropical volcanoes produce more global surface or tropospheric cooling. This is because the resulting sulphuric acid cloud in the upper atmosphere lasts between one and two years, and can cover much of the globe. However, their regional climatic impacts are difficult to predict, because dispersion of stratospheric sulphate aerosols depends heavily on atmospheric wind condi- tions at the time of eruption. Furthermore, the surface Decreased upward ux of 11 cooling effect is typically not uniform: because conti- energy due to absorption by aerosol cloud and emission nents cool more than the ocean, the summer monsoon at a low temperature can weaken, reducing rain over Asia and Africa. The cli- Re ected matic response is complicated further by the fact that Stratospheric Aerosols solar ux upper atmospheric clouds from tropical eruptions also (Lifetime 1-2 Years) Heating due absorb sunlight and heat from the Earth, which produc- Heating due to to absorption es more upper atmosphere warming in the tropics than Reactions absorption of of energy by on cloud energy from the cloud at high latitudes. particles Earth and lower destroy ozone atmosphere The largest volcanic eruptions of the past 250 years stim- Cooling because reduction of sunlight ulated scientific study. After the 1783 Laki eruption in overwhelms any Iceland, there were record warm summer temperatures Increased increased downward energy in Europe, followed by a very cold winter. Two large downward ux of emitted by volcanic energy due to eruptions, an unidentified one in 1809, and the 1815 emission from cloud Tambora eruption caused the Year Without a Summer aerosol cloud in 1816. Agricultural failures in Europe and the USA that year led to food shortages, famine and riots. Tropospheric Aerosols (Lifetime 1-3 Weeks) The largest eruption in more than 50 years, that of Agung in 1963, led to many modern studies, including observations and climate model calculations. Two subse- quent large eruptions, El Chichón in 1982 and Pinatubo in 1991, inspired the work that led to our current under- standing of the effects of volcanic eruptions on climate. FAQ 11.2, Figure 1 | Schematic of how large tropical or sub-tropical volcanoes (continued on next page) impact upper atmospheric (stratospheric) and lower atmospheric (tropospheric) temperatures. 1008 Near-term Climate Change: Projections and Predictability Chapter 11 FAQ 11.2 (continued) Volcanic clouds remain in the stratosphere only for a couple of years, so their impact on climate is correspondingly short. But the impacts of consecutive large eruptions can last longer: for example, at the end of the 13th century there were four large eruptions one every ten years. The first, in 1258 CE, was the largest in 1000 years. That sequence of eruptions cooled the North Atlantic Ocean and Arctic sea ice. Another period of interest is the three large, and several lesser, volcanic events during 1963 1991 (see Chapter 8 for how these eruptions affected atmo- spheric composition and reduced shortwave radiation at the ground. Volcanologists can detect when a volcano becomes more active, but they cannot predict whether it will erupt, or if it does, how much sulphur it might inject into the stratosphere. Nevertheless, volcanoes affect the ability to predict climate in three distinct ways. First, if a violent eruption injects significant volumes of sulphur dioxide into the stratosphere, this effect can be included in climate predictions. There are substantial challenges and sources of uncertainty involved, such as collecting good observations of the volcanic cloud, and calculating how it will move and change during its lifetime. But, based on observations, and successful modelling of recent eruptions, some of the effects of large eruptions can be included in predictions. The second effect is that volcanic eruptions are a potential source of uncertainty in our predictions. Eruptions cannot be predicted in advance, but they will occur, causing short-term climatic impacts on both local and global scales. In principle, this potential uncertainty can be accounted for by including random eruptions, or eruptions based on some scenario in our near-term ensemble climate predictions. This area of research needs further explora- tion. The future projections in this report do not include future volcanic eruptions. Third, the historical climate record can be used, along with estimates of observed sulphate aerosols, to test the fidelity of our climate simulations. While the climatic response to explosive volcanic eruptions is a useful analogue for some other climatic forcings, there are limitations. For example, successfully simulating the impact of one erup- tion can help validate models used for seasonal and interannual predictions. But in this way not all the mechanisms involved in global warming over the next century can be validated, because these involve long term oceanic feed- backs, which have a longer time scale than the response to individual volcanic eruptions. 11 11.3.6.3 Synthesis of Near-term Projections of Global Mean the CMIP5 projections based on the RCP scenarios. This difference Surface Air Temperature is at least partly attributable to higher aerosol concentrations in the SRES scenarios (see Section 11.3.6.1). Figure 11.25 provides a synthesis of near-term projections of global mean surface air temperature (GMST) from CMIP5, CMIP3 and studies 3. The CMIP3 and CMIP5 projections are ensembles of opportunity, that have attempted to use observations to quantify projection uncer- and it is explicitly recognized that there are sources of uncertain- tainty (see Section 11.3.2.1). On the basis of this evidence, an attempt ty not simulated by the models. Evidence of this can be seen by is made here to assess a likely range for GMST in the period 2016 comparing the Rowlands et al. (2012) projections for the A1B sce- 2035. Such an overall assessment is not straightforward. The following nario, which were obtained using a very large ensemble in which points must be taken into account: the physics parameterizations were perturbed in a single climate model, with the corresponding raw multi-model CMIP3 projec- 1. No likelihoods are associated with the different RCP scenarios. For tions. The former exhibit a substantially larger likely range than this reason, previous IPCC Assessment Reports have only present- the latter. A pragmatic approach to addressing this issue, which ed projections that are conditional on specific scenarios. Here we was used in the AR4 and is also used in Chapter 12, is to consider attempt a broader assessment across all four RCP scenarios. This is the 5 to 95% CMIP3/5 range as a likely rather than very likely possible only because, as discussed in Section 11.3.6.1, near-term range. projections of GMST are not especially sensitive to these different scenarios. 4. As discussed in Section 11.3.6.2, the RCP scenarios assume no underlying trend in total solar irradiance and no future volcanic 2. In the near term it is expected that increases in GMST will be eruptions. Future volcanic eruptions cannot be predicted and there driven by past and future increases in GHG concentrations and is low confidence in projected changes in solar irradiance (Chapter future decreases in anthropogenic aerosols, as found in all the RCP 8). Consequently the possible effects of future changes in natural scenarios. Figure 11.25c shows that in the near term the CMIP3 forcings are excluded from the assessment here. projections based on the SRES scenarios are generally cooler than 1009 Chapter 11 Near-term Climate Change: Projections and Predictability 5. As discussed in Section 11.3.2.1.1 observationally constrained rapidly in the near term than occurred over the hiatus period (see ASK projections (Gillett et al., 2013; Stott et al., 2013) are 10 to Box 9.2 and Annex II), which is consistent with more rapid warm- 15% cooler (median values for RCP4.5; 6 10% cooler for RCP8.5), ing. In addition, Box 9.2 includes an assessment that internal vari- and have a narrower range, than the corresponding raw (unini- ability is more likely than not to make a positive contribution to the tialized) CMIP5 projections. The reduced rate of warming in the increase in GMST in the near term. Internal variability is included ASK projections is related to evidence from Chapter 10 (Section in the CMIP5 projections, but because most of the CMIP5 simu- 10.3.1) that some CMIP5 models have a higher transient response lations do not reproduce the observed reduction in global mean to GHGs and a larger response to other anthropogenic forc- surface warming over the last 10 to 15 years, the distribution of ings (dominated by the effects of aerosols) than the real world CMIP5 near-term trends will not reflect this assessment and might, (medium confidence). These models may warm too rapidly as as a result, be biased low. This uncertainty, however, is somewhat GHGs increase and aerosols decline. counter balanced by the evidence of point 5, which suggests a high bias in the distribution of near-term trends. A further projection of 6. Over the last two decades the observed rate of increase in GMST GMST for the period 2016 2035 may be obtained by starting from has been at the lower end of rates simulated by CMIP5 models the observed GMST for 2012 (0.14°C relative to 1986 2005) and (Figure 11.25a). This hiatus in GMST rise is discussed in detail projecting increases at rates between the 5 to 95% CMIP5 range in Box 9.2 (Chapter 9), where it is concluded that the hiatus is of 0.11°C to 0.41°C per decade. The resulting range of 0.29°C to attributable, in roughly equal measure, to a decline in the rate of 0.69°C, relative to 1986 2005, is shown on Figure 11.25(c). increase in ERF and a cooling contribution from internal variability (expert judgment, medium confidence). The decline in the rate of Overall, in the absence of major volcanic eruptions which would increase in ERF is attributed primarily to natural (solar and vol- cause significant but temporary cooling and, assuming no significant canic) forcing but there is low confidence in quantifying the role future long term changes in solar irradiance, it is likely (>66% prob- of forcing trend in causing the hiatus, because of uncertainty in ability) that the GMST anomaly for the period 2016 2035, relative to the magnitude of the volcanic forcing trend and low confidence in the reference period of 1986 2005 will be in the range 0.3°C to 0.7°C the aerosol forcing trend. Concerning the higher rate of warming (expert assessment, to one significant figure; medium confidence). This in CMIP5 simulations it is concluded that there is a substantial range is consistent, to one significant figure, with the range obtained contribution from internal variability but that errors in ERF and by using CMIP5 5 to 95% model trends for 2012 2035. It is also con- in model responses may also contribute. There is low confidence sistent with the CMIP5 5 to 95% range for all four RCP scenarios of in this assessment because of uncertainties in aerosol forcing in 0.36°C to 0.79°C, using the 2006 2012 reference period, after the particular. upper and lower bounds are reduced by 10% to take into account the evidence noted under point 5 that some models may be too sensitive The observed hiatus has important implications for near-term pro- to anthropogenic forcing. The 0.3°C to 0.7°C range includes the likely 11 jections of GMST. A basic issue concerns the sensitivity of projec- range of the ASK projections and initialized predictions for RCP4.5. It tions to the choice of reference period. Figure 11.25b and c shows corresponds to a rate of change of GMST between 2012 and 2035 in the 5 to 95% ranges for CMIP5 projections using a 1986 2005 the range 0.12°C to 0.42°C per decade. The higher rates of change reference period (light grey), and the same projections using a can be associated with a significant positive contribution from internal 2006 2012 reference period (dark grey). The latter projections variability (Box 9.2) and/or high rates of increase in ERF (e.g., as found are cooler, and the effect of using a more recent reference period in RCP8.5). Note that an upper limit of 0.8°C on the 2016 2035 GMST appears similar to the effect of initialization (discussed in Section corresponds to a rate of change over the period 2012 2035 of 0.49°C 11.3.2.1.1 and shown in Figure 11.25c for RCP4.5). Using this more per decade, which is considered unlikely. The assessed rates of change recent reference period, the 5 to 95% range for the mean GMST are consistent with the AR4 SPM statement that For the next two dec- in 2016 2035 relative to 1986 2005 is 0.36°C to 0.79°C (using ades, a warming of about 0.2°C per decade is projected for a range of all RCP scenarios, weighted to ensure equal weights per model SRES emission scenarios . However, the implied rates of warming over and using an estimate of the observed GMST anomaly for (2006 the period from 1986 2005 to 2016 2035 are lower as a result of the 2012) (1986 2005) of 0.16°C). This range may be compared with hiatus: 0.10°C to 0.23°C per decade, suggesting the AR4 assessment the range of 0.48°C to 1.15°C obtained from the CMIP5 models was near the upper end of current expectations for this specific time using the original 1986 2005 reference period. interval. 7. In view of the sensitivity of projections to the reference period it The assessment here provides only a likely range for GMST. Possible is helpful to consider the possible rate of change of GMST in the reasons why the real world might depart from this range include: RF near term. The CMIP5 5 to 95% ranges for GMST trends in the departs significantly from the RCP scenarios, due to either natural (e.g., period 2012 2035 are 0.11°C to 0.41°C per decade. This range major volcanic eruptions, changes in solar irradiance) or anthropogenic is similar to, though slightly narrower than, the range found by (e.g., aerosol or GHG emissions) causes; processes that are poorly sim- Easterling and Wehner (2009) for the CMIP3 SRES A2 scenario over ulated in the CMIP5 models exert a significant influence on GMST. The the longer period 2000 2050. It may also be compared with recent latter class includes: a possible strong recovery from the recent hiatus rates in the observational record (e.g., ~0.26°C per decade for in GMST; the possibility that models might underestimate decadal vari- 1984 1998 and ~0.04°C per decade for hiatus period 1998 2012; ability (but see Section 9.5.3.1); the possibility that model sensitivity to See Box 9.2). The RCP scenarios project that ERF will increase more anthropogenic forcing may differ from that of the real world (see point 1010 Near-term Climate Change: Projections and Predictability Chapter 11 Global mean temperature near term projections relative to 1986 2005 2.5 Observations (4 datasets) (a) Temperature anomaly (°C) Historical (42 models) 2 RCP 2.6 (32 models) RCP 4.5 (42 models) 1.5 RCP 6.0 (25 models) RCP 8.5 (39 models) 1 0.5 0 0.5 Historical RCPs 1990 2000 2010 2020 2030 2040 2050 2.5 Indicative likely range for annual means (b) 3 Temperature anomaly (°C) ALL RCPs (5 95% range, two reference periods) 2 Relative to 1850 1900 ALL RCPs min max (299 ensemble members) Observational uncertainty (HadCRUT4) 1.5 Observations (4 datasets) 2 ALL RCPs Assessed likely range 1 for 2016 2035 mean 0.5 1 0 Assuming no future large volcanic eruptions 0.5 Historical RCPs 0 1990 2000 2010 2020 2030 2040 2050 1.5 Projections of 2016 2035 mean (c) Temperature anomaly (°C) Stott et al. Rowlands et al. 1 11 Meehl & Teng Using trends 0.5 B1 A1B A2 A1B 4.5 4.5 8.5 2.6 4.5 6.0 8.5 ALL ALL SRES CMIP3 Obs. Constrained RCPs CMIP5 Assessed 0 Key: 5% 17 83% 95% Figure 11.25 | Synthesis of near-term projections of global mean surface air temperature (GMST). (a) Simulations and projections of annual mean GMST 1986 2050 (anomalies relative to 1986 2005). Projections under all RCPs from CMIP5 models (grey and coloured lines, one ensemble member per model), with four observational estimates (Hadley Centre/Climate Research Unit gridded surface temperature data set 4 (HadCRUT4): Morice et al., 2012); European Centre for Medium range Weather Forecast (ECMWF) interim reanalysis of the global atmosphere and surface conditions (ERA-Interim): Simmons et al., 2010); Goddard Institute of Space Studies Surface Temperature Analysis (GISTEMP): Hansen et al., 2010); National Oceanic and Atmospheric Administration (NOAA): Smith et al., 2008)) for the period 1986 2012 (black lines). (b) As (a) but showing the 5 to 95% range of annual mean CMIP5 projections (using one ensemble member per model) for all RCPs using a reference period of 1986 2005 (light grey shade) and all RCPs using a reference period of 2006 2012, together with the observed anomaly for (2006 2012) to (1986 2005) of 0.16°C (dark grey shade). The percentiles for 2006 onwards have been smoothed with a 5-year running mean for clarity. The maximum and minimum values from CMIP5 using all ensemble members and the 1986 2005 reference period are shown by the grey lines (also smoothed). Black lines show annual mean observational estimates. The red hatched region shows the indicative likely range for annual mean GMST during the period 2016 2035 based on the ALL RCPs Assessed likely range for the 20-year mean GMST anomaly for 2016 2035, which is shown as a black bar in both (b) and (c) (see text for details). The temperature scale on the right hand side shows changes relative to a reference period of 1850-1900, assuming a warming of GMST between 1850-1900 and 1986-2005 of 0.61°C estimated from HadCRUT4.The temperature scale relative to the 1850-1900 period on the right-hand side assumes a warming of GMST prior to 1986 2005 of 0.61°C estimated from HadCRUT4. (c) A synthesis of projections for the mean GMST anomaly for 2016 2035 relative to 1986 2005. The box and whiskers represent the 66% and 90% ranges. Shown are unconstrained SRES CMIP3 and RCP CMIP5 projections; observationally constrained projections: Rowlands et al. (2012) for SRES A1B scenario, updated to remove simulations with large future volcanic eruptions; Meehl and Teng (2012) for RCP4.5 scenario, updated to include 14 CMIP5 models; Stott et al. (2013), based on six CMIP5 models with unconstrained 66% ranges for these six models shown as unfilled boxes; unconstrained projections for all four RCP scenarios using two reference periods as in panel b (light grey and dark grey shades, consistent with panel b); 90% range estimated using CMIP5 trends for the period 2012 2035 and the observed GMST anomaly for 2012; an overall likely (>66%) assessed range for all RCP scenarios. The dots for the CMIP5 estimates show the maximum and minimum values using all ensemble members. The medians (or maximum likelihood estimate for Rowlands et al. 2012) are indicated by a grey band. 1011 Chapter 11 Near-term Climate Change: Projections and Predictability Table 11.3 | Percentage of CMIP5 models for which the projected change in global annual mean GMST, which is shown as the red hatched area in Figure mean surface air temperature, relative to 1850-1900, crosses the specified temperature 11.25b. Note that this range does not take into account the expected levels, by the specified time periods and assuming the specified RCP scenarios. The pro- impact of any future volcanic eruptions. jected temperature change relative to the mean temperature in the period 1850-1900 is calculated using the models projected temperature change relative to 1986 2005 plus the observed temperature change between 1850 1900 and 1986 2005 of 0.61°C esti- The assessed likely range for GMST in the period 2016 2035 may also mated from the Hadley Centre/Climate Research Unit gridded surface temperature data be used to assess the likelihood that GMST will cross policy-relevant set 4 (HadCRUT4; Morice et al., 2012). The percentages in brackets use an alternative levels, relative to earlier time periods (Joshi et al., 2011). Using the reference period for the model projections of 2006 2012, together with the observed 1850 1900 period, and the observed temperature rise between 1850 temperature difference between 1986 2005 and 2006 2012 of 0.16°C. The definition of crossing is that the 20-year mean exceeds the specified temperature level. Note that 1900 and 1986 2005 of 0.61°C (estimated from the HadCRUT4 data these percentages should not be interpreted as likelihoods because there are other set (Morice et al., 2012) gives a likely range for the GMST anomaly sources of uncertainty (see discussion in Section 11.3.6.3). in 2016 2035 of 0.91°C 1.31°C, and supports the following conclu- sions: it is more likely than not that the mean GMST for the period Scenario Early (2016 2035) Mid (2046 2065) 2016 2035 will be more than 1°C above the mean for 1850 1900, Temperature +1.0°C and very unlikely that it will be more than 1.5°C above the 1850 1900 RCP 2.6 100% (84%) 100% (94%) mean (expert assessment, medium confidence). Additional information RCP 4.5 98% (93%) 100% (100%) about the possibility of GMST crossing specific temperature levels is RCP 6.0 96% (80%) 100% (100%) provided in Table 11.3, which shows the percentage of CMIP5 models RCP 8.5 100% (100%) 100% (100%) for which the projected change in GMST exceeds specific temperature levels, under each RCP scenario, in two time periods (early century: Temperature +1.5°C 2016 2035 and mid-century: 2046 2065), and also using the two RCP 2.6 22% (0%) 56% (28%) different reference periods discussed under point 6 and illustrated in RCP 4.5 17% (0%) 95% (86%) Figure 11.25. However, these percentages should not be interpreted as RCP 6.0 12% (0%) 92% (88%) likelihoods because as discussed in this section there are sources RCP 8.5 33% (5%) 100% (100%) of uncertainty not captured by the CMIP5 ensemble. Note finally that it Temperature +2.0°C is very likely that specific temperature levels will be crossed temporari- RCP 2.6 0% (0%) 16% (3%) ly in individual years before a permanent crossing is established (Joshi et al., 2011), but Table 11.3 is based on 20-year mean values. RCP 4.5 0% (0%) 43% (29%) RCP 6.0 0% (0%) 32% (20%) RCP 8.5 0% (0%) 95% (90%) Temperature +3.0°C 11 RCP 2.6 0% (0%) 0% (0%) RCP 4.5 0% (0%) 0% (0%) RCP 6.0 0% (0%) 0% (0%) RCP 8.5 0% (0%) 21% (5%) 5); and the possibility of abrupt changes in climate (see introduction to Sections 11.3.6 and 12.5.5). The assessment here has focused on 20-year mean values of GMST for the period 2016 2035. There is no unique method to derive a likely range for annual mean values from the range for 20-year means, so such calculations necessarily involve additional uncertainties (beyond those outlined in the previous paragraph), and lower confidence. Nev- ertheless, it is useful to attempt to estimate a range for annual mean values, which may be compared with raw model projections and, in the future, with observations. To do so, the following simple approach is used: (1) Starting in 2009 from the observed GMST anomaly for 2006 2012 of 0.16°C (relative to 1986 2005), linear trends are pro- jected over the period 2009 2035 with maximum and minimum gra- dients selected to be consistent with the 0.3°C to 0.7°C range for the mean GMST in the period 2016 2035; 2). To take into account the expected year-to-year variability of annual mean values, the resulting linear trends are offset by +/-0.1°C. The value of 0.1°C is based on the standard deviation of annual means in CMIP5 control runs (to one sig- nificant figure). These calculations provide an indicative likely range for 1012 Near-term Climate Change: Projections and Predictability Chapter 11 Box 11.2 | Ability of Climate Models to Simulate Observed Regional Trends The ability of models to simulate past climate change on regional scales can be used to investigate whether the multi-model ensemble spread covers the forcing and model uncertainties. Agreement between observed and simulated regional trends, taking natural variabil- ity and model spread into account, would build confidence in near-term projections. Although large-scale features are simulated well (see Chapter 10), on sub-continental and smaller scales the observed trends are, in general, more often in the tails of the distribution of modelled trends than would be expected by chance fluctuations (Bhend and Whetton, 2012; Knutson et al., 2013b; van Oldenborgh et al., 2013). Natural variability and model spread are larger at smaller scales (Stott et al., 2010), but this is not enough to bridge the gap between models and observations. Downscaling with Regional Climate Models (RCMs) does not affect seasonal mean trends except near mountains or coastlines in Europe (van Oldenborgh et al., 2009; van Haren et al., 2012). These results hold for both observed and modelled estimates of natural variability and for various analyses of the observations. Given the statistical nature of the comparisons, it is currently not possible to say in which regions observed discrepancies are due to coincidental natural variability and in which regions they are due to forcing or model deficiencies. These results show that in general the Coupled Model Intercomparison Project Phase 5 (CMIP5) ensemble cannot be taken as a reliable regional probability forecast, but that the true uncertainty can be larger than the model spread indicated in the maps in this chapter and Annex I. Temperature Räisänen (2007) and Yokohata et al. (2012) compared regional linear temperature trends during 1955 2005 (1961 2000) with cor- responding trends in the CMIP3 ensemble. They found that the range of simulated trends captured the observed trend in nearly all locations. Using another metric, Knutson et al., (2013b) found that CMIP5 models did slightly better than CMIP3 in reproducing linear trends (see also Figure 10.2, Section 10.3.1.1.2). The linear CMIP5 temperature trends are compared with the observed trends in Box 11.2, Figure 1a h. The rank histograms show the warm bias in global mean temperature (see Chapter 10) and some overconfidence, but within the inter-model spread. However, the apparent agreement appears to be for the wrong reason. Many of the models that appear to correctly simulate observed high regional trends do so because they have a high climate response (i.e., the global temperature rises quickly) and do not simulate the observed spatial pattern of trends (Kumar et al., 2013). To address this, Bhend and Whetton (2012) and van Oldenborgh et al. (2013) use another definition of the local trend: the regression of the local temperature on the (low-pass filtered) global mean temperature. This definition separates the local temperature response pattern from the global mean climate response. They find highly significant discrepancies between the CMIP3 and CMIP5 trend patterns and a variety of estimates of observed trend estimates. These discrepancies are defined relative to an error model that includes the (modelled or observed) natural variability, model spread and spatial autocorrelations. In the following, areas where the observed and modelled trends show marked differences are 11 noted. Areas of agreement are covered in Section 10.3.1.1.4. In December to February the observed Arctic amplification extends further south than modelled in Central Asia and northwestern North America. In June to August southern Europe and North Africa have warmed significantly faster than both CMIP3 and CMIP5 models simulated (van Oldenborgh et al., 2009); this also holds for the Middle East. The observed Indo-Pacific warm pool trend is significantly higher than the modelled trend year-round (Shin and Sardeshmukh, 2011; Williams and Funk, 2011), and the North Pacific and the southeastern USA and adjoining ocean trends were lower. Direct causes for many of these discrepancies are known (e.g., December to February circulation trends that differ between the observation and the models (Gillett et al., 2005; Gillett and Stott, 2009; van Olden- borgh et al., 2009; Bhend and Whetton, 2012) or teleconnections from other areas with trend biases (Deser and Phillips, 2009; Meehl et al., 2012a), but the causes of the underlying discrepancies are often unknown. Possibilities include observational uncertainties (note, however, that the areas where the observations warm more than the models do not correspond to areas of increased urbanization or irrigation; cf. Section 2.4.1.3), an underestimation of the low-frequency variability (Knutson et al. (2013b) show evidence that this is probably not the case for temperature outside the tropics), unrealistic local forcing (e.g., aerosols (Ruckstuhl and Norris, 2009)), or missing or misrepresented processes in models (e.g., fog (Vautard et al., 2009; Ceppi et al., 2012)). Precipitation In spite of the larger variability relative to the trends and observational uncertainties (cf. Section 2.5.1.2), annual mean regional linear precipitation trends have been found to differ significantly between observations and CMIP3 models, both in the zonal mean (Allan and Soden, 2007; Zhang et al., 2007b) and regionally (Räisänen, 2007). The comparison is shown in Box 11.2, Figure 1i p for the CMIP5 half-year seasons used in Annex I, following van Oldenborgh et al. (2013). In both half years the observations fall more often in the highest and lowest 5% than expected by chance fluctuations within the ensemble (grey area). The differences larger than the difference between the CRU and GPCC analyses (cf. Figure 2.29) are noted below. (continued on next page) 1013 Chapter 11 Near-term Climate Change: Projections and Predictability Box 11.2 (continued) In Europe there are large-scale differences between observed trends and trends, both in General Circulation Models (GCMs) and RCMs (Bhend and von Storch, 2008), which are ascribed to circulation change discrepancies in winter and in summer sea surface temperature (SST) trend biases (Lenderink et al., 2009; van Haren et al., 2012) and the misrepresentation of Summer North Atlantic Oscillation (NAO) teleconnections (Bladé et al., 2012). Central North America has become much wetter over 1950 2012, especially in winter, which is not simulated by the CMIP5 models. Larger observed northwest Australian rainfall increases than in CMIP3 in summer are driven by ozone forcings in two climate models (Kang et al., 2011) and aerosols in another (Rotstayn et al., 2012). The Guinea Coast has become drier in the observations than in the models. The CMIP5 patterns seem to reproduce the observed patterns somewhat better than the CMIP3 patterns (Bhend and Whetton, 2012), but the remaining discrepancies imply that CMIP5 projections cannot be used as reliable precipitation forecasts. a b c d 30% rank for temperature trends vs HadCRUT4 1950-2011 25% 20% 15% 10% 5% 0% 0% 10% 20% 30% 40% 50% 60% 70% 80% 90% 100% e f g h 30% rank for temperature trends vs HadCRUT4 1950-2011 25% 20% 15% 10% 5% 0% 0% 10% 20% 30% 40% 50% 60% 70% 80% 90% 100% (°C per century) percentile i j k l 30% rank for rel. precip. trends vs GPCC Oct-Mar 1950-2010 25% 20% 15% 10% 5% 0% 11 0% 10% 20% 30% 40% 50% 60% 70% 80% 90% 100% m n o p 30% rank for rel. precip. trends vs GPCC Apr-Sep 1950-2010 25% 20% 15% 10% 5% 0% 0% 10% 20% 30% 40% 50% 60% 70% 80% 90% 100% (% per century) percentile Box 11.2, Figure 1 | (a) Observed linear December to February temperature trend 1950 2012 (Hadley Centre/Climate Research Unit gridded surface temperature data set 4.1.1.0 (HadCRUT4.1.1.0, °C per century). ( b) The equivalent CMIP5 ensemble mean trend. (c) Quantile of the observed trend in the ensemble, and (d) the corresponding rank histogram, the grey band denotes the 90% band of intermodel fluctuations (following Annan and Hargreaves, 2010). (e h) Same for June to August. (i l) Same for October to March precipitation (Global Precipitation Climatology Centre (GPCC) v7) 1950 2010, % per century). (m p) Precipitation in April to September. Grid boxes where less than 50% of the years have observations are left white. (Based on Räisänen (2007) and van Oldenborgh et al. (2013).) Acknowledgements The authors thank Ed Hawkins (U. Reading, UK) for extensive input to discussions on the assessment of near-term global temperature and his work on key synthesis figures, and Jan Sedlacek (ETH, Switzerland) for his outstanding work on the production of numerous figures in this chapter. 1014 Near-term Climate Change: Projections and Predictability Chapter 11 References Alexander, L. V., and J. M. Arblaster, 2009: Assessing trends in observed and modelled Bauer, S. E., D. Koch, N. Unger, S. M. Metzger, D. T. Shindell, and D. G. Streets, 2007: climate extremes over Australia in relation to future projections. Int. J. Climatol., Nitrate aerosols today and in 2030: A global simulation including aerosols and 29, 417 435. tropospheric ozone. Atmos. Chem. Phys., 7, 5043 5059. Alexander, M. A., L. Matrosova, C. Penland, J. D. Scott, and P. Chang, 2008: Forecasting Bellouin, N., J. G. L. Rae, A. Jones, C. E. Johnson, J. M. Haywood, and O. Boucher, Pacific SSTs: Linear inverse model predictions of the PDO. J. Clim., 21, 385 402. 2011: Aerosol forcing in the CMIP5 simulations by Hadgem2-ES and the role of Alexandru, A., R. de Elia, and R. Laprise, 2007: Internal variability in regional climate ammonium nitrate. J. Geophys. Res. Atmos., doi:10.1029/2011JD016074. downscaling at the seasonal scale. Mon. Weather Rev., 135, 3221 3238. Bellucci, A., et al., 2013: Decadal climate predictions with a coupled OAGCM Alexeev, V. A., D. J. Nicolsky, V. E. Romanovsky, and D. M. Lawrence, 2007: An initialized with oceanic reanalyses. Clim. Dyn., 40, 1483 1497. evaluation of deep soil configurations in the CLM3 for improved representation Bender, F. A. M., A. M. L. Ekman, and H. Rodhe, 2010: Response to the eruption of of permafrost. Geophys. Res. Lett., 34, L09502. Mount Pinatubo in relation to climate sensitivity in the CMIP3 models. Clim. Allan, R. P., and B. J. Soden, 2007: Large discrepancy between observed and Dyn., 35, 875 886. simulated precipitation trends in the ascending and descending branches of the Berg, P., C. Moseley, and J.O. Haerter, 2013: Strong increase in convective tropical circulation. Geophys. Res. Lett., 34, L18705. precipitation in response to higher temperatures. Nature Geosci., 6, 181-185, Allan, R. P., B. J. Soden, V. O. John, W. Ingram, and P. Good, 2010: Current changes in DOI: 10.1038/ngeo1731. tropical precipitation. Environ. Res. Lett., 5, 025205. Berner, J., F. J. Doblas-Reyes, T. N. Palmer, G. Shutts, and A. Weisheimer, 2008: Impact Allen, M. R., and W. J. Ingram, 2002: Constraints on future changes in climate and of a quasi-stochastic cellular automaton backscatter scheme on the systematic the hydrologic cycle. Nature, 419, 224 232. error and seasonal prediction skill of a global climate model. Philos. Trans. R. Soc. Allen, M. R., P. A. Stott, J. F. B. Mitchell, R. Schnur, and T. L. Delworth, 2000: Quantifying London A, 366, 2561 2579. the uncertainty in forecasts of anthropogenic climate change. Nature, 407, Betts, R. A., et al., 2007: Projected increase in continental runoff due to plant 617 620. responses to increasing carbon dioxide. Nature, 448, 1037 1041, DOI 10.1038/ Allen, R., and S. Sherwood, 2011: The impact of natural versus anthropogenic nature06045. aerosols on atmospheric circulation in the Community Atmosphere Model. Clim. Bhend, J., and H. von Storch, 2008: Consistency of observed winter precipitation Dyn., 36, 1959 1978. trends in northern Europe with regional climate change projections. Clim. Dyn., Anderson-Teixeira, K., P. Snyder, T. Twine, S. Cuadra, M. Costa, and E. DeLucia, 31, 17 28. 2012: Climate-regulation services of natural and agricultural ecoregions of the Bhend, J., and P. Whetton, 2012: Consistency of simulated and observed regional Americas. Nature Clim. Change, doi:10.1038/nclimate1346. changes in temperature, sea level pressure and precipitation. Clim. Change, Andersson, C., and M. Engardt, 2010: European ozone in a future climate: doi:10.1007/s10584-012-0691-2. Importance of changes in dry deposition and isoprene emissions. J. Geophys. Bintanja, R., G.J. van Oldenborgh, S.S. Drijfhout, B. Wouters, and C. A. Katsman, 2013: Res., 115, D02303. Important role for ocean warming and increased ice-shelf melt in Antarctic sea- Anenberg, S. C., et al., 2012: Global air quality and health co-benefits of mitigating ice expansion. Nature Geosci, 6, 376 379. near-term climate change through methane and black carbon emission controls. Birner, T., 2010: Recent widening of the tropical belt from global tropopause statistics: Environ. Health Perspect., 120, 831 839. Sensitivities. J. Geophys. Res. Atmos., 115, DOI:10.1029/2010JD014664. Annan, J. D., and J. C. Hargreaves, 2010: Reliability of the CMIP3 ensemble. Geophys. Bitz, C., 2008: Some aspects of uncertainty in predicting sea ice thinning. In: Arctic Res. Lett., 37, L02703. Sea Ice Decline: Observations, Projections, Mechanisms, and Implications. Appelhans, T., A. Sturman, and P. Zawar-Reza, 2012: Synoptic and climatological Geophysical Monographs, 180. American Geophysical Union, Washington, DC, 11 controls of particulate matter pollution in a Southern Hemisphere coastal city. pp. 63 76. Int. J. Climatol., 33, 463-479. Bladé, I., D. Fortuny, G. J. van Oldenborgh, and B. Liebmann, 2012: The summer North Arblaster, J. M., and G. A. Meehl, 2006: Contributions of external forcings to southern Atlantic Oscillation in CMIP3 models and related uncertainties in projected annular mode trends. J. Clim., 19, 2896 2905. summer drying in Europe. J. Geophys. Res., 116, D16104. Arblaster, J. M., G. A. Meehl, and D. J. Karoly, 2011: Future climate change in the Bloomer, B. J., J. W. Stehr, C. A. Piety, R. J. Salawitch, and R. R. Dickerson, 2009: Southern Hemisphere: Competing effects of ozone and greenhouse gases. Observed relationships of ozone air pollution with temperature and emissions. Geophys. Res. Lett., 38, L02701. Geophys. Res. Lett., 36, L09803. Avise, J., R. G. Abraham, S. H. Chung, J. Chen, and B. Lamb, 2012: Evaluating the Boberg, F., and J. H. Christensen, 2012: Overestimation of Mediterranean summer effects of climate change on summertime ozone using a relative response factor temperature projections due to model deficiencies. Nature Clim. Change, 2(6), approach for policymakers. J. Air Waste Manage. Assoc., 62, 1061 1074. 433 436. Avise, J., J. Chen, B. Lamb, C. Wiedinmyer, A. Guenther, E. Salathé(c), and C. Mass, Boe, J. L., A. Hall, and X. Qu, 2009: September sea-ice cover in the Arctic Ocean 2009: Attribution of projected changes in summertime US ozone and PM2.5 projected to vanish by 2100. Nature Geosci., 2, 341 343. concentrations to global changes. Atmos. Chem. Phys., 9, 1111 1124. Boer, G. J., 2000: A study of atmosphere-ocean predictability on long time scales. Aw, J., and M. J. Kleeman, 2003: Evaluating the first-order effect of intraannual Clim. Dyn., 16, 469 477. temperature variability on urban air pollution. J. Geophys. Res., 108, 4365. Boer, G. J., 2004: Long time-scale potential predictability in an ensemble of coupled Baehr, J., K. Keller, and J. Marotzke, 2008: Detecting potential changes in the climate models. Clim. Dyn., 23, 29 44. meridional overturning circulation at 26°N in the Atlantic. Clim. Change, 91, Boer, G. J., 2011: Decadal potential predictability of twenty-first century climate. 11 27. Clim. Dyn., 36, 1119 1133. Baidya Roy, S., and R. Avissar, 2002: Impact of land use/land cover change on Boer, G. J., and S. J. Lambert, 2008: Multi-model decadal potential predictability of regional hydrometeorology in Amazonia,. J. Geophys. Res., 107(D20), LBA 4-1- precipitation and temperature. Geophys. Res. Lett., 35, L05706. LBA 4-12. DOI: 10.1029/2000JD000266. Boer, G. J., V. V. Kharin, and W. J. Merryfield, 2013: Decadal predictability and forecast Balmaseda, M., and D. Anderson, 2009: Impact of initialization strategies and skill. Clim. Dyn., doi:10.1007/s00382-013-1705-0. observations on seasonal forecast skill. Geophys. Res. Lett., 36, L01701. Boisier, J. P., et al., 2012: Attributing the impacts of land-cover changes in temperate Balmaseda, M. A., M. K. Davey, and D. L. T. Anderson, 1995: Decadal and seasonal regions on surface temperature and heat fluxes to specific causes: Results from dependence of ENSO prediction skill. J. Clim., 8, 2705 2715. the first LUCID set of simulations. J. Geophys. Res. Atmos., 117. Bates, B. C., Z. W. Kundzewicz, S. Wu, and J. P. Palutikof, 2008: Climate Change and Bollasina, M. A., Y. Ming, and V. Ramaswamy, 2011: Anthropogenic aerosols and Water. Technical Paper of the Intergovernmental Panel on Climate Change. IPCC, the weakening of the South Asian summer monsoon. Science, doi:10.1126/ 210 pp. science.1204994. Battisti, D., and E. Sarachik, 1995: Understanding and predicting ENSO. Rev. Bond, T. C., et al., 2013: Bounding the role of black carbon in the climate system: A Geophys., 1367 1376. scientific assessment. J. Geophys. Res., doi:10.1002/jgrd.50171. 1015 Chapter 11 Near-term Climate Change: Projections and Predictability Booth, B. B. B., N. J. Dunstone, P. R. Halloran, T. Andrews, and N. Bellouin, 2012: Carvalho, A., A. Monteiro, M. Flannigan, S. Solman, A. I. Miranda, and C. Borrego, Aerosols implicated as a prime driver of twentieth-century North Atlantic 2011: Forest fires in a changing climate and their impacts on air quality. Atmos. climate variability. Nature, 485, 534 534. Environ., 45, 5545 5553. Bounoua, L., F. G. Hall, P. J. Sellers, A. Kumar, G. J. Collatz, C. J. Tucker, and M. L. Imhoff, Cattiaux, J., P. Yiou, and R. Vautard, 2012: Dynamics of future seasonal temperature 2010: Quantifying the negative feedback of vegetation to greenhouse warming: trends and extremes in Europe: A multi-model analysis from CMIP. Clim. Dyn., A modeling approach. Geophys. Res. Lett., 27, L23701. 38(9 10), 1949-1964, DOI: 10.1007/s00382-001-1211-1. Brandefelt, J., and H. Kornich, 2008: Northern Hemisphere stationary waves in future Ceppi, P., S. C. Scherrer, A. M. Fischer, and C. Appenzeller, 2012: Revisiting Swiss climate projections. J. Clim., doi: 10.1175/2008JCLI2373.1, 6341-6353. temperature trends 1959 2008. Int. J. Climatol., 32, 203 213. Branstator, G., and H. Y. Teng, 2010: Two limits of initial-value decadal predictability Chalmers, N., E. J. Highwood, E. Hawkins, R. Sutton, and L. J. Wilcox, 2012: Aerosol in a CGCM. J. Clim., 23, 6292 6311. contribution to the rapid warming of near-term climate under RCP 2.6. Geophys. Branstator, G., and H. Y. Teng, 2012: Potential impact of initialization on decadal Res. Lett., 39, L18709. predictions as assessed for CMIP5 models. Geophys. Res. Lett., 39, L12703. Chang, C. Y., J. C. H. Chiang, M. F. Wehner, A. R. Friedman, and R. Ruedy, 2011: Sulfate Branstator, G., H. Y. Teng, G. A. Meehl, M. Kimoto, J. R. Knight, M. Latif, and A. aerosol control of tropical Atlantic climate over the twentieth century. J. Clim., Rosati, 2012: Systematic estimates of initial-value decadal predictability for six 24, 2540 2555. AOGCMs. J. Clim., 25, 1827 1846. Chen, J., J. Avise, A. Guenther, C. Wiedinmyer, E. Salathe, R. B. Jackson, and B. Lamb, Brocker, J., and L. A. Smith, 2007: Increasing the reliability of reliability diagrams. 2009a: Future land use and land cover influences on regional biogenic emissions Weather Forecast., 22, 651 661. and air quality in the United States. Atmos. Environ., 43, 5771 5780. Brohan, P., J. J. Kennedy, I. Harris, S. F. B. Tett, and P. D. Jones, 2006: Uncertainty Chen, J., et al., 2009b: The effects of global changes upon regional ozone pollution estimates in regional and global observed temperature changes: A new data set in the United States. Atmos. Chem. Phys., 9, 1125 1141. from 1850. J. Geophys. Res. Atmos., 111, DOI 10.1029/2005JD006548. Chevallier, M., and D. Salas-Melia, 2012: The role of sea ice thickness distribution in Brown, R. D., and P. W. Mote, 2009: The response of Northern Hemisphere snow the Arctic sea ice potential predictability: A diagnostic approach with a coupled cover to a changing climate. J. Clim., 22, 2124 2145. GCM. J. Clim., 25, 3025 3038. Brunner, D., S. Henne, C. A. Keller, S. Reimann, M. K. Vollmer, S. O Doherty, and M. Chikamoto, Y., M. Kimoto, M. Watanabe, M. Ishii, and T. Mochizuki, 2012a: Maione, 2012: An extended Kalman-filter for regional scale inverse emission Relationship between the Pacifc and Atlantic stepwise climate change during estimation. Atmos. Chem. Phys., 12, 3455 3478. the 1990s. Geophys. Res. Lett., 39, L21710. Brutel-Vuilmet, C., M. Menegoz, and G. Krinner, 2013: An analysis of present and Chikamoto, Y., et al., 2012b: Predictability of a stepwise shift in Pacific climate during future seasonal Northern Hemisphere land snow cover simulated by CMIP5 the late 1990s in hindcast experiments using MIROC. J. Meteorol. Soc. Jpn., 90A, coupled climate models. Cryosphere, 7, 67 80. 1 21. Bryan, F. O., G. Danabasoglu, N. Nakashiki, Y. Yoshida, D. H. Kim, J. Tsutsui, and S. Chin, M., T. Diehl, P. Ginoux, and W. Malm, 2007: Intercontinental transport of C. Doney, 2006: Response of the North Atlantic thermohaline circulation and pollution and dust aerosols: Implications for regional air quality. Atmos. Chem. ventilation to increasing carbon dioxide in CCSM3. J. Clim., 19, 2382 2397. Phys., 7, 5501 5517. Burgman, R., R. Seager, A. Clement, and C. Herweijer, 2010: Role of tropical Pacific Chou, C., J. Y. Tu, and P. H. Tan, 2007: Asymmetry of tropical precipitation change SSTs in global medieval hydroclimate: A modeling study. Geophys. Res. Lett., under global warming. Geophys. Res. Lett., 34, L17708. 37, L06705. Chou, C., J. D. Neelin, C. A. Chen, and J. Y. Tu, 2009: Evaluating the rich-get-richer Burke, E., and S. Brown, 2008: Evaluating uncertainties in the projection of future mechanism in tropical precipitation change under global warming. J. Clim., 22, drought. J. Hydrometeor, 9, 292 299. 1982 2005. Buser, C. M., H.R. Künsch, D. Lüthi, M. Wild, and C. Schär, 2009: Bayesian multi-model Christensen, J. H., et al., 2007: Regional climate projections. In: Climate Change projection of climate: Bias assumptions and interannual variability. Clim. Dyn., 2007: The Physical Science Basis. Contribution of Working Group I to the 11 33(6), 849-868, DOI:10.1007/s00382-009-0588-6. Fourth Assessment Report of the Intergovernmental Panel on Climate Change Butchart, N., et al., 2006: Simulations of anthropogenic change in the strength of the [Solomon, S., D. Qin, M. Manning, Z. Chen, M. Marquis, K. B. Averyt, M. Tignor Brewer-Dobson circulation. Clim. Dyn., 27, 727 741. and H. L. Miller (eds.)] Cambridge University Press, Cambridge, United Kingdom Butler, T. M., Z. S. Stock, M. R. Russo, H. A. C. Denier van der Gon, and M. G. Lawrence, and New York, NY, USA, pp. 847 940. 2012: Megacity ozone air quality under four alternative future scenarios. Atmos. Christidis, N., P. A. Stott, G. C. Hegerl, and R. A. Betts, 2013: The role of land use Chem. Phys., 12, 4413 4428. change in the recent warming of daily extreme temperatures. Geophys. Res. Butterbach-Bahl, K., M. Kahl, L. Mykhayliv, C. Werner, R. Kiese, and C. Li, 2009: A Lett., 40, 589 594. European-wide inventory of soil NO emissions using the biogeochemical models Chylek, P., C. K. Folland, G. Lesins, and M. K. Dubey, 2010: Twentieth century bipolar DNDC/Forest-DNDC. Atmos. Environ., 43, 1392 1402. seesaw of the Arctic and Antarctic surface air temperatures. Geophys. Res. Lett., Caesar, J., and J. A. Lowe, 2012: Comparing the impacts of mitigation versus non- 37, L08703. intervention scenarios on future temperature and precipitation extremes in the Chylek, P., C. K. Folland, G. Lesins, M. K. Dubey, and M. Y. Wang, 2009: Arctic air HadGEM2 climate model. J. Geophys. Res., 117, D15109. temperature change amplification and the Atlantic Multidecadal Oscillation. Callaghan, J., and S. B. Power, 2011: Variability and decline in the number of Geophys. Res. Lett., 36, L14801. severe tropical cyclones making land-fall over eastern Australia since the late Clark, R. T., J. M. Murphy, and S. J. Brown, 2010: Do global warming targets limit nineteenth century. Clim. Dyn., 37, 647 662. heatwave risk? Geophys. Res. Lett., 37, L17703. Callaghan, T. V., M. Johansson, O. Anisimov, H. H. Christiansen, A. Instanes, V. Colle, B. A., Z. Zhang, K.A. Lombardo, E. Chang, P. Liu, M. Zhang, and S. Hameed, Romanovsky, and S. Smith, 2011: Changing permafrost and its impacts. In: 2013: Historical and future predictions of eastern North America and western Snow, Water, Ice and Permafrost in the Arctic (SWIPA). Arctic Monitoring and Atlantic extratropical cyclones in CMIP5 during the cool Season. J. Clim., Assessment Program (AMAP). doi:10.1175/JCLI-D-12-00498.1. Cao, L., G. Bala, K. Caldeira, R. Nemani, and G. Ban-Weiss, 2009: Climate response Collins, M., 2002: Climate predictability on interannual to decadal time scales: The to physiological forcing of carbon dioxide simulated by the coupled Community initial value problem. Clim. Dyn., 19, 671 692. Atmosphere Model (CAM3.1) and Community Land Model (CLM3.0). Geophys. Collins, M., and B. Sinha, 2003: Predictability of decadal variations in the Res. Lett., 36, L10402. thermohaline circulation and climate. Geophys. Res. Lett., 30, 1306. Cao, L., G. Bala, K. Caldeira, R. Nemani, and G. Ban-Weiss, 2010: Importance of Collins, M., et al., 2006: Interannual to decadal climate predictability in the North carbon dioxide physiological forcing to future climate change. Proc. Natl. Acad. Atlantic: A multimodel-ensemble study. J. Clim., 19, 1195 1203. Sci. U.S.A., 107, 9513 9518. Comiso, J. C., C. L. Parkinson, R. Gersten, and L. Stock, 2008: Accelerated decline in Carlton, A. G., C. Wiedinmyer, and J. H. Kroll, 2009: A review of Secondary Organic the Arctic Sea ice cover. Geophys. Res. Lett., 35, L01703. Aerosol (SOA) formation from isoprene. Atmos. Chem. Phys., 9, 4987 5005. Cook, B. I., R. L. Miller, and R. Seager, 2009: Amplification of the North American Carlton, A. G., R. W. Pinder, P. V. Bhave, and G. A. Pouliot, 2010: To what extent can Dust Bowl drought through human-induced land degradation. Proc. Natl biogenic SOA be controlled? Environ. Sci. Technol., 44, 3376 3380. Acad. Sci. U.S.A., 106, 4997 5001. 1016 Near-term Climate Change: Projections and Predictability Chapter 11 Corti, S., A. Weisheimer, T.N. Palmer, F. J. Doblas-Reyes, and L. Magnusson, 2012: Doblas-Reyes, F. J., M. A. Balmaseda, A. Weisheimer, and T. N. Palmer, 2011: Decadal Reliability of decadal predictions. Geophys. Res. Lett., doi:10.1029/2012GL053354. climate prediction with the ECMWF coupled forecast system: Impact of ocean Cox, P., and D. Stephenson, 2007: Climate change - A changing climate for prediction. observations. J. Geophys. Res. Atmos, 116, D19111. Science, 317, 207 208. Doblas-Reyes, F. J., et al., 2009: Addressing model uncertainty in seasonal and annual Cox, W., and S. Chu, 1996: Assessment of interannual ozone variation in urban areas dynamical ensemble forecasts. Q. J. R. Meteorol. Soc., 135, 1538 1559. from a climatological perspective. Atmos. Environ., 30, 2615 2625. Doblas-Reyes, F. J., et al., 2013: Initialized near-term regional climate change Cravatte, S., T. Delcroix, D. Zhang, M. McPhaden, and J. Leloup, 2009: Observed prediction. Nature Commun., 4, 1715. freshening and warming of the western Pacific Warm Pool. Clim. Dyn., 33, Doherty, R., et al., 2009: Current and future climate- and air pollution-mediated 565 589. impacts on human health. Environ. Health, 8, doi: 10.1186/1476-069X-8-S1-S8. Dai, A., 2011: Drought under global warming: A review. WIREs Clim. Change, 2, Doherty, R. M., et al., 2013: Impacts of climate change on surface ozone and 45 65. intercontinental ozone pollution: A multi-model study. J. Geophys. Res. Atmos., Davis, S. M., and K. H. Rosenlof, 2012: A Multidiagnostic intercomparison of tropical- doi:10.1002/jgrd.50266. width time series using reanalyses and satellite observations. J. Clim., 25, 1061 Drijfhout, S. S., and W. Hazeleger, 2007: Detecting Atlantic MOC changes in an 1078. ensemble of climate change simulations. J. Clim., 20, 1571 1582. Dawson, J. P., P. N. Racherla, B. H. Lynn, P. J. Adams, and S. N. Pandis, 2009: Impacts Du, H., F. J. Doblas-Reyes, J. Garcia-Serrano, V. Guemas, Y. Soufflet, and B. Wouters, of climate change on regional and urban air quality in the eastern United States: 2012: Sensitivity of decadal predictions to the initial atmospheric and oceanic Role of meteorology. J. Geophys. Res., 114, D05308. perturbations. Clim. Dyn., 39, 2013 2023. de Noblet-Ducoudre, N., et al., 2012: Determining robust impacts of land-use- Dunstone, N. J., and D. M. Smith, 2010: Impact of atmosphere and sub-surface ocean induced land cover changes on surface climate over North America and Eurasia: data on decadal climate prediction. Geophys. Res. Lett., 37, L02709. Results from the first set of LUCID experiments. J. Clim., 25, 3261 3281. Dunstone, N. J., D. M. Smith, and R. Eade, 2011: Multi-year predictability of the DelSole, T., and X. Feng, 2013: The Shukla Gutzler method for estimating potential tropical Atlantic atmosphere driven by the high latitude North Atlantic Ocean. seasonal predictability. Mon. Weather Rev., 141, 822 832. Geophys. Res. Lett., 38, L14701. DelSole, T., X. S. Yang, and M. K. Tippett, 2013: Is unequal weighting significantly Durack, P. J., and S. E. Wijffels, 2010: Fifty-year trends in global ocean salinities and better than equal weighting for multi-model forecasting? Q. J. R. Meteorol. Soc., their relationship to broad-scale warming. J. Clim., 23, 4342 4362. 139, 176 183. Durack, P. J., S. E. Wijffels, and R. J. Matear, 2012: Ocean salinities reveal strong Delworth, T., and K. Dixon, 2006: Have anthropogenic aerosols delayed a greenhouse global water cycle intensification during 1950 to 2000. Science, 336, 455-458, gas-induced weakening of the North Atlantic thermohaline circulation? Geophys. doi: 10.1126/science.1212222. Res. Lett., doi:10.1029/2005GL024980, L02606. Dutra, E., C. Schar, P. Viterbo, and P. M. A. Miranda, 2011: Land-atmosphere coupling Delworth, T., V. Ramaswamy, and G. Stenchikov, 2005: The impact of aerosols on associated with snow cover. Geophys. Res. Lett., 38, L15707. simulated ocean temperature and heat content in the 20th century. Geophys. Eade, R., E. Hamilton, D. M. Smith, R. J. Graham, and A. A. Scaife, 2012: Forecasting Res. Lett., 32, L24709, doi: 10.1029/2005GL024457. the number of extreme daily events out to a decade ahead. J. Geophys. Res., Delworth, T. L., and F. Zeng, 2008: Simulated impact of altered Southern Hemisphere 117, D21110, doi:10.1029/2012JD018015. winds on the Atlantic Meridional Overturning Circulation. Geophys. Res. Lett., Easterling, D. R., and M. F. Wehner, 2009: Is the climate warming or cooling? 35, L20708, doi: 10.1029/2008GL035166. Geophys. Res. Lett., 36, L08706. Dentener, F., et al., 2005: The impact of air pollutant and methane emission controls El Nadi, A. H., 1974: The significance of leaf area in evapotranspiration. Ann. Bot, on tropospheric ozone and radiative forcing: CTM calculations for the period 38(3), 607 611. 1990 2030. Atmos. Chem. Phys., 5, 1731 1755. Emanuel, K., 2011: Global warming effects on U.S. hurricane damage. Weather Clim. Dentener, F., et al., 2006: The global atmospheric environment for the next Soc., 3, 261 268. generation. Environ. Sci. Technol., 40, 3586 3594. Evan, A. T., D. J. Vimont, A. K. Heidinger, J. P. Kossin, and R. Bennartz, 2009: The 11 Dery, S. J., M. A. Hernandez-Henriquez, J. E. Burford, and E. F. Wood, 2009: role of aerosols in the evolution of tropical North Atlantic Ocean Temperature Observational evidence of an intensifying hydrological cycle in northern Canada. anomalies. Science, 324, 778 781. Geophys. Res. Lett., 36, L13402. Eyring, V., et al., 2013: Long-term changes in tropospheric and stratospheric ozone Deser, C., and A. S. Phillips, 2009: Atmospheric circulation trends, 1950 2000: and associated climate impacts in CMIP5 simulations. J. Geophys., Res., 118. The relative roles of sea surface temperature forcing and direct atmospheric 5029-5060, doi:10.1002/jgrd.50316. radiative forcing. J. Clim., 22, 396 413. Eyring, V., et al., 2010: Multi-model assessment of stratospheric ozone return dates Deser, C., A. S. Phillips, and J. W. Hurrell, 2004: Pacific interdecadal climate variability: and ozone recovery in CCMVal-2 models. Atmos. Chem. Phys., 10, 9451 9472. Linkages between the tropics and the North Pacific during boreal winter since Fairlie, T. D., D. J. Jacob, and R. J. Park, 2007: The impact of transpacific transport of 1900. J. Clim., 17, 3109 3124. mineral dust in the United States. Atmos. Environ., 41, 1251 1266. Deser, C., A. Phillips, V. Bourdette, and H. Y. Teng, 2012: Uncertainty in climate change Fang, Y., et al., 2011: The impacts of changing transport and precipitation on projections: The role of internal variability. Clim. Dyn., 38, 527 546. pollutant distributions in a future climate. J. Geophys. Res., 116, D18303. Dharshana, K. G. T., S. Kravtsov, and J. D. W. Kahl, 2010: Relationship between synoptic Fasullo, J. T., 2010: Robust land-ocean contrasts in energy and water cycle feedbacks. weather disturbances and particulate matter air pollution over the United States. J. Clim., 23, 4677 4693. J. Geophys. Res. Atmos., 115, D24219, doi:10.1029/2010JD014852. Ferro, C. A. T., and T. E. Fricker, 2012: A bias-corrected decomposition of the Brier Diffenbaugh, N. S., and M. Ashfaq, 2010: Intensification of hot extremes in the score. Q. J. R. Meteorol. Soc., 138, 1954 1960. United States. Geophys. Res. Lett., 37, L15701. Feulner, G., and S. Rahmstorf, 2010: On the effect of a new grand minimum of solar Diffenbaugh, N. S., and M. Scherer, 2011: Observational and model evidence of activity on the future climate on Earth. Geophys. Res. Lett., 37, L05707, doi: global emergence of permanent, unprecedented heat in the 20th and 21st 10.1029/2010GL042710. centuries. Clim. Change, 107(3 4), 615 624. Field, C. B., R. B. Jackson, and H. A. Mooney, 1995: Stomatal responses to increased DiNezio, P., G. A. Vecchi, and A. Clement, 2013: Detectability of changes in the CO2 Implications from the plant to the global-scale. Plant Cell Environ., 18, Walker Circulation in response to global warming. J. Clim., doi:10.1175/JCLI- 1214 1225. D-12-00531.1. Findell, K. L., E. Shevliakova, P. C. D. Milly, and R. J. Stouffer, 2007: Modeled impact of DiNezio, P., A. Clement, G. Vecchi, B. Soden, and B. Kirtman, 2009: Climate response of anthropogenic land cover change on climate. J. Clim., 20, 3621 3634. the equatorial Pacific to global warming. J. Clim., doi: 10.1175/2009JCLI2982.1, Fiore, A. M., J. J. West, L. W. Horowitz, V. Naik, and M. D. Schwarzkopf, 2008: 4873 4892. Characterizing the tropospheric ozone response to methane emission controls Dlugokencky, E. J., E. G. Nisbet, R. Fisher, and D. Lowry, 2011: Global atmospheric and the benefits to climate and air quality. J. Geophys. Res., 113, D08307. methane: Budget, changes and dangers. Philos. Trans. R. Soc. London A, 369, Fiore, A. M., D. J. Jacob, B. D. Field, D. G. Streets, S. D. Fernandes, and C. Jang, 2002: 2058 2072. Linking ozone pollution and climate change: The case for controlling methane. Geophys. Res. Lett., 29, 1919. 1017 Chapter 11 Near-term Climate Change: Projections and Predictability Fiore, A. M., et al., 2012: Global air quality and climate. Chem. Soc. Rev., 41, 6663 Georgescu, M., D. B. Lobell, C. B. Field, and A. Mahalov, 2013: Simulated hydroclimatic 6683. impacts of projected Brazilian sugarcane expansion. Geophys. Res. Lett., 40, Fiore, A. M., et al., 2009: Multimodel estimates of intercontinental source-receptor 972-977, doi:10.1002/grl,50206. relationships for ozone pollution. J. Geophys. Res., 114, D04301. Gillett, N., R. Allan, and T. Ansell, 2005: Detection of external influence on sea level Fischer, E. M., and C. Schar, 2009: Future changes in daily summer temperature pressure with a  multi-model ensemble. Geophys. Res. Lett., 32, L19714, doi: variability: Driving processes and role for temperature extremes. Clim. Dyn., 33, 10.1029/2005GL023640. 917 935. Gillett, N., V. Arora, D. Matthews, and M. Allen, 2013: Constraining the ratio of Fischer, E. M., and C. Schar, 2010: Consistent geographical patterns of changes in global warming to cumulative CO2 emissions using CMIP5 simulations. J. Clim., high-impact European heatwaves. Nature Geosci., 3, 398 403. doi:10.1175/JCLI-D-12-00476.1. Fischer, E. M., J. Rajczak, and C. Schär, 2012: Changes in European summer Gillett, N. P., and D. W. J. Thompson, 2003: Simulation of recent Southern Hemisphere temperature variability revisited. Geophys. Res. Lett., 6, L19702. climate change. Science, 302, 273 275. Fischer, E. M., S. I. Seneviratne, P. L. Vidale, D. Luthi, and C. Schar, 2007: Soil moisture Gillett, N. P., and P. A. Stott, 2009: Attribution of anthropogenic influence on seasonal atmosphere interactions during the 2003 European summer heat wave. J. Clim., sea level pressure. Geophys. Res. Lett., 36, L23709. 20, 5081 5099. Goddard, L., et al., 2013: A verification framework for interannual-to-decadal Flanner, M. G., C. S. Zender, J. T. Randerson, and P. J. Rasch, 2007: Present-day climate predictions experiments. Clim. Dyn., 40, 245 272. forcing and response from black carbon in snow. J. Geophys. Res., 112, D11202, Goldenberg, S. B., C. W. Landsea, A. M. Mestas-Nunez, and W. M. Gray, 2001: The doi: 10.1029/2006JD008003. recent increase in Atlantic hurricane activity: Causes and implications. Science, Flannigan, M. D., M. A. Krawchuk, W. J. de Groot, B. M. Wotton, and L. M. Gowman, 293, 474 479. 2009: Implications of changing climate for global wildland fire. Int. J. Wildland Gosling, S. N., R. G. Taylor, N. W. Arnell, and M. C. Todd, 2011: A comparative Fire, 18, 483 507. analysis of projected impacts of climate change on river runoff from global and Fleming, E., C. Jackman, R. Stolarski, and A. Douglass, 2011: A model study of the catchment-scale hydrological models. Hydrol. Earth Syst. Sci., 15, 279 294. impact of source gas changes on the stratosphere for 1850 2100. Atmos. Chem. Granier, C., et al., 2006: Ozone pollution from future ship traffic in the Arctic northern Phys., 11, 8515 8541. passages. Geophys. Res. Lett., 33, L13807. Fogt, R. L., J. Perlwitz, A. J. Monaghan, D. H. Bromwich, J. M. Jones, and G. J. Marshall, Gray, L., et al., 2010: Solar Influences on climate. Rev. Geophys., 48, RG4001, doi: 2009: Historical SAM variability. Part II: Twentieth-century variability and trends 10.1029/2009/RG000282. from reconstructions, observations, and the IPCC AR4 Models. J. Clim., 22, Gregory, J., 2010: Long-term effect of volcanic forcing on ocean heat content. 5346 5365. Geophys. Res. Lett., doi:10.1029/2010GL045507, L22701. Folland, C., J. Knight, H. Linderholm, D. Fereday, S. Ineson, and J. Hurrell, 2009: Gregory, J. M., and J. F. B. Mitchell, 1995: Simulation of daily variability of surface- The summer North Atlantic Oscillation: Past, present, and future. J. Clim., doi: temperature and precipitation over Europe in the current and 2xco(2) climates 10.1175/2008JCLI2459.1, 1082 1103. using the Ukmo Climate Model. Q. J. R. Meteorol. Soc., 121, 1451 1476. Folland, C. K., A.W. Colman, D.M. Smith, O. Boucher, D. E. Parker, and J.-P. Vernier, Gregory, J. M., and P. M. Forster, 2008: Transient climate response estimated from 2013: High predictive skill of global surface temperature a year ahead. Geophys. radiative forcing and observed temperature change. J. Geophys. Res. Atmos., Res. Lett., 40, 761 767. 113, D23105, doi:10.1029/2008JD010405. Forkel, R. and R. Knoche, 2006: Regional climate change and its impact on Griffies, S. M., and K. Bryan, 1997: A predictability study of simulated North Atlantic photooxidant concentrations in multidecadal variability. Clim. Dyn., 13, 459 487. southern Germany: Simulations with a coupled regional climate-chemistry model. J. Grotzner, A., M. Latif, A. Timmermann, and R. Voss, 1999: Interannual to decadal Geophys. Res., 2006, 111, D12302. predictability in a coupled ocean-atmosphere general circulation model. J. Clim., Fowler, H. J., M. Ekstrom, S. Blenkinsop, and A. P. Smith, 2007: Estimating change in 12, 2607 2624. 11 extreme European precipitation using a multimodel ensemble. J. Geophys. Res. Grousset, F. E., P. Ginoux, A. Bory, and P. E. Biscaye, 2003: Case study of a Chinese Atmos., 112, D18104, doi: 10.1029/2007JD008619. dust plume reaching the French Alps. Geophys. Res. Lett., 30, L22701. Francis, J. A. H., E, 2007: Changes in the fabric of the Arctic s greenhouse blanket. Guemas, V., F. J. Doblas-Reyes, I. Andreu-Burillo, and M. Asif, 2013: Retrospective Environ. Res. Lett., doi:10.1088/1748-9326/2/4/045011. prediction of the global warming slowdown in the last decade. Nature Clim. Fyfe, J. C., N. P. Gillett, and G. J. Marshall, 2012: Human influence on extratropical Change, doi:10.1038/nclimate1863. Southern Hemisphere summer precipitation. Geophys. Res. Lett., 39, L23711. Guémas, V., F.J. Doblas-Reyes, F. Lienert, Y. Soufflet, and H. Du, 2012: Identifying Fyfe, J. C., W. J. Merryfield, V. Kharin, G. J. Boer, W. S. Lee, and K. von Salzen, 2011: the causes of the poor decadal climate prediction skill over the North Pacific. J. Skillful predictions of decadal trends in global mean surface temperature. Geophys. Res., 117, D20111. Geophys. Res. Lett., 38, L22801. Guenther, A., T. Karl, P. Harley, C. Wiedinmyer, P. I. Palmer, and C. Geron, 2006: Gaetani, M., and E. Mohino, 2013: Decadal prediction of the Sahelian precipitation Estimates of global terrestrial isoprene emissions using MEGAN (Model of in CMIP5 simulations. J. Clim., doi:10.1175/JCLI-D-12-00635.1. Emissions of Gases and Aerosols from Nature). Atmos. Chem. Phys., 6, 3181 Gangst, R., A. P. Weigel, M. A. Liniger, and C. Appenzeller, 2013: Comments on the 3210. evaluation of decadal predictions. Clim. Res., 55, 181 200. Guo, D. L., and H. Wang, 2012: A projection of permafrost degradation on the Ganguly, D., P. J. Rasch, H. Wang, and J.-H. Yoon, 2012: Climate response of the Tibetan Plateau during the 21st century. J. Geophys. Res., 117, D05106, South Asian monsoon system to anthropogenic aerosols. J. Geophys. Res., 117, doi:10.1029/2011JD016545. D13209. Gutowski, W. J., K. A. Kozak, R. W. Arritt, J. H. Christensen, J. C. Patton, and E. S. Takle, Gao, C. C., A. Robock, and C. Ammann, 2008: Volcanic forcing of climate over 2007: A possible constraint on regional precipitation intensity changes under the past 1500 years: An improved ice core-based index for climate models. J. global warming. J. Hydrometeorol., 8, 1382 1396. Geophys. Res. Atmos., 113, D23111, doi: 10.1029/2008JD010239. Gutowski, W. J., et al., 2008: Causes of observed changes in extremes and projections Garcia-Serrano, J., and F. J. Doblas-Reyes, 2012: On the assessment of near- of future changes. In: Weather and Climate Extremes in a Changing Climate. surface global temperature and North Atlantic multi-decadal variability in the Regions of Focus: North America, Hawaii, Caribbean, and U.S. Pacific Islands [T. ENSEMBLES decadal hindcast. Clim. Dyn., 39, 2025 2040. R. Karl, G. A. Meehl, D. M. Christopher, S. J. Hassol, A. M. Waple and W. L. Murray Garcia-Serrano, J., F. J. Doblas-Reyes, and C. A. S. Coelho, 2012: Understanding (eds.)]. U.S. Climate Change Science Program and the Subcommittee on Global Atlantic multi-decadal variability prediction skill. Geophys. Res. Lett., 39, Change Research. L18708, doi:10.1029/2012GL053283. Haarsma, R. J., and F. M. Selten, 2012: Anthropogenic changes in the Walker Gastineau, G., L. Li, and H. Le Treut, 2009: The Hadley and Walker Circulation changes Circulation and their impact on the extra-tropical planetary wave structure in in global warming conditions described by idealized atmospheric simulations. J. the Northern Hemisphere. Clim. Dyn., doi: 10.1007/s00382-012-1308-1. Clim., 22, 3993 4013. Haigh, J., A. Winning, R. Toumi, and J. Harder, 2010: An influence of solar spectral Georgescu, M., D. B. Lobell, and C. B. Field, 2009: Potential impact of U.S. biofuels on variations on radiative forcing of climate. Nature, 467, 696 699. regional climate. Geophys. Res. Lett., 36, L21806. Haigh, J. D., 1996: The impact of solar variability on climate. Science, 272, 981 984. 1018 Near-term Climate Change: Projections and Predictability Chapter 11 Haigh, J. D., M. Blackburn, and R. Day, 2005: The response of tropospheric circulation Hoerling, M., et al., 2011: On North American decadal climate for 2011 20. J. Clim., to perturbations in lower-stratospheric temperature. J. Clim., 18, 3672 3685. 24, 4519 4528. Hallquist, M., et al., 2009: The formation, properties and impact of secondary organic Hogrefe, C., et al., 2004: Simulating changes in regional air pollution over the eastern aerosol: Current and emerging issues. Atmos. Chem. Phys., 9, 5155 5236. United States due to changes in global and regional climate and emissions. J. Hanel, M., and T. A. Buishand, 2011: Analysis of precipitation extremes in an Geophys. Res., 109, D22301. ensemble of transient regional climate model simulations for the Rhine basin. Hohenegger, C., P. Brockhaus, C. S. Bretherton, and C. Schar, 2009: The soil Clim. Dyn., 36, 1135 1153. moisture-precipitation feedback in simulations with explicit and parameterized Hanlon, H. M., G. C. Hegerl, S. F. B. Tett, and D. M. Smith, 2013: Can a decadal convection. J. Clim., 22, 5003 5020. forecasting system predict temperature extreme indices? J. Clim., doi:10.1175/ Holland, M. M., J. Finnis, and M. C. Serreze, 2006: Simulated Arctic Ocean freshwater JCLI-D-12-00512.1. budgets in the twentieth and twenty-first centuries. J. Clim., 19, 6221 6242. Hansen, J., A. Lacis, R. Ruedy, and M. Sato, 1992: Potential climate impact of Mount- Holland, M. M., M.C. Serreze, and J. Stroeve, 2010: The sea ice mass budget of the Pinatubo eruption. Geophys. Res. Lett., 19, 215 218. Arctic and its future change as simulated by coupled climate models. Clim. Dyn., Hansen, J., R. Ruedy, M. Sato, and K. Lo, 2010: Global surface temperature change. 34, 185-200, doi: 10.1007/s00382-008-0493-4. Rev. Geophys., 48. Holland, M. M., J. Finnis, A. P. Barrett, and M. C. Serreze, 2007: Projected changes Hansen, J., M. Sato, R. Ruedy, A. Lacis, and V. Oinas, 2000: Global warming in the in arctic ocean freshwater budgets. J. Geophys. Res., 112, G04S55, doi: twenty-first century: An alternative scenario. Proc. Natl. Acad. Sci. U.S.A., 97, 10.1029/2006/JG000354. 9875 9880. Holmes, C. D., M. J. Prather, O. A. Svde, and G. Myhre, 2013: Future methane, Hansen, J., M. Sato and R. Ruedy, 2012: Perception of climate change. Proc. Natl. hydroxyl, and their uncertainties: Key climate and emission parameters for future Acad. Sci. U.S.A., 109(37), E2415 E2423. predictions. Atmos Chem Phys, 13, 285 302. Harder, J., J. Fontenla, P. Pilewskie, E. Richard, and T. Woods, 2009: Trends in solar Hoyle, C. R., et al., 2011: A review of the anthropogenic influence on biogenic spectral irradiance variability in the visible and infrared. Geophys. Res. Lett., 36, secondary organic aerosol. Atmos. Chem. Phys., 11, 321 343. L07801, doi: 10.1029/2008GL036797. Hsu, J., and M. Prather, 2010: Global long-lived chemical modes excited in a 3-D Hawkins, E., and R. Sutton, 2009: The potential to narrow uncertainty in chemistry transport model: Stratospheric N2O, NOy, O3 and CH4 chemistry. regional climate predictions. Bull. Am. Meteorol. Soc., 90, 1095-1107, doi: Geophys. Res. Lett., 37, L07805. 10.1175/2009BAMS2607.1. HTAP, 2010a: Hemispheric Transport of Air Pollution 2010, Part A: Ozone and Hawkins, E., and R. Sutton, 2011: The potential to narrow uncertainty in projections Particulate Matter. Air Pollution Studies No. 17. United Nations, New York, NY, of regional precipitation change. Clim. Dyn., 37, 407 418. USA, and Geneva, Swtzerland, 278 pp. Hawkins, E., and R. Sutton, 2012: Time of emergence of climate signals. Geophys. HTAP, 2010a, 2010b: Hemispheric Transport of Air Pollution 2010, Part C: Persistent Res. Lett., doi:10.1029/2011GL050087. Organic Pollutants. Air Pollution Studies No. 19. United Nations, New York, NY, Hawkins, E., J. Robson, R. Sutton, D. Smith, and N. Keenlyside, 2011: Evaluating USA, and Geneva, Switzerland, 278 pp. the potential for statistical decadal predictions of SSTs with a perfect model HTAP, 2010a, 2010c: Hemispheric Transport of Air Pollution 2010, Part B: Mercury. approach. Clim. Dyn., 37, 2495. Air Pollution Studies No. 18. United Nations, New York, NY, USA, and Geneva, Hazeleger, W., et al., 2013a: Multiyear climate predictions using two initialisation Switzerland, 278 pp. strategies. Geophys. Res. Lett., doi::10.1002/grl.50355. Hu, Y., L. Tao, and J. Liu, 2013: Poleward expansion of the Hadley Circulation in Hazeleger, W., et al., 2013b: Predicting multi-year North Atlantic Ocean variability. J. CMIP5 simulations. Adv. Atmos. Sci., 30, 790 795. Geophys. Res., doi:10.1002/grl.50355. Huang, H.-C., et al., 2008: Impacts of long-range transport of global pollutants and Heald, C. L., et al., 2008: Predicted change in global secondary organic aerosol precursor gases on U.S. air quality under future climatic conditions. J. Geophys. concentrations in response to future climate, emissions, and land use change. J. Res., 113, D19307. Geophys. Res., 113, D05211. Huebener, H., U. Cubasch, U. Langematz, T. Spangehl, F. Niehorster, I. Fast, and 11 Hedegaard, G. B., J. Brandt, J. H. Christensen, L. M. Frohn, C. Geels, K. M. Hansen, M. Kunze, 2007: Ensemble climate simulations using a fully coupled ocean- and M. Stendel, 2008: Impacts of climate change on air pollution levels in the troposphere-stratosphere general circulation model. Philos. Trans. R. Soc. London Northern Hemisphere with special focus on Europe and the Arctic. Atmos. Chem. A, doi: 10.1098/rsta.2007.2078, 2089-2101. Phys., 8, 3337 3367. Hungate, B. A., et al., 2002: Evapotranspiration and soil water content in a scrub-oak Heinrich, G., and A. Gobiet, 2011: The future of dry and wet spells in Europe: A woodland under carbon dioxide enrichment. Global Change Biol., 8, 289 298. comprehensive study based on the ENSEMBLES regional climate models. Int. J. Huntington, T. G., 2006: Evidence for intensification of the global water cycle: Review Climatol, doi:658 10.1002/joc.2421. and synthesis. J. Hydrol., 319, 83 95. Held, I., and B. Soden, 2006: Robust responses of the hydrological cycle to global Hurtt, G. C., et al., 2011: Harmonization of land-use scenarios for the period 1500 warming. J. Clim., 5686 5699. 2100: 600 years of global gridded annual land-use transitions, wood harvest, Henze, D. K., et al., 2012: Spatially Refined Aerosol Direct Radiative Forcing and resulting secondary lands. Clim. Change, 109, 117 161. Efficiencies. Environ. Sci. Technol., 46, 9511 9518. Huszar, P., et al., 2011: Effects of climate change on ozone and particulate matter Hermanson, L., and R. T. Sutton, 2010: Case studies in interannual to decadal climate over Central and Eastern Europe. Clim. Res., 50, 51 68. predictability. Clim. Dyn., 35, 1169 1189. Ihara, C., and Y. Kushnir, 2009: Change of mean midlatitude westerlies in 21st Hezel, P. j., X. Zhang, C.M. Bitz, and B. P. Kelly, 2012: Projected decline in snow depth century climate simulations. Geophys. Res. Lett., doi:10.1029/2009GL037674, on Arctic sea ice casued by progressively later autumn open ocean freeze-up this L13701. century. Geophys. Res. Lett., 39, L17505, doi:10.1029/2012GL052794. Im, E. S., W. J. Gutowski, and F. Giorgi, 2008: Consistent changes in twenty-first Ho, C. K., Hawkins, Shaffrey, and Underwood, 2012a: Statistical decadal predictions century daily precipitation from regional climate simulations for Korea using for sea surface temperatures: A benchmark for dynamical GCM predictions. two convection parameterizations. Geophys. Res. Lett., 35, L14706. Clim. Dyn., doi:10.1007/s00382-012-1531-9. Ineson, S., A. Scaife, J. Knight, J. Manners, N. Dunstone, L. Gray, and J. Haigh, 2011: Ho, C. K., D. B. Stephenson, M. Collins, C. A. T. Ferro, and S. J. Brown, 2012b: Calibration Solar forcing of winter climate variability in the Northern Hemisphere. Nature strategies: A source of additional uncertainty in climate change projections. Bull. Geosci., 4, 753 757. Am. Meteorol. Soc., 93, 21 26. ICPO, 2011: Decadal and Bias Correction for Decadal Climate Predictions. CLIVAR Hodnebrog, O., et al., 2012: Impact of forest fires, biogenic emissions and high Publication Series No.150, International CLIVAR Project Office. 6 pp. temperatures on the elevated Eastern Mediterranean ozone levels during the IPCC, 2007: Climate Change 2007: The Physical Science Basis. Contribution of hot summer of 2007. Atmos. Chem. Phys., 12, 8727 8750. Working Group I to the Fourth Assessment Report of the Intergovernmental Hodson, D. L. R., S.P.E. Keeley, A. West, J. Ridley, E. Hawkins, and H. T. Hewitt, 2012: Panel on Climate Change [Solomon, S., D. Qin, M. Manning, Z. Chen, M. Marquis, Identifying uncertainties in Arctic climate change projections. Clim. Dyn., K. B. Averyt, M. Tignor and H. L. Miller (eds.)] Cambridge University Press, doi:10.1007/s00382-012-1512-z. Cambridge, United Kingdom and New York, NY, USA, 996 pp. 1019 Chapter 11 Near-term Climate Change: Projections and Predictability Isaksen, I. S. A., et al., 2009: Atmospheric composition change: Climate-chemistry Katragkou, E., P. Zanis, I. Kioutsioukis, I. Tegoulias, D. Melas, B. C. Kruger, and E. interactions. Atmos. Environ., 43, 5138 5192. Coppola, 2011: Future climate change impacts on summer surface ozone from Ishii, M., and M. Kimoto, 2009: Reevaluation of historical ocean heat content regional climate-air quality simulations over Europe. J. Geophys. Res., 116, variations with an XBT depth bias correction. J. Oceanogr., 65, 287 299. D22307, doi:10.1029/2011JD015899. Ishii, M., M. Kimoto, K. Sakamoto, and S. Iwasaki, 2006: Steric sea level changes Kawase, H., T. Nagashima, K. Sudo, and T. Nozawa, 2011: Future changes in estimated from historical ocean subsurface temperature and salinity analyses. J. tropospheric ozone under Representative Concentration Pathways (RCPs). Oceanogr., 62, 155 170. Geophys. Res. Lett., 38, L05801. Ito, A., S. Sillman, and J. E. Penner, 2009: Global chemical transport model study of Keenlyside, N. S., and J. Ba, 2010: Prospects for decadal climate prediction. WIREs ozone response to changes in chemical kinetics and biogenic volatile organic Clim. Change, 1, 627 635. compounds emissions due to increasing temperatures: Sensitivities to isoprene Keenlyside, N. S., M. Latif, J. Jungclaus, L. Kornblueh, and E. Roeckner, 2008: nitrate chemistry and grid resolution. J. Geophys. Res., 114, D09301. Advancing decadal-scale climate prediction in the North Atlantic sector. Nature, Jacob, D. J., and D. A. Winner, 2009: Effect of climate change on air quality. Atmos. 453, 84 88. Environ., 43, 51 63. Keller, C. A., D. Brunner, S. Henne, M. K. Vollmer, S. O Doherty, and S. Reimann, Jacob, D. J., J. A. Logan, and P. P. Murti, 1999: Effect of rising Asian emissions on 2011: Evidence for under-reported western European emissions of the potent surface ozone in the United States. Geophys. Res. Lett., 26, 2175 2178. greenhouse gas HFC-23. Geophys. Res. Lett., 38, L15808. Jacob, D. J., et al., 1993: Factors regulating ozone over the United-States and its Kelly, J., P. A. Makar, and D. A. Plummer, 2012: Projections of mid-century summer air- export to the global atmosphere. J. Geophys. Res. Atmos., 98, 14817 14826. quality for North America: Effects of changes in climate and precursor emissions. Jacobson, M., 2008: Effects of wind-powered hydrogen fuel cell vehicles Atmos Chem Phys, 12, 5367 5390. on stratospheric ozone and global climate. Geophys. Res. Lett., Keppenne, C. L., M. M. Rienecker, N. P. Kurkowski, and D. A. Adamec, 2005: Ensemble doi:10.1029/2008GL035102, L14706. Kalman filter assimilation of temperature and altimeter data with bias correction Jacobson, M., 2010: Short-term effects of controlling fossil-fuel soot, biofuel soot and application to seasonal prediction. Nonlin. Process. Geophys., 12, 491 503. and gases, and methane on climate, Arctic ice, and air pollution health. J. Kesik, M., et al., 2006: Future scenarios of N2O and NO emissions from European Geophys. Res., D14209, doi:10.1029/2009JD013795. forest soils. J. Geophys., Res., 111, G02018. Jacobson, M., and D. Streets, 2009: Influence of future anthropogenic emissions Kharin, V. V., G. J. Boer, W. J. Merryfield, J. F. Scinocca, and W. S. Lee, 2012: Statistical on climate, natural emissions, and air quality. J. Geophys. Res., D08118, adjustment of decadal predictions in a changing climate. Geophys. Res. Lett., doi:10.1029/2008JD011476. 39, L19705. Jaffe, D. A., and N. L. Wigder, 2012: Ozone production from wildfires: A critical Kim, H. M., P. J. Webster, and J. A. Curry, 2012: Evaluation of short-term climate review. Atmos. Environ., 51, 1 10. change prediction in multi-model CMIP5 decadal hindcasts. Geophys. Res. Lett., Jai, L., and T. DelSole, 2012: Multi-year predictability of temperature and precipitation 39, L10701. in multiple climate models. Geophys. Res. Lett., 39, L17705. Kleeman, M. J., 2008: A preliminary assessment of the sensitivity of air quality in Jiang, X., Z.-L. Yang, H. Liao, and C. Wiedinmyer, 2010: Sensitivity of biogenic California to global change. Clim. Change, 87(Suppl 1), S273 S292. secondary organic aerosols to future climate change at regional scales: An Kleeman, R., Y. M. Tang, and A. M. Moore, 2003: The calculation of climatically online coupled simulation. Atmos. Environ., 44, 4891 4907. relevant singular vectors in the presence of weather noise as applied to the Jiang, X., C. Wiedinmyer, F. Chen, Z.-L. Yang, and J. C.-F. Lo, 2008: Predicted impacts ENSO problem. J. Atmos. Sci., 60, 2856 2868. of climate and land use change on surface ozone in the Houston, Texas, area. J. Klimont, Z., S. J. Smith, and J. Cofala, 2013: The last decade of global anthropogenic Geophys. Res., 113, D20312. sulfur dioxide: 2000 2011 emissions. Environ. Res. Lett., 8, 014003, Jickells, T. D., et al., 2005: Global iron connections between desert dust, ocean doi:10.1088/1748-9326/8/1/014003. biogeochemistry, and climate. Science, 308, 67 71. Kloster, S., F. Dentener, J. Feichter, F. Raes, U. Lohmann, E. Roeckner, and I. Fischer- 11 Joetzjer, E., H. Douville, C. Delire, and P. Ciais, 2012: Present-day and future Bruns, 2010: A GCM study of future climate response to aerosol pollution Amazonian precipitation in global climate models: CMIP5 versus CMIP3. Clim. reductions. Clim. Dyn., 34, 1177 1194. Dyn., doi:10.1007/s00382-012-1644-1. Kloster, S., et al., 2008: Influence of future air pollution mitigation strategies on total John, J. G., A. M. Fiore, V. Naik, L. W. Horowitz, and J. P. Dunne, 2012: Climate versus aerosol radiative forcing. Atmos. Chem. Phys., 8, 6405 6437. emission drivers of methane lifetime from 1860 2100. Atmos. Chem. Phys., 12, Knight, J., R. Allan, C. Folland, M. Vellinga, and M. Mann, 2005: A signature of 12021-12036, doi: 10.5194/acp-12-12021-2012. persistent natural thermohaline circulation cycles in observed climate. Geophys. Johnson, C. E., W. J. Collins, D. S. Stevenson, and R. G. Derwent, 1999: Relative roles Res. Lett., doi:10.1029/2005GL024233, L20708. of climate and emissions changes on future tropospheric oxidant concentrations. Knutson, T., and S. Manabe, 1995: Time-mean response over the tropical Pacific to J. Geophys. Res., 104, 18631 18645. increased CO2 in a coupled ocean-atmosphere model. J. Clim., 8, 2181 2199. Johnson, N. C., and S. P. Xie, 2010: Changes in the sea surface temperature threshold Knutson, T.R., and coauthors, 2013a: Dynamical Downscaling Projections of Late 21st for tropical convection. Nature Geosci., 3, 842 845. Century Atlantic Hurricane Activity CMIP3 and CMIP5 Model-based Scenarios. J. Jolliffe, I. T., 2007: Uncertainty and inference for verification measures. Weather Climate, doi:10.1175/JCLI-D-12-00539.1 Forecast., 22, 637 650. Knutson, T. R., F. Zeng, and A. T. Wittenberg 2013b: Multimodel Assessment of Jolliffe, I. T., and D. B. Stephenson, 2011: Forecast Verification: A Practitioner s Guide Regional Surface Temperature Trends: CMIP3 and CMIP5 Twentieth-Century in Atmospheric Science, 2nd ed. John Wiley & Sons, Hoboken, NJ, USA. Simulations. J. Clim., 26, 4168 4185. Jones, G., M. Lockwood, and P. Stott, 2012: What influence will future solar Knutson, T. R., et al., 2006: Assessment of Twentieth-Century regional surface activity changes over the 21st century have on projected global near-surface temperature trends using the GFDL CM2 coupled models. J. Clim., 19, 1624- temperature changes? J. Geophys. Res., 117, D05103, doi.1029/2011JD17013. 1651, doi: 10.1175/JCLI3709.1. Joshi, M., E. Hawkins, R. Sutton, J. Lowe, and D. Frame, 2011: Projections of when Knutson, T. R., et al., 2010: Tropical cyclones and climate change. Nature Geosci, 3, temperature change will exceed 2 degrees C above pre-industrial levels. Nature 157 163. Clim. Change, 1, 407 412. Knutti, R., D. Masson, and A. Gettelman, 2013: Climate model genealogy: Generation Jung, M., et al., 2010: Recent decline in the global land evapotranspiration trend due CMIP5 and how we got there. Geophys. Res. Lett., doi:10.1002/grl.50256. to limited moisture supply. Nature, 467, 951 954. Koster, R. D., et al., 2010: Contribution of land surface initialization to subseasonal Kang, S. M., L. M. Polvani, J. C. Fyfe, and M. Sigmond, 2011: Impact of polar ozone forecast skill: First results from a multi-model experiment. Geophys. Res. Lett., depletion on subtropical precipitation. Science, 332, 951 954. 37, L02402. Kao, S. C., and A. R. Ganguly, 2011: Intensity, duration, and frequency of precipitation Kroger, J., W. A. Muller, and J. S. von Storch, 2012: Impact of different ocean extremes under 21st-century warming scenarios. J. Geophys. Res., 116, D16119, reanalyses on decadal climate prediction. Clim. Dyn., 39, 795 810. doi:10.1029/2010JD015529. Krueger, O., and J.-S. von Storch, 2011: A simple empirical model for decadal climate prediction. J. Clim., 24, 1276 1283. Kumar, A., 2009: Finite samples and uncertainty estimates for skill measures for seasonal prediction. Mon. Weather Rev., 137, 2622 2631. 1020 Near-term Climate Change: Projections and Predictability Chapter 11 Kumar, S., V. Merwade, D. Niyogi, and J. L. Kinter III, 2013: Evaluation of temperature Lenderink, G., and E. v. Meijgaard, 2010: Linking increases in hourly precipitation and precipitation trends and long-term persistence in CMIP5 20th century extremes to atmospheric temperature and moisture changes. Environ. Res. Lett., climate simulations. J. Clim., doi:10.1175/JCLI-D-12-00259.1. 5(2), 025208. Laepple, T., S. Jewson, and K. Coughlin, 2008: Interannual temperature predictions Lenderink, G., E. van Meijgaard, and F. Selten, 2009: Intense coastal rainfall in the using the CMIP3 multi-model ensemble mean. Geophys. Res. Lett., 35, L10701. Netherlands in response to high sea surface temperatures: Analysis of the event Lamarque, J.-F., et al., 2011: Global and regional evolution of short-lived radiatively- of August 2006 from the  perspective of a changing climate. Clim. Dyn., 32, active gases and aerosols in the Representative Concentration Pathways. Clim. 19 33. Change, doi:10.1007/s10584-011-0155-0, 1 22. Lenderink, G., H.Y. Mok, T.C. Lee, and G. J. v. Oldenborgh, 2011: Scaling and trends of Lamarque, J. F., et al., 2013: The Atmospheric Chemistry and Climate Model hourly precipitation extremes in two different climate zones Hong Kong and Intercomparison Project (ACCMIP): Overview and description of models, the Netherlands. Hydrol. Earth Syst. Sci., 15(9), 3033 3041. simulations and climate diagnostics. Geosci. Model Dev., 6, 179-206, doi Leslie, L. M., D. J. Karoly, M. Leplastrier, and B. W. Buckley, 2007: Variability of tropical 10.5194/gmf-6-179-2013. cyclones over the southwest Pacific Ocean using a high-resolution climate Lambert, F. H., and J. C. H. Chiang, 2007: Control of land-ocean temperature model. Meteorol. Atmos. Phys., 97, 171 180. contrast by ocean heat uptake. Geophys. Res. Lett., 34, L13704, doi: Levermann, A., J. Schewe, V. Petoukhov, and H. Held, 2009: Basic mechanism for 10.1029/2007GL029755. abrupt monsoon transitions. Proc. Natl. Acad. Sci. U.S.A., 106, 20572 20577. Lambert, F. H., and M. J. Webb, 2008: Dependency of global mean precipitation on Levin, I., et al., 2010: The global SF6 source inferred from long-term high precision surface temperature. Geophys. Res. Lett., 35, L23803. atmospheric measurements and its comparison with emission inventories. Lammertsma, E. I., H. J. de Boer, S. C. Dekker, D. L. Dilcher, A. F. Lotter, and F. Wagner- Atmos. Chem. Phys., 10, 2655 2662. Cremer, 2011: Global CO2 rise leads to reduced maximum stomatal conductance Levy, H., L. W. Horowitz, Daniel Schwarzkopf, M. M., G. Y., N. J.-C., and V. Ramaswamy, in Florida vegetation. Proc. Acad. Sci. U.S.A., 108, 4035 4040. 2013: The roles of aerosol direct and indirect effects in past and future climate Lang, C., and D. W. Waugh, 2011: Impact of climate change on the frequency of change. J. Geophys. Res., doi:10.1002/jgrd.50192. Northern Hemisphere summer cyclones. J. Geophys. Res., 116, D04103, doi: Li, H. L., H. J. Wang, and Y. Z. Yin, 2012: Interdecadal variation of the West African 10.1029/2010JD014300. summer monsoon during 1979 2010 and associated variability. Clim. Dyn., Langner, J., M. Engardt, and C. Andersson, 2012a: European summer surface ozone doi:10.1007/s00382-012-1426-9. 1990 2100. Atmos. Chem. Phys.,12, 10097 10105. Li, S. L., and G. T. Bates, 2007: Influence of the Atlantic multidecadal oscillation on the Langner, J., M. Engardt, A. Baklanov, J. H. Christensen, M. Gauss, C. Geels, G. B. winter climate of East China. Adv. Atmos. Sci., 24, 126 135. Hedegaard, R. Nuterman, D. Simpson, J. Soares, M. Sofiev, P. Wind, and A. Zakey, Li, S. L., J. Perlwitz, X. W. Quan, and M. P. Hoerling, 2008: Modelling the influence 2012b: A multi-model study of impacts of climate change on surface ozone in of North Atlantic multidecadal warmth on the Indian summer rainfall. Geophys. Europe. Atmos. Chem. Phys., 12, 10423-10440. Res. Lett., 35, L05804. Lanzante, J. R., 2005: A cautionary note on the use of error bars. J. Clim., 18, 3699 Liao, H., W.-T. Chen, and J. H. Seinfeld, 2006: Role of climate change in global 3703. predictions of future tropospheric ozone and aerosols. J. Geophys. Res., 111, Latif, M., M. Collins, H. Pohlmann, and N. Keenlyside, 2006: A review of predictability D12304. studies of Atlantic sector climate on decadal time scales. J. Clim., 19, 5971 5987. Liao, K.-J., et al., 2007: Sensitivities of ozone and fine Particulate matter formation Latif, M., C. W. Boning, J. Willebrand, A. Biastoch, A. Alvarez-Garcia, N. Keenlyside, to emissions under the impact of potential future climate change. Environ. Sci. and H. Pohlmann, 2007: Decadal to multidecadal variability of the Atlantic MOC: Technol., 41, 8355 8361. Mechanisms and predictability. In: Ocean Circulation: Mechanisms and Impacts Liepert, B. G., and M. Previdi, 2009: Do models and observations disagree on the - Past and Future Changes of Meridional Overturning. AGU Monograph 173. rainfall response to global warming? J. Clim., 22, 3156 3166. [A. Schmittner, J. C. H. Chiang and S. R. Hemming (eds.)]. American Geophysical Lin, C. Y. C., D. J. Jacob, and A. M. Fiore, 2001: Trends in exceedances of the ozone air Union, Washington, DC, pp. 149 166. quality standard in the continental United States, 1980 1998. Atmos. Environ., 11 Lawrence, D. M., and A. G. Slater, 2010: The contribution of snow condition trends to 35, 3217 3228. future ground climate. Clim. Dyn., 34, 969 981. Lin, J.-T., D. J. Wuebbles, and X.-Z. Liang, 2008: Effects of intercontinental transport Lawrence, D. M., A. G. Slater, V. E. Romanovsky, and D. J. Nicolsky, 2008: Sensitivity on surface ozone over the United States: Present and future assessment with a of a model projection of near-surface permafrost degradation to soil column global model. Geophys. Res. Lett., 35, L02805. depth and representation of soil organic matter. J. Geophys. Res., 113, F02011, Liu, J., D. L. Mauzerall, L. W. Horowitz, P. Ginoux, and A. M. Fiore, 2009: Evaluating doi:10.1029/2007JF000883/ inter-continental transport of fine aerosols: (1) Methodology, global aerosol Lean, J. L., and D. H. Rind, 2009: How will Earth s surface temperature change in distribution and optical depth. Atmos. Environ., 43, 4327 4338. future decades? Geophys. Res. Lett., 36, L15708. Liu, J., J. A. Curry, H. Wang, M. Song, and R. M. Horton, 2012: Impact of declining Lee, J. D., et al., 2006a: Ozone photochemistry and elevated isoprene during the UK Arctic sea ice on winter snowfall. Proc. Natl. Acad. Sci. U.S.A., 109, 4074 4079. heatwave of august 2003. Atmos. Environ., 40, 7598-7613. Lobell, D. B., and M. B. Burke, 2008: Why are agricultural impacts of climate change Lee, S.-J., and E. H. Berbery, 2012: Land cover change effects on the climate of the La so uncertain? The importance of temperature relative to precipitation. Environ. Plata Basin. J. Hydrometeorol., 13, 84 102. Res. Lett., 3, L05804. Lee, T. C. K., F. W. Zwiers, X. B. Zhang, and M. Tsao, 2006b: Evidence of decadal Lockwood, M., 2010: Solar change and climate: An update in the light of the current climate prediction skill resulting from changes in anthropogenic forcing. J. Clim., exceptional solar minimum. Proc. R. Soc. London A, 466, 303 329. 19, 5305 5318. Lockwood, M., R. G. Harrison, M. J. Owens, L. Barnard, T. Woollings, and F. Steinhilber, Lei, H., D. J. Wuebbles, X.-Z. Liang, and S. Olsen, 2013: Domestic versus international 2011: The solar influence on the probability of relatively cold UK winters in the contributions on 2050 ozone air quality: How much is convertible by regional future. Environ. Res. Lett., 6, 034004, doi:10.1088/1748-9326/6/3/034004. control? Atmos. Environ., 68, 315 325. Logan, J. A., 1989: Ozone in rural areas of the United States. J. Geophys. Res., 94, Leibensperger, E. M., L. J. Mickley, and D. J. Jacob, 2008: Sensitivity of US air quality 8511 8532. to mid-latitude cyclone frequency and implications of 1980 2006 climate Lu, J., and M.Cai, 2009: Stabilization of the atmospheric boundary layer and the change. Atmos. Chem. Phys., 8, 7075 7086. muted global hydrological cycle response to global warming. J. Hydrometeor, Leibensperger, E. M., L. J. Mickley, D. J. Jacob, and S. R. H. Barrett, 2011a: 10, 347-352, doi: 10.1175/2008JHM1058.1. Intercontinental influence of NOx and CO emissions on particulate matter air Lu, J., G. Vecchi, and T. Reichler, 2007: Expansion of the Hadley Cell under global quality. Atmos. Environ., 45, 3318 3324. warming. Geophys. Res. Lett., doi:10.1029/2006GL028443, L06805. Leibensperger, E. M., L. J. Mickley, D. J. Jacob, W.-T. Chen, J. H. Seinfeld, A. Nenes, P. J. Lu, R. Y., and Y. H. Fu, 2010: Intensification of East Asian summer rainfall interannual Adams, D. G. Streets, N. Kumar, and D. Rind, 2012: Climatic effects of 1950 2050 variability in the twenty-first century simulated by 12 CMIP3 coupled models. J. changes in US anthropogenic aerosols Part 2: Climate response. Atmos. Chem. Clim., 23, 3316 3331. Phys., 12, 3349-3362. MacLeod, D. A., C Caminade, and A. P. Morse, 2013: Useful decadal climate prediction Lenderink, G., and E. Van Meijgaard, 2008: Increase in hourly precipitation extremes at regional scales? A look at the ENSEMBLES stream 2 decadal hindcasts. beyond expectations from temperature changes. Nature Geosci., 1, 511 514. Environ. Res. Lett., 7, 044012. 1021 Chapter 11 Near-term Climate Change: Projections and Predictability Magnusson, L., M. Balmaseda, S. Corti, F. Molteni, and T. Stockdale, 2013: Evaluation Meehl, G. A., J. M. Arblaster, and G. Branstator, 2012a: Mechanisms contributing of forecast strategies for seasonal and decadal forecasts in presence of to the warming hole and the consequent U.S. east-west differential of heat systematic model errors. Clim. Dyn., doi:10.1007/s00382-012-1599-2. extremes. J. Clim., 25, 6394 6408. Mahlstein, I., and R. Knutti, 2012: September Arctic sea ice predicted to disappear Meehl, G.A., A. Hu, J.M. Arblaster, J. Fasullo, and K.E. Trenberth, 2013a:  Externally near 2C global warming above present. J. Geophys. Res., 117, D06104. forced and internally generated decadal climate variability associated with the Mahlstein, I., R. Knutti, S. Solomon, and R. W. Portmann, 2011: Early onset of Interdecadal Pacific Oscillation, J.  Climate, 26, 7298-7310, doi:  http://dx.doi. significant local warming in low latitude countries. Environ. Res. Lett., 6, L06805. org/10.1175/JCLI-D-12-00548.1 Mahmud, A., M. Hixson, J. Hu, Z. Zhao, S. H. Chen, and M. J. Kleeman, 2010: Climate Meehl, G. A., J.M. Arblaster, and D. R. Marsh, 2013b: Could a future Grand Solar impact on airborne particulate matter concentrations in California using seven Minimum like the Maunder Minimum stop global warming? Geophys. Res. year analysis periods. Atmos. Chem. Phys., 10, 11097 11114. Lett., doi: 10.1002/grl.50361. Mahowald, N. M., 2007: Anthropocene changes in desert area: Sensitivity to climate Meehl, G. A., C. Tebaldi, G. Walton, D. Easterling, and L. McDaniel, 2009a: Relative model predictions. Geophys. Res. Lett., 34, L18817. increase of record high maximum temperatures compared to record low Mahowald, N. M., and C. Luo, 2003: A less dusty future? Geophys. Res. Lett., 30, minimum temperatures in the U. S. Geophys. Res. Lett., 36, L08703. 1903. Meehl, G. A., J. M. Arblaster, J. T. Fasullo, A. Hu, and K. E. Trenberth, 2011: Model- Mahowald, N. M., D. R. Muhs, S. Levis, P. J. Rasch, M. Yoshioka, C. S. Zender, and C. based evidence of deep-ocean heat uptake during surface-temperature hiatus Luo, 2006: Change in atmospheric mineral aerosols in response to climate: Last periods. Nature Clim. Change, 1, 360 364. glacial period, preindustrial, modern, and doubled carbon dioxide climates. J. Meehl, G. A., et al., 2007a: The WCRP CMIP3 multimodel dataset - A new era in Geophys. Res., 111, D10202. climate change research. Bull. Am. Meteorol. Soc., 88, 1383-1394. Makkonen, R., A. Asmi, V. M. Kerminen, M. Boy, A. Arneth, P. Hari, and M. Kulmala, Meehl, G. A., et al., 2013c: Climate change projections in CESM1(CAM5) compared 2012: Air pollution control and decreasing new particle formation lead to strong to CCSM4. J. Clim., doi:10.1175/JCLI-D-12-00572.1. climate warming. Atmos. Chem. Phys., 12, 1515 1524. Meehl, G. A., et al., 2012b: Climate system response to external forcings and climate Manabe, S., R. J. Stouffer, M. J. Spelman, and K. Bryan, 1991: Transient responses of change projections in CCSM4. J. Clim., 25, 3661 3683. a coupled Ocean Atmosphere Model to gradual changes of atmospheric CO2 .1. Meehl, G. A., et al., 2007b: Global climate projections. In: Climate Change 2007: The Annual mean response. J. Clim., 4, 785 818. Physical Science Basis. Contribution of Working Group I to the Fourth Assessment Manders, A. M. M., E. van Meijgaard, A. C. Mues, R. Kranenburg, L. H. van Ulft, and Report of the Intergovernmental Panel on Climate Change [Solomon, S., D. Qin, M. Schaap, 2012: The impact of differences in large-scale circulation output from M. Manning, Z. Chen, M. Marquis, K. B. Averyt, M. Tignor and H. L. Miller (eds.)] climate models on the regional modeling of ozone and PM. Atmos Chem Phys, Cambridge University Press, Cambridge, United Kingdom and New York, NY, 12, 9441 9458. USA, pp. 747 846. Mann, M., et al., 2009: Global signatures and dynamical origins of the Little Ice Age Meehl, G. A., et al., 2009b: Decadal prediction: Can it be skillful? Bull. Am. Meteorol. and Medieval Climate Anomaly. Science, 326, 1256 1260. Soc., 90, 1467 1485. Marengo, J. A., R. Jones, L. M. Alves, and M. C. Valverde, 2009: Future change of Meehl, G. A., et al., 2013d: Decadal climate prediction: An update from the trenches. temperature and precipitation extremes in South America as derived from the Bull. Am. Meteorol. Soc., doi:10.1175/BAMS-D-12-00241.1. PRECIS regional climate modeling system. Int. J. Climatol., 29, 2241 2255. Meinshausen, M., T. M. L. Wigley, and S. C. B. Raper, 2011a: Emulating atmosphere- Maslowski, W., J. C. Kinney, M. Higgens, and A. Roberts, 2012: The future of Arctic sea ocean and carbon cycle models with a simpler model, MAGICC6 Part 2: ice. Annu. Rev. Earth Planet. Sci., 40, 625 654. Applications. Atmos Chem Phys, 11, 1457 1471. Mason, S. J., 2004: On using climatology as a reference strategy in the Brier and Meinshausen, M., S. J. Smith, K. Calvin, and J. Daniel, 2011b: The RCP greenhouse ranked probability skill scores. Mon. Weather Rev., 132, 1891 1895. gas concentrations and their extensions from 1765 to 2300. Clim. Change, doi: Massonnet, T. Fichefet, H. Goosse, C. M. Bitz, G. Philippon-Berthier, M. M. Holland, 10.1007/s10584-011-0156 z. 11 and P.-Y. Barriat, 2012: Constraining projections of summer Arctic sea ice. Meleux, F., F. Solmon, and F. Giorgi, 2007: Increase in summer European ozone Cryosphere, 6, 1383 1394. amounts due to climate change. Atmos. Environ., 41, 7577 7587. Matei, D., J. Baehr, J. H. Jungclaus, H. Haak, W. A. Muller, and J. Marotzke, 2012a: Menon, S., and et al., 2008: Aerosol climate effects and air quality impacts from 1980 Multiyear prediction of monthly mean Atlantic Meridional Overturning to 2030. Environ. Res. Lett., 3, 024004. Circulation at 26.5 degrees N. Science, 335, 76 79. Merrifield, M. A., 2011: A shift in western tropical Pacific sea level trends during the Matei, D., H. Pohlmann, J. Jungclaus, W. Muller, H. Haak, and J. Marotzke, 2012b: Two 1990s. J. Clim., 24, 4126 4138. tales of initializing decadal climate prediction experiments with the ECHAM5/ Merryfield, W. J., et al., 2013: The Canadian Seasonal to Interannual Prediction MPI-OM Model. J. Clim., 25, 8502 8523. System. Part I: Models and initialization. Mon. Weather Rev., doi:10.1175/MWR- McCabe, G. J., M. A. Palecki, and J. L. Betancourt, 2004: Pacific and Atlantic Ocean D-12-00216.1. influences on multidecadal drought frequency in the United States. Proc. Natl. Mickley, L. J., D. J. Jacob, B. D. Field, and D. Rind, 2004: Effects of future climate Acad.Sci. U.S.A., 101, 4136 4141. change on regional air pollution episodes in the United States. Geophys. Res. McLandress, C., T. G. Shepherd, J. F. Scinocca, D. A. Plummer, M. Sigmond, A. I. Lett., 31, L24103. Jonsson, and M. C. Reader, 2011: Separating the dynamical effects of climate Mickley, L. J., E. M. Leibensperger, D. J. Jacob, and D. Rind, 2011: Regional warming change and ozone depletion. Part II. Southern Hemisphere troposphere. J. Clim., from aerosol removal over the United States: Results from a transient 2010 24, 1850 1868. 2050 climate simulation. Atmos. Environ., doi:10.1016/j.atmosenv.2011.07.030. Meehl, G., et al., 2006: Climate change projections for the twenty-first century and Miller, R., G. Schmidt, and D. Shindell, 2006: Forced annular variations in the 20th climate change commitment in the CCSM3. J. Clim., 2597 2616. century intergovernmental panel on climate change fourth assessment report Meehl, G. A., and A. X. Hu, 2006: Megadroughts in the Indian monsoon region and models. J. Geophys. Res.,D18101, doi:10.1029/2005JD006323. southwest North America and a mechanism for associated multidecadal Pacific Min, S.-K., and S.-K. Son, 2013: Multi-model attribution of the Southern Hemisphere sea surface temperature anomalies. J. Clim., 19, 1605 1623. Hadley Cell widening: Major role of ozone depletion. J. Geophys. Res., 118, Meehl, G. A., and J. M. Arblaster, 2011: Decadal variability of Asian-Australian 3007 3015. monsoon-ENSO-TBO relationships. J. Clim., 24, 4925 4940. Ming, Y., V. Ramaswamy, and G. Persad, 2010: Two opposing effects of absorbing Meehl, G. A., and J. M. Arblaster, 2012: Relating the strength of the tropospheric aerosols on global-mean precipitation. Geophys. Res. Lett., 37, L13701. biennial oscillation (TBO) to the phase of the Interdecadal Pacific Oscillation Ming, Y., V. Ramaswamy, and G. Chen, 2011: A model investigation of aerosol- (IPO). Geophys. Res. Lett., 39, L20716. induced changes in boreal winter extratropical circulation. J. Clim., Meehl, G. A., and H. Y. Teng, 2012: Case studies for initialized decadal hindcasts and doi:10.1175/2011jcli4111.1. predictions for the Pacific region. Geophys. Res. Lett., 39, L22705. Mishra, V., J. M. Wallace, and D. P. Lettenmaier, 2012: Relationship between hourly Meehl, G. A., A. X. Hu, and C. Tebaldi, 2010: Decadal prediction in the Pacific region. extreme precipitation and local air temperature in the United States. Geophys. J. Clim., 23, 2959 2973. Res. Lett., 39, L16403. Mochizuki, T., et al., 2012: Decadal prediction using a recent series of MIROC global climate models. J. Meteorol. Soc. Jpn., 90A, 373 383. 1022 Near-term Climate Change: Projections and Predictability Chapter 11 Mochizuki, T., et al., 2010: Pacific decadal oscillation hindcasts relevant to near-term Ottera, O. H., M. Bentsen, H. Drange, and L. L. Suo, 2010: External forcing as a climate prediction. Proc, Natl. Acad. Sci. U.S.A., 107, 1833 1837. metronome for Atlantic multidecadal variability. Nature Geosci., 3, 688 694. Monson, R. K., et al., 2007: Isoprene emission from terrestrial ecosystems in response Overland, J. E., and M. Wang, 2013: When will the summer Arctic be nearly ice free? to global change: Minding the gap between models and observations. Philos. Geophys. Res. Lett., doi:10.1002/grl.50316. Trans.R. Soc. A, 365, 1677 1695. Pacifico, F., S. P. Harrison, C. D. Jones, and S. Sitch, 2009: Isoprene emissions and Montzka, S., M. Krol, E. Dlugokencky, B. Hall, P. Jockel, and J. Lelieveld, 2011: Small climate. Atmos. Environ., 43, 6121 6135. interannual variability of global atmospheric hydroxyl. Science, 331, 67 69. Pacifico, F., G. A. Folberth, C. D. Jones, S. P. Harrison, and W. J. Collins, 2012: Sensitivity Morgenstern, O., et al., 2010: Anthropogenic forcing of the Northern Annular Mode of biogenic isoprene emissions to past, present, and future environmental in CCMVal-2 models. J. Geophys. Res., D00M03, doi:10.1029/2009JD013347. conditions and implications for atmospheric chemistry. J. Geophys. Res. Atmos., Morice, C. P., J. J. Kennedy, N. A. Rayner, and P. D. Jones, 2012: Quantifying 117, D22302. uncertainties in global and regional temperature change using an ensemble of Paeth, H., and F. Pollinger, 2010: Enhanced evidence in climate models for changes in observational estimates: The HadCRUT4 data set. J. Geophys. Res., 117, D08101, extratropical atmospheric circulation. Tellus A, 62, 647 660. doi: 10.1029/2011JD017187. Palmer, M. D., D. J. McNeall, and N. J. Dunstone, 2011: Importance of the deep ocean Msadek, R., K. Dixon, T. Delworth, and W. Hurlin, 2010: Assessing the predictability for estimating decadal changes in Earth s radiation balance. Geophys. Res. Lett., of the Atlantic meridional overturning circulation and associated fingerprints. 38, L13707. Geophys. Res. Lett., doi:10.1029/2010GL044517, L19608. Palmer, T. N., and A. Weisheimer, 2011: Diagnosing the causes of bias in climate Mues, A., A. Manders, M. Schaap, A. Kerschbaumer, R. Stern, and P. Builtjes, 2012: models why is it so hard? Geophys. Astrophys. Fluid Dyn., 105, 351 365. Impact of the extreme meteorological conditions during the summer 2003 in Palmer, T. N., R. Buizza, R. Hagedon, A. Lawrence, M. Leutbecher, and L. Smith, 2006: Europe on particulate matter concentrations. Atmos. Environ., 55, 377 391. Ensemble prediction: A pedagogical perspective. ECMWF Newslett., 106, 10 17. Muller, C. J., and P. A. O Gorman, 2011: An energetic perspective on the regional Palmer, T. N., et al., 2004: Development of a European multimodel ensemble system response of precipitation to climate change. Nature Clim. Change, 1, 266 271. for seasonal-to-interannual prediction (DEMETER). Bull. Am. Meteorol. Soc., 85, Muller, C. J., P. A. O Gorman, and L. E. Back, 2011: Intensification of precipitation 853-872. extremes with warming in a cloud-resolving model. J. Clim., 24, 2784 2800. Paolino, D. A., J. L. Kinter, B. P. Kirtman, D. H. Min, and D. M. Straus, 2012: The Impact Muller, W. A., et al., 2012: Forecast skill of multi-year seasonal means in the decadal of Land Surface and Atmospheric Initialization on Seasonal Forecasts with prediction system of the Max Planck Institute for Meteorology. Geophys. Res. CCSM. J. Clim., 25, 1007 1021. Lett., 39, L22707. Paulot, F., J. D. Crounse, H. G. Kjaergaard, J. H. Kroll, J. H. Seinfeld, and P. O. Wennberg, Murphy, J., et al., 2010: Towards prediction of decadal climate variability and change. 2009: Isoprene photooxidation: New insights into the production of acids and Proced. Environ. Sci., 1, 287 304. organic nitrates. Atmos. Chem. Phys., 9, 1479 1501. Murphy, J. M., B. B. B. Booth, M. Collins, G. R. Harris, D. M. H. Sexton, and M. J. Webb, Penner, J. E., H. Eddleman, and T. Novakov, 1993: Towards the development of a 2007: A methodology for probabilistic predictions of regional climate change global inventory for black carbon emissions. Atmos. Environ. A, 27, 1277 1295. from perturbed physics ensembles. Philos. Trans. R. Soc. A, 365, 1993 2028. Penner, J. E., M. J. Prather, I. S. A. Isaksen, J. S. Fuglestvedt, Z. Klimont, and D. S. Newman, M., 2007: Interannual to decadal predictability of tropical and North Stevenson, 2010: Short-lived uncertainty? Nature Geosci, 3(9), 587 588. Pacific sea surface temperatures. J. Clim., 20, 2333 2356. Persechino, A., J. Mignot, D. Swingedouw, S. Labetoulle, and E. Guilyardi, 2012: Newman, M., 2013: An empirical benchmark for decadal forecasts of global surface Decadal predictability of the Atlantic Meridional Overturning Circulation and temperature anomalies. J. Clim., doi:10.1175/JCLI-D-12-00590.1. climate in the IPSL-CM5A-LR model. Clim. Dyn, doi: 10.1007/s00382-012-1466- Nicolsky, D. J., V. E. Romanovsky, V. A. Alexeev, and D. M. Lawrence, 2007: Improved 1. modeling of permafrost dynamics in a GCM land-surface scheme. Geophys. Res. Pielke, R. A., et al., 2011: Land use/land cover changes and climate: Modeling Lett., 34, L08501. analysis and observational evidence. WIREs Clim. Change, 2, 828 850. Nolte, C. G., A. B. Gilliland, C. Hogrefe, and L. J. Mickley, 2008: Linking global to Pierce, D. W., P. J. Gleckler, T. P. Barnett, B. D. Santer, and P. J. Durack, 2012: The 11 regional models to assess future climate impacts on surface ozone levels in the fingerprint of human-induced changes in the ocean s salinity and temperature United States. J. Geophys. Res., 113, D14307. fields. Geophys. Res. Lett., 39, L21704, doi:10.1029/2012GL053389. Notz, D., 2009: The future of ice sheets and sea ice: Between reversible retreat and Pierce, D. W., T. P. Barnett, R. Tokmakian, A. Semtner, M. Maltrud, J. A. Lysne, and A. unstoppable loss. Proc. Natl. Acad. Sci. U.S.A., 106, 20590 20595. Craig, 2004: The ACPI Project, Element 1: Initializing a coupled climate model NRC, 2009: Global Sources of Local Pollution:An Assessment of Long-Range from observed conditions. Clim. Change, 62, 13 28. Transport of Key Air Pollutants to and from the United States. The National Pincus, R., C. P. Batstone, R. J. P. Hofmann, K. E. Taylor, and P. J. Glecker, 2008: Academies Press, Washington, DC. Evaluating the present day simulation of clouds, precipitation, and radiation in NRC, 2010: Greenhouse Gas Emissions: Methods to Support International Climate climate models. J. Geophys. Res., 113, D14209. Agreements. National Research Council, Washington, DC. Pinto, J., U. Ulbrich, G. Leckebusch, T. Spangehl, M. Reyers, and S. Zacharias, 2007: O Gorman, P. A., 2012: Sensitivity of tropical precipitation extremes to climate Changes in storm track and cyclone activity in three SRES ensemble experiments change. Nature Geosci., 5(10), 697 700. with the ECHAM5/MPI-OM1 GCM. Clim. Dyn., doi: 10.1007/s00382-007-0230- O Gorman, P. A., and T. Schneider, 2009: The physical basis for increases in 4, 195 210. precipitation extremes in simulations of 21st-century climate change. Proc. Natl. Pitman, A. J., et al., 2012: Effects of land cover change on temperature and rainfall Acad. Sci. U.S.A., 106, 14773 14777. extremes in multi-model ensemble simulations. Earth Syst. Dyn., 3, 213 231. O Gorman, P. A., R. P. Allan, M. P. Byrne, and M. Previdi, 2012: Energetic constraints Pitman, A. J., et al., 2009: Uncertainties in climate responses to past land cover on precipitation under climate change. Surv. Geophys., 33, 585 608. change: First results from the LUCID intercomparison study. Geophys. Res. Lett., Oman, L., et al., 2010: Multimodel assessment of the factors driving stratospheric 36, L14814. ozone evolution over the 21st century. J. Geophys. Res., 115, D24306, doi: Pohlmann, H., J. H. Jungclaus, A. Köhl, D. Stammer, and J. Marotzke, 2009: Initializing 10.1029/2010JD014362. decadal climate predictions with the GECCO oceanic synthesis: Effects on the Ordónez, C., H. Mathis, M. Furger, S. Henne, C. Hüglin, J. Staehelin, and A. S. H. Prévôt, North Atlantic. J. Clim., 22, 3926 3938. 2005: Changes of daily surface ozone maxima in Switzerland in all seasons Pohlmann, H., M. Botzet, M. Latif, A. Roesch, M. Wild, and P. Tschuck, 2004: Estimating from 1992 to 2002 and discussion of summer 2003. Atmos. Chem. Phys., 5, the decadal predictability of a coupled AOGCM. J. Clim., 17, 4463 4472. 1187 1203. Pohlmann, H., et al., 2013: Predictability of the mid-latitude Atlantic meridional Orlowsky, B., and S. I. Seneviratne, 2012: Global changes in extremes events: overturning circulation in a multi-model system. Clim. Dyn., doi:10.1007/ Regional and seasonal dimension. Climatic Change. Climate Change, 110(3 4), s00382-013-1663-6. 669 696. Polvani, L. M., M. Previdi, and C. Deser, 2011a: Large cancellation, due to ozone Ott, L., et al., 2010: Influence of the 2006 Indonesian biomass burning aerosols recovery, of future Southern Hemisphere atmospheric circulation trends. on tropical dynamics studied with the GEOS-5 AGCM. J. Geophys. Res., 115, Geophys. Res. Lett., 38, L04707. D14121. 1023 Chapter 11 Near-term Climate Change: Projections and Predictability Polvani, L. M., D. W. Waugh, G. J. P. Correa, and S.-W. Son, 2011b: Stratospheric ozone Rampal, P., J. Weiss, C. Dubois, and J. M. Campin, 2011: IPCC climate models depletion: The main driver of twentieth-century atmospheric circulation changes do not capture Arctic sea ice drift acceleration: Consequences in terms of in the Southern Hemisphere. J. Clim., 24, 795 812. projected sea ice thinning and decline. J. Geophys. Res., 116, C00D07, doi: Power, S., and R. Colman, 2006: Multi-year predictability in a coupled general 10.1029/2011JC007110. circulation model. Clim. Dyn., 26, 247 272. Randles, C. A., and V. Ramaswamy, 2010: Direct and semi-direct impacts of absorbing Power, S., T. Casey, C. Folland, A. Colman, and V. Mehta, 1999: Inter-decadal biomass burning aerosol on the climate of southern Africa: A Geophysical Fluid modulation of the impact of ENSO on Australia. Clim. Dyn., 15, 319 324. Dynamics Laboratory GCM sensitivity study. Atmos. Chem. Phys., 10, 9819 Power, S. B., 1995: Climate drift in a global ocean General-Circulation Model. J. Phys. 9831. Oceanogr., 25, 1025 1036. Rasmussen, D. J., A. M. Fiore, V. Naik, L. W. Horowitz, S. J. McGinnis, and M. G. Power, S. B., and G. Kociuba, 2011a: The impact of global warming on the Southern Schultz, 2012: Surface ozone-temperature relationships in the eastern US: A Oscillation Index. Clim. Dyn., 37, 1745 1754. monthly climatology for evaluating chemistry-climate models. Atmos. Environ., Power, S. B., and G. Kociuba, 2011b: What caused the observed twentieth-century doi:10.1016/j.atmosenv.2011.11.021. weakening of the Walker Circulation? J. Clim., 24, 6501 6514. Rind, D., 2008: The consequences of not knowing low-and high-latitude climate Power, S. B., M. Haylock, R. Colman, and X. Wang, 2006: The predictability of sensitivity. Bull. Am. Meteorol. Soc., doi: 10.1175/2007BAMS2520.1, 855 864. interdecadal changes in ENSO and ENSO teleconnections. J. Clim., 19, 4755 Roberts, C. D., and M. D. Palmer, 2012: Detectability of changes to the Atlantic 4771. meridional overturning circulation in the Hadley Centre Climate Models. Clim. Power, S. B., F. Delage, R. Colman, and A. Moise, 2012: Consensus on 21st century Dyn., 39, 2533-2546, doi: 10.1007/s00382-012-1306-3. rainfall projections in climate models more widespread than previously thought. Robock, A., 2000: Volcanic eruptions and climate. Rev. Geophys., 38, 191 219. J. Clim., doi::10.1175/JCLI-D-11-00354.1. Robson, J. I., R. T. Sutton, and D. M. Smith, 2012: Initialized decadal predictions of the Pozzer, A., et al., 2012: Effects of business-as-usual anthropogenic emissions on air rapid warming of the North Atlantic ocean in the mid 1990s. Geophys. Res. Lett., quality. Atmos Chem Phys, 12, 6915 6937. 39, L19713, doi: 10.1029/2012GL053370. Prather, M., et al., 2001: Atmospheric chemistry and greenhouse gases. In: Climate Roeckner, E., P. Stier, J. Feichter, S. Kloster, M. Esch, and I. Fischer-Bruns, 2006: Impact Change 2001: The Scientific Basis. Contribution of Working Group I to the Third of carbonaceous aerosol emissions on regional climate change. Clim. Dyn., 27, Assessment Report of the Intergovernmental Panel on Climate Change [J. T. 553 571. Houghton, Y. Ding, D. J. Griggs, M. Noquer, P. J. van der Linden, X. Dai, K. Maskell Roscoe, H. K., and J. D. Haigh, 2007: Influences of ozone depletion, the solar cycle and C. A. Johnson (eds.)]. Cambridge University Press, Cambridge, United and the QBO on the Southern Annular Mode. Q. J. R. Meteorol. Soc., 133, 1855 Kingdom and New York, NY, USA, pp. 239 287. 1864. Prather, M., et al., 2003: Fresh air in the 21st century? Geophys. Res. Lett., 30, 1100. Rotstayn, L. D., S. J. Jeffrey, M. A. Collier, S. M. Dravitzki, A. C. Hirst, J. I. Syktus, and Prather, M. J., and J. Hsu, 2010: Coupling of nitrous oxide and methane by global K. K. Wong, 2012: Aerosol- and greenhouse gas-induced changes in summer atmospheric chemistry. Science, 330, 952 954. rainfall and circulation in the Australasian region: A study using single-forcing Prather, M. J., C. D. Holmes, and J. Hsu, 2012: Reactive greenhouse gas scenarios: climate simulations. Atmos. Chem. Phys., 12, 6377 6404. Systematic exploration of uncertainties and the role of atmospheric chemistry. Rowell, D. P., 2011: Sources of uncertainty in future change in local precipitation. Geophys. Res. Lett., 39, L09803. Clim. Dyn., doi:10.1007/s00382-011-1210-2. Prather, M. J., et al., 2009: Tracking uncertainties in the causal chain from human Rowlands, D. J., et al., 2012: Broad range of 2050 warming from an observationally activities to climate. Geophys. Res. Lett., 36, L05707. constrained large climate model ensemble. Nature Geosci., 5, 256 260. Price, C., 2013: Lightning applications in weather and climate. Surv. Geophys., doi: Royal Society, 2008: Ground-Level Ozone in the 21st Century: Future Trends, Impacts 10.1007/s10712 012-9218-7. and Policy Implications.The Royal Society, London, United Kingdom. Prospero, J. M., 1999: Long-term measurements of the transport of African mineral Ruckstuhl, C., and J. R. Norris, 2009: How do aerosol histories affect solar dimming 11 dust to the southeastern United States: Implications for regional air quality. J. and brightening over Europe?: IPCC-AR4 models versus observations. J. Geophys. Res. Atmos., 104, 15917 15927. Geophys. Res. Atmos., 114, D00D04, doi: 1029/2008JD011066. Pye, H. O. T., H. Liao, S. Wu, L. J. Mickley, D. J. Jacob, D. K. Henze, and J. H. Seinfeld, Saha, S., et al., 2010: The NCEP Climate Forecast System Reanalysis. Bull. Am. 2009: Effect of changes in climate and emissions on future sulfate-nitrate- Meteorol. Soc., 91, 1015 1057. ammonium aerosol levels in the United States. J. Geophys. Res., 114, D01205. Sand, T., K. Berntsen, J. E. Kay, J. F. Lamarque, O. Seland, and A. Kirkevag, 2013: The Quesada, B., R. Vautard, P. Yiou, M. Hirschi, and S. Seneviratne, 2012: Asymmetric Arctic response to remote and local forcing of black carbon. Atmos Chem Phys, European summer heat predictability from wet and dry southern winters and 13, 211 224. springs. Nature Clim. Change, 2 (10), 736 741. Scaife, A. A., et al., 2012: Climate change projections and stratosphere-troposphere Quinn, P. K., et al., 2008: Short-lived pollutants in the Arctic: Their climate impact and interaction. Clim. Dyn., 38, 2089 2097. possible mitigation strategies. Atmos. Chem. Phys., 8, 1723 1735. Schaller, N., I. Mahlstein, J. Cermak, and R. Knutti, 2011: Analyzing precipitation Racherla, P. N., and P. J. Adams, 2006: Sensitivity of global tropospheric ozone and projections: A comparison of different approaches to climate model evaluation. fine particulate matter concentrations to climate change. J. Geophys. Res., 111, J. Geophys. Res., 116, D10118. D24103. Schar, C., P. L. Vidale, D. Luthi, C. Frei, C. Haberli, M. A. Liniger, and C. Appenzeller, Racherla, P. N., and P. J. Adams, 2008: The response of surface ozone to climate 2004: The role of increasing temperature variability in European summer change over the eastern United States. Atmos. Chem. Phys., 8, 871 885. heatwaves. Nature, 427, 332 336. Raes, F., and J. H. Seinfeld, 2009: New directions: Climate change and air pollution Scherrer, S. C., P. Ceppi, M. Croci-Maspoli, and C. Appenzeller, 2012: Snow-albedo abatement: A bumpy road. Atmos. Environ., 43, 5132 5133. feedback and Swiss spring temperature trends. Theor. Appl. Climatol., 110, Räisänen, J., 2008: Warmer climate: Less or more snow? Clim. Dyn., 30, 307 319. 509 516. Räisänen, J., 2007: How reliable are climate models? Tellus A, 59, 2 29. Schneider, E. K., B. Huang, Z. Zhu, D. G. DeWitt, J. L. Kinter, K. B.P., and J. Shukla, 1999: Räisänen, J., and L. Ruokolainen, 2006: Probabilistic forecasts of near-term climate Ocean data assimilation, initialization and predictions of ENSO with a coupled change based on a resampling ensemble technique. Tellus A, 58, 461 472. GCM. Mon. Weather Rev., 127, 1187 1207. Rajczak, J., P. Pall, and C. Schär, 2013: Projections of extreme precipitation events in Schneider, N., and A. J. Miller, 2001: Predicting western North Pacific Ocean climate. regional climate simulations for the European and Alpine regions. J. Geophys. J. Clim., 14, 3997 4002. Res., doi:10.1002/jgrd.50297. Schubert, S., M. J. Suarez, P. J. Pegion, R. D. Koster, and J. T. Bacmeister, 2004: On the Ramana, M. V., V. Ramanathan, Y. Feng, S. C. Yoon, S. W. Kim, G. R. Carmichael, and cause of the 1930s Dust Bowl. Science, 303, 1855 1859. J. J. Schauer, 2010: Warming influenced by the ratio of black carbon to sulphate Schweiger, A. J., R. W. Lindsay, S. Vavrus, and J. A. Francis, 2008: Relationships and the black-carbon source. Nature Geosci, 3, 542 545. between Arctic sea ice and clouds during autumn. J. Clim., 21, 4799 4810. Ramanathan, V., and Y. Feng, 2009: Air pollution, greenhouse gases and climate Screen, J. A., and I. Simmonds, 2010: The central role of diminishing sea ice in recent change: Global and regional perspectives. Atmos. Environ., 43, 37 50. Arctic temperature amplification. Nature, 464, 1334 1337. 1024 Near-term Climate Change: Projections and Predictability Chapter 11 Seager, R., Y. Kushnir, M. Ting, M. Cane, N. Naik, and J. Miller, 2008: Would advance Singleton, A., and R. Toumi, 2012: Super-Clausius-Clapeyron scaling of rainfall in a knowledge of 1930s SSTs have allowed prediction of the dust bowl drought? J. model squall line. Q. J. R. Meteorol. Soc., 139, 334 339. Clim., 21, 3261 3281. Skjth, C. A., and C. Geels, 2013: The effect of climate and climate change on Seager, R., N. Naik, W. Baethgen, A. Robertson, Y. Kushnir, J. Nakamura, and ammonia emissions in Europe. Atmos Chem Phys, 13, 117 128. S. Jurburg, 2010: Tropical oceanic causes of interannual to multidecadal Slater, A. G., and D. M. Lawrence, 2013: Diagnosing present and future permafrost precipitation variability in southeast South America over the past century. J. from climate models. J. Clim., doi:10.1175/JCLI-D-12-00341.1. Clim., 23, 5517 5539. Smith, D. M., and J. M. Murphy, 2007: An objective ocean temperature and salinity Selten, F., G. Branstator, H. Dijkstra, and M. Kliphuis, 2004: Tropical origins for analysis using covariances from a global climate model. J. Geophys. Res., 112, recent and future Northern Hemisphere climate change. Geophys. Res. Lett., C02022. doi:10.1029/2004GL020739, L21205. Smith, D. M., A. A. Scaife, and B. P. Kirtman, 2012: What is the current state of Semenov, V., M. Latif, J. Jungclaus, and W. Park, 2008: Is the observed NAO scientific knowledge with regard to seasonal and decadal forecasting? Environ. variability during the instrumental record unusual? Geophys. Res. Lett., Res. Lett., 7, 015602. doi:10.1029/2008GL033273, L11701. Smith, D. M., R. Eade, and H. Pohlmann, 2013a: A comparison of full-field and Semenov, V. A., M. Latif, D. Dommenget, N. S. Keenlyside, A. Strehz, T. Martin, and anomaly initialization for seasonal to decadal climate prediction. Clim. Dyn., W. Park, 2010: The impact of North Atlantic-Arctic multidecadal variability on doi:10.1007/s00382-013-1683-2. Northern Hemisphere surface air temperature. J. Clim., 23, 5668 5677. Smith, D. M., S. Cusack, A. W. Colman, C. K. Folland, G. R. Harris, and J. M. Murphy, Seneviratne, S. I., et al., 2010: Investigating soil moisture-climate interactions in a 2007: Improved surface temperature prediction for the coming decade from a changing climate: A review. Earth Sci. Rev., 99, 125 161. global climate model. Science, 317, 796 799. Seneviratne, S. I., et al., 2012: Changes in climate extremes and their impacts on the Smith, D. M., R. Eade, N. J. Dunstone, D. Fereday, J. M. Murphy, H. Pohlmann, and A. natural physical environment. In: IPCC Special Report on Extreme Events and A. Scaife, 2010: Skilful multi-year predictions of Atlantic hurricane frequency. Disasters (SREX). World Meteorological Organization, Geneva, Switzerland, pp. Nature Geosci., 3, 846 849. Serreze, M. C., A. P. Barrett, A. G. Slater, M. Steele, J. L. Zhang, and K. E. Trenberth, Smith, D. M., et al., 2013b: Real-time multi-model decadal climate predictions. Clim. 2007: The large-scale energy budget of the Arctic. J. Geophys. Res., 112, D11122, Dyn., doi:10.1007/s00382-012-1600 0. doi: 10.1029/2006JD008230. Smith, S. J., J. van Aardenne, Z. Klimont, R. J. Andres, A. Volke, and S. Delgado Arias, Sevellec, F., and A. Fedorov, 2012: Model bias reduction and the limits of oceanic 2011: Anthropogenic sulfur dioxide emissions: 1850 2005. Atmos Chem Phys, decadal predictability: Importance of the deep ocean. J. Clim., doi:10.1175/JCLI- 11, 1101 1116. D-12-00199.1. Smith, T. M., R. W. Reynolds, T. C. Peterson, and J. Lawrimore, 2008: Improvements Sheffield, J., and E. F. Wood, 2008: Projected changes in drought occurrence under to NOAA s historical merged land-ocean surface temperature analysis (1880 future global warming from multi-model, multi-scenario, IPCC AR4 simulations. 2006). J. Clim., 21, 2283 2296. Clim. Dyn., 31, 79 105. Soden, B. J., R. T. Wetherald, G. L. Stenchikov, and A. Robock, 2002: Global cooling Sheffield, J., E. F. Wood, and M. L. Roderick, 2012: Little change in global drought after the eruption of Mount Pinatubo: A test of climate feedback by water vapor. over the past 60 years. Nature, 491, 435 440. Science, 296, 727 730. Shi, Y., X. J. Gao, J. Wu, and F. Giorgi, 2011: Changes in snow cover over China in the Sohn, B. J., and S.-C. Park, 2010: Strengthened tropical circulations in past three 21st century as simulated by a high resolution regional climate model. Environ. decades inferred from water vapor transport. J. Geophys. Res., 115, D15112. Res. Lett., 6, 045401, doi: 10.1088/1748-9326/6/4/045401. Solomon, A., et al., 2011: Distinguishing the roles of natural and anthropogenically Shimpo, A., and M. Kanamitsu, 2009: Planetary scale land-ocean contrast of near- forced decadal climate variability. Bull. Am. Meteorol. Soc., 92, 141 156. surface air temperature and precipitation forced by present and future SSTs. J. Son, S., N. Tandon, L. Polvani, and D. Waugh, 2009a: Ozone hole and Southern Meteorol. Soc. Jpn., 87, 877 894. Hemisphere climate change. Geophys. Res. Lett., doi:10.1029/2009GL038671, Shin, S.-I., and P. D. Sardeshmukh, 2011: Critical influence of the pattern of tropical L15705. 11 ocean warming on remote climate trends. Clim. Dyn., 36, 1577 1591. Son, S., et al., 2009b: The impact of stratospheric ozone recovery on tropopause Shindell, D., et al., 2013: Radiative forcing in the ACCMIP historical and future height trends. J. Clim., doi: 10.1175/2008JCLI2215.1, 429 445. climate simulations. Atmos. Chem. Phys., 13, 2939-2974. Son, S. W., et al., 2008: The impact of stratospheric ozone recovery on the Southern Shindell, D., et al., 2012a: Simultaneously mitigating near-term climate change and Hemisphere westerly jet. Science, 320, 1486 1489. improving human health and food security. Science, 335, 183 189. Spracklen, D. V., L. J. Mickley, J. A. Logan, R. C. Hudman, R. Yevich, M. D. Flannigan, Shindell, D. T., and G. A. Schmidt, 2004: Southern Hemisphere climate response to and A. L. Westerling, 2009: Impacts of climate change from 2000 to 2050 on ozone changes and greenhouse gas increases. Geophys. Res. Lett., 31, L18209, wildfire activity and carbonaceous aerosol concentrations in the western United doi:10.1029/2004GL020724. States. J. Geophys. Res., 114, D20301. Shindell, D. T., A. Voulgarakis, G. Faluvegi, and G. Milly, 2012: Precipitation response Srokosz, M., et al., 2012: Past, present, and future changes in the Atlantic Meridional to regional radiative forcing. Atmos. Chem. Phys., 12, 6969 6982. Overturning Circulation. Bull. Am. Meteorol. Soc., 93, 1663 1676. Shindell, D. T., et al., 2006: Simulations of preindustrial, present-day, and 2100 Stainforth, D. A., et al., 2005: Uncertainty in predictions of the climate response to conditions in the NASA GISS composition and climate model G-PUCCINI. Atmos. rising levels of greenhouse gases. Nature, 433, 403 406. Chem. Phys., 6, 4427 4459. Stammer, D., 2006: Report of the First CLIVAR Workshop on Ocean Reanalysis. WCRP Sigmond, M., P. Kushner, and J. Scinocca, 2007: Discriminating robust and non-robust Informal Publication No. 9/2006. ICPO Publication Series No. 93. World Climate atmospheric circulation responses to global warming. J. Geophys. Res., D20121, Research Programme, World Meteorological Organization, Geneva, Switzerland. doi:10.1029/2006JD008270. Stan, C., and B. P. Kirtman, 2008: The influence of atmospheric noise and uncertainty Sillman, S., and P. J. Samson, 1995: Impact of temperature on oxidant photochemistry in ocean initial conditions on the limit of predictability in a coupled GCM. J. in urban, polluted rural and remote environments. J. Geophys. Res., 100, 11497 Clim., 21, 3487 3503. 11508. Staten, P. W., J. J. Rutz, T. Reichler, and J. Lu, 2011: Breaking down the tropospheric Sillmann, J., V.V. Kharin, F. W. Zwiers, and X. Zhang, 2013: Climate extreme indices in circulation response by forcing. Clim. Dyn., doi:10.1007/s00382-011-1267-y. the CMIP5 multi-model ensemble. Part 2: Future climate projections. J. Geophys. Stegehuis, A. I., R. Vautard, P. Ciais, R. Teuling, M. Jung, and P. Yiou, 2012: Summer Res., 118, 1 21. temperatures in Europe and land heat fluxes in observation-based data and Simmonds, I., and K. Keay, 2009: Extraordinary September Arctic sea ice reductions regional climate model simulations. Clim. Dyn., doi:10.1007/s00382-012- and their relationships with storm behavior over 1979 2008. Geophys. Res. 1559-x. Lett., 36, L19715. Steiner, A. L., S. Tonse, R. C. Cohen, A. H. Goldstein, and R. A. Harley, 2006: Influence Simmons, A. J., K. M. Willett, P. D. Jones, P. W. Thorne, and D. P. Dee, 2010: Low- of future climate and emissions on regional air quality in California. J. Geophys. frequency variations in surface atmospheric humidity, temperature, and Res., 111, D18303. precipitation: Inferences from reanalyses and monthly gridded observational data sets. J. Geophys. Res. Atmos., 115, D01110, doi:10.1029/2009JD012442. 1025 Chapter 11 Near-term Climate Change: Projections and Predictability Steiner, A. L., A. J. Davis, S. Sillman, R. C. Owen, A. M. Michalak, and A. M. Fiore, 2010: Swingedouw, D., J. Mignot, S. Labetoulle, E. Guilyardi, and G. Madec, 2013: Observed suppression of ozone formation at extremely high temperatures due Initialisation and predictability of the AMOC over the last 50 years in a climate to chemical and biophysical feedbacks. Proc. Natl. Acad. Sci. U.S.A., doi:10.1073/ model. Clim. Dyn., doi:10.1007/s00382-012-1516-8. pnas.1008336107. Szopa, S., D. A. Hauglustaine, R. Vautard, and L. Menut, 2006: Future global Stenchikov, G., T. Delworth, V. Ramaswamy, R. Stouffer, A. Wittenberg, and F. Zeng, tropospheric ozone changes and impact on European air quality. Geophys. Res. 2009: Volcanic signals in oceans. J. Geophys. Res. Atmos., doi:ARTN D16104, Lett., 33, L14805. 10.1029/2008JD011673, -. Tagaris, E., et al., 2007: Impacts of global climate change and emissions on regional Stenchikov, G., K. Hamilton, R. Stouffer, A. Robock, V. Ramaswamy, B. Santer, and ozone and fine particulate matter concentrations over the United States. J. H. Graf, 2006: Arctic Oscillation response to volcanic eruptions in the IPCC AR4 Geophys. Res., 112, D14312. climate models. J. Geophys. Res., 111, D07107, doi.1029/2005JD006286. Tai, A. P. K., L. J. Mickley, and D. J. Jacob, 2010: Correlations between fine particulate Stevenson, D., R. Doherty, M. Sanderson, C. Johnson, B. Collins, and D. Derwent, matter (PM2.5) and meteorological variables in the United States: Implications 2005: Impacts of climate change and variability on tropospheric ozone and its for the sensitivity of PM2.5 to climate change. Atmos. Environ., 44, 3976 3984. precursors. Faraday Discuss., 130, 41 57. Tai, A. P. K., L. J. Mickley, and D. J. Jacob, 2012a: Impact of 2000 2050 climate change Stevenson, D. S., et al., 2013: Tropospheric ozone changes, radiative forcing and on fine particulate matter (PM2.5) air quality inferred from a multi-model attribution to emissions in the Atmospheric Chemistry and Climate Model analysis of meteorological modes. Atmos. Chem. Phys., 12, 11329-11337, doi: Intercomparison Project (ACCMIP). Atmos. Chem. Phys., 13, 3063-2085. 10.5194/acp-12-11329-2012. doi:10.5194/acp-13-3063-2013. Tai, A. P. K., L. J. Mickley, D. J. Jacob, E. M. Leibensperger, L. Zhang, J. A. Fisher, and H. Stevenson, D. S., et al., 2006: Multimodel ensemble simulations of present-day and O. T. Pye, 2012b: Meteorological modes of variability for fine particulate matter near-future tropospheric ozone. J. Geophys. Res., 111, D08301. (PM2.5) air quality in the United States: Implications for PM2.5 sensitivity to Stockdale, T. N., 1997: Coupled ocean atmosphere forecasts in the presence of climate change. Atmos. Chem. Phys., 12, 3131 3145. climate drift. Mon. Weather Rev., 125, 809 818. Tao, Z., A. Williams, H.-C. Huang, M. Caughey, and X.-Z. Liang, 2007: Sensitivity of Stockdale, T. N., D. L. T. Anderson, J. O. S. Alves, and M. A. Balmaseda, 1998: Global U.S. surface ozone to future emissions and climate changes. Geophys. Res. Lett., seasonal rainfall forecasts using a coupled ocean-atmosphere model. Nature, 34, L08811. 392, 370 373. Tatebe, H., et al., 2012: Initialization of the climate model MIROC for decadal Stott, P., D. Stone, and M. Allen, 2004: Human contribution to the European heatwave prediction with hydographic data assimilation. J. Meteorol. Soc. Jpn., 90A, of 2003. Nature, 432, 610 614. 275 294. Stott, P., R. Sutton, and D. Smith, 2008: Detection and attribution of Atlantic salinity Taylor, C. M., A. Gounou, F. Guichard, P. P. Harris, R. J. Ellis, F. Couvreux, and M. De changes. Geophys. Res. Lett., doi:10.1029/2008GL035874, L21702. Kauwe, 2011: Frequency of Sahelian storm initiation enhanced over mesoscale Stott, P., P. Good, G. Jones, N. Gillet, and E. Hawkins, 2013: Upper range of climate soil-moisture patterns. Nature Geosci., 4, 430 433. warming projections are inconsistent with past warming. Environ. Res. Lett., 8, Taylor, K. E., R. J. Stouffer, and G. A. Meehl, 2012: An overview of Cmip5 and the 014024, doi:10.1088/1748-9326/8/1/014024. experiment design. Bull. Am. Meteorol. Soc., 93, 485 498. Stott, P., N. Gillett, G. Hegerl, D. Karoly, D. Stone, X. Zhang, and F. Zwiers, 2010: Tebaldi, C., J. M. Arblaster, and R. Knutti, 2011: Mapping model agreement on future Detection and attribution of climate change: A regional perspective. WIREs Clim. climate projections. Geophys. Res. Lett., 38, L23701. Change, 1, 192 211. Tegen, I., M. Werner, S. P. Harrison, and K. E. Kohfeld, 2004: Relative importance Stott, P. A., and J. A. Kettleborough, 2002: Origins and estimates of uncertainty in of climate and land use in determining present and future global soil dust predictions of twenty-first century temperature rise. Nature, 416, 723 726. emission. Geophys. Res. Lett., 31, L05105. Stott, P. A., and G. Jones, 2012: Observed 21st century temperatures further constrain Teng, H., W. M. Washington, G. Branstator, G. A. Meehl, and J.-F. Lamarque, 2012: decadal predictions of future warming. Atmos. Sci. Lett., 13, 151 156. Potential impacts of Asian carbon aerosols on future US warming. Geophys. Res. 11 Strahan, S., et al., 2011: Using transport diagnostics to understand chemistry Lett., 39, L11703. climate model ozone simulations. J. Geophys. Res. Atmos., 116, D17302, Teng, H. Y., G. Branstator, and G. A. Meehl, 2011: Predictability of the Atlantic doi:10.1029/2010/JD015360. Overturning Circulation and associated surface patterns in two CCSM3 climate Stroeve, J., M. M. Holland, W. Meier, T. Scambos, and M. Serreze, 2007: Arctic sea ice change ensemble experiments. J. Clim., 24, 6054 6076. decline: Faster than forecast. Geophys. Res. Lett., 34, L09501. Terray, L., 2012: Evidence for multiple drivers of North Atlantic multi-decadal climate Struzewska, J., and J. W. Kaminski, 2008: Formation and transport of photooxidants variability. Geophys. Res. Lett., 39, L19712. over Europe during the July 2006 heat wave - observations and GEM-AQ model Terray, L., L. Corre, S. Cravatte, T. Delcroix, G. Reverdin, and A. Ribes, 2012: Near- simulations. Atmos. Chem. Phys., 8, 721 736. surface salinity as nature s rain gauge to detect human influence on the tropical Sugi, M., and J. Yoshimura, 2012: Decreasing trend of tropical cyclone frequency in water cycle. J. Clim., 25, 958 977. 228-year high-resolution AGCM simulations. Geophys. Res. Lett., 39, L19805, Teuling, A. J., et al., 2010: Contrasting response of European forest and grassland doi: 10.1029/2012GL053360. energy exchange to heatwaves. Nature Geosci., 3, 722 727. Sugiura, N., et al., 2008: Development of a four-dimensional variational coupled Thompson, D. W. J., and S. Solomon, 2002: Interpretation of recent Southern data assimilation system for enhanced analysis and prediction of seasonal to Hemisphere climate change. Science, 296, 895 899. interannual climate variations. J. Geophys. Res. C, 113, C10017. Timmermann, A., S. McGregor, and F. Jin, 2010: Wind effects on past and Sugiura, N., et al., 2009: Potential for decadal predictability in the North Pacific future regional sea level trends in the Southern Indo-Pacific. J. Clim., doi: region. Geophys. Res. Lett., 36, L20701. 10.1175/2010JCLI3519.1, 4429-4437. Sun, J., and H. Wang, 2006: Relationship between Arctic Oscillation and Pacific Timmermann, A., et al., 2007: The influence of a weakening of the Atlantic Meridional Decadal Oscillation on decadal timescales. Chin. Sci. Bull., 51, 75 79. Overturning Circulation on ENSO. J. Clim., 20, 4899 4919. Sun, J., H. Wang, W. Yuan, and H. Chen, 2010: Spatial-temporal features of intense Timmreck, C., 2012: Modeling the climatic effects of large explosive volcanic snowfall events in China and their possible change. J. Geophys. Res., 115, eruptions. WIREs Clim. Change, 3, 545 564. D16110, doi: 10.1029/2009JD013541. Toyoda, T., et al., 2011: Impact of the assimilation of sea ice concentration data on Sun, Y., S. Solomon, A. Dai, and R. W. Portmann, 2007: How often will it rain? J. Clim., an atmosphere-ocean-sea ice coupled simulation of the Arctic Ocean climate. 20(19), 4801 4818. SOLA, 7, 37 40. Sushama, L., R. Laprise, and M. Allard, 2006: Modeled current and future soil Trenberth, K., and A. Dai, 2007: Effects of Mount Pinatubo volcanic eruption on thermal regime for northeast Canada. J. Geophys. Res. Atmos., 111, D18111, the hydrological cycle as an analog of geoengineering. Geophys. Res. Lett., 34, doi: 10.1029/20005JD007027. L15702, doi: 10.1029/2007GL030524. Sutton, R., and D. Hodson, 2005: Atlantic Ocean forcing of North American and Trenberth, K. E., and D. J. Shea, 2006: Atlantic hurricanes and natural variability in European summer climate. Science, doi: 10.1126/science.1109496, 115 118. 2005. Geophys. Res. Lett., 33, L12704. Sutton, R. T., B. W. Dong, and J. M. Gregory, 2007: Land/sea warming ratio in response to climate change: IPCC AR4 model results and comparison with observations. Geophys. Res. Lett., 34, L02701. 1026 Near-term Climate Change: Projections and Predictability Chapter 11 Trenberth, K. E., et al., 2007: Observations: Atmospheric surface and climate change. Vidale, P. L., D. Luethi, R. Wegmann, and C. Schaer, 2007: European summer climate In: Climate Change 2007: The Physical Science Basis. Contribution of Working variability in a heterogeneous multi-model ensemble. Clim. Change, 81, 209 Group I to the Fourth Assessment Report of the Intergovernmental Panel on 232. Climate Change [Solomon, S., D. Qin, M. Manning, Z. Chen, M. Marquis, K. B. Vieno, M., et al., 2010: Modelling surface ozone during the 2003 heat-wave in the Averyt, M. Tignor and H. L. Miller (eds.)] Cambridge University Press, Cambridge, UK. Atmos. Chem. Phys., 10, 7963 7978. United Kingdom and New York, NY, USA, pp. 235 336. Vikhliaev, Y., B. Kirtman, and P. Schopf, 2007: Decadal North Pacific bred vectors in a Tressol, M., et al., 2008: Air pollution during the 2003 European heat wave as seen coupled GCM. J. Clim., 20, 5744 5764. by MOZAIC airliners. Atmos. Chem. Phys., 8, 2133 2150. Villarini, G., and G. A. Vecchi, 2012: 21st century projections of North Atlantic tropical Troccoli, A., and T. N. Palmer, 2007: Ensemble decadal predictions from analysed storms from CMIP5 models. Nature Clim. Change, doi:Nature Climate Change initial conditions. Philos. Trans. R. Soc. A, 365, 2179 2191. :10:1038/NCLIMATE1530. Turner, A. J., A. M. Fiore, L. W. Horowitz, and M. Bauer, 2013: Summertime cyclones Villarini, G., and G. A. Vecchi, 2013: Projected increases in North Atlantic tropical over the Great Lakes Storm Track from 1860 2100: Variability, trends, and cyclone intensity from CMIP5 models. J. Clim., 26, 3231 3240. association with ozone pollution. Atmos. Chem. Phys., 13, 565 578. Villarini, G., G. A. Vecchi, T. R. Knutson, M. Zhao, and J. A. Smith, 2011: North Atlantic Tziperman, E., L. Zanna, and C. Penland, 2008: Nonnormal thermohaline circulation tropical storm frequency response to anthropogenic forcing: Projections and dynamics in a coupled ocean-atmosphere GCM. J. Phys. Oceanogr., 38, 588 604. sources of uncertainty. J. Clim., 24, 3224 3238. Ulbrich, U., J. Pinto, H. Kupfer, G. Leckebusch, T. Spangehl, and M. Reyers, 2008: Vollmer, M. K., et al., 2011: Atmospheric histories and global emissions of the Changing Northern Hemisphere storm tracks in an ensemble of IPCC climate anthropogenic hydrofluorocarbons HFC-365mfc, HFC-245fa, HFC-227ea, and change simulations. J. Clim., doi: 10.1175/2007JCLI1992.1, 1669 1679. HFC-236fa. J. Geophys. Res., 116, D08304. UNEP and WMO, 2011: Integrated Assessment of Black Carbon and Tropospheric Voulgarakis, A., et al., 2013: Analysis of present day and future OH and methane Ozone. United Nations Environment Programme & World Meteorological lifetime in the ACCMIP simulations. Atmos. Chem. Phys., 13, 2563 2587. Organization [Available at http://www.unep.org/dewa/Portals/67/pdf/ Vukovich, F. M., 1995: Regional-scale boundary layer ozone variations in the eastern BlackCarbon_SDM.pdf] United States and their association with meteorological variations. Atmos. Unger, N., 2012: Global climate forcing by criteria air pollutants. Annu. Rev. Environ. Environ., 29, 2259 2273. Resour., 37, 1-24. Wang, B., et al., 2013: Preliminary evaluations on skills of FGOALS-g2 in decadal Unger, N., D. T. Shindell, D. M. Koch, and D. G. Streets, 2006a: Cross influences of predictions. Adv. Atmos. Sci., 30(3), 674 683. ozone and sulfate precursor emissions changes on air quality and climate. Proc. Wang, H. J., J. Q. Sun, and K. Fan, 2007: Relationships between the North Pacific Natl. Acad. Sci. U.S.A., 103, 4377 4380. Oscillation and the typhoon/hurricane frequencies. Sci. China D, 50, 1409 1416. Unger, N., D. T. Shindell, D. M. Koch, M. Amann, J. Cofala, and D. G. Streets, 2006b: Wang, H. J., et al., 2012: Extreme climate in China: Facts, simulation and projection. Influences of man-made emissions and climate changes on tropospheric ozone, Meteorol. Z., 21(3), 279 304. methane, and sulfate at 2030 from a broad range of possible futures. J. Geophys. Wang, J., F. , et al., 2009: Impact of deforestation in the Amazon Basin on cloud Res. Atmos., 111, D12313, doi: 10.1029/2005JD006518. climatology. Proc. Natl. Acad. Sci., 106, 3670 3674. van der Linden, P., and J. F. B. Mitchell, 2009: ENSEMBLES: Climate change and Wang, M., J. Overland, and N. Bond, 2010: Climate projections for selected large its impacts. Summary of research and results from the ENSEMBLES project marine ecosystems. J. Mar. Syst., doi: 10.1016/j.jmarsys.2008.11.028, 258 266. [Available from the Met Office Hadley Centre, Fitzroy Road, Exeter EX1 3PB, Wang, M. Y., and J. E. Overland, 2009: A sea ice free summer Arctic within 30 years? United Kingdom]. Geophys. Res. Lett., 36, L07502, doi: 10.1029/2009GL037820. van Haren, R., G.J. van Oldenborgh, G. Lenderink, M. Collins, and W. Hazeleger, Wang, R. F., L. G. Wu, and C. Wang, 2011: Typhoon track changes associated with 2012: SST and circulation trend biases cause an underestimation of European global warming. J. Clim., 24, 3748 3752. precipitation trends precipitation trends. Clim. Dyn., 40, 1 20. Weaver, C. P., et al., 2009: A preliminary synthesis of modeled climate change impacts van Oldenborgh, G. J., P. Yiou, and R. Vautard, 2010: On the roles of circulation and on U.S. regional ozone concentrations. Bull. Am. Meteorol. Soc., 90, 1843 1863. 11 aerosols in the decline of mist and dense fog in Europe over the last 30 years. Weigel, A. P., R. Knutti, M. A. Liniger, and C. Appenzeller, 2010: Risks of model Atmos. Chem. Phys., 10, 4597 4609. weighting in multimodel climate projections. J. Clim., 23, 4175 4191. van Oldenborgh, G. J., F. J. Doblas-Reyes, B. Wouters, and W. Hazeleger, 2012: Decadal Weisheimer, A., T. N. Palmer, and F. J. Doblas-Reyes, 2011: Assessment of prediction skill in a multi-model ensemble. Clim. Dyn., 38, 1263 1280. representations of model uncertainty in monthly and seasonal forecast van Oldenborgh, G. J., F.J. Doblas-Reyes, S. S. Drijfhout, and E. Hawkins, 2013: ensembles. Geophys. Res. Lett., 38, L16703. Reliability of regional climate model trends. Environ. Res. Lett., 8, 014055. West, J. J., A. M. Fiore, L. W. Horowitz, and D. L. Mauzerall, 2006: Global health van Oldenborgh, G. J., et al., 2009: Western Europe is warming much faster than benefits of mitigating ozone pollution with methane emission controls. Proc. expected. Clim. Past, 5, 1 12. Natl. Acad. Sci. U.S.A., 103, 3988 3993. van Vuuren, D., et al., 2011: The representative concentration pathways: An overview. Wigley, T., et al., 2009: Uncertainties in climate stabilization. Clim. Change, 97, Clim. Change, doi:10.1007/s10584-011-0148-z, 1-27. 85 121. Vautard, R., P. Yiou, and G. van Oldenborgh, 2009: Decline of fog, mist and haze in Wild, M., J. Grieser, and C. Schaer, 2008: Combined surface solar brightening and Europe over the past 30 years. Nature Geosci., 2, 115 119. increasing greenhouse effect support recent intensification of the global land- Vautard, R., C. Honoré, M. Beekmann, and L. Rouil, 2005: Simulation of ozone during based hydrological cycle. Geophys. Res. Lett., 35, L17706. the August 2003 heat wave and emission control scenarios. Atmos. Environ., Wild, O., 2007: Modelling the global tropospheric ozone budget: Exploring the 39, 2957 2967. variability in current models. Atmos. Chem. Phys., 7, 2643 2660. Vavrus, S. J., M. M. Holland, A. Jahn, D. A. Bailey, and B. A. Blazey, 2012: Twenty-first- Wild, O., et al., 2012: Modelling future changes in surface ozone: A parameterized century Arctic climate change in CCSM4. J. Clim., 25, 2696 2710. approach. Atmos. Chem. Phys., 12, 2037-2054, doi: 10.5194/acp-12-2037-2012. Vecchi, G., and B. Soden, 2007: Global warming and the weakening of the tropical Wilks, D. S., 2006: Statistical Methods in the Atmospheric Sciences, Vol. 91. Academic circulation. J. Clim., doi: 10.1175/JCLI4258.1, 4316 4340. Press, Elsevier, San Diego, CA, USA, 627 pp. Vecchi, G., B. Soden, A. Wittenberg, I. Held, A. Leetmaa, and M. Harrison, 2006: Williams, A., and C. Funk, 2011: A westward extension of the warm pool leads to a Weakening of tropical Pacific atmospheric circulation due to anthropogenic westward extension of the Walker circulation, drying eastern Africa. Clim. Dyn., forcing. Nature, doi: 10.1038/nature04744, 73 76. 37, 2417 2435. Vecchi, G. A., et al., 2012: Technical comment on Multiyear prediction of monthly Williams, P. D., E. Guilyardi, R. Sutton, J. Gregory, and G. Madec, 2007: A new feedback mean Atlantic meridional overturning circulation at 26.5N. Science, 338, 604. on climate change from the hydrological cycle. Geophys. Res. Lett., 34, L08706. Vecchi, G.A., R. Msadek, W. Anderson, Y.-S. Chang, T. Delworth, K. Dixon, R. Gudgel, Woodward, S., D. L. Roberts, and R. A. Betts, 2005: A simulation of the effect of A. Rosati, W. Stern, G. Villarini, A. Wittenberg, X. Yang, F. Zeng, R. Zhang and climate change; induced desertification on mineral dust aerosol. Geophys. Res. S. Zhang (2013): Multi-year Predictions of North Atlantic Hurricane Frequency: Lett., 32, L18810. Promise and Limitations. J. Climate, doi:10.1175/JCLI-D-12-00464.1 Woollings, T., 2010: Dynamical influences on European climate: An uncertain future. Philos. Trans. R. Soc. A, doi: 10.1098/rsta.2010.0040, 3733 3756. 1027 Chapter 11 Near-term Climate Change: Projections and Predictability Woollings, T., and M. Blackburn, 2012: The North Atlantic jet stream under climate Zhang, S., A. Rosati, and T. Delworth, 2010a: The adequacy of observing systems in change and its relation to the NAO and EA patterns. J. Clim., 25, 886 902. monitoring the Atlantic Meridional Overturning Circulation and North Atlantic WMO, 2002: Standardised Verification System (SVS) for Long-Range Forecasts (LRF). Climate. J. Clim., 23, 5311 5324. New Attachment II-9 to the Manual on the GDPS (WMO-No. 485) [W. SVS-LRF Zhang, S., M. J. Harrison, A. Rosati, and A. A. Wittenberg, 2007a: System design and (ed.)]. World Meteorological Organization, Geneva, Switzerland. evaluation of coupled ensemble data assimilation for global oceanic climate WMO, 2010: Scientific Assessment of Ozone Depletion: 2010. Global Ozone Research studies. Mon. Weather Rev., 135, 3541 3564. and Monitoring Project-Report No. 52. 516. World Meteorological Organization, Zhang, X., et al., 2007b: Detection of human influence on twentieth-century Geneva, Switzerland. precipitation trends. Nature, 448, 461 465. Wu, B., and T. J. Zhou, 2012: Prediction of decadal variability of sea surface Zhang, X. D., 2010: Sensitivity of arctic summer sea ice coverage to global warming temperature by a coupled global climate model FGOALS_gl developed in LASG/ forcing: Towards reducing uncertainty in arctic climate change projections. Tellus IAP. Chin. Sci. Bull., 57, 2453 2459. A, 62, 220 227. Wu, P. L., R. Wood, J. Ridley, and J. Lowe, 2010: Temporary acceleration of the Zhang, Y., J. Wallace, and D. Battisti, 1997: ENSO-like interdecadal variability: 1900 hydrological cycle in response to a CO2 rampdown. Geophys. Res. Lett., 37, 93. J. Clim., 1004 1020. L12705. Zhang, Y., X. Y. Wen, and C. J. Jang, 2010b: Simulating chemistry-aerosol-cloud- Wu, S., L. J. Mickley, J. O. Kaplan, and D. J. Jacob, 2012: Impacts of changes in land radiation-climate feedbacks over the continental US using the online-coupled use and land cover on atmospheric chemistry and air quality over the 21st Weather Research Forecasting Model with chemistry (WRF/Chem). Atmos. century. Atmos. Chem. Phys., 12, 1597 1609. Environ., 44, 3568 3582. Wu, S., L. J. Mickley, D. J. Jacob, D. Rind, and D. G. Streets, 2008: Effects of 2000 2050 Zhang, Y., X.-M. Hu, L. R. Leung, and W. I. Gustafson, Jr., 2008: Impacts of regional changes in climate and emissions on global tropospheric ozone and the policy- climate change on biogenic emissions and air quality. J. Geophys. Res., 113, relevant background surface ozone in the United States. J. Geophys. Res., 113, D18310. D18312. Zhu, Y. L., H. J. Wang, W. Zhou, and J. H. Ma, 2011: Recent changes in the summer Wu, S., L. J. Mickley, D. J. Jacob, J. A. Logan, R. M. Yantosca, and D. Rind, 2007: Why precipitation pattern in East China and the background circulation. Clim. Dyn., are there large differences between models in global budgets of tropospheric 36, 1463 1473. ozone? J. Geophys. Res., 112, D05302. Xie, S., C. Deser, G. Vecchi, J. Ma, H. Teng, and A. Wittenberg, 2010: Global warming pattern formation: Sea surface temperature and rainfall. J. Clim., doi: 10.1175/2009JCLI3329.1, 966 986. Xin, X. G., T. W. Wu, and J. Zhang, 2013: Introduction of CMIP5 experiments carried out with the Climate System Models of Beijing Climate Center. Adv. Clim. Change Res, 4, 41 49. Xoplaki, E., P. Maheras, and J. Luterbacher, 2001: Variability of climate in Meridional Balkans during the periods 1675 1715 and 1780 1830 and its impact on human life. Clim. Change, 48, 581 615. Xu, Y., C.H. Xu, X.J. Gao, and Y. Luo, 2009: Projected changes in temperature and precipitation extremes over the Yangtze River Basin of China in the 21st century. Quat. Int., 208, 44-52. Yang, X., et al., 2013: A predictable AMO-like pattern in GFDL s fully-coupled ensemble initialization and decadal forecasting system. J. Clim., 26(2), 650-661. 11 Yeager, S., A. Karspeck, G. Danabasoglu, J. Tribbia, and H. Teng, 2012: A decadal prediction case study: Late 20th century North Atlantic ocean heat content. J. Clim., 25, 5173 5189. Yin, J. J., M. E. Schlesinger, and R. J. Stouffer, 2009: Model projections of rapid sea- level rise on the northeast coast of the United States. Nature Geosci., 2, 262 266. Yin, J. J., S. M. Griffies, and R. J. Stouffer, 2010: Spatial variability of sea level rise in twenty-first century projections. J. Clim., 23, 4585 4607. Yip, S., C. A. T. Ferro, D. B. Stephenson, and E. Hawkins, 2011: A simple, coherent framework for partitioning uncertainty in climate predictions. J. Clim., 24, 4634 4643. Yokohata, T., J. D. Annan, M. Collins, C. S. Jackson, M. Tobis, M. J. Webb, and J. C. Hargreaves, 2012: Reliability of multi-model and structurally different single- model ensembles. Clim. Dyn., 39, 599 616. Young, P. J., et al., 2013: Pre-industrial to end 21st century projections of tropospheric ozone from the Atmospheric Chemistry and Climate Model Intercomparison Project (ACCMIP). Atmos. Chem. Phys., 13, 2063 2090. Yue, X., H. J. Wang, H. Liao, and K. Fan, 2010: Simulation of dust aerosol radiative feedback using the GMOD: 2. Dust-climate interactions. J. Geophys. Res. Atmos., 115, D04201, doi: 10.1029/2009JD012063. Yue, X., H. Liao, H. Wang, S. Li, and J. Tang, 2011: Role of sea surface temperature responses in simulation of the climatic effect of mineral dust aerosol,. Atmos. Chem. Phys., 11, 6049-6069, doi: 10.5194/acp-11-6049-2011. Zanna, L., 2012: Forecast skill and predictability of observed Atlantic sea surface temperatures. J. Clim., 25, 5047 5056. Zeng, G., J. A. Pyle, and P. J. Young, 2008: Impact of climate change on tropospheric ozone and its global budgets. Atmos. Chem. Phys., 8, 369 387. Zeng, G., O. Morgenstern, P. Braesicke, and J. A. Pyle, 2010: Impact of stratospheric ozone recovery on tropospheric ozone and its budget. Geophys. Res. Lett., 37, L09805. Zhang, R., and T. L. Delworth, 2006: Impact of Atlantic multidecadal oscillations on India/Sahel rainfall and Atlantic hurricanes. Geophys. Res. Lett., 33, L17712. 1028 Long-term Climate Change: Projections, Commitments 12 and Irreversibility Coordinating Lead Authors: Matthew Collins (UK), Reto Knutti (Switzerland) Lead Authors: Julie Arblaster (Australia), Jean-Louis Dufresne (France), Thierry Fichefet (Belgium), Pierre Friedlingstein (UK/Belgium), Xuejie Gao (China), William J. Gutowski Jr. (USA), Tim Johns (UK), Gerhard Krinner (France/Germany), Mxolisi Shongwe (South Africa), Claudia Tebaldi (USA), Andrew J. Weaver (Canada), Michael Wehner (USA) Contributing Authors: Myles R. Allen (UK), Tim Andrews (UK), Urs Beyerle (Switzerland), Cecilia M. Bitz (USA), Sandrine Bony (France), Ben B.B. Booth (UK), Harold E. Brooks (USA), Victor Brovkin (Germany), Oliver Browne (UK), Claire Brutel-Vuilmet (France), Mark Cane (USA), Robin Chadwick (UK), Ed Cook (USA), Kerry H. Cook (USA), Michael Eby (Canada), John Fasullo (USA), Erich M. Fischer (Switzerland), Chris E. Forest (USA), Piers Forster (UK), Peter Good (UK), Hugues Goosse (Belgium), Jonathan M. Gregory (UK), Gabriele C. Hegerl (UK/Germany), Paul J. Hezel (Belgium/ USA), Kevin I. Hodges (UK), Marika M. Holland (USA), Markus Huber (Switzerland), Philippe Huybrechts (Belgium), Manoj Joshi (UK), Viatcheslav Kharin (Canada), Yochanan Kushnir (USA), David M. Lawrence (USA), Robert W. Lee (UK), Spencer Liddicoat (UK), Christopher Lucas (Australia), Wolfgang Lucht (Germany), Jochem Marotzke (Germany), François Massonnet (Belgium), H. Damon Matthews (Canada), Malte Meinshausen (Germany), Colin Morice (UK), Alexander Otto (UK/Germany), Christina M. Patricola (USA), Gwenaëlle Philippon- Berthier (France), Prabhat (USA), Stefan Rahmstorf (Germany), William J. Riley (USA), Joeri Rogelj (Switzerland/Belgium), Oleg Saenko (Canada), Richard Seager (USA), Jan Sedláèek (Switzerland), Len C. Shaffrey (UK), Drew Shindell (USA), Jana Sillmann (Canada), Andrew Slater (USA/Australia), Bjorn Stevens (Germany/USA), Peter A. Stott (UK), Robert Webb (USA), Giuseppe Zappa (UK/Italy), Kirsten Zickfeld (Canada/Germany) Review Editors: Sylvie Joussaume (France), Abdalah Mokssit (Morocco), Karl Taylor (USA), Simon Tett (UK) This chapter should be cited as: Collins, M., R. Knutti, J. Arblaster, J.-L. Dufresne, T. Fichefet, P. Friedlingstein, X. Gao, W.J. Gutowski, T. Johns, G. Krinner, M. Shongwe, C. Tebaldi, A.J. Weaver and M. Wehner, 2013: Long-term Climate Change: Projections, Com- mitments and Irreversibility. In: Climate Change 2013: The Physical Science Basis. Contribution of Working Group I to the Fifth Assessment Report of the Intergovernmental Panel on Climate Change [Stocker, T.F., D. Qin, G.-K. Plattner, M. Tignor, S.K. Allen, J. Boschung, A. Nauels, Y. Xia, V. Bex and P.M. Midgley (eds.)]. Cambridge University Press, Cambridge, United Kingdom and New York, NY, USA. 1029 Table of Contents Executive Summary.................................................................... 1031 12.5 Climate Change Beyond 2100, Commitment, Stabilization and Irreversibility................................. 1102 12.1 Introduction..................................................................... 1034 12.5.1 Representative Concentration Pathway Extensions................................................................ 1102 12.2 Climate Model Ensembles and Sources of 12.5.2 Climate Change Commitment.................................. 1102 Uncertainty from Emissions to Projections............ 1035 12.5.3 Forcing and Response, Time Scales of Feedbacks..... 1105 12.2.1 The Coupled Model Intercomparison Project Phase 5 and Other Tools........................................... 1035 12.5.4 Climate Stabilization and Long-term Climate Targets........................................................ 1107 12.2.2 General Concepts: Sources of Uncertainties............. 1035 Box 12.2: Equilibrium Climate Sensitivity and 12.2.3 From Ensembles to Uncertainty Quantification........ 1040 Transient Climate Response.................................................... 1110 Box 12.1: Methods to Quantify Model 12.5.5 Potentially Abrupt or Irreversible Changes............... 1114 Agreement in Maps.................................................................. 1041 12.2.4 Joint Projections of Multiple Variables..................... 1044 References ................................................................................ 1120 12.3 Projected Changes in Forcing Agents, Including Frequently Asked Questions Emissions and Concentrations................................... 1044 FAQ 12.1 Why Are So Many Models and Scenarios Used 12.3.1 Description of Scenarios........................................... 1045 to Project Climate Change?................................. 1036 12.3.2 Implementation of Forcings in Coupled Model FAQ 12.2 How Will the Earth s Water Cycle Change?........ 1084 Intercomparison Project Phase 5 Experiments........ 1047 FAQ 12.3 What Would Happen to Future Climate if We 12.3.3 Synthesis of Projected Global Mean Radiative Stopped Emissions Today?................................... 1106 Forcing for the 21st Century..................................... 1052 12.4 Projected Climate Change over the 21st Century.................................................................... 1054 12.4.1 Time-Evolving Global Quantities.............................. 1054 12.4.2 Pattern Scaling......................................................... 1058 12.4.3 Changes in Temperature and Energy Budget............ 1062 12.4.4 Changes in Atmospheric Circulation........................ 1071 12.4.5 Changes in the Water Cycle..................................... 1074 12.4.6 Changes in Cryosphere............................................ 1087 12.4.7 Changes in the Ocean.............................................. 1093 12 12.4.8 Changes Associated with Carbon Cycle Feedbacks and Vegetation Cover............................. 1096 12.4.9 Consistency and Main Differences Between Coupled Model Intercomparison Project Phase 3/Coupled Model Intercomparison Project Phase 5 and Special Report on Emission Scenarios/Representative Concentration Pathways ......................................... 1099 1030 Long-term Climate Change: Projections, Commitments and Irreversibility Chapter 12 Executive Summary Projections of Temperature Change This chapter assesses long-term projections of climate change for the Global mean temperatures will continue to rise over the 21st end of the 21st century and beyond, where the forced signal depends century if greenhouse gas (GHG) emissions continue unabat- on the scenario and is typically larger than the internal variability of ed. Under the assumptions of the concentration-driven RCPs, global the climate system. Changes are expressed with respect to a baseline mean surface temperatures for 2081 2100, relative to 1986 2005 will period of 1986 2005, unless otherwise stated. likely1 be in the 5 to 95% range of the CMIP5 models; 0.3°C to 1.7°C (RCP2.6), 1.1°C to 2.6°C (RCP4.5), 1.4°C to 3.1°C (RCP6.0), 2.6°C to Scenarios, Ensembles and Uncertainties 4.8°C (RCP8.5). Global temperatures averaged over the period 2081 2100 are projected to likely exceed 1.5°C above 1850-1900 for RCP4.5, The Coupled Model Intercomparison Project Phase 5 (CMIP5) RCP6.0 and RCP8.5 (high confidence), are likely to exceed 2°C above presents an unprecedented level of information on which to 1850-1900 for RCP6.0 and RCP8.5 (high confidence) and are more base projections including new Earth System Models with a likely than not to exceed 2°C for RCP4.5 (medium confidence). Temper- more complete representation of forcings, new Representative ature change above 2°C under RCP2.6 is unlikely (medium confidence). Concentration Pathways (RCP) scenarios and more output avail- Warming above 4°C by 2081 2100 is unlikely in all RCPs (high confi- able for analysis. The four RCP scenarios used in CMIP5 lead to a dence) except for RCP8.5, where it is about as likely as not (medium total radiative forcing (RF) at 2100 that spans a wider range than that confidence). {12.4.1, Tables 12.2, 12.3, Figures 12.5, 12.8} estimated for the three Special Report on Emission Scenarios (SRES) scenarios (B1, A1B, A2) used in the Fourth Assessment Report (AR4), Temperature change will not be regionally uniform. There is very RCP2.6 being almost 2 W m 2 lower than SRES B1 by 2100. The mag- high confidence2 that globally averaged changes over land will exceed nitude of future aerosol forcing decreases more rapidly in RCP sce- changes over the ocean at the end of the 21st century by a factor that narios, reaching lower values than in SRES scenarios through the 21st is likely in the range 1.4 to 1.7. In the absence of a strong reduction century. Carbon dioxide (CO2) represents about 80 to 90% of the total in the Atlantic Meridional Overturning, the Arctic region is project- anthropogenic forcing in all RCP scenarios through the 21st century. ed to warm most (very high confidence). This polar amplification is The ensemble mean total effective RFs at 2100 for CMIP5 concen- not found in Antarctic regions due to deep ocean mixing, ocean heat tration-driven projections are 2.2, 3.8, 4.8 and 7.6 W m 2 for RCP2.6, uptake and the persistence of the Antarctic ice sheet. Projected region- RCP4.5, RCP6.0 and RCP8.5 respectively, relative to about 1850, and al surface air temperature increase has minima in the North Atlantic are close to corresponding Integrated Assessment Model (IAM)-based and Southern Oceans in all scenarios. One model exhibits marked cool- estimates (2.4, 4.0, 5.2 and 8.0 W m 2). {12.2.1, 12.3, Table 12.1, Fig- ing in 2081 2100 over large parts of the Northern Hemisphere (NH), ures 12.1, 12.2, 12.3, 12.4} and a few models indicate slight cooling locally in the North Atlantic. Atmospheric zonal mean temperatures show warming throughout the New experiments and studies have continued to work towards troposphere, especially in the upper troposphere and northern high a more complete and rigorous characterization of the uncertain- latitudes, and cooling in the stratosphere. {12.4.2, 12.4.3, Table 12.2, ties in long-term projections, but the magnitude of the uncer- Figures 12.9, 12.10, 12.11, 12.12} tainties has not changed significantly since AR4. There is overall consistency between the projections based on CMIP3 and CMIP5, for It is virtually certain that, in most places, there will be more hot both large-scale patterns and magnitudes of change. Differences in and fewer cold temperature extremes as global mean temper- global temperature projections are largely attributable to a change in atures increase. These changes are expected for events defined as scenarios. Model agreement and confidence in projections depend on extremes on both daily and seasonal time scales. Increases in the fre- the variable and spatial and temporal averaging. The well-established quency, duration and magnitude of hot extremes along with heat stress 12 stability of large-scale geographical patterns of change during a tran- are expected; however, occasional cold winter extremes will continue to sient experiment remains valid in the CMIP5 models, thus justifying occur. Twenty-year return values of low temperature events are project- pattern scaling to approximate changes across time and scenarios ed to increase at a rate greater than winter mean temperatures in most under such experiments. Limitations remain when pattern scaling is regions, with the largest changes in the return values of low tempera- applied to strong mitigation scenarios, to scenarios where localized tures at high latitudes. Twenty-year return values for high temperature forcing (e.g., aerosols) are significant and vary in time and for varia- events are projected to increase at a rate similar to or greater than the bles other than average temperature and precipitation. {12.2.2, 12.2.3, rate of increase of summer mean temperatures in most regions. Under 12.4.2, 12.4.9, Figures 12.10, 12.39, 12.40, 12.41} RCP8.5 it is likely that, in most land regions, a current 20-year high temperature event will occur more frequently by the end of the 21st In this Report, the following terms have been used to indicate the assessed likelihood of an outcome or a result: Virtually certain 99 100% probability, Very likely 90 100%, 1 Likely 66 100%, About as likely as not 33 66%, Unlikely 0 33%, Very unlikely 0 10%, Exceptionally unlikely 0 1%. Additional terms (Extremely likely: 95 100%, More likely than not >50 100%, and Extremely unlikely 0 5%) may also be used when appropriate. Assessed likelihood is typeset in italics, e.g., very likely (see Section 1.4 and Box TS.1 for more details). In this Report, the following summary terms are used to describe the available evidence: limited, medium, or robust; and for the degree of agreement: low, medium, or high. 2 A level of confidence is expressed using five qualifiers: very low, low, medium, high, and very high, and typeset in italics, e.g., medium confidence. For a given evidence and agreement statement, different confidence levels can be assigned, but increasing levels of evidence and degrees of agreement are correlated with increasing confidence (see Section 1.4 and Box TS.1 for more details). 1031 Chapter 12 Long-term Climate Change: Projections, Commitments and Irreversibility century (at least doubling its frequency, but in many regions becoming Annual surface evaporation is projected to increase as global an annual or 2-year event) and a current 20-year low temperature event temperatures rise over most of the ocean and is projected to will become exceedingly rare. {12.4.3, Figures 12.13, 12.14} change over land following a similar pattern as precipitation. Decreases in annual runoff are likely in parts of southern Europe, the Changes in Atmospheric Circulation Middle East, and southern Africa by the end of the 21st century under the RCP8.5 scenario. Increases in annual runoff are likely in the high Mean sea level pressure is projected to decrease in high lati- northern latitudes corresponding to large increases in winter and tudes and increase in the mid-latitudes as global temperatures spring precipitation by the end of the 21st century under the RCP8.5 rise. In the tropics, the Hadley and Walker Circulations are likely scenario. Regional to global-scale projected decreases in soil moisture to slow down. Poleward shifts in the mid-latitude jets of about 1 and increased risk of agricultural drought are likely in presently dry to 2 degrees latitude are likely at the end of the 21st century under regions and are projected with medium confidence by the end of the RCP8.5 in both hemispheres (medium confidence), with weaker shifts 21st century under the RCP8.5 scenario. Prominent areas of projected in the NH. In austral summer, the additional influence of stratospheric decreases in evaporation include southern Africa and north western ozone recovery in the Southern Hemisphere opposes changes due to Africa along the Mediterranean. Soil moisture drying in the Mediterra- GHGs there, though the net response varies strongly across models and nean, southwest USA and southern African regions is consistent with scenarios. Substantial uncertainty and thus low confidence remains in projected changes in Hadley Circulation and increased surface tem- projecting changes in NH storm tracks, especially for the North Atlantic peratures, so surface drying in these regions as global temperatures basin. The Hadley Cell is likely to widen, which translates to broad- increase is likely with high confidence by the end of this century under er tropical regions and a poleward encroachment of subtropical dry the RCP8.5 scenario. In regions where surface moistening is projected, zones. In the stratosphere, the Brewer Dobson circulation is likely to changes are generally smaller than natural variability on the 20-year strengthen. {12.4.4, Figures 12.18, 12.19, 12.20} time scale. {12.4.5, Figures 12.23, 12.24, 12.25} Changes in the Water Cycle Changes in Cryosphere It is virtually certain that, in the long term, global precipitation It is very likely that the Arctic sea ice cover will continue shrink- will increase with increased global mean surface temperature. ing and thinning year-round in the course of the 21st century as Global mean precipitation will increase at a rate per degree Celsius global mean surface temperature rises. At the same time, in the smaller than that of atmospheric water vapour. It will likely increase by Antarctic, a decrease in sea ice extent and volume is expected, 1 to 3% °C 1 for scenarios other than RCP2.6. For RCP2.6 the range of but with low confidence. Based on the CMIP5 multi-model ensem- sensitivities in the CMIP5 models is 0.5 to 4% °C 1 at the end of the ble, projections of average reductions in Arctic sea ice extent for 2081 21st century. {12.4.1, Figures 12.6, 12.7} 2100 compared to 1986 2005 range from 8% for RCP2.6 to 34% for RCP8.5 in February and from 43% for RCP2.6 to 94% for RCP8.5 in Changes in average precipitation in a warmer world will exhibit September (medium confidence). A nearly ice-free Arctic Ocean (sea ice substantial spatial variation. Some regions will experience extent less than 1 × 106 km2for at least 5 consecutive years) in Septem- increases, other regions will experience decreases and yet ber before mid-century is likely under RCP8.5 (medium confidence), others will not experience significant changes at all. There is based on an assessment of a subset of models that most closely repro- high confidence that the contrast of annual mean precipitation duce the climatological mean state and 1979 2012 trend of the Arctic between dry and wet regions and that the contrast between sea ice cover. Some climate projections exhibit 5- to 10-year periods of wet and dry seasons will increase over most of the globe as sharp summer Arctic sea ice decline even steeper than observed over 12 temperatures increase. The general pattern of change indicates that the last decade and it is likely that such instances of rapid ice loss high latitude land masses are likely to experience greater amounts will occur in the future. There is little evidence in global climate models of precipitation due to the increased specific humidity of the warmer of a tipping point (or critical threshold) in the transition from a peren- troposphere as well as increased transport of water vapour from the nially ice-covered to a seasonally ice-free Arctic Ocean beyond which tropics by the end of this century under the RCP8.5 scenario. Many further sea ice loss is unstoppable and irreversible. In the Antarctic, the mid-latitude and subtropical arid and semi-arid regions will likely CMIP5 multi-model mean projects a decrease in sea ice extent that experience less precipitation and many moist mid-latitude regions will ranges from 16% for RCP2.6 to 67% for RCP8.5 in February and from likely experience more precipitation by the end of this century under 8% for RCP2.6 to 30% for RCP8.5 in September for 2081 2100 com- the RCP8.5 scenario. Globally, for short-duration precipitation events, a pared to 1986 2005. There is, however, low confidence in those values shift to more intense individual storms and fewer weak storms is likely as projections because of the wide inter-model spread and the inability as temperatures increase. Over most of the mid-latitude land-masses of almost all of the available models to reproduce the mean annual and over wet tropical regions, extreme precipitation events will very cycle, interannual variability and overall increase of the Antarctic sea likely be more intense and more frequent in a warmer world. The global ice areal coverage observed during the satellite era. {12.4.6, 12.5.5, average sensitivity of the 20-year return value of the annual maximum Figures 12.28, 12.29, 12.30, 12.31} daily precipitation increases ranges from 4% °C 1 of local temperature increase (average of CMIP3 models) to 5.3% oC 1  of local tempera- It is very likely that NH snow cover will reduce as global tem- ture increase (average of CMIP5 models) but regionally there are wide peratures rise over the coming century. A retreat of permafrost variations. {12.4.5, Figures 12.10, 12.22, 12.26, 12.27} extent with rising global temperatures is virtually certain. Snow 1032 Long-term Climate Change: Projections, Commitments and Irreversibility Chapter 12 cover changes result from precipitation and ablation changes, which driven by CO2 alone, global average temperature is projected to are sometimes opposite. Projections of the NH spring snow covered remain approximately constant for many centuries following a com- area by the end of the 21st century vary between a decrease of 7% plete cessation of emissions. The positive commitment from CO2 may (RCP2.6) and a decrease of 25% (RCP8.5), with a pattern that is fairly be enhanced by the effect of an abrupt cessation of aerosol emissions, consistent between models. The projected changes in permafrost are a which will cause warming. By contrast, cessation of emission of short- response not only to warming but also to changes in snow cover, which lived GHGs will contribute a cooling. {12.5.3, 12.5.4, Figures 12.44, exerts a control on the underlying soil. By the end of the 21st cen- 12.45, 12.46, FAQ 12.3} tury, diagnosed near-surface permafrost area is projected to decrease by between 37% (RCP2.6) and 81% (RCP8.5) (medium confidence). Equilibrium Climate Sensitivity and Transient Climate {12.4.6, Figures 12.32, 12.33} Response Changes in the Ocean Estimates of the equilibrium climate sensitivity (ECS) based on observed climate change, climate models and feedback analy- The global ocean will warm in all RCP scenarios. The strongest sis, as well as paleoclimate evidence indicate that ECS is likely ocean warming is projected for the surface in subtropical and tropi- in the range 1.5°C to 4.5°C with high confidence, extreme- cal regions. At greater  depth the warming is projected to  be most ly unlikely less than 1°C (high confidence) and very unlikely pronounced in the Southern Ocean. Best estimates of  ocean warm- greater than 6°C (medium confidence). The transient climate ing in the top one hundred meters are about 0.6°C (RCP2.6) to 2.0°C response (TCR) is likely in the range 1°C to 2.5C and extremely (RCP8.5), and about 0.3°C (RCP2.6) to 0.6°C (RCP8.5) at a depth of unlikely greater than 3°C, based on observed climate change about 1 km by the end of the 21st century. For RCP4.5 by the end of the and climate models. {Box 12.2, Figures 1, 2} 21st century, half of the energy taken up by the ocean is in the upper- most 700 m and 85% is in the uppermost 2000 m. Due to the long time Climate Stabilization scales of this heat transfer from the surface to depth, ocean warming will continue for centuries, even if GHG emissions are decreased or The principal driver of long-term warming is total emissions concentrations kept constant. {12.4.7, 12.5.2 12.5.4, Figure 12.12} of CO2 and the two quantities are approximately linearly related. The global mean warming per 1000 PgC (transient cli- It is very likely that the Atlantic Meridional Overturning Circu- mate response to cumulative carbon emissions (TCRE)) is likely lation (AMOC) will weaken over the 21st century but it is very between 0.8°C to 2.5°C per 1000 PgC, for cumulative emissions unlikely that the AMOC will undergo an abrupt transition or col- less than about 2000 PgC until the time at which temperatures lapse in the 21st century. Best estimates and ranges for the reduc- peak. To limit the warming caused by anthropogenic CO2 emissions tion from CMIP5 are 11% (1 to 24%) in RCP2.6 and 34% (12 to 54%) alone to be likely less than 2°C relative to the period 1861-1880, total in RCP8.5. There is low confidence in assessing the evolution of the CO2 emissions from all anthropogenic sources would need to be limit- AMOC beyond the 21st century. {12.4.7, Figure 12.35} ed to a cumulative budget of about 1000 PgC since that period. About half [445 to 585 PgC] of this budget was already emitted by 2011. Carbon Cycle Accounting for projected warming effect of non-CO2 forcing, a possible release of GHGs from permafrost or methane hydrates, or requiring When forced with RCP8.5 CO2 emissions, as opposed to the a higher likelihood of temperatures remaining below 2°C, all imply a RCP8.5 CO2 concentrations, 11 CMIP5 Earth System Models with lower budget. {12.5.4, Figures 12.45, 12.46, Box 12.2} interactive carbon cycle simulate, on average, a 50 ppm (min to max range 140 to +210 ppm) larger atmospheric CO2 concen- Some aspects of climate will continue to change even if temper- 12 tration and 0.2°C (min to max range 0.4 to +0.9°C) larger global atures are stabilized. Processes related to vegetation change, chang- surface temperature increase by 2100. {12.4.8, Figures 12.36, 12.37} es in the ice sheets, deep ocean warming and associated sea level rise and potential feedbacks linking for example ocean and the ice sheets Long-term Climate Change, Commitment and Irreversibility have their own intrinsic long time scales and may result in significant changes hundreds to thousands of years after global temperature is Global temperature equilibrium would be reached only after stabilized. {12.5.2 to 12.5.4} centuries to millennia if RF were stabilized. Continuing GHG emis- sions beyond 2100, as in the RCP8.5 extension, induces a total RF above Abrupt Change 12 W m 2 by 2300. Sustained negative emissions beyond 2100, as in RCP2.6, induce a total RF below 2 W m 2 by 2300. The projected warm- Several components or phenomena in the climate system could ing for 2281 2300, relative to 1986 2005, is 0.0°C to 1.2°C for RCP2.6 potentially exhibit abrupt or nonlinear changes, and some are and 3.0°C to 12.6°C for RCP8.5 (medium confidence). In much the same known to have done so in the past. Examples include the AMOC, way as the warming to a rapid increase of forcing is delayed, the cooling Arctic sea ice, the Greenland ice sheet, the Amazon forest and mon- after a decrease of RF is also delayed. {12.5.1, Figures 12.43, 12.44} soonal circulations. For some events, there is information on potential consequences, but in general there is low confidence and little con- A large fraction of climate change is largely irreversible on sensus on the likelihood of such events over the 21st century. {12.5.5, human time scales, unless net anthropogenic CO2 emissions Table 12.4} were strongly negative over a sustained period. For ­scenarios 1033 Chapter 12 Long-term Climate Change: Projections, Commitments and Irreversibility 12.1 Introduction GHGs may be specified and these gases may be chemically active in the atmosphere or be exchanged with pools in terrestrial and Projections of future climate change are not like weather forecasts. oceanic systems before ending up as an airborne concentration It is not possible to make deterministic, definitive predictions of how (see Figure 10.1 of AR4). climate will evolve over the next century and beyond as it is with short- term weather forecasts. It is not even possible to make projections of New types of model experiments have been performed, many the frequency of occurrence of all possible outcomes in the way that it coordinated by the Coupled Model Intercomparison Project Phase might be possible with a calibrated probabilistic medium-range weath- 5 (CMIP5) (Taylor et al., 2012), which exploit the addition of these er forecast. Projections of climate change are uncertain, first because new processes. Models may be driven by emissions of GHGs, or by they are dependent primarily on scenarios of future anthropogenic their concentrations with different Earth System feedback loops and natural forcings that are uncertain, second because of incomplete cut. This allows the separate assessment of different feedbacks in understanding and imprecise models of the climate system and finally the system and of projections of physical climate variables and because of the existence of internal climate variability. The term cli- future emissions. mate projection tacitly implies these uncertainties and dependencies. Nevertheless, as greenhouse gas (GHG) concentrations continue to Techniques to assess and quantify uncertainties in projections rise, we expect to see future changes to the climate system that are have been further developed but a full probabilistic quantifica- greater than those already observed and attributed to human activi- tion remains difficult to propose for most quantities, the exception ties. It is possible to understand future climate change using models being global, temperature-related measures of the system sensitiv- and to use models to characterize outcomes and uncertainties under ity to forcings, such as ECS and TCR. In those few cases, projections specific assumptions about future forcing scenarios. are presented in the form of probability density functions (PDFs). We make the distinction between the spread of a multi-model This chapter assesses climate projections on time scales beyond those ensemble, an ad hoc measure of the possible range of projections covered in Chapter 11, that is, beyond the mid-21st century. Informa- and the quantification of uncertainty that combines information tion from a range of different modelling tools is used here; from simple from models and observations using statistical algorithms. Just like energy balance models, through Earth System Models of Intermediate climate models, different techniques for quantifying uncertainty Complexity (EMICs) to complex dynamical climate and Earth System exist and produce different outcomes. Where possible, different Models (ESMs). These tools are evaluated in Chapter 9 and, where pos- estimates of uncertainty are compared. sible, the evaluation is used in assessing the validity of the projections. This chapter also summarizes some of the information on leading-order Although not an advance, as time has moved on, the baseline period measures of the sensitivity of the climate system from other chapters from which climate change is expressed has also moved on (a common and discusses the relevance of these measures for climate projections, baseline period of 1986 2005 is used throughout, consistent with commitments and irreversibility. the 2006 start-point for the RCP scenarios). Hence climate change is expressed as a change with respect to a recent period of history, rather Since the AR4 (Meehl et al., 2007b) there have been a number of than a time before significant anthropogenic influence. It should be advances: borne in mind that some anthropogenically forced climate change had already occurred by the 1986 2005 period (see Chapter 10). New scenarios of future forcings have been developed to replace the Special Report on Emissions Scenarios (SRES). The Represen- The focus of this chapter is on global and continental/ocean basin-scale tative Concentration Pathways (RCPs, see Section 12.3) (Moss et features of climate. For many aspects of future climate change, it is 12 al., 2010), have been designed to cover a wide range of possible possible to discuss generic features of projections and the processes magnitudes of climate change in models rather than being derived that underpin them for such large scales. Where interesting or unique sequentially from storylines of socioeconomic futures. The aim is changes have been investigated at smaller scales, and there is a level to provide a range of climate responses while individual socioeco- of agreement between different studies of those smaller-scale changes, nomic scenarios may be derived, scaled and interpolated (some these may also be assessed in this chapter, although where changes are including explicit climate policy). Nevertheless, many studies that linked to climate phenomena such as El Nino, readers are referred to have been performed since AR4 have used SRES and, where appro- Chapter 14. Projections of atmospheric composition, chemistry and air priate, these are assessed. Simplified scenarios of future change, quality for the 21st century are assessed in Chapter 11, except for CO2 developed strictly for understanding the response of the climate which is assessed in this chapter. An innovation for AR5 is Annex I: Atlas system rather than to represent realistic future outcomes, are also of Global and Regional Climate Projections, a collection of global and synthesized and the understanding of leading-order measures of regional maps of projected climate changes derived from model output. climate response such as the equilibrium climate sensitivity (ECS) A detailed commentary on each of the maps presented in Annex I is not and the transient climate response (TCR) are assessed. provided here, but some discussion of generic features is provided. New models have been developed with higher spatial resolution, Projections from regional models driven by boundary conditions from with better representation of processes and with the inclusion of global models are not extensively assessed but may be mentioned more processes, in particular processes that are important in simu- in this chapter. More detailed regional information may be found in lating the carbon cycle of the Earth. In these models, emissions of Chapter 14 and is also now assessed in the Working Group II report, where it can more easily be linked to impacts. 1034 Long-term Climate Change: Projections, Commitments and Irreversibility Chapter 12 12.2 Climate Model Ensembles and Sources of are performed with exactly the same model but with different initial Uncertainty from Emissions to Projections conditions, we choose only one ensemble member (usually the first but in cases where that was not available, the first available member is 12.2.1 The Coupled Model Intercomparison Project chosen) in order not to weight models with more ensemble members Phase 5 and Other Tools than others unduly in the multi-model synthesis. Rather than give an ­ exhaustive account of which models were used to make which figures, Many of the figures presented in this chapter and in others draw this summary information is presented as a guide to readers. on data collected as part of CMIP5 (Taylor et al., 2012). The project involves the worldwide coordination of ESM experiments including the In addition to output from CMIP5, information from a coordinated coordination of input forcing fields, diagnostic output and the host- set of simulations with EMICs is also used (Zickfeld et al., 2013) to ing of data in a distributed archive. CMIP5 has been unprecedented investigate long-term climate change beyond 2100. Even more sim- in terms of the number of modelling groups and models participating, plified energy balance models or emulation techniques are also used, the number of experiments performed and the number of diagnostics mostly to estimate responses where ESM experiments are not availa- collected. The archive of model simulations began being populated by ble (Meinshausen et al., 2011a; Good et al., 2013). An evaluation of mid-2011 and continued to grow during the writing of AR5. The pro- the models used for projections is provided in Chapter 9 of this Report. duction of figures for this chapter draws on a fixed database of simu- lations and variables that was available on 15 March 2013 (the same 12.2.2 General Concepts: Sources of Uncertainties as the cut-off date for the acceptance of the publication of papers). Different figures may use different subsets of models and there are The understanding of the sources of uncertainty affecting future cli- unequal numbers of models that have produced output for the differ- mate change projections has not substantially changed since AR4, but ent RCP scenarios. Figure 12.1 gives a summary of which output was many experiments and studies since then have proceeded to explore available from which model for which scenario. Where multiple runs and characterize those uncertainties further. A full characterization, Model/Variable tas psl pr clt hurs huss evspsbl rsut rlut rtmt rsdt mrro mrso tsl ta ua msft.yz sos sic snc tas_day pr_day ACCESS1-0 ACCESS1-3 bcc-csm1-1 bcc-csm1-1-m BNU-ESM CanESM2 CCSM4 CESM1-BGC CESM1-CAM5 CESM1-WACCM CMCC-CESM CMCC-CM CMCC-CMS CNRM-CM5 CSIRO-Mk3-6-0 EC-EARTH FGOALS-g2 FIO-ESM GFDL-CM3 GFDL-ESM2G GFDL-ESM2M GISS-E2-H-CC GISS-E2-H-P1 GISS-E2-H-P2 GISS-E2-H-P3 GISS-E2-R-CC GISS-E2-R-P1 GISS-E2-R-P2 GISS-E2-R-P3 12 HadGEM2-AO HadGEM2-CC HadGEM2-ES inmcm4 IPSL-CM5A-LR IPSL-CM5A-MR IPSL-CM5B-LR MIROC5 MIROC-ESM MIROC-ESM-CHEM MPI-ESM-LR MPI-ESM-MR MPI-ESM-P MRI-CGCM3 NorESM1-M NorESM1-ME 0 ensemble 1 ensemble 2 ensembles 3 ensembles 4 ensembles 5 or more ensembles Figure 12.1 | A summary of the output used to make the CMIP5 figures in this chapter (and some figures in Chapter 11). The climate variable names run along the horizontal axis and use the standard abbreviations in the CMIP5 protocol (Taylor et al., 2012, and online references therein). The climate model names run along the vertical axis. In each box the shading indicates the number of ensemble members available for historical, RCP2.6, RCP4.5, RCP6.0, RCP8.5 and pre-industrial control experiments, although only one ensemble member per model is used in the relevant figures. 1035 Chapter 12 Long-term Climate Change: Projections, Commitments and Irreversibility Frequently Asked Questions FAQ 12.1 | Why Are So Many Models and Scenarios Used to Project Climate Change? Future climate is partly determined by the magnitude of future emissions of greenhouse gases, aerosols and other natural and man-made forcings. These forcings are external to the climate system, but modify how it behaves. Future climate is shaped by the Earth s response to those forcings, along with internal variability inherent in the climate system. A range of assumptions about the magnitude and pace of future emissions helps scientists develop different emission scenarios, upon which climate model projections are based. Different climate models, mean- while, provide alternative representations of the Earth s response to those forcings, and of natural climate variabil- ity. Together, ensembles of models, simulating the response to a range of different scenarios, map out a range of possible futures, and help us understand their uncertainties. Predicting socioeconomic development is arguably even more difficult than predicting the evolution of a physical system. It entails predicting human behaviour, policy choices, technological advances, international competition and cooperation. The common approach is to use scenarios of plausible future socioeconomic development, from which future emissions of greenhouse gases and other forcing agents are derived. It has not, in general, been pos- sible to assign likelihoods to individual forcing scenarios. Rather, a set of alternatives is used to span a range of possibilities. The outcomes from different forcing scenarios provide policymakers with alternatives and a range of possible futures to consider. Internal fluctuations in climate are spontaneously generated by interactions between components such as the atmosphere and the ocean. In the case of near-term climate change, they may eclipse the effect of external per- turbations, like greenhouse gas increases (see Chapter 11). Over the longer term, however, the effect of external forcings is expected to dominate instead. Climate model simulations project that, after a few decades, different scenarios of future anthropogenic greenhouse gases and other forcing agents and the climate system s response to them will differently affect the change in mean global temperature (FAQ 12.1, Figure 1, left panel). Therefore, evaluating the consequences of those various scenarios and responses is of paramount importance, especially when policy decisions are considered. Climate models are built on the basis of the physical principles governing our climate system, and empirical under- standing, and represent the complex, interacting processes needed to simulate climate and climate change, both past and future. Analogues from past observations, or extrapolations from recent trends, are inadequate strategies for producing projections, because the future will not necessarily be a simple continuation of what we have seen thus far. Although it is possible to write down the equations of fluid motion that determine the behaviour of the atmo- sphere and ocean, it is impossible to solve them without using numerical algorithms through computer model simulation, similarly to how aircraft engineering relies on numerical simulations of similar types of equations. Also, many small-scale physical, biological and chemical processes, such as cloud processes, cannot be described by those equations, either because we lack the computational ability to describe the system at a fine enough resolution 12 to directly simulate these processes or because we still have a partial scientific understanding of the mechanisms driving these processes. Those need instead to be approximated by so-called parameterizations within the climate models, through which a mathematical relation between directly simulated and approximated quantities is estab- lished, often on the basis of observed behaviour. There are various alternative and equally plausible numerical representations, solutions and approximations for modelling the climate system, given the limitations in computing and observations. This diversity is considered a healthy aspect of the climate modelling community, and results in a range of plausible climate change projections at global and regional scales. This range provides a basis for quantifying uncertainty in the projections, but because the number of models is relatively small, and the contribution of model output to public archives is voluntary, the sampling of possible futures is neither systematic nor comprehensive. Also, some inadequacies persist that are common to all models; different models have different strength and weaknesses; it is not yet clear which aspects of the quality of the simulations that can be evaluated through observations should guide our evaluation of future model simulations. (continued on next page) 1036 Long-term Climate Change: Projections, Commitments and Irreversibility Chapter 12 FAQ 12.1 (continued) Models of varying complexity are commonly used for different projection problems. A faster model with lower resolution, or a simplified description of some climate processes, may be used in cases where long multi-century simulations are required, or where multiple realizations are needed. Simplified models can adequately represent large-scale average quantities, like global average temperature, but finer details, like regional precipitation, can be simulated only by complex models. The coordination of model experiments and output by groups such as the Coupled Model Intercomparison Project (CMIP), the World Climate Research Program and its Working Group on Climate Models has seen the science com- munity step up efforts to evaluate the ability of models to simulate past and current climate and to compare future climate change projections. The multi-model approach is now a standard technique used by the climate science community to assess projections of a specific climate variable. FAQ 12.1, Figure 1, right panels, shows the temperature response by the end of the 21st century for two illustrative models and the highest and lowest RCP scenarios. Models agree on large-scale patterns of warming at the surface, for example, that the land is going to warm faster than ocean, and the Arctic will warm faster than the tropics. But they differ both in the magnitude of their global response for the same scenario, and in small scale, regional aspects of their response. The magnitude of Arctic amplification, for instance, varies among different models, and a subset of models show a weaker warming or slight cooling in the North Atlantic as a result of the reduction in deepwater formation and shifts in ocean currents. There are inevitable uncertainties in future external forcings, and the climate system s response to them, which are further complicated by internally generated variability. The use of multiple scenarios and models have become a standard choice in order to assess and characterize them, thus allowing us to describe a wide range of possible future evolutions of the Earth s climate. Possible temperature responses in 2081-2100 to high emission scenario RCP8.5 Global surface temperature change (°C) Model mean global mean temperature change for high emission scenario RCP8.5 Possible temperature responses in 2081-2100 to low emission scenario RCP2.6 Model mean global mean temperature 12 change for low emission scenario RCP2.6 (°C) -2 -1.5 -1-0.5 0 0.5 1 1.5 2 3 4 5 7 9 11 FAQ 12.1, Figure 1 | Global mean temperature change averaged across all Coupled Model Intercomparison Project Phase 5 (CMIP5) models (relative to 1986 2005) for the four Representative Concentration Pathway (RCP) scenarios: RCP2.6 (dark blue), RCP4.5 (light blue), RCP6.0 (orange) and RCP8.5 (red); 32, 42, 25 and 39 models were used respectively for these 4 scenarios. Likely ranges for global temperature change by the end of the 21st century are indicated by vertical bars. Note that these ranges apply to the difference between two 20-year means, 2081 2100 relative to 1986 2005, which accounts for the bars being centred at a smaller value than the end point of the annual trajectories. For the highest (RCP8.5) and lowest (RCP2.6) scenario, illustrative maps of surface temperature change at the end of the 21st century (2081 2100 relative to 1986 2005) are shown for two CMIP5 models. These models are chosen to show a rather broad range of response, but this particular set is not representative of any measure of model response uncertainty. 1037 Chapter 12 Long-term Climate Change: Projections, Commitments and Irreversibility qualitative and even more so quantitative, involves much more than a use of the SRES scenarios (IPCC, 2000) developed using a sequential measure of the range of model outcomes, because additional sources approach, that is, socioeconomic factors feed into emissions scenarios of information (e.g., observational constraints, model evaluation, expert which are then used either to directly force the climate models or to judgement) lead us to expect that the uncertainty around the future determine concentrations of GHGs and other agents required to drive climate state does not coincide straightforwardly with those ranges. these models. This report also assesses outcomes of simulations that In fact, in this chapter we highlight wherever relevant the distinction use the new RCP scenarios, developed using a parallel process (Moss between model uncertainty evaluation, which encompasses the under- et al., 2010) whereby different targets in terms of RF at 2100 were standing that models have intrinsic shortcoming in fully and accurately selected (2.6, 4.5, 6.0 and 8.5 W m 2) and GHG and aerosol emissions representing the real system, and cannot all be considered independent consistent with those targets, and their corresponding socioeconom- of one another (Knutti et al., 2013), and a simpler descriptive quantifi- ic drivers were developed simultaneously (see Section 12.3). Rather cation, based on the range of outcomes from the ensemble of models. than being identified with one socioeconomic storyline, RCP scenarios are consistent with many possible economic futures (in fact, different Uncertainty affecting mid- to long-term projections of climatic changes combinations of GHG and aerosol emissions can lead to the same stems from distinct but possibly interacting sources. Figure 12.2 shows RCP). Their development was driven by the need to produce scenari- a schematic of the chain from scenarios, through ESMs to projections. os that could be input to climate model simulations more expediently Uncertainties affecting near-term projections of which some aspect while corresponding socioeconomic scenarios would be developed in are also relevant for longer-term projections are discussed in Section p ­ arallel, and to produce a wide range of model responses that may be 11.3.1.1 and shown in Figure 11.8. scaled and interpolated to estimate the response under other scenari- os, involving different measures of adaptation and mitigation. Future anthropogenic emissions of GHGs, aerosol particles and other forcing agents such as land use change are dependent on socioec- In terms of the uncertainties related to the RCP emissions scenarios, onomic factors including global geopolitical agreements to control the following issues can be identified: those emissions. Systematic studies that attempt to quantify the likely ranges of anthropogenic emission have been undertaken (Sokolov et No probabilities or likelihoods have been attached to the alterna- al., 2009) but it is more common to use a scenario approach of dif- tive RCP scenarios (as was the case for SRES scenarios). Each of ferent but plausible in the sense of technically feasible pathways, them should be considered plausible, as no study has questioned leading to the concept of scenario uncertainty. AR4 made extensive their technical feasibility (see Chapter 1). Representative Concentration Pathway (RCP) Target Radiative Diagnosed Radiative Forcing Forcing Earth System 12 Concentrations Models Climate Projections Diagnosed Emissions Emissions Figure 12.2 | Links in the chain from scenarios, through models to climate projections. The Representative Concentration Pathways (RCPs) are designed to sample a range of radiative forcing (RF) of the climate system at 2100. The RCPs are translated into both concentrations and emissions of greenhouse gases using Integrated Assessment Models (IAMs). These are then used as inputs to dynamical Earth System Models (ESMs) in simulations that are either concentration-driven (the majority of projection experiments) or emissions-driven (only for RCP8.5). Aerosols and other forcing factors are implemented in different ways in each ESM. The ESM projections each have a potentially different RF, which may be viewed as an output of the model and which may not correspond to precisely the level of RF indicated by the RCP nomenclature. Similarly, for concentration-driven experiments, the emissions consistent with those concentrations diagnosed from the ESM may be different from those specified in the RCP (diagnosed from the IAM). Different models produce different responses even under the same RF. Uncertainty propagates through the chain and results in a spread of ESM projections. This spread is only one way of assessing uncertainty in projections. Alternative methods, which combine information from simple and complex models and observations through statistical models or expert judgement, are also used to quantify that uncertainty. 1038 Long-term Climate Change: Projections, Commitments and Irreversibility Chapter 12 Despite the naming of the RCPs in terms of their target RF at 2100 means that these ensembles sample both modelling uncertainty and or at stabilization (Box 1.1), climate models translate concentra- internal variability jointly. tions of forcing agents into RF in different ways due to their differ- ent structural modelling assumptions. Hence a model simulation The ability of models to mimic nature is achieved by simplification of RCP6.0 may not attain exactly a RF of 6 W m 2; more accurately, choices that can vary from model to model in terms of the fundamental an RCP6.0 forced model experiment may not attain exactly the numeric and algorithmic structures, forms and values of parameteriza- same RF as was intended by the specification of the RCP6.0 forc- tions, and number and kinds of coupled processes included. Simplifi- ing inputs. Thus in addition to the scenario uncertainty there is cations and the interactions between parameterized and resolved pro- RF uncertainty in the way the RCP scenarios are implemented in cesses induce errors in models, which can have a leading-order impact climate models. on projections. It is possible to characterize the choices made when building and running models into structural indicating the numerical Some model simulations are concentration-driven (GHG concen- techniques used for solving the dynamical equations, the analytic form trations are specified) whereas some models, which have Earth of parameterization schemes and the choices of inputs for fixed or var- Systems components, convert emission scenarios into concen- ying boundary conditions and parametric indicating the choices trations and are termed emissions-driven. Different ESMs driven made in setting the parameters that control the various components by emissions may produce different concentrations of GHGs and of the model. The community of climate modellers has regularly col- aerosols because of differences in the representation and/or laborated in producing coordinated experiments forming multi-model p ­ arameterization of the processes responsible for the conversion ensembles (MMEs), using both global and regional model families, for of emissions into concentrations. This aspect may be considered a example, CMIP3/5 (Meehl et al., 2007a), ENSEMBLES (Johns et al., facet of forcing uncertainty, or may be compounded in the category 2011) and Chemistry Climate Model Validation 1 and 2 (CCM-Val-1 of model uncertainty, which we discuss below. Also, aerosol load- and 2; Eyring et al., 2005), through which structural uncertainty can be ing and land use changes are not dictated intrinsically by the RCP at least in part explored by comparing models, and perturbed physics specification. Rather, they are a result of the Integrated Assessment ensembles (PPEs, with e.g., Hadley Centre Coupled Model version 3 Model that created the emission pathway for a given RCP. (HadCM3; Murphy et al., 2004), Model for Interdiciplinary Research On Climate (MIROC; Yokohata et al., 2012), Community Climate System SRES and RCPs account for future changes only in anthropogenic forc- Model 3 (CCSM3; Jackson et al., 2008; Sanderson, 2011)), through ings. With regard to solar forcing, the 1985 2005 solar cycle is repeat- which uncertainties in parameterization choices can be assessed in a ed. Neither projections of future deviations from this solar cycle, nor given model. As noted below, neither MMEs nor PPEs represent an future volcanic RF and their uncertainties are considered. adequate sample of all the possible choices one could make in building a climate model. Also, current models may exclude some processes that Any climate projection is subject to sampling uncertainties that arise could turn out to be important for projections (e.g., methane clathrate because of internal variability. In this chapter, the prediction of, for release) or produce a common error in the representation of a particu- example, the amplitude or phase of some mode of variability that may lar process. For this reason, it is of critical importance to distinguish be important on long time scales is not addressed (see Sections 11.2 two different senses in which the uncertainty terminology is used or and 11.3). Any climate variable projection derived from a single simu- misused in the literature (see also Sections 1.4.2, 9.2.2, 9.2.3, 11.2.1 lation of an individual climate model will be affected by internal varia- and 11.2.2). A narrow interpretation of the concept of model uncer- bility (stemming from the chaotic nature of the system), whether it be tainty often identifies it with the range of responses of a model ensem- a variable that involves a long time average (e.g., 20 years), a snapshot ble. In this chapter this type of characterization is referred as model in time or some more complex diagnostic such as the variance comput- range or model spread. A broader concept entails the recognition of a ed from a time series over many years. No amount of time averaging fundamental uncertainty in the representation of the real system that 12 can reduce internal variability to zero, although for some EMICs and these models can achieve, given their necessary approximations and simplified models, which may be used to reproduce the results of more the limits in the scientific understanding of the real system that they complex model simulations, the representation of internal ­ ariability v encapsulate. When addressing this aspect and characterizing it, this is excluded from the model specification by design. For different chapter uses the term model uncertainty. variables, and different spatial and time scale averages, the relative importance of internal variability in comparison with other sources of The relative role of the different sources of uncertainty model, sce- uncertainty will be different. In general, internal variability becomes nario and internal variability as one moves from short- to mid- to more important on shorter time scales and for smaller scale variables long-term projections and considers different variables at different (see Section 11.3 and Figure 11.2). The concept of signal-to-noise ratio spatial scales has to be recognized (see Section 11.3). The three sourc- may be used to quantify the relative magnitude of the forced response es exchange relevance as the time horizon, the spatial scale and the (signal) versus internal variability (noise). Internal variability may be variable change. In absolute terms, internal variability is generally sampled and estimated explicitly by running ensembles of simulations estimated, and has been shown in some specific studies (Hu et al., with slightly different initial conditions, designed explicitly to represent 2012) to remain approximately constant across the forecast horizon, internal variability, or can be estimated on the basis of long control with model ranges and scenario/forcing variability increasing over runs where external forcings are held constant. In the case of both time. For forecasts of global temperatures after mid-century, scenario multi-model and perturbed physics ensembles (see below), there is an and model ranges dominate the amount of variation due to internally implicit perturbation in the initial state of each run considered, which generated variability, with scenarios accounting for the largest source 1039 Chapter 12 Long-term Climate Change: Projections, Commitments and Irreversibility of uncertainty in projections by the end of the century. For global aver- of possible values are explored and often statistical models that fit the age precipitation projections, scenario uncertainty has a much smaller relationship between parameter values and model output, that is, emu- role even by the end of the 21st century and model range maintains lators, are trained on the ensemble and used to predict the outcome the largest share across all projection horizons. For temperature and for unsampled parameter value combinations, in order to explore the precipitation projections at smaller spatial scales, internal variability parameter space more thoroughly that would otherwise be computa- may remain a significant source of uncertainty up until middle of the tionally affordable (Rougier et al., 2009). The space of a single model 21st century in some regions (Hawkins and Sutton, 2009, 2011; Rowell, simulations (even when filtered through observational constraints) can 2012; Knutti and Sedláèek, 2013). Within single model experiments, show a large range of outcomes for a given scenario (Jackson et al., the persistently significant role of internally generated variability for 2008). However, multi-model ensembles and perturbed physics ensem- regional projections even beyond short- and mid-term horizons has bles produce modes and distributions of climate responses that can been documented by analyzing relatively large ensembles sampling be different from one another, suggesting that one type of ensemble initial conditions (Deser et al., 2012a, 2012b). cannot be used as an analogue for the other (Murphy et al., 2007; Sanderson et al., 2010; Yokohata et al., 2010; Collins et al., 2011). 12.2.3 From Ensembles to Uncertainty Quantification Many studies have made use of results from these ensembles to charac- Ensembles like CMIP5 do not represent a systematically sampled terize uncertainty in future projections, and these will be assessed and family of models but rely on self-selection by the modelling groups. their results incorporated when describing specific aspects of future This opportunistic nature of MMEs has been discussed, for example, in climate responses. PPEs have been uniformly treated across the differ- Tebaldi and Knutti (2007) and Knutti et al. (2010a). These ensembles are ent studies through the statistical framework of analysis of computer therefore not designed to explore uncertainty in a coordinated manner, experiments (Sanso et al., 2008; Rougier et al., 2009; Harris et al., 2010) and the range of their results cannot be straightforwardly interpreted or, more plainly, as a thorough exploration of alternative responses as an exhaustive range of plausible outcomes, even if some studies reweighted by observational constraints (Murphy et al., 2004; Piani et have shown how they appear to behave as well calibrated probabil- al., 2005; Forest et al., 2008; Sexton et al., 2012). In all cases the con- istic forecasts for some large-scale quantities (Annan and Hargreaves, struction of a probability distribution is facilitated by the systematic 2010). Other studies have argued instead that the tail of distributions nature of the experiments. MMEs have generated a much more diver- is by construction undersampled (Räisänen, 2007). In general, the dif- sified treatment (1) according to the choice of applying weights to the ficulty in producing quantitative estimates of uncertainty based on different models on the basis of past performance or not (Weigel et al., multiple model output originates in their peculiarities as a statistical 2010) and (2) according to the choice between treating the different sample, neither random nor systematic, with possible dependencies models and the truth as indistinguishable or treating each model as among the members (Jun et al., 2008; Masson and Knutti, 2011; Pen- a version of the truth to which an error has been added (Annan and nell and Reichler, 2011; Knutti et al., 2013) and of spurious nature, that Hargreaves, 2010; Sanderson and Knutti, 2012). Many studies can be is, often counting among their members models with different degrees classified according to these two criteria and their combination, but of complexities (different number of processes explicitly represented or even within each of the four resulting categories different studies pro- parameterized) even within the category of general circulation models. duce different estimates of uncertainty, owing to the preponderance of a priori assumptions, explicitly in those studies that approach the Agreement between multiple models can be a source of information in problem through a Bayesian perspective, or only implicit in the choice an uncertainty assessment or confidence statement. Various methods of likelihood models, or weighting. This makes the use of probabilistic have been proposed to indicate regions where models agree on the and other results produced through statistical inference necessarily projected changes, agree on no change or disagree. Several of those dependent on agreeing with a particular set of assumptions (Sansom 12 methods are compared in Box 12.1. Many figures use stippling or et al., 2013), given the lack of a full exploration of the robustness of hatching to display such information, but it is important to note that probabilistic estimates to varying these assumptions. confidence cannot be inferred from model agreement alone. In summary, there does not exist at present a single agreed on and Perturbed physics experiments (PPEs) differ in their output interpret- robust formal methodology to deliver uncertainty quantification esti- ability for they can be, and have been, systematically constructed mates of future changes in all climate variables (see also Section 9.8.3 and as such lend themselves to a more straightforward treatment and Stephenson et al., 2012). As a consequence, in this chapter, state- through statistical modelling (Rougier, 2007; Sanso and Forest, 2009). ments using the calibrated uncertainty language are a result of the Uncertain parameters in a single model to whose values the output expert judgement of the authors, combining assessed literature results is known to be sensitive are targeted for perturbations. More often with an evaluation of models demonstrated ability (or lack thereof) it is the parameters in the atmospheric component of the model that in simulating the relevant processes (see Chapter 9) and model con- are varied (Collins et al., 2006a; Sanderson et al., 2008), and to date sensus (or lack thereof) over future projections. In some cases when a have in fact shown to be the source of the largest uncertainties in significant relation is detected between model performance and relia- large-scale response, but lately, with much larger computing power bility of its future projections, some models (or a particular parametric expense, also parameters within the ocean component have been per- configuration) may be excluded (e.g., Arctic sea ice; Section 12.4.6.1 turbed (Collins et al., 2007; Brierley et al., 2010). Parameters in the and Joshi et al., 2010) but in general it remains an open research ques- land surface schemes have also been subject to perturbation studies tion to find significant connections of this kind that justify some form (Fischer et al., 2011; Booth et al., 2012; Lambert et al., 2012). Ranges of weighting across the ensemble of models and produce aggregated 1040 Long-term Climate Change: Projections, Commitments and Irreversibility Chapter 12 Box 12.1 | Methods to Quantify Model Agreement in Maps The climate change projections in this report are based on ensembles of climate models. The ensemble mean is a useful quantity to characterize the average response to external forcings, but does not convey any information on the robustness of this response across models, its uncertainty and/or likelihood or its magnitude relative to unforced climate variability. In the IPCC AR4 WGI contribution (IPCC, 2007) several criteria were used to indicate robustness of change, most prominently in Figure SPM.7. In that figure, showing projected precipitation changes, stippling marked regions where at least 90% of the CMIP3 models agreed on the sign of the change. Regions where less than 66% of the models agreed on the sign were masked white. The resulting large white area was often misin- terpreted as indicating large uncertainties in the different models response to external forcings, but recent studies show that, for the most part, the disagreement in sign among models is found where projected changes are small and still within the modelled range of internal variability, that is, where a response to anthropogenic forcings has not yet emerged locally in a statistically significant way (Tebaldi et al., 2011; Power et al., 2012). A number of methods to indicate model robustness, involving an assessment of the significance of the change when compared to inter- nal variability, have been proposed since AR4. The different methods share the purpose of identifying regions with large, significant or robust changes, regions with small changes, regions where models disagree or a combination of those. They do, however, use different assumptions about the statistical properties of the model ensemble, and therefore different criteria for synthesizing the information from it. Different methods also differ in the way they estimate internal variability. We briefly describe and compare several of these methods here. Method (a): The default method used in Chapters 11,12 and 14 as well as in the Annex I (hatching only) is shown in Box 12.1, Figure 1a, and is based on relating the climate change signal to internal variability in 20-year means of the models as a reference3. Regions where the multi-model mean change exceeds two standard deviations of internal variability and where at least 90% of the models agree on the sign of change are stippled and interpreted as large change with high model agreement . Regions where the model mean is less than one standard deviation of internal variability are hatched and interpreted as small signal or low agreement of models . This can have various reasons: (1) changes in individual models are smaller than internal variability, or (2) although changes in individual models are significant, they disagree about the sign and the multi-model mean change remains small. Using this method, the case where all models scatter widely around zero and the case where all models agree on near zero change therefore are both hatched (e.g., precipitation change over the Amazon region by the end of the 21st century, which the following methods mark as inconsistent model response ). Method (b): Method (a) does not distinguish the case where all models agree on no change and the case where, for example, half of the models show a significant increase and half a decrease. The distinction may be relevant for many applications and a modification of method (a) is to restrict hatching to regions where there is high agreement among the models that the change will be small , thus eliminating the ambiguous interpretation small or low agreement in (a). In contrast to method (a) where the model mean is com- pared to variability, this case (b) marks regions where at least 80% of the individual models show a change smaller than two standard deviations of variability with hatching. Grid points where many models show significant change but don t agree are no longer hatched (Box 12.1, Figure 1b). 12 Method (c): Knutti and Sedláèek (2013) define a dimensionless robustness measure, R, which is inspired by the signal-to-noise ratio and the ranked probability skill score. It considers the natural variability and agreement on magnitude and sign of change. A value of ­ R = 1 implies perfect model agreement; low or negative values imply poor model agreement (note that by definition R can assume any negative value). Any level of R can be chosen for the stippling. For illustration, in Box 12.1, Figure 1c, regions with R > 0.8 are marked with small dots, regions with R > 0.9 with larger dots and are interpreted as robust large change . This yields similar results to method (a) for the end of the century, but with some areas of moderate model robustness (R > 0.8) already for the near-term projections, even though the signal is still within the noise. Regions where at least 80% of the models individually show no significant change are hatched and interpreted as changes unlikely to emerge from variability 4.There is less hatching in this method than in method (a), (continued on next page) 3 The internal variability in this method is estimated using pre-industrial control runs for each of the models which are at least 500 years long. The first 100 years of the pre-industrial are ignored. Variability is calculated for every grid point as the standard deviation of non-overlapping 20-year means, multiplied by the square root of 2 to account for the fact that the variability of a difference in means is of interest. A quadratic fit as a function of time is subtracted from these at every grid point to eliminate model drift. This is by definition the standard deviation of the difference between two independent 20-year averages having the same variance and estimates the variation of that difference that would be expected due to unforced internal variability. The median across all models of that quantity is used. 4 Variability in methods b d is estimated from interannual variations in the base period within each model. 1041 Chapter 12 Long-term Climate Change: Projections, Commitments and Irreversibility Box 12.1 (continued) DJF mean precipitation change (RCP8.5) 12 Box 12.1, Figure 1 | Projected change in December to February precipitation for 2016 2035 and 2081 2100, relative to 1986 2005 from CMIP5 models. The choice of the variable and time frames is just for illustration of how the different methods compare in cases with low and high signal-to-noise ratio (left and right column, respectively). The colour maps are identical along each column and only stippling and hatching differ on the basis of the different methods. Different methods for stippling and hatching are shown determined (a) from relating the model mean to internal variability, (b) as in (a) but hatching here indicates high agreement for small change , (c) by the robustness measure by Knutti and Sedláèek (2013), (d) by the method proposed by Tebaldi et al. (2011) and (e) by the method by Power et al. (2012). Detailed technical explanations for each method are given in the text. 39 models are used in all panels. 1042 Long-term Climate Change: Projections, Commitments and Irreversibility Chapter 12 Box 12.1 (continued) because it requires 80% of the models to be within variability, not just the model average. Regions where at least 50% of the models show significant change but R< 0.5 are masked as white to indicate models disagreeing on the projected change projections (Box 12.1, Figure 1c). Method (d): Tebaldi et al. (2011) start from IPCC AR4 SPM7 but separate lack of model agreement from lack of signal (Box 12.1, Figure 1e). Grid points are stippled and interpreted as robust large change when more than 50% of the models show significant change and at least 80% of those agree on the sign of change. Grid points where more than 50% of the models show significant change but less than 80% of those agree on the sign of change are masked as white and interpreted as unreliable . The results are again similar to the methods above. No hatching was defined in that method (Box 12.1 Figure 1d). (See also Neelin et al., 2006 for a similar approach applied to a specific regional domain.) Method (e): Power et al. (2012) identify three distinct regions using various methods in which projections can be very loosely described as either: statistically significant , small (relative to temporal variability) or zero, but not statistically significant or uncertain . The emphasis with this approach is to identify robust signals taking the models at face value and to address the questions: (1) What will change? (2) By how much? and (3) What will not change? The underlying consideration here is that statistical testing under the assumption of model independence provides a worthwhile, albeit imperfect, line of evidence that needs to be considered in conjunction with other evidence (e.g., degree of interdependence, ability of models to simulate the past), in order to assess the degree of confidence one has in a projected change. The examples given here are not exhaustive but illustrate the main ideas. Other methods include simply counting the number of models agreeing on the sign (Christensen et al., 2007), or varying colour hue and saturation to indicate magnitude of change and robustness of change separately (Kaye et al., 2012). In summary, there are a variety of ways to characterize magnitude or significance of change, and agreement between models. There is also a compromise to make between clarity and richness of information. Different methods serve different purposes and a variety of criteria can be justified to highlight specific properties of multi-model ensembles. Clearly only a subset of information regarding robust and uncertain change can be conveyed in a single plot. The methods above convey some important pieces of this information, but obviously more information could be provided if more maps with additional statistics were provided. In fact Annex I provides more explicit information on the range of projected changes evident in the models (e.g., the median, and the upper and lower quartiles). For most of the methods there is a necessity to choose thresholds for the level of agreement that cannot be identified objectively, but could be the result of individual, application-specific evaluations. Note also that all of the above methods measure model agreement in an ensemble of opportunity, and it is impossible to derive a confidence or likelihood statement from the model agreement or model spread alone, without considering consistency with observations, model dependence and the degree to which the relevant processes are understood and reflected in the models (see Section 12.2.3). The method used by Power et al. (2012) differs from the other methods in that it tests the statistical significance of the ensemble mean rather than a single simulation. As a result, the area where changes are significant increases with an increasing number of models. Already for the period centred on 2025, most of the grid points when using this method show significant change in the ensemble mean whereas in the other methods projections for this time period are classified as changes not exceeding internal variability. The 12 reason is that the former produces a statement about the mean of the distribution being significantly different from zero, equivalent to treating the ensemble as truth plus error , that is, assuming that the models are independent and randomly distributed around reality. Methods a d, on the other hand, use an indistinguishable interpretation, in which each model and reality are drawn from the same distribution. In that case, the stippling and hatching characterize the likelihood of a single member being significant or not, rather than the ensemble mean. There is some debate in the literature on how the multi-model ensembles should be interpreted statistically. This and past IPCC reports treat the model spread as some measure of uncertainty, irrespective of the number of models, which implies an indistinguishable interpretation. For a detailed discussion readers are referred to the literature (Tebaldi and Knutti, 2007; Annan and Hargreaves, 2010; Knutti et al., 2010a, 2010b; Annan and Hargreaves, 2011a; Sanderson and Knutti, 2012). 1043 Chapter 12 Long-term Climate Change: Projections, Commitments and Irreversibility future projections that are significantly different from straightforward significant correlations, likely because of the heterogeneity of the rela- one model one vote (Knutti, 2010) ensemble results. Therefore, most tion between the variables within those large averaged regions and of the analyses performed for this chapter make use of all available seasons. In Sexton et al. (2012) the spatial scale focussed on regions of models in the ensembles, with equal weight given to each of them Great Britain and correlation emerged as more significant, for exam- unless otherwise stated. ple, between summer temperatures and precipitation amounts. Fischer and Knutti (2013) estimated strong relationships between variables 12.2.4 Joint Projections of Multiple Variables making up impact relevant indices (e.g., temperature and humidi- ty) and showed how in some cases, uncertainties across models are While many of the key processes relevant to the simulation of single larger for a combined variable than if the uncertainties in the individ- variables are understood, studies are only starting to focus on assess- ual underlying variables were treated independently (e.g., wildfires), ing projections of joint variables, especially when extremes or varia- whereas in other cases the uncertainties in the combined variables are bility in the individual quantities are of concern. A few studies have smaller than in the individual ones (e.g., heat stress for humans). addressed projected changes in joint variables, for example, by combin- ing mean temperature and precipitation (Williams et al., 2007; Tebaldi Even while recognizing the need for joint multivariate projections, the and Lobell, 2008; Tebaldi and Sanso, 2009; Watterson, 2011; Watter- above limitations at this stage prevent a quantitative assessment for son and Whetton, 2011a; Sexton et al., 2012), linking soil moisture, most cases. A few robust qualitative relationships nonetheless emerge precipitation and temperature mean and variability (Seneviratne et al., from the literature and these are assessed, where appropriate, in the 2006; Fischer and Schär, 2009; Koster et al., 2009b, 2009c), combining rest of the chapter. For applications that are sensitive to relationships temperature and humidity (Diffenbaugh et al., 2007; Fischer and Schär, between variables, but still choose to use the multi-model framework 2010; Willett and Sherwood, 2012), linking summertime temperature to determine possible ranges for projections, sampling from univari- and soil moisture to prior winter snowpack (Hall et al., 2008) or linking ate ranges may lead to unrealistic results when significant correlations precipitation change to circulation, moisture and moist static energy exist. IPCC assessments often show model averages as best estimates, budget changes (Neelin et al., 2003; Chou and Neelin, 2004; Chou et but such averages can underestimate spatial variability, and more in al., 2006, 2009). Models may have difficulties simulating all relevant general they neither represent any of the actual model states (Knutti et interactions between atmosphere and land surface and the water cycle al., 2010a) nor do they necessarily represent the joint best estimate in a that determine the joint response, observations to evaluate models are multivariate sense. Impact studies usually need temporally and spatial- often limited (Seneviratne et al., 2010), and model uncertainties are ly coherent multivariate input from climate model simulations. In those therefore large (Koster et al., 2006; Boé and Terray, 2008; Notaro, 2008; cases, using each climate model output individually and feeding it into Fischer et al., 2011). In some cases, correlations between, for example, the impact model, rather than trying to summarise a multivariate distri- temperature and precipitation or accumulated precipitation and tem- bution from the MME and sample from it, is likely to be more consist- perature have found to be too strong in climate models (Trenberth and ent, assuming that the climate model itself correctly captures the spa- Shea, 2005; Hirschi et al., 2011). The situation is further complicated tial covariance, the temporal co-evolution and the relevant feedbacks. by the fact that model biases in one variable affect other variables. The standard method for model projections is to subtract model biases derived from control integrations (assuming that the bias remains con- 12.3 Projected Changes in Forcing Agents, stant in a future scenario integration). Several studies note that this Including Emissions and Concentrations may be problematic when a consistent treatment of biases in multiple variables is required (Christensen et al., 2008; Buser et al., 2009), but The experiments that form the basis of global future projections dis- there is no consensus at this stage for a methodology addressing this cussed in this chapter are extensions of the simulations of the observa- 12 problem (Ho et al., 2012). More generally the existence of structural tional record discussed in Chapters 9 and 10. The scenarios assessed in errors in models according to which an unavoidable discrepancy (Rou- AR5, introduced in Chapter 1, include four new scenarios designed to gier, 2007) between their simulations and reality cannot be avoided explore a wide range of future climate characterized by representative is relevant here, as well as for univariate projections. In the recent lit- trajectories of well-mixed greenhouse gas (WMGHG) concentrations erature an estimate of this discrepancy has been proposed through and other anthropogenic forcing agents. These are described further the use of MMEs, using each model in turn as a surrogate for reali- in Section 12.3.1. The implementation of forcing agents in model pro- ty, and measuring the distance between it and the other models of jections, including natural and anthropogenic aerosols, ozone and land the ensemble. Some summary statistic of these measures is then used use change are discussed in Section 12.3.2, with a strong focus on to estimate the distance between models and the real world (Sexton CMIP5 experiments. Global mean emissions, concentrations and RFs and Murphy, 2012; Sexton et al., 2012; Sanderson, 2013). Statistical applicable to the historical record simulations assessed in Chapters 8, frameworks to deal with multivariate projections are challenging even 9 and 10, and the future scenario simulations assessed here, are listed for just two variables, as they have to address a trade-off between in Annex II. Global mean RF for the 21st century consistent with these modelling the joint behavior at scales that are relevant for impacts scenarios, derived from CMIP5 and other climate model studies, is dis- that is, fine spatial and temporal scales, often requiring complex spa- cussed in Section 12.3.3. tio-temporal models and maintaining computational feasibility. In one instance (Tebaldi and Sanso, 2009) scales were investigated at the seasonal and sub-continental level, and projections of the forced response of ­temperature and precipitation at those scales did not show 1044 Long-term Climate Change: Projections, Commitments and Irreversibility Chapter 12 12.3.1 Description of Scenarios resulted from specific socioeconomic scenarios, that is, from storylines about future demographic and economic development, regionaliza- Long-term climate change projections reflect how human activities or tion, energy production and use, technology, agriculture, forestry, and natural effects could alter the climate over decades and centuries. In land use. All SRES scenarios assumed that no climate mitigation policy this context, defined scenarios are important, as using specific time would be undertaken. Based on these SRES scenarios, global climate series of emissions, land use, atmospheric concentrations or RF across models were then forced with corresponding WMGHG and aerosol multiple models allows for coherent climate model intercomparisons concentrations, although the degree to which models implemented and synthesis. Some scenarios present a simple stylized future (not these forcings differed (Meehl et al., 2007b, Table 10.1). The result- accompanied by a socioeconomic storyline) and are used for pro- ing climate projections, together with the socioeconomic scenarios on cess understanding. More comprehensive scenarios are produced by which they are based, have been widely used in further analysis by the Integrated Assessment Models (IAMs) as internally consistent sets of impact, adaptation and vulnerability research communities. emissions and socioeconomic assumptions (e.g., regarding population and socioeconomic development) with the aim of presenting sever- 12.3.1.3 The New Concentration Driven Representative al plausible future worlds (see Section 1.5.2 and Box 1.1). In general Concentration Pathway Scenarios, and Their Extensions it is these scenarios that are used for policy relevant climate change, impact, adaptation and mitigation analysis. It is beyond the scope of As introduced in Box 1.1 and mentioned in Section 12.1, a new parallel this report to consider the full range of currently published scenarios process for scenario development was proposed in order to facilitate and their implications for mitigation policy and climate targets that the interactions between the scientific communities working on cli- is covered by the Working Group III contribution to the AR5. Here, we mate change, adaptation and mitigation (Hibbard et al., 2007; Moss et focus on the RCP scenarios used within the CMIP5 intercomparison al., 2008, 2010; van Vuuren et al., 2011). These new scenarios, Repre- exercise (Taylor et al. 2012) along with the SRES scenarios (IPCC, 2000) sentative Concentration Pathways, are referred to as pathways in order developed for the IPCC Third Assessment Report (TAR) but still widely to emphasize that they are not definitive scenarios, but rather inter- used by the climate community. nally consistent sets of time-dependent forcing projections that could potentially be realized with more than one underlying socioeconomic 12.3.1.1 Stylized Concentration Scenarios scenario. The primary products of the RCPs are concentrations but they also provide gas emissions. They are representative in that they are one A 1% per annum compound increase of atmospheric CO2 concen- of several different scenarios, sampling the full range of published sce- tration until a doubling or a quadrupling of its initial value has been narios (including mitigation scenarios) at the time they were defined, widely used since the second phase of CMIP (Meehl et al., 2000) and that have similar RF and emissions characteristics. They are identified the Second Assessment Report (Kattenberg et al., 1996). This stylized by the approximate value of the RF (in W m 2) at 2100 or at stabiliza- scenario is a useful benchmark for comparing coupled model climate tion after 2100 in their extensions, relative to pre-industrial (Moss et sensitivity, climate feedback and transient climate response, but is not al., 2008; Meinshausen et al., 2011c). RCP2.6 (the lowest of the four, used directly for future projections. The exponential increase of CO2 also referred to as RCP3-PD) peaks at 3.0 W m 2 and then declines to concentration induces approximately a linear increase in RF due to 2.6 W m 2 in 2100, RCP4.5 (medium-low) and RCP6.0 (medium-high) a saturation effect of the strong absorbing bands (Augustsson and stabilize after 2100 at 4.2 and 6.0 W m 2 respectively, while RCP8.5 Ramanathan, 1977; Hansen et al., 1988; Myhre et al., 1998). Thus, a (highest) reaches 8.3 W m 2 in 2100 on a rising trajectory (see also linear ramp function in forcing results from these stylized pathways, Figure 12.3a which takes into account the efficacies of the various adding to their suitability for comparative diagnostics of the models anthropogenic forcings). The primary objective of these scenarios is to climate feedbacks and inertia. The CMIP5 intercomparison project provide all the input variables necessary to run comprehensive climate again includes such a stylized pathway, in which the CO2 concentration models in order to reach a target RF (Figure 12.2). These scenarios 12 reaches twice the initial concentration after 70 years and four times were developed using IAMs that provide the time evolution of a large the initial concentration after 140 years. The corresponding RFs are ensemble of anthropogenic forcings (concentration and emission of 3.7 W m 2 (Ramaswamy et al., 2001) and 7.4 W m 2 respectively with gas and aerosols, land use changes, etc.) and their individual RF values a range of +/-20% accounting for uncertainties in radiative transfer cal- (Moss et al., 2008, 2010; van Vuuren et al., 2011). Note that due to the culations and rapid adjustments (see Section 8.3.2.1), placing them substantial uncertainties in RF, these forcing values should be under- within the range of the RFs at the end of the 21st century for the stood as comparative labels , not as exact definitions of the forcing future scenarios presented below. The CMIP5 project also includes a that is effective in climate models. This is because concentrations or second stylized experiment in which the CO2 concentration is quadru- emissions, rather than the RF itself, are prescribed in the CMIP5 climate pled instantaneously, which allows a distinction between effective RFs model runs. The forcing as manifested in climate models is discussed and longer-term climate feedbacks (Gregory et al., 2004). in Section 12.3.3. 12.3.1.2 The Socioeconomic Driven Scenarios from the Special Various steps were necessary to turn the selected raw RCP scenarios Report on Emission Scenarios from the IAMs into data sets usable by the climate modelling commu- nity. First, harmonization with historical data was performed for emis- The climate change projections undertaken as part of CMIP3 and dis- sions of reactive gases and aerosols (Lamarque et al., 2010; Granier cussed in AR4 were based primarily on the SRES A2, A1B and B1 sce- et al., 2011; Smith et al., 2011), land use (Hurtt et al., 2011), and for narios (IPCC, 2000). These scenarios were developed using IAMs and GHG emissions and concentrations (Meinshausen et al., 2011c). Then 1045 Chapter 12 Long-term Climate Change: Projections, Commitments and Irreversibility Anthropogenic Radiative Forcing (W m-2) 12 SRES-B1 SRES-A1B 10 SRES-A2 RCP2.6 RCP4.5 8 RCP6.0 RCP8.5 6 4 2 0 a) 2000 2050 2100 2150 2200 2250 2300 Year Contribution of individual forcings to the total Contribution of individual forcings as a percentage of the total CO2 CO2 CH4 CH4 GHG GHG N2O N2O O3 O3 aerosol other aerosol other RCP8.5 (2100) RCP8.5 (2100) RCP6.0 (2100) RCP6.0 (2100) RCP4.5 (2100) RCP4.5 (2100) LU LU RCP2.6 (2100) RCP2.6 (2100) present day (2010) present day (2010) 1.0 0.0 1.0 2.0 3.0 4.0 5.0 6.0 7.0 8.0 60 40 20 0 20 40 60 80 100 b) c) (W m 2) (%) Figure 12.3 | (a) Time evolution of the total anthropogenic (positive) and anthropogenic aerosol (negative) radiative forcing (RF) relative to pre-industrial (about 1765) between 2000 and 2300 for RCP scenarios and their extensions (continuous lines), and SRES scenarios (dashed lines) as computed by the Integrated Assessment Models (IAMs) used to develop those scenarios. The four RCP scenarios used in CMIP5 are: RCP2.6 (dark blue), RCP4.5 (light blue), RCP6.0 (orange) and RCP8.5 (red). The three SRES scenarios used in CMIP3 are: B1 (blue, dashed), A1B (green, dashed) and A2 (red, dashed). Positive values correspond to the total anthropogenic RF. Negative values correspond to the forcing from all anthropogenic aerosol radiation interactions (i.e., direct effects only). The total RF of the SRES and RCP families of scenarios differs in 2000 because the number of forc- ings represented and our knowledge about them have changed since the TAR. The total RF of the RCP family is computed taking into account the efficacy of the various forcings 12 (Meinshausen et al., 2011a). (b) Contribution of the individual anthropogenic forcings to the total RF in year 2100 for the four RCP scenarios and at present day (year 2010). The individual forcings are gathered into seven groups: carbon dioxide (CO2), methane (CH4), nitrous oxide (N2O), ozone (O3), other greenhouse gases, aerosol (all effects unlike in (a), i.e., aerosol radiation and aerosol cloud interactions, aerosol deposition on snow) and land use (LU). (c) As in (b), but the individual forcings are relative to the total RF (i.e., RFx/ RFtot, in %, with RFx individual RFs and RFtot total RF). Note that the RFs in (b) and (c) are not efficacy adjusted, unlike in (a). The values shown in (a) are summarized in Table AII.6.8. The values shown in (b) and (c) have been directly extracted from data files (hosted at http://tntcat.iiasa.ac.at:8787/RcpDb/) compiled by the four modelling teams that developed the RCP scenarios and are summarized in Tables AII.6.1 to AII.6.3 for CO2, CH4 and N2O respectively. atmospheric chemistry runs were performed to estimate ozone and number and type of forcings included primarily depend on the exper- aerosol distributions (Lamarque et al., 2011). Finally, a single carbon iment. For instance, while the CO2 concentration is prescribed in most cycle model with a representation of carbon climate feedbacks was experiments, CO2 emissions are prescribed in some others (see Box 6.4 used in order to provide consistent values of CO2 concentration for and Section 12.3.2.1). Which of these forcings are included in individ- the CO2 emission provided by a different IAM for each of the scenari- ual CMIP5 models, and variations in their implementation, is described os. This methodology was used to produce consistent data sets across in Section 12.3.2.2. scenarios but does not provide uncertainty estimates for them. After these processing steps, the final RCP data sets comprise land use During this development process, the total RF and the RF of individual data, harmonized GHG emissions and concentrations, gridded reactive forcing agents have been estimated by the IAMs and made availa- gas and aerosol emissions, as well as ozone and aerosol abundance ble via the RCP database (Meinshausen et al., 2011c). Each individual fields. These data are used as forcings in individual climate models. The anthropogenic forcing varies from one scenario to another. They have 1046 Long-term Climate Change: Projections, Commitments and Irreversibility Chapter 12 been aggregated into a few groups in Figure 12.3b and c. The total 12.3.1.4 Comparison of Special Report on Emission Scenarios anthropogenic RF estimated by the IAMs in 2010 is about 0.15 W m 2 and Representative Concentration Pathway Scenarios lower than Chapter 8 s best estimate of ERF in 2010 (2.2 W m 2), the difference arising from a revision of the RF due to aerosols and land The four RCP scenarios used in CMIP5 lead to RF values that range from use in the current assessment compared to AR4. All the other individ- 2.3 to 8.0 W m 2 at 2100, a wider range than that of the three SRES ual forcings are consistent to within 0.02 W m 2. The change in CO2 scenarios used in CMIP3 which vary from 4.2 to 8.1 W m 2 at 2100 (see concentration is the main cause of difference in the total RF among Table AII.6.8 and Figure 12.3). The SRES scenarios do not assume any the scenarios (Figure 12.3b). The relative contribution5 of CO2 to the policy to control climate change, unlike the RCP scenarios. The RF of total anthropogenic forcing is currently (year 2010) about 80 to 90% RCP2.6 is hence lower by 1.9 W m 2 than the three SRES scenarios and and does not vary much across the scenarios (Figure 12.3c), as was very close to the ENSEMBLES E1 scenario (Johns et al., 2011). RCP4.5 also the case for SRES scenarios (Ramaswamy et al., 2001). Aerosols and SRES B1 have similar RF at 2100, and comparable time evolution have a large negative contribution to the total forcing (about 40 to (within 0.2 W m 2). The RF of SRES A2 is lower than RCP8.5 through- 50% in 2010), but this contribution decreases (in both absolute and out the 21st century, mainly due to a faster decline in the radiative relative terms) in the future for all the RCPs scenarios. This means that effect of aerosols in RCP8.5 than SRES A2, but they converge to within while anthropogenic aerosols have had a cooling effect in the past, 0.1 W m 2 at 2100. RCP6.0 lies in between SRES B1 and SRES A1B. their decrease in all RCP scenarios relative to current levels is expected Results obtained with one General Circulation Model (GCM) (Dufresne to have a net warming effect in the future (Levy II et al., 2013; see also et al., 2013) and with a reduced-complexity model (Rogelj et al., 2012) Figure 8.20). The 21st century decrease in the magnitude of future aer- confirm that the differences in temperature responses are consistent osol forcing was not as large and as rapid in the SRES scenarios (Figure with the differences in RFs estimates. RCP2.6, which assumes strong 12.3a). However, even in the SRES scenarios, aerosol effects were mitigation action, yields a smaller temperature increase than any SRES expected to have a diminishing role in the future compared to GHG scenario. The temperature increase with the RCP4.5 and SRES B1 sce- forcings, mainly because of the accumulation of GHG in the atmos- narios are close and the temperature increase is larger with RCP8.5 phere (Dufresne et al., 2005). Other forcings do not change much in than with SRES A2. The spread of projected global mean temperature the future, except CH4 which increases in the RCP8.5 scenario. Note for the RCP scenarios (Section 12.4.1) is considerably larger (at both that the estimates of all of these individual RFs are subject to many the high and low response ends) than for the three SRES scenarios uncertainties (see Sections 7.5, 8.5 and 11.3.6). In this section and in used in CMIP3 (B1, A1B and A2) as a direct consequence of the larger Table AII.6.8, the RF values for RCP scenarios are derived from pub- range of RF across the RCP scenarios compared to that across the lished equivalent-CO2 (CO2eq) concentration data that aggregates all three SRES scenarios (see analysis of SRES versus RCP global tempera- anthropogenic forcings including GHGs and aerosols. The conversion ture projections in Section 12.4.9 and Figure 12.40). to RF uses the formula: RF = 3.71/ln(2) ln(CO2eq/278) W m 2, where CO2eq is in ppmv. 12.3.2 Implementation of Forcings in Coupled Model Intercomparison Project Phase 5 Experiments The four RCPs (Meinshausen et al., 2011c) are based on IAMs up to the end of the 21st century only. In order to investigate longer-term climate The CMIP5 experimental protocol for long-term transient climate change implications, these RCPs were also extended until 2300. The experiments prescribes a common basis for a comprehensive set of extensions, formally named Extended Concentration Pathways (ECPs) anthropogenic forcing agents acting as boundary conditions in three but often simply referred to as RCP extensions, use simple assump- experimental phases historical, RCPs and ECPs (Taylor et al., 2012). tions about GHG and aerosol emissions and concentrations beyond To permit common implementations of this set of forcing agents in 2100 (such as stabilization or steady decline) and were designed as CMIP5 models, self-consistent forcing data time series have been com- hypothetical what-if scenarios, not as an outcome of an IAM assum- puted and provided to participating models (see Sections 9.3.2.2 and 12 ing socioeconomic considerations beyond 2100 (Meinshausen et al., 12.3.1.3) comprising emissions or concentrations of GHGs and related 2011c) (see Box 1.1). In order to continue to investigate a broad range compounds, ozone and atmospheric aerosols and their chemical pre- of possible climate futures, RCP2.6 assumes small constant net nega- cursors, and land use change. tive emissions after 2100 and RCP8.5 assumes stabilization with high emissions between 2100 and 2150, then a linear decrease until 2250. The forcing agents implemented in Atmosphere Ocean General Cir- The two middle RCPs aim for a smooth stabilization of concentrations culation Models (AOGCMs) and ESMs used to make long-term cli- by 2150. RCP8.5 stabilizes concentrations only by 2250, with CO2 mate projections in CMIP5 are summarized in Table 12.1. The number concentrations of approximately 2000 ppmv, nearly seven times the of CMIP5 models listed here is about double the number of CMIP3 pre-industrial level. As RCP2.6 implies net negative CO2 emissions after models listed in Table 10.1 of AR4 (Meehl et al., 2007b). around 2070 and throughout the extension, CO2 concentrations slowly reduce towards 360 ppmv by 2300. Natural forcings (arising from solar variability and aerosol emissions via volcanic activity) are also specified elements in the CMIP5 exper- imental protocol, but their future time evolutions are not prescribed 5 The range of the relative contribution of CO2 and aerosols to the total anthropogenic forcing is derived here from the RF values given by the IAMs and the best estimate assessed in Chapter 8. 1047 12 Table 12.1 | Radiative forcing agents in the CMIP5 multi-model global climate projections. See Table 9.A.1 for descriptions of the models and main model references. Earth System Models (ESMs) are highlighted in bold. Numeric superscripts 1048 indicate model-specific references that document forcing implementations. Forcing agents are mostly implemented in close conformance with standard prescriptions (Taylor et al., 2012) and recommended data sets (Lamarque et al., 2010; Cionni et al., 2011; Lamarque et al., 2011; Meinshausen et al., 2011c) provided for CMIP5. Variations in forcing implementations are highlighted with superscripts and expanded in the table footnotes. Entries mean: n.a.: Forcing agent is not included in either the historical or future scenario simulations; Y: Forcing agent included (via prescribed concentrations, distributions or time series data); E: Concentrations of forcing agent calculated interactively driven by prescribed emissions Chapter 12 or precursor emissions; Es: Concentrations of forcing agent calculated interactively constrained by prescribed surface concentrations. For a more detailed classification of ozone chemistry and ozone forcing implementations in CMIP5 models see Eyring et al. (2013). Forcing Agents Greenhouse Gases Aerosols Other Model Cloud Cloud Black Organic Land CO2 ce CH4 N2O Trop O3 Strat O3 CFCs SO4 Nitrate albedo lifetime Dust Volcanic Sea salt Solar carbon carbon use effect ac effect ac ACCESS-1.0 1 Yp Y Y Yb Yb Y E E E n.a. Y Y Y pd Y v5 Y pd n.a. Y ACCESS-1.3 1 Yp Y Y Yb Yb Y E E E n.a. Y Y n.a. Y v5 Y pd n.a. Y BCC-CSM1.1 2 Y/E p Y Y Y b Y b Y Y a Y a Y a n.a. n.a. n.a. Y a Y v0 Y a n.a. Y BCC-CSM1.1(m) 2 Y/E p Y Y Yb Yb Y Ya Ya Ya n.a. n.a. n.a. Ya Y v0 Ya n.a. Y BNU-ESM Y/E p Y Y Ya Ya Y Ya Ya Ya n.a. n.a. n.a. Ya Y v0 Ya n.a. Y b b so pd st,v0 pd CanCM4 Y Y Y Y Y Y E E E n.a. Y n.a. Y Y/E Y n.a. Y CanESM2 Y/E p Y Y Yb Yb Y E E E n.a. Y so n.a. Y pd Y/E st,v0 Y pd Y cr Y 3 p a a a a a a v0 a CCSM4 Y Y Y Y Y Y Y Y Y n.a. n.a. n.a. Y Y Y Y Y CESM1(BGC) 4 Y/E p Y Y Ya Ya Y Ya Ya Ya n.a. n.a. n.a. Ya Y v0 Ya Y Y CESM1(CAM5) 5 Y p Y Y Y a Y a Y E E E n.a. Y Y E Y v0 E Y Y CESM1(CAM5.1,FV2) 5 Yp Y Y Ya Ya Y E E E n.a. Y Y E Y v0 E Y Y CESM1(FASTCHEM) Y p Y a Y E E Y E Y a Y a n.a. n.a. n.a. Y a Y v0 Y a Y Y CESM1(WACCM) 6 Es p Es Es E/Es op E/Es op Es Y Y Y n.a. n.a. n.a. Ya Y v0 Ya Y Y 7 b b a so fx fx CMCC-CESM Y Y Y Y Y Y Y n.a. n.a. n.a. Y n.a. Y n.a. Y n.a. Y or CMCC-CM Y Y Y Yb Yb Y Ya n.a. n.a. n.a. Y so n.a. Y fx n.a. Y fx n.a. Y or CMCC-CMS Y Y Y Y b Y b Y Y a n.a. n.a. n.a. Y so n.a. Y fx n.a. Y fx n.a. Y or CNRM-CM5 8 Y Y Y Yc Yc Y Ye Ye Ye n.a. Y so,ic n.a. Ye Y v1 Ye n.a. Y CSIRO-Mk3.6.0 9 Y Y Y Y b Y b Y E E E n.a. Y Y Y pd Y v0 Y pd n.a. Y EC-EARTH 10 Y Y Y Yb Yb Y Ya Ya Ya n.a. n.a. n.a. Ya Y v1 Ya Y Y 11 b b a a a a a FGOALS-g2 Y Y Y Y Y Y Y Y Y n.a. Y Y Y n.a. Y n.a. Y FGOALS-s2 12 Y/E Y Y Yb Yb Y Ya Ya Ya n.a. n.a. n.a. Ya Y v0 Ya n.a. Y FIO-ESM Y/E Y Y Y a Y a Y Y a Y a Y a n.a. n.a. n.a. Y a Y v0 Y a n.a. Y GFDL-CM3 13 Yp Y/Es rc Y/Es rc E E Y/Es rc E E E n.a./E rc Y Y E pd Y/E st,v0 E pd Y Y GFDL-ESM2G Y/E p Y Y Yb Yb Y Ya Ya Ya n.a. n.a. n.a. Y fx Y v0 Y fx Y Y GFDL-ESM2M Y/E p Y Y Y b Y b Y Y a Y a Y a n.a. n.a. n.a. Y fx Y v0 Y fx Y Y GISS-E2-p1 14 Y Y Y Yd Yd Y Y Y Y Y Y n.a. Y fx Y v4 Y fx Y Y or GISS-E2-p2 14 Y Es/E hf Es E E Es/E hf E E E E Y n.a. Y pd Y v4 Y pd Y Y or Long-term Climate Change: Projections, Commitments and Irreversibility (continued on next page) Table 12.1 (continued) Forcing Agents Greenhouse Gases Aerosols Other Model Cloud Cloud Black Organic Land CO2 ce CH4 N2O Trop O3 Strat O3 CFCs SO4 Nitrate albedo lifetime Dust Volcanic Sea salt Solar carbon carbon use effect ac effect ac GISS-E2-p3 14 Y Es/E hf Es E E Es/E hf E E E E Y n.a. Y pd Y v4 Y pd Y Y or HadCM3 Yp Y Y Yb Yb Y E n.a. n.a. n.a. Y so n.a. n.a. Y v2 n.a. n.a. Y 15 p b b pd v2 pd HadGEM2-AO Y Y Y Y Y Y E E E n.a. Y Y Y Y Y Y Y HadGEM2-CC 16,17 Yp Y Y Yb Yb Y E E E n.a. Y Y Y pd Y v2 Y pd Y Y HadGEM2-ES 16 Y/E p Es Y E Y b Y E E E n.a. Y Y Y pd Y v2 Y pd Y Y INM-CM4 Y/E Y Y Yb Yb n.a. Y fx n.a. n.a. n.a. Y so n.a. n.a. Y v0 n.a. Y Y IPSL-CM5A-LR 18 Y/E p Y Y Y e Y e Y Y e Y e Y e n.a. Y n.a. Y e Y v1 Y e Y Y IPSL-CM5A-MR 18 Y/E p Y Y Ye Ye Y Ye Ye Ye n.a. Y n.a. Ye Y v1 Ye Y Y IPSL-CM5B-LR 18 Y p Y Y Y e Y e Y Y e Y e Y e n.a. Y n.a. Y e Y v1 Y e Y Y MIROC-ESM 19 Y/E p Y Y Yf Yf Y E E E n.a. Y ic Y ic Y pd Y v3 Y pd Y Y 19 p ic ic pd v3 pd MIROC-ESM-CHEM Y Y Y E E Y E E E n.a. Y Y Y Y Y Y Y MIROC4h 20 Yp Y Y Yg Yg Y E E E n.a. Y Y Y pd Y v3 Y pd Y cr Y MIROC5 20 Y p Y Y Y f Y f Y E E E n.a. Y ic Y ic Y pd Y v3 Y pd Y cr Y MPI-ESM-LR Y/E p Y Y Yb Yb Y Yh Yh Yh Yh n.a. n.a. Yh Y v0 Yh Y Y or Long-term Climate Change: Projections, Commitments and Irreversibility MPI-ESM-MR Yp Y Y Yb Yb Y Yh Yh Yh Yh n.a. n.a. Yh Y v0 Yh Y Y or MPI-ESM-P Yp Y Y Yb Yb Y Yh Yh Yh Yh n.a. n.a. Yh Y v0 Yh Y Y or 21 b b ic ic pd v0 pd MRI-CGCM3 Y Y Y Y Y Y E E E n.a. Y Y E E E Y Y MRI-ESM1 22 E Y Y E E Es E E E n.a. Y ic Y ic E pd E v0 E pd Y Y NorESM1-M 23 Yp Y Y Ya Ya Y E E E n.a. Y Y E Y/E st,v1 E pd Y Y NorESM1-ME 23 Y/E p Y Y Y a Y a Y E E E n.a. Y Y E Y/E st,v1 E pd Y Y Notes: Model-specific references relating to forcing implementations: 1 Dix et al. (2013) 12 Bao et al. (2013) 16 Jones et al. (2011) 13 17 2 Wu et al. (2013); Xin et al. (2013a, 2013b) Levy II et al. (2013) Hardiman et al. (2012) 3 Meehl et al. (2012); Gent et al. (2011) 14 Shindell et al. (2013a). GISS-E2-R and GISS-E2-H model variants are forced similarly and both 18 Dufresne et al. (2013) 4 Long et al. (2013); Meehl et al. (2012) represented here as GISS-E2. Both -R and -H model versions have three variants: in physics version 19 Watanabe et al. (2011) 1 (p1) aerosols and ozone are specified from pre-computed transient aerosol and ozone fields, in 20 5 Meehl et al. (2013) Komuro et al. (2012) physics version 2 (p2) aerosols and atmospheric chemistry are calculated online as a function of 21 6 Calvo et al. (2012); Meehl et al. (2012) atmospheric state and transient emissions inventories, while in physics version 3 (p3) atmospheric Yukimoto et al. (2012) 22 7 Cagnazzo et al. (2013) composition is calculated as for p2 but the aerosol impacts on clouds (and hence the aerosol indirect Adachi et al. (2013) 8 Voldoire et al. (2013) effect) is calculated interactively. In p1 and p2 variants the aerosol indirect effect is parameterized 23 Iversen et al. (2013); Kirkevag et al. (2013); Tjiputra et al. (2013) 9 Rotstayn et al. (2012) following Hansen et al. (2005b). 15 HadGEM2-AO is forced in a similar way to HadGEM2-ES and HadGEM2-CC following Jones et al. 10 Hazeleger et al. (2013) (2011), but tropospheric ozone, stratospheric ozone and land cover are prescribed. 11 Li et al. (2013c) (continued on next page) Chapter 12 1049 12 12 Table 12.1 (continued) f 1050 Additional notes: Ozone concentrations computed off-line by Kawase et al. (2011) using a CCM forced with CMIP5 emissions. ce g Ozone concentrations computed off-line by Sudo et al. (2003) for the historical period and Kawase et al. (2011) for the future. Separate entries for CO2 denote concentration-driven and emissions-driven experiments as indicated. ac Cloud albedo effect and Cloud lifetime effect are classical terms (as used in AR4) to describe indirect effects of radiative h Time dependent climatology based on simulations and observations; aerosols are distinguished only with respect to coarse Chapter 12 forcing associated with aerosols. They relate to the revised terminologies defined in Chapter 7 and used in AR5: Radiative and fine mode, and anthropogenic and natural origins, not with respect to composition. forcing from aerosol cloud interactions (RFaci) and Effective radiative forcing from aerosol cloud interactions (ERFaci) . op Separate entries denote different ozone chemistry precursors. RFaci equates to cloud albedo effect, while ERFaci is the effective forcing resulting from cloud albedo effect plus cloud so RFaci from sulphate aerosol only. lifetime effect, including all rapid adjustments to cloud lifetime and thermodynamics (Section 7.1.3, Figure 7.3). st Separate entries denote stratosphere and troposphere respectively. p Physiological forcing effect of CO via plant stomatal response and evapotranspiration (Betts et al., 2007) included. 2 ic Radiative effects of aerosols on ice clouds are represented. rc Separate entries denote different treatments used for radiation and chemistry respectively. pd Prognostic or diagnostic scheme for dust/sea salt aerosol with emissions/concentrations determined by the model state hf Separate entries denote treatment for historical and future (RCPs) respectively. rather than externally prescribed. a Three-dimensional tropospheric ozone, stratospheric ozone, methane, and/or aerosol distributions specified as monthly fx Fixed prescribed climatology of dust/sea salt aerosol concentrations with no year-to-year variability. 10-year mean concentrations, computed off-line using CAM-Chem a modified version of CAM3.5 with interactive chemistry v0 Explosive volcanic aerosol returns rapidly in future to zero (or near-zero) background, like that in the pre-industrial control driven with specified emissions for the historical period (Lamarque et al., 2010) and RCPs (Lamarque et al., 2011) with sea surface temperature and sea ice boundary conditions based on CCSM3 s projections for the closest corresponding AR4 experiment. v1 Explosive volcanic aerosol returns rapidly in future to constant (average volcano) background, the same as in the pre- scenarios. b industrial control experiment. Ozone prescribed using the original or slightly modified IGAC/SPARC ozone data set (Cionni et al., 2011); in some models this v2 Explosive volcanic aerosol returns slowly in future (over several decades) to constant (average volcano) background like that data set is modified to add a future solar cycle and in some models tropospheric ozone is zonally averaged. c Linearized 2D ozone chemistry scheme (Cariolle and Teyssedre, 2007) including transport and photochemistry, reactive to in the pre-industrial control experiment. v3 Explosive volcanic aerosol returns rapidly in future to near-zero background, below that in the pre-industrial control stratospheric chlorine concentrations but not tropospheric chemical emissions. d Ozone prescribed using the data set described in Hansen et al. (2007), with historical tropospheric ozone being calculated experiment. v4 by a CCM and stratospheric ozone taken from Randel and Wu (2007) in the past. Tropospheric ozone is held constant from Explosive volcanic aerosol set to zero in future, but constant (average volcano) background in the pre-industrial control 1990 onwards, while stratospheric ozone is constant from 1997 to 2003 and then returned linearly to its 1979 value over the experiment. period 2004 to 2050. v5 Explosive volcanic aerosol returns slowly in future (over several decades) to constant (average volcano) background, but zero e For IPSL-CM5 model versions, ozone and aerosol concentrations are calculated semi-offline with the atmospheric general background in the pre-industrial control experiment. circulation model including interactive chemistry and aerosol, following the four RCPs in the future (Dufresne et al., 2013; cr Land use change represented via crop change only. Szopa et al., 2013). The same aerosol concentration fields (but not ozone) are also prescribed for the CNRM-CM5 model. or Realistic time-varying orbital parameters for solar forcing (in historical period only for GISS-E2). Long-term Climate Change: Projections, Commitments and Irreversibility Long-term Climate Change: Projections, Commitments and Irreversibility Chapter 12 very precisely. A repeated 11-year cycle for total solar irradiance (Lean climate response driven with specified emissions or concentrations can and Rind, 2009) is suggested for future projections but the periodicity be derived from all participating models, while concentration-driven is not specified precisely as solar cycles vary in length. Some models ESM experiments also permit a policy-relevant diagnosis of the range include the effect of orbital variations as well, but most do not. For of anthropogenic carbon emissions compatible with the imposed con- volcanic eruptions, no specific CMIP5 prescription is given for future centration pathways (Hibbard et al., 2007; Moss et al., 2010). emissions or concentration data, the general recommendation being that volcanic aerosols should either be omitted entirely both from the WMGHG forcing implementations in CMIP5 concentration-driven control experiment and future projections or the same background experiments conform closely in almost all cases to the standard proto- volcanic aerosols should be prescribed in both. This provides a con- col (Table 12.1; CO2, CH4, N2O, chlorofluorocarbons (CFCs)), imposing sistent framework for model intercomparison given a lack of knowl- an effective control over the RF due to WMGHGs across the multi-mod- edge of when future large eruptions will occur. In general models have el ensemble, apart from the model spread arising from radiative trans- adhered to this guidance, but there are variations in the background fer codes (Collins et al., 2006b; Meehl et al., 2007b). The ability of ESMs volcanic aerosol levels chosen (zero or an average volcano back- to determine their own WMGHG concentrations in emissions-driven ground in general) and some cases, for example, Australian Commu- experiments means that RF due to WMGHGs is less tightly controlled nity Climate and Earth System Simulator (ACCESS)1.0 and ACCESS1.3 in such experiments. Even in concentration-driven experiments, many (Dix et al., 2013), where the background volcanic aerosol in future models implement some emissions-driven forcing agents (more often differs significantly from that in the control experiment, with a small aerosols, but also ozone in some cases), leading to a potentially great- effect on future RF. er spread in both the concentrations and hence RF of those emis- sions-driven agents. For the other natural aerosols (dust, sea-salt, etc.), no emission or concentration data are recommended. The emissions are potentially 12.3.2.2 Variations Between Model Forcing Implementations computed interactively by the models themselves and may change with climate, or prescribed from separate model simulations carried Apart from the distinction between concentration-driven and emis- out in the implementation of CMIP5 experiments, or simply held con- sions-driven protocols, a number of variations are present in the imple- stant. Natural aerosols (mineral dust and sea salt) are in a few cases mentation of forcing agents listed in Table 12.1, which generally arise prescribed with no year-to-year variation (giving no transient forcing due to constraining characteristics of the model formulations, various effect), in some cases prescribed from data sets computed off-line as computational efficiency considerations or local implementation deci- described above, and in other cases calculated interactively via prog- sions. In a number of models, off-line modelling using an aerosol chem- nostic or diagnostic calculations. The degree to which natural aerosol istry climate model has been used to convert emissions into concentra- emissions are interactive is effectively greater in some such models tions compatible with the specific model formulation or characteristics. than others, however, as mineral dust emissions are more constrained As a result, although detailed prescriptions are given for the forcing when land vegetation cover is specified (e.g., as in Commonwealth agents in CMIP5 experiments in emissions terms, individual modelling Scientific and Industrial Research Organisation (CSIRO)-Mk3.6.0) (Rot- approaches lead to considerable variations in their implementations stayn et al., 2012) than when vegetation is allowed to evolve dynami- and consequential RFs. This was also the case in the ENSEMBLES mul- cally (e.g., as in Hadley Centre new Global Environmental Model 2-ES ti-model projections, in which similar forcing agents to CMIP5 models (HadGEM2-ES)) (Jones et al., 2011) (Table 9.A.1). were applied but again with variations in the implementation of aer- osol, ozone and land use forcings, prescribing the SRES A1B and E1 12.3.2.1 Emissions-Driven versus Concentration Driven scenarios in a concentration-driven protocol (Johns et al., 2011) akin Experiments to the CMIP5 protocol. 12 A novel feature within the CMIP5 experimental design is that experi- Methane, nitrous oxide and CFCs (typically with some aggregation of ments with prescribed anthropogenic emissions are included in addi- the multiple gases) are generally prescribed in CMIP5 models as well- tion to classical experiments with prescribed concentration pathways mixed concentrations following the forcing data time series provid- for WMGHGs (Taylor et al., 2012). The essential features of these two ed for the given scenarios. In a number of models (CESM1(WACCM), classes of experiment are described in Box 6.4. The CMIP5 protocol GFDL-CM3, GISS-E2-p2, GISS-E2-p3, HadGEM2-ES and MRI-ESM1) the includes experiments in which ESMs (models possessing at least a three-dimensional concentrations in the atmosphere of some species carbon cycle, allowing for interactive calculation of atmospheric CO2 evolve interactively driven by the full emissions/sinks cycle (in some or compatible emissions) and AOGCMs (that do not possess such an cases constrained by prescribed concentrations at the surface, e.g., interactive carbon cycle) are both forced with WMGHG concentration HadGEM2-ES for methane). In cases where the full emissions/sinks pathways to derive a range of climate responses consistent with those cycle is modelled, the radiation scheme is usually passed the time-var- pathways from the two types of model. The range of climate responses ying 3-D concentrations, but some models prescribe different concen- including climate carbon cycle feedbacks can additionally be explored trations for the purpose of radiation. in ESMs driven with emissions rather than concentrations, analogous to Coupled Climate Carbon Cycle Model Intercomparison Project Eyring et al. (2013) document, in greater detail than Table 12.1, the (C4MIP) experiments (Friedlingstein et al., 2006) see Box 6.4. Results implementations of tropospheric and stratospheric ozone in CMIP5 from the two types of experiment cannot be compared directly, but models, including their ozone chemistry schemes and modifications they provide complementary information. Uncertainties in the forward applied to reference data sets in models driven by concentrations. In 1051 Chapter 12 Long-term Climate Change: Projections, Commitments and Irreversibility most models that prescribe ozone, concentrations are based on the lifetime (or second indirect) effect. Many CMIP5 models only include ­ original or slightly modified CMIP5 standard ozone data set comput- the interaction between sulphate aerosol and cloud, and the majority ­ ed as part of the International Global Atmospheric Chemistry/Strat- of them only model the effect of aerosols on cloud albedo rather than ospheric Processes and their Role in Climate (IGAC/SPARC) activity cloud lifetime (Table 12.1). No CMIP5 models represent urban aero- (Cionni et al., 2011). In the stratosphere, this data set is based on sol pollution explicitly so that is not listed in Table 12.1 (see Section observations of the past (Randel and Wu, 2007) continued into the 11.3.5.2 for discussion of future air quality). Only one model (GISS-E2) future with the multi-model mean of 13 chemistry climate models explicitly includes nitrate aerosol as a separate forcing, though it is (CCMs) projections following the SRES A1B (IPCC, 2000) and SRES also included within the total aerosol mixture in the Max Planck Insti- A1 adjusted halogen scenario (WMO, 2007). The stratospheric zonal tute-Earth System Model (MPI-ESM) model versions. mean ozone field is merged with a 3-D tropospheric ozone time series generated as the mean of two CCMs (Goddard Institute of Space Land use change is typically applied by blending anthropogenic land Studies-Physical Understanding of Composition-Climate Interactions surface disturbance via crop and pasture fraction changes with under- and Impacts (GISS-PUCCINI), Shindell et al., 2006; CAM3.5, Lamarque lying land cover maps of natural vegetation, but model variations et al., 2010) in the past and continued by one CCM (CAM3.5) in the in the underlying land cover maps and biome modelling mean that future. Some CMIP5 models (MIROC-ESM, MIROC4h, MIROC5 and the land use forcing agent is impossible to impose in a completely GISS-E2-p1) prescribe ozone concentrations using different data sets common way at present (Pitman et al., 2009). Most CMIP5 models rep- but again following just one GHG scenario in the future for the projec- resent crop and pasture disturbance separately, while some (Canadian tion of stratospheric ozone. In other models (e.g., Institut Pierre Simon Earth System Model (CanESM2), MIROC4h, MIROC5) represent crop Laplace (IPSL)-CM5, CCSM4) ozone is again prescribed, but supplied as but not pasture. Some models (e.g., HadGEM2-ES, MIROC-ESM and concentrations from off-line computations using a related CCM. Some MPI-ESM versions) allow a dynamical representation of natural vege- models determine ozone interactively from specified emissions via tation changes alongside anthropogenic disturbance (see also Sections on-line atmospheric chemistry (CESM1(FASTCHEM), CESM1(WACCM), 9.4.4.3 and 9.4.4.4). CNRM-CM5, GFDL-CM3, GISS-E2-p2, GISS-E2-p3, MIROC-ESM-CHEM, MRI-ESM1; and HadGEM2-ES for tropospheric ozone only). Computing Treatment of the CO2 emissions associated with land cover chang- ozone concentrations interactively allows the fast coupling between es is also model dependent. Some models do not account for land chemistry and climate to be captured, but modelling of chemistry pro- cover changes at all, some simulate the biophysical effects but are cesses is sometimes simplified (CNRM-CM5, CESM(FASTCHEM)) in still forced externally by land cover change induced CO2 emissions (in comparison with full complexity CCMs to reduce the computational emissions-driven simulations), while the most advanced ESMs simu- cost. Compared to CMIP3, in which all models prescribed ozone and late both biophysical effects of land cover changes and their associ- around half of them used a fixed ozone climatology, this leads to sub- ated CO2 emissions. stantial improvement to ozone forcings in CMIP5, although differences remain among the models with interactive chemistry. 12.3.3 Synthesis of Projected Global Mean Radiative Forcing for the 21st Century For atmospheric aerosols, either aerosol precursor emissions-driven or concentration-driven forcings are applied depending on individu- Quantification of future global mean RF is of interest as it is directly al model characteristics (see Sections 7.3 and 7.4 for an assessment related to changes in the global energy balance of the climate system of aerosols processes including aerosol radiation and aerosol cloud and resultant climate change. Chapter 8 discusses RF concepts and interactions). A larger fraction of models in CMIP5 than CMIP3 pre- methods for computing it that form the basis of analysis directly from scribe aerosol precursor emissions rather than concentrations. Many the output of model projections. 12 still prescribe concentrations pre-computed either using a directly relat- ed aerosol CCM or from output of another, complex, emissions-driven We assess three related estimates of projected global mean forc- aerosol chemistry model within the CMIP5 process. As for ozone, aer- ing and its range through the 21st century in the context of forcing osol concentrations provided from off-line simulations help to reduce estimated for the recent past (Figure 12.4). The estimates used are: the computational burden of the projections themselves. For several the total forcings for the defined RCP scenarios, harmonized to RF in of the concentration-driven models (CCSM4, IPSL-CM5A variants, the past (Meinshausen et al., 2011a; Meinshausen et al., 2011c); the MPI-ESM-LR, MPI-ESM-MR), additional emissions-driven simulations total effective radiative forcing (ERF) estimated from CMIP5 models have been undertaken to tailor the prescribed concentrations closely through the 21st century for the four RCP experiments (Forster et al., to the model s individual aerosol climate characteristics. Lamarque et 2013); and that estimated from models in the Atmospheric Chemistry al. (2010, 2011) provided the recommended CMIP5 aerosols data set and Climate Model Intercomparison Project (ACCMIP; Lamarque et which has been used in several of the models driven by concentrations. al., 2013 see Section 8.2.2 ) for RCP time-slice experiments (Shindell Compared with the CMIP3 models, a much larger fraction of CMIP5 et al., 2013b). Methodological differences mean that whereas CMIP5 models now incorporate black and organic carbon aerosol forcings. estimates include both natural and anthropogenic forcings based Also, a larger fraction of CMIP5 than CMIP3 models now includes a entirely on ERF, ACCMIP estimates anthropogenic composition forcing range of processes that combine in the effective RF from aerosol only (neglecting forcing changes due to natural, i.e., solar and volca- cloud interactions (ERFaci; see Section 7.1.3 and Figure 7.3). Previ- nic, and land use factors) based on a combination of ERF for aerosols ously such processes were generally termed aerosol indirect effects, and RF for WMGHG (see Section 8.5.3). Note also that total forcing usually separated into cloud albedo (or first indirect) effect and cloud for the defined RCP scenarios is based on Meinshausen et al. (2011c) 1052 Long-term Climate Change: Projections, Commitments and Irreversibility Chapter 12 but combining total anthropogenic ERF (allowing for efficacies of the CMIP3 models for the A1B scenario using the corresponding method v ­ arious anthropogenic forcings as in Figure 12.3) with natural (solar (Forster and Taylor, 2006). As for CMIP3 models, part of the forcing and volcanic) RF. spread in CMIP5 models (Forster et al., 2013) is consistent with differ- ences in GHG forcings arising from the radiative transfer codes (Col- The CMIP5 multi-model ensemble mean ERF at 2100 (relative to an lins et al., 2006b). Aerosol forcing implementations in CMIP5 models 1850 1869 base period) is 2.2, 3.8, 4.8 and 7.6 W m 2 respectively for also vary considerably, however (Section 12.3.2), leading to a spread RCP2.6, RCP4.5, RCP6.0 and RCP8.5 concentration-driven projections, in aerosol concentrations and forcings which contributes to the overall with a 1- range based on annual mean data for year 2100 of about model spread. A further small source of spread in CMIP5 results pos- +/-0.5 to 1.0 W m 2 depending on scenario (lowest for RCP2.6 and high- sibly arises from an underlying ambiguity in the CMIP5 experimental est for RCP8.5). The CMIP5-based ERF estimates are close to the total design regarding the volcanic forcing offset between the historical forcing at 2100 (relative to an 1850 1859 base period) of 2.4, 4.0, 5.2 experiment versus the pre-industrial control experiment. Most models and 8.0 W m 2 as defined for the four RCPs. implement zero volcanic forcing in the control experiment but some use constant negative forcing equal to the time-mean of historical The spread in ERF indicated from CMIP5 model results with specified volcanic forcing (see Table 12.1 and Section 12.3.2). The effect of this GHG concentration pathways is broadly consistent with that found for volcanic forcing offset persists into the future projections. 10 historical historical (ACCMIP) historical (CMIP5) RCP2.6 (ACCMIP) RCP2.6 RCP4.5 (ACCMIP) RCP2.6 (CMIP5) RCP6.0 (ACCMIP) 8 RCP4.5 RCP8.5 (ACCMIP) RCP4.5 (CMIP5) RCP6.0 RCP6.0 (CMIP5) 19 Radiative Forcing (W m-2) RCP8.5 6 RCP8.5 (CMIP5) 14 20C3M+A1B (CMIP3) +/- 1 Stdev 4 21 23 2 19 17 22 0 12 -2 1980 2000 2020 2040 2060 2080 2100 Year Figure 12.4 | Global mean radiative forcing (RF, W m 2) between 1980 and 2100 estimated by alternative methods. The baseline is circa 1850 but dependent on the methods. Dashed lines indicate the total anthropogenic plus natural (solar and volcanic) RF for the RCP scenarios as defined by Meinshausen et al. (2011c), taking into account the efficacies of the various anthropogenic forcings (Meinshausen et al., 2011a), normalized by the mean between 1850 and 1859. Solid lines are multi-model mean effective radiative forcing (ERF) realized in a subset of CMIP5 models for the concentration-driven historical experiment and RCP scenarios, normalized either with respect to the 1850 1869 base period or with respect to the pre-industrial control simulation (Forster et al., 2013). (The subset of CMIP5 models included is defined by Table 1 of Forster et al. (2013) but omitting the FGOALS-s2 (Flexible Global Ocean-Atmosphere-Land System) model, the historical and RCP simulations of which were subsequently withdrawn from the CMIP5 archive.) This CMIP5-based estimate assumes each model has an invariant climate feedback parameter, calculated from abrupt 4 × CO2 experiments using the method of Gregory et al. (2004). Each individual CMIP5 model s forcing estimate is an average over all available ensemble members, and a 1- inter-model range around the multi-model mean is shaded in light colour. Grey or coloured vertical bars illustrate the 1- range (68% confidence interval) of anthropogenic composition forcing (excluding natural and land use change forcings, based on ERF for aerosols combined with RF for WMGHG) estimated in ACCMIP models (Shindell et al., 2013b) for time slice experiments at 1980, 2000, 2030 (RCP8.5 only) and 2100 (all RCPs). The ACCMIP ranges plotted have been converted from the 5 to 95% ranges given in Shindell et al. (2013b) (Table 8) to a 1- range. Note that the ACCMIP bars at 1980 and 2100 are shifted slightly to aid clarity. The mean ERF diagnosed from 21 CMIP3 models for the SRES A1B scenario, as in Forster and Taylor (2006), is also shown (thick green line) with a 1- range (thinner green lines). The number of models included in CMIP3 and CMIP5 ensemble means is shown colour coded. (See Tables AII.6.8 to AII.6.10. Note that the CMIP5 model ranges given in Table AII.6.10 are based on decadal averages and therefore differ slightly from the ranges based on annual data shown in this figure.) 1053 Chapter 12 Long-term Climate Change: Projections, Commitments and Irreversibility ACCMIP projected forcing at 2030 (for RCP8.5) and 2100 (all RCPs) is (Figure 12.5 showing changes in concentration-driven model simu- systematically higher than corresponding CMIP5 ERF, although with lations). Temperature increases are almost the same for all the RCP some overlap between 1- ranges. CMIP5 and ACCMIP comprise dif- scenarios during the first two decades after 2005 (see Figure 11.25). ferent sets of models and they are related in many but not all cases At longer time scales, the warming rate begins to depend more on (Section 8.2.2). Confining analysis to a subset of closely related models the specified GHG concentration pathway, being highest (>0.3°C per also gives higher forcing estimates from ACCMIP compared to CMIP5 decade) in the highest RCP8.5 and significantly lower in RCP2.6, par- so the discrepancy in multi-model ensemble mean forcings appears ticularly after about 2050 when global surface temperature response unrelated to the different model samples associated with the two stabilizes (and declines thereafter). The dependence of global temper- methods of estimation. The discrepancy is thought to originate mostly ature rise on GHG forcing at longer time scales has been confirmed by from differences in the underlying methodologies used to estimate RF, several studies (Meehl et al., 2007b). In the CMIP5 ensemble mean, but is not yet well understood (see also Section 8.5.3). global warming under RCP2.6 stays below 2°C above 1850-1900 levels throughout the 21st century, clearly demonstrating the potential There is high confidence in projections from ACCMIP models (Shindell of mitigation policies (note that to translate the anomalies in Figure et al., 2013b) based on the GISS-E2 CMIP5 simulations (Shindell et al., 12.5 into anomalies with respect to that period, an assumed 0.61°C 2013a) and an earlier study with a version of the HadGEM2-ES model of observed warming since 1850 1900, as discussed in Section 2.4.3, related to that used in CMIP5 (Bellouin et al., 2011), consistent with should be added). This is in agreement with previous studies of aggres- understanding of the processes controlling nitrate formation (Adams sive mitigation scenarios (Johns et al., 2011; Meehl et al., 2012). Note, et al., 2001), that nitrate aerosols (which provide a negative forcing) however, that some individual ensemble members do show warming will increase substantially over the 21st century under the RCPs (Sec- exceeding 2°C above 1850-1900 (see Table 12.3). As for the other tion 8.5.3, Figure 8.20). The magnitude of total aerosol-related forcing pathways, global warming exceeds 2°C within the 21st century under (also negative in sign) will therefore tend to be underestimated in the RCP4.5, RCP6.0 and RCP8.5, in qualitative agreement with previous CMIP5 multi-model mean ERF, as nitrate aerosol has been omitted as a studies using the SRES A1B and A2 scenarios (Joshi et al., 2011). Global forcing from almost all CMIP5 models. mean temperature increase exceeds 4°C under RCP8.5 by 2100. The CMIP5 concentration-driven global temperature projections are broad- Natural RF variations are, by their nature, difficult to project reliably ly similar to CMIP3 SRES scenarios discussed in AR4 (Meehl et al., (see Section 8.4). There is very high confidence that Industrial Era nat- 2007b) and Section 12.4.9, although the overall range of the former ural forcing has been a small fraction of the (positive) anthropogenic is larger primarily because of the low-emission mitigation pathway forcing except for brief periods following large volcanic eruptions (Sec- RCP2.6 (Knutti and Sedláèek, 2013). tions 8.5.1 and 8.5.2). Based on that assessment and the assumption that variability in natural forcing remains of a similar magnitude and The multi-model global mean temperature changes under different character to that over the Industrial Era, total anthropogenic forcing RCPs are summarized in Table 12.2. The relationship between cumu- relative to pre-industrial, for any of the RCP scenarios through the 21st lative anthropogenic carbon emissions and global temperature is century, is very likely to be greater in magnitude than changes in natu- assessed in Section 12.5 and only concentration-driven models are ral (solar plus volcanic) forcing on decadal time scales. In summary, global mean forcing projections derived from climate models exhibit a substantial range for the given RCP scenarios in con- 12 17 centration-driven experiments, contributing to the projected global 12 39 mean temperature range (Section 12.4.1). Forcings derived from 25 42 12 ACCMIP models for 2100 are systematically higher than those estimat- 32 ed from CMIP5 models for reasons that are not fully understood but are partly due to methodological differences. The multi-model mean estimate of combined anthropogenic plus natural forcing from CMIP5 is consistent with indicative RCP forcing values at 2100 to within 0.2 42 models to 0.4 W m 2. 12.4 Projected Climate Change over the 21st Century Figure 12.5 | Time series of global annual mean surface air temperature anomalies 12.4.1 Time-Evolving Global Quantities (relative to 1986 2005) from CMIP5 concentration-driven experiments. Projections are shown for each RCP for the multi-model mean (solid lines) and the 5 to 95% range 12.4.1.1 Projected Changes in Global Mean Temperature and (+/-1.64 standard deviation) across the distribution of individual models (shading). Dis- continuities at 2100 are due to different numbers of models performing the exten- Precipitation sion runs beyond the 21st century and have no physical meaning. Only one ensemble member is used from each model and numbers in the figure indicate the number of A consistent and robust feature across climate models is a continua- different models contributing to the different time periods. No ranges are given for the tion of global warming in the 21st century for all the RCP scenarios RCP6.0 projections beyond 2100 as only two models are available. 1054 Long-term Climate Change: Projections, Commitments and Irreversibility Chapter 12 Table 12.2 | CMIP5 annual mean surface air temperature anomalies (°C) from the 1986 2005 reference period for selected time periods, regions and RCPs. The multi-model mean +/-1 standard deviation ranges across the individual models are listed and the 5 to 95% ranges from the models distribution (based on a Gaussian assumption and obtained by multiplying the CMIP5 ensemble standard deviation by 1.64) are given in brackets. Only one ensemble member is used from each model and the number of models differs for each RCP (see Figure 12.5) and becomes significantly smaller after 2100. No ranges are given for the RCP6.0 projections beyond 2100 as only two models are available. Using Hadley Centre/Climate Research Unit gridded surface temperature data set 4 (HadCRUT4) and its uncertainty estimate (5 to 95% confidence interval), the observed warming to the 1986 2005 reference period (see Section 2.4.3) is 0.61°C +/- 0.06°C (1850 1900), 0.30°C +/- 0.03°C (1961 1990), 0.11°C +/- 0.02°C (1980 1999). Decadal values are provided in Table AII.7.5, but note that percentiles of the CMIP5 distributions cannot directly be interpreted in terms of calibrated language. RCP2.6 (T in °C) RCP4.5 (T in °C) RCP6.0 (T in °C) RCP8.5 (T in °C) Global: 2046 2065 1.0 +/- 0.3 (0.4, 1.6) 1.4 +/- 0.3 (0.9, 2.0) 1.3 +/- 0.3 (0.8, 1.8) 2.0 +/- 0.4 (1.4, 2.6) 2081 2100 1.0 +/- 0.4 (0.3, 1.7) 1.8 +/- 0.5 (1.1, 2.6) 2.2 +/- 0.5 (1.4, 3.1) 3.7 +/- 0.7 (2.6, 4.8) 2181 2200 0.7 +/- 0.4 (0.1, 1.3) 2.3 +/- 0.5 (1.4, 3.1) 3.7 +/- 0.7 (-,-) 6.5 +/- 2.0 (3.3, 9.8) 2281 2300 0.6 +/- 0.3 (0.0, 1.2) 2.5 +/- 0.6 (1.5, 3.5) 4.2 +/- 1.0 (-,-) 7.8 +/- 2.9 (3.0, 12.6) Land: 2081 2100 1.2 +/- 0.6 (0.3, 2.2) 2.4 +/- 0.6 (1.3, 3.4) 3.0 +/- 0.7 (1.8, 4.1) 4.8 +/- 0.9 (3.4, 6.2) Ocean: 2081 2100 0.8 +/- 0.4 (0.2, 1.4) 1.5 +/- 0.4 (0.9, 2.2) 1.9 +/- 0.4 (1.1, 2.6) 3.1 +/- 0.6 (2.1, 4.0) Tropics: 2081 2100 0.9 +/- 0.3 (0.3, 1.4) 1.6 +/- 0.4 (0.9, 2.3) 2.0 +/- 0.4 (1.3, 2.7) 3.3 +/- 0.6 (2.2, 4.4) Polar: Arctic: 2081 2100 2.2 +/- 1.7 (-0.5, 5.0) 4.2 +/- 1.6 (1.6, 6.9) 5.2 +/- 1.9 (2.1, 8.3) 8.3 +/- 1.9 (5.2, 11.4) Polar: Antarctic: 2081 2100 0.8 +/- 0.6 (-0.2, 1.8) 1.5 +/- 0.7 (0.3, 2.7) 1.7 +/- 0.9 (0.2, 3.2) 3.1 +/- 1.2 (1.1, 5.1) included here. Warming in 2046 2065 is slightly larger under RCP4.5 TCR coincides with the assessed likely range of the TCR (see Section compared to RCP6.0, consistent with its greater total anthropogenic 12.4.1.2 below and Box 12.2). Based on this assessment, global mean forcing at that time (see Table A.II.6.12). For all other periods the mag- temperatures averaged in the period 2081 2100 are projected to nitude of global temperature change increases from RCP2.6 to RCP8.5. likely exceed 1.5°C above 1850-1900 for RCP4.5, RCP6.0 and RCP8.5 Beyond 2100, RCP2.6 shows a decreasing trend whereas under all (high confidence). They are also likely to exceed 2°C above 1850-1900 other RCPs warming continues to increase. Also shown in Table 12.2 for RCP6.0 and RCP8.5 (high confidence) and more likely than not are projected changes at 2081 2100 averaged over land and ocean to exceed 2°C for RCP4.5 (medium confidence). Temperature change separately as well as area-weighted averages over the Tropics (30°S above 2°C under RCP2.6 is unlikely but is assessed only with medium to 30°N), Arctic (67.5°N to 90°N) and Antarctic (90°S to 55°S) regions. confidence as some CMIP5 ensemble members do produce a global Surface air temperatures over land warm more than over the ocean, mean temperature change above 2°C. Warming above 4°C by 2081 and northern polar regions warm more than the tropics. The excess of 2100 is unlikely in all RCPs (high confidence) except RCP8.5. Under land mass in the Northern Hemisphere (NH) in comparison with the the latter, the 4°C global temperature level is exceeded in more than Southern Hemisphere (SH), coupled with the greater uptake of heat by half of ensemble members, and is assessed to be about as likely as not the Southern Ocean in comparison with northern ocean basins means (medium confidence). Note that the likelihoods of exceeding specific that the NH generally warms more than the SH. Arctic warming is much temperature levels show some sensitivity to the choice of reference greater than in the Antarctic, due to the presence of the Antarctic ice period (see Section 11.3.6.3). sheet and differences in local responses in snow and ice. Mechanisms behind these features of warming are discussed in Section 12.4.3. CMIP5 models on average project a gradual increase in global precip- Maps and time series of regional temperature changes are displayed in itation over the 21st century: change exceeds 0.05 mm day 1 (~2% Annex I and regional averages are discussed in Section 14.8.1. of global precipitation) and 0.15 mm day 1 (~5% of global precipi- 12 tation) by 2100 in RCP2.6 and RCP8.5, respectively. The relationship Global annual multi-model mean temperature changes above 1850- between global precipitation and global temperature is approximately 1900 are listed in Table 12.3 for the 2081 2100 period (assuming linear (Figure 12.6). The precipitation sensitivity, that is, the change of 0.61°C warming since 1850 1900 as discussed in Section 2.4.3) global precipitation with temperature, is about 1 to 3% °C 1 in most along with the percentage of 2081 2100 projections from the CMIP5 models, tending to be highest for RCP2.6 and RCP4.5 (Figure 12.7; models exceeding policy-relevant temperature levels under each RCP. note that only global values are discussed in this section, ocean and These complement a similar discussion for the near-term projections land changes are discussed in Section 12.4.5.2). These behaviours are in Table 11.3 which are based on the CMIP5 ensemble as well as consistent with previous studies, including CMIP3 model projections evidence (discussed in Sections 10.3.1, 11.3.2.1.1 and 11.3.6.3) that for SRES scenarios and AR4 constant composition commitment exper- some CMIP5 models have a higher sensitivity to GHGs and a larger iments (Meehl et al., 2007b), and ENSEMBLES multi-model results for response to other anthropogenic forcings (dominated by the effects SRES A1B and E1 scenarios (Johns et al., 2011). of aerosols) than the real world (medium confidence). The percent- age calculations for the long-term projections in Table 12.3 are based The processes that govern global precipitation changes are now well solely on the CMIP5 ensemble, using one ensemble member for each understood and have been presented in Section 7.6. They are briefly model. For these long-term projections, the 5 to 95% ranges of the summarized here and used to interpret the long-term projected chang- CMIP5 model ensemble are considered the likely range, an assess- es. The precipitation sensitivity (about 1 to 3% °C 1) is very different ment based on the fact that the 5 to 95% range of CMIP5 models from the water vapour sensitivity (~7% °C 1) as the main physical 1055 Chapter 12 Long-term Climate Change: Projections, Commitments and Irreversibility Table 12.3 | CMIP5 global annual mean temperature changes above 1850-1900 for the 2081 2100 period of each RCP scenario (mean, +/-1 standard deviation and 5 to 95% ranges based on a Gaussian assumption and obtained by multiplying the CMIP5 ensemble standard deviation by 1.64), assuming 0.61°C warming has occurred prior to 1986 2005 (second column). For a number of temperature levels (1°C, 1.5°C, 2°C, 3°C and 4°C), the proportion of CMIP5 model projections for 2081 2100 above those levels under each RCP scenario are listed. Only one ensemble member is used for each model. T (°C) T > +1.0°C T > +1.5°C T > +2.0°C T > +3.0°C T > +4.0°C 2081 2100 RCP2.6 1.6 +/- 0.4 (0.9, 2.3) 94% 56% 22% 0% 0% RCP4.5 2.4 +/- 0.5 (1.7, 3.2) 100% 100% 79% 12% 0% RCP6.0 2.8 +/- 0.5 (2.0, 3.7) 100% 100% 100% 36% 0% RCP8.5 4.3 +/- 0.7 (3.2, 5.4) 100% 100% 100% 100% 62% laws that drive these changes also differ. Water vapour increases are circulation, moisture and temperature (Mitchell et al., 1987; Boer, 1993; p ­ rimarily a consequence of the Clausius Clapeyron relationship asso- Vecchi and Soden, 2007; Previdi, 2010; O Gorman et al., 2012). Indeed, ciated with increasing temperatures in the lower troposphere (where the radiative cooling of the atmosphere is balanced by latent heat- most atmospheric water vapour resides). In contrast, future precipi- ing (associated with precipitation) and sensible heating. Since AR4, tation changes are primarily the result of changes in the energy bal- the changes in heat balance and their effects on precipitation have ance of the atmosphere and the way that these later interact with been analyzed in detail for a large variety of forcings, simulations and models (Takahashi, 2009a; Andrews et al., 2010; Bala et al., 2010; Ming et al., 2010; O Gorman et al., 2012; Bony et al., 2013). 0.3 a Precipitation change (mm day-1) RCP2.6 An increase of CO2 decreases the radiative cooling of the troposphere RCP4.5 and reduces precipitation (Andrews et al., 2010; Bala et al., 2010). On RCP6.0 longer time scales than the fast hydrological adjustment time scale 0.2 (Andrews et al., 2010; Bala et al., 2010; Cao et al., 2012; Bony et al., RCP8.5 2013), the increase of CO2 induces a slow increase of temperature and water vapour, thereby enhancing the radiative cooling of the atmos- phere and increasing global precipitation (Allen and Ingram, 2002; 0.1 Yang et al., 2003; Held and Soden, 2006). Even after the CO2 forcing stabilizes or begins to decrease, the ocean continues to warm, which then drives up global temperature, evaporation and precipitation. In addition, nonlinear effects also affect precipitation changes (Good et 0.0 al., 2012). These different effects explain the steepening of the precip- 0 1 2 3 4 5 6 itation versus temperature relationship in RCP2.6 and RCP4.5 scenari- Temperature change (°C) os (Figure 12.6), as RF stabilizes and/or declines from the mid-century (Figure 12.4). In idealized CO2 ramp-up/ramp-down experiments, this 0.3 effect produces an hydrological response overshoot (Wu et al., 2010). b Precipitation change (mm day-1) RCP2.6 An increase of absorbing aerosols warms the atmosphere and reduces precipitation, and the surface temperature response may be too small RCP4.5 to compensate this decrease (Andrews et al., 2010; Ming et al., 2010; 12 0.2 RCP6.0 Shiogama et al., 2010a). Change in scattering aerosols or incoming RCP8.5 solar radiation modifies global precipitation mainly via the response of the surface temperature (Andrews et al., 2009; Bala et al., 2010). 0.1 The main reasons for the inter-model spread of the precipitation sen- sitivity estimate among GCMs have not been fully understood. Never- theless, spread in the changes of the cloud radiative effect has been shown to have an impact (Previdi, 2010), although the effect is less 0.0 important for precipitation than it is for the climate sensitivity esti- 0 1 2 3 4 5 6 mate (Lambert and Webb, 2008). The lapse rate plus water vapour Temperature change (°C) feedback and the response of the surface heat flux (Previdi, 2010; O Gorman et al., 2012), the shortwave absorption by water vapour Figure 12.6 | Global mean precipitation (mm day 1) versus temperature (°C) changes (Takahashi, 2009b) or by aerosols, have been also identified as impor- relative to 1986 2005 baseline period in CMIP5 model concentrations-driven projec- tant factors. tions for the four RCPs for (a) means over decadal periods starting in 2006 and over- lapped by 5 years (2006 2015, 2011 2020, up to 2091 2100), each line representing a different model (one ensemble member per model) and (b) corresponding multi-model Global precipitation sensitivity estimates from observations are means for each RCP. very sensitive to the data and the time period considered. Some 1056 Long-term Climate Change: Projections, Commitments and Irreversibility Chapter 12 6 models have simulated all scenarios. To test the effect of undersam- CMIP5 CMIP5 CMIP5 ENSEMBLES Global Land Ocean Global pling, and to generate a consistent set of ­uncertainties across ­scenarios, 4 a step response method that estimates the total warming as sum of responses to small forcing steps (Good et al., 2011a) is used to emulate 2 23 CMIP5 models under the different scenarios (those 23 models that dP/dT (% °C-1) supplied the necessary simulations to compute the emulators, i.e., CO2 step change experiments). This provides means and ranges (5 to 95%) 0 that are comparable across scenarios (blue). See also Section 12.4.9 for a discussion focussed on the differences between CMIP3 and CMIP5 -2 projections of global average temperature changes. -4 For the CO2 concentration-driven simulations (Figure 12.8a), the dom- inant driver of uncertainty in projections of global temperature for the 2.6 4.5 6.0 8.5 2.6 4.5 6.0 8.5 2.6 4.5 6.0 8.5 E1 A1B higher RCPs beyond 2050 is the transient climate response (TCR), for RCP RCP RCP RCP2.6, which is closer to equilibrium by the end of the century, it is Figure 12.7 | Percentage changes over the 21st century in global, land and ocean pre- both the TCR and the equilibrium climate sensitivity (ECS). In a tran- cipitation per degree Celsius of global warming in CMIP5 model concentration-driven sient situation, the ratio of temperature to forcing is approximately projections for the four RCP scenarios. Annual mean changes are calculated for each constant and scenario independent (Meehl et al., 2007b, Appendix year between 2006 and 2100 from one ensemble member per model relative to its 10.A.1; Gregory and Forster, 2008; Knutti et al., 2008b; Good et al., mean precipitation and temperature for the 1986 2005 baseline period, and the gradi- 2013). Therefore, the uncertainty in TCR maps directly into the uncer- ent of a least-squares fit through the annual data is derived. Land and ocean derived values use global mean temperature in the denominator of dP/dT. Each coloured tainty in global temperature projections for the RCPs other than symbol represents a different model, the same symbol being used for the same model RCP2.6. The assessed likely range of TCR based on various lines of for different RCPs and larger black squares being the multi-model mean. Also shown evidence (see Box 12.2) is similar to the 5 to 95% percentile range for comparison are global mean results for ENSEMBLES model concentrations-driven of TCR in CMIP5. In addition, the assessed likely range of ECS is also projections for the E1 and A1B scenarios (Johns et al., 2011), in this case using a least- consistent with the CMIP5 range (see Box 12.2). There is little evidence squares fit derived over the period 2000 2099 and taking percentage changes relative to the 1980 1999 baseline period. Changes of precipitation over land and ocean are that the CMIP5 models are significantly over- or underestimating the discussed in Section 12.4.5.2. RF. The RF uncertainty is small compared to response uncertainty (see Figure 12.4), and is considered by treating the 5 to 95% as a likely rather than very likely range. Kuhlbrodt and Gregory (2012) suggest o ­ bservational studies suggest precipitation sensitivity values higher that models might be overestimating ocean heat uptake, as previously than model estimates (Wentz et al., 2007; Zhang et al., 2007), although suggested by Forest et al. (2006), but observationally constrained esti- more recent studies suggest consistent values (Adler et al., 2008; Li et mates of TCR are unaffected by that. The ocean heat uptake efficiency al., 2011b). does not contribute much to the spread of TCR (Knutti and Tomassini, 2008; Kuhlbrodt and Gregory, 2012). 12.4.1.2 Uncertainties in Global Quantities Therefore, for global mean temperature projections only, the 5 to 95% Uncertainties in global mean quantities arise from variations in internal range (estimated as 1.64 times the sample standard deviation) of the natural variability, model response and forcing pathways. Table 12.2 CMIP5 projections can also be interpreted as a likely range for future gives two measures of uncertainty in the CMIP5 model projections, temperature change between about 2050 and 2100. Confidence in this the standard deviation and the 5 to 95% range across the ensemble s assessment is high for the end of the century because the warming 12 distribution. Because CMIP5 was not designed to explore fully the then is dominated by CO2 and the TCR. Confidence is only medium for uncertainty range in projections (see Section 12.2), neither its stand- mid-century when the contributions of RF and initial conditions to the ard deviation nor its range can be interpreted directly as an uncer- total temperature response uncertainty are larger. The likely ranges are tainty statement about the corresponding real quantities, and other an expert assessment, taking into account many lines of evidence, in techniques and arguments to assess uncertainty in future projections much the same way as in AR4 (Figure SPM.5), and are not probabilistic. must be considered. Figure 12.8 summarizes the uncertainty ranges The likely ranges for 2046 2065 do not take into account the possible in global mean temperature changes at the end of the 21st century influence of factors that lead to near-term (2016 2035) projections of under the various scenarios quantified by various methods. Individual global mean surface temperature (GMST) that are somewhat cooler CMIP5 models are shown by red crosses. Red bars indicate mean and than the 5 to 95% model ranges (see Section 11.3.6), because the 5 to 95% percentiles based on assuming a normal distribution for the influence of these factors on longer term projections cannot be quan- CMIP5 sample (i.e., +/-1.64 standard deviations). Estimates from the tified. A few recent studies indicate that some of the models with the simple climate carbon cycle Model for the Assessment of Greenhouse strongest transient climate response might overestimate the near term Gas-Induced Climate Change (MAGICC; Meinshausen et al., 2011a; warming (Otto et al., 2013; Stott et al., 2013) (see Sections 10.8.1, Meinshausen et al., 2011b) calibrated to C4MIP (Friedlingstein et al., 11.3.2.1.1), but there is little evidence of whether and how much that 2006) carbon cycle models, assuming a PDF for climate sensitivity that affects the long-term warming response. One perturbed physics ensem- corresponds to the assessment of IPCC AR4 (Meehl et al., 2007b, Box ble combined with observations indicates warming that exceeds the 10.2), are given as yellow bars (Rogelj et al., 2012). Note that not all AR4 at the top end but used a relatively short time period of warming 1057 Chapter 12 Long-term Climate Change: Projections, Commitments and Irreversibility (50 years) to constrain the models projections (Rowlands et al., 2012) Temperature increase in 2081-2100 Temperature increase in 2081-2100 Concentration-driven (see Sections 11.3.2.1.1 and 11.3.6.3). GMSTs for 2081 2100 (rela- 7 relative to 1986 to 2005 (°C) tive to 1986 2005) for the CO2 concentration driven RCPs is therefore 6 a assessed to likely fall in the range 0.3°C to 1.7°C (RCP2.6), 1.1°C to 5 2.6°C (RCP4.5), 1.4°C to 3.1°C (RCP6.0), and 2.6°C to 4.8°C (RCP8.5) 4 estimated from CMIP5. Beyond 2100, the number of CMIP5 simula- 3 tions is insufficient to estimate a likely range. Uncertainties before 2 2050 are assessed in Section 11.3.2.1.1. The assessed likely range is 1 very similar to the range estimated by the pulse response model, sug- 0 gesting that the different sample of models for the different RCPs are RCP2.6 RCP4.5 RCP6.0 RCP8.5 not strongly affecting the result, and providing further support that Emission-driven 7 b relative to 1986 to 2005 (°C) this pulse response technique can be used to emulate temperature and 6 ocean heat uptake in Chapter 13 and Section 12.4.9. The results are 5 consistent with the probabilistic results from MAGICC, which for the 4 lower RCPs have a slightly narrower range due to the lack of inter- 3 nal variability in the simple model, and the fact that non-CO2 forcings 2 are treated more homogeneously than in CMIP5 (Meinshausen et al., 1 2011a, 2011b). This is particularly pronounced for RCP2.6 where the CMIP5 range is substantially larger, partly due to the larger fraction of 0 RCP2.6 RCP4.5 RCP6.0 RCP8.5 non-CO2 forcings in that scenario. Rogelj et al. 2012 Good et al. 2011 CMIP5 models 90% range 5-95th perc. range likely range -40 to +60% range The uncertainty estimate in AR4 for the SRES scenarios was 40% to 66% range 50th percentile around mean median +60% around the CMIP3 means (shown here in grey for comparison). multimodel mean That range was asymmetric and wider for the higher scenarios because it included the uncertainty in carbon cycle climate feedbacks. The SRES Figure 12.8 | Uncertainty estimates for global mean temperature change in 2081 scenarios are based on the assumption of prescribed emissions, which 2100 with respect to 1986 2005. Red crosses mark projections from individual CMIP5 then translates to uncertainties in concentrations that propagate models. Red bars indicate mean and 5 to 95% ranges based on CMIP5 (1.64 standard through to uncertainties in the temperature response. The RCP sce- deviations), which are considered as a likely range. Blue bars indicate 5 to 95% ranges from the pulse response emulation of 21 models (Good et al., 2011a). Grey bars mark narios assume prescribed concentrations. For scenarios that stabilize the range from the mean of CMIP5 minus 40% to the mean +60%, assessed as likely in (RCP2.6) that approach of constant fractional uncertainty underes- AR4 for the SRES scenarios. The yellow bars show the median, 17 to 83% range and 5 timates the uncertainty and is no longer applicable, mainly because to 95% range based on Rogelj et al. (2012). See also Figures 12.39 and 12.40. internal variability has a larger relative contribution to the total uncer- tainty (Good et al., 2013; Knutti and Sedláèek, 2013). For the RCPs, the carbon cycle climate feedback uncertainty is not included because compared between AR4 and AR5. The main reason is that uncertain- the simulations are driven by concentrations. Furthermore, there is no ties in carbon cycle feedbacks are not considered in the concentration clear evidence that distribution of CMIP5 global temperature changes driven RCPs. In contrast, the likely range in AR4 included those. The deviates from a normal distribution. For most other variables the shape assessed likely ranges are therefore narrower for the high RCPs. The of the distribution is unclear, and standard deviations are simply used differences in the projected warming are largely attributable to the dif- as an indication of model spread, not representing a formal uncertainty ference in scenarios (Knutti and Sedláèek, 2013), and the change in the 12 assessment. future and reference period, rather than to developments in modelling since AR4. A detailed comparison between the SRES and RCP scenarios Simulations with prescribed CO2 emissions rather than concentrations and the CMIP3 and CMIP5 models is given in Section 12.4.9. are only available for RCP8.5 (Figure 12.8b) and from MAGICC. The projected temperature change in 2100 is slightly higher and the uncer- 12.4.2 Pattern Scaling tainty range is wider as a result of uncertainties in the carbon cycle climate feedbacks. The CMIP5 range is consistent with the uncertainty 12.4.2.1 Definition and Use range given in AR4 for SRES A2 in 2100. Further details about emission versus concentration driven simulations are given in Section 12.4.8. In this chapter we show geographical patterns of projected changes in climate variables according to specific scenarios and time horizons. In summary, the projected changes in global temperature for 2100 in Alternative scenarios and projection times can be inferred from those the RCP scenarios are very consistent with those obtained by CMIP3 shown by using some established approximation methods. This is espe- for SRES in IPCC AR4 (see Section 12.4.9) when taking into account the cially the case for large-scale regional patterns of average temperature differences in scenarios. The likely uncertainty ranges provided here are and with additional caveats precipitation changes. In fact, pattern similar for RCP4.5 and RCP6.0 but narrower for RCP8.5 compared to scaling is an approximation that has been explicitly suggested in the AR4. There was no scenario as low as RCP2.6 in AR4. The uncertainties description of the RCPs (Moss et al., 2010) as a method for deriving in global temperature projections have not decreased significantly in impact-relevant regional projections for scenarios that have not been CMIP5 (Knutti and Sedláèek, 2013), but the assessed ranges cannot be simulated by global and regional climate models. It was first proposed 1058 Long-term Climate Change: Projections, Commitments and Irreversibility Chapter 12 by Santer et al. (1990) and revisited later by numerous studies (e.g., is given in Figure 12.9 for surface air temperature from each of the Huntingford and Cox, 2000). It relies on the existence of robust geo- CMIP5 models highlighting both similarities and differences between graphical patterns of change, emerging at the time when the response the responses of different models. The precipitation pattern was shown to external forcings emerges from the noise, and persisting across the to scale linearly with global average temperature to a sufficient accu- length of the simulation, across different scenarios, and even across racy in CMIP3 models (Neelin et al., 2006) for this to be useful for models, modulated by the corresponding changes in global average projections related to the hydrological cycle. Shiogama et al. (2010b) temperature. The robustness of temperature change patterns has find similar results with the caution that in the early stages of warming been amply documented from the original paper onward. An example aerosols modify the pattern. A more mixed evaluation can be found in Annual mean surface air temperature change (RCP4.5: 2081-2100) 12 Figure 12.9 | Surface air temperature change in 2081 2100 displayed as anomalies with respect to 1986 2005 for RCP4.5 from one ensemble member of each of the concen- tration-driven models available in the CMIP5 archive. 1059 Chapter 12 Long-term Climate Change: Projections, Commitments and Irreversibility Good et al. (2012), where some land areas in the low latitudes exhibit a for CMIP3 in Watterson and Whetton, 2011b), but uncertainty can be nonlinear relation to global average temperature, but, largely, average characterized, for example, by the inter-model spread in the pattern precipitation change over the remaining regions can be well approx- c(x). Recent applications of the methodology to probabilistic future imated by a grid-point specific linear function of global average tem- projections have in fact sought to fully quantify errors introduced by perature change. It is in the latter quantity that the dependence of the the approximation, on the basis of the available coupled model runs evolution of the change in time on the model (e.g., its climate sensitivi- (Harris et al., 2006). ty) and the forcing (e.g., the emission scenario) is encapsulated. Pattern scaling and its applications have been documented in IPCC In analytical terms, it is assumed that the following relation holds: WGI Reports before (IPCC, 2001, Section 13.5.2.1; Meehl et al., 2007b, Section 10.3.2). It has been used extensively for regional tempera- C (t,x) = TG(t) c(x) + R (t, x) ture and precipitation change projections, for example, Murphy et al. (2007), (Watterson, 2008), Giorgi (2008), Harris et al. (2006, 2010), May where the symbol x identifies the geographic location (model grid (2008a), Ruosteenoja et al. (2007), Räisänen and Ruokolainen (2006), point or other spatial coordinates) and possibly the time of year (e.g., Cabre et al. (2010) and impact studies, for example, as described in a June July August average). The index t runs along the length of the Dessai et al. (2005) and Fowler et al. (2007b). Recent studies have forcing scenario of interest. TG(t) indicates global average temperature focussed on patterns linked to warming at certain global average tem- change at time t under this scenario; c(x) is the time-invariant geo- perature change thresholds (e.g., May, 2008a; Sanderson et al., 2011) graphic pattern of change per 1°C global surface temperature change and patterns derived under the RCPs (Ishizaki et al., 2012). for the variable of interest (which represents the forced component of the change) and C (t,x) is the actual field of change for that variable There are basic limitations to this approach, besides a degradation of at the specific time t under this scenario. The R (t, x) is a residual term its performance as the regional scale of interest becomes finer and in and highlights the fact that pattern scaling cannot reconstruct model the presence of regionally specific forcings. Recent work with MIROC3.2 behaviour with complete accuracy due to both natural variability and (Shiogama et al., 2010a; Shiogama et al., 2010b) has revealed a depend- because of limitations of the methodology discussed below. This way, ence of the precipitation sensitivity (global average precipitation change regionally and temporally differentiated results under different scenar- per 1°C of global warming see Figure 12.6) on the scenario, due to the ios or climate sensitivities can be approximated by the product of a precipitation being more sensitive to carbon aerosols than WMGHGs. spatial pattern, constant over time, scenario and model characteristics, In fact, there are significant differences in black and organic carbon and a time evolving global mean change in temperature. Model and aerosol forcing between the emission scenarios investigated by Shiog- scenario dependence are thus captured through the global mean tem- ama et al. (2010a; 2010b). Levy II et al. (2013) confirm that patterns of perature response, and simple climate models calibrated against fully precipitation change are spatially correlated with the sources of aerosol coupled climate models can be used to simulate the latter, at a great emissions, in simulations where the indirect effect is represented. This saving in computational cost. The spatial pattern can be estimated is a behaviour that is linked to a more general limitation of pattern through the available coupled model simulations under the assump- scaling, which breaks down if aerosol forcing is significant. The effects tion that it does not depend on the specific scenario(s) used. of aerosols have a regional nature and are thus dependent on the future sources of pollution which are likely to vary geographically in the future The choice of the pattern in the studies available in the literature can be and are difficult to predict (May, 2008a). For example, Asian and North as simple as the ensemble average field of change (across models and/ American aerosol production are likely to have different time histories or across scenarios, for the coupled experiments available), normalized and future projections. Schlesinger et al. (2000) extended the method- by the corresponding change in global average temperature, choosing ology of pattern scaling by isolating and recombining patterns derived 12 a segment of the simulations when the signal has emerged from the by dedicated experiments with a coupled climate model where sulphate noise of natural variability from a baseline of reference (e.g., the last aerosols were increased for various regions in turn. More recently, in 20 years of the 21st century compared to pre-industrial or current cli- an extension of pattern scaling into a probabilistic treatment of model, mate) and taking the difference of two multi-decadal means. Similar scenario and initial condition uncertainties, Frieler et al. (2012) derived properties and results have been obtained using more sophisticated joint probability distributions for regionally averaged temperature and multivariate procedures that optimize the variance explained by the precipitation changes as linear functions of global average temperature pattern (Holden and Edwards, 2010). The validity of this approximation and additional predictors including regionally specific sulphate aerosol is discussed by Mitchell et al. (1999) and Mitchell (2003). Huntingford and black carbon emissions. and Cox (2000) evaluate the quality of the approximation for numer- ous variables, showing that the technique performs best for temper- Pattern scaling is less accurate for strongly mitigated stabilization ature, downward longwave radiation, relative humidity, wind speeds scenarios. This has been shown recently by May (2012), compar- and surface pressure while showing relatively larger limitations for ing patterns of temperature change under a scenario limiting global rainfall rate anomalies. Joshi et al. (2013) have recently shown that the warming since pre-industrial times to 2°C and patterns produced by accuracy of the approximation, especially across models, is improved a scenario that reaches 4.5°C of global average temperature change. by adding a second term, linear in the land sea surface warming ratio, The limitations of pattern scaling in approximating changes while the another quantity that can be easily estimated from existing coupled climate system approaches equilibrium have found their explanation in climate model simulations. There exist of course differences between Manabe and Wetherald (1980) and Mitchell et al. (1999). Both studies the patterns generated by different GCMs (documented for example point out that as the temperatures of the deep oceans reach equilibri- 1060 Long-term Climate Change: Projections, Commitments and Irreversibility Chapter 12 um (over multiple centuries) the geographical distribution of warming Pattern scaling has not been as thoroughly explored for quantities changes as well, for example, showing a larger warming of the high other than average temperature and precipitation. Impact relevant latitudes in the SH than in the earlier periods of the transient response, extremes, for example, seem to indicate a critical dependence on the relative to the global mean warming. More recently, Held et al. (2010) scale at which their changes are evaluated, with studies showing that showed how this slow warming pattern is in fact present during the some aspects of their statistics change in a close-to-linear way with initial transient response of the system as well, albeit with much small- mean temperature (Kharin et al., 2007; Lustenberger et al., 2013) while er amplitude. Further, Gillett et al. (2011) show how in a simulation in others have documented the dependence of their changes on moments which emissions cease, regional temperatures and precipitation pat- of their statistical distribution other than the mean (Ballester et al., terns exhibit ongoing changes, even though global mean temperature 2010a), which would make pattern scaling inadequate. remains almost constant. Wu et al. (2010) showed that the global pre- cipitation response shows a nonlinear response to strong mitigation 12.4.2.2 Coupled Model Intercomparison Project Phase 5 Patterns scenarios, with the hydrological cycle continuing to intensify even after Scaled by Global Average Temperature Change atmospheric CO2 concentration, and thus global average temperature, start decreasing. Regional nonlinear responses to mitigation scenari- On the basis of CMIP5 simulations, we show geographical patterns os of precipitation and sea surface temperatures (SSTs) are shown by (Figure 12.10) of warming and precipitation change and indicate Chadwick et al. (2013). measures of their variability across models and across RCPs. The pat- terns are scaled to 1°C global mean surface temperature change above Other areas where pattern scaling shows a lack of robustness are the the reference period 1986 2005 for 2081 2100 (first row) and for a edges of polar ice caps and sea ice extent, where at an earlier time in period of approximate stable temperature, 2181 2200 (thus excluding the simulation ice melts and regions of sharp gradient surface, while RCP8.5, which does not stabilize by that time) (second row). Spatial later in the simulation, in the absence of ice, the gradient will become correlation of fields of temperature and precipitation change range less steep. Different sea ice representations in models also make the from 0.93 to 0.99 when considering ensemble means under different location of such regions much less robust across the model ensembles RCPs. The lower values are found when computing correlation between and the scenarios. RCP2.6 and the higher RCPs, and may be related to the high ­ itigation m Temperature scaled by global T (oC per oC) Precipitation scaled by global T (% per oC) 12 Figure 12.10 | Temperature (left) and precipitation (right) change patterns derived from transient simulations from the CMIP5 ensembles, scaled to 1°C of global mean surface temperature change. The patterns have been calculated by computing 20-year averages at the end of the 21st (top) and 22nd (bottom) centuries and over the period 1986 2005 for the available simulations under all RCPs, taking their difference (percentage difference in the case of precipitation) and normalizing it, grid-point by grid-point, by the cor- responding value of global average temperature change for each model and scenario. The normalized patterns have then been averaged across models and scenarios. The colour scale represents degrees Celsius (in the case of temperature) and percent (in the case of precipitation) per 1°C of global average temperature change. Stippling indicates where the mean change averaged over all realizations is larger than the 95% percentile of the distribution of models. Zonal means of the geographical patterns are shown for each individual model for RCP2.6 (blue), 4.5 (light blue), 6.0 (orange) and 8.5 (red). RCP8.5 is excluded from the stabilization figures. The RCP2.6 simulation of the FIO-ESM (First Institute of Oceanography) model was excluded because it did not show any warming by the end of the 21st century, thus not complying with the method requirement that the pattern be estimated at a time when the temperature change signal from CO2 increase has emerged. 1061 Chapter 12 Long-term Climate Change: Projections, Commitments and Irreversibility enacted under RCP2.6 from early in the 21st century. Pattern corre- 2013). The phenomenon is predominantly a feature of the surface and lation varies between 0.91 and 0.98 for temperature and between lower atmosphere (Joshi et al., 2008). Studies have found it occurs due 0.91 and 0.96 for precipitation when comparing patterns computed to contrasts in surface sensible and latent fluxes over land (Sutton et by averaging and normalizing changes at the end of the 21st, 22nd al., 2007), land ocean contrasts in boundary layer lapse rate changes and 23rd centuries, with the largest value representing the correlation (Joshi et al., 2008), boundary layer relative humidity and associated between the patterns at the end of the 22nd and 23rd centuries, the low-level cloud cover changes over land (Doutriaux-Boucher et al., lowest representing the correlation between the pattern at the end 2009; Fasullo, 2010) and soil moisture reductions (Dong et al., 2009; of the 21st and the pattern at the end of the 23rd century. The zonal Clark et al., 2010) under climate change. The land sea warming con- means shown to the side of each plot represent each model by one line, trast is also sensitive to aerosol forcing (Allen and Sherwood, 2010; colour coding the four different scenarios. They show good agreement Joshi et al., 2013). Globally averaged warming over land and ocean of models and scenarios over low and mid-latitudes for temperature, is identified separately in Table 12.2 for the CMIP5 models and the but higher spread across models and especially across scenarios for the ratio of land to ocean warming is likely in the range of 1.4 to 1.7, areas subject to polar amplification, for which the previous discussion consistent with previous studies (Lambert et al., 2011). The CMIP5 mul- about the sensitivity of the patterns to the sea ice edge may be rele- ti-model mean ratio is approximately constant from 2020 through to vant. A comparison of the mean of the lines to their spread indicates 2100 (based on an update of Joshi et al., 2008 from available CMIP5 overall the presence of a strong mean signal with respect to the spread models). of the ensemble. Precipitation shows an opposite pattern of inter-mod- el spread, with larger variations in the low latitudes and around the Amplified surface warming in Arctic latitudes is also a consistent fea- equator, and smaller around the high latitudes. Precipitation has also ture in climate model integrations (e.g., Manabe and Stouffer, 1980). a lower signal-to-noise ratio (measured as above by comparing the This is often referred to as polar amplification, although numerous ensemble mean change magnitude to the spread across models and studies have shown that under transient forcing, this is primarily an scenarios of these zonal mean averages). Arctic phenomenon (Manabe et al., 1991; Meehl et al., 2007b). The lack of an amplified transient warming response in high Southern polar As already mentioned, although we do not explicitly use pattern scaling latitudes has been associated with deep ocean mixing, strong ocean in the sections that follow, we consider it a useful approximation when heat uptake and the persistence of the vast Antarctic ice sheet. In equi- the need emerges to interpolate or extrapolate results to different sce- librium simulations, amplified warming occurs in both polar regions. narios or time periods, noting the possibility that the scaling may break down at higher levels of global warming, and that the validity of the On an annual average, and depending on the forcing scenario (see approximation is limited to broad patterns of change, as opposed to Table 12.2), the CMIP5 models show a mean Arctic (67.5°N to 90°N) local scales. An important caveat is that pattern scaling only applies warming between 2.2 and 2.4 times the global average warming for to the climate response that is externally forced. The actual response 2081 2100 compared to 1986 2005. Similar polar amplification fac- is a combination of forced change and natural variability, which is not tors occurred in earlier coupled model simulations (e.g., Holland and and should not be scaled up or down by the application of this tech- Bitz, 2003; Winton, 2006a). This factor in models is slightly higher nique, which becomes important on small spatial scales and shorter than the observed central value, but it is within the uncertainty of time scales, and whose relative magnitude compared to the forced the best estimate from observations of the recent past (Bekryaev et component also depends on the variable (Hawkins and Sutton, 2009, al., 2010). The uncertainty is large in the observed factor because sta- 2011; Mahlstein et al., 2011; Deser et al., 2012a, 2012b; Mahlstein et tion records are short and sparse (Serreze and Francis, 2006) and the al., 2012) (see Section 11.2). One approach to produce projections that forced signal is contaminated by the noise of internal variability. By include both components is to estimate natural variability separately, contrast, model trends in surface air temperature are 2.5 to 5 times 12 scale the forced response and add the two. higher than observed over Antarctica, but here also the observational estimates have a very large uncertainty, so, for example, the CMIP3 12.4.3 Changes in Temperature and Energy Budget ensemble mean is consistent with observations within error estimates (Monaghan et al., 2008). Moreover, recent work suggests more wide- 12.4.3.1 Patterns of Surface Warming: Land Sea Contrast, spread current West Antarctic surface warming than previously esti- Polar Amplification and Sea Surface Temperatures mated (Bromwich et al., 2013). Patterns of surface air temperature change for various RCPs show The amplified Arctic warming in models has a distinct seasonal charac- widespread warming during the 21st century (Figure 12.11; see ter (Manabe and Stouffer, 1980; Rind, 1987; Holland and Bitz, 2003; Lu Annex I for seasonal patterns). A key feature that has been present and Cai, 2009; Kumar et al., 2010). Arctic amplification (defined as the ­throughout the history of coupled modelling is the larger warming over 67.5 N° to 90°N warming compared to the global average warming land compared to oceans, which occurs in both transient and equilib- for 2081 2100 versus 1986 2005) peaks in early winter (November rium climate change (e.g., Manabe et al., 1990). The degree to which to December) with a CMIP5 RCP4.5 multi-model mean warming for warming is larger over land than ocean is remarkably constant over 67.5°N to 90°N exceeding the global average by a factor of more than time under transient warming due to WMGHGs (Lambert and Chiang, 4. The warming is smallest in summer when excess heat at the Arctic 2007; Boer, 2011; Lambert et al., 2011) suggesting that heat capac- surface goes into melting ice or is absorbed by the ocean, which has ity differences between land and ocean do not play a major role in a relatively large thermal inertia. Simulated Arctic warming also has the land sea warming contrast (Sutton et al., 2007; Joshi et al., 2008, a consistent vertical structure that is largest in the lower troposphere 1062 Long-term Climate Change: Projections, Commitments and Irreversibility Chapter 12 Annual mean surface air temperature change Figure 12.11 | Multi-model ensemble average of surface air temperature change (compared to 1986 2005 base period) for 2046 2065, 2081 2100, 2181 2200 for RCP2.6, 4.5, 6.0 and 8.5. Hatching indicates regions where the multi-model mean change is less than one standard deviation of internal variability. Stippling indicates regions where the multi-model mean change is greater than two standard deviations of internal variability and where at least 90% of the models agree on the sign of change (see Box 12.1). The number of CMIP5 models used is indicated in the upper right corner of each panel. 12 (e.g., Manabe et al., 1991; Kay et al., 2012). This is in agreement with zontal latent heat transport by the atmosphere into the Arctic (Flan- recent observations (Serreze et al., 2009; Screen and Simmonds, 2010) nery, 1984; Alexeev et al., 2005; Cai, 2005; Langen and Alexeev, 2007; but contrary to an earlier study that suggested a larger warming aloft Kug et al., 2010), which warms primarily the lower troposphere. On (Graversen et al., 2008). The discrepancy in observed vertical structure average, CMIP3 models simulate enhanced latent heat transport (Held may reflect inadequacies in data sets (Bitz and Fu, 2008; Grant et al., and Soden, 2006), but north of about 65°N, the sensible heat transport 2008; Thorne, 2008) and sensitivity to the time period used for averag- declines enough to more than offset the latent heat transport increase ing (see also Box 2.3). (Hwang et al., 2011). Increased atmospheric heat transport into the Arctic and subsidence warming has been associated with a teleconnec- As also discussed in Box 5.1, there are many mechanisms that con- tion driven by enhanced convection in the tropical western Pacific (Lee tribute to Arctic amplification, some of which were identified in early et al., 2011). Ocean heat transport plays a role in the simulated Arctic modelling studies (Manabe and Stouffer, 1980). Feedbacks associat- amplification, with both large late 20th century transport (Mahlstein ed with changes in sea ice and snow amplify surface warming near and Knutti, 2011) and increases over the 21st century (Hwang et al., the poles (Hall, 2004; Soden et al., 2008; Graversen and Wang, 2009; 2011; Bitz et al., 2012) associated with higher amplification. As noted Kumar et al., 2010). The longwave radiation changes in the top of the by Held and Soden (2006), Kay et al. (2012), and Alexeev and Jackson atmosphere associated with surface warming opposes surface warm- (2012), diagnosing the role of various factors in amplified warming is ing at all latitudes, but less so in the Arctic (Winton, 2006a; Soden et complicated by coupling in the system in which local feedbacks inter- al., 2008). Rising temperature globally is expected to increase the hori- act with poleward heat transports. 1063 Chapter 12 Long-term Climate Change: Projections, Commitments and Irreversibility Although models consistently exhibit Arctic amplification as global discussed in Section 12.4.3.1. The tropospheric patterns are similar mean temperatures rise, the multitude of physical processes described to those in the TAR and AR4 with the RCP8.5 changes being up to above mean that they differ considerably in the magnitude. Previous several degrees warmer in the tropics compared to the A1B changes work has implicated variations across climate models in numerous fac- appearing in the AR4. Similar tropospheric patterns appear in the RCP tors including inversion strength (Boé et al., 2009a), ocean heat trans- 2.6 and 4.5 changes, but with reduced magnitudes, suggesting some port (Holland and Bitz, 2003; Mahlstein and Knutti, 2011), albedo feed- degree of scaling with forcing change in the troposphere, similar to back (Winton, 2006a), longwave radiative feedbacks (Winton, 2006a) behaviour discussed in the AR4 and Section 12.4.2. The consistency of and shortwave cloud feedback (Crook et al., 2011; Kay et al., 2012) tropospheric patterns over multiple generations of models indicates as playing a role in the across-model scatter in polar amplification. high confidence in these projected changes. The magnitude of amplification is generally higher in models with less extensive late 20th century sea ice in June, suggesting that the initial In the stratosphere, the models show similar tropical patterns of ice state influences the 21st century Arctic amplification. The pattern change, with magnitudes differing according to the degree of cli- of simulated Arctic warming is also associated with the initial ice state, mate forcing. Substantial differences appear in polar regions. In the and in particular with the location of the winter sea ice edge (Holland north, RCP8.5 and 4.5 yield cooling, though it is more significant in and Bitz, 2003; Räisänen, 2007; Bracegirdle and Stephenson, 2012). the RCP8.5 ensemble. In contrast, RCP2.6 shows warming, albeit weak This relationship has been suggested as a constraint on projected and with little significance. In the southern polar region, RCP 2.6 and Arctic warming (Abe et al., 2011; Bracegirdle and Stephenson, 2012), 4.5 both show significant warming, and RCP8.5 is the outlier, with sig- although, in general, the ability of models to reproduce observed cli- nificant cooling. The polar stratospheric warming, especially in the SH, mate and its trends is not a sufficient condition for attributing high is similar to that found by Butchart et al. (2010) and Meehl et al. (2012) confidence to the projection of future trends (see Section 9.8). in GCM simulations that showed effects of ozone recovery in deter- mining the patterns (Baldwin et al., 2007; Son et al., 2010). Eyring et Minima in surface warming occur in the North Atlantic and Southern al. (2013) find behaviour in the CMIP5 ensemble both for models with Oceans under transient forcing in part due to deep ocean mixed layers and without interactive chemistry that supports the contention that in those regions (Manabe et al., 1990; Xie et al., 2010). Trenberth and the polar stratospheric changes in Figure 12.12 are strongly influenced Fasullo (2010) find that the large biases in the Southern Ocean energy by ozone recovery. Overall, the stratospheric temperature changes do budget in CMIP3 coupled models negatively correlate with equilibrium not exhibit pattern scaling with global temperature change and are climate sensitivity (see Section 12.5.3), suggesting that an improved dependent on ozone recovery. mean state in the Southern Ocean is needed before warming there can be understood. In the equatorial Pacific, warming is enhanced Away from the polar stratosphere, there is physical and pattern consist- in a narrow band which previous assessments have described as El ency in temperature changes between different generations of models Nino-like , as may be expected from the projected decrease in atmos- assessed here and in the TAR and AR4. The consistency is especially clear pheric tropical circulations (see Section 12.4.4). However, DiNezio et al. in the northern high latitudes and, coupled with physical understanding, (2009) highlight that the tropical Pacific warming in the CMIP3 models indicates that some of the greatest warming is very likely to occur here. is not El Nino-like as the pattern of warming and associated tele- There is also consistency across generations of models in relatively large connections (Xie et al., 2010; Section 12.4.5.2) is quite distinct from warming in the tropical upper troposphere. Allen and Sherwood (2008) that of an El Nino event. Instead the pattern is of enhanced equatorial and Johnson and Xie (2010) have presented dynamic and thermody- warming and is due to a meridional minimum in evaporative damping namic arguments, respectively, for the physical robustness of the tropi- on the equator (Liu et al., 2005) and ocean dynamical changes that can cal behaviour. However, there remains uncertainty about the magnitude be decoupled from atmospheric changes (DiNezio et al., 2009) (see of warming simulated in the tropical upper troposphere because large 12 also further discussion in Section 12.4.7). observational uncertainties and contradictory analyses limit a confident assessment of model accuracy in simulating temperature trends in the In summary, there is robust evidence over multiple generations of tropical upper troposphere (Section 9.4.1.4.2). The combined evidence models and high confidence in these large-scale warming patterns. In indicates that relatively large warming in the tropical upper troposphere the absence of a strong reduction in the Atlantic Meridional Overturn- is likely, but with medium confidence. ing Circulation (AMOC), there is very high confidence that the Arctic region is projected to warm most. 12.4.3.3 Temperature Extremes 12.4.3.2 Zonal Average Atmospheric Temperature As the climate continues to warm, changes in several types of tem- perature extremes have been observed (Donat et al., 2013), and are Zonal temperature changes at the end of the 21st century show warm- expected to continue in the future in concert with global warming ing throughout the troposphere and, depending on the scenario, a mix (Seneviratne et al., 2012). Extremes occur on multiple time scales, from of warming and cooling in the stratosphere (Figure 12.12). The max- a single day or a few consecutive days (a heat wave) to monthly and imum warming in the tropical upper troposphere is consistent with seasonal events. Extreme temperature events are often defined by theoretical explanations and associated with a decline in the moist indices (see Box 2.4 for the common definitions used), for example, adiabatic lapse rate of temperature in the tropics as the climate warms percentage of days in a year when maximum temperature is above the (Bony et al., 2006). The northern polar regions also experience large 90th percentile of a present day distribution or by long period return warming in the lower atmosphere, consistent with the mechanisms values. Although changes in temperature extremes are a very robust 1064 Long-term Climate Change: Projections, Commitments and Irreversibility Chapter 12 Figure 12.12 | CMIP5 multi-model changes in annual mean zonal mean temperature in the atmosphere and ocean relative to 1986 2005 for 2081 2100 under the RCP2.6 (left), RCP4.5 (centre) and RCP8.5 (right) forcing scenarios. Hatching indicates regions where the multi-model mean change is less than one standard deviation of internal variability. Stippling indicates regions where the multi-model change mean is greater than two standard deviations of internal variability and where at least 90% of the models agree on the sign of change (see Box 12.1). 12 signature of anthropogenic climate change (Seneviratne et al., 2012), and Seneviratne, 2012; Sillmann et al., 2013), consistent with previous the magnitude of change and consensus among models varies with assessments (Solomon et al., 2007; Seneviratne et al., 2012). Figure the characteristics of the event being considered (e.g., time scale, mag- 12.13 shows multi-model mean changes in the absolute temperature nitude, duration and spatial extent) as well as the definition used to indices of the coldest day of the year and the hottest day of the year describe the extreme. and the threshold-based indices of frost days and tropical nights from the CMIP5 ensemble (Sillmann et al., 2013). A robust increase in warm Since the AR4 many advances have been made in establishing global temperature extremes and decrease in cold temperature extremes observed records of extremes (Alexander et al., 2006; Perkins et al., is found at the end of the 21st century, with the magnitude of the 2012; Donat et al., 2013) against which models can be evaluated to changes increasing with increased anthropogenic forcing. The coldest give context to future projections (Sillmann and Roeckner, 2008; Alex- night of the year undergoes larger increases than the hottest day in ander and Arblaster, 2009). Numerous regional assessments of future the globally averaged time series (Figure 12.13b and d). This tenden- changes in extremes have also been performed and a comprehensive cy is consistent with the CMIP3 model results shown in Figure 12.13, summary of these is given in Seneviratne et al. (2012). Here we sum- which use different models and the SRES scenarios (see Seneviratne marize the key findings from this report and assess updates since then. et al. (2012) for earlier CMIP3 results). Similarly, increases in the fre- quency of warm nights are greater than increases in the frequency It is virtually certain that there will be more hot and fewer cold extremes of warm days (Sillmann et al., 2013). Regionally, the largest increases as global temperature increases (Caesar and Lowe, 2012; Orlowsky in the coldest night of the year are projected in the high latitudes of 1065 Chapter 12 Long-term Climate Change: Projections, Commitments and Irreversibility the NH under the RCP8.5 scenario (Figure 12.13a). The subtropics and with the drier conditions, and the associated reduction in evaporative mid-latitudes exhibit the greatest projected changes in the hottest day cooling from the land surface projected over these areas (Kharin et al., of the year, whereas changes in tropical nights and the frequency of 2007). The representation of the latter constitutes a major source of warm days and warm nights are largest in the tropics (Sillmann et al., model uncertainty for projections of the absolute magnitude of tem- 2013). The number of frost days declines in all regions while significant perature extremes (Clark et al., 2010; Fischer et al., 2011). increases in tropical nights are seen in southeastern North America, the Mediterranean and central Asia. Winter cold extremes also warm more than the local mean temper- ature over northern high latitudes (Orlowsky and Seneviratne, 2012; It is very likely that, on average, there will be more record high than Sillmann et al., 2013) as a result of reduced temperature variability record cold temperatures in a warmer average climate. For example, related to declining snow cover (Gregory and Mitchell, 1995; Kjellstrom Meehl et al. (2009) find that the current ratio of 2 to 1 for record daily et al., 2007; Fischer et al., 2011) and decreases in land sea contrast high maxima to low minima over the USA becomes approximately 20 (de Vries et al., 2012). Changes in atmospheric circulation, induced by to 1 by the mid-21st century and 50 to 1 by late century in their model remote surface heating can also modify the temperature distribution simulation of the SRES A1B scenario. However, even at the end of the (Haarsma et al., 2009). Sillmann and Croci-Maspoli (2009) note that century daily record low minima continue to be broken, if in a small cold winter extremes over Europe are in part driven by atmospheric number, consistent with Kodra et al. (2011), who conclude that cold blocking and changes to these blocking patterns in the future lead to extremes will continue to occur in a warmer climate, even though their changes in the frequency and spatial distribution of cold temperature frequency will decline. extremes as global temperatures increase. Occasional cold winters will continue to occur (Räisänen and Ylhaisi, 2011). It is also very likely that heat waves, defined as spells of days with temperature above a threshold determined from historical climatology, Human discomfort, morbidity and mortality during heat waves depend will occur with a higher frequency and duration, mainly as a direct not only on temperature but also specific humidity. Heat stress, defined consequence of the increase in seasonal mean temperatures (Barnett as the combined effect of temperature and humidity, is expected to et al., 2006; Ballester et al., 2010a, 2010b; Fischer and Schär, 2010). increase along with warming temperatures and dominates the local Changes in the absolute value of temperature extremes are also very decrease in summer relative humidity due to soil drying (Diffenbaugh likely and expected to regionally exceed global temperature increases et al., 2007; Fischer et al., 2012b; Dunne et al., 2013). Areas with abun- by far, with substantial changes in hot extremes projected even for dant atmospheric moisture availability and high present-day temper- moderate (<2.5°C above present day) average warming levels (Clark atures such as Mediterranean coastal regions are expected to experi- et al., 2010; Diffenbaugh and Ashfaq, 2010). These changes often differ ence the greatest heat stress changes because the heat stress response from the mean temperature increase, as a result of changes in variabili- scales with humidity which thus becomes increasingly important to ty and shape of the temperature distribution (Hegerl et al., 2004; Meehl heat stress at higher temperatures (Fischer and Schär, 2010; Sherwood and Tebaldi, 2004; Clark et al., 2006). For example, summer tempera- and Huber, 2010; Willett and Sherwood, 2012). For some regions, sim- ture extremes over central and southern Europe are projected to warm ulated heat stress indicators are remarkably robust, because those substantially more than the corresponding mean local temperatures as models with stronger warming simulate a stronger decrease in atmos- a result of enhanced temperature variability at interannual to intrasea- pheric relative humidity (Fischer and Knutti, 2013). sonal time scales (Schär et al., 2004; Clark et al., 2006; Kjellstrom et al., 2007; Vidale et al., 2007; Fischer and Schär, 2009, 2010; Nikulin et Changes in rare temperature extremes can be assessed using extreme al., 2011; Fischer et al., 2012a). Several recent studies have also argued value theory based techniques (Seneviratne et al., 2012). Kharin et that the probability of occurrence of a Russian heat wave at least as al. (2007), in an analysis of CMIP3 models, found large increases 12 severe as the one in 2010 increases substantially (by a factor of 5 to in the 20-year return values of the annual maximum and minimum 10 by the mid-century) along with increasing mean temperatures and daily averaged surface air temperatures (i.e., the size of an event enhanced temperature variability (Barriopedro et al., 2011; Dole et al., that would be expected on average once every 20 years, or with a 2011). 5% chance every year) with larger changes over land than ocean. Figure 12.14 displays the end of 21st century change in the magni- Since the AR4, an increased understanding of mechanisms and feed- tude of these rare events from the CMIP5 models in the RCP2.6, 4.5 backs leading to projected changes in extremes has been gained and 8.5 scenarios (Kharin et al., 2013). Comparison to the changes in (Seneviratne et al., 2012). Climate models suggest that hot extremes summer mean temperature shown in Figure AI.5 and A1.7 of Annex are amplified by soil moisture-temperature feedbacks (Seneviratne et I Supplementary Material reveals that rare high temperature events al., 2006; Diffenbaugh et al., 2007; Lenderink et al., 2007; Vidale et are projected to change at rates similar to or slightly larger than the al., 2007; Fischer and Schär, 2009; Fischer et al., 2012a) in northern summertime mean temperature in many land areas. However, in much mid-latitude regions as the climate warms, consistent with previous of Northern Europe 20-year return values of daily high temperatures assessments. Changes in temperature extremes may also be impacted are projected to increase 2°C or more than JJA mean temperatures by changes in land sea contrast, with Watterson et al. (2008) show- under RCP8.5, consistent with previous studies (Sterl et al., 2008; ing an amplification of southern Australian summer warm extremes Orlowsky and Seneviratne, 2012). Rare low temperature events are over the mean due to anomalous temperature advection from warmer projected to experience significantly larger increases than the mean continental interiors. The largest increases in the magnitude of warm in most land regions, with a pronounced effect at high latitudes. Twen- extremes are simulated over mid-latitude continental areas, consistent ty-year return values of cold extremes increase significantly more than ­ 1066 Long-term Climate Change: Projections, Commitments and Irreversibility Chapter 12 b) Coldest daily Tmin (TNn) 18 8 historical RCP4.5 8 RCP2.6 RCP8.5 6 6 4 (oC) 4 2 2 0 0 CMIP3 B1 CMIP3 A1B CMIP3 A2 2 2 1960 1980 2000 2020 2040 2060 2080 2100 Year d) Warmest daily Tmax (TXx) 18 8 historical RCP4.5 8 RCP2.6 RCP8.5 6 6 (oC) 4 4 2 2 0 0 CMIP3 B1 CMIP3 A1B CMIP3 A2 2 2 1960 1980 2000 2020 2040 2060 2080 2100 Year f) Frost Days (FD) 5 5 18 historical RCP4.5 RCP2.6 RCP8.5 0 0 5 5 (days) 10 10 15 15 20 20 25 25 CMIP3 B1 CMIP3 A1B CMIP3 A2 1960 1980 2000 2020 2040 2060 2080 2100 Year h) Tropical Nights (TR) 18 historical RCP4.5 12 60 RCP2.6 RCP8.5 60 40 40 (days) 20 20 0 0 CMIP3 B1 CMIP3 A1B CMIP3 A2 1960 1980 2000 2020 2040 2060 2080 2100 Year Figure 12.13 | CMIP5 multi-model mean geographical changes (relative to a 1981 2000 reference period in common with CMIP3) under RCP8.5 and 20-year smoothed time series for RCP2.6, RCP4.5 and RCP8.5 in the (a, b) annual minimum of daily minimum temperature, (c, d) annual maximum of daily maximum temperature, (e, f) frost days (number of days below 0°C) and (g, h) tropical nights (number of days above 20°C). White areas over land indicate regions where the index is not valid. Shading in the time series represents the interquartile ensemble spread (25th and 75th quantiles). The box-and-whisker plots show the interquartile ensemble spread (box) and outliers (whiskers) for 11 CMIP3 model simulations of the SRES scenarios A2 (orange), A1B (cyan), and B1 (purple) globally averaged over the respective future time periods (2046 2065 and 2081 2100) as anomalies from the 1981 2000 reference period. Stippling indicates grid points with changes that are significant at the 5% level using a Wilcoxon signed-ranked test. (Updated from Sillmann et al. (2013), excluding the FGOALS-s2 model.) 1067 Chapter 12 Long-term Climate Change: Projections, Commitments and Irreversibility winter mean temperature changes, particularly over parts of North its ranges. The CMIP5 analysis shown in Figure 12.14 reinforces this America and Europe. Kharin et al. (2013) concluded from the CMIP5 assessment of large changes in the frequency of rare events, particu- models that it is likely that in most land regions a current 20 year max- larly in the RCP8.5 scenario (Kharin et al., 2013). imum temperature event is projected to become a one-in-two-year event by the end of the 21st century under the RCP4.5 and RCP8.5 There is high consensus among models in the sign of the future change scenarios, except for some regions of the high latitudes of the NH in temperature extremes, with recent studies confirming this conclu- where it is likely to become a one-in-five-year event (see also Senevi- sion from the previous assessments (Tebaldi et al., 2006; Meehl et al., ratne et al. (2012) Figure 3.5). Current 20-year minimum temperature 2007b; Orlowsky and Seneviratne, 2012; Seneviratne et al., 2012; Sill- events are projected to become exceedingly rare, with return periods mann et al., 2013). However, the magnitude of the change remains likely increasing to more than 100 years in almost all locations under uncertain owing to scenario and model (both structural and parame- RCP8.5 (Kharin et al., 2013). Section 10.6.1.1 notes that a number of ter) uncertainty (Clark et al., 2010) as well as internal variability. These detection and attribution studies since SREX suggest that the model uncertainties are much larger than corresponding uncertainties in the changes may tend to be too large for warm extremes and too small magnitude of mean temperature change (Barnett et al., 2006; Clark et for cold extremes and thus these likelihood statements are somewhat al., 2006; Fischer and Schär, 2010; Fischer et al., 2011). less strongly stated than a direct interpretation of model output and 12 Figure 12.14 | The CMIP5 multi-model median change in 20-year return values of annual warm temperature extremes (left-hand panels) and cold temperature extremes (right- hand panels) as simulated by CMIP5 models in 2081 2100 relative to 1986 2005 in the RCP2.6 (top), RCP4.5 (middle panels), and RCP8.5 (bottom) experiments. 1068 Long-term Climate Change: Projections, Commitments and Irreversibility Chapter 12 12.4.3.4 Energy Budget imbalance is always less than the RF because of the slow rate of ocean heat uptake.) Anthropogenic or natural perturbations to the climate system produce RFs that result in an imbalance in the global energy budget at the The rapid fluctuations that are simulated during the 20th century top of the atmosphere (TOA) and affect the global mean temperature originate from volcanic eruptions that are prescribed in the models (Section 12.3.3). The climate responds to a change in RF on multiple (see Section 12.3.2). These aerosols reflect solar radiation and thus time scales and at multiyear time scales the energy imbalance (i.e., decrease the amount of SW radiation absorbed by the Earth (Figure the energy heating or cooling the Earth) is very close to the ocean 12.15c). The minimum of shortwave (SW) radiation absorbed by the heat uptake due to the much lower thermal inertia of the atmosphere Earth during the period 1960 2000 is due mainly to two factors: a and the continental surfaces (Levitus et al., 2005; Knutti et al., 2008a; sequence of volcanic eruptions and an increase of the reflecting aer- Murphy et al., 2009; Hansen et al., 2011). The radiative responses of osol burden due to human activities (see Sections 7.5, 8.5 and 9.4.6). the fluxes at TOA are generally analysed using the forcing-feedback During the 21st century, the absorbed SW radiation monotonically framework and are presented in Section 9.7.2. increases for the RCP8.5 scenario, and increases and subsequently stabilizes for the other scenarios, consistent with what has been pre- CMIP5 models simulate a small increase of the energy imbalance at viously obtained with CMIP3 models and SRES scenarios (Trenberth the TOA over the 20th century (see Box 3.1, Box 9.2 and Box 13.1). The and Fasullo, 2009). The two main contributions to the SW changes are future evolution of the imbalance is very different depending on the the change of clouds (see Section 12.4.3.5) and the change of the cry- scenario (Figure 12.15a): for RCP8.5 it continues to increase rapidly, osphere (see Section 12.4.6) at high latitudes. In the longwave (LW) much less for RCP6.0, it is almost constant for RCP4.5 and decreases domain (Figure 12.15b), the net flux at TOA represents the opposite of for RCP2.6. This latter negative trend reveals the quasi-stabilization the flux that is emitted by the Earth s surface and atmosphere toward characteristic of RCP2.6. (In a transient scenario simulation, the TOA space, i.e., a negative anomaly represents an increase of the emitted (W m-2) Figure 12.15 | Time series of global and annual multi-model mean (a) net total radiation anomaly at the top of the atmosphere (TOA), (b) net longwave radiation anomaly at the TOA and (c) net shortwave radiation anomaly at the TOA from the CMIP5 concentration-driven experiments for the historical period (black) and the four RCP scenarios. All the fluxes are positive downward and units are W m 2. The anomalies are calculated relative to the 1900 1950 base period as this is a common period to all model experiments with few volcanic eruptions and relatively small trends. One ensemble member is used for each individual CMIP5 model and the +/- standard deviation across the distribution of individual models is shaded. 12 Figure 12.16 | Multi-model CMIP5 average changes in annual mean (left) net total radiation anomaly at the top of the atmosphere (TOA), (middle) net longwave radiation anomaly at the TOA and (right) net shortwave radiation anomaly at the TOA for the RCP4.5 scenario averaged over the periods 2081 2100. All fluxes are positive downward, units are W m 2. The net radiation anomalies are computed with respect to the 1900 1950 base period. Hatching indicates regions where the multi-model mean change is less than one standard deviation of internal variability. Stippling indicates regions where the multi-model mean change is greater than two standard deviations of internal variability and where at least 90% of models agree on the sign of change (see Box 12.1). 1069 Chapter 12 Long-term Climate Change: Projections, Commitments and Irreversibility LW radiation. The LW net flux depends mainly on two factors: the sur- Dufresne, 2005; Webb et al., 2006; Wyant et al., 2006). Since AR4, these face temperature and the magnitude of the greenhouse effect of the results have been confirmed along with the positive feedbacks due to atmosphere. During the 20th century, the rapid fluctuations of LW radi- high level clouds in the CMIP3 or CFMIP models (Zelinka and Hart- ation are driven by volcanic forcings, which decrease the absorbed SW mann, 2010; Soden and Vecchi, 2011; Webb et al., 2013) and CMIP5 radiation, surface temperature, and the LW radiation emitted by the models (Vial et al., 2013). Since AR4, the response of clouds has been Earth toward space. During the period 1960 2000, the fast increase of partitioned in a direct or rapid response of clouds to CO2 and a slow GHG concentrations also decreases the radiation emitted by the Earth. response of clouds to the surface temperature increase (i.e., the usual In response to this net heating of the Earth, temperatures warm and feedback response) (Gregory and Webb, 2008). The radiative effect of thereby increase emitted LW radiation although the change of the tem- clouds depends mainly on their fraction, optical depth and temper- perature vertical profile, water vapour, and cloud properties modulate ature. The contribution of these variables to the cloud feedback has this response (e.g., Bony et al., 2006; Randall et al., 2007). been quantified for the multi-model CMIP3 (Soden and Vecchi, 2011) and CFMIP1 database (Zelinka et al., 2012). These findings are con- 12.4.3.5 Clouds sistent with the radiative changes obtained with the CMIP5 models (Figure 12.16) and may be summarized as follows (see Section 7.2.5 This section provides a summary description of future changes in for more details). clouds and their feedbacks on climate. A more general and more pre- cise description and assessment of the role of clouds in the climate The dominant contributor to the SW cloud feedback is the change in system is provided in Chapter 7, in particular Section 7.2 for cloud pro- cloud fraction. The reduction of cloud fraction between 50°S and 50°N, cesses and feedbacks and Section 7.4 for aerosol cloud interactions. except along the equator and the eastern part of the ocean basins Cloud feedbacks and adjustments are presented in Section 7.2.5 and a (Figure 12.17), contributes to an increase in the absorbed solar radi- synthesis is provided in Section 7.2.6. Clouds are a major component ation (Figure 12.16c). Physical mechanisms and the role of different of the climate system and play an important role in climate sensitiv- parameterizations have been proposed to explain this reduction of ity (Cess et al., 1990; Randall et al., 2007), the diurnal temperature low-level clouds (Zhang and Bretherton, 2008; Caldwell and Breth- range (DTR) over land (Zhou et al., 2009), and land sea contrast (see erton, 2009; Brient and Bony, 2013; Webb et al., 2013). Poleward of Section 12.4.3.1). The observed global mean cloud RF is about 20 W 50°S, the cloud fraction and the cloud optical depth increases, thereby m 2 (Loeb et al., 2009) (see Section 7.2.1), that is, clouds have a net increasing cloud reflectance. This leads to a decrease of solar absorp- cooling effect. Current GCMs simulate clouds through various complex tion around Antarctica where the ocean is nearly ice free in summer p ­ arameterizations (see Section 7.2.3), and cloud feedback is a major (Figure 12.16c). However, there is low confidence in this result because source of the spread of the climate sensitivity estimate (Soden and GCMs do not reproduce the nearly 100% cloud cover observed there Held, 2006; Randall et al., 2007; Dufresne and Bony, 2008) (see Section and the negative feedback could be overestimated (Trenberth and 9.7.2). Fasullo, 2010) or, at the opposite, underestimated because the cloud optical depth simulated by models is biased high there (Zelinka et al., Under future projections the multi-model pattern of total cloud 2012). amount shows consistent decreases in the subtropics, in conjunction with a decrease of the relative humidity there, and increases at high In the LW domain, the tropical high cloud changes exert the dominant latitudes. Another robust pattern is an increase in cloud cover at all effect. A lifting of the cloud top with warming is simulated consistently latitudes in the vicinity of the tropopause, a signature of the increase of across models (Meehl et al., 2007b) which leads to a positive feed- the altitude of high level clouds in convective regions (Wetherald and back whereby the LW emissions from high clouds decrease as they Manabe, 1988; Meehl et al., 2007b; Soden and Vecchi, 2011; Zelinka cool (Figure 12.16b). The dominant driver of this effect is the increase 12 et al., 2012). Low-level clouds were identified as a primary cause of of tropopause height and physical explanations have been proposed inter-model spread in cloud feedbacks in CMIP3 models (Bony and (Hartmann and Larson, 2002; Lorenz and DeWeaver, 2007; Zelinka Figure 12.17 | CMIP5 multi-model changes in annual mean total cloud fraction (in %) relative to 1986 2005 for 2081 2100 under the RCP2.6 (left), RCP4.5 (centre) and RCP8.5 (right) forcing scenarios. Hatching indicates regions where the multi-model mean change is less than one standard deviation of internal variability. Stippling indicates regions where the multi-model mean change is greater than two standard deviations of internal variability and where 90% of the models agree on the sign of change (see Box 12.1). The number of CMIP5 models used is indicated in the upper right corner of each panel. 1070 Long-term Climate Change: Projections, Commitments and Irreversibility Chapter 12 and Hartmann, 2010). Although the decrease in cloudiness generally (see also Section 11.3.2.4 for near-term changes and Seneviratne et al. increases outgoing longwave radiation and partly offsets the effect of (2012) for an assessment of projected changes related to weather and cloud rising, the net effect is a consistent positive global mean LW climate extremes). cloud feedback across CMIP and CFMIP models. Global mean SW cloud feedbacks range from slightly negative to strongly positive (Soden and 12.4.4.1 Mean Sea Level Pressure and Upper-Air Winds Vecchi, 2011; Zelinka et al., 2012), with an inter-model spread in net cloud feedback being mainly attributable to low-level cloud changes. Sea level pressure gives an indication of surface changes in atmos- pheric circulation (Figure 12.18). As in previous assessments, a robust In summary, both the multi-model mean and the inter-model spread of feature of the pattern of change is a decrease in high latitudes and the cloud fraction and radiative flux changes simulated by the CMIP5 increases in the mid-latitudes, associated with poleward shifts in the models are consistent with those previously obtained by the CMIP3 SH mid-latitude storm tracks (Section 12.4.4.3) and positive trends models. These include decreases in cloud amount in the subtropics, in the annular modes (Section 14.5) as well as an expansion of the increases at high latitudes and increases in the altitude of high level Hadley Cell (Section 12.4.4.2). Similar patterns of sea level pressure clouds in convective regions. Many of these changes have been under- change are found in observed trends over recent decades, suggest- stood primarily as responses to large-scale circulation changes (see ing an already detectable change (Gillett and Stott, 2009; Section Section 7.2.6). 10.3.3.4), although the observed patterns are influenced by both natu- ral and anthropogenic forcing as well as internal variability and the 12.4.4 Changes in Atmospheric Circulation relative importance of these influences is likely to change in the future. Internal variability has been found to play a large role in uncertainties Projected changes in energy and water cycles couple with changes in of future sea level pressure projections, particularly at higher latitudes atmospheric circulation and mass distribution. Understanding this cou- (Deser et al., 2012a). pling is necessary to assess physical behaviour underlying projected changes, particularly at regional scales, revealing why changes occur In boreal winter, decreases of sea level pressure over NH high lati- and the realism of the changes. The focus in this section is on atmos- tudes are slightly weaker in the CMIP5 ensemble compared to previous pheric circulation behaviour that CMIP5 GCMs resolve well. Thus, the assessments, consistent with Scaife et al. (2012) and Karpechko and section includes discussion of extratropical cyclones but not tropical Manzini (2012), who suggest that improvements in the representation cyclones: extratropical cyclones are fairly well resolved by most CMIP5 of the stratosphere can influence this pattern. In austral summer, the GCMs, whereas tropical cyclones are not, requiring resolutions finer SH projections are impacted by the additional influence of stratospher- than used by the large majority of CMIP5 GCMs (see Section 9.5.4.3). ic ozone recovery (see Section 11.3.2.4.2) which opposes changes due Detailed discussion of tropical cyclones appears in Section 14.6.1 to GHGs. Under the weaker GHG emissions of RCP2.6, decreases in sea (see also Section 11.3.2.5.3 for near term changes and Section 3.4.4 level pressure over the SH mid-latitudes and increases over SH high in Seneviratne et al. (2012)). Regional detail concerning extratropical latitudes are consistent with expected changes from ozone recovery storm tracks, including causal processes, appears in Section 14.6.2 (Arblaster et al., 2011; McLandress et al., 2011; Polvani et al., 2011). For 12 Figure 12.18 | CMIP5 multi-model ensemble average of December, January and February (DJF, top row) and June, July and August (JJA, bottom row) mean sea level pressure change (2081 2100 minus 1986 2005) for, from left to right, RCP2.6, 4.5 and 8.5. Hatching indicates regions where the multi-model mean change is less than one standard deviation of internal variability. Stippling indicates regions where the multi-model mean change is greater than two standard deviations of internal variability and where at least 90% of models agree on the sign of change (see Box 12.1). 1071 Chapter 12 Long-term Climate Change: Projections, Commitments and Irreversibility Figure 12.19 | Coupled Model Intercomparison Project Phase 5 (CMIP5) multi-model ensemble average of zonal and annual mean wind change (2081 2100 minus 1986 2005) for, from left to right, Representative Concentration Pathway 2.6 (RCP2.6), 4.5 and 8.5. Black contours represent the multi-model average for the 1986 2005 base period. Hatching indicates regions where the multi-model mean change is less than one standard deviation of internal variability. Stippling indicates regions where the multi-model mean change is greater than two standard deviations of internal variability and where at least 90% of models agree on the sign of change (see Box 12.1). all other RCPs, the magnitude of SH extratropical changes scales with shift found in the multi-model mean under the low emissions scenario the RF, as found in previous model ensembles (Paeth and Pollinger, of RCP2.6 (Swart and Fyfe, 2012) and weak or poleward shifts in other 2010; Simpkins and Karpechko, 2012). RCPs (Swart and Fyfe, 2012; Wilcox et al., 2012). Eyring et al. (2013) note the sensitivity of the CMIP5 SH summertime circulation changes Large increases in seasonal sea level pressure are also found in regions to both the strength of the ozone recovery (simulated by some models of sub-tropical drying such as the Mediterranean and northern Africa interactively) and the rate of GHG increases. in DJF and Australia in JJA. Projected changes in the tropics are less consistent across the models; however, a decrease in the eastern equa- Although the poleward shift of the tropospheric jets are robust across torial Pacific and increase over the maritime continent, associated with models and likely under increased GHGs, the dynamical mechanisms a weakening of the Walker Circulation (Vecchi and Soden, 2007; Power behind these projections are still not completely understood and have and Kociuba, 2011b), is found in all RCPs. been explored in both simple and complex models (Chen et al., 2008; Lim and Simmonds, 2009; Butler et al., 2010). The shifts are associated Future changes in zonal and annual mean zonal winds (Figure 12.19) with a strengthening in the upper tropospheric meridional temperature are seen throughout the atmosphere with stronger changes in higher gradient (Wilcox et al., 2012) and hypotheses for associated changes RCPs. Large increases in winds are evident in the tropical stratosphere in planetary wave activity and/or synoptic eddy characteristics that 12 and a poleward shift and intensification of the SH tropospheric jet is impact on the position of the jet have been put forward (Gerber et seen under RCP4.5 and RCP8.5, associated with an increase in the al., 2012). Equatorward biases in the position of the SH jet (Section SH upper tropospheric meridional temperature gradient (Figure 12.12) 9.5.3.2), while somewhat improved over similar biases in the CMIP3 (Wilcox et al., 2012). In the NH, the response of the tropospheric jet models (Kidston and Gerber, 2010) still remain, limiting our confidence is weaker and complicated by the additional thermal forcing of polar in the magnitude of future changes. amplification (Woollings, 2008). Barnes and Polvani (2013) evaluate changes in the annual mean mid-latitude jets in the CMIP5 ensemble, In summary, poleward shifts in the mid-latitude jets of about 1 to 2 finding consistent poleward shifts in both hemispheres under RCP8.5 degrees latitude are likely at the end of the 21st century under RCP8.5 for the end of the 21st century. In the NH, the poleward shift is ~1°, in both hemispheres (medium confidence) with weaker shifts in the NH similar to that found for the CMIP3 ensemble (Woollings and Black- and under lower emission scenarios. Ozone recovery will likely weaken burn, 2012). In the SH, the annual mean mid-latitude jet shifts pole- the GHG-induced changes in the SH extratropical circulation in austral ward by ~2° under RCP8.5 at the end of the 21st century in the CMIP5 summer. multi-model mean (Barnes and Polvani, 2013), with a similar shift of 1.5° in the surface westerlies (Swart and Fyfe, 2012). A strengthen- 12.4.4.2 Planetary-Scale Overturning Circulations ing of the SH surface westerlies is also found under all RCPs except RCP2.6 (Swart and Fyfe, 2012), with largest changes in the Pacific Large-scale atmospheric overturning circulations and their interaction basin (Bracegirdle et al., 2013). In austral summer, ozone recovery off- with other atmospheric mechanisms are significant in determining trop- sets changes in GHGs to some extent, with a weak reversal of the jet ical climate and regional changes in response to enhanced RF. Observed 1072 Long-term Climate Change: Projections, Commitments and Irreversibility Chapter 12 changes in tropical atmospheric circulation are assessed in Section 2.7.5, Apart from changes in Hadley Circulation strength, a robust feature while Section 10.3.3 discusses attribution of these observed changes to in 21st century climate model simulations is an increase in the cell s anthropogenic forcing. Evidence is inconclusive on recent trends in the depth and width (Mitas and Clement, 2006; Frierson et al., 2007; Lu strength of the Hadley (Stachnik and Schumacher, 2011) and Walker et al., 2007; Lu et al., 2008), with the latter change translating to a Circulations (Vecchi et al., 2006; Sohn and Park, 2010; Merrifield, 2011; broadening of tropical regions (Seidel and Randel, 2007; Seidel et al., Luo et al., 2012; Tokinaga et al., 2012), though there is medium confi- 2008) and a poleward displacement of subtropical dry zones (Lu et dence of an anthropogenic influence on the observed widening of the al., 2007; Scheff and Frierson, 2012). The increase in the cell s depth Hadley Circulation (Hu and Fu, 2007; Johanson and Fu, 2009; Davis and is consistent with a tropical tropopause rise. The projected increase in Rosenlof, 2012). In the projections, there are indications of a weakening the height of the tropical tropopause and the associated increase in of tropical overturning of air as the climate warms (Held and Soden, meridional temperature gradients close to the tropopause slope have 2006; Vecchi and Soden, 2007; Gastineau et al., 2008, 2009; Chou and been proposed to be an important mechanism behind the Hadley cell Chen, 2010; Chadwick et al., 2012; Bony et al., 2013). In the SRES A1B expansion and the poleward displacement of the subtropical westerly scenario, CMIP3 models show a remarkable agreement in simulating a jet (Lu et al., 2008; Johanson and Fu, 2009). An increase in subtropical weakening of the tropical atmospheric overturning circulation (Vecchi and mid-latitude static stability has been found to be an important and Soden, 2007). CMIP5 models also show a consistent weakening factor widening the Hadley Cell by shifting baroclinic eddy activity and (Chadwick et al., 2012). Along the ascending branches of tropical over- the associated eddy-driven jet and subsidence poleward (Mitas and turning cells, a reduction in convective mass flux from the boundary Clement, 2006; Lu et al., 2008). The projected widening of the Hadley layer to the free atmosphere is implied by the differential response to Cell is consistent with late 20th century observations, where ~2° to 5° global warming of the boundary-layer moisture content and surface latitude expansion was found (Fu et al., 2006; Johanson and Fu, 2009). evaporation. This weakening of vertical motion along the ascending The consistency of simulated changes in CMIP3 and CMIP5 models and regions of both the tropical meridional and near-equatorial zonal cells the consistency of Hadley Cell changes with the projected tropopause is associated with an imbalance in the rate of atmospheric moisture rise and increase in subtropical and mid-latitude static stability indi- increase and that of global mean precipitation (Held and Soden, 2006). cate that a widening and weakening of the NH Hadley Cell by the late A reduction in the compensating climatological subsidence along the 21st century is likely. downward branches of overturning circulations, where the rate of increase of static stability exceeds radiative cooling, is implied. The zonally asymmetric Walker Circulation is projected to weaken under global warming (Power and Kociuba, 2011a, 2011b), more than Several mechanisms have been suggested for the changes in the inten- the Hadley Circulation (Lu et al., 2007; Vecchi and Soden, 2007). The sity of the tropical overturning circulation. The weakening of low-level consistency of the projected Walker Circulation slowdown from CMIP3 convective mass flux along ascending regions of tropical overturning to CMIP5 suggests that its change is robust (Ma and Xie, 2013). Almost cells has been ascribed to changes in the hydrologic cycle (Held and everywhere around the equatorial belt, changes in the 500 hPa ver- Soden, 2006; Vecchi and Soden, 2007). Advection of dry air from sub- tical motion oppose the climatological background motion, notably sidence regions towards the ascending branches of large-scale tropical over the maritime continent (Vecchi and Soden, 2007; Shongwe et al., circulation has been suggested to be a feasible mechanism weakening 2011). Around the Indo-Pacific warm pool, in response to a spatially ascent along the edges of convection regions (Chou et al., 2009). A uniform SST warming, the climatological upper tropospheric diver- deepening of the tropical troposphere in response to global warming gence weakens (Ma and Xie, 2013). Changes in the strength of the increases the vertical extent of convection, which has been shown to Walker Circulation also appear to be linked to differential warming increase the atmosphere s moist stability and thus also weakening between the Indian and Pacific Ocean warming at low latitudes (Luo et overturning cells (Chou and Chen, 2010). An imbalance between the al., 2012). Over the equatorial Pacific Ocean, where mid-tropospheric increase in diabatic heating of the troposphere and in static stabili- ascent is projected to strengthen, changes in zonal SST and hence sea 12 ty whereby the latter increases more rapidly has also been thought level pressure gradients induce low-level westerly wind anomalies that to play a role in weakening tropical ascent (Lu et al., 2008). Mean act to weaken the low-level branch of the Pacific Walker Circulation. advection of enhanced vertical stratification under GHG forcing which These projected changes in the tropical Pacific circulation are already involves cooling of convective regions and warming of subsidence occurring (Zhang and Song, 2006). However, the projected weakening regions has been shown to slow down tropical cells (Ma et al., 2012). of the Pacific Walker Cell does not imply an increase in the frequency The latest findings using CMIP5 models reveal that an increase in and/or magnitude of El Nino events (Collins et al., 2010). The consisten- GHGs (­ articularly CO2) contributes significantly to weakening tropi- p cy of simulated changes in CMIP3 and CMIP5 models and the consist- cal overturning cells by reducing radiative cooling in the upper atmos- ency of Walker Cell changes with equatorial SST and pressure-gradient phere (Bony et al., 2013). SST gradients have also been found to play changes that are already observed indicate that a weakening of the a role in altering the strength of tropical cells (Tokinaga et al., 2012; Walker Cell by the late 21st century is likely. Ma and Xie, 2013). Evidence has been provided suggesting that the SH Hadley Cell may strengthen in response to meridional SST gradients In the upper atmosphere, a robust feature of projected stratospheric featuring reduced warming in the SH subtropical oceans relative to the circulation change is that the Brewer Dobson circulation will likely NH, particularly over the Pacific and Indian Oceans (Ma and Xie, 2013). strengthen in the 21st century (Butchart et al., 2006, 2010; Li et al., The north-to-south SST warming gradients are a source of intermodel 2008; McLandress and Shepherd, 2009; Shepherd and McLandress, differences in their projections of changes in the SH Hadley Circulation. 2011). In a majority of model experiments, the projected changes in the large-scale overturning circulation in the stratosphere feature an 1073 Chapter 12 Long-term Climate Change: Projections, Commitments and Irreversibility intensification of tropical upward mass flux, which may extend to the between CMIP5 and CMIP3 projections under a variety of diagnostics upper stratosphere. The proposed driver of the increase in mass flux at and the physical consistency of the storm response with other climatic the tropical lower stratosphere is the enhanced propagation of wave changes gives high confidence that a poleward shift of several degrees activity, mainly resolved planetary waves, associated with a positive in SH storm tracks is likely by the end of the 21st century under the trend in zonal wind structure (Butchart and Scaife, 2001; Garcia and RCP8.5 scenario. Randel, 2008). In the 21st century, increases in wave excitation from diabatic heating in the upper tropical troposphere could reinforce the In the NH winter (Figure 12.20a, b), the CMIP5 multi-model ensemble wave forcing on the tropical upwelling branch of the stratospheric shows an overall reduced frequency of storms and less indication of mean meridional circulation (Calvo and Garcia, 2009). Parameterized a poleward shift in the tracks. The clearest poleward shift in the NH orographic gravity waves that result from strengthening of subtropical winter at the end of the 21st century occurs in the Asia-Pacific storm westerly jets and cause more waves to propagate into the lower strat- track, where intensification of the westerly jet promotes more intense osphere also play a role (Sigmond et al., 2004; Butchart et al., 2006). cyclones in an ensemble of CMIP5 models (Mizuta, 2012). Otherwise, The projected intensification in tropical upwelling is counteracted by changes in winter storm-track magnitude, as measured by band-pass enhanced mean extratropical/polar lower stratospheric subsidence. In sea level pressure fluctuations, show only small change relative to the NH high latitudes, the enhanced downwelling is associated with an interannual and inter-decadal variability by the end of the 21st century increase in stationary planetary wave activities (McLandress and Shep- in SRES A1B and RCP4.5 simulations for several land areas over the NH herd, 2009). The intensification of the stratospheric meridional residual (Harvey et al., 2012). Consistency in CMIP3 and CMIP5 changes seen circulation has already been reported in studies focussing on the last in the SH are absent in the NH (Chang et al., 2012a). Factors identified decades of the 20th century (Garcia and Randel, 2008; Li et al., 2008; that affect changes in the North Atlantic basin s storm track include Young et al., 2012). The projected increase in troposphere-to-strato- horizontal resolution (Colle et al., 2013) and how models simulate sphere mass exchange rate (Butchart et al., 2006) and stratospheric changes in the Atlantic s meridional overturning circulation (Catto et mixing associated with the strengthening of the Brewer Dobson circu- al., 2011; Woollings et al., 2012), the zonal jet and Hadley Circulation lation will likely result in a decrease in the mean age of air in the lower (Mizuta, 2012; Zappa et al., 2013) and subtropical upper troposphere stratosphere. In the mid-latitude lower stratosphere, quasi-horizontal temperature (Haarsma et al., 2013). Substantial uncertainty and thus mixing is a significant contributor to reducing the lifetimes of air. There low confidence remains in projecting changes in NH winter storm are some suggestions that the changes in stratospheric overturning tracks, especially for the North Atlantic basin. circulation could lead to a reduction in tropical ozone concentrations and an increase at high latitudes (Jiang et al., 2007) and an increase Additional analyses of CMIP3 GCMs have determined other changes in in the amplitude of the annual cycle of stratospheric ozone (Randel et properties of extratropical storms. Most analyses find that the frequen- al., 2007). cy of storms decreases in projected climates (Finnis et al., 2007; Favre and Gershunov, 2009; Dowdy et al., 2013), though the occurrence of 12.4.4.3 Extratropical Storms: Tracks and Influences on strong storms may increase in some regions (Pinto et al., 2007; Bengts- Planetary-Scale Circulation and Transports son et al., 2009; Ulbrich et al., 2009; Zappa et al., 2013). Many studies focus on behaviour of specific regions, and results of these studies are Since the AR4, there has been continued evaluation of changes in detailed in Section 14.6.2. extratropical storm tracks under projected warming using both CMIP3 and, more recently, CMIP5 simulations, as well as supporting studies Changes in extratropical storms in turn may influence other large-scale using single models or idealized simulations. CMIP3 analyses use a climatic changes. Kug et al. (2010) in a set of time-slice simulations variety of methods for diagnosing storm tracks, but diagnosis of chang- show that a poleward shift of storm tracks in the NH could enhance 12 es in the tracks appears to be relatively insensitive to methods used polar warming and moistening. The Arctic Oscillation (AO) is sensitive (Ulbrich et al., 2013). Analyses of SH storm tracks generally agree with to synoptic eddy vorticity flux, so that projected changes in storm earlier studies, showing that extratropical storm tracks will tend to tracks can alter the AO (Choi et al., 2010). The net result is that chang- shift poleward (Bengtsson et al., 2009; Gastineau et al., 2009; Gastin- es in extratropical storms alter the climate in which they are embed- eau and Soden, 2009; Perrie et al., 2010; Schuenemann and Cassano, ded, so that links between surface warming, extratropical storms and 2010; Chang et al., 2012b). The behaviour is consistent with a likely their influence on climate are more complex than simple responses to trend in observed storm-track behaviour (see Section 2.7.6). Similar changes in baroclinicity (O Gorman, 2010). behaviour appears in CMIP5 simulations for the SH (Figure 12.20c, d). In SH winter there is a clear poleward shift in storm tracks of several 12.4.5 Changes in the Water Cycle degrees and a reduction in storm frequency of only a few percent (not shown). The poleward shift at the end of the century is consistent with The water cycle consists of water stored on the Earth in all its phases, a poleward shift in the SH of the latitudes with strongest tropospheric along with the movement of water through the Earth s climate system. jets (Figure 12.19). This appears to coincide with shifts in baroclinic In the atmosphere, water occurs primarily as gaseous water vapour, dynamics governing extratropical storms (Frederiksen et al., 2011), but it also occurs as solid ice and liquid water in clouds. The ocean is though the degree of jet shift appears to be sensitive to bias in a mod- primarily liquid water, but is partly covered by ice in polar regions. Ter- el s contemporary-climate storm tracks (Chang et al., 2012a, 2012b). restrial water in liquid form appears as surface water (lakes, rivers), soil Although there is thus some uncertainty in the degree of shift, the moisture and groundwater. Solid terrestrial water occurs in ice sheets, consistency of behaviour with observation-based trends, consistency glaciers, frozen lakes, snow and ice on the surface and permafrost. 1074 Long-term Climate Change: Projections, Commitments and Irreversibility Chapter 12 RCP4.5: 2081-2100 RCP8.5: 2081-2100 a 29 b 29 Northern Hemisphere DJF c 29 d 29 Southern Hemisphere JJA 12 (number density per month per unit area) -3.9 -3.3 -2.7 -2.1 -1.5 -0.9 -0.3 0.3 0.9 1.5 2.1 2.7 3.3 3.9 Figure 12.20 | Change in winter, extratropical storm track density (2081 2100) (1986 2005) in CMIP5 multi-model ensembles: (a) RCP4.5 Northern Hemisphere December, January and February (DJF) and (b) RCP8.5 Northern Hemisphere DJF, (c) RCP4.5 Southern Hemisphere June, July and August (JJA) and (d) RCP8.5 Southern Hemisphere JJA. Storm-track computation uses the method of Bengtsson et al. (2006, their Figure 13a) applied to 6-hourly 850 hPa vorticity computed from horizontal winds in the CMIP5 archive. The number of models used appears in the upper right of each panel. DJF panels include data for December 1985 and 2080 and exclude December 2005 and December 2100 for in-season continuity. Stippling marks locations where at least 90% of the models agree on the sign of the change; note that this criterion differs from that used for many other figures in this chapter, due to the small number of models providing sufficient data to estimate internal variability of 20-year means of storm-track statistics. Densities have units (number density per month per unit area), where the unit area is equivalent to a 5° spherical cap (~106 km2). Locations where the scenario or contemporary-climate ensemble average is below 0.5 density units are left white. 1075 Chapter 12 Long-term Climate Change: Projections, Commitments and Irreversibility P ­ rojections of future changes in the water cycle are inextricably con- a broad-scale, quasi-unchanged RH response [to climate change] is nected to changes in the energy cycle (Section 12.4.3) and atmospheric uncontroversial (Randall et al., 2007). However, underlying this fairly circulation (Section 12.4.4). straightforward behaviour are changes in RH that can influence chang- es in cloud cover and atmospheric convection (Sherwood, 2010). More Saturation vapour pressure increases with temperature, but projected recent analysis provides further detail and insight on RH changes. Anal- future changes in the water cycle are far more complex than projected ysis of CMIP3 and CMIP5 models shows near-surface RH decreasing temperature changes. Some regions of the world will be subject to over most land areas as temperatures increase with the notable excep- decreases in hydrologic activity while others will be subject to increas- tion of parts of tropical Africa (O Gorman and Muller, 2010) (Figure es. There are important local seasonal differences among the responses 12.21). The prime contributor to these decreases in RH over land is the of the water cycle to climate change as well. larger temperature increases over land than over ocean in the RCP sce- narios (Joshi et al., 2008; Fasullo, 2010; O Gorman and Muller, 2010). At first sight, the water cycles simulated by CMIP3/5 models may The specific humidity of air originating over more slowly warming appear to be inconsistent, particularly at regional scales. Anthropogen- oceans will be governed by saturation temperatures of oceanic air. As ic changes to the water cycle are superimposed on complex naturally this air moves over land and is warmed, its relative humidity drops as varying modes of the climate (such as El Nino-Southern Oscillation any further moistening of the air over land is insufficient to maintain (ENSO), AO, Pacific Decadal Oscillation (PDO), etc.) aggravating the dif- constant RH, a behaviour Sherwood et al. (2010) term a last-satura- ferences between model projections. However, by careful consideration tion-temperature constraint. The RH decrease over most land areas by of the interaction of the water cycle with changes in other aspects of the end of the 21st century is consistent with a last-saturation-temper- the climate system, the mechanisms of change are revealed, increasing ature constraint and with observed behaviour during the first decade confidence in projections. of the current century (Section 2.5.5; Simmons et al., 2010). Land ocean differences in warming are projected to continue through the 12.4.5.1 Atmospheric Humidity 21st century, and although the CMIP5 projected changes are small, they are consistent with a last-saturation constraint, indicating with Atmospheric water vapour is the primary GHG in the atmosphere. Its medium confidence that reductions in near-surface RH over many land changes affect all parts of the water cycle. However, the amount of areas are likely. water vapour is dominated by naturally occurring processes and not significantly affected directly by human activities. A common experi- 12.4.5.2 Patterns of Projected Average Precipitation Changes ence from past modelling studies is that relative humidity (RH) remains approximately constant on climatological time scales and planetary Global mean precipitation changes have been presented in Section space scales, implying a strong constraint by the Clausius Clapeyron 12.4.1.1. The processes that govern large-scale changes in precipita- relationship on how specific humidity will change. The AR4 stated that tion are presented in Section 7.6, and are used here to interpret the 12 Figure 12.21 | Projected changes in near-surface relative humidity from the CMIP5 models under RCP8.5 for the December, January and February (DJF, left), June, July and August (JJA, middle) and annual mean (ANN, right) averages relative to 1986 2005 for the periods 2046 2065 (top row), 2081 2100 (bottom row). The changes are differences in relative humidity percentage (as opposed to a fractional or relative change). Hatching indicates regions where the multi-model mean change is less than one standard deviation of internal variability. Stippling indicates regions where the multi-model mean change is greater than two standard deviations of internal variability and where at least 90% of models agree on the sign of change (see Box 12.1). 1076 Long-term Climate Change: Projections, Commitments and Irreversibility Chapter 12 projected changes in RCP scenarios. Changes in precipitation extremes transport from oceans to land increases, and therefore the average P are presented in Section 12.4.5.5. Further discussion of regional chang- E over continents also increases (Liepert and Previdi, 2012). es, in particular the monsoon systems, is presented in Chapter 14. In the mid and high latitudes, a common feature across generations of Figure 12.22 shows the CMIP5 multi-model average percentage climate models is a simulated increased precipitation. The thermody- change in seasonal mean precipitation in the middle of the 21st namical component explains most of the projected increase (Emori and century, at the end of the 21st century and at the end of the 22nd Brown, 2005; Seager et al., 2010). This is consistent with theoretical century for the RCP8.5 scenario relative to the 1986 2005 average. explanations assuming fixed atmospheric flow patterns but increased Precipitation changes for all the scenarios are shown in Annex I Sup- water vapour in the lower troposphere (Held and Soden, 2006). In addi- plementary Material and scale approximately with the global mean tion to this thermodynamical effect, water transport may be modified temperature (Section 12.4.2). In many regions, changes in precipitation by the poleward shift of the storm tracks and by the increase of their exhibit strong seasonal characteristics so that, in regions where the intensity (Seager et al., 2010; Wu et al., 2011b), although confidence in sign of the precipitation changes varies with the season, the annual such changes in storm tracks may not be high (see Section 12.4.4). On mean values (Figure 12.10) may hide some of these seasonal changes, seasonal time scales, the minimum and maximum values of precipita- resulting in weaker confidence than seasonal mean values (Chou et al., tion both increase, with a larger increase of the maximum and there- 2013; Huang et al., 2013). fore an increase of the annual precipitation range (Seager et al., 2010; Chou and Lan, 2012). In particular, the largest changes over northern The patterns of multi-model precipitation changes displayed in Figure Eurasia and North America are projected to occur during winter. At 12.22 tend to smooth and decrease the spatial contrast of precip- high latitudes of the NH, the precipitation increase may lead to an itation changes simulated by each model, in particular over regions increase of snowfall in the colder regions and a decrease of snowfall where model results disagree. Thus the amplitude of the multi-model in the warmer regions due to the decreased number of freezing days ensemble mean precipitation response significantly underestimates (see Section 12.4.6.2). the median amplitude computed from each individual model (Neelin et al., 2006; Knutti et al., 2010a). The CMIP3/5 multi-model ensemble Most models simulate a large increase of the annual mean precipita- precipitation projections must be interpreted in the context of uncer- tion over the equatorial ocean and an equatorward shift of the Inter- tainty. Multi-model projections are not probabilistic statements about tropical Convergence Zone (ITCZ), in both summer and winter seasons, the likelihood of changes. Maps of multi-model projected changes are that are mainly explained by atmospheric circulation changes (Chou et smoothly varying but observed changes are and will continue to be al., 2009; Seager et al., 2010; Sobel and Camargo, 2011). The chang- much more granular. es of the atmospheric circulation have different origins. Along the margins of the convection zones, spatial inhomogeneities, including To analyze the patterns of projected precipitation changes, a useful local convergence feedback or the rate at which air masses from dry framework consists in decomposing them into a part that is related to regions tend to flow into the convection zone, can yield a considerable atmospheric circulation changes and a part that is related mostly to sensitivity in precipitation response (Chou et al., 2006; Neelin et al., water vapour changes, referred to as dynamical and thermodynamical 2006). Along the equator, atmosphere ocean interactions yield to a components, respectively. However, the definition of these two com- maximum of SST warming and a large precipitation increase there (Xie ponents may differ among studies. At the time of the AR4, the robust et al., 2010; Ma and Xie, 2013). Model studies with idealized configu- changes of the difference between precipitation and evaporation rations suggest that tropical precipitation changes should be interpret- (P E) were interpreted as a wet-get-wetter and dry-get-drier type ed as responses to changes of the atmospheric energy budget rather of response (Mitchell et al., 1987; Chou and Neelin, 2004; Held and than responses to changes of SST (Kang and Held, 2012). All of these Soden, 2006). The theoretical background, which is more relevant over atmospheric circulation changes, and therefore precipitation changes, 12 oceans than over land, is that the lower-tropospheric water vapour can differ considerably from model to model. This is the case over both increase with temperature enhances the moisture transported by ocean and land. For instance, the spread of model projections in the the circulation. This leads to additional moisture convergence within Sahel region, West Africa, is large in both the CMIP3 and CMIP5 mul- the convergence zones and to additional moisture divergence in the ti-model data base (Roehrig et al., 2013). descent zones, increasing the contrast in precipitation minus evapo- ration values between moisture convergence and divergence regions. In the subtropical dry regions, there is a robust decrease of P E that A weakening of the tropical overturning circulation (see Section is accounted for by the thermodynamic contribution (Chou and Neelin, 12.4.4.2) partially opposes this thermodynamic response (Chou and 2004; Held and Soden, 2006; Chou et al., 2009; Seager et al., 2010; Neelin, 2004; Held and Soden, 2006; Vecchi and Soden, 2007; Chou Bony et al., 2013). Over ocean, the spatial heterogeneity of temperature et al., 2009; Seager et al., 2010; Allan, 2012; Bony et al., 2013). At the increase impacts the lower-tropospheric water vapour increase, which regional scale the dynamic response may be larger than the thermo- impacts both the thermodynamic and the dynamic responses (Xie et dynamic response, and this has been analyzed in more detail since al., 2010; Ma and Xie, 2013). In addition, the pattern of precipitation the AR4 (Chou et al., 2009; Seager et al., 2010; Xie et al., 2010; Muller changes in dry regions may be different from that of P E because the and O Gorman, 2011; Chadwick et al., 2012; Scheff and Frierson, 2012; contribution of evaporation changes can be as large (but of opposite Bony et al., 2013; Ma and Xie, 2013). Over continents, this simple wet- sign) as the moisture transport changes (Chou and Lan, 2012; Scheff get-wetter and dry-get-drier type of response fails for some important and Frierson, 2012; Bony et al., 2013). This is especially the case over regions such as the Amazon. At the global scale, the net water vapour the subsidence regions during the warm season over land where the 1077 Chapter 12 Long-term Climate Change: Projections, Commitments and Irreversibility agreement between models is the smallest (Chou et al., 2009; Allan, of the troposphere and the large scale rising motion and hence reduc- 2012). A robust feature is the decline of precipitation on the poleward es precipitation in the convective regions. Over large landmasses, the flanks of the subtropical dry zones as a consequence of the Hadley Cell direct effect of CO2 on precipitation is the opposite owing to the small expansion, with possible additional decrease from a poleward shift of thermal inertia of land surfaces (Andrews et al., 2010; Bala et al., 2010; the mid latitude storm tracks (Seager et al., 2010; Scheff and Frierson, Cao et al., 2012; Bony et al., 2013). Regional precipitation changes are 2012). On seasonal time scales, the minimum and the maximum values also influenced by aerosol and ozone (Ramanathan et al., 2001; Allen of precipitation both increase, with a larger increase of the maximum et al., 2012; Shindell et al., 2013a) through both local and large-scale and therefore an increase of the annual precipitation range (Sobel and processes, including changes in the circulation. Stratospheric ozone Camargo, 2011; Chou and Lan, 2012). depletion contributes to the poleward expansion of the Hadley Cell and the related change of precipitation in the SH (Kang et al., 2011) Long-term precipitation changes are driven mainly by the increase of whereas black carbon and tropospheric ozone increases are major con- the surface temperature, as presented above, but other factors also tributors in the NH (Allen et al., 2012). Regional precipitation changes contribute to them. Recent studies suggest that CO2 increase has a sig- depend on regional forcings and on how models simulate their local nificant direct influence on atmospheric circulation, and therefore on and remote effects. Based on CMIP3 results, the inter-model spread global and tropical precipitation changes (Andrews et al., 2010; Bala et of the estimate of precipitation changes over land is larger than the al., 2010; Cao et al., 2012; Bony et al., 2013). Over the ocean, the pos- inter-scenario spread except in East Asia (Frieler et al., 2012). itive RF from increased atmospheric CO2 reduces the radiative cooling Seasonal mean percentage precipitation change (RCP8.5) 12 Figure 12.22 | Multi-model CMIP5 average percentage change in seasonal mean precipitation relative to the reference period 1986 2005 averaged over the periods 2045 2065, 2081 2100 and 2181 2200 under the RCP8.5 forcing scenario. Hatching indicates regions where the multi-model mean change is less than one standard deviation of internal variability. Stippling indicates regions where the multi-model mean change is greater than two standard deviations of internal variability and where at least 90% of models agree on the sign of change (see Box 12.1). 1078 Long-term Climate Change: Projections, Commitments and Irreversibility Chapter 12 Projected precipitation changes vary greatly between models, much over the equatorial Pacific and Indian Oceans and decreases over much more so than for temperature projections. Part of this variance is due to of the subtropical ocean. However, decreases are not projected to be genuine differences between the models including their ability to rep- larger than natural 20-year variations anywhere until the end of this licate observed precipitation patterns (see Section 9.4.1.1). However, a century under the RCP8.5 scenario. Decreased precipitation in the large part of it is also the result of the small ensemble size from each Mediterranean, Caribbean and Central America, southwestern United model (Rowell, 2012). This is especially true for regions of small pro- States and South Africa is likely under the RCP8.5 scenario and is pro- jected changes located between two regions: one experiencing signif- jected with medium confidence to be larger than natural variations by icant increases while the other experiences significant decreases. Indi- the end of the 22nd century in some seasons (Box 12.1). The CMIP3 vidual climate model realizations will differ in their projection of future models historical simulations of zonal mean precipitation trends were precipitation changes in these regions simply owing to their internal shown to underestimate observed trends (Gillett et al., 2004; Lambert variability (Deser et al., 2012b; Deser et al., 2012a). Multi-model pro- et al., 2005; Zhang et al., 2007; Liepert and Previdi, 2009) (see Section jections containing large numbers of realizations would tend to feature 10.3.2.2). Therefore it is more likely than not that the magnitude of the small changes in these regions, and hatching in Figure 12.22 indicates projected future changes in Figure 12.22 based on the multi-model regions where the projected multi-model mean change is less than one mean is underestimated. Observational uncertainties including limited standard deviation of internal variability (method (a), Box 12.1). Confi- global coverage and large natural variability, in addition to challenges dence in projections in regions of limited or no change in precipitation in precipitation modelling, limit confidence in assessment of climatic may be more difficult to obtain than confidence in regions of large pro- changes in precipitation. jected changes. However, Power et al. (2012) and Tebaldi et al. (2011) show that for some of the regions featuring small multi-model average 12.4.5.3 Soil Moisture projected changes, effective consensus in projections may be better than the metrics reported in AR4 would imply. Near-surface soil moisture is the net result of a suite of complex process- es (e.g., precipitation evapotranspiration, drainage, overland flow, infil- Since the AR4, progress has been made in the understanding of the tration), and heterogeneous and difficult-to-characterize aboveground processes that control large scale precipitation changes. There is high and belowground system properties (e.g., slope, soil texture). As a confidence that the contrast of seasonal mean precipitation between result, regional to global-scale simulations of soil moisture and drought dry and wet regions will increase in a warmer climate over most of remain relatively uncertain (Burke and Brown, 2008; Henderson-Sellers the globe although there may be regional exceptions to this general et al., 2008). The AR4 (Section 8.2.3.2) discussed the lack of assess- pattern. This response is particularly robust when considering P E ments of global-scale models in their ability to simulate soil moisture, changes as a function of atmospheric dynamical regimes. However, it and this problem appears to have persisted (Section 9.4.4.2). Further- is important to note that significant exceptions can occur in specific more, consistent multi-model projections of total soil moisture are diffi- regions especially along the equator and on the poleward edges of the cult to make owing to substantial differences between climate models subtropical dry zone. In these regions, atmospheric circulation changes in the depth of their soil. However, Koster et al. (2009a) argued that lead to shifts of the precipitation patterns. There is high confidence that once climatological statistics affecting soil moisture were accounted for, the contrast between wet and dry seasons will increase over most of different models tend to agree on soil moisture projections. the globe as temperatures increase. Over the mid- and high-latitude regions, projected precipitation increases in winter are larger than in The AR4 summarized multi-model projections of 21st century annual summer. Over most of the subtropical oceans, projected precipitation mean soil moisture changes as decreasing in the subtropics and Med- increases in summer are larger than in winter. iterranean region, and increasing in east Africa and central Asia. Dai (2013) found similar changes in an ensemble of 11 CMIP5 GCMs under The changes in precipitation shown in Figure 12.22 exhibit patterns RCP4.5. Figure 12.23 shows projected changes in surface soil moisture 12 that become more pronounced and confidence in them increases (upper 10 cm) in the CMIP5 ensemble at the end of the 21st century as temperatures increase. More generally, the spatial and temporal under the RCPs 2.6, 4.5, 6.0 and 8.5. We focus on this new CMIP5 changes in precipitation between two scenarios or within two peri- specification because it describes soil moisture at a consistent depth ods of a given scenario exhibit the pattern scaling behavior and lim- across all CMIP5 models. The broad patterns are moderately consist- itations described in Section 12.4.2. The patterns and the associated ent across the RCPs, with the changes tending to become stronger as multi-model spreads in CMIP5 for the RCP scenarios are very similar the strength of the forcing change increases. The agreement among to those in CMIP3 for the SRES scenarios discussed in the AR4, with CMIP5 models and the consistency with other physical features of the projections in CMIP5 being slightly more consistent over land than climate change indicate high confidence in certain regions where those from CMIP3 (Knutti and Sedláèek, 2013). The largest percentage surface soils are projected to dry. There is little-to-no confidence any- changes are at the high latitudes. By the end of the 21st century, over where in projections of moister surface soils. Under RCP8.5, with the the large northern land masses, increased precipitation is likely under largest projected change, individual ensemble members (not shown) the RCP8.5 scenario in the winter and spring poleward of 50°N. The show consistency across the ensemble for drying in the Mediterranean robustness across scenarios, the magnitude of the projected changes region, northeast and southwest South America, southern Africa, and versus natural variability and physical explanations described above southwestern USA. However, ensemble members show disagreement yield high confidence that the projected changes would be larger than on the sign of change in large regions such as central Asia or the high natural 20-year variations (see Box 12.1). In the tropics, precipitation northern latitudes. The Mediterranean, southwestern USA, northeast changes exhibit strong regional contrasts, with increased ­ recipitation p South America and southern African drying regions are consistent with 1079 Chapter 12 Long-term Climate Change: Projections, Commitments and Irreversibility Annual mean near-surface soil moisture change (2081-2100) Figure 12.23 | Change in annual mean soil moisture (mass of water in all phases in the uppermost 10 cm of the soil) (mm) relative to the reference period 1986 2005 projected for 2081 2100 from the CMIP5 ensemble. Hatching indicates regions where the multi-model mean change is less than one standard deviation of internal variability. Stippling indicates regions where the multi-model mean change is greater than two standard deviations of internal variability and where at least 90% of models agree on the sign of change (see Box 12.1). The number of CMIP5 models used is indicated in the upper right corner of each panel. projected widening of the Hadley Circulation that shifts downwelling, Projected changes in soil moisture from the CMIP3/5 models also show thus inhibiting precipitation in these regions. The large-scale drying in substantial seasonal variation. For example, soil moisture changes in the Mediterranean, southwest USA, and southern Africa appear across the North American midlatitudes, coupled with projected warming, generations of projections and climate models and is deemed likely increases the strength of land atmosphere coupling during spring and as global temperatures rise and will increase the risk of agricultural summer in 15 GCMs under RCP8.5 (Dirmeyer et al., 2013). For the drought. In addition, an analysis of CMIP3 and CMIP5 projections of Cline River watershed in western Canada, Kienzle et al. (2012) find soil moisture in five drought-prone regions indicates that the differ- decreases in summer soil moisture content, but annual increases aver- 12 ences in future forcing scenarios are the largest source of uncertain- aging 2.6% by the 2080s using a suite of CMIP3 GCMs simulating B1, ty in such regions rather than differences between model responses A1B and A2 scenarios to drive a regional hydrology model. Hansen et (Orlowsky and Seneviratne, 2012). al. (2007), using dynamical downscaling of one GCM running the A2 scenario, find summer soil moisture decreases in Mongolia of up to Other recent assessments include multi-model ensemble approaches, 6% due to increased potential evaporation in a warming climate and dynamical downscaling, and regional climate models applied around decreased precipitation and decreased precipitation. the globe and illustrate the variety of issues influencing soil moisture changes. Analyses of the southwestern USA using CMIP3 models Soil moisture projections in high latitude permafrost regions are crit- (Christensen and Lettenmaier, 2007; Seager et al., 2007) show consist- ically important for assessing future climate feedbacks from trace- ent projections of drying, primarily due to a decrease in winter precipi- gas emissions (Zhuang et al., 2004; Riley et al., 2011) and vegetation tation. In contrast, Kellomaki et al. (2010) find that SRES A2 projections changes (Chapin et al., 2005). In addition to changes in precipitation, for Finland yield decreased snow depth, but soil moisture generally snow cover and evapotranspiration, future changes in high-latitude increases, consistent with the general increase in precipitation occur- soil moisture also will depend on permafrost degradation, thermokarst ring in high northern latitudes. Kolomyts and Surova (2010), using pro- evolution, rapid changes in drainage (Smith et al., 2005), and changes jections from the CMIP3 models, GISS and HadCM2, under the SRES in plant communities and their water demands. Current understanding A2 forcing, show that vegetation type has substantial influence on the of these interacting processes at scales relevant to climate is poor, so development of pronounced drying over the 21st century in Middle that full incorporation in current GCMs is lacking. Volga Region forests. 1080 Long-term Climate Change: Projections, Commitments and Irreversibility Chapter 12 12.4.5.4 Runoff and Evaporation ­combination of evapotranspiration increases and precipitation decreas- es, with the overall reduction in river flow exacerbated by human water In the AR4, 21st century model-projected runoff consistently showed demands on the basin s supply. decreases in southern Europe, the Middle East, and southwestern USA and increases in Southeast Asia, tropical East Africa and at high north- A number of CMIP3 analyses have examined trends and seasonal shifts ern latitudes. The same general features appear in the CMIP5 ensemble in runoff. For example, Kienzle et al. (2012) studied climate change sce- of GCMs for all four RCPs shown in Figure 12.24, with the areas of most narios over the Cline River watershed in western Canada and projected robust change typically increasing with magnitude of forcing change. (1) spring runoff and peak streamflow up to 4 weeks earlier than in However, the robustness of runoff decreases in the southwestern USA 1961 1990; (2) significantly higher streamflow between October and is less in the CMIP5 models compared to the AR4. The large decreases June; and (3) lower streamflow between July and September. For the in runoff in southern Europe and southern Africa are consistent with Mediterranean basin, an ensemble of regional climate models driven changes in the Hadley Circulation and related precipitation decreases by several GCMs using the A1B scenario have a robust decrease in and warming-induced evapotranspiration increases. The high northern runoff emerging only after 2050 (Sanchez-Gomez et al., 2009). latitude runoff increases are likely under RCP8.5 and consistent with the projected precipitation increases (Figure 12.22). The consistency of Annual mean surface evaporation in the models assessed in AR4 changes across different generations of models and different forcing showed increases over most of the ocean and increases or decreases scenarios, together with the physical consistency of change indicates over land with largely the same pattern over land as increases and that decreases are also likely in runoff in southern Europe, the Middle decreases in precipitation. Similar behaviour occurs in an ensemble of East, and southern Africa in this scenario. CMIP5 models (Figure 12.25). Evaporation increases over most of the ocean and land, with prominent areas of decrease over land occurring A number of reports since the AR4 have updated findings from CMIP3 in southern Africa and northwestern Africa along the Mediterranean. models and analyzed a large set of mechanisms affecting runoff. Sev- The areas of decrease correspond to areas with reduced precipitation. eral studies have focussed on the Colorado River basin in the United There is some uncertainty about storm-track changes over Europe (see States (Christensen and Lettenmaier, 2007; McCabe and Wolock, 2007; Sections 12.4.3 and 14.6.2). However, the consistency of the decreas- Barnett and Pierce, 2008; Barnett et al., 2008) showing that runoff es across different generations of models and different forcing sce- reductions that do happen under global warming occur through a narios along with the physical basis for the precipitation decrease 12 Figure 12.24 | Change in annual mean runoff relative to the reference period 1986 2005 projected for 2081 2100 from the CMIP5 ensemble. Hatching indicates regions where the multi-model mean change is less than one standard deviation of internal variability. Stippling indicates regions where the multi-model mean change is greater than two standard deviations of internal variability and where at least 90% of models agree on the sign of change (see Box 12.1). The number of CMIP5 models used is indicated in the upper right corner of each panel. 1081 Chapter 12 Long-term Climate Change: Projections, Commitments and Irreversibility Figure 12.25 | Change in annual mean evaporation relative to the reference period 1986 2005 projected for 2081 2100 from the CMIP5 ensemble. Hatching indicates regions where the multi-model mean change is less than one standard deviation of internal variability. Stippling indicates regions where the multi-model mean change is greater than two standard deviations of internal variability and where at least 90% of models agree on the sign of change (see Box 12.1). The number of CMIP5 models used is indicated in the upper right corner of each panel. indicates that these decreases in annual mean evaporation are likely individual storms and fewer weak storms is projected (Seneviratne et under RCP8.5, but with medium confidence. Annual mean evapora- al., 2012). At seasonal or longer time scales, increased evapotranspira- tion increases over land in the northern high latitudes are consistent tion over land can lead to more frequent and more intense periods of with the increase in precipitation and the overall warming that would agricultural drought. increase potential evaporation. For the northern high latitudes, the physical consistency and the similar behaviour across multiple gener- A general relationship between changes in total precipitation and ations and forcing scenarios indicates that annual mean evaporation extreme precipitation does not exist (Seneviratne et al., 2012). Two increases there are likely, with high confidence. possible mechanisms controlling short-term extreme precipitation 12 amounts are discussed at length in the literature and are similar to the Evapotranspiration changes partly reflect changes in precipitation. thermodynamic and dynamical mechanisms detailed above for chang- However, some changes might come from altered biological processes. es in average precipitation. For example, increased atmospheric CO2 promotes stomatal closure and reduced transpiration (Betts et al., 2007; Cruz et al., 2010) which The first considers that extreme precipitation events occur when most can potentially yield increased runoff. There is potential for substan- of the available atmospheric water vapour rapidly precipitates out in a tial feedback between vegetation changes and regional water cycles, single storm. The maximum amount of water vapour in air (saturation) though the impact of such feedback remains uncertain at this point is determined by the Clausius Clapeyron relationship. As air temper- due to limitations on modelling crop and other vegetation processes in ature increases, this saturated amount of water also increases (Allen GCMs (e.g., Newlands et al., 2012) and uncertainties in plant response, and Ingram, 2002; Pall et al., 2007; Allan and Soden, 2008; Kendon et ecosystem shifts and land management changes. al., 2010). Kunkel et al. (2013) examined the CMIP5 model RCP4.5 and 8.5 projections for changes in maximum water vapour concentrations, 12.4.5.5 Extreme Events in the Water Cycle a principal factor controlling the probable bound on maximum precipi- tation, concluding that maximum water vapour changes are compara- In addition to the changes in the seasonal pattern of mean precipitation ble to mean water vapour changes but that the potential for changes described above, the distribution of precipitation events is projected to ­ in dynamical factors is less compelling. Such increases in atmospheric undergo profound changes (Gutowski et al., 2007; Sun et al., 2007; water vapour are expected to increase the intensity of individual pre- Boberg et al., 2010). At daily to weekly scales, a shift to more intense cipitation events, but have less impact on their frequency. As a result 1082 Long-term Climate Change: Projections, Commitments and Irreversibility Chapter 12 projected increases in extreme precipitation may be more reliable than a) Wettest consecutive five days (RX5day) similar projections of changes in mean precipitation in some regions (Kendon et al., 2010). historical RCP4.5 20 RCP2.6 RCP8.5 20 A second mechanism for extreme precipitation put forth by O Gorman Relative change (%) and Schneider (2009a, 2009b) is that such events are controlled by 15 15 anomalous horizontal moisture flux convergence and associated con- vective updrafts which would change in a more complicated fashion 10 10 in a warmer world (Sugiyama et al., 2010). Emori and Brown (2005) showed that the thermodynamic mechanism dominated over the dynamical mechanism nearly everywhere outside the tropical warm 5 5 pool. However, Utsumi et al. (2011) used gridded observed daily data to find that daily extreme precipitation monotonically increases with 0 0 temperature only at high latitudes, with the opposite behaviour in CMIP3 B1 CMIP3 A1B CMIP3 A2 the tropics and a mix in the mid-latitudes. Li et al. (2011a) found that 5 5 both mechanisms contribute to extreme precipitation in a high-res- 1960 1980 2000 2020 2040 2060 2080 2100 olution aquaplanet model with updrafts as the controlling element Year in the tropics and air temperature controlling the mid-latitudes con- sistent with the results by Chou et al. (2012). Using a high-resolution 18 regional model, Berg et al. (2009) found a seasonal dependence in Europe with the Clausius Clapeyron relationship providing an upper limit to daily precipitation intensity in winter but water availability rather than storage capacity is the controlling factor in summer. Addi- tionally, Lenderink and Van Meijgaard (2008) found that very short (sub-daily) extreme precipitation events increase at a rate twice the amount predicted by Clausius Clapeyron scaling in a very high-resolu- tion model over Europe suggesting that both mechanisms can interact jointly. Gastineau and Soden (2009) found in the CMIP3 models that the updrafts associated with the most extreme tropical precipitation events actually weaken despite an increase in the frequency of the heaviest rain rates further complicating simple mechanistic explana- tions. See also Sections 7.6.5 and 11.3.2.5.2. 18 Projections of changes in future extreme precipitation may be larger at the regional scales than for future mean precipitation, but natural variability is also larger causing a tendency for signal-to-noise ratios to decrease when considering increasingly extreme metrics. However, mechanisms of natural variability still are a large factor in assessing the robustness of projections (Kendon et al., 2008). In addition, large- scale circulation changes, which are uncertain, could dominate over the 12 above mechanisms depending on the rarity and type of events consid- ered. However, analysis of CMIP3 models suggests circulation changes are potentially insufficient to offset the influence of increasing atmos- pheric water vapour on extreme precipitation change over Europe at least on large spatial scales (Kendon et al., 2010). An additional shift of Figure 12.26 | (a, b) Projected percent changes (relative to the 1981 2000 refer- the storm track has been shown in models with a better representation ence period in common with CMIP3) from the CMIP5 models in RX5day, the annual of the stratosphere, and this is found to lead to an enhanced increase maximum five-day precipitation accumulation. (a) Global average percent change over in extreme rainfall over Europe in winter (Scaife et al., 2012). land regions for the RCP2.6, RCP4.5 and RCP8.5 scenarios. Shading in the time series represents the interquartile ensemble spread (25th and 75th quantiles). The box-and- Similar to temperature extremes (Section 12.4.3.3), the definition of whisker plots show the interquartile ensemble spread (box) and outliers (whiskers) for 11 CMIP3 model simulations of the SRES scenarios A2 (orange), A1B (cyan) and B1 a precipitation extreme depends very much on context and is often (purple) globally averaged over the respective future time periods (2046 2065 and used in discussion of particular climate-related impacts (Seneviratne 2081 2100) as anomalies from the 1981 2000 reference period. (b) Percent change et al. (2012), Box 3.1). Consistently, climate models project future epi- over the 2081 2100 period in the RCP8.5 scenario. (c) Projected change in annual sodes of more intense precipitation in the wet seasons for most of the CDD, the maximum number of consecutive dry days when precipitation is less than 1 land areas, especially in the NH and its higher latitudes, and the mon- mm, over the 2081 2100 period in the RCP8.5 scenario (relative to the 1981 2000 reference period) from the CMIP5 models. Stippling indicates gridpoints with changes soon regions of the world, and at a global average scale. The actual that are significant at the 5% level using a Wilcoxon signed-ranked test. (Updated from magnitude of the projected change is dependent on the model used, Sillmann et al. (2013), excluding the FGOALS-s2 model.) 1083 Chapter 12 Long-term Climate Change: Projections, Commitments and Irreversibility Frequently Asked Questions FAQ 12.2 | How Will the Earth s Water Cycle Change? The flow and storage of water in the Earth s climate system are highly variable, but changes beyond those due to natural variability are expected by the end of the current century. In a warmer world, there will be net increases in rainfall, surface evaporation and plant transpiration. However, there will be substantial differences in the changes between locations. Some places will experience more precipitation and an accumulation of water on land. In others, the amount of water will decrease, due to regional drying and loss of snow and ice cover. The water cycle consists of water stored on the Earth in all its phases, along with the movement of water through the Earth s climate system. In the atmosphere, water occurs primarily as a gas water vapour but it also occurs as ice and liquid water in clouds. The ocean, of course, is primarily liquid water, but the ocean is also partly covered by ice in polar regions. Terrestrial water in liquid form appears as surface water such as lakes and rivers soil moisture and groundwater. Solid terrestrial water occurs in ice sheets, glaciers, snow and ice on the surface and in permafrost and seasonally frozen soil. Statements about future climate sometimes say that the water cycle will accelerate, but this can be misleading, for strictly speaking, it implies that the cycling of water will occur more and more quickly with time and at all locations. Parts of the world will indeed experience intensification of the water cycle, with larger transports of water and more rapid movement of water into and out of storage reservoirs. However, other parts of the climate system will experience substantial depletion of water, and thus less movement of water. Some stores of water may even vanish. As the Earth warms, some general features of change will occur simply in response to a warmer climate. Those changes are governed by the amount of energy that global warming adds to the climate system. Ice in all forms will melt more rapidly, and be less pervasive. For example, for some simulations assessed in this report, summer Arctic sea ice disappears before the middle of this century. The atmosphere will have more water vapour, and observations and model results indicate that it already does. By the end of the 21st century, the average amount of water vapour in the atmosphere could increase by 5 to 25%, depending on the amount of human emissions of greenhouse gases and radiatively active particles, such as smoke. Water will evaporate more quickly from the surface. Sea level will rise due to expansion of warming ocean waters and melting land ice flowing into the ocean (see FAQ 13.2). These general changes are modified by the complexity of the climate system, so that they should not be expected to occur equally in all locations or at the same pace. For example, circulation of water in the atmosphere, on land and in the ocean can change as climate changes, concentrating water in some locations and depleting it in others. The changes also may vary throughout the year: some seasons tend to be wetter than others. Thus, model simu- lations assessed in this report show that winter precipitation in northern Asia may increase by more than 50%, whereas summer precipitation there is projected to hardly change. Humans also intervene directly in the water cycle, through water management and changes in land use. Changing population distributions and water practices would produce further changes in the water cycle. 12 Water cycle processes can occur over minutes, hours, days and longer, and over distances from metres to kilometres and greater. Variability on these scales is typically greater than for temperature, so climate changes in precipitation are harder to discern. Despite this complexity, projections of future climate show changes that are common across many models and climate forcing scenarios. Similar changes were reported in the AR4. These results collectively suggest well understood mechanisms of change, even if magnitudes vary with model and forcing. We focus here on changes over land, where changes in the water cycle have their largest impact on human and natural systems. Projected climate changes from simulations assessed in this report (shown schematically in FAQ 12.2, Figure 1) gen- erally show an increase in precipitation in parts of the deep tropics and polar latitudes that could exceed 50% by the end of the 21st century under the most extreme emissions scenario. In contrast, large areas of the subtropics could have decreases of 30% or more. In the tropics, these changes appear to be governed by increases in atmospheric water vapour and changes in atmospheric circulation that further concentrate water vapour in the tropics and thus promote more tropical rainfall. In the subtropics, these circulation changes simultaneously promote less rainfall despite warming in these regions. Because the subtropics are home to most of the world s deserts, these changes imply increasing aridity in already dry areas, and possible expansion of deserts. (continued on next page) 1084 Long-term Climate Change: Projections, Commitments and Irreversibility Chapter 12 FAQ 12.2 (continued) Increases at higher latitudes are governed by warmer temperatures, which allow more water in the atmosphere and thus, more water that can precipitate. The warmer climate also allows storm systems in the extratropics to transport more water vapour into the higher latitudes, without requiring substantial changes in typical wind strength. As indicated above, high latitude changes are more pronounced during the colder seasons. Whether land becomes drier or wetter depends partly on precipitation changes, but also on changes in surface evaporation and transpiration from plants (together called evapotranspiration). Because a warmer atmosphere can have more water vapour, it can induce greater evapotranspiration, given sufficient terrestrial water. However, increased carbon dioxide in the atmosphere reduces a plant s tendency to transpire into the atmosphere, partly counteracting the effect of warming. In the tropics, increased evapotranspiration tends to counteract the effects of increased precipitation on soil mois- ture, whereas in the subtropics, already low amounts of soil moisture allow for little change in evapotranspiration. At higher latitudes, the increased precipitation generally outweighs increased evapotranspiration in projected cli- mates, yielding increased annual mean runoff, but mixed changes in soil moisture. As implied by circulation changes in FAQ 12.2, Figure 1, boundaries of high or low moisture regions may also shift. A further complicating factor is the character of rainfall. Model projections show rainfall becoming more intense, in part because more moisture will be present in the atmosphere. Thus, for simulations assessed in this report, over much of the land, 1-day precipitation events that currently occur on average every 20 years could occur every 10 years or even more frequently by the end of the 21st century. At the same time, projections also show that precipi- tation events overall will tend to occur less frequently. These changes produce two seemingly contradictory effects: more intense downpours, leading to more floods, yet longer dry periods between rain events, leading to more drought. Wetter At high latitudes and at high elevation, further changes Drier y occur due to the loss of frozen water. Some of these are le ad H Wetter resolved by the present generation of global climate Drier Runoff Land models (GCMs), and some changes can only be inferred evaporation Wetter because they involve features such as glaciers, which typically are not resolved or included in the models. The warmer climate means that snow tends to start accu- mulating later in the fall, and melt earlier in the spring. Simulations assessed in this report show March to April FAQ 12.2, Figure 1 | Schematic diagram of projected changes in major com- snow cover in the Northern Hemisphere is projected to ponents of the water cycle. The blue arrows indicate major types of water move- decrease by approximately 10 to 30% on average by ment changes through the Earth s climate system: poleward water transport by the end of this century, depending on the greenhouse extratropical winds, evaporation from the surface and runoff from the land to the oceans. The shaded regions denote areas more likely to become drier or 12 gas scenario. The earlier spring melt alters the timing wetter. Yellow arrows indicate an important atmospheric circulation change by of peak springtime flow in rivers receiving snowmelt. the Hadley Circulation, whose upward motion promotes tropical rainfall, while As a result, later flow rates will decrease, potentially suppressing subtropical rainfall. Model projections indicate that the Hadley affecting water resource management. These features Circulation will shift its downward branch poleward in both the Northern and appear in GCM simulations. Southern Hemispheres, with associated drying. Wetter conditions are projected at high latitudes, because a warmer atmosphere will allow greater precipitation, Loss of permafrost will allow moisture to seep more with greater movement of water into these regions. deeply into the ground, but it will also allow the ground to warm, which could enhance evapotranspiration. However, most current GCMs do not include all the pro- cesses needed to simulate well permafrost changes. Studies analysing soils freezing or using GCM output to drive more detailed land models suggest substantial permafrost loss by the end of this century. In addition, even though current GCMs do not explicitly include glacier evolution, we can expect that glaciers will continue to recede, and the volume of water they provide to rivers in the summer may dwindle in some locations as they disappear. Loss of glaciers will also contribute to a reduction in springtime river flow. However, if annual mean precipitation increas- es either as snow or rain then these results do not necessarily mean that annual mean river flow will decrease. 1085 Chapter 12 Long-term Climate Change: Projections, Commitments and Irreversibility but there is strong agreement across the models over the direction of a simplified soil moisture model (Hoerling et al., 2012). The consecutive change (Tebaldi et al., 2006; Goubanova and Li, 2007; Chen and Knut- dry-day index (CDD) is the length of the longest period of consecutive son, 2008; Haugen and Iversen, 2008; May, 2008b; Kysely and Berano- days with precipitation less than 1 mm (Box 2.4, Table 1). CMIP5 pro- va, 2009; Min et al., 2011; Sillmann et al., 2013). Regional details are jected changes in CDD over the 2081 2100 period under the RCP8.5 less robust in terms of the relative magnitude of changes but remain in scenario (relative to the 1981 2000 reference period in common with good accord across models in terms of the sign of the change and the CMIP3) from the CMIP5 models are shown in Figure 12.26c and exhib- large-scale geographical patterns (Meehl et al., 2005a; CCSP, 2008a). In it patterns similar to projected changes in both precipitation and soil semi-arid regions of the midlatitudes and subtropics such as the Medi- moisture (Sillmann et al., 2013). Substantial increases in this measure terranean, the southwest USA, southwestern Australia, southern Africa of meteorological drought are projected in the Mediterranean, Central and a large portion of South America, the tendency manifested in the America, Brazil, South Africa and Australia while decreases are project- majority of model simulations is for longer dry periods and is consist- ed in high northern latitudes. ent with the average decreases shown in Figure 12.22. Figure 12.26 shows projected percent changes in RX5day, the annual maximum of Truly rare precipitation events can cause very significant impacts. The consecutive 5-day precipitation over land regions obtained from the statistics of these events at the tails of the precipitation distribution CMIP5 models (Box 2.4, Table 1). Globally averaged end of 21st centu- are well described by Extreme Value (EV) Theory although there are sig- ry changes over land range from 5% (RCP2.6) to 20% (RCP8.5) more nificant biases in the direct comparison of gridded model output and precipitation during very wet 5-day periods. Results from the CMIP3 actual station data (Smith et al., 2009). There is also strong evidence models are shown for comparison (see Section 12.4.9). Locally, the few that model resolution plays a key role in replicating EV quantities esti- regions where this index of extreme precipitation decreases in the late mated from gridded observational data, suggesting that high-resolu- 21st century RCP8.5 projection coincide with areas of robust decreases tion models may provide somewhat more confidence in projection of in the mean precipitation of Figure 12.22. changes in rare precipitation events (Fowler et al., 2007a; Wehner et al., 2011). Figure 12.27 shows the late 21st century changes per degree Drought is discussed extensively in the SREX report (Seneviratne et al., Celsius in local warming in 20-year return values of annual maximum 2012) and the conclusions about future drought risk described there daily precipitation relative to the late 20th century (left) and the asso- based on CMIP3 models are reinforced by the CMIP5 models. As noted ciated return periods of late 20th century 20-year return values at the in the SREX reports, assessments of changes in drought characteristics end of the 21st century from the CMIP5 models. Across future emission with climate change should be made in the context of specific impacts scenarios, the global average of the CMIP5 multi-model median return questions. The risk of future agricultural drought episodes is increased value sensitivity is an increase of 5.3% °C 1 (Kharin et al., 2013). The in the regions of robust soil moisture decrease described in Section CMIP5 land average is close to the CMIP3 value of 4% °C 1 report- 12.4.5.3 and shown in Figure 12.23. Other measures in the literature of ed by Min et al. (2011) for a subset of CMIP3 models. Corresponding future agricultural drought are largely focussed on the Palmer Drought with this change, the global average of return periods of late 20th Severity Index (Wehner et al., 2011; Schwalm et al., 2012; Dai, 2013) century 20-year return values is reduced from 20 years to 14 years for and project extreme drought as the normal climatological state by a 1°C local warming. Return periods are projected to be reduced by the end of the 21st century under the high emission scenarios in many about 10 to 20% °C 1 over the most of the mid-latitude land masses mid-latitude locations. However, this measure of agricultural drought with larger reductions over wet tropical regions (Kharin et al., 2013). has been criticized as overly sensitive to increased temperatures due to Hence, extreme precipitation events will very likely be more intense RP for present day 20-yr RV of daily precipitation Daily precipitation 20-yr RV change per 1°C warming under 1°C warming 12 31 31 Figure 12.27 | (Left) The CMIP5 2081 2100 multi-model ensemble median percent change in 20-year return values of annual maximum daily precipitation per 1°C of local warm- ing relative to the 1986 2005 reference period. (Right) The average 2081 2100 CMIP5 multi-model ensemble median of the return periods (years) of 1986 2005 20-year return values of annual maximum daily precipitation corresponding to 1°C of local warming. Regions of no change would have return periods of 20 years. 1086 Long-term Climate Change: Projections, Commitments and Irreversibility Chapter 12 and more frequent in these regions in a warmer climate. Reductions in 12.4.6 Changes in Cryosphere return values (or equivalently, increases in return period) are confined to ­ onvergent oceanic regions where circulation changes have reduced c 12.4.6.1 Changes in Sea Ice Cover the available water vapour. Based on the analysis of CMIP3 climate change simulations (e.g., Arzel Severe thunderstorms, associated with large hail, high winds, and tor- et al., 2006; Zhang and Walsh, 2006), the AR4 concludes that the Arctic nadoes, are another example of extreme weather associated with the and Antarctic sea ice covers are projected to shrink in the 21st cen- water cycle. The large-scale environments in which they occur are char- tury under all SRES scenarios, with a large range of model responses acterized by large Convective Available Potential Energy (CAPE) and (Meehl et al., 2007b). It also stresses that, in some projections, the deep tropospheric wind shear (Brooks et al., 2003; Brooks, 2009). Del Arctic Ocean becomes almost entirely ice-free in late summer during Genio et al. (2007), Trapp et al. (2007, 2009), and Van Klooster and Roe- the second half of the 21st century. These conclusions were confirmed bber (2009) found a general increase in the energy and decrease in the by further analyses of the CMIP3 archives (e.g., Stroeve et al., 2007; shear terms from the late 20th century to the late 21st century over the Bracegirdle et al., 2008; Lefebvre and Goosse, 2008; Boé et al., 2009b; USA using a variety of regional model simulations embedded in global Sen Gupta et al., 2009; Wang and Overland, 2009; Zhang, 2010b; NRC, model SRES scenario simulations. The relative change between these 2011; Körper et al., 2013). Figures 12.28 and 12.29 and the studies of two competing factors would tend to favour more environments that Maksym et al. (2012), Massonnet et al. (2012), Stroeve et al. (2012) and would support severe thunderstorms, providing storms are initiated. Wang and Overland (2012) show that the CMIP5 AOGCMs/ESMs as a Trapp et al. (2009), for example, found an increase in favourable thun- group also project decreases in sea ice extent through the end of this derstorm conditions for all regions of the USA east of the Rocky Moun- century in both hemispheres under all RCPs. However, as in the case of tains. Large variability in both the energy and shear terms means that CMIP3, the inter-model spread is considerable. statistical significance is not reached until late in the 21st century under high forcing scenarios. One way of assessing the possibility of a change In the NH, in accordance with CMIP3 results, the absolute rate of in the frequency of future thunderstorms is to look at historical records decrease of the CMIP5 multi-model mean sea ice areal coverage is of observed tornado, hail and wind occurrence with respect to the envi- greatest in September. The reduction in sea ice extent between the ronmental conditions (Brooks, 2013). This indicates that an increase in time periods 1986 2005 and 2081 2100 for the CMIP5 multi-model the fraction of severe thunderstorms containing non-tornadic winds average ranges from 8% for RCP2.6 to 34% for RCP8.5 in February would be consistent with the model projections of increased energy and from 43% for RCP2.6 to 94% for RCP8.5 in September. Medium and decreased shear, but there has not been enough research to make confidence is attached to these values as projections of sea ice extent a firm conclusion regarding future changes in frequency or magnitude. decline in the real world due to errors in the simulation of present-day sea ice extent (mean and trends see Section 9.4.3) and because Less work has been done on projected changes outside of the USA. of the large spread of model responses. About 90% of the available Marsh et al. (2009) found that mean energy decreased in the warm CMIP5 models reach nearly ice-free conditions (sea ice extent less than season in Europe while it increased in the cool season. Even though the 1 × 106 km2 for at least 5 consecutive years) during September in the energy decreases in the warm season, the number of days with favour- Arctic before 2100 under RCP8.5 (about 45% under RCP4.5). By the able environments for severe thunderstorms increases because of an end of the 21st century, the decrease in multi-model mean sea ice increasing number of days with relatively large values of available volume ranges from 29% for RCP2.6 to 73% for RCP8.5 in February energy. For Europe, with the Mediterranean Sea and Sahara Desert to and from 54% for RCP2.6 to 96% for RCP8.5 in September. Medium the south, questions remain about changes in boundary layer moisture, confidence is attached to these values as projections of the real world a main driver of the energy term. Niall and Walsh (2005) examined sea ice volume. In February, these percentages are much higher than changes in CAPE, which may be associated with hailstorm occurrence the corresponding ones for sea ice extent, which is indicative of a sub- 12 in southeastern Australia using a global model, and found little change stantial sea ice thinning. under warmer conditions. Leslie et al. (2008) reconsidered the south- eastern Australia hail question by nesting models with 1 km horizontal A frequent criticism of the CMIP3 models is that, as a group, they grid spacing and using sophisticated microphysical parameterizations strongly underestimate the rapid decline in summer Arctic sea ice and found an increase in the frequency of large hail by 2050 under the extent observed during the past few decades (e.g., Stroeve et al., 2007; SRES A1B scenario, but with extremely large internal variability in the Winton, 2011), which suggests that the CMIP3 projections of summer environments and hail size. Arctic sea ice areal coverage might be too conservative. As shown in Section 9.4.3 and Figure 12.28b, the magnitude of the CMIP5 mul- Overall, for all parts of the world studied, the results are suggestive of ti-model mean trend in September Arctic sea ice extent over the satel- a trend toward environments favouring more severe thunderstorms, lite era is more consistent with, but still underestimates, the observed but the small number of analyses precludes any likelihood estimate of one (see also Massonnet et al., 2012; Stroeve et al., 2012; Wang and this change. Overland, 2012; Overland and Wang, 2013). Owing to the shortness of the observational record, it is difficult to ascertain the relative influ- ence of natural variability on this trend. This hinders the comparison between modelled and observed trends, and hence the estimate of the sensitivity of the September Arctic sea ice extent to global surface tem- perature change (i.e., the decrease in sea ice extent per degree global 1087 Chapter 12 Long-term Climate Change: Projections, Commitments and Irreversibility warming) (Kay et al., 2011; Winton, 2011; Mahlstein and Knutti, 2012). (Overland et al., 2011; Collins et al., 2012; Hodson et al., 2012). For This sensitivity may be crucial for determining future sea ice losses. CMIP3 models, results indicate that the changes in Arctic sea ice mass Indeed, a clear relationship exists at longer than decadal time scales budget over the 21st century are related to the late 20th century mean in climate change simulations between the annual mean or September sea ice thickness distribution (Holland et al., 2010), average sea ice mean Arctic sea ice extent and the annual mean global surface tem- thickness (Bitz, 2008; Hodson et al., 2012), fraction of thin ice cover perature change for ice extents larger than ~1 × 106 km2 (e.g., Ridley (Boé et al., 2009b) and oceanic heat transport to the Arctic (Mahlstein et al., 2007; Zhang, 2010b; NRC, 2011; Winton, 2011; Mahlstein and and Knutti, 2011). For CMIP5 models, Massonnet et al. (2012) showed Knutti, 2012). This relationship is illustrated in Figure 12.30 for both that the time needed for the September Arctic sea ice areal coverage to CMIP3 and CMIP5 models. From this figure, it can be seen that the sea drop below a certain threshold is highly correlated with the September ice sensitivity varies significantly from model to model and is generally sea ice extent and annual mean sea ice volume averaged over the past larger and in better agreement among models in CMIP5. several decades (Figure 12.31a, b). The timing of a seasonally ice-free Arctic Ocean or the fraction of remaining sea ice in September at any A complete and detailed explanation for what controls the range of time during the 21st century were also found to correlate with the Arctic sea ice responses in models over the 21st century remains elu- past trend in September Arctic sea ice extent and the amplitude of the sive, but the Arctic sea ice provides an example where process-based mean seasonal cycle of sea ice extent (Boé et al., 2009b; Collins et al., constraints can be used to reduce the spread of model projections 2012; Massonnet et al., 2012) (Figure 12.31c, d). All these empirical Northern Hemisphere February Northern Hemisphere September a) Satellite obs. 1986 2005 avg: 15.5 x 106 km2 b) Satellite obs. 1986 2005 avg: 7.1 x106 km2 CMIP5 historical 1986 2005 avg: 15.9 x106 km2 CMIP5 historical 1986 2005 avg: 6.6 x106 km2 3 3 Sea ice extent change (10 km ) Sea ice extent change (10 km ) 2 2 2 2 1 1 6 6 0 0 1 1 2 Historical (39) 2 Historical (39) 3 RCP2.6 (29) 3 RCP2.6 (29) 4 RCP4.5 (39) 4 RCP4.5 (39) RCP6.0 (21) RCP6.0 (21) 5 RCP8.5 (37) 5 RCP8.5 (37) 6 Observations 6 Observations 7 7 1960 1980 2000 2020 2040 2060 2080 2100 1960 1980 2000 2020 2040 2060 2080 2100 Year Year Southern Hemisphere February Southern Hemisphere September c) Satellite obs. 1986 2005 avg: 3.3 x106 km2 d) Satellite obs. 1986 2005 avg: 19.0 x106 km2 CMIP5 historical 1986 2005 avg: 3.0 x106 km2 CMIP5 historical 1986 2005 avg: 17.8 x106 km2 3 3 Sea ice extent change (10 km ) Sea ice extent change (10 km ) 2 2 2 2 12 1 1 6 6 0 0 1 1 2 Historical (39) 2 Historical (39) 3 RCP2.6 (29) 3 RCP2.6 (29) 4 RCP4.5 (39) 4 RCP4.5 (39) RCP6.0 (21) RCP6.0 (21) 5 RCP8.5 (37) 5 RCP8.5 (37) 6 Observations 6 Observations 7 7 1960 1980 2000 2020 2040 2060 2080 2100 1960 1980 2000 2020 2040 2060 2080 2100 Year Year Figure 12.28 | Changes in sea ice extent as simulated by CMIP5 models over the second half of the 20th century and the whole 21st century under RCP2.6, RCP4.5, RCP6.0 and RCP8.5 for (a) Northern Hemisphere February, (b) Northern Hemisphere September, (c) Southern Hemisphere February and (d) Southern Hemisphere September. The solid curves show the multi-model means and the shading denotes the 5 to 95% range of the ensemble. The vertical line marks the end of CMIP5 historical climate change simulations. One ensemble member per model is taken into account in the analysis. Sea ice extent is defined as the total ocean area where sea ice concentration exceeds 15% and is calculated on the original model grids. Changes are relative to the reference period 1986 2005. The number of models available for each RCP is given in the legend. Also plotted (solid green curves) are the satellite data of Comiso and Nishio (2008, updated 2012) over 1979 2012. 1088 Long-term Climate Change: Projections, Commitments and Irreversibility Chapter 12 relationships can be understood on simple physical grounds (see the the potentially large imprint of natural variability on both observations aforementioned references for details). and model simulations when these two sources of information are to be compared (see Section 9.8.3). This latter point is particularly critical These results lend support for weighting/recalibrating the models if the past sea ice trend or sensitivity is used in performance metrics based on their present-day Arctic sea ice simulations. Today, the opti- given the relatively short observational period (Kay et al., 2011; Over- mal approach for constraining sea ice projections from climate models land et al., 2011; Mahlstein and Knutti, 2012; Massonnet et al., 2012; is unclear, although one notes that these methods should have a Stroeve et al., 2012). A number of studies have applied such metrics credible underlying physical basis in order to increase confidence in to the CMIP3 and CMIP5 models. Stroeve et al. (2007) and Stroeve et their results (see Section 12.2). In addition, they should account for al. (2012) rejected several CMIP3 and CMIP5 models, respectively, on a) 1986 2005 average (39) February September September February b) 2081 2100 average, RCP4.5 (39) February September September February c) 2081 2100 average, RCP8.5 (37) February September September February 12 (%) 0 20 40 60 80 100 Figure 12.29 | February and September CMIP5 multi-model mean sea ice concentrations (%) in the Northern and Southern Hemispheres for the periods (a) 1986 2005, (b) 2081 2100 under RCP4.5 and (c) 2081 2100 under RCP8.5. The model sea ice concentrations are interpolated onto a 1° × 1° regular grid. One ensemble member per model is taken into account in the analysis, and the multi-model mean sea ice concentration is shown where it is larger than 15%. The number of models available for each RCP is given in parentheses. The pink lines indicate the observed 15% sea ice concentration limits averaged over 1986 2005 (Comiso and Nishio, 2008, updated 2012). 1089 Chapter 12 Long-term Climate Change: Projections, Commitments and Irreversibility 12 12 CMIP3 (a) CMIP5 (b) September Arctic sea ice extent (10 km ) September Arctic sea ice extent (106 km 2) 10 10 2 6 8 8 6 6 4 4 2 2 0 0 0 1 2 3 4 5 0 1 2 3 4 5 Annual mean global surface warming (°C) Annual mean global surface warming (°C) Figure 12.30 | September Arctic sea ice extent as a function of the annual mean global surface warming relative to the period 1986 2005 for (a) CMIP3 models (all SRES sce- narios) and (b) CMIP5 models (all RCPs). The ice extents and global temperatures are computed on a common latitude-longitude grid for CMIP3 and on the original model grids for CMIP5. One ensemble member per model is taken into account in the analysis. A 21-year running mean is applied to the model output. The full black circle and vertical bar on the left-hand side of the y-axis indicate the mean and +/-2 standard deviations about the mean of the observed September Arctic sea ice extent over 1986 2005, respectively (Comiso and Nishio, 2008, updated 2012). The horizontal line corresponds to a nearly ice-free Arctic Ocean in September. the basis of their simulated late 20th century mean September Arctic ensemble members. Then, for each model, a +/-2 standard deviation sea ice extent. Wang and Overland (2009) selected a subset of CMIP3 interval is constructed around the ensemble mean or single realization models (and Wang and Overland (2012) did the same for the CMIP5 of the diagnostic considered. A model is retained if, for each diagnostic, models) based on their fidelity to the observed mean seasonal cycle of either this interval overlaps a +/-20% interval around the observed/rea- Arctic sea ice extent in the late 20th century and then scaled the chosen nalysed value of the diagnostic or at least one ensemble member from models to the recently observed September sea ice extent. Zhang that model gives a value for the diagnostic that falls within +/-20% of (2010b) retained a number of CMIP3 models based on the regression the observational/reanalysed data. The outcome is displayed in Figure between summer sea ice loss and Arctic surface temperature change. 12.31e for RCP8.5. Among the five selected models (ACCESS1.0, Boé et al. (2009b) and Mahlstein and Knutti (2012) did not perform a ACCESS1.3, GFDL-CM3, IPSL-CM5A-MR, MPI-ESM-MR), four project model selection but rather recalibrated the CMIP3 Arctic sea ice projec- a nearly ice-free Arctic Ocean in September before 2050 (2080) for tions on available observations of September Arctic sea ice trend and RCP8.5 (RCP4.5), the earliest and latest years of near disappearance sensitivity to global surface temperature change, respectively. Finally, of the sea ice pack being about 2040 and about 2060 (about 2040 Massonnet et al. (2012) selected a subset of CMIP5 models on the and 2100+), respectively. It should be mentioned that Maslowski et al. 12 basis of the four relationships illustrated in Figure 12.31a d. (2012) projected that it would take only until about 2016 to reach a nearly ice-free Arctic Ocean in summer, based on a linear extrapolation These various methods all suggest a faster rate of summer Arctic sea into the future of the recent sea ice volume trend from a hindcast sim- ice decline than the multi-model mean. Although they individually ulation conducted with a regional model of the Arctic sea ice ocean provide a reduced range for the year of near disappearance of the system. However, such an extrapolation approach is problematic as it September Arctic sea ice compared to the original CMIP3/CMIP5 mul- ignores the negative feedbacks that can occur when the sea ice cover ti-model ensemble, they lead to different timings (Overland and Wang, becomes thin (e.g., Bitz and Roe, 2004; Notz, 2009) and neglects the 2013). Consequently, the time interval obtained when combining all effect of year-to-year or longer-term variability (Overland and Wang, these studies remains wide: 2020 2100+ (2100+ = not before 2100) 2013). Mahlstein and Knutti (2012) encompassed the dependence of for the SRES A1B scenario and RCP4.5 (Stroeve et al., 2007, 2012; Boé sea ice projections on the forcing scenario by determining the annual et al., 2009b; Wang and Overland, 2009, 2012; Zhang, 2010b; Masson- mean global surface warming threshold for nearly ice-free conditions net et al., 2012) and 2020 2060 for RCP8.5 (Massonnet et al., 2012; in September. Their best estimate of ~2°C above the present derived Wang and Overland, 2012). The method proposed by Massonnet et from both CMIP3 models and observations is consistent with the 1.6 al. (2012) is applied here to the full set of models that provided the to 2.1°C range (mean value: 1.9°C) obtained from the CMIP5 model CMIP5 database with sea ice output. The natural variability of each subset shown in Figure 12.31e (see also Figure 12.30b). The reduction of the four diagnostics shown in Figure 12.31a d is first estimated in September Arctic sea ice extent by the end of the 21st century, aver- by averaging over all available models with more than one ensemble aged over this subset of models, ranges from 56% for RCP2.6 to 100% member the diagnostic standard deviations derived from the model for RCP8.5. 1090 Long-term Climate Change: Projections, Commitments and Irreversibility Chapter 12 RCP8.5, correlation = 0.82, p = 1e 09 RCP8.5, correlation = 0.64, p = 2e 05 2100 2100 a) b) 2080 2080 First year of near disappearance of September Arctic sea ice 2060 2060 2040 2040 2020 2020 2000 2000 2 3 4 5 6 7 8 9 10 11 10 15 20 25 30 35 40 45 September Arctic sea ice extent Annual mean Arctic sea ice volume 6 2 3 3 averaged over 1986 2005 (10 km ) averaged over 1986 2005 (10 km ) RCP8.5, correlation = 0.53, p = 0.0007 RCP8.5, correlation = 0.48, p = 0.002 2100 2100 c) d) 2080 2080 2060 2060 2040 2040 2020 2020 2000 2000 0 2 4 6 8 10 12 14 16 18 1600 1200 800 400 0 Amplitude of the seasonal cycle of Arctic sea ice extent Trend in September Arctic sea ice extent 6 2 3 2 averaged over 1986 2005 (10 km ) over 1979 2012 (10 km /decade) 12 e) 11 RCP8.5 September Arctic sea ice extent (10 km ) 10 2 6 9 8 7 6 5 4 12 3 2 1 0 2010 2020 2030 2040 2050 2060 2070 2080 2090 2100 Year Figure 12.31 | (a d) First year during which the September Arctic sea ice extent falls below 1 × 106 km2 in CMIP5 climate projections (37 models, RCP8.5) as a function of (a) the September Arctic sea ice extent averaged over 1986 2005, (b) the annual mean Arctic sea ice volume averaged over 1986 2005, (c) the amplitude of the 1986 2005 mean seasonal cycle of Arctic sea ice extent and (d) the trend in September Arctic sea ice extent over 1979 2012. The sea ice diagnostics displayed are calculated on the original model grids. The correlations and one-tailed p-values are computed from the multi-member means for models with several ensemble members (coloured crosses), but the ensemble mem- bers of individual models are also depicted (coloured dots). The vertical solid and dashed lines show the corresponding observations or bias-adjusted PIOMAS (Pan-Arctic Ice-Ocean Modelling and Assimilation System) reanalysis data (a, c and d: Comiso and Nishio, 2008, updated 2012; b: Schweiger et al., 2011) and the +/-20% interval around these data, respectively. (e) Time series of September Arctic sea ice extent (5-year running mean) as simulated by all CMIP5 models and their ensemble members under RCP8.5 (thin curves). The thick, coloured curves correspond to a subset of five CMIP5 models selected on the basis of panels a d following Massonnet et al. (2012) (see text for details). Note that each of these models provides only one ensemble member for RCP8.5. 1091 Chapter 12 Long-term Climate Change: Projections, Commitments and Irreversibility In light of all these results, it is very likely that the Arctic sea ice cover Snow cover extent change will continue to shrink and thin all year round during the 21st century 20 as the annual mean global surface temperature rises. It is also likely that the Arctic Ocean will become nearly ice-free in September before the middle of the century for high GHG emissions such as those corre- sponding to RCP8.5 (medium confidence). The potential irreversibility 0 of the Arctic sea ice loss and the possibility of an abrupt transition (%) toward an ice-free Arctic Ocean are discussed in Section 12.5.5.7. -20 In the SH, the decrease in sea ice extent between 1986 2005 and 2081 2100 projected by the CMIP5 models as a group varies from 16% for RCP2.6 to 67% for RCP8.5 in February and from 8% to 30% in September. In contrast with the NH, the absolute rate of decline is -40 greatest in wintertime. Eisenman et al. (2011) argue that this hemi- spheric asymmetry in the seasonality of sea ice loss is fundamentally related to the geometry of coastlines. For each forcing scenario, the relative changes in multi-model mean February and September Antarc- Figure 12.32 | Northern Hemisphere spring (March to April average) snow cover tic sea ice volumes by the end of the century are of the same order as extent change (in %) in the CMIP5 ensemble, relative to the simulated extent for the 1986 2005 reference period. Thick lines mark the multi-model average, shading indi- the corresponding ones for sea ice extent. About 75% of the available cates the inter-model spread (one standard deviation). The observed March to April CMIP5 models reach a nearly ice-free state in February within this cen- average snow cover extent for the 1986 2005 reference period is 32.6.106 km2 (Brown tury under RCP8.5 (about 60% under RCP4.5). For RCP8.5, only small and Robinson, 2011). portions of the Weddell and Ross Seas stay ice-covered in February during 2081 2100 in those models that do not project a seasonally ice-free Southern Ocean (see Figure 12.29c). Nonetheless, there is low that falls as snow and by increasing snowmelt, but projected increas- confidence in these Antarctic sea ice projections because of the wide es in precipitation over much of the northern high latitudes during range of model responses and the inability of almost all of the models winter months act to increase snow amounts. Whether snow cover- to reproduce the mean seasonal cycle, interannual variability and over- ing the ground will become thicker or thinner depends on the balance all increase of the Antarctic sea ice areal coverage observed during the between these competing factors. Both in the CMIP3 (Räisänen, 2008) satellite era (see Section 9.4.3; Maksym et al., 2012; Turner et al., 2013; and in the CMIP5 models (Brutel-Vuilmet et al., 2013), annual maxi- Zunz et al., 2013). mum SWE tends to increase or only marginally decrease in the coldest 12.4.6.2 Changes in Snow Cover and Frozen Ground Near-surface permafrost area Excluding ice sheets and glaciers, analyses of seasonal snow cover changes generally focus on the NH, where the configuration of the continents on the Earth induces a larger maximum seasonal snow cover extent (SCE) and a larger sensitivity of SCE to climate changes. Seasonal snow cover extent and snow water equivalent (SWE) respond (106 km2) to both temperature and precipitation. At the beginning and the end 12 of the snow season, SCE decreases are closely linked to a shortening of the seasonal snow cover duration, while SWE is more sensitive to snowfall amount (Brown and Mote, 2009). Future widespread reduc- tions of SCE, particularly in spring, are simulated by the CMIP3 models (Roesch, 2006; Brown and Mote, 2009) and confirmed by the CMIP5 ensemble (Brutel-Vuilmet et al., 2013). The NH spring (March-April average) snow cover area changes are coherent in the CMIP5 models although there is considerable scatter. Relative to the 1986 2005 ref- erence period, the CMIP5 models simulate a weak decrease of about Figure 12.33 | Northern Hemisphere near-surface permafrost area, diagnosed for 7 +/- 4% (one- inter-model dispersion) for RCP2.6 during the last two the available CMIP5 models by Slater and Lawrence (2013) following Nelson and Out- decades of the 21st century, while SCE decreases of about 13 +/- 4% are calt (1987) and using 20-year average bias-corrected monthly surface air temperatures simulated for RCP4.5, 15 +/- 5% for RCP6.0, and 25 +/- 8% for RCP8.5 and snow depths. Thick lines: multi-model average. Shading and thin lines indicate the inter-model spread (one standard deviation). The black line for the historical period is (Figure 12.32). There is medium confidence in these numbers because diagnosed from the average of the European Centre for Medium range Weather Fore- of the considerable inter-model scatter mentioned above and because cast (ECMWF) reanalysis of the global atmosphere and surface conditions (ERA), Japa- snow processes in global climate models are strongly simplified. nese ReAnalysis (JRA), Modern Era Retrospective-analysis for Research and Applications (MERRA) and Climate Forecast System Reanalysis and Reforecast (CFSRR) reanalyses Projections for the change in annual maximum SWE are more mixed. (Slater and Lawrence, 2013). Estimated present permafrost extent is between 12 and 17 million km2 (Zhang et al., 2000). Warming decreases SWE both by reducing the fraction of precipitation 1092 Long-term Climate Change: Projections, Commitments and Irreversibility Chapter 12 regions, while annual maximum SWE decreases are strong closer to the 12.4.7 Changes in the Ocean southern limit of the seasonally snow-covered area. 12.4.7.1 Sea Surface Temperature, Salinity and Ocean It is thus very likely (high confidence) that by the end of the 21st centu- Heat Content ry, NH spring snow cover extent will be substantially lower than today if anthropogenic climate forcing is similar to the stronger scenarios Projected increase of SST and heat content over the next two decades considered here. Conversely, there is only medium confidence in the is relatively insensitive to the emissions trajectory. However, projected latitudinal pattern of annual maximum SWE changes (increase or little outcomes diverge as the 21st century progresses. When SSTs increase change in the coldest regions, stronger decrease further to the South) as a result of external forcing, the interior water masses respond to because annual maximum SWE is influenced by competing factors the integrated signal at the surface, which is then propagated down to (earlier melt onset, higher solid precipitation rates in some regions). greater depth (Gleckler et al., 2006; Gregory, 2010). Changes in glob- ally averaged ocean heat content currently account for about 90% of The strong projected warming across the northern high latitudes in the change in global energy inventory since 1970 (see Box 3.1). Heat is climate model simulations has implications for frozen ground. Recent transported within the interior of the ocean by its large-scale general projections of the extent of near-surface permafrost (see Glossary) circulation and by smaller-scale mixing processes. Changes in trans- degradation continue to vary widely depending on the underlying ports lead to redistribution of existing heat content and can cause local climate forcing scenario and model physics, but virtually all of them cooling even though the global mean heat content is rising (Banks and indicate substantial near-surface permafrost degradation and thaw Gregory, 2006; Lowe and Gregory, 2006; Xie and Vallis, 2012). depth deepening over much of the permafrost area (Saito et al., 2007; Lawrence et al., 2008a, 2012; Koven et al., 2011, 2013; Eliseev et al., Figure 12.12 shows the multi-model mean projections of zonally aver- 2013; Slater and Lawrence, 2013). Permafrost at greater depths is less aged ocean temperature change under three emission scenarios. The directly relevant to the surface energy and water balance, and its deg- differences in projected ocean temperature changes for different RCPs radation naturally occurs much more slowly (Delisle, 2007). Climate manifest themselves more markedly as the century progresses. The models are beginning to represent permafrost physical processes and largest warming is found in the top few hundred metres of the subtrop- properties more accurately (Alexeev et al., 2007; Nicolsky et al., 2007; ical gyres, similar to the observed pattern of ocean temperature chang- Lawrence et al., 2008a; Rinke et al., 2008; Koven et al., 2009; Gout- es (Levitus et al., 2012, see also Section 3.2). Surface warming varies tevin et al., 2012), but there are large disagreements in the calculation considerably between the emission scenarios ranging from about 1°C of current frozen soil extent and active layer depth due to differenc- (RCP2.6) to more than 3°C in RCP8.5. Mixing and advection processes es in the land model physics in the CMIP5 ensemble (Koven et al., gradually transfer the additional heat to deeper levels of about 2000 2013). The projected changes in permafrost are a response not only m at the end of the 21st century. Depending on the emission scenario, to warming, but also to changes in snow conditions because snow global ocean warming between 0.5°C (RCP2.6) and 1.5°C (RCP8.5) properties and their seasonal evolution exert significant control on soil will reach a depth of about 1 km by the end of the century. The stron- thermal state (Zhang, 2005; Lawrence and Slater, 2010; Shkolnik et gest warming signal is found at the surface in subtropical and tropical al., 2010; Koven et al., 2013). Applying the surface frost index method regions. At depth the warming is most pronounced in the Southern (Nelson and Outcalt, 1987) to coupled climate model anomalies from Ocean. From an energy point of view, for RCP4.5 by the end of the 21st the CMIP5 models (Slater and Lawrence, 2013) yields a reduction of century, half of the energy taken up by the ocean is in the uppermost the diagnosed 2080 2099 near-surface permafrost area (continuous 700 m, and 85% is in the uppermost 2000 m. plus discontinuous near-surface permafrost) by 37 +/- 11% (RCP2.6), 51 +/- 13% (RCP4.5), 58 +/- 13% (RCP6.0), and 81+/-12% (RCP8.5), com- In addition to the upper-level warming, the patterns are further char- pared to the 1986 2005 diagnosed near-surface permafrost area, with acterized by a slight cooling in parts of the northern mid- and high 12 medium confidence in the numbers as such because of the strongly latitudes below 1000 m and a pronounced heat uptake in the deep simplified soil physical processes in current-generation global climate Southern Ocean at the end of the 21st century. The cooling may be models (Figure 12.33). The uncertainty range given here is the 1- linked to the projected decrease of the strength of the AMOC (see Sec- inter-model dispersion. Applying directly the model output to diag- tion 12.4.7.2; 13.4.1; Banks and Gregory, 2006). nose permafrost extent and its changes over the 21st century yields similar relative changes (Koven et al., 2013). In summary, based on The response of ocean temperatures to external forcing comprises high agreement across CMIP5 and older model projections, fundamen- mainly two time scales: a relatively fast adjustment of the ocean mixed tal process understanding, and paleoclimatic evidence (e.g., Vaks et layer and the slow response of the deep ocean (Hansen et al., 1985; al., 2013), it appears virtually certain (high confidence) that near-sur- Knutti et al., 2008a; Held et al., 2010). Simulations with coupled ocean face permafrost extent will shrink as global climate warms. However, atmosphere GCMs suggest time-scales of several millennia until the the amplitude of the projected reductions of near-surface permafrost deep ocean is in equilibrium with the external forcing (Stouffer, 2004; extent not only depends on the emission scenario and the global cli- Hansen et al., 2011; Li et al., 2013a). Thus, the long time-scale of the mate model response, but also very much on the permafrost-related ocean response to external forcing implies an additional commitment soil processes taken into account in the models. to warming for many centuries when GHG emissions are decreased or concentrations kept constant (see Section 12.5.2). Further assessment of ocean heat uptake and its relationship to projections of sea level rise is presented in Section 13.4.1. 1093 Chapter 12 Long-term Climate Change: Projections, Commitments and Irreversibility r ­esemblance to the climatological SSS field and is associated with an i ­ntensification of the global water cycle (see Sections 3.3.2.1, 10.4.2 and 12.4.5). The CMIP5 climate model projections available suggest that high SSS subtropical regions that are dominated by net evapora- tion are typically getting more saline; lower SSS regions at high lati- tudes are typically getting fresher. They also suggest a continuation of this trend in the Atlantic where subtropical surface waters become more saline as the century progresses (Figure 12.34) (see also Terray et al., 2012). At the same time, the North Pacific is projected to become less saline. 12.4.7.2 Atlantic Meridional Overturning Almost all climate model projections reveal an increase of high latitude Figure 12.34 | Projected sea surface salinity differences 2081 2100 for RCP8.5 rela- temperature and high latitude precipitation (Meehl et al., 2007b). Both tive to 1986 2005 from CMIP5 models. Hatching indicates regions where the multi- of these effects tend to make the high latitude surface waters lighter model mean change is less than one standard deviation of internal variability. Stippling and hence increase their stability. As seen in Figure 12.35, all models indicates regions where the multi-model mean change is greater than two standard show a weakening of the AMOC over the course of the 21st century deviations of internal variability and where at least 90% of the models agree on the sign of change (see Box 12.1). The number of CMIP5 models used is indicated in the (see Section 12.5.5.2 for further analysis). Projected changes in the upper right corner. strength of the AMOC at high latitudes appear stronger in Geophysical Fluid Dynamics Laboratory (GFDL) CM2.1 when density is used as a vertical coordinate instead of depth (Zhang, 2010a). Once the RF is sta- Durack and Wijffels (2010) and Durack et al. (2012) examined trends bilized, the AMOC recovers, but in some models to less than its pre-in- in global sea surface salinity (SSS) changes over the period 1950 dustrial level. The recovery may include a significant overshoot (i.e., a 2008. Their analysis revealed strong, spatially coherent trends in SSS weaker circulation may persist) if the anthropogenic RF is eliminated over much of the global ocean, with a pattern that bears striking (Wu et al., 2011a). Gregory et al. (2005) found that for all eleven models Atlantic Meridional Overturning Circulation at 30oN (Sv) 12 (Sv) Figure 12.35 | Multi-model projections of Atlantic Meridional Overturning Circulation (AMOC) strength at 30°N from 1850 through to the end of the RCP extensions. Results are based on a small number of CMIP5 models available. Curves show results from only the first member of the submitted ensemble of experiments. 1094 Long-term Climate Change: Projections, Commitments and Irreversibility Chapter 12 analysed (six from CMIP2/3 and five EMICs), the AMOC ­ eduction was r might enhance the average basal melting rate there from 0.2 m yr 1 to caused more by changes in surface heat flux than changes in surface almost 4 m yr 1 (Hellmer et al., 2012; see Section 13.4.4.2). freshwater flux. They further found that models with a stronger AMOC in their control run exhibited a larger weakening (see also Gregory and There are very few published analyses of CMIP5 climate projections Tailleux, 2011). focusing on the Southern Ocean. Meijers et al. (2012) found a wide variety of ACC responses to climate warming scenarios across CMIP5 Based on the assessment of the CMIP5 RCP simulations and on our models. Models show a high correlation between the changes in understanding gleaned from analysis of CMIP3 models, observations ACC strength and position, with a southward (northward) shift of the and our understanding of physical mechanisms, it is very likely that the ACC core as the ACC gets stronger (weaker). No clear relationship AMOC will weaken over the 21st century. Best estimates and ranges between future changes in wind stress and ACC strength was identi- for the reduction from CMIP5 are 11% (1 to 24%) in RCP2.6 and 34% fied, while the weakening of the ACC transport simulated at the end (12 to 54%) in RCP8.5. There is low confidence in assessing the evolu- of the 21st century by many models was found to correlate with the tion of the AMOC beyond the 21st century. strong decrease in the surface heat and freshwater fluxes in the ACC region (Meijers et al., 2012; Downes and Hogg, 2013). In agreement 12.4.7.3 Southern Ocean with the CMIP3 assessment (Sen Gupta et al., 2009), subtropical gyres generally strengthen under RCP4.5 and RCP8.5 and all expand south- A dominant and robust feature of the CMIP3 climate projections ward, inducing a southward shift of the northern boundary of the ACC assessed in AR4 is the weaker surface warming at the end of the 21st at most longitudes in the majority of CMIP5 models (Meijers et al., century in the Southern Ocean area compared to the global mean. Fur- 2012). As in CMIP3 climate projections, an overall shallowing of the thermore, the Antarctic Circumpolar Current (ACC) moves southward deep mixed layers that develop on the northern edge of the ACC in in most of the climate projections analysed in response to the simulat- winter is observed, with larger shallowing simulated by models with ed southward shift and strengthening of the SH mid-latitude westerlies deeper mixed layers during 1976 2005 (Sallée et al., 2013a). Sallée (Meehl et al., 2007b). et al. (2013b) reported a warming of all mode, intermediate and deep water masses in the Southern Ocean. The largest temperature increase The additional analyses of the CMIP3 model output performed since is found in mode and intermediate water layers. Consistently with the release of AR4 confirm and refine the earlier findings. The displace- CMIP3 projections (Downes et al., 2010), these water layers experience ment and intensification of the mid-latitude westerlies contribute to a a freshening, whereas bottom water becomes slightly saltier. Finally, large warming between 40°S and 60°S from the surface to mid-depths Sallée et al. (2013b) noted an enhanced upwelling of circumpolar deep (Fyfe et al., 2007; Sen Gupta et al., 2009). Part of this warming has water and an increased subduction of intermediate water that are been attributed to the southward translation of the Southern Ocean nearly balanced by interior processes (diapycnal fluxes). current system (Sen Gupta et al., 2009). Moreover, the wind changes influence the surface temperature through modifications of the latent A number of studies suggest that oceanic mesoscale eddies might and ­ensible heat fluxes and force a larger northward Ekman trans- s influence the response of the Southern Ocean circulation, meridional port of relatively cold polar surface water (Screen et al., 2010). This heat transport and deep water formation to changes in wind stress and also leads to a stronger upwelling that brings southward and upward surface buoyancy flux (Böning et al., 2008; Farneti et al., 2010; Downes relatively warm and salty deep water, resulting in a subsurface salinity et al., 2011; Farneti and Gent, 2011; Saenko et al., 2012; Spence et al., increase at mid-depths south of 50°S (Sen Gupta et al., 2009; Screen 2012). These eddies are not explicitly resolved in climate models and et al., 2010). their role in future circulation changes still needs to be precisely quan- tified. Some of the CMIP5 models have output the meridional overturn- Overall, CMIP3 climate projections exhibit a decrease in mixed layer ing due to the Eulerian mean circulation and that induced by parame- 12 depth at southern mid- and high latitudes by the end of the 21st centu- terized eddies, thus providing a quantitative estimate of the role of the ry. This feature is a consequence of the enhanced stratification resulting mesoscale circulation in a warming climate. On this basis, Downes and from surface warming and freshening (Lefebvre and Goosse, 2008; Sen Hogg (2013) found that, under RCP8.5, the strengthening (weakening) Gupta et al., 2009; Capotondi et al., 2012). Despite large inter-mod- of the upper (lower) Eulerian mean meridional overturning cell in the el differences, there is a robust weakening of Antarctic Bottom Water Southern Ocean is significantly correlated with the increased overlying production and its northward outflow, which is consistent with the wind stress and surface warming and is partly compensated at best by decrease in surface density and is manifest as a warming signal close changes in eddy-induced overturning. to the Antarctic margin that reaches abyssal depths (Sen Gupta et al., 2009). None of the CMIP3 and CMIP5 models include an interactive ice sheet component. When climate-ice sheet interactions are accounted for in In the vicinity of the Antarctic ice sheet, CMIP3 models project an aver- an EMIC under a 4 × CO2 scenario, the meltwater flux from the Antarc- age warming of ~0.5C° at depths of 200 500 m in 2091 2100 com- tic ice sheet further reduces the surface density close to Antarctica and pared to 1991 2000 for the SRES A1B scenario, which has the poten- the rate of Antarctic Bottom Water formation. This ultimately results tial to impact the mass balance of ice shelves (Yin et al., 2011). More in a smaller surface warming at high southern latitudes compared to detailed regional modelling using the SRES A1B scenario indicates a simulation in which the freshwater flux from the melting ice sheet is that a redirection of the coastal current into the cavities underlying not taken into account (Swingedouw et al., 2008). Nevertheless, in this the Filchner-Ronne ice shelf during the second half of the 21st century study, this effect becomes significant only after more than one century. 1095 Chapter 12 Long-term Climate Change: Projections, Commitments and Irreversibility 12.4.8 Changes Associated with Carbon Cycle Feedbacks uncertainty taken from 19 CMIP3 models and carbon cycle feedbacks and Vegetation Cover uncertainty taken from 10 C4MIP models, generating 190 model simu- lations (Meinshausen et al., 2011c; Meinshausen et al., 2011b). The Climate change may affect the global biogeochemical cycles changing emulation of the CMIP3/C4MIP models shows for the RCP8.5, a range the magnitude of the natural sources and sinks of major GHGs. Numer- of simulated CO2 concentrations of 794 to 1149 ppm (90% confidence ous studies investigated the interactions between climate change and level), extremely similar to what is obtained with the CMIP5 ESMs, the carbon cycle (e.g., Friedlingstein et al., 2006), methane cycle (e.g., with atmospheric concentration as high as 1150 ppm by 2100, that is, O Connor et al., 2010), ozone (Cionni et al., 2011) or aerosols (e.g., more than 200 ppm above the prescribed CO2 concentration. Carslaw et al., 2010). Many CMIP5 ESMs now include a representa- tion of the carbon cycle as well as atmospheric chemistry, allowing Global warming simulated by the E-driven runs show higher upper interactive projections of GHGs (mainly CO2 and O3) and aerosols. With ends than when atmospheric CO2 concentration is prescribed. For the such models, projections account for the imposed changes in anthro- models assessed here, the global surface temperature change (2081 pogenic emissions, but also for changes in natural sources and sinks 2100 average relative to 1986 2005 average) ranges between 2.6°C as they respond to changes in climate and atmospheric composition. If and 4.7°C, with a multi-model average of 3.7°C +/- 0.7°C for the con- included in ESMs, the impact on projected concentration, RF and hence centration driven simulations, while the emission driven simulations on climate can be quantified. Climate-induced changes on the carbon give a range of 2.5°C to 5.6°C, with a multi-model average of 3.9°C cycle are assessed below, while changes in natural emissions of CH4 +/- 0.9°C, that is, 5% larger than for the concentration driven runs. The are assessed in Chapter 6, changes in atmospheric chemistry in Chap- models that simulate the largest CO2 concentration by 2100 have the ter 11, and climate aerosol interactions are assessed in Chapter 7. largest warming amplification in the emission driven simulations, with an additional warming of more than 0.5°C. 12.4.8.1 Carbon Dioxide The uncertainty on the carbon cycle has been shown to be of com- As presented in Section 12.3, the CMIP5 experimental design includes, parable magnitude to the uncertainty arising from physical climate for the RCP8.5 scenario, experiments driven either by prescribed ­processes (Gregory et al., 2009). Huntingford et al. (2009) used a simple anthropogenic CO2 emissions or concentration. The historical and model to characterize the relative role of carbon cycle and climate sen- 21st century emission-driven simulations allow evaluating the cli- sitivity uncertainties in contributing to the range of future temperature mate response of the Earth system when atmospheric CO2 and the cli- changes, concluding that the range of carbon cycle processes represent mate response are interactively being calculated by the ESMs. In such about 40% of the physical feedbacks. Perturbed parameter ensembles ESMs, the atmospheric CO2 is calculated as the difference between systematically explore land carbon cycle parameter uncertainty and the imposed anthropogenic emissions and the sum of land and ocean illustrate that a wide range of carbon cycle responses are consistent carbon uptakes. As most of these ESMs account for land use changes with the same underlying model structures and plausible parameter and their CO2 emissions, the only external forcing is fossil fuel CO2 ranges (Booth et al., 2012; Lambert et al., 2012). Figure 12.37 shows emissions (along with all non-CO2 forcings as in the C-driven RCP8.5 how the comparable range of future climate change (SRES A1B) arises simulations). For a given ESM, the emission driven and concentration from parametric uncertainty in land carbon cycle and atmospheric driven simulations would show different climate projections if the feedbacks. The same ensemble shows that the range of atmospheric simulated atmospheric CO2 in the emission driven run is significantly CO2 in the land carbon cycle ensemble is wider than the full SRES con- different from the one prescribed for the concentration driven runs. centration range (B1 to A1FI scenario). This would happen if the ESMs carbon cycle is different from the one simulated by MAGICC6, the model used to calculate the CMIP5 GHGs The CMIP5 ESMs described above do not include the positive feed- 12 concentrations from the emissions for the four RCPs (Meinshausen et back arising from the carbon release from high latitudes permafrost al., 2011c). When driven by CO2 concentration, the ESMs can calculate thawing under a warming scenario, which could further increase the the fossil fuel CO2 emissions that would be compatible with the pre- atmospheric CO2 concentration and the warming. Two recent studies scribed atmospheric CO2 trajectory, allowing comparison with the set investigated the climate permafrost feedback from simulations with of CO2 emissions initially estimated by the IAMs (Arora et al., 2011; models of intermediate complexity (EMICs) that accounts for a per- Jones et al., 2013) (see Section 6.4.3, Box 6.4). mafrost carbon module (MacDougall et al., 2012; Schneider von Deim- ling et al., 2012). Burke et al. (2012) also estimated carbon loss from Figure 12.36 shows the simulated atmospheric CO2 and global aver- p ­ ermafrost, from a diagnostic of the present-day permafrost carbon age surface air temperature warming (relative to the 1986 2005 ref- store and future soil warming as simulated by CMIP5 models. However, erence period) for the RCP8.5 emission driven simulations from the this last study did not quantify the effect on global temperature. Each CMIP5 ESMs, compared to the concentration driven simulations from of these studies found that the range of additional warming due to the the same models. Most (seven out of eleven) of the models estimate a permafrost carbon loss is quite large, because of uncertainties in future larger CO2 concentration than the prescribed one. By 2100, the multi- high latitude soil warming, amount of carbon stored in permafrost model average CO2 concentration is 985 +/- 97 ppm (full range 794 soils, vulnerability of freshly thawed organic material, the proportion to 1142 ppm), while the CO2 concentration prescribed for the RCP8.5 of soil carbon that might be emitted as carbon dioxide via aerobic is 936 ppm. Figure 12.36 also shows the range of atmospheric CO2 decomposition or as methane via anaerobic decomposition (Schneider projections when the MAGICC6 model, used to provide the RCP con- von Deimling et al., 2012). For the RCP8.5, the additional warming centrations, is tuned to emulate combinations of climate sensitivity from permafrost ranges between 0.04°C and 0.69°C by 2100 although 1096 Long-term Climate Change: Projections, Commitments and Irreversibility Chapter 12 1200 7 a Atmospheric CO2 concentration b Global mean surface air temperature 1100 6 1000 5 CMIP5: CMIP5: 900 Emission-driven 4 Emission-driven 800 C Concentration-driven default Concentration-driven 3 Global Mean Temperature relative to 1986 2005 (°C) 700 600 2 500 1 400 CO2 concentration (ppm) 0 300 1 200 1100 c Atmospheric CO2 concentration d Global mean surface air temperature 6 1000 5 900 CMIP3 & C4MIP emulation: CMIP3 & C4MIP emulation: 90% 4 90% 800 68% Ranges 68% Ranges 50% 3 50% 700 C Concentration-driven default 600 2 500 1 400 0 300 1 200 1850 1900 1950 2000 2050 2100 1850 1900 1950 2000 2050 2100 Figure 12.36 | Simulated changes in (a) atmospheric CO2 concentration and (b) global averaged surface temperature (°C) as calculated by the CMIP5 Earth System Models (ESMs) for the RCP8.5 scenario when CO2 emissions are prescribed to the ESMs as external forcing (blue). Also shown (b, in red) is the simulated warming from the same ESMs when directly forced by atmospheric CO2 concentration (a, red white line). Panels (c) and (d) show the range of CO2 concentrations and global average surface temperature change simulated by the Model for the Assessment of Greenhouse Gas-Induced Climate Change 6 (MAGICC6) simple climate model when emulating the CMIP3 models climate sensitivity range and the Coupled Climate Carbon Cycle Model Intercomparison Project (C4MIP) models carbon cycle feedbacks. The default line in (c) is identical to the one in (a). there is medium confidence in these numbers as are the ones on the amount of carbon released (see Section 12.5.5.4) (MacDougall et al., 2012; Schneider von Deimling et al., 2012). 12.4.8.2 Changes in Vegetation Cover 12 Vegetation cover can also be affected by climate change, with forest C PA PC M cover potentially being decreasing (e.g., in the tropics) or increasing P C IP 3 (e.g., in high latitudes). In particular, the Amazon forest has been the subject of several studies, generally agreeing that future climate change would increase the risk tropical Amazon forest being replaced by seasonal forest or even savannah (Huntingford et al., 2008; Jones Figure 12.37 | Uncertainty in global mean temperature from Met Office Hadley Centre et al., 2009; Malhi et al., 2009). Increase in atmospheric CO2 would climate prediction model 3 (HadCM3) results exploring atmospheric physics and ter- partly reduce such risk, through increase in water efficiency under ele- restrial carbon cycle parameter perturbations under the SRES A1B scenario (Murphy et vated CO2 (Lapola et al., 2009; Malhi et al., 2009). Recent multi-model al., 2004; Booth et al., 2012). Relative uncertainties in the Perturbed Carbon Cycle (PCC, green plume) and Perturbed Atmospheric Processes (PAP, blue plume) on global mean estimates based on different CMIP3 climate scenarios and different anomalies of temperature (relative to the 1986 2005 period). The standard simulations dynamic global vegetation models predict a moderate risk of tropical from the two ensembles, HadCM3 (blue solid) and HadCM3C (green solid) are also forest reduction in South America and even lower risk for African and shown. Three bars are shown on the right illustrating the 2100 temperature anomalies Asian tropical forests (see also Section 12.5.5.6) (Gumpenberger et al., associated with the CMIP3/AR4 ensemble (black) the PAP ensemble (blue) and PCC 2010; Huntingford et al., 2013). ensemble (green). The ranges indicate the full range, 10th to 90th, 25th to 75th and 50th percentiles. 1097 Chapter 12 Long-term Climate Change: Projections, Commitments and Irreversibility 12 Difference in crop and pasture fraction (%) Change in surface air temperature (°C) Figure 12.38 | Impact of land use change on surface temperature. LUCID-CMIP5 experiments where six ESMs were forced either with or without land use change beyond 2005 under the RCP8.5 scenario. Left maps of changes in total crop and pasture fraction (%) in the RCP8.5 simulations between 2006 and 2100 as implemented in each ESM. Right maps show the differences in surface air temperature (averaged over the 2071 2100 period) between the simulations with and without land use change beyond 2005. Only statistically significant changes (p < 0.05) are shown. 1098 Long-term Climate Change: Projections, Commitments and Irreversibility Chapter 12 ESMs simulations with interactive vegetation confirmed known bio- heat fluxes were relatively small but significant in most of ESMs for physical feedback associated with large-scale changes in vegetation. regions with substantial land use changes. The scale of climatic effects In the northern high latitudes, warming-induced vegetation expansion reflects a small magnitude of land use changes in both the RCP2.6 and reduces surface albedo, enhancing the warming over these regions 8.5 scenarios and their limitation mainly to the tropical and subtropical (Falloon et al., 2012; Port et al., 2012), with potentially larger ampli- regions where differences between biophysical effects of forests and fication due to ocean and sea ice response (Swann et al., 2010). Over grasslands are less pronounced than in mid- and high latitudes. LUCID- tropical forest, reduction of forest coverage would reduce evapotran- CMIP5 did not perform similar simulations for the RCP4.5 or RCP6.0 spiration, also leading to a regional warming (Falloon et al., 2012; Port scenarios. As these two scenarios show a global decrease of land use et al., 2012). area, one might expect their climatic impact to be different from the one seen in the RC2.6 and RCP8.5. CMIP5 ESMs also include human induced land cover changes (deforest- ation, reforestation) affecting the climate system through changes in 12.4.9 Consistency and Main Differences Between land surface physical properties (Hurtt et al., 2011). Future changes Coupled Model Intercomparison Project Phase 3/ in land cover will have an impact on the climate system through bio- Coupled Model Intercomparison Project Phase 5 physical and biogeochemical processes (e.g., Pongratz et al., 2010). and Special Report on Emission Scenarios/ Biophysical processes include changes in surface albedo and changes Representative Concentration Pathways in partitioning between latent and sensible heat, while biogeochemi- cal feedbacks essentially include change in CO2 sources and sinks but In the experiments collected under CMIP5, both models and scenario could potentially also include changes in N2O or CH4 emissions. The bio- have changed with respect to CMIP3 making a comparison with earlier physical response to future land cover changes has been investigated results and the scientific literature they generated (on which some of within the SRES scenarios. Using the SRES A2 2100 land cover, Davin et this chapter s content is still based) complex. The set of models used al. (2007) simulated a global cooling of 0.14 K relatively to a simulation in AR4 (the CMIP3 models) have been superseded by the new CMIP5 with present-day land cover, the cooling being largely driven by change models (Table 12.1; Chapter 9) and the SRES scenarios have been in albedo. Regional analyses have been performed in order to quantify replaced by four RCPs (Section 12.3.1). In addition, the baseline period the biophysical impact of biofuels plantation generally finding a local used to compute anomalies has advanced 6 years, from 1980 1999 to to regional cooling when annual crops are replaced by bioenergy crops, 1986 2005. such as sugar cane (Georgescu et al., 2011; Loarie et al., 2011). How- ever, some energy crops require nitrogen inputs for their production, 4.0 leading inevitably to nitrous oxide (N2O) emissions, potentially reduc- Global mean temperature anomaly (°C) ing the direct cooling effect and the benefit of biofuels as an alterna- 3.5 tive to fossil fuel emissions. Such emission estimates are still uncertain, varying strongly for different crops, management methods, soil types 3.0 and reference systems (St. Clair et al., 2008; Smeets et al., 2009). 2.5 In the context of the Land-Use and Climate, IDentification of robust 2.0 impacts (LUCID) project (Pitman et al., 2009) ESMs performed addi- tional CMIP5 simulations in order to separate the biophysical from 1.5 the biogeochemical effects of land use changes in the RCP scenarios. SRES A1B RCP6.0 The LUCID CMIP5 experiments were designed to complement RCP8.5 1.0 2080 2099 - 1980 1999 2081 2100 - 1986 2005 and RCP2.6 simulations of CMIP5, both of which showing an intensi- 12 fication of land use change over the 21st century. The LUCID CMIP5 CMIP3 CMIP5 (em) CMIP3 (em) CMIP5 CMIP5+ analysis was focussed on a difference in climate and land-atmosphere Figure 12.39 | Global mean temperature anomalies at the end of the 21st century fluxes between the average of ensemble of simulations with and with- from General Circulation Model (GCM) experiments and emulators comparing CMIP3/ out land use changes by the end of 21st century (Brovkin et al., 2013). CMIP5 responses under SRES A1B and RCP6.0. The boxes and whiskers indicate the Due to different interpretation of land use classes, areas of crops and 5th percentile, mean value 1 standard deviation, mean, mean value + 1 standard deviation and 95th percentile of the distributions. The first box-and-whiskers on the pastures were specific for each ESM (Figure 12.38, left). On the global left is computed directly from the CMIP3 ensemble and corresponds to the numbers scale, simulated biophysical effects of land use changes projected in quoted in AR4. The emulated SRES A1B projections (second from left) of CMIP5 are the CMIP5 experiments with prescribed CO2 concentrations were not obtained by the method of Good et al. (2011a) and are calculated for the period 2080- significant. However, these effects were significant for regions with 2099 expressed with respect to the AR4 baseline period of 1980 1999. Because of the land use changes >10%. Only three out of six participating models, method, the subset of CMIP5 that are emulated are restricted to those with pre-indus- trial control, abrupt 4 × CO2, historical, RCP4.5 and RCP8.5 simulations. The emulated CanESM2, HadGEM2-ES and MIROC-ESM, reveal statistically signifi- RCP6.0 projections of CMIP3 (third from left, see also Figure 12.8) are from Knutti and cant changes in regional mean annual mean surface air temperature Sedláèek (2013) obtained using the method of Meinshausen et al. (2011b; 2011c) and for the RCP8.5 scenario (Figure 12.38, right). However, there is low are calculated for the slightly different future period 2081 2100 to be consistent with confidence on the overall effect as there is no agreement among the the rest of this chapter, and are expressed with respect to the AR5 baseline period of models on the sign of the global average temperature change due 1986 2005. The box-and-whiskers fourth from the left are a graphical representation of the numbers shown in Table 12.2. The final box-and-whiskers on the right is a combina- to the biophysical effects of land use changes (Brovkin et al., 2013). tion of CMIP5 model output and emulation of CMIP5 RCP6.0 numbers for those models Changes in land surface albedo, available energy, latent and sensible that did not run RCP6.0. 1099 Chapter 12 Long-term Climate Change: Projections, Commitments and Irreversibility It would be extremely costly computationally to rerun the full CMIP3 MAGICC models with parameters chosen to emulate the response of ensemble under the new RCPs and/or the full CMIP5 ensemble under the CMIP3 models to RCP6.0 forcing, with anomalies expressed with the old SRES scenarios in order to separate model and scenario effects. respect to the 1986 2005 baseline period (Figure 12.39). They too find In the absence of a direct comparison, we rely on simplified model- a larger mean response in the CMIP5 case but also a larger spread (+/-1 ling frameworks to emulate CMIP3/5 SRES/RCP model behaviour and standard deviation) in CMIP5. Uncertainties in the different approach- compare them. Figure 12.39 shows an emulation of the global mean es to emulating climate model simulations, for example estimating the temperature response at the end of the 21st century that one would non-GHG RF, and the small sample sizes of CMIP3 and CMIP5 make expect from the CMIP5 models if they were run under SRES A1B. In this it difficult to draw conclusions on the statistical significance of the case, anomalies are computed with respect to 1980 1999 for direct differences displayed in Figure 12.39, but the same uncertainties lead comparison with the values reported in AR4 (Meehl et al., 2007b) us to conclude that on the basis of these analyses there appears to which used that baseline. The method used to emulate the SRES A1B be no fundamental difference between the behaviour of the CMIP5 response of the CMIP5 is documented by Good et al. (2011a; 2013). ensemble, in comparison with CMIP3. Ensemble-mean A1B RF was computed from CMIP3 projections using the Forster and Taylor (2006) method, scaled to ensure consistency Meinshausen et al. (2011a; 2011b) tuned MAGICC6 to emulate 19 with the forcing required by the method. The simple model is only used GCMs from CMIP3. The results are temperature projections and their to predict the temperature difference between A1B and RCP8.5, and uncertainties (based on the empirical distribution of the ensemble) between A1B and RCP4.5 separately for each model. These differenc- under each of the RCPs, extended to year 2500 (under constant emis- es are then added to CMIP5 GCM simulations of RCP8.5 and RCP4.5 sions for the lowest RCP and constant concentrations for the remain- respectively, and averaged to give a single A1B estimate. The emulated ing three). In the same paper, an ensemble produced by combining CMIP5 SRES A1B results show a slightly larger mean response than the carbon cycle parameter calibration to nine C4MIP models with the 19 actual CMIP3 models, with a similar spread (+/-1 standard deviation is CMIP3 model parameter calibrations is also used to estimate the emis- used in this case). The main reason for this is the slightly larger mean sions implied by the various concentration pathways, had the CMIP3 transient climate response (TCR) in the subset of CMIP5 models avail- models included a carbon cycle component. Rogelj et al. (2012) used able in comparison with the AR4 CMIP3 models. An alternative emula- the same tool but performed a fully probabilistic analysis of the SRES tion is presented by Knutti and Sedláèek (2013) who use the simplified and RCP scenarios using a parameter space that is consistent with a 9 b 9 2090-2099 period RCP8.5 1980-1999 period SRES scenarios RCPs 8 8 Rogelj et al. (2012) IPCC AR4 values Temperature increase in 2090-2099 relative to pre-industrial (°C) 8 8 Temperature increase in 2090-2099 relative to 1980-1999 (°C) 7 7 Temperature increase relative to pre-industrial (°C) Temperature increase relative to 1980-1999 (°C) 7 7 6 6 6 6 5 5 RCP6 5 5 66% range for emission-driven RCPs 4 4 12 4 4 90% range for emission-driven RCPs best estimate likely range (-40 to +60% around mean) median median 3 3 3 3 66% range 2 RCP4.5 2 2 2 90% range 1 1 RCP3-PD 1 1 0 0 0 0 1950 2000 2050 2100 2150 2200 2250 2300 SRESB1 SRESA1T SRESB2 SRESA1B SRESA2 SRESA1FI RCP4.5 RCP8.5 RCP6 RCP3-PD Figure 12.40 | Temperature projections for SRES scenarios and the RCPs. (a) Time-evolving temperature distributions (66% range) for the four RCP scenarios computed with the ECS distribution from Rogelj et al. (2012) and a model setup representing closely the carbon-cycle and climate system uncertainty estimates of the AR4 (grey areas). Median paths are drawn in yellow. Red shaded areas indicate time periods referred to in panel b. (b) Ranges of estimated average temperature increase between 2090 and 2099 for SRES scenarios and the RCPs respectively. Note that results are given both relative to 1980 1999 (left scale) and relative to pre-industrial (right scale). Yellow ranges indicate results obtained by Rogelj et al. (2012). Colour-coding of AR4 ranges is chosen to be consistent with AR4 (Meehl et al., 2007b). RCP2.6 is labelled as RCP3-PD here. 1100 Long-term Climate Change: Projections, Commitments and Irreversibility Chapter 12 Figure 12.41 | Patterns of temperature (left column) and percent precipitation change (right column) for the CMIP3 models average (first row) and CMIP5 models average (second row), scaled by the corresponding global average temperature changes. The patterns are computed in both cases by taking the difference between the averages over the last 20 years of the 21st century experiments (2080 2099 for CMIP3 and 2081 2100 for CMIP5) and the last twenty years of the historic experiments (1980 1999 for CMIP3, 1986 2005 for CMIP5) and rescaling each difference by the corresponding change in global average temperature. This is done first for each individual model, and then the results are averaged across models. For the CMIP5 patterns, the RCP2.6 simulation of the FIO-ESM model was excluded because it did not show any warming by the end of the 21st century, thus not complying with the method requirement that the pattern be estimated at a time when the temperature change signal from CO2 increase has emerged. Stippling indicates a measure of significance of the difference between the two corresponding patterns obtained by a bootstrap exercise. Two subsets of the pooled set of CMIP3 and CMIP5 ensemble members of the same size as the original ensembles, but without distinguishing CMIP3 from CMIP5 members, were randomly sampled 500 times. For each random sample we compute the corresponding patterns and their difference, then the true difference is compared, grid-point by grid-point, to the distribution of the bootstrapped differences, and only grid-points at which the value of the difference falls in the tails of the bootstrapped distribution (less than the 2.5 percentiles or the 97.5 percentiles) are stippled. CMIP3/C4MIP but a more general uncertainty characterization for key Similar temperature change projections by the end of the 21st century 12 quantities like equilibrium climate sensitivity, similarly to the approach are obtained under RCP8.5 and SRES A1FI, RCP6 and SRES B2 and utilized by Meinshausen et al. (2009). Observational or other historical RCP4.5 and SRES B1. There remain large differences though in the tran- constraints are also used in this study and the analysis is consistent sient trajectories, with rates of change slower or faster for the different with the overall assessment of sources and ranges of uncertainties for pairs. These differences can be traced back to the interplay of the (neg- relevant quantities (equilibrium climate sensitivity above all) from AR4 ative) short-term effect of sulphate aerosols and the (positive) effect of (Meehl et al., 2007b , Box 10.2). Figure 12.40 summarizes results of this long-lived GHGs. Impact studies may be sensitive to the differences in probabilistic comparison for global temperature. The RCPs span a large these temporal profiles so care should be taken in approximating SRES range of stabilization, mitigation and non-mitigation pathways and with RCPs and vice versa. the resulting range of temperature changes are larger than those pro- duced under SRES scenarios, which do not consider mitigation options. While simple models can separate the effect of the scenarios and the The SRES results span an interval between just above 1.0°C and 6.5°C model response, no studies are currently available that allow an attri- when considering the respective likely ranges of all scenarios, including bution of the CMIP3-CMIP5 differences to changes in the transient B1 as the lowest and A1FI as the highest. Emissions under RCP8.5 are climate response, the carbon cycle, and the inclusion of new processes highest and the resulting temperature changes likely range from 4.0°C (chemistry, land surface, vegetation). The fact that these sets of CMIP3 to 6.1°C by 2100. The lowest RCP2.6 assumes significant mitigation and CMIP5 experiments do not include emission-driven runs would and the global temperature change likely remains below 2°C. suggest that differences in the representation of the carbon cycle are very unlikely to explain differences in the simulations, since the only 1101 Chapter 12 Long-term Climate Change: Projections, Commitments and Irreversibility effect of changes in the carbon cycle representation would affect the longer than the time it takes for the system to reach this perturbed ­ land surface, and thus would have only a minor effect on the climate state (see Glossary), for example, the climate change resulting from response at the global scale. the long residence time of a CO2 perturbation in the atmosphere. These results are discussed in Sections 12.5.2 to 12.5.4. Aspects of irreversi- Figure 12.41 shows a comparison of the patterns of warming and bility in the context of abrupt change, multiple steady states and hys- precipitation change from CMIP3 (using 23 models and three SRES teresis are discussed in Section 12.5.5 and in Chapter 13 for ice sheets scenarios) and CMIP5 (using 46 models and four RCPs), utilizing the and sea level rise. pattern scaling methodology (Section 12.4.2). The geographic patterns of mean change are very similar across the two ensembles of models, 12.5.1 Representative Concentration Pathway Extensions with pattern correlations of 0.98 for temperature and 0.90 for precipi- tation changes. However there exist significant differences in the abso- The CMIP5 intercomparison project includes simulations extending the lute values of the patterns, if not in their geographic shapes. A simple four RCP scenarios to the year 2300 (see Section 12.3.1). This allows bootstrapping exercise that pooled together all models and scenari- exploring the longer-term climate response to idealized GHG and aer- os and resampled 500 times the same numbers of models/scenarios osols forcings (Meinshausen et al., 2011c). Continuing GHG emissions divided into two groups, but without distinguishing CMIP3 from CMIP5 beyond 2100 as in the RCP8.5 extension induces a total RF above 12 (and thus SRES from RCPs) allows to compute a measure of signifi- W m 2 by 2300, while sustaining negative emissions beyond 2100, as cance of the actual differences in the patterns. Stippling in Figure 12.41 in the RCP2.6 extension, induces a total RF below 2 W m 2 by 2300. marks the large regions where the difference is significant for temper- The projected warming for 2281 2300, relative to 1986 2005, is 0.6°C ature and precipitation patterns. The temperature pattern from CMIP5 (range 0.0°C to 1.2°C) for RCP2.6, 2.5°C (range 1.5°C to 3.5°C) for shows significantly larger warming per degree Celsius of global mean RCP4.5, and 7.8°C (range 3.0°C to 12.6°C) for RCP8.5 (medium confi- temperature change in the NH and less warming per degree Celsius in dence, based on a limited number of CMIP5 simulations) (Figures 12.3 the SH compared to the corresponding pattern from CMIP3. For precip- and 12.5, Table 12.2). itation patterns, CMIP5 shows significantly larger increases per degree Celsius in the NH and significantly larger decreases per degree Celsius EMICs simulations have been performed following the same CMIP5 in the SH compared to CMIP3. Even in this case we do not have studies protocol for the historical simulation and RCP scenarios extended that allow tracing the source of these differences to specific changes in to 2300 (Zickfeld et al., 2013). These scenarios have been prolonged models configurations, processes represented or scenarios run. beyond 2300 to investigate longer-term commitment and irreversibility (see below). Up to 2300, projected warming and the reduction of the Knutti and Sedláèek (2013) attempt to identify or rule out at least AMOC as simulated by the EMICs are similar to those simulated by the some of these sources. Differences in model projections spread or its CMIP5 ESMs (Figures 12.5 and 12.42). counterpart, robustness, between CMIP3 and CMIP5 are discussed, and it is shown that by comparing the behaviour of only a subset 12.5.2 Climate Change Commitment of 11 models, contributed to the two CMIPs by the same group of institutions, the robustness of CMIP5 versus that of CMIP3 actually Climate change commitment, the fact that the climate will change decreases slightly. This would suggest that the enhanced robustness further after the forcing or emissions have been eliminated or held of CMIP5 is not clearly attributable to advances in modelling, and may c ­onstant, has attracted increased attention by scientists and poli- be a result of the fact that the CMIP5 ensemble contains different cymakers shortly before the completion of IPCC AR4 (Hansen et al., versions of the same model that are counted as independent in this 2005a; Meehl et al., 2005b, 2006; Wigley, 2005) (see also AR4 Section measure of robustness. 10.7.1). However, the argument that the surface response would lag 12 the RF due to the large thermal reservoir of the ocean in fact goes back A comparison of CMIP3 and CMIP5 results for extreme indices is pro- much longer (Bryan et al., 1982; Hansen et al., 1984, 1985; Siegenthal- vided in Sections 12.4.3.3 and Figure 12.13 for temperature extremes, er and Oeschger, 1984; Schlesinger, 1986; Mitchell et al., 2000; Weth- and Section 12.4.5.5 and Figure 12.26 for extremes in the water cycle. erald et al., 2001). The discussion in this section is framed largely in terms of temperature change, but other changes in the climate system (e.g., precipitation) are closely related to changes in temperature (see 12.5 Climate Change Beyond 2100, Sections 12.4.1.1 and 12.4.2). A summary of how past emissions relate Commitment, Stabilization and to future warming is also given in FAQ 12.3. Irreversibility The Earth system has multiple response time scales related to different This section discusses the long term (century to millennia) climate thermal reservoirs (see also Section 12.5.3). For a step change in forcing change based on the RCP scenario extensions and idealized scenari- (instantaneous increase in the magnitude of the forcing and constant os, the commitment from current atmospheric composition and from forcing after that), a large fraction of the total of the surface tempera- past emissions, the concept of cumulative carbon and the resulting ture response will be realized within years to a few decades (Brasseur constraints on emissions for various temperature targets. The term and Roeckner, 2005; Knutti et al., 2008a; Murphy et al., 2009; Hansen et ­irreversibility is used in various ways in the literature. This report defines al., 2011). The remaining response, realized over centuries, is controlled a perturbed state as irreversible on a given time scale if the recov- by the slow mixing of the energy perturbation into the ocean (Stouffer, ery time scale from this state due to natural processes is ­ ignificantly s 2004). The response time scale depends on the amount of ocean mixing 1102 Long-term Climate Change: Projections, Commitments and Irreversibility Chapter 12 Atmospheric CO2 A measure of constant composition commitment is the fraction of real- 2000 2050 2100 2150 2200 2250 2300 ized warming which can be estimated as the ratio of the warming at a 2000 a given time to the long-term equilibrium warming (e.g., Stouffer, 2004; RCP 8.5 Meehl et al., 2007b, Section 10.7.2; Eby et al., 2009; Solomon et al., 1500 2009). EMIC simulations have been performed with RCPs forcing up to (ppmv) RCP 6.0 RCP 4.5 1000 RCP 2.6 2300 prolonged until the end of the millennium with a constant forc- ing set at the value reached by 2300 (Figure 12.43). When the forcing 500 stabilizes, the fraction of realized warming is significantly below unity. However, the fraction of realized warming depends on the history of the forcing. For the RCP4.5 and RCP6.0 extension scenarios with early Surface air temperature change 10 stabilization, it is about 75% at the time of forcing stabilization; while b for RCP8.5, with stabilization occurring later, it is about 85% (see Figure 8 12.43); but for a 1% yr 1 CO2 increase to 2 × CO2 or 4 × CO2 and con- 6 stant forcing thereafter, the fraction of realized warming is much small- (oC) 4 er, about 40 to 70% at the time when the forcing is kept constant. The 2 fraction of realized warming rises typically by 10% over the century 0 following the stabilization of forcing. Due to the long time scales in the deep ocean, full equilibrium is reached only after hundreds to thou- Change in Atlantic meridional overturning circulation sands of years (Hansen et al., 1985; Gregory et al., 2004; Stouffer, 2004; 5 c Meehl et al., 2007b, Section 10.7.2; Knutti et al., 2008a; Danabasoglu 0 and Gent, 2009; Held et al., 2010; Hansen et al., 2011; Li et al., 2013a). (Sv) -5 Atmospheric CO2 2000 2200 2400 2600 2800 3000 -10 2000 a RCP 8.5 -15 1500 (ppmv) 2000 2050 2100 2150 2200 2250 2300 RCP 6.0 Year RCP 4.5 1000 RCP 2.6 Figure 12.42 | (a) Atmospheric CO2, (b) projected global mean surface temperature change and (c) projected change in the Atlantic meridional overturning circulation, as 500 simulated by EMICs for the four RCPs up to 2300 (Zickfeld et al., 2013). A 10-year smoothing was applied. Shadings and bars denote the minimum to maximum range. The dashed line on (a) indicates the pre-industrial CO2 concentration. Surface air temperature change 10 b 8 and the strength of climate feedbacks, and is longer for higher climate 6 sensitivity (Hansen et al., 1985; Knutti et al., 2005). The transient cli- (oC) mate response is therefore smaller than the equilibrium response, in 4 particular for high climate sensitivities. This can also be interpreted as 2 the ocean heat uptake being a negative feedback (Dufresne and Bony, 0 2008; Gregory and Forster, 2008). Delayed responses can also occur 12 due to processes other than ocean warming, for example, vegetation Fraction of realized warming change (Jones et al., 2009) or ice sheet melt that continues long after 3.5 c the forcing has been stabilized (see Section 12.5.3). 3.0 2.5 2.0 Several forms of commitment are often discussed in the literature. The 1.5 most common is the constant composition commitment , the warm- 1.0 ing that would occur after stabilizing all radiative constituents at a 0.5 given year (for example year 2000) levels. For year 2000 commitment, 0.0 AOGCMs estimated a most likely value of about 0.6°C for 2100 (rel- 2000 2200 2400 2600 2800 3000 ative to 1980 1999, AR4 Section 10.7.1). A present-day composition Year commitment simulation is not part of CMIP5, so direct comparison Figure 12.43 | (a) Atmospheric CO2, (b) projected global mean surface temperature with CMIP3 is not possible. However, the available CMIP5 results change and (c) fraction of realized warming calculated as the ratio of global tempera- based on the RCP4.5 extension with constant RF (see Section 12.5.1) ture change at a given time to the change averaged over the 2980 2999 time period, are consistent with those numbers, with an additional warming of as simulated by Earth System Models of Intermediate Complexity (EMICs) for the 4 RCPs up to 2300 followed by a constant (year 2300 level) radiative forcing up to the about 0.5°C 200 years after stabilization of the forcing (Figures 12.5 year 3000 (Zickfeld et al., 2013). A 10-year smoothing was applied. Shadings and bars and 12.42). denote the minimum to maximum range. The dashed line on (a) indicates the pre-indus- trial CO2 concentration. 1103 Chapter 12 Long-term Climate Change: Projections, Commitments and Irreversibility Constant emission commitment is the warming that would result even for strong reductions or complete elimination of CO2 emissions, from maintaining annual anthropogenic emissions at the current level. and might even increase temporarily for an abrupt reduction of the Few studies exist but it is estimated to be about 1°C to 2.5°C by 2100 short-lived aerosols (FAQ 12.3). The implications of this fact for climate assuming constant (year 2010) emissions in the future, based on the stabilization are discussed in Section 12.5.4. MAGICC model calibrated to CMIP3 and C4MIP models (Meinshausen et al., 2011a; Meinshausen et al., 2011b) (see FAQ 12.3). Such a scenar- New EMIC simulations with pre-industrial CO2 emissions and zero io is different from non-intervention economic scenarios, and it does not non-CO2 forcings after 2300 (Zickfeld et al., 2013) confirm this behav- stabilize global temperature, as any plausible emission path after 2100 iour (Figure 12.44) seen in many earlier studies (see above). Switching would cause further warming. It is also different from a constant cumu- off anthropogenic CO2 emissions in 2300 leads to a continuous slow lative emission scenario which implies zero emissions in the future. decline of atmospheric CO2, to a significantly slower decline of global temperature and to a continuous increase in ocean thermal expansion Another form of commitment involves climate change when anthropo- genic emissions are set to zero ( zero emission commitment ). Results from a variety of models ranging from EMICs (Meehl et al., 2007b; Weaver et al., 2007; Matthews and Caldeira, 2008; Plattner et al., 2008; Eby et al., 2009; Solomon et al., 2009; Friedlingstein et al., 2011) to ESMs (Frölicher and Joos, 2010; Gillett et al., 2011; Gillett et al., 2013) show that abruptly setting CO2 emissions to zero (keeping other forcings constant if accounted for) results in approximately constant global temperature for several centuries onward. Those results indicate that past emissions commit us to persistent warming for hundreds of years, continuing at about the level of warming that has been realized. On near equilibrium time scales of a few centuries to about a mil- lennium, the temperature response to CO2 emissions is controlled by climate sensitivity (see Box 12.2) and the cumulative airborne fraction of CO2 over these time scales. After about a thousand years (i.e., near thermal equilibrium) and cumulative CO2 emissions less than about 2000 PgC, approximately 20 to 30% of the cumulative anthropogenic carbon emissions still remain in the atmosphere (Montenegro et al., 2007; Plattner et al., 2008; Archer et al., 2009; Frölicher and Joos, 2010; Joos et al., 2013) (see Box 6.1) and maintain a substantial temperature response long after emissions have ceased (Friedlingstein and Solo- mon, 2005; Hare and Meinshausen, 2006; Weaver et al., 2007; Mat- thews and Caldeira, 2008; Plattner et al., 2008; Eby et al., 2009; Lowe et al., 2009; Solomon et al., 2009, 2010; Frölicher and Joos, 2010; Zickfeld et al., 2012). In the transient phase, on a 100- to 1000-year time scale, the approximately constant temperature results from a compensation between delayed commitment warming (Meehl et al., 2005b; Wigley, 2005) and the reduction in atmospheric CO2 resulting from ocean and 12 land carbon uptake as well as from the nonlinear dependence of RF on atmospheric CO2 (Meehl et al., 2007b; Plattner et al., 2008; Solomon et al., 2009; Solomon et al., 2010). The commitment associated with past emissions depends, as mentioned above, on the value of climate sensitivity and cumulative CO2 airborne fraction, but it also depends on the choices made for other RF constituents. In a CO2 only case and for equilibrium climate sensitivities near 3°C, the warming commitment (i.e., the warming relative to the time when emissions are stopped) is near zero or slightly negative. For high climate sensitivities, and in particular if aerosol emissions are eliminated at the same time, the commitment from past emission can be significantly positive, and is Figure 12.44 | (a) Compatible anthropogenic CO2 emissions up to 2300, followed by a superposition of a fast response to reduced aerosols emissions and zero emissions after 2300, (b) prescribed atmospheric CO2 concentration up to 2300 a slow response associated with high climate sensitivities (Brasseur followed by projected CO2 concentration after 2300, (c) global mean surface tempera- and Roeckner, 2005; Hare and Meinshausen, 2006; Armour and Roe, ture change and (d) ocean thermal expansion as simulated by Earth System Models of 2011; Knutti and Plattner, 2012; Matthews and Zickfeld, 2012) (see Intermediate Complexity (EMICs) for the four concentration driven RCPs with all forcings included (Zickfeld et al., 2013). A 10-year smoothing was applied. The drop in tempera- FAQ 12.3). In the real world, the emissions of CO2 and non-CO2 forcing ture in 2300 is a result of eliminating all non-CO2 forcings along with CO2 emissions. agents are of course coupled. All of the above studies support the con- Shadings and bars denote the minimum to maximum range. The dashed line on (b) clusion that temperatures would decrease only very slowly (if at all), indicates the pre-industrial CO2 concentration. 1104 Long-term Climate Change: Projections, Commitments and Irreversibility Chapter 12 over the course of the millennium. Larger forcings induce longer delays response of a model to an external forcing perturbation. However, before the Earth system would reach equilibrium. For RCP8.5, by year there are limitations to the concept of RF (Joshi et al., 2003; Shine et 3000 (700 years after emissions have ceased) global temperature has al., 2003; Hansen et al., 2005b; Stuber et al., 2005), and the separation decreased only by 1°C to 2°C (relative to its peak value by 2300) and of forcings and fast (or rapid) responses (e.g., clouds changing almost ocean thermal expansion has almost doubled (relative to 2300) and is instantaneously as a result of CO2-induced heating rates rather than still increasing (Zickfeld et al., 2013). as a response to the slower surface warming) is sometimes difficult (Andrews and Forster, 2008; Gregory and Webb, 2008). Equilibrium The previous paragraph discussed climate change commitment from warming also depends on the type of forcing (Stott et al., 2003; Hansen GHGs that have already been emitted. Another form of commitment et al., 2005b; Davin et al., 2007). ECS is time or state dependent in refers to climate change associated with heat and carbon that has some models (Senior and Mitchell, 2000; Gregory et al., 2004; Boer et gone into the land surface and oceans. This would be relevant to the al., 2005; Williams et al., 2008; Colman and McAvaney, 2009; Colman consequences of a one-time removal of all of the excess CO2 in the and Power, 2010), and in some but not all models climate sensitivity atmosphere and is computed by taking a transient simulation and from a slab ocean version differs from that of coupled models or the instantaneously setting atmospheric CO2 concentrations to initial effective climate sensitivity (see Glossary) diagnosed from a transient (pre-industrial) values (Cao and Caldeira, 2010). In such an extreme coupled integration (Gregory et al., 2004; Danabasoglu and Gent, case, there would be a net flux of CO2 from the ocean and land surface 2009; Li et al., 2013a). The computational cost of coupled AOGCMs is to the atmosphere, releasing an amount of CO2 representing about often prohibitively large to run simulations to full equilibrium, and only 30% of what was removed from the atmosphere, i.e., the airborne frac- a few models have performed those (Manabe and Stouffer, 1994; Voss tion applies equally to positive and negative emissions, and it depends and Mikolajewicz, 2001; Gregory et al., 2004; Danabasoglu and Gent, on the emissions history. A related form of experiment investigates 2009; Li et al., 2013a). Because of the time dependence of effective the consequences of an initial complete removal followed by sustained climate sensitivity, fitting simple models to AOGCMs over the first few removal of any CO2 returned to the atmosphere from the land sur- centuries may lead to errors when inferring the response on multi-cen- face and oceans, and is computed by setting atmospheric CO2 con- tury time scales. In the HadCM3 case the long-term warming would be centrations to pre-industrial values and maintaining this concentration underestimated by 30% if extrapolated from the first century (Gregory (Cao and Caldeira, 2010). In this case, only about one-tenth of the et al., 2004), in other models the warming of the slab and coupled pre-existing temperature perturbation persists for more than half of a model is almost identical (Danabasoglu and Gent, 2009). The assump- century. A similar study performed with a GFDL AOGCM where forcing tion that the response to different forcings is approximately additive was instantaneously returned to its pre-industrial value, found larger appears to be justified for large-scale temperature changes but limited residual warming, up to 30% of the pre-existing warming (Held et al., for other climate variables (Boer and Yu, 2003; Sexton et al., 2003; Gil- 2010). lett et al., 2004; Meehl et al., 2004; Jones et al., 2007). A more complete discussion of the concept of ECS and the limitations is given in Knutti Several studies on commitment to past emissions have demonstrat- and Hegerl (2008). The CMIP5 model estimates of ECS and TCR are ed that the persistence of warming is substantially longer than the also discussed in Section 9.7. Despite all limitations, the ECS and TCR lifetime of anthropogenic GHGs themselves, as a result of nonlinear remain key concepts to characterize the transient and near equilibrium absorption effects as well as the slow heat transfer into and out of warming as a response to RF on time scales of centuries. Their overall the ocean. In much the same way as the warming to a step increase of assessment is given in Box 12.2. forcing is delayed, the cooling after setting RF to zero is also delayed. Loss of excess heat from the ocean will lead to a positive surface air A number of recent studies suggest that equilibrium climate sensitiv- temperature anomaly for decades to centuries (Held et al., 2010; Solo- ities determined from AOGCMs and recent warming trends may sig- mon et al., 2010; Bouttes et al., 2013). nificantly underestimate the true Earth system sensitivity (see Glossa- 12 ry) which is realized when equilibration is reached on millennial time A more general form of commitment is the question of how much scales (Hansen et al., 2008; Rohling et al., 2009; Lunt et al., 2010; Pagani warming we are committed to as a result of inertia and hence com- et al., 2010; Rohling and Members, 2012). The argument is that slow mitments related to the time scales for energy system transitions and feedbacks associated with vegetation changes and ice sheets have other societal, economic and technological aspects (Grubb, 1997; their own intrinsic long time scales and are not represented in most Washington et al., 2009; Davis et al., 2010). For example, Davis et al. models (Jones et al., 2009). Additional feedbacks are mostly thought (2010) estimated climate commitment of 1.3°C (range 1.1°C to 1.4°C, to be positive but negative feedbacks of smaller magnitude are also relative to pre-industrial) from existing CO2-emitting devices under simulated (Swingedouw et al., 2008; Goelzer et al., 2011). The climate specific assumptions regarding their lifetimes. These forms of commit- sensitivity of a model may therefore not reflect the sensitivity of the ment, however, are strongly based on political, economic and social full Earth system because those feedback processes are not considered assumptions that are outside the domain of IPCC WGI and are not (see also Sections 10.8, 5.3.1 and 5.3.3.2; Box 5.1). Feedbacks deter- further considered here. mined in very different base state (e.g., the Last Glacial Maximum) differ from those in the current warm period (Rohling and Members, 12.5.3 Forcing and Response, Time Scales of Feedbacks 2012), and relationships between observables and climate sensitiv- ity are model dependent (Crucifix, 2006; Schneider von Deimling et Equilibrium climate sensitivity (ECS), transient climate response al., 2006; Edwards et al., 2007; Hargreaves et al., 2007, 2012). Esti- (TCR) and climate feedbacks are useful concepts to characterize the mates of climate sensitivity based on paleoclimate archives (Hansen 1105 Chapter 12 Long-term Climate Change: Projections, Commitments and Irreversibility Frequently Asked Questions FAQ 12.3 | What Would Happen to Future Climate if We Stopped Emissions Today? Stopping emissions today is a scenario that is not plausible, but it is one of several idealized cases that provide insight into the response of the climate system and carbon cycle. As a result of the multiple time scales in the climate system, the relation between change in emissions and climate response is quite complex, with some changes still occurring long after emissions ceased. Models and process understanding show that as a result of the large ocean inertia and the long lifetime of many greenhouse gases, primarily carbon dioxide, much of the warming would persist for centuries after greenhouse gas emissions have stopped. When emitted in the atmosphere, greenhouse gases get removed through chemical reactions with other reactive components or, in the case of carbon dioxide (CO2), get exchanged with the ocean and the land. These processes characterize the lifetime of the gas in the atmosphere, defined as the time it takes for a concentration pulse to decrease by a factor of e (2.71). How long greenhouse gases and aerosols persist in the atmosphere varies over a wide range, from days to thousands of years. For example, aerosols have a lifetime of weeks, methane (CH4) of about 10 years, nitrous oxide (N2O) of about 100 years and hexafluoroethane (C2F6) of about 10,000 years. CO2 is more complicated as it is removed from the atmosphere through multiple physical and biogeochemical processes in the ocean and the land; all operating at different time scales. For an emission pulse of about 1000 PgC, about half is removed within a few decades, but the remaining fraction stays in the atmosphere for much longer. About 15 to 40% of the CO2 pulse is still in the atmosphere after 1000 years. As a result of the significant lifetimes of major anthropogenic greenhouse gases, the increased atmospheric concen- tration due to past emissions will persist long after emissions are ceased. Concentration of greenhouse gases would not return immediately to their pre-industrial levels if emissions were halted. Methane concentration would return to values close to pre-industrial level in about 50 years, N2O concentrations would need several centuries, while CO2 would essentially never come back to its pre-industrial level on time scales relevant for our society. Changes in emissions of short-lived species like aerosols on the other hand would result in nearly instantaneous changes in their concentrations. The climate system response to the greenhouse gases 4 Ensemble Range: and aerosols forcing is characterized by an inertia, 90% driven mainly by the ocean. The ocean has a very large 85% 80% capacity of absorbing heat and a slow mixing between 3 68% Constant Emissions 50% Global surface warming (°C) the surface and the deep ocean. This means that it will take several centuries for the whole ocean to warm up and to reach equilibrium with the altered radiative forc- 2 ing. The surface ocean (and hence the continents) will Zero Emissions continue to warm until it reaches a surface temperature 1 in equilibrium with this new radiative forcing. The AR4 showed that if concentration of greenhouse gases were 12 held constant at present day level, the Earth surface 0 Constant Forcing would still continue to warm by about 0.6°C over the 21st century relative to the year 2000. This is the climate commitment to current concentrations (or constant 1950 2000 2050 2100 2150 Year composition commitment), shown in grey in FAQ 12.3, Figure 1. Constant emissions at current levels would fur- FAQ 12.3, Figure 1 | Projections based on the energy balance carbon ther increase the atmospheric concentration and result cycle model Model for the Assessment of Greenhouse Gas-Induced Climate in much more warming than observed so far (FAQ 12.3, Change (MAGICC) for constant atmospheric composition (constant forcing, Figure 1, red lines). grey), constant emissions (red) and zero future emissions (blue) starting in 2010, with estimates of uncertainty. Figure adapted from Hare and Mein- Even if anthropogenic greenhouses gas emissions were shausen (2006) based on the calibration of a simple carbon cycle climate halted now, the radiative forcing due to these long- model to all Coupled Model Intercomparison Project Phase 3 (CMIP3) and Coupled Climate Carbon Cycle Model Intercomparison Project (C4MIP) lived greenhouse gases concentrations would only models (Meinshausen et al., 2011a; Meinshausen et al., 2011b). Results are slowly decrease in the future, at a rate determined based on a full transient simulation starting from pre-industrial and using by the lifetime of the gas (see above). Moreover, the all radiative forcing components. The thin black line and shading denote the (continued on next page) observed warming and uncertainty. 1106 Long-term Climate Change: Projections, Commitments and Irreversibility Chapter 12 FAQ 12.3 (continued) climate response of the Earth System to that radiative forcing would be even slower. Global temperature would not respond quickly to the greenhouse gas concentration changes. Eliminating CO2 emissions only would lead to near constant temperature for many centuries. Eliminating short-lived negative forcings from sulphate aerosols at the same time (e.g., by air pollution reduction measures) would cause a temporary warming of a few tenths of a degree, as shown in blue in FAQ 12.3, Figure 1. Setting all emissions to zero would therefore, after a short warming, lead to a near stabilization of the climate for multiple centuries. This is called the commitment from past emissions (or zero future emission commitment). The concentration of GHG would decrease and hence the radiative forcing as well, but the inertia of the climate system would delay the temperature response. As a consequence of the large inertia in the climate and carbon cycle, the long-term global temperature is largely controlled by total CO2 emissions that have accumulated over time, irrespective of the time when they were emit- ted. Limiting global warming below a given level (e.g., 2°C above pre-industrial) therefore implies a given budget of CO2, that is, higher emissions earlier implies stronger reductions later. A higher climate target allows for a higher CO2 concentration peak, and hence larger cumulative CO2 emissions (e.g., permitting a delay in the necessary emis- sion reduction). Global temperature is a useful aggregate number to describe the magnitude of climate change, but not all changes will scale linearly global temperature. Changes in the water cycle for example also depend on the type of forcing (e.g., greenhouse gases, aerosols, land use change), slower components of the Earth system such as sea level rise and ice sheet would take much longer to respond, and there may be critical thresholds or abrupt or irreversible changes in the climate system. et al., 2008; Rohling et al., 2009; Lunt et al., 2010; Pagani et al., 2010; The latter idea of limiting peak warming is a more general concept Schmittner et al., 2011; Rohling and Members, 2012), most but not all than stabilization of temperature or atmospheric CO2, and one that is based on climate states colder than present, are therefore not neces- more realistic than an exact climate stabilization which would require sarily representative for an estimate of climate sensitivity today (see perpetual non-zero positive emissions to counteract the otherwise also Sections 5.3.1, 5.3.3.2, Box 5.1). Also it is uncertain on which time unavoidable long-term slow decrease in global temperature (Matsuno scale some of those Earth system feedbacks would become significant. et al., 2012a) (Figure 12.44). Equilibrium climate sensitivity undoubtedly remains a key quantity, 12.5.4.1 Background useful to relate a change in GHGs or other forcings to a global tempera- ture change. But the above caveats imply that estimates based on past The concept of stabilization is strongly linked to the ultimate objective climate states very different from today, estimates based on time scales of the UNFCCC, which is to achieve [ ] stabilization of greenhouse different than those relevant for climate stabilization (e.g., estimates gas concentrations in the atmosphere at a level that would prevent based on climate response to volcanic eruptions), or based on forcings dangerous anthropogenic interference with the climate system . Recent 12 other than GHGs (e.g., spatially non-uniform land cover changes, vol- policy discussions focussed on a global temperature increase, rather canic eruptions or solar forcing) may differ from the climate sensitivity than on GHG concentrations. The most prominent target currently dis- measuring the climate feedbacks of the Earth system today, and this cussed is the 2°C temperature target, that is, to limit global temper- measure, in turn, may be slightly different from the sensitivity of the ature increase relative to pre-industrial times to below 2°C. The 2°C Earth in a much warmer state on time scales of millennia. The TCR and target has been used first by the European Union as a policy target in the transient climate response to cumulative carbon emissions (TCRE) 1996 but can be traced further back (Jaeger and Jaeger, 2010; Randalls, are often more directly relevant to evaluate short term changes and 2010). Climate impacts however are geographically diverse (Joshi et emission reductions needed for stabilization (see Section 12.5.4). al., 2011) and sector specific, and no objective threshold defines when dangerous interference is reached. Some changes may be delayed or 12.5.4 Climate Stabilization and Long-term Climate irreversible, and some impacts are likely to be beneficial. It is thus not Targets possible to define a single critical threshold without value judgments and without assumptions on how to aggregate current and future This section discusses the relation between emissions and climate costs and benefits. Targets other than 2°C have been proposed (e.g., targets, in the context of the uncertainties characterizing both the 1.5°C global warming relative to pre-industrial), or targets based on transient and the equilibrium climate responses to emissions. Climate CO2 concentration levels, for example, 350 ppm (Hansen et al., 2008). targets considered here are both stabilizing temperature at a speci- The rate of change may also be important (e.g., for adaptation). This fied value and avoiding a warming beyond a predefined threshold. section does not advocate or defend any threshold, nor does it judge 1107 Chapter 12 Long-term Climate Change: Projections, Commitments and Irreversibility the economic or political feasibility of such goals, but simply assess- Also some models prescribe only CO2 emissions while others use multi es the implications of different illustrative climate targets on allowed gas scenarios, and the time horizons differ. The warming is usually carbon emissions, based on our current understanding of climate and larger if non-CO2 forcings are considered, since the net effect of the carbon cycle feedbacks. non-CO2 forcings is positive in most scenarios (Hajima et al., 2012). Not all numbers are therefore directly comparable. Matthews et al. (2009) 12.5.4.2 Constraints on Cumulative Carbon Emissions estimated the TCRE as 1°C to 2.1°C per 1000 PgC (TtC, or 1012 metric tonnes of carbon) (5 to 95%) based on the C4MIP model range (Figure The current RF from GHGs maintained indefinitely (i.e., the commit- 12.45a). The ENSEMBLES E1 show a range of 1°C to 4°C per 1000 PgC ment from constant greenhouse gas concentrations) would correspond (scaled from 0.5°C to 2°C for 500 PgC, Figure 12.45d) (Johns et al., to approximately 2°C warming. That, however, does not imply that the 2011). Rogelj et al. (2012) estimate a 5 to 95% range of about 1°C to commitment from past emissions has already exceeded 2°C. Part of the 2°C per 1000 PgC (Figure 12.45e) based on the MAGICC model cali- positive RF from GHGs is currently compensated by negative aerosol brated to the C4MIP model range and the likely range of 2°C to 4.5°C forcing, and stopping GHG emissions would lead to a decrease in the for climate sensitivity given in AR4. Allen et al. (2009) used a simple GHG forcing. Actively removing CO2 from the atmosphere, for example model and found 1.3°C to 3.9°C per 1000 PgC (5 to 95%) for peak by the combined use of biomass energy and carbon capture and stor- warming (Figure 12.45g) and 1.4°C to 2.5°C for TCRE. The EMICs TCRE age, would further accelerate the decrease in GHG forcing. simulations suggest a range of about 1.4°C to 2.5°C per 1000 PgC and a mean of 1.9°C per 1000 PgC (Zickfeld et al., 2013) (Figure 12.45h). The total amount of anthropogenic CO2 released in the atmosphere The results of Meinshausen et al. (2009) confirm the approximate lin- (often termed cumulative carbon emission) is a good indicator of the earity between temperature and CO2 emissions (Figure 12.45b). Their atmospheric CO2 concentration and hence of the global warming results are difficult to compare owing to the shorter time period con- response to CO2. The ratio of global temperature change to total cumu- sidered, but the model was found to be consistent with that of Allen et lative anthropogenic CO2 emissions (TCRE) is relatively constant over al. (2009). Zickfeld et al. (2009), using an EMIC, find a best estimate of time and independent of the scenario, but is model dependent as it about 1.5°C per 1000 PgC. Gillett et al. (2013) find a range of 0.8°C to depends on the model cumulative airborne fraction of CO2 and ECS/ 2.4°C per 1000 PgC in 15 CMIP5 models and derive an observationally TCR (Matthews and Caldeira, 2008; Allen et al., 2009; Gregory et al., constrained range of 0.7°C to 2.0°C per 1000 PgC. Results from much 2009; Matthews et al., 2009; Meinshausen et al., 2009; Zickfeld et al., earlier model studies support the near linear relationship of cumulative 2009; Bowerman et al., 2011; Knutti and Plattner, 2012; Zickfeld et al., emissions and global temperature, even though these studies did not 2012, 2013). This is consistent with an earlier study indicating that discuss the linear relationship. An example is given in Figure 12.45c the global warming potential of CO2 is approximately independent of based on data shown in IPCC TAR Figure 13.3 (IPCC, 2001) and IPCC the scenario (Caldeira and Kasting, 1993). The concept of a constant AR4 Figure 10.35 (Meehl et al., 2007b). The relationships between ratio of cumulative emissions of CO2 to temperature holds well only cumulative CO2 emissions and temperature in CMIP5 are shown in until temperatures peak (see Figure 12.45e) and only for smoothly var- Figure 12.45f for the 1% yr 1 CO2 increase scenarios and in Figure ying cumulative CO2 emissions (Gillett et al., 2013). It does not hold 12.45i for the RCP8.5 emission driven ESM simulations (Gillett et al., for stabilization on millennial time scales or for non-CO2 forcings, and 2013). Compatible emissions from concentration driven CMIP5 ESMs there is limited evidence for its applicability for cumulative emissions are discussed in Section 6.4.3.3. exceeding 2000 PgC owing to limited simulations available (Plattner et al., 2008; Hajima et al., 2012; Matsuno et al., 2012b; Gillett et al., 2013; Expert judgement based on the available evidence therefore suggests Zickfeld et al., 2013). For non-CO2 forcings with shorter atmospheric that the TCRE is likely between 0.8°C to 2.5°C per 1000 PgC, for cumu- life times than CO2 the rate of emissions at the time of peak warming lative CO2 emissions less than about 2000 PgC until the time at which 12 is more important than the cumulative emissions over time (Smith et temperature peaks. Under these conditions, and for low to medium al., 2012). estimates of climate sensitivity, the TCRE is nearly identical to the peak climate response to cumulative carbon emissions. For high climate Assuming constant climate sensitivity and fixed carbon cycle feed- sensitivity, strong carbon cycle climate feedbacks or large cumulative backs, long-term (several centuries to millennium) stabilization of emissions, the peak warming can be delayed and the peak response global temperatures requires eventually the stabilization of atmos- may be different from TCRE, but is often poorly constrained by models pheric concentrations (or decreasing concentrations if the temperature and observations. The range of TCRE assessed here is consistent with should be stabilized more quickly). This requires decreasing emissions other recent attempts to synthesize the available evidence (NRC, 2011; to near-zero (Jones et al., 2006; Meehl et al., 2007b; Weaver et al., Matthews et al., 2012). The results by Schwartz et al. (2010, 2012) 2007; Matthews and Caldeira, 2008; Plattner et al., 2008; Allen et al., imply a much larger warming for the carbon emitted over the historical 2009; Matthews et al., 2009; Meinshausen et al., 2009; Zickfeld et al., period and have been questioned by Knutti and Plattner (2012) for 2009; Friedlingstein et al., 2011; Gillett et al., 2011; Roeckner et al., neglecting the relevant response time scales and combining a transient 2011; Knutti and Plattner, 2012; Matsuno et al., 2012a). airborne fraction with an equilibrium climate sensitivity. The relationships between cumulative emissions and temperature for The TCRE can be compared to the temperature response to emissions various studies are shown in Figure 12.45. Note that some lines mark on a time scale of about 1000 years after emissions cease. This can the evolution of temperature as a function of emissions over time be estimated from the likely range of equilibrium climate sensitivity while other panels show peak temperatures for different simulations. (1.5°C to 4.5°C) and a cumulative CO2 airborne fraction after about 1108 Long-term Climate Change: Projections, Commitments and Irreversibility Chapter 12 1000 years of about 25 +/- 5% (Archer et al., 2009; Joos et al., 2013). The uncertainty in TCRE is caused by the uncertainty in the physical Again combining the extreme values would suggest a range of 0.6°C feedbacks and ocean heat uptake (reflected in TCR) and uncertainties to 2.7°C per 1000 PgC, and 1.5°C per 1000 PgC for an ECS of 3°C in carbon cycle feedbacks (affecting the cumulative airborne fraction and a cumulative airborne fraction of 25%. However, this equilibrium of CO2). TCRE only characterizes the warming due to CO2 emissions, estimate is based on feedbacks estimated for the present day climate. and contributions from non-CO2 gases need to be considered sepa- Climate and carbon cycle feedbacks may increase substantially on long rately when estimating likelihoods to stay below a temperature limit. time scales and for high cumulative CO2 emissions (see Section 12.5.3), Warming as a function of cumulative CO2 emissions is similar in the introducing large uncertainties in particular on the upper bound. Based four RCP scenarios, and larger than that due to CO2 alone, since non- on paleoclimate data and an analytical model, Goodwin et al. (2009) CO2 forcings contribute warming in these scenarios (compare Figure estimate a long term RF of 1.5 W m 2 for an emission of 1000 PgC. For 12.45 f, i) (Hajima et al., 2012). an equilibrium climate sensitivity of 3°C this corresponds to a warming of 1.2°C on millennial time scales, consistent with the climate carbon cycle models results discussed above. Global mean air surface temperature 6 6 a 7 b c Transient temperature increase Ranges: Transient temperature increase 5 6 90% 5 (°C) (rel. to 1860-1899) (°C) (rel. to 1860-1880) (°C) (rel. to 1850-1875) 68% 5 4 4 4 Medians 3 3 3 2 2 2 1 1 1 0 0 0 0 500 1000 1500 2000 2500 3000 1000 1500 2000 2500 3000 3500 0 500 1000 1500 2000 2500 3000 Cumulative CO2 emissions (PgC) Cumulative Kyoto-gas emissions 2000-2049 (PgCO2-eq) Cumulative diagnosed CO2 emissions (PgC) 6 6 6 Transient temperature increase Transient temperature increase d Median e f Transient temperature increase 5 5 66% range 5 (°C) (rel. to 1850-1875) 90% range (°C) (rel. to 1865-1875) (°C) (rel. to yr 0) 4 4 4 3 3 3 2 2 2 1 1 1 0 0 0 0 500 1000 1500 2000 2500 3000 0 500 1000 1500 2000 2500 3000 0 500 1000 1500 2000 2500 3000 Cumulative CO2 emissions (PgC) Cumulative CO2eq emissions (PgC-eq) Cumulative CO2 emissions (PgC) 6 6 6 Transient temperature increase g h i Transient temperature increase Peak CO2 induced warming (°C) (rel. to per-industrial) (°C) (rel. to 1850-1875) 5 5 5 (°C) (rel. to 1850-1875) 4 4 4 12 3 3 3 2 2 2 FF & industry Very likely (emis-driven) 1 Likely 1 1 Total (back- Most likely calculated) 0 0 0 0 500 1000 1500 2000 2500 3000 0 500 1000 1500 2000 2500 3000 0 500 1000 1500 2000 2500 3000 Cumulative CO2 emissions to 2200 (PgC) Cumulative diagnosed CO2 emissions (PgC) Cumulative CO2 emissions (PgC) Figure 12.45 | Global temperature change vs. cumulative carbon emissions for different scenarios and models. (a) Transient global temperature increase vs. cumulative CO2 emis- sions for Coupled Climate Carbon Cycle Model Intercomparison Project (C4MIP) (Matthews et al., 2009). (b) Maximum temperature increase until 2100 vs. cumulative Kyoto-gas emissions (CO2 equivalent; note that all other panels are given in C equivalent) (Meinshausen et al., 2009). (c) Transient temperature increase vs. cumulative CO2 emissions for IPCC TAR models (red, IPCC TAR Figure 13.3) and IPCC AR4 Earth System Models of Intermediate Complexity (EMICs, black: IPCC AR4 Figure 10.35). (d) As in (a) but for the ENSEMBLES E1 scenario (Johns et al., 2011). (e) Transient temperature increase for the RCP scenarios based on the Model for the Assessment of Greenhouse Gas-Induced Climate Change (MAGICC) model constrained to C4MIP, observed warming, and the IPCC AR4 climate sensitivity range (Rogelj et al., 2012). (f) Transient temperature change from the CMIP5 1% yr 1 concentration driven simulations. (g) Peak CO2 induced warming vs. cumulative CO2 emissions to 2200 (Allen et al., 2009; Bowerman et al., 2011). (h) Transient temperature increase from the new EMIC RCP simulations (Zickfeld et al., 2013). (i) Transient temperature change from the CMIP5 historical and RCP8.5 emission driven simulations (black) and transient temperature change in all concentration-driven CMIP5 RCP simulations with back-calculated emissions (red). Note that black lines in panel (i) do not include land use CO2 and that warming in (i) is higher than in (f) due to additional non-CO2 forcings. 1109 Chapter 12 Long-term Climate Change: Projections, Commitments and Irreversibility Box 12.2 | Equilibrium Climate Sensitivity and Transient Climate Response Equilibrium climate sensitivity (ECS) and transient climate response (TCR) are useful metrics summarizing the global climate system s temperature response to an externally imposed radiative forcing (RF). ECS is defined as the equilibrium change in annual mean global surface temperature following a doubling of the atmospheric CO2 concentration (see Glossary), while TCR is defined as the annual mean global surface temperature change at the time of CO2 doubling following a linear increase in CO2 forcing over a period of 70 years (see Glossary). Both metrics have a broader application than these definitions imply: ECS determines the eventual warming in response to stabilization of atmospheric composition on multi-century time scales, while TCR determines the warming expected at a given time following any steady increase in forcing over a 50- to 100-year time scale. ECS and TCR can be estimated from various lines of evidence. The estimates can be based on the values of ECS and TCR diagnosed from climate models (Section 9.7.1; Table 9.5), or they can be constrained by analysis of feedbacks in climate models (see Section 9.7.2), patterns of mean climate and variability in models compared to observations (Section 9.7.3.3), temperature fluctuations as reconstructed from paleoclimate archives (Sections 5.3.1 and 5.3.3.2; Box 5.1), observed and modelled short-term perturbations of the energy balance like those caused by volcanic eruptions (Section 10.8), and the observed surface and ocean temperature trends since pre-industrial (see Sections 10.8.1 and 10.8.2; Figure 10.20). For many applications, the limitations of the forcing-feedback analysis framework and the dependence of feedbacks on time scales and the climate state (see Section 12.5.3) must be kept in mind. Some studies estimate the TCR as the ratio of global mean temperature change to RF (Section 10.8.2.2) (Gregory and Forster, 2008; Padilla et al., 2011; Schwartz, 2012). Those estimates are scaled by the RF of 2 × CO2 (3.7 W m 2; Myhre et al., 1998) to be comparable to TCR in the following discussion. Aldrin et al. (2012) Newer studies of constraints based on the observed warming since Instrumental Bender et al. (2010) Lewis (2013) pre-industrial, analysed using simple and intermediate complexity Lin et al. (2010) Lindzen & Choi (2011) models, improved statistical methods, and several different and Otto et al. (2013) Murphy et al. (2009) newer data sets, are assessed in detail in Section 10.8.2. Together Olson et al. (2012) Schwartz (2012) with results from feedback analysis and paleoclimate constraints Tomassini et al. (2007) (Sections 5.3.1 and 5.3.3.2; Box 5.1), but without considering the CMIP based evidence, these studies show ECS is likely between 1.5°C to 4.5°C (medium confidence) and extremely unlikely less than 1.0°C (see Section 10.8.2). A few studies argued for very Climatological constraints Sexton et al. (2012) low values of climate sensitivity, but many of them have received criticism in the literature (see Section 10.8.2). Estimates based on AOGCMs and feedback analysis indicate a range of 2°C to Raw model range QUMP CMIP5 4.5°C, with the CMIP5 model mean at 3.2°C, similar to CMIP3. CAM3 MIROC5-CGCM-PPE A summary of published ranges and PDFs of ECS is given in Box CPDN-HadCM3 12.2, Figure 1. Distributions and ranges for the TCR are shown in CMIP3 AOGCMs CMIP5 AOGCMs Box 12.2, Figure 2. Palaeoclimate 12 Chylek & Lohmann (2008) Hargreaves et al. (2012) Simultaneously imposing different constraints from the observed Holden et al. (2010) ¨ Kohler et al. (2010) warming trends, volcanic eruptions, model climatology, and pale- Palaeosens (2012) Schmittner et al. (2012) oclimate, for example, by using a distribution obtained from the Last Glacial Maximum as a prior for the 20th century analysis, yields a more narrow range for climate sensitivity (see Figure Combination Aldrin et al. (2012) Libardoni & Forest (2013) 10.20; Section 10.8.2.5) (e.g., Annan and Hargreaves, 2006, Olson et al. (2012) 2011b; Hegerl et al., 2006; Aldrin et al., 2012). However, such methods are sensitive to assumptions of independence of the var- 0 1 2 3 4 5 6 7 8 9 10 Equilibrium Climate Sensitivity (°C) ious lines of evidence, which might have shared biases (Lemoine, 2010), and the assumption that each individual line of evidence Box 12.2, Figure 1 | Probability density functions, distributions and ranges is unbiased and its uncertainties are captured completely. Expert for equilibrium climate sensitivity, based on Figure 10.20b plus climatological elicitations for PDFs of climate sensitivity exist (Morgan and constraints shown in IPCC AR4 (Meehl et al., 2007b; Box 10.2, Figure 1), and Keith, 1995; Zickfeld et al., 2010), but have also received some results from CMIP5 (Table 9.5). The grey shaded range marks the likely 1.5°C to criticism (Millner et al., 2013). They are not used formally here 4.5°C range, and the grey solid line the extremely unlikely less than 1°C, the grey dashed line the very unlikely greater than 6°C. See Figure 10.20b and Chapter 10 because the experts base their opinion on the same studies as we Supplementary Material for full caption and details. Labels refer to studies since assess. The peer-reviewed literature provides no consensus on a AR4. Full references are given in Section 10.8. (continued on next page) 1110 Long-term Climate Change: Projections, Commitments and Irreversibility Chapter 12 Box 12.2 (continued) formal ­ tatistical method to combine different lines of evidence. All methods in general are sensitive to the assumed prior distributions. s These limitations are discussed in detail in Section 10.8.2. Based on the combined evidence from observed climate change including the observed 20th century warming, climate models, feed- back analysis and paleoclimate, ECS is likely in the range 1.5°C to 4.5°C with high confidence. The ­ ombined evidence increases c the confidence in this final assessment compared to that based on the observed warming and paleoclimate only. ECS is posi- tive, extremely unlikely less than 1°C (high confidence), and very Schwartz (2012) unlikely greater than 6°C (medium confidence). The upper limit of Libardoni & Forest (2011) the likely range is unchanged compared to AR4. The lower limit of Padilla et al (2011) the likely range of 1.5°C is less than the lower limit of 2°C in AR4. Gregory & Forster (2008) This change reflects the evidence from new studies of observed Stott & Forest (2007) temperature change, using the extended records in atmosphere Gillett et al (2013) Tung et al (2008) and ocean. These studies suggest a best fit to the observed sur- Probability / Relative Frequency (°C ) 1 Otto et al (2013) (a) face and ocean warming for ECS values in the lower part of the Otto et al (2013) (b) likely range. Note that these studies are not purely observation- Rogelj et al (2012) al, because they require an estimate of the response to RF from Harris et al (2013) Meinshausen et al (2009) models. In addition, the uncertainty in ocean heat uptake remains Knutti & Tomassini (2008) (a) substantial (see Section 3.2, Box 13.1). Accounting for short Knutti & Tomassini (2008) (b) term variability in simple models remains challenging, and it is 1.5 important not to give undue weight to any short time period that might be strongly affected by internal variability (see Box 9.2). On the other hand, AOGCMs show very good agreement with observed climatology with ECS values in the upper part of the 1 1.5°C to 4.5°C range (Section 9.7.3.3), but the simulation of key feedbacks like clouds remains challenging in those models. The Black histogram estimates from the observed warming, paleoclimate, and from CMIP5 models 0.5 Dashed lines climate models are consistent within their uncertainties, each is AR4 studies supported by many studies and multiple data sets, and in combi- nation they provide high confidence for the assessed likely range. Even though this assessed range is similar to previous reports 0 0 1 2 3 4 5 (Charney, 1979; IPCC, 2001), confidence today is much higher as Transient Climate Response (°C) a result of high quality and longer observational records with a clearer anthropogenic signal, better process understanding, more Box 12.2, Figure 2 | Probability density functions, distributions and ranges and better understood evidence from paleoclimate reconstruc- (5 to 95%) for the transient climate response from different studies, based on tions, and better climate models with higher resolution that cap- Figure 10.20a, and results from CMIP5 (black histogram; Table 9.5). The grey shaded range marks the likely 1°C to 2.5°C range, and the grey solid line marks ture many more processes more realistically. Box 12.2 Figure 1 12 the extremely unlikely greater than 3°C. See Figure 10.20a and Chapter 10 illustrates that all these lines of evidence individually support the Supplementary Material for full caption and details. Full references are given assessed likely range of 1.5°C to 4.5°C. in Section 10.8. The tails of the ECS distribution are now better understood. Multiple lines of evidence provide high confidence that an ECS value less than 1°C is extremely unlikely. The assessment that ECS is very unlikely greater than 6°C is an expert judgment informed by several lines of evidence. First, the comprehensive climate models used in the CMIP5 exercise produce an ECS range of 2.1°C to 4.7°C (Table 9.5), very similar to CMIP3. Second, comparisons of perturbed-physics ensembles against the observed climate find that models with ECS values in the range 3°C to 4°C show the smallest errors for many fields (Section 9.7.3.3). Third, there is increasing evidence that the aerosol RF of the 20th century is not strongly negative, which makes it unlikely that the observed warming was caused by a very large ECS in response to a very small net forcing. Fourth, multiple and at least partly independent observational constraints from the satellite period, instrumental period and palaeoclimate studies continue to yield very low probabilities for ECS larger than 6°C, particularly when including most recent ocean and atmospheric data (see Box 12.2, Figure 1). Analyses of observations and simulations of the instrumental period are estimating the effective climate sensitivity (a measure of the strengths of the climate feedbacks today, see Glossary), rather than ECS directly. In some climate models ECS tends to be higher than the effective climate sensitivity (see Section 12.5.3), because the feedbacks that are represented in the models (water vapour, lapse (continued on next page) 1111 Chapter 12 Long-term Climate Change: Projections, Commitments and Irreversibility Box 12.2 (continued) rate, albedo and clouds) vary with the climate state. On time scales of many centuries, additional feedbacks with their own intrinsic time scales (e.g., vegetation, ice sheets; see Sections 5.3.3 and 12.5.3) (Jones et al., 2009; Goelzer et al., 2011) may become important but are not usually modelled. The resulting Earth system sensitivity is less well constrained but likely to be larger than ECS (Hansen et al., 2008; Rohling et al., 2009; Lunt et al., 2010; Pagani et al., 2010; Rohling and Members, 2012), implying that lower atmospheric CO2 concentrations are needed to meet a given temperature target on multi-century time scales. A number of caveats, however, apply to those studies (see Section 12.5.3). Those long-term feedbacks have their own intrinsic time scales, and are less likely to be proportional to global mean temperature change. For scenarios of increasing RF, TCR is a more informative indicator of future climate than ECS (Frame et al., 2005; Held et al., 2010). This assessment concludes with high confidence that the TCR is likely in the range 1°C to 2.5°C, close to the estimated 5 to 95% range of CMIP5 (1.2°C to 2.4°C; see Table 9.5), is positive and extremely unlikely greater than 3°C. As with the ECS, this is an expert-assessed range, supported by several different and partly independent lines of evidence, each based on multiple studies, models and data sets. TCR is estimated from the observed global changes in surface temperature, ocean heat uptake and RF, the detection/attribution studies identifying the response patterns to increasing GHG concentrations (Section 10.8.1), and the results of CMIP3 and CMIP5 (Section 9.7.1). Estimating TCR suffers from fewer difficulties in terms of state- or time-dependent feedbacks (see Section 12.5.3), and is less affected by uncertainty as to how much energy is taken up by the ocean. Unlike ECS, the ranges of TCR estimated from the observed warming and from AOGCMs agree well, increasing our confidence in the assessment of uncertainties in projections over the 21st century. Another useful metric relating directly CO2 emissions to temperature is the transient climate response to cumulative carbon emission (TCRE) (see Sections 12.5.4 and 10.8.4). This metric is useful to determine the allowed cumulative carbon emissions for stabilization at a specific global temperature. TCRE is defined as the annual mean global surface temperature change per unit of cumulated CO2 emis- sions, usually 1000 PgC, in a scenario with continuing emissions (see Glossary). It considers physical and carbon cycle feedbacks and uncertainties, but not additional feedbacks associated for example with the release of methane hydrates or large amounts of carbon from permafrost. The assessment based on climate models as well as the observed warming suggests that the TCRE is likely between 0.8°C to 2.5°C per 1000 PgC (1012 metric tons of carbon), for cumulative CO2 emissions less than about 2000 PgC until the time at which temperatures peak. Under these conditions, and for low to medium estimates of climate sensitivity, the TCRE gives an accurate estimate of the peak global mean temperature response to cumulated carbon emissions. TCRE has the advantage of directly relating global mean surface temperature change to CO2 emissions, but as a result of combining the uncertainty in both TCR and the carbon cycle response, it is more uncertain. It also ignores non-CO2 forcings and the fact that other components of the climate system (e.g., sea level rise, ice sheets) have their own intrinsic time scales, resulting in climate change not avoided by limiting global temperature change. 12.5.4.3 Conclusions and Limitations The simplicity of the concept of a cumulative carbon emission budget 12 makes it attractive for policy (WBGU, 2009). The principal driver of long One difficulty with the concepts of climate stabilization and targets is term warming is the total cumulative emission of CO2 over time. To that stabilization of global temperature does not imply stabilization for limit warming caused by CO2 emissions to a given temperature target, all aspects of the climate system. For example, some models show sig- cumulative CO2 emissions from all anthropogenic sources therefore nificant hysteresis behaviour in the global water cycle, because global need to be limited to a certain budget. Higher emissions in earlier dec- precipitation depends on both atmospheric CO2 and temperature (Wu ades simply imply lower emissions by the same amount later on. This et al., 2010). Processes related to vegetation changes (Jones et al., is illustrated in the RCP2.6 scenario in Figure 12.46a/b. Two idealized 2009) or changes in the ice sheets (Charbit et al., 2008; Ridley et al., emission pathways with initially higher emissions (even sustained at 2010) as well as ocean acidification, deep ocean warming and asso- high level for a decade in one case) eventually lead to the same warm- ciated sea level rise (Meehl et al., 2005b; Wigley, 2005; Zickfeld et al., ing if emissions are then reduced much more rapidly. Even a stepwise 2013) (see Figure 12.44d), and potential feedbacks linking, for exam- emission pathway with levels constant at 2010 and zero near mid-cen- ple, ocean and the ice sheets (Gillett et al., 2011; Goelzer et al., 2011), tury would eventually lead to a similar warming as they all have iden- have their own intrinsic long time scales. Those will result in significant tical cumulative emissions. changes hundreds to thousands of years after global temperature is stabilized. Thermal expansion, in contrast to global mean temperature, However, several aspects related to the concept of a cumulative carbon also depends on the evolution of surface temperature (Stouffer and emission budget should be kept in mind. The ratio of global tempera- Manabe, 1999; Bouttes et al., 2013; Zickfeld et al., 2013). ture and cumulative carbon is only approximately constant. It is the result of an interplay of several compensating carbon cycle and climate 1112 Long-term Climate Change: Projections, Commitments and Irreversibility Chapter 12 feedback processes operating on different time scales (a cancellation of from past cumulative CO2 emissions and observed warming, is sup- variations in the increase in RF per ppm of CO2, the ocean heat uptake ported by process understanding of the carbon cycle and global energy efficiency and the airborne fraction) (Gregory et al., 2009; Matthews balance, and emerges as a robust result from the entire hierarchy of et al., 2009; Solomon et al., 2009). It depends on the modelled climate models. sensitivity and carbon cycle feedbacks. Thus, the allowed emissions for a given temperature target are uncertain (see Figure 12.45) (Matthews Using a best estimate for the TCRE would provide a most likely value et al., 2009; Zickfeld et al., 2009; Knutti and Plattner, 2012). Neverthe- for the cumulative CO2 emissions compatible with stabilization at a less, the relationship is nearly linear in all models. Most models do not given temperature. However, such a budget would imply about 50% consider the possibility that long term feedbacks (Hansen et al., 2007; probability for staying below the temperature target. Higher probabil- Knutti and Hegerl, 2008) may be different (see Section 12.5.3). Despite ities for staying below a temperature or concentration target require the fact that stabilization refers to equilibrium, the results assessed significantly lower budgets (Knutti et al., 2005; Meinshausen et al., here are primarily relevant for the next few centuries and may differ 2009; Rogelj et al., 2012). Based on the assessment of TCRE (assum- for millennial scales. Notably, many of these limitations apply similarly ing a normal distribution with a +/-1 standard deviation range of 0.8- to other policy targets, for example, stabilizing the atmospheric CO2 2.5°C per 1000 PgC), limiting the warming caused by anthropogenic concentration. CO2 emissions alone (i.e., ignoring other radiative forcings) to less than 2°C since the period 1861 1880 with a probability of >33%, >50% Non-CO2 forcing constituents are important, which requires either and >66%, total CO2 emissions from all anthropogenic sources would assumptions on how CO2 emission reductions are linked to changes need to be below a cumulative budget of about 1570 PgC, 1210 PgC in other forcings (Meinshausen et al., 2006; Meinshausen et al., 2009; and 1000 PgC since 1870, respectively. An amount of 515 [445 to 585] McCollum et al., 2013), or separate emission budgets and climate PgC was emitted between 1870 and 2011. Accounting for non-CO2 modelling for short-lived and long-lived gases. So far, many studies forcings contributing to peak warming, or requiring a higher likelihood ignored non-CO2 forcings altogether. Those that consider them find of temperatures remaining below 2°C, both imply lower cumulative significant effects, in particular warming of several tenths of a degree CO2 emissions. A possible release of GHGs from permafrost or meth- for abrupt reductions in emissions of short-lived species, like aerosols ane hydrates, not accounted for in current models, would also further (Brasseur and Roeckner, 2005; Hare and Meinshausen, 2006; Zickfeld reduce the anthropogenic CO2 emissions compatible with a given tem- et al., 2009; Armour and Roe, 2011; Tanaka and Raddatz, 2011) (see perature target. When accounting for the non-CO2 forcings as in the also FAQ 12.3). Other studies, which model reductions that explicitly RCP scenarios, compatible carbon emissions since 1870 are reduced target warming from short-lived non-CO2 species only, find important to about 900 PgC, 820 PgC and 790 PgC to limit warming to less than short-term cooling benefits shortly after the reduction of these species 2°C since the period 1861 1880 with a probability of >33%, >50%, (Shindell et al., 2012), but do not extend beyond 2030. and >66%, respectively. These estimates were derived by computing the fraction of CMIP5 ESMs and EMICs that stay below 2°C for given The concept of cumulative carbon also implies that higher initial emis- cumulative emissions following RCP8.5, as shown in TFE.8 Figure 1c. sions can be compensated by a faster decline in emissions later or by The non-CO2 forcing in RCP8.5 is higher than in RCP2.6. Because all negative emissions. However, in the real world short-term and long- likelihood statements in calibrated IPCC language are open intervals, term goals are not independent and mitigation rates are limited by the provided estimates are thus both conservative and consistent economic constraints and existing infrastructure (Rive et al., 2007; choices valid for non-CO2 forcings across all RCP scenarios. There is no Mignone et al., 2008; Meinshausen et al., 2009; Davis et al., 2010; RCP scenario which limits warming to 2°C with probabilities of >33% Friedlingstein et al., 2011; Rogelj et al., 2013). An analysis of 193 or >50%, and which could be used to directly infer compatible cumu- published emission pathways with an energy balance model (UNEP, lative emissions. For a probability of >66% RCP2.6 can be used as a 2010; Rogelj et al., 2011) is shown in Figure 12.46c, d. Those emission comparison. Combining the average back-calculated fossil fuel carbon 12 pathways that likely limit warming below 2°C (above pre-industrial) emissions for RCP2.6 between 2012 and 2100 (270 PgC) with the aver- by 2100 show emissions of about 31 to 46 Pg(CO2-eq) yr 1 and 17 to age historical estimate of 515 PgC gives a total of 785 PgC, i.e., 790 23 Pg(CO2-eq) yr 1 by 2020 and 2050, respectively. Median 2010 emis- PgC when rounded to 10 PgC. As the 785 PgC estimate excludes an sions of all models are 48 Pg(CO2-eq) yr 1. Note that, as opposed to explicit assessment of future land-use change emissions, the 790 PgC Figure 12.46a, b, many scenarios still have positive emissions in 2100. value also remains a conservative estimate consistent with the overall As these will not be zero immediately after 2100, they imply that the likelihood assessment. The ranges of emissions for these three likeli- warming may exceed the target after 2100. hoods based on the RCP scenarios are rather narrow, as they are based on a single scenario and on the limited sample of models available The aspects discussed above do not limit the robustness of the overall (TFE.8 Figure 1c). In contrast to TCRE they do not include observational scientific assessment, but highlight factors that need to be considered constraints or account for sources of uncertainty not sampled by the when determining cumulative CO2 emissions consistent with a given models. The concept of a fixed cumulative CO2 budget holds not just for temperature target. In conclusion, taking into account the available 2°C, but for any temperature level explored with models so far (up to information from multiple lines of evidence (observations, models and about 5°C; see Figures 12.44 to 12.46), with higher temperature levels process understanding), the near linear relationship between cumula- implying larger budgets. tive CO2 emissions and peak global mean temperature is well estab- lished in the literature and robust for cumulative total CO2 emissions up to about 2000 PgC. It is consistent with the relationship inferred 1113 Chapter 12 Long-term Climate Change: Projections, Commitments and Irreversibility Temperature increase relative to preindustrial (°C) CO2 emissions from fossil fuel and industry (GtC yr-1) 12 2.2 10 a 2 b 1.8 8 1.6 6 1.4 4 1.2 2 1 0 0.8 -2 2000 2010 2020 2030 2040 2050 2060 2070 2080 2090 2100 2000 2010 2020 2030 2040 2050 2060 2070 2080 2090 2100 Year Year Likely (>66%) temperature increase (T) during 21st century Median temperature increase per pathway group (min-max) from illustrative emission trajectories and RCPs and per RCP (median) 6 c d Temperature increase rel. to preindustrial (°C) 140 T<2°C Total GHG emission levels (GtCO2-eq yr-1) 2°C4°C 100 4 80 3 60 40 2 20 1 0 -20 0 2000 2010 2020 2030 2040 2050 2060 2070 2080 2090 2100 2000 2010 2020 2030 2040 2050 2060 2070 2080 2090 2100 Year Year Figure 12.46 | (a) CO2 emissions for the RCP2.6 scenario (black) and three illustrative modified emission pathways leading to the same warming. (b) Global temperature change relative to pre-industrial for the pathways shown in panel (a). (c) Grey shaded bands show Integrated Assessment Model (IAM) emission pathways over the 21st century. The pathways were grouped based on ranges of likely avoided temperature increase in the 21st century. Pathways in the darkest three bands likely stay below 2°C, 3°C, 4°C by 2100, respectively (see legend), while those in the lightest grey band are higher than that. Emission corridors were defined by, at each year, identifying the 15th to 85th percentile range of emissions and drawing the corresponding bands across the range. Individual scenarios that follow the upper edge of the bands early on tend to follow the lower edge of the band later on. Black-white lines show median paths per range. (d) Global temperature relative to pre-industrial for the pathways in (c). (Data in (c) and (d) based on Rogelj et al. (2011).) Coloured lines in (c) and (d) denote the four RCP scenarios. 12 12.5.5 Potentially Abrupt or Irreversible Changes times referred to as tipping points (Lenton et al., 2008)), beyond which abrupt or nonlinear transitions to a different state ensues. The term 12.5.5.1 Introduction irreversibility is used in various ways in the literature. The AR5 report defines a perturbed state as irreversible on a given time scale if the This report adopts the definition of abrupt climate change used in Syn- recovery time scale from this state due to natural processes is sig- thesis and Assessment Product 3.4 of the U.S. Climate Change Science nificantly longer than the time it takes for the system to reach this Program CCSP (CCSP, 2008b). We define abrupt climate change as a perturbed state (see Glossary). In that context, most aspects of the cli- large-scale change in the climate system that takes place over a few mate change resulting from CO2 emissions are irreversible, due to the decades or less, persists (or is anticipated to persist) for at least a few long residence time of the CO2 perturbation in the atmosphere and the decades, and causes substantial disruptions in human and natural sys- resulting warming (Solomon et al., 2009). These results are discussed tems (see Glossary). Other definitions of abrupt climate change exist. in Sections 12.5.2 to 12.5.4. Here, we also assess aspects of irreversi- For example, in the AR4 climate change was defined as abrupt if it bility in the context of abrupt change, multiple steady states and hys- occurred faster than the typical time scale of the responsible forcing. teresis, i.e., the question whether a change (abrupt or not) would be reversible if the forcing was reversed or removed (e.g., Boucher et al., A number of components or phenomena within the Earth system have 2012). Irreversibility of ice sheets and sea level rise are also assessed been proposed as potentially possessing critical thresholds (some- in Chapter 13. 1114 Long-term Climate Change: Projections, Commitments and Irreversibility Chapter 12 Table 12.4 | Components in the Earth system that have been proposed in the literature as potentially being susceptible to abrupt or irreversible change. Column 2 defines whether or not a potential change can be considered to be abrupt under the AR5 definition. Column 3 states whether or not the process is irreversible in the context of abrupt change, and also gives the typical recovery time scales. Column 4 provides an assessment, if possible, of the likelihood of occurrence of abrupt change in the 21st century for the respective components or phenomena within the Earth system, for the scenarios considered in this chapter. Potentially Change in climate Irreversibility if abrupt (AR5 Projected likelihood of 21st century change in scenarios considered system component forcing reversed definition) Atlantic MOC collapse Yes Unknown Very unlikely that the AMOC will undergo a rapid transition (high confidence) Ice sheet collapse No Irreversible for millennia Exceptionally unlikely that either Greenland or West Antarctic Ice sheets will suffer near-complete disintegration (high confidence) Permafrost carbon release No Irreversible for millennia Possible that permafrost will become a net source of atmospheric greenhouse gases (low confidence) Clathrate methane release Yes Irreversible for millennia Very unlikely that methane from clathrates will undergo catastrophic release (high confidence) Tropical forests dieback Yes Reversible within Low confidence in projections of the collapse of large areas of tropical forest centuries Boreal forests dieback Yes Reversible within Low confidence in projections of the collapse of large areas of boreal forest centuries Disappearance of Yes Reversible within Likely that the Arctic Ocean becomes nearly ice-free in September before mid-cen- summer Arctic sea ice years to decades tury under high forcing scenarios such as RCP8.5 (medium confidence) Long-term droughts Yes Reversible within Low confidence in projections of changes in the frequency and duration of megadroughts years to decades Monsoonal circulation Yes Reversible within Low confidence in projections of a collapse in monsoon circulations years to decades In this section we examine the main components or phenomena within In addition to the main threshold for a complete breakdown of the the Earth system that have been proposed in the literature as potential- circulation, others may exist that involve more limited changes, such as ly being susceptible to abrupt or irreversible change (see Table 12.4). a cessation of Labrador Sea deep water formation (Wood et al., 1999). Abrupt changes that arise from nonlinearities within the climate system Rapid melting of the Greenland ice sheet causes increases in freshwa- are inherently difficult to assess and their timing, if any, of future occur- ter runoff, potentially weakening the AMOC. None of the CMIP5 sim- rences is difficult to predict. Nevertheless, progress is being made ulations include an interactive ice sheet component. However, Jung- exploring the potential existence of early warning signs for abrupt cli- claus et al. (2006), Mikolajewicz et al. (2007), Driesschaert et al. (2007) mate change (see e.g., Dakos et al., 2008; Scheffer et al., 2009). and Hu et al. (2009) found only a slight temporary effect of increased melt water fluxes on the AMOC, that was either small compared to the 12.5.5.2 The Atlantic Meridional Overturning effect of enhanced poleward atmospheric moisture transport or only noticeable in the most extreme scenarios. EMICs for which the stability has been systematically assessed by suitably designed hysteresis experiments robustly show a threshold Although many more model simulations have been conducted since beyond which the Atlantic thermohaline circulation cannot be sus- the AR4 under a wide range of forcing scenarios, projections of the tained (Rahmstorf et al., 2005). This is also the case for one low-reso- AMOC behaviour have not changed. Based on the available CMIP5 lution ESM (Hawkins et al., 2011). However, proximity to this threshold models, EMICs and the literature, it remains very likely that the AMOC is highly model dependent and influenced by factors that are currently will weaken over the 21st century relative to pre-industrial. Best esti- 12 poorly understood. There is some indication that the CMIP3 climate mates and ranges for the reduction from CMIP5 are 11% (1 to 24%) models may generally overestimate the stability of the Atlantic Ocean in RCP2.6 and 34% (12 to 54%) in RCP8.5 (Weaver et al., 2012) (see circulation (Hofmann and Rahmstorf, 2009; Drijfhout et al., 2010). In Section 12.4.7.2, Figure 12.35). But there is low confidence in the mag- particular, De Vries and Weber (2005), Dijkstra (2007), Weber et al. nitude of the weakening. Drijfhout et al. (2012) show that the AMOC (2007), Huisman et al. (2010), Drijfhout et al. (2010) and Hawkins et decrease per degree global mean temperature rise varies from 1.5 to al. (2011) suggest that the sign of net freshwater flux into the Atlantic 1.9 Sv (106 m3 s 1) for the CMIP5 multi-model ensemble members they transported through its southern boundary via the overturning circu- considered depending on the scenario, but that the standard deviation lation determines whether or not the AMOC is in a mono-stable or in this regression is almost half the signal. bi-stable state. For the pre-industrial control climate of most of the CMIP3 models, Drijfhout et al. (2010) found that the salt flux was nega- The FIO-ESM model shows cooling over much of the NH that may be tive (implying a positive freshwater flux), indicating that they were in a related to a strong reduction of the AMOC in all RCP scenarios (even mono-stable regime. However, this is not the case in the CMIP5 models RCP2.6), but the limited output available from the model precludes where Weaver et al. (2012) found that the majority of the models were an assessment of the response and realism of this response. Hence in a bi-stable regime during RCP integrations. Observations suggest it is not included the overall assessment of the likelihood of abrupt that the present day ocean is in a bi-stable regime, thereby allowing changes. for multiple equilibria and a stable off state of the AMOC (Bryden et al., 2011; Hawkins et al., 2011). 1115 Chapter 12 Long-term Climate Change: Projections, Commitments and Irreversibility It is unlikely that the AMOC will collapse beyond the end of the 21st initiation thereof) during the 21st century and beyond is discussed in century for the scenarios considered but a collapse beyond the 21st detail in Sections 13.4.3 and 13.4.4. century for large sustained warming cannot be excluded.There is low confidence in assessing the evolution of the AMOC beyond the 21st 12.5.5.4 Permafrost Carbon Storage century. Two of the CMIP5 models revealed an eventual slowdown of the AMOC to an off state (Figure 12.35). But this did not occur abruptly. Since the IPCC AR4, estimates of the amount of carbon stored in permafrost have been significantly revised upwards (Tarnocai et al., As assessed by Delworth et al. (2008), for an abrupt transition of the 2009), putting the permafrost carbon stock to an equivalent of twice AMOC to occur, the sensitivity of the AMOC to forcing would have the atmospheric carbon pool (Dolman et al., 2010). Because of low to be far greater that seen in current models. Alternatively, significant carbon input at high latitudes, permafrost carbon is to a large part of ablation of the Greenland ice sheet greatly exceeding even the most Pleistocene (Zimov et al., 2006) or Holocene (Smith et al., 2004) origin, aggressive of current projections would be required (Swingedouw et and its potential vulnerability is dominated by decomposition (Eglin et al., 2007; Hu et al., 2009). While neither possibility can be excluded al., 2010). The conjunction of a long carbon accumulation time scale on entirely, it is unlikely that the AMOC will collapse beyond the end of one hand and potentially rapid permafrost thawing and carbon decom- the 21st century because of global warming based on the models and position under warmer climatic conditions (Zimov et al., 2006; Schuur range of scenarios considered. et al., 2009; Kuhry et al., 2010) on the other hand suggests poten- tial irreversibility of permafrost carbon decomposition (leading to an 12.5.5.3 Ice Sheets increase of atmospheric CO2 and/or CH4 concentrations) on time scales of hundreds to thousands of years in a warming climate. Indeed, recent As detailed in Section 13.4.3, all available modelling studies agree that observations (Dorrepaal et al., 2009; Kuhry et al., 2010) suggest that the Greenland ice sheet will significantly decrease in area and volume this process, induced by widespread permafrost warming and thaw- in a warmer climate as a consequence of increased melt rates not ing (Romanovsky et al., 2010), might be already occurring. However, compensated for by increased snowfall rates and amplified by positive the existing modelling studies of permafrost carbon balance under feedbacks. Conversely, the surface mass balance of the Antarctic ice future warming that take into account at least some of the essen- sheet is projected to increase in most projections because increased tial permafrost-related processes (Khvorostyanov et al., 2008; Wania snowfall rates outweigh melt increase (see Section 13.4.4). et al., 2009; Koven et al., 2011; Schaefer et al., 2011; MacDougall et al., 2012; Schneider von Deimling et al., 2012) do not yield coherent Irreversibility of ice sheet volume and extent changes can arise because results beyond the fact that present-day permafrost might become a of the surface-elevation feedback that operates when a decrease of the net emitter of carbon during the 21st century under plausible future elevation of the ice sheet induces a decreased surface mass balance warming scenarios (low confidence). This also reflects an insufficient (generally through increased melting), and therefore essentially applies understanding of the relevant soil processes during and after perma- to Greenland. As detailed in Section 13.4.3.3, several stable states of frost thaw, including processes leading to stabilization of unfrozen soil the Greenland ice sheet might exist (Charbit et al., 2008; Ridley et al., carbon (Schmidt et al., 2011), and precludes a firm assessment of the 2010; Langen et al., 2012; Robinson et al., 2012; Solgaard and Langen, amplitude of irreversible changes in the climate system potentially 2012), and the ice sheet might irreversibly shrink to a stable small- related to permafrost degassing and associated global feedbacks at er state once a warming threshold is crossed for a certain amount of this stage (see also Sections 6.4.3.4 and 6.4.7.2 and FAQ 6.1). time, with the critical duration depending on how far the temperature threshold has been exceeded. Based on the available evidence (see 12.5.5.5 Atmospheric Methane from Terrestrial and Oceanic Section 13.4.3.3), an irreversible decrease of the Greenland ice sheet Clathrates 12 due to surface mass balance changes appears very unlikely in the 21st century but likely on multi-centennial to millennial time scales in the Model simulations (Fyke and Weaver, 2006; Reagan and Moridis, 2007; strongest forcing scenarios. Lamarque, 2008; Reagan and Moridis, 2009) suggest that clathrate deposits in shallow regions (in particular at high latitude regions and in In theory (Weertman, 1974; Schoof, 2007) ice sheet volume and extent the Gulf of Mexico) are susceptible to destabilization via ocean warm- changes can be abrupt because of the grounding line instability that ing. However, concomitant sea level rise due to changes in ocean mass can occur in coastal regions where bedrock is retrograde (i.e., sloping enhances clathrate stability in the ocean (Fyke and Weaver, 2006). A towards the interior of the ice sheet) and below sea level (see Sec- recent assessment of the potential for a future abrupt release of meth- tion 4.4.4 and Box 13.2). This essentially applies to West Antarctica, ane was undertaken by the U.S. Climate Change Science Program (Syn- but also to parts of Greenland and East Antarctica. Furthermore, ice thesis and Assessment Product 3.4 see Brooke et al., 2008). They con- shelf decay induced by oceanic or atmospheric warming might lead to cluded that it was very unlikely that such a catastrophic release would abruptly accelerated ice flow further inland (De Angelis and Skvarca, occur this century. However, they argued that anthropogenic warming 2003). Because ice sheet growth is usually a slow process, such chang- will very likely lead to enhanced methane emissions from both terres- es could also be irreversible in the definition adopted here. The availa- trial and oceanic clathrates (Brooke et al., 2008). Although difficult to ble evidence (see Section 13.4) suggests that it is exceptionally unlikely formally assess, initial estimates of the 21st century positive feedback that the ice sheets of either Greenland or West Antarctica will suffer a from methane clathrate destabilization are small but not insignificant near-complete disintegration during the 21st century. More generally, (Fyke and Weaver, 2006; Archer, 2007; Lamarque, 2008). Nevertheless, the potential for abrupt and/or irreversible ice sheet changes (or the on multi-millennial time scales, the positive feedback to anthropogenic 1116 Long-term Climate Change: Projections, Commitments and Irreversibility Chapter 12 warming of such methane emissions is potentially larger (Archer and on the likelihood of this occurring are very high (Lenton et al., 2008; Buffett, 2005; Archer, 2007; Brooke et al., 2008). Once more, due to the Allen et al., 2010). This is mainly due to large gaps in knowledge con- difference between release and accumulation time scales, such emis- cerning relevant ecosystemic and plant physiological responses to sions are irreversible. See also FAQ 6.1. warming (Niinemets, 2010). The main response is a potential advance- ment of the boreal forest northward and the potential transition from 12.5.5.6 Tropical and Boreal Forests a forest to a woodland or grassland state on its dry southern edges in the continental interiors, leading to an overall increase in herbaceous 12.5.5.6.1 Tropical forests vegetation cover in the affected parts of the boreal zone (Lucht et al., 2006). The proposed potential mechanisms for decreased forest growth In today s climate, the strongest growth in the Amazon rainforest and/or increased forest mortality are: increased drought stress under occurs during the dry season when strong insolation is combined with warmer summer conditions in regions with low soil moisture (Barber et water drawn from underground aquifers that store the previous wet al., 2000; Dulamsuren et al., 2009, 2010); desiccation of saplings with season s rainfall (Huete et al., 2006). AOGCMs do not agree about how shallow roots due to summer drought periods in the top soil layers, the dry season length in the Amazon may change in the future under causing suppression of forest reproduction (Hogg and Schwarz, 1997); the SRES A1 scenario (Bombardi and Carvalho, 2009), but simulations leaf tissue damage due to high leaf temperatures during peak summer with coupled regional climate/potential vegetation models are consist- temperatures under strong climate warming; and increased insect, her- ent in simulating an increase in dry season length, a 70% reduction in bivory and subsequent fire damage in damaged or struggling stands the areal extent of the rainforest by the end of the 21st century using (Dulamsuren et al., 2008). The balance of effects controlling standing the SRES A2 scenario, and an eastward expansion of the Caatinga biomass, fire type and frequency, permafrost thaw depth, snow volume vegetation (Cook and Vizy, 2008; Sorensson et al., 2010). In addition, and soil moisture remains uncertain. Although the existence of, and the some models have demonstrated the existence of multiple equilibria of thresholds controlling, a potential critical threshold in the boreal forest the tropical South American climate vegetation system (e.g., Oyama are extremely uncertain, its existence cannot at present be ruled out. and Nobre, 2003). The transition could be abrupt when the dry season becomes too long for the vegetation to survive, although the resilience 12.5.5.7 Sea Ice of the vegetation to a longer dry period may be increased by the CO2 fertilization effect (Zelazowski et al., 2011). Deforestation may also Several studies based on observational data or model hindcasts sug- increase dry season length (Costa and Pires, 2010) and drier conditions gest that the rapidly declining summer Arctic sea ice cover might reach increase the likelihood of wildfires that, combined with fire ignition or might already have passed a tipping point (Lindsay and Zhang, 2005; associated with human activity, can undermine the forest s resiliency Wadhams, 2012; Livina and Lenton, 2013). Identifying Arctic sea ice tip- to climate change (see also Section 6.4.8.1). If climate change brings ping points from the short observational record is difficult due to high drier conditions closer to those supportive of seasonal forests rather interannual and decadal variability. In some climate projections, the than rainforest, fire can act as a trigger to abruptly and irreversibly decrease in summer Arctic sea ice areal coverage is not gradual but is change the ecosystem (Malhi et al., 2009). However, the existence of instead punctuated by 5- to10- year periods of strong ice loss (Holland refugia is an important determinant of the potential for the re-emer- et al., 2006; Vavrus et al., 2012; Döscher and Koenigk, 2013). Still, these gence of the vegetation (Walker et al., 2009). abrupt reductions do not necessarily require the existence of a tipping point in the system or further imply an irreversible behaviour (Amstrup Analysis of projected change in the climate biome space of current et al., 2010; Lenton, 2012). The 5- to 10-year events discussed by Hol- vegetation distributions suggest that the risk of Amazonian forest die- land et al. (2006) arise when large natural climate variability in the back is small (Malhi et al., 2009), a finding supported by modelling Arctic reinforces the anthropogenically-forced change (Holland et al., when strong carbon dioxide fertilization effects on Amazonian vegeta- 2008). Positive trends on the same time scale also occur when internal 12 tion are assumed (Rammig et al., 2010). However, the strength of CO2 variability counteracts the forced change until the middle of the 21st fertilization on tropical vegetation is poorly known (see Box 6.3). Uncer- century (Holland et al., 2008; Kay et al., 2011; Vavrus et al., 2012). tainty concerning the existence of critical thresholds in the Amazonian and other tropical rainforests purely driven by climate change therefore Further work using single-column energy-balance models (Merryfield remains high, and so the possibility of a critical threshold being crossed et al., 2008; Eisenman and Wettlaufer, 2009; Abbot et al., 2011) yielded in precipitation volume cannot be ruled out (Nobre and Borma, 2009; mixed results about the possibility of tipping points and bifurcations Good et al., 2011b, 2011c). Nevertheless, there is still some question as in the transition from perennial to seasonal sea ice cover. Thin ice and to whether a transition of the Amazonian or other tropical rainforests snow covers promote strong longwave radiative loss to space and high into a lower biomass state could result from the combined effects of ice growth rates (e.g., Bitz and Roe, 2004; Notz, 2009; Eisenman, 2012). limits to carbon fertilization, climate warming, potential precipitation These stabilizing negative feedbacks can be large enough to overcome decline in interaction with the effects of human land use. the positive surface albedo feedback and/or cloud feedback, which act to amplify the forced sea ice response. In such low-order models, the 12.5.5.6.2 Boreal forest emergence of multiple stable states with increased climate forcing is a parameter-dependent feature (Abbot et al., 2011; Eisenman, 2012). For Evidence from field observations and biogeochemical modelling make it example, Eisenman (2012) showed with a single-column energy-bal- scientifically conceivable that regions of the boreal forest could tip into ance model that certain parameter choices that cause thicker ice or a different vegetation state under climate warming, but ­ ncertainties u warmer ocean under a given climate forcing make the model more prone to bifurcations and hence irreversible behaviour. 1117 Chapter 12 Long-term Climate Change: Projections, Commitments and Irreversibility The reversibility of sea ice loss with respect to global or hemispher- years. Because the long-term droughts all ended, they are not irrevers- ic mean surface temperature change has been directly assessed in ible. Nonetheless transitions over years to a decade into a state of AOGCMs/ESMs by first raising the CO2 concentration until virtually all long-term drought would have impacts on human and natural systems. sea ice disappears year-round and then lowering the CO2 level at the same rate as during the ramp-up phase until it reaches again the initial AR4 climate model projections (Milly et al., 2008) and CMIP5 ensem- value (Armour et al., 2011; Boucher et al., 2012; Ridley et al., 2012; Li et bles (Figure 12.23) both suggest widespread drying and drought across al., 2013b). None of these studies show evidence of a bifurcation lead- most of southwestern North America and many other subtropical ing to irreversible changes in Arctic sea ice. AOGCMs have also been regions by the mid to late 21st century (see Section 12.4.5), although used to test summer Arctic sea ice recovery after either sudden or very without abrupt change. Some studies suggest that this subtropical rapid artificial removal, and all had sea ice return within a few years drying may have already begun in southwestern North America (Seager (Schröder and Connolley, 2007; Sedláèek et al., 2011; Tietsche et al., et al., 2007; Seidel and Randel, 2007; Barnett et al., 2008; Pierce et al., 2011). In the Antarctic, as a result of the strong coupling between the 2008). More recent studies (Hoerling et al., 2010; Seager and Vecchi, Southern Ocean s surface and the deep ocean, the sea ice areal cover- 2010; Dai, 2011; Seager and Naik, 2012) suggest that regional reduc- age in some of the models integrated with ramp-up and ramp-down tions in precipitation are due primarily to internal variability and that atmospheric CO2 concentration exhibits a significant lag relative to the the anthropogenic forced trends are currently weak in comparison. global or hemispheric mean surface temperature (Ridley et al., 2012; Li et al., 2013b), so that its changes may be considered irreversible on While previous long-term droughts in southwest North America arose centennial time scales. from natural causes, climate models project that this region will under- go progressive aridification as part of a general drying and poleward Diagnostic analyses of a few global climate models have shown abrupt expansion of the subtropical dry zones driven by rising GHGs (Held and sea ice losses in the transition from seasonal to year-round Arctic ice- Soden, 2006; Seager et al., 2007; Seager and Vecchi, 2010). The models free conditions after raising CO2 to very high levels (Winton, 2006b; project the aridification to intensify steadily as RF and global warm- Ridley et al., 2008; Li et al., 2013b), but without evidence for irreversi- ing progress without abrupt changes. Because of the very long life- ble changes. Winton (2006b, 2008) hypothesized that the small ice cap time of the anthropogenic atmospheric CO2 perturbation, such drying instability (North, 1984) could cause such an abrupt transition. With a induced by global warming would be largely irreversible on millennium low-order Arctic sea ice model, Eisenman and Wettlaufer (2009) also time scale (Solomon et al., 2009; Frölicher and Joos, 2010; Gillett et found an abrupt change behaviour in the transition from seasonal ice al., 2011) (see Sections 12.5.2 and 12.5.4). For example, Solomon et to year-round ice-free conditions, accompanied by an irreversible bifur- al. (2009) found in a simulation where atmospheric CO2 increases to cation to a new stable, annually ice-free state. They concluded that the 600 ppm followed by zero emissions, that the 15% reduction in pre- cause is a loss of the stabilizing effect of sea ice growth when the ice cipitation in areas such as southwest North America, southern Europe season shrinks in time. The Arctic sea ice may thus experience a sharp and western Australia would persist long after emissions ceased. This, transition to annually ice-free conditions, but the irreversible nature of however, is largely a consequence of the warming persisting for centu- this transition seems to depend on the model complexity and structure. ries after emissions cease rather than an irreversible behaviour of the water cycle itself. In conclusion, rapid summer Arctic sea ice losses are likely to occur in the transition to seasonally ice-free conditions. These abrupt changes 12.5.5.8.2 Monsoonal circulation might have consequences throughout the climate system as noted by Vavrus et al. (2011) for cloud cover and Lawrence et al. (2008b) for the Climate model simulations and paleo-reconstructions provide evidence high-latitude ground state. Furthermore, the interannual-to-decadal of past abrupt changes in Saharan vegetation, with the green Sahara 12 variability in the summer Arctic sea ice extent is projected to increase in conditions (Hoelzmann et al., 1998) of the African Humid Period (AHP) response to global warming (Holland et al., 2008; Goosse et al., 2009). during the mid-Holocene serving as the most recent example. However, These studies suggest that large anomalies in Arctic sea ice areal cov- Mitchell (1990) and Claussen et al. (2003) note that the mid-Holocene is erage, like the ones that occurred in 2007 and 2012, might become not a direct analogue for future GHG-induced climate change since the increasingly frequent. However, there is little evidence in global climate forcings are different: a increased shortwave forcing in the NH summer models of a tipping point (or critical threshold) in the transition from versus a globally and seasonally uniform atmospheric CO2 increase, a perennially ice-covered to a seasonally ice-free Arctic Ocean beyond respectively. Paleoclimate examples suggest that a strong radiative which further sea ice loss is unstoppable and irreversible. or SST forcing is needed to achieve a rapid climate change, and that the rapid changes are reversible when the forcing is withdrawn. Both 12.5.5.8 Hydrologic Variability: Long-Term Droughts and the abrupt onset and termination of the AHP were triggered when Monsoonal Circulation northern African summer insolation was 4.2% higher than present day, representing a local increase of about 19 W m 2 (deMenocal et 12.5.5.8.1 Long-term Droughts al., 2000). Note that the globally averaged radiative anthropogenic forcing from 1750 to 2011 (Table 8.6) is small compared to this local As noted in Section 5.5.5, long-term droughts (often called mega- increase in insolation. A rapid Saharan greening has been simulated in droughts, see Glossary) are a recurring feature of Holocene paleocli- a climate model of intermediate complexity forced by a rapid increase mate records in North America, East and South Asia, Europe, Africa and in ­ tmospheric CO2, with the overall extent of greening depending on a India. The transitions into and out of the long-term droughts take many the equilibrium atmospheric CO2 level reached (Claussen et al., 2003). 1118 Long-term Climate Change: Projections, Commitments and Irreversibility Chapter 12 Abrupt Saharan vegetation changes of the Younger Dryas are linked with a rapid AMOC weakening which is considered very unlikely during the 21st century and unlikely beyond that as a consequence of global warming. Studies with conceptual models (Zickfeld et al., 2005; Levermann et al., 2009) have shown that the Indian summer monsoon can operate in two stable regimes: besides the wet summer monsoon, a stable state exists which is characterized by low precipitation over India. These studies suggest that any perturbation of the radiative budget that tends to weaken the driving pressure gradient has the potential to induce abrupt transitions between these two regimes. Numerous studies with coupled ocean atmosphere models have explored the potential impact of anthropogenic forcing on the Indian monsoon (see also Section 14.2). When forced with anticipated increas- es in GHG concentrations, the majority of these studies show an inten- sification of the rainfall associated with the Indian summer monsoon (Meehl and Washington, 1993; Kitoh et al., 1997; Douville et al., 2000; Hu et al., 2000; May, 2002; Ueda et al., 2006; Kripalani et al., 2007; Sto- wasser et al., 2009; Cherchi et al., 2010). Despite the intensification of precipitation, several of these modelling studies show a weakening of the summer monsoon circulation (Kitoh et al., 1997; May, 2002; Ueda et al., 2006; Kripalani et al., 2007; Stowasser et al., 2009; Cherchi et al., 2010). The net effect is nevertheless an increase of precipitation due to enhanced moisture transport into the Asian monsoon region (Ueda et al., 2006). In recent years, studies with GCMs have also explored the direct effect of aerosol forcing on the Indian monsoon (Lau et al., 2006; Meehl et al., 2008; Randles and Ramaswamy, 2008; Collier and Zhang, 2009). Considering absorbing aerosols (black carbon) only, Meehl et al. (2008) found an increase in pre-monsoonal precipitation, but a decrease in summer monsoon precipitation over parts of South Asia. In contrast, Lau et al. (2006) found an increase in May June July precipi- tation in that region. If an increase in scattering aerosols only is consid- ered, the monsoon circulation weakens and precipitation is inhibited (Randles and Ramaswamy, 2008). More recently, Bollasina et al. (2011) showed that anthropogenic aerosols played a fundamental role in driv- ing the recent observed weakening of the summer monsoon. Given that the effect of increased atmospheric regional loading of aerosols is opposed by the concomitant increases in GHG concentrations, it is 12 unlikely that an abrupt transition to the dry summer monsoon regime will be triggered in the 21st century. Acknowledgements We especially acknowledge the input of Contributing Authors Urs Beyerle for maintaining the database of CMIP5 output, Jan Sedláèek for producing a large number of CMIP5 figures, and Joeri Rogelj for preparing synthesis figures. Chapter technical assistants Oliver Stebler, Franziska Gerber and Barbara Aellig, provided great help in assembling the chapter and Sébastien Denvil and Jérôme Raciazek provided tech- nical assistance in downloading the CMIP5 data. 1119 Chapter 12 Long-term Climate Change: Projections, Commitments and Irreversibility References Abbot, D. S., M. Silber, and R. T. Pierrehumbert, 2011: Bifurcations leading to summer Annan, J. D., and J. C. Hargreaves, 2011a: Understanding the CMIP3 multi-model Arctic sea ice loss. J. Geophys. Res., 116, D19120. ensemble. J. Clim., 24, 4529 4538. Abe, M., H. Shiogama, T. Nozawa, and S. Emori, 2011: Estimation of future surface Annan, J. D., and J. C. Hargreaves, 2011b: On the generation and interpretation of temperature changes constrained using the future-present correlated modes in probabilistic estimates of climate sensitivity. Clim. Change, 104, 423 436. inter-model variability of CMIP3 multimodel simulations. J. Geophys. Res., 116, Arblaster, J. M., G. A. Meehl, and D. J. Karoly, 2011: Future climate change in the D18104. Southern Hemisphere: Competing effects of ozone and greenhouse gases. Adachi, Y., et al., 2013: Basic performance of a new earth system model of the Geophys. Res. Lett., 38, L02701. Meteorological Research Institute (MRI-ESM1). Papers Meteorol. Geophys., Archer, D., 2007: Methane hydrate stability and anthropogenic climate change. doi:10.2467/mripapers.64. Biogeosciences, 4, 521 544. Adams, P. J., J. H. Seinfeld, D. Koch, L. Mickley, and D. Jacob, 2001: General circulation Archer, D., and B. Buffett, 2005: Time-dependent response of the global ocean model assessment of direct radiative forcing by the sulfate-nitrate-ammonium- clathrate reservoir to climatic and anthropogenic forcing. Geochem. Geophys. water inorganic aerosol system. J. Geophys. Res., 106, 1097 1111. Geosyst., 6, Q03002. Adler, R. F., G. J. Gu, J. J. Wang, G. J. Huffman, S. Curtis, and D. Bolvin, 2008: Archer, D., et al., 2009: Atmospheric lifetime of fossil fuel carbon dioxide. Annu. Rev. Relationships between global precipitation and surface temperature on Earth Planet. Sci., 37, 117 134. interannual and longer timescales (1979 2006). J. Geophys. Res., 113, D22104. Armour, K., and G. Roe, 2011: Climate commitment in an uncertain world. Geophys. Aldrin, M., M. Holden, P. Guttorp, R. B. Skeie, G. Myhre, and T. K. Berntsen, 2012: Res. Lett., 38, L01707. Bayesian estimation of climate sensitivity based on a simple climate model Armour, K., I. Eisenman, E. Blanchard-Wrigglesworth, K. McCusker, and C. Bitz, 2011: fitted to observations of hemispheric temperatures and global ocean heat The reversibility of sea ice loss in a state-of-the-art climate model. Geophys. Res. content. Environmetrics, 23, 253 271. Lett., 38, L16705. Alexander, L. V., and J. M. Arblaster, 2009: Assessing trends in observed and modelled Arora, V. K., et al., 2011: Carbon emission limits required to satisfy future climate extremes over Australia in relation to future projections. Int. J. Climatol., representative concentration pathways of greenhouse gases. Geophys. Res. 29, 417 435. Lett., 38, L05805. Alexander, L. V., et al., 2006: Global observed changes in daily climate extremes of Arzel, O., T. Fichefet, and H. Goosse, 2006: Sea ice evolution over the 20th and 21st temperature and precipitation. J. Geophys. Res., 111, D05109. centuries as simulated by current AOGCMs. Ocean Model., 12, 401 415. Alexeev, V., and C. Jackson, 2012: Polar amplification: Is atmospheric heat transport Augustsson, T., and V. Ramanathan, 1977: Radiative-convective model study of CO2 important? Clim. Dyn., doi:10.1007/s00382-012-1601-z. climate problem. J. Atmos. Sci., 34, 448 451. Alexeev, V., D. Nicolsky, V. Romanovsky, and D. Lawrence, 2007: An evaluation of Bala, G., K. Caldeira, and R. Nemani, 2010: Fast versus slow response in climate deep soil configurations in the CLM3 for improved representation of permafrost. change: Implications for the global hydrological cycle. Clim. Dyn., 35, 423 434. Geophys. Res. Lett., 34, L09502. Baldwin, M. P., M. Dameris, and T. G. Shepherd, 2007: Atmosphere How will the Alexeev, V. A., P. L. Langen, and J. R. Bates, 2005: Polar amplification of surface stratosphere affect climate change? Science, 316, 1576 1577. warming on an aquaplanet in ghost forcing experiments without sea ice Ballester, J., F. Giorgi, and X. Rodo, 2010a: Changes in European temperature feedbacks. Clim. Dyn., 24, 655 666. extremes can be predicted from changes in PDF central statistics. Clim. Change, Allan, R., and B. Soden, 2008: Atmospheric warming and the amplification of 98, 277 284. precipitation extremes. Science, 321, 1481 1484. Ballester, J., X. Rodo, and F. Giorgi, 2010b: Future changes in Central Europe heat Allan, R. P., 2012: Regime dependent changes in global precipitation. Clim. Dyn., waves expected to mostly follow summer mean warming. Clim. Dyn., 35, 1191 doi:10.1007/s00382-011-1134-x. 1205. Allen, C., et al., 2010: A global overview of drought and heat-induced tree mortality Banks, H. T., and J. M. Gregory, 2006: Mechanisms of ocean heat uptake in a coupled reveals emerging climate change risks for forests. Forest Ecol. Manage., 259, climate model and the implications for tracer based predictions of ocean heat 660 684. uptake. Geophys. Res. Lett., 33, L07608. Allen, M. R., and W. J. Ingram, 2002: Constraints on future changes in climate and Bao, Q., et al., 2013: The Flexible Global Ocean-Atmosphere-Land system model, the hydrologic cycle. Nature, 419, 224 232. Spectral Version 2: FGOALS-s2. Adv. Atmos. Sci., 30, 561 576. Allen, M. R., D. J. Frame, C. Huntingford, C. D. Jones, J. A. Lowe, M. Meinshausen, Barber, V., G. Juday, and B. Finney, 2000: Reduced growth of Alaskan white spruce and N. Meinshausen, 2009: Warming caused by cumulative carbon emissions in the twentieth century from temperature-induced drought stress. Nature, 405, towards the trillionth tonne. Nature, 458, 1163 1166. 668 673. 12 Allen, R. J., and S. C. Sherwood, 2008: Warming maximum in the tropical upper Barnes, E. A., and L. M. Polvani, 2013: Response of the midlatitude jets and of troposphere deduced from thermal winds. Nature Geosci., 1, 399 403. their variability to increased greenhouse gases in the CMIP5 models. J. Clim., Allen, R. J., and S. C. Sherwood, 2010: Aerosol-cloud semi-direct effect and land-sea doi:10.1175/JCLI-D-12-00536.1. temperature contrast in a GCM. Geophys. Res. Lett., 37, L07702. Barnett, D. N., S. J. Brown, J. M. Murphy, D. M. H. Sexton, and M. J. Webb, 2006: Allen, R. J., S. C. Sherwood, J. R. Norris, and C. S. Zender, 2012: Recent Northern Quantifying uncertainty in changes in extreme event frequency in response Hemisphere tropical expansion primarily driven by black carbon and tropospheric to doubled CO2 using a large ensemble of GCM simulations. Clim. Dyn., 26, ozone. Nature, 485, 350 354. 489 511. Amstrup, S., E. DeWeaver, D. Douglas, B. Marcot, G. Durner, C. Bitz, and D. Bailey, Barnett, T., and D. Pierce, 2008: When will Lake Mead go dry? Water Resour. Res., 2010: Greenhouse gas mitigation can reduce sea-ice loss and increase polar 44, W03201. bear persistence. Nature, 468, 955 958. Barnett, T. P., et al., 2008: Human-induced changes in the hydrology of the western Andrews, T., and P. M. Forster, 2008: CO2 forcing induces semi-direct effects with United States. Science, 319, 1080 1083. consequences for climate feedback interpretations. Geophys. Res. Lett., 35, Barriopedro, D., E. M. Fischer, J. Luterbacher, R. Trigo, and R. Garcia-Herrera, 2011: L04802. The hot summer of 2010: Redrawing the temperature record map of Europe. Andrews, T., P. M. Forster, and J. M. Gregory, 2009: A surface energy perspective on Science, 332, 220 224. climate change. J. Clim., 22, 2557 2570. Bekryaev, R. V., I. V. Polyakov, and V. A. Alexeev, 2010: Role of polar amplification Andrews, T., P. Forster, O. Boucher, N. Bellouin, and A. Jones, 2010: Precipitation, in long-term surface air temperature variations and modern Arctic warming. J. radiative forcing and global temperature change. Geophys. Res. Lett., 37, Clim., 23, 3888 3906. L14701. Bellouin, N., J. Rae, A. Jones, C. Johnson, J. Haywood, and O. Boucher, 2011: Aerosol Annan, J. D., and J. C. Hargreaves, 2006: Using multiple observationally-based forcing in the Hadley Centre CMIP5 simulations and the role of ammonium constraints to estimate climate sensitivity. Geophys. Res. Lett., 33, L06704. nitrate. J. Geophys. Res., 116, D20206. Annan, J. D., and J. C. Hargreaves, 2010: Reliability of the CMIP3 ensemble. Geophys. Bengtsson, L., K. I. Hodges, and E. Roeckner, 2006: Storm tracks and climate change. Res. Lett., 37, L02703. J. Clim., 19, 3518 3543. 1120 Long-term Climate Change: Projections, Commitments and Irreversibility Chapter 12 Bengtsson, L., K. I. Hodges, and N. Keenlyside, 2009: Will extratropical storms Brasseur, G., and E. Roeckner, 2005: Impact of improved air quality on the future intensify in a warmer climate? J. Clim., 22, 2276 2301. evolution of climate. Geophys. Res. Lett., 32, L23704. Berg, P., J. O. Haerter, P. Thejll, C. Piani, S. Hagemann, and J. H. Christensen, 2009: Brient, F., and S. Bony, 2013: Interpretation of the positive low-cloud feedback Seasonal characteristics of the relationship between daily precipitation intensity predicted by a climate model under global warming. Clim. Dyn., 40, 2415 2431. and surface temperature. J. Geophys. Res., 114, D18102. Brierley, C. M., M. Collins, and A. J. Thorpe, 2010: The impact of perturbations to Betts, R., et al., 2007: Projected increase in continental runoff due to plant responses ocean-model parameters on climate and climate change in a coupled model. to increasing carbon dioxide. Nature, 448, 1037 1041. Clim. Dyn., 34, 325 343. Bitz, C., and G. Roe, 2004: A mechanism for the high rate of sea ice thinning in the Bromwich, D. H., J. P. Nicolas, A. J. Monaghan, M. A. Lazzara, L. M. Keller, G. A. Arctic Ocean. J. Clim., 17, 3623 3632. Weidner, and A. B. Wilson, 2013: Central West Antarctica among the most rapidly Bitz, C., and Q. Fu, 2008: Arctic warming aloft is data set dependent. Nature, 455, warming regions on Earth. Nature Geosci., 6, 139 145. E3 E4. Brooke, E., D. Archer, E. Dlugokencky, S. Frolking, and D. Lawrence, 2008: Potential Bitz, C. M., 2008: Some aspects of uncertainty in predicting sea ice thinning. In: for abrupt changes in atmospheric methane. Abrupt Climate Change: A Report Arctic Sea Ice Decline: Observations, Projections, Mechanisms, and Implications by the U.S. Climate Change Science Program and the Subcommittee on Global [E. T. DeWeaver, C. M. Bitz and L. B. Tremblay (eds.)]. American Geophysical Change Research. U.S. Geological Survey, Washington, DC, pp. 163 201. Union, Washington, DC, pp. 63 76. Brooks, H. E., 2009: Proximity soundings for severe convection for Europe and the Bitz, C. M., J. K. Ridley, M. M. Holland, and H. Cattle, 2012: Global climate models United States from reanalysis data. Atmos. Res., 93, 546 553. and 20th and 21st century Arctic climate change. In: Arctic Climate Change The Brooks, H. E., 2013: Severe thunderstorms and climate change. Atmos. Res., 123, ACSYS Decade and Beyond [P. Lemke (ed.)]. Springer Science+Business Media, 129 138. Dordrecht, Netherlands, pp. 405 436. Brooks, H. E., J. W. Lee, and J. P. Craven, 2003: The spatial distribution of severe Boberg, F., P. Berg, P. Thejll, W. Gutowski, and J. Christensen, 2010: Improved thunderstorm and tornado environments from global reanalysis data. Atmos. confidence in climate change projections of precipitation evaluated using daily Res., 67 68, 73 94. statistics from the PRUDENCE ensemble. Clim. Dyn., 35, 1097 1106. Brovkin, V., et al., 2013: Effect of anthropogenic land-use and land cover changes Boé, J., and L. Terray, 2008: Uncertainties in summer evapotranspiration changes on climate and land carbon storage in CMIP5 projections for the 21st century. J. over Europe and implications for regional climate change. Geophys. Res. Lett., Clim., doi:10.1175/JCLI-D-12 00623.1. 35, L05702. Brown, R., and P. Mote, 2009: The response of Northern Hemisphere snow cover to Boé, J., A. Hall, and X. Qu, 2009a: Current GCMs unrealistic negative feedback in the a changing climate. J. Clim., 22, 2124 2145. Arctic. J. Clim., 22, 4682 4695. Brown, R. D., and D. A. Robinson, 2011: Northern Hemisphere spring snow cover Boé, J. L., A. Hall, and X. Qu, 2009b: September sea-ice cover in the Arctic Ocean variability and change over 1922 2010 including an assessment of uncertainty. projected to vanish by 2100. Nature Geosci., 2, 341 343. Cryosphere, 5, 219 229. Boer, G. J., 1993: Climate change and the regulation of the surface moisture and Brutel-Vuilmet, C., M. Menegoz, and G. Krinner, 2013: An analysis of present and energy budgets. Clim. Dyn., 8, 225 239. future seasonal Northern Hemisphere land snow cover simulated by CMIP5 Boer, G. J., 2011: The ratio of land to ocean temperature change under global coupled climate models. Cryosphere, 7, 67 80. warming. Clim. Dyn., 37, 2253 2270. Bryan, K., F. G. Komro, S. Manabe, and M. J. Spelman, 1982: Transient climate Boer, G. J., and B. Yu, 2003: Climate sensitivity and response. Clim. Dyn., 20, 415 429. response to increasing atmospheric carbon-dioxide. Science, 215, 56 58. Boer, G. J., K. Hamilton, and W. Zhu, 2005: Climate sensitivity and climate change Bryden, H. L., B. A. King, and G. D. McCarthy, 2011: South Atlantic overturning under strong forcing. Clim. Dyn., 24, 685 700. circulation at 24S. J. Mar. Res., 69, 38 55. Bollasina, M. A., Y. Ming, and V. Ramaswamy, 2011: Anthropogenic aerosols and the Burke, E., and S. Brown, 2008: Evaluating uncertainties in the projection of future weakening of the South Asian summer monsoon. Science, 334, 502 505. drought. J. Hydrometeorol., 9, 292 299. Bombardi, R., and L. Carvalho, 2009: IPCC global coupled model simulations of the Burke, E. J., C. D. Jones, and C. D. Koven, 2012: Estimating the permafrost-carbon- South America monsoon system. Clim. Dyn., 33, 893 916. climate response in the CMIP5 climate models using a simplified approach. J. Böning, C., A. Dispert, M. Visbeck, S. Rintoul, and F. Schwarzkopf, 2008: The response Clim., doi:10.1175/JCLI-D-12-00550.1. of the Antarctic Circumpolar Current to recent climate change. Nature Geosci., Buser, C. M., H. R. Kunsch, D. Luthi, M. Wild, and C. Schär, 2009: Bayesian multi- 1, 864 869. model projection of climate: Bias assumptions and interannual variability. Clim. Bony, S., and J. L. Dufresne, 2005: Marine boundary layer clouds at the heart of Dyn., 33, 849 868. tropical cloud feedback uncertainties in climate models. Geophys. Res. Lett., 32, Butchart, N., and A. A. Scaife, 2001: Removal of chlorofluorocarbons by increased L20806. mass exchange between the stratosphere and troposphere in a changing Bony, S., G. Bellon, D. Klocke, S. Sherwood, S. Fermepin, and S. Denvil, 2013: Robust climate. Nature, 410, 799 802. direct effect of carbon dioxide on tropical circulation and regional precipitation. Butchart, N., et al., 2006: Simulations of anthropogenic change in the strength of the 12 Nature Geosci., doi:10.1038/ngeo1799. Brewer-Dobson circulation. Clim. Dyn., 27, 727 741. Bony, S., et al., 2006: How well do we understand and evaluate climate change Butchart, N., et al., 2010: Chemistry-climate model simulations of twenty-first feedback processes? J. Clim., 19, 3445 3482. century stratospheric climate and circulation changes. J. Clim., 23, 5349 5374. Booth, B. B. B., et al., 2012: High sensitivity of future global warming to land carbon Butler, A. H., D. W. J. Thompson, and R. Heikes, 2010: The steady-state atmospheric cycle processes. Environ. Res. Lett., 7, 024002. circulation response to climate change-like thermal forcings in a simple General Boucher, O., et al., 2012: Reversibility in an Earth System model in response to CO2 Circulation Model. J. Clim., 23, 3474 3496. concentration changes. Environ. Res. Lett., 7, 024013. Cabre, M. F., S. A. Solman, and M. N. Nunez, 2010: Creating regional climate change Bouttes, N., J. M. Gregory, and J. A. Lowe, 2013: The reversibility of sea level rise. J. scenarios over southern South America for the 2020 s and 2050 s using the Clim., 26, 2502 2513. pattern scaling technique: Validity and limitations. Clim. Change, 98, 449 469. Bowerman, N., D. Frame, C. Huntingford, J. Lowe, and M. Allen, 2011: Cumulative Caesar, J., and J. A. Lowe, 2012: Comparing the impacts of mitigation versus non- carbon emissions, emissions floors and short-term rates of warming: Implications intervention scenarios on future temperature and precipitation extremes in the for policy. Philos. Trans. R. Soc. A, 369, 45 66. HadGEM2 climate model. J. Geophys. Res., 117, D15109. Bracegirdle, T., and D. Stephenson, 2012: Higher precision estimates of regional polar Cagnazzo, C., E. Manzini, P. G. Fogli, M. Vichi, and P. Davini, 2013: Role of stratospheric warming by ensemble regression of climate model projections. Clim. Dyn., 39, dynamics in the ozone carbon connection in the Southern Hemisphere. Clim. 2805 2821. Dyn., doi:10.1007/s00382-013-1745-5. Bracegirdle, T., W. Connolley, and J. Turner, 2008: Antarctic climate change over the Cai, M., 2005: Dynamical amplification of polar warming. Geophys. Res. Lett., 32, twenty first century. J. Geophys. Res., 113, D03103. L22710. Bracegirdle, T. J., et al., 2013: Assessment of surface winds over the Atlantic, Indian, Caldeira, K., and J. F. Kasting, 1993: Insensitivity of global warming potentials to and Pacific Ocean sectors of the Southern Ocean in CMIP5 models: Historical carbon-dioxide emission scenarios. Nature, 366, 251 253. bias, forcing response, and state dependence. J. Geophys. Res., 118, 547 562. Caldwell, P., and C. S. Bretherton, 2009: Response of a subtropical stratocumulus- capped mixed layer to climate and aerosol changes. J. Clim., 22, 20 38. 1121 Chapter 12 Long-term Climate Change: Projections, Commitments and Irreversibility Calvo, N., and R. R. Garcia, 2009: Wave forcing of the tropical upwelling in the lower Chou, C., J. D. Neelin, C. A. Chen, and J. Y. Tu, 2009: Evaluating the Rich-Get-Richer stratosphere under increasing concentrations of greenhouse gases. J. Atmos. mechanism in tropical precipitation change under global warming. J. Clim., 22, Sci., 66, 3184 3196. 1982 2005. Calvo, N., R. R. Garcia, D. R. Marsh, M. J. Mills, D. E. Kinnison, and P. J. Young, Chou, C., C. Chen, P.-H. Tan, and K.-T. Chen, 2012: Mechanisms for global warming 2012: Reconciling modeled and observed temperature trends over Antarctica. impacts on precipitation frequency and intensity. J. Clim., 25, 3291 3306. Geophys. Res. Lett., 39, L16803. Chou, C., J. C. H. Chiang, C.-W. Lan, C.-H. Chung, Y.-C. Liao, and C.-J. Lee, 2013: Cao, L., and K. Caldeira, 2010: Atmospheric carbon dioxide removal: Long-term Increase in the range between wet and dry season precipitation. Nature Geosci., consequences and commitment. Environ. Res. Lett., 5, 024011. 6, 263 267. Cao, L., G. Bala, and K. Caldeira, 2012: Climate response to changes in atmospheric Christensen, J. H., F. Boberg, O. B. Christensen, and P. Lucas-Picher, 2008: On the need carbon dioxide and solar irradiance on the time scale of days to weeks. Environ. for bias correction of regional climate change projections of temperature and Res. Lett., 7, 034015. precipitation. Geophys. Res. Lett., 35, L20709. Capotondi, A., M. Alexander, N. Bond, E. Curchitser, and J. Scott, 2012: Enhanced Christensen, J. H., et al., 2007: Regional climate projections. In: Climate Change upper ocean stratification with climate change in the CMIP3 models. J. Geophys. 2007: The Physical Science Basis. Contribution of Working Group I to the Res., 117, C04031. Fourth Assessment Report of the Intergovernmental Panel on Climate Change Cariolle, D., and H. Teyssedre, 2007: A revised linear ozone photochemistry [Solomon, S., D. Qin, M. Manning, Z. Chen, M. Marquis, K. B. Averyt, M. Tignor parameterization for use in transport and general circulation models: Multi- and H. L. Miller (eds.)] Cambridge University Press, Cambridge, United Kingdom annual simulations. Atmos. Chem. Phys., 7, 2183 2196. and New York, NY, USA, pp. 847 940. Carslaw, K., O. Boucher, D. Spracklen, G. Mann, J. Rae, S. Woodward, and M. Kulmala, Christensen, N., and D. Lettenmaier, 2007: A multimodel ensemble approach to 2010: A review of natural aerosol interactions and feedbacks within the Earth assessment of climate change impacts on the hydrology and water resources of system. Atmos. Chem. Phys., 10, 1701 1737. the Colorado River Basin. Hydrol. Earth Syst. Sci., 11, 1417 1434. Catto, J. L., L. C. Shaffrey, and K. I. Hodges, 2011: Northern Hemisphere extratropical Cionni, I., et al., 2011: Ozone database in support of CMIP5 simulations: Results and cyclones in a warming climate in the HiGEM high-resolution climate model. J. corresponding radiative forcing. Atmos. Chem. Phys., 11, 11267 11292. Clim., 24, 5336 5352. Clark, R. T., S. J. Brown, and J. M. Murphy, 2006: Modeling Northern Hemisphere CCSP, 2008a: Weather and Climate Extremes in a Changing Climate: A Report by the summer heat extreme changes and their uncertainties using a physics ensemble U.S. Climate Change Science Program and the Subcommittee on Global Change of climate sensitivity experiments. J. Clim., 19, 4418 4435. Research. Department of Commerce, NOAA s National Climatic Data Center, Clark, R. T., J. M. Murphy, and S. J. Brown, 2010: Do global warming targets limit College Park, MD, 164 pp. heatwave risk? Geophys. Res. Lett., 37, L17703. CCSP, 2008b: Abrupt Climate Change. A Report by the U.S. Climate Change Science Claussen, M., V. Brovkin, A. Ganopolski, C. Kubatzki, and V. Petoukhov, 2003: Climate Program and the Subcommittee on Global Change Research. U.S. Geological change in northern Africa: The past is not the future. Clim. Change, 57, 99 118. Survey, Washington, DC, 459 pp. Colle, B. A., Z. Zhang, K. A. Lombardo, E. Chang, P. Liu, and M. Zhang, 2013: Historical Cess, R., et al., 1990: Intercomparison and interpretation of climate feedback evaluation and future prediction of eastern North America and western Atlantic processes in 19 atmospheric general-circulation models. J. Geophys. Res., 95, extratropical cyclones in the CMIP5 models during the cool season. J. Clim., 16601 16615. doi:10.1175/JCLI-D-12-00498.1. Chadwick, R., I. Boutle, and G. Martin, 2012: Spatial patterns of precipitation change Collier, J., and G. Zhang, 2009: Aerosol direct forcing of the summer Indian monsoon in CMIP5: Why the rich don t get richer in the Tropics. J. Clim., doi:10.1175/JCLI- as simulated by the NCAR CAM3. Clim. Dyn., 32, 313 332. D-12-00543.1. Collins, M., C. M. Brierley, M. MacVean, B. B. B. Booth, and G. R. Harris, 2007: The Chadwick, R., P. Wu, P. Good, and T. Andrews, 2013: Asymmetries in tropical rainfall sensitivity of the rate of transient climate change to ocean physics perturbations. and circulation patterns in idealised CO2 removal experiments. Clim. Dyn., 40, J. Clim., 20, 2315 2320. 295 316. Collins, M., B. B. B. Booth, G. Harris, J. M. Murphy, D. M. H. Sexton, and M. J. Webb, Chang, E. K. M., Y. Guo, and X. Xia, 2012a: CMIP5 multimodel ensemble projection 2006a: Towards quantifying uncertainty in transient climate change. Clim. Dyn., of storm track change under global warming. J. Geophys. Res., 117, D23118. 27, 127 147. Chang, E. K. M., Y. Guo, X. Xia, and M. Zheng, 2012b: Storm track activity in IPCC Collins, M., R. E. Chandler, P. M. Cox, J. M. Huthnance, J. Rougier, and D. B. Stephenson, AR4/CMIP3 model simulations. J. Clim., 26, 246 260. 2012: Quantifying future climate change. Nature Clim. Change, 2, 403 409. Chapin, F., et al., 2005: Role of land-surface changes in Arctic summer warming. Collins, M., B. Booth, B. Bhaskaran, G. Harris, J. Murphy, D. Sexton, and M. Webb, Science, 310, 657 660. 2011: Climate model errors, feedbacks and forcings: A comparison of perturbed Charbit, S., D. Paillard, and G. Ramstein, 2008: Amount of CO2 emissions irreversibly physics and multi-model ensembles. Clim. Dyn., 36, 1737 1766. leading to the total melting of Greenland. Geophys. Res. Lett., 35, L12503. Collins, M., et al., 2010: The impact of global warming on the tropical Pacific ocean 12 Charney, J. G., 1979: Carbon Dioxide and Climate: A Scientific Assessment. National and El Nino. Nature Geosci., 3, 391 397. Academies of Science Press, Washington, DC, 22 pp. Collins, W. D., et al., 2006b: Radiative forcing by well-mixed greenhouse gases: Chen, C. T., and T. Knutson, 2008: On the verification and comparison of extreme Estimates from climate models in the Intergovernmental Panel on Climate rainfall indices from climate models. J. Clim., 21, 1605 1621. Change (IPCC) Fourth Assessment Report (AR4). J. Geophys. Res., 111, D14317. Chen, G., J. Lu, and D. M. W. Frierson, 2008: Phase speed spectra and the latitude Colman, R., and B. McAvaney, 2009: Climate feedbacks under a very broad range of of surface westerlies: Interannual variability and global warming trend. J. Clim., forcing. Geophys. Res. Lett., 36, L01702. 21, 5942 5959. Colman, R., and S. Power, 2010: Atmospheric radiative feedbacks associated with Cherchi, A., A. Alessandri, S. Masina, and A. Navarra, 2010: Effect of increasing CO2 transient climate change and climate variability. Clim. Dyn., 34, 919 933. levels on monsoons. Clim. Dyn., 37, 83 101. Comiso, J. C., and F. Nishio, 2008: Trends in the sea ice cover using enhanced and Choi, D. H., J. S. Kug, W. T. Kwon, F. F. Jin, H. J. Baek, and S. K. Min, 2010: Arctic compatible AMSR-E, SSM/I, and SMMR data. J. Geophys. Res., 113, C02S07. Oscillation responses to greenhouse warming and role of synoptic eddy Cook, K., and E. Vizy, 2008: Effects of twenty-first-century climate change on the feedback. J. Geophys. Res. Atmos., 115, D17103. Amazon rain forest. J. Clim., 21, 542 560. Chou, C., and J. D. Neelin, 2004: Mechanisms of global warming impacts on regional Costa, M., and G. Pires, 2010: Effects of Amazon and Central Brazil deforestation tropical precipitation. J. Clim., 17, 2688 2701. scenarios on the duration of the dry season in the arc of deforestation. Int. J. Chou, C., and C. Chen, 2010: Depth of convection and the weakening of tropical Climatol., 30, 1970 1979. circulation in global warming. J. Clim., 23, 3019 3030. Crook, J. A., P. M. Forster, and N. Stuber, 2011: Spatial patterns of modeled climate Chou, C., and C.-W. Lan, 2012: Changes in the annual range of precipitation under feedback and contributions to temperature response and polar amplification. J. global warming. J. Clim., 25, 222 235. Clim., 24, 3575 3592. Chou, C., J. D. Neelin, J. Y. Tu, and C. T. Chen, 2006: Regional tropical precipitation Crucifix, M., 2006: Does the Last Glacial Maximum constrain climate sensitivity? change mechanisms in ECHAM4/OPYC3 under global warming. J. Clim., 19, Geophys. Res. Lett., 33, L18701. 4207 4223. 1122 Long-term Climate Change: Projections, Commitments and Irreversibility Chapter 12 Cruz, F. T., A. J. Pitman, J. L. McGregor, and J. P. Evans, 2010: Contrasting regional Dong, B. W., J. M. Gregory, and R. T. Sutton, 2009: Understanding land-sea warming responses to increasing leaf-level atmospheric carbon dioxide over Australia. J. contrast in response to increasing greenhouse gases. Part I: Transient adjustment. Hydrometeorol., 11, 296 314. J. Clim., 22, 3079 3097. Dai, A., 2011: Drought under global warming: A review. WIREs Clim. Change, 2, Dorrepaal, E., S. Toet, R. van Logtestijn, E. Swart, M. van de Weg, T. Callaghan, and 45 65. R. Aerts, 2009: Carbon respiration from subsurface peat accelerated by climate Dai, A., 2013: Increasing drought under global warming in observations and models. warming in the subarctic. Nature, 460, 616 619. Nature Clim. Change, 3, 52 58. Döscher, R., and T. Koenigk, 2013: Arctic rapid sea ice loss events in regional coupled Dakos, V., M. Scheffer, E. H. van Nes, V. Brovkin, V. Petoukhov, and H. Held, 2008: climate scenario experiments. Ocean Sci., 9, 217 248. Slowing down as an early warning signal for abrupt climate change. Proc. Natl. Doutriaux-Boucher, M., M. J. Webb, J. M. Gregory, and O. Boucher, 2009: Carbon Acad. Sci. U.S.A., 105, 14308 14312. dioxide induced stomatal closure increases radiative forcing via a rapid Danabasoglu, G., and P. Gent, 2009: Equilibrium climate sensitivity: Is it accurate to reduction in low cloud. Geophys. Res. Lett., 36, L02703. use a slab ocean model? J. Clim., 22, 2494 2499. Douville, H., J. F. Royer, J. Polcher, P. Cox, N. Gedney, D. B. Stephenson, and P. J. Valdes, Davin, E. L., N. de Noblet-Ducoudre, and P. Friedlingstein, 2007: Impact of land cover 2000: Impact of CO2 doubling on the Asian summer monsoon: Robust versus change on surface climate: Relevance of the radiative forcing concept. Geophys. model-dependent responses. J. Meteorol. Soc. Jpn., 78, 421 439. Res. Lett., 34, L13702. Dowdy, A. J., G. A. Mills, B. Timbal, and Y. Wang, 2013: Changes in the risk of Davis, S., K. Caldeira, and H. Matthews, 2010: Future CO2 emissions and climate extratropical cyclones in Eastern Australia. J. Clim., 26, 1403 1417. change from existing energy infrastructure. Science, 329, 1330 1333. Downes, S., A. Budnick, J. Sarmiento, and R. Farneti, 2011: Impacts of wind stress on Davis, S. M., and K. H. Rosenlof, 2012: A multidiagnostic intercomparison of tropical- the Antarctic Circumpolar Current fronts and associated subduction. Geophys. width time series using reanalyses and satellite observations. J. Clim., 25, 1061 Res. Lett., 38, L11605. 1078. Downes, S. M., and A. M. Hogg, 2013: Southern Ocean circulation and eddy De Angelis, H., and P. Skvarca, 2003: Glacier surge after ice shelf collapse. Science, compensation in CMIP5 models. J. Clim., doi:10.1175/JCLI-D-12-00504.1. 299, 1560 1562. Downes, S. M., N. L. Bindoff, and S. R. Rintoul, 2010: Changes in the subduction of de Vries, H., R. J. Haarsma, and W. Hazeleger, 2012: Western European cold spells in Southern Ocean water masses at the end of the twenty-first century in eight current and future climate. Geophys. Res. Lett., 39, L04706. IPCC models. J. Clim., 23, 6526 6541. de Vries, P., and S. Weber, 2005: The Atlantic freshwater budget as a diagnostic for Driesschaert, E., et al., 2007: Modeling the influence of Greenland ice sheet melting the existence of a stable shut down of the meridional overturning circulation. on the Atlantic meridional overturning circulation during the next millennia. Geophys. Res. Lett., 32, L09606. Geophys. Res. Lett., 34, L10707. Del Genio, A. D., M.-S. Yao, and J. Jonas, 2007: Will moist convection be stronger in a Drijfhout, S., G. J. van Oldenborgh, and A. Cimatoribus, 2012: Is a decline of AMOC warmer climate? Geophys. Res. Lett., 34, L16703. causing the warming hole above the North Atlantic in observed and modeled Delisle, G., 2007: Near-surface permafrost degradation: How severe during the 21st warming patterns? J. Clim., 25, 8373 8379. century? Geophys. Res. Lett., 34, L09503. Drijfhout, S. S., S. Weber, and E. van der Swaluw, 2010: The stability of the MOC as Delworth, T. L., et al., 2008: The potential for abrupt change in the Atlantic meridional diagnosed from the model projections for the pre-industrial, present and future overturning circulation. In: Abrupt Climate Change: A Report by the U.S. Climate climate. Clim. Dyn., 37, 1575 1586. Change Science Program and the Subcommittee on Global Change Research, Dufresne, J.-L., et al., 2013: Climate change projections using the IPSL-CM5 Earth U.S. Geological Survey, Washington, DC, pp. 258 359. system model: From CMIP3 to CMIP5. Clim. Dyn., 40, 2123 2165. deMenocal, P., J. Ortiz, T. Guilderson, J. Adkins, M. Sarnthein, L. Baker, and M. Dufresne, J., J. Quaas, O. Boucher, S. Denvil, and L. Fairhead, 2005: Contrasts in the Yarusinsky, 2000: Abrupt onset and termination of the African Humid Period: effects on climate of anthropogenic sulfate aerosols between the 20th and the Rapid climate responses to gradual insolation forcing. Quaternary Science 21st century. Geophys. Res. Lett., 32, L21703. Reviews, 19, 347 361. Dufresne, J. L., and S. Bony, 2008: An assessment of the primary sources of spread Deser, C., A. Phillips, V. Bourdette, and H. Teng, 2012a: Uncertainty in climate change of global warming estimates from coupled atmosphere-ocean models. J. Clim., projections: The role of internal variability. Clim. Dyn., 38, 527 546. 21, 5135 5144. Deser, C., R. Knutti, S. Solomon, and A. S. Phillips, 2012b: Communication of the role Dulamsuren, C., M. Hauck, and M. Muhlenberg, 2008: Insect and small mammal of natural variability in future North American climate. Nature Clim. Change, 2, herbivores limit tree establishment in northern Mongolian steppe. Plant Ecol., 775 779. 195, 143 156. Dessai, S., X. F. Lu, and M. Hulme, 2005: Limited sensitivity analysis of regional Dulamsuren, C., M. Hauck, and C. Leuschner, 2010: Recent drought stress leads to climate change probabilities for the 21st century. J. Geophys. Res. Atmos., 110, growth reductions in Larix sibirica in the western Khentey, Mongolia. Global D19108. Change Biol., 16, 3024 3035. Diffenbaugh, N. S., and M. Ashfaq, 2010: Intensification of hot extremes in the Dulamsuren, C., et al., 2009: Water relations and photosynthetic performance in 12 United States. Geophys. Res. Lett., 37, L15701. Larix sibirica growing in the forest-steppe ecotone of northern Mongolia. Tree Diffenbaugh, N. S., J. S. Pal, F. Giorgi, and X. J. Gao, 2007: Heat stress intensification Physiol., 29, 99 110. in the Mediterranean climate change hotspot. Geophys. Res. Lett., 34, L11706. Dunne, J. P., R. J. Stouffer, and J. G. John, 2013: Reductions in labour capacity Dijkstra, H., 2007: Characterization of the multiple equilibria regime in a global from heat stress under climate warming. Nature Clim. Change, doi:10.1038/ ocean model. Tellus A, 59, 695 705. nclimate1827. DiNezio, P. N., A. C. Clement, G. A. Vecchi, B. J. Soden, and B. P. Kirtman, 2009: Climate Durack, P., and S. Wijffels, 2010: Fifty-year trends in global ocean salinities and their response of the equatorial Pacific to global warming. J. Clim., 22, 4873 4892. relationship to broad-scale warming. J. Clim., 23, 4342 4362. Dirmeyer, P. A., Y. Jin, B. Singh, and X. Yan, 2013: Evolving land-atmosphere Durack, P. J., S. E. Wijffels, and R. J. Matear, 2012: Ocean salinities reveal strong interactions over North America from CMIP5 simulations. J. Clim., doi:10.1175/ global water cycle intensification during 1950 to 2000. Science, 336, 455 458. JCLI-D-12-00454.1. Eby, M., K. Zickfeld, A. Montenegro, D. Archer, K. Meissner, and A. Weaver, 2009: Dix, M., et al., 2013: The ACCESS Coupled Model: Documentation of core CMIP5 Lifetime of anthropogenic climate change: Millennial time scales of potential simulations and initial results. Aust. Meteorol. Oceanogr. J., 63, 83-199. CO2 and surface temperature perturbations. J. Clim., 22, 2501 2511. Dole, R., et al., 2011: Was there a basis for anticipating the 2010 Russian heat wave? Edwards, T., M. Crucifix, and S. Harrison, 2007: Using the past to constrain the future: Geophys. Res. Lett., 38, L06702. How the palaeorecord can improve estimates of global warming. Prog. Phys. Dolman, A., G. van der Werf, M. van der Molen, G. Ganssen, J. Erisman, and B. Geogr., 31, 481 500. Strengers, 2010: A carbon cycle science update since IPCC AR-4. Ambio, 39, Eglin, T., et al., 2010: Historical and future perspectives of global soil carbon response 402 412. to climate and land-use changes. Tellus B, 62, 700 718. Donat, M. G., et al., 2013: Updated analyses of temperature and precipitation Eisenman, I., 2012: Factors controlling the bifurcation structure of sea ice retreat. J. extreme indices since the beginning of the twentieth century: The HadEX2 Geophys. Res., 117, D01111. dataset. J. Geophys. Res., 118, 2098 2118. Eisenman, I., and J. Wettlaufer, 2009: Nonlinear threshold behavior during the loss of Arctic sea ice. Proc. Natl. Acad. Sci. U.S.A., 106, 28 32. 1123 Chapter 12 Long-term Climate Change: Projections, Commitments and Irreversibility Eisenman, I., T. Schneider, D. S. Battisti, and C. M. Bitz, 2011: Consistent changes in Friedlingstein, P., and S. Solomon, 2005: Contributions of past and present human the sea ice seasonal cycle in response to global warming. J. Clim., 24, 5325 generations to committed warming caused by carbon dioxide. Proc. Natl. Acad. 5335. Sci. U.S.A., 102, 10832 10836. Eliseev, A., P. Demchenko, M. Arzhanov, and I. Mokhov, 2013: Transient hysteresis of Friedlingstein, P., S. Solomon, G. Plattner, R. Knutti, P. Ciais, and M. Raupach, 2011: near-surface permafrost response to external forcing. Clim. Dyn., doi:10.1007/ Long-term climate implications of twenty-first century options for carbon s00382 013 1672 5. dioxide emission mitigation. Nature Clim. Change, 1, 457 461. Emori, S., and S. Brown, 2005: Dynamic and thermodynamic changes in mean and Friedlingstein, P., et al., 2006: Climate-carbon cycle feedback analysis: Results from extreme precipitation under changed climate. Geophys. Res. Lett., 32, L17706. the C4MIP model intercomparison. J. Clim., 19, 3337 3353. Eyring, V., et al., 2005: A strategy for process-oriented validation of coupled Frieler, K., M. Meinshausen, M. Mengel, N. Braun, and W. Hare, 2012: A scaling chemistry-climate models. Bull. Am. Meteorol. Soc., 86, 1117 1133. approach to probabilistic assessment of regional climate. J. Clim., 25, 3117 Eyring, V., et al., 2013: Long-term ozone changes and associated climate impacts in 3144. CMIP5 simulations. J. Geophys. Res., doi:10.1002/jgrd.50316. Frierson, D., J. Lu, and G. Chen, 2007: Width of the Hadley cell in simple and Falloon, P. D., R. Dankers, R. A. Betts, C. D. Jones, B. B. B. Booth, and F. H. Lambert, comprehensive general circulation models. Geophys. Res. Lett., 34, L18804. 2012: Role of vegetation change in future climate under the A1B scenario Frölicher, T., and F. Joos, 2010: Reversible and irreversible impacts of greenhouse gas and a climate stabilisation scenario, using the HadCM3C Earth system model. emissions in multi-century projections with the NCAR global coupled carbon Biogeosciences, 9, 4739 4756. cycle-climate model. Clim. Dyn., 35, 1439 1459. Farneti, R., and P. Gent, 2011: The effects of the eddy-induced advection coefficient Fu, Q., C. M. Johanson, J. M. Wallace, and T. Reichler, 2006: Enhanced mid-latitude in a coarse-resolution coupled climate model. Ocean Model., 39, 135 145. tropospheric warming in satellite measurements. Science, 312, 1179 1179. Farneti, R., T. Delworth, A. Rosati, S. Griffies, and F. Zeng, 2010: The role of mesoscale Fyfe, J., O. Saenko, K. Zickfeld, M. Eby, and A. Weaver, 2007: The role of poleward- eddies in the rectification of the Southern Ocean response to climate change. J. intensifying winds on Southern Ocean warming. J. Clim., 20, 5391 5400. Phys. Oceanogr., 40, 1539 1557. Fyke, J., and A. Weaver, 2006: The effect of potential future climate change on the Fasullo, J. T., 2010: Robust land-ocean contrasts in energy and water cycle feedbacks. marine methane hydrate stability zone. J. Clim., 19, 5903 5917. J. Clim., 23, 4677 4693. Garcia, R. R., and W. J. Randel, 2008: Acceleration of the Brewer-Dobson circulation Favre, A., and A. Gershunov, 2009: North Pacific cyclonic and anticyclonic transients due to increases in greenhouse gases. J. Atmos. Sci., 65, 2731 2739. in a global warming context: Possible consequences for Western North American Gastineau, G., and B. J. Soden, 2009: Model projected changes of extreme wind daily precipitation and temperature extremes. Clim. Dyn., 32, 969 987. events in response to global warming. Geophys. Res. Lett., 36, L10810. Finnis, J., M. M. Holland, M. C. Serreze, and J. J. Cassano, 2007: Response of Northern Gastineau, G., H. Le Treut, and L. Li, 2008: Hadley circulation changes under global Hemisphere extratropical cyclone activity and associated precipitation to warming conditions indicated by coupled climate models. Tellus A, 60, 863 884. climate change, as represented by the Community Climate System Model. J. Gastineau, G., L. Li, and H. Le Treut, 2009: The Hadley and Walker circulation changes Geophys. Res., 112, G04S42. in global warming conditions described by idealized atmospheric simulations. J. Fischer, E. M., and C. Schär, 2009: Future changes in daily summer temperature Clim., 22, 3993 4013. variability: Driving processes and role for temperature extremes. Clim. Dyn., 33, Gent, P. R., et al., 2011: The Community Climate System Model Version 4. J. Clim., 917 935. 24, 4973 4991. Fischer, E. M., and C. Schär, 2010: Consistent geographical patterns of changes in Georgescu, M., D. Lobell, and C. Field, 2011: Direct climate effects of perennial high-impact European heatwaves. Nature Geosci., 3, 398 403. bioenergy crops in the United States. Proc. Natl. Acad. Sci. U.S.A., 109, 4307 Fischer, E. M., and R. Knutti, 2013: Robust projections of combined humidity and 4312. temperature extremes. Nature Clim. Change, 3, 126 130. Gerber, E. P., et al., 2012: Assessing and understanding the impact of stratospheric Fischer, E. M., D. M. Lawrence, and B. M. Sanderson, 2011: Quantifying uncertainties dynamics and variability on the Earth system. Bull. Am. Meteorol. Soc., 93, in projections of extremes A perturbed land surface parameter experiment. 845 859. Clim. Dyn., 37, 1381 1398. Gillett, N., M. Wehner, S. Tett, and A. Weaver, 2004: Testing the linearity of the Fischer, E. M., J. Rajczak, and C. Schär, 2012a: Changes in European summer response to combined greenhouse gas and sulfate aerosol forcing. Geophys. temperature variability revisited. Geophys. Res. Lett., 39, L19702. Res. Lett., 31, L14201. Fischer, E. M., K. W. Oleson, and D. M. Lawrence, 2012b: Contrasting urban and rural Gillett, N. P., and P. A. Stott, 2009: Attribution of anthropogenic influence on seasonal heat stress responses to climate change. Geophys. Res. Lett., 39, L03705. sea level pressure. Geophys. Res. Lett., 36, L23709. Flannery, B. P., 1984: Energy-balance models incorporating transport of thermal and Gillett, N. P., V. K. Arora, D. Matthews, and M. R. Allen, 2013: Constraining the ratio of latent energy. J. Atmos. Sci., 41, 414 421. global warming to cumulative CO2 emissions using CMIP5 simulations. J. Clim., Forest, C. E., P. H. Stone, and A. P. Sokolov, 2006: Estimated PDFs of climate system doi:10.1175/JCLI-D-12-00476.1. 12 properties including natural and anthropogenic forcings. Geophys. Res. Lett., 33, Gillett, N. P., V. K. Arora, K. Zickfeld, S. J. Marshall, and A. J. Merryfield, 2011: Ongoing L01705. climate change following a complete cessation of carbon dioxide emissions. Forest, C. E., P. H. Stone, and A. P. Sokolov, 2008: Constraining climate model Nature Geosci., 4, 83 87. parameters from observed 20th century changes. Tellus A, 60, 911 920. Giorgi, F., 2008: A simple equation for regional climate change and associated Forster, P., and K. Taylor, 2006: Climate forcings and climate sensitivities diagnosed uncertainty. J. Clim., 21, 1589 1604. from coupled climate model integrations. J. Clim., 19, 6181 6194. Gleckler, P. J., K. AchutaRao, J. M. Gregory, B. D. Santer, K. E. Taylor, and T. M. L. Wigley, Forster, P. M., T. Andrews, P. Good, J. M. Gregory, L. S. Jackson, and M. Zelinka, 2013: 2006: Krakatoa lives: The effect of volcanic eruptions on ocean heat content and Evaluating adjusted forcing and model spread for historical and future scenarios thermal expansion. Geophys. Res. Lett., 33, L17702. in the CMIP5 generation of climate models. J. Geophys. Res., 118, 1139 1150. Goelzer, H., P. Huybrechts, M. Loutre, H. Goosse, T. Fichefet, and A. Mouchet, 2011: Fowler, H., M. Ekstrom, S. Blenkinsop, and A. Smith, 2007a: Estimating change in Impact of Greenland and Antarctic ice sheet interactions on climate sensitivity. extreme European precipitation using a multimodel ensemble. J. Geophys. Res., Clim. Dyn., 37, 1005 1018. 112, D18104. Good, P., J. M. Gregory, and J. A. Lowe, 2011a: A step-response simple climate model Fowler, H. J., S. Blenkinsop, and C. Tebaldi, 2007b: Linking climate change modelling to reconstruct and interpret AOGCM projections. Geophys. Res. Lett., 38, L01703. to impacts studies: Recent advances in downscaling techniques for hydrological Good, P., J. M. Gregory, J. A. Lowe, and T. Andrews, 2013: Abrupt CO2 experiments modelling. Int. J. Climatol., 27, 1547 1578. as tools for predicting and understanding CMIP5 representative concentration Frame, D., B. Booth, J. Kettleborough, D. Stainforth, J. Gregory, M. Collins, and M. pathway projections. Clim. Dyn., 40, 1041 1053. Allen, 2005: Constraining climate forecasts: The role of prior assumptions. Good, P., C. Jones, J. Lowe, R. Betts, B. Booth, and C. Huntingford, 2011b: Quantifying Geophys. Res. Lett., 32, L09702. environmental drivers of future tropical forest extent. J. Clim., 24, 1337 1349. Frederiksen, C. S., J. S. Frederiksen, J. M. Sisson, and S. L. Osbrough, 2011: Australian Good, P., et al., 2012: A step-response approach for predicting and understanding winter circulation and rainfall changes and projections. Int. J. Clim. Change Strat. non-linear precipitation changes. Clim. Dyn., 39, 2789 2803. Manage., 3, 170 188. 1124 Long-term Climate Change: Projections, Commitments and Irreversibility Chapter 12 Good, P., et al., 2011c: A review of recent developments in climate change science. Hansen, J., M. Sato, P. Kharecha, G. Russell, D. Lea, and M. Siddall, 2007: Climate Part I: Understanding of future change in the large-scale climate system. Prog. change and trace gases. Philos. Trans. R. Soc. A, 365, 1925 1954. Phys. Geogr., 35, 281 296. Hansen, J., et al., 1984: Climate sensitivity: Analysis of feedback mechanisms. In: Goodwin, P., R. Williams, A. Ridgwell, and M. Follows, 2009: Climate sensitivity to the Climate Processes and Climate Sensitivity [J. Hansen and T. Takahashi (eds.)]. carbon cycle modulated by past and future changes in ocean chemistry. Nature American Geophysical Union, Washington, DC, pp. 130 163. Geosci., 2, 145 150. Hansen, J., et al., 1988: Global climate changes as forecast by Goddard Institute for Goosse, H., O. Arzel, C. Bitz, A. de Montety, and M. Vancoppenolle, 2009: Increased Space Studies 3-dimensional model. J. Geophys. Res. Atmos., 93, 9341 9364. variability of the Arctic summer ice extent in a warmer climate. Geophys. Res. Hansen, J., et al., 2008: Target atmospheric CO2: Where should humanity aim? Open Lett., 36, L23702. Atmos. Sci. J., 2, 217 231. Goubanova, K., and L. Li, 2007: Extremes in temperature and precipitation around Hansen, J., et al., 2005a: Earth s energy imbalance: Confirmation and implications. the Mediterranean basin in an ensemble of future climate scenario simulations. Science, 308, 1431 1435. Global Planet. Change, 57, 27 42. Hansen, J., et al., 2005b: Efficacy of climate forcings. J. Geophys. Res., 110, D18104. Gouttevin, I., G. Krinner, P. Ciais, J. Polcher, and C. Legout, 2012: Multi-scale validation Hardiman, S., N. Butchart, T. Hinton, S. Osprey, and L. Gray, 2012: The effect of a of a new soil freezing scheme for a land-surface model with physically-based well resolved stratosphere on surface climate: Differences between CMIP5 hydrology. Cryosphere, 6, 407 430. simulations with high and low top versions of the Met Office climate model. J. Granier, C., et al., 2011: Evolution of anthropogenic and biomass burning emissions Clim., 35, 7083 7099. at global and regional scales during the 1980 2010 period. Clim. Change, 109, Hare, B., and M. Meinshausen, 2006: How much warming are we committed to and 163 190. how much can be avoided? Clim. Change, 75, 111 149. Grant, A., S. Brönnimann, and L. Haimberger, 2008: Recent Arctic warming vertical Hargreaves, J. C., A. Abe-Ouchi, and J. D. Annan, 2007: Linking glacial and future structure contested. Nature, 455, E2 E3. climates through an ensemble of GCM simulations. Clim. Past, 3, 77 87. Graversen, R., and M. Wang, 2009: Polar amplification in a coupled climate model Hargreaves, J. C., J. D. Annan, M. Yoshimori, and A. Abe-Ouchi, 2012: Can the Last with locked albedo. Clim. Dyn., 33, 629 643. Glacial Maximum constrain climate sensitivity? Geophys. Res. Lett., 39, L24702. Graversen, R., T. Mauritsen, M. Tjernstrom, E. Kallen, and G. Svensson, 2008: Vertical Harris, G. R., M. Collins, D. M. H. Sexton, J. M. Murphy, and B. B. B. Booth, 2010: structure of recent Arctic warming. Nature, 541, 53 56. Probabilistic projections for 21st century European climate. Nat. Hazards Earth Gregory, J., and M. Webb, 2008: Tropospheric adjustment induces a cloud component Syst. Sci., 10, 2009 2020. in CO2 forcing. J. Clim., 21, 58 71. Harris, G. R., D. M. H. Sexton, B. B. B. Booth, M. Collins, J. M. Murphy, and M. J. Gregory, J., and P. Forster, 2008: Transient climate response estimated from radiative Webb, 2006: Frequency distributions of transient regional climate change from forcing and observed temperature change. J. Geophys. Res., 113, D23105. perturbed physics ensembles of general circulation model simulations. Clim. Gregory, J. M., 2010: Long-term effect of volcanic forcing on ocean heat content. Dyn., 27, 357 375. Geophys. Res. Lett., 37, L22701. Hartmann, D. L., and K. Larson, 2002: An important constraint on tropical cloud- Gregory, J. M., and J. F. B. Mitchell, 1995: Simulation of daily variability of surface- climate feedback. Geophys. Res. Lett., 29, 1951. temperature and precipitation over Europe in the current and 2xCO2 climates Harvey, B. J., L. C. Shaffrey, T. J. Woollings, G. Zappa, and K. I. Hodges, 2012: How using the UKMO climate model. Q. J. R. Meteorol. Soc., 121, 1451 1476. large are projected 21st century storm track changes? Geophys. Res. Lett., 39, Gregory, J. M., and R. Tailleux, 2011: Kinetic energy analysis of the response of the L18707. Atlantic meridional overturning circulation to CO2 forced climate change. Clim. Haugen, J., and T. Iversen, 2008: Response in extremes of daily precipitation and Dyn., 37, 893 914. wind from a downscaled multi-model ensemble of anthropogenic global climate Gregory, J. M., C. D. Jones, P. Cadule, and P. Friedlingstein, 2009: Quantifying carbon change scenarios. Tellus A, 60, 411 426. cycle feedbacks. J. Clim., 22, 5232 5250. Hawkins, E., and R. Sutton, 2009: The potential to narrow uncertainty in regional Gregory, J. M., et al., 2004: A new method for diagnosing radiative forcing and climate predictions. Bull. Am. Meteorol. Soc., 90, 1095 1107. climate sensitivity. Geophys. Res. Lett., 31, L03205. Hawkins, E., and R. Sutton, 2011: The potential to narrow uncertainty in projections Gregory, J. M., et al., 2005: A model intercomparison of changes in the Atlantic of regional precipitation change. Clim. Dyn., 37, 407 418. thermohaline circulation in response to increasing atmospheric CO2 Hawkins, E., R. Smith, L. Allison, J. Gregory, T. Woollings, H. Pohlmann, and B. de concentration. Geophys. Res. Lett., 32, L12703. Cuevas, 2011: Bistability of the Atlantic overturning circulation in a global Grubb, M., 1997: Technologies, energy systems and the timing of CO2 emissions climate model and links to ocean freshwater transport. Geophys. Res. Lett., 38, abatement An overview of economic issues. Energy Policy, 25, 159 172. L16699. Gumpenberger, M., et al., 2010: Predicting pan-tropical climate change induced Hazeleger, W., et al., 2013: Multiyear climate predictions using two initialisation forest stock gains and losses-implications for REDD. Environ. Res. Lett., 5, strategies. Geophys. Res. Lett., doi:10.1002/grl.50355. 014013. Hegerl, G., T. Crowley, W. Hyde, and D. Frame, 2006: Climate sensitivity constrained 12 Gutowski, W., K. Kozak, R. Arritt, J. Christensen, J. Patton, and E. Takle, 2007: A by temperature reconstructions over the past seven centuries. Nature, 440, possible constraint on regional precipitation intensity changes under global 1029 1032. warming. J. Hydrometeorol., 8, 1382 1396. Hegerl, G. C., F. W. Zwiers, P. A. Stott, and V. V. Kharin, 2004: Detectability of Haarsma, R. J., F. Selten, and G. J. van Oldenborgh, 2013: Anthropogenic changes of anthropogenic changes in annual temperature and precipitation extremes. J. the thermal and zonal flow structure over Western Europe and Eastern North Clim., 17, 3683 3700. Atlantic in CMIP3 and CMIP5 models. Clim. Dyn., doi:10.1007/s00382 013- Held, I., and B. Soden, 2006: Robust responses of the hydrological cycle to global 1734-8. warming. J. Clim., 19, 5686 5699. Haarsma, R. J., F. Selten, B. V. Hurk, W. Hazeleger, and X. L. Wang, 2009: Drier Held, I. M., M. Winton, K. Takahashi, T. Delworth, F. R. Zeng, and G. K. Vallis, 2010: Mediterranean soils due to greenhouse warming bring easterly winds over Probing the fast and slow components of global warming by returning abruptly summertime central Europe. Geophys. Res. Lett., 36, L04705. to preindustrial forcing. J. Clim., 23, 2418 2427. Hajima, T., T. Ise, K. Tachiiri, E. Kato, S. Watanabe, and M. Kawamiya, 2012: Climate Hellmer, H. H., F. Kauker, R. Timmermann, J. Determann, and J. Rae, 2012: Twenty- change, allowable emission, and Earth system response to representative first-century warming of a large Antarctic ice-shelf cavity by a redirected coastal concentration pathway scenarios. J. Meteorol. Soc. Jpn., 90, 417 433. current. Nature, 484, 225 228. Hall, A., 2004: The role of surface albedo feedback in climate. J. Clim., 17, 1550 1568. Henderson-Sellers, A., P. Irannejad, and K. McGuffie, 2008: Future desertification Hall, A., X. Qu, and J. Neelin, 2008: Improving predictions of summer climate change and climate change: The need for land-surface system evaluation improvement. in the United States. Geophys. Res. Lett., 35, L01702. Global and Planetary Change, 64, 129 138. Hansen, J., M. Sato, P. Kharecha, and K. von Schuckmann, 2011: Earth s energy Hibbard, K. A., G. A. Meehl, P. A. Cox, and P. Friedlingstein, 2007: A strategy for imbalance and implications. Atmos. Chem. Phys., 11, 13421 13449. climate change stabilization experiments. EOS Transactions AGU, 88, 217 221. Hansen, J., G. Russell, A. Lacis, I. Fung, D. Rind, and P. Stone, 1985: Climate response- Hirschi, M., et al., 2011: Observational evidence for soil-moisture impact on hot times Dependence on climate sensitivity and ocean mixing. Science, 229, extremes in southeastern Europe. Nature Geosci., 4, 17 21. 857 859. 1125 Chapter 12 Long-term Climate Change: Projections, Commitments and Irreversibility Ho, C. K., D. B. Stephenson, M. Collins, C. A. T. Ferro, and S. J. Brown, 2012: Calibration IPCC, 2000: IPCC Special Report on Emissions Scenarios. Prepared by Working Group strategies: A source of additional uncertainty in climate change projections. Bull. III of the Intergovernmental Panel on Climate Change. Cambridge University Am. Meteorol. Soc., 93, 21 26. Press, Cambridge, United Kingdom, and New York, NY, USA. Hodson, D. L. R., S. P. E. Keeley, A. West, J. Ridley, E. Hawkins, and H. T. Hewitt, IPCC, 2001: Climate Change 2001: The Scientific Basis. Contribution of Working 2012: Identifying uncertainties in Arctic climate change projections. Clim. Dyn., Group I to the Third Assessment Report of the Intergovernmental Panel on doi:10.1007/s00382-012-1512-z. Climate Change [J. T. Houghton, Y. Ding, D. J. Griggs, M. Noquer, P. J. van der Hoelzmann, P., D. Jolly, S. Harrison, F. Laarif, R. Bonnefille, and H. Pachur, 1998: Mid- Linden, X. Dai, K. Maskell and C. A. Johnson (eds.)]. Cambridge University Press, Holocene land-surface conditions in northern Africa and the Arabian Peninsula: Cambridge, United Kingdom and New York, NY, USA, 881 pp. A data set for the analysis of biogeophysical feedbacks in the climate system. IPCC, 2007: Climate Change 2007: The Physical Science Basis. Contribution of Global Biogeochem. Cycles, 12, 35 51. Working Group I to the Fourth Assessment Report of the Intergovernmental Hoerling, M., J. Eischeid, and J. Perlwitz, 2010: Regional precipitation trends: Panel on Climate Change [Solomon, S., D. Qin, M. Manning, Z. Chen, M. Marquis, Distinguishing natural variability from anthropogenic forcing. J. Clim., 23, K. B. Averyt, M. Tignor and H. L. Miller (eds.)]. Cambridge University Press, 2131 2145. Cambridge, United Kingdom and New York, NY, USA, 996 pp. Hoerling, M. P., J. K. Eischeid, X.-W. Quan, H. F. Diaz, R. S. Webb, R. M. Dole, and D. R. Ishizaki, Y., et al., 2012: Temperature scaling pattern dependence on representative Easterling, 2012: Is a transition to semipermanent drought conditions imminent concentration pathway emission scenarios. Clim. Change, 112, 535 546. in the US Great Plains? J. Clim., 25, 8380 8386. Iversen, T., et al., 2013: The Norwegian Earth System Model, NorESM1 M Part Hofmann, M., and S. Rahmstorf, 2009: On the stability of the Atlantic meridional 2: Climate response and scenario projections. Geosci. Model Dev., 6, 389 415. overturning circulation. Proc. Natl. Acad. Sci. U.S.A., 106, 20584 20589. Jackson, C. S., M. K. Sen, G. Huerta, Y. Deng, and K. P. Bowman, 2008: Error reduction Hogg, E., and A. Schwarz, 1997: Regeneration of planted conifers across climatic and convergence in climate prediction. J. Clim., 21, 6698 6709. moisture gradients on the Canadian prairies: Implications for distribution and Jaeger, C., and J. Jaeger, 2010: Three views of two degrees. Clim. Change Econ., 3, climate change. J. Biogeogr., 24, 527 534. 145 166. Holden, P. B., and N. R. Edwards, 2010: Dimensionally reduced emulation of an Jiang, X., S. J. Eichelberger, D. L. Hartmann, R. Shia, and Y. L. Yung, 2007: Influence of AOGCM for application to integrated assessment modelling. Geophys. Res. Lett., doubled CO2 on ozone via changes in the Brewer-Dobson circulation. J. Atmos. 37, L21707. Sci., 64, 2751 2755. Holland, M., C. Bitz, and B. Tremblay, 2006: Future abrupt reductions in the summer Johanson, C. M., and Q. Fu, 2009: Hadley Cell widening: Model simulations versus Arctic sea ice. Geophys. Res. Lett., 33, L23503. observations. J. Clim., 22, 2713 2725. Holland, M., M. Serreze, and J. Stroeve, 2010: The sea ice mass budget of the Arctic Johns, T. C., et al., 2011: Climate change under aggressive mitigation: The ENSEMBLES and its future change as simulated by coupled climate models. Clim. Dyn., 34, multi-model experiment. Clim. Dyn., 37, 1975 2003. 185 200. Johnson, N. C., and S.-P. Xie, 2010: Changes in the sea surface temperature threshold Holland, M. M., and C. M. Bitz, 2003: Polar amplification of climate change in for tropical convection. Nature Geosci., 3, 842 845. coupled models. Clim. Dyn., 21, 221 232. Jones, A., J. Haywood, and O. Boucher, 2007: Aerosol forcing, climate response and Holland, M. M., C. M. Bitz, B. Tremblay, and D. A. Bailey, 2008: The role of natural climate sensitivity in the Hadley Centre climate model. J. Geophys. Res., 112, versus forced change in future rapid summer Arctic ice loss. In: Arctic Sea D20211. Ice Decline: Observations, Projections, Mechanisms, and Implications [E. T. Jones, C., P. Cox, and C. Huntingford, 2006: Climate-carbon cycle feedbacks under DeWeaver, C. M. Bitz and L. B. Tremblay (eds.)]. American Geophysical Union, stabilization: Uncertainty and observational constraints. Tellus B, 58, 603 613. Washington, DC, pp. 133 150. Jones, C., J. Lowe, S. Liddicoat, and R. Betts, 2009: Committed terrestrial ecosystem Hu, A., G. Meehl, W. Han, and J. Yin, 2009: Transient response of the MOC and climate changes due to climate change. Nature Geosci., 2, 484 487. to potential melting of the Greenland ice sheet in the 21st century. Geophys. Jones, C. D., et al., 2013: 21st Century compatible CO2 emissions and airborne Res. Lett., 36, L10707. fraction simulated by CMIP5 Earth System models under 4 Representative Hu, Y., and Q. Fu, 2007: Observed poleward expansion of the Hadley circulation since Concentration Pathways. J. Clim., doi:10.1175/JCLI-D-12-00554.1. 1979. Atmos. Chem. Phys., 7, 5229 5236. Jones, C. D., et al., 2011: The HadGEM2 ES implementation of CMIP5 centennial Hu, Z.-Z., M. Latif, E. Roeckner, and L. Bengtsson, 2000: Intensified Asian summer simulations. Geosci. Model Dev., 4, 543 570. monsoon and its variability in a coupled model forced by increasing greenhouse Joos, F., et al., 2013: Carbon dioxide and climate impulse response functions for the gas concentrations. Geophys. Res. Lett., 27, 2681 2684. computation of greenhouse gas metrics: A multi-model analysis. Atmos. Chem. Hu, Z. Z., A. Kumar, B. Jha, and B. H. Huang, 2012: An analysis of forced and internal Phys., 13, 2793 2825. variability in a warmer climate in CCSM3. J. Clim., 25, 2356 2373. Joshi, M., E. Hawkins, R. Sutton, J. Lowe, and D. Frame, 2011: Projections of when Huang, P., S.-P. Xie, K. Hu, G. Huang, and R. Huang, 2013: Patterns of the seasonal temperature change will exceed 2°C above pre-industrial levels. Nature Clim. 12 response of tropical rainfall to global warming. Nature Geosci., 6, 357 361. Change, 1, 407 412. Huete, A. R., et al., 2006: Amazon rainforests green-up with sunlight in dry season. Joshi, M., K. Shine, M. Ponater, N. Stuber, R. Sausen, and L. Li, 2003: A comparison Geophys. Res. Lett., 33, L06405. of climate response to different radiative forcings in three general circulation Huisman, S., M. den Toom, H. Dijkstra, and S. Drijfhout, 2010: An indicator of the models: Towards an improved metric of climate change. Clim. Dyn., 20, 843 854. multiple equilibria regime of the Atlantic meridional overturning circulation. J. Joshi, M. M., F. H. Lambert, and M. J. Webb, 2013: An explanation for the difference Phys. Oceanogr., 40, 551 567. between twentieth and twenty-first century land sea warming ratio in climate Huntingford, C., and P. M. Cox, 2000: An analogue model to derive additional climate models. Clim. Dyn., doi:10.1007/s00382-013-1664-5. change scenarios from existing GCM simulations. Clim. Dyn., 16, 575 586. Joshi, M. M., M. J. Webb, A. C. Maycock, and M. Collins, 2010: Stratospheric water Huntingford, C., J. Lowe, B. Booth, C. Jones, G. Harris, L. Gohar, and P. Meir, 2009: vapour and high climate sensitivity in a version of the HadSM3 climate model. Contributions of carbon cycle uncertainty to future climate projection spread. Atmos. Chem. Phys., 10, 7161 7167. Tellus B, 61, 355 360. Joshi, M. M., J. M. Gregory, M. J. Webb, D. M. H. Sexton, and T. C. Johns, 2008: Huntingford, C., et al., 2008: Towards quantifying uncertainty in predictions of Mechanisms for the land/sea warming contrast exhibited by simulations of Amazon dieback . Philos. Trans. R. Soc. B, 363, 1857 1864. climate change. Clim. Dyn., 30, 455 465. Huntingford, C., et al., 2013: Simulated resilience of tropical rainforests to CO2 Jun, M., R. Knutti, and D. W. Nychka, 2008: Spatial analysis to quantify numerical induced climate change. Nature Geosci., 6, 268 273. model bias and dependence: How many climate models are there? J. Am. Stat. Hurtt, G., et al., 2011: Harmonization of land-use scenarios for the period 1500 Assoc. Appl. Case Stud., 103, 934 947. 2100: 600 years of global gridded annual land-use transitions, wood harvest, Jungclaus, J., H. Haak, M. Esch, E. Röckner, and J. Marotzke, 2006: Will Greenland and resulting secondary lands. Clim. Change, 109, 117 161. melting halt the thermohaline circulation? Geophys. Res. Lett., 33, L17708. Hwang, Y.-T., D. M. W. D.M.W. Frierson, B. J. Soden, and I. M. Held, 2011: Corrigendum Kang, S. M., and I. M. Held, 2012: Tropical precipitation, SSTs and the surface energy for Held and Soden (2006). J. Clim., 24, 1559 1560. budget: A zonally symmetric perspective. Clim. Dyn., 38, 1917 1924. Kang, S. M., L. M. Polvani, J. C. Fyfe, and M. Sigmond, 2011: Impact of polar ozone depletion on subtropical precipitation. Science, 332, 951 954. 1126 Long-term Climate Change: Projections, Commitments and Irreversibility Chapter 12 Karpechko, A. Y., and E. Manzini, 2012: Stratospheric influence on tropospheric Knutti, R., G. Abramowitz, M. Collins, V. Eyring, P. J. Gleckler, B. Hewitson, and L. climate change in the Northern Hemisphere. J. Geophys. Res., 117, D05133. Mearns, 2010b: Good practice guidance paper on assessing and combining Kattenberg, A., et al., 1996: Climate models Projections of future climate. In: multi model climate projections. Meeting Report of the Intergovernmental Panel Climate Change 1995: The Science of Climate Change. Contribution of WGI on Climate Change Expert Meeting on Assessing and Combining Multi-Model to the Second Assessment Report of the Intergovernmental Panel on Climate Climate Projections. IPCC Working Group I Technical Support Unit, University of Change [J. T. Houghton, L. G. Meira . A. Callander, N. Harris, A. Kattenberg and Bern, Bern, Switzerland. K. Maskell (eds.)]. Cambridge University Press, Cambridge, United Kingdom, and Knutti, R., et al., 2008b: A review of uncertainties in global temperature projections New York, NY, USA, pp. 285 357. over the twenty-first century. J. Clim., 21, 2651 2663. Kawase, H., T. Nagashima, K. Sudo, and T. Nozawa, 2011: Future changes in Kodra, E., K. Steinhaeuser, and A. R. Ganguly, 2011: Persisting cold extremes under tropospheric ozone under Representative Concentration Pathways (RCPs). 21st-century warming scenarios. Geophys. Res. Lett., 38, L08705. Geophys. Res. Lett., 38, L05801. Kolomyts, E., and N. Surova, 2010: Predicting the impact of global warming on soil Kay, J., M. Holland, and A. Jahn, 2011: Inter-annual to multi-decadal Arctic sea ice water resources in marginal forests of the middle Volga region. Water Resour., extent trends in a warming world. Geophys. Res. Lett., 38, L15708. 37, 89 101. Kay, J. E., M. M. Holland, C. Bitz, E. Blanchard-Wrigglesworth, A. Gettelman, A. Komuro, Y., et al., 2012: Sea-ice in twentieth-century simulations by new MIROC Conley, and D. Bailey, 2012: The influence of local feedbacks and northward heat coupled models: A comparison between models with high resolution and with transport on the equilibrium Arctic climate response to increased greenhouse ice thickness distribution. J. Meteorol. Soc. Jpn., 90A, 213 232. gas forcing in coupled climate models. J. Clim., 25, 5433 5450. Körper, J., et al., 2013: The effects of aggressive mitigation on steric sea level rise and Kaye, N., A. Hartley, and D. Hemming, 2012: Mapping the climate: Guidance on sea ice changes. Clim. Dyn., 40, 531 550. appropriate techniques to map climate variables and their uncertainty. Geosci. Koster, R., Z. Guo, R. Yang, P. Dirmeyer, K. Mitchell, and M. Puma, 2009a: On the Model Dev., 5, 245 256. nature of soil moisture in land surface models. J. Clim., 22, 4322 4335. Kellomaki, S., M. Maajarvi, H. Strandman, A. Kilpelainen, and H. Peltola, 2010: Model Koster, R., et al., 2006: GLACE: The Global Land-Atmosphere Coupling Experiment. computations on the climate change effects on snow cover, soil moisture and Part I: Overview. J. Hydrometeorol., 7, 590 610. soil frost in the boreal conditions over Finland. Silva Fennica, 44, 213 233. Koster, R. D., S. D. Schubert, and M. J. Suarez, 2009b: Analyzing the concurrence Kendon, E., D. Rowell, and R. Jones, 2010: Mechanisms and reliability of future of meteorological droughts and warm periods, with implications for the projected changes in daily precipitation. Clim. Dyn., 35, 489 509. determination of evaporative regime. J. Clim., 22, 3331 3341. Kendon, E., D. Rowell, R. Jones, and E. Buonomo, 2008: Robustness of future changes Koster, R. D., H. L. Wang, S. D. Schubert, M. J. Suarez, and S. Mahanama, 2009c: in local precipitation extremes. J. Clim., 17, 4280 4297. Drought-induced warming in the continental United States under different SST Kharin, V. V., F. W. Zwiers, X. B. Zhang, and G. C. Hegerl, 2007: Changes in temperature regimes. J. Clim., 22, 5385 5400. and precipitation extremes in the IPCC ensemble of global coupled model Koven, C., P. Friedlingstein, P. Ciais, D. Khvorostyanov, G. Krinner, and C. Tarnocai, 2009: simulations. J. Clim., 20, 1419 1444. On the formation of high-latitude soil carbon stocks: Effects of cryoturbation Kharin, V. V., F. W. Zwiers, X. Zhang, and M. Wehner, 2013: Changes in temperature and insulation by organic matter in a land surface model. Geophys. Res. Lett., and precipitation extremes in the CMIP5 ensemble. Clim. Change, doi:10.1007/ 36, L21501. s10584-013-0705-8. Koven, C. D., W. J. Riley, and A. Stern, 2013: Analysis of permafrost thermal dynamics Khvorostyanov, D., P. Ciais, G. Krinner, and S. Zimov, 2008: Vulnerability of east and response to climate change in the CMIP5 Earth system models. J. Clim., 26, Siberia s frozen carbon stores to future warming. Geophys. Res. Lett., 35, L10703. 1877 1900. Kidston, J., and E. P. Gerber, 2010: Intermodel variability of the poleward shift of the Koven, C. D., et al., 2011: Permafrost carbon-climate feedbacks accelerate global austral jet stream in the CMIP3 integrations linked to biases in 20th century warming. Proc. Natl. Acad. Sci. U.S.A., 108, 14769 14774. climatology. Geophys. Res. Lett., 37, L09708. Kripalani, R., J. Oh, A. Kulkarni, S. Sabade, and H. Chaudhari, 2007: South Asian Kienzle, S., M. Nemeth, J. Byrne, and R. MacDonald, 2012: Simulating the hydrological summer monsoon precipitation variability: Coupled climate model simulations impacts of climate change in the upper North Saskatchewan River basin, Alberta, and projections under IPCC AR4. Theor. Appl. Climatol., 90, 133 159. Canada. J. Hydrol., 412, 76 89. Kug, J., D. Choi, F. Jin, W. Kwon, and H. Ren, 2010: Role of synoptic eddy feedback Kirkevag, K., et al., 2013: Aerosol climate interactions in the Norwegian Earth on polar climate responses to the anthropogenic forcing. Geophys. Res. Lett., System Model NorESM1 M. Geosci. Model Dev., 6, 207 244. 37, L14704. Kitoh, A., S. Yukimoto, A. Noda, and T. Motoi, 1997: Simulated changes in the Asian Kuhlbrodt, T., and J. M. Gregory, 2012: Ocean heat uptake and its consequences for summer monsoon at times of increased atmospheric CO2. J. Meteorol. Soc. Jpn., the magnitude of sea level rise and climate change. Geophys. Res. Lett., 39, 75, 1019 1031. L18608. Kjellstrom, E., L. Barring, D. Jacob, R. Jones, G. Lenderink, and C. Schär, 2007: Kuhry, P., E. Dorrepaal, G. Hugelius, E. Schuur, and C. Tarnocai, 2010: Potential Modelling daily temperature extremes: Recent climate and future changes over remobilization of belowground permafrost carbon under future global warming. 12 Europe. Clim. Change, 81, 249 265. Permafr. Periglac. Process., 21, 208 214. Knutti, R., 2010: The end of model democracy? Clim. Change, 102, 395 404. Kumar, A., et al., 2010: Contribution of sea ice loss to Arctic amplification. Geophys. Knutti, R., and G. C. Hegerl, 2008: The equilibrium sensitivity of the Earth s Res. Lett., 37, L21701. temperature to radiation changes. Nature Geosci., 1, 735 743. Kunkel, K. E., T. R. Karl, D. R. Easterling, K. Redmond, J. Young, X. Yin, and P. Hennon, Knutti, R., and L. Tomassini, 2008: Constraints on the transient climate response 2013: Probable Maximum Precipitation (PMP) and climate change. Geophys. from observed global temperature and ocean heat uptake. Geophys. Res. Lett., Res. Lett., 40, 1402 1408. 35, L09701. Kysely, J., and R. Beranova, 2009: Climate-change effects on extreme precipitation Knutti, R., and G.-K. Plattner, 2012: Comment on Why hasn t Earth warmed as much in central Europe: Uncertainties of scenarios based on regional climate models. as expected? by Schwartz et al. 2010. J. Clim., 25, 2192 2199. Theor. Appl. Climatol., 95, 361 374. Knutti, R., and J. Sedláèek, 2013: Robustness and uncertainties in the new CMIP5 Lamarque, J.-F., et al., 2011: Global and regional evolution of short-lived radiatively- climate model projections. Nature Clim. Change, 3, 369 373. active gases and aerosols in the Representative Concentration Pathways. Clim. Knutti, R., D. Masson, and A. Gettelman, 2013: Climate model genealogy: Generation Change, 109, 191 212. CMIP5 and how we got there. Geophys. Res. Lett., 40, 1194 1199. Lamarque, J., 2008: Estimating the potential for methane clathrate instability in the Knutti, R., S. Krähenmann, D. Frame, and M. Allen, 2008a: Comment on Heat 1%-CO2 IPCC AR-4 simulations. Geophys. Res. Lett., 35, L19806. capacity, time constant, and sensitivity of Earth s climate system by S. E. Lamarque, J., et al., 2010: Historical (1850 2000) gridded anthropogenic and Schwartz. J. Geophys. Res., 113, D15103. biomass burning emissions of reactive gases and aerosols: Methodology and Knutti, R., F. Joos, S. Müller, G. Plattner, and T. Stocker, 2005: Probabilistic climate application. Atmos. Chem. Phys., 10, 7017 7039. change projections for CO2 stabilization profiles. Geophys. Res. Lett., 32, L20707. Lamarque, J. F., et al., 2013: The Atmospheric Chemistry and Climate Model Knutti, R., R. Furrer, C. Tebaldi, J. Cermak, and G. A. Meehl, 2010a: Challenges in Intercomparison Project (ACCMIP): Overview and description of models, combining projections from multiple climate models. J. Clim., 23, 2739 2758. simulations and climate diagnostics. Geosci. Model Dev., 6, 179 206. 1127 Chapter 12 Long-term Climate Change: Projections, Commitments and Irreversibility Lambert, F., and M. Webb, 2008: Dependency of global mean precipitation on surface Li, F., J. Austin, and J. Wilson, 2008: The strength of the Brewer-Dobson circulation temperature. Geophys. Res. Lett., 35, L16706. in a changing climate: Coupled chemistry-climate model simulations. J. Clim., Lambert, F. H., and J. C. H. Chiang, 2007: Control of land-ocean temperature contrast 21, 40 57. by ocean heat uptake. Geophys. Res. Lett., 34, L13704. Li, F., W. Collins, M. Wehner, D. Williamson, J. Olson, and C. Algieri, 2011a: Impact of Lambert, F. H., M. J. Webb, and M. J. Joshi, 2011: The relationship between land-ocean horizontal resolution on simulation of precipitation extremes in an aqua-planet surface temperature contrast and radiative forcing. J. Clim., 24, 3239 3256. version of Community Atmospheric Model (CAM3). Tellus, 63, 884 892. Lambert, F. H., N. P. Gillett, D. A. Stone, and C. Huntingford, 2005: Attribution studies Li, L., X. Jiang, M. Chahine, E. Olsen, E. Fetzer, L. Chen, and Y. Yung, 2011b: The of observed land precipitation changes with nine coupled models. Geophys. Res. recycling rate of atmospheric moisture over the past two decades (1988 2009). Lett., 32, L18704. Environ. Res. Lett., 6, 034018. Lambert, F. H., G. R. Harris, M. Collins, J. M. Murphy, D. M. H. Sexton, and B. B. B. Booth, Li, L. J., et al., 2013c: The Flexible Global Ocean-Atmosphere-Land System Model: 2012: Interactions between perturbations to different Earth system components Grid-point Version 2: FGOALS-g2. Adv. Atmos. Sci., 30, 543 560. simulated by a fully-coupled climate model. Clim. Dyn., doi:10.1007/s00382- Liepert, B. G., and M. Previdi, 2009: Do models and observations disagree on the 012-1618-3. rainfall response to global warming? J. Clim., 22, 3156 3166. Langen, P. L., and V. A. Alexeev, 2007: Polar amplification as a preferred response in Liepert, B. G., and M. Previdi, 2012: Inter-model variability and biases of the global an idealized aquaplanet GCM. Clim. Dyn., 29, 305 317. water cycle in CMIP3 coupled climate models. Environ. Res. Lett., 7, 014006. Langen, P. L., A. M. Solgaard, and C. S. Hvidberg, 2012: Self-inhibiting growth of the Lim, E. P., and I. Simmonds, 2009: Effect of tropospheric temperature change on the Greenland Ice Sheet. Geophys. Res. Lett., 39, L12502. zonal mean circulation and SH winter extratropical cyclones. Clim. Dyn., 33, Lapola, D. M., M. D. Oyama, and C. A. Nobre, 2009: Exploring the range of climate 19 32. biome projections for tropical South America: The role of CO2 fertilization and Lindsay, R., and J. Zhang, 2005: The thinning of Arctic sea ice, 1988 2003: Have we seasonality. Global Biogeochem. Cycles, 23, GB3003. passed a tipping point? J. Clim., 18, 4879 4894. Lau, K., M. Kim, and K. Kim, 2006: Asian summer monsoon anomalies induced by Liu, Z., S. J. Vavrus, F. He, N. Wen, and Y. Zhong, 2005: Rethinking tropical ocean aerosol direct forcing: The role of the Tibetan Plateau. Clim. Dyn., 26, 855 864. response to global warming: The enhanced equatorial warming. J. Clim., 18, Lawrence, D., and A. Slater, 2010: The contribution of snow condition trends to future 4684 4700. ground climate. Clim. Dyn., 34, 969 981. Livina, V. N., and T. M. Lenton, 2013: A recent tipping point in the Arctic sea-ice cover: Lawrence, D., A. Slater, and S. Swenson, 2012: Simulation of present-day and future Abrupt and persistent increase in the seasonal cycle since 2007. Cryosphere, 7, permafrost and seasonally frozen ground conditions in CCSM4. J. Clim., 25, 275 286. 2207 2225. Loarie, S. R., D. B. Lobell, G. P. Asner, Q. Z. Mu, and C. B. Field, 2011: Direct impacts Lawrence, D., A. Slater, V. Romanovsky, and D. Nicolsky, 2008a: Sensitivity of a on local climate of sugar-cane expansion in Brazil. Nature Clim. Change, 1, model projection of near-surface permafrost degradation to soil column depth 105 109. and representation of soil organic matter. J. Geophys. Res. Earth Surface, 113, Loeb, N. G., et al., 2009: Toward optimal closure of the Earth s Top-of-Atmosphere F02011. radiation budget. J. Clim., 22, 748 766. Lawrence, D., A. Slater, R. Tomas, M. Holland, and C. Deser, 2008b: Accelerated Arctic Long, M. C., K. Lindsay, S. Peacock, J. K. Moore, and S. C. Doney, 2013: Twentieth- land warming and permafrost degradation during rapid sea ice loss. Geophys. century oceanic carbon uptake and storage in CESM1(BGC). J. Clim., doi:10.1175/ Res. Lett., 35, L11506. JCLI-D-12-00184.1. Lean, J., and D. Rind, 2009: How will Earth s surface temperature change in future Lorenz, D. J., and E. T. DeWeaver, 2007: Tropopause height and zonal wind response decades? Geophys. Res. Lett., 36, L15708. to global warming in the IPCC scenario integrations. J. Geophys. Res. Atmos., Lee, S., T. Gong, N. Johnson, S. B. Feldstein, and D. Pollard, 2011: On the possible 112, D10119. link between tropical convection and the Northern Hemisphere Arctic surface air Lowe, J., C. Huntingford, S. Raper, C. Jones, S. Liddicoat, and L. Gohar, 2009: How temperature change between 1958 and 2001. J. Clim., 24, 4350 4367. difficult is it to recover from dangerous levels of global warming? Environ. Res. Lefebvre, W., and H. Goosse, 2008: Analysis of the projected regional sea-ice changes Lett., 4, 014012. in the Southern Ocean during the twenty-first century. Clim. Dyn., 30, 59 76. Lowe, J. A., and J. M. Gregory, 2006: Understanding projections of sea level rise in a Lemoine, D. M., 2010: Climate sensitivity distributions dependence on the possibility Hadley Centre coupled climate model. J. Geophys. Res., 111, C11014. that models share biases. J. Clim., 23, 4395 4415. Lu, J., and M. Cai, 2009: Seasonality of polar surface warming amplification in Lenderink, G., and E. Van Meijgaard, 2008: Increase in hourly precipitation extremes climate simulations. Geophys. Res. Lett., 36, L16704. beyond expectations from temperature changes. Nature Geosci., 1, 511 514. Lu, J., G. Vecchi, and T. Reichler, 2007: Expansion of the Hadley cell under global Lenderink, G., A. van Ulden, B. van den Hurk, and E. van Meijgaard, 2007: warming. Geophys. Res. Lett., 34, L06805. Summertime inter-annual temperature variability in an ensemble of regional Lu, J., G. Chen, and D. Frierson, 2008: Response of the zonal mean atmospheric 12 model simulations: Analysis of the surface energy budget. Clim. Change, 81, circulation to El Nino versus global warming. J. Clim., 21, 5835 5851. 233 247. Lucht, W., S. Schaphoff, T. Ebrecht, U. Heyder, and W. Cramer, 2006: Terrestrial Lenton, T., H. Held, E. Kriegler, J. Hall, W. Lucht, S. Rahmstorf, and H. Schellnhuber, vegetation redistribution and carbon balance under climate change. Carbon 2008: Tipping elements in the Earth s climate system. Proc. Natl. Acad. Sci. U.S.A., Balance Manage., 1, 1-6. 105, 1786 1793. Lunt, D., A. Haywood, G. Schmidt, U. Salzmann, P. Valdes, and H. Dowsett, 2010: Lenton, T. M., 2012: Arctic climate tipping points. Ambio, 41, 10 22. Earth system sensitivity inferred from Pliocene modelling and data. Nature Leslie, L. M., M. Leplastrier, and B. W. Buckley, 2008: Estimating future trends in Geosci., 3, 60 64. severe hailstorms over the Sydney Basin: A climate modelling study. Atmos. Res., Luo, J. J., W. Sasaki, and Y. Masumoto, 2012: Indian Ocean warming modulates Pacific 87, 37 51. climate change. Proc. Natl. Acad. Sci. U.S.A., 109, 18701 18706. Levermann, A., J. Schewe, V. Petoukhov, and H. Held, 2009: Basic mechanism for Lustenberger, A., R. Knutti, and E. M. Fischer, 2013: The potential of pattern scaling abrupt monsoon transitions. Proc. Natl. Acad. Sci. U.S.A., 106, 20572 20577. for projecting temperature-related extreme indices. Int. J. Climatol., doi:10.1002/ Levitus, S., J. Antonov, and T. Boyer, 2005: Warming of the world ocean, 1955 2003. joc.3659. Geophys. Res. Lett., 32, L02604. Ma, J., and S.-P. Xie, 2013: Regional patterns of sea surface temperature change: Levitus, S., et al., 2012: World ocean heat content and thermosteric sea level change A source of uncertainty in future projections of precipitation and atmospheric (0 2000 m), 1955 2010. Geophys. Res. Lett., 39, L10603. circulation. J. Clim., 26, 2482 2501. Levy II, H., L. W. Horowitz, M. D. Schwarzkopf, Y. Ming, J.-C. Golaz, V. Naik, and V. Ma, J., S.-P. Xie, and Y. Kosaka, 2012: Mechanisms for tropical tropospheric circulation Ramaswamy, 2013: The roles of aerosol direct and indirect effects in past and change in response to global warming. J. Clim., 25, 2979 2994. future climate change. J. Geophys. Res., doi:10.1002/jgrd.50192. MacDougall, A. H., C. A. Avis, and A. J. Weaver, 2012: Significant contribution to Li, C., J. S. von Storch, and J. Marotzke, 2013a: Deep-ocean heat uptake and climate warming from the permafrost carbon feedback. Nature Geosci., 5, equilibrium climate response. Clim. Dyn., 40, 1071 1086. 719 721. Li, C., D. Notz, S. Tietsche, and J. Marotzke, 2013b: The transient versus the equilibrium Mahlstein, I., and R. Knutti, 2011: Ocean heat transport as a cause for model response of sea ice to global warming. J. Clim., doi:10.1175/JCLI-D-12-00492.1. uncertainty in projected Arctic warming. J. Clim., 24, 1451 1460. 1128 Long-term Climate Change: Projections, Commitments and Irreversibility Chapter 12 Mahlstein, I., and R. Knutti, 2012: September Arctic sea ice predicted to disappear McLandress, C., and T. G. Shepherd, 2009: Simulated anthropogenic changes in the near 2°C global warming above present. J. Geophys. Res., 117, D06104. Brewer-Dobson circulation, including its extension to high latitudes. J. Clim., 22, Mahlstein, I., R. Knutti, S. Solomon, and R. W. Portmann, 2011: Early onset of 1516 1540. significant local warming in low latitude countries. Environ. Res. Lett., 6, 034009. McLandress, C., T. G. Shepherd, J. F. Scinocca, D. A. Plummer, M. Sigmond, A. I. Mahlstein, I., R. W. Portmann, J. S. Daniel, S. Solomon, and R. Knutti, 2012: Perceptible Jonsson, and M. C. Reader, 2011: Separating the dynamical effects of climate changes in regional precipitation in a future climate. Geophys. Res. Lett., 39, change and ozone depletion. Part II: Southern Hemisphere troposphere. J. Clim., L05701. 24, 1850 1868. Maksym, T., S. E. Stammerjohn, S. Ackley, and R. Massom, 2012: Antarctic sea ice A Meehl, G., and W. Washington, 1993: South Asian summer monsoon variability in a polar opposite? Oceanography, 25, 140 151. model with doubled atmospheric carbon-dioxide concentration. Science, 260, Malhi, Y., et al., 2009: Exploring the likelihood and mechanism of a climate-change- 1101 1104. induced dieback of the Amazon rainforest. Proc. Natl. Acad. Sci. U.S.A., 106, Meehl, G., J. Arblaster, and C. Tebaldi, 2005a: Understanding future patterns of 20610 20615. increased precipitation intensity in climate model simulations. Geophys. Res. Manabe, S., and R. Stouffer, 1980: Sensitivity of a global climate model to an increase Lett., 32, L18719. of CO2 concentration in the atmosphere. J. Geophys. Res., 85, 5529 5554. Meehl, G., J. Arblaster, and W. Collins, 2008: Effects of black carbon aerosols on the Manabe, S., and R. T. Wetherald, 1980: Distribution of climate change resulting from Indian monsoon. J. Clim., 21, 2869 2882. an increase in CO2 content of the atmosphere. J. Atmos. Sci., 37, 99 118. Meehl, G., et al., 2012: Climate system response to external forcings and climate Manabe, S., and R. Stouffer, 1994: Multiple-century response of a coupled ocean- change projections in CCSM4. J. Clim., 25, 3661 3683. atmosphere model to an increase of atmospheric carbon-dioxide. J. Clim., 7, Meehl, G. A., and C. Tebaldi, 2004: More intense, more frequent, and longer lasting 5 23. heat waves in the 21st century. Science, 305, 994 997. Manabe, S., K. Bryan, and M. J. Spelman, 1990: Transient-response of a global Meehl, G. A., G. J. Boer, C. Covey, M. Latif, and R. J. Stouffer, 2000: The Coupled Model ocean atmosphere model to a doubling of atmospheric carbon-dioxide. J. Phys. Intercomparison Project (CMIP). Bull. Am. Meteorol. Soc., 81, 313 318. Oceanogr., 20, 722 749. Meehl, G. A., C. Tebaldi, G. Walton, D. Easterling, and L. McDaniel, 2009: Relative Manabe, S., R. J. Stouffer, M. J. Spelman, and K. Bryan, 1991: Transient responses of a increase of record high maximum temperatures compared to record low coupled ocean atmosphere model to gradual changes of atmospheric CO2. Part minimum temperatures in the U. S. Geophys. Res. Lett., 36, L23701. I: Annual mean response. J. Clim., 4, 785 818. Meehl, G. A., W. M. Washington, C. M. Ammann, J. M. Arblaster, T. M. L. Wigley, and C. Marsh, P. T., H. E. Brooks, and D. J. Karoly, 2009: Preliminary investigation into the Tebaldi, 2004: Combinations of natural and anthropogenic forcings in twentieth- severe thunderstorm environment of Europe simulated by the Community century climate. J. Clim., 17, 3721 3727. Climate System Model 3. Atmos. Res., 93, 607 618. Meehl, G. A., et al., 2005b: How much more global warming and sea level rise? Maslowski, W., J. C. Kinney, M. Higgins, and A. Roberts, 2012: The future of Arctic sea Science, 307, 1769 1772. ice. In: Annual Review of Earth and Planetary Sciences [R. Jeanloz (ed.)]. Annual Meehl, G. A., et al., 2007a: The WCRP CMIP3 multimodel dataset - A new era in Reviews, Palo Alto, CA, USA, pp. 625 654. climate change research. Bull. Am. Meteorol. Soc., 88, 1383 1394. Masson, D., and R. Knutti, 2011: Climate model genealogy. Geophys. Res. Lett., 38, Meehl, G. A., et al., 2006: Climate change projections for the twenty-first century and L08703. climate change commitment in the CCSM3. J. Clim., 19, 2597 2616. Massonnet, F., T. Fichefet, H. Goosse, C. M. Bitz, G. Philippon-Berthier, M. Holland, Meehl, G. A., et al., 2013: Climate change projections in CESM1(CAM5) compared to and P. Y. Barriat, 2012: Constraining projections of summer Arctic sea ice. CCSM4. J. Clim., doi:10.1175/JCLI-D-12 00572.1. Cryosphere, 6, 1383 1394. Meehl, G. A., et al., 2007b: Global climate projections. In: Climate Change 2007: The Matsuno, T., K. Maruyama, and J. Tsutsui, 2012a: Stabilization of atmospheric carbon Physical Science Basis. Contribution of Working Group I to the Fourth Assessment dioxide via zero emissions-An alternative way to a stable global environment. Report of the Intergovernmental Panel on Climate Change [Solomon, S., D. Qin, Part 1: Examination of the traditional stabilization concept. Proc. Jpn. Acad. B, M. Manning, Z. Chen, M. Marquis, K. B. Averyt, M. Tignor and H. L. Miller (eds.)] 88, 368 384. Cambridge University Press, Cambridge, United Kingdom and New York, NY, Matsuno, T., K. Maruyama, and J. Tsutsui, 2012b: Stabilization of atmospheric carbon USA, pp. 747 846. dioxide via zero emissions-An alternative way to a stable global environment. Meijers, A. J. S., E. Shuckburgh, N. Bruneau, J.-B. Sallee, T. J. Bracegirdle, and Z. Wang, Part 2: A practical zero-emissions scenario. Proc. Jpn. Acad. B, 88, 385 395. 2012: Representation of the Antarctic Circumpolar Current in the CMIP5 climate Matthews, H., and K. Caldeira, 2008: Stabilizing climate requires near-zero emissions. models and future changes under warming scenarios. J. Geophys. Res., 117, Geophys. Res. Lett., 35, L04705. C12008. Matthews, H., N. Gillett, P. Stott, and K. Zickfeld, 2009: The proportionality of global Meinshausen, M., S. Raper, and T. Wigley, 2011a: Emulating coupled atmosphere- warming to cumulative carbon emissions. Nature, 459, 829 832. ocean and carbon cycle models with a simpler model, MAGICC6 Part 1: Model Matthews, H. D., and K. Zickfeld, 2012: Climate response to zeroed emissions of description and calibration. Atmos. Chem. Phys., 11, 1417 1456. 12 greenhouse gases and aerosols. Nature Clim. Change, 2, 338 341. Meinshausen, M., T. Wigley, and S. Raper, 2011b: Emulating atmosphere-ocean Matthews, H. D., S. Solomon, and R. Pierrehumbert, 2012: Cumulative carbon as and carbon cycle models with a simpler model, MAGICC6 Part 2: Applications. a policy framework for achieving climate stabilization. Philos. Trans. R. Soc. A, Atmos. Chem. Phys., 11, 1457 1471. 370, 4365 4379. Meinshausen, M., B. Hare, T. Wigley, D. Van Vuuren, M. Den Elzen, and R. Swart, May, W., 2002: Simulated changes of the Indian summer monsoon under enhanced 2006: Multi-gas emissions pathways to meet climate targets. Clim. Change, 75, greenhouse gas conditions in a global time-slice experiment. Geophys. Res. 151 194. Lett., 29, 1118. Meinshausen, M., et al., 2009: Greenhouse-gas emission targets for limiting global May, W., 2008a: Climatic changes associated with a global 2°C-stabilization warming to 2°C. Nature, 458, 1158 1162. scenario simulated by the ECHAM5/MPI-OM coupled climate model. Clim. Dyn., Meinshausen, M., et al., 2011c: The RCP greenhouse gas concentrations and their 31, 283 313. extensions from 1765 to 2300. Clim. Change, 109, 213 241. May, W., 2008b: Potential future changes in the characteristics of daily precipitation Merrifield, M. A., 2011: A shift in western tropical Pacific sea level trends during the in Europe simulated by the HIRHAM regional climate model. Clim. Dyn., 30, 1990s. J. Clim., 24, 4126 4138. 581 603. Merryfield, W. J., M. M. Holland, and A. H. Monahan, 2008: Multiple equilibria and May, W., 2012: Assessing the strength of regional changes in near-surface climate abrupt transitions in Arctic summer sea ice extent. In: Arctic Sea Ice Decline: associated with a global warming of 2°C. Clim. Change, 110, 619 644. Observations, Projections, Mechanisms, and Implications. American Geophysical McCabe, G., and D. Wolock, 2007: Warming may create substantial water supply Union, Washington, DC, pp. 151 174. shortages in the Colorado River basin. Geophys. Res. Lett., 34, L22708. Mignone, B., R. Socolow, J. Sarmiento, and M. Oppenheimer, 2008: Atmospheric McCollum, D., V. Krey, K. Riahi, P. Kolp, A. Grubler, M. Makowski, and N. Nakicenovic, stabilization and the timing of carbon mitigation. Clim. Change, 88, 251 265. 2013: Climate policies can help resolve energy security and air pollution Mikolajewicz, U., M. Vizcaino, J. Jungclaus, and G. Schurgers, 2007: Effect of ice sheet challenges. Clim. Change, doi:10.1007/s10584-013-0710-y. interactions in anthropogenic climate change simulations. Geophys. Res. Lett., 34, L18706. 1129 Chapter 12 Long-term Climate Change: Projections, Commitments and Irreversibility Millner, A., R. Calel, D. A. Stainforth, and G. MacKerron, 2013: Do probabilistic expert Niinemets, U., 2010: Responses of forest trees to single and multiple environmental elicitations capture scientists uncertainty about climate change? Clim. Change, stresses from seedlings to mature plants: Past stress history, stress interactions, 116, 427 436. tolerance and acclimation. Forest Ecol. Manage., 260, 1623 1639. Milly, P., J. Betancourt, M. Falkenmark, R. Hirsch, Z. Kundzewicz, D. Lettenmaier, and Nikulin, G., E. Kjellstrom, U. Hansson, G. Strandberg, and A. Ullerstig, 2011: Evaluation R. Stouffer, 2008: Stationarity is dead: Whither water management? Science, and future projections of temperature, precipitation and wind extremes over 319, 573 574. Europe in an ensemble of regional climate simulations. Tellus A, 63, 41 55. Min, S., X. Zhang, F. Zwiers, and G. Hegerl, 2011: Human contribution to more- Nobre, C., and L. Borma, 2009: Tipping points for the Amazon forest. Curr. Opin. intense precipitation extremes. Nature, 470, 378 381. Environ. Sustain., 1, 28 36. Ming, Y., V. Ramaswamy, and G. Persad, 2010: Two opposing effects of absorbing North, G., 1984: The small ice cap instability in diffuse climate models. J. Atmos. Sci., aerosols on global-mean precipitation. Geophys. Res. Lett., 37, L13701. 41, 3390 3395. Mitas, C., and A. Clement, 2006: Recent behavior of the Hadley cell and tropical Notaro, M., 2008: Statistical identification of global hot spots in soil moisture thermodynamics in climate models and reanalyses. Geophys. Res. Lett., 33, feedbacks among IPCC AR4 models. J. Geophys. Res., 113, D09101. L01810. Notz, D., 2009: The future of ice sheets and sea ice: Between reversible retreat and Mitchell, J. F. B., 1990: Is the Holocene a good analogue for greenhouse warming? unstoppable loss. Proc. Natl. Acad. Sci. U.S.A., 106, 20590 20595. J. Clim., 3, 1177 1192. NRC, 2011: Climate Stabilization Targets: Emissions, Concentrations, and Impacts Mitchell, J. F. B., C. A. Wilson, and W. M. Cunnington, 1987: On CO2 climate sensitivity over Decades to Millennia. National Academies Press, Washington, DC, 298 pp. and model dependence of results. Q. J. R. Meteorol. Soc., 113, 293 322. O Connor, F., et al., 2010: Possible role of wetlands, permafrost, and methane Mitchell, J. F. B., T. C. Johns, W. J. Ingram, and J. A. Lowe, 2000: The effect of stabilising hydrates in the methane cycle under future climate change: A review. Rev. atmospheric carbon dioxide concentrations on global and regional climate Geophys., 48, RG4005. change. Geophys. Res. Lett., 27, 2977 2980. O Gorman, P., and T. Schneider, 2009a: Scaling of precipitation extremes over a wide Mitchell, J. F. B., T. C. Johns, M. Eagles, W. J. Ingram, and R. A. Davis, 1999: Towards the range of climates simulated with an idealized GCM. J. Clim., 22, 5676 5685. construction of climate change scenarios. Clim. Change, 41, 547 581. O Gorman, P., and T. Schneider, 2009b: The physical basis for increases in precipitation Mitchell, T. D., 2003: Pattern scaling - An examination of the accuracy of the extremes in simulations of 21st-century climate change. Proc. Natl. Acad. Sci. technique for describing future climates. Clim. Change, 60, 217 242. U.S.A., 106, 14773 14777. Mizuta, R., 2012: Intensification of extratropical cyclones associated with the polar O Gorman, P., R. Allan, M. Byrne, and M. Previdi, 2012: Energetic constraints on jet change in the CMIP5 global warming projections. Geophys. Res. Lett., 39, precipitation under climate change. Surv. Geophys., 33, 585 608. L19707. O Gorman, P. A., 2010: Understanding the varied response of the extratropical storm Monaghan, A., D. Bromwich, and D. Schneider, 2008: Twentieth century Antarctic air tracks to climate change. Proc. Natl. Acad. Sci. U.S.A., 107, 19176 19180. temperature and snowfall simulations by IPCC climate models. Geophys. Res. O Gorman, P. A., and C. J. Muller, 2010: How closely do changes in surface and Lett., 35, L07502. column water vapor follow Clausius-Clapeyron scaling in climate change Montenegro, A., V. Brovkin, M. Eby, D. Archer, and A. Weaver, 2007: Long term fate of simulations? Environ. Res. Lett., 5, 025207. anthropogenic carbon. Geophys. Res. Lett., 34, L19707. Orlowsky, B., and S. I. Seneviratne, 2012: Global changes in extreme events: Regional Morgan, M. G., and D. W. Keith, 1995: Climate-change - Subjective judgments by and seasonal dimension. Clim. Change, 110, 669 696. climate experts. Environ. Sci. Technol., 29, A468 A476. Otto, A., et al., 2013: Energy budget constraints on climate response. Nature Geosci., Moss, R. H., et al., 2010: The next generation of scenarios for climate change research 6, 415-416. and assessment. Nature, 463, 747 756. Overland, J. E., and M. Wang, 2013: When will the summer Arctic be nearly sea ice Moss, R. H., et al., 2008: Towards new scenarios for analysis of emissions, climate free? Geophys. Res. Lett., doi:10.1002/grl.50316. change, impacts, and response strategies. In: IPCC Expert Meeting Report: Overland, J. E., M. Wang, N. A. Bond, J. E. Walsh, V. M. Kattsov, and W. L. Chapman, Towards New Scenarios. Intergovernmental Panel on Climate Change, Geneva, 2011: Considerations in the selection of global climate models for regional Switzerland, 132 pp. climate projections: The Arctic as a case study. J. Clim., 24, 1583 1597. Muller, C. J., and P. A. O Gorman, 2011: An energetic perspective on the regional Oyama, M. D., and C. A. Nobre, 2003: A new climate-vegetation equilibrium state for response of precipitation to climate change. Nature Clim. Change, 1, 266 271. Tropical South America. Geophys. Res. Lett., 30, 2199. Murphy, D. M., S. Solomon, R. W. Portmann, K. H. Rosenlof, P. M. Forster, and T. Wong, Padilla, L., G. Vallis, and C. Rowley, 2011: Probabilistic estimates of transient climate 2009: An observationally based energy balance for the Earth since 1950. J. sensitivity subject to uncertainty in forcing and natural variability. J. Clim., 24, Geophys. Res., 114, D17107. 5521 5537. Murphy, J., D. Sexton, D. Barnett, G. Jones, M. Webb, and M. Collins, 2004: Paeth, H., and F. Pollinger, 2010: Enhanced evidence in climate models for changes in Quantification of modelling uncertainties in a large ensemble of climate change extratropical atmospheric circulation. Tellus A, 62, 647 660. 12 simulations. Nature, 430, 768 772. Pagani, M., Z. Liu, J. LaRiviere, and A. Ravelo, 2010: High Earth-system climate Murphy, J. M., B. B. B. Booth, M. Collins, G. R. Harris, D. M. H. Sexton, and M. J. Webb, sensitivity determined from Pliocene carbon dioxide concentrations. Nature 2007: A methodology for probabilistic predictions of regional climate change Geosci., 3, 27 30. from perturbed physics ensembles. Philos. Trans. R. Soc. A, 365, 1993 2028. Pall, P., M. Allen, and D. Stone, 2007: Testing the Clausius-Clapeyron constraint on Myhre, G., E. Highwood, K. Shine, and F. Stordal, 1998: New estimates of radiative changes in extreme precipitation under CO2 warming. Clim. Dyn., 28, 351 363. forcing due to well mixed greenhouse gases. Geophys. Res. Lett., 25, 2715 2718. Pennell, C., and T. Reichler, 2011: On the effective number of climate models. J. Clim., Neelin, J. D., C. Chou, and H. Su, 2003: Tropical drought regions in global warming 24, 2358 2367. and El Nino teleconnections. Geophys. Res. Lett., 30, 2275. Perkins, S. E., L. V. Alexander, and J. R. Nairn, 2012: Increasing frequency, intensity Neelin, J. D., M. Munnich, H. Su, J. E. Meyerson, and C. E. Holloway, 2006: Tropical and duration of observed global heatwaves and warm spells. Geophys. Res. drying trends in global warming models and observations. Proc. Natl. Acad. Sci. Lett., 39, L20714. U.S.A., 103, 6110 6115. Perrie, W., Y. H. Yao, and W. Q. Zhang, 2010: On the impacts of climate change Nelson, F., and S. Outcalt, 1987: A computational method for prediction and and the upper ocean on midlatitude northwest Atlantic landfalling cyclones. J. regionalization of permafrost. Arct. Alpine Res., 19, 279 288. Geophys. Res., 115, D23110. Newlands, N. K., G. Espino-Hernández, and R. S. Erickson, 2012: Understanding crop Piani, C., D. J. Frame, D. A. Stainforth, and M. R. Allen, 2005: Constraints on climate response to climate variability with complex agroecosystem models. Int. J. Ecol., change from a multi-thousand member ensemble of simulations. Geophys. Res. 2012, 756242. Lett., 32, L23825. Niall, S., and K. Walsh, 2005: The impact of climate change on hailstorms in Pierce, D., et al., 2008: Attribution of declining Western US snowpack to human southeastern Australia. Int. J. Climatol., 25, 1933 1952. effects. J. Clim., 21, 6425 6444. Nicolsky, D., V. Romanovsky, V. Alexeev, and D. Lawrence, 2007: Improved modeling Pinto, J. G., U. Ulbrich, G. C. Leckebusch, T. Spangehl, M. Reyers, and S. Zacharias, of permafrost dynamics in a GCM land-surface scheme. Geophys. Res. Lett., 34, 2007: Changes in storm track and cyclone activity in three SRES ensemble L08501. experiments with the ECHAM5/MPI-OM1 GCM. Clim. Dyn., 29, 195 210. 1130 Long-term Climate Change: Projections, Commitments and Irreversibility Chapter 12 Pitman, A., et al., 2009: Uncertainties in climate responses to past land cover Ridley, J. K., J. A. Lowe, and H. T. Hewitt, 2012: How reversible is sea ice loss? change: First results from the LUCID intercomparison study. Geophys. Res. Lett., Cryosphere, 6, 193 198. 36, L14814. Riley, W. J., et al., 2011: Barriers to predicting changes in global terrestrial methane Plattner, G.-K., et al., 2008: Long-term climate commitments projected with climate- fluxes: Analyses using CLM4ME, a methane biogeochemistry model integrated carbon cycle models. J. Clim., 21, 2721 2751. in CESM. Biogeosciences, 8, 1925 1953. Polvani, L. M., M. Previdi, and C. Deser, 2011: Large cancellation, due to ozone Rind, D., 1987: The doubled CO2 climate - Impact of the sea-surface temperature- recovery, of future Southern Hemisphere atmospheric circulation trends. gradient. J. Atmos. Sci., 44, 3235 3268. Geophys. Res. Lett., 38, L04707. Rinke, A., P. Kuhry, and K. Dethloff, 2008: Importance of a soil organic layer for Arctic Pongratz, J., C. Reick, T. Raddatz, and M. Claussen, 2010: Biogeophysical versus climate: A sensitivity study with an Arctic RCM. Geophys. Res. Lett., 35, L13709. biogeochemical climate response to historical anthropogenic land cover change. Rive, N., A. Torvanger, T. Berntsen, and S. Kallbekken, 2007: To what extent can a Geophys. Res. Lett., 37, L08702. long-term temperature target guide near-term climate change commitments? Port, U., V. Brovkin, and M. Claussen, 2012: The influence of vegetation dynamics on Clim. Change, 82, 373 391. anthropogenic climate change. Earth Syst. Dyn., 3, 233 243. Robinson, A., R. Calov, and A. Ganopolski, 2012: Multistability and critical thresholds Power, S., and G. Kociuba, 2011a: The impact of global warming on the Southern of the Greenland ice sheet. Nature Clim. Change, 2, 429 432. Oscillation Index. Clim. Dyn., 37, 1745 1754. Roeckner, E., M. A. Giorgetta, T. Crueger, M. Esch, and J. Pongratz, 2011: Historical Power, S., F. Delage, R. Colman, and A. Moise, 2012: Consensus on twenty-first- and future anthropogenic emission pathways derived from coupled climate- century rainfall projections in climate models more widespread than previously carbon cycle simulations. Clim. Change, 105, 91 108. thought. J. Clim., 25, 3792 3809. Roehrig, R., D. Bouniol, F. Guichard, F. Hourdin, and J.-L. Redelsperger, 2013: The Power, S. B., and G. Kociuba, 2011b: What caused the observed twentieth-century present and future of the West African monsoon: A process-oriented assessment weakening of the Walker circulation? J. Clim., 24, 6501 6514. of CMIP5 simulations along the AMMA transect. J. Clim., doi:10.1175/JCLI-D- Previdi, M., 2010: Radiative feedbacks on global precipitation. Environ. Res. Lett., 12-00505.1. 5, 025211. Roesch, A., 2006: Evaluation of surface albedo and snow cover in AR4 coupled Rahmstorf, S., et al., 2005: Thermohaline circulation hysteresis: A model climate models. J. Geophys. Res., 111, D15111. intercomparison. Geophys. Res. Lett., 32, L23605. Rogelj, J., M. Meinshausen, and R. Knutti, 2012: Global warming under old and new Räisänen, J., 2007: How reliable are climate models? Tellus A, 59, 2 29. scenarios using IPCC climate sensitivity range estimates. Nature Clim. Change, Räisänen, J., 2008: Warmer climate: Less or more snow? Clim. Dyn., 30, 307 319. 2, 248 253. Räisänen, J., and L. Ruokolainen, 2006: Probabilistic forecasts of near-term climate Rogelj, J., D. L. McCollum, B. C. O Neill, and K. Riahi, 2013: 2020 emissions levels change based on a resampling ensemble technique. Tellus A, 58, 461 472. required to limit warming to below 2°C. Nature Clim. Change, 3, 405 412. Räisänen, J., and J. S. Ylhaisi, 2011: Cold months in a warming climate. Geophys. Res. Rogelj, J., et al., 2011: Emission pathways consistent with a 2°C global temperature Lett., 38, L22704. limit. Nature Clim. Change, 1, 413 418. Ramanathan, V., P. J. Crutzen, J. T. Kiehl, and D. Rosenfeld, 2001: Aerosols, climate, Rohling, E., and P. P. Members, 2012: Making sense of palaeoclimate sensitivity. and the hydrologic cycle. Science, 294, 2119 2124. Nature, 491, 683 691. Ramaswamy, V., et al., 2001: Radiative forcing of climate change. In: Climate Rohling, E., K. Grant, M. Bolshaw, A. Roberts, M. Siddall, C. Hemleben, and M. Change 2001: The Scientific Basis. Contribution of Working Group I to the Third Kucera, 2009: Antarctic temperature and global sea level closely coupled over Assessment Report of the Intergovernmental Panel on Climate Change [J. T. the past five glacial cycles. Nature Geosci., 2, 500 504. Houghton, Y. Ding, D. J. Griggs, M. Noquer, P. J. van der Linden, X. Dai, K. Maskell Romanovsky, V. E., S. L. Smith, and H. H. Christiansen, 2010: Permafrost thermal state and C. A. Johnson (eds.)]. Cambridge University Press, Cambridge, United in the polar Northern Hemisphere during the international polar year 2007 Kingdom and New York, NY, USA pp. 349-416. 2009: A synthesis. Permafr. Periglac. Process., 21, 106 116. Rammig, A., et al., 2010: Estimating the risk of Amazonian forest dieback. New Rotstayn, L. D., S. J. Jeffrey, M. A. Collier, S. M. Dravitzki, A. C. Hirst, J. I. Syktus, and Phytologist, 187, 694 706. K. K. Wong, 2012: Aerosol- and greenhouse gas-induced changes in summer Randall, D. A., et al., 2007: Climate models and their evaluation. In: Climate Change rainfall and circulation in the Australasian region: A study using single-forcing 2007: The Physical Science Basis. Contribution of Working Group I to the climate simulations. Atmos. Chem. Phys., 12, 6377 6404. Fourth Assessment Report of the Intergovernmental Panel on Climate Change Rougier, J., 2007: Probabilistic inference for future climate using an ensemble of [Solomon, S., D. Qin, M. Manning, Z. Chen, M. Marquis, K. B. Averyt, M. Tignor climate model evaluations. Clim. Change, 81, 247 264. and H. L. Miller (eds.)] Cambridge University Press, Cambridge, United Kingdom Rougier, J., D. M. H. Sexton, J. M. Murphy, and D. Stainforth, 2009: Analyzing the and New York, NY, USA, pp. 589 662. climate sensitivity of the HadSM3 climate model using ensembles from different Randalls, S., 2010: History of the 2°C climate target. WIREs Climate Change, 1, but related experiments. J. Clim., 22, 3540 3557. 598 605. Rowell, D. P., 2012: Sources of uncertainty in future changes in local precipitation. 12 Randel, W., and F. Wu, 2007: A stratospheric ozone profile data set for 1979 2005: Clim. Dyn., doi:10.1007/s00382 011 1210 2. Variability, trends, and comparisons with column ozone data. J. Geophys. Res., Rowlands, D. J., et al., 2012: Broad range of 2050 warming from an observationally 112, D06313. constrained large climate model ensemble. Nature Geosci., 5, 256 260. Randel, W. J., M. Park, F. Wu, and N. Livesey, 2007: A large annual cycle in ozone Ruosteenoja, K., H. Tuomenvirta, and K. Jylha, 2007: GCM-based regional above the tropical tropopause linked to the Brewer-Dobson circulation. J. Atmos. temperature and precipitation change estimates for Europe under four SRES Sci., 64, 4479 4488. scenarios applying a super-ensemble pattern-scaling method. Clim. Change, 81, Randles, C., and V. Ramaswamy, 2008: Absorbing aerosols over Asia: A Geophysical 193 208. Fluid Dynamics Laboratory general circulation model sensitivity study of model Saenko, O. A., A. S. Gupta, and P. Spence, 2012: On challenges in predicting bottom response to aerosol optical depth and aerosol absorption. J. Geophys. Res., 113, water transport in the Southern Ocean. J. Clim., 25, 1349 1356. D21203. Saito, K., M. Kimoto, T. Zhang, K. Takata, and S. Emori, 2007: Evaluating a high- Reagan, M., and G. Moridis, 2007: Oceanic gas hydrate instability and dissociation resolution climate model: Simulated hydrothermal regimes in frozen ground under climate change scenarios. Geophys. Res. Lett., 34, L22709. regions and their change under the global warming scenario. J. Geophys. Res., Reagan, M., and G. Moridis, 2009: Large-scale simulation of methane hydrate 112, F02S11. dissociation along the West Spitsbergen Margin. Geophys. Res. Lett., 36, L23612. Sallée, J.-B., E. Shuckburgh, N. Bruneau, A. J. S. Meijers, T. Bracegirdle, and Z. Wang, Ridley, J., J. Lowe, and D. Simonin, 2008: The demise of Arctic sea ice during 2013a: Assessment of Southern Ocean mixed-layer depths in CMIP5 models: stabilisation at high greenhouse gas concentrations. Clim. Dyn., 30, 333 341. Historical bias and forcing response. J. Geophys. Res., doi:10.1002/jgrc.20157. Ridley, J., J. Lowe, C. Brierley, and G. Harris, 2007: Uncertainty in the sensitivity Sallée, J.-B., E. Shuckburgh, N. Bruneau, A. J. S. Meijers, T. J. Bracegirdle, Z. Wang, of Arctic sea ice to global warming in a perturbed parameter climate model and T. Roy, 2013b: Assessment of Southern Ocean water mass circulation and ensemble. Geophys. Res. Lett., 34, L19704. characteristics in CMIP5 models: Historical bias and forcing response. J. Geophys. Ridley, J., J. Gregory, P. Huybrechts, and J. Lowe, 2010: Thresholds for irreversible Res., doi:10.1002/jgrc.20135. decline of the Greenland ice sheet. Clim. Dyn., 35, 1049 1057. 1131 Chapter 12 Long-term Climate Change: Projections, Commitments and Irreversibility Sanchez-Gomez, E., S. Somot, and A. Mariotti, 2009: Future changes in the Schuur, E., J. Vogel, K. Crummer, H. Lee, J. Sickman, and T. Osterkamp, 2009: The effect Mediterranean water budget projected by an ensemble of regional climate of permafrost thaw on old carbon release and net carbon exchange from tundra. models. Geophys. Res. Lett., 36, L21401. Nature, 459, 556 559. Sanderson, B. M., 2011: A multimodel study of parametric uncertainty in predictions Schwalm, C. R., et al., 2012: Reduction in carbon uptake during turn of the century of climate response to rising greenhouse gas concentrations. J. Clim., 25, 1362 drought in western North America. Nature Geosci., 5, 551 556. 1377. Schwartz, S., R. Charlson, R. Kahn, J. Ogren, and H. Rodhe, 2010: Why hasn t Earth Sanderson, B. M., 2013: On the estimation of systematic error in regression-based warmed as much as expected? J. Clim., 23, 2453 2464. predictions of climate sensitivity. Clim. Change, doi:10.1007/s10584 012 Schwartz, S., R. Charlson, R. Kahn, J. Ogren, and H. Rodhe, 2012: Reply to Comments 0671 6. on Why hasn t Earth warmed as much as expected? . J. Clim., 25, 2200 2204. Sanderson, B. M., and R. Knutti, 2012: On the interpretation of constrained climate Schwartz, S. E., 2012: Determination of Earth s transient and equilibrium climate model ensembles. Geophys. Res. Lett., 39, L16708. sensitivities from observations over the twentieth century: Strong dependence Sanderson, B. M., K. M. Shell, and W. Ingram, 2010: Climate feedbacks determined on assumed forcing. Surv. Geophys., 33, 745 777. using radiative kernels in a multi-thousand member ensemble of AOGCMs. Clim. Schweiger, A., R. Lindsay, J. Zhang, M. Steele, H. Stern, and R. Kwok, 2011: Uncertainty Dyn., 35, 1219 1236. in modeled Arctic sea ice volume. J. Geophys. Res., 116, C00D06. Sanderson, B. M., et al., 2008: Constraints on model response to greenhouse gas Screen, J., and I. Simmonds, 2010: The central role of diminishing sea ice in recent forcing and the role of subgrid-scale processes. J. Clim., 21, 2384 2400. Arctic temperature amplification. Nature, 464, 1334 1337. Sanderson, M. G., D. L. Hemming, and R. A. Betts, 2011: Regional temperature and Screen, J. A., N. P. Gillett, A. Y. Karpechko, and D. P. Stevens, 2010: Mixed layer precipitation changes under high-end (>= 4°C) global warming. Philos. Trans. R. temperature response to the Southern Annular Mode: Mechanisms and model Soc. A, 369, 85 98. representation. J. Clim., 23, 664 678. Sanso, B., and C. Forest, 2009: Statistical calibration of climate system properties. J. Seager, R., and G. A. Vecchi, 2010: Greenhouse warming and the 21st century R. Stat. Soc. C, 58, 485 503. hydroclimate of the southwestern North America. Proc. Natl. Acad. Sci. U.S.A., Sanso, B., C. E. Forest, and D. Zantedeschi, 2008: Inferring climate system properties 107, 21277 21282. using a computer model. Bayes. Anal., 3, 1 37. Seager, R., and N. Naik, 2012: A mechanisms-based approach to detecting recent Sansom, P. G., D. B. Stephenson, C. A. T. Ferro, G. Zappa, and L. Shaffrey, 2013: Simple anthropogenic hydroclimate change. J. Clim., 25, 236 261. uncertainty frameworks for selecting weighting schemes and interpreting multi- Seager, R., N. Naik, and G. A. Vecchi, 2010: Thermodynamic and dynamic mechanisms model ensemble climate change experiments. J. Clim., doi:10.1175/JCLI-D-12- for large-scale changes in the hydrological cycle in response to global warming. 00462.1. J. Clim., 23, 4651 4668. Santer, B. D., T. M. L. Wigley, M. E. Schlesinger, and J. F. B. Mitchell, 1990: Developing Seager, R., et al., 2007: Model projections of an imminent transition to a more arid Climate Scenarios from Equilibrium GCM Results. Max-Planck-Institut- climate in southwestern North America. Science, 316, 1181 1184. für-Meteorologie Report. Max-Planck-Institut-für-Meteorologie, Hamburg, Sedláèek, J., R. Knutti, O. Martius, and U. Beyerle, 2011: Impact of a reduced Arctic Germany, 29 pp. sea ice cover on ocean and atmospheric properties. J. Clim., 25, 307 319. Scaife, A. A., et al., 2012: Climate change projections and stratosphere-troposphere Seidel, D., and W. Randel, 2007: Recent widening of the tropical belt: Evidence from interaction. Clim. Dyn., 38, 2089 2097. tropopause observations. J. Geophys. Res., 112, D20113. Schaefer, K., T. Zhang, L. Bruhwiler, and A. Barrett, 2011: Amount and timing of Seidel, D. J., Q. Fu, W. J. Randel, and T. J. Reichler, 2008: Widening of the tropical belt permafrost carbon release in response to climate warming. Tellus B, 63, 165 in a changing climate. Nature Geosci., 1, 21 24. 180. Sen Gupta, A., A. Santoso, A. Taschetto, C. Ummenhofer, J. Trevena, and M. England, Schär, C., P. L. Vidale, D. Lüthi, C. Frei, C. Häberli, M. A. Liniger, and C. Appenzeller, 2009: Projected changes to the Southern Hemisphere ocean and sea ice in the 2004: The role of increasing temperature variability in European summer IPCC AR4 climate models. J. Clim., 22, 3047 3078. heatwaves. Nature, 427, 332 336. Seneviratne, S. I., D. Lüthi, M. Litschi, and C. Schär, 2006: Land-atmosphere coupling Scheff, J., and D. M. W. Frierson, 2012: Robust future precipitation declines in CMIP5 and climate change in Europe. Nature, 443, 205 209. largely reflect the poleward expansion of model subtropical dry zones. Geophys. Seneviratne, S. I., et al., 2010: Investigating soil moisture-climate interactions in a Res. Lett., 39, L18704. changing climate: A review. Earth Sci. Rev., 99, 125 161. Scheffer, M., et al., 2009: Early-warning signals for critical transitions. Nature, 461, Seneviratne, S. I., et al., 2012: Changes in climate extremes and their impacts on the 53 59. natural physical environment. In: Managing the Risks of Extreme Events and Schlesinger, M., 1986: Equilibrium and transient climatic warming induced by Disasters to Advance Climate Change Adaptation. A Special Report of Working increased atmospheric CO2. Clim. Dyn., 1, 35 51. Groups I and II of the Intergovernmental Panel on Climate Change (IPCC) [C. B. Schlesinger, M., et al., 2000: Geographical distributions of temperature change for Field, et al. (eds.)]. Cambridge University Press, Cambridge, United Kingdom, and 12 scenarios of greenhouse gas and sulfur dioxide emissions. Technol. Forecast. Soc. New York, NY, USA, pp. 109 230. Change, 65, 167 193. Senior, C. A., and J. F. B. Mitchell, 2000: The time-dependence of climate sensitivity. Schmidt, M. W. I., et al., 2011: Persistence of soil organic matter as an ecosystem Geophys. Res. Lett., 27, 2685 2688. property. Nature, 478, 49 56. Serreze, M., A. Barrett, J. Stroeve, D. Kindig, and M. Holland, 2009: The emergence of Schmittner, A., et al., 2011: Climate sensitivity estimated from temperature surface-based Arctic amplification. Cryosphere, 3, 11 19. reconstructions of the Last Glacial Maximum. Science, 334, 1385 1388. Serreze, M. C., and J. A. Francis, 2006: The Arctic amplification debate. Clim. Change, Schneider von Deimling, T., H. Held, A. Ganopolski, and S. Rahmstorf, 2006: Climate 76, 241 264. sensitivity estimated from ensemble simulations of glacial climate. Clim. Dyn., Sexton, D., H. Grubb, K. Shine, and C. Folland, 2003: Design and analysis of climate 27, 149 163. model experiments for the efficient estimation of anthropogenic signals. J. Clim., Schneider von Deimling, T., M. Meinshausen, A. Levermann, V. Huber, K. Frieler, D. 16, 1320 1336. Lawrence, and V. Brovkin, 2012: Estimating the near-surface permafrost-carbon Sexton, D. M. H., and J. M. Murphy, 2012: Multivariate probabilistic projections using feedback on global warming. Biogeosciences, 9, 649 665. imperfect climate models. Part II: Robustness of methodological choices and Schoof, C., 2007: Ice sheet grounding line dynamics: Steady states, stability, and consequences for climate sensitivity. Clim. Dyn., 2543 2558. hysteresis. J. Geophys. Res., 112, F03S28. Sexton, D. M. H., J. M. Murphy, M. Collins, and M. J. Webb, 2012: Multivariate Schröder, D., and W. M. Connolley, 2007: Impact of instantaneous sea ice removal in probabilistic projections using imperfect climate models. Part I: Outline of a coupled general circulation model. Geophys. Res. Lett., 34, L14502. methodology. Clim. Dyn., 2513 2542. Schuenemann, K. C., and J. J. Cassano, 2010: Changes in synoptic weather patterns Shepherd, T. G., and C. McLandress, 2011: A robust mechanism for strengthening and Greenland precipitation in the 20th and 21st centuries: 2. Analysis of 21st of the Brewer-Dobson circulation in response to climate change: Critical-layer century atmospheric changes using self-organizing maps. J. Geophys. Res., 115, control of subtropical wave breaking. J. Atmos. Sci., 68, 784 797. D05108. Sherwood, S. C., 2010: Direct versus indirect effects of tropospheric humidity changes on the hydrologic cycle. Environ. Res. Lett., 5, 025206. 1132 Long-term Climate Change: Projections, Commitments and Irreversibility Chapter 12 Sherwood, S. C., and M. Huber, 2010: An adaptability limit to climate change due to Soden, B. J., and I. M. Held, 2006: An assessment of climate feedbacks in coupled heat stress. Proc. Natl. Acad. Sci. U.S.A., 107, 9552 9555. ocean-atmosphere models. J. Clim., 19, 3354 3360. Sherwood, S. C., W. Ingram, Y. Tsushima, M. Satoh, M. Roberts, P. L. Vidale, and P. A. Soden, B. J., and G. A. Vecchi, 2011: The vertical distribution of cloud feedback in O Gorman, 2010: Relative humidity changes in a warmer climate. J. Geophys. coupled ocean-atmosphere models. Geophys. Res. Lett., 38, L12704. Res., 115, D09104. Sohn, B. J., and S.-C. Park, 2010: Strengthened tropical circulations in past three Shindell, D., et al., 2012: Simultaneously mitigating near-term climate change and decades inferred from water vapor transport. J. Geophys. Res., 115, D15112. improving human health and food security. Science, 335, 183 189. Sokolov, A. P., et al., 2009: Probabilistic forecast for twenty-first-century climate Shindell, D. T., et al., 2006: Simulations of preindustrial, present-day, and 2100 based on uncertainties in emissions (without policy) and climate parameters. J. conditions in the NASA GISS composition and climate model G-PUCCINI. Atmos. Clim., 23, 2230 2231. Chem. Phys., 6, 4427 4459. Solgaard, A. M., and P. L. Langen, 2012: Multistability of the Greenland ice sheet and Shindell, D. T., et al., 2013a: Interactive ozone and methane chemistry in GISS-E2 the effects of an adaptive mass balance formulation. Clim. Dyn., 39, 1599 1612. historical and future climate simulations. Atmos. Chem. Phys., 13, 2653 2689. Solomon, S., G. Plattner, R. Knutti, and P. Friedlingstein, 2009: Irreversible climate Shindell, D. T., et al., 2013b: Radiative forcing in the ACCMIP historical and future change due to carbon dioxide emissions. Proc. Natl. Acad. Sci. U.S.A., 106, climate simulations. Atmos. Chem. Phys., 13, 2939 2974. 1704 1709. Shine, K. P., J. Cook, E. J. Highwood, and M. M. Joshi, 2003: An alternative to radiative Solomon, S., J. Daniel, T. Sanford, D. Murphy, G. Plattner, R. Knutti, and P. Friedlingstein, forcing for estimating the relative importance of climate change mechanisms. 2010: Persistence of climate changes due to a range of greenhouse gases. Proc. Geophys. Res. Lett., 30, 2047. Natl. Acad. Sci. U.S.A., 107, 18354 18359. Shiogama, H., S. Emori, K. Takahashi, T. Nagashima, T. Ogura, T. Nozawa, and T. Solomon, S., et al., 2007: Technical Summary. In: Climate Change 2007: The Physical Takemura, 2010a: Emission scenario dependency of precipitation on global Science Basis. Contribution of Working Group I to the Fourth Assessment Report warming in the MIROC3.2 model. J. Clim., 23, 2404 2417. of the Intergovernmental Panel on Climate Change [Solomon, S., D. Qin, M. Shiogama, H., et al., 2010b: Emission scenario dependencies in climate change Manning, Z. Chen, M. Marquis, K. B. Averyt, M. Tignor and H. L. Miller (eds.)] assessments of the hydrological cycle. Clim. Change, 99, 321 329. Cambridge University Press, Cambridge, United Kingdom and New York, NY, Shkolnik, I., E. Nadyozhina, T. Pavlova, E. Molkentin, and A. Semioshina, 2010: Snow USA, pp. 19 92. cover and permafrost evolution in Siberia as simulated by the MGO regional Son, S. W., et al., 2010: Impact of stratospheric ozone on Southern Hemisphere climate model in the 20th and 21st centuries. Environ. Res. Lett., 5, 015005. circulation change: A multimodel assessment. J. Geophys. Res., 115, D00M07. Shongwe, M. E., G. J. van Oldenborgh, B. van den Hurk, and M. van Aalst, 2011: Sorensson, A., C. Menendez, R. Ruscica, P. Alexander, P. Samuelsson, and U. Projected changes in mean and extreme precipitation in Africa under global Willen, 2010: Projected precipitation changes in South America: A dynamical warming. Part II: East Africa. J. Clim., 24, 3718 3733. downscaling within CLARIS. Meteorol. Z., 19, 347 355. Siegenthaler, U., and H. Oeschger, 1984: Transient temperature changes due to Spence, P., O. A. Saenko, C. O. Dufour, J. Le Sommer, and M. H. England, 2012: increasing CO2 using simple models. Ann. Glaciol., 5, 153 159. Mechanisms maintaining Southern Ocean meridional heat transport under Sigmond, M., P. C. Siegmund, E. Manzini, and H. Kelder, 2004: A simulation of the projected wind forcing. J. Phys. Oceanogr., 42, 1923 1931. separate climate effects of middle-atmosphere and tropospheric CO2 doubling. St. Clair, S., J. Hillier, and P. Smith, 2008: Estimating the pre-harvest greenhouse gas J. Clim., 17, 2352 2367. costs of energy crop production. Biomass Bioenerg., 32, 442 452. Sillmann, J., and E. Roeckner, 2008: Indices for extreme events in projections of Stachnik, J. P., and C. Schumacher, 2011: A comparison of the Hadley circulation in anthropogenic climate change. Clim. Change, 86, 83 104. modern reanalyses. J. Geophys. Res., 116, D22102. Sillmann, J., and M. Croci-Maspoli, 2009: Present and future atmospheric blocking Stephenson, D. B., M. Collins, J. C. Rougier, and R. E. Chandler, 2012: Statistical and its impact on European mean and extreme climate. Geophys. Res. Lett., 36, problems in the probabilistic prediction of climate change. Environmetrics, 23, L10702. 364 372. Sillmann, J., V. V. Kharin, F. W. Zwiers, X. Zhang, and D. Bronaugh, 2013: Climate Sterl, A., et al., 2008: When can we expect extremely high surface temperatures? extremes indices in the CMIP5 multimodel ensemble: Part 2. Future climate Geophys. Res. Lett., 35, L14703. projections. J. Geophys. Res., 118, 2473 2493. Stott, P., G. Jones, and J. Mitchell, 2003: Do models underestimate the solar Simmons, A. J., K. M. Willett, P. D. Jones, P. W. Thorne, and D. P. Dee, 2010: Low- contribution to recent climate change? J. Clim., 16, 4079 4093. frequency variations in surface atmospheric humidity, temperature, and Stott, P., P. Good, G. A. Jones, N. Gillett, and E. Hawkins, 2013: The upper end of precipitation: Inferences from reanalyses and monthly gridded observational climate model temperature projections is inconsistent with past warming. data sets. J. Geophys. Res., 115, D01110. Environ. Res. Lett., 8, 014024. Simpkins, G. R., and A. Y. Karpechko, 2012: Sensitivity of the southern annular mode Stouffer, R., 2004: Time scales of climate response. J. Clim., 17, 209 217. to greenhouse gas emission scenarios. Clim. Dyn., 38, 563 572. Stouffer, R. J., and S. Manabe, 1999: Response of a coupled ocean-atmosphere model Slater, A. G., and D. M. Lawrence, 2013: Diagnosing present and future permafrost to increasing atmospheric carbon dioxide: Sensitivity to the rate of increase. J. 12 from climate models. J. Clim., doi:10.1175/JCLI-D-12-00341.1. Clim., 12, 2224 2237. Smeets, E. M. W., L. F. Bouwmanw, E. Stehfest, D. P. van Vuuren, and A. Posthuma, Stowasser, M., H. Annamalai, and J. Hafner, 2009: Response of the South Asian 2009: Contribution of N2O to the greenhouse gas balance of first-generation summer monsoon to global warming: Mean and synoptic systems. J. Clim., 22, biofuels. Global Change Biol., 15, 1 23. 1014 1036. Smith, L., Y. Sheng, G. MacDonald, and L. Hinzman, 2005: Disappearing Arctic lakes. Stroeve, J., M. Holland, W. Meier, T. Scambos, and M. Serreze, 2007: Arctic sea ice Science, 308, 1429 1429. decline: Faster than forecast. Geophys. Res. Lett., 34, L09501. Smith, L., et al., 2004: Siberian peatlands a net carbon sink and global methane Stroeve, J. C., V. Kattsov, A. Barrett, M. Serreze, T. Pavlova, M. Holland, and W. N. source since the early Holocene. Science, 303, 353 356. Meier, 2012: Trends in Arctic sea ice extent from CMIP5, CMIP3 and observations. Smith, R. L., C. Tebaldi, D. Nychka, and L. O. Mearns, 2009: Bayesian modeling of Geophys. Res. Lett., 39, L16502. uncertainty in ensembles of climate models. J. Am. Stat. Assoc., 104, 97 116. Stuber, N., M. Ponater, and R. Sausen, 2005: Why radiative forcing might fail as a Smith, S. J., J. van Aardenne, Z. Klimont, R. J. Andres, A. Volke, and S. Delgado Arias, predictor of climate change. Clim. Dyn., 24, 497 510. 2011: Anthropogenic sulfur dioxide emissions: 1850 2005. Atmos. Chem. Phys., Sudo, K., M. Takahashi, and H. Akimoto, 2003: Future changes in stratosphere- 11, 1101 1116. troposphere exchange and their impacts on future tropospheric ozone Smith, S. M., J. A. Lowe, N. H. A. Bowerman, L. K. Gohar, C. Huntingford, and M. simulations. Geophys. Res. Lett., 30, 2256. R. Allen, 2012: Equivalence of greenhouse-gas emissions for peak temperature Sugiyama, M., H. Shiogama, and S. Emori, 2010: Precipitation extreme changes limits. Nature Clim. Change, 2, 535 538. exceeding moisture content increases in MIROC and IPCC climate models. Proc. Sobel, A. H., and S. J. Camargo, 2011: Projected future seasonal changes in tropical Natl. Acad. Sci. U.S.A., 107, 571 575. summer climate. J. Clim., 24, 473 487. Sun, Y., S. Solomon, A. Dai, and R. W. Portmann, 2007: How often will it rain? J. Clim., Soden, B., I. Held, R. Colman, K. Shell, J. Kiehl, and C. Shields, 2008: Quantifying 20, 4801 4818. climate feedbacks using radiative kernels. J. Clim., 21, 3504 3520. 1133 Chapter 12 Long-term Climate Change: Projections, Commitments and Irreversibility Sutton, R. T., B. W. Dong, and J. M. Gregory, 2007: Land/sea warming ratio in response Ulbrich, U., G. C. Leckebusch, and J. G. Pinto, 2009: Extra-tropical cyclones in the to climate change: IPCC AR4 model results and comparison with observations. present and future climate: A review. Theor. Appl. Climatol., 96, 117 131. Geophys. Res. Lett., 34, L02701. Ulbrich, U., et al., 2013: Are greenhouse gas signals of Northern Hemisphere winter Swann, A. L., I. Y. Fung, S. Levis, G. B. Bonan, and S. C. Doney, 2010: Changes in Arctic extra-tropical cyclone activity dependent on the identification and tracking vegetation amplify high-latitude warming through the greenhouse effect. Proc. algorithm? Meteorol. Z., 22, 61 68. Natl. Acad. Sci. U.S.A., 107, 1295 1300. UNEP, 2010: The emissions gap report: Are the Copenhagen Accord pledges sufficient Swart, N. C., and J. C. Fyfe, 2012: Observed and simulated changes in the Southern to limit global warming to 2°C or 1.5°C? , 55 pp. Hemisphere surface westerly wind-stress. Geophys. Res. Lett., 39, L16711. Utsumi, N., S. Seto, S. Kanae, E. E. Maeda, and T. Oki, 2011: Does higher surface Swingedouw, D., P. Braconnot, P. Delecluse, E. Guilyardi, and O. Marti, 2007: temperature intensify extreme precipitation? Geophys. Res. Lett., 38, L16708. Quantifying the AMOC feedbacks during a 2xCO2 stabilization experiment with Vaks, A., et al., 2013: Speleothems reveal 500,000-year history of Siberian land-ice melting. Clim. Dyn., 29, 521 534. permafrost. Science, 340, 183 186. Swingedouw, D., T. Fichefet, P. Huybrechts, H. Goosse, E. Driesschaert, and M. Loutre, Van Klooster, S. L., and P. J. Roebber, 2009: Surface-based convective potential in 2008: Antarctic ice-sheet melting provides negative feedbacks on future climate the contiguous United States in a business-as-usual future climate. J. Clim., 22, warming. Geophys. Res. Lett., 35, L17705. 3317 3330. Szopa, S., et al., 2013: Aerosol and ozone changes as forcing for climate evolution van Vuuren, D. P., et al., 2011: RCP3 PD: Exploring the possibilities to limit global between 1850 and 2100. Clim. Dyn., 40, 2223 2250. mean temperature change to less than 2°C. Clim. Change, 109, 95 116. Takahashi, K., 2009a: Radiative constraints on the hydrological cycle in an idealized Vavrus, S., M. Holland, and D. Bailey, 2011: Changes in Arctic clouds during intervals radiative-convective equilibrium model. J. Atmos. Sci., 66, 77 91. of rapid sea ice loss. Clim. Dyn., 36, 1475 1489. Takahashi, K., 2009b: The global hydrological cycle and atmospheric shortwave Vavrus, S. J., M. M. Holland, A. Jahn, D. A. Bailey, and B. A. Blazey, 2012: Twenty-first- absorption in climate models under CO2 forcing. J. Clim., 22, 5667 5675. century Arctic climate change in CCSM4. J. Clim., 25, 2696 2710. Tanaka, K., and T. Raddatz, 2011: Correlation between climate sensitivity and aerosol Vecchi, G. A., and B. J. Soden, 2007: Global warming and the weakening of the forcing and its implication for the climate trap . Clim. Change, 109, 815 825. tropical circulation. Bull. Am. Meteorol. Soc., 88, 1529 1530. Tarnocai, C., J. Canadell, E. Schuur, P. Kuhry, G. Mazhitova, and S. Zimov, 2009: Soil Vecchi, G. A., B. J. Soden, A. T. Wittenberg, I. M. Held, A. Leetmaa, and M. J. Harrison, organic carbon pools in the northern circumpolar permafrost region. Global 2006: Weakening of tropical Pacific atmospheric circulation due to anthropogenic Biogeochem. Cycles, 23, GB2023. forcing. Nature, 441, 73 76. Taylor, K. E., R. J. Stouffer, and G. A. Meehl, 2012: A summary of the CMIP5 experiment Vial, J., J.-L. Dufresne, and S. Bony, 2013: On the interpretation of inter-model spread design. Bull. Am. Meteorol. Soc., 93, 485 498. in CMIP5 climate sensitivity estimates. Clim. Dyn., doi:10.1007/s00382-013- Tebaldi, C., and R. Knutti, 2007: The use of the multi-model ensemble in probabilistic 1725-9. climate projections. Philos. Trans. R. Soc. A, 365, 2053 2075. Vidale, P. L., D. Lüthi, R. Wegmann, and C. Schär, 2007: European summer climate Tebaldi, C., and D. B. Lobell, 2008: Towards probabilistic projections of climate variability in a heterogeneous multi-model ensemble. Clim. Change, 81, 209 change impacts on global crop yields. Geophys. Res. Lett., 35, L08705. 232. Tebaldi, C., and B. Sanso, 2009: Joint projections of temperature and precipitation Voldoire, A., et al., 2013: The CNRM-CM5.1 global climate model: Description and change from multiple climate models: A hierarchical Bayesian approach. J. R. basic evaluation. Clim. Dyn., 40, 2091 2121. Stat. Soc. A, 172, 83 106. Voss, R., and U. Mikolajewicz, 2001: Long-term climate changes due to increased Tebaldi, C., J. M. Arblaster, and R. Knutti, 2011: Mapping model agreement on future CO2 concentration in the coupled atmosphere-ocean general circulation model climate projections. Geophys. Res. Lett., 38, L23701. ECHAM3/LSG. Clim. Dyn., 17, 45 60. Tebaldi, C., K. Hayhoe, J. M. Arblaster, and G. A. Meehl, 2006: Going to the extremes. Wadhams, P., 2012: Arctic ice cover, ice thickness and tipping points. Ambio, 41, Clim. Change, 79, 185 211. 23 33. Terray, L., L. Corre, S. Cravatte, T. Delcroix, G. Reverdin, and A. Ribes, 2012: Near- Walker, R., et al., 2009: Protecting the Amazon with protected areas. Proc. Natl. Acad. surface salinity as nature s rain gauge to detect human influence on the tropical Sci. U.S.A., 106, 10582 10586. water cycle. J. Clim., 25, 958 977. Wang, M., and J. Overland, 2009: A sea ice free summer Arctic within 30 years? Thorne, P., 2008: Arctic tropospheric warming amplification? Nature, 455, E1 E2. Geophys. Res. Lett., 36, L07502. Tietsche, S., D. Notz, J. H. Jungclaus, and J. Marotzke, 2011: Recovery mechanisms of Wang, M., and J. E. Overland, 2012: A sea ice free summer Arctic within 30 years: An Arctic summer sea ice. Geophys. Res. Lett., 38, L02707. update from CMIP5 models. Geophys. Res. Lett., 39, L18501. Tjiputra, J. F., et al., 2013: Evaluation of the carbon cycle components in the Wania, R., I. Ross, and I. Prentice, 2009: Integrating peatlands and permafrost into Norwegian Earth System Model (NorESM). Geosci. Model Dev., 6, 301 325. a dynamic global vegetation model: 2. Evaluation and sensitivity of vegetation Tokinaga, H., S.-P. Xie, C. Deser, Y. Kosaka, and Y. M. Okumura, 2012: Slowdown and carbon cycle processes. Global Biogeochem. Cycles, 23, GB3015. 12 of the Walker circulation driven by tropical Indo-Pacific warming. Nature, 491, Washington, W., et al., 2009: How much climate change can be avoided by 439 443. mitigation? Geophys. Res. Lett., 36, L08703. Trapp, R. J., N. S. Diffenbaugh, and A. Gluhovsky, 2009: Transient response of severe Watanabe, S., et al., 2011: MIROC-ESM 2010: Model description and basic results of thunderstorm forcing to elevated greenhouse gas concentrations. Geophys. Res. CMIP5-20c3m experiments. Geosci. Model Dev., 4, 845 872. Lett., 36, L01703. Watterson, I. G., 2008: Calculation of probability density functions for temperature Trapp, R. J., N. S. Diffenbaugh, H. E. Brooks, M. E. Baldwin, E. D. Robinson, and J. S. Pal, and precipitation change under global warming. J. Geophys. Res., 113, D12106. 2007: Changes in severe thunderstorm environment frequency during the 21st Watterson, I. G., 2011: Calculation of joint PDFs for climate change with properties century caused by anthropogenically enhanced global radiative forcing. Proc. matching recent Australian projections. Aust. Meteorol. Oceanogr. J., 61, 211 Natl. Acad. Sci. U.S.A., 104, 19719 19723. 219. Trenberth, K. E., and D. J. Shea, 2005: Relationships between precipitation and Watterson, I. G., and P. H. Whetton, 2011a: Joint PDFs for Australian climate in surface temperature. Geophys. Res. Lett., 32, L14703. future decades and an idealized application to wheat crop yield. Aust. Meteorol. Trenberth, K. E., and J. T. Fasullo, 2009: Global warming due to increasing absorbed Oceanogr. J., 61, 221 230. solar radiation. Geophys. Res. Lett., 36, L07706. Watterson, I. G., and P. H. Whetton, 2011b: Distributions of decadal means of Trenberth, K. E., and J. T. Fasullo, 2010: Simulation of present-day and twenty-first- temperature and precipitation change under global warming. J. Geophys. Res., century energy budgets of the Southern Oceans. J. Clim., 23, 440 454. 116, D07101. Turner, J., T. J. Bracegirdle, T. Phillips, G. J. Marshall, and J. S. Hosking, 2013: An initial Watterson, I. G., J. L. McGregor, and K. C. Nguyen, 2008: Changes in extreme assessment of Antarctic sea ice extent in the CMIP5 models. J. Clim., 26, 1473 temperatures of Australasian summer simulated by CCAM under global 1484. warming, and the roles of winds and land-sea contrasts. Aust. Meteorol. Mag., Ueda, H., A. Iwai, K. Kuwako, and M. Hori, 2006: Impact of anthropogenic forcing on 57, 195 212. the Asian summer monsoon as simulated by eight GCMs. Geophys. Res. Lett., WBGU, 2009: Solving the Climate Dilemma: The Budget Approach. German Advisory 33, L06703. Council on Global Change, Berlin, 59 pp. 1134 Long-term Climate Change: Projections, Commitments and Irreversibility Chapter 12 Weaver, A., K. Zickfeld, A. Montenegro, and M. Eby, 2007: Long term climate Wyant, M. C., et al., 2006: A comparison of low-latitude cloud properties and their implications of 2050 emission reduction targets. Geophys. Res. Lett., 34, L19703. response to climate change in three AGCMs sorted into regimes using mid- Weaver, A. J., et al., 2012: Stability of the Atlantic meridional overturning circulation: tropospheric vertical velocity. Clim. Dyn., 27, 261 279. A model intercomparison. Geophys. Res. Lett., 39, L20709. Xie, P., and G. Vallis, 2012: The passive and active nature of ocean heat uptake in Webb, M., et al., 2006: On the contribution of local feedback mechanisms to the idealized climate change experiments. Clim. Dyn., 38, 667 684. range of climate sensitivity in two GCM ensembles. Clim. Dyn., 27, 17 38. Xie, S. P., C. Deser, G. A. Vecchi, J. Ma, H. Y. Teng, and A. T. Wittenberg, 2010: Global Webb, M. J., F. H. Lambert, and J. M. Gregory, 2013: Origins of differences in climate warming pattern formation: Sea surface temperature and rainfall. J. Clim., 23, sensitivity, forcing and feedback in climate models. Clim. Dyn., 40, 677 707. 966 986. Weber, S., et al., 2007: The modern and glacial overturning circulation in the Atlantic Xin, X., L. Zhang, J. Zhang, T. Wu, and Y. Fang, 2013a: Climate change projections over Ocean in PMIP coupled model simulations. Clim. Past, 3, 51 64. East Asia with BCC_CSM1.1 climate model under RCP scenarios. J. Meteorol. Weertman, J., 1974: Stability of the junction of an ice sheet and an ice shelf. J. Soc. Jpn., 4, 413-429. Glaciol., 13, 3 11. Xin, X., T. Wu, J. Li, Z. Wang, W. Li, and F. Wu, 2013b: How well does BCC_CSM1.1 Wehner, M., D. Easterling, J. Lawrimore, R. Heim, R. Vose, and B. Santer, 2011: reproduce the 20th century climate change in China? Atmos. Ocean. Sci. Lett., Projections of future drought in the continental United States and Mexico. J. 6, 21 26. Hydrometeorol., 12, 1359 1377. Yang, F. L., A. Kumar, M. E. Schlesinger, and W. Q. Wang, 2003: Intensity of hydrological Weigel, A., R. Knutti, M. Liniger, and C. Appenzeller, 2010: Risks of model weighting cycles in warmer climates. J. Clim., 16, 2419 2423. in multimodel climate projections. J. Clim., 23, 4175 4191. Yin, J., J. Overpeck, S. Griffies, A. Hu, J. Russell, and R. Stouffer, 2011: Different Wentz, F., L. Ricciardulli, K. Hilburn, and C. Mears, 2007: How much more rain will magnitudes of projected subsurface ocean warming around Greenland and global warming bring? Science, 317, 233 235. Antarctica. Nature Geosci., 4, 524 528. Wetherald, R., and S. Manabe, 1988: Cloud feedback processes in a General- Yokohata, T., M. Webb, M. Collins, K. Williams, M. Yoshimori, J. Hargreaves, and J. Circulation Model. J. Atmos. Sci., 45, 1397 1415. Annan, 2010: Structural similarities and differences in climate responses to CO2 Wetherald, R. T., R. J. Stouffer, and K. W. Dixon, 2001: Committed warming and its increase between two perturbed physics ensembles. J. Clim., 23, 1392 1410. implications for climate change. Geophys. Res. Lett., 28, 1535 1538. Yokohata, T., J. D. Annan, M. Collins, C. S. Jackson, M. Tobis, M. Webb, and J. C. Wigley, T. M. L., 2005: The climate change commitment. Science, 307, 1766 1769. Hargreaves, 2012: Reliability of multi-model and structurally different single- Wilcox, L. J., A. J. Charlton-Perez, and L. J. Gray, 2012: Trends in Austral jet position in model ensembles. Clim. Dyn., 39, 599 616. ensembles of high- and low-top CMIP5 models. J. Geophys. Res., 117, D13115. Young, P. J., K. H. Rosenlof, S. Solomon, S. C. Sherwood, Q. Fu, and J.-F. Lamarque, Willett, K., and S. Sherwood, 2012: Exceedance of heat index thresholds for 15 2012: Changes in stratospheric temperatures and their implications for changes regions under a warming climate using the wet-bulb globe temperature. Int. J. in the Brewer Dobson circulation, 1979 2005. J. Clim., 25, 1759 1772. Climatol., 32, 161 177. Yukimoto, S., et al., 2012: A new global climate model of the Meteorological Williams, J. W., S. T. Jackson, and J. E. Kutzbach, 2007: Projected distributions of Research Institute: MRI-CGCM3 Model description and basic performance. J. novel and disappearing climates by 2100 AD. Proc. Natl. Acad. Sci. U.S.A., 104, Meteorol. Soc. Jpn., 90A, 23 64. 5738 5742. Zappa, G., L. C. Shaffrey, K. I. Hodges, P. G. Sansom, and D. B. Stephenson, 2013: A Williams, K. D., W. J. Ingram, and J. M. Gregory, 2008: Time variation of effective multi-model assessment of future projections of North Atlantic and European climate sensitivity in GCMs. J. Clim., 21, 5076 5090. extratropical cyclones in the CMIP5 climate models. J. Clim., doi:10.1175/JCLI- Winton, M., 2006a: Amplified Arctic climate change: What does surface albedo D-12-00573.1. feedback have to do with it? Geophys. Res. Lett., 33, L03701. Zelazowski, P., Y. Malhi, C. Huntingford, S. Sitch, and J. Fisher, 2011: Changes in the Winton, M., 2006b: Does the Arctic sea ice have a tipping point? Geophys. Res. Lett., potential distribution of humid tropical forests on a warmer planet. Philos. Trans. 33, L23504. R. Soc. A, 369, 137 160. Winton, M., 2008: Sea ice-albedo feedback and nonlinear Arctic climate change. In: Zelinka, M., and D. Hartmann, 2010: Why is longwave cloud feedback positive? J. Arctic Sea Ice Decline: Observations, Projections, Mechanisms, and Implications Geophys. Res., 115, D16117. [E. T. DeWeaver, C. M. Bitz and L. B. Tremblay (eds.)]. American Geophysical Zelinka, M., S. Klein, and D. Hartmann, 2012: Computing and partitioning cloud Union, Washington, DC, pp. 111 131. feedbacks using Cloud property histograms. Part II: Attribution to changes in Winton, M., 2011: Do climate models underestimate the sensitivity of Northern cloud amount, altitude, and optical depth. J. Clim., 25, 3736 3754. Hemisphere sea ice cover? J. Clim., 24, 3924 3934. Zhang, M., and H. Song, 2006: Evidence of deceleration of atmospheric vertical WMO, 2007: Scientific assessment of ozone depletion. In: 2006, Global Ozone overturning circulation over the tropical Pacific. Geophys. Res. Lett., 33, L12701. Research and Monitoring Project. World Meteorological Organization, Geneva, Zhang, M. H., and C. Bretherton, 2008: Mechanisms of low cloud-climate feedback Switzerland, 572 pp. in idealized single-column simulations with the Community Atmospheric Model, Wood, R., A. Keen, J. Mitchell, and J. Gregory, 1999: Changing spatial structure of version 3 (CAM3). J. Clim., 21, 4859 4878. 12 the thermohaline circulation in response to atmospheric CO2 forcing in a climate Zhang, R., 2010a: Northward intensification of anthropogenically forced changes model. Nature, 399, 572 575. in the Atlantic meridional overturning circulation (AMOC). Geophys. Res. Lett., Woollings, T., 2008: Vertical structure of anthropogenic zonal-mean atmospheric 37, L24603. circulation change. Geophys. Res. Lett., 35, L19702. Zhang, T., 2005: Influence of the seasonal snow cover on the ground thermal regime: Woollings, T., and M. Blackburn, 2012: The North Atlantic jet stream under climate An overview. Rev. Geophys., 43, RG4002. change and its relation to the NAO and EA patterns. J. Clim., 25, 886 902. Zhang, T., J. A. Heginbottom, R. G. Barry, and J. Brown, 2000: Further statistics on the Woollings, T., J. M. Gregory, J. G. Pinto, M. Reyers, and D. J. Brayshaw, 2012: Response distribution of permafrost and ground ice in the Northern Hemisphere 1. Polar of the North Atlantic storm track to climate change shaped by ocean-atmosphere Geogr., 24, 126 131. coupling. Nature Geosci., 5, 313 317. Zhang, X., 2010b: Sensitivity of Arctic summer sea ice coverage to global warming Wu, P., R. Wood, J. Ridley, and J. Lowe, 2010: Temporary acceleration of the forcing: Towards reducing uncertainty in arctic climate change projections. Tellus hydrological cycle in response to a CO2 rampdown. Geophys. Res. Lett., 37, A, 62, 220 227. L12705. Zhang, X., and J. Walsh, 2006: Toward a seasonally ice-covered Arctic Ocean: Wu, P., L. Jackson, A. Pardaens, and N. Schaller, 2011a: Extended warming of Scenarios from the IPCC AR4 model simulations. J. Clim., 19, 1730 1747. the northern high latitudes due to an overshoot of the Atlantic meridional Zhang, X. B., et al., 2007: Detection of human influence on twentieth-century overturning circulation. Geophys. Res. Lett., 38, L24704. precipitation trends. Nature, 448, 461 U464. Wu, T., et al., 2013: Global carbon budgets simulated by the Beijing Climate Center Zhou, L. M., R. E. Dickinson, P. Dirmeyer, A. Dai, and S. K. Min, 2009: Spatiotemporal Climate System Model for the last century. J. Geophys. Res., doi:10.1002/ patterns of changes in maximum and minimum temperatures in multi-model jgrd.50320. simulations. Geophys. Res. Lett., 36, L02702. Wu, Y., M. Ting, R. Seager, H.-P. Huang, and M. A. Cane, 2011b: Changes in storm tracks and energy transports in a warmer climate simulated by the GFDL CM2.1 model. Clim. Dyn., 37, 53 72. 1135 Chapter 12 Long-term Climate Change: Projections, Commitments and Irreversibility Zhuang, Q., et al., 2004: Methane fluxes between terrestrial ecosystems and the atmosphere at northern high latitudes during the past century: A retrospective analysis with a process-based biogeochemistry model. Global Biogeochem. Cycles, 18, GB3010. Zickfeld, K., V. K. Arora, and N. P. Gillett, 2012: Is the climate response to CO2 emissions path dependent? Geophys. Res. Lett., 39, L05703. Zickfeld, K., B. Knopf, V. Petoukhov, and H. Schellnhuber, 2005: Is the Indian summer monsoon stable against global change? Geophys. Res. Lett., 32, L15707. Zickfeld, K., M. Eby, H. Matthews, and A. Weaver, 2009: Setting cumulative emissions targets to reduce the risk of dangerous climate change. Proc. Natl. Acad. Sci. U.S.A., 106, 16129 16134. Zickfeld, K., M. Morgan, D. Frame, and D. Keith, 2010: Expert judgments about transient climate response to alternative future trajectories of radiative forcing. Proc. Natl. Acad. Sci. U.S.A., 107, 12451 12456. Zickfeld, K., et al., 2013: Long-term climate change commitment and reversibility: An EMIC intercomparison. J. Clim., doi:10.1175/JCLI-D-12-00584.1. Zimov, S., S. Davydov, G. Zimova, A. Davydova, E. Schuur, K. Dutta, and F. Chapin, 2006: Permafrost carbon: Stock and decomposability of a globally significant carbon pool. Geophys. Res. Lett., 33, L20502. Zunz, V., H. Goosse, and F. Massonnet, 2013: How does internal variability influence the ability of CMIP5 models to reproduce the recent trend in Southern Ocean sea ice extent? Cryosphere, 7, 451 468. 12 1136 13 Sea Level Change Coordinating Lead Authors: John A. Church (Australia), Peter U. Clark (USA) Lead Authors: Anny Cazenave (France), Jonathan M. Gregory (UK), Svetlana Jevrejeva (UK), Anders Levermann (Germany), Mark A. Merrifield (USA), Glenn A. Milne (Canada), R. Steven Nerem (USA), Patrick D. Nunn (Australia), Antony J. Payne (UK), W. Tad Pfeffer (USA), Detlef Stammer (Germany), Alakkat S. Unnikrishnan (India) Contributing Authors: David Bahr (USA), Jason E. Box (Denmark/USA), David H. Bromwich (USA), Mark Carson (Germany), William Collins (UK), Xavier Fettweis (Belgium), Piers Forster (UK), Alex Gardner (USA), W. Roland Gehrels (UK), Rianne Giesen (Netherlands), Peter J. Gleckler (USA), Peter Good (UK), Rune Grand Graversen (Sweden), Ralf Greve (Japan), Stephen Griffies (USA), Edward Hanna (UK), Mark Hemer (Australia), Regine Hock (USA), Simon J. Holgate (UK), John Hunter (Australia), Philippe Huybrechts (Belgium), Gregory Johnson (USA), Ian Joughin (USA), Georg Kaser (Austria), Caroline Katsman (Netherlands), Leonard Konikow (USA), Gerhard Krinner (France), Anne Le Brocq (UK), Jan Lenaerts (Netherlands), Stefan Ligtenberg (Netherlands), Christopher M. Little (USA), Ben Marzeion (Austria), Kathleen L. McInnes (Australia), Sebastian H. Mernild (USA), Didier Monselesan (Australia), Ruth Mottram (Denmark), Tavi Murray (UK), Gunnar Myhre (Norway), J.P. Nicholas (USA), Faezeh Nick (Norway), Mahé Perrette (Germany), David Pollard (USA), Valentina Radiæ (Canada), Jamie Rae (UK), Markku Rummukainen (Sweden), Christian Schoof (Canada), Aimée Slangen (Australia/Netherlands), Jan H. van Angelen (Netherlands), Willem Jan van de Berg (Netherlands), Michiel van den Broeke (Netherlands), Miren Vizcaíno (Netherlands), Yoshihide Wada (Netherlands), Neil J. White (Australia), Ricarda Winkelmann (Germany), Jianjun Yin (USA), Masakazu Yoshimori (Japan), Kirsten Zickfeld (Canada) Review Editors: Jean Jouzel (France), Roderik van de Wal (Netherlands), Philip L. Woodworth (UK), Cunde Xiao (China) This chapter should be cited as: Church, J.A., P.U. Clark, A. Cazenave, J.M. Gregory, S. Jevrejeva, A. Levermann, M.A. Merrifield, G.A. Milne, R.S. Nerem, P.D. Nunn, A.J. Payne, W.T. Pfeffer, D. Stammer and A.S. Unnikrishnan, 2013: Sea Level Change. In: Climate Change 2013: The Physical Science Basis. Contribution of Working Group I to the Fifth Assessment Report of the Intergovernmental Panel on Climate Change [Stocker, T.F., D. Qin, G.-K. Plattner, M. Tignor, S.K. Allen, J. Boschung, A. Nauels, Y. Xia, V. Bex and P.M. Midgley (eds.)]. Cambridge University Press, Cambridge, United Kingdom and New York, NY, USA. 1137 Table of Contents Executive Summary.................................................................... 1139 13.6 Regional Sea Level Changes....................................... 1191 13.6.1 Regional Sea Level Changes, Climate Modes and 13.1 Components and Models of Sea Level Change..... 1142 Forced Sea Level Response...................................... 1191 13.1.1 Introduction and Chapter Overview......................... 1142 13.6.2 Coupled Model Intercomparison Project Phase 5 General Circulation Model Projections on Decadal 13.1.2 Fundamental Definitions and Concepts................... 1142 to Centennial Time Scales........................................ 1192 13.1.3 Processes Affecting Sea Level.................................. 1143 13.6.3 Response to Atmospheric Pressure Changes............ 1193 13.1.4 Models Used to Interpret Past and Project Future 13.6.4 Response to Freshwater Forcing............................... 1193 Changes in Sea Level............................................... 1144 13.6.5 Regional Relative Sea Level Changes....................... 1194 13.2 Past Sea Level Change.................................................. 1145 13.6.6 Uncertainties and Sensitivity to Ocean/Climate Model Formulations and Parameterizations............. 1197 13.2.1 The Geological Record............................................. 1145 13.2.2 The Instrumental Record (~1700 2012).................. 1146 13.7 Projections of 21st Century Sea Level Extremes and Waves..................................................... 1200 13.3 Contributions to Global Mean Sea Level Rise 13.7.1 Observed Changes in Sea Level Extremes................ 1200 During the Instrumental Period................................. 1150 13.7.2 Projections of Sea Level Extremes............................ 1200 13.3.1 Thermal Expansion Contribution.............................. 1150 13.7.3 Projections of Ocean Waves..................................... 1202 13.3.2 Glaciers.................................................................... 1151 13.3.3 Greenland and Antarctic Ice Sheets......................... 1153 13.8 Synthesis and Key Uncertainties............................... 1204 13.3.4 Contributions from Water Storage on Land.............. 1155 13.3.5 Ocean Mass Observations from the Gravity References ................................................................................ 1206 Recovery and Climate Experiment........................... 1156 13.3.6 Budget of Global Mean Sea Level Rise.................... 1156 Frequently Asked Questions Box 13.1: The Global Energy Budget...................................... 1159 FAQ 13.1 Why Does Local Sea Level Change Differ from the Global Average?.................................... 1148 13.4 Projected Contributions to Global Mean FAQ 13.2 Will the Greenland and Antarctic Ice Sheets Sea Level........................................................................... 1161 Contribute to Sea Level Change over the Rest of the Century?............................................. 1177 13.4.1 Ocean Heat Uptake and Thermal Expansion............ 1161 13.4.2 Glaciers.................................................................... 1163 Supplementary Material 13.4.3 Greenland Ice Sheet................................................. 1165 Supplementary Material is available in online versions of the report. 13.4.4 Antarctic Ice Sheet................................................... 1170 Box 13.2: History of the Marine Ice-Sheet Instability Hypothesis ................................................................................ 1175 13.4.5 Anthropogenic Intervention in Water Storage on Land.................................................................... 1176 13.5 Projections of Global Mean Sea Level Rise............ 1179 13.5.1 Process-Based Projections for the 21st Century....... 1179 13 13.5.2 Semi-Empirical Projections for the 21st Century...... 1182 13.5.3 Confidence in Likely Ranges and Bounds................. 1184 13.5.4 Long-term Scenarios................................................ 1186 1138 Sea Level Change Chapter 13 Executive Summary c ­hanges have made only a small contribution; the rate of ground- water depletion has increased and now exceeds the rate of reservoir This chapter considers changes in global mean sea level, regional sea impoundment. Since 1993, when observations of all sea level com- level, sea level extremes, and waves. Confidence in projections of global ponents are available, the sum of contributions equals the observed mean sea level rise has increased since the Fourth Assessment Report global mean sea level rise within uncertainties (high confidence). (AR4) because of the improved physical understanding of the compo- {Chapters 3, 4, 13.3.6, Figure 13.4, Table 13.1} nents of sea level, the improved agreement of process-based models with observations, and the inclusion of ice-sheet dynamical changes. There is high confidence in projections of thermal expansion and Greenland surface mass balance, and medium confidence Past Sea Level Change in projections of glacier mass loss and Antarctic surface mass balance. There has been substantial progress in ice-sheet modelling, Paleo sea level records from warm periods during the last 3 particularly for Greenland. Process-based model calculations of contri- million years indicate that global mean sea level has exceeded butions to past sea level change from ocean thermal expansion, gla- 5 m above present (very high confidence)1 when global mean cier mass loss and Greenland ice-sheet surface mass balance are con- temperature was up to 2°C warmer than pre-industrial (medium sistent with available observational estimates of these contributions confidence). There is very high confidence that maximum global over recent decades. Ice-sheet flowline modelling is able to reproduce mean sea level during the last interglacial period (~129 to 116 ka) the observed acceleration of the main outlet glaciers in the Green- was, for several thousand years, at least 5 m higher than present and land ice sheet, thus allowing estimates of the 21st century dynamical high confidence that it did not exceed 10 m above present, implying response (medium confidence). Significant challenges remain in the substantial contributions from the Greenland and Antarctic ice sheets. process-based projections of the dynamical response of marine-termi- This change in sea level occurred in the context of different orbital forc- nating glaciers and marine-based sectors of the Antarctic ice sheet. ing and with high latitude surface temperature, averaged over several Alternative means of projection of the Antarctic ice-sheet contribution thousand years, at least 2°C warmer than present (high confidence) (extrapolation within a statistical framework and informed judgement) {5.3.4, 5.6.1, 5.6.2, 13.2.1} provide medium confidence in a likely range. There is currently low confidence in projecting the onset of large-scale grounding line insta- Proxy and instrumental sea level data indicate a transition in bility in the marine-based sectors of the Antarctic ice sheet. {13.3.1 to the late 19th century to the early 20th century from relative- 13.3.3, 13.4.3, 13.4.4} ly low mean rates of rise over the previous two millennia to higher rates of rise (high confidence). It is likely2 that the rate The sum of thermal expansion simulated by Coupled Model of global mean sea level rise has continued to increase since Intercomparison Project phase 5 (CMIP5) Atmosphere Ocean the early 20th century, with estimates that range from 0.000 General Circulation Models (AOGCMs), glacier mass loss com- [ 0.002 to 0.002] mm yr 2 to 0.013 [0.007 to 0.019] mm yr 2. It puted by global glacier models using CMIP5 climate change is very likely that the global mean rate was 1.7 [1.5 to 1.9] mm yr 1 simulations, and estimates of land water storage explain 65% between 1901 and 2010 for a total sea level rise of 0.19 [0.17 to 0.21] of the observed global mean sea level rise for 1901 1990 and m. Between 1993 and 2010, the rate was very likely higher at 3.2 [2.8 90% for 1971 2010 and 1993 2010 (high confidence). When to 3.6] mm yr 1; similarly high rates likely occurred between 1920 and observed climate parameters are used, the glacier models indicate a 1950. {3.7.2, 3.7.4, 5.6.3, 13.2.1, 13.2.2, Figure 13.3} larger Greenland peripheral glacier contribution in the first half of the 20th century such that the sum of thermal expansion, glacier mass Understanding of Sea Level Change loss and changes in land water storage and a small ongoing Antarctic ice-sheet contribution are within 20% of the observations throughout Ocean thermal expansion and glacier melting have been the the 20th century. Model-based estimates of ocean thermal expansion dominant contributors to 20th century global mean sea level and glacier contributions indicate that the greater rate of global mean rise. Observations since 1971 indicate that thermal expansion and gla- sea level rise since 1993 is a response to radiative forcing (RF, both ciers (excluding Antarctic glaciers peripheral to the ice sheet) explain anthropogenic and natural) and increased loss of ice-sheet mass and 75% of the observed rise (high confidence). The contribution of the not part of a natural oscillation (medium confidence). {13.3.6, Figures Greenland and Antarctic ice sheets has increased since the early 1990s, 13.4, 13.7, Table 13.1} partly from increased outflow induced by warming of the immediate- ly adjacent ocean. Natural and human-induced land water storage 13 In this Report, the following summary terms are used to describe the available evidence: limited, medium, or robust; and for the degree of agreement: low, medium, or 1 high. A level of confidence is expressed using five qualifiers: very low, low, medium, high, and very high, and typeset in italics, e.g., medium confidence. For a given evi- dence and agreement statement, different confidence levels can be assigned, but increasing levels of evidence and degrees of agreement are correlated with increasing confidence (see Section 1.4 and Box TS.1 for more details). In this Report, the following terms have been used to indicate the assessed likelihood of an outcome or a result: Virtually certain 99 100% probability, Very likely 2 90 100%, Likely 66 100%, About as likely as not 33 66%, Unlikely 0 33%, Very unlikely 0 10%, Exceptionally unlikely 0 1%. Additional terms (Extremely likely: 95 100%, More likely than not >50 100%, and Extremely unlikely 0 5%) may also be used when appropriate. Assessed likelihood is typeset in italics, e.g., very likely (see Section 1.4 and Box TS.1 for more details). 1139 Chapter 13 Sea Level Change The Earth s Energy Budget (medium confidence). This assessment is based on medium confidence in the modelled contribution from thermal expansion and low con- Independent estimates of effective RF of the climate system, fidence in the modelled contribution from ice sheets. The amount of the observed heat storage, and surface warming combine to ocean thermal expansion increases with global warming (0.2 to 0.6 give an energy budget for the Earth that is closed within uncer- m °C 1) but the rate of the glacier contribution decreases over time tainties (high confidence), and is consistent with the likely range as their volume (currently 0.41 m sea level equivalent) decreases. Sea of climate sensitivity. The largest increase in the storage of heat in level rise of several meters could result from long-term mass loss by the climate system over recent decades has been in the oceans; this ice sheets (consistent with paleo data observations of higher sea levels is a powerful observation for the detection and attribution of climate during periods of warmer temperatures), but there is low confidence in change. {Boxes 3.1, 13.1} these projections. Sea level rise of 1 to 3 m per degree of warming is projected if the warming is sustained for several millennia (low confi- Global Mean Sea Level Rise Projections dence). {13.5.4, Figures 13.4.3, 13.4.4} It is very likely that the rate of global mean sea level rise during The available evidence indicates that sustained global warming the 21st century will exceed the rate observed during 1971 greater than a certain threshold above pre-industrial would lead 2010 for all Representative Concentration Pathway (RCP) sce- to the near-complete loss of the Greenland ice sheet over a mil- narios due to increases in ocean warming and loss of mass from lennium or more, causing a global mean sea level rise of about glaciers and ice sheets. Projections of sea level rise are larger 7 m. Studies with fixed ice-sheet topography indicate the threshold than in the AR4, primarily because of improved modeling of is greater than 2°C but less than 4°C (medium confidence) of global land-ice contributions. For the period 2081 2100, compared to mean surface temperature rise with respect to pre-industrial. The one 1986 2005, global mean sea level rise is likely (medium confi- study with a dynamical ice sheet suggests the threshold is greater dence) to be in the 5 to 95% range of projections from process- than about 1°C (low confidence) global mean warming with respect to based models, which give 0.26 to 0.55 m for RCP2.6, 0.32 to pre-industrial. We are unable to quantify a likely range. Whether or not 0.63 m for RCP4.5, 0.33 to 0.63 m for RCP6.0, and 0.45 to 0.82 m a decrease in the Greenland ice sheet mass loss is irreversible depends for RCP8.5. For RCP8.5, the rise by 2100 is 0.52 to 0.98 m with a on the duration and degree of exceedance of the threshold. Abrupt and rate during 2081 2100 of 8 to 16 mm yr 1. We have considered the irreversible ice loss from a potential instability of marine-based sectors evidence for higher projections and have concluded that there is cur- of the Antarctic ice sheet in response to climate forcing is possible, but rently insufficient evidence to evaluate the probability of specific levels current evidence and understanding is insufficient to make a quantita- above the assessed likely range. Based on current understanding, only tive assessment. {5.8, 13.3, 13.4 } the collapse of marine-based sectors of the Antarctic ice sheet, if initi- ated, could cause global mean sea level to rise substantially above the Regional Sea Level Change Projections likely range during the 21st century. This potential additional contribu- tion cannot be precisely quantified but there is medium confidence It is very likely that in the 21st century and beyond, sea level that it would not exceed several tenths of a meter of sea level rise change will have a strong regional pattern, with some places during the 21st century. {13.5.1, Table 13.5, Figures 13.10, 13.11} experiencing significant deviations of local and regional sea level change from the global mean change. Over decadal periods, Some semi-empirical models project a range that overlaps the the rates of regional sea level change as a result of climate variability process-based likely range while others project a median and can differ from the global average rate by more than 100% of the 95th percentile that are about twice as large as the process- global average rate. By the end of the 21st century, it is very likely based models. In nearly every case, the semi-empirical model that over about 95% of the world ocean, regional sea level rise will 95th percentile is higher than the process-based likely range. be positive, and most regions that will experience a sea level fall are Despite the successful calibration and evaluation of semi-empirical located near current and former glaciers and ice sheets. About 70% of models against the observed 20th century sea level record, there is the global coastlines are projected to experience a relative sea level no consensus in the scientific community about their reliability, and change within 20% of the global mean sea level change. {13.6.5, Fig- consequently low confidence in projections based on them. {13.5.2, ures 13.18 to 13.22} 13.5.3, Figure 13.12} Projections of 21st Century Sea Level Extremes and It is virtually certain that global mean sea level rise will con- Surface Waves 13 tinue beyond 2100, with sea level rise due to thermal expan- sion to continue for many centuries. The amount of longer term It is very likely that there will be a significant increase in the sea level rise depends on future emissions. The few available occurrence of future sea level extremes in some regions by 2100, process-based models that go beyond 2100 indicate global mean sea with a likely increase in the early 21st century. This increase will level rise above the pre-industrial level to be less than 1 m by 2300 primarily be the result of an increase in mean sea level (high confi- for greenhouse gas concentrations that peak and decline and remain dence), with the frequency of a particular sea level extreme increasing below 500 ppm CO2-eq, as in scenario RCP2.6. For a radiative forcing by an order of magnitude or more in some regions by the end of the that corresponds to above 700 ppm CO2-eq but below 1500 ppm, as 21st century. There is low confidence in region-specific projections of in the scenario RCP8.5, the projected rise is 1 m to more than 3 m storminess and associated storm surges. {13.7.2, Figure 13.25} 1140 Sea Level Change Chapter 13 It is likely (medium confidence) that annual mean significant wave heights will increase in the Southern Ocean as a result of enhanced wind speeds. Southern Ocean generated swells are likely to affect heights, periods, and directions of waves in adjacent basins. It is very likely that wave heights and the duration of the wave season will increase in the Arctic Ocean as a result of reduced sea-ice extent. In general, there is low confidence in region-specific projections due to the low confidence in tropical and extratropical storm projections, and to the challenge of downscaling future wind fields from coarse-resolu- tion climate models. {13.7.3; Figure 13.26} 13 1141 Chapter 13 Sea Level Change 13.1 Components and Models of Sea Despite changes in the scenarios between the four Assessments, the Level Change sea level projections for 2100 (compared to 1990) for the full range of scenarios were remarkably similar, with the reduction in the upper 13.1.1 Introduction and Chapter Overview end in more recent reports reflecting the smaller increase in radiative forcing (RF) in recent scenarios due to smaller GHG emissions and the Changes in sea level occur over a broad range of temporal and spatial inclusion of aerosols, and a reduction in uncertainty in projecting the scales, with the many contributing factors making it an integral meas- contributions: 31 to 110 cm in the FAR, 13 to 94 cm in the SAR, 9 to ure of climate change (Milne et al., 2009; Church et al., 2010). The pri- 88 cm in the TAR and 18 to 59 cm in AR4 (not including a possible mary contributors to contemporary sea level change are the expansion additional allowance for a dynamic ice-sheet response). of the ocean as it warms and the transfer of water currently stored on land to the ocean, particularly from land ice (glaciers and ice sheets) Results since the AR4 show that for recent decades, sea level has contin- (Church et al., 2011a). Observations indicate the largest increase in the ued to rise (Section 3.7). Improved and new observations of the ocean storage of heat in the climate system over recent decades has been in (Section 3.7) and the cryosphere (Chapter 4) and their representation the oceans (Section 3.2) and thus sea level rise from ocean warming in models have resulted in better understanding of 20th century sea is a central part of the Earth s response to increasing greenhouse gas level rise and its components (this chapter). Records of past sea level (GHG) concentrations. changes constrain long-term land-ice response to warmer climates as well as extend the observational record to provide a longer context for The First IPCC Assessment Report (FAR) laid the groundwork for much current sea level rise (Section 5.6). of our current understanding of sea level change (Warrick and Oer- lemans, 1990). This included the recognition that sea level had risen This chapter provides a synthesis of past and contemporary sea level during the 20th century, that the rate of rise had increased compared change at global and regional scales. Drawing on the published ref- to the 19th century, that ocean thermal expansion and the mass loss ereed literature, including as summarized in earlier chapters of this from glaciers were the main contributors to the 20th century rise, that Assessment, we explain the reasons for contemporary change and during the 21st century the rate of rise was projected to be faster than assess confidence in and provide global and regional projections of during the 20th century, that sea level will not rise uniformly around likely sea level change for the 21st century and beyond. We discuss the world, and that sea level would continue to rise well after GHG the primary factors that cause regional sea level to differ from the emissions are reduced. They also concluded that no major dynamic global average and how these may change in the future. In addition, response of the ice sheets was expected during the 21st century, leav- we address projected changes in surface waves and the consequences ing ocean thermal expansion and the melting of glaciers as the main of sea level and climate change for extreme sea level events. contributors to the 21st century rise. The Second Assessment Report (SAR) came to very similar conclusions (Warrick et al., 1996). 13.1.2 Fundamental Definitions and Concepts By the time of the Third Assessment Report (TAR), coupled Atmos- The height of the ocean surface at any given location, or sea level, is phere Ocean General Circulation Models (AOGCMs) and ice-sheet measured either with respect to the surface of the solid Earth (relative models largely replaced energy balance climate models as the primary sea level (RSL)) or a geocentric reference such as the reference ellipsoid techniques supporting the interpretation of observations and the pro- (geocentric sea level). RSL is the more relevant quantity when consider- jections of sea level (Church et al., 2001). This approach allowed con- ing the coastal impacts of sea level change, and it has been measured sideration of the regional distribution of sea level change in addition to using tide gauges during the past few centuries (Sections 13.2.2 and the global average change. By the time of the Fourth Assessment Report 3.7) and estimated for longer time spans from geological records (Sec- (AR4), there were more robust observations of the variations in the rate tions 13.2.1 and 5.6). Geocentric sea level has been measured over the of global average sea level rise for the 20th century, some understand- past two decades using satellite altimetry (Sections 13.2.2 and 3.7). ing of the variability in the rate of rise, and the satellite altimeter record was long enough to reveal the complexity of the time-variable spatial A temporal average for a given location, known as Mean Sea Level distribution of sea level (Bindoff et al., 2007). Nevertheless, three cen- (MSL; see Glossary), is applied to remove shorter period variability. tral issues remained. First, the observed sea level rise over decades Apart from Section 13.7, which considers high-frequency changes in was larger than the sum of the individual contributions estimated from ocean surface height, the use of sea level elsewhere in this chapter observations or with models (Rahmstorf et al., 2007, 2012a), although refers to MSL. It is common to average MSL spatially to define global in general the uncertainties were large enough that there was no sig- mean sea level (GMSL; see Glossary). In principle, integrating RSL 13 nificant contradiction. Second, it was not possible to make confident change over the ocean area gives the change in ocean water volume, projections of the regional distribution of sea level rise. Third, there was which is directly related to the processes that dominate sea level insufficient understanding of the potential contributions from the ice change (changes in ocean temperature and land-ice volume). In con- sheets. In particular, the AR4 recognized that existing ice-sheet models trast, a small correction ( 0.15 to 0.5 mm yr 1) needs to be subtracted were unable to simulate the recent observations of ice-sheet acceler- from altimetry observations to estimate ocean water volume change ations and that understanding of ice-sheet dynamics was too limited (Tamisiea, 2011). Local RSL change can differ significantly from GMSL to assess the likelihood of continued acceleration or to provide a best because of spatial variability in changes of the sea surface and ocean estimate or an upper bound for their future contributions. floor height (see FAQ 13.1 and Section 13.6). 1142 Sea Level Change Chapter 13 13.1.3 Processes Affecting Sea Level the global ocean. The coupled atmosphere ocean system can also adjust to temperature anomalies associated with surface freshwater This chapter focusses on processes within the ocean, atmosphere, land anomalies through air sea feedbacks, resulting in dynamical adjust- ice, and hydrological cycle that are climate sensitive and are expected ments of sea level (Okumura et al., 2009; Stammer et al., 2011). Water to contribute to sea level change at regional to global scales in the mass exchange between land and the ocean also results in patterns coming decades to centuries (Figure 13.1). Figure 13.2 is a navigation of sea level change called sea level fingerprints (Clark and Lingle, aid for the different sections of this chapter and sections of other chap- 1977; Conrad and Hager, 1997; Mitrovica et al., 2001) due to change in ters that are relevant to sea level change. the gravity field and vertical movement of the ocean floor associated with visco-elastic Earth deformation (Farrell and Clark, 1976). These Changes in ocean currents, ocean density and sea level are all tightly changes in mass distribution also affect the Earth s inertia tensor and coupled such that changes at one location impact local sea level and therefore rotation, which produces an additional sea level response sea level far from the location of the initial change, including changes (Milne and Mitrovica, 1998). in sea level at the coast in response to changes in open-ocean tem- perature (Landerer et al., 2007; Yin et al., 2010). Although both tem- There are other processes that affect sea level but are not associated perature and salinity changes can contribute significantly to region- with contemporary climate change. Some of these result in changes al sea level change (Church et al., 2010), only temperature change that are large enough to influence the interpretation of observational produces a significant contribution to global average ocean volume records and sea level projections at regional and global scales. In par- change due to thermal expansion or contraction (Gregory and Lowe, ticular, surface mass transfer from land ice to oceans during the last 2000). Regional atmospheric pressure anomalies also cause sea level deglaciation contributes significantly to present-day sea level change to vary through atmospheric loading (Wunsch and Stammer, 1997). All due to the ongoing visco-elastic deformation of the Earth and the cor- of these climate-sensitive processes cause sea level to vary on a broad responding changes of the ocean floor height and gravity (referred to range of space and time scales from relatively short-lived events, such as glacial isostatic adjustment (GIA)) (Lambeck and Nakiboglu, 1984; as waves and storm surges, to sustained changes over several decades Peltier and Tushingham, 1991). Ice sheets also have long response or centuries that are associated with atmospheric and ocean modes of times and so continue to respond to past climate change (Section climate variability (White et al., 2005; Miller and Douglas, 2007; Zhang 13.1.5). and Church, 2012). Anthropogenic processes that influence the amount of water stored in Water and ice mass exchange between the land and the oceans leads the ground or on its surface in lakes and reservoirs, or cause changes in to a change in GMSL. A signal of added mass to the ocean propagates land surface characteristics that influence runoff or evapotranspiration rapidly around the globe such that all regions experience a sea level rates, will perturb the hydrological cycle and cause sea level change change within days of the mass being added (Lorbacher et al., 2012). (Sahagian, 2000; Wada et al., 2010). Such processes include water In addition, an influx of freshwater changes ocean temperature and impoundment (dams, reservoirs), irrigation schemes, and groundwater salinity and thus changes ocean currents and local sea level ­ Stammer, ( depletion (Section 13.4.5). 2008; Yin et al., 2009), with signals taking decades to propagate around 13 Figure 13.1 | Climate-sensitive processes and components that can influence global and regional sea level and are considered in this chapter. Changes in any one of the com- ponents or processes shown will result in a sea level change. The term ocean properties refers to ocean temperature, salinity and density, which influence and are dependent on ocean circulation. Both relative and geocentric sea level vary with position. Note that the geocenter is not shown. 1143 Chapter 13 Sea Level Change 13.1.4 Models Used to Interpret Past and Project Future Changes in Sea Level AOGCMs have components representing the ocean, atmosphere, land, and cryosphere, and simulate sea surface height changes relative to the geoid resulting from the natural forcings of volcanic eruptions and changes in solar irradiance, and from anthropogenic increases in GHGs and aerosols (Chapter 9). AOGCMs also exhibit internally generated climate variability, including such modes as the El Nino-Southern Oscil- lation (ENSO), the Pacific Decadal Oscillation (PDO), the North Atlantic Oscillation (NAO) and others that affect sea level (White et al., 2005; Zhang and Church, 2012). Critical components for global and regional changes in sea level are changes in surface wind stress and air sea heat and freshwater fluxes (Lowe and Gregory, 2006; Timmermann et al., 2010; Suzuki and Ishii, 2011) and the resultant changes in ocean density and circulation, for instance in the strength of the Atlantic Meridional Overturning Circulation (AMOC) (Yin et al., 2009; Lorbacher et al., 2010; Pardaens et al., 2011a). As in the real world, ocean density, circulation and sea level are dynamically connected in AOGCMs and evolve together. Offline models are required for simulating glacier and ice-sheet changes (Section 13.1.4.1). Geodynamic surface-loading models are used to simulate the RSL response to past and contemporary changes in surface water and land- ice mass redistribution and contemporary atmospheric pressure chang- es. The sea surface height component of the calculation is based solely on water mass conservation and perturbations to gravity, with no con- siderations of ocean dynamic effects. Application of these models has focussed on annual and interannual variability driven by contemporary changes in the hydrological cycle and atmospheric loading (Clarke et al., 2005; Tamisiea et al., 2010), and on secular trends associated with past and contemporary changes in land ice and hydrology (Lambeck et al., 1998; Mitrovica et al., 2001; Peltier, 2004; Riva et al., 2010). Figure 13.2 | Schematic representation of key linkages between processes and com- ponents that contribute to sea level change and are considered in this report. Colouring Semi-empirical models (SEMs) project sea level based on statistical of individual boxes indicates the types of models and approaches used in projecting the relationships between observed GMSL and global mean temperature contribution of each process or component to future sea level change. The diagram also (Rahmstorf, 2007a; Vermeer and Rahmstorf, 2009; Grinsted et al., serves as an index to the sections in this Assessment that are relevant to the assessment 2010) or total RF (Jevrejeva et al., 2009, 2010). The form of this rela- of sea level projections via the section numbers given at the bottom of each box. Note tionship is motivated by physical considerations, and the parameters gravity and solid Earth effects change the shape of the ocean floor and surface and thus are required to infer changes in ocean water volume from both relative and geocentric are determined from observational data hence the term semi-em- sea level observations. pirical (Rahmstorf et al., 2012b). SEMs do not explicitly simulate the underlying processes, and they use a characteristic response time that could be considerably longer than the time scale of interest (Rahm- Sea level changes due to tectonic and coastal processes are beyond storf, 2007a) or one that is explicitly determined by the model (Grin- the scope of this chapter. With the exception of earthquakes, which sted et al., 2010). can cause rapid local changes and tsunamis (Broerse et al., 2011) and secular RSL changes due to post-seismic deformation (Watson et al., Storm-surge and wave-projection models are used to assess how 2010), tectonic processes cause, on average, relatively low rates of sea changes in storminess and MSL impact sea level extremes and wave 13 level change (order 0.1 mm yr 1 or less; Moucha et al., 2008). Sediment climates. The two main approaches involve dynamical (Lowe et al., transfer and compaction (including from ground water depletion) in 2010) and statistical models (Wang et al., 2010). The dynamical the coastal zone are particularly important in deltaic regions (Blum and models are forced by near-surface wind and mean sea level pressure Roberts, 2009; Syvitski et al., 2009). Although they can dominate sea fields derived from regional or global climate models (Lowe et al., level change in these localized areas, they are less important as a source 2010). of sea level change at regional and global scales and so are not consid- ered further in this chapter (see discussion in Working Group II, Chapter In this chapter, we use the term process-based models (see Glossary) 5). Estimates of sediment delivery to the oceans (Syvitski and Kettner, to refer to sea level and land-ice models (Section 13.1.4.1) that aim 2011) suggest a contribution to GMSL rise of order 0.01 mm yr 1. to simulate the underlying processes and interactions, in contrast to 1144 Sea Level Change Chapter 13 semi-empirical models which do not. Although these two approaches line migration robustly so that results do not depend to an unreason- are distinct, semi-empirical methods are often employed in compo- able extent on model resolution (Durand et al., 2009; Goldberg et al., nents of the process-based models (e.g., glacier models in which sur- 2009; Morlighem et al., 2010; Cornford et al., 2013; Pattyn et al., 2013). face mass balance is determined by a degree-day method (Braithwaite One-dimensional flowline models have been developed to the stage and Olesen, 1989)). that modelled iceberg calving is comparable with observations (Nick et al., 2009). The success of this modelling approach relies on the ability 13.1.4.1 Models Used to Project Changes in Ice Sheets of the model s computational grid to evolve to continuously track the and Glaciers migrating calving front. Although this is relatively straightforward in a one-dimensional model, this technique is difficult to incorporate into The representation of glaciers and ice sheets within AOGCMs is not three-dimensional ice-sheet models that typically use a computational yet at a stage where projections of their changing mass are routinely grid that is fixed in space. available. Additional process-based models use output from AOGCMs to evaluate the consequences of projected climate change on these The main challenge faced by models attempting to assess sea level ice masses. change from glaciers is the small number of glaciers for which mass budget observations are available (about 380) (Cogley, 2009a) (see The overall contribution of an ice mass to sea level involves changes Sections 4.3.1 and 4.3.4) as compared to the total number (the Ran- to either its surface mass balance (SMB) or changes in the dynamics dolph Glacier Inventory contains more than 170,000) (Arendt et al., of ice flow that affect outflow (i.e., solid ice discharge) to the ocean. 2012). Statistical techniques are used to derive relations between SMB is primarily the difference between snow accumulation and the observed SMB and climate variables for the small sample of surveyed melt and sublimation of snow and ice (ablation). An assessment of glaciers, and then these relations are used to upscale to regions of the observations related to this mass budget can be found in Section 4.4.2. world. These techniques often include volume area scaling to estimate Although some ice-sheet models used in projections incorporate both glacier volume from their more readily observable areas. Although effects, most studies have focussed on either SMB or flow dynamics. tidewater glaciers may also be affected by changes in outflow related It is assumed that the overall contribution can be found by summing to calving, the complexity of the associated processes means that most the contributions calculated independently for these two sources, studies limit themselves to assessing the effects of SMB changes. which is valid if they do not interact significantly. Although this can be addressed using a correction term to SMB in ice-sheet projections over the next century, such interactions become more important on longer 13.2 Past Sea Level Change time scales when, for example, changes in ice-sheet topography may significantly affect SMB or dynamics. 13.2.1 The Geological Record Projecting the sea level contribution of land ice requires comparing the Records of past sea level change provide insight into the sensitivity model results with a base state that assumes no significant sea level of sea level to past climate change as well as context for understand- contribution. This base state is taken to be either the pre-industrial ing current changes and evaluating projected changes. Since the AR4, period or, because of our scant knowledge of the ice sheets before important progress has been made in understanding the amplitude the advent of satellites, the late 20th century. In reality, even at these and variability of sea level during past intervals when climate was times, the ice sheets may have been contributing to sea level change warmer than pre-industrial, largely through better accounting of the (Huybrechts et al., 2011; Box and Colgan, 2013) and this contribution, effects of proxy uncertainties and GIA on coastal sequences (Kopp although difficult to quantify, should be included in the observed sea et al., 2009, 2013; Raymo et al., 2011; Dutton and Lambeck, 2012; level budget (Gregory et al., 2013b). Lambeck et al., 2012; Raymo and Mitrovica, 2012) (Chapter 5). Here we summarize the constraints provided by the record of past sea level Regional Climate Models (RCMs), which incorporate or are coupled variations during times when global temperature was similar to or to sophisticated representations of the mass and energy budgets warmer than today. associated with snow and ice surfaces, are now the primary source of ice-sheet SMB projections. A major source of uncertainty lies in the 13.2.1.1 The Middle Pliocene ability of these schemes to adequately represent the process of inter- nal refreezing of melt water within the snowpack (Bougamont et al., There is medium confidence that during the warm intervals of the 2007; Fausto et al., 2009). These models require information on the middle Pliocene (3.3 to 3.0 Ma), global mean surface temperatures state of the atmosphere and ocean at their lateral boundaries, which were 2°C to 3.5°C warmer than for pre-industrial climate (Section 13 are derived from reanalysis data sets or AOGCMs for past climate, or 5.3.1). There are multiple lines of evidence that GMSL during these from AOGCM projections of future climate. middle Pliocene warm periods was higher than today, but low agree- ment on how high it reached (Section 5.6.1). The most robust lines of Models of ice dynamics require a fairly complete representation of evidence come from proximal sedimentary records that suggest peri- stresses within an ice mass in order to represent the response of ice odic deglaciation of the West Antarctic ice sheet (WAIS) and parts of flow to changes at the marine boundary and the governing longitudinal the East Antarctic ice sheet (EAIS) (Naish et al., 2009; Passchier, 2011) stresses (Schoof, 2007a). For Antarctica, there is also a need to employ and from ice-sheet models that suggest near-complete deglaciation high spatial resolution (<1 km) to capture the dynamics of grounding of the Greenland ice sheet, WAIS and partial deglaciation of the EAIS 1145 Chapter 13 Sea Level Change (Pollard and DeConto, 2009; Hill et al., 2010; Dolan et al., 2011). The contribution from the Antarctic ice sheet to the global mean sea level assessment by Chapter 5 suggests that GMSL was above present, but during the last interglacial period, but this is not yet supported by that it did not exceed 20 m above present, during the middle Pliocene observational and model evidence. warm periods (high confidence). There is medium confidence for a sea level fluctuation of up to 4 m 13.2.1.2 Marine Isotope Stage 11 during the LIG, but regional sea level variability and uncertainties in sea level proxies and their ages cause differences in the timing and During marine isotope stage 11 (MIS 11; 401 to 411 ka), Antarctic ice amplitude of the reported fluctuation (Kopp et al., 2009, 2013; Thomp- core and tropical Pacific paleo temperature estimates suggest that son et al., 2011). For the time interval during the LIG in which GMSL global temperature was 1.5°C to 2.0°C warmer than pre-industrial was above present, there is high confidence that the maximum 1000- (low confidence) (Masson-Delmotte et al., 2010). Studies of the mag- year average rate of GMSL rise associated with the sea level fluctua- nitude of sea level highstands from raised shorelines attributed to MIS tion exceeded 2 m kyr 1 but that it did not exceed 7 m kyr 1 (Chapter 5) 11 have generated highly divergent estimates. Since the AR4, stud- (Kopp et al., 2013). Faster rates lasting less than a millennium cannot ies have accounted for GIA effects (Raymo and Mitrovica, 2012) or be ruled out by these data. Therefore, there is high confidence that reported elevations from sites where the GIA effects are estimated to there were intervals when rates of GMSL rise during the LIG exceeded be small (Muhs et al., 2012; Roberts et al., 2012). From this evidence, the 20th century rate of 1.7 [1.5 to 1.9] mm yr 1. our assessment is that MIS 11 GMSL reached 6 to 15 m higher than present (medium confidence), requiring a loss of most or all of the 13.2.1.4 The Late Holocene present Greenland ice sheet and WAIS plus a reduction in the EAIS of up to 5 m equivalent sea level if sea level rise was at the higher end Since the AR4, there has been significant progress in resolving the sea of the range. level history of the last 7000 years. RSL records indicate that from ~7 to 3 ka, GMSL likely rose 2 to 3 m to near present-day levels (Chapter 13.2.1.3 The Last Interglacial Period 5). Based on local sea level records spanning the last 2000 years, there is medium confidence that fluctuations in GMSL during this interval New data syntheses and model simulations since the AR4 indicate have not exceeded ~ +/-0.25 m on time scales of a few hundred years that during the Last Interglacial Period (LIG, ~129 to 116 ka), global (Section 5.6.3, Figure 13.3a). The most robust signal captured in salt mean annual temperature was 1°C to 2oC warmer than pre-industrial marsh records from both Northern and Southern Hemispheres sup- (medium confidence) with peak global annual sea surface tempera- ports the AR4 conclusion for a transition from relatively low rates of tures (SSTs) that were 0.7°C +/- 0.6°C warmer (medium confidence) change during the late Holocene (order tenths of mm yr 1) to modern (Section 5.3.4). High latitude surface temperature, averaged over sev- rates (order mm yr 1) (Section 5.6.3, Figure 13.3b). However, there eral thousand years, was at least 2°C warmer than present (high con- is variability in the magnitude and the timing (1840 1920) of this fidence) (Section 5.3.4). There is robust evidence and high agreement increase in both paleo and instrumental (tide gauge) records (Section that under the different orbital forcing and warmer climate of the LIG, 3.7). By combining paleo sea level records with tide gauge records at sea level was higher than present. There have been a large number of the same localities, Gehrels and Woodworth (2013) concluded that estimates of the magnitude of LIG GMSL rise from localities around sea level began to rise above the late Holocene background rate the globe, but they are generally from a small number of RSL recon- between 1905 and 1945, consistent with the conclusions by Lambeck structions, and do not consider GIA effects, which can be substantial et al. (2004). (Section 5.6.2). Since the AR4, two approaches have addressed GIA effects in order to infer LIG sea level from RSL observations at coast- 13.2.2 The Instrumental Record (~1700 2012) al sites. Kopp et al. (2009, 2013) obtained a probabilistic estimate of GMSL based on a large and geographically broadly distributed data- The instrumental record of sea level change is mainly comprised of base of LIG sea level indicators. Their analysis accounted for GIA effects tide gauge measurements over the past two to three centuries (Figures (and their uncertainties) as well as uncertainties in geochronology, the 13.3b and 13.3c) and, since the early 1990s, of satellite-based radar interpretation of sea level indicators, and regional tectonic uplift and altimeter measurements (Figure 13.3d). subsidence. Kopp et al. (2013) concluded that GMSL was 6.4 m (95% probability) and 7.7 m (67% probability) higher than present, and 13.2.2.1 The Tide Gauge Record (~1700 2012) with a 33% probability that it exceeded 8.8 m. The other approach, taken by Dutton and Lambeck (2012), used data from far-field sites The number of tide gauges has increased since the first gauges at 13 that are tectonically stable. Their estimate of 5.5 to 9 m LIG GMSL is some northern European ports were installed in the 18th century; consistent with the probabilistic estimates made by Kopp et al. (2009, Southern Hemisphere (SH) measurements started only in the late 19th 2013). Chapter 5 thus concluded there is very high confidence that the century. Section 3.7 assesses 20th century sea level rise estimates maximum GMSL during the LIG was at least 5 m higher than present from tide gauges (Douglas, 2001; Church and White, 2006, 2011; and high confidence it did not exceed 10 m. The best estimate is 6 m Jevrejeva et al., 2006, 2008; Holgate, 2007; Ray and Douglas, 2011), higher than present. Chapter 5 also concluded from ice-sheet model and concludes that even though different strategies were developed simulations and elevation changes derived from a new Greenland ice to account for inhomogeneous tide gauge data coverage in space and core that the Greenland ice sheet very likely contributed between 1.4 time, and to correct for vertical crustal motions (also sensed by tide and 4.3 m sea level equivalent. This implies with medium confidence a gauges, in addition to sea level change and variability), it is very likely 1146 Sea Level Change Chapter 13 Figure 13.3 | (a) Paleo sea level data for the last 3000 years from Northern and Southern Hemisphere sites. The effects of glacial isostatic adjustment (GIA) have been removed from these records. Light green = Iceland (Gehrels et al., 2006), purple = Nova Scotia (Gehrels et al., 2005), bright blue = Connecticut (Donnelly et al., 2004), blue = Nova Scotia (Gehrels et al., 2005), red = United Kingdom (Gehrels et al., 2011), green = North Carolina (Kemp et al., 2011), brown = New Zealand (Gehrels et al., 2008), grey = mid-Pacific Ocean (Woodroffe et al., 2012). (b) Paleo sea level data from salt marshes since 1700 from Northern and Southern Hemisphere sites compared to sea level reconstruction from tide gauges (blue time series with uncertainty) (Jevrejeva et al., 2008). The effects of GIA have been removed from these records by subtracting the long-term trend (Gehrels and 13 Woodworth, 2013). Ordinate axis on the left corresponds to the paleo sea level data. Ordinate axis on the right corresponds to tide gauge data. Green and light green = North Carolina (Kemp et al., 2011), orange = Iceland (Gehrels et al., 2006), purple = New Zealand (Gehrels et al., 2008), dark green = Tasmania (Gehrels et al., 2012), brown = Nova Scotia (Gehrels et al., 2005). (c) Yearly average global mean sea level (GMSL) reconstructed from tide gauges by three different approaches. Orange from Church and White (2011), blue from Jevrejeva et al. (2008), green from Ray and Douglas (2011) (see Section 3.7). (d) Altimetry data sets from five groups (University of Colorado (CU), National Oceanic and Atmospheric Administration (NOAA), Goddard Space Flight Centre (GSFC), Archiving, Validation and Interpretation of Satellite Oceanographic (AVISO), Commonwealth Scientific and Industrial Research Organisation (CSIRO)) with mean of the five shown as bright blue line (see Section 3.7). (e) Comparison of the paleo data from salt marshes (purple symbols, from (b)), with tide gauge and altimetry data sets (same line colours as in (c) and (d)). All paleo data were shifted by mean of 1700 1850 derived from the Sand Point, North Carolina data. The Jevrejeva et al. (2008) tide gauge data were shifted by their mean for 1700 1850; other two tide gauge data sets were shifted by the same amount. The altimeter time series has been shifted vertically upwards so that their mean value over the 1993 2007 period aligns with the mean value of the average of all three tide gauge time series over the same period. 1147 Chapter 13 Sea Level Change Frequently Asked Questions FAQ 13.1 | Why Does Local Sea Level Change Differ from the Global Average? Shifting surface winds, the expansion of warming ocean water, and the addition of melting ice can alter ocean cur- rents which, in turn, lead to changes in sea level that vary from place to place. Past and present variations in the distribution of land ice affect the shape and gravitational field of the Earth, which also cause regional fluctuations in sea level. Additional variations in sea level are caused by the influence of more localized processes such as sedi- ment compaction and tectonics. Along any coast, vertical motion of either the sea or land surface can cause changes in sea level relative to the land (known as relative sea level). For example, a local change can be caused by an increase in sea surface height, or by a decrease in land height. Over relatively short time spans (hours to years), the influence of tides, storms and climatic variability such as El Nino dominates sea level variations. Earthquakes and landslides can also have an effect by causing changes in land height and, sometimes, tsunamis. Over longer time spans (decades to centuries), the influ- ence of climate change with consequent changes in volume of ocean water and land ice is the main contributor to sea level change in most regions. Over these longer time scales, various processes may also cause vertical motion of the land surface, which can also result in substantial changes in relative sea level. Since the late 20th century, satellite measurements of the height of the ocean surface relative to the center of the Earth (known as geocentric sea level) show differing rates of geocentric sea level change around the world (see FAQ 13.1, Figure 1). For example, in the western Pacific Ocean, rates were about three times greater than the global mean value of about 3 mm per year from 1993 to 2012. In contrast, those in the eastern Pacific Ocean are lower than the global mean value, with much of the west coast of the Americas experiencing a fall in sea surface height over the same period. (continued on next page) Year Year Year 1960 1980 2000 1960 1980 2000 1960 1980 2000 500 Sea level (mm) 250 0 250 San Francisco Charlottetown Stockholm 500 14 12 Charlottetown Stockholm 10 Sea level change (mm yr-1) San Francisco 8 6 Manila 4 2 0 2 Antofagasta Pago Pago 4 6 8 10 12 500 14 Antofagasta Manila Pago Pago Sea level (mm) 250 13 0 -250 -500 1960 1980 2000 1960 1980 2000 1960 1980 2000 Year Year Year FAQ13.1, Figure 1 | Map of rates of change in sea surface height (geocentric sea level) for the period 1993 2012 from satellite altimetry. Also shown are relative sea level changes (grey lines) from selected tide gauge stations for the period 1950 2012. For comparison, an estimate of global mean sea level change is also shown (red lines) with each tide gauge time series. The relatively large, short-term oscillations in local sea level (grey lines) are due to the natural climate variability described in the main text. For example, the large, regular deviations at Pago Pago are associated with the El Nino-Southern Oscillation. 1148 Sea Level Change Chapter 13 FAQ 13.1 (continued) Much of the spatial variation shown in FAQ 13.1, Figure 1 is a result of natural climate variability such as El Nino and the Pacific Decadal Oscillation over time scales from about a year to several decades. These climate variations alter surface winds, ocean currents, temperature and salinity, and hence affect sea level. The influence of these processes will continue during the 21st century, and will be superimposed on the spatial pattern of sea level change associated with longer term climate change, which also arises through changes in surface winds, ocean currents, temperature and salinity, as well as ocean volume. However, in contrast to the natural variability, the longer term trends accu- mulate over time and so are expected to dominate over the 21st century. The resulting rates of geocentric sea level change over this longer period may therefore exhibit a very different pattern from that shown in FAQ 13.1, Figure 1. Tide gauges measure relative sea level, and so they include changes resulting from vertical motion of both the land and the sea surface. Over many coastal regions, vertical land motion is small, and so the long-term rate of sea level change recorded by coastal and island tide gauges is similar to the global mean value (see records at San Francisco and Pago Pago in FAQ 13.1, Figure 1). In some regions, vertical land motion has had an important influence. For example, the steady fall in sea level recorded at Stockholm (FAQ 13.1, Figure 1) is caused by uplift of this region after the melting of a large (>1 km thick) continental ice sheet at the end of the last Ice Age, between ~20,000 and ~9000 years ago. Such ongoing land deformation as a response to the melting of ancient ice sheets is a significant contributor to regional sea level changes in North America and northwest Eurasia, which were covered by large continental ice sheets during the peak of the last Ice Age. In other regions, this process can also lead to land subsidence, which elevates relative sea levels, as it has at Char- lottetown, where a relatively large increase has been observed, compared to the global mean rate (FAQ 13.1, Figure 1). Vertical land motion due to movement of the Earth s tectonic plates can also cause departures from the global mean sea level trend in some areas most significantly, those located near active subduction zones, where one tec- tonic plate slips beneath another. For the case of Antofagasta (FAQ 13.1, Figure 1) this appears to result in steady land uplift and therefore relative sea level fall. In addition to regional influences of vertical land motion on relative sea level change, some processes lead to land motion that is rapid but highly localized. For example, the greater rate of rise relative to the global mean at Manila (FAQ 13.1, Figure 1) is dominated by land subsid- ence caused by intensive groundwater pumping. Land subsidence due to natural and anthropogenic processes, such as the extraction of groundwater or hydrocarbons, is common in many coastal regions, particularly in large river deltas. 3.0 2.0 1.0 0.0 0.2 0.4 0.6 0.8 1.0 1.1 1.2 1.3 It is commonly assumed that melting ice from glaciers Sea level change (mm yr ) -1 or the Greenland and Antarctic ice sheets would cause FAQ13.1, Figure 2 | Model output showing relative sea level change due to globally uniform sea level rise, much like filling a bath melting of the Greenland ice sheet and the West Antarctic ice sheet at rates of tub with water. In fact, such melting results in region- 0.5 mm yr 1 each (giving a global mean value for sea level rise of 1 mm yr 1). al variations in sea level due to a variety of processes, The modelled sea level changes are less than the global mean value in areas including changes in ocean currents, winds, the Earth s near the melting ice but enhanced further afield. (Adapted from Milne et al., 2009) gravity field and land height. For example, computer models that simulate these latter two processes predict a regional fall in relative sea level around the melting ice sheets, because the gravitational attraction between ice and ocean water is reduced, and the land tends to rise as the ice melts (FAQ 13.1, Figure 2). However, further away from the ice sheet melting, sea level rise is enhanced, 13 compared to the global average value. In summary, a variety of processes drive height changes of the ocean surface and ocean floor, resulting in distinct spatial patterns of sea level change at local to regional scales. The combination of these processes produces a complex pattern of total sea level change, which varies through time as the relative contribution of each process changes. The global average change is a useful single value that reflects the contribution of climatic processes (e.g., land-ice melting and ocean warming), and represents a good estimate of sea level change at many coastal loca- tions. At the same time, however, where the various regional processes result in a strong signal, there can be large departures from the global average value. 1149 Chapter 13 Sea Level Change that the long-term trend estimate in GMSL is 1.7 [1.5 to 1.9] mm 13.3 Contributions to Global Mean Sea Level yr 1 between 1901 and 2010 for a total sea level rise of 0.19 [0.17 Rise During the Instrumental Period to 0.21] m (Figure 13.3c). Interannual and decadal-scale variability is superimposed on the long-term MSL trend, and Chapter 3 noted that In order to assess our understanding of the causes of observed changes discrepancies between the various published MSL records are present and our confidence in projecting future changes we compare obser- at these shorter time scales. vational estimates of contributions with results derived from AOGCM experiments, beginning in the late 19th century, forced with estimated Section 3.7 also concludes that it is likely that the rate of sea level past time-dependent anthropogenic changes in atmospheric compo- rise increased from the 19th century to the 20th century. Taking this sition and natural forcings due to volcanic aerosols and variations in evidence in conjunction with the proxy evidence for a change of rate solar irradiance (Section 10.1). This period and these simulations are (Sections 5.6.3 and 13.2.1; Figure 13.3b), there is high confidence that often referred to as historical. the rate of sea level rise has increased during the last two centu- ries, and it is likely that GMSL has accelerated since the early 1900 s. 13.3.1 Thermal Expansion Contribution Because of the presence of low-frequency variations (e.g., multi-dec- adal variations seen in some tide gauge records; Chambers et al. 13.3.1.1 Observed (2012)), sea level acceleration results are sensitive to the choice of the analysis time span. When a 60-year oscillation is modelled along Important progress has been realized since AR4 in quantifying the with an acceleration term, the estimated acceleration in GMSL (twice observed thermal expansion component of global mean sea level rise. the quadratic term) computed over 1900 2010 ranges from 0.000 This progress reflects (1) the detection of systematic time-dependent [ 0.002 to 0.002] mm yr 2 in the Ray and Douglas (2011) record, to depth biases affecting historical expendable bathythermograph data 0.013 [0.007 to 0.019] mm yr 2 in the Jevrejeva et al. (2008) record, (Gouretski and Koltermann, 2007) (Chapter 3), (2) the newly available and 0.012 [0.009 to 0.015] mm yr 2 in the Church and White (2011) Argo Project ocean (temperature and salinity) data with almost global record. For comparison, Church and White (2011) estimated the accel- coverage (not including ice-covered regions and marginal seas) of the eration term to be 0.009 [0.004 to 0.014] mm yr 2 over the 1880 2009 oceans down to 2000 m since 2004 2005, and (3) estimates of the time span when the 60-year cycle is not considered. deep-ocean contribution using ship-based data collected during the World Ocean Circulation Experiment and revisit cruises (Johnson and 13.2.2.2 The Satellite Altimeter Record (1993 2012) Gruber, 2007; Johnson et al., 2007; Purkey and Johnson, 2010; Kouke- tsu et al., 2011). The high-precision satellite altimetry record started in 1992 and pro- vides nearly global (+/-66°) sea level measurements at 10-day inter- For the period 1971 2010, the rate for the 0 to 700 m depth range is vals. Ollivier et al. (2012) showed that Envisat, which observes to 0.6 [0.4 to 0.8] mm yr 1 (Section 3.7.2 and Table 3.1). Including the +/-82° latitude, provides comparable GMSL estimates. Although there deep-ocean contribution for the same period increases the value to are slight differences at interannual time scales in the altimetry-based 0.8 [0.5 to 1.1] mm yr 1 (Table 13.1). Over the altimetry period (1993 GMSL time series produced by different groups (Masters et al., 2012), 2010), the rate for the 0 to 700 m depth range is 0.8 [0.5 to 1.1] mm there is very good agreement on the 20-year long GMSL trend (Figure yr 1 and 1.1 [0.8 to 1.4] mm yr 1 when accounting for the deep ocean 13.3d). After accounting for the ~ 0.3 mm yr 1 correction related (Section 3.7.2, Table 3.1, Table 13.1). to the increasing size of the global ocean basins due to GIA (Peltier, 2009), a GMSL rate of 3.2 [2.8 to 3.6] mm yr 1 over 1993 2012 is found 13.3.1.2 Modelled by the different altimetry data processing groups. The current level of precision is derived from assessments of all source of errors affecting GMSL rise due to thermal expansion is approximately proportional to the altimetric measurements (Ablain et al., 2009) and from tide gauge the increase in ocean heat content (Section 13.4.1). Historical GMSL comparisons (Beckley et al., 2010; Nerem et al., 2010). Chapter 3 con- rise due to thermal expansion simulated by CMIP5 models is shown cludes that the GMSL trend since 1993 is very likely higher compared in Table 13.1 and Figure 13.4a. The model spread is due to uncertainty to the mean rates over the 20th century, and that it is likely that GMSL in RF and modelled climate response (Sections 8.5.2, 9.4.2.2, 9.7.2.5 rose between 1920 and 1950 at a rate comparable to that observed and 13.4.1). since 1993. This recent higher rate is also seen in tide gauge data over the same period, but on the basis of observations alone it does not In the time mean of several decades, there is a negative volcanic forc- necessarily reflect a recent acceleration, considering the previously ing if there is more volcanic activity than is typical of the long term, and 13 reported multi-decadal variations of mean sea level. The rapid increase a positive forcing if there is less. In the decades after major volcanic in GMSL since 2011 is related to the recovery from the 2011 La Nina eruptions, the rate of expansion is temporarily enhanced, as the ocean event (Section 13.3.5) (Boening et al., 2012). recovers from the cooling caused by the volcanic forcing (Church et al., 2005; Gregory et al., 2006) (Figure 13.4a). During 1961 1999, a period when there were several large volcanic eruptions, the CMIP3 simula- tions with both natural and anthropogenic forcing have substantially smaller increasing trends in the upper 700 m than those with anthro- pogenic forcing only (Domingues et al., 2008) because the natural vol- canic forcing tends to cool the climate system, thus reducing ocean 1150 Sea Level Change Chapter 13 Table 13.1 | Global mean sea level budget (mm yr 1) over different time intervals from observations and from model-based contributions. Uncertainties are 5 to 95%. The Atmo- sphere Ocean General Circulation Model (AOGCM) historical integrations end in 2005; projections for RCP4.5 are used for 2006 2010. The modelled thermal expansion and glacier contributions are computed from the CMIP5 results, using the model of Marzeion et al. (2012a) for glaciers. The land water contribution is due to anthropogenic intervention only, not including climate-related fluctuations. Source 1901 1990 1971 2010 1993 2010 Observed contributions to global mean sea level (GMSL) rise Thermal expansion 0.8 [0.5 to 1.1] 1.1 [0.8 to 1.4] Glaciers except in Greenland and Antarcticaa 0.54 [0.47 to 0.61] 0.62 [0.25 to 0.99] 0.76 [0.39 to 1.13] Glaciers in Greenlanda 0.15 [0.10 to 0.19] 0.06 [0.03 to 0.09] 0.10 [0.07 to 0.13]b Greenland ice sheet 0.33 [0.25 to 0.41] Antarctic ice sheet 0.27 [0.16 to 0.38] Land water storage 0.11 [ 0.16 to 0.06] 0.12 [0.03 to 0.22] 0.38 [0.26 to 0.49] Total of contributions 2.8 [2.3 to 3.4] Observed GMSL rise 1.5 [1.3 to 1.7] 2.0 [1.7 to 2.3] 3.2 [2.8 to 3.6] Modelled contributions to GMSL rise Thermal expansion 0.37 [0.06 to 0.67] 0.96 [0.51 to 1.41] 1.49 [0.97 to 2.02] Glaciers except in Greenland and Antarctica 0.63 [0.37 to 0.89] 0.62 [0.41 to 0.84] 0.78 [0.43 to 1.13] Glaciers in Greenland 0.07 [ 0.02 to 0.16] 0.10 [0.05 to 0.15] 0.14 [0.06 to 0.23] Total including land water storage 1.0 [0.5 to 1.4] 1.8 [1.3 to 2.3] 2.8 [2.1 to 3.5] Residual c 0.5 [0.1 to 1.0] 0.2 [ 0.4 to 0.8] 0.4 [ 0.4 to 1.2] Notes: a Data for all glaciers extend to 2009, not 2010. b This contribution is not included in the total because glaciers in Greenland are included in the observational assessment of the Greenland ice sheet. c Observed GMSL rise modelled thermal expansion modelled glaciers observed land water storage. heat uptake (Levitus et al., 2001). The models including natural forcing energy budget and RF of the climate system (Box 13.1), we have high are closer to observations, though with a tendency to underestimate confidence in the projections of thermal expansion using AOGCMs. the trend by about 10% (Sections 9.4.2.2 and 10.4.1). 13.3.2 Glaciers Gregory (2010) and Gregory et al. (2013a) proposed that AOGCMs underestimate ocean heat uptake in their historical simulations 13.3.2.1 Observed because their control experiments usually omit volcanic forcing, so the imposition of historical volcanic forcing on the simulated climate Glaciers are defined here as all land-ice masses, including those system represents a time mean negative forcing relative to the con- peripheral to (but not including) the Greenland and Antarctic ice trol climate. The apparent long persistence of the simulated oceanic sheets. The term glaciers and ice caps was applied to this category ­cooling following the 1883 eruption of Krakatau (Delworth et al., 2005; in the AR4. Changes in aggregate glacier volume have conventional- Gleckler et al., 2006a, 2006b; Gregory et al., 2006) is a consequence ly been determined by various methods of repeat mapping of surface of this bias, which also causes a model-dependent underestimate of elevation to detect elevation (and thus volume) change. Mass changes up to 0.2 mm yr 1 of thermal expansion on average during the 20th are determined by compilation and upscaling of limited direct observa- century (Gregory et al., 2013a, 2013b). This implies that CMIP5 results tions of surface mass balance (SMB). Since 2003, gravity observations may be similarly underestimated, depending on the details of the indi- from Gravity Recovery and Climate Experiment (GRACE) satellites have vidual model control runs. Church et al. (2013) proposed a correction been used to detect mass change of the world s glaciers. of 0.1 mm yr 1 to the model mean rate, which we apply in the sea level budget in Table 13.1 and Figure 13.7. The corrected CMIP5 model mean The combined records indicate that a net decline of global glacier rate for 1971 2010 is close to the central observational estimate; the volume began in the 19th century, before significant anthropogenic model mean rate for 1993 2010 exceeds the central observational RF had started, and was probably the result of warming associated 13 estimate but they are not statistically different given the uncertainties with the termination of the Little Ice Age (Crowley, 2000; Gregory et (Table 13.1 and Figure 13.4a). This correction is not made to projec- al., 2006, 2013b). Global rates of glacier volume loss did not increase tions of thermal expansion because it is very small compared with the significantly during much of the 20th century (Figure 4.12). In part this projected increase in the rate (Section 13.5.1). may have been because of an enhanced rate of loss due to unforced high-latitude variability early in the century, while anthropogenic In view of the improvement in observational estimates of thermal warming was still comparatively small (Section 13.3.2.2). It is likely expansion, the good agreement of historical model results with obser- that anthropogenic forcing played a statistically significant role in vational estimates, and their consistency with understanding of the acceleration of global glacier losses in the latter decades of the 20th 1151 Chapter 13 Sea Level Change 13 Figure 13.4 | Comparison of modelled and observed components of global mean sea level change since 1900. Changes in glaciers, ice sheets and land water storage are shown as positive sea level rise when mass is added to the ocean. (a) Ocean thermal expansion. Individual CMIP5 Atmosphere Ocean General Circulation Model (AOGCM) simulations are shown in grey, the AOGCM average is black, observations in teal with the 5 to 95% uncertainties shaded. (b) Glaciers (excluding Antarctic peripheral glaciers). Model simulations by Marzeion et al. (2012a) with input from individual AOGCMs are shown in grey with the average of these results in bright purple. Model simulations by Marzeion et al. (2012a) forced by observed climate are shown in light blue. The observational estimates by Cogley (2009b) are shown in green (dashed) and by Leclercq et al. (2011) in red (dashed). (c) Changes in land water storage (yellow/orange, the sum of groundwater depletion and reservoir storage) start at zero in 1900. The Greenland ice sheet (green), the Antarctic ice sheet (blue) and the sum of the ice sheets (red), start at zero at the start of the record in 1991. (d) The rate of change (19-year centred trends) for the terms in (a) (c), and for the ice sheets (5-year centred trends). All curves in (a) and (b) are shown with zero time-mean over the period 1986 2005 and the colours in (d) are matched to earlier panels. (Updated from Church et al., 2013) 1152 Sea Level Change Chapter 13 century relative to rates in the 19th century (Section 10.5.2.2). It is also not reproduced by AOGCM experiments (Section 10.2). In our analysis likely that, during the 20th century, the progressive loss of glacier area of the budget of GMSL rise (Section 13.3.6), we take the difference significantly restricted the rate of mass loss (Gregory et al., 2013b). between the simulations using AOGCM forcing and the simulation using observations as an estimate of the influence of unforced climate The earliest sea level assessments recognized that glaciers have been variability on global glacier mass balance (Figure 13.4b). significant contributors to GMSL rise (Meier, 1984). As assessed in Chapter 4, observations, improved methods of analysis and a new, There is medium confidence in the use of glacier models to make globally complete inventory indicate that glaciers, including those global projections based on AOGCM results. The process-based under- around the ice-sheet peripheries, very likely continue to be significant standing of glacier surface mass balance, the consistency of models contributors to sea level, but are also highly variable on annual to dec- and observations of glacier changes, and the evidence that AOGCM cli- adal time scales. It is assumed that all glacier losses contribute to sea mate simulations can provide realistic input all give confidence, which level rise, but the potential role of terrestrial interception of runoff, on the other hand is limited because the set of well-observed glaciers either in lakes formed following future ice retreat or in groundwater, is a very small fraction of the total. has yet to be evaluated. For the period 2003 2009, the sea level con- tribution of all glaciers globally, including those glaciers surrounding 13.3.3 Greenland and Antarctic Ice Sheets the periphery of the two ice sheets, is 0.71 [0.64 to 0.79] mm yr 1 sea level equivalent (SLE) (Section 4.3.3, Table 4.4). Depending on the 13.3.3.1 Observed Mass Balance method used, however, loss-rate measurements of the two ice sheets can be very difficult to separate from losses from the peripheral gla- The Greenland ice sheet s mass balance is comprised of its surface ciers. To avoid double counting, total cryospheric losses are determined mass balance and outflow, whereas Antarctica s mass budget is domi- by adding estimates of glacier losses excluding the peripheral glaciers nated by accumulation and outflow in the form of calving and ice flow to losses from the ice sheets including their peripheral glaciers. The sea into floating (and therefore sea level neutral) ice shelves. Knowledge of level contribution of all glaciers excluding those glaciers surrounding the contribution of the Greenland and Antarctic ice sheets to observed the periphery of the two ice sheets was 0.54 [0.47-0.61] mm yr-1 SLE sea level changes over the last two decades comes primarily from sat- for 1901-1990, 0.62 [0.25-0.99] mm yr-1 SLE for 1971-2009, 0.76 [0.39- ellite and airborne surveys. Three main techniques are employed: the 1.13] mm yr-1 SLE for 1993-2009, and 0.83 [0.46-1.20] mm yr-1 SLE for mass budget method, repeat altimetry, and gravimetric methods that 2005-2009 (Section 4.3.3.4, Table 13.1). measure temporal variations in the Earth s gravity field (Section 4.4.2). 13.3.2.2 Modelled Observations indicate that the Greenland contribution to GMSL has very likely increased from 0.09 [ 0.02 to 0.20] mm yr 1 for 1992 2001 Global glacier mass balance models are calibrated using data from the to 0.59 [0.43 to 0.76] mm yr 1 for 2002 2011 (Section 4.4.3, Figure few well-observed glaciers. Approximately 100 glacier mass balance 13.4). The average rate of the Antarctica contribution to sea level rise records are available in any given year over the past half-century; only likely increased from 0.08 [ 0.10 to 0.27] mm yr 1 for 1992 2001 to 17 glaciers exist with records of 30 years or more (Dyurgerov and Meier, 0.40 [0.20 to 0.61] mm yr 1 for 2002 2011 (Section 4.4.3). For the 2005; Kaser et al., 2006; Cogley, 2012). Confidence in these models for budget period 1993 2010, the combined contribution of the ice sheets projections of future change (Section 13.4.2) depends on their ability is 0.60 [0.42 to 0.78] mm yr 1. For comparison, the AR4 s assessment to reproduce past observed glacier change using corresponding cli- for the period 1993 2003 was 0.21 +/- 0.07 mm yr 1 for Greenland and mate observations as the forcing (Raper and Braithwaite, 2005; Meier 0.21 +/- 0.35 mm yr 1 for Antarctica. et al., 2007; Bahr et al., 2009; Radiæ and Hock, 2011; Marzeion et al., 2012b; 2012a; Giesen and Oerlemans, 2013). Model validation is chal- 13.3.3.2 Modelled Surface Mass Balance lenging owing to the scarcity of independent observations (unused in model calibration), but uncertainties have been evaluated by methods Projections of changes in the SMB of the Antarctic and Greenland ice such as cross validation of hindcast projections for individual glaciers sheets are obtained from RCM or downscaled AOGCM simulations drawn from the sample of glacier observations averaged for calibration (Sections 13.4.3.1 and 13.4.4.1). A spatial resolution of a few tens (Marzeion et al., 2012a; Radiæ et al., 2013). kilometres or finer is required in order to resolve the strong gradi- ents in SMB across the steep slopes of the ice-sheet margins. Although Confidence in the use of AOGCM climate simulations as input to simulations of SMB at particular locations may have errors of 5 to 20% glacier projections is gained from the agreement since the mid-20th compared with in situ observations, there is good agreement between century of glacier models forced by AOGCM simulations with gla- methods involving RCMs and observational methods of evaluating ice- 13 cier models forced by observations (Marzeion et al., 2012a) (Figure sheet mass balance (Shepherd et al., 2012). In the present climate, 13.4b). In the earlier 20th century, around the 1930s, glaciers at high for both Greenland and Antarctica, the mean SMB over the ice-sheet northern latitudes lost mass at an enhanced rate (Oerlemans et al., area is positive, giving a negative number when expressed as sea level 2011; Leclercq et al., 2012); in the model, observed forcings produced equivalent (SLE). larger glacier losses than did AOGCM forcings (Marzeion et al., 2012a) (Figure 13.4d). This is judged likely to be due to an episode of unforced, In Greenland, the average and standard deviation of accumulation regionally variable warming around Greenland (Box, 2002; Chylek et (precipitation minus sublimation) estimates for 1961 1990 is 1.62 al., 2004) rather than to RF of the climate system, and is consequently +/- 0.21 mm yr 1 SLE from the models in Table 13.2, agreeing with 1153 Chapter 13 Sea Level Change p ­ ublished observation-based accumulation maps, for example 1.42 +/- that the dominant contribution is internally generated regional climate 0.11 mm yr 1 SLE by Bales et al. (2009) and 1.63 +/- 0.23 mm yr 1 SLE by variability, which is not expected to be reproduced by AOGCM histori- Burgess et al. (2010). For SMB (accumulation minus runoff, neglecting cal simulations (Section 10.2). We have high confidence in projections drifting snow erosion, which is small), the models give 0.92 +/- 0.26 of future warming in Greenland because of the agreement of models mm yr 1 SLE for 1961 1990 (Table 13.2). in predicting amplified warming at high northern latitudes (Sections 12.4.3.1, 14.8.2) for well-understood physical reasons, although there All of these models indicate that Greenland ice sheet SMB showed no remains uncertainty in the size of the amplification, and we have significant trend from the 1960s to the 1980s, then started becoming high confidence in projections of increasing surface melting (Section less positive (becoming less negative expressed as SLE) in the early 13.4.3.1) because of the sensitivity to warming demonstrated by SMB 1990s, on average by 3% yr 1. This results in a statistically significant models of the past. and increasing (i.e., becoming more positive) contribution to the rate of GMSL rise (SMB trend column of Table 13.2, Figure 13.5). The largest All Greenland SMB simulations for the first half of the 20th century trends are found in models with coupled snow and atmosphere sim- depend on reconstructions of meteorological variability over the ice ulations using the Regional Atmospheric Climate Model 2 (RACMO2) sheet made using empirical relationships based on observations from and the Modele Atmosphérique Régional (MAR). Van den Broeke et coastal stations and estimates of accumulation from ice cores. Despite al. (2009) concluded that the mass loss during 2000 2008 is equally the similar input data sets in all cases, the various climate reconstruction split between SMB and dynamical change. Rignot et al. (2011) indicat- and SMB methods used have led to a range of results (Fettweis et al., ed that SMB change accounts for about 60% of the mass loss since 2008; Wake et al., 2009; Hanna et al., 2011; Box, 2013; Box and Colgan, 1992 and Sasgen et al. (2012) showed that SMB change, simulated by 2013; Box et al., 2013; Gregory et al., 2013b). For 1901 1990, Hanna et RACMO2 (Ettema et al., 2009, an earlier version of the model in Table al. (2011) have a time-mean GMSL contribution of 0.3 mm yr 1, while 13.2), accounts for about 60% of the observed rate of mass loss during Box and Colgan (2013) have a weakly positive contribution and the 2002 2010, with an observational estimate of the increase in ice out- others are about zero. In all cases, there is substantial variability associ- flow accounting for the remainder. This satisfactory consistency, within ated with regional climate fluctuations, in particular the warm episode uncertainties, in estimates for the Greenland ice-sheet mass budget in the 1930s, during which glaciers retreated in southeastern Greenland gives confidence in SMB simulations of the past, and hence also in the (Bjork et al., 2012). Chylek et al. (2004) argued that this episode was similar models used for projections of SMB changes (Section 13.4.3.1). associated with the NAO rather than with global climate change. This recent trend towards increasingly less positive SMB is caused In Antarctica, accumulation (precipitation minus sublimation) approx- almost entirely by increased melting and subsequent runoff, with vari- imates SMB because surface melting and runoff are negligible in the ability in accumulation being comparatively small (Sasgen et al., 2012; present climate (Section 4.4.2.1.1). There are uncertainties in model- Vernon et al., 2013). This tendency is related to pronounced regional and observation-based estimates of Antarctic SMB. Global climate warming, which may be attributed to some combination of anthro- models do not account for snow hydrology or for drifting snow pro- pogenic climate change and anomalous regional variability in recent cesses which remove an estimated 7% of the accumulated snow (Len- years (Hanna et al., 2008; 2012; Fettweis et al., 2013). Greenland SMB aerts et al., 2012), and the ice sheet s steep coastal slopes are not well models forced by boundary conditions from AOGCM historical simula- captured by coarse-resolution models. Observation-based estimates tions (Rae et al., 2012; Fettweis et al., 2013) do not show statistically rely on sparse accumulation measurements with very little coverage significant trends towards increasing contributions to GMSL, implying in high-accumulation areas. For the Antarctic ice sheet and ice shelves Table 13.2 | Surface mass balance (SMB) and rates of change of SMB of the Greenland ice sheet, calculated from ice-sheet SMB models using meteorological observations and reanalyses as input, expressed as sea level equivalent (SLE). A negative SLE number for SMB indicates that accumulation exceeds runoff. A positive SLE for SMB anomaly indicates that accumulation has decreased, or runoff has increased, or both. Uncertainties are one standard deviation. Uncertainty in individual model results reflects temporal variability (1 standard deviations of annual mean values indicated); the uncertainty in the model average is 1 standard deviation of variation across models. Time-Mean SMB Anomaly (With Respect Time-Mean SMB Rate of Change of SMB to 1961 1990 Time-Mean SMB)b Reference and Model a 1961 1990 1991 2010 mm yr 1 SLE mm yr 1 SLE mm yr 2 SLE 1971 2010 1993 2010 2005 2010 RACMO2, Van Angelen et al. (2012), 11 km RCM 1.13 +/- 0.30 0.04 +/- 0.01 0.07 +/- 0.33 0.23 +/- 0.30 0.47 +/- 0.24 13 MAR, Fettweis et al. (2011), 25 km RCM 1.17 +/- 0.31 0.05 +/- 0.01 0.12 +/- 0.38 0.36 +/- 0.33 0.64 +/- 0.22 PMM5, Box et al. (2009), 25 km RCM 0.98 +/- 0.18 0.02 +/- 0.01 0.00 +/- 0.19 0.10 +/- 0.22 0.23 +/- 0.21 ECMWFd, Hanna et al. (2011), 5 km PDD 0.77 +/- 0.27 0.02 +/- 0.01 0.02 +/- 0.28 0.12 +/- 0.27 0.24 +/- 0.19 SnowModel, Mernild and Liston (2012), 5 km EBM 0.54 +/- 0.21 0.03 +/- 0.01 0.09 +/- 0.25 0.19 +/- 0.24 0.36 +/- 0.23 Model Average 0.92 +/- 0.26 0.03 +/- 0.01 0.06 +/- 0.05 0.20 +/- 0.10 0.39 +/- 0.17 Notes: a The approximate spatial resolution is stated and the model type denoted by PDD = positive degree day, EBM = Energy Balance Model, RCM = Regional Climate Model. b Difference from the time-mean SMB of 1961 1990. This difference equals the sea level contribution from Greenland SMB changes if the ice sheet is assumed to have been near zero mass balance during 1961 1990 (Hanna et al., 2005; Sasgen et al., 2012). 1154 Sea Level Change Chapter 13 Figure 13.5 | Annual mean surface mass balance (accumulation minus ablation) for the Greenland ice sheet, simulated by five regional climate models for the period 1960 2010. together, CMIP3 AOGCMs simulate SMB for 1979 2000 of 7.1 +/- 1.5 Lemieux-Dudon et al., 2010; Stenni et al., 2011). The absence of a sig- mm yr 1 SLE (Connolley and Bracegirdle, 2007; Uotila et al., 2007), the nificant trend in Antarctic precipitation up to the present is not incon- mean being about 10% larger in magnitude than observation-based sistent with the expected relationship, because observed temperature estimates, for instance 6.3 mm yr 1 SLE from Vaughan et al. (1999). trends over the majority of the continent are weak (Section 10.5.2.1) For the SMB of the grounded ice sheet alone, four global reanalysis and trends in Antarctic precipitation simulated for recent decades are models, with resolutions of 38 to 125 km (Bromwich et al., 2011), give much smaller than interannual variability (van den Broeke et al., 2006; 5.2 +/- 0.5 mm yr 1 SLE for 1979 2010, which compares well with Uotila et al., 2007). Taking all these considerations together, we have an observational estimate of 4.9 +/- 0.1 mm yr 1 SLE for 1950 2000 medium confidence in model projections of a future Antarctic SMB (Arthern et al., 2006). Because of higher accumulation near the coast, increase, implying a negative contribution to GMSL rise (see also Sec- the regional climate model RACMO2 gives the somewhat larger value tions 13.4.4.1, 13.5.3 and 14.8.15). of 5.5 +/- 0.3 mm yr 1 SLE for 1979 2000 (Lenaerts et al., 2012). This relatively good agreement, combined with the similarity of the geo- 13.3.4 Contributions from Water Storage on Land graphical distribution of modelled and observed SMB, give medium confidence in the realism of the RCM SMB simulation. Changes in water storage on land in response to climate change and variability (i.e., water stored in rivers, lakes, wetlands, the vadose zone, Some global reanalyses have been shown to contain spurious trends in aquifers and snow pack at high latitudes and altitudes) and from direct various quantities in the SH related to changes in the observing systems, human-induced effects (i.e., storage of water in reservoirs and ground- for example, new satellite observations (Bromwich et al., 2007; 2011). water pumping) have the potential to contribute to sea level change. In the RCMs and in global reanalyses that are not affected by spurious Based on satellite observations of the Northern Hemisphere (NH) trends, no significant trend is present in accumulation since 1980 (Sec- snowpack, Biancamaria et al. (2011) found no significant trend in the tion 4.4.2.3). This agrees with observation-based studies (Monaghan contribution of snow to sea level. Estimates of climate-related changes et al., 2006; Anschütz et al., 2009) (Chapter 4) and implies that Ant- in land water storage over the past few decades rely on global hydro- arctic SMB change has not contributed significantly to recent changes logical models because corresponding observations are inadequate in the rate of GMSL rise. Likewise, CMIP3 historical simulations do not (Milly et al., 2010). In assessing the relation between terrestrial water exhibit any systematic trend in Antarctic precipitation during the late storage and climate, Milly et al. (2003) and Ngo-Duc et al. (2005) found 20th century (Uotila et al., 2007). No observational assessments have no long-term climatic trend in total water storage, but documented 13 been made of variability in SMB for the whole ice sheet for the earlier interannual to decadal fluctuations, equivalent to several millimetres part of the 20th century, or of its longer term mean. of sea level. Recent studies have shown that interannual variability in observed GMSL correlates with ENSO indices (Nerem et al., 2010) General Circulation Model (GCM) and Regional Circulation Model and is inversely related to ENSO-driven changes of terrestrial water (RCM) projections consistently indicate significant Antarctic warming storage, especially in the tropics (Llovel et al., 2011). During El Nino and concomitant increase in precipitation. We have high confidence in events, sea level (and ocean mass) tends to be higher because ocean expecting a relationship between these quantities on physical grounds precipitation increases and land precipitation decreases in the tropics (Section 13.4.4.1) and from ice core evidence (Van Ommen et al., 2004; (Cazenave et al., 2012). The reverse happens during La Nina events, as 1155 Chapter 13 Sea Level Change seen during 2010 2011, when there was a decrease in GMSL due to a yr-1 for 1971 to 2010 and 0.38 [0.26 to 0.49] mm yr-1 for 1993 to 2010 temporary increase in water storage on the land, especially in Austral- (Table 13.1). ia, northern South America, and southeast Asia (Boening et al., 2012) (Section 13.3.5). 13.3.5 Ocean Mass Observations from Gravity Recovery and Climate Experiment Direct human interventions on land water storage also induce sea level changes (Sahagian, 2000; Gornitz, 2001; Huntington, 2008; Lettenmai- As discussed in Chapter 3, it has been possible to directly estimate er and Milly, 2009). The largest contributions come from impoundment changes in ocean mass using satellite gravity data from GRACE since in reservoirs and groundwater withdrawal. Over the past half-century, 2002 (Chambers et al., 2004, 2010; Chambers, 2006; Cazenave et al., storage in tens of thousands of reservoirs has offset some of the sea 2009; Leuliette and Miller, 2009; Llovel et al., 2010). These measure- level rise that would otherwise have occurred. Chao et al. (2008) esti- ments represent the sum of total land ice plus land water components, mated that the nearly 30,000 reservoirs built during the 20th century and thus provide an independent assessment of these contributions. resulted in nominal reservoir storage up to 2007 equivalent to ~23 However, GRACE is also sensitive to mass redistribution associated with mm of sea level fall (mostly since 1940), with a stabilization in recent GIA and requires that this effect (on the order of 0.7 to 1.3 mm yr 1 years. Chao et al. further assumed that the reservoirs were 85% full, when averaged over the ocean domain) (Paulson et al., 2007; Peltier, and by including seepage into groundwater as estimated from a model, 2009; Chambers et al., 2010; Tamisiea, 2011) be removed before esti- they obtained a total of 30 mm of sea level fall (equivalent to a rate mating the ocean-mass component. Most recent estimates (Leuliette of sea level fall of 0.55 mm yr 1 from 1950 to 2000). Their seepage and Willis, 2011; von Schuckmann and Le Traon, 2011) report a global estimate was argued to be unrealistically large, however, because it mean ocean mass increase of 1.8 [1.4 to 2.2] mm yr 1 over 2003 2012 assumes aquifers are infinite and have no interfering boundary con- after correcting for the GIA component. The associated error results ditions (Lettenmaier and Milly, 2009; Konikow, 2013). Chao et al. from the low signal-to-noise ratio over the ocean domain and uncer- (2008) argued that sedimentation of reservoirs does not reduce their tainty in the model-based GIA correction (Quinn and Ponte, 2010). sea level contribution, but their argument is disputed (Gregory et al., 2013b). Lettenmaier and Milly (2009) suggested a loss of capacity due Chapter 3 notes that, in terms of global averages, the sum of the con- to sedimentation at 1% yr 1. Given the uncertainty about them, neither tribution to GMSL due to change in global ocean mass (the barystatic the seepage nor the effect of sedimentation is included in the budget contribution), measured by GRACE, and the contribution due to global (Section 13.3.6). Here the (negative) GMSL contribution from reservoir ocean thermal expansion (the thermosteric contribution), measured by storage is estimated as 85% [70 to 100%] of the nominal capacity the Argo Project, agrees within uncertainties with the GMSL change (with the lower limit coming from Pokhrel et al. (2012)). observed by satellite altimetry (Leuliette and Willis, 2011; von Schuck- mann and Le Traon, 2011), although there is still a missing contribution Konikow (2011) estimated that human-induced groundwater deple- from expansion in the deep ocean below 2000 m. These data sets have tion contributed 0.26 +/- 0.07 mm yr 1 to GMSL rise over 1971 2008 allowed an investigation of the cause of variability in sea level over and 0.34 +/- 0.07 mm yr 1 over 1993 2008 (based mostly on obser- the last few years (Figure 13.6). In particular, Boening et al. (2012) con- vational methods), whereas Wada et al. (2012) estimated values of cluded that the decrease in GMSL over 2010 2011 followed by a rapid 0.42 +/- 0.08 mm yr 1 over 1971 2008 and 0.54 +/- 0.09 mm yr 1 over increase since 2011 was related to the 2011 La Nina event, where- 1993 2008 (based on modelling of water fluxes). The average of by changes in land/ocean precipitation patterns caused a temporary these two series with the difference as a measure of the uncertainty is increase in water storage on the land (and corresponding decrease in used in the sea level budget (Section 13.3.6). Pokhrel et al. (2012) esti- GMSL) during the La Nina event, especially in Australia, northern South mated a larger groundwater depletion, but Konikow (2013) (disputed America and southeast Asia (Boening et al., 2012). by Pohkrel et al. (2013)) argued that their underlying assumptions of defining depletion as equivalent to groundwater use, and allowing 13.3.6 Budget of Global Mean Sea Level Rise unlimited extraction to meet water demand, led to substantial over- estimates of depletion. Drawing on Sections 13.3.1 to 13.3.5, the budget of GMSL rise (Table 13.1, Figure 13.7) is analysed using models and observations for the In summary, climate-related changes in water and snow storage on periods 1901 1990 (the 20th century, excluding the period after 1990 land do not show significant long-term trends for the recent decades. when ice-sheet contributions to GMSL rise have increased; Sections However, direct human interventions in land water storage (reservoir 4.4 and 13.3.3.1), since 1971 (when significantly more ocean data impoundment and groundwater depletion) have each contributed at became available and systematic glacier reconstructions began), and 13 least several tenths of mm yr 1 of sea level change (Figure 13.4, Table since 1993 (when precise satellite sea level altimetry began). The 13.1). Reservoir impoundment exceeded groundwater depletion for the 2005 2010 period when Argo and GRACE data are available is short majority of the 20th century but groundwater depletion has increased and strongly affected by interannual climate variability, as discussed in and now exceeds current rates of impoundment, contributing to an the previous subsection (Section 13.3.5 and Figure 13.6). Such varia- increased rate of GMSL rise. The net contribution for the 20th century bility is not externally forced and is therefore not expected to be repro- is estimated by adding the average of the two groundwater depletion duced in AOGCM historical experiments. For the contribution from land estimates to the reservoir storage term (Figure 13.4c). The trends are water storage (Figure 13.4c) we use the estimated effect of human -0.11 [-0.16 to -0.06] mm yr-1 for 1901-1990, 0.12 [0.03 to 0.22] mm intervention and neglect effects from climate-related variation, which 1156 Sea Level Change Chapter 13 25 Total SSH (Altimetry) Ocean Mass (GRACE) 20 Thermosteric (Argo) GRACE + Argo 15 10 GMSL (mm) 5 0 5 10 15 2005 2006 2007 2008 2009 2010 2011 2012 2013 year Figure 13.6 | Global mean sea level from altimetry from 2005 to 2012 (blue line). Ocean mass changes are shown in green (as measured by Gravity Recovery and Climate Experiment (GRACE)) and thermosteric sea level changes (as measured by the Argo Project) are shown in red. The black line shows the sum of the ocean mass and thermosteric contributions. (Updated from Boening et al., 2012) are unimportant on multi-decadal time scales (Section 13.3.4). Contri- to estimate ice-sheet contributions with high confidence before the butions due to runoff from thawed permafrost, change in atmospheric 1990s, and ocean data sampling is too sparse to permit an estimate of moisture content, and sedimentation in the ocean are not considered global mean thermal expansion before the 1970s. However, a closed in the budget because they are negligible compared with observed observational GMSL budget since the 1970s can be demonstrated GMSL rise and the uncertainties. with reasonable estimates of ice-sheet contributions (Church et al., 2011a; Moore et al., 2011) (Table 13.1, Figure 13.7). For 1971 2010, For 1993 2010, allowing for uncertainties, the observed GMSL rise is the observed contributions from thermal expansion and mass loss from consistent with the sum of the observationally estimated contributions glaciers (not including those in Antarctica) alone explain about 75% of (high confidence) (Table 13.1, Figure 13.7e). The two largest terms are the observed GMSL (high confidence). ocean thermal expansion (accounting for about 35% of the observed GMSL rise) and glacier mass loss (accounting for a further 25%, not AOGCM-based estimates of thermal expansion, which agree well including that from Greenland and Antarctica). Observations indicate with observations since 1971, observational estimates of the glacier an increased ice-sheet contribution over the last two decades (Sections contribution, and the estimated change in land water storage (Figure 4.4.2.2, 4.4.2.3 and 13.3.3.1) (Shepherd et al., 2012). The closure of 13.4c), which is relatively small, can all be made from the start of 13 the observational budget since 1993, within uncertainties, represents the 20th century (Sections 13.3.1.2, 13.3.2.2 and 13.3.4, Table 13.1). a significant advance since the AR4 in physical understanding of the Model estimates of Greenland ice-sheet SMB changes give an uncer- causes of past GMSL change, and provides an improved basis for criti- tain but relatively small contribution during most of the 20th century, cal evaluation of models of these contributions in order to assess their increasing since the early 1990s (Section 13.3.3.2). There could be a reliability for making projections. small constant contribution from the Antarctic ice sheet (Huybrechts et al., 2011; Gregory et al., 2013b) due to long-term adjustment to The observational budget cannot be rigorously assessed for 1901 1990 climate change in previous millennia. Any secular rate of sea level rise or 1971 2010 because there is insufficient observational information in the late Holocene was small (order of few tenths mm yr 1) (Section 1157 Chapter 13 Sea Level Change 13 Figure 13.7 | (a) The observed and modelled sea level for 1900 to 2010. (b) The rates of sea level change for the same period, with the satellite altimeter data shown as a red dot for the rate. (c) The observed and modelled sea level for 1961 to 2010. (d) The observed and modelled sea level for 1990 to 2010. Panel (e) compares the sum of the observed contributions (orange) and the observed sea level from the satellite altimeter data (red). The estimates of global mean sea level are from Jevrejeva et al. (2008), Church and White (2011), and Ray and Douglas (2011), with the shading indicating the uncertainty estimates (two standard deviations). The satellite altimeter data since 1993 are shown in red. The grey lines in panels (a)-(d) are the sums of the contributions from modelled ocean thermal expansion and glaciers (excluding glaciers peripheral to the Antarctic ice sheet; from Marzeion et al., 2012a), plus changes in land-water storage (see Figure 13.4). The black line is the mean of the grey lines plus a correction of thermal expansion for the omission of volcanic forcing in the AOGCM control experiments (see Section 13.3.1.2). The dashed black line (adjusted model mean) is the sum of the corrected model mean thermal expan- sion, the change in land water storage, the Marzeion et al. (2012a) glacier estimate using observed (rather than modelled) climate (see Figure 13.4), and an illustrative long-term ice-sheet contribution (of 0.1 mm yr 1). The dotted black line is the adjusted model mean but now including the observed ice-sheet contributions, which begin in 1993. Because the observational ice-sheet estimates include the glaciers peripheral to the Greenland and Antarctic ice sheets (from Section 4.4), the contribution from glaciers to the adjusted model mean excludes the peripheral glaciers to avoid double counting. (Figure and caption updated from Church et al., 2013). 1158 Sea Level Change Chapter 13 13.2.1.4), probably less than 0.2 mm yr 1 (see discussion in Gregory the model mean is within 20% of the observed GMSL rise for the 20th et al., (2013b). Including these ice-sheet contributions (but omitting century (Figure 13.7a,c, dashed line), and 10% since 1993 (Figure Antarctic SMB variations, for which no observationally based infor- 13.7d, dashed line; Church et al. (2013)). When the observed ice- mation for the ice sheet as a whole is available for the majority of sheet contributions since 1992 are included as well, the sum is almost the 20th century), GMSL rise during the 20th century can be account- equivalent to the observed rise (dotted line in Figure 13.7). Both obser- ed for within uncertainties, including the observation that the linear vations and models have a maximum rate of rise in the 1930 1950 trend of GMSL rise during the last 50 years is little larger than for the period, a minimum rate in the 1960s and a maximum rate over the last 20th century, despite the increasing anthropogenic forcing (Gregory two decades (Figure 13.7b). This agreement provides evidence that the et al., 2013b). Model-based attribution of sea level change to RFs is larger rate of rise since 1990, with a significant component of ocean discussed in Section 10.4.3. thermal expansion (Figure 13.4d), results from increased RF (both nat- ural and anthropogenic) and increased ice-sheet discharge, rather than The sum of CMIP5 AOGCM thermal expansion (Section 13.3.1.2), gla- a natural oscillation (medium confidence) (Church et al., 2013). cier model results with CMIP5 AOGCM input (not including glaciers in Antarctica; Section 13.3.2.2; Marzeion et al., (2012a)), and anthropo- In summary, the evidence now available gives a clearer account of genic intervention in land water storage (Section 13.3.4) accounts for observed GMSL change than in previous IPCC assessments, in two about 65% of the observed rate of GMSL rise for 1901 1990, and 90% respects. First, reasonable agreement can be demonstrated throughout for 1971 2010 and 1993 2010 (high confidence) (Table 13.1; Figure the period since 1900 between GMSL rise as observed and as calcu- 13.7). In all periods, the residual is small enough to be attributed to the lated from the sum of contributions. From 1993, all contributions can ice sheets (Section 13.3.3.2). be estimated from observations; for earlier periods, a combination of models and observations is needed. Second, when both models and The unusually warm conditions in the Arctic during the 1930s (Chylek observations are available, they are consistent within uncertainties. et al., 2004), which are attributed to unforced climate variability (Del- These two advances give confidence in the 21st century sea level pro- worth and Knutson, 2000) and are therefore not expected to be sim- jections. The ice-sheet contributions have the potential to increase sub- ulated by AOGCMs, likely produced a greater mass loss by glaciers in stantially due to rapid dynamical change (Sections 13.1.4.1, 13.4.3.2 high northern latitudes (Section 13.3.2.2). The difference between the and 13.4.4.2) but have been relatively small up to the present (Sections glacier mass loss calculated with the Marzeion et al. (2012a) model 4.4 and 13.3.3.2). Therefore, the closure of the sea level budget to date when it is forced with observed climate rather than AOGCM simulated does not test the reliability of ice-sheet models in projecting future climate (the purple and blue curves in Figure 13.4b) is an estimate of rapid dynamical change; we have only medium confidence in these this effect. models, on the basis of theoretical and empirical understanding of the relevant processes and observations of changes up to the present If the glacier model results for observational input are used (Marzeion (13.4.3.2, 13.4.4.2). et al. (2012a), not including glaciers in Antarctica) and an illustrative value of 0.1 mm yr 1 is included for a long-term Antarctic ­ ontribution, c Box 13.1 | The Global Energy Budget The global energy balance is a fundamental aspect of the Earth s climate system. At the top of the atmosphere (TOA), the boundary of the climate system, the balance involves shortwave radiation received from the Sun, and shortwave radiation reflected and longwave radiation emitted by the Earth (Section 1.2.2). The rate of storage of energy in the Earth system must be equal to the net downward radiative flux at the TOA. The TOA fluxes (Section 2.3) have been measured by the Earth Radiation Budget Experiment (ERBE) satellites from 1985 to 1999 (Wong et al., 2006) and the Cloud and the Earth s Radiant Energy System (CERES) satellites from March 2000 to the present (Loeb et al., 2009). The TOA radiative flux measurements are highly precise, allowing identification of changes in the Earth s net energy budget from year to year within the ERBE and CERES missions (Kato, 2009; Stackhouse et al., 2010; Loeb et al., 2012), but the absolute calibration of the instruments is not sufficiently accurate to allow determination of the absolute TOA energy flux or to provide continuity across missions (Loeb et al., 2009). 13 The ocean has stored more than 90% of the increase in energy in the climate system over recent decades (Box 3.1), resulting in ocean thermal expansion and hence sea level rise (Sections 3.7, 9.4 and 13.3.1). Thus the energy and sea level budgets are linked and must be consistent (Church et al., 2011b). This Box focusses on the Earth s global energy budget since 1970 when better global observational data coverage is available. The RFs (from Chapter 8), the global averaged surface temperatures (Hadley Centre/Climate Research Unit gridded surface temperature data set 4 (HadCRUT4) (Morice et al., 2012), and the rate of energy storage are relative to the time mean of 1860 to 1879. Otto et al. (2013) used an energy imbalance over this reference period of 0.08 +/- 0.03 W m 2, which is subtracted from the observed energy storage. (continued on next page) 1159 Chapter 13 Sea Level Change Box 13.1 (continued) Since 1970, the effective radiative forcing (ERF) of the climate system has been positive as a result of increased greenhouse gas (GHG) concentrations (well-mixed and short-lived GHGs, tropospheric and stratospheric ozone, and stratospheric water vapour) and a small increase in solar irradiance (Box 13.1, Figure 1a). This positive ERF has been partly compensated by changes in tropospheric aerosols which predominantly reflect sunlight and modify cloud properties and structure in ways that tend to reinforce the negative ERF, although black carbon produces positive forcing. Explosive volcanic eruptions (such as El Chichón in Mexico in 1982 and Mt. Pinatubo in the Philippines in 1991) can inject sulphur dioxide into the stratosphere, giving rise to stratospheric aerosol, which persists for several years. This reflects some of the incoming solar radiation, and thus gives a further negative forcing. Changes in surface albedo from land-use change have also led to a greater reflection of shortwave radiation back to space and hence a negative forcing. Since 1970, the net ERF of the climate system (including black carbon on snow and combined contrails and contrail-induced cirrus, not shown) has increased (Chapter 8), resulting in a cumulative total energy inflow (Box 13.1, Figure 1a). From 1971 to 2010, the total energy inflow (relative to the reference period 1860-1879) is estimated to be 790 [105 to 1,370] ZJ (1 ZJ = 1021 J). Year Year Box 13.1, Figure 1 | The Earth s energy budget from 1970 through 2011. (a) The cumulative energy flux into the Earth system from changes in well-mixed and short- lived greenhouse gases, solar forcing, changes in tropospheric aerosol forcing, volcanic forcing and surface albedo (relative to 1860 1879) are shown by the coloured lines and these are added to give the cumulative energy inflow (black; including black carbon on snow and combined contrails and contrail-induced cirrus, not shown 13 separately). (b) The cumulative total energy inflow from (a, black) is balanced by the sum of the warming of the Earth system (blue; energy absorbed in warming the ocean, the atmosphere and the land and in the melting of ice) and an increase in outgoing radiation inferred from changes in the global averaged surface temperature. The sum of these two terms is given for a climate feedback parameter of 0.82, 1.23 and 2.47 W m 2 °C 1 (corresponding to an equilibrium climate sensitivity of 4.5, 3.0 and 1.5C, respectively). The energy budget would be closed for a particular value of if that line coincided with the total energy inflow. For clarity, all uncertainties (shading) shown are for a likely range. If the ERF were fixed, the climate system would eventually warm sufficiently that the radiative response would balance the ERF, and there would be zero net heat flux into the system. As the ERF is increasing, the ocean s large capacity to store heat means the climate system is not in equilibrium (Hansen et al., 2005), and continues to store energy (Box 3.1 and Box 13.1, Figure 1b). This storage provides (continued on next page) 1160 Sea Level Change Chapter 13 Box 13.1 (continued) strong evidence of a changing climate. The majority of this additional heat is in the upper 700 m of the ocean but there is also warming in the deep and abyssal ocean (Box 3.1). The associated thermal expansion of the ocean has contributed about 40% of the observed sea level rise since 1971 (Sections 13.3.1, 13.3.6; Church et al., (2011b)). A small amount of additional heat has been used to warm the continents, warm and melt glacial and sea ice, and warm the atmosphere. The estimated increase in energy in the Earth system between 1971 and 2010 is 274 [196 to 351] ZJ (Box 3.1). As the climate system warms, energy is lost to space through increased outgoing radiation. This radiative response by the system is pre- dominantly due to increased thermal grey-body radiation emitted by the atmosphere and surface, but is modified by climate feedbacks, such as changes in water vapour, surface albedo and clouds, which affect both outgoing longwave and reflected shortwave radiation. Following Murphy et al. (2009), Box 13.1, Figure 1b relates the cumulative total energy inflow to the Earth system to the change in energy storage and the cumulative outgoing radiation. Calculation of the latter is based on the observed globally averaged surface temperature change T relative to a reference temperature for which the Earth system would be in radiative balance. This temperature change is multiplied by the climate feedback parameter , which in turn is related to the equilibrium climate sensitivity. For equilibrium climate sensitivities of 4.5°C, 3.0°C to 1.5°C (Box 12.2) and an ERF for a doubled CO2 concentration of 3.7 +/- 0.74 W m 2 (Sections 8.1, 8.3), the corresponding estimates of the climate feedback parameter are 0.82, 1.23 and 2.47 W m 2 °C 1. In addition to these forced variations in the Earth s energy budget, there is also internal variability on decadal time scales. Observations and models indicate that because of the comparatively small heat capacity of the atmosphere, a decade of steady or even decreasing surface temperature can occur in a warming world (Easterling and Wehner, 2009; Palmer et al., 2011). General Circulation Model simulations indicate that these periods are associated with a transfer of heat from the upper to the deeper ocean, of order 0.1 W m 2 (Katsman and van Oldenborgh, 2011; Meehl et al., 2011), with a near steady (Meehl et al., 2011) or an increased radiation to space (Katsman and van Oldenborgh, 2011), again of order 0.1 W m 2. Although these natural fluctuations represent a large amount of heat, they are significantly smaller than the anthropogenic forcing of the Earth s energy budget (Huber and Knutti, 2012), particularly when looking at time scales of several decades or more (Santer et al., 2011). These independent estimates of ERF, observed heat storage, and surface warming combine to give an energy budget for the Earth that is consistent with the assessed likely range of climate sensitivity (1.5°C to 4.5°C; Box 12.2) to within estimated uncertainties (high confidence). Quantification of the terms in the Earth s energy budget and verification that these terms balance over recent decades provides strong evidence for our understanding of anthropogenic climate change. Changes in the Earth s energy storage are a powerful observation for the detection and attribution of climate change (Section 10.3) (Gleckler et al., 2012; Huber and Knutti, 2012). 13.4 Projected Contributions to Global content is approximately proportional to the global mean SAT change Mean Sea Level from equilibrium (Gregory, 2000; Meehl et al., 2007; Rahmstorf, 2007a; Gregory and Forster, 2008; Katsman et al., 2008; Schwartz, 2012), with 13.4.1 Ocean Heat Uptake and Thermal Expansion the constant of proportionality (in W m 2 °C 1) being the ocean heat uptake efficiency k. More than 90% of the net energy increase of the climate system on multiannual time scales is stored in the ocean (Box 3.1). GMSL rise due The ocean heat uptake efficiency quantifies the effect of ocean heat to thermal expansion is approximately proportional to the increase in uptake on moderating time-dependent climate change; neglecting the ocean heat content. The constant of proportionality is 0.11 +/- 0.01 m small fraction of heat stored elsewhere in the climate system, the sur- per 1024 J for the ensemble of CMIP5 models (Kuhlbrodt and Gregory, face warming can be approximated as F/(a+k), where F is the RF and 2012); it depends on the vertical and latitudinal distribution of warm- a is the climate feedback parameter (Raper et al., 2002), and hence the ing in the ocean, because the expansion of sea water per degree Cel- rate of ocean heat uptake is approximately kF/(a+k). In CMIP3 and sius of warming is greater at higher temperature and higher pressure CMIP5, the model spread in projections of surface warming is dominat- 13 (Russell et al., 2000; Hallberg et al., 2012; Körper et al., 2013; Perrette ed by the spread in F and a, but the spread in k accounts for a substan- et al., 2013). tial part of the spread in projections of ocean heat uptake (Dufresne and Bony, 2008; Gregory and Forster, 2008; Knutti and Tomassini, 2008; For the early decades of the 21st century, the upper ocean dominates Geoffroy et al., 2012; Sriver et al., 2012; Forster et al., 2013). the ocean heat uptake, and ocean heat content rises roughly linearly with global mean surface air temperature (SAT) change (Pardaens et The spread in k relates to differences among models in heat-transport al., 2011b; Körper et al., 2013). On multi-decadal time scales under processes within the ocean. The warming spreads downwards from scenarios of steadily increasing RF, the rate of increase of ocean heat the surface over time, and the greatest increases in projected ocean 1161 Chapter 13 Sea Level Change heat content occur where the warming penetrates most deeply, in the 2011b; Körper et al., 2013), and half as much in RCP2.6 as in RCP8.5 Southern Ocean and the North Atlantic (Figure 12.12; Section 12.4.7.1) (Yin, 2012) (Section 13.5.1). The integrating effect means that ther- (Kuhlbrodt and Gregory, 2012). Changes in convection and the large- mal expansion depends not only on the cumulative total, but also on scale vertical circulation are particularly important to heat uptake in the pathway of CO2 emissions; reducing emissions earlier rather than the North Atlantic (Banks and Gregory, 2006; Rugenstein et al., 2013). later, for the same cumulative total, leads to a larger mitigation of sea Heat is also transported vertically by eddies, especially in the Southern level rise due to thermal expansion (Zickfeld et al., 2012; Bouttes et Ocean, and by turbulent mixing. These processes are parameterized in al., 2013). The integrating effect also means that annual time series of models when they occur at unresolved scales. Observed ocean heat global ocean thermal expansion show less interannual variability than uptake has been used in conjunction with observed global SAT change time series of global SAT. For the present assessment of GMSL rise, to constrain the ocean effective thermal diffusivity representing all projections of ocean heat uptake and thermal expansion up to 2100 unresolved vertical transports in simple climate models and EMICs have been derived from the CMIP5 AOGCMs (Yin, 2012). Methods are (Forest et al., 2008; Knutti and Tomassini, 2008; Marèelja, 2010; Soko- described in Section 13.5.1 and the Supplementary Material and the lov et al., 2010). The simulated ocean vertical temperature profile and results for ocean heat uptake are shown in Figure 13.8, and for thermal the depth of penetration of the warming in AOGCMs have also been expansion in Table 13.5 and Figures 13.10 and 13.11. evaluated by comparison with observations, and both bear a relation- ship to k (Hallberg et al., 2012; Kuhlbrodt and Gregory, 2012). Such Ocean heat uptake efficiency is not constant on time scales of many comparisons suggest that model projections might be biased towards decades or in scenarios of stable or decreasing RF (Rahmstorf, 2007a; overestimating ocean heat uptake and thermal expansion for a given Schewe et al., 2011; Bouttes et al., 2013). A good representation of surface warming (Sections 9.4.2.2, 10.8.3 and 13.3.1.2). The physical AOGCM behaviour is obtained by distinguishing a shallow layer, which causes of this tendency are unclear. Although the simulated vertical is associated with surface temperature variations on decadal time temperature profile is affected by the model representation of vertical scales, from a deep layer, which has the majority of the heat capacity heat transport processes, Brierley et al. (2010) found only a small effect (Hansen et al., 1985; Knutti et al., 2008; Held et al., 2010; Olivié et on k from variation of model parameters that influence interior heat al., 2012; Schwartz, 2012; Bouttes et al., 2013; Geoffroy et al., 2013). transport. Ocean heat uptake and thermal expansion take place not only while atmospheric GHG concentrations are rising, but continue for many cen- Because the ocean integrates the surface heat flux, thermal expan- turies to millennia after stabilization of RF, at a rate which declines sion projections following different scenarios do not significantly on a centennial time scale (Stouffer, 2004; Meehl et al., 2005; 2007; diverge for several decades. Scenarios assuming strong mitigation of Solomon et al., 2009; Hansen et al., 2011; Meehl et al., 2012; Schwartz, GHG emissions begin to show a reduced rate of thermal expansion 2012; Bouttes et al., 2013; Li et al., 2013; Zickfeld et al., 2013). This is beyond about 2040; the amount by 2100 is about one third less than because the time scale for warming the deep ocean is much longer in a non-mitigation scenario (Washington et al., 2009; Pardaens et al., than for the shallow ocean (Gregory, 2000; Held et al., 2010). 4 RCP2.6 RCP4.5 RCP6.0 RCP8.5 For individual scenarios, in their own colors: 3 Mean and 5-95% range from TOA radiation Ocean heat uptake (YJ) Mean and 5-95% range from ocean temperature 20 2 12 20 14 1 13 0 2000 2020 2040 2060 2080 2100 Year Figure 13.8 | Heat uptake by the climate system during the 21st century relative to 1986 2005 projected by CMIP5 Atmosphere Ocean General Circulation Models (AOGCMs) under RCP scenarios (1 YJ = 1024 J). The heat uptake is diagnosed by two different methods. The thick solid lines, and the coloured ranges for RCP2.6 and RCP8.5, are the time- and global integral of the net downward radiative flux perturbation at the top of the atmosphere, from the 21 AOGCMs used to make the global mean sea level projections (in some cases estimated from other scenarios, as described in the Supplementary Material). The broken solid lines, and the thin solid lines delimiting ranges for RCP2.6 and RCP8.5, are the global volume integral of ocean temperature change, in a smaller and different set of AOGCMs for each scenario. The difference between the two diagnoses is due partly to the different sets of models (which is a consequence of diagnostics available in the CMIP5 data set), and partly to heat uptake in other parts of the simulated climate system than the ocean water. In both methods, climate drift in the pre-industrial control run has been subtracted. 1162 Sea Level Change Chapter 13 The rate and the stabilization time scale for thermal expansion depend 13.1), future projections should ideally assess the peripheral glaciers on the GHG stabilization level. For the highest scenario (RCP8.5), GMSL separately, as these are too small and dynamically responsive to be rise due to thermal expansion can exceed 2 m above the pre-industrial modelled adequately with coarse-grid, non-dynamic ice-sheet SMB level by the year 2500 (Section 12.5.2, Figure 12.44, Figure 13.14a), models. The peripheral glaciers surrounding both the Greenland and and is still rising at that time. Changes in ocean circulation, particularly Antarctic ice sheets are thus included in the process-based models due to a reduction in deep water formation, can also have a large described above, but for projections shown in Table 13.5, the Antarctic effect on global ocean heat uptake, and may relate nonlinearly to peripheral glaciers are included with the Antarctic ice sheet where- global surface warming (Levermann et al., 2005; Fluckiger et al., 2006; as the Greenland peripheral glaciers are included with the remaining Vellinga and Wood, 2008). As the rate of ocean heat uptake decreases, world s glaciers. Projected losses from glaciers peripheral to both ice the surface warming approaches the level determined by the equilibri- sheets are listed separately in Table 13.3. um climate sensitivity. Several glacier loss projections derived from model types other than On a multi-millennial time scale, the range from Earth System Models process-based models have been published since 2007; their pro- of Intermediate Complexity suggests that thermal expansion contrib- jections range from 0.08 to 0.39 m SLE by 2100 (Table 13.3). These utes between 0.20 to 0.63 m per degree Celsius of global mean tem- used methods of projecting future losses from glaciers developed in perature increase (Meehl et al., 2007; Zickfeld et al., 2013) (Section response to the absence of a global compilation of glacier observations 12.5.2 and Figure 13.14a). The median of the six models of 0.42 m after 2005 and the absence of a globally complete glacier inventory to °C 1 is consistent with a thermal expansion of 0.38 m °C 1 that would provide geographic boundary conditions for conventional modelling. result from a uniform increase in ocean temperature from the presently These methods include extrapolation from observed rates (Meier et observed temperature and salinity distribution (Levitus et al., 2009). al., 2007), semi-empirical methods applied to sea level change compo- Uncertainty arises due to the different spatial distribution of the warm- nents (Jevrejeva et al., 2012b), kinematic (or limit seeking ) projections ing in models and the dependence of the expansion on local tempera- (Pfeffer et al., 2008), and power-law scaling estimates based on re-es- ture and salinity. tablishing equilibrium accumulation-area ratios (AARs) from initial non-equilibrium AARs (Bahr et al., 2009). Strengths of these approach- 13.4.2 Glaciers es include the fact that observations used to calibrate extrapolation and semi-empirical projection partially account for future dynamically The 21st century sea level contribution from glaciers presented in the forced losses, that semi-empirical methods use modelled future forc- AR4 assessment ranged from 0.06 to 0.15 m SLE by 2100 across a ings as guidance for projections, and that AAR equilibration has strong range of scenarios (Meehl et al., 2007). The Randolph Glacier Inventory physical and theoretical underpinnings and gives generalized but (RGI) (Arendt et al., 2012) has improved projections of glacier contribu- robust projections. These strengths partially offset the weaknesses of tion to sea level rise by providing the first globally complete account- these models, which include, in the case of extrapolation and semi-em- ing of glacier location, area, and area-elevation distribution (hypsom- pirical projection, an assumption of statistical stationarity that may not etry). Several analyses of scenario-dependent SMB glacier projections be valid, while the AAR equilibration approach gives only a final steady (referred to here as process-based models) have been produced using state value, so that rates or cumulative losses at any intermediate time the RGI, including Marzeion et al. (2012a), Giesen and Oerlemans must be estimated by area-response time scaling. However, these (2013), and Radiæ et al. (2013). The Marzieon and Radiæ approaches alternate methods are valuable because of their construction on fun- each used different suites of CMIP5 AOGCM models to calculate SMB damental and robust principles together with their use of the limited terms from RCP forcings, and the model by Slangen and van de Wal available information to produce projections that, although imprecise, (2011) was used to calculate SMB terms from RCP forcings (Supple- are transparent, and require less detailed input information or knowl- mentary Material 13.SM.1). Giesen and Oerlemans (2013) used CRU edge of details of complex processes in comparison to process-based forcing but calculated SMB from three different combinations of varia- models. tions in modelled temperature, precipitation, and atmospheric trans- missivity. Only their results for varying temperature are shown here. Published results from process-based models are shown in Table 13.3. Machguth et al. (2013) is also included in Table 13.3, but this projection Glacier contributions at 2100, expressed as SLE, range between 0.04 represents changes in Greenland peripheral glaciers only, and is not and 0.11 m for Special Report on Emission Scenarios (SRES) A1B, 0.07 included in the global glacier summaries. Although these details differ and 0.17 m for RCP2.6, between 0.07 and 0.20 m for RCP4.5, between among the models, all share a generally common time-evolving struc- 0.07 and 0.20 m for RCP6.0, and between 0.12 and 0.26 m for RCP8.5. ture, with SMB initially determined by model-generated climate forcing applied to a subset of global glaciers, the ensuing volume change con- The projections derived from alternative models are also shown in Table 13 verted to area change via volume-area scaling, and this result upscaled 13.3; the mean and range of these models listed here is 0.24 [0.08 to to a new global distribution and hypsometry to create initial conditions 0.39] m SLE, consistent with the process-based models. Results from for the subsequent time step. These methods are described further in the process-based models, plotted as time series and grouped by forc- Section13.5.1 and in the Supplementary Material. Related results are ing scenario, are shown in Figure 13.9. See Table 13.3 for specific start/ shown in Table 13.5 and Figures 13.10 and 13.11. end dates for each projection. Although the peripheral glaciers surrounding the ice sheets are includ- Unresolved uncertainties in the projection of glacier contributions to ed with the ice sheets in assessment of present-day changes (Table sea level rise include the potential for near-term dynamic response 1163 Chapter 13 Sea Level Change from marine-terminating glaciers and interception of terrestrial lost 7.65 Gt yr 1 between 1996 and 2007, with 94% of that loss coming runoff. Of the about 734,000 km2 of global glacier area exclusive of from rapid tidewater retreat (Rasmussen et al., 2011); the loss from this that peripheral to the Greenland and Antarctic ice sheets, 280,500 single 1000 km2 glacier is 1.3% of the global cryospheric component km2 (38%) drains through marine-terminating outlets (Gardner et of sea level rise during 1993 2010 (Table 13.1) and 0.7% of total sea al., 2013). Although the long-term potential for dynamic discharge level rise. The observations required to estimate the potential for sim- from glaciers (as opposed to ice sheets) is limited by their small total ilar dynamic response from other glacier regions do not exist at this volume, dynamic losses may be an important component of total sea time, but the dynamic contribution could be large on the century time level rise on the decade-to-century scale. In Alaska, Columbia Glacier scale. If the basin-wide thinning rate observed at Columbia Glacier Table 13.3 | Twenty-first century sea level rise projections for global glaciers, from process-based surface mass balance models, and from alternate model strategies. Dates for beginning and end of model period are as shown; mean and 5% to 95% confidence sea level equivalents are shown in metres. Process-based models all use variations on Atmo- sphere Ocean General Circulation Model (AOGCM) mass balance forcing applied to inventoried glacier hypsometries on a subset of global glaciers and upscaling by power-law techniques to the global total. Calving and rapid dynamic response are not included in any of the models except for Jevrejeva et al. (2012b), where calving losses are present to a limited degree in input data, and NRC (2012), where calving is explicitly included in future losses. Other model details are discussed in the text. Contribution to Global Peripheral Glacier Mean Sea Level Rise (SLR) (PG) Contribution Starting End Projected SLR (m) from Gla- Greenland Ice Antarctic Ice Reference Model Date Date ciers except Antarctic PGs Sheet PG (m) Sheet PG (m) [5 to 95%] 5 to 95% 5 to 95% Process-based Surface Mass Balance (SMB) Models Mean confidence confidence confidence Scenario RCP2.6 Marzeion et al. (2012a) 1986 2005 2099 0.12 [0.07 0.17] 0.007 0.02 0.02 0.04 Mean Slangen and van de Wal (2011) 2000 2099 0.10 [0.07 0.13] 0.004 0.007 0.02 0.03 Scenario RCP4.5 Marzeion et al. (2012a) 1986 2005 2099 0.14 [0.08 0.20] 0.009 0.022 0.02 0.04 Mean Radic et al. (2013) 2006 2099 0.13 [0.07 0.20] 0.0 0.024 0.02 Slangen and van de Wal (2011) 2000 2099 0.12 [0.07 0.17] 0.005 0.01 0.03 0.04 Scenario RCP6.0 Marzeion et al. (2012a) 1986 2005 2099 0.15 [0.09 0.20] 0.01 0.022 0.02 0.04 Mean Slangen and van de Wal (2011) 2000 2099 0.14 [0.07 0.20] 0.006 0.01 0.04 Scenario RCP8.5 Marzeion et al. (2012a) 1986 2005 2099 0.18 [0.12 0.25] 0.015 0.025 0.02 0.05 Mean Radic et al. (2013) 2006 2099 0.19 [0.12 0.26] 0.009 0.031 0.02 0.03 Slangen and van de Wal (2011) 2000 2099 0.18 [0.12 0.24] 0.008 0.015 0.04 0.06 Scenario A1B Giesen and Oerlemans (2013) 2012 2099 0.08 [0.04 0.11] 0.004 0.021 0.01 0.04 Scenario A1B and RCP4.5 Machguth et al. (2013)a 2000 2098 0.006 0.011 Alternate Models Meier et al. (2007) Extrapolation with fixed rate 2006 2100 0.3 [0.08 0.13] Extrapolation with fixed acceleration 2006 2100 0.24 [0.11 0.37] 13 Pfeffer et al. (2008) Low-range projection 2007 2100 0.17 High-range projection 2007 2100 0.24 Bahr et al. (2009) AAR fixed at present values Find equilibrium value 0.18 [0.15 0.27] AAR declines at current rate Find equilibrium value 0.38 [0.35 0.39] National Research Generalized linear model 2010 2100 0.14 [0.13 0.16] Council (2012) extrapolation, variable rate Jeverjeva et al. (2012b) Semi-empirical projection 2009 2100 0.26 of components of sea level rise, forced by radiation Notes a This projection represents changes in Greenland peripheral glaciers only, and is not included in the global glacier summaries. 1164 Sea Level Change Chapter 13 250 RCP 2.6 RCP 4.5 200 191 GMSLR (mm) 150 145 141 150 110 100 63 50 50 46 0 250 239 RCP 6.0 / A1B RCP 8.5 214 200 183 170 GMSLR (mm) 150 136 108 100 93 80 65 66 59 50 0 2000 2020 2040 2060 2080 2100 2000 2020 2040 2060 2080 2100 Years Years Figure 13.9 | Time series plots for process-based model projections of sea level contributions from global glaciers (in mm), including peripheral glaciers surrounding the Greenland ice sheet but excluding the glaciers surrounding the Antarctic ice sheet. Projections are grouped by forcing scenario as indicated on the plots. Results are plotted for a common time interval of 2011 to 2099. Colours correspond to particular model analyses: red = Marzeion et al. (2012a); blue = Slangen and van de Wal (2011); green = Radiæ et al. (2013); black = Giesen and Oerlemans (2013). Individual Atmosphere Ocean General Circulation Model (AOGCM) projections are plotted for each analysis, so the ranges of the curves at 2099 are different than those listed in Table 13.3, where 5 to 95% confidence limits are shown. In the panel showing results for RCP6.0 and A1B forcings, only Geisen and Oerlemans (black lines) use the A1B forcing. over the past 25 years (about 5 m yr 1) were to occur over the area of with no delay or interception by surface or aquifer storage. Although global glaciers draining through marine outlets (280,500 km2) during this probably will not apply to discharge from glaciers located near the next 89 years (2011 2100), the sea level contribution would be coasts (e.g., Canadian Arctic, Patagonia, Alaska, ice-sheet peripheries), approximately 30 cm SLE, compatible with Jeverajeva et al s (2012b) runoff from interior regions (e.g., Alps, High Mountain Asia) may be projected loss of 26 cm SLE from glaciers. Although this is a rough significantly intercepted before reaching the ocean. Whether terrestrial calculation and an upper bound, because drainage through marine interception has any significant effect on the net glacier contribution outlets does not guarantee tidewater instability, it indicates that the to sea level rise is undetermined at this time. potential for a significant sea level response to dynamic retreat of gla- ciers cannot be rejected a priori. 13.4.3 Greenland Ice Sheet 13 Completion of the global glacier inventory has allowed large improve- 13.4.3.1 Surface Mass Balance Change ments in assessment and modelling, but further uncertainties related to the inventory remain to be resolved, including those arising from the Greenland SMB is positive in the present climate but shows a decreas- size cutoff decided for the inventory (Bahr and Radiæ, 2012). Another ing trend (Section 13.3.3.2), which implies an increasing contribution source of uncertainty is interception of glacier runoff by land hydrolo- to GMSL rise. Like the AR4, all recent studies have indicated that the gy. Despite rapidly growing knowledge of changes in terrestrial water future sea level contribution from Greenland SMB change will be storage, especially through increased reliability of GRACE observations, increasingly positive because the increase in ablation (mostly runoff) glacier mass loss is still generally assumed to flow directly to the ocean, outweighs that in accumulation (mostly snowfall), and that scenarios 1165 Chapter 13 Sea Level Change of greater RF lead to a larger sea level contribution. Precipitation is pro- projected contribution to GMSL rise (Table 13.4). Yoshimori and Abe- jected to increase at about 5% per °C of annual-mean warming over Ouchi (2012) found that the inter-model spread in global mean SAT Greenland, but the increase in snowfall is smaller because the fraction change accounts for about 60% of the spread in the change of project- of rain increases as temperature rises (Gregory and Huybrechts, 2006; ed Greenland ablation. Two important contributions to the remaining Fettweis et al., 2013). spread are the weakening of the AMOC, which affects the magnitude of warming over Greenland, and the SAT of Greenland in the model We compare post-AR4 studies of Greenland SMB change using control climate, which affects the sensitivity of melting to warming time-dependent simulations of the 21st century by CMIP3 AOGCMs (Yoshimori and Abe-Ouchi, 2012; Fettweis et al., 2013). for scenario SRES A1B and CMIP5 AOGCMs for scenario RCP4.5 (Table 13.4). The time-integral of the Greenland SMB anomaly with respect to Ablation is computed using either an EBM, which may be stand-alone a reference period is interpreted as a contribution to GMSL rise, on the or part of a regional climate model, or from surface air temperature assumption that the ice sheet was in approximate mass balance during using an empirical temperature index method, mostly commonly the the reference period (see discussion in Sections 13.1.4.1 and 13.3.3.2); positive-degree-day (PDD) method, in which melting is proportional to this assumption can be avoided only if ice-sheet outflow is also mod- the time-integral of temperature above the freezing point. Meltwater elled. Making this assumption, the Greenland SMB contribution lies in production increases faster than linearly with temperature increase the range 0.00 to 0.13 m for these two scenarios. because of reduced albedo due to refreezing of meltwater in the snow- pack and expansion of the area of bare ice (van Angelen et al., 2012; The spread in the magnitude and patterns of Greenland climate change Fettweis et al., 2013; Franco et al., 2013). The simulation of this posi- projected by the AOGCMs causes a large part of the spread in the tive albedo feedback on mass loss depends sensitively on the model Table 13.4 | Contribution to sea level rise from change in the surface mass balance of the Greenland ice sheet during the 21st century. Where given, ranges are 5 to 95% esti- mated from the published results and indicate the uncertainty due to the climate change modelling by Atmosphere Ocean General Circulation Model (AOGCMs), except where noted otherwise. Contribution to Global Mean Sea Level Rise Reference Modela starting from up to amount (m)b rate (mm yr 1)b Scenario SRES A1B, CMIP3 AOGCMs AR4 (Meehl et al., 2007)c 20 km PDD 1990 2090 2099 0.01 0.08d 0.3 1.9d Bengtsson et al. (2011) e 60 km (T213) EBM 1959 1989 2069 2099 1.4 Fettweis et al. (2008)f TI from 25 km EBM 1970 1999 2090 2099 0.03 0.05 0.3 1.0 Graversen et al. (2011) 10 km PDD 2000 2100 0.02 0.08 0.00 0.17g 0.0 2.1g Mernild et al. (2010) 25 km EBM 1980 1999 2070 2079 0.02 0.5 Rae et al. (2012)h 25 km EBM 1980 1999 2090 2099 0.01, 0.04, 0.06 0.3,1.2,1.5 Seddik et al. (2012)i 10 kme PDD 2004 2104 0.02, 0.04 Yoshimori and Abe-Ouchi (2012) 1 2 km TI 1980 1999 2090 2099 0.02 0.13 0.2 2.0 Scenario RCP4.5, CMIP5 AOGCMs Fettweis et al. (2013)c 25 km RCM 1980 1999 2100 0.02 0.11 0.1 1.2 in 2080 2099 Gregory and Huybrechts (2006)c,j 20 km PDD 1980 1999 2100 0.00 0.06 0.0 0.8 in 2080 2099 Van Angelen et al. (2012) k 11 km RCM 1960 1990 2100 0.11 l 1.7l in 2079 2098 Yoshimori and Abe-Ouchi (2012)j 1 2 km TI 1980 1999 2090 2099 0.00 0.11 0.0 1.8 Notes: a The spatial resolution is stated and the surface mass balance (SMB) method denoted by TI = temperature index, PDD = positive degree day, EBM = Energy Balance Model. b The amount of sea level rise is the time-integral of the SMB anomaly from the period or date labelled starting from to the one labelled up to . Unless otherwise indicated, the SMB anomaly is calculated relative to the mean SMB for the starting from period, and the rate of sea level rise is the SMB anomaly in the up to period. c These results are estimated from global mean surface air temperature (SAT) change, using formulae fitted to results from a Greenland SMB model. d The SMB anomaly is relative to the late 19th century. e This experiment used time-slices, with boundary conditions from the European Centre for Medium range Weather Forecasts (ECMWF) and Hamburg 5 (ECHAM5) GCM, rather than a simulation 13 of the complete century; thus, results are not available for the amount. f Fettweis et al. (2008) and Franco et al. (2011) used a hybrid approach: they derived a regression relationship from simulations of the recent past using a Regional Climate Model (RCM), incorporating an EBM, between annual anomalies in Greenland climate and in Greenland SMB, then applied this relationship to project future SMB from projected future climate anomalies. The method assumes that a relationship derived from past variability will also hold for future forced climate change. g Range including uncertainty in choice of emission scenario (B1, A1B or A2), SMB modelling and ice-sheet dynamical modelling, as well as uncertainty in climate modelling. h Results are given for the Hadley Centre Regional Model 3P (HadRM3P), High-Resolution Hamburg climate model 5 (HIRHAM5) and the Modele Atmosphérique Régional (MAR) RCMs driven with the same boundary conditions from the ECHAM5/MPI-OM AOGCM. i Results are given for two ice sheet models (Elmer/Ice, SImulation COde for POLythermal Ice Sheets (SICOPOLIS)) using the same AOGCM climate boundary conditions. The resolution given is for SICOPOLIS; Elmer/Ice has variable resolution. j Results calculated from CMIP5 AOGCMs by the same method as used in the paper. k These results were obtained from the model of Van Angelen et al. (2012) using boundary conditions from the HadGEM2-ES AOGCM and are shown by Fettweis et al. (2013). l With respect to 1992 2011 as a reference period, during which there is a significant simulated trend in SMB (Section 13.3.3.2), the amount is 0.07 m and the rate 1.4 mm yr 1. 1166 Sea Level Change Chapter 13 snow-albedo parameterization (Rae et al., 2012). Goelzer et al. (2013) face elevation. As a consequence, a nonlinear increase in ice loss from projected 14 to 31% more runoff during the 21st century when using Greenland with increasing regional RF is found across different scenar- an EBM than when using a PDD method, mainly because of the omis- ios (Driesschaert et al., 2007). This nonlinearity arises from the increase sion of the albedo feedback in the latter. However, other studies using in both the length of the ablation season and the daily amount of temperature index methods (Graversen et al., 2011; Yoshimori and melting as the ice-sheet surface lowers. This SMB-surface elevation Abe-Ouchi, 2012) have ranges extending to higher values than those feedback is also the main reason for the threshold behaviour of the from EBMs, indicating that this is not the only difference between Greenland ice sheet on multi-millennial time scales (Section 13.4.3.3). these classes of methods (Table 13.4). Medium-to-low confidence is assigned to the models representation SMB simulations are also particularly sensitive to the treatment of of the atmospheric and ocean circulation and sea-ice changes. On mul- meltwater refreezing (Bougamont et al., 2007; Rae et al., 2012). The ti-centennial time scales, Swingedouw et al. (2008) found enhanced ice pore space in the present-day percolation zone could accommodate loss from Greenland in a coupled simulation (compared to the uncou- 1 to 5 mm SLE of refrozen meltwater over the next several decades pled version) in which ice topography and meltwater flux influence the (Harper et al., 2012), and the importance of meltwater refreezing will ocean and atmospheric circulation as well as sea-ice distribution. Viz- become greater as melting becomes prevalent in areas where it has caíno et al. (2010) found reduced ice loss due to the coupling, mainly previously been rare. On the other hand, refreezing will be restricted, caused by the effect of topographic changes on the surface tempera- and runoff consequently increased, by the expansion of the area of ture, but less pronounced in amplitude compared with Swingedouw bare ice (Fettweis et al., 2013). et al. (2008). Both the atmospheric circulation and the ocean currents, especially in coastal areas, are poorly resolved by these models. It is Another source of model spread is the representation of topography, therefore likely that the time scales associated with ocean transport which is lower when represented at coarser resolution. This allows processes are distorted and there is low confidence that these feed- precipitation to spread further inland because of reduced topograph- backs, although existent, can be quantified accurately by the applied ic barriers (Bengtsson et al., 2011), and enhances ablation because models. there is more area at lower, warmer altitudes (Bengtsson et al., 2011; Seddik et al., 2012). Most of the models in Table 13.4 use a fixed The AMOC exerts a strong influence on regional climate around the Greenland topography, and thus cannot simulate the positive feedback Greenland ice sheet and consequently its SMB. Most CMIP5 models on ablation that can be expected as the ice-sheet surface becomes show a reduction of the AMOC under future warming during the 21st lower. Dynamical models are required to simulate this effect (Section century and beyond (Section 12.4.7.2). Although coupled climate ice 13.4.3.2). sheet models show some influence of meltwater from Greenland on the AMOC, the uncertainty between models with respect to the AMOC For the present assessment of GMSL rise, changes in Greenland ice response to warming is significantly larger than the difference between sheet SMB up to 2100 have been computed from global mean SAT simulations with or without this feedback within one model. change projections derived from the CMIP5 AOGCMs, following meth- ods described in Section 13.5.1 and the Supplementary Material. The In the coupled climate ice sheet model applied by Mikolajewicz et al. distribution of results, shown in Table 13.5 and Figures 13.10 and (2007a) and Vizcaíno et al. (2008), the AMOC shows a strongly non- 13.11, covers the ranges obtained using the methods of Fettweis et al. linear response to global warming. A weak AMOC reduction is found (2013), Gregory and Huybrechts (2006), and Yoshimori and Abe-Ouchi for 1%-per-year-CO2-increase scenarios up to 560 and 840 ppm, and (2012). a near-complete cessation of the AMOC for 1120 ppm. As a conse- quence, after 600 years of integration, the sea level contribution for On multi-centennial to millennial time scales, feedbacks between the 1120 ppm scenario is similar to that of the 560 ppm scenario, but regional climate and the ice sheet become increasingly relevant, espe- doubles for the medium scenario, which stabilizes at 840 ppm. In the cially under strong climate change scenarios, thus requiring coupled most recent model version (Vizcaíno et al., 2010), the AMOC shows a climate ice-sheet models to capture potential feedbacks beyond the strong weakening of the AMOC in all scenarios (~60% reduction in year 2100. These models apply a reduced spatial resolution in order to 560 ppm scenario; ~80% for 1120 ppm). The total sea level contribu- be computationally efficient enough to evaluate longer time scales and tion from Greenland, including the effect of the AMOC weakening, is to combine the different climatic components. Consistent with regional ~1 m (corresponding to an average rate of 1.7 mm yr 1) for 560 ppm climate models for the 21st century, they project an increasingly nega- CO2 and ~3 m (5 mm yr 1) for 1120 ppm CO2. tive mass balance for the Greenland ice sheet for all warming scenarios which is mainly due to a decreasing SMB (Ridley et al., 2005; Wing- Even though the AMOC weakening in the model by Huybrechts et al. 13 uth et al., 2005; Driesschaert et al., 2007; Mikolajewicz et al., 2007a; (2011) is less pronounced (10 to 25%), the ice loss through melting is Swingedouw et al., 2008; Vizcaíno et al., 2008, 2010; Huybrechts et significantly weaker in this model. During the first 1000 years of inte- al., 2011; Goelzer et al., 2013). The main feedbacks between climate gration, the Greenland ice sheet contributes 0.36 m (corresponding to and the ice sheet arise from changes in ice elevation, atmospheric and an average rate of 0.36 mm yr 1) for 560 ppm CO2 and 2.59 m (2.59 ocean circulation, and sea-ice distribution. mm yr 1) for 1120 ppm CO2. In Huybrechts et al. (2011), the respective increases in global mean SAT are 2.4°C (2 × CO2) and 6.3°C (4 × CO2) Comparing the different feedbacks, high confidence can be assigned after 1000 years with respect to pre-industrial. This relatively weak to the models ability to capture the feedback between SMB and sur- warming response to GHG forcing compared to CMIP5 models and 1167 Chapter 13 Sea Level Change the climate model used in Vizcaíno et al. (2010) explains the relatively which equates to SLR of 4 to 8 mm after scaling (by a factor of ~6) small sea level response. to all outlet glaciers based on observed mass loss (van den Broeke et al., 2009). Total projected SLR then varies between 10 and 45 mm at Using the same model as Huybrechts et al. (2011), albeit with a slightly 2100 if successive retreats are specified with a notional repeat interval higher polar warming, Goelzer et al. (2012) computed temperatures between 50 and 10 years. and sea level under the SRES scenarios B1, A1B and A2, with subse- quent GHG stabilization after the year 2100. As in Huybrechts et al. Goelzer et al. (2013) implemented the Nick et al. (2013) retreat chro- (2011), the ice-sheet evolution is dominated by the SMB. They find sea nology within a 5-km resolution ice-sheet model along with their own level contributions of 1.4, 2.6 and 4.2 m in the year 3000 for the sce- generalization for including unsampled outlet glaciers. Associated SLR narios B1, A1B and B2, which correspond to mean rates of sea level at 2100 is projected to vary between 8 and 18 mm. Graversen et al. rise of 1.4 mm yr 1, 2.6 mm yr 1, and 4.2 mm yr 1, respectively. (2011) attempted to capture the effect of increased outflow by enhanc- ing basal sliding and generated SLR of 9 to 24 mm at 2100. In summary, coupled climate-ice sheet models consistently show an increasingly negative mass balance of the Greenland ice sheet due Two estimates of the effect of dynamical change on Greenland s con- mainly to a decreasing SMB under warming scenarios on centennial tribution to SLR by 2100 have been made on the basis of physical time scales beyond 2100. On multi-millennial time scales, these models intuition. Pfeffer et al. (2008) developed a low scenario by assuming show a threshold temperature beyond which the melting of the Green- a first-decade doubling of outlet glacier velocity throughout the ice land ice sheet self-amplifies and the ice volume is reduced to less than sheet that equates to 93 mm SLR, while a high scenario that assumes 30% of its present volume (Section 13.4.3.3). an order of magnitude increase on the same time scale contributes 467 mm. Katsman et al. (2011) used a similar methodology to obtain an 13.4.3.2 Dynamical Change estimate of 100 mm SLR. Observations suggest three main mechanisms by which climate change Based primarily on Nick et al. (2013), we assess the upper limit of the can affect the dynamics of ice flow in Greenland (Sections 4.4.3 and likely range of this dynamical effect to be 85 mm for RCP8.5 and 63 4.4.4): by directly affecting ice loss (outflow) through the calving of mm for all other RCP scenarios for the year 2100. We have medium icebergs and marine melt from marine-terminating outlet glaciers; by confidence in this as an upper limit because it is compatible with Kats- altering basal sliding through the interaction of surface melt water with man et al. (2011), the low scenario of Pfeffer et al. (2008), and Price et the glacier bed; and indirectly through the interaction between SMB al. (2011) in the probable event of a sub-decadal recurrence interval. and ice flow. We assess the consequences of each of these processes. Although the likely upper limit is less than the high scenario of Pfef- fer et al. (2008), process modelling gives no support to the order of Section 4.4.3.2 presents the observational basis on which concerns magnitude increase in flow on which this scenario is based. It is higher about increased ice loss by calving and marine melt are based. In par- than the contributions found by Goelzer et al. (2013) and Graversen ticular, recent increases in loss are thought to be linked to the migration et al. (2011) for which there are two potential explanations. First, the of subtropical water masses around the coast of Greenland (Holland generalization used to extrapolate from the modelled sample to all et al., 2008) and its occupation of coastal fjords (Straneo et al., 2010; outlet glaciers differs. Nick et al. (2013) used a scaling similar to the Christoffersen et al., 2011). Output from 19 AOGCMs under scenario independently derived value of Price et al. (2011), while the implied A1B showed warming of 1.7°C to 2.0°C around Greenland over the scaling used by Goelzer et al. (2013) is substantially lower. Second, course of the 21st century (Yin et al., 2011), suggesting that the trend Goelzer et al. (2013) suggested that surface ice melt and calving each towards increased outflow triggered by warming coastal waters will remove marginal ice (see below), implying that by not including sur- continue. face melt, overall mass loss by dynamics may be over predicted by the flowline model of Nick et al. (2013). At present, these studies cannot be Although projections of outflow are at a fairly early stage, literature reconciled and we therefore use the more inclusive range. now exists to make an assessment. Flowline modelling has successfully simulated the observed retreat and associated acceleration of the main The lower limit of the likely range is assessed as 20 mm for RCP8.5 and outlet glaciers of the Greenland ice sheet (Helheim and Petermann 14 mm for all other RCP scenarios. This reflects the individual outlet Glaciers (Nick et al., 2009, 2012); Jakobshavn Isbrae (Vieli and Nick, glacier projections of Nick et al. (2013) but uses a lower generalization 2011)). The same model has been used to project mass loss from these more similar to that found by Goelzer et al. (2013). This assessment of glaciers (Nick et al., 2013), as well as Kangerdlugssuaq Glacier, using the lower limit is compatible with Price et al. (2011) and Graversen et 13 ocean and atmosphere forcing based on scenarios A1B and RCP8.5. al. (2011). At 2100, total projected SLR spans 8 to 13 mm for A1B and 11 to 17 mm for RCP8.5. These figures generalize to 40 to 63 mm and 57 to 85 Section 4.4.3.2 assesses understanding of the link between abundant mm, respectively, for the whole ice sheet based on a simple scaling summer meltwater, lubrication of the ice-sheet base, and enhanced between modelled and total ice-sheet area (a factor of ~5). Price et ice flow. Although this mechanism appears important in modulating al. (2011) modelled the century-scale response of the ice sheet to the present-day ice flow, it is not supported as the cause of recent mass observed recent retreat of three outlet glaciers (Jakobshavn Isbrae, and loss. Goelzer et al. (2013) incorporated a parameterization of this effect Helheim and Kangerdlugssuaq Glaciers). At 2100, the projected SLR based on field observations, which results in less than a millimetre SLR associated with the three modelled outlet glaciers is 0.6 to 1.4 mm, by 2100 in their projections. Bindschadler et al. (2013) reported a suite 1168 Sea Level Change Chapter 13 of experiments assessing this effect in an eight-model ensemble, but narios by year 2100. The latter are assumed to have uniform SLR in the their parameterization appears overly simplistic and may well exag- absence of literature allowing these scenarios to be assessed individu- gerate the importance of the effect. These projections do not incor- ally, although dependency on scenario is expected to exist. In addition, porate the effect on ice flow of the latent heat released by increased mass loss associated with SMB-height feedback is likely to contribute future quantities of melt water within the ice sheet (Phillips et al., a further 0 to 15% of SMB (in itself scenario dependent). This equates 2010; 2013), for which no projections are currently available. Basal to, for example, 0 to 14 mm by 2100 based on the central estimate of lubrication is therefore assessed as making an insignificant contribu- RCP8.5. The peripheral glaciers of Greenland are not included here but tion to the likely range of SLR over the next century and is omitted in are in the assessment of global glaciers contribution to SLR (Section the remainder of the assessment. We have medium confidence in pro- 13.4.2). All the available literature suggests that this dynamical contri- jections of this effect primarily because recent improvements in pro- bution to sea level rise will continue well beyond 2100. cess-based understanding show that it has little contribution to mass loss (Section 4.4.3.2); the potential of latent-heat effects in the future 13.4.3.3 Possible Irreversibility of Greenland Ice Loss and limits a higher level of confidence. Associated Temperature Threshold Finally, we assess the level of interaction between SMB change and ice A number of model results agree in showing that the Greenland ice flow. In AR4, this effect is assessed as 0 +/- 10% (likely range) of SMB, sheet, like other climatic subsystems (Lenton et al., 2008; Levermann et based on Huybrechts and de Wolde (1999) and Gregory and Huybrechts al., 2012) (see Section 12.5.5), exhibits a strongly nonlinear and poten- (2006). This assessment included both the positive feedback between tially irreversible response to surface warming. The mechanism of this SMB and the height of the ice sheet, and a countering negative feed- threshold behaviour is the SMB-height feedback (Section 13.4.3.2); back involving ice flow and depletion effects. The latter effect is partly that is, as the surface is lowered due to ice loss, the associated warm- included in our assessment of the direct impacts of climate change ing of the near surface increases ablation, leading to further ice loss. on ice flow, and we therefore limit our assessment to the SMB-height This feedback is small but not negligible in the 21st century (Section feedback. Few studies explicitly determine this effect, but Goelzer et al. 13.4.3.2) and becomes important for projections for the 22nd centu- (2013) reported that it amounts to 5 to 15% of SMB over the course of ry (Goelzer et al. 2013) and beyond. This nonlinear behaviour may be the 21st century, which we extend slightly (0 to 15%) to reflect possi- accelerated by a reduced surface albedo caused by surface melting ble interaction with mass loss by calving (Goelzer et al., 2013). which tends to further decrease the surface mass balance (Box et al., 2012) (Section 13.4.3.1). Goelzer et al. (2013) and Gillet-Chaulet et al. (2012) suggested that SMB and ice dynamics cannot be assessed separately because of the Although the mean SMB of the Greenland ice sheet is positive, in a strong interaction between ice loss and climate due to, for instance, steady state it must be balanced by ice outflow, so the ice sheet must calving and SMB. The current assessment has by necessity separated extend to the coast. In a warmer climate, the mean SMB is reduced these effects because the type of coupled ice sheet-climate models (Section 13.4.3.1) and the steady-state ice sheet will have a lower sur- needed to make a full assessment do not yet exist. These interactions face and volume. Models show a threshold in surface warming beyond may well combine to reduce SLR in comparison to the assessed range which self-amplifying feedbacks result in a partial or near-complete because of the mass-depletion effect of retreating outlet glaciers. ice loss on Greenland (Greve, 2000; Driesschaert et al., 2007; Charbit Another source of uncertainty is the bedrock topography of Greenland, et al., 2008; Ridley et al., 2010; Robinson et al., 2012). If a temperature although recent improvements in data coverage (Bamber et al., 2013) above this threshold is maintained over a multi-millennial time period, suggest that the majority of the ice sheet rests on bedrock above sea the majority of the Greenland ice sheet will be lost by changes in SMB level and the number of deep bedrock troughs penetrating into the on a millennial to multi-millennial time scale (equivalent to a sea level interior of Greenland are limited, thus limiting the potential for marine rise of about 7 m; Table 4.1). During the Middle Pliocene warm inter- ice-sheet instability (see Box 13.2). vals, when global mean temperature was 2°C to 3.5°C higher than pre-industrial, ice-sheet models suggest near-complete deglaciation of Although not strictly comparable because they contain a different Greenland (Hill et al., 2010). balance of ice-dynamical effects, the assessment is consistent with Bindschadler et al. (2013), who reported an extensive model inter-com- A simplifying assumption is that the threshold is the warming required parison exercise in which standardized experiments are combined to with the current ice-sheet topography to reduce the mean SMB to zero, represent the impact of climate change under RCP8.5 on the Green- on the argument that the ice sheet margin must then retreat from the land ice sheet. The resultant projection included contributions from coast. Using this criterion, Gregory and Huybrechts (2006) estimat- lubrication, marine melt and SMB-coupling and generated a mean SLR ed that the SMB threshold occurs for a GMST increase of 3.1 [1.9 to 13 at 2100 of 162 mm over five models, or 53 mm if an outlier with anom- 4.6] °C (4.5 [3.0 to 6.0] °C for Greenland surface temperature) above alously high response is removed (including SMB results in SLR at 2100 pre-industrial (assumed to be a steady state). More recent studies have of 223 and 114 mm for five- and four-model means, respectively). This found thresholds below or in the lower part of this range. In a coupled comparison provides further weight to our confidence. ice sheet climate model of intermediate complexity, Huybrechts et al. (2011) found this threshold at 2.5°C for annual average Greenland In summary, dynamical change within the Greenland ice sheet is likely SAT. Comparing three regional climate models, Rae et al. (2012) found (medium confidence) to lead to SLR during the next century with a a strong dependence of the threshold on the model formulation of the range of 20 to 85 mm for RCP8.5, and 14 to 63 mm for all other sce- SMB. Based on the model s performance against observations and the 1169 Chapter 13 Sea Level Change physical detail of its surface scheme, MAR is considered the most real- exceeded 2oC pre-industrial, the Greenland ice sheet contributed no istic model, and yields a threshold value 2.8 [2.1 to 3.4] °C for changes more than ~4 m to GMSL. This could indicate that the threshold for in Greenland annual average temperature compared to pre-industrial. near-complete deglaciation had not been passed, or that it was not Using MAR driven with output from various CMIP5 AOGCMs, Fettweis greatly exceeded so that the rate of mass loss was low; however, the et al. (2013) evaluated the threshold as ~3°C in GMST above 1980 forcing responsible for the LIG warming was orbital rather than from 1999 (hence about 3.5°C relative to pre-industrial), and found that it CO2 (van de Berg et al., 2011), so it is not a direct analogue and the is not exceeded in the 21st century under the RCP4.5 scenario but is applicable threshold may be different. Studies with fixed-topography reached around 2070 under the RCP8.5 scenario. ice sheets indicate a threshold of 2°C or above of global warming with respect to pre-industrial for near-complete loss of the Greenland ice Some of the uncertainty in the threshold results from the value assumed sheet, while the one study (and therefore low confidence) presently for the steady state ice-sheet SMB (see Table 13.2), and whether this available with a dynamical ice sheet suggests that the threshold could is assumed to be pre-industrial or a more recent period. For 400 Gt yr 1 be as low as about 1°C (Robinson et al. 2012). Recent studies with (Fettweis et al., 2013), the parametrization of Greenland ice sheet SMB fixed-topography ice sheets indicate that the threshold is less than used for present assessment of 21st century changes (Section 13.4.3.1, about 4°C (medium confidence because of multiple studies). With cur- Supplementary Material) gives a global warming threshold of 3.0 [2.1 rently available information, we do not have sufficient confidence to to 4.1] °C with respect to 1860-1879 (the reference period used in Box assign a likely range for the threshold. If the threshold is exceeded 13.1); for 225 Gt yr 1 (Gregory and Huybrechts, 2006, following Church temporarily, an irreversible loss of part or most of the Greenland ice et al., 2001), the threshold is 2.1 [1.5 to 3.0] °C. sheet could result, depending on the duration and amount that the threshold is exceeded. Although a negative SMB is a sufficient condition for passing the threshold, it will overestimate the value of the threshold quantita- 13.4.4 Antarctic Ice Sheet tively, because the SMB height feedback (even without passing the threshold) means that the steady-state SMB is reduced by more than 13.4.4.1 Surface Mass Balance Change is calculated assuming fixed topography. The actual SMB change will depend on the dynamical response of the ice sheet that determines its Because the ice loss from Antarctica due to surface melt and runoff is topography. Constraining simulations with a dynamic ice-sheet model about 1% of the total mass gain from snowfall, most ice loss occurs to changes during the last interglacial, Robinson et al. (2012) estimat- through solid ice discharge into the ocean. In the 21st century, ablation ed the threshold as 1.6 [0.9 to 2.8] °C global averaged temperature is projected to remain small on the Antarctic ice sheet because low above pre-industrial. In these simulations, they find that the thresh- surface temperatures inhibit surface melting, except near the coast and old is passed when southeastern Greenland has a negative SMB. The on the Antarctic Peninsula, and meltwater and rain continue to freeze near-complete ice loss then occurs through ice flow and SMB. in the snowpack (Ligtenberg et al., 2013). Projections of Antarctic SMB changes over the 21st century thus indicate a negative contribution The complete loss of the ice sheet is not inevitable because it has a to sea level because of the projected widespread increase in snowfall long time scale (tens of millennia near the threshold and a millennium associated with warming air temperatures (Krinner et al., 2007; Uotila or more for temperatures a few degrees above the threshold). If the et al., 2007; Bracegirdle et al., 2008). Several studies (Krinner et al., surrounding temperatures decline before the ice sheet is eliminated, 2007; Uotila et al., 2007; Bengtsson et al., 2011) have shown that the the ice sheet might regrow. In the context of future GHG emissions, the precipitation increase is directly linked to atmospheric warming via the time scale of ice loss is competing with the time scale of temperature increased moisture holding capacity of warmer air, and is therefore decline after a reduction of GHG emissions (Allen et al., 2009; Solomon larger for scenarios of greater warming. The relationship is exponential, et al., 2009; Zickfeld et al., 2009). The outcome therefore depends on resulting in an increase of SMB as a function of Antarctic SAT change both the CO2 concentration and on how long it is sustained. Charbit evaluated in various recent studies with high-resolution (~60 km) et al. (2008) found that loss of the ice sheet is inevitable for cumula- models as 3.7% °C 1 (Bengtsson et al., 2011), 4.8% °C 1 (Ligtenberg tive emissions above about 3000 GtC, but a partial loss followed by et al., 2013) and ~7% °C 1 (Krinner et al., 2007). These agree well with regrowth occurs for cumulative emissions less than 2500 GtC. Ridley the sensitivity of 5.1 +/- 1.5% °C 1 (one standard deviation) of CMIP3 et al. (2010) identified three steady states of the ice sheet. If the CO2 AOGCMs (Gregory and Huybrechts, 2006). concentration is returned to pre-industrial when more than 20 to 40% of the ice sheet has been lost, it will regrow only to 80% of its original The effect of atmospheric circulation changes on continental-mean volume due to a local climate feedback in one region; if 50% or more, SMB is an order of magnitude smaller than the effect of warming, but 13 it regrows to 20 to 40% of the original. Similar states with ice volume circulation changes can have a large influence on regional changes around 20%, 60 to 80% and 100% of the initial ice volume are also in accumulation, particularly near the ice-sheet margins (Uotila et al., found in other models (Langen et al., 2012; Robinson et al., 2012). If 2007) where increased accumulation may induce additional ice flow all the ice is lost, temperatures must decline to below a critical thresh- across the grounding line (Huybrechts and De Wolde, 1999; Gregory old for regrowth of the ice sheet (Robinson et al., 2012; Solgaard and and Huybrechts, 2006; Winkelmann et al., 2012). Simulated SMB is Langen, 2012). strongly and nonlinearly influenced by ocean surface temperature and sea-ice conditions (Swingedouw et al., 2008). This dependence means On the evidence of paleo data and modelling (Section 5.6.2.3, 13.2.1), that the biases in the model-control climate may distort the SMB sen- it is likely that during the LIG, when global mean temperature never sitivity to climate change, suggesting that more accurate predictions 1170 Sea Level Change Chapter 13 may be obtained from regional models by using boundary conditions In a similar experiment but allowing GHG concentrations to reach constructed by combining observed present-day climate with projected 1120 ppm CO2 before being stabilized, both models show a net posi- climate change (Krinner et al., 2008). There is a tendency for higher tive sea level contribution after 600 years of integration. Huybrechts et resolution models to simulate a stronger future precipitation increase al. (2011) found a weak sea level contribution during the first 500 years because of better representation of coastal and orographic precipita- of integration followed by a stronger and relatively constant long-term tion processes (Genthon et al., 2009). average rate of ~2 mm yr 1 after 1000 years of integration up to a total contribution of ~4 m SLE after 3000 years of integration. Although For the present assessment of GMSL rise, changes in Antarctic ice-sheet they found some grounding line retreat due to basal ice-shelf melt, the SMB up to 2100 have been computed from global mean SAT change multi-centennial evolution of the ice sheet is dominated by changes in projections derived from the CMIP5 AOGCMs, using the range of sensi- SMB whereas the solid-ice discharge after an initial increase shows a tivities of precipitation increase to atmospheric warming summarized significant decrease during the scenario. above, and the ratio of Antarctic to global warming evaluated from CMIP3 AOGCMs by Gregory and Huybrechts (2006) (see also Section For the same scenario, Vizcaíno et al. (2010) found that the initial mass 13.5.1 and Supplementary Material). The results are shown in Table gain is followed by a weak mass loss. After 250 years of integration, 13.5 and Figures 13.10 and 13.11. The projected change in ice outflow the contribution is stronger and relatively constant at a rate of about 3 is affected by the SMB because of the influence of topography on ice mm yr 1, corresponding to a net contribution of 1.2 m to global mean dynamics (Section 13.4.4.2 and Supplementary Material). Ozone recov- sea level rise after 600 years. ery, through its influence on atmospheric circulation at high southern latitudes (Section 10.3.3.3), may offset some effects of GHG increase The same model as in Huybrechts et al. (2011), although with a slightly in the 21st century, but Antarctic precipitation is nonetheless projected stronger polar amplification, was applied to the three SRES scenarios to increase (Polvani et al., 2011). Bintanja et al. (2013) suggested that used in the AR4 (B1, A1B, A2) with stabilization in the year 2100 (Goel- Antarctic warming and precipitation increase may be suppressed in zer et al., 2012). For the lowest scenario (B1), they found practically the future by expansion of Antarctic sea ice, promoted by freshening no net Antarctic contribution to sea level in the year 3000. Under the of the surface ocean, caused by basal melting of ice shelves, and they medium scenario (A1B), the ice sheet contributes 0.26 m, and under conducted an AOGCM sensitivity test of this hypothesis. We consider the highest scenario (A2), it contributes 0.94 m SLE in the year 3000. these possibilities in Section 13.5.3. These simulations include a negative feedback on the regional climate Beyond the year 2100, regional climate simulations run at high spatial by ice-sheet melt through which summer temperatures can be signif- resolution (5 to 55 km) but without climate-ice sheet feedbacks includ- icantly reduced over Antarctica (Swingedouw et al., 2008). However, ed show a net ice gain until the year 2200 (Ligtenberg et al., 2013). due to the coarse resolution and the high polar amplification, there During the 22nd century, the ice gain is equivalent to an average rate is low confidence in the model s representation of oceanic circulation of sea level fall of 1.2 mm yr 1 for the A1B scenario and 0.46 mm yr 1 changes around Antarctica. for the E1 scenario. In both models (Vizcaíno et al., 2010; Huybrechts et al., 2011), the ice For multi-centennial to multi-millennial projections, feedbacks between sheets are not equilibrated with the surrounding climate after the inte- the ice sheet and regional climate need to be accounted for. This is cur- gration period under the 1120 ppm CO2 forcing. Though GHG concen- rently done using ice-sheet models coupled to climate models of inter- trations were stabilized after 120 years of integration, the Antarctic ice mediate complexity, which have a significantly lower spatial resolution sheet continues to contribute to sea level rise at a relatively constant in the atmospheric component than regional climate models used to rate for another 480 years in Vizcaíno et al. (2010) and 2880 years in assess future SMB within the 21st century. These coarser resolution Huybrechts et al. (2011). This is consistent with a positive sea level models capture the increase in snowfall under future warming, but contribution from Antarctica during past warmer climates (Sections the regional distribution is represented less accurately. Accordingly, 13.2.1 and 13.5.4). there is low confidence in their ability to model spatial melting and accumulation patterns accurately. In contrast, medium confidence can In summary, both coupled ice sheet-climate models consistently show be assigned to the models projection of total accumulation on Ant- that for high-emission scenarios, the surface melt increases and leads arctica, as it is controlled by the large-scale moisture transport toward to an ice loss on multi-centennial time scales. The long time period the continent. over which the Antarctic ice sheet continues to lose mass indicates a potential role of the feedback between climate and ice sheet. Con- In idealized scenarios of 1% increase of CO2 yr 1 up to 560 ppm with sistent with regional climate models for the 21st and 22nd centuries, 13 subsequent stabilization, Vizcaíno et al. (2010) and Huybrechts et al. both coarse-resolution coupled models show a positive SMB change (2011) found an initial increase of ice volume due to additional snow- for most of the first 100 years of climate change. Due to the inertia in fall during the first 600 years of integration. In both models, the chang- the climate system, regional temperatures continue to rise after that. es in SMB dominate the mass changes during and beyond the first Together with enhanced solid ice discharge, this results in mass loss of 100 years. After 600 years of integration, Vizcaíno et al. (2010) found a the ice sheet. The corresponding decline in surface elevation increases mass gain corresponding to a sea level fall of 0.15 m ( 0.25 mm yr 1 on the surface temperature and leads to additional ice loss. average). For the same experiment and the same period, Huybrechts et al. (2011) found a sea level fall of 0.08 m ( 0.13 mm yr 1 on average). 1171 Chapter 13 Sea Level Change 13.4.4.2 Dynamical Change There is good evidence linking the focus of current Antarctic mass loss in the Amundsen Sea sector of the WAIS (containing Pine Island and The Antarctic ice sheet represents the largest potential source of future Thwaites Glaciers) (Shepherd and Wingham, 2007; Rignot et al., 2008; SLR; the West Antarctic ice sheet alone has the potential to raise sea Pritchard et al., 2009) to the presence of relatively warm Circumpolar level by ~4.3 m (Fretwell et al., 2013). The rate at which this reser- Deep Water on the continental shelf (Thoma et al., 2008; Jenkins et al., voir will be depleted and cause sea level to rise, however, is not easily 2010). However, it is not possible to determine whether this upwelling quantifiable. In this section, we focus on dynamical changes (i.e., those was related directly or indirectly to a rise in global mean temperature. related to the flow of the ice sheet) that affect SLR by altering the flux Yin et al. (2011) assessed output from 19 AOGCMs under scenario A1B of ice across the grounding line (or outflow) that separates ice resting to determine how subsurface temperatures are projected to evolve on bedrock (some of which does not currently displace ocean water) around the West and East Antarctic ice sheets. They showed decad- from floating ice shelves (which already displace ocean water and have al-mean warming of 0.4°C to 0.7°C and 0.4°C to 0.9°C around West only a negligible effect on sea level) (Jenkins and Holland, 2007). and East Antarctica, respectively (25th to 75th percentiles of ensemble) by the end of the 21st century. More detailed regional modelling using Issues associated with the inability of models to reproduce recently scenario A1B illustrates the potential for warm water to invade the observed changes in the dynamics of the Antarctic ice sheet prevented ocean cavity underlying the Filchner-Ronne ice shelf in the second half the AR4 from quantifying the effect of these changes on future sea of the 21st century, with an associated 20-fold increase in melt (Hellmer level. Since the AR4, progress has been made in understanding the et al., 2012). Based on the limited literature, there is medium confi- observations (Sections 4.4.3 and 4.4.4), and projections are becoming dence that oceanic processes may potentially trigger further dynamical available. It must be stressed, however, that this field has yet to reach change particularly in the latter part of the 21st century, while there the same level of development that exists for modelling many other is also medium confidence that atmospheric change will not affect components of the Earth system. There is an underlying concern that dynamics outside of the Antarctic Peninsula during the 21st century. observations presage the onset of large-scale grounding line retreat in what is termed the Marine Ice Sheet Instability (MISI; Box 13.2), and Several process-based projections of the future evolution of Pine Island much of the research assessed here attempts to understand the appli- Glacier have now been made, and some of the issues that this mod- cability of this theoretical concept to projected SLR from Antarctica. elling faced (such as the need for sub-kilometre resolution to ensure consistent results; Cornford et al. (2013), Durand et al. (2009)) are There are three distinct processes that could link climate change to being resolved (Pattyn et al., 2013). In experiments using an idealized dynamical change of the Antarctic ice sheet and potentially trigger increase in marine melt, Joughin et al. (2010) demonstrated only lim- increased outflow. These may operate directly through the increased ited (~25 km) retreat of the grounding line before a new equilibrium potential for melt ponds to form on the upper surface of ice shelves, position was established. Gladstone et al. (2012) used a flowline model which may destabilize them, or by increases in submarine melt expe- forced with ocean-model output (Hellmer et al., 2012) to identify two rienced by ice shelves as a consequence of oceanic warming, which modes of retreat: one similar to that identified by Joughin et al. (2010), leads to their thinning, as well as indirectly by coupling between SMB and a second characterized by complete collapse from 2150 onwards. and ice flow. Section 4.4.3.2 presents the observational basis on which More sophisticated ice-flow modelling (albeit with idealized forcing) understanding of these processes is based, while their potential future suggests grounding line retreat of ~100 km in 50 years (Cornford et al., importance is assessed here. Literature on the two mechanisms directly 2013). These studies support the theoretical finding of Gudmundsson linked to climate change will be assessed first, followed by their rela- et al. (2012) that grounding line retreat, if triggered, does not inev- tion to outflow change and lastly SMB coupling. itably lead to MISI but may halt if local buttressing from ice rises or channel sidewalls is sufficient. Parizek et al. (2013) used a flowline There is strong evidence that regional warming and increased melt model to study Thwaites Glacier and found that grounding line retreat water ponding in the Antarctic Peninsula led to the collapse of ice is possible only under extreme ocean forcing. It is also thought that shelves along the length of peninsula (Cook and Vaughan, 2010), most considerably less back pressure is exerted by Thwaites ice shelf in notably the Larsen B ice shelf (MacAyeal et al., 2003). Substantial local comparison to Pine Island s (Rignot, 2001; 2008), which may make it warming (~5 to 7 °C) would, however, be required before the main less sensitive to forcing by submarine melt (Schoof, 2007a; Goldberg Antarctic ice shelves (the Ross and Filchner-Ronne ice shelves) would et al., 2012). Based on this literature, there is high confidence that become threatened (Joughin and Alley, 2011). An assessment of the the retreat of Pine Island Glacier (if it occurs) can be characterized by AR4 AOGCM ensemble under scenario A1B yielded an Antarctic con- a SLR measured in centimetres by 2100, although there is low con- tinental-average warming rate of 0.034 +/- 0.01°C yr 1 (Bracegirdle et fidence in the models ability to determine the probability or timing 13 al., 2008), suggesting that the required level of warming may not be of any such retreat. There is also medium confidence (in the light of approached by the end of the 21st century. Using an intermediate com- the limited literature) that Thwaites Glacier is probably less prone to plexity model with scenario A2, Fyke et al. (2010) found that melt starts undergo ocean-driven grounding line retreat than its neighbour in the to reach significant levels over these ice shelves around 2100 to 2300. 21st century. No process-based modelling is available on which to be Barrand et al. (2013) made a process-based assessment of the effect base projections of EAIS glaciers currently losing mass, such as Totten of ice-shelf collapse on outflow from the Antarctic Peninsula, which and Cook Glaciers. yields a range of SLR at 2100 between 10 and 20 mm, with a bounding maximum of 40 mm. Bindschadler et al. (2013) reported a model inter-comparison exercise on the impact of climate change under RCP8.5 on the Antarctic ice 1172 Sea Level Change Chapter 13 sheet. The resultant projection includes contributions from increased and low-melt experiment of Bindschadler et al. (2013) (~100 and 69 marine melt in the Amundsen Sea and Amery sectors, and generat- mm, respectively). The base projection of the NRC (2012) (157 to 323 ed a mean SLR at 2100 of ~100 mm over four models (with overall mm including future SMB change), however, is less compatible. This SLR of 81 mm when SMB change was included). There is, however, moderate level of consistency across a range of techniques suggests low confidence in the projection because of the unproven ability of medium confidence in this assessment. We assess this as the likely (as many of the contributing models to simulate grounding line motion. opposed to very likely) range because it is based primarily on pertur- Bindschadler et al. (2013) also reported idealized experiments in which bations of the ice sheet s present-day state of mass imbalance and ice-shelf melt is increased by 2, 20 and 200 m yr 1. The resulting five- does not include the potentially large increases in outflow that may be model mean SLR of 69, 693 and 3477 mm by 2100, respectively, can be associated with the MISI discussed below. considered only as a general indication because of the shortcomings of the contributing models (e.g., two do not include ice shelves) which The probability of extensive grounding line retreat being both triggered may be offset by the use of a multi-model mean. Although grounding and continuing to the extent that it contributes to significant SLR in the line migration in the 20 m yr 1 experiment is extensive in some models 21st century is very poorly constrained, as the results of a recent expert and consistent with what might be expected under MISI (Bindschadler elicitation indicate (Bamber and Aspinall, 2013). We have medium con- et al., 2013), the 200 m yr 1 experiment is unrealistic, even if used as a fidence, however, that this probability lies outside of the likely range of proxy for the improbable atmosphere-driven collapse of the major ice SLR. Five arguments support this assessment. First, the partial loss of shelves, and is not considered further. Pine Island Glacier is included by Little et al. (2013a) in their range and the full loss of the ice stream (if it were to occur) is thought to raise We now assess two alternatives to process-based modelling that sea level by centimetres only (consistent with the use of the Little et exist in the literature: the development of plausible high-end projec- al. (2013a) 5 to 95% range as the assessed likely range). Second, the tions based on physical intuition (Pfeffer et al., 2008; Katsman et al., current grounding line position of the neighbouring Thwaites Glacier 2011) and the use of a probabilistic framework for extrapolating cur- appears to be more stable than that of Pine Island Glacier (Parizek rent observations of the ice sheet s mass budget (Little et al., 2013a; et al., 2013) so that its potentially large contribution to SLR by 2100 2013b). Pfeffer et al. (2008) postulated a possible but extreme scenario is assessed to have a significantly lower probability. Third, there is a of 615 mm SLR based on vastly accelerated outflow in the Amund- low probability that atmospheric warming in the 21st century will lead sen Sea sector and East Antarctica, whereas a more plausible scenario to extensive grounding line retreat outside of the Antarctic Peninsula involving reduced acceleration in the Amundsen Sea sector yields 136 because summer air temperatures will not rise to the level where sig- mm. Katsman et al. (2011) used similar assumptions in a modest sce- nificant surface melt and ponding occur. Fourth, although this retreat nario that generates SLR of 70 to 150 mm, and a severe scenario may be triggered by oceanic warming during the 21st century (in par- that attempts to capture the consequences of the collapse of the WAIS ticular, under the Filchner-Ronne ice shelf), current literature suggests through the MISI and has a SLR contribution of 490 mm. The NRC that this may occur late in the century (Hellmer et al., 2012), reducing (2012) extrapolated mass-budget observations of the ice sheet to gen- the time over which enhanced outflow could affect end-of-century SLR. erate a projection of 157 to 323 mm (including future SMB change), Finally, there are theoretical grounds to believe that grounding line with an additional 77 to 462 mm accounting for 21st-century increases retreat may stabilize (Gudmundsson et al., 2012) so that MISI (and in outflow (summing as 234 to 785 mm). associated high SLR) is not inevitable. Little et al. (2013a) applied a range of linear growth rates to pres- We next assess the magnitude of potential SLR at 2100 in the event that ent-day SMB and outflow observations of Antarctic sectors (Rignot et MISI affects the Antarctic ice sheet. Bindschadler et al. (2013), Katsman al., 2008; Shuman et al., 2011; Zwally and Giovinetto, 2011), which et al. (2011), the NRC (2012), and Pfeffer et al. (2008) presented con- are then weighted using a continental-scale observational synthesis trasting approaches that can be used to make this assessment, which (Shepherd et al., 2012) (consistent with the assessment of Chapter 4). are upper-end estimates of 693, 490, 785 and 615 mm, respectively. In the case of Pine Island Glacier, growth rates are based on the pro- Together this literature suggests with medium confidence that this con- cess-based modelling of Joughin et al. (2010). Within this framework, tribution would be several tenths of a metre. The literature does not SLR at 2100 has a 5 to 95% range of 20 to 185 mm for dynamical offer a means of assessing the probability of this contribution, however, change only, and 86 to 133 mm when SMB change is included (based other than our assessment (above) that it lies above the likely range. on Uotila et al. (2007)). Projections for the Antarctic Peninsula are consistent with the process-based modelling of Barrand et al. (2013). Literature investigating the relation between the SLR generated by Further, Little et al. (2013a) found that the upper (95%) limit of the dynamical change and emission scenario does not currently exist. There projected range can only approach 400 mm under scenarios expected is also a lack of literature on the relation between emission scenario 13 for MISI (such as the immediate collapse of Pine Island and Thwaites and the intrusions of warm water into ice-shelf cavities thought to be Glaciers or all marine-based sectors experiencing the same rates of important in triggering observed mass loss (Jacobs et al., 2011) and mass loss as Pine Island Glacier). potentially important in the future (Hellmer et al., 2012). It is therefore premature to attach a scenario dependence to projections of dynami- Our assessment of the likely range of SLR is based on the weighted cal change, even though such a dependency is expected to exist. 5-95% range (-20 to 185 mm) of Little et al. (2013), which is consist- ent with the lower scenarios of Katsman et al. (2011) (70 to 150 mm) Likely increases in snowfall over the next century (Section 13.4.4.1) and Pfeffer et al. (2008) (136 mm), and with the RCP8.5 projection will affect the amount of ice lost by outflow across the grounding 1173 Chapter 13 Sea Level Change line because of local changes in ice thickness and stress regime (Huy- downward sloping bedrock (Bamber et al., 2009). As detailed in Box brechts and De Wolde, 1999). This effect was incorporated in AR4 pro- 13.2, large areas of the WAIS may therefore be subject to potential jections for 2100 as a compensatory mass loss amounting to 0 to 10% ice loss via the MISI. As it is the case for other potential instabilities of the SMB mass gain (Gregory and Huybrechts, 2006). Winkelmann within the climate system (Section 12.5.5), there are four lines of evi- et al. (2012) re-evaluated the effect and reported a range of 15 to dence to assess the likelihood of a potential occurrence: theoretical 35% for the next century (30 to 65% after 500 years). The two studies understanding, present-day observations, numerical simulations, and are difficult to compare because of differences in model physics and paleo records. experimental design so that the use of their joint range (0 to 35%) is an appropriate assessment of the likely range of this effect. This range The MISI is based on a number of studies that indicated the theoreti- is supported by Barrand et al. (2013), who quantified the effect for the cal existence of the instability (Weertman, 1961; Schoof, 2007a) (see Antarctic Peninsula as ~15% of SMB. Moreover, because this contribu- also Box 13.2). The most fundamental derivation, that is, starting from tion relies on similar physics to the grounding line migration discussed a first-principle ice equation, states that in one-dimensional ice flow above, it is appropriate to assume that their uncertainties are corre- the grounding line between grounded ice sheet and floating ice shelf lated. Winkelmann et al. (2012) showed that the fractional size of this cannot be stable on a landward sloping bed. The limitation of the compensatory effect is independent of scenario. Accounting for this one-dimensional case disregards possible stabilizing effects of the ice effect equates to SLR of 0 to 32 mm by 2100 based on the SMB range shelves (Dupont and Alley, 2005). Although it is clear that ice shelves over all emission-scenario projections in Section 13.5.1.1. that are laterally constrained by embayments inhibit ice flow into the ocean, the effect has not been quantified against the MISI. The same Beyond the 21st century, only projections with coarse-resolution ice is true for other potentially stabilizing effects such as sedimentation sheet climate models are available (Vizcaíno et al., 2010; Huybrechts near the grounding line (Alley et al., 2007) or the influence of large- et al., 2011). Confidence in the ability of these two models to cap- scale bedrock roughness (i.e., topographic pinning points) on ice flow. ture both change in the oceanic circulation around Antarctica and Although these stabilizing effects need to be further investigated and the response of the ice sheet to these changes, especially a poten- quantified against the destabilizing effect of the MISI, no studies are tial grounding line retreat, is low. The model applied by Vizcaíno et al. available that would allow dismissing the MISI on theoretical grounds. (2010) lacks a dynamic representation of ice shelves. Because dynam- ic ice discharge from Antarctica occurs predominately through ice Although direct observations of ice dynamics are available, they are shelves, it is likely that the projections using this model considerably neither detailed enough nor cover a sufficiently long period to allow underestimate the Antarctic contribution. the monitoring of the temporal evolution of an MISI. Most Antarctic ice loss that has been detected during the satellite period has come In summary, it is likely that dynamical change within the Antarctic ice from the WAIS (Rignot et al., 2008; Pritchard et al., 2012). Some studies sheet will lead to SLR during the next century with a range of 20 have found an acceleration of ice loss (Rignot et al., 2011) as well as to 185 mm. SLR beyond the likely range is poorly constrained and enhanced basal ice-shelf melting (Pritchard et al., 2012), but the short considerably larger increases are possible (the underlying probability period of observations does not allow one to either dismiss or confirm distribution is asymmetric towards larger rise), which will probably be that these changes are associated with destabilization of WAIS. associated with the MISI (Box 13.2). Although the likelihood of such SLR cannot yet be assessed more precisely than falling above the likely Paleo records suggest that WAIS may have deglaciated several times range, literature suggests (with medium confidence) that its potential during warm periods of the last 5 million years, but they contain no magnitude is several tenths of a metre. We are unable to assess SLR information on rates (Naish et al., 2009). Although coarse-resolution as a function of emission scenario, although a dependency of SLR on models are in principle capable of modelling the MISI, there is medium scenario is expected to exist. In addition, coupling between SMB and confidence in their ability to simulate the correct response time to dynamical change is likely to make a further contribution to SLR of 0 to external perturbations on decadal to centennial time scales (Pattyn et 35% of the SMB. All the available literature suggests that this dynami- al., 2013). One of these models (Pollard and DeConto, 2009) repro- cal contribution to sea level rise will continue well beyond 2100. duced paleo records of deglaciation with a forced ice-sheet model at 40 km resolution and parameterized ice flow across the grounding line 13.4.4.3 Possible Irreversibility of Ice Loss from West Antarctica according to Schoof (2007a). These simulations showed a sea level rise of about 7 m over time spans of 1000 to 7000 years with approxi- Due to relatively weak snowfall on Antarctica and the slow ice motion mately equal contributions from West and East Antarctica. However, no in its interior, it can be expected that the WAIS would take at least sev- available model results or paleo records have indicated the possibility 13 eral thousand years to regrow if it was eliminated by dynamic ice dis- of self-accelerated ice discharge from these regions. charge. Consequently any significant ice loss from West Antarctic that occurs within the next century will be irreversible on a multi-centen- In summary, ice-dynamics theory, numerical simulations, and paleo nial to millennial time scale. We discuss here the possibility of abrupt records indicate that the existence of a marine-ice sheet instability asso- dynamic ice loss from West Antarctica (see Section 12.5.5 for definition ciated with abrupt and irreversible ice loss from the Antarctic ice sheet of abrupt). is possible in response to climate forcing. However, theoretical consid- erations, current observations, numerical models, and paleo records cur- Information on the ice and bed topography of WAIS suggests that it rently do not allow a quantification of the timing of the onset of such an has about 3.3 m of equivalent global sea level grounded on areas with instability or of the magnitude of its multi-century contribution. 1174 Sea Level Change Chapter 13 Box 13.2 | History of the Marine Ice-Sheet Instability Hypothesis Marine ice sheets rest on bedrock that is submerged below sea level (often by 2 to 3 km). The most well-researched marine ice sheet is the West Antarctic ice sheet (WAIS) where approximately 75% of the ice sheet s area currently rests on bedrock below sea level. The East Antarctic ice sheet (EAIS), however, also has appreciable areas grounded below sea level (~35%), in particular around the Totten and Cook Glaciers. These ice sheets are fringed by floating ice shelves, which are fed by flow from grounded ice across a grounding line (GL). The GL is free to migrate both seawards and landwards as a consequence of the local balance between the weight of ice and displaced ocean water. Depending on a number of factors, which include ice-shelf extent and geometry, ice outflow to the ocean generally (but not always) increases with ice thickness at the GL. Accordingly, when the ice sheet rests on a bed that deepens towards the ice-sheet interior (see Box 13.2, Figure 1a), the ice outflow to the ocean will generally increase as the GL retreats. It is this feature that gives rise to the Marine Ice-Sheet Instability (MISI), which states that a GL cannot remain stable on a landward-deepening slope. Even if snow accumulation and outflow were initially in balance (Box 13.2, Figure 1b), natural fluctuations in climate cause the GL to fluctuate slightly (Box 13.2, Figure 1c). In the case of a retreat, the new GL position is then associated with deeper bedrock and thicker ice, so that outflow increases (Box 13.2, Figure 1d). This increased outflow leads to further, self-sustaining retreat until a region of shallower, seaward-sloping bedrock is reached. Stable configurations can therefore exist only where the GL rests on slopes that deepen towards the ocean. A change in climate can therefore potentially force a large-scale retreat of the GL from one bedrock ridge to another further inland. (continued on next page) 13 Box 13.2, Figure 1 | Schematic of the processes leading to the potentially unstable retreat of a grounding line showing (a) geometry and ice fluxes of a marine ice sheet, (b) the grounding line in steady state, (c) climate change triggering mass outflow from the ice sheet and the start of grounding line retreat and (d) self-sustained retreat of the grounding line. 1175 Chapter 13 Sea Level Change Box 13.2 (continued) The MISI has a long history based on theoretical discussions that were started by Weertman (1974) and Mercer (1978), and has seen many refinements over the subsequent years. The advent of satellite-based observations has given fresh impetus to this debate, in particular work on the GL retreat and associated thinning of Pine Island (PIG), Thwaites (TG) and Smith Glaciers (all part of the WAIS), which are collectively responsible for most of Antarctica s present mass loss (Rignot et al., 2008). These observations highlighted the need to develop a better understanding of the MISI to make more accurate projections of the ice sheet s future contribution to sea level rise. Early studies of the MISI were not based on a formal derivation from the basic laws of mechanics thought to control ice-sheet flow and the robustness of their results was therefore difficult to assess. An open question was the expected impact of changes at the GL on the ice-sheet flow (Hindmarsh, 1993). Recently, however, a more complete analysis from first principles has been developed that suggests that the fundamental relation between thickness and flux at the GL exists and has a power of ~5 (i.e., that a 10% increase in thickness leads to a 60% increase in flux) (Schoof, 2007b, 2011). This analysis, however, does not include ice shelves that occupy laterally constrained embayments, which is often the case (for instance at PIG). In such situations, drag from ice-shelf sidewalls may suppress the positive feedback between increasing ice thickness and ice flux at the GL (Dupont and Alley, 2005; Goldberg et al., 2009; Gudmundsson et al., 2012). Other factors that could suppress the instability include a sea level fall adjacent to the GL resulting from the isostatic and gravitational effects of ice loss (Gomez et al., 2010b). Two processes that could trigger GL retreat are particularly relevant to contemporary polar climate change. The first is the presence of warmer ocean water under ice shelves, which leads to enhanced submarine ice-shelf melt (Jacobs et al., 2011). The second is the pres- ence of melt water ponds on the surface of the ice shelf, which can cause stress concentrations allowing fractures to penetrate the full ice-shelf thickness. This process appears to have been a primary factor in the collapse of the Larsen B Ice Shelf (LBIS) over the course of two months in 2002 (MacAyeal et al., 2003). The collapse of the LBIS provided a natural demonstration of the linkage between the structural integrity of an ice shelf and the flow of grounded ice draining into it. Following the breakup of LBIS, the speeds of the glaciers feeding the collapsed portion of the shelf increased two- to eightfold, while the flow of glaciers draining into a surviving sector was unaltered (Rignot et al., 2004; Scambos et al., 2004; Rott et al., 2011). This indicates that a mechanical link does indeed exist between shelf and sheet, and has important implications for the future evolution of the far more significant PIG and TG systems of the WAIS. The recent strides made in placing MISI on a sound analytical footing are, however, limited to the analysis of steady states. Numerical modelling is needed to simulate the GL retreat rates that are required to make accurate SLR projections. There are major challenges in designing models whose results are not controlled by the details of their numerical design. Problems arise at the GL because, in addi- tion to flotation, basal traction is dramatically reduced as the ice loses contact with the underlying bedrock (Pattyn et al., 2006). This is a topic of active research, and a combination of more complete modelling of the GL stress regime (Favier et al., 2012) and the use of high-resolution (subkilometre) models (Durand et al., 2009; Cornford et al., 2013) shows promise towards resolving these problems. Much progress has also been made by using model inter-comparison as a means of understanding these effects (Pattyn et al., 2013). 13.4.5 Anthropogenic Intervention in Water Storage assuming that the groundwater extraction estimates of Wada et al. on Land (2010) can be scaled up in the future with global population. These two possibilities indicate a range of about 20 to 90 mm for the contribution The potential future effects that human activities have on changing of groundwater depletion to GMSL rise. water storage on land, thus affecting sea level, have been little stud- ied in the published peer-reviewed scientific literature. For depletion of For the rate of impoundment of water in reservoirs, we evaluate two groundwater arising from extraction (for agriculture and other uses), possibilities. The first assumes it will continue throughout the 21st cen- 13 we consider two possibilities. The first assumes that this contribution to tury (e.g., Lempériere, 2006) at the average rate of 0.2 +/- 0.05 mm yr 1 GMSL rise continues throughout the 21st century at the rate of 0.40 +/- SLE (mean +/- SD) estimated for 1971 2010 using data updated from 0.11 mm yr 1 (mean +/- SD) assessed for 2001 2008 by Konikow (2011), Chao et al. (2008), giving a negative contribution to GMSL rise of 19 amounting to 38 [21 to 55] mm by 2081 2100 relative to 1986 2005. [ 11 to 27] mm by 2081 2100 relative to 1986 2005. The second The second uses results from land surface hydrology models (Wada et assumes it will be zero after 2010 (i.e., no further net impoundment), al., 2012) with input from climate and socioeconomic projections for as shown for the 1990s and 2000s by Lettenmaier and Milly (2009) SRES scenarios, yielding 70 [51 to 90] mm for the same time interval. (see Section 13.3.4 for discussion). A zero contribution implies a bal- Because of the improved treatment of groundwater recharge by Wada ance between further construction of reservoir capacity and reduction et al. (2012), this is less than Rahmstorf et al. (2012b) obtained by ­ of storage volume by sedimentation, each of which could plausibly 1176 Sea Level Change Chapter 13 Frequently Asked Questions FAQ 13.2: Will the Greenland and Antarctic Ice Sheets Contribute to Sea Level Change over the Rest of the Century? The Greenland, West and East Antarctic ice sheets are the largest reservoirs of freshwater on the planet. As such, they have contributed to sea level change over geological and recent times. They gain mass through accumulation (snowfall) and lose it by surface ablation (mostly ice melt) and outflow at their marine boundaries, either to a float- ing ice shelf, or directly to the ocean through iceberg calving. Increases in accumulation cause global mean sea level to fall, while increases in surface ablation and outflow cause it to rise. Fluctuations in these mass fluxes depend on a range of processes, both within the ice sheet and without, in the atmosphere and oceans. Over the course of this century, however, sources of mass loss appear set to exceed sources of mass gain, so that a continuing positive contribution to global sea level can be expected. This FAQ summarizes current research on the topic and provides indicative magnitudes for the various end-of-century (2081-2100 with respect to 1986-2005) sea level contributions from the full assessment, which are reported as the two-in-three probability level across all emission scenarios. Over millennia, the slow horizontal flow of an ice sheet carries mass from areas of net accumulation (generally, in the high-elevation interior) to areas of net loss (generally, the low-elevation periphery and the coastal perimeter). At present, Greenland loses roughly half of its accumulated ice by surface ablation, and half by calving. Antarctica, on the other hand, loses virtually all its accumulation by calving and submarine melt from its fringing ice shelves. Ice shelves are floating, so their loss has only a negligible direct effect on sea level, although they can affect sea level indirectly by altering the mass budget of their parent ice sheet (see below). In East Antarctica, some studies using satellite radar altimetry suggest that snowfall has increased, but recent atmospheric modelling and satellite measurements of changes in gravity find no significant increase. This apparent disagreement may be because relatively small long-term trends are masked by the strong interannual variability of snowfall. Projections suggest a substantial increase in 21st century Antarctic snowfall, mainly because a warmer atmosphere would be able to carry more moisture into polar regions. Regional changes in atmospheric circulation probably play a secondary role. For the whole of the Antarctic ice sheet, this process is projected to contribute between 0 and 70 mm to sea level fall. Currently, air temperatures around Antarctica are too cold for substantial surface ablation. Field and satellite-based observations, however, indicate enhanced outflow manifested as ice-surface lowering in a few localized coastal regions. These areas (Pine Island and Thwaites Glaciers in West Antarctica, and Totten and Cook Glaciers in East Antarctica) all lie within kilometre-deep bedrock troughs towards the edge of Antarctica s continental shelf. The increase in outflow is thought to have been triggered by regional changes in ocean circulation, bringing warmer water in contact with floating ice shelves. On the more northerly Antarctic Peninsula, there is a well-documented record of ice-shelf collapse, which appears to be related to the increased surface melting caused by atmospheric warming over recent decades. The subsequent thinning of glaciers draining into these ice shelves has had a positive but minor effect on sea level, as will any further such events on the Peninsula. Regional projections of 21st century atmospheric temperature change suggest that this process will probably not affect the stability of the large ice shelves of both the West and East Antarctica, although these ice shelves may be threatened by future oceanic change (see below). Estimates of the contribution of the Antarctic ice sheets to sea level over the last few decades vary widely, but great strides have recently been made in reconciling the observations. There are strong indications that enhanced outflow (primarily in West Antarctica) currently outweighs any increase in snow accumulation (mainly in East Ant- arctica), implying a tendency towards sea level rise. Before reliable projections of outflow over the 21st century can be made with greater confidence, models that simulate ice flow need to be improved, especially of any changes in 13 the grounding line that separates floating ice from that resting on bedrock and of interactions between ice shelves and the ocean. The concept of marine ice-sheet instability is based on the idea that the outflow from an ice sheet resting on bedrock below sea level increases if ice at the grounding line is thicker and, therefore, faster flowing. On bedrock that slopes downward towards the ice-sheet interior, this creates a vicious cycle of increased outflow, causing ice at the grounding line to thin and go afloat. The grounding line then retreats down slope into thicker ice that, in turn, drives further increases in outflow. This feedback could potentially result in the rapid loss of parts of the ice sheet, as grounding lines retreat along troughs and basins that deepen towards the ice sheet s interior. 1177 Chapter 13 Sea Level Change FAQ 13.2 (continued) Future climate forcing could trigger such an unstable collapse, which may then continue independently of climate. This potential collapse might unfold over centuries for individual bedrock troughs in West Antarctica and sectors of East Antarctica. Much research is focussed on understanding how important this theoretical concept is for those ice sheets. Sea level could rise if the effects of marine instability become important, but there is not enough evidence at present to unambiguously identify the precursor of such an unstable retreat. Change in outflow is projected to contribute between 20 (i.e., fall) and 185 mm to sea level rise by year 2100, although the uncertain impact of marine ice-sheet instability could increase this figure by several tenths of a metre. Overall, increased snowfall seems set to only partially offset sea level rise caused by increased outflow. In Greenland, mass loss through more surface ablation and outflow dominates a possible recent trend towards increased accumulation in the interior. Estimated mass loss due to surface ablation has doubled since the early 1990s. This trend is expected to continue over the next century as more of the ice sheet experiences surface abla- tion for longer periods. Indeed, projections for the 21st century suggest that increasing mass loss will dominate over weakly increasing accumulation. The refreezing of melt water within the snow pack high up on the ice sheet offers an important (though perhaps temporary) dampening effect on the relation between atmospheric warming and mass loss. Although the observed response of outlet glaciers is both complex and highly variable, iceberg calving from many of Greenland s major outlet glaciers has increased substantially over the last decade, and constitutes an appreciable additional mass loss. This seems to be related to the intrusion of warm water into the coastal seas around Green- land, but it is not clear whether this phenomenon is related to inter-decadal variability, such as the North Atlantic (continued on next page) 80°W 80°W 60° 0° ° 80°N 20° 20°W 20°W 0° 20°E 40°E Sea level contribution Sea level contribution 17th 83rd (percentile) -40 -20 0 20 40 60 (mm SLR) <-150 0 >150 (mm yr -1 w.e.) m ) 60°S S 70°N 70°N 0°N N 60°S 60°N 60°N 0 125 250 500 <-1000 0 >1000 0 250 500 1000 40°W (mm yr -1 w.e.) 140°W 160°W 180° 160°E 140°E 13 km km FAQ 13.2, Figure 1 | Illustrative synthesis of projected changes in SMB and outflow by 2100 for (a) Greenland and (b) Antarctic ice sheets. Colours shown on the maps refer to projected SMB change between the start and end of the 21st century using the RACMO2 regional atmospheric climate model under future warming scenarios A1B (Antarctic) and RCP4.5 (Greenland). For Greenland, average equilibrium line locations during both these time periods are shown in purple and green, respectively. Ice-sheet margins and grounding lines are shown as black lines, as are ice-sheet sectors. For Greenland, results of flowline modelling for four major outlet glaciers are shown as inserts, while for Antarctica the coloured rings reflect projected change in outflow based on a probabilistic extrapolation of observed trends. The outer and inner radius of each ring indicate the upper and lower bounds of the two-thirds probability range of the contribution, respectively (scale in upper right); red refers to mass loss (sea level rise) while blue refers to mass gain (sea level fall). Finally, the sea level contribution is shown for each ice sheet (insert located above maps) with light grey referring to SMB (model experiment used to generate the SMB map is shown as a dashed line) and dark grey to outflow. All projections refer to the two-in-three probability range across all scenarios. 1178 Sea Level Change Chapter 13 FAQ 13.2 (continued) Oscillation, or a longer term trend associated with greenhouse gas induced warming. Projecting its effect on 21st century outflow is therefore difficult, but it does highlight the apparent sensitivity of outflow to ocean warming. The effects of more surface melt water on the lubrication of the ice sheet s bed, and the ability of warmer ice to deform more easily, may lead to greater rates of flow, but the link to recent increases in outflow is unclear. Change in the net difference between surface ablation and accumulation is projected to contribute between 10 and 160 mm to sea level rise in 2081-2100 (relative to 1986-2005), while increased outflow is projected to contribute a fur- ther 10 to 70 mm (Table 13.5). The Greenland ice sheet has contributed to a rise in global mean sea level over the last few decades, and this trend is expected to increase during this century. Unlike Antarctica, Greenland has no known large-scale instabilities that might generate an abrupt increase in sea level rise over the 21st century. A threshold may exist, however, so that continued shrinkage might become irreversible over multi-centennial time scales, even if the climate were to return to a pre-industrial state over centennial time scales. Although mass loss through the calving of icebergs may increase in future decades, this process will eventually end when the ice margin retreats onto bedrock above sea level where the bulk of the ice sheet resides. have a rate of about 1% yr 1 of existing capacity (Lempériere, 2006; using parameterizations derived from the results of process-based Lettenmaier and Milly, 2009). These two possibilities together indicate models of these components (note that glaciers on Antarctica are cov- a range of about 0 to 30 mm of GMSL fall for the contribution of res- ered by the Antarctic ice-sheet SMB projection, and are therefore not ervoir impoundment. included in the glacier projections) (Sections 13.4.2, 13.4.3.1, 13.4.4.1 and Supplementary Material). According to the assessment in Section Our assessment thus leads to a range of 10 to +90 mm for the net con- 12.4.1.2, global mean SAT change is likely to lie within the 5 to 95% tribution to GMSL rise from anthropogenic intervention in land water range of the projections of CMIP5 models. Following this assessment, storage by 2081 2100 relative to 1986 2005. This range includes the the 5 to 95% range of model results for each of the GMSL rise contri- range of 0 to 40 mm assumed by Katsman et al. (2008). Because of the butions that is projected on the basis of CMIP5 results is interpreted limited information available, we do not have sufficient confidence to as the likely range. give ranges for individual RCP scenarios. Possible ice-sheet dynamical changes by 2100 are assessed from the published literature (Sections 13.4.3.2 and 13.4.4.2), which as yet pro- 13.5 Projections of Global Mean Sea Level Rise vides only a partial basis for making projections related to particular scenarios. They are thus treated as independent of scenario, except Process-based projections for GMSL rise during the 21st century, given that a higher rate of change is used for Greenland ice sheet outflow in Section 13.5.1, are the sum of contributions derived from models under RCP8.5. Projections of changes in land water storage due to that were evaluated by comparison with observations in Section 13.3 human intervention are also treated as independent of emissions sce- and used to project the contributions in Section 13.4. Projections nario, because we do not have sufficient information to give ranges of GMSL rise by semi-empirical models (SEMs) are given in Section for individual scenarios. The scenario-independent treatment does not 13.5.2. We compare these two and other approaches in Section 13.5.3 imply that the contributions concerned will not depend on the scenario and assess the level of confidence that we can place in each approach. followed, only that the current state of knowledge does not permit Longer term projections are discussed in Section 13.5.4. a quantitative assessment of the dependence. For each of these con- tributions, our assessment of the literature provides a 5-95% range 13.5.1 Process-Based Projections for the 21st Century for the late 21st century (2100 for Greenland and Antarctic ice-sheet dynamics, 2081-2100 for land water storage). For consistency with the The process-based projections of GMSL rise for each RCP scenario are treatment of the CMIP5-derived results, we interpret this range as the based on results from 21 CMIP5 AOGCMs from which projections of likely range. We assume that each of these contributions begins from 13 SAT change and thermal expansion are available (see Section 13.4.1). its present-day rate and that the rate increases linearly in time, in order Where CMIP5 results were not available for a particular AOGCM to interpolate from the present day to the late 21st century (see Sup- and scenario, they were estimated (Good et al., 2011; 2013) (Section plementary Material for details). 12.4.1.2; Supplementary Material). The projections of thermal expan- sion do not include an adjustment for the omission of volcanic forcing The likely range of GMSL rise given for each RCP combines the uncer- in AOGCM spin-up (Section 13.3.4.2), as this is uncertain and relatively tainty in global climate change, represented by the CMIP5 ensemble small (about 10 mm during the 21st century). Changes in glacier and (Section 12.4.1.2), with the uncertainties in modelling the contributions ­ ice-sheet SMB are calculated from the global mean SAT projections to GMSL. The part of the uncertainty related to the magnitude of global 1179 Chapter 13 Sea Level Change climate change is correlated among all the scenario-dependent contri- tional ranges, perhaps related to the simulated rate of climatic warm- butions, while the methodological uncertainties are treated as inde- ing being greater than has been observed (Box 9.2). In the projections, pendent (see also Supplementary Material). the rate of rise initially increases. In RCP2.6 it becomes roughly con- stant (central projection 4.5 mm yr 1) before the middle of the century, The sum of the projected contributions gives the likely range for future and subsequently declines slightly. The rate of rise becomes roughly GMSL rise. The median projections for GMSL in all scenarios lie within a constant in RCP4.5 and RCP6.0 by the end of the century, whereas range of 0.05 m until the middle of the century (Figure 13.11), because acceleration continues throughout the century in RCP8.5, reaching 11 the divergence of the climate projections has a delayed effect owing to [8 to 16] mm yr 1 in 2081 2100. the time-integrating characteristic of sea level. By the late 21st century (over an interval of 95 years, between the 20-year mean of 2081 2100 In all scenarios, thermal expansion is the largest contribution, account- and the 20-year mean of 1986 2005), they have a spread of about ing for about 30 to 55% of the projections. Glaciers are the next largest, 0.25 m, with RCP2.6 giving the least amount of rise (0.40 [0.26 to accounting for 15-35% of the projections. By 2100, 15 to 55% of the 0.55] m) (likely range) and RCP8.5 giving the most (0.63 [0.45 to 0.82] present volume of glaciers outside Antarctica is projected to be elim- m). RCP4.5 and RCP6.0 are very similar at the end of the century (0.47 inated under RCP2.6, and 35 to 85% under RCP8.5 (Table 13.SM.2). [0.32 to 0.63] m and 0.48 [0.33 to 0.63]] m respectively), but RCP4.5 SMB change on the Greenland ice sheet makes a positive contribu- has a greater rate of rise earlier in the century than RCP6.0 (Figure tion, whereas SMB change in Antarctica gives a negative contribution 13.10 and Table 13.5). At 2100, the likely ranges are 0.44 [0.28 0.61] (Sections 13.4.3.1 and 13.4.4.1). The positive contribution due to rapid m (RCP2.6), 0.53 [0.36 0.71] m (RCP4.5), 0.55 [0.38 0.73] m (RCP6.0), dynamical changes that result in increased ice outflow from both ice and 0.74 [0.52 0.98] m (RCP8.5). sheets together has a likely range of 0.03 to 0.20 m in RCP8.5 and 0.03 to 0.19 m in the other RCPs. There is a relatively small positive contri- In all scenarios, the rate of rise at the start of the RCP projections bution from human intervention in land water storage, predominantly (2007 2013) is about 3.7 mm yr 1, slightly above the observational due to increasing extraction of groundwater. range of 3.2 [2.8 to 3.6] mm yr 1 for 1993 2010, because the modelled contributions for recent years, although consistent with observations for 1993 2010 (Section 13.3), are all in the upper part of the observa- 1.2 Sum 2081-2100 relative to 1986-2005 Thermal expansion 1.0 Glaciers Greenland ice sheet (including dynamics) Antarctic ice sheet (including dynamics) Global mean sea level rise (m) Land water storage 0.8 Greenland ice-sheet rapid dynamics Antarctic ice-sheet rapid dynamics 0.6 0.4 0.2 0.0 A1B RCP2.6 RCP4.5 RCP6.0 RCP8.5 13 Figure 13.10 | Projections from process-based models with likely ranges and median values for global mean sea level rise and its contributions in 2081 2100 relative to 1986 2005 for the four RCP scenarios and scenario SRES A1B used in the AR4. The contributions from ice sheets include the contributions from ice-sheet rapid dynamical change, which are also shown separately. The contributions from ice-sheet rapid dynamical change and anthropogenic land water storage are treated as having uniform probability distributions, and as independent of scenario (except that a higher rate of change is used for Greenland ice-sheet outflow under RCP8.5). This treatment does not imply that the contributions concerned will not depend on the scenario followed, only that the current state of knowledge does not permit a quantitative assessment of the dependence. See discussion in Sec- tions 13.5.1 and 13.5.3 and Supplementary Material for methods. Only the collapse of the marine-based sectors of the Antarctic ice sheet, if initiated, could cause global mean sea level (GMSL) to rise substantially above the likely range during the 21st century. This potential additional contribution cannot be precisely quantified but there is medium confidence that it would not exceed several tenths of a meter of sea level rise. 1180 Sea Level Change Chapter 13 (a) RCP2.6 RCP4.5 1.0 1.0 Sum Thermal expansion Global mean sea level rise (m) Global mean sea level rise (m) 0.8 Glaciers 0.8 Greenland ice sheet Antarctic ice sheet 0.6 Greenland ice-sheet rapid dynamics 0.6 Antarctic ice-sheet rapid dynamics Land water storage 0.4 0.4 0.2 0.2 0.0 0.0 2000 2020 2040 2060 2080 2100 2000 2020 2040 2060 2080 2100 Year Year RCP6.0 RCP8.5 1.0 1.0 Global mean sea level rise (m) Global mean sea level rise (m) 0.8 0.8 0.6 0.6 0.4 0.4 0.2 0.2 0.0 0.0 2000 2020 2040 2060 2080 2100 2000 2020 2040 2060 2080 2100 Year Year RCP2.6 RCP4.5 (b) 15 Sum 15 Thermal expansion Glaciers sea level rise (mm yr-1) sea level rise (mm yr-1) Greenland ice sheet Rate of global mean Rate of global mean Antarctic ice sheet 10 Greenland ice-sheet rapid dynamics 10 Antarctic ice-sheet rapid dynamics Land water storage 5 5 0 0 2000 2020 2040 2060 2080 2100 2000 2020 2040 2060 2080 2100 Year Year RCP6.0 RCP8.5 15 15 sea level rise (mm yr-1) sea level rise (mm yr-1) Rate of global mean Rate of global mean 10 10 5 5 0 0 13 2000 2020 2040 2060 2080 2100 2000 2020 2040 2060 2080 2100 Year Year Figure 13.11 | Projections from process-based models of (a) global mean sea level (GMSL) rise relative to 1986 2005 and (b) the rate of GMSL rise and its contributions as a function of time for the four RCP scenarios and scenario SRES A1B. The lines show the median projections. For GMSL rise and the thermal expansion contribution, the likely range is shown as a shaded band. The contributions from ice sheets include the contributions from ice-sheet rapid dynamical change, which are also shown separately. The time series for GMSL rise plotted in (a) are tabulated in Annex II (Table AII.7.7), and the time series of GMSL rise and all of its contributions are available in the Supplementary Material. The rates in (b) are calculated as linear trends in overlapping 5-year periods. Only the collapse of the marine-based sectors of the Antarctic ice sheet, if initiated, could cause GMSL to rise substantially above the likely range during the 21st century. This potential additional contribution cannot be precisely quantified but there is medium confidence that it would not exceed several tenths of a metre of sea level rise. 1181 Chapter 13 Sea Level Change Table 13.5 | Median values and likely ranges for projections of global mean sea level (GMSL) rise and its contributions in metres in 2081 2100 relative to 1986 2005 for the four RCP scenarios and SRES A1B, GMSL rise in 2046 2065 and 2100, and rates of GMSL rise in mm yr 1 in 2081 2100. See Section 13.5.1 concerning how the likely range is defined. Because some of the uncertainties in modelling the contributions are treated as uncorrelated, the sum of the lower bound of contributions does not equal the lower bound of the sum, and similarly for the upper bound (see Supplementary Material). Because of imprecision from rounding, the sum of the medians of contributions may not exactly equal the median of the sum. The net contribution (surface mass balance (SMB) + dynamics) for each ice sheet, and the contribution from rapid dynamical change in both ice sheets together, are shown as additional lines below the sum; they are not contributions in addition to those given above the sum. The contributions from ice-sheet rapid dynamical change and anthropogenic land water storage are treated as having uniform probability distributions, uncorrelated with the magnitude of global climate change (except for the interaction between Antarctic ice sheet SMB and outflow), and as independent of scenario (except that a higher rate of change is used for Greenland ice sheet outflow under RCP8.5). This treatment does not imply that the contributions concerned will not depend on the scenario followed, only that the current state of knowledge does not permit a quantitative assess- ment of the dependence. Regional sea level change is expected in general to differ from the global mean (see Section 13.6). SRES A1B RCP2.6 RCP4.5 RCP6.0 RCP8.5 Thermal expansion 0.21 [0.16 to 0.26] 0.14 [0.10 to 0.18] 0.19 [0.14 to 0.23] 0.19 [0.15 to 0.24] 0.27 [0.21 to 0.33] Glaciersa 0.14 [0.08 to 0.21] 0.10 [0.04 to 0.16] 0.12 [0.06 to 0.19] 0.12 [0.06 to 0.19] 0.16 [0.09 to 0.23] Greenland ice-sheet SMBb 0.05 [0.02 to 0.12] 0.03 [0.01 to 0.07] 0.04 [0.01 to 0.09] 0.04 [0.01 to 0.09] 0.07 [0.03 to 0.16] Antarctic ice-sheet SMBc 0.03 [ 0.06 to 0.01] 0.02 [ 0.04 to 0.00] 0.02 [ 0.05 to 0.01] 0.02 [ 0.05 to 0.01] 0.04 [ 0.07 to 0.01] Greenland ice-sheet 0.04 [0.01 to 0.06] 0.04 [0.01 to 0.06] 0.04 [0.01 to 0.06] 0.04 [0.01 to 0.06] 0.05 [0.02 to 0.07] rapid dynamics Antarctic ice-sheet 0.07 [ 0.01 to 0.16] 0.07 [ 0.01 to 0.16] 0.07 [ 0.01 to 0.16] 0.07 [ 0.01 to 0.16] 0.07 [ 0.01 to 0.16] rapid dynamics Land water storage 0.04 [ 0.01 to 0.09] 0.04 [ 0.01 to 0.09] 0.04 [ 0.01 to 0.09] 0.04 [ 0.01 to 0.09] 0.04 [ 0.01 to 0.09] Global mean sea level 0.52 [0.37 to 0.69] 0.40 [0.26 to 0.55] 0.47 [0.32 to 0.63] 0.48 [0.33 to 0.63] 0.63 [0.45 to 0.82] rise in 2081 2100 Greenland ice sheet 0.09 [0.05 to 0.15] 0.06 [0.04 to 0.10] 0.08 [0.04 to 0.13] 0.08 [0.04 to 0.13] 0.12 [0.07 to 0.21] Antarctic ice sheet 0.04 [ 0.05 to 0.13] 0.05 [ 0.03 to 0.14] 0.05 [ 0.04 to 0.13] 0.05 [ 0.04 to 0.13] 0.04 [ 0.06 to 0.12] Ice-sheet rapid dynamics 0.10 [0.03 to 0.19] 0.10 [0.03 to 0.19] 0.10 [0.03 to 0.19] 0.10 [0.03 to 0.19] 0.12 [0.03 to 0.20] Rate of global mean 8.1 [5.1 to 11.4] 4.4 [2.0 to 6.8] 6.1 [3.5 to 8.8] 7.4 [4.7 to 10.3] 11.2 [7.5 to 15.7] sea level rise Global mean sea level 0.27 [0.19 to 0.34] 0.24 [0.17 to 0.32] 0.26 [0.19 to 0.33] 0.25 [0.18 to 0.32] 0.30 [0.22 to 0.38] rise in 2046 2065 Global mean sea 0.60 [0.42 to 0.80] 0.44 [0.28 to 0.61] 0.53 [0.36 to 0.71] 0.55 [0.38 to 0.73] 0.74 [0.52 to 0.98] level rise in 2100 Only the collapse of the marine-based sectors of the Antarctic ice sheet, if initiated, could cause GMSL to rise substantially above the likely range during the 21st century. This potential additional contribution cannot be precisely quantified but there is medium confidence that it would not exceed several tenths of a meter of sea level rise. Notes: a Excluding glaciers on Antarctica but including glaciers peripheral to the Greenland ice sheet. b Including the height SMB feedback. c Including the interaction between SMB change and outflow. 13.5.2 Semi-Empirical Projections for the 21st Century The semi-empirical approach regards a change in sea level as an integrated response of the entire climate system, reflecting changes The development of semi-empirical models (SEMs) was motivated in the dynamics and thermodynamics of the atmosphere, ocean and by two problems. First, process-based modelling was incomplete in cryosphere; it does not explicitly attribute sea level rise to its individual the AR4 because of the unavailability of ice-sheet dynamical models physical components. SEMs use simple physically motivated relation- which could be used to simulate the observed recent accelerations in ships, with various analytical formulations and parameters determined ice flow and make projections with confidence (Meehl et al., 2007) from observational time series, to predict GMSL for the 21st century (Sections 13.1.4.1, 13.4.3.2 and 13.4.4.2). Second, in all previous IPCC (Figure 13.12 and Table 13.6) and beyond, from either global mean SAT assessments, observed GMSL rise during the 20th century could not be (Rahmstorf, 2007a; Horton et al., 2008; Vermeer and Rahmstorf, 2009; completely accounted for by the contributions to GMSL from thermal Grinsted et al., 2010; Rahmstorf et al., 2012b) or RF (Jevrejeva et al., expansion, glaciers and ice sheets. For example, the AR4 assessed the 2009; 2010, 2012a). 13 mean observational rate for 1961 2003 as 1.8 +/- 0.5 mm yr 1, and the sum of contributions as 1.1 +/- 0.5 mm yr 1 (Bindoff et al., 2007; Hegerl SEMs are designed to reproduce the observed sea level record over et al., 2007). With the central estimates, only about 60% of observed their period of calibration, as this provides them with model param- sea level rise was thus explained, and the potential implication was eters needed to make projections (Rahmstorf, 2007a; Jevrejeva et al., that projections using process-based models which reproduce only 2009; Vermeer and Rahmstorf, 2009; Grinsted et al., 2010). A test of the those known contributions would underestimate future sea level rise predictive skill of the models requires simulating a part of the observed (Rahmstorf, 2007a; Jevrejeva et al., 2009; Grinsted et al., 2010). SEMs record that has not been used for calibration. For instance, Rahmstorf do not aim to solve the two problems that motivated their develop- (2007b) calibrated for 1880 1940 and predicted 1940 2000, obtaining ment, but instead provide an alternative approach for projecting GMSL. results within 0.02 m of observed. Jevrejeva et al. (2012b) ­calibrated 1182 Sea Level Change Chapter 13 to predict 1961 2003, the model of Bittermann et al. (2013) over- a) b) 1.5 1.5 estimates the GMSL data set of Jevrejeva et al. (2008) by 75%, but Global mean sea level rise (m) makes an accurate estimate for the Church and White (2011) data set, although these two data sets have similar rates of sea level rise in the 1.0 1.0 predicted period. The central projections of Rahmstorf et al. (2012b) for 2100 under RCP4.5 (Table 13.6) for calibration with the GMSL data set of Church and White (2006) are about 0.2 m more than for cali- 0.5 0.5 bration with the Church and White (2011) data set, although the two Church and White (2006, 2011) data sets differ at all times by less than one standard deviation. The ranges of the projections by Grinsted 0 0 et al. (2010) and Jevrejeva et al. (2010, 2012a, 2012b) allow for the 1 2 3 4 5 6 7 8 9 1 2 3 4 5 6 7 8 9 uncertainty in the GMSL reconstructions through the use of an uncer- c) d) tainty covariance matrix in determining the model parameters. Grin- 1.5 1.5 sted et al. (2010) also investigated the sensitivity to the temperature Global mean sea level rise (m) data set used as predictor, and Jevrejeva et al. (2010) investigated the sensitivity to RF as predictor (Table 13.6). In the latter case, three data 1.0 1.0 sets gave median projections under RCP4.5 for 2100 within a range of about +/-0.20 m. 0.5 0.5 SEM projections will be biased unless contributions to past GMSL rise which correlate with but are not physically related to contemporary changes in the predictor variable (either global mean SAT change or RF) 0 0 are subtracted from the observational sea level record before the cali- 1 2 3 4 5 6 7 8 9 1 2 3 4 5 6 7 8 9 bration (Vermeer and Rahmstorf, 2009; Jevrejeva et al., 2012b; Rahm- Figure 13.12 | Median and range (5 to 95%) for projections of global mean sea level storf et al., 2012b; Orliæ and Pasariæ, 2013). These include groundwater rise (metres) in 2081 2100 relative to 1986 2005 by semi-empirical models for (a) depletion due to anthropogenic intervention and storage of water by RCP2.6, (b) RCP4.5, (c) RCP6.0 and (d) RCP8.5. Blue bars are results from the models dams (Section 13.3.4), ongoing adjustment of the Greenland and Ant- using RCP temperature projections, red bars are using RCP radiative forcing (RF). The arctic ice sheets to climate change in previous centuries and millen- numbers on the horizontal axis refer to the literature source of the projection and the sea level reconstruction used for calibration (for studies using RCP temperature projec- nia (Section 13.3.6), and the effects of internally generated regional tions) or reconstruction of RF (for studies using RCP RF). (1) Rahmstorf et al. (2012b), climate variability on glaciers (Marzeion et al., 2012a; Church et al., with Kemp et al. (2011); (2) Schaeffer et al. (2012); (3) Rahmstorf et al. (2012b), with 2013, Sections 13.3.2.2 and 13.3.6) and ice sheets (Section 13.3.3.2). Church and White (2006); (4) Rahmstorf et al. (2012b), with Church and White (2011); For instance, Jevrejeva et al. (2012b) found that their median projec- (5) Rahmstorf et al. (2012b), with Jevrejeva et al. (2008); (6) Grinsted et al. (2010), tions for 2100 were reduced by 0.02 to 0.10 m by excluding some such with Moberg et al. (2005); (7) Jevrejeva et al. (2012a), with Goosse et al. (2005); (8) Jevrejeva et al. (2012a), with Crowley et al. (2003); (9) Jevrejeva et al. (2012a) with Tett contributions. et al. (2007). Also shown for comparison is the median (thick black line) and likely range (horizontal grey bar) (as defined in Section 13.5.1) from the process-based projections Making projections with a SEM assumes that sea level change in the (Table 13.5), which are assessed as having medium confidence, in contrast to SEMs, future will have the same relationship as it has had in the past to RF or which are assessed as having low confidence (Section 13.5.3). global mean temperature change. The appropriate choice for the for- mulation of the SEM may depend on the nature of the climate forcing up to 1950 and predicted 0.03 m (about 25%) less than observed for and the time scale, and potentially nonlinear physical processes may 1950 2009, and 3.8 mm yr 1 for 1993 2010, which is about 20% more not scale in the future in ways which can be calibrated from the past than observed. (von Storch et al., 2008; Vermeer and Rahmstorf, 2009; Rahmstorf et al., 2012b; Orliæ and Pasariæ, 2013). Two such effects that could lead The GMSL estimates used for calibrating the SEMs are based on the to overestimated or underestimated projections by SEMs have been existing sparse network of long tide-gauge records, and are thus uncer- discussed in the literature. tain, especially before the late 19th century; these uncertainties are reflected in the observational estimates of the rate of GMSL rise (Sec- First, AOGCMs indicate that the ocean heat uptake efficiency tends tions 3.7 and 13.2.2). Consequently, the projections may be sensitive to decline as warming continues and heat penetrates more deeply to the statistical treatment of the temporal variability in the instrumen- (Gregory and Forster, 2008). A linear scaling of the rate of global ocean 13 tal record of sea level change (Holgate et al., 2007; Rahmstorf, 2007b; heat uptake with global SAT determined from the past, as proposed by Schmith et al., 2007). Rahmstorf et al. (2012b) reported that GMSL Rahmstorf (2007a), will thus overestimate future time-integrated heat projections for the RCP4.5 scenario for 2100 (Table 13.6) varied by content change and the consequent global ocean thermal expansion +/-0.04 m when the embedding dimension used for temporal smoothing on a century time scale (Orliæ and Pasariæ, 2013). Rahmstorf (2007a) during the calibration was varied within a range of 0 to 25 years. found that the linear scaling overestimated by 0.12 m (about 30%) the thermal expansion simulated by a climate model with a 3D ocean from Furthermore, there is some sensitivity to the choice of data sets used 1990 to 2100 under scenario SRES A1FI. Furthermore, the Rahmstorf for calibration. For instance, when calibrated up to 1960 and used (2007a) model is inadequate for simulating sea level variations of the 1183 Chapter 13 Sea Level Change last millennium (von Storch et al., 2008), which arise predominantly the climate becomes warmer, tending to give an increase in sensitiv- from episodic volcanic forcing, rather than the sustained forcing on ity (Rahmstorf et al., 2012b) (Section 13.4.2). Estimating the balance multi-decadal time scales for which it was intended. In both applica- of these two effects will require detailed modelling of glacier SMB. tions, the AOGCM behaviour is more accurately reproduced by taking The absence of a multidecadal acceleration in the rate of glacier mass into account the vertical profile of warming, at least by distinguishing loss in observations of the 20th and simulations of the 21st centuries the upper (mixed layer) and lower (thermocline) layers (Vermeer and (Section 4.3.3) (Radic and Hock, 2010; Marzeion et al., 2012a), despite Rahmstorf, 2009; Held et al., 2010) (Section 13.4.1), or by introducing a rising global temperatures, suggests that the reduction in sensitivity relaxation time scale for sea level rise (Jevrejeva et al., 2012b). may dominate (Gregory et al., 2013b). Second, the sensitivity of glaciers to warming will tend to decrease as 13.5.3 Confidence in Likely Ranges and Bounds the area most prone to ablation and the remaining volume decrease, partly counteracted by lowering of the surface due to thinning (Huss et The AR4 (Meehl et al., 2007) presented process-model-based pro- al., 2012) Section 13.4.2). On the other hand, glaciers at high latitudes jections of GMSL rise for the end of the 21st century, but did not that currently have negligible surface melting will begin to ablate as provide a best estimate or likely range principally because scientific Table 13.6 | Global mean sea level (GMSL) rise (metres) projected by semi-empirical models and compared with the IPCC AR4 and AR5 projections. In each case the results have a probability distribution whose 5th, 50th and 95th percentiles are shown in the columns as indicated. The AR5 5 to 95% process-based model range is interpreted as a likely range (medium confidence) (Section 13.5.1). From To 5% 50% 95% Scenario SRES A1B IPCC AR4a 1990 2100 0.22 0.37 0.50 IPCC AR4a,b 1990 2100 0.22 0.43 0.65 IPCC AR5 (also in Table 13.5) 1996 2100 0.42 0.60 0.80 Rahmstorf (2007a) c 1990 2100 0.85 Horton et al. (2008)d 2000 2100 0.62 0.74 0.88 Vermeer and Rahmstorf (2009) 1990 2100 0.98 1.24 1.56 Grinsted et al. (2010) with Brohan et al. (2006) 1990 2100 0.32 0.83 1.34 temperature for calibration Grinsted et al. (2010) with Moberg et al. (2005) 1990 2100 0.91 1.12 1.32 temperature for calibration Jevrejeva et al. (2010) with Crowley et al. (2003) 1990 2100 0.63 0.86 1.06 forcing for calibration Jevrejeva et al. (2010) with Goosse et al. (2005) 1990 2100 0.60 0.75 1.15 forcing for calibration Jevrejeva et al. (2010) with Tett et al. (2007) 1990 2100 0.87 1.15 1.40 forcing for calibration Scenario RCP4.5 IPCC AR5 (also in Table 13.5) 1986 2005 2081 2100 0.32 0.47 0.63 Grinsted et al. (2010) calibrated with 1986 2005 2081 2100 0.63 0.88 1.14 Moberg et al. (2005) temperature Rahmstorf et al. (2012b) calibrated with 1986 2005 2081 2100 0.79 0.86 0.93 Church and White (2006) GMSL Rahmstorf et al. (2012b) calibrated with 1986 2005 2081 2100 0.57 0.63 0.68 Church and White (2011) GMSL Rahmstorf et al. (2012b) calibrated with 1986 2005 2081 2100 0.82 0.97 1.12 Jevrejeva et al. (2008) GMSL Rahmstorf et al. (2012b) calibrated with proxy data 1986 2005 2081 2100 0.56 0.88 1.24 Jevrejeva et al. (2012a) calibrated with 1986 2005 2081 2100 0.43 0.56 0.69 13 Goosse et al. (2005) radiative forcing Jevrejeva et al. (2012a) calibrated with Crow- 1986 2005 2081 2100 0.48 0.65 0.80 ley et al. (2003) radiative forcing Jevrejeva et al. (2012a) calibrated with 1986 2005 2081 2100 0.65 0.85 1.05 Tett et al. (2007) radiative forcing Schaeffer et al. (2012) 1986 2005 2081 2100 0.58 0.80 1.05 Notes: a Extrapolated to 2100 using the projected rates of sea level rise for 2090 2099 in Table 10.7 of Meehl et al. (2007). b Including scaled-up ice-sheet discharge given in Table 10.7 of Meehl et al. (2007) and extrapolated to 2100 as an illustration of the possible magnitude of this effect. c Uncertainty range not given. d The mean value and the range are shown for semi-empirical model projections based on results from 11 GCMs. 1184 Sea Level Change Chapter 13 u ­ nderstanding at the time was not sufficient to allow an assessment of that are about twice as large as the process-based models. In nearly the possibility of future rapid changes in ice-sheet dynamics (on time every case, the SEM 95-percentile is above the process-based likely scales of a few decades, Section 4.4.5). Future rapid changes in ice- range (Figure 13.12). Two physical explanations have been suggest- sheet outflow were consequently not included in the ranges given by ed for the higher projections. First, the contribution from accelerated the AR4. For the SRES A1B scenario, the AR4 range was 0.21 to 0.48 m, calving of tidewater glaciers may be substantial and included in SEMs and for the highest emissions scenario, A1FI, it was 0.26 to 0.59 m. The but not process-based models (Jevrejeva et al., 2012b); however, this AR4 also noted that if ice-sheet outflow increased linearly with global could account for only 0.1 to 0.2 m of additional GMSL rise. Second, mean surface air temperature, the AR4 maximum projections would be SEMs may allow for rapid ice-sheet dynamical change (Section 4.4.4) raised by 0.1 to 0.2 m. The AR4 was unable to exclude larger values or in response to future climate change (Grinsted et al., 2010; Little et to assess their likelihood. al., 2013a). In order for large ice-sheet dynamical changes to be pre- dictable by SEMs, two conditions must be met. First, these changes Since the publication of the AR4, upper bounds of up to 2.4 m for must have contributed substantially to sea level rise during the period GMSL rise by 2100 have been estimated by other approaches, namely of calibration. This is very unlikely to be the case, because it is very SEMs (Section 13.5.2), evidence from past climates (Section 13.2.1) likely that dynamical changes have contributed only a small part of and physical constraints on ice-sheet dynamics (Sections 13.4.3.2 and the observed sea level rise during the 20th century, rising to about 13.4.4.2). The broad range of values reflects the different methodolo- 15% during 1993 2010 (Section 13.3.6). Second, the changes must gies for obtaining the upper bound, involving different constraining fac- have a link to global surface temperature or RF. Current understanding tors and sources of evidence. In particular, the upper bound is strongly of recent dynamical changes in Greenland and Antarctica is that they affected by the choice of probability level, which in some approaches is have been triggered by local changes in ocean temperature (Holland unknown because the probability of the underlying assumptions is not et al., 2008; Thoma et al., 2008; Jacobs et al., 2011), but a link has quantified (Little et al., 2013b). not been demonstrated between these changes and global climate change or its drivers. Consequently there is great uncertainty regard- The confidence that can be placed in projections of GMSL rise and its ing whether recent ice-sheet dynamical changes indicate a long-term upper bound by the various approaches must be considered. Confidence trend or instead arise from internal variability (Bamber and Aspinall, arises from the nature, quantity, quality and consistency of the evidence. 2013). Hence there is no evidence that ice-sheet dynamical change is the explanation for the higher GMSL rise projections of SEMs, implying The first approach is based on process-based projections, which use that either there is some other contribution which is presently uniden- the results from several models for each contribution (Sections 13.4 tified or underestimated by process-based models, or that the projec- and 13.5.1; Table 13.5). There is medium evidence in support of this tions of SEMs are overestimates (cf. Section 13.5.2). Because of the approach, arising from our understanding of the modelled physical limited or medium evidence supporting SEMs, and the low agreement processes, the consistency of the models with wider physical under- about their reliability, we have low confidence in their projections. standing of those processes as elements of the climate system (e.g., Box 13.1), the consistency of modelled and observed contributions The third approach uses paleo records of sea level change that show (Sections 13.3.1 to 13.3.5), the consistency of observed and modelled that rapid GMSL rise has occurred during glacial terminations, at GMSL (Section 13.3.6), and the consistency of process-based projec- rates that averaged about 10 mm yr 1 over centuries, with at least tions based on the CMIP5 ensemble of AOGCMs, which have a range one instance (Meltwater Pulse 1A) that exceeded 40 mm yr 1 (Section of 50 to 60% of the ensemble mean under a given scenario (Table 5.6.3), but this rise was primarily from much larger ice-sheet sourc- 13.5). Considering this evidence, we have medium confidence in the es that no longer exist. Contributions from these vanished ice sheets process-based projections. could have continued even after sea level and climate had reached interglacial states, if the Greenland and Antarctic ice sheets contracted The second approach uses SEMs (Section 13.5.2, Table 13.6), which during the termination to smaller sizes than at present. During past make projections by calibrating a physically motivated relationship interglacial periods, only the Greenland and Antarctic ice sheets were between GMSL and some other parameter of the climate system in present. For the time interval during the LIG in which GMSL was above the past and applying it to the future, without quantifying the con- present, there is high confidence that the maximum 1000-year average tributory physical processes. If we had no physical understanding of rate of GMSL rise during these periods exceeded 2 m kyr 1 but did the causes of sea level rise, the semi-empirical approach to projections not exceed 7 m kyr 1 (Kopp et al., 2013) (Sections 5.6.2 and 13.2.1.3). would be the only possible one, but extrapolation beyond the range Because climate variations during interglacial periods had different of calibration implies uncertainty that is difficult to quantify, owing to forcings from anthropogenic climate change, they give only a limited the assumption that sea level change in the future will have the same basis for predictions of the future, and we do not consider that they 13 relationship as it has had in the past to RF or global mean tempera- provide upper bounds for GMSL rise during the 21st century. ture change (Section 13.5.2). As a result, there is low agreement and no consensus in the scientific community about the reliability of SEM The fourth approach is concerned particularly with the contribution projections, despite their successful calibration and evaluation against from ice-sheet dynamical change, for which it considers kinematic the observed 20th century sea level record. limits. Pfeffer et al. (2008) argued that scenarios of GMSL rise exceed- ing 2 m by 2100 are physically untenable, ruling out, for example, the For a given RCP, some SEMs project a range that overlaps the process- heuristic argument of Hansen et al. (2007) giving 5 m by 2100. Pfeffer based likely range while others project a median and 95-percentile et al. (2008) constructed scenarios of 0.8 m and 2.0 m, and Katsman 1185 Chapter 13 Sea Level Change et al. (2011) of 1.15 m, for GMSL rise by 2100, including ice-sheet The time mean rate of GMSL rise during the 21st century is very likely to rapid dynamical acceleration. Although these authors considered their exceed the rate of 2.0 [1.7 to 2.3] mm yr 1 observed during 1971 2010, scenarios to be physically possible, they are unable to quantify their because the process-based GMSL projections indicate a significantly likelihood, because the probability of the assumptions on which they greater rate even under the RCP2.6 scenario, which has the lowest depend cannot be estimated from observations of the response of the RF. It has been asserted that the acceleration of GMSL rise implied by Greenland and Antarctic ice sheets to climate change or variability on the IPCC AR4 projections is inconsistent with the observed magnitude century time scales. These scenarios involve contributions of ~0.5 m of acceleration during the 20th century (Boretti, 2011, 2012b, 2012a, from Antarctica. This is much greater than any process-based projec- 2012c, 2013a, 2013b. 2013c; Boretti and Watson, 2012; Parker, 2013a, tions of dynamical ice-sheet change (Section 13.4.4.2), and would 2013b, 2013c). Refuting this argument, Hunter and Brown (2013) show require either a sustained high increase in outflow in all marine-based that the acceleration projected in the AR4 is consistent with observa- sectors or the localized collapse of the ice sheet in the Amundsen Sea tions since 1990s. Present understanding of the contributions to GMSL sector (Little et al., 2013a). rise (Section 13.3) gives an explanation of the rate of 20th century GMSL rise and confidence in the process-based projections, which indi- In summary, we have greater confidence in the process-based projec- cate a greater rate of rise in the 21st century because of increasing tions than in the other approaches, and our assessment is that GMSL forcing. rise during the 21st century for each RCP scenario is likely (medium confidence) to lie within the 5 to 95% range given by the process- The improved agreement of process-based models with observations based projections (Section 13.5.1 and Table 13.5; see Section 13.5.4 and physical understanding represents progress since the AR4, in which for following centuries), which are consistent with the likely ranges there was insufficient confidence to give likely ranges for 21st century projected for global mean surface air temperature change (Section GMSL rise, as we have done here. For scenario SRES A1B, which was 12.4.1.2). We are not able to assess a very likely range on the same assessed in the AR4, the likely range on the basis of science assessed basis, because there is no assessment available of the very likely range in the AR5 is 0.60 [0.42 to 0.80] m by 2100 relative to 1986 2005, and for global mean SAT change, and because we cannot robustly quantify 0.57 [0.40 to 0.76] m by 2090 2099 relative to 1990. Compared with the probability of ice-sheet dynamical changes which would give rise the AR4 projection of 0.21 to 0.48 m for the same scenario and period, to greater values. the largest increase is from the inclusion of rapid changes in Greenland and Antarctic ice sheet outflow, for which the combined likely range is Under the RCP8.5 scenario, which has the highest RF, the likely range 0.03 to 0.21 m by 2091 2100 (assuming uncorrelated uncertainties). reaches 0.98 m by 2100 relative to 1986 2005. Observations do not These terms were omitted in the AR4 because a basis to make projec- show an increase in Antarctic precipitation, which is projected by tions was not available in published literature at that time. The contri- models and makes a negative contribution to the projected GMSL rise bution from thermal expansion is similar to the AR4 projection and has (Table 13.5). The recovery of Antarctic stratospheric ozone concentra- smaller uncertainty. The contribution from glaciers is larger than in the tion and increased basal melting of Antarctic ice shelves have both AR4 primarily because of the greater estimate of the present glacier been suggested as giving rise to mechanisms whereby the Antarctic volume in new inventories (although the glacier area estimate is sim- warming and precipitation increase might be suppressed with respect ilar, Table 4.1), and the Greenland SMB contribution is larger because to CMIP5 projections (Section 13.4.4.1). If the Antarctic precipitation of recent improvement in models of relevant surface processes. Further increase is omitted from the process-based projections, the likely range progress on modelling each of the contributions is still needed in order for RCP8.5 at 2100 reaches 1.03 m (assuming uncorrelated errors). to attain high confidence in GMSL projections, in particular concerning Higher values for 2100 are given in the scientific literature on the basis the probability distribution of GMSL above the likely ranges. of various approaches: 1.15 m (Katsman et al., 2011), 1.21 m (Schaef- fer et al., 2012) (for RCP4.5), 1.40 m (National Research Council, 2012), 13.5.4 Long-term Scenarios 1.65 m (Jevrejeva et al., 2012b) (for RCP8.5), 1.79 m (Vermeer and Rahmstorf, 2009) (for SRES A1FI), 1.90 m (Rahmstorf et al., 2012b) Less information is available on climate change beyond the year 2100 (with proxy calibration, for RCP8.5), 2.0 m (Pfeffer et al., 2008), 2.25 than there is up to the year 2100. However, the ocean and ice sheets m (Sriver et al., 2012), and 2.4 m (Nicholls et al., 2011). Considering will continue to respond to changes in external forcing on multi-centen- this inconsistent evidence, we conclude that the probability of specific nial to multi-millennial time scales. For the period up to the year 2500, levels above the likely range cannot be reliably evaluated. available physical model projections discussed in Sections 13.4.1-4 are combined into an assessment of future sea level rise. Paleo simulations Only the collapse of marine-based sectors of the Antarctic ice sheet are combined with paleo data to estimate the sea level commitment 13 could cause GMSL rise substantially above the likely range during the on a multi-millennial time scale beyond 2500 for different levels of 21st century. Expert estimates of contributions from this source have sustained increases in global mean temperature. a wide spread (Bamber and Aspinall, 2013), indicating a lack of con- sensus on the probability for such a collapse. The potential additional The RCPs, as applied in Chapter 12 and Sections 13.4 and 13.5.1, are contribution to GMSL rise also cannot be precisely quantified, but defined up to the year 2100. Their extension up to the year 2300 is there is medium confidence that, if a collapse were initiated, it would used to project long-term climate change (Section 12.3.1.3) (Mein- not exceed several tenths of a metre during the 21st century (Section shausen et al., 2011), but they are not directly derived from integrated 13.4.4.2). assessment models. In simulations that are reported here up to the year 2500, the RF has generally been kept constant at the 2300 level 1186 Sea Level Change Chapter 13 except for RCP2.6, in which the forcing continues to decline at the ed beyond the year 2100 because of the small number of available 2300 rate. Some model simulations of ice sheets and ocean warming simulations, the fact that different scenarios were combined within assessed here have used scenarios different from the RCP scenarios. one scenario group, and the overall low confidence in the ability of Because of the limited number of available simulations, sea level pro- the coarse-resolution ice-sheet models to capture the dynamic ice dis- jections beyond the year 2100 have thus been grouped into three cat- charge from Greenland and Antarctica, as discussed below. The range egories according to their GHG concentration in the 22nd century: low for the total sea level change was obtained by taking the sum of con- scenarios in which atmospheric GHG concentrations peak and decline tributions that result in the lowest and the highest sea level rise and and do not exceed values that are equivalent to 500 ppm CO2, medium thereby covers the largest possible model spread. scenarios with concentrations between 500 and 700 ppm CO2-eq, and high scenarios above 700 ppm. As a consequence, the model spread Except for the glacier models (Section 13.4.2), the models used here shown in Figure 13.13 and Table 13.8 combines different scenarios for the period beyond 2100 are different from the models used for the and is not merely due to different model physics. The low scenarios 21st century (Sections 13.4.1, 13.4.3, 13.4.4, and 13.5.1). Generally, include RCP2.6, SRES B1 and scenarios with 0.5 and 2% yr 1 increases the model spread for the total sea level contribution in 2100 is slightly in CO2 followed by no emissions after 450 ppm has been reached, and lower than the likely range provided in Section 13.5.1 (light red bars the commitment scenarios, CC, in Goelzer et al. (2013) which stabilize in Figure 13.13). This is due to the ice-sheet models, particularly of CO2 at present-day levels. In a number of the low scenarios, the global the Antarctic ice sheet, as coarse-resolution model results for thermal mean temperature peaks during the 21st century and declines there- expansion cover the range of the CMIP5 projections (light blue vertical after. These peak-and-decline scenarios include RCP2.6 as well as all lines in Figure 13.13 and Table 13.7.) and the glacier contribution is scenarios with no GHG emissions after a specified year. Even in these the same. scenarios sea level continues to rise up to the year 2500 in accordance with the multi-millennial sea level commitment of about 2 m °C 1 as Projections beyond 2100 show positive contributions to sea level from discussed in Section 13.5.4.2. The medium scenarios include RCP4.5 thermal expansion, glaciers and changes in Greenland ice sheet SMB. as well as scenarios with 1% yr 1 increase in CO2 up to 560 ppm and Due to enhanced accumulation under warming, the Antarctic ice sheet SRES-B1 and SRES-A1B. The high scenarios include RCP6.0 and RCP8.5 SMB change makes a negative contribution to sea level in scenarios as well as 1120 ppm scenarios and SRES A2. Also included are scenar- below 700 ppm CO2-eq. These results were obtained with fully cou- ios with 0.5 and 2% increase in CO2 and a SRES A2 scenario with zero pled climate ice sheet models which need to apply a relatively low emissions after 1200 and 1120 ppm have been reached, respectively. spatial resolution. In light of the discussion in Section 13.3.3.2 and the assessment of the 21st century changes in Section 13.4.4.1, there 13.5.4.1 Multi-centennial Projections is low confidence in this result. For scenarios above 700 ppm CO2-eq, Antarctic SMB change is contributing positively to GMSL. The multi-centennial sea level contributions from ocean expansion and the cryospheric components are discussed in Sections 13.4.1 to As discussed in Sections 13.4.3.2 and 13.4.4.2, there is medium con- 13.4.4. A synthesis of these contributions is provided in Table 13.8 and fidence in the ability of coupled ice sheet climate models to project Figure 13.13 for the end of each century until the year 2500. Thermal sea level contributions from dynamic ice-sheet changes in Greenland expansion contributions (dark blue bars, Figure 13.13) were obtained and Antarctica for the 21st century. In Greenland, dynamic mass loss is from coarse-resolution coupled climate models (Vizcaíno et al., 2008; limited by topographically defined outlets regions. Furthermore, solid Solomon et al., 2009; Gillett et al., 2011; Schewe et al., 2011; Zick- ice discharge induced from interaction with the ocean is self-limiting feld et al., 2013). For comparison, the full model spread of the CMIP5 because retreat of the ice sheet results in less contact with the ocean models which were integrated beyond 2100 is provided in Table 13.7 and less mass loss by iceberg calving (Pfeffer et al., 2008; Graversen and as light blue bars in Figure 13.13. Even though the models used et al., 2011; Price et al., 2011). By contrast, the bedrock topography of for the long-term projections (Table 13.8) are less complex compared Antarctica is such that parts of the retreating ice sheet will remain in to the CMIP5 models, their model spread for the different periods and contact with the ocean. In particular, due to topography that is sloping scenarios encompasses the CMIP5 spread, which provides medium landward, especially in West Antarctica, enhanced rates of mass loss confidence in the application of the less complex models beyond 2300. are expected as the ice retreats. Contributions from the Greenland and Antarctic ice sheets were Although the model used by Huybrechts et al. (2011) is in principle obtained with climate models of comparable complexity coupled to capable of capturing grounding line motion of marine ice sheets (see ice-sheet models (Vizcaíno et al., 2010; Huybrechts et al., 2011; Goelzer Box 13.2), low confidence is assigned to the model s ability to cap- et al., 2012). Glacier projections were obtained by application of the ture the associated time scale and the perturbation required to ini- 13 method by Marzeion et al. (2012a) to the CMIP5 model output for sce- tiate a retreat (Pattyn et al., 2013). The model used by Vizcaino et al. narios and models that were integrated up to the year 2300. For 2400 (2010) does not represent ice-shelf dynamics and is thus lacking a and 2500, the same model spread as for 2300 is shown. This is proba- fundamental process that can trigger the instability. As stated by the bly underestimating the glacier s sea level contribution beyond 2300. authors, low confidence is thus also assigned to the model s ability to project future solid ice discharge from Antarctica. It is thus likely The ranges of sea level contributions provided in Figure 13.13 and that the values depicted in Figure 13.13 systematically underestimate Table 13.8 only represent the model spread and cannot be interpreted Antarctica s future contribution. As detailed in Section 13.5.4.2, simu- as uncertainty ranges. An uncertainty assessment cannot be provid- lations of the last 5 Myr (Pollard and DeConto, 2009) indicate that on 1187 Chapter 13 Sea Level Change Figure 13.13 | Sea level projections beyond the year 2100 are grouped into three categories according to the concentration of GHG concentration (in CO2-eq) in the year 2100 (upper panel: >700 ppm including RCP6.0 and RCP8.5; middle panel: 500 700 ppm including RCP4.5; lower panel: <500 ppm including RCP2.6). Colored bars show the full model spread. Horizontal lines provide the specific model simulations. The different contributions are given from left to right as thermal expansion from the CMIP5 simulations up to 2300 (as used for the 21st century projections, section 13.5.1, light blue, with the median indicated by the horizontal bar), thermal expansion for the models considered in this section (dark blue), glaciers (light green), Greenland ice sheet (dark green), Antarctic ice sheet (orange), and the total contribution (red). The range provided for the total sea level change represents the maximum possible spread that can be obtained from the four different contributions. Light red-shaded bars show the likely range for the 21st century total sea level projection of the corresponding scenarios from Figure 13.10 with the median as the horizontal line. In the upper panel, the left light red bar corresponds to RCP6.0 and the right light red bar corresponds to RCP8.5. m ­ ulti-millennial time scales, the Antarctic ice sheet loses mass for ele- of 0.27 to 1.51 m computed by the process-based models. Using a 13 vated temperatures, in contrast to the projections until the year 2500 d ­ ifferent semi-empirical approach, Jevrejeva et al. (2012a) obtained a ­ for the low and medium scenarios. 90% confidence range of 0.13 to 1.74 m for RCP2.6 in the year 2500, which encloses the model spread of 0.50 to 1.02 m for the low scenario The model spread of total sea level change in 2300 ranges from 0.41 from the process-based models. For the medium and high scenarios, to 0.85 m for the low scenario (Table 13.8). Using an SEM, Schaef- however, they obtained ranges of 0.72 to 4.3 m and 1.0 to 11.5 m, fer et al. (2012) obtained a significantly larger 90% confidence range respectively, which are significantly higher than the corresponding pro- of 1.3 to 3.3 m for the RCP2.6 scenario. The RCP4.5 scenario, for cess-based model spread of 0.18 to 2.32 m and 1.51 to 6.63 m (Table which they obtained a range of 2.3 to 5.5 m, is categorized here as 13.8). Because projections of land water storage are not available for a medium scenario, and is also significantly higher than the range years beyond 2100 these were not included here. 1188 Sea Level Change Chapter 13 The higher estimates from the SEMs than the process-based models up to 1°C and 0.34 m °C 1 between 2°C and 4°C) apart from the abrupt used here for the long-term projections are consistent with the relation threshold of ice loss between 0.8°C and 2.2°C above pre-industrial between the two modelling approaches for the 21st century (Figure (90% confidence interval in the particular model calculations reported 13.12). Section 13.5.3 concluded that the limited or medium evidence here) (Figure 13.14c). This represents a change from a fully ice-covered supporting SEMs, and the low agreement about their reliability, pro- Greenland to an essentially ice-free state, reducing the ice sheet to vides low confidence in their projections for the 21st century. We note around 10% of present-day volume and raising sea level by over 6 m here that the confidence in the ability of SEMs is further reduced with (Ridley et al., 2005; Ridley et al., 2010). The threshold temperature is the length of the extrapolation period and the deviation of the future lower than estimates obtained from the assumption that the threshold forcing from the forcing of the learning period (Schaeffer et al., 2012), coincides with a negative total SMB of the Greenland ice sheet (see thus decreasing confidence over the long time frames considered here. Section 13.4.3.3 for a more complete discussion). For increasing global mean SAT, sea level is virtually certain to contin- The Antarctic ice sheet contribution comes from a simulation of the ue to rise beyond the year 2500 as shown by available process-based last 5 million years (Pollard and DeConto, 2009), which is in good model simulations of thermal expansion and ice sheets that were com- agreement with regional paleo records (Naish et al., 2009). The sen- puted beyond 2500 (Rahmstorf and Ganopolski, 1999; Ridley et al., sitivity of the ice sheet was extracted from this model simulation by 2005; Winguth et al., 2005; Driesschaert et al., 2007; Mikolajewicz et correlating the ice volume with the global mean temperature which al., 2007b; Swingedouw et al., 2008; Vizcaíno et al., 2008; Solomon et forces the simulation. The standard deviation of the resulting scatter al., 2009; Vizcaíno et al., 2010; Gillett et al., 2011; Goelzer et al., 2011; is used as a measure of uncertainty (Figure 13.14d). Uncertainty arises Huybrechts et al., 2011; Schewe et al., 2011). from uncertainty in the forcing data, the ice physics representation, and from the time-dependent nature of the simulation. For example, 13.5.4.2 Multi-Millennial Projections the existence of hysteresis behavior on the sub-continental scale can lead to different contributions for the same temperature increase. The Here sea level commitment in response to a global mean tempera- Antarctic ice sheet shows a relatively constant commitment of 1.2 m ture increase on a multi-millennial time scale is assessed. Figure 13.14 °C 1. Paleorecords indicate that a potential hysteresis behaviour of East shows the sea level rise after several millennia of constant global mean Antarctica requires a temperature increase above 4°C and is thereby temperature increase above pre-industrial. The thermal expansion of outside of the scope discussed here (Foster and Rohling, 2013). the ocean was taken from 1000-year integrations with six coupled cli- mate models as used in the AR4 (models Bern2D, CGoldstein, CLIMate In order to compare the model results with past sea level anomalies for and BiosphERe-2 (CLIMBER-2), Massachusetts Institute of Technology the temperature range up to 4°C, we focus on the three previous peri- (MIT), MoBidiC, and Loch-Vecode-Ecbilt-CLio-agIsm Model (LOVE- ods of warmer climates and higher sea levels than pre-industrial that CLIM) in Figure 10.34 in Meehl et al. (2007)). These yield a rate of sea were assessed in Sections 5.6.1, 5.6.2 and 13.2.1: the middle Pliocene, level change in the range of 0.20 to 0.63 m °C 1 (Figure 13.14a). For MIS 11, and the LIG (Figure 13.14e). In each case, there is reasonable reference, a spatially uniform increase of ocean temperature yields a agreement between the model result of a long-term sea level response global mean sea level rise of 0.38 m °C 1 when added to observed for a given temperature with the information from the paleo record. data (Levitus et al., 2009) (black dots in Figure 13.14a). Uncertainty arises due to the different spatial distribution of the warming in models The ability of the physical models to reproduce paleo sea level records and the dependence of the expansion on local temperature and salin- on a multi-millennial time scale provides confidence in applying them ity. The contribution for glaciers was obtained with the models from to millennial time frames. After 2000 years, the sea level contribution Mazeion et al. (2012a) and Radic and Hock (2011) by integration with will be largely independent of the exact warming path during the first fixed boundary conditions corresponding to different global mean SAT century. As can be seen from Figure 10.34 of AR4, the oceanic heat levels for 3000 years. content will be largely equilibrated after 2000 years; the same is true for the glacier component. The situation for Antarctica is slightly more As detailed in Sections 13.4.3.2 and 13.4.4.2, there is low confidence in complicated, but as can be inferred from Pollard and DeConto (2009), the ability of current Antarctic ice-sheet models to capture the tempo- much of the retreat of the West Antarctic ice sheet will have already ral response to changes in external forcing on a decadal to centennial occurred by 2000 years, especially if the warming occurs on a decadal time scale. On multi-centennial to multi-millennial time scales, however, to centennial time scale. The opposite and smaller trend in East Ant- these models can be validated against paleo sea level records. The con- arctic ice volume due to increased snowfall in a warmer environment tributions from the Greenland ice sheet were computed with a dynamic will also have largely equilibrated (Uotila et al., 2007; Winkelmann et ice-sheet model coupled to an energy-moisture balance model for the al., 2012). 13 SMB (Robinson et al., 2012). The model s parameters were constrained by comparison with SMB estimates and topographic data for the pres- The most significant difference arises from the contribution of the ent day and with estimated summit-elevation changes from ice-core Greenland ice sheet. Consistent with previous estimates (Huybrechts records for the Last Interglacial period (LIG), in order to ensure that the et al., 2011; Goelzer et al., 2012), the rate of the sea level contribution coupled model ensemble has a realistic sensitivity to climatic change. from Greenland increases with temperature. The transient simulations The parameter spread leads to a spread in ice-sheet responses (dark for an instantaneous temperature increase show a quasi-quadratic green lines in Figure 13.14c). The contribution to sea level commitment dependence of the sea level contribution on this temperature increase from the Greenland ice sheet is relatively weak (on average 0.18 m °C 1 after 2000 years (Figure 13.14h) (Robinson et al. 2012). The results are 1189 Chapter 13 Sea Level Change quantitatively consistent with previous estimates on a millennial time Greenland mean temperature increase (Gregory and Huybrechts, 2006, scale (Huybrechts et al., 2011; Goelzer et al., 2012). The sea level contri- black dot in Figure 13.14h). Taken together, these results imply that a bution of the Greenland ice sheet after 2000 years of integration at 560 sea level rise of 1 to 3 m °C 1 is expected if the warming is sustained for ppm was plotted against the average Greenland temperature divided several millennia (low confidence) (Figure 13.14e, 13.14j). by the standard polar amplification of 1.5 between global mean and 0.42 m °C-1 0.42 m °C-1 1.2 m °C-1 1.2 m °C-1 1.8 m °C-1 2.3 m °C-1 2.3 m °C-1 13 ° ° Figure 13.14 | (Left column) Multi-millennial sea level commitment per degree Celsius of warming as obtained from physical model simulations of (a) ocean warming, (b) mountain glaciers and (c) the Greenland and (d) the Antarctic ice sheets. (e) The corresponding total sea level commitment, compared to paleo estimates from past warm periods (PI = pre-industrial, LIG = last interglacial period, M11 = Marine Isotope Stage 11, Plio = Mid-Pliocene). Temperatures are relative to pre-industrial. Dashed lines provide linear approximations in (d) and (e) with constant slopes of 1.2, 1.8 and 2.3 m °C 1. Shading as well as the vertical line represents the uncertainty range as detailed in the text. (Right column) 2000-year-sea level commitment. The difference in total sea level commitment (j) compared to the fully equilibrated situation (e) arises from the Greenland ice sheet which equilibrates on tens of thousands of years. After 2000 years one finds a nonlinear dependence on the temperature increase (h) consistent with coupled climate ice sheet simulations by Huybrechts et al. (2011) (black dot). The total sea level commitment after 2000 years is quasi-linear with a slope of 2.3 m °C 1. 1190 Sea Level Change Chapter 13 Table 13.7 | Median and model spread of the thermal expansion of CMIP5 comprehensive climate models. RCP2.6 belongs to the low scenarios as shown in Figure 13.13 and Table 13.8; RCP4.5 is a medium scenario and RCP8.5 a high scenario . The model spread in Table 13.8 encloses the CMIP5 model spread for all scenarios. Sea level contributions are provided in metres. Mean 2191 2200 Mean 2291 2300 Scenario No. of Models Median Model Spread No. of Models Median Model Spread RCP2.6 3 0.19 m 0.15 0.22 m 3 0.21 m 0.15 0.25 m RCP4.5 7 0.39 m 0.30 0.47 m 6 0.54 m 0.38 0.66 m RCP8.5 2 0.85m 0.80 0.90 m 2 1.34 m 1.26 1.41 m Table 13.8 | Model spread of sea level contribution and total sea level change for low, medium and high scenarios as defined in the text and shown in Figure 13.13. As detailed in the text, there is low confidence in the ice-sheet models ability to project rapid dynamical change in the Antarctic ice sheet, which may result in a systematic underestimation of the ice-sheet contributions. The unit of all sea level contributions is metres. Contribution Scenario 2100 2200 2300 2400 2500 Thermal expansion Low 0.07 to 0.31 m 0.08 to 0.41 m 0.08 to 0.47 m 0.09 to 0.52 m 0.09 to 0.57 m Glaciers Low 0.15 to 0.18 m 0.19 to 0.23 m 0.22 to 0.26 m 0.22 to 0.26 mb 0.22 to 0.26 mb Greenland ice sheet Low 0.05 ma 0.10 ma 0.15 ma 0.21 ma 0.26 ma Antarctic ice sheet Low 0.01 m a 0.02 m a 0.03 m a 0.05 m a 0.07 ma Total Low 0.26 to 0.53 m 0.35 to 0.72 m 0.41 to 0.85 m 0.46 to 0.94 m 0.50 to 1.02 m Thermal expansion Medium 0.09 to 0.39 m 0.17 to 0.62 m 0.20 to 0.81 m 0.22 to 0.98 m 0.24 to 1.13 m Glaciers Medium 0.15 to 0.19 m 0.21 to 0.25 m 0.25 to 0.29 m 0.25 to 0.29 m b 0.25 to 0.29 mb Greenland ice sheet Medium 0.02 to 0.09 m 0.05 to 0.24 m 0.08 to 0.44 m 0.11 to 0.65 m 0.14 to 0.91 m Antarctic ice sheet Medium 0.07 to 0.01 m 0.17 to 0.02 m 0.25 to 0.03 m 0.36 to 0.02 m 0.45 to 0.01 m Total Medium 0.19 to 0.66 m 0.26 to 1.09 m 0.27 to 1.51 m 0.21 to 1.90 m 0.18 to 2.32 m Thermal expansion High 0.08 to 0.55 m 0.23 to 1.20 m 0.29 to 1.81 m 0.33 to 2.32 m 0.37 to 2.77 m Glaciers High 0.17 to 0.19 m 0.25 to 0.32 m 0.30 to 0.40 m 0.30 to 0.40 mb 0.30 to 0.40 mb Greenland ice sheet High 0.02 to 0.09 m 0.13 to 0.50 m 0.31 to 1.19 m 0.51 to 1.94 m 0.73 to 2.57 m Antarctic ice sheet High 0.07 to 0.00 m 0.04 to 0.01 m 0.02 to 0.19 m 0.06 to 0.51 m 0.11 to 0.88 m Total High 0.21 to 0.83 m 0.58 to 2.03 m 0.92 to 3.59 m 1.20 to 5.17 m 1.51 to 6.63 m Notes: a The value is based on one simulation only. b Owing to lack of available simulations the same interval used as for the year 2300. 13.6 Regional Sea Level Changes decades from tide gauges appear to be steric (Levitus et al., 2005, 2009; Lombard et al., 2005a, 2005b; Ishii and Kimoto, 2009; Stammer Regional sea level changes may differ substantially from a global et al., 2013). Moreover, steric changes observed during the altimetry average, showing complex spatial patterns which result from ocean era appear to be primarily thermosteric in nature, although haloster- dynamical processes, movements of the sea floor, and changes in ic effects, which can reduce or enhance thermosteric changes, are gravity due to water mass redistribution (land ice and other terres- also important in some regions (e.g., Atlantic Ocean, Bay of Bengal). trial water storage) in the climate system. The regional distribution is Ocean models and ocean reanalysis-based results (Carton et al., 2005; associated with natural or anthropogenic climate modes rather than Wunsch and Heimbach, 2007; Stammer et al., 2011) as well as ocean factors causing changes in the global average value, and include such circulation models without data assimilation (Lombard et al., 2009) processes as a dynamical redistribution of water masses and a change confirm these results. of water mass properties caused by changes in winds and air pressure, air sea heat and freshwater fluxes and ocean currents. Because the Observations and ocean reanalysis (Stammer et al., 2011; 2013) also characteristic time scales of all involved processes are different, their agree in showing that steric spatial patterns over the last half of the 13 relative contribution to net regional sea level variability or change will 20th century fluctuate in space and time as part of modes of the cou- depend fundamentally on the time scale considered. pled ocean atmosphere system such as ENSO, the NAO, and the PDO (Levitus et al., 2005; Lombard et al., 2005a; Di Lorenzo et al., 2010; 13.6.1 Regional Sea Level Changes, Climate Modes and Lozier et al., 2010; Zhang and Church, 2012). In these cases, regional Forced Sea Level Response sea level variability is associated with changing wind fields and result- ing changes in the ocean circulation (Kohl and Stammer, 2008). For As discussed in Chapter 3, most of the regional sea level changes example, the large rates of sea level rise in the western tropical Pacific observed during the recent altimetry era or reconstructed during past and of sea level fall in the eastern Pacific over the period 1993 2010 1191 Chapter 13 Sea Level Change correspond to an increase in the strength of the trade winds in the Toward the end of the 21st century, the CMIP5 results indicate that it central and eastern tropical Pacific over the same period (Timmermann is possible that the interannual to decadal variability of dynamic sea et al., 2010; Merrifield and Maltrud, 2011; Nidheesh et al., 2012). The level can weaken in some parts of the world ocean, for example, the long-term sea level trend from 1958 to 2001 in the tropical Pacific can western low-latitude Pacific and parts of the Indian Ocean, whereas it also be explained as the ocean s dynamical response to variations in could be amplified in other parts, for example, the North Pacific, the the wind forcing (Qiu and Chen, 2006; Timmermann et al., 2010). eastern tropical Pacific, the eastern subtropical Atlantic and the Arctic (Figure 13.15b). Spatial variations in trends in regional sea level may also be specific to a particular sea or ocean basin. For example, a sea level rise of 5.4 Longer-than-decadal-time-scale regional sea level changes can increas- +/- 0.3 mm yr 1 in the region between Japan and Korea from 1993 to ingly be expected to result from long-term changes in the wind field, 2001 is nearly two times the GMSL trend, with more than 80% of this changes in the regional and global ocean heat and freshwater content rise being thermosteric (Kang et al., 2005). Han et al. (2010) found that and the associated dynamical adjustment (with associated redistribu- regional changes of sea level in the Indian Ocean that have emerged tion of ocean properties), and (to a lesser extent) from atmospheric since the 1960s are driven by changing surface winds associated with pressure. The CMIP5 projections of steric sea level changes toward the a combined enhancement of Hadley and Walker Cells. end of the 21st century reveal a clear regional pattern in dynamical sea level change (Figure 13.16), in which the Southern Ocean shows a net 13.6.2 Coupled Model Intercomparison Project Phase 5 decline relative to the global mean, while the remaining global ocean General Circulation Model Projections on Decadal displays complex ridge-and-trough structures superimposed on a gen- to Centennial Time Scales erally rising sea level (Yin, 2012). For example, in the North Atlantic, the largest sea level rise is along and north of the North Atlantic Current, CMIP5 projections of regional sea level provide information primar- but less so further to the south in the center of the warmer subtropical ily about dynamical sea level changes resulting from increased heat gyre. A similar dipole pattern was observed in CMIP3 results there due uptake and changes in the wind forcing. On decadal time scales, the to a weakening of the AMOC which leads to a local steric sea level rise CMIP5 model ensemble identifies strong interannual variability (up to east of North America, resulting in more water on the shelf and directly 8 cm, root-mean square (RMS)) associated with ENSO and dynamics of impacting northeastern North America (Levermann et al., 2005; Lan- the equatorial current system in the tropical Pacific and Indian Oceans derer et al., 2007; Yin et al., 2010). A similar pattern can be observed in (Figure 13.15a). Similar variability in the amplitude of sea level change the North Pacific, but here and in other parts of the world ocean (e.g., but due to other climate modes is also apparent in the North Atlantic Southern Ocean), regional sea level patterns are largely the result of Current and in parts of the Southern Ocean. changes in wind forcing, associated changes in the circulation, and an associated redistribution of heat and freshwater. Some regional chang- es can also be expected to result from modifications in the expansion a) coefficient due to changes in the ocean s regional heat content (Kuhl- 70 60°N brodt and Gregory, 2012). 60 30°N 50 The CMIP5 ensemble indicates that regions showing an enhanced sea 0° 40 level toward the end of the 21st century coincide with those showing 30 the largest uncertainty (Figure 13.16b). Although this also appeared in 30°S 20 the earlier CMIP3 SRES A1B results, the CMIP5 results, by comparison, show a general reduction in the ensemble spread, especially in high lati- 60°S 10 tudes. On a global average, this reduction is from 5.7 cm to 2.1 cm, RMS. 90°E 180° 90°W 0° 0 (mm) b) The contribution of changes of global ocean heat storage to regional 10.0 60°N steric sea level anomalies is virtually certain to increase with time as 7.5 the climate warming signal increasingly penetrates into the deep ocean 30°N 5.0 (Pardaens et al., 2011a). For the last three decades of the 21st centu- 2.5 ry, the AR4 climate model ensemble mean shows a significant heat 0° 0.0 storage increase (Yin et al., 2010), about half of which is stored in the 2.5 30°S ocean below 700 m depth. Recent detection of ongoing changes in the 13 5.0 ocean salinity structure (Durack and Wijffels, 2010) (Section 3.3.2) may 60°S 7.5 also contribute to future regional steric sea level changes. Halosteric 10.0 90°E 180° 90°W 0° effects can dominate in some regions, especially in regions of high- (mm) latitude water mass formation where long-term heat and freshwater Figure 13.15 | (a) Root-mean square (RMS) interannual dynamic sea level variability changes are expected to occur (e.g., in the subpolar North Atlantic, the (millimetres) in a CMIP5 multi-model ensemble (21 models), built from the historically Arctic, the Southern Ocean) (Yin et al., 2010; Pardaens et al., 2011a). forced experiments during the period 1951 2005. (b) Changes in the ensemble aver- Because of an anticipated increase in atmospheric moisture transport age interannual dynamic sea level variability (standard deviation (SD), in millimetres) evaluated over the period 2081 2100 relative to the reference period 1986 2005. The from low to high latitudes (Pardaens et al., 2003), halosteric anoma- projection data (2081 2100) are from the CMIP5 RCP4.5 experiment. lies are positive in the Arctic Ocean and dominate regional sea level 1192 Sea Level Change Chapter 13 a) anomalies there (Yin et al., 2010). It is likely that future thermosteric 0.30 changes will dominate the steric variations in the Southern Ocean, and 60°N 0.24 strong compensation between thermosteric and halosteric change will 30°N characterize the Atlantic (Pardaens et al., 2011a). 0.18 0° 13.6.3 Response to Atmospheric Pressure Changes 0.12 30°S 0.06 Regional sea level also adjusts to regional changes in atmospheric sea 60°S level pressure relative to its instantaneous mean over the ocean. Over 0.00 90°E 180° 90°W 0° time scales longer than a few days, the adjustment is nearly isostatic. (m) b) Sea level pressure is projected to increase over the subtropics and 0.30 mid-latitudes (depressing sea level) and decrease over high latitudes 60°N (raising sea level), especially over the Arctic (order several millibars), 0.24 30°N by the end of the 21st century associated with a poleward expansion 0.18 of the Hadley Circulation and a poleward shift of the storm tracks of 0° several degrees latitude (Section 12.4.4) (Held and Soden, 2006). These 0.12 changes may therefore contribute positively to the sea level rise in the 30°S Arctic in the range of up to 1.5 cm and about 2.5 cm for RCP4.5 and 0.06 60°S RCP8.5, respectively (Yin et al., 2010) (Figure 13.17). In contrast, air 90°E 180° 90°W 0° 0.00 pressure changes oppose sea level rise in mid- and low latitudes albeit (m) with small amplitudes. Air pressure may also influence regional sea Figure 13.16 | (a) Ensemble mean projection of the time-averaged dynamic and steric level elsewhere, as demonstr ated by sea level changes in the Mediter- sea level changes for the period 2081 2100 relative to the reference period 1986 ranean in the second half of the 20th century (Tsimplis et al., 2005). 2005, computed from 21 CMIP5 climate models (in metres), using the RCP4.5 experi- ment. The figure includes the globally averaged steric sea level increase of 0.18 +/- 0.05 13.6.4 Response to Freshwater Forcing m. (b) Root-mean square (RMS) spread (deviation) of the individual model result around the ensemble mean (metres). Note that the global mean is different from the value in Table 13.5, by less than 0.01 m, because a slightly different set of CMIP5 models was Enhanced freshwater fluxes derived from an increase in ice-sheet melt- used (see the Supplementary Material). water at high latitudes results in a regional pattern of sea level rise a) b) 60°N 60°N 30°N 30°N 0° 0° 30°S 30°S 60°S 60°S 90°E 180° 90°W 0° 90°E 180° 90°W 0° c) d) 60°N 60°N 30°N 30°N 0° 0° 30°S 30°S 13 60°S 60°S 90°E 180° 90°W 0° 90°E 180° 90°W 0° (m) 0.025 0.015 0.005 0.005 0.015 0.025 Figure 13.17 | Projected ensemble mean sea level change (metres) due to changes in atmospheric pressure loading over the period from 1986 2005 to 2081 2100 for (a) RCP4.5 and (b) RCP8.5 (contour interval is 0.005 m). Standard deviation of the model ensemble due to the atmospheric pressure loading for (c) RCP4.5 and (d) RCP8.5 (contour interval is 0.005 m). 1193 Chapter 13 Sea Level Change originating from adjustments in ocean dynamics and in the solid earth. subject to relative sea level fall of about an order of magnitude greater Neither effect is included in CMIP5 models, although the latter adjust- than the equivalent GMSL rise from these mass contributions, whereas ment is computed off line here. in the far field the sea level rise is larger (up to about 30%) than the global average rise (Mitrovica et al., 2001, 2009; Gomez et al., 2010a). 13.6.4.1 Dynamic Ocean Response to Cryospheric Freshwater Gomez et al. (2010a) and Mitrovica et al. (2011) showed that differenc- Forcing es in the maximum predicted rise (relative to the global mean) between published results is due to the accuracy with which water expulsion The addition of freshwater from glaciers and ice sheets to the ocean from the deglaciated marine basins is calculated. These changes are in leads to an instantaneous increase in global mean sea level, but addition to the ongoing response to past changes (e.g., glacial isostatic because it is communicated around the ocean basins via a dynamical adjustment in response to the last deglaciation). Mitrovica et al. (2001) adjustment, it is not instantaneously globally uniform (Kawase, 1987; suggested that the lower rates of sea level change inferred from tide Cane, 1989). For the addition of mass, the barotropic adjustment of gauge records at European sites relative to the global average were the ocean takes place in a few days (Gower, 2010; Lorbacher et al., consistent with 20th century melting from Greenland. Similarly, Geh- 2012). The addition of freshwater to the ocean from melting of the rels and Woodworth (2013) suggested that the larger magnitude of Greenland ice sheet results in an additional basin-wide steric response the early 20th century sea level acceleration observed in Australia and of the North Atlantic within months and is communicated to the global New Zealand, as compared with the North Atlantic, may represent a ocean via boundary waves, equatorial Kelvin waves, and westward fingerprint of the increased melt contributions of Greenland and Arctic propagating baroclinic Rossby waves on decadal time scales (Stammer, glaciers in the 1930s. Nevertheless, current rates of ice-sheet melting 2008). A similar response but with a different pattern can be observed are difficult to distinguish from dynamic variability (Kopp et al., 2010; from Antarctic meltwater input. In both cases, an associated complete Hay et al., 2013), but it is likely that with further ice-sheet melting baroclinic adjustment of the global ocean might take as long as sev- they will begin to dominate the regional patterns of sea level change eral centuries. The adjustment of the ocean to high-latitude meltwater toward the end of the 21st century, especially under climate forcing input also involves atmospheric teleconnections; such a response to conditions for which ice-sheet melting contributes more than 20-cm Greenland meltwater pulses could lead to sea level changes in the equivalent sea level rise (Kopp et al., 2010). These changes are in addi- Pacific within months (Stammer et al., 2011). On longer-than-decadal tion to the ongoing response to past changes (e.g., GIA in response to time scales, the freshwater input to the North Atlantic raises sea level the last deglaciation; Figure 13.18a). in the Arctic Ocean and leads to an anomalous southward Bering Strait throughflow, transporting colder, fresher water from the Arctic Ocean Water mass redistributions associated with land hydrology changes into the North Pacific (Hu et al., 2010) and causing North Pacific cool- other than those from land ice may also produce spatially variable fin- ing (Okumura et al., 2009). Meltwater forcing in the subpolar North gerprints in sea level (Fiedler and Conrad, 2010). In particular, region- Atlantic also causes changes of the AMOC (Section 12.4.7.2), which al changes in the terrestrial storage of water can lead to a sea level in turn causes dynamical changes of sea level in the North Atlantic, response on interannual and longer time scales, specifically near large particularly in its northwestern region (Lorbacher et al., 2010). The river basins (Riva et al., 2010). combination of this dynamic sea level rise and the global mean sea level rise makes the northeastern North American coast vulnerable to 13.6.5 Regional Relative Sea Level Changes some of the fastest and largest sea level rises during this century (Yin et al., 2009). Regional relative sea level change projections can be estimated from a combination of the various contributions to sea level change described 13.6.4.2 Earth and Gravitational Response to Contemporary above, emerging from the ocean, atmospheric pressure loading and Surface Water Mass Redistribution the solid Earth. Deformational, rotational and gravitational responses to mass redis- Over the next few decades, regional relative sea level changes over tribution between the cryosphere, the land and the oceans produce most parts of the world are likely to be dominated by dynamical chang- distinctive regional departures from GMSL, referred to as sea level es (mass redistribution and steric components) resulting from natu- fingerprints (Mitrovica et al., 2001, 2009; Gomez et al., 2010a; Riva ral variability, although exceptions are possible at sites near rapidly et al., 2010) (Section 13.1, FAQ 13.1). Many existing studies of these melting ice sheets where static effects could become large. However, effects have not defined a specific rate of ice-sheet mass loss (Mitro- towards the end of the 21st century, regional patterns in sea level from vica et al., 2001) or are based on end-member scenarios of ice retreat, all other contributions will progressively emerge and eventually domi- 13 such as from the WAIS (Bamber et al., 2009; Mitrovica et al., 2009; nate over the natural variability. Gomez et al., 2010a) and marine-based parts of the East Antarctic ice sheet (Gomez et al., 2010a). Bamber and Riva (2010) calculated the Ensemble mean estimates of relative sea level change during the sea level fingerprint of all contemporary land-ice melt and each of its period 2081 2100 relative to 1986 2000 resulting from GIA and from major components. Spada et al. (2013) examined the regional sea level glacier and ice-sheet melting for RCP4.5 and RCP8.5 scenarios (Figure pattern from future ice melt based on the A1B scenario. 13.18) suggest that for the 21st century, past, present and future loss of land-ice mass will very likely be an important contributor to spatial As can be seen from Figure 13.18, a characteristic of the sea level fin- patterns in relative sea level change, leading to rates of maximum rise gerprints is that regions adjacent to the source of the mass loss are at low-to-mid latitudes. Hu et al. (2011) and Sallenger et al. (2012) also 1194 Sea Level Change Chapter 13 a) Figure 13.20 shows ensemble mean regional relative sea level change 60°N between 1986 2005 and 2081 2100 for RCPs 2.6, 6.0 and 8.5. 30°N It is very likely that over about 95% of the world ocean, regional rela- tive sea level rise will be positive, while most regions that will experi- 0° ence a sea level fall are located near current and former glaciers and ice sheets. Figure 13.21b shows that over most of the oceans (except 30°S for limited regions around western Antarctica, Greenland, and high 60°S Arctic regions), estimated regional sea level changes are significant at the 90% confidence limit. Local sea level changes deviate more 90°E 180° 90°W 0° than 10% and 25% from the global mean projection for as much as b) 30% and 9% of the ocean area, respectively, indicating that spatial 60°N 30°N a) 0° 60°N 30°S 30°N 60°S 0° 90°E 180° 90°W 0° c) 30°S 60°N 60°S 30°N 90°E 180° 90°W 0° b) 0° 60°N 30°S 30°N 60°S 0° 90°E 180° 90°W 0° 30°S (m) 0.1 0.0 0.1 0.2 0.3 60°S Figure 13.18 | Ensemble mean regional contributions to sea level change (metres) 90°E 180° 90°W 0° from (a) glacial isostatic adjustment (GIA), (b) glaciers and (c) ice-sheet surface mass c) balance (SMB). Panels (b) and (c) are based on information available from scenario 60°N RCP4.5. All panels represent changes between the periods 1986 2000 and 2081 2100. 30°N 0° suggested that steric and dynamical sea level changes can potentially increase the sea level near the northeastern coast of North America 30°S and in the western Pacific. Considerable uncertainties remain, howev- er, in both the sea level budget and in the regional expression of sea 60°S level rise. In addition, local sea level rise can also partly be compensat- 90°E 180° 90°W 0° ed by vertical land movement resulting from GIA, especially in some (m) formerly glaciated high-latitude regions where high rates of land uplift may lead to a decrease of relative sea level. For example, Johansson et 0.4 0.2 0.0 0.2 0.4 0.6 0.8 al. (2014) reported a 29 cm sea level rise in the Gulf of Finland and 27 13 Figure 13.19 | (a) Ensemble mean regional relative sea level change (m) evaluated cm fall in the Bay of Bothnia. from 21 models of the CMIP5 scenario RCP 4.5, including atmospheric loading, plus land-ice, GIA and terrestrial water sources, between 1986 2005 and 2081 2100. The ensemble mean regional relative sea level change between Global mean is 0.48 m, with a total range of -1.74 to +0.71 m. (b) The local, lower 90% uncertainty bound (p=0.05) for RCP4.5 scenario sea level rise (plus non-scenario com- 1986 2005 and 2081 2100 for the RCP4.5 scenario (not including ponents). (c) The local, upper 90% uncertainty bound (p=0.95) for RCP4.5 scenario sea the dynamic ocean contribution in response to the influx of freshwater level rise (plus non-scenario components). Note that the global mean is different from associated with land-ice loss and changes in terrestrial ground water) the value in Table 13.5, by less than 0.01 m, because a slightly different set of CMIP5 reveals that many regions are likely to experience regional sea level models was used (see the Supplementary Material) and that panels (b) and (c) contain changes that differ substantially from the global mean (Figure 13.19). local uncertainties not present in global uncertainties. 1195 Chapter 13 Sea Level Change a) b) 60°N 60°N 30°N 30°N 0° 0° 30°S 30°S 60°S 60°S 90°E 180° 90°W 0° 90°E 180° 90°W 0° c) d) 60°N 60°N 30°N 30°N 0° 0° 30°S 30°S 60°S 60°S 90°E 180° 90°W 0° 90°E 180° 90°W 0° (m) 0.4 0.2 0.0 0.2 0.4 0.6 0.8 Figure 13.20 | Ensemble mean regional relative sea level change (metres) evaluated from 21 CMIP5 models for the RCP scenarios (a) 2.6, (b) 4.5, (c) 6.0 and (d) 8.5 between 1986 2005 and 2081 2100. Each map includes effects of atmospheric loading, plus land ice, glacial isostatic adjustment (GIA) and terrestrial water sources. a) 50 60°N v ­ ariations can be large. Regional changes in sea level reach values of 30 up to 30% above the global mean value in the Southern Ocean and 30°N around North America, between 10 and 20% in equatorial regions 10 0° and up to 50% below the global mean in the Arctic region and some 10 regions near Antarctica (Figure 13.21a). 30°S 30 Figure 13.22 shows that, between 1986 2005 and 2081 2100, sea 60°S 50 level changes along the world s coastlines associated with the RCP4.5 90°E 180° 90°W 0° (%) and RCP8.5 scenarios have a substantially skewed non-Gaussian dis- b) tribution, with significant coastal deviations from the global mean. 60°N 4 When the coastlines around Antarctica and Greenland are excluded (Figure 13.22b), many negative changes disappear, but the general 30°N 2 structures of the global histograms remain. In general, changes along 0° 0 the coastlines will range from about 30 cm to 55 cm for an RCP 4.5 scenario, peaking near 50 cm, and from about 40 cm to more than 80 30°S 2 cm under a RCP 8.5 scenario, peaking near 65 cm. About 68% and 60°S 4 72% of the coastlines will experience a relative sea level change within +/-20% of the GMSL change for RCP4.5 and RCP8.5, respectively. In 13 90°E 180° 90°W 0° (std. err.) both cases, the maximum of the histogram is slightly higher than the GMSL, whereas the arithmetic mean is lower. Only some coastlines will Figure 13.21 | (a) Percentage of the deviation of the ensemble mean regional relative sea level change between 1986 2005 and 2081 2100 from the global mean value. The experience a sea level rise of up to about 40% above GMSL change. figure was computed for RCP4.5, but to first order is representative for all RCPs. (b) Total RCP4.5 sea level change (plus all other components) divided by the combined standard Figure 13.23 shows the combination of the natural variability (annual error of all components (see Supplementary Material Section 13.SM.2). Assuming a mean) and the CMIP5 projected sea level rise for the RCP4.5 scenar- normal distribution, or a t-distribution given the number of models as an approximation io for a number of locations distributed around the world. For exam- of the number of degrees of freedom, a region passes the 90% confidence level where the change is greater than 2 standard errors, which is most of the ocean except for ple, at Pago Pago (14°S,195°E) in the western equatorial Pacific, the limited regions around western Antarctica, Greenland and high Arctic regions. 1196 Sea Level Change Chapter 13 a) 13.6.6 Uncertainties and Sensitivity to Ocean/Climate Model Formulations and Parameterizations 0.12 RCP4.5 RCP8.5 Uncertainties of climate models are discussed in detail in Chapter 9. Fraction of total coastline 0.10 Sea level is a property of the ocean connected to nearly all dynamical and thermodynamical processes over the full ocean column, from the 0.08 surface fluxes to the ocean bottom. Although many of the process- es are to first order correctly simulated in climate models, differences 0.06 between models (Figure 13.24) indicate that uncertainties in simulated and projected steric sea level (globally and regionally) remain poorly 0.04 understood. Moreover, the spread in ocean heat uptake efficiency among models is responsible for 50% of the spread in heat uptake 0.02 (Kuhlbrodt and Gregory, 2012). In addition, some processes are not part of the CMIP5 simulations, such as the dynamical response of the 0.00 0.4 0.2 0.0 0.2 0.4 0.6 0.8 1.0 ocean to meltwater input or the GIA/rotational/gravitational processes b) associated with this ice mass loss. Stammer and Hüttemann (2008) showed that coupled climate models that do not include the effect of changes in atmospheric moisture content on sea level pressure will 0.12 RCP4.5 underestimate future regional atmospheric pressure loading effects by RCP8.5 Fraction of total coastline up to 2 cm. Other uncertainties result from GIA/rotational/gravitational 0.10 effects as well as from uncertainties in air sea fluxes. 0.08 Improvements in the skill of a sea level projection require (1) better 0.06 parameterizations of unresolved physical processes, (2) improved numerical algorithms for such processes as temperature and salinity 0.04 advection, (3) refined grid resolution to better represent such features as boundary currents and mesoscale eddies, and (4) the elimination of 0.02 obsolete assumptions that have a direct impact on sea level (Griffies and Greatbatch, 2012). Among the many limiting approximations 0.00 made in ocean models, the Boussinesq approximation has been found 0.4 0.2 0.0 0.2 0.4 0.6 0.8 1.0 to only marginally impact regional patterns (i.e., deviations from global m SSH change (2081-2100 minus 1986-2005) mean) when directly compared to non-Boussinesq simulations (Losch et al., 2004), thus lending greater confidence in Boussinesq models Figure 13.22 | (a) Histograms of the deviation of the ensemble mean regional rela- for addressing questions of regional sea level change. Furthermore, tive sea level change (Figure 13.20) along all coastlines (represented by the closest model grid point) between 1986 2005 and 2081 2100 from the global mean value. for global sea level, the now-standard a posteriori adjustment (Great- Shown are results for RCP4.5 (blue) and RCP8.5 (pink), respectively. (b) Same as in (a) batch, 1994; Griffies and Greatbatch, 2012) accurately incorporates but excluding Antarctic and Greenland coastlines. Vertical dashed lines represent global the missing global steric effect. The representation of dense overflows mean sea level changes for the two RCPs. can also affect sea level simulations, and is particularly problematic in many ocean models used for climate studies, with direct impacts on the simulated vertical patterns of ocean heat uptake (Legg et al., 2009). h ­ istorical record indicates that annual variability in mean sea level has been about 21 cm (5 to 95% range). Projections by individual climate Coarse-resolution ocean climate simulations require a parameter- models indicate that it is very likely that a similar range of natural vari- ization of mesoscale and smaller eddies, but the parameterizations ability will continue through the 21st century (Figure 13.15b). However, as well as the details of their numerical implementations can great- by 2100, the average projected sea level for the RCP4.5 scenario of ly impact the simulation. As shown by Hallberg and Gnanadesikan 0.52 [0.32 to 0.70] m is greater than any observations of annual mean (2006) and Farneti et al. (2010), coarse-resolution climate models may sea level in the instrumental record. Of all the examples shown, the be overestimating the Antarctic Circumpolar Current response to wind greatest sea level increase will be in New York, which is representative changes. Better implementations of eddy parameterizations reduce of the enhanced sea level rise there due to ocean processes and GIA in such biases (Farneti and Gent, 2011; Gent and Danabasoglu, 2011), 13 the region (compare Figures 13.16 and 13.18). The figure also reveals and they form the basis for some, but not all, of the CMIP5 simulations. the large spatial inhomogeneity of interannual to decadal variability. In Moreover, Vinogradov and Ponte (2011) suggested that as one consid- each case, monthly variability and extreme sea levels from winds and ers regional sea level variability and its relevant dynamics and forcing, waves associated with weather phenomena (Section 13.7) need to be mesoscale ocean features become important factors on a sub-decadal considered in addition to these projections of regional sea level. time scale. Suzuki et al. (2005) compared changes in mean dynam- ic sea level in 2080 2100 relative to 1980 2000 as obtained from a low- and a high-resolution ocean component of a coupled model and concluded that although changes are comparable between runs, the 1197 Chapter 13 Sea Level Change h ­ igh-resolution model captures enhanced details owing to resolving GIA models such as the mantle viscosity structure. Each of the many ocean eddy dynamics. uncertainties and errors results in considerable spread in the projected patterns of sea level change (Figure 13.24) (Pardaens et al., 2011a; Even with a perfect ocean model, skill in sea level projections depends Slangen et al., 2012). In addition to ocean climate model formulations on skill of the coupled climate model in which errors impacting sea and parameterizations, uncertainty in predictions of sea level change level may originate from non-ocean components. Furthermore, initiali- may be associated with specified freshwater forcing. Whether or not zation is fundamental to the prediction problem, particularly for simu- an ocean model is coupled with an ice-sheet model, the forcing should lation of low-frequency climate variability modes (Meehl et al., 2010). distinguish between runoff and iceberg flux. Martin and Adcroft (2010) Projections of land-ice melting and the resultant sea level rise patterns reported the only attempt thus far to explicitly represent iceberg drift also have large uncertainties, with additional uncertainties arising from and melting in a fully coupled climate model. a) San Francisco b) New York c) IJmuiden 1.2 1.0 0.8 0.6 0.4 0.2 0.0 (m) d) Bay of Bengal e) Kanmen, China f) Brest 1.2 1.0 0.8 0.6 0.4 0.2 0.0 (m) g) Mar del Plata, Argentina h) Fremantle i) Pago Pago 1.2 1.0 0.8 0.6 0.4 0.2 0.0 (m) 13 1970 1980 1990 1970 1980 1990 1970 1980 1990 2000 2010 2020 2030 2040 2050 2060 2070 2080 2090 2100 2000 2010 2020 2030 2040 2050 2060 2070 2080 2090 2100 2000 2010 2020 2030 2040 2050 2060 2070 2080 2090 2100 Figure 13.23 | Observed and projected relative sea level change (compare Figure 13.20) near nine representative coastal locations for which long tide-gauge measurements are available. The observed in situ relative sea level records from tide gauges (since 1970) are plotted in yellow, and the satellite record (since 1993) is provided as purple lines. The projected range from 21 CMIP5 RCP4.5 scenario runs (90% uncertainty) is shown by the shaded region for the period 2006 2100, with the bold line showing the ensemble mean. Coloured lines represent three individual climate model realizations drawn randomly from three different climate models used in the ensemble. Station locations of tide gauges are: (a) San Francisco: 37.8°N, 122.5°W; (b) New York: 40.7°N, 74.0°W; (c) Ijmuiden: 52.5°N, 4.6°E; (d) Haldia: 22.0°N, 88.1°E; (e) Kanmen, China: 28.1°N, 121.3°E; (f) Brest: 48.4°N, 4.5°W; (g) Mar del Plata, Argentina: 38.0°S, 57.5°W; (h) Fremantle: 32.1°S, 115.7°E; (i) Pago Pago: 14.3°S, 170.7°W. Vertical bars at the right sides of each panel represent the ensemble mean and ensemble spread (5 to 95%) of the likely (medium confidence) sea level change at each respective location at the year 2100 inferred from RCPs 2.6 (dark blue), 4.5 (light blue), 6.0 (yellow) and 8.5 (red). 1198 Sea Level Change Chapter 13 13 Figure 13.24 | Projected relative sea level change (in m) from the combined global steric plus dynamic topography and glacier contributions for the RCP4.5 scenario over the period from 1986 2005 to 2081 2100 for each individual climate model used in the production of Figure 13.16a. 1199 Chapter 13 Sea Level Change 13.7 Projections of 21st Century Sea Level but the limited geographical coverage of studies and uncertainties Extremes and Waves associated with storminess changes prevent a general assessment. The global tropical cyclone frequency will likely decrease or remain roughly Climate change will affect sea levels extremes and ocean waves in two constant, but it is more likely than not that the frequency of the most principal ways. First, because extratropical and tropical storms are one intense storms will increase in some ocean basins (Chapter 14). Uncer- of the key drivers of sea level extremes and waves, future changes in tainties in projections of cyclone frequency and tracks make it difficult intensity, frequency, duration, and path of these storms will impact to project how these changes will impact particular regions. Similarly, them. Second, sea level rise adds to the heights of sea level extremes, while the SREX and the current assessment (Chapter 14) find that it is regardless of any changes in the storm-related component. MSL likely that there has been a poleward shift in the main northern and change may also accentuate the threat of coastal inundation due to southern extra-tropical cyclone tracks during the last 50 years, and that changes in wave runup. Observations of changes in sea level extremes regional changes may be substantial, there is only low confidence in and waves are discussed in Chapter 3. Sea level extremes at the coast region-specific projections. occur mainly in the form of storm surges and tsunamis, but because the latter are not climate driven, we assess only projections for sea level 13.7.2.2 Projections Based on Dynamical and Statistical extremes based on estimates of future storminess and MSL change. Approaches 13.7.1 Observed Changes in Sea Level Extremes Projected changes in storm surges (relative to MSL) have been assessed by applying climate model forcing to storm-surge models. As discussed in the AR4 (Bindoff et al., 2007) and confirmed by more Return periods of sea level extremes (see Glossary) exceeding a given recent studies (Menéndez and Woodworth, 2010), statistical analyses threshold level, referred to as return levels, are used in quantifying of tide-gauge observations have shown an increase in observed sea projected changes. Using three regionally downscaled GCMs for A2, level extremes worldwide that are caused primarily by an increase B2 and A1B scenarios, Debernard and Roed (2008) found an 8 to 10% in MSL (Chapter 3). Dominant modes of climate variability, particu- increase in the 99th percentile surge heights between 1961 1990 and larly ENSO and NAO, also have a measureable influence on sea level 2071 2100, mainly during the winter season, along the coastlines of extremes in many regions (Lowe et al., 2010; Walsh et al., 2011). These the eastern North Sea and the northwestern British Isles, and decreas- impacts are due to sea level anomalies associated with climate modes, es south of Iceland. Using a downscaled GCM under an A1B scenario, as well as mode-related changes in storminess. There has been some Wang et al. (2008) projected a significant increase in wintertime storm indication that the amplitude and phase of major tidal constituents surges around most of Ireland between 1961 1990 and 2031 2060. have exhibited long-term change (Jay, 2009; Muller et al., 2011), but Sterl et al. (2009) concatenated the output from a 17-member ensem- their impacts on extreme sea level are not well understood. Using par- ble of A1B simulations from a GCM over the periods 1950 2000 and ticle size analysis of cores collected in the Mackenzie Delta in the Arctic 2050 2100 into a single longer time series to estimate 10,000-year region, Vermaire et al. (2013) inferred increased storm surge activity in return levels of surge heights along the Dutch coastline. No statis- the region during the last approximately 150 years, which they related tically significant change in this value was projected for the 21st to the annual mean temperature anomaly in the NH and a decrease in century because projected wind speed changes were not associated summer sea-ice extent. with the maximum surge-generating northerlies. Using an ensemble of three climate models under A2 simulations, Colberg and McInnes 13.7.2 Projections of Sea Level Extremes (2012) found that changes in the 95th percentile sea level height (with respect to mean sea level) across the southern Australian coast 13.7.2.1 Recent Assessments of Projections of Sea Level Extremes in 2081 2100 compared to 1981 2000 were small (+/-0.1 m), mostly negative, and despite some inter-model differences, resembled the The AR4 assessed projections of storm surges for a few regions (Europe, changes in wind patterns simulated by the climate models (McInnes Australia, the Bay of Bengal) based on a limited number of dynamical et al., 2011). These studies demonstrate that the results are sensitive modelling studies (Christensen et al., 2007). Although these results to the particular choice of GCM or RCM, therefore identifying uncer- generally indicated higher magnitude surges in future scenarios, there tainties associated with the projections. For the tropical east coast of was low confidence in these projections because of the wide spread in Australia, Harper et al. (2009) found that a 10% increase in tropical underlying AOGCM and RCM projections. cyclone intensity for 2050 led to increases in the 100-year return level (including tides) that at most locations were smaller than 0.1 m with Studies since the AR4 have further assessed the relative contributions respect to mean sea level. 13 of sea level rise and storminess on projected sea level extremes. Lowe et al. (2010) concluded that the increases in the observed sea level Several regional storm-surge studies have considered the relative con- extremes in the 20th century occurred primarily through an increase tribution of the two main causative factors on changes in future sea in MSL, and that the same applies to projections for the 21st centu- level extremes (e.g., McInnes et al. (2009, 2013) for the southeastern ry. The IPCC Special Report on Managing the Risks of Extreme Events coast of Australia; Brown et al. (2010) for the eastern Irish Sea; Woth et and Disasters to Advance Climate Change Adaptation (SREX) assess- al. (2006) for the North Sea; Lowe et al. (2009) for the United Kingdom ment concluded that it is very likely that MSL rise will contribute to coast). They concluded that sea level rise has a greater potential than an increase in future sea level extremes (Seneviratne et al., 2012). It meteorological changes to increase sea level extremes by the end of noted that changes in storminess may also affect sea level extremes the 21st century in these locations. Unnikrishnan et al. (2011) used 1200 Sea Level Change Chapter 13 RCM simulations to force a storm-surge model for the Bay of Bengal relationship between hurricane-induced storm surges, sea level rise and found that the combined effect of MSL rise of 4 mm yr 1 and RCM and hurricane intensification through increased SSTs for three mod- projections for the A2 scenario (2071 2100) gave an increase in 100- elled major historical cyclones, concluding that the dynamic interaction year return levels of total sea level (including tides) between 0.40 to of surge and sea level rise lowered or amplified the surge at different 0.67 m (about 15 to 20%) along the northern part of the east coast points within a shallow coastal bay. of India, except around the head of the bay, compared to those in the base line (1961 1990) scenario. Higher mean sea levels can significantly decrease the return period for exceeding given threshold levels. For a network of 198 tide gauges Using six hypothetical hurricanes producing approximate 100-year covering much of the globe, Hunter (2012) determined the factor by return levels, Smith et al. (2010) found that in the regions of large which the frequency of sea levels exceeding a given height would be surges on the southeastern Louisiana coast, the effect of MSL rise increased for a MSL rise of 0.5 m (Figure 13.25a). These calculations added linearly to the simulated surges. However, in the regions of have been repeated here (Figure 13.25b) using regional RSL projec- moderate surges (2 3 m), particularly in wetland-fronted areas, the tions and their uncertainty using the RCP4.5 scenario (Section 13.6, increase in surge height was 1 3 m larger than the increase in mean Figure 13.19a). This multiplication factor depends exponentially on the sea level rise. They showed that sea level rise alters the speed of prop- inverse of the Gumbel scale parameter (a factor that describes the sta- agation of surges and their amplification in different regions of the tistics of sea level extremes caused by the combination of tides and coast. For the Gulf of Mexico, Mousavi et al. (2011) developed a simple storm surges) (Coles and Tawn, 1990). The scale parameter is generally 60°N 30°N 0° 30°S 60°S 60°E 120°E 180° 120°W 60°W 0° 1 10 100 1000 60°N 30°N 0° 30°S 13 60°S 60°E 120°E 180° 120°W 60°W 0° 1 10 100 1000 Figure 13.25 | The estimated multiplication factor (shown at tide gauge locations by colored dots), by which the frequency of flooding events of a given height increase for (a) a mean sea level (MSL) rise of 0.5 m (b) using regional projections of MSL for the RCP4.5 scenario, shown in Figure13.19a. 1201 Chapter 13 Sea Level Change large where tides and/or storm surges are large, leading to a small In general, there is low confidence in projections of future storm multiplication factor, and vice versa. Figure 13.25a shows that a 0.5 conditions (Chapters 12 and 14) and hence in projections of ocean m MSL rise would likely result in the frequency of sea level extremes waves. Nevertheless, there has been continued progress in translating increasing by an order of magnitude or more in some regions. The mul- climate model outputs into wind wave projections. In the AR4, project- tiplication factors are found to be similar or slightly higher, in general, ed changes in global SWHs were based on a single statistical model when accounting for regional MSL projections (Figure 13.25b). Specifi- (Wang and Swail, 2006). The projected conditions were consistent with cally, in regions having higher regional projections of MSL, such as the increased wind speeds associated with mid-latitude storms, but they east coast of Canada and the USA (where GIA results in a larger sea considered only a limited five-member ensemble for a single future level rise) and/or in regions of large uncertainty (e.g. in regions near emission scenario (SRES A2); wave parameters other than SWH were the former Laurentide ice sheet where the GIA uncertainty is large), the not considered. multiplication factor is higher, whereas in regions having lower region- al projections of MSL, such as the northwest region of North America Since the AR4, global wave climate projections for the end of the 21st (where the land is rising due to present changes in glaciers and ice- century have been made by dynamically downscaling CMIP3 AOGCM caps), the multiplication factor is lower. In another study, large increas- results. A multi-model ensemble based on dynamical models forced es in the frequency of sea level extremes for 2050 were found for a with various GHG emission scenarios (SRES A1B: Mori et al. (2010), network of sites around the USA coastline based on semi-empirical Fan et al. (2013), Semedo et al. (2013); SRES A2: Hemer et al. (2012a), MSL rise projections and 20th century statistics of extremes (Tebaldi et as well as the statistical model of Wang and Swail (2006) forced with al., 2012). Using projected time series of tides, MSL rise, components of emission scenarios IS92a and SRES A2 and B2, has been constructed sea level fluctuations from projected MSLP and wind stress fields, and as part of the Coordinated Ocean Wave Climate Project (COWCLIP) a contribution for ENSO variability through projected SSTs for the 21st (Hemer et al., 2013). In general, the ensemble projected changes of century, Cayan et al. (2008) showed that for high-end scenarios of MSL annual mean SWH (Figure 13.26a) resemble the statistical projections rise, the frequency and magnitude of extremes along the California of Wang and Swail (2006) under an A2 scenario. The largest change coast increases considerably relative to those experienced in the 20th is projected to be in the Southern Ocean, where mean SWHs at the century. end of the 21st century are approximately 5 to 10% higher than the present-day mean. SWH increase in this region reflects the projected In summary, dynamical and statistical methods on regional scales strengthening of the westerlies over the Southern Ocean, particu- show that it is very likely that there will be an increase in the occur- larly during austral winter (Figure 13.26c). Another region of SWH rence of future sea level extremes in some regions by 2100, with a increase in the ensembles is in the tropical South Pacific associated likely increase in the early 21st century. The combined effects of MSL with a projected strengthening of austral winter easterly trade winds rise and changes in storminess will affect future extremes. There is in the CMIP3 multi-model data set (Figure 13.26c). Negligible change high confidence that extremes will increase with MSL rise yet there is or a mean SWH decrease is projected for all other ocean basins, with low confidence in region-specific projections in storminess and storm decreases identified in the trade wind region of the North Pacific, the surges. mid-latitude westerlies in all basins, and in the trade and monsoon wind regions of the Indian Ocean. Hemer et al. (2013) found that var- 13.7.3 Projections of Ocean Waves iance of wave climate projections associated with wave downscaling methodology dominated other sources of variance within the projec- Changes in ocean wave conditions are determined by changes in the tions such as the climate scenario or climate model uncertainties. Mori major wind systems, especially in the main areas affected by tropi- et al. (2013) reported similar findings. cal and extra-tropical storms. Based on in situ and satellite altimeter observations and wave model hindcasts, it is likely that mean signif- Three CMIP3-based model projections (Mori et al., 2010; Hemer et al., icant wave heights (SWH, defined as the average of the highest one 2012b; Fan et al., 2013) were used to compare projections of wave third of wave heights) have increased in regions of the North Pacific direction and period (Hemer et al., 2013). Wave direction (Figure and the North Atlantic over the past half century, and in the South- 13.26d) exhibits clockwise rotation in the tropics, consistent with a ern Ocean since the mid 1980s (Chapter 3, Seneviratne et al., 2012). higher contribution from northward propagating swell from the South- The limited observational wave record makes it difficult to separate ern Ocean. Wave period (Figure 13.26e) shows an increase over the long-term trends from multi decadal variability (Young et al., 2011). A eastern Pacific, which is also attributed to enhanced wave generation number of studies have related changes in wind wave climatologies in the Southern Ocean and northward swell propagation. A projected to modes of climate variability such as ENSO (Allan and Komar, 2006; decrease in wave periods in the North Atlantic and western and central 13 Adams et al., 2008; Menéndez et al., 2008), the NAO (Woolf et al., North Pacific is symptomatic of weaker wind forcing in these regions. 2002; Izaguirre et al., 2010), and the Southern Annular Mode (SAM) (Hemer et al., 2010; Izaguirre et al., 2011). Although anthropogenic SWH projections based on CMIP5 winds for emission scenarios RCP4.5 influences have been considered (Wang et al., 2009), it is likely that and RCP8.5 (Dobrynin et al., 2012) exhibit similar regional patterns for reported SWH trends over the past half-century largely reflect natural the end of the 21st century to the CMIP3 results presented in Figure variations in wind forcing. Recent reductions in summer sea ice extent 13.26A. Dobrynin et al. (2012) reported SWH increases in the Arctic have resulted in enhanced wave activity in the Arctic Ocean due to Ocean, an area not considered by Hemer et al. (2013), and in basins increased fetch area and longer duration of the open-water season connected to the Southern Ocean, particularly for RCP8.5. The proba- (Francis et al., 2011; Overeem et al., 2011). bility of extreme wave heights is projected to increase in the SH, the 1202 Sea Level Change Chapter 13 o 60 N a) o 30 N 0o o 30 S o 60 S 60oE 120oE 180oW 120oW 60oW 0o b) c) -10 -5 0 5 10 HS (%) d) e) 13 -10 -5 0 5 10 -0.25 0 0.25 °Anti clockwise °Clockwise TM (s) Figure 13.26 | Projected changes in wind wave conditions (~2075 2100 compared with ~1980 2009) derived from the Coordinated Ocean Wave Climate Projection (COWCLIP) Project (Hemer et al., 2013). (a) Percentage difference in annual mean significant wave height. (b) Percentage difference in means of January to March significant wave height. (c) Percentage difference in means of July to September significant wave height. Hashed regions indicate projected change is greater than the 5-member ensemble standard deviation. (d) As for (a), but displaying absolute changes in mean wave direction, with positive values representing projected clockwise rotation relative to displayed vectors, and colours shown only where ensemble members agree on sign of change. (e) As for (a), but displaying absolute changes in mean wave period. The symbol ~ is used to indicate that the reference periods differ slightly for the various model studies considered. 1203 Chapter 13 Sea Level Change Arctic and Indian Oceans, but decrease in the North and Equatorial 13.8 Synthesis and Key Uncertainties Atlantic and in the Pacific. In addition to wind changes, the project- ed loss of summer sea ice extent in the Arctic Ocean is very likely to There has been significant progress in our understanding of sea level increase overall wave activity there (Manson and Solomon, 2007; change since the AR4. Paleo data now provide high confidence that Overeem et al., 2011). sea levels were substantially higher when GHG concentrations were higher or surface temperatures were warmer than pre-industrial. The Model intercomparisons are starting to identify common features of combination of paleo sea level data and long tide gauge records global wave projections but in general there is low confidence in wave confirms that the rate of rise has increased from low rates of change model projections because of uncertainties regarding future wind during the late Holocene (order tenths of mm yr 1) to rates of almost states, particularly storm geography, the limited number of model sim- 2 mm yr 1 averaged over the 20th century, with a likely continuing ulations used in the ensemble averages, and the different methodolo- acceleration during the 20th century (Figure 13.27). Since 1993, the gies used to downscale climate model results to regional scales (Hemer sum of observed contributions to sea level rise is in good agreement et al., 2012a). Despite these uncertainties, it appears likely (medium with the observed rise. confidence) that enhanced westerly surface winds in the SH (discussed in Chapter 12) will lead to enhanced wave generation in that region by Understanding of the components that contribute to total sea level the end of the 21st century. rise has improved significantly. For the 20th century, the range from an ensemble of such process-based models encompasses the observed A number of dynamical wave projection studies have been carried out rise when allowances are made for lack of inclusion of volcanic forcing with a regional focus. For the Mediterranean Sea, Lionello et al. (2008; in AOGCM control simulations, natural climate variability, and a pos- 2010) projected a widespread shift of the wave height distribution to sible small long-term ice-sheet contribution. Ice-sheet contributions to lower values by the mid-21st century under an SRES A1B scenario, the 20th century sea level rise were small, however, and this agreement implying a decrease in mean and extreme wave heights. Caires et al. is thus not an evaluation of ice-sheet models. Nevertheless, there has (2008) and Debernard and Red (2008) reported a decrease (4 to 6% been significant improvement in accounting for important physical of present values) in the annual 99th percentile SWH south of Iceland processes in ice-sheet models, particularly of the dynamical response by the end of the 21st century, and an increase (6 to 8%) along the of individual glacier systems to warmer ocean waters in the immediate North Sea east coast (SRES A2, B2, A1B scenarios). Grabemann and vicinity of the outlet glaciers. Although there are as yet no complete Weisse (2008) found increases (up to 18% of present values) in annual simulations of regional ocean temperature changes near ice sheets 99th percentile SWH in the North Sea by the end of the 21st century, and of the ice-sheet response to realistic climate change forcing, the with an increase in the frequency of extreme wave events over large publications to date have allowed an assessment of the likely range of areas of the southern and eastern North Sea (SRES A2, B2 scenarios). sea level rise for the 21st century (Figure 13.27). Charles et al. (2012) projected a general decrease in wave heights in the Bay of Biscay by the end of the 21st century (SRES A2, A1B, B1 scenarios), accompanied by clockwise rotations in winter swell (attrib- uted to a projected northward shift in North Atlantic storm tracks) and summer sea and intermediate waves (attributed to a projected slack- ening of westerly winds). Along the Portuguese coast, Andrade et al. (2007) found little projected change in SWH and a tendency for a more northerly wave direction than present (SRES A2 scenario). In the Pacific, multi-model projections by Graham et al. (2013) (SRES A2 scenario) indicate a decrease in boreal winter upper-quantile SWHs over the mid-latitude North Pacific by the end of the 21st century asso- ciated with a projected decrease in wind speeds along the southern flank of the main westerlies. There is a less robust tendency for higher extreme waves at higher latitudes. On the southeastern Australian coast, Hemer et al. (2012b) used multi-model projections (SRES A2 and B1 scenarios) to identify a decrease in mean SWH (<0.2 m) by the end of the 21st century compared to present due to a projected decrease 13 in regional storm wave energy, and a shift to a more southerly wave direction, consistent with a projected southward shift of the subtropi- cal ridge in the forcing fields. Figure 13.27 | Compilation of paleo sea level data, tide gauge data, altimeter data (from Figure 13.3), and central estimates and likely ranges for projections of global mean sea level rise for RCP2.6 (blue) and RCP8.5 (red) scenarios (Section 13.5.1), all relative to pre-industrial values. 1204 Sea Level Change Chapter 13 These observations, together with our current scientific understanding and projections of future climate and sea level, imply that it is virtually certain that sea level will continue to rise during the 21st century and beyond. For the first few decades of the 21st century, regional sea level change will be dominated by climate variability superimposed on the climate change signal. For all scenarios, the rate of 21st century GMSL rise is very likely to exceed the average rate during the 20th century. For the RCP8.5 scenario, the projected rate of GMSL rise by the end of the 21st century will approach average rates experienced during the deglaciation of the Earth after the Last Glacial Maximum. These rates imply a significant transfer of mass from the ice sheets to the oceans and associated regional departures of sea level rise from the global average, in addition to the regional patterns from changing atmos- phere ocean interactions. Sea level rise has already led to a significant increase in the return frequency of sea level extremes at many locations, and it is very likely that this will continue during the 21st century, although there is low confidence in projections of changes in storminess. The first assess- ment of surface waves indicates a likely (medium confidence) increase in the height of waves in the Southern Ocean. Despite this progress, significant uncertainties remain, particularly related to the magnitude and rate of the ice-sheet contribution for the 21st century and beyond, the regional distribution of sea level rise, and the regional changes in storm frequency and intensity. For coastal planning, sea level rise needs to be considered in a risk management framework, requiring knowledge of the frequency of sea level variabili- ty (from climate variability and extreme events) in future climates, pro- jected changes in mean sea level, and the uncertainty of the sea level projections (Hunter, 2010, 2012), as well as local issues such as the compaction of sediments in deltaic regions and the changing supply of these sediments to maintain the height of the deltas (Syvitski et al., 2009). Although improved understanding has allowed the projection of a likely range of sea level rise during the 21st century, it has not been possible to quantify a very likely range or give an upper bound to future rise. The potential collapse of ice shelves, as observed on the Antarctic Peninsula (Rignot et al., 2004; Scambos et al., 2004; Rott et al., 2011), could lead to a larger 21st century rise of up to several tenths of a metre. Sea level will continue to rise for centuries, even if GHG concentra- tions are stabilized, with the amount of rise dependent on future GHG emissions. For higher emission scenarios and warmer temperatures, surface melting of the Greenland ice sheet is projected to exceed accu- mulation, leading to its long-term decay and a sea level rise of metres, consistent with paleo sea level data. 13 Acknowledgements We thank Lea Crosswell and Louise Bell for their assistance in drafting a number of diagrams in this chapter and Jorie Clark for assistance with managing chapter references. 1205 Chapter 13 Sea Level Change References Ablain, M., A. Cazenave, G. Valladeau, and S. Guinehut, 2009: A new assessment of Bintanja, R., G. J. van Oldenborgh, S. S. Drijfhout, B. Wouters, and C. A. Katsman, the error budget of global mean sea level rate estimated by satellite altimetry 2013: Important role for ocean warming and increased ice-shelf melt in Antarctic over 1993 2008. Ocean Sci., 5, 193 201. sea-ice expansion. Nature Geosci., 6, 376 379. Adams, P. N., D. L. Inman, and N. E. Graham, 2008: Southern California deep-water Bittermann, K., S. Rahmstorf, M. Perrette, and M. Vermeer, 2013: Predictability of wave climate: Characterization and application to coastal processes. J. Coast. 20th century sea-level rise from past data. Environ. Res. Lett., 8, 014013. Res., 24, 1022 1035. Bjork, A. A., et al., 2012: An aerial view of 80 years of climate-related glacier Allan, J. C., and P. D. Komar, 2006: Climate controls on US West Coast erosion fluctuations in southeast Greenland. Nature Geosci., 5, 427 432. processes. J. Coast. Res., 22, 511 529. Blum, M. D., and H. H. Roberts, 2009: Drowning of the Mississippi Delta due to Allen, M. R., D. J. Frame, C. Huntingford, C. D. Jones, J. A. Lowe, M. Meinshausen, insufficient sediment supply and global sea-level rise. Nature Geosci., 2, 488 and N. Meinshausen, 2009: Warming caused by cumulative carbon emissions 491. towards the trillionth tonne. Nature, 458, 1163 1166. Boening, C., J. K. Willis, F. W. Landerer, R. S. Nerem, and J. Fasullo, 2012: The 2011 La Alley, R. B., S. Anandakrishnan, T. K. Dupont, B. R. Parizek, and D. Pollard, 2007: Nina: So strong, the oceans fell. Geophys. Res. Lett., 39, L19602. Effect of sedimentation on ice-sheet grounding-line stability. Science, 315, Boretti, A., 2011: The measured rate of rise of sea levels is not increasing and climate 1838 1841. models should be revised to match the experimental evidence. R. Soc. Publish. Andrade, C., H. O. Pires, R. Taborda, and M. C. Freitas, 2007: Projecting future changes eLett., July 12, 2011. in wave climate and coastal response in Portugal by the end of the 21st century. Boretti, A., 2012a: Short term comparison of climate model predictions and satellite J. Coast. Res., SI 50, 263 257. altimeter measurements of sea levels. Coast. Eng., 60, 319 322. Anschütz, H., et al., 2009: Revisiting sites of the South Pole Queen Maud Land Boretti, A., 2012b: Is there any support in the long term tide gauge data to the Traverses in East Antarctica: Accumulation data from shallow firn cores. J. claims that parts of Sydney will be swamped by rising sea levels? Coast. Eng., Geophys. Res. Atmos., 114, D012204. 64, 161 167. Arendt, A., et al., 2012: Randolph Glacier Inventory [v2.0]: A dataset of global glacier Boretti, A. A., 2012c: Discussion of Natalya N. Warner, Philippe E. Tissot, Storm outlines. Global Land Ice Measurements from Space, Boulder CO, USA. Digital flooding sensitivity to sea level rise for Galveston Bay, Texas , Ocean Eng. 44 Media. (2012), 23 32. Ocean Eng., 55, 235 237. Arthern, R., D. P. Winebrenner, and D. G. Vaughan, 2006: Antarctic snow accumulation Boretti, A., and T. Watson, 2012: The inconvenient truth: Ocean levels are not mapped using polarization of 4.3 cm wavelength microwave emission. J. accelerating in Australia or over the world. Energy Environ., 23, 801 817. Geophys. Res. Atmos., 111, D06107. Boretti, A., 2013a: Discussion of Christine C. Shepard, Vera N. Agostini, Ben Gilmer, Bahr, D. B., and V. Radiæ, 2012: Significant contribution to total mass from very small Tashya Allen, Jeff Stone, William Brooks and Michael W. Beck. Reply: Evaluating glaciers. Cryosphere, 6, 763 770. alternative future sea-level rise scenarios, Nat. Hazards, 2012 doi:10.1007/ Bahr, D. B., M. Dyurgerov, and M. F. Meier, 2009: Sea-level rise from glaciers and ice s11069-012-0160 2. Nat. Hazards, 65, 967 975. caps: A lower bound. Geophys. Res. Lett., 36, L03501. Boretti, A., 2013b: Discussion of J.A.G. Cooper, C. Lemckert, Extreme sea level rise Bales, R. C., et al., 2009: Annual accumulation for Greenland updated using ice core and adaptation options for coastal resort cities: A qualitative assessment from data developed during 2000 2006 and analysis of daily coastal meteorological the Gold Coast, Australia. Ocean Coast. Manage., 78, 132 135. data. J. Geophys. Res. Atmos., 114, D06116. Boretti, A. A.,, 2013c: Discussion of Dynamic System Model to Predict Global Sea- Bamber, J., and R. Riva, 2010: The sea level fingerprint of recent ice mass fluxes. Level Rise and Temperature Change by Mustafa M. Aral, Jiabao Guan, and Biao Cryosphere, 4, 621 627. Chang. J. Hydrol. Eng., 18, 370 372. Bamber, J. L., and W. P. Aspinall, 2013: An expert judgement assessment of future sea Bougamont, M., et al., 2007: The impact of model physics on estimating the surface level rise from the ice sheets. Nature Clim. Change, 3, 424 427. mass balance of the Greenland ice sheet. Geophys. Res. Lett., 34, L17501. Bamber, J. L., R. E. M. Riva, B. L. A. Vermeersen, and A. M. LeBrocq, 2009: Reassessment Bouttes, N., J. M. Gregory, and J. A. Lowe, 2013: The reversibility of sea-level rise. J. of the potential sea-level rise from a collapse of the West Antarctic Ice Sheet. Clim., 26, 2502 2513. Science, 324, 901 903. Box, J. E., 2002: Survey of Greenland instrumental temperature records: 1873 2001. Bamber, J. L., et al., 2013: A new bed elevation dataset for Greenland. Cryosphere, Int. J. Climatol., 22, 1829 1847. 7, 499 510. Box, J. E., 2013: Greenland ice sheet mass balance reconstruction. Part II: Surface Banks, H. T., and J. M. Gregory, 2006: Mechanisms of ocean heat uptake in a coupled mass balance (1840 2010). J. Clim., 26, 6974-6989. climate model and the implications for tracer based predictions of ocean heat Box, J. E., and W. Colgan, 2013: Greenland ice sheet mass balance reconstruction. uptake. Geophys. Res. Lett., 33, L07608. Part III: Marine ice loss and total mass balance (1840 2010). J. Clim., 26, 6990- Barrand, N. E., et al., 2013: Computing the volume response of the Antarctic 7002. Peninsula ice sheet to warming scenarios to 2200. J. Glaciol., 55, 397 409. Box, J. E., L. Yang, D. H. Bromwich, and L. S. Bai, 2009: Greenland ice sheet surface air Beckley, B. D., et al., 2010: Assessment of the Jason-2 Extension to the TOPEX/ temperature variability: 1840 2007. J. Clim., 22, 4029 4049. Poseidon, Jason-1 sea-surface height time series for global mean sea level Box, J. E., X. Fettweis, J. C. Stroeve, M. Tedesco, D. K. Hall, and K. Steffen, 2012: monitoring. Mar. Geodesy, 33, 447 471. Greenland ice sheet albedo feedback: Thermodynamics and atmospheric drivers. Bengtsson, L., S. Koumoutsaris, and K. Hodges, 2011: Large-scale surface mass Cryosphere, 6, 821 839. balance of ice sheets from a comprehensive atmosphere model. Surv. Geophys., Box, J. E., et al., 2013: Greenland ice sheet mass balance reconstruction. Part I: Net 32, 459 474. snow accumulation (1600 2009). J. Clim., 26, 3919-3934. Biancamaria, S., A. Cazenave, N. M. Mognard, W. Llovel, and F. Frappart, 2011: Bracegirdle, T. J., W. M. Connolley, and J. Turner, 2008: Antarctic climate change over 13 Satellite-based high latitude snow volume trend, variability and contribution to the twenty first century. J. Geophys. Res. Atmos., 113, D03103. sea level over 1989/2006. Global Planet. Change, 75, 99 107. Braithwaite, R. J., and O. B. Olesen, 1989: Calculation of glacier ablation from air Bindoff, N. L., et al., 2007: Observations: Oceanic climate change and sea level. temperature, West Greenland. In: Glacier Fluctuations and Climatic Change [J. In: Climate Change 2007: The Physical Science Basis. Contribution of Working Oerlemans (ed.)]. Kluwer Academic, Dordrecht, Netherlands, pp. 219 233. Group I to the Fourth Assessment Report of the Intergovernmental Panel on Brierley, C., M. Collins, and A. Thorpe, 2010: The impact of perturbations to ocean- Climate Change [Solomon, S., D. Qin, M. Manning, Z. Chen, M. Marquis, K. B. model parameters on climate and climate change in a coupled model. Clim. Averyt, M. Tignor and H. L. Miller (eds.)] Cambridge University Press, Cambridge, Dyn., 34, 325 343. United Kingdom and New York, NY, USA, pp. 385 432. Broerse, D. B. T., L. L. A. Vermeersen, R. E. M. Riva, and W. van der Wal, 2011: Ocean Bindschadler, R. A., et al., 2013: Ice-sheet model sensitivities to environmental contribution to co-seismic crustal deformation and geoid anomalies: Application forcing and their use in projecting future sea level (The SeaRISE Project). J. to the 2004 December 26 Sumatra-Andaman earthquake. Earth Planet. Sci. Lett., Glaciol., 59, 195 224. 305, 341 349. 1206 Sea Level Change Chapter 13 Brohan, P., J. J. Kennedy, I. Harris, S. F. B. Tett, and P. D. Jones, 2006: Uncertainty Church, J. A., et al., 2001: Changes in sea level. Climate Change 2001: The Scientific estimates in regional and global observed temperature changes: A new data set Basis. Contribution of Working Group I to the Third Assessment Report of the from 1850. J. Geophys. Res. Atmos., 111, D12106. Intergovernmental Panel on Climate Change [J. T. Houghton, Y. Ding, D. J. Griggs, Bromwich, D. H., J. P. Nicolas, and A. J. Monaghan, 2011: An assessment of M. Noquer, P. J. van der Linden, X. Dai, K. Maskell and C. A. Johnson (eds.)]. precipitation changes over Antarctica and the Southern Ocean since 1989 in Cambridge University Press, Cambridge, United Kingdom and New York, NY, contemporary global reanalyses. J. Clim., 24, 4189 4209. USA, pp. 639 693. Bromwich, D. H., R. L. Fogt, K. I. Hodges, and J. E. Walsh, 2007: A tropospheric Church, J. A., et al., 2011b: Revisiting the Earth s sea-level and energy budgets from assessment of the ERA-40, NCEP, and JRA-25 global reanalyses in the polar 1961 to 2008. Geophys. Res. Lett., 38, L18601. regions. J. Geophys. Res. Atmos., 112, D10111. Chylek, P., J. E. Box, and G. Lesins, 2004: Global warming and the Greenland ice Brown, J., A. Souza, and J. Wolf, 2010: Surge modelling in the eastern Irish Sea: sheet. Clim. Change, 63, 201 221. Present and future storm impact. Ocean Dyn., 60, 227 236. Clark, J. A., and C. S. Lingle, 1977: Future sea-level changes due to West Antarctic ice Burgess, E. W., R. R. Forster, J. E. Box, E. Mosley-Thompson, D. H. Bromwich, R. C. sheet fluctuations. Nature, 269, 206 209. Bales, and L. C. Smith, 2010: A spatially calibrated model of annual accumulation Clarke, P. J., D. A. Lavallee, G. Blewitt, T. M. van Dam, and J. M. Wahr, 2005: Effect rate on the Greenland Ice Sheet (1958 2007). Journal of Geophys. Res. Earth of gravitational consistency and mass conservation on seasonal surface mass Surf., 115, F02004. loading models. Geophys. Res. Lett., 32, L08306. Caires, S., J. Groeneweg, and A. Sterl, 2008: Past and future changes in North Sea Cogley, G., 2009a: Geodetic and direct mass-balance measurements: Comparison extreme waves. In: Proceedings of the 31st International Conference on Coastal and joint analysis. Ann. Glaciol., 50, 96 100. Engineering, Vols. 1-5 [J.M. Smith (ed.)], World Scientific Publishing Company, Cogley, G., 2012: The future of the world s glaciers. In: Future Climates of the World, Singapore, pp. 547 559. 2nd ed. [A. Henderson-Sellers and K. McGuffie (eds.)]. Elsevier, Amsterdam, Cane, M. A., 1989: A mathmatical note on Kawase study of deep ocean. J. Phys. Netherlands, and Philadelphia, PA, USA, pp. 197 222. Oceanogr., 19, 548 550. Cogley, J. G., 2009b: A more complete version of the World Glacier Inventory. Ann. Carton, J. A., B. S. Giese, and S. A. Grodsky, 2005: Sea level rise and the warming of Glaciol., 50, 32 38. the oceans in the Simple Ocean Data Assimilation (SODA) ocean reanalysis. J. Colberg, F., and K. L. McInnes, 2012: The impact of future changes in weather Geophys. Res. Oceans, 110, C09006. patterns on extreme sea levels over southern Australia. J. Geophys. Res. Oceans, Cayan, D., P. Bromirski, K. Hayhoe, M. Tyree, M. Dettinger, and R. Flick, 2008: Climate 117, C08001. change projections of sea level extremes along the California coast. Clim. Coles, S. G., and J. A. Tawn, 1990: Statistics of coastal flood prevention. Philos. Trans. Change, 87, 57 73. R. Soc. London A, 332, 457 476. Cazenave, A., et al., 2009: Sea level budget over 2003 2008: A reevaluation from Connolley, W. M., and T. J. Bracegirdle, 2007: An Antarctic assessment of IPCC AR4 GRACE space gravimetry, satellite altimetry and Argo. Global Planet. Change, coupled models. Geophys. Res. Lett., 34, L22505. 65, 83 88. Conrad, C. P., and B. H. Hager, 1997: Spatial variations in the rate of sea level rise Cazenave, A., et al., 2012: Estimating ENSO influence on the global mean sea level, caused by the present-day melting of glaciers and ice sheets. Geophys. Res. Lett., 1993 2010. Mar. Geodesy, 35 (SI1), 82 97. 24, 1503 1506. Chambers, D. P., 2006: Evaluation of new GRACE time variable gravity data over the Cook, A. J., and D. G. Vaughan, 2010: Overview of areal changes of the ice shelves on ocean. Geophys. Res. Lett., 33, L17603. the Antarctic Peninsula over the past 50 years. Cryosphere, 4, 77 98. Chambers, D. P., J. Wahr, and R. S. Nerem, 2004: Preliminary observations of global Cornford, S. L., et al., 2013: Adaptive mesh, finite volume modeling of marine ice ocean mass variations with GRACE. Geophys. Res. Lett., 31, L13310. sheets. J. Comput. Phys., 232, 529 549. Chambers, D. P., M. A. Merrifield, and R. S. Nerem, 2012: Is there a 60-year oscillation Crowley, T., 2000: Causes of climate change over the past 1000 years. Science, 289, in global mean sea level? Geophys. Res. Lett., 39, L18607. 270 277. Chambers, D. P., J. M. Wahr, M. Tamisiea, and R. S. Nerem, 2010: Ocean mass from Crowley, T. J., S. K. Baum, K.-Y. Kim, G. C. Hegerl, and W. T. Hyde, 2003: Modeling GRACE and glacial isostatic adjustment. J. Geophys. Res., 115, B11415. ocean heat content changes during the last millennium. Geophys. Res. Lett., Chao, B. F., Y. H. Wu, and Y. S. Li, 2008: Impact of artificial reservoir water impoundment 30, 1932. on global sea level. Science, 320, 212 214. Debernard, J. B., and L. P. Red, 2008: Future wind, wave and storm surge climate in Charbit, S., D. Paillard, and G. Ramstein, 2008: Amount of CO2 emissions irreversibly the Northern Seas: A revisit. Tellus A, 60, 427 438. leading to the total melting of Greenland. Geophys. Res. Lett., 35, L12503. Delworth, T. L., and T. R. Knutson, 2000: Simulation of early 20th century global Charles, E. D., D. Idier, P. Delecluse, M. Deque, and G. Le Cozannet, 2012: Climate warming. Science, 287, 2246 2250. change impact on waves in the Bay of Biscay, France. Ocean Dyn., 62, 831 848. Delworth, T. L., V. Ramaswamy, and G. L. Stenchikov, 2005: The impact of aerosols Christensen, J. H., et al., 2007: Regional climate projections. In: Climate Change on simulated ocean temperature and heat content in the 20th century. Geophys. 2007: The Physical Science Basis. Contribution of Working Group I to the Res. Lett., 32, L24709. Fourth Assessment Report of the Intergovernmental Panel on Climate Change Di Lorenzo, E., et al., 2010: Central Pacific El Nino and decadal climate change in the [Solomon, S., D. Qin, M. Manning, Z. Chen, M. Marquis, K. B. Averyt, M. Tignor North Pacific Ocean. Nature Geosci., 3, 762 765. and H. L. Miller (eds.)] Cambridge University Press, Cambridge, United Kingdom Dobrynin, M., J. Murawsky, and S. Yang, 2012: Evolution of the global wind wave and New York, NY, USA, pp. 849 925. climate in CMIP5 experiments. Geophys. Res. Lett., 39, L18606. Christoffersen, P., et al., 2011: Warming of waters in an East Greenland fjord prior Dolan, A. M., A. M. Haywood, D. J. Hill, H. J. Dowsett, S. J. Hunter, D. J. Lunt, and S. J. to glacier retreat: Mechanisms and connection to large-scale atmospheric Pickering, 2011: Sensitivity of Pliocene ice sheets to orbital forcing. Palaeogeogr. conditions. Cryosphere, 5, 701 714. Palaeoclimatol. Palaeoecol., 309, 98 110. Church, J. A., and N. J. White, 2006: A 20th century acceleration in global sea-level Domingues, C. M., J. A. Church, N. J. White, P. J. Gleckler, S. E. Wijffels, P. l. M. Barker, rise. Geophys. Res. Lett., 33, L01602. and J. R. Dunn, 2008: Improved estimates of upper-ocean warming and multi- Church, J. A., and N. J. White, 2011: Sea-level rise from the late 19th to the early 21st decadal sea-level rise. Nature, 453, 1090 1093. 13 century. Surv. Geophys., 32, 585 602. Donnelly, J. P., P. Cleary, P. Newby, and R. Ettinger, 2004: Coupling instrumental and Church, J. A., N. J. White, and J. M. Arblaster, 2005: Significant decadal-scale impact geological records of sea-level change: Evidence from southern New England of of volcanic eruptions on sea level and ocean heat content. Nature, 438, 74 77. an increase in the rate of sea-level rise in the late 19th century. Geophys. Res. Church, J. A., P. L. Woodworth, T. Aarup, and W. S. Wilson, (eds.) 2010: Understanding Lett., 31, L05203. Sea-Level Rise and Variability. Wiley-Blackwell, Hoboken, NJ, USA, 428 pp. Douglas, B. C., 2001: Sea level change in the era of the recording tide gauge. In: Sea Church, J. A., D. Monselesan, J. M. Gregory, and B. Marzeion, 2013: Evaluating the Level Rise, History and Consequences. International Geophysics Series, Volume ability of process based models to project sea-level change. Environ. Res. Lett., 75 [B. Douglas, M. S. Kearney and S. P. Leatherman (eds.)]. Academic Press, San 8, 015051. Diego, CA, USA, pp. 37 64. Church, J. A., J. M. Gregory, N. J. White, S. M. Platten, and J. X. Mitrovica, 2011a: Driesschaert, E., et al., 2007: Modeling the influence of Greenland ice sheet melting Understanding and projecting sea level change. Oceanography, 24, 130 143. on the Atlantic meridional overturning circulation during the next millennia. Geophys. Res. Lett., 34, L10707. 1207 Chapter 13 Sea Level Change Dufresne, J. L., and S. Bony, 2008: An assessment of the primary sources of spread Franco, B., X. Fettweis, M. Erpicum, and S. Nicolay, 2011: Present and future climates of global warming estimates from coupled atmosphere-ocean models. J. Clim., of the Greenland ice sheet according to the IPCC AR4 models. Clim. Dyn., 36, 21, 5135 5144. 1897 1918. Dupont, T. K., and R. B. Alley, 2005: Assessment of the importance of ice-shelf Fretwell, P., et al., 2013: Bedmap2: Improved ice bed, surface and thickness datasets buttressing to ice-sheet flow. Geophys. Res. Lett., 32, L04503. for Antarctica. Cryosphere, 7, 375 393. Durack, P. J., and S. E. Wijffels, 2010: Fifty-year trends in global ocean salinities and Fyke, J. G., L. Carter, A. Mackintosh, A. J. Weaver, and K. J. Meissner, 2010: Surface their relationship to broad-scale warming. J. Clim., 23, 4342 4362. melting over ice shelves and ice sheets as assessed from modeled surface air Durand, G., O. Gagliardini, T. Zwinger, E. Le Meur, and R. C. A. Hindmarsh, 2009: Full temperatures. J. Clim., 23, 1929 1936. Stokes modeling of marine ice sheets: Influence of the grid size. Ann. Glaciol., Gardner, A. S., et al., 2013: A reconciled estimate of glacier contributions to sea level 50, 109 114. rise: 2003 to 2009. Science, 340, 852-857. Dutton, A., and K. Lambeck, 2012: Ice volume and sea level during the last Gehrels, R., and P. L. Woodworth, 2013: When did modern rates of  sea-level rise interglacial. Science, 337, 216 219. start? Global Planet. Change, 100, 263 277. Dyurgerov, M. B., and M. F. Meier, 2005: Glaciers and the changing Earth system: Gehrels, W. R., B. Hayward, R. M. Newnham, and K. E. Southall, 2008: A 20th century A 2004 snapshot. Occasional Paper. Institute of Arctic and Alpine Research, acceleration of sea-level rise in New Zealand. Geophys. Res. Lett., 35, L02717. University of Colorado, Boulder,CO, USA. Gehrels, W. R., D. A. Dawson, J. Shaw, and W. A. Marshall, 2011: Using Holocene Easterling, D. R., and M. F. Wehner, 2009: Is the climate warming or cooling? relative sea-level data to inform future sea-level predictions: An example from Geophys. Res. Lett., 36, L08706. southwest England. Global Planet. Change, 78, 116 126. Ettema, J., M. R. van den Broeke, E. van Meijgaard, W. J. van de Berg, J. L. Bamber, Gehrels, W. R., et al., 2005: Onset of recent rapid sea-level rise in the western Atlantic J. E. Box, and R. C. Bales, 2009: Higher surface mass balance of the Greenland Ocean. Quat. Sci. Rev., 24, 2083 2100. ice sheet revealed by high-resolution climate modeling. Geophys. Res. Lett., 36, Gehrels, W. R., et al., 2006: Rapid sea-level rise in the North Atlantic Ocean since the L12501.    first half of the nineteenth century. Holocene, 16, 949 965. Fan, Y., I. M. Held, S. J. Lin, and X. and Wang, 2013: Ocean warming effect on surface Gehrels, W. R., et al., 2012: Nineteenth and twentieth century sea-level changes in gravity wave climate change for the end of the 21st century. J. Clim., 26, 6046- Tasmania and New Zealand. Earth Planet. Sci. Lett., 315, 94 102. 6066. Gent, P. R., and G. Danabasoglu, 2011: Response to increasing southern hemisphere Farneti, R., and P. R. Gent, 2011: The effects of the eddy-induced advection coefficient winds in CCSM4. J. Clim., 24, 4992 4998. in a coarse-resolution coupled climate model. Ocean Model., 39, 135 145. Genthon, C., G. Krinner, and H. Castebrunet, 2009: Antarctic precipitation and Farneti, R., T. L. Delworth, A. J. Rosati, S. M. Griffies, and F. Zeng, 2010: The role of climate-change predictions: Horizontal resolution and margin vs plateau issues. mesoscale eddies in the rectification of the Southern Ocean response to climate Ann. Glaciol., 50, 55 60. change. J. Phys. Oceanogr., 40, 1539 1557. Geoffroy, O., D. Saint-Martin, and A. Ribes, 2012: Quantifying the source of spread in Farrell, W. E., and J. A. Clark, 1976: On postglacial sea level. Geophys. J. R. Astron. climate change experiments. Geophys. Res. Lett., 39, L24703. Soc., 46, 647 667. Geoffroy, O., D. Saint-Martin, D. J. L. Olivie, A. Voldoire, G. Belon, and S. Tyteca, 2013: Fausto, R. S., A. P. Ahlstrom, D. van As, S. J. Johnsen, P. L. Langen, and K. Steffen, Transient climate response in a two-box energy-balance model.  Part I: Analytical 2009: Improving surface boundary conditions with focus on coupling snow solution and parameter calibration using CMIP5. J. Clim., 26, 1841-1857. densification and meltwater retention in large-scale ice-sheet models of Giesen, R. H., and J. Oerlemans, 2013: Climate-model induced differences in the 21st Greenland. J. Glaciol., 55, 869 878. century global and regional glacier contributions to sea-level rise. Clim. Dyn., Favier, L., O. Gagliardini, G. Durand, and T. Zwinger, 2012: A three-dimensional full 41, 3283 3300. Stokes model of the grounding line dynamics: Effect of a pinning point beneath Gillet-Chaulet, F., et al., 2012: Greenland ice sheet contribution to sea-level rise from the ice shelf. Cryosphere, 6, 101 112. a new-generation ice-sheet model. Cryosphere, 6, 1561 1576. Fettweis, X., E. Hanna, H. Gallee, P. Huybrechts, and M. Erpicum, 2008: Estimation of Gillett, N. P., V. K. Arora, K. Zickfeld, S. J. Marshall, and A. J. Merryfield, 2011: Ongoing the Greenland ice sheet surface mass balance for the 20th and 21st centuries. climate change following a complete cessation of carbon dioxide emissions. Cryosphere, 2, 117 129. Nature Geosci., 4, 83 87. Fettweis, X., A. Belleflame, M. Erpicum, B. Franco, and S. Nicolay, 2011: Estimation of Gladstone, R. M., et al., 2012: Calibrated prediction of Pine Island Glacier retreat the sea level rise by 2100 resulting from changes in the surface mass balance of during the 21st and 22nd centuries with a coupled flowline model. Earth Planet. the Greenland ice sheet. Clim. Change Geophys. Found. Ecol. Effects [J. Blanco Sci. Lett., 333, 191 199. and H. Kheradmand (eds.)]. Croatia: Intech, pp. 503 520. Gleckler, P. J., K. AchutaRao, J. M. Gregory, B. D. Santer, K. E. Taylor, and T. M. L. Wigley, Fettweis, X., B. Franco, M. Tedesco, J. H. van Angelen, J. T. M. Lenaerts, M. R. van 2006a: Krakatoa lives: The effect of volcanic eruptions on ocean heat content den Broeke, and H. Gallee, 2013: Estimating Greenland ice sheet surface mass and thermal expansion. Geophys. Res. Lett., 33, L17702. balance contribution to future sea level rise using the regional atmospheric Gleckler, P. J., T. M. L. Wigley, B. D. Santer, J. M. Gregory, K. AchutaRao, and K. E. model MAR. Cryosphere, 7, 469 489. Taylor, 2006b: Volcanoes and climate: Krakatoa s signature persists in the ocean. Fiedler, J. W., and C. P. Conrad, 2010: Spatial variability of sea level rise due to water Nature, 439, 675. impoundment behind dams. Geophys. Res. Lett., 37, L12603. Gleckler, P. J., et al., 2012: Human-induced global ocean warming on multidecadal Fluckiger, J., R. Knutti, and J. W. C. White, 2006: Oceanic processes as potential trigger timescales. Nature Clim. Change, 2, 524 529. and amplifying mechanisms for Heinrich events. Paleoceanography, 21, PA2014. Goelzer, H., P. Huybrechts, M. F. Loutre, H. Goosse, T. Fichefet, and A. Mouchet, 2011: Forest, C. E., P. H. Stone, and A. P. Sokolov, 2008: Constraining climate model Impact of Greenland and Antarctic ice sheet interactions on climate sensitivity. parameters from observed 20th century changes. Tellus A, 60, 911 920. Clim. Dyn., 37, 1005 1018. Forster, P. M., T. Andrews, I. Goodwin, J. M. Gregory, L. S. Jackson, and M. Zelinka, Goelzer, H., P. Huybrechts, S. C. B. Raper, M. F. Loutre, H. Goosse, and T. Fichefet, 2012: 2013: Evaluating adjusted forcing and model spread for historical and future Millennial total sea-level commitments projected with the Earth system model 13 scenarios in the CMIP5 generation of climate models. J. Geophys. Res., 118, of intermediate complexity LOVECLIM. Environ. Res. Lett., 7, 045401. 1139 1150. Goelzer, H., et al., 2013: Sensitivity of Greenland ice sheet projections to model Foster, G. L., and E. J. Rohling, 2013: Relationship between sea level and climate formulations. J. Glaciol., 59, 733-749. forcing by CO2 on geological timescales. Proc. Natl. Acad. Sci. U.S.A., 110, 1209 Goldberg, D., D. M. Holland, and C. Schoof, 2009: Grounding line movement and ice 1214. shelf buttressing in marine ice sheets. J. Geophys. Res. Earth Surf., 114, F04026. Francis, O. P., G. G. Panteleev, and D. E. Atkinson, 2011: Ocean wave conditions in Goldberg, D. N., C. M. Little, O. V. Sergienko, A. Gnanadesikan, R. Hallberg, and M. the Chukchi Sea from satellite and in situ observations. Geophys. Res. Lett., 38, Oppenheimer, 2012: Investigation of land ice-ocean interaction with a fully L24610. coupled ice-ocean model: 2. Sensitivity to external forcings. J. Geophys. Res. Franco, B., X. Fettweis, and M. Erpicum, 2013: Future projections of the Greenland ice Earth Surf., 117, F02038. sheet energy balance driving the surface melt. Cryosphere, 7, 1 18. Gomez, N., J. X. Mitrovica, P. Huybers, and P. U. Clark, 2010a: Sea level as a stabilizing factor for marine-ice-sheet grounding lines. Nature Geosci., 3, 850 853. 1208 Sea Level Change Chapter 13 Gomez, N., J. X. Mitrovica, M. E. Tamisiea, and P. U. Clark, 2010b: A new projection Hanna, E., S. H. Mernild, J. Cappelen, and K. Steffen, 2012: Recent warming in of sea level change in response to collapse of marine sectors of the Antarctic Ice Greenland in a long-term instrumental (1881 2012) climatic context: I. Sheet. Geophys. J. Int., 180, 623 634. Evaluation of surface air temperature records. Environ. Res. Lett., 7, 045404. Good, P., J. M. Gregory, and J. A. Lowe, 2011: A step-response simple climate model Hanna, E., P. Huybrechts, I. Janssens, J. Cappelen, K. Steffen, and A. Stephens, 2005: to reconstruct and interpret AOGCM projections. Geophys. Res. Lett., 38, L01703. Runoff and mass balance of the Greenland ice sheet: 1958 2003. J. Geophys. Good, P., J. M. Gregory, J. A. Lowe, and T. Andrews, 2013: Abrupt CO2 experiments Res. Atmos., 110, D13108. as tools for predicting and understanding CMIP5 representative concentration Hanna, E., et al., 2008: Increased runoff from melt from the Greenland Ice Sheet: A pathway projections. Clim. Dyn., 40, 1041 1053. response to global warming. J. Clim., 21, 331 341. Goosse, H., H. Renssen, A. Timmermann, and R. S. Bradley, 2005: Internal and forced Hanna, E., et al., 2011: Greenland Ice Sheet surface mass balance 1870 to 2100 climate variability during the last millennium: A model-data comparison using based on twentieth century reanalysis, and links with global climate forcing. J. ensemble simulations. Quat. Sci. Rev., 24, 1345 1360. Geophys. Res., 116, D24121. Gornitz, V., 2001: Impoundment, groundwater mining, and other hydrologic Hansen, J., M. Sato, P. Kharecha, and K. von Schuckmann, 2011: Earth s energy transformations: Impacts on global sea level rise. Sea Level Rise, History and imbalance and implications. Atmos. Chem. Phys., 11, 13421 13449. Consequences. International Geophysics Series, Volume 75 [B. Douglas, M. S. Hansen, J., G. Russell, A. Lacis, I. Fung, D. Rind, and P. Stone, 1985: Climate response Kearney and S. P. Leatherman (eds.)]. Academic Press, San Diego, CA, USA, pp. times dependence on climate sensitivity and ocean mixing. Science, 229, 97 119. 857 859. Gouretski, V., and K. P. Koltermann, 2007: How much is the ocean really warming? Hansen, J., M. Sato, P. Kharecha, G. Russell, D. Lea, and M. Siddall, 2007: Climate Geophys. Res. Lett., 34, L01610. change and trace gases. Philos. Trans. R. Soc. London A, 365, 1925 1954. Gower, J. F. R., 2010: Comment on Response of the global ocean to Greenland and Hansen, J., et al., 2005: Earth s energy imbalance: Confirmation and implications. Antarctic ice melting by D. Stammer. J. Geophys. Res. Oceans, 115, C10009. Science, 308, 1431 1435. Grabemann, I., and R. Weisse, 2008: Climate change impact on extreme wave Harper, B., T. Hardy, L. Mason, and R. Fryar, 2009: Developments in storm tide conditions in the North Sea: An ensemble study. Ocean Dyn., 58, 199 212. modelling and risk assessment in the Australian region. Nat. Hazards, 51, 225 Graham, N. E., D. R. Cayan, P. Bromirski, and R. Flick, 2013: Multi-model projections 238. of 21st century North Pacific winter wave climate under the IPCC A2 scenario. Harper, J., N. Humphrey, W. T. Pfeffer, J. Brown, and X. Fettweis, 2012: Greenland Clim. Dyn., 40, 1335 1360. ice-sheet contribution to sea-level rise buffered by meltwater storage in firn. Graversen, R. G., S. Drijfhout, W. Hazeleger, R. van de Wal, R. Bintanja, and M. Helsen, Nature, 491, 240-243. 2011: Greenland s contribution to global sea level rise by the end of the 21st Hay, C. C., E. Morrow, R. E. Kopp, and J. X. Mitrovica, 2013: Estimating the sources century. Clim. Dyn., 37, 1427 1442. of global sea level rise with data assimilation techniques. Proc. Natl. Acad. Sci. Greatbatch, R. J., 1994: A note on the representation of steric sea-levels in models U.S.A., 110, 3692 3699. that conserve volume rather than mass. J. Geophys. Res. Oceans, 99, 12767 Hegerl, G. C., et al., 2007: Understanding and attributing climate change. In: Climate 12771. Change 2007: The Physical Science Basis. Contribution of Working Group I to the Gregory, J. M., 2000: Vertical heat transports in the ocean and their effect on time- Fourth Assessment Report of the Intergovernmental Panel on Climate Change dependent climate change. Clim. Dyn., 16, 501 515. [Solomon, S., D. Qin, M. Manning, Z. Chen, M. Marquis, K. B. Averyt, M. Tignor Gregory, J. M., 2010: Long-term effect of volcanic forcing on ocean heat content. and H. L. Miller (eds.)]. Cambridge University Press, Cambridge, United Kingdom Geophys. Res. Lett., 37, L22701. and New York, NY, USA, pp. 663 745. Gregory, J. M., and J. A. Lowe, 2000: Predictions of global and regional sea-level Held, I. M., and B. J. Soden, 2006: Robust responses of the hydrological cycle to rise using AOGCMs with and without flux adjustment. Geophys. Res. Lett., 27, global warming. J. Clim., 19, 5686 5699. 3069 3072. Held, I. M., M. Winton, K. Takahashi, T. Delworth, F. R. Zeng, and G. K. Vallis, 2010: Gregory, J. M., and P. Huybrechts, 2006: Ice-sheet contributions to future sea-level Probing the fast and slow components of global warming by returning abruptly change. Philos. R. Soc. London A, 364, 1709 1731. to preindustrial forcing. J. Clim., 23, 2418 2427. Gregory, J. M., and P. M. Forster, 2008: Transient climate response estimated from Hellmer, H. H., F. Kauker, R. Timmermann, J. Determann, and J. Rae, 2012: Twenty- radiative forcing and observed temperature change. J.  Geophys. Res. Atmos., first-century warming of a large Antarctic ice-shelf cavity by a redirected coastal 113, D23105. current. Nature, 485, 225 228. Gregory, J. M., J. A. Lowe, and S. F. B. Tett, 2006: Simulated global-mean sea-level Hemer, M. A., J. A. Church, and J. R. Hunter, 2010: Variability and trends in the changes over the last half-millennium. J. Clim., 19, 4576 4591. directional wave climate of the Southern Hemisphere. Int. J. Climatol., 30, 475 Gregory, J. M., et al., 2013a: Climate models without pre-industrial volcanic forcing 491. underestimate historical ocean thermal expansion. Geophys. Res. Lett., 40, 1 5. Hemer, M. A., J. Katzfey, and C. Trenham, 2012a: Global dynamical projections of Gregory, J. M., et al., 2013b: Twentieth-century global-mean sea level rise: Is the surface ocean wave climate for a future high greenhouse gas emission scenario. whole greater than the sum of the parts? J. Clim., 26, 4476-4499. Ocean Model., 70, 221-245. Greve, R., 2000: On the response of the Greenland ice sheet to greenhouse climate Hemer, M. A., K. L. McInnes, and R. Ranasinghe, 2012b: Projections of climate change- change. Clim. Change, 46, 289 303. driven variations in the offshore wave climate off southeastern Australia. Int. J. Griffies, S. M., and R. J. Greatbatch, 2012: Physical processes that impact the Climatol., 33, 1615-1632. evolution of global mean sea level in ocean climate models. Ocean Model., 51, Hemer, M. A., Y. Fan, N. Mori, A. Semedo, and X. L. Wang, 2013: Projected future 37 72. changes in wind-wave climate in a multi-model ensemble. Nature Clim. Change, Grinsted, A., J. C. Moore, and S. Jevrejeva, 2010: Reconstructing sea level from paleo 3, 471 476. and projected temperatures 200 to 2100 AD. Clim. Dyn., 34, 461 472. Hill, D. J., A. M. Dolan, A. M. Haywood, S. J. Hunter, and D. K. Stoll, 2010: Sensitivity Gudmundsson, G. H., J. Krug, G. Durand, F. L., and O. Gagliardini, 2012: The stability of the Greenland ice sheet to Pliocene sea surface temperatures. Stratigraphy, of grounding lines on retrograde slopes. Cryosphere Discuss., 6, 2597 2619. 7, 111 121. 13 Hallberg, R., and A. Gnanadesikan, 2006: The role of eddies in determining the Hindmarsh, R. C. A., 1993: Qualitative dynamics of marine ice sheets. Ice Clim. Syst. structure and response of the wind-driven southern hemisphere overturning: I, 12, 68 99. Results from the Modeling Eddies in the Southern Ocean (MESO) project. J. Phys. Holgate, S., S. Jevrejeva, P. Woodworth, and S. Brewer, 2007: Comment on A semi- Oceanogr., 36, 2232 2252. empirical approach to projecting future sea-level rise . Science, 317, 2. Hallberg, R., A. Adcroft, J. Dunne, J. Krasting, and R. J. Stouffer, 2013: Sensitivity of Holgate, S. J., 2007: On the decadal rates of sea level change during the twentieth 21st century global-mean steric sea level rise to ocean model formulation. J. century. Geophys. Res. Lett., 34, L01602. Clim., 26, 2947-2956. Holland, D. M., R. H. Thomas, B. De Young, M. H. Ribergaard, and B. Lyberth, 2008: Han, W. Q., et al., 2010: Patterns of Indian Ocean sea-level change in a warming Acceleration of Jakobshavn Isbrae triggered by warm subsurface ocean waters. climate. Nature Geosci., 3, 546 550. Nature Geosci., 1, 659 664. 1209 Chapter 13 Sea Level Change Horton, R., C. Herweijer, C. Rosenzweig, J. P. Liu, V. Gornitz, and A. C. Ruane, 2008: Joughin, I., B. E. Smith, and D. M. Holland, 2010: Sensitivity of 21st century sea level Sea level rise projections for current generation CGCMs based on the semi- to ocean-induced thinning of Pine Island Glacier, Antarctica. Geophys. Res. Lett., empirical method. Geophys. Res. Lett., 35, L02715. 37, L20502. Hu, A., G. A. Meehl, W. Han, and J. Yin, 2011: Effect of the potential melting of the Kang, S. K., J. Y. Cherniawsky, M. G. G. Foreman, H. S. Min, C. H. Kim, and H. W. Greenland Ice Sheet on the Meridional Overturning Circulation and global Kang, 2005: Patterns of recent sea level rise in the East/Japan Sea from satellite climate in the future. Deep-Sea Res. Pt. Ii, 58, 1914 1926. altimetry and in situ data. J. Geophys. Res. Oceans, 110, C07002. Hu, A. X., et al., 2010: Influence of Bering Strait flow and North Atlantic circulation Kaser, G., J. G. Cogley, M. B. Dyurgerov, M. F. Meier, and A. Ohmura, 2006: Mass on glacial sea-level changes. Nature Geosci., 3, 118 121. balance of glaciers and ice caps: Consensus estimates for 1961 2004. Geophys. Huber, M., and R. Knutti, 2012: Anthropogenic and natural warming inferred from Res. Lett., 33, L19501. changes in earth s energy balance. Nature Geosci., 5, 31 36. Kato, S., 2009: Interannual variability of the global radiation budget. J. Clim., 22, Hunter, J., 2010: Estimating sea-level extremes under conditions of uncertain sea- 4893 4907. level rise. Clim. Change, 99, 331 350. Katsman, C., W. Hazeleger, S. Drijfhout, G. Oldenborgh, and G. Burgers, 2008: Climate Hunter, J., 2012: A simple technique for estimating an allowance for uncertain sea- scenarios of sea level rise for the northeast Atlantic Ocean: A study including level rise. Clim. Change, 113, 239 252. the effects of ocean dynamics and gravity changes induced by ice melt. Clim. Hunter, J. R., and M. J. I. Brown, 2013: Discussion of Boretti, A., Is there any support Change, 91, 351 374. in the long term tide gauge data to the claims that parts of Sydney will be Katsman, C. A., and G. J. van Oldenborgh, 2011: Tracing the upper ocean s missing swamped by rising sea levels? . Coastal Eng., 75, 1 3. heat. Geophys. Res. Lett., 38, L14610. Huntington, T. G., 2008: Can we dismiss the effect of changes in land-based water Katsman, C. A., et al., 2011: Exploring high-end scenarios for local sea level rise to storage on sea-level rise? Hydrol. Proc., 22, 717 723. develop flood protection strategies for a low-lying delta - the Netherlands as an Huss, M., R. Hock, A. Bauder, and M. Funk, 2012: Conventional versus reference- example. Clim. Dyn., 109, 617 645. surface mass balance. J. Glaciol., 58, 278 286. Kawase, M., 1987: Establishment of deep ocean circulation driven by deep-water. J. Huybrechts, P., and J. De Wolde, 1999: The dynamic response of the Greenland and Phys. Oceanogr., 17, 2294 2317. Antarctic ice sheets to multiple-century climatic warming. J. Clim., 12, 2169 Kemp, A. C., B. P. Horton, J. P. Donnelly, M. E. Mann, M. Vermeer, and S. Rahmstorf, 2188. 2011: Climate related sea-level variations over the past two millennia. Proc. Huybrechts, P., H. Goelzer, I. Janssens, E. Driesschaert, T. Fichefet, H. Goosse, and M. Natl. Acad. Sci. U.S.A., 108, 11017 11022. F. Loutre, 2011: Response of the Greenland and Antarctic ice sheets to multi- Knutti, R., and L. Tomassini, 2008: Constraints on the transient climate response millennial greenhouse warming in the earth system model of intermediate from observed global temperature and ocean heat uptake. Geophys. Res. Lett., complexity LOVECLIM. Surv. Geophys., 32, 397 416. 35, L09701. Ishii, M., and M. Kimoto, 2009: Reevaluation of historical ocean heat content Knutti, R., S. Krahenmann, D. J. Frame, and M. R. Allen, 2008: Comment on Heat variations with time-varying XBT and MBT depth bias corrections. J. Oceanogr., capacity, time constant, and sensitivity of Earth s climate system by S. E. 65, 287 299. Schwartz. J. Geophys. Res. Atmos., 113, D15103. Izaguirre, C., F. J. Méndez, M. Menéndez, and I. J. Losada, 2011: Global extreme wave Kohl, A., and D. Stammer, 2008: Decadal sea level changes in the 50-year GECCO height variability based on satellite data. Geophys. Res. Lett., 38, L10607. ocean synthesis. J. Clim., 21, 1876 1890. Izaguirre, C., F. J. Méndez, M. Menéndez, A. Luceno, and I. J. Losada, 2010: Extreme Konikow, L. F., 2011: Contribution of global groundwater depletion since 1900 to wave climate variability in southern Europe using satellite data. J. Geophys. Res. sea-level rise. Geophys. Res. Lett., 38, L17401. Oceans, 115, C04009. Konikow, L. F., 2013: Comment on Model estimates of sea-level change due to Jacobs, S. S., A. Jenkins, C. F. Giulivi, and P. Dutrieux, 2011: Stronger ocean circulation anthropogenic impacts on terrestrial water storage by Pokhrel et al. Nature and increased melting under Pine Island Glacier ice shelf. Nature Geosci., 4, Geosci., 6, 2. 519 523. Kopp, R. E., F. J. Simons, J. X. Mitrovica, A. C. Maloof, and M. Oppenheimer, 2009: Jay, D. A., 2009: Evolution of tidal amplitudes in the eastern Pacific Ocean. Geophys. Probabilistic assessment of sea level during the last interglacial stage. Nature, Res. Lett., 36, L04603. 462, 863 868. Jenkins, A., and D. Holland, 2007: Melting of floating ice and sea level rise. Geophys. Kopp, R. E., F. J. Simons, J. X. Mitrovica, A. C. Maloof, and M. Oppenheimer 2013: A Res. Lett., 34, L16609. probabilistic assessment of sea level variations within the last interglacial stage. Jenkins, A., P. Dutrieux, S. S. Jacobs, S. D. McPhail, J. R. Perrett, A. T. Webb, and D. Geophys. J. Int., 193, 711 716. White, 2010: Observations beneath Pine Island Glacier in West Antarctica and Kopp, R. E., J. X. Mitrovica, S. M. Griffies, J. J. Yin, C. C. Hay, and R. J. Stouffer, 2010: implications for its retreat. Nature Geosci., 3, 468 472. The impact of Greenland melt on local sea levels: A partially coupled analysis Jevrejeva, S., A. Grinsted, and J. C. Moore, 2009: Anthropogenic forcing dominates of dynamic and static equilibrium effects in idealized water-hosing experiments. sea level rise since 1850. Geophys. Res. Lett., 36, L20706. Clim. Change, 103, 619 625. Jevrejeva, S., J. C. Moore, and A. Grinsted, 2010: How will sea level respond to Körper, J., et al., 2013: The effect of aggressive mitigation on sea level rise and sea changes in natural and anthropogenic forcings by 2100? Geophys. Res. Lett., ice changes. Clim. Dyn., 40, 531 550. 37, L07703. Kouketsu, S., et al., 2011: Deep ocean heat content changes estimated from Jevrejeva, S., J. C. Moore, and A. Grinsted, 2012a: Sea level projections to AD 2500 observation and reanalysis product and their influence on sea level change. J. with a new generation of climate change scenarios. Global Planet. Change, Geophys. Res. Oceans, 116, C03012. 80 81, 14 20. Krinner, G., O. Magand, I. Simmonds, C. Genthon, and J. L. Dufresne, 2007: Simulated Jevrejeva, S., J. C. Moore, and A. Grinsted, 2012b: Potential for bias in 21st century Antarctic precipitation and surface mass balance at the end of the twentieth and semiempirical sea level projections. J. Geophys. Res., 117, D20116. twenty-first centuries. Clim. Dyn., 28, 215 230. Jevrejeva, S., A. Grinsted, J. C. Moore, and S. Holgate, 2006: Nonlinear trends and Krinner, G., B. Guicherd, K. Ox, C. Genthon, and O. Magand, 2008: Influence of 13 multiyear cycles in sea level records. J. Geophys. Res. Oceans, 111, C09012. oceanic boundary conditions in simulations of Antarctic climate and surface Jevrejeva, S., J. C. Moore, A. Grinsted, and P. L. Woodworth, 2008: Recent global sea mass balance change during the coming century. J. Clim., 21, 938 962. level acceleration started over 200 years ago? Geophys. Res. Lett., 35, L08715. Kuhlbrodt, T., and J. M. Gregory, 2012: Ocean heat uptake and its consequences for Johansson, M. M., H. Pellikka, K. K. Kahma, and K. Ruosteenoja, 2013: Global sea the magnitude of sea level rise and climate change. Geophys. Res. Lett., 39, level rise scenarios adapted to the Finnish coast. J. Mar. Syst., 129, 35 46. L18608. Johnson, G. C., and N. Gruber, 2007: Decadal water mass variations along 20 W in Lambeck, K., and S. M. Nakiboglu, 1984: Recent global changes in sea level. Geophys. the Northeastern Atlantic Ocean. Prog. Oceanogr., 73, 277 295. Res. Lett., 11, 959 961. Johnson, G. C., S. Mecking, B. M. Sloyan, and S. E. Wijffels, 2007: Recent bottom Lambeck, K., C. Smither, and M. Ekman, 1998: Tests of glacial rebound models for water warming in the Pacific Ocean. J. Clim., 20, 5365 5375. Fennoscandinavia based on instrumented sea- and lake-level records. Geophys. Joughin, I., and R. B. Alley, 2011: Stability of the West Antarctic ice sheet in a J. Int., 135, 375 387. warming world. Nature Geosci., 4, 506 513. 1210 Sea Level Change Chapter 13 Lambeck, K., A. Purcell, and A. Dutton, 2012: The anatomy of interglacial sea levels: Llovel, W., S. Guinehut, and A. Cazenave, 2010: Regional and interannual variability The relationship between sea levels and ice volumes during the Last Interglacial. in sea level over 2002 2009 based on satellite altimetry, Argo float data and Earth Planet. Sci. Lett., 315, 4 11. GRACE ocean mass. Ocean Dyn., 60, 1193 1204. Lambeck, K., M. Anzidei, F. Antonioli, A. Benini, and A. Esposito, 2004: Sea level in Llovel, W., et al., 2011: Terrestrial waters and sea level variations on interannual time Roman time in the Central Mediterranean and implications for recent change. scale. Global Planet. Change, 75, 76 82. Earth Planet. Sci. Lett., 224, 563 575. Loeb, N. G., et al., 2009: Toward optimal closure of the earth s top-of-atmosphere Landerer, F. W., J. H. Jungclaus, and J. Marotzke, 2007: Regional dynamic and steric radiation budget. J. Clim., 22, 748 766. sea level change in response to the IPCC-A1B scenario. J. Phys. Oceanogr., 37, Loeb, N. G., et al., 2012: Observed changes in top-of-the-atmosphere radiation and 296 312. upper-ocean heating consistent within uncertainty. Nature Geosci., 5, 110 113. Langen, P. L., A. M. Solgaard, and C. S. Hvidberg, 2012: Self-inhibiting growth of the Lombard, A., G. Garric, and T. Penduff, 2009: Regional patterns of observed sea level Greenland Ice Sheet. Geophys. Res. Lett., 39, L12502. change: Insights from a 1/4A degrees global ocean/sea-ice hindcast. Ocean Leclercq, P. W., J. Oerlemans, and J. G. Cogley, 2011: Estimating the glacier Dyn., 59, 433 449. contribution to sea-level rise for the period 1800 2005. Surv. Geophys., 32, Lombard, A., A. Cazenave, P. Y. Le Traon, and M. Ishii, 2005a: Contribution of thermal 519 535. expansion to present-day sea-level change revisited. Global Planet. Change, 47, Leclercq, P. W., A. Weidick, F. Paul, T. Bolch, M. Citterio, and J. Oerlemans, 2012: 1 16. Brief communication Historical glacier length changes in West Greenland . Lombard, A., A. Cazenave, K. DoMinh, C. Cabanes, and R. S. Nerem, 2005b: Cryosphere, 6, 1339 1343. Thermosteric sea level rise for the past 50 years: Comparison with tide gauges Legg, S., et al., 2009: Improving oceanic overflow representation in climate models. and inference on water mass contribution. Global Planet. Change, 48, 303 312. Bull. Am. Meteorol. Soc., 90, 657 670. Lorbacher, K., J. Dengg, C. W. Boning, and A. Biastoch, 2010: Regional patterns of Lemieux-Dudon, B., et al., 2010: Consistent dating for Antarctic and Greenland ice sea level change related to interannual variability and multidecadal trends in the cores. Quat. Sci. Rev., 29, 8 20. Atlantic meridional overturning circulation. J. Clim., 23, 4243 4254. Lempériere, F., 2006: The role of dams in the XXI century: Achieving a sustainable Lorbacher, K., S. J. Marsland, J. A. Church, S. M. Griffies, and D. Stammer, 2012: Rapid development target. Int. J. Hydropower Dams, 13, 99 108. barotrophic sea-level rise from ice-sheet melting scenarios. J. Geophys. Res., Lenaerts, J. T. M., M. R. van den Broeke, W. J. van de Berg, E. van Meijgaard, and 117, C06003. P. Kulpers Munneke, 2012: A new high-resolution surface mass balance map Losch, M., A. Adcroft, and J. M. Campin, 2004: How sensitive are coarse general of Antarctica (1989 2009) based on regional atmospheric climate modeling. circulation models to fundamental approximations in the equations of motion? Geophys. Res. Lett., 39, L04501. J. Phys. Oceanogr., 34, 306 319. Lenton, T. M., H. Held, E. Kriegler, J. W. Hall, W. Lucht, S. Rahmstorf, and H. J. Lowe, J. A., and J. M. Gregory, 2006: Understanding projections of sea level rise in Schellnhuber, 2008: Tipping elements in the Earth s climate system. Proc. Natl. a Hadley Centre coupled climate model. J. Geophys. Res. Oceans, 111, C11014. Acad. Sci. U.S.A., 105, 1786 1793. Lowe, J. A., et al., 2009: UK Climate Projections science report: Marine and coastal Lettenmaier, D. P., and P. C. D. Milly, 2009: Land waters and sea level. Nature Geosci., projections. M. O. H. Centre, Ed. 2, 452 454. Lowe, J. A., et al., 2010: Past and future changes in extreme sea levels and waves. Leuliette, E. W., and L. Miller, 2009: Closing the sea level rise budget with altimetry, In: Understanding Sea-Level Rise and Variability [J. A. Church, P. L. Woodworth, T. Argo, and GRACE. Geophys. Res. Lett., 36, L04608. Aarup and W. S. Wilson (eds.)]. Wiley-Blackwell, Hoboken, NJ, USA, pp. 326 375. Leuliette, E. W., and J. K. Willis, 2011: Balancing the sea level budget. Oceanography, Lozier, M. S., V. Roussenov, M. S. C. Reed, and R. G. Williams, 2010: Opposing decadal 24, 122 129. changes for the North Atlantic meridional overturning circulation. Nature Levermann, A., A. Griesel, M. Hofmann, M. Montoya, and S. Rahmstorf, 2005: Geosci., 3, 728 734. Dynamic sea level changes following changes in the thermohaline circulation. MacAyeal, D. R., T. A. Scambos, C. L. Hulbe, and M. A. Fahnestock, 2003: Catastrophic Clim. Dyn., 24, 347 354. ice-shelf break-up by an ice-shelf-fragment- capsize mechanism. J. Glaciol., 49, Levermann, A., et al., 2012: Potential climatic transitions with profound impact 22 36. on Europe Review of the current state of six tipping elements of the climate Machguth, H., et al., 2013: The future sea-level rise contribution of Greenland s system . Clim. Change, 110, 845 878. glaciers and ice caps. Environ. Res. Lett., 8, 025005. Levitus, S., J. Antonov, and T. Boyer, 2005: Warming of the world ocean, 1955 2003. Manson, G. K., and S. M. Solomon, 2007: Past and future forcing of Beaufort sea Geophys. Res. Lett., 32, L02604. coastal change. Atmos. Ocean, 45, 107 122. Levitus, S., J. I. Antonov, J. L. Wang, T. L. Delworth, K. W. Dixon, and A. J. Broccoli, 2001: Marèelja, S., 2010: The timescale and extent of thermal expansion of the global Anthropogenic warming of Earth s climate system. Science, 292, 267 270. ocean due to climate change. Ocean Sci., 6, 179 184. Levitus, S., J. I. Antonov, T. P. Boyer, R. A. Locarnini, H. E. Garcia, and A. V. Mishonov, Martin, T., and A. Adcroft, 2010: Parameterizing the fresh-water flux from land ice 2009: Global ocean heat content 1955 2008 in light of recently revealed to ocean with interactive icebergs in a coupled climate model. Ocean Model., instrumentation problems. Geophys. Res. Lett., 36, L07608. 34, 111 124. Li, C., J. S. von Storch, and J. Marotzke, 2013: Deep-ocean heat uptake and equilibrium Marzeion, B., A. H. Jarosch, and M. Hofer, 2012a: Past and future sea-level changes climate response. Clim. Dyn., 40, 1071 1086. from the surface mass balance of glaciers. Cryosphere, 6, 1295 1322. Ligtenberg, S. R. M., W. J. van de Berg, M. R. van den Broeke, J. G. L. Rae, and E. van Marzeion, B., M. Hofer, A. H. Jarosch, G. Kaser, and T. Mölg, 2012b: A minimal Meijgaard, 2013: Future surface mass balance of the Antarctic ice sheet and model for reconstructing interannual mass balance variability of glaciers in the its influence on sea level change, simulated by a regional atmospheric climate European Alps. Cryosphere, 6, 71 84. model. Clim. Dyn., 41, 867 884. Masson-Delmotte, V., et al., 2010: EPICA Dome C record of glacial and interglacial Lionello, P., M. B. Galati, and E. Elvini, 2010: Extreme storm surge and wind wave intensities. Quat. Sci. Rev., 29, 113 128. climate scenario simulations at the Venetian littoral. Phys. Chem. Earth Pts. Masters, D., R. S. Nerem, C. Choe, E. Leuliette, B. Beckley, N. White, and M. Ablain, A/B/C, 40 41, 86 92. 2012: Comparison of global mean sea level time series from TOPEX/Poseidon, 13 Lionello, P., S. Cogo, M. B. Galati, and A. Sanna, 2008: The Mediterranean surface Jason-1, and Jason-2. Mar. Geodesy, 35, 20 41. wave climate inferred from future scenario simulations. Global Planet. Change, McInnes, K., I. Macadam, G. Hubbert, and J. O Grady, 2009: A modelling approach for 63, 152 162. estimating the frequency of sea level extremes and the impact of climate change Little, C. M., M. Oppenheimer, and N. M. Urban, 2013a: Upper bounds on twenty- in southeast Australia. Nat. Hazards, 51, 115 137. first-century Antarctic ice loss assessed using a probabilistic framework. Nature McInnes, K., I. Macadam, G. Hubbert, and J. G. O Grady, 2013: An assessment of Clim. Change, 7, 654-659. current and future vulnerability to coastal inundation due to sea level extremes Little, C. M., N. M. Urban, and M. Oppenheimer, 2013b: Probabilistic framework for in Victoria, southeast Australia. Int. J. Climatol., 33, 33 47. assessing the ice sheet contribution to sea level change. Proc. Natl. Acad. Sci. McInnes, K. L., T. A. Erwin, and J. M. Bathols, 2011: Global climate model projected U.S.A., 110, 3264 3269. changes in 10 m wind due to anthropogenic climate change. Atmos. Sci. Lett., 12, 325 333. 1211 Chapter 13 Sea Level Change Meehl, G. A., A. X. Hu, and C. Tebaldi, 2010: Decadal Prediction in the Pacific Region. Moore, J. C., S. Jevrejeva, and A. Grinsted, 2011: The historical global sea level J. Clim., 23, 2959 2973. budget. Ann. Glaciol., 52, 8 14. Meehl, G. A., J. M. Arblaster, J. T. Fasullo, A. Hu, and K. E. Trenberth, 2011: Model- Mori, N., T. Shimura, T. Yasuda, and H. Mase, 2013: Multi-model climate projections based evidence of deep-ocean heat uptake during surface-temperature hiatus of ocean surface variables under different climate scenarios Future change of periods. Nature Clim. Change, 1, 360 364. waves, sea level, and wind. Ocean Eng., 71, 122-129. Meehl, G. A., et al., 2005: How much more global warming and sea level rise? Mori, N., T. Yasuda, H. Mase, T. Tom, and Y. Oku, 2010: Projection of extreme wave Science, 307, 1769 1772. climate change under global warming. Hydrol. Res. Lett., 4, 15 19. Meehl, G. A., et al., 2007: Global climate projections.In: Climate Change 2007: The Morice, C. P., J. J. Kennedy, N. A. Rhayner, and P. D. Jones, 2012: Quantifying Physical Science Basis. Contribution of Working Group I to the Fourth Assessment uncertainties in global and regional temperature change using an ensemble of Report of the Intergovernmental Panel on Climate Change [Solomon, S., D. Qin, observational estimates: The HadCRUT4 data set. J. Geophys. Res. Atmos., 117, M. Manning, Z. Chen, M. Marquis, K. B. Averyt, M. Tignor and H. L. Miller (eds.)]. D08101. Cambridge University Press, Cambridge, United Kingdom and New York, NY, Morlighem, M., E. Rignot, H. Seroussi, E. Larour, and H. Ben Dhia, 2010: Spatial USA, pp. 755 828. patterns of basal drag inferred using control methods from a full-Stokes and Meehl, G. A., et al., 2012: Relative outcomes of climate change mitigation related simpler models for Pine Island Glacier, West Antarctica. Geophys. Res. Lett., 37, to global temperature versus sea-level rise. Nature Clim. Change, 2, 576 580. L14502. Meier, M. F., 1984: Contribution of small glaciers to global sea level. Science, 226, Moucha, R., A. M. Forte, J. X. Mitrovica, D. B. Rowley, S. Quere, N. A. Simmons, and S. 1418 1421. P. Grand, 2008: Dynamic topography and long-term sea-level variations: There Meier, M. F., et al., 2007: Glaciers dominate eustatic sea-level rise in the 21st century. is no such thing as a stable continental platform. Earth Planet. Sci. Lett., 271, Science, 317, 1064 1067. 101 108. Meinshausen, M., et al., 2011: The RCP greenhouse gas concentrations and their Mousavi, M., J. Irish, A. Frey, F. Olivera, and B. Edge, 2011: Global warming and extensions from 1765 to 2300. Clim. Change, 109, 213 241. hurricanes: The potential impact of hurricane intensification and sea level rise Menéndez, M., and P. L. Woodworth, 2010: Changes in extreme high water levels on coastal flooding. Clim. Change, 104, 575 597. based on a quasi-global tide-gauge data set. J. Geophys. Res. Oceans, 115, Muhs, D. R., J. M. Pandolfi, K. R. Simmons, and R. R. Schumann, 2012: Sea-level C10011. history of past interglacial periods from uranium-series dating of corals, Curacao, Menéndez, M., F. J. Méndez, I. J. Losada, and N. E. Graham, 2008: Variability of extreme Leeward Antilles islands. Quat. Res., 78, 157 169. wave heights in the northeast Pacific Ocean based on buoy measurements. Muller, M., B. K. Arbic, and J. X. Mitrovica, 2011: Secular trends in ocean tides: Geophys. Res. Lett., 35, L22607. Observations and model results. J. Geophys. Res. Oceans, 116, C05013. Mercer, J. H., 1978: West Antarctic ice sheet and CO2 greenhouse effect: A threat of Murphy, D. M., S. Solomon, R. W. Portmann, K. H. Rosenlof, P. M. Forster, and T. disaster. Nature, 271, 321 325. Wong, 2009: An observationally based energy balance for the Earth since 1950. Mernild, S. H., and G. E. Liston, 2012: Greenland freshwater runoff. Part II: Distribution J. Geophys. Res. Oceans, 114, D17107. and trends, 1960 2010. J. Clim., 25, 6015. Naish, T., et al., 2009: Obliquity-paced Pliocene West Antarctic ice sheet oscillations. Mernild, S. H., G. E. Liston, C. A. Hiemstra, and J. H. Christensen, 2010: Greenland Nature, 458, 322 328. ice sheet surface mass-balance modeling in a 131-yr perspective, 1950 2080. J. National Research Council, 2012: Sea-Level Rise for the Coasts of California, Oregon, Hydrometeorol., 11, 3 25. and Washington: Past, Present, and Future. The National Academies Press, Merrifield, M. A., and M. E. Maltrud, 2011: Regional sea level trends due to a Pacific Washington, DC. trade wind intensification. Geophys. Res. Lett., 38, L21605. Nerem, R. S., D. P. Chambers, C. Choe, and G. T. Mitchum, 2010: Estimating mean Mikolajewicz, U., M. Vizcaíno, J. Jungclaus, and G. Schurgers, 2007a: Effect of ice sea level change from the TOPEX and Jason altimeter missions. Mar. Geodesy, sheet interactions in anthropogenic climate change simulations. Geophys. Res. 33, 435 446. Lett., 34, L18706. Ngo-Duc, T., K. Laval, J. Polcher, A. Lombard, and A. Cazenave, 2005: Effects of land Mikolajewicz, U., M. Groger, E. Maier-Reimer, G. Schurgers, M. Vizcaíno, and A. water storage on global mean sea level over the past half century. Geophys. Res. Winguth, 2007b: Long-term effects of anthropogenic CO2 emissions simulated Lett., 32, L09704. with a complex earth system model. Clim. Dyn., 28, 599 634. Nicholls, R. J., et al., 2011: Sea-level rise and its possible impacts given a beyond Miller, L., and B. C. Douglas, 2007: Gyre-scale atmospheric pressure variations and 4 degrees C world in the twenty-first century. Philos. Trans. R. Soc. London A, their relation to 19th and 20th century sea level rise. Geophys. Res. Lett., 34, 369, 161 181. L16602. Nick, F. M., A. Vieli, I. M. Howat, and I. Joughin, 2009: Large-scale changes in Milly, P. C. D., A. Cazenave, and M. C. Gennero, 2003: Contribution of climate-driven Greenland outlet glacier dynamics triggered at the terminus. Nature Geosci., change in continental water storage to recent sea-level rise. Proc. Natl. Acad. Sci. 2, 110 114. U.S.A., 100, 13158 13161. Nick, F. M., et al., 2012: The response of Petermann Glacier, Greenland, to large Milly, P. C. D., et al., 2010: Terrestrial water-storage contributions to sea-level rise calving events, and its future stability in the context of atmospheric and oceanic and variability. In: Understanding Sea-Level Rise and Variability [J. A. Church, P. warming. J. Glaciol., 58, 229 239. L. Woodworth, T. Aarup and W. S. Wilson (eds.)]. Wiley-Blackwell, Hoboken, NJ, Nick, F. M., et al., 2013: Future sea-level rise from Greenland s major outlet glaciers USA, pp. 226 255. in a warming climate. Nature, 497, 235 238. Milne, G. A., and J. X. Mitrovica, 1998: Postglacial sea-level change on a rotating Nidheesh, A. G., M. Lengaine, J. Vialard, A. S. Unnikrishnam, and H. Dayan, 2013: Earth. Geophys. J. Int., 133, 1 19. Decadal and long-term sea level variability in the tropical Indo-Pacific Ocean. Milne, G. A., W. R. Gehrels, C. W. Hughes, and M. E. Tamisiea, 2009: Identifying the Clim. Dyn., 41, 381 402. causes of sea-level change. Nature Geosci., 2, 471 478. Oerlemans, J., J. Jania, and L. Kolondra, 2011: Application of a minimal glacier model Mitrovica, J. X., N. Gomez, and P. U. Clark, 2009: The sea-level fingerprint of West to Hansbreen, Svalbard. Cryosphere, 5, 1 11. 13 Antarctic collapse. Science, 323, 753 753. Okumura, Y. M., C. Deser, A. Hu, A. Timmermann, and S. P. Xie, 2009: North Pacific Mitrovica, J. X., M. E. Tamisiea, J. L. Davis, and G. A. Milne, 2001: Recent mass climate response to freshwater forcing in the Subarctic North Atlantic: Oceanic balance of polar ice sheets inferred from patterns of global sea-level change. and atmospheric pathways. J. Clim., 22, 1424 1445. Nature, 409, 1026 1029. Olivié, D. J. L., G. P. Peters, and D. Saint-Martin, 2012: Atmosphere Response Time Mitrovica, J. X., N. Gomez, E. Morrow, C. Hay, K. Latychev, and M. E. Tamisiea, 2011: Scales Estimated from AOGCM Experiments. J. Clim., 25, 7956 7972. On the robustness of predictions of sea-level fingerprints. Geophys. J. Int., 187, Ollivier, A., Y. Faugere, N. Picot, M. Ablain, P. Femenias, and J. Benveniste, 2012: 729 742. Envisat Ocean Altimeter becoming relevant for mean sea level trend studies. Moberg, A., D. M. Sonechkin, K. Holmgren, N. M. Datsenko, and W. Karlen, 2005: Mar. Geodesy, 35, 118 136. Highly variable Northern Hemisphere temperatures reconstructed from low- and Orliæ, M., and Z. Pasariæ, 2013: Semi-empirical versus process-based sea-level high-resolution proxy data. Nature, 433, 613 617. projections for the twenty-first century. Nature Clim. Change, 8, 735-738. Monaghan, A. J., et al., 2006: Insignificant change in Antarctic snowfall since the Otto, A., et al., 2013: Energy budget contraints on climate response. Nature Geosci., International Geophysical Year. Science, 313, 827 831. 6, 415 416. 1212 Sea Level Change Chapter 13 Overeem, I., R. S. Anderson, C. W. Wobus, G. D. Clow, F. E. Urban, and N. Matell, 2011: Pritchard, H. D., R. J. Arthern, D. G. Vaughan, and L. A. Edwards, 2009: Extensive Sea ice loss enhances wave action at the Arctic coast. Geophys. Res. Lett., 38, dynamic thinning on the margins of the Greenland and Antarctic ice sheets. L17503. Nature, 461, 971 975. Palmer, M. D., D. J. McNeall, and N. J. Dunstone, 2011: Importance of the deep ocean Pritchard, H. D., S. R. M. Ligtenberg, H. A. Fricker, D. G. Vaughan, M. R. van den Broeke, for estimating decadal changes in Earth s radiation balance. Geophys. Res. Lett., and L. Padman, 2012: Antarctic ice-sheet loss driven by basal melting of ice 38, L13707. shelves. Nature, 484, 502 505. Pardaens, A., J. M. Gregory, and J. Lowe, 2011a: A model study of factors influencing Purkey, S. G., and G. C. Johnson, 2010: Warming of global abyssal and deep southern projected changes in regional sea level over the twenty-first century. Clim. Dyn., ocean waters between the 1990s and 2000s: Contributions to global heat and 36, 2015 2033. sea level rise budgets. J. Clim., 23, 6336 6351. Pardaens, A. K., H. T. Banks, J. M. Gregory, and P. R. Rowntree, 2003: Freshwater Qiu, B., and S. M. Chen, 2006: Decadal variability in the large-scale sea surface height transports in HadCM3. Clim. Dyn., 21, 177 195. field of the South Pacific Ocean: Observations and causes. J. Phys. Oceanogr., 36, Pardaens, A. K., J. A. Lowe, S. Brown, R. J. Nicholls, and D. de Gusmao, 2011b: Sea- 1751 1762. level rise and impacts projections under a future scenario with large greenhouse Quinn, K. J., and R. M. Ponte, 2010: Uncertainty in ocean mass trends from GRACE. gas emission reductions. Geophys. Res. Lett., 38, L12604. Geophys. J. Int., 181, 762 768. Parizek, B. R., et al., 2013: Dynamic (in)stability of Thwaites Glacier, West Antarctica. Radic, V., and R. Hock, 2010: Regional and global volumes of glaciers derived from J. Geophys. Res. Earth Surf., 118, 638 655. statistical upscaling of glacier inventory data. J. Geophys. Res. Earth Surf., 115, Parker, A., 2013a: Comment to M Lichter and D Felsenstein, Assessing the costs of F01010. sea-level rise and extreme flooding at the local level: A GIS-based approach. Radiæ, V., and R. Hock, 2011: Regionally differentiated contribution of mountain Ocean Coast. Manage., 78, 138 142. glaciers and ice caps to future sea-level rise. Nature Geosci., 4, 91 94. Parker, A., 2013b: Sea level trends at locations of the United States with more than Radiæ, V., A. Bliss, A. D. Beedlow, R. Hock, E. Miles, and J. G. Cogley, 2013: Regional 100 years of recording. Nat. Hazards, 65, 1011 1021. and global projections of the 21st century glacier mass changes in response to Parker, A., 2013c: Comment to Shepard, CC, Agostini, VN, Gilmer, B., Allen, T., Stone, climate scenarios from global climate models. Clim. Dyn., doi:10.1007/s00382- J., Brooks, W., Beck, MW: Assessing future risk: Quantifying the effects of sea 013-1719-7. level rise on storm surge risk for the southern shores of Long Island. Nat. Rae, J. G. L., et al., 2012: Greenland ice sheet surface mass balance: Evaluating Hazards, 65, 977 980. simulations and making projections with regional climate models. Cryosphere, Passchier, S., 2011: Linkages between East Antarctic Ice Sheet extent and Southern 6, 1275 1294. Ocean temperatures based on a Pliocene high-resolution record of ice-rafted Rahmstorf, S., 2007a: A semi-empirical approach to projecting future sea-level rise. debris off Prydz Bay, East Antarctica. Paleoceanography, 26, Pa4204. Science, 315, 368 370. Pattyn, F., A. Huyghe, S. De Brabander, and B. De Smedt, 2006: Role of transition Rahmstorf, S., 2007b: Response to comments on A semi-empirical approach to zones in marine ice sheet dynamics. J. Geophys. Res. Earth Surf., 111, F02004. projecting future sea-level rise . Science, 317, 1866. Pattyn, F., et al., 2013: Grounding-line migration in plan-view marine ice-sheet Rahmstorf, S., and A. Ganopolski, 1999: Long-term global warming scenarios models: Results of the ice2sea MISMIP3d intercomparison. J. Glaciol., 59, 410 computed with an efficient coupled climate model. Clim. Change, 43, 353 367. 422. Rahmstorf, S., M. Perrette, and M. Vermeer, 2012a: Testing the robustness of semi- Paulson, A., S. J. Zhong, and J. Wahr, 2007: Inference of mantle viscosity from GRACE empirical sea level projections. Clim. Dyn., 39, 861 875. and relative sea level data. Geophys. J. Int., 171, 497 508. Rahmstorf, S., G. Foster, and A. Cazenave, 2012b: Comparing climate projections to Peltier, W. R., 2004: Global glacial isostasy and the surface of the ice-age earth: The observations up to 2011. Environ. Res. Lett., 7, 044035. ICE-5G (VM2) model and GRACE. Annu. Rev. Earth Planet. Sci., 32, 111 149. Rahmstorf, S., A. Cazenave, J. A. Church, J. E. Hansen, R. F. Keeling, D. E. Parker, and Peltier, W. R., 2009: Closure of the budget of global sea level rise over the GRACE era: R. C. J. Somerville, 2007: Recent climate observations compared to projections. The importance and magnitudes of the required corrections for global glacial Science, 316, 709 709. isostatic adjustment. Quat. Sci. Rev., 28, 1658 1674. Raper, S. C. B., and R. J. Braithwaite, 2005: The potential for sea level rise: New Peltier, W. R., and A. M. Tushingham, 1991: Influence of glacial isostatic-adjustment estimates from glacier and ice cap area and volume distributions. Geophys. Res. on tide gauge measurements of secular sea-level change. J. Geophys. Res. Solid Lett., 32, L05502. Earth Planets, 96, 6779 6796. Raper, S. C. B., J. M. Gregory, and R. J. Stouffer, 2002: The role of climate sensitivity Perrette, M., F. W. Landerer, R. Riva, K. Frieler, and M. Meinshausen, 2013: A scaling and ocean heat uptake on AOGCM transient temperature response. J. Clim., 15, approach to project regional sea level rise and its uncertainties. Earth Syst. Dyn., 124 130. 4, 11 29. Rasmussen, L. A., H. Conway, R. M. Krimmel, and R. Hock, 2011: Surface mass Pfeffer, W. T., J. T. Harper, and S. O Neel, 2008: Kinematic constraints on glacier balance, thinning and iceberg production, Columbia Glacier, Alaska, 1948 2007. contributions to 21st-century sea-level rise. Science, 321, 1340 1343. J. Glaciol., 57, 431 440. Phillips, T., H. Rajaram, and K. Steffen, 2010: Cryo-hydrologic warming: A potential Ray, R. D., and B. C. Douglas, 2011: Experiments in reconstructing twentieth-century mechanism for rapid thermal response of ice sheets. Geophys. Res. Lett., 37, sea levels. Prog. Oceanogr., 91, 495 515. L20503. Raymo, M. E., and J. X. Mitrovica, 2012: Collapse of polar ice sheets during the stage Phillips, T., H. Rajaram, W. Colgan, K. Steffen, and W. Abdalati, 2013: Evaluation of 11 interglacial. Nature, 483, 453 456. cryo-hydrologic warming as an explanation for increased ice velocities in the Raymo, M. E., J. X. Mitrovica, M. J. O Leary, R. M. DeConto, and P. L. Hearty, 2011: wet snow zone, Sermeq Avannarleq, West Greenland. J. Geophys. Res., 118, Departures from eustasy in Pliocene sea-level records. Nature Geosci., 4, 328 1241 1256. 332. Pokhrel, Y. N., N. Hanasaki, P. J. F. Yeh, T. J. Yamada, S. Kanae, and T. Oki, 2012: Model Ridley, J., J. M. Gregory, P. Huybrechts, and J. Lowe, 2010: Thresholds for irreversible estimates of sea-level change due to anthropogenic impacts on terrestrial water decline of the Greenland ice sheet. Clim. Dyn., 35, 1065 1073. storage. Nature Geosci., 5, 389 392. Ridley, J. K., P. Huybrechts, J. M. Gregory, and J. A. Lowe, 2005: Elimination of the 13 Pokhrel, Y. N., N. Hanasaki, P. J. F. Yeh, T. J. Yamada, S. Kanae, and T. Oki, 2013: Greenland ice sheet in a high CO2 climate. J. Clim., 18, 3409 3427. Overestimated water storage Reply. Nature Geosci., 6, 2 3. Rignot, E., 2001: Evidence for rapid retreat and mass loss of Thwaites Glacier, West Pollard, D., and R. M. DeConto, 2009: Modelling West Antarctic ice sheet growth and Antarctica. J. Glaciol., 47, 213 222. collapse through the past five million years. Nature, 458, 329 332. Rignot, E., 2008: Changes in West Antarctic ice stream dynamics observed with ALOS Polvani, L. M., M. Previdi, and C. Deser, 2011: Large cancellation, due to ozone PALSAR data. Geophys. Res. Lett., 35, L12505. recovery, of future Southern Hemisphere atmospheric circulation trends. Rignot, E., I. Velicogna, M. R. van den Broeke, A. Monaghan, and J. Lenaerts, 2011: Geophys. Res. Lett., 38, L04707. Acceleration of the contribution of the Greenland and Antarctic ice sheets to sea Price, S. F., A. J. Payne, I. M. Howat, and B. E. Smith, 2011: Committed sea-level rise level rise. Geophys. Res. Lett., 38, L05503. for the next century from Greenland ice sheet dynamics during the past decade. Rignot, E., G. Casassa, P. Gogineni, W. Krabill, A. Rivera, and R. Thomas, 2004: Proc. Natl. Acad. Sci. U.S.A., 108, 8978 8983. Accelerated ice discharge from the Antarctic Peninsula following the collapse of Larsen B ice shelf. Geophys. Res. Lett., 31, L18401. 1213 Chapter 13 Sea Level Change Rignot, E., J. L. Bamber, M. R. Van Den Broeke, C. Davis, Y. H. Li, W. J. Van De Berg, and Slangen, A. B. A., and R. S. W. van de Wal, 2011: An assessment of uncertainties E. Van Meijgaard, 2008: Recent Antarctic ice mass loss from radar interferometry in using volume-area modelling for computing the twenty-first century glacier and regional climate modelling. Nature Geosci., 1, 106 110. contribution to sea-level change. Cryosphere, 5, 673 686. Riva, R. E. M., J. L. Bamber, D. A. Lavallee, and B. Wouters, 2010: Sea-level fingerprint Slangen, A. B. A., C. A. Katsman, R. S. W. van de Wal, L. L. A. Vermeersen, and R. of continental water and ice mass change from GRACE. Geophys. Res. Lett., 37, E. M. Riva, 2012: Towards regional projections of twenty-first century sea-level L19605. change based on IPCC SRES scenarios. Clim. Dyn., 38, 1191 1209. Roberts, D. L., P. Karkanas, Z. Jacobs, C. W. Marean, and R. G. Roberts, 2012: Melting Smith, J. M., M. A. Cialone, T. V. Wamsley, and T. O. McAlpin, 2010: Potential impact of ice sheets 400,000 yr ago raised sea level by 13 m: Past analogue for future sea level rise on coastal surges in southeast Louisiana. Ocean Eng., 37, 37 47. trends. Earth Planet. Sci. Lett., 357, 226 237. Sokolov, A. P., C. E. Forest, and P. H. Stone, 2010: Sensitivity of climate change Robinson, A., R. Calov, and A. Ganopolski, 2012: Multistability and critical thresholds projections to uncertainties in the estimates of observed changes in deep-ocean of the Greenland ice sheet. Nature Clim. Change, 2, 429 432. heat content. Clim. Dyn., 34, 735 745. Rott, H., F. Muller, T. Nagler, and D. Floricioiu, 2011: The imbalance of glaciers after Solgaard, A. M., and P. L. Langen, 2012: Multistability of the Greenland ice sheet and disintegration of Larsen-B ice shelf, Antarctic Peninsula. Cryosphere, 5, 125 134. the effects of an adaptive mass balance formulation. Clim. Dyn., 39, 1599 1612. Rugenstein, M., M. Winton, R. J. Stouffer, S. M. Griffies, and R. W. Hallberg, 2013: Solomon, S., G.-K. Plattner, R. Knutti, and P. Friedlingstein, 2009: Irresversible climate Northern high latitude heat budget decomposition and transient warming. J. change due to carbon dioxide emissions. Proc. Natl. Acad. Sci. U.S.A., 106, Clim., 26, 609-621. 1704 1709. Russell, G. L., V. Gornitz, and J. R. Miller, 2000: Regional sea-level changes projected Spada, G., J. L. Bamber, and R. T. W. L. Hurkmans, 2013: The gravitationally consistent by the NASA/GISS atmosphere-ocean model. Clim. Dyn., 16, 789 797. sea-level fingerprint of future terrestrial ice loss. Geophys. Res. Lett., 40, 482 Sahagian, D., 2000: Global physical effects of anthropogenic hydrological alterations: 486. Sea level and water redistribution. Global Planet. Change, 25, 39 48. Sriver, R. L., N. M. Urban, R. Olson, and K. Keller, 2012: Toward a physically plausible Sallenger, A. H., K. S. Doran, and P. A. Howd, 2012: Hotspot of accelerated sea-level upper bound of sea-level rise projections. Clim. Change, 115, 893 902. rise on the Atlantic coast of North America. Nature Clim. Change, 2, 884-888. Stackhouse, P. W., T. Wong, N. G. Loeb, D. P. Kratz, A. C. Wilber, D. R. Doelling, and L. C. Santer, B. D., et al., 2011: Separating signal and noise in atmospheric temperature Nguyen, 2010: Earth radiation budget at top-of-atmosphere. Bull. Am. Meteorol. changes: The importance of timescale. J. Geophys. Res. Atmos., 116, D22105. Soc., 90, S33 S34. Sasgen, I., et al., 2012: Timing and origin of recent regional ice-mass loss in Stammer, D., 2008: Response of the global ocean to Greenland and Antarctic ice Greenland. Earth Planet. Sci. Lett., 333, 293 303. melting. J. Geophys. Res. Oceans, 113, C06022. Scambos, T. A., J. A. Bohlander, C. A. Shuman, and P. Skvarca, 2004: Glacier Stammer, D., and S. Huttemann, 2008: Response of regional sea level to atmospheric acceleration and thinning after ice shelf collapse in the Larsen B embayment, pressure loading in a climate change scenario. J. Clim., 21, 2093 2101. Antarctica. Geophys. Res. Lett., 31, L18402. Stammer, D., A. Cazenave, R. M. Ponte, and M. E. Tamisiea, 2013: Causes for Schaeffer, M., W. Hare, S. Rahmstorf, and M. Vermeer, 2012: Long-term sea-level rise contemporary regional sea level changes. In: Annual Review of Marine Science, implied by 1.5°C and 2°C warming levels. Nature Clim. Change, 2, 867 870. Vol. 5 [C. A. Carlson and S. J. Giovannoni (eds.)]. Annual Reviews, Palo Alto, CA, Schewe, J., A. Levermann, and M. Meinshausen, 2011: Climate change under USA, pp. 21 46. a scenario near 1.5 °C of global warming: Monsoon intensification, ocean Stammer, D., N. Agarwal, P. Herrmann, A. Kohl, and C. R. Mechoso, 2011: Response warming and steric sea level rise. Earth Syst. Dyn., 2, 25 35. of a coupled ocean-atmosphere model to Greenland ice melting. Surv. Geophys., Schmith, T., S. Johansen, and P. Thejll, 2007: Comment on A semi-empirical approach 32, 621 642. to projecting future sea-level rise . Science, 317, 1866. Stenni, B., et al., 2011: Expression of the bipolar see-saw in Antarctic climate records Schoof, C., 2007a: Marine ice-sheet dynamics. Part 1. the case of rapid sliding. J. Fluid during the last deglaciation. Nature Geosci., 4, 46 49. Mech., 573, 27 55. Sterl, A., H. van den Brink, H. de Vries, R. Haarsma, and E. van Meijgaard, 2009: An Schoof, C., 2007b: Ice sheet grounding line dynamics: Steady states, stability, and ensemble study of extreme North Sea storm surges in a changing climate. Ocean hysteresis. J. Geophys. Res. Earth Surf., 112, F03S28. Sci. Discuss., 6, 1031 1059. Schoof, C., 2011: Marine ice sheet dynamics. Part 2. A Stokes flow contact problem. Stouffer, R. J., 2004: Time scales of climate response. J. Clim., 17, 209 217. J. Fluid Mech., 679, 122 155. Straneo, F., et al., 2010: Rapid circulation of warm subtropical waters in a major Schwartz, S. E., 2012: Determination of Earth s transient and equilibrium climate glacial fjord in East Greenland. Nature Geosci., 3, 182 186. sensitivities from observations over the twentieth century: Strong dependence Suzuki, T., and M. Ishii, 2011: Regional distribution of sea level changes resulting on assumed forcing. Surv. Geophys., 33, 745 777. from enhanced greenhouse warming in the Model for Interdisciplinary Research Seddik, H., R. Greve, T. Zwinger, F. Gillet-Chaulet, and O. Gagliardini, 2012: Simulations on Climate version 3.2. Geophys. Res. Lett., 38, L02601. of the Greenland ice sheet 100 years into the future with the full Stokes model Suzuki, T., et al., 2005: Projection of future sea level and its variability in a high- Elmer/Ice. J. Glaciol., 58, 427 440. resolution climate model: Ocean processes and Greenland and Antarctic ice- Semedo, A., R. Weisse, A. Beherens, A. Sterl, L. Bengtson, and H. Gunther, 2013: melt contributions. Geophys. Res. Lett., 32, L19706. Projection of global wave climate change towards the end of the 21st century. Swingedouw, D., T. Fichefet, P. Huybrechts, H. Goosse, E. Driesschaert, and M. F. J. Clim., 26, 8269-8288. Loutre, 2008: Antarctic ice-sheet melting provides negative feedbacks on future Seneviratne, S.I., N. Nicholls, D. Easterling, C.M. Goodess, S. Kanae, J. Kossin, Y. Luo, climate warming. Geophys. Res. Lett., 35, L17705. J. Marengo, K. McInnes, M. Rahimi, M. Reichstein, A. Sorteberg, C. Vera, and X. Syvitski, J. P. M., and A. Kettner, 2011: Sediment flux and the Anthropocene. Philos. Zhang, 2012: Changes in climate extremes and their impacts on the natural Trans. R. Soc. London A, 369, 957 975. physical environment. In: Managing the Risks of Extreme Events and Disasters Syvitski, J. P. M., et al., 2009: Sinking deltas due to human activities. Nature Geosci., to Advance Climate Change Adaptation [Field, C.B., V. Barros, T.F. Stocker, D. Qin, 2, 681 686. D.J. Dokken, K.L. Ebi, M.D. Mastrandrea, K.J. Mach, G.-K. Plattner, S.K. Allen, M. Tamisiea, M. E., 2011: Ongoing glacial isostatic contributions to observations of sea 13 Tignor, and P.M. Midgley (eds.)]. A Special Report of Working Groups I and II of level change. Geophys. J. Int., 186, 1036 1044. the Intergovernmental Panel on Climate Change (IPCC). Cambridge University Tamisiea, M. E., E. M. Hill, R. M. Ponte, J. L. Davis, I. Velicogna, and N. T. Vinogradova, Press, Cambridge, UK, and New York, NY, USA, pp. 109-230. 2010: Impact of self attraction and loading on the annual cycle in sea level. J. Shepherd, A., and D. Wingham, 2007: Recent sea-level contributions of the Antarctic Geophys. Res., 115, C07004. and Greenland ice sheets. Science, 315, 1529 1532. Tebaldi, C., B. H. Strauss, and C. E. Zervas, 2012: Modelling sea level rise impacts on Shepherd, A., et al., 2012: A reconciled estimate of ice-sheet mass balance. Science, storm surges along US coasts. Environ. Res. Lett., 7, 2 11. 338, 1183 1189. Tett, S. F. B., et al., 2007: The impact of natural and anthropogenic forcings on climate Shuman, C. A., E. Berthier, and T. A. Scambos, 2011: 2001 2009 elevation and mass and hydrology since 1550. Clim. Dyn., 28, 3 34. losses in the Larsen A and B embayments, Antarctic Peninsula. J. Glaciol., 57, Thoma, M., A. Jenkins, D. Holland, and S. Jacobs, 2008: Modelling circumpolar deep 737 754. water intrusions on the Amundsen Sea continental shelf, Antarctica. Geophys. Res. Lett., 35, L18602. 1214 Sea Level Change Chapter 13 Thompson, W. G., H. A. Curran, M. A. Wilson, and B. White, 2011: Sea-level Wang, S., R. McGrath, J. Hanafin, P. Lynch, T. Semmler, and P. Nolan, 2008: The impact oscillations during the last interglacial highstand recorded by Bahamas corals. of climate change on storm surges over Irish waters. Ocean Model., 25, 83 94. Nature Geosci., 4, 684 687. Wang, X. L., and V. R. Swail, 2006: Climate change signal and uncertainty in Timmermann, A., S. McGregor, and F. F. Jin, 2010: Wind effects on past and future projections of ocean wave heights. Clim. Dyn., 26, 109 126. regional sea level trends in the Southern Indo-Pacific. J. Clim., 23, 4429 4437. Wang, X. L., V. R. Swail, and A. Cox, 2010: Dynamical versus statistical downscaling Tsimplis, M., E. Alvarez-Fanjul, D. Gomis, L. Fenoglio-Marc, and B. Perez, 2005: methods for ocean wave heights. Int. J. Climatol., 30, 317 332. Mediterranean Sea level trends: Atmospheric pressure and wind contribution. Wang, X. L., V. R. Swail, F. Zwiers, X. Zhang, and Y. Feng, 2009: Detection of external Geophys. Res. Lett., 32, L20602. influence on trends of atmospheric storminess and northern oceans wave Unnikrishnan, A. S., M. R. R. Kumar, and B. Sindhu, 2011: Tropical cyclones in the Bay heights. Clim. Dyn., 32, 189 203. of Bengal and extreme sea-level projections along the east coast of India in a Warrick, R. A., and J. Oerlemans, 1990: Sea level rise. In: Climate Change: The IPCC future climate scenario. Curr. Sci. (India), 101, 327 331. Scientific Assessment [J. T. Houghton, G. J. Jenkins and J. J. Ephraum (eds.)]. Uotila, P., A. H. Lynch, J. J. Cassano, and R. I. Cullather, 2007: Changes in Antarctic net Cambridge University Press, Cambridge, United Kingdom, and New York, NY, precipitation in the 21st century based on Intergovernmental Panel on Climate USA, pp. 260 281. Change (IPCC) model scenarios. J. Geophys. Res. Atmos., 112, D10107. Warrick, R. A., C. Le Provost, M. F. Meier, J. Oerlemans, and P. L. Woodworth, 1996: van Angelen, J. H., et al., 2012: Sensitivity of Greenland ice sheet surface mass Changes in sea level. In: Climate Change 1995: The Science of Climate Change. balance to surface albedo parameterization: A study with a regional climate Contribution of WGI to the Second Assessment Report of the Intergovernmental model. Cryosphere, 6, 1531 1562. Panel on Climate Change [J. T. Houghton, L. G. Meira . A. Callander, N. Harris, van de Berg, W. J., M. van den Broeke, J. Ettema, E. van Meijgaard, and F. Kaspar, A. Kattenberg and K. Maskell (eds.)]. Cambridge University Press, Cambridge, 2011: Significant contribution of insolation to Eemian melting of the Greenland United Kingdom, and New York, NY, USA, pp. 359 405. ice sheet. Nature Geosci., 4, 679 683. Washington, W. M., et al., 2009: How much climate change can be avoided by van den Broeke, M., W. J. van de Berg, and E. van Meijgaard, 2006: Snowfall in mitigation? Geophys. Res. Lett., 36, L08703. coastal West Antarctica much greater than previously assumed. Geophys. Res. Watson, C., et al., 2010: Twentieth century constraints on sea level change and Lett., 33, L02505. earthquake deformation at Macquarie Island. Geophys. J. Int., 182, 781 796. van den Broeke, M., et al., 2009: Partitioning recent Greenland mass loss. Science, Weertman, J., 1961: Stability of Ice-Age ice sheets. J. Geophys. Res., 66, 3783 3792. 326, 984 986. Weertman, J., 1974: Stability of the junction of an ice sheet and an ice shelf. J. Van Ommen, T. D., V. Morgan, and M. A. J. Curran, 2004: Deglacial and Holocene Glaciol., 13, 3 11. changes in accumulation at Law Dome, East Antarctica. Ann. Glaciol., 39, 395 White, N. J., J. A. Church, and J. M. Gregory, 2005: Coastal and global averaged sea 365. level rise for 1950 to 2000. Geophys. Res. Lett., 32, L01601. Vaughan, D. G., J. L. Bamber, M. Giovinetto, J. Russell, and A. P. R. Cooper, 1999: Winguth, A., U. Mikolajewicz, M. Groger, E. Maier-Reimer, G. Schurgers, and M. Reassessment of net surface mass balance in Antarctica. J. Clim., 12, 933 946. Vizcaíno, 2005: Centennial-scale interactions between the carbon cycle and Vellinga, M., and R. Wood, 2008: Impacts of thermohaline circulation shutdown in anthropogenic climate change using a dynamic Earth system model. Geophys. the twenty-first century. Clim. Change, 91, 43 63. Res. Lett., 32, L23714. Vermaire, J. C., M. F. J. Pisaric, J. R. Thienpont, C. J. C. Mustaphi, S. V. Kokelj, and J. P. Winkelmann, R., A. Levermann, M. A. Martin, and K. Frieler, 2012: Increased future Smol, 2013: Arctic climate warming and sea ice declines lead to increased storm ice discharge from Antarctica owing to higher snowfall. Nature, 492, 239 242. surge activity. Geophys. Res. Lett., 40, 1386-1390. Wong, T., B. A. Wielecki, R. B. I. Lee, G. L. Smith, K. A. Bush, and J. K. Willis, 2006: Vermeer, M., and S. Rahmstorf, 2009: Global sea level linked to global temperature. Reexamination of the observed decadal variability of the earth radiation budget Proc. Natl. Acad. Sci. U.S.A., 106, 21527 21532. using altitude-corrected ERBE/ERBS nonscanner WFOV data. J. Clim., 19, 4028 Vernon, C. L., J. L. Bamber, J. E. Box, M. R. van den Broeke, X. Fettweis, E. Hanna, 4040. and P. Huybrechts, 2013: Surface mass balance model intercomparison for the Woodroffe, C. D., H. V. McGregor, K. Lambeck, S. G. Smithers, and D. Fink, 2012: Mid- Greenland ice sheet. Cryosphere, 7, 599 614. Pacific microatolls record sea-level stability over the past 5000 yr. Geology, 40, Vieli, A., and F. M. Nick, 2011: Understanding and modelling rapid dynamic changes 951 954. of tidewater outlet glaciers: Issues and implications. Surv. Geophys., 32, 437 Woolf, D. K., P. G. Challenor, and P. D. Cotton, 2002: Variability and predictability of 458. the North Atlantic wave climate. J. Geophys. Res. Oceans, 107, C103145. Vinogradov, S. V., and R. M. Ponte, 2011: Low-frequency variability in coastal sea Woth, K., R. Weisse, and H. von Storch, 2006: Climate change and North Sea storm level from tide gauges and altimetry. J. Geophys. Res. Oceans, 116, C07006. surge extremes: An ensemble study of storm surge extremes expected in a Vizcaíno, M., U. Mikolajewicz, J. Jungclaus, and G. Schurgers, 2010: Climate changed climate projected by four different regional climate models. Ocean modification by future ice sheet changes and consequences for ice sheet mass Dyn., 56, 3 15. balance. Clim. Dyn., 34, 301 324. Wunsch, C., and D. Stammer, 1997: Atmospheric loading and the oceanic inverted Vizcaíno, M., U. Mikolajewicz, M. Groger, E. Maier-Reimer, G. Schurgers, and A. M. E. barometer effect. Rev. Geophys., 35, 79 107. Winguth, 2008: Long-term ice sheet-climate interactions under anthropogenic Wunsch, C., and P. Heimbach, 2007: Practical global oceanic state estimation. greenhouse forcing simulated with a complex Earth System Model. Clim. Dyn., Physica D, 230, 197 208. 31, 665 690. Yin, J., 2012: Century to multi-century sea level rise projections from CMIP5 models. von Schuckmann, K., and P. Y. Le Traon, 2011: How well can we derive Global Ocean Geophys. Res. Lett., 39, L17709. Indicators from Argo data? Ocean Sci., 7, 783 791. Yin, J. J., M. E. Schlesinger, and R. J. Stouffer, 2009: Model projections of rapid sea- von Storch, H., E. Zorita, and J. F. Gonzalez-Rouco, 2008: Relationship between level rise on the northeast coast of the United States. Nature Geosci., 2, 262 266. global mean sea-level and global mean temperature in a climate simulation of Yin, J. J., S. M. Griffies, and R. J. Stouffer, 2010: Spatial variability of sea level rise in the past millennium. Ocean Dyn., 58, 227 236. twenty-first century projections. J. Clim., 23, 4585 4607. Wada, Y., L. P. H. van Beek, C. M. van Kempen, J. W. T. M. Reckman, S. Vasak, and M. Yin, J. J., J. T. Overpeck, S. M. Griffies, A. X. Hu, J. L. Russell, and R. J. Stouffer, 2011: 13 F. P. Bierkens, 2010: Global depletion of groundwater resources. Geophys. Res. Different magnitudes of projected subsurface ocean warming around Greenland Lett., 37, L20402. and Antarctica. Nature Geosci., 4, 524 528. Wada, Y., L. P. H. van Beek, F. C. S. Weiland, B. F. Chao, Y. H. Wu, and M. F. P. Bierkens, Yoshimori, M., and A. Abe-Ouchi, 2012: Sources of spread in multi-model projections 2012: Past and future contribution of global groundwater depletion to sea-level of the Greenland ice-sheet surface mass balance. J. Clim., 25, 1157 1175. rise. Geophys. Res. Lett., 39, L09402. Young, I. R., S. Zieger, and A. V. Babanin, 2011: Global trends in wind speed and wave Wake, L. M., P. Huybrechts, J. E. Box, E. Hanna, I. Janssens, and G. A. Milne, 2009: height. Science, 332, 451 455. Surface mass-balance changes of the Greenland ice sheet since 1866. Ann. Zhang, X. B., and J. A. Church, 2012: Sea level trends, interannual and decadal Glaciol., 50, 178 184. variability in the Pacific Ocean. Geophys. Res. Lett., 39, L21701. Walsh, K. J. E., K. McInnes, and J. L. McBride, 2011: Climate change impacts on Zickfeld, K., V. K. Arora, and N. P. Gillett, 2012: Is the climate response to CO2 tropical cyclones and extreme sea levels in the South Pacific a regional emissions path dependent? Geophys. Res. Lett., 39, L05703. assessment. Global Planet. Change, 80 81, 149 164. 1215 Chapter 13 Sea Level Change Zickfeld, K., M. Eby, H. D. Matthews, and A. J. Weaver, 2009: Setting cumulative emissions targets to reduce the risk of dangerous climate change. Proc. Natl. Acad. Sci. U.S.A., 106, 16129 16134. Zickfeld, K., et al., 2013: Long-term climate change commitment and reversibility: An EMIC intercomparison. J. Clim., 26, 5782-5809. Zwally, H. J., and M. B. Giovinetto, 2011: Overview and assessment of Antarctic Ice- Sheet mass balance estimates: 1992 2009. Surv. Geophys., 32, 351 376. 13 1216 Climate Phenomena and their Relevance for Future Regional Climate Change 14 Coordinating Lead Authors: Jens Hesselbjerg Christensen (Denmark), Krishna Kumar Kanikicharla (India) Lead Authors: Edvin Aldrian (Indonesia), Soon-Il An (Republic of Korea), Iracema Fonseca Albuquerque Cavalcanti (Brazil), Manuel de Castro (Spain), Wenjie Dong (China), Prashant Goswami (India), Alex Hall (USA), Joseph Katongo Kanyanga (Zambia), Akio Kitoh (Japan), James Kossin (USA), Ngar-Cheung Lau (USA), James Renwick (New Zealand), David B. Stephenson (UK), Shang-Ping Xie (USA), Tianjun Zhou (China) Contributing Authors: Libu Abraham (Qatar), Tércio Ambrizzi (Brazil), Bruce Anderson (USA), Osamu Arakawa (Japan), Raymond Arritt (USA), Mark Baldwin (UK), Mathew Barlow (USA), David Barriopedro (Spain), Michela Biasutti (USA), Sébastien Biner (Canada), David Bromwich (USA), Josephine Brown (Australia), Wenju Cai (Australia), Leila V. Carvalho (USA/Brazil), Ping Chang (USA), Xiaolong Chen (China), Jung Choi (Republic of Korea), Ole Bssing Christensen (Denmark), Clara Deser (USA), Kerry Emanuel (USA), Hirokazu Endo (Japan), David B. Enfield (USA), Amato Evan (USA), Alessandra Giannini (USA), Nathan Gillett (Canada), Annamalai Hariharasubramanian (USA), Ping Huang (China), Julie Jones (UK), Ashok Karumuri (India), Jack Katzfey (Australia), Erik Kjellström (Sweden), Jeff Knight (UK), Thomas Knutson (USA), Ashwini Kulkarni (India), Koteswara Rao Kundeti (India), William K. Lau (USA), Geert Lenderink (Netherlands), Chris Lennard (South Africa), Lai-yung Ruby Leung (USA), Renping Lin (China), Teresa Losada (Spain), Neil C. Mackellar (South Africa), Victor Magana (Mexico), Gareth Marshall (UK), Linda Mearns (USA), Gerald Meehl (USA), Claudio Menéndez (Argentina), Hiroyuki Murakami (USA/Japan), Mary Jo Nath (USA), J. David Neelin (USA), Geert Jan van Oldenborgh (Netherlands), Martin Olesen (Denmark), Jan Polcher (France), Yun Qian (USA), Suchanda Ray (India), Katharine Davis Reich (USA), Belén Rodriguez de Fonseca (Spain), Paolo Ruti (Italy), James Screen (UK), Jan Sedláèek (Switzerland) Silvina Solman (Argentina), Martin Stendel (Denmark), Samantha Stevenson (USA), Izuru Takayabu (Japan), John Turner (UK), Caroline Ummenhofer (USA), Kevin Walsh (Australia), Bin Wang (USA), Chunzai Wang (USA), Ian Watterson (Australia), Matthew Widlansky (USA), Andrew Wittenberg (USA), Tim Woollings (UK), Sang-Wook Yeh (Republic of Korea), Chidong Zhang (USA), Lixia Zhang (China), Xiaotong Zheng (China), Liwei Zou (China) Review Editors: John Fyfe (Canada), Won-Tae Kwon (Republic of Korea), Kevin Trenberth (USA), David Wratt (New Zealand) This chapter should be cited as: Christensen, J.H., K. Krishna Kumar, E. Aldrian, S.-I. An, I.F.A. Cavalcanti, M. de Castro, W. Dong, P. Goswami, A. Hall, J.K. Kanyanga, A. Kitoh, J. Kossin, N.-C. Lau, J. Renwick, D.B. Stephenson, S.-P. Xie and T. Zhou, 2013: Climate Phenomena and their Relevance for Future Regional Climate Change. In: Climate Change 2013: The Physical Sci- ence Basis. Contribution of Working Group I to the Fifth Assessment Report of the Intergovernmental Panel on Climate Change [Stocker, T.F., D. Qin, G.-K. Plattner, M. Tignor, S.K. Allen, J. Boschung, A. Nauels, Y. Xia, V. Bex and P.M. Midgley (eds.)]. Cambridge University Press, Cambridge, United Kingdom and New York, NY, USA. 1217 Table of Contents Executive Summary.................................................................... 1219 14.7 Additional Phenomena of Relevance....................... 1253 14.7.1 Pacific South American Pattern............................... 1253 14.1 Introduction..................................................................... 1222 14.7.2 Pacific North American Pattern............................... 1253 14.1.1 Monsoons and Tropical Convergence Zones............ 1222 14.7.3 Pacific Decadal Oscillation/Inter-decadal 14.1.2 Modes of Climate Variability.................................... 1222 Pacific Oscillation..................................................... 1253 14.1.3 Tropical and Extratropical Cyclones.......................... 1223 14.7.4 Tropospheric Biennial Oscillation............................. 1253 14.1.4 Summary of Climate Phenomena and their Impact 14.7.5 Quasi-Biennial Oscillation........................................ 1254 on Regional Climate................................................. 1223 14.7.6 Atlantic Multi-decadal Oscillation............................ 1254 Box 14.1: Conceptual Definitions and Impacts of Modes of Climate Variability............................................................... 1223 14.7.7 Assessment Summary.............................................. 1255 14.2 Monsoon Systems.......................................................... 1225 14.8 Future Regional Climate Change............................... 1255 14.2.1 Global Overview...................................................... 1225 14.8.1 Overview.................................................................. 1255 14.2.2 Asian-Australian Monsoon...................................... 1227 14.8.2 Arctic....................................................................... 1257 14.2.3 American Monsoons................................................ 1232 14.8.3 North America.......................................................... 1258 14.2.4 African Monsoon..................................................... 1234 14.8.4 Central America and Caribbean............................... 1260 14.2.5 Assessment Summary.............................................. 1234 14.8.5 South America.......................................................... 1261 14.8.6 Europe and Mediterranean...................................... 1264 14.3 Tropical Phenomena...................................................... 1235 14.8.7 Africa....................................................................... 1266 14.3.1 Convergence Zones.................................................. 1235 14.8.8 Central and North Asia............................................. 1268 14.3.2 Madden Julian Oscillation....................................... 1237 14.8.9 East Asia.................................................................. 1269 14.3.3 Indian Ocean Modes................................................ 1237 14.8.10 West Asia................................................................. 1271 14.3.4 Atlantic Ocean Modes.............................................. 1239 14.8.11 South Asia................................................................ 1272 14.3.5 Assessment Summary.............................................. 1240 14.8.12 Southeast Asia......................................................... 1273 14.8.13 Australia and New Zealand...................................... 1273 14.4 El Nino-Southern Oscillation...................................... 1240 14.8.14 Pacific Islands Region............................................... 1275 14.4.1 Tropical Pacific Mean State...................................... 1240 14.8.15 Antarctica................................................................ 1276 14.4.2 El Nino Changes over Recent Decades and in the Future............................................................ 1240 References ................................................................................ 1290 14.4.3 Teleconnections....................................................... 1243 14.4.4 Assessment Summary.............................................. 1243 Frequently Asked Questions FAQ 14.1 How Is Climate Change 14.5 Annular and Dipolar Modes........................................ 1243 Affecting Monsoons?............................................ 1228 14.5.1 Northern Modes....................................................... 1244 FAQ 14.2 How Are Future Projections in Regional 14.5.2 Southern Annular Mode........................................... 1245 Climate Related to Projections of Global Means?................................................................... 1256 14.5.3 Assessment Summary.............................................. 1246 Box 14.2: Blocking.................................................................... 1246 Supplementary Material Supplementary Material is available in online versions of the report. 14.6 Large-scale Storm Systems......................................... 1248 14.6.1 Tropical Cyclones..................................................... 1248 14.6.2 Extratropical Cyclones............................................. 1251 14.6.3 Assessment Summary.............................................. 1252 14 1218 Climate Phenomena and their Relevance for Future Regional Climate Change Chapter 14 Executive Summary precipitation are projected to increase. There is medium confidence that the increase of the Indian summer monsoon rainfall and its extremes This chapter assesses the scientific literature on projected changes in throughout the 21st century will be the largest among all monsoons. major climate phenomena and more specifically their relevance for {14.2.2, 14.8.9, 14.8.11, 14.8.13} future change in regional climates, contingent on global mean temper- atures continue to rise. There is low confidence in projections of changes in precipita- tion amounts for the North American and South American monsoons, Regional climates are the complex result of processes that vary strong- but medium confidence that the North American monsoon will arrive ly with location and so respond differently to changes in global-scale and persist later in the annual cycle, and high confidence in expansion influences. The following large-scale climate phenomena are increas- of the South American monsoon area. {14.2.3, 14.8.3, 14.8.4, 14.8.5} ingly well simulated by climate models and so provide a scientific basis for understanding and developing credibility in future regional There is low confidence in projections of a small delay in the devel- climate change. A phenomenon is considered relevant to regional cli- opment of the West African rainy season and an intensification of mate change if there is confidence that it has influence on the regional late-season rains. Model limitations in representing central features climate and there is confidence that the phenomenon will change, par- of the West African monsoon result in low confidence in future projec- ticularly under the Representative Concentration Pathway 4.5 (RCP4.5) tions. {14.2.4, 14.8.7} or higher end scenarios. {Table 14.3} Tropical Phenomena Monsoon Systems Based on models ability to reproduce general features of the There is growing evidence of improved skill of climate models Indian Ocean Dipole and agreement on future projections, the in reproducing climatological features of the global mon- tropical Indian Ocean is likely to feature a zonal (east west) soon. Taken together with identified model agreement on pattern of change in the future with reduced warming and future changes, the global monsoon, aggregated over all mon- decreased precipitation in the east, and increased warming and soon systems, is likely1 to strengthen in the 21st century with increased precipitation in the west, directly influencing East increases in its area and intensity, while the monsoon circula- Africa and Southeast Asia precipitation. {14.3, 14.8.7, 14.8.12} tion weakens. Monsoon onset dates are likely to become earlier or not to change much and monsoon retreat dates are likely to A newly identified robust feature in model simulations of trop- delay, resulting in lengthening of the monsoon season in many ical precipitation over oceans gives medium confidence that regions. {14.2.1} annual precipitation change follows a warmer-get-wetter pattern, increasing where warming of sea surface temperature Future increase in precipitation extremes related to the monsoon is exceeds the tropical mean and vice versa. There is medium con- very likely in South America, Africa, East Asia, South Asia, Southeast fidence in projections showing an increase in seasonal mean precipi- Asia and Australia. Lesser model agreement results in medium confi- tation on the equatorial flank of the Inter-Tropical Convergence Zone dence2 that monsoon-related interannual precipitation variability will (ITCZ) affecting parts of Central America, the Caribbean, South Ameri- increase in the future. {14.2.1, 14.8.5, 14.8.7, 14.8.9, 14.8.11, 14.8.12, ca, Africa and West Asia despite shortcomings in many models in simu- 14.8.13} lating the ITCZ. There is medium confidence that the frequency of zon- ally oriented South Pacific Convergence Zone events will increase, with Model skill in representing regional monsoons is lower compared to the South Pacific Convergence Zone (SPCZ) lying well to the northeast the global monsoon and varies across different monsoon systems. of its average position, a feature commonly reproduced in models that There is medium confidence that overall precipitation associated with simulate the SPCZ realistically, resulting in reduced precipitation over the Asian-Australian monsoon will increase but with a north south many South Pacific island nations. Similarly there is medium confi- asymmetry: Indian and East Asian monsoon precipitation is projected dence that the South Atlantic Convergence Zone will shift southwards, to increase, while projected changes in Australian summer monsoon leading to an increase in precipitation over southeastern South Amer- precipitation are small. There is medium confidence that the Indian ica and a reduction immediately north thereof. {14.3, 14.8.4, 14.8.5, summer monsoon circulation will weaken, but this is compensated by 14.8.7, 14.8.11, 14.8.14} increased atmospheric moisture content, leading to more precipitation. ­ For the East Asian summer monsoon, both monsoon circulation and In this Report, the following terms have been used to indicate the assessed likelihood of an outcome or a result: Virtually certain 99 100% probability, Very likely 90 100%, 1 Likely 66 100%, About as likely as not 33 66%, Unlikely 0 33%, Very unlikely 0 10%, Exceptionally unlikely 0 1%. Additional terms (Extremely likely: 95 100%, More likely than not >50 100%, and Extremely unlikely 0 5%) may also be used when appropriate. Assessed likelihood is typeset in italics, e.g., very likely (see Section 1.4 and Box TS.1 for more details). In this Report, the following summary terms are used to describe the available evidence: limited, medium, or robust; and for the degree of agreement: low, medium, or high. 2 A level of confidence is expressed using five qualifiers: very low, low, medium, high, and very high, and typeset in italics, e.g., medium confidence. For a given evidence and agreement statement, different confidence levels can be assigned, but increasing levels of evidence and degrees of agreement are correlated with increasing confidence (see Section 1.4 and Box TS.1 for more details). 14 1219 Chapter 14 Climate Phenomena and their Relevance for Future Regional Climate Change There is low confidence in projections of future changes in the Despite systematic biases in simulating storm tracks, most Madden Julian Oscillation owing to poor ability of the models to models and studies are in agreement on the future changes in simulate it and its sensitivity to ocean warming patterns. The implica- the number of extratropical cyclones (ETCs). The global number tions for future projections of regional climate extremes in West Asia, of ETCs is unlikely to decrease by more than a few percent. A South Asia, Southeast Asia and Australia are therefore highly uncertain small poleward shift is likely in the Southern Hemisphere (SH) when associated with the Madden Julian Oscillation. {14.3, 14.8.10, storm track. It is more likely than not, based on projections with 14.8.11, 14.8.12, 14.8.13} medium confidence, that the North Pacific storm track will shift pole- ward. However, it is unlikely that the response of the North Atlantic There is low confidence in the projections of future changes for the storm track is a simple poleward shift. There is low confidence in the tropical Atlantic, both for the mean and interannual modes, because magnitude of regional storm track changes, and the impact of such of systematic errors in model simulations of current climate. The impli- changes on regional surface climate. It is very likely that increases in cations for future changes in Atlantic hurricanes and tropical South Arctic, Northern European, North American and SH winter precipitation American and West African precipitation are therefore uncertain. {14.3, by the end of the 21st century (2081 2100) will result from more pre- 14.6.1, 14.8.5, 14.8.7 } cipitation in ETCs associated with enhanced extremes of storm-related precipitation. {14.6, 14.8.2, 14.8.3, 14.8.5, 14.8.6, 14.8.13, 14.8.15} The realism of the representation of El Nino-Southern Oscilla- tion (ENSO) in climate models is increasing and models simulate Blocking ongoing ENSO variability in the future. Therefore there is high confidence that ENSO very likely remains as the dominant mode Increased ability in simulating blocking in models and higher of interannual variability in the future and due to increased agreement on projections indicate that there is medium confi- moisture availability, the associated precipitation variability on dence that the frequency of Northern and Southern Hemisphere regional scales likely intensifies. An eastward shift in the patterns blocking will not increase, while trends in blocking intensity and of temperature and precipitation variations in the North Pacific and persistence remain uncertain. The implications for blocking-related North America related to El Nino and La Nina (teleconnections), a fea- regional changes in North America, Europe and Mediterranean and ture consistently simulated by models, is projected for the future, but Central and North Asia are therefore also uncertain. {14.8.3, 14.8.6, with medium confidence, while other regional implications including 14.8.8, Box 14.2} those in Central and South America, the Caribbean, Africa, most of Asia, Australia and most Pacific Islands are more uncertain. However, Annular and Dipolar Modes of Variability natural modulations of the variance and spatial pattern of ENSO are so large in models that confidence in any specific projected change in Models are generally able to simulate gross features of annular its variability in the 21st century remains low. {14.4, 14.8.3, 14.8.4, and dipolar modes. Model agreement in projections indicates 14.8.5, 14.8.7, 14.8.9, 14.8.11, 14.8.12, 14.8.13, 14.8.14} that future boreal wintertime North Atlantic Oscillation is very likely to exhibit large natural variations and trend of similar Cyclones magnitude to that observed in the past and is likely to become slightly more positive on average, with some, but not well doc- Based on process understanding and agreement in 21st century umented, implications for winter conditions in the Arctic, North projections, it is likely that the global frequency of occurrence America and Eurasia. The austral summer/autumn positive trend of tropical cyclones will either decrease or remain essentially in Southern Annular Mode is likely to weaken considerably as unchanged, concurrent with a likely increase in both global stratospheric ozone recovers through the mid-21st century with mean tropical cyclone maximum wind speed and precipitation some, but not well documented, implications for South Ameri- rates. The future influence of climate change on tropical cyclones ca, Africa, Australia, New Zealand and Antarctica. {14.5.1, 14.5.2, is likely to vary by region, but the specific characteristics of the 14.8.2, 14.8.3, 14.8.5, 14.8.6, 14.8.7, 14.8.8, 14.8.13, 14.8.15} changes are not yet well quantified and there is low confidence in region-specific projections of frequency and intensity. How- Atlantic Multi-decadal Oscillation ever, better process understanding and model agreement in specific regions provide medium confidence that precipitation will be more Multiple lines of evidence from paleo reconstructions and model extreme near the centres of tropical cyclones making landfall in North simulations indicate that the Atlantic Multi-decadal Oscillation and Central America; East Africa; West, East, South and Southeast (AMO) is unlikely to change its behaviour in the future as the Asia as well as in Australia and many Pacific islands. Improvements in mean climate changes. However, natural fluctuations in the AMO model resolution and downscaling techniques increase confidence in over the coming few decades are likely to influence regional climates projections of intense storms, and the frequency of the most intense at least as strongly as will human-induced changes, with implications storms will more likely than not increase substantially in some basins. for Atlantic major hurricane frequency, the West African wet season, {14.6, 14.8.3, 14.8.4, 14.8.7, 14.8.9, 14.8.10, 14.8.11, 14.8.12, 14.8.13, North American and European summer conditions. {14.7.6, 14.2.4, 14.8.14} 14.6.1, 14.8.3, 14.8.6} 14 1220 Climate Phenomena and their Relevance for Future Regional Climate Change Chapter 14 Pacific South American Pattern Understanding of underlying physical mechanisms and the pro- jected sea surface temperatures in the equatorial Indo-Pacific regions gives medium confidence that future changes in the mean atmospheric circulation for austral summer will project on this pattern, thereby influencing the South American Conver- gence Zone and precipitation over southeastern South America. {14.7.2, 14.8.5} 14 1221 Chapter 14 Climate Phenomena and their Relevance for Future Regional Climate Change 14.1 Introduction regional climates but also influence the global atmospheric circula- tion. Section 14.3 presents an assessment of these and other important Regional climates are the complex outcome of local physical processes tropical phenomena. and the non-local responses to large-scale phenomena such as the El Nino-Southern Oscillation (ENSO) and other dominant modes of cli- 14.1.2 Modes of Climate Variability mate variability. The dynamics of regional climates are determined by local weather systems that control the net transport of heat, moisture Regional climates are strongly influenced by modes of climate variabil- and momentum into a region. Regional climate is interpreted in the ity (see Box 14.1 for definitions of mode, regime and teleconnection). widest sense to mean the whole joint probability distribution of cli- This chapter assesses major modes such as El Nino-Southern Oscil- mate variables for a region including the time mean state, the variance lation (ENSO, Section 14.4), the North Atlantic Oscillation/Northern and co-variance and the extremes. Annular Mode (NAO/NAM) and Southern Annular Mode (SAM) in the extratropics (Section 14.5) and various other well-known modes such This chapter assesses the physical basis of future regional climate as the Pacific North American (PNA) pattern, Pacific Decadal Oscillation change in the context of changes in the following types of phenom- (PDO), Atlantic Multi-decadal Oscillation (AMO), etc. (Section 14.7). ena: monsoons and tropical convergence zones, large-scale modes of Many of these modes are described in previous IPCC reports (e.g., Sec- climate variability and tropical and extratropical cyclones. Assessment tion 3.6 of AR4 WG1). Chapter 2 gives operational definitions of mode of future changes in these phenomena is based on climate model indices (Box 2.5, Table 1) and an assessment of observed historical projections (e.g., the Coupled Model Intercomparison Project Phase behaviour (Section 2.7.8). Climate models are generally able to sim- 3 (CMIP3) and CMIP5 multi-model ensembles described in Chapter ulate the gross features of many of the modes of variability (Section 12) and an understanding of how well such models represent the key 9.5), and so provide useful tools for understanding how modes might processes in these phenomena. More generic processes relevant to change in the future (Müller and Roeckner, 2008; Handorf and Dethloff, regional climate change, such as thermodynamic processes and land 2009). atmosphere feedback processes, are assessed in Chapter 12. Local pro- cesses such as snow albedo feedback, moisture feedbacks due to local Modes and regimes provide a simplified description of variations in vegetation, effects of steep complex terrain etc. can be important for the climate system. In the simplest paradigm, variations in climate var- changes but are in general beyond the scope of this chapter. The main iables are described by linear projection onto a set of mode indices focus here is on large-scale atmospheric phenomena rather than more (Baldwin et al., 2009; Baldwin and Thompson, 2009; Hurrell and Deser, local feedback processes or impacts such as floods and droughts. 2009). For example, a large fraction of interannual variance in Northern Hemisphere (NH) sea level pressure is accounted for by linear combi- Sections 14.1.1 to 14.1.3 introduce the three main classes of phenom- nations of the NAM and the PNA modes (Quadrelli and Wallace, 2004). ena addressed in this Assessment and then Section 14.1.4 summarizes Alternatively, the nonlinear regime paradigm considers the probability their main impacts on precipitation and surface temperature. Specif- distribution of local climate variables to be a multi-modal mixture of ic climate phenomena are then addressed in Sections 14.2 to 14.7, distributions related to a discrete set of regimes/types (Palmer, 1999; which build on key findings from the Fourth Assessment Report, AR4 Cassou and Terray, 2001; Monahan et al., 2001). (IPCC, 2007a), and provide an assessment of process understanding and how well models simulate the phenomenon and an assessment of There is ongoing debate on the relevance of the different paradigms future projections for the phenomena. In Section 14.8, future regional (Stephenson et al., 2004; Christiansen, 2005; Ambaum, 2008; Fereday climate changes are assessed, and where possible, interpreted in terms et al., 2008), and care is required when interpreting these constructs of future changes in phenomena. In particular, the relevance of the var- (Monahan et al., 2009; Takahashi et al., 2011). ious phenomena addressed in this chapter for future climate change in the regions covered in Annex I are emphasized. The regions are those Modes of climate variability may respond to climate change in one or defined in previous regional climate change assessments (IPCC, 2007a, more of the following ways: 2007b, 2012). Regional Climate Models (RCMs) and other downscaling tools required for local impact assessments are assessed in Section 9.6 No change the modes will continue to behave as they have done and results from these studies are used where such supporting infor- in the recent past. mation adds additional relevant details to the assessment. Index changes the probability distributions of the mode indices 14.1.1 Monsoons and Tropical Convergence Zones may change (e.g., shifts in the mean and/or variance, or more com- plex changes in shape such as changes in local probability density, The major monsoon systems are associated with the seasonal move- e.g., frequency of regimes). ment of convergence zones over land, leading to profound season- al changes in local hydrological cycles. Section 14.2 assesses current Spatial changes the climate patterns associated with the modes understanding of monsoonal behaviour in the present and future cli- may change spatially (e.g., new flavours of ENSO; see Section mate, how monsoon characteristics are influenced by the large-scale 14.4 and Supplementary Material) or the local amplitudes of the tropical modes of variability and their potential changes and how the climate patterns may change (e.g., enhanced precipitation for a monsoons in turn affect regional extremes. Convergence zones over given change in index (Bulic and Kucharski, 2012)). 14 the tropical oceans not only play a fundamental role in determining 1222 Climate Phenomena and their Relevance for Future Regional Climate Change Chapter 14 Structural changes the types and number of modes and their and modes of climate variability. Both types of cyclone can produce mutual dependencies may change; completely new modes could extreme wind speeds and precipitation (see Section 3.4, IPCC Spe- in principle emerge. cial Report on Managing the Risks of Extreme Events and Disasters to Advance Climate Change Adaptation (SREX; IPCC, 2012)). Sections An assessment of changes in modes of variability can be problematic 14.6.1 and 14.6.2 assess the recent progress in scientific understand- for several reasons. First, interpretation depends on how one separates ing of how these important weather systems are likely to change in modes of variability from forced changes in the time mean or variations the future. in the annual cycle (Pezzulli et al., 2005; Compo and Sardeshmukh, 2010). Modes of variability are generally defined using indices based 14.1.4 Summary of Climate Phenomena and their Impact on either detrended anomalies (Deser et al., 2010b) or anomalies on Regional Climate obtained by removing the time mean over a historical reference period (see Box 2.5). The mode index in the latter approach will include Box 14.1, Figure 1 illustrates the large-scale climate phenomena changes in the mean, whereas by definition there is no trend in a mode assessed in this chapter. Many of the climate phenomena are evident index when it is based on detrended anomalies. Second, it can be diffi- in the map of annual mean rainfall (central panel). The most abundant cult to separate natural variations from forced responses, for example, annual rainfall occurs in the tropical convergence zones: Inter-Tropical warming trends in the N. Atlantic during the 20th century that may be Convergence Zone (ITCZ) over the Pacific, Atlantic and African equato- due to trends in aerosol and other forcings rather than natural internal rial belt (see Section 14.3.1.1), South Pacific Convergence Zone (SPCZ) variability (see Sections 14.6.2 and 14.7.1). Finally, modes of climate over central South Pacific (see Section 14.3.1.2) and South Atlantic variability are nonlinearly related to one another (Hsieh et al., 2006) Convergence Zone (SACZ) over Southern South America and Southern and this relationship can change in time (e.g., trends in correlation Atlantic (see Section 14.3.1.3). In the global monsoon domain (white between ENSO and NAO indices). contours on the map), large amounts of precipitation occur but only in certain seasons (see Section 14.2.1). Local maxima in precipitation Even when the change in a mode of variability index does not con- are also apparent over the major storm track regions in mid-latitudes tribute greatly to mean regional climate change, a climate mode may (see Section 14.7.2). Box 14.1 Figure 1 also illustrates surface air tem- still play an important role in regional climate variability and extremes. perature (left panels) and precipitation (right panels) teleconnection Natural variations, such as those due to modes of variability, are a patterns for ENSO (in December to February and June to August; see major source of uncertainty in future projections of mean regional Section 14.4), NAO (in December to February; see Section 14.5.1) and climate (Deser et al., 2012). Furthermore, changes in the extremes of SAM (in September to November; see Section 14.5.2). The telecon- regional climate are likely to be sensitive to small changes in variance nection patterns were obtained by taking the correlation between or shape of the distribution of the mode indices or the mode spatial monthly gridded temperature and precipitation anomalies and indi- patterns (Coppola et al., 2005; Scaife et al., 2008). ces for the modes (see Box 14.1 definitions). It can be seen that all three modes have far-reaching effects on temperature and precipita- 14.1.3 Tropical and Extratropical Cyclones tion in many parts of the world. Box 14.1, Table 1 briefly summarizes the main regional impacts of different well-known modes of climate Tropical and extratropical cyclones (TCs and ETCs) are important variability. weather phenomena intimately linked to regional climate phenomena Box 14.1 | Conceptual Definitions and Impacts of Modes of Climate Variability This box briefly defines key concepts used to interpret modes of variability (below) and summarizes regional impacts associated with well-known modes (Box 14.1, Table 1 and Box 14.1, Figure 1). The terms below are used to describe variations in time series variables reported at a set of geographically fixed spatial locations, for example, a set of observing stations or model grid points (based on the more complete statistical and dynamical interpretation in Stephenson et al. (2004)). Climate indices Time series constructed from climate variables that provides an aggregate summary of the state of the climate system. For example, the difference between sea level pressure in Iceland and the Azores provides a simple yet useful historical NAO index (see Section 14.5 and Box 2.5 for definitions of this and other well-known observational indices). Because of their maximum variance properties, climate indices are often defined using principal components. Principal component A linear combination of a set of time series variables that has maximum variance subject to certain normalization constraints. Principal components are widely used to define optimal climate indices from gridded datasets (e.g., the Arctic Oscillation (AO) index, defined as the leading principal component of NH sea level pressure; Section 14.5). (continued on next page) 14 1223 Chapter 14 Climate Phenomena and their Relevance for Future Regional Climate Change Box 14.1 (continued) Climate pattern A set of coefficients obtained by projection (regression) of climate variables at different spatial locations onto a climate index time series. Empirical Orthogonal Function The climate pattern obtained if the climate index is a principal component. It is an eigenvector of the covariance matrix of gridded climate data. Teleconnection A statistical association between climate variables at widely separated, geographically fixed spatial locations. Teleconnections are caused by large spatial structures such as basin-wide coupled modes of ocean atmosphere variability, Rossby wave-trains, mid-latitude jets and storm-tracks, etc. Teleconnection pattern A correlation map obtained by calculating the correlation between variables at different spatial locations and a climate index. It is the special case of a climate pattern obtained for standardized variables and a standardized climate index, that is, the variables and index are each centred and scaled to have zero mean and unit variance. One-point teleconnection maps are made by choosing a variable at one of the locations to be the climate index. (continued on next page) Box 14.1, Table 1 | Regional climate impacts of fundamental modes of variability. Mode Regional Climate Impacts Global impact on interannual variability in global mean temperature. Influences severe weather and tropical cyclone activity worldwide. The diverse El Nino ENSO flavours present different teleconnection patterns that induce large impacts in numerous regions from polar to tropical latitudes (Section 14.4). Influences surface air temperature and precipitation over the entire North American continent and extratropical North Pacific. Modulates ENSO rainfall PDO teleconnections, e.g., Australian climate (Section 14.7.3). Modulates decadal variability in Australian rainfall, and ENSO teleconnections to rainfall, surface temperature, river flow and flood risk over Australia, IPO New Zealand and the SPCZ (Section 14.7.3). Influences the N. Atlantic jet stream, storm tracks and blocking and thereby affects winter climate in over the N. Atlantic and surrounding landmasses. NAO The summer NAO (SNAO) influences Western Europe and Mediterranean basin climates in the season (Section 14.5.1). Modulates the intensity of mid-latitude storms throughout the Northern Hemisphere and thereby influences North America and Eurasia climates as well as NAM sea ice distribution across the Arctic sea (Section 14.5.1). NPO Influences winter air temperature and precipitation over much of western North America as well as Arctic sea ice in the Pacific sector (Section 14.5.1). Influences temperature over Antarctica, Australia, Argentina, Tasmania and the south of New Zealand and precipitation over southern South America, SAM New Zealand, Tasmania, Australia and South Africa (Section 14.5.2). Influences the jet stream and storm tracks over the Pacific and North American sectors, exerting notable influences on the temperature and precipitation in PNA these regions on intraseasonal and interannual time scales (Section 14.7.2). PSA Influences atmospheric circulation over South America and thereby has impacts on precipitation over the continent (Section 14.7.1). Influences air temperatures and rainfall over much of the Northern Hemisphere, in particular, North America and Europe. It is associated with multidecadal AMO variations in Indian, East Asian and West African monsoons, the North African Sahel and northeast Brazil rainfall, the frequency of North American droughts and Atlantic hurricanes (Section 14.7.6). Influences seasonal hurricane activity in the tropical Atlantic on both decadal and interannual time scales. Its variability is influenced by other modes, AMM particularly ENSO and NAO (Section 14.3.4). Affects the West African Monsoon, the oceanic forcing of Sahel rainfall on both decadal and interannual time-scales and the spatial extension of drought AN in South Africa (Section 14.3.4). Associated with the intensity of Northwest Pacific monsoon, the tropical cyclone activity over the Northwest Pacific and anomalous rainfall over East Asia IOB (Section 14.3.3). Associated with droughts in Indonesia, reduced rainfall over Australia, intensified Indian summer monsoon, floods in East Africa, hot summers over Japan, and IOD anomalous climate in the extratropical Southern Hemisphere (Section 14.3.3). TBO Modulates the strength of the Indian and West Pacific monsoons. Affects droughts and floods over large areas of south Asia and Australia (Section 14.7.4). Modulates the intensity of monsoon systems around the globe and tropical cyclone activity in the Indian, Pacific and Atlantic Oceans. Associated with enhanced MJO rainfall in Western North America, northeast Brazil, Southeast Africa and Indonesia during boreal winter and Central America/Mexico and Southeast Asia during boreal summer (Section 14.3.2). Strongly affects the strength of the northern stratospheric polar vortex as well as the extratropical troposphere circulation, occurring preferentially QBO in boreal winter (Section 14.7.5). BLC Associated with cold air outbreaks, heat-waves, floods and droughts in middle and high latitudes of both hemispheres (Box 14.2). Notes: AMM: Atlantic Meridional Mode IOB: Indian Ocean Basin pattern NAO: North Atlantic Oscillation QBO: Quasi-Biennial Oscillation AMO: Atlantic Multi-decadal Oscillation IOD: Indian Ocean Dipole pattern NPO: North Pacific Oscillation SAM: Southern Annular Mode AN: Atlantic Nino pattern IPO: Interdecadal Pacific Oscillation PDO: Pacific Decadal Oscillation TBO: Tropospheric Biennial Oscillation BLC: Blocking events MJO: Madden-Julian Oscillation PNA: Pacific North America pattern 14 ENSO: El Nino-Southern Oscillation NAM: Northern Annular Mode PSA: Pacific South America pattern 1224 Climate Phenomena and their Relevance for Future Regional Climate Change Chapter 14 Box 14.1 (continued) Mode of climate variability Underlying space time structure with preferred spatial pattern and temporal variation that helps account for the gross features in vari- ance and for teleconnections. A mode of variability is often considered to be the product of a spatial climate pattern and an associated climate index time series. Climate regime A set of similar states of the climate system that occur more frequently than nearby states due to either more persistence or more often recurrence. In other words, a cluster in climate state space associated with a local maximum in the probability density function. Temperature Annual precipitation Precipitation Winter storm-tracks Monsoon precipitation domains NAO DJF NAO DJF SOI DJF SOI DJF SOI JJA SOI JJA SAM SON SAM SON 0 10 30 50 70 90 120 150 200 250 300 400 -0.8 -0.6 -0.4 -0.2 0.2 0.4 0.6 0.8 -0.8 -0.6 -0.4 -0.2 0.2 0.4 0.6 0.8 Box 14.1, Figure 1 | Global distribution of average annual rainfall (in cm/year) from 1979 2010 Global Precipitation Climatology Project (GPCP) database, monsoon precipitation domain (white contours) as defined in Section 14.2.1, and winter storm-tracks in both hemispheres (black arrows). In left (right) column seasonal cor- relation maps of North Atlantic Oscillation (NAO), Southern Oscillation Index (SOI, the atmospheric component of El Nino-Southern Oscillation (ENSO)) and Southern Annular Mode (SAM) mode indexes vs. monthly temperature (precipitation) anomalies in boreal winter (December, January and February (DJF)), austral winter (June, July and August (JJA)) and austral spring (September, October and November (SON)). Black contours indicate a 99% significance level. The mode indices were taken from National Oceanic and Atmospheric Administration (NOAA, http://www.esrl.noaa.gov/psd/data/climateindices/list/), global temperatures from NASA Goddard Institute of Space Studies Surface Temperature Analysis (GISTEMP, http://data.giss.nasa.gov/gistemp/) and global precipitations from GPCP (http://www.esrl.noaa.gov/psd/data/ gridded/data.gpcp.html). 14.2 Monsoon Systems 14.2.1 Global Overview Monsoons are a seasonal phenomenon responsible for producing the The global land monsoon precipitation displays a decreasing trend majority of wet season rainfall within the tropics. The precipitation over the last half-century, with primary contributions from the weak- characteristics over the Asian-Australian, American and African mon- ened summer monsoon systems in the NH (Wang and Ding, 2006). soons can be viewed as an integrated global monsoon system, asso- The combined global ocean land monsoon precipitation has inten- ciated with a global-scale atmospheric overturning circulation (Tren- sified during 1979 2008, mainly due to an upward trend in the NH berth et al., 2000). In Section 14.2.1, changes in precipitation of the summer oceanic monsoon precipitation (Zhou et al., 2008b; Hsu et al., global monsoon system are assessed. Changes in regional monsoons 2011; Wang et al., 2012b). Because the fractional increase in monsoon are assessed in Sections 14.2.2 to 14.2.4. area is greater than that in total precipitation, the ratio of the latter to the former (a measure of the global monsoon intensity) exhibits a 14 1225 Chapter 14 Climate Phenomena and their Relevance for Future Regional Climate Change ­decreasing trend (Hsu et al., 2011). CMIP5 models generally reproduce imum 5-day precipitation total (R5d) and consecutive dry days (CDD) the observed global monsoon domain, but the disparity between the all indicate that intense precipitation will increase at larger rates than best and poorest models is large (Section 9.5.2.4). those of mean precipitation (Figure 14.1). The standard deviation of interannual variability in seasonal average precipitation (Psd) is pro- In the CMIP5 models the global monsoon area (GMA), the global jected to increase by many models but some models show a decrease monsoon total precipitation (GMP) and the global monsoon precipi- in Psd. This is related to uncertainties in projections of future chang- tation intensity (GMI) are projected to increase by the end of the 21st es in tropical sea surface temperature (SST). Regarding seasonality, century (2081 2100, Hsu et al., 2013; Kitoh et al., 2013; Figure 14.1). CMIP5 models project that monsoon onset dates will come earlier or See Supplementary Material Section 14.SM.1.2 for the definitions of not change much while monsoon retreat dates will delay, resulting in a GMA, GMP and GMI. The CMIP5 model projections show an expan- lengthening of the monsoon season in many regions. sion of GMA mainly over the central to eastern tropical Pacific, the southern Indian Ocean and eastern Asia. In all RCP scenarios, GMA is CMIP5 models show a decreasing trend of lower-troposphere wind very likely to increase, and GMI is likely to increase, resulting in a very convergence (dynamical factor) throughout the 20th and 21st centu- likely increase in GMP, by the end of the 21st century (2081 2100). The ries (Figure 14.2d). With increased moisture (see also Section 12.4), 100-year median changes in GMP are +5%, +8%, +10%, and +16% the moisture flux convergence shows an increasing trend from 1980 in RCP2.6, RCP4.5, RCP6.0, and RCP8.5 scenarios, respectively. Indices through the 21st century (Figure 14.2c). Surface evaporation shows of precipitation extremes such as simple daily precipitation intensity a similar trend (Figure 14.2b) associated with warmer SSTs. There- index (SDII), defined as the total precipitation divided by the number fore, the global monsoon precipitation increases (Figure 14.2a) due to of days with precipitation greater than or equal to 1 mm, annual max- increases in moisture flux convergence and surface evaporation despite Figure 14.1 | (Upper) Observed (thick contour) and simulated (shading) global monsoon domain, based on the definition of Wang et al. (2011). The observations are based on GPCP v2.2 data (Huffman et al., 2009), and the simulations are based on 26 CMIP5 multi-model mean precipitation with a common 2.5 by 2.5 degree grid in the present day (1986 2005) and the future (2080 2099; RCP8.5 scenario). Orange (dark blue) shading shows monsoon domain only in the present day (future). Light blue shading shows monsoon domain in both periods. (Lower) Projected changes for the future (2080 2099) relative to the present day (1986-2005) in the global monsoon area (GMA) and global monsoon intensity (GMI), global monsoon total precipitation (GMP), standard deviation of interannual variability in seasonal average precipitation (Psd), simple daily precipitation intensity index (SDII), seasonal maximum 5-day precipitation total (R5d), seasonal maximum consecutive dry days (CDD) and monsoon season duration (DUR), under the RCP2.6 (dark blue; 18 models), RCP4.5 (light blue; 24 models), RCP6.0 (orange; 14 models) and RCP8.5 scenarios (red; 26 models). Units are % except for DUR (days). Box-and-whisker plots show the 10th, 25th, 50th, 75th and 90th percentiles. All of the indices are calculated for the summer season (May to September in the Northern Hemisphere; November to March in the Southern Hemisphere). The indices of Psd, SDII, R5d and CDD calculated for each model s original grid, and then averaged over the monsoon domains determined by 14 each model at the present-day. The indices of DUR are calculated for seven regional monsoon domains based on the criteria proposed by Wang and LinHo (2002) using regionally averaged climatological cycles of precipitation, and then their changes are averaged with weighting based on their area at the present day. 1226 Climate Phenomena and their Relevance for Future Regional Climate Change Chapter 14 Figure 14.2 | Time series of simulated anomalies, smoothed with a 20-year running mean over the global land monsoon domain for (a) precipitation (mm day 1), (b) evaporation (mm day 1), (c) water vapour flux convergence in the lower (below 500 hPa) troposphere (mm day 1), and (d) wind convergence in the lower troposphere (10 3 kg m 2 s 1), relative to the present-day (1986 2005), based on CMIP5 multi-model monthly outputs. Historical (grey; 29 models), RCP2.6 (dark blue; 20 models), RCP4.5 (light blue; 24 models), RCP6.0 (orange; 16 models), and RCP8.5 (red; 24 models) simulations are shown in the 10th and 90th percentile (shading), and in all model averages (thick lines). a weakened monsoon circulation. Besides greenhouse gases (GHGs), precipitation of the East Asian summer (EAS) monsoon, while more monsoons are affected by changes in aerosol loadings (Ramanathan than 95% of models project an increase in heavy precipitation events et al., 2005). The aerosol direct forcing may heat the atmosphere but (Figure 14.4). All models and all scenarios project an increase in both cools the surface, altering atmospheric stability and inducing horizon- the mean and extreme precipitation in the Indian summer monsoon tal pressure gradients that modulate the large-scale circulation and (referred to as SAS in Figures 14.3 and 14.4) . In these two regions, hence monsoon rainfall (Lau et al., 2008). However, the representation the interannual standard deviation of seasonal mean precipitation of aerosol forcing differs among models, and remains an important also increases. Over the Australian-Maritime Continent (AUSMC) source of uncertainty (Chapter 7 and Section 12.2.2), particularly in monsoon region, agreement among models is low. Figure 14.5 shows some regional monsoon systems. the time-series of circulation indices representing EAS, Indian (IND), Western North Pacific (WNP) and Australian (AUS) summer monsoon 14.2.2 Asian-Australian Monsoon systems. The Indian monsoon circulation index is likely to decrease in the 21st century, while a slight increase in the East Asian monsoon The seasonal variation in the thermal contrast between the large Eur- circulation is projected. Scatter among models is large for the western asian landmass and the Pacific-Indian Oceans drives the powerful North Pacific and Australian monsoon circulation change. Asian-Australian monsoon (AAM) system (Figure 14.3), which consists of five major subsystems: Indian (also known as South Asian), East Factors that limit the confidence in quantitative assessment of mon- Asian, Maritime Continent, Australian, and Western North Pacific mon- soon changes include sensitivity to model resolution (Cherchi and soons. More than 85% of CMIP5 models show an increase in mean Navarra, 2007; Klingaman et al., 2011), model biases (Levine and 14 1227 Chapter 14 Climate Phenomena and their Relevance for Future Regional Climate Change Frequently Asked Questions FAQ 14.1 | How is Climate Change Affecting Monsoons? Monsoons are the most important mode of seasonal climate variation in the tropics, and are responsible for a large fraction of the annual rainfall in many regions. Their strength and timing is related to atmospheric moisture con- tent, land sea temperature contrast, land cover and use, atmospheric aerosol loadings and other factors. Overall, monsoonal rainfall is projected to become more intense in future, and to affect larger areas, because atmospheric moisture content increases with temperature. However, the localized effects of climate change on regional mon- soon strength and variability are complex and more uncertain. Monsoon rains fall over all tropical continents: Asia, Australia, the Americas and Africa. The monsoon circulation is driven by the difference in temperature between land and sea, which varies seasonally with the distribution of solar heating. The duration and amount of rainfall depends on the moisture content of the air, and on the configuration and strength of the atmospheric circulation. The regional distribution of land and ocean also plays a role, as does topography. For example, the Tibetan Plateau through variations in its snow cover and surface heating modu- lates the strength of the complex Asian monsoon systems. Where moist on-shore winds rise over mountains, as they do in southwest India, monsoon rainfall is intensified. On the lee side of such mountains, it lessens. Since the late 1970s, the East Asian summer monsoon has been weakening and not extending as far north as it used to in earlier times , as a result of changes in the atmospheric circulation. That in turn has led to increasing drought in northern China, but floods in the Yangtze River Valley farther south. In contrast, the Indo-Australian and West- ern Pacific monsoon systems show no coherent trends since the mid-20th century, but are strongly modulated by the El Nino-Southern Oscillation (ENSO). Similarly, changes observed in the South American monsoon system over the last few decades are strongly related to ENSO variability. Evidence of trends in the North American monsoon system is limited, but a tendency towards heavier rainfalls on the northern side of the main monsoon region has been observed. No systematic long-term trends have been observed in the behaviour of the Indian or the African monsoons. The land surface warms more rapidly than the ocean surface, so that surface temperature contrast is increasing in most regions. The tropical atmospheric overturning circulation, however, slows down on average as the climate warms due to energy balance constraints in the tropical atmosphere. These changes in the atmospheric circulation lead to regional changes in monsoon intensity, area and timing. There are a number of other effects as to how (continued on next page) (a) present (b) future solar radiation solar radiation weaker circulation aerosols changes in aerosols more rain land use moisture land use enhanced moisture warm cool warmer warm FAQ 14.1, Figure 1 | Schematic diagram illustrating the main ways that human activity influences monsoon rainfall. As the climate warms, increasing water vapour transport from the ocean into land increases because warmer air contains more water vapour. This also increases the potential for heavy rainfalls. Warming-related changes in large-scale circulation influence the strength and extent of the overall monsoon circulation. Land use change and atmospheric aerosol loading can also affect the amount of solar radiation that is absorbed in the atmosphere and land, potentially moderating the land sea temperature difference. 14 1228 Climate Phenomena and their Relevance for Future Regional Climate Change Chapter 14 FAQ 14.1 (continued) climate change can influence monsoons. Surface heating varies with the intensity of solar radiation absorption, which is itself affected by any land use changes that alter the reflectivity (albedo) of the land surface. Also, chang- ing atmospheric aerosol loadings, such as air pollution, affect how much solar radiation reaches the ground, which can change the monsoon circulation by altering summer solar heating of the land surface. Absorption of solar radiation by aerosols, on the other hand, warms the atmosphere, changing the atmospheric heating distribution. The strongest effect of climate change on the monsoons is the increase in atmospheric moisture associated with warming of the atmosphere, resulting in an increase in total monsoon rainfall even if the strength of the monsoon circulation weakens or does not change. Climate model projections through the 21st century show an increase in total monsoon rainfall, largely due to increasing atmospheric moisture content. The total surface area affected by the monsoons is projected to increase, along with the general poleward expansion of the tropical regions. Climate models project from 5% to an approxi- mately 15% increase of global monsoon rainfall depending on scenarios. Though total tropical monsoon rainfall increases, some areas will receive less monsoon rainfall, due to weakening tropical wind circulations. Monsoon onset dates are likely to be early or not to change much and the monsoon retreat dates are likely to delay, resulting in lengthening of the monsoon season. Future regional trends in monsoon intensity and timing remain uncertain in many parts of the world. Year-to-year variations in the monsoons in many tropical regions are affected by ENSO. How ENSO will change in future and how its effects on monsoon will change also remain uncertain. However, the projected overall increase in mon- soon rainfall indicates a corresponding increase in the risk of extreme rain events in most regions. Turner, 2012; Bollasina and Ming, 2013), poor skill in simulating the Ramanathan, 2006; Lau et al., 2008; Bollasina et al., 2011), land use Madden Julian Oscillation (MJO; Section 9.1.3.3) and uncertainties in (Niyogi et al., 2010; see also Chapter 10) and SSTs (Annamalai et al., projected ENSO changes (Collins et al., 2010; Section 14.4) and in the 2013). An increase in extreme rainfall events occurred at the expense representation of aerosol effects (Section 9.4.6). of weaker rainfall events (Goswami et al., 2006) over the central Indian region, and in many other areas (Krishnamurthy et al., 2009). With a 14.2.2.1 Indian Monsoon declining number of monsoon depressions (Krishnamurthy and Ajay- amohan, 2010), the upward trend in extreme rainfall events may be The Indian summer monsoon is known to have undergone abrupt due to enhanced moisture content (Goswami et al., 2006) or warmer shifts in the past millennium, giving rise to prolonged and intense SSTs in the tropical Indian Ocean (Rajeevan et al., 2008). droughts (Meehl and Hu, 2006; Sinha et al., 2011; see also Chapter 2). The observed recent weakening tendency in seasonal rainfall and CMIP3 projections show suppressed rainfall over the equatorial Indian the regional re-distribution has been partially attributed to factors Ocean (Cai et al., 2011e; Turner and Annamalai, 2012), and an increase such as changes in black carbon and/or sulphate aerosols (Chung and in seasonal mean rainfall over India (Ueda et al., 2006; Annamalai Figure 14.3 | Regional land monsoon domain based on 26 CMIP5 multi-model mean precipitation with a common 2.5° × 2.5° grid in the present-day (1986 2005). For regional divisions, the equator separates the northern monsoon domains (North America Monsoon System (NAMS), North Africa (NAF), Southern Asia (SAS) and East Asian summer (EAS)) from the southern monsoon domains (South America Monsoon System (SAMS), South Africa (SAF), and Australian-Maritime Continent (AUSMC)), 60°E separates NAF from SAS, 14 and 20°N and 100°E separates SAS from EAS. All the regional domains are within 40°S to 40°N. 1229 Chapter 14 Climate Phenomena and their Relevance for Future Regional Climate Change Figure 14.4 | Changes in precipitation indices over the regional land monsoon domains of (upper) East Asian summer (EAS), (middle) Southern Asia (SAS), and (lower) Australian- Maritime Continent (AUSMC) based on CMIP5 multi-models. (Left) Time series of observed and model-simulated summer precipitation anomalies (%) relative to the present-day average. All the time series are smoothed with a 20-year running mean. For the time series of simulations, all model averages are shown by thick lines for the historical (grey; 40 models), RCP2.6 (dark blue; 24 models), RCP4.5 (light blue; 34 models), RCP6.0 (orange; 20 models), and RCP8.5 scenarios (red; 32 models). Their intervals between 10th and 90th percentiles are shown by shading for RCP2.6 and RCP8.5 scenarios. For the time series of observations, Climate Research Unit (CRU) TS3.2 (update from Mitchell and Jones, 2005; dark blue), Global Precipitation Climatology Centre (GPCC) v6 (Becker et al., 2013; deep green), GPCC Variability Analysis of Surface Climate Observations (VASClimO; Beck et al., 2005; light green), Highly Resolved Observational Data Integration Towards the Evaluation of Water Resources (APHRODITE) v1101 (Yatagai et al., 2012; only for EAS and SAS regions; light blue), Global Precipitation Climatology Project (GPCP) v2.2 (updated from Huffman et al., 2009; black), and Climate Prediction Center (NOAA) Merged Analysis of Precipitation (CMAP) v1201 (updated from Xie and Arkin, 1997; black with dots) are shown. GPCC v6 with dot line, GPCC VASClimO, GPCP v2.2 and CMAP v1201 are calculated using all grids for the period of 1901 2010, 1951 2000, 1979 2010, 1979 2010, respectively. CRU TS3.2, GPCC v6 with solid line, and APHRODITE v1101, are calculated using only grid boxes (2.5° in longitude/latitude) where at least one observation site exists for more than 80% of the period of 1921 2005, 1921 2005, and 1951 2005, respectively. (Right) Projected changes for the future (2080-2099) relative to the present-day average in averaged precipitation (Pav), standard deviation of interannual variability in seasonal average precipitation (Psd), simple precipitation daily intensity index (SDII), seasonal maximum 5-day precipitation total (R5d), seasonal maximum consecutive dry days (CDD), monsoon onset date (ONS), retreat date (RET), and duration (DUR), under the RCP2.6 (18 models), RCP4.5 (24 models), RCP6.0 (14 models) and RCP8.5 scenarios (26 models). Units are % in Pav, Psd, SDII, R5d, and CDD; days in ONS, RET, and DUR. Box-whisker plots show the 10th, 25th, 50th, 75th and 90th percentiles. All of the indices are calculated for the summer season (May to September in the Northern Hemisphere; November to March in the Southern Hemisphere). The indices of Pav, Psd, SDII, R5d and CDD are calculated for each model s original grid, and then averaged over the monsoon domains determined by each model at the present day. The indices of ONS, RET and DUR are calculated based 14 on the criteria proposed by Wang and LinHo (2002) using regionally averaged climatological cycles of precipitation. 1230 Climate Phenomena and their Relevance for Future Regional Climate Change Chapter 14 Figure 14.5 | Time series of summer monsoon indices (21-year running mean) relative to the base period average (1986 2005). Historical (gray), RCP4.5 (light blue) and RCP8.5 (red) simulations by 39 CMIP5 model ensembles are shown in 10th and 90th (shading), and 50th (thick line) percentiles. (a) East Asian summer monsoon (defined as June, July and August (JJA) sea level pressure difference between 160°E and 110°E from 10°N to 50°N), (b) Indian summer monsoon (defined as meridional differences of the JJA 850 hPa zonal winds averaged over 5°N to 15°N, 40°E to 80°E and 20°N to 30°N, 60°E to 90°E), (c) western North Pacific summer monsoon (defined as meridional differences of the JJA 850 hPa zonal winds averaged over 5°N to 15°N, 100°E to 130°E and 20°N to 30°N, 110°E to 140°E), (d) Australian summer monsoon (defined as December, January and February (DJF) 850 hPa zonal wind anomalies averaged over 10°S to 0°, 120°E to 150°E). (See Wang et al. (2004) and Zhou et al. (2009c) for indices definitions.) et al., 2007; Turner et al., 2007a; Kumar et al., 2011b; Sabade et al., 14.2.2.2 East Asian Monsoon 2011). These results are generally confirmed by CMIP5 projections (Chaturvedi et al., 2012). The projected changes in Indian monsoon The East Asian monsoon is characterized by a wet season and rainfall increase with the anthropogenic forcing among RCPs (May, southerly flow in summer and by dry cold northerly flow in winter. 2011; see Figure 14.4; SAS). The East Asian summer (EAS) monsoon circulation has experienced an inter-decadal weakening from the 1960s to the 1980s (Hori et In a suite of models that realistically simulate ENSO monsoon rela- al., 2007; Li et al., 2010a), associated with deficient rainfall in North tionships, normal monsoon years are likely to become less frequent China and excessive rainfall in central East China along 30°N (Hu, in the future, but there is no clear consensus about the occurrence of 1997; Wang, 2001; Gong and Ho, 2002; Yu et al., 2004). The summer extreme monsoon years (Turner and Annamalai, 2012). CMIP3 models monsoon circulation has begun to recover in recent decades (Liu et indicate ENSO monsoon relationships to persist in the future (Kumar al., 2012a; Zhu et al., 2012). The summer rainfall amount over East et al., 2011b), but there is low confidence in the projection of ENSO Asia shows no clear trend during the 20th century (Zhang and Zhou, variability (Section 14.4). Sub-seasonal scale monsoon variability is 2011), although significant trends may be found in local station linked to the MJO but again the confidence in the future projection of records (Wang et al., 2006). The winter monsoon circulation weakened MJO remains low (Section 14.3.2). significantly after the 1980s (Wang et al., 2009a; Wang and Chen, 2010). See Supplementary Material Sections 14.SM.1.3 to 14.SM.1.7 CMIP5 models project an increase in mean precipitation as well as for additional discussions of natural variability. its interannual variability and extremes (Figure 14.4; SAS). All models project an increase in heavy precipitation events but disagree on CDD CMIP3 models show reasonable skill in simulating large-scale circula- changes. Regarding seasonality, model agreement is high on an earlier tion of the EAS monsoon (Boo et al., 2011), but their performance is onset and later retreat, and hence longer duration. The monsoon cir- poor in reproducing the monsoon rainband (Lin et al., 2008a; Li and culation weakens in the future (Figure 14.5; IND) but the precipitation Zhou, 2011). Only a few CMIP3 models reproduce the Baiu rainband increases. Like the global monsoon (Section 14.2.1), the precipitation (Ninomiya, 2012) and high-resolution models (Kitoh and Kusunoki, ­ increase is largely due to the increased moisture flux from ocean to 2008) show better performance than low resolution CMIP3 type land. models in simulating the monsoon rainband (Kitoh and Kusunoki, 14 1231 Chapter 14 Climate Phenomena and their Relevance for Future Regional Climate Change 2008). CMIP3 models show large uncertainties in projections of mon- stayn et al., 2007; Shi et al., 2008b; Smith et al., 2008), whereas over soon precipitation and circulation (Ding et al., 2007; Kripalani et al., northeast Australia, summer rainfall has decreased markedly since 2007a) but the simulation of interannual variability of the EAS mon- around 1980 (Li et al., 2012a). soon circulation has improved from CMIP3 to CMIP5 (Sperber et al., 2012). Climate change may bring a change in the position of the mon- Models in general show skill in representing the gross spatial char- soon rain band (Li et al., 2010a). acteristics of Australian monsoon summer precipitation (Moise et al., 2005). Further, atmospheric General Circulation Models (GCMs) forced CMIP5 projections indicate a likely increase in both the circulation by SST anomalies can skilfully reproduce monsoon-related zonal wind (Figure 14.5) and rainfall of the EAS monsoon (Figure 14.4) throughout variability over recent decades (Zhou et al., 2009a). Recent analysis of the 21st century. This is different from other Asian-Australian mon- the skill of a suite of CMIP3 models showed a good representation in soon subsystems, where the increase in precipitation (Figure 14.4) the ensemble mean, but a very large range of biases across individu- is generally associated with weakening monsoon circulation (Figure al models (more than a factor of 6; Colman et al., 2011). Most CMIP 14.5). Interannual variability of seasonal mean rainfall is very likely to models have biases in monsoon seasonality, but CMIP5 models gener- increase except for RCP2.6 (Figure 14.4). Heavy precipitation events ally perform better than CMIP3 (Jourdain et al., 2013). (SDII and R5d) are also very likely to increase. CMIP5 models project an earlier monsoon onset and longer duration but the spread among In climate change projections, overall changes in tropical Australian models is large (Figure 14.4). rainfall are small, with substantial uncertainties (Figure 14.4; Moise et al., 2012; see also Figure 14.27). Using a group of CMIP5 models that 14.2.2.3 Maritime Continent Monsoon exhibit a realistic present-day climatology, most projections using the RCP8.5 scenario produced 5% to 20% more monsoon rainfall by the Interaction between land and water characterizes the Maritime Con- late 21st century compared to the pre-industrial period (Jourdain et tinent region located between the Asian continent and Australia. It al., 2013). Most CMIP3 model projections suggest delayed monsoon provides a land bridge along which maximum convection marches onset and reduced monsoon duration over northern Australia. Weaker from the Asian summer monsoon regime (generally peaking in June, model agreement is seen over the interior of the Australian continent, July and August) to the Australian summer monsoon system (generally where ensembles show an approximate 7-day delay of both the onset peaking in December, January and February). and retreat with little change in duration (Zhang et al., 2013a). CMIP5 model agreement in changes of monsoon precipitation seasonality is Phenomena such as the MJO (Tangang et al., 2008; Section 14.3.2; low (Figure 14.4). Hidayat and Kizu, 2010; Salahuddin and Curtis, 2011), and ENSO (Aldri- an and Djamil, 2008; Moron et al., 2010; Section 14.4) influence Mari- 14.2.2.5 Western North Pacific Monsoon time Continent Monsoon variability. Rainfall extremes in the Maritime Continent are strongly influenced by diurnal rainfall variability (Qian, The western North Pacific summer monsoon (WNPSM) occupies a 2008; Qian et al., 2010a; Robertson et al., 2011; Ward et al., 2011) as broad oceanic region of the South China and Philippine Seas, featuring well as the MJO. There have been no obvious trends in extreme rainfall a monsoon trough and a subtropical anticyclonic ridge to the north indices in Indonesia, except evidence of a decrease in some areas in (Zhang and Wang, 2008). annual rainfall and an increase in the ratio of the wet to dry season rainfall (Aldrian and Djamil, 2008). The western North Pacific monsoon does not show any trend during 1950 1999. Since the late 1970s, the correlation has strengthened Modelling the Maritime Continent monsoon is a challenge because between interannual variability in the western North Pacific monsoon of the coarse resolution of contemporary large-scale coupled climate and ENSO (Section 14.4), a change mediated by Indian Ocean SST models (Aldrian and Djamil, 2008; Qian, 2008). Most CMIP3 models (Huang et al., 2010; Xie et al., 2010a). This occurred despite a weaken- tend to simulate increasing precipitation in the tropical central Pacific ing of the Indian monsoon ENSO correlation in this period (Wang et but declining trends over the Maritime Continent for June to August al., 2008a). (Ose and Arakawa, 2011), consistent with a decreasing zonal SST gra- dient across the equatorial Pacific and a weakening Walker Circulation CMIP5 models project little change in western North Pacific monsoon (Collins et al., 2010). Projections of CMIP5 models are consistent with circulation (Figure 14.5) but enhanced precipitation (Figures AI.66-67; those of CMIP3 models, with decreasing precipitation during boreal Figure 12.22; but see also Figure 14.24) due to increased moisture con- summer and increasing precipitation during boreal winter, but model vergence (Chapter 12). agreement is not high (Figures AI.66-67; Figure 12.22, but see also Figure 14.27). 14.2.3 American Monsoons 14.2.2.4 Australian Monsoon The American monsoons, the North America Monsoon System (NAMS) and the South America Monsoon System (SAMS), are associated with Some indices of the Australian summer monsoon (Wang et al., 2004; Li large inter-seasonal differences in precipitation, humidity and atmos- et al., 2012a) show a clear post-1980 reduction, but another index by pheric  circulation (Vera et al., 2006; Marengo et al., 2010a). NAMS Kajikawa et al. (2010) does not fully exhibit this change. Over north- and SAMS indices are often, though not always, defined in terms of 14 west Australia, summer rainfall has increased by more than 50% (Rot- precipitation characteristics (Wang and LinHo, 2002). 1232 Climate Phenomena and their Relevance for Future Regional Climate Change Chapter 14 Figure 14.6 | As in Figure 14.4, except for (upper) North America Monsoon System (NAMS) and (lower) South America Monsoon System (SAMS). 14.2.3.1 North America Monsoon System 1994; Mock and Brunelle-Daines, 1999; Harrison et al., 2003; Poore et al., 2005; Metcalfe et al., 2010). The warm season precipitation in northern Mexico and the southwest- ern USA is strongly influenced by the NAMS. It has been difficult to Over the coming century, CMIP5 simulations generally project a precip- simulate many important NAMS-related phenomenon in global cli- itation reduction in the core zone of the monsoon (Figures AI.27 and mate models (Castro et al., 2007; Lin et al., 2008b; Cerezo-Mota et al., Figure 14.6), but this signal is not particularly consistent across models, 2011), though the models capture gross-scale features associated with even under the RCP8.5 scenario (Cook and Seager, 2013). Thus con- the NAMS seasonal cycle (Liang et al., 2008b; Gutzler, 2009). See Sup- fidence in projections of monsoon precipitation changes is currently plementary Material Section 14.SM.1.8 for a more detailed discussion low. CMIP5 models have no consensus on future changes of monsoon of NAMS dynamics. timing (Figure 14.6). Temperature increases are consistently project- ed in all models (Annex I). This will likely increase the frequency of In the NAMS core region, no distinct precipitation trends have been extreme summer temperatures (Diffenbaugh and Ashfaq, 2010; Ander- seen over the last half of the 20th century (Anderson et al., 2010; son, 2011; Duffy and Tebaldi, 2012), together with projected increase Arriaga-Ramirez and Cavazos, 2010), due to countervailing trends in in consecutive dry days (Figure 14.6). increasing intensity and decreasing frequency of events, as well as the decreasing length of the monsoon season itself (Englehart and 14.2.3.2 South America Monsoon System Douglas, 2006). However, monsoonal stream flow in western Mexico has been decreasing, possibly as a result of changing precipitation The SAMS mainly influences precipitation in the South American trop- characteristics or antecedent hydrological conditions rather than over- ics and subtropics (Figure 14.1). The main characteristics of SAMS all precipitation  amounts (Gochis et al., 2007). There has also been onset are increased humidity flux from the Atlantic Ocean over north- a systematic delay in monsoon onset, peak and termination (Grantz ern South America, an eastward shift of the subtropical high, strong et al., 2007) as well as an increase in extreme precipitation events northwesterly moisture flux east of the tropical Andes, and establish- associated with land-falling hurricanes (Cavazos et al., 2008). Finally, ment of the Bolivian High (Raia and Cavalcanti, 2008; Marengo et al., positive trends in NAMS precipitation amounts have been detected 2010a; Silva and Kousky, 2012). Recent SAMS indices have been cal- in the northern fringes of the core area, that is, Arizona and western culated based on different variables, such as a large scale index (Silva New Mexico (Anderson et al., 2010), consistent with northward NAMS and Carvalho, 2007), moisture flux (Raia and Cavalcanti, 2008), and expansion during relatively warm periods in the Holocene (Petersen, wind (Gan et al., 2006), in addition to precipitation (Nieto-Ferreira and 14 1233 Chapter 14 Climate Phenomena and their Relevance for Future Regional Climate Change Rickenbach, 2010; Seth et al., 2010; Kitoh et al., 2013). As seen below, tion is known to limit the ability to capture the mesoscale squall line conclusions regarding SAMS changes can depend on the index chosen. systems that form a central element in the maintenance of the rainy season (Ruti and Dell Aquila, 2010; see also Section 14.8.7). Therefore, SAMS duration and amplitude obtained from the observed large-scale projections of the West African monsoon rainfall appear to be uncer- index have both increased in the last 32 years (Jones and Carvalho, tain, reflected by considerable model deficiencies and spread in the 2013). Increase of extreme precipitation and consecutive dry days have projections (Figure 14.7). Note that this figure is based on a somewhat been observed in the SAMS region from 1969 to 2009 (Skansi et al., eastward extended area for the West African monsoon (NAF in Figure 2013). The overall annual cycle of  precipitation in the SAMS region, 14.3), seen as a component of the global monsoon system (Section including SAMS onset and demise, is generally well represented by 14.2.1). The limitations of model simulations in the region arising from models (Bombardi and Carvalho, 2009; Seth et al., 2010; Kitoh et al., the lack of convective organization (Kohler et al., 2010) leading to the 2013). Extreme precipitation indices in SAMS region are also well sim- underestimation of interannual variability (Scaife et al., 2009) imply ulated by CMIP5 models (Kitoh et al., 2013). CMIP5 models subjected that confidence in projections of the African monsoon is low. to historical forcing show increases in SAMS amplitude, earlier onset and later demise during the 1951 2005 period (Jones and Carvalho, The limited information that could be deduced from CMIP3 has not 2013). Using a precipitation based index, precipitation increases in improved much in CMIP5. Figure 14.7 largely confirms the findings austral summer but decreases in austral spring, indicating delayed based on CMIP3. The CMIP5 model ensemble projects a modest SAMS onset in the CMIP3 projections (Seth et al., 2011). CMIP5 projec- change in the onset date (depending on the scenario) and a small tions based on the global precipitation index (Section 14.2.1) consist- delay in the retreat date, leading to a small increase in the duration ently show small precipitation increases and little change in onset and of the rainy season. The delay in the monsoon retreat is larger in the retreat (Kitoh et al., 2013; Figure 14.6). On the other hand, when using high-end emission scenarios. The interannual variance and the 5-day a different index, earlier onsets and later demises and thus, longer rain intensity show a robust increase, while a small increase in dry day duration of the SAMS by the end of the 21st century (2081 2100) has periods is less significant. been found (Jones and Carvalho, 2013). Thus there is medium con- fidence that SAMS overall precipitation will remain unchanged. The 14.2.5 Assessment Summary different estimates of changes in timing underscores potential uncer- tainties related to SAMS timing due to differences in SAMS indices. The It is projected that global monsoon precipitation will likely strengthen models do show significant and robust increases in extreme precipi- in the 21st century with increase in its area and intensity while the tation indices in the SAMS region, such as seasonal maximum 5-day monsoon circulation weakens. Precipitation extremes including pre- precipitation total and number of consecutive dry days (Figure 14.6), cipitation intensity and consecutive dry days are likely to increase at leading to medium confidence in projections of these characteristics. higher rates than those of mean precipitation. Overall, CMIP5 models project that the monsoon onset will be earlier or not change much 14.2.4 African Monsoon and the monsoon retreat dates will delay, resulting in a lengthening of the monsoon season. Such features are likely to occur in most of In Africa, monsoon circulation affects precipitation in West Africa Asian-Australian Monsoon regions. where notable upper air flow reversals are observed. East and south African precipitation is generally described by variations in the tropi- There is medium confidence that overall precipitation associated with cal convergence zone rather than as a monsoon feature. This section the Asian-Australian monsoon will increase but with a north-south covers the West African monsoon, and Section 14.8.7 also covers the asymmetry: Indian and East Asian monsoon precipitation is projected latter two regions. to increase, while projected changes in Australian summer monsoon precipitation are small. There is medium confidence that the Indian The West African monsoon develops during northern spring and summer monsoon circulation will weaken, but this is compensated by summer, with a rapid northward jump of the rainfall belt from along increased atmospheric moisture content, leading to more precipita- the Gulf of Guinea at 5°N in May to June to the Sahel at 10°N in July tion. For the East Asian summer monsoon, both monsoon circulation to August. Factors influencing the West African monsoon include inter- and precipitation are projected to increase. There is low confidence annual to decadal variations, land processes and the direct response to that over the Maritime Continent boreal summer rainfall will decrease radiative forcing. Cross-equatorial tropical Atlantic SST patterns influ- and boreal winter rainfall will increase. There is low confidence that ence the monsoon flow and moistening of the boundary layer, so that changes in the tropical Australian monsoon rainfall are small. There a colder northern tropical Atlantic induces negative rainfall anomalies is low confidence that Western North Pacific summer monsoon circu- (Biasutti et al., 2008; Giannini et al., 2008; Xue et al., 2010; Rowell, lation changes are small, but with increased rainfall due to enhanced 2011). moisture. There is medium confidence in an increase of Indian summer monsoon rainfall and its extremes throughout the 21st century under In CMIP3 simulations, rainfall is projected to decrease in the early part all RCP scenarios. Their percentage change ratios are the largest and but increase towards the end of the rainy season, implying a small model agreement is highest among all monsoon regions. delay in the monsoon season and an intensification of late-season rains (Biasutti and Sobel, 2009; Biasutti et al., 2009; Seth et al., 2010). CMIP5 There is low confidence in projections of American monsoon precipi- models, on the other hand, simulate the variability of tropical Atlantic tation changes but there is high confidence in increases of precipita- 14 SST patterns with little credibility (Section 9.4.2.5.2) and model resolu- tion extremes, of wet days and consecutive dry days. There is medium 1234 Climate Phenomena and their Relevance for Future Regional Climate Change Chapter 14 Figure 14.7 | As in Figure 14.4, except for (upper) North Africa (NAF) and (lower) South Africa (SAF). ­confidence in precipitation associated with the NAMS will arrive later a convergence zone, rainfall may decrease because of the increased in the annual cycle, and persist longer. Projections of changes in the horizontal gradient in specific humidity and the resultant increase in timing and duration of the SAMS remain uncertain. There is high confi- dry advection into the convergence zone (Neelin et al., 2003). dence in the expansion of SAMS, resulting from increased temperature and humidity. Although these arguments based on moist atmospheric dynamics call for changes in tropical convection to be organized around the clima- Based on how models represent known drivers of the West African tological rain band, studies since AR4 show that such changes in a monsoon, there is low confidence in projections of its future develop- warmer climate also depend on the spatial pattern of SST warming. ment based on CMIP5. Confidence is low in projections of a small delay As a result of the SST pattern effect, rainfall change does not generally in the onset of the West African rainy season with an intensification of project onto the climatological convergence zones, especially for the late-season rains. annual mean. In CMIP3/5 model projections, annual rainfall change over tropical oceans follows a warmer-get-wetter pattern, increas- ing where the SST warming exceeds the tropical mean and vice versa 14.3 Tropical Phenomena (Figure 14.8, Xie et al., 2010b; Sobel and Camargo, 2011; Chadwick et al., 2013). Differences among models in the SST warming pattern are 14.3.1 Convergence Zones an important source of uncertainty in rainfall projections, accounting for a third of inter-model variability in annual precipitation change in Section 7.6 presents a radiative perspective of changes in convection the tropics (Ma and Xie, 2013). (including the differences between GHG and aerosol forcings), and Sec- tion 12.4.5.2 discusses patterns of precipitation change on the global Figure 14.8 presents selected indices for several robust patterns of SST scale. The emphasis here is on regional aspects of tropical changes. warming for RCP8.5. They include greater warming in the NH than in Tropical convection over the oceans, averaged for a month or longer, the Southern Hemisphere (SH), a pattern favouring rainfall increase at is organized into long and narrow convergence zones, often anchored locations north of the equator and decreases to the south (Friedman et by SST structures. Latent heat release in convection drives atmospher- al., 2013); enhanced equatorial warming (Liu et al., 2005) that anchors ic circulation and affects global climate. In model experiments where a pronounced rainfall increase in the equatorial Pacific; reduced warm- spatially uniform SST warming is imposed, precipitation increases in ing in the subtropical Southeast Pacific that weakens convection there; these tropical convergence zones (Xie et al., 2010b), following the decreased zonal SST gradient across the equatorial Pacific (see Sec- wet-get-wetter paradigm (Held and Soden, 2006). On the flanks of tion 14.4) and increased westward SST gradient across the ­ quatorial e 14 1235 Chapter 14 Climate Phenomena and their Relevance for Future Regional Climate Change Indian Ocean (see Section 14.3.3) that together contribute to the 2007) and extratropical influences (Kang et al., 2008; Fuèkar et al., weakened Walker cells. 2013). Many models show an unrealistic double-ITCZ pattern over the tropical Pacific and Atlantic, with excessive rainfall south of the equa- Changes in tropical convection affect the pattern of SST change tor (Section 9.4.2.5.1). This bias needs to be kept in mind in assessing (Chou et al., 2005) and such atmospheric and oceanic perturbations ITCZ changes in model projections, especially for boreal spring when are inherently coupled. The SST pattern effect dominates the annual the model biases are largest. rainfall change while the wet-get-wetter effect becomes important for seasonal mean rainfall in the summer hemisphere (Huang et al., 2013). The global zonal mean ITCZ migrates back and forth across the equator This is equivalent to an increase in the annual range of precipitation following the sun. In CMIP5, seasonal mean rainfall is projected to in a warmer climate (Chou et al., 2013). Given uncertainties in SST increase on the equatorward flank of the ITCZ (Figure 14.9). The co-mi- warming pattern, the confidence is generally higher for seasonal than gration of rainfall increase with the ITCZ is due to the wet-get-wetter annual mean changes in tropical rainfall. effect while the equatorward displacement is due to the SST pattern effect (Huang et al., 2013). 14.3.1.1 Inter-Tropical Convergence Zone 14.3.1.2 South Pacific Convergence Zone The Inter-Tropical Convergence Zone (ITCZ) is a zonal band of persis- tent low-level convergence, atmospheric convection, and heavy rainfall. The South Pacific Convergence Zone (SPCZ, Widlansky et al., 2011) Over the Atlantic and eastern half of the Pacific, the ITCZ is displaced extends southeastward from the tropical western Pacific to French Pol- north of the equator due to ocean atmosphere interaction (Xie et al., ynesia and the SH mid-latitudes, contributing most of the yearly rainfall 2 (%) 1.5 1 Temperature ( oC) 0.5 0 0.5 1 1.5 2 NH SH EQ SE PAC IO r(T,Precip) PAC PAC Zonal Zonal Figure 14.8 | (Upper panel) Annual mean precipitation percentage change (dP/P in green/gray shade and white contours at 20% intervals), and relative SST change (colour contours at intervals of 0.2°C; negative dashed) to the tropical (20°S to 20°N) mean warming in RCP8.5 projections, shown as 23 CMIP5 model ensemble mean. (Lower panel) Sea surface temperature (SST) warming pattern indices in the 23-model RCP8.5 ensemble, shown as the 2081 2100 minus 1986 2005 difference. From left: Northern (EQ to 60°N) minus Southern (60°S to EQ) Hemisphere; equatorial (120°E to 60°W, 5°S to 5°N) and Southeast (130°W to 70°W, 30°S to 15°S) Pacific relative to the tropical mean warming; zonal SST gradient in the equatorial Pacific (120°E to 180°E minus 150°W to 90°W, 5°S to 5°N) and Indian (50°E to 70°E, 10°S to 10°N minus 90°E to 110°S, 10°S to EQ) Oceans. (Rightmost) Spatial correlation (r) between relative SST change and precipitation percentage change (dP/P) in the tropics (20°S to 20°N) in each model. (The spatial correlation for 14 the multi-model ensemble mean fields in the upper panel is 0.63). The circle and error bar indicate the ensemble mean and +/-1 standard deviation, respectively. The upper panel is a CMIP5 update of Ma and Xie (2013), and see text for indices in the lower panel. 1236 Climate Phenomena and their Relevance for Future Regional Climate Change Chapter 14 SPCZ events would have major implications for regional climate, possi- bly leading to longer dry spells in the southwest Pacific. 14.3.1.3 South Atlantic Convergence Zone The South Atlantic Convergence Zone (SACZ) extends from the Amazon region through southeastern Brazil towards the Atlantic Ocean during austral summer (Cunningham and Cavalcanti, 2006; Carvalho et al., 2011; de Oliveira Vieira et al., 2013). Floods or dry conditions in south- eastern Brazil are often related to SACZ variability (Muza et al., 2009; Lima et al., 2010; Vasconcellos and Cavalcanti, 2010). A subset of CMIP models simulate the SACZ (Vera and Silvestri, 2009; Seth et al., 2010; Yin et al., 2012) and its variability as a dipolar structure (Junquas et al., 2012; Cavalcanti and Shimizu, 2012). A southward displacement of SACZ and intensification of the southern centre of the precipitation dipole are suggested in projections of CMIP3 and CMIP5 models (Seth et al., 2010; Junquas et al., 2012; Cavalcanti and Shimizu, 2012). This displacement is consistent with the increased precipitation over southeastern South America, south of 25°S, project- ed for the second half of the 21st century, in CMIP3, CMIP5 and region- ( ) al models (Figure AI.34, Figure 14.21). It is also consistent with the southward displacement of the Atlantic subtropical high (Seth et al., 2010) related to the southward expansion of the Hadley Cell (Lu et al., Figure 14.9 | Seasonal cycle of zonal mean tropical precipitation change (2081 2100 in RCP8.5 minus 1986 2005) in CMIP5 multi-model ensemble (MME) mean. Eighteen 2007). Pacific SST warming and the strengthening of the Pacific South CMIP5 models were used. Stippling indicates that more than 90% models agree on the American (PSA)-like wave train (Section 14.6.2) are potential mech- sign of MME change. The red curve represents the meridional maximum of the climato- anisms for changes in the dipolar pattern resulting in SACZ change logical rainfall. (Adapted from Huang et al., 2013.) (Junquas et al., 2012). This change is also supported by the intensifica- tion and increased frequency of the low level jet over South America in future projections (Soares and Marengo, 2009; Seth et al., 2010). to the many South Pacific island nations under its influence. The SPCZ is most pronounced during austral summer (December, January and 14.3.2 Madden Julian Oscillation February (DJF)). The MJO is the dominant mode of tropical intraseasonal (20 to 100 Zonal and meridional SST gradients, trade wind strength, and sub- days) variability (Zhang, 2005). The MJO modulates tropical cyclone sidence over the eastern Pacific are important mechanisms for SPCZ activity (Frank and Roundy, 2006), contributes to intraseasonal fluctu- orientation and variability (Takahashi and Battisti, 2007; Lintner and ations of the monsoons (Maloney and Shaman, 2008), and excites tel- Neelin, 2008; Vincent et al., 2011; Widlansky et al., 2011). Many GCMs econnection patterns outside the tropics (L Heureux and Higgins, 2008; simulate the SPCZ as lying east west, giving a double-ITCZ structure Lin et al., 2009). Simulation of the MJO by GCMs remains challenging, and missing the southeastward orientation (Brown et al., 2012a). but with some improvements made in recent years (Section 9.5.2.3). The majority of CMIP models simulate increased austral summer mean Possible changes in the MJO in a future warmer climate have just precipitation in the SPCZ, with decreased precipitation at the eastern begun to be explored with models that simulate the phenomenon. In edge of the SPCZ (Brown et al., 2012a; Brown et al., 2012b). The posi- the Max Planck Institute Earth System Model, MJO variance increas- tion of the SPCZ varies on interannual to decadal time scales, shifting es appreciably with increasing warming (Schubert et al., 2013). The northeast in response to El Nino (Folland et al., 2002; Vincent et al., change in MJO variance is highly sensitive to the spatial pattern of SST 2011). Strong El Nino events induce a zonally oriented SPCZ locat- warming (Maloney and Xie, 2013). In light of the low skill in simulating ed well northeast of its average position, while more moderate ENSO MJO, and its sensitive to SST warming pattern, which in itself is subject (Section 14.4) events are associated with movement of the SPCZ to the to large uncertainties, it is currently not possible to assess how the northeast or southwest, without a change in its orientation. MJO will change in a warmer climate. Models from both CMIP3 and CMIP5 that simulate the SPCZ well show 14.3.3 Indian Ocean Modes a consistent tendency towards much more frequent zonally oriented SPCZ events in future (Cai et al., 2012b). The mechanism appears to be The tropical Indian Ocean SST exhibits two modes of interannual vari- associated with a reduction in near-equatorial meridional SST gradient, ability (Schott et al., 2009; Deser et al., 2010b): the Indian Ocean Basin a robust feature of modelled SST response to anthropogenic forcing (IOB) mode featuring a basin-wide structure of the same sign, and the (Widlansky et al., 2013). An increased frequency of zonally oriented Indian Ocean Dipole (IOD) mode with largest amplitude in the eastern ­ 14 1237 Chapter 14 Climate Phenomena and their Relevance for Future Regional Climate Change Indian Ocean off Indonesia, and weaker anomalies of the opposite Sea cyclones (Evan et al., 2011b). In the equatorial Indian Ocean, coral polarity over the rest of the basin (Box 2.5). Both modes are statisti- isotope records off Indonesia indicate a reduced SST warming and/or cally significantly correlated with ENSO (Section 14.4). CMIP models increased salinity during the 20th century (Abram et al., 2008). From simulate both modes well (Section 9.5.3.4.2). ship-borne surface measurements, an easterly wind change especially during July to October has been observed over the past six decades, a The formation of IOB is linked to ENSO via an atmospheric bridge and result consistent with a reduction of marine cloudiness in the east and surface heat flux adjustment (Klein et al., 1999; Alexander et al., 2002). a decreasing precipitation trend over the maritime continent (Tokinaga Ocean atmosphere interactions within the Indian Ocean are impor- et al., 2012). Atmospheric reanalysis products have difficulty represent- tant for the long persistence of this mode (Izumo et al., 2008; Wu et ing these changes (Han et al., 2010). al., 2008; Du et al., 2009). The basin mode affects the termination of ENSO events (Kug and Kang, 2006), it induces coherent atmospheric The projected changes over the equatorial Indian Ocean include east- anomalies in the summer following El Nino (Xie et al., 2009), including erly wind anomalies, a shoaling thermocline (Vecchi and Soden, 2007a; supressed convection (Wang et al., 2003) and reduced tropical cyclone Du and Xie, 2008) and reduced SST warming in the east (Stowasser et activity (Du et al., 2011) over the Northwest Pacific and anomalous al., 2009), a result confirmed by CMIP5 multi-model analysis (Zheng rainfall over East Asia (Huang et al., 2004). et al., 2013; Figure 14.10). The change in zonal SST gradient, in turn, reinforces the easterly wind change, indicative of a positive feedback IOD develops in July to November and involves Bjerknes feedback between them as envisioned by Bjerknes (1969). This coupled pattern between zonal SST gradient, zonal wind and thermocline tilt along the is most pronounced during July to November, and is broadly consistent equator (Saji et al., 1999; Webster et al., 1999). A positive IOD event with the observed changes in the equatorial Indian Ocean. (with negative SST anomalies off Sumatra) is associated with droughts in Indonesia, reduced rainfall over Australia, intensified Indian summer In one CMIP3 model, the IOB mode and its capacitor effect persist monsoon, increased precipitation in East Africa and anomalous con- longer, through summer into early fall towards the end of the century ditions in the extratropical SH (Yamagata et al., 2004). Most CMIP3 (2081 2100, Zheng et al., 2011). This increased persistence intensifies models are able to reproduce the general features of the IOD, including ENSO s influence on the Northwest Pacific summer monsoon. The con- its phase lock onto the July to November season, while detailed analy- fidence level of this relationship is low due to the lack of multi-model sis of CMIP5 simulations are not yet available (Section 9.5.3.4.2) studies. Basin-mean SST has risen steadily for much of the 20th century, a trend The IOD variability in SST remains nearly unchanged in future projec- captured by CMIP3 20th century simulations (Alory et al., 2007). The tions of CMIP3 and CMIP5 (Ihara et al., 2009; Figure 14.11a) despite SST increase over the North Indian Ocean since about 1930 is notice- the easterly wind change that lifts the thermocline (Figure 14.10b) ably weaker than for the rest of the basin. This spatial pattern is sug- and intensifies thermocline feedback on SST in the eastern equatorial gestive of the effects of reduced surface solar radiation due to Asian Indian Ocean. The global increase in atmospheric dry static stability brown clouds (Chung and Ramanathan, 2006) and it affects Arabian weakens the atmospheric response to zonal SST gradient changes, Latitude (mm per month) Longitude Figure 14.10 | September to November changes in a 22-model CMIP5 ensemble (2081 2100 in RCP8.5 minus 1986 2005 in historical run). (a) Sea surface temperature (SST, colour contours at 0.1°C intervals) relative to the tropical mean (20°S to 20°N), and precipitation (shading and white contours at 20 mm per month intervals). (b) Surface wind velocity (m s 1), and sea surface height deviation from the global mean (contours, centimetres). Over the equatorial Indian Ocean, ocean atmospheric changes form Bjerknes feedback, with 14 the reduced SST warming and suppressed convection in the east. (Updated with CMIP5 from Xie et al., 2010b.) 1238 Climate Phenomena and their Relevance for Future Regional Climate Change Chapter 14 1.5 90% 1.3 75% 50% Standard deviation 1.1 25% 10% 0.9 0.7 0.5 (a) IOD Variance (b) Zonal Wind Variance (c) SSH Variance Figure 14.11 | CMIP5 multi-model ensemble mean standard deviations of interannual variability for September to November in pre-industrial (PiControl; blue bars) and RCP8.5 (red) runs: (a) the Indian Ocean dipole index defined as the western (50°E to 70°E, 10°S to 10°N) minus eastern (90°E to 110°E, 10°S to 0°) SST difference; (b) zonal wind in the central equatorial Indian Ocean (70°E to 90°E, 5°S to 5°N); and (c) sea surface height in the eastern equatorial Indian Ocean (90°E to 110°E, 10°S to 0°). The standard deviation is normalized by the pre-industrial (PiControl) value for each model before ensemble average. Blue box-and-whisker plots show the 10th, 25th, 50th, 75th and 90th percentiles of 51-year windows for PiControl, representing natural variability. Red box-and-whisker plots represent inter-model variability for RCP8.5, based on the nearest rank. (Adapted from Zheng et al., 2013.) countering the enhanced thermocline feedback (Zheng et al., 2010). Over the past century, the Atlantic has experienced a pronounced The weakened atmospheric feedback is reflected in a decrease in IOD and persistent warming trend. The warming has brought detectable variance in both zonal wind and the thermocline depth (Zheng et al., changes in atmospheric circulation and rainfall patterns in the region. 2013; Figure 14.11b, c ). In particular, the ITCZ has shifted southward and land precipitation has increased over the equatorial Amazon, equatorial West Africa, and 14.3.4 Atlantic Ocean Modes along the Guinea coast, while it has decreased over the Sahel (Deser et al., 2010a; Tokinaga and Xie, 2011; see also Sections 2.5 and 2.7 ). The Atlantic features a northward-displaced ITCZ (Section 14.3.1.1), Atlantic Nino variability has weakened by 40% in amplitude from 1960 and a cold tongue that develops in boreal summer. Climate models to 1999, associated with a weakening of the equatorial cold tongue generally fail to simulate these characteristics of tropical Atlantic cli- (Tokinaga and Xie, 2011). mate (Section 9.5.3.3). The biases severely limit model skill in simu- lating modes of Atlantic climate variability and in projecting future The CMIP3 20th century climate simulations generally capture the climate change in the Atlantic sector. In-depth analysis of the CMIP5 warming trend of the basin-averaged SST over the tropical Atlantic. A projections of Atlantic Ocean Modes has not yet been fully explored, majority of the models also seem to capture the secular trend in the but see Section 12.4.3. tropical Atlantic SST inter-hemispheric gradient and, as a result, the southward shift of the Atlantic ITCZ over the past century (Chang et The inter-hemispheric SST gradient displays pronounced interannual to al., 2011). decadal variability (Box 2.5, Figure 2), referred to as the Atlantic merid- ional mode (AMM; Servain et al., 1999; Chiang and Vimont, 2004; Xie Many CMIP3 model simulations with the A1B emission scenario show and Carton, 2004). A thermodynamic feedback between surface winds, only minor changes in the SST variance associated with the AMM. evaporation and SST (WES; Xie and Philander, 1994) is fundamental to However, the few models that give the best AMM simulation over the the AMM (Chang et al., 2006). This mode affects precipitation in north- 20th century project a weakening in future AMM activity (Breugem et eastern Brazil by displacing the ITCZ (Servain et al., 1999; Chiang and al., 2006), possibly due to the northward shift of the ITCZ (Breugem Vimont, 2004; Xie and Carton, 2004), and Atlantic hurricane activity et al., 2007). At present, model projections of future change in AMM (Vimont and Kossin, 2007; Smirnov and Vimont, 2011). activity is considered highly uncertain because of the poorly simulat- ed Atlantic ITCZ. In fact, uncertainty in projected changes in Atlantic The Atlantic Nino mode represents interannual variability in the equa- meridional SST gradient limits the confidence in regional climate pro- torial cold tongue, akin to ENSO (Box 2.5, Figure 2). Bjerknes feedback jections surrounding the tropical Atlantic Ocean (Good et al., 2008). is considered important for energizing the mode (Zebiak, 1993; Carton and Huang, 1994; Keenlyside and Latif, 2007). This mode affects the A majority of CMIP3 models forced with the A1B emission scenario West Africa Monsoon (Vizy and Cook, 2002; Giannini et al., 2003). project no major change in Atlantic Nino activity in the 21st century, 14 1239 Chapter 14 Climate Phenomena and their Relevance for Future Regional Climate Change while a few models project a sizable decrease in future activity (Breu- 14.4 El Nino-Southern Oscillation gem et al., 2006). The ENSO is a coupled ocean atmosphere phenomenon naturally CMIP5 projections show an accelerated SST warming over much of the occurring at the interannual time scale over the tropical Pacific (see tropical Atlantic (Figure 12.11). RCP8.5 projections of the inter-hemi- Box 2.5, Supplementary Material Section 14.SM.2, and Figure 14.12). spheric SST gradient change within the basin, however, are not consist- ent among CMIP5 models as future GHG increase dominates over the 14.4.1 Tropical Pacific Mean State anthropogenic aerosol effect. SST in the western tropical Pacific has increased by up to 1.5°C per cen- 14.3.5 Assessment Summary tury, and the warm pool has expanded (Liu and Huang, 2000; Huang and Liu, 2001; Cravatte et al., 2009). Studies disagree on how the east There is medium confidence that annual rainfall changes over tropi- west SST gradient along the equator has changed, some showing a cal oceans follow a warmer-get-wetter pattern, increasing where the strengthening (Cane et al., 1997; Hansen et al., 2006; Karnauskas et al., SST warming exceeds the tropical mean and vice versa. One third of 2009; An et al., 2011) and others showing a weakening (Deser et al., inter-model differences in precipitation projection are due to those in 2010a; Tokinaga et al., 2012), because of observational uncertainties SST pattern. The SST pattern effect on precipitation change is a new associated with limited data sampling, changing measurement tech- finding since AR4. niques, and analysis procedures. Most CMIP3 and CMIP5 models also disagree on the response of zonal SST gradient across the equatorial The wet-get-wetter effect is more obvious in the seasonal than annual Pacific (Yeh et al., 2012). rainfall change in the tropics. Confidence is generally higher in sea- sonal than in annual mean changes in tropical precipitation. There is The Pacific Ocean warms more near the equator than in the subtropics medium confidence that seasonal rainfall will increase on the equator- in CMIP3 and CMIP5 projections (Liu et al., 2005; Gastineau and Soden, ward flank of the current ITCZ; that the frequency of zonally oriented 2009; Widlansky et al., 2013; Figure 14.12) because of the difference SPCZ events will increase, with the SPCZ lying well to the northeast in evaporative damping (Xie et al., 2010b). Other oceanic changes of its average position during those events; and that the SACZ shifts include a basin-wide thermocline shoaling (Vecchi and Soden, 2007a; southwards, in conjunction with the southward displacement of the DiNezio et al., 2009; Collins et al., 2010; Figure 14.12), a weakening of South Atlantic subtropical high, leading to an increase in precipitation surface currents, and a slight upward shift and strengthening of the over southeastern South America. equatorial undercurrent (Luo and Rothstein, 2011; Sen Gupta et al., 2012). A weakening of tropical atmosphere circulation during the 20th Owing to models ability to reproduce general features of IOD and century was documented in observations and reanalyses (Vecchi et al., agreement on future projections, it is likely that the tropical Indian 2006; Zhang and Song, 2006; Vecchi and Soden, 2007a; Bunge and Ocean will feature a zonal pattern with reduced (enhanced) warm- Clarke, 2009; Karnauskas et al., 2009; Yu and Zwiers, 2010; Tokinaga ing and decreased (increased) rainfall in the east (west), a pattern et al., 2012) and in CMIP models (Vecchi and Soden, 2007a; Gastineau especially pronounced during August to November. The Indian Ocean and Soden, 2009). The Pacific Walker Circulation, however, intensified dipole mode will very likely remain active, with interannual variability during the most recent two decades (Mitas and Clement, 2005; Liu and unchanged in SST but decreasing in thermocline depth. There is low Curry, 2006; Mitas and Clement, 2006; Sohn and Park, 2010; Li and confidence in changes in the summer persistence of the Indian Ocean Ren, 2012; Zahn and Allan, 2011; Zhang et al., 2011a), illustrating the SST response to ENSO and in ENSO s influence on summer climate over effects of natural variability. the Northwest Pacific and East Asia. 14.4.2 El Nino Changes over Recent Decades and The observed SST warming in the tropical Atlantic represents a reduc- in the Future tion in spatial variation in climatology: the warming is weaker north than south of the equator; and the equatorial cold tongue weakens The amplitude modulation of ENSO at longer time scales has been both in the mean and interannual variability. There is low confidence observed in reconstructed instrumental records (Gu and Philander, in projected changes over the tropical Atlantic, both for the mean and 1995; Wang, 1995; Mitchell and Wallace, 1996; Wang and Wang, 1996; interannual modes, because of large errors in model simulations of Power et al., 1999; An and Wang, 2000; Yeh and Kirtman, 2005; Power current climate. and Smith, 2007; Section 5.4.1), in proxy records (Cobb et al., 2003; Braganza et al., 2009; Li et al., 2011c; Yan et al., 2011), and is also There is low confidence in how MJO will change in the future due to simulated by coupled GCMs (Lau et al., 2008; Wittenberg, 2009). Some the poor skill of models in simulating MJO and the sensitivity of its studies have suggested that the modulation was due to changes in change to SST warming patterns that are themselves subject to large mean climate conditions in the tropical Pacific (An and Wang, 2000; uncertainties in the projections. Fedorov and Philander, 2000; Wang and An, 2001, 2002; Li et al., 2011c), as observed since the 1980s (An and Jin, 2000; An and Wang, 2000; Fedorov and Philander, 2000; Kim and An, 2011). With three events during 2000-2010, which meets intensity in Nino4 being larger than in Nino3, two events during 1990-2000 and only two events are 14 found for 1950-1990 the maximum SST warming during El Nino now 1240 Climate Phenomena and their Relevance for Future Regional Climate Change Chapter 14 (a) Normal conditions Walker circula on Warm pool Cold tongue e Thermoclin Upwelling El Nino Conditions (b) El Nino conditions (SST anomalies) Thermocline Thermocline Upwelling Upwelling Climate change (c) Climate change (SST anomalies) e e Thermoclin Thermoclin Upwelling Upwelling Figure 14.12 | Idealized schematic showing atmospheric and oceanic conditions of the tropical Pacific region and their interactions during normal conditions, El Nino condi- tions, and in a warmer world. (a) Mean climate conditions in the tropical Pacific, indicating sea surface temperatures (SSTs), surface wind stress and associated Walker Circulation, the mean position of convection and the mean upwelling and position of the thermocline. (b) Typical conditions during an El Nino event. SSTs are anomalously warm in the east; convection moves into the central Pacific; the trade winds weaken in the east and the Walker Circulation is disrupted; the thermocline flattens and the upwelling is reduced. (c) The likely mean conditions under climate change derived from observations, theory and coupled General Circulation Models (GCMs). The trade winds weaken; the thermocline flattens and shoals; the upwelling is reduced although the mean vertical temperature gradient is increased; and SSTs (shown as anomalies with respect to the mean tropical-wide warm- ing) increase more on the equator than off. Diagrams with absolute SST fields are shown on the left, diagrams with SST anomalies are shown on the right. For the climate change fields, anomalies are expressed with respect to the basin average temperature change so that blue colours indicate a warming smaller than the basin mean, not a cooling (Collins et al., 2010). 14 1241 Chapter 14 Climate Phenomena and their Relevance for Future Regional Climate Change Figure 14.13 | Intensities of El Nino and La Nina events for the last 60 years in the eastern equatorial Pacific (Nino3 region) and in the central equatorial Pacific (Nino4 region), and the estimated linear trends, obtained from Extended Reconstructed Sea Surface Temperature v3 (ERSSTv3). appears to occur more often in the central Pacific (Figure 14.13; Ashok not significantly distinguished from natural modulations (Stevenson, et al., 2007; Kao and Yu, 2009; Kug et al., 2009; Section 9.5.3.4.1 and 2012; Figure 14.14). Because the change in tropical mean conditions Supplementary Material Section 14.SM.2; Yeh et al., 2009), with global (especially the zonal gradient) in a warming climate is model depend- impacts that are distinct from standard El Nino events where the ent (Section 14.4.1), changes in ENSO intensity for the 21st century maximum warming is over the eastern Pacific (Kumar et al., 2006a; (Solomon and Newman, 2011; Hu et al., 2012a) are uncertain (Figure Ashok et al., 2007; Kao and Yu, 2009; Hu et al., 2012b). During the past 14.14). Future changes in ENSO depend on competing changes in cou- century, an increasing trend in ENSO amplitude was also observed (Li pled ocean atmospheric feedback (Philip and Van Oldenborgh, 2006; et al., 2011c; Vance et al., 2012), possibly caused by a warming climate Collins et al., 2010; Vecchi and Wittenberg, 2010), and on the dynami- (Zhang et al., 2008; Kim and An, 2011) although other reconstructions cal regime a given model is in. There is high confidence, however, that in this data-sparse region dispute this trend (Giese and Ray, 2011). ENSO will remain the dominant mode of natural climate variability in the 21st century (Collins et al., 2010; Guilyardi et al. 2012; Kim and Yu Long coupled GCM simulations show that decadal-to-centennial mod- 2012; Stevenson 2012). ulations of ENSO can be generated without any change in external forcing (Wittenberg, 2009; Yeh et al., 2011), with multi-decadal epochs of anomalous ENSO behaviour. The modulations result from nonlinear processes in the tropical climate system (Timmermann et al., 2003), the interaction with the mean climate state (Ye and Hsieh, 2008; Choi et al., 2009, 2011, 2012), or from random changes in ENSO activity triggered by chaotic atmospheric variability (Power and Colman, 2006; Power et al., 2006). There is little consensus as to whether the decadal modula- tions of ENSO properties (amplitude and spatial pattern) during recent decades are due to anthropogenic effects or natural variability. Instru- mental SST records are available back to the 1850s, but good observa- tions of the coupled air sea feedbacks that control ENSO behaviour including subsurface temperature and current fluctuations, and air sea exchanges of heat, momentum and water are available only after the late 1970s, making observed historical variations in ENSO feedbacks highly uncertain (Chen, 2003; Wittenberg, 2004). CMIP5 models show some improvement compared to CMIP3, espe- Figure 14.14 | Standard deviation in CMIP5 multi-model ensembles of sea surface cially in ENSO amplitude (Section 9.5.3.4.1). Selected CMIP5 models temperature variability over the eastern equatorial Pacific Ocean (Nino3 region: 5°S- that simulate well strong El Nino events show a gradual increase of El 5°N, 150°W-90°W), a measure of El Nino amplitude, for the pre-industrial (PI) control Nino intensity, especially over the central Pacific (Kim and Yu, 2012). and 20th century (20C) simulations, and 21st century projections using RCP4.5 and CMIP3 models suggested a westward shift of SST variability in future RCP8.5. Thirty-one models are used for the ensemble average. Open circles indicate multi-model ensemble means, and the red cross symbol is the observed standard devia- projections (Boer, 2009; Yeh et al., 2009). Generally, however, future tion for January 1870 December 2011 obtained from HadISSTv1. The linear trend and changes in El Nino intensity in CMIP5 models are model dependent climatological mean of seasonal cycle have been removed. Box-whisker plots show the 14 (Guilyardi et al., 2012; Kim and Yu, 2012; Stevenson et al., 2012), and 16th, 25th, 50th, 75th, and 84th percentiles. 1242 Climate Phenomena and their Relevance for Future Regional Climate Change Chapter 14 14.4.3 Teleconnections v ­ariability remains the same (Trenberth 2011; Section 12.4.5). This applies to ENSO-induced precipitation variability but the possibility of There is little improvement in the CMIP5 ensemble relative to CMIP3 changes in ENSO teleconnections complicates this general conclusion, in the amplitude and spatial correlation metrics of precipitation tele- making it somewhat regional-dependent (Seager et al. 2012) connections in response to ENSO, in particular within regions of strong observed precipitation teleconnections (equatorial South America, the 14.4.4 Assessment Summary western equatorial Pacific and a southern section of North America; Langenbrunner and Neelin, 2013). Scenario projections in CMIP3 and ENSO shows considerable inter-decadal modulations in amplitude CMIP5 showed a systematic eastward shift in both El Nino- and La and spatial pattern within the instrumental record. Models without Nina-induced teleconnection patterns over the extratropical NH changes in external forcing display similar modulations, and there is (Meehl and Teng, 2007; Stevenson et al., 2012; Figure 14.15), which little consensus on whether the observed changes in ENSO are due to might be due to the eastward migration of tropical convection centres external forcing or natural variability (see also Section 10.3.3 for an associated with the expansion of the warm pool in a warm climate attribution discussion). (Muller and Roeckner, 2006; Müller and Roeckner, 2008; Cravatte et al., 2009; Kug et al., 2010), or changes in the mid-latitude mean cir- There is high confidence that ENSO will remain the dominant mode of culation (Meehl and Teng, 2007). Some models produced an intensi- interannual variability with global influences in the 21st century, and fied ENSO teleconnection pattern over the North Atlantic region in a due to changes in moisture availability ENSO-induced rainfall variabil- warmer climate (Müller and Roeckner, 2008; Bulic et al., 2012) and a ity on regional scales will intensify. There is medium confidence that weakened teleconnection pattern over the North Pacific (Stevenson, ENSO-induced teleconnection patterns will shift eastward over the 2012). It is unclear whether the eastward shift of tropical convection is North Pacific and North America. There is low confidence in changes in related to longitudinal shifts in El Nino maximum SST anomalies (see the intensity and spatial pattern of El Nino in a warmer climate. Supplementary Material Section 14.SM.2) or to changes in the mean state in the tropical Pacific. Some coupled GCMs, which do not show an increase in the central Pacific warming during El Nino in response 14.5 Annular and Dipolar Modes to a warming climate, do not produce a substantial change in the lon- gitudinal location of tropical convection (Müller and Roeckner, 2008; The North Atlantic Oscillation (NAO), the North Pacific Oscillation (NPO) Yeh et al., 2009). and the Northern and Southern Annular Modes (NAM and SAM) are dominant modes of variability in the extratropics. These modes are the In a warmer climate, the increase in atmospheric moisture intensifies focus of much research attention, especially in impact studies, where temporal variability of precipitation even if atmospheric circulation they are often used as aggregate descriptors of past regional ­climate ­ 20th century (historical) 21st century (RCP 4.5) (e) El Nino Aleutian Low shift: RCP 4.5 (a) o 60 N (c) o 60 N 4 Northwest Northeast 3 4 o o 30 N 30 N 2 SLP (hPa) 1 3 o 0o Lat 0 0 1 2 o 2 30 S 30oS 3 1 4 Lat (°N) o 60 S El Nino DJF 60oS El Nino DJF 0 (b) o 60 N (d) o 60 N 4 3 1 30oN 30oN 2 2 SLP (hPa) 1 0o 0o Lat 0 1 3 2 30oS 30oS 3 4 60oS La Nina DJF 60oS La Nina DJF 4 Southwest Southeast o E oE 120 o 80 o o E oE 120 o 80 o 12 0 160 160oW W W 12 0 160 160oW W W 10 0 10 Lon Lon Lon (°E) Figure 14.15 | Changes to sea level pressure (SLP) teleconnections during December, January and February (DJF) in the CMIP5 models. (a) SLP anomalies for El Nino during the 20th century. (b) SLP anomalies for La Nina during the 20th century. (c) SLP anomalies for El Nino during RCP4.5. (d) SLP anomalies for La Nina during RCP4.5. Maps in (a) (d) are stippled where more than two thirds of models agree on the sign of the SLP anomaly ((a),( b): 18 models; (c),(d): 12 models), and hatched where differences between the RCP4.5 multi-model mean SLP anomaly exceed the 60th percentile (red-bordered regions) or are less than the 40th percentile (blue-bordered regions) of the distribution of 20th century ensemble means. In all panels, El Nino (La Nina) periods are defined as years having DJF Nino3.4 SST above (below) one standard deviation relative to the mean of the detrended time series. For ensemble mean calculations, all SLP anomalies have been normalized to the standard deviation of the ensemblemember detrended Nino3.4 SST. (e) Change in the centre of mass of the Aleutian Low SLP anomaly, RCP4.5 20th century. The Aleutian Low SLP centre of mass is a vector with two elements (lat, lon), and is defined as the sum of 14 (lat, lon) weighted by the SLP anomaly, over all points in the region 180°E to 120°E, 40°N to 60°N having a negative SLP anomaly during El Nino. 1243 Chapter 14 Climate Phenomena and their Relevance for Future Regional Climate Change trends and variations over many parts of the world. For example, since even considered NAO to be a source of natural variability that needs to IPCC (2007a) more than 2000 scientific articles have been published, be removed before detection and attribution of anthropogenic chang- which include NAO, AO, or NAM in either the title or abstract. This es (Zhang et al., 2006). Detection of regional surface air temperature assessment focusses on recent research on these modes that is most response to anthropogenic forcing has been found to be robust to the relevant for future regional climate change. Past behaviour of these exclusion of model-simulated AO and PNA changes (Wu and Karoly, modes inferred from observations is assessed in Section 2.7.8. 2007). Model projections of wintertime European precipitation have been shown to become more consistent with observed trends after 14.5.1 Northern Modes removal of trends due to NAO (Bhend and von Storch, 2008). Underes- timation of trends in NAO can lead to biases in projections of regional The NAO is a well-established dipolar mode of climate variability climate, for example, Arctic sea ice (Koldunov et al., 2010). having opposite variations in sea level pressure between the Atlan- tic subtropical high and the Iceland/Arctic low (Wanner et al., 2001; Underestimation of NAO long-term variability may be due to missing Hurrell et al., 2003; Budikova, 2009). It is strongly associated with the or poorly represented processes in climate models. Recent observation- tropospheric jet, storms (see Section 14.6.2), and blocking that deter- al and modelling studies have helped to confirm that the lower strat- mine the weather and climate over the North Atlantic and surround- osphere plays an important role in explaining recent more negative ing continents (Hurrell and Deser, 2009; Box 14.2). The NAO exists in NAO winters and long-term trends in NAO (Scaife et al., 2005; Dong boreal summer as well as in boreal winter, albeit with different physical et al., 2011; Ouzeau et al., 2011; Schimanke et al., 2011). This is sup- characteristics (Sun et al., 2008; Folland et al., 2009). ported by evidence that seasonal forecasts of NAO can be improved by inclusion of the stratospheric Quasi-Biennial Oscillation (QBO; Boer Over the North Pacific, there is a similar wintertime dipolar mode and Hamilton, 2008; Marshall and Scaife, 2010). Other studies have known as the NPO associated with north south displacements of the found that observed changes in stratospheric water vapour changes Asian-Pacific jet stream and the Pacific storm track. The NPO influences from 1965 1995 led to an impact on NAO simulated by a model, and winter air temperature and precipitation over much of western North have suggested that changes in stratospheric water vapour may be America as well as sea ice over the Pacific sector of the Arctic, more so another possible pathway for communicating tropical forcing to the than either ENSO (Section 14.4) or the PNA (Linkin and Nigam, 2008). extratropics (Joshi et al., 2006; Bell et al., 2009). There is growing evi- dence that future NAO projections are sensitive to how climate models These dipolar modes have been interpreted as the regional manifes- resolve stratospheric processes and troposphere stratosphere interac- tation of an annular mode in sea level pressure known as the Arctic tions (Sigmond and Scinocca, 2010; Scaife et al., 2011a; Karpechko and Oscillation (AO; Thompson and Wallace, 1998) or the Northern Annular Manzini, 2012). Mode (NAM; Thompson and Wallace, 2000). The AO (NAM at 1000 hPa) index and the NAO index (see Box 2.5, Table 1) are strongly correlated Several recent studies of historical data have found a positive associa- but the AO spatial pattern is more zonally symmetric and so differs tion between solar activity and NAO (Haigh and Roscoe, 2006; Kodera from the NAO over the N. Pacific (Ambaum et al., 2001; Feldstein and et al., 2008; Lockwood et al., 2010), while other studies have found Franzke, 2006). Hereafter, the term NAO is used to denote NAO, AO and little imprint of solar and volcanic forcing on NAO (Casty et al., 2007). NAM in boreal winter unless further distinction is required. Positive associations between NAO and solar forcing have been repro- duced in recent modelling studies (Lee et al., 2008; Ineson et al., 2011) Climate models are generally able to simulate the gross features of NAO but no significant changes were found in CMIP5 projections of NAO and NPO (see Section 9.5.3.2). It has been argued that these modes due to changes in solar irradiance or aerosol forcing. may be a preferred pattern of response to climate change (Gerber et al., 2008). However, this is not supported by a detailed examination Observational studies have noted weakening of NAO during periods of of the vertical structure of the simulated global warming response reduced Arctic sea ice (Strong et al., 2009; Wu and Zhang, 2010). Sev- (Woollings, 2008). Hori et al. (2007) noted that NAO variability did not eral modelling studies have also shown a negative NAO response to change substantially in the Special Report on Emission Scenarios (SRE- the partial removal of sea ice in the Arctic or high latitudes (Kvamsto et S)-A1B and 20th century scenarios and so concluded that the trend in al., 2004; Magnusdottir et al., 2004; Seierstad and Bader, 2009; Deser the NAO index (defined relative to a historical mean state) is a result et al., 2010c; Screen et al., 2012). However, the strength and timing of of an anthropogenic trend in the basic mean state rather than due the response to sea ice loss varies considerably between studies, and to changes in NAO variability. However, other research indicates that can be hard to separate from common responses to warming of the there is a coherent two-way interaction between the trend in the mean troposphere and from natural climate variability. The impact of sea ice state and the NAO-like modes of variability the mode and/or regime loss in individual years on NAO is small and hard to detect (Bluthgen structure change due to changes in the mean state (Branstator and et al., 2012). Reviews of the emerging literature on this topic can be Selten, 2009 ; Barnes and Polvani, 2013). Section 14.6.2 assesses the found in Budikova (2009) and Bader et al. (2011). jet and storm track changes associated with the projected responses. The NPO contributes to the excitation of ENSO events via the Seasonal Model simulations have underestimated the magnitude of the large Footprinting Mechanism (SFM; Anderson, 2003; Vimont et al., 2009; positive trend from 1960-2000 in winter NAO observations, which Alexander et al., 2010). Some studies indicate that warm events in the now appears to be more likely due to natural variability rather than central tropical Pacific Ocean may in turn excite the NPO (Di Lorenzo 14 anthropogenic influences (see Section 10.3.3.2). Some studies have et al., 2009). 1244 Climate Phenomena and their Relevance for Future Regional Climate Change Chapter 14 Recent multi-model studies of NAO (Hori et al., 2007; Karpechko, 2010; Some differences in model projections can be accounted for by chang- Zhu and Wang, 2010; Gillett and Fyfe, 2013) reconfirm the small pos- es in the NAO spatial pattern, for example, northeastward shifts in itive response of boreal winter NAO indices to GHG forcing noted in NAO centres of action have been found to be important for estimating earlier studies reported in AR4 (Kuzmina et al., 2005; Miller et al., 2006; the trend in the NAO index (Ulbrich and Christoph, 1999; Hu and Wu, Stephenson et al., 2006). Projected trends in wintertime NAO indices are 2004). Individual model simulations have shown the spatial extent of generally found to have small amplitude compared to natural internal NAO influence decreases with GHG forcing (Fischer-Bruns et al., 2009), variations (Deser et al., 2012). Furthermore, there is substantial vari- a positive feedback between jet and storm tracks that enhances a ation in NAO projections from different climate models. For example, poleward shift in the NAO pattern (Choi et al., 2010), and changes in one study found no significant NAO trends in two simulations with the the NAO pattern but with no changes in the propagation conditions for ECHAM4/OPYC3 model (Fischer-Bruns et al., 2009), whereas another Rossby waves (Brandefelt, 2006). One modelling study found a trend study found a strong positive trend in NAO in the ECHAM5/MPI-OM in the correlation between NAO and ENSO during the 21st century SRES A1B simulations (Müller and Roeckner, 2008). The model depend- (Muller and Roeckner, 2006). Such changes in the structure of NAO ence of the response is an important source of uncertainty in the region- and/or its interaction with other modes of variability would could lead al climate change response (Karpechko, 2010). A multi-model study of to important regional climate impacts. 24 climate model projections suggests that there are no major changes in the NPO due to greenhouse warming (Furtado et al., 2011). 14.5.2 Southern Annular Mode Figures 14.16a, b summarize the wintertime NAO and NAM indices The Southern Annular Mode (SAM, also known as Antarctic Oscillation, simulated by models participating in the CMIP5 experiment (Gillett AAO), is the leading mode of climate variability in the SH extratropics, and Fyfe, 2013). The multi-model mean of the NAO and NAM indices describing fluctuations in the latitudinal position and strength of the are similar and exhibit small linear trends in agreement with those mid-latitude eddy-driven westerly jet (see Box 2.5; Section 9.5.3.2). shown for the NAM index in AR4 (AR4, Figure 10.17a). The multi-model SAM variability has a major influence on the climate of Antarctica, mean projected increase of around 1 to 2 hPa from 1850 to 2100 is A ­ ustralasia, southern South America and South Africa (Watterson, smaller than the spread of around 2 to 4 hPa between model simula- 2009; Thompson et al., 2011 and references therein). tions (Figure 14.16). (a) NAO (b) NAM (c) SAM (d) (e) (f) Figure 14.16 | Summary of multi-model ensemble simulations of wintertime (December to February) mean North Atlantic Oscillation (NAO), Northern Annular Mode (NAM) and Southern Annular Mode (SAM) sea level pressure (SLP) indices for historical and RCP4.5 scenarios produced by 39 climate models participating in CMIP5. Panels (a) (c) show time series of the ensemble mean (black line) and inter-quartile range (grey shading) of the mean index for each model. Panels (d) (f) show scatter plots of individual model 2081 2100 time means versus 1986 2005 time means (black crosses) together with ( 2,+2) standard error bars. The NAO index is defined here as the difference of regional averages: (90°W to 60°E, 20°N to 55°N) minus (90°W to 60°E, 55°N to 90°N) (see Stephenson et al., 2006). The NAM and SAM are defined as zonal indices: NAM as the difference in zonal mean SLP at 35°N and 65°N (Li and Wang, 2003) and SAM as the difference in zonal mean SLP at 40°S and 65°S (Gong and Wang, 1999). All indices have been centred to have zero time mean from 1861 1900. Comparison of simulated and observed trends from 1961 2011 is shown in Figure 10.13. 14 1245 Chapter 14 Climate Phenomena and their Relevance for Future Regional Climate Change The physical mechanisms of the SAM are well understood, and the the current observed SAM changes are neutralized or reversed during SAM is well represented in climate models, although the detailed spa- austral summer (Perlwitz et al., 2008; Son et al., 2010; Polvani et al., tial and temporal characteristics vary between models (Raphael and 2011; Bracegirdle et al., 2013). Figure 14.16 shows the projected Holland, 2006). In the past few decades the SAM index has exhibited ensemble-mean future SAM index evolution during DJF from a suite of a positive trend in austral summer and autumn (Figure 14.16, Mar- CMIP5 models, suggesting that the recent positive trend will weaken shall, 2007; Jones et al., 2009b), a change attributed to the effects of considerably as stratospheric ozone concentrations recover over south- ozone depletion and, to a lesser extent, the increase in GHGs (Thomp- ern high latitudes. son et al., 2011, see also Section 10.3.3.3). It is likely that these two factors will continue to be the principal drivers into the future, but Projected 21st century changes in the SAM, and the closely associated as the ozone hole recovers they will be competing to push the SAM SH eddy-driven jet position, vary by season (Gillett and Fyfe, 2013), in opposite directions (Arblaster et al., 2011; Thompson et al., 2011; and are sensitive to the rate of ozone recovery (Son et al., 2010; Eyring Bracegirdle et al., 2013), at least during late austral spring and summer, et al., 2013) and to GHG emissions scenario (Swart and Fyfe, 2012; when ozone depletion has had its greatest impact on the SAM. The Eyring et al., 2013). In the RCP2.6 scenario, with small increases in SAM is also influenced by teleconnections to the tropics, primarily GHGs, ozone recovery may dominate in austral summer giving a small associated with ENSO (Carvalho et al., 2005; L Heureux and Thompson, projected equatorward jet shift (Eyring et al., 2013) with little change 2006). Changes to the tropical circulation, and to such teleconnections, in the annual mean jet position (Swart and Fyfe, 2012). In RCP8.5 large as the climate warms could further affect SAM variability (Karpechko GHG increases are expected to dominate, giving an ongoing poleward et al., 2010). See Supplementary Material Section 14.SM.3.1 for further shift of the SH jet in all seasons (Swart and Fyfe, 2012; Eyring et al., details on the observed variability of SAM. 2013). In RCP4.5 the influences of ozone recovery and GHG increas- es are expected to approximately balance in austral summer, with an The CMIP3 models projected a continuing positive trend in the SAM in ongoing poleward jet shift projected in the other seasons (Swart and both summer and winter (Miller et al., 2006). However, those models Fyfe, 2012; Eyring et al., 2013; Gillett and Fyfe, 2013). generally had poor simulations of stratospheric ozone, and tended to underestimate natural variability and to misrepresent observed trends 14.5.3 Assessment Summary in the SAM, indicating that care should be taken in interpretation of their future SAM projections (Fogt et al., 2009). Arblaster et al. (2011) Future boreal wintertime NAO is very likely to exhibit large natural var- showed that there can be large differences in the sensitivity of these iations and trend of similar magnitude to that observed in the past; is models to CO2 increases, which affects their projected trends in the very likely to be differ quantitatively from individual climate model pro- SAM. jections; is likely to become slightly more positive (on average) due to increases in GHGs. The austral summer/autumn positive trend in SAM Since the AR4 a number of chemistry climate models (CCMs) have is likely to weaken considerably as ozone depletion recovers through to been run that have fully interactive stratospheric chemistry, although the mid-21st century. There is medium confidence from recent studies unlike coupled atmosphere ocean models they are usually not coupled that projected changes in NAO and SAM are sensitive to boundary to the oceans (see also Sections 9.1.3.2.8 and 9.4.6.2). The majority processes, which are not yet well represented in many climate models of CCMs and coupled models, which generally compare well to rea- currently used for projections, for example, stratosphere-troposphere nalyses (Gerber et al., 2010) although many exhibit biases in their interaction, ozone chemistry, solar forcing and atmospheric response placement of the SH eddy-driven jet (Wilcox et al., 2012; Bracegirdle to Arctic sea ice loss. There is low confidence in projections of other et al., 2013), indicate that through to at least the mid-21st century modes such as the NPO due to the small number of modelling studies. Box 14.2 | Blocking Atmospheric blocking is associated with persistent, slow-moving high-pressure systems that interrupt the prevailing westerly winds of middle and high latitudes and the normal eastward progress of extratropical storm systems. Overall, blocking activity is more frequent at the exit zones of the jet stream and shows appreciable seasonal variability in both hemispheres, reaching a maximum in winter spring and a minimum in summer autumn (e.g., Wiedenmann et al., 2002). In the Northern Hemisphere (NH), the preferred locations for winter blocking are the North Atlantic and North Pacific, whereas continental blocks are relatively more frequent in summer (Tyrlis and Hoskins, 2008; Barriopedro et al., 2010). Southern Hemisphere (SH) blocking is less frequent than in the NH, and it tends to be concentrated over the Southeast Pacific and the Indian Ocean (Berrisford et al., 2007). Blocking is a complex phenomenon that involves large- and small-scale components of the atmospheric circulation, and their mutual interactions. Although there is not a widely accepted blocking theory, transient eddy activity is considered to play an important role in blocking occurrence and maintenance through feedbacks between the large-scale flow and synoptic eddies (e.g., Yamazaki and Itoh, 2009). (continued on next page) 14 1246 Climate Phenomena and their Relevance for Future Regional Climate Change Chapter 14 Box 14.2 (continued) Blocking is an important component of intraseasonal variability in the extratropics and causes climate anomalies over large areas of Europe (Trigo et al., 2004; Masato et al., 2012), North America (Carrera et al., 2004), East Asia (e.g., Wang et al., 2010; Cheung et al., 2012), high-latitude regions of the SH (Mendes et al., 2008) and Antarctica (Massom et al., 2004; Scarchilli et al., 2011). Blocking can also be responsible for extreme events (e.g., Buehler et al., 2011; Pfahl and Wernli, 2012), such as cold spells in winter (e.g., 2008 in China, Zhou et al., 2009d; or 2010 in Europe, Cattiaux, 2010) and summer heat waves in the NH (e.g., 2010 in Russia, Matsueda, 2011; Lupo et al., 2012) and in southern Australia (Pezza et al., 2008). At interannual time scales, there are statistically significant relationships between blocking activity and several dominant modes of atmospheric variability, such as the NAO (Section 14.5.1) and wintertime blocking in the Euro-Atlantic sector (Croci-Maspoli et al., 2007a; Luo et al., 2010), the winter PNA (Section 14.7.1) and blocking frequency in the North Pacific (Croci-Maspoli et al., 2007a), or the SAM (Section 14.5.2) and winter blocking activity near the New Zealand sector (Berrisford et al., 2007). Multi-decadal variability in winter blocking over the North Atlantic and the North Pacific seem to be related, respectively, with the Atlantic Meridional Overturning Circulation (AMOC; Häkkinen et al., 2011; Section 14.7.6) and the Pacific Decadal Oscillation (PDO; Chen and Yoon, 2002; Section 14.7.3), although this remains an open question. Other important scientific issues related to the blocking phenomenon include the mechanisms of blocking onset and maintenance, two- way interactions between blocking and stratospheric processes (e.g., Martius et al., 2009; Woollings et al., 2010), influence on blocking of slowly varying components of the climate system (sea surface temperature (SST), sea ice, etc., Liu et al., 2012b), and external forcings. The most consistent long-term observed trends in blocking for the second half of the 20th century are the reduced winter activity over the North Atlantic (e.g., Croci-Maspoli et al., 2007b), which is consistent with the observed increasing North Atlantic Oscillation (NAO) trend from the 1960s to the mid-1990s (Section 2.7.8), as well as an eastward shift of intense winter blocking over the Atlantic and Pacific Oceans (Davini et al., 2012). The apparent decreasing trend in SH blocking activity (e.g., Dong et al., 2008) seems to be in agreement with the upward trend in the SAM. The AR4 (Section 8.4.5) reported a tendency for General Circulation Models (GCMs) to underestimate NH blocking frequency and per- sistence, although most models were able to capture the preferred locations for blocking occurrence and their seasonal distributions. Several intercomparison studies based on a set of CMIP3 models (Scaife et al., 2010; Vial and Osborn, 2012) revealed some progress in the simulation of NH blocking activity, mainly in the North Pacific, but only modest improvements in the North Atlantic. In the SH, blocking frequency and duration was also underestimated, particularly over the Australia New Zealand sector (Matsueda et al., 2010). CMIP5 models still show a general blocking frequency underestimation over the Euro-Atlantic sector, and some tendency to overesti- mate North Pacific blocking (Section 9.5.2.2), with considerable inter-model spread (Box 14.2, Figure 1). Model biases in the mean flow, rather than in variability, can explain a large part of the blocking underestimation and they are usually evidenced as excessive zonality of the flow or systematic shifts in the latitude of the jet stream (Matsueda et al., 2010; Scaife et al., 2011b; Barnes and Hartmann, 2012; Vial and Osborn, 2012; Anstey et al., 2013; Dunn-Sigouin and Son, 2013). Increasing the horizontal resolution in atmospheric GCMs with prescribed SSTs has been shown to significantly reduce blocking biases, particularly in the Euro-At- lantic sector and Australasian sectors (e.g., Matsueda et al., 2010; Jung et al., 2011; Dawson et al., 2012; Berckmans et al., 2013), while North Pacific blocking could be more sensitive to systematic errors in tropical SSTs (Hinton et al., 2009). Also blocking biases are smaller in those CMIP5 models with higher horizontal and vertical resolution (Anstey et al., 2013). However, the improvement of blocking sim- ulation with increasing horizontal resolution is less clear in coupled models than in atmospheric GCMs with prescribed SSTs, indicating that both SSTs and the relative coarse resolution in OGCM (Scaife et al., 2011b) are important causes of blocking biases. Most CMIP3 models projected significant reductions in NH annual blocking frequency (Barnes et al., 2012), particularly during winter, but CMIP5 models seem to indicate weaker decreases in the future (Dunn-Sigouin and Son, 2013) and a more complex response than that reported for CMIP3 models, including possible regional increases of blocking frequency in summer (Cattiaux et al., 2013; Masato et al., 2013). There is high agreement that winter blocking frequency over the North Atlantic and North Pacific will not increase under enhanced GHG concentrations (Barnes et al., 2012; Dunn-Sigouin and Son, 2013). Future strengthening of the zonal wind and merid- ional jet displacements may partially account for some of the projected changes in blocking frequency over the ocean basins of both hemispheres (Matsueda et al., 2010; Barnes and Hartmann, 2012; Dunn-Sigouin and Son, 2013). Future trends in blocking intensity and persistence are even more uncertain, with no clear signs of significant changes. How the location and frequency of blocking events will evolve in future are both critically important for understanding regional climate change in particular with respect to extreme conditions (e.g., Sillmann et al., 2011; de Vries et al., 2013). (continued on next page) 14 1247 Chapter 14 Climate Phenomena and their Relevance for Future Regional Climate Change Box 14.2 (continued) In summary, the increased ability in simulating blocking in some models indicate that there is medium confidence that the frequency of NH and SH blocking will not increase, while trends in blocking intensity and persistence remain uncertain. The implications for blocking related regional changes in North America, Europe and Mediterranean and Central and North Asia are therefore also uncertain [Box 14.2 and 14.8.3, 14.8.6, 14.8.8] CMIP5 models 1961-1990 15 Blocking frequency (%) 10 5 0 -90 -60 -30 0 30 60 90 120 150 180 210 240 270 Longitude REANALYSIS BCC-CSM1-1 BCC-CSM1-1-M BNU-ESM CanESM2 CCSM4 CMCC-CM CMCC-CESM CMCC-CMS CNRM-CM5 EC-EARTH FGOALS-g2 FGOALS-s2 GFDL-CM3 GFDL-ESM2M HadGEM2-CC IPSL-CM5A-LR IPSL-CM5A-MR IPSL-CM5B-LR MIROC5 MIROC-ESM MIROC-ESM-CHEM MPI-ESM-MR MPI-ESM-LR MPI-ESM-P MRI-CGCM3 NorESM1-M Box 14.2, Figure 1 | Annual mean blocking frequency in the NH (expressed in % of time, that is, 1% means about 4 days per year) as simulated by a set of CMIP5 models (colour lines) for the 1961 1990 period of one run of the historical simulation. Grey shading shows the mean model result plus/minus one standard deviation. Black thick line indicates the observed blocking frequency derived from the National Centers for Environmental Prediction/National Center for Atmospheric Research (NCEP/NCAR) reanalysis. Only CMIP5 models with available 500 hPa geopotential height daily data at http://pcmdi3.llnl.gov/esgcet/home.htm have been used. Block- ing is defined as in Barriopedro et al. (2006), which uses a modified version of the(Tibaldi and Molteni, 1990) index. Daily data was interpolated to a common regular 2.5° × 2.5° longitude latitude grid before detecting blocking. 14.6 Large-scale Storm Systems q ­ uantification of natural variability in these measures (Knutson et al., 2010; Lee et al., 2012; Seneviratne et al., 2012). Observed regional cli- 14.6.1 Tropical Cyclones mate variability generally represents a complex convolution of natural and anthropogenic factors, and the response of tropical cyclones to The potential for regional changes in future tropical cyclone frequency, each factor is not yet well understood (see also Section 10.6.1.5 and track and intensity is of great interest, not just because of the associat- Supplementary Material Section 14.SM.4.1.2). For example, the steady ed negative effects, but also because tropical cyclones can play a major long-term increase in tropical Atlantic SST due to increasing GHGs can role in maintaining regional water resources (Jiang and Zipser, 2010; be dominated by shorter-term decadal variability forced by both exter- Lam et al., 2012; Prat and Nelson, 2012). Past and projected increases nal and internal factors (Mann and Emanuel, 2006; Baines and Folland, in human exposure to tropical cyclones in many regions (Peduzzi et al., 2007; Evan et al., 2009, 2011a; Ting et al., 2009; Zhang and Delworth, 2012) heightens the interest further. 2009; Chang et al., 2011; Solomon and Newman, 2011; Booth et al., 2012; Camargo et al., 2012; Villarini and Vecchi, 2012). Similarly, tropi- 14.6.1.1 Understanding the Causes of Past and Projected cal upper-tropospheric temperatures, which modulate tropical cyclone Regional Changes potential intensity (Emanuel, 2010), can be forced by slowly evolving changes in the stratospheric circulation of ozone (Brewer Dobson Detection of past trends in measures of tropical cyclone activi- circulation) due to climate change with occasional large amplitude 14 ty is constrained by the quality of historical records and uncertain and persistent changes forced by volcanic eruptions (Thompson and 1248 Climate Phenomena and their Relevance for Future Regional Climate Change Chapter 14 S ­ olomon, 2009; Evan, 2012). This convolution of anthropogenic and increase by about 20% within 100 km of the cyclone centre. However, natural factors, as represented in a climate model, has also been shown inter-model differences in regional projections lead to lower confidence to be useful in prediction of Atlantic tropical storm frequency out to a in basin-specific projections, and confidence is particularly low for pro- few years (Smith et al., 2010). jections of frequency within individual basins. For example, a recent study by Ying et al. (2012) showed that numerical projections of 21st In addition to greenhouse warming scenarios, tropical cyclones can century changes in tropical cyclone frequency in the western North also respond to anthropogenic forcing via different and possibly unex- Pacific range broadly from 70% to +60%, while there is better model pected pathways. For example, increasing anthropogenic emissions of agreement in measures of mean intensity and precipitation, which are black carbon and other aerosols in South Asia has been linked to a projected to change in the region by 3% to +18% and +5% to +30%, reduction of SST gradients in the Northern Indian Ocean (Chung and respectively. The available modelling studies that are capable of pro- Ramanathan, 2006; Meehl et al., 2008), which has in turn been linked ducing very strong cyclones typically project substantial increases in to a weakening of the vertical wind shear in the region. Evan et al. the frequency of the most intense cyclones and it is more likely than (2011b) linked the reduced wind shear to the observed increase in the not that this increase will be larger than 10% in some basins (Emanuel number of very intense storms in the Arabian Sea, including five very et al., 2008; Bender et al., 2010; Knutson et al., 2010, 2013; Yamada severe cyclones that have occurred since 1998, but the fundamental et al., 2010; Murakami et al., 2012). It should be emphasized that this cause of this proposed linkage is not yet certain (Evan et al., 2012; metric is generally more important to physical and societal impacts Wang et al., 2012a). Furthermore, it is possible that a substantial part than overall frequency or mean intensity. of the multi-decadal variability of North Atlantic SST is radiatively forced, via the cloud albedo effect, by what are essentially pollution As seen in Tables 14.SM.1 to 14.SM.4 of the Supplementary Material, aerosols emitted from North America and Europe (Baines and Folland, as well as the previous assessments noted above, model projections 2007; Booth et al., 2012), although the relative contribution of this often vary in the details of the models and the experiments performed, forcing to the observed variability has been questioned (Zhang et al., and it is difficult to objectively assess their combined results to form a 2013b). Note that in the North Atlantic, the evidence suggests that the consensus, particularly by region. It is useful to do this after normaliz- reduction of pollution aerosols is linked to tropical SST increases, while ing the model output using a combination of objective and subjective in the northern Indian Ocean, increases in aerosol pollution have been expert judgements. The results of this are shown in Figure 14.17, and linked to reduced vertical wind shear. Both of these effects (increasing are based on a subjective normalization of the model output to four SST and reduced shear) have been observed to be related to increased common metrics under a common future scenario projected through tropical cyclone activity. the 21st century. The global assessment is essentially the same as Knutson et al. (2010) and the assessment of projections in the west- Finally, in addition to interannual-to-multi-decadal forcing of tropi- ern North Pacific is essentially unchanged from Ying et al. (2012). The cal Atlantic SST via radiative dimming (Evan et al., 2009; Evan et al., annual frequency of tropical cyclones is generally projected to decrease 2011a), dust aerosols have a large and more immediate in situ effect or remain essentially unchanged in the next century in most regions on the regional thermodynamic and kinematic environment (Dunion although as noted above, the confidence in the projections is lower and Marron, 2008; Dunion, 2011), and Saharan dust storms whose in specified regions than global projections. The decrease in storm frequency has been linked to atmospheric CO2 concentration (Mahow- frequency is apparently related to a projected decrease of upward ald, 2007) have also been linked to reduced strengthening of tropical deep convective mass flux and increase in the saturation deficit of cyclones (Dunion and Velden, 2004; Wu, 2007). Direct in situ relation- the middle troposphere in the tropics associated with global warming ships have also been identified between aerosol pollution concentra- (Bengtsson et al., 2007; Emanuel et al., 2008, 2012; Zhao et al., 2009; tions and tropical cyclone structure and intensity (Khain et al., 2008, Held and Zhao, 2011; Murakami et al., 2012; Sugi et al., 2012; Sugi and 2010; Rosenfeld et al., 2011). Thus, when assessing changes in tropical Yoshimura, 2012). cyclone activity, it is clear that detection and attribution aimed simply at long-term linear trends forced by increasing well-mixed GHGs is not A number of experiments that are able to simulate intense tropical adequate to provide a complete picture of the potential anthropogenic cyclones project increases in the frequency of these storms in some contributions to the changes in tropical cyclone activity that have been regions, although there are presently only limited studies to assess and observed (Section 10.6). there is insufficient data to draw from in most regions to make a con- fident assessment (Figure  14.17). Confidence is somewhat better in 14.6.1.2 Regional Numerical Projections the North Atlantic and western North Pacific basins where an increase in the frequency of the strongest storms is more likely than not. The Similar to observational analyses, confidence in numerical simulations models generally project an increase in mean lifetime-maximum inten- of tropical cyclone activity (Supplementary Material Tables 14.SM.1 sity of simulated storms (Supplementary Material Table 14.SM.3), to 14.SM.4) is reduced when model spatial domain is reduced from which is consistent with a projected increase in the frequency in the global to region-specific (IPCC SREX Box 3.2; see also Section 9.5.4.3). more intense storms (Elsner et al., 2008). The projected increase in The assessment provided by Knutson et al. (2010) of projections based intensity concurrent with a projected decrease in frequency can be on the SRES A1B scenario concluded that it is likely that the global argued to result from a difference in scaling between projected chang- frequency of tropical cyclones will either decrease or remain essential- es in surface enthalpy fluxes and the Clausius Clapeyron relationship ly unchanged while mean intensity (as measured by maximum wind associated with the moist static energy of the middle troposphere speed) increases by +2 to +11% and tropical cyclone rainfall rates (Emanuel et al., 2008). The increase in rainfall rates associated with 14 1249 Chapter 14 Climate Phenomena and their Relevance for Future Regional Climate Change tropical cyclones is a consistent feature of the numerical models under is modulated in most regions by known modes of atmosphere ocean greenhouse warming as atmospheric moisture content in the tropics variability. The details of the relationships vary by region (e.g., El Nino and tropical cyclone moisture convergence is projected to increase events tend to cause track shifts in western North Pacific typhoons and (Trenberth et al., 2005, 2007a; Allan and Soden, 2008; Knutson et al., tend to suppress Atlantic storm genesis and development). Similarly, 2010, 2013). Although no broad-scale, detectable long-term changes in it has been demonstrated that accurate modelling of tropical cyclone tropical cyclone rainfall rates have been reported, preliminary evidence activity fundamentally depends on the model s ability to reproduce for a detectable anthropogenic increase in atmospheric moisture con- these modes of variability (e.g., Yokoi and Takayabu, 2009). Reliable tent over ocean regions has been reported by Santer et al. (2007). projections of future tropical cyclone activity, both global and region- al, will then depend critically on reliable projections of the behaviour A number of studies since the AR4 have attempted to project future of these modes of variability (e.g., ENSO) under global warming, as changes in tropical cyclone tracks and genesis at inter- or intra-ba- well as an adequate understanding of their physical links with tropical sin scale (Leslie et al., 2007; Vecchi and Soden, 2007b; Emanuel et al., cyclones. At present there is still uncertainty in their projected behav- 2008; Yokoi and Takayabu, 2009; Zhao et al., 2009; Li et al., 2010b; iours (e.g., Section 14.4). Murakami and Wang, 2010; Lavender and Walsh, 2011; Murakami et al., 2011a, 2013). These studies suggest that projected changes in The reduction in signal-to-noise ratio that accompanies changing focus tropical cyclone activity are strongly correlated with projected changes from global to regional scales also lengthens the emergence time in the spatial pattern of tropical SST (Sugi et al., 2009; Chauvin and scale (i.e., the time required for a trend signal to rise above the nat- Royer, 2010; Murakami et al., 2011b; Zhao and Held, 2012) and asso- ural variability in a statistically significant way). Based on changes in ciated weakening of the Pacific Walker Circulation (Vecchi and Soden, tropical cyclone intensity predicted by idealized numerical simulations 2007a), indicating that reliable projections of regional tropical cyclone with carbon dioxide (CO2)-induced tropical SST warming, Knutson and activity depend critically on the reliability of the projected pattern of Tuleya (2004) suggested that clearly detectable increases may not be SST changes. However, assessing changes in regional tropical cyclone manifest for decades to come. The more recent high-resolution dynam- frequency is still limited because confidence in projections critically ical downscaling study of Bender et al. (2010) supports this argument depend on the performance of control simulations (Murakami and and suggests that the predicted increases in the frequency of the Sugi, 2010), and current climate models still fail to simulate observed strongest Atlantic storms may not emerge as a statistically ­ ignificant s temporal and spatial variations in tropical cyclone frequency (Walsh et signal until the latter half of the 21st century under the SRES A1B emis- al., 2012). As noted above, tropical cyclone genesis and track variability sionscenario. However, regional forcing by agents other than GHGs, Western North Pacific North Atlantic 50 200 % Eastern North Pacific 50 % Change North Indian % Change 0 50 50 0 % Change % Change 50 insf. d. insf. d. 0 0 50 -100 % I II IIII IV 50 I II IIII IV 50 I II IIII IV I II IIII IV South Pacific South Indian 50 50 % Change % Change insf. d. insf. d. 0 0 Tropical Cyclone (TC) Metrics: 50 I All TC frequency 50 II Category 4-5 TC frequency I II IIII IV III Lifetime Maximum Intensity I II IIII IV IV Precipitation rate SOUTHERN HEMISPHERE GLOBAL NORTHERN HEMISPHERE 50 50 50 % Change % Change % Change insf. d. insf. d. 0 0 0 insf. d. insf. d. 50 50 50 I II IIII IV I II IIII IV I II IIII IV Figure 14.17 | General consensus assessment of the numerical experiments described in Supplementary Material Tables 14.SM.1 to 14.SM.4. All values represent expected percent change in the average over period 2081 2100 relative to 2000 2019, under an A1B-like scenario, based on expert judgement after subjective normalization of the model projections. Four metrics were considered: the percent change in (I) the total annual frequency of tropical storms, (II) the annual frequency of Category 4 and 5 storms, (III) the mean Lifetime Maximum Intensity (LMI; the maximum intensity achieved during a storm s lifetime) and (IV) the precipitation rate within 200 km of storm centre at the time of LMI. For each metric plotted, the solid blue line is the best guess of the expected percent change, and the coloured bar provides the 67% (likely) confidence interval for this value (note that this interval 14 ranges across 100% to +200% for the annual frequency of Category 4 and 5 storms in the North Atlantic). Where a metric is not plotted, there are insufficient data (denoted insf. d. ) available to complete an assessment. A randomly drawn (and coloured) selection of historical storm tracks are underlain to identify regions of tropical cyclone activity. 1250 Climate Phenomena and their Relevance for Future Regional Climate Change Chapter 14 such as anthropogenic aerosols, is known to affect the regional climat- the large-scale flow. However, there is evidence that the amplitude of ic conditions differently (e.g., Zhang and Delworth, 2009), and there the tropical and polar warming are largely determined by atmospheric is evidence that tropical cyclone activity may have changed in some poleward heat fluxes set by local processes (Hwang and Frierson, 2010). regions because of effects from anthropogenic aerosol pollution. The fidelity of the emergence time scales projected under A1B warming Local processes could prove important for the storm track response thus depends on the fidelity of A1B aerosol projections, which are in certain regions, for example, sea ice loss (Kvamsto et al., 2004; Sei- known to be uncertain (Forster et al., 2007; Haerter et al., 2009). erstad and Bader, 2009; Deser et al., 2010c; Bader et al., 2011) and spatial changes in SSTs (Graff and LaCasce, 2012). Local land sea con- 14.6.2 Extratropical Cyclones trast in warming also has a local influence on baroclinicity along the eastern continental coastlines (Long et al., 2009; McDonald, 2011). It is Some agreement on the response of storm tracks to anthropogenic not clear how the storm track responds to multiple forcings with some forcing started to emerge in the climate model projections from CMIP3, studies suggesting a linear response (Lim and Simmonds, 2009) while with many models projecting a poleward shift of the storm tracks (Yin, others suggest more complex interaction (Butler et al., 2010). 2005) and an expansion of the tropics (Lu et al., 2007). As stated in AR4 (Meehl et al., 2007) this response is particularly clear in the SH, but less The projected increase in moisture content in a warmer atmosphere is clear in the NH. Although clearer in zonal mean responses (Yin, 2005), also likely to have competing effects. Latent heating has been shown regional responses at different longitudes differ widely from this in to play a role in invigorating individual ETCs, especially in the down- many models (Ulbrich et al., 2008). There is a strong two-way coupling stream development over eastern ocean (Dacre and Gray, 2009; Fink between storm tracks and the large-scale circulation (Lorenz and Hart- et al., 2009, 2012). However, there is evidence that the overall effect of mann, 2003; Robinson, 2006; Gerber and Vallis, 2007), which results in moistening is to weaken ETCs by improving the efficiency of poleward associated shifts in storm tracks and westerly jet streams (Raible, 2007; heat transport and hence reducing the dry baroclinicity (Frierson et al., Athanasiadis et al., 2010). 2007; O Gorman and Schneider, 2008; Schneider et al., 2010; Lucarini and Ragone, 2011). Consistent with this, studies have shown that pre- 14.6.2.1 Process Understanding of Future Changes cipitation is projected to increase in ETCs despite no increase in wind speed intensity of ETCs (Bengtsson et al., 2009; Zappa et al., 2013b). Future storm track change is the result of several different competing dynamical influences (Held, 1993; O Gorman, 2010; Woollings, 2010). 14.6.2.2 Regional Projections It is becoming apparent that the relatively modest storm track response in many models reflects the partial cancelling of opposing tendencies Large-scale projections of ETCs are assessed in Section 12.4.4.3. This (Son and Lee, 2005; Lim and Simmonds, 2009; Butler et al., 2010). section complements this by presenting a more detailed assessment of regional changes. One of the most important factors is the change in the meridional temperature gradient from which ETCs draw most of their energy. Individual model projections of regional storm track changes are often This gradient is projected to increase in the upper troposphere due to comparable with the magnitude of interannual natural variability and tropical amplification and decrease in the lower troposphere due to so the changes are expected to be relevant for regional climate. How- polar amplification, and it is still unclear whether this will lead to an ever, the magnitude of the response is model dependent at any given overall increase or decrease in ETC activity. The projected response can location, especially over land (Harvey et al., 2012). There is also disa- involve an increase in eddy activity at upper levels and a decrease at greement between different cyclone/storm track identification meth- lower levels (Hernandez-Deckers and von Storch, 2010), although in ods, even when applied to the same data (Raible et al., 2008; Ulbrich other models changes in low level eddy activity are more in line with et al., 2009), although in the response to anthropogenic forcing, these the upper level wind changes (Mizuta et al., 2011; Wu et al., 2011; differences appear mainly in the statistics of weak cyclones (Ulbrich et Mizuta, 2012). The projected warming pattern also changes vertical al., 2013). Conversely, when the same method is applied to different temperature gradients leading to increased stability at low latitudes models the spread between the model responses is often larger than and decreased stability at higher latitudes, and there is some modelling the ensemble mean response, especially in the NH (Ulbrich et al., 2008; evidence that this may be a strong factor in the response (Lu et al., Laine et al., 2009). 2008, 2010; Kodama and Iwasaki, 2009; Lim and Simmonds, 2009). Increasing depth of the troposphere might also be important for future The poleward shift of the SH storm track remains one of the most changes (Lorenz and DeWeaver, 2007). reproducible projections, yet even here there is considerable quan- titative uncertainty. This is partly associated with the varied model Uncertainties in the projections of large-scale warming contribute to biases in jet latitude (Kidston and Gerber, 2010) although factors such uncertainty in the storm track response (Rind, 2008). Several mecha- as the varied cloud response may play a role (Trenberth and ­Fasullo, nisms have been proposed to explain how the storm tracks respond to 2010). Many models project a similar poleward shift in the North the large-scale changes, including changes in eddy phase speed (Chen Pacific (Bengtsson et al., 2006; Ulbrich et al., 2008; Catto et al., 2011), et al., 2007, 2008; Lu et al., 2008), eddy source regions (Lu et al., 2010) although this is often weaker compared to natural variability and often and eddy length scales (Kidston et al., 2011) with a subsequent effect varies considerably between ensemble members (Pinto et al., 2007; on wave-breaking characteristics (Riviere, 2011). Furthermore, changes McDonald, 2011). Poleward shifts are generally less clear at the surface in the large-scale warming might also be partly due to changes in the than in the upper troposphere (Yin, 2005; McDonald, 2011; Chang et 14 storm tracks due to the two-way coupling between storm tracks and 1251 Chapter 14 Climate Phenomena and their Relevance for Future Regional Climate Change al., 2012), which reduces their relevance for regional impacts. However, response to forcing (Chang et al., 2012). Some models with improved a shift in extreme surface winds is still detectable in the zonal mean, representation of the stratosphere have shown a markedly different especially in the subtropics and the southern high latitudes (Gastineau circulation response in the NH, with consequences for Atlantic/Euro- and Soden, 2009). A weakening of the Mediterranean storm track is a pean storm activity in particular (Scaife et al., 2011a). Concerns over particularly robust response (Pinto et al., 2007; Loeptien et al., 2008; the skill of many models in representing both the stratosphere and the Ulbrich et al., 2009; Donat et al., 2011) for which increasing static ocean mean that confidence in NH storm track projections remains stability is important (Raible et al., 2010). In general, the storm track low. Higher horizontal resolution can improve ETC representation, yet response in summer is weaker than in winter with less consistency there are still relatively few high-resolution global models which have between models (Lang and Waugh, 2011). been used for storm track projections (Geng and Sugi, 2003; Bengtsson et al., 2009; Catto et al., 2011; Colle et al., 2013; Zappa et al., 2013a). The response of the North Atlantic storm track is more complex than a Several studies have used RCMs to simulate storms at high resolu- poleward shift in many models, with an increase in storm activity and a tion in particular regions. In multi-model experiments over Europe, the downstream extension of the storm track into Europe (Bengtsson et al., ETC response is more sensitive to the choice of driving GCM than the 2006; Pinto et al., 2007; Ulbrich et al., 2008; Catto et al., 2011; McDon- choice of RCM (Leckebusch et al., 2006; Donat et al., 2011), highlight- ald, 2011). In some models this regional response is important (Ulbrich ing the importance of large-scale circulation uncertainties. There has et al., 2009), with storm activity over Western Europe increasing by been little work on potential changes to mesoscale storm systems, 50% (McDonald, 2011) or by an amount comparable to the natural although it has been suggested that polar lows may reduce in frequen- variability (Pinto et al., 2007; Woollings et al., 2012). The return periods cy due to an increase in static stability (Zahn and von Storch, 2010). of intense cyclones are shortened (Della-Marta and Pinto, 2009) with Higher resolution runs of one climate model also suggest an increase impact on potential wind damage (Leckebusch et al., 2007; Donat et in intensity of autumn ETCs due to increased transitioning of Atlantic al., 2011) and economic losses (Pinto et al., 2012). This response is hurricanes (Haarsma et al., 2013). related to the local minimum in warming in the North Atlantic ocean, which serves to increase the meridional temperature gradient on its 14.6.3 Assessment Summary southern side (Laine et al., 2009; Catto et al., 2011). The minimum in warming arises due to the weakening of northward ocean heat trans- The influence of past and future climate change on tropical cyclones is ports by the Atlantic Meridional Overturning Circulation (AMOC), likely to vary by region, but the specific characteristics of the changes and the varying AMOC responses of the models can account for a are not yet well understood, and the substantial influence of ENSO large fraction of the variance in the Atlantic storm track projections and other known climate modes on global and regional tropical (Woollings et al., 2012). CMIP5 models show a similar, albeit weaker cyclone activity emphasizes the need for more reliable assessments of extension of the storm track towards Europe, flanked by reductions in future changes in the characteristics of these modes. Recent advanc- cyclone activity on both the northern and southern sides (Harvey et al., es in understanding and phenomenological evidence for shorter-term 2012; Zappa et al., 2013b). Despite large biases in the mean state, the effects on tropical cyclones from aerosol forcing are providing increas- model responses were found to agree with one another within sam- ingly greater confidence that anthropogenic forcing has had a measur- pling variation caused by natural variability (Sansom et al., 2013). Colle able effect on tropical cyclone activity in certain regions. Shorter term et al. (2013) noted similar reductions but also found that the higher increases such as those observed in the Atlantic over the past 30 to 40 resolution CMIP5 models gave more realistic ETC performance in the years appear to be robust and have been hypothesized to be related, in historical period. The best 7 models were found to give projections of part, to regional external forcing by GHGs and aerosols, but the more increased 10 to 20% increase in cyclone track density over the eastern steady century-scale trends that may be expected from CO2 forcing USA, including 10 to 40% more intense (<980 hPa) cyclones. alone are much more difficult to assess given the data uncertainty in the available tropical cyclone records. There is general agreement that there will be a small global reduction in ETC numbers (Ulbrich et al., 2009). In individual regions there can Although projections under 21st century greenhouse warming indicate be much larger changes which are comparable to natural variations, that it is likely that the global frequency of tropical cyclones will either but these changes are not reproduced by the majority of the models decrease or remain essentially unchanged, concurrent with a likely (e.g., Donat et al., 2011). ETC intensities are particularly sensitive to the increase in both global mean tropical cyclone maximum wind speed method and quantity used to define them, so there is little consensus and rainfall rates, there is low confidence in region-specific projections on changes in intensity (Ulbrich et al., 2009). While there are indica- of frequency and intensity. Still, based on high-resolution modelling tions that the absolute values of pressure minima deepen in future studies, the frequency of the most intense storms, which are associ- scenario simulations (Lambert and Fyfe, 2006), this is often associated ated with particularly extensive physical effects, will more likely than with large-scale pressure changes rather than changes in the pres- not increase substantially in some basins under projected 21st century sure gradients or winds associated with ETCs (Bengtsson et al., 2009; warming and there is medium confidence that tropical cyclone rainfall Ulbrich et al., 2009; McDonald, 2011). The CMIP5 model projections rates will increase in every affected region. show little evidence of change in the intensity of winds associated with ETCs (Zappa et al., 2013b). The global number of ETCs is unlikely to decrease by more than a few percent due to anthropogenic change. A small poleward shift is likely There are systematic storm track biases common to many models, in the SH storm track, but the magnitude is model dependent. There 14 which might have some influence on the projected storm track is only medium confidence in projections of storm track shifts in the 1252 Climate Phenomena and their Relevance for Future Regional Climate Change Chapter 14 Northern Hemisphere. Nevertheless, model results suggests that it is agreement with the observations. The model-projected future evolu- more likely than not that the N. Pacific storm track will shift poleward, tion of the PNA pattern has not yet been fully assessed and therefore and that it is unlikely that the N. Atlantic storm track will respond with confidence in its future development is low. a simple poleward shift. There is low confidence in the magnitude of regional storm track changes, and the impact of such changes on 14.7.3 Pacific Decadal Oscillation/Inter-decadal regional surface climate. Pacific Oscillation The Pacific Decadal Oscillation (PDO, Box 2.5) refers to the leading 14.7 Additional Phenomena of Relevance Empirical Orthogonal Function (EOF) of monthly SST anomalies over the North Pacific (north of 20°N) from which the globally averaged 14.7.1 Pacific South American Pattern SST anomalies have been subtracted (Mantua et al., 1997). It exhibits anomalies of one sign along the west coast of North America and of The PSA pattern is a teleconnection from the tropics to SH extratrop- opposite sign in the western and central North Pacific (see also Sec- ics through Rossby wave trains (Box 2.5). This pattern induces atmos- tion 9.5.3.6 and Chapter 11). The PDO is closely linked to fluctuations pheric circulation anomalies over South America, affecting extreme in the strength of the wintertime Aleutian Low Pressure System. The precipitation (Drumond and Ambrizzi, 2005; Cunningham and Cav- time scale of the PDO is around 20 to 30 years, with changes of sign alcanti, 2006; Muza et al., 2009; Vasconcellos and Cavalcanti, 2010). between positive and negative polarities in the 1920s, the late 1940s, PSA trends for recent decades depend on the choice of indices and are the late 1970s and around 2000. hence uncertain (Section 2.7.8). The PSA pattern is reproduced in many model simulations (Solman and Le Treut, 2006; Vera and Silvestri, 2009; The extension of the PDO to the whole Pacific basin is known as the Bates, 2010; Rodrigues et al., 2011; Cavalcanti and Shimizu, 2012). Inter-decadal Pacific Oscillation (IPO, Power et al., 1999). The IPO is nearly identical in form to the PDO in the NH but is defined globally, as The intensification and westward displacement of the PSA wave pat- a leading EOF (principal component) of 13-year lowpass-filtered global tern in projections of CMIP3 may be related to the increase in frequen- SST anomalies (Parker et al., 2007) and has substantial amplitude in cy and intensity of positive SST anomalies in the tropical Pacific by the the tropical and southern Pacific. The time series of the PDO and IPO end of the 21st century (2081 2100, Junquas et al., 2012). These per- correlate highly on an annual basis. The PDO/IPO pattern is considered turbations of the PSA characteristics are linked with changes in SACZ to be the result of internal climate variability (Schneider and Cornuelle, dipole precipitation and affect South America precipitation (Section 2005; Alexander, 2010) and has not been observed to exhibit a long- 14.3.1.3). The PSA pattern occurrence and implications for precipita- term trend. The PDO/IPO is associated with ENSO modulations, with tion increase in the southeastern South America have been associated more El Nino activity during the positive PDO/IPO and more La Nina with the zonally asymmetric part of the global SST change in the equa- activity during the negative PDO/IPO. torial Indian western Pacific Oceans (Junquas et al., 2013). At the time of the AR4, little had been published on modelling of the Process understanding of the formation of the PSA gives medium con- PDO/IPO or of its evolution in future. In a recent study, Furtado et al. fidence that future changes in the mean atmospheric circulation for (2011) found that the PDO/IPO did not exhibit major changes in spa- austral summer will project on the PSA pattern thereby influencing the tial or temporal characteristics under GHG warming in most of the 24 SACZ and precipitation over southeastern South America. However, the CMIP3 models used, although some models indicated a weak shift literature is not sufficient to assess more general changes in PSA, and toward more occurrences of the negative phase of the PDO/IPO by confidence is low in its future projections. the end of the 21st century (2081 2100, Lapp et al., 2012). However, given that the models strongly underestimate the PDO/IPO connection 14.7.2 Pacific North American Pattern with tropical Indo-Pacific SST variations (Furtado et al., 2011; Lienert et al., 2011), the credibility of the projections remains uncertain. Further- The PNA pattern, as defined in Box 2.5, Table 1 and portrayed in Box more, internal variability is so high that it is hard to detect any forced 2.5, Figure 2 is a prominent mode of atmospheric variability over the changes in the Aleutian Low for the next half a century (Deser et al., North Pacific and the North American land mass. This phenomenon 2012; Oshima et al., 2012). Therefore confidence is low in projections exerts notable influences on the temperature and precipitation varia- of future changes in PDO/IPO. bility in these regions (e.g., Nigam, 2003). The PNA pattern is related to ENSO events in the tropical Pacific (see Section 14.4), and also serves 14.7.4 Tropospheric Biennial Oscillation as a bridge linking ENSO and NAO variability (see Li and Lau, 2012). The data records indicate a significant positive trend in the wintertime The Tropospheric Biennial Oscillation (TBO; Meehl, 1997) is a proposed PNA index over the past 60 years (see Table 2.14 and Box 2.5, Figure 1). mechanism for the biennial tendency in large-scale drought and floods of south Asia and Australia. Multiple studies imply that TBO involves Stoner et al. (2009) have assessed the capability of 22 CMIP3 GCMs the Asian-Australian monsoon, the IOD and ENSO (Sections 14.2.2, in replicating the essential aspects of the observed PNA pattern. Their 14.3.3 and 14.4; see also Supplementary Material Section 14.SM.5.2). results indicate that a majority of the models overestimate the fraction of variance explained by the PNA pattern, and that the spatial charac- The IPO (Section 14.7.3) affects the decade-to-decade strength of the teristics of PNA patterns simulated in 14 of the 22 models are in good TBO. A major contributor to recent change in the TBO comes from 14 1253 Chapter 14 Climate Phenomena and their Relevance for Future Regional Climate Change increase of SST in the Indian Ocean that contributes to stronger trade There are limited published results on the future behaviour of the QBO, winds in the Pacific, one of the processes previously identified with using CMIP5 models. On the basis of the recent literature, it is uncer- strengthening the TBO (Meehl and Arblaster, 2012). Thus, prediction of tain how the period or amplitude of the QBO may change in future and decadal variability assessed in Chapter 11 that can be associated, for confidence in the projections remains low. example, with the IPO (e.g., Meehl et al., 2010) can influence the accu- racy of shorter-term predictions of the TBO across the entire Indo-Pacif- 14.7.6 Atlantic Multi-decadal Oscillation ic region (Turner et al., 2011), but the relevance for longer time scales is uncertain. The AMO (Box 2.5; see also Section 9.5.3.3.2) is a fluctuation seen in the instrumental SST record throughout the North Atlantic Ocean and Since AR4, little work has been done to document the ability of climate is related to variability in the thermohaline circulation (Knight et al., models to simulate the TBO. However, Li et al. (2012b) showed that 2005). Area-mean North Atlantic SST shows variations with a range of there is an overall improvement in the seasonality of monsoons rainfall about 0.4°C (see Box 2.5) and a warming of a similar magnitude since related to changes in the TBO from CMIP3 to CMIP5, with most CMIP5 1870. The AMO has a quasi-periodicity of about 70 years, although the models better simulating both the monsoon timing and the very low approximately 150-year instrumental record possesses only a few dis- rainfall rates outside of the monsoon season (see also Section 14.2.2). tinct phases warm during approximately 1930 1965 and after 1995, In addition they concluded that the India-Australia link seems to be and cool between 1900 1930 and 1965 1995. The phenomenon has robust in all models. also been referred to as Atlantic Multidecadal Variability to avoid the implication of temporal regularity. Along with secular trends and Pacif- With regard to possible future behaviour of the TBO, no analysis ic variability, the AMO is one of the principal features of multidecadal using multiple GCMs has been made since the AR4. In models that variability in the instrumental climate record. more accurately simulate the TBO in the present-day climate, the TBO strengthens in a future warmer climate (Nanjundiah et al., 2005). How- The AR4 highlighted a number of important links between the AMO ever, as with ENSO (Section 14.4) and IOD (Section 14.3.3), internally and regional climates. Subsequent research using observational and generated decadal variability complicates the interpretation of such paleoclimate records, and climate models, has confirmed and expand- future changes. Therefore, it remains unclear how future changes in the ed upon these connections, such as West African monsoon and Sahel TBO will emerge and how this may influence regional climate in the rainfall (Mohino et al., 2011; Section 14.2.4), summer climate in North future. Confidence in the projected future changes in TBO remains low. America (Seager et al., 2008; Section 14.8.3; Feng et al., 2011) and Europe (Folland et al., 2009; Ionita et al., 2012; Section 14.8.6), the 14.7.5 Quasi-Biennial Oscillation Arctic (Chylek et al., 2009; Mahajan et al., 2011), and Atlantic major hurricane frequency (Chylek and Lesins, 2008; Zhang and Delworth, The QBO is a near-periodic, large-amplitude, downward propagating 2009; Section 14.6.1). Further, the list of AMO influences around the oscillation in zonal winds in the equatorial stratosphere (Baldwin et globe has been extended to include decadal variations in many other al., 2001). It is driven by vertically propagating internal waves that regions (e.g., Zhang and Delworth, 2006; Kucharski et al., 2009a, are generated in the tropical troposphere (Plumb, 1977). The QBO has 2009b; Huss et al., 2010; Marullo et al., 2011; Wang et al., 2011). substantial effects on the global stratospheric circulation, in particular the strength of the northern stratospheric polar vortex as well as the Paleo reconstructions of Atlantic temperatures show AMO-like variabili- extratropical troposphere (Boer, 2009; Marshall and Scaife, 2009; Gar- ty well before the instrumental era, as noted in the AR4 (Chapter 6; see finkel and Hartmann, 2011). These extratropical effects occur primarily also Section 5.4.2). Recent analyses confirm this, and suggest potential in winter when the stratosphere and troposphere are strongly coupled for intermittency in AMO variability (Saenger et al., 2009; Zanchettin et (Anstey and Shepherd, 2008; Garfinkel and Hartmann, 2011). al., 2010; Chylek et al., 2012). Control simulations of climate models run for hundreds or thousands of years also show long-lived Atlantic mul- It has been unclear how the QBO will respond to future climate change ti-decadal variability (Menary et al., 2012). These lines of evidence sug- related to GHG increase and recovery of stratospheric ozone. Climate gest that AMO variability will continue into the future. No fundamental models assessed in the AR4 did not simulate the QBO as they lacked changes in the characteristics of North Atlantic multi-decadal variability the necessary vertical resolution (Kawatani et al., 2011). Recent model in the 21st century are seen in CMIP3 models (Ting et al., 2011). studies without using gravity wave parameterization (Kawatani et al., 2011; Kawatani et al., 2012) showed that the QBO period and ampli- Many studies have diagnosed a trend towards a warm North Atlantic tude may become longer and weaker, and the downward penetration in recent decades additional to that implied by global climate forcings into the lowermost stratosphere may be more curtailed in a warmer (Knight, 2009; Polyakov et al., 2010). It is unclear exactly when the climate. This finding is attributed to the effect of increased equatorial current warm phase of the AMO will terminate, but may occur within upwelling (stronger Brewer Dobson circulation; Butchart et al., 2006; the next few decades, leading to a cooling influence in the North Atlan- Garcia and Randel, 2008; McLandress and Shepherd, 2009; Okamoto tic and offsetting some of the effects characterizing global warming et al., 2011) dominating the effect of increased wave forcing (more (Keenlyside et al., 2008; see also Section 11.3.3.3). convective activity). Two studies with gravity wave parameterization, however, gave conflicting results depending on the simulated ­ hanges c Some similarity in the shape of the instrumental time series of global in the intensity of the Brewer Dobson circulation (Watanabe and and NH mean surface temperatures and the AMO has long been noted. 14 Kawatani, 2012). By removing an estimate of the effect of interannual variability phe­ 1254 Climate Phenomena and their Relevance for Future Regional Climate Change Chapter 14 nomena like ENSO (Section 14.4), AMO transitions have been shown to Research Institute (MRI) model (Endo et al., 2012; Mizuta et al., 2012). have the potential to produce large and abrupt changes in hemispheric To facilitate a direct comparison across the scenarios, the precipitation temperatures (Thompson et al., 2010). Estimates of the AMO s contri- changes are normalized by the global annual mean surface air temper- bution to recent climate change are uncertain, however, as attribution ature changes in each scenario. Published results using other downscal- of the observed AMO requires a model (physical or conceptual) whose ing methods are also assessed when found essential to illustrate issues assumptions are nearly always difficult to verify (Knight, 2009). related to regional climate change. 14.7.7 Assessment Summary Regional climate projections are generally more uncertain than pro- jections of global mean temperature but the sources of uncertainty Literature is generally insufficient to assess future changes in behav- are similar (see Chapters 8, 11, and 12) yet differ in relative impor- iour of the PNA, PSA, TBO, QBO and PDO/IPO. Confidence in the pro- tance. For example, natural variability (Deser et al., 2012), aerosol jections of changes in these modes is therefore low. However, process forcing (Chapter 7) and land use/cover changes (DeFries et al., 2002; understanding of the formation of the PSA gives medium confidence Moss et al., 2010) all become more important sources of uncertain- that future changes in the mean atmospheric circulation for austral ty on a regional scale. Regional climate assessments incur additional summer will project on the PSA pattern thereby influencing the SACZ uncertainty due to the cascade of uncertainty through the hierarchy and precipitation over Southeastern South America. of models needed to generate local information (cf. downscaling in Section 9.6). Calibration (bias correction) of model output to match Paleoclimate reconstructions and long model simulations indicate that local observations is an additional important source of uncertainty in the AMO is unlikely to change its behaviour in the future as the mean regional climate projections (e.g., Ho et al., 2012), which should be climate changes. However, natural fluctuations in the AMO over the considered when interpreting the regional projections. Therefore, the coming few decades are likely to influence regional climates at least as model spread shown in Annex 1 should not be interpreted as the final strongly as will human-induced changes. uncertainty in the observable regional climate change response. Table 14.2 summarizes the assessed confidence in the ability of CMIP5 14.8 Future Regional Climate Change models to represent regional scale present-day climate (temperature and precipitation, based on Chapter 9), the main controlling phenom- 14.8.1 Overview ena for weather and climate in that region and the assessed result- ing confidence in the future projections. There is generally less confi- The following sections assess future climate projections for several dence in projections of precipitation than of temperature. For example, regions, and relate them, where possible, to projected changes in the in Annex I, the temperature projections for 2081 2100 are almost major climate phenomena assessed in Sections 14.2 to 14.7. The region- always above the model estimates of natural variability, whereas the al climate change assessments are mainly of mean surface air temper- precipitation projections less frequently rise above natural variability. ature and mean precipitation based primarily on multi-model ensemble Although some projections are robust for reasons that are well under- projections from general circulation models. Reference is made to the stood (e.g., the projected increase in precipitation at high latitudes), appropriate projection maps from CMIP5 (Taylor et al., 2011c) present- many other regions have precipitation projections that vary in sign and ed in Annex I: Atlas of Global and Regional Climate Projections. Annex magnitude across the models. These issues are further discussed in Sec- I uses smaller sub-regions similar to those introduced by SREX (Sen- tion 12.4.5.2. Details on how the confidence table is constructed are eviratne et al., 2012). Table 14.1 presents a quantitative summary of found in the Supplementary Material. the regional area averages over three projection periods (2016 2035, 2046 2065 and 2081 2100 with respect to the reference period 1986 Credibility in regional climate change projections is increased if it 2005, representing near future, middle century and end of century) for is possible to find key drivers of the change that are known to be the RCP4.5 scenario. The 26 land regions assessed here are presented in well-simulated and well-projected by climate models. Table 14.3 Seneviratne et al., 2012, page 12 and the coordinates can be found from summarizes the assessment of how major climate phenomena might their online Appendix 3.A. Added to this are six additional regions con- be relevant for future regional climate change. For each entry in the taining the two polar regions, the Caribbean, Indian Ocean and Pacific table, the relevance is based on an assessment of confidence in future Island States (see Annex I for further details). Table 14.1 identifies the change in the phenomenon and the confidence in how the phenome- smaller sub-domains grouped within the somewhat large regions that non influences regional climate. For example, NAO is assigned high rel- are discussed in Sections 14.8.2 to 14.8.15. Tables for RCP2.6, RCP6.0 evance (red) for the Arctic region because NAO is known to influence and RCP8.5 scenarios are presented in Supplementary Material Tables the Arctic and there is high confidence that the NAO index will increase 14.SM.1a to 14.SM.1c. For continental-scale regions, projected chang- in response to anthropogenic forcing. If there is low confidence in how es in mean precipitation between (2081 2100) and (1986 2005) are a phenomenon might change (e.g., ENSO) but high confidence that compared in two generations of models forced under two comparable it has a strong regional impact, then the cell in the table is assigned emission scenarios: RCP4.5 in CMIP5 versus A1B in CMIP3. In contrast medium relevance (yellow). It can be seen from the table that there are to the Annex, the seasons here are chosen differently for each region many cases where major phenomena are assessed to have high (red) so as to best capture the regional features such as monsoons. Down- or medium (yellow) relevance for future regional climate change. See scaling issues are illustrated in panels showing results from an ensem- Supplementary Material Section 14.SM.6.1 for more details on how ble of high-resolution time-slice experiments with the Meteorological this relevance table was constructed. 14 1255 Chapter 14 Climate Phenomena and their Relevance for Future Regional Climate Change Frequently Asked Questions FAQ 14.2 | How Are Future Projections in Regional Climate Related to Projections of Global Means? The relationship between regional climate change and global mean change is complex. Regional climates vary strongly with location and so respond differently to changes in global-scale influences. The global mean change is, in effect, a convenient summary of many diverse regional climate responses. Heat and moisture, and changes in them, are not evenly distributed across the globe for several reasons: External forcings vary spatially (e.g., solar radiation depends on latitude, aerosol emissions have local sources, land use changes regionally, etc.). Surface conditions vary spatially, for example, land/sea contrast, topography, sea surface temperatures, soil mois- ture content. Weather systems and ocean currents redistribute heat and moisture from one region to another. Weather systems are associated with regionally important climate phenomena such as monsoons, tropical conver- gence zones, storm tracks and important modes of climate variability (e.g., El Nino-Southern Oscillation (ENSO), North Atlantic Oscillation (NAO), Southern Annular Mode (SAM), etc.). In addition to modulating regional warm- ing, some climate phenomena are also projected to change in the future, which can lead to further impacts on regional climates (see Table 14.3). Projections of change in surface temperature and precipitation show large regional variations (FAQ 14.2, Figure 1). Enhanced surface warming is projected to occur over the high-latitude continental regions and the Arctic ocean, (continued on next page) (a) (b) (°C per °C) (°C per °C) 0.5 1 1.5 2 0 0.25 0.5 0.75 1 1.25 1.5 1.75 2 (c) (d) (%) (% per °C) 0 0.5 1 1.5 2 -12 -9 -6 -3 0 3 6 9 12 FAQ 14.2, Figure 1 | Projected 21st century changes in annual mean and annual extremes (over land) of surface air temperature and precipitation: (a) mean surface temperature per °C of global mean change, (b) 90th percentile of daily maximum temperature per °C of global average maximum temperature, (c) mean precipitation (in % per °C of global mean temperature change), and (d) fraction of days with precipitation exceeding the 95th percentile. Sources: Panels (a) and (c) projected changes in means between 1986 2005 and 2081 2100 from CMIP5 simulations under RCP4.5 scenario (see Chapter 12, Figure 12.41); Panels (b) and (d) projected changes 14 in extremes over land between 1980 1999 and 2081 2100 (adapted from Figures 7 and 12 of Orlowsky and Seneviratne, 2012). 1256 Climate Phenomena and their Relevance for Future Regional Climate Change Chapter 14 FAQ 14.2 (continued) while over other oceans and lower latitudes changes are closer to the global mean (FAQ 14.2, Figure 1a). For example, warming near the Great Lakes area of North America is projected to be about 50% greater than that of the global mean warming. Similar large regional variations are also seen in the projected changes of more extreme temperatures (FAQ 14.2, Figure 1b). Projected changes in precipitation are even more regionally variable than changes in temperature (FAQ 14.2, Figure 1c, d), caused by modulation from climate phenomena such as the monsoons and tropical convergence zones. Near-equatorial latitudes are projected to have increased mean precipi- tation, while regions on the poleward edges of the subtropics are projected to have reduced mean precipitation. Higher latitude regions are projected to have increased mean precipitation and in particular more extreme precipi- tation from extratropical cyclones. Polar regions illustrate the complexity of processes involved in regional climate change. Arctic warming is projected to increase more than the global mean, mostly because the melting of ice and snow produces a regional feedback by allowing more heat from the Sun to be absorbed. This gives rise to further warming, which encourages more melting of ice and snow. However, the projected warming over the Antarctic continent and surrounding oceans is less marked in part due to a stronger positive trend in the Southern Annular Mode. Westerly winds over the mid- latitude southern oceans have increased over recent decades, driven by the combined effect of loss of stratospheric ozone over Antarctica, and changes in the atmosphere s temperature structure related to increased greenhouse gas concentrations. This change in the Southern Annular Mode is well captured by climate models and has the effect of reducing atmospheric heat transport to the Antarctic continent. Nevertheless, the Antarctic Peninsula is still warming rapidly, because it extends far enough northwards to be influenced by the warm air masses of the westerly wind belt. 14.8.2 Arctic 2010). Finally, warmer temperatures have been sustained in pan-Arctic land areas where a declining NAO over the past decade ought to have This section is concerned with temperature and precipitation dimen- caused cooling (Semenov, 2007; Turner et al., 2007b). Since AR4, evi- sions of Arctic climate change, and their links to climate phenomena. dence has also emerged that precipitation has trended upward in most The reader is referred elsewhere for information on sea ice loss (Sec- pan-Arctic land areas over the past few decades (e.g., Pavelsky and tions 4.2.2, 5.5.2 and Chapter 10), and projections of sea ice change Smith, 2006; Rawlins et al., 2010), though the evidence remains mixed (Sections 9.4.3, 9.8.3 and Chapters 11 and 12). (e.g., Dai et al., 2009). Increasing ETC activity over the Canadian Arctic has also been observed (Section 2.6.4). Arctic climate is affected by three modes of variability: NAO (Section 14.5.1), PDO (Section 14.7.3) and AMO (Section 14.7.6). The NAO Since AR4, there has been progress in adapting RCMs for polar appli- index correlates positively with temperatures in the northeastern Eur- cations (Wilson et al., 2012). These models have been evaluated with asian sector, and correlates negatively with temperatures in the Baffin regard to their ability to simulate Arctic clouds, surface heat fluxes, and Bay and Canadian Archipelago, but exhibits little relationship with boundary layer processes (Tjernstrom et al., 2004; Inoue et al., 2006; central Arctic temperatures (Polyakov et al., 2003). The PDO plays a Rinke et al., 2006). They have been used to improve simulations of role in temperature variability of Alaska and the Yukon (Hartmann and Arctic-specific climate processes, such as glacial mass balance (Zhang Wendler, 2005). The AMO is positively associated with SST throughout et al., 2007). A few regional models have been used for Arctic climate the Arctic (Chylek et al., 2009; Levitus et al., 2009; Chylek et al., 2010) change projections (e.g., Zahn and von Storch, 2010; Koenigk et al., (Mahajan et al., 2011). ETCs are also mainly responsible for winter 2011; Döscher and Koenigk, 2012). For information on GCM quality in precipitation in the region (see Table 14.3). the Arctic, see Chapter 9 and the brief summary of assessed confidence in the CMIP5 models in Table 14.2. The surface and lower troposphere in the Arctic and surrounding land areas show regional warming over the past three decades of about 1°C The CMIP5 model simulations exhibit an ensemble-mean polar ampli- per decade significantly greater than the global mean trend (Figures fied warming, especially in winter, similar to CMIP3 model simulations 2.22 and 2.25). According to temperature reconstructions, this signal (Bracegirdle and Stephenson, 2012; see also Box 5.1). For RCP4.5, is highly unusual: Temperatures averaged over the Arctic over the past ensemble-mean winter warming rises to 5.0°C over pan-Arctic land few decades are significantly higher than any seen over the past 2000 areas by the end of the 21st century (2081 2100), and about 7.0°C years (Kaufman et al., 2009). Temperatures 11 ka were greater than the over the Arctic Sea (Table14.1). Throughout the century, the warming 20th century mean, but this is probably a strongly forced signal, since exceeds simulated estimates of internal variability (Figure AI.8). The summer solar radiation was 9% greater than present (Miller et al., RCP4.5 ensemble-mean warming is more modest in JJA (Table 14.1), 14 1257 Chapter 14 Climate Phenomena and their Relevance for Future Regional Climate Change reaching about 2.2°C by century s end over pan-Arctic land areas, and 2007; Vimont and Kossin, 2007; Smirnov and Vimont, 2011). ETCs are 1.5°C over the Arctic Sea. The summer warming exceeds variability also mainly responsible for winter precipitation, especially in the north- estimates by about mid-century (Figure AI.9). These simulated anthro- ern half of NA. See Table 14.3 for a summary of this information. pogenic seasonal warming patterns match qualitatively the observed warming patterns over the past six decades (AMAP, 2011), and the A general surface warming over NA has been documented over the observed warming patterns are likely to be at least partly anthropo- last century (see Section 2.4). It is particularly large over Alaska and genic in origin (Section 10.3.1.1.4). Given the magnitude of future northern Western NA during winter and spring and the northern part projected changes relative to variability, and the presence of anthro- of Eastern NA during summer (Zhang et al., 2011b). There is also a pogenic signals already, it is likely future Arctic surface temperature cooling tendency over Central and Eastern NA (i.e., the warming hole changes will continue to be strongly influenced by the anthropogenic discussed in Section 2.4.1) during spring, though it is absent in lower forcing over the coming decades. tropospheric temperature (cf. Figure 2.25). The warming has coincided with a general decline in NA snow extent and depth (Brown and Mote, The CMIP5 models robustly project precipitation increases in the 2009; McCabe and Wolock, 2010; Kapnick and Hall, 2012). Consistent pan-Arctic (both land and sea) region over the 21st century, as did with surface temperature trends, temperature extremes also exhibit their CMIP3 counterparts (Kattsov et al., 2007; Rawlins et al., 2010). secular changes. Cold days and nights have decreased in the last half Under the RCP4.5 scenario, the cold season, ensemble mean precip- century, while warm days and nights have increased (see Chapter 2). itation increases about 25% by the century s end (Table 14.1), due These changes are especially apparent for nightly extremes (Vincent to enhanced precipitation in ETCs (Table 14.3). However, this signal et al., 2007). It is unclear whether there have been mean precipita- does not rise consistently above the noise of simulated variability tion trends over the last 50 years (Section 2.5.1; Zhang et al., 2011b). until mid 21st century (Figure AI.10). During the warm season, precip- However, precipitation extremes increased, especially over Central and itation increases are smaller, about 15% (Table 14.1), though these Eastern NA (see Section 2.6.2 and Seneviratne et al., 2012). signals also rise above variability by mid 21st century (Figure AI.11). The inter-model spread in the precipitation increase is generally as large as Table 14.2 provides an assessment of GCM quality for simulations the ensemble mean signal itself (similar to CMIP3 model behaviour, of temperature, precipitation, and main phenomena in NA s regions. Holland and Webster, 2007), so the magnitude of the future increase Regarding regional modelling experiments since AR4, biases have is uncertain. However, since nearly all models project a large precipi- decreased somewhat as resolutions increase. The North American tation increase rising above the variability year-round, it is likely the Regional Climate Change Assessment Program created a simulation pan-Arctic region will experience a statistically significant increase in suite for NA at 50-km resolution. When forced by reanalyses, this suite precipitation by mid-century (see also Table 14.2). The small projected generally reproduces climate variability within observational error increase in the NAO is likely to affect Arctic precipitation (and tem- (Leung and Qian, 2009; Wang et al., 2009b; Gutowski, 2010; Mearns perature) patterns in the coming century (Section 14.5.1; Table 14.3), et al., 2012).Other regional modelling experiments covering parts or though the importance of these signals relative to anthropogenic sig- all of NA have shown improvements as resolution increases (Liang et nals described here is unclear. al., 2008a; Lim et al., 2011; Yeung et al., 2011), including for extremes (Kawazoe and Gutowski, 2013). Bias reductions are large for snowpack In summary: It is likely Arctic surface temperature changes will be in topographically complex Western NA, as revealed by 2- to 20-km res- strongly influenced by anthropogenic forcing over the coming decades olution regional simulations (Qian et al., 2010b; Salathe Jr. et al., 2010; dominating natural variability such as induced by NAO. It is likely the Pavelsky et al., 2011; Rasmussen et al., 2011). Thus there has been pan-Arctic region will experience a significant increase in precipitation substantial progress since AR4 in understanding the value of regional by mid-century due mostly to enhanced precipitation in ETCs. modelling in simulating NA climate. The added value of using regional models to simulate climate change is discussed in Section 9.6.6. 14.8.3 North America NA warming patterns in RCP4.5 CMIP5 projections are generally sim- The climate of North America (NA) is affected by the following phenom- ilar to those of CMIP3 (Figures AI.4 and AI.5, Table 14.1). In winter, ena: NAO (Section 14.5.1), ENSO (Section 14.4), PNA (Section 14.7.2), warming is greatest in Alaska, Canada, and Greenland (Figures AI.12 PDO (Section 14.7.3), NAMS (Section 14.2.3), TCs and ETCs (Section and AI.16), while in summer, maximum warming shifts south, to West- 14.6). The NAO affects temperature and precipitation over Eastern NA ern, Central, and Eastern NA. Examining near-term (2016 2035) CMIP5 during winter (Hurrell et al., 2003). Positive PNA brings warmer tem- projections of the less sensitive models (25th percentile, i.e., upper peratures to northern Western NA and Alaska in winter, cooler tem- left maps in Figures AI.12, AI.13, AI.16, AI.17, AI.20 AI.21, AI.24 and peratures to the southern part of Eastern NA, and dry conditions to AI.25), the warming generally exceeds natural variability estimates. much of Eastern NA (Nigam, 2003). The PNA can also be excited by Exceptions are Alaska, parts of Western, Central, and Eastern NA, and ENSO-related SST anomalies (Horel and Wallace, 1981; Nigam, 2003). Canada and Greenland during winter, when natural variability linked The PDO is linked to decadal climate anomalies resembling those of to wintertime storms is particularly large. By 2046 2065, warming in the PNA. The NAMS brings excess summer rainfall to Central America all regions exceeds the natural variability estimate for all models. Thus and Mexico and the southern portion of Western NA (Gutzler, 2004). it is very likely the warming signal will be large compared to natural TCs also impact the Gulf Coast and Eastern NA (see Section 14.6.1). variability in all NA regions throughout the year by mid-century. This The AMM and AMO may affect their frequency and intensity (Landsea warming generally leads to a two- to four fold increase in simulated 14 et al., 1999; Goldenberg et al., 2001; Cassou et al., 2007; Emanuel, heat wave frequency over the 21st century (e.g., Lau and Nath, 2012). 1258 Climate Phenomena and their Relevance for Future Regional Climate Change Chapter 14 Anthropogenic climate change may also bring systematic cold-season the NAMS are likewise uncertain, though there is medium confidence precipitation changes. As with previous models, CMIP5 projections the phenomenon will move to later in the annual cycle (Section 14.2.3, generally agree in projecting a winter precipitation increase over the Table 14.3). As there is medium confidence tropical cyclones will be northern half of NA (Figure 14.18 and AI.19). This is associated with associated with greater rainfall rates, the Gulf and East coasts of NA increased atmospheric moisture, increased moisture convergence, and may be impacted by greater precipitation when tropical cyclones occur a poleward shift in ETC activity (Section 14.6.2 and Table 14.3). The (Table 14.3). change is consistent with CMIP3 model projections of positive NAO trends (Table 14.3; Hori et al., 2007; Karpechko, 2010; Zhu and Wang, CMIP3 models showed a 21st century precipitation decrease across 2010). Winter precipitation increases extend southward into the USA much of southwestern NA, accompanied by a robust evaporation (northern portions of SREX regions 3 to 5; Neelin et al., 2013) but increase characteristic of mid-latitude continental warming (Seager with decreasing strength relative to natural variability. This behaviour et al., 2007; Seager and Vecchi, 2010) and an increase in drought fre- is qualitatively reproduced in higher resolution simulations (Figure quency (Sheffield and Wood, 2008; Gutzler and Robbins, 2011). When 14.18). downscaled, CMIP3 models showed less drying in the region (Gao et al., 2012c) and an extreme precipitation increase, despite overall Warm-season precipitation also exhibits significant increases in drying (Dominguez et al., 2012). CMIP5 models do not consistently Alaska, northern Canada, and Eastern NA by century s end (Figures show such a precipitation decrease in this region (Neelin et al., 2013). 14.18, AI.19, AI.22). However, CMIP5 models disagree on the sign This is one of the few emerging differences between the two ensem- of the precipitation change over the rest of NA (Figures AI.26 and bles in climate projections over NA. However, the CMIP5 models AI.27), consistent with CMIP3 results (Figure 14.18; Neelin et al., 2006; still show a strong decrease in soil moisture here (Dai, 2013), due to Rauscher et al., 2008; Seth et al., 2010). One set of high resolution increasing evaporation. simulatons (Endo et al., 2012) shows a tendency towards more precipi- tation than either CMIP3 or CMIP5 models (Figure 14.18), suggesting In summary, it is very likely that by mid-century the anthropogenic the simulated warm-season precipitation change in the region may be warming signal will be large compared to natural variability such as resolution-dependent. Future precipitation changes associated with that stemming from the NAO, ENSO, PNA, PDO, and the NAMS in all Figure 14.18 | Maps of precipitation changes for North America in 2080 2099 with respect to 1986 2005 in June, July and August (above) and December to February (below) in the SRES A1B scenario with 24 CMIP3 models (left), and in the RCP4.5 scenario with 39 CMIP5 models (middle). Right figures are the precipitation changes in 2075 2099 with respect to 1979 2003 in the SRES A1B scenario with the 12 member 60 km mesh Meteorological Research Institute (MRI)-Atmospheric General Circulation Model 3.2 (AGCM3.2) multi-physics, multi-SST ensembles (Endo et al., 2012). Precipitation changes are normalized by the global annual mean surface air temperature changes in each scenario. Light hatching denotes where more than 66% of models (or members) have the same sign with the ensemble mean changes, while dense hatching denotes where more than 90% of models (or members) have the same sign with the ensemble mean changes. 14 1259 Chapter 14 Climate Phenomena and their Relevance for Future Regional Climate Change NA regions throughout the year. It is likely that the northern half of NA i ­nfluence trade winds over the Tropical North Atlantic and can combine will experience an increase in precipitation over the 21st century, due with ENSO to modulate the summer Western Hemisphere warm pool in large part to a precipitation increase within ETCs. (e.g., Enfield et al., 2006). Table 14.3 summarizes the main phenomena and their relevance to climate change over the CAC. 14.8.4 Central America and Caribbean Because inter-decadal  climate variations can be large in the CAC The Central America and the Caribbean (CAC) region is affected by region, precipitation trends must be interpreted carefully. From 1950 several phenomena, including the ITCZ (Section 14.3.1.1), NAMS (Sec- to 2003, negative trends were seen in several data sets in the Carib- tion 14.2.3.1), ENSO (Section 14.4) and TCs (Section 14.6.1; Table 14.3; bean region and parts of Central America (Neelin et al., 2006). How- also Gamble and Curtis, 2008). The annual cycle results from air sea ever, regarding secular trends (1901 2005), this signal was identified interactions over the Western Hemisphere warm pool in the tropical only in the Caribbean region (Trenberth et al., 2007b). Prolonged dry eastern north Pacific and the Intra Americas Seas (Amador et al., 2006; or wet periods are related to decadal variability of the adjacent Pacific Wang et al., 2007). The Caribbean Low Level Jet is a key element of the and Atlantic (Mendoza et al., 2007; Seager et al., 2009; Mendez and region s summer climate (Cook and Vizy, 2010) and is controlled by Magana, 2010), and the intensity of easterlies over the region. For the size and intensity of the Western Hemisphere warm pool (Wang et instance, increased easterly surface winds over Puerto Rico from 1950 al., 2008b). It is also modulated by SST gradients between the eastern to 2000 disrupted a pattern of inland moisture convergence, leading to equatorial Pacific and tropical Atlantic (Taylor et al., 2011d). ENSO is a dramatic precipitation decrease (Comarazamy and Gonzalez, 2011). the main driver of climate variability, with El Nino being associated with dry conditions and La Nina with wet conditions (Karmalkar et Table 14.2 provides an overall assessment of GCM quality for simula- al., 2011). Other teleconnection patterns, such as the NAO (Section tions of temperature, precipitation and main phenomena in the CAC 14.5.1) and the strength of boreal winter convection over the Amazon, sub-regions. Annual cycles of temperature and precipitation are well Figure 14.19 | Maps of precipitation changes for Central America and Caribbean in 2080 2099 with respect to 1986 2005 in June to September (above) and December to March (below) in the SRES A1B scenario with 24 CMIP3 models (left), and in the RCP4.5 scenario with 39 CMIP5 models (middle). Right figures are the precipitation changes in 2075 2099 with respect to 1979 2003 in the SRES A1B scenario with the 12 member 60 km mesh Meteorological Research Institute (MRI)-Atmospheric General Circulation Model 3.2 (AGCM3.2) multi-physics, multi-SST ensembles (Endo et al., 2012). Precipitation changes are normalized by the global annual mean surface air temperature changes in each scenario. Light hatching denotes where more than 66% of models (or members) have the same sign with the ensemble mean changes, while dense hatching denotes where more 14 than 90% of models (or members) have the same sign with the ensemble mean changes. 1260 Climate Phenomena and their Relevance for Future Regional Climate Change Chapter 14 simulated by CMIP5 models, though precipitation from June to Octo- p ­ recipitation will decrease in the Caribbean region, over the coming ber is underestimated (Figure 9.38). Regional models also simulate century. However, there is only medium confidence that Central Amer- temperature and precipitation climatologies, and the magnitude and ica will experience a decrease in precipitation. annual cycle of the Caribbean Low-Level Jet reasonably well (Campbell et al., 2010; Taylor et al., 2013). 14.8.5 South America CMIP3 models generally projected a precipitation reduction over much South America (SA) is affected by several climate phenomena. ENSO of the Caribbean region, consistent with the observed negative trend (Section 14.4) and Atlantic Ocean modes (Section 14.3.4) have a role since 1950 (Neelin et al., 2006; Rauscher et al., 2008). The subtropics in interannual variability of many regions. The SAMS (Section 14.2.3.2) are generally expected to dry as global climate warms (Held and Soden, is responsible for rainfall over large areas, while the SACZ (Section 2006), but in both CMIP3 and CMIP5 models the CAC region shows the 14.3.1.3) and Atlantic ITCZ (Section 14.3.1.1) also affect precipitation. greatest drying. Future drying may also be related to strengthening Teleconnections such as the PSA (Section 14.7.1), the SAM (Section of the Caribbean Low-Level Jet (Taylor et al., 2013) and subsidence 14.5.2) with related ETCs (Section 14.6.2) and the IOD (Section 14.3.3) over the Caribbean region associated with warmer SSTs in the tropical also influence climate variability. Table 14.3 summarizes the main phe- Pacific than Atlantic (Taylor et al., 2011d). A high-resolution regional nomena and their assessed relevance to climate change over SA. Ocean GCM using a CMIP3 ensemble for boundary conditions confirms that the Intra American Seas circulation weakens by similar rate as the Positive minimum temperature trends have been observed in SA reduction in Atlantic Meridional Overturning (Liu et al., 2012c). This (Alexander et al., 2006; Marengo and Camargo, 2008; Rusticucci and weakening causes the Gulf of Mexico to warm less than other oceans. Renom, 2008; Marengo et al., 2009; Seneviratne et al., 2012; Skansi et al., 2013). Glacial retreat in the tropical Andes was observed in the last Downscaling experiments for the region have shown a mid-21st centu- three decades (Vuille et al., 2008; Rabatel et al., 2013). In contrast to ry warming between 2°C and 3°C (Vergara et al., 2007; Rauscher et al., the warming over the continental interior, a prominent but localized 2008; Karmalkar et al., 2011). Precipitation decreases over most of the coastal cooling was detected during the past 30 to 50 years, extending CAC region, similar to the signal in driving global models (Campbell et from central Peru (Gutiérrez et al., 2011) to northern (Schulz et al., al., 2010; Hall et al., 2012). However, only a few downscaling studies 2012) and central Chile (Falvey and Garreaud, 2009). Observed pre- took into account key elements of the region s climate, such as east- cipitation changes include a significant increase in precipitation during erly wave activity, TCs, or interannual variability mechanisms linked to the 20th century over the southern sector of southeastern SA, a nega- ENSO (Karmalkar et al., 2011). tive trend in SACZ continental area (Section 2.5.1; Barros et al., 2008), a negative trend in mean precipitation and precipitation extremes in By century s end, CMIP5 models project greatest warming in the CAC central-southern Chile, and a positive trend in southern Chile (Haylock region in JJA. Warming is projected to be larger over Central America et al., 2006; Quintana and Aceituno, 2012). Other detected changes than the Caribbean in summer and winter (Figures AI.24, AI.2, Table include positive extreme precipitation trends in southeastern SA, cen- 14.1). From October to March, ensemble mean projections indicate pre- tral-northern Argentina and northwestern Peru and Ecuador (Section cipitation decrease in northern Central America, including Mexico. In the 2.6.2; Haylock et al., 2006; Dufek et al., 2008; Marengo et al., 2009; Re Caribbean precipitation is projected to decrease in the south (consistent and Barros, 2009; Skansi et al., 2013). with the observed trends) but to increase in the north (Figure AI.26). From April to September, the projected zone of precipitation reduction Table 14.2 provides an overall assessment of GCM quality for simula- expands over the entire CAC region, and this signal is generally larger tions of temperature, precipitation and main phenomena in the sub-re- than the models estimates of natural variability (Figure AI.27). Precipi- gions of SA. In general, GCM results are consistent with observed tem- tation changes projected by CMIP3, CMIP5 and a high-resolution model perature tendencies (e.g., Haylock et al., 2006). Trends toward warmer show a similar reduction in parts of Mexico and the southern Caribbe- nights in CMIP3 models (Marengo et al., 2010b; Rusticucci et al., 2010) an in DJFM, and in Central America and the Caribbean in JJAS (Figure are consistent with observed trends. CMIP3 models, however, do not 14.19). The CMIP5 ensemble shows greater agreement in the DJFM pre- simulate the cooling ocean and warming land trends observed in the cipitation increase in the northern Caribbean sector than CMIP3. These last 30 years along subtropical western SA noted above. The number of projected changes are also reflected in Table 14.1. Figures AI.26, AI.27 warm nights in SA is well represented in CMIP5 simulations (Sillmann and Figure 14.19 suggest an intensification and southward displace- et al., 2013). CMIP5 models reproduce the annual cycle of precipitation ment of the East Pacific ITCZ, which can contribute to drying in southern over SA, though the multi-model mean underestimates rainfall over Central America (Karmalkar et al., 2011). some areas (Figure 9.38). In tropical SA, rainy season precipitation is better reproduced in the CMIP5 ensemble than CMIP3 (Figure 9.39). ENSO will continue to influence CAC climate, but changes in ENSO CMIP3 models were able to simulate extreme precipitation indices frequency or intensity remain uncertain (Section 14.4). Projected drier over SA (Rusticucci et al., 2010), but CMIP5 models improved them conditions may also be related to decreased frequency of TCs, though globally (Sillmann et al., 2013). CMIP5 also improved simulations the associated rainfall rate of these systems are higher in future pro- of precipitation indices in the SAMS region (Kitoh et al., 2013; Sec- jections (Section 14.6.1). tion 14.2.3.2). The main precipitation features are well represented by regional models in several areas of SA (Solman et al., 2008; Alves In summary, owing to model agreement on projections and the and Marengo, 2010; Chou et al., 2012; Solman et al., 2013). However, degree of consistency with observed trends, it is likely warm-season regional models underestimate daily precipitation intensity in the La 14 1261 Chapter 14 Climate Phenomena and their Relevance for Future Regional Climate Change Plata Basin and in eastern Northeastern SA in DJF and almost over the over SA, except in parts of Argentina, and a reduction of cold nights whole continent in JJA (Carril et al., 2012). over the whole continent (Marengo et al., 2009). CMIP5 projections confirm the results of CMIP3 in AR4 and SREX (see Section 12.4.9). Regarding future projections, CMIP5 models indicate higher temper- atures over all of SA, with the largest changes in southeastern Ama- CMIP5 results confirm precipitation changes projected by CMIP3 zonia by century s end. (Figures AI.28, AI.29, Table 14.1). Temperature models in the majority of SA regions, with increased confidence, as changes projected by RCMs forced by a suite of CMIP3 models agree more models agree in the changes (AI.30, AI.34, AI31, AI35). Inter-mod- that the largest warming occurs over the southern Amazon during aus- el spread in precipitation also decreased in some SA regions from tral winter. Regional models project a greater frequency of warm nights CMIP3 to CMIP5 (Blázquez and Nunez, 2012). CMIP5 precipitation ­ Figure 14.20 | Maps of precipitation changes for South America in 2080 2099 with respect to 1986 2005 in June to September (above) and December to March (below) in the SRES A1B scenario with 24 CMIP3 models (left), and in the RCP4.5 scenario with 39 CMIP5 models (middle). Right figures are the precipitation changes in 2075 2099 with respect to 1979 2003 in the SRES A1B scenario with the 12-member 60-km mesh Meteorological Research Institute (MRI)- Atmospheric General Circulation Model 3.2 (AGCM3.2) multi- physics, multi-SST ensembles (Endo et al., 2012). Precipitation changes are normalized by the global annual mean surface air temperature changes in each scenario. Light hatching 14 denotes where more than 66% of models (or members) have the same sign with the ensemble mean changes, while dense hatching denotes where more than 90% of models (or members) have the same sign with the ensemble mean changes. 1262 Climate Phenomena and their Relevance for Future Regional Climate Change Chapter 14 Precipitation change A1B DJF (a) 50 (c) 40 30 40 20 20 10 0 0 -10 -20 -20 -30 -40 ETA HADCM3 -40 LMDZ IPSL -50 SESA SACZ S-Amazonia REMO ECHAM5 PROMES HADCM3 REGCM3 ECHAM5 Precipitation change A1B JJA RECGM3 HADCM3 (b) RCA ECHAM5 1 RCA ECHAM5 2 50 (d) 40 RCA ECHAM5 3 40 20 LMDZ ECHAM5 30 20 0 10 -20 0 -10 -40 -20 -60 -30 -40 -80 -50 SESA SACZ S-Amazonia Figure 14.21 | (a) December, January and February (DJF) and (b) June, July and August (JJA) relative precipitation change in 2071 2100 with respect to 1961 1990 in the A1B scenario from an ensemble of 10 Regional Climate Models (RCMs) participating in the Europe South America Network for Climate Change Assessment and Impact Studies-La Plata Basin (CLARIS-LPB) Project. Hatching denotes areas where 8 out of 10 RCMs agree in the sign of the relative change. (c) DJF and (d) JJA dispersion among regional model projections of precipitation changes averaged over land grid points in Southeastern South America (SESA, 35°S to 25°S, 60°W to 50°W), South Atlantic Convergence Zone (SACZ, 25°S to 15°S, 45°W to 40°W) and southern Amazonia (15°S to 10°S, 65°W to 55°W), indicated by the boxes in (a). projections for the end of the twenty-first century (2081 2100) show a Brazil, there is less agreement. Results from high-resolution or regional precipitation increase from October to March over the southern part of models forced by CMIP3 models provides further indication the pro- Southeast Brazil and the La Plata Basin, the extreme south of Chile, the jected changes are robust. A high-resolution regional model ensem- northwest coast of SA, and the Atlantic ITCZ, extending to a small area ble projects precipitation increases during austral summer over the La of the northeastern Brazil coast (Figures AI.30 and AI.34). Reduced Plata Basin region, northwestern SA and southernmost Chile, and a October to March rainfall is projected in the extreme northern region decrease over northern SA, eastern Amazonia, eastern Brazil, central of SA, eastern Brazil, and central Chile. In eastern Amazonia and north- Chile and the Altiplano (Figure 14.21). Other regional models also pro- eastern Brazil, CMIP5 models show both drying and moistening. This ject a precipitation increase over the Peruvian coast and Ecuador and uncertainty can also be seen in Table 14.1. a reduction in the Amazon Basin (Marengo et al., 2010b; Marengo et al., 2012). Figure 14.20 confirms that changes in northwestern, southwestern and southeastern SA are consistent among CMIP3 and CMIP5 ensembles From April to September, the CMIP5 ensemble projects precipitation and a high-resolution model ensemble, which gives more confidence in increases over the La Plata Basin and northwestern SA near the coast these results. However, in eastern Amazonia and northeast and eastern (Figures AI31, AI35). In contrast, a reduction is projected for northeast 14 1263 Chapter 14 Climate Phenomena and their Relevance for Future Regional Climate Change Brazil and eastern Amazonia. Precipitation is projected to decrease in with the SAM s projected positive trend (Reboita et al., 2009; Sec- Central Chile, but to increase over extreme southern areas. In CMIP3 tion 14.5.2) impacts zones of cyclogenesis off the southeast SA coast models, a precipitation reduction in the central Andes resulted from a (Kruger et al., 2011; Section 12.4.4 ). moisture transport decrease from the continental interior to the Alti- plano (Minvielle and Garreaud, 2011). CMIP3 and CMIP5 models are In summary, it is very likely temperatures will increase over the whole consistent in projecting drier conditions in eastern Amazonia during continent, with greatest warming projected in southern Amazonia. It is the dry season and wetter conditions in western Amazonia (Malhi et likely there will be an increase (reduction) in frequency of warm (cold) al., 2008; Cook et al., 2011). The Amazon forest s future is discussed in nights in most regions. It is very likely precipitation will increase in the Section 12.5.5.6.1. Areas of maximum change in CMIP5 are consistent southern sector of southeastern and northwestern SA, and decrease with those of CMIP3 in JJA, agreeing also with a high-resolution model in Central Chile and extreme north of the continent. It is very likely ensemble (Figure 14.20). Increased precipitation in southeastern SA that less rainfall will occur in eastern Amazonia, northeast and east- is projected by a high-resolution model ensemble in all four seasons ern Brazil during the dry season. However, in the rainy season there (Blázquez et al., 2012). The austral winter precipitation increase over is medium confidence in the precipitation changes over these regions. the La Plata Basin and southern Chile, and the reduction in eastern There is high confidence in an increase of precipitation extremes. Amazonia and northeast Brazil, are also projected by RCMs (Figure 14.21) as in CMIP5 models. A relevant result from a RCM is the pre- 14.8.6 Europe and Mediterranean cipitation decrease over most of SA north of 20°S in austral spring, suggesting a longer dry season (Sörensson et al., 2010; see also Section This section assesses regional climate change in Europe and the North 14.2.3.2). Note that average CMIP5 spatial values in Table 14.1 are African and West Asian rims of the Mediterranean basin. Area-average consistent with changes seen in the maps, unless for the west coast of summaries are presented for the three sub-regions of Northern Europe SA, where there are spatial variations within the area and the values (NEU), Central Europe (CEU) and Mediterranean (MED) (cf. Tables 14.1 do not reflect the changes. to 14.2). Regional model projections and a high-resolution model ensemble The most relevant climate phenomena for this region are NAO (Section indicate an increase in the number of consecutive dry days in north- 14.5.1), ETCs (Section 14.6.2) and blocking (Box 14.2, Folland et al., eastern SA (Marengo et al., 2009; Kitoh et al., 2011). An increase in 2009; Feliks et al., 2010; Dole et al., 2011; Mariotti and Dell Aquila, heavy precipitation events over almost the entire continent, especially 2012). These phenomena also interact with longer time-scale North Amazonia, southern Brazil and northern Argentina, is projected by a Atlantic ocean-atmosphere phenomena such as the AMO (Section high-resolution model ensemble (Kitoh et al., 2011) and in subtropical ­ 14.7.6, Mariotti and Dell Aquila, 2012; Sutton and Dong, 2012). Other areas of South America by regional models (Marengo et al., 2009). phenomena have minor influence in limited sectors of the region (see Seneviratne et al. (2012) indicated low to medium confidence in CMIP3 Supplementary Material Section 14.SM.6.3). SA precipitation trends. However, the increased ability of CMIP5 models to represent extremes (Kitoh et al., 2013) provides higher confidence in Recent 1981-2012 trends in annual mean temperature in each sub- the signals discussed above (Section 14.2.3.2), consistent with global region exceed the global mean land trend as can be inferred from changes in land areas (Section 12.4.5). Figure 2.22. Consistent with previous AR4 conclusions (Section 11.3), recent studies of extreme events (Section 2.6.1) point to a very likely Precipitation changes projected over SA are consistent with El Nino increase of the number of warm days and nights, and decrease of the influences, for example, rainfall increase over southeastern and north- number of cold days and nights, since 1950 in Europe. Heat waves western SA and decrease over eastern Amazonia. However, CMIP3 can be amplified by drier soil conditions resulting from warming (Vau- models could not represent certain features of ENSO well (Roxy et al., tard et al., 2007; Seneviratne et al., 2010; Hirschi et al., 2011). Several 2013) and there is no consensus about future ENSO behaviour (Coelho studies (Section 2.6.2.1) also indicate general increases in the intensity and Goddard, 2009; Collins et al., 2010) even with CMIP5 results (Sec- and frequency of extreme precipitation especially in winter during the tion 14.4). As the various types of ENSO produce different impacts on last four decades however there are inconsistencies between studies, SA (Ashok et al., 2007; Hill et al., 2011; Tedeschi et al., 2013), future regions and seasons. ENSO effects remain uncertain. It is very likely that ENSO remains the dominant mode of interannual variability in the future (Section 14.4.2). The ability of climate models to simulate the climate in this region Therefore, regions in SA currently influenced by Pacific SST will contin- has improved in many important aspects since AR4 (see Figure 9.38). ue to experience ENSO effects on precipitation and temperature. Particularly relevant for this region are increased model resolution and a better representation of the land surface processes in many of the Projected precipitation increases in the southern sector of south- models that participated in the recent CMIP5 experiment. Table 14.2 eastern SA are consistent with changes in the SACZ dipole (Section provides an assessment of the CMIP5 quality for simulations of tem- 14.3.1.3) and PSA (Section 14.7.1). Increased precipitation in this perature, precipitation, and main phenomena in the region. The CMIP5 region may also have a contribution from a more frequent and intense projections reveal warming in all seasons for the three sub-regions, Low Level Jet (Nunez et al., 2009; Soares and Marengo, 2009). CMIP3 while precipitation projections are more variable across sub-regions model analyses show little impact on extreme precipitation from SAM and seasons. In the winter half year (October to March), NEU and CEU changes toward century s end, except in Patagonia (Menendez and are projected to have increased mean precipitation associated with 14 Carril, 2010). However, the southward shift of stormtracks associated ­ increased atmospheric moisture, increased moisture convergence and 1264 Climate Phenomena and their Relevance for Future Regional Climate Change Chapter 14 intensification in ETC activity (Section 14.6.2 and Table 14.3) and no pling (natural variability), even for the 2021 2050 time frame (Déqué change or a moderate reduction in the MED. In the summer half year et al., 2012). Other studies based on CMIP3 projections suggest that (April to September) , NEU and CEU mean precipitation are projected GHG-forced changes in the MED are likely to become distinguisha- to have only small changes whereas there is a notable reduction in ble from the noise created by internal decadal variations in decades MED (see Table 14.1, Figures AI.36 to AI.37 and AI.42-AI.43). Figure beyond 2020 2030 (Giorgi and Bi, 2009). It has been also shown using 14.22 illustrates that the precipitation changes are broadly consistent an ensemble of RCM simulations that the removal of NAO-related var- with the findings CMIP3. iability leads to an earlier emergence of change in seasonal mean tem- peratures for some regions in Europe (Kjellström et al., 2013). Hence, in High-resolution projections from the Japanese high-resolution model the near term, decadal predictability is likely to be critically dependent ensemble also agree with these findings and are consistent with on the regional impacts of modes of variability internal to the climate d ­ ownscaling results from coordinated multi-model GCM/RCM exper- system (Section 11.3). However, it has been shown that NAO trends do iments (e.g., ENSEMBLES, Déqué et al., 2012). In general, regional cli- not account for a large fraction of the long-term future change in mean mate change amplitudes for temperature and precipitation follow the temperature or precipitation (Stephenson et al., 2006) and that large- global warming amplitude although modulated both by changes in the scale atmospheric circulation changes in CMIP5 models are not the large-scale circulation and by regional feedback processes (Kjellstrom main driver of the warming projected in Europe by the end of the cen- et al., 2011), which confirms assessments in AR4 (Christensen et al., tury (2081 2100; Cattiaux et al., 2013). Therefore, changes in climate 2007). phenomena contribute to the uncertainty in the near-term projections rather than long-term changes in this region (Table 14.3), further sup- Some new investigations have focussed on the uncertainties associat- porting the credibility in model projection (Table 14.2). ed with model projections. A large ensemble of RCM-GCM shows that the temperature response is robust in spite of a considerable uncer- Recent studies have clearly identified a possible amplification of tem- tainty related to choice of model combination (GCM/RCM) and sam- perature extremes by changes in soil moisture (Jaeger and Seneviratne, Figure 14.22 | Maps of precipitation changes for Europe and Mediterranean in 2080 2099 with respect to 1986 2005 in June to August (above) and December to Febru- ary (below) in the SRES A1B scenario with 24 CMIP3 models (left), and in the RCP4.5 scenario with 39 CMIP5 models (middle). Right figures are the precipitation changes in 2075 2099 with respect to 1979 2003 in the SRES A1B scenario with the 12 member 60 km mesh Meteorological Research Institute (MRI)-Atmospheric General Circulation Model 3.2 (AGCM3.2) multi-physics, multi-sea surface temperature (SST) ensembles (Endo et al., 2012). Precipitation changes are normalized by the global annual mean surface air temperature changes in each scenario. Light hatching denotes where more than 66% of models (or members) have the same sign with the ensemble mean changes, while dense hatching denotes where more than 90% of models (or members) have the same sign with the ensemble mean changes. 14 1265 Chapter 14 Climate Phenomena and their Relevance for Future Regional Climate Change 2010; Hirschi et al., 2011), acting as a mechanism that further mag- interannual variability (Janicot et al., 2011). When evaluating models nifies the intensity and frequency of heat waves given the projected their ability to reproduce such characteristics of the African monsoon enhance of summer drying conditions. This is in line with the assessed is essential. A large effect of natural multi-decadal SST and warming of results presented in SREX (Seneviratne et al., 2012). At the other end the oceans on Sahel rainfall is very likely (Hoerling et al., 2006; Ting et of the spectrum, studies indicate that European winter variability may al., 2009, 2011; Mohino et al., 2011; Rodriguez-Fonseca et al., 2011). be related to sea ice reductions in the Barents-Kara Sea (Petoukhov and Semenov, 2010) and CMIP5 models in projections for the future in East Africa experiences a semi-annual rainfall cycle, driven by the ITCZ general exhibit a similar relation until the summer sea ice has almost movement across the equator. Direct links between the region s rainfall disappeared (Yang and Christensen, 2012). Although the mechanism and ENSO have been demonstrated (Giannini et al., 2008) and refer- behind this relation remains unclear this suggests that cold winters ences therein), but variations in Indian Ocean SST (phases of the IOD) in Europe will continue to occur in coming decades, despite an overall are recognized as the dominant driver of east African rainfall variability warming. (Marchant et al., 2007). This feature acts to enhance rainfall through either anomalous low-level easterly flow of moist air into the continent Although climate models have improved fidelity in simulating aspects (Shongwe et al., 2011), or a weakening of the low-level westerly flow of regional climates over Europe and the Mediterranean, the spread in over the northern Indian Ocean that transports moisture away from projections is still substantial, partly due to large amounts of natural the continent (Black et al., 2003). Although the effect of the IOD is variability in this region (particularly NAO and AMO), besides the inher- evident in the short rainy season, Shongwe et al. (2011) do not find a ent model deficiencies . similar relationship for the long rains. Williams and Funk (2011), how- ever, argue for a reduction in the long rains over Kenya and Ethiopia in In summary, there is high confidence in model projections of mean response to warmer Indian Ocean SSTs. temperature in this region. It is very likely that temperatures will con- tinue to increase throughout the 21st century over all of Europe and Variability in southern Africa s climate is strongly influenced by its adja- the Mediterranean region. It is likely that winter mean temperature cent oceans (Rouault et al., 2003; Hansingo and Reason, 2008, 2009; will rise more in NEU than in CEU or MED, whereas summer warming Hermes and Reason, 2009) as well as by ENSO (Vigaud et al., 2009; will likely be more intense in MED and CEU than in NEU. The length, Pohl et al., 2010). Although it is generally observed that El Nino events frequency, and/or intensity of warm spells or heat waves are assessed correspond to conditions of below-average rainfall over much of south- to be very likely to increase throughout the whole region. There is ern Africa (Mason, 2001; Giannini et al., 2008; Manatsa et al., 2008) the medium confidence in an annual mean precipitation increase in NEU ENSO teleconnection is not linear, but rather has complex influence in and CEU, while a decrease is likely in MED summer mean precipitation. which a number of regimes of local rainfall response can be identified (Fauchereau et al., 2009). The extreme southwestern parts of southern 14.8.7 Africa Africa receive rainfall in austral winter brought by mid-latitude frontal systems mostly associated with passing ETCs, but the majority of the The African continent encompasses a variety of climatic zones. Here region experiences a single summer rainfall season occurring between the continent is divided into four major sub-regions: Sahara (SAH), November and April. A semi-permanent zone of sub-tropical conver- Western Africa (WAF), Eastern Africa (EAF) and Southern Africa (SAF). gence is a major contributor to summer rainfall in sub-tropical southern A fifth Mediterranean region to the north of Sahara is discussed in Africa (Fauchereau et al., 2009; Vigaud et al., 2012). Section 14.8.6. In tropical latitudes, rainfall follows insolation (this sim- plified picture is modified by the presence of orography, especially in Because of its exceptional magnitude and its clear link to global SST, the Great Horn of Africa, the geography of the coastline, and by the 20th century decadal rainfall variability in the Sahel is a test of GCMs oceans). The most relevant phenomena affecting climate variability are ability to produce realistic long-term changes in tropical precipitation. the monsoons (Section 14.2.4), ENSO (Section 14.4), Indian and Atlan- Despite biases in the region (Cook and Vizy, 2006) the CMIP3 coupled tic Ocean SSTs (IOD, Section 14.3.3; AMM Section 14.3.4; AMO Section models overall can capture the observed correlation between Sahel 14.7.6) and the atmospheric Walker Circulation (Section 2.7.5). Tropical rainfall and basin-wide area averaged SST variability (Biasutti et al., cyclones impact East African and Madagascan coastal regions (Section 2008) even though individual models may fail, especially at interan- 14.6.1) and ETCs clearly impact southern Africa (Section 14.6.2). nual time scales (Lau et al., 2006; Joly et al., 2007). Recently, Ackerley et al. (2011) used a perturbed physics ensemble and reached a similar Sub-Saharan Sahelian climate is dominated by the monsoonal system result for the role of atmospheric sulphate, confirming previous results that brings rainfall to the region during only one season (Polcher et al., (Rotstayn and Lohmann, 2002; Held et al., 2005). Since AR4, only lim- 2011). Most of the rain between May/June and September comes from ited information about improved performance has been documented mesoscale squall line systems that travel short distances in their life- and only in WAF have major efforts been focussing on relating model time (~1000 to 2000 km), and whose distribution is somewhat modi- behaviour with ability to simulate local climate processes in such fied by the synoptic scale African Easterly Wave (Ruti and Dell Aquila, details. However, in a comparative study of the ability of CMIP3 and 2010). The onset of the rainy season in West Africa is a key parame- CMIP5 to simulate multiple SST Africa teleconnections, Rowell (2013) ter triggering changes in the vegetation and surface properties, that found varying degrees of success in simulating these. In particular, no implies feedbacks to the local atmospheric heat and moisture cycle. clear indication of an improvement in the CMIP5 models vs. the CMI3 The length and frequency of dry spells as well as the length or cumu- models was identified. 14 lated rainfall of the season also affect this. All are affected by a large 1266 Climate Phenomena and their Relevance for Future Regional Climate Change Chapter 14 In projections of the 21st century, the CMIP3 models produced both The relevance of a local effect is supported by several lines of evidence. significant drying and significant moistening (Held et al., 2005; Biasutti There is observational evidence that local soil moisture gradients can and Giannini, 2006; Cook and Vizy, 2006; Lau et al., 2006), and the trigger convective systems and that these surface contrasts are as mechanisms by which a model dries or wets the Sahel are not fully important as topography for generating these systems, which bring understood (Cook, 2008). At least qualitatively, the CMIP3 ensem- most of the rain to the region (Taylor et al., 2011a, 2011b). Additional ble simulates a more robust response during the pre-onset and the evidence comes from simulations of future rainfall changes in West demise portion of the rainy season (Biasutti and Sobel, 2009; Seth et Africa by RCMs subject to coupled model-derived boundary conditions al., 2011). Rainfall is projected to decrease in the early phase of the (Patricola and Cook, 2010), documenting a wetting response of the seasons implying a small delay in the main rainy season, but is pro- Sahel to increased GHG in the absence of other forcings. But the rela- jected to increase at the end of the season implying an intensifica- tive importance of this effect versus the response to SST trends is not tion of late-season rains (d Orgeval et al., 2006), although this appears well quantified, mostly due to the limitation of using a single RCM. to be less robust in the CMIP5 models (Section 14.2.4). Projections of a change in the timing of the rains is common to other monsoon regions An evaluation of six GCMs over East Africa by Conway et al. (2007) (Li et al., 2006; Biasutti and Sobel, 2009; Seth et al., 2011), including reveals no clear multi-model trend in mean annual rainfall by the southern Africa. 2080s, but some indications of increased SON and decreased March, Figure 14.23 | Maps of precipitation changes for Africa in 2080 2099 with respect to 1986 2005 in June to September (above) and December to March (below) in the SRES A1B scenario with 24 CMIP3 models (left), and in the RCP4.5 scenario with 39 CMIP5 models (middle). Right figures are the precipitation changes in 2075 2099 with respect to 1979 2003 in the SRES A1B scenario with the 12-member 60-km mesh Meteorological Research Institute (MRI)-Atmospheric Generl Circulation Model 3.2 (AGCM3.2) multi- physics, multi-sea surface temperature (SST) ensembles (Endo et al., 2012). Precipitation changes are normalized by the global annual mean surface air temperature changes in each scenario. Light hatching denotes where more than 66% of models (or members) have the same sign with the ensemble mean changes, while dense hatching denotes where 14 more than 90% of models (or members) have the same sign with the ensemble mean changes. 1267 Chapter 14 Climate Phenomena and their Relevance for Future Regional Climate Change April and May (MAM) rainfall are noted. They found inconsisten- F ­ igures AI.40 to AI.51). This is consistent with the results from CMIP3 cy in how the models represent changes in the IOB and consequent as depicted in Figure 14.23 in the West African monsoon wet season changes in rainfall over East Africa. Shongwe et al. (2011) analysed an and austral summer. High resolution information provided by the Japa- ensemble of 12 CMIP3 GCMs (forced with A1B emissions). They found nese high-resolution model ensemble also matches this finding. widespread increases in short season (OND) rainfall including extreme precipitation across the region, with statistically significant ensemble In summary, given models ability to capture local processes, large mean increases. For the long rains (MAM), similar changes in the sign scale climate evolution and their linkages, it is very likely that all of and magnitude of mean and extreme seasonal rainfall were seen, but Africa will continue to warm during the 21st century. The overall qual- model skill in simulating the MAM season is relatively poor. The chang- ity of the CMIP5 models imply, that SAH already very dry is very likely es shown for the short rains are consistent with a differential warming to remain very dry. But there is low confidence in projection statements of 21st century Indian Ocean SSTs, which leads to a positive IOD-like about drying or wetting of WAF. Owing to models ability to capture state (see Section 14.3.3). The atmospheric consequence of this is a the overall monsoonal behaviour, there is medium confidence in pro- weakening of the descending branch of the East African Walker Cell jections of a small delay in the rainy season with an increase at the and enhancement of low-level moisture convergence over east Africa end of the season. There is medium confidence in projections showing (Vecchi and Soden, 2007a; Shongwe et al., 2011). little change in mean precipitation in EAF and reduced precipitation in the Austral winter in SAF, as models tend to represent Indian Ocean In an assessment of 19 CMIP3 models run with the A1B emissions SST developments with credibility. Likewise, increasing rainfall in EAF forcing, Giannini et al. (2008) note a tendency toward a persistent El is likely for the short rainy season, but low confidence exists in projec- Nino-like pattern (see Section 14.4) in the equatorial Pacific along with tions regarding drying or wetting in the long rainy season. a decrease in rainfall over southern Africa. Dynamical downscaling of a single GCM by Engelbrecht et al. (2011) shows for the austral 14.8.8 Central and North Asia winter an intensification of the southern edge of the subtropical high pressure belt resulting in southward displacement of the mid-latitude This area mostly covering the interior of a large continent extending systems that bring frontal rain to the south western parts of the con- from the Tibetan plateau to the Arctic is mainly influenced by weath- tinent, thus resulting in decrease in rainfall. The decrease in summer er systems coming from the west or south, giving some dependency rainfall is consistent with high-resolution (18 km) RCM simulations on the AAM (Section 14.2.2) on the one hand and NAO/NAM (Sec- done by Haensler et al. (2011) which indicate widespread reductions in tion 14.5.1) on the other, with associated atmospheric blocking as an rainfall over southern Africa under the A1B scenario. additional phenomenon of influence related to the latter (Box 14.2). In particular, the variability and long-term change of the climate system Shongwe et al. (2009) identified reduction in spring (SON) rainfall in central Asia and northern Asia are closely related to variations of throughout the eastern parts of southern Africa. There is good consen- the NAO and NAM (Takaya and Nakamura, 2005; Knutson et al., 2006; sus amongst the models used, with the spring anomalies indicating a Popova and Shmakin, 2010; Sung et al., 2010; Table 14.3). trend toward later onset of the summer rainy season. Autumn (MAM) reductions are shown for most of southern Africa while eastern South As a part of the polar amplification, large warming trends in recent Africa experiences no change and eastern parts of southern Africa decades are observed in the northern Asian sector (e.g., Figure 2.22). show a small increase. The warming trend was particularly strong in the cold season (Novem- ber to March), with an increase of 2.4°C per 50 years in the mid-lat- Table 14.2 provides an overall assessment of CMIP5 quality for simula- itude semi-arid area of Asia, where the annual rainfall is within the tions of temperature, precipitation, and main phenomena in the differ- range of 200 to 600 mm over the period of 1901 2009 (Huang et ent sub-regions of Africa. Overall, confidence in the projected precipi- al., 2012). The observations indicate some increasing trends of heavy tation changes is at best medium. This is owing to the overall modest precipitation events in northern Asia, but no spatially coherent trends ability of models to capture the most important phenomena having a in central Asia (Seneviratne et al., 2012). strong control on African climates (Table 14.3). The CMIP5 models generally have difficulties in representing the mean The ability of climate models to simulate historical climate, its change, climate expressed as the climatological means of both temperature and its variability, has improved in many aspects since the AR4 (see and precipitation (Table 14.2) for the sub-regions represented in this Section 9.6.1). But for Africa there is no clear evidence that the modest area, which is partly related to the poor resolution unable to resolve increase in resolution and a better representation of the land surface the complex mountainous terrain dominating this region. But the processes in many CMIP5 models have resulted in marked improve- scarceness of observational data and issues related to how these best ments (e.g., Figure 9.39). The CMIP5 models projection for this century can be compared with coarse resolution models adds to the uncertain- is further warming in all seasons in the considered four sub-regions, ty regarding model quality. while precipitation show some distinct sub-regional and seasonally dependent changes. In the October to March half year all four regions The model projections presented in AR4 (Section 11.4) indicated strong are projected to receive practically unaltered precipitation amounts by warming in northern Asia during winter and in central Asia during 2081 2100, although somewhat elevated in RCP8.5. In the April to summer. Precipitation was projected to increase throughout the year September half year SAH, WAF and EAF will experience little change in northern Asia with the largest fractional increase during winter. For 14 but a quite notable reduction in SAF is projected (see Table 14.1, central Asia, a majority of the CMIP3 models projected ­ ecreasing d 1268 Climate Phenomena and their Relevance for Future Regional Climate Change Chapter 14 Figure 14.24 | Maps of precipitation changes for Central, North, East and South Asia in 2080 2099 with respect to 1986 2005 in June to September (above) and December to March (below) in the SRES A1B scenario with 24 CMIP3 models (left), and in the RCP4.5 scenario with 39 CMIP5 models (middle). Right figures are the precipitation changes in 2075 2099 with respect to 1979 2003 in the SRES A1B scenario with the 12-member 60-km mesh Meteorological Research Institute (MRI)-Atmospheric General Circulation Model 3.2 (AGCM3.2) multi-physics, multi-sea surface temperature (SST) ensembles (Endo et al., 2012). Precipitation changes are normalized by the global annual mean surface air temperature changes in each scenario. Light hatching denotes where more than 66% of models (or members) have the same sign with the ensemble mean changes, while dense hatching denotes where more than 90% of models (or members) have the same sign with the ensemble mean changes. p ­ recipitation during spring and summer. Seneviratne et al. (2012) indi- variability (Table 14.2), and therefore suggests that confidence in the cate increases in all precipitation extreme indices for northern Asia and sign of the projected change in future precipitation is medium. in the 20-year return value of annual maximum daily precipitation for central Asia. These projections are supported by output from CMIP5 In summary, all the areas are projected to warm, a stronger than global models subject to various RCP scenarios (see Annex I). CMIP5 project- mean warming trend is projected for northern Asia during winter. ed temperature increase in Central Asia of comparable magnitude in For central Asia, warming magnitude is similar between winter and both JJA and in DJF. In North Asia, temperatures rise more in DJF than summer. Precipitation in northern Asia will very likely increase, where- in JJA, while less annual variation is found over Central Asia and the as the precipitation over central Asia is likely to increase. Extreme pre- Tibetan Plateau (Table 14.1, Figures AI.12 to AI.13, AI.52 to AI.55 and cipitation events will likely increase in both regions. AI.56 to AI.57). 14.8.9 East Asia With an RCM Sato et al. (2007) projected precipitation decreases over northern Mongolia and increases over southern Mongolia in July. Soil Summer is the rainy season for East Asia. The Meiyu-Changma-Baiu moisture over Mongolia decreases in July as a result of the combined rain band is the defining feature of East Asian summer climate, extend- effect of decreased precipitation and increased potential evaporation ing from eastern China through central Japan (Ding and Chan, 2005; due to rising surface temperature. In North Asia, all CMIP5 models pro- Zhou et al., 2009b). The summer rain band is anchored by the sub- jects an increase in precipitation in the winter half year, and summer tropical westerly jet (Sampe and Xie, 2010), and located on the north- half year precipitation is also projected to increase (Table 14.1; Figures western flank of the western North Pacific subtropical high (Zhou and AI.14 to AI.15). In Central Asia and the Tibetan Plateau, model agree- Yu, 2005). The wintertime circulation is characterized by monsoonal ment is lower on changes both for winter and summer precipitation northerlies between the Siberian High and the Aleutian Low. (Figure 14.24; Table 14.1; Figures AI.54 to AI.55 and AI.58 to AI.59). The ability of these CMIP5 models to simulate precipitation over this region Both the East Asian summer and winter monsoon circulations have varies (Table 14.3). The reasonable level of agreement in projections of experienced an inter-decadal scale weakening after the 1970s due to precipitation to be positive and significantly above the 20-year natural natural variability of the coupled climate system, leading to enhanced 14 1269 Chapter 14 Climate Phenomena and their Relevance for Future Regional Climate Change (a) PRCTOC (c) R95 30 N 30 N 0 0 30 S 30 S 60 E 90 E 180 E 150 E 60 E 90 E 180 E 150 E - 80 -48 -16 16 48 80 (% per 50 yr) Figure 14.25 | Linear trend for local summer (a) total precipitation and (b) R95 (summer total precipitation when PR >95th percentile) during 1961 2006. The unit is % per 50-years. The trends statistically significant at the 5% level are dotted. The daily precipitation data over Australia and China are produced by the Australian Water Availability Project (AWAP, Jones et al., 2009a) and National Climate Centre China of China Meteorological Administration (Wu and Gao, 2013), respectively, while that over the other area is compiled by the Highly Resolved Observational Data Integration Towards the Evaluation of Water Resources (APHRODITE) project (Yatagai et al., 2012). The resolution of precipitation data set is 0.5° × 0.5°. Local summer is defined as June, July and August in the Northern Hemisphere, and December, January and February in the Southern Hemisphere. mean and extreme precipitation along the Yangtze River Valley (30°N) in the winter half year (see Table 14.1 and Figures AI.56 to AI.59). but deficient mean precipitation in North China in summer (Figure CMIP3 models projections indicated a decrease of winter precipita- 14.25), and a warmer climate in winter. The observed monsoon circu- tion extending northeastward from South China Sea to south of Japan lation changes are partly reproduced by GCMs driven by PDO-related under SRES A1B scenario, changes seen in CMIP5 projections but with SST patterns but the quality of precipitation simulation is poor (Zhou smaller spatial coverage (Figure 14.24). et al., 2008a; Li et al., 2010a; Zhou and Zou, 2010). An increase of extreme precipitation is projected over East Asia in a In AR4, the regional warming is projected to be above the global warmer climate (Jiang et al., 2011; Lee et al., 2011; Li et al., 2011a, mean in East Asia (Christensen et al., 2007). It is very likely that heat 2011b). A high-resolution model projects an increase of Meiyu pre- waves/hot spells in summer will be of longer duration, more intense cipitation in May through July, Changma precipitation over Korean and more frequent, but very cold days are very likely to decrease in peninsula in May, and Baiu precipitation over Japan in July (Kusunoki frequency. The precipitation is likely to increase in both boreal winter and Mizuta, 2008), and an increase of heavy precipitation over East and summer, while the frequency of intense precipitation events is very Asia under SRES A1B scenario (Kusunoki and Mizuta, 2008; Endo, likely to increase. Extreme rainfall and winds associated with tropical 2012). CMIP3 models project a late withdrawal of Baiu (Kitoh and cyclones are likely to increase . CMIP5 results support many of these Uchiyama, 2006), as has been observed in eastern and western Japan assessments. (Endo, 2010). There is a significant increase in mean, daily maximum and minimum temperatures in southeastern China, associated with a More recent analysis suggested that CMIP3 models projected decrease in the number of frost days and an increase in the heat wave increased summer precipitation in amount and intensity over East duration under SRES A2 scenario (Chen et al., 2011). The CMIP5 model Asia (Figure 14.24 for SRES A1B scenario) due to enhanced mois- projections also indicate an increase of temperature in both boreal ture convergence in a warmer climate (Ding et al., 2007; Sun and winter and summer over East Asia for RCP4.5 (Table 14.1). A decrease Ding, 2010; Chen et al., 2011; Kusunoki and Arakawa, 2012), along of the annual and seasonal maximum wind speeds is found under SRES with an increase in interannual variability (Lu and Fu, 2010). CMIP5 A2 scenario due to both the reduced intensity of cold waves and the projections for RCP4.5 support those from AR4 for summer (Figure reduced intensity of the winter monsoons (Jiang and Zhao, 2013). 14 14.24), with 90% of the models projecting a precipitation increase 1270 Climate Phenomena and their Relevance for Future Regional Climate Change Chapter 14 The future warming patterns simulated by RCMs essentially follow and robust (Alpert et al., 2008; AlSarmi and Washington, 2011; Tanar- those of the driving GCMs (e.g., Dairaku et al., 2008). For summer pre- hte et al., 2012). cipitation, however, RCM downscaling usually shows different regional details due to more realistic topographic forcing than in GCMs (Gao et The ability of climate models to simulate historical climate, its change al., 2008, 2012a). The uncertainty of precipitation projection in eastern and its variability, has improved in many important aspects since the China is larger than that in western China (Gao et al., 2012b). RCM AR4 (see Figure 9.39 in Chapter 9). CMIP5 models tend to be able to downscaling indicates that both the seasonal mean summer rain- reproduce the basic climate state of the region as well as the main fall and extreme precipitation around Japan Islands are projected to phenomena affecting it with some fidelity (Table 14.2), but the region increase (Im et al., 2008; Iizumi et al., 2012). is at the fringes of the influence of different drivers of European, Asian and African climates and remains poorly analysed in the peer-reviewed Projections with a 5-km RCM show that the heaviest hourly precipita- literature with respect to climate model performances. tion is projected to increase even in the near future (2030s) when tem- perature increase is modest (Kitoh et al., 2009). A southwest expan- The CMIP5 model projections for this century are for further warming sion of the subtropical anticyclone over the northwestern Pacific Ocean in all seasons, while precipitation shows some distinct sub-regional associated with El Nino-like mean state changes in the Pacific and a and seasonally dependent changes, characterized by model scatter. In dry air intrusion in the mid-troposphere from the Asian continent gives both winter (October to March) and summer (April to September) pre- a favourable condition for intense precipitation in the Baiu season in cipitation in general is projected to decrease, (see Table 14.1, Figures Japan (Kanada et al., 2010). Increased water vapour supply from the AI.52 to AI.55). However, the various interacting dynamical influenc- south of the Baiu front and an intensified frontal zone with intense es on precipitation of the region (that models have varying success mean updrafts contribute to the increased occurrence of intense daily in capturing in the current climate) results in uncertainty in both the precipitation during the late Baiu season (Kanada et al., 2012). patterns and magnitude of future precipitation change. Indeed, while the overall pattern of change has remained the same between CMIP3 In summary, based on CMIP5 model projections, there is medium con- and CMIP5, the confidence has decreased somewhat and the boundary fidence that with an intensified East Asian summer monsoon, summer between the Mediterranean decreases and the general mid-latitude precipitation over East Asia will increase (Table 14.3). Under RCP4.5 increase to the north has shifted closer to the region (Figures 14.26 and scenario, precipitation increase is likely over East Asia during the Mei- AI.54 to AI.55). So, although the Mediterranean side still appears likely yu-Changma-Baiu season in May to July, and precipitation extremes to become drier, the likely precipitation changes for the interior land are very likely to increase over the eastern Asian continent in all sea- masses are less clear and the intensified and northward shifting ITCZ sons and over Japan in summer. However, there is only low confidence may imply an increase in precipitation in the most southern part of the in more specific details of the projected changes due to the limited Arabian Peninsula. Overall, the projections by the end of the century skill of CMIP5 models in simulating monsoon features such as the East (2081 2100) indicates little overall change, although with a tendency Asian monsoon rainband (Table 14.2). for reduced precipitation, particular in the high end scenarios (Figures AI.5 to AI.55). However, regardless of the sign of precipitation change 14.8.10 West Asia in the high mountain regions of the interior, the influence of warming on the snow pack will very likely cause important changes in the timing This region extends from the Mediterranean to the western fringes of and amount of the spring melt (Diffenbaugh et al., 2013). South Asia, covering the Middle East and the Arabian Peninsula and includes large areas of barren desert. The climate over this region Recent downscaling results (Lionello et al., 2008; Evans, 2009; Jin et varies from arid to semi-arid and precipitation is primarily received in al., 2010; Dai, 2011) suggest that the eastern Mediterranean will expe- the cold season. rience a decrease in precipitation during the rainy season due to a northward displacement of the storm tracks (Section 14.6.2). A north- The western part of the region is on the margin of Atlantic and Medi- ward shift in the ITCZ results in more precipitation in the southern part, terranean influences, primarily the NAO (Section 14.5.1) during winter not previously being seriously affected by it. A moderate change in the months, and indirectly the monsoon heat low (Section 14.2.2.1) in the annual cycle of precipitation has also been simulated by some models. summer months. Precipitation in this region comes largely from pass- Precipitation and temperature statistics in RCMs for an area consist- ing ETCs (Section 14.6.2). Land-falling TCs (Section 14.6.1) that occa- ing of the western part of the Arab Peninsula was assessed by Black sionally influence the eastern part of the Arabian Peninsula are notable (2009) and Onol and Semazzi (2009) confirming GCM-based findings. extreme events. Pacific Ocean variability, associated with ENSO (Sec- Increased drought duration has been projected (Kim and Byun, 2009). tion 14.2.4), and the ITCZ (Section 14.3.1) are also known to impact Inland from the Mediterranean coastal areas, resolution of the terrain weather and climate in different parts of West Asia. becomes more important and, while downscaled results (Evans, 2008; Marcella and Eltahir, 2011; Lelieveld et al., 2012) broadly agree with In recent decades, there appears to be a weak but non-significant GCM projections, higher resolution results in some differences associ- downward trend in mean precipitation (Zhang et al., 2005; Alpert et al., ated with mountain barrier jets (Evans, 2008; see also Figure 14.26). 2008; AlSarmi and Washington, 2011; Tanarhte et al., 2012), although intense weather events appear to be increasing (Alpert et al., 2002; In summary, since AR4 climate models appear to have only modest- Yosef et al., 2009). In contrast, upward temperature trends are notable ly improved fidelity in simulating aspects of large-scale climate 14 1271 Chapter 14 Climate Phenomena and their Relevance for Future Regional Climate Change Figure 14.26 | Maps of precipitation changes for West Asia in 2080 2099 with respect to 1986 2005 in June, July and August (above) and December, January and February (below) in the SRES A1B scenario with 24 CMIP3 models (left), and in the RCP4.5 scenario with 39 CMIP5 models (middle). The figures on the right are the precipitation changes in 2075 2099 with respect to 1979 2003 in the SRES A1B scenario with the 12-member 60-km mesh Meteorological Research Institute (MRI)-Atmospheric General Circulation Model 3.2 (AGCM3.2) multi-physics, multi-sea surface temperature (SST) ensembles (Endo et al., 2012). Precipitation changes are normalized by the global annual mean surface air temperature changes in each scenario. Light hatching denotes where more than 66% of models (or members) have the same sign with the ensemble mean changes, while dense hatching denotes where more than 90% of models (or members) have the same sign with the ensemble mean changes. p ­henomena influencing regional climates over West Asia. Model warm pool (Annamalai et al., 2013). The increase in the number of agreement, however, indicates that it is very likely that temperatures monsoon break days over India (Dash et al., 2009), and the decline in will continue to increase. But at the same time, model agreement on the number of monsoon depressions (Krishnamurthy and Ajayamohan, projected precipitation changes have reduced, resulting in medium 2010), are consistent with the overall decrease in seasonal mean rain- confidence in projections showing an overall reduction in precipitation. fall. The frequency of heavy precipitation events is increasing (Rajeevan et al., 2008; Krishnamurthy et al., 2009; Sen Roy, 2009; Pattanaik and 14.8.11 South Asia Rajeevan, 2010), while light rain events are decreasing (Goswami et al., 2006). From June through September, the Indian summer monsoon (Section 14.2.2.1) dominates South Asia, while the northeast winter monsoon CMIP models reasonably simulate the annual cycle of precipitation and contributes substantially to annual rainfall over southeastern India and temperature over South Asia (Table 14.2; Figure 9.38) but are limited Sri Lanka. The winter weather systems are also important in northern in simulating fine structures of rainfall variability on sub-seasonal and parts of South Asia, that is, the western Himalayas. sub-regional scales (Turner and Annamalai, 2012). CMIP5 models show improved skill in simulating monsoon variability compared to CMIP3 Seasonal mean rainfall shows interdecadal variability, noticeably a (Sperber et al., 2012; Section 14.2.2). declining trend with more frequent deficit monsoons (Kulkarni, 2012). There are regional inhomogeneities: precipitation decreased over cen- Summer precipitation changes in South Asia are consistent overall tral India along the monsoon trough (Figure 14.25) thought to be due between CMIP3 and CMIP5 (Figure 14.24), but model scatter is large to a number of factors (Section 14.2.2) including black carbon, sul- in winter precipitation change (Figures 14.24 and AI.62). Changes in phate aerosols (Chung and Ramanathan, 2007; Bollasina et al., 2011), the summer monsoon dominate annual rainfall (see Section 14.2.2). 14 land use changes (Niyogi et al., 2010) and SST rise over the Indo-Pacific The CMIP3 multi-model ensemble shows an increase in summer 1272 Climate Phenomena and their Relevance for Future Regional Climate Change Chapter 14 ­precipitation (Kumar et al., 2011a; May, 2011; Sabade et al., 2011), the dry seasons increased (Aldrian and Djamil, 2008). This appears to although there are wide variations among model projections (Annam- be at least in part consistent with an upward trend of the IOD. While alai et al., 2007; Kripalani et al., 2007b). Spatially, the rainfall increase an increasing frequency of extreme events has been reported in the is stronger over northern parts of South Asia, Bangladesh and Sri northern parts of South East Asia, decreasing trends in such events are Lanka, with a weak decrease over Pakistan (Turner and Annamalai, reported in Myanmar (Chang, 2011); see also Figure 14.25. 2012). In RCP6.0 and RCP8.5 scenarios, frequency of extreme precip- itation days shows consistent increasing trends in 2060 and beyond For a given region, strong seasonality in change is observed. In Penin- (Chaturvedi et al., 2012; Figure AI.63). In six CMIP3 models, precipita- sular Malaya during the southwest monsoon season, total rainfall and tion anomalies during Indian summer monsoon breaks strengthen in a the frequency of wet days decreased, but rainfall intensity increased in warmer climate, but changes in the timing and duration of active/break much of the region (Deni et al., 2010). During the northeast monsoon, spells are variable among models (Mandke et al., 2007). Note that the total rainfall, the frequency of extreme rainfall events, and rainfall active/break spells of the monsoon are related to the MJO (see Section intensity all increased over the peninsula (Suhaila et al., 2010). 14.3.2), a phenomenon that models simulate poorly (Section 9.5.2.3; Lin et al., 2008a; Sperber and Annamalai, 2008). High-resolution model simulations are necessary to resolve com- plex terrain such as in Southeast Asia (Nguyen et al., 2012; Section High-resolution RCM and GCM projections showed an overall increase 14.2.2.4). In a RCM downscaling simulation using the A1B emission of precipitation over a large area of peninsular India (Rupa Kumar et scenario (Chotamonsak et al., 2011), regional average rainfall was al., 2006; Stowasser et al., 2009; Kumar et al., 2011a), but a significant projected to increase, consistent with a combination of the warmer reduction in orographic rainfall in both seasonal mean and extreme getting wetter mechanism (Section 14.3.1), an increase in summer events on west coasts of India (Rajendran and Kitoh, 2008; Ashfaq et monsoon, though there is a lack of consensus on future ENSO changes. al., 2009; Kumar et al., 2013). Such spatial variations in projected pre- The spatial pattern of change is similar to that projected in the AR4 cipitation near orography are noticeable in Figure 14.24 on the back- (Christensen et al., 2007, Section 11.4). ground of the overall increase. The median increase in temperature over land ranges from 0.8°C in CMIP5 models project a clear increase in temperature over India RCP2.6 to 3.2°C in RCP8.5 by the end of this century (2081 2100). A especially in winter (Figures AI.60 to AI.61), with enhanced warming moderate increase in precipitation is projected for the region: 1% in during night than day (Kumar et al., 2011a) and over northern India RCP2.6 increasing to 8% in RCP8.5 by 2100 (Table 14.1, Supplemen- (Kulkarni, 2012). In summer, extremely hot days and nights are project- tary Material Table 14.SM.1a to 14.SM.1c, Figures 14.27 and AI.64 to ed to increase. Table 14.1 summarizes the projected temperature and AI.65). On islands neighbouring the southeast tropical Indian Ocean, precipitation changes for SAS in the RCP4.5 scenario based on CMIP5. rainfall is projected to decrease during July to November (the IOD prev- alent season), consistent with a slower oceanic warming in the east In summary, there is high confidence in projected rise in temperature. than in the west tropical Indian Ocean, despite little change projected There is medium confidence in summer monsoon precipitation increase in the IOD (Section 14.3.3). in the future over South Asia. Model projections diverge on smaller regional scales. In summary, warming is very likely to continue with substantial sub-re- gional variations. There is medium confidence in a moderate increase in 14.8.12 Southeast Asia rainfall, except on Indonesian islands neighbouring the southeast Indian Ocean. Strong regional variations are expected because of terrain. Southeast Asia features a complex range of terrains and land sea con- trasts. Across the region, temperature has been increasing at a rate of 14.8.13 Australia and New Zealand 0.14°C to 0.20°C per decade since the 1960s (Tangang et al., 2007), coupled with a rising number of hot days and warm nights, and a The climate of Australia is a mix of tropical and extratropical influenc- decline in cooler weather (Manton et al., 2001; Caesar et al., 2011). es. Northern Australia lies in the tropics and is strongly affected by A positive trend in the occurrence of heavy (top 10% by rain amount) the Australian monsoon circulation (Section 14.2.2) and ENSO (Section and light (bottom 5%) rain events and a negative trend in moderate 14.4). Southern Australia extends into the extratropical westerly circu- (25 to 75%) rain events has been observed (Lau and Wu, 2007). Annual lation and is also affected by the middle latitude storm track (Section total wet-day rainfall has increased by 22 mm per decade, while rain- 14.6.2), the SAM (Section 14.5.2), mid-latitude transient wave propa- fall from extreme rain days has increased by 10 mm per decade (Alex- gation, and remotely by the IOD (Section 14.3.3) and ENSO. ander et al., 2006; Caesar et al., 2011). Eastern northeastern Australian rainfall is strongly influenced by the Several large-scale phenomena influence the climate of this region. ENSO cycle, with La Nina years typically associated with wet conditions While ENSO (Section 14.4) influence is predominant in East Malay- and more frequent and intense tropical cyclones in summer, and El sia and areas east of it, Maritime continent monsoon (Section 14.2.3) Nino years with drier than normal conditions, most notably in spring. influences the climate in Peninsular Malaya. The impact of the IOD The SAM plays a significant role in modulating southern Australian (Section 14.3.3) is more prominent in eastern Indonesia. Thus climate rainfall, the positive SAM being associated with generally above-nor- variability and trends differ vastly across the region and between mal rainfall during summer (Hendon et al., 2007; Thompson et al., seasons. Between 1955 and 2005 the ratio of rainfall in the wet to 2011), but in winter with reduced rainfall, particularly in Southwest 14 1273 Chapter 14 Climate Phenomena and their Relevance for Future Regional Climate Change Figure 14.27 | Maps of precipitation changes for Southeast Asia, Australia and New Zealand in 2080 2099 with respect to 1986 2005 in June to September (above) and Decem- ber to March (below) in the SRES A1B scenario with 24 CMIP3 models (left), and in the RCP4.5 scenario with 39 CMIP5 models (middle). Right figures are the precipitation changes in 2075 2099 with respect to 1979 2003 in the SRES A1B scenario with the 12-member 60- km mesh Meteorological Research Institute (MRI)-Atmospheric General Circulation Model 3.2 (AGCM3.2) multi-physics, multi-sea surface temperature (SST) ensembles (Endo et al., 2012). Precipitation changes are normalized by the global annual mean surface air temperature changes in each scenario. Light hatching denotes where more than 66% of models (or members) have the same sign with the ensemble mean changes, while dense hatching denotes where more than 90% of models (or members) have the same sign with the ensemble mean changes. Western Australia (Hendon et al., 2007; Meneghini et al., 2007; Pezza eastern Australia (Murphy and Timbal, 2008). Over southwest Western et al., 2008; Risbey et al., 2009; Cai et al., 2011c). Rossby wavetrains Australia, the decrease in winter rainfall since the late 1960s of about induced by tropical convective anomalies associated with the IOD (Cai 20% have led to an even bigger (~50%) drop in inflow into dams. The et al., 2009), and associated with ENSO through its coherence with rainfall decline has been linked to changes in large-scale mean sea the IOD (Cai et al., 2011b) also have a strong impact, leading to lower level pressure (Bates et al., 2008), shifts in synoptic systems (Hope et winter and spring rainfall particularly over Southeastern Australia al., 2006), changes in baroclinicity (Frederiksen and Frederiksen, 2007), during positive IOD and El Nino events. Along the eastern seaboard, the SAM (Cai and Cowan, 2006; Meneghini et al., 2007), land cover ETCs (Section 14.6.2) exert a strong influence on the regional climate, changes (Timbal and Arblaster, 2006), anthropogenic forcing (Timbal while ENSO and other teleconnections play a lesser role (Risbey et al., et al., 2006), Indian Ocean warming (England et al., 2006) and tele- 2009; Dowdy et al., 2012). connection to Antarctic precipitation (van Ommen and Morgan, 2010). Significant trends have been observed in Australian rainfall over recent Over southeastern Australia, the decreasing rainfall trend is largest decades (Figure 14.25), varying vastly by region and season. Increasing in autumn with sustained declines during the drought of 1997 2009, summer rainfall and decreasing temperature trends over northwest especially in May (Cai and Cowan, 2008; Murphy and Timbal, 2008; Cai Australia have raised the question of whether aerosols originating in et al., 2012a). The exact causes remain contentious, and for the decrease the NH play a role (Rotstayn et al., 2007; Shi et al., 2008b; Smith et al., in May, may include ENSO variability and long-term Indian Ocean 2008; Rotstayn et al., 2009; Cai et al., 2011d), but there is no consensus warming (Cai and Cowan, 2008; Ummenhofer et al., 2009b), a weak- at present. By contrast, a prominent rainfall decline has been expe- ening of the subtropical storm track due to decreasing baroclinic insta- rienced in austral winter over southwest Western Australia (Cai and bility of the subtropical jet (Frederiksen et al., 2010; Frederiksen et al., 14 Cowan, 2006; Bates et al., 2008) and in mid-to-late autumn over south- 2011a, 2011b) and a poleward shift the ocean atmosphere ­ irculation c 1274 Climate Phenomena and their Relevance for Future Regional Climate Change Chapter 14 (Smith and Timbal, 2012; Cai and Cowan, 2013). The well-documented projections (Barnes et al., 2010). The influence of poleward expansion poleward expansion of the subtropical dry zone (Seidel et al., 2008; of the subtropical high-pressure belt is projected to lead to drier condi- Johanson and Fu, 2009; Lucas et al., 2012), particularly in April and tions in parts of the country (Figure 14.27; Table 14.1), and a decrease May, is shown to account for much of the April May reduction (Cai in westerly wind strength in northern regions. Such projections imply et al., 2012a). Rainfall trends over southeastern Australia in spring, far increased seasonality of rainfall in many regions of New Zealand (Reis- weaker but with a signature in the subtropical ridge (Cai et al., 2011a; inger et al., 2010). Both flood and drought occurrence is projected Timbal and Drosdowsky, 2012), have been shown to be linked with to approximately double over New Zealand during the 21st century, trends and variability in the IOD (Cai et al., 2009; Ummenhofer et al., under the SRES A1B scenario. Temperatures are projected to rise at 2009b). Antarctic proxy data that capture both eastern Australian rain- about 70% of the global rate, because of the buffering effect of the fall and ENSO variability (Vance et al., 2012) show a predominance of oceans around New Zealand. Temperature rises are projected to be El Nino/drier conditions in the 20th century than was the average over smallest in spring (SON) while the season of greatest warming varies the last millennium. by region around the country. Continued decreases in frost frequen- cy, and increases in the frequency of high-temperature extremes, are On seasonal to decadal time scales, New Zealand precipitation is expected, but have not been quantified (Reisinger et al., 2010). modulated by the SAM (Kidston et al., 2009; Thompson et al., 2011), ENSO (Kidson and Renwick, 2002; Ummenhofer and England, 2007) In summary, based on understanding of recent trends and on CMIP5 and the IPO (Griffiths, 2007). Increased westerly flow across New Zea- results, it is likely that cool season precipitation will decrease over land, associated with negative SAM and with El Nino events, leads southern Australia associated in part with trends in the SAM, the IOD to increased rainfall and generally lower than normal temperatures and a poleward shift and expansion of the subtropical dry zone. It is in western regions. The positive SAM and La Nina conditions are gen- very likely that Australia will continue to warm through the 21st cen- erally associated with increased rainfall in the north and east of the tury, at a rate similar to the global land surface mean. The frequency country, and warmer than normal conditions. On longer time scales, of very warm days is very likely to increase through this century, across a drying trend since 1979 across much of New Zealand during austral the whole country. summer is consistent with recent trends in the SAM and to a lesser extent ENSO and the IPO (Griffiths, 2007; Ummenhofer et al., 2009a). It is very likely that temperatures will continue to rise over New Zea- In western regions, however, the drying is accompanied by a trend land. Precipitation is likely to increase in western regions in winter and towards increased heavy rainfall (Griffiths, 2007). Temperatures over spring, but the magnitude of change is likely to remain comparable to New Zealand have risen by just under 1°C over the past century (Dean that of natural climate variability through the rest of the century. In and Stott, 2009). The upward trend has been modulated by an increase summer and autumn, it is as likely as not that precipitation amounts in the frequency of cool southerly wind flows over the country since the will change. 1950s, without which the observed warming is consistent with large- scale anthropogenic forcing (Dean and Stott, 2009). 14.8.14 Pacific Islands Region A recent analysis (Irving et al., 2012; their Figure 9) shows that climate The Pacific Islands region includes the northwest tropical Pacific, and projections over Australia using CMIP5 models, which generally sim- the tropical southwest Pacific. North of the Equator, the wet season ulate the climate of Australia well (Watterson et al., 2013), are highly occurs from May to November. In the south, the wet seasons occurs consistent with existing CMIP3-derived projections. The projected from November to April. changes include a further 1.0 to 5.0°C temperature rise by the year 2070 (relative to 1990); a long-term drying over southern areas during The phenomena mainly responsible for climate variations in the Pacif- winter, particularly in the southwest (Figure 14.27), that is consistent ic Islands are ENSO (Section 14.4), the SPCZ (Section 14.3.1.2), the with an upward trend of the SAM (Pitman and Perkins, 2008; Shi et al., ITCZ (Section 14.3.1.1) and the WNPSM (Section 14.2.2.5). During El 2008a; Cai et al., 2011c); a long-term rainfall decline over southern and Nino events, the ITCZ and SPCZ move closer to the equator, rainfall eastern areas during spring, in part consistent with a upward trend of decreases in western regions and increases in the central Pacific, and the IOD index (Smith and Chandler, 2010; Zheng et al., 2010; Weller tropical cyclone numbers tend to increase and to occur farther east and Cai, 2013; Zheng et al., 2013). Precipitation change in northeast than normal (Diamond et al., 2012). During La Nina, the western trop- Australia remains uncertain (Moise et al., 2012), related to the lack of ical Pacific tends to experience above-average numbers of tropical consensus over how ENSO may change (Collins et al., 2010; Section cyclones (Nicholls et al., 1998; Lavender and Walsh, 2011). 14.4). In terms of climate extremes, more frequent hot days and nights and less frequent cold days and nights are projected (Alexander and The seasonal evolution of the SPCZ has a strong influence on the Arblaster, 2009). Changes in the intensity and frequency of extreme seasonality of the climate of the southern tropical Pacific, particularly rainfall events generally follow the mean rainfall change (Kharin et al., during the wet season. The SPCZ moves northward during moderate 2007), although there is an increase in most regions in the intensity of El Nino events and southward during La Nina events (Folland et al., short duration extremes (e.g., Alexander and Arblaster, 2009). 2002; Vincent et al., 2011). During El Nino events, southwest Pacific Island nations experience an increased occurrence of forest fires and For New Zealand, future climate projections suggest further increases droughts (Salinger et al., 2001; Kumar et al., 2006b), and an increased in the westerlies in winter and spring, though model biases in jet lat- probability of tropical cyclone damage, as tropical cyclogenesis tends itude in the present climate reduce confidence in the detail of future to reside within 6° to 10° south of the SPCZ (Vincent et al., 2011). 14 1275 Chapter 14 Climate Phenomena and their Relevance for Future Regional Climate Change Nauru experiences drought during La Nina as the SPCZ and ITCZ move et al., 2012a), it has recently been questioned (Widlansky et al., 2013; to the west (Brown et al., 2012c). During strong El Nino events (e.g., see Section 14.3.1). There are two competing mechanisms, the wet 1982/1983, 1997/1998) the SPCZ undergoes an extreme swing of up regions getting wetter and the warmest getting wetter, or coldest get- to 10 degrees towards the equator and collapses to a more zonally ting drier paradigms. These two mechanisms compete within much of oriented structure (Vincent et al., 2011; Section 14.3.2). The impacts the SPCZ region. Based on a multi-model ensemble of 55 greenhouse from these zonal SPCZ events are much more severe than those from warming experiments, in which model biases were corrected, tropical moderate El Nino events (Vincent et al., 2011; Cai et al., 2012b), and SST changes between 2°C to 3°C resulted in a 5% decrease of austral can induce massive droughts and food shortages (Barnett, 2011). summer moisture convergence in the current SPCZ region (Widlansky et al., 2013). This projects a diminished rainy season for most Southwest Temperatures have increased at a rate between 0.1°C and 0.2°C per Pacific island nations. In Samoa and neighbouring islands, summer rain- decade throughout the Pacific Islands during the 20th century (Folland fall may decrease on average by 10 to 20% during the 21st century as et al., 2003). Changes in temperature extremes have followed those of simulated by the hierarchy of bias-corrected atmospheric model experi- mean temperatures (Manton et al., 2001; Griffiths et al., 2005). During ments. Less rainfall, combined with increasing surface temperatures and 1961 2000, locations to the northeast of the SPCZ became wetter, enhanced potential evaporation, could increase the chance for longer- with the largest trends occurring in the eastern Pacific Ocean (east of term droughts in the region. Such projections are completely opposite 160°W), while locations to the southwest of the SPCZ became drier to those based on direct model outputs (Figure 14.27). (Griffiths et al., 2003), indicative of a northeastward shift of the SPCZ. Trends in the frequency of rain days were generally similar to those of Recent downscaling experiments support the above conclusion regard- total annual rainfall (Manton et al., 2001; Griffiths et al., 2003). Since ing the impact of biases on the SPCZ change, and suggest that the 1980, western Pacific monsoon- and ITCZ-related rain during June to projected intensification of the ITCZ may have uncertainties of a simi- August has decreased (Hennessy et al., 2011). lar nature (Chapter 7 of Hennessy et al., 2011). In these experiments a bias correction is applied to average sea surface temperatures, and the Future projections for tropical Pacific Island nations are based on direct atmosphere is forced with the correct climatological seasonal cycle outputs from a suite of CMIP3 models, updated using CMIP5 wherever together with warming derived from large-scale model outputs. The available (Brown et al., 2011; Hennessy et al., 2011; Irving et al., 2011; results show opposite changes in much of the SPCZ and some of the Moise and Delage, 2011; Perkins, 2011; Perkins et al., 2012). These ITCZ regions, resulting in much lower confidence in rainfall projections. projections carry a large uncertainty, even in the sign of change, as discussed below and as evident in Table 14.1. Despite the uncertainty, there is general agreement in model projec- tions regarding an increase in rainfall along the equator (Tables 14.1 Annual average air and sea surface temperature are projected to con- and 14.2), and regarding a faster warming rate in the equatorial Pacific tinue to increase for all tropical Pacific countries. By 2055, under the than the off-equatorial regions (Xie et al., 2010b). A potential conse- high A2 emissions scenario, the increase is projected to be 1°C to 2°C. quence is an increase in the frequency of the zonal SPCZ events (Cai A rise in the number of hot days and warm nights is also projected, and et al., 2012b). a decline in cooler weather, as already observed (Manton et al., 2001). For a low-emission scenario, the lower range decreases about 0.5C In summary, based on CMIP3 and CMIP5 model projections and while the upper range reduces by between 0.2°C and 0.5°C. recently observed trends, it is very likely that temperatures, including the frequency and magnitude of extreme high temperatures, will con- To a large extent, the response of the ITCZ, the SPCZ, and the WNPSM tinue to increase through the 21st century. In equatorial regions, the to greenhouse warming will determine how rainfall patterns will consistency across model projections suggests that rainfall is likely to change in tropical Pacific. In northwestern and near-equatorial regions, increase. However, given new model results and physical insights since rainfall during all seasons is projected to increase in the 21st century. the AR4, the rainfall outlook is uncertain in regions directly affected by Wet season increases are consistent with the expected intensification the SPCZ and western portion of the ITCZ. of the WNPSM and the ITCZ (Smith et al., 2012a). For the southwest- ern tropical Pacific, the CMIP3 and CMIP5 ensemble mean change in 14.8.15 Antarctica summer rainfall is far smaller than the inter-model range (Brown et al., 2012b; Widlansky et al., 2013). There is a projected intensification Much of the climate variability of Antarctica is modulated by the South- in the western part of the SPCZ and near the equator with little mean ern Annular Mode (SAM, Section 14.5.2), the high-latitude atmospher- change in SPCZ position (Brown et al., 2012a; Brown et al., 2012b). ic response to ENSO (Section 14.4) and interactions between the two For the southern group of the Cook Islands, the Solomon Islands, and (Stammerjohn et al., 2008; Fogt et al., 2011; see also Sections 2.7 Tuvalu, average rainfall during the wet season is projected to increase; and 10.3.3). Signatures of the SAM and ENSO in Antarctic tempera- and for Vanuatu, Tonga, Samoa, Niue, Fiji, a decrease in dry season ture, snow accumulation and sea ice have been documented by many rainfall is accompanied by an increase in the wet season, indicating an observational and modelling studies (Bromwich et al., 2004; Guo et intensified seasonal cycle. al., 2004; Kaspari et al., 2004; van den Broeke and van Lipzig, 2004; Marshall, 2007). Extreme rainfall days are likely to occur more often in all regions related to an intensification of the ITCZ and the SPCZ (Perkins, 2011). Although The positive SAM is associated on average with warmer conditions over 14 the intensification appears to be reproduced in CMIP5 models (Brown the Peninsula and colder conditions over East Antarctica, with a mixed 1276 Climate Phenomena and their Relevance for Future Regional Climate Change Chapter 14 and generally non-significant impact over West Antarctica (Kwok and Antarctica (Bracegirdle et al., 2008), but the spatial pattern of precipi- Comiso, 2002; Thompson and Solomon, 2002; van den Broeke and van tation change remains uncertain. Lipzig, 2004). ENSO is associated with circulation anomalies over the southeast Pacific that primarily affect West Antarctica (Bromwich et al., In summary, consistency across CMIP5 projections suggests it is very 2004; Guo et al., 2004; Turner, 2004). ENSO variability tends to produce likely that Antarctic temperatures will increase through the rest of out-of-phase variations between the western and eastern sectors of the century, but more slowly than the global mean rate of increase West Antarctica (Bromwich et al., 2004; Kaspari et al., 2004), in associ- (Table 14.1). SSTs of the oceans around Antarctica are likely to rise ation with the PSA pattern (Section 14.7.1). more slowly than surface air temperature over the Antarctic land mass. As temperatures rise, it is also likely that precipitation will increase The positive summer/autumn trend in the SAM index in recent dec- (Table 14.1), up to 20% or more over the East Antarctic. However, given ades (Section 14.5.2) has been related to the contrasting temperature known difficulties associated with correctly modelling Antarctic cli- trend patterns observed in these two seasons, with warming in the mate, and uncertainties associated with future SAM and ENSO trends east and north of the Antarctic Peninsula and cooling (or no significant and the extent of Antarctic sea ice, precipitation projections have only temperature change) over much of East Antarctica (Turner et al., 2005; medium confidence. Thompson et al., 2011). The high polarity of the SAM is also consistent with the significant increase in snow accumulation observed in the southern part of the Peninsula (Thomas et al., 2008). Unlike the eastern Antarctic Peninsula, its western coast shows maxi- mum warming in austral winter (when the SAM does not exhibit any significant trend), which has been attributed to reduced sea ice con- centrations in the Bellingshausen Sea. Recent studies have emphasized the role of tropical SST forcing not directly linked to ENSO to explain the prominent spring- and wintertime atmospheric warming in West Antarctica (Ding et al., 2011; Schneider et al., 2012). There is further evidence of tropical SST influence on Antarctic temperatures and pre- cipitation on decadal to inter-decadal time scales (Monaghan and Bromwich, 2008; Okumura et al., 2012). Modelling of Antarctic climate remains challenging, in part because of the nature of the high-elevation ice sheet in the east Antarctic and its effects on regional climate (Section 9.4.1.1). Moreover, modelling ice properties themselves, for both land ice and sea ice, is an area that is still developing despite improvements in recent years (Vancoppenolle et al., 2009; Picard et al., 2012; Section 9.4.3). Modelling the role of the stratosphere and of ozone recovery is critical for Antarctic climate, as stratospheric change is intimately linked to trends in the SAM (Section 14.5.2). The projected easing of the positive SAM trend in austral summer (Section 14.5.2) may act to delay future loss of Antarctic sea ice (Bitz and Polvani, 2012; Smith et al., 2012b). It is unclear what effect ENSO will have on future Antarctic climate change as the ENSO response to climate change remains uncertain (see 12.4.4.1 and 14.5.2 for more information). Seasonally, changes in the strength of the circumpo- lar westerlies are also expected during the 21st century as a result of changes in the semi-annual oscillation caused by alterations in the mid- to high-latitude temperature gradient in the SH. Bracegirdle et al. (2008) considered modelled circulation changes over the Southern Ocean and found a more pronounced strengthening of the autumn peak of the semi-annual oscillation compared with the spring peak. Future changes in surface temperature over Antarctica are likely to be smaller than the global mean, and much smaller than those projected for the Arctic, because of the buffering effect of the southern oceans, and the thermal mass of the east Antarctic ice sheet (Section 12.4.6). Warming is likely to bring increased precipitation on average across 14 1277 Chapter 14 Climate Phenomena and their Relevance for Future Regional Climate Change Table 14.1 | Temperature and precipitation projections by the CMIP5 global models. The figures shown are averages over SREX regions (Seneviratne et al., 2012) of the projections by a set of 42 global models for the RCP4.5 scenario. Added to the SREX regions are a six other regions including the two Polar Regions, the Caribbean, Indian Ocean and Pacific Island States (see Annex I for further details). The 26 SREX regions are: Alaska/NW Canada (ALA), Eastern Canada/Greenland/Iceland (CGI), Western North America (WNA), Central North America (CNA), Eastern North America (ENA), Central America/Mexico (CAM), Amazon (AMZ), NE Brazil (NEB), West Coast South America (WSA), Southeastern South America (SSA), Northern Europe (NEU), Central Europe (CEU), Southern Europe/the Mediterranean (MED), Sahara (SAH), Western Africa (WAF), Eastern Africa (EAF), Southern Africa (SAF), Northern Asia (NAS), Western Asia (WAS), Central Asia (CAS), Tibetan Plateau (TIB), Eastern Asia (EAS), Southern Asia (SAS), Southeastern Asia (SEA), Northern Australia (NAS) and Southern Australia/New Zealand (SAU). The area-mean temperature and precipitation responses are first averaged for each model over the 1986 2005 period from the historical simulations and the 2016 2035, 2046 2065 and 2081 2100 periods of the RCP4.5 experiments. Based on the difference between these two periods, the table shows the 25th, 50th and 75th percentiles, and the lowest and highest response among the 42 models, for temperature in degrees Celsius and precipitation as a percent change. Regions in which the middle half (25 to 75%) of this distribution is all of the same sign in the precipitation response are coloured light brown for decreasing precipitation and light green for increas- ing precipitation. Information is provided for land areas contained in the boxes unless otherwise indicated. The temperature responses are averaged over the boreal winter and summer seasons; December, January and February (DJF) and June, July and August (JJA) respectively. The precipitation responses are averaged over half year periods, boreal winter; October, November, December, January, February and March (ONDJFM) and summer; April, May, June, July, August and September (AMJJAS). RCP4.5     Temperature (°C) Precipitation (%) REGION MONTH a Year min 25% 50% 75% max min 25% 50% 75% max (land) DJF 2035 0.6 1.5 1.7 2.2 4.2 3 7 9 11 19   2065 0.4 3.0 3.4 4.5 8.0 5 14 17 21 37   2100 0.9 3.7 5.0 6.2 10.0 2 18 24 30 50   JJA 2035 0.3 0.8 1.0 1.2 3.0 3 4 5 7 20   2065 0.5 1.3 1.8 2.3 4.8 1 7 10 12 34   2100 0.3 1.8 2.2 3.0 6.0 2 10 13 17 39   Annual 2035 0.4 1.3 1.5 1.7 3.8 1 5 6 8 20     2065 0.3 2.4 2.8 3.5 6.4 3 11 13 15 35     2100 0.4 3.0 3.9 4.7 7.8 2 14 17 21 43 (sea) DJF 2035 0.2 2.2 2.8 3.3 6.7 1 7 9 15 25   2065 0.5 4.2 5.1 6.8 11.4 2 14 18 25 39   2100 2.2 5.4 7.0 9.1 14.8 10 23 26 37 48   JJA 2035 0.1 0.5 0.6 0.7 1.9 3 4 6 7 17   2065 0.0 0.8 1.2 1.4 2.9 2 9 11 14 23   2100 0.3 1.2 1.5 2.1 4.0 3 12 16 18 29   Annual 2035 0.2 1.5 2.0 2.3 4.7 0 6 8 9 21     2065 0.1 2.9 3.7 4.7 7.4 1 11 13 20 28     2100 1.0 3.7 4.9 6.5 9.3 7 16 21 26 37 High latitudes Canada/ DJF 2035 0.2 1.2 1.7 1.9 3.1 0 4 5 9 14 Greenland/ 2065 0.6 2.8 3.4 3.9 6.6 3 9 12 15 21 Iceland 2100 0.5 3.2 4.6 5.6 8.1 2 11 15 22 32   JJA 2035 0.1 0.7 1.0 1.2 3.0 0 2 3 4 8   2065 0.5 1.3 1.8 2.3 4.5 2 5 6 9 16   2100 0.2 1.7 2.3 3.0 5.6 1 6 9 12 20   Annual 2035 0.2 1.1 1.3 1.6 2.9 0 3 4 6 9     2065 0.4 2.0 2.5 2.9 5.2 3 7 9 11 17     2100 0.2 2.6 3.2 4.0 6.4 0 10 11 15 22 North Asia DJF 2035 0.5 1.1 1.5 2.2 4.0 2 6 8 10 22   2065 1.2 2.3 3.0 3.6 6.0 5 11 14 18 34   2100 0.2 3.0 3.8 4.9 7.8 5 13 18 22 44   JJA 2035 0.1 0.8 1.0 1.4 2.5 1 2 4 6 16   2065 0.8 1.5 2.0 2.7 4.4 1 5 8 10 21   2100 0.8 1.9 2.4 3.5 5.1 3 6 9 12 30   Annual 2035 0.4 1.1 1.3 1.6 3.0 1 4 5 7 18     2065 0.8 2.0 2.4 2.9 4.9 2 8 9 12 25     2100 0.2 2.5 3.2 3.8 5.8 1 10 12 15 35 14 (continued on next page) 1278 Climate Phenomena and their Relevance for Future Regional Climate Change Chapter 14 Table 14.1 (continued) RCP4.5     Temperature (°C) Precipitation (%) REGION MONTHa Year min 25% 50% 75% max min 25% 50% 75% max North America Alaska/ DJF 2035 0.0 1.1 1.7 2.4 3.4 1 3 5 8 12 NW Canada 2065 1.2 2.8 3.6 4.8 7.4 3 9 11 17 29   2100 2.3 3.5 4.8 5.9 9.7 7 11 17 21 42   JJA 2035 0.3 0.7 1.0 1.4 2.8 1 2 5 7 16   2065 0.7 1.3 1.8 2.3 4.9 2 6 10 12 29   2100 0.9 1.8 2.2 3.1 5.2 2 9 12 16 34   Annual 2035 0.4 1.0 1.4 1.8 2.8 0 3 6 7 14     2065 1.4 2.1 2.7 3.6 5.2 4 8 10 13 28     2100 1.7 2.5 3.5 4.3 6.7 3 11 14 17 33 West North DJF 2035 0.4 0.7 1.1 1.5 2.5 2 0 3 4 8 America 2065 0.9 1.7 2.2 2.6 4.0 3 3 4 6 11   2100 1.3 2.2 2.6 3.4 5.2 4 4 6 8 17   JJA 2035 0.3 0.9 1.1 1.3 2.1 6 1 1 3 9   2065 0.8 1.7 2.0 2.6 3.4 7 1 1 4 10   2100 0.9 2.1 2.5 3.4 4.6 8 1 2 6 10   Annual 2035 0.3 0.8 1.0 1.3 1.9 4 1 2 3 6     2065 0.9 1.7 2.0 2.5 3.4 3 1 3 5 11     2100 1.1 2.0 2.6 3.4 4.3 4 2 4 6 14 Central North DJF 2035 0.1 0.7 1.1 1.6 2.9 8 1 1 5 11 America 2065 0.9 1.6 2.2 2.7 4.2 7 1 4 7 17   2100 1.2 2.0 2.7 3.6 4.9 6 1 4 9 18   JJA 2035 0.3 0.8 1.1 1.4 2.3 7 2 0 3 9   2065 0.9 1.7 2.1 2.5 3.5 16 1 2 5 12   2100 1.0 2.1 2.5 3.1 4.6 13 1 2 5 13   Annual 2035 0.4 0.9 1.1 1.3 2.0 4 1 1 3 7     2065 1.0 1.7 2.0 2.4 3.4 7 0 3 4 14     2100 1.1 2.0 2.6 3.1 4.3 4 0 3 6 10 Eastern North DJF 2035 0.0 0.8 1.1 1.7 2.2 6 0 3 7 12 America 2065 0.9 1.7 2.4 2.8 4.1 2 4 7 9 18   2100 0.7 2.2 2.9 3.8 4.8 4 6 9 12 20   JJA 2035 0.1 0.8 1.0 1.2 1.9 4 0 3 5 9   2065 0.8 1.5 2.0 2.4 3.9 6 2 4 6 14   2100 1.0 2.0 2.5 3.1 4.8 7 2 5 7 14   Annual 2035 0.4 0.8 1.1 1.3 1.9 4 1 3 5 9     2065 1.0 1.7 2.1 2.4 3.5 1 3 5 7 14     2100 1.0 2.1 2.7 3.1 4.2 2 4 7 9 14 (continued on next page) 14 1279 Chapter 14 Climate Phenomena and their Relevance for Future Regional Climate Change Table 14.1 (continued) RCP4.5     Temperature (°C) Precipitation (%) REGION MONTH a Year min 25% 50% 75% max min 25% 50% 75% max Central America Central DJF 2035 0.3 0.6 0.8 0.9 1.3 8 3 1 2 10 America 2065 0.7 1.2 1.5 1.7 2.1 15 4 2 3 10   2100 1.0 1.6 1.8 2.4 2.7 22 5 0 2 11   JJA 2035 0.5 0.7 0.8 1.0 1.4 8 3 1 2 7   2065 1.1 1.3 1.6 1.9 2.5 15 6 2 1 6   2100 1.1 1.6 2.0 2.5 3.2 17 6 2 1 12   Annual 2035 0.4 0.7 0.9 0.9 1.3 8 3 1 1 6     2065 1.0 1.3 1.5 1.8 2.4 14 6 2 1 6     2100 1.2 1.6 1.9 2.5 3.0 17 5 2 1 9 Caribbean DJF 2035 0.3 0.5 0.6 0.7 1.0 13 4 0 3 8 (land and sea) 2065 0.6 1.0 1.2 1.4 1.8 14 6 1 3 16   2100 0.7 1.2 1.4 1.9 2.4 22 6 0 5 15   JJA 2035 0.3 0.5 0.6 0.7 1.1 17 9 6 0 11   2065 0.7 0.9 1.1 1.4 2.0 25 16 11 4 16   2100 0.7 1.1 1.3 1.8 2.5 36 18 10 3 13   Annual 2035 0.3 0.5 0.6 0.7 1.1 12 5 3 1 8     2065 0.6 0.9 1.1 1.4 1.9 19 11 5 2 17     2100 0.7 1.2 1.4 1.9 2.4 29 10 5 1 14 South America Amazon DJF 2035 0.4 0.7 0.8 0.9 1.6 12 2 0 2 4   2065 0.8 1.3 1.6 1.9 3.0 22 3 1 2 6   2100 0.7 1.7 2.0 2.5 3.7 22 4 1 1 8   JJA 2035 0.5 0.8 1.0 1.1 1.8 14 3 0 2 5   2065 1.0 1.5 1.8 2.1 3.3 25 4 1 2 11   2100 1.3 1.8 2.2 2.8 4.2 31 4 1 1 9   Annual 2035 0.4 0.8 0.9 1.0 1.8 13 2 0 1 4     2065 0.9 1.4 1.7 2.1 3.3 23 3 1 1 7     2100 1.0 1.8 2.1 2.8 4.0 25 4 1 1 7 Northeast DJF 2035 0.4 0.6 0.7 0.9 1.3 10 2 1 3 17 Brazil 2065 0.8 1.3 1.5 1.7 2.3 15 5 0 4 21   2100 0.8 1.6 1.8 2.4 2.9 17 5 1 5 25   JJA 2035 0.3 0.7 0.8 1.0 1.7 16 6 3 2 15   2065 0.8 1.4 1.6 1.9 3.0 29 10 5 1 18   2100 1.1 1.7 1.9 2.5 3.3 39 14 9 4 27   Annual 2035 0.4 0.7 0.8 0.9 1.4 11 3 0 3 13     2065 0.8 1.4 1.6 1.8 2.6 17 6 2 3 20     2100 1.0 1.7 1.9 2.5 3.1 19 7 3 3 26 West Coast DJF 2035 0.5 0.7 0.8 0.9 1.2 4 1 1 3 6 South America 2065 0.9 1.2 1.5 1.7 2.1 7 1 1 4 7   2100 1.0 1.6 1.9 2.2 2.9 8 0 2 5 9   JJA 2035 0.5 0.7 0.9 0.9 1.3 9 1 0 2 7   2065 1.1 1.3 1.5 1.8 2.5 10 2 1 2 9   2100 1.3 1.6 1.9 2.4 3.0 11 2 1 4 11   Annual 2035 0.5 0.7 0.8 0.9 1.2 4 0 1 2 5     2065 1.0 1.2 1.5 1.7 2.3 6 1 1 2 5     2100 1.1 1.5 1.8 2.3 2.8 7 0 2 4 7 14 (continued on next page) 1280 Climate Phenomena and their Relevance for Future Regional Climate Change Chapter 14 Table 14.1 (continued) RCP4.5     Temperature (°C) Precipitation (%) REGION MONTH a Year min 25% 50% 75% max min 25% 50% 75% max Southeastern DJF 2035 0.2 0.6 0.7 0.9 1.4 7 0 2 4 10 South America 2065 0.7 1.1 1.3 1.6 2.4 6 0 3 6 15   2100 0.6 1.3 1.7 2.2 3.0 6 1 4 7 18   JJA 2035 0.0 0.4 0.6 0.8 1.2 12 1 2 6 19   2065 0.4 1.0 1.2 1.5 2.1 13 1 5 7 17   2100 0.9 1.3 1.5 1.9 2.7 18 1 4 8 27   Annual 2035 0.3 0.5 0.6 0.8 1.3 6 0 1 4 12     2065 0.6 1.0 1.3 1.6 2.3 6 1 3 6 13     2100 0.7 1.3 1.6 2.2 2.7 8 1 4 7 17 Europe Northern Europe DJF 2035 0.3 0.6 1.3 2.3 3.0 4 2 4 6 12   2065 0.5 1.8 2.7 3.5 5.7 1 3 8 11 24   2100 3.2 2.6 3.4 4.4 6.0 2 7 11 14 25   JJA 2035 0.2 0.6 0.9 1.3 2.6 6 2 4 6 11   2065 0.0 1.2 1.8 2.5 3.6 10 2 3 8 18   2100 1.1 1.6 2.2 3.0 4.7 4 2 5 8 23   Annual 2035 0.1 0.8 1.1 1.6 2.7 2 2 3 6 12     2065 0.5 1.6 2.0 2.8 3.8 5 3 5 9 17     2100 2.3 2.1 2.7 3.5 4.5 1 5 8 10 24 Central Europe DJF 2035 0.4 0.6 1.2 1.7 2.5 4 0 3 5 11   2065 0.3 1.4 2.1 2.7 3.6 3 2 6 10 17   2100 0.8 2.0 2.6 3.4 5.1 4 3 7 11 18   JJA 2035 0.3 0.9 1.1 1.5 2.4 8 3 0 4 9   2065 0.4 1.7 2.0 2.6 4.3 13 4 1 3 8   2100 0.4 2.0 2.7 3.0 4.6 16 6 0 5 13   Annual 2035 0.3 0.7 1.1 1.4 2.3 3 1 2 3 8     2065 0.4 1.5 1.9 2.4 3.2 6 0 3 5 9     2100 0.3 2.0 2.6 3.1 4.0 5 0 4 6 14 Southern Europe/ DJF 2035 0.1 0.6 0.8 1.0 1.5 11 4 2 2 8 Mediterranean 2065 0.1 1.2 1.5 1.8 2.3 15 6 3 0 7   2100 0.2 1.5 2.0 2.4 3.0 19 7 4 1 9   JJA 2035 0.6 0.9 1.2 1.4 2.9 16 7 4 1 5   2065 1.0 1.9 2.2 2.6 4.3 24 12 9 4 5   2100 1.2 2.3 2.8 3.3 5.5 28 17 11 6 2   Annual 2035 0.3 0.8 1.0 1.2 2.0 12 4 2 0 3     2065 0.7 1.5 1.7 2.1 3.1 14 8 5 2 3     2100 0.6 2.0 2.3 2.7 4.0 19 10 6 3 4 Africa Sahara DJF 2035 0.1 0.8 1.0 1.1 1.5 43 11 2 6 33   2065 0.6 1.5 1.7 2.0 2.5 29 15 7 1 92   2100 0.7 1.8 2.2 2.6 3.1 42 14 7 4 98   JJA 2035 0.4 0.9 1.1 1.2 2.0 25 5 3 8 45   2065 0.9 1.7 2.0 2.4 3.5 31 11 1 14 70   2100 1.1 2.2 2.4 3.2 4.5 28 15 1 10 108   Annual 2035 0.4 0.9 1.0 1.1 1.5 25 7 0 7 45     2065 1.0 1.6 1.8 2.2 2.8 31 11 3 8 57   2100 1.0 2.0 2.2 2.9 3.8 27 14 6 9 86 (continued on next page) 14 1281 Chapter 14 Climate Phenomena and their Relevance for Future Regional Climate Change Table 14.1 (continued) RCP4.5     Temperature (°C) Precipitation (%) REGION MONTHa Year min 25% 50% 75% max min 25% 50% 75% max West Africa DJF 2035 0.4 0.8 0.9 1.0 1.3 5 1 2 3 9   2065 0.9 1.4 1.6 1.9 2.7 10 1 4 5 7   2100 1.3 1.7 2.0 2.5 3.6 5 1 4 6 11   JJA 2035 0.6 0.7 0.8 0.9 1.2 4 0 1 2 6   2065 1.0 1.3 1.5 1.9 2.6 9 1 2 3 6   2100 0.9 1.6 1.8 2.6 3.3 12 0 2 4 9   Annual 2035 0.6 0.7 0.8 0.9 1.2 4 1 1 3 8     2065 1.1 1.3 1.5 1.9 2.5 10 0 2 4 6     2100 1.0 1.6 1.9 2.6 3.2 8 1 3 4 8 East Africa DJF 2035 0.4 0.7 0.8 1.0 1.2 4 1 1 5 10   2065 0.8 1.3 1.5 1.8 2.5 3 1 3 7 19   2100 1.0 1.6 1.9 2.4 3.2 6 1 5 10 25   JJA 2035 0.5 0.7 0.9 1.0 1.2 8 3 0 2 12   2065 0.8 1.4 1.6 1.9 2.4 10 4 1 3 18   2100 0.7 1.7 2.0 2.5 3.1 12 4 0 5 19   Annual 2035 0.5 0.7 0.8 0.9 1.2 5 2 1 3 10     2065 1.0 1.3 1.6 1.9 2.4 6 2 1 6 17     2100 1.0 1.6 2.0 2.5 3.1 7 2 2 8 21 Southern DJF 2035 0.6 0.7 0.9 1.1 1.3 11 4 2 0 3 Africa 2065 1.0 1.4 1.7 2.0 2.6 19 5 3 1 4   2100 1.1 1.8 2.1 2.7 3.3 19 7 3 1 5   JJA 2035 0.5 0.8 0.9 1.0 1.5 18 9 4 1 9   2065 1.1 1.5 1.7 2.0 2.5 29 13 8 3 4   2100 1.4 1.8 2.1 2.6 3.3 29 18 9 3 12   Annual 2035 0.6 0.8 0.9 1.0 1.4 13 5 2 0 4     2065 1.1 1.5 1.7 2.1 2.6 15 7 4 1 4     2100 1.4 1.8 2.1 2.7 3.3 20 7 5 1 5 West Indian DJF 2035 0.3 0.5 0.6 0.7 1.0 10 0 2 3 10 Ocean 2065 0.6 1.0 1.1 1.3 1.8 10 1 2 5 13   2100 0.8 1.2 1.4 1.8 2.3 9 1 2 6 22   JJA 2035 0.4 0.5 0.6 0.7 1.0 5 1 2 5 12   2065 0.6 0.9 1.1 1.3 1.8 7 1 1 5 12   2100 0.7 1.2 1.4 1.8 2.3 7 0 2 5 19   Annual 2035 0.3 0.5 0.6 0.7 1.0 5 1 2 3 7     2065 0.6 1.0 1.1 1.3 1.8 4 1 2 4 11     2100 0.8 1.2 1.4 1.8 2.2 5 0 2 5 19 Asia West Asia DJF 2035 0.0 0.8 1.1 1.4 1.8 12 0 3 6 14   2065 0.5 1.5 1.9 2.3 3.2 10 1 2 7 21   2100 0.6 1.9 2.4 2.9 3.8 11 3 4 9 20   JJA 2035 0.2 0.9 1.1 1.3 2.1 10 2 1 5 55   2065 1.1 1.7 2.1 2.6 4.0 20 6 3 2 51   2100 1.2 2.0 2.7 3.4 4.7 29 6 1 4 60   Annual 2035 0.1 0.9 1.0 1.2 1.8 9 2 3 4 27     2065 0.7 1.7 1.9 2.3 3.2 12 2 0 4 27     2100 0.9 2.1 2.5 3.1 4.1 19 2 1 6 28 (continued on next page) 14 1282 Climate Phenomena and their Relevance for Future Regional Climate Change Chapter 14 Table 14.1 (continued) RCP4.5     Temperature (°C) Precipitation (%) REGION MONTHa Year min 25% 50% 75% max min 25% 50% 75% max Central Asia DJF 2035 0.1 0.8 1.3 1.6 2.4 6 0 4 8 19   2065 0.6 1.7 2.4 2.9 4.0 9 2 4 10 17   2100 1.0 2.3 2.7 3.3 5.4 12 1 5 12 25   JJA 2035 0.3 0.9 1.1 1.4 2.1 13 3 2 6 17   2065 1.1 1.7 2.1 2.6 4.3 22 5 1 6 16   2100 0.9 2.1 2.7 3.4 5.0 17 3 1 5 18   Annual 2035 0.2 0.8 1.1 1.3 2.0 6 1 2 6 13     2065 0.7 1.7 2.2 2.5 3.6 13 2 2 6 16     2100 0.8 2.2 2.6 3.2 4.8 12 4 4 8 18 Eastern Asia DJF 2035 0.3 0.8 1.0 1.3 2.3 9 1 1 3 7   2065 0.8 1.6 2.0 2.5 3.4 5 3 5 9 16   2100 0.9 2.1 2.7 3.1 4.7 9 5 9 15 30   JJA 2035 0.4 0.7 0.9 1.1 1.6 3 0 2 3 6   2065 0.7 1.4 1.9 2.3 3.1 2 3 6 8 18   2100 0.7 1.8 2.2 2.8 3.9 1 4 7 11 24   Annual 2035 0.3 0.9 0.9 1.1 1.7 3 0 2 3 7     2065 0.9 1.6 1.9 2.2 3.0 1 4 6 8 18     2100 0.7 1.9 2.4 3.0 3.9 1 5 7 11 21 Tibetan DJF 2035 0.0 0.9 1.2 1.5 2.2 3 2 4 8 15 Plateau 2065 0.9 1.9 2.3 2.9 3.9 1 6 8 12 17   2100 1.4 2.3 2.8 3.5 5.5 2 6 11 16 25   JJA 2035 0.4 0.9 1.1 1.3 2.3 5 1 3 5 12   2065 1.0 1.7 2.1 2.5 4.4 3 2 6 9 25   2100 0.9 2.2 2.5 3.1 5.4 4 5 9 13 37   Annual 2035 0.3 0.9 1.2 1.4 2.0 2 1 4 5 11     2065 1.0 1.8 2.2 2.6 3.6 1 4 7 9 22     2100 0.9 2.2 2.6 3.3 4.9 1 6 9 14 32 South Asia DJF 2035 0.1 0.7 1.0 1.1 1.4 18 6 1 4 8   2065 0.6 1.6 1.8 2.3 2.6 17 3 4 7 13   2100 1.4 2.0 2.3 3.0 3.7 14 0 8 14 28   JJA 2035 0.3 0.6 0.7 0.9 1.3 3 2 3 6 9   2065 0.9 1.1 1.3 1.7 2.6 3 5 7 11 33   2100 0.7 1.4 1.7 2.2 3.3 7 8 10 13 37   Annual 2035 0.2 0.7 0.8 1.0 1.3 2 1 3 4 7     2065 0.8 1.4 1.6 1.9 2.5 2 3 7 9 26     2100 1.3 1.7 2.1 2.7 3.5 3 6 10 12 27 North Indian DJF 2035 0.1 0.5 0.6 0.7 1.0 16 3 1 7 22 Ocean 2065 0.5 1.0 1.2 1.5 1.9 7 1 5 15 33   2100 0.8 1.3 1.5 2.0 2.5 9 5 9 20 41   JJA 2035 0.2 0.5 0.6 0.7 1.0 8 1 2 5 16   2065 0.6 1.0 1.2 1.4 1.9 7 2 6 9 23   2100 0.8 1.3 1.4 1.9 2.5 10 5 8 12 36   Annual 2035 0.2 0.5 0.6 0.7 1.0 5 0 1 4 12     2065 0.5 1.0 1.1 1.4 1.9 4 3 6 9 22     2100 0.9 1.3 1.5 2.0 2.5 5 5 9 13 38 (continued on next page) 14 1283 Chapter 14 Climate Phenomena and their Relevance for Future Regional Climate Change Table 14.1 (continued) RCP4.5     Temperature (°C) Precipitation (%) REGION MONTH a Year min 25% 50% 75% max min 25% 50% 75% max Southeast DJF 2035 0.3 0.5 0.7 0.8 1.1 2 1 2 4 12 Asia (land) 2065 0.6 1.1 1.3 1.6 2.2 1 1 3 8 13   2100 0.8 1.4 1.6 2.2 3.0 5 2 6 9 19   JJA 2035 0.3 0.6 0.7 0.8 1.2 3 0 1 3 7   2065 0.7 1.1 1.2 1.5 2.2 2 0 3 7 13   2100 0.8 1.4 1.5 2.0 2.7 3 2 4 9 19   Annual 2035 0.3 0.6 0.7 0.8 1.2 2 0 1 3 8     2065 0.7 1.1 1.2 1.6 2.2 1 1 3 7 13     2100 0.8 1.4 1.6 2.1 2.7 2 2 5 10 18 Southeast DJF 2035 0.3 0.5 0.6 0.7 1.1 3 0 2 3 9 Asia (sea) 2065 0.6 0.9 1.1 1.3 1.9 4 0 3 6 10   2100 0.9 1.2 1.4 1.7 2.5 5 1 3 6 11   JJA 2035 0.3 0.5 0.6 0.6 1.0 4 0 1 2 7   2065 0.7 0.9 1.1 1.3 1.9 2 2 3 5 9   2100 0.9 1.2 1.4 1.7 2.5 1 2 3 6 16   Annual 2035 0.3 0.5 0.6 0.7 1.0 4 0 2 3 8     2065 0.6 1.0 1.1 1.3 1.9 2 1 3 5 7     2100 0.9 1.2 1.4 1.7 2.5 3 2 4 6 9 Australia North Australia DJF 2035 0.2 0.6 0.9 1.1 1.9 20 5 2 3 8   2065 0.6 1.2 1.5 2.1 3.4 18 6 0 3 12   2100 1.1 1.6 2.0 2.6 4.0 31 8 4 3 9   JJA 2035 0.4 0.8 0.9 1.1 1.4 48 10 4 1 15   2065 0.9 1.4 1.6 1.9 2.3 53 15 7 1 17   2100 0.9 1.7 2.0 2.5 2.9 46 19 8 2 11   Annual 2035 0.3 0.7 0.9 1.1 1.6 24 6 3 1 7     2065 0.7 1.3 1.6 1.9 2.6 21 7 2 2 11     2100 1.0 1.7 2.0 2.5 3.4 33 9 4 1 8 South Australia/ DJF 2035 0.1 0.6 0.8 1.0 1.2 27 5 2 2 7 New Zealand 2065 0.4 1.2 1.5 1.7 2.2 18 4 0 2 11   2100 0.7 1.5 1.8 2.3 3.0 17 6 2 2 8   JJA 2035 0.2 0.6 0.7 0.8 1.0 22 3 1 1 4   2065 0.6 1.1 1.2 1.4 1.6 21 6 3 2 11   2100 0.7 1.4 1.6 1.8 2.4 20 9 3 2 7   Annual 2035 0.1 0.6 0.7 0.8 1.0 24 3 2 1 5     2065 0.6 1.1 1.3 1.5 1.7 18 5 1 1 10     2100 0.9 1.5 1.8 2.0 2.4 17 9 2 2 7 (continued on next page) 14 1284 Climate Phenomena and their Relevance for Future Regional Climate Change Chapter 14 Table 14.1 (continued) RCP4.5     Temperature (°C) Precipitation (%) REGION MONTH a Year min 25% 50% 75% max min 25% 50% 75% max The Pacific Northern DJF 2035 0.2 0.5 0.6 0.7 0.9 7 2 0 3 11 Tropical Pacific 2065 0.7 1.0 1.1 1.4 1.9 4 2 1 6 12   2100 0.9 1.2 1.4 1.7 2.4 6 1 1 5 20   JJA 2035 0.3 0.5 0.6 0.7 1.0 11 2 1 3 8   2065 0.6 0.9 1.0 1.3 2.0 9 2 2 5 9   2100 0.8 1.1 1.4 1.8 2.6 11 1 2 4 16   Annual 2035 0.3 0.5 0.6 0.7 1.0 8 2 1 3 7     2065 0.6 1.0 1.1 1.3 1.9 7 1 1 4 9     2100 0.9 1.2 1.4 1.7 2.4 8 0 1 4 18 Equatorial Pacific DJF 2035 0.1 0.5 0.6 0.7 1.2 9 1 7 11 44   2065 0.5 1.0 1.2 1.4 2.5 4 5 12 19 226   2100 0.4 1.2 1.5 1.8 3.3 27 7 16 29 309   JJA 2035 0.1 0.5 0.6 0.7 1.1 18 5 10 14 40   2065 0.7 1.0 1.1 1.4 2.3 0 11 15 25 143   2100 0.5 1.2 1.5 1.8 2.9 19 13 23 33 125   Annual 2035 0.1 0.5 0.7 0.7 1.1 11 3 7 12 40     2065 0.7 1.0 1.2 1.4 2.3 1 7 12 24 194     2100 0.5 1.2 1.4 1.8 2.9 23 13 19 29 225 Southern Pacific DJF 2035 0.3 0.4 0.5 0.6 0.9 7 1 1 2 6   2065 0.6 0.8 1.0 1.2 1.5 22 0 2 4 6   2100 0.8 1.0 1.3 1.5 2.0 24 1 3 5 8   JJA 2035 0.3 0.4 0.5 0.6 0.9 10 0 1 3 8   2065 0.6 0.8 1.0 1.1 1.6 18 1 1 4 7   2100 0.8 1.0 1.2 1.5 2.1 17 2 2 4 10   Annual 2035 0.3 0.4 0.5 0.6 0.9 8 0 1 2 7     2065 0.6 0.8 1.0 1.1 1.6 21 0 2 3 5     2100 0.8 1.1 1.2 1.5 2.0 21 0 2 4 6 Antarctica (land) DJF 2035 0.1 0.5 0.6 0.8 1.3 3 1 3 4 8   2065 0.1 1.0 1.3 1.6 2.3 7 3 5 8 14   2100 0.5 1.5 1.7 2.1 3.1 5 4 8 10 17   JJA 2035 0.5 0.6 0.8 0.9 1.8 3 2 5 6 13   2065 0.1 1.2 1.4 1.8 2.5 1 6 8 13 16   2100 0.3 1.5 1.9 2.4 3.8 1 9 12 15 23   Annual 2035 0.1 0.5 0.7 0.9 1.3 3 2 4 5 9     2065 0.0 1.1 1.3 1.7 2.3 3 4 7 10 14     2100 0.1 1.5 1.8 2.3 3.2 3 7 9 13 21 (sea) DJF 2035 0.3 0.2 0.4 0.5 0.7 1 1 3 3 5   2065 0.4 0.5 0.6 0.9 1.3 0 3 4 5 8   2100 0.3 0.6 0.9 1.2 1.8 0 4 5 7 11   JJA 2035 0.7 0.4 0.6 1.0 1.9 0 2 2 4 5   2065 0.6 0.7 1.1 1.6 3.3 2 4 5 7 10   2100 0.8 1.1 1.4 2.2 3.8 3 5 7 10 13   Annual 2035 0.4 0.3 0.5 0.7 1.3 0 2 2 4 5     2065 0.5 0.5 0.8 1.2 2.3 2 3 4 6 9     2100 0.5 0.8 1.2 1.7 2.6 1 4 6 9 12 Notes: 14 a Precipitation changes cover 6 months; ONDJFM and AMJJAS for winter and summer (Northern Hemisphere). 1285 Chapter 14 Climate Phenomena and their Relevance for Future Regional Climate Change Table 14.2 | Assessed confidence (high, medium, low) in climate projections of regional temperature and precipitation change from the multi-model ensemble of CMIP5 models for the RCP4.5 scenario. Column 1 refers to the SREX regions (cf. Seneviratne et al., 2012, page 12. The region s coordinates can be found from their online Appendix 3.A) and six additional regions including the two polar regions, the Caribbean, Indian Ocean and Pacific Island States (see Annex I for further details). Columns 2 to 4 show confidence in models ability to simulate present-day mean temperature and precipitation as well as the most important phenomena for that region based on Figures 9.39, 9.40, and 9.45. In column 4, the individual phenomena are listed, with associated confidence levels shown below, in the same order as the phenomena. Note that only phenomena assessed in Figure 9.45 are listed. Column 5 is an interpretation of the relevance of the main climate phenomena for future regional climate change, based on Table 14.3. Note that the SREX regions are smaller than the regions listed in Table 14.3. Columns 6 and 7 express confidence in projected temperature and precipitation changes, based solely on model agreement for 2080 2099 vs. 1985 2005, as listed in Table 14.1 and in the maps shown in Annex I. The confidence is assessed for two periods for temperature (DJF and JJA) and two-half year periods for precipitation (October to March and April to September). When the projections are consistent with no significant change, it is marked by an asterisk (*) and the assigned confidence is medium. Further details on how confidence levels have been assigned are provided in the Supplementary Material (Section 14.SM.6.1). Present Future Relevance of SREX Region Temperature Precipitation Main Phenomenon Temperature Precipitation Main Phenomena 1. ALA M L PNA/PDO H H/H H/H M/M 2. CGI H M NAO H H/H H/H H 3. WNA M L PNA/ENSO/PDO/Monsoon M-H H/H M/M* M/M/M/M 4. CNA L H PNA/ENSO M-H H/H M*/M* M/M 5. ENA H H PNA/ENSO/NAO/Monsoon M-H H/H H/M M/M/H/M 6. CAM H M ENSO/TC M-H H/H M*/M* M/H 7. AMZ H L ENSO M H/H M*/M* M 8. NEB H M ENSO M-H H/H M*/L M 9. WSA M L ENSO /SAM M-H H/H L/M* M/M 10. SSA H L ENSO/SAM M-H H/H L/L M/M 11. NEU M H NAO/blocking H H/H H/L H/L 12. CEU H M NAO/blocking H H/H M/M* H/L 13. MED H H NAO/blocking H H/H L/M H/L 14. SAH M L NAO H H/H M*/M* H 15. WAF M L Monsoon/AMO M H/H L/M* M/M 16. EAF H L IOD M H/H M*/M* M 17. SAF H L SAM/TC H H/H M*/L M/H 18. NAS M L NAO/Blocking M H/H H/H H/L 19. WAS H L NAO/IOD/TC M-H H/H M*/M* H/M/H 20. CAS M L N/A N/A H/H M*/M* 21. TIB M L Monsoon M H/H H/H M 22. EAS M M ENSO/Monsoon/TC M-H H/H M/H M/M/H 23. SAS M M Monsoon/IOD/ENSO/TC/MJO L-H H/H M*/H M/M/M/H/L 24. SEA H M Monsoon/IOD/ENSO/TC/MJO L-H H/H M/M M/M/M/H/L 14 (continued on next page) 1286 Climate Phenomena and their Relevance for Future Regional Climate Change Chapter 14 Table 14.2 (continued) Present Future Relevance of SREX Region Temperature Precipitation Main Phenomenon Temperature Precipitation Main Phenomena 25. NAU H M ENSO/Monsoon/TC/IOD/MJO L-H H/H M*/M* M/M/H/M/L 26. SAU H L SAM M-H H/H M*/M* M 1. Arctic (land) H L NAO H H/H H/H H 2. Arctic (sea) H L NAO H H/H H/H H 3. Antarctic (land) M M SAM L-H H/H H/H M 4. Antarctic (sea) M M SAM L-H H/H H/H M 5. Caribbean H L TC/ENSO M-H H/H M*/M H/M 6. West Indian H M IOD N/A H/H M*/M* Ocean M 7. North Indian H M Monsoon/MJO N/A H/H L/M Ocean M/L 8. SE Asia (sea) H M Monsoon/IOD/ENSO/TC/MJO L-H H/H L/M M/M/M/H/L 9. Northern H L ENSO/TC M-H H/H M*/M* Tropical Pacific M/H 10. Equatorial H M ENSO/MJO M-H H/H M/M Tropical Pacific M/L 11. Southern H H ENSO//MJO M-H H/H M*/M* Tropical Pacific M/L 14 1287 14 Table 14.3 | Summary of the relevance of projected changes in major phenomena for mean change in future regional climate. The relevance is classified into high (red), medium (yellow), low (cyan), and no obvious relevance (grey), based 1288 on confidence that there will be a change in the phenomena ( HP for high, MP for medium, LP for low), and confidence in the impact of the phenomena on each region ( HI for high, MI for medium, LI for low). More information on how these assessments have been constructed is given in the Supplementary Material (Section 14.SM.6.1). Chapter 14 Phenomena Monsoon Systems Tropical Phenomenaa ENSO Annular and Dipolar Modes Tropical Cyclones Extratropical Cyclonesb Regions Section MP see Section 14.2 HP/MP/LP/LP See Section 14.3 LP See Section 14.4 HP See Section 14.5 MP See Section 14.6.1 MP/HP See Section 14.6.2 Arctic 14.8.2 HP/HI MP/HI The small projected increase Projected increase in precipitation in NAO is likely to contrib- in extratropical cyclones is likely ute to wintertime changes in to enhance mean precipitation. temperature and precipitation. North America 14.8.3 MP/HI HP/LI LP/HI HP/MI MP/HI MP/HI It is likely the number of Projected ITCZ shifts unre- Likely changes in N. American The small projected increase in the Projected increases in extreme Projected increases in precipita- consecutive dry days will lated to ENSO changes will impact precipitation if ENSO changes. NAO index is likely to contribute to precipitation near the centres tion in extratropical cyclones will increase, and overall water temperature and precipita- wintertime temperature and pre- of tropical cyclones making lead to large increases in availability will be reduced. tion, especially in winter. cipitation changes in NE America. landfall along the western coast wintertime precipitation over the of the USA and Mexico, the northern third of the continent. Gulf Mexico, and the eastern coast of the USA and Canada. Central America 14.8.4 MP/HI HP/HI LP/HI MP/HI and Caribbean Projected reduction in Reduced mean precipitation Reduced mean precipitation if More extreme precipitation near mean precipitation . in southern Central America if El Nino events become more the centres of tropical cyclones there is a southward displace- frequent and/or intense. making landfall along the ment of the East Pacific ITCZ. eastern and western coasts. South America 14.8.5 MP/HI HP/HI LP/HI HP/HI HP/HI Projected increase in extre- Projected increase in the mean Reduced mean precipita- Poleward shift of storm tracks Southward displacement me precipitation and in the precipitation in the southeast tion in eastern Amazonia due to projected positive trend of cyclogenesis activity extension of monsoon area. due to the projected southward and increased precipitation in SAMS phase leads to less increases the precipitation displacement of the SACZ. in the La Plata Basin. precipitation in central Chile and in the extreme south. increased precipitation in the southern tip of South America. Europe and 14.8.6 HP/HI MP/HI Mediterranean Projected increase in the NAO will Enhanced extremes of storm- lead to enhanced winter warming related precipitation and decreased and precipitation over NW Europe. frequency of storm-related precipi- tation over the E. Mediterranean. Africa 14.8.7 MP/HI HP/LI LP/HI HP/HI MP/HI HP/HI Projected enhancement Enhanced precipitation in parts Increased precipitation in Enhanced winter warming Projected increase in extreme Enhanced extremes of storm- of summer precipita- of East Africa due to pro- East Africa and decreased over southern Africa due to precipitation near the centres related precipitation and decreased tion in West Africa. jected shifts in ITCZ. Modified precipitation and enhanced projected increase in SAM. of tropical cyclones making frequency of storm-related precipi- precipitation in West or East warming in southern Africa if landfall along the eastern coast tation over southwestern Africa. Africa according to variations in El Nino events become more (including Madagascar). Atlantic or Indian Ocean SSTs. frequent and/or intense. Central and 14.8.8 MP/MI HP/LI North Asia Projected enhancement Projected enhancement in winter in summer mean warming over North Asia. precipitation. East Asia 14.8.9 MP/MI LP/HI MP/HI MP/MI Enhanced summer precipita- Enhanced warming if El Projected increase in extreme Projected reduction in tion due to intensification Nino events become more precipitation near the centres of midwinter precipitation. of East Asian summer frequent and/or intense. tropical cyclones making landfall monsoon circulation. in Japan, along coasts of east China Sea and Sea of Japan. (continued on next page) Climate Phenomena and their Relevance for Future Regional Climate Change Table 14.3 (continued) Phenomena Monsoon Systems Tropical Phenomenaa ENSO Annular and Dipolar Modes Tropical Cyclones Extratropical Cyclonesb Regions Section MP see Section 14.2 HP/MP/LP/LP See Section 14.3 LP See Section 14.4 HP See Section 14.5 MP See Section 14.6.1 MP/HP See Section 14.6.2 West Asia 14.8.10 HP/LI MP/HI MP/LI Enhanced precipitation in southern Projected increase in extreme Projected decrease in mean parts of West Asia due to projected precipitation near the centres of precipitation due to north- northward shift in ITCZ. tropical cyclones making landfall ward shift of storm tracks. on the Arabian Peninsula. South Asia 14.8.11 MP/MI LP/MI LP/HI MP/HI Enhanced summer Strengthened break mon- Enhanced warming and Projected increase in extreme precipitation associated soon precipitation anomalies increased summer season precipitation near the centres with Indian Monsoon. associated with MJO. rainfall variability due to ENSO. of tropical cyclones making landfall along coasts of Bay of Bengal and Arabian Sea. Southeast Asia 14.8.12 LP/MI HP/MI LP/HI MP/HI Decrease in precipitation Projected changes in Reduction in mean precipitation Projected increase in extreme over Maritime continent. IOD-like warming pattern will and enhanced warming if El precipitation near the centres of reduce mean precipitation in Nino events become more tropical cyclones making landfall Indonesia during Jul-Oct. frequent and/or intense. along coasts of South China Sea, Gulf of Thailand, and Andaman Sea. Australia and 14.8.13 MP/LI HP/LI LP/HI HP/MI MP/HI HP/HI New Zealand Mean monsoon pre- More frequent zonal SPCZ Reduced precipitation in North Increased warming and More extreme precipitation Projected increase in extremes cipitation may increase episodes may reduce pre- and East Australia and NZ if reduced precipitation in NZ and near the centres of tropical of storm-related precipitation. over northern Australia. cipitation in NE Australia. El Nino events become more South Aust. due to projected cyclones making landfall along frequent and/or intense. positive trend in SAM. the eastern, western, and northern coasts of Australia. Pacific Islands 14.8.14 HP/LI HP/LI HP/HI Region Increased mean precipitation along Increased mean precipita- More extreme precipitation near equator with ITCZ intensification. tion in central/east Pacific if the centres of tropical cyclones Climate Phenomena and their Relevance for Future Regional Climate Change More frequent zonal SPCZ episodes El Nino events become more passing over or near Pacific islands. leading to reduced precipitation in frequent and/or intense. southwest and increases in east. Antarctica 14.8.15 LP/MI HP/HI HP/MI Increased warming over Increased warming over Increased precipitation in Antarctic Peninsula and Antarctic Peninsula and west coastal areas due to projected reduced across central Pacific Antarctic related to positive poleward shift of storm track. if El Nino events become more trend projected in SAM. frequent and/or intense. Chapter 14 1289 14 Chapter 14 Climate Phenomena and their Relevance for Future Regional Climate Change References Abram, N. J., M. K. Gagan, J. E. Cole, W. S. Hantoro, and M. Mudelsee, 2008: Recent Annamalai, H., J. Hafner, K. P. Sooraj, and P. Pillai, 2013: Global warming shifts intensification of tropical climate variability in the Indian Ocean. Nature Geosci., monsoon circulation, drying South Asia. J. Clim., 26, 2701 2718. 1, 849 853. Anstey, J. A., and T. G. Shepherd, 2008: Response of the northern stratospheric polar Ackerley, D., B. B. B. Booth, S. H. E. Knight, E. J. Highwood, D. J. Frame, M. R. Allen, vortex to the seasonal alignment of QBO phase transitions. Geophys. Res. Lett., and D. P. Rowell, 2011: Sensitivity of twentieth-century Sahel rainfall to sulfate 35, L22810. aerosol and CO2 forcing. J. Clim., 24, 4999 5014. Anstey, J. A., et al., 2013: Multi-model analysis of Northern Hemisphere winter Aldrian, E., and Y. S. Djamil, 2008: Spatio-temporal climatic change of rainfall in east blocking, Part I: Model biases and the role of resolution. J. Geophys. Res. Atmos., Java Indonesia. Int. J. Climatol., 28, 435 448. 118, doi: 10.1002/jgrd.50231. Alexander, L. V., and J. M. Arblaster, 2009: Assessing trends in observed and modelled Arblaster, J. M., G. A. Meehl, and D. J. Karoly, 2011: Future climate change in the climate extremes over Australia in relation to future projections. Int. J. Climatol., Southern Hemisphere: Competing effects of ozone and greenhouse gases. 29, 417 435. Geophys. Res. Lett., 38, L02701. Alexander, L. V., et al., 2006: Global observed changes in daily climate extremes of Arriaga-Ramirez, S., and T. Cavazos, 2010: Regional trends of daily precipitation temperature and precipitation. J. Geophys. Res. Atmos., 111, D05109. indices in northwest Mexico and southwest United States. J. Geophys. Res., 115, Alexander, M., D. Vimont, P. Chang, and J. Scott, 2010: The impact of extratropical D144111. atmospheric variability on ENSO: Testing the seasonal footprinting mechanism Ashfaq, M., S. Ying, T. Wen-wen, R. J. Trapp, G. Xueijie, J. S. Pal, and N. S. Diffenbaugh, using coupled model experiments. J. Clim., 23, 2885 2901. 2009: Suppression of South Asian summer monsoon precipitation in the 21st Alexander, M., I. Blade, M. Newman, J. Lanzante, N. Lau, and J. Scott, 2002: The century. Geophys. Res. Lett., doi:10.1029/2008gl036500. atmospheric bridge: The influence of ENSO teleconnections on air-sea interaction Ashok, K., S. K. Behera, S. A. Rao, H. Y. Weng, and T. Yamagata, 2007: El Nino Modoki over the global oceans. J. Clim., 15, 2205 2231. and its possible teleconnection. J. Geophys. Res. Oceans, 112, C11007. Alexander, M. A., 2010: Extratropical air-sea interaction, SST variability and the Athanasiadis, P. J., J. M. Wallace, and J. J. Wettstein, 2010: Patterns of wintertime Pacific Decadal Oscillation (PDO). In: Climate Dynamics: Why Does Climate jet stream variability and their relation to the storm tracks. J. Atmos. Sci., 67, Vary? [D. S. a. F. Bryan (ed.)]. American Geophysical Union, Washingon, DC, pp. 1361 138. 123 148. Bader, J., M. D. S. Mesquita, K. I. Hodges, N. Keenlyside, S. Osterhus, and M. Miles, Allan, R., and B. Soden, 2008: Atmospheric warming and the amplification of 2011: A review on Northern Hemisphere sea-ice, storminess and the North precipitation extremes. Science, 321, 1481 1484. Atlantic Oscillation: Observations and projected changes. Atmos. Res., 101, Alory, G., S. Wijffels, and G. Meyers, 2007: Observed temperature trends in the Indian 809 834. Ocean over 1960 1999 and associated mechanisms. Geophys. Res. Lett., 34, Baines, P. G., and C. K. Folland, 2007: Evidence for a rapid global climate shift across L02606. the late 1960s. J. Clim., 20, 2721 2744. Alpert, P., S. Krichak, H. Shafir, D. Haim, and I. Osetinsky, 2008: Climatic trends to Baldwin, M., D. Stephenson, and I. Jolliffe, 2009: Spatial weighting and iterative extremes employing regional modeling and statistical interpretation over the E. projection methods for EOFs. J. Clim., 22, 234 243. Mediterranean. Global Planet. Change, 63, 163 170. Baldwin, M. P., and D. W. J. Thompson, 2009: A critical comparison of stratosphere- Alpert, P., et al., 2002: The paradoxical increase of Mediterranean extreme daily troposphere coupling indices. Q. J. R. Meteorol. Soc., 135, 1661 1672. rainfall in spite of decrease in total values. Geophys. Res. Lett., 29, 31 34. Baldwin, M. P., et al., 2001: The quasi-biennial oscillation. Rev. Geophys., 39, 179 AlSarmi, S., and R. Washington, 2011: Recent observed climate change over the 229. Arabian Peninsula. J. Geophys. Res. Atmos., 116, D11109. Barnes, E., J. Slingo, and T. Woollings, 2012: A methodology for the comparison of Alves, L. M., and J. A. Marengo, 2010: Assessment of regional seasonal predictability blocking climatologies across indices, models and climate scenarios. Clim. Dyn., using the PRECIS regional climate modeling system over South America. Theor. 38, 2467 2481. Appl. Climatol., 100, 337 350. Barnes, E. A., and D. L. Hartmann, 2012: Detection of Rossby wave breaking and its Amador, J. A., E. J. Alfaro, O. G. Lizano, and V. O. Magana, 2006: Atmospheric forcing response to shifts of the midlatitude jet with climate change. J. Geophys. Res. of the eastern tropical Pacific: A review. Prog. Oceanogr., 69, 101 142. Atmos., 117, D09117. AMAP, 2011: Snow, Water, Ice and Permafrost in the Arctic (SWIPA): Climate Change Barnes, E. A., and L. Polvani, 2013: Response of the midlatitude jets and of their and the Cryosphere, Arctic Monitoring and Assessment Programme, Oslo, variability to increased greenhouse gases in the CMIP5 models. J. Clim., 26, Norway, 538 pp. 7117 7135. Ambaum, M., B. Hoskins, and D. Stephenson, 2001: Arctic oscillation or North Barnes, E. A., D. L. Hartmann, D. M. W. Frierson, and J. Kidston, 2010: Effect of latitude Atlantic oscillation? J. Clim., 14, 3495 3507. on the persistence of eddy-driven jets. Geophys. Res. Lett., 37, L11804. Ambaum, M. H. P., 2008: Unimodality of wave amplitude in the Northern Hemisphere. Barnett, J., 2011: Dangerous climate change in the Pacific Islands: Food production J. Atmos. Sci., 65, 1077 1086. and food security. Region. Environ. Change, 11, S229 S237. An, S.-I., J.-W. Kim, S.-H. Im, B.-M. Kim, and J.-H. Park, 2011: Recent and future sea Barriopedro, D., R. Garcia-Herrera, A. R. Lupo, and E. Hernandez, 2006: A climatology surface temperature trends in the tropical Pacific warm pool and cold tongue of Northern Hemisphere blocking. J. Clim., 19, 1042 1063. regions. Clim. Dyn., doi:10.1007/s00382-011-1129-7. Barriopedro, D., R. García-Herrera, J. F. González-Rouco, and R. M. Trigo, 2010: An, S. I., and B. Wang, 2000: Interdecadal change of the structure of the ENSO mode Application of blocking diagnosis methods to General Circulation Models. Part and its impact on the ENSO frequency. J. Clim., 13, 2044 2055. II: Model simulations. Clim. Dyn., 35, 1393 1409. An, S. I., and F. F. Jin, 2000: An Eigen analysis of the interdecadal changes in the Barros, V. R., M. Doyle, and I. Camilloni, 2008: Precipitation trends in southeastern structure and frequency of ENSO mode. Geophys. Res. Lett., 27, 2573 2576. South America: Relationship with ENSO phases and the low-level circulation. Anderson, B., J. Wang, G. Salvucci, S. Gopal, and S. Islam, 2010: Observed trends Theor. Appl. Climatol., 93, 19 33. in summertime precipitation over the southwestern United States. J. Clim., 23, Bates, B., P. Hope, B. Ryan, I. Smith, and S. Charles, 2008: Key findings from the Indian 1937 1944. Ocean Climate Initiative and their impact on policy development in Australia. Anderson, B. T., 2003: Tropical Pacific sea surface temperatures and preceding Clim. Change, 89, 339 354. sea level pressure anomalies in the subtropical North Pacific. J. Geophys. Res. Bates, S. C., 2010: Seasonal influences on coupled ocean-atmosphere variability in Atmos., 108, 4732. the tropical Atlantic ocean. J. Clim., 23, 582 604. Anderson, B. T., 2011: Near-term increase in frequency of seasonal temperature Beck, C., J. Grieser, and B. Rudolf, 2005: A new monthly precipitation climatology extremes prior to the 2 degree C global warming target. Clim. Change, 108, for the global land areas for the period 1951 to 2000. In: Climate Status Report 581 589. 2004. German Weather Service, Offenbach, Germany, pp. 181 190. Annamalai, H., K. Hamilton, and K. R. Sperber, 2007: The South Asian summer 14 monsoon and its relationship with ENSO in the IPCC AR4 simulations. J. Clim., 20, 1071 1092. 1290 Climate Phenomena and their Relevance for Future Regional Climate Change Chapter 14 Becker, A., P. Finger, A. Meyer-Christoffer, B. Rudolf, K. Schamm, U. Schneider, and M. Booth, B. B. B., N. J. Dunstone, P. R. Halloran, T. Andrews, and N. Bellouin, 2012: Ziese, 2013: A description of the global land-surface precipitation data products Aerosols implicated as a prime driver of twentieth-century North Atlantic of the Global Precipitation Climatology Centre with sample applications climate variability. Nature, 484, 228 232. including centennial (trend) analysis from 1901 present. Earth Syst. Sci. Data, Bracegirdle, T. J., and D. B. Stephenson, 2012: Higher precision estimates of regional 5, 71 99. polar warming by ensemble regression of climate model projections. Clim. Dyn., Bell, C. J., L. J. Gray, A. J. Charlton-Perez, M. M. Joshi, and A. A. Scaife, 2009: 39, 2805 2821. Stratospheric communication of El Nino teleconnections to European winter. J. Bracegirdle, T. J., W. M. Connolley, and J. Turner, 2008: Antarctic climate change over Clim., 22, 4083 4096. the twenty first century. J. Geophys. Res., 113, D03103. Bender, M. A., T. R. Knutson, R. E. Tuleya, J. J. Sirutis, G. A. Vecchi, S. T. Garner, and I. Bracegirdle, T. J., et al., 2013: Assessment of surface winds over the Atlantic, Indian, M. Held, 2010: Modeled impact of anthropogenic warming on the frequency of and Pacific Ocean sectors of the Southern Ocean in CMIP5 models: Historical intense Atlantic hurricanes. Science, 327, 454 458. bias, forcing response, and state dependence. J. Geophys. Res. Atmos., 118, Bengtsson, L., K. I. Hodges, and E. Roeckner, 2006: Storm tracks and climate change. 547 562. J. Clim., 19, 3518 3543. Braganza, K., J. Gergis, S. Power, J. Risbey, and A. Fowler, 2009: A multiproxy index of Bengtsson, L., K. I. Hodges, and N. Keenlyside, 2009: Will extratropical storms the El Nino-Southern Oscillation, AD 1525 1982. J. Geophys. Res. Atmos., 114, intensify in a warmer climate? J. Clim., 22, 2276 2301. D05106. Bengtsson, L., K. I. Hodges, M. Esch, N. Keenlyside, L. Kornblueh, J.-J. Luo, and T. Brandefelt, J., 2006: Atmospheric modes of variability in a changing climate. J. Clim., Yamagata, 2007: How may tropical cyclones change in a warmer climate? Tellus 19, 5934 5943. A, 59, 539 561. Branstator, G., and F. Selten, 2009: Modes of Variability and Climate Change. J. Berckmans, J., T. Woollings, M.-E. Demory, P.-L. Vidale, and M. Roberts, 2013: Clim., 22, 2639 2658. Atmospheric blocking in a high resolution climate model: Influences of mean Breugem, W., W. Hazeleger, and R. Haarsma, 2006: Multimodel study of tropical state, orography and eddy forcing. Atmos. Sci. Lett., 14, 34 40. Atlantic variability and change. Geophys. Res. Lett., doi:10.1029/2006GL027831. Berrisford, P., B. J. Hoskins, and E. Tyrlis, 2007: Blocking and Rossby wave-breaking Breugem, W., W. Hazeleger, and R. Haarsma, 2007: Mechanisms of northern tropical on the dynamical tropopause in the Southern Hemisphere. J. Atmos. Sci., 64, Atlantic variability and response to CO2 doubling. J. Clim., doi:DOI 10.1175/ 2881 2898. JCLI4137.1, 2691 2705. Bhend, J., and H. von Storch, 2008: Consistency of observed winter precipitation Bromwich, D. H., A. J. Monaghan, and Z. C. Guo, 2004: Modeling the ENSO modulation trends in northern Europe with regional climate change projections. Clim. Dyn., of Antarctic climate in the late 1990s with the polar MM5. J. Clim., 17, 109 132. 31, 17 28. Brown, J., A. Moise, and R. Colman, 2012a: The South Pacific Convergence Zone Biasutti, M., and A. Giannini, 2006: Robust Sahel drying in response to late 20th in CMIP5 simulations of historical and future climate. Clim. Dyn., doi:10.1007/ century forcings. Geophys. Res. Lett., 33, L11706. s00382-012-1591-x, 1 19. Biasutti, M., and A. H. Sobel, 2009: Delayed seasonal cycle and African monsoon in a Brown, J., A. Moise, and F. Delage, 2012b: Changes in the South Pacific Convergence warmer climate. Geophys. Res. Lett., 36, L23707. Zone in IPCC AR4 future climate projections. Clim. Dyn., 39, 1 19. Biasutti, M., A. H. Sobel, and S. J. Camargo, 2009: The role of the Sahara Low in Brown, J., S. Power, F. Delage, R. Colman, A. Moise, and B. Murphy, 2011: Evaluation summertime Sahel rainfall variability and change in the CMIP3 models. J. Clim., of the South Pacific Convergence Zone in IPCC AR4 climate model simulations 22, 5755 5771. of the twentieth century. J. Clim., 24, 1565 1582. Biasutti, M., I. Held, A. Sobel, and A. Giannini, 2008: SST forcings and Sahel rainfall Brown, J., et al., 2012c: Implications of CMIP3 model biases and uncertainties for variability in simulations of the twentieth and twenty-first centuries. J. Clim., climate projections in the western tropical Pacific. Clim. Change, doi:10.1007/ 21, 3471 3486. s10584-012-0603-5, 1 15. Bitz, C. M., and L. M. Polvani, 2012: Antarctic climate response to stratospheric Brown, R., and P. Mote, 2009: The response of Northern Hemisphere snow cover to a ozone depletion in a fine resolution ocean climate model. Geophys. Res. Lett., changing climate. J. Clim., doi:10.1175/2008JCLI2665.1, 2124 2145. 39, L20705. Budikova, D., 2009: Role of Arctic sea ice in global atmospheric circulation: A review. Bjerknes, J., 1969: Atmospheric teleconnections from the Equatorial Pacific. Mon. Global Planet. Change, 68, 149 163. Weather Rev., 97, 163 172. Buehler, T., C. C. Raible, and T. F. Stocker, 2011: The relationship of winter season Black, E., 2009: The impact of climate change on daily precipitation statistics in North Atlantic blocking frequencies to extreme cold and dry spells in the ERA- Jordan and Israel. Atmos. Sci. Lett., 10, 192 200. 40. Tellus A, 63, 212 222. Black, E., J. Slingo, and K. Sperber, 2003: An observational study of the relationship Bulic, I., and F. Kucharski, 2012: Delayed ENSO impact on spring precipitation over between excessively strong short rains in coastal East Africa and Indian Ocean the North/Atlantic European region. Clim. Dyn., 38, 2593 2612. SST. Mon. Weather Rev., 131, 74 94. Bulic, I., C. Brankovic, and F. Kucharski, 2012: Winter ENSO teleconnections in a Blázquez, J., and M. Nunez, 2012: Analysis of uncertainties in future climate warmer climate. Clim. Dyn., 38, 1593 1613. projections for South America: Comparison of WCRP-CMIP3 and WCRP-CMIP5 Bunge, L., and A. J. Clarke, 2009: A verified estimation of the El Nino index Nino-3.4 models. Clim. Dyn., doi:10.1007/s00382-012-1489-7, 1-18. since 1877. J. Clim., 22, 3979 3992. Blázquez, J., M. N. Nunez, and S. Kusunoki, 2012: Climate projections and Butchart, N., et al., 2006: Simulations of anthropogenic change in the strength of the uncertainties over South America from MRI/JMA global model experiments. Brewer-Dobson circulation. Clim. Dyn., 27, 727 741. Atmos. Clim. Sci., 2, 381 400. Butler, A. H., D. W. J. Thompson, and R. Heikes, 2010: The steady-state atmospheric Bluthgen, J., R. Gerdes, and M. Werner, 2012: Atmospheric response to the extreme circulation response to climate change-like thermal forcings in a simple General Arctic sea ice conditions in 2007. Geophys. Res. Lett., 39, L02707. Circulation Model. J. Clim., 23, 3474 3496. Boer, G., 2009: Changes in interannual variability and decadal potential predictability Caesar, J., et al., 2011: Changes in temperature and precipitation extremes over the under global warming. J. Clim., 22, 3098 3109. Indo-Pacific region from 1971 to 2005. Int. J. Climatol., 31, 791 801. Boer, G. J., and K. Hamilton, 2008: QBO influence on extratropical predictive skill. Cai, W., and T. Cowan, 2008: Dynamics of late autumn rainfall reduction over Clim. Dyn., 31, 987 1000. southeastern Australia. Geophys. Res. Lett., 35, L09708. Bollasina, M., and Y. Ming, 2013: The general circulation model precipitation Cai, W., and T. Cowan, 2013: Southeast Australia autumn rainfall reduction: A bias over the southwestern equatorial Indian Ocean and its implications for climate-change induced poleward shift of ocean-atmosphere circulation. J. simulating the South Asian monsoon. Clim. Dyn., 40, 823 838. Clim., 26, 189 205. Bollasina, M. A., Y. Ming, and V. Ramaswamy, 2011: Anthropogenic aerosols and the Cai, W., T. Cowan, and A. Sullivan, 2009: Recent unprecedented skewness towards weakening of the south Asian summer monsoon. Science, 334, 502 505. positive Indian Ocean Dipole occurrences and its impact on Australian rainfall. Bombardi, R. J., and L. M. V. Carvalho, 2009: IPCC global coupled model simulations Geophys. Res. Lett., 36, L11705. of the South America monsoon system. Clim. Dyn., 33, 893 916. Cai, W., P. van Rensch, and T. Cowan, 2011a: Influence of global-scale variability on Boo, K. O., G. Martin, A. Sellar, C. Senior, and Y. H. Byun, 2011: Evaluating the East the subtropical ridge over southeast Australia. J. Clim., 24, 6035 6053. Asian monsoon simulation in climate models. J. Geophys. Res., 116, D01109. 14 1291 Chapter 14 Climate Phenomena and their Relevance for Future Regional Climate Change Cai, W., T. Cowan, and M. Thatcher, 2012a: Rainfall reductions over Southern Chang, C., J. Chiang, M. Wehner, A. Friedman, and R. Ruedy, 2011: Sulfate aerosol Hemisphere semi-arid regions: The role of subtropical dry zone expansion. Sci. control of tropical Atlantic climate over the twentieth century. J. Clim., 24, Rep., 2, doi: 10.1038/srep00702. 2540 2555. Cai, W., P. van Rensch, T. Cowan, and H. H. Hendon, 2011b: Teleconnection pathways Chang, E. K. M., Y. Guo, and X. Xia, 2012: CMIP5 multimodel ensemble projection of ENSO and the IOD and the mechanisms for impacts on Australian rainfall. J. of storm track change under global warming. J. Geophys. Res. Atmos., 117, doi: Clim., 24, 3910 3923. 10.1029/2012jd018578. Cai, W., P. van Rensch, S. Borlace, and T. Cowan, 2011c: Does the Southern Annular Chang, P., et al., 2006: Climate fluctuations of tropical coupled systems - The role of Mode contribute to the persistence of the multidecade long drought over ocean dynamics. J. Clim., 19, 5122 5174. southwest Western Australia? Geophys. Res. Lett., 38, L14712. Chaturvedi, R. K., J. Joshi, M. Jayaraman, G. Bala, and N. H. Ravindranath, 2012: Multi- Cai, W., T. Cowan, A. Sullivan, J. Ribbe, and G. Shi, 2011d: Are anthropogenic aerosols model climate change projections for India under Representative Concentration responsible for the northwest Australia summer rainfall increase? A CMIP3 Pathways (RCPs): A preliminary analysis. Curr. Sci., 103, 791 802. perspective and implications. J. Clim., 24, 2556 2564. Chauvin, F., and J.-F. Royer, 2010: Role of the SST Anomaly structures in response Cai, W., et al., 2012b: More extreme swings of the South Pacific convergence zone of cyclogenesis to global warming. In: Hurricanes and Climate Change [J. due to greenhouse warming. Nature, 488, 365 369. B. Elsner, R. E. Hodges, J. C. Malmstadt and K. N. Scheitlin (eds.)]. Springer Cai, W. J., and T. Cowan, 2006: SAM and regional rainfall in IPCC AR4 models: Can Science+Business Media, Dordrecht, Netherlands, pp. 39 56. anthropogenic forcing account for southwest Western Australian winter rainfall Chen, D., 2003: A comparison of wind products in the context of ENSO prediction. reduction? Geophys. Res. Lett., 33, doi: 10.1029/2006gl028037. Geophys. Res. Lett., 30, doi: 10.1029/2002GL016121. Cai, W. J., A. Sullivan, and T. Cowan, 2011e: Interactions of ENSO, the IOD, and the Chen, G., I. M. Held, and W. A. Robinson, 2007: Sensitivity of the latitude of the SAM in CMIP3 Models. J. Clim., 24, 1688 1704. surface westerlies to surface friction. J. Atmos. Sci., 64, 2899 2915. Camargo, S., M. Ting, and Y. Kushnir, 2012: Influence of local and remote SST on Chen, G., J. Lu, and D. M. W. Frierson, 2008: Phase speed spectra and the latitude North Atlantic tropical cyclone potential intensity Clim. Dyn., 40, 1515 1529. of surface westerlies: Interannual variability and global warming trend. J. Clim., Campbell, J. D., M. A. Taylor, T. S. Stephenson, R. A. Watson, and F. S. Whyte, 2010: 21, 5942 5959. Future climate of the Caribbean from a regional climate model. Int. J. Climatol., Chen, T.-C., and J.-h. Yoon, 2002: Interdecadal variation of the North Pacific 31, 1866 1878. wintertime blocking. Mon. Weather Rev., 130, 3136 3143. Cane, M. A., et al., 1997: Twentieth-century sea surface temperature trends. Science, Chen, W., Z. Jiang, L. Li, and P. Yiou, 2011: Simulation of regional climate change 275, 957 960. under the IPCC A2 scenario in southeast China. Clim. Dyn., 36, 491 507. Carrera, M. L., R. W. Higgins, and V. E. Kousky, 2004: Downstream weather impacts Cherchi, A., and A. Navarra, 2007: Sensitivity of the Asian summer monsoon to associated with atmospheric blocking over the northeast Pacific. J. Clim., 17, the horizontal resolution: Differences between AMIP-type and coupled model 4823 4839. experiments. Clim. Dyn., 28, 273 290. Carril, A. F., et al., 2012: Performance of a multi-RCM ensemble for South Eastern Cheung, H. N., W. Zhou, H. Y. Mok, and M. C. Wu, 2012: Relationship between Ural South America. Clim. Dyn., 39, 2747 2768. Siberian blocking and the East Asian winter monsoon in relation to the Arctic Carton, J., and B. Huang, 1994: Warm events in the tropical Atlantic. J. Phys. Oscillation and the El Nino Southern Oscillation. J. Clim., 25, 4242 4257. Oceanogr., 24, 888 903. Chiang, J., and D. Vimont, 2004: Analogous Pacific and Atlantic meridional modes of Carvalho, L. M. V., C. Jones, and T. Ambrizzi, 2005: Opposite phases of the antarctic tropical atmosphere-ocean variability. J. Clim., 4143 4158. oscillation and relationships with intraseasonal to interannual activity in the Choi, D. H., J. S. Kug, W. T. Kwon, F. F. Jin, H. J. Baek, and S. K. Min, 2010: Arctic tropics during the austral summer. J. Clim., 18, 702 718. Oscillation responses to greenhouse warming and role of synoptic eddy Carvalho, L. M. V., A. E. Silva, C. Jones, B. Liebmann, P. L. Silva Dias, and H. R. Rocha, feedback. J. Geophys. Res. Atmos., 115, doi: 10.1029/2010jd014160. 2011: Moisture transport and intraseasonal variability in the South America Choi, J., S. An, and S. Yeh, 2012: Decadal amplitude modulation of two types of ENSO Monsoon System. Clim. Dyn., 36, 1865 1880. and its relationship with the mean state. Clim. Dyn., 38, 2631 2644. Cassou, C., and L. Terray, 2001: Dual influence of Atlantic and Pacific SST anomalies Choi, J., S. I. An, B. Dewitte, and W. W. Hsieh, 2009: Interactive feedback between the on the North Atlantic/Europe winter climate. Geophys. Res. Lett., 28, 3195 3198. Tropical Pacific Decadal Oscillation and ENSO in a Coupled General Circulation Cassou, C., C. Deser, and M. A. Alexander, 2007: Investigating the impact of Model. J. Clim., 22, 6597 6611. reemerging sea surface temperature anomalies on the winter atmospheric Choi, J., S.-I. An, J.-S. Kug, and S.-W. Yeh, 2011: The role of mean state on changes in circulation over the North Atlantic. J. Clim., 20, 3510 3526. El Nino s flavor. Clim. Dyn., 37, 1205 1215. Castro, C. L., R. A. Pielke Sr., and J. O. Adegoke, 2007: Investigation of the summer Chotamonsak, C., E. P. Salathe, Jr., J. Kreasuwan, S. Chantara, and K. Siriwitayakorn, climate of the contiguous United States and Mexico using the Regional 2011: Projected climate change over Southeast Asia simulated using a WRF Atmospheric Modeling System (RAMS). Part I: Model climatology (1950 2002). regional climate model. Atmos. Sci. Lett., 12, 213 219. J. Clim., 20, 3844 3865. Chou, C., J. D. Neelin, U. Lohmann, and J. Feichter, 2005: Local and remote impacts of Casty, C., C. C. Raible, T. F. Stocker, H. Wanner, and J. Luterbacher, 2007: A European aerosol climate forcing on tropical precipitation. J. Clim., 18, 4621 4636. pattern climatology 1766 2000. Clim. Dyn., 29, 791 805. Chou, C., J. C. H. Chiang, C.-W. Lan, C.-H. Chung, Y.-C. Liao, and C.-J. Lee, 2013: Cattiaux, J., H. Douville, and Y. Peings, 2013: European temperatures in CMIP5: Increase in the range between wet and dry season precipitation. Nature Geosci., Origins of present-day biases and future uncertainties. Clim. Dyn., doi:10.1007/ 6, 263 267. s00382-013-1731-y, 1 19. Chou, S., et al., 2012: Downscaling of South America present climate driven by Catto, J. L., L. C. Shaffrey, and K. I. Hodges, 2011: Northern Hemisphere extratropical 4-member HadCM3 runs. Clim. Dyn., 38, 635 653. cyclones in a warming climate in the HiGEM High-Resolution Climate Model. J. Christensen, J. H., et al., 2007: Regional climate projections. In: Climate Change Clim., 24, 5336 5352. 2007: The Physical Science Basis. Contribution of Working Group I to the Cavalcanti, I. F. A., and M. H. Shimizu, 2012: Climate fields over South America and Fourth Assessment Report of the Intergovernmental Panel on Climate Change variability of SACZ and PSA in HadGEM-ES. Am. J. Clim. Change, 1, 132 144. [Solomon, S., D. Qin, M. Manning, Z. Chen, M. Marquis, K. B. Averyt, M. Tignor Cavazos, T., C. Turrent, and D. P. Lettenmaier, 2008: Extreme precipitation trends and H. L. Miller (eds.)] Cambridge University Press, Cambridge, United Kingdom associated with tropical cyclones in the core of the North American monsoon. and New York, NY, USA, pp. 847 940. Geophys. Res. Lett., 35, doi: 10.1029/2008GL035832. Christiansen, B., 2005: The shortcomings of nonlinear principal component analysis Cerezo-Mota, R., M. Allen, and R. Jones, 2011: Mechanisms controlling precipitation in identifying circulation regimes. J. Clim., 18, 4814 4823. in the northern portion of the North American monsoon. J. Clim., 24, 2771 2783. Chung, C. E., and V. Ramanathan, 2006: Weakening of North Indian SST gradients Chadwick, R., I. Boutle, and G. Martin, 2013: Spatial patterns of precipitation change and the monsoon rainfall in India and the Sahel. J. Clim., 19, 2036 2045. in CMIP5: Why the rich don t get richer in the tropics. J. Clim., 26, 3803 3822. Chung, C. E., and V. Ramanathan, 2007: Relationship between trends in Chang, C.-H., 2011: Preparedness and storm hazards in a global warming world: land precipitation and tropical SST gradient. Geophys. Res. Lett., 34, doi: Lessons from Southeast Asia. Nat. Hazards, 56, 667 679. 10.1029/2007gl030491. 14 Chylek, P., and G. Lesins, 2008: Multidecadal variability of Atlantic hurricane activity: 1851 2007. J. Geophys. Res., 113, D22106. 1292 Climate Phenomena and their Relevance for Future Regional Climate Change Chapter 14 Chylek, P., C. K. Folland, G. Lesins, and M. Dubey, 2010: The 20th Century bipolar Davini, P., C. Cagnazzo, S. Gualdi, and A. Navarra, 2012: Bidimensional diagnostics, seesaw of the Arctic and Antarctic surface air temperatures. Geophys. Res. Lett., variability, and trends of Northern Hemisphere blocking. J. Clim., 25, 6496 6509. 37, doi: 10.1029/2010GL042793. Dawson, A., T. N. Palmer, and S. Corti, 2012: Simulating regime structures in weather Chylek, P., C. K. Folland, G. Lesins, M. Dubey, and M. Wang, 2009: Arctic air and climate prediction models. Geophys. Res. Lett., 39, L21805. temperature change amplification and the Atlantic Multidecadal Oscillation. de Oliveira Vieira, S., P. Satyamurty, and R. V. Andreoli, 2013: On the South Atlantic Geophys. Res. Lett., 36, doi: 10.1029/ 2009GL038777. Convergence Zone affecting southern Amazonia in austral summer. Atmos. Sci. Chylek, P., C. Folland, L. Frankcombe, H. Dijkstra, G. Lesins, and M. Dubey, 2012: Lett., 14, 1 6. Greenland ice core evidence for spatial and temporal variability of the Atlantic de Vries, H., T. Woollings, J. Anstey, R. J. Haarsma, and W. Hazeleger, 2013: Multidecadal Oscillation. Geophys. Res. Lett., 39, L09705. Atmospheric blocking and its relation to jet changes in a future climate. Clim. Cobb, K. M., C. D. Charles, H. Cheng, and R. L. Edwards, 2003: El Nino/Southern Dyn., doi:10.1007/s00382-013-1699-7, 1 12. Oscillation and tropical Pacific climate during the last millennium. Nature, 424, Dean, S., and P. Stott, 2009: The effect of local circulation variability on the detection 271 276. and attribution of New Zealand temperature trends. J. Clim., 22, 6217 6229. Coelho, C. A. S., and L. Goddard, 2009: El Nino-induced tropical droughts in climate DeFries, R., L. Bounoua, and G. Collatz, 2002: Human modification of the landscape change projections. J. Clim., 22, 6456 6476. and surface climate in the next fifty years. Global Change Biol., 8, 438 458. Colle, B. A., Z. Zhang, K. A. Lombardo, E. Chang, P. Liu, and M. Zhang, 2013: Historical Della-Marta, P. M., and J. G. Pinto, 2009: Statistical uncertainty of changes in winter evaluation and future prediction of eastern North America and western Atlantic storms over the North Atlantic and Europe in an ensemble of transient climate extratropical cyclones in the CMIP5 models during the cool season. J. Clim., 26, simulations. Geophys. Res. Lett., 36, doi: 10.1029/2009gl038557. 6882 6903. Deni, S. M., J. Suhaila, W. Z. W. Zin, and A. A. Jemain, 2010: Spatial trends of dry Collins, M., et al., 2010: The impact of global warming on the tropical Pacific ocean spells over Peninsular Malaysia during monsoon seasons. Theor. Appl. Climatol., and El Nino. Nature Geosci., 3, 391 397. 99, 357 371. Colman, R. A., A. F. Moise, and L. I. Hanson, 2011: Tropical Australian climate and the Déqué, M., S. Somot, E. Sanchez-Gomez, C. M. Goodess, D. Jacob, G. Lenderink, and Australian monsoon as simulated by 23 CMIP3 models. J. Geophys. Res. Atmos., O. B. Christensen, 2012: The spread amongst ENSEMBLES regional scenarios: 116, doi: 10.1029/2010jd015149. Regional climate models, driving general circulation models and interannual Comarazamy, D. E., and J. E. Gonzalez, 2011: Regional long-term climate change variability. Clim. Dyn., 38, 951 964. (1950 2000) in the midtropical Atlantic and its impacts on the hydrological Deser, C., A. S. Phillips, and M. A. Alexander, 2010a: Twentieth century tropical cycle of Puerto Rico. J. Geophys. Res. Atmos., 116, doi: 10.1029/2010jd015414. sea surface temperature trends revisited. Geophys. Res. Lett., 37, doi: Compo, G. P., and P. D. Sardeshmukh, 2010: Removing ENSO-related variations from 10.1029/2010gl043321. the climate record. J. Clim., 23, 1957 1978. Deser, C., M. A. Alexander, S.-P. Xie, and A. S. Phillips, 2010b: Sea surface temperature Conway, D., C. Hanson, R. Doherty, and A. Persechino, 2007: GCM simulations of variability: Patterns and mechanisms. Annu. Rev. Mar. Sci., 2, 115 143. the Indian Ocean dipole influence on East African rainfall: Present and future. Deser, C., R. Tomas, M. Alexander, and D. Lawrence, 2010c: The seasonal atmospheric Geophys. Res. Lett., 34, doi: 10.1029/2006GL027597. response to projected Arctic sea ice loss in the late twenty-first century. J. Clim., Cook, B., N. Zeng, and J.-H. Yoon, 2011: Will Amazonia dry out? Magnitude and 23, 333 351. causes of change from IPCC Climate Model Projections. Earth Interact., 16, Deser, C., A. Phillips, V. Burdette, and H. Teng, 2012: Uncertainty in climate change 1 27. projections: The role of internal variability. Clim. Dyn., 38, 527 546. Cook, B. I., and R. Seager, 2013: The response of the North American Monsoon to Di Lorenzo, E., et al., 2009: Nutrient and salinity decadal variations in the central increased greenhouse gas forcing. J. Geophys. Res., 118, and eastern North Pacific. Geophys. Res. Lett., 36, doi: 10.1029/2009GL038261. Cook, K., 2008: Climate science: The mysteries of Sahel droughts. Nature Geosci., Diamond, H. J., A. M. Lorrey, and J. A. Renwick, 2012: A southwest Pacific tropical 1, 647 648. cyclone climatology and linkages to the El Nino Southern Oscillation. J. Clim., Cook, K. H., and E. K. Vizy, 2006: Coupled model simulations of the west African 26, 3 25. monsoon system: Twentieth- and twenty-first-century simulations. J. Clim., 19, Diffenbaugh, N. S., and M. Ashfaq, 2010: Intensification of hot extremes in the 3681 3703. United States. Geophys. Res. Lett., 37, L15701. Cook, K. H., and E. K. Vizy, 2010: Hydrodynamics of the Caribbean Low-Level Jet and Diffenbaugh, N. S., M. Scherer, and M. Ashfaq, 2013: Response of snow-dependent its relationship to precipitation. J. Clim., 23, 1477 1494. hydrologic extremes to continued global warming. Nature Clim. Change, 3, Coppola, E., F. Kucharski, F. Giorgi, and F. Molteni, 2005: Bimodality of the North 379 384. Atlantic Oscillation in simulations with greenhouse gas forcing. Geophys. Res. DiNezio, P. N., A. C. Clement, G. A. Vecchi, B. J. Soden, and B. P. Kirtman, 2009: Climate Lett., 32, doi: 10.1029/2005gl024080. response of the equatorial Pacific to global warming. J. Clim., 22, 4873 4892. Cravatte, S., T. Delcroix, D. Zhang, M. McPhaden, and J. Leloup, 2009: Observed Ding, Q., E. Steig, D. Battisti, and M. Kuttel, 2011: Winter warming in West Antarctica freshening and warming of the western Pacific Warm Pool. Clim. Dyn., 33, caused by central tropical Pacific warming. Nature Geosci., 4, 398 403. 565 589. Ding, Y., and J. C. L. Chan, 2005: The East Asian summer monsoon: An overview. Croci-Maspoli, M., C. Schwierz, and H. Davies, 2007a: Atmospheric blocking: Space- Meteorol. Atmos. Phys., 89, 117 142. time links to the NAO and PNA. Clim. Dyn., 29, 713 725. Ding, Y., G. Ren, Z. Zhao, Y. Xu, Y. Luo, Q. Li, and J. Zhang, 2007: Detection, causes and Croci-Maspoli, M., C. Schwierz, and H. C. Davies, 2007b: A multifaceted climatology projection of climate change over China: An overview of recent progress. Adv. of atmospheric blocking and its recent linear trend. J. Clim., 20, 633 649. Atmos. Sci., doi:DOI 10.1007/s00376-007-0954-4, 954 971. Cunningham, C. A. C., and I. F. D. Cavalcanti, 2006: Intraseasonal modes of variability Dole, R., M. Hoerling, J. Perlwitz, J. Eischeid, and P. Pegion, 2011: Was there a basis affecting the South Atlantic Convergence Zone. Int. J. Climatol., 26, 1165 1180. for anticipating the 2010 Russian heat wave? Geophys. Res. Lett., L06702, doi d Orgeval, T., J. Polcher, and L. Li, 2006: Uncertainties in modelling future hydrological 10.1029/2010GL046582. change over West Africa. Clim. Dyn., 26, 93 108. Dominguez, F., E. Rivera, D. P. Lettenmaier, and C. L. Castro, 2012: Changes in winter Dacre, H. F., and S. L. Gray, 2009: The spatial distribution and evolution characteristics precipitation extremes for the western United States under a warmer climate as of North Atlantic Cyclones. Mon. Weather Rev., 137, 99 115. simulated by regional climate models. Geophys. Res. Lett., 39, L05803. Dai, A., 2011: Drought under global warming: A review. WIREs Clim. Change, 2, Donat, M. G., G. C. Leckebusch, S. Wild, and U. Ulbrich, 2011: Future changes in 45 65. European winter storm losses and extreme wind speeds inferred from GCM and Dai, A., 2013: Increasing drought under global warming in observations and models. RCM multi-model simulations. Nat. Hazards Earth Syst. Sci., 11, 1351 1370. Nature Clim. Change, 3, 52 58. Dong, B., R. T. Sutton, and T. Woollings, 2011: Changes of interannual NAO variability Dai, A., T. Qian, K. E. Trenberth, and J. D. Milliman, 2009: Changes in continental in response to greenhouse gases forcing. Clim. Dyn., 37, 1621 1641. freshwater discharge from 1948 to 2004. J. Clim., 22, 2773 2792. Dong, L., T. J. Vogelsang, and S. J. Colucci, 2008: Interdecadal trend and ENSO-related Dairaku, K., S. Emori, and T. Nozawa, 2008: Impacts of global warming on hydrological interannual variability in Southern Hemisphere blocking. J. Clim., 21, 3068 3077. cycles in the Asian monsoon region. Adv. Atmos. Sci., 25, 960 973. Döscher, R., and T. Koenigk, 2012: Arctic rapid sea ice loss events in regional coupled Dash, S. K., M. A. Kulkarni, U. C. Mohanty, and K. Prasad, 2009: Changes in the climate scenario experiments. Ocean Sci. Discuss., 9, 2327 2373. 14 characteristics of rain events in India. J. Geophys. Res. Atmos., 114, D10109. 1293 Chapter 14 Climate Phenomena and their Relevance for Future Regional Climate Change Dowdy, A. J., G. A. Mills, B. Timbal, and Y. Wang, 2012: Changes in the risk of Evan, A. T., D. J. Vimont, A. K. Heidinger, J. P. Kossin, and R. Bennartz, 2009: The extratropical cyclones in eastern Australia. J. Clim., 26, 1403 1417. role of aerosols in the evolution of tropical North Atlantic Ocean temperature Drumond, A. R. M., and T. Ambrizzi, 2005: The role of SST on the South American anomalies. Science, 324, 778 781. atmospheric circulation during January, February and March 2001. Clim. Dyn., Evans, J. P., 2008: Changes in water vapor transport and the production of 24, 781 791. precipitation in the eastern Fertile Crescent as a result of global warming. J. Du, Y., and S.-P. Xie, 2008: Role of atmospheric adjustments in the tropical Indian Hydrometeorol., 9, 1390 1401. Ocean warming during the 20th century in climate models. Geophys. Res. Lett., Evans, J. P., 2009: 21st century climate change in the Middle East. Clim. Change, 35, doi: 10.1029/2008GL033631. 92, 417 432. Du, Y., L. Yang, and S. Xie, 2011: Tropical Indian Ocean influence on Northwest Pacific Eyring, V., et al., 2013: Long-term ozone changes and associated climate impacts in tropical cyclones in summer following strong El Nino. J. Clim., 24, 315 322. CMIP5 simulations. J. Geophys. Res. Atmos, doi:10.1002/jgrd.50316. Du, Y., S. P. Xie, G. Huang, and K. M. Hu, 2009: Role of air-sea interaction in the Falvey, M., and R. D. Garreaud, 2009: Regional cooling in a warming world: Recent long persistence of El Nino-induced north Indian Ocean warming. J. Clim., 22, temperature trends in the southeast Pacific and along the west coast of 2023 2038. subtropical South America (1979 2006). J. Geophys. Res. Atmos., 114, D04102. Dufek, A. S., T. Ambrizzi, and R. P. Rocha, 2008: Are reanalysis data useful for Fauchereau, N., B. Pohl, C. Reason, M. Rouault, and Y. Richard, 2009: Recurrent daily calculating climate indices over South America? Ann. NY Acad. Sci., 1146, OLR patterns in the Southern Africa/Southwest Indian Ocean region, implications 87 104. for South African rainfall and teleconnections. Clim. Dyn., 32, 575 591. Duffy, P. B., and C. Tebaldi, 2012: Increasing prevalence of extreme summer Fedorov, A. V., and S. G. Philander, 2000: Is El Nino changing? Science, 288, 1997 temperatures in the U.S. Clim. Change, 111, 487 495. 2002. Dunion, J., and C. Velden, 2004: The impact of the Saharan air layer on Atlantic Feldstein, S. B., and C. Franzke, 2006: Are the North Atlantic Oscillation and the tropical cyclone activity. Bull. Am. Meteorol. Soc., 85, 353 365. Northern Annular Mode distinguishable? J. Atmos. Sci., 63, 2915 2930. Dunion, J., and C. Marron, 2008: A reexamination of the Jordan mean tropical Feliks, Y., M. Ghil, and A. W. Robertson, 2010: Oscillatory climate modes in the eastern sounding based on awareness of the Saharan air layer: Results from 2002. J. Mediterranean and their synchronization with the North Atlantic Oscillation. J. Clim., 21, 5242 5253. Clim., 23, 4060 4079. Dunion, J. P., 2011: Rewriting the climatology of the tropical North Atlantic and Feng, S., Q. Hu, and R. Oglesby, 2011: Influence of Atlantic sea surface temperatures Caribbean Sea atmosphere. J. Clim., 24, 893 908. on persistent drought in North America. Clim. Dyn., 37, 569 586. Dunn-Sigouin, E., and S.-W. Son, 2013: Northern Hemisphere blocking frequency and Fereday, D. R., J. R. Knight, A. A. Scaife, C. K. Folland, and A. Philipp, 2008: Cluster duration in the CMIP5 models. J. Geophys. Res. Atmos., 118, 1179 1188. analysis of North Atlantic-European circulation types and links with tropical Elsner, J. B., J. P. Kossin, and T. H. Jagger, 2008: The increasing intensity of the Pacific sea surface temperatures. J. Clim., 21, 3687 3703. strongest tropical cyclones. Nature, 455, 92 95. Fink, A., S. Pohle, J. Pinto, and P. Knippertz, 2012: Diagnosing the influence of diabatic Emanuel, K., 2007: Environmental factors affecting tropical cyclone power processes on the explosive deepening of extratropical cyclones. Geophys. Res. dissipation. J. Clim., 20, 5497 5509. Lett., 39, doi: 10.1029/2012GL051025. Emanuel, K., 2010: Tropical cyclone activity downscaled from NOAA-CIRES reanalysis, Fink, A. H., T. Bruecher, V. Ermert, A. Krueger, and J. G. Pinto, 2009: The European 1908 1958. J. Adv. Model. Earth Syst., 2, 12. storm Kyrill in January 2007: Synoptic evolution, meteorological impacts and Emanuel, K., R. Sundararajan, and J. Williams, 2008: Hurricanes and global warming: some considerations with respect to climate change. Nat. Hazards Earth Syst. Results from downscaling IPCC AR4 simulations. Bull. Am. Meteorol. Soc., 89, Sci., 9, 405 423. 347 367. Fischer-Bruns, I., D. F. Banse, and J. Feichter, 2009: Future impact of anthropogenic Emanuel, K., S. Solomon, D. Folini, S. Davis, and C. Cagnazzo, 2012: Influence of sulfate aerosol on North Atlantic climate. Clim. Dyn., 32, 511 524. tropical tropopause layer cooling on Atlantic hurricane activity. J. Clim., 26, Fogt, R., D. Bromwich, and K. Hines, 2011: Understanding the SAM influence on the 2288 2301. South Pacific ENSO teleconnection. Clim. Dyn., 36, 1555 1576. Endo, H., 2010: Long-term changes of seasonal progress in Baiu rainfall using 109 Fogt, R. L., J. Perlwitz, A. J. Monaghan, D. H. Bromwich, J. M. Jones, and G. J. Marshall, years (1901 2009) daily station data. Sola, 7, 5 8. 2009: Historical SAM variability. Part II: Twentieth-century variability and Ttrends Endo, H., 2012: Future changes of Yamase bringing unusually cold summers over from reconstructions, observations, and the IPCC AR4 models. J. Clim., 22, 5346 northeastern Japan in CMIP3 multi-models. J. Meteorol. Soc. Jpn., 90A, 123-136. 5365. Endo, H., A. Kitoh, T. Ose, R. Mizuta, and S. Kusunoki, 2012: Future changes and Folland, C., M. Salinger, N. Jiang, and N. Rayner, 2003: Trends and variations in South uncertainties in Asian precipitation simulated by multiphysics and multi sea Pacific island and ocean surface temperatures. J. Clim., 16, 2859 2874. surface temperature ensemble experiments with high-resolution Meteorological Folland, C. K., J. A. Renwick, M. J. Salinger, and A. B. Mullan, 2002: Relative Research Institute atmospheric general circulation models (MRI-AGCMs). J. influences of the Interdecadal Pacific Oscillation and ENSO on the South Pacific Geophys. Res., 117, D16118. Convergence Zone. Geophys. Res. Lett., 29, doi: 10.1029/2001GL014201. Enfield, D., S. K. Lee, and C. Wang, 2006: How are large Western Hemisphere warm Folland, C. K., J. Knight, H. W. Linderholm, D. Fereday, S. Ineson, and J. W. Hurrell, pools formed? Prog. Oceanogr., 70, 346 365. 2009: The summer North Atlantic Oscillation: Past, present, and future. J. Clim., Engelbrecht, C. J., F. A. Engelbrecht, and L. L. Dyson, 2011: High-resolution model- 22, 1082 1103. projected changes in mid-tropospheric closed-lows and extreme rainfall events Forster, P., et al., 2007: Changes in atmospheric constituents and in radiative forcing. over southern Africa. Int. J. Climatol., 33, 173 187. In: Climate Change 2007: The Physical Science Basis. Contribution of Working England, M. H., C. C. Ummenhofer, and A. Santoso, 2006: Interannual rainfall Group I to the Fourth Assessment Report of the Intergovernmental Panel on extremes over southwest Western Australia linked to Indian ocean climate Climate Change [Solomon, S., D. Qin, M. Manning, Z. Chen, M. Marquis, K. B. variability. J. Clim., 19, 1948 1969. Averyt, M. Tignor and H. L. Miller (eds.)] Cambridge University Press, Cambridge, Englehart, P. J., and A. V. Douglas, 2006: Defining intraseasonal rainfall variability United Kingdom and New York, NY, USA, pp. 129 234. within the North American monsoon. J. Clim., 19, 4243 4253. Frank, W., and P. Roundy, 2006: The role of tropical waves in tropical cyclogenesis. Evan, A., 2012: Atlantic hurricane activity following two major volcanic eruptions. J. Mon. Weather Rev., 134, 2397 2417. Geophys. Res. Atmos., 117, doi: 10.1029/2011JD016716. Frederiksen, C. S., J. S. Frederiksen, J. M. Sisson, and S. L. Osbrough, 2011a: Changes Evan, A., G. Foltz, D. Zhang, and D. Vimont, 2011a: Influence of African dust on ocean- and projections in the annual cycle of the Southern Hemisphere circulation, atmosphere variability in the tropical Atlantic. Nature Geosci., 4, 762 765. storm tracks and Australian rainfall. Int. J. Clim. Change Impacts Respons., 2, Evan, A. T., J. P. Kossin, C. E. Chung, and V. Ramanathan, 2011b: Arabian Sea tropical 143 162. cyclones intensified by emissions of black carbon and other aerosols. Nature, Frederiksen, C. S., J. S. Frederiksen, J. M. Sisson, and S. L. Osbrough, 2011b: Australian 479, 94 97. winter circulation and rainfall changes and projections. Int. J. Clim. Change Strat. Evan, A. T., J. P. Kossin, C. Chung, and V. Ramanathan, 2012: Evan et al. reply to Wang Manage., 3, 170 188. et al. (2012), Intensified Arabian Sea tropical storms . Nature, 489, E2 E3. Frederiksen, J. S., and C. S. Frederiksen, 2007: Interdecadal changes in southern 14 hemisphere winter storm track modes. Tellus A, 59, 599 617. 1294 Climate Phenomena and their Relevance for Future Regional Climate Change Chapter 14 Frederiksen, J. S., C. S. Frederiksen, S. L. Osbrough, and J. M. Sisson, 2010: Causes of Gong, D. Y., and C. H. Ho, 2002: The Siberian High and climate change over middle to changing Southern Hemispheric weather systems. In: Managing Climate Change high latitude Asia. Theor. Appl. Climatol., 72, 1 9. [I. Jupp, P. Holper and W. Cai (eds.)]. CSIRO Publishing, Collingwood, Victoria, Good, P., J. A. Lowe, M. Collins, and W. Moufouma-Okia, 2008: An objective tropical Australia, pp. 85 98. Atlantic sea surface temperature gradient index for studies of south Amazon Friedman, A. R., Y. T. Hwang, J. C. H. Chiang, and D. M. W. Frierson, 2013: dry-season climate variability and change. Philos. Trans. R. Soc. London B, 363, Interhemispheric temperature asymmetry over the 20th century and in future 1761 1766. projections. J. Clim., doi:10.1175/JCLI-D-12-00525.1. Goswami, B. N., V. Venugopal, D. Sengupta, M. S. Madhusoodanan, and P. K. Frierson, D. M. W., I. M. Held, and P. Zurita-Gotor, 2007: A gray-radiation aquaplanet Xavier, 2006: Increasing trend of extreme rain events over India in a warming moist GCM. Part II: Energy transports in altered climates. J. Atmos. Sci., 64, environment. Science, 314, 1442 1445. 1680 1693. Graff, L., and J. LaCasce, 2012: Changes in the extratropical storm tracks in response Fuèkar, N. S., S.-P. Xie, R. Farneti, E. A. Maroon, and D. M. W. Frierson, 2013: Influence to changes in SST in an AGCM. J. Clim., 25, 1854 1870. of the extratropical ocean circulation on the intertropical convergence zone in Grantz, K., B. Rajagopalan, M. Clark, and E. Zagona, 2007: Seasonal shifts in the an idealized coupled general circulation model. J. Clim., 26, 4612 4629. North American monsoon. J. Clim., 20, 1923 1935. Furtado, J., E. Di Lorenzo, N. Schneider, and N. A. Bond, 2011: North Pacific decadal Griffiths, G., M. Salinger, and I. Leleu, 2003: Trends in extreme daily rainfall across variability and climate change in the IPCC AR4 models. J. Clim., 24, 3049 3066. the South Pacific and relationship to the South Pacific Convergence Zone. Int. J. Gamble, D. W., and S. Curtis, 2008: Caribbean precipitation: Review, model and Climatol., 23, 847 869. prospect. Prog. Phys. Geogr., 32, 265 276. Griffiths, G., et al., 2005: Change in mean temperature as a predictor of extreme Gan, M. A., V. B. Rao, and M. C. L. Moscati, 2006: South American monsoon indices. temperature change in the Asia-Pacific region. Int. J. Climatol., 25, 1301 1330. Atmos. Sci. Lett., 6, 219 223. Griffiths, G. M., 2007: Changes in New Zealand daily rainfall extremes 1930 - 2004. Gao, X., Y. Shi, and F. Giorgi, 2012a: A high resolution simulation of climate change Weather Clim., 27, 3 44. over China. Sci. China Earth Sci., 54, 462 472. Gu, D. F., and S. G. H. Philander, 1995: Secular changes of annual and interannual Gao, X., Y. Shi, R. Song, F. Giorgi, Y. Wang, and D. Zhang, 2008: Reduction of future variability in the tropics during the past century. J. Clim., 8, 864 876. monsoon precipitation over China: Comparison between a high resolution RCM Guilyardi, E., H. Bellenger, M. Collins, S. Ferrett, W. Cai, and A. Wittenberg, 2012: A simulation and the driving GCM. Meteorol. Atmos. Phys., 100, 73 86. first look at ENSO in CMIP5. CLIVAR Exchanges, 58, 29-32. Gao, X., Y. Shi, D. Zhang, J. Wu, F. Giorgi, Z. Ji, and Y. Wang, 2012b: Uncertainties in Guo, Z. C., D. H. Bromwich, and K. M. Hines, 2004: Modeled antarctic precipitation. monsoon precipitation projections over China: Results from two high-resolution Part II: ENSO modulation over West Antarctica. J. Clim., 17, 448 465. RCM simulations. Clim. Res., 2, 213. Gutiérrez, D., et al., 2011: Coastal cooling and increased productivity in the main Gao, Y., L. R. Leung, E. P. Salathé, F. Dominguez, B. Nijssen, and D. P. Lettenmaier, upwelling zone off Peru since the mid-twentieth century. Geophys. Res. Lett., 2012c: Moisture flux convergence in regional and global climate models: 38, L07603. Implications for droughts in the southwestern United States under climate Gutowski, W. J. et al., 2010: Regional, extreme monthly precipitation simulated by change. Geophys. Res. Lett., 39, L09711. NARCCAP RCMs. J. Hydrometeorol., 11, 1373 1379. Garcia, R., and W. J. Randel, 2008: Acceleration of the Brewer Dobson circulation Gutzler, D. S., 2004: An index of interannual precipitation variability in the core of the due to increases in greenhouse gases. J. Atmos. Sci., 65, 2731 2739. North American monsoon region. J. Clim., 17, 4473 4480. Garfinkel, C. I., and D. L. Hartmann, 2011: The influence of the Quasi-Biennial Gutzler, D. S., and T. O. Robbins, 2011: Climate variability and projected change in Oscillation on the troposphere in wintertime in a hierarchy of models, Part 1: the western United States: Regional downscaling and drought statistics. Clim. Simplified dry GCMs. J. Atmos. Sci., 68, 1273 1289. Dyn., 37, 835 849. Gastineau, G., and B. J. Soden, 2009: Model projected changes of extreme Gutzler, D. S., L. N. Long, J. Schemm, S. B. Roy, M. Bosilovich, J. C. Collier, M. Kanamitsu, wind events in response to global warming. Geophys. Res. Lett., 36, doi: P. Kelly, D. Lawrence, M. I. Lee, R. L. Sánchez, B. Mapes, K. Mo, A. Nunes, E. A. 10.1029/2009gl037500. Ritchie, J. Roads, S. Schubert, H. Wei, and G. J. Zhang, 2009: Simulations of the Geng, Q. Z., and M. Sugi, 2003: Possible change of extratropical cyclone activity 2004 North American Monsoon: NAMAP2. J. Climate, 22, 6716-6740. due to enhanced greenhouse gases and sulfate aerosols - Study with a high- Haarsma, R. J., et al., 2013: More hurricanes to hit Western Europe due to global resolution AGCM. J. Clim., 16, 2262 2274. warming. Geophys. Res. Lett., doi:10.1002/grl.50360. Gerber, E. P., and G. K. Vallis, 2007: Eddy-zonal flow interactions and the persistence Haensler, A., S. Hagemann, and D. Jacob, 2011: The role of the simulation setup in of the zonal index. J. Atmos. Sci., 64, 3296 3311. a long-term high-resolution climate change projection for the southern African Gerber, E. P., L. M. Polvani, and D. Ancukiewicz, 2008: Annular mode time scales region. Theor. Appl. Climatol., 106, 153 169. in the Intergovernmental Panel on Climate Change Fourth Assessment Report Haerter, J., E. Roeckner, L. Tomassini, and J. von Storch, 2009: Parametric models. Geophys. Res. Lett., 35, doi: 10.1029/2008gl035712. uncertainty effects on aerosol radiative forcing. Geophys. Res. Lett., 36, doi: Gerber, E. P., et al., 2010: Stratosphere-troposphere coupling and annular 10.1029/2009GL039050. mode variability in chemistry-climate models. J. Geophys. Res., 115, doi: Haigh, J. D., and H. K. Roscoe, 2006: Solar influences on polar modes of variability. 10.1029/2009jd013770. Meteorol. Z., 15, 371 378. Giannini, A., R. Saravanan, and P. Chang, 2003: Oceanic forcing of Sahel rainfall on Häkkinen, S., P. B. Rhines, and D. L. Worthen, 2011: Atmospheric blocking and atlantic interannual to interdecadal time scales. Science, 302, 1027 1030. multidecadal ocean variability. Science, 334, 655 659. Giannini, A., M. Biasutti, I. Held, and A. Sobel, 2008: A global perspective on African Hall, T., A. Sealy, T. Stephenson, S. Kusunoki, M. Taylor, A. A. Chen, and A. Kitoh, 2012: climate. Clim. Change, 90, 359 383. Future climate of the Caribbean from a super-high-resolution atmospheric Giese, B., and S. Ray, 2011: El Nino variability in simple ocean data assimilation general circulation model. Theor. Appl. Climatol., doi:10.1007/s00704-012-0779- (SODA), 1871 2008. J. Geophys. Res. Oceans, 116, 10.1029/2010JC006695. 7, 1 17. Gillett, N. P., and J. C. Fyfe, 2013: Annular mode changes in the CMIP5 simulations. Han, W., et al., 2010: Patterns of Indian Ocean sea-level change in a warming Geophys. Res. Lett., 40, . climate. Nature Geosci., 3, 546 550. Giorgi, F., and X. Bi, 2009: Time of emergence (TOE) of GHG-forced precipitation Handorf, K., and Dethloff, 2009: Atmospheric teleconnections and flow regimes change hot-spots. Geophys. Res. Lett., 36, doi:10.1029/2009GL037593. under future climate projections. 237 255. Gochis, D. J., L. Castillo-Brito, and J. Shuttleworth, 2007: Correlations between sea- Hansen, J., M. Sato, R. Ruedy, K. Lo, D. W. Lea, and M. Medina-Elizade, 2006: Global surface temperatures and warm season streamflow in northwest Mexico. Int. J. temperature change. Proc. Natl. Acad. Sci. U.S.A., 103, 14288 14293. Climatol., 27, 883 901. Hansingo, K., and C. Reason, 2008: Modelling the atmospheric response to SST Goldenberg, S. B., C. Landsea, A. M. Mestas-Nunez, and W. M. Gray, 2001: The recent dipole patterns in the South Indian Ocean with a regional climate model. increase in Atlantic hurricane activity: Causes and implications. Science, 293, Meteorol. Atmos. Phys., 100, 37 52. 474 479. Hansingo, K., and C. Reason 2009: Modelling the atmospheric response over Gong, D. Y., and S. W. Wang, 1999: Definition of Antarctic Oscillation Index. Geophys. southern Africa to SST forcing in the southeast tropical Atlantic and southwest Res. Lett., 26, 459 462. subtropical Indian Oceans. Int. J. Climatol., 29, 1001 1012. 14 1295 Chapter 14 Climate Phenomena and their Relevance for Future Regional Climate Change Harrison, S. P., et al., 2003: Mid-Holocene climates of the Americas: A dynamical Hu, Z. Z., 1997: Interdecadal variability of summer climate over East Asia and its response to changed seasonality. Clim. Dyn., 20, 663 688. association with 500 hPa height and global sea surface temperature. J. Geophys. Hartmann, B., and G. Wendler, 2005: The Significance of the 1976 Pacific climate shift Res. Atmos., 102, 19403 19412. in the climatology of Alaska. J. Clim., 18, 4824 4839. Hu, Z. Z., and Z. H. Wu, 2004: The intensification and shift of the annual North Atlantic Harvey, B. J., L. C. Shaffrey, T. J. Woollings, G. Zappa, and K. I. Hodges, 2012: How Oscillation in a global warming scenario simulation. Tellus A, 56, 112 124. large are projected 21st century storm track changes? Geophys. Res. Lett., 39, Huang, B., and Z. Liu, 2001: Temperature trend of the last 40 yr in the upper Pacific L18707. Ocean. J. Clim., 14, 3738 3750. Haylock, M. R., et al., 2006: Trends in total and extreme South American rainfall in Huang, G., K. M. Hu, and S. P. Xie, 2010: Strengthening of tropical Indian Ocean 1960 2000 and links with sea surface temperature. J. Clim., 19, 1490 1512. teleconnection to the northwest Pacific since the mid-1970s: An atmospheric Held, I., and M. Zhao, 2011: The response of tropical cyclone statistics to an increase GCM study. J. Clim., 23, 5294 5304. in CO2 with fixed sea surface temperatures. J. Clim., 24, 5353 5364. Huang, J., X. Guan, and F. Ji, 2012: Enhanced cold-season warming in semi-arid Held, I., T. Delworth, J. Lu, K. Findell, and T. Knutson, 2005: Simulation of Sahel drought regions. Atmos. Chem. Phys. Discuss., 12, 4627 4653. in the 20th and 21st centuries. Proc. Natl. Acad. Sci. U.S.A., 102, 17891 17896. Huang, P., S.-P. Xie, K. Hu, G. Huang, and R. Huang, 2013: Patterns of the seasonal Held, I. M., 1993: Large-scale dynamics and global warming. Bull. Am. Meteorol. response of tropical rainfall to global warming. Nature Geosci., 6, 357 361. Soc., 74, 228 241. Huang, R., W. Chen, B. Yang, and R. Zhang, 2004: Recent advances in studies of the Held, I. M., and B. J. Soden, 2006: Robust responses of the hydrological cycle to interaction between the east Asian winter and summer monsoons and ENSO global warming. J. Clim., 19, 5686 5699. cycle. Adv. Atmos. Sci., 21, 407 424. Hendon, H. H., D. W. J. Thompson, and M. C. Wheeler, 2007: Australian rainfall Huffman, G. J., R. F. Adler, D. T. Bolvin, and G. Gu, 2009: Improving the global and surface temperature variations associated with the Southern Hemisphere precipitation record: GPCP Version 2.1. Geophys. Res. Lett., 36, L17808. annular mode. J. Clim., 20, 2452 2467. Hurrell, J. W., and C. Deser, 2009: North Atlantic climate variability: The role of the Hennessy, K., S. Power, and G. Cambers, Eds., 2011: Climate change in the Pacific: North Atlantic Oscillation. J. Mar. Syst., 78, 28 41. Scientific Assessment and New Research. Regional Overview (Volume 1) and Hurrell, J. W., Y. Kushnir, G. Ottersen, and M. Visbeck, 2003: An overview of the North Country Reports (Volume 2). Australian Bureau of Meteorology (BoM) and Atlantic Oscillation. In: The North Atlantic Oscillation: Climate Significance Commonwealth Scientific and Industrial Organisation (CSIRO), Melbourne, and Environmental Impact [J. W. Hurrell, Y. Kushnir, M. Visbeck and G. Ottersen Australia. (eds.)]. American Geophysical Union, Washington, DC, pp. 1 35. Hermes, J., and C. Reason, 2009: Variability in sea-surface temperature and winds in Huss, M., R. Hock, A. Bauder, and M. Funk, 2010: 100-year mass changes in the Swiss the tropical south-east Atlantic Ocean and regional rainfall relationships. Int. J. Alps linked to the Atlantic Multidecadal Oscillation. Geophys. Res. Lett., 37, doi: Climatol., 29, 11 21. 10.1029/2010GL042616. Hernandez-Deckers, D., and J.-S. von Storch, 2010: Energetics responses to increases Hwang, Y.-T., and D. M. W. Frierson, 2010: Increasing atmospheric poleward in greenhouse gas concentration. J. Clim., 23, 3874 3887. energy transport with global warming. Geophys. Res. Lett., 37, doi: Hidayat, R., and S. Kizu, 2010: Influence of the Madden-Julian Oscillation on 10.1029/2010GL045440. Indonesian rainfall variability in austral summer. Int. J. Climatol., 30, 1816 1825. Ihara, C., Y. Kushnir, M. Cane, and V. de la Pena, 2009: Climate Change over the Hill, K. J., A. S. Taschetto, and M. H. England, 2011: Sensitivity of South American Equatorial Indo-Pacific in Global Warming. J. Clim., 22, 2678 2693. summer rainfall to tropical Pacific Ocean SST anomalies. Geophys. Res. Lett., Iizumi, T., F. Uno, and M. Nishimori, 2012: Climate downscaling as a source of 38, L01701. uncertainty in projecting local climate change impacts. J. Meteorol. Soc. Jpn., Hinton, T. J., B. J. Hoskins, and G. M. Martin, 2009: The influence of tropical sea 90B, 83 90. surface temperatures and precipitation on North Pacific atmospheric blocking. Im, E. S., J. B. Ahn, W. T. Kwon, and F. Giorgi, 2008: Multi-decadal scenario simulation Climate Dynamics, 33, 549-563. over Korea using a one-way double-nested regional climate model system. Part Hirschi, M., et al., 2011: Observational evidence for soil-moisture impact on hot 2: Future climate projection (2021 2050). Clim. Dyn., 30, 239 254. extremes in southeastern Europe. Nature Geosci., 4, 17 21. Ineson, S., A. A. Scaife, J. R. Knight, J. C. Manners, N. J. Dunstone, L. J. Gray, and Ho, C. K., D. B. Stephenson, M. Collins, C. A. T. Ferro, and S. J. Brown, 2012: Calibration J. D. Haigh, 2011: Solar forcing of winter climate variability in the Northern strategies: A source of additional uncertainty in climate change projections. Bull. Hemisphere. Nature Geosci., 4, 753 757. Am. Meteorol. Soc., 93, 21 26. Inoue, J., J. Liu, and J. A. Curry, 2006: Intercomparison of arctic regional climate Hoerling, M., J. Hurrell, J. Eischeid, and A. Phillips, 2006: Detection and attribution models: Modeling clouds and radiation for SHEBA in May 1998. J. Clim., 19, of twentieth-century northern and southern African rainfall change. J. Clim., 19, 4167 4178. 3989 4008. Ionita, M., G. Lohmann, N. Rimbu, S. Chelcea, and M. Dima, 2012: Interannual to Holland, G. J., and P. J. Webster, 2007: Heightened tropical cyclone activity in the decadal summer drought variability over Europe and its relationship to global North Atlantic: Natural variability or climate trend? Philos. Trans. R. Soc. London sea surface temperature. Clim. Dyn., 38, 363 377. A, 365, 2695 2716. IPCC, 2007a: Climate Change 2007: The Physical Science Basis. Contribution of Hope, P. K., W. Drosdowsky, and N. Nicholls, 2006: Shifts in the synoptic systems Working Group I to the Fourth Assessment Report of the Intergovernmental influencing southwest Western Australia. Clim. Dyn., 26, 751 764. Panel on Climate Change [Solomon, S., D. Qin, M. Manning, Z. Chen, M. Marquis, Horel, J. D., and J. M. Wallace, 1981: Planetary-scale atmospheric phenomena K. B. Averyt, M. Tignor and H. L. Miller (eds.)] Cambridge University Press, associated with the Southern Oscillation. Mon. Weather Rev., 109, 813 829. Cambridge, United Kingdom and New York, NY, USA,996 pp. Hori, M. E., D. Nohara, and H. L. Tanaka, 2007: Influence of Arctic Oscillation towards IPCC, 2007b: Climate Change 2007: Impacts, Adaptation and Vulnerability. the Northern Hemisphere surface temperature variability under the global Contribution of Working Group II to the Fourth Assessment Report of the warming scenario. J. Meteorol. Soc. Jpn., 85, 847 859. Intergovernmental Panel on Climate Change (IPCC) [M. L. Parry, O. F. Canziani, Hsieh, W. W., A. Wu, and A. Shabbar, 2006: Nonlinear atmospheric teleconnections. J. P. Palutikof, P. J. van der Linden and C. E. Hanson (eds.)]. Cambridge University Geophys. Res. Lett., 33, doi: 10.1029/2005gl025471. Press, Cambridge, United Kingdom and New York, NY, USA, 976 pp. Hsu, P.-C., T. Li, and B. Wang, 2011: Trends in global monsoon area and precipitation IPCC, 2012: Managing the Risks of Extreme Events and Disasters to Advance in the past 30 years. Geophys. Res. Lett., 38, doi: 10.1029/2011GL046893. Climate Change Adaptation. A Special Report of Working Groups I and II of the Hsu, P.-C., T. Li, H. Murakami, and A. Kitoh, 2013: Future change of the global Intergovernmental Panel on Climate Change [C. B. Field, V. Barros, T. F. Stocker, monsoon revealed from 19 CMIP5 models. J. Geophys. Res. Atmos., 118, doi: D. Qin, D. J. Dokken, K. L. Ebi, M. D. Mastrandrea, K. J. Mach, G.-K. Plattner, 10.1002/jgrd.50145. S. K. Allen, M. Tignor and P.M. Midgley (eds.)]. Cambridge University Press, Hu, Z., A. Kumar, B. Jha, and B. Huang, 2012a: An Analysis of Forced and internal Cambridge, United Kingdom and New York, NY, USA, 582 pp. variability in a warmer climate in CCSM3. J. Clim., 25, 2356 2373. Irving, D., P. Whetton, and A. Moise, 2012: Climate projections for Australia: A first Hu, Z., A. Kumar, B. Jha, W. Wang, B. Huang, and B. Huang, 2012b: An analysis glance at CMIP5. Aust. Mereorol. Oceanogr. J., 62, 211 225. of warm pool and cold tongue El Ninos: Air-sea coupling processes, global Irving, D., et al., 2011: Evaluating global climate models for the Pacific island region. 14 influences, and recent trends. Clim. Dyn., 38, 2017 2035. Clim. Res., 49, 169 187. 1296 Climate Phenomena and their Relevance for Future Regional Climate Change Chapter 14 Izumo, T., C. D. Montegut, J. J. Luo, S. K. Behera, S. Masson, and T. Yamagata, 2008: Karpechko, A. Y., 2010: Uncertainties in future climate attributable to uncertainties The role of the western Arabian Sea upwelling in Indian monsoon rainfall in future Northern Annular Mode trend. Geophys. Res. Lett., 37, doi: variability. J. Clim., 21, 5603 5623. 10.1029/2010gl044717. Jaeger, E. B., and S. I. Seneviratne, 2010: Impact of soil moisture atmosphere Karpechko, A. Y., and E. Manzini, 2012: Stratospheric influence on tropospheric coupling on European climate extremes and trends in a regional climate model. climate change in the Northern Hemisphere. J. Geophys. Res., 117, doi: Clim. Dyn., 36, 1919 1939. 10.1029/2011JD017036. Janicot, S., et al., 2011: Intraseasonal variability of the West African monsoon. Karpechko, A. Y., N. P. Gillett, L. J. Gray, and M. Dall Amico, 2010: Influence of ozone Atmos. Sci. Lett., 12, 58 66. recovery and greenhouse gas increases on Southern Hemisphere circulation. J. Jiang, H., and E. Zipser, 2010: Contribution of tropical cyclones to the global Geophys. Res., 115, D22117. precipitation from eight seasons of TRMM data: Regional, seasonal, and Kaspari, S., P. A. Mayewski, D. A. Dixon, V. B. Spikes, S. B. Sneed, M. J. Handley, and interannual variations. J. Clim., 23, 1526 1543. G. S. Hamilton, 2004: Climate variability in West Antarctica derived from annual Jiang, Y. L., and Z. Zhao, 2013: Maximum wind speed changes over China. Acta accumulation-rate records from ITASE firn/ice cores. Annals of Glaciology, 39, Meteorol. Sin., 27, 63 74. 585 594. Jiang, Z., J. Song, L. Li, W. Chen, Z. Wang, and J. Wang, 2011: Extreme climate events Kattsov, V. M., J. E. Walsh, W. L. Chapman, V. A. Govorkova, T. V. Pavlova, and X. D. in China: IPCC-AR4 model evaluation and projection. Clim. Change, 110, 385 Zhang, 2007: Simulation and projection of arctic freshwater budget components 401. by the IPCC AR4 global climate models. J. Hydrometeorol., 8, 571 589. Jin, F., A. Kitoh, and P. Alpert, 2010: Water cycle changes over the Mediterranean: A Kaufman, D. S., et al., 2009: Recent warming reverses long-term Arctic cooling. comparison study of a super-high-resolution global model with CMIP3. Philos. Science, 325, 1236 1239. Trans. R. Soc. London A, 68, 5137 5149. Kawatani, Y., K. Hamilton, and S. Watanabe, 2011: The Quasi-Biennial Oscillation in a Johanson, C. M., and Q. Fu, 2009: Hadley cell widening: Model simulations versus double CO2 climate. J. Atmos. Sci., 68, 265 283. observations. J. Clim., 22, 2713 2725. Kawatani, Y., K. Hamilton, and A. Noda, 2012: The effects of changes in sea surface Joly, M., A. Voldoire, H. Douville, P. Terray, and J.-F. Royer, 2007: African monsoon temperature and CO2 concentration on the Quasi-Biennial Oscillation. J. Atmos. teleconnections with tropical SSTs: Validation and evolution in a set of IPCC4 Sci., 69, 1734 1749. simulations. Clim. Dyn., 29, 1 20. Kawazoe, S., and W. Gutowski, 2013: Regional, very heavy daily precipitation in Jones, C., and L. M. V. Carvalho, 2013: Climate change in the South American NARCCAP simulations. J. Hydrometeorol., doi:10.1175/jhm-d-12-068.1. Monsoon System: Present climate and CMIP5 projections. J. Clim., doi:10.1175/ Keenlyside, N., and M. Latif, 2007: Understanding equatorial Atlantic interannual JCLI-D-12-00412.1. variability. J. Clim., 20, 131 142. Jones, D. A., W. Wang, and R. Fawcett, 2009a: High-quality spatial climate data-sets Keenlyside, N., M. Latif, J. Jungclaus, L. Kornblueh, and E. Roeckner, 2008: Advancing for Australia. Aust. Meteorol. Oceanogr. J., 58, 233 248. decadal-scale climate prediction in the North Atlantic sector. Nature, 453, Jones, J. M., R. L. Fogt, M. Widmann, G. J. Marshall, P. D. Jones, and M. Visbeck, 84 88. 2009b: Historical SAM variability. Part I: Century-ength seasonal reconstructions. Khain, A., B. Lynn, and J. Dudhia, 2010: Aerosol effects on intensity of landfalling J. Clim., 22, 5319 5345. hurricanes as seen from simulations with the WRF model with spectral bin Joshi, M. M., A. J. Charlton, and A. A. Scaife, 2006: On the influence of stratospheric microphysics. J. Atmos. Sci., 67, 365 384. water vapor changes on the tropospheric circulation. Geophys. Res. Lett., 33, Khain, A., N. Cohen, B. Lynn, and A. Pokrovsky, 2008: Possible aerosol effects on doi: 10.1029/2006gl025983. lightning activity and structure of hurricanes. J. Atmos. Sci., 65, 3652 3677. Jourdain, N., A. Gupta, A. Taschetto, C. Ummenhofer, A. Moise, and K. Ashok, 2013: Kharin, V. V., F. W. Zwiers, X. Zhang, and G. C. Hegerl, 2007: Changes in temperature The Indo-Australian monsoon and its relationship to ENSO and IOD in reanalysis and precipitation extremes in the IPCC ensemble of global coupled model data and the CMIP3/CMIP5 simulations. Clim. Dyn., doi:10.1007/s00382-013- simulations. J. Clim., 20, 1419 1444. 1676-1, 1 30. Kidson, J. W., and J. A. Renwick, 2002: Patterns of convection in the tropical Pacific Jung, T., et al., 2011: High-resolution global climate simulations with the ECMWF and their influence on New Zealand weather. Int. J. Climatol., 22, 151 174. model in project Athena: Experimental design, model climate, and seasonal Kidston, J., and E. P. Gerber, 2010: Intermodel variability of the poleward shift of the forecast skill. J. Clim., 25, 3155 3172. austral jet stream in the CMIP3 integrations linked to biases in 20th century Junquas, C., C. Vera, L. Li, and H. Treut, 2012: Summer precipitation variability over climatology. Geophys. Res. Lett., 37. Southeastern South America in a global warming scenario. Climate Dynamics, Kidston, J., J. A. Renwick, and J. McGregor, 2009: Hemispheric-scale seasonality of 38, 1867-1883. the Southern Annular Mode and impacts on the climate of New Zealand. J. Clim., Junquas, C., C. S. Vera, L. Li, and H. Treut, 2013: Impact of projected SST changes 22, 4759 4770. on summer rainfall in southeastern South America. Clim. Dyn., 40, 1569 1589. Kidston, J., G. K. Vallis, S. M. Dean, and J. A. Renwick, 2011: Can the increase in the Kajikawa, Y., B. Wang, and J. Yang, 2010: A multi-time scale Australian monsoon eddy length scale under global warming cause the poleward shift of the jet index. Int. J. Climatol., 30, 1114 1120. streams? J. Clim., 24, 3764 3780. Kanada, S., M. Nakano, and T. Kato, 2010: Changes in mean atmospheric structures Kim, B. M., and S. I. An, 2011: Understanding ENSO regime behavior upon an around Japan during July due to global warming in regional climate experiments Increase in the warm-pool temperature using a simple ENSO model. J. Clim., using a cloud resolving model. Hydrol. Res. Lett., 4, 11 14. 24, 1438 1450. Kanada, S., M. Nakano, and T. Kato, 2012: Projections of future changes in Kim, D., and H. Byun, 2009: Future pattern of Asian drought under global warming precipitation and the vertical structure of the frontal zone during the Baiu scenario. Theor. Appl. Climatol., 98, 137 150. season in the vicinity of Japan using a 5-km-mesh regional climate model. J. Kim, S. T., and J.-Y. Yu, 2012: The two types of ENSO in CMIP5 models. Geophys. Res. Meteorol. Soc. Jpn., 90A, 65 86. Lett., doi:10.1029/2012GL052006. Kang, S., I. Held, D. Frierson, and M. Zhao, 2008: The response of the ITCZ to Kitoh, A., and T. Uchiyama, 2006: Changes in onset and withdrawal of the East Asian extratropical thermal forcing: Idealized slab-ocean experiments with a GCM. J. summer rainy season by multi-model global warming experiments. J. Meteorol. Clim., 21, 3521 3532. Soc. Jpn., 84, 247 258. Kao, H. Y., and J. Y. Yu, 2009: Contrasting Eastern-Pacific and Central-Pacific types of Kitoh, A., and S. Kusunoki, 2008: East Asian summer monsoon simulation by a 20-km ENSO. J. Clim., 22, 615 632. mesh AGCM. Clim. Dyn., 31, 389 401. Kapnick, S., and A. Hall, 2012: Causes of recent changes in western North American Kitoh, A., S. Kusunoki, and T. Nakaegawa, 2011: Climate change projections snowpack. Clim. Dyn., 38, 1885 1899. over South America in the late 21st century with the 20 and 60 km mesh Karmalkar, A. V., R. S. Bradley, and H. F. Diaz, 2011: Climate change in Central Meteorological Research Institute atmospheric general circulation model (MRI- America and Mexico: Regional climate model validation and climate change AGCM). J. Geophys. Res. Atmos., 116, D06105. projections. Clim. Dyn., 37, 605 629. Kitoh, A., T. Ose, K. Kurihara, S. Kusunoki, M. Sugi, and KAKUSHIN Team-3 Modeling Karnauskas, K. B., R. Seager, A. Kaplan, Y. Kushnir, and M. A. Cane, 2009: Observed Group, 2009: Projection of changes in future weather extremes using super- strengthening of the zonal sea surface temperature gradient across the high-resolution global and regional atmospheric models in the KAKUSHIN 14 equatorial Pacific Ocean. J. Clim., 22, 4316 4321. Program: Results of preliminary experiments. Hydrol. Res. Lett., 3, 49 53. 1297 Chapter 14 Climate Phenomena and their Relevance for Future Regional Climate Change Kitoh, A., H. Endo, K. Krishna Kumar, I. F. A. Cavalcanti, P. Goswami, and T. Zhou, Kug, J. S., S. I. An, Y. G. Ham, and I. S. Kang, 2010: Changes in El Nino and La Nina 2013: Monsoons in a changing world regional perspective in a global context. J. teleconnections over North Pacific-America in the global warming simulations. Geophys. Res. Atmos., 118, doi: 10.1002/jgrd.50258. Theor. Appl. Climatol., 100, 275 282. Kjellstrom, E., G. Nikulin, U. Hansson, G. Strandberg, and A. Ullerstig, 2011: 21st Kulkarni, A., 2012: Weakening of Indian summer monsoon rainfall in warming century changes in the European climate: Uncertainties derived from an environment. Theor. Appl. Climatol., doi:10.1007/s00704-012-0591-4. ensemble of regional climate model simulations. Tellus A, 63, 24 40. Kumar, K., S. Patwardhan, A. Kulkarni, K. Kamala, K. Rao, and R. Jones, 2011a: Kjellström, E., P. Thejll, M. Rummukainen, J. H. Christensen, F. Boberg, C. O. B, and C. Simulated projections for summer monsoon climate over India by a high- Fox Maule, 2013: Emerging regional climate change signals for Europe under resolution regional climate model (PRECIS). Curr. Sci., 101, 312 326. varying large-scale circulation conditions. Clim. Res., 56, 103 119. Kumar, K., et al., 2011b: The once and future pulse of Indian monsoonal climate. Klein, S. A., B. J. Soden, and N.-C. Lau, 1999: Remote sea surface temperature Clim. Dyn., 36, 2159 2170. variations during ENSO: Evidence for a tropical atmospheric bridge. J. Clim., 12, Kumar, K. K., B. Rajagopalan, M. Hoerling, G. Bates, and M. Cane, 2006a: Unraveling 917 932. the mystery of Indian Monsoon failure during El Nino. Science, 314, 115 119. Klingaman, N. P., S. J. Woolnough, H. Weller, and J. M. Slingo, 2011: The impact of Kumar, P., et al., 2013: Downscaled climate change projections with uncertainty finer-resolution air-sea coupling on the Intraseasonal Oscillation of the Indian assessment over India using a high resolution multi-model approach. Sci. Total monsoon. J. Clim., 24, 2451 2468. Environ., doi:10.1016/j.scitotenv.2013.01.051. Knight, J., 2009: The Atlantic Multidecadal Oscillation inferred from the forced Kumar, V., R. Deo, and V. Ramachandran, 2006b: Total rain accumulation and rain- climate response in coupled general ciculation models. J. Clim., 22, 1610 1625. rate analysis for small tropical Pacific islands: A case study of Suva, Fiji. Atmos. Knight, J. R., R. J. Allan, C. K. Folland, M. Vellinga, and M. E. Mann, 2005: A signature Sci. Lett., 7, 53 58. of persistent natural thermohaline circulation cycles in observed climate. Kusunoki, S., and R. Mizuta, 2008: Future changes in the Baiu rain band projected by Geophys. Res. Lett., 32, L20708. a 20-km mesh global atmospheric model: Sea surface temperature dependence. Knutson, T. R., and R. E. Tuleya, 2004: Impact of CO2-induced warming on simulated Sola, 4, 85 88. hurricane intensity and precipitation: Sensitivity to the choice of climate model Kusunoki, S., and O. Arakawa, 2012: Change in the precipitation intensity of the East and convective parameterization. J. Clim., 17, 3477 3495. Asian summer monsoon projected by CMIP3 models. Clim. Dyn., 38, 2055 2072. Knutson, T. R., et al., 2006: Assessment of twentieth-century regional surface Kuzmina, S. I., L. Bengtsson, O. M. Johannessen, H. Drange, L. P. Bobylev, and M. temperature trends using the GFDL CM2 coupled models. J. Clim., 19, 1624 W. Miles, 2005: The North Atlantic Oscillation and greenhouse-gas forcing. 1651. Geophys. Res. Lett., 32, doi: 10.1029/2004gl021064. Knutson, T. R., et al., 2010: Tropical cyclones and climate change. Nature Geosci., 3, Kvamsto, N., P. Skeie, and D. Stephenson, 2004: Impact of labrador sea-ice extent on 157 163. the North Atlantic oscillation. Int. J. Climatol., 24, 603 612. Knutson, T. R., et al., 2013: Dynamical downscaling projections of 21st century Kwok, R., and J. C. Comiso, 2002: Southern ocean climate and sea ice anomalies Atlantic hurricane activity: CMIP3 and CMIP5 model-based scenarios. J. Clim., associated with the Southern Oscillation. J. Clim., 15, 487 501. 26, 6591 6617. L Heureux, M. L., and D. W. J. Thompson, 2006: Observed relationships between the Kodama, C., and T. Iwasaki, 2009: Influence of the SST rise on baroclinic instability El Nino Southern Oscillation and the extratropical zonal-mean circulation. J. wave activity under an aquaplanet condition. J. Atmos. Sci., 66, 2272 2287. Clim., 19, 276 287. Kodera, K., M. E. Hori, S. Yukimoto, and M. Sigmond, 2008: Solar modulation of the L Heureux, M. L., and R. W. Higgins, 2008: Boreal winter links between the Madden- Northern Hemisphere winter trends and its implications with increasing CO2. Julian oscillation and the Arctic oscillation. J. Clim., 21, 3040 3050. Geophys. Res. Lett., 35, doi: 10.1029/2007gl031958. Laine, A., M. Kageyama, D. Salas-Melia, G. Ramstein, S. Planton, S. Denvil, and S. Koenigk, T., R. Döscher, and G. Nikulin, 2011: Arctic future scenario experiments with Tyteca, 2009: An energetics study of wintertime Northern Hemisphere storm a coupled regional climate model. Tellus A, 63, 69 86. tracks under 4 × CO(2) conditions in two ocean-atmosphere coupled models. Kohler, M., N. Kalthoff, and C. Kottmeier, 2010: The impact of soil moisture J. Clim., 22, 819 839. modifications on CBL characteristics in West Africa: A case-study from the Lam, H., M. H. Kok, and K. K. Y. Shum, 2012: Benefits from typhoons the Hong Kong AMMA campaign. Q. J. R. Meteorol. Soc., 136, 442 455. perspective. Weather, 67, 16 21. Koldunov, N. V., D. Stammer, and J. Marotzke, 2010: Present-day Arctic sea ice Lambert, S. J., and J. C. Fyfe, 2006: Changes in winter cyclone frequencies and variability in the Coupled ECHAM5/MPI-OM Model. J. Clim., 23, 2520 2543. strengths simulated in enhanced greenhouse warming experiments: Results Kripalani, R., J. Oh, and H. Chaudhari, 2007a: Response of the East Asian summer from the models participating in the IPCC diagnostic exercise. Clim. Dyn., 26, monsoon to doubled atmospheric CO2: Coupled climate model simulations and 713 728. projections under IPCC AR4. Theor. Appl. Climatol., 87, 1 28. Landsea, C. W., R. A. Pielke, A. Mestas-Nunez, and J. A. Knaff, 1999: Atlantic basin Kripalani, R. H., J. H. Oh, A. Kulkarni, S. S. Sabade, and H. S. Chaudhari, 2007b: hurricanes: Indices of climatic changes. Clim. Change, 42, 89 129. South Asian summer monsoon precipitation variability: Coupled climate model Lang, C., and D. W. Waugh, 2011: Impact of climate change on the frequency of simulations and projections under IPCC AR4. Theor. Appl. Climatol., 90, 133 159. Northern Hemisphere summer cyclones. J. Geophys. Res. Atmos., 116, D04103. Krishnamurthy, C. K. B., U. Lall, and H. H. Kwon, 2009: Changing frequency and Langenbrunner, B., and J. D. Neelin, 2013: Analyzing ENSO teleconnections in CMIP intensity of rainfall extremes over India from 1951 to 2003. J. Clim., 22, 4737 models as a measure of model fidelity in simulating precipitation. J. Clim., 4746. doi:10.1175/jcli-d-12-00542.1. Krishnamurthy, V., and R. S. Ajayamohan, 2010: Composite structure of monsoon Lapp, S. L., J. M. St. Jacques, E. M. Barrow, and D. J. Sauchyn, 2012: GCM projections low pressure systems and its relation to Indian rainfall. J. Clim., 23, 4285 4305. for the Pacifi Decadal Oscillation under greenhouse forcing for the early 21st Kruger, L. F., R. P. da Rocha, M. S. Reboita, and T. Ambrizzi, 2011: RegCM3 nested century. International Journal of Climatology, 32, 1423 1442. in the HadAM3 scenarios A2 and B2: projected changes in cyclogeneses, Lau, K., S. Shen, K. Kim, and H. Wang, 2006: A multimodel study of the twentieth- temperature and precipitation over South Atlantic Ocean. Clim. Change, 113, century simulations of Sahel drought from the 1970s to 1990s. J. Geophys. Res. 599 621. Atmos., 111. Kucharski, F., A. Bracco, J. Yoo, A. Tompkins, L. Feudale, P. Ruti, and A. Dell Aquila, Lau, K., et al., 2008: The Joint Aerosol-Monsoon Experiment A new challenge for 2009a: A Gill-Matsuno-type mechanism explains the tropical Atlantic influence monsoon climate research. Bull. Am. Meteorol. Soc., doi:10.1175/BAMS-89-3- on African and Indian monsoon rainfall. Q. J. R. Meteorol. Soc., 135, 569 579. 369, 369 383. Kucharski, F., et al., 2009b: The CLIVAR C20C project: Skill of simulating Indian Lau, K. M., and H. T. Wu, 2007: Detecting trends in tropical rainfall characteristics, monsoon rainfall on interannual to decadal timescales. Does GHG forcing play a 1979 2003. Int. J. Climatol., 27, 979 988. role? Clim. Dyn., 33, 615 627. Lau, N.-C., and M. J. Nath, 2012: A model study of heat waves over North America: Kug, J.-S., and I.-S. Kang, 2006: Interactive Feedback between ENSO and the Indian Meteorological aspects and projections for the 21st Century. J. Clim., 25, 4761 Ocean. J. Clim., 19, 1784 1801. 4784. Kug, J.-S., F.-F. Jin, and S.-I. An, 2009: Two types of El Nino events: Cold tongue El Lavender, S., and K. Walsh, 2011: Dynamically downscaled simulations of Australian 14 Nino and warm pool El Nino. J. Clim., 22, 1499 1515. region tropical cyclones in current and future climates. Geophys. Res. Lett., 38, doi: 10.1029/2011GL047499. 1298 Climate Phenomena and their Relevance for Future Regional Climate Change Chapter 14 Leckebusch, G. C., U. Ulbrich, L. Froehlich, and J. G. Pinto, 2007: Property loss Lim, E.-P., and I. Simmonds, 2009: Effect of tropospheric temperature change on potentials for European midlatitude storms in a changing climate. Geophys. Res. the zonal mean circulation and SH winter extratropical cyclones. Clim. Dyn., 33, Lett., 34, doi: 10.1029/2006gl027663. 19 32. Leckebusch, G. C., B. Koffi, U. Ulbrich, J. G. Pinto, T. Spangehl, and S. Zacharias, 2006: Lim, Y.-K., L. B. Stefanova, S. C. Chan, S. D. Schubert, and J. J. O Brien, 2011: Analysis of frequency and intensity of European winter storm events from a High-resolution subtropical summer precipitation derived  from dynamical multi-model perspective, at synoptic and regional scales. Clim. Res., 31, 59 74. downscaling of the NCEP/DOE reanalysis:how much small-scale information is Lee, J. N., S. Hameed, and D. T. Shindell, 2008: The northern annular mode in summer added by a regional model? Clim. Dyn., 37, 1061 1080. and its relation to solar activity variations in the GISS ModelE. J. Atmos. Sol. Lima, K., P. Satyamurty, and J. Fernández, 2010: Large-scale atmospheric conditions Terres. Phys., 70, 730 741. associated with heavy rainfall episodes in Southeast Brazil. Theor. Appl. Climatol., Lee, T.-C., K.-Y. Chan, H.-S. Chan, and M.-H. Kok, 2011: Projections of extreme rainfall 101, 121 135. in Hong Kong in the 21st century. Acta Meteorol. Sin., 25, 691 709. Lin, H., G. Brunet, and J. Derome, 2009: An observed connection between the North Lee, T.-C., T. R. Knutson, H. Kamahori, and M. Ying, 2012: Impacts of climate change Atlantic Oscillation and the Madden-Julian Oscillation. J. Clim., 22, 364 380. on tropical cyclones in the western North Pacific basin. Part I: Past observations. Lin, J. L., et al., 2008a: Subseasonal variability associated with Asian summer Trop. Cyclone Res. Rev., 1, 213 230. monsoon simulated by 14 IPCC AR4 coupled GCMs. J. Clim., 21, 4541 4567. Lelieveld, J., et al., 2012: Climate change and impacts in the Eastern Mediterranean Lin, J. L., et al., 2008b: North American monsoon and convectively coupled equatorial and the Middle East. Clim. Change, 114, 667 687. waves simulated by IPCC AR4 coupled GCMs. J. Clim., 21, 2919 2937. Leslie, L., D. Karoly, M. Leplastrier, and B. Buckley, 2007: Variability of tropical Linkin, M., and S. Nigam, 2008: The North Pacific Oscillation-West Pacific cyclones over the southwest Pacific Ocean using a high-resolution climate teleconnection pattern: Mature-phase structure and winter impacts. J. Clim., 21, model. Meteorol. Atmos. Phys., 97, 171 180. 1979 1997. Leung, L. R., and Y. Qian, 2009: Atmospheric rivers induced heavy precipitation Lintner, B., and J. Neelin, 2008: Eastern margin variability of the South Pacific and flooding in the western U.S. simulated by the WRF regional climate model. Convergence Zone. Geophys. Res. Lett., 35, doi: 10.1029/2008gl034298. Geophys. Res. Lett., 36, L03820. Lionello, P., S. Planton, and X. Rodo, 2008: Preface: Trends and climate change in the Levine, R. C., and A. G. Turner, 2012: Dependence of Indian monsoon rainfall on Mediterranean region. Global Planet. Change, 63, 87 89. moisture fluxes across the Arabian Sea and the impact of coupled model sea Liu, H. W., T. J. Zhou, Y. X. Zhu, and Y. H. Lin, 2012a: The strengthening East Asia surface temperature biases. Clim. Dyn., 38,  2167-2190. summer monsoon since the early 1990s. Chinese Science Bulletin, 57, 1553 Levitus, S., G. Matishov, D. Seidov, and I. Smolyar, 2009: Barents Sea multidecadal 1558. variability. Geophys. Res. Lett., 36, L19604. Liu, J., J. A. Curry, H. Wang, M. Song, and R. M. Horton, 2012b: Impact of declining Li, B., and T. J. Zhou, 2011: El Nino-Southern Oscillation-related principal interannual Arctic sea ice on winter snowfall. Proc. Natl. Acad. Sci. U.S.A., 109, 4074 4079. variability modes of early and late summer rainfall over East Asia in sea surface Liu, J. P., and J. A. Curry, 2006: Variability of the tropical and subtropical ocean temperature-driven atmospheric general circulation model simulations. J. surface latent heat flux during 1989 2000. Geophys. Res. Lett., 33, doi: Geophys. Res. Atmos., 116, 15. 10.1029/2005gl024809. Li, G., and B. Ren, 2012: Evidence for strengthening of the tropical Pacific ocean Liu, Y., S.-K. Lee, B. A. Muhling, J. T. Lamkin, and D. B. Enfield, 2012c: Significant surface wind speed during 1979 2001. Theor. Appl. Climatol., doi:10.1007/ reduction of the Loop Current in the 21st century and its impact on the Gulf of s00704-0110-463-3. Mexico. J. Geophys. Res., 117, C05039. Li, H., A. Dai, T. Zhou, and J. Lu, 2010a: Responses of East Asian summer monsoon Liu, Z., and B. Huang, 2000: Cause of tropical Pacific warming trend. Geophys. Res. to historical SST and atmospheric forcing during 1950 2000. Clim. Dyn., 34, Lett., 27, 1935 1938. 501 514. Liu, Z., S. Vavrus, F. He, N. Wen, and Y. Zhong, 2005: Rethinking tropical ocean Li, H. M., L. Feng, and T. J. Zhou, 2011a: Multi-model projection of July-August response to global warming: The enhanced equatorial warming. J. Clim., 18, climate extreme changes over China under CO2 doubling. Part II: Temperature. 4684 4700. Adv. Atmos. Sci., 28, 448 463. Lockwood, M., R. G. Harrison, T. Woollings, and S. K. Solanki, 2010: Are cold winters Li, H. M., L. Feng, and T. J. Zhou, 2011b: Multi-model projection of July-August in Europe associated with low solar activity? Environ. Res. Lett., 5, doi: climate extreme changes over China under CO2 doubling. Part I: precipitation. 10.1088/1748-9326/5/2/024001. Adv. Atmos. Sci., 28, 433 447. Loeptien, U., O. Zolina, S. Gulev, M. Latif, and V. Soloviov, 2008: Cyclone life cycle Li, J., and J. Wang, 2003: A modified zonal index and its physical sense. Geophys. Res. characteristics over the Northern Hemisphere in coupled GCMs. Clim. Dyn., 31, Lett., 30, doi: 10.1029/2003GL017441. 507 532. Li, J., J. Feng, and Y. Li, 2012a: A possible cause of decreasing summer rainfall in Long, Z., W. Perrie, J. Gyakum, R. Laprise, and D. Caya, 2009: Scenario changes northeast Australia. Int. J. Climatol., 32, 995 1005. in the climatology of winter midlatitude cyclone activity over eastern North Li, J. B., et al., 2011c: Interdecadal modulation of El Nino amplitude during the past America and the Northwest Atlantic. J. Geophys. Res. Atmos., 114, doi: millennium. Nature Clim. Change, 1, 114 118. 10.1029/2008jd010869. Li, T., P. Liu, X. Fu, B. Wang, and G. Meehl, 2006: Spatiotemporal structures and Lorenz, D. J., and D. L. Hartmann, 2003: Eddy-zonal flow feedback in the Northern mechanisms of the tropospheric biennial oscillation in the Indo-Pacific warm Hemisphere winter. J. Clim., 16, 1212 1227. ocean regions. J. Clim., 19, 3070 3087. Lorenz, D. J., and E. T. DeWeaver, 2007: Tropopause height and zonal wind response Li, T., M. Kwon, M. Zhao, J. Kug, J. Luo, and W. Yu, 2010b: Global warming shifts Pacific to global warming in the IPCC scenario integrations. J. Geophys. Res. Atmos., tropical cyclone location. Geophys. Res. Lett., 37, doi: 10.1029/2010GL045124. 112, doi: 10.1029/2006jd008087. Li, Y., and N. Lau, 2012: Impact of ENSO on the atmospheric variability over the north Lu, J., G. A. Vecchi, and T. Reichler, 2007: Expansion of the Hadley cell under global Atlantic in late winter Role of transient eddies. J. Clim., 25, 320 342. warming. Geophys. Res. Lett., 34, doi: 10.1029/2006gl028443. Li, Y., N. C. Jourdain, A. S. Taschetto, C. C. Ummenhofer, K. Ashok, and A. Sen Gupta, Lu, J., G. Chen, and D. M. W. Frierson, 2008: Response of the zonal mean atmospheric 2012b: Evaluation of monsoon seasonality and the tropospheric biennial circulation to El Nino versus global warming. J. Clim., 21, 5835 5851. oscillation transitions in the CMIP models. Geophys. Res. Lett., 39, L20713. Lu, J., G. Chen, and D. M. W. Frierson, 2010: The position of the mid latitude storm Liang, X.-Z., K. E. Kunkel, G. A. Meehl, R. G. Jones, and J. X. L. Wang, 2008a: Regional track and eddy-driven westerlies in Aquaplanet AGCMs. J. Atmos. Sci., 67, 3984 climate models downscaling analysis of general circulation models present 4000. climate biases propagation into future change projections. Geophys. Res. Lett., Lu, R., and Y. Fu, 2010: Intensification of East Asian summer rainfall interannual 35, L08709. variability in the twenty-first century simulated by 12 CMIP3 coupled models. J. Liang, X.-Z., J. Zhu, K. E. Kunkel, M. Ting, and J. X. L. Wang, 2008b: Do GCMs simulate Clim., doi:10.1175/2009JCLI3130.1, 3316 3331. the North American monsoon precipitation seasonal-interannual variability? J. Lucarini, V., and F. Ragone, 2011: Energetics of climate models: Net energy balance and Clim., 21, 4424 4448. meridional enthalpy transport. Rev. Geophys., 49, doi: 10.1029/2009RG000323. Lienert, F., J. C. Fyfe, and W. J. Marryfield, 2011: Do climate models capture the Lucas, C., H. Nguyen, and B. Timbal, 2012: An observational analysis of tropical influences on North Pacific sea surface temperature variability? J. Clim., southern hemisphere tropical expansion. J. Geophys. Res., 117, doi: 14 24, 6203 6209. 10.1029/2011JD017033. 1299 Chapter 14 Climate Phenomena and their Relevance for Future Regional Climate Change Luo, D., W. Zhou, and K. Wei, 2010: Dynamics of eddy-driven North Atlantic Marshall, A. G., and A. A. Scaife, 2010: Improved predictability of stratospheric Oscillations in a localized shifting jet: Zonal structure and downstream blocking. sudden warming events in an atmospheric general circulation model Clim. Dyn., 34, 73 100. with enhanced stratospheric resolution. J. Geophys. Res. Atmos., 115, doi: Luo, Y., and L. M. Rothstein, 2011: Response of the Pacific ocean circulation to 10.1029/2009jd012643. climate change. Atmosphere-ocean, 49, 235 244. Marshall, G. J., 2007: Half-century seasonal relationships between the Southern Lupo, A. R., I. I. Mokhov, M. G. Akperov, A. V. Chernokulsky, and H. Athar, 2012: A Annular Mode and Antarctic temperatures. Int. J. Climatol., 27, 373 383. dynamic analysis of the role of the planetary- and synoptic-scale in the summer Martius, O., L. M. Polvani, and H. C. Davies, 2009: Blocking precursors to stratospheric of 2010 blocking episodes over the European part of Russia. Adv. Meteorol., sudden warming events. Geophys. Res. Lett., 36, L14806. 2012, 11. Marullo, S., V. Artale, and R. Santoleri, 2011: The SST multidecadal variability in the Ma, J., and S.-P. Xie, 2013: Regional patterns of sea surface temperature change: Atlantic Mediterranean region and its relation to AMO. J. Clim., 24, 4385 4401. A source of uncertainty in future projections of precipitation and atmospheric Masato, G., B. J. Hoskins, and T. J. Woollings, 2012: Wave-breaking characteristics of circulation. J. Clim., 26, 2482 2501. midlatitude blocking. Q. J. R. Meteorol. Soc., 138, 1285 1296. Magnusdottir, G., C. Deser, and R. Saravanan, 2004: The effects of North Atlantic SST Masato, G., B. J. Hoskins, and T. Woollings, 2013: Winter and summer Northern and sea ice anomalies on the winter circulation in CCM3. Part I: Main features Hemisphere blocking in CMIP5 models. J. Clim., doi:10.1175/jcli-d-12-00466.1. and storm track characteristics of the response. J. Clim., 17, 857 876. Mason, S., 2001: El Nino, climate change, and Southern African climate. Mahajan, S., R. Zhang, and T. L. Delworth, 2011: Impact of the Atlantic meridional Environmetrics, 12, 327 345. overturning circulation (AMOC) on Arctic surface air temperature and sea ice Massom, R. A., M. J. Pook, J. C. Comiso, N. Adams, J. Turner, T. Lachlan-Cope, and T. T. variability. J. Clim., 24, 6573 6581. Gibson, 2004: Precipitation over the interior East Antarctic ice sheet related to Mahowald, N., 2007: Anthropocene changes in desert area: Sensitivity to climate midlatitude blocking-high activity. J. Clim., 17, 1914 1928. model predictions. Geophys. Res. Lett., 34, doi: 10.1029/2007GL030472. Matsueda, M., 2011: Predictability of Euro-Russian blocking in summer of 2010. Malhi, Y., J. T. Roberts, R. A. Betts, T. J. Killeen, W. Li, and C. A. Nobre, 2008: Climate Geophys. Res. Lett., 38, L06801. change, deforestation, and the fate of the Amazon. Science, 319, 169 172. Matsueda, M., H. Endo, and R. Mizuta, 2010: Future change in Southern Hemisphere Maloney, E. D., and J. Shaman, 2008: Intraseasonal variability of the West African summertime and wintertime atmospheric blockings simulated using a monsoon and Atlantic ITCZ. J. Clim., 21, 2898 2918. 20-km-mesh AGCM. Geophys. Res. Lett., 37, L02803. Maloney, E. D., and S.-P. Xie, 2013: Sensitivity of MJO activity to the pattern of May, W., 2011: The sensitivity of the Indian summer monsoon to a global warming climate warming. J. Adv. Model. Earth Syst., 5, 32 47. of 2 degrees C with respect to pre-industrial times. Clim. Dyn., 37, 1843 1868. Manatsa, D., W. Chingombe, H. Matsikwa, and C. H. Matarira, 2008: The superior McCabe, G., and D. Wolock, 2010: Long-term variability in Northern Hemisphere influence of Darwin Sea level pressure anomalies over ENSO as a simple drought snow cover and associations with warmer winters. Clim. Change, doi:10.1007/ predictor for Southern Africa. Theor. Appl. Climatol., 92, 1 14. s10584-009-9675-2, 141 153. Mandke, S. K., A. K. Sahai, M. A. Shinde, S. Joseph, and R. Chattopadhyay, 2007: McDonald, R. E., 2011: Understanding the impact of climate change on Northern Simulated changes in active/break spells during the Indian summer monsoon Hemisphere extra-tropical cyclones. Clim. Dyn., 37, 1399 1425. due to enhanced CO2 concentrations: Assessment from selected coupled McLandress, C., and T. G. Shepherd, 2009: Simulated anthropogenic changes in the atmosphere-ocean global climate models. Int. J. Climatol., 27, 837 859. Brewer Dobson circulation, including its extension to high latitudes. J. Clim., Mann, M. E., and K. A. Emanuel, 2006: Atlantic hurricane trends linked to climate 22, 1516 1540. change. Eos Trans., 87, 233 241. Mearns, L. O., R. Arritt, S. Biner, M. Bukovsky, S. Stain, and et al., 2012: The North Manton, M. J., et al., 2001: Trends in extreme daily rainfall and temperature in American regional climate change assessment program: Overview of phase I Southeast Asia and the South Pacific: 1961 1998. Int. J. Climatol., 21, 269 284. results. Bull. Am. Meteorol. Soc., 93, 1337 1362. Mantua, N. J., S. R. Hare, Y. Zhang, J. M. Wallace, and R. C. Francis, 1997: A Pacific Meehl, G., and H. Teng, 2007: Multi-model changes in El Nino teleconnections over interdecadal climate oscillation with impacts on salmon production. Bull. Am. North America in a future warmer climate. Clim. Dyn., 29, 779 790. Meteorol. Soc., 78, 1069 1079. Meehl, G., J. Arblaster, and W. Collins, 2008: Effects of Black Carbon Aerosols on the Marcella, M. P., and E. A. B. Eltahir, 2011: Modeling the summertime climate of Indian Monsoon. J. Clim., 21, 2869 2882. Southwest Asia: The role of land surface processes in shaping the climate of Meehl, G., A. Hu, and C. Tebaldi, 2010: Decadal Prediction in the Pacific Region. J. semiarid regions. J. Clim., 25, 704 719. Clim., 23, 2959 2973. Marchant, R., C. Mumbi, S. Behera, and T. Yamagata, 2007: The Indian Ocean Meehl, G. A., 1997: The south Asian monsoon and the tropospheric biennial dipole the unsung driver of climatic variability in East Africa. Afr. J. Ecol., 45, oscillation. J. Clim., 10, 1921 1943. 4 16. Meehl, G. A., and A. Hu, 2006: Megadroughts in the Indian monsoon region and Marengo, J., et al., 2010a: Recent developments on the South American Monsoon southwest North America and a mechanism for associated multidecadal Pacific system. Int. J. Climatol., 32, 1 21. sea surface temperature anomalies. J. Clim., 19, 1605 1623. Marengo, J., et al., 2012: Development of regional future climate change scenarios Meehl, G. A., and J. M. Arblaster, 2012: Relating the strength of the tropospheric in South America using the Eta CPTEC/HadCM3 climate change projections: biennial oscillation (TBO) to the phase of the Interdecadal Pacific Oscillation Climatology and regional analyses for the Amazon, Sao Francisco and the (IPO). Geophys. Res. Lett., 39, L20716. Paraná River basins. Clim. Dyn., 38, 1829 1848. Meehl, G. A., et al., 2007: Global climate projections. In: Climate Change 2007: The Marengo, J. A., and C. C. Camargo, 2008: Surface air temperature trends in Southern Physical Science Basis. Contribution of Working Group I to the Fourth Assessment Brazil for 1960 2002. Int. J. Climatol., 28, 893 904. Report of the Intergovernmental Panel on Climate Change [Solomon, S., D. Qin, Marengo, J. A., R. Jones, L. M. Alves, and M. C. Valverde, 2009: Future change of M. Manning, Z. Chen, M. Marquis, K. B. Averyt, M. Tignor and H. L. Miller (eds.)] temperature and precipitation extremes in South America as derived from the Cambridge University Press, Cambridge, United Kingdom and New York, NY, PRECIS regional climate modeling system. Int. J. Climatol., 29, 2241 2255. USA, pp. 747 846. Marengo, J. A., M. Rusticucci, O. Penalba, and M. Renom, 2010b: An intercomparison Menary, M., W. Park, K. Lohmann, M. Vellinga, M. Palmer, M. Latif, and J. Jungclaus, of observed and simulated extreme rainfall and temperature events during the 2012: A multimodel comparison of centennial Atlantic meridional overturning last half of the twentieth century: Part 2: Historical trends. Clim. Change, 98, circulation variability. Clim. Dyn., 38, 2377 2388. 509 529. Mendes, M. C. D., R. M. Trigo, I. F. A. Cavalcanti, and C. C. Da Camara, 2008: Blocking Mariotti, A., and A. Dell Aquila, 2012: Decadal climate variability in the Mediterranean episodes in the Southern Hemisphere: Impact on the climate of adjacent region: Roles of large-scale forcings and regional processes. Clim. Dyn., 38, continental areas. Pure Appl. Geophys., 165, 1941 1962. 1129 1145. Mendez, M., and V. Magana, 2010: Regional aspects of prolonged meteorological Marshall, A. G., and A. A. Scaife, 2009: Impact of the QBO on surface winter climate. droughts over Mexico and Central America. J. Clim., 23, 1175 1188. J. Geophys. Res., 114, doi: 10.1029/ 2009jd011737. Mendoza, B., V. Garcia-Acosta, V. Velasco, E. Jauregui, and R. Diaz-Sandoval, 2007: Frequency and duration of historical droughts from the 16th to the 19th centuries 14 in the Mexican Maya lands, Yucatan Peninsula. Clim. Change, 83, 151 168. 1300 Climate Phenomena and their Relevance for Future Regional Climate Change Chapter 14 Meneghini, B., I. Simmonds, and I. N. Smith, 2007: Association between Australian Murakami, H., and M. Sugi, 2010: Effect of model resolution on tropical cyclone rainfall and the Southern Annular Mode. Int. J. Climatol., 27, 109 121. climate projections. Sola, 6, 73 76. Menendez, C. G., and A. Carril, 2010: Potential changes in extremes and links with Murakami, H., B. Wang, and A. Kitoh, 2011a: Future change of Western North Pacific the Southern Annular Mode as simulated by a multi-model ensemble. Clim. typhoons: Projections by a 20-km-mesh global atmospheric model. J. Clim., 24, Change, 98, 359 377. 1154 1169. Metcalfe, S. E., M. D. Jones, S. J. Davies, A. Noren, and A. MacKenzie, 2010: Climate Murakami, H., R. Mizuta, and E. Shindo, 2011b: Future changes in tropical cyclone variability over the last two millennia in the North American Monsoon, recorded activity projected by multi-physics and multi-SST ensemble experiments using in laminated lake sediments from Laguna de Juanacatlan, Mexico. Holocene, the 60-km-mesh MRI-AGCM. Clim. Dyn., doi:10.1007/s00382-011-1223-x. 20, 1195 1206. Murakami, H., M. Sugi, and A. Kitoh, 2013: Future changes in tropical cyclone activity Miller, G. H., et al., 2010: Temperature and precipitation history of the Arctic. Q. Sci. in the North Indian Ocean projected by high-resolution MRI-AGCMs. Clim. Dyn., Rev., 29, 1679 1715. 40, 1949 1968. Miller, R. L., G. A. Schmidt, and D. T. Shindell, 2006: Forced annular variations in Murakami, H., et al., 2012: Future changes in tropical cyclone activity projected by the 20th century intergovernmental panel on climate change fourth assessment the new high-resolution MRI-AGCM. J. Clim., 25, 3237 3260. report models. J. Geophys. Res. Atmos., 111, doi: 10.1029/2005jd006323. Murphy, B. F., and B. Timbal, 2008: A review of recent climate variability and climate Minvielle, M., and R. D. Garreaud, 2011: Projecting rainfall changes over the South change in southeastern Australia. Int. J. Climatol., 28, 859 879. American altiplano. J. Clim., 24, 4577 4583. Muza, M. N., L. M. V. Carvalho, C. Jones, and B. Liebmann, 2009: Intraseasonal and Mitas, C. M., and A. Clement, 2005: Has the Hadley cell been strengthening in recent interannual variability of extreme dry and wet events over southeastern South decades? Geophys. Res. Lett., 32, doi: 10.1029/2004gl021765. America and the subtropical Atlantic during austral summer. J. Clim., 22, 1682 Mitas, C. M., and A. Clement, 2006: Recent behavior of the Hadley cell and tropical 1699. thermodynamics in climate models and reanalyses. Geophys. Res. Lett., 33, doi: Nanjundiah, R., V. Vidyunmala, and J. Srinivasan, 2005: The impact of increase in CO2 10.1029/2005gl024406. on the simulation of tropical biennial oscillations (TBO) in 12 coupled general Mitchell, T. D., and P. D. Jones, 2005: An improved method of constructing a database circulation models. Atmos. Sci. Lett., 6, 183 191. of monthly climate observations and associated high-resolution grids. Int. J. Neelin, J., C. Chou, and H. Su, 2003: Tropical drought regions in global warming and Climatol., 25, 693 712. El Nino teleconnections. Geophys. Res. Lett., 30, doi: 10.1029/2003GL018625. Mitchell, T. P., and J. M. Wallace, 1996: ENSO seasonality: 1950 78 versus 1979 92. Neelin, J. D., M. Munnich, H. Su, J. E. Meyerson, and C. E. Holloway, 2006: Tropical J. Clim., 9, 3149 3161. drying trends in global warming models and observations. Proc. Natl. Acad. Sci., Mizuta, R., 2012: Intensification of extratropical cyclones associated with the polar 103, 6110 6115. jet change in the CMIP5 global warming projections. Geophys. Res. Lett., 39, doi: Neelin, J. D., B. Langenbrunner, J. E. Meyerson, A. Hall, and N. Berg, 2013: California 10.1029/2012GL053032. winter precipitation change under global warming in CMIP5 models. J. Clim., Mizuta, R., M. Matsueda, H. Endo, and S. Yukimoto, 2011: Future change in 26, 6238 6256. extratropical cyclones associated with change in the upper troposphere. J. Clim., Nguyen, K., J. Katzfey, and J. McGregor, 2012: Global 60 km simulations with CCAM: 24, 6456 6470. Evaluation over the tropics. Clim. Dyn., 39, 637 654. Mizuta, R., et al., 2012: Climate simulations using MRI-AGCM3.2 with 20-km grid. J. Nicholls, N., C. Landsea, and J. Gill, 1998: Recent trends in Australian region tropical Meteorol. Soc. Jpn., 90A, 233 258. cyclone activity. Meteorol. Atmos. Phys., 65, 197 205. Mock, C. J., and A. R. Brunelle-Daines, 1999: A modern analogue of western United Nieto-Ferreira, R., and T. Rickenbach, 2010: Regionality of monsoon onset in South States summer palaeoclimate at 6000 years before present. Holocene, 9, 541 America: A three-stage conceptual model. Int. J. Climatol., 31, 1309 1321. 545. Nigam, S., 2003: Teleconnections. In: Encyclopedia of Atmospheric Sciences [J. A. P. Mohino, E., S. Janicot, and J. Bader, 2011: Sahel rainfall and decadal to multi-decadal J. R. Holton and J. A. Curry (eds.)]. Academic Press, San Diego, CA, USA, pp. sea surface temperature variability. Clim. Dyn., 37, 419 440. 2243 2269. Moise, A., and F. Delage, 2011: New climate model metrics based on object- Ninomiya, K., 2012: Characteristics of intense rainfalls over southwestern Japan orientated pattern matching of rainfall. J. Geophys. Res. Atmos., 116, doi: in the Baiu season in the CMIP3 20th century simulation and 21st century 10.1029/2010JD015318. projection. J. Meteorol. Soc. Jpn., 90A, 327 338. Moise, A. F., R. A. Colman, and J. R. Brown, 2012: Behind uncertainties in projections Niyogi, D., C. Kishtawal, S. Tripathi, and R. S. Govindaraju, 2010: Observational of Australian tropical climate: Analysis of 19 CMIP3 models. J. Geophys. Res. evidence that agricultural intensification and land use change may be reducing Atmos., 117, doi: 10.1029/2011jd017365. the Indian summer monsoon rainfall. Water Resources Research, 46, W03533, Moise, A. F., R. A. Colman, and H. Zhang, 2005: Coupled model simulations of doi: 03510.01029/02008wr007082. current Australian surface climate and its changes under greenhouse warming: Nunez, M. N., S. A. Solman, and M. F. Cabre, 2009: Regional climate change An analysis of 18 CMIP2 models. Aust. Meteorol. Mag., 54, 291 307. experiments over southern South America. II: Climate change scenarios in the Monaghan, A. J., and D. H. Bromwich, 2008: Advances in describing recent Antarctic late twenty-first century. Clim. Dyn., 32, 1081 1095. climate variablity. Bull. Am. Meteorol. Soc., 89, 1295 1306. O Gorman, P. A., 2010: Understanding the varied response of the extratropical storm Monahan, A. H., L. Pandolfo, and J. C. Fyfe, 2001: The preferred structure of variability tracks to climate change. Proc. Natl. Acad. Sci. U.S.A., 107, 19176 19180. of the Northern Hemisphere atmospheric circulation. Geophys. Res. Lett., 28, O Gorman, P. A., and T. Schneider, 2008: Energy of midlatitude transient eddies in 1019 1022. idealized simulations of changed climates. J. Clim., 21, 5797 5806. Monahan, A. H., J. C. Fyfe, M. H. P. Ambaum, D. B. Stephenson, and G. R. North, Okamoto, K., K. Sato, and H. Akiyoshi, 2011: A study on the formation and trend of the 2009: Empirical Orthogonal Functions: The medium is the message. J. Clim., 22, Brewer-Dobson circulation. J. Geophys. Res., 116, doi: 10.1029/2010JD014953. 6501 6514. Okumura, Y. M., D. Schneider, C. Deser, and R. Wilson, 2012: Decadal-interdecadal Moron, V., A. W. Robertson, and J.-H. Qian, 2010: Local versus regional-scale climate variability over Antarctica and linkages to the tropics: Analysis of ice characteristics of monsoon onset and post-onset rainfall over Indonesia. Clim. core, instrumental, and tropical proxy data. J. Clim., 25, 7421 7441. Dyn., 34, 281 299. Onol, B., and F. Semazzi, 2009: Regionalization of climate change simulations over Moss, R. H., et al., 2010: The next generation of scenarios for climate change research the Eastern Mediterranean. J. Clim., 22, 1944 1961. and assessment. Nature, 463, 747 756. Orlowsky, B., and S. Seneviratne, 2012: Global changes in extreme events: Regional Muller, W. A., and E. Roeckner, 2006: ENSO impact on midlatitude circulation and seasonal dimension. Clim. Change, 110, 669 696. patterns in future climate change projections. Geophys. Res. Lett., 33, doi: Ose, T., and O. Arakawa, 2011: Uncertainty of future precipitation change due to 10.1029/2005gl025032. global warming associated with sea surface temperature change in the tropical Müller, W. A., and E. Roeckner, 2008: ENSO teleconnections in projections of future Pacific. J. Meteorol. Soc. Jpn., 89, 539 552. climate in ECHAM5/MPI-OM. Clim. Dyn., 31, 533 549. Oshima, K., Y. Tanimoto, and S. P. Xie, 2012: Regional patterns of wintertime SLP Murakami, H., and B. Wang, 2010: Future change of North Atlantic tropical cyclone change over the North Pacific and their uncertainty in CMIP3 multi-model tracks: Projection by a 20 km-mesh global atmospheric model. J. Clim., 23, projections. J. Meteorol. Soc. Jpn., 90, 385 396. 14 2699 2721. 1301 Chapter 14 Climate Phenomena and their Relevance for Future Regional Climate Change Ouzeau, G., J. Cattiaux, H. Douville, A. Ribes, and D. Saint-Martin, 2011: European Polyakov, I., V. Alexeev, U. Bhatt, E. Polyakova, and X. Zhang, 2010: North Atlantic cold winter 2009 2010: How unusual in the instrumental record and how warming: Patterns of long-term trend and multidecadal variability. Clim. Dyn., reproducible in the ARPEGE-Climat model? Geophys. Res. Lett., 38, 6. 34, 439 457. Palmer, T. N., 1999: A nonlinear dynamical perspective on climate prediction. J. Clim., Polyakov, I. V., et al., 2003: Variability and trends of air temperature and pressure in 12, 575 591. the maritime Arctic, 1875 2000. J. Clim., 16, 2067 2077. Parker, D., C. Folland, A. Scaife, J. Knight, A. Colman, P. Baines, and B. Dong, 2007: Poore, R. Z., M. J. Pavich, and H. D. Grissino-Mayer, 2005: Record of the North Decadal to multidecadal variability and the climate change background. J. American southwest monsoon from Gulf of Mexico sediment cores. Geology, Geophys. Res., 112, D18115. 33, 209 212. Patricola, C., and K. Cook, 2010: Northern African climate at the end of the twenty- Popova, V. V., and A. B. Shmakin, 2010: Regional structure of surface-air temperature first century: An integrated application of regional and global climate models. fluctuatoons in Northern Eurasia in the latter half of the 20th and early 21st Clim. Dyn., 35, 193 212. centuries. Izvestiya Atmos. Ocean. Phys., 46, 144 158. Pattanaik, D. R., and M. Rajeevan, 2010: Variability of extreme rainfall events over Power, S., and R. Colman, 2006: Multi-year predictability in a coupled general India during southwest monsoon season. Meteorol. Appl., 17, 88 104. circulation model. Clim. Dyn., 26, 247 272. Pavelsky, T., S. Kapnick, and A. Hall, 2011: Accumulation and melt dynamics of Power, S., M. Haylock, R. Colman, and X. Wang, 2006: The predictability of snowpack from a multiresolution regional climate model in the central Sierra interdecadal changes in ENSO activity and ENSO teleconnections. J. Clim., 19, Nevada, California. J. Geophys. Res. Atmos., 116, D16115. 4755 4771. Pavelsky, T. M., and L. C. Smith, 2006: Intercomparison of four global precipitation Power, S., T. Casey, C. Folland, A. Colman, and V. Mehta, 1999: Inter-decadal data sets and their correlation with increased Eurasian river discharge to the modulation of the impact of ENSO on Australia. Clim. Dyn., 15, 319 324. Arctic Ocean. J. Geophys. Res. Atmos., 111, D21112. Power, S. B., and I. N. Smith, 2007: Weakening of the Walker Circulation and apparent Peduzzi, P., et al., 2012: Global trends in tropical cyclone risk. Nature Clim. Change, dominance of El Nino both reach record levels, but has ENSO really changed? 2, 289 294. Geophys. Res. Lett., 34, L18702. Perkins, S., 2011: Biases and model agreement in projections of climate extremes Prat, O. P., and B. R. Nelson, 2012: Precipitation contribution of tropical cyclones in over the tropical Pacific. Earth Interactions, 15, 1-36. the Southeastern United States from 1998 to 2009 using TRMM satellite data. Perkins, S., D. Irving, J. Brown, S. Power, A. Moise, R. Colman, and I. Smith, 2012: J. Clim., 26, 1047 1062. CMIP3 ensemble climate projections over the western tropical Pacific based on Qian, J.-H., 2008: Why precipitation is mostly concentrated over islands in the model skill. Clim. Res., 51, 35 58. Maritime Continent. J. Atmos. Sci., 65, 1428 1441. Perlwitz, J., S. Pawson, R. L. Fogt, J. E. Nielsen, and W. D. Neff, 2008: Impact of Qian, J.-H., A. W. Robertson, and V. Moron, 2010a: Interactions among ENSO, the stratospheric ozone hole recovery on Antarctic climate. Geophys. Res. Lett., 35, Monsoon, and Diurnal Cycle in rainfall variability over Java, Indonesia. J. Atmos. doi: 10.1029/2008gl033317. Sci., 67, 3509 3524. Petersen, K. L., 1994: A warm and wet Little Climatic Optimum and a cold and dry Qian, Y., S. J. Ghan, and L. R. Leung, 2010b: Downscaling hydroclimate changes Little Ice Age in the southern Rocky Mountains, U.S.A. Clim. Change, 26, 243 over the Western US based on CAM subgrid scheme and WRF regional climate 269. simulations. Int. J. Climatol., 30, 675 693. Petoukhov, V., and V. A. Semenov, 2010: A link between reduced Barents-Kara sea Quadrelli, R., and J. M. Wallace, 2004: A simplified linear framework for interpreting ice and cold winter extremes over northern continents. J. Geophys. Res. Atmos., patterns of Northern Hemisphere wintertime climate variability. J. Clim., 17, 115, D21111. 3728 3744. Pezza, A. B., T. Durrant, I. Simmonds, and I. Smith, 2008: Southern Hemisphere Quintana, J. M., and P. Aceituno, 2012: Changes in the rainfall regime along the synoptic behavior in extreme phases of SAM, ENSO, sea ice extent, and southern extratropical west coast of South America (Chile): 30 43oS. Atmosfera, 25, 1 22. Australia rainfall. J. Clim., 21, 5566 5584. Rabatel, A., et al., 2013: Current state of glaciers in the tropical Andes: A multi- Pezzulli, S., D. Stephenson, and A. Hannachi, 2005: The variability of seasonality. J. century perspective on glacier evolution and climate change. Cryosphere, 7, Clim., 18, 71 88. 81 102. Pfahl, S., and H. Wernli, 2012: Quantifying the relevance of atmospheric blocking Raia, A., and I. F. A. Cavalcanti, 2008: The life cycle of the South American Monsoon for co-located temperature extremes in the Northern Hemisphere on (sub-)daily System. J. Clim., 21, 6227 6246. time scales. Geophys. Res. Lett., doi:10.1029/2012GL052261. Raible, C., 2007: On the relation between extremes of midlatitude cyclones Philip, S., and G. Van Oldenborgh, 2006: Shifts in ENSO coupling processes under and the atmospheric circulation using ERA40. Geophys. Res. Lett., 34, doi: global warming. Geophys. Res. Lett., 33, doi: 10.1029/2006GL026196. 10.1029/2006GL029084. Picard, G., F. Domine, G. Krinner, L. Arnaud, and E. Lefebvre, 2012: Inhibition of the Raible, C. C., B. Ziv, H. Saaroni, and M. Wild, 2010: Winter synoptic-scale variability positive snow-albedo feedback by  precipitation in interior Antarctica  Nature over the Mediterranean Basin under future climate conditions as simulated by Clim. Change, doi:10.1038/NCLIMATE1590. the ECHAM5. Clim. Dyn., 35, 473 488. Pinto, J. G., M. K. Karreman, K. Born, P. M. Della-Marta, and M. Klawa, 2012: Raible, C. C., P. M. Della-Marta, C. Schwierz, H. Wernli, and R. Blender, 2008: Northern Loss potentials associated with European windstorms under future climate Hemisphere extratropical cyclones: A comparison of detection and tracking conditions. Clim. Res., 54, 1 20. methods and different reanalyses. Mon. Weather Rev., 136, 880 897. Pinto, J. G., U. Ulbrich, G. C. Leckebusch, T. Spangehl, M. Reyers, and S. Zacharias, Rajeevan, M., J. Bhate, and A. K. Jaswal, 2008: Analysis of variability and trends of 2007: Changes in storm track and cyclone activity in three SRES ensemble extreme rainfall events over India using 104 years of gridded daily rainfall data. experiments with the ECHAM5/MPI-OM1 GCM. Clim. Dyn., 29, 195 210. Geophys. Res. Lett., 35, doi: 10.1029/2008gl035143. Pitman, A. J., and S. E. Perkins, 2008: Regional projections of future seasonal and Rajendran, K., and A. Kitoh, 2008: Indian summer monsoon in future climate annual changes in rainfall and temperature over Australia based on skill- projection by a super high-resolution global model. Curr. Sci., 95, 1560 1569. selected AR4 models. Earth Interact., 12, 1 50. Ramanathan, V., et al., 2005: Atmospheric brown clouds: Impacts on South Asian Plumb, R. A., 1977: The interaction of two internal waves with the mean flow: climate and hydrological cycle. Proc. Natl. Acad. Sci. U.S.A., doi: 10.1073/ Implications for the theory of the quasi-biennial oscillation. J. Atmos. Sci., 34, pnas.0500656102, 5326 5333. 1847 1858. Raphael, M. N., and M. M. Holland, 2006: Twentieth century simulation of the Pohl, B., N. Fauchereau, C. Reason, and M. Rouault, 2010: Relationships between southern hemisphere climate in coupled models. Part 1: Large scale circulation the Antarctic Oscillation, the Madden - Julian Oscillation, and ENSO, and variability. Clim. Dyn., 26, 217 228. Consequences for Rainfall Analysis. J. Clim., 23, 238 254. Rasmussen, R., et al., 2011: High-resolution coupled climate runoff simulations Polcher, J., et al., 2011: AMMA s contribution to the evolution of prediction and of seasonal snowfall over Colorado; A process study of current and warmer decision-making systems for West Africa. Atmos. Sci. Lett., 12, 2 6. climate. J. Clim., 24, 3015 3048. Polvani, L. M., M. Previdi, and C. Deser, 2011: Large cancellation, due to ozone Rauscher, S. A., F. Giorgi, N. S. Diffenbaugh, and A. Seth, 2008: Extension and recovery, of future Southern Hemisphere atmospheric circulation trends. Intensification of the Meso-American mid-summer drought in the twenty-first 14 Geophys. Res. Lett., 38, doi: 10.1029/2011gl046712. century. Clim. Dyn., 31, 551 571. 1302 Climate Phenomena and their Relevance for Future Regional Climate Change Chapter 14 Rawlins, M. A., et al., 2010: Analysis of the Arctic system for freshwater cycle Saenger, C., A. Cohen, D. Oppo, R. Halley, and J. Carilli, 2009: Surface-temperature intensification: Observations and expectations. J. Clim., 23, 5715 5737. trends and variability in the low-latitude North Atlantic since 1552. Nature Re, M., and V. Barros, 2009: Extreme rainfalls in SE South America. Clim. Change, Geosci., 2, 492 495. 96, 119 136. Saji, N. H., B. N. Goswami, P. N. Vinayachandran, and T. Yamagata, 1999: A dipole Reboita, M. S., T. Ambrizzi, and R. P. da Rocha, 2009: Relationship between the mode in the tropical Indian Ocean. Nature, 401, 360 363. southern annular mode and southern hemisphere atmospheric systems. Rev. Salahuddin, A., and S. Curtis, 2011: Climate extremes in Malaysia and the equatorial Brasil. Meteorol., 24, doi: 10.1590/S0102-77862009000100005. South China Sea. Global Planet. Change, 78, 83 91. Reisinger, A., A. B. Mullan, M. Manning, D. Wratt, and R. Nottage, 2010: Global Salathe Jr, E. P., L. R. Leung, Y. Qian, and Y. Zhang, 2010: Regional climate model and local climate change scenarios to support adaptation in New Zealand. projections for the State of Washington. Clim. Change, 102, 51 75. In: Climate Change Adaptation in New Zealand: Future Scenarios and Some Salinger, M. J., J. A. Renwick, and A. B. Mullan, 2001: Interdecadal Pacific Oscillation Sectoral Perspectives [R. A. C. Nottage, D. S. Wratt, J. F. Bornman, and K. Jones and South Pacific climate. Int. J. Climatol., 21, 1705 1722. (eds.)] VUW Press, Wellington, New Zealand, pp. 26 43. Sampe, T., and S.-P. Xie, 2010: Large-scale dynamics of the Meiyu-Baiu rainband: Rind, D., 2008: The consequences of not knowing low-and high-latitude climate Environmental forcing by the westerly jet. J. Climate, 23, 113 134. sensitivity. Bull. Am. Meteorol. Soc., 89, 855 864. Sansom, P. G., D. B. Stephenson, C. A. T. Ferro, G. Zappa, and L. Shaffrey, 2013: Simple Rinke, A., et al., 2006: Evaluation of an ensemble of Arctic regional climate models: uncertainty frameworks for selecting weighting schemes and interpreting multi- Spatiotemporal fields during the SHEBA year. Clim. Dyn., 26, 459 472. model ensemble climate change experiments. J. Clim., doi:10.1175/JCLI-D-12 Risbey, J. S., M. J. Pook, P. C. McIntosh, M. C. Wheeler, and H. H. Hendon, 2009: On 00462.1. the remote drivers of rainfall variability in Australia. Mon. Weather Rev., 137, Santer, B. D., et al., 2007: Identification of human-induced changes in atmospheric 3233 3253. moisture content. Proc. Natl. Acad. Sci. U.S.A., 104, 15248 15253. Riviere, G., 2011: A dynamical interpretation of the poleward shift of the jet streams Sato, T., F. Kimura, and A. Kitoh, 2007: Projection of global warming onto regional in global warming scenarios. J. Atmos. Sci., 68, 1253 1272. precipitation over Mongolia using a regional climate model. J. Hydrol., 333, Robertson, A. W., et al., 2011: The Maritime Continent monsoon. In: The Global 144 154. Monsoon System: Research and Forecast, 2nd ed. [C. P. Chang, Y. Ding, N. C. Scaife, A., et al., 2011a: Climate change projections and stratosphere troposphere Lau, R. H. Johnson, B. Wang and T. Yasunari (eds.)] World Scientific Singapore, interaction. Clim. Dyn., 38, 2089 2097. pp. 85 98. Scaife, A., et al., 2009: The CLIVAR C20C project: Selected twentieth century climate Robinson, W. A., 2006: On the self-maintenance of midlatitude jets. J. Atmos. Sci., events. Clim. Dyn., 33, 603 614. 63, 2109 2122. Scaife, A. A., J. R. Knight, G. K. Vallis, and C. K. Folland, 2005: A stratospheric influence Rodrigues, R. R., R. J. Haarsma, E. J. D. Campos, and T. Ambrizzi, 2011: The impacts of on the winter NAO and North Atlantic surface climate. Geophys. Res. Lett., 32, inter El Nino variability on the tropical Atlantic and northeast Brazil climate. J. doi: 10.1029/2005gl023226. Clim., 24, 3402 3422. Scaife, A. A., C. K. Folland, L. V. Alexander, A. Moberg, and J. R. Knight, 2008: European Rodriguez-Fonseca, B., et al., 2011: Interannual and decadal SST-forced responses of climate extremes and the North Atlantic Oscillation. J. Clim., 21, 72 83. the West African monsoon. Atmos. Sci. Lett., 12, 67 74. Scaife, A. A., T. Wollings, J. Knight, G. Martin, and T. Hinton, 2010: Atmospheric Rosenfeld, D., M. Clavner, and R. Nirel, 2011: Pollution and dust aerosols modulating blocking and mean biases in 18 climate models. Journal of Climate, 23, 6143- tropical cyclones intensities. Atmos. Res., 102, 66 76. 6152. Rotstayn, L., and U. Lohmann, 2002: Tropical rainfall trends and the indirect aerosol Scaife, A. A., et al., 2011b: Improved Atlantic winter blocking in a climate model. effect. J. Clim., 15, 2103 2116. Geophys. Res. Lett., 38, L23703. Rotstayn, L. D., et al., 2007: Have Australian rainfall and cloudiness increased due Scarchilli, C., M. Frezzotti, and P. Ruti, 2011: Snow precipitation at four ice core sites to the remote effects of Asian anthropogenic aerosols? J. Geophys. Res. Atmos., in East Antarctica: Provenance, seasonality and blocking factors. Clim. Dyn., 37, 112, D09202. 2107 2125. Rotstayn, L. D., et al., 2009: Improved simulation of Australian climate and ENSO- Schimanke, S., J. Koerper, T. Spangehl, and U. Cubasch, 2011: Multi-decadal variability related climate variability in a GCM with an interactive aerosol treatment. Int. J. of sudden stratospheric warmings in an AOGCM. Geophys. Res. Lett., 38, L01801. Climatol., doi:10.1002/joc.1952. Schneider, D., C. Deser, and Y. Okumura, 2012: An assessment and interpretation of Rouault, M., P. Florenchie, N. Fauchereau, and C. Reason, 2003: South East tropical the observed warming of West Antarctica in the austral spring. Clim. Dyn., 38, Atlantic warm events and southern African rainfall. Geophys. Res. Lett., 30, doi: 323 347. 10.1029/2002GL014840. Schneider, N., and B. Cornuelle, 2005: The forcing of the Pacific decadal oscillation. Rowell, D. P., 2011: Sources of uncertainty in future changes in local precipitation. J. Clim., 18, 4355 4373. Clim. Dyn., 39, 1929 1950. Schneider, T., P. A. O Gorman, and X. J. Levine, 2010: Water vapor and the dynamics Rowell, D. P., 2013: Simulating SST teleconnections to Africa: What is the state of the of climate changes. Rev. Geophys., 48, RG3001. art? J. Clim., doi:10.1175/jcli-d-12 00761.1. Schott, F. A., S.-P. Xie, and J. P. McCreary, 2009: Indian Ocean circulation and climate Roxy, M., N. Patil, K. Ashok, and K. Aparna, 2013: Revisiting the Indian summer variability. Rev. Geophys., 47, RG1002. monsoon-ENSO links in the IPCC AR4 projections: A cautionary outlook. Global Schubert, J. J., B. Stevens, and T. Crueger, 2013: The Madden-Julian Oscillation as Planet. Change, doi:10.1016/j.gloplacha.2013.02.003, early on-line release. simulated by the MPI Earth System Model: Over the last and into the next Rupa Kumar, K., et al., 2006: High-resolution climate change scenarios for India for millennium. J. Adv. Model. Earth Syst., 5, 71 84. the 21st century. Curr. Sci., 90, 334 345. Schulz, N., J. P. Boisier, and P. Aceituno, 2012: Climate change along the arid coast of Rusticucci, M., and M. Renom, 2008: Variability and trends in indices of quality- northern Chile. Int. J. Climatol., 32, 1803 1814. controlled daily temperature extremes in Uruguay. Int. J. Climatol., 28, 1083 Screen, J. A., I. Simmonds, C. Deser, and R. Tomas, 2012: The atmospheric response to 1095. three decades of observed Arctic sea ice loss. J. Clim., 26, 1230 1248. Rusticucci, M., J. Marengo, O. Penalba, and M. Renom, 2010: An intercomparison Seager, R., and G. Vecchi, 2010: Greenhouse warming and the  21st century of model-simulated in extreme rainfall and temperature events during the last hydroclimate of southwestern North America. Proc. Natl. Acad. Sci. U.S.A., 107, half of the twentieth century. Part 1: Mean values and variability. Clim. Change, 21277 21282. 98, 493 508. Seager, R., Y. Kushnir, M. Ting, M. Cane, N. Naik, and J. Miller, 2008: Would advance Ruti, P., and A. Dell Aquila, 2010: The twentieth century African easterly waves in knowledge of 1930s SSTs have allowed prediction of the dust bowl drought? J. reanalysis systems and IPCC simulations, from intra-seasonal to inter-annual Clim., 21, 3261 3281. variability. Clim. Dyn., 35, 1099 1117. Seager, R., N. Naik, and L. Vogel, 2012: Does Global Warming Cause Intensified Sabade, S., A. Kulkarni, and R. Kripalani, 2011: Projected changes in South Asian Interannual Hydroclimate Variability? J. Clim., 25, 3355-3372   summer monsoon by multi-model global warming experiments. Theor. Appl. Seager, R., et al., 2007: Model projections of an imminent transition to a more arid Climatol., 103, 543 565. climate in southwestern North America. Science, 316, 1181 1184. Seager, R., et al., 2009: Mexican drought: An observational modeling and tree ring 14 study of variability and climate change. Atmosfera, 22, 1 31. 1303 Chapter 14 Climate Phenomena and their Relevance for Future Regional Climate Change Seidel, D. J., Q. Fu, W. J. Randel, and T. J. Reichler, 2008: Widening of the tropical belt Smith, I. N., and B. Timbal, 2012: Links between tropical indices and southern in a changing climate. Nature Geosci., 1, 21 24. Australian rainfall. Int. J. Climatol., 32, 33 40. Seierstad, I. A., and J. Bader, 2009: Impact of a projected future Arctic Sea Ice Smith, I. N., L. Wilson, and R. Suppiah, 2008: Characteristics of the northern reduction on extratropical storminess and the NAO. Clim. Dyn., 33, 937 943. Australian rainy season. J. Clim., 21, 4298 4311. Semenov, V. A., 2007: Structure of temperature variability in the high latitudes of the Smith, I. N., A. F. Moise, and R. Colman, 2012a: Large scale circulation features in the Northern Hemisphere. Izvestiya Atmos. Ocean. Phys., 43, 687 695. tropical Western Pacific and their representation in climate models. J. Geophys. Sen Gupta, A., A. Ganachaud, S. McGregor, J. N. Brown, and L. Muir, 2012: Drivers of Res., 117, doi: 10.1029/2011JD016667. the projected changes to the Pacific Ocean equatorial circulation. Geophys. Res. Smith, K. L., L. M. Polvani, and D. R. Marsh, 2012b: Mitigation of 21st century Lett., 39, L09605. Antarctic sea ice loss by stratospheric ozone recovery. Geophys. Res. Lett., 39, Sen Roy, S., 2009: A spatial analysis of extreme hourly precipitation patterns in India. doi: 10.1029/2012GL053325. Int. J. Climatol., 29, 345 355. Soares, W. R., and J. A. Marengo, 2009: Assessments of moisture fluxes east of Seneviratne, S., et al., 2010: Investigating soil moisture-climate interactions in a the Andes in South America in a global warming scenario. Int. J. Climatol., 29, changing climate: A review. Earth Sci. Rev., 95, 125 161. 1395 1414. Seneviratne, S. I., et al., 2012: Changes in climate extremes and their impacts on the Sobel, A., and S. Camargo, 2011: Projected future seasonal changes in tropical natural physical environment. In:  Managing the Risks of Extreme Events and summer climate. J. Clim., 24, 473 487. Disasters to Advance Climate Change Adaptation. A Special Report of Working Sohn, B., and S. Park, 2010: Strengthened tropical circulations in past three Groups I and II of the Intergovernmental Panel on Climate Change (IPCC) [C. B. decades inferred from water vapor transport. J. Geophys. Res. Atmos., 115, doi: Field, V. Barros, T. F. Stocker, D. Qin, D. J. Dokken, K. L. Ebi, M. D. Mastrandrea, K. J. 10.1029/2009JD013713. Mach, G. -K. Plattner, S. K. Allen, M. Tignor and P. M. Midgley (eds.)]. Cambridge Solman, S., M. Nunez, and M. Cabré, 2008: Regional climate change experiments University Press, Cambridge, United Kingdom, and New York, NY, USA, pp. over southern South America. I: Present climate. Clim. Dyn., 30, 533 552. 109 230. Solman, S., et al., 2013: Evaluation of an ensemble of regional climate model Servain, J., I. Wainer, J. McCreary, and A. Dessier, 1999: Relationship between the simulations over South America driven by the ERA-Interim reanalysis: Model equatorial and meridional modes of climatic variability in the tropical Atlantic. performance and uncertainties. Clim. Dyn., doi:10.1007/s00382-013-1667-2, Geophys. Res. Lett., 26, 485 488. 1 19. Seth, A., M. Rojas, and S. A. Rauscher, 2010: CMIP3 projected changes in the annual Solman, S. A., and H. Le Treut, 2006: Climate change in terms of modes of atmospheric cycle of the South American Monsoon. Clim. Change, 98, 331 357. variability and circulation regimes over southern South America. Clim. Dyn., 26, Seth, A., S. A. Rauscher, M. Rojas, A. Giannini, and S. J. Camargo, 2011: Enhanced 835 854. spring convective barrier for monsoons in a warmer world? Clim. Change, 104, Solomon, A., and M. Newman, 2011: Decadal predictability of tropical Indo-Pacific 403 414. Ocean temperature trends due to anthropogenic forcing in a coupled climate Sheffield, J., and E. F. Wood, 2008: Projected changes in drought occurrence under model. Geophys. Res. Lett., 38, doi: 10.1029/2010GL045978. future global warming from multi-model, multi-scenario, IPCC AR4 simulations. Son, S. W., and S. Y. Lee, 2005: The response of westerly jets to thermal driving in a Clim. Dyn., 31, 79 105. primitive equation model. J. Atmos. Sci., 62, 3741 3757. Shi, G., J. Ribbe, W. Cai, and T. Cowan, 2008a: An interpretation of Australian rainfall Son, S. W., et al., 2010: Impact of stratospheric ozone on Southern Hemisphere projections. Geophys. Res. Lett., 35, L02702. circulation change: A multimodel assessment. J. Geophys. Res., 115, D00M07. Shi, G., W. Cai, T. Cowan, J. Ribbe, L. Rotstayn, and M. Dix, 2008b: Variability and trend Sörensson, A. A., C. Menéndez, R. Ruscica, P. Alexander, P. Samuelsson, and U. of North West Australia rainfall: Observations and coupled climate modeling. J. Willén, 2010: Projected precipitation changes in South America: A dynamical Clim., 21, 2938 2959. downscaling within CLARIS. . Meteorol. Z., 19, 347 355. Shongwe, M., G. van Oldenborgh, B. van den Hurk, and M. van Aalst, 2011: Projected Sperber, K., and H. Annamalai, 2008: Coupled model simulations of boreal summer changes in mean and extreme precipitation in Africa under global warming. Part intraseasonal (30 50 day) variability, Part 1: Systematic errors and caution on II: East Africa. J. Clim., 24, 3718 3733. use of metrics. Clim. Dyn., 31, 345 372. Shongwe, M. E., G. J. van Oldenborgh, B. van den Hurk, B. de Boer, C. A. S. Coelho, Sperber, K. R., et al., 2012: The Asian summer monsoon: An intercomparison of and M. K. van Aalst, 2009: Projected changes in mean and extreme precipitation CMIP5 vs. CMIP3 simulations of the late 20th century. Clim. Dyn., doi:10.1007/ in Africa under global warming. Part I: Southern Africa. J. Clim., 22, 3819 3837. s00382-012-1607-6, 1 34. Sigmond, M., and J. F. Scinocca, 2010: The influence of the basic state on the Northern Stammerjohn, S. E., D. G. Martinson, R. C. Smith, X. Yuan, and D. Rind, 2008: Trends Hemisphere circulation response to climate change. J. Clim., 23, 1434 1446. in Antarctic annual sea ice retreat and advance and their relation to El Nino- Sillmann, J., M. Croci-Maspoli, M. Kallache, and R. W. Katz, 2011: Extreme cold winter Southern Oscillation and Southern Annular Mode variability. J. Geophys. Res., temperatures in Europe under the influence of North Atlantic atmospheric 113, C03S90. blocking. J. Clim., 24, 5899 5913. Stephenson, D., A. Hannachi, and A. O Neill, 2004: On the existence of multiple Sillmann, J., V. V. Kharin, X. Zhang, F. W. Zwiers, and D. Bronaugh, 2013: Climate climate regimes. Q. J. R. Meteorol. Soc., 130, 583 605. extremes indices in the CMIP5 multimodel ensemble: Part 1. Model evaluation Stephenson, D., V. Pavan, M. Collins, M. Junge, and R. Quadrelli, 2006: North Atlantic in the present climate. J. Geophys. Res. Atmos., 118, 1716 1733. Oscillation response to transient greenhouse gas forcing and the impact on Silva, A. E., and L. M. V. Carvalho, 2007: Large-scale index for South America European winter climate: A CMIP2 multi-model assessment. Clim. Dyn., 27, Monsoon (LISAM). Atmos. Sci. Lett., 8, 51 57. 401 420. Silva, V. B. S., and V. E. Kousky, 2012: The South American Monsoon System: Stevenson, S., B. Fox-Kemper, M. Jochum, R. Neale, C. Deser, and G. Meehl, 2012: Climatology and variability. Chapter 5 in: Modern Climatology [S.-Y. Wang (ed.)], Will there be a significant change to El Nino in the twenty-first century? J. Clim., pp 123-152. 25, 2129 2145. Sinha, A., et al., 2011: A global context for megadroughts in monsoon Asia during Stevenson, S. L., 2012: Significant changes to ENSO strength and impacts  in the past millennium. Quat. Sci. Rev., 30, 47 62. the twenty-first century: Results from CMIP5. Geophys. Res. Lett., Skansi, M. d. l. M., et al., 2013: Warming and wetting signals emerging from analysis doi:10.1029/2012GL052759. of changes in climate extreme indices over South America. Global Planet. Stoner, A. M. K., K. Hayhoe, and D. J. Wuebbles, 2009: Assessing General Circulation Change, 100, 295 307. Model simulations of atmospheric teleconnection patterns. J. Clim., 22, 4348 Smirnov, D., and D. Vimont, 2011: Variability of the Atlantic Meridional Mode during 4372. the Atlantic hurricane season. J. Clim., 24, 1409 1424. Stowasser, M., H. Annamalai, and J. Hafner, 2009: Response of the South Asian Smith, D. M., R. Eade, N. J. Dunstone, D. Fereday, J. M. Murphy, H. Pohlmann, and A. summer monsoon to global warming: Mean and synoptic systems. J. Clim., 22, A. Scaife, 2010: Skilful multi-year predictions of Atlantic hurricane frequency. 1014 1036. Nature Geosci, 3, 846 849. Strong, C., G. Magnusdottir, and H. Stern, 2009: Observed feedback between winter Smith, I., and E. Chandler, 2010: Refining rainfall projections for the Murray Darling sea ice and the North Atlantic Oscillation. J. Clim., 22, 6021 6032. 14 Basin of south-east Australia the effect of sampling model results based on Sugi, M., and J. Yoshimura, 2012: Decreasing trend of tropical cyclone frequency in performance. Clim. Change, 102, 377 393. 228-year high-resolution AGCM simulations. Geophys. Res. Lett., 39, L19805. 1304 Climate Phenomena and their Relevance for Future Regional Climate Change Chapter 14 Sugi, M., H. Murakami, and J. Yoshimura, 2009: A reduction in global tropical cyclone Timbal, B., and J. M. Arblaster, 2006: Land cover change as an additional forcing to frequency due to global warming. Sola, 5, 164 167. explain the rainfall decline in the south west of Australia. Geophys. Res. Lett., Sugi, M., H. Murakami, and J. Yoshimura, 2012: On the mechanism of tropical cyclone 33, L07717. frequency changes due to global warming. J. Meteorol. Soc. Jpn., 90A, 397 408. Timbal, B., and W. Drosdowsky, 2012: The relationship between the decline of South- Suhaila, J., S. M. Deni, W. Z. W. Zin, and A. A. Jemain, 2010: Spatial patterns and eastern Australian rainfall and the strengthening of the subtropical ridge. Int. J. trends of daily rainfall regime in Peninsular Malaysia during the southwest and Climatol., doi:10.1002/joc.3492. northeast monsoons: 1975 2004. Meteorol. Atmos. Phys., 110, 1 18. Timbal, B., J. M. Arblaster, and S. Power, 2006: Attribution of the late-twentieth- Sun, J., H. Wang, and W. Yuan, 2008: Decadal variations of the relationship between century rainfall decline in southwest Australia. J. Clim., 19, 2046 2062. the summer North Atlantic Oscillation and middle East Asian air temperature. J. Timmermann, A., F. F. Jin, and J. Abshagen, 2003: A nonlinear theory for El Nino Geophys. Res. Atmos., 113, D15107. bursting. J. Atmos. Sci., 60, 152 165. Sun, Y., and Y. H. Ding, 2010: A projection of future changes in summer precipitation Ting, M., Y. Kushnir, R. Seager, and C. Li, 2009: Forced and internal twentieth-century and monsoon in East Asia. Science China Earth Sciences, 53, 284 300. SST trends in the north Atlantic. J. Clim., 22, 1469 1481. Sung, M.-K., G.-H. Lim, and J.-S. Kug, 2010: Phase asymmetric downstream Ting, M., Y. Kushnir, R. Seager, and C. Li, 2011: Robust features of Atlantic multi- development of the North Atlantic Oscillation and its impact on the East Asian decadal variability and its climate impacts. Geophys. Res. Lett., 38, L17705. winter monsoon. J. Geophys. Res., 115, doi: 10.1029/2009JD013153. Tjernstrom, M., et al., 2004: Modeling the Arctic boundary layer: An evalutation of Sutton, R. T., and B. Dong, 2012: Atlantic Ocean influence on a shift in European six ARCMIP regional-scale models with data from the SHEBA project. Bound. climate in the 1990s. Nature Geosci., 5, 788 792. Layer Meteorol., 117, 337 381. Swart, N. C., and J. C. Fyfe, 2012: Observed and simulated changes in the Southern Tokinaga, H., and S. P. Xie, 2011: Weakening of the equatorial Atlantic cold tongue Hemisphere surface westerly wind-stress. Geophys. Res. Lett., 39, L16711. over the past six decades. Nature Geosci., 4, 222 226. Takahashi, K., and D. S. Battisti, 2007: Processes controlling the mean tropical Pacific Tokinaga, H., S. Xie, A. Timmermann, S. McGregor, T. Ogata, H. Kubota, and Y. precipitation pattern. Part II: The SPCZ and the southeast Pacific dry zone. J. Okumura, 2012: Regional patterns of tropical Indo-Pacific climate change: Clim., 20, 5696 5706. Evidence of the Walker Circulation weakening. J. Clim., 25, 1689 1710. Takahashi, K., A. Montecinos, K. Goubanova, and B. Dewitte, 2011: ENSO regimes: Trenberth, K.E., 2011: Changes in precipitation with climate change. Climate Res., Reinterpreting the canonical and Modoki El Nino. Geophys. Res. Lett., 38, doi: 47, 123-138. 10.1029/2011gl047364. Trenberth, K., and J. Fasullo, 2010: Simulation of present-day and twenty-first- Takaya, K., and H. Nakamura, 2005: Mechanisms of intraseasonal amplification of century energy budgets of the southern oceans. J. Clim., 23, 440 454. the cold Siberian high. J. Atmos. Sci., 62, 4423 4440. Trenberth, K., J. Fasullo, and L. Smith, 2005: Trends and variability in column- Tanarhte, M., P. Hadjinicolaou, and J. Lelieveld, 2012: Intercomparison of temperature integrated atmospheric water vapor. Clim. Dyn., 24, 741 758. and precipitation data sets based on observations in the Mediterranean and the Trenberth, K., C. Davis, and J. Fasullo, 2007a: Water and energy budgets of Middle East. J. Geophys. Res. Atmos., 117, doi: 10.1029/2011JD017293. hurricanes: Case studies of Ivan and Katrina. J. Geophys. Res. Atmos., 112, doi: Tangang, F. T., L. Juneng, and S. Ahmad, 2007: Trend and interannual variability of 10.1029/2006JD008303. temperature in Malaysia: 1961 2002. Theor. Appl. Climatol., 89, 127 141. Trenberth, K. E., D. P. Stepaniak, and J. M. Caron, 2000: The global monsoon as seen Tangang, F. T., et al., 2008: On the roles of the northeast cold surge, the Borneo through the divergent atmospheric circulation. J. Clim., 13, 3969 3993. vortex, the Madden-Julian Oscillation, and the Indian Ocean Dipole during the Trenberth, K. E., et al., 2007b: Observations: Surface and atmospheric climate change. extreme 2006/2007 flood in southern Peninsular Malaysia. Geophys. Res. Lett., In: Climate Change 2007: The Physical Science Basis. Contribution of Working 35, L14S07. Group I to the Fourth Assessment Report of the Intergovernmental Panel on Taylor, C., A. Gounou, F. Guichard, P. Harris, R. Ellis, F. Couvreux, and M. De Kauwe, Climate Change [Solomon, S., D. Qin, M. Manning, Z. Chen, M. Marquis, K. B. 2011a: Frequency of Sahelian storm initiation enhanced over mesoscale soil- Averyt, M. Tignor and H. L. Miller (eds.)] Cambridge University Press, Cambridge, moisture patterns. Nature Geosci., 4, 430 433. United Kingdom and New York, NY, USA, pp. 235 336. Taylor, C., et al., 2011b: New perspectives on land-atmosphere feedbacks from the Trigo, R. M., I. F. Trigo, C. C. DaCamara, and T. J. Osborn, 2004: Climate impact of African Monsoon Multidisciplinary Analysis. Atmos. Sci. Lett., 12, 38 44. the European winter blocking episodes from the NCEP/NCAR Reanalyses. Clim. Taylor, K. E., R. J. Stouffer, and G. A. Meehl, 2011c: An overview of CMIP5 and the Dyn., 23, 17 28. experiment design. Bull. Am. Meteorol. Soc., 93, 485 498. Turner, A., K. Sperber, J. Slingo, G. A. Meehl, C. R. Mechoso, M. Kimoto, and A. Taylor, M. A., F. S. Whyte, T. S. Stephenson, and C. J.D, 2013: Why dry? Investigating the Giannini, 2011: Modelling monsoons: Understanding and predicting current and future evolution of the Caribbean Low Level Jet to explain projected Caribbean future behaviour. World Scientific Series on Asia-Pacific Weather and Climate, drying. Int. J. Climatol., 33, 784 792. Vol. 5. The Global Monsoon System: Research and Forecast, 2nd ed. [C. P. Chang, Taylor, M. A., T. S. Stephenson, A. Owino, A. A. Chen, and J. D. Campbell, 2011d: Y. Ding, N.-C. Lau, R. H. Johnson, B. Wang and T. Yasunari (eds.)]. World Scientific Tropical gradient influences on Caribbean rainfall. J. Geophys. Res., 116, D00Q08. Publication Company, Singapore, 608 pp. Tedeschi, R. G., I. F. A. Cavalcanti, and A. M. Grimm, 2013: Influences of two types of Turner, A. G., and H. Annamalai, 2012: Climate change and the South Asian summer ENSO on South American precipitation. Int. J. Climatol., 33, 1382 1400. monsoon. Nature Clim. Change, 2, 587 595. Thomas, E. R., G. J. Marshall, and J. R. McConnell, 2008: A doubling in snow Turner, A. G., P. M. Inness, and J. M. Slingo, 2007a: The effect of doubled CO2 and accumulation in the western Antarctic Peninsula since 1850. Geophys. Res. Lett., model basic state biases on the monsoon-ENSO system. I: Mean response and 35, L01706. interannual variability. Q. J. R. Meteorol. Soc., 133, 1143 1157. Thompson, D., and S. Solomon, 2009: Understanding recent stratospheric climate Turner, J., 2004: The El Nino southern oscillation and Antarctica. Int. J. Climatol., change. J. Clim., 22, 1934 1943. 24, 1 31. Thompson, D. W. J., and J. M. Wallace, 1998: The Arctic Oscillation signature in the Turner, J., J. E. Overland, and J. E. Walsh, 2007b: An Arctic and Antarctic perspective wintertime geopotential height and temperature fields. Geophys. Res. Lett., 25, on recent climate change. Int. J. Climatol., 27, 277 293. 1297 1300. Turner, J., et al., 2005: Antarctic climate change during the last 50 years. Int. J. Thompson, D. W. J., and J. M. Wallace, 2000: Annular modes in the extratropical Climatol., 25, 279 294. circulation. Part I: Month-to-month variability. J. Clim., 13, 1000 1016. Tyrlis, E., and B. J. Hoskins, 2008: Aspects of a Northern Hemisphere atmospheric Thompson, D. W. J., and S. Solomon, 2002: Interpretation of recent Southern blocking climatology. J. Atmos. Sci., 65, 1638 1652. Hemisphere climate change. Science, 296, 895 899. Ueda, H., A. Iwai, K. Kuwako, and M. E. Hori, 2006: Impact of anthropogenic forcing Thompson, D. W. J., J. M. Wallace, J. J. Kennedy, and P. D. Jones, 2010: An abrupt drop on the Asian summer monsoon as simulated by eight GCMs. Geophys. Res. Lett., in Northern Hemisphere sea surface temperature around 1970. Nature, 467, 33, doi: 10.1029/2005gl025336. 444 447. Ulbrich, U., and M. Christoph, 1999: A shift of the NAO and increasing storm track Thompson, D. W. J., S. Solomon, P. J. Kushner, M. H. England, K. M. Grise, and D. J. activity over Europe due to anthropogenic greenhouse gas forcing. Clim. Dyn., Karoly, 2011: Signatures of the Antarctic ozone hole in Southern Hemisphere 15, 551 559. surface climate change. Nature Geosci., 4, 741 749. Ulbrich, U., G. C. Leckebusch, and J. G. Pinto, 2009: Extra-tropical cyclones in the 14 present and future climate: A review. Theor. Appl. Climatol., 96, 117 131. 1305 Chapter 14 Climate Phenomena and their Relevance for Future Regional Climate Change Ulbrich, U., J. G. Pinto, H. Kupfer, G. C. Leckebusch, T. Spangehl, and M. Reyers, 2008: Vizy, E., and K. Cook, 2002: Development and application of a mesoscale climate Changing northern hemisphere storm tracks in an ensemble of IPCC climate model for the tropics: Influence of sea surface temperature anomalies on the change simulations. J. Clim., 21, 1669 1679. West African monsoon. J. Geophys. Res. Atmos., 107, ACL 2-1-ACL 2 22. Ulbrich, U., et al., 2013: Are Greenhouse Gas Signals of Northern Hemisphere winter Vuille, M., B. Francou, P. Wagnon, I. Juen, G. Kaser, B. G. Mark, and R. S. Bradley, 2008: extra-tropical cyclone activity dependent on the identification and tracking Climate change and tropical Andean glaciers: Past, present and future. Earth Sci. methodology? Meteorol. Z., 22, 61-68. Rev., 89, 79 96. Ummenhofer, C. C., and M. H. England, 2007: Interannual extremes in New Zealand Walsh, K., K. McInnes, and J. McBride, 2012: Climate change impacts on tropical precipitation linked to modes of Southern Hemisphere climate variability. J. cyclones and extreme sea levels in the South Pacific - A regional assessment. Clim., 20, 5418 5440. Global Planet. Change, 80 81, 149 164. Ummenhofer, C. C., A. Sen Gupta, and M. H. England, 2009a: Causes of late Wang, B., 1995: Interdecadal changes in El-Nino onset in the last four decades. J. twentieth-century trends in New Zealand precipitation. J. Clim., 22, 3 19. Clim., 8, 267 285. Ummenhofer, C. C., M. H. England, P. C. McIntosh, G. A. Meyers, M. J. Pook, J. S. Risbey, Wang, B., and Y. Wang, 1996: Temporal structure of the Southern Oscillation as A. S. Gupta, and A. S. Taschetto, 2009b: What causes southeast Australia s worst revealed by waveform and wavelet analysis. J. Clim., 9, 1586 1598. droughts? Geophys. Res. Lett., 36, doi: 10.1029/2008gl036801. Wang, B., and S. I. An, 2001: Why the properties of El Nino changed during the late van den Broeke, M. R., and N. P. M. van Lipzig, 2004: Changes in Antarctic 1970s. Geophys. Res. Lett., 28, 3709 3712. temperature, wind and precipitation in response to the Antarctic Oscillation. Wang, B., and S. I. An, 2002: A mechanism for decadal changes of ENSO behavior: Ann. Glaciol., 39, 119 126. Roles of background wind changes. Clim. Dyn., 18, 475 486. van Ommen, T. D., and V. Morgan, 2010: Snowfall increase in coastal East Antarctica Wang, B., and LinHo, 2002: Rainy season of the Asian-Pacific summer monsoon. J. linked with southwest Western Australian drought. Nature Geosci, 3, 267 272. Clim., 15, 386 398. Vance, T. R., T. D. van Ommen, M. A. J. Curran, C. T. Plummer, and A. D. Moy, 2012: Wang, B., and Q. Ding, 2006: Changes in global monsoon precipitation over the past A millennial proxy record of ENSO and eastern Australian rainfall from the Law 56 years. Geophys. Res. Lett., 33, L06711. Dome ice core, East Antarctica. J. Clim., 26, 710 725. Wang, B., R. G. Wu, and T. Li, 2003: Atmosphere-warm ocean interaction and its Vancoppenolle, M., T. Fichefet, H. Goosse, S. Bouillon, G. Madec, and M. A. M. impacts on Asian-Australian monsoon variation. J. Clim., 16, 1195 1211. Maqueda, 2009: Simulating the mass balance and salinity of arctic and antarctic Wang, B., I. S. Kang, and J. Y. Lee, 2004: Ensemble simulations of Asian-Australian sea ice. 1. Model description and validation. Ocean Model., 27, 33 53. monsoon variability by 11 AGCMs. J. Clim., 17, 803 818. Vasconcellos, F. C., and I. F. A. Cavalcanti, 2010: Extreme precipitation over Wang, B., Q. Ding, and J. Jhun, 2006: Trends in Seoul (1778 2004) summer Southeastern Brazil in the austral summer and relations with the Southern precipitation. Geophys. Res. Lett., 33, L15803. Hemisphere annular mode. Atmos. Sci. Lett., 11, 21 26. Wang, B., J. Yang, and T. J. Zhou, 2008a: Interdecadal changes in the major modes Vautard, R., et al., 2007: Summertime European heat and drought waves induced of Asian-Australian monsoon variability: Strengthening relationship with ENSO by wintertime Mediterranean rainfall deficit. Geophys. Res. Lett., 34, doi: since the late 1970s. J. Clim., 21, 1771 1789. 10.1029/2006GL028001. Wang, B., H.-J. Kim, K. Kikuchi, and A. Kitoh, 2011: Diagnostic metrics for evaluation Vecchi, G., and A. Wittenberg, 2010: El Nino and our future climate: Where do we of annual and diurnal cycles. Clim. Dyn., 37, 941 955. stand? WIREs Clim Change, 1, 260 270. Wang, B., S. Xu, and L. Wu, 2012a: Intensified Arabian Sea tropical storms. Nature, Vecchi, G. A., and B. J. Soden, 2007a: Global warming and the weakening of the 489, E1 E2. tropical circulation. J. Clim., 20, 4316 4340. Wang, B., J. Liu, H.-J. Kim, P. J. Webster, and S.-Y. Yim, 2012b: Recent change of the Vecchi, G. A., and B. J. Soden, 2007b: Increased tropical Atlantic wind shear in model global monsoon precipitation (1979 2008). Clim. Dyn., 39, 1123 1135. projections of global warming. Geophys. Res. Lett., 34, L08702. Wang, C., S. K. Lee, and D. B. Enfield, 2007: Impact of the Atlantic warm pool on the Vecchi, G. A., B. J. Soden, A. T. Wittenberg, I. M. Held, A. Leetmaa, and M. J. Harrison, summer climate of the Western Hemisphere. J. Clim., 20, 5021 5040. 2006: Weakening of tropical Pacific atmospheric circulation due to anthropogenic Wang, C., S. K. Lee, and D. B. Enfield, 2008b: Climate response to anomalously large forcing. Nature, 441, 73 76. and small Atlantic warm pools during the summer. J. Clim., 21, 2437 2450. Vera, C., and G. Silvestri, 2009: Precipitation interannual variability in South America Wang, H., 2001: The weakening of the Asian monsoon circulation after the end of from the WCRP-CMIP3 multi-model dataset. Clim. Dyn., 32, 1003 1014. 1970 s. Adv. Atmos. Sci., 376 386. Vera, C., et al., 2006: Toward a unified view of the American Monsoon Systems. J. Wang, L., and W. Chen, 2010: How well do existing indices measure the strength of Clim., 19, 4977 5000. the East Asian winter monsoon? Adv. Atmos. Sci., 27, 855 870. Vergara, W., et al., 2007: Visualizing future climate in Latin America: Results from Wang, L., R. Huang, L. Gu, W. Chen, and L. Kang, 2009a: Interdecadal variations the application of the Earth Simulator. In: Latin America and Caribbean Region of the east Asian winter monsoon and their association with quasi-stationary Sustainable Development Working Paper No. 30. The World Bank, Washington, planetary wave activity. J. Clim., 22, 4860 4872. DC, 82 pp. Wang, L., W. Chen, W. Zhou, J. C. L. Chan, D. Barriopedro, and R. Huang, 2010: Effect Vial, J., and T. Osborn, 2012: Assessment of atmosphere-ocean general circulation of the climate shift around mid 1970s on the relationship between wintertime model simulations of winter northern hemisphere atmospheric blocking. Clim. Ural blocking circulation and East Asian climate. Int. J. Climatol., 30, 153 158. Dyn., 39, 95 112. Wang, S. Y., R. R. Gillies, E. S. Takle, and W. J. Gutowski, 2009b: Evaluation of Vigaud, N., B. Pohl, and J. Crétat, 2012: Tropical-temperate interactions over southern precipitation in the Intermountain Region as simulated by the NARCCAP Africa simulated by a regional climate model. Clim. Dyn., doi:10.1007/s00382- regional climate models. Geophys. Res. Lett., 36, L11704. 012-1314-3, 1 22. Wang, X., C. Z. Wang, W. Zhou, D. X. Wang, and J. Song, 2011: Teleconnected Vigaud, N., Y. Richard, M. Rouault, and N. Fauchereau, 2009: Moisture transport influence of North Atlantic sea surface temperature on the El Nino onset. Clim. between the South Atlantic Ocean and southern Africa: Relationships with Dyn., 37, 663 676. summer rainfall and associated dynamics. Clim. Dyn., 32, 113 123. Wanner, H., et al., 2001: North Atlantic Oscillation Concepts and studies. Surveys Villarini, G., and G. A. Vecchi, 2012: Twenty-first-century projections of North Atlantic in Geophysics, 22, 321 382. tropical storms from CMIP5 models. Nature Clim. Change, 2, 604 607. Ward, P., M. Marfai, Poerbandono, and E. Aldrian, 2011: Climate adaptation in the Vimont, D., M. Alexander, and A. Fontaine, 2009: Midlatitude excitation of tropical city of Jakarta. Chapter 13 in: Climate Adaptation and Flood Risk in Coastal variability in the Pacific: The role of thermodynamic coupling and seasonality. J. Cities [J. Aerts, W. Botzen, M. Bowman, P. Ward and P. Dircke (eds.)]. Routledge Clim., 22, 518 534. Earthscan, Amsterdam, Netherlands, 330 pp. Vimont, D. J., and J. P. Kossin, 2007: The Atlantic Meridional Mode and hurricane Watanabe, S., and Y. Kawatani, 2012: Sensitivity of the QBO to mean tropical activity. Geophys. Res. Lett., 34, L07709. upwelling under a changing climate simulated with an Earth System Model. J. Vincent, E., M. Lengaigne, C. Menkes, N. Jourdain, P. Marchesiello, and G. Madec, Meteorol. Soc. Jpn. II, 90A, 351 360. 2011: Interannual variability of the South Pacific Convergence Zone and Watterson, I., A. C. Hirst, and L. D. Rotstayn, 2013: A skill-score based evaluation of implications for tropical cyclone genesis. Clim. Dyn., 36, 1881 1896. simulated Australian climate. Australian Meteorol. Oceanogr. J., 63, 181-190. 14 Vincent, L. A., W. A. van Wijngaarden, and R. Ropkinson, 2007: Surface temperature and humidity trends in Canda for 1953 2005. J. Clim., 20, 5100 5113. 1306 Climate Phenomena and their Relevance for Future Regional Climate Change Chapter 14 Watterson, I. G., 2009: Components of precipitation and temperature anomalies and Xie, S. P., Y. Du, G. Huang, X. T. Zheng, H. Tokinaga, K. M. Hu, and Q. Y. Liu, 2010a: change associated with modes of the Southern Hemisphere. Int. J. Climatol., 29, Decadal shift in El Nino influences on Indo-western Pacific and east Asian 809 826. climate in the 1970s. J. Clim., 23, 3352 3368. Webster, P. J., A. M. Moore, J. P. Loschnigg, and R. R. Leben, 1999: Coupled ocean- Xie, S. P. D., C. Deser, G. A. Vecchi, J. Ma, H. Teng, and A. T. Wittenberg, 2010b: Global atmosphere dynamics in the Indian Ocean during 1997 98. Nature, 401, 356 warming pattern formation: Sea surface temperature and rainfall. J. Clim., 23, 360. 966 986. Weller, E., and W. Cai, 2013: Realism of the Indian Ocean Dipole in CMIP5 models: Xu, Y., X.-J. Gao, and F. Giorgi, 2009: Regional variability of climate change hot-spots The implication for climate projections. J. Clim., 26, 6649 6659. in East Asia. Adv. Atmos. Sci., 26, 783 792. Widlansky, M., P. Webster, and C. Hoyos, 2011: On the location and orientation of the Xue, Y., et al., 2010: Intercomparison and analyses of the climatology of the West South Pacific Convergence Zone. Clim. Dyn., 36, 561 578. African Monsoon in the West African Monsoon Modeling and Evaluation project Widlansky, M. J., et al., 2013: Changes in South Pacific rainfall bands in a warming (WAMME) first model intercomparison experiment. Clim. Dyn., 35, 3 27. climate. Nature Clim. Change, 3, 417 423. Yamada, Y., K. Oouchi, M. Satoh, H. Tomita, and W. Yanase, 2010: Projection of changes Wiedenmann, J. M., A. R. Lupo, I. I. Mokhov, and E. A. Tikhonova, 2002: The climatology in tropical cyclone activity and cloud height due to greenhouse warming: Global of blocking anticyclones for the Northern and Southern Hemispheres: Block cloud-system-resolving approach. Geophys. Res. Lett., 37, L07709. intensity as a diagnostic. J. Clim., 15, 3459 3473. Yamagata, T., S. K. Behera, J.-J. Luo, S. Masson, M. Jury, and S. A. Rao, 2004: Coupled Wilcox, L. J., A. J. Charlton-Perez, and L. J. Gray 2012: Trends in Austral jet ocean-atmosphere variability in the tropical Indian Ocean. Earth Clim. Ocean- position in ensembles of high- and low-top CMIP5 models. J. Geophys. Res., Atmos. Interact., American Geophysical Union, 189 212. doi:10.1029/2012JD017597. Yamazaki, A., and H. Itoh, 2009: Selective absorption mechanism for the maintenance Williams, A., and C. Funk, 2011: A westward extension of the warm pool leads to a of blocking. Geophys. Res. Lett., 36, L05803. westward extension of the Walker circulation, drying eastern Africa. Clim. Dyn., Yan, H., L. G. Sun, Y. H. Wang, W. Huang, S. C. Qiu, and C. Y. Yang, 2011: A record 37, 2417 2435. of the Southern Oscillation Index for the past 2,000 years from precipitation Wilson, A. B., D. H. Bromwich, and K. M. Hines, 2012: Evaluation of Polar WRF proxies. Nature Geosci., 4, 611 614. forecasts on the Arctic System Reanalysis domain:2. Atmopsheric hydrologic Yang, S., and J. H. Christensen, 2012: Arctic sea ice reduction and European cold cycle. J. Geophys. Res., 17, D04107. winters in CMIP5 climate change experiments. Geophys. Res. Lett., 39, L20707. Wittenberg, A., 2004: Extended wind stress analyses for ENSO. J. Clim., 17, 2526 Yatagai, A., K. Kamiguchi, O. Arakawa, A. Hamada, N. Yasutomi, and A. Kitoh, 2012: 2540. APHRODITE: Constructing a long-term daily gridded precipitation dataset for Wittenberg, A. T., 2009: Are historical records sufficient to constrain ENSO Asia based on a dense network of rain gauges. Bull. Am. Meteorol. Soc., 93, simulations? Geophys. Res. Lett., 36, L12702. 1401 1415. Woollings, T., 2008: Vertical structure of anthropogenic zonal-mean atmospheric Ye, Z. Q., and W. W. Hsieh, 2008: Changes in ENSO and associated overturning circulation change. Geophys. Res. Lett., 35, L19702. circulations from enhanced greenhouse gases by the end of the twentieth Woollings, T., 2010: Dynamical influences on European climate: An uncertain future. century. J. Clim., 21, 5745 5763. Philos. Trans. R. Soc. London A, 368, 3733 3756. Yeh, S.-W., Y.-G. Ham, and J.-Y. Lee, 2012: Changes in the tropical Pacific SST Trend Woollings, T., A. Charlton-Perez, S. Ineson, A. G. Marshall, and G. Masato, 2010: from CMIP3 to CMIP5 and its implication of ENSO. J. Clim., 25, 7764 7771. Associations between stratospheric variability and tropospheric blocking. J. Yeh, S.-W., B. P. Kirtman, J.-S. Kug, W. Park, and M. Latif, 2011: Natural variability of Geophys. Res. Atmos., 115, D06108. the central Pacific El Nino event on multi-centennial timescales. Geophys. Res. Woollings, T., J. Gregory, J. Pinto, M. Reyers, and D. Brayshaw, 2012: Response of Lett., 38, L02704. the North Atlantic storm track to climate change shaped by ocean-atmosphere Yeh, S. W., and B. P. Kirtman, 2005: Pacific decadal variability and decadal ENSO coupling. Nature Geosci., 5, 313 317. amplitude modulation. Geophys. Res. Lett., 32, L05703. Wu, J., and X. J. Gao, 2013: A gridded daily observation dataset over China region Yeh, S. W., J. S. Kug, B. Dewitte, M. H. Kwon, B. P. Kirtman, and F. F. Jin, 2009: El Nino and comparison with the other datasets. Chin. J. Geophys (in Chinese), 56, in a changing climate. Nature, 461, 511 515. 1102 1111. Yeung, J. K., J. A. Smith, G. Villarini, A. A.N., M. L. Baeck, and W. F. Krajewski, 2011: Wu, L. G., 2007: Impact of Saharan air layer on hurricane peak intensity. Geophys. Analyses of the warm season rainfall climatology of the northeastern US using Res. Lett., 34, doi: 10.1029/2007GL029564. regional climate model simulations and radar rainfall fields. Adv. Water Resour., Wu, Q., and X. Zhang, 2010: Observed forcing-feedback processes between Northern 34, 184 204. Hemisphere atmospheric circulation and Arctic sea ice coverage. J. Geophys. Res. Yin, J. H., 2005: A consistent poleward shift of the storm tracks in simulations of 21st Atmos., 115., doi: 10.1029/2009jd013574. century climate. Geophys. Res. Lett., 32, 4. Wu, Q. G., and D. J. Karoly, 2007: Implications of changes in the atmospheric Yin, L., R. Fu, E. Shevliakova, and R. Dickinson, 2012: How well can CMIP5 simulate circulation on the detection of regional surface air temperature trends. Geophys. precipitation and its controlling processes over tropical South America? Clim. Res. Lett., 34, L08703. Dyn., doi:10.1007/s00382-012-1582 y. Wu, R., B. P. Kirtman, and V. Krishnamurthy, 2008: An asymmetric mode of tropical Ying, M., T. R. Knutson, H. Kamahori, and T.-C. Lee, 2012: Impacts of climate change Indian Ocean rainfall variability in boreal spring. J. Geophys. Res. Atmos., 113, on tropical cyclones in the Western North Pacific Basin. Part II: Late twenty-first D05104. century projections. Trop. Cyclone Res. Rev., 1, 231 241. Wu, Y., M. Ting, R. Seager, H.-P. Huang, and M. A. Cane, 2011: Changes in storm Yokoi, S., and Y. Takayabu, 2009: Multi-model projection of global warming impact tracks and energy transports in a warmer climate simulated by the GFDL CM2.1 on tropical cyclone genesis frequency over the western north Pacific. J. Meteorol. model. Clim. Dyn., 37, 53 72. Soc. Jpn., 87, 525 538. Xie, P., and P. A. Arkin, 1997: Global precipitation: A 17-year monthly analysis based Yosef, Y., H. Saaroni, and P. Alpert, 2009: Trends in daily rainfall intensity over Israel on gauge observations, satellite estimates, and numerical model outputs. Bull. 1950/1 2003/4. Open Atmos. Sci. J., 3, 196 203. Am. Meteorol. Soc., 78, 2539 2558. Yu, B., and F. W. Zwiers, 2010: Changes in equatorial atmospheric zonal circulations Xie, S.-P., et al., 2007: A regional ocean atmosphere model for Eastern Pacific in recent decades. Geophys. Res. Lett., 37, L05701. climate: Toward reducing tropical biases. J. Clim., 20, 1504 1522. Yu, R. C., B. Wang, and T. J. Zhou, 2004: Tropospheric cooling and summer monsoon Xie, S. P., and S. G. H. Philander, 1994: A coupled ocean-atmosphere model of weakening trend over East Asia. Geophys. Res. Lett., 31, L22212. relevance to the ITCZ in the eastern Pacific. Tellus A, 46, 340 350. Zahn, M., and H. von Storch, 2010: Decreased frequency of North Atlantic polar lows Xie, S. P., and J. A. Carton, 2004: Tropical Atlantic variability: Patterns, mechanisms, associated with future climate warming. Nature, 467, 309 312. and impacts. Earth Clim. Ocean-Atmos. Interact., American Geophysical Union, Zahn, M., and R. Allan, 2011: Changes in water vapor transports of the ascending 121 142. branch of the tropical circulation. J. Geophys. Res. Atmos., 116, doi: Xie, S. P., K. Hu, J. Hafner, H. Tokinaga, Y. Du, G. Huang, and T. Sampe, 2009: Indian 10.1029/2011JD016206. Ocean capacitor effect on Indo-western Pacific climate during the summer Zanchettin, D., A. Rubino, and J. Jungclaus, 2010: Intermittent multidecadal-to- following El Nino. J. Clim., 22, 730 747. centennial fluctuations dominate global temperature evolution over the last 14 millennium. Geophys. Res. Lett., 37, L14702. 1307 Chapter 14 Climate Phenomena and their Relevance for Future Regional Climate Change Zappa, G., L. C. Shaffrey, and K. I. Hodges, 2013a: The ability of CMIP5 models to Zhou, T. J., D. Y. Gong, J. Li, and B. Li, 2009b: Detecting and understanding the multi- simulate North Atlantic extratropical cyclones. J. Clim., doi:10.1175/jcli-d-12- decadal variability of the East Asian Summer Monsoon Recent progress and 00501.1. state of affairs. Meteorol. Z., 18, 455 467. Zappa, G., L. C. Shaffrey, K. I. Hodges, P. G. Sansom, and D. B. Stephenson, 2013b: Zhou, T. J., et al., 2009c: The CLIVAR C20C project: Which components of the Asian- A multi-model assessment of future projections of North Atlantic and European Australian monsoon circulation variations are forced and reproducible? Clim. extratropical cyclones in the CMIP5 climate models. J. Clim., doi:10.1175/jcli-d- Dyn., 33, 1051 1068. 12-00573.1. Zhou, W., J. C. L. Chan, W. Chen, J. Ling, J. G. Pinto, and Y. Shao, 2009d: Synoptic-scale Zebiak, S. E., 1993: Air sea interaction in the equatorial Atlantic region. J. Clim., 6, controls of persistent low temperature and icy weather over southern China in 1567 1586. January 2008. Mon. Weather Rev., 137, 3978 3991. Zhang, C., 2005: Madden-Julian Oscillation. Rev. Geophys., 43, RG2003. Zhu, C., B. Wang, W. Qian, and B. Zhang, 2012: Recent weakening of northern East Zhang, H., P. Liang, A. Moise, and L. Hanson, 2013a: The response of summer Asian summer monsoon: A possible response to global warming. Geophys. Res. monsoon onset/retreat in Sumatra-Java and tropical Australia region to global Lett., 39, doi: 10.1029/2012GL051155. warming in CMIP3 models. Clim. Dyn., 40, 377 399. Zhu, Y. L., and H. J. Wang, 2010: The Arctic and Antarctic Oscillations in the IPCC AR4 Zhang, J., U. S. Bhatt, W. V. Tangborn, and C. S. Lingle, 2007: Climate downscaling Coupled Models. Acta Meteorol. Sin., 24, 176 188. for estimating glacier mass balances in northwestern North America: Validation with a USGS benchmark glacier. Geophys. Res. Lett., 34, L21505. Zhang, L., L. Wu, and L. Yu, 2011a: Oceanic origin of a recent La Nia-like trend in the tropical Pacific. Adv. Atmos. Sci., 28, 1109 1117. Zhang, L. X., and T. J. Zhou, 2011: An assessment of monsoon precipitation changes during 1901 2001. Clim. Dyn., 37, 279 296. Zhang, M. H., and H. Song, 2006: Evidence of deceleration of atmospheric vertical overturning circulation over the tropical Pacific. Geophys. Res. Lett., 33, L12701. Zhang, Q., Y. Guan, and H. Yang, 2008: ENSO amplitude change in observation and coupled models. Adv. Atmos. Sci., 25, 361 366. Zhang, R., and T. L. Delworth, 2006: Impact of Atlantic multidecadal oscillations on India/Sahel rainfall and Atlantic hurricanes. Geophys. Res. Lett., 33, L17712. Zhang, R., and T. L. Delworth, 2009: A new method for attributing climate variations over the Atlantic Hurricane Basin s main development region. Geophys. Res. Lett., 36, L06701. Zhang, R., et al., 2013b: Have aerosols caused the observed Atlantic multidecadal variability? J. Atmos. Sci., 70, 1135 1144. Zhang, S., and B. Wang, 2008: Global summer monsoon rainy seasons. Int. J. Climatol., 28, 1563 1578. Zhang, X., R. Brown, L. Vincent, W. Skinner, Y. Feng, and E. Mekis, 2011b: Canadian climate trends, 1950 2007. Canadian Biodiversity: Ecosystem Status and Trends 2012, Technical Thematic Report No. 5. Canadian Councils of Resource Ministers, Ottowa, iv + 21p. Zhang, X., et al., 2005: Trends in Middle East climate extreme indices from 1950 to 2003. J. Geophys. Res. Atmos., 110, doi: 10.1029/2005JD006181. Zhang, X. B., F. W. Zwiers, and P. A. Stott, 2006: Multimodel multisignal climate change detection at regional scale. J. Clim., 19, 4294 4307. Zhao, M., and I. Held, 2012: TC-permitting GCM simulations of hurricane frequency response to sea surface temperature anomalies projected for the late twenty- first century. J. Clim., 25, 2995 3009. Zhao, M., I. M. Held, S. J. Lin, and G. A. Vecchi, 2009: Simulations of global hurricane climatology, interannual variability, and response to global warming using a 50-km resolution GCM. J. Clim., 22, 6653 6678. Zheng, X.-T., S.-P. Xie, and Q. Liu, 2011: Response of the Indian Ocean basin mode and its capacitor effect to global warming. J. Clim., 24, 6146 6164. Zheng, X.-T., Y. Du, L. Liu, G. Huang, and Q. Liu, 2013: Indian Ocean Dipole response to global warming in the CMIP5 multi-model ensemble. J. Clim., 26, 6067 6080. Zheng, X. T., S. P. Xie, G. A. Vecchi, Q. Y. Liu, and J. Hafner, 2010: Indian Ocean Dipole response to global warming: Analysis of ocean-atmospheric feedbacks in a coupled model. J. Clim., 23, 1240 1253. Zhou, T., B. Wu, and B. Wang, 2009a: How well do atmospheric general circulation models capture the leading modes of the interannual variability of the Asian- Australian monsoon? J. Clim., 22, 1159 1173. Zhou, T., R. Yu, H. Li, and B. Wang, 2008a: Ocean forcing to changes in global monsoon precipitation over the recent half-century. J. Clim., 21, 3833 3852. Zhou, T. J., and R. C. Yu, 2005: Atmospheric water vapor transport associated with typical anomalous summer rainfall patterns in China. J. Geophys. Res. Atmos., 110, D08104. Zhou, T. J., and L. W. Zou, 2010: Understanding the predictability of East Asian summer monsoon from the reproduction of land-sea thermal contrast change in AMIP-type simulation. J. Clim., 23, 6009 6026. Zhou, T. J., L. X. Zhang, and H. M. Li, 2008b: Changes in global land monsoon area and total rainfall accumulation over the last half century. Geophys. Res. Lett., 14 35, L16707. 1308 Introduction Chapter 2 Annexes AI Annex I: Atlas of Global and Regional Climate Projections Editorial Team: Geert Jan van Oldenborgh (Netherlands), Matthew Collins (UK), Julie Arblaster (Australia), Jens Hesselbjerg Christensen (Denmark), Jochem Marotzke (Germany), Scott B. Power (Australia), Markku Rummukainen (Sweden), Tianjun Zhou (China) Advisory Board: David Wratt (New Zealand), Francis Zwiers (Canada), Bruce Hewitson (South Africa) Review Editor Team: Pascale Delecluse (France), John Fyfe (Canada), Karl Taylor (USA) This annex should be cited as: IPCC, 2013: Annex I: Atlas of Global and Regional Climate Projections [van Oldenborgh, G.J., M. Collins, J. Arblaster, J.H. Christensen, J. Marotzke, S.B. Power, M. Rummukainen and T. Zhou (eds.)]. In: Climate Change 2013: The Physical Sci- ence Basis. Contribution of Working Group I to the Fifth Assessment Report of the Intergovernmental Panel on Climate Change [Stocker, T.F., D. Qin, G.-K. Plattner, M. Tignor, S.K. Allen, J. Boschung, A. Nauels, Y. Xia, V. Bex and P.M. Midgley (eds.)]. Cambridge University Press, Cambridge, United Kingdom and New York, NY, USA. 1311 Table of Contents Introduction and Scope............................................................ 1313 AI Technical Notes........................................................................... 1313 References ................................................................................ 1314 Atlas ......................................................................................... 1317 Figures AI.4 to AI.7: World........................................................... 1318 Figures AI.8 to AI.11: Arctic.......................................................... 1322 Figures AI.12 to AI.15: High latitudes.......................................... 1326 Figures AI.16 to AI.19: North America (West).............................. 1330 Figures AI.20 to AI.23: North America (East)................................ 1334 Figures AI.24 to AI.27: Central America and Caribbean............... 1338 Figures AI.28 to AI.31: Northern South America.......................... 1342 Figures AI.32 to AI.35: Southern South America.......................... 1346 Figures AI.36 to AI.39: North and Central Europe........................ 1350 Figures AI.40 to AI.43: Mediterranean and Sahara...................... 1354 Figures AI.44 to AI.47: West and East Africa................................ 1358 Figures AI.48 to AI.51: Southern Africa and West Indian Ocean............................................................................... 1362 Figures AI.52 to AI.55: West and Central Asia.............................. 1366 Figures AI.56 to AI.59: Eastern Asia and Tibetan Plateau............. 1370 Figures AI.60 to AI.63: South Asia................................................ 1374 Figures AI.64 to AI.67: Southeast Asia......................................... 1378 Figures AI.68 to AI.71: Australia and New Zealand...................... 1382 Figures AI.72 to AI.75: Pacific Islands region............................... 1386 Figures AI.76 to AI.79: Antarctica................................................ 1390 Supplementary Material Supplementary Material is available in online versions of the report. 1312 Atlas of Global and Regional Climate Projections Annex I Introduction and Scope are discussed in Sections 11.3.1 and 12.2.2 to 12.2.3. The reliability of past trends is assessed in Box 11.2, which concludes that the time This Annex presents a series of figures showing global and regional series and maps cannot be interpreted literally as probability density patterns of climate change computed from global climate model functions. They should not be interpreted as forecasts . output gathered as part of the Coupled Model Intercomparison Project Phase 5 (CMIP5; Taylor et al., 2012). Maps of surface air temperature Projections of future climate change are conditional on assumptions of change and relative precipitation change (i.e., change expressed as a climate forcing, affected by shortcomings of climate models and inevi- AI percentage of mean precipitation) in different seasons are presented tably also subject to internal variability when considering specific peri- for the globe and for a number of different sub-continental-scale ods. Projected patterns of climate change may differ from one climate regions. Twenty-year average changes for the near term (2016 2035), model generation to the next due to improvements in models. Some for the mid term (2046 2065) and for the long term (2081 2100) are model-inadequacies are common to all models, but so are many pat- given, relative to a reference period of 1986 2005. Time series for tem- terns of change across successive generations of models, which gives perature and relative precipitation changes are shown for global land some confidence in projections. The information presented is intended and sea averages, the 26 sub-continental SREX (IPCC Special Report on to be only a starting point for anyone interested in more detailed infor- Managing the Risks of Extreme Events and Disasters to Advance Cli- mation on projections of future climate change and complements the mate Change Adaptation) regions (IPCC, 2012) augmented with polar assessment in Chapters 11, 12 and 14. regions and the Caribbean, two Indian Ocean and three Pacific Ocean regions. In total this Annex gives projections for 35 regions, 2 variables Technical Notes and 2 seasons. The projections are made under the Representative Concentration Pathway (RCP) scenarios, which are introduced in Chap- Data and Processing: The figures have been constructed using the ter 1 with more technical detail given in Section 12.3 (also note the CMIP5 model output available at the time of the AR5 cut-off for discussion of near-term biases in Sections 11.3.5.1 and 11.3.6.1). Maps accepted papers (15 March 2013). This data set comprises 32/42/25/39 are shown only for the RCP4.5 scenario; however, the time series pre- scenario experiments for RCP2.6/4.5/6.0/8.5 from 42 climate models sented show how the area-average response varies among the RCP2.6, (Table AI.1). Only concentration-driven experiments are used (i.e., those RCP4.5, RCP6.0 and RCP8.5 scenarios. Spatial maps for the other RCP in which concentrations rather than emissions of greenhouse gases are scenarios and additional seasons are presented in the Annex I Supple- prescribed) and only one ensemble member from each model is select- mentary Material. Figures AI.1 and AI.2 give a graphical explanation ed, even if multiple realizations exist with different initial conditions of aspects of both the time series plots and the spatial maps. While and different realizations of natural variability. Hence each model is some of the background to the information presented is given here, given equal weight. Maps from only one scenario (RCP4.5) are shown discussion of the maps and time series, as well as important additional but time series are included from all four RCPs. Maps from other RCPs background, is provided in Chapters 9, 11, 12 and 14. Figure captions are presented in the Annex I Supplementary Material. on each page of the Atlas reference the specific sub-sections in the report relevant to the regions considered on that page. Reference Period: Projections are expressed as anomalies with respect to the reference period of 1986 2005 for both time series and The projection of future climate change involves the careful evaluation spatial maps (i.e., differences between the future period and the ref- of models, taking into account uncertainties in observations and con- erence period). Thus the changes are relative to the climate change sideration of the physical basis of the findings, in order to characterize that has already occurred since the pre-industrial period and which is the credibility of the projections and assess their sensitivity to uncer- discussed in Chapters 2 and 10. For quantities where the trend is larger tainties. As discussed in Chapter 9, different climate models have vary- than the natural variability such as large-area temperature changes, a ing degrees of success in simulating past climate variability and mean more recent reference period would give better estimates (see Section state when compared to observations. Verification of regional trends 11.3.6.1); for quantities where the natural variability is much larger is discussed in Box 11.2 and provides further information on the cred- than the trend a longer reference period would be preferable. ibility of model projections. The information presented in this Annex is based entirely on all available CMIP5 model output with equal weight Equal Model Weighting: Model evaluation uses a multitude of tech- given to each model or version with different parameterizations. niques (see Chapter 9) and there is no consensus in the community about how to use this information to assign likelihood to different Complementary methods for making quantitative projections, in which model projections. Consequently, the different CMIP5 models used for model output is combined with information about model performance the projections in the Atlas are all considered to give equally likely pro- using statistical techniques, exist and should be considered in impacts jections in the sense of one model, one vote . Models with variations studies (see Sections 9.8.3, 11.3.1 and 12.2.2 to 12.2.3). Although in physical parameterization schemes are treated as distinct models. results from the application of such methods can be assessed along- side the projections from CMIP5 presented here, it is beyond the scope Variables: Two variables have been plotted: surface air temperature of this Annex. Nor do the simple maps provided represent a robust change and relative precipitation change. The relative precipitation estimate of the uncertainty associated with the projections. Here the change is defined as the percentage change from the 1986 2005 ref- range of model spread is provided as a simple, albeit imperfect, guide erence period in each ensemble member. For the time series, the vari- to the range of possible futures (including the effect of natural vari- ables are first averaged over the domain and then the changes from ability). Alternative approaches used to estimate projection uncertainty the reference period are computed. This implies that in regions with 1313 Annex I Atlas of Global and Regional Climate Projections large climatological precipitation gradients, the change is generally of ensemble members is shown, on the right the 75th percentile. The dominated by the areas with the most precipitation. median is shown in the middle (different from similar plots in Chapters 11 and 12 and the time series which show the multi-model mean). Seasons: For temperature, the standard meteorological seasons June The distribution combines the effects of natural variability and model to August and December to February are shown, as these often corre- spread. The colour scale is kept constant over all maps. spond roughly with the warmest and coldest seasons. The annual mean and remaining seasons, March to May and September to October can Hatching: Hatching indicates regions where the magnitude of the AI be found in the Annex I Supplementary Material. For precipitation, the change of the 20-year mean is less than 1 standard deviation of mod- half-years April to September and October to March are shown so that el-estimated present-day natural variability of 20-year mean differ- in most monsoon areas the local rain seasons are entirely contained ences. The natural variability is estimated using all pre-industrial con- within the seasonal range plotted. Because the seasonal average is trol runs which are at least 500 years long. The first 100 years of the computed first, followed by the percentile change, these numbers are pre-industrial are ignored. The natural variability is then calculated for dominated by the rainy months within the half-year. The annual means every grid point as the standard deviation of non-overlapping 20-year are included in the Supplementary Material. means after a quadratic fit is subtracted at every grid point to eliminate model drift. This is multiplied by the square root of 2, a factor that Regions: In addition to the global maps, the areas defined in the SREX arises as the comparison is between two distributions of numbers. The (IPCC, 2012) are plotted with the addition of six regions containing the median across all models of that quantity is used. This characterizes Caribbean, Indian Ocean and Pacific Island States and land and sea the typical difference between two 20-year averages that would be areas of the two polar regions. For regions containing large land-areas, expected due to unforced internal variability. The hatching is applied averages are computed only over land grid points only. For ocean to all maps so, for example, if the 25th percentile of the distribution regions, averages are computed over both land and ocean grid points of model projections is less than 1 standard deviation of natural vari- (see figure captions). A grid box is considered land if the land fraction ability, it is hatched. is larger than 50% and sea if it is smaller than this. SREX regions with long coastlines (west coast of South America, North Europe, South- The hatching can be interpreted as some indication of the strength of east Asia) therefore include some influence of the ocean. Note that the future anomalies from present-day climate, when compared to the temperature and precipitation over islands may be very different from strength of present day internal 20-year variability. It either means that those over the surrounding sea. the change is relatively small or that there is little agreement between models on the sign of the change. It is presented only as a guide Time Series: For each of the resulting areas the areal mean is comput- to assessing the strength of change as the difference between two ed on the original model grid using land, sea or all points, depending on 20-year intervals. Using other measures of natural variability would the definition of the region (see above). As an indication of the model give smaller or larger hatched areas, but the colours underneath the uncertainty and natural variability, the time series of each model and hatching would not be very different. Other methods of hatching and scenario over the common period 1900 2100 are shown on the top of stippling are possible (see Box 12.1) and, in cases where such informa- the page as anomalies relative to 1986 2005 (the seasons December tion is critical, it is recommended that thorough attention is paid to to February and October to March are counted towards the second assessing significance using a statistical test appropriate to the prob- year in the interval). The multi-model ensemble means are also shown. lem being considered. Finally, for the period 2081 2100, the 20-year means are computed and the box-and-whisker plots show the 5th, 25th, 50th (median), Scenarios: Spatial patterns of changes for scenarios other than RCP4.5 75th and 95th percentiles sampled over the distribution of the 20-year can be found in the Annex I Supplementary Material. means of the model time series indicated in Table AI.1, including both natural variability and model spread. In the 20-year means the natu- ral variability is suppressed relative to the annual values in the time References series whereas the model uncertainty is the same. Note that owing to a smaller number of models, the box-and-whisker plots for the RCP2.6 IPCC, 2012: Managing the Risks of Extreme Events and Disasters to Advance Climate Change Adaptation. A Special Report of Working Groups I and II of the Intergov- scenario and especially the RCP6.0 scenario are less certain than those ernmental Panel on Climate Change [C. B. Field, V. Baros, T. F. Stocker, D. Qin, D. for RCP4.5 and RCP8.5. J. Dokken, K. L. Ebi, M. D. Mastrandrea, K .J. Mach, G.-K. Plattner, S. K. Allen, M. Tignor and P. M. Midgley (eds.)]. Cambridge University Press, Cambridge, United Spatial Maps: The maps in the Atlas show, for an area encompassing Kingdom, and New York, NY, USA, 582 pp. two or three regions, the difference between the periods 2016 2035, Taylor, K. E., R. J. Stouffer, and G. A. Meehl, 2012: A summary of the CMIP5 experi- ment design. Bull. Am. Meteorol. Soc., 93, 485 498. 2046 2065 and 2081 2100 and the reference period 1986 2005. As local projections of climate change are uncertain, a measure of the range of model projections is shown in addition to the median response of the model ensemble interpolated to a common 2.5° grid (the interpolation was done bilinearly for surface air temperature and first order conservatively for precipitation). It should again be empha- sized (see above) that this range does not represent the full uncertainty in the projection. On the left, the 25th percentile of the distribution 1314 Atlas of Global and Regional Climate Projections Annex I Table AI.1 | The CMIP5 models used in this Annex for each of the historical and RCP scenario experiments. A number in each column is the identifier of the single ensemble member from that model that is used. A blank indicates no run was used, usually because that scenario run was not available. For the pre-industrial control column (piControl), a tas indicates that those control simulations are used in the estimate of internal variability of surface air temperature and a pr indicates that those control simulations are used in the estimate of precipitation internal variability. CMIP5 Model Name piControl Historical RCP2.6 RCP4.5 RCP6.0 RCP8.5 ACCESS1-0 tas/pr 1 1 1 ACCESS1-3 tas/pr 1 1 1 AI bcc-csm1-1 tas/pr 1 1 1 1 1 bcc-csm1-1-m 1 1 1 1 BNU-ESM tas/pr 1 1 1 1 CanESM2 tas/pr 1 1 1 1 CCSM4 tas/pr 1 1 1 1 1 CESM1-BGC tas/pr 1 1 1 CESM1-CAM5 1 1 1 1 1 CMCC-CM 1 1 1 CMCC-CMS tas/pr 1 1 1 CNRM-CM5 tas/pr 1 1 1 1 CSIRO-Mk3-6-0 tas/pr 1 1 1 1 1 EC-EARTH 8 8 8 8 FGOALS-g2 tas/pr 1 1 1 1 FIO-ESM tas/pr 1 1 1 1 1 GFDL-CM3 tas/pr 1 1 1 1 1 GFDL-ESM2G tas/pr 1 1 1 1 1 GFDL-ESM2M tas/pr 1 1 1 1 1 GISS-E2-H p1 1 1 1 1 1 GISS-E2-H p2 tas/pr 1 1 1 1 1 GISS-E2-H p3 tas/pr 1 1 1 1 1 GISS-E2-H-CC 1 1 GISS-E2-R p1 1 1 1 1 1 GISS-E2-R p2 pr 1 1 1 1 1 GISS-E2-R p3 pr 1 1 1 1 1 GISS-E2-R-CC 1 1 HadGEM2-AO 1 1 1 1 1 HadGEM2-CC 1 1 1 HadGEM2-ES 2 2 2 2 2 inmcm4 tas/pr 1 1 1 IPSL-CM5A-LR tas/pr 1 1 1 1 1 IPSL-CM5A-MR 1 1 1 1 1 IPSL-CM5B-LR 1 1 1 MIROC5 tas/pr 1 1 1 1 1 MIROC-ESM tas/pr 1 1 1 1 1 MIROC-ESM-CHEM 1 1 1 1 1 MPI-ESM-LR tas/pr 1 1 1 1 MPI-ESM-MR tas/pr 1 1 1 1 MPI-ESM-P tas/pr MRI-CGCM3 tas/pr 1 1 1 1 1 NorESM1-M tas/pr 1 1 1 1 1 NorESM1-ME 1 1 1 1 1 Number of models 42 32 42 25 39 1315 Annex I Atlas of Global and Regional Climate Projections Variable Region Season Temperature change World (land) December-February RCP8.5 8 8 RCP6.0 Thick lines: 95%-tile RCP4.5 RCP2.6 Ensemble mean 75%-tile AI 6 6 historical Median Units 25%-tile 4 Thin lines: Individual 4 5%-tile (°C) model simulations 2 2 0 0 -2 -2 1900 1950 2000 2050 2100 2081-2100 mean Year Figure AI.1 | Explanation of the features of a typical time series figure presented in Annex I. Variable Scenario Time period Season Percentile of multi model distribution Units Colour scale indicates changes with respect to 1986-2005 average Figure AI.2 | Explanation of the features of a typical spatial map presented in Annex I. Hatching indicates regions where the magnitude of the 25th, median or 75th p ­ ercentile of the 20-year mean change is less than 1 standard deviation of model-estimated natural variability of 20-year mean differences. 1316 Atlas of Global and Regional Climate Projections Annex I Atlas AI Figure AI.3 | Overview of the SREX, ocean and polar regions used. Figures AI.4 to AI.7: World Figures AI.44 to AI.47: West and East Africa Figures AI.8 to AI.11: Arctic Figures AI.48 to AI.51: Southern Africa and West Indian Ocean Figures AI.12 to AI.15: High latitudes Figures AI.52 to AI.55: West and Central Asia Figures AI.16 to AI.19: North America (West) Figures AI.56 to AI.59: Eastern Asia and Tibetan Plateau Figures AI.20 to AI.23: North America (East) Figures AI.60 to AI.63: South Asia Figures AI.24 to AI.27: Central America and Caribbean Figures AI.64 to AI.67: Southeast Asia Figures AI.28 to AI.31: Northern South America Figures AI.68 to AI.71: Australia and New Zealand Figures AI.32 to AI.35: Southern South America Figures AI.72 to AI.75: Pacific Islands region Figures AI.36 to AI.39: North and Central Europe Figures AI.76 to AI.79: Antarctica Figures AI.40 to AI.43: Mediterranean and Sahara 1317 Annex I Atlas of Global and Regional Climate Projections Temperature change World (land) December-February Temperature change World (sea) December-February RCP8.5 RCP8.5 8 RCP6.0 8 8 RCP6.0 8 RCP4.5 RCP4.5 RCP2.6 RCP2.6 6 historical 6 6 historical 6 4 4 4 4 (°C) (°C) 2 2 2 2 AI 0 0 0 0 -2 -2 -2 -2 1900 1950 2000 2050 2100 2081-2100 mean 1900 1950 2000 2050 2100 2081-2100 mean Figure AI.4 | (Top left) Time series of temperature change relative to 1986 2005 averaged over land grid points over the globe in December to February. (Top right) Same for sea grid points. Thin lines denote one ensemble member per model, thick lines the CMIP5 multi-model mean. On the right-hand side the 5th, 25th, 50th (median), 75th and 95th percentiles of the distribution of 20-year mean changes are given for 2081 2100 in the four RCP scenarios. (Below) Maps of temperature changes in 2016 2035, 2046 2065 and 2081 2100 with respect to 1986 2005 in the RCP4.5 scenario. For each point, the 25th, 50th and 75th percentiles of the distribution of the CMIP5 ensemble are shown; this includes both natural variability and inter-model spread. Hatching denotes areas where the 20-year mean differences of the percentiles are less than the standard deviation of model-estimated present-day natural variability of 20-year mean differences. Sections 9.4.1.1, 9.6.1.1, 10.3.1.1.4, 11.3.2.1.2, 11.3.3.1, Box 11.2, 12.4.3.1 and 12.4.7 contain relevant information regarding the evaluation of models in this region, the model spread in the context of other methods of projecting changes and the role of modes of variability and other climate phenomena. 1318 Atlas of Global and Regional Climate Projections Annex I Temperature change World (land) June-August Temperature change World (sea) June-August RCP8.5 RCP8.5 8 RCP6.0 8 8 RCP6.0 8 RCP4.5 RCP4.5 RCP2.6 RCP2.6 6 historical 6 6 historical 6 4 4 4 4 (°C) (°C) 2 2 2 2 AI 0 0 0 0 -2 -2 -2 -2 1900 1950 2000 2050 2100 2081-2100 mean 1900 1950 2000 2050 2100 2081-2100 mean Figure AI.5 | (Top left) Time series of temperature change relative to 1986 2005 averaged over land grid points over the globe in June to August. (Top right) Same for sea grid points. Thin lines denote one ensemble member per model, thick lines the CMIP5 multi-model mean. On the right-hand side the 5th, 25th, 50th (median), 75th and 95th percentiles of the distribution of 20-year mean changes are given for 2081 2100 in the four RCP scenarios. (Below) Maps of temperature changes in 2016 2035, 2046 2065 and 2081 2100 with respect to 1986 2005 in the RCP4.5 scenario. For each point, the 25th, 50th and 75th percentiles of the distribution of the CMIP5 ensemble are shown; this includes both natural variability and inter-model spread. Hatching denotes areas where the 20-year mean differences of the percentiles are less than the standard deviation of model-estimated present-day natural variability of 20-year mean differences. Sections 9.4.1.1, 9.6.1.1, 10.3.1.1.4, 11.3.2.1.2, 11.3.3.1, Box 11.2, 12.4.3.1 and 12.4.7 contain relevant information regarding the evaluation of models in this region, the model spread in the context of other methods of projecting changes and the role of modes of variability and other climate phenomena. 1319 Annex I Atlas of Global and Regional Climate Projections Precipitation change World (land) October-March Precipitation change World (sea) October-March 25 25 25 25 RCP8.5 RCP8.5 20 RCP6.0 20 20 RCP6.0 20 RCP4.5 RCP4.5 RCP2.6 RCP2.6 15 historical 15 15 historical 15 10 10 10 10 (%) (%) 5 5 5 5 AI 0 0 0 0 -5 -5 -5 -5 -10 -10 -10 -10 -15 -15 -15 -15 1900 1950 2000 2050 2100 2081-2100 mean 1900 1950 2000 2050 2100 2081-2100 mean Figure AI.6 | (Top left) Time series of relative change relative to 1986 2005 in precipitation averaged over land grid points over the globe in October to March. (Top right) Same for sea grid points. Thin lines denote one ensemble member per model, thick lines the CMIP5 multi-model mean. On the right-hand side the 5th, 25th, 50th (median), 75th and 95th percentiles of the distribution of 20-year mean changes are given for 2081 2100 in the four RCP scenarios. (Below) Maps of precipitation changes in 2016 2035, 2046 2065 and 2081 2100 with respect to 1986 2005 in the RCP4.5 scenario. For each point, the 25th, 50th and 75th percentiles of the distribution of the CMIP5 ensemble are shown; this includes both natural variability and inter-model spread. Hatching denotes areas where the 20-year mean differences of the percentiles are less than the standard deviation of model-estimated present-day natural variability of 20-year mean differences. Sections 9.4.1.1, 9.6.1.1, 10.3.2.2, 11.3.2.3.1, Box 11.2, 12.4.5.2, 14.2 contain relevant information regarding the evaluation of models in this region, the model spread in the context of other methods of projecting changes and the role of modes of variability and other climate phenomena. 1320 Atlas of Global and Regional Climate Projections Annex I Precipitation change World (land) April-September Precipitation change World (sea) April-September 25 25 25 25 RCP8.5 RCP8.5 20 RCP6.0 20 20 RCP6.0 20 RCP4.5 RCP4.5 RCP2.6 RCP2.6 15 historical 15 15 historical 15 10 10 10 10 (%) (%) 5 5 5 5 0 0 0 0 AI -5 -5 -5 -5 -10 -10 -10 -10 -15 -15 -15 -15 1900 1950 2000 2050 2100 2081-2100 mean 1900 1950 2000 2050 2100 2081-2100 mean Figure AI.7 | (Top left) Time series of relative change relative to 1986 2005 in precipitation averaged over land grid points over the globe in April to September. (Top right) Same for sea grid points. Thin lines denote one ensemble member per model, thick lines the CMIP5 multi-model mean. On the right-hand side the 5th, 25th, 50th (median), 75th and 95th percentiles of the distribution of 20-year mean changes are given for 2081 2100 in the four RCP scenarios. (Below) Maps of precipitation changes in 2016 2035, 2046 2065 and 2081 2100 with respect to 1986 2005 in the RCP4.5 scenario. For each point, the 25th, 50th and 75th percentiles of the distribution of the CMIP5 ensemble are shown; this includes both natural variability and inter-model spread. Hatching denotes areas where the 20-year mean differences of the percentiles are less than the standard deviation of model-estimated present-day natural variability of 20-year mean differences. Sections 9.4.1.1, 9.6.1.1, 10.3.2.2, 11.3.2.3.1, Box 11.2, 12.4.5.2, 14.2 contain relevant information regarding the evaluation of models in this region, the model spread in the context of other methods of projecting changes and the role of modes of variability and other climate phenomena. 1321 Annex I Atlas of Global and Regional Climate Projections Temperature change Arctic (land) December-February Temperature change Arctic (sea) December-February 25 RCP8.5 25 25 RCP8.5 25 RCP6.0 RCP6.0 20 RCP4.5 20 20 RCP4.5 20 RCP2.6 RCP2.6 historical historical 15 15 15 15 10 10 10 10 (°C) (°C) 5 5 5 5 AI 0 0 0 0 -5 -5 -5 -5 -10 -10 -10 -10 1900 1950 2000 2050 2100 2081-2100 mean 1900 1950 2000 2050 2100 2081-2100 mean Figure AI.8 | (Top left) Time series of temperature change relative to 1986 2005 averaged over land grid points in the Arctic (67.5°N to 90°N) in December to February. (Top right) Same for sea grid points. Thin lines denote one ensemble member per model, thick lines the CMIP5 multi-model mean. On the right-hand side the 5th, 25th, 50th (median), 75th and 95th percentiles of the distribution of 20-year mean changes are given for 2081 2100 in the four RCP scenarios. (Below) Maps of temperature changes in 2016 2035, 2046 2065 and 2081 2100 with respect to 1986 2005 in the RCP4.5 scenario. For each point, the 25th, 50th and 75th percentiles of the distribution of the CMIP5 ensemble are shown; this includes both natural variability and inter-model spread. Hatching denotes areas where the 20-year mean differences of the percentiles are less than the standard deviation of model-estimated present-day natural variability of 20-year mean differences. Sections 9.4.1.1, 9.6.1.1, 10.3.1.1.4, 11.3.2.1.2, Box 11.2, 12.4.3.1, 14.8.2 contain relevant information regarding the evaluation of models in this region, the model spread in the context of other methods of projecting changes and the role of modes of variability and other climate phenomena. 1322 Atlas of Global and Regional Climate Projections Annex I Temperature change Arctic (land) June-August Temperature change Arctic (sea) June-August 25 RCP8.5 25 25 RCP8.5 25 RCP6.0 RCP6.0 20 RCP4.5 20 20 RCP4.5 20 RCP2.6 RCP2.6 historical historical 15 15 15 15 10 10 10 10 (°C) (°C) 5 5 5 5 0 0 0 0 AI -5 -5 -5 -5 -10 -10 -10 -10 1900 1950 2000 2050 2100 2081-2100 mean 1900 1950 2000 2050 2100 2081-2100 mean Figure AI.9 | (Top left) Time series of temperature change relative to 1986 2005 averaged over land grid points in the Arctic (67.5°N to 90°N) in June to August. (Top right) Same for sea grid points. Thin lines denote one ensemble member per model, thick lines the CMIP5 multi-model mean. On the right-hand side the 5th, 25th, 50th (median), 75th and 95th percentiles of the distribution of 20-year mean changes are given for 2081 2100 in the four RCP scenarios. (Below) Maps of temperature changes in 2016 2035, 2046 2065 and 2081 2100 with respect to 1986 2005 in the RCP4.5 scenario. For each point, the 25th, 50th and 75th percentiles of the distribution of the CMIP5 ensemble are shown; this includes both natural variability and inter-model spread. Hatching denotes areas where the 20-year mean differences of the percentiles are less than the standard deviation of model-estimated present-day natural variability of 20-year mean differences. Sections 9.4.1.1, 9.6.1.1, 10.3.1.1.4, 11.3.2.1.2, Box 11.2, 12.4.3.1, 14.8.2 contain relevant information regarding the evaluation of models in this region, the model spread in the context of other methods of projecting changes and the role of modes of variability and other climate phenomena. 1323 Annex I Atlas of Global and Regional Climate Projections Precipitation change Arctic (land) October-March Precipitation change Arctic (sea) October-March 120 120 120 120 RCP8.5 RCP8.5 100 RCP6.0 100 100 RCP6.0 100 RCP4.5 RCP4.5 RCP2.6 RCP2.6 80 historical 80 80 historical 80 60 60 60 60 (%) (%) 40 40 40 40 20 20 20 20 AI 0 0 0 0 -20 -20 -20 -20 -40 -40 -40 -40 1900 1950 2000 2050 2100 2081-2100 mean 1900 1950 2000 2050 2100 2081-2100 mean Figure AI.10 | (Top left) Time series of relative change relative to 1986 2005 in precipitation averaged over land grid points in the Arctic (67.5°N to 90°N) in October to March. (Top right) Same for sea grid points. Thin lines denote one ensemble member per model, thick lines the CMIP5 multi-model mean. On the right-hand side the 5th, 25th, 50th (median), 75th and 95th percentiles of the distribution of 20-year mean changes are given for 2081 2100 in the four RCP scenarios. (Below) Maps of precipitation changes in 2016 2035, 2046 2065 and 2081 2100 with respect to 1986 2005 in the RCP4.5 scenario. For each point, the 25th, 50th and 75th percentiles of the distribution of the CMIP5 ensemble are shown; this includes both natural variability and inter-model spread. Hatching denotes areas where the 20-year mean differences of the percentiles are less than the standard deviation of model-estimated present-day natural variability of 20-year mean differences. Sections 9.4.1.1, 9.6.1.1, 11.3.2.3.1, Box 11.2, 12.4.5.2, 14.8.2 contain relevant information regarding the evaluation of models in this region, the model spread in the context of other methods of projecting changes and the role of modes of variability and other climate phenomena. 1324 Atlas of Global and Regional Climate Projections Annex I Precipitation change Arctic (land) April-September Precipitation change Arctic (sea) April-September 120 120 120 120 RCP8.5 RCP8.5 100 RCP6.0 100 100 RCP6.0 100 RCP4.5 RCP4.5 RCP2.6 RCP2.6 80 historical 80 80 historical 80 60 60 60 60 (%) (%) 40 40 40 40 20 20 20 20 AI 0 0 0 0 -20 -20 -20 -20 -40 -40 -40 -40 1900 1950 2000 2050 2100 2081-2100 mean 1900 1950 2000 2050 2100 2081-2100 mean Figure AI.11 | (Top left) Time series of relative change relative to 1986 2005 in precipitation averaged over land grid points in the Arctic (67.5°N to 90°N) in April to September. (Top right) Same for sea grid points. Thin lines denote one ensemble member per model, thick lines the CMIP5 multi-model mean. On the right-hand side the 5th, 25th, 50th (median), 75th and 95th percentiles of the distribution of 20-year mean changes are given for 2081 2100 in the four RCP scenarios. (Below) Maps of precipitation changes in 2016 2035, 2046 2065 and 2081 2100 with respect to 1986 2005 in the RCP4.5 scenario. For each point, the 25th, 50th and 75th percentiles of the distribution of the CMIP5 ensemble are shown; this includes both natural variability and inter-model spread. Hatching denotes areas where the 20-year mean differences of the percentiles are less than the standard deviation of model-estimated present-day natural variability of 20-year mean differences. Sections 9.4.1.1, 9.6.1.1, 11.3.2.3.1, Box 11.2, 12.4.5.2, 14.8.2 contain relevant information regarding the evaluation of models in this region, the model spread in the context of other methods of projecting changes and the role of modes of variability and other climate phenomena. 1325 Annex I Atlas of Global and Regional Climate Projections Temperature change Canada/Greenland/Iceland December-February Temperature change North Asia December-February RCP8.5 RCP8.5 15 RCP6.0 15 15 RCP6.0 15 RCP4.5 RCP4.5 RCP2.6 RCP2.6 10 historical 10 10 historical 10 (°C) (°C) 5 5 5 5 AI 0 0 0 0 -5 -5 -5 -5 1900 1950 2000 2050 2100 2081-2100 mean 1900 1950 2000 2050 2100 2081-2100 mean Figure AI.12 | (Top left) Time series of temperature change relative to 1986 2005 averaged over land grid points in Canada/Greenland/Iceland (50°N to 85°N, 105°W to 10°W) in December to February. (Top right) Same for land grid points in North Asia (50°N to 70°N, 40°E to 180°E). Thin lines denote one ensemble member per model, thick lines the CMIP5 multi-model mean. On the right-hand side the 5th, 25th, 50th (median), 75th and 95th percentiles of the distribution of 20-year mean changes are given for 2081 2100 in the four RCP scenarios. (Below) Maps of temperature changes in 2016 2035, 2046 2065 and 2081 2100 with respect to 1986 2005 in the RCP4.5 scenario. For each point, the 25th, 50th and 75th percentiles of the distribution of the CMIP5 ensemble are shown; this includes both natural variability and inter-model spread. Hatching denotes areas where the 20-year mean differences of the percentiles are less than the standard deviation of model-estimated present-day natural variability of 20-year mean differences. Sections 9.4.1.1, 9.6.1.1, 10.3.1.1.4, 11.3.2.1.2, Box 11.2, 14.8.2, 14.8.8 contain relevant information regarding the evaluation of models in this region, the model spread in the context of other methods of projecting changes and the role of modes of variability and other climate phenomena. 1326 Atlas of Global and Regional Climate Projections Annex I Temperature change Canada/Greenland/Iceland June-August Temperature change North Asia June-August RCP8.5 RCP8.5 15 RCP6.0 15 15 RCP6.0 15 RCP4.5 RCP4.5 RCP2.6 RCP2.6 10 historical 10 10 historical 10 (°C) (°C) 5 5 5 5 AI 0 0 0 0 -5 -5 -5 -5 1900 1950 2000 2050 2100 2081-2100 mean 1900 1950 2000 2050 2100 2081-2100 mean Figure AI.13 | (Top left) Time series of temperature change relative to 1986 2005 averaged over land grid points in Canada/Greenland/Iceland (50°N to 85°N, 105°W to 10°W) in June to August. (Top right) Same for land grid points in North Asia (50°N to 70°N, 40°E to 180°E). Thin lines denote one ensemble member per model, thick lines the CMIP5 multi-model mean. On the right-hand side the 5th, 25th, 50th (median), 75th and 95th percentiles of the distribution of 20-year mean changes are given for 2081 2100 in the four RCP scenarios. (Below) Maps of temperature changes in 2016 2035, 2046 2065 and 2081 2100 with respect to 1986 2005 in the RCP4.5 scenario. For each point, the 25th, 50th and 75th percentiles of the distribution of the CMIP5 ensemble are shown; this includes both natural variability and inter-model spread. Hatching denotes areas where the 20-year mean differences of the percentiles are less than the standard deviation of model-estimated present-day natural variability of 20-year mean differences. Sections 9.4.1.1, 9.6.1.1, 10.3.1.1.4, 11.3.2.1.2, Box 11.2, 14.8.2, 14.8.8 contain relevant information regarding the evaluation of models in this region, the model spread in the context of other methods of projecting changes and the role of modes of variability and other climate phenomena. 1327 Annex I Atlas of Global and Regional Climate Projections Precipitation change Canada/Greenland/Iceland October-March Precipitation change North Asia October-March 100 100 100 100 RCP8.5 RCP8.5 RCP6.0 RCP6.0 80 RCP4.5 80 80 RCP4.5 80 RCP2.6 RCP2.6 historical historical 60 60 60 60 (%) 40 40 (%) 40 40 AI 20 20 20 20 0 0 0 0 -20 -20 -20 -20 1900 1950 2000 2050 2100 2081-2100 mean 1900 1950 2000 2050 2100 2081-2100 mean Figure AI.14 | (Top left) Time series of relative change relative to 1986 2005 in precipitation averaged over land grid points in Canada/Greenland/Iceland (50°N to 85°N, 105°W to 10°W) in October to March. (Top right) Same for land grid points in North Asia (50°N to 70°N, 40°E to 180°E). Thin lines denote one ensemble member per model, thick lines the CMIP5 multi-model mean. On the right-hand side the 5th, 25th, 50th (median), 75th and 95th percentiles of the distribution of 20-year mean changes are given for 2081 2100 in the four RCP scenarios. (Below) Maps of precipitation changes in 2016 2035, 2046 2065 and 2081 2100 with respect to 1986 2005 in the RCP4.5 scenario. For each point, the 25th, 50th and 75th percentiles of the distribution of the CMIP5 ensemble are shown; this includes both natural variability and inter-model spread. Hatching denotes areas where the 20-year mean differences of the percentiles are less than the standard deviation of model-estimated present-day natural variability of 20-year mean differences. Sections 9.4.1.1, 9.6.1.1, 10.3.2.2, 11.3.2.3.1, Box 11.2, 12.4.5.2, 14.8.2, 14.8.8 contain relevant information regarding the evaluation of models in this region, the model spread in the context of other methods of projecting changes and the role of modes of variability and other climate phenomena. 1328 Atlas of Global and Regional Climate Projections Annex I Precipitation change Canada/Greenland/Iceland April-September Precipitation change North Asia April-September 100 100 100 100 RCP8.5 RCP8.5 RCP6.0 RCP6.0 80 RCP4.5 80 80 RCP4.5 80 RCP2.6 RCP2.6 historical historical 60 60 60 60 (%) 40 40 (%) 40 40 20 20 20 20 AI 0 0 0 0 -20 -20 -20 -20 1900 1950 2000 2050 2100 2081-2100 mean 1900 1950 2000 2050 2100 2081-2100 mean Figure AI.15 | (Top left) Time series of relative change relative to 1986 2005 in precipitation averaged over land grid points in Canada/Greenland/Iceland (50°N to 85°N, 105°W to 10°W) in April to September. (Top right) Same for land grid points in North Asia (50°N to 70°N, 40°E to 180°E). Thin lines denote one ensemble member per model, thick lines the CMIP5 multi-model mean. On the right-hand side the 5th, 25th, 50th (median), 75th and 95th percentiles of the distribution of 20-year mean changes are given for 2081 2100 in the four RCP scenarios. (Below) Maps of precipitation changes in 2016 2035, 2046 2065 and 2081 2100 with respect to 1986 2005 in the RCP4.5 scenario. For each point, the 25th, 50th and 75th percentiles of the distribution of the CMIP5 ensemble are shown; this includes both natural variability and inter-model spread. Hatching denotes areas where the 20-year mean differences of the percentiles are less than the standard deviation of model-estimated present-day natural variability of 20-year mean differences. Sections 9.4.1.1, 9.6.1.1, 10.3.2.2, 11.3.2.3.1, Box 11.2, 12.4.5.2, 14.8.2, 14.8.8 contain relevant information regarding the evaluation of models in this region, the model spread in the context of other methods of projecting changes and the role of modes of variability and other climate phenomena. 1329 Annex I Atlas of Global and Regional Climate Projections Temperature change Alaska/NW Canada December-February Temperature change West North America December-February 20 RCP8.5 20 20 RCP8.5 20 RCP6.0 RCP6.0 RCP4.5 RCP4.5 15 RCP2.6 15 15 RCP2.6 15 historical historical 10 10 10 10 (°C) (°C) 5 5 5 5 AI 0 0 0 0 -5 -5 -5 -5 -10 -10 -10 -10 1900 1950 2000 2050 2100 2081-2100 mean 1900 1950 2000 2050 2100 2081-2100 mean Figure AI.16 | (Top left) Time series of temperature change relative to 1986 2005 averaged over land grid points in Alaska/NW Canada (60°N to 72.6°N, 168°W to 105°W) in December to February. (Top right) Same for land grid points in West North America (28.6°N to 60°N, 130°W to 105°W). Thin lines denote one ensemble member per model, thick lines the CMIP5 multi-model mean. On the right-hand side the 5th, 25th, 50th (median), 75th and 95th percentiles of the distribution of 20-year mean changes are given for 2081 2100 in the four RCP scenarios. (Below) Maps of temperature changes in 2016 2035, 2046 2065 and 2081 2100 with respect to 1986 2005 in the RCP4.5 scenario. For each point, the 25th, 50th and 75th percentiles of the distribution of the CMIP5 ensemble are shown; this includes both natural variability and inter-model spread. Hatching denotes areas where the 20-year mean differences of the percentiles are less than the standard deviation of model-estimated present-day natural variability of 20-year mean differences. Sections 9.4.1.1, 9.6.1.1, 10.3.1.1.4, Box 11.2, 14.8.3 contain relevant information regarding the evaluation of models in this region, the model spread in the context of other methods of projecting changes and the role of modes of variability and other climate phenomena. 1330 Atlas of Global and Regional Climate Projections Annex I Temperature change Alaska/NW Canada June-August Temperature change West North America June-August 20 RCP8.5 20 20 RCP8.5 20 RCP6.0 RCP6.0 RCP4.5 RCP4.5 15 RCP2.6 15 15 RCP2.6 15 historical historical 10 10 10 10 (°C) (°C) 5 5 5 5 0 0 0 0 AI -5 -5 -5 -5 -10 -10 -10 -10 1900 1950 2000 2050 2100 2081-2100 mean 1900 1950 2000 2050 2100 2081-2100 mean Figure AI.17 | (Top left) Time series of temperature change relative to 1986 2005 averaged over land grid points in Alaska/NW Canada (60°N to 72.6°N, 168°W to 105°W) in June to August. (Top right) Same for land grid points in West North America (28.6°N to 60°N, 130°W to 105°W). Thin lines denote one ensemble member per model, thick lines the CMIP5 multi-model mean. On the right-hand side the 5th, 25th, 50th (median), 75th and 95th percentiles of the distribution of 20-year mean changes are given for 2081 2100 in the four RCP scenarios. (Below) Maps of temperature changes in 2016 2035, 2046 2065 and 2081 2100 with respect to 1986 2005 in the RCP4.5 scenario. For each point, the 25th, 50th and 75th percentiles of the distribution of the CMIP5 ensemble are shown; this includes both natural variability and inter-model spread. Hatching denotes areas where the 20-year mean differences of the percentiles are less than the standard deviation of model-estimated present-day natural variability of 20-year mean differences. Sections 9.4.1.1, 9.6.1.1, 10.3.1.1.4, Box 11.2, 14.8.3 contain relevant information regarding the evaluation of models in this region, the model spread in the context of other methods of projecting changes and the role of modes of variability and other climate phenomena. 1331 Annex I Atlas of Global and Regional Climate Projections Precipitation change Alaska/NW Canada October-March Precipitation change West North America October-March 100 100 100 100 RCP8.5 RCP8.5 RCP6.0 RCP6.0 80 RCP4.5 80 80 RCP4.5 80 RCP2.6 RCP2.6 60 historical 60 60 historical 60 40 40 40 40 (%) (%) 20 20 20 20 AI 0 0 0 0 -20 -20 -20 -20 -40 -40 -40 -40 1900 1950 2000 2050 2100 2081-2100 mean 1900 1950 2000 2050 2100 2081-2100 mean Figure AI.18 | (Top left) Time series of relative change relative to 1986 2005 in precipitation averaged over land grid points in Alaska/NW Canada (60°N to 72.6°N, 168°W to 105°W) in October to March. (Top right) Same for land grid points in West North America (28.6°N to 60°N, 130°W to 105°W). Thin lines denote one ensemble member per model, thick lines the CMIP5 multi-model mean. On the right-hand side the 5th, 25th, 50th (median), 75th and 95th percentiles of the distribution of 20-year mean changes are given for 2081 2100 in the four RCP scenarios. (Below) Maps of precipitation changes in 2016 2035, 2046 2065 and 2081 2100 with respect to 1986 2005 in the RCP4.5 scenario. For each point, the 25th, 50th and 75th percentiles of the distribution of the CMIP5 ensemble are shown; this includes both natural variability and inter-model spread. Hatching denotes areas where the 20-year mean differences of the percentiles are less than the standard deviation of model-estimated present-day natural variability of 20-year mean differences. Sections 9.4.1.1, 9.6.1.1, Box 11.2, 12.4.5.2, 14.2.3.1, 14.8.3 contain relevant information regarding the evaluation of models in this region, the model spread in the context of other methods of projecting changes and the role of modes of variability and other climate phenomena. 1332 Atlas of Global and Regional Climate Projections Annex I Precipitation change Alaska/NW Canada April-September Precipitation change West North America April-September 100 100 100 100 RCP8.5 RCP8.5 RCP6.0 RCP6.0 80 RCP4.5 80 80 RCP4.5 80 RCP2.6 RCP2.6 60 historical 60 60 historical 60 40 40 40 40 (%) (%) 20 20 20 20 AI 0 0 0 0 -20 -20 -20 -20 -40 -40 -40 -40 1900 1950 2000 2050 2100 2081-2100 mean 1900 1950 2000 2050 2100 2081-2100 mean Figure AI.19 | (Top left) Time series of relative change relative to 1986 2005 in precipitation averaged over land grid points in Alaska/NW Canada (60°N to 72.6°N, 168°W to 105°W) in April to September. (Top right) Same for land grid points in West North America (28.6°N to 60°N, 130°W to 105°W). Thin lines denote one ensemble member per model, thick lines the CMIP5 multi-model mean. On the right-hand side the 5th, 25th, 50th (median), 75th and 95th percentiles of the distribution of 20-year mean changes are given for 2081 2100 in the four RCP scenarios. (Below) Maps of precipitation changes in 2016 2035, 2046 2065 and 2081 2100 with respect to 1986 2005 in the RCP4.5 scenario. For each point, the 25th, 50th and 75th percentiles of the distribution of the CMIP5 ensemble are shown; this includes both natural variability and inter-model spread. Hatching denotes areas where the 20-year mean differences of the percentiles are less than the standard deviation of model-estimated present-day natural variability of 20-year mean differences. Sections 9.4.1.1, 9.6.1.1, Box 11.2, 12.4.5.2, 14.2.3.1, 14.8.3 contain relevant information regarding the evaluation of models in this region, the model spread in the context of other methods of projecting changes and the role of modes of variability and other climate phenomena. 1333 Annex I Atlas of Global and Regional Climate Projections Temperature change Central North America December-February Temperature change Eastern North America December-February RCP8.5 RCP8.5 10 RCP6.0 10 10 RCP6.0 10 RCP4.5 RCP4.5 RCP2.6 RCP2.6 historical historical 5 5 5 5 (°C) (°C) 0 0 0 0 AI -5 -5 -5 -5 1900 1950 2000 2050 2100 2081-2100 mean 1900 1950 2000 2050 2100 2081-2100 mean Figure AI.20 | (Top left) Time series of temperature change relative to 1986 2005 averaged over land grid points in Central North America (28.6°N to 50°N, 105°W to 85°W) in December to February. (Top right) Same for land grid points in Eastern North America (25°N to 50°N, 85°W to 60°W). Thin lines denote one ensemble member per model, thick lines the CMIP5 multi-model mean. On the right-hand side the 5th, 25th, 50th (median), 75th and 95th percentiles of the distribution of 20-year mean changes are given for 2081 2100 in the four RCP scenarios. (Below) Maps of temperature changes in 2016 2035, 2046 2065 and 2081 2100 with respect to 1986 2005 in the RCP4.5 scenario. For each point, the 25th, 50th and 75th percentiles of the distribution of the CMIP5 ensemble are shown; this includes both natural variability and inter-model spread. Hatching denotes areas where the 20-year mean differences of the percentiles are less than the standard deviation of model-estimated present-day natural variability of 20-year mean differences. Sections 9.4.1.1, 9.6.1.1, 10.3.1.1.4, Box 11.2, 14.8.3 contain relevant information regarding the evaluation of models in this region, the model spread in the context of other methods of projecting changes and the role of modes of variability and other climate phenomena. 1334 Atlas of Global and Regional Climate Projections Annex I Temperature change Central North America June-August Temperature change Eastern North America June-August RCP8.5 RCP8.5 10 RCP6.0 10 10 RCP6.0 10 RCP4.5 RCP4.5 RCP2.6 RCP2.6 historical historical 5 5 5 5 (°C) (°C) 0 0 0 0 AI -5 -5 -5 -5 1900 1950 2000 2050 2100 2081-2100 mean 1900 1950 2000 2050 2100 2081-2100 mean Figure AI.21 | (Top left) Time series of temperature change relative to 1986 2005 averaged over land grid points in Central North America (28.6°N to 50°N, 105°W to 85°W) in June to August. (Top right) Same for land grid points in Eastern North America (25°N to 50°N, 85°W to 60°W). Thin lines denote one ensemble member per model, thick lines the CMIP5 multi-model mean. On the right-hand side the 5th, 25th, 50th (median), 75th and 95th percentiles of the distribution of 20-year mean changes are given for 2081 2100 in the four RCP scenarios. (Below) Maps of temperature changes in 2016 2035, 2046 2065 and 2081 2100 with respect to 1986 2005 in the RCP4.5 scenario. For each point, the 25th, 50th and 75th percentiles of the distribution of the CMIP5 ensemble are shown; this includes both natural variability and inter- model spread. Hatching denotes areas where the 20-year mean differences of the percentiles are less than the standard deviation of model-estimated present-day natural variability of 20-year mean differences. Sections 9.4.1.1, 9.6.1.1, 10.3.1.1.4, Box 11.2, 14.8.3 contain relevant information regarding the evaluation of models in this region, the model spread in the context of other methods of projecting changes and the role of modes of variability and other climate phenomena. 1335 Annex I Atlas of Global and Regional Climate Projections Precipitation change Central North America October-March Precipitation change Eastern North America October-March 80 RCP8.5 80 80 RCP8.5 80 RCP6.0 RCP6.0 60 RCP4.5 60 60 RCP4.5 60 RCP2.6 RCP2.6 historical historical 40 40 40 40 20 20 20 20 (%) (%) 0 0 0 0 AI -20 -20 -20 -20 -40 -40 -40 -40 -60 -60 -60 -60 1900 1950 2000 2050 2100 2081-2100 mean 1900 1950 2000 2050 2100 2081-2100 mean Figure AI.22 | (Top left) Time series of relative change relative to 1986 2005 in precipitation averaged over land grid points in Central North America (28.6°N to 50°N, 105°W to 85°W) in October to March. (Top right) Same for land grid points in Eastern North America (25°N to 50°N, 85°W to 60°W). Thin lines denote one ensemble member per model, thick lines the CMIP5 multi-model mean. On the right-hand side the 5th, 25th, 50th (median), 75th and 95th percentiles of the distribution of 20-year mean changes are given for 2081 2100 in the four RCP scenarios. (Below) Maps of precipitation changes in 2016 2035, 2046 2065 and 2081 2100 with respect to 1986 2005 in the RCP4.5 scenario. For each point, the 25th, 50th and 75th percentiles of the distribution of the CMIP5 ensemble are shown; this includes both natural variability and inter-model spread. Hatching denotes areas where the 20-year mean differences of the percentiles are less than the standard deviation of model-estimated present-day natural variability of 20-year mean differences. Sections 9.4.1.1, 9.6.1.1, Box 11.2, 14.8.3 contain relevant information regarding the evaluation of models in this region, the model spread in the context of other methods of projecting changes and the role of modes of variability and other climate phenomena. 1336 Atlas of Global and Regional Climate Projections Annex I Precipitation change Central North America April-September Precipitation change Eastern North America April-September 80 RCP8.5 80 80 RCP8.5 80 RCP6.0 RCP6.0 60 RCP4.5 60 60 RCP4.5 60 RCP2.6 RCP2.6 historical historical 40 40 40 40 20 20 20 20 (%) (%) 0 0 0 0 AI -20 -20 -20 -20 -40 -40 -40 -40 -60 -60 -60 -60 1900 1950 2000 2050 2100 2081-2100 mean 1900 1950 2000 2050 2100 2081-2100 mean Figure AI.23 | (Top left) Time series of relative change relative to 1986 2005 in precipitation averaged over land grid points in Central North America (28.6°N to 50°N, 105°W to 85°W) in April to September. (Top right) Same for land grid points in Eastern North America (25°N to 50°N, 85°W to 60°W). Thin lines denote one ensemble member per model, thick lines the CMIP5 multi-model mean. On the right-hand side the 5th, 25th, 50th (median), 75th and 95th percentiles of the distribution of 20-year mean changes are given for 2081 2100 in the four RCP scenarios. (Below) Maps of precipitation changes in 2016 2035, 2046 2065 and 2081 2100 with respect to 1986 2005 in the RCP4.5 scenario. For each point, the 25th, 50th and 75th percentiles of the distribution of the CMIP5 ensemble are shown; this includes both natural variability and inter-model spread. Hatching denotes areas where the 20-year mean differences of the percentiles are less than the standard deviation of model-estimated present-day natural variability of 20-year mean differences. Sections 9.4.1.1, 9.6.1.1, Box 11.2, 14.8.3 contain relevant information regarding the evaluation of models in this region, the model spread in the context of other methods of projecting changes and the role of modes of variability and other climate phenomena. 1337 Annex I Atlas of Global and Regional Climate Projections Temperature change Central America December-February Temperature change Caribbean (land and sea) December-February RCP8.5 RCP8.5 RCP6.0 RCP6.0 6 RCP4.5 6 6 RCP4.5 6 RCP2.6 RCP2.6 historical historical 4 4 4 4 (°C) (°C) 2 2 2 2 AI 0 0 0 0 -2 -2 -2 -2 1900 1950 2000 2050 2100 2081-2100 mean 1900 1950 2000 2050 2100 2081-2100 mean Figure AI.24 | (Top left) Time series of temperature change relative to 1986 2005 averaged over land grid points in Central America (68.8°W, 11.4°N; 79.7°W, 1.2°S; 116.3°W, 28.6°N; 90.3°W, 28.6°N) in December to February. (Top right) Same for all grid points in Caribbean (land and sea) (68.8°W, 11.4°N; 85.8°W, 25°N, 60°W, 25°N, 60°W, 11.44°N). Thin lines denote one ensemble member per model, thick lines the CMIP5 multi-model mean. On the right-hand side the 5th, 25th, 50th (median), 75th and 95th percentiles of the distribution of 20-year mean changes are given for 2081 2100 in the four RCP scenarios. (Below) Maps of temperature changes in 2016 2035, 2046 2065 and 2081 2100 with respect to 1986 2005 in the RCP4.5 scenario. For each point, the 25th, 50th and 75th percentiles of the distribution of the CMIP5 ensemble are shown; this includes both natural variability and inter-model spread. Hatching denotes areas where the 20-year mean differences of the percentiles are less than the standard deviation of model-estimated present-day natural variability of 20-year mean differences. Sections 9.4.1.1, 9.6.1.1, 10.3.1.1.4, Box 11.2, 14.8.4 contain relevant information regarding the evaluation of models in this region, the model spread in the context of other methods of projecting changes and the role of modes of variability and other climate phenomena. 1338 Atlas of Global and Regional Climate Projections Annex I Temperature change Central America June-August Temperature change Caribbean (land and sea) June-August RCP8.5 RCP8.5 RCP6.0 RCP6.0 6 RCP4.5 6 6 RCP4.5 6 RCP2.6 RCP2.6 historical historical 4 4 4 4 (°C) (°C) 2 2 2 2 AI 0 0 0 0 -2 -2 -2 -2 1900 1950 2000 2050 2100 2081-2100 mean 1900 1950 2000 2050 2100 2081-2100 mean Figure AI.25 | (Top left) Time series of temperature change relative to 1986 2005 averaged over land grid points in Central America (68.8°W, 11.4°N; 79.7°W, 1.2°S; 116.3°W, 28.6°N; 90.3°W, 28.6°N) in June to August. (Top right) Same for all grid points in Caribbean (land and sea) (68.8°W, 11.4°N; 85.8°W, 25°N, 60°W, 25°N, 60°W, 11.44°N). Thin lines denote one ensemble member per model, thick lines the CMIP5 multi-model mean. On the right-hand side the 5th, 25th, 50th (median), 75th and 95th percentiles of the distribution of 20-year mean changes are given for 2081 2100 in the four RCP scenarios. (Below) Maps of temperature changes in 2016 2035, 2046 2065 and 2081 2100 with respect to 1986 2005 in the RCP4.5 scenario. For each point, the 25th, 50th and 75th percentiles of the distribution of the CMIP5 ensemble are shown; this includes both natural variability and inter-model spread. Hatching denotes areas where the 20-year mean differences of the percentiles are less than the standard deviation of model-estimated present-day natural variability of 20-year mean differences. Sections 9.4.1.1, 9.6.1.1, 10.3.1.1.4, Box 11.2, 14.8.4 contain relevant information regarding the evaluation of models in this region, the model spread in the context of other methods of projecting changes and the role of modes of variability and other climate phenomena. 1339 Annex I Atlas of Global and Regional Climate Projections Precipitation change Central America October-March Precipitation change Caribbean (land and sea) October-March 150 150 150 150 RCP8.5 RCP8.5 RCP6.0 RCP6.0 RCP4.5 RCP4.5 100 RCP2.6 100 100 RCP2.6 100 historical historical 50 50 50 50 (%) (%) 0 0 0 0 AI -50 -50 -50 -50 -100 -100 -100 -100 1900 1950 2000 2050 2100 2081-2100 mean 1900 1950 2000 2050 2100 2081-2100 mean Figure AI.26 | (Top left) Time series of relative change relative to 1986 2005 in precipitation averaged over land grid points in Central America (68.8°W,11.4°N; 79.7°W, 1.2°S; 116.3°W,28.6°N; 90.3°W,28.6°N) in October to March. (Top right) Same for all grid points in Caribbean (land and sea) (68.8°W, 11.4°N; 85.8°W, 25°N, 60°W, 25°N, 60°W, 11.44°N). Thin lines denote one ensemble member per model, thick lines the CMIP5 multi-model mean. On the right-hand side the 5th, 25th, 50th (median), 75th and 95th percentiles of the distribution of 20-year mean changes are given for 2081 2100 in the four RCP scenarios. (Below) Maps of precipitation changes in 2016 2035, 2046 2065 and 2081 2100 with respect to 1986 2005 in the RCP4.5 scenario. For each point, the 25th, 50th and 75th percentiles of the distribution of the CMIP5 ensemble are shown; this includes both natural variability and inter-model spread. Hatching denotes areas where the 20-year mean differences of the percentiles are less than the standard deviation of model-estimated present-day natural variability of 20-year mean differences. Sections 9.4.1.1, 9.6.1.1, Box 11.2, 12.4.5.2, 14.2.3.1, 14.8.4 contain relevant information regarding the evaluation of models in this region, the model spread in the context of other methods of projecting changes and the role of modes of variability and other climate phenomena. 1340 Atlas of Global and Regional Climate Projections Annex I Precipitation change Central America April-September Precipitation change Caribbean (land and sea) April-September 150 150 150 150 RCP8.5 RCP8.5 RCP6.0 RCP6.0 RCP4.5 RCP4.5 100 RCP2.6 100 100 RCP2.6 100 historical historical 50 50 50 50 (%) (%) 0 0 0 0 AI -50 -50 -50 -50 -100 -100 -100 -100 1900 1950 2000 2050 2100 2081-2100 mean 1900 1950 2000 2050 2100 2081-2100 mean Figure AI.27 | (Top left) Time series of relative change relative to 1986 2005 in precipitation averaged over land grid points in Central America (68.8°W, 11.4°N; 79.7°W, 1.2°S; 116.3°W, 28.6°N; 90.3°W, 28.6°N) in April to September. (Top right) Same for all grid points in Caribbean (land and sea) (68.8°W, 11.4°N; 85.8°W, 25°N, 60°W, 25°N, 60°W, 11.44°N). Thin lines denote one ensemble member per model, thick lines the CMIP5 multi-model mean. On the right-hand side the 5th, 25th, 50th (median), 75th and 95th percentiles of the distribution of 20-year mean changes are given for 2081 2100 in the four RCP scenarios. (Below) Maps of precipitation changes in 2016 2035, 2046 2065 and 2081 2100 with respect to 1986 2005 in the RCP4.5 scenario. For each point, the 25th, 50th and 75th percentiles of the distribution of the CMIP5 ensemble are shown; this includes both natural variability and inter-model spread. Hatching denotes areas where the 20-year mean differences of the percentiles are less than the standard deviation of model-estimated present-day natural variability of 20-year mean differences. Sections 9.4.1.1, 9.6.1.1, Box 11.2, 12.4.5.2, 14.2.3.1, 14.8.4 contain relevant information regarding the evaluation of models in this region, the model spread in the context of other methods of projecting changes and the role of modes of variability and other climate phenomena. 1341 Annex I Atlas of Global and Regional Climate Projections Temperature change Amazon December-February Temperature change North-East Brazil December-February 12 12 12 12 RCP8.5 RCP8.5 10 RCP6.0 10 10 RCP6.0 10 RCP4.5 RCP4.5 RCP2.6 RCP2.6 8 historical 8 8 historical 8 6 6 6 6 (°C) (°C) 4 4 4 4 AI 2 2 2 2 0 0 0 0 -2 -2 -2 -2 -4 -4 -4 -4 1900 1950 2000 2050 2100 2081-2100 mean 1900 1950 2000 2050 2100 2081-2100 mean Figure AI.28 | (Top left) Time series of temperature change relative to 1986 2005 averaged over land grid points in the Amazon (20°S, 66.4°W; 1.24°S, 79.7°W; 11.44°N, 68.8°W; 11.44°N, 50°W; 20°S, 50°W) in December-February. (Top right) Same for land grid points in northeast Brazil (20°S to EQ, 50°W to 34°W). Thin lines denote one ensemble member per model, thick lines the CMIP5 multi-model mean. On the right-hand side the 5th, 25th, 50th (median), 75th and 95th percentiles of the distribution of 20-year mean changes are given for 2081 2100 in the four RCP scenarios. (Below) Maps of temperature changes in 2016 2035, 2046 2065 and 2081 2100 with respect to 1986 2005 in the RCP4.5 scenario. For each point, the 25th, 50th and 75th percentiles of the distribution of the CMIP5 ensemble are shown; this includes both natural variability and inter-model spread. Hatching denotes areas where the 20-year mean differences of the percentiles are less than the standard deviation of model-estimated present-day natural variability of 20-year mean differences. Sections 9.4.1.1, 9.6.1.1, 10.3.1.1.4, Box 11.2, 14.8.5 contain relevant information regarding the evaluation of models in this region, the model spread in the context of other methods of projecting changes and the role of modes of variability and other climate phenomena. 1342 Atlas of Global and Regional Climate Projections Annex I Temperature change Amazon June-August Temperature change North-East Brazil June-August 12 12 12 12 RCP8.5 RCP8.5 10 RCP6.0 10 10 RCP6.0 10 RCP4.5 RCP4.5 RCP2.6 RCP2.6 8 historical 8 8 historical 8 6 6 6 6 (°C) (°C) 4 4 4 4 2 2 2 2 AI 0 0 0 0 -2 -2 -2 -2 -4 -4 -4 -4 1900 1950 2000 2050 2100 2081-2100 mean 1900 1950 2000 2050 2100 2081-2100 mean Figure AI.29 | (Top left) Time series of temperature change relative to 1986 2005 averaged over land grid points in the Amazon (20°S, 66.4°W; 1.24°S, 79.7°W; 11.44°N, 68.8°W; 11.44°N, 50°W; 20°S, 50°W) in June to August. (Top right) Same for land grid points in northeast Brazil (20°S to EQ, 50°W to 34°W). Thin lines denote one ensemble member per model, thick lines the CMIP5 multi-model mean. On the right-hand side the 5th, 25th, 50th (median), 75th and 95th percentiles of the distribution of 20-year mean changes are given for 2081 2100 in the four RCP scenarios. (Below) Maps of temperature changes in 2016 2035, 2046 2065 and 2081 2100 with respect to 1986 2005 in the RCP4.5 scenario. For each point, the 25th, 50th and 75th percentiles of the distribution of the CMIP5 ensemble are shown; this includes both natural variability and inter-model spread. Hatching denotes areas where the 20-year mean differences of the percentiles are less than the standard deviation of model-estimated present-day natural variability of 20-year mean differences. Sections 9.4.1.1, 9.6.1.1, 10.3.1.1.4, Box 11.2, 14.8.5 contain relevant information regarding the evaluation of models in this region, the model spread in the context of other methods of projecting changes and the role of modes of variability and other climate phenomena. 1343 Annex I Atlas of Global and Regional Climate Projections Precipitation change Amazon October-March Precipitation change North-East Brazil October-March 150 150 150 150 RCP8.5 RCP8.5 RCP6.0 RCP6.0 RCP4.5 RCP4.5 100 RCP2.6 100 100 RCP2.6 100 historical historical 50 50 50 50 (%) (%) 0 0 0 0 AI -50 -50 -50 -50 -100 -100 -100 -100 1900 1950 2000 2050 2100 2081-2100 mean 1900 1950 2000 2050 2100 2081-2100 mean Figure AI.30 | (Top left) Time series of relative change relative to 1986 2005 in precipitation averaged over land grid points in the Amazon (20°S, 66.4°W; 1.24°S, 79.7°W; 11.44°N, 68.8°W; 11.44°N, 50°W; 20°S, 50°W) in October to March. (Top right) Same for land grid points in northeast Brazil (20°S to EQ, 50°W to 34°W). Thin lines denote one ensemble member per model, thick lines the CMIP5 multi-model mean. On the right-hand side the 5th, 25th, 50th (median), 75th and 95th percentiles of the distribution of 20-year mean changes are given for 2081 2100 in the four RCP scenarios. (Below) Maps of precipitation changes in 2016 2035, 2046 2065 and 2081 2100 with respect to 1986 2005 in the RCP4.5 scenario. For each point, the 25th, 50th and 75th percentiles of the distribution of the CMIP5 ensemble are shown; this includes both natural variability and inter-model spread. Hatching denotes areas where the 20-year mean differences of the percentiles are less than the standard deviation of model-estimated present-day natural variability of 20-year mean differences. Sections 9.4.1.1, 9.6.1.1, 11.3.2.1.2, Box 11.2, 14.2.3.2, 14.8.5 contain relevant information regarding the evaluation of models in this region, the model spread in the context of other methods of projecting changes and the role of modes of variability and other climate phenomena. 1344 Atlas of Global and Regional Climate Projections Annex I Precipitation change Amazon April-September Precipitation change North-East Brazil April-September 150 150 150 150 RCP8.5 RCP8.5 RCP6.0 RCP6.0 RCP4.5 RCP4.5 100 RCP2.6 100 100 RCP2.6 100 historical historical 50 50 50 50 (%) (%) 0 0 0 0 AI -50 -50 -50 -50 -100 -100 -100 -100 1900 1950 2000 2050 2100 2081-2100 mean 1900 1950 2000 2050 2100 2081-2100 mean Figure AI.31 | (Top left) Time series of relative change relative to 1986 2005 in precipitation averaged over land grid points in the Amazon (20°S, 66.4°W; 1.24°S, 79.7°W; 11.44°N, 68.8°W; 11.44°N, 50°W; 20°S, 50°W) in April to September. (Top right) Same for land grid points in northeast Brazil (20°S to EQ, 50°W to 34°W). Thin lines denote one ensemble member per model, thick lines the CMIP5 multi-model mean. On the right-hand side the 5th, 25th, 50th (median), 75th and 95th percentiles of the distribution of 20-year mean changes are given for 2081 2100 in the four RCP scenarios. (Below) Maps of precipitation changes in 2016 2035, 2046 2065 and 2081 2100 with respect to 1986 2005 in the RCP4.5 scenario. For each point, the 25th, 50th and 75th percentiles of the distribution of the CMIP5 ensemble are shown; this includes both natural variability and inter-model spread. Hatching denotes areas where the 20-year mean differences of the percentiles are less than the standard deviation of model-estimated present-day natural variability of 20-year mean differences. Sections 9.4.1.1, 9.6.1.1, 11.3.2.1.2, Box 11.2, 14.2.3.2, 14.8.5 contain relevant information regarding the evaluation of models in this region, the model spread in the context of other methods of projecting changes and the role of modes of variability and other climate phenomena. 1345 Annex I Atlas of Global and Regional Climate Projections Temperature change West Coast South America December-February Temperature change Southeastern South America December-February 8 8 8 8 RCP8.5 RCP8.5 RCP6.0 RCP6.0 6 RCP4.5 6 6 RCP4.5 6 RCP2.6 RCP2.6 historical historical 4 4 4 4 (°C) (°C) 2 2 2 2 AI 0 0 0 0 -2 -2 -2 -2 -4 -4 -4 -4 1900 1950 2000 2050 2100 2081-2100 mean 1900 1950 2000 2050 2100 2081-2100 mean Figure AI.32 | (Top left) Time series of temperature change relative to 1986 2005 averaged over land grid points in the west coast of South America (79.7°W, 1.2°S; 66.4°W, 20°S; 72.1°W, 50°S; 67.3°W, 56.7°S; 82.0°W, 56.7°S; 82.2°W, 0.5°N) in December to February. (Top right) Same for land grid points in southeastern South America (39.4°W, 20°S; 39.4°W, 56.6°S; 67.3°W, 56.7°S; 72.1°W, 50°S; 66°W, 20°S). Thin lines denote one ensemble member per model, thick lines the CMIP5 multi-model mean. On the right-hand side the 5th, 25th, 50th (median), 75th and 95th percentiles of the distribution of 20-year mean changes are given for 2081 2100 in the four RCP scenarios. (Below) Maps of temperature changes in 2016 2035, 2046 2065 and 2081 2100 with respect to 1986 2005 in the RCP4.5 scenario. For each point, the 25th, 50th and 75th percentiles of the distribution of the CMIP5 ensemble are shown; this includes both natural variability and inter-model spread. Hatching denotes areas where the 20-year mean differences of the percentiles are less than the standard deviation of model-estimated present-day natural variability of 20-year mean differences. Sections 9.4.1.1, 9.6.1.1, 10.3.1.1.4, Box 11.2, 14.8.5 contain relevant information regarding the evaluation of models in this region, the model spread in the context of other methods of projecting changes and the role of modes of variability and other climate phenomena. 1346 Atlas of Global and Regional Climate Projections Annex I Temperature change West Coast South America June-August Temperature change Southeastern South America June-August 8 8 8 8 RCP8.5 RCP8.5 RCP6.0 RCP6.0 6 RCP4.5 6 6 RCP4.5 6 RCP2.6 RCP2.6 historical historical 4 4 4 4 (°C) (°C) 2 2 2 2 0 0 0 0 AI -2 -2 -2 -2 -4 -4 -4 -4 1900 1950 2000 2050 2100 2081-2100 mean 1900 1950 2000 2050 2100 2081-2100 mean Figure AI.33 | (Top left) Time series of temperature change relative to 1986 2005 averaged over land grid points in the west coast of South America (79.7°W, 1.2°S; 66.4°W, 20°S; 72.1°W, 50°S; 67.3°W, 56.7°S; 82.0°W, 56.7°S; 82.2°W, 0.5°N) in June to August. (Top right) Same for land grid points in southeastern South America (39.4°W, 20°S; 39.4°W, 56.6°S; 67.3°W, 56.7°S; 72.1°W, 50°S; 66°W, 20°S). Thin lines denote one ensemble member per model, thick lines the CMIP5 multi-model mean. On the right-hand side the 5th, 25th, 50th (median), 75th and 95th percentiles of the distribution of 20-year mean changes are given for 2081 2100 in the four RCP scenarios. (Below) Maps of temperature changes in 2016 2035, 2046 2065 and 2081 2100 with respect to 1986 2005 in the RCP4.5 scenario. For each point, the 25th, 50th and 75th percentiles of the distribution of the CMIP5 ensemble are shown; this includes both natural variability and inter-model spread. Hatching denotes areas where the 20-year mean differences of the percentiles are less than the standard deviation of model-estimated present-day natural variability of 20-year mean differences. Sections 9.4.1.1, 9.6.1.1, 10.3.1.1.4, Box 11.2, 14.8.5 contain relevant information regarding the evaluation of models in this region, the model spread in the context of other methods of projecting changes and the role of modes of variability and other climate phenomena. 1347 Annex I Atlas of Global and Regional Climate Projections Precipitation change West Coast South America October-March Precipitation change Southeastern South America October-March 120 120 120 120 RCP8.5 RCP8.5 100 RCP6.0 100 100 RCP6.0 100 RCP4.5 RCP4.5 80 RCP2.6 80 80 RCP2.6 80 historical historical 60 60 60 60 40 40 40 40 (%) (%) 20 20 20 20 AI 0 0 0 0 -20 -20 -20 -20 -40 -40 -40 -40 -60 -60 -60 -60 1900 1950 2000 2050 2100 2081-2100 mean 1900 1950 2000 2050 2100 2081-2100 mean Figure AI.34 | (Top left) Time series of relative change relative to 1986 2005 in precipitation averaged over land grid points in the west coast of South America (79.7°W, 1.2°S; 66.4°W, 20°S; 72.1°W, 50°S; 67.3°W, 56.7°S; 82.0°W, 56.7°S; 82.2°W, 0.5°N) in October to March. (Top right) Same for land grid points in southeastern South America (39.4°W, 20°S; 39.4°W, 56.6°S; 67.3°W, 56.7°S; 72.1°W, 50°S; 66°W, 20°S). Thin lines denote one ensemble member per model, thick lines the CMIP5 multi-model mean. On the right-hand side the 5th, 25th, 50th (median), 75th and 95th percentiles of the distribution of 20-year mean changes are given for 2081 2100 in the four RCP scenarios. (Below) Maps of precipitation changes in 2016 2035, 2046 2065 and 2081 2100 with respect to 1986 2005 in the RCP4.5 scenario. For each point, the 25th, 50th and 75th percentiles of the distribution of the CMIP5 ensemble are shown; this includes both natural variability and inter-model spread. Hatching denotes areas where the 20-year mean differences of the percentiles are less than the standard deviation of model-estimated present-day natural variability of 20-year mean differences. Sections 9.4.1.1, 9.6.1.1, Box 11.2, 12.4.5.2, 14.8.5 contain relevant information regarding the evaluation of models in this region, the model spread in the context of other methods of projecting changes and the role of modes of variability and other climate phenomena. 1348 Atlas of Global and Regional Climate Projections Annex I Precipitation change West Coast South America April-September Precipitation change Southeastern South America April-September 120 120 120 120 RCP8.5 RCP8.5 100 RCP6.0 100 100 RCP6.0 100 RCP4.5 RCP4.5 80 RCP2.6 80 80 RCP2.6 80 historical historical 60 60 60 60 40 40 40 40 (%) (%) 20 20 20 20 0 0 0 0 AI -20 -20 -20 -20 -40 -40 -40 -40 -60 -60 -60 -60 1900 1950 2000 2050 2100 2081-2100 mean 1900 1950 2000 2050 2100 2081-2100 mean Figure AI.35 | (Top left) Time series of relative change relative to 1986 2005 in precipitation averaged over land grid points in the west coast of South America (79.7°W, 1.2°S; 66.4°W, 20°S; 72.1°W, 50°S; 67.3°W, 56.7°S; 82.0°W, 56.7°S; 82.2°W, 0.5°N) in April to September. (Top right) Same for land grid points in southeastern South America (39.4°W, 20°S; 39.4°W, 56.6°S; 67.3°W, 56.7°S; 72.1°W, 50°S; 66°W, 20°S). Thin lines denote one ensemble member per model, thick lines the CMIP5 multi-model mean. On the right-hand side the 5th, 25th, 50th (median), 75th and 95th percentiles of the distribution of 20-year mean changes are given for 2081 2100 in the four RCP scenarios. (Below) Maps of precipitation changes in 2016 2035, 2046 2065 and 2081 2100 with respect to 1986 2005 in the RCP4.5 scenario. For each point, the 25th, 50th and 75th percentiles of the distribution of the CMIP5 ensemble are shown; this includes both natural variability and inter-model spread. Hatching denotes areas where the 20-year mean differences of the percentiles are less than the standard deviation of model-estimated present-day natural variability of 20-year mean differences. Sections 9.4.1.1, 9.6.1.1, Box 11.2, 12.4.5.2, 14.8.5 contain relevant information regarding the evaluation of models in this region, the model spread in the context of other methods of projecting changes and the role of modes of variability and other climate phenomena. 1349 Annex I Atlas of Global and Regional Climate Projections Temperature change North Europe December-February Temperature change Central Europe December-February RCP8.5 RCP8.5 15 RCP6.0 15 15 RCP6.0 15 RCP4.5 RCP4.5 RCP2.6 RCP2.6 10 historical 10 10 historical 10 5 5 5 5 (°C) (°C) 0 0 0 0 AI -5 -5 -5 -5 -10 -10 -10 -10 1900 1950 2000 2050 2100 2081-2100 mean 1900 1950 2000 2050 2100 2081-2100 mean Figure AI.36 | (Top left) Time series of temperature change relative to 1986 2005 averaged over land grid points in North Europe (10°W, 48°N; 10°W, 75°N; 40°E, 75°N; 40°E, 61.3°N) in December to February. (Top right) Same for land grid points in Central Europe (10°W, 45°N; 10°W, 48°N; 40°E, 61.3°N; 40°E, 45°N). Thin lines denote one ensemble member per model, thick lines the CMIP5 multi-model mean. On the right-hand side the 5th, 25th, 50th (median), 75th and 95th percentiles of the distribution of 20-year mean changes are given for 2081 2100 in the four RCP scenarios. (Below) Maps of temperature changes in 2016 2035, 2046 2065 and 2081 2100 with respect to 1986 2005 in the RCP4.5 scenario. For each point, the 25th, 50th and 75th percentiles of the distribution of the CMIP5 ensemble are shown; this includes both natural variability and inter-model spread. Hatching denotes areas where the 20-year mean differences of the percentiles are less than the standard deviation of model-estimated present-day natural variability of 20-year mean differences. Sections 9.4.1.1, 9.6.1.1, 10.3.1.1.4, 10.3, Box 11.2, 14.8.6 contain relevant information regarding the evaluation of models in this region, the model spread in the context of other methods of projecting changes and the role of modes of variability and other climate phenomena. 1350 Atlas of Global and Regional Climate Projections Annex I Temperature change North Europe June-August Temperature change Central Europe June-August RCP8.5 RCP8.5 15 RCP6.0 15 15 RCP6.0 15 RCP4.5 RCP4.5 RCP2.6 RCP2.6 10 historical 10 10 historical 10 5 5 5 5 (°C) (°C) 0 0 0 0 AI -5 -5 -5 -5 -10 -10 -10 -10 1900 1950 2000 2050 2100 2081-2100 mean 1900 1950 2000 2050 2100 2081-2100 mean Figure AI.37 | (Top left) Time series of temperature change relative to 1986 2005 averaged over land grid points in North Europe (10°W, 48°N; 10°W, 75°N; 40°E, 75°N; 40°E, 61.3°N) in June to August. (Top right) Same for land grid points in Central Europe (10°W, 45°N; 10°W, 48°N; 40°E, 61.3°N; 40°E, 45°N). Thin lines denote one ensemble member per model, thick lines the CMIP5 multi-model mean. On the right-hand side the 5th, 25th, 50th (median), 75th and 95th percentiles of the distribution of 20-year mean changes are given for 2081 2100 in the four RCP scenarios. (Below) Maps of temperature changes in 2016 2035, 2046 2065 and 2081 2100 with respect to 1986 2005 in the RCP4.5 scenario. For each point, the 25th, 50th and 75th percentiles of the distribution of the CMIP5 ensemble are shown; this includes both natural variability and inter-model spread. Hatching denotes areas where the 20-year mean differences of the percentiles are less than the standard deviation of model-estimated present-day natural variability of 20-year mean differences. Sections 9.4.1.1, 9.6.1.1, 10.3.1.1.4, 10.3, Box 11.2, 14.8.6 contain relevant information regarding the evaluation of models in this region, the model spread in the context of other methods of projecting changes and the role of modes of variability and other climate phenomena. 1351 Annex I Atlas of Global and Regional Climate Projections Precipitation change North Europe October-March Precipitation change Central Europe October-March 80 80 80 80 RCP8.5 RCP8.5 RCP6.0 RCP6.0 60 RCP4.5 60 60 RCP4.5 60 RCP2.6 RCP2.6 40 historical 40 40 historical 40 20 20 20 20 (%) (%) 0 0 0 0 AI -20 -20 -20 -20 -40 -40 -40 -40 -60 -60 -60 -60 1900 1950 2000 2050 2100 2081-2100 mean 1900 1950 2000 2050 2100 2081-2100 mean Figure AI.38 | (Top left) Time series of relative change relative to 1986 2005 in precipitation averaged over land grid points in North Europe (10°W, 48°N; 10°W, 75°N; 40°E, 75°N; 40°E, 61.3°N) in October to March. (Top right) Same for land grid points in Central Europe (10°W, 45°N; 10°W, 48°N; 40°E, 61.3°N; 40°E, 45°N). Thin lines denote one ensemble member per model, thick lines the CMIP5 multi-model mean. On the right-hand side the 5th, 25th, 50th (median), 75th and 95th percentiles of the distribution of 20-year mean changes are given for 2081 2100 in the four RCP scenarios. (Below) Maps of precipitation changes in 2016 2035, 2046 2065 and 2081 2100 with respect to 1986 2005 in the RCP4.5 scenario. For each point, the 25th, 50th and 75th percentiles of the distribution of the CMIP5 ensemble are shown; this includes both natural variability and inter-model spread. Hatching denotes areas where the 20-year mean differences of the percentiles are less than the standard deviation of model-estimated present-day natural variability of 20-year mean differences. Sections 9.4.1.1, 9.6.1.1, Box 11.2, 12.4.5.2, 14.8.6 contain relevant information regarding the evaluation of models in this region, the model spread in the context of other methods of projecting changes and the role of modes of variability and other climate phenomena. 1352 Atlas of Global and Regional Climate Projections Annex I Precipitation change North Europe April-September Precipitation change Central Europe April-September 80 80 80 80 RCP8.5 RCP8.5 RCP6.0 RCP6.0 60 RCP4.5 60 60 RCP4.5 60 RCP2.6 RCP2.6 40 historical 40 40 historical 40 20 20 20 20 (%) (%) 0 0 0 0 AI -20 -20 -20 -20 -40 -40 -40 -40 -60 -60 -60 -60 1900 1950 2000 2050 2100 2081-2100 mean 1900 1950 2000 2050 2100 2081-2100 mean Figure AI.39 | (Top left) Time series of relative change relative to 1986 2005 in precipitation averaged over land grid points in North Europe (10°W, 48°N; 10°W, 75°N; 40°E, 75°N; 40°E, 61.3°N) in April to September. (Top right) Same for land grid points in Central Europe (10°W, 45°N; 10°W, 48°N; 40°E, 61.3°N; 40°E, 45°N). Thin lines denote one ensemble member per model, thick lines the CMIP5 multi-model mean. On the right-hand side the 5th, 25th, 50th (median), 75th and 95th percentiles of the distribution of 20-year mean changes are given for 2081 2100 in the four RCP scenarios. (Below) Maps of precipitation changes in 2016 2035, 2046 2065 and 2081 2100 with respect to 1986 2005 in the RCP4.5 scenario. For each point, the 25th, 50th and 75th percentiles of the distribution of the CMIP5 ensemble are shown; this includes both natural variability and inter-model spread. Hatching denotes areas where the 20-year mean differences of the percentiles are less than the standard deviation of model-estimated present-day natural variability of 20-year mean differences. Sections 9.4.1.1, 9.6.1.1, Box 11.2, 12.4.5.2, 14.8.6 contain relevant information regarding the evaluation of models in this region, the model spread in the context of other methods of projecting changes and the role of modes of variability and other climate phenomena. 1353 Annex I Atlas of Global and Regional Climate Projections Temperature change South Europe/Mediterranean December-February Temperature change Sahara December-February 12 12 12 12 RCP8.5 RCP8.5 10 RCP6.0 10 10 RCP6.0 10 RCP4.5 RCP4.5 RCP2.6 RCP2.6 8 historical 8 8 historical 8 6 6 6 6 (°C) (°C) 4 4 4 4 AI 2 2 2 2 0 0 0 0 -2 -2 -2 -2 -4 -4 -4 -4 1900 1950 2000 2050 2100 2081-2100 mean 1900 1950 2000 2050 2100 2081-2100 mean Figure AI.40 | (Top left) Time series of temperature change relative to 1986 2005 averaged over land grid points in the region South Europe/Mediterranean (30°N to 45°N, 10°W to 40°E) in December to February. (Top right) Same for land grid points in the Sahara (15°N to 30°N, 20°W to 40°E). Thin lines denote one ensemble member per model, thick lines the CMIP5 multi-model mean. On the right-hand side the 5th, 25th, 50th (median), 75th and 95th percentiles of the distribution of 20-year mean changes are given for 2081 2100 in the four RCP scenarios. (Below) Maps of temperature changes in 2016 2035, 2046 2065 and 2081 2100 with respect to 1986 2005 in the RCP4.5 scenario. For each point, the 25th, 50th and 75th percentiles of the distribution of the CMIP5 ensemble are shown; this includes both natural variability and inter-model spread. Hatching denotes areas where the 20-year mean differences of the percentiles are less than the standard deviation of model-estimated present-day natural variability of 20-year mean differences. Sections 9.4.1.1, 9.6.1.1, 10.3.1.1.4, Box 11.2, 14.8.6, 14.8.7 contain relevant information regarding the evaluation of models in this region, the model spread in the context of other methods of projecting changes and the role of modes of variability and other climate phenomena. 1354 Atlas of Global and Regional Climate Projections Annex I Temperature change South Europe/Mediterranean June-August Temperature change Sahara June-August 12 12 12 12 RCP8.5 RCP8.5 10 RCP6.0 10 10 RCP6.0 10 RCP4.5 RCP4.5 RCP2.6 RCP2.6 8 historical 8 8 historical 8 6 6 6 6 (°C) (°C) 4 4 4 4 2 2 2 2 AI 0 0 0 0 -2 -2 -2 -2 -4 -4 -4 -4 1900 1950 2000 2050 2100 2081-2100 mean 1900 1950 2000 2050 2100 2081-2100 mean Figure AI.41 | (Top left) Time series of temperature change relative to 1986 2005 averaged over land grid points in the region South Europe/Mediterranean (30°N to 45°N, 10°W to 40°E) in June to August. (Top right) Same for land grid points in the Sahara (15°N to 30°N, 20°W to 40°E). Thin lines denote one ensemble member per model, thick lines the CMIP5 multi-model mean. On the right-hand side the 5th, 25th, 50th (median), 75th and 95th percentiles of the distribution of 20-year mean changes are given for 2081 2100 in the four RCP scenarios. (Below) Maps of temperature changes in 2016 2035, 2046 2065 and 2081 2100 with respect to 1986 2005 in the RCP4.5 scenario. For each point, the 25th, 50th and 75th percentiles of the distribution of the CMIP5 ensemble are shown; this includes both natural variability and inter-model spread. Hatching denotes areas where the 20-year mean differences of the percentiles are less than the standard deviation of model-estimated present-day natural variability of 20-year mean differences. Sections 9.4.1.1, 9.6.1.1, 10.3.1.1.4, Box 11.2, 14.8.6, 14.8.7 contain relevant information regarding the evaluation of models in this region, the model spread in the context of other methods of projecting changes and the role of modes of variability and other climate phenomena. 1355 Annex I Atlas of Global and Regional Climate Projections Precipitation change South Europe/Mediterranean October-March Precipitation change Sahara October-March 100 100 RCP8.5 700 RCP8.5 700 RCP6.0 RCP6.0 RCP4.5 600 RCP4.5 600 RCP2.6 RCP2.6 50 historical 50 historical 500 500 400 400 (%) (%) 0 0 300 300 AI 200 200 -50 -50 100 100 0 0 -100 -100 -100 -100 1900 1950 2000 2050 2100 2081-2100 mean 1900 1950 2000 2050 2100 2081-2100 mean Figure AI.42 | (Top left) Time series of relative change relative to 1986 2005 in precipitation averaged over land grid points in the region South Europe/Mediterranean (30°N to 45°N, 10°W to 40°E) in October to March. (Top right) Same for land grid points in the Sahara (15°N to 30°N, 20°W to 40°E). Thin lines denote one ensemble member per model, thick lines the CMIP5 multi-model mean. On the right-hand side the 5th, 25th, 50th (median), 75th and 95th percentiles of the distribution of 20-year mean changes are given for 2081 2100 in the four RCP scenarios. Note different scales. (Below) Maps of precipitation changes in 2016 2035, 2046 2065 and 2081 2100 with respect to 1986 2005 in the RCP4.5 scenario. For each point, the 25th, 50th and 75th percentiles of the distribution of the CMIP5 ensemble are shown; this includes both natural variability and inter-model spread. Hatching denotes areas where the 20-year mean differences of the percentiles are less than the standard deviation of model-estimated present-day natural variability of 20-year mean differences. Sections 9.4.1.1, 9.6.1.1, Box 11.2, 12.4.5.2, 14.8.6, 14.8.7 contain relevant information regarding the evaluation of models in this region, the model spread in the context of other methods of projecting changes and the role of modes of variability and other climate phenomena. 1356 Atlas of Global and Regional Climate Projections Annex I Precipitation change South Europe/Mediterranean April-September Precipitation change Sahara April-September 100 100 RCP8.5 700 RCP8.5 700 RCP6.0 RCP6.0 RCP4.5 600 RCP4.5 600 RCP2.6 RCP2.6 50 historical 50 historical 500 500 400 400 (%) (%) 0 0 300 300 200 200 AI -50 -50 100 100 0 0 -100 -100 -100 -100 1900 1950 2000 2050 2100 2081-2100 mean 1900 1950 2000 2050 2100 2081-2100 mean Figure AI.43 | (Top left) Time series of relative change relative to 1986 2005 in precipitation averaged over land grid points in the region South Europe/Mediterranean (30°N to 45°N, 10°W to 40°E) in April to September. (Top right) Same for land grid points in the Sahara (15°N to 30°N, 20°W to 40°E). Thin lines denote one ensemble member per model, thick lines the CMIP5 multi-model mean. On the right-hand side the 5th, 25th, 50th (median), 75th and 95th percentiles of the distribution of 20-year mean changes are given for 2081 2100 in the four RCP scenarios. Note different scales. (Below) Maps of precipitation changes in 2016 2035, 2046 2065 and 2081 2100 with respect to 1986 2005 in the RCP4.5 scenario. For each point, the 25th, 50th and 75th percentiles of the distribution of the CMIP5 ensemble are shown; this includes both natural variability and inter-model spread. Hatching denotes areas where the 20-year mean differences of the percentiles are less than the standard deviation of model-estimated present-day natural variability of 20-year mean differences. Sections 9.4.1.1, 9.6.1.1, Box 11.2, 12.4.5.2, 14.8.6, 14.8.7 contain relevant information regarding the evaluation of models in this region, the model spread in the context of other methods of projecting changes and the role of modes of variability and other climate phenomena. 1357 Annex I Atlas of Global and Regional Climate Projections Temperature change west Africa December-February Temperature change East Africa December-February 8 8 8 8 RCP8.5 RCP8.5 RCP6.0 RCP6.0 6 RCP4.5 6 6 RCP4.5 6 RCP2.6 RCP2.6 historical historical 4 4 4 4 (°C) (°C) 2 2 2 2 AI 0 0 0 0 -2 -2 -2 -2 -4 -4 -4 -4 1900 1950 2000 2050 2100 2081-2100 mean 1900 1950 2000 2050 2100 2081-2100 mean Figure AI.44 | (Top left) Time series of temperature change relative to 1986 2005 averaged over land grid points in West Africa (11.4°S to 15°N, 20°W to 25°E) in December to February. (Top right) Same for land grid points in East Africa (11.3°S to 15°N, 25°E to 52°E). Thin lines denote one ensemble member per model, thick lines the CMIP5 multi- model mean. On the right-hand side the 5th, 25th, 50th (median), 75th and 95th percentiles of the distribution of 20-year mean changes are given for 2081 2100 in the four RCP scenarios. (Below) Maps of temperature changes in 2016 2035, 2046 2065 and 2081 2100 with respect to 1986 2005 in the RCP4.5 scenario. For each point, the 25th, 50th and 75th percentiles of the distribution of the CMIP5 ensemble are shown; this includes both natural variability and inter-model spread. Hatching denotes areas where the 20-year mean differences of the percentiles are less than the standard deviation of model-estimated present-day natural variability of 20-year mean differences. Sections 9.4.1.1, 9.6.1.1, 10.3.1.1.4, Box 11.2, 14.8.7 contain relevant information regarding the evaluation of models in this region, the model spread in the context of other methods of projecting changes and the role of modes of variability and other climate phenomena. 1358 Atlas of Global and Regional Climate Projections Annex I Temperature change west Africa June-August Temperature change East Africa June-August 8 8 8 8 RCP8.5 RCP8.5 RCP6.0 RCP6.0 6 RCP4.5 6 6 RCP4.5 6 RCP2.6 RCP2.6 historical historical 4 4 4 4 (°C) (°C) 2 2 2 2 0 0 0 0 AI -2 -2 -2 -2 -4 -4 -4 -4 1900 1950 2000 2050 2100 2081-2100 mean 1900 1950 2000 2050 2100 2081-2100 mean Figure AI.45 | (Top left) Time series of temperature change relative to 1986 2005 averaged over land grid points in West Africa (11.4°S to 15°N, 20°W to 25°E) in June to August. (Top right) Same for land grid points in East Africa (11.3°S to 15°N, 25°E to 52°E). Thin lines denote one ensemble member per model, thick lines the CMIP5 multi-model mean. On the right-hand side the 5th, 25th, 50th (median), 75th and 95th percentiles of the distribution of 20-year mean changes are given for 2081 2100 in the four RCP scenarios. (Below) Maps of temperature changes in 2016 2035, 2046 2065 and 2081 2100 with respect to 1986 2005 in the RCP4.5 scenario. For each point, the 25th, 50th and 75th percentiles of the distribution of the CMIP5 ensemble are shown; this includes both natural variability and inter-model spread. Hatching denotes areas where the 20-year mean differences of the percentiles are less than the standard deviation of model-estimated present-day natural variability of 20-year mean differences. Sections 9.4.1.1, 9.6.1.1, 10.3.1.1.4, Box 11.2, 14.8.7 contain relevant information regarding the evaluation of models in this region, the model spread in the context of other methods of projecting changes and the role of modes of variability and other climate phenomena. 1359 Annex I Atlas of Global and Regional Climate Projections Precipitation change west Africa October-March Precipitation change East Africa October-March 80 80 80 80 RCP8.5 RCP8.5 RCP6.0 RCP6.0 60 RCP4.5 60 60 RCP4.5 60 RCP2.6 RCP2.6 40 historical 40 40 historical 40 20 20 20 20 (%) (%) 0 0 0 0 AI -20 -20 -20 -20 -40 -40 -40 -40 -60 -60 -60 -60 1900 1950 2000 2050 2100 2081-2100 mean 1900 1950 2000 2050 2100 2081-2100 mean Figure AI.46 | (Top left) Time series of relative change relative to 1986 2005 in precipitation averaged over land grid points in West Africa (11.4°S to 15°N, 20°W to 25°E) in October to March. (Top right) Same for land grid points in East Africa (11.3°S to 15°N, 25°E to 52°E). Thin lines denote one ensemble member per model, thick lines the CMIP5 multi-model mean. On the right-hand side the 5th, 25th, 50th (median), 75th and 95th percentiles of the distribution of 20-year mean changes are given for 2081 2100 in the four RCP scenarios. (Below) Maps of precipitation changes in 2016 2035, 2046 2065 and 2081 2100 with respect to 1986 2005 in the RCP4.5 scenario. For each point, the 25th, 50th and 75th percentiles of the distribution of the CMIP5 ensemble are shown; this includes both natural variability and inter-model spread. Hatching denotes areas where the 20-year mean differences of the percentiles are less than the standard deviation of model-estimated present-day natural variability of 20-year mean differences. Sections 9.4.1.1, 9.6.1.1, 11.3.2.1.2, Box 11.2, 12.4.5.2, 14.2.4, 14.8.7 contain relevant information regarding the evaluation of models in this region, the model spread in the context of other methods of projecting changes and the role of modes of variability and other climate phenomena. 1360 Atlas of Global and Regional Climate Projections Annex I Precipitation change west Africa April-September Precipitation change East Africa April-September 80 80 80 80 RCP8.5 RCP8.5 RCP6.0 RCP6.0 60 RCP4.5 60 60 RCP4.5 60 RCP2.6 RCP2.6 40 historical 40 40 historical 40 20 20 20 20 (%) (%) 0 0 0 0 AI -20 -20 -20 -20 -40 -40 -40 -40 -60 -60 -60 -60 1900 1950 2000 2050 2100 2081-2100 mean 1900 1950 2000 2050 2100 2081-2100 mean Figure AI.47 | (Top left) Time series of relative change relative to 1986 2005 in precipitation averaged over land grid points in West Africa (11.4°S to 15°N, 20°W to 25°E) in April to September. (Top right) Same for land grid points in East Africa (11.3°S to 15°N, 25°E to 52°E). Thin lines denote one ensemble member per model, thick lines the CMIP5 multi-model mean. On the right-hand side the 5th, 25th, 50th (median), 75th and 95th percentiles of the distribution of 20-year mean changes are given for 2081 2100 in the four RCP scenarios. (Below) Maps of precipitation changes in 2016 2035, 2046 2065 and 2081 2100 with respect to 1986 2005 in the RCP4.5 scenario. For each point, the 25th, 50th and 75th percentiles of the distribution of the CMIP5 ensemble are shown; this includes both natural variability and inter-model spread. Hatching denotes areas where the 20-year mean differences of the percentiles are less than the standard deviation of model-estimated present-day natural variability of 20-year mean differences. Sections 9.4.1.1, 9.6.1.1, 11.3.2.1.2, Box 11.2, 12.4.5.2, 14.2.4, 14.8.7 contain relevant information regarding the evaluation of models in this region, the model spread in the context of other methods of projecting changes and the role of modes of variability and other climate phenomena. 1361 Annex I Atlas of Global and Regional Climate Projections Temperature change Southern Africa December-February Temperature change West Indian Ocean December-February RCP8.5 RCP8.5 8 RCP6.0 8 8 RCP6.0 8 RCP4.5 RCP4.5 RCP2.6 RCP2.6 6 historical 6 6 historical 6 4 4 4 4 (°C) (°C) 2 2 2 2 AI 0 0 0 0 -2 -2 -2 -2 1900 1950 2000 2050 2100 2081-2100 mean 1900 1950 2000 2050 2100 2081-2100 mean Figure AI.48 | (Top left) Time series of temperature change relative to 1986 2005 averaged over land grid points in Southern Africa (35°S to 11.4°S, 10°W to 52°E) in December to February. (Top right) Same for sea grid points in the West Indian Ocean (25°S to 5°N, 52°E to 75°E). Thin lines denote one ensemble member per model, thick lines the CMIP5 multi-model mean. On the right-hand side the 5th, 25th, 50th (median), 75th and 95th percentiles of the distribution of 20-year mean changes are given for 2081 2100 in the four RCP scenarios. (Below) Maps of temperature changes in 2016 2035, 2046 2065 and 2081 2100 with respect to 1986 2005 in the RCP4.5 scenario. For each point, the 25th, 50th and 75th percentiles of the distribution of the CMIP5 ensemble are shown; this includes both natural variability and inter-model spread. Hatching denotes areas where the 20-year mean differences of the percentiles are less than the standard deviation of model-estimated present-day natural variability of 20-year mean differences. Sections 9.4.1.1, 9.6.1.1, 10.3.1.1.4, Box 11.2, 14.8.7 contain relevant information regarding the evaluation of models in this region, the model spread in the context of other methods of projecting changes and the role of modes of variability and other climate phenomena. 1362 Atlas of Global and Regional Climate Projections Annex I Temperature change Southern Africa June-August Temperature change West Indian Ocean June-August RCP8.5 RCP8.5 8 RCP6.0 8 8 RCP6.0 8 RCP4.5 RCP4.5 RCP2.6 RCP2.6 6 historical 6 6 historical 6 4 4 4 4 (°C) (°C) 2 2 2 2 AI 0 0 0 0 -2 -2 -2 -2 1900 1950 2000 2050 2100 2081-2100 mean 1900 1950 2000 2050 2100 2081-2100 mean Figure AI.49 | (Top left) Time series of temperature change relative to 1986 2005 averaged over land grid points in Southern Africa (35°S to 11.4°S, 10°W to 52°E) in June to August. (Top right) Same for sea grid points in the West Indian Ocean (25°S to 5°N, 52°E to 75°E). Thin lines denote one ensemble member per model, thick lines the CMIP5 multi-model mean. On the right-hand side the 5th, 25th, 50th (median), 75th and 95th percentiles of the distribution of 20-year mean changes are given for 2081 2100 in the four RCP scenarios. (Below) Maps of temperature changes in 2016 2035, 2046 2065 and 2081 2100 with respect to 1986 2005 in the RCP4.5 scenario. For each point, the 25th, 50th and 75th percentiles of the distribution of the CMIP5 ensemble are shown; this includes both natural variability and inter-model spread. Hatching denotes areas where the 20-year mean differences of the percentiles are less than the standard deviation of model-estimated present-day natural variability of 20-year mean differences. Sections 9.4.1.1, 9.6.1.1, 10.3.1.1.4, Box 11.2, 14.8.7 contain relevant information regarding the evaluation of models in this region, the model spread in the context of other methods of projecting changes and the role of modes of variability and other climate phenomena. 1363 Annex I Atlas of Global and Regional Climate Projections Precipitation change Southern Africa October-March Precipitation change West Indian Ocean October-March 80 80 80 80 RCP8.5 RCP8.5 60 RCP6.0 60 60 RCP6.0 60 RCP4.5 RCP4.5 RCP2.6 RCP2.6 40 historical 40 40 historical 40 20 20 20 20 (%) (%) 0 0 0 0 -20 -20 -20 -20 AI -40 -40 -40 -40 -60 -60 -60 -60 -80 -80 -80 -80 1900 1950 2000 2050 2100 2081-2100 mean 1900 1950 2000 2050 2100 2081-2100 mean Figure AI.50 | (Top left) Time series of relative change relative to 1986 2005 in precipitation averaged over land grid points in Southern Africa (35°S to 11.4°S, 10°W to 52°E) in October to March. (Top right) Same for sea grid points in the West Indian Ocean (25°S to 5°N, 52°E to 75°E). Thin lines denote one ensemble member per model, thick lines the CMIP5 multi-model mean. On the right-hand side the 5th, 25th, 50th (median), 75th and 95th percentiles of the distribution of 20-year mean changes are given for 2081 2100 in the four RCP scenarios. (Below) Maps of precipitation changes in 2016 2035, 2046 2065 and 2081 2100 with respect to 1986 2005 in the RCP4.5 scenario. For each point, the 25th, 50th and 75th percentiles of the distribution of the CMIP5 ensemble are shown; this includes both natural variability and inter-model spread. Hatching denotes areas where the 20-year mean differences of the percentiles are less than the standard deviation of model-estimated present-day natural variability of 20-year mean differences. Sections 9.4.1.1, 9.6.1.1, Box 11.2, 12.4.5.2, 14.8.7 contain relevant information regarding the evaluation of models in this region, the model spread in the context of other methods of projecting changes and the role of modes of variability and other climate phenomena. 1364 Atlas of Global and Regional Climate Projections Annex I Precipitation change Southern Africa April-September Precipitation change West Indian Ocean April-September 80 80 80 80 RCP8.5 RCP8.5 60 RCP6.0 60 60 RCP6.0 60 RCP4.5 RCP4.5 RCP2.6 RCP2.6 40 historical 40 40 historical 40 20 20 20 20 (%) (%) 0 0 0 0 -20 -20 -20 -20 AI -40 -40 -40 -40 -60 -60 -60 -60 -80 -80 -80 -80 1900 1950 2000 2050 2100 2081-2100 mean 1900 1950 2000 2050 2100 2081-2100 mean Figure AI.51 | (Top left) Time series of relative change relative to 1986 2005 in precipitation averaged over land grid points in Southern Africa (35°S to 11.4°S, 10°W to 52°E) in April to September. (Top right) Same for sea grid points in the West Indian Ocean (25°S to 5°N, 52°E to 75°E). Thin lines denote one ensemble member per model, thick lines the CMIP5 multi-model mean. On the right-hand side the 5th, 25th, 50th (median), 75th and 95th percentiles of the distribution of 20-year mean changes are given for 2081 2100 in the four RCP scenarios. (Below) Maps of precipitation changes in 2016 2035, 2046 2065 and 2081 2100 with respect to 1986 2005 in the RCP4.5 scenario. For each point, the 25th, 50th and 75th percentiles of the distribution of the CMIP5 ensemble are shown; this includes both natural variability and inter-model spread. Hatching denotes areas where the 20-year mean differences of the percentiles are less than the standard deviation of model-estimated present-day natural variability of 20-year mean differences. Sections 9.4.1.1, 9.6.1.1, Box 11.2, 12.4.5.2, 14.8.7 contain relevant information regarding the evaluation of models in this region, the model spread in the context of other methods of projecting changes and the role of modes of variability and other climate phenomena. 1365 Annex I Atlas of Global and Regional Climate Projections Temperature change West Asia December-February Temperature change Central Asia December-February RCP8.5 RCP8.5 10 RCP6.0 10 10 RCP6.0 10 RCP4.5 RCP4.5 RCP2.6 RCP2.6 historical historical 5 5 5 5 (°C) (°C) AI 0 0 0 0 -5 -5 -5 -5 1900 1950 2000 2050 2100 2081-2100 mean 1900 1950 2000 2050 2100 2081-2100 mean Figure AI.52 | (Top left) Time series of temperature change relative to 1986 2005 averaged over land grid points in West Asia (15°N to 50°N, 40°E to 60°E) in December to February. (Top right) Same for land grid points in Central Asia (30°N to 50°N, 60°E to 75°E). Thin lines denote one ensemble member per model, thick lines the CMIP5 multi-model mean. On the right-hand side the 5th, 25th, 50th (median), 75th and 95th percentiles of the distribution of 20-year mean changes are given for 2081 2100 in the four RCP scenarios. (Below) Maps of temperature changes in 2016 2035, 2046 2065 and 2081 2100 with respect to 1986 2005 in the RCP4.5 scenario. For each point, the 25th, 50th and 75th percentiles of the distribution of the CMIP5 ensemble are shown; this includes both natural variability and inter-model spread. Hatching denotes areas where the 20-year mean differences of the percentiles are less than the standard deviation of model-estimated present-day natural variability of 20-year mean differences. Sections 9.4.1.1, 9.6.1.1, 10.3.1.1.4, Box 11.2, 14.8.8, 14.8.10 contain relevant information regarding the evaluation of models in this region, the model spread in the context of other methods of projecting changes and the role of modes of variability and other climate phenomena. 1366 Atlas of Global and Regional Climate Projections Annex I Temperature change West Asia June-August Temperature change Central Asia June-August RCP8.5 RCP8.5 10 RCP6.0 10 10 RCP6.0 10 RCP4.5 RCP4.5 RCP2.6 RCP2.6 historical historical 5 5 5 5 (°C) (°C) 0 0 0 0 AI -5 -5 -5 -5 1900 1950 2000 2050 2100 2081-2100 mean 1900 1950 2000 2050 2100 2081-2100 mean Figure AI.53 | (Top left) Time series of temperature change relative to 1986 2005 averaged over land grid points in West Asia (15°N to 50°N, 40°E to 60°E) in June to August. (Top right) Same for land grid points in Central Asia (30°N to 50°N, 60°E to 75°E). Thin lines denote one ensemble member per model, thick lines the CMIP5 multi-model mean. On the right-hand side the 5th, 25th, 50th (median), 75th and 95th percentiles of the distribution of 20-year mean changes are given for 2081 2100 in the four RCP scenarios. (Below) Maps of temperature changes in 2016 2035, 2046 2065 and 2081 2100 with respect to 1986 2005 in the RCP4.5 scenario. For each point, the 25th, 50th and 75th percentiles of the distribution of the CMIP5 ensemble are shown; this includes both natural variability and inter-model spread. Hatching denotes areas where the 20-year mean differences of the percentiles are less than the standard deviation of model-estimated present-day natural variability of 20-year mean differences. Sections 9.4.1.1, 9.6.1.1, 10.3.1.1.4, Box 11.2, 14.8.8, 14.8.10 contain relevant information regarding the evaluation of models in this region, the model spread in the context of other methods of projecting changes and the role of modes of variability and other climate phenomena. 1367 Annex I Atlas of Global and Regional Climate Projections Precipitation change West Asia October-March Precipitation change Central Asia October-March RCP8.5 RCP8.5 200 RCP6.0 200 200 RCP6.0 200 RCP4.5 RCP4.5 RCP2.6 RCP2.6 150 historical 150 150 historical 150 100 100 100 100 (%) (%) 50 50 50 50 AI 0 0 0 0 -50 -50 -50 -50 1900 1950 2000 2050 2100 2081-2100 mean 1900 1950 2000 2050 2100 2081-2100 mean Figure AI.54 | (Top left) Time series of relative change relative to 1986 2005 in precipitation averaged over land grid points in West Asia (15°N to 50°N, 40°E to 60°E) in October to March. (Top right) Same for land grid points in Central Asia (30°N to 50°N, 60°E to 75°E). Thin lines denote one ensemble member per model, thick lines the CMIP5 multi-model mean. On the right-hand side the 5th, 25th, 50th (median), 75th and 95th percentiles of the distribution of 20-year mean changes are given for 2081 2100 in the four RCP scenarios. (Below) Maps of precipitation changes in 2016 2035, 2046 2065 and 2081 2100 with respect to 1986 2005 in the RCP4.5 scenario. For each point, the 25th, 50th and 75th percentiles of the distribution of the CMIP5 ensemble are shown; this includes both natural variability and inter-model spread. Hatching denotes areas where the 20-year mean differences of the percentiles are less than the standard deviation of model-estimated present-day natural variability of 20-year mean differences. Sections 9.4.1.1, 9.6.1.1, Box 11.2, 12.4.5.2, 14.8.8, 14.8.10 contain relevant information regarding the evaluation of models in this region, the model spread in the context of other methods of projecting changes and the role of modes of variability and other climate phenomena. 1368 Atlas of Global and Regional Climate Projections Annex I Precipitation change West Asia April-September Precipitation change Central Asia April-September RCP8.5 RCP8.5 200 RCP6.0 200 200 RCP6.0 200 RCP4.5 RCP4.5 RCP2.6 RCP2.6 150 historical 150 150 historical 150 100 100 100 100 (%) (%) 50 50 50 50 AI 0 0 0 0 -50 -50 -50 -50 1900 1950 2000 2050 2100 2081-2100 mean 1900 1950 2000 2050 2100 2081-2100 mean Figure AI.55 | (Top left) Time series of relative change relative to 1986 2005 in precipitation averaged over land grid points in West Asia (15°N to 50°N, 40°E to 60°E) in April to September. (Top right) Same for land grid points in Central Asia (30°N to 50°N, 60°E to 75°E). Thin lines denote one ensemble member per model, thick lines the CMIP5 multi- model mean. On the right-hand side the 5th, 25th, 50th (median), 75th and 95th percentiles of the distribution of 20-year mean changes are given for 2081 2100 in the four RCP scenarios. (Below) Maps of precipitation changes in 2016 2035, 2046 2065 and 2081 2100 with respect to 1986 2005 in the RCP4.5 scenario. For each point, the 25th, 50th and 75th percentiles of the distribution of the CMIP5 ensemble are shown; this includes both natural variability and inter-model spread. Hatching denotes areas where the 20-year mean differences of the percentiles are less than the standard deviation of model-estimated present-day natural variability of 20-year mean differences. Sections 9.4.1.1, 9.6.1.1, Box 11.2, 12.4.5.2, 14.8.8, 14.8.10 contain relevant information regarding the evaluation of models in this region, the model spread in the context of other methods of projecting changes and the role of modes of variability and other climate phenomena. 1369 Annex I Atlas of Global and Regional Climate Projections Temperature change Eastern Asia December-February Temperature change Tibetan Plateau December-February 12 RCP8.5 12 12 RCP8.5 12 RCP6.0 RCP6.0 10 RCP4.5 10 10 RCP4.5 10 RCP2.6 RCP2.6 8 historical 8 8 historical 8 6 6 6 6 (°C) (°C) 4 4 4 4 2 2 2 2 AI 0 0 0 0 -2 -2 -2 -2 -4 -4 -4 -4 1900 1950 2000 2050 2100 2081-2100 mean 1900 1950 2000 2050 2100 2081-2100 mean Figure AI.56 | (Top left) Time series of temperature change relative to 1986 2005 averaged over land grid points in Eastern Asia (20°N to 50°N, 100°E to 145°E) in December to February. (Top right) Same for land grid points on the Tibetan Plateau (30°N to 50°N, 75°E to 100°E). Thin lines denote one ensemble member per model, thick lines the CMIP5 multi-model mean. On the right-hand side the 5th, 25th, 50th (median), 75th and 95th percentiles of the distribution of 20-year mean changes are given for 2081 2100 in the four RCP scenarios. (Below) Maps of temperature changes in 2016 2035, 2046 2065 and 2081 2100 with respect to 1986 2005 in the RCP4.5 scenario. For each point, the 25th, 50th and 75th percentiles of the distribution of the CMIP5 ensemble are shown; this includes both natural variability and inter-model spread. Hatching denotes areas where the 20-year mean differences of the percentiles are less than the standard deviation of model-estimated present-day natural variability of 20-year mean differences. Sections 9.4.1.1, 9.6.1.1, 10.3.1.1.4, Box 11.2, 14.8.8, 14.8.9 contain relevant information regarding the evaluation of models in this region, the model spread in the context of other methods of projecting changes and the role of modes of variability and other climate phenomena. 1370 Atlas of Global and Regional Climate Projections Annex I Temperature change Eastern Asia June-August Temperature change Tibetan Plateau June-August 12 RCP8.5 12 12 RCP8.5 12 RCP6.0 RCP6.0 10 RCP4.5 10 10 RCP4.5 10 RCP2.6 RCP2.6 8 historical 8 8 historical 8 6 6 6 6 (°C) (°C) 4 4 4 4 2 2 2 2 AI 0 0 0 0 -2 -2 -2 -2 -4 -4 -4 -4 1900 1950 2000 2050 2100 2081-2100 mean 1900 1950 2000 2050 2100 2081-2100 mean Figure AI.57 | (Top left) Time series of temperature change relative to 1986 2005 averaged over land grid points in Eastern Asia (20°N to 50°N, 100°E to 145°E) in June to August. (Top right) Same for land grid points on the Tibetan Plateau (30°N to 50°N, 75°E to 100°E). Thin lines denote one ensemble member per model, thick lines the CMIP5 multi-model mean. On the right-hand side the 5th, 25th, 50th (median), 75th and 95th percentiles of the distribution of 20-year mean changes are given for 2081 2100 in the four RCP scenarios. (Below) Maps of temperature changes in 2016 2035, 2046 2065 and 2081 2100 with respect to 1986 2005 in the RCP4.5 scenario. For each point, the 25th, 50th and 75th percentiles of the distribution of the CMIP5 ensemble are shown; this includes both natural variability and inter-model spread. Hatching denotes areas where the 20-year mean differences of the percentiles are less than the standard deviation of model-estimated present-day natural variability of 20-year mean differences. Sections 9.4.1.1, 9.6.1.1, 10.3.1.1.4, Box 11.2, 14.8.8, 14.8.9 contain relevant information regarding the evaluation of models in this region, the model spread in the context of other methods of projecting changes and the role of modes of variability and other climate phenomena. 1371 Annex I Atlas of Global and Regional Climate Projections Precipitation change Eastern Asia October-March Precipitation change Tibetan Plateau October-March 100 100 100 100 RCP8.5 RCP8.5 80 RCP6.0 80 80 RCP6.0 80 RCP4.5 RCP4.5 RCP2.6 RCP2.6 60 historical 60 60 historical 60 40 40 40 40 (%) (%) 20 20 20 20 AI 0 0 0 0 -20 -20 -20 -20 -40 -40 -40 -40 1900 1950 2000 2050 2100 2081-2100 mean 1900 1950 2000 2050 2100 2081-2100 mean Figure AI.58 | (Top left) Time series of relative change relative to 1986 2005 in precipitation averaged over land grid points in Eastern Asia (20°N to 50°N, 100°E to 145°E) in October to March. (Top right) Same for land grid points on the Tibetan Plateau (30°N to 50°N, 75°E to 100°E). Thin lines denote one ensemble member per model, thick lines the CMIP5 multi-model mean. On the right-hand side the 5th, 25th, 50th (median), 75th and 95th percentiles of the distribution of 20-year mean changes are given for 2081 2100 in the four RCP scenarios. (Below) Maps of precipitation changes in 2016 2035, 2046 2065 and 2081 2100 with respect to 1986 2005 in the RCP4.5 scenario. For each point, the 25th, 50th and 75th percentiles of the distribution of the CMIP5 ensemble are shown; this includes both natural variability and inter-model spread. Hatching denotes areas where the 20-year mean differences of the percentiles are less than the standard deviation of model-estimated present-day natural variability of 20-year mean differences. Sections 9.4.1.1, 9.6.1.1, Box 11.2, 14.2.2.2, 14.8.8, 14.8.9 contain relevant information regarding the evaluation of models in this region, the model spread in the context of other methods of projecting changes and the role of modes of variability and other climate phenomena. 1372 Atlas of Global and Regional Climate Projections Annex I Precipitation change Eastern Asia April-September Precipitation change Tibetan Plateau April-September 100 100 100 100 RCP8.5 RCP8.5 80 RCP6.0 80 80 RCP6.0 80 RCP4.5 RCP4.5 RCP2.6 RCP2.6 60 historical 60 60 historical 60 40 40 40 40 (%) (%) 20 20 20 20 0 0 0 0 AI -20 -20 -20 -20 -40 -40 -40 -40 1900 1950 2000 2050 2100 2081-2100 mean 1900 1950 2000 2050 2100 2081-2100 mean Figure AI.59 | (Top left) Time series of relative change relative to 1986 2005 in precipitation averaged over land grid points in Eastern Asia (20°N to 50°N, 100°E to 145°E) in April to September. (Top right) Same for land grid points on the Tibetan Plateau (30°N to 50°N, 75°E to 100°E). Thin lines denote one ensemble member per model, thick lines the CMIP5 multi-model mean. On the right-hand side the 5th, 25th, 50th (median), 75th and 95th percentiles of the distribution of 20-year mean changes are given for 2081 2100 in the four RCP scenarios. (Below) Maps of precipitation changes in 2016 2035, 2046 2065 and 2081 2100 with respect to 1986 2005 in the RCP4.5 scenario. For each point, the 25th, 50th and 75th percentiles of the distribution of the CMIP5 ensemble are shown; this includes both natural variability and inter-model spread. Hatching denotes areas where the 20-year mean differences of the percentiles are less than the standard deviation of model-estimated present-day natural variability of 20-year mean differences. Sections 9.4.1.1, 9.6.1.1, Box 11.2, 14.2.2.2, 14.8.8, 14.8.9 contain relevant information regarding the evaluation of models in this region, the model spread in the context of other methods of projecting changes and the role of modes of variability and other climate phenomena. 1373 Annex I Atlas of Global and Regional Climate Projections Temperature change South Asia December-February Temperature change North Indian Ocean December-February RCP8.5 RCP8.5 8 RCP6.0 8 8 RCP6.0 8 RCP4.5 RCP4.5 RCP2.6 RCP2.6 6 historical 6 6 historical 6 4 4 4 4 (°C) (°C) 2 2 2 2 AI 0 0 0 0 -2 -2 -2 -2 1900 1950 2000 2050 2100 2081-2100 mean 1900 1950 2000 2050 2100 2081-2100 mean Figure AI.60 | (Top left) Time series of temperature change relative to 1986 2005 averaged over land grid points in South Asia (60°E, 5°N; 60°E, 30°N; 100°E, 30°N; 100°E, 20°E; 95°E, 20°N; 95°E, 5°N) in December to February. (Top right) Same for sea grid points in the North Indian Ocean (5°N to 30°N, 60°E to 95°E). Thin lines denote one ensemble member per model, thick lines the CMIP5 multi-model mean. On the right-hand side the 5th, 25th, 50th (median), 75th and 95th percentiles of the distribution of 20-year mean changes are given for 2081 2100 in the four RCP scenarios. (Below) Maps of temperature changes in 2016 2035, 2046 2065 and 2081 2100 with respect to 1986 2005 in the RCP4.5 scenario. For each point, the 25th, 50th and 75th percentiles of the distribution of the CMIP5 ensemble are shown; this includes both natural variability and inter-model spread. Hatching denotes areas where the 20-year mean differences of the percentiles are less than the standard deviation of model-estimated present-day natural variability of 20-year mean differences. Sections 9.4.1.1, 9.6.1.1, 10.3.1.1.4, Box 11.2, 14.8.11 contain relevant information regarding the evaluation of models in this region, the model spread in the context of other methods of projecting changes and the role of modes of variability and other climate phenomena. 1374 Atlas of Global and Regional Climate Projections Annex I Temperature change South Asia June-August Temperature change North Indian Ocean June-August RCP8.5 RCP8.5 8 RCP6.0 8 8 RCP6.0 8 RCP4.5 RCP4.5 RCP2.6 RCP2.6 6 historical 6 6 historical 6 4 4 4 4 (°C) (°C) 2 2 2 2 AI 0 0 0 0 -2 -2 -2 -2 1900 1950 2000 2050 2100 2081-2100 mean 1900 1950 2000 2050 2100 2081-2100 mean Figure AI.61 | (Top left) Time series of temperature change relative to 1986 2005 averaged over land grid points in South Asia (60°E, 5°N; 60°E, 30°N; 100°E, 30°N; 100°E, 20°E; 95°E, 20°N; 95°E, 5°N) in June to August. (Top right) Same for sea grid points in the North Indian Ocean (5°N to 30°N, 60°E to 95°E). Thin lines denote one ensemble member per model, thick lines the CMIP5 multi-model mean. On the right-hand side the 5th, 25th, 50th (median), 75th and 95th percentiles of the distribution of 20-year mean changes are given for 2081 2100 in the four RCP scenarios. (Below) Maps of temperature changes in 2016 2035, 2046 2065 and 2081 2100 with respect to 1986 2005 in the RCP4.5 scenario. For each point, the 25th, 50th and 75th percentiles of the distribution of the CMIP5 ensemble are shown; this includes both natural variability and inter-model spread. Hatching denotes areas where the 20-year mean differences of the percentiles are less than the standard deviation of model-estimated present-day natural variability of 20-year mean differences. Sections 9.4.1.1, 9.6.1.1, 10.3.1.1.4, Box 11.2, 14.8.11 contain relevant information regarding the evaluation of models in this region, the model spread in the context of other methods of projecting changes and the role of modes of variability and other climate phenomena. 1375 Annex I Atlas of Global and Regional Climate Projections Precipitation change South Asia October-March Precipitation change North Indian Ocean October-March RCP8.5 RCP8.5 150 RCP6.0 150 150 RCP6.0 150 RCP4.5 RCP4.5 RCP2.6 RCP2.6 100 historical 100 100 historical 100 (%) (%) 50 50 50 50 AI 0 0 0 0 -50 -50 -50 -50 1900 1950 2000 2050 2100 2081-2100 mean 1900 1950 2000 2050 2100 2081-2100 mean Figure AI.62 | (Top left) Time series of relative change relative to 1986 2005 in precipitation averaged over land grid points in South Asia (60°E, 5°N; 60°E, 30°N; 100°E, 30°N; 100°E, 20°E; 95°E, 20°N; 95°E, 5°N) in October to March. (Top right) Same for sea grid points in the North Indian Ocean (5°N to 30°N, 60°E to 95°E). Thin lines denote one ensemble member per model, thick lines the CMIP5 multi-model mean. On the right-hand side the 5th, 25th, 50th (median), 75th and 95th percentiles of the distribution of 20-year mean changes are given for 2081 2100 in the four RCP scenarios. (Below) Maps of precipitation changes in 2016 2035, 2046 2065 and 2081 2100 with respect to 1986 2005 in the RCP4.5 scenario. For each point, the 25th, 50th and 75th percentiles of the distribution of the CMIP5 ensemble are shown; this includes both natural variability and inter-model spread. Hatching denotes areas where the 20-year mean differences of the percentiles are less than the standard deviation of model-estimated present-day natural variability of 20-year mean differences. Sections 9.4.1.1, 9.6.1.1, Box 11.2, 14.2.2.1, 14.8.11 contain relevant information regarding the evaluation of models in this region, the model spread in the context of other methods of projecting changes and the role of modes of variability and other climate phenomena. 1376 Atlas of Global and Regional Climate Projections Annex I Precipitation change South Asia April-September Precipitation change North Indian Ocean April-September RCP8.5 RCP8.5 150 RCP6.0 150 150 RCP6.0 150 RCP4.5 RCP4.5 RCP2.6 RCP2.6 100 historical 100 100 historical 100 (%) (%) 50 50 50 50 AI 0 0 0 0 -50 -50 -50 -50 1900 1950 2000 2050 2100 2081-2100 mean 1900 1950 2000 2050 2100 2081-2100 mean Figure AI.63 | (Top left) Time series of relative change relative to 1986 2005 in precipitation averaged over land grid points in South Asia (60°E, 5°N; 60°E, 30°N; 100°E, 30°N; 100°E, 20°E; 95°E, 20°N; 95°E, 5°N) in April to September. (Top right) Same for sea grid points in the North Indian Ocean (5°N to 30°N, 60°E to 95°E). Thin lines denote one ensemble member per model, thick lines the CMIP5 multi-model mean. On the right-hand side the 5th, 25th, 50th (median), 75th and 95th percentiles of the distribution of 20-year mean changes are given for 2081 2100 in the four RCP scenarios. (Below) Maps of precipitation changes in 2016 2035, 2046 2065 and 2081 2100 with respect to 1986 2005 in the RCP4.5 scenario. For each point, the 25th, 50th and 75th percentiles of the distribution of the CMIP5 ensemble are shown; this includes both natural variability and inter-model spread. Hatching denotes areas where the 20-year mean differences of the percentiles are less than the standard deviation of model-estimated present-day natural variability of 20-year mean differences. Sections 9.4.1.1, 9.6.1.1, Box 11.2, 14.2.2.1, 14.8.11 contain relevant information regarding the evaluation of models in this region, the model spread in the context of other methods of projecting changes and the role of modes of variability and other climate phenomena. 1377 Annex I Atlas of Global and Regional Climate Projections Temperature change Southeast Asia (land) December-February Temperature change Southeast Asia (sea) December-February 7 7 7 7 RCP8.5 RCP8.5 6 RCP6.0 6 6 RCP6.0 6 RCP4.5 RCP4.5 5 RCP2.6 5 5 RCP2.6 5 historical historical 4 4 4 4 3 3 3 3 (°C) (°C) 2 2 2 2 AI 1 1 1 1 0 0 0 0 -1 -1 -1 -1 -2 -2 -2 -2 1900 1950 2000 2050 2100 2081-2100 mean 1900 1950 2000 2050 2100 2081-2100 mean Figure AI.64 | (Top left) Time series of temperature change relative to 1986 2005 averaged over land grid points in Southeast Asia (10°S to 20°N, 95°E to 155°E) in December to February. (Top right) Same for sea grid points. Thin lines denote one ensemble member per model, thick lines the CMIP5 multi-model mean. On the right-hand side the 5th, 25th, 50th (median), 75th and 95th percentiles of the distribution of 20-year mean changes are given for 2081 2100 in the four RCP scenarios. (Below) Maps of temperature changes in 2016 2035, 2046 2065 and 2081 2100 with respect to 1986 2005 in the RCP4.5 scenario. For each point, the 25th, 50th and 75th percentiles of the distribution of the CMIP5 ensemble are shown; this includes both natural variability and inter-model spread. Hatching denotes areas where the 20-year mean differences of the percentiles are less than the standard deviation of model-estimated present-day natural variability of 20-year mean differences. Sections 9.4.1.1, 9.6.1.1, 10.3.1.1.4, Box 11.2, 14.8.12 contain relevant information regarding the evaluation of models in this region, the model spread in the context of other methods of projecting changes and the role of modes of variability and other climate phenomena. 1378 Atlas of Global and Regional Climate Projections Annex I Temperature change Southeast Asia (land) June-August Temperature change Southeast Asia (sea) June-August 7 7 7 7 RCP8.5 RCP8.5 6 RCP6.0 6 6 RCP6.0 6 RCP4.5 RCP4.5 5 RCP2.6 5 5 RCP2.6 5 historical historical 4 4 4 4 3 3 3 3 (°C) (°C) 2 2 2 2 1 1 1 1 AI 0 0 0 0 -1 -1 -1 -1 -2 -2 -2 -2 1900 1950 2000 2050 2100 2081-2100 mean 1900 1950 2000 2050 2100 2081-2100 mean Figure AI.65 | (Top left) Time series of temperature change relative to 1986 2005 averaged over land grid points in Southeast Asia (10°S to 20°N, 95°E to 155°E) in June to August. (Top right) Same for sea grid points. Thin lines denote one ensemble member per model, thick lines the CMIP5 multi-model mean. On the right-hand side the 5th, 25th, 50th (median), 75th and 95th percentiles of the distribution of 20-year mean changes are given for 2081 2100 in the four RCP scenarios. (Below) Maps of temperature changes in 2016 2035, 2046 2065 and 2081 2100 with respect to 1986 2005 in the RCP4.5 scenario. For each point, the 25th, 50th and 75th percentiles of the distribution of the CMIP5 ensemble are shown; this includes both natural variability and inter-model spread. Hatching denotes areas where the 20-year mean differences of the percentiles are less than the standard deviation of model-estimated present-day natural variability of 20-year mean differences. Sections 9.4.1.1, 9.6.1.1, 10.3.1.1.4, Box 11.2, 14.8.12 contain relevant information regarding the evaluation of models in this region, the model spread in the context of other methods of projecting changes and the role of modes of variability and other climate phenomena. 1379 Annex I Atlas of Global and Regional Climate Projections Precipitation change Southeast Asia (land) October-March Precipitation change Southeast Asia (sea) October-March RCP8.5 RCP8.5 60 RCP6.0 60 60 RCP6.0 60 RCP4.5 RCP4.5 RCP2.6 RCP2.6 40 historical 40 40 historical 40 20 20 20 20 (%) (%) 0 0 0 0 AI -20 -20 -20 -20 -40 -40 -40 -40 1900 1950 2000 2050 2100 2081-2100 mean 1900 1950 2000 2050 2100 2081-2100 mean Figure AI.66 | (Top left) Time series of relative change relative to 1986 2005 in precipitation averaged over land grid points in Southeast Asia (10°S to 20°N, 95°E to 155°E) in October to March. (Top right) Same for sea grid points. Thin lines denote one ensemble member per model, thick lines the CMIP5 multi-model mean. On the right-hand side the 5th, 25th, 50th (median), 75th and 95th percentiles of the distribution of 20-year mean changes are given for 2081 2100 in the four RCP scenarios. (Below) Maps of precipitation changes in 2016 2035, 2046 2065 and 2081 2100 with respect to 1986 2005 in the RCP4.5 scenario. For each point, the 25th, 50th and 75th percentiles of the distribution of the CMIP5 ensemble are shown; this includes both natural variability and inter-model spread. Hatching denotes areas where the 20-year mean differences of the percentiles are less than the standard deviation of model-estimated present-day natural variability of 20-year mean differences. Sections 9.4.1.1, 9.6.1.1, Box 11.2, 14.2.2.3, 14.2.2.5, 14.8.12 contain relevant information regarding the evaluation of models in this region, the model spread in the context of other methods of projecting changes and the role of modes of variability and other climate phenomena. 1380 Atlas of Global and Regional Climate Projections Annex I Precipitation change Southeast Asia (land) April-September Precipitation change Southeast Asia (sea) April-September RCP8.5 RCP8.5 60 RCP6.0 60 60 RCP6.0 60 RCP4.5 RCP4.5 RCP2.6 RCP2.6 40 historical 40 40 historical 40 20 20 20 20 (%) (%) 0 0 0 0 AI -20 -20 -20 -20 -40 -40 -40 -40 1900 1950 2000 2050 2100 2081-2100 mean 1900 1950 2000 2050 2100 2081-2100 mean Figure AI.67 | (Top left) Time series of relative change relative to 1986 2005 in precipitation averaged over land grid points in Southeast Asia (10°S to 20°N, 95°E to 155°E) in April to September. (Top right) Same for sea grid points. Thin lines denote one ensemble member per model, thick lines the CMIP5 multi-model mean. On the right-hand side the 5th, 25th, 50th (median), 75th and 95th percentiles of the distribution of 20-year mean changes are given for 2081 2100 in the four RCP scenarios. (Below) Maps of precipitation changes in 2016 2035, 2046 2065 and 2081 2100 with respect to 1986 2005 in the RCP4.5 scenario. For each point, the 25th, 50th and 75th percentiles of the distribution of the CMIP5 ensemble are shown; this includes both natural variability and inter-model spread. Hatching denotes areas where the 20-year mean differences of the percentiles are less than the standard deviation of model-estimated present-day natural variability of 20-year mean differences. Sections 9.4.1.1, 9.6.1.1, Box 11.2, 14.2.2.3, 14.2.2.5, 14.8.12 contain relevant information regarding the evaluation of models in this region, the model spread in the context of other methods of projecting changes and the role of modes of variability and other climate phenomena. 1381 Annex I Atlas of Global and Regional Climate Projections Temperature change North Australia December-February Temperature change South Australia/New Zealand December-February 8 RCP8.5 8 8 RCP8.5 8 RCP6.0 RCP6.0 RCP4.5 RCP4.5 6 RCP2.6 6 6 RCP2.6 6 historical historical 4 4 4 4 (°C) (°C) 2 2 2 2 AI 0 0 0 0 -2 -2 -2 -2 -4 -4 -4 -4 1900 1950 2000 2050 2100 2081-2100 mean 1900 1950 2000 2050 2100 2081-2100 mean Figure AI.68 | (Top left) Time series of temperature change relative to 1986 2005 averaged over land grid points in North Australia (30°S to 10°S, 110°E to 155°E) in December to February. (Top right) Same for land grid points in South Australia/New Zealand (50°S to 30°S, 110°E to 180°E). Thin lines denote one ensemble member per model, thick lines the CMIP5 multi-model mean. On the right-hand side the 5th, 25th, 50th (median), 75th and 95th percentiles of the distribution of 20-year mean changes are given for 2081 2100 in the four RCP scenarios. (Below) Maps of temperature changes in 2016 2035, 2046 2065 and 2081 2100 with respect to 1986 2005 in the RCP4.5 scenario. For each point, the 25th, 50th and 75th percentiles of the distribution of the CMIP5 ensemble are shown; this includes both natural variability and inter-model spread. Hatching denotes areas where the 20-year mean differences of the percentiles are less than the standard deviation of model-estimated present-day natural variability of 20-year mean differences. Sections 9.4.1.1, 9.6.1.1, 10.3.1.1.4, Box 11.2, 14.8.13 contain relevant information regarding the evaluation of models in this region, the model spread in the context of other methods of projecting changes and the role of modes of variability and other climate phenomena. 1382 Atlas of Global and Regional Climate Projections Annex I Temperature change North Australia June-August Temperature change South Australia/New Zealand June-August 8 RCP8.5 8 8 RCP8.5 8 RCP6.0 RCP6.0 RCP4.5 RCP4.5 6 RCP2.6 6 6 RCP2.6 6 historical historical 4 4 4 4 (°C) (°C) 2 2 2 2 0 0 0 0 AI -2 -2 -2 -2 -4 -4 -4 -4 1900 1950 2000 2050 2100 2081-2100 mean 1900 1950 2000 2050 2100 2081-2100 mean Figure AI.69 | (Top left) Time series of temperature change relative to 1986 2005 averaged over land grid points in North Australia (30°S to 10°S, 110°E to 155°E) in June to August. (Top right) Same for land grid points in South Australia/New Zealand (50°S to 30°S, 110°E to 180°E). Thin lines denote one ensemble member per model, thick lines the CMIP5 multi-model mean. On the right-hand side the 5th, 25th, 50th (median), 75th and 95th percentiles of the distribution of 20-year mean changes are given for 2081 2100 in the four RCP scenarios. (Below) Maps of temperature changes in 2016 2035, 2046 2065 and 2081 2100 with respect to 1986 2005 in the RCP4.5 scenario. For each point, the 25th, 50th and 75th percentiles of the distribution of the CMIP5 ensemble are shown; this includes both natural variability and inter-model spread. Hatching denotes areas where the 20-year mean differences of the percentiles are less than the standard deviation of model-estimated present-day natural variability of 20-year mean differences. Sections 9.4.1.1, 9.6.1.1, 10.3.1.1.4, Box 11.2, 14.8.13 contain relevant information regarding the evaluation of models in this region, the model spread in the context of other methods of projecting changes and the role of modes of variability and other climate phenomena. 1383 Annex I Atlas of Global and Regional Climate Projections Precipitation change North Australia October-March Precipitation change South Australia/New Zealand October-March 200 200 RCP8.5 RCP8.5 RCP6.0 100 RCP6.0 100 150 RCP4.5 150 RCP4.5 RCP2.6 RCP2.6 historical historical 100 100 50 50 (%) (%) 50 50 0 0 AI 0 0 -50 -50 -50 -50 -100 -100 1900 1950 2000 2050 2100 2081-2100 mean 1900 1950 2000 2050 2100 2081-2100 mean Figure AI.70 | (Top left) Time series of relative change relative to 1986 2005 in precipitation averaged over land grid points in North Australia (30°S to 10°S, 110°E to 155°E) in October to March. (Top right) Same for land grid points in South Australia/New Zealand (50°S to 30°S, 110°E to 180°E). Thin lines denote one ensemble member per model, thick lines the CMIP5 multi-model mean. On the right-hand side the 5th, 25th, 50th (median), 75th and 95th percentiles of the distribution of 20-year mean changes are given for 2081 2100 in the four RCP scenarios. Note different scales. (Below) Maps of precipitation changes in 2016 2035, 2046 2065 and 2081 2100 with respect to 1986 2005 in the RCP4.5 scenario. For each point, the 25th, 50th and 75th percentiles of the distribution of the CMIP5 ensemble are shown; this includes both natural variability and inter-model spread. Hatching denotes areas where the 20-year mean differences of the percentiles are less than the standard deviation of model-estimated present-day natural variability of 20-year mean differences. Sections 9.4.1.1, 9.6.1.1, Box 11.2, 14.2.2.4, 14.8.13 contain relevant information regarding the evaluation of models in this region, the model spread in the context of other methods of projecting changes and the role of modes of variability and other climate phenomena. 1384 Atlas of Global and Regional Climate Projections Annex I Precipitation change North Australia April-September Precipitation change South Australia/New Zealand April-September 350 350 RCP8.5 RCP8.5 300 RCP6.0 300 100 RCP6.0 100 RCP4.5 RCP4.5 250 RCP2.6 250 RCP2.6 historical historical 200 200 50 50 150 150 (%) (%) 100 100 0 0 50 50 AI 0 0 -50 -50 -50 -50 -100 -100 1900 1950 2000 2050 2100 2081-2100 mean 1900 1950 2000 2050 2100 2081-2100 mean Figure AI.71 | (Top left) Time series of relative change relative to 1986 2005 in precipitation averaged over land grid points in North Australia (30°S to 10°S, 110°E to 155°E) in April to September. (Top right) Same for land grid points in South Australia/New Zealand (50°S to 30°S, 110°E to 180°E). Thin lines denote one ensemble member per model, thick lines the CMIP5 multi-model mean. On the right-hand side the 5th, 25th, 50th (median), 75th and 95th percentiles of the distribution of 20-year mean changes are given for 2081 2100 in the four RCP scenarios. Note different scales. (Below) Maps of precipitation changes in 2016 2035, 2046 2065 and 2081 2100 with respect to 1986 2005 in the RCP4.5 scenario. For each point, the 25th, 50th and 75th percentiles of the distribution of the CMIP5 ensemble are shown; this includes both natural variability and inter-model spread. Hatching denotes areas where the 20-year mean differences of the percentiles are less than the standard deviation of model-estimated present-day natural variability of 20-year mean differences. Sections 9.4.1.1, 9.6.1.1, Box 11.2, 14.2.2.4, 14.8.13 contain relevant information regarding the evaluation of models in this region, the model spread in the context of other methods of projecting changes and the role of modes of variability and other climate phenomena. 1385 Annex I Atlas of Global and Regional Climate Projections Temperature change Northern Tropical Pacific December-February Temperature change Equatorial Pacific December-February Temperature change Southern Tropical Pacific December-February 8 8 8 8 8 8 RCP8.5 RCP8.5 RCP8.5 RCP6.0 RCP6.0 RCP6.0 6 RCP4.5 6 6 RCP4.5 6 6 RCP4.5 6 RCP2.6 RCP2.6 RCP2.6 historical historical historical 4 4 4 4 4 4 (°C) (°C) (°C) 2 2 2 2 2 2 0 0 0 0 0 0 -2 -2 -2 -2 -2 -2 AI -4 -4 -4 -4 -4 -4 1900 1950 2000 2050 2100 2081-2100 mean 1900 1950 2000 2050 2100 2081-2100 mean 1900 1950 2000 2050 2100 2081-2100 mean Figure AI.72 | (Top left) Time series of temperature change relative to 1986 2005 averaged over all grid points in the Northern Tropical Pacific (5°N to 25°N, 155°E to 150°W) in December to February. Top middle: same for all grid points in the Equatorial Pacific (5°S to 5°N, 155°E to 150°W). (Top right) Same for all grid points in the Southern Tropical Pacific (5°S to 5°N, 155°E to 150°W). Thin lines denote one ensemble member per model, thick lines the CMIP5 multi-model mean. On the right-hand side the 5th, 25th, 50th (median), 75th and 95th percentiles of the distribution of 20-year mean changes are given for 2081 2100 in the four RCP scenarios. (Below) Maps of temperature changes in 2016 2035, 2046 2065 and 2081 2100 with respect to 1986 2005 in the RCP4.5 scenario. For each point, the 25th, 50th and 75th percentiles of the distribution of the CMIP5 ensemble are shown; this includes both natural variability and inter-model spread. Hatching denotes areas where the 20-year mean differences of the percentiles are less than the standard deviation of model-estimated present-day natural variability of 20-year mean differences. Sections 9.4.1.1, 9.6.1.1, 10.3.1.1.4, Box 11.2, 12.4.3.1, 14.4.1, 14.8.14 contain relevant information regarding the evaluation of models in this region, the model spread in the context of other methods of projecting changes and the role of modes of variability and other climate phenomena. 1386 Atlas of Global and Regional Climate Projections Annex I Temperature change Northern Tropical Pacific June-August Temperature change Equatorial Pacific June-August Temperature change Southern Tropical Pacific June-August 8 8 8 8 8 8 RCP8.5 RCP8.5 RCP8.5 RCP6.0 RCP6.0 RCP6.0 6 RCP4.5 6 6 RCP4.5 6 6 RCP4.5 6 RCP2.6 RCP2.6 RCP2.6 historical historical historical 4 4 4 4 4 4 (°C) (°C) (°C) 2 2 2 2 2 2 0 0 0 0 0 0 -2 -2 -2 -2 -2 -2 -4 1900 1950 2000 2050 -4 2100 2081-2100 mean -4 1900 1950 2000 2050 -4 2100 2081-2100 mean -4 1900 1950 2000 2050 -4 2100 2081-2100 mean AI Figure AI.73 | (Top left) Time series of temperature change relative to 1986 2005 averaged over all grid points in the Northern Tropical Pacific (5°N to 25°N, 155°E to 150°W) in June to August. Top middle: same for all grid points in the Equatorial Pacific (5°S to 5°N, 155°E to 150°W). (Top right) Same for all grid points in the Southern Tropical Pacific (5°S to 5°N, 155°E to 150°W). Thin lines denote one ensemble member per model, thick lines the CMIP5 multi-model mean. On the right-hand side the 5th, 25th, 50th (median), 75th and 95th percentiles of the distribution of 20-year mean changes are given for 2081 2100 in the four RCP scenarios. (Below) Maps of temperature changes in 2016 2035, 2046 2065 and 2081 2100 with respect to 1986 2005 in the RCP4.5 scenario. For each point, the 25th, 50th and 75th percentiles of the distribution of the CMIP5 ensemble are shown; this includes both natural variability and inter-model spread. Hatching denotes areas where the 20-year mean differences of the percentiles are less than the standard deviation of model-estimated present-day natural variability of 20-year mean differences. Sections 9.4.1.1, 9.6.1.1, 10.3.1.1.4, Box 11.2, 12.4.3.1, 14.4.1, 14.8.14 contain relevant information regarding the evaluation of models in this region, the model spread in the context of other methods of projecting changes and the role of modes of variability and other climate phenomena. 1387 Annex I Atlas of Global and Regional Climate Projections Precipitation change Northern Tropical Pacific October-March Precipitation change Equatorial Pacific October-March Precipitation change Southern Tropical Pacific October-March 1000 1000 100 RCP8.5 100 RCP8.5 100 RCP8.5 100 RCP6.0 RCP6.0 RCP6.0 80 RCP4.5 80 800 RCP4.5 800 80 RCP4.5 80 RCP2.6 RCP2.6 RCP2.6 60 historical 60 historical 60 historical 60 40 40 600 600 40 40 (%) (%) (%) 20 20 20 20 400 400 0 0 0 0 -20 -20 200 200 -20 -20 -40 -40 -40 -40 0 0 AI -60 -60 -60 -60 1900 1950 2000 2050 2100 2081-2100 mean 1900 1950 2000 2050 2100 2081-2100 mean 1900 1950 2000 2050 2100 2081-2100 mean Figure AI.74 | (Top left) Time series of relative change relative to 1986 2005 in precipitation averaged over all grid points in the Northern Tropical Pacific (5°N to 25°N, 155°E to 150°W) in October to March. Top middle: same for all grid points in the Equatorial Pacific (5°S to 5°N, 155°E to 150°W). (Top right) Same for all grid points in the Southern Tropical Pacific (5°S to 5°N, 155°E to 150°W). Thin lines denote one ensemble member per model, thick lines the CMIP5 multi-model mean. On the right-hand side the 5th, 25th, 50th (median), 75th and 95th percentiles of the distribution of 20-year mean changes are given for 2081 2100 in the four RCP scenarios. Note different scales. (Below) Maps of precipitation changes in 2016 2035, 2046 2065 and 2081 2100 with respect to 1986 2005 in the RCP4.5 scenario. For each point, the 25th, 50th and 75th percentiles of the distribution of the CMIP5 ensemble are shown; this includes both natural variability and inter-model spread. Hatching denotes areas where the 20-year mean differences of the percentiles are less than the standard deviation of model-estimated present-day natural variability of 20-year mean differences. Sections 9.4.1.1, 9.6.1.1, 11.3.2.1.2, Box 11.2, 12.4.5.2, 14.8.14 contain relevant information regarding the evaluation of models in this region, the model spread in the context of other methods of projecting changes and the role of modes of variability and other climate phenomena. 1388 Atlas of Global and Regional Climate Projections Annex I Precipitation change Northern Tropical Pacific April-September Precipitation change Equatorial Pacific April-September Precipitation change Southern Tropical Pacific April-September 1000 1000 100 RCP8.5 100 RCP8.5 100 RCP8.5 100 RCP6.0 RCP6.0 RCP6.0 80 RCP4.5 80 800 RCP4.5 800 80 RCP4.5 80 RCP2.6 RCP2.6 RCP2.6 60 historical 60 historical 60 historical 60 40 40 600 600 40 40 (%) (%) (%) 20 20 20 20 400 400 0 0 0 0 -20 -20 200 200 -20 -20 -40 -40 -40 -40 0 0 -60 -60 -60 -60 AI 1900 1950 2000 2050 2100 2081-2100 mean 1900 1950 2000 2050 2100 2081-2100 mean 1900 1950 2000 2050 2100 2081-2100 mean Figure AI.75 | (Top left) Time series of relative change relative to 1986 2005 in precipitation averaged over all grid points in the Northern Tropical Pacific (5°N to 25°N, 155°E to 150°W) in April to September. Top middle: same for all grid points in the Equatorial Pacific (5°S to 5°N, 155°E to 150°W). (Top right) Same for all grid points in the Southern Tropical Pacific (5°S to 5°N, 155°E to 150°W). Thin lines denote one ensemble member per model, thick lines the CMIP5 multi-model mean. On the right-hand side the 5th, 25th, 50th (median), 75th and 95th percentiles of the distribution of 20-year mean changes are given for 2081 2100 in the four RCP scenarios. Note different scales. (Below) Maps of precipitation changes in 2016 2035, 2046 2065 and 2081 2100 with respect to 1986 2005 in the RCP4.5 scenario. For each point, the 25th, 50th and 75th percentiles of the distribution of the CMIP5 ensemble are shown; this includes both natural variability and inter-model spread. Hatching denotes areas where the 20-year mean differences of the percentiles are less than the standard deviation of model-estimated present-day natural variability of 20-year mean differences. Sections 9.4.1.1, 9.6.1.1, 11.3.2.1.2, Box 11.2, 12.4.5.2, 14.8.14 contain relevant information regarding the evaluation of models in this region, the model spread in the context of other methods of projecting changes and the role of modes of variability and other climate phenomena. 1389 Annex I Atlas of Global and Regional Climate Projections Temperature change Antarctica (land) December-February Temperature change Antarctica (sea) December-February 8 RCP8.5 8 8 RCP8.5 8 RCP6.0 RCP6.0 RCP4.5 RCP4.5 6 RCP2.6 6 6 RCP2.6 6 historical historical 4 4 4 4 (°C) (°C) 2 2 2 2 AI 0 0 0 0 -2 -2 -2 -2 -4 -4 -4 -4 1900 1950 2000 2050 2100 2081-2100 mean 1900 1950 2000 2050 2100 2081-2100 mean Figure AI.76 | (Top left) Time series of temperature change relative to 1986 2005 averaged over land grid points in Antarctica (90°S to 50°S) in December to February. (Top right) Same for sea grid points. Thin lines denote one ensemble member per model, thick lines the CMIP5 multi-model mean. On the right-hand side the 5th, 25th, 50th (median), 75th and 95th percentiles of the distribution of 20-year mean changes are given for 2081 2100 in the four RCP scenarios. (Below) Maps of temperature changes in 2016 2035, 2046 2065 and 2081 2100 with respect to 1986 2005 in the RCP4.5 scenario. For each point, the 25th, 50th and 75th percentiles of the distribution of the CMIP5 ensemble are shown; this includes both natural variability and inter-model spread. Hatching denotes areas where the 20-year mean differences of the percentiles are less than the standard deviation of model-estimated present-day natural variability of 20-year mean differences. Sections 9.4.1.1, 9.6.1.1, 10.3.1.1.4, Box 11.2, 12.4.3.1, 14.8.15 contain relevant information regarding the evaluation of models in this region, the model spread in the context of other methods of projecting changes and the role of modes of variability and other climate phenomena. 1390 Atlas of Global and Regional Climate Projections Annex I Temperature change Antarctica (land) June-August Temperature change Antarctica (sea) June-August 8 RCP8.5 8 8 RCP8.5 8 RCP6.0 RCP6.0 RCP4.5 RCP4.5 6 RCP2.6 6 6 RCP2.6 6 historical historical 4 4 4 4 (°C) (°C) 2 2 2 2 0 0 0 0 AI -2 -2 -2 -2 -4 -4 -4 -4 1900 1950 2000 2050 2100 2081-2100 mean 1900 1950 2000 2050 2100 2081-2100 mean Figure AI.77 | (Top left) Time series of temperature change relative to 1986 2005 averaged over land grid points in Antarctica (90°S to 50°S) in June to August. (Top right) Same for sea grid points. Thin lines denote one ensemble member per model, thick lines the CMIP5 multi-model mean. On the right-hand side the 5th, 25th, 50th (median), 75th and 95th percentiles of the distribution of 20-year mean changes are given for 2081 2100 in the four RCP scenarios. (Below) Maps of temperature changes in 2016 2035, 2046 2065 and 2081 2100 with respect to 1986 2005 in the RCP4.5 scenario. For each point, the 25th, 50th and 75th percentiles of the distribution of the CMIP5 ensemble are shown; this includes both natural variability and inter-model spread. Hatching denotes areas where the 20-year mean differences of the percentiles are less than the standard deviation of model-estimated present-day natural variability of 20-year mean differences. Sections 9.4.1.1, 9.6.1.1, 10.3.1.1.4, Box 11.2, 12.4.3.1, 14.8.15 contain relevant information regarding the evaluation of models in this region, the model spread in the context of other methods of projecting changes and the role of modes of variability and other climate phenomena. 1391 Annex I Atlas of Global and Regional Climate Projections Precipitation change Antarctica (land) October-March Precipitation change Antarctica (sea) October-March RCP8.5 RCP8.5 60 RCP6.0 60 60 RCP6.0 60 RCP4.5 RCP4.5 RCP2.6 RCP2.6 40 historical 40 40 historical 40 (%) (%) 20 20 20 20 AI 0 0 0 0 -20 -20 -20 -20 1900 1950 2000 2050 2100 2081-2100 mean 1900 1950 2000 2050 2100 2081-2100 mean Figure AI.78 | (Top left) Time series of relative change relative to 1986 2005 in precipitation averaged over land grid points in Antarctica (90°S to 50°S) in October to March. (Top right) Same for sea grid points. Thin lines denote one ensemble member per model, thick lines the CMIP5 multi-model mean. On the right-hand side the 5th, 25th, 50th (median), 75th and 95th percentiles of the distribution of 20-year mean changes are given for 2081 2100 in the four RCP scenarios. (Below) Maps of precipitation changes in 2016 2035, 2046 2065 and 2081 2100 with respect to 1986 2005 in the RCP4.5 scenario. For each point, the 25th, 50th and 75th percentiles of the distribution of the CMIP5 ensemble are shown; this includes both natural variability and inter-model spread. Hatching denotes areas where the 20-year mean differences of the percentiles are less than the standard deviation of model-estimated present-day natural variability of 20-year mean differences. Sections 9.4.1.1, 9.6.1.1, 10.3.2.2, Box 11.2, 12.4.5.2, 14.8.15 contain relevant information regarding the evaluation of models in this region, the model spread in the context of other methods of projecting changes and the role of modes of variability and other climate phenomena. 1392 Atlas of Global and Regional Climate Projections Annex I Precipitation change Antarctica (land) April-September Precipitation change Antarctica (sea) April-September RCP8.5 RCP8.5 60 RCP6.0 60 60 RCP6.0 60 RCP4.5 RCP4.5 RCP2.6 RCP2.6 40 historical 40 40 historical 40 (%) (%) 20 20 20 20 AI 0 0 0 0 -20 -20 -20 -20 1900 1950 2000 2050 2100 2081-2100 mean 1900 1950 2000 2050 2100 2081-2100 mean Figure AI.79 | (Top left) Time series of relative change relative to 1986 2005 in precipitation averaged over land grid points in Antarctica (90°S to 50°S) in April to September. (Top right) Same for sea grid points. Thin lines denote one ensemble member per model, thick lines the CMIP5 multi-model mean. On the right-hand side the 5th, 25th, 50th (median), 75th and 95th percentiles of the distribution of 20-year mean changes are given for 2081 2100 in the four RCP scenarios. (Below) Maps of precipitation changes in 2016 2035, 2046 2065 and 2081 2100 with respect to 1986 2005 in the RCP4.5 scenario. For each point, the 25th, 50th and 75th percentiles of the distribution of the CMIP5 ensemble are shown; this includes both natural variability and inter-model spread. Hatching denotes areas where the 20-year mean differences of the percentiles are less than the standard deviation of model-estimated present-day natural variability of 20-year mean differences. Sections 9.4.1.1, 9.6.1.1, 10.3.2.2, Box 11.2, 12.4.5.2, 14.8.15 contain relevant information regarding the evaluation of models in this region, the model spread in the context of other methods of projecting changes and the role of modes of variability and other climate phenomena. 1393 AII Annex II: Climate System Scenario Tables Editorial Team: Michael Prather (USA), Gregory Flato (Canada), Pierre Friedlingstein (UK/Belgium), Christopher Jones (UK), Jean-François Lamarque (USA), Hong Liao (China), Philip Rasch (USA) Contributors: Olivier Boucher (France), François-Marie Bréon (France), Tim Carter (Finland), William Collins (UK), Frank J. Dentener (EU/Netherlands), Edward J. Dlugokencky (USA), Jean-Louis Dufresne (France), Jan Willem Erisman (Netherlands), Veronika Eyring (Germany), Arlene M. Fiore (USA), James Galloway (USA), Jonathan M. Gregory (UK), Ed Hawkins (UK), Chris Holmes (USA), Jasmin John (USA), Tim Johns (UK), Fiona Lo (USA), Natalie Mahowald (USA), Malte Meinshausen (Germany), Colin Morice (UK), Vaishali Naik (USA/India), Drew Shindell (USA), Steven J. Smith (USA), David Stevenson (UK), Peter W. Thorne (USA/Norway/UK), Geert Jan van Oldenborgh (Netherlands), Apostolos Voulgarakis (UK/Greece), Oliver Wild (UK), Donald Wuebbles (USA), Paul Young (UK) This annex should be cited as: IPCC, 2013: Annex II: Climate System Scenario Tables [Prather, M., G. Flato, P. Friedlingstein, C. Jones, J.-F. Lamarque, H. Liao and P. Rasch (eds.)]. In: Climate Change 2013: The Physical Science Basis. Contribution of Working Group I to the Fifth Assessment Report of the Intergovernmental Panel on Climate Change [Stocker, T.F., D. Qin, G.-K. Plattner, M. Tignor, S.K. Allen, J. Boschung, A. Nauels, Y. Xia, V. Bex and P.M. Midgley (eds.)]. Cambridge University Press, Cambridge, United Kingdom and New York, NY, USA. 1395 Table of Contents Introduction ................................................................................ 1397 Chemical Abbreviations and Symbols.................................. 1397 List of Tables................................................................................ 1398 References ................................................................................ 1400 Tables ......................................................................................... 1401 AII.1: Historical Climate System Data.......................................... 1401 AII AII.2: Anthropogenic Emissions................................................... 1410 AII.3: Natural Emissions.............................................................. 1421 AII.4: Abundances of the Well-Mixed Greenhouse Gases............ 1422 AII.5: Column Abundances, Burdens, and Lifetimes..................... 1428 AII.6: Effective Radiative Forcing................................................. 1433 AII.7: Environmental Data........................................................... 1437 1396 Climate System Scenario Tables Annex II Introduction 6.4.3, 11.3.5 and 12.3). Thus, projected changes in greenhouse gases (GHGs), aerosols and ERF evaluated in this report may differ from the Annex II presents, in tabulated form, data related to historical and pro- published RCPs and from what was used in the CMIP5 runs, and these jected changes in the climate system that are assessed in the chap- are denoted RCP&. The CMIP5 climate projections used for the most ters of this report (see Section 1.6). It also includes some comparisons part the RCP concentration pathways for well-mixed greenhouse gases with the Third Assessment Report (TAR) and Fourth Assessment Report (WMGHG) and the emissions pathways for ozone (O3) and aerosol pre- (AR4) results. These data include values for emissions into the atmo- cursors. Such differences are discussed in the relevant chapters and sphere, atmospheric abundances and burdens (integrated abundance), noted in the tables. effective radiative forcing (ERF; includes adjusted forcing from aero- sols, see Chapters 7 and 8), and global mean surface temperatures and For each species, the abundances (given as dry air mole fraction: ppm sea level. Projections from 2010 to 2100 focus on the RCP scenarios = micromoles per mole (10 6); ppb = nanomoles per mole (10 9); and (Moss et al., 2010; Lamarque et al., 2010; 2011; Meinshausen et al., ppt = picomoles per mole (10 12)), burdens (global total in grams, 1 Tg 2011a; van Vuuren et al., 2011; see also Chapters 1, 6, 8, 11, 12 and = 1012 g), average column amount (1 Dobson Unit (DU) = 2.687 × 1016 13). Projections also include previous IPCC scenarios (IPCC Scenarios molecules per cm2), AOD (mean aerosol optical depth at 550 nm), ERF 1992a (IS92a), Special Report on Emission Scenarios (SRES) A2 and B1, (effective radiative forcing, W m 2), and other climate system quantities AII TAR Appendix II) and some alternative near-term scenarios for meth- are calculated for scenarios using methodologies based on the latest ane (CH4) and short-lived pollutants that impact climate or air quality. climate chemistry and climate carbon models (see Chapters 2, 6, 7, Emissions from biomass burning are included as anthropogenic. ERF 8, 10, 11 and 12). Results are shown for individual years (e.g., 2010 from land use change is also included in some tables. = year 2010) and decadal averages (e.g., 2020d = average of years 2016 through 2025), although some 10-year periods are different, see Where uncertainties or ranges are presented here, they are noted in table notes. Year 2011 is the last year for observed quantities (denoted each table as being a recommended value or model ensemble mean/ 2011* or 2011obs). Results are shown as global mean values except median with a 68% confidence interval (16 to 84%, +/-1 for a normal for environmental data focussing on air quality (Tables AII.7.1 AII.7.4), distribution) or 90% confidence interval (5 to 95%, +/-1.645 for a which give regional mean surface abundances of O3 and fine particu- normal distribution) or statistics (standard deviation, percentiles, or late matter with diameter less than 2.5 m (PM2.5). Results for global minimum/maximum) of an ensemble of models. In some cases these mean surface temperature (Tables AII.7.5 and AII.7.6) show only raw are a formal evaluation of uncertainty as assessed in the chapters, but CMIP5 data or data from previous assessments. For best estimates of in other cases (specifically Tables AII.2.1, 3.1, 4.1, 5.1, 6.10, 7.1 to 7.5) near-term and long-term temperature change see Chapters 11 and 12, they just describe the statistical results from the available models, and respectively. Results for global mean sea level rise (Table AII.7.7) are the referenced chapters must be consulted for the assessed uncertainty assessed values with uncertainties described in Chapter 13. or confidence level of these results. In the case of Table AII.7.5, for example, the global mean surface temperature change (°C) relative to 1986 2005 is a statistical summary of the spread in the Coupled Chemical Abbreviations and Symbols Model Intercomparison Project (CMIP) ensembles for each of the sce- narios: model biases and model dependencies are not accounted for; Well Mixed Greenhouse Gases (WMGHG) the percentiles do not correspond to the assessed uncertainty derived in Chapters 11 (Section 11.3.6.3) and 12 (Section 12.4.1); and statisti- CO2 carbon dioxide (KP, Kyoto Protocol gas) cal spread across models cannot be interpreted in terms of calibrated CH4 methane (KP) language (Section 12.2). N2O nitrous oxide (KP) HFC hydrofluorocarbon1 (a class of compounds: HFC-32, HFC- The Representative Concentration Pathway (RCP) scenarios for emis- 134a, ) (KP) sions include only anthropogenic sources and use a single model to PFC perfluorocarbon (a class of compounds: CF4, C2F6, ) (KP) project from emissions to abundances to radiative forcing to climate SF6 sulphur hexafluoride (KP) change (Meinshausen et al., 2011a; 2011b). We include projected NF3 nitrogen trifluoride (KP) changes in natural carbon dioxide (CO2) sources and sinks for 2010 CFC chlorofluorocarbon (a class of compounds: CFCl3, CF2Cl2, ) 2100 based on this assessment (Chapters 6 and 12). Present-day natu- (MP, Montreal Protocol gas) ral and anthropogenic emissions of CH4 and nitrous oxide (N2O) are HCFC hydrochlorofluorocarbon1 (a class of compounds: HCFC-22, assessed and used to scale the RCP anthropogenic emissions to be con- HCFC-141b, ) (MP) sistent with these best estimates (Chapters 6 and 11). Current model CCl4 carbon tetrachloride (MP) evaluations of atmospheric chemistry and the carbon cycle, including CH3CCl3 methyl chloroform (MP) results from the CMIP5 and Atmospheric Chemistry and Climate Model Intercomparison Project (ACCMIP) projects, are used to project future composition and ERF separately from the RCP model (see Sections 1 A few HFCs and HCFCs are very short lived in the atmosphere and therefore not well mixed. 1397 Annex II Climate System Scenario Tables Ozone and Aerosols, and their Precursors Table AII.2.21: Anthropogenic OC aerosols emissions (Tg yr 1) Table AII.2.22: Anthropogenic BC aerosols emissions (Tg yr 1) O3 ozone (both stratospheric and tropospheric) Table AII.2.23: Anthropogenic nitrogen fixation (Tg-N yr 1) NOx sum of NO (nitric oxide) and NO2 (nitrogen dioxide) NH3 ammonia AII.3: Natural Emissions CO carbon monoxide NMVOC a class of compounds comprising all non-methane volatile Table AII.3.1a: Net land (natural and land use) CO2 emissions (PgC organic compounds (i.e., hydrocarbons that may also contain yr 1) oxygen, also known as biogenic VOC or NMHC) Table AII.3.1b: Net ocean CO2 emissions (PgC yr 1) OH hydroxyl radical PM2.5 any aerosols with diameter less than 2.5 m AII.4: Abundances of the Well-Mixed Greenhouse Gases BC black carbon aerosol OC organic carbon aerosol Table AII.4.1: CO2 abundance (ppm) SO2 sulphur dioxide, a gas Table AII.4.2: CH­ abundance (ppb) 4 SOx oxidized sulphur in gaseous form, including SO2 Table AII.4.3: N2O abundance (ppb) AII SO4= sulphate ion, usually as sulphuric acid or ammonium sul- Table AII.4.4: SF6 abundance (ppt) phate in aerosol Table AII.4.5: CF4 abundance (ppt) Table AII.4.6: C2F6 abundance (ppt) Table AII.4.7: C6F14 abundance (ppt) List of Tables Table AII.4.8: HFC-23 abundance (ppt) Table AII.4.9: HFC-32 abundance (ppt) AII.1: Historical Climate System Data Table AII.4.10: HFC-125 abundance (ppt) Table AII.4.11: HFC-134a abundance (ppt) Table AII.1.1a: Historical abundances of the Kyoto greenhouse gases Table AII.4.12: HFC-143a abundance (ppt) Table AII.1.1b: Historical abundances of the Montreal Protocol green- Table AII.4.13: HFC-227ea abundance (ppt) house gas (all ppt) Table AII.4.14: HFC-245fa abundance (ppt) Table AII.1.2: Historical effective radiative forcing (ERF) (W m 2), Table AII.4.15: HFC-43-10mee abundance (ppt) including land use change (LUC) Table AII.4.16: Montreal Protocol greenhouse gas abundances (ppt) Table AII.1.3: Historical global decadal-mean global surface-air t ­ emperature (°C) relative to 1961 1990 average AII.5: Column Abundances, Burdens, and Lifetimes AII.2: Anthropogenic Emissions Table AII.5.1: Stratospheric O3 column changes (DU) Table AII.5.2: Tropospheric O3 column changes (DU) Table AII.2.1a: Anthropogenic CO2 emissions from fossil fuels and Table AII.5.3: Total aerosol optical depth (AOD) other industrial sources (FF) (PgC yr 1) Table AII.5.4: Absorbing aerosol optical depth (AAOD) Table AII.2.1b: Anthropogenic CO2 emissions from agriculture, ­forestry, Table AII.5.5: Sulphate aerosol atmospheric burden (TgS) land use (AFOLU) (PgC yr 1) Table AII.5.6: OC aerosol atmospheric burden (Tg) Table AII.2.1c: Anthropogenic total CO2 emissions (PgC yr 1) Table AII.5.7: BC aerosol atmospheric burden (Tg) Table AII.2.2: Anthropogenic CH4 emissions (Tg yr 1) Table AII.5.8: CH4 atmospheric lifetime (yr) against loss by tropo- Table AII.2.3: Anthropogenic N2O emissions (TgN yr 1) spheric OH Table AII.2.4: Anthropogenic SF6 emissions (Gg yr 1) Table AII.5.9: N2O atmospheric lifetime (yr) Table AII.2.5: Anthropogenic CF4 emissions (Gg yr 1) Table AII.2.6: Anthropogenic C2F6 emissions (Gg yr 1) AII.6: Effective Radiative Forcing Table AII.2.7: Anthropogenic C6F14 emissions (Gg yr 1) Table AII.2.8: Anthropogenic HFC-23 emissions (Gg yr 1) Table AII.6.1: ERF from CO2 (W m 2) Table AII.2.9: Anthropogenic HFC-32 emissions (Gg yr 1) Table AII.6.2: ERF from CH4 (W m 2) Table AII.2.10: Anthropogenic HFC-125 emissions (Gg yr 1) Table AII.6.3: ERF from N2O (W m 2) Table AII.2.11: Anthropogenic HFC-134a emissions (Gg yr 1) Table AII.6.4: ERF from all HFCs (W m 2) Table AII.2.12: Anthropogenic HFC-143a emissions (Gg yr 1) Table AII.6.5: ERF from all PFCs and SF6 (W m 2) Table AII.2.13: Anthropogenic HFC-227ea emissions (Gg yr 1) Table AII.6.6: ERF from Montreal Protocol greenhouse gases (W m 2) Table AII.2.14: Anthropogenic HFC-245fa emissions (Gg yr 1) Table AII.6.7a: ERF from stratospheric O3 changes since 1850 (W m 2) Table AII.2.15: Anthropogenic HFC-43-10mee emissions (Gg yr 1) Table AII.6.7b: ERF from tropospheric O3 changes since 1850 (W m 2) Table AII.2.16: Anthropogenic CO emissions (Tg yr 1) Table AII.6.8: Total anthropogenic ERF from published RCPs and SRES Table AII.2.17: Anthropogenic NMVOC emissions (Tg yr 1) (W m 2) Table AII.2.18: Anthropogenic NOX emissions (TgN yr 1) Table AII.6.9: ERF components relative to 1850 (W m 2) derived from Table AII.2.19: Anthropogenic NH3 emissions (TgN yr 1) ACCMIP Table AII.2.20: Anthropogenic SOX emissions (TgS yr 1) Table AII.6.10: Total anthropogenic plus natural ERF (W m 2) from CMIP5 and CMIP3, including historical 1398 Climate System Scenario Tables Annex II AII.7: Environmental Data Table AII.7.1: Global mean surface O3 change (ppb) Table AII.7.2: Surface O3 change (ppb) for HTAP regions Table AII.7.3: Surface O3 change (ppb) from CMIP5/ACCMIP for continental regions Table AII.7.4: Surface particulate matter change (log10[PM2.5 (microgram/m3)]) from CMIP5/ACCMIP for continental regions Table AII.7.5: CMIP5 (RCP) and CMIP3 (SRES A1B) global mean surface temperature change (°C) relative to 1986 2005 reference period Table AII.7.6: Global mean surface temperature change (°C) relative to 1990 from the TAR Table AII.7.7: Global mean sea level rise (m) with respect to 1986 2005 at 1 January on the years indicated AII 1399 Annex II Climate System Scenario Tables References Calvin, K., et al., 2012: The role of Asia in mitigating climate change: Results from the Prather, M. J., C. D. Holmes, and J. Hsu, 2012: Reactive greenhouse gas scenarios: Asia modeling exercise. Energy Econ., 34, S251 S260. Systematic exploration of uncertainties and the role of atmospheric chemistry. Cionni, I., V. Eyring, J. Lamarque, W. Randel, D. Stevenson, F. Wu, G. Bodeker, T. Geophys. Res. Lett., 39, L09803. Shepherd, D. Shindell, and D. Waugh, 2011: Ozone database in support of CMIP5 Rogelj, J., et al., 2011: Emission pathways consistent with a 2°C global temperature simulations: Results and corresponding radiative forcing. Atmos. Chem. Phys., limit. Nature Clim. Change, 1, 413 418. 11, 11267 11292. Shindell, D.T., J.-F. Lamarque, M. Schulz, M. Flanner, et al., 2013: Radiative forcing in Cofala, J., M. Amann, Z. Klimont, K. Kupiainen, and L. Hoglund-Isaksson, 2007: the ACCMIP historical and future climate simulations. Atmos. Chem. Phys., 13, Scenarios of global anthropogenic emissions of air pollutants and methane until 2939 2974. 2030. Atmos. Environ., 41, 8486 8499. Stevenson, D. S., et al., 2013: Tropospheric ozone changes, radiative forcing and Dentener, F., D. Stevenson, J. Cofala, R. Mechler, M. Amann, P. Bergamaschi, F. Raes, attribution to emissions in the Atmospheric Chemistry and Climate Model and R. Derwent, 2005: The impact of air pollutant and methane emission Intercomparison Project (ACCMIP). Atmos. Chem. Phys., 13, 3063 3085. controls on tropospheric ozone and radiative forcing: CTM calculations for the van Vuuren, D. P., et al., 2008: Temperature increase of 21st century mitigation period 1990-2030. Atmos. Chem. Phys., 5, 1731 1755. scenarios. Proc. Natl. Acad. Sci. U.S.A., 105, 15258 15262. Dentener, F., et al., 2006: The global atmospheric environment for the next van Vuuren, D., et al., 2011: The representative concentration pathways: An overview. generation. Environ. Sci. Technol., 40, 3586 3594. Clim. Change, 109, 5 31. AII Douglass, A. and V. Fioletov, 2010: Stratospheric Ozone and Surface Ultraviolet Voulgarakis, A., et al., 2013: Analysis of present day and future OH and methane Radiation in Scientific Assessment of Ozone Depletion: 2010. Global Ozone lifetime in the ACCMIP simulations. 21 Atmos. Chem. Phys., 13, 2563 2587. Research and Monitoring Project-Report No. 52.World Meteorological Wild, O., A.M. Fiore et al., 2012: Modelling future changes in surface ozone: A Organization, Geneva, Switzerland. parameterized approach. Atmos. Chem. Phys., 12, 2037 2054. Erisman, J. W., M. A. Sutton, J. Galloway, Z. Klimont, and W. Winiwarter, 2008: How a WMO. 2010. Scientific Assessment of Ozone Depletion: 2010. Global Ozone Research century of ammonia synthesis changed the world. Nature Geosci., 1, 636 639. and Monitoring Project Report No. 52. World Meteorological Organization, Eyring, V., et al., 2013: Long-term ozone changes and associated climate impacts in Geneva, Switzerland. CMIP5 simulations. J. Geophys. Res., doi:10.1002/jgrd.50316. Young, P. J., et al., 2013: Pre-industrial to end 21st century projections of tropospheric Fiore, A. M., et al., 2012: Global air quality and climate. Chem. Soc. Rev., 41, 6663 ozone from the Atmospheric Chemistry and Climate Model Intercomparison 6683. Project (ACCMIP). Atmos. Chem. Phys., 13, 2063 2090. Fleming, E., C. Jackman, R. Stolarski and A. Douglass, 2011: A model study of the impact of source gas changes on the stratosphere for 1850-2100. Atmos. Chem. Phys., 11, 8515 8541. Forster, P. M., T. Andrews, P. Good, J. M. Gregory, L. S. Jackson, and M. Zelinka, 2013: Evaluating adjusted forcing and model spread for historical and future scenarios in the CMIP5 generation of climate models. J. Geophys. Res., 118, 1139 1150. Friedlingstein, P., et al., 2006: Climate-carbon cycle feedback analysis: Results from the C4MIP model intercomparison. J. Clim., 19, 3337 3353. Holmes, C. D., M. J. Prather, A.O. Svde, and G. Myhre, 2013: Future methane, hydroxyl, and their uncertainties: Key climate and emission parameters for future predictions. Atmos. Chem. Phys., 13, 285 302. HTAP, 2010. Hemispheric Transport of Air Pollution 2010, Part A: Ozone and Particulate Matter. United Nations, Geneva, Switzerland. Jones, C. D., et al., 2013: 21st Century compatible CO2 emissions and airborne fraction simulated by CMIP5 Earth System models under 4 Representative Concentration Pathways. J. Clim., doi:10.1175/JCLI-D-12-00554.1. Lamarque, J. F., G. P. Kyle, M. Meinshausen, K. Riahi, S. J. Smith, D. P. Van Vuuren, A. J. Conley, and F. Vitt, 2011: Global and regional evolution of short-lived radiatively- active gases and aerosols in the Representative Concentration Pathways. Clim. Change, 109, 191 212. Lamarque, J. F., et al., 2010: Historical (1850-2000) gridded anthropogenic and biomass burning emissions of reactive gases and aerosols: methodology and application. Atmos. Chem. Phys., 10, 7017 7039. Lamarque, J. F., et al., 2013: The Atmospheric Chemistry and Climate Model Intercomparison Project (ACCMIP): Overview and description of models, simulations and climate diagnostics. Geosci. Model Dev., 6, 179 206. Meinshausen, M., T. M. L. Wigley, and S. C. B. Raper, 2011b: Emulating atmosphere- ocean and carbon cycle models with a simpler model, MAGICC6-Part 2: Applications. Atmos. Chem. Phys., 11, 1457 1471. Meinshausen, M., et al., 2011a: The RCP greenhouse gas concentrations and their extensions from 1765 to 2300. Clim. Change, 109, 213 241. Moss, R. H., et al., 2010: The next generation of scenarios for climate change research and assessment. Nature, 463, 747 756. Prather, M., et al., 2001: Atmospheric chemistry and greenhouse gases. In: Climate Change 2001: The Scientific Basis. Contribution of Working Group I to the Third Assessment Report of the Intergovernmental Panel on Climate Change [J. T. Houghton, Y. Ding, D. J. Griggs, M. Noquer, P. J. van der Linden, X. Dai, K. Maskell and C. A. Johnson (eds.)]. Cambridge University Press, Cambridge, United Kingdom and New York, NY, USA, pp. 239 287. Prather, M., et al., 2003: Fresh air in the 21st century? Geophys. Res. Lett., 30, 1100. 1400 Climate System Scenario Tables Annex II Tables AII.1: Historical Climate System Data Table AII.1.1a | Historical abundances of the Kyoto greenhouse gases Year CO2 (ppm) CH4 (ppb) N2O (ppb) Year CO2 (ppm) CH4 (ppb) N2O (ppb) PI* 278 +/- 2 722 +/- 25 270 +/- 7 PI* 278 +/- 2 722 +/- 25 270 +/- 7 1755 276.7 723 272.8 1959 316.0 1251 292.1 1760 276.5 726 274.1 1960 316.7 1263 292.4 1765 276.6 730 274.2 1961 317.4 1275 292.5 1770 277.3 733 273.7 1962 318.0 1288 292.5 1775 278.0 736 273.1 1963 318.5 1301 292.6 1780 278.2 739 272.4 1964 319.0 319.0 292.6 1785 278.6 742 271.9 1965 319.7 1328 292.7 AII 1790 280.0 745 271.8 1966 320.6 1343 292.9 1795 281.4 748 272.1 1967 321.5 1357 293.3 1800 282.6 751 272.6 1968 322.5 1372 293.8 1805 283.6 755 272.1 1969 323.5 1388 294.4 1810 284.2 760 271.4 1970 324.6 1403 295.2 1815 284.0 765 271.5 1971 325.6 1419 296.0 1820 283.3 769 272.9 1972 326.8 1435 296.9 1825 283.1 774 274.1 1973 328.0 1451 297.8 1830 283.8 779 273.7 1974 329.2 1467 298.4 1835 283.9 784 270.5 1975 330.2 1483 299.0 1840 284.1 789 269.6 1976 331.3 1500 299.4 1845 285.8 795 270.3 1977 332.7 1516 299.8 1850 286.8 802 270.4 1978 334.3 1532 300.2 1855 286.4 808 270.6 1979 336.2 1549 300.7 1860 286.1 815 271.7 1980 338.0 1567 301.3 1865 286.3 823 272.3 1981 339.3 1587 302.0 1870 288.0 831 273.0 1982 340.5 1607 303.0 1875 289.4 839 274.7 1983 342.1 1626 303.9 1880 289.8 847 275.8 1984 343.7 1643 304.5 1885 290.9 856 277.2 1985 345.2 1657 305.5 1890 293.1 866 278.3 1986 346.6 1670 305.9 1895 295.4 877 277.7 1987 348.4 1682 306.3 1900 296.2 891 277.3 1988 350.5 1694 306.7 1905 297.4 912 279.2 1989 352.2 1704 307.8 1910 299.3 935 280.8 1990 353.6 1714 308.7 1915 301.1 961 282.7 1991 354.8 1725 309.3 1920 303.3 990 285.1 1992 355.7 1733 309.8 1925 304.7 1020 284.3 1993 356.6 1738 310.1 1930 306.6 1049 284.9 1994 358.0 1743 310.4 1935 308.4 1077 286.6 1995 359.9 1747 311.0 1940 310.4 1102 287.7 1996 361.4 1751 311.8 1945 310.9 1129 288.0 1997 363.1 1757 312.7 1950 311.2 1162 287.6 1998 365.2 1765 313.7 1955 313.4 1207 289.6 1999 367.2 1771 314.7 1956 314.0 1217 290.4 2000 368.7 1773 315.6 1957 314.6 1228 291.2 2001 370.2 1773 316.3 1958 315.3 1239 291.7 2002 372.3 1774 317.0 (continued on next page) 1401 Annex II Climate System Scenario Tables Table AII.1.1a (continued) Year CO2 (ppm) CH4 (ppb) N2O (ppb) Year SF6 (ppt) CF4 (ppt) C2F6 (ppt) C6F14 (ppt) NF3 (ppt) PI* 278 +/- 2 722 +/- 25 270 +/- 7 PI* 0 35 0 0 2003 374.5 1776 317.6 1900 0 35 0 0 2004 376.6 1776 318.3 1910 0 35 0.1 0 2005 378.7 1776 319.1 1920 0 35 0.1 0 2006 380.8 1776 319.8 1930 0 36 0.2 0 2007 382.7 1781 320.6 1940 0 37 0.3 0 2008 384.6 1787 321.4 1950 0 39 0.5 0 2005 378.7 1776 319.1 1960 0.1 43 0.6 0 2006 380.8 1776 319.8 1970 0.3 51 0.8 0 2007 382.7 1781 320.6 1980 0.8 60 1.2 0 2008 384.6 1787 321.4 1990 2.4 68 1.9 0 AII 2009 386.4 1792 322.3 2000 4.5 76 2.9 0 2010 388.4 1798 323.2 2005 5.6 75 3.7 0 0.3 2011* 390.5 +/- 0.3 1803 +/- 4 324 +/- 1 2010 7.0 78.3 4.1 0 2011* 7.3 +/- 0.1 79.0 4.2 0 0.6 HFC-23 HFC-32 HFC-125 HFC-134a HFC-143a HFC-227ea HFC-245fa HFC-43-10mee Year (ppt) (ppt) (ppt) (ppt) (ppt) (ppt) (ppt) (ppt) PI* 0 0 0 0 0 0 0 0 1940 0.1 0 0 0 0 0 0 0 1950 0.3 0 0 0 0 0 0 0 1960 0.7 0 0 0 0 0 0 0 1970 1.6 0 0 0 0 0 0 0 1980 3.7 0 0 0 0.2 0 0 0 1990 7.9 0 0.1 0 0.6 0 0 0 2000 14.8 0 1.3 14 3.1 0.1 0 0 2010 23.2 4.1 8.2 58 10.9 0.6 1.1 0 2011* 24.0 4.9 9.6 63 +/- 1 12.0 0.65 1.24 0 Notes: Abundances are mole fraction of dry air for the lower, well-mixed atmosphere (ppm = micromoles per mole, ppb = nanomoles per mole, ppt = picomoles per mole). Values refer to single-year average. Uncertainties (5 to 95% confidence intervals) are given for 2011 only when more than one laboratory reports global data. Pre-industrial (PI*, taken to be 1750 for GHG) and present day (2011*) abundances are from Chapter 2, Tables 2.1 and 2.SM.1; see also Chapter 6 for Holocene variability (10 ppm CO2, 40 ppb CH4, 10 ppb N2O). Intermediate data for CO2, CH4 and N2O are from Chapters 2 and 8, Figure 8.6. See also Appendix 1.A. Intermediate data for the F-gases are taken from Meinshausen et al. (2011). 1402 Climate System Scenario Tables Annex II Table AII.1.1b | Historical abundances of the Montreal Protocol greenhouse gases (all ppt) Year CFC-11 CFC-12 CFC-113 CFC-114 CFC-115 CCl4 CH3CCl3 HCFC-22 PI* 0 0 0 0 0 0 0 0 1960 9.5 29.5 1.9 3.8 0.0 52.1 1.5 2.1 1965 23.5 58.8 3.1 5.0 0.0 64.4 4.7 4.9 1970 52.8 114.3 5.5 6.5 0.2 75.9 16.2 12.1 1975 106.1 203.1 10.4 8.3 0.6 85.5 40.0 23.8 1980 161.9 297.4 19.0 10.7 1.3 93.3 81.6 42.5 1985 205.4 381.2 37.3 12.9 2.8 99.6 106.1 62.7 1990 256.2 477.5 67.6 15.4 4.7 106.5 127.2 88.2 1995 267.4 523.8 83.6 16.1 6.8 103.2 110.3 113.6 2000 261.7 541.0 82.3 16.5 7.9 98.6 49.7 139.5 2005 251.6 542.7 78.8 16.6 8.3 93.7 20.1 165.5 2010 240.9 532.5 75.6 16.4 8.4 87.6 8.3 206.8 AII 2011* 238 +/- 1 528+/-2 74.3+/-0.5 15.8 8.4 86+/-2 6.4+/-0.4 213+/-2 Year HCFC-141b HCFC-142b Halon 1211 Halon 1202 Halon 1301 Halon 2402 CH3Br CH3Cl PI* 0 0 0 0 0 0 1960 0.0 0.0 0.00 0.00 0.00 0.00 6.5 510 1965 0.0 0.0 0.00 0.00 0.00 0.00 6.7 528 1970 0.0 0.0 0.02 0.00 0.00 0.02 0.0 540 1975 0.0 0.2 0.12 0.01 0.04 0.06 7.4 546 1980 0.0 0.4 0.42 0.01 0.24 0.15 7.7 548 1985 0.0 0.7 1.04 0.02 0.74 0.26 8.2 549 1990 0.0 1.2 2.27 0.03 1.66 0.41 8.6 550 1995 2.7 6.3 3.34 0.04 2.63 0.52 9.2 550 2000 11.8 11.4 4.02 0.04 2.84 0.50 8.9 550 2005 17.5 15.1 4.26 0.02 3.03 0.48 7.9 550 2010 20.3 20.5 4.07 0.00 3.20 0.46 7.2 550 2011* 21.4+/-0.5 21.2+/-0.5 4.07 0.00 3.23 0.45 7.1 534 Notes: See Table AII.1.1a. For present-day (2011*) see Chapter 2. Intermediate years are from Scenario A1, WMO Ozone Assessment (WMO, 2010). 1403 Annex II Climate System Scenario Tables Table AII.1.2 | Historical effective radiative forcing (ERF) (W m 2), including land use change (LUC) GHG O3 O3 Aerosol H2O BC Con Year CO2 LUC Solar Volcano Other* (Trop) (Strat) (Total) (Strat) Snow trails 1750 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.001 1751 0.023 0.004 0.000 0.000 0.002 0.000 0.000 0.000 0.000 0.014 0.000 1752 0.024 0.006 0.001 0.000 0.004 0.001 0.000 0.000 0.000 0.029 0.000 1753 0.024 0.007 0.001 0.000 0.005 0.001 0.000 0.000 0.000 0.033 0.000 1754 0.025 0.008 0.002 0.000 0.007 0.002 0.000 0.001 0.000 0.043 0.000 1755 0.026 0.010 0.002 0.000 0.009 0.002 0.000 0.001 0.000 0.054 0.664 1756 0.026 0.011 0.003 0.000 0.011 0.002 0.000 0.001 0.000 0.055 0.000 1757 0.027 0.013 0.003 0.000 0.013 0.003 0.000 0.001 0.000 0.048 0.000 1758 0.028 0.014 0.003 0.000 0.014 0.003 0.000 0.001 0.000 0.050 0.000 1759 0.028 0.015 0.004 0.000 0.016 0.004 0.000 0.001 0.000 0.102 0.000 AII 1760 0.029 0.016 0.004 0.000 0.018 0.004 0.000 0.001 0.000 0.112 0.060 1761 0.029 0.017 0.005 0.000 0.020 0.004 0.000 0.002 0.000 0.016 1.093 1762 0.029 0.017 0.005 0.000 0.021 0.005 0.001 0.002 0.000 0.007 0.300 1763 0.029 0.018 0.006 0.000 0.023 0.005 0.001 0.002 0.000 0.018 0.093 1764 0.028 0.018 0.006 0.000 0.025 0.006 0.001 0.002 0.000 0.022 0.021 1765 0.026 0.018 0.006 0.000 0.027 0.006 0.001 0.002 0.000 0.054 0.003 1766 0.024 0.018 0.007 0.000 0.029 0.006 0.001 0.002 0.000 0.048 0.000 1767 0.022 0.018 0.007 0.000 0.030 0.007 0.001 0.003 0.000 0.036 0.000 1768 0.020 0.018 0.008 0.000 0.032 0.007 0.001 0.003 0.000 0.016 0.000 1769 0.017 0.018 0.008 0.000 0.034 0.008 0.001 0.003 0.000 0.050 0.000 1770 0.014 0.018 0.009 0.000 0.036 0.008 0.001 0.003 0.000 0.081 0.000 1771 0.011 0.018 0.009 0.000 0.038 0.008 0.001 0.003 0.000 0.055 0.000 1772 0.008 0.018 0.009 0.000 0.039 0.009 0.001 0.003 0.000 0.052 0.070 1773 0.005 0.018 0.010 0.000 0.041 0.009 0.001 0.003 0.000 0.016 0.020 1774 0.003 0.018 0.010 0.000 0.043 0.010 0.001 0.004 0.000 0.002 0.005 1775 0.001 0.018 0.011 0.000 0.045 0.010 0.001 0.004 0.000 0.038 0.001 1776 0.001 0.018 0.011 0.000 0.046 0.010 0.001 0.004 0.000 0.045 0.000 1777 0.002 0.018 0.011 0.000 0.048 0.011 0.001 0.004 0.000 0.036 0.000 1778 0.003 0.018 0.012 0.000 0.050 0.011 0.001 0.004 0.000 0.017 0.067 1779 0.003 0.018 0.012 0.000 0.052 0.012 0.001 0.004 0.000 0.034 0.071 1780 0.003 0.018 0.013 0.000 0.054 0.012 0.002 0.004 0.000 0.069 0.018 1781 0.004 0.018 0.013 0.000 0.055 0.012 0.002 0.005 0.000 0.057 0.004 1782 0.004 0.018 0.014 0.000 0.057 0.013 0.002 0.005 0.000 0.028 0.001 1783 0.006 0.018 0.014 0.000 0.059 0.013 0.002 0.005 0.000 0.065 7.857 1784 0.009 0.018 0.014 0.000 0.061 0.014 0.002 0.005 0.000 0.059 0.522 1785 0.012 0.018 0.015 0.000 0.062 0.014 0.002 0.005 0.000 0.046 0.121 1786 0.017 0.018 0.015 0.000 0.064 0.014 0.002 0.005 0.000 0.022 0.027 1787 0.021 0.018 0.016 0.000 0.066 0.015 0.002 0.005 0.000 0.001 0.002 1788 0.027 0.018 0.016 0.000 0.068 0.015 0.002 0.006 0.000 0.034 0.133 1789 0.033 0.019 0.017 0.000 0.070 0.016 0.002 0.006 0.000 0.033 0.041 1790 0.038 0.019 0.017 0.000 0.071 0.016 0.002 0.006 0.000 0.058 0.009 1791 0.044 0.019 0.017 0.000 0.073 0.016 0.002 0.006 0.000 0.056 0.001 1792 0.050 0.020 0.018 0.000 0.075 0.017 0.002 0.006 0.000 0.051 0.000 1793 0.055 0.020 0.018 0.000 0.077 0.017 0.002 0.006 0.000 0.065 0.000 1794 0.060 0.021 0.019 0.000 0.079 0.018 0.002 0.006 0.000 0.064 0.157 1795 0.066 0.022 0.019 0.000 0.080 0.018 0.002 0.007 0.000 0.027 0.000 1796 0.070 0.023 0.020 0.000 0.082 0.018 0.002 0.007 0.000 0.033 0.781 1797 0.075 0.023 0.020 0.000 0.084 0.019 0.002 0.007 0.000 0.043 0.071 1798 0.079 0.024 0.020 0.000 0.086 0.019 0.002 0.007 0.000 0.045 0.016 1404 Climate System Scenario Tables Annex II Table AII.1.2 | (continued) GHG O3 O3 Aerosol H2O BC Con Year CO2 LUC Solar Volcano Other* (Trop) (Strat) (Total) (Strat) Snow trails 1799 0.084 0.025 0.021 0.000 0.087 0.020 0.003 0.007 0.000 0.047 0.002 1800 0.088 0.025 0.021 0.000 0.089 0.020 0.003 0.007 0.000 0.055 0.000 1801 0.092 0.026 0.022 0.000 0.091 0.020 0.003 0.007 0.000 0.021 0.154 1802 0.096 0.026 0.022 0.000 0.093 0.021 0.003 0.008 0.000 0.010 0.048 1803 0.099 0.026 0.023 0.000 0.095 0.021 0.003 0.008 0.000 0.033 0.011 1804 0.103 0.026 0.023 0.000 0.096 0.022 0.003 0.008 0.000 0.040 0.230 1805 0.106 0.026 0.023 0.000 0.098 0.022 0.003 0.008 0.000 0.046 0.070 1806 0.109 0.026 0.024 0.000 0.100 0.022 0.003 0.008 0.000 0.036 0.016 1807 0.112 0.026 0.024 0.000 0.102 0.023 0.003 0.008 0.000 0.057 0.002 1808 0.114 0.026 0.025 0.000 0.104 0.023 0.003 0.008 0.000 0.065 0.000 1809 0.116 0.026 0.025 0.000 0.105 0.024 0.003 0.009 0.000 0.065 6.947 AII 1810 0.117 0.026 0.025 0.000 0.107 0.024 0.003 0.009 0.000 0.070 2.254 1811 0.118 0.027 0.026 0.000 0.109 0.024 0.003 0.009 0.000 0.072 0.836 1812 0.119 0.027 0.026 0.000 0.111 0.025 0.003 0.009 0.000 0.072 0.308 1813 0.118 0.028 0.027 0.000 0.112 0.025 0.004 0.009 0.000 0.069 0.109 1814 0.117 0.029 0.027 0.000 0.114 0.026 0.004 0.009 0.000 0.064 0.000 1815 0.115 0.030 0.028 0.000 0.116 0.026 0.004 0.009 0.000 0.062 11.629 1816 0.113 0.031 0.028 0.000 0.118 0.026 0.004 0.010 0.000 0.052 4.553 1817 0.110 0.032 0.028 0.000 0.120 0.027 0.004 0.010 0.000 0.048 2.419 1818 0.107 0.034 0.029 0.000 0.121 0.027 0.004 0.010 0.000 0.053 0.915 1819 0.104 0.035 0.029 0.000 0.123 0.028 0.004 0.010 0.000 0.054 0.337 1820 0.101 0.037 0.030 0.000 0.125 0.028 0.004 0.010 0.000 0.059 0.039 1821 0.099 0.038 0.030 0.000 0.127 0.028 0.004 0.010 0.000 0.065 0.000 1822 0.097 0.040 0.031 0.000 0.128 0.029 0.004 0.010 0.000 0.066 0.000 1823 0.096 0.041 0.031 0.000 0.130 0.029 0.004 0.011 0.000 0.068 0.000 1824 0.097 0.042 0.031 0.000 0.132 0.030 0.004 0.011 0.000 0.059 0.000 1825 0.098 0.043 0.032 0.000 0.134 0.030 0.005 0.011 0.000 0.052 0.000 1826 0.100 0.044 0.032 0.000 0.136 0.030 0.005 0.011 0.000 0.044 0.000 1827 0.103 0.045 0.033 0.000 0.137 0.031 0.005 0.011 0.000 0.018 0.000 1828 0.106 0.045 0.033 0.000 0.139 0.031 0.005 0.011 0.000 0.008 0.000 1829 0.109 0.045 0.034 0.000 0.141 0.032 0.005 0.011 0.000 0.006 0.000 1830 0.111 0.045 0.034 0.000 0.143 0.032 0.005 0.012 0.000 0.002 0.000 1831 0.113 0.044 0.034 0.000 0.145 0.032 0.005 0.012 0.000 0.002 1.538 1832 0.114 0.043 0.035 0.000 0.146 0.033 0.005 0.012 0.000 0.020 1.229 1833 0.114 0.041 0.035 0.000 0.148 0.033 0.005 0.012 0.000 0.035 0.605 1834 0.114 0.039 0.036 0.000 0.150 0.034 0.005 0.012 0.000 0.038 0.223 1835 0.113 0.037 0.036 0.000 0.152 0.034 0.005 0.012 0.000 0.033 4.935 1836 0.112 0.036 0.037 0.000 0.153 0.034 0.005 0.012 0.000 0.017 1.445 1837 0.112 0.035 0.037 0.000 0.155 0.035 0.006 0.013 0.000 0.055 0.523 1838 0.112 0.035 0.037 0.000 0.157 0.035 0.006 0.013 0.000 0.051 0.192 1839 0.114 0.036 0.038 0.000 0.159 0.036 0.006 0.013 0.000 0.028 0.069 1840 0.117 0.037 0.038 0.000 0.161 0.036 0.006 0.013 0.000 0.027 0.047 1841 0.121 0.038 0.039 0.000 0.162 0.036 0.006 0.013 0.000 0.007 0.013 1842 0.127 0.040 0.039 0.000 0.164 0.037 0.006 0.013 0.000 0.006 0.003 1843 0.135 0.041 0.039 0.000 0.166 0.037 0.006 0.013 0.000 0.013 0.052 1844 0.142 0.042 0.040 0.000 0.168 0.038 0.006 0.014 0.000 0.024 0.014 1845 0.149 0.043 0.040 0.000 0.169 0.038 0.006 0.014 0.000 0.026 0.003 1846 0.155 0.044 0.041 0.000 0.171 0.038 0.006 0.014 0.000 0.024 0.071 1847 0.160 0.044 0.041 0.000 0.173 0.039 0.007 0.014 0.000 0.062 0.020 1848 0.163 0.045 0.042 0.000 0.175 0.039 0.007 0.014 0.000 0.018 0.005 1405 Annex II Climate System Scenario Tables Table AII.1.2 | (continued) GHG O3 O3 Aerosol H2O BC Con Year CO2 LUC Solar Volcano Other* (Trop) (Strat) (Total) (Strat) Snow trails 1849 0.166 0.046 0.042 0.000 0.177 0.040 0.007 0.014 0.000 0.043 0.001 1850 0.167 0.046 0.042 0.000 0.178 0.040 0.007 0.014 0.000 0.024 0.100 1851 0.167 0.047 0.043 0.000 0.180 0.040 0.007 0.015 0.000 0.016 0.075 1852 0.166 0.048 0.044 0.000 0.182 0.041 0.007 0.015 0.000 0.020 0.025 1853 0.164 0.049 0.045 0.000 0.184 0.041 0.007 0.015 0.000 0.011 0.025 1854 0.162 0.050 0.046 0.000 0.185 0.041 0.007 0.016 0.000 0.010 0.000 1855 0.160 0.051 0.047 0.000 0.187 0.042 0.007 0.016 0.000 0.027 0.050 1856 0.158 0.052 0.048 0.000 0.189 0.042 0.007 0.016 0.000 0.037 0.975 1857 0.156 0.054 0.049 0.000 0.191 0.042 0.008 0.016 0.000 0.037 1.500 1858 0.155 0.055 0.050 0.000 0.192 0.043 0.008 0.017 0.000 0.020 0.725 1859 0.154 0.057 0.050 0.000 0.194 0.043 0.008 0.017 0.000 0.007 0.275 AII 1860 0.154 0.058 0.051 0.000 0.196 0.043 0.008 0.017 0.000 0.029 0.125 1861 0.153 0.060 0.052 0.000 0.198 0.044 0.008 0.018 0.000 0.036 0.075 1862 0.153 0.061 0.053 0.000 0.199 0.044 0.008 0.018 0.000 0.013 0.350 1863 0.154 0.062 0.054 0.000 0.201 0.044 0.008 0.018 0.000 0.006 0.250 1864 0.156 0.063 0.055 0.000 0.203 0.045 0.008 0.018 0.000 0.017 0.125 1865 0.158 0.064 0.056 0.000 0.205 0.045 0.009 0.019 0.000 0.018 0.050 1866 0.162 0.066 0.057 0.000 0.206 0.045 0.009 0.019 0.000 0.021 0.025 1867 0.167 0.067 0.058 0.000 0.208 0.046 0.009 0.019 0.000 0.037 0.000 1868 0.173 0.068 0.059 0.000 0.210 0.046 0.009 0.020 0.000 0.039 0.000 1869 0.180 0.070 0.059 0.000 0.212 0.046 0.009 0.020 0.000 0.005 0.025 1870 0.188 0.071 0.060 0.000 0.213 0.047 0.009 0.020 0.000 0.028 0.025 1871 0.195 0.073 0.061 0.000 0.215 0.047 0.009 0.020 0.000 0.025 0.025 1872 0.202 0.075 0.062 0.000 0.217 0.047 0.009 0.021 0.000 0.012 0.025 1873 0.208 0.077 0.063 0.000 0.219 0.048 0.010 0.021 0.000 0.015 0.075 1874 0.212 0.079 0.064 0.000 0.220 0.048 0.010 0.021 0.000 0.000 0.050 1875 0.215 0.081 0.065 0.000 0.222 0.049 0.010 0.022 0.000 0.015 0.025 1876 0.218 0.083 0.066 0.000 0.224 0.049 0.010 0.022 0.000 0.029 0.150 1877 0.219 0.084 0.067 0.000 0.226 0.049 0.010 0.022 0.000 0.033 0.125 1878 0.219 0.086 0.067 0.000 0.227 0.050 0.010 0.022 0.000 0.041 0.075 1879 0.221 0.088 0.068 0.000 0.229 0.050 0.010 0.023 0.000 0.044 0.050 1880 0.222 0.089 0.069 0.000 0.231 0.050 0.011 0.023 0.000 0.039 0.025 1881 0.224 0.091 0.070 0.000 0.233 0.051 0.011 0.023 0.000 0.007 0.025 1882 0.228 0.092 0.071 0.000 0.234 0.051 0.011 0.024 0.000 0.019 0.025 1883 0.232 0.094 0.072 0.000 0.236 0.052 0.011 0.024 0.000 0.031 1.175 1884 0.238 0.096 0.073 0.000 0.238 0.052 0.011 0.024 0.000 0.018 3.575 1885 0.244 0.098 0.074 0.000 0.240 0.053 0.011 0.024 0.000 0.002 1.575 1886 0.250 0.100 0.075 0.000 0.241 0.053 0.011 0.025 0.000 0.014 0.900 1887 0.258 0.102 0.075 0.000 0.243 0.053 0.012 0.025 0.000 0.033 0.925 1888 0.266 0.104 0.076 0.000 0.245 0.054 0.012 0.025 0.000 0.037 0.550 1889 0.274 0.106 0.077 0.000 0.247 0.054 0.012 0.026 0.000 0.041 0.725 1890 0.283 0.107 0.078 0.000 0.248 0.055 0.012 0.026 0.000 0.041 0.975 1891 0.293 0.108 0.079 0.000 0.250 0.055 0.012 0.026 0.000 0.020 0.750 1892 0.302 0.109 0.080 0.000 0.252 0.056 0.012 0.026 0.000 0.004 0.550 1893 0.311 0.110 0.081 0.000 0.254 0.056 0.013 0.027 0.000 0.035 0.225 1894 0.319 0.111 0.082 0.000 0.255 0.057 0.013 0.027 0.000 0.072 0.100 1895 0.325 0.111 0.083 0.000 0.257 0.057 0.013 0.027 0.000 0.052 0.025 1896 0.330 0.112 0.083 0.000 0.259 0.058 0.013 0.028 0.000 0.023 0.450 1897 0.334 0.113 0.084 0.000 0.261 0.058 0.013 0.028 0.000 0.003 0.425 1898 0.336 0.114 0.085 0.000 0.262 0.059 0.014 0.028 0.000 0.012 0.300 1406 Climate System Scenario Tables Annex II Table AII.1.2 | (continued) GHG O3 O3 Aerosol H2O BC Con Year CO2 LUC Solar Volcano Other* (Trop) (Strat) (Total) (Strat) Snow trails 1899 0.337 0.115 0.086 0.000 0.264 0.059 0.014 0.028 0.000 0.017 0.125 1900 0.339 0.117 0.087 0.000 0.266 0.060 0.014 0.029 0.000 0.028 0.050 1901 0.341 0.120 0.088 0.000 0.268 0.061 0.014 0.029 0.000 0.043 0.025 1902 0.344 0.123 0.089 0.000 0.270 0.061 0.015 0.030 0.000 0.048 0.500 1903 0.349 0.127 0.090 0.000 0.272 0.062 0.015 0.030 0.000 0.036 1.800 1904 0.355 0.130 0.091 0.000 0.274 0.062 0.015 0.031 0.000 0.011 0.800 1905 0.362 0.134 0.092 0.000 0.276 0.063 0.016 0.032 0.000 0.016 0.325 1906 0.369 0.138 0.092 0.000 0.278 0.063 0.016 0.032 0.000 0.028 0.175 1907 0.376 0.141 0.093 0.000 0.280 0.064 0.016 0.033 0.000 0.001 0.225 1908 0.383 0.145 0.094 0.000 0.282 0.064 0.017 0.033 0.000 0.020 0.250 1909 0.389 0.148 0.095 0.001 0.284 0.065 0.017 0.034 0.000 0.002 0.100 1910 0.395 0.151 0.096 0.001 0.286 0.065 0.017 0.035 0.000 0.006 0.075 AII 1911 0.400 0.155 0.097 0.001 0.288 0.066 0.018 0.035 0.000 0.032 0.050 1912 0.406 0.159 0.098 0.001 0.289 0.066 0.018 0.035 0.000 0.045 0.475 1913 0.412 0.163 0.100 0.001 0.290 0.067 0.019 0.035 0.000 0.042 0.600 1914 0.419 0.167 0.101 0.001 0.291 0.068 0.019 0.035 0.000 0.033 0.250 1915 0.427 0.171 0.102 0.001 0.292 0.068 0.019 0.035 0.000 0.013 0.100 1916 0.436 0.175 0.103 0.001 0.293 0.069 0.020 0.035 0.000 0.068 0.075 1917 0.445 0.180 0.104 0.001 0.294 0.069 0.020 0.035 0.000 0.086 0.050 1918 0.453 0.185 0.105 0.001 0.296 0.070 0.021 0.035 0.000 0.121 0.050 1919 0.460 0.189 0.107 0.002 0.297 0.071 0.021 0.035 0.000 0.073 0.050 1920 0.466 0.193 0.108 0.001 0.298 0.071 0.022 0.035 0.000 0.039 0.225 1921 0.472 0.196 0.109 0.001 0.302 0.072 0.022 0.036 0.000 0.012 0.200 1922 0.476 0.199 0.110 0.002 0.305 0.073 0.022 0.036 0.000 0.013 0.075 1923 0.481 0.201 0.111 0.002 0.309 0.073 0.023 0.036 0.000 0.025 0.025 1924 0.486 0.203 0.113 0.002 0.313 0.074 0.023 0.036 0.000 0.029 0.075 1925 0.491 0.205 0.114 0.002 0.317 0.075 0.024 0.036 0.000 0.015 0.075 1926 0.497 0.207 0.115 0.002 0.321 0.076 0.024 0.036 0.000 0.020 0.050 1927 0.503 0.210 0.116 0.002 0.325 0.076 0.025 0.036 0.000 0.063 0.050 1928 0.510 0.214 0.117 0.002 0.328 0.077 0.025 0.037 0.000 0.033 0.125 1929 0.517 0.218 0.119 0.002 0.332 0.078 0.025 0.037 0.000 0.028 0.250 1930 0.523 0.222 0.120 0.003 0.336 0.079 0.026 0.037 0.000 0.048 0.150 1931 0.530 0.226 0.122 0.003 0.338 0.080 0.026 0.037 0.000 0.009 0.125 1932 0.536 0.230 0.124 0.003 0.340 0.081 0.027 0.038 0.000 0.016 0.200 1933 0.542 0.234 0.126 0.003 0.341 0.081 0.027 0.038 0.000 0.029 0.175 1934 0.548 0.237 0.128 0.003 0.343 0.082 0.027 0.039 0.000 0.027 0.100 1935 0.555 0.241 0.130 0.003 0.345 0.083 0.028 0.039 0.000 0.008 0.100 1936 0.563 0.244 0.133 0.003 0.347 0.084 0.028 0.040 0.000 0.068 0.075 1937 0.570 0.247 0.135 0.003 0.349 0.085 0.029 0.040 0.000 0.089 0.075 1938 0.577 0.251 0.137 0.003 0.350 0.086 0.029 0.040 0.000 0.080 0.125 1939 0.584 0.254 0.139 0.004 0.352 0.087 0.029 0.041 0.000 0.094 0.100 1940 0.590 0.257 0.141 0.004 0.354 0.088 0.030 0.041 0.000 0.070 0.075 1941 0.595 0.261 0.143 0.004 0.358 0.089 0.030 0.042 0.000 0.057 0.050 1942 0.598 0.264 0.146 0.004 0.362 0.090 0.030 0.042 0.000 0.030 0.100 1943 0.599 0.267 0.148 0.004 0.366 0.092 0.031 0.043 0.000 0.005 0.100 1944 0.599 0.270 0.150 0.004 0.370 0.093 0.031 0.043 0.001 0.011 0.050 1945 0.599 0.273 0.152 0.004 0.374 0.094 0.032 0.043 0.001 0.019 0.050 1946 0.599 0.276 0.154 0.005 0.378 0.095 0.032 0.044 0.001 0.025 0.050 1947 0.598 0.279 0.156 0.005 0.382 0.096 0.032 0.044 0.002 0.093 0.050 1948 0.598 0.283 0.158 0.005 0.386 0.097 0.033 0.045 0.002 0.146 0.050 1407 Annex II Climate System Scenario Tables Table AII.1.2 | (continued) GHG O3 O3 Aerosol H2O BC Con Year CO2 LUC Solar Volcano Other* (Trop) (Strat) (Total) (Strat) Snow trails 1949 0.601 0.287 0.161 0.005 0.390 0.099 0.033 0.045 0.002 0.123 0.075 1950 0.604 0.291 0.163 0.005 0.394 0.100 0.034 0.046 0.002 0.110 0.075 1951 0.608 0.296 0.168 0.005 0.409 0.102 0.034 0.046 0.002 0.037 0.050 1952 0.615 0.302 0.173 0.006 0.424 0.103 0.035 0.047 0.002 0.045 0.100 1953 0.623 0.308 0.178 0.006 0.439 0.105 0.036 0.047 0.003 0.025 0.075 1954 0.631 0.315 0.183 0.006 0.455 0.106 0.036 0.048 0.003 0.003 0.100 1955 0.641 0.323 0.188 0.006 0.470 0.108 0.037 0.048 0.003 0.015 0.050 1956 0.651 0.332 0.193 0.007 0.485 0.109 0.038 0.049 0.003 0.064 0.025 1957 0.662 0.341 0.198 0.007 0.500 0.111 0.038 0.050 0.004 0.129 0.025 1958 0.673 0.349 0.203 0.007 0.515 0.112 0.039 0.050 0.004 0.194 0.000 1959 0.685 0.358 0.208 0.008 0.530 0.114 0.040 0.051 0.004 0.159 0.000 AII 1960 0.698 0.366 0.213 0.008 0.546 0.116 0.041 0.051 0.004 0.151 0.125 1961 0.709 0.374 0.218 0.008 0.563 0.117 0.041 0.051 0.004 0.110 0.275 1962 0.719 0.383 0.223 0.009 0.580 0.119 0.042 0.051 0.004 0.051 0.325 1963 0.727 0.392 0.228 0.009 0.598 0.120 0.043 0.050 0.005 0.038 1.150 1964 0.735 0.402 0.233 0.010 0.615 0.122 0.044 0.050 0.005 0.019 1.800 1965 0.748 0.412 0.239 0.011 0.632 0.123 0.045 0.050 0.005 0.008 1.075 1966 0.762 0.424 0.244 0.011 0.650 0.125 0.046 0.050 0.006 0.012 0.575 1967 0.778 0.437 0.249 0.012 0.667 0.126 0.047 0.049 0.007 0.055 0.375 1968 0.794 0.451 0.254 0.013 0.684 0.127 0.048 0.049 0.008 0.086 0.675 1969 0.811 0.466 0.259 0.014 0.701 0.129 0.049 0.049 0.009 0.077 0.850 1970 0.828 0.483 0.264 0.014 0.719 0.130 0.050 0.049 0.009 0.092 0.425 1971 0.846 0.500 0.270 0.016 0.722 0.131 0.050 0.049 0.009 0.082 0.150 1972 0.865 0.519 0.277 0.017 0.725 0.132 0.051 0.049 0.009 0.076 0.100 1973 0.885 0.538 0.284 0.018 0.728 0.134 0.052 0.049 0.010 0.044 0.200 1974 0.904 0.558 0.290 0.019 0.732 0.135 0.053 0.050 0.010 0.023 0.325 1975 0.920 0.578 0.297 0.021 0.735 0.136 0.054 0.050 0.010 0.006 0.750 1976 0.938 0.598 0.304 0.022 0.738 0.137 0.055 0.050 0.010 0.003 0.350 1977 0.960 0.617 0.310 0.024 0.741 0.138 0.056 0.050 0.011 0.040 0.125 1978 0.987 0.636 0.317 0.026 0.745 0.138 0.057 0.051 0.011 0.129 0.200 1979 1.018 0.656 0.324 0.027 0.748 0.139 0.058 0.051 0.012 0.167 0.225 1980 1.046 0.675 0.330 0.029 0.751 0.140 0.059 0.051 0.012 0.150 0.125 1981 1.066 0.696 0.335 0.031 0.763 0.141 0.061 0.051 0.012 0.147 0.125 1982 1.085 0.717 0.339 0.033 0.775 0.141 0.062 0.050 0.012 0.094 1.325 1983 1.110 0.737 0.343 0.035 0.788 0.142 0.063 0.050 0.012 0.091 1.875 1984 1.136 0.757 0.348 0.037 0.800 0.143 0.064 0.049 0.013 0.016 0.750 1985 1.158 0.776 0.352 0.038 0.812 0.143 0.065 0.049 0.014 0.011 0.325 1986 1.180 0.795 0.356 0.040 0.824 0.144 0.065 0.049 0.015 0.012 0.350 1987 1.208 0.813 0.360 0.042 0.836 0.144 0.066 0.048 0.016 0.015 0.250 1988 1.240 0.832 0.365 0.044 0.848 0.145 0.067 0.048 0.017 0.095 0.200 1989 1.266 0.853 0.369 0.046 0.861 0.145 0.067 0.047 0.018 0.151 0.150 1990 1.287 0.872 0.373 0.048 0.873 0.146 0.068 0.047 0.019 0.118 0.150 1991 1.305 0.888 0.375 0.050 0.878 0.146 0.068 0.046 0.019 0.126 1.350 1992 1.318 0.900 0.376 0.052 0.883 0.146 0.069 0.045 0.020 0.137 3.025 1993 1.332 0.909 0.378 0.054 0.888 0.147 0.069 0.045 0.022 0.063 1.225 1994 1.354 0.916 0.379 0.055 0.893 0.147 0.069 0.044 0.024 0.027 0.500 1995 1.381 0.923 0.380 0.056 0.897 0.147 0.070 0.043 0.025 0.020 0.250 1996 1.404 0.930 0.382 0.057 0.902 0.148 0.070 0.043 0.027 0.003 0.175 1997 1.428 0.937 0.383 0.057 0.907 0.148 0.070 0.042 0.028 0.016 0.125 1998 1.459 0.944 0.385 0.057 0.912 0.148 0.071 0.041 0.029 0.062 0.075 1408 Climate System Scenario Tables Annex II Table AII.1.2 | (continued) GHG O3 O3 Aerosol H2O BC Con Year CO2 LUC Solar Volcano Other* (Trop) (Strat) (Total) (Strat) Snow trails 1999 1.489 0.952 0.386 0.056 0.917 0.148 0.071 0.041 0.031 0.104 0.050 2000 1.510 0.957 0.388 0.056 0.922 0.149 0.071 0.040 0.033 0.127 0.050 2001 1.532 0.961 0.389 0.055 0.920 0.149 0.071 0.040 0.033 0.114 0.050 2002 1.563 0.965 0.390 0.055 0.918 0.149 0.071 0.040 0.033 0.108 0.050 2003 1.594 0.969 0.391 0.054 0.916 0.149 0.071 0.040 0.034 0.042 0.075 2004 1.624 0.973 0.393 0.053 0.913 0.149 0.071 0.040 0.038 0.012 0.050 2005 1.654 0.976 0.394 0.053 0.911 0.149 0.071 0.040 0.040 0.011 0.075 2006 1.684 0.981 0.395 0.052 0.909 0.150 0.071 0.040 0.042 0.016 0.100 2007 1.711 0.986 0.396 0.052 0.907 0.150 0.071 0.040 0.044 0.017 0.100 2008 1.736 0.992 0.398 0.051 0.904 0.150 0.072 0.040 0.046 0.025 0.100 2009 1.762 0.999 0.399 0.051 0.902 0.150 0.072 0.040 0.044 0.027 0.125 AII 2010 1.789 1.005 0.400 0.050 0.900 0.150 0.072 0.040 0.048 0.001 0.100 2011 1.816 1.015 0.400 0.050 0.900 0.150 0.073 0.040 0.050 0.030 0.125 Notes: See Figure 8.18, also Sections 8.1 and 11.3.6.1. To get the total ERF (effective radiative forcing) all components can be summed. Small negative values for CO2 prior to 1800 are due to uncertainty in PI values. GHG other* includes only WMGHG. Aerosol is the sum of direct and indirect effects. LUC includes land use land cover change. Contrails combines aviation contrails (~20% of total) and contrail-induced cirrus. Values are annual average. Table AII.1.3 | Historical global decadal mean global surface air temperature (°C) relative to 1961 1990 average HadCRUT4 GISS NCDC Year Lower (5%) Median (50%) Upper (95%) Median (50%) Median (50%) 1850d 0.404 0.320 0.243 1860 d 0.413 0.335 0.263 1870d 0.326 0.258 0.195 1880d 0.363 0.297 0.237 0.296 0.291 1890 d 0.430 0.359 0.299 0.361 0.370 1900d 0.473 0.410 0.353 0.418 0.434 1910d 0.448 0.387 0.334 0.435 0.430 1920 d 0.297 0.242 0.193 0.311 0.311 1930d 0.166 0.116 0.070 0.172 0.161 1940d 0.047 0.002 +0.042 0.085 0.063 1950 d 0.106 0.061 0.017 0.134 0.136 1960d 0.093 0.054 0.014 0.104 0.086 1970d 0.113 0.077 0.041 0.058 0.060 1980d +0.052 +0.095 +0.135 +0.118 +0.109 1990d +0.221 +0.270 +0.318 +0.275 +0.272 2000d +0.400 +0.453 +0.508 +0.472 +0.450 1986 2005 +0.61 +/- 0.06 N/A N/A minus 1850 1900 1986 2005 +0.66 +/- 0.06 +0.66 +0.66 minus 1886 1905 1986 2005 +0.30 +/- 0.03 +0.31 +0.30 minus 1961 1990 1986 2005 +0.11 +/- 0.02 +0.11 +0.11 minus 1980 1999 1946 2012 +0.38 +/- 0.04 +0.40 +0.39 minus 1880 1945 Notes: Decadal average (1990d = 1990 1999) median global surface air temperatures from HadCRUT4, GISS and NCDC analyses. See Chapter 2, Sections 2.4.3 and 2.SM.4.3.3, Table 2.7, Figures 2.19, 2.20, 2.21 and 2.22, and also Figure 11.24a. Confidence intervals (5 to 95% for HadCRUT4 only) take into account measurement, sampling, bias and coverage uncertainties. Also shown are temperature increases between the CMIP5 reference period (1986 2005) and four earlier averaging periods, where 1850 1900 is the early instrumental temperature record. Uncertainties in these temperature differences are 5 to 95% confidence intervals. 1409 Annex II Climate System Scenario Tables AII.2: Anthropogenic Emissions See discussion of Figure 8.2 and Section 11.3.5. Table AII.2.1a | Anthropogenic CO2 emissions from fossil fuels and other industrial sources (FF) (PgC yr 1) Year RCP2.6 RCP4.5 RCP6.0 RCP8.5 A2 B1 IS92a RCP2.6& RCP4.5& RCP6.0& RCP8.5& 2000d 6.82 6.82 6.82 6.82 6.90 6.90 7.10 6.92 +/- 0.80 6.98 +/- 0.81 6.76 +/- 0.71 6.98 +/- 0.81 2010 d 8.61 8.54 8.39 8.90 8.46 8.50 8.68 8.38 +/- 1.03 8.63 +/- 1.07 7.66 +/- 1.64 8.27 +/- 1.68 2020d 9.00 9.79 8.99 11.38 11.01 10.00 10.26 8.46 +/- 1.38 10.24+/-1.69 8.33 +/- 1.82 10.30 +/- 1.87 2030d 7.21 10.83 9.99 13.79 13.53 11.20 11.62 6.81 +/- 1.49 10.93+/-1.83 9.20 +/- 1.55 12.36 +/- 2.25 2040 d 4.79 11.25 11.47 16.69 15.01 12.20 12.66 4.61 +/- 1.60 11.82+/-1.84 10.04 +/- 1.42 15.09 +/- 2.15 2050d 3.21 10.91 13.00 20.03 16.49 11.70 13.70 2.96 +/- 1.80 11.37+/-1.84 11.14 +/- 1.55 18.15 +/- 2.56 2060d 1.55 9.42 14.73 23.32 18.49 10.20 14.68 1.77 +/- 1.06 9.96 +/- 2.17 13.22 +/- 2.05 21.49 +/- 2.42 2070d 0.26 7.17 16.33 25.75 20.49 8.60 15.66 0.75 +/- 0.90 7.86 +/- 1.94 14.57 +/- 1.88 23.62 +/- 2.43 AII 2080 d 0.39 4.62 16.87 27.28 22.97 7.30 17.00 0.09 +/- 0.99 5.17 +/- 1.77 15.51 +/- 2.29 24.47 +/- 2.70 2090d 0.81 4.19 14.70 28.24 25.94 6.10 18.70 0.30 +/- 1.09 5.13 +/- 1.53 14.24 +/- 1.81 25.30 +/- 2.86 2100d 0.92 4.09 13.63 28.68 28.91 5.20 20.40 0.63 +/- 1.17 4.64 +/- 1.34 12.78 +/- 1.35 25.28 +/- 2.73 Notes: Decadal mean values (2010d = average of 2005 2014) are used for emissions because linear interpolation between decadal means conserves total emissions. Data are taken from RCP database (Meinshausen et al., 2011a; http://www.iiasa.ac.at/web-apps/tnt/RcpDb) and may be different from yearly snapshots; for 2100 the average (2095 2100) is used. SRES A2 and B1 and IS92a are taken from TAR Appendix II. RCPn.n& values are inferred from ESMs used in CMIP5. The model mean and standard deviation is shown. ESM fossil emissions are taken from 14 models as described in Jones et al. (2013) although not every model has performed every scenario. See Chapter 6, Sections 6.4.3, and 6.4.3.3, and Figure 6.25. Table AII.2.1b | Anthropogenic CO2 emissions from agriculture, forestry, land use (AFOLU) (PgC yr 1) Year RCP2.6 RCP4.5 RCP6.0 RCP8.5 SRES-A2 SRES-B1 IS92a 2000d 1.21 1.21 1.21 1.21 1.07 1.07 1.30 2010 d 1.09 0.94 0.93 1.08 1.12 0.78 1.22 2020d 0.97 0.41 0.38 0.91 1.25 0.63 1.14 2030d 0.79 0.23 0.43 0.74 1.19 0.09 1.04 2040 d 0.51 0.21 0.67 0.65 1.06 0.48 0.92 2050d 0.29 0.23 0.48 0.58 0.93 0.41 0.80 2060d 0.55 0.19 0.27 0.50 0.67 0.46 0.54 2070d 0.55 0.11 0.04 0.42 0.40 0.42 0.28 2080 d 0.55 0.02 0.20 0.31 0.25 0.60 0.12 2090d 0.59 0.03 0.24 0.20 0.21 0.78 0.06 2100d 0.50 0.04 0.18 0.09 0.18 0.97 0.10 Notes: See Table AII.2.1a. Table AII.2.1c | Anthropogenic total CO2 emissions (PgC yr 1) Year RCP2.6 RCP4.5 RCP6.0 RCP8.5 2000d 8.03 8.03 8.03 8.03 2010 d 9.70 9.48 9.32 9.98 2020d 9.97 10.20 9.37 12.28 2030d 8.00 11.06 9.57 14.53 2040 d 5.30 11.46 10.80 17.33 2050d 3.50 11.15 12.52 20.61 2060d 2.10 9.60 14.46 23.83 2070 d 0.81 7.27 16.29 26.17 2080d 0.16 4.65 17.07 27.60 2090d 0.23 4.22 14.94 28.44 2100 d 0.42 4.13 13.82 28.77 Notes: See Table AII.2.1a. 1410 Climate System Scenario Tables Annex II Table AII.2.2 | Anthropogenic CH4 emissions (Tg yr 1) Year RCP2.6 RCP4.5 RCP6.0 RCP8.5 A2 B1 IS92a RCP2.6& RCP4.5& RCP6.0& RCP8.5& PI 202 +/- 28 202 +/- 28 202 +/- 28 202 +/- 28 2010 total 554 +/- 56 554 +/- 56 554 +/- 56 554 +/- 56 2010anthrop 352 +/- 45 352 +/- 45 352 +/- 45 352 +/- 45 2010d 322 322 321 345 370 349 433 352 +/- 45 352 +/- 45 352 +/- 45 352 +/- 45 2020d 267 334 315 415 424 377 477 268 +/- 34 366 +/- 47 338 +/- 43 424 +/- 54 2030 d 238 338 326 484 486 385 529 246 +/- 31 370 +/- 47 354 +/- 45 490 +/- 63 2040d 223 337 343 573 542 381 580 235 +/- 30 368 +/- 47 373 +/- 47 585 +/- 75 2050d 192 331 354 669 598 359 630 198 +/- 25 361 +/- 46 385 +/- 49 685 +/- 88 2060 d 169 318 362 738 654 342 654 174 +/- 22 346 +/- 44 395 +/- 50 754 +/- 96 2070d 161 301 359 779 711 324 678 169 +/- 22 328 +/- 42 390 +/- 50 790 +/-101 2080d 155 283 336 820 770 293 704 162 +/- 21 306 +/- 39 369 +/- 47 832 +/-106 2090 d 149 274 278 865 829 266 733 155 +/- 20 298 +/- 38 293 +/- 37 882 +/-113 AII 2100 d 143 267 250 885 889 236 762 148 +/- 19 290 +/- 37 267 +/- 34 899 +/-115 Year MFR CLE MFR* CLE* RogL RogU AMEL AMEU 2000d 366 366 303 303 2010 d 193 335 332 333 2020d 208 383 240 390 294 350 2030d 339 478 229 443 217 428 293 376 2040 d 295 404 2050d 178 454 291 426 2060d 275 434 2070 d 254 436 2080d 201 430 2090d 183 417 2100 d 121 385 167 406 Notes: For all anthropogenic emissions see Box 1.1 (Figure 4), Section 8.2.2, Figure 8.2, Sections 11.3.5.1.1 to 3, 11.3.5.2, 11.3.6.1. Ten-year average values (2010d = average of 2005 2014; but 2100d = average of 2095 2100) are given for RCP-based emissions, but single-year emissions are shown for other scenarios. RCPn.n = harmonized anthropogenic emissions as reported. SRES A2 and B1 and IS92a are from TAR Appendix II. AR5 RCPn.n& emissions have +/- 1-s (16 to 84% confidence) uncertainties and are based on the methodology of Prather et al. (2012) updated with CMIP5 results (Holmes et al., 2013; Voulgarakis et al., 2013). Projections of CH4 lifetimes are harmonized based on PI (1750) and PD (2010) budgets that include uncertainties in lifetimes and abundances. All projected RCP abundances for CH4 and N2O (Tables AII.4.2 to AII.4.3) rescale each of the RCP emissions by a fixed factor equal to the ratio of RCP to AR5 anthropogenic emissions at year 2010 to ensure harmonization between total emissions, lifetimes and observed abundances. Natural emissions are kept constant but included as additional uncertainty. Independent emission estimates are shown as follows: MFR/CLE are the maximum feasible reduction and current legislation scenarios from Dentener et al. (2005; 2006), while MFR*/CLE* are the similarly labeled scenarios from Cofala et al. (2007). REFL/REFU are lower/upper bounds from the reference scenario of van Vuuren et al. (2008), while POLL/POLU are the lower/upper bounds from their policy scenario. AMEL/AMEU are lower/upper bounds from Calvin et al. (2012). RogL/RogU are lower/upper bounds from Rogelj et et. (2011). 1411 Annex II Climate System Scenario Tables Table AII.2.3 | Anthropogenic N2O emissions (TgN yr 1) Year RCP2.6 RCP4.5 RCP6.0 RCP8.5 A2 B1 IS92a RCP2.6& RCP4.5& RCP6.0& RCP8.5& PI 9.1 +/- 1.0 9.1 +/- 1.0 9.1 +/- 1.0 9.1 +/- 1.0 2010 total 15.7 +/- 1.1 15.7 +/- 1.1 15.7 +/- 1.1 15.7 +/- 1.1 2010anthrop 6.5 +/- 1.3 6.5 +/- 1.3 6.5 +/- 1.3 6.5 +/- 1.3 2010d 7.7 7.8 8.0 8.25 8.1 7.5 6.2 6.5 +/- 1.3 6.5 +/- 1.3 6.5 +/- 1.3 6.5 +/- 1.3 2020d 7.4 8.2 8.1 9.5 9.6 8.1 7.1 6.1 +/- 1.2 6.8 +/- 1.3 6.3 +/- 1.2 7.7 +/- 1.5 2030 d 7.3 8.5 8.8 10.7 10.7 8.2 7.7 6.1 +/- 1.2 7.1 +/- 1.4 7.0 +/- 1.4 8.6 +/- 1.7 2040d 7.1 8.7 9.7 11.9 11.3 8.3 8.0 6.0 +/- 1.2 7.2 +/- 1.4 7.8 +/- 1.5 9.6 +/- 1.9 2050d 6.3 8.6 10.5 12.7 12.0 8.3 8.3 5.2 +/- 1.0 7.1 +/- 1.4 8.4 +/- 1.6 10.3 +/- 2.0 2060d 5.8 8.5 11.3 13.4 12.9 7.7 8.3 4.8 +/- 0.9 7.1 +/- 1.4 9.1 +/- 1.8 10.8 +/- 2.1 2070d 5.7 8.4 12.0 13.9 13.9 7.4 8.4 4.8 +/- 0.9 7.0 +/- 1.3 9.6 +/- 1.9 11.2 +/- 2.2 2080d 5.6 8.2 12.3 14.5 14.8 7.0 8.5 4.7 +/- 0.9 6.8 +/- 1.3 9.9 +/- 1.9 11.7 +/- 2.3 AII 2090 d 5.5 8.1 12.4 15.2 15.7 6.4 8.6 4.6 +/- 0.9 6.8 +/- 1.3 9.9 +/- 1.9 12.3 +/- 2.4 2100d 5.3 8.1 12.2 15.7 16.5 5.7 8.7 4.4 +/- 0.9 6.7 +/- 1.3 9.8 +/- 1.9 12.6 +/- 2.4 Notes: See Table AII.2.2. Table AII.2.4 | Anthropogenic SF6 emissions (Gg yr 1) Year RCP2.6 RCP4.5 RCP6.0 RCP8.5 A2 B1 2000d 5.70 5.70 5.70 5.70 6.20 6.20 2010d 6.14 5.68 7.43 6.93 7.60 5.60 2020d 2.87 3.02 9.19 8.12 9.70 5.70 2030d 1.96 2.89 9.58 9.83 11.60 7.20 2040d 1.53 3.32 9.68 11.14 13.70 8.90 2050 d 0.76 3.77 9.78 12.07 16.00 10.40 2060d 0.51 4.28 9.92 13.69 18.80 10.90 2070d 0.42 4.87 10.05 13.72 19.80 9.50 2080 d 0.32 5.53 10.00 14.79 20.70 7.10 2090d 0.19 5.99 9.86 15.96 23.40 6.50 2100d 0.07 6.25 9.37 16.79 25.20 6.50 Notes: For this and all following emissions tables, see Table AII.2.2. RCPn.n = harmonized anthropogenic emissions as reported by RCPs (Lamarque et al., 2010; 2011; Meinshausen et al., 2011a). SRES A2 and B1 and IS92a from TAR Appendix II. Table AII.2.5 | Anthropogenic CF4 emissions (Gg yr 1) Year RCP2.6 RCP4.5 RCP6.0 RCP8.5 A2 B1 2000d 11.62 11.62 11.62 11.62 12.60 12.60 2010d 13.65 10.69 19.10 11.04 20.30 14.50 2020d 12.07 8.77 22.84 11.67 25.20 15.70 2030d 7.36 8.47 23.46 12.29 31.40 16.60 2040 d 5.06 8.68 23.77 12.22 37.90 18.50 2050d 2.95 9.04 23.73 12.37 45.60 20.90 2060d 2.24 8.95 23.70 11.89 56.00 23.10 2070d 2.07 9.04 23.45 11.81 63.60 22.50 2080d 1.52 9.51 22.91 11.58 73.20 21.30 2090d 1.22 10.50 21.98 11.14 82.80 22.50 2100d 1.11 11.05 20.56 10.81 88.20 22.20 1412 Climate System Scenario Tables Annex II Table AII.2.6 | Anthropogenic C2F6 emissions (Gg yr 1) Year RCP2.6 RCP4.5 RCP6.0 RCP8.5 A2 B1 2000d 2.43 2.43 2.43 2.43 1.30 1.30 2010d 4.29 2.34 2.62 2.50 2.00 1.50 2020d 4.98 1.76 2.66 2.61 2.50 1.60 2030d 2.33 1.80 2.69 2.75 3.10 1.70 2040 d 1.15 1.94 2.63 2.74 3.80 1.80 2050d 0.55 2.03 2.56 2.79 4.60 2.10 2060d 0.34 2.03 2.49 2.71 5.60 2.30 2070 d 0.26 1.99 2.50 2.74 6.40 2.20 2080d 0.16 1.93 2.36 2.74 7.30 2.10 2090d 0.10 1.97 2.26 2.68 8.30 2.20 2100 d 0.09 2.01 2.09 2.63 8.80 2.20 AII Table AII.2.7 | Anthropogenic C6F14 emissions (Gg yr 1) Year RCP2.6 RCP4.5 RCP6.0 RCP8.5 2000d 0.213 0.213 0.213 0.213 2010d 0.430 0.430 0.429 0.430 2020d 0.220 0.220 0.220 0.220 2030d 0.123 0.123 0.123 0.123 2040d 0.112 0.112 0.112 0.112 2050 d 0.109 0.109 0.109 0.109 2060d 0.108 0.108 0.108 0.108 2070d 0.106 0.106 0.106 0.106 2080 d 0.103 0.103 0.103 0.103 2090d 0.097 0.097 0.097 0.097 2100d 0.090 0.088 0.088 0.090 Table AII.2.8 | Anthropogenic HFC-23 emissions (Gg yr 1) Year RCP2.6 RCP4.5 RCP6.0 RCP8.5 A2 B1 2000 d 10.4 10.4 10.4 10.4 13.0 13.0 2010d 9.1 9.1 9.1 9.1 15.0 15.0 2020d 2.4 2.4 2.4 2.4 5.0 5.0 2030 d 0.7 0.7 0.7 0.7 2.0 2.0 2040d 0.4 0.4 0.4 0.4 2.0 2.0 2050d 0.3 0.3 0.3 0.3 1.0 1.0 2060 d 0.1 0.1 0.1 0.1 1.0 1.0 2070d 0.1 0.1 0.1 0.1 1.0 1.0 2080d 0.0 0.0 0.0 0.0 1.0 1.0 2090 d 0.0 0.0 0.0 0.0 1.0 1.0 2100d 0.0 0.0 0.0 0.0 1.0 1.0 1413 Annex II Climate System Scenario Tables Table AII.2.9 | Anthropogenic HFC-32 emissions (Gg yr 1) Year RCP2.6 RCP4.5 RCP6.0 RCP8.5 A2 B1 2000d 3.5 3.5 3.5 3.5 0.0 0.0 2010 d 20.1 20.1 20.1 20.1 4.0 3.0 2020d 55.4 55.4 55.4 55.4 6.0 6.0 2030d 71.2 71.2 71.2 71.2 9.0 8.0 2040d 78.8 78.8 78.8 78.8 11.0 10.0 2050d 76.5 76.5 76.5 76.5 14.0 14.0 2060d 83.6 83.6 83.6 83.6 17.0 14.0 2070d 92.7 92.7 92.7 92.7 20.0 14.0 2080 d 95.4 95.4 95.4 95.4 24.0 14.0 2090d 91.0 91.0 91.0 91.0 29.0 14.0 2100d 82.7 82.7 82.7 82.7 33.0 13.0 AII Table AII.2.10 | Anthropogenic HFC-125 emissions (Gg yr 1) Year RCP2.6 RCP4.5 RCP6.0 RCP8.5 A2 B1 IS92a 2000d 8 8 8 8 0 0 0 2010 d 29 18 10 32 11 11 1 2020d 82 29 9 63 21 21 9 2030d 108 32 9 79 29 29 46 2040d 122 31 10 99 35 36 111 2050 d 122 30 10 115 46 48 175 2060d 138 27 11 128 56 48 185 2070d 157 24 11 139 66 48 194 2080 d 165 24 12 144 79 48 199 2090d 161 23 12 147 94 46 199 2100d 150 23 12 148 106 44 199 Table AII.2.11 | Anthropogenic HFC-134a emissions (Gg yr 1) Year RCP2.6 RCP4.5 RCP6.0 RCP8.5 A2 B1 IS92a 2000 d 72 72 72 72 80 80 148 2010d 146 140 139 153 166 163 290 2020d 173 184 153 255 252 249 396 2030 d 193 208 159 331 330 326 557 2040d 209 229 163 402 405 414 738 2050d 203 248 167 461 506 547 918 2060 d 225 246 172 506 633 550 969 2070d 252 260 175 553 758 544 1020 2080d 263 299 177 602 915 533 1047 2090 d 256 351 175 651 1107 513 1051 2100d 239 400 171 696 1260 486 1055 1414 Climate System Scenario Tables Annex II Table AII.2.12 | Anthropogenic HFC-143a emissions (Gg yr 1) Year RCP2.6 RCP4.5 RCP6.0 RCP8.5 A2 B1 2000d 7.5 7.5 7.5 7.5 0.0 0.0 2010d 23.1 14.0 7.0 23.2 9.0 8.0 2020d 59.1 17.4 5.4 34.1 16.0 15.0 2030d 74.7 20.3 6.0 38.5 22.0 21.0 2040d 81.8 23.1 6.6 45.1 27.0 26.0 2050d 79.0 25.6 7.1 49.8 35.0 35.0 2060d 86.1 25.9 7.7 52.3 43.0 35.0 2070d 94.2 28.2 8.3 54.1 51.0 35.0 2080d 95.1 33.5 8.7 52.7 61.0 35.0 2090d 88.7 39.6 9.0 50.2 73.0 34.0 2100d 79.2 45.1 9.1 47.3 82.0 32.0 AII Table AII.2.13 | Anthropogenic HFC-227ea emissions (Gg yr 1) Year RCP2.6 RCP4.5 RCP6.0 RCP8.5 A2 B1 2000d 1.7 1.7 1.7 1.7 0.0 0.0 2010d 7.0 5.3 6.9 8.5 12.0 13.0 2020d 2.6 1.4 2.5 2.7 17.0 18.0 2030d 0.9 0.3 0.8 0.7 21.0 24.0 2040d 0.8 0.2 0.7 0.7 26.0 30.0 2050d 0.4 0.1 0.3 0.4 32.0 39.0 2060d 0.2 0.0 0.1 0.2 40.0 40.0 2070d 0.1 0.0 0.1 0.1 48.0 39.0 2080d 0.1 0.0 0.1 0.1 58.0 38.0 2090d 0.1 0.0 0.0 0.1 70.0 36.0 2100d 0.1 0.0 0.0 0.1 80.0 34.0 Table AII.2.14 | Anthropogenic HFC-245fa emissions (Gg yr 1) Year RCP2.6 RCP4.5 RCP6.0 RCP8.5 A2 B1 2000d 11 11 11 11 0 0 2010d 42 46 53 74 59 60 2020d 32 86 65 143 79 80 2030d 7 95 67 186 98 102 2040d 0 97 68 181 121 131 2050d 0 95 69 163 149 173 2060d 0 87 70 150 190 173 2070d 0 82 71 138 228 170 2080d 0 80 70 129 276 166 2090d 0 81 68 123 334 159 2100d 0 83 65 130 388 150 1415 Annex II Climate System Scenario Tables Table AII.2.15 | Anthropogenic HFC-43-10mee emissions (Gg yr 1) Year RCP2.6 RCP4.5 RCP6.0 RCP8.5 A2 B1 2000d 0.6 0.6 0.6 0.6 0.0 0.0 2010 d 5.6 5.6 5.6 5.6 7.0 6.0 2020d 7.2 7.2 7.2 7.2 8.0 7.0 2030d 8.1 8.1 8.1 8.1 8.0 8.0 2040 d 9.4 9.4 9.4 9.1 9.0 9.0 2050d 10.8 10.8 10.8 10.4 11.0 11.0 2060d 11.1 11.1 11.1 12.1 12.0 11.0 2070 d 11.0 11.0 11.0 13.9 14.0 11.0 2080d 11.0 11.0 10.9 16.2 16.0 11.0 2090d 10.7 10.7 10.7 18.9 19.0 11.0 2100 d 10.5 10.5 10.5 21.4 22.0 10.0 AII Table AII.2.16 | Anthropogenic CO emissions (Tg yr 1) Year RCP2.6 RCP4.5 RCP6.0 RCP8.5 A2 B1 IS92a 2000d 1071 1071 1071 1071 877 877 1048 2010d 1035 1041 1045 1054 977 789 1096 2020d 984 997 1028 1058 1075 751 1145 2030d 930 986 1030 1019 1259 603 1207 2040d 879 948 1046 960 1344 531 1282 2050d 825 875 1033 907 1428 471 1358 2060d 779 782 996 846 1545 459 1431 2070d 718 678 939 799 1662 456 1504 2080d 668 571 879 759 1842 426 1576 2090d 638 520 835 721 2084 399 1649 2100d 612 483 798 694 2326 363 1722 Year MFR CLE REFL REFU POLL POLU 2000d 977 977 708 1197 706 1197 2010 d 771 1408 769 1408 2020d 755 1629 705 1611 2030d 729 904 707 1865 592 1803 2040 d 695 2165 620 2002 2050d 591 2487 482 2218 2060d 504 2787 363 2409 2070 d 450 3052 328 2558 2080d 438 3279 268 2635 2090d 410 3510 259 2714 2100 d 363 3735 253 2796 1416 Climate System Scenario Tables Annex II Table AII.2.17 | Anthropogenic NMVOC emissions (Tg yr 1) Year RCP2.6 RCP4.5 RCP6.0 RCP8.5 A2 B1 IS92a CLE MFR 2000d 213 213 213 213 141 141 126 147 147 2010d 216 209 215 217 155 141 142 2020d 213 197 214 224 179 140 158 2030d 202 201 217 225 202 131 173 146 103 2040d 192 201 222 218 214 123 188 2050d 179 191 220 209 225 116 202 2060d 167 180 214 202 238 111 218 2070d 152 167 204 194 251 103 234 2080 d 140 152 193 189 275 99 251 2090d 132 145 182 182 309 96 267 2100d 126 141 174 177 342 87 283 AII Table AII.2.18 | Anthropogenic NOX emissions (TgN yr 1) Year RCP2.6 RCP4.5 RCP6.0 RCP8.5 CLE MFR 2000d 38.5 38.5 38.5 38.5 53.4 53.4 2010d 43.5 42.4 43.1 43.5 2020d 47.5 43.5 43.3 48.1 2030d 50.8 45.2 46.2 52.1 69.8 69.8 2040d 53.2 46.3 49.8 55.6 2050d 55.5 46.4 53.0 58.4 2060d 58.4 46.0 56.5 60.6 2070d 61.2 45.2 59.5 62.4 2080d 63.3 44.3 60.9 63.8 2090 d 65.2 43.9 62.1 65.3 2100d 67.0 43.6 61.8 66.9 Year MFR CLE REFL REFU POLL POLU 2000 d 38.0 38.0 29.1 41.6 29.1 41.6 2010d 26.0 50.2 23.9 50.1 2020d 26.3 60.4 21.6 59.2 2030d 23.1 42.9 24.4 71.8 16.5 67.4 2040 d 21.5 86.3 14.1 75.3 2050d 17.0 101.7 11.6 83.3 2060d 13.2 115.7 11.4 89.8 2070 d 12.0 127.5 10.5 94.6 2080d 11.5 137.2 9.6 97.2 2090d 12.0 146.2 8.8 100.1 2100d 13.0 155.0 8.0 104.0 Notes: Odd nitrogen (NOx) emissions occur as NO or NO2, measured here as Tg of N. 1417 Annex II Climate System Scenario Tables Table AII.2.19 | Anthropogenic NH3 emissions (TgN yr 1) Year RCP2.6 RCP4.5 RCP6.0 RCP8.5 CLE MFR 2000d 38.5 38.5 38.5 38.5 53.4 53.4 2010 d 43.5 42.4 43.1 43.5 2020d 47.5 43.5 43.3 48.1 2030d 50.8 45.2 46.2 52.1 69.8 69.8 2040d 53.2 46.3 49.8 55.6 2050d 55.5 46.4 53.0 58.4 2060d 58.4 46.0 56.5 60.6 2070d 61.2 45.2 59.5 62.4 2080 d 63.3 44.3 60.9 63.8 2090d 65.2 43.9 62.1 65.3 2100d 67.0 43.6 61.8 66.9 AII Table AII.2.20 | Anthropogenic SOX emissions (TgS yr 1) Year RCP2.6 RCP4.5 RCP6.0 RCP8.5 A2 B1 IS92a 2000d 55.9 55.9 55.9 55.9 69.0 69.0 79.0 2010d 54.9 54.8 55.8 51.9 74.7 73.9 95.0 2020d 44.5 50.3 49.9 47.6 99.5 74.6 111.0 2030d 30.8 43.2 42.7 42.3 112.5 78.2 125.8 2040d 20.9 35.0 41.9 33.5 109.0 78.5 139.4 2050d 16.0 26.5 37.8 26.8 105.4 68.9 153.0 2060d 13.8 21.0 34.0 23.0 89.6 55.8 151.8 2070d 11.9 16.7 23.5 20.3 73.7 44.3 150.6 2080d 9.9 13.2 15.9 18.3 64.7 36.1 149.4 2090d 8.0 12.0 12.7 14.9 62.5 29.8 148.2 2100d 6.7 11.4 10.8 13.1 60.3 24.9 147.0 Year MFR CLE REFL REFU POLL POLU 2000d 55.6 55.6 50.6 76.4 50.6 76.4 2010d 53.1 81.8 52.7 78.7 2020 d 56.9 84.8 47.7 77.8 2030d 17.9 58.8 60.1 86.7 29.8 76.3 2040d 52.5 82.9 19.0 72.0 2050 d 44.2 72.3 12.4 61.7 2060d 32.8 73.9 9.5 52.9 2070d 30.5 77.7 7.8 49.8 2080 d 29.6 81.1 6.2 50.5 2090d 22.8 84.5 5.1 52.5 2100d 18.0 88.0 4.0 54.0 Notes: Anthropogenic sulphur emissions as SO2, measured here as Tg of S. 1418 Climate System Scenario Tables Annex II Table AII.2.21 | Anthropogenic OC aerosols emissions (Tg yr 1) Year RCP2.6 RCP4.5 RCP6.0 RCP8.5 A2 B1 IS92a MFR* CLE* 2000 d 35.6 35.6 35.6 35.6 81.4 81.4 81.4 35.0 35.0 2010d 36.6 34.6 36.2 35.6 89.3 74.5 85.2 29.2 34.6 2020d 36.6 30.8 36.1 34.5 97.0 71.5 89.0 28.6 32.6 2030 d 35.3 29.2 36.0 33.2 111.4 59.9 93.9 27.9 30.9 2040d 32.3 28.0 36.4 31.6 118.1 54.2 99.8 2050d 30.3 26.8 36.5 30.1 124.7 49.5 105.8 2060d 29.6 25.0 35.7 28.5 133.9 48.6 111.5 2070 d 28.2 22.8 34.4 27.4 143.1 48.3 117.2 2080d 27.0 20.7 33.4 26.4 157.2 46.0 122.9 2090d 26.4 19.9 32.7 25.1 176.2 43.8 128.6 2100 d 25.5 19.5 32.2 24.1 195.2 41.0 134.4 AII Notes: For both MFR* and CLE* 23 Tg is added to Cofala et al. (2007) values to include biomass burning. Table AII.2.22 | Anthropogenic BC aerosols emissions (Tg yr 1) Year RCP2.6 RCP4.5 RCP6.0 RCP8.5 A2 B1 IS92a MFR* CLE* 2000d 7.88 7.88 7.88 7.88 12.40 12.40 12.40 7.91 7.91 2010 d 8.49 8.13 8.13 8.06 13.60 11.30 13.00 6.31 8.01 2020d 8.27 7.84 7.77 7.66 14.80 10.90 13.60 5.81 7.41 2030d 7.03 7.36 7.53 7.04 17.00 9.10 14.30 5.41 7.01 2040d 5.80 6.81 7.39 6.22 18.00 8.30 15.20 2050d 5.00 6.21 7.07 5.67 19.00 7.50 16.10 2060d 4.46 5.56 6.48 5.22 20.40 7.40 17.00 2070d 3.99 4.88 5.75 4.88 21.80 7.40 17.90 2080 d 3.70 4.23 5.15 4.66 24.00 7.00 18.70 2090d 3.55 4.01 4.70 4.43 26.80 6.70 19.60 2100d 3.39 3.88 4.41 4.27 29.70 6.20 20.50 Notes: For both MFR* and CLE* 2.6 Tg added to Cofala et al. (2007) values to include biomass burning. Table AII.2.23 | Anthropogenic nitrogen fixation (Tg-N yr 1) SRES A1 FAO2000 FAO2000 Tilman Tubiello Year Historical SRES A2 SRES B1 SRES B2 + Biofuel Baselinea Improved a 2001 a 2007 a 1910 0.0 1920 0.2 1925 0.6 1930 0.9 1935 1.3 1940 2.2 1950 3.7 1955 6.8 1960 9.5 1965 18.7 1970 31.6 1971 33.3 1972 36.2 1973 39.1 1974 38.6 1975 43.7 1419 Annex II Climate System Scenario Tables Table AII.2.23 (continued) SRES A1 FAO2000 FAO2000 Tilman Tubiello Year Historical SRES A2 SRES B1 SRES B2 + Biofuel Baselinea Improved a 2001 a 2007 a 1975 43.7 1976 46.4 1977 49.9 1978 53.8 1979 57.4 1980 60.6 1981 60.3 1982 61.3 1983 67.1 1984 70.9 AII 1985 70.2 1986 72.5 1987 75.8 1988 79.5 1989 78.9 1990 77.1 1991 75.5 1992 73.7 1993 72.3 1994 72.4 1995 78.5 1996 82.6 77.8 77.8 1997 81.4 1998 82.8 1999 84.9 2000 82.1 87.0 2001 82.9 2002 85.2 2003 90.2 2004 91.7 2005 94.2 2007 98.4 2010 104.1 101.9 101.7 96.5 2015 106.8 88.0 2020 122.6 110.7 111.2 100.9 135.0 2030 141.1 117.6 118.4 103.3 124.5 96.2 2040 153.3 130.7 122.2 103.5 2050 165.5 131.1 123.2 101.9 236.0 2060 171.3 134.0 121.4 99.2 2070 177.0 132.1 117.5 95.6 2080 180.1 138.1 111.6 91.5 205 2090 186.0 146.5 108.8 91.3 2100 192.5 149.8 104.1 91.0 Notes: (a) See Chapter 6, Figure 6.30 and Erisman et al. (2008) for details and sources. 1420 Climate System Scenario Tables Annex II AII.3: Natural Emissions Table AII.3.1a | Net land (natural and land use) CO2 emissions (PgC yr 1) Year RCP2.6& RCP4.5& RCP6.0& RCP8.5& 2000 d 1.02 +/- 0.87 1.14 +/- 0.87 0.92 +/- 0.93 1.14 +/- 0.87 2010d 1.49 +/- 1.02 1.85 +/- 0.96 1.03 +/- 1.65 1.30 +/- 1.64 2020d 1.24 +/- 1.35 2.83 +/- 1.47 1.79 +/- 1.95 1.43 +/- 1.82 2030 d 1.28 +/- 1.53 2.84 +/- 1.59 2.37 +/- 1.54 1.76 +/- 2.22 2040d 1.21 +/- 1.33 3.25 +/- 1.58 2.27 +/- 1.46 2.15 +/- 2.13 2050d 1.00 +/- 1.53 3.07 +/- 1.54 1.98 +/- 1.57 2.35 +/- 2.45 2060 d 0.76 +/- 0.83 2.80 +/- 1.83 2.46 +/- 2.01 2.71 +/- 2.38 2070d 0.68 +/- 0.84 2.59 +/- 1.73 2.40 +/- 2.06 2.57 +/- 2.42 2080d 0.15 +/- 0.81 2.04 +/- 1.48 2.22 +/- 2.12 1.96 +/- 2.64 2090 d 0.03 +/- 0.99 2.12 +/- 1.38 2.77 +/- 1.96 1.63 +/- 2.70 AII 2100d 0.36 +/- 0.95 1.54 +/- 1.25 2.13 +/- 1.32 1.27 +/- 2.90 Notes: Ten-year average values are shown (2010d = average of 2005 2014). CO2 emissions are inferred from ESMs used in CMIP5 (Jones et al., 2013). See notes Table AII.2.1a and Chapter 6, Sections 6.4.3 and 6.4.3.3 and Figure 6.24. Table AII.3.1b | Net ocean CO2 emissions (PgC yr 1) Year RCP2.6& RCP4.5& RCP6.0& RCP8.5& 2000d 2.09 +/- 0.19 2.14 +/- 0.32 2.10 +/- 0.17 2.14 +/- 0.32 2010 d 2.44 +/- 0.22 2.50 +/- 0.42 2.44 +/- 0.20 2.53 +/- 0.43 2020d 2.70 +/- 0.26 2.75 +/- 0.46 2.59 +/- 0.22 3.02 +/- 0.51 2030d 2.59 +/- 0.30 2.98 +/- 0.52 2.69 +/- 0.22 3.47 +/- 0.54 2040 d 2.22 +/- 0.32 3.16 +/- 0.56 2.88 +/- 0.27 3.96 +/- 0.67 2050d 1.83 +/- 0.33 3.22 +/- 0.60 3.16 +/- 0.31 4.47 +/- 0.76 2060d 1.52 +/- 0.30 3.12 +/- 0.63 3.52 +/- 0.36 4.92 +/- 0.84 2070d 1.23 +/- 0.23 2.82 +/- 0.61 3.79 +/- 0.41 5.24 +/- 0.97 2080 d 0.99 +/- 0.27 2.46 +/- 0.59 4.02 +/- 0.44 5.40 +/- 1.14 2090d 0.85 +/- 0.26 2.22 +/- 0.53 3.96 +/- 0.43 5.45 +/- 1.18 2100d 0.77 +/- 0.26 2.14 +/- 0.47 3.84 +/- 0.42 5.44 +/- 1.22 Notes: See Table AII.3.1.a. 1421 Annex II Climate System Scenario Tables AII.4: Abundances of the Well-Mixed Greenhouse Gases Table AII.4.1 | CO2 abundance (ppm) Year Observed RCP2.6 RCP4.5 RCP6.0 RCP8.5 A2 B1 IS92a Min RCP8.5& Max PI 278 +/- 2 278 278 278 278 278 278 278 2011obs 390.5 +/- 0.3 2000 368.9 368.9 368.9 368.9 368 368 368 2005 378.8 378.8 378.8 378.8 378.8 2010 389.3 389.1 389.1 389.3 388 387 388 366 394 413 2020 412.1 411.1 409.4 415.8 416 411 414 386 425 449 2030 430.8 435.0 428.9 448.8 448 434 442 412 461 496 2040 440.2 460.8 450.7 489.4 486 460 472 443 504 555 2050 442.7 486.5 477.7 540.5 527 485 504 482 559 627 AII 2060 441.7 508.9 510.6 603.5 574 506 538 530 625 713 2070 437.5 524.3 549.8 677.1 628 522 575 588 703 810 2080 431.6 531.1 594.3 758.2 690 534 615 651 790 914 2090 426.0 533.7 635.6 844.8 762 542 662 722 885 1026 2100 420.9 538.4 669.7 935.9 846 544 713 794 985 +/- 97 1142 Notes: For observations (2011obs) see Chapter 2; and for projections see Box 1.1 (Figure 2), Sections 6.4.3.1, 11.3.1.1, 11.3.5.1.1. RCPn.n refers to values taken directly from the published RCP scenarios using the MAGICC model (Meinshausen et al., 2011a; 2011b). These are harmonized to match observations up to 2005 (378.8 ppm) and project future abundances thereafter. RCP8.5& shows the average and assessed 90% confidence interval for year 2100, plus the min-max full range derived from the CMIP5 archive for all years (P. Friedlingstein, based on Friedlingstein et al., 2006). 11 ESMs participated (BCC-CSM-1, CanESM2, CESM1-BGC, GFDL-ESM2G, HadGem-2ES, INMCM4, IPSLCM5-LR, MIROC-ESM, MPI-ESM-LR, MRI-ESM1, and Nor-ESM1-ME), running the RCP8.5 anthropogenic emission scenario forced by the RCP8.5 climate change scenario (see Figure 12.36). All abundances are mid-year. Projected values for SRES A2 and B1 and IS92 are the average of reference models taken from the TAR Appendix II. Table AII.4.2 | CH4 abundance (ppb) Year RCP2.6 RCP4.5 RCP6.0 RCP8.5 A2 B1 IS92a RCP2.6& RCP4.5& RCP6.0& RCP8.5& PI 720 720 720 720 722 +/- 25 722 +/- 25 722 +/- 25 722 +/- 25 2011obs 1803 +/- 4 1803 +/- 4 1803 +/- 4 1803 +/- 4 2000 1751 1751 1751 1751 1760 1760 1760 2010 1773 1767 1769 1779 1861 1827 1855 1795 +/- 18 1795 +/- 18 1795 +/- 18 1795 +/- 18 2020 1731 1801 1786 1924 1997 1891 1979 1716 +/- 23 1847 +/- 21 1811 +/- 22 1915 +/- 25 2030 1600 1830 1796 2132 2163 1927 2129 1562 +/- 38 1886 +/- 28 1827 +/- 28 2121 +/- 44 2040 1527 1842 1841 2399 2357 1919 2306 1463 +/- 50 1903 +/- 37 1880 +/- 36 2412 +/- 74 2050 1452 1833 1895 2740 2562 1881 2497 1353 +/- 60 1899 +/- 47 1941 +/- 48 2784 +/- 116 2060 1365 1801 1939 3076 2779 1836 2663 1230 +/- 71 1872 +/- 59 1994 +/- 61 3152 +/- 163 2070 1311 1745 1962 3322 3011 1797 2791 1153 +/- 78 1824 +/- 72 2035 +/- 77 3428 +/- 208 2080 1285 1672 1940 3490 3252 1741 2905 1137 +/- 88 1756 +/- 87 2033 +/- 94 3624 +/- 250 2090 1268 1614 1819 3639 3493 1663 3019 1135 +/- 98 1690 +/- 100 1908 +/- 111 3805 +/- 293 2100 1254 1576 1649 3751 3731 1574 3136 1127 +/- 106 1633 +/- 110 1734 +/- 124 3938 +/- 334 Notes: RCPn.n refers to values taken directly from the published RCP scenarios using the MAGICC model (Meinshausen et al., 2011b) and initialized in year 2005 at 1754 ppb. Values for SRES A2 and B1 and IS92 are from the TAR Appendix II. RCPn.n& values are best estimates with uncertainties (68% confidence intervals) from Chapter 11 (Section 11.3.5) based on Holmes et al. (2013) and using RCP& emissions and uncertainties tabulated above. For RCP& the PI, year 2011 and year 2010 values are based on observations. RCP models used slightly different PI abundances than recommended here (Table AII.1.1, Chapter 2). 1422 Climate System Scenario Tables Annex II Table AII.4.3 | N2O abundance (ppb) Year RCP2.6 RCP4.5 RCP6.0 RCP8.5 A2 B1 IS92a RCP2.6& RCP4.5& RCP6.0& RCP8.5& PI 272 272 272 272 270 +/- 7 270 +/- 7 270 +/- 7 270 +/- 7 2011 obs 324 +/- 1 324 +/- 1 324 +/- 1 324 +/- 1 2000 316 316 316 316 316 316 316 2010 323 323 323 323 325 324 324 323 +/- 3 323 +/- 3 323 +/- 3 323 +/- 3 2020 329 330 330 332 335 333 333 330 +/- 4 331 +/- 4 331 +/- 4 332 +/- 4 2030 334 337 337 342 347 341 343 336 +/- 5 339 +/- 5 338 +/- 5 342 +/- 6 2040 339 344 345 354 360 349 353 342 +/- 6 346 +/- 7 346 +/- 7 353 +/- 8 2050 342 351 355 367 373 357 363 346 +/- 8 353 +/- 9 355 +/- 9 365 +/- 11 2060 343 356 365 381 387 363 372 349 +/- 9 360 +/- 10 364 +/- 11 377 +/- 13 2070 344 361 376 394 401 368 381 351 +/- 10 365 +/- 12 374 +/- 13 389 +/- 16 2080 344 366 386 408 416 371 389 352 +/- 11 370 +/- 13 384 +/- 15 401 +/- 18 2090 344 369 397 421 432 374 396 353 +/- 11 374 +/- 14 393 +/- 17 413 +/- 21 AII 2100 344 372 406 435 447 375 403 354 +/- 12 378 +/- 16 401 +/- 19 425 +/- 24 Notes: See notes Table AII.4.2. Table AII.4.4 | SF6 abundance (ppt) Year RCP2.6 RCP4.5 RCP6.0 RCP8.5 A2 B1 Obs 2011 obs 7.3 +/- 0.1 2010 7.0 6.9 7.0 7.0 7 7 2020 8.9 8.7 10.3 9.9 11 9 2030 9.7 9.7 14.1 13.4 15 12 2040 10.4 10.9 17.9 17.6 20 15 2050 10.8 12.3 21.7 22.1 26 19 2060 11.0 13.8 25.6 27.2 32 23 2070 11.2 15.6 29.5 32.6 40 27 2080 11.3 17.6 33.4 38.1 48 30 2090 11.4 19.9 37.3 44.1 56 33 2100 11.4 22.3 41.0 50.5 65 35 Notes: Projected SF6 and PFC abundances (Tables AII.4.4 to AII.4.7) taken directly from RCPs (Meinshausen et al., 2011a). Observed values shown for year 2011. Table AII.4.5 | CF4 abundance (ppt) Year RCP2.6 RCP4.5 RCP6.0 RCP8.5 A2 B1 Obs 2011 obs 79.0 2010 84 83 85 83 92 91 2020 93 90 99 91 107 101 2030 99 95 115 99 125 111 2040 103 101 130 107 148 122 2050 106 107 146 115 175 135 2060 108 113 162 123 208 150 2070 109 119 177 131 246 164 2080 110 125 193 138 291 179 2090 111 131 207 146 341 193 2100 112 138 222 153 397 208 1423 Annex II Climate System Scenario Tables Table AII.4.6 | C2F6 abundance (ppt) Year RCP2.6 RCP4.5 RCP6.0 RCP8.5 A2 B1 Obs 2011 obs 4.2 2010 4.1 3.9 3.9 3.9 4 4 2020 6.2 4.8 5.0 5.0 5 4 2030 7.9 5.5 6.2 6.1 6 5 2040 8.6 6.3 7.3 7.2 7 6 2050 8.9 7.1 8.4 8.4 9 7 2060 9.1 7.9 9.4 9.6 11 8 2070 9.2 8.8 10.5 10.7 14 8 2080 9.3 9.6 11.5 11.8 17 9 2090 9.3 10.4 12.5 13.0 20 10 2100 9.3 11.3 13.4 14.1 23 11 AII Table AII.4.7 | C6F14 abundance (ppt) Year RCP2.6 RCP4.5 RCP6.0 RCP8.5 2010 0.07 0.07 0.07 0.07 2020 0.13 0.13 0.13 0.13 2030 0.16 0.16 0.16 0.16 2040 0.18 0.18 0.18 0.18 2050 0.20 0.20 0.20 0.20 2060 0.21 0.21 0.21 0.21 2070 0.23 0.23 0.23 0.23 2080 0.25 0.25 0.25 0.25 2090 0.27 0.27 0.27 0.27 2100 0.28 0.28 0.28 0.28 Table AII.4.8 | HFC-23 abundance (ppt) Year RCP2.6 RCP4.5 RCP6.0 RCP8.5 A2 B1 RCP2.6& RCP4.5& RCP6.0& RCP8.5& 2011 obs 24.0 24.0 24.0 24.0 2010 22.9 22.9 22.9 22.9 26 26 23.2 +/- 1 23.2 +/- 1 23.2 +/- 1 23.2 +/- 1 2020 27.2 27.2 27.2 27.2 33 33 26.6 +/- 1 26.6 +/- 1 26.6 +/- 1 26.6 +/- 1 2030 27.0 27.0 27.1 27.1 35 35 26.3 +/- 1 26.3 +/- 1 26.3 +/- 1 26.3 +/- 1 2040 26.5 26.5 26.6 26.6 35 35 25.7 +/- 1 25.8 +/- 1 25.8 +/- 1 25.8 +/- 1 2050 25.8 25.9 25.9 26.0 35 35 24.9 +/- 1 25.0 +/- 1 25.1 +/- 1 25.1 +/- 1 2060 25.0 25.1 25.1 25.3 35 34 24.0 +/- 1 24.2 +/- 1 24.3 +/- 1 24.4 +/- 1 2070 24.1 24.2 24.4 24.6 34 34 23.0 +/- 1 23.4 +/- 1 23.4 +/- 1 23.6 +/- 1 2080 23.3 23.3 23.5 23.8 34 33 22.1 +/- 1 22.5 +/- 1 22.6 +/- 1 22.8 +/- 1 2090 22.4 22.5 22.7 23.0 34 33 21.2 +/- 1 21.6 +/- 1 21.8 +/- 1 22.1 +/- 1 2100 21.6 21.6 21.9 22.3 33 32 20.3 +/- 1 20.8 +/- 1 21.0 +/- 1 21.3 +/- 1 Notes: RCPn.n HFC abundances (Tables AII.4.8 to AII.4.15) are as reported (Meinshausen et al., 2011a). SRES A2 and B1 and IS92a (where available) are taken from TAR Appendix II. Observed values are shown for 2011 (see Chapter 2, and Table AII.1.1). The AR5 RCPn.n& abundances are calculated starting with observed abundances (adopted for 2010) and future tropospheric OH changes using the methodology of Prather et al. (2012), updated for uncertainty in lifetime and scenario changes in OH using Holmes et al. (2013) and ACCMIP results (Stevenson et al., 2013; Voulgarakis et al., 2013). Projected RCP& abundances are best estimates with 68% confidence range as uncertainties. See also notes Tables AII.4.2 and AII.5.9. 1424 Climate System Scenario Tables Annex II Table AII.4.9 | HFC-32 abundance (ppt) Year RCP2.6 RCP4.5 RCP6.0 RCP8.5 A2 B1 RCP2.6& RCP4.5& RCP6.0& RCP8.5& 2011 obs 4.9 4.9 4.9 4.9 2010 5.7 5.7 5.7 5.7 1 1 4.1 +/- 0 4.1 +/- 0 4.1 +/- 0 4.1 +/- 0 2020 21.0 21.0 21.1 21.1 3 3 23.8 +/- 2 24.0 +/- 2 24.0 +/- 2 24.0 +/- 2 2030 34.7 35.2 35.5 35.8 4 4 38.1 +/- 5 39.1 +/- 5 39.1 +/- 5 39.2 +/- 5 2040 41.1 41.9 42.4 43.6 6 5 44.7 +/- 6 46.7 +/- 6 46.9 +/- 6 47.8 +/- 6 2050 41.9 42.8 43.9 46.2 7 7 44.3 +/- 7 47.6 +/- 7 48.2 +/- 7 50.3 +/- 8 2060 43.1 43.8 45.6 48.8 9 8 45.0 +/- 7 49.6 +/- 8 50.6 +/- 8 53.8 +/- 8 2070 47.9 48.1 50.7 54.7 11 8 49.4 +/- 8 54.9 +/- 8 56.8 +/- 9 60.3 +/- 9 2080 51.3 50.5 54.0 58.6 14 8 53.8 +/- 9 58.2 +/- 9 61.4 +/- 10 64.7 +/- 10 2090 51.0 49.6 52.8 58.2 17 8 54.0 +/- 9 56.9 +/-10 60.6 +/- 10 64.4 +/- 11 2100 47.5 45.6 47.4 53.8 20 8 50.5 +/- 9 51.8 +/- 9 55.2 +/- 10 59.6 +/- 11 AII Table AII.4.10 | HFC-125 abundance (ppt) Year RCP2.6 RCP4.5 RCP6.0 RCP8.5 A2 B1 IS92a RCP2.6& RCP4.5& RCP6.0& RCP8.5& 2011obs 9.6 9.6 9.6 9.6 2010 7.1 6.4 5.7 7.7 2 2 0 8.2 +/- 1 8.2 +/- 1 8.2 +/- 1 8.2 +/- 1 2020 27.4 14.3 7.6 25.7 8 8 2 30.9 +/- 1 16.3 +/- 1 9.6 +/- 1 27.6 +/- 1 2030 60.0 23.2 9.2 48.5 16 16 12 64.1 +/- 3 25.2 +/- 2 10.9 +/- 1 51.0 +/- 3 2040 90.5 29.7 10.6 72.0 24 24 40 95.5 +/- 7 31.9 +/- 3 12.2 +/- 1 75.9 +/- 5 2050 114.5 34.0 11.8 97.6 34 33 87 119.5 +/- 11 36.6 +/- 4 13.3 +/- 2 103 +/- 8 2060 133.4 36.0 12.9 122.9 45 43 137 139.0 +/- 15 39.0 +/- 5 14.4 +/- 2 130 +/- 12 2070 154.8 35.8 13.9 147.1 58 49 177 160.8 +/- 20 39.4 +/- 6 15.5 +/- 2 156 +/- 16 2080 176.2 34.8 14.8 168.7 72 54 210 183.2 +/- 24 39.1 +/- 6 16.6 +/- 2 180 +/- 20 2090 192.3 34.0 15.5 185.8 89 57 236 200.9 +/- 29 38.7 +/- 7 17.4 +/- 3 199 +/- 25 2100 200.2 33.2 15.8 198.9 107 58 255 210.5 +/- 34 38.1 +/- 7 18.0 +/- 3 215 +/- 30 Table AII.4.11 | HFC-134a abundance (ppt) Year RCP2.6 RCP4.5 RCP6.0 RCP8.5 A2 B1 IS92a RCP2.6& RCP4.5& RCP6.0& RCP8.5& 2011 63 +/- 1 63 +/- 1 63 +/- 1 63 +/- 1 2010 56 56 56 56 55 55 94 58 +/- 3 58 +/- 3 58 +/- 3 58 +/- 3 2020 96 95 90 112 111 108 183 97 +/- 5 98 +/- 5 91 +/- 5 117 +/- 5 2030 122 129 109 180 170 165 281 123 +/- 9 132 +/- 9 110 +/- 8 184 +/- 11 2040 142 154 121 245 231 223 401 143 +/- 12 157 +/- 12 122 +/- 10 249 +/- 17 2050 153 175 129 311 299 293 537 150 +/- 15 178 +/- 16 130 +/- 12 314 +/- 24 2060 160 187 135 370 382 352 657 155 +/- 16 192 +/- 19 137 +/- 14 373 +/- 32 2070 175 193 141 423 480 380 743 168 +/- 18 200 +/- 21 143 +/- 15 427 +/- 39 2080 191 205 144 471 594 391 807 184 +/- 21 216 +/- 23 148 +/- 16 476 +/- 47 2090 200 229 144 517 729 390 850 193 +/- 23 242 +/- 26 150 +/- 18 524 +/- 56 2100 199 262 141 561 877 379 878 192 +/- 25 275 +/- 30 148 +/- 19 570 +/- 64 1425 Annex II Climate System Scenario Tables Table AII.4.12 | HFC-143a abundance (ppt) Year RCP2.6 RCP4.5 RCP6.0 RCP8.5 A2 B1 RCP2.6& RCP4.5& RCP6.0& RCP8.5& 2011 12.0 12.0 12.0 12.0 2010 10.2 9.4 8.4 10.8 3 2 11 +/- 1 11 +/- 1 11 +/- 1 11 +/- 1 2020 33.9 17.8 10.1 28.2 10 9 37 +/- 1 19 +/- 1 12 +/- 1 29 +/- 1 2030 72.1 26.8 12.1 46.8 20 18 75 +/- 2 28 +/- 1 14 +/- 1 48 +/- 1 2040 109.9 36.0 14.0 65.6 32 29 13 +/- 4 38 +/- 1 16 +/- 1 67 +/- 2 2050 142.1 45.4 16.0 85.7 45 43 144 +/- 6 47 +/- 2 18 +/- 1 88 +/- 3 2060 168.6 54.0 18.1 105.2 62 57 170 +/- 8 56 +/- 3 20 +/- 1 107 +/- 4 2070 196.1 61.4 20.1 123.2 81 68 197 +/- 11 64 +/- 3 22 +/- 1 126 +/- 6 2080 222.2 69.7 22.2 138.7 103 77 223 +/- 14 73 +/- 4 24 +/- 2 142 +/- 8 2090 242.0 80.2 24.0 150.2 129 85 243 +/- 17 85 +/- 5 26 +/- 2 154 +/- 9 2100 252.9 92.6 25.6 157.9 157 90 254 +/- 20 98 +/- 6 28 +/- 2 163 +/- 11 AII Table AII.4.13 | HFC-227ea abundance (ppt) Year RCP2.6 RCP4.5 RCP6.0 RCP8.5 A2 B1 RCP2.6& RCP4.5& RCP6.0& RCP8.5& 2011 0.65 0.65 0.65 0.65 2010 1.43 1.28 1.42 1.56 2 2 0.6 +/- 0.1 0.6 +/- 0.1 0.6 +/- 0.1 0.6 +/- 0.1 2020 2.81 2.10 2.78 3.30 5 6 2.0 +/- 0.1 1.5 +/- 0.1 2.0 +/- 0.1 2.4 +/- 0.1 2030 2.48 1.71 2.44 2.77 10 10 2.0 +/- 0.1 1.3 +/- 0.1 2.0 +/- 0.1 2.2 +/- 0.1 2040 2.09 1.35 2.04 2.29 14 15 1.8 +/- 0.1 1.1 +/- 0.1 1.8 +/- 0.1 2.0 +/- 0.2 2050 1.74 1.06 1.68 1.92 19 21 1.6 +/- 0.2 1.0 +/- 0.1 1.6 +/- 0.2 1.8 +/- 0.2 2060 1.35 0.81 1.31 1.55 25 27 1.3 +/- 0.2 0.8 +/- 0.1 1.3 +/- 0.2 1.5 +/- 0.2 2070 1.04 0.61 1.01 1.23 32 31 1.1 +/- 0.2 0.6 +/- 0.1 1.1 +/- 0.2 1.3 +/- 0.2 2080 0.81 0.45 0.78 0.99 40 34 0.9 +/- 0.2 0.5 +/- 0.1 0.9 +/- 0.2 1.1 +/- 0.2 2090 0.63 0.34 0.59 0.79 49 35 0.8 +/- 0.2 0.4 +/- 0.1 0.8 +/- 0.2 0.9 +/- 0.2 2100 1.43 1.28 1.42 1.56 2 2 0.6 +/- 0.2 0.3 +/- 0.1 0.6 +/- 0.2 0.8 +/- 0.2 Table AII.4.14 | HFC-245fa abundance (ppt) Year RCP2.6 RCP4.5 RCP6.0 RCP8.5 A2 B1 RCP2.6& RCP4.5& RCP6.0& RCP8.5& 2011 1.24 1.24 1.24 1.24 2010 7.5 7.3 8.2 9.5 8 8 1 +/- 0.2 1 +/- 0.2 1 +/- 0.2 1 +/- 0.2 2020 12.1 19.3 18.1 31.5 17 17 10.2 +/- 1 18.9 +/- 2 16.4 +/- 2 31.0 +/- 4 2030 7.4 28.2 21.3 51.2 23 23 6.6 +/- 1.5 29.2 +/- 4 21.6 +/- 3 53.1 +/- 8 2040 2.3 31.2 22.6 61.7 29 29 2.2 +/- 1.0 33.0 +/- 6 23.7 +/- 4 63.8 +/- 10 2050 0.6 31.9 23.3 62.0 36 38 0.7 +/- 0.5 34.1 +/- 7 24.6 +/- 5 64.4 +/- 12 2060 0.2 30.6 23.8 59.1 46 43 0.2 +/- 0.2 32.9 +/- 7 25.3 +/- 5 61.7 +/- 13 2070 0.0 28.2 24.2 55.3 58 44 0.1 +/- 0.1 30.8 +/- 7 25.9 +/- 5 58.1 +/- 13 2080 0.0 26.4 24.3 51.5 72 43 0.0 +/- 0.1 29.3 +/- 7 26.4 +/- 6 54.4 +/- 12 2090 0.0 25.8 23.6 48.0 88 42 0.0 +/- 0.0 28.6 +/- 6 26.0 +/- 6 51.0 +/- 12 2100 0.0 26.0 22.3 47.3 105 40 0.0 +/- 0.0 28.6 +/- 6 24.9 +/- 6 50.6 +/- 11 1426 Climate System Scenario Tables Annex II Table AII.4.15 | HFC-43-10mee abundance (ppt) Year RCP2.6 RCP4.5 RCP6.0 RCP8.5 A2 B1 RCP2.6& RCP4.5& RCP6.0& RCP8.5& 2011 2010 0.52 0.52 0.52 0.52 1 1 0.0 +/- 0.0 0.0 +/- 0.0 0.0 +/- 0.0 0.0 +/- 0.0 2020 1.46 1.46 1.46 1.47 2 1 1.2 +/- 0.1 1.2 +/- 0.1 1.2 +/- 0.1 1.2 +/- 0.1 2030 2.09 2.11 2.12 2.14 2 2 2.0 +/- 0.2 2.1 +/- 0.2 2.1 +/- 0.2 2.1 +/- 0.2 2040 2.61 2.64 2.66 2.68 3 2 2.7 +/- 0.3 2.8 +/- 0.3 2.8 +/- 0.3 2.8 +/- 0.3 2050 3.13 3.17 3.22 3.23 3 3 3.3 +/- 0.4 3.4 +/- 0.4 3.4 +/- 0.4 3.4 +/- 0.4 2060 3.56 3.61 3.70 3.83 4 3 3.7 +/- 0.6 3.9 +/- 0.6 4.0 +/- 0.6 4.1 +/- 0.6 2070 3.78 3.81 3.96 4.52 4 4 3.9 +/- 0.7 4.3 +/- 0.7 4.3 +/- 0.7 4.9 +/- 0.7 2080 3.89 3.88 4.08 5.27 5 4 4.1 +/- 0.8 4.4 +/- 0.8 4.6 +/- 0.8 5.8 +/- 0.9 2090 3.93 3.87 4.10 6.14 6 4 4.2 +/- 0.8 4.5 +/- 0.8 4.7 +/- 0.9 6.7 +/- 1.0 2100 3.91 3.81 3.99 7.12 7 4 4.2 +/- 0.9 4.4 +/- 0.9 4.6 +/- 0.9 7.9 +/- 1.2 AII Table AII.4.16 | Montreal Protocol greenhouse gas abundances (ppt) Year CFC-11 CFC-12 CFC-113 CFC-114 CFC-115 CCl4 CH3CCl3 HCFC-22 2011* 238 +/- 1 528 +/- 2 74.5 +/- 0.5 15.8 8.4 86 +/- 2 6.4 +/- 0.4 213 +/- 2 2010 240.9 532.5 75.6 16.4 8.4 87.6 8.3 206.8 2020 213.0 492.8 67.4 15.8 8.4 70.9 1.5 301.8 2030 182.6 448.0 59.9 15.1 8.4 54.4 0.2 265.4 2040 153.5 405.8 53.3 14.4 8.4 40.3 0.0 151.0 2050 127.2 367.3 47.4 13.6 8.4 29.2 0.0 71.1 2060 104.4 332.4 42.1 12.9 8.3 20.0 0.0 31.5 2070 85.2 300.7 37.4 12.3 8.3 13.6 0.0 13.7 2080 69.1 272.1 33.3 11.6 8.2 9.3 0.0 5.9 2090 55.9 246.2 29.6 11.1 8.2 6.3 0.0 2.6 2100 45.1 222.8 26.3 10.5 8.1 4.3 0.0 1.1 Year HCFC-141b HCFC-142b Halon 1211 Halon 1202 Halon 1301 Halon 2402 CH3Br CH3Cl 2011* 21.4 +/- 0.5 21.2 +/- 0.5 4.07 0.00 3.23 0.45 7.1 534 2010 20.3 20.5 4.07 0.00 3.20 0.46 7.2 550 2020 30.9 30.9 3.08 0.00 3.29 0.38 7.1 550 2030 34.4 31.2 2.06 0.00 3.19 0.27 7.1 550 2040 27.9 23.3 1.30 0.00 2.97 0.18 7.1 550 2050 19.3 14.9 0.78 0.00 2.71 0.12 7.1 550 2060 12.4 9.0 0.46 0.00 2.43 0.07 7.1 550 2070 7.7 5.2 0.26 0.00 2.16 0.05 7.1 550 2080 4.7 3.0 0.15 0.00 1.90 0.03 7.1 550 2090 2.9 1.7 0.08 0.00 1.66 0.02 7.1 550 2100 1.7 0.9 0.05 0.00 1.44 0.01 7.1 550 Notes: Present day (2011*) is from Chapter 2; projections are from Scenario A1, WMO Ozone Assessment (WMO 2010). 1427 Annex II Climate System Scenario Tables AII.5: Column Abundances, Burdens, and Lifetimes Table AII.5.1 | Stratospheric O3 column changes (DU) Year Obs RCP2.6 RCP4.5 RCP6.0 RCP8.5 1850 17 17 17 17 1980 11 15 15 15 15 2000 269 +/- 8 276 +/- 9 276 +/- 9 276 +/- 9 276 +/- 9 2010 0 2 1 1 2 2020 4 0 3 2 2030 8 4 7 5 2040 9 7 10 9 2050 12 10 13 12 2060 13 12 14 15 AII 2070 13 11 15 16 2080 12 11 16 15 2090 13 12 16 18 2100 15 13 17 20 Notes: Observed O3 columns and trends taken from WMO (Douglass and Filetov, 2010), subtracting tropospheric column O3 (Table AII.5.2) with uncertainty estimates driven by polar variability. CMIP5 RCP results are from Eyring et al. (2013). The multi-model mean is derived from the CMIP5 models with predictive (interactive or semi-offline) stratospheric and tropospheric ozone chemistry. The absolute value is shown for year 2000. All other years are differences relative to (minus) year 2000. The multi-model standard deviation is shown only for year 2000; it does not change much over time; and, representing primarily the spread in absolute O3 column, it is larger than the standard deviation of the changes (not evaluated here). All models used the same projections for ozone- depleting substances. Near-term differences in projected O3 columns across scenarios reflect model sampling (i.e., different sets of models contributing to each RCP), while long-term changes reflect changes in N2O, CH4 and climate. See Section 11.3.5.1.2. Table AII.5.2 | Tropospheric O3 column changes (DU) Year CMIP5 ACCMIP RCP2.6 RCP4.5 RCP6.0 RCP8.5 RCP2.6 RCP4.5 RCP6.0 RCP8.5 1850 10.2 10.2 10.2 10.2 8.9 8.9 8.9 8.9 1980 2.0 2.0 2.0 2.0 1.3 1.3 1.3 1.3 2000 31.1 +/- 3.3 31.1 +/- 3.3 31.1 +/- 3.3 31.1 +/- 3.3 30.8 +/- 2.1 30.8 +/- 2.1 30.8 +/- 2.1 30.8 +/- 2.1 2010 1.1 0.6 0.8 0.8 2020 1.0 0.9 1.0 2.1 2030 0.6 1.5 1.4 3.5 1.3 1.0 0.1 1.8 2040 0.5 1.6 2.1 4.5 2050 0.0 1.7 2.4 5.7 2060 0.7 1.3 2.6 7.1 2070 1.6 0.5 2.3 8.1 2080 2.5 0.1 2.0 8.9 2090 2.8 0.4 1.5 9.5 2100 3.1 0.5 1.1 10.2 5.4 2.2 2.6 5.3 (continued on next page) 1428 Climate System Scenario Tables Annex II Table AII.5.2 (continued) Year A2 B1 IS92a CLE MFR 1850 1980 2000 34.0 34.0 34.0 32.6 32.6 2010 1.7 0.8 1.5 2020 4.2 1.6 3.1 2030 6.8 1.9 4.7 1.5 +/- 0.8 1.4 +/- 0.4 2040 8.6 1.8 6.1 2050 10.2 1.0 7.6 2060 11.7 0.0 8.9 2070 13.2 0.9 10.0 2080 15.3 1.9 11.1 2090 18.0 2.8 12.1 AII 2100 20.8 3.9 13.2 Notes: RCP results from CMIP5 (Eyring et al., 2013) and ACCMIP (Young et al., 2013). For ACCMIP all models have interactive tropospheric ozone chemistry and are included, in contrast to the CMIP5 multi- model mean which includes only those models with predictive (interactive or semi-offline) stratospheric and tropospheric ozone chemistry. The absolute value is shown for year 2000. All other years are differences relative to (minus) year 2000. The multi-model standard deviation is shown only for year 2000; it does not change much over time; and, representing primarily the spread in absolute O3 columns, it is larger than the standard deviation of the changes across individual models (not evaluated here). SRES values are from TAR Appendix II. CLE/MFR scenarios are from Dentener et al. (2005, 2006): CLE includes climate change, MFR does not. See Section 11.3.5.1.2. Table AII.5.3 | Total aerosol optical depth (AOD) Year (Min) Historical (Max) RCP2.6 RCP4.5 RCP6.0 RCP8.5 1860 d 0.056 0.101 0.161 0.094 0.101 0.092 0.100 1870d 0.058 0.102 0.162 0.095 0.102 0.094 0.101 1180d 0.058 0.102 0.163 0.095 0.102 0.094 0.101 1890 d 0.059 0.104 0.164 0.098 0.104 0.096 0.103 1900d 0.058 0.105 0.166 0.099 0.105 0.097 0.104 1910d 0.059 0.107 0.169 0.101 0.107 0.099 0.106 1920 d 0.060 0.108 0.170 0.102 0.108 0.100 0.107 1930d 0.061 0.110 0.173 0.104 0.110 0.101 0.109 1940d 0.061 0.111 0.175 0.105 0.111 0.103 0.110 1950 d 0.060 0.115 0.181 0.108 0.115 0.106 0.113 1960d 0.064 0.122 0.192 0.116 0.122 0.113 0.120 1970d 0.065 0.130 0.204 0.123 0.130 0.120 0.128 1980 d 0.066 0.135 0.221 0.127 0.135 0.124 0.133 1990d 0.068 0.138 0.231 0.129 0.138 0.126 0.135 2000d 0.068 0.136 0.232 0.127 0.136 0.124 0.134 2010d 0.127 0.137 0.124 0.133 2020 d 0.123 0.134 0.122 0.132 2030d 0.117 0.130 0.119 0.130 2040d 0.111 0.126 0.118 0.126 2050 d 0.108 0.123 0.117 0.124 2060d 0.106 0.119 0.116 0.121 2070d 0.105 0.116 0.110 0.120 2080 d 0.103 0.114 0.107 0.118 2090d 0.102 0.112 0.106 0.118 2100d 0.101 0.111 0.105 0.117 Number of models 21 15 21 13 19 Notes: Multi-model decadal global means (2030d = 2025 2034, 2100d = 2095 2100) from CMIP5 models reporting AOD. The numbers of models for each experiment are indicated in the bottom row. The full range of models (given only for historical period for AOD and AAOD) is large and systematic in that models tend to scale relative to one another. Historical estimates for different RCPs vary because of the models included. RCP4.5 included the full set of CMIP5 models contributing aerosol results (21). The standard deviation of the models is 28% (AOD) and 62% (AAOD) (N. Mahowald, CMIP5 archive; Lamarque et al., 2013; Shindell et al., 2013). See Sections 11.3.5.1.3 and 11.3.6.1. 1429 Annex II Climate System Scenario Tables Table AII.5.4 | Absorbing aerosol optical depth (AAOD) Year (Min) Historical (Max) RCP2.6 RCP4.5 RCP6.0 RCP8.5 1860d 0.00050 0.0035 0.0054 0.0033 0.0035 0.0031 0.0035 1870 d 0.00060 0.0035 0.0054 0.0033 0.0035 0.0032 0.0036 1180d 0.00060 0.0036 0.0054 0.0034 0.0036 0.0032 0.0036 1890d 0.00060 0.0036 0.0055 0.0035 0.0036 0.0033 0.0037 1900 d 0.00070 0.0037 0.0056 0.0035 0.0037 0.0033 0.0038 1910d 0.00070 0.0038 0.0057 0.0036 0.0038 0.0034 0.0038 1920d 0.00070 0.0038 0.0058 0.0036 0.0038 0.0034 0.0039 1930d 0.00070 0.0038 0.0057 0.0036 0.0038 0.0034 0.0038 1940 d 0.00070 0.0038 0.0057 0.0036 0.0038 0.0034 0.0039 1950d 0.00070 0.0038 0.0058 0.0036 0.0038 0.0034 0.0039 1960d 0.00080 0.0040 0.0059 0.0038 0.0040 0.0036 0.0040 AII 1970 d 0.00090 0.0042 0.0065 0.0040 0.0042 0.0038 0.0043 1980d 0.00100 0.0046 0.0073 0.0044 0.0046 0.0042 0.0046 1990d 0.00110 0.0049 0.0079 0.0047 0.0049 0.0044 0.0049 2000 d 0.00120 0.0050 0.0084 0.0048 0.0050 0.0045 0.0051 2010d 0.0050 0.0051 0.0046 0.0051 2020d 0.0050 0.0050 0.0045 0.0050 2030 d 0.0047 0.0049 0.0045 0.0049 2040d 0.0043 0.0048 0.0044 0.0047 2050d 0.0041 0.0046 0.0044 0.0046 2060 d 0.0039 0.0044 0.0043 0.0045 2070d 0.0037 0.0042 0.0041 0.0044 2080d 0.0037 0.0040 0.0039 0.0043 2090d 0.0036 0.0039 0.0038 0.0043 2100 d 0.0036 0.0039 0.0038 0.0042 Number of models 14 11 14 10 12 Notes: See notes Table AII.5.3. Table AII.5.5 | Sulphate aerosol atmospheric burden (TgS) Year (Min) Historical (Max) RCP2.6 RCP4.5 RCP6.0 RCP8.5 1860d 0.09 0.61 1.42 0.60 0.61 0.57 0.60 1870 d 0.10 0.62 1.45 0.62 0.62 0.59 0.61 1180d 0.12 0.65 1.49 0.64 0.65 0.61 0.64 1890d 0.16 0.68 1.57 0.67 0.68 0.64 0.66 1900 d 0.21 0.73 1.65 0.73 0.73 0.70 0.72 1910d 0.23 0.79 1.80 0.79 0.79 0.76 0.78 1920d 0.23 0.83 1.84 0.83 0.83 0.80 0.81 1930 d 0.24 0.87 1.94 0.88 0.87 0.85 0.86 1940d 0.25 0.93 2.05 0.95 0.93 0.91 0.92 1950d 0.27 1.03 2.21 1.05 1.03 1.01 1.01 1960 d 0.31 1.25 2.67 1.29 1.25 1.24 1.23 1970d 0.35 1.48 3.14 1.52 1.48 1.45 1.47 1980d 0.37 1.58 3.33 1.62 1.58 1.54 1.58 1990d 0.37 1.59 3.31 1.63 1.59 1.55 1.60 2000 d 0.37 1.55 3.17 1.59 1.55 1.53 1.56 (continued on next page) 1430 Climate System Scenario Tables Annex II Table AII.5.5 | (continued) Year (Min) Historical (Max) RCP2.6 RCP4.5 RCP6.0 RCP8.5 2010d 1.57 1.59 1.52 1.54 2020 d 1.43 1.54 1.43 1.51 2030d 1.21 1.44 1.33 1.44 2040d 1.03 1.31 1.34 1.31 2050 d 0.94 1.16 1.29 1.20 2060d 0.90 1.05 1.24 1.13 2070d 0.86 0.96 1.06 1.08 2080 d 0.81 0.88 0.92 1.05 2090d 0.76 0.85 0.86 0.98 2100d 0.71 0.83 0.80 0.94 Number of models 18 12 18 10 16 AII Notes: See notes Table AII.5.3. The standard deviation of the models is about 50% for sulphate, OC and BC aerosol loadings (N. Mahowald, CMIP5 archive; Lamarque et al., 2013; Shindell et al., 2013). Table AII.5.6 | OC aerosol atmospheric burden (Tg) Year (Min) Historical (Max) RCP2.6 RCP4.5 RCP6.0 RCP8.5 1860d 0.34 1.08 2.7 1.09 1.08 1.13 1.12 1870 d 0.35 1.09 2.7 1.10 1.09 1.14 1.13 1180d 0.36 1.09 2.7 1.11 1.09 1.15 1.14 1890d 0.35 1.10 2.8 1.12 1.10 1.16 1.15 1900 d 0.36 1.11 2.8 1.12 1.11 1.16 1.15 1910d 0.33 1.10 2.8 1.11 1.10 1.15 1.15 1920d 0.34 1.08 2.7 1.09 1.08 1.12 1.13 1930 d 0.33 1.07 2.6 1.07 1.07 1.11 1.12 1940d 0.33 1.07 2.6 1.07 1.07 1.11 1.12 1950d 0.36 1.08 2.6 1.08 1.08 1.11 1.12 1960 d 0.41 1.13 2.7 1.13 1.13 1.17 1.17 1970d 0.46 1.20 2.9 1.22 1.20 1.26 1.24 1980d 0.54 1.28 3.1 1.32 1.28 1.36 1.33 1990 d 0.53 1.38 3.3 1.44 1.38 1.48 1.43 2000d 0.53 1.41 3.5 1.47 1.41 1.52 1.46 2010d 1.59 1.21 1.55 1.29 2020d 1.59 1.12 1.56 1.26 2030 d 1.56 1.08 1.55 1.25 2040d 1.47 1.06 1.57 1.22 2050d 1.41 1.04 1.57 1.20 2060 d 1.40 1.01 1.56 1.17 2070d 1.36 0.96 1.55 1.14 2080d 1.33 0.92 1.55 1.13 2090 d 1.32 0.90 1.54 1.10 2100d 1.30 0.89 1.55 1.09 Number of models 19 12 19 10 17 Notes: See notes Table AII.5.5. 1431 Annex II Climate System Scenario Tables Table AII.5.7 | BC aerosol atmospheric burden (Tg) Year (Min) Historical (Max) RCP2.6 RCP4.5 RCP6.0 RCP8.5 1860d 0.037 0.059 0.127 0.058 0.059 0.057 0.059 1870 d 0.039 0.063 0.133 0.062 0.063 0.061 0.064 1180d 0.040 0.068 0.139 0.066 0.068 0.065 0.069 1890d 0.043 0.075 0.149 0.070 0.075 0.070 0.076 1900 d 0.045 0.082 0.156 0.076 0.082 0.075 0.083 1910d 0.048 0.089 0.167 0.081 0.089 0.081 0.091 1920d 0.049 0.092 0.167 0.083 0.092 0.082 0.095 1930 d 0.049 0.090 0.161 0.082 0.090 0.081 0.092 1940d 0.051 0.091 0.162 0.082 0.091 0.082 0.093 1950d 0.053 0.094 0.165 0.085 0.094 0.085 0.096 1960 d 0.061 0.102 0.179 0.094 0.102 0.094 0.105 AII 1970d 0.071 0.115 0.201 0.107 0.115 0.107 0.117 1980d 0.088 0.141 0.245 0.130 0.141 0.130 0.144 1990d 0.098 0.157 0.274 0.146 0.157 0.145 0.161 2000 d 0.101 0.164 0.293 0.153 0.164 0.152 0.169 2010d 0.170 0.174 0.157 0.170 2020d 0.169 0.174 0.152 0.164 2030 d 0.144 0.166 0.147 0.153 2040d 0.120 0.155 0.144 0.138 2050d 0.103 0.141 0.138 0.127 2060 d 0.091 0.126 0.127 0.118 2070d 0.081 0.110 0.113 0.110 2080d 0.075 0.094 0.101 0.106 2090 d 0.071 0.087 0.092 0.102 2100d 0.068 0.084 0.087 0.099 Number of models 19 13 19 11 17 Notes: See notes Table AII.5.5. Table AII.5.8 | CH4 atmospheric lifetime (yr) against loss by tropospheric OH Year RCP2.6& RCP4.5& RCP6.0& RCP8.5& RCP2.6^ RCP4.5^ RCP6.0^ RCP8.5^ 2000 11.2 +/- 1.3 11.2 +/- 1.3 11.2 +/- 1.3 11.2 +/- 1.3 11.2 +/- 1.3 11.2 +/- 1.3 11.2 +/- 1.3 11.2 +/- 1.3 2010 11.2 +/- 1.3 11.2 +/- 1.3 11.2 +/- 1.3 11.2 +/- 1.3 2020 11.0 +/- 1.3 11.2 +/- 1.3 11.2 +/- 1.3 11.2 +/- 1.3 2030 10.8 +/- 1.3 11.3 +/- 1.4 11.3 +/- 1.4 11.4 +/- 1.4 10.6 +/- 1.4 11.4 +/- 2.1 11.1 +/- 1.4 11.2 +/- 1.4 2040 10.6 +/- 1.3 11.3 +/- 1.4 11.4 +/- 1.4 11.8 +/- 1.4 2050 10.2 +/- 1.3 11.3 +/- 1.4 11.5 +/- 1.4 12.2 +/- 1.5 2060 9.9 +/- 1.3 11.2 +/- 1.4 11.6 +/- 1.4 12.6 +/- 1.6 2070 9.9 +/- 1.4 11.2 +/- 1.5 11.8 +/- 1.5 12.6 +/- 1.7 2080 10.4 +/- 1.5 11.1 +/- 1.5 11.9 +/- 1.6 12.6 +/- 1.8 2090 10.4 +/- 1.6 10.9 +/- 1.6 11.7 +/- 1.7 12.6 +/- 1.8 2100 10.6 +/- 1.6 10.7 +/- 1.6 11.4 +/- 1.8 12.5 +/- 1.9 10.7 +/- 1.6 10.1 +/- 1.5 11.1 +/- 1.8 12.1 +/- 2.0 Notes: RCPn.n& lifetimes based on best estimate with uncertainty for 2000 2010 (Prather et al., 2012) and then projecting changes in key factors (Holmes et al., 2013). All uncertainties are 68% confidence intervals. RCPn.n^ lifetimes are from ACCMIP results (Voulgarakis et al., 2013) scaled to 11.2 +/- 1.3 yr for year 2000; the ACCMIP mean and standard deviation in 2000 are 9.8 +/- 1.5 yr. Projected ACCMIP values combine the present day uncertainty with the model standard deviation of future change. Note that the total atmospheric lifetime of CH4 must include other losses (e.g., stratosphere, surface, tropospheric chlorine), and for 2010 it is 9.1 +/- 0.9 yr, see Chapter 8, Section 11.3.5.1.1. 1432 Climate System Scenario Tables Annex II Table AII.5.9 | N2O atmospheric lifetime (yr) Year RCP2.6& RCP4.5& RCP6.0& RCP8.5& 2010 131 +/- 10 131 +/- 10 131 +/- 10 131 +/- 10 2020 130 +/- 10 131 +/- 10 131 +/- 10 131 +/- 10 2030 130 +/- 10 130 +/- 10 130 +/- 10 130 +/- 10 2040 130 +/- 10 130 +/- 10 130 +/- 10 129 +/- 10 2050 129 +/- 10 129 +/- 10 129 +/- 10 129 +/- 10 2060 129 +/- 10 129 +/- 10 129 +/- 10 128 +/- 10 2070 129 +/- 11 128 +/- 11 128 +/- 10 128 +/- 11 2080 128 +/- 11 128 +/- 11 128 +/- 11 127 +/- 11 2090 128 +/- 11 128 +/- 11 127 +/- 11 127 +/- 11 2100 128 +/- 11 127 +/- 11 127 +/- 11 126 +/- 11 Notes: RCPn.n& lifetimes based on projections from Fleming et al. (2011) and Prather et al. (2012). All uncertainties are 68% confidence intervals. AII AII.6: Effective Radiative Forcing Table AII.6.1 | ERF from CO2 (W m 2) Year RCP2.6 RCP4.5 RCP6.0 RCP8.5 A2 B1 IS92a 2000 1.51 1.51 1.51 1.51 1.50 1.50 1.50 2010 1.80 1.80 1.80 1.80 1.78 1.77 1.78 2020 2.11 2.09 2.07 2.15 2.16 2.09 2.13 2030 2.34 2.40 2.32 2.56 2.55 2.38 2.48 2040 2.46 2.70 2.58 3.03 2.99 2.69 2.83 2050 2.49 2.99 2.90 3.56 3.42 2.98 3.18 2060 2.48 3.23 3.25 4.15 3.88 3.20 3.53 2070 2.43 3.39 3.65 4.76 4.36 3.37 3.89 2080 2.35 3.46 4.06 5.37 4.86 3.49 4.25 2090 2.28 3.49 4.42 5.95 5.39 3.57 4.64 2100 2.22 3.54 4.70 6.49 5.95 3.59 5.04 Notes: RCPn.n ERF based on RCP published projections (Tables AII.4.1 to AII.4.3) and TAR formula for RF. See Chapter 8, Figure 8.18, Section 11.3.5, 11.3.6.1, Figure 12.3. SRES A2 and B1 and IS92a calculated from abundances in Tables AII.4.1 to AII.4.3. Table AII.6.2 | ERF from CH4 (W m 2) Year RCP2.6 RCP4.5 RCP6.0 RCP8.5 A2 B1 IS92a 2000 0.47 0.47 0.47 0.47 0.48 0.48 0.48 2010 0.48 0.48 0.48 0.48 0.51 0.50 0.51 2020 0.47 0.49 0.49 0.54 0.56 0.53 0.56 2030 0.42 0.50 0.49 0.61 0.62 0.54 0.61 2040 0.39 0.51 0.51 0.70 0.68 0.54 0.67 2050 0.36 0.50 0.53 0.80 0.75 0.52 0.73 2060 0.32 0.49 0.54 0.90 0.81 0.51 0.78 2070 0.30 0.47 0.55 0.97 0.88 0.49 0.82 2080 0.29 0.44 0.54 1.01 0.95 0.47 0.85 2090 0.28 0.42 0.50 1.05 1.01 0.44 0.88 2100 0.27 0.41 0.44 1.08 1.07 0.41 0.92 Notes: See notes Table AII.6.1. 1433 Annex II Climate System Scenario Tables Table AII.6.3 | ERF from N2O (W m 2) Year RCP2.6 RCP4.5 RCP6.0 RCP8.5 A2 B1 IS92a 2000 0.15 0.15 0.15 0.15 0.15 0.15 0.15 2010 0.17 0.17 0.17 0.17 0.17 0.17 0.17 2020 0.19 0.19 0.19 0.19 0.20 0.20 0.20 2030 0.20 0.21 0.21 0.23 0.24 0.22 0.23 2040 0.22 0.23 0.24 0.26 0.28 0.25 0.26 2050 0.23 0.25 0.26 0.30 0.32 0.27 0.29 2060 0.23 0.27 0.29 0.34 0.36 0.29 0.32 2070 0.23 0.28 0.33 0.38 0.40 0.30 0.34 2080 0.23 0.30 0.36 0.42 0.44 0.31 0.37 2090 0.23 0.31 0.39 0.46 0.49 0.32 0.39 2100 0.23 0.32 0.41 0.49 0.53 0.32 0.41 AII Notes: See notes Table AII.6.1. Table AII.6.4 | ERF from all HFCs (W m 2) Year Historical RCP2.6 RCP4.5 RCP6.0 RCP8.5 2011* 0.019 2010 0.019 0.019 0.019 0.020 2020 0.038 0.034 0.030 0.044 2030 0.056 0.046 0.036 0.069 2040 0.071 0.055 0.040 0.091 2050 0.083 0.061 0.042 0.110 2060 0.092 0.064 0.044 0.128 2070 0.104 0.066 0.046 0.144 2080 0.116 0.069 0.047 0.159 2090 0.124 0.074 0.047 0.171 2100 0.126 0.080 0.046 0.182 Notes: See Table 8.3, 8.A.1, Section 11.3.5.1.1. ERF is calculated from RCP published abundances (Meinshausen et al., 2011a; http://www.iiasa.ac.at/web-apps/tnt/RcpDb) and AR5 radiative efficiencies (Chapter 8). Table AII.6.5 | ERF from all PFCs and SF6 (W m 2) Year Historical RCP2.6 RCP4.5 RCP6.0 RCP8.5 2011* 0.009 2010 0.009 0.009 0.010 0.009 2020 0.012 0.011 0.013 0.012 2030 0.014 0.013 0.017 0.015 2040 0.015 0.014 0.021 0.019 2050 0.015 0.016 0.025 0.022 2060 0.016 0.017 0.029 0.026 2070 0.016 0.019 0.033 0.031 2080 0.016 0.021 0.038 0.035 2090 0.016 0.023 0.042 0.039 2100 0.016 0.026 0.045 0.044 Notes: See notes Table AII.6.4. 1434 Climate System Scenario Tables Annex II Table AII.6.6 | ERF from Montreal Protocol greenhouse gases (W m 2) Table AII.6.7a | ERF from stratospheric O3 changes since 1850 (W m 2) Year Historical WMO A1 Year AR5 CCMVal-2 2011* 0.328 1960 0.0 2020 0.33 +/- 0.01 1980 0.033 2030 0.29 +/- 0.01 2000 0.079 2040 0.24 +/- 0.01 2011* 0.05 2050 0.20 +/- 0.01 2050 0.055 2060 0.17 +/- 0.02 2100 0.075 2070 0.15 +/- 0.02 Notes: 2080 0.13 +/- 0.02 AR5 results are from Chapter 8, see also Sections 11.3.5.1.2, 11.3.6.1. CCMVal-2 results (Cionni et al. 2011) are the multi-model average (13 chemistry climate models) running a single 2090 0.11 +/- 0.02 scenario for stratospheric change: REF-B2 scenario of CCMVal-2 with SRES A1B climate scenario. 2100 0.10 +/- 0.02 Notes: See Table 8.3, 8.A.1. ERF is calculated from AR5 radiative efficiency and projected abundances AII in Scenario A1 of WMO/UNEP assessment (WMO 2010). The 68% confidence interval shown is approximated by combining uncertainty in the radiative efficiency of each gas (+/-6.1%) and the decay of each gas since 2010 from Table AII.4.16 (+/-15%). All sources of uncertainty are assumed to be independent (see Chapters 2 and 8). Table AII.6.7b | ERF from tropospheric O3 changes since 1850 (W m 2) Year AR5 RCP2.6 RCP4.5 RCP6.0 RCP8.5 1980 0.31 +/- 0.05 0.31 +/- 0.05 0.31 +/- 0.05 0.31 +/- 0.05 2000 0.36 0.36 0.36 0.36 2011* 0.40 2030 0.32 0.38 0.36 0.44 2100 0.17 0.27 0.27 0.60 +/- 0.11 Notes: AR5 results from Chapter 8; see also Sections 11.3.5.1.2, 11.3.6.1. Model mean results from ACCMIP (Stevenson et al., 2013) using a consistent model set (FGKN), which is similar to the all-model mean. Standard deviation across models shown for 1980s decade is similar for all scenarios except for RCP8.5 at 2100, which is twice as large. Table AII.6.8: Total anthropogenic ERF from published RCPs and SRES (W m 2) AR5 Year RCP2.6 RCP4.5 RCP6.0 RCP8.5 A2 A1B B1 IS92a Historical 1850 0.12 0.12 0.12 0.12 0.06 1990 1.23 1.23 1.23 1.23 1.03 1.03 1.03 1.03 1.60 2000 1.45 1.45 1.45 1.45 1.33 1.33 1.33 1.31 1.87 2010 1.81 1.81 1.78 1.84 1.74 1.65 1.73 1.63 2.25 2020 2.25 2.25 2.15 2.32 2.04 2.16 2.15 2.00 2030 2.52 2.67 2.52 2.91 2.56 2.84 2.56 2.40 2040 2.65 3.07 2.82 3.61 3.22 3.61 2.93 2.82 2050 2.64 3.42 3.20 4.37 3.89 4.16 3.30 3.25 2060 2.55 3.67 3.58 5.13 4.71 4.79 3.65 3.76 2070 2.47 3.84 4.11 5.89 5.56 5.28 3.92 4.24 2080 2.41 3.90 4.60 6.60 6.40 5.62 4.09 4.74 2090 2.35 3.91 4.93 7.32 7.22 5.86 4.18 5.26 2100 2.30 3.94 5.15 7.97 8.07 6.05 4.19 5.79 Notes: Derived from RCP published CO2-eq concentrations that aggregate all anthropogenic forcings including greenhouse gases plus aerosols. These results may not be directly comparable to ERF values used in AR5 because of how aerosol indirect effects are included, but results are similar to those derived using ERF in Chapter 12 (see Figure 12.4). Comparisons with the TAR Appendix II (SRES A2 and B1) may not be equivalent because those total RF values (TAR II.3.11) were made using the TAR Chapter 9 Simple Model, not always consistent with the individual components in that appendix (TAR II.3.1 to 9). See Chapter 1, Sections 11.3.6.1, 12.3.1.3 and 12.3.1.4, Figures 1.15 and 12.3. For AR5 Historical, see Table AII.1.2 and Chapter 8. 1435 Annex II Climate System Scenario Tables Table AII.6.9: ERF components relative to 1850 (W m 2) derived from ACCMIP Year WMGHG Ozone Aerosol ERF Net 1930 0.58 +/- 0.04 0.09 +/- 0.03 0.24 +/- 0.06 0.44 +/- 0.07 1980 1.56 +/- 0.10 0.30 +/- 0.10 0.90 +/- 0.22 1.00 +/- 0.26 2000 2.30 +/- 0.14 0.33 +/- 0.11 1.17 +/- 0.28 1.51 +/- 0.33 2030 RCP8.5 3.64 +/- 0.22 0.43 +/- 0.12 0.91 +/- 0.22 3.20 +/- 0.33 2100 RCP2.6 2.83 +/- 0.17 0.14 +/- 0.07 0.12 +/- 0.06* 2.86 +/- 0.19 2100 RCP4.5 4.33 +/- 0.26 0.23 +/- 0.09 0.12 +/- 0.06* 4.44 +/- 0.28 2100 RCP6.0 5.60 +/- 0.34 0.25 +/- 0.05 0.12 +/- 0.06* 5.74 +/- 0.35 2100 RCP8.5 8.27 +/- 0.50 0.55 +/- 0.18 0.12 +/- 0.03 8.71 +/- 0.53 Notes: Radiative forcing and adjusted forcing from the ACCMIP results (Shindell et al., 2013) are given for all well-mixed greenhouse gases (WMGHG), ozone, aerosols, and the net. Original 90% confidence intervals have been reduced to 68% confidence to compare with the CMIP5 model standard deviations in Table AII.6.10. Some uncertainty ranges (*) are estimated from the 2100 RCP8.5 results (see Chapter 12). See Sections 11.3.5.1.3 and 11.3.6.1, Figure 12.4. AII Table AII.6.10 | Total anthropogenic plus natural ERF (W m 2) from CMIP5 and CMIP3, including historical Year SRES A1B RCP2.6& RCP4.5& RCP6.0& RCP8.5& 1850sH 0.19 +/- 0.19 0.12 +/- 0.07 1986 2005 H 1.51 +/- 0.44 1.34 +/- 0.50 1986 2005 1.51 +/- 0.44 1.31 +/- 0.47 1.30 +/- 0.48 1.29 +/- 0.51 1.30 +/- 0.47 2010d 2.18 +/- 0.53 1.97 +/- 0.50 1.91 +/- 0.53 1.90 +/- 0.54 1.96 +/- 0.53 2020 d 2.58 +/- 0.57 2.33 +/- 0.47 2.27 +/- 0.51 2.16 +/- 0.55 2.43 +/- 0.52 2030d 3.15 +/- 0.60 2.50 +/- 0.51 2.61 +/- 0.54 2.41 +/- 0.60 2.92 +/- 0.57 2040d 3.77 +/- 0.72 2.64 +/- 0.47 2.98 +/- 0.55 2.72 +/- 0.58 3.52 +/- 0.60 2050 d 4.32 +/- 0.73 2.65 +/- 0.47 3.25 +/- 0.56 3.07 +/- 0.61 4.21 +/- 0.63 2060d 4.86 +/- 0.74 2.57 +/- 0.50 3.50 +/- 0.59 3.40 +/- 0.60 4.97 +/- 0.68 2070d 5.32 +/- 0.79 2.51 +/- 0.50 3.65 +/- 0.58 3.90 +/- 0.65 5.70 +/- 0.76 2080 d 5.71 +/- 0.81 2.40 +/- 0.46 3.71 +/- 0.55 4.27 +/- 0.69 6.31 +/- 0.81 2090d 6.00 +/- 0.83 2.44 +/- 0.49 3.78 +/- 0.58 4.64 +/- 0.71 7.13 +/- 0.89 2081 2100 5.99 +/- 0.78 2.40 +/- 0.46 3.73 +/- 0.56 4.56 +/- 0.70 7.02 +/- 0.92 Notes: CMIP5 historical and RCP results (Forster et al., 2013) are shown with CMIP3 SRES A1B results (Forster and Taylor, 2006). The alternative results for 1986 2005 with CMIP5 are derived from: all models contributing historical experiments (1986 2005H), and the subsets of models contributing to each RCP experiment (next line, 1986 2005). For SRES A1B the same set of models is used from 1850 to 2100. Values are 10-year averages (2090d = 2086 2095) and show multi-model means and standard deviations. See Chapter 12, Section 12.3 and discussion of Figure 12.4, also Sections 8.1, 9.3.2.2, 11.3.6.1 and 11.3.6.3. Due to lack of reporting, for RCP8.5 the 2081 2100 result contains one fewer model than the 2090d decade, and for A1B the 1850s result has just 5 models and the 2081 2100 result has 3 fewer models than the 2090d decade. 1436 Climate System Scenario Tables Annex II AII.7: Environmental Data Table AII.7.1 | Global mean surface O3 change (ppb) HTAP SRES Year RCP2.6 RCP4.5 RCP6.0 RCP8.5 A2 B1 CLE MFR 2000 27.2 +/- 2.9 27.2 +/- 2.9 27.2 +/- 2.9 27.2 +/- 2.9 27.2 +/- 2.9 27.2 +/- 2.9 28.7 28.7 2010 0.1 0.1 0.0 0.1 1.2 0.6 2020 0.3 0.2 0.2 0.6 2.8 1.1 2030 1.1 0.1 0.3 1.0 4.4 1.3 0.7 +/- 1.4 2.3 +/- 1.1 2040 1.5 0.3 0.3 1.2 5.3 1.3 2050 1.9 0.8 0.4 1.5 6.2 0.8 2060 2.4 1.3 0.5 1.8 7.1 0.2 2070 3.0 1.9 1.0 1.9 8.0 0.5 2080 3.5 2.5 1.5 1.9 9.2 1.1 AII 2090 3.8 2.8 2.1 1.9 10.6 1.7 2100 4.2 3.0 2.8 1.9 11.9 2.5 CMIP5 ACCMIP Year RCP2.6 RCP4.5 RCP6.0 RCP8.5 RCP2.6 RCP4.5 RCP6.0 RCP8.5 2000 30.0 +/- 4.2 30.0 +/- 4.2 30.0 +/- 4.2 30.0 +/- 4.2 28.1 +/- 3.1 28.1 +/- 3.2 28.1+/- 3.1 28.1 +/- 3.1 2010 0.4 0.2 0.6 0.1 2020 0.9 0.3 0.9 0.7 2030 1.8 0.2 1.1 1.5 1.4 0.3 0.6 1.7 2040 2.3 0.3 1.2 2.0 2050 2.9 0.9 1.5 2.5 2060 4.0 1.7 1.9 2.9 2070 5.4 2.8 2.8 3.1 2080 6.4 3.7 3.9 3.0 2090 6.9 4.1 4.8 2.8 2100 7.2 4.3 5.6 2.7 6.3 3.5 4.9 3.4 Notes: HTAP results are from Wild et al. (2012) and use the published O3 sensitivities to regional emissions from the HTAP multi-model study (HTAP 2010) and scale those O3 changes to the RCP emission scenarios. The +/-1 standard deviation (68% confidence interval) over the range of 14 parametric models is shown for year 2000 and is similar for all years. Results from the SRES A2 and B1 scenarios are from the TAR OxComp studies diagnosed by Wild (Prather et al., 2001; 2003). CLE and MFR results (Dentener et al., 2005; 2006) include uncertainty (standard deviation of model results) in the change since year 2000, and CLE alone includes climate effects. The CMIP5 and ACCMIP results are from V. Naik and A. Fiore based on Fiore et al. (2012) and include the standard deviation over the models in year 2000, which is similar for following years. This does not necessarily reflect the uncertainty in the projected change, which may be smaller, see Fiore et al. (2012). The difference in year 2000 between CMIP5 (4 models) and ACCMIP (12 models) reflect different model biases. Even though ACCMIP only has three decades (2000, 2030, 2100), the greater number of models (5 to 11 depending on time slice and scenario) makes this a more robust estimate. See Chapter 11, ES, Section 11.3.5.2.2. 1437 Annex II Climate System Scenario Tables Table AII.7.2 | Surface O3 change (ppb) for HTAP regions North America Year RCP2.6 RCP4.5 RCP6.0 RCP8.5 A2 B1 2000 36.1 +/- 3.2 36.1 +/- 3.2 36.1 +/- 3.2 36.1 +/- 3.2 36.1 +/- 3.2 36.1 +/- 3.2 2010 0.8 1.1 0.1 1.5 1.5 0.4 2020 1.9 2.3 0.9 1.4 3.6 0.5 2030 3.7 2.7 1.5 1.1 5.3 0.1 2040 4.6 3.2 1.9 1.1 6.2 0.8 2050 5.6 3.9 2.4 0.9 6.9 1.9 2060 6.5 4.6 3.0 0.7 7.9 2.9 2070 7.5 5.3 4.0 0.7 8.8 3.8 2080 8.2 6.1 4.9 0.7 10.3 4.5 2090 8.5 6.4 5.7 0.8 12.2 5.2 AII 2100 8.9 6.6 6.7 0.9 13.9 6.1 Europe Year RCP2.6 RCP4.5 RCP6.0 RCP8.5 A2 B1 2000 37.8 +/- 3.7 37.8 +/- 3.7 37.8 +/- 3.7 37.8 +/- 3.7 37.8 +/- 3.7 37.8 +/- 3.7 2010 0.5 0.3 0.1 0.7 1.5 0.3 2020 1.4 1.3 0.7 0.2 3.7 0.6 2030 3.0 1.4 1.1 0.1 5.7 0.2 2040 3.8 1.9 1.5 0.1 6.7 0.3 2050 4.6 2.7 2.0 0.3 7.7 1.2 2060 5.6 3.5 2.6 0.4 8.8 2.1 2070 6.6 4.3 3.3 0.4 9.8 3.0 2080 7.5 5.1 4.2 0.2 11.3 3.8 2090 8.0 5.6 5.2 0.1 13.4 4.6 2100 8.5 6.0 6.4 0.2 15.1 5.6 South Asia Year RCP2.6 RCP4.5 RCP6.0 RCP8.5 A2 B1 2000 39.6 +/- 3.4 39.6 +/- 3.4 39.6 +/- 3.4 39.6 +/- 3.4 39.6 +/- 3.4 39.6 +/- 3.4 2010 1.5 1.4 0.3 1.4 2.7 1.8 2020 1.6 2.2 0.0 3.9 6.1 3.3 2030 0.5 3.4 0.6 5.0 8.9 3.9 2040 0.3 3.5 0.1 5.5 10.4 4.1 2050 0.2 2.9 0.0 5.2 11.7 2.9 2060 0.1 1.1 0.4 5.1 12.7 1.5 2070 1.0 1.2 0.2 4.9 13.6 0.1 2080 2.6 3.9 1.7 4.9 14.5 1.5 2090 4.4 5.0 3.0 4.1 15.1 3.0 2100 6.8 6.0 4.7 4.0 15.0 4.6 (continued on next page) 1438 Climate System Scenario Tables Annex II Table AII.7.2 | (continued) East Asia Year RCP2.6 RCP4.5 RCP6.0 RCP8.5 A2 B1 2000 35.6 +/- 2.7 35.6 +/- 2.7 35.6 +/- 2.7 35.6 +/- 2.7 35.6 +/- 2.7 35.6 +/- 2.7 2010 1.0 0.6 0.5 1.3 2.0 1.1 2020 0.5 0.6 0.4 2.5 4.6 1.9 2030 1.4 0.2 0.6 2.8 6.8 2.1 2040 2.7 0.8 1.4 1.8 8.0 2.0 2050 3.8 2.5 1.4 1.4 9.1 0.9 2060 4.8 3.6 0.9 1.4 10.2 0.3 2070 6.0 4.6 0.7 1.2 11.2 1.4 2080 6.9 5.5 2.2 1.0 12.5 2.4 2090 7.4 5.8 3.5 0.7 13.9 3.4 AII 2100 8.0 6.0 4.9 0.5 14.9 4.6 Notes: HTAP results from Wild et al. (2012); see Table AII.7.1. Table AII.7.3 | Surface O3 change (ppb) from CMIP5/ACCMIP for continental regions Africa CMIP5 ACCMIP Year RCP2.6 RCP4.5 RCP6.0 RCP8.5 RCP2.6 RCP4.5 RCP6.0 RCP8.5 2000 33.8 +/- 4.3 33.8 +/- 4.3 33.8 +/- 4.3 33.8 +/- 4.3 33.1 +/- 4.1 33.1 +/- 4.1 33.1 +/- 4.1 33.1 +/- 4.1 2010 0.7 0.1 1.2 0.2 2020 1.0 0.2 1.5 0.9 2030 1.9 0.5 1.8 1.7 1.4 0.9 1.3 2.4 2040 2.0 0.6 1.8 2.6 2050 2.3 0.2 2.0 3.2 2060 2.6 0.3 2.2 3.7 2070 3.2 1.2 2.8 4.0 2080 3.6 2.3 3.7 4.1 2090 4.1 3.0 4.5 4.1 2100 4.8 3.3 5.2 4.1 4.9 2.9 4.9 5.0 Australia CMIP5 ACCMIP Year RCP2.6 RCP4.5 RCP6.0 RCP8.5 RCP2.6 RCP4.5 RCP6.0 RCP8.5 2000 23.3 +/- 4.6 23.3 +/- 4.6 23.3 +/- 4.6 23.3 +/- 4.6 23.7 +/- 3.5 23.7 +/- 3.5 23.7 +/- 3.5 23.7 +/- 3.5 2010 1.3 1.1 0.8 0.9 2020 1.7 1.4 1.0 0.6 2030 2.3 1.3 1.4 0.0 1.8 0.4 1.4 0.9 2040 2.6 1.2 1.7 0.5 2050 3.0 1.5 1.9 0.9 2060 3.7 1.9 2.0 1.5 2070 4.4 2.4 2.5 1.8 2080 5.0 2.9 3.1 1.9 2090 5.0 3.1 3.5 1.9 2100 5.2 3.2 4.0 2.0 4.3 2.5 4.0 3.1 (continued on next page) 1439 Annex II Climate System Scenario Tables Table AII.7.3 | (continued) Central Eurasia CMIP5 ACCMIP Year RCP2.6 RCP4.5 RCP6.0 RCP8.5 RCP2.6 RCP4.5 RCP6.0 RCP8.5 2000 38.7 +/- 5.3 38.7 +/- 5.3 38.7 +/- 5.3 38.7 +/- 5.3 32.5 +/- 6.2 32.5 +/- 6.2 32.5 +/- 6.2 32.5 +/- 6.2 2010 0.6 0.6 0.6 0.5 2020 1.6 1.2 1.2 0.5 2030 3.2 1.3 1.4 1.4 1.9 0.1 0.3 1.8 2040 4.5 1.9 1.7 1.6 2050 5.7 2.9 2.2 1.8 2060 7.2 4.2 3.0 2.8 2070 9.1 5.4 4.3 3.0 2080 10.6 6.5 6.0 2.9 AII 2090 11.2 6.8 7.2 2.6 2100 11.5 7.0 8.1 2.6 8.5 3.8 5.6 4.3 Europe CMIP5 ACCMIP Year RCP2.6 RCP4.5 RCP6.0 RCP8.5 RCP2.6 RCP4.5 RCP6.0 RCP8.5 2000 40.4 +/- 6.0 40.4 +/- 6.0 40.4 +/- 6.0 40.4 +/- 6.0 33.6 +/- 5.2 33.6 +/- 5.2 33.6 +/- 5.2 33.6 +/- 5.2 2010 0.4 0.5 0.5 0.4 2020 1.5 1.3 1.2 0.3 2030 3.2 1.7 1.7 1.1 1.6 0.6 0.4 2.3 2040 4.6 2.4 2.3 1.4 2050 6.1 3.5 3.0 1.8 2060 8.0 4.9 4.1 2.4 2070 10.4 6.3 5.8 2.6 2080 12.2 7.6 7.6 2.3 2090 13.0 8.0 9.2 2.1 2100 13.4 8.1 10.3 2.0 9.4 3.5 7.2 4.9 East Asia CMIP5 ACCMIP Year RCP2.6 RCP4.5 RCP6.0 RCP8.5 RCP2.6 RCP4.5 RCP6.0 RCP8.5 2000 46.3 +/- 4.9 46.3 +/- 4.9 46.3 +/- 4.9 46.3 +/- 4.9 41.0 +/- 5.5 41.0 +/- 5.5 41.0 +/- 5.5 41.0 +/- 5.5 2010 0.8 0.6 0.1 1.1 2020 0.1 0.8 0.1 2.7 2030 2.3 0.5 0.4 3.8 1.8 1.0 0.4 3.2 2040 3.9 0.9 1.1 3.8 2050 5.8 3.3 1.0 3.7 2060 8.0 5.4 0.2 3.9 2070 10.2 7.3 1.6 3.6 2080 12.1 8.8 4.0 3.3 2090 13.2 9.4 6.3 2.9 2100 13.9 9.6 8.0 2.8 11.4 5.9 6.6 4.6 (continued on next page) 1440 Climate System Scenario Tables Annex II Table AII.7.3 | (continued) Middle East CMIP5 ACCMIP Year RCP2.6 RCP4.5 RCP6.0 RCP8.5 RCP2.6 RCP4.5 RCP6.0 RCP8.5 2000 45.9 +/- 3.1 45.9 +/- 3.1 45.9 +/- 3.1 45.9 +/- 3.1 45.7 +/- 5.4 45.7 +/- 5.4 45.7 +/- 5.4 45.7 +/- 5.4 2010 0.4 0.5 0.7 0.5 2020 1.5 0.4 1.4 2.5 2030 3.3 0.6 1.6 3.8 2.8 0.9 1.1 4.1 2040 3.6 0.2 2.0 4.4 2050 4.6 0.9 2.6 4.7 2060 6.0 2.7 3.5 5.2 2070 8.1 4.9 4.2 5.1 2080 9.9 7.1 5.9 5.1 AII 2090 11.3 8.4 8.2 4.8 2100 12.4 9.0 9.9 4.6 11.7 7.5 9.8 5.0 North America CMIP5 ACCMIP Year RCP2.6 RCP4.5 RCP6.0 RCP8.5 RCP2.6 RCP4.5 RCP6.0 RCP8.5 2000 40.7 +/- 5.1 40.7 +/- 5.1 40.7 +/- 5.1 40.7 +/- 5.1 34.3 +/- 5.5 34.3 +/- 5.5 34.3 +/- 5.5 34.3 +/- 5.5 2010 0.9 1.2 0.6 1.0 2020 2.1 2.4 1.4 0.5 2030 4.3 2.8 1.8 0.1 2.5 0.7 0.8 1.3 2040 5.7 3.6 2.5 0.3 2050 7.2 4.6 3.1 0.6 2060 9.1 5.8 4.4 1.0 2070 11.4 7.1 6.2 1.2 2080 13.2 8.3 8.1 1.2 2090 13.8 8.5 9.6 1.0 2100 14.1 8.8 10.9 0.9 10.5 4.7 8.7 3.4 South America CMIP5 ACCMIP Year RCP2.6 RCP4.5 RCP6.0 RCP8.5 RCP2.6 RCP4.5 RCP6.0 RCP8.5 2000 25.3 +/- 4.2 25.3 +/- 4.2 25.3 +/- 4.2 25.3 +/- 4.2 23.7 +/- 3.9 23.7 +/- 3.9 23.7 +/- 3.9 23.7 +/- 3.9 2010 1.4 0.6 1.2 0.3 2020 2.1 1.2 1.8 0.3 2030 2.9 1.2 2.1 0.6 2.3 0.6 1.8 1.2 2040 2.9 1.3 2.3 1.1 2050 3.2 1.7 2.6 1.3 2060 3.6 2.5 2.9 1.5 2070 4.3 3.6 3.5 1.5 2080 5.1 4.5 4.2 1.1 2090 5.5 5.0 4.7 0.7 2100 5.7 5.2 5.3 0.4 5.0 4.0 5.2 2.0 (continued on next page) 1441 Annex II Climate System Scenario Tables Table AII.7.3 | (continued) South Asia CMIP5 ACCMIP Year RCP2.6 RCP4.5 RCP6.0 RCP8.5 RCP2.6 RCP4.5 RCP6.0 RCP8.5 2000 34.4 +/- 3.9 34.4 +/- 3.9 34.4 +/- 3.9 34.4 +/- 3.9 33.7 +/- 4.6 33.7 +/- 4.6 33.7 +/- 4.6 33.7 +/- 4.6 2010 1.3 0.9 0.1 1.3 2020 1.4 1.6 0.2 3.1 2030 0.7 2.7 0.1 3.9 0.6 2.3 0.4 4.6 2040 0.6 2.8 0.3 4.0 2050 0.4 1.6 0.4 3.6 2060 0.5 0.7 0.3 3.2 2070 2.0 3.2 0.5 2.9 2080 3.9 5.7 2.0 2.7 AII 2090 5.7 6.7 3.3 2.2 2100 7.1 7.3 4.5 1.9 7.2 6.1 4.5 3.6 Notes: See notes for Table AII.7.1. For definition of regions, see Figure 11.23 and Fiore et al. (2012). Table AII.7.4 | Surface particulate matter change (log10[PM2.5 (microgram/m3)]) from CMIP5/ACCMIP for continental regions Africa Year RCP2.6 RCP4.5 RCP6.0 RCP8.5 2000 1.17 +/- 0.23 2030 0.00 0.04 0.01 0.01 2050 0.02 0.02 0.01 2100 0.00 0.01 0.03 0.02 Australia Year RCP2.6 RCP4.5 RCP6.0 RCP8.5 2000 0.65 +/- 0.32 2030 0.04 0.03 0.01 0.01 2050 0.06 0.02 0.04 2100 0.00 0.00 0.03 0.01 Central Eurasia Year RCP2.6 RCP4.5 RCP6.0 RCP8.5 2000 0.59 +/- 0.17 2030 0.07 0.01 0.05 0.06 2050 0.12 0.08 0.09 2100 0.13 0.11 0.11 0.12 Europe Year RCP2.6 RCP4.5 RCP6.0 RCP8.5 2000 0.81 +/- 0.09 2030 0.20 0.10 0.13 0.24 2050 0.31 0.25 0.33 2100 0.32 0.28 0.37 0.38 (continued on next page) 1442 Climate System Scenario Tables Annex II Table AII.7.4 | (continued) East Asia Year RCP2.6 RCP4.5 RCP6.0 RCP8.5 2000 1.04 +/- 0.16 2030 0.04 0.02 0.01 0.01 2050 0.24 0.07 0.17 2100 0.31 0.33 0.21 0.30 Middle East Year RCP2.6 RCP4.5 RCP6.0 RCP8.5 2000 1.10 +/- 0.27 2030 0.06 0.02 0.05 0.03 2050 0.08 0.06 0.03 AII 2100 0.11 0.11 0.10 0.12 North America Year RCP2.6 RCP4.5 RCP6.0 RCP8.5 2000 0.51 +/- 0.15 2030 0.16 0.10 0.10 0.15 2050 0.20 0.16 0.17 2100 0.20 0.19 0.24 0.21 South America Year RCP2.6 RCP4.5 RCP6.0 RCP8.5 2000 0.71 +/- 0.11 2030 0.05 0.04 0.04 0.03 2050 0.10 0.05 0.07 2100 0.11 0.11 0.09 0.12 South Asia Year RCP2.6 RCP4.5 RCP6.0 RCP8.5 2000 1.02 +/- 0.11 2030 0.04 0.02 0.03 0.05 2050 0.05 0.07 0.00 2100 0.16 0.24 0.06 0.11 Notes: Decadal average of the log10[PM2.5] values are given only where results include at least four models from either ACCMIP or CMIP5. Results are from A. Fiore and V. Naik based on Fiore et al. (2012) using the CMIP5/ACCMIP archive. Due to the very large systematic spread across models, the statistics were calculated for the log values, but Figure 11.23 shows statistics for direct PM2.5 values. Owing to the large spatial variations no global average is given. Model mean and standard deviation are shown for year 2000; differences in log10[PM2.5] are shown for 2030, 2050 and 2100. See notes for Table AII.7.3 and Figure 11.23 for regions; see also Chapter 11, ES. 1443 Annex II Climate System Scenario Tables Table AII.7.5 | CMIP5 (RCP) and CMIP3 (SRES A1B) global mean surface temperature change (°C) relative to 1986 2005 reference period. Results here are a statistical sum- mary of the spread in the CMIP ensembles for each of the scenarios. They do not account for model biases and model dependencies, and the percentiles do not correspond to the assessed uncertainty in Chapters 11 (11.3.6.3) and 12 (12.4.1). The statistical spread across models cannot be interpreted as uncertainty ranges or in terms of calibrated language (Section 12.2). RCP2.6 RCP4.5 Years 5% 17% 50% 83% 95% 5% 17% 50% 83% 95% 1850 1990 0.61 0.61 1986 2005 0.00 0.00 2010d 0.19 0.33 0.36 0.52 0.62 0.22 0.26 0.36 0.48 0.59 2020 d 0.36 0.45 0.55 0.81 1.07 0.39 0.48 0.59 0.74 0.83 2030d 0.47 0.56 0.74 1.02 1.24 0.56 0.69 0.82 1.10 1.22 2040d 0.51 0.68 0.88 1.25 1.50 0.64 0.86 1.04 1.35 1.57 2050d 0.49 0.71 0.94 1.37 1.65 0.84 1.05 1.24 1.63 1.97 2060 d 0.36 0.69 0.93 1.48 1.71 0.90 1.13 1.44 1.90 2.19 AII 2070d 0.20 0.70 0.89 1.49 1.71 0.98 1.20 1.54 2.07 2.32 2080d 0.15 0.62 0.94 1.44 1.79 0.98 1.27 1.62 2.25 2.54 2090 d 0.18 0.58 0.94 1.53 1.79 1.06 1.33 1.68 2.29 2.59 RCP6.0 RCP8.5 Years 5% 17% 50% 83% 95% 5% 17% 50% 83% 95% 1850 1990 0.61 0.61 1986 2005 0.00 0.00 2010d 0.21 0.26 0.36 0.47 0.64 0.23 0.29 0.37 0.47 0.62 2020d 0.33 0.40 0.55 0.70 0.90 0.37 0.51 0.66 0.84 0.99 2030 d 0.40 0.59 0.74 0.92 1.17 0.65 0.77 0.94 1.29 1.39 2040d 0.59 0.73 0.95 1.21 1.41 0.93 1.13 1.29 1.68 1.77 2050d 0.69 0.92 1.15 1.52 1.81 1.20 1.48 1.70 2.19 2.37 2060 d 0.88 1.08 1.32 1.78 2.18 1.55 1.88 2.16 2.74 2.99 2070d 1.08 1.28 1.58 2.14 2.52 1.96 2.25 2.60 3.31 3.61 2080d 1.33 1.56 1.81 2.58 2.88 2.31 2.65 3.05 3.93 4.22 2090 d 1.51 1.72 2.03 2.92 3.24 2.63 2.96 3.57 4.45 4.81 SRES A1B Years 5% 17% 50% 83% 95% 1850 1990 0.61 1986 2005 0.00 2010d 0.15 0.22 0.34 0.44 0.62 2020d 0.27 0.37 0.52 0.76 0.91 2030 d 0.47 0.59 0.82 1.04 1.38 2040d 0.65 0.90 1.11 1.36 1.79 2050d 0.92 1.14 1.55 1.65 2.14 2060 d 1.12 1.40 1.75 1.98 2.67 2070d 1.40 1.60 2.14 2.39 3.12 2080d 1.61 1.80 2.30 2.75 3.47 2090 d 1.76 1.96 2.54 3.05 3.84 Notes: This spread in the model ensembles (as shown in Figures 11.26a and 12.5, and discussed in Section 11.3.6) is not a measure of uncertainty. For the AR5 assessment of global mean surface temperature changes and uncertainties see: Section 11.3.6.3 and Figure 11.25 for the near-term (2016 2035) temperatures; and Section 12.4.1 and Tables 12.2 3 for the long term (2081 2100). See discussion about uncertainty and ensembles in Section 12.2, which explains how model spread is not equivalent to uncertainty. Results here are shown for the CMIP5 archive (Annex I, frozen as of March 15, 2013) for the RCPs and the similarly current CMIP3 archive for SRES A1B, which is not the same set of models used in AR4 (Figure SPM.5). Ten-year averages are shown (2030d = 2026 2035). Temperature changes are relative to the reference period (1986 2005, defined as zero in this table), using CMIP5 for all four RCPs (G. J. van Oldenborgh, http://climexp.knmi.nl/t; see Annex I for listing of models included) and CMIP3 for SRES A1B (22 models). The warming from early instrumental record (1850 1900) to the modern reference period (1986 2005) is derived from HadCRUT4 observations as 0.61°C (C. Morice; see Chapter 2 and Table AII.1.3). 1444 Climate System Scenario Tables Annex II Table AII.7.6 | Global mean surface temperature change (°C) relative to 1990 from the TAR Years A1B A1T A1FI A2 B1 B2 IS92a A1B PI* 0.33 0.33 0.33 0.33 0.33 0.33 0.33 0.33 1990 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 2000 0.16 0.16 0.16 0.16 0.16 0.16 0.15 0.16 2010 0.30 0.40 0.32 0.35 0.34 0.39 0.27 0.30 2020 0.52 0.71 0.55 0.50 0.55 0.66 0.43 0.52 2030 0.85 1.03 0.85 0.73 0.77 0.93 0.61 0.85 2040 1.26 1.41 1.27 1.06 0.98 1.18 0.80 1.26 2050 1.59 1.75 1.86 1.42 1.21 1.44 1.00 1.59 2060 1.97 2.04 2.50 1.85 1.44 1.69 1.26 1.97 2070 2.30 2.25 3.10 2.33 1.63 1.94 1.52 2.30 2080 2.56 2.41 3.64 2.81 1.79 2.20 1.79 2.56 2090 2.77 2.49 4.09 3.29 1.91 2.44 2.08 2.77 AII 2100 2.95 2.54 4.49 3.79 1.98 2.69 2.38 2.95 Notes: Single-year estimates of mean surface air temperature warming relative to the reference period 1990 for the SRES scenarios evaluated in the TAR. The pre-industrial estimates are for 1750, and all results are based on a simple climate model. See TAR Appendix II. Table AII.7.7 | Global mean sea level rise (m) with respect to 1986 2005 at 1 January on the years indicated. Values shown as median and likely range; see Section 13.5.1. Year SRES A1B RCP2.6 RCP4.5 RCP6.0 RCP8.5 2007 0.03 [0.02 to 0.04] 0.03 [0.02 to 0.04] 0.03 [0.02 to 0.04] 0.03 [0.02 to 0.04] 0.03 [0.02 to 0.04] 2010 0.04 [0.03 to 0.05] 0.04 [0.03 to 0.05] 0.04 [0.03 to 0.05] 0.04 [0.03 to 0.05] 0.04 [0.03 to 0.05] 2020 0.08 [0.06 to 0.10] 0.08 [0.06 to 0.10] 0.08 [0.06 to 0.10] 0.08 [0.06 to 0.10] 0.08 [0.06 to 0.11] 2030 0.12 [0.09 to 0.16] 0.13 [0.09 to 0.16] 0.13 [0.09 to 0.16] 0.12 [0.09 to 0.16] 0.13 [0.10 to 0.17] 2040 0.17 [0.13 to 0.22] 0.17 [0.13 to 0.22] 0.17 [0.13 to 0.22] 0.17 [0.12 to 0.21] 0.19 [0.14 to 0.24] 2050 0.23 [0.17 to 0.30] 0.22 [0.16 to 0.28] 0.23 [0.17 to 0.29] 0.22 [0.16 to 0.28] 0.25 [0.19 to 0.32] 2060 0.30 [0.21 to 0.38] 0.26 [0.18 to 0.35] 0.28 [0.21 to 0.37] 0.27 [0.19 to 0.35] 0.33 [0.24 to 0.42] 2070 0.37 [0.26 to 0.48] 0.31 [0.21 to 0.41] 0.35 [0.25 to 0.45] 0.33 [0.24 to 0.43] 0.42 [0.31 to 0.54] 2080 0.44 [0.31 to 0.58] 0.35 [0.24 to 0.48] 0.41 [0.28 to 0.54] 0.40 [0.28 to 0.53] 0.51 [0.37 to 0.67] 2090 0.52 [0.36 to 0.69] 0.40 [0.26 to 0.54] 0.47 [0.32 to 0.62] 0.47 [0.33 to 0.63] 0.62 [0.45 to 0.81] 2100 0.60 [0.42 to 0.80] 0.44 [0.28 to 0.61] 0.53 [0.36 to 0.71] 0.55 [0.38 to 0.73] 0.74 [0.53 to 0.98] 1445 AIII Annex III: Glossary Editor: Serge Planton (France) This annex should be cited as: IPCC, 2013: Annex III: Glossary [Planton, S. (ed.)]. In: Climate Change 2013: The Physical Science Basis. Contribution of Working Group I to the Fifth Assessment Report of the Intergovernmental Panel on Climate Change [Stocker, T.F., D. Qin, G.-K. Plattner, M. Tignor, S.K. Allen, J. Boschung, A. Nauels, Y. Xia, V. Bex and P.M. Midgley (eds.)]. Cambridge University Press, Cambridge, United Kingdom and New York, NY, USA. 1447 Annex III Glossary Aerosol radiation interaction An interaction of aerosol directly This glossary defines some specific terms as the Lead Authors intend with radiation produce radiative effects. In this report two levels of radia- them to be interpreted in the context of this report. Red, italicized tive forcing (or effect) are distinguished: words indicate that the term is defined in the Glossary. Radiative forcing (or effect) due to aerosol radiation interac- tions (RFari) The radiative forcing (or radiative effect, if the pertur- bation is internally generated) of an aerosol perturbation due directly Abrupt climate change A large-scale change in the climate system to aerosol radiation interactions, with all environmental variables that takes place over a few decades or less, persists (or is anticipated to remaining unaffected. Traditionally known in the literature as the direct persist) for at least a few decades and causes substantial disruptions in aerosol forcing (or effect). human and natural systems. Effective radiative forcing (or effect) due to aerosol-radiation Active layer The layer of ground that is subject to annual thawing and interactions (ERFari) The final radiative forcing (or effect) from freezing in areas underlain by permafrost. the aerosol perturbation including the rapid adjustments to the ini- tial change in radiation. These adjustments include changes in cloud Adjustment time See Lifetime. See also Response time. caused by the impact of the radiative heating on convective or larger- Advection Transport of water or air along with its properties (e.g., tem- scale atmospheric circulations, traditionally known as semi-direct aero- perature, chemical tracers) by winds or currents. Regarding the general sol forcing (or effect). distinction between advection and convection, the former describes trans- The total effective radiative forcing due to both aerosol cloud and port by large-scale motions of the atmosphere or ocean, while convection aerosol radiation interactions is denoted aerosol effective radiative describes the predominantly vertical, locally induced motions. forcing (ERFari+aci). See also Aerosol cloud interaction. Aerosol A suspension of airborne solid or liquid particles, with a typical Afforestation Planting of new forests on lands that historically have size between a few nanometres and 10 m that reside in the atmosphere not contained forests. For a discussion of the term forest and related terms for at least several hours. For convenience the term aerosol, which includes such as afforestation, reforestation and deforestation, see the IPCC Special both the particles and the suspending gas, is often used in this report in Report on Land Use, Land-Use Change and Forestry (IPCC, 2000). See also its plural form to mean aerosol particles. Aerosols may be of either natural AIII the report on Definitions and Methodological Options to Inventory Emis- or anthropogenic origin. Aerosols may influence climate in several ways: sions from Direct Human-induced Degradation of Forests and Devegeta- directly through scattering and absorbing radiation (see Aerosol radiation tion of Other Vegetation Types (IPCC, 2003). interaction) and indirectly by acting as cloud condensation nuclei or ice nuclei, modifying the optical properties and lifetime of clouds (see Aero- Airborne fraction The fraction of total CO2 emissions (from fossil fuel sol cloud interaction). and land use change) remaining in the atmosphere. Aerosol cloud interaction A process by which a perturbation to Air mass A widespread body of air, the approximately homogeneous aerosol affects the microphysical properties and evolution of clouds properties of which (1) have been established while that air was situated through the aerosol role as cloud condensation nuclei or ice nuclei, par- over a particular region of the Earth s surface, and (2) undergo specific ticularly in ways that affect radiation or precipitation; such processes can modifications while in transit away from the source region (AMS, 2000). also include the effect of clouds and precipitation on aerosol. The aerosol Albedo The fraction of solar radiation reflected by a surface or object, perturbation can be anthropogenic or come from some natural source. The often expressed as a percentage. Snow-covered surfaces have a high radiative forcing from such interactions has traditionally been attributed albedo, the albedo of soils ranges from high to low, and vegetation-cov- to numerous indirect aerosol effects, but in this report, only two levels of ered surfaces and oceans have a low albedo. The Earth s planetary albedo radiative forcing (or effect) are distinguished: varies mainly through varying cloudiness, snow, ice, leaf area and and Radiative forcing (or effect) due to aerosol cloud interactions cover changes. (RFaci) The radiative forcing (or radiative effect, if the perturbation is Alkalinity A measure of the capacity of an aqueous solution to neutral- internally generated) due to the change in number or size distribution ize acids. of cloud droplets or ice crystals that is the proximate result of an aero- sol perturbation, with other variables (in particular total cloud water Altimetry A technique for measuring the height of the Earth s surface content) remaining equal. In liquid clouds, an increase in cloud droplet with respect to the geocentre of the Earth within a defined terrestrial refer- concentration and surface area would increase the cloud albedo. This ence frame (geocentric sea level). effect is also known as the cloud albedo effect, first indirect effect, or Annular modes See Northern Annular Mode (NAM) and Southern Twomey effect. It is a largely theoretical concept that cannot readily be Annular Mode (SAM). isolated in observations or comprehensive process models due to the rapidity and ubiquity of rapid adjustments. Anthropogenic Resulting from or produced by human activities. Effective radiative forcing (or effect) due to aerosol cloud inter- Atlantic Multi-decadal Oscillation/Variability (AMO/AMV) A actions (ERFaci) The final radiative forcing (or effect) from the aero- multi-decadal (65- to 75-year) fluctuation in the North Atlantic, in which sol perturbation including the rapid adjustments to the initial change sea surface temperatures showed warm phases during roughly 1860 to in droplet or crystal formation rate. These adjustments include changes 1880 and 1930 to 1960 and cool phases during 1905 to 1925 and 1970 to in the strength of convection, precipitation efficiency, cloud fraction, 1990 with a range of approximately 0.4°C. See AMO Index, Box 2.5. lifetime or water content of clouds, and the formation or suppression Atmosphere The gaseous envelope surrounding the Earth. The dry of clouds in remote areas due to altered circulations. atmosphere consists almost entirely of nitrogen (78.1% volume mixing The total effective radiative forcing due to both aerosol cloud and ratio) and oxygen (20.9% volume mixing ratio), together with a number aerosol radiation interactions is denoted aerosol effective radiative of trace gases, such as argon (0.93% volume mixing ratio), helium and forcing (ERFari+aci). See also Aerosol radiation interaction. radiatively active greenhouse gases such as carbon dioxide (0.035% 1448 Glossary Annex III volume mixing ratio) and ozone. In addition, the atmosphere contains the Black carbon (BC) Operationally defined aerosol species based on g ­ reenhouse gas water vapour, whose amounts are highly variable but typi- measurement of light absorption and chemical reactivity and/or thermal cally around 1% volume mixing ratio. The atmosphere also contains clouds stability. It is sometimes referred to as soot. and aerosols. Blocking Associated with persistent, slow-moving high-pressure sys- Atmosphere Ocean General Circulation Model (AOGCM) See tems that obstruct the prevailing westerly winds in the middle and high Climate model. latitudes and the normal eastward progress of extratropical transient storm systems. It is an important component of the intraseasonal climate Atmospheric boundary layer The atmospheric layer adjacent to the variability in the extratropics and can cause long-lived weather conditions Earth s surface that is affected by friction against that boundary surface, such as cold spells in winter and summer heat waves. and possibly by transport of heat and other variables across that surface (AMS, 2000). The lowest 100 m of the boundary layer (about 10% of the Brewer Dobson circulation The meridional overturning circulation boundary layer thickness), where mechanical generation of turbulence is of the stratosphere transporting air upward in the tropics, poleward to the dominant, is called the surface boundary layer or surface layer. winter hemisphere, and downward at polar and subpolar latitudes. The Brewer Dobson circulation is driven by the interaction between upward Atmospheric lifetime See Lifetime. propagating planetary waves and the mean flow. Attribution See Detection and attribution. Burden The total mass of a gaseous substance of concern in the atmo- Autotrophic respiration Respiration by photosynthetic (see photo­ sphere. synthesis) organisms (e.g., plants and algaes). 13 C Stable isotope of carbon having an atomic weight of approximately Basal lubrication Reduction of friction at the base of an ice sheet 13. Measurements of the ratio of 13C/12C in carbon dioxide molecules are or glacier due to lubrication by meltwater. This can allow the glacier or used to infer the importance of different carbon cycle and climate pro- ice sheet to slide over its base. Meltwater may be produced by pressure- cesses and the size of the terrestrial carbon reservoir. induced melting, friction or geothermal heat, or surface melt may drain to C Unstable isotope of carbon having an atomic weight of approxi- 14 the base through holes in the ice. mately 14, and a half-life of about 5700 years. It is often used for dating Baseline/reference The baseline (or reference) is the state against purposes going back some 40 kyr. Its variation in time is affected by the AIII which change is measured. A baseline period is the period relative to which magnetic fields of the Sun and Earth, which influence its production from anomalies are computed. The baseline concentration of a trace gas is that cosmic rays (see Cosmogenic radioisotopes). measured at a location not influenced by local anthropogenic emissions. Calving The breaking off of discrete pieces of ice from a glacier, ice Bayesian method/approach A Bayesian method is a method by sheet or an ice shelf into lake or seawater, producing icebergs. This is a which a statistical analysis of an unknown or uncertain quantity(ies) is car- form of mass loss from an ice body. See also Mass balance/budget (of ried out in two steps. First, a prior probability distribution for the uncertain glaciers or ice sheets). quantity(ies) is formulated on the basis of existing knowledge (either by Carbonaceous aerosol Aerosol consisting predominantly of organic eliciting expert opinion or by using existing data and studies). At this first substances and black carbon. stage, an element of subjectivity may influence the choice, but in many cases, the prior probability distribution can be chosen as neutrally as pos- Carbon cycle The term used to describe the flow of carbon (in various sible, in order not to influence the final outcome of the analysis. In the forms, e.g., as carbon dioxide) through the atmosphere, ocean, terrestrial second step, newly acquired data are used to update the prior distribution and marine biosphere and lithosphere. In this report, the reference unit for into a posterior distribution. The update is carried out either through an the global carbon cycle is GtC or equivalently PgC (1015g). analytic computation or though numeric approximation, using a theorem Carbon dioxide (CO2) A naturally occurring gas, also a by-product of formulated by and named after the British mathematician Thomas Bayes burning fossil fuels from fossil carbon deposits, such as oil, gas and coal, (1702 1761). of burning biomass, of land use changes and of industrial processes (e.g., Biological pump The process of transporting carbon from the ocean s cement production). It is the principal anthropogenic greenhouse gas that surface layers to the deep ocean by the primary production of marine phy- affects the Earth s radiative balance. It is the reference gas against which toplankton, which converts dissolved inorganic carbon (DIC) and nutrients other greenhouse gases are measured and therefore has a Global Warming into organic matter through photosynthesis. This natural cycle is limited Potential of 1. primarily by the availability of light and nutrients such as phosphate, nitrate Carbon dioxide (CO2) fertilization The enhancement of the growth and silicic acid, and micronutrients, such as iron. See also Solubility pump. of plants as a result of increased atmospheric carbon dioxide (CO2) con- Biomass The total mass of living organisms in a given area or volume; centration. dead plant material can be included as dead biomass. Biomass burning is Carbon Dioxide Removal (CDR) Carbon Dioxide Removal meth- the burning of living and dead vegetation. ods refer to a set of techniques that aim to remove CO2 directly from the Biome A biome is a major and distinct regional element of the bio- atmosphere by either (1) increasing natural sinks for carbon or (2) using sphere, typically consisting of several ecosystems (e.g., forests, rivers, chemical engineering to remove the CO2, with the intent of reducing the ponds, swamps within a region). Biomes are characterized by typical com- atmospheric CO2 concentration. CDR methods involve the ocean, land and munities of plants and animals. technical systems, including such methods as iron fertilization, large-scale afforestation and direct capture of CO2 from the atmosphere using engi- Biosphere (terrestrial and marine) The part of the Earth system neered chemical means. Some CDR methods fall under the category of comprising all ecosystems and living organisms, in the atmosphere, on geoengineering, though this may not be the case for others, with the dis- land (terrestrial biosphere) or in the oceans (marine biosphere), including tinction being based on the magnitude, scale, and impact of the particular derived dead organic matter, such as litter, soil organic matter and oceanic CDR activities. The boundary between CDR and mitigation is not clear and detritus. 1449 Annex III Glossary there could be some overlap between the two given current definitions imbalance persists and until all components of the climate system have (IPCC, 2012, p. 2). See also Solar Radiation Management (SRM). adjusted to a new state. The further change in temperature after the com- position of the atmosphere is held constant is referred to as the constant CFC See Halocarbons. composition temperature commitment or simply committed warming or Chaotic A dynamical system such as the climate system, governed by warming commitment. Climate change commitment includes other future nonlinear deterministic equations (see Nonlinearity), may exhibit erratic or changes, for example, in the hydrological cycle, in extreme weather events, chaotic behaviour in the sense that very small changes in the initial state in extreme climate events, and in sea level change. The constant emission of the system in time lead to large and apparently unpredictable changes commitment is the committed climate change that would result from keep- in its temporal evolution. Such chaotic behaviour limits the predictabil- ing anthropogenic emissions constant and the zero emission commitment ity of the state of a nonlinear dynamical system at specific future times, is the climate change commitment when emissions are set to zero. See also although changes in its statistics may still be predictable given changes in Climate change. the system parameters or boundary conditions. Climate feedback An interaction in which a perturbation in one Charcoal Material resulting from charring of biomass, usually retain- climate quantity causes a change in a second, and the change in the ing some of the microscopic texture typical of plant tissues; chemically it second quantity ultimately leads to an additional change in the first. A consists mainly of carbon with a disturbed graphitic structure, with lesser negative feedback is one in which the initial perturbation is weakened amounts of oxygen and hydrogen. by the changes it causes; a positive feedback is one in which the initial perturbation is enhanced. In this Assessment Report, a somewhat narrower Chronology Arrangement of events according to dates or times of definition is often used in which the climate quantity that is perturbed is occurrence. the global mean surface temperature, which in turn causes changes in the Clathrate (methane) A partly frozen slushy mix of methane gas and global radiation budget. In either case, the initial perturbation can either ice, usually found in sediments. be externally forced or arise as part of internal variability. See also Climate Feedback Parameter. Clausius Clapeyron equation/relationship The thermodynamic relationship between small changes in temperature and vapour pressure Climate Feedback Parameter A way to quantify the radiative in an equilibrium system with condensed phases present. For trace gases response of the climate system to a global mean surface temperature AIII such as water vapour, this relation gives the increase in equilibrium (or change induced by a radiative forcing. It varies as the inverse of the effec- saturation) water vapour pressure per unit change in air temperature. tive climate sensitivity. Formally, the Climate Feedback Parameter (a; units: W m 2 °C 1) is defined as: a = (Q F)/T, where Q is the global mean Climate Climate in a narrow sense is usually defined as the average radiative forcing, T is the global mean air surface temperature, F is the weather, or more rigorously, as the statistical description in terms of the heat flux into the ocean and represents a change with respect to an mean and variability of relevant quantities over a period of time rang- unperturbed climate. ing from months to thousands or millions of years. The classical period for averaging these variables is 30 years, as defined by the World Meteorologi- Climate forecast See Climate prediction. cal Organization. The relevant quantities are most often surface variables Climate index A time series constructed from climate variables that such as temperature, precipitation and wind. Climate in a wider sense is provides an aggregate summary of the state of the climate system. For the state, including a statistical description, of the climate system. example, the difference between sea level pressure in Iceland and the Climate carbon cycle feedback A climate feedback involving Azores provides a simple yet useful historical NAO index. Because of their changes in the properties of land and ocean carbon cycle in response to cli- optimal properties, climate indices are often defined using principal com- mate change. In the ocean, changes in oceanic temperature and circulation ponents linear combinations of climate variables at different locations could affect the atmosphere ocean CO2 flux; on the continents, climate that have maximum variance subject to certain normalisation constraints change could affect plant photosynthesis and soil microbial respiration (e.g., the NAM and SAM indices which are principal components of North- and hence the flux of CO2 between the atmosphere and the land biosphere. ern Hemisphere and Southern Hemisphere gridded pressure anomalies, respectively). See Box 2.5 for a summary of definitions for established Climate change Climate change refers to a change in the state of the observational indices. See also Climate pattern. climate that can be identified (e.g., by using statistical tests) by changes in the mean and/or the variability of its properties, and that persists for an Climate model (spectrum or hierarchy) A numerical representa- extended period, typically decades or longer. Climate change may be due tion of the climate system based on the physical, chemical and biological to natural internal processes or external forcings such as modulations of properties of its components, their interactions and feedback processes, the solar cycles, volcanic eruptions and persistent anthropogenic changes and accounting for some of its known properties. The climate system can in the composition of the atmosphere or in land use. Note that the Frame- be represented by models of varying complexity, that is, for any one com- work Convention on Climate Change (UNFCCC), in its Article 1, defines ponent or combination of components a spectrum or hierarchy of models climate change as: a change of climate which is attributed directly or indi- can be identified, differing in such aspects as the number of spatial dimen- rectly to human activity that alters the composition of the global atmo- sions, the extent to which physical, chemical or biological processes are sphere and which is in addition to natural climate variability observed over explicitly represented or the level at which empirical parametrizations comparable time periods . The UNFCCC thus makes a distinction between are involved. Coupled Atmosphere Ocean General Circulation Models climate change attributable to human activities altering the atmospheric (AOGCMs) provide a representation of the climate system that is near or composition, and climate variability attributable to natural causes. See also at the most comprehensive end of the spectrum currently available. There Climate change commitment, Detection and Attribution. is an evolution towards more complex models with interactive chemistry and biology. Climate models are applied as a research tool to study and Climate change commitment Due to the thermal inertia of the simulate the climate, and for operational purposes, including monthly, sea- ocean and slow processes in the cryosphere and land surfaces, the climate sonal and interannual climate predictions. See also Earth System Model, would continue to change even if the atmospheric composition were held Earth-System Model of Intermediate Complexity, Energy Balance Model, fixed at today s values. Past change in atmospheric composition leads to Process-based Model, Regional Climate Model and Semi-empirical model. a committed climate change, which continues for as long as a radiative 1450 Glossary Annex III Climate pattern A set of spatially varying coefficients obtained by Climate sensitivity parameter See climate sensitivity. projection (regression) of climate variables onto a climate index time Climate system The climate system is the highly complex system series. When the climate index is a principal component, the climate pat- consisting of five major components: the atmosphere, the hydrosphere, tern is an eigenvector of the covariance matrix, referred to as an Empirical the cryosphere, the lithosphere and the biosphere, and the interactions Orthogonal Function (EOF) in climate science. between them. The climate system evolves in time under the influence of Climate prediction A climate prediction or climate forecast is the its own internal dynamics and because of external forcings such as vol- result of an attempt to produce (starting from a particular state of the canic eruptions, solar variations and anthropogenic forcings such as the climate system) an estimate of the actual evolution of the climate in changing composition of the atmosphere and land use change. the future, for example, at seasonal, interannual or decadal time scales. Climate variability Climate variability refers to variations in the mean Because the future evolution of the climate system may be highly sensitive state and other statistics (such as standard deviations, the occurrence of to initial conditions, such predictions are usually probabilistic in nature. See extremes, etc.) of the climate on all spatial and temporal scales beyond also Climate projection, Climate scenario, Model initialization and Predict- that of individual weather events. Variability may be due to natural internal ability. processes within the climate system (internal variability), or to variations Climate projection A climate projection is the simulated response of in natural or anthropogenic external forcing (external variability). See also the climate system to a scenario of future emission or concentration of Climate change. greenhouse gases and aerosols, generally derived using climate models. Cloud condensation nuclei (CCN) The subset of aerosol particles Climate projections are distinguished from climate predictions by their that serve as an initial site for the condensation of liquid water, which can dependence on the emission/concentration/radiative forcing scenario lead to the formation of cloud droplets, under typical cloud formation con- used, which is in turn based on assumptions concerning, for example, ditions. The main factor that determines which aerosol particles are CCN at future socioeconomic and technological developments that may or may a given supersaturation is their size. not be realized. See also Climate scenario. Cloud feedback A climate feedback involving changes in any of the Climate regime A state of the climate system that occurs more fre- properties of clouds as a response to a change in the local or global mean quently than nearby states due to either more persistence or more frequent surface temperature. Understanding cloud feedbacks and determining recurrence. In other words, a cluster in climate state space associated with their magnitude and sign require an understanding of how a change in cli- AIII a local maximum in the probability density function. mate may affect the spectrum of cloud types, the cloud fraction and height, Climate response See Climate sensitivity. the radiative properties of clouds, and finally the Earth s radiation budget. At present, cloud feedbacks remain the largest source of uncertainty in Climate scenario A plausible and often simplified representation of climate sensitivity estimates. See also Cloud radiative effect. the future climate, based on an internally consistent set of climatological relationships that has been constructed for explicit use in investigating Cloud radiative effect The radiative effect of clouds relative to the the potential consequences of anthropogenic climate change, often serv- identical situation without clouds. In previous IPCC reports this was called ing as input to impact models. Climate projections often serve as the raw cloud radiative forcing, but that terminology is inconsistent with other uses material for constructing climate scenarios, but climate scenarios usually of the forcing term and is not maintained in this report. See also Cloud require additional information such as the observed current climate. A cli- feedback. mate change scenario is the difference between a climate scenario and the CO2-equivalent See Equivalent carbon dioxide. current climate. See also Emission scenario, scenario. Cold days/cold nights Days where maximum temperature, or nights Climate sensitivity In IPCC reports, equilibrium climate sensitivity where minimum temperature, falls below the 10th percentile, where the (units: °C) refers to the equilibrium (steady state) change in the annual respective temperature distributions are generally defined with respect to global mean surface temperature following a doubling of the atmospheric the 1961 1990 reference period. For the corresponding indices, see Box equivalent carbon dioxide concentration. Owing to computational con- 2.4. straints, the equilibrium climate sensitivity in a climate model is sometimes estimated by running an atmospheric general circulation model coupled Compatible emissions Earth System Models that simulate the land to a mixed-layer ocean model, because equilibrium climate sensitivity is and ocean carbon cycle can calculate CO2 emissions that are compatible largely determined by atmospheric processes. Efficient models can be run with a given atmospheric CO2 concentration trajectory. The compatible to equilibrium with a dynamic ocean. The climate sensitivity parameter emissions over a given period of time are equal to the increase of carbon (units: °C (W m 2) 1) refers to the equilibrium change in the annual global over that same period of time in the sum of the three active reservoirs: the mean surface temperature following a unit change in radiative forcing. atmosphere, the land and the ocean. The effective climate sensitivity (units: °C) is an estimate of the global Confidence The validity of a finding based on the type, amount, quality, mean surface temperature response to doubled carbon dioxide concen- and consistency of evidence (e.g., mechanistic understanding, theory, data, tration that is evaluated from model output or observations for evolv- models, expert judgment) and on the degree of agreement. Confidence is ing non-equilibrium conditions. It is a measure of the strengths of the expressed qualitatively (Mastrandrea et al., 2010). See Figure 1.11 for the climate feedbacks at a particular time and may vary with forcing history levels of confidence and Table 1.1 for the list of likelihood qualifiers. See and climate state, and therefore may differ from equilibrium climate also Uncertainty. sensitivity. Convection Vertical motion driven by buoyancy forces arising from The transient climate response (units: °C) is the change in the global static instability, usually caused by near-surface cooling or increases in mean surface temperature, averaged over a 20-year period, centred at salinity in the case of the ocean and near-surface warming or cloud-top the time of atmospheric carbon dioxide doubling, in a climate model radiative cooling in the case of the atmosphere. In the atmosphere con- simulation in which CO2 increases at 1% yr 1. It is a measure of the vection gives rise to cumulus clouds and precipitation and is effective at strength and rapidity of the surface temperature response to green- both scavenging and vertically transporting chemical species. In the ocean house gas forcing. convection can carry surface waters to deep within the ocean. 1451 Annex III Glossary Cosmogenic radioisotopes Rare radioactive isotopes that are cre- climate variables. In all cases, the quality of the driving model remains an ated by the interaction of a high-energy cosmic ray particles with atoms important limitation on the quality of the downscaled information. nuclei. They are often used as indicator of solar activity which modulates Drought A period of abnormally dry weather long enough to cause a the cosmic rays intensity or as tracers of atmospheric transport processes, serious hydrological imbalance. Drought is a relative term; therefore any and are also called cosmogenic radionuclides. discussion in terms of precipitation deficit must refer to the particular Cryosphere All regions on and beneath the surface of the Earth and precipitation-related activity that is under discussion. For example, short- ocean where water is in solid form, including sea ice, lake ice, river ice, age of precipitation during the growing season impinges on crop produc- snow cover, glaciers and ice sheets, and frozen ground (which includes tion or ecosystem function in general (due to soil moisture drought, also permafrost). termed agricultural drought), and during the runoff and percolation season primarily affects water supplies (hydrological drought). Storage changes Dansgaard Oeschger events Abrupt events characterized in Green- in soil moisture and groundwater are also affected by increases in actual land ice cores and in palaeoclimate records from the nearby North Atlantic evapotranspiration in addition to reductions in precipitation. A period with by a cold glacial state, followed by a rapid transition to a warmer phase, an abnormal precipitation deficit is defined as a meteorological drought. A and a slow cooling back to glacial conditions. Counterparts of Dansgaard megadrought is a very lengthy and pervasive drought, lasting much longer Oeschger events are observed in other regions as well. than normal, usually a decade or more. For the corresponding indices, see Deforestation Conversion of forest to non-forest. For a discussion of Box 2.4. the term forest and related terms such as afforestation, reforestation, and Dynamical system A process or set of processes whose evolution in deforestation see the IPCC Special Report on Land Use, Land-Use Change time is governed by a set of deterministic physical laws. The climate system and Forestry (IPCC, 2000). See also the report on Definitions and Meth- is a dynamical system. See also Abrupt climate change, Chaotic, Nonlinear- odological Options to Inventory Emissions from Direct Human-induced ity and Predictability. Degradation of Forests and Devegetation of Other Vegetation Types (IPCC, 2003). Earth System Model (ESM) A coupled atmosphere ocean general circulation model in which a representation of the carbon cycle is includ- Deglaciation/glacial termination Transitions from full glacial con- ed, allowing for interactive calculation of atmospheric CO2 or compatible ditions (ice age) to warm interglacials characterized by global warming emissions. Additional components (e.g., atmospheric chemistry, ice sheets, AIII and sea level rise due to change in continental ice volume. dynamic vegetation, nitrogen cycle, but also urban or crop models) may be Detection and attribution Detection of change is defined as the included. See also Climate model. process of demonstrating that climate or a system affected by climate has Earth System Model of Intermediate Complexity (EMIC) A cli- changed in some defined statistical sense, without providing a reason for mate model attempting to include all the most important earth system that change. An identified change is detected in observations if its likeli- processes as in ESMs but at a lower resolution or in a simpler, more ideal- hood of occurrence by chance due to internal variability alone is deter- ized fashion. mined to be small, for example, <10%. Attribution is defined as the pro- cess of evaluating the relative contributions of multiple causal factors to Earth System sensitivity The equilibrium temperature response of a change or event with an assignment of statistical confidence (Hegerl et the coupled atmosphere ocean cryosphere vegetation carbon cycle al., 2010). system to a doubling of the atmospheric CO2 concentration is referred to as Earth System sensitivity. Because it allows slow components (e.g., ice Diatoms Silt-sized algae that live in surface waters of lakes, rivers and sheets, vegetation) of the climate system to adjust to the external pertur- oceans and form shells of opal. Their species distribution in ocean cores is bation, it may differ substantially from the climate sensitivity derived from often related to past sea surface temperatures. coupled atmosphere ocean models. Direct (aerosol) effect See Aerosol radiation interaction. Ecosystem An ecosystem is a functional unit consisting of living Direct Air Capture Chemical process by which a pure CO2 stream is organisms, their non-living environment, and the interactions within and produced by capturing CO2 from the ambient air. between them. The components included in a given ecosystem and its spa- tial boundaries depend on the purpose for which the ecosystem is defined: Diurnal temperature range The difference between the maximum in some cases they are relatively sharp, while in others they are diffuse. and minimum temperature during a 24-hour period. Ecosystem boundaries can change over time. Ecosystems are nested within Dobson Unit (DU) A unit to measure the total amount of ozone in a other ecosystems, and their scale can range from very small to the entire vertical column above the Earth s surface (total column ozone). The number biosphere. In the current era, most ecosystems either contain people as of Dobson Units is the thickness in units of 10 5 m that the ozone column key organisms, or are influenced by the effects of human activities in their would occupy if compressed into a layer of uniform density at a pressure environment. of 1013 hPa and a temperature of 0°C. One DU corresponds to a column of Effective climate sensitivity See Climate sensitivity. ozone containing 2.69 × 1020 molecules per square metre. A typical value for the amount of ozone in a column of the Earth s atmosphere, although Effective radiative forcing See Radiative forcing. very variable, is 300 DU. Efficacy A measure of how effective a radiative forcing from a given Downscaling Downscaling is a method that derives local- to regional- anthropogenic or natural mechanism is at changing the equilibrium global scale (10 to 100 km) information from larger-scale models or data analyses. mean surface temperature compared to an equivalent radiative forc- Two main methods exist: dynamical downscaling and empirical/statistical ing from carbon dioxide. A carbon dioxide increase by definition has an downscaling. The dynamical method uses the output of regional climate e ­ fficacy of 1.0. Variations in climate efficacy may result from rapid adjust- models, global models with variable spatial resolution or high-resolution ments to the applied forcing, which differ with different forcings. global models. The empirical/statistical methods develop statistical rela- Ekman pumping Frictional stress at the surface between two fluids tionships that link the large-scale atmospheric variables with ­local/­regional (atmosphere and ocean) or between a fluid and the adjacent solid sur- face (the Earth s surface) forces a circulation. When the resulting mass 1452 Glossary Annex III t ­ransport is converging, mass conservation requires a vertical flow away dimensional or two-dimensional model if changes to the energy budget from the surface. This is called Ekman pumping. The opposite effect, in case with respect to latitude, or both latitude and longitude, are explicitly con- of divergence, is called Ekman suction. The effect is important in both the sidered. See also Climate model. atmosphere and the ocean. Energy budget (of the Earth) The Earth is a physical system with Ekman transport The total transport resulting from a balance between an energy budget that includes all gains of incoming energy and all losses the Coriolis force and the frictional stress due to the action of the wind on of outgoing energy. The Earth s energy budget is determined by measur- the ocean surface. See also Ekman pumping. ing how much energy comes into the Earth system from the Sun, how much energy is lost to space, and accounting for the remainder on Earth Electromagnetic spectrum Wavelength or energy range of all elec- and its atmosphere. Solar radiation is the dominant source of energy into tromagnetic radiation. In terms of solar radiation, the spectral irradiance is the Earth system. Incoming solar energy may be scattered and reflected the power arriving at the Earth per unit area, per unit wavelength. by clouds and aerosols or absorbed in the atmosphere. The transmitted El Nino-Southern Oscillation (ENSO) The term El Nino was initially radiation is then either absorbed or reflected at the Earth s surface. The used to describe a warm-water current that periodically flows along the average albedo of the Earth is about 0.3, which means that 30% of the coast of Ecuador and Peru, disrupting the local fishery. It has since become incident solar energy is reflected into space, while 70% is absorbed by identified with a basin-wide warming of the tropical Pacific Ocean east of the Earth. Radiant solar or shortwave energy is transformed into sensible the dateline. This oceanic event is associated with a fluctuation of a global- heat, latent energy (involving different water states), potential energy, and scale tropical and subtropical surface pressure pattern called the Southern kinetic energy before being emitted as infrared radiation. With the average Oscillation. This coupled atmosphere ocean phenomenon, with preferred surface temperature of the Earth of about 15°C (288 K), the main outgoing time scales of two to about seven years, is known as the El Nino-Southern energy flux is in the infrared part of the spectrum. See also Energy balance, Oscillation (ENSO). It is often measured by the surface pressure anomaly Latent heat flux, Sensible heat flux. difference between Tahiti and Darwin or the sea surface temperatures in Ensemble A collection of model simulations characterizing a climate the central and eastern equatorial Pacific. During an ENSO event, the pre- prediction or projection. Differences in initial conditions and model formu- vailing trade winds weaken, reducing upwelling and altering ocean cur- lation result in different evolutions of the modelled system and may give rents such that the sea surface temperatures warm, further weakening the information on uncertainty associated with model error and error in initial trade winds. This event has a great impact on the wind, sea surface tem- conditions in the case of climate forecasts and on uncertainty associated AIII perature and precipitation patterns in the tropical Pacific. It has climatic with model error and with internally generated climate variability in the effects throughout the Pacific region and in many other parts of the world, case of climate projections. through global teleconnections. The cold phase of ENSO is called La Nina. For the corresponding indices, see Box 2.5. Equilibrium and transient climate experiment An equilibrium climate experiment is a climate model experiment in which the model is Emission scenario A plausible representation of the future develop- allowed to fully adjust to a change in radiative forcing. Such experiments ment of emissions of substances that are potentially radiatively active provide information on the difference between the initial and final states (e.g., greenhouse gases, aerosols) based on a coherent and internally con- of the model, but not on the time-dependent response. If the forcing is sistent set of assumptions about driving forces (such as demographic and allowed to evolve gradually according to a prescribed emission scenario, socioeconomic development, technological change) and their key relation- the time-dependent response of a climate model may be analysed. Such ships. Concentration scenarios, derived from emission scenarios, are used an experiment is called a transient climate experiment. See also Climate as input to a climate model to compute climate projections. In IPCC (1992) projection. a set of emission scenarios was presented which were used as a basis for the climate projections in IPCC (1996). These emission scenarios are Equilibrium climate sensitivity See Climate sensitivity. referred to as the IS92 scenarios. In the IPCC Special Report on Emission Equilibrium line The spatially averaged boundary at a given moment, Scenarios (Nakiæenoviæ and Swart, 2000) emission scenarios, the so-called usually chosen as the seasonal mass budget minimum at the end of SRES scenarios, were published, some of which were used, among others, summer, between the region on a glacier where there is a net annual loss as a basis for the climate projections presented in Chapters 9 to 11 of IPCC of ice mass (ablation area) and that where there is a net annual gain (accu- (2001) and Chapters 10 and 11 of IPCC (2007). New emission scenarios mulation area). The altitude of this boundary is referred to as equilibrium for climate change, the four Representative Concentration Pathways, were line altitude (ELA). developed for, but independently of, the present IPCC assessment. See also Climate scenario and Scenario. Equivalent carbon dioxide (CO2) concentration The concentra- tion of carbon dioxide that would cause the same radiative forcing as Energy balance The difference between the total incoming and total a given mixture of carbon dioxide and other forcing components. Those outgoing energy. If this balance is positive, warming occurs; if it is nega- values may consider only greenhouse gases, or a combination of green- tive, cooling occurs. Averaged over the globe and over long time periods, house gases and aerosols. Equivalent carbon dioxide concentration is a this balance must be zero. Because the climate system derives virtually all metric for comparing radiative forcing of a mix of different greenhouse its energy from the Sun, zero balance implies that, globally, the absorbed gases at a particular time but does not imply equivalence of the corre- solar radiation, that is, incoming solar radiation minus reflected solar radi- sponding climate change responses nor future forcing. There is generally ation at the top of the atmosphere and outgoing longwave radiation emit- no connection between equivalent carbon dioxide emissions and resulting ted by the climate system are equal. See also Energy budget. equivalent carbon dioxide concentrations. Energy Balance Model (EBM) An energy balance model is a sim- Equivalent carbon dioxide (CO2) emission The amount of carbon plified model that analyses the energy budget of the Earth to compute dioxide emission that would cause the same integrated radiative forcing, changes in the climate. In its simplest form, there is no explicit spatial over a given time horizon, as an emitted amount of a greenhouse gas or dimension and the model then provides an estimate of the changes in a mixture of greenhouse gases. The equivalent carbon dioxide emission is globally averaged temperature computed from the changes in radiation. obtained by multiplying the emission of a greenhouse gas by its Global This zero-dimensional energy balance model can be extended to a one- Warming Potential for the given time horizon. For a mix of greenhouse 1453 Annex III Glossary gases it is obtained by summing the equivalent carbon dioxide emissions Fossil fuel emissions Emissions of greenhouse gases (in particular of each gas. Equivalent carbon dioxide emission is a common scale for carbon dioxide), other trace gases and aerosols resulting from the combus- comparing emissions of different greenhouse gases but does not imply tion of fuels from fossil carbon deposits such as oil, gas and coal. equivalence of the corresponding climate change responses. See also Framework Convention on Climate Change See United Nations Equivalent carbon dioxide concentration. Framework Convention on Climate Change (UNFCCC). Evapotranspiration The combined process of evaporation from the Free atmosphere The atmospheric layer that is negligibly affected by Earth s surface and transpiration from vegetation. friction against the Earth s surface, and which is above the atmospheric Extended Concentration Pathways See Representative Concentra- boundary layer. tion Pathways. Frozen ground Soil or rock in which part or all of the pore water is External forcing External forcing refers to a forcing agent outside the frozen. Frozen ground includes permafrost. Ground that freezes and thaws climate system causing a change in the climate system. Volcanic eruptions, annually is called seasonally frozen ground. solar variations and anthropogenic changes in the composition of the General circulation The large-scale motions of the atmosphere and atmosphere and land use change are external forcings. Orbital forcing is the ocean as a consequence of differential heating on a rotating Earth. also an external forcing as the insolation changes with orbital parameters General circulation contributes to the energy balance of the system eccentricity, tilt and precession of the equinox. through transport of heat and momentum. Extratropical cyclone A large-scale (of order 1000 km) storm in General Circulation Model (GCM) See Climate model. the middle or high latitudes having low central pressure and fronts with strong horizontal gradients in temperature and humidity. A major cause Geoengineering Geoengineering refers to a broad set of methods and of extreme wind speeds and heavy precipitation especially in wintertime. technologies that aim to deliberately alter the climate system in order to alleviate the impacts of climate change. Most, but not all, methods seek Extreme climate event See Extreme weather event. to either (1) reduce the amount of absorbed solar energy in the climate Extreme sea level See Storm surge. system (Solar Radiation Management) or (2) increase net carbon sinks from the atmosphere at a scale sufficiently large to alter climate (Carbon Extreme weather event An extreme weather event is an event that AIII Dioxide Removal). Scale and intent are of central importance. Two key is rare at a particular place and time of year. Definitions of rare vary, but characteristics of geoengineering methods of particular concern are that an extreme weather event would normally be as rare as or rarer than the they use or affect the climate system (e.g., atmosphere, land or ocean) 10th or 90th percentile of a probability density function estimated from globally or regionally and/or could have substantive unintended effects observations. By definition, the characteristics of what is called extreme that cross national boundaries. Geoengineering is different from weather weather may vary from place to place in an absolute sense. When a pat- modification and ecological engineering, but the boundary can be fuzzy tern of extreme weather persists for some time, such as a season, it may (IPCC, 2012, p. 2). be classed as an extreme climate event, especially if it yields an average or total that is itself extreme (e.g., drought or heavy rainfall over a season). Geoid The equipotential surface having the same geopotential at each latitude and longitude around the world (geodesists denoting this poten- Faculae Bright patches on the Sun. The area covered by faculae is great- tial W0) that best approximates the mean sea level. It is the surface of er during periods of high solar activity. reference for measurement of altitude. In practice, several variations of Feedback See Climate feedback. definitions of the geoid exist depending on the way the permanent tide (the zero-frequency gravitational tide due to the Sun and Moon) is consid- Fingerprint The climate response pattern in space and/or time to a spe- ered in geodetic studies. cific forcing is commonly referred to as a fingerprint. The spatial patterns of sea level response to melting of glaciers or ice sheets (or other changes in Geostrophic winds or currents A wind or current that is in balance surface loading) are also referred to as fingerprints. Fingerprints are used with the horizontal pressure gradient and the Coriolis force, and thus is out- to detect the presence of this response in observations and are typically side of the influence of friction. Thus, the wind or current is directly parallel estimated using forced climate model simulations. to isobars and its speed is proportional to the horizontal pressure gradient. Flux adjustment To avoid the problem of coupled Atmosphere Ocean Glacial interglacial cycles Phase of the Earth s history marked by General Circulation Models (AOGCMs) drifting into some unrealistic cli- large changes in continental ice volume and global sea level. See also Ice mate state, adjustment terms can be applied to the atmosphere-ocean age and Interglacials. fluxes of heat and moisture (and sometimes the surface stresses resulting Glacial isostatic adjustment (GIA) The deformation of the Earth from the effect of the wind on the ocean surface) before these fluxes are and its gravity field due to the response of the earth ocean system to imposed on the model ocean and atmosphere. Because these adjustments changes in ice and associated water loads. It is sometimes referred to as are pre-computed and therefore independent of the coupled model inte- glacio-hydro isostasy. It includes vertical and horizontal deformations of gration, they are uncorrelated with the anomalies that develop during the the Earth s surface and changes in geoid due to the redistribution of mass integration. during the ice ocean mass exchange. Forest A vegetation type dominated by trees. Many definitions of the Glacier A perennial mass of land ice that originates from compressed term forest are in use throughout the world, reflecting wide differences in snow, shows evidence of past or present flow (through internal deforma- biogeophysical conditions, social structure and economics. For a discussion tion and/or sliding at the base) and is constrained by internal stress and of the term forest and related terms such as afforestation, reforestation and friction at the base and sides. A glacier is maintained by accumulation of deforestation see the IPCC Report on Land Use, Land-Use Change and For- snow at high altitudes, balanced by melting at low altitudes and/or dis- estry (IPCC, 2000). See also the Report on Definitions and Methodological ­ charge into the sea. An ice mass of the same origin as glaciers, but of Options to Inventory Emissions from Direct Human-induced Degradation of continental size, is called an ice sheet. For the purpose of simplicity in this Forests and Devegetation of Other Vegetation Types (IPCC, 2003). Assessment Report, all ice masses other than ice sheets are referred to as 1454 Glossary Annex III glaciers. See also Equilibrium line and Mass balance/budget (of glaciers or winds near the surface, and with rising air near the equator in the so-called ice sheets). Inter-Tropical Convergence Zone. Global dimming Global dimming refers to a widespread reduction of Halocarbons A collective term for the group of partially halogenated solar radiation received at the surface of the Earth from about the year organic species, which includes the chlorofluorocarbons (CFCs), hydro- 1961 to around 1990. chlorofluorocarbons (HCFCs), hydrofluorocarbons (HFCs), halons, methyl chloride and methyl bromide. Many of the halocarbons have large Global Global mean surface temperature An estimate of the global mean Warming Potentials. The chlorine and bromine-containing halocarbons are surface air temperature. However, for changes over time, only anomalies, also involved in the depletion of the ozone layer. as departures from a climatology, are used, most commonly based on the area-weighted global average of the sea surface temperature anomaly and Halocline A layer in the oceanic water column in which salinity changes land surface air temperature anomaly. rapidly with depth. Generally saltier water is denser and lies below less salty water. In some high latitude oceans the surface waters may be colder Global Warming Potential (GWP) An index, based on radiative than the deep waters and the halocline is responsible for maintaining properties of greenhouse gases, measuring the radiative forcing following water column stability and isolating the surface waters from the deep a pulse emission of a unit mass of a given greenhouse gas in the present- waters. See also Thermocline. day atmosphere integrated over a chosen time horizon, relative to that of carbon dioxide. The GWP represents the combined effect of the differing Halosteric See Sea level change. times these gases remain in the atmosphere and their relative effective- HCFC See Halocarbons. ness in causing radiative forcing. The Kyoto Protocol is based on GWPs from pulse emissions over a 100-year time frame. Heat wave A period of abnormally and uncomfortably hot weather. See also Warm spell. Greenhouse effect The infrared radiative effect of all infrared-absorb- ing constituents in the atmosphere. Greenhouse gases, clouds, and (to a Heterotrophic respiration The conversion of organic matter to small extent) aerosols absorb terrestrial radiation emitted by the Earth s carbon dioxide by organisms other than autotrophs. surface and elsewhere in the atmosphere. These substances emit infra- HFC See Halocarbons. red radiation in all directions, but, everything else being equal, the net amount emitted to space is normally less than would have been emitted Hindcast or retrospective forecast A forecast made for a period in AIII in the absence of these absorbers because of the decline of temperature the past using only information available before the beginning of the fore- with altitude in the troposphere and the consequent weakening of emis- cast. A sequence of hindcasts can be used to calibrate the forecast system sion. An increase in the concentration of greenhouse gases increases the and/or provide a measure of the average skill that the forecast system has magnitude of this effect; the difference is sometimes called the enhanced exhibited in the past as a guide to the skill that might be expected in the greenhouse effect. The change in a greenhouse gas concentration because future. of anthropogenic emissions contributes to an instantaneous radiative forc- Holocene The Holocene Epoch is the latter of two epochs in the Qua- ing. Surface temperature and troposphere warm in response to this forcing, ternary System, extending from 11.65 ka (thousand years before 1950) to gradually restoring the radiative balance at the top of the atmosphere. the present. It is also known as Marine Isotopic Stage (MIS) 1 or current Greenhouse gas (GHG) Greenhouse gases are those gaseous con- interglacial. stituents of the atmosphere, both natural and anthropogenic, that absorb Hydroclimate Part of the climate pertaining to the hydrology of a and emit radiation at specific wavelengths within the spectrum of terres- region. trial radiation emitted by the Earth s surface, the atmosphere itself, and by clouds. This property causes the greenhouse effect. Water vapour (H2O), Hydrological cycle The cycle in which water evaporates from the carbon dioxide (CO2), nitrous oxide (N2O), methane (CH4) and ozone (O3) oceans and the land surface, is carried over the Earth in atmospheric are the primary greenhouse gases in the Earth s atmosphere. Moreover, circulation as water vapour, condenses to form clouds, precipitates over there are a number of entirely human-made greenhouse gases in the ocean and land as rain or snow, which on land can be intercepted by trees atmosphere, such as the halocarbons and other chlorine- and bromine- and vegetation, provides runoff on the land surface, infiltrates into soils, containing substances, dealt with under the Montreal Protocol. Beside CO2, recharges groundwater, discharges into streams and ultimately flows out N2O and CH4, the Kyoto Protocol deals with the greenhouse gases sul- into the oceans, from which it will eventually evaporate again. The vari- phur hexafluoride (SF6), hydrofluorocarbons (HFCs) and perfluorocarbons ous systems involved in the hydrological cycle are usually referred to as (PFCs). For a list of well-mixed greenhouse gases, see Table 2.A.1. hydrological systems. Gross Primary Production (GPP) The amount of carbon fixed by the Hydrosphere The component of the climate system comprising liquid autotrophs (e.g. plants and algaes). surface and subterranean water, such as oceans, seas, rivers, fresh water lakes, underground water, etc. Grounding line The junction between a glacier or ice sheet and ice shelf; the place where ice starts to float. This junction normally occurs over Hypsometry The distribution of land or ice surface as a function of a finite zone, rather than at a line. altitude. Gyre Basin-scale ocean horizontal circulation pattern with slow flow Ice age An ice age or glacial period is characterized by a long-term circulating around the ocean basin, closed by a strong and narrow (100 to reduction in the temperature of the Earth s climate, resulting in growth of 200 km wide) boundary current on the western side. The subtropical gyres ice sheets and glaciers. in each ocean are associated with high pressure in the centre of the gyres; Ice albedo feedback A climate feedback involving changes in the the subpolar gyres are associated with low pressure. Earth s surface albedo. Snow and ice have an albedo much higher (up to Hadley Circulation A direct, thermally driven overturning cell in the ~0.8) than the average planetary albedo (~0.3). With increasing tempera- atmosphere consisting of poleward flow in the upper troposphere, subsid- tures, it is anticipated that snow and ice extent will decrease, the Earth s ing air into the subtropical anticyclones, return flow as part of the trade overall albedo will decrease and more solar radiation will be absorbed, warming the Earth further. 1455 Annex III Glossary Ice core A cylinder of ice drilled out of a glacier or ice sheet. Iron fertilization Deliberate introduction of iron to the upper ocean intended to enhance biological productivity which can sequester addition- Ice sheet A mass of land ice of continental size that is sufficiently thick al atmospheric carbon dioxide into the oceans. to cover most of the underlying bed, so that its shape is mainly determined by its dynamics (the flow of the ice as it deforms internally and/or slides at Irreversibility A perturbed state of a dynamical system is defined as its base). An ice sheet flows outward from a high central ice plateau with irreversible on a given timescale, if the recovery timescale from this state a small average surface slope. The margins usually slope more steeply, and due to natural processes is significantly longer than the time it takes for the most ice is discharged through fast flowing ice streams or outlet glaciers, system to reach this perturbed state. In the context of WGI, the time scale in some cases into the sea or into ice shelves floating on the sea. There are of interest is centennial to millennial. See also Tipping point. only two ice sheets in the modern world, one on Greenland and one on Isostatic or Isostasy Isostasy refers to the response of the earth to Antarctica. During glacial periods there were others. changes in surface load. It includes the deformational and gravitational Ice shelf A floating slab of ice of considerable thickness extending from response. This response is elastic on short time scales, as in the earth the coast (usually of great horizontal extent with a very gently sloping ocean response to recent changes in mountain glaciation, or viscoelastic surface), often filling embayments in the coastline of an ice sheet. Nearly on longer time scales, as in the response to the last deglaciation following all ice shelves are in Antarctica, where most of the ice discharged into the the Last Glacial Maximum. See also Glacial Isostatic Adjustment (GIA). ocean flows via ice shelves. Isotopes Atoms of the same chemical element that have the same the Ice stream A stream of ice with strongly enhanced flow that is part of number of protons but differ in the number of neutrons. Some proton an ice sheet. It is often separated from surrounding ice by strongly sheared, neutron configurations are stable (stable isotopes), others are unstable crevassed margins. See also Outlet glacier. undergoing spontaneous radioactive decay (radioisotopes). Most elements have more than one stable isotope. Isotopes can be used to trace transport Incoming solar radiation See Insolation. processes or to study processes that change the isotopic ratio. Radioiso- Indian Ocean Dipole (IOD) Large scale mode of interannual variabil- topes provide in addition time information that can be used for radiometric ity of sea surface temperature in the Indian Ocean. This pattern manifests dating. through a zonal gradient of tropical sea surface temperature, which in one Kyoto Protocol The Kyoto Protocol to the United Nations Framework extreme phase in boreal autumn shows cooling off Sumatra and warming AIII Convention on Climate Change (UNFCCC) was adopted in 1997 in Kyoto, off Somalia in the west, combined with anomalous easterlies along the Japan, at the Third Session of the Conference of the Parties (COP) to the equator. UNFCCC. It contains legally binding commitments, in addition to those Indirect aerosol effect See Aerosol-cloud interaction. included in the UNFCCC. Countries included in Annex B of the Protocol (most Organisation for Economic Cooperation and Development countries Industrial Revolution A period of rapid industrial growth with far- and countries with economies in transition) agreed to reduce their anthro- reaching social and economic consequences, beginning in Britain during pogenic greenhouse gas emissions (carbon dioxide, methane, nitrous the second half of the 18th century and spreading to Europe and later to oxide, hydrofluorocarbons, perfluorocarbons, and sulphur hexafluoride) by other countries including the United States. The invention of the steam at least 5% below 1990 levels in the commitment period 2008 2012. The engine was an important trigger of this development. The industrial revolu- Kyoto Protocol entered into force on 16 February 2005. tion marks the beginning of a strong increase in the use of fossil fuels and emission of, in particular, fossil carbon dioxide. In this report the terms pre- Land surface air temperature The surface air temperature as mea- industrial and industrial refer, somewhat arbitrarily, to the periods before sured in well-ventilated screens over land at 1.5 m above the ground. and after 1750, respectively. Land use and Land use change Land use refers to the total of Infrared radiation See Terrestrial radiation. arrangements, activities and inputs undertaken in a certain land cover type (a set of human actions). The term land use is also used in the sense of the Insolation The amount of solar radiation reaching the Earth by lati- social and economic purposes for which land is managed (e.g., grazing, tude and by season measured in W m 2. Usually insolation refers to the timber extraction and conservation). Land use change refers to a change radiation arriving at the top of the atmosphere. Sometimes it is specified in the use or management of land by humans, which may lead to a change as referring to the radiation arriving at the Earth s surface. See also Total in land cover. Land cover and land use change may have an impact on Solar Irradiance. the surface albedo, evapotranspiration, sources and sinks of greenhouse Interglacials or interglaciations The warm periods between ice age gases, or other properties of the climate system and may thus give rise to glaciations. Often defined as the periods at which sea levels were close radiative forcing and/or other impacts on climate, locally or globally. See to present sea level. For the Last Interglacial (LIG) this occurred between also the IPCC Report on Land Use, Land-Use Change, and Forestry (IPCC, about 129 and 116 ka (thousand years) before present (defined as 1950) 2000). although the warm period started in some areas a few thousand years Land water storage Water stored on land other than in glaciers and earlier. In terms of the oxygen isotope record interglaciations are defined ice sheets (that is water stored in rivers, lakes, wetlands, the vadose zone, as the interval between the midpoint of the preceding termination and aquifers, reservoirs, snow and permafrost). Changes in land water storage the onset of the next glaciation. The present interglaciation, the Holocene, driven by climate and human activities contribute to sea level change. started at 11.65 ka before present although globally sea levels did not approach their present position until about 7 ka before present. La Nina See El Nino-Southern Oscillation. Internal variability See Climate variability. Lapse rate The rate of change of an atmospheric variable, usually tem- perature, with height. The lapse rate is considered positive when the vari- Inter-Tropical Convergence Zone (ITCZ) The Inter-Tropical Conver- able decreases with height. gence Zone is an equatorial zonal belt of low pressure, strong convection and heavy precipitation near the equator where the northeast trade winds Last Glacial Maximum (LGM) The period during the last ice age when meet the southeast trade winds. This band moves seasonally. the glaciers and ice sheets reached their maximum extent, approximately ­ 1456 Glossary Annex III 21 ka ago. This period has been widely studied because the radiative forc- period 1400 CE and 1900 CE. Currently available reconstructions of aver- ings and boundary conditions are relatively well known. age Northern Hemisphere temperature indicate that the coolest periods at the hemispheric scale may have occurred from 1450 to 1850 CE. Last Interglacial (LIG) See Interglacials. Longwave radiation See Terrestrial radiation. Latent heat flux The turbulent flux of heat from the Earth s surface to the atmosphere that is associated with evaporation or condensation of Madden Julian Oscillation (MJO) The largest single component of water vapour at the surface; a component of the surface energy budget. tropical atmospheric intraseasonal variability (periods from 30 to 90 days). The MJO propagates eastwards at around 5 m s 1 in the form of a large- Lifetime Lifetime is a general term used for various time scales char- scale coupling between atmospheric circulation and deep convection. As it acterizing the rate of processes affecting the concentration of trace gases. progresses, it is associated with large regions of both enhanced and sup- The following lifetimes may be distinguished: pressed rainfall, mainly over the Indian and western Pacific Oceans. Each Turnover time (T) (also called global atmospheric lifetime) is the MJO event lasts approximately 30 to 60 days, hence the MJO is also known ratio of the mass M of a reservoir (e.g., a gaseous compound in the as the 30- to 60-day wave, or the intraseasonal oscillation. atmosphere) and the total rate of removal S from the reservoir: T = M/S. Marine-based ice sheet An ice sheet containing a substantial region For each removal process, separate turnover times can be defined. In that rests on a bed lying below sea level and whose perimeter is in contact soil carbon biology, this is referred to as Mean Residence Time. with the ocean. The best known example is the West Antarctic ice sheet. Adjustment time or response time (Ta) is the time scale character- Mass balance/budget (of glaciers or ice sheets) The balance izing the decay of an instantaneous pulse input into the reservoir. The between the mass input to the ice body (accumulation) and the mass loss term adjustment time is also used to characterize the adjustment of (ablation and iceberg calving) over a stated period of time, which is often the mass of a reservoir following a step change in the source strength. a year or a season. Point mass balance refers to the mass balance at a Half-life or decay constant is used to quantify a first-order exponential particular location on the glacier or ice sheet. Surface mass balance is the decay process. See Response time for a different definition pertinent to difference between surface accumulation and surface ablation. The input climate variations. and output terms for mass balance are: The term lifetime is sometimes used, for simplicity, as a surrogate for Accumulation All processes that add to the mass of a glacier. The adjustment time. AIII main contribution to accumulation is snowfall. Accumulation also In simple cases, where the global removal of the compound is directly includes deposition of hoar, freezing rain, other types of solid precipita- proportional to the total mass of the reservoir, the adjustment time tion, gain of wind-blown snow, and avalanching. equals the turnover time: T = Ta. An example is CFC-11, which is Ablation Surface processes that reduce the mass of a glacier. The removed from the atmosphere only by photochemical processes in the main contributor to ablation is melting with runoff but on some gla- stratosphere. In more complicated cases, where several reservoirs are ciers sublimation, loss of wind-blown snow and avalanching are also involved or where the removal is not proportional to the total mass, significant processes of ablation. the equality T = Ta no longer holds. Carbon dioxide (CO2) is an extreme example. Its turnover time is only about 4 years because of the rapid Discharge/outflow Mass loss by iceberg calving or ice discharge exchange between the atmosphere and the ocean and terrestrial biota. across the grounding line of a floating ice shelf. Although often treated However, a large part of that CO2 is returned to the atmosphere within as an ablation term, in this report iceberg calving and discharge is con- a few years. Thus, the adjustment time of CO2 in the atmosphere is sidered separately from surface ablation. actually determined by the rate of removal of carbon from the surface Mean sea level The surface level of the ocean at a particular point layer of the oceans into its deeper layers. Although an approximate averaged over an extended period of time such as a month or year. Mean value of 100 years may be given for the adjustment time of CO2 in the sea level is often used as a national datum to which heights on land are atmosphere, the actual adjustment is faster initially and slower later referred. on. In the case of methane (CH4), the adjustment time is different from the turnover time because the removal is mainly through a chemical Medieval Climate Anomaly (MCA) See Medieval Warm Period. reaction with the hydroxyl radical (OH), the concentration of which Medieval Warm Period (MWP) An interval of relatively warm con- itself depends on the CH4 concentration. Therefore, the CH4 removal ditions and other notable climate anomalies such as more extensive rate S is not proportional to its total mass M. drought in some continental regions. The timing of this interval is not Likelihood The chance of a specific outcome occurring, where this clearly defined, with different records showing onset and termination of might be estimated probabilistically. This is expressed in this report using a the warmth at different times, and some showing intermittent warmth. standard terminology, defined in Table 1.1. See also Confidence and Uncer- Most definitions lie within the period 900 to 1400 CE. Currently available tainty. reconstructions of average Northern Hemisphere temperature indicate that the warmest period at the hemispheric scale may have occurred from 950 Lithosphere The upper layer of the solid Earth, both continental and to 1250 CE. Currently available records and temperature reconstructions oceanic, which comprises all crustal rocks and the cold, mainly elastic indicate that average temperatures during parts of the MWP were indeed part of the uppermost mantle. Volcanic activity, although part of the litho- warmer in the context of the last 2 kyr, though the warmth may not have sphere, is not considered as part of the climate system, but acts as an been as ubiquitous across seasons and geographical regions as the 20th external forcing factor. See also Isostatic. century warming. It is also called Medieval Climate Anomaly. Little Ice Age (LIA) An interval during the last millennium charac- Meridional Overturning Circulation (MOC) Meridional (north terized by a number of extensive expansions of mountain glaciers and south) overturning circulation in the ocean quantified by zonal (east west) moderate retreats in between them, both in the Northern and Southern sums of mass transports in depth or density layers. In the North Atlantic, Hemispheres. The timing of glacial advances differs between regions and away from the subpolar regions, the MOC (which is in principle an observ- the LIA is, therefore, not clearly defined in time. Most definitions lie in the able quantity) is often identified with the thermohaline circulation (THC), 1457 Annex III Glossary which is a conceptual and incomplete interpretation. It must be borne in Mode of climate variability Underlying space time structure with mind that the MOC is also driven by wind, and can also include shallower preferred spatial pattern and temporal variation that helps account for the overturning cells such as occur in the upper ocean in the tropics and sub- gross features in variance and for teleconnections. A mode of variability tropics, in which warm (light) waters moving poleward are transformed to is often considered to be the product of a spatial climate pattern and an slightly denser waters and subducted equatorward at deeper levels. associated climate index time series. Metadata Information about meteorological and climatological data Mole fraction Mole fraction, or mixing ratio, is the ratio of the number concerning how and when they were measured, their quality, known prob- of moles of a constituent in a given volume to the total number of moles lems and other characteristics. of all constituents in that volume. It is usually reported for dry air. Typical values for well-mixed greenhouse gases are in the order of mol mol 1 Methane (CH4) Methane is one of the six greenhouse gases to be miti- (parts per million: ppm), nmol mol 1 (parts per billion: ppb), and fmol mol 1 gated under the Kyoto Protocol and is the major component of natural gas (parts per trillion: ppt). Mole fraction differs from volume mixing ratio, and associated with all hydrocarbon fuels, animal husbandry and agricul- often expressed in ppmv etc., by the corrections for non-ideality of gases. ture. This correction is significant relative to measurement precision for many Metric A consistent measurement of a characteristic of an object or greenhouse gases (Schwartz and Warneck, 1995). activity that is otherwise difficult to quantify. Within the context of the Monsoon A monsoon is a tropical and subtropical seasonal reversal in evaluation of climate models, this is a quantitative measure of agreement both the surface winds and associated precipitation, caused by differential between a simulated and observed quantity which can be used to assess heating between a continental-scale land mass and the adjacent ocean. the performance of individual models. Monsoon rains occur mainly over land in summer. Microwave Sounding Unit (MSU) A microwave sounder on National Montreal Protocol The Montreal Protocol on Substances that Deplete Oceanic and Atmospheric Administration (NOAA) polar orbiter satellites, the Ozone Layer was adopted in Montreal in 1987, and subsequently that estimates the temperature of thick layers of the atmosphere by mea- adjusted and amended in London (1990), Copenhagen (1992), Vienna suring the thermal emission of oxygen molecules from a complex of emis- (1995), Montreal (1997) and Beijing (1999). It controls the consump- sion lines near 60 GHz. A series of nine MSUs began making this kind tion and production of chlorine- and bromine-containing chemicals that of measurement in late 1978. Beginning in mid 1998, a follow-on series destroy stratospheric ozone, such as chlorofluorocarbons, methyl chloro- AIII of instruments, the Advanced Microwave Sounding Units (AMSUs), began form, carbon tetrachloride and many others. operation. Near-surface permafrost A term  frequently  used in climate model Mineralization/Remineralization The conversion of an element applications to refer to permafrost at depths close to the ground surface from its organic form to an inorganic form as a result of microbial decom- (typically down to 3.5 m). In modelling studies,  near-surface permafrost position. In nitrogen mineralization, organic nitrogen from decaying plant is usually diagnosed from 20 or 30 year climate averages, which  is  dif- and animal residues (proteins, nucleic acids, amino sugars and urea) is ferent from the conventional definition of permafrost. Disappearance of converted to ammonia (NH3) and ammonium (NH4+) by biological activity. near-surface permafrost in a location does not preclude the longer-term Mitigation A human intervention to reduce the sources or enhance the persistence of permafrost at greater depth. See also Active layer, Frozen sinks of greenhouse gases. ground and Thermokarst. Mixing ratio See Mole fraction. Near-term climate forcers (NTCF) Near-term climate forcers (NTCF) refer to those compounds whose impact on climate occurs primarily within Model drift Since model climate differs to some extent from observed the first decade after their emission. This set of compounds is primarily climate, climate forecasts will typically drift from the initial observation- composed of those with short lifetimes in the atmosphere compared to based state towards the model s climate. This drift occurs at different time well-mixed greenhouse gases, and has been sometimes referred to as scales for different variables, can obscure the initial-condition forecast short lived climate forcers or short-lived climate pollutants. However, the information and is usually removed a posteriori by an empirical, usually common property that is of greatest interest to a climate assessment is linear, adjustment. the timescale over which their impact on climate is felt. This set of com- Model hierarchy See Climate model (spectrum or hierarchy). pounds includes methane, which is also a well-mixed greenhouse gas, as well as ozone and aerosols, or their precursors, and some halogenated Model initialization A climate forecast typically proceeds by integrat- species that are not well-mixed greenhouse gases. These compounds do ing a climate model forward in time from an initial state that is intended not accumulate in the atmosphere at decadal to centennial timescales, to reflect the actual state of the climate system. Available observations of and so their effect on climate is predominantly in the near term following the climate system are assimilated into the model. Initialization is a com- their emission. plex process that is limited by available observations, observational errors and, depending on the procedure used, may be affected by uncertainty in Nitrogen deposition Nitrogen deposition is defined as the nitrogen the history of climate forcing. The initial conditions will contain errors that transferred from the atmosphere to the Earth s surface by the processes of grow as the forecast progresses, thereby limiting the time for which the wet deposition and dry deposition. forecast will be useful. See also Climate prediction. Nitrous oxide (N2O) One of the six greenhouse gases to be mitigat- Model spread The range or spread in results from climate models, ed under the Kyoto Protocol. The main anthropogenic source of nitrous such as those assembled for Coupled Model Intercomparison Project oxide is agriculture (soil and animal manure management), but important Phase 5 (CMIP5). Does not necessarily provide an exhaustive and formal c ­ ontributions also come from sewage treatment, combustion of fossil fuel, e ­ stimate of the uncertainty in feedbacks, forcing or projections even when and chemical industrial processes. Nitrous oxide is also produced naturally expressed numerically, for example, by computing a standard deviation of from a wide variety of biological sources in soil and water, particularly the models responses. In order to quantify uncertainty, information from microbial action in wet tropical forests. observations, physical constraints and expert judgement must be com- bined, using a statistical framework. 1458 Glossary Annex III Nonlinearity A process is called nonlinear when there is no simple pro- Pacific Decadal Oscillation (PDO) The pattern and time series of portional relation between cause and effect. The climate system contains the first empirical orthogonal function of sea surface temperature over the many such nonlinear processes, resulting in a system with potentially very North Pacific north of 20°N. The PDO broadened to cover the whole Pacific complex behaviour. Such complexity may lead to abrupt climate change. Basin is known as the Inter-decadal Pacific Oscillation. The PDO and IPO See also Chaotic and Predictability. exhibit similar temporal evolution. See also Pacific Decadal Variability. North Atlantic Oscillation (NAO) The North Atlantic Oscillation con- Pacific decadal variability Coupled decadal-to-inter-decadal vari- sists of opposing variations of surface pressure near Iceland and near the ability of the atmospheric circulation and underlying ocean in the Pacific Azores. It therefore corresponds to fluctuations in the strength of the main Basin. It is most prominent in the North Pacific, where fluctuations in the westerly winds across the Atlantic into Europe, and thus to fluctuations in strength of the winter Aleutian Low pressure system co-vary with North the embedded extratropical cyclones with their associated frontal systems. Pacific sea surface temperatures, and are linked to decadal variations in See NAO Index, Box 2.5. atmospheric circulation, sea surface temperatures and ocean circulation throughout the whole Pacific Basin. Such fluctuations have the effect of Northern Annular Mode (NAM) A winter fluctuation in the ampli- modulating the El Nino-Southern Oscillation cycle. Key measures of Pacific tude of a pattern characterized by low surface pressure in the Arctic and decadal variability are the North Pacific Index (NPI), the Pacific Decadal strong mid-latitude westerlies. The NAM has links with the northern polar Oscillation (PDO) index and the Inter-decadal Pacific Oscillation (IPO) vortex into the stratosphere. Its pattern has a bias to the North Atlantic and index, all defined in Box 2.5. its index has a large correlation with the North Atlantic Oscillation index. See NAM Index, Box 2.5. Pacific North American (PNA) pattern An atmospheric large-scale wave pattern featuring a sequence of tropospheric high and low pressure Ocean acidification Ocean acidification refers to a reduction in the pH anomalies stretching from the subtropical west Pacific to the east coast of of the ocean over an extended period, typically decades or longer, which North America. See PNA pattern index, Box 2.5. is caused primarily by uptake of carbon dioxide from the atmosphere, but can also be caused by other chemical additions or subtractions from the Paleoclimate Climate during periods prior to the development of mea- ocean. Anthropogenic ocean acidification refers to the component of pH suring instruments, including historic and geologic time, for which only reduction that is caused by human activity (IPCC, 2011, p. 37). proxy climate records are available. Ocean heat uptake efficiency This is a measure (W m 2 °C 1) of Parameterization In climate models, this term refers to the technique AIII the rate at which heat storage by the global ocean increases as global of representing processes that cannot be explicitly resolved at the spatial mean surface temperature rises. It is a useful parameter for climate change or temporal resolution of the model (sub-grid scale processes) by relation- experiments in which the radiative forcing is changing monotonically, ships between model-resolved larger-scale variables and the area- or time- when it can be compared with the Climate Feedback Parameter to gauge averaged effect of such subgrid scale processes. the relative importance of climate response and ocean heat uptake in Percentiles The set of partition values which divides the total popula- determining the rate of climate change. It can be estimated from such an tion of a distribution into 100 equal parts, the 50th percentile correspond- experiment as the ratio of the rate of increase of ocean heat content to the ing to the median of the population. global mean surface air temperature change. Permafrost Ground (soil or rock and included ice and organic material) Organic aerosol Component of the aerosol that consists of organic that remains at or below 0°C for at least two consecutive years. See also compounds, mainly carbon, hydrogen, oxygen and lesser amounts of other Near-surface permafrost. elements. See also Carbonaceous aerosol. pH pH is a dimensionless measure of the acidity of water (or any solu- Outgoing longwave radiation Net outgoing radiation in the infra- tion) given by its concentration of hydrogen ions (H+). pH is measured on red part of the spectrum at the top of the atmosphere. See also Terrestrial a logarithmic scale where pH = log10(H+). Thus, a pH decrease of 1 unit radiation. corresponds to a 10-fold increase in the concentration of H+, or acidity. Outlet glacier A glacier, usually between rock walls, that is part of, and Photosynthesis The process by which plants take carbon dioxide from drains an ice sheet. See also Ice stream. the air (or bicarbonate in water) to build carbohydrates, releasing oxygen Ozone Ozone, the triatomic form of oxygen (O3), is a gaseous atmospher- in the process. There are several pathways of photosynthesis with different ic constituent. In the troposphere, it is created both naturally and by photo- responses to atmospheric carbon dioxide concentrations. See also Carbon chemical reactions involving gases resulting from human activities (smog). dioxide fertilization. Tropospheric ozone acts as a greenhouse gas. In the stratosphere, it is cre- Plankton Microorganisms living in the upper layers of aquatic systems. ated by the interaction between solar ultraviolet radiation and molecular A distinction is made between phytoplankton, which depend on photo- oxygen (O2). Stratospheric ozone plays a dominant role in the stratospheric synthesis for their energy supply, and zooplankton, which feed on phyto- radiative balance. Its concentration is highest in the ozone layer. plankton. Ozone hole See Ozone layer. Pleistocene The Pleistocene Epoch is the earlier of two epochs in the Ozone layer The stratosphere contains a layer in which the concentra- Quaternary System, extending from 2.59 Ma to the beginning of the Holo- tion of ozone is greatest, the so-called ozone layer. The layer extends from cene at 11.65 ka. about 12 to 40 km above the Earth s surface. The ozone concentration Pliocene The Plionece Epoch is the last epoch of the Neogene System reaches a maximum between about 20 and 25 km. This layer has been and extends from 5.33 Ma to the beginning of the Pleistocene at 2.59 Ma. depleted by human emissions of chlorine and bromine compounds. Every year, during the Southern Hemisphere spring, a very strong depletion of Pollen analysis A technique of both relative dating and environmental the ozone layer takes place over the Antarctic, caused by anthropogenic reconstruction, consisting of the identification and counting of pollen types chlorine and bromine compounds in combination with the specific meteo- preserved in peat, lake sediments and other deposits. See also Proxy. rological conditions of that region. This phenomenon is called the Ozone hole. See also Montreal Protocol. 1459 Annex III Glossary Precipitable water The total amount of atmospheric water vapour in Radiative effect The impact on a radiation flux or heating rate (most a vertical column of unit cross-sectional area. It is commonly expressed in commonly, on the downward flux at the top of atmosphere) caused by terms of the height of the water if completely condensed and collected in the interaction of a particular constituent with either the infrared or solar a vessel of the same unit cross section. radiation fields through absorption, scattering and emission, relative to an otherwise identical atmosphere free of that constituent. This quanti- Precursors Atmospheric compounds that are not greenhouse gases or fies the impact of the constituent on the climate system. Examples include aerosols, but that have an effect on greenhouse gas or aerosol concen- the aerosol radiation interactions, cloud radiative effect, and greenhouse trations by taking part in physical or chemical processes regulating their effect. In this report, the portion of any top-of-atmosphere radiative effect production or destruction rates. that is due to anthropogenic or other external influences (e.g., volcanic Predictability The extent to which future states of a system may be eruptions or changes in the sun) is termed the instantaneous radiative forc- predicted based on knowledge of current and past states of the system. ing. Because knowledge of the climate system s past and current states is gen- Radiative forcing Radiative forcing is the change in the net, down- erally imperfect, as are the models that utilize this knowledge to produce a ward minus upward, radiative flux (expressed in W m 2) at the tropopause climate prediction, and because the climate system is inherently nonlinear or top of atmosphere due to a change in an external driver of climate and chaotic, predictability of the climate system is inherently limited. Even change, such as, for example, a change in the concentration of carbon diox- with arbitrarily accurate models and observations, there may still be limits ide or the output of the Sun. Sometimes internal drivers are still treated as to the predictability of such a nonlinear system (AMS, 2000). forcings even though they result from the alteration in climate, for example Prediction quality/skill Measures of the success of a prediction aerosol or greenhouse gas changes in paleoclimates. The traditional radia- against observationally based information. No single measure can sum- tive forcing is computed with all tropospheric properties held fixed at their marize all aspects of forecast quality and a suite of metrics is considered. unperturbed values, and after allowing for stratospheric temperatures, if Metrics will differ for forecasts given in deterministic and probabilistic perturbed, to readjust to radiative-dynamical equilibrium. Radiative forc- form. See also Climate prediction. ing is called instantaneous if no change in stratospheric temperature is accounted for. The radiative forcing once rapid adjustments are accounted Pre-industrial See Industrial Revolution. for is termed the effective radiative forcing. For the purposes of this report, Probability Density Function (PDF) A probability density function radiative forcing is further defined as the change relative to the year 1750 AIII is a function that indicates the relative chances of occurrence of different and, unless otherwise noted, refers to a global and annual average value. outcomes of a variable. The function integrates to unity over the domain Radiative forcing is not to be confused with cloud radiative forcing, which for which it is defined and has the property that the integral over a sub- describes an unrelated measure of the impact of clouds on the radiative domain equals the probability that the outcome of the variable lies within flux at the top of the atmosphere. that sub-domain. For example, the probability that a temperature anomaly Rapid adjustment The response to an agent perturbing the climate defined in a particular way is greater than zero is obtained from its PDF system that is driven directly by the agent, independently of any change by integrating the PDF over all possible temperature anomalies greater in the global mean surface temperature. For example, carbon dioxide and than zero. Probability density functions that describe two or more variables aerosols, by altering internal heating and cooling rates within the atmo- simultaneously are similarly defined. sphere, can each cause changes to cloud cover and other variables thereby Process-based Model Theoretical concepts and computational meth- producing a radiative effect even in the absence of any surface warming or ods that represent and simulate the behaviour of real-world systems cooling. Adjustments are rapid in the sense that they begin to occur right derived from a set of functional components and their interactions with away, before climate feedbacks which are driven by warming (although each other and the system environment, through physical and mechanistic some adjustments may still take significant time to proceed to completion, processes occurring over time. See also Climate model. for example those involving vegetation or ice sheets). It is also called the rapid response or fast adjustment. For further explanation on the concept, Projection A projection is a potential future evolution of a quantity or see Sections 7.1 and 8.1. set of quantities, often computed with the aid of a model. Unlike predic- tions, projections are conditional on assumptions concerning, for example, Rapid climate change See Abrupt climate change. future socioeconomic and technological developments that may or may Rapid dynamical change (of glaciers or ice sheets) Changes in not be realized. See also Climate prediction and Climate projection. glacier or ice sheet mass controlled by changes in flow speed and dis- Proxy A proxy climate indicator is a record that is interpreted, using charge rather than by accumulation or ablation. This can result in a rate physical and biophysical principles, to represent some combination of of mass change larger than that due to any imbalance between accumula- climate-related variations back in time. Climate-related data derived in tion and ablation. Rapid dynamical change may be initiated by a climatic this way are referred to as proxy data. Examples of proxies include pollen t ­rigger, such as incursion of warm ocean water beneath an ice shelf, or analysis, tree ring records, speleothems, characteristics of corals and vari- thinning of a grounded tidewater terminus, which may lead to reactions ous data derived from marine sediments and ice cores. Proxy-data can be within the glacier system, that may result in rapid ice loss. See also Mass calibrated to provide quantitative climate information. balance/budget (of glaciers or ice sheets). Quasi-Biennal Oscillation (QBO) A near-periodic oscillation of the Reanalysis Reanalyses are estimates of historical atmospheric tem- equatorial zonal wind between easterlies and westerlies in the tropical perature and wind or oceanographic temperature and current, and other stratosphere with a mean period of around 28 months. The alternating quantities, created by processing past meteorological or oceanographic wind maxima descend from the base of the mesosphere down to the tro- data using fixed state-of-the-art weather forecasting or ocean circulation popause, and are driven by wave energy that propagates up from the tro- models with data assimilation techniques. Using fixed data assimilation posphere. avoids effects from the changing analysis system that occur in operational analyses. Although continuity is improved, global reanalyses still suffer Quaternary The Quaternary System is the latter of three systems that from changing coverage and biases in the observing systems. make up the Cenozoic Era (65 Ma to present), extending from 2.59 Ma to the present, and includes the Pleistocene and Holocene epochs. 1460 Glossary Annex III Rebound effect When CO2 is removed from the atmosphere, the CO2 RCP8.5 One high pathway for which radiative forcing reaches great- concentration gradient between atmospheric and land/ocean carbon reser- er than 8.5 W m 2 by 2100 and continues to rise for some amount of voirs is reduced. This leads to a reduction or reversal in subsequent inher- time (the corresponding ECP assuming constant emissions after 2100 ent rate of removal of CO2 from the atmosphere by natural carbon cycle and constant concentrations after 2250) processes on land and ocean. For further description of future scenarios, see Box 1.1. Reconstruction (of climate variable) Approach to reconstructing Reservoir A component of the climate system, other than the atmo- the past temporal and spatial characteristics of a climate variable from sphere, which has the capacity to store, accumulate or release a substance predictors. The predictors can be instrumental data if the reconstruction is of concern, for example, carbon, a greenhouse gas or a precursor. Oceans, used to infill missing data or proxy data if it is used to develop paleoclimate soils and forests are examples of reservoirs of carbon. Pool is an equivalent reconstructions. Various techniques have been developed for this purpose: term (note that the definition of pool often includes the atmosphere). The linear multivariate regression based methods and nonlinear Bayesian and absolute quantity of the substance of concern held within a reservoir at a analog methods. specified time is called the stock. Reforestation Planting of forests on lands that have previously Resolution In climate models, this term refers to the physical distance contained forests but that have been converted to some other use. For (metres or degrees) between each point on the grid used to compute the a discussion of the term forest and related terms such as afforestation, equations. Temporal resolution refers to the time step or time elapsed reforestation and deforestation, see the IPCC Report on Land Use, Land- between each model computation of the equations. Use Change and Forestry (IPCC, 2000). See also the Report on Definitions and Methodological Options to Inventory Emissions from Direct Human- Respiration The process whereby living organisms convert organic induced Degradation of Forests and Devegetation of Other Vegetation matter to carbon dioxide, releasing energy and consuming molecular Types (IPCC, 2003). oxygen. Region A region is a territory characterized by specific geographical Response time The response time or adjustment time is the time and climatological features. The climate of a region is affected by regional needed for the climate system or its components to re-equilibrate to a and local scale features like topography, land use characteristics and lakes, new state, following a forcing resulting from external processes. It is very as well as remote influences from other regions. See also Teleconnection. different for various components of the climate system. The response time AIII of the troposphere is relatively short, from days to weeks, whereas the Regional Climate Model (RCM) A climate model at higher resolu- stratosphere reaches equilibrium on a time scale of typically a few months. tion over a limited area. Such models are used in downscaling global cli- Due to their large heat capacity, the oceans have a much longer response mate results over specific regional domains. time: typically decades, but up to centuries or millennia. The response Relative humidity The relative humidity specifies the ratio of actual time of the strongly coupled surface troposphere system is, therefore, water vapour pressure to that at saturation with respect to liquid water or slow compared to that of the stratosphere, and mainly determined by the ice at the same temperature. See also Specific humidity. oceans. The biosphere may respond quickly (e.g., to droughts), but also very slowly to imposed changes. See lifetime for a different definition of Relative sea level Sea level measured by a tide gauge with respect to response time pertinent to the rate of processes affecting the concentra- the land upon which it is situated. See also Mean sea level and Sea level tion of trace gases. change. Return period An estimate of the average time interval between Representative Concentration Pathways (RCPs) Scenarios that occurrences of an event (e.g., flood or extreme rainfall) of (or below/above) include time series of emissions and concentrations of the full suite of a defined size or intensity. See also Return value. greenhouse gases and aerosols and chemically active gases, as well as land use/land cover (Moss et al., 2008). The word representative signifies that Return value The highest (or, alternatively, lowest) value of a given each RCP provides only one of many possible scenarios that would lead to variable, on average occurring once in a given period of time (e.g., in 10 the specific radiative forcing characteristics. The term pathway emphasizes years). See also Return period. that not only the long-term concentration levels are of interest, but also River discharge See Streamflow. the trajectory taken over time to reach that outcome. (Moss et al., 2010). Runoff That part of precipitation that does not evaporate and is not RCPs usually refer to the portion of the concentration pathway extend- transpired, but flows through the ground or over the ground surface and ing up to 2100, for which Integrated Assessment Models produced returns to bodies of water. See also Hydrological cycle. corresponding emission scenarios. Extended Concentration Pathways (ECPs) describe extensions of the RCPs from 2100 to 2500 that were Scenario A plausible description of how the future may develop based calculated using simple rules generated by stakeholder consultations, on a coherent and internally consistent set of assumptions about key driv- and do not represent fully consistent scenarios. ing forces (e.g., rate of technological change, prices) and relationships. Note that scenarios are neither predictions nor forecasts, but are useful to Four RCPs produced from Integrated Assessment Models were selected provide a view of the implications of developments and actions. See also from the published literature and are used in the present IPCC Assess- Climate scenario, Emission scenario, Representative Concentration Path- ment as a basis for the climate predictions and projections presented ways and SRES scenarios. in Chapters 11 to 14: Sea ice Ice found at the sea surface that has originated from the freez- RCP2.6 One pathway where radiative forcing peaks at approxi- ing of seawater. Sea ice may be discontinuous pieces (ice floes) moved on mately 3 W m 2 before 2100 and then declines (the corresponding ECP the ocean surface by wind and currents (pack ice), or a motionless sheet assuming constant emissions after 2100) attached to the coast (land-fast ice). Sea ice concentration is the fraction RCP4.5 and RCP6.0 Two intermediate stabilization pathways in of the ocean covered by ice. Sea ice less than one year old is called first- which radiative forcing is stabilized at approximately 4.5 W m 2 and year ice. Perennial ice is sea ice that survives at least one summer. It may 6.0 W m 2 after 2100 (the corresponding ECPs assuming constant con- be subdivided into second-year ice and multi-year ice, where multiyear ice centrations after 2150) has survived at least two summers. 1461 Annex III Glossary Sea level change Sea level can change, both globally and locally due Soil temperature The temperature of the soil. This can be measured or to (1) changes in the shape of the ocean basins, (2) a change in ocean modelled at multiple levels within the depth of the soil. volume as a result of a change in the mass of water in the ocean, and (3) Solar activity General term describing a variety of magnetic phenome- changes in ocean volume as a result of changes in ocean water density. na on the Sun such as sunspots, faculae (bright areas), and flares (emission Global mean sea level change resulting from change in the mass of the of high-energy particles). It varies on time scales from minutes to millions ocean is called barystatic. The amount of barystatic sea level change due to of years. See also Solar cycle. the addition or removal of a mass of water is called its sea level equivalent (SLE). Sea level changes, both globally and locally, resulting from changes Solar ( 11-year ) cycle A quasi-regular modulation of solar activity in water density are called steric. Density changes induced by tempera- with varying amplitude and a period of between 8 and 14 years. ture changes only are called thermosteric, while density changes induced Solar radiation Electromagnetic radiation emitted by the Sun with a by salinity changes are called halosteric. Barystatic and steric sea level spectrum close to the one of a black body with a temperature of 5770 K. changes do not include the effect of changes in the shape of ocean basins The radiation peaks in visible wavelengths. When compared to the ter- induced by the change in the ocean mass and its distribution. See also restrial radiation it is often referred to as shortwave radiation. See also Relative Sea Level and Thermal expansion. Insolation and Total solar irradiance (TSI). Sea level equivalent (SLE) The sea level equivalent of a mass of Solar Radiation Management (SRM) Solar Radiation Management water (ice, liquid or vapour) is that mass, converted to a volume using a refers to the intentional modification of the Earth s shortwave radiative density of 1000 kg m 3, and divided by the present-day ocean surface area budget with the aim to reduce climate change according to a given metric of 3.625 × 1014 m2. Thus, 362.5 Gt of water mass added to the ocean will (e.g., surface temperature, precipitation, regional impacts, etc). Artificial cause 1 mm of global mean sea level rise. See also Sea level change. injection of stratospheric aerosols and cloud brightening are two examples Seasonally frozen ground See Frozen ground. of SRM techniques. Methods to modify some fast-responding elements of the longwave radiative budget (such as cirrus clouds), although not strictly Sea surface temperature (SST) The sea surface temperature is the speaking SRM, can be related to SRM. SRM techniques do not fall within subsurface bulk temperature in the top few metres of the ocean, measured the usual definitions of mitigation and adaptation (IPCC, 2012, p. 2). See by ships, buoys and drifters. From ships, measurements of water samples in also Solar radiation, Carbon Dioxide Removal (CDR) and Geoengineering. buckets were mostly switched in the 1940s to samples from engine intake AIII water. Satellite measurements of skin temperature (uppermost layer; a Solubility pump Solubility pump is an important physicochemical pro- fraction of a millimetre thick) in the infrared or the top centimetre or so in cess that transports dissolved inorganic carbon from the ocean s surface the microwave are also used, but must be adjusted to be compatible with to its interior. This process controls the inventory of carbon in the ocean. the bulk temperature. The solubility of gaseous carbon dioxide can alter carbon dioxide concen- trations in the oceans and the overlying atmosphere. See also Biological Semi-direct (aerosol) effect See Aerosol radiation interaction. pump. Semi-empirical model Model in which calculations are based on a Source Any process, activity or mechanism that releases a greenhouse combination of observed associations between variables and theoretical gas, an aerosol or a precursor of a greenhouse gas or aerosol into the considerations relating variables through fundamental principles (e.g., atmosphere. conservation of energy). For example, in sea level studies, semi-empirical models refer specifically to transfer functions formulated to project future Southern Annular Mode (SAM) The leading mode of variability of global mean sea level change, or contributions to it, from future global Southern Hemisphere geopotential height, which is associated with shifts mean surface temperature change or radiative forcing. in the latitude of the midlatitude jet. See SAM Index, Box 2.5. Sensible heat flux The turbulent or conductive flux of heat from the Southern Oscillation See El Nino-Southern Oscillation (ENSO). Earth s surface to the atmosphere that is not associated with phase chang- South Pacific Convergence Zone (SPCZ) A band of low-level con- es of water; a component of the surface energy budget. vergence, cloudiness and precipitation ranging from the west Pacific warm Sequestration See Uptake. pool south-eastwards towards French Polynesia, which is one of the most significant features of subtropical Southern Hemisphere climate. It shares Shortwave radiation See Solar radiation. some characteristics with the ITCZ, but is more extratropical in nature, Significant wave height The average trough-to-crest height of the especially east of the Dateline. highest one third of the wave heights (sea and swell) occurring in a par- Spatial and temporal scales Climate may vary on a large range of ticular time period. spatial and temporal scales. Spatial scales may range from local (less than Sink Any process, activity or mechanism that removes a greenhouse 100 000 km2), through regional (100 000 to 10 million km2) to continental gas, an aerosol or a precursor of a greenhouse gas or aerosol from the (10 to 100 million km2). Temporal scales may range from seasonal to geo- atmosphere. logical (up to hundreds of millions of years). Slab-ocean model A simplified representation in a climate model of Specific humidity The specific humidity specifies the ratio of the mass the ocean as a motionless layer of water with a depth of 50 to 100 m. of water vapour to the total mass of moist air. See also Relative humidity. Climate models with a slab ocean can be used only for estimating the equi- SRES scenarios SRES scenarios are emission scenarios developed by librium response of climate to a given forcing, not the transient evolution Nakiæenoviæ and Swart (2000) and used, among others, as a basis for some of climate. See also Equilibrium and transient climate experiment. of the climate projections shown in Chapters 9 to 11 of IPCC (2001) and Snow cover extent The areal extent of snow covered ground. Chapters 10 and 11 of IPCC (2007). The following terms are relevant for a better understanding of the structure and use of the set of SRES scenarios: Snow water equivalent (SWE) The depth of liquid water that would result if a mass of snow melted completely. Scenario family Scenarios that have a similar demographic, soci- etal, economic and technical change storyline. Four scenario families Soil moisture Water stored in the soil in liquid or frozen form. comprise the SRES scenario set: A1, A2, B1 and B2. 1462 Glossary Annex III Illustrative Scenario A scenario that is illustrative for each of dardized variables and a standardized climate index, that is, the variables the six scenario groups reflected in the Summary for Policymakers of and index are each centred and scaled to have zero mean and unit vari- Nakiæenoviæ and Swart (2000). They include four revised marker scenar- ance. One-point teleconnection maps are made by choosing a variable at ios for the scenario groups A1B, A2, B1, B2 and two additional scenar- one of the locations to be the climate index. See also Teleconnection. ios for the A1FI and A1T groups. All scenario groups are equally sound. Terrestrial radiation Radiation emitted by the Earth s surface, the Marker Scenario A scenario that was originally posted in draft form atmosphere and the clouds. It is also known as thermal infrared or long- on the SRES website to represent a given scenario family. The choice of wave radiation, and is to be distinguished from the near-infrared radia- markers was based on which of the initial quantifications best reflected tion that is part of the solar spectrum. Infrared radiation, in general, has a the storyline, and the features of specific models. Markers are no more distinctive range of wavelengths (spectrum) longer than the wavelength of likely than other scenarios, but are considered by the SRES writing team the red light in the visible part of the spectrum. The spectrum of terrestrial as illustrative of a particular storyline. They are included in revised form radiation is almost entirely distinct from that of shortwave or solar radia- in Nakiæenoviæ and Swart (2000). These scenarios received the closest tion because of the difference in temperature between the Sun and the scrutiny of the entire writing team and via the SRES open process. Sce- Earth atmosphere system. See also Outgoing longwave radiation. narios were also selected to illustrate the other two scenario groups. Thermal expansion In connection with sea level, this refers to the Storyline A narrative description of a scenario (or family of sce- increase in volume (and decrease in density) that results from warming narios), highlighting the main scenario characteristics, relationships water. A warming of the ocean leads to an expansion of the ocean volume between key driving forces and the dynamics of their evolution. and hence an increase in sea level. See also Sea level change. Steric See Sea level change. Thermocline The layer of maximum vertical temperature gradient in the ocean, lying between the surface ocean and the abyssal ocean. In sub- Stock See Reservoir. tropical regions, its source waters are typically surface waters at higher Storm surge The temporary increase, at a particular locality, in the latitudes that have subducted (see Subduction) and moved equatorward. height of the sea due to extreme meteorological conditions (low atmo- At high latitudes, it is sometimes absent, replaced by a halocline, which is spheric pressure and/or strong winds). The storm surge is defined as being a layer of maximum vertical salinity gradient. the excess above the level expected from the tidal variation alone at that Thermohaline circulation (THC) Large-scale circulation in the ocean AIII time and place. that transforms low-density upper ocean waters to higher-density interme- Storm tracks Originally, a term referring to the tracks of individual diate and deep waters and returns those waters back to the upper ocean. cyclonic weather systems, but now often generalized to refer to the main The circulation is asymmetric, with conversion to dense waters in restrict- regions where the tracks of extratropical disturbances occur as sequences ed regions at high latitudes and the return to the surface involving slow of low (cyclonic) and high (anticyclonic) pressure systems. upwelling and diffusive processes over much larger geographic regions. The THC is driven by high densities at or near the surface, caused by cold Stratosphere The highly stratified region of the atmosphere above the temperatures and/or high salinities, but despite its suggestive though troposphere extending from about 10 km (ranging from 9 km at high lati- common name, is also driven by mechanical forces such as wind and tides. tudes to 16 km in the tropics on average) to about 50 km altitude. Frequently, the name THC has been used synonymously with Meridional Streamflow Water flow within a river channel, for example expressed Overturning Circulation. in m3 s 1. A synonym for river discharge. Thermokarst The process by which characteristic landforms result from Subduction Ocean process in which surface waters enter the ocean the thawing of ice-rich permafrost or the melting of massive ground ice. interior from the surface mixed layer through Ekman pumping and lateral Thermosteric See Sea level change. advection. The latter occurs when surface waters are advected to a region where the local surface layer is less dense and therefore must slide below Tide gauge A device at a coastal or deep-sea location that continu- the surface layer, usually with no change in density. ously measures the level of the sea with respect to the adjacent land. Time averaging of the sea level so recorded gives the observed secular changes Sunspots Dark areas on the Sun where strong magnetic fields reduce of the relative sea level. the convection causing a temperature reduction of about 1500 K com- pared to the surrounding regions. The number of sunspots is higher during Tipping point In climate, a hypothesized critical threshold when global periods of higher solar activity, and varies in particular with the solar cycle. or regional climate changes from one stable state to another stable state. The tipping point event may be irreversible. See also Irreversibility. Surface layer See Atmospheric boundary layer. Total solar irradiance (TSI) The total amount of solar radiation in Surface temperature See Global mean surface temperature, Land watts per square metre received outside the Earth s atmosphere on a surface air temperature and Sea surface temperature. s ­ urface normal to the incident radiation, and at the Earth s mean distance Talik A layer of year-round unfrozen ground that lies in permafrost from the Sun. areas. Reliable measurements of solar radiation can only be made from space Teleconnection A statistical association between climate variables at and the precise record extends back only to 1978. The generally accept- widely separated, geographically-fixed spatial locations. Teleconnections ed value is 1368 W m 2 with an accuracy of about 0.2%. It has recently are caused by large spatial structures such as basin-wide coupled modes been estimated to 1360.8 +/- 0.5 W m 2 for the solar minimum of 2008. of ocean atmosphere variability, Rossby wave-trains, mid-latitude jets and Variations of a few tenths of a percent are common, usually associ- storm tracks, etc. See also Teleconnection pattern. ated with the passage of sunspots across the solar disk. The solar cycle variation of TSI is of the order of 0.1% (AMS, 2000). Changes in the Teleconnection pattern A correlation map obtained by calculating ultraviolet part of the spectrum during a solar cycle are comparatively the correlation between variables at different spatial locations and a cli- larger (percent) than in TSI. See also Insolation. mate index. It is the special case of a climate pattern obtained for stan- 1463 Annex III Glossary Transient climate response See Climate sensitivity. Volatile Organic Compounds (VOC) Important class of organic chemical air pollutants that are volatile at ambient air conditions. Other Transient climate response to cumulative CO2 emissions (TCRE) terms used to represent VOCs are hydrocarbons (HCs), reactive organic The transient global average surface temperature change per unit cumu- gases (ROGs) and non-methane volatile organic compounds (NMVOCs). lated CO2 emissions, usually 1000 PgC. TCRE combines both information NMVOCs are major contributors (together with NOx and CO) to the forma- on the airborne fraction of cumulated CO2 emissions (the fraction of the tion of photochemical oxidants such as ozone. total CO2 emitted that remains in the atmosphere), and on the transient climate response (TCR). Walker Circulation Direct thermally driven zonal overturning circula- tion in the atmosphere over the tropical Pacific Ocean, with rising air in the Tree rings Concentric rings of secondary wood evident in a cross sec- western and sinking air in the eastern Pacific. tion of the stem of a woody plant. The difference between the dense, small- celled late wood of one season and the wide-celled early wood of the Warm days/warm nights Days where maximum temperature, or following spring enables the age of a tree to be estimated, and the ring nights where minimum temperature, exceeds the 90th percentile, where widths or density can be related to climate parameters such as tempera- the respective temperature distributions are generally defined with respect ture and precipitation. See also Proxy. to the 1961 1990 reference period. For the corresponding indices, see Box 2.4. Trend In this report, the word trend designates a change, generally monotonic in time, in the value of a variable. Warm spell A period of abnormally hot weather. For the corresponding indices, see Box 2.4. See also Heat wave. Tropopause The boundary between the troposphere and the strato- sphere. Water cycle See Hydrological cycle. Troposphere The lowest part of the atmosphere, from the surface to Water mass A body of ocean water with identifiable properties (tem- about 10 km in altitude at mid-latitudes (ranging from 9 km at high lati- perature, salinity, density, chemical tracers) resulting from its unique for- tudes to 16 km in the tropics on average), where clouds and weather phe- mation process. Water masses are often identified through a vertical or nomena occur. In the troposphere, temperatures generally decrease with horizontal extremum of a property such as salinity. North Pacific Intermedi- height. See also Stratosphere. ate Water (NPIW) and Antarctic Intermediate Water (AAIW) are examples of water masses. AIII Turnover time See Lifetime. Weathering The gradual removal of atmospheric CO2 through disso- Uncertainty A state of incomplete knowledge that can result from a lution of silicate and carbonate rocks. Weathering may involve physical lack of information or from disagreement about what is known or even processes (mechanical weathering) or chemical activity (chemical weath- knowable. It may have many types of sources, from imprecision in the data ering). to ambiguously defined concepts or terminology, or uncertain projections of human behaviour. Uncertainty can therefore be represented by quantita- Well-mixed greenhouse gas See Greenhouse gas. tive measures (e.g., a probability density function) or by qualitative state- Younger Dryas A period 12.85 to 11.65 ka (thousand years before ments (e.g., reflecting the judgment of a team of experts) (see Moss and 1950), during the deglaciation, characterized by a temporary return to Schneider, 2000; Manning et al., 2004; Mastrandrea et al., 2010). See also colder conditions in many locations, especially around the North Atlantic. Confidence and Likelihood. United Nations Framework Convention on Climate Change (UNFCCC) The Convention was adopted on 9 May 1992 in New York and signed at the 1992 Earth Summit in Rio de Janeiro by more than 150 countries and the European Community. Its ultimate objective is the sta- bilisation of greenhouse gas concentrations in the atmosphere at a level that would prevent dangerous anthropogenic interference with the climate system . It contains commitments for all Parties. Under the Convention, Parties included in Annex I (all OECD countries and countries with econo- mies in transition) aim to return greenhouse gas emissions not controlled by the Montreal Protocol to 1990 levels by the year 2000. The convention entered in force in March 1994. In 1997, the UNFCCC adopted the Kyoto Protocol. Uptake The addition of a substance of concern to a reservoir. The uptake of carbon containing substances, in particular carbon dioxide, is often called (carbon) sequestration. Urban heat island (UHI) The relative warmth of a city compared with surrounding rural areas, associated with changes in runoff, effects on heat retention, and changes in surface albedo. Ventilation The exchange of ocean properties with the atmospheric surface layer such that property concentrations are brought closer to equi- librium values with the atmosphere (AMS, 2000), and the processes that propagate these properties into the ocean interior. 1464 Glossary Annex III References AMS, 2000: AMS Glossary of Meteorology, 2nd ed. American Meteorological Society, Nakiæenoviæ, N., and R. Swart (eds.), 2000: Special Report on Emissions Scenarios. A Boston, MA, http://amsglossary.allenpress.com/glossary/browse. Special Report of Working Group III of the Intergovernmental Panel on Climate Hegerl, G. C., O. Hoegh-Guldberg, G. Casassa, M. P. Hoerling, R. S. Kovats, C. Parmesan, Change. Cambridge University Press, Cambridge, United Kingdom and New D. W. Pierce, and P. A. Stott, 2010: Good practice guidance paper on detection York, NY, USA, 599 pp. and attribution related to anthropogenic climate change. In: Meeting Report of Schwartz, S.E., and P. Warneck, 1995: Units for use in atmospheric chemistry. Pure the Intergovernmental Panel on Climate Change Expert Meeting on Detection Appl. Chem., 67, 1377 1406. and Attribution of Anthropogenic Climate Change [T. F. Stocker, C. B. Field, D. Qin, V. Barros, G.-K. Plattner, M. Tignor, P. M. Midgley and K. L. Ebi (eds.)]. IPCC Working Group I Technical Support Unit, University of Bern, Bern, Switzerland. IPCC, 1992: Climate Change 1992: The Supplementary Report to the IPCC Scientific Assessment [J. T. Houghton, B. A. Callander and S. K. Varney (eds.)]. Cambridge University Press, Cambridge, United Kingdom and New York, NY, USA, 116 pp. IPCC, 1996: Climate Change 1995: The Science of Climate Change. Contribution of Working Group I to the Second Assessment Report of the Intergovernmental Panel on Climate Change [J. T. Houghton., L. G. Meira . A. Callander, N. Harris, A. Kattenberg and K. Maskell (eds.)]. Cambridge University Press, Cambridge, United Kingdom and New York, NY, USA, 572 pp. IPCC, 2000: Land Use, Land-Use Change, and Forestry. Special Report of the Intergovernmental Panel on Climate Change [R. T. Watson, I. R. Noble, B. Bolin, N. H. Ravindranath, D. J. Verardo, and D. J. Dokken (eds.)]. Cambridge University Press, Cambridge, United Kingdom and New York, NY, USA, 377 pp. IPCC, 2001: Climate Change 2001: The Scientific Basis. Contribution of Working Group I to the Third Assessment Report of the Intergovernmental Panel on Climate Change [T. Houghton, Y. Ding, D. J. Griggs, M. Noquer, P. J. van der Linden, X. Dai, K. Maskell and C. A. Johnson (eds.)]. Cambridge University Press, AIII Cambridge, United Kingdom and New York, NY, USA, 881 pp. IPCC, 2003: Definitions and Methodological Options to Inventory Emissions from Direct Human-Induced Degradation of Forests and Devegetation of Other Vegetation Types [Penman, J., M. Gytarsky, T. Hiraishi, T. Krug, D. Kruger, R. Pipatti, L. Buendia, K. Miwa, T. Ngara, K. Tanabe and F. Wagner (eds.)]. The Institute for Global Environmental Strategies (IGES), Japan, 32 pp. IPCC, 2007: Climate Change 2007: The Physical Science Basis. Contribution of Working Group I to the Fourth Assessment Report of the Intergovernmental Panel on Climate Change. [Solomon, S., D. Qin, M. Manning, Z. Chen, M. Marquis, K. B. Averyt, M. Tignor and H. L. Miller (eds.)]. Cambridge University Press, Cambridge, United Kingdom and New York, NY, USA, 996 pp. IPCC, 2011: Workshop Report of the Intergovernmental Panel on Climate Change Workshop on Impacts of Ocean Acidification on Marine Biology and Ecosystems [C. B. Field, V. Barros, T. F. Stocker, D. Qin, K.J. Mach, G.-K. Plattner, M. D. Mastrandrea, M. Tignor and K. L. Ebi (eds.)]. IPCC Working Group II Technical Support Unit, Carnegie Institution, Stanford, CA, USA, 164 pp. IPCC, 2012: Meeting Report of the Intergovernmental Panel on Climate Change Expert Meeting on Geoengineering [O. Edenhofer, R. Pichs-Madruga, Y. Sokona, C. Field, V. Barros, T. F. Stocker, Q. Dahe, J. Minx, K. Mach, G.-K. Plattner, S. Schlo mer, G. Hansen and M. Mastrandrea (eds.)]. IPCC Working Group III Technical Support Unit, Potsdam Institute for Climate Impact Research, Potsdam, Germany, 99 pp. Manning, M., et al., 2004: IPCC Workshop on Describing Scientific Uncertainties in Climate Change to Support Analysis of Risk of Options. Workshop Report. IPCC Working Group I Technical Support Unit, Boulder, CO, USA, 138 pp. Mastrandrea, M. D., C. B. Field, T. F. Stocker, O. Edenhofer, K. L. Ebi, D. J. Frame, H. Held, E. Kriegler, K. J. Mach, P. R. Matschoss, G.-K. Plattner, G. W. Yohe, and F. W. Zwiers, 2010: Guidance Note for Lead Authors of the IPCC Fifth Assessment Report on Consistent Treatment of Uncertainties. Intergovernmental Panel on Climate Change (IPCC). http://www.ipcc.ch. Moss, R., and S. Schneider, 2000: Uncertainties in the IPCC TAR: Recommendations to Lead Authors for More Consistent Assessment and Reporting. In: IPCC Supporting Material: Guidance Papers on Cross Cutting Issues in the Third Assessment Report of the IPCC. [Pachauri, R., T. Taniguchi, and K. Tanaka (eds.)]. Intergovernmental Panel on Climate Change, Geneva, pp. 33 51. Moss, R., et al., 2008: Towards new scenarios for analysis of emissions, climate change, impacts and response strategies. Intergovernmental Panel on Climate Change, Geneva, 132 pp. Moss, R. et al., 2010: The next generation of scenarios for climate change research and assessment. Nature, 463, 747 756. 1465 AIV Annex IV: Acronyms This annex should be cited as: IPCC, 2013: Annex IV: Acronyms. In: Climate Change 2013: The Physical Science Basis. Contribution of Working Group I to the Fifth Assessment Report of the Intergovernmental Panel on Climate Change [Stocker, T.F., D. Qin, G.-K. Plattner, M. Tignor, S.K. Allen, J. Boschung, A. Nauels, Y. Xia, V. Bex and P.M. Midgley (eds.)]. Cambridge University Press, Cam- bridge, United Kingdom and New York, NY, USA. 1467 Annex IV Acronyms mol Micromole ARFI Aerosol Radiative Forcing over India 20C3M 20th Century Climate in Coupled Models ari Aerosol Radiation Interactions AABW Antarctic Bottom Water ARM Atmospheric Radiation Measurement AAIW Antarctic Intermediate Water ARTIST Arctic Radiation and Turbulence Interaction Study AAO Antarctic Oscillation ATL3 Atlantic 3 AATSR Advanced Along Track Scanning Radiometer ATSR Along Track Scanning Radiometer ABA AMSR Bootstrap Algorithm AUSMC Australian-Maritime Continent ACC Antarctic Circumpolar Current AVHRR Advanced Very High Resolution Radiometer ACCENT Atmospheric Composition Change: a European AVISO Archiving, Validation and Interpretation of Network Satellite Oceanographic Data aci Aerosol Cloud Interactions BATS Bermuda Atlantic Time Series Study ACRIM Active Cavity Radiometer Irradiance Monitor BC Black Carbon ACW Antarctic Circumpolar Wave BCC Beijing Climate Center AeroCom Aerosol Model Intercomparison BCC-CSM Beijing Climate Center-Climate System Model AERONET Aerosol Robotic Network BDC Brewer Dobson Circulation A-FORCE Aerosol Radiative Forcing in East Asia BECCS Bio-Energy with Carbon-Capture and Storage Aircraft Campaign BMI Basin Mean Index AGAGE Advanced Global Atmospheric Gases Experiment BNF Biological Nitrogen Fixation AGCM Atmospheric General Circulation Model BOM Bureau of Meteorology AGTP Absolute Global Temperature Change Potential C2Cl4 Tetrachloroethene AGWP Absolute Global Warming Potential C4MIP Coupled Climate Carbon Cycle Model AIC Aircraft-Induced Cirrus Intercomparison Project AIV ALOHA A Long-term Oligotrophic Habitat Assessment CaCO3 Calcium Carbonate AMIP Atmospheric Model Intercomparison Project CALIOP Cloud-Aerosol Lidar with Orthogonal Polarization AMM Atlantic Meridional Mode CALIPSO Cloud-Aerosol Lidar and Infrared Pathfinder Satellite Observations AMO Atlantic Multi-decadal Oscillation CAM Community Atmosphere Model AMOC Atlantic Meridional Overturning Circulation CAMS Climate Anomaly Monitoring System AMSR Advanced Microwave Scanning Radiometer CanESM Canadian Earth System Model AMSU Advanced Microwave Sounding Unit CASTNET Clean Air Status and Trends Network AMV Atlantic Multi-decadal Variability CCCma Canadian Centre for Climate Modelling AO Arctic Oscillation and Analysis AOD Aerosol Optical Depth CCl4 Carbon Tetrachloride AOGCM Atmosphere Ocean General Circulation Model CCM Chemistry Climate Model APHRODITE Asian Precipitation Highly Resolved CCMVal Chemistry Climate Model Validation Observational Data Integration Towards Evaluation CCN Cloud Condensation Nuclei AR4 IPCC Fourth Assessment Report CCR Carbon Climate Response ARCPAC Aerosol, Radiation, and Cloud Processes affecting Arctic Climate CCSM Community Climate System Model ARCTAS Arctic Research of the Composition of the CCSR Centre for Climate System Research Troposphere from Aircraft and Satellites CDD Consecutive Dry Days 1468 Acronyms Annex IV CDIAC Carbon Dioxide Information Analysis Center COCO CCSR Ocean Component Model CDR Carbon Dioxide Removal COHMAP Cooperative Holocene Mapping Project CDW Circumpolar Deep Water CORE Coordinated Ocean-ice Reference Experiments CE Common Era COWCLIP Coordinated Ocean Wave Climate Project CERES Cloud and the Earth s Radiant Energy System COWL Cold Ocean/Warm Land CESM Community Earth System Model CPC Climate Prediction Center (NOAA) CESM1 BGC Community Earth System Model 1 CPR Cloud Profiling Radar Biogeochemical CRE Cloud Radiative Effect CF4 Perfluoromethane CRU Climatic Research Unit CFC Chlorofluorocarbon CRUTEM4 Climatic Research Unit Gridded Dataset of CFC-11 Trichlorofluoromethane (CFCl3) Global Historical Near-Surface Air TEMperature Anomalies Over Land Version 4 CFC-113 Trichlorotrifluoroethane (CF2ClCFCl2) CS Complex Ocean Sediment Model CFC-12 Dichlorodifluoromethane (CF2Cl2) CSFR Climate Forecast System Reanalysis CFMIP Cloud Feedback Model Intercomparison Project CSIRO Commonwealth Scientific and Industrial CFSRR Climate Forecast System Reanalysis and Research Organisation Reforecast CWC Cumulative Warming Commitment CGCM Coupled General Circulation Model DCESS Danish Center for Earth System Science CH2Cl2 Dichloromethane DIC Dissolved Inorganic Carbon CH3Br Bromomethane DJF December, January and February CH3CCl3 Methyl Chloroform DMI Directional Movement Index CH3Cl Chloromethane DMS Dimethyl Sulphide CH4 Methane AIV DO Dissolved Oxygen; also Dansgaard-Oeschger CLIMAP Climate: Long-range Investigation, Mapping, and Prediction DOC Dissolved Organic Carbon CLIMBER-2 Climate and Biosphere Model DOE Department of Energy CLIO Coupled Large-scale Ice-Ocean Model DTR Diurnal Temperature Range CLM4C Community Land Model for Carbon DU Dobson Units CLM4CN Community Land Model for Carbon Nitrogen EAS East Asian Summer CMAP CPC Merged Analysis of Precipitation EASM East Asian Summer Monsoon CMDL Climate Monitoring and Diagnostics Laboratory EBC Equivalent Black Carbon (NOAA) EBM Energy Balance Model CMIP3 Coupled Model Intercomparison Project Phase 3 ECBILT Coupled Atmosphere Ocean Model from de Bilt CMIP5 Coupled Model Intercomparison Project Phase 5 ECHAM ECMWF and Hamburg CNRM Centre National de Recherches Météorologiques ECHO-G ECHAM4+HOPE-G CO Carbon Monoxide ECMWF European Centre for Medium Range CO2 Carbon Dioxide Weather Forecasts CO32 Carbonate ECS Equilibrium Climate Sensitivity COADS Comprehensive Ocean Atmosphere Data Set EDGAR Emission Database for Global Atmospheric Research COBE-SST Centennial in situ Observation-Based Estimates of Sea Surface Temperature EMIC Earth System Model of Intermediate Complexity 1469 Annex IV Acronyms ENSO El Nino-Southern Oscillation GHCNDEX Global Historical Climatology Network-Daily Gridded Data Set of Climate Extremes EOF Empirical Orthogonal Function GHCNv3 Global Historical Climatology Network Version 3 ERA-40 ECMWF 40-year ReAnalysis GHG Greenhouse Gas ERBE Earth Radiation Budget Experiment GI Greenland Interstadial ERBS Earth Radiation Budget Satellite GIA Glacial Isostatic Adjustment ERF Effective Radiative Forcing GIS Greenland Ice Sheet ERFaci Effective Radiative Forcing due to Aerosol Cloud Interactions GISP Greenland Ice Sheet Project ERFari Effective Radiative Forcing due to Aerosol GISS Goddard Institute of Space Studies Radiation Interactions GISTEMP Goddard Institute for Space Studies Surface ERS European Remote Sensing (Satellite) Temperature Analysis ERSST Extended Reconstructed Sea Surface Temperature GL Grounding Line ESA European Space Agency GLODAP Global Ocean Data Analysis Project ESM Earth System Model GLS Generalized Least Squares ESMR Electrically Scanning Microwave Radiometer GMA Global Monsoon Area ESRL Earth System Research Library (NOAA) GMD Global Monitoring Division (NOAA) ESTOC European Station for Time Series in the Ocean GMI Global Monsoon Precipitation Intensity ETC Extratropical Cyclone GMP Global Monsoon Total Precipitation FACE Free-Air CO2 Enrichment GMSL Global Mean Sea Level FAO Food and Agriculture Organization (UN) GMST Global Mean Surface Temperature FAR IPCC First Assessment Report GOCCP GCM-Oriented CALIPSO Cloud Product FGOALS1 Flexible Global Ocean Atmosphere Land GOGA Global Ocean Global Atmosphere AIV System Model Version 1 GOME Global Ozone Monitoring Experiment FIO First Institute of Oceanography GOMOS Global Ozone Monitoring by Occultation of Stars FLUXNET Global Network of Flux Towers GOSAT Greenhouse Gases Observing Satellite FTIR Fourier Transform Infrared Spectroscopy GPCC Global Precipitation Climatology Centre FTS Fourier-Transform Spectrometer GPCP Global Precipitation Climatology Project FWCC Freshwater Content Changes GPH Geopotential Height GCAM Global Change Assessment Model GPP Gross Primary Productivity GCM General Circulation Model GPS Global Positioning System GCP Global Cost Potential GRACE Gravity Recovery and Climate Experiment GCRM Global Cloud-Resolving Models GRISLI Grenoble Ice Shelf and Land Ice Model GEISA Gestion et Etude des Informations GS Greenland Stadial Spectroscopiques Atmosphériques GSFC Goddard Space Flight Centre GENIE-1 Grid Enabled Integrated Earth System Model-1 Gt Gigatonnes GeoMIP G1 Geoengineering Model Intercomparison Project G1 GTP Global Temperature Change Potential GFDL Geophysical Fluid Dynamics Laboratory GUESS General Ecosystem Simulator GFED Global Fire Emissions Database GWD Gravity-Wave Drag GHCN Global Historical Climatology Network GWP Global Warming Potential 1470 Acronyms Annex IV HadAT2 Hadley Centre Atmospheric Temperature IMBIE Ice-sheet Mass Balance Intercomparison Data Set 2 Experiment HadCM Hadley Centre Climate Prediction Models IMPROVE US Interagency Monitoring of Protected Visual Environments HadCRUT4 Hadley Centre Climatic Research Unit Gridded Surface Temperature Data Set 4 INMCM4 Institute for Numerical Mathematics Coupled Model 4 HadEX Hadley Centre Gridded Data Set Of Temperature And Precipitation Extremes IOB Indian Ocean Basin HadGEM1 Hadley Centre New Global Environmental IOBM Indian Ocean Basin Mode Model 1 IOD Indian Ocean Dipole HadGEM2-ES Hadley Centre New Global Environmental IODM Indian Ocean Dipole Mode Model 2-Earth System IPA International Permafrost Association HadGHCND Hadley Centre Gridded Daily Temperatures Data Set IPO Inter-decadal Pacific Oscillation HadISST Hadley Centre Interpolated SST IPSL Institut Pierre Simon Laplace HadNMAT2 Hadley Centre Night Marine Air Temperatures IPY International Polar Year Data Set Version 2 IR Infrared HadSLP2r Hadley Centre Sea Level Pressure Data Set 2r IRF Impulse Response Function HadSST3 Hadley Centre Sea Surface Temperature Data ISCCP International Satellite Cloud Climatology Project Set Version 3 ITCZ Inter-Tropical Convergence Zone HALOE Halogen Occultation Experiment ITF Indonesian Throughflow HCFC Hydrochlorofluorocarbon IUK Iterative Universal Kriging HCO3 Bicarbonate Ion JIMAR Joint Institute for Marine and Atmospheric HF Hickey Frieden (Radiometer) Research HFC Hydrofluorocarbon JJA June, July and August AIV HIPPO HIAPER Pole-to-Pole Observations JMA Japan Meteorological Agency HIRHAM5 High-Resolution Hamburg Climate Model 5 JPL Jet Propulsion Laboratory HITRAN High-Resolution Transmission Molecular ka 1000 Years ago Absorption KCM Knowledge Capture and Modeling HOAPS Hamburg Ocean Atmosphere Parameters and Fluxes from Satellite kyr 1000 Years HOT Hawaii Ocean Time Series LAC Light-Absorbing Carbon HYDE History Database of the Environment LBIS Larsen B Ice Shelf HY-INT Hydroclimatic Intensity LBL Line-by-line (models) HYLAND Hybrid Land Terrestrial Ecosystem Model LGM Last Glacial Maximum IAM Integrated Assessment Model LIA Little Ice Age IASI Infrared Atmospheric Sounder Interferometer LIG Last Interglacial ICE Ice Cloud and Land Elevation LISAM Large scale Index for South America Monsoon ICESat Ice, Cloud and Land Elevation Satellite LLGHG Long-Lived Greenhouse Gas ICOADS International Comprehensive Ocean-Atmosphere LMM Late Maunder Minimum Data Set LNADW Lower North Atlantic Deep Water IGAC International Global Atmospheric Chemistry LOSU Level of Scientific Understanding IMAGE Integrated Model to Assess the Global LOVECLIM Loch Vecode-Ecbilt-Clio-Agism Model Environment 1471 Annex IV Acronyms LPB La Plata Basin MLOST Merged Land Ocean Surface Temperature (Analysis) LPJ Lund-Potsdam-Jena Dynamic Global Model MLS Microwave Limb Sounder LRF Long-Range Forecast MME Multi-Model Ensemble LS Lower Stratosphere MMF Multiscale Modelling Framework LSAT Land-Surface Air Temperature MMM Multi-Model Mean LSW Labrador Sea Water MMTS Maximum Minimum Temperature Systems LUC Land Use and Climate MOC Meridional Overturning Circulation LUCID Land Use and Climate, Identification of Robust Impacts MOCAGE Modele de Chimie Atmosphérique a Grande Echelle LULC Land Use and Land Cover MODIS Moderate Resolution Imaging Spectrometer LULCC Land Use and Land Cover Change MOHC Met Office Hadley Centre LWCRE Longwave Cloud Radiative Effect MOPITT Measurements of Pollutants in the Troposphere LWR Longwave Radiation MPI Max Planck Institute MAGICC Model for the Assessment of Greenhouse Gas Induced Climate Change MPIOM Max Planck Institute Ocean Model MAM March, April and May MPWP Mid-Pliocene Warm Period MAR Modele Atmosphérique Régional MRI Meteorological Research Institute of Japan Meteorological Agency MARGO Multiproxy Approach for the Reconstruction of the Glacial Ocean Surface MSL Mean Sea Level MAT Marine Air Temperatures MSSS Mean Square Skill Score MBT Mechanical Bathythermograph MSU Microwave Sounding Unit MCA Medieval Climate Anomaly Mt Megatonnes AIV MDA Mineral Dust Aerosol MT Mid-Tropospheric MDT Mean Dynamic Topography MTCO Mean Temperature of the Coldest Month MEA Millennium Ecosystem Assessment MTWA Mean Temperature of the Warmest Month MERRA Modern Era Reanalysis for Research MW Microwave and Applications MXD Maximum Latewood Density MESSAGE Model for Energy Supply Strategy Alternatives Ma Million Years ago and their General Environmental Impact Myr Million Years MFR Maximum Feasible Reduction N2O Nitrous Oxide MHD Mace Head NADW North Atlantic Deep Water MIP Model Intercomparison Project NAM Northern Annular Mode MIPAS Michelson Interferometer for Passive Atmospheric Sounding NAMP National Air Quality Monitoring Programme (India) MIROC Model for Interdisciplinary Research on Climate NAMS North American Monsoon System MISI Marine Ice Sheet Instability NAO North Atlantic Oscillation MISR Multi-angle Imaging Spectro-Radiometer NASA National Aeronautics and Space Administration MIT Massachusetts Institute of Technology NCAR National Center for Atmospheric Research MJO Madden Julian Oscillation NCEP National Centers for Environmental Prediction MLD Mixed Layer Depth NEC North Equatorial Current 1472 Acronyms Annex IV NEEM North Greenland Eemian Ice Drilling OLS Ordinary Least Squares NEWS Global Nutrient Export from WaterSheds OMI Ozone Monitoring Instrument NF3 Nitrogen Trifluoride ONDJFM October, November, December, January, February and March NGRIP North Greenland Ice Core Project ORC Oceanic Reservoir Correction NH Northern Hemisphere PAGES 2k Past Global Changes 2k NIWA National Institute of Water and Atmospheric Research PARASOL Polarization and Anisotropy of Reflectances for Atmospheric Sciences Coupled with Observations NMAT Nighttime Marine Air Temperature from Lidar NMVOC Non-Methane Volatile Organic Compound PATMOS-x Pathfinder Atmospheres Extended Data Set NNR NCEP NCAR PBAPs Primary Biological Aerosol Particles NOAA National Oceanic and Atmospheric Administration PCM Parallel Climate Model NODC National Oceanic Data Center pCO2 Partial Pressure of Carbon Dioxide NorESM Norwegian Earth System Model PDF Probability Density Function NOx Reactive Nitrogen Oxides (the Sum PDO Pacific Decadal Oscillation of NO and NO2) PDSI Palmer Drought Severity Index NPI North Pacific Index PETM Paleocene Eocene Thermal Maximum NPIW North Pacific Intermediate Water PFC Perfluorocarbon NPP Net Primary Productivity PG Peripheral Glacier NSIDC National Snow and Ice Data Center Pg Petagram NT1 National Aeronautics and Space Administration (NASA) Team Algorithm, Version 1 PM10 Particulate Matter with Aerodynamic Diameter <10 m NT2 National Aeronautics and Space Administration (NASA) Team Algorithm, Version 2 PM2.5 Particulate Matter with Aerodynamic AIV Diameter <2.5 m NTCF Near-Term Climate Forcer PMEL Pacific Marine Environmental Laboratory O(1D) Oxygen Radical in the 1D Excited State PMIP3 Paleoclimate Modelling Intercomparison O3 Ozone Project Phase III OA Ocean Atmosphere; also Other Anthropogenic PMOD Physikalisch-Meteorologisches (Forcings) Observatorium Davos OAC Ocean Atmosphere Carbon Cycle PNA Pacific North American (Pattern) OAFlux Objectively Analyzed Air Sea Heat Fluxes POA Primary Organic Aerosol OAGCMs Ocean Atmosphere General Circulation Models POC Particulate Organic Carbon OAV Ocean Atmosphere Vegetation POLDER Polarization and Directionality of the OC Organic Carbon Earth s Reflectance OCN Oceanic Carbon and Nutrient Cycling (Model) PPE Perturbed-Parameter Ensemble ODP Ocean Drilling Program PRCE Peak Response to Cumulative Emissions ODS Ozone-Depleting Substance PREMOS Precision Monitor Sensor OH Hydroxyl Radical PSA Pacific South American (Pattern) OHC Ocean Heat Content PSMSL Permanent Service for Mean Sea Level OHR Ocean Heating Rate PSS Practical Salinity Scale OLR Outgoing Longwave Radiation PSS78 Practical Salinity Scale 1978 1473 Annex IV Acronyms PUCCINI Physical Understanding of Composition-Climate SBA SSM/I Bootstrap Algorithm Interactions and Impacts SBUV Solar Backscatter Ultraviolet QBO Quasi-Biennial Oscillation SC Solar Cycle R95p (R99p) Amount of Precipitation from Days >95th (99th) SCA Snow-Covered Area Percentile SCD Snow Cover Duration RACMO2 Regional Atmospheric Climate Model 2 SCE Snow Cover Extent RAOBCORE Radiosone Observation Correction using Reanalyses SCIA Scanning Imaging Absorption Spectrometer for Atmospheric Chartography RAPID/MOCHA Rapid Climate Change-Meridional Overturning Circulation and Heatflux Array SCIAMACHY Scanning Imaging Absorption Spectrometer for Atmospheric Chartography RATPAC Radiosonde Atmospheric Temperature Products for Assessing Climate SD Snow Depth; also Statistical Downscaling RCM Regional Climate Model SDGVM Sheffield Dynamic Global Vegetation Model RCP Representative Concentration Pathway SDII Simple Daily Precipitation Intensity Index RE Radiative Efficiency SeaWiFS Sea-viewing Wide Field-of-view Sensor REMBO Regional, Moisture-Balance Orographic Model SEM Semi-Empirical Model REML Restricted Maximum Likelihood SF6 Sulphur Hexafluoride RF Radiative Forcing SH Southern Hemisphere RFaci Radiative Forcing from Aerosol Cloud Interactions SICOPOLIS Simulation Code for Polythermal Ice Sheets RGI Randolph Glacier Inventory SIM Spectral Irradiance Monitor RH Relative Humidity SIO Scripps Institution of Oceanography RICH Radiosonde Innovation Composite SLE Sea Level Equivalent Homogenization SLP Sea Level Pressure AIV RMIB Royal Meteorological Institute of Belgium SLR Sea Level Rise RMS Root Mean Square SMB Surface Mass Balance RMSE Root Mean Square Error SMMR Scanning Multichannel Microwave Radiometer RO Radio Occultation SMOS Soil Moisture and Ocean Salinity RSCA Relative Snow-Covered Area SNO Simultaneous Nadir Overpass RSL Relative Sea Level SO2 Sulphur Dioxide RSS Remote Sensing System SO2F2 Sulphuryl Fluoride RX5day/RX1day Annual Maximum 5-Day/1-Day Precipitation SO42 Sulfate S/N Signal-to-Noise (Ratio) SOA Secondary Organic Aerosol SACZ South Atlantic Convergence Zone SOI Southern Oscillation Index SAGE Stratospheric Aerosol and Gas Experiment or SOLSTICE Solar Stellar Irradiance Comparison Experiment Centre for Sustainability and the Global Environment SON September, October and November SAM Southern Annular Mode SORCE Solar Radiation and Climate Experiment SAMS South American Monsoon System SPARC Stratospheric Processes and their Role in Climate Chemistry Climate Model Validation SAMW Sub-Antarctic Mode Water SPCZ South Pacific Convergence Zone SAR IPCC Second Assessment Report SAT Surface Air Temperature 1474 Acronyms Annex IV SPEI Standardised Precipitation Evapotranspiration TOPEX Topography Experiment Index TRANSCOM Atmospheric Tracer Transport Model SPI Standardised Precipitation Index Intercomparison Project SPRINTARS Spectral Radiation-Transport Model for TRIFFID Top-down Representation of Interactive Foliage Aerosol Species and Flora Including Dynamics SRALT Satellite Radar Altimetry TRUTHS Traceable Radiometry Underpinning Terrestrial and Helio Studies SRES IPCC Special Report on Emission Scenarios TRW Tree-Ring Width SREX IPCC Special Report on Managing the Risk of Extreme Events and Disasters to Advance Climate TSI Total Solar Irradiance Change Adaptation TTD Transit Time Distribution SRM Solar Radiation Management TW Tidewater SSH Sea Surface Height UAH University of Alabama in Huntsville SSI Spectral Solar Irradiance UARS Upper Atmosphere Research Satellite SSM/I Special Sensor Microwave/Imager UCI University of California, Irvine SSR Surface Solar Radiation UHI Urban Heat Island SSS Sea Surface Salinity UNADW Upper North Atlantic Deep Water SST Sea Surface Temperature UNEP United Nations Environment Programme SSU Stratospheric Sounding Unit UOHC Upper (0 700 m) Ocean Heat Content STAR Center for Satellite Applications and Research USHCN US Historical Climatology Network STMW Subtropical Mode Water UTLS Upper Troposphere/Lower Stratosphere SVS Standard Verification System (WMO) UV Ultraviolet SWCRE Shortwave Cloud Radiative Effect UVic University of Victoria SWE Snow Water Equivalent AIV VasClimO Variability Analyses of Surface Climate SWH Significant Wave Height Observations SWR Solar Shortwave Radiation VEGAS Terrestrial Vegetation and Carbon Model TBO Tropospheric Biennial Oscillation VIIRS Visible Infrared Imaging Radiometer Suite Tg Teragrams VLM Vertical Land Motion T/P TOPEX/Poseidon VOC Volatile Organic Compound TANSO Thermal and Near Infrared Sensor for VOS Voluntary Observing Ship Carbon Observation W Watts TAR IPCC Third Assessment Report WAIS West Antarctic Ice Sheet TC Tropical Cyclone; also Total Carbon WASWind Wave- and Anemometer-Based Sea Surface Wind TCCON Total Carbon Column Observing Network WCRP World Climate Research Programme TCR Transient Climate Response WMGHG Well-Mixed Greenhouse Gas TCRE Transient Climate Response to Cumulative CO2 WMO World Meteorological Organization Emissions WOCE World Ocean Circulation Experiment TES Tropospheric Emission Spectrometer WSG Western Subarctic Gyre TIM Total Irradiance Monitor XBT Expendable Bathythermograph TNI Trans-Nino Index TOA Top of the Atmosphere TOMS Total Ozone Mapping Spectrometer 1475 Annex V: Contributors to the AV IPCC WGI Fifth Assessment Report This annex should be cited as: IPCC, 2013: Annex V: Contributors to the IPCC WGI Fifth Assessment Report. In: Climate Change 2013: The Physical Sci- ence Basis. Contribution of Working Group I to the Fifth Assessment Report of the Intergovernmental Panel on Climate Change [Stocker, T.F., D. Qin, G.-K. Plattner, M. Tignor, S.K. Allen, J. Boschung, A. Nauels, Y. Xia, V. Bex and P.M. Midgley (eds.)]. Cambridge University Press, Cambridge, United Kingdom and New York, NY, USA. 1477 Annex V Contributors to the IPCC WGI Fifth Assessment Report ALLEN, Simon K. ARRITT, Raymond Coordinating Lead Authors, Lead Authors, IPCC WGI TSU, University of Bern Iowa State University Review Editors and Contributing Authors Switzerland USA are listed alphabetically by surname. ALLISON, Ian ARTAXO, Paulo Antarctic Climate and Ecosystems University of Sao Paulo Cooperative Research Centre Brazil AAMAAS, Borgar Australia BAEHR, Johanna Center for International Climate and AMBRIZZI, Tércio University of Hamburg Environmental Research Oslo University of Sao Paulo Germany Norway Brazil BAHR, David B. ABE-OUCHI, Ayako AN, Soon-Il University of Colorado Boulder University of Tokyo Yonsei University USA Japan Republic of Korea BALA, Govindasamy ABIODUN, Babatunde ANAV, Alessandro Indian Institute of Science University of Cape Town University of Exeter India South Africa UK BALAN SAROJINI, Beena ABRAHAM, Libu ANCHUKAITIS, Kevin University of Reading Qatar Meteorological Department Woods Hole Oceanographic Institution UK Qatar USA BALDWIN, Mark ACHUTARAO, Krishna Mirle ANDERSON, Bruce University of Exeter Indian Institute of Technology Boston University UK India USA BAMBER, Jonathan ADEDOYIN, Akintayo John ANDREWS, Oliver University of Bristol University of Botswana University of East Anglia UK Botswana UK BARINGER, Molly ADLER, Robert F. ANDREWS, Timothy National Oceanic and Atmospheric University of Maryland Met Office Hadley Centre Administration, Atlantic Oceanographic and USA UK Meteorological Laboratory AHLSTRÖM, Anders USA AOKI, Shigeru Lund University Hokkaido University BARLOW, Mathew Sweden Japan University of Massachusetts ALDRIAN, Edvin USA AOYAMA, Michio Agency for Meteorology, Climatology and Meteorological Research Institute BARRIOPEDRO, David Geophysics Japan Universidad Complutense de Madrid Indonesia AV Spain ARAKAWA, Osamu ALDRIN, Magne University of Tsukuba BARTHOLY, Judit Norwegian Computing Center and University Japan Eötvös Loránd University of Oslo Hungary Norway ARBLASTER, Julie Bureau of Meteorology BARTLEIN, Patrick J. ALEXANDER, Lisa V. Australia University of Oregon University of New South Wales USA Australia ARCHER, David University of Chicago BATES, Nicholas R. ALLAN, Richard P. USA Bermuda Biological Station University of Reading Bermuda UK ARENDT, Anthony A. University of Alaska Fairbanks BEER, Jürg ALLAN, Robert USA Eawag - Swiss Federal Institute of Aquatic Met Office Hadley Centre Science and Technology UK ARORA, Vivek Switzerland Environment Canada ALLEN, Myles R. Canada University of Oxford UK 1478 Contributors to the IPCC WGI Fifth Assessment Report Annex V BELLOUIN, Nicolas BOPP, Laurent BROOKS, Harold E. University of Reading Laboratoire des Sciences du Climat et de National Oceanic and Atmospheric UK l Environnement, Institut Pierre Simon Administration, National Severe Storms Laplace Laboratory BENEDETTI, Angela France USA European Centre for Medium-Range Weather Forecasts BORGES, Alberto Vieira BROVKIN, Victor UK Université de Liege Max Planck Institute for Meteorology Belgium Germany BENITO, Gerardo Consejo Superior de Investigaciones BOUCHER, Olivier BROWN, Josephine Cientificas Laboratoire de Météorologie Dynamique, Bureau of Meteorology Spain Institut Pierre Simon Laplace Australia France BEYERLE, Urs BROWN, Ross ETH Zurich BOUSQUET, Philippe Environment Canada Switzerland Laboratoire des Sciences du Climat et de Canada l Environnement, Institut Pierre Simon BIASUTTI, Michela BROWNE, Oliver Laplace Columbia University University of Edinburgh France USA UK BOUWMAN, Lex BINDOFF, Nathaniel L. BRUHWILER, Lori M. PBL Netherlands Environmental Assessment University of Tasmania National Oceanic and Atmospheric Agency Australia Administration, Earth System Research Netherlands Laboratory BINER, Sébastien BOX, Jason E. USA Ouranos Consortium on Regional Geological Survey of Denmark and Greenland Climatology and Adaptation to Climate BRUTEL-VUILMET, Claire Denmark Change Laboratoire de Glaciologie et Géophysique Canada BOYER, Timothy de l`Environnement, Université Joseph Fourier National Oceanic and Atmospheric France BITZ, Cecilia M. Administration, National Oceanographic Data University of Washington BYRNE, Robert H. Center USA University of South Florida USA USA BLAKE, Donald R. BRACONNOT, Pascale University of California Irvine CAI, Wenju Laboratoire des Sciences du Climat et de USA CSIRO Marine and Atmospheric Research l Environnement, Institut Pierre Simon Australia BODAS-SALCEDO, Alejandro Laplace Met Office Hadley Centre France CALDEIRA, Kenneth UK Carnegie Institution for Science BRAUER, Achim USA BOER, George J. GFZ German Research Centre for AV Environment Canada Geosciences CAMERON-SMITH, Philip Canada Germany Lawrence Livermore National Laboratory USA BOJARIU, Roxana BRÉON, François-Marie National Meteorological Administration Laboratoire des Sciences du Climat et de CAMILLONI, Ines Romania l Environnement, Institut Pierre Simon Universidad de Buenos Aires Laplace Argentina BONAN, Gordon France National Center for Atmospheric Research CAMPOS, Edmo USA BRETHERTON, Christopher University of Sao Paulo University of Washington Brazil BONY, Sandrine USA Laboratoire de Météorologie Dynamique, CANADELL, Josep Institut Pierre Simon Laplace BROMWICH, David H. CSIRO Marine and Atmospheric Research France Ohio State University Australia USA BOOTH, Ben B.B. CANE, Mark Met Office Hadley Centre BRÖNNIMANN, Stefan Columbia University UK University of Bern USA Switzerland 1479 Annex V Contributors to the IPCC WGI Fifth Assessment Report CAO, Long CHEVALLIER, Frédéric COLLINS, William Zhejiang University Laboratoire des Sciences du Climat et de University of Reading China l Environnement, Institut Pierre Simon UK Laplace CARRASCO, Jorge COLLINS, William France Direccion Meteorologica de Chile Lawrence Berkeley National Laboratory Chile CHHABRA, Abha USA Indian Space Research Organisation CARSON, Mark COMISO, Josefino C. India University of Hamburg National Aeronautics and Space Germany CHIKAMOTO, Yoshimitsu Administration, Goddard Space Flight Center University of Hawaii USA CARTER, Tim USA Finnish Environment Institute COOK, Edward Finland CHOI, Jung Columbia University Seoul National University USA CARVALHO, Leila V. Republic of Korea University of California Santa Barbara COOK, Kerry H. USA CHOU, Sin Chan University of Texas National Institute for Space Research USA CATTO, Jennifer Brazil Monash University COOLEY, Sarah Australia CHRISTENSEN, Jens Hesselbjerg Woods Hole Oceanographic Institution Danish Meteorological Institute USA CAVALCANTI, Iracema F.A. Denmark National Institute for Space Research COOPER, Owen R. Brazil CHRISTENSEN, Ole Bssing Cooperative Institute for Research in Danish Meteorological Institute Environmental Sciences CAZENAVE, Anny Denmark USA Laboratoire d Etudes en Géophysique et Océanographie Spatiales CHRISTIDIS, Nikolaos CORTI, Susanna France Met Office Hadley Centre Institute of Atmospheric Sciences and UK Climate CHADWICK, Robin Italy Met Office Hadley Centre CHURCH, John A. UK CSIRO Marine and Atmospheric Research COX, Peter Australia University of Exeter CHAMBERS, Don UK University of South Florida CIAIS, Philippe USA Laboratoire des Sciences du Climat et de CROWLEY, Thomas l Environnement, Institut Pierre Simon Braeheads Institute CHANG, Ping Laplace UK Texas A&M University France USA CUBASCH, Ulrich AV CLARK, Peter U. Freie Universität Berlin CHAPPELLAZ, Jérôme Oregon State University Germany Laboratoire de Glaciologie et Géophysique USA de l`Environnement, Université Joseph Fourier CUNNINGHAM, Stuart France CLEVELAND, Cory Scottish Association of Marine Science University of Montana UK CHARABI, Yassine Abdul-Rahman USA Sultan Qaboos University DAI, Aiguo Oman CLIFTON, Olivia University at Albany Columbia University USA CHEN, Deliang USA University of Gothenburg DALSOREN, Stig B. Sweden COGLEY, J. Graham Center for International Climate and Trent University Environmental Research Oslo CHEN, Xiaolong Canada Norway Institute of Atmospheric Physics, Chinese Academy of Sciences COLLINS, Matthew DANIEL, John S. China University of Exeter National Oceanic and Atmospheric UK Administration, Earth System Research Laboratory USA 1480 Contributors to the IPCC WGI Fifth Assessment Report Annex V DAVIS, Robert E. DINEZIO, Pedro EASTERLING, David R. University of Virginia University of Hawaii National Oceanic and Atmospheric USA USA Administration, Cooperative Institute for Climate and Satellites DAVIS, Sean M. DING, Yihui USA National Oceanic and Atmospheric National Climate Center, China Administration, Earth System Research Meteorological Administration EBY, Michael Laboratory China University of Victoria USA Canada DLUGOKENCKY, Edward J. DE CASTRO, Manuel National Oceanic and Atmospheric EDWARDS, R. Lawrence Universidad de Castilla-La Mancha Administration, Earth System Research University of Minnesota Spain Laboratory USA USA DE DECKKER, Patrick ELISEEV, Alexey Australian National University DOBLAS-REYES, Francisco Russian Academy of Sciences Australia Institució Catalana de Recerca i Estudis Russian Federation Avançats and Institut Catala de Ciencies del DE ELÍA, Ramón EMANUEL, Kerry Clima Université du Québec a Montréal and Massachusetts Institute of Technology Spain Ouranos Consortium USA Canada DOKKEN, Trond EMORI, Seita Uni Research Norway DE MENEZES, Viviane Vasconcellos National Institute for Environmental Studies Norway University of Tasmania Japan Australia DOMINGUES, Catia M. ENDO, Hirokazu Antarctic Climate and Ecosystems DE VERNAL, Anne Meteorological Research Institute Cooperative Research Centre Université du Québec a Montréal Japan Australia Canada ENFIELD, David B. DONAT, Markus G. DEFRIES, Ruth University of Miami University of New South Wales Columbia University USA Australia USA ERISMAN, Jan Willem DONEY, Scott C. DEL GENIO, Anthony Louis Bolk Institute Woods Hole Oceanographic Institution National Aeronautics and Space Netherlands USA Administration, Goddard Institute for Space EUSKIRCHEN, Eugenie S. Studies DONG, Wenjie University of Alaska Fairbanks USA Beijing Normal University USA China DELCROIX, Thierry EVAN, Amato Laboratoire d Etudes en Géophysique et DORE, John Scripps Institution of Oceanography Océanographie Spatiales Montana State University USA AV France USA EYRING, Veronika DELECLUSE, Pascale DOWSETT, Harry J. DLR German Aerospace Center Météo-France U.S. Geological Survey Germany France USA FACCHINI, Maria Cristina DELMONTE, Barbara DRIOUECH, Fatima Institute of Atmospheric Sciences and University of Milano-Bicocca Direction de la Météorologie Nationale Climate Italy Morocco Italy DELSOLE, Tim DUFRESNE, Jean-Louis FASULLO, John George Mason University Laboratoire de Météorologie Dynamique, National Center for Atmospheric Research USA Institut Pierre Simon Laplace USA France DENTENER, Frank J. FEELY, Richard A. European Commission, Joint Research Center DURACK, Paul J. National Oceanic and Atmospheric EU Lawrence Livermore National Laboratory Administration, Pacific Marine Environmental USA DESER, Clara Laboratory National Center for Atmospheric Research USA USA 1481 Annex V Contributors to the IPCC WGI Fifth Assessment Report FEINGOLD, Graham FREELAND, Howard GEHRELS, W. Roland National Oceanic and Atmospheric Fisheries and Oceans Canada University of York Administration, Earth System Research Canada UK Laboratory FRIEDLINGSTEIN, Pierre GERLAND, Sebastian USA University of Exeter Norwegian Polar Institute FETTWEIS, Xavier UK Norway Université de Liege FRÖHLICH, Claus GHAN, Steven Belgium Physikalisch-Meteorologisches Pacific Northwest National Laboratory FICHEFET, Thierry Observatorium Davos, World Radiation USA Université catholique de Louvain Center GIANNINI, Alessandra Belgium Switzerland Columbia University FINE, Rana FUGLESTVEDT, Jan USA University of Miami Center for International Climate and GIESEN, Rianne USA Environmental Research Oslo Utrecht University Norway FIOLETOV, Vitali Netherlands Environment Canada FUZZI, Sandro GILLETT, Nathan Canada Institute of Atmospheric Sciences and Environment Canada Climate FIORE, Arlene M. Canada Italy Columbia University and Lamont-Doherty GINOUX, Paul Earth Observatory FYFE, John National Oceanic and Atmospheric USA Environment Canada Administration, Geophysical Fluid Dynamics Canada FISCHER, Erich M. Laboratory ETH Zurich GALLOWAY, James USA Switzerland University of Virginia GLECKLER, Peter J. USA FISCHER, Hubertus Lawrence Livermore National Laboratory University of Bern GANOPOLSKI, Andrey USA Switzerland Potsdam Institute for Climate Impact GONZÁLEZ ROUCO, Jesús Fidel Research FLANNER, Mark Universidad Complutense de Madrid Germany University of Michigan Spain USA GAO, Xuejie GONZÁLEZ-DÁVILA, Melchor National Climate Center, China FLATO, Gregory Universidad de Las Palmas de Gran Canaria Meteorological Administration Environment Canada Spain China Canada GOOD, Peter GARCÍA-SERRANO, Javier FLEITMANN, Dominik Met Office Hadley Centre Institut Catala de Ciencies del Clima AV University of Reading UK Spain UK GOOD, Simon GARDNER, Alex S. FOREST, Chris E. Met Office Hadley Centre Clark University Pennsylvania State University UK USA USA GOODESS, Clare GARZOLI, Silvia FORSTER, Piers University of East Anglia National Oceanic and Atmospheric University of Leeds UK Administration, Atlantic Oceanographic and UK Meteorological Laboratory GOOSSE, Hugues FOSTER, Gavin USA Université catholique de Louvain University of Southampton Belgium GATES, Lydia UK Freie Universität Berlin GOSWAMI, Prashant FRAME, David Germany CSIR Centre for Mathematical Modelling and Victoria University of Wellington Computer Simulation GBOBANIYI, Emiola New Zealand India Swedish Meteorological and Hydrological Institute Sweden 1482 Contributors to the IPCC WGI Fifth Assessment Report Annex V GOVIN, Aline GUTOWSKI, William J. HEIMANN, Martin MARUM Center for Marine Environmental Iowa State University Max Planck Institute for Biogeochemistry Sciences USA Germany Germany GUTZLER, David HEINZE, Christoph GRANIER, Claire University of New Mexico University of Bergen Laboratoire Atmospheres, Milieux, USA Norway Observations Spatiales, Institut Pierre Simon HAAS, Christian HELD, Isaac Laplace York University National Oceanic and Atmospheric France Canada Administration, Geophysical Fluid Dynamics GRAVERSON, Rune Grand Laboratory HAGEN, Jon Ove Stockholm University USA University of Oslo Sweden Norway HEMER, Mark GRAY, Lesley CSIRO Marine and Atmospheric Research HAIGH, Joanna University of Oxford Australia Imperial College London UK UK HENSE, Andreas GREGORY, Jonathan M. University of Bonn HAIMBERGER, Leopold University of Reading and Met Office Hadley Germany University of Vienna Centre Austria HEWITSON, Bruce UK University of Cape Town HALL, Alex GREVE, Ralf South Africa University of California Los Angeles Hokkaido University USA HEZEL, Paul J. Japan Université catholique de Louvain HANNA, Edward GRIFFIES, Stephen Belgium University of Sheffield National Oceanic and Atmospheric UK HO, Shu-Peng (Ben) Administration, Geophysical Fluid Dynamics National Center for Atmospheric Research Laboratory HANSINGO, Kabumbwe USA USA University of Zambia Zambia HOCK, Regine GRUBER, Nicolas University of Alaska Fairbanks ETH Zurich HARGREAVES, Julia USA Switzerland Japan Agency for Marine-Earth Science and Technology HODGES, Kevin I. GRUBER, Stephan Japan University of Reading University of Zurich UK Switzerland HARIHARASUBRAMANIAN, Annamalai University of Hawaii HODNEBROG, Oivind GUEMAS, Virginie USA Center for International Climate and Institut Catala de Ciencies del Clima Environmental Research Oslo AV Spain HARRISON, Sandy Norway Macquarie University GUILYARDI, Eric Australia HOLGATE, Simon J. Laboratoire d Océanographie et du Climat, Sea Level Research Foundation Institut Pierre Simon Laplace HARTMANN, Dennis L. UK France University of Washington USA HOLLAND, David GULEV, Sergey New York University P.P. Shirshov Institute of Oceanology HAWKINS, Ed USA Russian Federation University of Reading UK HOLLAND, Elisabeth A. GUPTA, Anil K. University of the South Pacific Wadia Institute of Himalayan Geology HAYWOOD, Alan Fiji India University of Leeds UK HOLLAND, Greg GURNEY, Kevin National Center for Atmospheric Research Arizona State University HEGERL, Gabriele C. USA USA University of Edinburgh UK 1483 Annex V Contributors to the IPCC WGI Fifth Assessment Report HOLLAND, Marika M. HURRELL, Jim JAKOB, Christian National Center for Atmospheric Research National Center for Atmospheric Research Monash University USA USA Australia HOLLIS, Chris HURTT, George JANSEN, Eystein GNS Science University of Maryland University of Bergen New Zealand USA Norway HOLMES, Christopher HUSS, Matthias JANSSEN, Emily University of California Irvine University of Fribourg University of Illinois USA Switzerland USA HOOSE, Corinna HUYBRECHTS, Philippe JEVREJEVA, Svetlana Karlsruhe Institute of Technology Vrije Universiteit Brussel National Oceanography Centre Germany Belgium UK HOPWOOD, Brett HYDES, David JOHN, Jasmin Oak Ridge National Laboratory National Oceanography Centre National Oceanic and Atmospheric USA UK Administration, Geophysical Fluid Dynamics Laboratory HORTON, Ben ILYINA, Tatiana USA Rutgers University Max Planck Institute for Meteorology USA Germany JOHNS, Tim Met Office Hadley Centre HOUGHTON, Richard A. IMBERS QUINTANA, Jara UK Woods Hole Research Center University of Oxford USA UK JOHNSON, Gregory C. National Oceanic and Atmospheric HOUSE, Joanna I. INFANTI, Johnna Administration, Pacific Marine Environmental University of Bristol University of Miami Laboratory UK USA USA HOUWELING, Sander INGRAM, William JONES, Andy Utrecht University University of Oxford Met Office Hadley Centre Netherlands UK UK HU, Yongyun ISHII, Masayoshi JONES, Christopher Peking University Meteorological Research Institute Met Office Hadley Centre China Japan UK HUANG, Jianping IVANOVA, Detelina JONES, Julie Lanzhou University Lawrence Livermore National Laboratory University of Sheffield China USA UK HUANG, Ping JACOB, Daniel AV JOOS, Fortunat Institute of Atmospheric Physics, Chinese Harvard University University of Bern Academy of Sciences USA Switzerland China JACOBS, Stanley JOSEY, Simon A. HUBER, Markus Columbia University National Oceanography Centre ETH Zurich USA UK Switzerland JACOBSON, Andrew D. JOSHI, Manoj HUNKE, Elizabeth Northwestern University University of East Anglia Los Alamos National Laboratory USA UK USA JAIN, Atul JOUGHIN, Ian HUNTER, John R. University of Illinois University of Washington University of Tasmania USA USA Australia JAIN, Suman JOUSSAUME, Sylvie HUNTER, Stephen University of Zambia Laboratoire des Sciences du Climat et de University of Leeds Zambia l Environnement, Institut Pierre Simon UK Laplace France 1484 Contributors to the IPCC WGI Fifth Assessment Report Annex V JOUZEL, Jean KATZFEY, Jack KLEIN, Stephen A. Laboratoire des Sciences du Climat et de CSIRO Marine and Atmospheric Research Lawrence Livermore National Laboratory l Environnement, Institut Pierre Simon Australia USA Laplace KAZMIN, Alexander KLEIN GOLDEWIJK, Kees France P.P. Shirshov Institute of Oceanology Utrecht University and PBL Netherlands JUNGCLAUS, Johann Russian Federation Environmental Assessment Agency Max Planck Institute for Meteorology Netherlands KEELING, Ralph Germany Scripps Institution of Oceanography KLEIN TANK, Albert M.G. KAGEYAMA, Masa USA Royal Netherlands Meteorological Institute Laboratoire des Sciences du Climat et de Netherlands KENNEDY, John J. l Environnement, Institut Pierre Simon Met Office Hadley Centre KLEYPAS, Joan Laplace UK National Center for Atmospheric Research France USA KENT, Elizabeth C. KANIKICHARLA, Krishna Kumar National Oceanography Centre KLIMONT, Zbigniew Indian Institute of Tropical Meteorology UK International Institute for Applied Systems India Analysis KERMINEN, Veli-Matti KANYANGA, Joseph Katongo Austria Finnish Meteorological Institute Zambia Meteorological Department Finland KLOSTER, Silvia Zambia Max Planck Institute for Meteorology KEY, Robert M. KANZOW, Torsten Germany Princeton University GEOMAR Helmholtz Centre for Ocean USA KNIGHT, Jeff Research Met Office Hadley Centre Germany KHARIN, Viatcheslav UK Environment Canada KAPLAN, Alexey Canada KNUTSON, Thomas Columbia University National Oceanic and Atmospheric USA KHATIWALA, Samar Administration, Geophysical Fluid Dynamics Columbia University KAPLAN, Jed O. Laboratory USA EPFL Lausanne USA Switzerland KIMOTO, Masahide KNUTTI, Reto University of Tokyo KARL, David ETH Zurich Japan University of Hawaii Switzerland USA KINNE, Stefan KOCH, Dorothy Max Planck Institute for Meteorology KARUMURI, Ashok U.S. Department of Energy Germany Indian Institute of Tropical Meteorology USA India KIRSCHKE, Stefanie KOIKE, Makoto AV Laboratoire des Sciences du Climat et de KASER, Georg University of Tokyo l Environnement, Institut Pierre Simon University of Innsbruck Japan Laplace Austria France KONDO, Yutaka KASPAR, Frank University of Tokyo KIRTMAN, Ben Deutscher Wetterdienst Japan University of Miami Germany USA KONIKOW, Leonard KATO, Etsushi U.S. Geological Survey KITOH, Akio National Institute for Environmental Studies USA University of Tsukuba Japan Japan KOPP, Robert KATSMAN, Caroline Rutgers University KJELLSTRÖM, Erik Royal Netherlands Meteorological Institute USA Swedish Meteorological and Hydrological Netherlands Institute KÖRPER, Janina KATTSOV, Vladimir Sweden Freie Universität Berlin Voeikov Main Geophysical Observatory Germany Russian Federation 1485 Annex V Contributors to the IPCC WGI Fifth Assessment Report KOSSIN, James P. LANDAIS, Amaëlle LEE, Tong National Oceanic and Atmospheric Laboratoire des Sciences du Climat et de National Aeronautics and Space Administration, Cooperative Institute for l Environnement, Institut Pierre Simon Administration, Jet Propulsion Laboratory Meteorological Satellite Studies Laplace USA USA France LEMKE, Peter KOSTIANOY, Andrey LANDERER, Felix Alfred Wegener Institute for Polar and Marine P.P. Shirshov Institute of Oceanology National Aeronautics and Space Research Russian Federation Administration, Jet Propulsion Laboratory Germany USA KOVEN, Charles LENAERTS, Jan Lawrence Berkeley National Laboratory LASSEY, Keith Utrecht University USA National Institute of Water and Atmospheric Netherlands Research KRAVITZ, Ben LENDERINK, Geert New Zealand Pacific Northwest National Laboratory Royal Netherlands Meteorological Institute USA LAU, Ngar-Cheung Netherlands National Oceanic and Atmospheric KRINNER, Gerhard LENNARD, Chris Administration, Geophysical Fluid Dynamics Laboratoire de Glaciologie et Géophysique University of Cape Town Laboratory de l`Environnement, Université Joseph Fourier South Africa USA France LENTON, Andrew LAU, William K. KROEZE, Carolien CSIRO Marine and Atmospheric Research National Aeronautics and Space Wageningen University and Open Universiteit Australia Administration, Goddard Institute for Space Nederland Studies LEULIETTE, Eric Netherlands USA National Oceanic and Atmospheric KULKARNI, Ashwini Administration, Center for Satellite LAW, Rachel M. Indian Institute of Tropical Meteorology Applications and Research CSIRO Marine and Atmospheric Research India USA Australia KUNDETI, Koteswara Rao LEUNG, Lai-yung Ruby LAWRENCE, David M. Indian Institute of Tropical Meteorology Pacific Northwest National Laboratory National Center for Atmospheric Research India USA USA KUSHNIR, Yochanan LEVERMANN, Anders LE BROCQ, Anne Columbia University Potsdam Institute for Climate Impact University of Exeter USA Research UK Germany KWOK, Ronald LE QUÉRÉ, Corinne National Aeronautics and Space LI, Camille University of East Anglia Administration, Jet Propulsion Laboratory University of Bergen UK USA Norway AV LEBSOCK, Matthew KWON, Won-Tae LI, Hongmei National Aeronautics and Space National Institute of Meteorological Research Max Planck Institute for Meteorology Administration, Jet Propulsion Laboratory Republic of Korea Germany USA LAKEN, Benjamin LIAO, Hong LEE, David Instituto de Astrofisica de Canarias Institute of Atmospheric Physics, Chinese Manchester Metropolitan University Spain Academy of Sciences UK China LAMARQUE, Jean-François LEE, Kitack National Center for Atmospheric Research LIDDICOAT, Spencer Pohang University of Science and Technology USA Met Office Hadley Centre Republic of Korea UK LAMBECK, Kurt LEE, Robert W. Australian National University LIGTENBERG, Stefan University of Reading Australia Utrecht University UK Netherlands 1486 Contributors to the IPCC WGI Fifth Assessment Report Annex V LIN, Renping MACKELLAR, Neil C. MATTHEWS, H. Damon Institute of Atmospheric Physics, Chinese University of Cape Town Concordia University Academy of Sciences South Africa Canada China MAGANA, Victor MAURITZEN, Cecilie LITTLE, Christopher M. Universidad Nacional Autonoma de Mexico Center for International Climate and Princeton University Mexico Environmental Research Oslo USA Norway MAHLSTEIN, Irina LO, Fiona Federal Office of Meteorology and MAYORGA, Emilio Cornell University Climatology MeteoSwiss University of Washington USA Switzerland USA LOCKWOOD, Mike MAHOWALD, Natalie MCGREGOR, Shayne University of Reading Cornell University University of New South Wales UK USA Australia LOEB, Norman G. MAKI, Takashi MCINNES, Kathleen L. National Aeronautics and Space Meteorological Research Institute CSIRO Marine and Atmospheric Research Administration, Langley Research Center Japan Australia USA MARENGO, José MEARNS, Linda LOHMANN, Ulrike National Institute for Space Research National Center for Atmospheric Research ETH Zurich Brazil USA Switzerland MARKUS, Thorsten MEARS, Carl A. LOMAS, Mark R. National Aeronautics and Space Remote Sensing Systems University of Sheffield Administration, Goddard Space Flight Center USA UK USA MEEHL, Gerald LOSADA, Teresa MARLAND, Gregg National Center for Atmospheric Research Universidad de Castilla-La Mancha Appalachian State University USA Spain USA MEINSHAUSEN, Malte LOTT, Fraser MAROTZKE, Jochem Potsdam Institute for Climate Impact Met Office Hadley Centre Max Planck Institute for Meteorology Research UK Germany Germany LU, Jian MARSHALL, Gareth MELTON, Joe R. George Mason University British Antarctic Survey Environment Canada USA UK Canada LUCAS, Christopher MARSTON, George MENDOZA, Blanca Bureau of Meteorology University of Reading Universidad Nacional Autonoma de Mexico Australia UK Mexico AV LUCHT, Wolfgang MARZEION, Ben MENÉNDEZ, Claudio Potsdam Institute for Climate Impact University of Innsbruck Universidad de Buenos Aires Research Austria Argentina Germany MASSOM, Rob MENÉNDEZ, Melisa LUNT, Daniel J. Australian Antarctic Division Universidad de Cantabria University of Bristol Australia Spain UK MASSON-DELMOTTE, Valérie MENNE, Matthew LUO, Yiqi Laboratoire des Sciences du Climat et de National Oceanic and Atmospheric University of Oklahoma l Environnement, Institut Pierre Simon Administration, National Climatic Data USA Laplace Center France USA LUTERBACHER, Jürg Justus-Liebig University Giessen MASSONNET, François MERCHANT, Christopher J. Germany Université catholique de Louvain University of Edinburgh Belgium UK 1487 Annex V Contributors to the IPCC WGI Fifth Assessment Report MERNILD, Sebastian H. MORDY, Calvin MYHRE, Gunnar Los Alamos National Laboratory National Oceanic and Atmospheric Center for International Climate and USA Administration, Pacific Marine Environmental Environmental Research Oslo Laboratory Norway MERRIFIELD, Mark A. USA University of Hawaii MYNENI, Ranga B. USA MORICE, Colin P. Boston University Met Office Hadley Centre USA METZL, Nicolas UK Laboratoire d Océanographie et du Climat, NAIK, Vaishali Institut Pierre Simon Laplace MOTE, Philip National Oceanic and Atmospheric France Oregon State University Administration, Geophysical Fluid Dynamics USA Laboratory MILNE, Glenn A. USA University of Ottawa MOTTRAM, Ruth Canada Danish Meteorological Institute NAISH, Tim Denmark Victoria University of Wellington MIN, Seung-Ki New Zealand Pohang University of Science and Technology MSADEK, Rym Republic of Korea National Oceanic and Atmospheric NAKAJIMA, Teruyuki Administration, Geophysical Fluid Dynamics University of Tokyo MITCHELL, Daniel Laboratory Japan University of Oxford USA UK NATH, Mary Jo MUDELSEE, Manfred National Oceanic and Atmospheric MITROVICA, Jerry X. Alfred Wegener Institute for Polar and Marine Administration, Geophysical Fluid Dynamics Harvard University Research Laboratory USA Germany USA MOBERG, Anders MÜLLER, Stefanie NEELIN, J. David Stockholm University Freie Universität Berlin University of California Los Angeles Sweden Germany USA MOHOLDT, Geir MUHS, Daniel R. NEREM, R. Steven Scripps Institution of Oceanography U.S. Geological Survey Cooperative Institute for Research in USA USA Environmental Sciences MOKHOV, Igor I. USA MULITZA, Stefan A.M. Obukhov Institute of Atmospheric MARUM Center for Marine Environmental NICHOLAS, J.P. Physics Sciences Ohio State University Russian Federation Germany USA MOKSSIT, Abdalah MUNHOVEN, Guy NICK, Faezeh Direction de la Météorologie Nationale Université de Liege UNIS - The University Centre in Svalbard AV Morocco Belgium Norway MÖLG, Thomas MURAKAMI, Hiroyuki NIELSEN, Claus J. Technical University Berlin University of Hawaii University of Oslo Germany USA Norway MONSELESAN, Didier MURPHY, Daniel NIWA, Yosuke CSIRO Marine and Atmospheric Research National Oceanic and Atmospheric Meteorological Research Institute Australia Administration, Earth System Research Japan MONTZKA, Stephen A. Laboratory NOJIRI, Yukihiro National Oceanic and Atmospheric USA National Institute for Environmental Studies Administration, Earth System Research MURRAY, Tavi Japan Laboratory Swansea University USA NORBY, Richard J. UK Oak Ridge National Laboratory MORAK, Simone MYHRE, Cathrine Lund USA University of Reading Norwegian Institute for Air Research UK NORRIS, Joel R. Norway Scripps Institution of Oceanography USA 1488 Contributors to the IPCC WGI Fifth Assessment Report Annex V NUNN, Patrick D. PALMER, Tim PERRETTE, Mahé University of New England University of Oxford Potsdam Institute for Climate Impact Australia UK Research Germany O CONNOR, Fiona PARK, Geun-Ha Met Office Hadley Centre Korea Institute of Ocean Science and PETERS, Glen P. UK Technology Center for International Climate and Republic of Korea Environmental Research Oslo O DOWD, Colin Norway National University of Ireland, Galway PARK, Geun-Ha Ireland National Oceanic and Atmospheric PETERS, Wouter Administration, Atlantic Oceanographic and Wageningen University O NEILL, Brian C. Meteorological Laboratory Netherlands National Center for Atmospheric Research USA USA PETERSCHMITT, Jean-Yves PARKER, David E. Laboratoire des Sciences du Climat et de OLAFSSON, Jon Met Office Hadley Centre l Environnement, Institut Pierre Simon University of Iceland UK Laplace Iceland France PARRENIN, Frédéric OLESEN, Martin Laboratoire de Glaciologie et Géophysique PEYLIN, Philippe Danish Meteorological Institute de l`Environnement, Université Joseph Fourier Laboratoire des Sciences du Climat et de Denmark France l Environnement, Institut Pierre Simon ORR, James Laplace PATRA, Prabir Laboratoire des Sciences du Climat et de France Japan Agency for Marine-Earth Science and l Environnement, Institut Pierre Simon Technology PFEFFER, W. Tad Laplace Japan University of Colorado Boulder France USA PATRICOLA, Christina M. ORSI, Alejandro Texas A&M University PHILIPPON-BERTHIER, Gwenaëlle Texas A&M University USA Laboratoire des Sciences du Climat et de USA l Environnement, Institut Pierre Simon PAUL, Frank OSBORN, Timothy Laplace University of Zurich University of East Anglia France Switzerland UK PIAO, Shilong PAVLOVA, Tatiana OTTO, Alexander Peking University Voeikov Main Geophysical Observatory University of Oxford China Russian Federation UK PIERCE, David PAYNE, Antony J. OTTO, Friederike Scripps Institution of Oceanography University of Bristol University of Oxford USA UK UK AV PIPER, Stephen PEARSON, Paul N. OTTO-BLIESNER, Bette Scripps Institution of Oceanography Cardiff University National Center for Atmospheric Research USA UK USA PITMAN, Andy PENNER, Joyce OVERDUIN, Pier Paul University of New South Wales University of Michigan Alfred Wegener Institute for Polar and Marine Australia USA Research PLANTON, Serge Germany PEREGON, Anna Météo-France Laboratoire des Sciences du Climat et de OVERLAND, James France l Environnement, Institut Pierre Simon National Oceanic and Atmospheric Laplace PLATTNER, Gian-Kasper Administration, Pacific Marine Environmental France IPCC WGI TSU, University of Bern Laboratory Switzerland USA PERLWITZ, Judith Cooperative Institute for Research in POLCHER, Jan PAINTER, Jeff Environmental Sciences Laboratoire de Météorologie Dynamique, Lawrence Livermore National Laboratory USA Institut Pierre Simon Laplace USA France 1489 Annex V Contributors to the IPCC WGI Fifth Assessment Report POLLARD, David RAE, Jamie RAYNER, Peter Pennsylvania State University Met Office Hadley Centre University of Melbourne USA UK Australia POLSON, Debbie RAHIMZADEH, Fatemeh REASON, Chris University of Edinburgh Islamic Republic of Iran Meteorological University of Cape Town UK Organization South Africa Iran POLYAKOV, Igor REICH, Katharine Davis University of Alaska Fairbanks RAHMSTORF, Stefan University of California Los Angeles USA Potsdam Institute for Climate Impact USA Research PONGRATZ, Julia REID, Jeffrey Germany Max Planck Institute for Meteorology U.S. Naval Research Laboratory Germany RÄISÄNEN, Jouni USA University of Helsinki POULTER, Benjamin REN, Jiawen Finland Laboratoire des Sciences du Climat et de Cold and Arid Regions Environmental and l Environnement, Institut Pierre Simon RAMASWAMY, Venkatachalam Engineering Research Institute, Chinese Laplace National Oceanic and Atmospheric Academy of Sciences France Administration, Geophysical Fluid Dynamics China Laboratory POWER, Scott B. RENWICK, James USA Bureau of Meteorology Victoria University of Wellington Australia RAMESH, Rengaswamy New Zealand Physical Research Laboratory PRABHAT REVERDIN, Gilles India Lawrence Berkeley National Laboratory Laboratoire d Océanographie et du Climat, USA RANDALL, David Institut Pierre Simon Laplace Colorado State University France PRATHER, Michael USA University of California Irvine RHEIN, Monika USA RANDEL, William J. University of Bremen National Center for Atmospheric Research Germany PROWSE, Terry USA Environment Canada RIBES, Aurélien Canada RASCH, Philip Météo-France Pacific Northwest National Laboratory France PURKEY, Sarah G. USA University of Washington RICHTER, Andreas USA RAUSER, Florian University of Bremen Max Planck Institute for Meteorology Germany QIAN, Yun Germany Pacific Northwest National Laboratory RICHTER, Carolin AV USA RAVISHANKARA, A.R. World Meteorological Organization National Oceanic and Atmospheric Switzerland QIN, Dahe Administration, Earth System Research Co-Chair IPCC WGI, China Meteorological RIDGWELL, Andy Laboratory Administration University of Bristol USA China UK RAY, Suchanda QIU, Bo RIGBY, Matthew CSIR Centre for Mathematical Modelling and University of Hawaii University of Bristol Computer Simulation USA UK India QUINN, Terrence RIGNOT, Eric RAYMOND, Peter A. University of Texas National Aeronautics and Space Yale University USA Administration, Jet Propulsion Laboratory USA USA RADIÆ, Valentina RAYNAUD, Dominique University of British Columbia RILEY, William J. Laboratoire de Glaciologie et Géophysique Canada Lawrence Berkeley National Laboratory de l`Environnement, Université Joseph Fourier USA France 1490 Contributors to the IPCC WGI Fifth Assessment Report Annex V RINGEVAL, Bruno RUSTICUCCI, Matilde SCHMIDTKO, Sunke Utrecht University Universidad de Buenos Aires University of East Anglia Netherlands Argentina UK RINTOUL, Stephen R. RUTI, Paolo SCHMITT, Raymond CSIRO Marine and Atmospheric Research Italian National Agency for New Woods Hole Oceanographic Institution Australia Technologies, Energy and Sustainable USA Economic Development ROBINSON, David SCHMITTNER, Andreas Italy Rutgers University Oregon State University USA SABINE, Christopher USA National Oceanic and Atmospheric ROBOCK, Alan SCHOOF, Christian Administration, Pacific Marine Environmental Rutgers University University of British Columbia Laboratory USA Canada USA RÖDENBECK, Christian SCHULZ, Jörg SAENKO, Oleg Max Planck Institute for Biogeochemistry EUMETSAT Environment Canada Germany Germany Canada RODRIGUES, Luis R.L. SCHULZ, Michael SALZMANN, Ulrich Institut Catala de Ciencies del Clima MARUM Center for Marine Environmental Northumbria University Spain Sciences UK Germany RODRÍGUEZ DE FONSECA, Belén SAMSET, Bjrn Universidad Complutense de Madrid SCHULZ, Michael Center for International Climate and Spain Norwegian Meteorological Institute Environmental Research Oslo Norway RODWELL, Mark Norway European Centre for Medium-Range Weather SCHULZWEIDA, Uwe SANTER, Benjamin D. Forecasts Max Planck Institute for Meteorology Lawrence Livermore National Laboratory UK Germany USA ROEMMICH, Dean SCHURER, Andrew SARR, Abdoulaye Scripps Institution of Oceanography University of Edinburgh National Meteorological Agency of Senegal USA UK Senegal ROGELJ, Joeri SCHUUR, Edward SATHEESH, S.K. ETH Zurich University of Florida Indian Institute of Science Switzerland USA India ROHLING, Eelco SCINOCCA, John SAUNOIS, Marielle Australian National University Environment Canada Laboratoire des Sciences du Climat et de Australia Canada l Environnement, Institut Pierre Simon ROJAS, Maisa Laplace SCREEN, James AV Universidad de Chile France University of Exeter Chile UK SAVARINO, Joël ROMANOU, Anastasia Laboratoire de Glaciologie et Géophysique SEAGER, Richard Columbia University de l`Environnement, Université Joseph Fourier Columbia University USA France USA ROTH, Raphael SCAIFE, Adam A. SEBBARI, Rachid University of Bern Met Office Hadley Centre Direction de la Météorologie Nationale Switzerland UK Morocco ROTSTAYN, Leon SCHÄR, Christoph SEDLÁÈEK, Jan CSIRO Marine and Atmospheric Research ETH Zurich ETH Zurich Australia Switzerland Switzerland RUMMUKAINEN, Markku SCHMIDT, Hauke SEIDEL, Dian J. Swedish Meteorological and Hydrological Max Planck Institute for Meteorology National Oceanic and Atmospheric Institute Germany Administration, Air Resources Laboratory Sweden USA 1491 Annex V Contributors to the IPCC WGI Fifth Assessment Report SEMENOV, Vladimir SIMMONS, Adrian STAMMER, Detlef Russian Academy of Sciences European Centre for Medium-Range Weather University of Hamburg Russian Federation Forecasts Germany UK SEXTON, David STAMMERJOHN, Sharon Met Office Hadley Centre SITCH, Stephen University of Colorado Boulder UK University of Exeter USA UK SHAFFREY, Len C. STEFFEN, Konrad University of Reading SLANGEN, Aimée Swiss Federal Institute for Forest, Snow and UK CSIRO Marine and Atmospheric Research Landscape Research WSL Australia Switzerland SHAKUN, Jeremy Boston College SLATER, Andrew STENDEL, Martin USA National Snow and Ice Data Center Danish Meteorological Institute USA Denmark SHAO, XueMei Institute of Geographic Sciences and Natural SMERDON, Jason STEPHENS, Graeme Resources Research, Chinese Academy of Columbia University National Aeronautics and Space Sciences USA Administration, Jet Propulsion Laboratory China USA SMIRNOV, Dmitry SHARP, Martin Russian Academy of Sciences STEPHENSON, David B. University of Alberta Russian Federation University of Exeter Canada UK SMITH, Doug SHEPHERD, Theodore Met Office Hadley Centre STEVENS, Bjorn University of Reading UK Max Planck Institute for Meteorology UK Germany SMITH, Sharon SHERWOOD, Steven Natural Resources Canada STEVENSON, David S. University of New South Wales Canada University of Edinburgh Australia UK SMITH, Steven J. SHIKLOMANOV, Nikolay Pacific Northwest National Laboratory STEVENSON, Samantha George Washington University USA University of Hawaii USA USA SMITH, Thomas M. SHIMADA, Koji National Oceanic and Atmospheric STIER, Philip Tokyo University of Marine Science and Administration, Center for Satellite University of Oxford Technology Applications and Research UK Japan USA STÖBER, Uwe SHINDELL, Drew SODEN, Brian J. University of Bremen AV National Aeronautics and Space University of Miami Germany Administration, Goddard Institute for Space USA STOCKER, Benjamin D. Studies SOLMAN, Silvina University of Bern USA Universidad de Buenos Aires Switzerland SHINE, Keith Argentina STOCKER, Thomas F. University of Reading SOLOMINA, Olga Co-Chair IPCC WGI, University of Bern UK Russian Academy of Sciences Switzerland SHIOGAMA, Hideo Russian Federation STORELVMO, Trude National Institute for Environmental Studies SPAHNI, Renato Yale University Japan University of Bern USA SHONGWE, Mxolisi Switzerland STOTT, Peter A. South African Weather Service SPERBER, Kenneth Met Office Hadley Centre South Africa Lawrence Livermore National Laboratory UK SILLMANN, Jana USA Environment Canada Canada 1492 Contributors to the IPCC WGI Fifth Assessment Report Annex V STRAMMA, Lothar TETT, Simon VAN DEN HURK, Bart GEOMAR Helmholtz Centre for Ocean University of Edinburgh Royal Netherlands Meteorological Institute Research UK Netherlands Germany TEULING, Adriaan J. (Ryan) VAN DER WERF, Guido STUBENRAUCH, Claudia Wageningen University VU University Amsterdam Laboratoire de Météorologie Dynamique, Netherlands Netherlands Institut Pierre Simon Laplace THOMPSON, Rona L. VAN NOIJE, Twan France Norwegian Institute for Air Research Royal Netherlands Meteorological Institute SUGA, Toshio Norway Netherlands Tohoku University THORNE, Peter W. VAN OLDENBORGH, Geert Jan Japan Nansen Environmental and Remote Sensing Royal Netherlands Meteorological Institute SUTTON, Rowan Center Netherlands University of Reading Norway VAN VUUREN, Detlef UK THORNTON, Peter PBL Netherlands Environmental Assessment SWART, Neil Oak Ridge National Laboratory Agency University of Victoria USA Netherlands Canada TIMMERMANN, Axel VAUGHAN, David G. TAKAHASHI, Taro University of Hawaii British Antarctic Survey Columbia University USA UK USA TJIPUTRA, Jerry VAUTARD, Robert TAKAYABU, Izuru Uni Research Norway Laboratoire des Sciences du Climat et de Meteorological Research Institute Norway l Environnement, Institut Pierre Simon Japan Laplace TRENBERTH, Kevin France TAKEMURA, Toshihiko National Center for Atmospheric Research Kyushu University USA VAVRUS, Steve Japan University of Wisconsin TÜRKEª, Murat USA TALLEY, Lynne D. Çanakkale Onsekiz Mart University Scripps Institution of Oceanography Turkey VECCHI, Gabriel USA National Oceanic and Atmospheric TURNER, John Administration, Geophysical Fluid Dynamics TANGANG, Fredolin British Antarctic Survey Laboratory National University of Malaysia UK USA Malaysia UMMENHOFER, Caroline VELICOGNA, Isabella TANHUA, Toste Woods Hole Oceanographic Institution University of California Irvine GEOMAR Helmholtz Centre for Ocean USA USA Research AV UNNIKRISHNAN, Alakkat S. Germany VERNIER, Jean-Paul National Institute of Oceanography National Aeronautics and Space TANS, Pieter India Administration, Langley Research Center National Oceanic and Atmospheric VAN ANGELEN, Jan H. USA Administration, Earth System Research Utrecht University Laboratory VESALA, Timo Netherlands USA University of Helsinki VAN DE BERG, Willem Jan Finland TARASOV, Pavel Utrecht University Freie Universität Berlin VINTHER, Bo M. Netherlands Germany University of Copenhagen VAN DE WAL, Roderik Denmark TAYLOR, Karl Utrecht University Lawrence Livermore National Laboratory VITERBO, Pedro Netherlands USA Instituto de Meteorologia VAN DEN BROEKE, Michiel Portugal TEBALDI, Claudia Utrecht University Climate Central, Inc. VIZCAÍNO, Miren Netherlands USA Delft University of Technology Netherlands 1493 Annex V Contributors to the IPCC WGI Fifth Assessment Report VON SCHUCKMANN, Karina WANIA, Rita WILD, Oliver Institut Français de Recherche pour Austria Lancaster University l Exploitation de la Mer UK WANNER, Heinz France University of Bern WILLETT, Kate M. VON STORCH, Hans Switzerland Met Office Hadley Centre University of Hamburg UK WANNINKHOF, Rik Germany National Oceanic and Atmospheric WILLIAMS, Keith VOULGARAKIS, Apostolos Administration, Atlantic Oceanographic and Met Office Hadley Centre Imperial College London Meteorological Laboratory UK UK USA WINKELMANN, Ricarda WADA, Yoshihide WARD, Daniel S. Potsdam Institute for Climate Impact Utrecht University Cornell University Research Netherlands USA Germany WADHAMS, Peter WATTERSON, Ian WINKER, David University of Cambridge CSIRO Marine and Atmospheric Research National Aeronautics and Space UK Australia Administration, Langley Research Center USA WAELBROECK, Claire WEAVER, Andrew J. Laboratoire des Sciences du Climat et de University of Victoria WINTHER, Jan-Gunnar l Environnement, Institut Pierre Simon Canada Norwegian Polar Institute Laplace Norway WEBB, Mark France Met Office Hadley Centre WITTENBERG, Andrew WALSH, Kevin UK National Oceanic and Atmospheric University of Melbourne Administration, Geophysical Fluid Dynamics WEBB, Robert Australia Laboratory National Oceanic and Atmospheric USA WANG, Bin Administration, Earth System Research University of Hawaii Laboratory WOLF-GLADROW, Dieter USA USA Alfred Wegener Institute for Polar and Marine Research WANG, Chunzai WEHNER, Michael Germany National Oceanic and Atmospheric Lawrence Berkeley National Laboratory Administration, Atlantic Oceanographic and USA WOOD, Simon N. Meteorological Laboratory University of Bath WEISHEIMER, Antje USA UK University of Oxford WANG, Fan UK WOODWORTH, Philip L. Institute of Oceanology, Chinese Academy of National Oceanography Centre WEISS, Ray F. Sciences UK AV Scripps Institution of Oceanography China USA WOOLLINGS, Tim WANG, Hui-Jun University of Reading WHITE, Neil J. Institute of Atmospheric Physics, Chinese UK CSIRO Marine and Atmospheric Research Academy of Sciences Australia WORBY, Anthony China CSIRO Marine and Atmospheric Research WIDLANSKY, Matthew WANG, Junhong Australia University of Hawaii National Center for Atmospheric Research USA WRATT, David USA National Institute of Water and Atmospheric WIJFFELS, Susan WANG, Muyin Research CSIRO Marine and Atmospheric Research National Oceanic and Atmospheric New Zealand Australia Administration, Joint Institute for the Study WUEBBLES, Donald of the Atmosphere and Ocean WILD, Martin University of Illinois USA ETH Zurich USA Switzerland WANG, Xiaolan L. WYANT, Matthew Environment Canada University of Washington Canada USA 1494 Contributors to the IPCC WGI Fifth Assessment Report Annex V XIAO, Cunde ZHANG, Hua ZWARTZ, Dan Chinese Academy of Meteorological Sciences, National Climate Center, China Victoria University of Wellington China Meteorological Administration Meteorological Administration New Zealand China China ZWIERS, Francis XIE, Shang-Ping ZHANG, Jianglong University of Victoria Scripps Institution of Oceanography University of North Dakota Canada USA USA YASHAYAEV, Igor ZHANG, Lixia Bedford Institute of Oceanography Institute of Atmospheric Physics, Chinese Canada Academy of Sciences China YASUNARI, Tetsuzo Nagoya University ZHANG, Rong Japan National Oceanic and Atmospheric Administration, Geophysical Fluid Dynamics YEH, Sang-Wook Laboratory Hanyang University USA Republic of Korea ZHANG, Tingjun YIN, Jianjun Cooperative Institute for Research in University of Arizona Environmental Sciences USA USA YOKOYAMA, Yusuke ZHANG, Xiao-Ye University of Tokyo Chinese Academy of Meteorological Sciences, Japan China Meteorological Administration YOSHIMORI, Masakazu China University of Tokyo ZHANG, Xuebin Japan Environment Canada YOUNG, Paul Canada Lancaster University ZHAO, Lin UK Cold and Arid Regions Environmental and YU, Lisan Engineering Research Institute, Chinese Woods Hole Oceanographic Institution Academy of Sciences USA China ZACHOS, James ZHAO, Zong-Ci University of California Santa Cruz National Climate Center, China USA Meteorological Administration China AV ZAEHLE, Sönke Max Planck Institute for Biogeochemistry ZHENG, Xiaotong Germany Ocean University of China China ZAPPA, Giuseppe University of Reading ZHOU, Tianjun UK Institute of Atmospheric Physics, Chinese Academy of Sciences ZENG, Ning China University of Maryland USA ZICKFELD, Kirsten Simon Fraser University ZHAI, Panmao Canada National Climate Center, China Meteorological Administration ZOU, Liwei China Institute of Atmospheric Physics, Chinese Academy of Sciences ZHANG, Chidong China University of Miami USA 1495 Annex VI: Expert Reviewers AVI of the IPCC WGI Fifth Assessment Report This annex should be cited as: IPCC, 2013: Annex VI: Expert Reviewers of the IPCC WGI Fifth Assessment Report. In: Climate Change 2013: The Physi- cal Science Basis. Contribution of Working Group I to the Fifth Assessment Report of the Intergovernmental Panel on Climate Change [Stocker, T.F., D. Qin, G.-K. Plattner, M. Tignor, S.K. Allen, J. Boschung, A. Nauels, Y. Xia, V. Bex and P.M. Midgley (eds.)]. Cambridge University Press, Cambridge, United Kingdom and New York, NY, USA. 1497 Annex VI Expert Reviewers of the IPCC WGI Fifth Assessment Report AAMAAS, Borgar ANDREAE, Meinrat O. ARTUSO, Florinda Center for International Climate and Max Planck Institute for Chemistry Italian National Agency for New Environmental Research Oslo Germany Technologies, Energy and Sustainable Norway Economic Development ANDREU-BURILLO, Isabel Italy ABRAHAM, JOHN Institut Catala de Ciencies del Clima University of St. Thomas Spain ASMI, Ari USA University of Helsinki ANDREWS, Oliver David Finland ADAM, Hussein University of East Anglia Wad Medani Ahlia College UK AUAD, Guillermo Sudan Bureau of Ocean Energy Management ANEL CABANELAS, Juan Antonio USA AGREN, Göran University of Oxford Swedish University of Agricultural Sciences UK AUCAMP, Pieter Sweden Ptersa Environmental ANENBERG, Susan Management Consultants ALEXANDER, Lisa U.S. Environmental Protection Agency South Africa University of New South Wales USA Australia AZAR, Christian ANNAMALAI, H. Chalmers University of Technology ALEYNIK, Dmitry University of Hawaii Sweden Scottish Association for Marine Science USA UK BADER, David ANNAN, James Lawrence Livermore National Laboratory ALLAN, Richard Japan Agency for Marine-Earth USA University of Reading Science and Technology UK Japan BADIOU, Pascal Ducks Unlimited Canada ALLEN, Simon K. APITULEY, Arnoud Canada IPCC WGI TSU, University of Bern Royal Netherlands Meteorological Institute Switzerland Netherlands BAHN, Michael University of Innsbruck ALLEY, Richard B. APPENZELLER, Christof Austria Pennsylvania State University Federal Office of Meteorology and USA Climatology MeteoSwiss BAKAN, Stephan Switzerland Max Planck Institute for Meteorology ALLISON, Ian Germany Antarctic Climate and Ecosystems ARBLASTER, Julie Cooperative Research Centre Bureau of Meteorology BALTENSPERGER, Urs Australia Australia Paul Scherrer Institute Switzerland ALORY, Gaël ARNETH, Almut Laboratoire d Etudes en Géophysique Karlsruhe Institute of Technology BAMBER, Jonathan et Océanographie Spatiales Germany University of Bristol France UK ARORA, Vivek ALPERT, Alice Environment Canada BAN-WEISS, George Massachusetts Institute of Technology Canada Lawrence Berkeley National Laboratory USA and University of Southern California ARTALE, Vincenzo USA AMJAD, Muhammad Italian National Agency for New Global Change Impact Studies Centre Technologies, Energy and Sustainable BARKER, Stephen Pakistan Economic Development Cardiff University AVI Italy UK ANDEREGG, William Stanford University ARTINANO, Begona BARNETT, Tim USA Centro de Investigaciones Energéticas, Scripps Institution of Oceanography Medioambientales y Tecnológicas USA ANDERSEN, Bo Spain Norwegian Space Centre BARRETT, Jack Norway Imperial College London (retired) UK 1498 Expert Reviewers of the IPCC WGI Fifth Assessment Report Annex VI BARRETT, Peter BHANDARI, Medani BONNET, Sophie Victoria University of Wellington Syracuse University Université du Québec New Zealand USA Canada BARRY, Roger BINDOFF, Nathaniel L. BONY, Sandrine National Snow and Ice Data Center University of Tasmania Laboratoire de Météorologie Dynamique, USA Australia Institut Pierre Simon Laplace France BATES, J. Ray BINTANJA, Richard University College Dublin Royal Netherlands Meteorological Institute BOOTH, Ben Ireland Netherlands Met Office Hadley Centre UK BATES, Timothy BLADÉ, Ileana National Oceanic and Atmospheric Universitat de Barcelona BOSILOVICH, Michael Administration, Pacific Marine Spain National Aeronautics and Space Environmental Laboratory Administration, Goddard Space Flight Center BLANCO, Juan A. USA USA Universidad Pública de Navarra BEKKI, Slimane Spain BOUCHER, Olivier Laboratoire Atmospheres, Milieux, Laboratoire de Météorologie Dynamique, BLATTER, Heinz Observations Spatiales, Institut Institut Pierre Simon Laplace ETH Zurich Pierre Simon Laplace France Switzerland France BOULDIN, Jim BLOMQVIST, Sven BELLOUIN, Nicolas University of California Davis Stockholm University Met Office Hadley Centre USA Sweden UK BOURBONNIERE, Richard BODAS-SALCEDO, Alejandro BELTRAN, Catherine Environment Canada Met Office Hadley Centre Université Pierre et Marie Curie Canada UK France BOURLES, Bernard BODE, Antonio BENNARTZ, Ralf Institut de Recherche pour le Développement Instituto Espanol de Oceanografia University of Wisconsin France Spain USA BOUSQUET, Philippe BOEHM, Christian Reiner BERNHARD, Luzi Laboratoire des Sciences du Imperial College London Swiss Federal Institute for Forest, Snow Climat et de l Environnement, UK and Landscape Research WSL Institut Pierre Simon Laplace Switzerland BOENING, Carmen France National Aeronautics and Space BERNHARDT, Karl-Heinz BOWEN, Melissa Administration, Jet Propulsion Laboratory Leibniz Society of Sciences at Berlin University of Auckland USA Germany New Zealand BOERSMA, Klaas Folkert BERNIER, Pierre BOYER, Timothy Royal Netherlands Meteorological Institute Natural Resources Canada National Oceanic and Atmospheric and Eindhoven University of Technology Canada Administration, National Netherlands Oceanographic Data Center BERNTSEN, Terje BOGNER, Jean E. USA University of Oslo University of Illinois Norway BRACEGIRDLE, Thomas USA British Antarctic Survey BERTHIER, Etienne BOKO, Michel UK AVI Laboratoire d Etudes en Géophysique Université d Abomey Calavi et Océanographie Spatiales BRACONNOT, Pascale Benin France Laboratoire des Sciences du BOLLASINA, Massimo Climat et de l Environnement, BETTS, Richard National Oceanic and Atmospheric Institut Pierre Simon Laplace Met Office Hadley Centre Administration, Geophysical France UK Fluid Dynamics Laboratory BRAESICKE, Peter BETZ, Gregor USA University of Cambridge Karlsruhe Institute of Technology UK Germany 1499 Annex VI Expert Reviewers of the IPCC WGI Fifth Assessment Report BREGMAN, Abraham BUTLER, James CASSOU, Christophe Royal Netherlands Meteorological Institute National Oceanic and Atmospheric Centre Européen de Recherche et de Netherlands Administration, Earth System Formation Avancée en Calcul Scientifique Research Laboratory France BRENDER, Pierre USA Laboratoire des Sciences du Climat CEARRETA, Alejandro et de l Environnement, Institut Pierre CAESAR, John Universidad del Pais Vasco Simon Laplace and AgroParisTech Met Office Hadley Centre Spain France UK CERMAK, Jan BREWER, Michael CAGNAZZO, Chiara Ruhr-Universität Bochum National Oceanic and Atmospheric Institute of Atmospheric Sciences and Climate Germany Administration, National Italy CERVARICH, Matthew Climatic Data Center CAI, Rongshuo University of Illinois USA Third Institute of Oceanography, USA BRIERLEY, Christopher State Oceanic Administration CHADWICK, Robin University College London China Met Office Hadley Centre UK CAI, Zucong UK BRIFFA, Keith Nanjing Normal University CHARLESWORTH, Mark University of East Anglia China Keele University UK CAINEY, Jill UK BROMWICH, David UK CHARLSON, Robert Ohio State University CALVO, Natalia University of Washington USA Universidad Complutense de Madrid USA BROOKS, Harold Spain CHARPENTIER LJUNGQVIST, Fredrik National Oceanic and Atmospheric CAMERON-SMITH, Philip Stockholm University Administration, National Severe Lawrence Livermore National Laboratory Sweden Storms Laboratory USA USA CHAUVIN, Fabrice CANDELA, Lucila Météo-France BROVKIN, Victor Universitat Politecnica de Catalunya France Max Planck Institute for Meteorology Spain Germany CHAZETTE, Patrick CAO, Jianting Laboratoire des Sciences du BROWN, Jaclyn General Institute of Water Resources Climat et de l Environnement, CSIRO Marine and Atmospheric Research and Hydropower Planning and Design, Institut Pierre Simon Laplace Australia Ministry of Water Resources France BROWN, Josephine China CHE, Tao Bureau of Meteorology CARDIA SIMOES, Jefferson Cold and Arid Regions Environmental Australia Universidade Federal do Rio Grande do Sul and Engineering Research Institute, BROWN, Simon Brazil Chinese Academy of Sciences Met Office Hadley Centre China CARDINAL, Damien UK Université Pierre et Marie Curie CHEN, Xianyao BURKETT, Virginia France First Institute of Oceanography, U.S. Geological Survey State Oceanic Administration CARTER, Timothy USA China Finnish Environment Institute AVI BURT, Peter Finland CHERCHI, Annalisa University of Greenwich Centro Euromediterraneo per i CASELDINE, Chris UK Cambiamenti Climatici and Istituto University of Exeter Nazionale di Geofisica e Vulcanologia BURTON, David UK Italy Burton Systems Software CASSARDO, Claudio USA CHHABRA, Abha University of Torino Indian Space Research Organisation BUTENHOFF, Christopher Italy India Portland State University USA 1500 Expert Reviewers of the IPCC WGI Fifth Assessment Report Annex VI CHIKAMOTO, Megumi COGLEY, J. Graham CRIMMINS, Allison University of Hawaii Trent University U.S. Environmental Protection Agency USA Canada USA CHIKAMOTO, Yoshimitsu COLE, Julia CRISTINI, Luisa University of Hawaii University of Arizona University of Hawaii USA USA USA CHOU, Chia COLLIER, Mark CROK, Marcel Academia Sinica CSIRO Marine and Atmospheric Research Netherlands Taiwan, China Australia CURRY, Charles CHRISTIAN, James COLLINS, Matthew University of Victoria Fisheries and Oceans Canada University of Exeter Canada Canada UK CURTIS, Jeffrey CHRISTOPHERSEN, Oyvind COLLINS, William University of Illinois Climate and Pollution Agency University of Reading USA Norway UK DAI, Aiguo CHRISTY, John COLMAN, Robert University at Albany and National University of Alabama Bureau of Meteorology Center for Atmospheric Research USA Australia USA CHURCH, John COLOSE, Chris DAIRAKU, Koji CSIRO Marine and Atmospheric Research University at Albany National Research Institute for Earth Australia USA Science and Disaster Prevention Japan CHYLEK, Petr COOPER, Owen Los Alamos National Laboratory Cooperative Institute for Research DAMERIS, Martin USA in Environmental Sciences DLR German Aerospace Center USA Germany CIRANO, Mauro Federal University of Bahia COPSTEIN WALDEMAR, Celso DANIEL, John Brazil Porto Alegre Municipality, National Oceanic and Atmospheric Environmental Department Administration, Earth System CIURO, Darienne Brazil Research Laboratory University of Illinois USA USA CORTESE, Giuseppe GNS Science DANIELS, Emma CLARK, Robin New Zealand Wageningen University Met Office Hadley Centre Netherlands UK CORTI, Susanna European Centre for Medium-Range DANIS, François CLAUSSEN, Martin Weather Forecasts and Institute of Laboratoire de Météorologie Dynamique, Max Planck Institute for Meteorology Atmospheric Sciences and Climate Institut Pierre Simon Laplace Germany Italy France CLERBAUX, Cathy COTRIM DA CUNHA, Leticia DAUTRAY, Robert Laboratoire Atmospheres, Milieux, Rio de Janeiro State University Académie des Sciences Observations Spatiales, Institut Brazil France Pierre Simon Laplace France COUMOU, Dim DAVIDSON, Eric Potsdam Institute for Climate Woods Hole Research Center CLIFT, Peter AVI Impact Research USA Louisiana State University Germany USA DAVIES, Michael COVEY, Curt Coldwater Consulting Ltd COAKLEY, James Lawrence Livermore National Laboratory Canada Oregon State University USA USA DAY, Jonathan CRAWFORD, James University of Reading COFFEY, Michael USA UK National Center for Atmospheric Research USA 1501 Annex VI Expert Reviewers of the IPCC WGI Fifth Assessment Report DE ELIA, Ramon DEWITT, David G. DOSTAL, Paul Ouranos Consortium on Regional Climatology Columbia University DLR German Aerospace Center and Adaptation to Climate Change USA Germany Canada DIAZ MOREJON, Cristobal Felix DOWNES, Stephanie DE SAEDELEER, Bernard Ministry of Science, Technology Australian National University Université catholique de Louvain and the Environment Australia Belgium Cuba DOYLE, Moira Evelina DE VRIES, Hylke DICKENS, Gerald Universidad de Buenos Aires Royal Netherlands Meteorological Institute Rice University Argentina Netherlands USA DRAGONI, Walter DEAN, Robert DIEDHIOU, Arona University of Perugia University of Florida Institut de Recherche pour le Développement Italy USA France DRIJFHOUT, Sybren DEL GENIO, Anthony DIMA, Mihai Royal Netherlands Meteorological Institute National Aeronautics and University of Bucharest Netherlands Space Administration, Goddard Romania DU, Enzai Institute for Space Studies DING, Yihui Peking University USA National Climate Center, China China DELPLA, Ianis Meteorological Administration DUAN, Anmin Laboratoire d Etude et de Recherche China Institute of Atmospheric Physics, en Environnement et Santé DING, Yongjian Chinese Academy of Sciences France Cold and Arid Regions Environmental China DELSOLE, Timothy and Engineering Research Institute, DUCE, Robert George Mason University Chinese Academy of Sciences Texas A&M University USA China USA DELWORTH, Thomas DITLEVSEN, Peter DUDOK DE WIT, Thierry National Oceanic and Atmospheric University of Copenhagen Université d Orléans Administration, Geophysical Denmark France Fluid Dynamics Laboratory DOHERTY, Ruth USA DUNNE, Eimear University of Edinburgh Finnish Meteorological Institute DEMORY, Marie-Estelle UK Finland University of Reading DOLE, Randall UK DUNSTONE, Nick National Oceanic and Atmospheric Met Office Hadley Centre DÉQUÉ, Michel Administration, Earth System UK Météo-France Research Laboratory France USA DURACK, Paul Lawrence Livermore National Laboratory DERKSEN, Chris DOLMAN, Han USA Environment Canada VU University Amsterdam Canada Netherlands DWYER, Ned University College Cork DESIATO, Franco DOMINGUES, Catia M. Ireland Institute for Environmental Antarctic Climate and Ecosystems Protection and Research Cooperative Research Centre EASTERBROOK, Don AVI Italy Australia Western Washington University USA DEVARA, Panuganti C.S. DONAHUE, Neil Indian Institute of Tropical Meteorology Carnegie Mellon University EBI, Kristie India USA Stanford University USA DEWALS, Benjamin DONNER, Leo Université de Liege National Oceanic and Atmospheric EISEN, Olaf Belgium Administration, Geophysical Alfred Wegener Institute for Fluid Dynamics Laboratory Polar and Marine Research USA Germany 1502 Expert Reviewers of the IPCC WGI Fifth Assessment Report Annex VI EISENMAN, Ian FEINGOLD, Graham FORBES, Donald University of California San Diego National Oceanic and Atmospheric Bedford Institute of Oceanography USA Administration, Earth System Canada Research Laboratory EKHOLM, Tommi FOREST, Chris USA VTT Technical Research Centre of Finland Pennsylvania State University Finland FEIST, Dietrich USA Max Planck Institute for Biogeochemistry ELDEVIK, Tor FORSTER, Piers Germany University of Bergen University of Leeds Norway FERRONE, Andrew UK Karlsruhe Institute of Technology ELJADID, Ali Geath FOSTER, James Germany Al-Fath University National Aeronautics and Space Libya FESER, Frauke Administration, Goddard Space Flight Center Helmholtz-Zentrum Geesthacht USA EMANUEL, Kerry Germany Massachusetts Institute of Technology FOUNTAIN, Andrew USA FEULNER, Georg Portland State University Potsdam Institute for Climate USA ENOMOTO, Hiroyuki Impact Research National Institute of Polar Research FRANKLIN, James Germany Japan CLF-Chem Consulting SPRL FICHEFET, Thierry Belgium ERICKSON, David Université catholique de Louvain Oak Ridge National Laboratory FRANKS, Stewart Belgium USA University of Newcastle Australia FIELD, Christopher Australia ESPINOZA, Jhan Carlo Carnegie Institution for Science Instituto Geofísico del Perú FREDERIKSEN, Carsten USA Peru Bureau of Meteorology FISCHER, Andreas Australia ESSERY, Richard Federal Office of Meteorology and University of Edinburgh FREDERIKSEN, Jorgen Climatology MeteoSwiss UK CSIRO Marine and Atmospheric Research Switzerland Australia EVANS, Michael Neil FISCHER, Hubertus University of Maryland FREE, Melissa University of Bern USA National Oceanic and Atmospheric Switzerland Administration, Air Resources Laboratory EVANS, Wayne FISCHLIN, Andreas USA York University ETH Zurich Canada FREELAND, Howard Switzerland Fisheries and Oceans Canada EXBRAYAT, Jean-François FISHER, Joshua Canada University of New South Wales National Aeronautics and Space Australia FREPPAZ, Michele Administration, Jet Propulsion Laboratory University of Torino EYNAUD, Frédérique USA Italy Université Bordeaux 1 FLORES, José-Abel France FRIEDLINGSTEIN, Pierre Universidad de Salamanca University of Exeter FAHEY, David Spain UK National Oceanic and Atmospheric FLOSSMANN, Andrea Administration, Earth System FROELICHER, Thomas AVI Université Blaise Pascal Research Laboratory Princeton University France USA USA FOLBERTH, Gerd FAN, Jiwen FRONZEK, Stefan Met Office Hadley Centre Pacific Northwest National Laboratory Finnish Environment Institute UK USA Finland FOLLAND, Christopher FARAGO, Tibor FRÜH, Barbara Met Office Hadley Centre St. Istvan University Deutscher Wetterdienst UK Hungary Germany 1503 Annex VI Expert Reviewers of the IPCC WGI Fifth Assessment Report FU, Joshua Xiouhua GARIMELLA, Sarvesh GILLETT, Nathan University of Hawaii Massachusetts Institute of Technology Environment Canada USA USA Canada FU, Weiwei GARREAUD, René GINOUX, Paul Danish Meteorological Institute Universidad de Chile National Oceanic and Atmospheric Denmark Chile Administration, Geophysical Fluid Dynamics Laboratory FUGLESTVEDT, Jan GATTUSO, Jean-Pierre USA Center for International Climate and Observatoire Océanologique de Villefranche Environmental Research Oslo sur Mer, Université Pierre et Marie Curie GIORGETTA, Marco Norway France Max Planck Institute for Meteorology Germany FUKASAWA, Masao GAUCI, Vincent Japan Agency for Marine-Earth The Open University GLIKSON, Andrew Science and Technology UK Australian National University Japan Australia GAYO, Eugenia M. FUNG, Inez Centro de Investigaciones del GODIN-BEEKMANN, Sophie University of California Berkeley Hombre en el Desierto Laboratoire Atmospheres, Milieux, USA Chile Observations Spatiales, Institut Pierre Simon Laplace FUNK, Martin GEDNEY, Nicola France ETH Zurich Met Office Hadley Centre Switzerland UK GOLAZ, Jean-Christophe National Oceanic and Atmospheric FYFE, John GEHRELS, Roland Administration, Geophysical Environment Canada Plymouth University Fluid Dynamics Laboratory Canada UK USA GAALEMA, Stephen GERBER, Stefan GONG, Daoyi Black Forest Engineering, LLC University of Florida Beijing Normal University USA USA China GAGLIARDINI, Olivier GERLAND, Sebastian GONZALEZ, Patrick Laboratoire de Glaciologie et Géophysique Norwegian Polar Institute U.S. National Park Service de l`Environnement, Université Joseph Fourier Norway USA France GERVAIS, François GOOD, Peter GAJEWSKI, Konrad Université François-Rabelais de Tours Met Office Hadley Centre University of Ottawa France UK Canada GETTELMAN, Andrew GOOD, Simon GALDOS, Marcelo National Center for Atmospheric Research Met Office Hadley Centre Brazilian Bioethanol Science and USA UK Technology Laboratory GHAN, Steven Brazil GOODESS, Clare Pacific Northwest National Laboratory University of East Anglia GALLEGO, David USA UK Universidad Pablo de Olavide GHOSH, Sucharita Spain GOOSSE, Hugues Swiss Federal Institute for Forest, Snow Université catholique de Louvain GANOPOLSKI, Andrey and Landscape Research WSL Belgium Potsdam Institute for Climate Switzerland AVI Impact Research GORIS, Nadine GIFFORD, Roger Germany University of Bergen and Bjerknes CSIRO Plant Industry Centre for Climate Research GAO, Xuejie Australia Norway National Climate Center, China GILBERT, Denis Meteorological Administration GOSWAMI, Santonu Fisheries and Oceans Canada China Oak Ridge National Laboratory Canada USA GARCIA-HERRERA, Ricardo Universidad Complutense de Madrid Spain 1504 Expert Reviewers of the IPCC WGI Fifth Assessment Report Annex VI GOWER, James HAEBERLI, Wilfried HARTMANN, Jens Fisheries and Oceans Canada University of Zurich University of Hamburg Canada Switzerland Germany GRAY, Vincent HAFEZ, Yehia HASANEAN, Hosny New Zealand King Abdulaziz University King Abdulaziz University Saudi Arabia Saudi Arabia GREGORY, Jonathan University of Reading and Met HAGEN, David L. HASSLER, Birgit Office Hadley Centre AcrossTech Cooperative Institute for Research UK USA in Environmental Sciences USA GREWE, Volker HAGOS, Samson DLR German Aerospace Center Pacific Northwest National Laboratory HAWKINS, Ed Germany USA University of Reading UK GRIFFIES, Stephen HAIGH, Joanna National Oceanic and Atmospheric Imperial College London HAYASAKA, Tadahiro Administration, Geophysical UK Tohoku University Fluid Dynamics Laboratory Japan HAJIMA, Tomohiro USA Japan Agency for Marine-Earth HAYWOOD, Jim GRIGGS, David Science and Technology Met Office Hadley Centre and Monash University Japan University of Exeter Australia UK HALL, Dorothy GRIMM, Alice National Aeronautics and Space HEGERL, Gabriele Federal University of Parana Administration, Goddard Space Flight Center University of Edinburgh Brazil USA UK GRINSTED, Aslak HALLBERG, Robert HEIM, Richard University of Copenhagen National Oceanic and Atmospheric National Oceanic and Atmospheric Denmark Administration, Geophysical Administration, National Fluid Dynamics Laboratory Climatic Data Center GRUBER, Nicolas USA USA ETH Zurich Switzerland HALLORAN, Paul HEINTZENBERG, Jost Met Office Hadley Centre Leibniz Institute for Tropospheric Research GRUBER, Stephan UK Germany University of Zurich Switzerland HAN, Dawei HEINZE, Christoph University of Bristol University of Bergen and Bjerknes GUGLIELMIN, Mauro UK Centre for Climate Research University of Insubria Norway Italy HANSEN, Bogi Faroe Marine Research Institute HERTWICH, Edgar GUILYARDI, Eric Faroe Islands Norwegian University of Laboratoire d Océanographie et du Science and Technology Climat, Institut Pierre Simon Laplace HAO, Aibing Norway France Ministry of Land and Resources China HEWITSON, Bruce GUTTORP, Peter University of Cape Town University of Washington and HARGREAVES, Julia South Africa Norwegian Computing Center Japan Agency for Marine-Earth AVI USA Science and Technology HIGGINS, Paul Japan American Meteorological Society GUTZLER, David USA University of New Mexico HARNISCH, Jochen USA KfW HIRST, Anthony Germany CSIRO Marine and Atmospheric Research HAARSMA, Reindert Australia Royal Netherlands Meteorological Institute HARPER, Joel Netherlands University of Montana USA 1505 Annex VI Expert Reviewers of the IPCC WGI Fifth Assessment Report HISDAL, Hege HOWARD, William INOUE, Toshiro Norwegian Water Resources Australian National University University of Tokyo and Energy Directorate Australia Japan Norway HREN, Michael IRVINE, Peter HOCK, Regine University of Connecticut Institute for Advanced Sustainability Studies University of Alaska Fairbanks USA Germany USA HU, Aixue ISE, Takeshi HODSON, Dan National Center for Atmospheric Research University of Hyogo University of Reading USA Japan UK HU, Zeng-Zhen ISHII, Masao HOERLING, Martin National Oceanic and Atmospheric Meteorological Research Institute National Oceanic and Atmospheric Administration, National Weather Service Japan Administration, Earth System USA ISHIZUKA, Shigehiro Research Laboratory HUANG, Jianping Forestry and Forest Products Institute USA Lanzhou University Japan HÖGBERG, Peter China ITO, Akihiko Swedish University of Agricultural Sciences HUANG, Lei National Institute for Environmental Studies Sweden National Climate Center, China Japan HOLGATE, Simon Meteorological Administration ITO, Takamitsu National Oceanography Centre China Georgia Institute of Technology UK HUANG, Lin USA HOLLIS, Christopher Environment Canada ITOH, Kiminori GNS Science Canada Yokohama National University New Zealand HUDSON, James Japan HOLTSLAG, Albert A.M. Desert Research Institute IVERSEN, Trond Wageningen University USA European Centre for Medium-Range Netherlands HUGGEL, Christian Weather Forecasts, UK and HÖNISCH, Bärbel University of Zurich Norwegian Meteorological Institute Columbia University Switzerland Norway USA HUGHES, Malcolm JACKSON, Laura HOPE, Pandora University of Arizona Met Office Hadley Centre Bureau of Meteorology USA UK Australia HUNTER, John JACOBEIT, Jucundus HOROWITZ, Larry Antarctic Climate and Ecosystems University of Augsburg National Oceanic and Atmospheric Cooperative Research Centre Germany Administration, Geophysical Australia JACOBSON, Mark Z. Fluid Dynamics Laboratory HURST, Dale Stanford University USA Cooperative Institute for Research USA HOURDIN, Frédéric in Environmental Sciences JAENICKE, Ruprecht Laboratoire de Météorologie Dynamique, USA Johannes Gutenberg University Mainz Institut Pierre Simon Laplace HUYBRECHTS, Philippe Germany France Vrije Universiteit Brussel AVI JAIN, Sharad K. HOUSE, Joanna Belgium Indian Institute of Technology Roorkee University of Bristol INCECIK, Selahattin India UK Istanbul Technical University JEONG, Myeong-Jae HOUWELING, Sander Turkey Gangneung-Wonju National University Utrecht University INGRAM, William Republic of Korea Netherlands Met Office Hadley Centre and HOVLAND, Martin University of Oxford University of Bergen UK Norway 1506 Expert Reviewers of the IPCC WGI Fifth Assessment Report Annex VI JIANG, Dabang JOYCE, Terrence KAROLY, David Institute of Atmospheric Physics, Woods Hole Oceanographic Institution University of Melbourne Chinese Academy of Sciences USA Australia China JUCKES, Martin KARPECHKO, Alexey JIANG, Jonathan Science and Technologies Facility Council Finnish Meteorological Institute National Aeronautics and Space UK Finland Administration, Jet Propulsion Laboratory JYLHÄ, Kirsti KATBEH-BADER, Nedal USA Finnish Meteorological Institute Ministry of Environment Affairs JOHANSSON, Daniel Finland Palestine Chalmers University of Technology KÄÄB, Andreas KATO, Etsushi Sweden University of Oslo National Institute for Environmental Studies JOHN, Jasmin Norway Japan National Oceanic and Atmospheric KAGEYAMA, Masa KAUFMANN, Robert Administration, Geophysical Laboratoire des Sciences du Boston University Fluid Dynamics Laboratory Climat et de l Environnement, USA USA Institut Pierre Simon Laplace KAUPPINEN, Jyrki JOHNS, Tim France University of Turku Met Office Hadley Centre KAHN, Brian Finland UK National Aeronautics and Space KAVANAGH, Christopher JOHNSON, Jennifer Administration, Jet Propulsion Laboratory International Atomic Energy Agency Stanford University USA Monaco USA KAHN, Ralph KAWAI, Hiroyasu JOHNSON, Nathaniel National Aeronautics and Space Port and Airport Research Institute University of Hawaii Administration, Goddard Space Flight Center Japan USA USA KAWAMIYA, Michio JONES, Christopher KALESCHKE, Lars Japan Agency for Marine-Earth Science Met Office Hadley Centre University of Hamburg and Technology UK Germany Japan JONES, Gareth S. KANAKIDOU, Maria KAWAMURA, Kenji Met Office Hadley Centre University of Crete National Institute of Polar Research UK Greece Japan JONES, Philip KANAYA, Yugo KAYE, Neil University of East Anglia Japan Agency for Marine-Earth Met Office Hadley Centre UK Science and Technology UK Japan JOOS, Fortunat KEELING, Ralph University of Bern KANDEL, Robert Scripps Institution of Oceanography Switzerland Laboratoire de Météorologie Dynamique, USA Institut Pierre Simon Laplace JOSEY, Simon France KEEN, Richard National Oceanography Centre University of Colorado Boulder (retired) UK KANG, Shichang USA Institute of Tibetan Plateau Research, JOSHI, Manoj Chinese Academy of Sciences KEENLYSIDE, Noel University of East Anglia China University of Bergen and Bjerknes AVI UK Centre for Climate Research KANG, Sok Kuh JOUGHIN, Ian Norway Korea Ocean Research and University of Washington Development Institute KELLER, Charles USA Republic of Korea Los Alamos National Laboratory (retired) JOUSSAUME, Sylvie USA KARLSSON, Per Erik Laboratoire des Sciences du Climat et de Swedish Environmental Research Institute KENDON, Elizabeth l Environnement, Institut Pierre Simon Laplace Sweden Met Office Hadley Centre France UK 1507 Annex VI Expert Reviewers of the IPCC WGI Fifth Assessment Report KENNEDY, John KIRKEVAG, Alf KONFIRST, Matthew Met Office Hadley Centre Norwegian Meteorological Institute American Association for the Advancement UK Norway of Science and National Science Foundation USA KENT, Elizabeth KITOH, Akio National Oceanography Centre Meteorological Research Institute KONOVALOV, Vladimir UK Japan Russian Academy of Sciences Russian Federation KESKIN, Siddik Sinan KJELLSTRÖM, Erik Marmara University Swedish Meteorological and KOPP, Robert Turkey Hydrological Institute Rutgers University Sweden USA KHALIL, Mohammad Aslam Khan Portland State University KLEIN TANK, Albert KORTELAINEN, Pirkko USA Royal Netherlands Meteorological Institute Finnish Environment Institute Netherlands Finland KHESHGI, Haroon ExxonMobil Research and Engineering KLINGER, Lee KREASUWUN, Jiemjai USA USA Chiang Mai University Thailand KHMELINSKII, Igor KLOTZBACH, Philip Universidade do Algarve Colorado State University KREIENKAMP, Frank Portugal USA Climate & Environment Consulting Potsdam GmbH KHOSRAWI, Farahnaz KNUTSON, Thomas Germany Stockholm University National Oceanic and Atmospheric Sweden Administration, Geophysical KRINNER, Gerhard Fluid Dynamics Laboratory Laboratoire de Glaciologie et Géophysique KILADIS, George USA de l`Environnement, Université Joseph Fourier National Oceanic and Atmospheric France Administration, Earth System KNUTTI, Reto Research Laboratory ETH Zurich KRIPALANI, Ramesh USA Switzerland Indian Institute of Tropical Meteorology India KIM, Daehyun KOBASHI, Takuro Columbia University National Institute of Polar Research KRISTJÁNSSON, Jón Egill USA Japan University of Oslo Norway KIM, Seong-Joong KOBAYASHI, Shigeki Korea Polar Research Institute Toyota Central R&D Labs., Inc. KRIVOVA, Natalie Republic of Korea Japan Max Planck Institute for Solar System Research KINDLER, Pascal KOBAYASHI, Taiyo Germany University of Geneva Japan Agency for Marine-Earth Switzerland Science and Technology KUHN, Nikolaus J. Japan University of Basel KING, Andrew Switzerland University of New South Wales KOH, Tieh-Yong Australia Nanyang Technological University KULSHRESTHA, Umesh Singapore Jawaharlal Nehru University KING, Matt India University of Tasmania and KÖHLER, Peter Newcastle University Alfred Wegener Institute for KUSANO, Kanya Australia Polar and Marine Research Nagoya University AVI Germany Japan KINTER, James Institute of Global Environment KOMEN, Gerbrand KUSUNOKI, Shoji and Society, Inc. Royal Netherlands Meteorological Meteorological Research Institute USA Institute and Utrecht University (retired) Japan Netherlands KIRCHENGAST, Gottfried LAGERLOEF, Gary University of Graz KONDO, Yutaka Earth & Space Research Austria University of Tokyo USA Japan 1508 Expert Reviewers of the IPCC WGI Fifth Assessment Report Annex VI LAKEN, Benjamin LECLERCQ, Paul LEVY II, Hiram Instituto de Astrofisíca de Canarias Utrecht University National Oceanic and Atmospheric Spain Netherlands Administration, Geophysical Fluid Dynamics Laboratory (retired) LAMBERT, Fabrice LEE, Arthur USA Korea Institute of Ocean Chevron Corporation Science and Technology USA LEWIS, Nicholas Republic of Korea UK LEE, Jae Hak LAMBERT, Francis Hugo Korea Institute of Ocean LEWITT, Martin University of Exeter Science and Technology American Geophysical Union UK Republic of Korea USA LANDUYT, William LEE, Sai Ming LI, Can ExxonMobil Research and Engineering Hong Kong Observatory University of Maryland and National USA China Aeronautics and Space Administration, Goddard Space Flight Center LANE, Tracy LEE, Seoung Soo USA International Hydropower Association National Oceanic and Atmospheric UK Administration, Earth System LI, Jui-Lin (Frank) Research Laboratory National Aeronautics and Space LANG, Herbert USA Administration, Jet Propulsion Laboratory ETH Zurich USA Switzerland LEE, Tsz-Cheung Hong Kong Observatory LI, Qingxiang LARTER, Robert China National Meteorological Information Center, British Antarctic Survey China Meteorological Administration UK LEGG, Sonya China Princeton University LAW, Beverly USA LI, Shenggong Oregon State University Institute of Geographic Sciences USA LEMKE, Peter and Natural Resources Research, Alfred Wegener Institute for LAW, Katharine Chinese Academy of Sciences Polar and Marine Research Laboratoire Atmospheres, Milieux, China Germany Observations Spatiales, Institut LI, Shuanglin Pierre Simon Laplace LENAERTS, Jan Institute of Atmospheric Physics, France Utrecht University Chinese Academy of Sciences Netherlands LAWRENCE, Judy China Victoria University of Wellington LENDERINK, Geert LI, Weiping New Zealand Royal Netherlands Meteorological Institute National Climate Center, China Netherlands LAWRENCE, Mark Meteorological Administration Institute for Advanced Sustainability Studies LEROY, Suzanne China Germany Brunel University LI, Weiwei UK LAXON, Seymour University of Illinois University College London LEVIN, Ingeborg USA UK University of Heidelberg LI, Yueqing Germany LE QUÉRÉ, Corinne Institute of Plateau Meteorology, China University of East Anglia LEVITUS, Sydney Meteorological Administration UK National Oceanic and Atmospheric China AVI Administration, National LEAITCH, Warren Richard LI, Zhanqing Oceanographic Data Center Environment Canada University of Maryland USA Canada USA LEVY, Julian LECK, Caroline LIAO, Hong Levy Environmental Consulting, Ltd. Stockholm University Institute of Atmospheric Physics, USA Sweden Chinese Academy of Sciences China 1509 Annex VI Expert Reviewers of the IPCC WGI Fifth Assessment Report LIN, Hai LOEW, Alexander MÄDER, Claudia Environment Canada Max Planck Institute for Meteorology Federal Environment Agency Canada Germany Germany LIN, Jialin LOFGREN, Brent MAGGI, Valter Ohio State University National Oceanic and Atmospheric University of Milano-Bicocca USA Administration, Great Lakes Italy Environmental Research Laboratory LINDERHOLM, Hans W. MAHLSTEIN, Irina USA University of Gothenburg Federal Office of Meteorology and Sweden LOHMANN, Gerrit Climatology MeteoSwiss Alfred Wegener Institute for Switzerland LINDSAY, Ron Polar and Marine Research University of Washington MAHOWALD, Natalie Germany USA Cornell University LOHMANN, Ulrike USA LIOU, Kuo-Nan ETH Zurich University of California Los Angeles MAKI, Takashi Switzerland USA Meteorological Research Institute LOOKYAT TAYLOR, Helen Japan LITTLE, Christopher World Science Data Base Princeton University MANN, Michael USA USA Pennsylvania State University LÓPEZ MORENO, Juan Ignacio USA LIU, Hongyan Instituto Pirenaico de Ecología Peking University MANNING, Martin Spain China Victoria University of Wellington LOUGH, Janice New Zealand LIU, Ke Xiu Australian Institute of Marine Science National Marine Data and MANZINI, Elisa Australia Information Service Max Planck Institute for Meteorology China LUCE, Charles Germany U.S. Forest Service LIU, Qiyong MARAUN, Douglas USA China CDC GEOMAR Helmholtz Centre China LÜTHI, Martin for Ocean Research ETH Zurich Germany LIU, Shaw Switzerland Academia Sinica MARBAIX, Philippe Taiwan, China LUNT, Daniel Université catholique de Louvain University of Bristol Belgium LIU, Xiaohong UK Pacific Northwest National Laboratory MARENGO, José USA LUO, Jing-Jia National Institute for Space Research Bureau of Meteorology Brazil LJUNGQVIST, Fredrik Australia Stockholm University MARINOVA, Dora Sweden LUPO, Anthony Curtin University University of Missouri Australia LLOYD, Philip USA Cape Peninsula University of Technology MARIOTTI, Annarita South Africa MA, Zhuguo National Oceanic and Atmospheric Institute of Atmospheric Physics, Administration, Climate Program Office LO, Yueh-Hsin Chinese Academy of Sciences USA AVI National Taiwan University China Taiwan, China MAROTZKE, Jochem MACCRACKEN, Michael Max Planck Institute for Meteorology LOBELL, David Climate Institute Germany Stanford University USA USA MARSH, Robert MACGREGOR, Joseph University of Southampton LOEB, Norman University of Texas UK National Aeronautics and Space USA Administration, Langley Research Center USA 1510 Expert Reviewers of the IPCC WGI Fifth Assessment Report Annex VI MARTIN, Eric MCLEAN, John MEYSSIGNAC, Benoit Météo-France James Cook University Laboratoire d Etudes en Géophysique France Australia et Océanographie Spatiales France MARTIN, Gill MEEHL, Gerald Met Office Hadley Centre National Center for Atmospheric Research MICKLEY, Loretta UK USA Harvard University USA MARTÍN MÍGUEZ, Belén MEIER, Walter Centro Tecnológico del Mar National Snow and Ice Data Center MIELIKÄINEN, Kari Spain USA Finnish Forest Research Institute Finland MARTIN-VIDE, Javier MEIYAPPAN, Prasanth Universitat de Barcelona University of Illinois MILLER, Benjamin R. Spain USA Cooperative Institute for Research in Environmental Sciences MARTY, Christoph MELSOM, Arne USA WSL Institute for Snow and Norwegian Meteorological Institute Avalanche Research SLF Norway MIMS, Forrest Switzerland Geronimo Creek Observatory MÉNDEZ, Carlos USA MASSONNET, François Instituto Venezolano de Université catholique de Louvain Investigaciones Científicas MIN, Seung-Ki Belgium Venezuela CSIRO Marine and Atmospheric Research Australia MATEI, Daniela MENGE, Duncan Max Planck Institute for Meteorology Princeton University MING, Jing Germany USA National Climate Center, China Meteorological Administration MATSUNO, Taroh MENZEL, W. Paul China Japan Agency for Marine-Earth University of Wisconsin Science and Technology USA MING, Yi Japan National Oceanic and Atmospheric MERCHANT, Christopher Administration, Geophysical MATSUOKA, Kenichi University of Edinburgh Fluid Dynamics Laboratory Norwegian Polar Institute UK USA Norway MEREDITH, Michael MINSCHWANER, Kenneth MATTHEWS, Paul British Antarctic Survey New Mexico Institute of University of Nottingham UK Mining and Technology UK MERLIS, Timothy USA MAURITSEN, Thorsten Princeton University and National MITCHELL, John Max Planck Institute for Meteorology Oceanic and Atmospheric Administration, Met Office Hadley Centre Germany Geophysical Fluid Dynamics Laboratory UK USA MAY, Wilhelm MOBERG, Anders Danish Meteorological Institute MERRYFIELD, William Stockholm University Denmark Environment Canada Sweden Canada MCELROY, Charles Thomas MÖHLER, Ottmar York University MESINGER, Fedor Karlsruhe Institute of Technology Canada University of Maryland Germany USA MCINNES, Kathleen AVI MOLINIÉ, Gilles CSIRO Marine and Atmospheric Research METCALFE, Daniel Laboratoire de Glaciologie et Géophysique Australia Swedish University of Agricultural Sciences de l`Environnement, Université Joseph Fourier Sweden MCKAY, Nicholas France University of Arizona METELKA, Ladislav MONAHAN, Adam USA Czech Hydrometeorological Institute University of Victoria Czech Republic MCKITRICK, Ross Canada University of Guelph Canada 1511 Annex VI Expert Reviewers of the IPCC WGI Fifth Assessment Report MONCKTON OF BRENCHLEY, Christopher MURATA, Akihiko NEU, Urs Science and Public Policy Institute Japan Agency for Marine-Earth Swiss Academy of Sciences UK Science and Technology Switzerland Japan MONTZKA, Stephen NEVISON, Cynthia National Oceanic and Atmospheric MURPHY, Brad University of Colorado Boulder Administration, Earth System Bureau of Meteorology USA Research Laboratory Australia NEWBERY, David USA MURPHY, Daniel University of Bern MOORTHY, K. Krishna National Oceanic and Atmospheric Switzerland Indian Space Research Organisation Administration, Earth System NEWBURY, Thomas Dunning India Research Laboratory Amercian Association for the USA MOOSDORF, Nils Advancement of Science and U.S. University of Hamburg MUSCHELER, Raimund Department of the Interior (retired) Germany Lund University USA Sweden MORGENSTERN, Olaf NICHOLLS, Robert National Institute of Water and MUTHALAGU, Ravichandran University of Southampton Atmospheric Research Indian National Centre for Ocean UK New Zealand Information Services NITSCHE, Helga India MORI, Nobuhito Deutscher Wetterdienst Kyoto University MYHRE, Gunnar Germany Japan Center for International Climate and NODA, Akira Environmental Research Oslo MORICE, Colin Japan Agency for Marine-Earth Norway Met Office Hadley Centre Science and Technology UK NABBEFELD, Birgit Japan DLR German Aerospace Center MORRISON, Hugh OBBARD, Jeffrey Germany National Center for Atmospheric Research National University of Singapore USA NAIK, Vaishali Singapore National Oceanic and Atmospheric MOTE, Philip OBROCHTA, Stephen Administration, Geophysical Oregon State University University of Tokyo Fluid Dynamics Laboratory USA Japan USA MSADEK, Rym O CONNOR, Fiona NAKAEGAWA, Tosiyuki National Oceanic and Atmospheric Met Office Hadley Centre Meteorological Research Institute Administration, Geophysical UK Japan Fluid Dynamics Laboratory OGREN, John USA NAKAJIMA, Teruyuki National Oceanic and Atmospheric University of Tokyo MUDELSEE, Manfred Administration, Earth System Japan Alfred Wegener Institute for Research Laboratory Polar and Marine Research NASSAR, Ray USA Germany Environment Canada OGURA, Tomoo Canada MUELLER, Christoph National Institute for Environmental Studies Justus Leibig University Giessen NAUELS, Alexander Japan Germany IPCC WGI TSU, University of Bern OHBA, Masamichi AVI Switzerland MÜLLER, Rolf Central Research Institute of Forschungszentrum Jülich NEELIN, J. David Electric Power Industry Germany University of California Los Angeles Japan USA MÜLLER, Wolfgang OHMURA, Atsumu Max Planck Institute for Meteorology NESJE, Atle ETH Zurich Germany University of Bergen and Bjerknes Switzerland Centre for Climate Research MULLER, Christian OHNEISER, Christian Norway Belgian Institute for Space Aeronomy Shell International B.V. Belgium Netherlands 1512 Expert Reviewers of the IPCC WGI Fifth Assessment Report Annex VI O ISHI, Ryouta PAN, Genxing PELLIKKA, Hilkka University of Tokyo Nanjing Agricultural University Finnish Meteorological Institute Japan China Finland OLIVIÉ, Dirk PANDEY, Dhananjai Kumar PERLWITZ, Judith University of Oslo National Centre for Antarctic Cooperative Institute for Research Norway and Ocean Research in Environmental Sciences India USA OLIVIER, Jos Netherlands Environmental PARKER, Albert PEROVICH, Donald Assessment Agency University of Ballarat Cold Regions Research and Netherlands Australia Engineering Laboratory USA OOUCHI, Kazuyoshi PARKER, David Japan Agency for Marine-Earth Met Office Hadley Centre PETERS, Glen Science and Technology UK Center for International Climate and Japan Environmental Research Oslo PARRISH, David Norway OPPENHEIMER, Michael National Oceanic and Atmospheric Princeton University Administration, Earth System PETERS, Karsten USA Research Laboratory Monash University USA Australia OREOPOULOS, Lazaros National Aeronautics and Space PASSCHIER, Sandra PETIT, Michel Administration, Goddard Space Flight Center Montclair State University Conseil général de l Economie,de l Industrie, USA USA de l Energie et des Technologies France ORLIC, Mirko PATTYN, Frank University of Zagreb Université libre de Bruxelles PFEFFER, W. Tad Croatia Belgium University of Colorado Boulder USA ORLOWSKY, Boris PAUL, Frank ETH Zurich University of Zurich PHILIPONA, Rolf Switzerland Switzerland Federal Office of Meteorology and Climatology MeteoSwiss OSBORN, Timothy PAVAN, Valentina Switzerland University of East Anglia Environmental Agency of Emilia-Romagna UK Italy PIACENTINI, Rubén D. Universidad Nacional de Rosario OSTROM, Nathaniel PAVELSKY, Tamlin Argentina Michigan State University University of North Carolina USA USA PINCUS, Robert University of Colorado Boulder OVERPECK, Jonathan PAYNE, Antony USA University of Arizona University of Bristol USA UK PLANTON, Serge Météo-France OWENS, John PAYNTER, David France 3M Company National Oceanic and Atmospheric USA Administration, Geophysical PLATTNER, Gian-Kasper Fluid Dynamics Laboratory IPCC WGI TSU, University of Bern PABÓN-CAICEDO, José Daniel USA Switzerland Universidad Nacional de Colombia Colombia PEARSON, David PLUMMER, David AVI Met Office Hadley Centre Environment Canada PADMAN, Laurence UK Canada Earth & Space Research USA PEDERSEN, Jens Olaf Pepke POERTNER, Hans Technical University of Denmark Alfred Wegener Institute for PALMER, Matthew Denmark Polar and Marine Research Met Office Hadley Centre Germany UK PELEJERO, Carles Institució Catalana de Recerca i Estudis POHLMANN, Holger Avançats and Institut de Ciencies del Mar Max Planck Institute for Meteorology Spain Germany 1513 Annex VI Expert Reviewers of the IPCC WGI Fifth Assessment Report POITOU, Jean QUINN, Patricia RANDALL, David Laboratoire des Sciences du Climat et de National Oceanic and Atmospheric Colorado State University l Environnement, Institut Pierre Simon Administration, Pacific Marine USA Laplace and Société Française de Physique Environmental Laboratory RAPER, Sarah France USA Manchester Metropolitan University POKHREL, Samir RABATEL, Antoine UK Indian Institute of Tropical Meteorology Laboratoire de Glaciologie et Géophysique RASCH, Philip India de l`Environnement, Université Joseph Fourier Pacific Northwest National Laboratory France POLLACK, Henry USA University of Michigan RADIÆ, Valentina RAUPACH, Michael USA University of British Columbia CSIRO Marine and Atmospheric Research Canada POLONSKY, Alexander Australia Marine Hydrophysical Institute RADUNSKY, Klaus RAVISHANKARA, A.R. Ukraine Umweltbundesamt National Oceanic and Atmospheric Austria PONGRATZ, Julia Administration, Earth System Max Planck Institute for Meteorology RAHAMAN, Hasibur Research Laboratory Germany Indian National Centre for Ocean USA Information Services PORTMANN, Robert RAWLS, Alec India National Oceanic and Atmospheric USA Administration, Earth System RAHIMZADEH, Fatemeh RAYNER, Nick Research Laboratory Islamic Republic of Iran Met Office Hadley Centre USA Meteorological Organization UK Iran POULTER, Benjamin RAYNER, Peter Laboratoire des Sciences du RAHMSTORF, Stefan University of Melbourne Climat et de l Environnement, Potsdam Institute for Climate Australia Institut Pierre Simon Laplace Impact Research France Germany REAY, David University of Edinburgh POWER, Scott RAIBLE, Christoph UK Bureau of Meteorology University of Bern Australia Switzerland REIS, Stefan Centre for Ecology & Hydrology PRATHER, Michael RÄISÄNEN, Jouni UK University of California Irvine University of Helsinki USA Finland REISINGER, Andy New Zealand Agricultural PRENTICE, Iain Colin RÄISÄNEN, Petri GHG Research Centre Macquarie University and Imperial College Finnish Meteorological Institute New Zealand Australia Finland REISMAN, John P. PRINN, Ronald RAJEEVAN, Madhavan Nair OSS Foundation Massachusetts Institute of Technology Government of India, Ministry USA USA of Earth Sciences India REMEDIOS, John PUEYO, Salvador University of Leicester Institut Catala de Ciencies del Clima RAMASWAMY, Venkatachalam UK Spain National Oceanic and Atmospheric AVI Administration, Geophysical REMER, Lorraine QIAO, Bing Fluid Dynamics Laboratory National Aeronautics and Space China Waterborne Transport USA Administration, Goddard Space Flight Center Research Institute USA China RAMSTEIN, Gilles Laboratoire des Sciences du REN, Guoyu QUAAS, Johannes Climat et de l Environnement, National Climate Center, China University of Leipzig Institut Pierre Simon Laplace Meteorological Administration Germany France China 1514 Expert Reviewers of the IPCC WGI Fifth Assessment Report Annex VI RENWICK, James RODHE, Henning RUPP, David Victoria University of Wellington Stockholm University Oregon State University New Zealand Sweden USA REUTEN, Christian ROGELJ, Joeri RUSSELL, Andrew RWDI AIR Inc. ETH Zurich Brunel University Canada Switzerland UK RIAHI, Keywan ROHLING, Eelco Johan RUTI, Paolo Michele International Institute for National Oceanography Centre Italian National Agency for New Applied Systems Analysis UK Technologies, Energy and Sustainable Austria Economic Development ROJAS, Maisa Italy RIBES, Aurélien Universidad de Chile Météo-France Chile SAHU, Lokesh Kumar France Physical Research Laboratory ROMANOVSKY, Vladimir India RIDDICK, Stuart University of Alaska Fairbanks Cornell University USA SAKAGUCHI, Koichi USA University of Arizona RONCHAIL, Josyane USA RIDLEY, Jeff Laboratoire d Océanographie et du Climat, Met Office Hadley Centre Institut Pierre Simon Laplace SALAS Y MELIA, David UK France Météo-France France RIGNOT, Eric ROSEN, Sergiu Dov University of California Irvine Israel Oceanographic and SALZMANN, Nadine USA Limnological Research University of Zurich and University of Fribourg Israel Switzerland RIGOR, Ignatius University of Washington ROSENFELD, Daniel SAMANTA, Arindam USA Hebrew University of Jerusalem Atmospheric and Environmental Research Israel USA RINGEVAL, Bruno Utrecht University ROSENLOF, Karen SANCHEZ GONI, Maria Fernanda Netherlands National Oceanic and Atmospheric Université Bordeaux 1 Administration, Earth System France RITZ, Christoph Research Laboratory Swiss Academy of Sciences SANDERSON, Benjamin USA Switzerland National Center for Atmospheric Research ROTSTAYN, Leon USA RIVERA, Andrés CSIRO Marine and Atmospheric Research Centro de Estudios Científicos SANYAL, Swarnali Australia Chile University of Illinois ROTT, Helmut USA ROBAA, S.M. University of Innsbruck Cairo University SAROFIM, Marcus Austria Egypt U.S. Environmental Protection Agency ROWELL, David USA ROBERTS, Chris Met Office Hadley Centre Met Office Hadley Centre SATHEESH, S.K. UK UK Indian Institute of Science ROY, Indrani India ROBERTSON, Iain University of Exeter Swansea University SATOH, Masaki AVI UK UK University of Tokyo ROY, Shouraseni Japan ROBOCK, Alan University of Miami Rutgers University SAUCHYN, David USA USA University of Regina RUMMUKAINEN, Markku Canada ROBSON, Jonathan Swedish Meteorological and Hydrological University of Reading SAULO, Celeste Institute and Lund University UK Universidad de Buenos Aires Sweden Argentina 1515 Annex VI Expert Reviewers of the IPCC WGI Fifth Assessment Report SAUNDERS, Roger SCHWARTZ, Stephen E. SHAO, Andrew Met Office Hadley Centre Brookhaven National Laboratory University of Washington UK USA USA SAUSEN, Robert SCHWARZKOPF, M. Daniel SHAO, XueMei DLR German Aerospace Center National Oceanic and Atmospheric Institute of Geographic Sciences Germany Administration, Geophysical and Natural Resources Research, Fluid Dynamics Laboratory Chinese Academy of Sciences SAVOLAINEN, Ilkka USA China VTT Technical Research Centre of Finland Finland SCHWEIGER, Axel SHELL, Karen University of Washington Oregon State University SCAIFE, Adam USA USA Met Office Hadley Centre UK SEDLÁÈEK, Jan SHERWIN, Toby ETH Zurich Scottish Association for Marine Science SCHMID, Beat Switzerland UK Pacific Northwest National Laboratory USA SEHAT KASHANI, Saviz SHERWOOD, Steven Islamic Azad University University of New South Wales SCHMIDT, Gavin Iran Australia National Aeronautics and Space Administration, Goddard SEIBERT, Petra SHEVLIAKOVA, Elena Institute for Space Studies University of Vienna Princeton University USA Austria USA SCHNEEBELI, Martin SEIDEL, Dian SHI, Zongbo WSL Institute for Snow and National Oceanic and Atmospheric University of Birmingham Avalanche Research SLF Administration, Air Resources Laboratory UK Switzerland USA SHIBATA, Kiyotaka SCHNEIDER, Johannes SELVARAJ, Kandasamy Meteorological Research Institute Max Planck Institute for Chemistry Xiamen University Japan Germany China SHINDELL, Drew SCHOENWIESE, Christian-D. SEN, Omer L. National Aeronautics and Goethe University Istanbul Technical University Space Administration, Goddard Germany Turkey Institute for Space Studies USA SCHOLES, Robert SENEVIRATNE, Sonia Council for Scientific and Industrial Research ETH Zurich SHINE, Keith South Africa Switzerland University of Reading UK SCHRAMA, Ernst SENSOY, Serhat Delft University of Technology Turkish State Meteorological Service SHIOGAMA, Hideo Netherlands Turkey National Institute for Environmental Studies Japan SCHULZ, Michael SENTMAN, Lori Norwegian Meteorological Institute National Oceanic and Atmospheric SHKOLNIK, Igor Norway Administration, Geophysical Voeikov Main Geophysical Observatory Fluid Dynamics Laboratory Russian Federation SCHUMANN, Ulrich USA DLR German Aerospace Center SHMAKIN, Andrey Germany SERPIL, Yaä¾an Russian Academy of Sciences AVI Turkish State Meteorological Service Russian Federation SCHUSTER, Gregory Turkey National Aeronautics and Space SHUMAN, Bryan Administration, Langley Research Center SETH, Anji University of Wyoming USA University of Connecticut USA USA SCHUUR, Edward SICRE, Marie-Alexandrine University of Florida SEXTON, David Laboratoire des Sciences du Climat et de USA Met Office Hadley Centre l Environnement, Institut Pierre Simon Laplace UK France 1516 Expert Reviewers of the IPCC WGI Fifth Assessment Report Annex VI SIDDALL, Mark SMITH, Steven STEBLER, Oliver University of Bristol Pacific Northwest National Laboratory ETH Zurich UK USA Switzerland SIEGLE, Eduardo SNIDERMAN, Kale STEIG, Eric Universidade de Sao Paulo University of Melbourne University of Washington Brazil Australia USA SIEVERING, Herman SOLOMINA, Olga STEINFELDT, Reiner National Oceanic and Atmospheric Russian Academy of Sciences University of Bremen Administration, Earth System Russian Federation Germany Research Laboratory and University SOLOMON, Susan STENDEL, Martin of Colorado Boulder Massachusetts Institute of Technology Danish Meteorological Institute USA USA Denmark SILLMANN, Jana SOMERVILLE, Richard STEPEK, Andrew Environment Canada Scripps Institution of Oceanography Royal Netherlands Meteorological Institute Canada USA Netherlands SIMMONDS, Ian SONG, Shaojie STEPHENS, Graeme University of Melbourne Massachusetts Institute of Technology National Aeronautics and Space Australia USA Administration, Jet Propulsion Laboratory SIMMONS, Adrian USA SPAHNI, Renato European Centre for Medium-Range University of Bern STEPHENSON, David Weather Forecasts Switzerland University of Exeter UK UK SPARRENBOM, Charlotte SINGER, S. Fred Lund University STERL, Andreas University of Virginia Sweden Royal Netherlands Meteorological Institute USA Netherlands SPARROW, Michael SLANGEN, Aimée Scientific Committee on Antarctic Research STERN, Harry Utrecht University UK University of Washington Netherlands USA SPORYSHEV, Petr SMEDSRUD, Lars Henrik Voeikov Main Geophysical Observatory STEVENSON, David Bjerknes Centre for Climate Research Russian Federation University of Edinburgh Norway UK SRIKANTHAN, Ramachandran SMITH, Doug Physical Research Laboratory STEWART, Ronald Met Office Hadley Centre India University of Manitoba UK Canada SRIVER, Ryan SMITH, Ian University of Illinois STIER, Philip CSIRO Marine and Atmospheric Research USA University of Oxford Australia UK SROKOSZ, Meric SMITH, Leonard National Oceanography Centre STÖBER, Uwe London School of Economics UK University of Bremen and Political Science Germany UK STAGER, Jay Curt Paul Smith s College STOCKDALE, Timothy SMITH, Sharon USA European Centre for Medium- AVI Natural Resources Canada Range Weather Forecasts Canada STAHLE, David UK University of Arkansas SMITH, Stephen USA STOCKER, Benjamin Committee on Climate Change University of Bern UK STAINFORTH, David Switzerland London School of Economics SMITH, Stephen G.G. and Political Science STOCKER, Thomas F. UK UK Co-Chair IPCC WGI, University of Bern Switzerland 1517 Annex VI Expert Reviewers of the IPCC WGI Fifth Assessment Report STONE, Dáithí SUNDQUIST, Eric TANAKA, Hiroshi Lawrence Berkeley National Laboratory U.S. Geological Survey University of Tsukuba USA USA Japan STONE, Reynold SUTTON, Rowan TANAKA, Katsumasa University of the West Indies University of Reading ETH Zurich Trinidad and Tobago UK Switzerland STOTT, Peter SVENSSON, Gunilla TANG, Qi Met Office Hadley Centre Stockholm University Cornell University UK Sweden USA STOUFFER, Ronald SWEENEY, Conor TAPIADOR, Francisco J. National Oceanic and Atmospheric University College Dublin Universidad de Castilla-La Mancha Administration, Geophysical Ireland Spain Fluid Dynamics Laboratory SWIETLICKI, Erik TARASOV, Lev USA Lund University Memorial University of Newfoundland STOY, Paul Sweden Canada Montana State University SWINGEDOUW, Didier TAYLOR, Jeffrey USA Laboratoire des Sciences du National Ecological Observatory Network STRAUSS, Benjamin Climat et de l Environnement, USA Climate Central Institut Pierre Simon Laplace TELFORD, Richard USA France University of Bergen STUBENRAUCH, Claudia TACHIIRI, Kaoru Norway Laboratoire de Météorologie Dynamique, Japan Agency for Marine-Earth TERRAY, Laurent Institut Pierre Simon Laplace Science and Technology Centre Européen de Recherche et de France Japan Formation Avancée en Calcul Scientifique STUMM, Dorothea TAKAHASHI, Ken France International Centre for Integrated Instituto Geofísico del Perú TETT, Simon Mountain Development Peru University of Edinburgh Nepal TAKAHASHI, Kiyoshi UK SU, Hui National Institute for Environmental Studies THIELEN, Dirk National Aeronautics and Space Japan Instituto Venezolano de Administration, Jet Propulsion Laboratory TAKAYABU, Izuru Investigaciones Científicas USA Meteorological Research Institute Venezuela SUBRAMANIAN, Aneesh Japan THOMAS, Robert Scripps Institution of Oceanography TAKAYABU, Yukari SIGMA Space USA University of Tokyo USA SUGI, Masato Japan THOMASON, Larry Japan Agency for Marine-Earth TAKEMURA, Toshihiko National Aeronautics and Space Science and Technology Kyushu University Administration, Langley Research Center Japan Japan USA SUGIYAMA, Masahiro TALARICO, Franco THOMPSON, Erica Central Research Institute of University of Siena London School of Economics Electric Power Industry Italy and Political Science AVI Japan UK TALLAKSEN, Lena M. SUN, Jianqi University of Oslo THOMPSON, Rona Institute of Atmospheric Physics, Norway Norwegian Institute for Air Research Chinese Academy of Sciences Norway China TAMISIEA, Mark National Oceanography Centre THORNE, Peter SUN, Junying UK National Oceanic and Atmospheric Chinese Academy of Meteorological Sciences, Administration, National China Meteorological Administration Climatic Data Center China USA 1518 Expert Reviewers of the IPCC WGI Fifth Assessment Report Annex VI TIAN, Jian TSUTSUI, Junichi VAN YPERSELE, Jean-Pascal University of Illinois Central Research Institute of Université catholique de Louvain USA Electric Power Industry Belgium Japan TIGNOR, Melinda VANAGS, Andrejs IPCC WGI TSU, University of Bern TURCQ, Bruno The Space Exploration Society Switzerland Institut de Recherche pour le Développement USA France TILYA, Faustine Fidelis VAQUERO, José Manuel Tanzania Meteorological Agency TURNER, Andrew Universidad de Extremadura United Republic Of Tanzania University of Reading Spain UK TITUS, James G. VAUGHAN, David U.S. Environmental Protection Agency TZEDAKIS, Chronis British Antarctic Survey USA University College London UK UK TKALICH, Pavel VAUGHAN, Naomi National University of Singapore UNNINAYAR, Sushel University of East Anglia Singapore National Aeronautics and Space UK Administration, Goddard Space Flight Center TOKINAGA, Hiroki VELDERS, Guus USA University of Hawaii National Institute for Public USA URREGO, Dunia H. Health and the Environment Université Bordeaux 1 Netherlands TOMASEK, Bradley France University of Illinois VERHEGGEN, Bart USA VAN DEN HURK, Bart ECN Energy Research Institute Royal Netherlands Meteorological Institute of the Netherlands TOMOZEIU, Rodica Netherlands Netherlands Environmental Agency of Emilia-Romagna Italy VAN DER LINDEN, Paul VERHOEF, Anne Met Office Hadley Centre University of Reading TONITTO, Christina UK UK Cornell University USA VAN DER WERF, Guido VERLEYEN, Elie VU University Amsterdam Ghent University TOTTERDELL, Ian Netherlands Belgium Met Office Hadley Centre UK VAN HUISSTEDEN, Ko VIDAL, Jean-Philippe VU University Amsterdam Institut National de Recherche TRAINER, Michael Netherlands en Sciences et Technologies pour National Oceanic and Atmospheric l Environnement et l Agriculture Administration, Earth System VAN KESTEREN, Line France Research Laboratory IPCC Synthesis Report TSU USA Netherlands VIGNATI, Elisabetta European Commission Joint Research Centre TRANVIK, Lars VAN NOIJE, Twan Italy Uppsala University Royal Netherlands Meteorological Institute Sweden Netherlands VINITNANTHARAT, Soydoa King Mongkut s University of TRENBERTH, Kevin VAN OLDENBORGH, Geert Jan Technology Thonburi National Center for Atmospheric Research Royal Netherlands Meteorological Institute Thailand USA Netherlands VISSER, Hans TROUET, Valerie VAN OMMEN, Tasman AVI PBL Netherlands Environmental University of Arizona Australian Antarctic Division Assessment Agency USA Australia Netherlands TSUSHIMA, Yoko VAN VELTHOVEN, Peter VOIGT, Thomas Met Office Hadley Centre Royal Netherlands Meteorological Institute Federal Environment Agency UK Netherlands Germany VAN WEELE, Michiel Royal Netherlands Meteorological Institute Netherlands 1519 Annex VI Expert Reviewers of the IPCC WGI Fifth Assessment Report VOLLMER, Martin WANG, Kaicun WATTERSON, Ian Swiss Federal Laboratories for Materials Beijing Normal University CSIRO Marine and Atmospheric Research Science and Technology EMPA China Australia Switzerland WANG, Minghuai WEBB, David VON SCHUCKMANN, Karina Pacific Northwest National Laboratory National Oceanography Centre Institut Français de Recherche USA UK pour l Exploitation de la Mer WANG, Pinxian WEBB, Mark France Tongji University Met Office Hadley Centre WAGNON, Patrick China UK Laboratoire de Glaciologie et Géophysique WANG, Shaowu WEBB, Robert de l`Environnement, Université Joseph Fourier Peking University National Oceanic and Atmospheric France China Administration, Earth System WAHL, Eugene Research Laboratory WANG, Tijian National Oceanic and Atmospheric USA Nanjing University Administration, National China WEEDON, Graham Climatic Data Center Met Office Hadley Centre USA WANG, Ting UK Lehigh University WAHL, Terje USA WEISHEIMER, Antje Norwegian Space Centre European Centre for Medium- Norway WANG, Xiaolan Range Weather Forecasts Environment Canada WAHL, Thomas UK Canada University of Siegen WEISS, Jérôme Germany WANG, Xuemei Laboratoire de Glaciologie et Géophysique Sun Yat-sen University WALISER, Duane de l`Environnement, Université Joseph Fourier China National Aeronautics and Space France Administration, Jet Propulsion Laboratory WANG, Yingping WEISSE, Ralf USA CSIRO Marine and Atmospheric Research Helmholtz-Zentrum Geesthacht Australia WALLINGTON, Timothy Germany Ford Motor Company WANG, Yongguang WENDISCH, Manfred USA National Climate Center, China University of Leipzig Meteorological Administration WALTER, Andreas Germany China Deutscher Wetterdienst WESTRA, Seth Germany WANG, Zhaomin University of Adelaide Nanjing University of Information WANG, Bin Australia Science and Technology Institute of Atmospheric Physics, Chinese China WEYHENMEYER, Gesa Academy of Sciences and Tsinghua University Uppsala University China WANLISS, James Sweden Presbyterian College WANG, Chien USA WHETTON, Penny Massachusetts Institute of Technology CSIRO Marine and Atmospheric Research USA WANNER, Heinz Australia University of Bern WANG, Dongxiao Switzerland WHITE, Neil South China Sea Institute of Oceanology, CSIRO Marine and Atmospheric Research AVI Chinese Academy of Sciences WATERLAND, Robert Australia China E. I. du Pont de Nemours & Co. Inc. USA WIELICKI, Bruce WANG, Hailong National Aeronautics and Space Pacific Northwest National Laboratory WATSON, Phil Administration, Langley Research Center USA NSW Government Office of USA Environment and Heritage WANG, Junye Australia WILD, Oliver Rothamsted Research Lancaster University UK WATSON, Thomas UK Australia 1520 Expert Reviewers of the IPCC WGI Fifth Assessment Report Annex VI WILLETT, Kate WURZLER, Sabine YU, Zicheng Met Office Hadley Centre Landesamt für Natur, Umwelt und Lehigh University UK Verbraucherschutz NRW USA Germany WILLIAMS, Keith YUKIMOTO, Seiji Met Office Hadley Centre XIA, Chaozong Meteorological Research Institute UK State Forestry Administration Japan China WILLIAMS, Paul ZAEHLE, Sönke University of Reading XIA, Yu Max Planck Institute for Biogeochemistry UK IPCC WGI TSU, University of Bern Germany Switzerland WILLIAMS, Richard G. ZAHN, Matthias Liverpool University XIE, Shang-Ping University of Reading UK Scripps Institution of Oceanography UK USA WILLIAMS, S. Jeffress ZAPPA, Giuseppe U.S. Geological Survey XU, Chong-Yu University of Reading USA University of Oslo UK Norway WILSON, Rob ZEMP, Michael University of St Andrews XU, Kuan-Man University of Zurich UK National Aeronautics and Space Switzerland Administration, Langley Research Center WITTENBERG, Andrew ZENG, Xubin USA National Oceanic and Atmospheric University of Arizona Administration, Geophysical XU, Xiaobin USA Fluid Dynamics Laboratory Chinese Academy of Meteorological Sciences, ZHANG, Chengyi USA China Meteorological Administration National Climate Center, China China WOLFF, Eric Meteorological Administration British Antarctic Survey XU, Ying China UK National Climate Center, China ZHANG, De-er Meteorological Administration WOOD, Richard National Climate Center, China China Met Office Hadley Centre Meteorological Administration UK XU, Yongfu China Institute of Atmospheric Physics, WOOD, Robert ZHANG, Gan Chinese Academy of Sciences University of Washington University of Illinois China USA USA YABI, Ibouraima WOODS, Thomas ZHANG, Guang Université d Abomey Calavi University of Colorado Boulder Scripps Institution of Oceanography Benin USA USA YASUNARI, Tetsuzo WOODWORTH, Philip ZHANG, Hua Nagoya University National Oceanography Centre National Climate Center, China Japan UK Meteorological Administration YDE, Jacob Clement China WORDEN, Helen Sogn og Fjordane University College National Center for Atmospheric Research ZHANG, Rong Norway USA National Oceanic and Atmospheric YOKOUCHI, Yoko Administration, Geophysical WRATT, David AVI National Institute for Environmental Studies Fluid Dynamics Laboratory National Institute of Water and Japan USA Atmospheric Research New Zealand YOSHIMORI, Masakazu ZHANG, Tianyu University of Tokyo National Marine Environmental WU, Tonghua Japan Forecasting Center Cold and Arid Regions Environmental China and Engineering Research Institute, YU, Rucong Chinese Academy of Sciences China Meteorological Administration ZHANG, Xiangdong China China University of Alaska Fairbanks USA 1521 Annex VI Expert Reviewers of the IPCC WGI Fifth Assessment Report ZHANG, Xuebin ZWEIFEL, Roman CSIRO Marine and Atmospheric Research Swiss Federal Institute for Forest, Snow Australia and Landscape Research WSL Switzerland ZHANG, Xuebin Environment Canada ZWIERS, Francis Canada University of Victoria Canada ZHAO, Xuepeng (Tom) National Oceanic and Atmospheric Administration, National Climatic Data Center USA ZHAO, Zong-Ci National Climate Center, China Meteorological Administration China ZHENG, Jingyun Institute of Geographic Sciences and Natural Resources Research, Chinese Academy of Sciences China ZHOU, Guangsheng Chinese Academy of Meteorological Sciences, China Meteorological Administration China ZHOU, Limin East China Normal University China ZHOU, Tianjun Institute of Atmospheric Physics, Chinese Academy of Sciences China ZHU, Bin Nanjing University of Information Science and Technology China ZICKFELD, Kirsten Simon Fraser University Canada ZORITA, Eduardo Helmholtz-Zentrum Geesthacht Germany ZUIDEMA, Paquita AVI University of Miami USA ZUO, Juncheng HoHai University China 1522 Index This index should be cited as: IPCC, 2013: Index. In: Climate Change 2013: The Physical Science Basis. Contribution of Working Group I to the Fifth Assessment Report of the Intergovernmental Panel on Climate Change [Stocker, T.F., D. Qin, G.-K. Plattner, M. Tignor, S.K. Allen, J. Boschung, A. Nauels, Y. Xia, V. Bex and P.M. Midgley (eds.)]. Cambridge University Press, Cambridge, United Kingdom and New York, NY, USA. 1523 Index climate indices, changes in, 211-212 changes in, 333-334, 367, 368 Note: * indicates the term also appears in projections, 106, 1281-1282, 1288, 1358-1365 decadal trends, 329-330 the Glossary (Annex III). Bold page numbers Air quality, 684-685, 955, 1001-1002 drift, 328-329 indicate page spans for entire chapters. climate-driven changes, 999-1000, 1005-1006 extent and concentration, 324-326, 325, 326 Italicized page numbers denote tables, figures extreme weather and, 1005 irreversible changes, 1115, 1117-1118 and boxed material. projections, 24, 88-89, 957, 996-1004 models, 16, 18, 744, 787-790, 787-789 Aircraft. See Aviation projections, 24-25, 956, 1032, 1087-1092, 1089- Albedo*, 126 1091 cloud albedo effect, 578, 610, 1048-1050 salinity effects on, 271-273 A snow, 321, 358, 359, 757 seasonality, 329 surface, 628, 662, 686-687, 687, 819 summary, 9, 10, 319, 367 Abrupt climate change*, 70-72, 151, 386-387, 432- urban, 687 thickness and volume, 319, 327-328, 328 435, 1114-1119 Altimetry*, 286, 287, 348-349 Asia abrupt glacial events, 483 Ammonia, 1418 climate indices, changes in, 211-212 paleoclimate*, 386-387, 432-435, 434 Ammonium, 605-606 precipitation extremes, 211-212 permafrost thawing and, 530-531 Annular modes*, 233-235, 900-901, 900, 1243-1246 projections, 106, 1268-1273, 1278, 1282-1284, projections, 88, 1005, 1033, 1114-1119 projections, 108, 1220, 1288-1289 1288-1289, 1366-1381 summary, 1005, 1115 Antarctic ice sheet, 9, 25, 29, 137, 320, 351-353, 909 Asian-Australian monsoon, 1227-1232, 1230-1231 Aerosols*, 151, 174-180, 571-657 dynamical change, 1172-1174 Atlantic Meridional Mode (AMM), 802, 1224 absorption on snow and ice, 574, 617-618, 685, ice loss, 351-353, 352-353, 367, 381-382 Atlantic Meridional Overturning Circulation 685 irreversible changes, 71-72, 356, 1174 (AMOC), 8, 282-284, 782-783 aerosol-climate feedbacks, 574, 605-606 mass balance*, 348, 1139, 1170-1171 irreversibility and, 70, 433-435, 1115-1116, 1115 aerosol-cloud interactions*, 127, 573, 578, 606- models, 753, 1171 paleoclimate*, 386-387, 433-435, 456 614, 607, 618-621, 623, 625-626, 683-685 observed changes, 351-353, 352-353 projections, 24, 956, 973-974, 995, 1033, 1094- aerosol optical depth (AOD), 161, 174-176, 176, paleoclimate*, 387, 428-431, 1174 1095 179, 596-599, 599, 692, 757, 794-795, 794-795, polar amplification, 397 variability, 801, 802, 806 1429-1430 sea level equivalent, 320, 321, 352-354 Atlantic Multi-decadal Oscillation/Variability aerosol-radiation interactions*, 574, 576, 578, sea level rise and, 1139, 1154-1155, 1170-1176, (AMO/AMV)*, 230, 233-235, 801, 802, 806, 869, 604-605, 614-618, 615, 617, 622, 682-683, 683 1177-1179, 1182 1254-1255 aviation contrails, 574, 592-594, 686 West Antarctic (WAIS), 320, 332, 349, 352-354, impacts, 1224 carbonaceous*, 606 357, 1174, 1175 projections, 108, 971-973, 972, 1220 climate relevant properties, 573, 602-604, 622-623 Antarctic region, 151, 939, 1276-1277 Atlantic Nino, 233, 803, 806, 1224, 1239-1240 cloud condensation nuclei (CCN)*, 603-604, 609, bottom water, 279-280 Atlantic Ocean 886 circulation, 284 carbon storage, 495 composition and mixing state, 602-603 ice shelves, 320, 353, 367 hurricanes, 809 effective radiative forcing (ERF), 574, 576-578, oceans, 279-280 modes, 1239-1240 577-578, 614-624, 619-621, 1404-1409 paleoclimate*, 387, 420, 459-460 salinity, 271, 280 feedbacks, 574, 576-578, 577, 605-606 polar amplification, 385, 396-398 temperature, 280 formation and types, 595 projections, 106, 1277, 1285, 1289, 1390-1393 tropical, models, 787 general concepts, 595-606, 595, 597, 622-623 Weddell Sea, 280 variability, 233-235 glaciation effect, 578 Antarctic sea ice, 9, 25, 69, 319, 330-335 water mass properties, 279 in situ surface measurements, 176-180, 177 changes in, 333-334, 368, 906-909, 908, 931 Atlantic Ocean Multidecadal Variability, 233-234 lifetime effects, 578, 609-610 drift, 332 Atmosphere*, 5, 159-254 mineral dust (MDA), 394, 600, 605, 617 extent and concentration, 330, 331, 332 free*, 197-198, 197-200 models, 16, 608-609, 744, 752, 757, 794-795 models, 787-790, 787-789 global reanalyses*, 185-186 new terminology, 578, 578 projections, 1089, 1092 models, 144, 746, 747, 748-750, 756-757, 760-777 observations, 161, 175, 595-599, 596, 598 seasonality and trends, 332-335 observations, 5, 6, 159-254 organic*, 1048-1050, 1052, 1419, 1428 Anthropogenic climate change*. See Detection and projections, 19-24, 28, 980-993 paleoclimate*, 394 attribution of climate change radiation budget, 161, 180-186 precipitation effects, 624-627 Aragonite, 94-95, 533 summary of observations, 5, 130, 161-163 projections, 1000-1001, 1002-1003, 1007-1008, Arctic region temperature, 4-5, 6, 66-68, 161-162, 187-201, 984 1048-1050, 1052 anthropogenic influence, 19, 956 See also Hydrological cycle; Temperature radiative forcing*, 13-14, 14, 127, 186, 574, 576- climate projections, 956, 1031, 1062-1064 Atmospheric chemistry, 669-675 578, 577, 614-621, 662, 675, 682-686, 1007, 1048- ocean salinity, 271-273 Atmospheric circulation, 163, 223-232, 899-901, 1050, 1052, 1404-1409 polar amplification, 385, 396-398, 1031, 1062- 899-900 sea spray, 599-601, 605 1064 attribution of changes, 871, 899-901, 899-900, size and optical properties, 603 projections, 106, 1257-1258, 1278, 1288, 1322- 937-938 sources, 599-601 1324 geopotential height, 223, 223, 226 thermodynamic effect, 578 temperature, 9, 10, 20, 931, 956, 1062-1064, 1257- jets, storm tracks and weather types, 229-230 volcanic aerosols, 14, 662, 691-693 1258, 1278 projections, 88, 90, 956, 972-975, 988-990, 989- Index See also specific aerosols Arctic sea ice, 9, 10, 69, 136-137, 319, 323-330 990, 1032, 1071-1074, 1071-1072 Africa, 1266-1268, 1267 attribution of changes, 19, ,870, 906-909, 908, sea level pressure (SLP), 223-224, 223-224, 1071- African monsoon, 1234, 1235 931, 938 1072, 1071 1524 Index stratospheric circulation, 230 permafrost*, 480, 526-528 observations, 50-52 surface wind speed, 224-226, 225 sinks*, 93, 468, 470-472, 471, 480, 495-503, 503, observed changes, 11-12, 12, 132-134, 132, 161, teleconnections*, 233, 805, 1224, 1243, 1243 519-523, 538-539, 543, 551-552 165-167, 166, 467 tropical circulation, 226-230, 899-900, 989-990, total, 178 observed changes, last millennium, 485-486, 486 989, 1073 transient climate response to emissions (TCRE), ocean absorption of, 11, 12, 26-27, 291-293, 295- upper-air winds, 226 16-17, 1108-1109 300, 300, 472, 472-473, 495-499 variability in, 163, 230-232, 231-235 See also Black carbon ocean sink for, 495-499, 496, 519-520 Atmospheric composition, 126, 161, 165-180 Carbon cycle*, 11-12, 96-97, 470-480, 502-504 paleoclimate*, 385, 391-394, 399-400, 400, 457, aerosols*, 161, 174-180, 576 before fossil fuel era, 480-486 459-460, 468, 483-484, 483 clouds, 576 carbon removal/storage techniques, 469, 546-552, permafrost*, 27, 530-531 gases, 161, 165-170, 166 547 projections, 19, 26-27, 27-28, 28, 148, 156, 468, models, 17-18 climate-carbon cycle feedback*, 514-523, 515, 514-528, 524, 662, 1048-1050, 1096-1097, 1097, observed changes, 165-180 516-518, 551-552 1422 projections, 996-1004 in climate models, 16, 468, 516-518, 751-752, 792- proxy methods and data, 394, 457 See also specific constituents 794 radiative forcing*, 13, 14, 126, 165, 661, 676-678, Attribution of climate change. See Detection and commitments, 543-546 678, 1048-1050, 1404-1409, 1433 attribution feedbacks, 26, 475-480, 477-478, 514-523, 515- rapid adjustments* to, 590 Australia and New Zealand, 106, 1273-1275, 1274, 518, 520 regional budgets, 503 1284, 1289, 1382-1385 geoengineering and, 469, 546-552 summary, 11-12, 12 monsoon, 1230-1231, 1232 global, 470-473, 471 temperature and, 398-399 Aviation contrails/cloud effect, 574, 592-594, 686 long-term, 543-546, 543 timescale of persistence in atmosphere, 469 models, 502-504, 514-528, 516-518, 520-522, Carbon Dioxide Removal (CDR)*, 29, 469, 546-551, 524-529, 744, 751-752, 757, 792-794, 793-794 547 B nitrogen cycle and, 475-480, 476-479, 537-539, methods, 547-550, 548-549, 632-633 538 side effects, 633 Baseline/reference*, 1034 observations, 11-12, 12, 50-53 summary, 552 Bayesian method/approach*, 83, 755 ocean carbon balance, 498-499 Carbon monoxide (CO), 13, 14, 174, 1416 Biogeochemical cycles, 11-12, 465-570 paleoclimate*, 468 lifetime and global warming potential, 718, 740 before fossil fuel era, 480-486 perturbations and uncertainties, 96-97 radiative forcing*, 662 carbon removal/storage techniques, 546-552 projections, 26-27, 93-95, 96-97, 468-469, 523- Carbon tetrachloride (CCl4), 169-170, 678, 733 connections of carbon, nitrogen, and oxygen 528, 542-546, 1033, 1096-1099, 1097-1098 Caribbean region. See Central America and cycles, 475-480, 477-479 regional fluxes, 499-502, 500-501 Caribbean ocean, 259, 291-301, 312 sensitivity of, 503-504, 504-505 Cement production, 489 overview, 11-12, 470-480 since industrial revolution, 474-475, 486-504 Central America and Caribbean, 106, 1260-1261, projections, 93-95, 96-97, 468-469, 514-539 sinks*, 468, 470-472, 471, 480, 495-503, 503, 519- 1260, 1280, 1288, 1338-1341 since industrial revolution, 474-475, 486-514 523 Central and North Asia, 106, 1268-1269, 1269 See also Carbon cycle summary, 11-12, 467-469 Chaotic system*, 955, 959, 1033 Biological pump*, 472 terrestrial processes and feedbacks, 502-504, 503- Chlorocarbons, 733 Biomass* burning, 507, 509, 600-601, 616, 663, 671, 505 Chlorofluorocarbons (CFCs), 161, 169-170, 672, 714 Carbon dioxide (CO2)*, 166-167 1403, 1427 Black carbon*, 600, 616, 685, 685, 1432 air-sea fluxes, 497, 498, 499-501, 500-501 lifetime and radiative efficiency, 731 global warming potential, 740 airborne fraction*, 495 radiative forcing*, 127, 661, 678, 679, 1048-1050 metrics, 718 atmosphere-to-land fluxes, 501-502 Chloroform, 733 projections, 955, 1048-1050, 1419 atmospheric concentration, 11-12, 12, 28, 161, Circulation radiative forcing*, 1048-1050, 1052, 1404-1409 166-167, 166-167, 467, 476, 1401-1402 atmospheric, 163, 223-232, 899-901, 899-900, Blocking*, 229-230, 796, 1220, 1224, 1246-1248 atmospheric, growth rate, 491-494, 493-494 937-938, 956, 1032 Brewer-Dobson circulation*, 90, 163, 230, 1073- atmospheric, residence times, 472-473 models, 773-774, 782-784, 810-813 1074, 1248 13C/12C ratio, 476 oceanic, 258, 281-285, 283, 433-435, 481, 995, Bromocarbons, 733 carbon cycle and, 470-473 1094-1095 Budgets. See Energy budget; Radiation budget climate change commitment and, 27-28, 28, 1033 planetary-scale overturning circulations, 1072- compatible emissions*, 523-528, 526-529 1074 current rate of rise as unprecedented, 385 projections, 90, 956, 972-975, 989-991, 989-990, C emissions, 486-488, 487, 544-545, 1108-1109, 995, 1071-1074, 1071-1074, 1094-1095 1109, 1410 Clathrates*, 70-71, 1115, 1116-1117 Carbon emissions metrics, 716-717, 731 Clausius-Clapeyron equation/relationship*, 208, cumulative emissions, 1108-1109, 1109, 1112- emissions, natural, 1421 1083 1113, 1114 feedbacks, 26 Climate* dissolved inorganic carbon (DIC), 95, 472, 497, fertilization*, 475, 501, 502 key concepts, 123-129 546-552 glacial-interglacial changes, 385, 480-483, 482, weather and, 123-126, 914-917 land storage, 26, 93 483 Climate change* Index models, 502-504 global budget, 488-494 baseline period*, 1034 oceanic, 259, 291-293, 294, 300, 301, 472 industrial era, 474-475 direct observations of, 124, 130 organic, 1048-1050, 1052, 1419, 1431 lifetime and radiative efficiency, 731 drivers of, 13-14, 14, 124, 126, 170-174, 1033 1525 Index general concepts, 119-158, 124-125 downscaling*, 744, 810-817 probability and, 961-962, 974-975 historical overview of assessments, 124-125 drift*, 967-970, 978 quality/skill*, 85-86, 86, 958, 960-961, 964-965, indicators of, 130-137, 130, 164 dynamic global vegetation, 752, 791 966-978, 976-977, 1008-1009 irreversible aspects of, 28, 70-72, 129, 386-387, Earth System Models*, 16, 19, 26-27, 146, 468, retrospective, 85 433-435, 469, 1033 516, 518, 520, 523-526, 524-529, 743-745, 746, scientific basis for, 958 long-term, 19-20, 89-93, 1029-1136 747, 751-753, 822-823, 822-823 summary, 955, 1011-1012, 1011 multiple lines of evidence for, 121, 129-130 Earth System Models of Intermediate Complexity temperature, 973, 975, 977-978, 977 near-term, 85-89, 953-1029 (EMICs)*, 744-745, 746-748, 748 See also Climate projections observations, summarized, 4-12, 130 emergent constraints, 826-827 Climate projections*, 19-29, 79-108, 125, 953-1136 sun and, 394-395, 885-886 ensemble*, 146, 754-755, 793, 966, 1041-1043 abrupt change*, 1033, 1114-1119 timescales, 28, 125, 128-129, 128, 1033, 1105- evaluation, 15-16, 75-76, 741-866 air quality, 957, 996-1004 1107 evaluation, limitations of, 755-756 atlas of, 1311-1393 21st century projections, 1054-1102 evaluation, observations used in, 756-758 atmosphere and land surface, 980-994, 996-1004 weather vs., 123-126 experimental strategies and intercomparisons, atmospheric circulation, 90, 956, 972-975, 988- Climate change commitment*, 27-29, 28, 105, 128- 128, 759-760, 759 990, 989-990, 1032, 1033, 1071-1074, 1071-1072 129, 129, 1033, 1102-1105, 1103 extremes, 806-809 carbon cycle, 93-95, 96-97, 514-534, 1033, 1096- constant composition, 1103 flux adjustments*, 825 1099, 1097-1098 stabilization scenarios, 102-105, 1107-1113 global, 810-814, 811-813 climate models and, 79-81, 958, 978, 997-998, zero emission commitment, 1104, 1104, 1106- initialization*, 754, 760, 770, 796, 958 1013-1014, 1035-1044, 1036-1037, 1047-1052 1107 land, 750-751, 752, 790-791 climate models, consistency and differences, 1099- Climate change projections. See Climate projections long-term simulations, 15 1102, 1099-1101 Climate feedbacks*. See Feedbacks model errors, 62-63, 771-772, 809-810, 815, 1039 climate stabilization and targets, 27-29, 102-105, Climate forcing. See Radiative forcing multi-model ensembles (MMEs), 755, 817-819, 1033, 1107-1113 Climate forecast. See Climate predictions 967, 970, 1039 clouds, 1070-1071, 1070 Climate indices*, 1223 new components of, 751-753 commitment and irreversibility, 1033, 1102-1119, extreme events, 221-222 ocean, 750, 751-752, 777-787 1106-1107, 1114-1119 indices of climate variability, 230-232, 231-235 overview, 746-753, 1036-1037 comparison with observations, 64-65 regional changes in, 209-213, 211-212 parameterizations*, 748, 750 cryosphere*, 92, 92-93, 956, 995-996, 1087-1093, Climate models*, 15-16, 75-76, 741-866 performance, assessment of, 753-758, 809-810, 1088-1092 advances in, 121-122, 142-150, 748-753, 749-750, 821-827, 822-825 data sources and, 155-158, 155-157 824-825 performance, climate sensitivity and, 820-821 energy budget*, 1069-1071, 1069-1070 aerosols, 744, 752, 794-795 performance metrics, 765-766, 766-767 ensemble*, 1041-1043 assumptions, 146, 754, 755 perturbed-parameter, 755, 1040 equilibrium climate sensitivity, 1033, 1105-1107, atmosphere models, 748-750, 760-777 process-based*, 98-99, 806, 1144-1145 1110-1112 Atmosphere-Ocean General Circulation Models projections from, 19-29, 79-81, 127-128, 523-528, extremes, 956, 990-993, 990-991, 1003-1004, (AOGCMs)*, 83, 405, 516, 746, 747, 810-813, 822- 825-827, 958, 978, 997-998, 1014-1015, 1035- 1064-1068, 1067-1068 823, 822-823, 919, 1144 1044, 1047-1052 global, 19-29, 1054-1058 Atmospheric Chemistry and Climate Model proxy methods*, 388, 394, 404, 457 global projections, 1318-1321 Intercomparison Project (ACCMIP), 958, 1052- reanalyses*, 143-144, 185-186, 756-758, 760 greenhouse gases, 955, 998-1000, 1006-1007, 1054 recent and longer-term records in, 760-795 1048-1050 Atmospheric General Circulation Models (AGCMs), regional-scale, 15, 748, 810-817, 816, 1013-1014 hydrological cycle, 44-45, 88, 91-92, 91, 956, 984- 813 resolution*, 57, 753, 809 988, 985, 987, 1032, 1074-1087 capabilities of, 143-145, 144-150 sea ice, 744, 751, 787-790 initialization, 85, 960-961, 968-969 carbon cycle, 516-518, 751-752, 792-794 semi-empirical*, 99-100, 1140, 1144-1145 joint multivariate projections, 1044 chemistry-climate interactions, 752, 1052 summary, 15-16, 18, 743-746, 822-823 key concepts, 959-962, 1036-1037, 1084-1085, climate sensitivity and feedbacks, 745, 817-821, temperature, 743, 760-761, 767-773, 777-778 1106-1107, 1256-1257 817-819 top-down vs. bottom up, 886 long-term, 1029-1136 climate simulations, 122, 147-150, 743, 767-809, trend models, 179-180 long-term, 21st century, 1054-1102 959-961, 1013-1014 uncertainties*, 139-142, 140-141, 809-810, 815, long-term, beyond 2100, 1102-1119 climate variability and, 61-62, 129, 142-143, 230- 1035-1040, 1038, 1197-1198 long-term projections, 89-93 232, 743, 769-770, 795-806 vegetation, 752, 791 model agreement, 1041-1043 comparison of, 16, 27, 29, 523-526, 1099-1102, See also specific topics and models near-term, 978-1012 1099-1101 Climate patterns*, 232-235, 1224 near-term projections, 85-89 comparison with observations, 74, 146, 822-823, Climate penalty, 685 oceans, 93, 956, 993-995, 993-994, 1033, 1093- 1013-1014 Climate phenomena, 105-108, 106, 1217-1308 1095 confidence in, 743-745, 762, 768, 769-772, 793, See also Regional climate change pattern scaling, 1058-1062, 1061 806, 813, 822, 824-825 Climate predictions*, 953-1028 precipitation, 7, 956, 984-986, 985, 992-993, 992, Coupled Model Intercomparison Project Phase 5 concepts and terms, 959-961 1014-1015, 1032, 1278-1287 (CMIP5), 19-20, 21, 79-81, 146, 514-523, 516-518, decadal prediction, 955, 958, 966-978 precipitation, long-term, 91-92, 91, 1032, 1055- 521-522, 670, 745, 747-748, 756-759, 759-760, hindcasts*, 965, 967, 970, 973-974, 975 1057, 1057, 1076-1079, 1078 Index 766, 818-819, 822-823, 968-969, 971-978, 1031, initialization, 85, 961-962, 968-969, 975, 975 probability in, 961-962 1035, 1047-1052, 1048-1050, 1099-1102 near-term, 963-978 quality/skill*, 85-86, 86, 958, 960-961, 976-977 development and tuning, 144, 749-750 predictability studies, 962-965, 963 radiative forcing*, 79-80, 700-701, 701, 955, 1526 Index 1005-1010, 1006-1007, 1046-1052, 1048-1050 614, 607, 618-623, 623, 625-626, 683-685 1251-1253, 1288-1289 reference period, 958, 1034, 1313 anthropogenic sources of moisture, 592-595 models, 743, 807 regional projections, 956, 957, 1001-1002, 1001- aviation-induced cloudiness, 574, 592-594, 686 observations, 7 1003, 1014-1015, 1217-1308, 1288-1289, 1322- cloud albedo effect, 578, 610, 1048-1050 projections, 7, 107-108, 108, 110, 113, 956, 992- 1393 cloud condensation nuclei (CCN)*, 603-604, 608, 993, 993, 1219, 1249-1253, 1250, 1288-1289 scenarios, 955, 956, 997, 1031, 1034, 1045-1047 886 tropical, 7, 107-108, 108, 113, 162, 216-217, 216, sea level change*, 7, 25-26, 26, 98-101, 125, 1140, cloud convection effects, 573, 585 807, 871, 913-914, 938, 956, 992-993, 993, 1220, 1150-1191 cloud feedbacks*, 587-592, 819-820 1248-1251, 1288-1289 sensitivity of, 979, 1007 cloud lifetime effect, 1048-1050 summary, 19-29, 955-957, 1009-1012, 1011-1012, cloud radiative effect (CRE)*, 580-582, 582, 585- 1031-1033 586, 764, 765 D temperature, 7, 955-956, 973-974, 980-984, 981- cold clouds, 611-612 983, 1006, 1006, 1010-1012, 1012-1014, 1278- cosmic ray effects on, 613-614, 691 Dansgaard-Oeschger (DO) events*, 432-433 1287 effects on Earth s radiation budget, 580-582, 582 Deforestation*, 50, 55, 1008 temperature, long-term, 89-90, 1031-1032, 1054- feedbacks, 573-574, 576-578, 577, 587-592 Detection and attribution of climate change*, 7, 1057, 1054-1056, 1062-1068, 1063 formation and types, 576, 578-580, 579-581 17-19, 125, 867-952 transient climate response, 1033, 1110-1112 general concepts, 578-595, 593-594 anthropogenic radiative forcings, 13-14, 14, 17, tropical cyclones, 993-994, 993 geoengineering methods, 628 146, 617, 661-662, 675-688, 932-934, 1005-1008 uncertainties*, 115, 955, 978-1039, 979, 1004- ice clouds, 585 atmosphere and surface, 878-901 1012, 1034, 1035-1040, 1038, 1057-1058, 1058 lifetime effects, 578, 609-610 atmospheric circulation, 871, 899-901, 899-900, vs. predictions, 978 liquid clouds, 585, 609-611 931, 937-938 See also Regional climate change; specific topics mixed-phase clouds, 585 atmospheric temperatures, 869-870, 878-893 Climate regime*, 1225 models, 16, 573, 582-587, 591-592, 592, 608-611, climate models and, 825, 869, 872, 875-876 Climate scenarios*, 29, 131-132, 147-150, 1031, 743, 762-766, 764 climate system properties, 871, 920-927 1034, 1036-1037, 1045-1047 observations, 578-595 combination of evidence, 871, 924-926, 931 comparison of, 1047 opacity, 590 context, 151, 872-874 tables, 1395-1445 precipitation effects, 624-627 cryosphere*, 870, 906-910, 931, 936-937 uncertainty*, 1038-1039, 1038 in present-day climate system, 578-582 definition, 872-873 See also Emissions scenarios processes, 582-587, 592 Earth system properties, 926-927 Climate sensitivity*, 82-85, 164, 745, 817-821 projections, 1070-1071, 1070 extremes, 110, 871, 910-917, 911 equilibrium climate sensitivity (ECS), 16, 81, 82-85, radiative forcing (CRF)*, 126, 126, 576-578, 577, fingerprinting, 873-874, 877-878, 894-895 385, 405-407, 405-406, 817-819, 817, 821, 920- 618-621, 682-684 greenhouse gases, 127, 150, 869, 887, 932 926, 925, 1110-1112 sea-ice interactions, 590 human attribution, 7, 17-19, 121, 125, 127, 869- probability density functions (PDFs)*, 134-135, water vapour feedbacks, 574 871, 927-931, 932-939 134 Cold days/cold nights*, 162, 210-212, 221 hydrological cycle, 72, 870, 895-899, 931, 935-936 transient climate response (TCR), 16-17, 84-85, projections, 86, 956, 990, 1065-1066, 1067 irreversibility and, 28 128, 817-818, 821, 920-921, 925, 1110-1112 Commitment. See Climate change commitment lessons from the past, 919-920 Climate simulations, 122, 147-150, 743, 767-795, Compatible emissions*, 523-528, 526-529, 1104 methods, 872-878, 875-876, 894-895 959-961, 1013-1014 Confidence*, 4, 36, 139-142, 142 models, 825, 869, 872, 875-876 Climate stabilization, 27-29, 102-105, 1033, 1107- Contrails, 574, 592-594, 686 multi-century to millennia, 917-920, 938 1113 Cosmic rays, 573, 613-614, 691 multi-variable approaches, 878, 927 Climate system*, 15, 15-19, 60-78, 871, 920-931 Cryosphere*, 9, 69, 317-382 null hypothesis, 878 climate models, 15-16 area, volume, and sea level equivalents, 321-322 ocean properties, 293-294, 870, 901-906, 926, environmental data, 1437-1445 attribution of changes, 870, 906-910, 931, 936-937 934-935 historical data, 1401-1409 components, 321, 321, 322 precipitation, 72, 870, 871, 896-897, 897-898 nonlinear, chaotic nature of, 955, 960, 1033 feedbacks, 27, 321, 358, 359, 757 regional changes, 888-891, 889, 919, 938-939 observed changes, 4-12, 37-52 frozen ground*, 320, 362-366, 367 scaling factors, 873-874 quantification of responses, 16-17 glaciers*, 319, 335-344, 367 sea level change, 870, 905, 1156, 1176-1179 responses of, 16-17, 81, 1004 ice sheets*, 320, 344-357, 367 single-step and multi-step attribution, 878 scenario tables, 1395-1445 impact of changes in, 321-323 solar irradiance and forcing, 885-886 transient climate response, 16-17, 920-921, 925 irreversible changes, 71-72 summary, 869-871, 893, 927-931, 932-939 warming of, 4-5, 5, 6-7, 198-199 lake and river ice, 320, 361-362, 367 temperature, 17-19, 60, 869-870, 871, 878-893, Climate targets, 102-105, 1033, 1107-1113 observation methods, 323, 335-338, 338, 368 918-920, 930, 932-934 Climate variability*, 121, 138, 142-143, 164, 232- observations, 9, 10, 136-137, 317-382 time series methods, 874-877, 887-888, 895, 1223 235, 795-806, 959 projections, 24-25, 88, 323, 956, 995-996 weather and climate events, 914-917 indices of, 230-232, 231-235 projections, long-term, 92, 92-93, 1032-1033, whole climate system, 927-931, 930 interannual-to-centennial, 799-806, 806 1087-1093, 1088-1092 Dimethyl sulphide (DMS), 601 internal, 61-62, 769-770, 919, 923, 959 sea ice*, 319, 323-335, 367, 870 Direct air capture*, 550 modes of*, 415-416, 744, 801-803, 1220, 1222- seasonal snow, 320, 358-361, 358-360 Diurnal temperature range (DTR). See Temperature 1223, 1223-1225, 1288-1289 summary, 319-320, 367-368, 367 Doha Amendment, 169 Index patterns of*, 232-235, 900-901, 900, 1243-1246 Cyclones, 110, 162, 1220, 1248-1253 Downscaling*. See Climate models Clouds, 208, 571-657 attribution of changes, 871, 913-914, 938 Drivers of climate change, 13-14, 53-59, 392-393 aerosol-cloud interactions*, 164, 573, 578, 606- extratropical*, 113, 217-220, 743, 913, 1220, long-term, 1033 1527 Index near-term, 170-174, 668 projections, 106, 991, 1264-1266, 1265, 1281, permafrost-climate, 27 summary, 13-14, 14, 124, 126 1288, 1350-1357 projections, 24 uncertainties, 114 severe storms, 217 snow-albedo, 321, 358, 359, 757 Droughts*, 110, 112, 212, 214-215, 1118 temperature, 939, 991 timescales of, 128-129, 128, 1105-1107 attribution of changes, 912-913 wind speeds, 217, 220 water vapour, 586-587, 587, 667, 819 megadroughts, 110, 112, 422, 423-424 Evaporation, 205, 269-270 Fingerprints*, 873-874, 877-878, 894-895 models, 807-809 projections, 91-92, 573, 986-988, 1032, 1081- Fires, 542, 693, 752 observations, 7, 162, 211-212, 212 1082, 1082 Floods, 112, 214, 290, 915-916, 915 paleoclimate*, 386, 422-425, 423-424 Extratropical circulation, 415-416, 773 paleoclimate, 386, 422-425, 424 projections, 7, 91-92, 110, 986, 1086, 1118 Extratropical cyclones*, 113, 217-220, 743, 913, Forests*, 543, 1115, 1117 Dust, 394, 600, 605, 1048-1050 1220, 1251-1253, 1288-1289 deforestation*, 50, 55, 1008 Extremes, 72-73, 109-113, 121, 134-136, 162-163, potential irreversible changes, 70-71 209-222 Fossil fuel emissions*, 467, 477, 489, 616 E air pollution and, 1005 compatible emissions, 93, 94, 523-528, 526-529 attribution of changes, 110, 871, 910-917, 911, Frequently Asked Questions (FAQs) Earth system 931 Are climate models getting better, and how would energy budget, 1069-1071, 1069-1070, 1140, changes in, 209-222, 218-219 we know?, 824-825 1159-1161 confidence levels, 134-136, 135 Are glaciers in mountain regions disappearing?, properties, 926-927 cyclones, 113, 217 345-346 responses and feedbacks, 388, 395, 398-415 extratropical storms, 217-220, 1074, 1075 Climate is always changing. How do we determine El Nino-Southern Oscillation (ENSO)*, 106-107, fraction of attributable risk, 47 the causes of observed changes?, 894-895 232, 233-235, 1240-1243 hydrological cycle, 110-112, 213-216, 912-913, Could geoengineering counteract climate change Atlantic Nino, 233, 803, 806, 1224, 1239-1240 1082-1087 and what side effects might occur?, 632-634 changes, 1240-1242, 1242 indices of, 221-222 Could rapid release of methane and carbon dioxide impacts, 1224 models, 15, 744, 758, 806-809, 808 from thawing permafrost or ocean warming indices, 231, 232, 233-234 observations, 46-50, 110, 162-163, 164, 209-222 substantially increase warming?, 530-531 models, 15, 744, 803-805, 804, 806, 1220 precipitation, 23, 110-112, 211-212, 626-627, 807, Do improvements in air quality have an effect on paleoclimate*, 386, 415-416, 416 808, 871, 912, 956, 991, 992 climate change?, 684-685 projections, 23, 106-107, 1240-1243, 1242, 1259, probability density functions (PDFs)*, 134-135, Have there been any changes in climate extremes?, 1288-1289 134 218-219 tropical Pacific mean state, 1240, 1241 projections, 956, 990-993, 990-991, 1003-1004, How are future projections in regional climate variability, 129, 744, 806 1031-1032, 1064-1068, 1067-1068, 1082-1087 related to projections of global means?, 1256- Electromagnetic spectrum*, 126 regional, 211-212 1257 Emission metrics, 17, 58-59, 59, 662-663, 710-720, sea level, 7, 101, 110, 258, 290-291, 290, 1140, How do aerosols affect climate and climate 731-738 1200-1204 change?, 622-623 application of, 716-720 severe local weather, 216 How do clouds affect climate and climate change?, concepts, 710-716, 710-712 small-scale, 163 593-594 by sector, 719-720, 720 SREX, 7, 110, 209, 212-214, 217 How do volcanic eruptions affect climate and our Emissions scenarios*, 516-517, 523-528, 662-663, temperature, 109-112, 209-212, 209-212, 211- ability to predict climate?, 1008-1009 997, 1106-1107, 1410-1421 212, 218-219, 871, 910-912, 931, 990-992, 990- How do we know the world has warmed?, 198-199 compatible emissions*, 523-528, 526-529, 1104 991, 1031-1032, 1064-1068, 1067-1068 How does anthropogenic ocean acidification relate Representative Concentration Pathways (RCPs)*, tropical storms, 216-217, 216 to climate change, 297-298 79-81, 147-150, 468, 523-526, 524-529, 1045- waves, 1141 How important is water vapour to climate change?, 1047, 1100 666-667 SRES scenarios*, 131-132, 146-147, 149-150, 955, How is climate change affecting monsoons?, 1228- 997, 1045, 1100 F 1229 zero emission commitment, 1104, 1104, 1106- How is sea ice changing in the Arctic and 1107 Feedbacks*, 16, 57-58, 82-85, 127, 128 Antarctic?, 333-334 Energy budget of the Earth*, 67-68, 1140, 1159- carbon cycle*, 26, 475-480, 477-478, 514-523, How unusual is current sea level rate of change?, 1161 515-518, 520 430-431 glaciers and, 344 climate*, 57-58, 817-821, 817-819 How will the Earth s water cycle change?, 1084- projections, 1069-1071, 1069-1070 climate-carbon cycle, 514-523, 515, 516-518, 551- 1085 Energy inventory (global), 257, 264-265 552 If understanding of the climate system has Equilibrium climate experiment*, 128 climate-vegetation, 752, 791 increased, why hasn t the range of temperature Equilibrium climate sensitivity (ECS), 16, 81, 82-85, cloud and aerosol, 573-574, 576-578, 577, 587- projections been reduced?, 140-141 385, 405-407, 405-406, 817-819, 817, 821, 920- 592, 593-594, 605-606 If you cannot predict the weather next month, how 926, 925, 1110-1112 cryosphere*, 27, 321, 358, 359, 757 can you predict climate for the coming decade?, projections, 81, 1033, 1105-1107 distinguished from forcing and rapid adjustments, 964-965 summary, 1110-1112 576-578 Is the ocean warming?, 266-267 Europe and Mediterranean, 1264-1266, 1265 Earth System (global and hemispheric scales), 388, Is the Sun a major driver of recent changes in Index climate indices, changes in, 211-212 395, 398-415 climate?, 392-393 flood frequency, 424, 915-916, 915 models, 16, 19, 26, 514-521, 516-518, 817-821, Is there evidence for changes in the Earth s water precipitation extremes, 211-212, 213, 991 818 cycle?, 269-270 1528 Index What happens to carbon dioxide after it is emitted Global Warming Potential (GWP)*, 17, 663, 710- Texas (2011), 212, 916 to the atmosphere?, 544-545 714, 711-712 Hindcasts*, 965, 970, 973-974, 975 What would happen to future climate if we GRACE satellite mission, 349, 351-353, 380, 1156, precipitation, 976 stopped emissions today?, 1106-1107 1157 sea surface temperature*, 967 When will human influence on climate become Gravity field. See GRACE satellite mission Holocene* . See Paleoclimate obvious on local scales?, 928-929 Greenhouse effect*, 124, 127, 666-667 Human effects on climate, 7, 17-19, 121, 127, 928- Why are so many models and scenarios used to Greenhouse gases (GHGs)*, 126, 127, 161, 165-170, 929 project climate change?, 1036-1037 385 carbon cycle, 467-468 Why does local sea level change differ from the anthropogenic*, 17, 27-28, 391, 869, 887, 932, detection and attribution studies, 867-952 global average?, 1148-1149 1410-1420 irreversible aspects of, 28, 469 Will the Greenland and Antarctic ice sheets commitment and irreversibility, 1033 ocean acidification, 293-294, 295-298 contribute to sea level change over the rest of the emissions scenarios, 516-517, 523-528, 662-663, oceanic carbon dioxide, 292-293, 293 century?, 1177-1179 997-1001, 1410-1421 radiative forcing*, 13, 14, 17, 146, 617, 661-662, Freshwater ice, 320, 361-362 feedbacks, 17, 128, 667 675-688 Frozen ground*, 320, 362-366, 367 glacial-interglacial changes, 385, 480-483, 482, See also Detection and attribution permafrost*, 320, 362-364, 362-363 483 Humidity, 162, 201, 205-208, 206, 870 seasonally frozen, 320, 364-366, 365-366 global trends, 164 in climate models, 819 lifetimes, 128-129, 128 projections, 956, 987, 988, 1032, 1076, 1076 observed changes, 4, 11-12, 132-134, 132-133, relative*, 987, 988, 1076 G 164, 165-170 specific*, 206, 206, 956, 987, 988, 1032 observed changes, last millennium, 485-486, 486 surface, 205-206, 206 Geoengineering*, 29, 98, 546-552, 632, 632-634 paleoclimate*, 385, 391-398, 483-484, 483 tropospheric, 206-208 Carbon Dioxide Removal (CDR)*, 469, 547-551, projections, 19, 27-28, 148, 955, 997-1001, 1006- Hurricanes, 809, 994 548-549, 632-633 1007, 1410-1420, 1422-1427 See also Cyclones carbon sequestration in ocean, 549-550 radiative forcing*, 13-14, 14, 126, 164, 165, 391- Hydrochlorofluorocarbons (HCFCs), 161, 170, 1403, climate response and, 629-635, 629-631 398, 470, 661, 675-676, 1404-1409 1427 side effects and risks, 29, 575, 627-628, 632-634 since industrial revolution, 486-514 lifetime and radiative efficiency, 661, 731 Solar Radiation Management (SRM)*, 29, 469, spectral properties, 675-676 Hydrofluorocarbons (HFCs), 168-169, 998, 1402 574-575, 627-635, 629-631, 633-634, 693 well-mixed, 165-170, 166, 661, 668, 676-679, 677- atmospheric concentration, 161, 168-169, 168 volcanic eruptions as analogues for, 693 678, 1006-1007 lifetime and radiative efficiency, 732-733 Geopotential height, 223, 223, 226 See also Emissions; specific gases projections, 1414-1416, 1424-1427 Glaciation Greenland ice sheet, 9, 137, 320, 349-351, 397, 909 radiative forcing*, 678, 679, 1434 future, 387, 435 attribution of changes, 870, 909, 931 Hydrological cycle*, 17, 72, 162, 201-208 glacial-interglacial cycles*, 385, 399-402, 480-483, dynamical change, 1168-1169 abrupt/irreversible changes, 1115, 1118-1119 482-483 loss of (possibility), 71-72, 353, 363, 1140, 1169- attribution of changes, 17, 72, 870, 895-899, 931, last glacial termination, 389, 400-401, 428-432 1170 935-936 Glaciers*, 319, 335-344, 345-346, 367 mass balance*, 347, 380-381, 1139, 1153-1155, changes in, 42-45, 269-270, 273 abrupt glacial events, 483 1154-1155, 1165-1168, 1166 extremes, 110-112, 213-216, 912-913, 1082-1087, anthropogenic influence, 19 models, 753, 1166-1168 1083, 1086 attribution of changes, 870, 909-910, 931 observed changes, 349-351, 350, 357, 367, 368 greenhouse effect and, 666 calving*, 335, 336, 337, 342, 343 paleoclimate*, 387, 1170 land water storage, 1151, 1155-1156, 1176-1179, current area and volume, 335, 336-337 projected loss of, 29 1182 deglaciation*, 385, 400 projections, 25, 1140, 1165-1170 observations, 40-46, 42-45, 162, 164, 201-208 dynamic change potential, 1164-1165 sea level equivalent, 320, 321, 350, 353-354 oceans and, 265, 273 equilibrium line*, 338, 345-346 sea level rise and, 1139, 1140, 1153-1154, 1154- paleoclimate*, 386, 421-422 greenhouse gases and, 480-483 1155, 1165-1170, 1177-1179, 1182 projections, 20-23, 88, 956, 984-988, 985, 987, mass balance/budget*, 319, 341-344, 343, 1151, thresholds and irreversibility, 71-72, 1169-1170 1084-1085 1153 projections, long-term, 44-45, 91-92, 91, 1032, measurement methods, 335-338, 338 1074-1087, 1082-1087, 1083, 1086 models, 1145, 1163-1164 H proxy data, 421-422 observed changes, 9, 319, 338-344, 339-340 radiative forcing*, 624-625 paleoclimate, 385, 421 Hadley Circulation*, 226-229, 227, 871, 899-900, surface hydrology, 790-791, 897-899 projections, 24, 25, 1145, 1164-1165 899 See also Precipitation; Water vapour sea level change and, 367, 1139, 1151-1153, 1151, projections, 90, 956, 989-990, 989, 1032, 1073 1163-1165, 1164-1165, 1182 Halocarbons*, 13, 14, 675, 717 sea level equivalent, 319, 321 radiative forcing*, 678-679, 678 I summary, 9, 24, 137, 319, 367 Halogenated alcohols and ethers, 734-737 volume and mass changes, 338-344, 339-344 Halons, 733 Ice, 136-137, 319-320 Global Damage Potential (GDP), 715 Heat flux, 182, 274-275, 786 aerosol absorption on, 574 Global dimming*, 161, 183-184, 794 Heat waves*, 5, 7, 110, 211-212, 212 annual melt rates, 264 Index Global Positioning System (GPS), 143, 196, 207 attribution of changes, 915, 916, 939 freshwater ice, 320, 361-362, 367 Global Temperature change Potential (GTP), 17, projections, 110, 1066 river and lake ice, 320, 361-362, 367 663, 712-714, 714-715, 720 Russia (2010), 212, 915, 916 sea ice*, 319, 323-335, 367, 870 1529 Index See also Glaciers K paleoclimate, 385, 485 Ice age*, 386, 389, 413 permafrost*, 508, 530-531, 541-542 Ice clouds, 585 Kyoto Protocol*, 715 projections, 24, 27, 148, 156, 468-469, 539-542, Ice cores*, 391-394, 432, 485 Kyoto Protocol gases, 161, 166-170, 997, 1005, 540, 997-998, 999, 1048-1050, 1411, 1422 Ice nuclei, 604 1401-1402 radiative forcing*, 13, 14, 126, 661, 662, 674-675, Ice sheets*, 320, 344-357, 367, 1177-1179 677, 678, 1048-1050, 1433 Antarctic, 9, 25, 29, 137, 320, 321, 351-353, 352- Methane hydrate, 542 353, 356-357, 368, 909, 1170-1176 L Methyl chloroform (CH3CCl3), 678, 733 attribution of changes, 870, 909-910, 931 Methylene chloride (CH2CH2), 733 basal lubrication*, 354-355 Lake ice, 320, 361-362, 367 Metrics* calving*, 355 Land carbon storage, 26, 93 emission metrics, 17, 58-59, 59, 662-663, 710-720, causes of changes, 353-355 Land surface, 790-791 731-738 climate-ice sheet interactions, 402-403 Land surface air temperature*, 162, 164, 187-189, model performance metrics, 765-766, 766-767 dynamics and stability, 25, 1159, 1168-1169, 187 Microwave Sounding Unit (MSU), 194-196, 195 1172-1174, 1175-1176, 1179 Land use and land use change*, 127, 162, 188-189, Mineral dust aerosol (MDA), 394, 600, 605, 617 Greenland, 9, 25, 29, 137, 320, 321, 349-351, 350, 686-688 Mitigation*, 27-29 357, 870, 909, 1165-1170 carbon dioxide emissions, 467, 474-475, 489-491, Models. See Climate models grounding line*, 347, 351, 353, 357 490-492 Modes of climate variability*, 415-416, 744, 801- ice loss, 320, 349-353, 353-354, 380-382 future scenarios, 523 803, 1222-1223 irreversible changes, 29, 71-72, 355-356, 433, land cover, 686-687 definitions and impacts, 1223-1225 1115, 1116, 1169-1170, 1174 land water storage, 1151, 1155-1156, 1176-1179, projections, 1220, 1288-1289 marine ice-sheet instability hypothesis (MISI), 1182 regional impacts, 1224 1175-1176 models, 752, 791 responses to climate change, 1222-1223 mass balance/budget*, 344-353, 347-348, 380- projections, 1006-1007, 1038, 1048-1050, 1052, Monsoons*, 105, 1222, 1225-1235, 1228-1229, 382, 1139 1099 1288-1289 measurement techniques, 347-349, 347-348 radiative forcing*, 662, 686-688, 687, 1048-1050, abrupt/irreversible changes, 1115, 1118-1119 models, 25-26, 753, 1145 1052, 1404-1409 African, 1234, 1235 observed changes, 9, 10, 320, 346-353, 347-348 urban effects, 162, 188-189 American, 1232-1234, 1233 ocean interactions, 354, 355, 356-357 Land water storage, 1151, 1155-1156, 1176-1179, Asian-Australian, 1227-1232, 1230-1231 paleoclimate*, 387, 426-431, 1170, 1174 1182 East Asian, 1230-1231, 1231-1232 polar amplification, 397, 907 Lapse rate*, 586-587, 587, 819 Indian, 1229-1231 processes, 354-355 Likelihood*, 36, 139-142 models, 15, 798-799, 799, 1219 projections, 25, 29, 1145, 1165-1176 See also Confidence; Uncertainty observations, 163, 227 rapid changes, 355-357 Long-term climate change, 19-20, 89-93, 1029- overview, 1225-1227, 1226-1227 sea level change and, 29, 355, 367, 1139, 1145, 1136 paleoclimate*, 387, 401-402, 401, 421-422 1151, 1153-1155, 1154-1155, 1165-1176, 1177- See also Climate projections projections, 23, 105, 107, 1118-1119, 1219, 1225- 1179, 1182 1235, 1288-1289 sea level equivalents, 321, 352-354, 353 Montreal Protocol*, 661, 672, 678 subsurface melting, 356-357 M Montreal Protocol gases, 161, 170, 678, 1403, 1427, summary, 320, 353-354, 367 1435 Ice shelves*, 320, 353, 367 Madden-Julian Oscillation (MJO)*, 796-798, 798, Indian Ocean, 233-235, 280, 495 1220, 1224, 1237 models, 787 Mediterranean region. See Europe and N projections, 1219 Mediterranean Indian Ocean Dipole (IOD)*, 233-235, 1220, 1237- Meridional Overturning Circulation (MOC). See Natural forcings, 13-14, 14 1239 Atlantic Meridional Overturning Circulation Near-term climate change, 85-89, 953-1029 impacts, 1224 Methane (CH4)*, 11, 165, 167, 385, 486, 508-510 See also Climate projections models, 744, 805, 806 anthropogenic, 509, 663, 955, 1411 Near-term climate forcers (NTCFs)*, 668, 717-718 projections, 1237-1239, 1238-1239 atmospheric changes, 505-508 New Zealand. See Australia and New Zealand Indonesian Throughflow, 284-285 atmospheric concentration, 156, 161, 166-167, Nitrate aerosols, 605-606, 616-617, 1048-1050 Industrial Revolution*, 474-475, 486-514, 697-698 167, 1401-1402 Nitrogen, 93, 127, 468, 535-539, 538 Insolation*, 794-795 clathrates*, 70-71, 1115, 1116-1117 global budgets, 510-514, 511-512 Inter-Tropical Convergence Zone (ITCZ)*, 387, 786, couplings and feedbacks, 674-675 Nitrogen cycle, 475-480, 477-479 1077, 1219, 1236 glacial, 482-483, 483 projections, 535-539, 536-540 Iron fertilization*, 481, 543 global budget, 505-510, 507-508 Nitrogen dioxide (NO2), 174, 174 Irreversibility*, 27-29, 70-72, 129, 386-387, 433-435, growth rate, 385, 506, 506 Nitrogen fertilizers, 469, 510, 512, 535-536, 536 469 industrial era, 475 Nitrogen fixation, 475, 477, 511, 514, 1419-1420 ice sheets*, 29, 71-72, 355-356, 433, 1115, 1116, lifetime and radiative efficiency, 731, 1432 Nitrogen oxides, 717-718, 739 1154, 1169-1170 methane cycle, 473-474, 474, 752 Nitrogen trifluoride (NF3), 169, 678, 679, 733 long-term projections, 1033, 1114-1119 models, 509-510, 752 Nitrous oxide (N2O)*, 11, 167-168, 475 Index paleoclimate perspective, 386-387, 433-435 natural sources, 508-509 atmosphere burden and growth rate, 385, 510- sea level and, 29 observed changes, 11, 133, 134, 161, 165-166, 512, 511-513 Islands. See Pacific islands 166, 167, 467, 505-508 1530 Index atmospheric concentration, 161, 167-168, 168, freshwater content, 257, 272, 273 monitoring sites, 173 476, 1401-1402 freshwater fluxes, 275-276, 276, 994 ozone hole*, 171, 752 feedbacks and sensitivity, 512-514, 513 heat content, 17, 18, 257, 260-263, 262, 264, 266, projections, 24, 542, 957, 997, 1000, 1001-1002, glacial, 482-483, 483 301, 779-781, 782, 901-903, 902 1048-1050, 1428, 1438-1442 global budget, 510-514, 511-512 heat content, modeling, 743 radiative forcing*, 13, 17, 127, 661-662, 670-672, global warming potential, 717 heat content, projections, 1162 672, 679-681, 1048-1050, 1404-1409, 1434 lifetime and radiative efficiency, 731, 1433 heat fluxes, 274-275, 786 stratospheric, 161, 171-172, 172, 672-674, 681- observed changes, 11, 133, 134, 161, 166, 167- heat uptake*, 93, 267, 821, 1161-1163, 1162 682, 681, 774-775, 999, 1048-1050, 1078, 1428 168, 467-468, 486 human influences, 17, 292-294, 293 tropospheric, 161, 172-173, 670-672, 672-673, paleoclimate*, 385, 485 inertia and, 958 679-681, 680-681, 684, 775, 998-999, 1048-1050, projections, 148, 157, 469, 535-537, 537, 998, iron deposition/fertilization*, 481, 543 1428-1429 1048-1050, 1412, 1423 irreversible changes, 433-435 Ozone-depleting substances, 161, 169-170 radiative forcing*, 13, 14, 126, 127, 661, 675, 677- mass observations, 1156, 1157 678, 678, 1048-1050 models, 750, 751-752, 753, 758, 777-787 Non-methane volatile organic compounds nitrogen concentration, 475 P (NMVOCs)*, 13, 14, 174, 996, 1000, 1417 nutrients, 298-300 Nonlinearity*, 955, 960, 1033 observations, 8, 10, 22, 255-315, 302 Pacific Decadal Oscillation (PDO)*, 230, 231, 233- North America observations, capabilities and methods, 144, 302, 235, 1253 climate indices, changes in, 211-212, 212 311-316 impacts, 1224 cyclones, 217 ocean-atmosphere coupling, 753, 1118-1119 models, 806, 806, 1253 monsoon, 1233, 1233 ocean heating rate (OHR), 182, 183 predictions, 971, 972 precipitation extremes, 211-212, 213 oxygen concentrations, 259, 294-298, 300-301, Pacific Decadal Variability*, 233-235, 972 projections, 106, 1258-1260, 1259, 1279, 1288, 300, 469, 535, 870, 905-906 Pacific Islands region, 106, 1275-1276, 1285, 1289, 1334-1337 oxygen projections, 532-534, 534-535 1386-1389 North Atlantic Oscillation (NAO)*, 230, 231, 233- paleoclimate*, 433-435, 456, 484, 783-784 Pacific/North American (PNA) pattern*, 231, 233- 235, 354, 1244-1245 precipitation and, 275-276, 276 235, 806, 1224, 1253 impacts, 1224 projections, 24, 88, 468, 469, 519-520, 528-532, Pacific Ocean, 271, 280, 495 models, 744, 801, 806 956, 993-995, 993-994 circulation systems, 281-282 paleoclimate*, 386, 415-416 projections, long-term, 93, 1033, 1093-1095 tropical, mean state, 743, 786-787 projections, 989, 1220, 1244-1245, 1245 salinity, 8, 257, 265-273, 280, 301, 870, 903-905, Pacific/South American (PSA) index, 231, 233-235 summary, 806 904, 994, 994, 1094, 1094 Pacific/South American (PSA) pattern, 1221, 1224, North Pacific Oscillation (NPO), 801, 1224 solubility/biological pumps*, 472 1253 Northern Annular Mode (NAM)*, 233-234, 900, summary, 257-259, 301-302, 302 Paleoclimate*, 124, 383-464 900, 1244 surface temperature, 5, 6, 777-779, 778-780 8.2 ka event, 389, 434 impacts, 1224 temperature, 5, 6, 68-69, 257, 260-265, 266-267, abrupt change and irreversibility, 386-387, 432- models, 415, 806 901-903, 902, 993-995, 993-994 435, 434 paleoclimate*, 415-416 temperature projections, 24 carbon dioxide, 385, 391-394, 399-400, 400, 457, projections, 108, 989, 1245, 1245 thermal expansion*, 99, 99, 1139, 1143, 1150- 459-460, 468, 483-484, 483 summary, 806 1151, 1159, 1161-1163, 1180, 1182 droughts*, 386, 422, 423-424 thermal forcing, 354 Earth system responses and feedbacks, 388, 395, upper ocean salinity, 268-273 398-415 O upper ocean temperature, 257, 258, 261-262, 261, equilibrium climate sensitivity, 923-924 263, 265, 301, 870, 901 floods, 386, 422-425, 424 Observations. See specific topics warming (observed), 8, 10, 17, 24, 257, 260-265, glacial-interglacial cycles, 385, 399-402, 480-483, Oceans, 8, 255-315 280 482-483 acidification*, 11, 12, 12, 52, 69, 136, 259, 295- warming rates, 263, 263 greenhouse gases, 385, 391-398, 483-484, 483 296, 300, 751, 870, 905-906 water exchange between ocean basins, 284-285 Holocene*, 389, 417-425, 428-435, 434, 776-777, acidification, anthropogenic influence, 293-294, water mass properties*, 258, 278-281 776-777, 1146 295-298 wave heights, 258, 277-278 ice sheets*, 387, 426-428, 1170, 1174 acidification projections, 22, 27, 94, 105, 469, 528- wind stress, 276-278, 784-785, 784-785 interglacials*, 386, 407-409, 425-428, 1146 532, 532 See also Sea level; Sea level change last 2,000 years, 389, 409-415, 409-410 attribution of changes, 870, 901-906, 926, 934-935 Optimal fingerprinting, 877-878 Last Glacial Maximum (LGM)*, 385, 389, 394, 403- biogeochemical changes, 259, 291-301 Orbital forcing, 385-388, 399, 400 407, 404, 776-777, 776-777 carbon balance, 300, 301, 498-499 Oxygen (O2) last glacial termination, 389, 400-401, 428-432 carbon dioxide absorption, 11, 12, 26, 51-52, 93, atmospheric concentration, 476, 480, 1437 Last Interglacial (LIG), 385, 389, 407-409, 408, 259, 291-293, 293, 295-300, 300, 472, 495-499, dissolved in oceans, 95, 259, 294-298, 300-301, 425-428, 427, 1146 751, 870 300, 469, 905-906 last millennium, 917-920, 918 CDR methods and, 549-550, 551 feedbacks, 480 Little Ice Age*, 386, 389, 413 circulation, 258, 281-285, 283, 481, 956 oceanic, projections, 532-534, 534-535 Medieval Warm Period*, 5, 386, 389 circulation, projections, 994-995, 1094-1095 Ozone*, 1000 methods, 385, 388 Index deep and bottom waters, 263, 279-280 depletion, 739, 869, 937, 998-999, 1000, 1078 models, 388, 403-405, 411-415, 413-414, 456-464, evaporation, 274-275, 275, 276 long-term trends, 172-173 776-777, 820-821 fluxes, 258, 273-278 models, 744, 752, 757, 774-775, 775 modes of climate variability*, 386, 415-416 1531 Index next glacial inception, 387, 435 runoff*, 91-92, 204-205, 956, 1081, 1081 distinguished from feedbacks, 573 ocean circulation, 433-435, 456, 783-784 summary, 5, 7-8 drivers of, 124 orbital forcing, 385, 386, 388, 399 trends, 202-203, 215, 624, 898 effective (ERF)*, 53, 574, 576-578, 578, 614-621, periods assessed, 389 warmer-get-wetter, 1219, 1240 619-621, 661, 770, 1052-1053, 1160-1161, 1404- Pliocene*, 1145-1146 wet-get-wetter, 624 1409, 1433-1436 polar amplification, 385, 396-398 See also Monsoons effective (ERF)*, defined, 664-665, 665 pre-industrial perspectives, 388-398, 389 Predictability*, 131, 953-1029 effective (ERF)*, probability density function*, 697 proxy methods*, 388, 394, 403-404, 457-458 near-term predictions, 963-978 effective (ERF)*, total anthropogenic, 661 radiative forcing*, 385, 388-398 prediction quality/skill*, 85-86, 86, 958, 960-961, emission metrics, 710-720, 711, 731-738 reconstructions*, 77-78, 411-415, 414-415 966-978 external*, 388-398, 917-919 sea level, 47, 385, 425-432, 427-429, 1139, 1145- terminology, 960 geographic distribution, 702-709, 703-705 1150, 1147 See also Climate predictions global mean, 89, 693-701, 696-697 temperature, 385-386, 395, 409-415, 417-420, Principal component, 1223 Global Warming Potential and Temperature change 461-464 Probability density functions (PDFs)*, 134-135, Potential, 663, 710-714 uncertainties*, 404, 411-412 134, 697 industrial-era, 661-662, 697-698, 697-698, 705- volcanic forcing, 390, 391 Probability in climate predictions/projections, 708, 705 Particulate matter. See Aerosols 961-962 land surface changes, 686-688, 1404-1409 Pattern scaling, 1058-1062, 1061 Projections. See Climate projections limitations of, 667-668 Perfluorocarbons (PFCs), 161, 168-169, 679, 733- Proxy methods*, 388, 394, 404, 457-458 models, 146, 700-701, 701, 818 734, 1000 natural forcings, 13-14, 14, 55-56, 126, 662, 688- Permafrost*, 320, 362-364 693, 760, 1008 active layer*, 364-366, 365 Q orbital forcing, 385, 386, 387, 388, 399, 400 carbon storage in, 480, 526-528 paleoclimate*, 130, 385, 388-398 irreversible changes, 70-71, 1115, 1116 Quasi-Biennial Oscillation (QBO)*, 230, 744, 806, polar amplification, 396-398 methane from, 508, 530-531, 541-542 806, 1224, 1254 pre-industrial, 388-398 models, 752 projections, 79-81, 662-663, 700-701, 701, 955, near-surface*, 996 1005-1010, 1006-1007, 1044-1054, 1048-1050, observed changes, 9 R 1053 permafrost-climate feedback, 27 radiative transfer codes, 675-676 projections, 25, 27, 468, 541-542, 997 Radiation scenarios, 79-81, 1046-1047, 1046 projections, long-term, 1032-1033, 1092, 1093 radiative imbalance, 264 solar forcing, 388-391, 885-886, 1007, 1404-1409 subsea, 364 surface solar (SSR), 183-184, 184, 185-186 solar irradiance, 14, 14, 126-127, 126, 662, 688- temperature, 9, 25, 362-364, 362-363 surface thermal and net, 184-185 691, 885-886 Perturbed physics experiments (PPEs), 1040 top of the atmosphere (TOA), 180-181, 580-582, spatial and temporal patterns, 662, 702-709, 703- Phosphorus, 542 618, 620, 765, 1069, 1069 705, 709 Photosynthesis, 470, 471-472, 475, 478, 480, 502, Radiation budget, 161, 180-186, 576 summary, 13-14, 56-57, 57, 126, 129, 661-663, 545 cloud effects on, 580-582, 582 693-701, 1052-1054, 1159-1161 Polar amplification, 385, 396-398, 907, 1031, 1062- global mean, 127, 181, 182-183, 183 surface albedo and energy budget, 360-361, 662, 1064 rapid adjustments and, 573, 576 686-687, 687 Polynyas, 329, 332-334 surface, changes in, 183-186, 184 time evolution of, 698-700, 698 Precipitation, 201-204 Radiative effect*, 573, 576, 578, 1161 timescales and, 128-129, 128 aerosol effects, 624-627 cloud radiative effect (CRE)*, 580-582, 582, 585- uncertainties*, 667, 694-698, 694, 955, 1004- attribution of changes, 72, 870, 871, 896-897, 897- 586, 764, 765 1008, 1005-1006 898 Radiative efficiency, 717, 731-738 volcanic, 390, 391, 662, 691-693, 692-693, 923, extremes, 5, 7, 23, 110-112, 162, 211-212, 213- Radiative forcing (RF)*, 13-14, 14, 53-57, 54, 127, 1007, 1404-1409 214, 573, 626-627, 807, 808, 871, 912, 956, 991, 659-740, 1404-1409, 1433-1436 well-mixed greenhouse gases, 164, 661, 668, 676- 992, 1082-1087 aerosols*, 13-14, 14, 576-578, 577, 614-623, 682- 679, 677-678 extremes, indices of, 221 684, 1404-1409 See also specific gases and components extremes, physical basis for changes in, 626-627 aircraft and contrails, 574, 592-594, 686 Radiosonde records, 194-196, 195, 200-201, 206- global changes and projections, 1320-1321 anthropogenic, 13, 13-14, 14, 17, 146, 617, 661- 207 global distribution of, 1225 662, 675-688, 932-934, 1005-1008 Rapid adjustments*, 355-357, 573, 576, 590, 605, global warming effects on, 624, 625 atmospheric carbon dioxide, 13 661, 664-665, 665, 1005 large-scale changes, 201-204, 202-203, 624 atmospheric chemistry, 669-675 See also Abrupt climate change models, 743, 761-762, 763, 811-813, 811-813, calculation methodologies, 668-669, 669 Rebound effect*, 546 1013-1014 climate response, 395 Region(s)*, 1222 observations, 5, 7, 8, 22, 162, 201-204 clouds, 576-578, 577, 580-582, 582, 585-586, 620- atlas (map), 1317 ocean precipitation, 275-276, 276 622 carbon cycle feedbacks, 522 projections, 7, 20-23, 22, 573, 956, 984-986, 985, common properties of forcing compounds, 668 radiative forcing*, 705-708, 705 991, 1014-1015, 1278-1287 comparison of previous reports, 696 Regional climate change, 73-74, 105-108, 106, projections, global, 1320-1321 concentration/emission changes, 668-669 1217-1308 Index projections, long-term, 91-92, 91, 1032, 1055- concept, 53, 661, 664-668 annular and dipolar modes, 108, 1220, 1243-1246, 1057, 1057, 1076-1079, 1078 confidence levels, 694-695, 694-695 1288-1289 regional, 573, 1219-1220 definitions, 664-665, 665 Atlantic Multi-decadal Oscillation (AMO), 1220 1532 Index Atlantic Ocean modes, 1239-1240 sea surface, 267-268, 268, 270, 1094, 1094 contributions to, 11, 25-26, 288, 291, 1139, 1142- blocking, 1220, 1224, 1246-1248 trends in, 257, 273 1145, 1150-1179, 1177-1179, 1182 changes and projections, 1322-1393 upper ocean, regional changes, 271-273, 301 extremes, 7, 101, 110, 112, 258, 290-291, 290, climate indices, 209-213, 211-212 upper ocean, subsurface, 268-271 1140, 1200-1204 climate system, 930 Satellite-based methods, 164, 175, 182, 191, 207, freshwater forcing and, 1193-1194 CO2 budgets, 501 208 glaciers and, 367, 1139, 1151-1153, 1151, 1163- CO2 fluxes, 499, 500 altimetry*, 286, 287, 348-349 1165, 1164-1165, 1182, 1184 confidence in projections, 1286-1287 GRACE, 349, 351-353, 380, 1156, 1157 global average, 10, 11, 1148-1149 cyclones, 1220, 1248-1251, 1288-1289 Microwave Sounding Unit (MSU), 194-196, 195 global mean sea level rise, 90, 1140, 1152, 1156- El Nino-Southern Oscillation (ENSO)*, 106-107, sea level measurement, 1150 1159, 1157-1158, 1179-1191 1240-1243, 1241-1243, 1288-1289 Scenarios*. See Climate scenarios; Emissions ice sheets* and, 367, 1139, 1140, 1151, 1153- extreme events, 211-212 scenarios 1155, 1154-1155, 1159, 1165-1176, 1177-1179, global means and, 1256-1257 Scientific method, 123 1182 in Holocene (paleoclimate), 417-425 Sea ice*, 69, 136-137, 323-335, 333-334, 367, 481 instrumental record (1700-2012), 1146-1161 large-scale storm systems, 1248-1253, 1250 aerosol absorption on, 617-618 land water storage and, 1151, 1155-1156, 1176- models, 748, 810-817, 816, 1013-1014, 1219 Antarctic, 9, 18, 69, 319, 330-335, 333-334, 368, 1179, 1182 modes of climate variability*, 1222-1223, 1223- 906-909, 908, 1092 long-term scenarios, 98-101, 1186-1191, 1188, 1225 Arctic, 9, 10, 18, 24-25, 69, 271-273, 319, 323- 1190-1191 monsoon systems*, 105, 1219, 1222, 1225-1235, 330, 333-334, 367, 368, 906-908, 908, 1087-1092, measurements, 1146-1150 1288-1289 1089-1091 models, 1139-1140, 1142, 1144-1145, 1179-1183, Pacific South American pattern, 1221, 1253 attribution of changes, 870, 906-909, 908, 931, 1180-1184, 1192-1193, 1192-1193 precipitation, 1032, 1078-1079 936-937 models, compared with observations, 1152, 1158 projections, 956, 1001-1002, 1001-1003, 1014- as climate change indicator, 136-137 nonuniformity of, 26 1015, 1031, 1032, 1078-1079, 1255-1277, 1256, cloud interactions, 590 observed changes, 4, 10, 11, 46, 110, 124, 136, 1278-1289 drift, 328-329, 332 137, 157-158, 258, 291, 301, 1151, 1198 projections, summary, 1288-1289 extent and concentration, 324-326, 325-326, 330, ocean heat content/uptake*, 905, 1161-1163, sea level, 100-101, 101, 288-289, 1140, 1191- 331-332 1162, 1183-1184 1199, 1194-1197, 1195-1199 irreversible changes, 1115, 1117-1118 ocean mass observations, 1156, 1157 temperature, 89-90, 869, 888-891, 889, 919, 930, land-fast ice, 329, 334-335 ocean waves, 1202-1204, 1203 938-939, 1278-1285 models, 18, 20, 744, 751, 787-790, 787-789 paleoclimate*, 46, 47, 385, 425-432, 427-429, tropical cyclones, 1248-1251, 1288-1289 observations, 40 1139, 1145-1150, 1147 tropical phenomena, 105-106, 1219-1220, 1222, observed changes, 136-137, 319, 367, 368, 386 past sea level change, 1139, 1145-1150, 1147 1235-1240, 1288-1289 paleoclimate*, 420-421 process-based projections, 99-100, 1179-1180, See also specific regions projections, 20, 21-22, 24-25, 956, 995-996 1180-1182 Regional Climate Models (RCMs)*, 748, 810-817, projections, long-term, 92, 92, 1032, 1087-1092, processes and linkages, 1143-1144, 1143-1144 816, 1013-1014, 1145, 1222 1088-1091 projected extremes, 1200-1204, 1201, 1203 Representative Concentration Pathways (RCPs)*, rate of decrease, 319, 386 projections, 7, 20, 23, 25-26, 26, 97-101, 125, 137, 19-20, 22, 25, 79-81, 147-150, 468, 523-526, 524- salinity effects on, 271-273 157, 1140, 1150-1191, 1445 529, 1045-1047, 1100 sea level equivalent, 321 projections with loss of Greenland ice sheet, 1140, compared with SRES, 149-150, 997 summary, 9, 319, 367 1169-1170 described, 29 thickness and volume, 319, 327-328, 328, 330-332 rate of, 258, 289-290, 291, 430-431 extensions, 1102, 1103 trends, 329-330, 331, 333-334, 335 regional changes, 288-289, 1191-1199 projections and, 955-956, 1031, 1034, 1045-1047, Sea level, 11, 127, 1137-1216 regional projections, 100-101, 101, 1140, 1194- 1100 anomalies, 286, 287 1197, 1195-1199 uncertainties*, 1004-1005, 1005-1006, 1038- geocentric, 1142, 1143 satellite altimeter record (1993-2012), 1150 1039, 1038 irreversible aspects of, 29 semi-empirical projections, 99-100, 1182-1184, Respiration*, 470, 471-472, 477-478, 545 mean*, 1142, 1151, 1156-1159 1184 River and lake ice, 320, 361-362, 367 measurement, 285-286, 312, 1142, 1146-1150 summary, 1139-1141, 1204-1205, 1204 River discharge. See Streamflow models, 779-781, 781, 1139-1140, 1192-1193 thermal expansion* and, 99, 99, 1139, 1143, 1150- Runoff*, 91-92, 204-205, 956, 1081, 1081 processes affecting, 1143-1144, 1143-1144 1151, 1151, 1159, 1161-1163, 1180, 1182 projections, 20, 23, 26 timescales, 1142 relative (RSL)*, 1142, 1143, 1194-1197, 1195- uncertainties*, 47-49,1197-1198, 1204-1205 S 1199 Sea level equivalent (SLE)*, 319, 320, 321, 344, storm-surge models, 1200-1202 349-350, 350, 352-354, 353, 1153 Salinity (of oceans), 257, 265-273, 269-270, 280, trends in, 286-288, 287, 289, 291 Sea level pressure (SLP), 223-224, 223-224, 871, 301, 904-905, 904 Sea level change*, 12, 47-49, 98-101, 258, 285-289, 901 attribution of changes, 870, 904-905 1137-1216 projections, 1071-1072, 1071 before fossil fuel era, 481 atmospheric pressure change and, 1193, 1193 Sea salt, 1048-1050 defined, 265 attribution of changes, 19, 110, 870, 905, 1156, Sea spray aerosols, 599-601 measurement, 312 1176-1179 Sea surface temperature (SST)*, 164, 190-194, 190- Index models, 778-779, 778, 783 budget, 1156-1159, 1157-1158 193, 480-481 projections, 994, 994 commitment, 28, 1140 models, 777-779, 778-780 sea ice and, 271-273 confidence in projections, 1184-1186 observations, 5, 6 1533 Index paleoclimate*, 416, 420, 422, 458 South American Convergence Zone, 1221 T projections, 994-995, 1093 South Atlantic Convergence Zone (SACZ), 1237 proxy methods, 458 South-east Asia, 106, 211-212, 1273, 1274, 1378- Teleconnections*, 233, 1224, 1243, 1243 tropical phenomena and, 1235, 1236 1381 models, 805, 806 variability, 107 South Pacific Convergence Zone (SPCZ)*, 1219, Temperature, 5, 187-201, 926 Sectors 1236-1237 anomalies, 197, 461-462, 768, 1059 emission metrics and impacts, 719-720, 720 Southern Annular Mode (SAM)*, 231, 233-235, atmosphere and surface, 4-5, 6, 60-68, 161-162, radiative forcing and temperature, 663 354, 871, 900-901, 900, 937, 1245-1246 187-201, 869-870, 984 Snow, ice and frozen ground, 320, 358-360, 367 impacts, 1224 attribution of changes, 17-19, 60, 869-870, 871, aerosol absorption on, 574, 617-618, 685 models, 415-416, 801, 806 878-893, 918-920, 930, 932-934 attribution of changes, 870, 906-910, 931, 936-937 paleoclimate*, 386, 415-416 cold days/cold nights*, 86, 162, 210-212, 221, 956, frozen ground, 320, 362-366, 367 projections, 108, 1220, 1245, 1246 990, 1065-1066, 1067 glaciers*, 9, 24, 319, 335-344 summary, 806 commitment, 20 ice sheets and shelves*, 9, 320, 344-357, 367 Southern Ocean, 273, 783, 1141 diurnal temperature range (DTR)*, 188 models, 790, 790 polar amplification, 396-398 evidence for warming, 198-199 observed changes, 4, 320 projections, 24, 1095 extremes, 19, 109-112, 209-212, 209-212, 218- projections, 24-25, 92, 92-93, 996, 1032-1033, temperature, 354, 387, 780 219, 807, 808, 871, 910-912, 931, 1064-1068 1092-1093, 1092 SRES scenarios*, 131-132, 146-147, 149-150, 955, free atmosphere, 196-201, 197-201, 984 river and lake ice, 320, 361-362, 367 997, 1034, 1045, 1100 geoengineering and, 29, 574-575 seasonal snow, 320, 358-361, 358-360 Stabilization. See Climate stabilization global changes and projections, 1318-1319 snow albedo, 321, 358, 359 Storm surge*, 1200-1202 global diurnal temperature range (DTR), 162 snow cover (Northern Hemisphere), 9, 10, 24, 25, Storm tracks*, 229, 743, 773, 956 global instrumental record, 881-885, 882 92, 93, 320, 358, 358-359, 367, 870, 910, 931, 937, projections, 1074, 1075, 1220 global mean surface air surface temperature, 131- 996, 1092-1093, 1092 Stratosphere*, 130 132, 132, 955-956, 1409 snow-cryosphere interactions, 360-361 aerosols*, 627-628, 693 global mean surface air temperature, 23 snowfall, 204, 358-361 Brewer-Dobson circulation*, 163, 230, 1073-1074, global mean surface temperature (GMST)*, 20, 21, Soil moisture*, 790-791, 897 1248 23, 90, 121, 161-162, 164, 192-194, 192-194, 385, projections, 91-92, 956, 988, 1079-1080, 1080 ozone, 161, 171-172, 172, 672-674, 681-682, 681, 878-880, 879, 1011 Solar activity*, 393 774-775, 1000, 1048-1050, 1078, 1428 global mean surface temperature, models, 743, Solar forcing, 388-391, 885-886, 1007, 1048-1050 stratospheric-tropospheric relations, 753 769-772 Solar irradiance, 14, 14, 19, 126-127, 126, 392-393, temperature, 162, 197, 892-893, 893 global mean surface temperature, variability, 887- 688-691, 885-886 water vapour, 161, 170-171, 171, 661-662, 681- 888, 888-889 global dimming*, 161, 183-184, 794 682 global temperature change potential, 17, 663, 712- measurement, 689-690, 689 Streamflow*, 204-205 714, 714-715, 720 paleoclimate*, 388-391 Sulphate aerosols, 81, 605-606, 616 global warming potential*, 17, 663, 710-714, 711- projections, 86, 690, 955-956 Sulphur cycle, 537, 539 712 radiative forcing*, 662, 688-691, 1404-1409 Sulphur dioxide (SO2), 127, 538, 684, 794, 1402 heat waves*, 110, 162, 211-212, 212, 915, 916, surface solar radiation (SSR), 183-184, 184, 185- geoengineering with, 627 939 186 models, 744, 794, 795 interannual variability, 5, 6, 207-208 total (TSI)*, 19, 388-391, 394-395, 662, 689-690, Sulphur hexafluoride (SF6), 161, 168, 169, 733 land-surface air temperature (LSAT)*, 162, 164, 689 projections, 1412, 1423 187-189, 187 variations, 689-690, 689 radiative forcing*, 678, 679, 1434 last 2,000 years, 409-415, 409-410 Solar radiation*, 126-127, 126, 662 Surface marine air temperature (MAT), 191 projections, 543, 662 climate projections*, 980-993 mitigation*, 27-28 See also Orbital forcing land surface changes, 684-688 models, 15-16, 20, 743-745, 760-761, 761-762, Solar Radiation Management (SRM)*, 29, 469, models, 131-132, 132, 750-751 767-771, 768, 769-772, 777-779, 778-780, 807, 574-575, 627-635, 633-634 observations, 5, 6-7, 130, 159-254 810, 811-813, 1013-1014 cirrus thinning, 628 wind speed, 224-226 observed changes, 4, 6-7, 22, 121, 124, 131-132, climate response and, 629-635, 629-631 See also Atmosphere; Hydrological cycle 132, 187-201, 878-881, 879-881 cloud brightening, 628 Surface air temperature, 760-761, 761, 974-975, observed variability, 393, 744, 869 impacts on carbon cycle, 551-552 980-984, 981-982 oceans, 5, 6, 8, 10, 68-69, 257, 260-265, 266-267, side effects and risks, 575, 627-628, 634 Surface fluxes, 784-786, 784-785, 897 274-275, 280, 311, 311-312, 901-903, 902 stratospheric aerosols, 627-628, 693 Surface solar radiation (SSR), 183-184, 184, 185- paleoclimate*, 385-386, 395, 398-399, 409-420, summary, 635 186 418-419, 461-464 surface albedo, 628 Surface temperature*, 5, 6-7, 60-66, 60, 187-194, radiative forcing overview, 62 Solubility pump*, 472 461-462, 577, 760-761, 761-762, 878-881, 879- sea surface temperature (SST)*, 6, 107, 164, 190- South America 881 194, 190-193, 777-779, 778-780, 806, 994-995, climate indices, changes in, 211-212 interannual variability, 6, 207-208 1093 monsoon, 1233-1234, 1233 projections, 980-984, 981-982 summary, 5, 6-7, 161-162 precipitation extremes, 211-212, 213 surface*, 5, 6-7, 60-66, 60, 161-162, 187-194, 461- Index projections, 106, 1261-1264, 1262-1263, 1280- 462, 577, 743, 878-881, 879-881 1281, 1288, 1338-1349 surface air temperature, 760-761, 761, 974-975, 980-984, 981-982 1534 Index trends, 194, 194, 197-201, 222, 880, 895 993, 1220, 1248-1251, 1288-1289 Volcanic eruptions, 15, 86, 140, 393-394, 691-693 upper air, 162, 194-201, 197, 772-773, 774 Tropical Indian Ocean Variability, 233-235 as analogues, 693 upper ocean, 257, 258, 261-262, 261, 263, 265, Tropics, 1217, 1219-1220, 1235-1240 climate prediction and, 1008-1009 301, 301 atmospheric circulation, 226-230, 989-990, 989, models, 391 warm days/warm nights*, 86, 162, 210-212, 221- 1073 projections, 693, 1007 222, 956, 990, 1065-1066, 1067 convergence zones, 421-422, 1219, 1221, 1222, volcanic forcing, 390, 391, 662, 691-693, 692-693, warming hiatus, 61-63, 769-772, 798, 909 1235-1237, 1236-1237 923, 1007, 1048-1050, 1404-1409 warming hole in N. America, 212 extratropical modes, 415-416 Temperature projections, 7, 20, 21-23, 125, 155, paleoclimate*, 415, 420 929, 1278-1287, 1444-1445 precipitation, 1219 W extremes, 991-992, 991, 1031-1032, 1064-1068, projections, 1235-1240, 1288-1289 1067-1068 tropical modes, 415 Walker Circulation*, 163, 226-229, 227 free atmospheric temperature, 984 tropical Pacific mean state, 1240, 1241 projections, 90, 991, 1032, 1073 global mean surface temperature, 972, 980-984, tropical phenomena, 105-106, 1219-1220, 1222, Warm days/warm nights*, 162, 210-212, 221-222 981, 1010-1012, 1012-1013, 1444-1445 1235-1240, 1288-1289 projections, 86, 956, 990, 1065-1066, 1067 global projections, 1318-1319 tropical storms, 216-217 Warmer-get-wetter pattern, 1219, 1240 long-term, 89-90, 1031-1032, 1054-1057, 1054- warmer-get-wetter pattern, 1219, 1240 Water cycle. See Hydrological cycle 1056, 1062-1068, 1063, 1065, 1067-1068 Tropopause*, 226, 228 Water vapour, 207, 624, 666-667, 896 near-term, 85-86, 87, 955-956, 980-984, 993-995, Troposphere*, 130 feedbacks, 586-587, 587, 667, 819 993-994, 1009-1012, 1011-1012 humidity, 206-208 projections, 1076, 1076 ocean temperature, 956, 993-995, 993-994 ozone, 161, 172-173, 670-672, 672-673, 679-681, radiative forcing*, 126, 661-662, 666-667 regional projections, 1014, 1031 680-681, 684, 775, 998-999, 1048-1050, 1428- stratospheric, 161, 170-171, 171, 661-662, 681- skill in, 974, 977-978, 977 1429 682 summary, 955-956, 1009-1012, 1011-1012 stratospheric-tropospheric relations, 753 tropospheric, 207, 207, 265 surface air temperature, 974-975, 980-984, 981- temperature, 5, 162, 195, 197, 772-773, 774, 891- water vapour-lapse rate, 586-587, 587, 819 982 892 See also Humidity timescale, 28 water vapour, 207, 207 Wave height, 258, 277-278, 1141 uncertainties*, 140-141, 1006, 1006 Tropospheric Biennial Oscillation (TBO), 805, 1224, projections, 101, 1202-1204, 1203 zonal average, 1064, 1065 1253-1254 Weather, 229-230 See also Regional climate change climate and, 123-126, 914-917 Thematic Focus Elements (TFEs) Wetlands, 539-541 Carbon cycle perturbations and uncertainties, U Winds 96-97 mid-latitude westerlies, 956 Climate extremes, 109-113 Uncertainty*, 36, 114-115, 121, 139-142, 140-141 models, 784-785, 784-785 Climate sensitivity and feedbacks, 82-85 carbon cycle*, 96-97 projections, 1072, 1072 Climate targets and stabilization, 102-105 climate models*, 139-142, 140-141, 809-810, 815, upper-air, 226 Comparing projections from previous IPCC 1035-1040, 1038, 1197-1198 wave height and, 258, 277-278, 1141 assessments with observations, 64-65 climate projections, 115, 955, 978-980, 979, 1004- wind speeds, 217, 220, 224-226, 225 Irreversibility and abrupt change, 70-72 1012, 1035-1040, 1038, 1057-1058, 1058, 1197- wind stress (oceanic), 276-278, 784-785, 784-785 Sea level change: scientific understanding and 1198 uncertainties*, 47-49 in observations, 36, 114, 165, 810 The changing energy budget of the global climate quantification, 1040-1044 system, 67-68 scenario uncertainty, 1038-1039, 1038 Water cycle change, 42-45 sea level change*, 47-49 Thermal expansion*, 1139, 1143, 1150-1151, 1159 temperature projections, 140-141 projections, 99, 99, 1161-1163, 1180, 1182 See also Variability Thermal radiation, 184-185 Urban albedo, 687 Tide gauge* records, 285-286, 1146-1150, 1201 Urban heat islands*, 162, 188-189 Timescales, 28, 125, 128-129, 128, 1033, 1105-1107 Tipping points*. See Irreversibility Top of the atmosphere (TOA) radiation, 180-181, V 580-582, 618, 620, 765, 1069, 1069 Transient climate response (TCR), 128, 817-818, Variability, 121, 129-130, 138, 163, 164, 232-235 821, 871, 920-921, 925 internal*, 61-62, 138, 769-770, 869, 919, 923 projections, 81, 84-85, 1033 models, 795-806 summary, 16-17, 1110-1112 natural, 121, 129-132, 138, 140 Transient climate response to cumulative CO2 paleoclimate*, 386 emissions (TCRE)*, 16-17, 871, 926-927 See also Climate variability projections, 102-104, 1033, 1108-1109, 1113 Vegetation Trend models, 179-180 models, 752, 791 Index Tropical Atlantic Ocean Variability, 233-235 projections, 1097-1099, 1098 Tropical cyclones, 7, 107-108, 108, 110, 162, 216- Volatile organic compounds (VOCs)*, 127, 718, 740 217, 216, 807, 871, 913-914, 938, 956, 992-993, Volcanic aerosols, 14, 662, 691-693 1535